Urban Heat Island (UHI) Mitigation: Hot and Humid Regions [1st ed.] 9789813340497, 9789813340503

Citation preview

Advances in 21st Century Human Settlements

Napoleon Enteria Matteos Santamouris Ursula Eicker   Editors

Urban Heat Island (UHI) Mitigation Hot and Humid Regions

Advances in 21st Century Human Settlements Series Editor Bharat Dahiya, College of Interdisciplinary Studies, Thammasat University, Bangkok, Thailand Editorial Board Andrew Kirby, Arizona State University, Tempe, USA Erhard Friedberg, Sciences Po-Paris, France Rana P. B. Singh, Banaras Hindu University, Varanasi, India Kongjian Yu, Peking University, Beijing, China Mohamed El Sioufi, Monash University, Australia Tim Campbell, Woodrow Wilson Center, USA Yoshitsugu Hayashi, Chubu University, Kasugai, Japan Xuemei Bai, Australian National University, Australia Dagmar Haase, Humboldt University, Germany

Indexed by SCOPUS This Series focuses on the entire spectrum of human settlements – from rural to urban, in different regions of the world, with questions such as: What factors cause and guide the process of change in human settlements from rural to urban in character, from hamlets and villages to towns, cities and megacities? Is this process different across time and space, how and why? Is there a future for rural life? Is it possible or not to have industrial development in rural settlements, and how? Why does ‘urban shrinkage’ occur? Are the rural areas urbanizing or is that urban areas are undergoing ‘ruralisation’ (in form of underserviced slums)? What are the challenges faced by ‘mega urban regions’, and how they can be/are being addressed? What drives economic dynamism in human settlements? Is the urban-based economic growth paradigm the only answer to the quest for sustainable development, or is there an urgent need to balance between economic growth on one hand and ecosystem restoration and conservation on the other – for the future sustainability of human habitats? How and what new technology is helping to achieve sustainable development in human settlements? What sort of changes in the current planning, management and governance of human settlements are needed to face the changing environment including the climate and increasing disaster risks? What is the uniqueness of the new ‘socio-cultural spaces’ that emerge in human settlements, and how they change over time? As rural settlements become urban, are the new ‘urban spaces’ resulting in the loss of rural life and ‘socio-cultural spaces’? What is leading the preservation of rural ‘socio-cultural spaces’ within the urbanizing world, and how? What is the emerging nature of the rural-urban interface, and what factors influence it? What are the emerging perspectives that help understand the human-environment-culture complex through the study of human settlements and the related ecosystems, and how do they transform our understanding of cultural landscapes and ‘waterscapes’ in the 21st Century? What else is and/or likely to be new vis-à-vis human settlements – now and in the future? The Series, therefore, welcomes contributions with fresh cognitive perspectives to understand the new and emerging realities of the 21st Century human settlements. Such perspectives will include a multidisciplinary analysis, constituting of the demographic, spatio-economic, environmental, technological, and planning, management and governance lenses. If you are interested in submitting a proposal for this series, please contact the Series Editor, or the Publishing Editor: Bharat Dahiya ([email protected]) or Loyola D’Silva ([email protected])

More information about this series at http://www.springer.com/series/13196

Napoleon Enteria Matteos Santamouris Ursula Eicker •

Editors

Urban Heat Island (UHI) Mitigation Hot and Humid Regions

123

•

Editors Napoleon Enteria College of Engineering and Technology Mindanao State University—Iligan Institute of Technology Iligan, Philippines

Matteos Santamouris Faculty of Built Environment University New South Wales Sydney, NSW, Australia

Ursula Eicker Gina Cody School of Engineering and Computer Science Concordia University Montreal, QC, Canada

ISSN 2198-2546 ISSN 2198-2554 (electronic) Advances in 21st Century Human Settlements ISBN 978-981-33-4049-7 ISBN 978-981-33-4050-3 (eBook) https://doi.org/10.1007/978-981-33-4050-3 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The rapid development of urban areas in hot and humid regions has led to increases in urban temperatures, a decrease of urban ventilation, and a transformation of the once green outdoor environment into solar-energy-absorbing concrete and asphalt. This situation has increased the discomfort both outdoor and indoor and decreased air quality. Also, the energy consumption and CO2 emissions of urban areas are still increasing despite many efforts to improve efficiency of buildings and energy systems. The term urban heat island (UHI) refers to the current increase in urban temperatures due to high thermal load, problems related to urban ventilation, and the increased usage of asphalt and concrete. UHIs negatively affect the health of urban dwellers and increase urban cooling energy consumption. The presence of UHIs, in combination with global warming, means that the temperatures in metropolitan areas within hot and humid regions are expected to increase, as the demand for more thermal comfort and better ventilation results in increased usage of air-conditioning and ventilation systems. The UHI phenomenon in hot and humid regions affects the daily lives of the populations living in these areas—it increases the urban temperature, which results in increased discomfort. Furthermore, as the outdoor temperature increases, the operation of air-conditioning systems increases, which further affects the outdoor temperature. This will increase the urban temperature, which complicates the UHI phenomenon in hot and humid regions as energy and environmental concerns become interconnected. Hence, passive and active concepts and technologies are being implemented to mitigate the effects of UHIs in hot and humid regions. The research and development of concepts and technologies intended to mitigate the effects of UHIs has advanced especially in countries within hot and humid regions, as urban centers experience temperature increases, especially during the hot summer season. As UHIs are expected to be of growing concern in many urban areas in hot and humid countries, the development and application of UHI mitigation concepts and technologies will have a significant impact on public health and energy consumption.

v

vi

Preface

This book compiles the concepts and technologies associated with the mitigation of UHIs that are applicable in hot and humid regions. Several experts in the field were invited to contribute chapters on the reduction of UHIs in different areas to provide readers, researchers, and policymakers with insights into the concepts and technologies that should be considered when planning and constructing urban centers and buildings. This book offers solutions for the problem of increasing UHIs in hot and humid climates. The chapters discuss passive and active methods that can be incorporated during urban planning, urban renewal, building design, and building retrofitting processes. We acknowledge with gratitude each of the global experts who have fully supported and contributed chapters. With their support, this book has become a guide for urban planners, building designers, and policymakers with regard to the consideration of the urban heat island (UHI) phenomenon in hot and humid regions. We are grateful to Springer and the staff for the support given to us from this book’s conceptualization through to its publication. We are also thankful to our families for their support during the entire process of producing this book. We hope that with this book, urban planning and building design in hot and humid regions will not complicate the UHI problem. Hence, it will contribute to lessening the impact of UHIs through the application of the latest concepts and technologies for the reduction of urban temperatures. Iligan, Philippines Sydney, Australia Montreal, Canada

Napoleon Enteria Matteos Santamouris Ursula Eicker

Contents

Morphology of Buildings and Cities in Hot and Humid Regions . . . . . . Napoleon Enteria, Odinah Cuartero-Enteria, Mattheos Santamouris, and Ursula Eicker

1

Assessment of the Effects of Urban Heat Island on Buildings . . . . . . . . Liangzhu (Leon) Wang and Chang Shu

15

Urban Heat Island Monitoring with Global Navigation Satellite System (GNSS) Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jorge Mendez-Astudillo, Lawrence Lau, Isaac Yu Fat Lun, Yu-Ting Tang, and Terry Moore An Estimation of Air-Conditioning Energy-Saving Effects Through Urban Thermal Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yujiro Hirano and Tsuyoshi Fujita

43

61

Urban Heat Island, Contributing Factors, Public Responses and Mitigation Approaches in the Tropical Context of Malaysia . . . . . . 107 Nasrin Aghamohammadi, Logaraj Ramakreshnan, Chng Saun Fong, and Nik Meriam Sulaiman Urban Heat Island Studies in Hot and Humid Climates: A Review of the State of Art in Latin-America . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Massimo Palme Urban Heat Island Simulation and Monitoring in the Hot and Humid Climate Cities of Guayaquil and Durán, Ecuador . . . . . . . . . . . . . . . . . 143 Jaqueline Litardo, Massimo Palme, Mercy Borbor-Cordova, Rommel Caiza, Rubén Hidalgo-Leon, María del Pilar Cornejo-Rodriguez, and Guillermo Soriano Optimization of Urban Cooling Strategies for Parking Lots in Hot and Dry Climates: Case Study of Las Vegas and Adelaide . . . . . . . . . . 169 Ehsan Sharifi, Phillip Zawarus, and Steffen Lehmann

vii

viii

Contents

Urban Heat Island and Mitigation in Tropical India . . . . . . . . . . . . . . . 183 Priyadarsini Rajagopalan The Hot Climate of the Middle East . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Parham A. Mirzaei and Reihaneh Aghamolaei Urban Heat Island Effects and Mitigation Strategies in Saudi Arabian Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Yusuf A. Aina, Irshad M. Parvez, Abdul-Lateef Balogun, and Elhadi Adam Monitoring Urban Heat Islands in Selected Cities of the Gulf Region Based on Nighttime MODIS LST Data (2003–2018) . . . . . . . . . . . . . . . 249 Abdullah Al-Fazari, Ahmed El-Kenawy, Noura Al-Nasiri, and Mohamed Hereher Revisiting Urban Heat Island Effects in Coastal Regions: Mitigation Strategies for the Megacity of Istanbul . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Mustafa Dihkan, Fevzi Karsli, Abdulaziz Guneroglu, and Nilgun Guneroglu

About the Editors

Napoleon Enteria is Professor of mechanical engineering at Mindanao State University—Iligan Institute of Technology, Philippines. He has worked as research specialist at Building Research Institute, a research staff member at Tohoku University for industry–government–academe collaboration, a scientist at the Solar Energy Research Institute of Singapore of the National University of Singapore, and a global center of excellence researcher at the Wind Engineering Research Center of the Tokyo Polytechnic University, Japan. He founded the Enteria Grün Energietechnik, a research and technology consulting for tropical climate. He has participated in collaborative projects with research institutes, universities, and companies across several countries. He is Associate Editor with some of the international journals. He also serves as Guest Editor of some of the important special research issues. Matteos Santamouris is Scientia Professor and Professor of high-performance architecture at the University of New South Wales, Australia. He is also Professor at the University of Athens, Greece, and Visiting Professor at The Cyprus Institute, Metropolitan University of London, Tokyo Polytechnic University, Bolzano University, Brunel University London, and National University of Singapore. He has served as Director of the Laboratory of Building Energy Research at the University of Athens and is Former President of the National Center of Renewable and Energy Savings of Greece. He works as Editor in Chief and Member of the editorial board of several international ix

x

About the Editors

journals. He has been Guest Editor for twelve special issues of various scientific journals. He is considered an expert by various international research institutions. Ursula Eicker is the Canada Excellence Research Chair (CERC) for Next-Generation Cities at Concordia University, Montréal. She has held leadership positions at the Stuttgart University of Applied Sciences and its Centre for Sustainable Energy Technologies. She has been leading international research projects in the fields of energy efficiency in buildings and sustainable energy supply systems for more than two decades. At Concordia, she leads an ambitious research program to establish pathways toward new tools, technologies, and strategies for zero-carbon cities. Her research interests include the renewable energy systems, smart buildings, urban energy simulation, sustainable transportation, and smart waste and wastewater management. From solar modules to cooling systems to tech industrialization, she has the distinctive experience of working across sectors, from the laboratory to the factory and beyond.

Morphology of Buildings and Cities in Hot and Humid Regions Napoleon Enteria, Odinah Cuartero-Enteria, Mattheos Santamouris, and Ursula Eicker

Abstract Hot and humid regions consist of the tropical climate, Middle Eastern climate, and Mediterranean climate. Such regions are normally located near the equator but also include dessert regions located far from the equator, such as the Gobi Desert. These regions experience uncomfortable thermal comfort levels due to the high outdoor air temperature and, in some cases, high humidity. This situation makes it challenging to provide thermal comfort in these regions. The increased economic activities in most of the countries in hot and humid regions have changed the morphology of urban areas, cities, buildings, and houses. The increase in urbanization affects the outdoor and indoor environments of buildings and houses. The increasing urban temperature due to the increase of heat generation from people, cars, appliances, and other human activities affect the chemical and biological situations of urban areas. The increasing outdoor air temperature due to urban heat generation (aka, urban heat island) in hot and humid regions worsens the already unpleasant outdoor air conditions. It has also resulted in an increase in the use of air conditioning systems and energy consumption as the heat sink temperature (outdoor air) increases. With this, the difference between the indoor air and outdoor air temperature has increased. Keywords Hot and humid regions · Urban heat island (UHI) · Mitigation techniques · Cities and urban centers · Buildings and houses

N. Enteria (B) Iligan Institute of Technology, Mindanao State University, 9200 Iligan, Philippines e-mail: [email protected]; [email protected] O. Cuartero-Enteria Surigao Del Sur State University—Cantilan Campus, 8317 Cantilan, Philippines M. Santamouris University of New South Wales, Sydney, NSW 2052, Australia U. Eicker Concordia University, Montreal, QC H3G 1M8, Canada © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_1

1

2

N. Enteria et al.

1 Introduction The economic development of different countries in hot and humid regions has different accounts. The development of countries in hot and humid tropical countries in South East Asia has centered on the development of natural resources [1] and commercial and manufacturing activities [2], which have resulted in a better standard of living and urbanization. The case of Middle Eastern countries has resulted in the booming production of oils, which has resulted in the development of metropolitan areas [3, 4]. Situations in other regions in Latin America, North America, and the Mediterranean are due to increased economic activities owing to a combination of natural resources development and manufacturing-commercial operations, which, in turn, have resulted in urban development and an influx of people [5, 6]. The increase of populations of urban areas, either due to permanent migration or movement during working hours, has led to extensive energy consumption [7] for transportation, houses, food establishment, offices, health centers, etc. [8]. The increase of building energy consumption has resulted in an increase in heat emission as the energy consumed in those establishments is intended to produce better indoor comfortable conditions (e.g., better thermal comfort, air quality, lighting, and energy for work equipment and personal gadgets) [9]. In addition, the systems that transport workers and goods create environmental concerns [10–12] in addition to climatic conditions (e.g., heating due to the absorption of solar energy by urban structures) [13]. Due to the large influx of people in concentrated commercial, trade, manufacturing, and urban areas, heat generation has increased [14, 15]. The flourishing of urban areas and centers has resulted in high concentrations of people, transportation services (public and private), restaurants, and other amenities to provide comfort to the people in the surrounding areas [16]. Large-scale and quickly developing urban centers have created stress related to supporting the requirements of the general population living and working around these areas. In turn, the situation has resulted in environmental degradation [17, 18]. Environmental degradation has affected the environment’s ability to support a healthy population as emissions increase [19]. The rapid development of urban areas and cities, which have resulted in unplanned urban planning and zoning, have created an imbalance between natural and artificial structures around densely populated areas, which, in turn, has resulted in unhealthy environmental conditions [20]. In hot and humid regions, these conditions have furthered the localized heating of urban areas owing to heat generation, absorption of solar energy, and the effect of hampered urban ventilation [21–23]. The resultant heat increase—the so-called urban heat island (UHI)—further complicates the situation of hot and humid regions [24, 25]. This has resulted in the further utilization of artificial mechanical cooling and ventilation systems [26], which generates additional urban anthropogenic heat [27]. Due to the limitations of horizontal space development, vertical development has become more widespread in urban areas and cities. High-rise buildings containing offices, residential complexes, amenities, and other vertical structures, have become

Morphology of Buildings and Cities in Hot and Humid Regions

3

the norm in highly urbanized areas; these structures lead to a high building density and contribute to UHIs [28]. The vertical development of urban areas also increases the anthropogenic heat and hampers the flow of natural air ventilation while increasing pedestrian-level airspeed and lowering solar energy absorption due to sun-shading [29, 30]. Buildings affect solar irradiation absorption, which either helps with heating or cooling urban areas [31]. In compact cities, the street-level thermal environment affects the outdoor thermal comfort, the urban environment, and pollutant dispersion [32]. With the large-scale influx of people in urban areas and cities, housing developments in nearby areas flourish to cater to the needs of the people in the middle and upper echelons of society [33–35]. The development of subdivisions eliminates the natural vegetation of the areas around the urban areas and cities [36]. The alteration of the land to cater to the needs of urban growth has resulted in increased air temperature [37]. Hence, the preservation of nature should be considered in all land development projects [38]. Otherwise, it will result in biodiversity loss [36].

2 Buildings and Houses The buildings and houses in hot and humid regions evolved since the start of civilization until the present generation. Numerous structures have been changed regarding their design, selection of materials, methods of construction, and operation. From passively operated buildings and houses in previous years to the advanced and smart-operated buildings and houses in modern times are typical in the regions.

2.1 Old Situation In previous years, buildings and houses were designed and constructed based on the available materials, safety considerations, and climatic conditions. In the Middle Eastern climate, the buildings and houses are designed based on the available materials, such as mud, clay, and stones, and the hot climate [39–42]. In the hot and humid climates of tropical regions, buildings and houses are typically constructed using wood [43–45]. Variations of the design and construction can be seen based on records [46]. From these designs and methods of construction, it can be concluded that the design of old houses and buildings is solely based on the materials present and the climatic conditions [47]. The maintenance of safety and a comfortable environment based on the building materials, design, and construction were important considerations in ancient times [48–50]. In the hot climate of the Middle East, parts of South America, and the Mediterranean area, different cooling and ventilation techniques were applied [51– 53]. Local materials that could minimize heat transfer (thus minimizing the indoor

4

N. Enteria et al.

heating effect) were used [54, 55]. Natural ventilation was also applied based on the design of buildings and houses [51].

2.2 Present Situation The structural design of buildings and houses in hot and humid regions has changed as science and technology have changed [56–58]. Today, buildings and houses are mostly designed and constructed using concrete, steel, and glass [59]. These designs absorb a lot of solar energy and increase the indoor temperature [60]. Buildings in hot and dry regions such as the Middle East become solar energy absorber due to the building materials and glass facades. Thus, the application of solar energy reflectors [61] and thermal storage [62] could minimize indoor heating. The same pattern (e.g., the application of sun shading and heat insulation) has been observed in other hot and humid regions, such as tropical regions of Asia and Latin America [57]. Because of the demand for better indoor thermal comfort, air quality, and energy conservation, buildings nowadays are designed to have air handling systems that can provide the needed indoor thermal and air quality environment for different building requirements [63]. With the structure of buildings absorbing a higher percentage of solar energy, the energy demand for maintaining a comfortable indoor environment has caused the buildings in hot and humid regions to consume a large percentage of the energy required for the building sector [64]. Buildings situated in central areas or urban centers are expected to continuously operate air handling systems to maintain a comfortable indoor environment [65]. Hence, making the indoor environment thermally comfortable makes the outdoor environment more uncomfortable due to energy consumption and heat emission [66, 67].

2.3 Future Situation Environmental concerns have become intense due to global warming and climate change, which are caused by large amounts of greenhouse gas emissions, to which the building sector has contributed a sizable percentage [68, 69]. Buildings and houses are to be designed to minimize the absorption of solar energy to avoid indoor heating [70, 71]. Buildings are designed to be sustainable by utilizing recyclable and organic materials in their construction [72, 73]. With the application of advanced building technologies in hot and humid regions and with the consideration of climatic conditions such green walling [74] and smart windows for passive ventilation [75], buildings will become energy efficient and sustainable, thereby having a smaller impact on the outdoor environment [76]. This will create smart buildings whose operation depends on the changing requirements of the occupants and the outdoor environment [77].

Morphology of Buildings and Cities in Hot and Humid Regions

5

Due to concerns about the environmental impact and energy requirements for providing the needed indoor environment, buildings and houses are to be energyefficient with clean energy generating capabilities [78]. In the future, buildings and houses are expected to be this to minimize energy waste while providing comfortable air quality in the indoor environment [79]. With advancements of building technologies, buildings and houses are expected to minimize their contribution to increasing the surrounding temperature from the solar energy absorption [11], constraining urban ventilation [80], and emitting heat from their air handling systems [81].

3 Urban Centers and Cities Civilization starts when people build cities for trade, commerce, government centers, and areas for living. The convergence of people and buildings changes the structure of urban centers and cities. This situation creates stress on the environment, as cities and urban centers become unsustainable due to the environmental impact created within an outside its boundaries to cater to the needs of the population.

3.1 Old Situation At the start of civilization, cities were created that tended to change the balance of the interactions between the people and the environment, as the people came to demand more resources from the environment to support their existence [82, 83]. The availability of different resources and infrastructures resulted in an increase in cities’ populations, which also resulted in an increase in environmental concerns [84]. Cities of the old times were built near the available needed resources and materials (e.g., water, food, mud, clay, stones, timber) [85]. The development of old cities affected the resources available in nearby areas and resulted in the destruction of natural resources such as water resources, soils, and other resources [86, 87]. The rapid development of older cities and urban areas created discomfort in hot and humid regions as a large influx of people and a build-up of different infrastructures created different methods to maintain thermally comfortable environments [88]. Old technologies (e.g., wind catchers, sun shading, evaporative cooling, prevailing wind ventilation) were created to minimize the effect of air temperature [89–91]. The innovations of the people living in these times in hot and humid regions contributed to minimizing the effects of the increase in urban temperature [92].

6

N. Enteria et al.

3.2 Present Situation With the rapid development of urban planning and building sciences, the zonal planning of urban areas has become popular [93, 94]. In hot and humid regions, the redevelopment of cities and urban areas depends on each country’s capabilities through proper investigation [23, 95]. In highly developed and economically stable countries, existing cities have been redeveloped with proper urban planning to cater to the needs of the local populace [96], minimize the concentration of man-made structures [97], and apply greening around urban areas [98]. However, in developing countries, proper urban redevelopment has become a concern, as it will involve massive investment and the involvement of different stakeholders [99, 100]. Proper urban planning has resulted in the minimization of the increase of urban temperature, air pollution, and the usage of air conditioning and ventilation systems [101]. Moreover, it also contributed to the greening of different urban areas, which resulted in an increase in urban air quality [102]. In well-planned urban centers and cities, urban ventilation has contributed to a reduction in air pollution, urban temperature from solar energy absorption, and heat emitted from air handling systems, equipment, devices, and people [22, 103]. With proper urban planning, the use of the public transportation systems can be minimized as people can use different transportation modes, such as walking, biking [104].

3.3 Future Situation With the global concern of energy and environment [105, 106] coupled with increasing urban population [107], proper urban planning will become an important consideration in the redevelopment of urban areas and cities, to minimize the usage of common urban transportation methods [108] and typical energy sources by promoting clean energy sources [109, 110]. Urban areas are expected to minimize the build-up of heat, pollution, and the utilization of different urban greening technologies, which can contribute to the minimization of UHIs [9, 111–113]. This is possible by synergizing natural and artificial structures to be built side by side [114, 115]. It can also be done by means of utilizing solar energy for different applications [116]. The application of advanced urban planning and building sciences could create a positive impact on the comfort, wellbeing, and health of the urban environment and the people living in these environments [117, 118]. This creates a healthy population and minimizes negative impacts on the environment by using smart technologies [119–121]. Redeveloped and well-planned urban centers and cities attract investment and development as people tend to be more productive (e.g., no traffic, comfortable environment, a healthy population, and lower pollution) [78, 122, 123]. This situation has a great impact on whole countries, as well-planned urban centers and cities have

Morphology of Buildings and Cities in Hot and Humid Regions

7

a greater effect on the economy through which the concept of smart cities will be realized [124–126].

4 Conclusions This chapter describes the evolution of cities and buildings in hot and humid regions. Hot and humid regions’ development further contributes to the increase of UHIs. As these regions are already hot, the situation will become more complex if it is not properly addressed soon. Hence, to minimize the effect of UHIs in already hot and humid regions, proper urban planning will be introduced. Urban greening will be an important component with zoning to minimize the traffic situation. Application of different technologies to reduce pollutant emissions, and the use of building materials that minimizes the absorption of solar energy will be minimize the increase of urban temperature. With this, it is expected that current urban planning practices will be reviewed, and future urban planning and zoning methods will be strictly implemented. With the dynamic economic situation and technologies under development, the development of urban centers and cities will be prepared to apply future technologies through which the environmental, economic, and technological demands of urban dwellers will be addressed.

References 1. Shangle A, Solaymani S (2020) Responses of monetary policies to oil price changes in Malaysia. Energy 200:117553 2. Rogozhina N (2020) Intra-regional migration of labor resources in Southeast Asia. Mirovaya Ekonomika I Mezhdunarodnye Otnosheniya 64:111–119 3. Alshubiri FN, Tawfik OI, Jamil SA (2020) Impact of petroleum and non-petroleum indices on financial development in Oman. Financ Innov. https://doi.org/10.1186/s40854-020-00180-7 4. Moghaddam SNM, Rafieian M (2019) Urban development as a marionette? Oil income and urban development in post-revolutionary Iran. Int Dev Plann Rev. https://doi.org/10.3828/ idpr.2019.33 5. Ferreira P, Pereira E, Silva M (2020) The relationship between oil prices and the Brazilian stock market. Phys A 545:123745 6. Bilgili F, Kocak E, Bulut U (2020) The shale gas production and economic growth in local economies across the US. Environ Sci Pollut Res 27:12001–12006 7. Bristow DN, Kennedy CA (2013) The energy for growing and maintaining cities. Ambio 42:41–51 8. Boex J, Malik AA, Brookins D, Edwards B, Zaidi H (2019) The political economy of urban governance in Asian cities: delivering water, sanitation and solid waste management services. In: Dahiya B, Das A (eds) New urban agenda in Asia-Pacific. Advances in 21st century human settlements. Springer, Singapore 9. Yuan J, Yamanaka T, Kobayashi T, Kitakaze H, Emura K (2019) Effect of highly reflective building envelopes on outdoor environment temperature and indoor thermal loads using CFD

8

10.

11.

12.

13. 14.

15.

16. 17. 18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

N. Enteria et al. and numerical analysis. In: E3S web of conferences. https://doi.org/10.1051/e3sconf/201911 106031 Breuer JL, Samsun RC, Peters R, Stolten D (2020) The impact of diesel vehicles on NOx and PM10 emissions from road transport in urban morphological zones: a case study in North Rhine-Westphalia, Germany. Sci Total Environ 727:138583 Huang X, Liu D, Li N, Wang J, Zhang Z, Zhong M (2020) Single novel Ca0.5 Mg10.5 (HPO3 )8 (OH)3 F3 coating for efficient passive cooling in the natural environment. Sol Energy 202:164–170 Ly BT, kajii Y, Nguyen TYL, Shoji K, Van DA, Do TN, Nghiem TD, Sakamoto Y (2020) Characteristics of roadside volatile organic compounds in an urban area dominated by gasoline vehicles, a case study in Hanoi. Chemosphere 254:126749 Xue Y, Wang Y, Peng H, Wang H, Shen J (2020) The impact of building configurations and anthropogenic heat on daily urban air temperature cycles. Build Environ 169:106564 Veena K, Parammasivam KM, Venkatesh TN (2020) Urban heat island studies: current status in India and a comparison with the international studies. J Earth Syst Sci. https://doi.org/10. 1007/s12040-020-1351-y Wu S, Zhang C, Yang Z, Rong Y, Wang Y (2020) Research on surface temperature inversion and spatiotemporal distribution characteristics based on Landsat data. IOP Conf Ser Earth Environ Sci 450:012031. https://doi.org/10.1088/1755-1315/450/1/012031 Gupta P, Jadon SS (2019) Effect of changing lifestyle on urban pattern. AIP Conf Proc 2158:020024. https://doi.org/10.1063/1.5127148 Nichol JE, Choi SY, Wong MS, Abbas S (2020) Temperature change and urbanization in a multi-nucleated megacity: China’s pearl river delta. Urban Clim 31:100592 Pham NM, Huynh TL, Nasir MA (2020) Environmental consequences of population, affluence and technological progress for European countries: a malthusian view. J Environ Manage 260:110143 Robinson ES, Gu P, Ye Q, Li HZ, Shah RU, Apte JS, Robinson AL, Presto AA (2018) Restaurant impacts on outdoor air quality: elevated organic aerosol mass from restaurant cooking with neighborhood-scale plume extents. Environ Sci Technol 52:9285–9294 Nimish G, Bharath HA, Lalitha A (2020) Exploring temperature indices by deriving relationship between land surface temperature and urban landscape. Remote Sens Appl Soc Environ 18:100299 Yuan C, Adelia AS, Mei S, He W, Li XX, Norford L (2020) Mitigating intensity of urban heat island by better understanding on urban morphology and anthropogenic heat dispersion. Build Environ 176:106876 Chatterjee S, Khan A, Dinda A, Mithun SK, Khatun R, Akbari H, Kusaka H, Mitra C, Bhatti SS, Doan QV, Wang Y (2019) Simulating micro-scale thermal interactions in different building environments for mitigating urban heat islands. Sci Total Environ 663:610–631 Palme M, Clemente C, Cellurale M, Carrasco C, Salvati A (2019) Mitigation strategies of the urban heat island intensity in Mediterranean climates: simulation studies in Rome (Italy) and Valparaiso (Chile). IOP Conf Ser Earth Environ Sci 323:012025. https://doi.org/10.1088/ 1755-1315/323/1/012025 Le MT, Cao TAT, Tran NAQ, Sadriavich SI, Nguyen TKP, Le TKM (2019) Case study of GIS application in analyzing urban heating island phenomena in tropical climate country. IOP Conf Ser Mat Sci Eng. https://doi.org/10.1088/1757-899X/661/1/012090 Ramakreshnan L, Aghamohammadi N, Fong CS, Ghaffarianhoseini A, Wong LP, Sulaiman NM (2019) Empirical study on temporal variations of canopy-level urban heat island effect in the tropical city of Greater Kuala Lumpur. Sustain Cities Soc 44:748–762 Zheng S, Guldmann JM, Liu Z, Zhao L, Wang J, Pan X, Zhao D (2020) Predicting the influence of subtropical trees on urban wind through wind tunnel tests and numerical simulations. Sustain Cities Soc 57:102116 Adelia AS, Yuan C, Liu L, Shan RQ (2019) Effects of urban morphology on anthropogenic heat dispersion in tropical high-density residential areas. Energy Buildings 186:368–383

Morphology of Buildings and Cities in Hot and Humid Regions

9

28. Budhiraja B, Agrawal G, Pathak P (2020) Urban heat island effect of a polynuclear megacity Delhi—compactness and thermal evaluation of four-cities. Urban Clim 32:100634 29. Barbosa GS, Drach PRC, Corbella OD (2019) Intraurban temperature variations: urban morphologies of the densification process of Copacabana Neighborhood, Brazil. Climate. https://doi.org/10.3390/cli7050065 30. Sharmin T, Steemers K, Matzarakis A (2017) Microclimate modelling in assessing the impact of urban geometry on urban thermal environment. Sustain Cities Soc 34:293–308 31. Pacifici M, Rama F, de Castro Martins KR (2019) Analysis of temperature variability within outdoor urban spaces multiple scales. Urban Clim 27:90–104 32. Wang X, Li Y, Yang X, Chan PW, Nichol J, Li Q (2018) The street air warming phenomenon in a high-rise compact city. Atmosphere. https://doi.org/10.3390/atmos9100402 33. Aulia DN, Suryani L (2020) Gated community typology based on growth and development in Medan City, Indonesia. IOP Conf Ser Earth Environ Sci 452:012154. https://doi.org/10. 1088/1755-1315/452/1/012154 34. Ortiz Báez P, Boisson S, Torres M, Bogaert J (2020) Analysis of the urban-rural gradient terminology and its imaginaries in a Latin-American context. Theor Empirical Res Urban Manage 15:81–98 35. Hovhannisyan Y, Devadoss S (2020) Effects of urbanization on food demand in China. Empirical Econ 58:699–721 36. Asabere SB, Acheampong RA, Asiagbor G, Beckers SC, Erasmi S, Schanze J, Sauer D (2020) Urbanization, land use transformation and spatio-environmental impacts: analysis of trends and implications in major metropolitan regions of Ghana. Land Use Policy 96:104707 37. Mushore TD, Odindi J, Dube T, Mutanga O (2017) Understanding the relationship between urban outdoor temperatures and indoor air-conditioning energy demand in Zimbabwe. Sustain Cities Soc 34:97–108 38. Xu X, Jiang B, Chen M, Bai Y, Yang G (2020) Strengthening the effectiveness of nature reserves in representing ecosystem services: the Yangtze river economic belt in China. Land Use Policy 96:104717 39. Shabahang S, Vale B, Gjerde M (2018) The problem of the modern built environment and enhanced urban warming in Iran. In: Kaparaju P, Howlett R, Littlewood J, Ekanyake C, Vlacic L (eds) Sustainability in energy and buildings 2018. KES-SEB 2018. Smart innovation, systems and technologies, vol 131. Springer, Cham 40. Ainash N (2017) On the issue of interior research and objective environment in the home of the ancient tribes of Kazakhstan. Man India 97:791–796 41. Takhirov S, Gilani A, Quigley B, Myagkova L (2017) Detailed numerical analysis of a historic building based on its current condition captured by laser scans and material tests. In: 6th ECCOMAS thematic conference on computational methods in structural dynamics and earthquake engineering, Rhodes Island, Greece, June 15–17, 2017 42. Utaliev SA (2017) Production and use specifics of carved decorations in the architecture of medieval khwarezm (XIII-XIV centuries AD). Annales d’Universite ‘Valahia’ Targoviste, Section d’Archeologie et d’Histoire 19:57–66 43. Hermawan PE, Setyowati E (2015) Thermal comfort of wood-wall house in coastal and mountainous region in tropical area. Procedia Eng 125:725–731 44. Das P, Chaaruchandra K, Sudhakar P, Satya S (2012) Traditional bamboo houses of NorthEastern region: a field study of Assam & Mizoram. Key Eng Mater 517:197–202 45. Rashid R, Hamdan Bin Ahmed M (2010) Bangladesh traditional house construction technology is influenced by changing the use of materials—a field study was conducted in Bangladesh. In: Proceedings of the 2nd international postgraduate conference on infrastructure and environment, Hong Kong, China, June 1–2, 2010 46. Millaire JF, Eastaugh E (2014) Geophysical survey on the coast of Peru: the early prehispanic city of Gallinazo Group in the Viru Valley. Latin Am Antiq 25:239–255 47. Vongvilay X, Shin JE, Kang YH, Kim ED, Choi JH (2015) The influence of French colonial rule on Lao architecture with a focus on residential buildings. J Asian Archit Build Eng 14:279–286

10

N. Enteria et al.

48. Grove R, Pim JE, Serrano M, Cidrás D, Viles H, Sanmartín P (2020) Pastoral stone enclosures as biological cultural heritage: Galician and Cornish examples of community conservation. Land. https://doi.org/10.3390/land9010009 49. Jayadi A, Mikhriani NI (2020) Green pesantren: an islamic model of environmental responsible (case study of pondok pesantren al qodir and al imdad yogyakarta). Int J Psychosoc Rehabil 24:818–824 50. Rosenberg D, Love S, Hubbard E, Klimscha F (2020) 7,200 years old constructions and mudbrick technology: the evidence from Tel Tsaf, Jordan Valley, Israel. PLoS ONE 15:e0227288 51. Shaeri J, Yaghoubi M, Aflaki A, Habibi A (2018) Evaluation of thermal comfort in traditional houses in a Tropical climate. Buildings. https://doi.org/10.3390/buildings8090126 52. Leccese F, Mattoccia A, Rocca M, Rubio R, Salvadori G (2015) A parametric approach to design a wooden climatic responsive village in Atacama Desert (Chile). Build Simul Appl 2015:231–238 53. Tabriz SN, Jahandideh M, Douzdouzani Y, A’zami A, Aliyev F (2009) Analysing of badgir as a sustainable ventilation system in traditional Iranian buildings. In: 29th biennial solar world congress of the International Solar Energy Society, Johannesburg, South Africa, Oct 11–14, 2009 54. Mohamad AF (2020) Comparative study of traditional and modern building techniques in Siwa Oasis, Egypt—case study: affordable residential building using appropriate building technique. Case Stud Constr Mater 12:e00311 55. Siahaan F (2020) Identification of application of biological architecture in the North Nias’s traditional house “Omo Hada” in Indonesia. IOP Conf Ser Earth Environ Sci. https://doi.org/ 10.1088/1755-1315/452/1/012016 56. Panchabikesan K, Joybari MM, Haghighat F, Ramalingam V, Ding Y (2020) Feasibility study on the year-round operation of PCM based free cooling systems in tropical climatic conditions. Energy 192:116695 57. Tuck NW, Zaki SA, Hagishima A, Rijal HB, Yakub F (2020) Affordable retrofitting methods to achieve thermal comfort for a terrace house in Malaysia with a hot-humid climate. Energy Buildings 223:110072 58. dos Santos GH, Mendes N (2015) Numerical analysis of hygrothermal performance of reflective insulated roof coatings. Appl Therm Eng 81:66–73 59. Marsono AKB, Balasbaneh AT (2015) Combinations of building construction materials for residential building for the global warming mitigation for Malaysia. Constr Build Mater 85:100–108 60. Shastry V, Mani M, Tenorio R (2012) Impacts of modern transitions on thermal comfort in vernacular dwellings in warm-humid climate of Sugganahalli (India). Indoor Built Environ. https://doi.org/10.1177/1420326X12461801 61. Saber HH, Maref W (2019) Energy performance of cool roofs followed by development of practical design tool. Front Energy Res. https://doi.org/10.3389/fenrg.2019.00122 62. Solgi E, Memarian S, Moud GN (2018) Financial viability of PCMs in countries with low energy cost: a case study of different climates in Iran. Energy Buildings 173:128–137 63. Imran MS, Ibrahim SH, Baharun A, Abidin WAWZ (2016) Performance of low cost alternative radiant cooling panel in Malaysia. Int J Appl Eng Res 11:7333–7342 64. Aldossary NA, Rezgui Y, Kwan A (2014) Energy consumption patterns for domestic buildings in hot climates using Saudi Arabia as case study field: multiple case study analyses. In: 2014 international conference on computing in civil and building engineering, Orlando, Florida, United States, June 23–25, 2014 65. Cheong KW, Chong KY (2001) Development and applications of an indoor air quality audit to an air-conditioned building in Singapore. Build Environ 36:181–188 66. Chen M, Ma Z, Liu M (2020) The field survey on local heat island effect of precision airconditioning. In: Wang Z, Zhu Y, Wang F, Wang P, Shen C, Liu J (eds) Proceedings of the 11th international symposium on heating, ventilation and air conditioning (ISHVAC 2019), ISHVAC 2019. Environmental science and engineering. Springer, Singapore

Morphology of Buildings and Cities in Hot and Humid Regions

11

67. Girgis N, Elariane S, Elrazik MA (2016) Evaluation of heat exhausts impacts on pedestrian thermal comfort. Sustain Cities Soc 27:152–159 68. Houghton A (2011) Health impact assessments a tool for designing climate change resilience into green building and planning projects. J Green Build 6:66–87 69. Hillman T, Ramaswami A (2010) Greenhouse gas emission footprints and energy use benchmarks for eight U.S. cities. Environ Sci Technol 44:1902–1910 70. Tan Y, Peng J, Curcija DC, Hart R, Jonsson JC, Selkowitz S (2020) Parametric study of the impact of window attachments on air conditioning energy consumption. Sol Energy 202:136– 143 71. Abuhussain MA, Chow DHC, Sharples S (2019) Sensitivity energy analysis for the Saudi residential buildings envelope codes under future climate change scenarios: the case for the hot and humid region in Jeddah. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/ 1755-1315/329/1/012039 72. Scrucca F, Ingrao C, Maalouf C, Moussa T, Polidori G, Messineo A, Arcidiacono C, Asdrubali F (2020) Energy and carbon footprint assessment of production of hemp hurds for application in buildings. Environ Impact Assess Rev 84:106417 73. Streimikiene D, Skulskis V, Balezentis T, Agnusdei GP (2020) Uncertain multi-criteria sustainability assessment of green building insulation materials. Energy Buildings 219:110021 74. Schettini E, Campiotti CA, Scarascia Mugnozza G, Blanco I, Vox G (2018) Green walls for building microclimate control. In: International symposium on greener cities for more efficient ecosystem services in a climate changing world, Bologne, Italy, Sept 12–15, 2017 75. Tan Z, Deng X (2020) An optimized window control strategy for naturally ventilated residential buildings in warm climates. Sustain Cities Soc 57:102118 76. Caponigro M, Manoloudis A, Papadopoulos AM (2020) Developing a strategy for energy efficiency in the Egyptian building sector. IOP Conf Ser Earth Environ Sci. https://doi.org/ 10.1088/1755-1315/410/1/012076 77. Leung MY, Wang C, Wei X (2020) Structural model for the relationships between indoor built environment and behaviors of residents with dementia in care and attention homes. Build Environ 169:106532 78. Bowley W, Evins R (2020) Assessing energy and emissions savings for space conditioning, materials and transportation for a high-density mixed-use building. J Build Eng 31:101386 79. Emdadi Z, Asim N, Yarmo MA, Shamsudin R, Masita Mohammad M, Kamaruzaman Sopian K (2016) Green material prospects for passive evaporative cooling systems: geopolymers. Energies. https://doi.org/10.3390/en9080586 80. Yang F, Chen L (2020) Pedestrian wind in high-rise residential quarters. In: High-rise urban form and microclimate. The urban book series. Springer, Singapore. https://doi.org/10.1007/ 978-981-15-1714-3_5 81. Ghaddar Z, Ghali K, Ghaddar N (2017) Impact of integrating desiccant dehumidification processes to conventional AC system on urban microclimate and energy use in Beirut city. Energy Convers Manage 153:374–390 82. Fouache E, Pavlopoulos K (2010) The interplay between environment and people from neolithic to classical times in Greece and Albania. In: Martini I, Chesworth W (eds) Landscapes and societies. Springer, Dordrecht 83. Gajdoš P (2002) The city and its development in the social-spatial context. Sociologia 34:305– 326 84. Ortman SG, Cabaniss AHF, Sturm JO, Bettencourt LMA (2014) The pre-history of urban scaling. PLoS ONE 9:e87902 85. Mirsky A (1982) Influence of geologic factors on ancient Mesopotamian civilization. J Geol Educ 30:294–299 86. Mele C (2019) Human settlements and sustainability: a crucial and open issue. In: E3S web of conferences. https://doi.org/10.1051/e3sconf/201911900012 87. Miller JN, Pansic N, Malec S (2001) Stream restoration in the urban environment. In: Proceedings of the 2001 wetlands engineering and river restoration conference, Reno, NV, United States, Aug 31, 2001

12

N. Enteria et al.

88. Praseeda KI, Mani M, Venkatarama Reddy BV (2014) Assessing impact of material transition and thermal comfort models on embodied and operational energy in vernacular dwellings (India). Energy Procedia 54:342–351 89. Shalaby HM, Sherif A, Altan H (2018) The impact of the informal area expansion on the urban natural ventilation of Alexandria. Innov Infrastruct Solutions. https://doi.org/10.1007/ s41062-017-0109-0 90. Sharma AK (2017) Historic city—a case of resilient built environment. Procedia Eng 180:1103–1109 91. Nahi N, Singery M (2015) Investigating the effect of climatic factors on the spatial structure of old texture of Yazd city: a specimen of a sustainable urban texture. In: Sayigh A (ed) Renewable energy in the service of mankind, vol I. Springer, Cham 92. Ariffin NAM, Behaz A, Denan Z (2018) Thermal comfort studies on houses in hot arid climates. IOP Conf Ser Mater Sci Eng. https://doi.org/10.1088/1757-899X/401/1/012028 93. Kapoor P, Saberi O, Oliver N (2019) Green urban development: a methodology to calculate site and infrastructure related GHG emissions. IOP Conf Ser Earth Environ Sci. https://doi. org/10.1088/1755-1315/297/1/012004 94. Marino FPR, Lembo F, Fanuele V (2019) Towards more sustainable patterns of urban development. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/297/1/ 012028 95. Siba MAJA, Maruthaveeran S, Ujang N (2020) Investigating the use and constraints associated with green outdoor environment at workplaces: what do the office employees in Kuala Lumpur say? Urban For Urban Greening 51:126692 96. Wang Y, Fukuda H (2019) Sustainable urban regeneration for shrinking cities: a case from Japan. Sustainability. https://doi.org/10.3390/su11051505 97. Tian Y, Wu H, Zhang G, Wang L, Zheng D, Li S (2020) Perceptions of ecosystem services, disservices and willingness-to-pay for urban green space conservation. J Environ Manage 260:110140 98. Venter ZS, Krog NH, Barton DN (2020) Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway. Sci Total Environ 709:136193 99. Zadeh AV (2020) Evaluation of the prospective role of affordable housing in regeneration of historical districts of Iranian cities to alleviate socio-spatial segregation. In: Arefian F, Moeini S (eds) Urban heritage along the silk roads. The urban book series. Springer, Cham 100. Mosciaro M, Pereira A (2019) Reinforcing uneven development: the financialisation of Brazilian urban redevelopment projects. Urban Stud. https://doi.org/10.1177/004209801982 9428 101. Tsai IT, Ghazal S (2017) Urban geometry, building energy consumption and pedestrian mobility: an integrated modeling approach. In: 2nd IEEE international conference on intelligent transportation engineering, Singapore, Sept 1–3, 2017 102. Richards DR, Fung TK, Belcher RN, Edwards PJ (2020) Differential air temperature cooling performance of urban vegetation types in the tropics. Urban For Urban Green 50:126651 103. Yola L (2020) Canyon effects in urban configurations: tropical context study. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/436/1/012028 104. Wang C, Wu Y, Shi X, Li Y, Zhu S, Jin X, Zhou X (2020) Dynamic occupant density models of commercial buildings for urban energy simulation. Build Environ 169:106549 105. Feng C, Zheng CJ, Shan ML (2020) The clarification for the features, temporal variations, and potential factors of global carbon dioxide emissions. J Clean Prod 255:120250 106. Mahlooji M, Gaudard L, Ristic B, Madami K (2020) The importance of considering resource availability restrictions in energy planning: what is the footprint of electricity generation in the Middle East and North Africa (MENA)? Sci Total Environ 717:135035 107. Ahmed HA, Singh SK, Kumar M, Maina MS, Dzwairo (Bloodless) R, Lal D (2020) Impact of urbanization and land cover change on urban climate: case study of Nigeria. Urban Clim 32:100600 108. Zahraei SM, Kurniawan JH, Cheah L (2020) A foresight study on urban mobility: Singapore in 2040. Foresight 22:37–52

Morphology of Buildings and Cities in Hot and Humid Regions

13

109. Nathaniel S, Anyanwu O, Shah M (2020) Renewable energy, urbanization, and ecological footprint in the Middle East and North Africa region. Environ Sci Pollut Res 27:14601–14613 110. Taghizadeh-Hesary F (2020) The impacts of air pollution on health and economy in Southeast Asia. Energies. https://doi.org/10.3390/en13071812 111. Beraldi R, Neri L, Costa F, Facini O, Rapparini F, Carriero G (2019) Ecophysiological and micromorphological characterization of green roof vegetation for urban mitigation. Urban For Urban Greening 37:24–32 112. Hussain MRM, Yusoff NH, Tukiman I, Samah MAA (2019) Community perception and participation of urban farming activities. Int J Recent Technol Eng 8:341–345 113. Moss JL, Doick KJ, Smith S, Shahrestani M (2019) Influence of evaporative cooling by urban forests on cooling demand in cities. Urban For Urban Greening 37:65–73 114. Escobedo FJ, Giannico V, Jim CY, Sanesi G, Lafortezza R (2019) Urban forests, ecosystem services, green infrastructure and nature-based solutions: nexus or evolving metaphors? Urban For Urban Greening 37:3–12 115. Nastran M, Kobal M, Eler K (2019) Urban heat islands in relation to green land use in European cities. Urban For Urban Greening 37:33–41 116. Nasir SD, Xu W, Vital B, Pantua C, Zhou B, Calautit J, Hughes B (2019) Urban road and pavement solar collector system for heat island mitigation: assessing the beneficial impact on outdoor temperature. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/ 463/1/012038 117. Buccolieri R, Santiago JL, Rivas E, Saanchez B (2019) Review on urban tree modelling in CFD simulations: aerodynamics, deposition and thermal effects. Urban For Urban Greening 37:56–64 118. Liu J, wang Y, Zimmer C, Kang J, Yu T (2019) Factors associated with soundscape experiences in urban green spaces: a case study in Rostock, Germany. Urban For Urban Greening 37:135– 146 119. Liu C, Ren L, Wu L, Guo M (2020) Measuring the smart growth pattern for medium-sized cities. J Urban Plann Dev. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000569 120. Tu Q, Cheng L, Yuan T, Cheng Y, Li M (2020) The constrained reliable shortest path problem for electric vehicles in the urban transportation network. J Cleaner Prod 261:121130 121. Sallustio L, Perone A, Vizzarri M, Corona P, Fares S, Cocozza C, Tognetti R, Lasserre B, Marchetti M (2019) The green side of the grey: assessing greenspaces in built-up areas of Italy. Urban For Urban Greening 37:147–153 122. Zhao L, Wang S, Wei J, Peng ZR (2020) Hierarchical linear model for investigating effect of built environment on bus transit. J Urban Plann Dev. https://doi.org/10.1061/(ASCE)UP. 1943-5444.0000568 123. Pierer C, Creutzig F (2019) Star-shaped cities alleviate trade-off between climate change mitigation and adaptation. Environ Res Lett 14:085011 124. Liu F, Tait S, Schellart A, Mayfield M, Boxall J (2020) Reducing carbon emissions by integrating urban water systems and renewable energy sources at a community scale. Renew Sustain Energy Rev 123:109767 125. Mboup G (2019) Africa’s smart city foundation: urbanization, urban form and structure, land tenure and basic infrastructures. In: Mboup G, Oyelaran-Oyeyinka B (eds) Smart economy in smart african cities. Advances in 21st century human settlements. Springer, Singapore 126. Yamagata Y, Seya H (2013) Simulating a future smart city: a integrated land use-energy model. Appl Energy 112:1466–1474

Assessment of the Effects of Urban Heat Island on Buildings Liangzhu (Leon) Wang and Chang Shu

Abstract Climate change and global warming have been indisputable as supported by mounting evidence of more extended, severe, and frequent occurrences of extreme weather events (EHEs), in particular, summertime heatwaves in recent years. EHEs often interact with buildings in urban area centers, which are densely packed by building blocks with vulnerable populations: the homeless, elderly, children, socially disadvantaged people, the physically challenged, or the sick, creating a unique natural phenomenon, urban heat island (UHI). This chapter covers a comprehensive effort to assess the UHI impacts on buildings and the potentially vulnerable populations through a series of surveys and field measurements in schools and hospitals, and a multi-scale climatic modeling framework from global and regional climates, urban microclimate, to building scale simulations. General methodologies are reported in detail for a better understanding of the levels of impacts by UHIs on buildings, e.g., excessively high indoor temperatures, energy demands and peak loads, and on people, e.g., indoor overheating risks. The effort is essential for developing measures and strategies to mitigate the UHI impacts on buildings and occupants for the current and future climates. Keywords Climate change · Urban heat island · Extreme heat event · Vulnerable · Survey · Field measurement · Overheating · Thermal comfort · Energy load · Mitigation · WRF · UHI · Microclimate · Weather forecasting · Multi-scale simulation · Digital twin · CFD · Urban building energy model

1 Introduction It is unequivocal that the global climate has been consistently warming and projected to worsen in the future [1]. Furthermore, extreme climate events such as heatwaves are L. (Leon) Wang (B) · C. Shu Department of Building, Civil and Environmental Engineering, Centre for Zero Energy Building Studies, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, QC H3G 1M8, Canada e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_2

15

16

L. (Leon) Wang and C. Shu

projected to increase in frequency and intensity [2]. Overheating of building interior spaces as may arise from such climate change and extreme heat events (EHEs) have been identified as a major concern to the comfort and health of building occupants, particularly of the vulnerable people, such as the homeless, elderly, children, socially disadvantaged people, the physically challenged or the sick. Urban area centers that are subject to the urban heat island (UHI) effects may exacerbate the risk of the overheating events in that the indoor thermal conditions can reach excessive values over a prolonged period. In a recent heatwave of June 30–July 7, 2018, up to 66 deaths were reported in Montreal with most of them being older residents, such as those people who suffered from mental or chronic illness and addiction more easily than the others, as they were left without access to air conditioning in vulnerable communities of the city center [3]. Buildings play a significant role in limiting the risk of overheating events [4]. Buildings influence the indoor thermal conditions to which occupants are exposed most of the time, given the fact that people spend approximately 80–90% of their time indoors [5]. Buildings that house vulnerable people and/or with poor management of indoor thermal conditions will suffer the most from the effects of overheating. It was found that most of the 66 heat-related deaths during the 2018 extreme heat event in Montreal happens in the community, and still, around 11 happened in hospitals [6, 7]. The resilience of hospitals against EHEs may help to reduce the mortality and morbidity of vulnerable groups of people, e.g., the elderly, sick, and those having mental illnesses [8]. The high indoor temperature in schools may also violate the academic performance of the children students aged between 8–14 [9]. The risk of overheating in mild climate area has been quantified by simulation studies, and more field monitoring are needed to cope with the future overheating problem due to the increase of I.T. equipment usage in classrooms and global warming trend [10]. The severity of the indoor conditions depends on many factors of buildings: types (houses, retirement homes, apartment buildings, schools, hospitals, etc.), internal space usage (occupant density, internal heat gains), construction characteristics (insulation levels, window proportions, solar shading, the orientation of facades), and building operation (air-conditioning use, natural ventilation, etc.) [11]. However, studies on building indoor thermal conditions as relating to the outdoor conditions are still minimal to enable the healthcare and building code organizations to establish threshold exposure limit of temperature and relative humidity to protect the health of the vulnerable population, which could be attributed to the following limitations and challenges: (1) There is a significant lack of field monitoring data of outdoor and indoor thermal environments for different building types. As a result, no reliable benchmarking data are available to support the assessment of the resilience level of the existing building stocks against overheating and the establishment of threshold overheating exposure limit criteria. (2) There are limited simulation studies for establishing correlations between indoor and outdoor conditions, and the development of climate-adaptive mitigation strategies for developing associated guidelines against overheating. Accurate whole building performance simulations require adequate validations against field monitoring data. (3) The whole building simulations also need accurate and detailed inputs of surrounding ambient conditions, which

Assessment of the Effects of Urban Heat Island on Buildings

17

were often based on global/regional-scale weather and climate change data in the previous studies without considering the impacts from local microclimate environment down to building scales [12]. A scientific challenge remains to derive reliable climate change information at a spatial resolution that is relevant for building-scale impact assessments (e.g., 100 km) and downscaled to regional level also taking into account the uncertainty in projections as contributed due to the existence of multiple Global Climate Models (GCMs) and greenhouse gas emission scenarios. This chapter introduces a showcase study in Montreal, Canada, to assess the overheating risks in buildings as a result of urban heat island effects. In this study, (1) a series of multi-year field measurements on multiple buildings are conducted to determine the indoor condition exposure levels as related to the outdoor conditions to help set up temperature and humidity threshold limits for vulnerable occupant health; (2) A series of simulations, calibrations, and validations based on the field measurement and urban-scale microclimate data are conducted. (3) A novel integrated regionalurban-building-scale simulation platform is developed to study the impact of current weather and future climate change on building indoor environments as a result of the urban heat island. Note that for generality, this chapter focuses on the introduction of approaches and methodologies instead of specific data obtained from this study.

2 Field Measurements of UHI Effects on Buildings 2.1 Building Selection and Site Visits In this study, field monitoring is carried out for a limited number of school and hospital buildings for three years. Therefore, determining the best combination of buildings as regards to the most vulnerable to EHEs can be a significant challenge to ensure capturing both the EHE and indoor overheating problems during the longterm monitoring program. A five-step guideline for the screening and selection of buildings for field monitoring is given in Fig. 1. In the first step, a vast building database for all the hospital buildings and school buildings in Montreal were obtained from the official institutes. A total of 200 hospitals and 396 school buildings across the Montreal island were provided at first with their locations (Fig. 2). In the second step, a pre-screening of the building database was conducted to further reduce the scope for building selection. An investigation on previous heatrelated deaths during EHEs showed that the location and distribution of emergency calls and the heat-related deaths [7, 13] attributed to EHEs; these are highly related to the urban heat island intensity as given in Fig. 2. The large dataset of buildings were first filtered by the types of buildings. For hospital buildings, only three types of hospitals with long-term residents are considered, i.e., residential and long-term care centres (CHSLD), hospital center (C.H.), and rehabilitation center (C.R.). For

18

L. (Leon) Wang and C. Shu

Fig. 1 Procedure for screening and selection of buildings for field monitoring

1. Building Database 2. Building Pre-screen 3. Detailed Survey 4. On-site Visiting

5. Select for Monitoring

Building Database 200 hospitals

Building Pre-screen 61 candidates

Detailed Survey & Site Visiting 12 candidates

a) Hospital

Different types of hospitals CHSLD : A-E CH : A-F CR :A A C F

396 schools

b) School

Only public schools are considered, private ones are not included.

62 candidates

A A

B

C

B D E D E

15 candidates From different school boards SB1: A-H SB2: A-F SB3: A D H A

A B C G

D E A F Heat-related death in 2018. Building sites.

F

E

B C

Select Buildings s and Rooms for Field Monitoring

Fig. 2 Distribution of the school and hospital buildings in Montreal island for survey and site visiting screening

Assessment of the Effects of Urban Heat Island on Buildings

19

school buildings, only preschools and primary schools with students aged between 8 and 14 are considered. An investigation on previous heat-related deaths during EHEs showed the location and distribution of emergency calls and the heat-related deaths attributed to EHEs; these are also highly related to the urban heat island intensity [7, 13]. Most of the health events (i.e., heat-related deaths and emergency calls) happened in areas with intensive heat island problems, indicating dwellings in these areas may have had a higher exposure during the EHE, and the buildings in these areas may be more vulnerable to overheating issues. Therefore, the location of the buildings and their surrounding environment become an essential criterion for the pre-screening of the buildings. The surrounding conditions of the buildings can be first studied from Google Earth (G.E.) and street views. A graphic set of buildings were created from the southern view from Google street views to study the orientation of the buildings and to figure out if there are imperious or natural green open spaces, parks, tall plants and high buildings adjacent to the buildings. The surrounding information can also be confirmed later during the site visiting. After the building pre-screen process, 61 hospitals and 62 schools are targeted plotted in Fig. 2 to show their locations to compare with the heat-related death locations in 2018. A graphic set of each of the buildings was created from the southern view on Google street maps, and the buildings were filtered using the following criteria: 1. Schools mainly with children aged 8–14; 2. Hospitals with long-term residents; 3. The building location is close to those sites where deaths had been previously noted; 4. Buildings with the longer façade facing the north–south direction 5. Buildings that were not close to green areas or parks 6. Buildings located in a high-density neighborhood and close to major streets or parking lots having large areas of impervious land cover without any shading. For the reduced set of candidate buildings, a building information survey campaign was prepared for gathering detailed information in the third step. A building information survey form was distributed to the buildings to obtain information on construction details, building equipment, and related information. The survey sheet also contained information in which the study objectives were provided and that to explain the possibility for building managers to support the study. The building information survey form is organized into five sections: 1. General information of buildings: building name construction year, number of floors, number of occupants, etc. 2. Building performance and occupant behavior: thermal comfort and historical heat-related health events, building activities, overheating complaints, and relevant measures to mitigate impacts of overheating. 3. HVAC system: type of system, fresh air system, cooling system, ventilation, etc. 4. Building envelope: type of envelope construction, materials, window type, window-wall-ratio, etc. 5. Building plans.

20

L. (Leon) Wang and C. Shu

The fourth step is to conduct on-site visits and gather first-hand building information. For hospital buildings, three types of hospital buildings with long-term residents are considered including CHSLD, CH and C.R. For school buildings, a total of 15 school buildings from three school boards are visited. The selection of schools is only limited to preschool or primary schools with the ages of the students between 8 and 14. A total of 14 residential buildings are provided for the site visiting. The visits were conducted in July 2019 for hospital sites, September 2019 for schools, and February 2020 for residential buildings. Due to the breakout of COVID-19, we have only completed site visits to six residential buildings. Most of the visited residential buildings are in the north and east of Montreal Island. At last, in the fifth step, decisions can be made after a comprehensive analysis of all the information from the previous steps to evaluate the visited buildings. The overall distribution of selected buildings, the real conditions of the building, and the willingness of collaborations of the building owners should be considered comprehensively.

2.2 Summary of Building Information and Selection Results As is mentioned in the previous section, the building information survey consists of five parts covering comprehensive aspects of the building. But it was found that it is hard to know the real performance of the buildings and hard to conclude the occupant behavior and the HVAC system based on the concise answers to the survey sheet. Although the building information survey is conducted before the site visiting, it seems much efficient to analyze and extract useful information from the survey forms after the site visitings. We therefore first classified the buildings into two groups of categories according to the site investigations: (i) buildings with overheating complaints and (ii) without complaints, as shown in Table 1. Then the potential factors considered in the survey forms are analyzed to find out the most valuable cases to study the overheating problems in the summer. After the survey and site visiting, it was found that a cooling system is seldom used in schools. Among the 15 buildings visited, only SB1-H has a cooling system in a new building section. Most of the school buildings have fresh air supply to the corridor, gym, and basement. The buildings are usually cooled through passive Table 1 Overheating complaints in the visited buildings

Bldg. types

With complaints

Few complaints

Hospitals

CHSLD-A, B CH-A, B, D, F

CHSLD-C, D, E CH-B, C, E CR-A

Schools

SB1-A, B, D, G SB2-A, B, D, E

SB1-C, E, F, H SB2-C, F SB3-A

Assessment of the Effects of Urban Heat Island on Buildings

21

approaches like cross-ventilation (SB1-F, SB2-B, C) and night ventilation (SB3-A). The thermal comfort of the building is highly related to the surrounding environment. The orientation of the rooms and the distribution of adjacent plants are the most important factors affecting the indoor conditions. The overheating complaints that happened in school buildings are majorly on the top floor of the buildings. The vulnerable rooms are majorly facing the south (SB1-B, SB2-A), southwest (SB1-D), southeast (SB2-B, D, E), and west (SB1-A, G). Most of the overheating complaints happen in buildings with large window-wall-ratios, e.g., 40–50% (SB1-A, D, G; SB2-A, D, E) and >50% (SB1-B). SB2-D and E seem to have the most severe overheating complaints. They are therefore selected, the teachers and students use words like “melting” to describe their feelings in the southeast facing classrooms on the morning of July and September. These two buildings are next to each other, with large impervious playgrounds on the southeast side of the building but tall trees on the west or south side of the building providing external shadings to the building envelope. Another two schools (SB1-A, D) from SB1 have a similar problem and are also selected. SB3-A is chosen because it is a typical building in SB3 with the Building Automation System (BAS) installed to monitor the air temperature in the fresh air system. SB2-D is selected because this building is built in three distinctive construction year periods. For the new section built in 2014, they have temperature sensors in each room and well managed fresh air system, while for the old section, there are no plants close to the building, and there are major complaints in the south and west-facing rooms due to its large windows. On the other hand, for the hospital buildings, the HVAC system is much more complicated, and most of them have cooled down the corridor, and the rooms are cooled down by opening the doors. Window air conditioning units are often used for individual offices or patient rooms. Therefore, unlike the schools, most of the thermal comforts in hospital buildings are highly related to the design and operation of the HVAC system. Some of the hospitals (CHSLD-A, B; CH-A, D) reported there is insufficient fresh air supply in the corridor. The selection of hospital building does not restrict to the building with severe overheating problems: CHSLD-A, B, and CH-D are selected because they have the most severe overheating complaints. The thermal feeling in CH-D is quite different in different areas of the building, and the occupants in CHSLD-A are complaining it is “hot and stuffy” in both public areas and patient rooms. CHSLD-B has a fresh air supply only for the halls but not in the corridors. CH-C is a historical building with few central systems for cooling, but there is surprisingly good thermal comfort. This might be related to its spacious corridors, efficient natural ventilation, and the terracotta claddings. It is, therefore, be selected for further study. CHSLD-C is selected as a positive case study because it has a well-managed air conditioning system and ideal indoor thermal feeling. At last, to cover more overheating vulnerable groups of patients, CR-A is selected since it is for kids with mental illness. For the residential buildings, most of the visited buildings have overheating complaints; we, therefore, selected the buildings according to the locations of the buildings.

22 Fig. 3 Distribution of selected buildings for field monitoring

L. (Leon) Wang and C. Shu

Selected building distributions 6 schools 6 hospitals CH

CHSLD

CR

3 residential

15 buildings in total 11 weather stations

Share one weather station

The distribution of the three types of buildings selected for field monitoring is plotted in Fig. 3. A total of 15 buildings are selected, including six schools, six hospitals, and three residential buildings. The buildings are distributed over the Montreal Island so that the urban-scale thermal condition patterns can be captured through the field monitoring. For those buildings close to each other, only one weather station needs to be installed. Therefore only 11 weather stations are required for the 15 buildings.

2.3 Field Monitoring Devices On-site weather stations (Fig. 4) will be placed on the roofs of the selected buildings to collect the local weather parameters, including air temperature, relative humidity, solar radiation, wind speed and direction, gust speed, and precipitation. These weather stations will formulate an urban weather observation network to capture the urban scale climate patterns, e.g., urban heat island. The indoor air temperature and relative humidity in these buildings will also be monitored to analyze the indoor thermal comfort and capture the interaction of building indoor overheating with the urban heat patterns. The data may reveal the indoor and outdoor interactions for the development of mitigation strategies later. It can also be used for the validation of mesoscale climate models and building thermal and energy simulation results. For the outdoor weather condition monitoring, the HOBO Onset RX3004 data logger with LCD and GSM/HSPA cellular communications was applied so that the data can be transferred automatically through an online cloud platform. The selected weather station is composed of temperature and humidity sensor (S-THB-M002),

Assessment of the Effects of Urban Heat Island on Buildings

23

Fig. 4 Outdoor weather stations for monitoring UHIs

pyrometer (S-LIB-M003), wind speed, and wind direction sensors (RM Young Wind Monitor Sensor), rain gauge sensor (S-RGB-M002). Two models of the indoor sensors from HOBO onset are used for this study: MX1101 (without CO2 concentration sensor) and MX1102 (with CO2 concentration sensor). These data loggers are small-powered, self-contained sensors with LCD screen and onboard memory. The configuration and data downloading can be conducted through Bluetooth, and the wireless communication range is 100 m (Fig. 5).

3 Regional Scale UHI Simulations 3.1 WRF Simulation Methodology The non-hydrostatic (V4.0) version of the Weather Research and Forecasting (WRF) model coupled to the Noah land surface model (LSM, [14–16]) is used for the modeling of urban meteorology surrounding the Montreal city area from June 01 to August 31, 2018. Three high spatial resolution WRF model experiments were

24

L. (Leon) Wang and C. Shu

Fig. 5 Indoor sensors for monitoring indoor overheating conditions during UHIs

conducted, each one covering the same three-month summertime period from June 01 to August 31, 2018, to evaluate the WRF model’s ability to reproduce the diurnal cycle of near-surface meteorology and accumulated precipitation under present-day weather conditions. All WRF-experiments share the same numerical domain that is composed of three two-way nested domains with 276 × 296, 250 × 283, and 391 × 364 grid points, distanced 9, 3, and 1 km, respectively. The innermost domain includes the metropolitan area of Montreal. The vertical dimension is split into 40 eta levels, with 14 within the lowest 1.5 km to characterize planetary boundary layer processes better. The planetary boundary layer is parameterized with the two-order closure MellorYamada-Janjic [17] turbulent parameterization. Radiative processes are parameterized with the proposed scheme for the shortwave radiation [18] and with the Rapid Radiative Transfer Model [19] for the longwave radiation. The bulk urban parameterization [20] included in the Noah LSM is used as the first WRF-experiment (hereafter denoted as Noah-BULK WRF-experiment) to represent zero-order effects of urban surfaces. This urban physics-option assumes common values for the entire urban domain and presupposes an urban fraction of one in each urban grid cell. Despite its simplicity, the bulk urban parameterization has been successfully employed in real-time weather forecasts (e.g., [20]). The second WRFexperiment is performed with the multilayer UCM building effect parameterization (BEP; developed by [21]) that is coupled to the Noah LSM to characterize the impacts of urban surfaces (henceforth denoted as Noah-bep WRF-experiment). Unlike the bulk urban parameterization, the multilayer UCM BEP represents the urban geometry utilizing infinitely long street canyons and recognizes three different urban surfaces in the urban canopy layer, namely, roofs, roads, and vertical walls. Urban surfaces interact directly with WRF through the whole urban canopy layer, and buildings (vertically distributed) are considered sources and sinks of heat and momentum

Assessment of the Effects of Urban Heat Island on Buildings

25

from ground surface up to the highest building present in the urban domain. Finally, the third WRF-simulation (henceforth denoted as MOD WRF-experiment) is also performed with the multilayer UCM BEP but this time the urban domain for the city area is updated with the 2010 land cover dataset. Moderate Resolution Imaging Spectroradiometer (MODIS) land cover classification is used to characterize the nonurban land use categories in all WRF-experiments and the urban domain for Noah-BULK and Noah-BEP WRF-experiments.

3.2 Regional UHI Impacts on Buildings The spatial distribution of the 2 m air temperature over the Montreal island on July 03 from 09:00 to 17:00 is plotted in Fig. 6. The distributions show that the temperature of the east and north island is much higher than the west island. To compare the thermal condition at different locations for the hospitals and schools, the weather condition is extracted from the locations of the buildings, and the humidex is calculated and plotted in Fig. 7. To show the difference of different locations, the number of hours above a threshold of the humidex is calculated. Humidex higher than 45 °C is considered as dangerous, which is used as the threshold to evaluate the overheating hours as a result of UHI. The simulation is performed to reproduce the three months of the indoor condition in the buildings visited. The standard equivalent temperature (SET) developed from

Fig. 6 1-km resolution simulation results and distribution of schools and hospitals in Montreal

26

L. (Leon) Wang and C. Shu

Fig. 7 Outdoor thermal condition of the school and hospital buildings

the two-node bioheat model [22] is used to evaluate the indoor thermal comfort. It indicates as “hot” when SET is above 34.5 °C. Therefore it is used as the threshold for assessing the overheating (“hot”) hours (Fig. 8). The BS EN 15251 standard [23] established adaptive temperature criteria for the building indoor overheating. The outdoor running mean temperature is calculated according to [24]. As shown in Fig. 9, in general, the hospitals may have more overheating hours but still within the limit of the 438 h. The schools at different

Fig. 8 Indoor thermal risk evaluated by SET for the school and hospital buildings selected

Assessment of the Effects of Urban Heat Island on Buildings

27

Fig. 9 Indoor thermal evaluated by the BS EN 15251 standard [23] adaptive temperature criteria for the school and hospital buildings selected

locations may have very different overheating hours. The three school buildings SB2C, SB1-E, and SB2-F, have much more overheating hours than the other schools. The two schools SB2-C, SB1-E, may exceed the limit of 438 h/year for the criteria Category-1, which is for vulnerable people.

4 Urban Scale and Microclimate UHI Simulations 4.1 Urban Scale Simulation Methodology To study the UHI effects on buildings to the sub-km urban scale (in meters), we developed an urban-microclimate-building simulation model. Figure 10 shows the workflow of the model consisting of four different layers: input data, simulation engine, dynamic integration of building and microclimate models, and result. Three sets of input data are required for the simulation: 3D model of the city, Building’s

28

L. (Leon) Wang and C. Shu Input data

Simulation Engine

Coupling strategy

CityFFD:

Output

Local microclimate Summer hotspots Pedestrian wind comfort Outdoor thermal stress, etc.

CityBEM: Building energy analysis Cooling/heating load Indoor thermal stress Wall temperature, etc.

Fig. 10 The workflow of dynamic urban building and microclimate simulation

properties, and weather data. The 3D model of the city is generated by the integration of OpenStreetMap (OSM), and G.E. Building’s footprint data is provided by the OSM website and/or Microsoft buildings’ footprint data. The OSM file is then enriched by the building’s height information obtained from GE API. Building’s properties, including age and usage data, are provided by official datasets and joined with the OSM file using the QGIS tool. Then, the building’s age/usage data are used for creating the building archetype library for the estimation of non-geometrical properties required for building thermal and energy simulations. Weather data, including air temperature, solar radiation, wind speed, and wind direction, is provided by a nearby weather station. The second layer of the model is the simulation engine. Two in-house models, CityFFD [25–28] and CityBEM [29], are used for dynamic urban building and microclimate simulation. The third layer of the model is the dynamic

Assessment of the Effects of Urban Heat Island on Buildings

29

integration of CityFFD and CityBEM. The ping-pong coupling strategy [30] is used for the integration of the two models. The last layer is the output data and simulation result. CityFFD provides urban microclimate data, including local air temperature and wind patterns. Finding urban hotspots during UHI days, outdoor thermal comfort and pedestrian wind comfort are some of the valuable information presented by the CityFFD model. CityBEM result includes the building’s energy analysis, transient cooling/heating load estimation, indoor air temperature, indoor thermal comfort, wall temperature. The present model is used for the dynamic simulation of an urban area in the downtown of Montreal (Fig. 11). The size of the urban area is 550 m × 600 m and includes 255 buildings. The CityFFD and CityBEM simulation is conducted for 15 days of summer 2019 (06/24/2019–07/08/2019). The 3D city model is generated using the integrated OSM/GE model. Figure 11b shows the modified OSM model, and the real geometry provided by G.E. The initial height of the buildings provided by the OSM is inaccurate, which is improved by the presented method. Building’s Fig. 11 a Actual buildings on G.E., b modified OSM model, c construction year of the modeled buildings

(a)

(b)

(c)

30

L. (Leon) Wang and C. Shu

Fig. 12 Input weather data for CityFFD and CityBEM simulations: a air temperature, and solar radiation, b wind speed and direction

usage and age data are obtained from PAU shapefile and joined with the OSM file using the QGIS tool. Figure 11c shows the color map of the year of construction of buildings after joining the data with the OSM file. Buildings are constructed from 1870 to 2015. The total time spent on preparing the input data is about 15 min for creating the 3D model and 15 min to join the PAU data with the OSM file. Weather data are the last input data required to start the simulation. Measured weather data by the closest weather station is used in this work. McTavish weather station is a local weather station in the region of study, which is shown by red color in Fig. 11a. The data are available on Environment Canada’s weather website. The input weather data for the period of study (06/24/2019–07/08/2019) are shown in Fig. 12. The temperature varied between 15 and 32 °C during this period. Most of the days are sunny, with high daily temperatures. Therefore a significant part of buildings’ energy consumption is expected for cooling down the buildings. After preparing all input data, the fully integrated CityFFD/CityBEM tool is used for the urban microclimate and building thermal/energy simulations to the study the UHI impacts on building thermal conditions and associated energy consumptions. The computational domain and grid of the CityFFD model are shown in Fig. 13. If H is the height of the tallest building, according to the AIJ guidelines [31], the size of the computational domain in horizontal and vertical directions is 10H and 5H, respectively. In this case, the height of the tallest building in the area is 112.5 m. Therefore, the size of the domain is 2250 m ×2250 m ×562.5 m. The total number of grids is 4.2 million, and the grid resolution near the buildings is around 4 m. A finer mesh is used near the buildings to capture physical phenomena in the urban area. Vertical boundary conditions vary based on the weather data, and at each time step, two domain boundaries are considered as inflows, and the others are as outflows. The boundary condition of the top of the domain is symmetry. Wall boundary condition is applied to the buildings’ surfaces and also the floor of the computational domain. The time step of transferring data between CityFFD and CityBEM models is 1 h. At each time step, CityFFD simulates the steady-state urban microclimate with the time step of 2 s. CityFFD simulations are completely independent of each other, and at each time step, a new simulation starts with new B.C.s from the hourly weather data

Assessment of the Effects of Urban Heat Island on Buildings

31

Fig. 13 Left: Computational domain and grid of the CityFFD/CityBEM model, Right: 3D model of the buildings and triangles on each façade

available, and the B.C.s provided from CityBEM. CityBEM simulates 1-h transient urban building energy performance with the internal time step of 5 min. The initial wall temperature for CityBEM transient simulation is obtained from the previous time step. In CityBEM, each building is modeled as a single block, as shown in Fig. 10. To study the impacts of UHI on building energy consumptions, all buildings are assumed to be with a set-point temperature of 24 °C. Each façade of the building is divided into multiple triangles (Fig. 13). CityFFD calculated an average air temperature and wind components near each triangle, which are transferred to the CityBEM model. CityBEM also calculates the surface temperature of each triangle and is used by CityFFD as the wall B.C.s. The computational time of CityFFD steady-state simulation is 20 min (4.8 min per 1 million grid) on a P.C. with 16 GB RAM and the Intel(R) Core(T.M.) i7-6700 [email protected] GHz and the NVIDIA GeForce GTX 970 graphic card. The computational time of 1-h transient simulation of CityBEM is 2 min on the same P.C. The total time to prepare the input data and run the integrated model for 15 days with a time step of 1-h is 132.5 h (5 days and 12 h).

4.2 Urban UHI Microclimate Impacts on Buildings In the urban microclimate scale, UHIs have major impacts on local microclimate thermal conditions near buildings and building surfaces, and more importantly, building energy consumptions for cooling down buildings. Building’s cooling load consists of four components: direct solar radiation, transmission load, ventilation/infiltration load, and internal load. Building envelope thermal properties, local weather, solar radiation, and occupancy schedules are the major parameters that affect a building’s cooling load. In this section, the transient cooling load of buildings is studied and compared between buildings with different usage and ages. Figure 14 shows the contour map of the cooling load of buildings at four different hours of 07/04/2019. The maximum outdoor air temperature during the studied period occurs

32

L. (Leon) Wang and C. Shu

Fig. 14 Contour map of the cooling load of buildings at 07/04/2019: a midnight, b 6 am, c noon, d 6 pm

on 07/04/2019—6 pm (Table 2), and the daily change of buildings cooling load is studied on this day. Table 2 shows the corresponding weather was hot and sunny, with a diurnal temperature range of 10 °C and a light breeze. As shown in Fig. 14, during the night time, the cooling load of all buildings is lower than 15 W/m2 . The outdoor air temperature is relatively low during the night and is close to the indoor set-point temperature. Therefore, the transmission and infiltration loads are small at night. Zero solar heat gain is another reason for Table 2 Weather data at different times of the day 07/04/2019 for the input of urban scale simulation Time

Ta

Solar radiation

Wind speed

Wind direction

07/04/2019 12 am

26.43

0

3.56

307.59

07/04/2019 6 am

22.24

66.4

2.39

283

07/04/2019 12 pm

29.49

917.5

2.62

232.5

07/04/2019 6 pm

32.2

345.3

1.96

186.4

Assessment of the Effects of Urban Heat Island on Buildings

33

lower cooling load at the night time compared to the day time. The new commercial buildings that are not occupied during the night and are better insulated represent a lower cooling load (10 W/m2 ) because they are occupied during the night time and are not well-insulated. During the day, the cooling load of buildings gradually rises because of the increasing solar radiation and increased outdoor air temperature. The maximum cooling load (34 W/m2 ) occurs at 6 pm for a commercial building constructed in 1880. During the working hours, old commercial buildings show a higher cooling load because of the low insulation of these buildings and higher IHG. To study the two-way interaction of buildings and microclimate, the buildings’ surface temperature, local air temperature, and local wind around buildings at different times of the day (07/04/2019) are presented in Fig. 15. The spatial variations of buildings’ surface temperatures are higher than 10 °C at all times under the study. The building’s envelope properties (U-value and solar absorptance), and local weather conditions (air temperature, and wind speed) are the main factors that affect the buildings’ surface temperature. For the newer buildings with lower U-value, the rate of heat transfer is lower, and the exterior surface of the wall is less affected by the indoor condition. Therefore, the surface temperature of newly constructed buildings is higher than old buildings. Three buildings with different years of construction are marked in Fig. 15. The red, orange, and blue buildings which are constructed in 2014, 2009, and 1884, respectively. The surface temperature of the buildings is 43 °C, 40 °C, and 31 °C which shows the 12 °C temperature difference between two new and old buildings. The surface temperature of the buildings directly affects the local air temperature around the buildings through the heat flux from the building surface to the ambient air. By observing the distribution of air temperature at different times of the day, we find that the local air temperature is higher around buildings with higher surface temperatures. The spatial variation of the outdoor air temperature is more than 15 °C. The higher local air temperature and building surface temperature directly affect the cooling load of the building through the longwave radiation and convective heat transfer. On the other hand, higher building’ surface temperature and local air temperature also affect local pedestrian thermal comfort directly. Wind speed is another microclimate parameter that affects building surface temperature and cooling load of the building through the local convective heat transfer. Wind speed and direction around the buildings depend on the free stream wind and building topologies and configurations. The right column of Fig. 15 shows the distribution of the wind at different times of the day. Lower wind speed leads to lower convective and higher diffusion effects. So, in the area with lower wind speed, the building’s surface temperature has a higher impact on the local air temperature. Figure 16a shows the contour map of the local convective heat transfer coefficient (h conv ) at the midnight of 07/04/2019. The range of the variation of h conv is about 3−20 W/m2 K. It indicates the importance of the inclusion of the microclimate for urban building thermal/energy analysis: under the similar temperature difference between the building surface and the outdoor air, the maximum convective heat transfer can be six times higher than the minimum. Figure 16b shows the convective heat transfer coefficient variation at four different locations for the selected region

34

L. (Leon) Wang and C. Shu

Fig. 15 Left: buildings’ surface temperature. Middle: air temperature. Right: wind speed at different times of the day 07/04/2019: a midnight, b 6 am, c noon, d 6 pm

Assessment of the Effects of Urban Heat Island on Buildings

35

Fig. 16 a Contour map of local convective heat transfer coefficient (hconv ) at the midnight of 07/04/2019; b variation of hconv with time at four locations of the selected area (07/04/2019)

on the hottest day (07/04/2019). Points 1 and 2 are close to two different facades of the same building located at the corner of the urban area. As a result of its location, higher wind speed is observed near the building corners. At midnight, the h conv of point 2 is four times greater than point 1. Points 3 and 4 are close to a building in the middle of the selected region, whereas the latter is often under higher wind speed than the former. So, it is shown that local wind speed and h conv around this facade of point 4 is always higher than the façade of point 3. These results show that the local wind speed and h conv can be significantly different on different sides of a building. Furthermore, this leads to significantly different local air and building surface temperatures, and thus building cooling loads. In conclusion, buildings and their surrounding microclimates interact with each other closely through local wind speed, direction, local heat transfer conditions, air, and building surface temperatures and eventually building thermal and energy loads. It is essential to include urban microclimate analysis for the assessment of urban heat island impacts on buildings.

5 A Web-Based Multi-scale Simulation Portal All the analysis in the previous sections are integrated into a web-based multiscale simulation portal, which includes real-time field measurements from weather stations, regional-urban-building scale simulation platform, and visualization platform. The web portal is developed based on the Tridium’s Niagara Framework, an operating system of the Internet of Things (IoT), and an open environment connecting various devices and systems. It enables us to connect and control devices while normalizing, visualizing, and analyzing data from nearly anywhere or anything. This section briefly introduces the general functionalities and features of the web portal.

36

L. (Leon) Wang and C. Shu

Figure 17 shows that the city and building topologies and information are shown on the website. The urban model is based on Mapbox with accurate 3D building data from Google Earth and OpenStreetMap. Real-time field monitoring data from the weather stations installed can also be collected and visualized, as shown in the right figure of Fig. 17. While weather stations provide discrete real-time weather data for measuring UHI effects in the city, regional forecasted 48-h weather data are retrieved and visualized on the same web portal from the Canadian Meteorological Centre (CMC) (Fig. 18). These forecasted weather are updated automatically and available in real-time on the website to show more specific weather conditions of the city than those available from regular weather services. The forecasted CMC data are also used as the input boundary conditions for the urban microclimate analysis through CityFFD and CityBEM.

Fig. 17 City and building information and field measurements from weather stations

Fig. 18 Forecasted regional map of summertime heat and UHI effects in Montreal

Assessment of the Effects of Urban Heat Island on Buildings

37

The website portal also supports the Regional-Urban-Microclimate simulations by CityFFD and CityBEM. The urban and building information, such as building geometry, vegetation, and ground topology information (Fig. 19), are integrated into the website so that the domain, terrain, and 3D buildings for the simulations can be extracted directly from the website. The input files of the simulations can be downloaded directly through a simple drag and select operation from the website, as shown in Fig. 20. Once the multi-scale simulations are completed, the obtained microclimate analysis results through CityFFD can be uploaded to the website and visualized directly among the 3D buildings and digital cities on the web portal. Figure 21 shows an example of the calculated 3D wind distribution around an urban region through velocity contours and particle tracing. All the results can be interactively visualized at different 2D planes and 3D volumetrical results. The UHI impacts on building thermal and energy performances can be obtained through CityBEM simulations, and the results can also be interactively visualized through the website portal. Figure 22 shows an example of the simulation results uploaded from CityBEM, which are visualized interactively together with the 3D building models for the study of the UHI impacts on buildings and their surrounding environments.

Fig. 19 Urban terrain, building, and their shading information

Fig. 20 Direct generation of multi-scale simulation input files

38

L. (Leon) Wang and C. Shu

Fig. 21 Interactive visualization of urban microclimate simulation from the CityFFD engine

Fig. 22 Interactive visualization of urban thermal and energy simulation results from the CityBEM simulation engine

6 Conclusions In this chapter, we introduce the study of the assessment of urban heat island impacts on buildings through a series of field measurements, multi-scale simulations, and analysis. In the field measurement campaign, the first round site visiting of the three types of buildings, i.e., hospitals, schools, and residential social housing, has been completed, and the building information has been collected through the building survey and site visits. The vulnerable and typical buildings are selected through a comprehensive consideration of both the vulnerability to overheating and the spatial distribution of the buildings. Fifteen buildings have been selected for the further investigation of field monitoring and simulation studies. The installation of the field

Assessment of the Effects of Urban Heat Island on Buildings

39

monitoring equipment is still in progress. The installation at one hospital building and two school buildings have been completed. The next step for the field monitoring task is to complete the installations of the weather stations and indoor sensors at the rest of the selected buildings. For the simulation task, both the regional and urban scale thermal environment, energy modeling, and the whole building energy and dynamic thermal simulation have progressed well. The urban scale weather condition is simulated through the mesoscale regional climate model WRF, and three models have been validated and compared with each other. At last, the multilayer urban parameterization model with modified urban land cover performs the best, and the model will be used for the long-term prediction of the weather condition in the selected urban area. For the development of the fast urban scale or microclimate simulation tool CityFFD and the urban building thermal/energy model CityBEM, the models have been applied and validated with more practical cases. The two models can be integrated to consider the microclimate impact on buildings to deliver more detailed building environment and accurate predictions on building performances. A webbased multi-scale simulation portal is developed as an integrated and interactive platform for all the previously-stated efforts. The platform provides essential functions of the 3D digital city simulation model generation, the visualizations of realtime field measurements, forecasted regional weather data, and urban microclimate and building thermal and energy performances under the impacts of the urban heat island.

References 1. IPCC, Climate Change (2014) Synthesis report. Contribution of Working Groups I-III to the fifth assessment report of the IPCC. https://doi.org/10.1016/S0022-0248(00)00575-3 2. IPCC, Summary for policymakers. In: Climate change 2014: impacts, adaptation and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to fifth assessment report of the Intergovernmental Panel on Climate Change, p 33 (2013). https://doi.org/10.1017/ CBO9781107415324 3. Laframboise K (2018) Heat wave blamed for 53 deaths in Montreal. Global News. https://glo balnews.ca/news/4338998/heat-wave-blamed-53-deaths-montreal/ 4. Loughnan M, Carroll M, Tapper NJ (2015) The relationship between housing and heat wave resilience in older people. Int J Biometeorol 59:1291–1298. https://doi.org/10.1007/s00484014-0939-9 5. IOM climate change, the indoor environment, and health (2011). National Academies Press, D.C. https://doi.org/10.17226/13115 6. Direction régionale de santé publique de Montréal, Canicule: Juillet 2018 - Montréal, Bilan préliminaire, Montréal, Québec (2018) 7. Lamothe F, Roy M, Racine-Hamel S-É (2019) Enquête épidémiologique - Vague de chaleur à l’été 2018 à Montréal, Montréal, Québec. https://doi.org/9782550840206 8. Xu Z, Cheng J, Hu W, Tong S (2018) Heatwave and health events: a systematic evaluation of different temperature indicators, heatwave intensities and durations. Sci Total Environ 630:679– 689. https://doi.org/10.1016/j.scitotenv.2018.02.268

40

L. (Leon) Wang and C. Shu

9. Barrett P, Davies F, Zhang Y, Barrett L (2015) The impact of classroom design on pupils’ learning: final results of a holistic, multi-level analysis. Build Environ 89:118–133. https://doi. org/10.1016/j.buildenv.2015.02.013 10. Jenkins DP, Peacock AD, Banfill PFG (2009) Will future low-carbon schools in the UK have an overheating problem? Build Environ 44:490–501. https://doi.org/10.1016/j.buildenv.2008. 04.012 11. Quinn A, Tamerius JD, Perzanowski M, Jacobson JS, Goldstein I, Acosta L, Shaman J (2014) Predicting indoor heat exposure risk during extreme heat events. Sci Total Environ 490:686– 693. https://doi.org/10.1016/j.scitotenv.2014.05.039 12. Gracik S, Heidarinejad M, Liu J, Srebric J (2015) Effect of urban neighborhoods on the performance of building cooling systems. Build Environ 90:15–29. https://doi.org/10.1016/j.buildenv. 2015.02.037 13. The Institut national de santé publique du Québec, Îlots de chaleur/fraicheur urbains et température de surface, INSPQ (2018) 14. Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the PennState-NCAR MM5 modeling system. Part II: preliminary model validation. Mon Weather Rev 129:587–604. https://doi.org/10.1175/1520-0493(2001)1292.0.CO;2 15. Chen F, Dudhia J (2001) Coupling and advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569–585. https://doi.org/10.1175/1520-0493(2001)1292. 0.CO;2 16. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the national centers for environmental prediction operational mesoscale eta model. J Geophys Res D Atmos 108:1–16. https://doi. org/10.1029/2002jd003296 17. Janjic ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulent closure schemes. Mon Weather Rev 927–945 18. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107 19. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res D Atmos 102:16663–16682. https://doi.org/10.1029/97jd00237 20. Liu Y, Chen F, Warner T, Basara J (2006) Verification of a mesoscale data-assimilation and forecasting system for the Oklahoma city area during the joint urban 2003 field project. J Appl Meteorol Climatol 45:912–929 21. Martilli A, Clappier A, Rotach MW (2002) An urban surface exchange parameterisation for mesoscale models. Boundary-Layer Meteorol 104:261–304 22. Gagge AP, Fobelets AP, Berglund L (1986) A standard predictive index of human reponse to thermal enviroment. Am Soc Heating Refrig Air-Conditioning Eng 709–731 23. BS EN 15251 (2007) Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics 24. CIBSE (2013) The limits of thermal comfort: avoiding overheating in European buildings. https://doi.org/10.1017/CBO9781107415324.004 25. Mortezazadeh M, (Leon) Wang L (2017) SLAC—a semi-Lagrangian artificial compressibility solver for steady-state incompressible flows. Int J Numer Methods Heat Fluid Flow (Under Review) 26. Mortezazadeh M, (Leon) Wang L (2019) An adaptive time-stepping semi-Lagrangian method for incompressible flows. Numer Heat Transf Part B Fundam 75:1–18. https://doi.org/10.1080/ 10407790.2019.1591860 27. Mortezazadeh M, (Leon) Wang L (2017) A high-order backward forward sweep interpolating algorithm for semi-Lagrangian method. Int J Numer Methods Fluids 84:584–597. https://doi. org/10.1002/fld.4362

Assessment of the Effects of Urban Heat Island on Buildings

41

28. Mortezazadeh M, (Leon) Wang L (2020) Solving city and building microclimates by fast fluid dynamics with large timesteps and coarse meshes. Build Environ 179:106955. https://doi.org/ 10.1016/j.buildenv.2020.106955 29. Katal A, Mortezazadeh M, (Leon) Wang L (2019) Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations. Appl Energy 250:1402–1417. https://doi.org/10.1016/j.apenergy.2019.04.192 30. Hensen J (1995) Modelling coupled heat and airflow: ping-pong versus onions. In: Proceedings of 16th conference on implementing the results of ventilation research, pp 253–262 31. Tominaga Y, Mochida A, Yoshie R, Kataoka H, Nozu T, Yoshikawa M, Shirasawa T (2008) AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. J Wind Eng Ind Aerodyn 96:1749–1761

Urban Heat Island Monitoring with Global Navigation Satellite System (GNSS) Data Jorge Mendez-Astudillo, Lawrence Lau, Isaac Yu Fat Lun, Yu-Ting Tang, and Terry Moore

Abstract The Urban Heat Island (UHI) effect occurs when the temperature in an urban area is higher than the temperature in a rural area. UHIs are generally monitored using remote sensing techniques such as satellite imagery or using temperature sensors deployed in a metropolitan area. In this chapter, a newly proposed methodology to monitor the UHI intensity from Global Navigation Satellite Systems (GNSS) data is described. As the GNSS signal travels from the satellite to the receiver it propagates through the troposphere the travelling signal is delayed by the troposphere. The tropospheric delay is proportional to environmental variables. The tropospheric delay in zenith direction (ZTD) is estimated as part of the Precise Point Positioning (PPP) technique. Therefore, this chapter shows how to process GNSS data to obtain ZTD and how to obtain temperature at an urban and a rural site simultaneously from the ZTD. The advantages of using GNSS data is its availability and many GNSS networks have been deployed in different cities so no need to deploy sensor networks. Furthermore, GNSS signal is less affected by bad weather conditions than satellite imagery.

J. Mendez-Astudillo International Doctoral Innovation Center, University of Nottingham Ningbo China, Ningbo, China L. Lau (B) Department of Civil Engineering, University of Nottingham Ningbo China, Ningbo, China e-mail: [email protected]; [email protected] Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China I. Y. F. Lun Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Y.-T. Tang School of Geographical Sciences, University of Nottingham Ningbo China, Ningbo, China T. Moore The Nottingham Geospatial Institute, The University of Nottingham, Nottingham, UK © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_3

43

44

J. Mendez-Astudillo et al.

Keywords Urban heat island · GNSS · GNSS remote sensing · Zenith tropospheric delay

1 Introduction The Global Navigation Satellite System (GNSS) is formed by several constellations of satellites transmitting electromagnetic signals to receivers around the earth. The receivers then use this data to find location. The GNSS constellations are the American Global Positioning System (GPS), the Russian Global’naya Navigatsionnaya Sputnikovaya Sistema (GLONASS), the European Galileo and the Chinese BeiDou Navigation Satellite System (BDS) [1]. GPS is operating for civilian purposes since 1983. It has 24 operational satellites orbiting around the earth in six nearly circular orbits, inclined to 55°. The communication between the satellites and the receivers is done using the Code Division Multiple Access (CDMA) principle. GPS receivers are currently embedded in many hand-held devices such as smartphones, tablets and navigation systems in cars or ships [2]. GLONASS is operating for civilian purposes since 1995. It has 24 operating satellites which are placed in three circular orbital planes with and inclination of 64.8°. The communication between the satellites and the receivers uses the Frequency Division Multiple Access (FDMA) channel access method. Many hand-held devices such as smartphones have GLONASS receivers embedded [1]. Galileo went live in 2016, it has been created by the European Union. In the year 2018 there 22 satellites in orbit in three orbital planes, inclined by 56°. The communication between satellites and receivers is achieved with a Binary Offset Carrier of rate (1,1) (BOC (1,1)) modulation. Hand-held devices such as smartphones and navigation systems in Europe have embedded Galileo receivers [3]. BDS started operating in the Asia–Pacific region in 2012. As of 2018 there were 23 satellite in orbit, the full constellation of 35 satellites is expected to be fully operative by 2020. BDS satellites are in Geosynchronous Earth Orbit (GEO), Medium Earth Orbit (MEO) and Inclined Geosynchronous Satellites Orbit (IGSO) with an inclination of 55°. It implements the Quadrature Phase-Shift Keying (QPSK) modulation technique [4]. The radio signal sent from the satellite to the receiver in all the systems previously described is affected as it propagates through the atmosphere. The Ionosphere and the Troposphere cause delays to the signals. The effect of the troposphere needs to be compensated to increase positioning accuracy. However, it can be used to monitor environmental variables in the troposphere because the tropospheric delay is proportional to the refractivity of the troposphere. In this chapter a newly proposed algorithm to estimate temperature from GNSS data and monitor the Urban Heat Island (UHI) effect is proposed. First, the environmental variables defining the troposphere are defined, then, the effect of the troposphere to GNSS signals is described. Finally, the algorithm to monitor the UHI from GNSS data is described in this chapter.

Urban Heat Island Monitoring with Global Navigation …

45

2 The Troposphere The atmosphere is divided into several layers, including the ionosphere and the troposphere. The ionosphere is the ionized part of earth’s upper atmosphere and is found from 60–1000 km above earth’s surface. The troposphere is the layer closest to the earth’s surface and is where all weather phenomena are located. The troposphere has altitudes reaching up to more than 20 km at the equator, and 7 km or more at the poles. The troposphere contains approximately 75% of Earth’s total atmospheric mass and 99% of its total water vapour [5]. Apart from water vapour, the troposphere comprises nitrogen (78%), and oxygen (20%) with the remaining 2% made up of other gases [5, 6]. The composition of gases within the troposphere is essentially homogenous, except for water vapour which shows high temporal and spatial variability. There are no ionized gases in the troposphere; therefore, for electromagnetic waves in the radio-frequency spectrum (up to 15 GHz of fundamental frequency), the troposphere is a non-dispersive medium [7]. The environmental variables present in the troposphere are: pressure, water vapor and temperature.

2.1 Pressure in the Troposphere Pressure (P) is defined as force per unit of area. According to the International Standard Atmosphere at Mean Sea Level, the pressure is 1013.25 hPa [8]. By definition 1013.25 hPa, equals a pressure of 1 kg/cm2 of surface area or one atmosphere. Due to gravity and decreased air density, air pressure decreases exponentially with increased height above the surface. The pressure at the surface of the earth can be measured using a barometer, and at different altitudes, using radiosondes.

2.2 Relative Humidity (RH) in the Troposphere Humidity is defined as the amount of water vapour in the air. Its temporal and spatial distribution is highly heterogeneous (highly variable) horizontally and vertically. For these reasons, it is hard to model or simulate with simple mathematical models. Furthermore, it is found that humidity at an altitude as high as 10 km above the earth’s surface. Several measures are used to characterize water vapour: • Water vapour pressure—expressed in hPa or mbar among other units. • Absolute humidity—the amount of water vapour in the air, expressed in g/m3 . • Specific humidity—the ratio between the density of water vapour and the density of wet air. • Relative humidity—the ratio of water vapour pressure to saturation vapour pressure, expressed in %.

46

J. Mendez-Astudillo et al.

• Mixing ratio—the ratio of the density of water vapour to dry air. Humidity is mostly measured as relative humidity, by sensors at surface level, such as weather stations. Another measurement typically available with humidity sensors is the Dew Point, which is the temperature at which enough water vapour is in the air for saturation to occur.

2.3 Temperature in the Troposphere The temperature T varies depending on several factors, including altitude, latitude, and time of the day. In the tropospheric region, temperature decreases with increasing altitude, at a rate of about −5 to −7 K/km. The decrease of temperature is due mainly to greater heat absorption by the sun-heated earth’s surface, which via conduction then heats up the air closer to the ground. At a certain altitude—the first boundary of the tropopause—the temperature increases at a different rate to its decrease. After that, when the stratosphere layer is reached, the temperature remains constant. Temperature varies depending on the latitude and the day of the year. The yearly profile of temperatures at different latitudes in the northern hemisphere and in the southern hemisphere are shown in Figs. 1 and 2, respectively. As shown in Fig. 1, the temperature has its maximum in the summertime, between the June and August and the minimum is found between the February and March. The stations in the arctic region located within the latitudes of 70° and 80° to the north, exhibit extreme temperatures; during winter, the average temperature is around

Fig. 1 Annual temperature profiles for weather stations in the northern hemisphere

Urban Heat Island Monitoring with Global Navigation …

47

Fig. 2 Annual temperature profiles for weather stations in the southern hemisphere

−30 °C, while in the summer the temperatures reach an average of 0 °C. On the other hand, stations near the equator show smaller temperature fluctuation between summer and winter. It can be seen in Fig. 1 that their temperatures hardly fluctuate through the year. Figure 2 shows the behaviour of the temperatures throughout the year for stations in the southern hemisphere. Figure 2 shows the yearly temperature profiles for southern hemisphere stations, which are the opposite of those for the northern hemisphere. The coldest temperatures are found between the 6th and 8th month. While the hottest temperature is found during the 12th and 1st month.

2.4 Profile of P, T and RH Versus Height The profile of the variables in the troposphere, P (air pressure), T (air temperature) and RH (relative humidity) versus altitude is shown in Fig. 3. Figure 3 shows that temperature decreases as altitude increases up to the tropopause. Then, the temperature increases at a different rate, until the top of the tropopause. The tropopause is defined as the layer between the first Lapse Rate Tropopause (LRT1) and the second Lapse rate tropopause (LRT2). After which the temperature remains constant. According to the International Standard Atmosphere the average sea level temperature is 27 °C, while the minimum at the tropopause is −64 °C [8]. Pressure decreases in an exponential fashion—as an inverse to altitude—and reaches almost 0 kN/m2 at a point in the tropopause layer. Humidity also

48

J. Mendez-Astudillo et al.

Fig. 3 Profiles of temperature, air pressure and relative humidity versus altitude

behaves exponentially, decreasing up to 10 km altitude (this 10 km layer is where most of the moisture of air is concentrated) and then behaving in an irregular way through higher altitudes.

3 Effect of the Troposphere to GNSS Signals GNSS signals are susceptible to experiencing delays during their transmission from the satellite to the receiver. The troposphere induces a delay to the GNSS traveling signal called the tropospheric delay. In positioning applications, the tropospheric delay is translated into positioning errors. Therefore, it is important to understand this delay. Mathematically, the propagation delay, ρtr opo in meters, is defined as an integral of the refractive index, n, of the media along the ray path, s, between the satellite W and the receiver R [1, 4, 7], as shown in Eq. 1. W ρ tr opo =

(n − 1)ds

(1)

R

The refractive index n is defined as the ratio of the propagation velocity of the signal in a respective medium v and the propagation velocity of the signal in the vacuum c. The tropospheric delay can be written in terms of the refractivity of the troposphere N as [1]:

Urban Heat Island Monitoring with Global Navigation …

49

Table 1 Values for the empirically determined refractivity constants (as K/hPa) k1

k2

k3

Thayer in [10]

77.6

72

3.75 × 105

Smith and Weintraub in [11]

77.61

64.8

3.77 × 105

Bevis et al. in [12]

77.6

70.4

3.73 × 105

ρ tr opo = 10

−6

W N ds

(2)

R

The refractivity of the troposphere N depends on environmental variables at the point of measurement. In the next equation. k1 , k2 and k3 are empirically determined coefficients, p is the air pressure in hPa, T is the absolute temperature in Kelvin and e is the water vapour partial pressure in hPa. Z d and Z w are unit-less compressibility factors for dry air and water vapour, respectively [9].    p−e e e  Z d−1 + k2 · + k3 · 2 Z w−1 N = k1 · T T T

(3)

The compressibility factors are corrections to account for the deviation of atmospheric constituents from an ideal gas. For an ideal gas, the compressibility factors equal to 1. For simplicity, it is assumed that the troposphere constitutes an ideal gas. Values for the empirically determined refractivity constants k 1 , k 2 and k 3 have been investigated by several authors [10–12], and their results are tabulated in Table 1. Since each of the environmental parameters that are needed to calculate the refractivity of the troposphere using Eq. 3 depend on altitude, the refractivity profile will also depend on altitude.

3.1 Profile of the Refractivity Versus Altitude In order to compute the profile of N versus altitude, 10 years of radiosonde data have been collected and processed for 12 radiosonde stations at different latitudes [13]. Values at the same height are averaged, and a single value per height is used. The values of p, T and e, at different altitudes are input into Eq. 3 to compute the refractivity at different heights. It is found that all the profiles of the refractivity of the troposphere have a similar shape and values, as summarized in Fig. 4. The mathematical expression of the relationship between N and altitude is described in the following equation: N = N0 e−Nh Z

(4)

50

J. Mendez-Astudillo et al.

Fig. 4 a Profiles of refractivity versus geodetic height, at different latitudes; b fit to exponential in order to find the mathematical relation between refractivity and height

where N0 is the refractivity at ground level and Nh is the ratio at which the refractivity decreases with altitude. A value for N0 and Nh can be obtained for different latitudes.

3.2 Tropospheric Delay The first term in Eq. 3 is the refractivity caused by the induced dipole moment of the dry constituents of the atmosphere. The second term is the induced dipole moment of water vapour, and the third term shows the effect of the permanent dipole of the water vapour molecules [14]. Therefore, the tropospheric delay can also be separated into two components, the dry component, related to temperature and air pressure, and the wet component related to the amount of water vapour available in the troposphere. The dry component is relatively stable, while the wet component fluctuates and varies a lot [15]. Therefore, the tropospheric delay can be written as: ∇ρ tr opo = 10

−6

w Ndr y ds + 10 r

−6

w Nwet ds

(5)

r

3.3 Zenith Tropospheric Delay The total delay in the line of sight between a GNSS receiver and a GNSS satellite is derived as the sum of the hydrostatic (or dry) and wet delays, in the zenith direction, multiplied by respective mapping functions and a gradient correction [16]. Equation (6) represents the tropospheric delay, where ρhz and ρωz are the hydrostatic

Urban Heat Island Monitoring with Global Navigation …

51

and wet zenith delays, respectively, with associated hydrostatic and wet mapping functions m h () and m ω (). Symbol ε represents elevation angle of the satellite. ρ tr opo = ρ hz · m h () + ρ ωz () + m g ()[G N cos α + G E sin α]

(6)

The term m g ()[G N cos α + G E sin α] is called the tropospheric gradient correction and accounts for the azimuthal dependence of the tropospheric path delay, m g () stands for the gradient mapping function, with respect to the elevation angle α. G N and G E denote the horizontal delay gradients in the north and east directions, respectively. Alpha is the azimuth angle of the received signal, measured from east to north. The tropospheric delay in the zenith direction is called the Zenith Tropospheric Delay (ZTD). It can be determined as an integral of refractivity N, in the zenith direction [17].  N ds (7) Z T D = 10−6 zenith dir ection

The ZTD is defined as the sum of the Zenith Hydrostatic Delay (ZHD) and the Zenith Wet Delay (ZWD) [1]. The ZTD is related to the tropospheric delay as described in Eq. 8 [1]. Z T D = Z H D ∗ m h () + Z W D ∗ m w ()

(8)

where m h () and m w () are the hydrostatic and wet mapping functions, depending on the elevation angle. The ZTD can also be defined as the integral of the refractivity over a vertical column of the neutral atmosphere, as represented in Eq. (9) [18]. ρ is the density of air, z is the geometric height, R is the gas constant, T is the temperature, zsite is the height of the receiver with respect to the ground and ztop is the height of the troposphere. Z T D = 10

−6

   ztop k3 k1 Rd ρd + k2 + Rw ρw δz T

(9)

zsite

where subscripts d and w represent dry and wet components, respectively. Symbols k1 , k2 and k3 are empirically determined constants, with k1 = 7.76 × 10–1 K/Pa, k2 = 7.04 × 10–1 K/Pa and k3 = 3.739 × 103 K2 /Pa [6]. In order to solve the integral, it is important to use appropriate geometric heights for z, rather than the geopotential heights widely used in meteorology [18].

52

J. Mendez-Astudillo et al.

3.4 Estimation of ZTD ZTD is a by-product of the GNSS Precise Point Positioning (PPP) technique [19] which estimates the positioning coordinates and other parameters. There are two main estimators used, the Extended Kalman Filter (EKF) [20] and the least squares adjustment [21]. There are different implementations of the PPP technique, a study of different software and online-services implementing the PPP technique has shown that both, online services, and standalone PPP implementations provide similar estimations [22]. The ZTD is required as input for the algorithm, therefore, it can be estimated with a chosen software or online-service.

4 Urban Heat Island Monitoring Using GNSS Data The Urban Heat Island effect (UHI) happens when an urban area is warmer than a rural area [23] because of the built structures releasing heat to the atmosphere and because of anthropogenic heat sources such as air conditioning units or transportation networks. The metric of the UHI effect is the Intensity of the UHI (UHII). The UHII can be monitored using GNSS data by the following 4 steps: • Data collection simultaneously in an urban and a rural area. Therefore, it is necessary to classify the stations as being in an urban or a rural area. • Processing of GNSS data to obtain the Zenith Tropospheric Delay (ZTD) and location coordinates of the station. The Precise Point Positioning technique (PPP) is used. • Calculation of temperature using ZTD in the urban and the rural stations • Computation of the UHII.

4.1 Classification of the Type of Environment Around the Station There are many definitions for urban and rural areas. Some definitions are in terms of economic activity, land cover or geographic location. Typically, urban areas are densely populated and densely constructed [24]. Also, in urban areas, there are anthropogenic sources of heat, such as the means of transportation and air conditioning units, among others. In contrast, a rural area has few built structures, is mostly covered by nature and the numbers and types of anthropogenic heat sources are reduced. Usually rural and urban areas are attached to each other, with rural areas found outside the city or urban area [24]. A classification framework depending on land cover surrounding a station to be used in urban climatology studies has been used in UHI studies [25, 26]. The classification defines 17 Local Climate Zones summarized in Table 2.

Dense mix of low-rise buildings (1–3 stories) Few or no trees

LCZ 6: Open low-rise

LCZ 4: Open high-rise

LZC zone

LCZ 3: Compact low-rise

Example

LCZ 5: Open mid-rise

Dense mix of tall buildings (more than 10 stories) Few or no trees

LCZ1: Compact high-rise

LCZ 2: Dense mix of Compact mid mid-rise rise buildings (3–9 stories) Few or no trees

Definition

LZC zone

Table 2 LCZ defined for UHI studies [25]

Open arrangement of low-rise buildings (1–3 stories)

Open arrangement of midrise buildings (3–9 stories) Low plants, scattered trees

Open arrangement of tall buildings (10 or more stories) Low plants, scattered trees

Definition

Example

(continued)

Urban Heat Island Monitoring with Global Navigation … 53

Low-rise and midrise industrial structures Few or no trees

Definition

LCZ B: Lightly wooded Scattered trees landscape of trees Natural forest or urban park

LCZ 10: Heavy industry

LZC zone

LCZ 9: Sparse Sparsely built arrangement of small or medium-sized buildings in natural setting

Example

LCZ A: Dense Heavily wooded trees landscape of trees Natural forest or urban park

Dense mix of single-story buildings Few or no trees

LCZ 7: Lightweight low-rise

LCZ 8: Large Open low-rise arrangement of large low-rise buildings (1–3 stories)

Definition

LZC zone

Table 2 (continued) Example

(continued)

54 J. Mendez-Astudillo et al.

LCZ E: Bare Featureless rock or paved landscape of rock or paved cover Few or no trees Urban transportation

Featureless landscape of grass of herbaceous plants Few or no trees Agriculture or urban park

Featureless landscape of soil or sand cover Few or no trees Natural desert

Definition

LCZ G: Water Water bodies, large or small Lakes, reservoirs, rivers, lagoons

LZC zone

LCZ D: Low plants

Example LCZ F: Bare soil or sand

Definition

LCZ C: Bush, Open scrub arrangement of bushes, shrubs and short, woody trees Natural scrubland or agriculture

LZC zone

Table 2 (continued) Example

Urban Heat Island Monitoring with Global Navigation … 55

56

J. Mendez-Astudillo et al.

If a meteorological or a GNSS stations is located in LCZ1-3,10 they would be considered urban stations. Moreover, if a station is located in LCZ 5-9 they would be considered urban or rural depending on the amount of anthropogenic heat sources. If a station in LCZ 5-9 is compared with a station LCZ1, it would be colder. However, if compared with a station surrounded by nature it would be expected to be warmer. LCZ A, B, C, D, E, F and G are considered rural areas because there are not many built structures and there are only a few anthropogenic heat sources.

4.2 Algorithm to Calculate Temperature from GNSS Data The algorithm developed to calculate temperature from GNSS data is shown in the block diagram in Fig. 5. The algorithm requires values for the height of the troposphere (S), air pressure at the place of measurement (P) and the water vapour partial pressure (e) at the site of measurement. These values are obtained from radiosonde data and are called the universal parameters.

4.2.1

Calculation of Universal Parameters

Height of the Troposphere (S) The height of the troposphere can be calculated in two ways, using radiosonde data only or using GNSS and radiosonde data. The height of the troposphere can be defined in terms of the First and Second Lapse Rate Tropopause (LRT1 and LRT2). According to the World Meteorological Organization (WMO), the LRT1 is defined as the lowest level at which the lapse rate (change of temperature with height) decreases to 2 °C/km or less, provided also that the averaged lapse rate between this level and levels within the next 2 km vertically does not exceed 2 °C/km. If above the LRT1 the average lapse rate between any level and all higher levels with 1 km exceeds 3 °C/km, then a second tropopause (LRT2) is defined. LRT2 may be either within or

Fig. 5 Block diagram of the algorithm developed to estimate temperature from GNSS data

Urban Heat Island Monitoring with Global Navigation …

57

above the 1 km layer [27]. Radiosonde data is used to compute the vertical profile of the temperature and with the WMO’s definition, the height of LRT2 is found. A second technique used to calculate the height of the troposphere S requires the profile of the refractivity previously obtained with radiosonde data, N0 and Nh are needed as inputs (cf. Sect. 1) [28]. The ZTD is also an input into Eq. 11.  Str op =

ln

−Nh N0

Z T D × 10−3 + exp−Nh Z site

 (10)

−Nh

Pressure (P) The pressure is obtained either from radiosonde sounding at its lowest level or from weather stations. In this algorithm, the pressure is considered as constant at a given latitude because its annual fluctuation is negligible. Water Vapor Partial Pressure at Different Heights e(z) e(z) is the partial pressure of water vapor in any gas mixture in equilibrium with solid or liquid water. This variable cannot be obtained with direct measurements such as weather sensors or radiosondes. Therefore, several models have been developed to estimate this parameter. For example, a model in terms of the ZWD [29]. Another model based on the Clausius-Clapeyron relation for gases is Antoine’s model [20] defined as: B

10 A− C+T (z) e(z) = 0.75

(11)

where A, B and C are Antoine’s constants (A = 8.071, B = 1730.63 and C = 233.43). T(z) is the temperature in Kelvin at height Z [20]. Equation 12 is valid for temperatures greater than 0 °C and lower than 100 °C. It is assumed that urban temperatures in the metropolitan areas under study are between that range most of the time. Negative temperatures are assumed to yield 0 hPa of water vapor partial pressure.

4.2.2

Calculation of Local Parameters

In Sect. 2, the refractivity of the troposphere is defined in terms of the environmental variables surrounding the GNSS receiver. Equation (12) defines the profile of the refractivity in the troposphere. The troposphere causes a delay to the signal in the zenith direction ZTD (in meters) which can be expressed as an integral of the total refractivity N along propagation path s from receiver r to satellite w [1]: Z T D = 10

−6

w N ds r

(12)

58

J. Mendez-Astudillo et al.

A local N 0 (N0 _local) is found using N h , Ztrop (height of the tropopause), the altitude of the receiver (Zrec ) and the ZTD in the following equation: N0 _local =

q

ZT D ztr op Nh z dz zr ec exp

(13)

N0 _local is the value of the refractive index near the surface of the place of measurement.

4.2.3

Calculation of Temperature

The temperature of the site near the receiver is calculated equating to zero the equation of refractivity using local parameters (Eq. 14). Temperature is estimated solving the quadratic equation described in Eq. 14: N0 _localT 2 − T (k1 P − (k1 + k2 )e) − k3 e = 0

(14)

4.3 Calculation of the UHI Intensity (UHII) from GNSS Data The UHI intensity is measured by subtracting the temperature at an urban station with the temperature at a rural station. The UHII can only be calculated by comparing temperatures obtained at the same time. The diurnal cycle of the UHII can be studied with GNSS data since it is available every 30 s. Mathematically, the UHII obtained from GNSS data is defined as: U H I I = Tgnss (U R B AN ) − Tgnss (RU R AL)

(15)

where Tgnss (Location) is the temperature in ºC obtained with the algorithm at the chosen location.

References 1. Hofmann-Wellenhof B, Lichtenegger H, Wasle E (2008) GNSS-global navigation satellite systems GPS, GLONASS, Galileo and more. Springer, Vienna, Austria 2. National Coordination Office for Spaced-Based Positioning, Navigation and Timing (2020, May 19) GPS the global positioning system. Available: https://www.gps.gov/systems/gps/ space/ 3. EGSA (2020, May 19) Galileo programme. Available: https://www.gsa.europa.eu/galileo/pro gramme

Urban Heat Island Monitoring with Global Navigation …

59

4. BeiDou Navigation Satellite System (2020, May 19) BeiDou navigation satellite system. Available: https://en.beidou.gov.cn/ 5. Speight JG (2017) Environmental organic chemistry for engineers. Elsevier/ButterworthHeinemann, Amsterdam, Boston 6. Liang J (2013) Chemical modeling for air resources fundamentals, applications and corroborative analysis. Academic Press, Cambridge, p 298 7. Seeber G (2008) Satellite geodesy foundations, methods and applications, 2nd edn. de Gruyter, Berlin 8. International Organization for Standarization (1975) International standard atmosphere, 2533 9. Essen L, Froome KD (1951) Dielectric constant and refractive index of air and its principal constituents at 24,000 Mc./s. Nature 167:512–513 10. Thayer G (1974) An improved equation for the radio refractive index of air. Radio Sci 9:211–222 11. Smith E, Weintraub S (1953) The constants in the equation of atmospheric refractive index at radio frequencies. Proc Inst Radio Eng 41(8):1035–1037 12. Bevis M, Businger S, Chiswell S (1994) GPS meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteorol 33:379–386 13. Mendez Astudillo J (2020) Investigation into UHI monitoring with GNSS sensor network. Ph.D., School of Civil Engineering, University of Nottingham, Ningbo, China 14. Davis J, Herring T, Shapiro I, Rogers A, Elgered G (1985) Geodesy by radio interferometry: effects of atmospheric modeling errors on estimates of baseline length. Radio Sci 20:1593–1607 15. Hoffmann P, Krueger O, Schlünzen KH (2012) A statistical model for the urban heat island and its application to a climate change scenario. Int J Climatol 32(8):1238–1248 16. McCarthy D, Pétit G (2004) IERS conventions (2003) 17. Wilgan K, Hurter F, Geiger A, Rohm W, Bosy J (2016) Tropospheric refractivity and zenith path delays from least-square collocation of meteorological and GNSS data. J Geodesy 91(2):1–18 18. Vedel H, Mogensen KS, Huang X-Y (2001) Calculation of zenith delays from meteorological data comparison of NWP model, radiosonde and GPS delays. Phys Chem Earth (A) 26(6– 8):497–502 19. Heroux P, Kouba J (2001) GPS precise point positioning using IGS orbit products. Phys Chem Earth (A) 26(6–8):573–578 20. Tolman BW (2008) GPS precise absolute positioning via Kalman filtering. In: ION GNSS 21st international technical meeting of the satellite division, Savannah, GA 21. Moritz H (1972) Advanced least-squares methods. In: Reports of the Department of Geodetic Science, The Ohio State University, Columbus, Ohio. Available: https://earthsciences.osu.edu/ sites/earthsciences.osu.edu/files/report-175.pdf 22. Mendez Astudillo J, Lau L, Tang YT, Moore T (2018) Analysing the Zenith tropospheric delay estimates in on-line precise point positioning (PPP) services and PPP software packages. Sensors (Basel) 18(2) 23. Roth M (2013) Urban heat island. In: Fernando HJS (ed) Handbook of environmental fluid dynamics, vol II. Taylor & Francis Group, UK, pp 143–159 24. Memon RA, Leung DYC, Liu C-H (2009) An investigation of urban heat island intensity (UHII) as an indicator of urban heating. Atmos Res 94(3):491–500 25. Stewart ID, Oke TR (2012) Local climate zones for urban temperature studies. Bull Am Meteorol Soc 93:1879–1900 26. Stewart ID, Oke TR, Krayenhoff ES (2014) Evaluation of the ‘local climate zone’ scheme using temperature obervations and model simulations. Int J Climatol 34:1062–1080 27. WMO (1957) Lapse rate tropopause 28. Mendez Astudillo J, Lau L, Tang YT, Moore T (2020) A novel approach for the determination of the height of the tropopause from ground-based GNSS obsevartions. Remote Sens 12(293) 29. Younes SA-M (2016) Modeling investigation of wet tropospheric delay error and precipitable water vapor content in Egypt. Egypt J Remote Sens Space Sci 19:333–342

An Estimation of Air-Conditioning Energy-Saving Effects Through Urban Thermal Mitigation Yujiro Hirano and Tsuyoshi Fujita

Abstract This chapter introduces the evaluation cases of the urban heat island (UHI) countermeasures that we conducted in Tokyo. As Tokyo is hot and humid compared to many European and North American cities, measures to mitigate the severe thermal environment have been considered. We evaluated UHI countermeasures on the urban/city block scale and have thus far reported the results in mainly domestic journals. The countermeasures include general-purpose methods that can be used in other areas. We therefore present our evaluation as a case study. First, we propose a method for quantifying energy consumption by taking into consideration the spatial and temporal distributions of both air temperature and human activities. Next, we propose an estimation method for fractional vegetation cover (FVC) and an urban climate simulation method using FVC data. We then quantified the UHI mitigation and energy reduction effects of urban greening. Since the results are only valid for Tokyo, it is important to carry out similar evaluations in cities where urbanization is progressing rapidly, using the methods proposed in this study. Keywords Urban heat island · Air-conditioning · Energy saving · Fractional vegetation cover · Urban greening · Meteorological simulation · Satellite remote sensing · Temperature sensitivity · Estimation method

1 Introduction One of the serious problems arising from the urban heat island (UHI) phenomena in urban areas that are located in hot, humid regions is increased energy consumption due to the use of air conditioning [1–4]. Increased energy consumption is problematic because it increases CO2 emissions, the reduction of which is at the core of Y. Hirano (B) National Institute for Environmental Studies, Fukushima Environmental Creation Centre, 10-2 Fukasaku, Miharu Town, Tamura District, Fukushima, Japan e-mail: [email protected] T. Fujita National Institute for Environmental Studies, Tsukuba, Japan © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_4

61

62

Y. Hirano and T. Fujita

global warming countermeasures. At present, since many rapidly urbanizing cities are located in hot, humid areas, UHI mitigation measures are urgently needed. In fact, in many cities, various evaluations have already been conducted on the energysaving effects of UHI mitigation measures such as high-albedo coatings and urban greening [5]. These studies can provide useful information for considering UHI countermeasures in the context of the urbanization process in hot, humid cities, such as Tokyo, which, due to its higher summer temperature compared with many European and North American cities, sees a seriously problematic seasonal UHI-related increase in cooling energy consumption [6, 7]. Hence, Tokyo’s experience with UHI countermeasures can be utilized in other hot and humid cities. In order to conserve energy through UHI mitigation, it is crucial to select areaappropriate mitigation measures. For example, in an area where the influence of the UHI is stronger in winter than in summer, a mitigation measure that decreases the temperature throughout the year may cause an increase in energy consumption. As explained in Sect. 2 of this chapter, in Tokyo, the increase in energy consumption for cooling is large in the commercial sector due to the UHI; however, it is outpaced by the decrease in energy consumption for space/water heating in the residential sector [8]. Therefore, while various UHI mitigation measures would be effective in densely built-up areas in the city center, it is important to select measures that do not cool the winter temperatures in the surrounding residential areas. This evaluation result was obtained under Tokyo’s climatic conditions; however, the situation varies by city. In hotter, more humid areas than Tokyo, there is a high possibility that energy savings can be realized even in residential areas through a year-round temperature drop. Therefore, although Tokyo’s research results can be used to develop individual countermeasure technologies and evaluation methods, it is extremely important to evaluate appropriate mitigation measures based on specific regional conditions. Air-conditioning load simulation is often used to estimate energy consumption according to an area’s specific climatic conditions (e.g., Refs. [9–11]). We have evaluated the effect of UHI countermeasures on energy consumption by using a combined urban canopy and building energy model [12–16]. This model makes it possible to calculate city block-scale air-conditioning loads that include interaction with the outside atmosphere by incorporating an air-conditioning load calculation model [17, 18] into a one-dimensional vertical atmospheric model [19, 20]. We used this model to evaluate the effects of rooftop greening and high-albedo paint on office districts, fire-resistant residential districts, and wooden residential districts [15]. We conducted field observations to verify the effect of using high-albedo paint and incorporated it as a parameter in this model [21, 22]. In addition, we performed a more detailed simulation evaluation of rooftop greening using the evaporation efficiency parameter that was obtained from the heat balance observation of rooftop greening [16, 23]. These research results may provide valuable information for improving the environment in various cities that are situated in hot, humid regions where urbanization is progressing rapidly. However, the results are not necessarily applicable to other regions because the effects depend on various factors such as climatic conditions, buildings’ thermal performance, lifestyles, and so on. In addition, air-conditioning load simulations

An Estimation of Air-Conditioning Energy-Saving …

(a) The target area (approximately 2-km grid)

63

(b) Computational domain (unequal spacing grid)

宗宲宮宼宲季宅室宼

Fig. 1 Study area. a The target area, with an approximately 2-km grid. b The computational domain, with a variable interval grid. Solid circles in a mark the observation points of the high-density urban climate observation network

require many input parameters (e.g., walls’ heat insulation, air-conditioning system type, the area ratio of windows on walls, etc.). Calculation results strongly depend on setting parameter values, but it is difficult to obtain precise parameter values using this method because there is a wide variety of building types in cities, and in many cases, there is a lack of surveys reflecting existing buildings’ current conditions. It is therefore necessary to propose a broadly applicable alternative method. Against this backdrop, the following sections will introduce our evaluation study, which quantifies the effects of UHI countermeasures on energy consumption. We have published the results of our UHI evaluations in domestic journals to share knowledge with various stakeholders such as domestic researchers, policymakers, planners, and others. However, because urbanization is rapidly progressing in many cities in hot, humid regions other than Japan, it is necessary to evaluate UHI countermeasures elsewhere, as was done in the case of Tokyo. Our Tokyo evaluation cases can provide useful information to readers whose concerns are outside Japan. Naturally, any evaluation method should be appropriately selected and applied according to various area-specific conditions, such as targeted mitigation measures, the spatiotemporal scale of evaluation, and the availability of data. As an example, we believe that our method has significance due to its applicability to various foreign regions. This chapter introduces the evaluation of urban greening as a case study of UHI mitigation measures. Specifically, in Sect. 2, we propose an estimation method for energy consumption that reflects the temperature distribution. In Sect. 3, we propose an estimation method for fractional vegetation cover (FVC) on an urban land surface. In Sect. 4, we propose a simulation method for the urban climate using FVC data. In Sect. 5, we evaluate the UHI mitigation effects and the energy-saving effects of urban greening by combining the methods proposed in Sects. 2–4. The study area

64

Y. Hirano and T. Fujita

is shown in Fig. 1. This study focused on primary energy consumption for space cooling, space heating, and water heating in the commercial and residential sectors.

2 Evaluation Method for the UHI Effect on Energy Consumption We proposed a method to calculate the energy consumption that corresponds to the temperature distribution. With this method, it is possible to calculate energy consumption based on the spatial and temporal distribution of both air temperature and energy consumption behavior, which change from moment to moment, depending on the season and the time. Using this method, we evaluated the effect of the UHI on energy consumption as an exemplar case study. This has already been published in an international journal, so only an overview will be given here (see Ref. [8] for details). This method can also be used to estimate the energy-saving effects of various UHI mitigation measures. Section 5 presents a case study in which the energy-saving effect of UHI mitigation measures through urban greening was calculated using this method.

2.1 Evaluation Method A number of studies have evaluated the effect of the UHI on energy consumption. A method based on energy consumption sensitivity with respect to air temperature [24, 25] is used as a relatively simple evaluation method compared to the air-conditioning load calculation. Knowledge of the temperature sensitivity of energy consumption is useful for predicting energy demand under various weather conditions, such as hot versus cool summers. However, since in these studies, the relationship between energy consumption data from the supply side (e.g., electric power plants or city– gas supply companies) and the average temperature of the supply area was analyzed, spatial distribution was not considered. This knowledge is insufficient for assessing the impacts of the UHI, which must consider an urban area’s local temperature changes. On the other hand, numerous studies have been conducted on the spatial distribution of energy consumption [26–29]. In these studies, spatial data reflecting the floor area or the number of households were multiplied by the specific energy consumption. In this research, specific energy consumption is defined as the amount of energy consumed per standard unit of basic indicators, such as floor area or the number of households. However, this method is not suitable for evaluating the impacts of the UHI because the influence of temperature cannot be considered.

An Estimation of Air-Conditioning Energy-Saving …

65

We therefore proposed a method for evaluating the impacts of the UHI on energy consumption by considering both temperature sensitivity and spatial distribution, as shown in Fig. 2. The procedure is explained below. First, we established estimation equations by expressing specific energy consumption as a function of air temperature. Then, we calculated the temporal and spatial distributions of air temperature using a meteorological model. This calculation was carried out for two cases: the first case is the present case, which was carried out under the present condition, and the second case is the no-UHI case, which was carried out under the condition that there are no urban effects. For both cases, the distributions of specific energy consumption were then estimated by applying the estimation equations to each grid cell of temperature. The temporal and spatial distributions of energy consumption for both cases were estimated by multiplication with grid cell-based distribution data reflecting the floor area or the number of households. For the no-UHI case, the estimation results indicate energy consumption determined under the assumption that there would be no urban effects on temperature, whereas urban activities are considered to be under the

Surface parameters - Present case

Meteorological simulations - No-UHI case - Countermeasure case

Temperature distributions

Estimation equations: E = F(T) E : Specific energy consumption T : Temperature

- Present case - No-UHI case - Countermeasure case

Basic indicators

Specific energy consumptions

- Floor area - Number of households

- Present case - No-UHI case - Countermeasure case

- Present case - No-UHI case - Countermeasure case

Spatial and temporal distributions of energy consumption

Comparison Fig. 2 Method for assessing the impact of UHI on energy consumption [8]

66

Y. Hirano and T. Fujita

present conditions. Therefore, the UHI effect on energy consumption was quantified by comparing each case’s results. In this section, the no-UHI case is compared with the present case to quantify the effect of the UHI on energy consumption. However, the energy-saving effect of the UHI mitigation measure can be quantified using the UHI countermeasure case for comparison. An example of the evaluation case is presented in Sect. 5. As explained in the Introduction, we also conducted a UHI assessment using airconditioning load calculations as an alternative method for evaluating the energy impact of UHIs [12–16]. We confirmed the validity of the energy consumption calculation method through the inter-comparison of these methods [30].

2.2 Generating the Estimation Equations Estimation equations for specific energy consumption have been established based on the correspondence of the seasonal variation patterns of specific energy consumption with those of air temperature. The specific energy consumption data used in this study reflect the amount of primary energy consumed for space cooling, space heating, and water heating per standard unit of floor area in the commercial sector and per the number of households in the residential sector. This study evaluated seven building types in the commercial sector, namely hospitals, office buildings, retail stores, hotels, amusement facilities, cultural facilities, and schools, and two building types in the residential sector, namely detached houses and apartment buildings. We obtained monthly and hourly specific energy consumption values using annual specific energy consumption data and seasonal and diurnal variation pattern data for each building type. The method for generating estimation equations is explained below through an example of space cooling in office buildings at 14:00. The relationship between these data is shown in Fig. 3, in which it can be observed that the slope changes around April. In general, as long as an air conditioner is in operation, the relationship is Fig. 3 Scatter diagram of the specific energy consumption data versus monthly and hourly mean temperatures of space cooling in office buildings at 14:00. The numbers at the side of each point indicate the months [8]

An Estimation of Air-Conditioning Energy-Saving …

67

likely to be linear. The change in slope appears because the air conditioner begins to operate at an increase in temperature that is beyond a certain boundary. This tendency appeared for space cooling and space heating for all building types except schools, in which air conditioners are hardly operated during the summer vacation in August. This relationship was approximated through the combination of regression lines (shown as the solid line in Fig. 3) using the following procedure. The procedure for creating regression equations for the broken lines is as follows: 1. The monthly specific energy consumption data were sorted by temperature. 2. Regression equations were created for all combinations that divide the data into two groups. 3. The root mean square errors (RMSE) were calculated when using the regression equations as estimation equations. 4. The combination with the smallest RMSE was selected. This procedure is explained in detail in Ref. [8]. In the case of water heating, because the relationships between temperature and specific energy consumption were found to be linear, ordinary single-regression equations were established as estimation equations, instead of combining equations. Figure 4 shows an example of the residential sector at 20:00 and the commercial sector at 14:00. This method of creating estimation equations using broken lines is employed as a general method for calculating temperature sensitivity [31].

2.3 Numerical Simulations of the UHI Effect The UHI effect in Tokyo was simulated for each month using a mesoscale meteorological model. This study used the Colorado State University Mesoscale Model (CSU-MM), which was developed by Pielke [32] and improved by Ulrickson and Mass [33], and Kessler and Douglas [34]. This model assumes an incompressible fluid, a hydrostatic equilibrium, and the Boussinesq approximation, and it employs a terrain-following coordinate system. Kessler and Douglas provided further details in their article [34]. We used a modified version of the CSU-MM, i.e., a version that improved the methods to input surface parameters and anthropogenic heat [26]. This model has already been utilized in numerous urban climate simulations [9, 26, 35–39]. The Tokyo metropolitan area is divided into grid cells, with a grid size of about 2 km × 2 km (see Fig. 1). To simulate the local circulations developed around the target area, the computational domain was set at 500 km × 500 km with a variableinterval grid system; the grid interval becomes larger in regions that are far from the target area. Surface parameters, i.e., albedo, evaporation efficiency, roughness length, density, specific heat capacity, and the heat diffusion coefficient, were set for each grid cell on the basis of their land-use ratio. Table 1 shows the surface parameters for each land-use type; the parameters were taken from Ichinose et al. [26]. For the

68

Y. Hirano and T. Fujita

Fig. 4 Scatter diagrams of specific energy consumption versus temperatures, and estimation equations as examples of a the commercial sector at 14:00 and b the residential sector at 20:00 [8]

An Estimation of Air-Conditioning Energy-Saving …

69

Table 1 Surface parameter values for each land-use type [8] Land-use types

Roughness Evaporation Albedo Density Specific Heat Land-cover length efficiency [–] [g/cm3 ] heat diffusion elements [cm] [–] [J/g/K] coefficient [cm2 /s]

Rice paddy 10

0.50

0.17

1.8

1.173

0.0053

(Rice paddy)

Vegetable field

10

0.30

0.17

1.8

1.173

0.0053

(Vegetable field)

Orchard

50

0.30

0.16

1.8

1.173

0.0053

Lawn and park

50

0.30

0.16

1.8

1.173

0.0053

Forest

50

0.35

0.16

1.8

1.173

0.0053

Tree

Wasteland

15

0.20

0.14

1.8

1.173

0.0053

Soil

Building lot

50

0.00

0.18

2.4

0.880

0.0072

Building

Traffic use

50

0.00

0.18

2.1

0.880

0.0038

Road

Other use

20

0.03

0.18

1.8

1.173

0.0035

River/Lake

0

1.00

0.08

1.0

4.190

0.0033

Seastrand

5

0.25

0.18

1.8

1.173

0.0053

Sea surface

0

1.00

0.08

1.0

4.190

0.0033

(Water)

present case, we obtained the actual land-use data from the Digital National Land Information; the data were compiled by the Geographical Survey Institute of Japan. The surface parameter values for the aggregated grid data for the mesoscale model were computed using the land-use data for a 100-m grid by applying the values listed in Table 1 and calculating the area-weighted averages in each grid cell. In addition, for the present case, using the same method as Urano et al. [35], anthropogenic heat was expressed as the diurnal variation of energy consumption, of which the diurnal average was 30 W/m2 for “built-up area” and “traffic use.” The simulation was started at 0:00 and carried out for 48 h. The calculation time step was 20 s. The initial air temperature and humidity values were obtained from the monthly averages of the observation data obtained from the Japan Meteorological Agency’s Automated Meteorological Data Acquisition System (AMeDAS) stations. The wind directions and velocities of geostrophic wind and the vertical gradients of potential temperatures were derived from upper-air observation data at Tateno, which is the observation point that is the nearest to Tokyo. Sea surface temperatures were obtained from the Japan Oceanographic Data Center’s temperature statistics in a one-degree mesh. These initial values were set to be homogeneous in the horizontal direction. Although it is possible to set the spatial distribution of these initial conditions, it would have been difficult to arrange initial values that are suitable for the no-UHI case. Therefore, calculations were started under simplified initial conditions, and the second day’s calculation results were used for the analysis.

70

Y. Hirano and T. Fujita

Fig. 5 Simulation result for the present case in a July and b January at 15:00 LST [8]

Figure 5 provides an example of the simulation results for horizontal distributions of wind and temperature near the ground surface for the present case in July at 15:00. This figure shows that the penetration of the sea breeze was well represented, and a large horizontal temperature gradient appeared around the coastal areas of Tokyo Bay due to the sea breeze front. Next, we carried out monthly simulations for the no-UHI case. In this case, after the land-use categories for “built-up area,” “traffic use,” and “other use” were changed equitably into “forest” and “wasteland,” the surface parameters were set using the same method as in the present case. Anthropogenic heat was not provided in the no-UHI case. Figure 6 shows the horizontal distribution of the temperature differences between the present case and the no-UHI case in July at 15:00. It can be seen that the increase in temperature due to the effect of the UHI reached 2.5 °C in the northwestern region of the Tokyo metropolitan area. On the other hand, the UHI in the central city area (around the center of the target area) was less conspicuous than in the inland area, probably due to the effect of the sea breeze.

2.4 Calculating Energy Consumption Energy consumption in the present case and the no-UHI case were estimated using the method shown in Fig. 2. First, we prepared grid cell-based floor area data and number of household data. Floor area data were generated by multiplying the building area by the number of stories and aggregating these into each grid cell using building polygon data from the Tokyo Metropolitan Government City Planning Map System. The number of household data for detached houses and apartment buildings were generated by distributing statistical data reflecting the number of households in each

An Estimation of Air-Conditioning Energy-Saving …

71

Fig. 6 Differences in air temperature between each case in a July and b January at 15:00 LST in °C (the present case—the no-UHI case) [8]

administrative district (Tokyo’s 23 wards) among the grids according to their floor areas. The statistical data reflecting the number of households were obtained from the Housing and Land Survey; the data were compiled by the Ministry of Internal Affairs and Communications. Second, grid cell-based monthly and hourly specific energy consumptions were estimated by applying the estimation equations established in Sect. 2.2 to the simulated temperature data obtained in Sect. 2.3. Energy consumption was then calculated by multiplying these specific energy consumptions by the floor area or the number of households for the commercial or residential sector. This estimation procedure was carried out for the present case and the no-UHI case. Thus, the effects of the UHI on energy consumption can be quantified by comparing both cases’ estimation results.

2.5 Comparison of Annual Energy Consumption Figure 7 shows the Tokyo metropolitan area’s total annual energy consumption for both the present case and the no-UHI case, as well as the difference between the two. In the commercial sector, the increase in the energy consumption for space cooling was up to 8490 TJ/year, suggesting that the UHI has a large impact on space cooling, as pointed out in a previous publication [6, 7]. However, the increase in the energy consumption for space cooling was almost balanced out by a decrease in the spaceand water-heating energy consumptions, which were approximately 6830 TJ/year and 660 TJ/year, respectively. Meanwhile, total energy consumption increased by approximately 1000 TJ/year. It can be concluded, then, that in the commercial sector, energy consumption increases due to the effects of the UHI.

72

Y. Hirano and T. Fujita

Fig. 7 Total annual amount of a energy consumption for both cases and b the difference between them [8]

On the other hand, in the residential sector, due to the effect of the UHI, the energy consumption for space heating decreased by approximately 7300 TJ/year, while energy consumption for water heating decreased by approximately 5650 TJ/year. In contrast, the space-cooling energy consumption increased by approximately 3920 TJ/year, which is a small change compared to the decreases in space- and water-heating energy consumption. Overall, annual energy consumption decreased by approximately 9000 TJ/year in the residential sector. Combining annual energy consumption in both sectors, it is found that annual energy consumption decreases in the entire target area due to the effect of the UHI. Using approximate values, energy consumption increased by 1.0% in the commercial sector and decreased by 8.0% in the residential sector, while total energy consumption decreased by 3.7%. With respect to each energy use, energy consumption for space cooling increased by 27.5% and energy consumption for space heating decreased by 18.4%, while energy consumption for water heating decreased by 6.7% in the total of both sectors. Figure 8 shows the differences in monthly energy consumption in each case. A comparison of the monthly peaks shows that the increases in energy consumption for space cooling and the decreases for space heating are approximately the same in both sectors, i.e., approximately 1300 TJ/month in the commercial sector and

Fig. 8 Differences between monthly energy consumption (the present case—the no-UHI case) in a the commercial sector and b the residential sector [8]

An Estimation of Air-Conditioning Energy-Saving …

73

approximately 1000 TJ/month in the residential sector. However, there are differences in the durations for which these influences are observed. In the commercial sector, the influence of the UHI on space cooling is observed mainly from May to October, while its influence on space heating is observed mainly from November to March. On the other hand, in the residential sector, the influence of the UHI on space cooling and space heating are observed mainly from July to September and from November to April, respectively. These differences in the periods in and durations for which the influence of the UHI are observed cause tendency differences in both sectors, as shown in Fig. 8. In the case of water-heating energy consumption, although the influence of the UHI was hardly observed in the commercial sector, energy consumption decreased throughout the year in the residential sector. This difference in energy consumption between the two sectors resulted from the fact that the energy consumed by bathing/showering in the residential sector is susceptible to temperature. However, during summer, the decrease in energy consumption for water heating is less than the increase in energy consumption for cooling in both sectors, implying that the energy-saving effect is expected to occur due to the UHI mitigation measure, which is effective in summer. The results of this study show that commercial energy consumption increased, while residential energy consumption decreased. This suggests that for the city center, where commercial buildings are densely concentrated, it may be possible to reduce energy consumption using scale or city block-scale mitigation measures, for example, high-albedo coatings or rooftop greenings. On the other hand, on the urban scale, which includes residential areas, it is important to select a mitigation measure that is effective in summer, for example, ensuring urban greening using deciduous trees or creating an urban ventilation path using seasonal wind. It should be noted that this evaluation is a case study of Tokyo. Although the evaluation method proposed in this paper appears to be general purpose, the results are only applicable to the Tokyo metropolitan area.

3 Land Surface Vegetation Analysis Using Satellite Remote Sensing According to the results presented in the previous section, in order to conserve energy in Tokyo, it is appropriate, due to its climatic conditions, to select countermeasures that mitigate the thermal environment in summer while avoiding getting cold during winter. A typical example is urban greening using deciduous trees. Thus, in the following sections, we will introduce a case study of the evaluation of the UHI mitigation effect of urban greening. This section analyzes vegetation distribution using satellite remote sensing for incorporation into the meteorological model.

74

Y. Hirano and T. Fujita

3.1 Normalized Difference Vegetation Index (NDVI) on Urban Surfaces Satellite remote sensing technology is often used as a method for efficiently grasping the spatial distribution of vegetation and its seasonal changes. The normalized difference vegetation index (NDVI) is a well-known, widely-applied method for determining the distribution of vegetation using satellite remote sensing. We have already analyzed the characteristics of the NDVI under different land use and land cover conditions in and around urban areas [40]. In the analysis, the reflectance of each land-cover category and the NDVI were linked and analyzed on the NDVI contour line on the visible near-infrared band coordinate. The details are omitted, and the characteristics of the NDVI by land use in urban areas are briefly shown. The definition formula of the NDVI is given in Eq. (1): N DV I =

N I R O B S − VO B S N I R O B S + VO B S

(1)

where V and NIR are the reflectance in the visible and near-infrared bands, respectively, and the subscript OBS is the observed value. The numerator is the difference in reflectance between the near-infrared and visible regions. This expresses the amount of vegetation by utilizing plant leaves’ unique reflection property, which allows them to absorb visible light and reflect nearinfrared rays. The denominator is the sum of these two bands; dividing by the overall brightness decreases susceptibility to the effects of terrain and building shadows. However, because the physical meaning of the NDVI is unclear, it is usually used as a relative indicator. In particular, it is known to have a high correlation with FVC and is often used for estimating FVC. Since it has been pointed out that the NDVI has a non-linear relationship with respect to FVC [41–43], it is necessary to examine the method for calculating FVC. Moreover, because it is known that non-green pixel coverage has a large effect on the NDVI [44, 45], there is high NDVI variation on urban land surfaces where the land-cover components are complex. Therefore, we used actual satellite remote sensing data to clarify the characteristics of the NDVI on the urban land surface, and we estimated the FVC.

3.2 Seasonal NDVI Variation Pattern on the Urban Land Surface The satellite data used in this study are optical sensor data collected by the Japanese Earth Resources Satellite-1 (JERS-1). Although the operation of JERS-1 has been concluded, the analysis introduced here can be applied to the data from various subsequent Earth observation satellites. We used the JERS-1 data from Band 2 (0.63– 0.69 μm) for the visible band (V-band) and that from Band 3 (0.76–0.86 μm) for the

An Estimation of Air-Conditioning Energy-Saving …

75

near-infrared band (NIR-band). Multitemporal data were used to analyze seasonal changes in vegetation, as discussed in Sect. 5. The data used in this study are shown in Table 2. Ideally, data from the same year should be used, but it was difficult to obtain data without clouds. For this reason, we used the data for 4 years, from 1994 to 1997, without distinguishing the specific year. To compare data from multiple scenes, it is necessary to eliminate the effect of differences in irradiance due to weather conditions. Therefore, atmospheric corrections should be made using the radiative transfer model and converted to surface reflectance for all scenes. This is difficult to accomplish because no accurate information about the atmospheric conditions at the time of data acquisition is available. Although it is possible to obtain an approximate value of the correction coefficient, assuming a standard atmospheric condition, the level of accuracy would be insufficient for comparing different scenes. Therefore, the method of relative atmospheric correction [46, 47] was applied by extracting the points that did not change seasonally and matching the data levels relatively. We used the data for July 5, which is the leafing stage, as a reference, and we performed matching as follows. First, using image interpretation, we selected 50 areas, such as asphalt, concrete, and water surface, where the reflectance would not change seasonally (Fig. 9a). Next, the average value of the pixels contained in each of these areas was calculated, and a regression equation was created using the data for July 5 as the objective variable and other data as explanatory variables (Fig. 9b). Then, the matched data were converted into radiance using the conversion formula that was used to convert the original satellite data into radiance. The ground surface reflectance was then calculated by performing atmospheric correction using the radiative transfer model MODTRAN 3.7, based on the US standard atmospheric model. Figure 10 shows the seasonal variation pattern of the NDVI, as calculated using the above-mentioned matched data. Although the individual patterns of each pixel are reflected when they are finally incorporated into the meteorological model, Fig. 10 shows only the average value for each land use. As a result, the forests, wastelands, riverbeds, and residential areas were the largest in July and the smallest in February, Table 2 Overview of satellite data

Satellite data (Path/Row)

JERS-1, OPS (Path: 064, Row: 241)

Utilized band

Visible band: Band 2 (0.63–0.69 μm) Near-infrared band: Band 3 (0.76–0.86 μm)

Data acquisition date

February 23, 1997 March 22, 1995 July 5, 1997 August 14, 1994 October 1, 1997 December 24, 1994

76

Y. Hirano and T. Fujita

(b) Created regression equations (example of 2/23 vs. 7/5) July 5, 1997

(a) Selected areas

y=42.2+1.26x (R=0.957)

Band 2

y=38.1+1.25x (R=0.970)

Band 3

February 23, 1997 Fig. 9 An example of the data matching process [40]

Fig. 10 Seasonal variation patterns in the NDVI by land-use category [40]

and the overall patterns were similar, although the ranges of fluctuation and the magnitude of absolute values differed. This fluctuation range and absolute value seem to accurately represent the amount of vegetation in each land use. On the other hand, the patterns for “rice paddy” and “vegetable fields” are clearly different from other land-use patterns. Hence, this is considered to be an artificial fluctuation pattern due to cultivation. The NDVI shows almost no seasonal change for built-up areas and rivers/lakes where there is almost no vegetation. Based on these facts, although it is often pointed out that the physical meaning of the NDVI is unclear, it seems it accurately expresses the amount of vegetation in urban areas with complex surface cover.

An Estimation of Air-Conditioning Energy-Saving …

77

3.3 FVC Method Based on the NDVI We have already proposed a method for estimating the FVC based on the NDVI, which takes urban surface characteristics [48] into account. Here, we provide an overview of our method. As mentioned above, the physical meaning of the NDVI is unclear, and it is pointed out that the NDVI is non-linear with respect to the FVC. Thus, the nonlinear relationship between the NDVI and the FVC is expressed through various functions, all of which show good agreement [42, 43]. However, such a non-linear regression method is limited in applicability because it requires data that reflect the FVC that was used as an explained variable in the FVC estimation. On the other hand, under such conditions, the method for calculating the FVC from the NDVI using the data of each pure pixel of green and non-green cover is employed [49–51]. A pure pixel consists of only a single land-cover category. This is a practical method because pure pixel data are often easily obtained from an image. However, in many existing studies, conversion from the NDVI to the FVC was performed via linear stretching, making the non-linearity of the NDVI with respect to the FVC a factor of error. In this study, we proposed a relational expression between the NDVI and the FVC by extracting pure pixels from an image and applying a linear mixture model, which is a method of more directly expressing the area ratio. The method is described as follows. Two preconditions were set: (1) each pixel has uniform coverage of the green and non-green surfaces, and (2) the category of the non-green surface can be set according to the land-use data. Setting such preconditions is practical because, in many cases, land-use data have been prepared in urban areas, and vegetation that cannot be reflected in land-use data, such as garden trees and roadside trees, may become problematic during UHI analysis. The definition of the NDVI is shown in Eq. (1). However, as mentioned above, the NDVI does not directly indicate the FVC. Therefore, we applied a linear mixture model to directly express the area ratio. As described above, this method assumes that the green and non-green cover within each pixel constitute a uniform land-cover category. In this explanation, it is assumed that the ground surface is composed of vegetation and soil. In this case, Eq. (2) can be assumed, if the effects of topographical shadows are ignored: VO B S = αVv + (1 − α)Vs N I R O B S = α N I Rv + (1 − α)N I Rs

(2)

Here, α: FVC, and the subscripts v and s denote the reflectance of pure pixels on the green and non-green surfaces (soil surface in this example), respectively. However, in reality, α cannot be fixed by Eq. (2) because it varies depending on topography and building shadow. Substituting Eq. (2) into Eq. (1) and solving for α yields the following equation:

78

Y. Hirano and T. Fujita

a × N DV I + b c × N DV I + d a = Vs + N I Rs

α=

b = Vs − N I Rs c = Vs − Vv + N I Rs − N I Rv d = Vs − Vv − N I Rs + N I Rv

(3)

where a, b, c, and d are constants and can be calculated based on the data of each pure pixel. Therefore, when each pure pixel is obtained, the FVC can be calculated from the NDVI using Eq. (3). Since the NDVI is a normalized index, fluctuation factors that proportionally affect both visible and near-infrared bands are removed, decreasing susceptibility to shade and shadow. For this reason, although Eq. (2) alone includes the shade and shadow arising from both the topography and the building as an error factor, as described above, Eq. (3) is robust to the influence of the shadow with respect to the NDVI. The estimation formula in Eq. (3) is superior to the nonlinear regression method [42, 43] because it theoretically expresses the relationship between the FVC and the NDVI, and because the FVC can be calculated using pure pixel data only.

3.4 FVC Estimation Result The FVC was estimated based on Eq. (3). We selected the data from July 5, when the NDVI was at the maximum shown in Fig. 10, as the data for the analysis of the leafing stage. When simulating seasonal changes, including the effect of deciduous trees, as discussed in Sect. 5, the data in Fig. 10 was interpolated by month, and the same FVC estimation method was applied to all the data (see Sect. 5.1). First, for each land-cover category shown in Table 1, the reflectance on pure pixels was extracted from the image data (Table 3). However, it was difficult to extract pure pixels on the road given the resolution of the satellite data. Therefore, the road was classified as urban land cover, without being distinguished from the building lot. Figure 11 shows the relationship between the NDVI and land coverage by landcover category, which was obtained by substituting the values in Table 3 into Eq. (3). Table 3 Extracted pure pixel data [48, 55]

Land-cover elements Tree

Visible band V (%)

Near-infrared band NIR (%)

2.62

44.13

Urban land cover, road

19.24

22.73

Residence

13.54

17.47

Soil

14.11

20.19

3.51

4.91

Water

An Estimation of Air-Conditioning Energy-Saving … Fig. 11 Relationship between the NDVI and the FVC [48]

79

Tree + Urban land cover / Road (

Tree + Residence

FVC䠄䠂䠅

Tree + Soil Tree + Water

NDVI It can be seen that, except for the combination of vegetation and water, both are almost linear; however, there is some variation when the FVC is low. In addition, in the case of a combination of vegetation and water, the NDVI is higher than in other categories when both are mixed to the same extent. The relationship between the NDVI and the FVC shown in Fig. 11 was applied to each pixel of the NDVI data to estimate the FVC (Fig. 12). The combinations of land-cover categories are set according to the land-use data, as shown in Table 4. The FVC data were created for the entire satellite data application range (see Fig. 1), but Fig. 12 shows only the target region. To verify this FVC estimation result, we compared the existing survey data for each special ward (Tokyo’s 23 wards) (Fig. 13). The survey was conducted in each ward using aerial photography interpretation, according to the Tokyo Metropolitan Government’s manual. Since this survey includes different survey years for each Fig. 12 Estimated FVC in the target area [48, 55]

80 Table 4 Combinations of land-cover elements [55]

Y. Hirano and T. Fujita Land-use types

Combination of land-cover elements (Vegetation + non-vegetation)

Forest/grassland, developed land, park/green zone

Tree + Soil

Rice paddy

(Rice paddy)

Vegetable field

(Vegetable field)

Built-up area, other public area, Any other areas

Tree + Urban land cover

Residential areas

Tree + Residence

Traffic use

Tree + Road

River/lake

Tree + Water

Sea

(Water)

ward, it would have been difficult to perform strict precision verification. However, these values are in good agreement and are considered to be sufficiently usable for the UHI simulation that is presented in the next section.

4 UHI Simulation Incorporating Fractional Vegetation Cover Data In the previous section, we created the FVC data for Tokyo using satellite remote sensing. In this section, we propose a method for conducting meteorological simulations by incorporating these FVC data. In previous meteorological simulations, it was common to set the land cover in meteorological models using land-use data. On the other hand, by utilizing realistic FVC data obtained through satellite remote sensing, it became possible to perform simulations that reflect vegetation that could not be captured in land-use data, such as street trees and planting trees in residential areas.

4.1 Urban Climate Simulations in Existing Research Many studies have evaluated the UHI mitigation effect of vegetation using urban climate simulations. In particular, in the evaluation of a scale of several tens of kilometers, including the entire city, the mesoscale meteorological model has been used to accurately represent the effects of local circulation in an area [9, 26, 35–39]. However, in evaluations using the mesoscale meteorological model, there is room for consideration as to whether the ground surface setting is realistic. In many studies, land-use data were used to set the ground surface of the meteorological model, but

An Estimation of Air-Conditioning Energy-Saving …

81

FVC (%)

Ward (year) Chiyoda (1995) Chuo (1996) Minato (1995) Shinjuku (1995) Bunkyo (1995) Taito (1996) Sumida (1990) Koutou(1992) Shinagawa (1994) Meguro (1992) Ota (1997) Setagaya (1997) Shibuya (1998) Nakano (1992) Suginami (1997) Toshima (1997) Kita (1998) Arakawa (1987) Itabashi (1994) Nerima (1996) Adachi (1994) Katsushika (1988) Edogawa (1993)

Survey result by each ward

This study

Fig. 13 Comparison of the existing survey results for each ward [48]

this method can neither express the effects of planting trees on residential land nor that of roadside trees that cannot be captured in land-use data. According to Tokyo statistics, the land-use rate in Tokyo’s wards is 0.1% for forests, 0.9% for wilderness, 1.4% for agricultural land, and 6.1% for parks. The parks include playgrounds, amusement parks, tennis courts, etc., so not all of them are green areas. On the other hand, according to the aerial photography survey of each ward in Tokyo, the FVC is about 15–20% (see Fig. 13). Therefore, it is clear that most of the vegetation in central Tokyo cannot be captured in land-use data. In addition, land-use data cannot express the effects of leaf fall. From the thermal environment evaluation viewpoint, the significance of vegetation is different in

82

Y. Hirano and T. Fujita

summer versus in winter and hence, summer-only evaluation is insufficient. For example, Sailor [52] compared the effects of vegetation by performing summer and winter simulations, without considering seasonal changes in vegetation. Therefore, the FVC data obtained by satellite remote sensing, as described in the previous section, were used as data for creating a realistic vegetation distribution. We have already proposed a UHI simulation method that combines the FVC distribution obtained by remote sensing with a mesoscale meteorological model [53]. To reiterate, this method enables evaluation based on a realistic vegetation distribution that cannot be grasped from land-use data, thus making it possible to perform simulations that also reflect the effects of fallen leaves in winter. An example of the evaluation of seasonal changes will be introduced in the next section.

4.2 Method for Incorporating FVC Data into the Meteorological Model In a simulation that covers the entire city, it is difficult to model the geometric shapes of the urban ground surface in detail. Therefore, in order to express ground surface coverage, it is common to divide the ground surface into grids and set parameters (called surface parameters) that represent the physical characteristics in each grid. However, in reality, it is difficult to accurately obtain the distribution of surface parameters. Therefore, in the conventional method, it is common to set the ground surface parameters for each land-use type and obtain the distribution of the ground surface parameters from the land-use data. The simulation performed in Sect. 2 was also based on this method. However, as mentioned above, most of the vegetation in urban areas cannot be reflected in land-use data. We propose a method in which the coverage category of the non-green surface is set using land-use data, and the ratio of mixed green surface is given in the FVC data. Figure 14 shows an outline of the proposed method [53]. Table 1 shows the dataset of surface parameters by land use that was utilized in previous studies [26]. In this study, these values were used as they are. However, these land-use items were set as land-cover elements, as shown in italics in Table 1, and they served as the parameters for each type of land cover. For example, forests can be regarded as a set of trees, so we redefined the parameters of forests as the parameters of the “Tree” land-cover category. Regarding building land, various land-cover categories such as asphalt, concrete, and roof tiles are mixed, even on non-green covered surfaces. Since it is difficult to grasp these individually, and they are averaged within the grid, here, we assumed the averaged categories of “Urban land cover” and “Residence.” Next, as shown in Table 4, combinations of land-cover categories for green and non-green land cover were set for each category, other than for agricultural land and sea, in the land-use data. However, for the agricultural lands and the sea areas that are indicated in parenthesis in Table 4, the FVC data were not used, and the parameters of each land use in Table 1 were used as before. This is because the appropriate parameters

An Estimation of Air-Conditioning Energy-Saving …

83

Fig. 14 Parameterization of the ground surface in the conventional method and in the present method, which includes FVC data [53]

of vegetation as a land-cover element could not be obtained for the agricultural lands. However, according to the Tokyo statistics, the area under agricultural lands is as small as 1.4% of the Tokyo ward area; thus, it is not considered to be a large error factor in this study’s target area. The surface parameters of the meteorological model were set by determining the combination of green and non-green cover categories for each grid of land-use data based on Table 4 and by giving the area ratio of green and non-green cover as the FVC data shown in Fig. 12. The calculation results using this ground surface parameter are introduced in the following subsections.

84

Y. Hirano and T. Fujita

4.3 Calculating and Verifying the Current Situation A simulation reflecting the current vegetation was conducted using the abovementioned surface parameters incorporating the FVC data (the standard case) [54, 55]. The simulation’s analysis target day was selected from the period July 29 to August 1, 1999, which represents typical sunny summer days. Since this study did not aim to represent a specific day’s meteorological phenomenon, the composite data for representing one day were compiled using the average of the selected period. The initial and boundary conditions were set based on the same data source as the simulation that is described in Sect. 2. The calculation results are shown in Fig. 15, in which it can be seen that a local high-temperature region occurs in the northern part of the target area during the daytime. Observational data also indicate that the northwestern part of Tokyo tends to experience high temperatures during the summer days [56, 57], which is a finding that is in good agreement. In order to verify the effectiveness of the FVC data application method that is proposed in this section, a simulation was performed using only the conventional land-use data, and the reproducibility was compared. In the case of land-use data only, the land surface parameters for each land-use category in Table 1 were directly applied to the land-use data. All other calculation conditions were the same, as in the case of applying the FVC data. In order to verify the accuracy of these calculation results, we compared the results with the temperature observation data (Fig. 16). The data used for verification were the observation data from the high-density urban climate observation network (see Fig. 1a). It can be seen that the temperature rises above the actual value when the ground surface parameters are set based only on the land-use data. This is because, as mentioned above, most of the vegetation in Tokyo cannot be captured in land-use data. In comparison, it can be seen that the case in which the FVC data is applied has a lower temperature and is closer to the observed value. Therefore, compared

Fig. 15 Simulation result at 9:00, 15:00, and 21:00 LST [54]

85

Calculated value (oC)

An Estimation of Air-Conditioning Energy-Saving …

Observed value (oC)

Observed value (oC)

Green coverage data applied

Land use data only

Fig. 16 Scatter diagrams of the calculated versus the observed temperatures [54]

with the conventional method, the effect of vegetation in the city is accurately represented by applying the FVC data obtained through satellite remote sensing, and the reproducibility of the current situation is observably improved.

4.4 UHI Mitigation Effect of Urban Vegetation We evaluated the UHI mitigation effect of the present urban vegetation. As an example of evaluating the climate mitigation effect of vegetation in Tokyo using a meteorological model, there is a research case in which the UHI mitigation effect of greening was evaluated [58]; however, this evaluation was based on given greening scenarios. In order to achieve applicability to more specific mitigation measures, it is necessary to evaluate the actual vegetation distribution. The method employed in this study can evaluate the effect of the actual vegetation using FVC data from satellite remote sensing. For comparison, a simulation was conducted under the assumption that there was no vegetation, and this was compared with the current simulation results (as the no-vegetation case). In the no-vegetation case, all grids of FVC data were set to 0%, and the method described above was applied to set the distribution of the ground surface parameters. However, agricultural lands used the wasteland parameters. Since this research aimed to evaluate the effect of urban green space, the area outside the FVC data (see Fig. 1b) was not changed. Figure 17 shows the daytime temperature differences between the two cases. It can be seen that vegetation reduced the temperature by more than 1.5 °C over a wide area in the western part of the target area. It is thought that this result is due to the fact that there are many low-rise residential areas in this area, and the FVC is relatively high. On the other hand, in the eastern part of the target area, the temperature reduction

86

Y. Hirano and T. Fujita

Fig. 17 Temperature reduction effect of the current vegetation at 15:00. The solid line rectangle represents the grid illustrated in Fig. 18, and the dashed rectangle represents the drawing area in Fig. 23 [54, 55]

Altitude (m)

(Standard case) – (No-veg. case) [oC]

Standard case No-vegetation case Potential temperature (K) Fig. 18 Vertical profile of the potential temperature in each case [54]

effect is smaller than in the west, at approximately 0.5 °C. Since the east includes dense urban areas in central Tokyo, dense residential areas in downtown areas, and reclaimed land in Tokyo Bay, low FVC is considered to be one of the factors. In addition, the sea breeze seems to have influenced this distribution form. It can also be seen that the cooling effect diminishes near rivers. This is because the vegetation’s evapotranspiration effect is weakened due to the relatively low temperature and high humidity near the sea breeze penetration area and in the vicinity of the river.

An Estimation of Air-Conditioning Energy-Saving …

87

Fig. 19 Changes in wind direction and speed due to vegetation (15:00, at a height of 100 m) [54]

Figure 18 shows the vertical distribution of the temperature potential resulting from this calculation. This figure shows an example of a grid in a residential area where the effect of lowering the temperature is considerable (the rectangle in Fig. 17). This vertical profile corresponds well with Yoshikado’s [59] observation results and is considered to accurately represent the target area’s typical temperature profile. In this figure, it can be seen that the vegetation effect reaches a height of approximately 600–800 m at 15:00. The effects of vegetation are diffused into the upper atmosphere due to the development of the daytime convective mixed layer. On the other hand, at night, the atmosphere is stable, and vegetation affects altitudes of approximately 100 m. Figure 19 shows changes in the wind direction and speed in the upper layer caused by vegetation. The wind speed ratio (the isoline in Fig. 19) is less than 100% in most areas, indicating that vegetation weakens wind speed mainly due to the decrease in sea breeze that is associated with the decrease in temperature. In addition, based on the vector difference (arrows in Fig. 19), it seems that the divergent component is relatively strong and centered on the northern part of the target area. This is because the effect of temperature reduction due to vegetation weakens the components of wind convergence that accompany the UHI. The layers near the ground were also plotted in the same way as in Fig. 19, but the distribution was complex and no clear features appeared. It is inferred that this is mainly because the lower layer is affected by the change in roughness due to vegetation, and the upper layer is strongly affected by the thermal effect due to the temperature change. Since it is difficult to discuss the former with a model of this scale, the lower layer illustration was omitted here. We examined the diurnal variation pattern of the temperature reduction effect of vegetation. To illustrate the temporal change, the average value of each block shown in Fig. 20 was calculated. Figure 21 shows the temperature that was calculated in each case and the temperature differences between the two cases. In this figure, it

88 Fig. 20 Division into blocks for plotting in Figs. 21 and 22 [54]

Y. Hirano and T. Fujita

Hokubu

Yamanote Tobu

Seibu

Shitamachi Nanbu

Toshin

Fig. 21 The diurnal variation patterns in each case and the differences between them [54]. The values given in parentheses in the legend indicate the FVC for each block

can be seen that the temperature reduction effect of vegetation is significant during the daytime. The decrease in temperature is mainly caused by an increase in the latent heat flux due to evapotranspiration; therefore, the effect is large during the day because of high solar radiation coupled with high air temperature. The climatic effect of vegetation would be significant in urban areas because the daytime thermal environment in summer becomes the most serious problem in relation to energy consumption for air conditioning and the peak load of electric power. Comparing each block, the temperature reduction effect is small in the Shitamachi, Tobu, and Nanbu blocks, and large in the Seibu Hokubu and Yamanote blocks. Based on this spatial distribution, it can be said that the sea breeze exerts a sizable influence, as described above. However, since the FVC is higher in the western part of the target

An Estimation of Air-Conditioning Energy-Saving …

89

Fig. 22 Difference between the case where the FVC was set as 15% and the standard case. The symbols are the same as in Fig. 21 [54]

h (FVC 15% case) – (No-veg. case)

area, both effects should be distinguished and clarified. Therefore, in order to clarify the susceptibility of vegetation to such regional conditions, a similar simulation was conducted with a uniform FVC of 15% (Fig. 22). In this case, the FVC was set to 15% for all grids, other than the water area. Daytime temperature variations by block were more than 1 °C, even after the FVC was set to be uniform, as shown in Fig. 22. Thus, the sea breeze is believed to have exerted a considerable influence. In order to clarify the effect of vegetation on the thermal environment, Fig. 23 shows the diurnal variation of the surface heat balance in the western part of the target area (the dashed rectangle in Fig. 17). The change in heat balance due to vegetation occurs because the latent heat flux increases owing to the effect of evapotranspiration, and the surface temperature decreases with the decrease in the sensible heat flux. The latent heat flux increased by approximately 250 W/m2 around 12:00 to 13:00. It should be noted that previous observations in Tokyo also reported that latent heat flux was generated in office districts and residential areas [60], which is consistent with this result. The above examination shows that vegetation contributes to UHI mitigation in urban areas. The evaluation method in this section can be applied to a more specific

Fig. 23 Diurnal variation patterns of surface heat balance in a the standard case and b the novegetation case [55]

90

Y. Hirano and T. Fujita

evaluation of the effects of urban greening. An example of an evaluation case study is presented in the next section.

5 Evaluation of the UHI Mitigation Measure of Greening Using Deciduous Trees The impact assessment in Sect. 2 showed that under Tokyo’s climatic conditions, it is appropriate to take measures to mitigate the thermal environment in summer and avoid getting cold during winter. In Sect. 3, using satellite remote sensing, we analyzed the seasonal variation of the NDVI and proposed a method for estimating the FVC from the NDVI. In Sect. 4, we proposed a method for linking the current FVC data with urban climate simulations, and we evaluated the UHI mitigation effect of urban green areas. In this section, by combining these methods, the energy-saving effect of urban greening using deciduous trees is evaluated, with consideration of seasonal changes.

5.1 Setting up a Greening Scenario First, a greening scenario was set up [61]. In order to evaluate the potential of UHI mitigation and energy conservation through greening, it is necessary to consider the area where trees can be planted under the current land-use conditions. However, because it is difficult to accurately extract the area where trees can be planted for the entire Tokyo area, we assumed the following greening measures as a scenario. In this research, we assume the greening of the ground that can maintain the current urban activities, and we target only non-building land. Even on non-building land, there are areas where trees cannot be planted, such as parking lots and areas that are in contact with buildings. Therefore, in the four building types, namely offices, commercial facilities, condominiums, and detached houses, it was assumed that the upper 5% of the (FVC)/(non-building coverage) ratio is the upper limit of the greening of existing facilities. These four building types occupy 70% of the building land in the target area. Figure 24 shows a scatter plot of the non-building coverage ratio and the FVC for these four building types. The FVC is the data derived from satellite remote sensing in Sect. 2. For this examination, we used the data from July 5, when the NDVI was the largest. The non-building coverage ratio was calculated from the building polygon data within the Geographic Information System data created by the Tokyo Metropolitan Government. The boundary where the (FVC)/(non-building coverage) ratio is the upper 5% on this scatter plot is marked by the dashed line in Fig. 24. In this study, this dashed line is assumed to be the upper limit of greening at existing facilities.

An Estimation of Air-Conditioning Energy-Saving …

91

Fig. 24 Scatter plot of the non-building coverage ratio and the FVC [61]

For facilities other than the above-mentioned four building types, there was little data, which made it difficult to perform the same examination. For these building types and outdoor use sites, it was therefore assumed that trees could be planted on up to 20% of the non-built-up land; this value references a calculation by the green buildings committee within the Tokyo Metropolitan Government. The Tokyo Metropolitan Government’s estimation was also used for other building types. These setting values are listed in Table 5. The current level for grids was maintained, for which the current FVC data have already exceeded the values in Table 5. Figure 25 shows the greening scenario. According to the FVC data (Fig. 12), the current FVC in Tokyo is approximately 13.4%, whereas, according to the greening scenario, the FVC after greening was 25.2%. Assuming that the current land use is unchanged, this is considered to be the upper limit of greening in Tokyo. Incidentally, this scenario presents an example of UHI evaluation, and it should be kept in mind that the actual greening possibility must be considered separately. Next, we examined the setting of seasonal variations in vegetation. The FVC was calculated from the NDVI shown in Fig. 10 using Eq. (3) and then interpolated by month. Figure 26 shows only the average value for each land use, but all grids have different patterns for the actual calculation. The FVC data for the greening case was set by providing seasonal changes to the greening scenario. The seasonal variation pattern of the greening area was set as shown by the bold line in Fig. 26. This pattern was created based on the seasonal

92 Table 5 Setting the greening scenario [61]

Y. Hirano and T. Fujita Land-use types

FVC after greening

Office buildings

3.6

54.1% of the non-building area

Commercial buildings

5.4

44.8% of the non-building area

Detached houses

20.7

49.6% of the non-building area

Apartment houses

13.4

50.4% of the non-building area

Other building areas

17.8

20% of the non-building area

Outdoor use Road

(a) FVC after greening

Area ratio (%)

5.8

20%

19.8

10%

Park

6.6

50%

Unused land

3.9

100%

Forest

1.1

100%

(b) Increment of FVC due to greening

Fig. 25 Greening scenario that was set for evaluation in this study [61]

forest variation pattern because the areas to which the “Forest” category refer are considered to be mainly covered with trees, although evergreen trees and bare land are mixed. On the other hand, the greening scenario in this study assumes greening using deciduous trees, and in summer, it must match Fig. 25. Therefore, the seasonal variation pattern of the greening area (the bold line in Fig. 26) was set by linearly transforming the “forest” pattern, so that the minimum value becomes 0% and the

Fig. 26 Seasonal variation in FVC for the current land use and the greening area [61]

93

FVC (%)

An Estimation of Air-Conditioning Energy-Saving …

Greening area Residential area Built-up area

Month Rice paddy

Wasteland

Vegetable field Forest

River and lake Riverbed

maximum value becomes 100%. For the greening case, the increment of the monthly FVC due to greening was then set by multiplying the greening part (Fig. 25b) by this seasonal change pattern and adding the product to the monthly FVC data for the standard case. Note that this is just a greening scenario that was set up as an evaluation example. The actual significance of the UHI evaluation method that is proposed in this study is to enable various actors, such as local governments, planners, and civic groups, to evaluate specific greening plans.

5.2 Classification of Surface Wind Systems and Seasonal Variation Patterns When expressing seasonal changes, it is necessary to consider the seasonal differences in weather conditions. In particular, it is known that the surface wind system strongly affects the formation of the UHI, so the influence of seasonal winds cannot be ignored. Therefore, it is necessary to select analysis target days for the urban climate simulation with consideration to seasonal winds. For this purpose, the surface wind system generated in the Kanto plain was classified [62] by applying Suzuki and Kawamura’s [63] cluster analysis method to wind direction and wind speed data for the Kanto plain centering on Tokyo. The seasonality of each wind system type is also shown. However, although Suzuki and Kawamura’s [63] analysis shows the difference in the frequency of appearance of each wind system type depending on the time, they do not consider diurnal variation patterns, which constitute an important key for classification for the Kanto plain, given that it is strongly affected by land and sea breezes. Therefore, in this research, cluster analysis was performed, including the diurnal variation pattern.

94

Y. Hirano and T. Fujita

In this study, we used the data for 3 years, from 1989 to 1991. The method is as follows. First, the wind direction and wind speed data were decomposed into east– west and north–south components, which were designated as U and V, respectively. Here, U and V at time t from observation point i on the target day a are U ati and V ati , respectively. Next, for all these target days, the “distance” from other target days was defined as follows: Dab =

T N  1  (Uati − Ubti )2 + (Vati − Vbti )2 T · N t=1 i=1

(4)

where Dab is the distance between target days a and b, N is the number of observation points, and T is the number of wind system diagrams used for each target day. In this study, we used the wind system diagram at 6:00, 9:00, 12:00, 15:00, 18:00, and 21:00 on each target day (T = 6). These target days were defined as clusters, and the clusters were integrated in order of increasing distance. The integrated clusters formed new clusters, and clusters with smaller distances were integrated again. By repeating this process and gradually increasing the integrated distance, the target days were grouped into several clusters. The distance between the new cluster and other clusters when the clusters were integrated was defined as follows: Dck =

n a Dak + n b Dbk na + nb

(5)

where Dck is the distance between the new cluster c, which is a combination of clusters a and b, and another cluster k, and na , and nb represent the number of days included in clusters a and b, respectively. The wind system diagrams were created from the U and V components of each cluster in stages integrated into seven clusters (Fig. 27). The frequency of the appearance of each wind system type was counted by month, and the seasonal changes were clarified (Fig. 28).

Fig. 27 Wind system diagrams classified by cluster analysis [62]. The numbers shown in the figure indicate the frequency of the appearance of each wind system type

95

Number of days (3 years)

An Estimation of Air-Conditioning Energy-Saving …

Month

Fig. 28 Monthly appearance frequency of each wind system type [62]

The characteristics of each wind system will now be discussed. Although only the example at 15:00 is shown in Fig. 27, the analysis results are considered by referring to the diurnal variation of the wind system, the weather chart, and the meteorological conditions of each cluster [62]. In this figure, wind systems with a relatively high frequency of occurrence are E-, F-, and A-types. The E-type wind system, which has a high frequency of appearance, is generally calm and has little seasonality. In this study’s definition of distance in the cluster analysis, the clusters were integrated across a small distance on a day with a low wind speed; thus, calm days were grouped into one cluster. Following the E-type, the F- and A-types also appear frequently. These are considered to be typical summer and winter wind systems, respectively. Other types occur less frequently, but B-, C-, and D-types occur mainly in winter, while the G-type occurs during summer. In addition, B- and D-types represent typical winter days, but they see more sunny days than the A-type and are classified into different clusters. In particular, for type B, the weather chart showed the atmospheric pressure distribution in the west as high and in the east as low, which is characteristic of winter. The C-type also occurs mainly in winter, but the wind direction is different. We investigated the weather chart and confirmed that the C-type was the wind system when the Boso Front [64] occurred. Both G- and F-types are summer types, but the wind speeds are different. Both were affected by the average sea breeze in the summer, but the G-type had a distinctive southern high-pressure and northern low-pressure distribution. The above examinations confirmed that the results of the cluster analysis were reasonable. In this study, we selected E-, F-, and A-types, which have a high frequency of occurrence among the wind system types, as simulation targets.

96

Y. Hirano and T. Fujita

5.3 UHI Mitigation Effect of Urban Greening We conducted monthly UHI simulations using the same mesoscale meteorological model as in Sects. 2 and 4 [65]. The evaluation targets were the current FVC (the standard case) and the increased FVC based on the greening scenario (the greening cases). The above-mentioned monthly FVC data for both cases (see Fig. 26) were applied to the ground surface of the mesoscale model using the method described in Sect. 4. The meteorological conditions to be calculated were set as follows, based on the results of the cluster analysis described above (see Figs. 27 and 28). In the summer simulation, the calculation conditions were set according to the average value of the days classified into E- and F-types from April to September, and the monthly meteorological simulation was conducted. At that time, the E- and F-types were simulated separately, and when analyzing the results, the temperature obtained by weighted averaging the calculated results according to each wind system’s frequency of appearance was used. Similarly, in winter, simulations were conducted on the days classified into E- and A-types from October to March, and the results were weighted averaged by appearance frequency. Figure 29 shows the temperature differences between the current case and the greening case based on the calculation results for July. In this figure, it can be seen that the temperature reduction effect is large mainly in the northwestern part of the target area, while it is smaller in the coastal area than in the inland area. The temperature cooling effect was greatest in the rectangular grid shown in Fig. 29; this grid had a maximum temperature reduction effect of approximately 0.7 °C (at 13:00). Figure 30 shows the monthly and hourly variation patterns of the temperature reduction effect in this grid. There is a large temperature reduction effect mainly during the daytime in summer. In this study’s deciduous tree greening, it is assumed that all tree planting sites fall in February (see Fig. 26), so the temperature reduction effect is 0 °C. Based on these examinations, the effect of greening using deciduous

(Greening case) - (Standard case) [oC] Fig. 29 Calculation result for the temperature reduction effect by greening at 9:00, 15:00, and 21:00. The solid line rectangle represents the grid illustrated in Fig. 30 [65]

An Estimation of Air-Conditioning Energy-Saving …

97

Fig. 30 Monthly and hourly temperature reduction by greening [65]

(Greening case) - (Standard case) [oC]

trees can be represented as a UHI mitigation measure that is effective during summer without leading to cooling during winter.

5.4 Evaluating the Energy-Saving Effects Based on the temperature distribution obtained through the above-mentioned simulation, the energy-saving effect was calculated according to the method that was introduced in Sect. 2 (see Fig. 2). This method combines the monthly and hourly specific energy consumption by building type and energy use, the monthly and hourly temperature grid, the floor area, and the number of households grid data. Using this method, energy consumption can be calculated based on the spatial and temporal distributions of both temperature and energy consumption. The same data mentioned in Sect. 2 were used for the specific energy consumption estimation equation, the building floor area in the commercial sector, and the number of households in the residential sector. Figure 31 presents examples of the calculation results. Although a calculation was performed for each building type, the results were complex; hence, the following discussion is based on the results that were compiled for the residential and commercial sectors. Figure 32 shows the difference in monthly energy consumption between the current case and the greening case for the entire target area. It can be seen that in the commercial sector, energy-saving effects have been obtained over a long period, from April to October, centering on July. The annual energy consumption reduction for space cooling in the commercial sector was calculated to be 1340 TJ/year. On the other hand, in the residential sector, although the energy consumption for space cooling decreased, the energy consumption for water heating also increased to an unignorable amount. The energy-saving effect occurred between June and

98

Y. Hirano and T. Fujita

Fig. 31 Examples of the calculation results for energy consumption in each grid

September, which was shorter than in the commercial sector. In addition, the increase in energy consumption for water heating in the residential sector was smaller than the energy-saving effect of space cooling during the summer, but the period was slightly longer. In April and November, energy consumption for space heating also increased slightly. In the residential sector, the energy consumption for water heating is relatively large because bathing/showering account for a large proportion of hot water demand. Although the temperature does not significantly affect the hot water demand itself, the temperature of tap water is strongly affected by the air temperature, so the energy consumption for water heating is very susceptible to air temperature. This effect causes an increase in energy consumption due to UHI mitigation, even in summer. For this reason, although this study evaluated the UHI mitigation measure using deciduous trees, the cooling energy savings were almost offset by the increased energy consumption effect of heating in the residential sector. For the total value of the three energy applications, an energy-saving effect of about 80 TJ/year for the residential sector, approximately 1170 TJ/year for the commercial sector, and about 1260 TJ/year for both sectors can be expected. In order to examine the spatial distribution of the energy-saving effect, we calculated the change in annual energy consumption in both sectors by grid cells (Fig. 33). As a result, the energy-saving effect is generated over a wide range near Marunouchi, but in the densely-populated area on the west side, there is an area where the energysaving effect is larger locally. The peak magnitude of the energy-saving effect on a grid basis was seen in Ikebukuro, Shinjuku, Shibuya, and Marunouchi, in descending order. The energy consumption is very large around Marunouchi, which is the center

An Estimation of Air-Conditioning Energy-Saving …

99

[ TJ/mon ]

(a) Commercial sector

Cooling Heating Hot water

Month

[ TJ/mon ]

(b) Residential sector

Cooling Heating Hot water

Month Fig. 32 Differences in monthly energy consumption in each case in a the commercial sector and b the residential sector [65] Fig. 33 Spatial distribution of the differences in monthly energy consumption in each case [65]

Ikebukuro Shinjuku

[ TJ/km2/yr ] Marunouchi

Shibuya

(Greening case - Standard case)

100

Y. Hirano and T. Fujita

of Tokyo. However, the UHI mitigation effect is not large because there is little room for greening (see Fig. 25b), and Marunouchi is affected by sea breeze (see Fig. 29). On the other hand, the UHI mitigation effect was relatively large in the inland residential area on the west side of the target area (see Fig. 29). Therefore, a large peak in the energy-saving effect occurred in dense commercial areas within residential areas such as in Ikebukuro, Shinjuku, and Shibuya. However, the peak magnitude may depend on accidental grid divisions. Thus, in areas where local energy-saving effects are large, it is important to conduct detailed evaluations using atmospheric simulations and air-conditioning load simulations on finer block scales, and link them to realistic greening plans. Figure 34 shows the hourly energy consumption for cooling in July, heating in November, and hot water supply in July. It can be seen that there is a sizable energy consumption reduction during the daytime in the commercial sector. As show in Fig. 30, the UHI mitigation effect of greening is strong during the daytime in summer; thus, it is likely to have an effect on cooling in the commercial sector. In contrast, although there was a concern that the space-/water-heating energy consumption in the residential sector would increase, it is found to be relatively less affected compared to the commercial sector. Therefore, urban greening is deemed an appropriate measure in Tokyo. In general, some UHI countermeasures may be effective during the day (e.g., increasing evapotranspiration and albedo, or using sea breeze) and may also be effective during the night (e.g., preventing heat storage on urban surfaces or using katabatic winds from mountains). However, considering that commercial energy consumption increases due to the UHI, the former should be given higher priority under the regional conditions in Tokyo. To contribute to the efforts to resolve global climate-related issues, it is important to focus on and quantify CO2 emission reduction. Thus, CO2 emission reductions

Fig. 34 Hourly energy consumption in each case and the differences between them in terms of a space cooling in July, b space heating in November, and c water heating in July

An Estimation of Air-Conditioning Energy-Saving …

101

(a) Commercial sector

[t-CO2/mon]

0

1 2 3 4 5 6 7 8 9 10 1112 Month

-5000

-10000 -15000

(b) Residential sector [t-CO2/mon]

4000 2000 0 -2000

1 2 3 4

5

6 7 8 9 101112 Month

-4000

Space cooling

-6000

Space heating

-8000

Water heating (Greening case - Standard case)

Fig. 35 Monthly CO2 emission reduction in each case in a the commercial sector and b the residential sector

were calculated by assuming the ratio of energy sources for each energy use based on the existing data [66–68]. Actually, the patterns of primary energy consumption were found to be similar to CO2 emission patterns, and monthly CO2 reductions are shown here as an example (Fig. 35). The other results are almost identical. However, the balance between CO2 emissions from fuel and electricity are expected to change due to future improvements in power generation efficiency and the introduction of renewable energy. Therefore, it is important to not only evaluate energy conservation but also CO2 reduction.

6 Conclusion In this chapter, we presented an example of the evaluation of energy consumption for UHI countermeasures in Tokyo. In Sect. 2, we proposed a method for estimating the distribution of energy consumption according to the temperature distribution. This method makes it possible

102

Y. Hirano and T. Fujita

to calculate energy consumption by associating the spatial and temporal changes of both temperature and human activity, which show continuous change. Using this method, we conducted an impact assessment and found that in Tokyo’s climate, UHI increases energy consumption in the commercial sector and decreases it in the residential sector. Therefore, it is highly possible that various UHI mitigation measures can conserve energy in central Tokyo, but it is necessary to select mitigation measures that will not cause cooling during winter in the surrounding residential areas. This demonstrates that appropriate measures differ according to climatic and human activity conditions. Therefore, when introducing measures in other areas where urbanization is in progress, it is necessary to carry out a similar careful pre-assessment. In Sect. 3, we used the NDVI obtained from satellite remote sensing to clarify the seasonal pattern of vegetation in urban land use, and then we estimated the FVC. We proposed an FVC estimation method that considers the characteristics of an urban land surface composed of complex covering components. This method can express the non-linear relationship between the NDVI and the FVC with the values of visible and near-infrared pure pixels. Based on satellite data, the method would be particularly useful in areas where urbanization is currently in progress, since there may be insufficient local information on land use and land cover. In Sect. 4, we proposed a method for incorporating the FVC data into the meteorological model and conducted a simulation using the FVC data described in Sect. 3. It was confirmed that this method improved reproducibility. The effect of current vegetation on the urban climate in Tokyo was clarified by expressing the effect of a realistic vegetation distribution based on satellite remote sensing. This method made it possible to represent the effects of small vegetation such as street trees and garden trees that cannot be captured in land-use data. Therefore, as satellite data are available in many other cities, this method can be used as a general-purpose urban climate simulation method. In Sect. 5, we evaluated the energy-saving effect of greening by combining the methods proposed in Sects. 2–4. Although this evaluation was carried out based on the greening scenario that was set up as an example, this method can be used to evaluate local governments’ specific greening plans. It was shown that greening deciduous trees would not result in increased energy consumption, even in the residential sector. There is a high possibility that the CO2 reduction effect will be obtained by greening in many realistic urban blocks where residential and commercial buildings are mixed. As this result is based on an evaluation of Tokyo, it cannot, as mentioned above, be applied to other areas where there are different climatic conditions. Therefore, similar evaluations are required for each city wherein UHI mitigation measures are to be implemented. The method presented in this study can be utilized as a general method for evaluating the influence of UHI countermeasures on urban energy consumption. Since energy saving is an important global warming countermeasure, attention must be directed towards the CO2 reduction effect. Hence, evaluating the CO2 reduction effect is an important next step, following the evaluation of the energy-saving effect. We recently evaluated the energy savings and CO2 reduction potential of UHI countermeasures using an urban block-scale model, and this examination has

An Estimation of Air-Conditioning Energy-Saving …

103

already been published in international journals (see Refs. [14–16] for details). In the block-scale simulation, it was possible to evaluate the effects of building-level measures such as rooftop greening and high-albedo walls by coupling a local atmospheric model and an air-conditioning load calculation model. However, it is difficult to evaluate CO2 reduction for the entire city with various building types using urban block-scale simulation. Therefore, we are developing integrated evaluation methods by combining the method introduced in this chapter with more detailed simulations. The energy consumption estimation method shown in this research may also be applicable to district heating and cooling planning and electric energy management in microgrids. In Japan, in the wake of the 2011 Great East Japan Earthquake and the accident at the Fukushima nuclear power plant, the significance of regional-scale renewable energy and distributed energy systems has been strongly recognized from both environmental and disaster prevention perspectives. In recent years, we have also been conducting research on the planning and evaluation of regional energy management systems in the context of reconstruction town development in tsunami-affected areas [69]. Detailed demand predictions are necessary to expand the system’s supply area or introduce a similar system in other regions. In particular, because renewable energy, such as solar and wind power generation, is unstable, unpredictable, and uncontrollable, it is necessary to create a plan based on detailed demand-side predictions. Furthermore, to introduce such a system into a hot, humid area, plans must be made in consideration of the urban block’s thermal environment as well as building energy saving. Therefore, we plan to integrate the method proposed in this study with detailed building and meteorological models, and utilize it for planning and evaluation in various regions.

References 1. Davies M, Steadman P, Oreszczyn T (2008) Strategies for the modification of the urban climate and the consequent impact on building energy use. Energy Policy 36:4548–4551 2. Rizwan AM, Dennis LYC, Liu C (2008) A review on the generation, determination and mitigation of urban heat island. J Environ Sci 20:120–128 3. Santamouris M (2014) On the energy impact of urban heat island and global warming on buildings. Energy Build 82:100–113 4. Li X, Zhou Y, Yu S, Jia G, Li H, Li W (2019) Urban heat island impacts on building energy consumption: a review of approaches and findings. Energy 174:407–419 5. Santamouris M (2014) Cooling the cities—a review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments. Sol Energy 103:682–703 6. Ojima T (1990/91) Changing Tokyo metropolitan area and its heat island model. Energy Build 15–16:191–203 7. Ichinose T, Matsumoto F, Kataoka K (2008) Counteracting urban heat islands in Japan. In: Droege P (ed) Urban energy transition from fossil fuels to renewable power. Elsevier, Amsterdam, pp 365–380 8. Hirano Y, Fujita T (2012) Evaluation of the impact of the urban heat island on residential and commercial energy consumption in Tokyo. Energy 37:371–383 9. Dhakal S, Hanaki K (2002) Improvement of urban thermal environment by managing heat discharge sources and surface modification in Tokyo. Energy Build 34:13–23

104

Y. Hirano and T. Fujita

10. Akbari H, Konopacki S (2005) Calculating energy-saving potentials of heat-island reduction strategies. Energy Policy 33:721–756 11. Assimakopoulos MN, Mihalakakou G, Flocas HA (2007) Simulating the thermal behaviour of a building during summer period in the urban environment. Renew Energy 32:1805–1816 12. Ohashi Y, Genchi Y, Kikegawa Y, Kondo H, Yoshikado H, Hirano Y (2007) Influence of airconditioning waste heat on air temperature in Tokyo office areas during summer—numerical experiments using an urban canopy model coupled with a building energy model. J Appl Meteorol Climatol 46(1):66–81 13. Ohashi Y, Kuroyanagi H, Kikegawa Y, Oka K, Shigeta Y, Hirano Y, Kusaka H, Chen F (2010) Influence of differences in urban structure and electric power demand on atmospheric thermal environment in Osaka urban area. J Hydrosci Hydraul Eng 28(1):87–101 14. Hirano Y, Yoshida Y (2016) Assessing the effects of CO2 reduction strategies on heat islands in urban areas. Sustain Cities Soc 26:383–392 15. Hirano Y, Fujita T (2016) Simulating the CO2 reduction caused by decreasing the air conditioning load in an urban area. Energy Build 114(15):87–95 16. Hirano Y, Ihara T, Gomi K, Fujita T (2019) Simulation-based evaluation of the effect of green roofs in office building districts on mitigating the urban heat island effect and reducing CO2 emissions. Sustainability 11(7) 17. Kikegawa Y, Genchi Y, Yoshikado H, Kondo H (2003) Development of a numerical simulation system toward comprehensive assessments of urban warming countermeasures including their impacts upon the urban buildings’ energy-demands. Appl Energy 76:449–466 18. Kikegawa Y, Genchi Y, Kondo H, Hanaki K (2006) Impacts of city-block-scale countermeasures against urban heat-island phenomena upon a building’s energy-consumption for air-conditioning. Appl Energy 83:649–668 19. Kondo H, Liu FH (1998) A study on the urban thermal environment obtained through onedimensional urban canopy model. J Jpn Soc Atmos Environ 33:179–192 (in Japanese) 20. Kondo H, Genchi Y, Kikegawa Y, Ohashi Y, Yoshikado H, Komiyama H (2005) Development of a multi-layer urban canopy model for the analysis of energy consumption in a big city: structure of the urban canopy model and its basic performance. Boundary-Layer Meteorol 116:395–421 21. Hirano Y, Niitsu K, Ohashi Y, Ichinose T (2004) Observation of surface temperature and surface heat budget on concrete coated with high-albedo paints. Pap Environ Inf Sci 18:247–252 (in Japanese) 22. Hirano Y, Ohashi Y, Fujino T (2005) Evaluation of cooling effects of high-albedo paints by data analysis and numerical simulation of surface heat budget. Pap Environ Inf Sci 19:31–36 (in Japanese) 23. Hirano Y, Ichinose T (2006) Observation and characterization of the surface heat budget on a rooftop planted with sedum. Environ Eng Res 43:661–672 (in Japanese) 24. Fung WY, Lam KS, Hung WT, Pang SW, Lee YL (2006) Impact of urban temperature on energy consumption of Hong Kong. Energy 31:2623–2637 25. Ihara T, Genchi Y, Sato T, Yamaguchi K, Endo Y (2008) City-block-scale sensitivity of electricity consumption to air temperature and air humidity in business districts of Tokyo, Japan. Energy 33:1634–1645 26. Ichinose T, Shimodozono K, Hanaki K (1999) Impact of anthropogenic heat on urban climate in Tokyo. Atmos Environ 33:3897–3909 27. Yamaguchi Y, Shimoda Y, Mizuno M (2007) Proposal of a modeling approach considering urban form for evaluation of city level energy management. Energy Build 39:580–592 28. Hamilton IG, Davies M, Steadman P, Stone A, Ridley I, Evans S (2009) The significance of the anthropogenic heat emissions of London’s buildings: a comparison against captured shortwave solar radiation. Build Environ 44:807–817 29. Narumi D, Kondo A, Shimoda Y (2009) Effects of anthropogenic heat release upon the urban climate in a Japanese megacity. Environ Res 109:421–431 30. Hirano Y, Kikegawa Y, Genchi Y, Kondo H (2006) A comparison of the estimation methods used to determine the air temperature sensitivity of energy consumption by air-conditioning and hot water systems in offices and residences. Energy Resour 27(3):218–224 (in Japanese)

An Estimation of Air-Conditioning Energy-Saving …

105

31. Hirano Y, Gomi K, Nakamura S, Yoshida Y, Narumi D, Fujita T (2017) Analysis of the impact of regional temperature pattern on the energy consumption in the commercial sector in Japan. Energy Build 149:160–170 32. Pielke RA (1974) A three-dimensional numerical model of the sea breezes over South Florida. Mon Weather Rev 102:115–139 33. Ulrickson BL, Mass CF (1990) Numerical investigation of mesoscale circulations over the Los Angeles Basin. Part I: a verification study. Mon Weather Rev 118:2138–2161 34. Kessler RC, Douglas SG (1992) User’s guide to the systems applications international mesoscale model (version 2.0). Systems Applications International, California. SYSAPP-92085 35. Urano A, Ichinose T, Hanaki K (1999) Thermal environment simulation for three dimensional replacement of urban activity. J Wind Eng Ind Aerodyn 81:197–210 36. Ichinose T (2003) Regional warming related to land use change during recent 135 years in Japan. J Global Environ Eng 9:19–39 37. Hirano Y, Yasuoka Y, Ichinose T (2004) Urban climate simulation by incorporating satellitederived vegetation cover distribution into a mesoscale meteorological model. Theor Appl Climatol 79:175–184 38. Shu J, Ichinose T, Yang X, Jiang T, Zhang C (2005) Monitoring and numerical simulation of urban heat island in Shanghai, China. In: Feng H, Yu L, Solecki W (eds) Urban dimensions of environmental change—science, exposures, policies and technologies. Science Press, China, pp 67–78 39. Hirano Y, Ichinose T, Imura H, Shiraki Y (2009) Simulation evaluation of mitigation effect of urban heat island by watering. Annual J Hydraul Eng 53:307–312 (in Japanese) 40. Hirano Y (2001) Characterization of normalized vegetation index of urban land use and its seasonal variation pattern. J Architect Plann (Transactions of AIJ) (548):75–82 (in Japanese) 41. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62:241–252 42. Dymond JR, Stephens PR, Newsome PF, Wilde RH (1992) Percentage vegetation cover of a degrading rangeland from SPOT. Int J Remote Sens 13:1999–2007 43. Purevdorj T, Tateishi R, Ishiyama T, Honda Y (1998) Relationships between percent vegetation cover and vegetation indices. Int J Remote Sens 19:3519–3535 44. Yun D, Hoyano A (1998) Index to extract vegetation cover ratio in a pixel in urban areas. J Remote Sens Soc Jpn 18(3):228–240 (in Japanese) 45. Montandon LM, Small EE (2008) The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sens Environ 112:1835–1845 46. Caselles V, López García MJ (1989) An alternative simple approach to estimate atmospheric correction in multitemporal studies. Int J Remote Sens 10:1127–1134 47. Awaya Y, Tanaka N (1999) Seasonal spectral changes in cool temperate forests: an analysis using landsat TM images. J Jpn Soc Photogrammetry Remote Sens 38(4):35–46 (in Japanese) 48. Hirano Y, Yasuoka Y, Shibasaki R (2002) Pragmatic approach for estimation of vegetation cover ratio in urban area using NDVI. J Remote Sens Soc Jpn 22(2):163–174 (in Japanese) 49. Qi J, Marsett RC, Moran MS, Goodrich DC, Heilman P, Kerr YH, Dedieu G, Chehbouni A, Zhang XX (2000) Spatial and temporal dynamics of vegetation in the San Pedro River basin area. Agric For Meteorol 105:55–68 50. Matsui T, Lakshmi V, Small EE (2005) The effects of satellite-derived vegetation cover variability on simulated land–atmosphere interactions in the NAMS. J Clim 18(1):21–40 51. Wang X, Sun Z, Zhou A-G (2014) Alpine cold vegetation response to climate change in the western Nyainqentanglha range in 1972–2009. Sci World J 514736 52. Sailor DJ (1998) Simulations of annual degree day impacts of urban vegetative augmentation. Atmos Environ 32:43–52 53. Hirano Y, Yasuoka Y, Ichinose T (2004) Urban climate simulation by incorporating satellitederived vegetation cover distribution into a mesoscale meteorological model. Theoret Appl Climatol 79:175–184

106

Y. Hirano and T. Fujita

54. Hirano Y, Yasuoka Y, Ichinose T (2003) Evaluation of the mitigation effects on summer thermal stress by urban vegetation in Tokyo metropolitan area. Proc Hydraul Eng 47:7–12 (in Japanese) 55. Hirano Y, Yasuoka Y, Ichinose T (2004) Evaluation of effect of vegetation on urban heat island by using satellite remote sensing data and mesoscale meteorological model. Environ Sci 17(5):343–358 (in Japanese) 56. Hirano Y, Kaya Y (1998) An analysis of the spatio-temporal structure of urban air temperature in the southern part of Kanto plain. Theory Appl GIS 6(2):1–10 (in Japanese) 57. Hirano Y, Ichinose T, Kaya Y (1999) Estimation of temperature distribution and its application to evaluation of urban thermal environment in Tokyo. Environ Eng Res 36:49–59 (in Japanese) 58. Takeuchi T, Hirano Y, Ichinose T (2003) The effects of green open space on the urban heat island in Tokyo. J Japan Inst Landscape Architect 66(5):893–896 (in Japanese) 59. Yoshikado H (1992) Numerical study of the daytime urban effect and its interaction with the sea breeze. J Appl Meteorol 31:1146–1164 60. Moriwaki R, Kanda M (2004) Seasonal and diurnal fluxes of radiation, heat, water vapor, and carbon dioxide over a suburban area. J Appl Meteorol 43(11):1700–1710 61. Hirano Y (2005) Urban heat island mitigation and its effects on energy consumption: a case study of urban greening in the Tokyo metropolitan area on typical summer and winter days. J Environ Eng (Transactions of AIJ) 70(591):75–82 (in Japanese) 62. Hirano Y, Shibasaki R (2001) Classification of the wind systems in Kanto plain by using cluster analysis. Proc Hydraul Eng 45:235–240 (in Japanese) 63. Suzuki R, Kawamura T (1989) Surface airflow patterns and their relation to synoptic pressure fields in central Japan. Geogr Rev Jpn 62A:375–388 (in Japanese) 64. Mizuno T, Kondo H, Yoshikado H (1993) Study on mechanism and cause of local front formed across the Tokyo Bay—relation to air pollution. Tenki 40(3):171–180 (in Japanese) 65. Hirano Y, Imura H (2009) Year-round evaluation of urban heat island mitigation and its effects on energy consumption by urban greening using deciduous trees. Pap Environ Inf Sci 23:179– 184 (in Japanese) 66. Hirano Y, Tonooka Y, Ikaga T, Kikegawa Y, Fujinuma Y, Shimoda Y (2008) A comparative analysis of the unit energy consumption in the commercial sector. J Environ Eng (Transactions of AIJ) 73(633):1331–1339 (in Japanese) 67. Hirano Y, Inaba R, Sakai K, Hayabuchi Y, Osako M (2013) Detailed estimation of energy consumption in the commercial sector by business type. Environ Sci 26(5):430–439 68. Hirano Y, Ihara T, Hara M, Honjo K (2020) Estimation of direct and indirect household CO2 emissions in 49 japanese cities with consideration of regional conditions. Sustainability 12(11) 69. Hirano Y, Nakamura S, Gomi K, Togawa T, Fujita T, Oba M (2018) Introduction of low-carbon community energy systems by combining information networks and cogeneration-type district heating and cooling systems. In: Ghenai C, Salameh T (eds) Sustainable air conditioning systems. IntechOpen, UK, pp 99–120

Urban Heat Island, Contributing Factors, Public Responses and Mitigation Approaches in the Tropical Context of Malaysia Nasrin Aghamohammadi, Logaraj Ramakreshnan, Chng Saun Fong, and Nik Meriam Sulaiman Abstract Urban Heat Island (UHI) is a notable thermal phenomenon of any tropical city in relation to increased urbanization. It records a positive urban thermal balance due to higher air temperatures in the densely built areas compared to the rural or sub-urban peripheries under the same climate conditions. The rapid infrastructure development in high-risk areas of tropical cities will be exposing the urban population to extreme heat. As predicted by International Panel on Climate Change (IPCC) climate change scenario, some of the cities in Southeast Asia may be as much as 4 °C warmer by 2050. Being a Southeast Asian country, this would be a consequential threat to the capital cities of Malaysia which suffered inevitable territorial urban development that manifested into formation of severe UHIs with an average gain in surface temperature of 8.47 °C between 1997 and 2013. The increasing surface temperature is mainly associated with the reduction in vegetation cover, open burning, forest fires, land use changes, land clearing, industrial and traffic emissions. Besides, it also exhibits the potential to emerge as one of the public health menace with reduced outdoor thermal comfort levels, heat exhaustions, heat N. Aghamohammadi (B) Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: [email protected] L. Ramakreshnan · C. S. Fong · N. M. Sulaiman Department of Social and Preventive Medicine, Faculty of Medicine, Centre for Occupational and Environmental Health, University of Malaya, 50603 Kuala Lumpur, Malaysia e-mail: [email protected] C. S. Fong e-mail: [email protected]; [email protected] N. M. Sulaiman e-mail: [email protected] L. Ramakreshnan · C. S. Fong Institute for Advanced Studies, University of Malaya, 50603 Kuala Lumpur, Malaysia N. M. Sulaiman Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_5

107

108

N. Aghamohammadi et al.

cramps and respiratory ailments among the tropical city dwellers in various urban settings. To overcome this, a number of mitigation approaches such as increase of vegetation cover, replacement of cooling pavement materials and architectural innovations are studied as viable UHI remedies in the context of Malaysia. In addition, target driven assessments are intended to meet the city population’s health needs to assist in designing initiatives to effectively reduce UHI effects. In line with these, this chapter would provide the state-of-art of UHI, known contributing factors and impacts, community needs and other mitigation efforts targeting at urban temperature reductions via case study approaches in the context of Malaysia. Keywords Mitigation approaches · Public responses · Sustainable tropical city · Outdoor thermal comfort · Urban heat island · Urban microclimate

1 Introduction Cities and emerging townships play a crucial economic role in improving people’s liveability by leveraging upon their strength of advanced infrastructure development and job opportunities. Indeed, with an addition of almost 1.5 million people per week, future estimates highlight that about 66% of the earth’s population will be accumulated in world-major cities [1]. As a result, major cities in the world are experiencing unprecedented growth both horizontally and vertically to cater escalating socioeconomic demands which has profound consequences on urban climate and sustainability [2, 3]. Population explosion coupled with the rapid developments in these cities bring some notable perturbations in the energy balance that modify the surrounding thermal environment [4, 5]. The rapid infrastructure development in high-risk areas of tropical cities will be exposing the urban population to extreme heat. One of such manifestations is the artificial warming of the urban climate system, known as Urban Heat Island (UHI) [6, 7]. As predicted by Intergovernmental Panel on Climate Change’s (IPCC) scenario, some of the cities in the Southeast Asia may be as much as 4 °C warmer by 2050. Being a Southeast Asian country, this would be a consequential threat to the capital cities of Malaysia which suffered inevitable territorial urban development that manifested into formation of severe UHIs with an average gain in surface temperature of 8.47 °C between 1997 and 2013 in the heart of Klang Valley [8]. Apparently, the urban heating phenomenon grabbed a noticeable attention as the country was occasionally experiencing many consequential impacts of rising temperatures such as flash flood, heat waves, water shortages and even rare episodes of hail storms [9]. Indeed, earlier observations elucidated that the capital city of Kuala Lumpur (KL) was getting hotter by 0.6 °C per decade [10]. Other than this, it placed an immense burden on public health with reduced outdoor thermal comfort levels, heat exhaustions, heat cramps and respiratory ailments among city dwellers in various urban settings. To overcome this, a number of mitigation approaches and target driven assessments are studied as viable UHI remedies in the context of Malaysia. Therefore, this chapter would provide the state-of-art of UHI, known

Urban Heat Island, Contributing Factors, Public Responses …

109

contributing factors and impacts and mitigation efforts targeting at urban temperature reductions via case study approaches in the context of Malaysia.

2 Essence of UHI UHI phenomenon is a type urban thermal pollution which occurs due to the absorbance and reflectance properties of urban materials in densely built environment compared to its rural peripheries [11]. Thermally massive urban materials alter the biophysical properties of urban surfaces that modify sensible heat dissipation, convection efficiency and evaporative cooling effects in the cities [12]. In addition, other aspects such as urban structures (orientation, sky view factor, aspect ratio), urban fabric (land use, road network, parks) and urban metabolism (energy and material flow) are equally modified resulting in the enhancement of solar radiation absorption and the reduction of latent heat release that lead to the exacerbation of UHI effects [13, 14]. As a consequence, UHI adds additional warmth to the urban atmosphere that creates a contrasting thermal environment compared to the rural fringes [15]. Studies across the metropolitan cities have reported that the UHI Intensity (UHII) can vary between 0 and 2 °C [15, 16] and can reach up to 10 °C in the extreme cases [17]. Notwithstanding the pivotal influence of UHI on global warming, IPCC has recognized this phenomenon as an emerging human-driven threat to the Earth’s climate regimes [18]. Meanwhile, experts have deduced that elevating urban temperatures are not an exclusive product of urbanization or population-induced UHI phenomenon alone [19], whereas it is a product of interrelated factors comprises of air pollution [20, 21], microclimate conditions [22, 23] and other anthropogenic activities [24, 25]. For instance, encapsulation of greenhouse gases in the cities’ atmosphere due to anthropogenic and industrial emissions is deemed to be one of the main contributors to urban heating as it absorbs excessive solar energy which will be retained to warm the Earth’s surface [26]. While registering intense UHI effects, cities that comprises of higher aspect ratio canopies (ratio of building height and street width) also accumulates higher pollution concentration levels due to lower air exchange rate and heavy traffic load. The trapped urban aerosols contributes to radiative forcing on the Earth’s climate system that consequently alter global temperature distributions and precipitation patterns [27]. Besides being economically established, Malaysia is envisioned to be among the top 20 most liveable cities in the world by year 2020. Yet, the rapid urbanization over the past few decades have led to the deterioration of the natural and built environment, particularly in the city centre. The formation of Urban Heat Island (UHI) is prominent with observable urban-rural air temperature difference. A typical UHI profile is presented in Fig. 1.

110

N. Aghamohammadi et al.

Fig. 1 Urban Heat Island profile

3 UHI Status in the Cities of Malaysia The first foundation for UHI assessment in Malaysia was in the 70s by Sani, who is the pioneer UHI researcher in Malaysia [28]. Sani’s in situ data collection using temperature traverse technique in and around KL for about two decades revealed that the temperature distributions were normally higher in the city centre compared to the surrounding rural areas [29]. In 2004, Elsayed innovatively combined both traverse survey method and weather station networks method and identified an increase of 1.5 °C in UHII for KL city compared to a similar study done by Sani in 1985 [30]. This study demonstrated the emergence of more heat islands in the city centre while many cool islands disappeared due to the absorption and retention of heat by thermally bulk urban materials. After a decade, Yusuf et al. reported an average gain of 8.4 °C in surface temperature between 1997 and 2013 in Greater KL using remote sensing applications [8]. By integrating satellite data with the Geographical Information System (GIS), Shaharuddin et al. found that the intermediate monsoon during daytime of October displayed the highest UHI intensity of 14.5 °C in Klang Valley [31]. Later on, Shaharuddin et al. demonstrated the formation of UHIs in the urban areas of Kepong, Jinjang, Segambut, Sentul, Setapak and Bangsar with surface temperatures exceeding 32 °C in accordance with different land use patterns in 1988 [32]. Apart from this, Hashim et al. retrieved remotely sensed data of both surface temperature and land cover for Selangor in 1988 and discovered that the fast expanding areas such as Kajang, Cheras and Bandar Baru Bangi have the highest surface temperatures (27.5 °C) due to intense thermal energy responses of artificial impervious surface covers such as asphalt and concrete [33]. Morris et al. applied a single-layer urban canopy model coupled with the WRF/NOAH LSM modelling system in Putrajaya and discovered that the UHII varied temporally and spatially with a maximum magnitude of 3.1 °C in 2012 [34]. In another study, Morris et al. used the same simulation approach to investigate the local urban climate changes over a decade (1999–2011) in Putrajaya and concluded that the canopy layer temperature of the area was increasing at the rate of 1.66 °C per decade whereas the prevailing UHII

Urban Heat Island, Contributing Factors, Public Responses …

111

Fig. 2 Diurnal variations of canopy-level UHII in GKL

of the area was approximately 2.1 °C [35]. By using Automated Principal Weather Stations’ coverage, Ramakreshnan et al. observed the highest UHI intensity of 1.7 °C in Petaling Jaya (PJ) compared to another urban station, Subang (SUB), both located in Greater KL [15] (Fig. 2).

4 Contributing Factors to UHI A number of studies devoted towards exploring the contributing factors of UHII. Reduction in the vegetation, adjacency to a green park and rapid land conversion to commercial and residential areas are deemed to be the main factors to high urban temperatures. In Shah Alam, Buyadi et al. reported that the built-up areas in adjacent to the National Botanic Garden showed an increase of 0.42 °C in surface radiant temperature between 1991 and 2001 due to the replacement of natural vegetation by man-made urban materials such as concrete, stone, metal and asphalt for over a decade [36]. Reduction in the vegetation declines evapotranspiration that dissipates heat from the soil and urban surfaces. In another study, Buyadi et al. estimated about 7.2 °C of surface temperature increase in the built-up area of Shah Alam city centre over a period of 18 years (1991–2009) with a relative decrease of 17.48% of vegetation due to rapid urban sprawl [37]. Meanwhile, land use changes and climate were also described as the contributing factors of UHI in the local context. For instance, Salleh et al. further evaluated land use changes and historical climate data to quantify the urbanization and its impacts towards the thermal behaviour of Putrajaya [38]. They found that the surface temperature in the well-built areas increased steadily

112

N. Aghamohammadi et al.

Table 1 A summary of factors of UHI Studies

Study area

Factors

Results

Buyadi et al.

Shah Alam

Vegetation loss and urbanization

Increase of 0.42 °C between 1991 and 2001

Buyadi et al.

Shah Alam

Vegetation loss (17.48% loss)

Increase of 7.2 °C between 1991 and 2009

Salleh et al.

Putra Jaya

Land use changes and climate

Increase of 4.85 °C between 1999 and 2006

Thani et al.

Putra Jaya

Landscape morphology, urban geometry, land cover and land use activities

Highest temperature of 39 °C at the boulevard area with impermeable, paved surfaces and with adjacent buildings

(4.85 °C) between 1999 and 2006 due to heavy urbanization activities between 2006 and 2009. Further comparison with global heating data indicated that the remarkably high sea levels in 2006 may have contributed to the peaking temperature record in that year before it declined in the following years [38]. In Putrajaya, Thani et al. identified that the highest temperatures (39 °C) were recorded at the boulevard area with impermeable, paved surfaces and buildings which were intensely warmed by solar radiation and tend to store heat more rapidly than natural materials [39]. The forested land situated at the northern part of Putrajaya comparatively registered the lowest temperatures (32.5 °C) during the study period. The summary of the results of the reviewed studies were presented in Table 1.

5 The Deterioration of Outdoor Thermal Comfort Level in Tropical Cities The deterioration of outdoor thermal comfort is a foreseeable threat for tropical cities as it is the most documented direct effect of UHI on public health in various urban settings [40]. It is postulated that future urbanization will influence the daily surface temperature variations in terms of narrower temperature gap [41]. Despite the worsening of the situation, the lack of studies focusing on outdoor thermal comfort within the Southeast Asia region is upsetting. Through their critical appraisal, Fong et al. [40] identified that scientific studies related to outdoor thermal comfort in the tropical region of Southeast Asia are mostly concentrated in Malaysia (10) and Singapore (6) while countries such as Indonesia (3), Vietnam (1) and Thailand (1) have three or lesser studies. The tropical countries which experience hot and humid climate annually are at higher risk from the aforementioned narrow temperature gap. Due to the complexity of the interaction in between the natural environment, built environment and the human occupants, it is not easy to set a common ground for comparing the outdoor thermal comfort level within the tropical climate especially due to the lack of research in this area. As such, a critical appraisal by Fong et al.

Urban Heat Island, Contributing Factors, Public Responses …

113

[40] has highlighted the limitations and suggested holistic approach for the conduct of outdoor thermal comfort research in the tropical context of South East Asia. Among one of the crucial findings, the lack of validation to associate the physiological and psychological outdoor thermal comfort level hinders the identification of the actual outdoor thermal comfort level among urban communities. This is especially important in a tropical climate where the communities are found to exhibit higher tolerance towards the hot-humid climate due to acclimatization and thermal adaptation. Besides the deterioration in outdoor thermal comfort level, the health impact which arises due to UHI is also a rising concern [40]. From a large-scale survey involving 1050 respondents, Wong et al. [42] identified that majority of the respondents (n = 947, 90.2%) reported respiratory problems followed by heat exhaustion (n = 873, 83.1%) and heat cramps (n = 766, 72.9%) while depression (n = 679, 64.7%) is the most reported psychological symptom. However, more scholarly studies are clearly needed to obtain research evidence to elucidate UHI-driven physical and psychological health impacts in future.

6 UHI Mitigation Approaches in Malaysia 6.1 State-of-Art of the UHI Mitigation Studies in Malaysia Many mitigation approaches were studied as potential UHI remedies in Malaysia. A growing interest was devoted on evaluating the potential remedies for UHI effect to create a conducive urban microclimate. In an attempt to enhance the microclimate and thermal comfort sensation of urban parks, Nasir et al. simulated three different scenarios of tree shades in Shah Alam and identified that the dense and matured trees sustained the microclimate of the park by lowering both air temperature (max. 0.2 °C) and mean radiant temperature (max. 15.8 °C), thereby increasing the relative humidity and maintaining the wind flow [43]. In another approach of studying the influence of land cover profile on urban cooling, Buyadi et al. discussed that the cooling effect increased with the distance from the park boundary. The study deduced that about 3.17 °C of cooling intensity provided by the green areas was only significant within 500 m distance from the parks [44]. Ahmed et al. examined the influence of street canyon characteristics and the morphology of blocks in determining the surface temperature distributions in Putrajaya [45]. They found that clustered trees along the street were effective in reducing 9% of the surface temperature compared to scattered and isolated trees. At the same time, 5 °C of surface temperature reductions were recorded in areas where a high-albedo with polished white granite materials covered the building walls. In addition, high surface temperature reductions (3–5 °C) and wall temperature reductions (9 °C) were recorded in areas where the taller buildings and trees in the boulevard provide shading effects during the intense solar radiation [45]. In another study, Benrazavi et al. examined the role of different pavement materials on urban heat reduction in Putrajaya on a clear

114

N. Aghamohammadi et al.

and sunny day. A significant surface temperature reduction of 15.6% (9.00 a.m. to 12.00 p.m.) and 13.7% (12.00 p.m. to 3.00 p.m.) was recorded by Blue Impala under shaded location compared to near water or open space [46]. This study also provides a solid evidence that material’s thermal behaviour changes according to location and time. Therefore, a careful selection of materials in specific regions should be done to assist the efforts on urban temperature reduction. Besides, Davis et al. described an architectural invention of building houses on triangular land within larger hexagonalshaped land, which is known as ‘Honeycomb Townships’ developed at University Putra Malaysia as thermal comfort housings that require no air conditioning even on the hottest days of the year [10]. As a viable mitigation for UHI in Malaysia, this technology was targeted to reduce the electricity consumption spent on air conditioners and potentially save Malaysian Ringgit (MYR) 200 billion over the next 30 years that is the equivalent of USD 48,934,405.59. However, the efficiency of this town planning tool on enhancing the thermal comfort in cities is still under research and needs real time data for validation after the commencement and completion of the project. In Muar district, Johor, Malaysia, Rajagopalan et al. investigated the effect of urban geometry and wind flow on UHI intensity in the city centre [47]. Numerical simulations of various urban geometry modifications showed that step up configuration was the most effective geometry to distribute the wind evenly along the leeward side of the buildings. This improved the overall natural ventilation and thermal comfort at pedestrian level. Contemporarily, a significant number of researchers also attempted to study the feasibility of adopting green technologies implemented in the temperate countries to the local context. For instance, Sanusi et al. conducted a preliminary investigation to explore soil temperatures at various depths to identify the role of soil as heat sinks in Selangor [48]. This preliminary study provided a baseline data to evaluate the potential of Earth Air Heat Exchanger (EAHE) technology as a cooling means in various building typologies in Malaysia, particularly when the air temperature went beyond 34 °C [48]. As a conclusion, the role of vegetation on urban temperature reduction often gains the interest of local researchers although the use of different pavement materials and architectural innovations are occasionally studied as viable remedies. Nonetheless, future studies need to focus more on the creative and innovative use of greeneries in the form of green roofs, green façades, green corridors and green pavements to reduce the intensification of UHI effects in Malaysia.

6.2 UHI Mitigation Potential in the Context of Urban Design and Greening The consequences of poor urban planning are impacting the urban thermal environment and the communities within it. UHI directly increases the surface air temperature in the city centre and prolonged the time needed for heat dissipation due to

Urban Heat Island, Contributing Factors, Public Responses …

115

the urban canyon effect. Indirectly, UHI is encouraging the over-dependence on airconditioning to provide cooling that result in a spike in building energy usage that also contributes to global greenhouse gas emission. Hence, rapid measures need to be undertaken now before the situation reaches an irreversible point. From the perspective of urban planning, researchers from various countries and climate have highlighted the role of urban design and greening to address the UHI impact. While other countries from different climate are struggling to achieve a balance in urban design between the summer and winter, the urban design and greening approach in the tropical climate is much straightforward. The influences of urban configurations on the formation of heat island and thermal comfort conditions have brought research attention to understand the relationship between urban design and microclimate. Urban outdoor spaces are important because they accommodate daily pedestrian traffic and various outdoor activities that encourage the growth in both social and economic aspects besides contributing greatly to urban liveability and vitality. Under these circumstances, ensuring that pedestrians are well served by outdoor spaces is essential to high-quality urban living. Over the past decades, making outdoor spaces attractive to people and encourages the usage of it has been increasingly recognized as a goal in urban planning and design. Among many factors that determine the quality of outdoor spaces, the outdoor microclimate is an important issue which is greatly influenced by urban design. It has been identified that shading or exposure are the most important factors to determine the comfort level in a hot and humid climate country. Thus, modifying the physical features of the city in relation to solar access and wind orientation would alter the above-mentioned variables which determine the outdoor thermal comfort level near ground surface. Such approach emphasizes the important role of urban planners and urban designers in creating a favourable urban microclimate from the early design stage. Integrating climatic considerations into urban planning and design would contribute significantly to the sustainable urban development and mitigate the adverse effects of increased urban surface air temperature. In terms of urban design, aspect ratio is one of the key parameters in determining the canyon geometry which will influence the heat dissipation rate as well as wind-access near ground surface level. The aspect ratio is defined as the ratio between the average height (H) of the canyon walls and the canyon width (W) [49] as illustrated in Fig. 3. The canyon is considered uniform if it has an aspect ratio of approximately equal to 1, shallow if the canyon has an aspect ratio below 0.5, and deep if the aspect ratio equals 2. Night time air temperature has been directly linked to the magnitude of the aspect ratio, which means that higher aspect ratio results in higher night time air temperature [50–54]. Radiative losses [51, 55] and penetration of the undisturbed wind [56] are lessened in deep canyons. Shading has been reported as the main reason behind the reduced level of thermal discomfort in cities. A significant reduction has been shown in physiological equivalent temperature (PET), an index representing thermal comfort conditions, from shade enhancement and increased aspect ratio. Narrow streets are known to provide better shading from neighbouring buildings for pedestrians on sidewalks than wide streets. A simulation study with RayMan model of five different locations in the hot-humid climate of Colombo, Sri Lanka indicated

116

N. Aghamohammadi et al.

Fig. 3 Illustration of aspect ratio (H/W)

that deep street canyons (i.e., highly shaded streets) present the greatest potential in ameliorating heat stress at outdoor urban spaces [57]. The findings confirmed that tall buildings effectively increase the comfort level during the daytime owing to their shading effect at the street level. Urban canyons tend to channel the wind similar to natural canyons and play a key role in enhancing wind speed and dissipating excess heat from urban areas. The cooling benefits of the wind contribute to the reduction of heat island and thermal stress in cities. Wind speed of 1–1.5 m/s is found to reduce air temperature by almost 2 °C [58]. Wind pattern is highly governed by the placement, geometry, and shape of the built-up and open areas in the city. As one of the determinant factors of canyon geometry, aspect ratio controls air movement at the pedestrian level. Deep canyons have lower air temperatures and offer more favourable thermal condition for pedestrians during summer due to the lower level of exposure to the sun [59]. Consequently, the compact urban form will result in better thermal conditions during summer. However, it should be emphasized that the alteration towards aspect ratio to improve the microenvironment may not be entirely practical because of various reasons such as high expenditure costs, issues with environmentally sensitive/protected area, etc. Thus, the aspect ratio should be considered at the earlier stages of the urban planning process.

Urban Heat Island, Contributing Factors, Public Responses …

117

On the other hand, urban greening is also considered as another effective measures in ameliorating urban heat impact in a tropical climate. Cities are considered hostile environments for greenery because of the high level of impervious surfaces, reduced level of soil moisture, lack of nutrient and rooting volume, and presence of air/water pollutants [60]. Relevant literature indicated that application of vegetation in urban areas would alter the microclimate parameters such as air temperature, relative humidity, wind pattern and precipitation [61]. Therefore, urban greening has always been recommended as an important adaption strategy and significant approach to mitigate the heat island phenomenon and reduce the health-related consequences of increased air temperatures. Extensive field-based measurements revealed that green areas are usually cooler than the surrounding built-up areas, leading to a temperature difference of up to 1–7 °C. The vegetation introduced to the urban environment cools cities via shading, evapotranspiration, and alteration of wind pattern. Shading from tree canopies provides cooling to the atmosphere by intercepting solar radiation and preventing the rise in air/surface temperature. However, it is important to note that the quality of shading depends on the placement, canopy height, leaf size, and structure of the tree. Besides that, trees can also provide other environmental benefits such as filtering the air pollutants, reducing ambient noise level and stabilizing soil content. On top of that, the introduction of trees would also result in numerous economic benefits. For example, several studies showed significant summer energy saving by providing shade trees next to buildings. In the tropical city of Bangalore, India, street segments with trees recorded lower ambient air temperature by 5.6 °C on average [62]. Shade trees cool the environment by reducing the use of artificial air-conditioning and decreasing air temperature [63]. A field measurement conducted in a hot-humid climate condition of Singapore showed a temperature difference of 1.5–2.8 °C between tree canopies and surrounding areas. All the simulation and field-based measurement studies supported the effective role of street trees on outdoor pedestrian thermal comfort, but many researchers argued that this condition might not be the case in all directions around the buildings. Certain studies stated that the cooling effect of tree canopies are maximum in EastWest oriented streets where thermal discomfort is the greatest. Planting street trees is a promising strategy for reducing air and surface temperature as well as decreasing the summer time discomfort. However, as mentioned earlier, the position of the tree in relation to the buildings, the distance between the tree and building, the height, form, and structure of the tree canopy have to be considered in maximizing their impact on the improvement of urban climate. The cooling potential of the trees is not only influenced by the attributes of the tree but also depend on the surrounding environments such as geometry, building heights and density as well as the surface materials. In fact, cooling benefits of street trees are highly localized and vary spatially and temporally. In summary, various urban design and greening measures have shown to be reliable approaches in mitigating the localized urban heat impact, especially among tropical hot-humid cities. In future studies, the application of 3D computational fluid dynamic urban microclimate software such as ENVI-MET should be encouraged to identify

118

N. Aghamohammadi et al.

local urban heat island as well as simulating various mitigation measures to improve the urban microclimate. On top of that, more studies can be conducted to bridge the gap pertaining the best practice of urban design and greening. By doing this, the impact from the urban heat island phenomenon can be contained and reduced to ensure high level of living standard within the tropical city.

7 Conclusion In conclusion, this chapter provided the state-of-art of UHI, known contributing factors and impacts, community needs and other mitigation efforts targeting at urban temperature reductions via case study approaches in the context of Malaysia. The formation of UHI is being studied as early as in the 70 s and the intensity of UHI were found to range in between 1.5 and 14.5 °C depending on the type of measuring approaches and the selected study area. A number of studies were also devoted towards exploring the contributing factors of UHII. In general, the reduction in the vegetation, adjacency to a green park and rapid land conversion to commercial and residential areas are deemed to be the main factors to high urban temperatures. In return, the warmer city centre because of the UHI phenomenon causes the deterioration of outdoor thermal comfort level. Besides the deterioration in outdoor thermal comfort level, the health impact which arises because of UHI is also a rising concern. However, more scholarly studies are clearly needed to obtain research evidence to elucidate UHI-driven physical and psychological health impacts in future. As the impact from UHI towards the natural environment, built environment as well as the human occupants within the urban setting is supported by prominent evidences, many researches were dedicated to identify mitigation approaches as potential UHI remedies in the context of Malaysia. In particular, various urban design and greening measures have shown to be reliable approaches in mitigating the localized urban heat impact, especially among tropical hot-humid cities. In future studies, the application of 3D computational fluid dynamic urban microclimate software should be encouraged to identify local urban heat island hotspots as well as simulating the various mitigation measures to improve the urban microclimate. On top of that, more studies can be conducted to bridge the gap pertaining the best practice of urban design and greening in the context of Malaysia to ensure the urban development to be align with the United Nation’s Sustainable Development Goal. Acknowledgements The authors would like to express their gratitude to the University of Malaya as this study is supported by University of Malaya Grand Challenges Research Grant (GC002A15SUS), University of Malaya Living Lab Grant Programme (UMLL038-18SUS) and University of Malaya Partnership Grant (RK003-2017). Competing Interest None.

Urban Heat Island, Contributing Factors, Public Responses …

119

References 1. U.U. Nations (2018) United Nations Population Division, New York, World Urbanization Prospects: The 2018 Revision. Last accessed May 2018 2. Jamei Y, Rajagopalan P, Sun QC (2019) Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Sci Total Environ 659:1335–1351 3. Qi JD, He BJ, Wang M, Zhu J, Fu WC (2019) Do grey infrastructures always elevate urban temperature? No, utilizing grey infrastructures to mitigate urban heat island effects. Sustain Cities Soc 46:101392 4. Li K, Chen Y, Wang M, Gong A (2019) Spatial-temporal variations of surface urban heat island intensity induced by different definitions of rural extents in China. Sci Total Environ 669:229–247 5. Yang Q, Huang X, Tang Q (2019) The footprint of urban heat island effect in 302 Chinese cities: temporal trends and associated factors. Sci Total Environ 655:652–662 6. Gunawardena K, Kershaw T, Steemers K (2019) Simulation pathway for estimating heat island influence on urban/suburban building space-conditioning loads and response to facade material changes. Build Environ 150:195–205 7. Rizvi SH, Alam K, Iqbal MJ (2019) Spatio-temporal variations in urban heat island and its interaction with heat wave. J Atmos Sol-Terr Phys 185:50–57 8. Yusuf YA, Pradhan B, Idrees MO (2014) Spatio-temporal assessment of urban heat island effects in Kuala Lumpur Metropolitan city using landsat images. J Indian Soc Remote Sens 42(4):829–837 9. Ramakreshnan L, Aghamohammadi N, Fong C, Ghaffarianhoseini A, Ghaffarianhoseini A, Wong L, Hassan N, Sulaiman N (2018) A critical review of Urban Heat Island phenomenon in the context of Greater Kuala Lumpur, Malaysia. Sustain Cities Soc 39:99–113 10. Davis MP, Reimann GP, Ghazali M (2005) Reducing urban heat island effect with thermal comfort housing and honeycomb townships. Paper presented at sustainable building South East Asia conference, Malaysia (2005, April). Retrieved from https://www.irbnet.de/daten/ico nda/CIB_DC23495.pdf 11. Sultana S, Satyanarayana ANV (2020) Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000–2018 over a sub-tropical Indian City. Sustain Cities Soc 52:101846 12. Cao C, Lee XH, Liu SD, Schultz N, Xiao W, Zhang M, Zhao L (2016) Urban heat islands in China enhanced by haze pollution. Nat Commun 7:7 13. He BJ, Zhao ZQ, Shen LD, Wang HB, Li LG (2019) An approach to examining performances of cool/hot sources in mitigating/enhancing land surface temperature under different temperature backgrounds based on landsat 8 image. Sustain Cities Soc 44:416–427 14. Jato-Espino D (2019) Spatiotemporal statistical analysis of the Urban Heat Island effect in a Mediterranean region. Sustain Cities Soc 46:101427 15. Ramakreshnan L, Aghamohammadi N, Fong CS, Ghaffarianhoseini A, Wong LP, Sulaiman NM (2019) Empirical study on temporal variations of canopy-level Urban Heat Island effect in the tropical city of Greater Kuala Lumpur. Sustain Cities Soc 44:748–762 16. Huang Y, Yuan M, Lu Y (2019) Spatially varying relationships between surface urban heat islands and driving factors across cities in China. Environ Plan B: Urban Anal City Sci 46(2):377–394 17. Levermore G, Parkinson J (2019) The urban heat island of London, an empirical model. Build Serv Eng Res Technol 40(3):290–295 18. Ramakreshnan L, Aghamohammadi N, Fong CS, Ghaffarianhoseini A, Wong LP, Noor RM, Hanif NR, Aziz WNAWA, Sulaiman NM, Hassan N (2019) A qualitative exploration on the awareness and knowledge of stakeholders towards Urban Heat Island phenomenon in Greater Kuala Lumpur: critical insights for urban policy implications. Habitat Int 86:28–37

120

N. Aghamohammadi et al.

19. Mika J, Forgo P, Lakatos L, Olah AB, Rapi S, Utasi Z (2018) Impact of 1.5 K global warming on urban air pollution and heat island with outlook on human health effects. Curr Opin Environ Sustain 30:151–159 20. Agarwal M, Tandon A (2010) Modeling the effects of urban heat islands generated mesoscale wind on air pollution dispersion in a patchy atmosphere. Nat Resour Model 23(1):48–78 21. Wang Y, Du H, Xu Y, Lu D, Wang X, Guo Z (2018) Temporal and spatial variation relationship and influence factors on surface urban heat island and ozone pollution in the Yangtze River Delta, China. Sci Total Environ 631:921–933 22. Huang LM, Li HT, Zha DH, Zhu JY (2008) A fieldwork study on the diurnal changes of urban microclimate in four types of ground cover and urban heat island of Nanjing, China. Build Environ 43(1):7–17 23. Salata F, Golasi I, Petitti D, de Lieto Vollaro E, Coppi M, de Lieto Vollaro A (2017) Relating microclimate, human thermal comfort and health during heat waves: an analysis of heat island mitigation strategies through a case study in an urban outdoor environment. Sustain Cities Soc 30:79–96 24. Du HY, Wang DD, Wang YY, Zhao XL, Qin F, Jiang H, Cai YL (2016) Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Sci Total Environ 571:461–470 25. Varentsov M, Konstantinov P, Baklanov A, Esau I, Miles V, Davy R (2018) Anthropogenic and natural drivers of a strong winter urban heat island in a typical Arctic city. Atmos Chem Phys 18(23):17573–17587 26. Wu H, Wang T, Wang QG, Riemer N, Cao Y, Liu C, Ma CX, Xie X (2019) Relieved air pollution enhanced urban heat island intensity in the Yangtze River Delta. China Aerosol Air Qual Res 19(12):2683–2696 27. Ford HL, Ravelo AC (2019) Estimates of pliocene tropical pacific temperature sensitivity to radiative greenhouse gas forcing. Paleoceanogr Paleoclimatol 34(1):2–15 28. Sani S (1972) Some aspects of urban microclimate of Kuala Lumpur, West Malaysia. Akademika 1:85–92 29. Sani S (1984) Urban development and changing patterns of night-time temperatures in the Kuala Lumpur, Petaling Jaya area, Malaysia. J Teknologi 5:27–36 30. Elsayed IS (2012) A study on the urban heat island of the city of Kuala Lumpur, Malaysia. J King Abdulaziz Univ 23(2):121 31. Shaharuddin A, Noorazuan MH, Takeuchi W, Noraziah A (2014) The effects of urban heat islands on human comfort: a case of Klang Valley Malaysia. Glob J Adv Pure Appl Sci 2:1–8 32. Shaharuddin A, Noorazuan MH, Yaakob MJ (2009) Fenomena Pulau Haba Bandar dan isu alam sekitar di Bandaraya Kuala Lumpur. Geografia: Malaysian J Soc Space 5(3):57–67 33. Hashim N, Ahmad A, Abdullah M (2007) Mapping Urban Heat Island phenomenon, remote sensing approach. J Inst Eng Malaysia 68(3):25–30 34. Morris KI, Aekbal Salleh S, Chan A, Ooi MCG, Abakr YA, Oozeer MY, Duda M (2015) Computational study of urban heat island of Putrajaya, Malaysia. Sustain Cities Soc 19:359–372 35. Morris KI, Chan A, Salleh SA, Ooi MCG, Oozeer MY, Abakr YA (2016) Numerical study on the urbanisation of Putrajaya and its interaction with the local climate, over a decade. Urban Clim 16:1–24 36. Buyadi SNA, Mohd WMNW, Misni A (2013) Green spaces growth impact on the urban microclimate. Procedia-Soc Behav Sci 105:547–557 37. Buyadi SNA, Mohd WMNW, Misni A (2013) Impact of land use changes on the surface temperature distribution of area surrounding the National Botanic Garden, Shah Alam. Procedia-Soc Behav Sci 101:516–525 38. Salleh SA, Latif ZA, Mohd WMNW, Chan A (2013) Factors contributing to the formation of an urban heat island in Putrajaya, Malaysia. Procedia-Soc Behav Sci 105:840–850 39. Thani SKSO, Mohamad NHN, Abdullah SMS (2013) The influence of urban landscape morphology on the temperature distribution of Hot-Humid Urban Centre. Procedia-Soc Behav Sci 85:356–367

Urban Heat Island, Contributing Factors, Public Responses …

121

40. Fong CS, Aghamohammadi N, Ramakreshnan L, Sulaiman NM, Mohammadi P (2019) Holistic recommendations for future outdoor thermal comfort assessment in tropical Southeast Asia: a critical appraisal. Sustain Cities Soc 46:101428 41. Doan QV, Kusaka H, Ho QB (2016) Impact of future urbanization on temperature and thermal comfort index in a developing tropical city: Ho Chi Minh City. Urban Clim 17:20–31 42. Wong LP, Alias H, Aghamohammadi N, Aghazadeh S, Sulaiman NMN (2017) Urban heat island experience, control measures and health impact: a survey among working community in the city of Kuala Lumpur. Sustain Cities Soc 35:660–668 43. Nasir RA, Ahmad SS, Zain-Ahmed A, Ibrahim N (2015) Adapting human comfort in an Urban Area: the role of tree shades towards urban regeneration. Procedia-Soc Behav Sci 170:369–380 44. Buyadi SNA, Mohd WMNW, Misni A (2014) Quantifying green space cooling effects on the urban microclimate using Remote Sensing and GIS techniques. In: FIG Congress 2014 engaging the challenges—enhancing the relevance, Kuala Lumpur, Malaysia, 16–21 June 2014 45. Ahmed AQ, Ossen DR, Jamei E, Manaf NA, Said I, Ahmad MH (2014) Urban surface temperature behaviour and heat island effect in a tropical planned city. Theoret Appl Climatol 119(3–4):493–514 46. Benrazavi RS, Binti Dola K, Ujang N, Sadat Benrazavi N (2016) Effect of pavement materials on surface temperatures in tropical environment. Sustain Cities Soc 22:94–103 47. Rajagopalan P, Lim KC, Jamei E (2014) Urban heat island and wind flow characteristics of a tropical city. Sol Energy 107:159–170 48. Sanusi ANZ, Shao L, Zamri AAA (2014) Seeking underground for potential heat sink in Malaysia for Earth Air Heat Exchanger (EAHE) application. Aust J Basic Appl Sci 8(8):54–57 49. Oke TR (1988) The urban energy balance. Prog Phys Geogr 12(4):471–508 50. Chatzidimitriou A, Yannas S (2017) Street canyon design and improvement potential for urban open spaces; the influence of canyon aspect ratio and orientation on microclimate and outdoor comfort. Sustain Cities Soc 33:85–101 51. Qaid A, Bin Lamit H, Ossen DR, Shahminan RNR (2016) Urban heat island and thermal comfort conditions at micro-climate scale in a tropical planned city. Energy Build 133:577–595 52. Memon RA, Leung DYC, Liu CH (2009) An investigation of urban heat island intensity (UHII) as an indicator of urban heating. Atmos Res 94(3):491–500 53. Ahmed AQ, Ossen DR, Jamei E, Abd Manaf N, Said I, Ahmad MH (2015) Urban surface temperature behaviour and heat island effect in a tropical planned city. Theor Appl Climatol 119(3–4):493–514 54. Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol 23(1):1–26 55. Oke TR (1981) Canyon geometry and the nocturnal urban heat-island—comparison of scale model and field observations. J Climatol 1(3):237 56. Wang W, Ng E, Yuan C, Raasch S (2017) Large-eddy simulations of ventilation for thermal comfort—a parametric study of generic urban configurations with perpendicular approaching winds. Urban Clim 20:202–227 57. Hwang RL, Lin TP, Matzarakis A (2011) Seasonal effects of urban street shading on long-term outdoor thermal comfort. Build Environ 46(4):863–870 58. Erell E, Pearlmutter D, Williamson T (2012) Urban microclimate: designing the spaces between buildings. Routledge 59. Johansson E, Emmanuel R (2006) The influence of urban design on outdoor thermal comfort in the hot, humid city of Colombo, Sri Lanka. Int J Biometeorol 51(2):119–133 60. Pataki DE, McCarthy HR, Litvak E, Pincetl S (2011) Transpiration of urban forests in the Los Angeles metropolitan area. Ecol Appl 21(3):661–677 61. Byrne LB, Bruns MA, Kim KC (2008) Ecosystem properties of urban land covers at the aboveground–belowground interface. Ecosystems 11(7):1065–1077 62. Vailshery LS, Jaganmohan M, Nagendra H (2013) Effect of street trees on microclimate and air pollution in a tropical city. Urban For Urban Green 12(3):408–415 63. Akbari H, Pomerantz M, Taha H (2001) Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Sol Energy 70(3):295–310

Urban Heat Island Studies in Hot and Humid Climates: A Review of the State of Art in Latin-America Massimo Palme

Abstract Urban heat island is a phenomenon affecting cities across the world. While in cold climates it could be regarded as an even beneficious process, in temperate climates and especially in the inter-tropical latitude range, the increase in urban temperature can generate risks for health, outdoor and indoor discomfort, and an increase in buildings energy needs. This chapter provide a state of art review of UHI studies conducted recently in Latin-American area, with special focus on tropical climate cities. First step is determining which big Latin-American cities are placed in tropical or subtropical climates. Then, Journals articles, Book Chapters and Proceedings are investigated to establish the state of art, putting in evidence which kind of methods are used in determining UHI intensities, which impacts are searched, and which mitigation strategies are proposed. Keywords Tropical climate · Urban heat island · Latin-America

1 Introduction Tropical climates are defined by Koppen-Gaiger [1] classification as climates with average temperature of 18 °C or higher and significant precipitations (more than 60 mm per month during the wet season). Classification method consider three subcategories: rainforest, monsoon and savannah, depending on the precipitations during the dry season. In Central and South America, each of these subcategories regard large parts of the territory and important cities are placed there. Rainforest climate (Af climate) can be found in large parts of Brazil, Peru, Colombia, Ecuador, Venezuela, Guyana, Suriname and French Guyana. It is also present in some zones of Bolivia, Paraguay, México, Costa Rica, Honduras, Nicaragua, Guatemala, and M. Palme (B) Universidad Católica del Norte, Escuela de Arquitectura, Antofagasta, Chile e-mail: [email protected] Centro de Investigación Tecnológica del Agua en el Desierto, Universidad Católica del Norte, Antofagasta, Chile © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_6

123

124

M. Palme

Belize. Parts of Haiti, Dominican Republic and smaller islands of Trinidad and Tobago also present this kind of climate. Reference cities located in this climate are Medellin in Colombia and Salvador de Bahia in Brazil. Monsoon climate (Am) can be found in large parts of Brazil, Peru, Colombia, Venezuela, Bolivia, Paraguay, Suriname, Guyana, French Guyana, Panama, Costa Rica, Nicaragua, Honduras, Dominican Republic, Puerto Rico, Guatemala and Mexico. A reference city is Santo Domingo in the Dominican Republic. Savannah climate (Aw or As) can be found in Brazil, Venezuela, Bolivia, Paraguay, Ecuador, Mexico, Haiti, Dominican Republic, Salvador, and Cuba. Some zones of Colombia and Peru also present this climate. Many important cities locate there: Caracas (Venezuela), Guayaquil (Ecuador), Santa Cruz (Bolivia), Asunción (Paraguay), Guatemala City, San José (Costa Rica), and even a megalopolis like Rio de Janeiro (Brazil) (Fig. 1).

Fig. 1 Latin-America Koppen Gaiger map (from Peel et al.)

Urban Heat Island Studies in Hot and Humid Climates: A Review …

125

Many other Latin American big cities are in subtropical wet climates (Cfa or Cwa): for example, Guadalajara, Sao Paulo, Belo Horizonte, Florianopolis and Puerto Alegre, to name just a few that locates at the border with tropical climate. Moreover, Latin American cities are suffering fast urbanization processes. Table 1 show the most populated cities in Latin America in 2018. If in 1950 only Buenos Aires had more than 5 million of inhabitants, there are today 6 megalopolises (more than 10 million of people), 9 cities with more than 5 million and 35 cities with more than 2 million of inhabitants [2, 3]. 21 of these 35 cities are placed in tropical climates and other 6 in subtropical wet climates. Only 8 cities stand in other climates (basically temperate by altitude, like Mexico City or arid like in the case of Lima). LatinAmerica, moreover, presents an impressive rate of growth of urban population, due to the changing economies of the countries that move people from the towns to the megacities [4]. Table 1 Latin American cities with more than 2,000,000 and climate classification CITY

CRITERION USED

COUNTRY

INHABITANTS

CLIMATE

126

M. Palme

2 UHI Studies in Latin American Tropical Climates Despite the increasing trend of urbanization processes and the number of cities in tropical climates, urban climatology and especially urban heat island studies in Latin America are still a few. A literature search conducted in the first semester 2020 using “urban heat island”, “tropical climate” and “Latin America” as key words in the SCOPUS (abstract, title and key words) and WEB OD SCIENCE (all fields) database lead to no results. Using only “urban heat island” and “Latin America”, WEB OF SCIENCE tool found only 2 papers and SCOPUS 6. Changing “Latin America” for “South America” results ware 7 and 13 respectively. A literature search conducted in SCIELO database in English, Spanish and Portuguese with “urban heat island”, “isla de calor” and “ilha de calor” as key word lead only to 35, 26 and 20 results respectively. However, some of that results were actually studies not focused on Latin America, beside the use of the specific key word. After eliminating the incidental and double results, we kept 33 records. Two records, not detected by key-words search, was also added from other reviews [5, 6]. Besides of these records, other studies were conducted recently on this topic. In 2019 Springer published a book titled “Urban Climates in Latin-America”, edited by Chilean geographers Cristian Henriquez and Hugo Romero. The book is divided in three sections, focusing respectively on urban heat island, comfort and thermal zoning (part 1), air pollution and urban climate (part 2), disasters, health and resilience (part 3). Other sources of valuable information analyzed were the Proceedings of the UHI Countermeasures Conferences (2014 and 2016) and the International Conference on Urban Climate (2018). Some more papers were added form other conferences. Between all materials obtained, we filter that papers focused on tropical climates, to be finally analyzed. We decided to include studies conducted in cities like Mexico City and Bogotá, which has climates mitigated by altitude but still similar to tropical climates. Table 2 resume the number of records obtained and analyzed in deep in this review. Table 2 Resources analyzed in this review

Source

Records obtained

Records selected and analysed

Journals

35

18

Books chapters

16

4

Proceedings

16

11

Total

67

33

Urban Heat Island Studies in Hot and Humid Climates: A Review …

127

2.1 Continental Studies Krellenberg et al. [7] realized a summary for policy-makers envisioning adaptation to climate change in Latin-American cities and focusing especially on megacities like Bogotá, Buenos Aires, México City, Lima, Santiago and Sao Paulo. They find a relation among urban expansion and global warming, and proposes urban greening as one of the most important actions to take facing climate change. Sarricolea and Meseguer-Rúiz [8] analyzed the urban climate of 8 South-American megacities (Sao Paulo, Mexico City, Buenos Aires, Rio de Janeiro, Lima, Bogota, Santiago and Belo Horizonte): they found UHI intensities between 3 and 8 K and SUHI of 5.5–15 K.

2.2 Mexico In Mexico, Barrientos-Gonzalez et al. [9] found a difference of 5.0 K in the average air temperature of a specific urban microenvironment in Chetumal, Quintana-Rojo (Mexico) respect to rural environment. Jauregui [10] found an increasing trend in urban temperature in Mexico City and discussed the impact of the land-use changes on the urban climate. Then extend the results to other Mexican cities [11]. Recently, Vargas and Magaña [12] studied the Metropolitan Area of Mexico City and found an historical increasing trend in UHI intensity. Rosas and Bartorila [13] recommend the use of urban greening and remark the importance of landscape preservation, analyzing the case of Madero City in Mexico. Colunga et al. [14] studied the case of Queretaro (Mexico) and proposed the city greening as main strategy to reduce UHI of the city. They found that an increase in the 50% of the green areas could reduce the UHI in more than 2 K.

2.3 Brazil In Brazil, De Lucerna et al. [15] analyzed the case of Rio de Janeiro and found a difference in the LST of about 2.5 K respect to rural or low-density environments and about 4.0 degrees respect to zones with vegetation. They also analyzed the effect of sea breezes on the spatial distribution of the SUHI across the city. Da Silva et al. [16] recorded air temperatures in urban transects for the city of Uberlandia, Minas Gerais (Brazil). UHI intensity was found to be higher in winter (about 2.2 K) than in summer (about 1.2°). Cardoso and Amorim [17] recorded also air temperature in urban transects for the city of Presidente Prudente, Sao Paulo (Brazil). Results show a maximum average UHI intensity of 4.4 K and a correlation between urban morphology and soil cover (LCZ based) and the UHI. Vieira de Azevedo et al. [18] recorded air temperature during a whole year in a specific site in Petrolina (Brazil). They found a maximum UHI intensity of 5.5 K.

128

M. Palme

Amorim [19] analyzed LST for the cases of the middle-sized cities of Presidente Prudente, Rosana, Paranavaí and Nova Andradina in Brazil. She found SUHI in the range 4–6 K for all cases. Nakata-Osaki et al. [20] analyzed the cities of Rio Prieto and Bauru (Brazil) and discussed the importance of the H/W relation in determining the maximum intensity of the UHI. Kruger [21] found 1.2 K of UHI intensity for the case of Curitiba (Brazil) and discussed the impacts on the indoor and outdoor comfort. Correa et al. [22] used remote sensing techniques to find the SUHI for the case of Manaus (Brazil). Average SUHI intensity is found to be in the range 4–6 K. Duarte et al. [23] evaluate the benefits introduced in San Paulo (Brazil) by the development of a green infrastructure, using the tool Envi-Met and data monitoring. Moreira et al. [24] discussed the relation between SUHI and UHI in Penápolis, Brazil, comparing LST and monitored data, finding a maximum nocturnal UHI intensity of 3–4 K. Denser city sectors have higher UHI intensities while areas with higher vegetation cover have lower UHI. Piffer et al. [25] find for Paranavai, Brazil, a maximum SUHI of 14–15 K, depending on surface cover. Urban vegetation is proposed as the most effective strategy in reducing SUHI. Oliveira et al. [26] reported observation data for Sao Paulo by fixed stations and found 6–8 K UHI intensity, depending on cloudiness and air circulation patterns. Vianna and Romero [27] compare the cases of Brasilia and Singapore, founding for Brasila up to 6 K of UHI intensity. They propose vegetation as the most important mitigation strategy. Silva et al. [28] present the importance of building and city design in generating sensitivity to UHI by discussing the case of Lucio Costa Brasilia design. They found a sensitivity to UHI in this kind of modernism cities. Abreu et al. [29, 30] discusses the Phsycological Equivalent Temperature in Santos, Sao Paulo and Campinas, Brazil. They suggest shading and ventilations as urgent mitigation actions to be taken. For the case of Campinas, they tested 40 species of trees as shadowing providers, concluding that perceived temperature can be lowered in 4°. Cantuaria and Romero [31] explore the UHI impact on comfort in residential areas of Brasilia. They propose vegetation and air circulation as strategies to mitigate the phenomenon. Silva and Romero [32] discuss for Brasilia the impact of canyon morphology on thermal comfort and suggest the orientation of streets can help improving the perceived temperatures. Alchapar et al. [33] compared the cases of Mendoza and Campinas by using simulations and proposes green areas and cool materials as mitigation strategy. Mendonça and Lombardo [34] studied the case of Florianópolis in Brazil.

2.4 Ecuador and Colombia Palme et al. [35] developed a pioneer study for Ecuador, simulating the UHI intensity in the city of Guayaquil and finding a maximum nocturnal intensity of about 5 K. They also proposed a simulation methodology to account for UHI impact on buildings’ energy needs and tested it in different south American cities [36]. Palme et al. [37] also report simulations results for four cities of the South American Pacific coast,

Urban Heat Island Studies in Hot and Humid Climates: A Review …

129

detecting UHI intensities of 1–4 K, and estimating the impact on buildings’ energy needs for cooling. Litardo et al. [38] conducted an exhaustive study on the UHI of the city of Durán, using simulation and monitoring. UHI intensity seems to depend on morphological factor and anthropogenic heat release by vehicles. They proposed mitigation strategies as city greening and cool materials for roofs and pavements. Soto-Estrada [39] conducted the first study for the city of Medellín, finding an average urban heat island intensity of 4.3 K, especially pronounced in dense areas. Ramírez and Lucas-Sousa [40] used eight meteorological stations and found a correlation between population density and UHI intensity in Bogotá. They also suggest that a value of 14,500 inhabitants per km2 relates to 1 K of UHI intensity.

3 Discussion Different methodologies are used in analysed studies. We can at least divide the studies in the following categories: • • • •

studies that use monitoring stations to assess UHI intensity studies that focus on SUHI and uses remote sensing techniques to assess it studies that use simulation tools to generate scenarios studies that compare results obtained by different methodologies.

Respect to impacts and proposals, we can at least divide studies in: • • • •

studies that focus on the proposal of adaptation/mitigation strategies studies that focus on urban climatology and compare different locations studies that focus on assessing a specific case of study studies that focus on the impact of UHI on some aspects (indoor and outdoor comfort, buildings’ energy needs, sensitivity to heat waves) (Table 3).

Figures 2, 3, 4, 5 and 6 identify analysed cities. Studies focused on cities of different size: from more than 20,000,000 inhabitants (Sao Paulo, Mexico City) to 18,000 (Rosana). We grouped the cities in four categories: megacities (more than 4,000,000), big cities (1,000,000–4,000,000), intermediate cities (200,000–1,000,000) and small cities (less than 200,000). Monitoring studies can be grouped in two main categories: studies that uses fixed meteorological stations [22, 40] and studies that uses mobile stations [16, 17] to record environmental variables. Both have been used in analysed studies. One of the advantages of the fixed monitoring is the capability to obtain continuous temporal series of data. The most important advantage of mobile monitoring is the obtention of spatially defined information, however it is not easy to use this information because of the temporal shift that is obviously characterizing the transects characterization. Simulation studies use basically two tools: Envimet [23] and UWG [33, 35]. Envimet is used to specific location studies, while UWG is used to establish patterns and extract results from simulation to be applied to whole cities. UWG is very used

Mexico

Jáuregui et al. (2005)

Rio de Janeiro

Brazil

Mexico

De Lucerna et al. (2013)

Barrientos et al. (2019)

Chetumal

Bogotá

Ramírez and Lucas Colombia (2018)

Various cities

Mexico City

Queretaro

Mexico

Mexico

Colunga et al. (2015)

Curitiba

Jáuregui et al. (2004)

Brazil

Kruger (2016)

AntofagastaValparaíso, Lima, Guayaquil

Manaus

Chile, Peru, Ecuador

Palme et a. (2019)

City

Correa et al. (2016) Brazil

Country

Study

Table 3 Main findings of reviewed studies

Monitoring

Remote sensing

Monitoring (fixed)

Monitoring (fixed)

Monitoring

Remote sensing Monitoring (fixed)

Remote sensing

Monitoring

Simulation

Method

5K

2.5–4 K

1–3 K

2–3 K

5–8 K

4–6 K

1–5 K

1–5 K

1–4 K

UHI or SUHI intensity

Urban morphology

Building density

Population density Urban morphology

Land use, City size

Land use change Population

Not discussed

Land use, LCZ

Urban morphology,

Urban morphology Anthropogenic heat

UHI or SUHI drivers

Mitigation strategies

Indoor comfort

Not discussed

Not discussed

Not discussed

Climate Comfort

Not discussed

Outdoor comfort

Thermal comfort

(continued)

Cool materials

Vegetation Breezes

Not discussed

Not discussed

Vegetation Breezes

Not discussed

Vegetation

Not discussed

Buildings Breezes energy needs Vegetation Cool materials

Impact

130 M. Palme

Latin-America

Sarricolea and Meseguer (2019)

Brazil

Mexico

Latin-America

Ecuador

Brazil

Nakata et al. (2016)

Rosas and Bartorila (2019)

Krellenberg et al. (2014)

Litardo et al. (2020)

Cardoso and Amorim (2018)

Guayaquil

Campinas Santos Sao Paulo

Palme et al. (2016) Ecuador

Abreu et al. (2014) Brazil

Presidente Prudente

Durán

Megacities

Madero

Rio Prieto Bauru

Argentina Brazil Mendoza Campinas

Sao Paulo

Megacities

City

Alchapar et al. (2019)

Duarte et al. (2014) Brazil

Country

Study

Table 3 (continued)

Monitoring (fixed)

Simulation

Monitoring (mobile)

Simulation Monitoring (fixed)

Review

Monitoring

Monitoring Simulation

Simulation

Simulation

Review

Method

Not provided

2–4 K

4.4 K

2–3 K

4–8 K

Not provided

2–4 K

3–4 K

2.6–5.5 K

3–8 K

UHI or SUHI intensity

Not discussed

Urban morphology

Urban morphology LCZ

Anthropogenic heat Urban morphology

Land use change

Land use change

Urban morphology H/W

Urban morphology

Building density

Land use change Urban morphology

UHI or SUHI drivers

Urban design

Vegetation

Not discussed

Vegetation

Vegetation

Vegetation Breezes Cool materials

Mitigation strategies

Vegetation

Outdoor comfort

(continued)

Shading Vegetation

Building Not energy needs discussed

Not discussed

Building Vegetation energy needs Cool materials

Energy

Outdoor comfort

Outdoor comfort

Comfort Energy

Not discussed

Comfort

Impact

Urban Heat Island Studies in Hot and Humid Climates: A Review … 131

Brazil

Brazil

Mexico

Brazil

Brazil

Brazil

Brazil

Brazil

Brazil

Da Silva (2018)

Oliveira et al. (2018)

Vargas and Magaña (2020)

Piffer et al. (2018)

Moreira et al. (2019)

Vianna and Romero (2016)

Silva et al. (2016)

Cantuaria and Romero (2014)

Silva and Romero (2014)

Palme et al. (2017) Chile, Ecuador, Peru

Country

Study

Table 3 (continued)

Antofagasta Valparaíso Lima Guayaquil

Brasilia

Brasilia

Brasilia

Brasilia

Penápolis

Paranavai

Mexico City Metropolitan Area

Sao Paulo

Uberlandia

City

Simulation

Simulation

Monitoring (fixed) Simulation

Remote sensing Simulation

Remote sensing Monitoring (mobile)

Remote sensing Monitoring (mobile)

Remote sensing

Remote sensing

Monitoring (fixed)

Monitoring (mobile)

Method

Land use change

Cloudiness Air circulation

Urban morphology

UHI or SUHI drivers

0.5–4 K

Not provided

4–5 K

Not provided

6K

3–4 K

Urban morphology Anthropogenic heat

Urban morphology

Building design

Building design City design

Building density

Building density

14–15 K (max) Land use change

3.2 K

6–8 K

1.2–2.2 K

UHI or SUHI intensity

Energy

Outdoor comfort

Indoor and Outdoor comfort

Not discussed

Not discussed

Not discussed

Not discussed

Not discussed

Not discussed

Not discussed

Impact

(continued)

Breezes Urban design

Street design

Vegetation Breezes

Not discussed

Vegetation

Breezes Vegetation

Vegetation

Breezes Vegetation

Breezes

Not discussed

Mitigation strategies

132 M. Palme

Brazil

P. Prudente, Rosana, Paranavaí, N. Andradina

Florianópolis

Amorim (2017)

Brazil

Mendonça and Lombardo

Medellín

Petrolina

Colombia

Soto-Estrada (2020)

City

Vieira et al. (2017) Brazil

Country

Study

Table 3 (continued)

Remote sensing

Monitoring (fixed)

Remote Sensing

Remote Sensing

Method

8–11 K (max)

5.3 K

4–6 K

3–6 K

UHI or SUHI intensity

Urban size

Not discussed

Land use change

Land use change

UHI or SUHI drivers

Not discussed

Outdoor comfort

Not discussed

Not discussed

Impact

Not discussed

Not discussed

Vegetation

Vegetation

Mitigation strategies

Urban Heat Island Studies in Hot and Humid Climates: A Review … 133

134

M. Palme

Fig. 2 Analysed megacities, more than 4,000,000 inhabitants

in connection with BPS tools, helping to obtain estimations of building energy needs [36]. Envi-met seems to be more suitable to analyse outdoor comfort and local climate descriptions. Some very valuable contributions reviewed the study conducted ap to date and used monitoring to calibrate and validate simulations [38]. Such studies are very important because once validated, the models and methodologies can be applied to other cases. Other studies compare UHI and SUHI [24] or different cities placed in similar [27, 30] or different climates [33, 35].

Urban Heat Island Studies in Hot and Humid Climates: A Review …

135

Fig. 3 Analysed big cities, 1,000,000–4,000,000 inhabitants

Most of consulted studies indicates land use change as one the most important driving factors of UHI [13, 25, 39]. Population density is also associated with increase in UHI intensity, so it appears that in this region the densification processes are not associated with an adequate infrastructure development: denser urban areas shows higher values of anthropogenic heat emission [10, 11, 40]. Many studies also concluded that the way our buildings are designed today has an impact on the resulting urban climate [9, 19, 20]. About the half of the analysed studies did not focused on impacts on our lives of urban heat island, just recorded its intensity. The other half, divides in studies

136

M. Palme

Fig. 4 Analysed intermediate cities, 200,000–1,000,000 inhabitants

investigating outdoor [21, 28, 29] and indoor [9, 31] comfort and studies investigating buildings’ energy needs [36, 38]. Different strategies of mitigation are proposed depending on specific location of selected studies. If available, wind circulation is one of the preferred strategies [15, 26, 35]. City greening, cool materials and reduction of anthropogenic heat generated by transportation systems are other strategies considered [14, 31]. Some studies also suggest interventions on building and city design, considering the interactions between the fabrics and the environment [20, 32, 37]. Almost all revised studies agree in establishing the need of new paradigms of the urban living. Future cities

Urban Heat Island Studies in Hot and Humid Climates: A Review …

137

Fig. 5 Analysed small cities, less than 200,000 inhabitants

should be invented [41] taking into account climate-resilient strategies, including bioclimatic building design, nature-based solutions, green and blue infrastructure development and improving energy efficiency of any city-related sector (transport, residential, commercial, industrial).

138

M. Palme

Fig. 6 Latin-American tropical and sub-tropical cities analysed by studies included in this review

4 Conclusion In Latin-America studies on UHI are still few. Most have been developed in Brazil, some in Chile, Argentina, and Mexico. Very few studies have been conducted in Colombia, Ecuador, or another country. Only very few studies propose mitigation strategies and connect them to observations. The region needs of complementarity between urban climatology science and urban design practice. Connections within academic disciplines and among academia and practitioner are also needed. The relation with the public stakeholders is a key factor in improving the impact of science on our lives. Because some of the most populated cities of the world are placed in this area, it appears very urgent to start a dialogue between all these sectors, focused to the understanding of the relations between local climates, building performance, and urban metabolism [42]. Interventions on the built environment should be planned as a result of the collaborative work involving practitioners, stakeholders, academia, and the civil society as whole.

Urban Heat Island Studies in Hot and Humid Climates: A Review …

139

References 1. Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644 2. United Nations (2018) World urbanization prospects. https://population.un.org/wup/. Accessed Nov 2019 3. World Population Review (2019) https://worldpopulationreview.com/world-cities/. Accessed Nov 2019 4. Inostroza L, Bauer R, Csaplovics (2013) Urban Sprawl and fragmentation in Latin-Americ: a dynamic quantification and characterization of spatial patterns. J Environ Manage 5. Santamouris M (2020) Recent progress on urban overheating and heat island research. Integrated assessment of the energy, environmental, vulnerability and health impact. Synergies with the global climate change. Energy Build 207 6. Santamouris M et al (2020) Heat mitigation technologies can improve sustainability in cities. An holistic experimental and numerical impact assessment of urban overheating and related heat mitigation strategies on energy consumption, indoor comfort, vulnerability and heat-related mortality and morbidity in cities. Energy Build 217 7. Krellenberg K et al (2014) Adaptation to climate change in megacities of Latin America. Project Report, Economic Commission for the Latin America and the Caribbean 8. Sarricolea P, Meseguer-Ruiz O (2019) Urban climate of large cities: comparison of the urban heat island effect in Latin-America. In: Henriquez C, Romero H (eds) Urban Climates in Latin-America. Springer Nature 9. Barrientos-Gonzalez R et al (2019) Indoor temperature validation of low-income detached dwellings under tropical weather conditions. Climate 7(8):96 10. Jáuregui E (2004) Impact of land-use changes on the climate of the Mexico City Region. Investigaciones Geográficas 55 11. Jáuregui E (2005) Possible impact of urbanization on the termal climate of some large cities in Mexico. Atmósfera 18 12. Vargas N, Magaña V (2020) Warm spells and climate risk to human health in the Mexico City Metropolitan Area. J Am Meteorol Soc 12:351–365 13. Rosas M, Bartorila M (2019) Forestation contributions to urban sustainability in tropical cities. Nuevo Amanecer wetland, Ciudad Madero, Mexico. Nova Sci 9(19):528–550 14. Colunga M et al (2015) The role of urban vegetation in temperature and heat island effects in Queretaro city, Mexico. Atmosfera 28 15. De Lucena AJ, Correa O, de Almeida JR, de Faria L, Nobrega L (2013) Urban climate and clues of heat island events in the metropolitan area of Rio de Janeiro. Theoret Appl Climatol 111(3–4):497–511 16. Da Silva VJ (2018) Mobile transect for indentification of intre-urbana heat islands in Urberlandia, Brazil. Revista Ambiente Agua 13(4) 17. Cardoso R, Amorim M (2018) Urban heat island análisis using the local climate zone scheme in Presidente Prudente, Brazil. Investigacioens Geográficas 69:107–118 18. Vieira P, Bezerra P, Leitao M, Santos C (2017) Thermal comfort level assessment in urban area of Petrolina-PE County. BrazilRevista Brasileira de Meteorologia 32(4) 19. Amorim M (2017) Remote sensing of surface heat islands. Examples of small and mediumsized cities in the tropical environment, Brazil. Finisterra-Revista Portuguesa de Geografia 105 20. Nakata-Osaki C, Lucas da Souza L, Souto Rodrigues D (2016) The impact of the urban canyon geometry in the nocturnal heat island intensity: analysis by a simplified model adapted to a GIS. Ambiente Construido 16(3) 21. Kruger E (2016) Effects of the urban heat island on outdoor and indoor thermal comfort for the climatic conditions of Curitiba. Engenharia Sanitaria e Ambiental 21(3) 22. Correa et al (2016) Heat Islands in Manaus City: study with remote sensing datam modeling and meteorological data. Revista Brasileira de Meteorologia 31

140

M. Palme

23. Duarte D, Shinzato P, Gusson C, Alves C (2014) The impact of vegetation on urban microclimate to counteract the increase in built density in a tropical changing climate. In: Proceedings of the 4th international conference on counteracting urban heat island, Venice, Italy 24. Moreira J, Amorim C, Garcia F (2019) Analysis of intensity and hourly evolution of the heat and dry islands in a tropical environment, Brazil. Revista de Geografia e Ordenamento do Território 17 25. Piffer L, Amorim M, Montenegro A (2018) Thermal infrared remote sensing to assess the urban surface heat island effect in Brazil and the USA. In: International conference on urban climate, NY, Aug 2018 26. Oliveira A et al (2018) Observational investigation of the urban heat island in the metropolitan region of São Paulo City, Brazil. In: International conference on urban climate, NY, Aug 2018 27. Vianna E, Romero M (2016) The process of formation of heat islands: a parallel Among Brasilia and Singapore. In: Proceedings of the international conference on urban heat island countermeasures, Singapore, May 2016 28. Silva C, Ferrari I, Markiewicz J (2016) The Urban Sectorization of Lúcio Costa’s Modernism and the Emergency of Heat Islands in the Capital of Brazil. In: Proceedings of the international conference on urban heat island countermeasures, Singapore, May 2016 29. Abreu-Harbich V, Chebel-Labaki L, Matzarakis A (2014) Thermal bioclimate analysis for improvement of urban climate in tropical cities: case of Campinas, Sao Paulo and Santos, São Paulo state. In: Proceedings of the 4th international conference on counteracting urban heat island, Venice, Italy 30. Abreu-Harbich V, Chebel Labaki L, Lotufo Bueno-Bartholomei C (2014) How much does the shade provided by different trees collaborate to control the urban heat island in tropical climates?—a study in Campinas, Brazil. In: Proceedings of the 4th international conference on counteracting urban heat island, Venice, Italy 31. Cantuaria GAC, Romero MAB (2014) Urban heat island in residential areas of Brasilia. In: Proceedings of the 4th international conference on counteracting urban heat island, Venice, Italy 32. Silva C, Romero M (2014) Urban cavity method: microclimatic evaluation of urban canyon in tropical climate. Case Study: Federal District-Brazil. In: Proceedings of the 4th international conference on counteracting urban heat island, Venice, Italy 33. Alchapar N, Correa E, Pezzuto C, Salvati A (2019) Evaluación del modelo Urban Weather Generator en clima árido y tropical: aplicaciones en Mendoza, Argentina y Campinas, Brasil. In: Proceedings of IBPSA-Latam conference, Mendoza, Oct 2019 34. Mendonça M, Lombardo M (2009) El clima urbano de ciudades subtropicales costeras atlánticas: el caso de la conurbación de Florianópolis. Revista de Geografía Norte Grande 44:129–141 35. Palme M, Villacreses G, Lobato A, Córdovez M, Macías J (2016) Estimating the urban heat island effect in the city of Guayaquil. In: Proceedings of the FICUP conference, Quito, Sept 2016 36. Palme M, Inostroza L, Villacreses G, Lobato A, Carrasco C (2019) Urban climate in the South-American coastal cities of Guayaquil, Lima, Antofagasta and Valparaíso, and its impact on the energy efficiency of buildings. In: Henriquez C, Romero H (eds) Urban climates in Latin-America. Springer Nature 37. Palme M, Lobato A, Carrasco C (2016) Quantitative analysis of factors contributing to urban heat island effect in cities of Latin-American Pacific Coast. Procedia Eng 169:199–206 38. Litardo J, Palme M, Borbor M, Caiza R, Macías J, Hidalgo R (submitted) Urban heat island intensity and building’s energy needs in Durán, Ecuador: simulation studies and proposal of mitigation strategies. Sustainable Cities and Society

Urban Heat Island Studies in Hot and Humid Climates: A Review …

141

39. Soto-Estrada E (2020) Estimation of the urban heat island in Medellin, Colombia. Revista internacional de contaminación ambiental 35(2) 40. Ramírez E, Lucas Sousa L (2018) Population density and urban heat island in Bogotá, Colombia. In: International conference on urban climate, NY, Aug 2018 41. Batty M (2018) Inventing future cities. MIT Press, Cambridge 42. Palme M, Salvati A (2020) Sustainability and urban metabolsim. Sustainability

Urban Heat Island Simulation and Monitoring in the Hot and Humid Climate Cities of Guayaquil and Durán, Ecuador Jaqueline Litardo, Massimo Palme, Mercy Borbor-Cordova, Rommel Caiza, Rubén Hidalgo-Leon, María del Pilar Cornejo-Rodriguez, and Guillermo Soriano Abstract Urban Heat Island (UHI) research has been increasingly impacting science during the last decades. As most of humanity is living in cities, urban climatology is a consolidating field that attract more and more interest with time. Higher temperature in cities are involved in many processes and have impacts on energy needs, thermal comfort, public health and air pollution. Especially in tropical climates, where temperature and humidity ranges are high per se, the impact of urbanization processes can be even deeper. Despite of the high urbanization rates and the climate in which most populated cities of Latin-America are placed, there are only few studies that estimates UHI intensities and proposes mitigation strategies conducted in the region up to now. Here we will briefly look at the most important studies on UHI for tropical cities, then we will describe the pioneer studies that we conducted in the cities of Guayaquil and Durán, Ecuador. Keywords Ecuador · Urban weather generator · Urban heat island · Tropical climate

J. Litardo · R. Hidalgo-Leon · G. Soriano Facultad de Ingeniería Mecánica y Ciencias de la Producción, ESPOL, Guayaquil, Ecuador Centro de Energías Renovables y Alternativas, ESPOL, Guayaquil, Ecuador M. Palme (B) Escuela de Arquitectura, Universidad Católica del Norte, Antofagasta, Chile e-mail: [email protected] Centro de Investigación Tecnológica del Agua en el Desierto, Universidad Católica del Norte, Antofagasta, Chile M. Borbor-Cordova · R. Caiza · M. del Pilar Cornejo-Rodriguez Pacific International Center for Disaster Risk Reduction, ESPOL, Guayaquil, Ecuador Facultad de Ingeniería Marítima y Ciencias del Mar, ESPOL, Guayaquil, Ecuador © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_7

143

144

J. Litardo et al.

1 Introduction Latin-American region present impressive rates of urban growth, lacks of regulations and norms, and higher exposition of the poor population sectors to environmental risks. Many megalopolises of the region, are placed in tropical areas [1]. However, systematic reviews put in evidence the lack of knowledge on UHI in Latin-America [2–4]. Urban heat island is a phenomenon that in tropical climates can have impacts more disastrous than in other climates. Tropical cities are exposed to high temperatures and humidity ranges that make difficult to leave the excess of heat of the human body. So, an increase of 2–3° can be very difficult to be assumed, because people already live in limiting conditions. The probability of happening of a heat stress event [5] is higher than in colder or dryer climates. Moreover, urban climate can also generate conditions favorable to food bacteria proliferation and vector borne diseases such as dengue [6, 7]. Tropical climate can be found in almost the 50% of the Latin-American region. Countries like Brazil, Mexico, Colombia, Venezuela, Ecuador and Peru have most of their territory in such climates and many big cities are placed there. Despite of this, UHI studies in tropical climates have been developed mostly in Asia [2]. In Latin-American, studies on UHI concentrates in Brazil, Mexico, Argentina and Chile [4]. Other countries with tropical megalopolis, like Ecuador, still need the implementation of such kind of studies. Existing research recognizes the relevance of quantifying the UHI effect in cities worldwide. Most of the research on UHI have focused considerable efforts to propose strategies that take into account the climate, the morphology, and even the socio-economic and political factors of the studied zone. However, studies for tropical wet climate cities from Latin America are limited, and the results obtained for other cases cannot be extrapolated. As a fast-growing country, Ecuador has the potential to incorporate sustainable construction practices in tropical wet cities such as Guayaquil and Duran. Results from earlier studies have shown the existence of the UHI phenomenon in some coastal and tropical cities of South Asia and Africa [2]. Kotharkar and Surawar [8] explored the UHI effect in Nagpur, India. The maximum nocturnal mean canopy UHI intensity was observed during March (3.62 °C), when the temperatures increase gradually until reach May, the month with peak temperatures. A subsequent study identified critical areas in Nagpur, where UHI intensities ranged between 1.76 and 4.09 °C in the winter season [9]. It was also observed that compact zones with higher percentage of build land coverage presented higher temperatures than open and sparsely building zones. Similarly, in Bangkok, Thailand, zones with high-rise buildings and their physical characteristics contributed to cause the effect of UHI [10]. Studies that were carried out in the African cities showed almost the same results. An example of this are the cities of Accra and Kumasi in Ghana, where the intensities of UHI did not exceed the 2.50 °C [11]. In the case of Accra, the influence of the urban growth caused an increase of 0.64 °C in the intensity of UHI between 2002 and 2017. Although extensive research has been carried out on the UHI phenomenon

Urban Heat Island Simulation and Monitoring in the Hot …

145

in these regions, governments need to be more aware of this problem and promote the incorporation of policies that help mitigate the effect. Previous research has established the advantages of including green spaces and blue spaces into urban environments [12–14]. It has also been observed that the incorporation of these two strategies playing together have a crucial role in providing several benefits including cooling [15]. The increase in areas of grass and tree coverage ratios, mainly in hot climates, are measures that reduce the effect of UHI [16, 17] by providing cooling and simultaneously, reducing the peak cooling loads [18]. Furthermore, the cooling effect could be exploited if the vegetation is planted in the areas of urban core [19]. Impervious areas and roofs are the surfaces that are most exposed to solar radiation in cities [20] because large proportion of these surfaces present low emissivity. Recent evidence indicate that the use of high-albedo and green roofs could reduce the energy use related to HVAC systems [21]. Also, the implementation of these roofs allowed for reductions of about 2 °C in intensities of UHI in Singapore [22]. However, this benefit could be considerably reduced in urban centers with highrise buildings [17]. The incorporation of shading in buildings is another relevant strategy to reduce the thermal loads associated with direct solar radiation that increase the effect of UHI in hot and humid climates [23].

2 Case Study: The Urban System Guayaquil-Samborondón-Durán Guayaquil, Durán, and Samborondón are cities of the Province of Guayas, Ecuador, where Guayaquil is the province’s capital. Both Durán and Samborondón are part of the Metropolitan District of Guayaquil, which is located in the coastal gulf of Guayaquil. These cities are linked by the Guayas, Daule and Babahoyo rivers. These three rivers form the Guayas River Basin. A series of bridges interconnect these cities, making it possible to communicate via terrestrial with other Ecuadorian cities. Guayaquil is the largest of the three cities with a surface of 2493.86 km2 , followed by Durán with 300.19 km2 , and Samborondón with 230.48 km2 [24]. Together, these three cities represent approximately 1.2% of the total surface of continental Ecuador. Figure 1 shows the geographic location of these cities. According to the projections from the National Institute of Statistics and Censuses [25], Guayaquil would reach a population of around 2.72 million people in 2020, based on the data from the last population census carried out in 2010. Likewise, Durán and Samborondón would reach a population of about 316,000 and 102,400 inhabitants, respectively, in the same year. If that is the case, in 2020, Guayaquil will concentrate 16% of the Ecuadorian population, Durán 1.8%, and Samborondón 0.6%. The Metropolitan District of Guayaquil plays an essential role in Ecuador’s economy, being Guayaquil, the city with the highest tax revenues among them [26]. It

146

J. Litardo et al.

Fig. 1 Location of Guayaquil-Samborondón-Durán urban system in the Gulf of Guayaquil

indicates that Guayaquil generates a relevant part of the Ecuadorian employment and is crucial for the different sectors of the Ecuadorian productive economic system, such as the commercial, industrial, transport, construction, and others. Also, Guayaquil has a seaport, which is the fundamental point of Ecuador’s foreign trade. Due to their geographical location, these cities compart a tropical climate. According to the Köppen-Gaiger [27] classification, these cities are included among the Aw group (tropical savanna climate, also known as tropical wet climate). One of the characteristics of the climate type of these zones is that it presents two well defined seasons: wet (Jan–Apr) and dry (May–Dec). Average monthly temperatures in these cities range from 23 to 27 °C throughout the year, with the highest temperatures occurring in the wet season [28]. Similarly, the average annual relative humidity is around 70%, where the lowest monthly averages occur in the dry season. These cities have an average annual global insolation of approximately 4.5 kWh/m2 [29], and a low wind resource with wind velocities below the 3 m/s [30]. Surface Urban Heat Island of the urban cluster is quite strong (Fig. 2).

3 Methodology We conducted recently some pioneering studies in Ecuador, using simulation strategies to estimate Guayaquil and Durán urban heat island. We used the tool Urban

Urban Heat Island Simulation and Monitoring in the Hot …

147

Fig. 2 Land surface temperature of Guayaquil metropolitan area (Landsat)

Weather Generator (UWG), developed at MIT [31, 32]. We also started a preliminary monitoring of both cities, to understand the effect of urban morphologies and anthropogenic heat generation on the UHI intensity. Here we describe the simulation strategy we used in the aforementioned studies and the monitoring strategy we adopted to obtain first calibration results for the case of Durán.

3.1 Selection of Samples for Simulation One of the critical points in performing urban weather simulation is the samples selection. Different strategies can be used, depending on the objective of the specific assessment. A first possibility is to choose the samples in order to maximize the morphological differences among city’s neighborhoods. This is the strategy followed by Palme et al. for the case of Guayaquil [33]. The city was analyzed following its historical development, then eight representative urban morphologies were selected,

148

J. Litardo et al.

reflecting the variety of architectural solutions used during the city expansion period considered. Another strategy, very different from the first, can be the random selection of a bigger number of samples (the bigger, the better) and then a clustering process that permit to obtain a smaller number of clusters composed by similar samples. Clustering process can be conducted considering only morphological characters [34, 35] or considering other factors too (materials, anthropogenic heat from vehicles), as in the case of the study for the case of Durán [36]. The main advantage of the first strategy is that urban weather files provided by UWG will show more difference between samples, that means, can be used to test best and worst scenarios. The main advantage of the second strategy is to be representative of the city as whole, by grouping random samples. If this strategy is followed, special attention has to be putted on the number of samples. The optimum number of samples depends on the city size and on the uniformity of the same city in terms of morphology, construction materials, traffic, etcetera, so it cannot be expressed as a general rule. Stewart and Oke [37] established a classification of what they called the Local Climate Zones (LCZ). Each LCZ has its own geometric and surface cover properties. In the case of Duran city, this classification can serve as a guideline to identify the type of urban morphology obtained by the clustering methodology. Cluster 1 represents an urban morphology of a sparse arrangement of single-story buildings, the green surface is the highest compared to the other clusters and has a low-level of traffic. Clusters 2 and 4 are characterized by an open arrangement of low-rise buildings (1–3 stories), with low ratios of green coverage and low-mid levels of traffic. Cluster 3 has the highest value of SCR, presenting a dense mix of low-rise buildings (1–3 stories), few or no trees, and mid-level of vehicular traffic (Figs. 3, 4 and 5).

3.2 Urban Weather Generator Simulations Among different tools that can be used to analyse urban climates, UWG has the capability to produce a weather file to be used in other simulations, e.g. building performance simulation [34, 35]. Another characteristic of the tool is to be parametric, so it is very useful especially for studies that follow the second strategy described above. Main parameters required as input by UWG relates to urban form, buildings’ materials, anthropogenic heat generation, and vegetation. Particularly interesting are the morphological parameters, as evidenced by multiples studies [38–41]. Tables 1 and 2 show the parameters used in UWG simulations following the described strategies for the cases of Guayaquil and Durán respectively. While the first study we conducted in Guayaquil used a fixed value for anthropogenic heat generation by vehicles, in the Durán study we decided to estimate with more precision the heat produced by traffic. We followed the strategy proposed by Grimmond [42] and Quah and Roth [43]. The first step in this process was to determine the energy emitted per meter travelled of the vehicle based on consuming fuel type (EV), given by the equation:

Urban Heat Island Simulation and Monitoring in the Hot …

149

Fig. 3 Sample selection for the first study conducted in Guayaquil (adapted from Palme et al. [33])

EV ij =

N H C j .ρ j F Ei j

(1)

where parameters i and j represent the vehicle class and fuel type, respectively; N H C j is the net heat of combustion according to j; ρ j is the density according to j; F E i j is the mean fuel economy, according to i and j. Then, the anthropogenic heat

150

J. Litardo et al.

Fig. 4 Sample distribution and clustering for the study conducted in Durán (adapted from Litardo et al. [36])

Urban Heat Island Simulation and Monitoring in the Hot …

Fig. 5 Details of samples used in the Durán study case (from Litardo et al. [36])

151

152

J. Litardo et al.

Table 1 UWG parameter values used in Guayaquil study Site

SCR

WBH

FSR

TCR

VCR

Roof Albedo

Wall Albedo

Road Albedo

Traffic (W/m2 )

1

0.100

25.6

2.6

0.04

0.04

0.32

0.15

0.150

50

2

0.77

6

1.5

0.17

0.17

0.51

0.58

0.15

50

3

0.73

6.2

1.4

0.02

0.02

0.51

0.58

0.15

50

4

0.6

6

1.5

0.04

0.04

0.51

0.38

0.2

50

5

0.41

3

0.1

0.01

0.01

0.48

0.58

0.15

50

6

0.61

29.3

2.9

0.1

0.1

0.42

0.58

0.1

50

7

0.2

6

0.2

0.18

0.18

0.42

0.58

0.15

50

8

0.71

3

0.7

0.11

0.11

0.51

0.58

0.2

50

Table 2 UWG parameters used in Durán study Cluster

SCR

WBH

FSR

TCR

VCR

Roof Albedo

Wall Albedo

Road Albedo

Traffic (W/m2 )

1

0.099

2.780

0.164

0.031

0.235

0.363

0.175

0.130

13.000

2

0.236

3.108

0.396

0.060

0.066

0.519

0.194

0.169

14.889

3

0.683

4.212

1.819

0.022

0.003

0.426

0.200

0.200

30.000

4

0.398

3.762

0.796

0.060

0.039

0.411

0.198

0.155

30.167

from traffic Q V T , is calculated through the equation:  Q V T (h) =

 n .d V (h).E i j k .A i jk V T i jk 3600

(2)

where h is local time; k is the road segment; n V T i jk (h) is the hourly total number according to ijk at hour h; dk is vehicle distance traveled on-road segment k; A is the dimension of the study area. According to the traffic in Duran, the vehicles circulating use three fuel types which are 92-octane gasoline (j = 1), diesel (j = 2) and 87-octane gasoline (j = 3). For this study, light vehicles (i = 1) are considered automobiles, and heavy vehicles (i = 2) are massive passenger and goods transport systems. Diesel fuel is used by heavy vehicles; diesel, 92-octane gasoline, and 87-octane gasoline fuels are used by light vehicles [44, 45]. The types of vehicles in Duran are similar to those presented in Quah and Roth [43]. Therefore, the FEij values used for light and heavy vehicles were 10,416.67 m/L and 13,333.33 m/L, respectively. The traffic data for the determination of QVT in Z25 were obtained from Salvador and Jácome [46]. Finally, we proceeded to calculate the QVT, for each route-vehicle class, where QVT max. corresponded to the sum of the QVT of each route, depending on the vehicle class.

Urban Heat Island Simulation and Monitoring in the Hot …

153

3.3 Monitoring Several researchers have used similar methodologies in pursuit of meteorological data for UHI analysis [9, 47]. These methodologies have been mainly introduced by the World Meteorological Organization and Oke [48] and are summarized in the following items: 1. Selection of representative areas of LCZ or clusters. In this step, transition areas between one LCZ and others should be avoided. Likewise, areas that could have a microclimatic influence should be avoided (i.e., areas adjacent to water bodies or areas with extensive vegetation). 2. The recommended screen height for non-urban stations is between 1.25 and 2 m. For urban stations, this is also acceptable, but height could be greater while sensors remain accessible and secure. 3. Temperature sensors must have radiation shields and ventilation to avoid the radiation effects on measures. The effect of UHI on cities is reflected in the difference in air temperature between urban and rural areas, as mentioned previously. In view of this, it is vital to know the air temperature levels in relevant points of the cities under study, which can be done using sensors and dataloggers. To provide observations to validate the modeled UHI temperatures, we followed the methodology described above. We chose 8 possible zones to mount temperature/humidity data loggers in 2 zones per cluster. The dataloggers used for the study were the HOBO ONSET MX2302. Each one of these devices is linked to two sensors that acquire information on air temperature and relative humidity in both urban and rural areas. The sensors have an operating range of −40 to 70 °C for temperature and 0–100% RH for relative humidity. It has an accuracy of ±0.2 °C for temperature and ±2.5% RH for relative humidity, which are suitable for both indoor and outdoor applications. Similarly, the resolution is 0.04 °C for temperature and 0.01% RH for relative humidity. Interference of solar radiation on data storage is avoided by placing each of the devices in white outdoor plastic boxes with holes for ventilation. An RS3-B solar radiation shield protected the air temperature and relative humidity sensors. One of the main advantages of these devices is that the data can be downloaded through a mobile application on portable equipment, such as a cell phone, using a Bluetooth 4.0 communication model. It avoids the use of cables for data downloading, which reduces the danger of falls of people who perform these activities using ladders. It is because the devices are regularly placed more than one meter above the ground. The interconnection distance between the portable equipment and devices is a maximum of 30.5 m. The logging interval was set to 60 min, recording one measure of temperature and relative humidity per hour. The power consumption of the device is 1 mW with a memory capacity of 128 kB. 9 HOBO devices in the same number of boxes were mounted in the studied city. 8 were placed in the urban area and 1 in the rural area, as can be observed in Fig. 6. We chose a height between 2 and 2.5 m above the ground for the location of the 9 devices. In several papers,

154

J. Litardo et al.

Fig. 6 Location of monitoring stations (adapted from Litardo et al. [36])

the authors recommend these heights to have easy access to the devices in case of maintenance/revision as well as to avoid theft of the devices, acts of vandalism, and keeping them away from heating sources and air conditioning units. The field measurements in Duran city were conducted over a 6-month period from July 2019 to January 2020. During these periods, it was feasible to obtain observations for the dry and wet seasons in Duran.

4 Results of Simulation and Monitoring 4.1 Guayaquil Results of Guayaquil simulation [33] show that the city has a maximum UHI intensity of about 4K at night. Differences between sectors suggest that the denser areas should present higher values of UHI intensity. In this first study we find specific patterns depending on the cloudiness of the sky (Tables 3 and 4). Respect to impacts of UHI, we developed a set of simulations on different residential building typologies [49] and we found that Guayaquil residential buildings could increase their cooling needs in a range 15–60% depending on building size, orientation and construction materials used. In another study [50], we found also

Urban Heat Island Simulation and Monitoring in the Hot …

155

Table 3 Results of simulated UHI intensity, sunny day in February (from Palme et al. 2016) 6.00

12.00

18.00

24.00

T (°C)

UHI (°C)

T (°C)

UHI (°C)

T (°C)

UHI (°C)

T (°C)

UHI (°C)

Location 1

25.5

1.9

30.0

− 0.7

32.5

2.5

30.0

4.5

Location 2

26.2

2.7

29.5

− 1.2

32.2

2.2

30.0

4.5

Location 3

26.1

2.6

30.0

− 0.7

32.7

2.7

30.2

4.7

Location 4

26.0

2.5

30.0

− 0.7

32.7

2.7

30.1

4.6

Location 5

25.6

2.0

30.4

− 0.3

33.0

3.0

29.7

4.2

Location 6

25.5

1.9

29.5

− 1.2

31.7

1.7

29.5

4.0

Location 7

25.5

1.9

29.7

− 1.0

32.3

2.3

29.7

4.2

Location 8

26.1

2.6

30.0

− 0.7

32.6

2.6

30.2

4.7

Table 4 Results of simulated UHI intensity, cloudy day in February (from Palme et al. 2016) 6.00

12.00

18.00 UHI (°C)

T (°C)

24.00

T (°C)

UHI (°C)

T (°C)

UHI (°C)

T (°C)

UHI (°C)

Location 1

27.0

2.8

29.0

0.2

30.0

2.0

28.7

3.5

Location 2

27.0

2.8

28.2

− 0.6

29.7

1.7

28.0

2.8

Location 3

27.0

2.8

29.0

0.2

30.0

2.0

28.2

3.0

Location 4

27.0

2.8

29.0

0.2

30.0

2.0

28.2

3.0

Location 5

26.2

2.0

29.1

0.3

31.0

3.0

27.8

2.6

Location 6

26.2

2.0

28.2

− 0.6

29.5

1.5

27.4

2.2

Location 7

26.5

2.3

28.2

− 0.6

29.7

1.7

27.4

2.2

Location 8

27.0

2.8

29.0

0.2

30.0

2.0

28.2

3.0

that Guayaquil case could be extremely sensitive to the anthropogenic heat variation, differing in that form other south American cities placed in other climates (such as Lima or Santiago de Chile). This was one of the reasons to model with more detail on traffic the case study of Durán.

4.2 Durán Results of Durán simulation [36] also indicates that sectors with denser morphologies and higher anthropogenic heat generation present higher UHI intensities (Fig. 7). Figure 8 compares the hourly values of urban air temperatures measured in the field in at least one zone of clusters 2, 3, and 4, and the urban air temperatures modelled by the UWG. In all cases, we registered 48 data of temperature (1 measure per hour), between January 1 and 3, 2020. This period is representative of the wet season. It can be seen from Fig. 8 that the modelled temperatures follow the pattern from the

156

J. Litardo et al.

Fig. 7 Simulated UHI evolution for a 30 days period (authors elaboration)

Fig. 8 Hourly values of urban air temperatures from field measurement compared to a cluster 2, b cluster 3, and c cluster 4 modelled in UWG

measured temperatures. We also quantified the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE), summarized in Table 5. Overall, the Table 5 RMSE and MAPE between the urban air temperatures measured in the field and the urban air temperatures modelled by the UWG

RMSE (°C)

MAPE (%)

Cluster 2 versus S6

1.9

6.3

Cluster 3 versus S8

2.2

7.1

Cluster 4 versus S1

2.1

6.9

Cluster 4 versus S7

1.8

5.7

Urban Heat Island Simulation and Monitoring in the Hot …

157

Fig. 9 Impacts on cooling needs of two buildings typologies (authors elaboration)

MAPE and the RMSE did not exceed 7.1% and 2.2 °C, respectively, and were higher for Cluster 3. Respect to impacts on building sector, we simulated two kind of buildings (residential and small commercial) and obtain an increase in cooling needs of about 15–20% for commercial buildings and about 40–50% for residential buildings (Fig. 9).

5 Mitigation Strategies Proposal In the aforementioned studies, we have developed a comprehensive methodology based on of the urban configuration, the solar energy, and the anthropogenic heat sources (buildings, transport, materials, etc.) that allows identifying the most influencing factors of the UHI. Results should be used to improve the general mitigation strategies facing climate change that the Durán city authorities are putting in practice. Subsequently, heat mitigation strategies for the city of Duran are analyzed, based upon the 4 clusters obtained in this study together with the land use zone, and social considerations of the population. Social variables come from census data at the urban sector level allows identifying the highest density neighborhood, where are located the children and elders (age-dependency), level of education, and access to social security (Fig. 10). The following type of strategies for urban heat mitigation are considered: • Urban planning considering urban forestry; design of public space infrastructure, shading with urban forestry; • Ecological infrastructure: urban forestry in parks, lineal park (walking trails and rain garden) along the channels specific local trees and vegetation. • Adaptive and material into buildings and surfaces. • Preventive health and disaster risk management at neighborhood level. In Table 6 there are some examples of how the strategies can be applied to the specific neighborhood of Duran. Strategy followed for the Durán mitigation strategy proposal could be applied also to Guayaquil case and extended to other locations with similar climate and urban

158 Fig. 10 Spatial distribution at social variables at urban sector level: a population density, b age-dependency inhabitants with age less than 15 years old and older than 65 years old, c level of education, d Population with access to social security (authors)

J. Litardo et al.

Urban Heat Island Simulation and Monitoring in the Hot … Fig. 10 (continued)

159

160

J. Litardo et al.

Table 6 Mitigation strategies by clusters Cluster

Zone

Neighborhood

Social-conditions characteristics

Mitigation strategy

1 Low

288 hectáreas

Low density, high age dependency, low level education, low access to social security

Green infrastructure, program for low income eco-climatic housing, health care and risk reduction community program

2 Medium

28 Ha/Nuevo horizonte

Low density, high age dependency, very low education, and low access to social security

Urban planning with lineal and open park, low income eco-climatic housing, health care and risk reduction community program

3 High

El Recreo

High density, low age dependency, high level of education, medium access to social security

Improvement of building materials, roof garden, water management, rain garden, etc

4 Medium

Hector Cobos

High density, high age dependency, very low education and low access to social security

Ecological infrastructure along the natural channel, greening the open park areas, urban garden

Industrial zone

Low density, industrial land zone

Materials for buildings, reflective pavement, energy efficiency equipment, cargo transportation regulations and scheduling

characteristics. For a complete assessment, studies on social vulnerability are also needed to estimate the multifactorial dimension of UHI risk.

6 Climate Change and Land Planning at Municipality Level Local governments are playing an important role in developing actions in regards to climate change and land planning, those local actions contribute to the Sustainable Development Goals [51], to the Paris Agreement of Climate Change [52], and to the New Urban Agenda (NUA) proposed by the Habitat III Conference. All these

Urban Heat Island Simulation and Monitoring in the Hot …

161

international policies pointed out the importance for cities to assess their vulnerabilities and risk to climate events such as flooding, urban heat island, landslides, and epidemics. There is a growing consensus that reducing urban vulnerability to the impacts of climate change will entail building resilience and improve public health [53]. However, local governments in the Latin America region face a limited capacity to address these urban issues as integrated plans of adaptation and mitigation to climate change and disaster risk reduction [54]. Urban heat island and extreme heat emerge as some of the leading causes of weather-related human mortality worldwide. However, little is known about the heat impact on intermediate cities in Latin America. The Intergovernmental Panel on Climate Change [55] have argued, with high confidence, that heat waves will increase in frequency, intensity and duration increasing heat-related morbidity and mortality in cities worldwide [56]. In this section, we present how a collaborative research program between the local government of Duran and an interdisciplinary group of researchers developed a multihazard approach assessing the vulnerability to floods, UHI and landslides within the city. The collaborative stakeholder engagement process initiated in 2017, when Duran city officers from the risk management office attended a technical training on geographic information systems tools for urban land planning and vulnerability mapping. After this initial contact were months of dialogues, technical discussion and finally after signing an inter-institutional Memorandum of Understanding between the Escuela Superior Politécnica del Litoral (ESPOL) and the Municipality of Durán, the project entitled “Climate Resilience for Duran: building strategies to reduce the vulnerability to hydroclimatic risks (RESCLIMA)” was executed in close collaboration with the Duran Office of Disaster Risk Management. A critical aspect considered in this project was the Urban and Territorial Planning (UTP) which by Ecuadorian policy is a responsibility of the local governments [57]. Since 2019, by law municipalities have to include in their UTP actions and measures for adaptation, mitigation and disaster risk reduction [58]. RESCLIMA project, was based on the climate risk framework proposed by the Intergovernmental Panel on Climate Change (IPCC), aiming to reduce the impact of the hydro-climatic risks and protect human health while considering land urban planning, and identifying adaptation and mitigation strategies on the local context. Regarding the UHI, different phases involved this study: (1) Mapping hazards of urban heat island using tools such as: Urban Weather Generator (UWG) and a temperature sensors network on the city, (2) Assessing the socio-ecological vulnerability of Duran using a spatial analysis, and (3) Identifying “low regret” strategies through urban planning, community participation and risk reduction measures [59]. Low-regret actions are relatively low-cost measures and provide relatively large benefits under predicted future climates. Examples of low-regret measure in built environment are: (i) to reduce internal heat gain especially during heatwaves e.g. by creating green roofs and walls and (ii) to reduce the risk of flooding by avoiding building in high risk areas. During the project, it was evidenced that a cross cutting process of capacity building, knowledge development and communication, raised awareness to city stakeholders and community about the potential impacts of UHI on human health and

162

J. Litardo et al.

increase on energy consumption. Subsequently, through workshops and discussion city officers, researchers, community and other city representatives identified some measures that could integrated in the Duran UTP. Some of the low-regret actions can be applied to different city aspects as adaptation strategies, mitigation or disaster risk reduction measures, are described as follow. Respect to heat risks research and monitoring for the city (adaptation and risk reduction measures), RESCLIMA project investigated the risks of heat stress to residents and type of housing, identifying urban sectors that are most vulnerable to heat stress related to the percentage of elderly, young, and people with disabilities within Duran, and mapping UHI vulnerability as function of exposure, sensitive population and adaptive capacity of the communities. Actions proposed to improve the knowledge on UHI impacts in Durán are: • to develop a network of temperature sensors and meteorological station that provide weather data to generate weather indicators for UHI and heat waves within the city; and • to design parks and open spaces working with the Disaster Risk Office to undertake an analysis of the relationship between weather conditions and fire risk in open spaces and parks. Respect to urban policy and building codes guidelines targeting UHI (Urban climate change policy and climate change mitigation measures), it appears important: • to develop ordinances to ensure that criteria for sustainability will be included in the housing codes and residential development; • to develop incentives (green taxes) or building codes to promote a sustainable design, to provide a comfortable internal environment with the least use of energy over their lifetime; and • to develop guidelines that promote cooling systems that maximize use of natural ventilation and low-carbon cooling techniques. Respect to practical actions and synergies with flood control measures (Adaptation, mitigation and disaster risk reduction measures), recommendations are: • to ensure that where possible enhancements to biodiversity include increased planting for shade in open spaces; • to promote guidelines for green infrastructure to enhance tree cover in parks and linear parks along the natural channels on Duran; • to work with strategic health authorities to implement a local heatwave plan and to include actions to manage air pollution health risks in these plans, as high air pollution levels often coincide with heatwaves; • to implement transport and routes analysis for public transportation and cargo to reduce the traffic jam, improve air quality and reduce heat due to high rate of vehicles; • to build awareness among workers and residents over the wider impacts of high temperatures, such as increased risk of bacterial contamination of fresh food, increase the cases of vector-borne diseases (dengue, zika, chikungunya, other),

Urban Heat Island Simulation and Monitoring in the Hot …

163

higher morbidity and mortality of climate sensitive diseases (acute respiratory infection, cardiovascular and metabolic diseases); and • to enhance levels of coordination and information exchange between public health and disaster risk prevention officers to develop heat awareness and prevention measures (Fig. 11). Stakeholder engagement established in RESCLIMA has generated actions and policy recommendations that can be integrated in the Duran Urban and Territorial Planning. For instance, disaster risk reduction needs to be based on the vulnerability and risk assessment of the hydro-climatological hazards that the city faces. The adaptation strategies to climate change are related to the green infrastructure, trees cover and open space restauration for biodiversity and flood control.

Fig. 11 a Capacity building for city officers to apply geographic information systems for mapping risks and vulnerability to floods and heat waves; b Durán city technical staff are in training with the sensors and data downloading for the meteorological station that was implemented as part of the RESCLIMA project; c the meteorological station installed in Durán; and, d one of the nine HOBO dataloggers installed in different points of Durán city

164

J. Litardo et al.

Mitigation measures oriented to materials and green building codes reduce use on energy consumption at the same time that improve thermal comfort at micro-urban level. Finally, the importance of science, innovation and communication to build a resilient city with the participation of city officers, decision makers, researchers, community and private sector is a critical component that contribute to the urban governance and the achievement of the SDG. The RESCLIMA project revealed that in general policy makers have little awareness and knowledge of UHI impacts whereas practitioners have some understanding of a technical portfolio that can be applied to the urban site. Unfortunately, there is no explicit mention to the UHI issues on the Urban and Territorial Planning diagnostic or proposed measures. This transdisciplinary research may be one of the few studies in Ecuador that integrated a multi-hazard approach with adaptation and mitigation strategies at city level providing impactful insights and information to many local governments to devise necessary actions and capacity building program to create UHI awareness amongst the policy makers and city stakeholders. The need to develop a strategy for capacity building in topics related to the New Urban Agenda linked to the climate action and urban land planning within the cities on Ecuador should be strengthen between local government and academia.

7 Conclusion Previous research on heat vulnerability mapping [60–63] have assessed that short term emergency planning and disaster risk management can reduce the impact on the morbidity and mortality of the vulnerable population (age-dependency). Thus, a vulnerability analysis of the city to UHI is an important tool for city decision makers, urban planners, and health and social services. Measures for short term heat mitigation may include water provision for houses and parks, and health promotion campaigns around the UHI and related diseases. These interventions target elder population, families with children, and people with low education, no access to social and health care system. Long term mitigation includes urban planning to increase green parks, lineal parks around channels, increase the albedo of urban surfaces, and improve the thermal and optical performance of building materials. Especial emphasis should be putted on the construction of a Green Infrastructure, which has many benefits for a city inhabitant [64, 65]. A general policy recommendation is to include the vulnerability analysis to UHI, and UHI assessment into the City Plan or Land Planning, with integrated actions between health department, risk management, and urban planning. Main conclusions, drawn from the stakeholder engagement process between the representatives of the Duran local government, the Escuela Superior Politécnica del Litoral with their international partners and local community groups, that can be applied to other cities are:

Urban Heat Island Simulation and Monitoring in the Hot …

165

• The development of plans and strategies to build resilience should be done integrating actions to face climate change and disaster risk reduction within a multihazard approach established in their Urban and Territorial Planning. • Incorporating the natural ecosystems as part of the urban landscape, ecosystems services, green infrastructure as well as nature-based solutions that increasing tree coverages, keeping and restoring natural ecosystems can generate positive synergies that reduce the risk of floods, heat islands, landslides, improving urban health and the well-being of its inhabitants. • Implementing energy efficiency policies and guidelines for housing, transport and industrial activity, can reduce greenhouse gas emissions and generate co-benefits by reducing air pollution and the impact on human health. • Developing strategic alliances between local governments with local universities, research centers, and innovation networks will generate the enabling conditions of information, scientific knowledge, and technology required to build a resilient city and communities. Acknowledgements This paper is part of the project “Climate Resilience for Duran: Strategies to reduce vulnerability to hydroclimatic risks (RESCLIMA)” at the Pacific International Centre for Disaster Risk Reduction (CIP4 DRR), funded by the Municipality of Duran (Ecuador) and Escuela Superior Politecnica del Litoral (ESPOL).

References 1. United Nations (2018) World urbanization prospects. https://population.un.org/wup/. Accessed on Nov 2019 2. Santamouris M (2015) Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions. Sci Total Environ 512–513:582–598 3. Santamouris M et al (2020) Heat mitigation technologies can improve sustainability in cities. An holistic experimental and numerical impact assessment of urban overheating and related heat mitigation strategies on energy consumption, indoor comfort, vulnerability and heat-related mortality and morbidity in cities. Energy Buildings 217 4. Palme M (Submitted) Urban heat island studies in hot and humid climates: a review of the state of art in South America. In: Urban heat island mitigation. Hot and humid cities. Springer, Berlin 5. Knutson T, Ploshay J (2016) Detection of anthropogenic influence on summertime heat stress index. Climatic Change 138:25–29 6. Rosenghini W, Mandonça F, Ceccato P (2019) Urban climate and dengue epidemics in Brazil. In: Urban climate in Latin-America. Springer, Berlin 7. Real-Cotto J (2017) Factors related to the dynamics of dengue in Guayaquil based on historical trends. Anales de la Facultad de Medicina 78(1) 8. Kotharkar R, Surawar M (2016) Land use, land cover, and population density impact on the formation of canopy urban heat islands through traverse survey in the Nagpur urban area, India. J Urban Plan Dev 142:4015003 9. Kotharkar R, Bagade A (2018) Evaluating urban heat island in the critical local climate zones of an Indian city. Landscape Urban Plann 169:92–104 10. Pakarnseree R, Chunkao K, Bualert S (2018) Physical characteristics of Bangkok and its urban heat island phenomenon. Build Environ 143:561–569

166

J. Litardo et al.

11. Buo INK (2019) Urban sprawl dynamics and urban heat islands (UHI) in Ghana 12. Gunawardena K, Kershaw T, Steemers K (2019) Simulation pathway for estimating heat island influence on urban/suburban building space-conditioning loads and response to facade material changes. Build Environ 150:195–205 13. Hu L, Li Q (2020) Greenspace, bluespace, and their interactive influence on urban thermal environments. Environ Res Lett 15:034041 14. Zhao TF, Fong KF (2017) Characterization of different heat mitigation strategies in landscape to fight against heat island and improve thermal comfort in hot–humid climate (Part I): measurement and modelling. Sus Cit and Soc 32:523–531 15. Gunawardena KR, Wells MJ, Kershaw T (2017) Utilising green and bluespace to mitigate urban heat island intensity. Sci Total Environ 584:1040–1055 16. Hami A, Abdi B, Zarehaghi D, Maulan SB (2019) Assessing the thermal comfort effects of green spaces: a systematic review of methods, parameters, and plants’ attributes. Sustain Cities Soc 49:101634 17. Kim H, Gu D, Kim HY (2018) Effects of urban heat island mitigation in various climate zones in the United States. Sustain Cities Soc 41:841–852 18. Giridharan R, Emmanuel R (2018) The impact of urban compactness, comfort strategies and energy consumption on tropical urban heat island intensity: a review. Sustain Cities Soc 40:677– 687 19. Yue W, Liu X, Zhou Y, Liu Y (2019) Impacts of urban configuration on urban heat island: an empirical study in China mega-cities. Sci Total Environ 671:1036–1046 20. Mohajerani A, Bakaric J, Jeffrey-Bailey T (2017) The urban heat island effect, its causes, and mitigation, with reference to the thermal properties of asphalt concrete. J Environ Manage 197:522–538 21. He JF, Liu JY, Zhuang DF, Zhang W, Liu ML (2007) Assessing the effect of land use/land cover change on the change of urban heat island intensity. Theoret Appl Climatol 90:2017–2226 22. Li XX, Norford LK (2016) Evaluation of cool roof and vegetations in mitigating urban heat island in a tropical city, Singapore. Urban Clim 16:59–74 23. Lima I, Scalco V, Lamberts R (2019) Estimating the impact of urban densification on high-rise office building cooling loads in a hot and humid climate. Energy Build 182:30–44 24. INEC (2010) Ecuador. Población, superficie (km2), densidad poblacional a nivel parroquial 25. INEC (2020) Proyección de la Población Ecuatorian, por años calendario, según cantones 2010– 2020 [WWW document]. Proyecciones Poblacionales. https://www.ecuadorencifras.gob.ec/ proyecciones-poblacionales/ 26. Senplades (2015) Agenda Zona. Zona 8 - Guayaquil 27. INAMHI (2020) Red de estaciones automáticas hidrometeorológicas [WWW document]. https://186.42.174.236/InamhiEmas/ 28. Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci Discuss 4:439–473 29. Vaca D, Ordóñez F (2018) Mapa Solar del Ecuador 30. Meteonorm (2020) Weather report for the cities of Guayaquil, Duran and Samborondon. Guayaquil 31. Bueno B, Norford L, Hidalgo J, Pigeon G (2013) The urban weather generator. J Build Perform Simul 6(4):269–281 32. Mao J, Nordford L (in press) Urban weather generator: physics-based microclimate simulation for performance-oriented urban planning. In: Urban microclimate modelling for comfort and energy studies. Springer, Berlin 33. Palme M, Villacreses G, Lobato A, Córdovez M, Macías J (2016) Estimating the urban heat island effect in the city of Guayaquil. In: Proceedings of the FICUP conference, Quito, Sept 2016 34. Palme M, Inostroza L, Salvati A (2018) Technomass and cooling demand in South America: a superlinear relationship? Build Res Inf 46(Issue 8: Urban form, density & microclimate):864– 880

Urban Heat Island Simulation and Monitoring in the Hot …

167

35. Palme M, Inostroza L, Villacreses G, Lobato A, Carrasco C (2019) Urban climate in the South-American coastal cities of Guayaquil, Lima, Antofagasta and Valparaíso, and its impact on the energy efficiency of buildings. In: Henriquez C, Romero H (eds) Urban climates in Latin-America. Springer Nature, Berlin 36. Litardo J, Palme M, Borbor M, Caiza R, Macías J, Hidalgo R, Soriano G (2020) Urban heat island intensity and building’s energy needs in Durán, Ecuador: simulation studies and proposal of mitigation strategies. Sustain Cities Soc 62:102387 37. Stewart I, Oke TR (2012) Local climate zones for urban temperatura studies. Bull Am Meteorol Soc 93:1879–1900 38. Salvati A, Monti P, Coch H, Cecere C (2019) Climatic performance of urban textures: analysis tools for a mediterranean urban context. Energy Buildings 185:162–179 39. Salvati A, Palme M, Chiesa G, Kolokotroni M (2020) Built form, urban climate and building energy modelling: case studies in Rome and Antofagasta. J Build Perform Simul 13(2) 40. Mao J, Fu Y, Afshari A, Armstrong PR, Norford LK (2018) Optimization-aided calibration of an urban microclimate model under uncertainty. Build Environ 143:390–403 41. Mao J, Yang JH, Afshari A, Norford LK (2017) Global sensitivity analysis of an urban microclimate system under uncertainty: design and case study. Build Environ 124:153–170 42. Grimmond CSB (1992) The suburban energy balance: methodological considerations and results for a mid-latitude west coast city under winter and spring conditions. Int J Climatol 12(5):481–497 43. Quah AKL, Roth M (2012) Diurnal and weekly variation of anthropogenic heat emissions in a tropical city, Singapore. Atmos Environ 46:92–103 44. Ruiz P, Paúl E, Villaba Ramírez RA (2016) Evaluar el comportamiento del combustible ECOPAÍS a una altura mayor de 2500MSNM y su comportamiento en el combustible extra y E10. Escuela Superior Politécnica de Chimborazo 45. Sierra JC (2016) Estimating road transport fuel consumption in Ecuador. Energy Policy 92:359– 368 46. Salvador H, Jácome J (2019) Data city: Análisis de movilidad para la toma de decisiones. Escuela Superior Politécnica Del Litoral 47. Doick KJ, Peace A, Hutchings TR (2014) The role of one large greenspace in mitigating London’s nocturnal urban heat island. Sci Total Environ 493:662–671 48. Oke TR (2006) Initial guidance to obtain representative meteorological observations at urban sites. World Meteorological Organization instruments and observation methods report no. 81 49. Palme M, Carrasco C, Lobato A (2016) Quantitative analysis of factors contributing to urban heat island effect in cities of Latin-American Pacific coast. Procedia Eng 169:199–206 50. Palme M, Inostroza L, Villacreses G, Lobato A, Carrasco C (2017) Form urban climate to buildings’ energy consumption. Enhancing building performance simulation by including the urban heat island effect. Energy Buildings 145 51. United Nations (2019) The sustainable development goals report. 52. UNFCC (2015) Adoption of the Paris agreement 3, pp 54–67 53. Leal W, Echevarria L, Neht A, Klavins M, Morgan EA (2018) Coping with the impacts of urban heat islands. A literature based study on understanding urban heat vulnerability and the need for resilience in cities in a global climate change context. J Clean Prod 171:1140–1149 54. Reckien D, Salvia M, Heidrich O, Church JM, Pietrapertosa F, De Gregorio-Hurtado S, D’Alonzo V, Foley A, Simoes SG, Krkoška Lorencová E, Orru H, Orru K, Wejs A, Flacke J, Olazabal M, Geneletti D, Feliu E, Vasilie S, Nador C, Krook-Riekkola A, Matosovi´c M, Fokaides PA, Ioannou BI, Flamos A, Spyridaki NA, Balzan MV, Fülöp O, Paspaldzhiev I, Grafakos S, Dawson R (2018) How are cities planning to respond to climate change? Assessment of local climate plans from 885 cities in the EU-28. J Clean Prod 191:207–219 55. IPCC (2007) Intergovernmental Panel on Climate Change fourth assessment report. Intergovernmental Panel on Climate Change, Geneva, Switzerland. Cambridge University Press, Cambridge; UK. Available from: www.ipcc.ch 56. Chow WTL, Chuang W-C, Gober P (2012) Vulnerability to extreme heat in metropolitan Phoenix: spatial, temporal, and demographic dimensions. Prof Geogr 64:286–302

168

J. Litardo et al.

57. COOTAD (2018) Codigo organico de organizacion territorial autonomia y decentralizacion (cootad). Ecuador 58. COA (2019) Código Organico Ambiental. Ecuador 59. Cornejo-Rodriguez MP, Borbor-Cordova M (2020) Diseñando Estrategias para la Resiliencia en Ciudades. Guayaquil 60. Vescovi L, Rebetez M (2005) Assessing public health risk due to extremely high temperature events: climate and social parameters. Clim Res 30(1):71–78 61. Reid CE et al (2009) Mapping community determinants of heat vulnerability. Environ Health Perspect 117(11):1730–1736 62. Rinner C et al (2010) The role of maps in neighborhood-level heat vulnerability assessment for the city of Toronto. Cartography Geogr Inf Sci 37(1):31–44 63. Hattis D, Ogneva-Himmelberger Y, Ratick S (2012) The spatial variability of heat-related mortality in Massachusetts. Appl Geogr 33:45–52 64. Pappalardo V et al (2017) The potential of green infrastructure application in urban runoff control for land use planning: a preliminary evaluation from a southern Italy case study. Ecosyst Serv 26:345–354 65. Privitera R, La Rosa D (2018) Reducing seismic vulnerability and energy demand of cities through green infrastructure Sustainability (Switzerland) 10(8)

Optimization of Urban Cooling Strategies for Parking Lots in Hot and Dry Climates: Case Study of Las Vegas and Adelaide Ehsan Sharifi, Phillip Zawarus, and Steffen Lehmann

Abstract Urban microclimates are distinguished by the balance between solar gain and heat lost from building envelope and ground surfaces, by convective heat exchange, and by the generation of anthropogenic heat within the city. Global climate change and the urban heat island (UHI) effect—whereby cities are up to 8 °C hotter than their surrounding countryside—carry growing threats to outdoor living, public health, and urban energy demand. Urban heat stress intensifies in cities with hot and dry summer climates such as Las Vegas (USA) and Adelaide (Australia), where the temperature goes frequently above 36 °C (97 °F). Both cities have a dry, hot, and arid climate. Possible adaptation countermeasures include cool surfaces, urban greenery, and active cooling with the consideration of higher demand for water and energy, and potential winter cooling penalties. Large open-air parking lots appear in many modern cities around shopping malls, hospitals and public venues and provide essential access to these public facilities. In this context, a comparative study of different cooling strategies informs more effective decision making for the design and implementation of UHI adaptation and mitigation strategies. This chapter compares urban cooling strategies for typical parking lots in Downtown Las Vegas, Adelaide CBD and the suburban context. Cool surface materials, tree canopy, evaporative cooling and shading scenarios are estimated, and cooling benefits and side effects of each intervention are discussed. The research shows that planting trees between car parking spaces is vital to most urban environments, especially for parking lots where it leads to 1–5 °C summer cooling. Keywords UHI cooling · Parking lot · Public space · Hot and dry climate · Optimization · Climate change adaptation · Tree canopy E. Sharifi (B) School of Architecture and Built Environment, University of Adelaide, Adelaide, SA, Australia e-mail: [email protected] P. Zawarus · S. Lehmann School of Architecture, University of Nevada, Las Vegas, NV, USA e-mail: [email protected] S. Lehmann e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_8

169

170

E. Sharifi et al.

1 Introduction By 2050, over 68% of global population will live in cities [1]. Compared with the current urbanization rate of 54%, almost all the expected population growth will take place in cities. As a result, higher densities will emerge in existing cities and new urban areas are required to accommodate around two billion new urban residents worldwide. More natural landscape will be replaced by building mass and impermeable surfaces, creating environmental voids in future cities [2, 3]. Climate change projections indicate that surface temperature in South Australia is likely to increase between 1.3 °C (B1 low growth low emission scenario) and 3.1 °C (A2 high growth, high emission scenario) by 2050, compared with 2000 [4]. Even more significant projections indicate a likely surface temperature increase of 2 °C (B1) and 5 °C (A2) by 2070 in the city of Las Vegas, Southern Nevada [5]. Cities are affected by the dangerous urban heat island (UHI) effect as an additional heat source. The interplay between higher urban densities and the increased risk of UHI effect has been described by Bay and Lehmann [6]. The human-made hot spots can result in higher densities being significantly warmer, compared to their suburban fringe and peri-urban hinterlands. The urban-rural/suburban temperature difference frequently reaches 4 °C and can peak at more than 10 °C [7–9]. The convergence of regional warming and the UHI effect can significantly impact citizens’ health, wellbeing and quality of public life in existing and future cities [10, 11]. Heatrelated vulnerability and the exposure of dangerous climatic conditions may rise tremendously putting more lives under threat [12]. This chapter is built on the extensive research of the authors on the UHI adaptation and mitigation strategies in Australian and North American cities since 2010, with a particular focus on human outdoor thermal comfort and urban resilience to climate change.

2 Background A recent report by the Urban Land Institute [13] highlights that: • More cities are likely to be at risk of extreme heat because of climate change, global warming and increased urban development. • Extreme heat is a pressing public health risk, particularly for low-income and elderly communities. Cool design strategies, combined with public health and emergency responses, can help offset heat-related mortalities. • Without intervention, the current and potential future impacts of extremely high temperatures—on real estate developments, infrastructure, and the economy— could be substantial.

Optimization of Urban Cooling Strategies for Parking Lots …

171

• Widespread adoption of mitigation strategies could help reduce the urban warming trends currently occurring in cities, leaving them to contend with a more manageable increase, rather than the up to 5 °C (10 °F) increase currently projected for some cities due to the urban heat island effect. Extensive literature on the UHI effect indicates that the artificial increase of temperature in cities is happening because of changes in radiative energy and water budget in the built environment [14, 15]. Three atmospheric layers are affected namely: • Surface layer (buildings and land surfaces), • Canopy layer (below the canopy of trees or at human scale), • Boundary layer (up to 1500 m above the ground surface). Impermeable surfaces such as asphalt and paving tend to absorb and store solar energy in their thermal mass during the day and emit stored energy in the form of heat (via infrared waves) during the night, causing the built environment to remain relatively hotter than the rural or suburban counterparts. Oke [16] enumerate major contributing factors as: • Urban geometry affects energy balance and heat exchange in cities by affecting shadow and wind patterns. It affects the exposure of materials to sunlight and the consequent heat storage in thermal mass. The complex radiation exchange between building mass and adjacent atmosphere can also change the intensity and patterns of airflow in urban canyons. • Urban surface cover materials affect the heat absorption and reflection rate. Thermodynamic specification, color, texture and density of materials are effective factors. • Urban greenery affects water and heat exchange via evapotranspiration. Typology, distribution and intensity of urban greenery are influential factors. • Urban metabolism and anthropogenic (human-made) heat, mainly related to energy consumption for indoor air-conditioning and vehicular transportation. • Local air turbulence mixes the air in each layer with other adjacent layers in and above the built environment. Due to the complexity of the UHI effect and variations in the measurement methods (air temperature, surface temperature or simulation via fixed stations, mobile traverse or software), UHI intensity is recorded between 2 °C and 10 °C across different cities around the world. As Fig. 1 shows, the UHI in many Australian and American cities—such as Las Vegas, Phoenix, Atlanta, Adelaide, Melbourne and Sydney—has already reached temperature differences above 4 °C. Detailed magnitude of the UHI effect is presented in Fig. 1 [17–30]. Due to the regional climate and the UHI effect, public spaces in Adelaide and Las Vegas are increasingly warmer in summer than humans’ thermal comfort, pushing citizens into air-conditioned buildings and creating an ever-increasing rise in outdoor temperatures.

172

E. Sharifi et al.

UHI intensity (˚C)

Fig. 1 Variation of the intensity of the UHI effect in Australian and North American cities

Adelaide

Las Vegas 10

Phoenix

8 Hobart

Atlanta

6 4

Sydney

New York

2 0

Melbourne

Houston

Brisbane

Chicago

Canberra

Boise Darwin

3 Methods and Results This chapter is built on the extensive research of the authors on North American and Australian UHIs at the University of Nevada Las Vegas and at the University of Adelaide. The main aim of this chapter is to translate the findings of the authors’ previously published research, and form an end-user friendly decision making tool, to select the most appropriate cooling intervention for parking lots in different urban contexts in both cities, Las Vegas and Adelaide (extendable to cities with similar climate conditions). This chapter is based on the results from previous publications of the authors in addition to new surface and air temperature field measurements.

3.1 Landsat 7 and 8 Remote Sensing Remote sensing results of Landsat 7 and Landsat 8 urban surface temperature related to asphalt, paving, grass and tree canopy classes in Adelaide in 2001–2002 and 2013– 2014 [31]. This study was at the urban scale and data resolution was 30 m (see the source publication for detailed methods). Related results are presented in Table 1 and Fig. 2. Figure 2 indicates that among the common surfaces used in parking lots, a tree canopy leads to an average 4–5 °C lower surface temperature than impermable surfaces such as black asphalt, concrete or pavers; also the surface temperature is 1– 3 °C lower than at permeable surfaces such as grass cover, or protected or bare natural soil. The maximum recorded surface temperature variation was 18.6 °C between the tree canopy and black asphalt.

Optimization of Urban Cooling Strategies for Parking Lots …

173

Table 1 Variation in temperature of common urban surfaces in Adelaide 2001–2002 and 2013–2014 [31, p. 442] Tree canopy (°C) Winter 2001

Min.

Winter 2014

Summer 2001

26.0 24.0 22.0 20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0

ΔT (surface cover - air) (°C)

ΔT (surface cover - air) (°C)

Summer 2014

18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0

9

Permeable surfaces (°C)

Concrete sand pavers (°C)

Asphalt (°C)

Average air T (°C) 11

11.41

9.85

11.9

Max.

13.47

15.28

16.51

16.55

Var.

4.47

3.87

6.66

4.65

Min.

18.3

21.55

21.93

23.11

Max.

23.18

25.31

27.23

27.19

Var.

4.88

3.76

5.3

4.08

Min.

31.03

34.2

36.66

36.68

Max.

40.67

43.15

43.3

43.31

Var.

9.64

8.95

6.64

6.63

Min.

33.67

35.73

39.12

40.64

Max.

43.81

46.96

45.82

46.15

Var.

10.14

11.23

6.7

5.51

10

23

30

Summer

Tree canopy (°C) Permeable surfaces (°C) Impermable surfaces (°C) Natural land (°C)

ADL Hot 2001

ADL Hot 2014

Winter Tree canopy (°C) Permeable surfaces (°C) Impermable surfaces (°C) Natural land (°C)

ADL Cold 2001

ADL Cold 2014

Fig. 2 Normalized surface temperature of urban cover classes in hot and cold seasons in Adelaide 2001–2002 and 2013–2014 (5% error lines)

174

E. Sharifi et al.

3.2 Air Temperate Field Measurement The temperature reduction capacity of potential surface cover setup of parking lots in the Adelaide and Las Vegas context has been tested in studies for Mason Lakes Campus of the University of South Australia. The air temperature and relative humidity data in 7 different spatial configurations with a range of urban greenery, permeable and impermeable surface covers was collected between August 2017 and December 2018. EXTECH RTH10 data loggers (16,000 readings of air temperature and relative humidity, recorded every 30 min, resolution 0.1 °C and 0.1%RH, Accuracy ±1 °C and ±3%RH). Sensors were attached to a plastic pole at 1.2 m from the ground and were protected by a two-layer solar shield. The location and setup of the sensors are presented in Fig. 3.

Fig. 3 Sensor locations at the Mawson Lakes campus of University of South Australia 2016–2017

Optimization of Urban Cooling Strategies for Parking Lots …

175

Results indicate that a combination of tree canopy shade and evaporative cooling effect under a well-established tree (h = 6 m; foliage canopy = 6 m; coverage 80%) in location 21 and 32 can result in a daily air temperature reduction of 1 °C compared with permeable surface covers such as mulch on soil and more than 1.4 °C compared with impermeable surfaces such as concrete, asphalt and pavers (see Fig. 4). A maximum air temperature variation of 12.4 °C (30 min average) was recorded between sensor locations 34 and 32 in November 2017 during a summer heatwave in Adelaide. The results also indicate that during hot summer days, the cooling effect of a well-established tree is similar to a well-irrigated grass cover surface. Unlike the overall UHI effect, temperature variation in human scale tends to peak in summer up to 12 °C and decrease to less than 1 °C in winter. Thus, trees can be used as the most effective passive outdoor climate moderators in cities with a hot and dry climate like Adelaide and Las Vegas. 10.0

3-HOURLY ΔT (°C)

8.0 6.0 4.0 2.0 0.0 -2.0 -4.0 -6.0 -8.0 LO33-LO32

LO22-LO21

LO33-LO32

LO22-LO21

3.0

DAILY ΔT (°C)

2.5 2.0 1.5 1.0 0.5 0.0 -0.5

Fig. 4 Daily and 3-hourly (average) air temperature variation between tree canopy vicinity (location 21 and 32) and paving (location 22 and 33) in Adelaide, 2017–2018

176

E. Sharifi et al.

4 Cooling Capacity of Different Interventions Effective cooling of public spaces can be achieved via a combination of various strategies. Each cooling strategy listed below provides specific opportunities and constraints. For example, permeable paving is more effective in cities with wet winter and dry summer climate, also in cities with temperate climate where there is a reasonable rainfall. Where Adelaide may use permeable surfaces for urban cooling with an annual 536 mm rainfall, Las Vegas would benefit less from permeable surfaces due to its limited rainfall of annual 106 mm. Furthermore, the cooling effect of natural turf is highly dependent on its irrigation state and availability of water. A summary of the cooling capacities of various strategies is presented in Table 2. Table 2 Relative effect of different cooling strategies adopted from [32, p. 33] to use in open air parking lots Cool paving

Cool seals

Permeable Grass Tree Exposed Misting paving cover canopy water fans bodies

Shading structures

33

33

20

20

19

N/A

N/A

15

Maximum 2.5 effect on air temp. around the point of application (°C)

1.5

2

2

4

4

8

4

Rainfall dependent Less efficient in humid climates

Availability of water supply Grass cover is very water demanding Horticultural and ecosystem maintenance

Maximum effect on surface temp. (°C)

Main Changes in constraints reflectance over time (aging, dirt accumulation) Complex reflectance in street canyons Maximum effect in city scale air temp. (°C)

2 °C

Effect is temporary

N/A

N/N

Optimization of Urban Cooling Strategies for Parking Lots …

177

5 Urban Cooling Context-Intervention Matrix for Parking Lots in Adelaide and Las Vegas Application and effectiveness of urban cooling techniques including cool and green surface covers, evaporative cooling and shading vary with locational context. Depending on the state of development, aspect ratio and sky view factor a range of urban cooling guidelines may become more appropriate and cost-effective in downtown/CBD and suburban contexts. Studies have found that strategic planning and design of parking lots with the objective of maintaining a healthy tree canopy can effectively retrofit existing lots with enough soil volume with limited or no impact to the loss of parking space (Southern Nevada 2012). When considering trees for parking lots it is important to utilize appropriate tree species, soil conditions, and planter design. The following should also be applied for parking lot specific trees: • Choose trees with minimal litter, or sharp spines and thorns due to the proximity to pedestrian and vehicle traffic. • Use moderate to fast growing trees with large canopies to optimize shading and storm water benefits. • Select native or appropriate species that can tolerate reflected heat. • Avoid using palm trees, as they provide minimum shading to the interior of the parking lot. Where and how these trees are planted in parking lots will greatly affect their ability to live healthy lives and provide environmental, social, and economic benefits. Planter strips or fingers are preferred over planting diamonds since they provide a more desirable soil volume (Southern Nevada 2012). Las Vegas is one of the sunniest, driest, and least humid locations in North America, and Adelaide is Australia’s driest capital city. Both cities encounter particularly warm and dry summers. Las Vegas has an average of 134 days with air temperature higher than 32 °C (90 °F, in July), with an annual rainfall of only 110 mm and only 27 rainy days per year. Adelaide experiences its highest monthly mean maximum temperature of 29.5 °C and averages 10.5 h of daily sunshine during summer, when rainfall can be as low as 15.4 mm during February. Both cities have more than 3– 5 days over 42 °C (107 °F) every year, with the record summer temperature of above 47 °C (117 °F). Table 3 shows different cooling strategies for open air parking lots in Las Vegas and Adelaide with an effectiveness score of 1–3 for each intervention. It is worth noting that a combination of these interventions can be used for more significant microclimate cooling. • Utilizing surface water and other evaporative cooling strategies are highly recommended in Adelaide. Relatively low rainfall during summer makes water sensitive urban design principles essential to ensure evaporative cooling during summer. In Las Vegas, the surface water, although limited, can be supplemental to standard irrigation practices. Most native trees with large canopies, once matured and

Winter

Temperate CBD and cool Suburbs 1

H

3

3

3

2

1

M

3

2

3

3

1

1

1

2

1

2

1

2

1

1

3

3

3

3

Parking lot Sky Cool paving High Green Tree context View albedo envelope canopy Factor High High Permeable surface treatments albedo emittance paving paving paving

Hot and Mild and Downtown M dry—extended dry—short Suburbs H

Summer

Climate

Adelaide Hot and temperate

Las Vegas

City

1

2

1

1

1

1

1

1

Surface Misting water fan cooling

Evaporative cooling

3

3

3

3

Shading structures

Table 3 Parking lot cooling strategies for Las Vegas and Adelaide, with an effectiveness score of 1 to 3 for each intervention (with 3 being the most effective)

178 E. Sharifi et al.

Optimization of Urban Cooling Strategies for Parking Lots …

•

•

•

•

179

established, become less reliant on frequent watering from municipal sources and can be sustained on surface water from rain events. Summer days in Las Vegas and Adelaide usually have high solar radiation intensity and UV levels. Thus, increased tree canopy and shading are the best strategies, especially in Las Vegas, where the dense tree canopy cover is only 2.9% and more scattered green cover is 12.9%. Adelaide’s tree canopy is higher at 20.3% (including its vast parklands surrounding the CBD) and less than 15% in the suburbs, still far away from the ideal 30% commonly recommended in the urban cooling strategies. Maximum daily temperature in Las Vegas and Adelaide regularly surpasses 32 °C (90 °F) in summer and reaches 45 °C (113 °F) or more during heatwaves, which happens more and more frequently every year in the context of global warming. Misting fans for temporary cooling are highly effective, particularly when mixed with shading. With an annual average rainfall of 564 mm, permeable paving is an essential strategy for urban cooling, while addressing stormwater management in Adelaide. In Las Vegas, permeable paving may not be effective for major flooding: due to the very low annual rainfall of 110 mm, however, it will benefit root growth with less compacted soils. High emittance permeable paving is the best practice to radiate away the urban while promoting extensive root growth and healthy tree canopy for achieving the best results. However, high albedo permeable paving is only recommended to be used in low traffic areas, such as parking stalls, where the glare effect, slippery surfaces, and load impact are not relevant to users.

6 Conclusions Urban centers that are up to 4 °C (7 °F) warmer in the daytime and up to 12 °C (22 °F) warmer at night than surrounding rural or suburban areas due to hard, impermeable surfaces like black asphalt on roads, sidewalks, parking lots and roofs that absorb and store heat. This temperature increase results in higher energy costs for cooling of buildings, lower quality of life, and decreased air and water quality (Sharifi and Lehmann 2014). Is it possible through the appropriate design of buildings, urban structures and infrastructure to decrease the temperature of cities? The School of Architecture at the University of Nevada Las Vegas has conducted extensive research in the urban heat island effect on car-parking lots in Las Vegas and formulated recommendations for the City of Las Vegas and the City of Henderson how to plan parking lots that absorb and store less solar radiation, therefore create less UHI effect. Planners and architects must consider the impact that rising temperatures and excessive heat waves are having on urban development and apply strategies to mitigate UHI effects. Extreme heat is emerging as a growing risk factor that requires suitable planning consideration, and the experts are responding with design

180

E. Sharifi et al.

approaches, technologies, and new policies (building codes) to mitigate the impacts and help protect human health. The UHI effect is dangerous, because it can increase heat-related mortality and morbidity up to 100%, especially among low-income populations and the elderly [12]. The research provides evidence that trees are beneficial in the urban environment to mitigate the UHI effect. The maximum amount that trees can reduce surface temperatures is 19 °C (35 °F); and they can reduce summer air temperatures in cities from 1 to 5 °C (2–9 °F). Matt Santamouris has extensively written on the urban microclimate [33, 34] and notes: Buildings play a huge role in the creation of the urban heat island phenomenon increasing the temperature of cities. Urban heat island is caused because of the inappropriate use of absorbing materials like black asphalt and dark exterior roofing materials, the high density of buildings reducing wind penetration, generated anthropogenic heat, a lack of greenery and water, and excess use of impervious surfaces that store solar heat then re-emit it at night-time back into the air. Urban overheating has a significant impact on energy usage and the environmental quality of urban space, increasing the ecological footprint of cities and raising the risk of heat related mortality and morbidity. It also seriously affects the quality of life of vulnerable and low-income households, increasing substantially indoor temperatures during extreme events and placing people’s health and life under threat.

Scientific studies estimate that because of climate change and the resulting increase in solar gain, the energy consumption of buildings may double by 2050, and the temperature of cities may increase up to 4–5 °C. Architects and planners are responsible for mitigating the UHI effect through the use of additional greenery in cities, be it integrated into buildings, on roof tops, or in the city infrastructure; they can also specify the use of advanced cool materials in open spaces and the exterior envelope of buildings (using the albedo and infrared emittance effects), the use of water sources, and solar control and shading of the open urban spaces. All these measures together can reduce the peak ambient temperature of cities by −3 °C and more. Our research shows that planting trees between car parking spaces is vital to most urban environments and can lead to −1 to −5 °C (−34 to −41 °F) summer cooling for parking lots in Las Vegas and Adelaide. Acknowledgements The authors are grateful for support this research has received from the Urban Futures Lab at the University of Nevada, Las Vegas.

References 1. DESA (2018) World urbanization prospects: highlights. Department of Economic and Social Affairs Population Division, Editor. United Nations, New York 2. UN-Habitat (2014) Planning for climate change. A strategic values-based approach for urban planners. In: Cities and Climate Change, United Nations Human Settlements Programme, London 3. World Bank (2013) Turn down the heat: climate extremes, regional impacts, and the case for resilience. Washington, DC

Optimization of Urban Cooling Strategies for Parking Lots …

181

4. Cleugh H et al (2011) Climate change in Australia: technical report 2011. CSIRO Aspendale VIC 5. Maurer EP (2007) Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two emissions scenarios. Clim Change 82(3):309–325 6. Bay JHP, Lehmann S (2017) Growing compact: urban form, density and sustainability. Taylor & Francis 7. Gartland L (2008) Heat islands: understanding and mitigating heat in urban areas. Earthscan, Washington, DC 8. Oke TR (1988) The urban energy balance. Prog Phys Geogr 12(4):471–508 9. Wong NH, Jusuf SK, Tan CL (2011) Integrated urban microclimate assessment method as a sustainable urban development and urban design tool. Landsc Urban Plan 100(4):386–389 10. Guest CS et al (1999) Climate and mortality in Australia: retrospective study, 1979–1990, and predicted impacts in five major cities in 2030. Clim Res 13(1):1–15 11. Stone B (2012) City and the coming climate: climate change in the places we live. Cambridge University Press, New York 12. Sanderson M et al (2017) The use of climate information to estimate future mortality from high ambient temperature: a systematic literature review. PLoS ONE 12(7):e0180369 13. Burgess K, Foster E (2019) SCORCHED: extreme heat and real estate. Urban Land Institute, Washington, DC 14. Erell E, Pearlmutter D, Williamson T (2011) Urban microclimate: designing the spaces between buildings. Earthscan, London 15. Santamouris M, Kolokosta DD (2015) Urban microclimates: mitigaing urban heat. In: Lehmann S (ed) Low carbon cities. Routledge, New York, pp 282–292 16. Oke TR (2006) Initial guidance to obtain representative meteorological observations at Urban Sites: instruments and observing methods. IOM Report No. 81. 2006, World Meteorological Organization, Canada 17. Soltani A, Sharifi E (2017) Daily variation of urban heat island effect and its correlations to urban greenery: A case study of Adelaide. Front Archit Res 6(4):529–538 18. Guan H et al (2013) Characterisation, interpretation and implications of the Adelaide Urban Heat Island. Flinders University, Adelaide 19. Clay R et al (2016) Urban Heat Island traverses in the City of Adelaide, South Australia. Urban Clim 17:89–101 20. Coutts AM, Beringer J, Tapper NJ (2008) Investigating the climatic impact of urban planning strategies through the use of regional climate modelling: a case study for Melbourne, Australia. Int J Climatol J R Meteorol Soc 28(14):1943–1957 21. Santamouris M et al (2018) On the energy impact of urban heat island in Sydney: climate and energy potential of mitigation technologies. Energy Build 166:154–164 22. Deilami K, Kamruzzaman M (2017) Modelling the urban heat island effect of smart growth policy scenarios in Brisbane. Land Use Policy 64:38–55 23. Mahmuda S, Webb R (2016) Climate adaptation and urban planning for heat islands: a case study of the Australian Capital Territory. Aust Plan 53(2):127–142 24. Wang C et al (2018) Assessing local climate zones in arid cities: the case of Phoenix, Arizona and Las Vegas, Nevada. ISPRS J Photogramm Remote Sens 141:59–71 25. Bornstein R, Lin Q (2000) Urban heat islands and summertime convective thunderstorms in Atlanta: three case studies. Atmos Environ 34(3):507–516 26. Gaffin SR et al (2008) Variations in New York city’s urban heat island strength over time and space. Theoret Appl Climatol 94(1):1–11 27. Streutker DR (2003) Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sens Environ 85(3):282–289 28. Haddad S et al (2018) Mitigation of urban overheating in three Australian cities (Darwin, Alice Springs and Western Sydney). In: Engaging architectural science: meeting the challenges of higher density. The Architectural Science Association (ANZAScA) Melbourne, Australia 29. Kim H, Gu D, Kim HY (2018) Effects of Urban Heat Island mitigation in various climate zones in the United States. Sustain Cities Soc 41:841–852

182

E. Sharifi et al.

30. Black A, Stephen H (2014) Relating temperature trends to the normalized difference vegetation index in Las Vegas. GISci Remote Sens 51(4):468–482 31. Sharifi E et al (2017) Landsat surface temperature data analysis for urban heat resilience: case study of Adelaide. In: Geertman S et al (eds) Planning support science for smarter urban futures. Springer, Gewerbestrasse, pp 433–448 32. Osmond P, Sharifi E (2017) Guides to urban cooling strategies. Low Carbon Living CRC, Sydney 33. Santamouris M, Kolokotsa D (2016) Urban climate mitigation techniques. Routledge 34. Santamouris M (2018) Minimizing energy consumption, energy poverty and global and local climate change in the built environment: innovating to zero: causalities and impacts in a zero concept world. Elsevier Science

Urban Heat Island and Mitigation in Tropical India Priyadarsini Rajagopalan

Abstract Urban heat island (UHI) studies using ground-based observations are limited in the tropical cities of India. This chapter reviews the UHI studies in the tropical cities located in the southern and central states of the subcontinent, namely Tamil Nadu, Karnataka, Kerala and Maharashtra. In tropical cities that experience high latent heat fluxes, the thermal environments are also affected by the heterogenous nature of urban settings. Majority of the UHI studies were conducted for a short period using either non-standard stations or mobile surveys, or a combination of these, with the reported UHI intensities ranging from 1.76 to 4.6 °C. Comparison between studies are difficult due to the variation in the methodology and the way results are presented. Ground-based measurements deployed in both micro and macro scales informed by high resolution remote sensing outputs will help to address the gap in the current knowledge. Dense network of stations installed using crowd sourcing approach are proven to be beneficial if uncertainties are carefully addressed. This chapter also discusses the application of UHI mitigation strategies established in similar climatic conditions and land use patterns. Mitigation actions including tree planting, use of appropriate materials as well as enhancing ventilation should be carefully chosen according to the geometry and orientation of the streets. Keywords Urban heat island · Tropical India · Field measurements · Air temperature · Mitigation · Land use classification

1 Introduction The world population is projected to increase to 9.1 billion by 2050 [1]. With the increase in population in many developing countries, there has been exponentially growing demand in basic amenities such as housing, sanitation and transport. The challenges paused by climate change which has been negligible in the developing P. Rajagopalan (B) Sustainable Building Innovation (SBi) Lab, School of Property, Construction and Project Management, RMIT University, Melbourne, Australia e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_9

183

184

P. Rajagopalan

countries has become increasingly alarming in recent years with majority of the growth occurring in developing countries. The contribution of low-income, highly populated regions to global carbon emission has been negligible in comparison to developed countries. However, the growing economic development in the developing countries has resulted in significant changes in the carbon emission patterns. It is well recognized that as the size of cities increases, the local climate gets modified. Urban Heat Island (UHI) effect is the phenomena where densely built city centres are hotter than the surrounding rural or suburban areas. UHI is a major challenge in the tropics where 40% of the population lives [2]. The population density varies considerably with majority of the people concentrated in urban areas as a result of intensified migration from rural areas for jobs. The people living in the tropics are highly vulnerable to the detrimental effects of climate change as they already experience high temperature. Even though a large number of studies has explored UHI, so far the focus has mainly been temperate climates. Despite the hot and humid conditions causing uncomfortable environments in the region, detailed knowledge of tropical urban climate and ways to improve the microclimate is limited. This chapter aims to review and analyse the characteristics of UHI studies performed in the southern and central states located in the hot and humid climatic zones in India. The analysis mainly focused on studies that reported canopy layer UHI that uses ground-based measurements, although specific studies that employ remote sensing techniques for surface UHI measurements are also briefly mentioned in this chapter to provide an overall context.

2 Urban Energy Balance Figure 1 shows a typical profile of UHI illustrating the difference in temperature build up in rural, sparsely built and densely built urban environments. The thermodynamic behaviour of the air and surface temperatures is caused by the difference in energy balance [3]. The energy balance is associated with the physical process that involves

Fig. 1 Representation of urban heat island

Urban Heat Island and Mitigation in Tropical India

185

Fig. 2 Energy balance of urban environment

the surface and the boundary layer and result from radiative, heat and moisture exchanges between the natural and artificial surfaces and the atmosphere. In urban areas, turbulent intensities are higher than rural areas due to the roughness induced by buildings. The surface energy balance affecting the urban area (Fig. 2) can be expressed as in Eq. 1 which describes the heat exchanges between different surfaces. Q ∗ + Q F = Q H + Q E + Q S + Q A

(1)

Q* represents the net all-wave radiation including short- and long-wave; QF is the anthropogenic heat flux that is usually generated by buildings and human activities such as air conditioning, traffic; QH is the convective (or turbulent) sensible heat flux which is the energy that transferred from surfaces to the air; QE is the latent heat flux resulting from evaporation; QS is the net storage heat flux or heat stored in materials and QA is the net horizontal heat advection which is the energy required for air velocity. The latent heat flux (QE ) represents the amount of energy for evaporation and transpiration commonly associated with natural surfaces, vegetation and water bodies. The main difference in the tropical UHI is associated with the surface energy balance due to excess heating by direct solar radiation as a result of high sun angle that heat urban structures. Tropical UHIs are usually smaller in magnitude in comparison to temperate cities [4, 5]. Furthermore, daytime UHI is found to be negative in tropical cities during pre-monsoon period because of low-vegetation cover in non-urban surroundings. The studies conducted during wet season is much lesser compared to dry season because it is relatively easier to carry out observations under dry conditions [4].

186

P. Rajagopalan

3 India—Climate, Topography India, experiencing a vast variety of climate zones is one of the most climatically diverse countries in the world with the climates ranging from tropical in the south, temperate and alpine in the Himalayan north to arid in the northwestern Thar desert. The country’s climate is strongly influenced by the Thar Desert and the Himalayas mountain range [6]. The tropical wet or tropical monsoon climate that covers southwestern strip including Kerala and Goa is characterised by moderate to high temperatures throughout the year, and heavy rainfall in the monsoon seasons. Tropical wet and dry climate, where summer is very hot is more common in the rest of the south and inland areas. Hot semi-arid climate prevails in the region including central Maharashtra and Karnataka, inland Tamil Nadu and western Andhra Pradesh. The northwest area of Rajasthan has hot desert climate. Humid subtropical climate prevails in some parts of north India and most of northeast India. This climate has hot summers and coldest winters where temperature can go below zero degree. For the tropical monsoon climate where the temperature doesn’t fall below 18 °C, heavy seasonal rainfall with around 2000 mm is prevalent. March to May is the summer season, while June–August is monsoon. Pre-monsoon time is normally the hottest period of the year. In an analysis of the effect of urbanization on 15 cities of India, Rao et al. [7] reported a general increase of relative humidity and rainfall and decrease of wind speed, sunshine hours and cloud cover.

Fig. 3 Map of the cities investigated in this study

Urban Heat Island and Mitigation in Tropical India

187

Figure 3 illustrates the map of India showing the hot and humid cities investigated in this chapter, majority of which lie in the coastal areas. Over the coastal sites, high humidity builds towards monsoon season along with increased temperature. In a review analysing heat island of Asian and Australian cities, Santamouris [8] note that UHI intensity during the summer is reduced in coastal cities due to the presence of see breeze that helps to transfer cool air into the city enhancing wind speed. However, Desai and Dhorde [9] reported significant decrease in wind speed along with increase in temperature and humidity, resulting in extreme thermal discomfort in the western coastal regions in India. This, to some degree may be due to the rise in sea surface temperature. Desai and Dhorde [9] also reported that highly developed urban centres experienced maximum thermal discomfort by year 2014, while rapidly expanding urban centres showed increasing trend in thermal discomfort. Table 1 shows the cities investigated in this chapter along with their topography and climatic classification. Additionally, minimum, maximum and average temperatures as well as relative humidity and annual rainfall are summarised in the table. Table 1 Climatic characteristics of the cities Cities

Koppen Average climate annual classification temperature (°C) min–max

Average Annual Altitude Geography annual rainfall (m) relative (mm) humidity % min–max (avg)

Chennai

Aw (Tropical 19.4–38.3 savanna-dry winter)

57–78 (70)

1400

6

Coastal

Bangalore

Aw (Tropical 14.3–33.7 savanna-dry winter)

45–79 (63)

970

920

Landlocked

Thiruvananthapuram Aw (Tropical 22.4–31.5 savanna-dry winter)

69–85 (78)

1754

10

Coastal

Ernakulam

Am (Tropical monsoon)

75–90 (82.5) 67–89

2978

0

Coastal

Mumbai

Aw (Tropical savanna-dry winter)

19–32.8

69–86 (75)

2260

14

Coastal

Pune

BSh (Hot semi-arid)

12.2–37.4

36–82 (75)

763

560

Hilly

Nagpur

As (Tropical savanna-dry summer)

12.5–42.7

24–80 (53)

1094

310

Landlocked

Source [10, 11]

22.3–33

188

P. Rajagopalan

4 Urban Built Environment in India As a result of rapid urbanization in India, dramatic land-use changes have taken place in many of the major cities. For example, the vegetation area in Chennai which constituted 70% in 1901 has declined to 48% by 2011 [12]. Similarly, in Bangalore, the built-up area has increased by six times and the green cover has reduced from 68% in 1973 to merely 25% in 2012 [13]. In a recent study in Bangalore conducted using temporal remote sensing data by Ramachandra et al. [14], it was revealed that during 1973–2017, there was 1028% increase in urban area with the reduction of 79% of water bodies and 88% vegetation. Building materials used in the Indian cities include concrete, stones, bricks, wood, steel, clay tiles and other composite materials. In the urbanisation process, the older buildings, usually one to two storeyed with sloping roofs and made of stones and bricks (some of them with small courtyard inside) were replaced by four to five storey mid-rise blocks with concrete roofs. This subsequently cleared the way for 20–30 storeyed tall building blocks in the later years. However, the street widths were not increased along with the increase in building heights. The development in the last few decades remained mostly unplanned. Proliferation of multi-storeyed buildings with modern materials, without respecting the context is a common sight in many cities. Vernacular practices which are proven to be sustainable for many years are repeatedly ignored. Due to the heterogenous and dynamic nature and of the canopy layer, thermal and radiative properties of artificial and natural surfaces, the UHI assessment becomes very complex. Additionally, the canopy layer models tested for mid latitude cities are not applicable for tropical cities [15]. For these reasons, there is an urgent need for developing coordinated policy framework informed by evidence-based research.

5 UHI Monitoring Methods UHIs are better understood and measured as either surface heat island or atmospheric heat island. The canopy layer UHI refers to the air temperature differences observed close to the ground and is typically more pronounced at night due to the re-radiation of heat from buildings and roads into urban canyons. On the other hand, during daytime, the urban–rural difference is not that prominent, especially in high density areas, because tall buildings promote shading of surfaces [16]. Depending on the type of UHI under investigations, various monitoring techniques ranging from analysis of weather data to detailed field monitoring using fixed sensors or mobile traverses are used. The technique of monitoring remotely through satellite images has been extensively used to cover a larger area. The number of studies using satellite imagery has increased significantly in the recent years due to the abundance of freely available satellite data. Remote sensing has the capability to capture data for large geographic area. A study on surface UHI intensity across 44 major cities of India by Raj et al.

Urban Heat Island and Mitigation in Tropical India

189

[17] showed different patterns in the tropical, temperate and arid climate regions. For tropical cities like Thiruvananthapuram and Visakhapatnam, UHI was intense during day and weak during night compared to cities with arid and temperate climates. The increasing night-time results highlight the increasing trend in the anthropogenic activities causing high surface heat islands during nights. Investigating canopy layer UHI is necessary to study the impact on building energy consumption thermal comfort, and air quality. Santamouris et al. [8] note that due to the considerable variation in the duration of the measurements, the number of measuring stations used, measurement season, the format of the reported UHI intensity etc. between the reported studies, comparing between studies has become very difficult. In addition, the UHI intensities measured using non-standard measuring equipment or mobile stations were significantly higher than that measured using standard measuring stations [8]. This is because standard meteorological stations are installed in relatively undisturbed areas whereas mobile equipment and non-standard stations are installed in dense urban areas where thermal balance is significantly affected by the characteristics of local environment.

6 UHI Studies Using Weather Data UHI studies using long-term weather data collected from Indian Meteorological Department mainly involved cities in the state of Tamil Nadu [18, 19], Maharashtra [20] and western coastal cities [9]. Amirtham [21] analysed the air temperature and comfort trends of the Chennai metropolitan area using historic climate records from two meteorological stations: Meenambakkam meteorological station representing the rural and Nungambakkam meteorological station representing the urban area. Analysis of 20 years’ data from year 1988 to 2008 showed that the urban station had a significant increasing trend during the daytime (0.06 °C/year) compared to rural station (0.02 °C/year). Higher nighttime temperature was observed in the urban station. In recent years (2002–2008), a decreasing trend was observed in the diurnal temperature difference. The impact on thermal discomfort is also discussed in this study. The nighttime environment which used to be comfortable previously have shown significant discomfort trend in recent years. Recently Jeganathan et al. [22] investigated the trends in seasonal and annual temperature variations in Tamil Nadu for the period of 1969–2016, using the recordings from 17 weather stations. Majority of the stations which have experienced high warming in recent years, showed positive trends in maximum, minimum and average temperatures; and the warming was more pronounced during post monsoon seasons. Most of the coastal stations showed increase in maximum temperature compared to inland stations. However, high increase in minimum temperatures in comparison to coastal stations were found in inland stations. For accurate comparison and analysis of UHI, selection of reference station in coastal cities and the distance from the sea is an important aspect and need to be same for rural and urban stations [8]. Maral and Mukhopadyay [20] analysed the maximum and minimum temperature of Mumbai

190

P. Rajagopalan

metropolitan area using 32 years’ data (from 1976–2007). Four stations: one in the Mumbai city district’ one in the suburban district centre, and two away from the city on the periphery were selected for analysis. The suburban station Santacruz was warmer than both peripheral stations and the city stations Colaba. The coastal area was found to have higher daytime temperature due to the significant land-see breeze circulation as a result of the heating of the air above. During the day, pressure gradient generates sea breeze from cooler sea to warmer land. The air flow is reversed in the night. In summary, time series analysis showed strong and statistically significant increasing trend due to urbanisation. While the above-mentioned studies focused on individual cities, in a larger scale study, the trends in thermal discomfort indices from 1969 to 2014 in seven cities in the western coastal region were analysed by Desai and Dhorde [9]. Southern part displayed substantial increase in thermal discomfort, more in the monsoon season than pre-monsoon season. These results highlighted the need for further research on mitigation and adaptation strategies. The next section discusses the studies in different cities that used field measurements and mobile surveys.

7 UHI Studies in Different Cities 7.1 Chennai The state of Tamil Nadu has a long fertile coastal plane in the east, mix of hills and plains in the northern part, arid planes in the central and the south-central regions. The climates range from semi-arid to dry sub-humid. With a distinct period of rainfall, the state receives maximum rainfall during the post monsoon season (October to December). Majority of the UHI studies in Tamil Nadu were conducted in Chennai, which is the capital city located on the coastal plain. Few studies that are conducted in Chennai focused on UHI investigation using mobile traverses. In an early study conducted in 1987, Sundersingh [23] used mobile traverses to collect temperature, humidity and wind data from 77 locations in Chennai using three routes. The difference between cool pockets and heat islands ranged from 2.5 to 4 °C. Also, the heat pockets showed minimum humidity values while the cool pockets showed higher humidity values. The proposed mitigation measures included introducing cool pockets within the heat islands, development controls facilitating proper ventilation, preventing multi-storey apartments along the coastline and designing canyons such a way to promote ventilation. Another mobile survey carried out in the recent year by Jeganathan et al. [22] divided the Chennai metropolitan area using a grid network to characterize various land use patterns. The results showed that the temperature differences between the fringes and the central part of the city were in the range of 3–4.5 °C. Population of Chennai metropolitan area had grown rapidly in the last 20 years. As

Urban Heat Island and Mitigation in Tropical India

191

the city grows, the intensity shows an increasing tendency as demonstrated by these studies. Generally, the number of field-based studies reported were very limited due to the substantial resources required to conduct such studies. Field measurements were conducted in Thyagaraja Nagar (T Nagar), a planned residential area which has become one of the busiest shopping districts in Chennai [21, 24]. Data on air temperature and relative humidity were recorded on a typical winter day from nine different locations having different urban morphology. The results showed that East West oriented narrow street canyon (with aspect ratio 3.1) located near high density builtup areas had the highest temperature during the afternoon. In addition, wider streets that has an H/W ratio of 0.44 caused high temperature due to direct solar radiation. The minimum temperatures were recorded at locations with high percentage of vegetation cover and less hard surfaces. While up to 3.2 °C difference was found between the different sites at 2 pm, the diurnal variation between sites also varied ranging from 3.3 °C at location with an aspect ratio of 0.36 and 7 °C at location with an aspect ratio of 3.1. The maximum temperatures were found to be associated with the presence of dense built spaces that have hard and impervious surfaces. The presence of anthropogenic heat in the form of heavy traffic also increased the temperature at certain points. Figure 4 shows a view of one of the busiest streets in the T Nagar area. Like many other growing India cities, once-rural outskirts have turned into suburbs in the last two decades. The boom in the Information Technology (IT) industry caused rapid development of areas like Navalur which used to be a small village. Proximity to employment opportunities and entertainment venues as well as good road Infrastructure continues to attract more residents to this suburb. Figure 5 shows

Fig. 4 View of a busy street in T Nagar (Source United News of India)

192

Fig. 5 New urban developments in Chennai

P. Rajagopalan

Urban Heat Island and Mitigation in Tropical India

193

the view of some new IT parks and residential estates in the new suburbs. To date, no studies focusing on these new developments are reported.

7.2 Bangalore Bangalore, the capital of Karnataka is one of the fastest changing cities and a global IT hub in India. Previously known as the garden city of India due to the presence of many parks and water bodies, Bangalore has been transformed into a city filled with private real estate developments and commercial districts by extensive clearing of trees and green spaces. Due to its elevation, Bangalore used to have a pleasant and equable climate throughout the whole year. However, a warming tendency is recorded in the last decade. In a remote sensing-based study conducted using Landsat and MODIS data [25], urban areas consisting of industrial, commercial and residential land were found to exhibit the highest temperature in Bangalore. High temperatures were also observed in open ground; the lowest temperature across all years was observed in water bodies and vegetation. The minimum and maximum surface temperatures computed were 28.29 °C and 34.29 °C respectively in 2007. This study reported an increase of 2– 2.5 °C during the years 2000 to 2009. Only a handful of studies that adopted field monitoring were reported in Bangalore. Vailshery et al. [26] compared the environmental conditions in roads with and without trees. The ambient air temperature in treelined road sections were up to 5.6 °C lower than the temperature without trees. Air temperature with trees ranged from 23.1 to 34.2 °C, while those without trees ranged from 23.4 to 38.3 °C. There was a marked difference in road surface temperature, segments with trees ranging from 23 to 56 °C, and without trees ranging from 27 to 62 °C. Figure 6 shows an example of a tree lined narrow street in HSR layout, a suburb in the southeast of Bangalore. A field measurement by the Energy Research Institute [27] investigated the UHI in nine different pockets of Bangalore in residential and commercial areas including IT parks. The measurement was conducted during winter and summer using air temperature, globe temperature, humidity sensors, infra-red guns and thermal images. In the month of March, the maximum air temperature in the residential zones ranged from 31 to 34 °C. The study that focused on the effect of urban planning and green cover on air temperatures concluded that for residential building development, H/W ratio of μ + 0.5 ∗ δ

(8)

0 < LST ≤ μ + 0.5 ∗ δ

(9)

where μ and δ are the mean and standard deviation of LST in the study area, respectively. To further characterize the UHI phenomenon and to derive UHI parameters like magnitude/intensity, spatial extent and distribution within the study area, Urban Heat Island Intensity (UHII) was calculated from the LST values for the entire study area [28–33]. The UHII was calculated for the study area using Eq. (10). UHII was subsequently used to calculate the UHII index (UHIII) using Eq. (11) [34]. Ample representative samples were extracted from the surrounding desert area of the city to calculate the mean area of the desert to be used in UHIII derivation. UHII = Toa − Tmd

(10)

where Toa is the temperature of other areas and Tmd is the mean temperature of the bare earth/desert. UHII was then used to derive UHIII using Eq. (11). UHIII = Toa − Tmd/Tmd

(11)

The UHIII values were used to divide the study area into two zones of low UHIII and high UHIII based on mean plus one standard deviation. The LST and NDVI values for 2014 and 2019 within the UHIII zones were compared to highlight the trend of LST and NDVI in the zones. Statistical analysis, including correlation coefficient

240

Y. A. Aina et al.

and p-values, was carried out to further understand the dynamics of LST and UHI in the study area.

3 Challenges of UHI in a Desert Environment: The Case of Saudi Cities One of the current challenges of environmental sustainability in Saudi Arabia is the increasing trend of high energy consumption [35, 36]. In 2014, the energy consumption per capita was threefold the global average [36] and billions of dollars are spent on subsidy [37]. The energy consumption pattern is related to UHI effects as increases in energy usage drive further the processes of temperature increase through human activities. Moreover, some of the strategies for mitigating the UHI effects might require water, which is made available through further energy consumption by desalination. These challenges were highlighted by Gober [38] that the relationship between energy and water and their links to urban growth, environment and economic activities should be considered in achieving sustainability in desert regions. Howarth et al. [39] illustrated similar challenges by examining the relationship between increasing summer temperatures, high electricity consumption and air conditioning. As regards urban growth and UHI effects, Saudi Arabia’s urbanization has already surpassed 80% [35]. The spatial expansion of major Saudi cities has also been remarkable, around 40% from 1985 to 2000 and 50% from 2000 to 2015 [40, 41]. Though the concomitant increase in green areas has led to the occurrence of cool spots in the expanding cities [11, 42, 43], studies have shown trends of increasing temperatures especially the maximum temperatures [39, 44–46]. Rahman et al. [46] found that the mean temperature of the built-up area in Dammam increased from 36.42 °C in 1990 to 44.12 °C in 2014, which is higher than the increment of other land cover classes. In Jeddah, the minimum and maximum temperatures (both in summer and winter sessions) increased by, at least 2 °C, from 1986 to 2016 [11]. In a global study, Sobrino and Irakulis [47] showed that the mean Surface Urban Heat Island (SUHI) of Riyadh, Jeddah and Dammam were 2.6, 0.43 and −0.04 respectively. Riyadh was ranked fifteenth, based on mean SUHI, out of 71 urban agglomerations [47]. Similar to other cities, UHI in Saudi major cities is driven by impervious surfaces, economic and industrial activities, lack of green areas and population density [12, 48, 49].

4 UHI Mitigation Strategies The adoption and implementation of climate change mitigation plans are one of the targets of the United Nations Sustainable Development Goals (SDG11) [50], so UHI mitigation strategies should be established for urban areas. Newman [51] argued that cities need ‘water sensitive design’ and ‘biophilic urbanism’ in taking action against

Urban Heat Island Effects and Mitigation Strategies …

241

climate change. He argued that the strategies might include decoupling the use of fossil fuel from economic development and the adoption of water recycling, electric transport and urban greenery [51]. Aflaki et al. [52] identified the UHI mitigation strategies as the use of highly reflective materials/surfaces, urban morphology design and urban greening. Similarly, Wang et al. [53] and Yang et al. [54] asserted the effectiveness of cool roofs, green roofs, surface materials and urban morphology design in reducing UHI effects. He et al. [55] highlighted the co-benefits of sponge city development in UHI mitigation. It can improve urban greening, reduce flooding and regulate temperature regimes [55]. In Saudi Arabia, urban morphology is characterized by wide streets and extensive use of space that exposes the built environment to direct solar radiation. Moreover, Aljoufie and Tiwari [56] showed, in a case study of Jeddah, the lack of adequate green infrastructure in Saudi cities. With the inauguration of the Saudi Vision 2030, the government has embarked on a drive to enhance the environmental sustainability of the built environment through urban greenery and improved urban morphology design [37http://vision2030.gov.sa/en]. Green Buildings Council and Green Buildings Forum have been established to foster sustainable building practices [57]. Several urban greenery projects have been fully or partially implemented like the Wadi Hanifah wetlands in Riyadh, the Yanbu 12-km waterfront project and the initiative to plant trees along road centerlines in the cities [37]. Addas and Alserayhi [58] indicated that the Jeddah Municipality has the goal of increasing the open space per capita to 3.9 m2 from 3.47 m2 .

5 Exploring the Effectiveness of the Strategies The results of the LST and UHI analysis show the relationship between impervious surfaces, vegetation and LST over five years as summarized in Table 1. There is a significant drop in the LST and a steady increase in the NDVI values suggesting an increase in vegetation in the study area. The LST in 2014 ranges from 31.43 to 58.56 whereas in 2019 the range is from 30.44 to 54.33, same is the case with the NDVI values which ranges between −0.10 to 0.30 in 2014 and −0.34 to 0.48 in 2019 suggesting thriving vegetation. The area under vegetation had increased from 37.88 km2 in 2014 to 50.47 km2 in 2019. Figure 1 shows the spatial distribution of normalized LST and NDVI values in 2014 and 2019. Table 1 Temporal descriptive statistics of LST and NDVI in the study area Year

LST (Min)

LST (Max)

LST (Mean)

LST (SD)

NDVI (Min)

NDVI (Max)

NDVI (Mean)

NDVI (SD)

2014

31.43

58.56

48.75

2.63

−0.10

0.30

0.058

0.022

2019

30.44

54.33

47.69

2.02

−0.34

0.48

0.061

0.02

242

Y. A. Aina et al.

Fig. 1 Normalized LST and NDVI in 2014 and 2019

The delineation of UHI and Non-UHI (NUHI) zones revealed a stark difference in the LST and NDVI ranges in the respective zones as shown in Table 2. What is interesting is the marked difference in the ranges of LST and NDVI within UHI and NUHI zones within the same image and in between images collected at different times. The LST ranges between 50.06 to 58.56 and from 48.7 to 54.33 in the areas

Urban Heat Island Effects and Mitigation Strategies … Table 2 Variations in LST and NDVI within the UHI and NUHI zones in the study area

243 Zone

2014

2019

UHI

50.06

48.7

NUHI

31.43

30.44

UHI

58.56

54.33

NUHI

50.06

48.69

LST (Mean)

UHI

51.31

49.67

NUHI

47.34

46.67

LST (SD)

UHI

0.966

0.795

NUHI

2.16

1.67

LST (Min) LST (Max)

NDVI (Min)

UHI

−0.01

−0.049

NUHI

−0.158

−0.341

0.24

0.44

NDVI (Max)

UHI NUHI

0.45

0.48

NDVI (Mean)

UHI

0.053

0.061

NUHI

0.06

0.062

UHI

0.015

0.02

NUHI

0.025

0.03

NDVI (SD)

mapped as urban heat island zone in 2014 and 2019 respectively whereas in the NonUrban heat island zone the LST range between 31.43–50.06 in 2014 and 30.44–48.69 in 2019. The distribution and spatial extent of UHIII in the study are shown in Fig. 2. The study area was divided into zones of low and high UHIII values to determine the influence of vegetation on the temperature regimes in each zone and to seek the trend of how these variables correlate. Sufficient samples were collected from low and high UHIII zones which were later used to extract LST and NDVI. The correlation was positive in the high UHIII zone and negative in the low UHIII zone in both the years. The correlation coefficients of 0.08 and −0.29 were observed for high and low UHIII zones respectively for 2014 whereas the correlation coefficients of 2019 high and low UHIII zones were recorded at 0.059 and 0.0047 (Fig. 3). The spatial distribution of UHII over time and space has significantly reduced over the two sampled periods. In 2014, the high UHIII zones were concentrated in the north, NNE, SSE and South encompassing a major part of the study area whereas low UHIII constitutes mostly the residential and recreational part in the center to the west of the study area. In 2019, the high UHII zones were mainly concentrated in the north while UHI intensity had significantly reduced in the rest of the areas. The low UHIII zones that mainly constituted the urban center and was concentrated in the western part of the study area in 2014 has extended to the eastern limits of the study area in 2019. The UHIII hotspot in the northern areas of the study area can be attributed to the construction of a new airport in that area around that time. The results are in contrast with the findings of Chen and Zhang [34] in which 0.5 was

244

Y. A. Aina et al.

Fig. 2 Spatial distribution of UHIII values in 2014 and 2019

Fig. 3 Correlation coefficients and p-values of the relationships between LST and NDVI in UHIII zones in 2014 and 2019

Urban Heat Island Effects and Mitigation Strategies …

245

used to delineate high UHIII and low UHIII zones. Most of the values in the case of Jeddah are below 0.5. This might be due to the desert environment where desert soil surrounds the city of Jeddah unlike the tropical area where vegetated rural areas surround the city of Kunming, China [34].

6 Conclusion This chapter has highlighted the challenges in mitigating UHI effects in Saudi Arabia and the need to consider the relationship between energy and water in addressing UHI mitigation. The case study of Jeddah showed the effort by the government to reduce UHI through urban greening. There has been an increase in the area covered by vegetation and a corresponding decrease in UHI effects. As asserted by Addas and Alserayhi [58], a quarter of the planned open spaces is yet to be completed. It is expected that further implementation of the plan will lead to more success in reducing the UHI. The government could explore more mitigation strategies such as cool and green roofs and sponge city development. The proposed Taliah Channel project in Jeddah [59] can be a step in achieving sponge city development.

References 1. Carpio M, Gonzalez A, Gonzalez M, Verichev K (in press) Influence of pavements on the urban heat island phenomenon: a scientific evolution analysis. Energy Build 2. Litardo J, Palme M, Borbor-Cordova M, Caiza R, Macías J, Hidalgo-Leon R, Soriano G (2020) Urban Heat Island intensity and buildings’ energy needs in Duran, Ecuador: simulation studies and proposal of mitigation strategies. In: Sustainable cities and society, p 102387 3. Bahi H, Mastouri H, Radoine H (2020) Review of methods for retrieving urban heat islands. Mater Today: Proc 4. Oke TR, Mills G, Christen A, Voogt JA (2017) Urban climates. Cambridge University Press 5. Chakraborty T, Lee X (2019) A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability. Int J Appl Earth Obs Geoinf 74:269–280 6. Ghadban M, Baayoun A, Lakkis I, Najem S, Saliba NA, Shihadeh A (2020) A novel method to improve temperature forecast in data-scarce urban environments with application to the Urban Heat Island in Beirut. Urban Clim 33:100648 7. Sun R, Lü Y, Yang X, Chen L (2019) Understanding the variability of urban heat islands from local background climate and urbanization. J Clean Prod 208:743–752 8. Liu Y, Li Q, Yang L, Mu K, Zhang M, Liu J (2020) Urban heat island effects of various urban morphologies under regional climate conditions. Sci Total Environ 140589 9. Manoli G, Fatichi S, Schläpfer M, Yu K, Crowther TW, Meili N, Burlando P, Katul GG, BouZeid E (2019) Magnitude of urban heat islands largely explained by climate and population. Nature 573(7772):55–60 10. Nuruzzaman M (2015) Urban heat island: causes, effects and mitigation measures-a review. Int J Environ Monit Anal 3(2):67–73 11. Parvez IM, Aina YA, Balogun AL (in press) The influence of urban form on the spatiotemporal variations in land surface temperature in an arid coastal city. Geocarto Int 1–20

246

Y. A. Aina et al.

12. Li Y, Sun Y, Li J, Gao C (in press) Socioeconomic drivers of urban heat island effect: empirical evidence from major Chinese cities. Sustain Cities Soc 13. Gunawardena K, Kershaw T, Steemers K (2019) Simulation pathway for estimating heat island influence on urban/suburban building space-conditioning loads and response to facade material changes. Build Environ 150:195–205 14. He BJ (2019) Towards the next generation of green building for urban heat island mitigation: Zero UHI impact building. Sustain Cities Soc 50:101647 15. Kolokotroni M, Ren X, Davies M, Mavrogianni A (2012) London’s urban heat island: Impact on current and future energy consumption in office buildings. Energy Build 47:302–311 16. Wang Y, Wang A, Zhai J, Tao H, Jiang T, Su B, Yang J, Wang G, Liu Q, Gao C, Kundzewicz ZW (2019) Tens of thousands additional deaths annually in cities of China between 1.5 C and 2.0 C warming. Nat Commun 10(1):1–7 17. Stone B Jr (2012) The city and the coming climate: climate change in the places we live. Cambridge University Press 18. Pyrgou A, Santamouris M (2018) Increasing probability of heat-related mortality in a Mediterranean city due to urban warming. Int J Environ Res Publ Health 15(8):1571 19. Wu H, Liu Z, Yang Y, Bai S (2016) Characterizing thermal impacts of pavement materials on Urban Heat Island (UHI) Effect. DEStech Trans Eng Technol Res 20. Zander KK, Cadag JR, Escarcha J, Garnett ST (2018) Perceived heat stress increases with population density in urban Philippines. Environ Res Lett 13(8):084009 21. Jiménez-Muñoz JC, Cristóbal J, Sobrino JA, Sòria G, Ninyerola M, Pons X (2008) Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermalinfrared data. IEEE Trans Geosci Remote Sens 47(1):339–349 22. Cristóbal J, Jiménez-Muñoz JC, Sobrino JA, Ninyerola M, Pons X (2009) Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. J Geophys Res: Atmos 114(D8) 23. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434–440 24. Barsi JA, Schott JR, Palluconi FD, Hook SJ (2005) Validation of a web-based atmospheric correction tool for single thermal band instruments. In: Earth observing systems X, vol 5882. International Society for Optics and Photonics, p 58820E 25. Sobrino JA, Raissouni N, Li ZL (2001) A comparative study of land surface emissivity retrieval from NOAA data. Remote Sens Environ 75(2):256–266 26. Ma Y, Kuang Y, Huang N (2010) Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery. Int J Appl Earth Obs Geoinf 12(2):110–118 27. Guha S, Govil H, Mukherjee S (2017) Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India. J Appl Remote Sens 11(3):036020 28. Streutker DR (2002) A remote sensing study of the urban heat island of Houston, Texas. Int J Remote Sens 23(13):2595–2608 29. Streutker DR (2003) Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sens Environ 85(3):282–289 30. Rajasekar U, Weng Q (2009) Urban heat island monitoring and analysis by data mining of MODIS imageries. ISPRS J Photogramm Remote Sens 64(1):86–96 31. Rajasekar U, Weng Q (2009) Spatio-temporal modelling and analysis of urban heat islands by using Landsat TM and ETM+ imagery. Int J Remote Sens 30(13):3531–3548 32. Imhoff ML, Zhang P, Wolfe RE, Bounoua L (2010) Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens Environ 114(3):504–513 33. Keramitsoglou I, Kiranoudis CT, Ceriola G, Weng Q, Rajasekar U (2011) Identification and analysis of urban surface temperature patterns in Greater Athens, Greece, using MODIS imagery. Remote Sens Environ 115(12):3080–3090 34. Chen X, Zhang Y (2017) Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustain Cities Soc 32:87–99

Urban Heat Island Effects and Mitigation Strategies …

247

35. Abubakar IR, Aina YA (2018) Achieving sustainable cities in Saudi Arabia: juggling the competing urbanization challenges. In: E-planning and collaboration: concepts, methodologies, tools, and applications. IGI Global, pp 234–255 36. Abubakar IR, Dano UL (2020) Sustainable urban planning strategies for mitigating climate change in Saudi Arabia. Environ Dev Sustain 22:5129–5152 37. Aina YA, Wafer A, Ahmed F, Alshuwaikhat HM (2019) Top-down sustainable urban development? Urban governance transformation in Saudi Arabia. Cities 90:272–281 38. Gober P (2010) Desert urbanization and the challenges of water sustainability. Curr Opin Environ Sustain 2(3):144–150 39. Howarth N, Odnoletkova N, Alshehri T, Almadani A, Lanza A, Patzek T (2020) Staying cool in A warming climate: temperature, electricity and air conditioning in Saudi Arabia. Climate 8(1):4 40. Alqurashi AF, Kumar L, Sinha P (2016) Urban land cover change modelling using time-series satellite images: a case study of urban growth in five cities of Saudi Arabia. Remote Sens 8(10):838 41. Aina YA, Adam E, Ahmed F, Wafer A, Alshuwaikhat HM (2019) Using multisource data and the VIS model in assessing the urban expansion of Riyadh city, Saudi Arabia. Eur J Remote Sens 52(1):557–571 42. Alghamdi AS, Moore TW (2015) Detecting temporal changes in Riyadh’s urban heat island. Pap Appl Geogr 1(4):312–325 43. Aina YA, Adam EM, Ahmed F (2017) Spatiotemporal variations in the impacts of urban land use types on urban heat island effects: the case of Riyadh, Saudi Arabia. Int Arch Photogramm Remote Sens Spat Inf Sci 42 44. Hereher ME (2016) Recent trends of temperature and precipitation proxies in Saudi Arabia: implications for climate change. Arab J Geosci 9(11):575 45. Aina YA, Parvez IM, Balogun AL (2017) Examining the effect of land use on the spatiotemporal dynamics of urban temperature in an industrial city: a landsat imagery analysis. In: Global changes and natural disaster management: geo-information technologies. Springer, Cham, pp 3–15 46. Rahman MT, Aldosary AS, Mortoja M (2017) Modeling future land cover changes and their effects on the land surface temperatures in the Saudi Arabian eastern coastal city of Dammam. Land 6(2):36 47. Sobrino JA, Irakulis I (2020) A methodology for comparing the surface urban heat island in selected urban agglomerations around the world from Sentinel-3 SLSTR data. Remote Sens 12(12):2052 48. Rahman MT (2018) Examining and modelling the determinants of the rising land surface temperatures in Arabian desert cities: an example from Riyadh, Saudi Arabia. J Settl Spat Planning 9:1–10 49. Parvez MI, Aina YA (2019) Exploring the influence of land use type and population density on urban heat island intensity. In: Advances in remote sensing and geo informatics applications. Springer, Cham, p 113 50. Abubakar IR, Aina YA (2019) The prospects and challenges of developing more inclusive, safe, resilient and sustainable cities in Nigeria. Land Use Policy 87:104105 51. Newman P (2020) Cool planning: How urban planning can mainstream responses to climate change. Cities 103:102651 52. Aflaki A, Mirnezhad M, Ghaffarianhoseini A, Ghaffarianhoseini A, Omrany H, Wang ZH, Akbari H (2017) Urban heat island mitigation strategies: a state-of-the-art review on Kuala Lumpur, Singapore and Hong Kong. Cities 62:131–145 53. Wang Y, Berardi U, Akbari H (2016) Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy Build 114:2–19 54. Yang J, Pyrgou A, Chong A, Santamouris M, Kolokotsa D, Lee SE (2018) Green and cool roofs’ urban heat island mitigation potential in tropical climate. Sol Energy 173:597–609 55. He BJ, Zhu J, Zhao DX, Gou ZH, Qi JD, Wang J (2019) Co-benefits approach: opportunities for implementing sponge city and urban heat island mitigation. Land Use Policy 86:147–157

248

Y. A. Aina et al.

56. Aljoufie M, Tiwari A (2015) Valuing ‘green infrastructure’ in Jeddah: a city lost in ‘grey’ infrastructure. J Archit Urban 39(4):248–259 57. MOMRA (2016) The national report for the third UN conference on housing and sustainable urban development (HABITAT III) for the Kingdom of Saudi Arabia. Riyadh, Saudi Arabia: Ministry of Municipality and Rural Affairs 58. Addas A, Alserayhi G (2020) Quantitative evaluation of public open space per inhabitant in the Kingdom of Saudi Arabia: a case study of the city of Jeddah. SAGE Open 10(2):2158244020920608 59. Bogis A, Bork D, Miller P (2018) Are green infrastructure strategies suitable in arid climates? A design feasibility study from Jeddah City. Saudi Arabia. Preprints 2018:2018090013

Monitoring Urban Heat Islands in Selected Cities of the Gulf Region Based on Nighttime MODIS LST Data (2003–2018) Abdullah Al-Fazari, Ahmed El-Kenawy, Noura Al-Nasiri, and Mohamed Hereher Abstract The urban heat island (UHI) effect is the most obvious atmospheric modification related to urbanization and development. During the past twenty years, the Gulf Cooperation Council (GCC) states (Saudi Arabia, Kuwait, Bahrain, Qatar, United Arab Emirates, and Oman) have witnessed tremendous urban developments and urban expansions. At the same time these countries are experiencing some of the highest rates of economic growth in the world accompanied by accelerated living standards due to the huge reserves of oil production and refining processes. The present study aims to delineate the urban heat island effect (UHI) of the major cities in the GCC during the last two decades, including Dammam, Kuwait, Manama, Doha, Dubai, and Bawshar. Thermal infrared data from 736 images covering the period 2003–2018 were utilized to highlight the nighttime land surface temperature (LST) trends. Images were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Aqua satellite on the basis of 8-day composite imaging. LST measurements were conducted within the city centers for the nighttime images. It is observed that Dubai and Doha cities have a considerable warming and nighttime trends give more indications on UHI effects. MODIS data proved to be sufficient for giving an insight overview for the warming of the urban environment in the Gulf region. Keywords UHI · MODIS · Nighttime temperature · LST · GCC

1 Introduction The continuous rapid and huge urban development in the world has significantly increased in the last few decades in different cities around the world. Population of cities exceeds 55% of the total population in the world [1] and by 2050 this number will increase to 68% compared with only 30% of the population lived in cities in A. Al-Fazari · A. El-Kenawy · N. Al-Nasiri · M. Hereher (B) Geography Department, College of Arts and Social Sciences, Sultan Qaboos University, Muscat, Oman e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_12

249

250

A. Al-Fazari et al.

the 1950s [2]. It is a fact that urbanization leads to many environmental issues, such as global warming, air pollution, and environmental degradation, which harmfully affect the quality and comfort of urban life and the urban population [3]. The extent of city’s built environment, population size and density, anthropogenic activity, and socioeconomic aspects play a critical role in determining the effect of the urbanization on temperature variation [4–6]. One of the most widely observed effects of this dynamic impact is the UHI phenomenon [6, 7]. UHI can be defined as the urban area that records relatively high temperatures compared to its urban environment, and these urban islands arise mainly as a consequence of the marked acceleration of the urbanization and the rapid changes in the patterns of land use within the city. For example, the shrinking of green areas and their replacement with residential or commercial buildings, which leads to fundamental changes in the thermal balance in those urban areas and their surrounding environments. As a result, some economic and social effects with UHIs may lead to an increase in the air temperature in those areas and to a higher demand for air conditioning and may lead to an increase in pollution levels within the city [8]. UHI is a climate phenomenon in which urban areas have a higher temperature than the surrounding rural areas, due to various human activities. There are many factors that work on the emergence and development of UHI, which can be monitored by tracking the spatial and temporal changes in the LST. Some differences between city centers could be as much as 5 °C more than the rural surroundings [9]. The impacts of UHIs result from the human features and activities of urban areas where urban natural surfaces are changed to roofs, buildings, road networks and infrastructure during the urbanization process. Thus, differences in surface material, land use, and random expansion in urban areas affect local temperatures and make urban areas warmer compared to the surrounding non-urban areas. Therefore, it is important to monitor these urban heat islands to implement measures and mitigate their negative effects [10]. However, the traditional method of monitoring with ground measurements is limited in spatial coverage. Furthermore, weather data are obtained mainly from airports and meteorological stations that may be located on the outskirts of cities and these stations are under the influence of the heat islands. Also, it may be difficult to obtain data with adequate spatial coverage through the ground measurements due to their scattered spatial distribution, or due to the small number of monitoring stations [11]. According to the diverse environmental, economic, and social impacts of UHIs, there is a need arises to study UHIs, especially with the rapid progress in remote sensing science and its technologies. This technology will provide some of the outputs with high spatial and temporal resolution, which allow detailed urban climate studies and may help decision-makers in properly formulating their plans with the goals of sustainable development [12]. The satellite imageries provided opportunities to obtain terrestrial data with varying degrees of temporal resolution and a reasonable spatial coverage, and attempts were made to extract Earth’s surface temperature from remote sensing data for several decades using a variety of satellites, e.g., Landsat, AVHRR, MODIS, etc. [4, 13, 14]. In light of this global trend, especially with the abundance of remote sensing data, it is feasible to monitor UHIs for a number of major cities in the GCC

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

251

countries during the past two decades (2003–2018) depending on data acquired from the MODIS satellite, which provides images of the LST with a high spatial (1 km) and temporal (half-day) resolutions. This study generally aims to assess and monitor the trends of UHIs in six selected cities in the GCC countries represented by Dammam, Kuwait, Manama, Doha, Dubai, and Bawshar for 16 years (2003–2018) in order to determine the most influenced city in the GCC countries with relating these UHI to population change.

2 An Overview About MODIS The Earth Observing System (EOS) is the focus of NASA’s Earth Sciences mission, and the name of the first satellite (EOS AM-1) that was launched in December 1999 on Terra, which has five remote sensing sensors on board. Among the most important sensors are MODIS and ASTER. MODIS is a widely used sensor for scientific studies. Two models of this sensor were launched, the first model was launched on the satellite (Terra) in December 1999, and the second model was launched on satellite Aqua in May 2002. Both probes can be used to collect information related to daily changes on the surface of the Earth, oceans, and glaciers as well as different climatic conditions [15]. MODIS collects data in 36 spectral bands in the visible, infrared, shortwave infrared, and thermal infrared portions of the spectrum (from 0.4 to 14.8 µm). MODIS has been designed to provide data for large-scale terrestrial changes, including changes in cloud cover and measurement of the amount of radiation that occurs on land or oceans and the lower atmosphere, as well as its high capacity to measure the LST. It is worth mentioning that MODIS works on imaging the entire earth in just one or two days and, at the same time it provides high radiation accuracy (12 bit). The spatial resolution of bands 1 and 2 is (250 m), while the spatial resolution in the bands from 3 to 7 (500 m), and the bands from 8 to 36 reach the spatial resolution of 1 km. MODIS contains a group of products, including those that contain a composite image for 8 days, and some of which contain composite images for 16 days. MODIS data are being processed to provide a global coverage of various elements of the Earth, vegetation index data, chlorophyll absorption data, aerosol data, soil discrimination data, and data related to clouds and snow characteristics and others. It is worth mentioning that the use of satellite data from MODIS is one of the modern directions in the field of observing natural phenomena and dynamics of the Earth’s surface. In addition, MODIS has many advantages over other satellites, hence it is preferred to be used in this study to the following: 1. One of the data provided by MODIS is the night data, and this corresponds to the objective of the study, especially since the best time to observe UHIs is at night, whereas in the daytime the effect of solar radiation is significant. Hence,

252

2.

3.

4.

5.

A. Al-Fazari et al.

it is better to eliminate the emitted energy from the earth’s objects from the solar energy. At nighttime, the satellite measures the potential energy of the objects. Most of the previous measurements made to measure the UHIs used the Landsat satellite data, which may not be suitable for time series investigation because the data of the Landsat are not periodically available since 1980s and gaps of imaging occur for many regions in the world. Landsat measurements also are image dependent, where radiometric data could be influenced by the climatic factors accompanying the day of image acquisition. Moreover, nighttime data of the Landsat are also not available. The high temporal frequency of the MODIS data (8-day composite) enables frequent temporal coverage (736 images) of the study area for the entire period (16 years), which is not suitable from the Landsat satellite. The large geographic coverage MODIS data support the aims of this study, where each scene covers an area of 1200 km × 1200 km compared to 185 km × 185 km in Landsat. This advantage gives the opportunity to acquire data for the entire study area at the same time and conditions. Technical processes that took place by sophisticated treatments and algorithms in NASA laboratories to eliminate the effects of the aerosols, clouds, and smoke, making MODIS data of high confident and accurate estimations.

3 Selected Cities Over the past 40 years, the GCC States have witnessed tremendous transformations from traditional economies and societies to modern development countries [16]. The revenues of the oil and gas production in the GCC countries have enabled exceptional and accelerated growth in all aspects of life [17]. The GCC countries are, thus, among the most highly urbanized areas in the world, with more than 70% of the population living in cities and about 100% being urban with Kuwait and Qatar. The wealth of the economy and the improvement in the living conditions of the population have led to the enormous urban expansion in the region, which has made the area attractive for investment and migrant workers, as well as internal migration to urban areas. In addition, the GCC countries are among the highest in the world’s GDP per capita [16]. According to the Gulf Statistics Center, the total population of the GCC countries increased from 44.3 million in 2010 to about 53.5 million in 2016, with an increase of 9.2 million (20.8%) during that period. These numbers are not distributed equally between them where the population of the Kingdom of Saudi Arabia constituted 59.4% of the total population of the GCC countries in 2016, on the other hand, the population of Bahrain constituted only 2.7% in the same year. Moreover, the total population of the GCC countries is expected to increase from 53.5 million in 2016 to about 106.8 million in 2033, assuming that the annual population growth rate continues at the same level.

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

253

Fig. 1 The locations of the selected GCC cities

These six countries, located in southwestern Asia, are characterized by dry climates. In particular, they share the same climatic conditions, which are characterized by the long summers, high temperatures, and scant rainfall [18]. Moreover, during the summer months, many of these countries experience major heat waves and high level of humidity. In the current study, six major cities in the GCC were chosen. These cities include the capital city of Kuwait, Dammam in the Kingdom of Saudi Arabia, Manama in Bahrain, Doha in Qatar, Dubai in the United Arab Emirates, and Bawshar in the Sultanate of Oman. These cities were selected as a result of their attraction to a large number of the population of each country, and the rapid growth of human activities. The locations and characteristics of the studied cities are shown in Fig. 1 and Table 1.

4 Urban Heat Islands: A Review Despite the importance to study the phenomenon of urban heat islands and their effects, the studies that discussed the urban heat islands of the GCC cities using MODIS satellite data are very few. The following are some of the previous local and regional studies.

254

A. Al-Fazari et al.

Table 1 The main characteristics of the selected cities Name of the city

Latitude

Longitude Area (km2 )

Kuwait

29° 22 47° 59 42.9096 25.2276 N E

60.9

Dammam 26° 23 49° 59 1139 57.3000 3.6960 E N

Population Population % from the References (2005) (2018) total population of the country 2018 254,503

573,000

745,658 (2004)

1,024,409 (2017) 561,880

Manama

26° 12 3.6000 N

50° 36 25.1928 E

39.3 153,395 (2001)

Doha

25° 17 9.9816 N

51° 32 5.3412 E

143.8 339,847 (2004)

Dubai

25° 16 55° 17 37.1532 46.4964 N E

4794

Bawshar

23° 33 58° 23  20.27 N 56.04 E

406

1,321,453

150,420 (2003)

13.5

3.06

[33]

[34]

37.3

[35]

956,457 (2015)

39.7

[36]

3,192,275

32

[37]

10.4

[38]

477,121

Omar [19] analyzed the heat islands in Dammam using remote sensing and geographic information systems. The study relied on four images from the Landsat5 with addressing the patterns of land use in the city. The study indicated that the patterns of heat islands which existed in the center of Dammam differ from those which existed in the east and west of Dammam and most influenced by the pattern of land use. The study also found that the heat islands were concentrated over industrial areas in the city. Al Jumaie [20] assessed the role of afforestation projects in the city of Makkah to mitigate the phenomenon of heat islands. The author identified heat islands in summer and winter and calculated the green areas in these seasons with the use of two images from the Landsat-8 in 2013. He indicated that the Mecca city had been suffering from heat islands included cold heat islands, temperate heat islands, relatively moderate heat islands, warm heat islands, strong heat islands and extremely strong heat islands. Ismail [21] studied the UHI in Helwan city near Cairo, Egypt between 2000 and 2016 using MODIS images, suggesting the lack of a clear seasonal pattern for the evolution of the surface heat island intensity. Also, the study indicated that the temperature of the urbanized area in Helwan city was higher than the industrial areas in the north and south in all seasons. El Kenawy et al. [22] studied the performance of LSTs product retrieved from the MODIS Aqua platform over Egypt for the period 2002–2015 with the aid of air temperature data from 34 meteorological stations. Their results indicated better performance of the nighttime

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

255

LSTs compared to the daytime LSTs. Specifically, while nighttime LST tended to underestimate the minimum air temperature during winter, spring, and autumn on the order of 1.3 °C, 1.2 °C, and 1.4 °C, respectively, daytime LST markedly overestimated the maximum air temperature in all seasons, with values mostly above 5 °C. Hereher [23] operated MODIS LST data acquired between 2003 and 2014 to extract monthly surface air temperatures for the deserts in Oman. The results showed that there is a strong correlation between MODIS data and the data from meteorological stations, and that MODIS data could be reliable, especially during the nighttime in all seasons of the year. In the same manner, Hereher and El Kenawy [39] utilized MODIS LST data to extrapolate max/min daily surface air temperatures for Egypt between 2003 and 2015 by applying regression analysis with air temperature data from ground stations. Results indicated that the regression coefficients were as high as 0.8 between minimum air temperature and night LST data, compared to 0.77 between maximum air temperature and daytime LST. Furthermore, MODIS data showed a reasonable result in producing regional monthly and annual as well as diurnal surface air temperatures maps for Egypt. Hereher [24] assessed the spatial variability of surface temperature over the Greater Cairo, Egypt through the analysis of MODIS satellite data for the period 2002–2015. He utilized ancillary data for albedo and vegetation land cover. He showed that vegetation, topography, and surface albedo have negative correlations with LST. In addition, his results showed that the vegetation/LST correlation has the maximum regression coefficient (R2 = 0.68) and albedo/LST has the minimum (R2 = 0.03). He also observed that the industrial lands exhibit the highest LST, which confirms the impact of the human activity on the emergence of urban heat islands. Rasul [25] studied the spatial and temporal variation of the surface urban heat islands of the city of Irbil in Iraq using LST data from satellite imagery of Landsat 4, 5, 7 and 8 between 1992 and 2013. The study found that during the daytime in summer, autumn and winter, densely built-up areas had lower LST acting as cool islands compared to the non-urbanized area around the city. Kikon, Singh [26] studied the relationship between land surface temperatures obtained from Landsat data and ground cover/land use with the aid of field data and meteorological records in order to assess the temporal variations in rising trends of UHI in the city of Noida, India. The study showed that during the year 2000, the total built-up area was 28.17 km2 , which has increased to 88.35 km2 in 2013 with a negative relationship between the normalized difference built-up index (NDVI), and emissivity and temperature, while there was a positive relationship among the normalized difference built-up index (NDBI), Albedo and temperature. Prado [27] utilized the LST from MODIS to study the UHI in some cities in the state of Texas, USA including El Paso, Dallas, Houston, and San Antonio during the period (2000– 2008). They indicated that UHI can be seen during the nighttime at all the year, and the intensities of the UHI are larger in night times of spring and summer seasons than those of fall and winter seasons. The study also found that Aqua/MODIS LST products in the night time (1:30 am) is the best for mapping the UHI for all four cities and that the spatial extent and pattern of the UHI differs from the expected UHI usually centered in downtown of a city. Bonafoni and Keeratikasikorn [28] studied the relation between the urban density and land surface temperature using

256

A. Al-Fazari et al.

237 MODIS LST images for Bangkok City for the years 2004, 2008, 2012 and 2016. Their results indicated that the mean LST decreases away from the city center, but the daytime and the nighttime trends are different in both shape and values. Through the previous studies, the following findings can be summarized: 1. Most of the studies of the urban heat islands were carried out by the Landsat satellite data, and a few of those studies were done using MODIS satellite data; the reason for this may be due to that the MODIS is a modern satellite compared to other observation satellites. 2. MODIS provides LST nighttime data that can outperform better than other satellites, adding more value to the current study assessment. 3. There are a few studies which implemented MODIS data in monitoring the land surface temperature of the GCC region, except for what he has been done by Hereher [40] for monitoring the change in land surface temperature of the western region of the Kingdom of Saudi Arabia using the product MYD11A2 in order to determine climate change in that region. 4. Some of the previous studies confirm that urban heat islands could be measured with a greater accuracy at nighttime than during the daytime, and this is the principle carried out in the current study. 5. The current study will fill the gap in the scientific references and literature in the study area of UHI, whether in the subject, the methodology, or the data used.

5 Materials and Methods The present study relies on observing the UHIs of GCC cities using the LST 8-day composite product from the Aqua satellite (MYD11A2). Images of this product are provided in 1 km spatial resolution. The Aqua satellite overpass the imaged region around 1:30 PM in its ascending mode and at 1:30 AM in its descending mode [14]. Therefore, this satellite is an ideal readout for the high and low temperatures of any point on the earth’s land surface. In this study, a total of 736 images were acquired from the Land Processes Distributed Active Archive Center: (https://lpdaac.usgs. gov/) for the period Jan. 2003 to Dec. 2018 as one composite image each 8 days. Data were downloaded from the latest version (6) in GeoTiff format. Data processing steps were carried using ERDAS Imagine, ArcMap and SPSS Software.

5.1 Retrieval of LST In order to extract the LST of the studied cities, the total 736 individual images were stacked together using ERDAS IMAGINE software to produce a unique file image

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

257

containing 736 layers. Each layer represents 8-day composite for the period 2003– 2018. Then in ERDAS Modeler the digital number (DN) value of each pixel in the stacked image was converted to Kelvin and then to degree Celsius.

5.2 Selection of UHI Locations In order to determine the pixels of high anomalies or UHIs (in this case the pixels experiencing an increased trend in LST), a normalization approach was performed to the 736-layrs image. At the beginning we extracted the area of each city using the polygonal shapefile of this city. Therefore, a separate file was produced for each city consisting of 736 layers. We then determined the average, the maximum, and the minimum value for each pixel in the 736-layers of each city and then we applied the normalization equation in order to determine locations of high trends in LST in each city. The resulted number ranges from 0 values for pixels with no change in the time-series LST and pixels of high trend in LST, which correspond to UHI have values close to 1.0 [29]. The following equation was applied to calculate the normalized temperature values which represent UHIs: N L ST =

l ST mean − L ST min L ST max − L ST min

where NLST is the normalized surface temperature, corresponding to the UHI, LSTmean represents the mean land surface temperature for a given pixel, LSTmax is the maximum, while LSTmin represents the minimum value in the image of that city. NLST values range between 0.0 and 1.0, where pixels experiencing UHI have values close to 1.0 in each city. Figure 2 shows the main steps of the work methodology in this study.

5.3 Assessment of the Temperature Trends for Each UHI Once the pixels of high normalization values were identified, the locations of these pixels were highlighted and then in the time series image of LST in degree Celsius (736 layers), the LST profile was exported to Excel in order to determine the LST trend for each UHI in every city. The decadal change in LST was, then estimated and the UHIs were compared for the studied cities. Also, the change in the UHIs was linked to some human variables (demographic data) in the selected cities.

258

Fig. 2 Flow chart diagram of the study methodology

A. Al-Fazari et al.

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

259

6 Results 6.1 The Capital City of Kuwait Kuwait City is one of the major cities along the Arabian Gulf coast. The city is the home of the most governmental offices, the headquarters of most corporations and banks. The city also includes the political, cultural, and economic centers of the country. The mean LST nighttime in Kuwait City during the study period indicate that large areas of the city have a mean LST of 20 °C, whereas the remaining regions of the capital show lower LST (Fig. 3). The normalization LST image indicates a few pixels of high values (0.957), meaning that these pixels have UHI effect (Fig. 4). These hotspot pixels represent the commercial center in the city center at the Al Mubarakiya market, Sheraton Kuwait Hotel, Four Seasons Hotel, Al Shaya Tower, Shuwaikh Health Zone, Shuwaikh Educational District, Kaifan and Al Fayhaa area, and Al Nuzha area (Fig. 5). The time trend of the hotspot pixels of Kuwait City shows that there are clear seasonal fluctuations in temperature during the whole study period (January 2003–July 2018). The trend of time series analysis indicates an average value of 20.94 °C, and the lowest value of the mean LST 4.22 °C in the January 2018, while the highest value of the mean temperature is 33.81 °C in July 2016. The decadal LST change in the UHI of Kuwait City is 0.8 °C (Fig. 6).

Fig. 3 The mean nighttime LST in Kuwait City during the period 2003–2018

260

A. Al-Fazari et al.

Fig. 4 The values and locations of the UHIs in Kuwait City, during the period (2003–2018)

Fig. 5 The locations of the observed UHIs in Kuwait City during the period 2003–2018

6.2 Dammam Dammam City is one of the major industrial regions, particularly for the oil industry, along the Arabian Gulf coast. It is the capital of the Eastern Province of Saudi Arabia. The mean LST nighttime of Dammam City indicates a rise in the temperature in urban areas compared to surrounding deserts. The temperature increases also gradually as

Monitoring Urban Heat Islands in Selected Cities of the Gulf … Night Mean Temparture - Kuwait

261 Trend = 0.8 °C/decade

35 25

°C 15 5 -5

Years

Fig. 6 The trend of the nighttime LST in Kuwait City (2003–2018). The red line indicates the fitted linear regression line

we go to the center of these urban clusters in the city center (Fig. 7). The mean LST ranged between 4.43 °C in February 2017 and 32.15 °C in August 2017 with an average LST value of 19.27 °C for the period 2003-2018. For the UHI for Dammam, the normalization equation shows some regions with UHI of 0.957, mostly in the industrial zone of the city, the sewage station, the central market, Al Khalidiya area, and King Fahd International Airport (Fig. 8). Figure 9 shows these locations where the UHIs are formed. The time series trends of the nighttime LST for Dammam UHI (Fig. 10) shows a general increasing trend between 2003 and 2018 with a decadal increase rate of 1.1 °C.

Fig. 7 The mean nighttime LST in Dammam during the period 2003–2018

262

A. Al-Fazari et al.

Fig. 8 The locations of the UHIs in Dammam City, during the period 2003–2018

Fig. 9 The locations of the observed UHIs in Dammam City during the period 2003–2018

Monitoring Urban Heat Islands in Selected Cities of the Gulf … Night Mean Temparture - Dammam

°C

263 Trend = 1.1 °C/decade

35 30 25 20 15 10 5 0

Years

Fig. 10 The trend of the nighttime LST in Dammam City (2003–2018). The red line indicates the fitted linear regression line

6.3 Manama Manama City is the focal point of the Bahraini economy. This state is mainly dependent upon the petroleum as the mainstay of its economy. However, industry, tourism and trade are growing drastically in this small island. The value of the mean LST in the Al Manama city ranges between 7.51 and 20.3 °C. The distribution of the mean LST in Manama between 2003 and 2018 is shown in (Fig. 11). Furthermore, the urban heat islands that were monitored in the city occur in the central market area, Al Salmiya area, and the area where the main hotels are located. The UHI

Fig. 11 The mean nighttime LST in Al Manama during the period 2003–2018

264

A. Al-Fazari et al.

Fig. 12 The locations of the UHIs in Al Manama City, during the period 2003–2018

value approach 0.957-0.958 in the normalization image (Fig. 12). Locations where urban heat islands observed in Manama City are shown in (Fig. 13). The trend of the nighttime LST for Manama City during the period 2003 to 2018 indicates an average value of 21.64 °C with the lowest value 7.51 °C in December 2006 and the highest

Fig. 13 shows the locations of the observed UHIs in Al Manama City during the period

Monitoring Urban Heat Islands in Selected Cities of the Gulf … Night Mean Temparture - Manama

°C

265 Trend = 1.2 °C/decade

35 30 25 20 15 10 5

Years

Fig. 14 The trend of the nighttime LST in Al Manamah City (2003–2018). The red line indicates the fitted linear regression line

value was 32.56 °C in August 2017. The nighttime LST time series trend (Fig. 14) reveals an increasing rate of 1.2 °C/decade.

6.4 Doha Doha City is the capital of Qatar, where the majority of the country’s population and urban land occur. The average LST in Doha ranges between 23 and 6.9 °C (Fig. 15). The map of UHI in Doha indicates that the majority of Doha area has high values (0.948). However, the maximum normalization values are 0.964–0.965 in the old

Fig. 15 The mean nighttime LST in Doha City during the period 2003–2018

266

A. Al-Fazari et al.

Fig. 16 The locations of the UHIs monitored in Doha City during the period 2003–2018

and new area of Doha International Airport, and Souq Al-Haraj (Fig. 16). Figure 17 shows these locations where the UHIs are located. On the other hand, the pattern of time series analysis of the mean LST in Doha showed clear seasonal variations over the entire study period (Fig. 18). The time series analysis of the nighttime LST for Doha City indicates an average value of 21.62 °C. However, it reached the lowest value (6.9 °C) in February 2017 and the highest value (32.42 °C) in July 2017. The decadal nighttime trend reveals an increase by 1.5 °C/decade.

6.5 Dubai Dubai is a major business district in the Arabian Peninsula. It is also a global transport hub for passengers and cargo. The city is famous for its sky-scrapers and renowned malls. As the urbanization in the city was tremendously increased in the past three decades, the LST of the city increased accordingly in comparison to the non-urban land covers. The LST was observed to increase in urban agglomerations of the city center. Figure 19 shows the average night LST in Dubai during the study period (2003–2018), while the value of the average LST ranges between 25 and 5.54 °C. The normalization of the LST data reveals that there are many regions of Dubai experiencing UHIs. Figure 20 shows the highest UHI values in Dubai (0.966–0.972). The

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

267

Fig. 17 shows the locations of the observed UHIs in Doha City during the period Night Mean Temparture -Doha

°C

Trend = 1.5 °C/decade

35 30 25 20 15 10 5

Years

Fig. 18 The trend of the nighttime LST in Doha City (2003–2018). The red line indicates the fitted linear regression line

UHI regions in Dubai occur in Abu Hail region, Al Ras commercial area including, Ras Al Khor industrial area, Zabeel District, the Emirates Towers area, Jumeirah Tower area, the Dubai International Convention and Exhibition Center, and the Jebel Ali Port area (Fig. 21). The trend of the LST in Dubai shows clear seasonal LST fluctuations 2003–2018 (Fig. 22). The trend of time series analysis of the mean nighttime

268

A. Al-Fazari et al.

Fig. 19 The mean nighttime LST in Dubai City during the period 2003–2018

Fig. 20 The locations of the UHIs monitored in Dubai City for the period 2003–2018

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

269

Fig. 21 The locations of the observed UHIs in Dubai City (2003–2018)

Night Mean Temparture - Dubai

°C

Trend = 1.9 °C/decade

35 30 25 20 15 10 5

Years

Fig. 22 The trend of the nighttime LST in Dubai City (2003–2018). The red line indicates the fitted linear regression line

LST for Dubai City indicates an average value of 19.77 °C. However, it reached the lowest value 5.54 °C in February 2008, while the highest LST was 31.14 °C in August 2013. The decadal increase in LST is 1.9 °C.

270

A. Al-Fazari et al.

Fig. 23 The mean LST in Bawshar region during the period 2003–2018

6.6 Bawshar Bawshar is one of the districts at Muscat Governorate in the Sultanate of Oman. Bawshar has the hottest nighttime temperatures of all the studied cities. As seen in (Fig. 23), most areas in the city exceed 25 °C. Moreover, the mean LST values range from 11.2° to 26.5° C during the study period. As for the UHIs, the highest values in the normalization image (Fig. 24) range between 0.966 and 0.972. These areas occur at the northern Azaiba region, Ghala industrial area, Al-Irfan City area where the Oman Waste Water Services Company ‘Haya Water’ is located, and AlAnsab Residential Area. Figure 25 shows these locations where UHIs are located. The trend analysis indicates an average value of 24.41 °C. Minimum LST value is 11.22 °C in January 2006, while the maximum value 33.65 °C was recorded in May 2010. The decadal nighttime LST trend for Bawshar is estimated at 0.75 °C/decade (Fig. 26).

7 Discussions The results indicate that all the studied cities suffer from the existence and impact UHIs with varying intensities from one city to another at the GCC countries. The urban surfaces, in contrast to rural surfaces, have higher radiation absorption, higher thermal conductivity and capability to release the heat stored during the day to be

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

Fig. 24 The locations of the UHIs monitored in Bawshar during the period 2003–2018

Fig. 25 The locations of the observed UHIs in Wilayate Bawshar during the period

271

272

A. Al-Fazari et al. Night Mean Temparture - Bawshar

Trend = 0.75°C/decade

35 30 25 °C 20 15 10

Years

Fig. 26 The trend of the nighttime LST in Bawshar (2003–2018). The red line indicates the fitted linear regression line

emitted at night [30]. The results of the current study showed that Dubai has a considerable warming than any other city. The nighttime LST trend is estimated at 1.9 °C/decade. This is accompanied by tremendous urbanization and development of Dubai as one of the major business centers in the Middle East. Thousands of kilometers of asphalt roads and hundreds of skyscrapers of concrete and metals were constructed in the city accompanied by an increase in the population from 1.3 million in 2005 to more than 3.1 million in 2018. Furthermore, there are many towers in Dubai and the tallest tower in the world which is Burj Khalifa at more than 800 meters high. In fact, high buildings produce a dynamic structure that absorbs energy and change air distribution, increasing the amount of energy required to heat the urban environment. In fact, this result corresponds to the results that Arnfield [7] and Deilami, Kamruzzaman [31]. Heat storage and surface heating in rural areas, on the other hand, are mitigated by the cooling effect of larger areal coverage of green spaces and typically a greater abundance of water surfaces [32]. Doha and Manama share the higher decadal increase in the LST after Dubai city, where in Doha, the nighttime LST trend is estimated at 1.5 °C/decade and in Manama it is 1.2 °C/decade. This is accompanied by an increase of population in Doha from 260 thousand in 1997 to 950 thousand in 2015 and an increase in Manama from 150 thousand in 2001 to 560 thousand in 2018. Dammam city encountered an increase in its nighttime temperatures of 1.1 °C/decade. On the other hand, both Kuwait City and Bawshar in Oman have comparable lower LST trends during the study period. The nighttime LST trend approached 0.8 °C/decade for Kuwait City and 0.75 °C/decade for Bawshar. This may be attributed to the nature of the urban expansion and population growth of the two cities. Kuwait City population increased from 250 thousand in 2005 to 573 thousand in 2018, while in Bawshar, the population increased from 150 thousand in 2003 to 475 thousand in 2018, which reveals that both Kuwait and Bawshar have the least population increase in the studied cities. It is obvious that the highest UHIs occurred at the cities experienced highest population growth. The statistical analysis of the observed results at the seasonal and annual levels (Table 2) reveals high significant correlations, particularly for the annual, spring, summer and fall seasons (p < 0.01) for the majority of the studied locations with the except of Bawshar region. This indicates the high confidence and reliability of the results. Comparing the results

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

273

Table 2 The statistical significance of the LST trends in the studied cities City

Annual

Winter

Spring

Summer

Fall

Kuwait

0.001

0.467

0.053

0.001

0.043

Dammam

0.000

0.364

0.018

0.000

0.022

Doha

0.000

0.034

0.000

0.000

0.001

Dubai

0.000

0.002

0.000

0.002

0.000

Manama

0.000

0.146

0.000

0.000

0.002

Bawshar

0.158

0.436

0.315

0.111

0.165

from the present study with the observation of Hereher [24] for the Greater Cairo reveals that LST trends in the GCC countries are lower than those in the Greater Cairo region, particularly of the industrial zone at Helwan (4 °C in 13 years). The study asserts on the reliability of MODIS data for environmental monitoring and assessmnent [39, 40].

8 Conclusions The nighttime LST for all studied cities showed a distinctive pattern, where the urban lands have noticeable higher LST than surrounding non-urban and rural regions. Maximum UHI is observed in Dubai City (1.9 °C/decade) and the minimum is observed in Bawshar in Oman (0.75 °C/decade). Major UHIs in the study areas occur in airports, central markets, and commercial zones. Dubai is the highest city among the study cities in terms of the normalized value which represent UHIs as it reached its highest value, and this may be due to what distinguishes Dubai from the other cities studied from being an economic city, dominated by urbanization and remarkable urban density, which made it an attractive city for a lot of immigrants and tourist, especially during the last few years. The population statistics indicated that Dubai had an annual growth rate of 6.7% during the study period. All studied cities share the similarity of increasing trends in temperature but with different values. Perhaps this is since the GCC countries are common in climatic conditions, especially as they located within the hottest climatic range. It is important to say that the MODIS satellite data used in this study demonstrated the efficiency of remote sensing in measuring and retrieving the LST. In addition, the accuracy and reliability of the MODIS data would encourage researchers and specialists to use them with studies that comply with the criteria that were taken in this study.

274

A. Al-Fazari et al.

9 Recommendations It is highly recommended to relocate the industrial areas from city center to the outskirts of the cities, which will help to reduce the temperatures of those cities. It is recommended to spread green covers as well as the parks and garden. This will help to mitigate and reduce the impact of UHIs in the cities. The planners should consider the building heights which may cause the increasing in temperature, especially in Dubai and Doha, as they recorded in this study the strongest UHIs values. It is recommended to use the satellite data to get some of the climate indicators. That is because some of these satellites can provide data which may not be covered by traditional meteorological stations due to their limitations and their presence in a specific geographical area. On the other hand, satellite images provide information for any point on the earth.

References 1. United Nations (2018) 2018 revision of world urbanization prospects. United Nations, New York 2. Golden JS, Kaloush K (2005) A hot night in the big city. Public Works 136(13):40–43 3. Ash C, Jasny BR, Roberts L, Stone R, Sugden AM (2008) Reimagining cities. American Association for the Advancement of Science 4. Chen X-L, Zhao H-M, Li P-X, Yin Z-Y (2006) Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens Environ 104(2):133–146 5. DeFries R (2008) Terrestrial vegetation in the coupled human-earth system: contributions of remote sensing. Annu Rev Environ Resour 33:369–390 6. Oke TR (1973) City size and the urban heat island. Atmos Environ 7(8):769–779 7. Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol 23(1):1–26 8. Kato S, Yamaguchi Y (2005) Analysis of urban heat-island effect using ASTER and ETM + Data: separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sens Environ 99(1–2):44–54 9. United Nations (2010) World population prospects, the 2010 Revision. Department of Economic and Social Affairs, Population Division, New York 10. Aina Y, Adam E, Ahmed F (2017) Spatiotemporal variations in the impacts of urban land use types on urban heat island effects: the case of Riyadh, Saudi Arabia. Int Arch Photogramm Remote Sens Spat Inf Sci 42 11. Li X, Kamarianakis Y, Ouyang Y, Turner BL II, Brazel A (2017) On the association between land system architecture and land surface temperatures: evidence from a Desert Metropolis— Phoenix, Arizona, USA. Landsc Urban Plan 163:107–120 12. Kaufmann RK, Seto KC, Schneider A, Liu Z, Zhou L, Wang W (2007) Climate response to rapid urban growth: evidence of a human-induced precipitation deficit. J Clim 20(10):2299– 2306; Nuruzzaman M (2015) Urban heat island: causes, effects and mitigation measures—a review. Int J Environ Monit Anal 3(2):67–73 13. Price JC (1984) Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. J Geophys Res: Atmos 89(D5):7231– 7237; Clinton N, Gong P (2013) MODIS detected surface urban heat islands and sinks: global locations and controls. Remote Sens Environ 134:294–304

Monitoring Urban Heat Islands in Selected Cities of the Gulf …

275

14. Zhang J, Wang Y, Li Y (2006) A C ++ program for retrieving land surface temperature from the data of Landsat TM/ETM + band6. Comput Geosci 32(10):1796–1805 15. Maiyas MA (2013) Principles of remote sensing, 1 edn. Sana’a, The Republic of Yemen: Aden University Printing and Publishing House, pp 1–392 16. Ramadan E (2015) Sustainable urbanization in the Arabian Gulf region: problems and challenges. Arts Soc Sci J 6(109):2 17. Raouf MA (2008) Climate change threats, opportunities, and the GCC countries. Middle East Inst Policy Brief 12:1–17 18. Al-Rashed MF, Sherif MM (2000) Water resources in the GCC countries: an overview. Water Resour Manage 14(1):59–75 19. Omar BM (2007) The heat island of Dammam: a study using geographic information systems and remote sensing in the second national conference on geographic information systems, Saudi Arabia, pp 1–84 20. Al Jumaie ZM (2015) The role of afforestation projects in limiting the phenomenon of heat islands in the city of Makkah, a study using remote sensing technology and geographical information systems. Human Soc Sci 83:153–172 21. Ismail HMA (2017) The evolution of the surface heat islands in Helwan City during the period (2000–2016): a study in the urban climate using remote sensing techniques. Geogr Messages 444:75 22. El Kenawy AM, Hereher ME, Robaa SM (2019) An assessment of the accuracy of MODIS land surface temperature over Egypt using ground-based measurements. Remote Sens 11(20):2369 23. Hereher ME (2019) Estimation of monthly surface air temperatures from MODIS LST time series data: application to the deserts in the Sultanate of Oman. Environ Monit Assess 191(9):592 24. Hereher ME (2017) Retrieving spatial variations of land surface temperatures from satellite data–Cairo region, Egypt. Geocarto Int 32(5):556–568 25. Rasul AO (2016) Remote sensing of surface urban cool and heat island dynamics in Erbil, Iraq, between 1992 and 2013 in Department of Geography. University of Leicester, Leicester, UK 26. Kikon N, Singh P, Singh SK, Vyas A (2016) Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data. Sustain Cities Soc 22:19–28 27. Prado D (2010) Characterizing urban heat island phenomenon of four Texas cities using MODIS LST products. The University of Texas, San Antonio 28. Bonafoni S, Keeratikasikorn C (2018) Land surface temperature and urban density: Multiyear modeling and relationship analysis using MODIS and Landsat data. Remote Sens 10(9):1471 29. Haashemi S, Weng Q, Darvishi A, Alavipanah SK (2016) Seasonal variations of the surface urban heat island in a semi-arid city. Remote Sens 8(4):352 30. Weng Q (2001) A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. Int J Remote Sens 22(10):1999–2014; Gartland LM (2012) Heat islands: understanding and mitigating heat in urban areas. Routledge 31. Deilami K, Kamruzzaman M, Liu Y (2018) Urban heat island effect: a systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int J Appl Earth Observ Geoinf 67:30–42 32. Tam BY, Gough WA, Mohsin T (2015) The impact of urbanization and the urban heat island effect on day to day temperature variation. Urban Clim 12:1–10 33. Central Statistical Bureau (2005) General population census. Kuwait. viewed on 24 May 2020. Retrieved from: https://www.csb.gov.kw/Pages/Statistics_en?ID=6&ParentCatID=%201 34. General Authority for Statistics (2004) Census of 2004—Eastern Region. Kingdom of Saudi Arabia. viewed on 24 May 2020. Retrieved from: https://www.stats.gov.sa/en/13 35. Central Statistics Organization (2001) Basic results population, housing, buildings & establishments census. Bahrain. Viewed on 25 May 2020. Retrieved from: https://www.data.gov.bh/ ar/ResourceCenter/ 36. Ministry of Development Planning & Statistics (2004) Population & housing census-2004. Qatar. Viewed on 25 May 2020. Retrieved from: https://www.psa.gov.qa/en/statistics1/statistic ssite/census/pages/default.aspx

276

A. Al-Fazari et al.

37. Dubai Statistical Center (2018) Population and vital statistics. Dubai, UAE. Viewed on 24 May 2020. Retrieved from: https://www.dsc.gov.ae/enus/Publications/Pages/publicationdeta ils.aspx?PublicationId=5 38. National Center for Statistics and Information (2020) Population statistics-data portal. Oman. Viewed on 26 May 2020. Retrieved from: https://data.gov.om/OMPOP2016/population?indica tor=1000140 39. Hereher ME, El Kenawy A (2020) Extrapolation of daily air temperatures of Egypt from MODIS LST data. Geocarto Int 1–17 40. Hereher ME (2016) Recent trends of temperature and precipitation proxies in Saudi Arabia: implications for climate change. Arab J Geosci 9(11):575

Revisiting Urban Heat Island Effects in Coastal Regions: Mitigation Strategies for the Megacity of Istanbul Mustafa Dihkan, Fevzi Karsli, Abdulaziz Guneroglu, and Nilgun Guneroglu

Abstract Throughout history, the city of Istanbul has been an important settlement for different civilizations with its geographical location connecting two continents. This metropolis with a population of over 16 million has more than 50% of total economic activity for Turkey. Especially in the last 50 years, the city has been under a catastrophic anthropogenic pressure because of its historical, geographical, and economic attractiveness. These pressures on the city caused some environmental problems due to planning. Some of these problems are intensive urbanization; increase in impervious surface, pollution, traffic, and Urban Heat Island (UHI). The impact of UHI, as a result of wrong urban planning activities causes several adversities in terms of human health, energy efficiency, and ecological sustainability. Factors such as land use/cover (LULC) changes, canyon effect, surface covering material selection, intensive energy usage are effective in the emergence of the UHI effect. In recent years, increasing environmental awareness, international regulations, and developments in landscape planning have led to the emergence of planning strategies to reduce UHI impact. The existence of the UHI effect in Istanbul has been demonstrated and modeled in previous studies. Accordingly, the presence of the UHI effect was identified in both the European and Asian study areas. In the statistical modeling studies, it has been shown that the UHI effect is mainly due to changes in land cover M. Dihkan (B) · F. Karsli Department of Geomatics, Faculty of Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey e-mail: [email protected] F. Karsli e-mail: [email protected] A. Guneroglu Department of Marine Ecology, Faculty of Marine Science, Karadeniz Technical University, 61530 Camburnu/Trabzon, Turkey e-mail: [email protected] N. Guneroglu Department of Landscape Architecture, Faculty of Forestry, Karadeniz Technical University, 61080 Trabzon, Turkey e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 N. Enteria et al. (eds.), Urban Heat Island (UHI) Mitigation, Advances in 21st Century Human Settlements, https://doi.org/10.1007/978-981-33-4050-3_13

277

278

M. Dihkan et al.

usage components, urban impervious surfaces, green cover, bare soil, and agricultural areas. In this study, various scenarios of land cover elements, which are stated to reduce the UHI effect in Asian and European study areas of Istanbul, have been created. For this purpose, green corridors that are designed in accordance with the urban texture of Istanbul are proposed. As a result of the analyses, UHI intensity mitigation was observed in the European and Asian study regions above 2.5 °C on average. As a result, in addition to land cover usage changes, increasing water surfaces, the use of effective wind corridors, avoiding high buildings, and preferring reflective coating surface materials in landscape design and planning activities are considered to be of utmost importance in terms of UHI reduction. Keywords LULC · UHI · Mitigation · Landscape · Istanbul

1 Introduction The world has a unique ecological balance formed by various natural interactions that have been going on for centuries. Due to the rapidly increasing population and intensive industrialization activities in the last century, human-induced effects have become more effective on this balance than ever before and created a situation where environmental dynamics are unsustainable. Research has revealed that the urban population has reached 50% from 34% in the last 50 years and it is predicted that the total population living in urban areas will be around 3 billion in the 2030s [1]. In urban environments, serious changes occur in the character of Land Use/Land Cover (LULC). To summarize these; The effects such as the proportional increase of impermeable surfaces, the decrease of vegetation cover, a significant change of water drainage systems, reduction of water-covered/moist surfaces, the formation of an artificial urban morphology formed by dense and high-rise buildings, and wind ventilation can be counted. In this process, man-made objects built-in urbanized areas replace natural land cover elements by changing their geometric properties, surface morphology, positioning characteristics, chemical structure, and impermeability. This situation changes the radiative and thermal character of the surfaces and causes the surface energy balance in the urban areas to differ significantly from the rural and suburban areas that surround the urban periphery. Due to the changing surface energy balance, a unique micro-climate system can be observed in urban areas. The effects of these microclimate systems, which are felt more especially in crowded and dense metropolitan cities, have been investigated in the literature for many years and it has been determined that the urban areas are warmer than the rural environments [2]. Researches have revealed that the annual average temperature value of a city with a population of more than one million may be 1–3 °C higher than the surrounding rural areas and this temperature difference may rise to 12 °C under the conditions of calm wind and clear sky [3]. This phenomenon is described in the literature as the concept of Urban Heat Island (UHI) and is considered as the most important indicator of regional climate change [4]. UHI phenomenon, with

Revisiting Urban Heat Island Effects in Coastal Regions …

279

its effects on microclimate, can cause global warming [5], extreme meteorological events such as storm and heavy rain [6], increase in energy demand in urban areas [7] and heatwave originated diseases and deaths [8]. A significant number of studies have been conducted on the detection, characterization, factors causing UHI formation, effects of UHI on urban environment and methods for UHI mitigation in various metropolises from past to present [9–13].

1.1 Main Causes of UHI Formation To explain the UHI formation process and to develop methods for mitigation, it is very important to reveal the effects such as socio-economical, geographical and climatological factors involved in UHI formation as a result of changing of rural-urban surface energy balance. While these factors cause the urbansurface and atmosphere to warm up, they also prevent the cooling process, causing the urban areas to be characterized by higher temperature values than the rural areas which are known as the UHI effect (Fig. 1). A quantitative explanation of the warming process in question is possible by mathematically modeling the changes occurring in the surface energy balance components and the factors that caused this change in these regions. The surface energy balance can be formulated as in Eq. (1). Convection + Evaporation + H eat Storage = N et Radiation + H uman H eat (1) Relationships between factors given in Eq. (1) and surface energy balance components were summarized in Table 1. When the physical effects caused by these factors are considered as variables, it is very important to examine whether these variables

Fig. 1 Factors causing UHI formation. Modified from Pradhan and Pattanasri [14]

280 Table 1 Various factors that change the surface energy balance in the urban environment and cause UHI formation

M. Dihkan et al. Factors causing UHI formation Effects on surface energy balance Vegetation deficiency

Reduces the evaporation level

Intensive use of impervious surfaces

Reduces the evaporation level

The increased thermal emissivity of urban materials

Increases heat storage capacity

Low solar reflectance value of urban materials

Increases the amount of net radiation

Urban heat-retaining design geometry

Increases the amount of net radiation

Wind speed impeding design of Reduces the amount of urban geometry convection Increased air pollution

Increases the amount of net radiation

Increased energy use

Increases the amount of human heat

have positive or negative effects on the surface energy balance to reveal their relationship with UHI formation [15]. In this way, to reduce or eliminate the effect of UHI in the cities, it is possible to determine which variable and at what rate should be criticized and make correct interventions. Considering the urban environment, the mentioned socio-economic, geographical, and climatic factors; LULC changes can be expressed as population increase, canyon effect, decrease in wind speed, pollution, increase in human heat, and energy use. The main factor affecting UHI formation is the changing of LULC patterns in urbanized regions due to intensive construction activities [16, 17]. For this reason, in urban areas; It can be stated that changes on the surfaces represented by 4 main LULC classes such as Impervious Surface (IS), Vegetation (V), Bare Soil and Agricultural Area (BSAA) and Water (V) have a significant effect on UHI formation. Today, with increasing population density in urban areas, the numbers and surface areas of various man-made objects (buildings, roads, various parking areas, roofs, etc.) with impermeable surface coatings are rapidly increasing. Such structures are quite different from natural land objects with their spatial geometrical features and surface morphologies. Many materials that make up urban impervious surfaces (asphalt, concrete, metal, glass, tile, etc.) reflect much less backward than the natural rural surfaces of shortwave solar radiation that reaches its surface due to its radiative, thermal, physical and chemical properties [18, 19]. This situation increases the amount of solar radiation absorbed and the net radiation value in equilibrium Eq. (1) [20]. Moreover, components commonly used in impervious surface coatings have lower thermal emissivity values than materials that make up natural rural surfaces. The low level of thermal diffusivity, which expresses the ability of the materials to lose the heat they hold in their bodies, also positively affects the amount of net radiation. Besides, urban land objects have a very high drainage ability compared

Revisiting Urban Heat Island Effects in Coastal Regions …

281

to rural surfaces due to smooth geometric designs and surface morphologies. These conditions, even if the urban surfaces are partially permeable, cause an insufficient amount of water to penetrate the lower layers of the surface and dry the surfaces quickly. The evapotranspiration capabilities of these low humidity surface layers are also low. Therefore, the heat loss on these surfaces is minimal [21, 22]. Urban impermeable surfaces cause the surface energy balance to change according to the rural area, by transforming the incoming solar radiation directly into the sensible heat flux and increasing the amount of net radiation. The increase in impervious surfaces in urban areas often leads to a decrease in vegetation coverage. Vegetation is a component that has a significant effect on surface energy balance with its radiative and thermal properties [23]. The vegetation-covered surfaces can transform the incoming solar radiation into a latent heat flux by evapotranspiration. In this way, they can ensure that sensible heat fluxes remain at low levels [24]. The energy consumed by the hidden heat flux causes the surface to remain cool [25]. The canopy (shadow effect) created by high and dense vegetation types over buildings, roads, roofs, windows, pavements, etc. can reduce the short wave radiation amount reaching the surface and can have an important effect on net radiation amount in equilibrium [26]. However, vegetation can prevent the increase of net radiation amount by clearing various pollutants produced from a human origin in urban areas [27]. Therefore, vegetation cover is one of the most effective components of surface energy balance in rural areas with all these radiative and thermal properties. Therefore, when it is aimed to reduce the urban-rural surface energy balance difference and UHI effect, vegetation can be used as an important variable. In particular, the transition zone between urban areas and rural areas has a limited effect on the surface energy balance of urban areas. However, this transition zone, consisting of bare soil and agricultural areas (BSAA), is extremely effective on the rural surface energy balance. The natural structure and moisture content of such soil surfaces can significantly affect the energy exchange processes. During the daytime, dry soil surfaces turn short-wave solar radiation into sensible heat flux and increase the net amount of radiation. On the other hand, especially at night time, humid soil surfaces can affect the surface energy balance by creating a latent heat flux via evapotranspiration to cool the surrounding surfaces. For example, on completely wet soil surfaces, 80% more evapotranspiration processes can occur under favorable conditions (with sufficient energy presence) compared to moist soil surfaces [3]. Water masses are very important in terms of storing and transporting energy with their dynamic and thermal properties. Especially due to the complex physical structure of the air/water interface, the transfer of heat energy can take place both by conduction/radiation, and convection/advection. This situation brings along the fact that the water surfaces have a great absorption character, but also have a very low level of thermal reflection. Due to its unique properties, water surfaces show high thermal stability and they get warm very slowly in summer. However, in relatively shallow water bodies with limited depth, thermal stability can not be achieved due to horizontal and vertical mixing. Evaporation occurring on open water surfaces (rivers, lakes, dams, etc.), as well as evapotranspiration resulting from moisture in plant, soil,

282

M. Dihkan et al.

and other surface layers, is very effective in the formation of surface energy balance [18]. With the migration from rural to urban and the increase in the number of people per unit area in urban areas, the population triggers many factors, especially LULC changes, leading to energy balance change and UHI formation. In addition to its indirect effects, the population factor may affect the energy equilibrium state in Eq. (1) by directly increasing the heat flow from the human source with the increase in the number of people living in the urban area. Thus, due to this effect, the amount of anthropogenic heat energy that occurs in winter times in some crowded metropolises can equal or exceed the amount of net radiation [28]. For this reason, the effect of population density on urban climate and UHI formation has been a common research topic in the literature [20, 29, 30]. The population factor, which is directly or indirectly involved in the formation of UHI, with all its processes, should be considered as an important variable in the processes for UHI characterization and mitigation. Urbanized areas differ significantly in terms of surface morphology and topography compared to their surrounding rural environments. Urban structures (buildings, roads, parking areas, etc.) that replace natural surfaces create a virtually artificial urban topography by changing the rural surface morphology. High-rise buildings and roads, which form an important part of urban morphology, form a geometriclike shape that is defined as an urban canyon in the literature [18]. Urban canyons with their radiative, thermal, humidity and aerodynamic properties; It can increase the total surface area exposed to energy exchange processes, affect the positional distribution of solar radiation coming to and reflected from the surface, causing radiative interactions between their surfaces, reducing the upward longwave radiation flux, controlling turbulence and average flow structure [3]. With all these features, while the canyon system increases the amount of short wavelength radiation, it can decrease the albedo level regardless of the reflectance capabilities of the surface materials. Therefore, they can significantly decrease the surface cooling capacity in urban areas, especially after sunset, and lead to the emergence of the UHI effect in the night times. The urban canyon effect can be increased or decreased depending on many parameters related to the geometry created by the building masses and streets; building heights, street widths, height-width ratios, height - street width ratios, and lateral surface areas, etc. The three-dimensional geometry of urban canyons causes energy exchange processes to take place in a rather complex structure. For this reason, many dynamic fluid models have been developed to numerically model the canyon effect [31–34]. Urban location and topography can lead to the differentiation of the amount of solar radiation reaching the surface according to the existing geometry. It can also change the airflow character on the surface. In the case of inclined surface condition, the amount of instantaneous radiation received by the surface may differ depending on the angle formed between the normal of the inclined surface and the beam coming to the surface due to the cosine rule. As this angular value decreases, the amount of solar radiation received by the surface will increase and decrease as it grows. Therefore, the slope values for the surface can directly affect the amount of radiation received depending on the aspect of the surface and the latitude [35]. Also, different

Revisiting Urban Heat Island Effects in Coastal Regions …

283

heating and cooling characteristics of the valley surfaces in the rough areas can create a unique wind regime by causing temperature and pressure differences. During the daytime, the warming air mass rises over the slopes at the bottom of the valley and on the lateral sides, after moving towards the top of the valley center, it descends back to the bottom and creates a transverse valley circulation. The transverse valley circulation performs considerable heat transfer from the active surfaces surrounding the valley, resulting in warming of the entire valley atmosphere. This causes the valley atmosphere, which is at the same level as the flat terrain (lowland, etc.) atmosphere, to be warmer. During the night time, the valley surface cools with longwave radiation propagation, and the cooling air masses in the lower layers of the valley begin to flow from the valley down to the plains with the effect of gravity. This situation, which is called valley inversion, causes the coldest air masses to settle in the bottom regions with the lowest elevation values, while the temperature values increase in the high parts of the valley. Due to all these interactions in urban areas, land level, slope, aspect, etc. topographic factors can affect UHI formation. Another factor contributing to the formation of UHI is wind as it affects the energy balance. Artificial urban topography and surface morphology created by tall solid structures, especially built on natural wind corridors, can often reduce wind speed or change direction [36]. As a result of the decrease in the wind speed, heat transfer from urban surfaces to air is reduced due to weak turbulence and the cooling capacity of the surfaces decreases significantly. While this situation causes more heat energy to be retained on urban surfaces from sunrise to sunset, it slows down the cooling process by preventing the decrease in the amount of heat on the surface after sunset [37]. However, apart from the winds mentioned above, in a city under the influence of UHI, various local breezes may occur. Such winds should not be considered as the cause of UHI, but rather as a result. For example, in coastal cities, the cool air on the sea can create such local breezes with the trend of flow towards the inner city and UHI center [38]. Therefore, the changes that occur in the speed and direction of the wind factor and the local effects caused by UHI on each other should be taken into account in the analysis and interpretation processes. Various pollutants (aerosols and gases) present in the urban atmosphere can produce a decrease in shortwave solar radiation and an increase in longwave radiation emitted from the atmosphere, depending on the types and quantities [39]. In the first case, while the polluted atmosphere holds an important part of the short-wave radiation that comes with intense atmospheric scattering-absorption-reflection properties, it can also change its spectral and directional character by creating a filter effect [40]. In the second case, solar radiation absorbed from the polluted urban atmosphere throughout the day causes an increase in the downward longwave infrared radiation from the atmosphere. This leads to an increase in the amount of net radiation during the night time [41]. Due to the opposite effects of these two conditions on net radiation, net radiation increase can occur only during the daytime if the amount of longwave radiation is higher. Due to such interactions at night times the net radiation may contribute to increase UHI formation. Anthropogenic heat flux refers to the amount of sensible heat produced by various activities performed by humans in urban areas and is equal to the total amount of heat emitted from three different

284

M. Dihkan et al.

components. These components are the heat generated by the combustion of fuels used in various vehicles, the heat produced by establishments (residential buildings, commercial buildings, industrial zones, power plants, etc.) and heat emitted from human metabolism which has less impact than the other two components [42]. Although the amount of human heat in urban areas cannot be measured directly, it can be calculated with the help of energy consumption per capita and population data, which is almost completely dependent on it [43]. In most of the studies on urban energy balance in the past, the human-induced heat component was generally ignored and not taken into account for two reasons. These are the previous studies carried out in the summer when human-induced heat production is very low, and the anthropogenic heat is negligibly low compared to other energy balance components. Especially in winter seasons, the anthropogenic heat, which arises due to the rapidly increasing population in urban areas and the energy consumption triggered by it, has become an effective component in the surface energy balance [44, 45]. For this reason, human-induced heat is one of the factors evaluated in the description of the UHI formation mechanism.

1.2 UHI Detection and Characterization In the literature, the UHI effect is classified in various categories according to the data acquisition methods and data types used in the studies for its detection. According to measurement techniques, UHI can be categorized into three types. The first one is Canopy Layer UHI (CLUHI) which is based on the measurements obtained from the atmospheric layer between the land surface and canopy surface of city morphology, the second one is Boundary Layer UHI (BLUHI), in this case, UHI measurements were based on upper boundary layer of the atmosphere, the third type is surface UHI (SUHI) and it is modeled with lower atmosphere or land surface layer parameters such as land surface temperature, surface humidity, etc. [46, 47]. Radiative cooling differences between urban and rural in the boundary and canopy layers of the atmosphere reach its maximum level at night, under windless and clear sky conditions. Under these conditions, boundary, and canopy UHI effects can be observed at the maximum level. Although the surface UHI effect can occur both during the day and night hours, it can reach the maximum level with the effect of high solar radiation, especially during the day. To record near-surface temperatures in Canopy layer UHI detection, it is possible to benefit from meteorology stations located at appropriate heights (~1.3 m) and mobile transect transitions made with various land vehicles mounted on temperature sensors. In Boundary layer UHI detection; special platforms such as high towers, radiosonde, air observation balloon, or temperature sensors placed on various aircraft are used. Remote sensing and thermal imaging technologies are mostly used to determine the effect of SUHI. In thermal remote sensing techniques, land surface temperature (LST) values can be calculated using thermal infrared regions of the electromagnetic spectrum by sensors placed on various aircraft or satellites.

Revisiting Urban Heat Island Effects in Coastal Regions …

285

Different approaches are used to calculate intensity values for BLUHI, CLUHI, and SUHI in the UHI detection process. BLUHI and CLUHI intensities are calculated based on point-based in-situ observations in relevant atmospheric layers in urban-rural areas. For this purpose, intensity values are obtained by using rural reference atmospheric temperature/humidity values. In determining the rural reference temperature value, many factors such as the geographical location and spatial distribution of the measurement points, measurement timing differences, local effects of other meteorological parameters (wind, humidity, etc.) on the temperature can also be included in the evaluation. In the detection and characterization of UHI, it is very difficult to apply the approaches proposed in the literature to data sets obtained in different regions and times. The reason for this is the unique settlement style, geographical location, geological structure, climatic condition, spatial distribution, and land cover/usage characteristics of each city. Some methods are proposed in the literature to solve this complex situation mentioned above. In this way, by defining the UHI characteristic, spatial distribution parameters such as UHI location, coverage area, and magnitude were tried to be determined [4, 12, 48, 49]. Remote sensing and thermal imaging techniques are used extensively in SUHI analyzes and evaluations, as they present data from large areas in the appropriate format and relatively effective ways. In a city under the influence of UHI, there is a temperature difference between the urban area and the rural area surrounding it, and this difference varies depending on the location. In order to characterize the existence of UHI, this difference and its location-based change must have a certain characteristic structure. Due to the nature of the UHI effect, it is expected that the LST change depending on the location from rural to urban transition, represents a continuous logarithmic or Gaussian function form. Based on this principle, Streutker [4] expressed this effect of SUHI as it is in Eq. (2). The representation of the urban-rural heat surface preferred in such approaches allows for more precise and accurate calculation of both rural reference temperatures (T RURAL ) and SUHI intensities (T SUHI ) calculated according to Eq. (2) [12]. T0 (x, y) = TRU R AL (x, y) + TSU H I (x, y)

(2)

1.3 Modeling the UHI Effect In cities facing the UHI phenomena, it is very important to model the UHI effect successfully when it is aimed to develop appropriate approaches to analyze the UHI formation mechanisms and reduce its impact. At this stage, after evaluating many factors that can be effective in the UHI formation (see Sect. 1.1) depending on the specific socio-economic, climatic and geographical characteristics of each city, it is necessary to establish quantitative models between effective parameters and the UHI intensities. For this purpose, several approaches to modeling the UHI effect have been

286

M. Dihkan et al.

developed in some of the UHI related studies in the literature. In such researches, various models and methods that take into account the impact of UHI in urban planning processes are emphasized. Besides, thanks to the developed models, scenarios that may occur as a result of intervening or not interfering with the factors causing the UHI effect in the relevant city can be simulated. In this way, in a study to reduce the UHI effect, it will be possible to determine what factor to interfere with which factor. In general approaches for modeling the UHI effect, it can be expressed in two main categories; numerical or experimental methods. Experimental techniques are based on statistical algorithms, various parameterization techniques, engineering formulas, and various qualitative concepts. Experimental models can model various changes on the basic features of the data by making use of stationary and highly representative training data. Statistical approaches are the most common techniques in experimental models and they are capable of representing relational impacts between each factor and UHI. The reason for this is that such models provide highly useful and representative quantitative information on various factors that have an important role in UHI formation [50, 51]. Many studies have been conducted in the literature to model both atmospheric UHI and surface UHI effects with statistical approaches. In these studies, using various regression models, effective factors such as LULC, urban geometry, population, etc. in UHI formation, can be statistically modeled [52]. In the statistical model developed for European and North American countries by Oke [53], revealed that there is a strong relationship between UHI intensities and the logarithm of population growth amounts. However, various criteria expressing urban geometry are also used in the statistical modeling of the UHI effect. Urban geometry related parameters, such as sky view factor [54], the height-width ratio of urban canyons [55], building heights [15], can be included in various statistical modeling studies of UHI effect. Recently, using remote sensing, GIS, and computer technologies together, dense and multi-dimensional datasets with high surface representation can be produced. Complex relationships between UHI intensities and multidimensional datasets can be modeled using advanced interpolation and multidimensional linear regression techniques [56–59]. For example, Sherafati et al. [60] modeled the relationship between various urban development parameters and UHI using artificial neural networks and support vector regression techniques for the city of Tehran. Similarly, Zhou et al. [61] characterized the UHI effect with the gauss-based approach proposed by Streutker [4] during the night period and developed a statistical model with the help of the support vector regression technique among the various factors effective in UHI formation with the UHI parameters. Thanks to the usage of the expressed techniques integrated with spatial information within GIS environments, the interactions of the UHI effect and factors involved can be revealed by location-based quantitative statistical analysis [62]. For this purpose, many datasets that can characterize the urban environment such as high spatial resolution LULC data [63], various topographic data, and threedimensional building models produced using remote sensing technologies can be utilized with the help of GIS tools. It is very important to use three-dimensional data with sufficient spatial resolution in UHI models developed especially in the case of densely urbanized areas [64]. Three-dimensional datasets with high spatial resolution, produced by the recently developed photogrammetry and LiDAR technologies,

Revisiting Urban Heat Island Effects in Coastal Regions …

287

enable the characteristic features of urban geometry to be revealed very quickly and with high accuracy [65].

1.4 UHI Mitigation Strategies In the last 50 years, it has been revealed that the UHI phenomenon has an undeniable effect on the environmental and socio-economic dynamics of urban areas, with many types of research on UHI detection, characterization, and modeling of effective factors in UHI formation. The awareness created in this way leads to the spread of scientific research on UHI mitigation and the implementation of various methodologies proposed in these studies by governments, local decision-makers, and civil society. Due to its nature, the UHI effect may occur due to the effects of many direct or indirect factors, such as the geographical location, topography, climatic characteristics, LULC character, population, and urban design of urban areas. Among these, mitigation strategies can be developed using various anthropogenic factors such as LULC structure, urban morphology, design, energy use, and pollution. Therefore, the proposed strategies for UHI mitigation can be expressed under four general headings, taking into account the climatic and environmental characteristics of urban areas in scientific research. These are (1) Approaches based on the use of building elements with high cooling capacity thanks to their radiative and thermal properties in buildings and other artificial impervious surfaces, (2) techniques developed based on the use of LULC classes with high cooling capacity, especially green vegetation and water, in urban landscape and planning activities, (3) approaches to designing the building morphology to provide adequate ventilation and cooling in urban design and planning activities, (4) techniques based on the reduction of environmental pollutants and air pollution by taking various measures. In these approaches, increasing the solar reflectance, emittance, and evapotranspiration amounts of urban surfaces is the main goal [9]. For this purpose, many studies conducted using real data or mesoscale simulation models, it has been revealed that the average ambient temperature decreases around 0.3–0.5 K in response to every 0.1 increase in albedo level, depending on the unique characteristics of urban areas [66]. Therefore, the potential and efficiency of the mitigation approach to be chosen for a city under the influence of UHI is directly related to many climatological, optical, thermal, and hydrological parameters specific to the city. Solar radiation intensity reaching the surface, which is one of the important climatological variables, affects the amount of heat that artificial impervious surfaces can absorb or emit. Also, as a function of the spectral wavelength of incoming solar radiation, changes may occur in the reflectance, transmittance, and absorbance amounts of the surfaces (depending on the structural properties of surface materials such as moisture and color) used in UHI mitigation. Similarly, ambient temperature directly affects the amount of sensible and latent heat flux formed by convection between the natural/artificial surface covers preferred for UHI mitigation and the near-surface layers of the atmosphere. Due to seasonality, decreasing evapotranspiration levels in

288

M. Dihkan et al.

urban areas together with low ambient temperatures in winter leads to a decrease in the efficiency of vegetation-based mitigation instruments such as green roofs, green corridors, parks, and increase in summer [67]. Wind speed and atmospheric turbulence affect sensible heat flux from urban surfaces and the evapotranspiration level of green plant species utilized for UHI mitigation in a directly proportional relationship. However, high wind speed can increase the rate of evapotranspiration by accelerating the transfer of water vapor from moist soil [68]. Therefore, in cities under the influence of UHI, where mitigation instruments such as green roofs and moist soil are preferred, wind speed and turbulence should be taken into account as very important variables. Two other important variables that depend on the urban climate character and have an impact on the efficiency of mitigation activities are atmospheric water vapor and precipitation. High atmospheric relative humidity levels can reduce the amount of latent heat flux by preventing evapotranspiration on surfaces created for mitigation purposes such as green roofs. Precipitation can increase the amount of latent heat flux significantly by increasing the moisture rate in the soil and surfaces. Various optical parameters should also be taken into account in the application of techniques for the mitigation of the UHI effect in urban areas. The albedo and emissivity values of the surface covering elements (green plants, various roof, and floor coverings, etc.) selected for mitigation have the potential to significantly change the surface thermal energy balance. Reflective surface pavements with a high albedo level can stay cool by getting less sensible heat fluxes to their bodies with low absorbance values and have a strong UHI mitigation potential. Surface pavements with high emissivity can cool rapidly by emitting a significant amount of heat through infrared radiation emission. For example, green covers created with suitable green plants can provide significant UHI mitigation with emissivity values between 0.9 0.95 depending on the plant type and planting style. The thermal capacity and transmittance level of the surfaces can be expressed with the overall heat transfer coefficient (U) parameter. In an urban area under the influence of UHI, the heat transfer level of the covering materials to be used on building roofs and other artificial surfaces directly affects the mitigation potential. Especially in UHI mitigation, sensible heat flux can be reduced significantly thanks to the characteristic low overall heat transfer coefficient values of green vegetation used as a covering on buildings and other artificial surfaces. This can contribute to mitigation processes by keeping the surfaces cool. In urban surfaces, especially latent heat losses due to evaporation are directly related to water vapor transfer between plants and soil. This relationship depends on various hydrological parameters. Latent heat transfer with the diffusion of water vapor from the soil depends on water content and temperature values. At this point, soil moisture content of the soil must be above the wilting point. Water vapor transfer from soil to plant and the air is dependent on the soil surface, leaf surfaces, and vapor pressure levels in ambient air. However, another parameter controlling the water vapor transfer and evapotranspiration process for a vegetation mass is the canopy resistance value. The canopy resistance level can be obtained by proportioning leaf resistance value to leaf area index (LAI) when the canopy is considered as a large leaf. LAI index is a key parameter that determines the value of canopy resistance and thus

Revisiting Urban Heat Island Effects in Coastal Regions …

289

the loss of latent heat flux through evapotranspiration [69]. Therefore, more efficient results can be obtained in UHI mitigation planting activities by using canopies with a high LAI index [70]. Considering all these characteristic features of urban areas, UHI mitigation approaches have been researched and categorized in the literature [71–75]. The proposed methodologies can be expressed in four general categories considering the UHI mitigation efficiency and practicality of implementation by decision-makers in different urban areas. These, (1) techniques based on greening, (2) techniques based on the use of cool paving materials, (3) techniques based on the design of building morphology and street geometries, (4) techniques based on the use of water masses in the surface pavements of buildings and other IS outer layers.

1.4.1

UHI Mitigation Techniques Based on Greening

In researches and applications for UHI mitigation, the cooling effect of vegetation through evapotranspiration and shading is widely used. For this purpose, techniques based on different vegetation applications such as green roofs, green facades, urban parks, and street trees, ground vegetation (private green in gardens) have been developed [72]. However, to achieve maximum mitigation efficiency, planning of vegetation cover is important based on patch size, edge, shape, and connectivity [76]. However, considering the specific conditions of each urban region, in the selection of the plant species; factors such as height and canopy propagation of vegetation, size and growth rate of roots, sun, soil, water and temperature requirements, components such as leaves, fruits and flowers, low maintenance and irrigation requirements must be taken into account. Roofs constitute a significant part of impervious surface areas in dense urban areas [77]. Therefore, green roofs created by covering these surfaces with living green vegetation are a powerful UHI mitigation instrument [78–80]. Depending on the vegetation types used, green roofs are divided into two groups: intensive (containing small trees and shrubs) and extensive (covered with a thin vegetation layer). In the extensive green roof concept, evergreen plant types that require low maintenance, preferably resistant to extreme weather conditions, drought-tolerant, and have a high covering capacity are used. In the intensive green roof concept, many plant types suitable for the relevant urban area can be used, including large trees and bushes used in a traditional garden or park design. In intensive green roofs installation, structural reinforcement and irrigation support are generally required in buildings. Although such applications are more costly, the formed roof gardens give buildings an additional recreation space function for residents. Thanks to the green roof applications, the water-permeable potential of urban surfaces increases, and the water remains in the soil. In this way, successful UHI mitigation results can be obtained, especially on hot summer days [81]. Another UHI mitigation approach used in the urban landscape based on vegetation is green facades and green walls created by using climbing and trailing plants. Such techniques are divided into three groups as wall-climbing, hanging-down, and modular type. Applications that are carried out

290

M. Dihkan et al.

by freely growing climbing or hanging plant species on building facades are more common [82]. In particular, more effective results can be achieved in glass-covered buildings and low-rise buildings. With the green facades applications, a significant decrease can be achieved in the surface temperatures of the building’s exterior walls and indoor temperature values [83]. In addition, thanks to green facades applications, strong insulation can be provided for buildings. The insulation effect increases with increasing leaf area density and thickness. However, achieving adequate UHI mitigation efficiency depends on a sufficient amount of irrigation and suitable climatic conditions, depending on the type of plant utilized [84]. Another greening-based UHI mitigation technique utilizes the ability of plants such as trees or shrubs to cool the surfaces through evapotranspiration and create solar shade by reducing the amount of solar radiation reaching vertical and horizontal impervious surfaces. The parks and urban forests used in the urban landscape can be used for this purpose with the park cool island (PCI) effect they create. The PCI effect may vary depending on the type of park, whether the park contains a water body or irrigation. Therefore, parks with high and closed tree canopies in the afternoon, multi-use and savannah-type parks immediately after sunset, parks with open grass design during the night and close to sunrise, create maximum PCI effect. It has been revealed that multi-use, savannah, and open parking areas with higher sky view factor value can contribute more to the nocturnal cooling processes [25]. For this reason, to provide effective nocturnal cooling, it is appropriate to evaluate the compositions made with different types of parks [85]. In the literature, the spatial impact area of the city parks used in UHI mitigation has been extensively investigated, and it has been revealed that the parks can produce different levels of cooling effect, up to 1000 m from the park boundaries, depending on the relevant city characteristics [86–88]. In another UHI mitigation approach based on cooling by tree shading, landscape designs such as green corridors formed by a series of trees planted around the building or on the edges of roads and parking lots are used [89]. In urban areas, along with buildings, other objects with the high impervious surface area are roads and parking lots. These surfaces, with their low albedo, emissivity, and high dryness (with fast water drainage capabilities), can quickly heat up and retain high levels of heat. Therefore, it can make a significant contribution to UHI mitigation by shading such surfaces with high trees. In such urban landscape designs, positioning the trees at strategic points on the east and west facades of the buildings will create maximum shadow and cooling effects. Similarly, keeping the air condition units in the shade will increase energy efficiency [90]. At this point, it is suggested that suitable tree species should be located at a distance of minimum 1.5–3 m and maximum 9–15 m with buildings, by projecting their growth. The selection of deciduous trees, especially for urban areas with a cold winter climate, will create a cooling effect in the summer period, as well as provide heat gain for the buildings in the winter period. Similarly, tree corridors located along the edges of roads and parking lots, and preferably with tree heights of 6–12 m, can significantly contribute to the UHI mitigation by keeping the surfaces cool and moist [91]. In addition, such tree corridor designs can significantly reduce heating and air pollution from motor vehicles. [92] demonstrated that the surface temperatures

Revisiting Urban Heat Island Effects in Coastal Regions …

291

decreased by 27.5 °C, atmospheric near surface temperatures by 5.6 °C, and the SO2 ratio in the air by 65% in a tropical city through the application of this technique. Another mitigation tool that is applied by using vegetation in urban areas under the UHI effect is ground greening activities. Permeable grass pavers, which are widely used for this purpose, can be applied in urban forests and parks, along with trees, on the surfaces of landscape elements such as pedestrian parkways, parking lots, pedestrian walkways, driveways, patios, fire lanes. Although significant solar radiation is absorbed due to ground greening, a significant amount of this heat can be consumed through evapotranspiration under appropriate climatological and hydrological conditions. At this point, it is essential that the surface has sufficient soil moisture levels. Researches have revealed that ground greening provides a significant amount of cooling after sunset and during the night. However, it has been determined that the landscape designs to be applied with a small number of trees will increase the spatial spreading distance of the resulting cooling effect [93]. Moreover, the soil moisture level of the urban surface to which the technique will be applied is very important in obtaining effective results from soil greening techniques. In these areas, sufficient soil moisture levels should be maintained using irrigation or other methods. Such techniques can contribute significantly to UHI mitigation, especially in urban areas with hot and dry climates (such as the Mediterranean climate) [94].

1.4.2

UHI Mitigation Techniques Based on the Use of Cool Paving Materials

Many pavements such as building roofs and walls, such as roads, streets, driveways, sidewalks, parking lots, runways, plazas, playgrounds, constitute a large portion of urban surfaces areally [95]. Pavement materials such as concrete, asphalt, brick, or tile produced mostly in the traditional structure are used in these areas. Such pavements can absorb a significant amount of solar radiation with their low solar reflectance and thermal emittance values and contribute significantly to the formation of UHI in the city. From this point of view, many kinds of research have been conducted on the development and use of new generation materials with high albedo and thermal emittance for UHI mitigation in such surface pavements. Doulos et al. [96] investigated 93 cool pavement materials that can be obtained naturally for use on outdoor urban surfaces and have relatively high albedo values (mostly not exceeding 0.75), and found that these materials can decrease the temperature values by certain rates. In subsequent studies, with the development of very high albedo artificial white materials and high IR reflective colored materials, the temperature values were further decreased and contributed to the UHI mitigation. In order to provide UHI mitigation by using such materials, many design concepts have been developed such as cool roofs, cool facades, cool pavements, water-retentive pavements [97]. Cool roofs can be designed and applied by using such materials in building roofs with different slope and design. Open and impervious roof surfaces can reach quite high-temperature

292

M. Dihkan et al.

values, especially in summer. By determining and using suitable cool roofing materials for cities with different climatic characteristics, surface and ambient temperatures can be reduced and positive contribution can be made to energy-saving and thermal comfort levels of buildings [77, 98]. Cool-colored roofing materials designed in recent years, with their pigment and multi-layered structure, can reflect the near IR wavelength portion of solar radiation reaching their surface at a much higher rate [99]. Generally, building roof types are divided into two categories: low-sloped and steep-sloped. Low-sloped roofs are mostly used in buildings such as commercial, industrial, office, etc. Liquid coating and single-ply membrane applications can be applied to create cool roofs concept in such structures. In this way, the approximate solar reflectance value of the roof coverings can be increased to 65% and the thermal emittance level to 80–90%. Coating elements such as tiles, metal sheets, or asphalt shingles are used extensively in steep-slope roofs. Thanks to the use of new generation coating types with cool-colored pigments instead of traditional coating types, it has been demonstrated that solar reflectance values can reach 70% [100] for tiles, 90% [101] for metal sheets, 65% [102] for asphalt shingles. Also, it is important that the coating and paint materials used in building facades have a structure that can contribute to UHI mitigation thanks to their radiative and thermal properties. Pisello et al. [103] demonstrated that by applying a cool roofing membrane in a prototype building in Italy with a Mediterranean climate, the indoor temperature decreased by 2.6 °C and the roof surface temperature by 19.8 °C. However, in the study of [103], with the combination of the cool facade paint application and the cool roofing membrane application in the south-facing facade, a temperature drop up to 3.1 and 11.4 ° C can be reached in the indoor air and outdoor walls temperatures respectively. Although the materials used in cool roof and cool facade applications can offer high efficiency and relatively inexpensive solutions in UHI mitigation, it has been revealed that a certain decrease in reflectance values may occur after a few years due to weathering and aging [104, 105]. It is very important to evaluate the cost-benefit status of UHI mitigation by projecting such changes that occur over time. Similarly, materials such as asphalt, concrete, stone parquet, which constitute an important part of ground pavements in urban areas, play an important role in surface energy balance and heating processes with their radiative and thermal properties [106]. Among such ground pavement materials, solar reflectance values of conventional asphalt surfaces vary between 0.10 and 0.18 (depending on the type of aggregate used in the asphalt mix), and solar reflectance values of conventional concrete surfaces vary between 0.25 and 0.30. Replacing such materials with new generation cool pavements alternatives(for asphalt pavements: using light-colored aggregates, cool-colored asphalt mixed with pigment or sealant, using light-colored tree resin in place of asphalt, for concrete pavements: using white cement or cement blended with light-colored slag) with high albedo and reflectivity allows the surface and near-surface air temperatures to be significantly reduced [107, 108]. However, traditional urban ground pavements have high water impermeability and rapid drainage ability. This situation causes the dryness level to be too high and evaporation level to be very low. To eliminate this problem and increase the evaporative cooling level, permeable and water-retentive pavement types have been developed. Permeable ground pavements, which contain

Revisiting Urban Heat Island Effects in Coastal Regions …

293

specially designed more voids, allow water to penetrate and leak into the lower layers of the surface, thus increasing the moisture level there. Various types of permeable pavements include porous asphalt pavements, pervious concrete pavements, pervious cast concrete pavements, permeable interlocking concrete pavements can be considered [109]. Besides, the use of plants such as grass in vegetated permeable pavements designs enables higher evaporation potential and higher latent heat loss. Water retentive pavements have been developed based on the principle of absorbing moisture and increasing evaporation by using various water-retentive materials in the lower layers of asphalt or concrete surfaces. In this way, it is possible to increase the cooling ability of the surface. In experimental studies from literature, it has been revealed that the road surface temperature can be reduced by 25 °C with the application of water retentive pavements [110]. Therefore, when all these cool pavements approaches are applied in favorable conditions and combination with each other, they can make a significant contribution to UHI mitigation.

1.4.3

UHI Mitigation Techniques Based on the Design of Building Morphology and Street Geometries

Urban morphology and geometry-related parameters such as the geometric design, morphology, orientation, solar radiation angle of incidence, and directions towards active wind directions of urban canyons formed by buildings and streets in urban areas can have a significant impact on UHI. Thus, various UHI mitigation techniques have been developed based on intervention in these parameters. The amount of solar radiation reaching the urban surfaces, wind-induced airflow, and turbulence can be controlled via these parameters [111]. At this point, the effect of different scenarios on the energy balance was investigated in urban design and parcel dimensioning studies conducted by considering horizontal (areal) and vertical (height) densities and street orientations of the buildings. In such studies, it has been demonstrated that building height and width values are more effective than orientation [112]. Especially in the summer, the h/w aspect ratio (AR) and orientation of the streets can have a significant influence on the cooling energy consumption level [113]. While the cooling energy consumption level is lower in the streets with North-South orientation and high aspect ratio value due to the mutual cooling effect of the building facades, it can be higher in East-West oriented and wide streets [114]. In the literature, it is recommended to place the buildings perpendicular to the prevailing wind directions in order to increase the reduced wind speed and vertical turbulence due to the impact of narrow streets and tall buildings. In this way, the UHI effect can be reduced [115]. The morphological structure created by high buildings and narrow streets in urban canyons formed by roads and pathways surrounded by the buildings, walls and roofs prevent air circulation and ensures that heat is trapped. The h/w aspect ratio value of urban canyons can be highly effective in mitigation strategies [116]. However, depending on the h/w factor, quite complex interactions can occur between the cool pavement solutions (see Sect. 1.4.2) used for UHI mitigation purposes and the canyon geometry. It has been demonstrated that pavements albedo values that are increased

294

M. Dihkan et al.

for UHI mitigation can only increase the value of urban canyon albedo if the urban canyon h/w AR is ≤1.0 [117]. Otherwise (h/w AR > 1.0), cool pavements surfaces with high albedo can create a reflector effect in the canyon geometry, causing the air temperature to rise. This situation can cause sensible heat flux to increase during the daytime and heat emission during the nighttime. For this reason, the urban canyons in this geometry are often among the urban areas where UHI mitigation techniques are most difficult to apply because of the possibility of having lower albedo value than the road and building pavements they contain. Therefore, when using cool pavement materials with high albedo values for UHI mitigation in such urban areas, it is very important to reveal the exact mitigation potential by evaluating site-specific parameters such as sky view factor, AR, building insulation. Although the UHI mitigation performance achieved through the use of vegetation-based techniques varies according to different street designs, it has been demonstrated by simulations that this approach has a significant temperature lowering effect. In the researches, a temperature decrease up to 24 Kcan be reached by using a large central row of trees and galleries (h/w = 1) in a North-South oriented street canyon and a 22 K by using a narrow tree row in an east-west oriented street canyon (h/w = 2), compared to the treeless situation. In addition, it has been determined that such approaches can increase the circulation level in the canyon at different levels depending on the tree density and geometry [118, 119].

1.4.4

UHI Mitigation Techniques Based on the Use of Water Bodies

In the literature, techniques based on the use of water masses for the purpose of UHI mitigation in urban areas are increasingly researched and various applications are carried out. The Water Cool Island (WCI) concept created by the water masses with its high evaporation potential has the ability to consume a significant amount of heat energy. In addition, waterways such as rivers and canals can transfer heat from the city center to cool rural areas outside the city. The effect of water bodies on urban ambient temperature has been investigated in many experimental studies. Especially in hot summer periods, it has been determined that the air temperature above the water surface can be up to 5 °C lower than the urban environment around the water mass, depending on factors such as surface area, depth, wind speed, and direction. Especially in waterways such as rivers or canals that are discharged to large water bodies such as sea or lake, this cooling effect is found to be present with sea breeze blows even at a distance of several hundred meters horizontally and up to 80 meters in vertical [120, 121]. However, the cooling effect associated with the WCI concept for water bodies can vary depending on the type of water body (marine, estuarine, riverine, lacustrine, and palustrine), its geometry (whether it is simple, complex, square, circular, circular) or its mobility. Comparatively, simply shaped water bodies to complex-shaped water bodies, flowing/dispersed water bodies to stagnant water bodies, deep water bodies to shallow water bodies and vegetation surrounded water bodies tobuildings surrounded water bodies have more cooling ability [122–124].

Revisiting Urban Heat Island Effects in Coastal Regions …

295

2 Case Study of Istanbul Megacity In every period of history, coastal regions have been very attractive areas for people with their economic, socio-cultural, and environmental features [125]. Therefore, many densely populated cities are located in these areas and the urban sprawl effect is felt in these cities. Coastal Istanbul megacity is one of the unique examples of such cities in the world due to its unique cultural heritage and geographical situation (Fig. 2). The city of Istanbul (historical peninsula), with its special location in the region where the ancient Asian and European continents meet, has the longest coastal strip of Turkey. The megacity of Istanbul is the most crowded city in Turkey and Europe with about 15 million inhabitants. The unplanned urbanization activities brought with this extremely dense population caused the LULC character of the city to change drastically and the IS areas in the city to reach high dimensions (Fig. 3). This makes the city of Istanbul a very convenient study area for the investigation of the UHI phenomenon. In this study, the results data obtained by Dihkan et al. [12] were used for the proposed SUHI mitigation approach for the coastal Istanbul megacity. In the first stage of the study, Dihkan et al. [12] determined and characterized the SUHI effect using remote sensing techniques in Istanbulcity. Using the approach proposed in the study, quantitative parameters related to the magnitude and spatial distribution of the SUHI effect in the city were calculated for different temporal periods (Table 2).

Fig. 2 Study area

296

M. Dihkan et al.

Fig. 3 Istanbul LULC Map of 23.06.2011 Table 2 SUHI Parameters for each sub-region of Istanbul (Asia and Europe) Region

Period

SUHI magnitude (a0 ) (K)

SUHI centre location (x 0 , y0 ) (m)

SUHI orientation angle (F)0

SUHI spatial extent (ax , ay ) (m)

R2

Istanbul Asia

14.06.2002

3.70

683480, 4544267

15.8499

21170, 16900

0.64

12.06.2007

3.77

683771, 4543914

23.2059

21942, 17591

0.65

17.06.2009

4.77

683421, 4544600

13.51

28615, 20899

0.55

23.06.2011

5.37

681630, 4543000

17.2469

30991, 21298

0.53

05.06.1987

2.88

665850, 4550808

29.9344

7052, 8148

0.64

14.06.2002

2.90

663938, 4551019

6.4650

7303, 8231

0.65

12.06.2007

3.12

663563, 4549553

11584, 10323

0.63

17.06.2009

4.18

664588, 4548025

2.7948

19748, 14764

0.59

23.06.2011

4.96

663132, 4547598

15.8499

23676, 16094

0.57

Istanbul Europe

76.59

Revisiting Urban Heat Island Effects in Coastal Regions …

297

In the second stage, it statistically modeled the relationship between SUHI intensities and seven potential factors that affect the SUHI formation: four LULC classes [Impervious Surface (IS), Vegetation (V), Bare Soil and Agriculture (BSAA), Water (W)], Digital Elevation Model (DEM), Population Density (PD), Mean Building Height (MBH) and Electrical Energy Consumption (EEC). In the research, all data layers were represented in raster format with 210 m grid size, and statistical analyzes were performed on sample grids up to 8% of the total number of grids representing the study area. Due to the unique morphology created by the Bosphorus crossing, Dihkan et al. [12] investigated the existence and spatial distribution of SUHI by dividing the city into two sub-study areas, namely Asia and Europe. In the analyzes carried out at different periods in both sub-study areas, it was revealed that the city was under heavy SUHI effect for many years. Dihkan et al. [12] revealed that LULC, MBH, DEM, and EEC factors were highly correlated with SUHI intensities as a result of OLS multiple regression analysis performed between seven selected factors (4 LULC class, DEM, PD, EEC) and SUHI (Table 3). When the regression model parameters calculated for the two sub-study areas are examined, it is understood that three independent variables related to LULC, IS, V and BSAA are effective in SUHI formation. Therefore, in this study, a greeningbased UHI mitigation strategy was applied. For this purpose, the green corridors are designed to go through the regions under the influence of the intensive SUHI by examining the SUHI density map shown in Fig. 5a. In the proposed landscape design, minimum intervention to the city’s existing structure (street geometry, topography, building style, etc.) was aimed, and variables such as existing road networks, dominant wind direction, and coastal morphology were also taken into account. It is recommended that greenery based UHI mitigation instruments such as green roofs, vertical gardens, roads, and park plantings are used together in the designed green corridors (Fig. 4). After this intervention to the LULC variables, the inverse solution was made by using the increased V intensities and the decreasing IS and BSAA intensities in regression models and SUHI intensities were recalculated. Thus, after the implementation of the UHI mitigation approach proposed for Istanbul Asia and Europe sub-study areas, a changed SUHI density map was obtained (Fig. 5b). The coastal Istanbul megacity is experiencing significant problems arising from intense and unplanned urbanization. In Istanbul metropolis, which has an intense UHI effect on both the European and Anatolian sides, a landscape and urban planning design have been proposed using green corridor networks to solve this problem. It is aimed to reduce the UHI effect in the city with this proposed greening-based mitigation technique. From this point of view, the total amount of green areas in the UHI identified regions of Istanbul city has been increased by 14.47% for the European sub-study area and 4.59% for the Anatolian sub-study area. In Fig. 5, the change in SUHI intensity maps can be seen with the effect of this intervention. When evaluated on SUHI intensities, it was calculated that an average decrease of 2.7 °C occurred in the European region and an average of 3.2 °C in the Anatolian region.

Sample grid count

893

513

City

Istanbul Asia

Istanbul Europe

0.89

0.92

R2

837.90

1973.03

F IS 0.201533 0.179535

Constant −9.620311 −11.686661

0.104267

0.091347

V

Independent variable coefficients (p < 0.05)

Table 3 OLS Multiple regression parameters for Istanbul Asia and Europe sub-study areas

0.143733

0.181734

BSSA

0.009701

−0.008139

DEM

−0.010914

−0.018138

MBH

298 M. Dihkan et al.

Revisiting Urban Heat Island Effects in Coastal Regions …

299

Fig. 4 Recommended green roads and corridors for Istanbul megacity

Fig. 5 For the Istanbul megacity, a existing SUHI and b mitigated SUHI effect

3 Conclusion In the twenty-first century, a significant part of the world population has been living in urban areas. Therefore, most of the basic human needs such as housing, work, nutrition, and entertainment, that are increasing day by day, are faced in megacities. When the attractiveness of the coastal zones due to its characteristics is added to this, the metropolises in the coastal zones are the cities where the fast urbanization and urban sprawl effect are felt most intensely. This intense stress caused by human

300

M. Dihkan et al.

activities often makes it inevitable for the UHI effect to occur in such megacities. It has been revealed in the researches that Istanbul coastal megacity has also been under the influence of UHI for many years and this effect is due to the high rate of LULC change and urban geometry. From this point of view, a mitigation technique based on green corridor landscape designs was designed for the city of Istanbul and tested on the existing statistical models. The results showed that the correct mitigation approach can only be designed thanks to the success and guidance of the techniques used for the detection, characterization, and modeling of the UHI phenomenon, thereby achieving a high level of UHI mitigation efficiency. When evaluated in general, the UHI effect must be characterized firstly in Istanbul and other megacities that receive dense population and rapidly urbanize with similar characteristics. In this way, the easiest and most effective mitigation solution will be provided for the determined UHI impact zone. In a UHI mitigation process, geographical, climatological, and hydrological variables must be taken into account for these megacities. A significant portion of the UHI-influenced megacities in Europe and the world are located in regions with a sub-tropical and Mediterranean climate. In the mitigation of the summer UHI effect, which is more intense in these regions, more efficient results can be obtained with greening-based and cool pavements techniques. UHI mitigation technologies such as green roofs, reflective roofs, and cool pavements can provide a high level of mitigation efficiency by reducing the amount of net radiation. However, in mitigation strategies applied, costs are still frightening today. This situation may cause megacities and policymakers to not be eager enough in UHI mitigation activities, especially in developing countries. At this point, relatively more economical solutions can be provided, especially with cool and reflective pavements. In new urbanized areas, especially street geometry-based UHI mitigation approaches can be applied more easily by policymakers. Therefore, techniques for UHI detection, characterization, and modeling, which have been demonstrated through scientific research, should be included in urban design and planning processes and master plans should be created for these cities based on the scenarios for many years to come. By using landscape designs such as green corridors in coastal megacities such as Istanbul, the circulation of cool air mass on coastal waters with hot air mass in local urban areas under the UHI effect can be achieved. In this way, it can contribute to the cooling of these regions. Considering the prevailing wind directions in such designs, highly efficient UHI mitigation results can be obtained. In the future, the efficiency of mitigation strategies should be investigated by using high-dimensional advanced simulation models, which can incorporate the unique geographical, climatic and hydrological conditions of urban areas and factors influencing UHI formation in the solution. At this point, with the developments in remote sensing technologies, it will be possible to use higher spatial and temporal resolution data structures related to these variables in models. Besides, mitigation efficiency can be increased in the future by developing hybrid techniques based on the use of different mitigation approaches. In such techniques, advanced decision-making algorithms such as deep learning, which can provide automated solutions by evaluating high dimensional dense data structures related to multiple variables, should be utilized. The success of the methodologies for the mitigation of the UHI phenomenon,

Revisiting Urban Heat Island Effects in Coastal Regions …

301

which has serious negative effects on public health and energy efficiency, primarily depends on the initiatives that politicians and policymakers will take at this point. Only in this way, the social awareness of urban residents and society can be ensured. From this point of view, it is very important to support UHI mitigation approaches that are specifically recommended for urban areas through scientific research, with suitable laws and regulations. Acknowledgements This study was financially supported by The Scientific and Technological Research Council of Turkey (TÜB˙ITAK) through the project numbered 112Y038. The authors are also grateful to the Global Land Cover Facility (GLCF) for providing the Landsat TM satellite data.

References 1. United Nations (2018) Department of economic and social affairs, population division world urbanization prospects 2018, Highlights (ST/ESA/SER.A/421). New York 2. Howard L (1833) The climate of London: deduced from meteorological observations, made at different places in the neighbourhood of the metropolis, vol 1 3. Oke TR (1982) The energetic basis of the urban heat island. Q J R Meteorol Soc. https://doi. org/10.1002/qj.49710845502 4. Streutker DR (2003) Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sens Environ. https://doi.org/10.1016/S0034-4257(03)00007-5 5. Alcoforado MJ, Andrade H (2008) Global warming and the urban heat island. Urban ecology. Springer, Boston, pp 249–262 6. Grady Dixon P, Mote TL (2003) Patterns and causes of Atlanta’s urban heat island-initiated precipitation. J Appl Meteorol. https://doi.org/10.1175/1520-0450(2003)042%3c1273:PAC OAU%3e2.0.CO;2 7. Santamouris M, Cartalis C, Synnefa A, Kolokotsa D (2015) On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—a review. Energy Build. https://doi.org/10.1016/j.enbuild.2014.09.052 8. Hondula DM, Georgescu M, Balling RC (2014) Challenges associated with projecting urbanization-induced heat-related mortality. Sci Total Environ 490:538–544. https://doi.org/ 10.1016/j.scitotenv.2014.04.130 9. Akbari H, Kolokotsa D (2016) Three decades of urban heat islands and mitigation technologies research. Energy Build. https://doi.org/10.1016/j.enbuild.2016.09.067 10. Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol 23:1–26. https://doi.org/10. 1002/joc.859 11. Deilami K, Kamruzzaman M, Liu Y (2018) Urban heat island effect: a systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int J Appl Earth Obs Geoinf 67:30–42. https://doi.org/10.1016/j.jag.2017.12.009 12. Dihkan M, Karsli F, Guneroglu N, Guneroglu A (2018) Evaluation of urban heat island effect in Turkey. Arab J Geosci 11:186. https://doi.org/10.1007/s12517-018-3533-3 13. Memon RA, Leung DYC, Chunho L (2008) A review on the generation, determination and mitigation of urban heat island. J Environ Sci (China) 20:120–128. https://doi.org/10.1016/ s1001-0742(08)60019-4 14. Pradhan S, Pattanasri N (2009) Two-dimensional retrieval in synchronized media streams. In: United States environmental protection agency (EPA), pp 236–240 15. Giridharan R, Ganesan S, Lau SSY (2004) Daytime urban heat island effect in high-rise and high-density residential developments in Hong Kong. Energy Build. https://doi.org/10.1016/ j.enbuild.2003.12.016

302

M. Dihkan et al.

16. Shudo H, Sugiyama J, Yokoo N, Oka T (1997) A study on temperature distribution influenced by various land uses. Energy Build. https://doi.org/10.1016/s0378-7788(96)01035-3 17. Weng Q (2003) Fractal analysis of satellite-detected urban heat island effect. Photogramm E. Remote Sens 18. Oke TR (1987) Boundary layer climates, 2nd edn. 19. Asaeda T, Ca VT (1993) The subsurface transport of heat and moisture and its effect on the environment: a numerical model. Boundary-Layer Meteorol 65:159–179. https://doi.org/10. 1007/BF00708822 20. Sailor DJ, Fan H (2002) Modeling the diurnal variability of effective albedo for cities. Atmos Environ. https://doi.org/10.1016/S1352-2310(01)00452-6 21. Oke TR, Cleugh HA (1987) Urban heat storage derived as energy balance residuals. BoundaryLayer Meteorol 39:233–245. https://doi.org/10.1007/BF00116120 22. Grimmond CSB, Oke TR (1991) An evapotranspiration-interception model for urban areas. Water Resour Res 27:1739–1755. https://doi.org/10.1029/91WR00557 23. Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483. https:// doi.org/10.1016/j.rse.2003.11.005 24. Taha H (1997) Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy Build 25:99–103. https://doi.org/10.1016/S0378-7788(96)00999-1 25. Spronken-Smith RA, Oke TR (1998) The thermal regime of urban parks in two cities with different summer climates. Int J Remote Sens 19:2085–2104. https://doi.org/10.1080/014311 698214884 26. Dimoudi A, Nikolopoulou M (2003) Vegetation in the urban environment: microclimatic analysis and benefits. Energy Build 35:69–76. https://doi.org/10.1016/S0378-7788(02)000 81-6 27. Rao PS, Gavane AG, Ankam SS, Ansari MF, Pandit VI, Nema P (2004) Performance evaluation of a green belt in a petroleum refinery: a case study. Ecol Eng. https://doi.org/10.1016/ j.ecoleng.2004.06.013 28. Magee N, Curtis J, Wendler G (1999) The urban heat island effect at Fairbanks. Theor Appl Climatol, Alaska. https://doi.org/10.1007/s007040050109 29. Hogan AW, Ferrick MG (1998) Observations in nonurban heat islands. J Appl Meteorol. https://doi.org/10.1175/1520-0450(1998)037%3c0232:OINHI%3e2.0.CO;2 30. Karl TR, Diaz HF, Kukla G (1988) Urbanization: its detection and effect in the United States climate record. J Clim 1:1099–1123. https://doi.org/10.1175/1520-0442(1988)001%3c1099: UIDAEI%3e2.0.CO;2 31. Nunez M, Oke TR (1977) The energy balance of an urban canyon. J Appl Meteorol 16:11–19. https://doi.org/10.1175/1520-0450(1977)016%3c0011:TEBOAU%3e2.0.CO;2 32. Voogt JA, Oke TR (1991) Validation of an urban canyon radiation model for nocturnal longwave fluxes. Boundary-Layer Meteorol. https://doi.org/10.1007/BF00118866 33. Arnfield AJ, Grimmond CSB (1998) An urban canyon energy budget model and its application to urban storage heat flux modeling. Energy Build 27:61–68. https://doi.org/10.1016/S03787788(97)00026-1 34. Arnfield AJ (2000) A simple model of urban canyon energy budget and its validation. Phys Geogr. https://doi.org/10.1080/02723646.2000.10642712 35. Monteith JL, Unsworth MH (1990) Principles of environmental physics, 2nd edn. Edward Arnold, London. https://doi.org/10.1016/B978-0-12-386910-4.00026-3 36. Hare FK, Landsberg HE (1983) The urban climate. Geogr Rev 73:242. https://doi.org/10. 2307/214663 37. Kidder SQ, Essenwanger OM (1995) The effect of clouds and wind on the difference in nocturnal cooling rates between urban and rural areas. J Appl Meteorol 34:2440–2448. https:// doi.org/10.1175/1520-0450(1995)034%3c2440:TEOCAW%3e2.0.CO;2 38. Yoshikado H (1990) Vertical structure of the sea breeze penetrating through a large urban complex. J Appl Meteorol. https://doi.org/10.1175/1520-0450(1990)029%3c0878: VSOTSB%3e2.0.CO;2

Revisiting Urban Heat Island Effects in Coastal Regions …

303

39. Estournel C, Vehil R, Guedalia D, Fontan J, Druilhet A (1983) Observations and modeling of downward radiative fluxes (solar and infrared) in urban/rural areas. J Clim Appl Meteorol 22:134–142. https://doi.org/10.1175/1520-0450(1983)022%3c0134:OAMODR%3e2.0. CO;2 40. Stanhill G, Kalma JD (1995) Solar dimming and urban heating at Hong Kong. Int J Climatol 15:933–941. https://doi.org/10.1002/joc.3370150807 41. Rouse WR, Noad D, McCutcheon J (1973) Radiation, temperature and atmospheric emissivities in a polluted urban atmosphere at Hamilton, Ontario. J Appl Meteorol. https://doi.org/ 10.1175/1520-0450(1973)012%3c0798:rtaaei%3e2.0.co;2 42. Sailor DJ, Lu L (2004) A top–down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas. Atmos Environ 38:2737–2748. https://doi.org/10. 1016/j.atmosenv.2004.01.034 43. Schmid HP, Cleugh HA, Grimmond CSB, Oke TR (1991) Spatial variability of energy fluxes in suburban terrain. Boundary-Layer Meteorol. https://doi.org/10.1007/BF00183956 44. Khan SM, Simpson RW (2001) Effect of a heat island on the meteorology of a complex urban airshed. Boundary-Layer Meteorol 100:487–506. https://doi.org/10.1023/A:1019284332306 45. Ichinose T, Shimodozono K, Hanaki K (1999) Impact of anthropogenic heat on urban climate in Tokyo. Atmos Environ 33:3897–3909. https://doi.org/10.1016/S1352-2310(99)00132-6 46. Grimmond CSB (2006) Progress in measuring and observing the urban atmosphere. Theor Appl Climatol 84:3–22. https://doi.org/10.1007/s00704-005-0140-5 47. Voogt J, Oke T (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86:370–384. https://doi.org/10.1016/S0034-4257(03)00079-8 48. Rajasekar U, Weng Q (2009) Spatio-temporal modelling and analysis of urban heat islands by using Landsat TM and ETM+ imagery. Int J Remote Sens 30:3531–3548. https://doi.org/ 10.1080/01431160802562289 49. Anniballe R, Bonafoni S, Pichierri M (2014) Spatial and temporal trends of the surface and air heat island over Milan using MODIS data. Remote Sens Environ. https://doi.org/10.1016/ j.rse.2014.05.005 50. Giridharan R, Lau SSY, Ganesan S, Givoni B (2007) Urban design factors influencing heat island intensity in high-rise high-density environments of Hong Kong. Build Environ. https:// doi.org/10.1016/j.buildenv.2006.09.011 51. Balázs B, Unger J, Gál T, Sümeghy Z, Geiger J, Szegedi S (2009) Simulation of the mean urban heat island using 2D surface parameters: empirical modelling, verification and extension. Meteorol Appl. https://doi.org/10.1002/met.116 52. Eliasson I (1996) Urban nocturnal temperatures, street geometry and land use. Atmos Environ 53. Oke TR (1973) City size and the urban heat island. Atmos Environ. https://doi.org/10.1016/ 0004-6981(73)90140-6 54. Bärring L, Mattsson JO, Lindqvist S (1985) Canyon geometry, street temperatures and urban heat island in malmö, sweden. J Climatol. https://doi.org/10.1002/joc.3370050410 55. Goh KC, Chang CH (1999) The relationship between height to width ratios and the heat island intensity at 22:00 h for Singapore. Int J Climatol 19:1011–1023. https://doi.org/10.1002/(SIC I)1097-0088(199907)19:9%3c1011:AID-JOC411%3e3.0.CO;2-U 56. Svensson MK, Eliasson I, Holmer B (2002) A GIS based empirical model to simulate air temperature variations in the Göteborg urban area during the night. Clim Res. https://doi.org/ 10.3354/cr022215 57. Alcoforado M-J, Andrade H (2006) Nocturnal urban heat island in Lisbon (Portugal): main features and modelling attempts. Theor Appl Climatol 84:151–159. https://doi.org/10.1007/ s00704-005-0152-1 58. László E, Szegedi S (2015) A multivariate linear regression model of mean maximum urban heat island: a case study of Beregszász (Berehove), Ukraine. Idojaras 119:409–423 59. Vicente-Serrano SM, Cuadrat-Prats JM, Saz-Sánchez MA (2005) Spatial patterns of the urban heat island in Zaragoza (Spain). Clim Res. https://doi.org/10.3354/cr030061

304

M. Dihkan et al.

60. Sherafati SA, Saradjian MR, Niazmardi S (2013) Urban heat island growth modeling using artificial neural networks and support vector regression: a case study of Tehran, Iran. ISPRS— Int Arch Photogramm Remote Sens Spat Inf Sci XL-1/W3:399–403. https://doi.org/10.5194/ isprsarchives-XL-1-W3-399-2013 61. Zhou J, Zhou J, Chen Y, Wang J, Zhan W, Wang J (2011) Maximum nighttime urban heat island (UHI) intensity simulation by integrating remotely sensed data and meteorological observations. IEEE J Sel Top Appl Earth Obs Remote Sens. https://doi.org/10.1109/JSTARS. 2010.2070871 62. Szymanowski M, Kryza M (2012) Local regression models for spatial interpolation of urban heat island-an example from Wrocław. Theor Appl Climatol, SW Poland. https://doi.org/10. 1007/s00704-011-0517-6 63. Carlson TN, Traci Arthur S (2000) The impact of land use—land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Glob Planet Change 25:49–65. https://doi.org/10.1016/S0921-8181(00)00021-7 64. Unger J (2006) Modelling of the annual mean maximum urban heat island using 2D and 3D surface parameters. Clim Res 30:215–226. https://doi.org/10.3354/cr030215 65. Chun B, Guldmann JM (2014) Spatial statistical analysis and simulation of the urban heat island in high-density central cities. Landsc Urban Plan. https://doi.org/10.1016/j.landurbplan. 2014.01.016 66. Sailor DJ, Elley TB, Gibson M (2012) Exploring the building energy impacts of green roof design decisions—a modeling study of buildings in four distinct climates. J Build Phys 35:372–391. https://doi.org/10.1177/1744259111420076 67. Jim CY, He H (2010) Coupling heat flux dynamics with meteorological conditions in the green roof ecosystem. Ecol Eng. https://doi.org/10.1016/j.ecoleng.2010.04.018 68. Tsang SW, Jim CY (2011) Theoretical evaluation of thermal and energy performance of tropical green roofs. Energy 36:3590–3598. https://doi.org/10.1016/j.energy.2011.03.072 69. Bonan G (2015) Ecological climatology: concepts and applications. Cambridge Univ Press. https://doi.org/10.21425/f58433332 70. Takebayashi H, Moriyama M (2007) Surface heat budget on green roof and high reflection roof for mitigation of urban heat island. Build Environ 42:2971–2979. https://doi.org/10.1016/ j.buildenv.2006.06.017 71. Gartland L (2012) Heat islands: understanding and mitigating heat in urban areas 72. Kleerekoper L, van Esch M, Salcedo TB (2012) How to make a city climate-proof, addressing the urban heat island effect. Resour Conserv Recycl 64:30–38. https://doi.org/10.1016/j.res conrec.2011.06.004 73. Gago EJ, Roldan J, Pacheco-Torres R, Ordóñez J (2013) The city and urban heat islands: a review of strategies to mitigate adverse effects. Renew Sustain Energy Rev 74. Wong NH, Jusuf SK (2013) Urban heat island and mitigation strategies at city and building level. In: Hien Wong N, Kardinal Jusuf S (eds) Advances in the development of cool materials for the built environment. Bentham Science Publishers, pp 3–32 75. Nuruzzaman M (2015) Urban heat island: causes, effects and mitigation measures—a review. Int J Environ Monit Anal 3:67. https://doi.org/10.11648/j.ijema.20150302.15 76. Chen A, Yao XA, Sun R, Chen L (2014) Effect of urban green patterns on surface urban cool islands and its seasonal variations. Urban For Urban Green 13:646–654. https://doi.org/10. 1016/j.ufug.2014.07.006 77. Akbari H, Rose LS (2008) Urban Surfaces and Heat Island Mitigation Potentials. J HumanEnvironment Syst 11:85–101. https://doi.org/10.1618/jhes.11.85 78. Kikegawa Y, Genchi Y, Kondo H, Hanaki K (2006) Impacts of city-block-scale countermeasures against urban heat-island phenomena upon a building’s energy-consumption for air-conditioning. Appl Energy 83:649–668. https://doi.org/10.1016/j.apenergy.2005.06.001 79. Li D, Bou-Zeid E, Oppenheimer M (2014) The effectiveness of cool and green roofs as urban heat island mitigation strategies. Environ Res Lett 9:055002. https://doi.org/10.1088/17489326/9/5/055002

Revisiting Urban Heat Island Effects in Coastal Regions …

305

80. Heusinger J, Weber S (2015) Comparative microclimate and dewfall measurements at an urban green roof versus bitumen roof. Build Environ 92:713–723. https://doi.org/10.1016/j. buildenv.2015.06.002 81. Oberndorfer E, Lundholm J, Bass B, Coffman RR, Doshi H, Dunnett N, Gaffin S, Köhler M, Liu KKY, Rowe B (2007) Green Roofs as Urban Ecosystems: Ecological Structures, Functions, and Services. Bioscience 57:823–833. https://doi.org/10.1641/B571005 82. Wong NH, Tan AYK, Tan PY, Sia A, Wong NC (2010) Perception studies of vertical greenery systems in Singapore. J Urban Plan Dev. https://doi.org/10.1061/(ASCE)UP.1943-5444.000 0034 83. Cheng CY, Cheung KKS, Chu LM (2010) Thermal performance of a vegetated cladding system on facade walls. Build Environ 45:1779–1787. https://doi.org/10.1016/j.buildenv. 2010.02.005 84. Hoelscher M-T, Nehls T, Jänicke B, Wessolek G (2016) Quantifying cooling effects of facade greening: Shading, transpiration and insulation. Energy Build 114:283–290. https://doi.org/ 10.1016/j.enbuild.2015.06.047 85. Yang X, Zhao L (2015) Diurnal Thermal Behavior of Pavements, Vegetation, and Water Pond in a Hot-Humid City. Buildings 6:2. https://doi.org/10.3390/buildings6010002 86. Ca VT, Asaeda T, Abu EM (1998) Reductions in air conditioning energy caused by a nearby park. Energy Build 29:83–92. https://doi.org/10.1016/S0378-7788(98)00032-2 87. Yu C, Hien WN (2006) Thermal benefits of city parks. Energy Build 38:105–120. https://doi. org/10.1016/j.enbuild.2005.04.003 88. Skoulika F, Santamouris M, Kolokotsa D, Boemi N (2014) On the thermal characteristics and the mitigation potential of a medium size urban park in Athens, Greece. Landsc Urban Plan 123:73–86. https://doi.org/10.1016/j.landurbplan.2013.11.002 89. Guneroglu N, Acar C, Dihkan M, Karsli F, Guneroglu A (2013) Green corridors and fragmentation in South Eastern Black Sea coastal landscape. Ocean Coast Manag 83. https://doi. org/10.1016/j.ocecoaman.2013.02.025 90. McPherson EG, Simpson JR (2000) Carbon dioxide reduction through urban forestry: guidelines for professional and volunteer tree planters 91. McPherson EG (2001) Sacramento’s parking lot shading ordinance: environmental and economic costs of compliance. Landsc Urban Plan 57:105–123. https://doi.org/10.1016/ S0169-2046(01)00196-7 92. Vailshery LS, Jaganmohan M, Nagendra H (2013) Effect of street trees on microclimate and air pollution in a tropical city. Urban For Urban Green 12:408–415. https://doi.org/10.1016/ j.ufug.2013.03.002 93. Vaz Monteiro M, Doick KJ, Handley P, Peace A (2016) The impact of greenspace size on the extent of local nocturnal air temperature cooling in London. Urban For Urban Green 16:160–169. https://doi.org/10.1016/j.ufug.2016.02.008 94. Oliveira S, Andrade H, Vaz T (2011) The cooling effect of green spaces as a contribution to the mitigation of urban heat: a case study in Lisbon. Build Environ 46:2186–2194. https:// doi.org/10.1016/j.buildenv.2011.04.034 95. Akbari H, Menon S, Rosenfeld A (2009) Global cooling: increasing world-wide urban albedos to offset CO2 . Clim Change 94:275–286. https://doi.org/10.1007/s10584-008-9515-9 96. Doulos L, Santamouris M, Livada I (2004) Passive cooling of outdoor urban spaces. The role of materials. Sol Energy 77:231–249. https://doi.org/10.1016/j.solener.2004.04.005 97. Kolokotroni M, Kolokotsa DD (2013) Energy and environmental aspects of cool materials. In: Kolokotroni M, Kolokotsa D-D (eds) Advances in the development of cool materials for the built environment. Bentham Science Publishers, pp 231–272 98. Pisello AL, Castaldo VL, Piselli C, Pignatta G, Cotana F (2015) Combined thermal effect of cool roof and cool façade on a prototype building. Energy Procedia 78:1556–1561. https:// doi.org/10.1016/j.egypro.2015.11.205 99. Levinson R, Berdahl P, Akbari H, Miller W, Joedicke I, Reilly J, Suzuki Y, Vondran M (2007) Methods of creating solar-reflective nonwhite surfaces and their application to residential roofing materials. Sol Energy Mater Sol Cells 91:304–314. https://doi.org/10.1016/j.solmat. 2006.06.062

306

M. Dihkan et al.

100. Chen A (2020) Cool colors, cool roofs, part 2. Science beat. https://www2.lbl.gov/ScienceArticles/Archive/sb/Aug-2004/3_coolroofs-2.html. Accessed 17 June 2020 101. Cool Colors, Cool Roofs, part 2. Science Beat. https://www2.lbl.gov/Science-Articles/Arc hive/sb/Aug-2004/3_coolroofs-2.html. Accessed 17 June 2020 102. Gas Pacific, Company Electric (2006) Inclusion of solar reflectance and thermal emittance prescriptive requirements for residential roofs in title 24. Pacific Gas and Electric Company, Sacramento, CA 103. Pisello AL, Castaldo VL, Piselli C, Fabiani C, Cotana F (2017) Thermal performance of coupled cool roof and cool façade: experimental monitoring and analytical optimization procedure. Energy Build 157:35–52. https://doi.org/10.1016/j.enbuild.2017.04.054 104. Sleiman M, Kirchstetter TW, Berdahl P, Gilbert HE, Quelen S, Marlot L, Preble CV, Chen S, Montalbano A, Rosseler O, Akbari H, Levinson R, Destaillats H (2014) Soiling of building envelope surfaces and its effect on solar reflectance—Part II: development of an accelerated aging method for roofing materials. Sol Energy Mater Sol Cells 122:271–281. https://doi.org/ 10.1016/j.solmat.2013.11.028 105. Sleiman M, Chen S, Gilbert HE, Kirchstetter TW, Berdahl P, Bibian E, Bruckman LS, Cremona D, French RH, Gordon DA, Emiliani M, Kable J, Ma L, Martarelli M, Paolini R, Prestia M, Renowden J, Marco Revel G, Rosseler O, Shiao M, Terraneo G, Yang T, Yu L, Zinzi M, Akbari H, Levinson R, Destaillats H (2015) Soiling of building envelope surfaces and its effect on solar reflectance—Part III: Interlaboratory study of an accelerated aging method for roofing materials. Sol Energy Mater Sol Cells 143:581–590. https://doi.org/10.1016/j.solmat. 2015.07.031 106. Qin Y (2015) A review on the development of cool pavements to mitigate urban heat island effect. Renew Sustain Energy Rev 52:445–459. https://doi.org/10.1016/j.rser.2015.07.177 107. Pomerantz M, Akbari H, Chang S-C, Levinson R, Pon B (2003) Examples of cooler reflective streets for urban heat-island mitigation: Portland cement concrete and chip seals. Berkeley, CA 108. Belshe M, Kaloush KE, Golden JS, Mamlouk MS (2008) The urban heat island effect and impact of asphalt rubber friction course overlays on Portland cement concrete pavements in the Phoenix area. GeoCongress 2008. American Society of Civil Engineers, Reston, VA, pp 1032–1040 109. Li H, Harvey JT, Holland TJ, Kayhanian M (2013) The use of reflective and permeable pavements as a potential practice for heat island mitigation and stormwater management. Environ Res Lett 8:015023. https://doi.org/10.1088/1748-9326/8/1/015023 110. Ministry to promote water-retentive pavement for heat island control. https://www.japanfs. org/en/news/archives/news_id025750.html. Accessed 17 June 2020 111. Nabil A, Mardaljevic J (2006) Useful daylight illuminances: a replacement for daylight factors. Energy Build 38:905–913. https://doi.org/10.1016/j.enbuild.2006.03.013 112. Kristl Ž, Krainer A (2001) Energy evaluation of urban structure and dimensioning of building site using iso-shadow method. Sol Energy 70:23–34. https://doi.org/10.1016/S0038-092 X(00)00113-4 113. Johansson E (2006) Influence of urban geometry on outdoor thermal comfort in a hot dry climate: a study in Fez, Morocco. Build Environ 41:1326–1338. https://doi.org/10.1016/j.bui ldenv.2005.05.022 114. Krüger E, Pearlmutter D, Rasia F (2010) Evaluating the impact of canyon geometry and orientation on cooling loads in a high-mass building in a hot dry environment. Appl Energy 87:2068–2078. https://doi.org/10.1016/j.apenergy.2009.11.034 115. Ratti C, Di Sabatino S, Britter R (2006) Urban texture analysis with image processing techniques: winds and dispersion. Theor Appl Climatol 84:77–90. https://doi.org/10.1007/s00 704-005-0146-z 116. Memon RA, Leung DYC, Liu C-H (2010) Effects of building aspect ratio and wind speed on air temperatures in urban-like street canyons. Build Environ 45:176–188. https://doi.org/10. 1016/j.buildenv.2009.05.015

Revisiting Urban Heat Island Effects in Coastal Regions …

307

117. Qin Y (2015) Urban canyon albedo and its implication on the use of reflective cool pavements. Energy Build 96:86–94. https://doi.org/10.1016/j.enbuild.2015.03.005 118. Shashua-Bar L, Hoffman ME (2000) Vegetation as a climatic component in the design of an urban street. Energy Build 31:221–235. https://doi.org/10.1016/S0378-7788(99)00018-3 119. Ali-Toudert F, Mayer H (2007) Effects of asymmetry, galleries, overhanging façades and vegetation on thermal comfort in urban street canyons. Sol Energy 81:742–754. https://doi. org/10.1016/j.solener.2006.10.007 120. Murakawa S, Sekine T, Narita K, Nishina D (1991) Study of the effects of a river on the thermal environment in an urban area. Energy Build 16:993–1001. https://doi.org/10.1016/ 0378-7788(91)90094-J 121. Manteghi G, Limit H Bin, Remaz D (2015) Water bodies an urban microclimate: a review. Mod Appl Sci 9. https://doi.org/10.5539/mas.v9n6p1 122. Steeneveld GJ, Koopmans S, Heusinkveld BG, Theeuwes NE (2014) Refreshing the role of open water surfaces on mitigating the maximum urban heat island effect. Landsc Urban Plan 121:92–96. https://doi.org/10.1016/j.landurbplan.2013.09.001 123. Doick KJ, Peace A, Hutchings TR (2014) The role of one large greenspace in mitigating London’s nocturnal urban heat island. Sci Total Environ 493:662–671. https://doi.org/10. 1016/j.scitotenv.2014.06.048 124. Du S, Xiong Z, Wang Y-C, Guo L (2016) Quantifying the multilevel effects of landscape composition and configuration on land surface temperature. Remote Sens Environ 178:84–92. https://doi.org/10.1016/j.rse.2016.02.063 125. Dihkan M, Karsli F, Guneroglu A, Guneroglu N (2015) Evaluation of surface urban heat island (SUHI) effect on coastal zone: the case of Istanbul Megacity. Ocean Coast Manage 118. https://doi.org/10.1016/j.ocecoaman.2015.03.008