Dust and Health: Challenges and Solutions 3031212088, 9783031212086

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Emerging Contaminants and Associated Treatment Technologies

Ali Al-Dousari Muhammad Zaffar Hashmi   Editors

Dust and Health Challenges and Solutions

Emerging Contaminants and Associated Treatment Technologies Series Editors Muhammad Zaffar Hashmi, Department of Chemistry, COMSATS University Islamabad, Pakistan Vladimir Strezov, Department of Environmental Sciences, Macquarie University Sydney, NSW, Australia

Emerging Contaminants and Associated Treatment Technologies focuses on contaminant matrices (air, land, water, soil, sediment), the nature of pollutants (emerging, well-known, persistent, e-waste, nanomaterials, etc.), health effects (e.g., toxicology, occupational health, infectious diseases, cancer), treatment technologies (bioremediation, sustainable waste management, low cost technologies), and issues related to economic development and policy. The book series includes current, comprehensive texts on critical national and regional environmental issues of emerging contaminants useful to scientists in academia, industry, planners, policy makers and governments from diverse disciplines. The knowledge captured in this series will assist in understanding, maintaining and improving the biosphere in which we live. The scope of the series includes monographs, professional books and graduate textbooks, edited volumes and books devoted to supporting education on environmental pollution at the graduate and post-graduate levels.

Ali Al-Dousari  •  Muhammad Zaffar Hashmi Editors

Dust and Health Challenges and Solutions

Editors Ali Al-Dousari Environment & Life Sciences Research Center Kuwait Institute for Scientific Research Safat, Kuwait

Muhammad Zaffar Hashmi Department of Chemistry COMSATS University Islamabad Islamabad, Pakistan

ISSN 2524-6402     ISSN 2524-6410 (electronic) Emerging Contaminants and Associated Treatment Technologies ISBN 978-3-031-21208-6    ISBN 978-3-031-21209-3 (eBook) https://doi.org/10.1007/978-3-031-21209-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Dust in deserts is driven by wind due to the prolonged drought and degradation in arid regions. Large quantities of dust can be transported thousands of kilometers from its source. Understanding the various effects of aeolian dust on various surfaces, such as solar panels and wind turbines, is a challenge that requires a comprehensive understanding of both the physical and chemical properties of dust. Dust has an impact on health, aviation, agricultural output, and the economy. According to recent aeolian studies, dust is a regional environmental and economic issue. Unfortunately, few researchers in arid places have focused on these challenges as part of the natural system. The book (Dust and Health: Challenges and Solutions) aims to provide a comprehensive view of the various health aspects of dust and aerosols and to provide various strategies and solutions that can help control the negative effects of indoor and outdoor air pollutants on urban areas. Most of the deposited dust in dust traps in desert areas is due to re-suspension in accordance with many recent studies using the depositional features of radionuclides 7Be, 210Pb, 40K, and 137Cs. In the Middle East and China, few studies have shown that the presence of native vegetation and green belts can help minimize the effects of dust and mobile sand on the environment. This is the main reason why the region's green belts and grasslands are considered ideal locations for controlling air pollutants. The existence of rich native vegetation in the region is the only element that can prevent the re-suspension and play a significant role in outdoor and interior air pollution control methods for current and future metropolitan areas. Aside from preventing the air pollutants from entering the environment, the presence of dust and sand in aeolian deposits can also help rehabilitate marine life. These deposits are known to have high levels of nutrients and organic materials, which can act as a healing factor due to their high concentration of nutrients, seeds, and organic materials, which act as a primary healing element.

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This book is the first comprehensive analysis of the various aspects of dust's health effects and health issues covering topics such as PM2.5, PM10, NO2, O3, and SO2. It also provides a variety of epidemiological studies that examine the link between dust and various health conditions. Safat, Kuwait Islamabad, Pakistan

Ali Al-Dousari Muhamad Zaffar Hashmi

Introduction

The global dust belt is composed of various regions, including the Northern Desert of Africa, the Middle East, and the western portion of China (Gobi and Taklimakan deserts), that are known to be the most important sources of sand and dust storms (SDS) (Formenti et  al., 2021; Shi et  al., 2021). The horizontal visibility of these storms in terms of atmospheric aerosols is a key factor that can be used to determine their intensity (Dagsson-Waldhauserova et al., 2014). The availability of data (more than 50  years) on the horizontal visibility of SDS through the Global Basic Observing Network GBON, which is a network of observation stations operated by the World Meteorological Organization (WMO), has been regarded as a justification for the widespread use of these data (Baddock et al., 2014). The SDS events frequency is an indicator of the environmental variations that have taken place, especially in arid and semi-arid regions excluding vegetation coverage differences and the impact of human interferences (Gao et al., 1997; Goudie & Middleton, 2001). This information can help strategic planners identify areas of their operations that are most affected by the dust (Mesbahzadeh et  al., 2020). The effects of various environmental factors on human health are considered to be severe. According to Smith et  al. (1999), around 25–33 million deaths could be caused by various diseases. The organic and inorganic pollutants of dust can spread through two different pathways. These particles are composed of various elements. They are divided into two clusters: trace and major elements consisting of 16 elements (Chabukdhara & Nema, 2013). These elements are copper (Cu), manganese (Mn), mercury (Hg), zinc (Zn), chromium (Cr), vanadium (V), lead (Pb), arsenic (As), nickel (Ni), cadmium (Cd), and cobalt (Co) (Li et al., 2018). These heavy metals are known to cause negative effects on the environment due to their unfriendly and noxious relation to the atmosphere (Klavinš et al., 2000). They can also lead to various diseases, such as respiratory and cardiac issues. Due to their non-biodegradable properties, these heavy metals cannot be properly recognized by body tissues and skin cells and can lead to death and various contagious and infectious diseases (Zheng et al., 2010; Wu et al., 2010).

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Road dirt is made up of tiny particles that are produced by various mechanical processes, such as crushing, squeezing, and the impact of mining handling, explosion, and decrepitating of organic and inorganic materials like rock, ore, and metals (Khan & Strand, 2018). It also contains various organic and non-organic compounds that are harmful to human health and contribute significantly to air pollution (Li et al., 2018). Road dust particles are produced in varying numbers depending on the various factors that affect their composition. They can be found in different locations and include the number of people using the roads, the number of cars, and the locations of parks and business establishments (Mohmand et al., 2015). Dust indoors is made up of a diverse combination of biological and inorganic components (Kurt-­ Karakus, 2012). The various combination of gases, particles, and vapors come from different sources, such as inside and outside sources (Morawska & He, 2014). Outside dust contributes to the accumulation of dust in the building. Outside dust is believed to account for over 85% of indoor dust (Kurt-Karakus, 2012). Aside from directly affecting the Earth’s atmosphere, dust particles can also have negative effects on human health. These include respiratory and lung problems, which can often be triggered by prolonged exposure and inhalation of dust particles (Pope & Dockery, 2006; Hoek et al., 2013). Moreover, dust particles can also be used as carriers for various toxic and allergic substances. Dust particles are also known to transport various toxic substances and allergens, which are either contained in (or adsorbed to) dust particles, for instance, trace elements (such as Cu, Co, Mn, Ni, Pb, P, Zn, Ti, Ba, and Sr). Eventually, once dust particles are deposited on dry or wet depositions, they may contaminate soil and water supplies as well as food sources. The presence of vegetation can help prevent soil particles from being transported and lifted by the wind. It can also reduce the wind speed and the erosivity of soil, forming a mechanical barrier to stop soil particles from being lifted and transported. The threshold for wind speed is expected to increase exponentially with higher vegetation density and coverage (Shi et al., 2004). Despite the uncertainty surrounding the effects of human activities on the SDS, it is believed that the impacts of these activities will rise in the coming decades. The effects of human activities on the development and occurrence of SDS can vary depending on the ecosystem’s characteristics, for instance, the role of precipitation in regulating the soil moisture content influences particle adhesion and the growth of vegetation (Gao et al., 2012). The World Meteorological Organization (WMO) defines SDS as powerful and turbulent particles that can lift and disperse at great heights and reduce visibility at eye level (1.8 m) to less than 1000 m (McTainsh & Pitblado, 1987). Sand and dust storms (SDS) are categorized into three categories in reference to visibility, and dimensions, namely small, intermediate, and extensive (Al-Dousari et al., 2022). Many studies have shown a strong correlation between SDS triggering asthma attacks in children and adults. These effects were observed in different countries such as Saudi Arabia, Kuwait, Australia, Greece, and the USA (Meo et al., 2013; Thalib & Al-Taiar, 2012; Samoli et al., 2011; Grineski et al., 2011; Rutherford et al., 1999). In addition, these effects were also observed in other breathing conditions

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such as chronic obstructive pulmonary disease (COPD) exacerbations and allergic rhinitis (Tam et al., 2012; Meltzer et al., 2012). Hashizume et al. (2020) identified a 9% increase in respiratory disease hospital admissions 3 days after an Asian dust episode, as well as a 14.5 and 8.5% increase in asthma and pneumonia admissions during dust episodes. The presence of dust particles in the environment can cause various pathological pathways to develop. Possible pathways include direct physical injury to respiratory epithelial cells by dust particles, which causes an inflammatory response and increased oxidative stress, resulting in genetic damage and poor respiratory health outcomes (Meng and Zhang, 2007). Recent studies have revealed that dust and aerosols can have harmful effects on health, such as increased mortality rates and hospitalizations (Geravandi et  al., 2017; Neisi et  al., 2017). Particulate matter is known to cause inflammation and respiratory disease, and it can also lead to pulmonary transformation. Particulate matter is associated with respiratory mortality and morbidity by generating oxidative and inflammation stress (Hashizume et  al., 2020; Hasunuma et  al., 2021). Large-scale soil losses can affect the properties of the desert, which can hinder the succession and recolonization of specific organisms (Bowker et al., 2006; Pointing & Belnap, 2014). Climate change is predicted to increase the frequency and magnitude of dust events that can occur in the desert, which could also alter the properties of the desert (Neff et al., 2008; Prospero et al., 2012). The environment has changed significantly since the Industrial Revolution occurred in the eighteenth century which was supposed to improve the standard of living, but it has resulted in various environmental problems (Patnaik, 2018). One of these is air pollution, which has been taken as a major consideration. It can be classified as a combination of biological and chemical compounds that can cause damage to the atmosphere (Florentina & Ion, 2011). Due to the absence of a comprehensive book on the health issues related to dust, this book serves as a starting point and a building block to arrange for a full understanding of the various health aspects associated or related to dust.

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Meo, S. A., Al-Kheraiji, M. F. A., Alfaraj, Z. F., Alwehaibi, N. A., & Aldereihim, A. A. (2013). Respiratory and general health complaints in subjects exposed to sandstorm at Riyadh, Saudi Arabia. Pakistan Journal of Medical Sciences Quarterly, 29(2), 642–646. Mesbahzadeh, T., Salajeghe, A., Sardoo, F.  S., et  al. (2020). Climatology of dust days in the Central Plateau of Iran. Natural Hazards, 104, 1801–1817. https://doi.org/10.1007/ s11069-­020-­04248-­6 Mohmand, J., Eqani, S.  A. M.  A. S., Fasola, M., Alamdar, A., Mustafa, I., Ali, N., & Shen, H. (2015). Human exposure to toxic metals via contaminated dust: Bio-accumulation trends and their potential risk estimation. Chemosphere, 132, 142–151. Morawska, L., & He, C. (2014). Indoor particles, combustion products and fibres. In Indoor air pollution (pp. 37–68). Springer. Neff, J.  C., Ballantyne, A.  P., & Farmer, G.  L. (2008). Increasing eolian dust deposition in the western United States linked to human activity. Nature Geoscience, 1, 189–195. Neisi, A., Vosoughi Niri, M., Idani, E., et al. (2017). Comparison of normal and dusty day impacts on fractional exhaled nitric oxide and lung function in healthy children in Ahvaz, Iran. Environmental Science and Pollution Research, 24(13), 12360–12371. https://doi.org/10.1007/ s11356-­017-­8853-­4 Patnaik, R. (2018). Impact of industrialization on environment and sustainable solutions  – Reflections from a south Indian region. IOP Conference Series: Earth and Environmental Science, 120, 012016. https://doi.org/10.1088/1755-­1315/120/1/012016 Pointing, S. B., & Belnap, J. (2014). Disturbance to desert soil ecosystems contributes to dust-­ mediated impacts at regional scales. Biodiversity and Conservation, 24, 1659–1667. Pope, C. A., III, & Dockery, D. W. (2006). Health effects of fine particulate air pollution: Lines that connect. Journal of the Air & Waste Management Association, 56, 709–742. Prospero, J. M., Bullard, J. E., & Hodgkins, R. (2012). High-latitude dust over the North Atlantic: Inputs from Icelandic proglacial dust storms. Science, 335, 1078–1082. Rutherford, S., Clark, E., McTainsh, G., Simpson, R., & Mitchell, C. (1999). Characteristics of rural dust events shown to impact on asthma severity in Brisbane, Australia. International Journal of Biometeorology, 42(4), 217–225. Samoli, E., Nastos, P. T., Paliatsos, A. G., Katsouyanni, K., & Priftis, K. N. (2011). Acute effects of air pollution on pediatric asthma exacerbation: Evidence of association and effect modification. Environmental Research, 111(3), 418–424. Shi, P., Yan, P., Yuan, Y., & Nearing, M. A. (2004). Wind erosion research in China: Past, present and future. Progress in Physical Geography, 28(3), 366–386. Shi, L., Zhang, J., Yao, F., Zhang, D., & Guo, H. (2021). Drivers to dust emissions over dust belt from 1980 to 2018 and their variation in two global warming phases. Science of the Total Environment, 767, 144860. Smith, K. R., Corvalán, C. F., & Kjellström, T. (1999). How much global ill health is attributable to environmental factors? Epidemiology, 10(5), 573–584. Tam, W. W. S., Wong, T. W., Wong, A. H. S., & Hui, D. S. C. (2012). Effect of dust storm events on daily emergency admissions for respiratory diseases. Respirology, 17(1), 143–148. Thalib, L., & Al-Taiar, A. (2012). Dust storms and the risk of asthma admissions to hospitals in Kuwait. Science of the Total Environment, 433, 347–351. Wu, G., Kang, H., Zhang, X., Shao, H., Chu, L., & Ruan, C. (2010). A critical review on the bio-­ removal of hazardous heavy metals from contaminated soils: Issues, progress, eco-­ environmental concerns and opportunities. Journal of Hazardous Materials, 174(1–3), 1–8. Zheng, N., Liu, J., Wang, Q., & Liang, Z. (2010). Health risk assessment of heavy metal exposure to street dust in the zinc smelting district, Northeast of China. Science of the Total Environment, 408(4), 726–733.

Acknowledgments

Special thanks to Higher Education Commission of Pakistan NRPU projects 7958 and 7964. Further thanks to Pakistan Science Foundation project PSF/Res/CP/C-­ CUI/Envr (151) and Pakistan Academy of Sciences projects.

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1 Dust  Effects and Human Health������������������������������������������������������������    1 Tariq Ali, Syed Ali Mustjab Akber Shah Eqani, Muhammad Sadiq, Tassawur Khanam, Irfan Ullah, Siwatt Pongpiachan, Muhammad Faseeh Ullah, Umar Farooq, and Muhammad Zafar Hashmi 2 Quantification  of the Inhaled Deposited Dose During Sand and Dust Storms��������������������������������������������������������������������������������������   17 Tareq Hussein and Jakob Löndahl 3 Sources,  Drivers, and Impacts of Sand and Dust Storms: A Global View������������������������������������������������������������������������������������������   31 Ali Darvishi Boloorani, Masoud Soleimani, Ramin Papi, Najmeh Neysani Samany, Pari Teymouri, and Zahra Soleimani 4 Exposure  of Dust Storms and Air Pollution (PM10, PM2.5) and Associated Health Risk in the Arid Region������������������������������������   51 Ali Al-Hemoud 5 Dust  and Eye Inflictions��������������������������������������������������������������������������   79 Muhammad Zaffar Hashmi, Qinza Qadeer, Umar Farooq, and Siwatt Pongpiachan 6 Epidemiology  of Dust Effects: Review and Challenges������������������������   93 Barrak Alahmad, Haitham Khraishah, Souzana Achilleos, and Petros Koutrakis 7 Dust  Storm and Infant Health����������������������������������������������������������������  113 Parya Broomandi, Kairat Davletov, Jong Ryeol Kim, and Ferhat Karaca 8 Dust  and Microorganisms: Their Interactions and Health Effects ����  137 Jun Noda, Kozo Morimoto, Satoshi Mitarai, and Teruya Maki

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9 Microbiology  of Sand and Dust Storms and the Effects on Human Health in Iran and Other Persian Gulf Countries������������  157 Ali Darvishi Boloorani, Zahra Soleimani, Pari Teymouri, Najmeh Neysani Samany, Masoud Soleimani, and Ramin Papi 10 Assessment  of the Airborne Microbes in the Dust of the Arabian Gulf Region ��������������������������������������������������������������������  187 K. Y. Mataqi and B. Mathew 11 Pollen  Prevalence and Health Impact in Kuwait����������������������������������  215 M. I. Ibrahim, A. Al-Hemoud, N. Al-Dousari, and M. Ahmed 12 Dust  and Health: Control Methods and Strategies������������������������������  231 Ali Al-Dousari, Modi Ahmed, Abdulaziz Alshareeda, Noor Al-Dousari, Salem Alateeqi, and Abeer Alsaleh Index������������������������������������������������������������������������������������������������������������������  247

Chapter 1

Dust Effects and Human Health Tariq Ali, Syed Ali Mustjab Akber Shah Eqani, Muhammad Sadiq, Tassawur Khanam, Irfan Ullah, Siwatt Pongpiachan, Muhammad Faseeh Ullah, Umar Farooq, and Muhammad Zafar Hashmi

Abstract  The societal evolution not only provides the progress of mankind, but also brings the serious environmental pollution. Industrial revolution changes environmental conditions, and environmental conditions are closely related with human health. Poor environmental conditions cause serious human health problems such as skin diseases, respiratory diseases, hearth diseases, stomach and lungs cancer, etc., which can lead to death. Dust is made of fine particles of solid matter, these dust particles are different pollutants, which come from both natural and anthropogenic sources and show different results in atmosphere and then in human health simultaneously. Dust comes from natural (desert storms) and anthropogenic (fossil fuel combustion, grinding, redecoration, etc.) sources; it can hold on in the air from hours to days. Human exposed by dust in three ways ingesting, resuspended dust particle inhalation, and dermal absorption. Different research work performed in different areas to examine the dust relation with human health, on the bases of published data on dust and human health I concluded that dust is the major cause of almost all serious health problems (skin, eye, heart, stomach and respiratory diseases, etc.), which can lead to death. Heavy metals present in the dust increase its toxicity due their non-biodegradability and carcinogenic property. Human health risk, which induced by dust, depends on exposure area, dust type, and dust doze. Keywords  Dust effects · Human health T. Ali · S. A. M. A. S. Eqani · M. Sadiq · T. Khanam · I. Ullah · M. F. Ullah Department of Biosciences, COMSATS University, Islamabad, Pakistan S. Pongpiachan Center for Research & Development of Disaster Prevention & Management, National Institute of Development Administration, Bangkok, Thailand U. Farooq Department of Chemistry, COMSATS University Islamabad, Abbottabad, Pakistan M. Z. Hashmi (*) Department of Chemistry, COMSATS University, Islamabad, Pakistan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Al-Dousari, M. Z. Hashmi (eds.), Dust and Health, Emerging Contaminants and Associated Treatment Technologies, https://doi.org/10.1007/978-3-031-21209-3_1

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T. Ali et al.

1 Introduction Because of the fast-enhancing factors of urbanization and industrialization, different dust pollutants have increased poor atmosphere in crowded areas (Amato et al., 2010). Along with related statement, environment always closely related with human health. In negative point of view, all environmental factors show drastic impact to human health and are responsible for lot of diseases approximately about 25–33% and 09 million deaths noted (Smith et al., 1999). In the economic world, it also caused many losses in the year 2015. The Lancet Commission given reports on health- and pollution-related issues and in result many dangerous impacts are created, which lead to the death in huge level (Entwistle et al., 2019). Dust are sub-particles of solid matter. Actually, these dust particles are different pollutants that come from different sources and show different results in atmosphere and then in human health, simultaneously. It is wonderful research that some of them are made from skin cells is about 50% (van Bronswijk, 1981). All these pollutants (dust particles comes from different sources) exist everywhere in the environment. And without care, these are the enemies of respiratory and cardiac system of humans. (Hess-Kosa, 2002). Dust particles with inorganic and organic pollutant characteristics can spread through two separate pathways and pollute the environment. These particles consist of 16 elements that are divided into two groups: elements trace and major (Chabukdhara & Nema, 2013). These elements are manganese (Mn), zinc (Zn), mercury (Hg), chromium (Cr), copper (Cu), lead (Pb), vanadium (V), nickel (Ni), arsenic (As), cobalt (Co), and cadmium (Cd) (Li et al., 2018). These strong metals are very serious pollutants because of their noxious, constancy, and un-friendly relation with the atmosphere (Klavinš et  al., 2000). These heavy metals are non-­ biodegradable and not recognized by skin cells and body tissues as well, which lead to the different contagious and infectious diseases that disrupt different parts of the human body, i.e. heart, lungs, and lead to the death (Zheng et  al., 2010; Wu et al., 2010). Generally, dust particles having less than 10 μm in diameter which can penetrate in respiratory system and less than 2.5 μm (PM2.5, inhalable particles that infiltrate in the alveoli of lung), and then less than 100 nm are small in size but cover a very large area due to their existence and strong effect (Brunekreef & Holgate, 2002). Thus, these particles are penetrated in the bronchial ways and disrupt respiratory tract and sometimes paralyze some functional organs of the body, which cause different serious health problems as asthma and pneumonia that further rise the cardiovascular problems (Sandstrom & Forsberg, 2008). Road dirt is made up of compact particles formed by any mechanical material processing, such as squeezing, grinding, fast impact, handling, explosion, and decrepitation of organic and inorganic materials like rock, ore, and metals (Khan & Strand, 2018). It also contains some micro elements and organic materials that are detrimental to human health, and it contributes significantly to air pollution (Li

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et al., 2018). The amounts of components in road dust vary dramatically relay on the use of land, population multiplicity, human activities, transport position, numbers of automobiles, business regions, and park sites (Mohmand et al., 2015). Heavy metals, dangerous air pollutants (NO2, SO2), poisonous organic compounds, and other poisonous particles can be found in high concentrations in road dust in industrial locations (Pereira et al., 2007). Individuals who work in industrial complexes (ICs) and those persons who dwell near ICs are thus facing industrial pollution (Shi et al., 2008). Because of the lower particle size and inherent mobility of urban road dust in windy weather conditions, individuals are at danger of both direct and indirect exposure; direct exposure from dust can occur through intake of air and ingestion (Wei & Yang, 2010). Indoor dust is made up of a diverse mixture of biological and inorganic particles (Kurt-Karakus, 2012). It is a complex combination of different vapors, particles, and gases that come from both inside and outside sources (Morawska & He, 2014). Outside dust is thought to account for almost 85% of indoor dust (Kurt-Karakus, 2012) to name a few: crustal minerals, road dust, construction operations, fuel (coal, petroleum) combustion, industrial pollution, and automotive traffic. Furthermore, household activities, such as redecorating or heating, play a significant role in the entry of contaminates into inside sources (Morawska & He, 2014). Home dust is considered as a significant source of trace element exposure, especially for youngsters (Lisiewicz et  al., 2000), by inadvertent ingesting, resuspended dust particle inhalation, and dermal absorption (Zota et al., 2011). Dust that is not released from definable point sources, such as industrial smokestacks, is referred to as fugitive dust. Open fields, streets, and storage piles are all potential sources (Khan & Strand, 2018). The considerations highlighted in the phrasing of the problem, such as exposure doze and risk identification, are reflected in the exposure assessment. Chemical entry into the body of human can be split into three pathways (oral, respiratory, and cutaneous). As a result, the measurement of exposure doze can be separated into three parts; skin contact, inhalation, and ingestion (Fitzpatrick et  al., 2017). Inhalation appears to play a significantly less role in the overall dose absorbed by children at home than ingestion and cutaneous absorption (Kurt-Karakus, 2012). On a daily basis, around two billion people are exposed to microscopic particles carried by the wind (Sprigg, 2016). The majority of studies on health-related particulate matter (PM) research to date have focused on the effects of anthropogenically created PM (such as PM from combustion engines) (Ezzati, 2005), but just a little amount of research has been done on the effects of PM emanating from dust storms (naturally occurring pollutants). Huge amounts of dust are transported throughout the world by air from the nine major desert sources (Tanaka & Chiba, 2006). Comprehensive studies evaluated that worldwide dust emissions vary by a factor of somewhat more than two. Despite the fact that extreme values range from 1 × 103 Tg year−1 to 3 × 103 Tg year−1 being recorded for the previous two decades (Miller et al., 2004). Estimates of other source locations’ contributions vary by study and are more difficult to come

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by, principally because each source area has its own cyclical cycle (Engelstaedter et al., 2006). However, all studies looking at these issues concur that the principal source is North Africa (over 50%) (Ginoux et al., 2004). Expanded surrounding dust concentration in the air is temporally connected with worsening in pleasantness of the air and the substantial probability of detrimental effects on human health in areas of the world in the path of dust-loaded wind (Engelstaedter et al., 2006). Dust particles can linger in the air for hours or even days (Goudie, 2014).

2 Human Health Risks High-dust concentrations have been related to adverse human health effects. The aim of this review is to realize further about the global correlation of dust and human health. Papers that look into the possible link between dust and health. Dust is associated with multitude of adverse health impacts, including respiratory, cardiovascular, and cardiopulmonary illnesses. It demonstrates a disparity in between locations more exposed to dust and the areas where health impacts are most examined.

2.1 Human Health Risks Assessment Risk assessment for human health is the process of determining the nature and likelihood of unfavorable health impact in individuals who may now or in the future be exposed to toxins in degraded environmental media (Khan & Strand, 2018). In a human health problem assessment, the total amount of time spent listening to heavy metal is calculated. The following are the four major routes; exposure to heavy metals in soil and dust by humans: (a) soil and substrate dust particles are directly ingested (Ding); (b) soil and suspended dust particles are inhaled through the mouth and nose (Dinh); (c) cutaneous absorption of heavy metals in particles adhered to exposed skin (Dinh); and (d) cutaneous absorption of heavy metals in particles adhered (Ddermal). Among the metals tested in this study, only Ni is cancer causing. These metals, on the other hand, were identified in small levels and with varying degrees of contamination in urban soil and road dust. As a result, only non-carcinogenic metals (Mn, Zn, Pb, Ni, and Cu Zn) were examined in industrial area soil and road dust. The receptors for exposure risk assessment were recognized as babies, children, teenagers, and adults. The USEPA (United States Environmental Protection Agency) and the OME (Ontario Ministry of the Environment) in Canada used equations to establish receptor settings and calculate heavy metal intake across exposure pathways.

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2.2 Dust Effects on Human Health The rapid rise of urbanization and industrialization has increased the risk of dust particle pollution in road regions (Amato et al., 2010). Concerns regarding the effect of the environment on human health have grown significantly in recent years. Environmental risk factors, according to Smith et al. (1999), account for 25 to 33% of the worldwide load of disease. Heavy metal exposure can cause organelles to collapse and the immune system to weaken, leaving the body prone to a variety of diseases. As a result of their early childhood stage and child-specific activities, newborns and small kids are at more risk to heavy metal exposure and toxicity than adults. The first few years of life are when the brain grows and differentiates the most, and when children’s gastrointestinal absorption of pollutants is higher than adults (around 50 versus 8%). Babies may inhale greater amounts of dust because of their increased hand to mouth activity (several dust-contaminated things were touched and mouthed) and dragging on the ground surface, and breathing rates per unit of BM (body mass) that are higher than mature ones (Zota et  al., 2011). Furthermore, children’s resistant to toxins is lower than that of mature one, heightening the risk of negative health consequences (Kurt-Karakus, 2012). The health impacts of dust can be split into two distinct categories: short- and long-term impacts. Human health problems that happened during or soon after a dust storm are characterized as short-term effects, whereas long-term consequences, on the other hand, are described as human health risks that occur after repeated dust storm episodes over a lengthy period of time (Aghababaeian et  al., 2021). People who have had diabetes, hypertension, cerebrovascular disease, or respiratory disorders are more proven to develop a disease (Kashima et al., 2017).

2.3 Effects on Immune System Immune system is made up of a complex web of cells and tissues, and is always on the lookout for intruders, it expanded to the complete body and consists different types of proteins, cells, tissues, and organs. Essentially, it can differentiate our body tissue from foreign tissue or self from non-self. Our survival is depended on immune system; if the immune system is weak or affected, pathogens can easily attack and cause different diseases. Immune system protects us and keeps us healthy as we float by a sea of infectious agents (pathogens) (Tim Newman et al., 2018). Human respiratory and immunological systems are both harmed by dust. Desert dust is often believed to have the ability to (1) cause direct physical harm-ness to deep respiratory system (effects the walls of alveoli and bronchial epithelial cells); (2) influence oxidative stress and the release of pro-inflammatory cytokines in respiratory epithelial cells; (3) harm DNA molecule (the major contributors to DNA damage may be organic chemicals and the insoluble particle-core); and (4) cause a chemical reaction in the respiratory epithelial cells (Meng et al., 2007).

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2.4 Effects on Nervous System The nervous system is a web of nerves and cells that connects the brain to the rest of the body. It controls how the body reacts to internal and external stimuli. Topographically, the nervous system is separated into two sections: the central nervous system (CNS) and the peripheral nervous system (PNS) (Bazira et al., 2021). Organic dust exposure in the workplace has been linked to malignancies in the CNS in parents (Patel et al., 2020). Organic dust exposure in the home has been linked to a rise incidence of CNS malignancies in children. Grain handlers, cooks, textile workers, and carpenters are all exposed to high levels of organic dust from animal, plant, or microbial sources, with varying outcomes for juvenile leukemia and CNS cancers (Wigle et  al., 2009). Characterizing the neurotoxicity of environmental micro- and nanoparticles is critical for creating a strategy for analyzing the potential for planetary and intergalactic dust to be toxic (Krisanova et al., 2013). The ability to absorb dangerous compounds from the environment, as well as the composition, shape, charge, surface properties, aggregation state, and composition, among other aspects, determines particle neurotoxicity (Borisova et al., 2018).

2.5 Respiratory Diseases The respiratory system is a biological system made up of organs and structures that work together to exchange gases. Air intake by nose or mouth and transmitted down the wind pipe transports small dust particles to the lungs. Deposition of particle sizes is routinely correlated with disorders in three respiratory system regions (nasopharyngeal region, tracheobronchial region, and bottom lung tissue region), which are separated into three sections (nasopharyngeal region, tracheobronchial region, and bottom lung tissue region) (Roy et al., 2004). The respiratory system of humans is immediately affected by dust from air pollution. Respirable dust is defined as dust particles smaller than 10 μm that can pass through the human lung and cause major health problems (Batsungneon et al., 2011). Big particles are frequently caught in the top portion of respiratory tract (nasopharyngeal area, tracheobronchial region), whereas small particles may penetrate lower lung tissue (Zhang et al., 2016). Wood dust is believed to be inhaled by at least two million individuals every day all over the world. Wood dust exposure, in particular, declines the pulmonary functions and increases the incidence of respiratory disorders. Wood includes a variety of microorganisms (including fungus), poisons, and chemical substances, all of which can have a negative impact on human health. These compounds are known to produce irritation on the mouth and throat, chest tightness, irritating dermatitis, urticaria, alveolitis, and impairment of pulmonary functioning (Osman et al., 2009).

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2.6 Lungs Even in the twenty-first century, millions of individuals operate in a dusty environment on a regular basis. They are exposed to a wide range of health risks, including smoke, gases, and dust, all of which are risk factors for occupational illness development. The cement business contributes to the growth of structures in this advanced and contemporary world, but it gives rise to dust in the process. Lung function disability, long-term obstructive pulmonary disease, restrictive lung disease, pneumoconiosis, and lung cancer are all caused by cement dust (Meo et al., 2004).

2.7 Asthma Asthma is persistent irritant respiratory track disease marked by coughing, wheezing, shortness of breath, reversible airway constriction, and hyper responsiveness of the airways (Highashi et al., 2014). In the last few years, the number of persons with persistent cough without wheezing or shortness of breath has raised, as has the pervasiveness of asthma among adults in Japan. In Japan, the majority of patients in this group have cough alternative asthma (Fukutomi et  al., 2010). Cough variant asthma is a forerunner to asthma, non-asthmatic is called atopic, bronchodilator-­ resistant long-term cough linked with atopy. Chronic cough can be caused by super sensitivity to environmental components like chemicals, cold air, and smoking (Ternesten-Hasseus et al., 2011; Matsumoto et al., 2012). Kosa is a pollutant to the environment that can produce cough in adult individuals with respiratory diseases (asthma, cough variant asthma, or atopic cough). Our initial findings recognize that there are significantly more cough-positive individuals during kosa times than during non-kosa seasons (Highashi et al., 2014).

2.8 Stomach Diseases The stomach located on the left side of the upper abdomen absorbs food from the esophagus and then produces acid and enzymes to break down the meal. Environmental variables are thought to involve in stomach cancer etiology. The presence of stomach cancer is linked to low-socioeconomic position, dietary components such as salted fish and nitrates, and high amounts of ionizing radiation exposure (Nomura et al., 1982). Numerous epidemiologic studies have discovered a rise incidence of stomach cancer in workers who work in a dusty workplace. Coal miners, asbestos insulation workers, rubber workers, steel polishers, and other abrasive workers had higher risks of stomach cancer mortality (Ames et al., 1983). A study of occupational mortality in Washington state discovered an increased incidence of stomach cancer in males who worked with various forms of dust (e.g.,

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carpenters, cabinetmakers, brick masons, plasterers, lathers, and metal molders). Dust causes stomach cancer; however, the mechanism through which dust causes cancer is unknown (Wright et al., 1988).

2.9 Cardiovascular Diseases Many of the trace elements found in dust are harmful at high quantities and may act as disease initiators or promoters, like cancer and heart problems (Tong et al., 1998). In epidemiological research, airborne particles are linked to negative health consequences and lead to excess mortality. High levels of ultrafine (0.1 m diameter) particles in the environment may promote alveolar inflammation and, as a result, worsening of pre-existing cardiovascular disorders (Penttinen et al., 2001). Due to industrialization and cars, the ASD-PM (Asian Sand Dust-Particulate Matter) aerosol transports massive concentration of wind-eroded soil particles with high-metal contents. Local inflammatory reactions are triggered by pro-inflammatory and cytotoxic cytokines, which result in a high incidence of cardiovascular disease (Lee et al., 2019).

2.10 Eye Diseases The eyes are directly exposed to pollutants during a storm. The eyes are specifically fragile and due to the dense neuronal innervation on the ocular surface, it is vulnerable to direct contact with air pollutants (Tuominen et al., 2003). Eye redness, discomfort, and a foreign body sensation are all indications of PM exposure. Conjunctivitis, dry eye, and blepharitis are common eye illnesses caused by PM exposure. PM is thought to cause and exacerbate ocular surface illnesses, as well as damage to the ocular surface due to oxidative stress, toxicity, and immunological and inflammatory responses (SY Choi et al., 2019). Conjunctivitis is the most common eye-related disorders that are strongly linked to air pollution exposure. Eye irritation and itching are common symptoms of conjunctivitis; conjunctivitis is a condition in which the conjunctiva becomes irritated or inflamed. Conjunctivitis has been linked to degree of outside air toxicant or airborne allergens like fungal spores and pollen grains in the past studies (Novaes et al., 2007; Bourcier et al., 2003). Fume from biomass burning (e.g., charcoal, wood, and dung) has also been examined as a potential cause of conjunctivitis and other eye diseases (Ezzati et  al., 2000). When compared to amounts of these and allergens toxicant over long-time period, ADS occurrences are defined by massive amount of air toxicants and allergens. Conjunctivitis is very likely to develop as an outcome of exposure during ADS episodes (CJ Ma et al., 2001). There have been few epidemiological studies on the effects of ADS on eye health, and no significant findings have been reached (Yang et al., 2006).

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2.11 Skin Diseases Human skin is rapidly growing organ; epidermal keratinocytes involve in the formation of a basic protective barrier against environmental contaminants. When epidermal keratinocytes in the basal layer of the epidermis move upward and finally evolve into cornified cells in the epidermal stratum corneum, the epidermal permeability barrier is established (Elias et al., 2002; Feingold et al., 2007). The disturbance of the epidermal permeability barrier is linked to atopic dermatitis, which is likely due to raised penetration of environmental contaminants through the stratum corneum, resulting provocative skin situations. Human epidermal keratinocytes (HEK) secrete a variety of cytokines and autacoids that promote inflammation in response to toxic chemicals or organic substances, which can undermine the epidermal permeability barrier’s integrity (Monteiro-Riviere et al., 2004). Atopic dermatitis, for example, is characterized by intense itching and type 2 immunity-linked super sensitivity to widely disseminated allergens, such as those produced from house dust mites (HDM) (Serhan et al., 2019). Fine dust particles (FDP) have an impact on the cause of inflammatory skin diseases such as acne, psoriasis, and atopic dermatitis, as well as skin ageing, which is manifested by wrinkles and melanogenesis (Krutmann et al., 2014; Yang et al., 2014). FDP-induced skin ageing is connected to increased oxidative stress, which is mediated mostly by ROS and impairs skin functions such as DNA repair and pathogen entry prevention (Wang et al., 2017).

2.12 Maternity and Reproduction Although it has been established that desert particle pollution has a deleterious effect on pregnancy (Stieb et  al., 2012), the implications of dust events on pregnancy have received little attention. Single study looked at the effects of Saharan dust events on pregnancy issues (preeclampsia and bacteriuria) and results (birth weight and gestational age at delivery) in a cohort of births in Barcelona, Spain (Dadvand et al., 2011). The authors discovered no statistically significant negative effects of Saharan dust events on the complexities and results of their study’s pregnant women, However, there is a statistically significant increase in gestational age at delivery when compared to the number of episodic days in the third trimester and throughout the pregnancy (0.8 and 0.5 days, respectively). In animal investigation, natural dust, as well as Asian and Arizona dust, was shown to have a deleterious impact on the reproductive system of male mice, with sperm production on daily basis significantly reduced but no significant changes in blood testosterone concentrations (Yoshida et al., 2009).

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2.13 Carcinogenic Effects Wood dust exposure has been linked with different health issues, including lung diseases and other ailments (Acheson et al., 1968). Wood dust has been related to different diseases, including cancer (Barcenas et al., 2005). As a result, the IARC (International Agency for Research on Cancer) recognized wood dust as a category I human cancer-causing material in 1995 (IRAC 1995). Exposure to wood dust varies greatly among communities, and there is no evidence that it is neither particular to a single industry or occupation nor to a single malignancy. Exposure to wood dust, on the other hand, has been related to adenocarcinoma (ADCN). Currently, wood dust exposure has a significant effect on occupational health, with prevalence rates ranging from 10 to 15%. While occupational exposure to wood dust may increase the mortality rate for some employees, it may also have an effect on the general population’s mortality rate. As a result of the health and social consequences of wood exposure, it is critical to assess and provide appropriate professional and occupational protection (Chamorro et al., 2012a, b). Elements of dust such as lead (Pb), arsenic (As), nickel (Ni), cadmium (Cd), and chromium (Cr), all of which are known to cause cancer, have been found in big amount in dust and may increase the chances of cancer in both adults and children (Kim et al., 2015). Dust contains As, Cr (VI), Cd, Ni, and Pb, which are carcinogenic to both adults and children (Li et al., 2018).

2.14 Lung Cancer Around the world, lung cancer is the major cause of cancer mortality (17.8%) and overall cancer incidence (12.8%) (Smith et al., 2000). In the United States, in 2004, 93,110 males and 80,660 females are predicted to be diagnosed with lung cancer, accounting for 13% male and 12% female of all newly recognized cancer patients, respectively. Lung cancer is anticipated to kill 91,930 males (32% of deaths caused by cancer) and 68,510 females (25% all cancer-related deaths) in the US in 2004, it makes the major factor of cancer-related death (Jemal et al., 2004). Tobacco usage is linked to 85% of all lung cancer cases. Because cancer affects just a small fraction of long-term smokers, it has been suggested that other factors play a role in tobacco carcinogenesis. Individual differences in the potential to metabolize tobacco carcinogens and repair DNA damage after genotoxic exposure, for example, can be examined through different tests as maker of DNA repair pathways (Amos et al., 1999). The condition has been associated to a variety of dietary components, as well as environmental and occupational exposures (Haus et al., 2001). Wood dust is one source of potential exposure. Softwood (conifers) makes up about two-thirds of the wood used in industry, while hardwood (deciduous trees) makes up the remaining one-third, with the majority of the harvested hardwood being used as fuel. In 1990, the United States generated 24% of the world’s sawed wood (IARC 1995). Between

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1980 and 1983, the National Occupational Exposure Survey (NOES) projected that the lumber and wood products industry employed 2.1% of the total workforce in the United States (de la Hoz et al., 1997).

3 Conclusion In this busy life, we are exposed by dust all the time everywhere. On the basis of published data on dust, I concluded that dust is the serious problem of the world nowadays and also in future. Dust contaminates the environment which cause health problems for human, almost all the human health problems are related to dust exposure directly or indirectly. The peoples of urban and industrial areas are at high risk as compare to rural and agricultural area peoples due to more toxic environmental pollutants. Childs are more vulnerable for dust exposure than adults due their invading ability and more hand mouth touching activities. Health problems caused by dust depend on dust area, type, and doze.

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Chapter 2

Quantification of the Inhaled Deposited Dose During Sand and Dust Storms Tareq Hussein and Jakob Löndahl

Abstract  Exposure to dust particles during a sand and dust storm (SDS) can be harmful for health. The first step to understand the health effects of such exposure is to quantify the amount of deposited dust particles in the respiratory tracts. In this book chapter, a brief summary is presented about: (1) the outbreak mechanisms dust particles and being airborne, (2) physical and chemical characteristics of dust particles, and (3) toxins and health effects of dust particles. This summary was followed by a simple description about the exposure pathways with a focus on the inhaled deposited dose in the respiratory tract. The regional inhaled deposited dose was quantified by a simple model. The simple dose model was applied to quantify the dose rate for adult males or females being exposed to dust particles (i.e., SDS scenario) and undergoing common activities (resting and exercising). Keywords  Quantification · Inhaled deposited dose · Sand and dust storms

1 Introduction The increased episodes of dust and atmospheric dust concentrations have a significant impact on the albedo and short-wave radiation over the African and Arabian regions leading to higher surface reflection (Satheesh et  al., 2006), especially in Iraq, Palestine, and Kuwait (Singer et  al., 2003; Al-Dousari, 2009; Al-Hemoud T. Hussein (*) Department of Physics, The University of Jordan, Amman, Jordan Institute for Atmospheric and Earth System Research (INAR/Physics), University of Helsinki, Helsinki, Finland e-mail: [email protected]; [email protected] J. Löndahl Department of Design Sciences, Lund University, Lund, Sweden e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Al-Dousari, M. Z. Hashmi (eds.), Dust and Health, Emerging Contaminants and Associated Treatment Technologies, https://doi.org/10.1007/978-3-031-21209-3_2

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et  al., 2020). This also has a socioeconomic impact on oil sector, photovoltaic energy efficiency, and health (Al-Hemoud et  al., 2020; Al-Dousari et  al., 2018, 2019). For example, aerosol dust causes direct and indirect adverse effects for fauna, flora, and human health in the regional scale (Abd El-Wahab et al., 2018). Besides affecting the Earth’s atmosphere directly and indirectly, dust particles have adverse health effects such as cardiorespiratory and lung problems that often have been associated with long-term exposure and inhalation of dust particles (Hoek et al., 2013; Pope III & Dockery, 2006). The number of people who are regularly affected by natural dusts is increasing. Linking health effects to dust particles on a large scale is challenging. At population level, exposure to natural dusts usually occurs at low and chronic levels; therefore, attribution of morbidity from such sources is difficult. Dust particles may include trace metals, fluoride, radioactive elements, natural asbestiform compounds, silicates, alkali salts, and bioallergens. In urban areas, exposure to dust particles occurs as a mixture with other pollutants. Thus, the effect of chemical content in natural dusts may not be easily differentiated from those resulting from individual exposures (e.g., smoking, indoor air pollution, anthropogenic sources, industry, etc.). The emphasis of this chapter is on dust particles’ personal exposure rather than occupational groups. Firstly, the physical–chemical characteristics and geographical distribution sources of dust are briefly introduced. Secondly, the personal exposure assessment is described. Thirdly, the exposure to dust particles is quantified during selected scenarios of human activities.

2 Atmospheric Dust Particles: Sources and Geographical Distribution Dust particles in the atmosphere are originated from certain geographical regions. The physical–chemical properties vary with the nature of the source; and thus, the environmental impacts and health effects of dust particles vary accordingly. During certain seasons, the dust particles’ concentration might reach extreme high levels forming a moving wall of dust and debris, which is a phenomenon called sand and dust storm (SDS), affecting visibility and imposing severe environmental impacts and health effects. There is a need of investigating the impact on air quality and health effects of dust particles in different areas by applying comparable methodologies in order to characterize exposure (Querol et al., 2019). An SDS is defined as an aeolian processes that occur wherever there is a supply of granular material (typically inorganic grains with diameter smaller than 70 μm) and sufficiently strong wind to move the grains (Kok et al., 2012; Middleton, 2017). An SDS episode is understood to occur in three modes: (1) saltation, (2) creep, and (3) suspension (Gillette et al., 1974; Shao et al., 1993). In practice, dust particles are susceptible to turbulent fluctuations and can remain airborne for a short-term, when the grain diameter is within the range 20–70  μm, or long-term, when the grain

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diameter smaller than 20 μm (Natsagdorj et al., 2003; Thomas et al., 2005; Evan et al., 2011; Kok et al., 2012). In general, dust particles consist of insoluble minerals. The mineralogical and physical–chemical characteristics of dust particles vary with the source. Dust particles are of medium to coarse grade with an approximate range of 10–50 μm. As a result of dry deposition and settling, there is a gradient in the modal particle size of windborne dust particles. The fine dust particles may be transported across thousands of kilometers before being deposited. In other words, closer to the source, the mean dust particle size is coarser. The coarse fraction (diameter 2.5–10 μm) predominates in dust particles. Eventually, short-term suspended dust particles have local effects. Long-term suspended dust particles can stay airborne for up to several weeks and travel thousands of kilometers from their source region having regional effects and impacts such as affecting weather and climate, air quality, ecosystem productivity, hydrological cycle, and many components of the Earth system in addition to severe human health effects (Gillette & Walker, 1977; Small et  al., 2001; Zender et  al., 2003; Miller et  al., 2006; Goudie, 2009; Karanasiou et  al., 2012; Kok et  al., 2012; Rezazadeh et al., 2013; Zoljoodi et al., 2013; Almasi et al., 2014; Goudie, 2014; Díaz et al., 2017; Gherboudj et al., 2017; Middleton, 2017). The major sources of SDS are located in the Northern Hemisphere concentrated within the so-called ‘Afro-Asian belt’, which includes the north Africa, the Middle East, Iran, Afghanistan, Pakistan, Mongolia, and China (Middleton, 1986a, b; Herman et al., 1997; Torres et al., 1998; Prospero et al., 2002; Furman, 2003; Léon & Legrand, 2003; Goudie, 2009). The Middle Eastern and North African Dust Emission Potential (MENA-DEP) spatiotemporal variability was characterized as: low-dust emission areas, moderate dust emission areas, and high-dust emission areas (Gherboudj et  al., 2017). Accordingly, high- and moderate-dust emission areas in north Africa are: Chad, Niger, Mauritania, Occidental Sahara, west and north Algeria, south Tunisia, north-west and central Libya, central Egypt, Sudan, and African horn. In the Middle East, the high- and moderate-dust emission areas are Jordan, Syria, east Iraq, and Arabian Peninsula. Central Iran, west Afghanistan, south-west Pakistan, and West India are also characterized as high- and moderate-­ dust emission areas. There are other ways to characterize the dust source areas based on the temporal variability, strength, period, etc. (Rezazadeh et  al., 2013; Alam et al., 2014; Nabavi et al., 2016; Naimabadi et al., 2016; Khaniabadi et al., 2017; Rashki et al., 2015). Based on the width and the shape of the dust outbreak, Al-Dousari et al. (2017) classified SDS episodes in the north-east of the Arabian Peninsula (during 2000–2017) according to three major types and 12 subtypes of dust storms trajectories. The Eastern Mediterranean region is frequently affected by SDS episodes. The SDS episodes have been reported more frequently during the past decades (Hamidi et  al., 2013; Keramat et  al., 2011; Kutiel & Furman, 2003; Hussein et  al., 2011, 2014, 2017, 2018; Kchih et  al., 2015; Munir et  al., 2017; Amarloei et  al., 2019; Abdulwahed et  al., 2019; Saeifar & Alijani, 2019; Fountoukis et  al., 2020). The main reason is referred to the impact of climate change and its consequences by

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desertification, deforestation, wetland destruction, increased population growth and anthropogenic emissions, food insecurity, and water shortage (Amiraslani & Dragovich, 2011; Notaro et al., 2015; Rezazadeh et al., 2013). Since the beginning of this century, the Eastern Mediterranean has suffered of warming and a drying episode (Notaro et al., 2015), which led to an increased potential to collapse the Fertile Crescent (namely Iraq and Syria). As a result of the climate change and the global warming impacts, a pronounced variability in dust activity was reported with an abrupt regime shift from an inactive dust period (1998–2005) to an active dust period (2007–2013) in the Arabian Peninsula (Aba et al., 2018; Notaro et al., 2015; Doronzo et al., 2016, 2019). During the previous decades, anthropogenic aerosol concentrations have had an increasing trend in the Middle East (Givati & Rosenfeld, 2007). These aerosols slowed down the conversion of cloud droplets into raindrops and snowflakes (i.e., decreasing precipitation). Accordingly, this escalated the desertification process in the Middle East causing increased frequency of dust episodes and atmospheric dust particle concentrations.

3 Health Effects of Dust Particles The most interesting to human health is the fraction finer than 10 μm in diameter (i.e., PM10), which is potentially respirable. Particles smaller than 4 μm in diameter are able to penetrate deep into the lung. The capacity of dust particles to trigger health effects varies with their specific characteristics (e.g., size, dispersal patterns, and chemical constituents) and toxicity (Erel et al., 2007; Rodríguez et al., 2011; Karanasiou et al., 2012; Morman & Plumlee, 2013; Goudie, 2014; Querol et al., 2019; Wang et al., 2020). The mineralogy of dust particles is very complex and has a regional and seasonal variation (Escudero et  al., 2005; Querol et  al., 2009, 2019; Onishi et  al., 2012; Rezazadeh et al., 2013; Hussein et al., 2020; Li et al., 2020; Wang et al., 2020). In general, dust particles contain a variety of oxides (such as SiO2, Al2O3, CaO, Fe2O3, MgO, K2O, and Na2O). Silica (SiO2) is the dominant component of dust particles. Inhalation can lead to development of silicosis, which is a fibrotic lung disease that affects small airways and alveoli and might lead to death as a result of cardiopulmonary failure. Silicosis sufferers are at high risk of contracting tuberculosis infection. Dust particles play a role as a carrier for some toxins and allergens, which is either contained in (or adsorbed to) dust particles. For instance, trace elements (such as Zn, Cu, Mn, Co, Ni, Pb, P, Ti, Sr, and Ba), which are a major group of toxins, can be found in dust particles. Pathogens and bioallergens may also get adsorbed to dust particles. For example, dust particles often may include bacteria and fungi, plant allergens, and animal allergens all of which trigger allergic responses in susceptible subjects. Eventually, dust particles are deposited (via either dry or wet deposition) and may contaminate soil, water supplies, and food sources. This is known as the indirect effects of elements in natural dusts affecting the community health.

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4 Personal Exposure Pathways Exposure pathways are identified as inhalation, dermal, and ingestion. In practice, dermal and ingestion exposure can be lowered by using protective cloths and avoiding direct contact with contaminated surfaces. However, reducing inhalation exposure can be difficult, because dust particles span over a wide range of particle diameters (as discussed before, contained within the accumulation mode and the coarse mode fractions). It is already known that accumulation mode particles have the highest penetration factor through any material and the longest life-time being airborne. Therefore, inhalation exposure to dust particles might not be efficiently reduced by using typical filtration methods. Besides that, exposure to dust particles might also occur indoors as outdoor aerosols migrate from the outdoor air via the air exchange process. Inhalation exposure requires understanding on the physiology of the lungs and the respiratory tract, which include the nasopharynx (nose, mouth, and larynx), the tracheobronchial (conducting airways from the larynx to the terminal bronchioles), and pulmonary (bronchioles, alveolar ducts, alveolar sacs, and alveoli) regions (McClellan, 2000; Kreyling & Scheuch, 2000). The nasopharynx humidifies and equilibrates the incoming air temperature and operates as an initial barrier to larger particles. The tracheobronchial region is lined with mucous covered ciliated cells, which provide clearance mechanisms for particles. These clearance mechanisms are: (1) absorption of dissolved material and (2) particle transport to the larynx, where they are swallowed, or to the lymphatic system, where they are disposed of. The pulmonary region is the place of gas exchange between the lungs and the blood. During the inhalation process, particles are transported through the nose, which is an effective filter for larger particles, or the mouth, which is not. Mouth breathing occurs during exercise or heavy activities allowing large particles to enter the respiratory system (Schultz et al., 2000). The chemical stability of the particles plays a role in their clearance and the toxicological action (Adamson et al., 1999; Kreyling et al., 2007). Insoluble particles are removed by two main pathways: (1) coughing, (2) the mucociliary escalator through phagocytosis by alveolar macrophages, retention, and sequestration. Soluble particles may dissociate and dissolve reacting or interacting with airway fluid or cells.

5 Inhaled Deposited Dose The health effects assessment of inhaled dust particles is based on the evaluation of the exposure levels followed by understanding the deposited dose in the respiratory tract. The deposited dose can be directly measured by monitoring the inhaled and exhaled particle concentrations. Alternatively, the deposited dose can be estimated by means of mathematical models. The widely available are the Multiple Path Particle Dosimetry model (MPPD, Chemical Industry Institute of Toxicology,

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Research Triangle Park, NC) and the International Commission on Radiological Protection model (ICRP, 1994). As suggested by Hussein et  al. (2013, 2015), the deposited dose (D) can be expressed as t1 D p 1

D    VE • DF • nN0 • dlogD p dt , t2 D p 2



(2.1)

where VE is the minute ventilation (or breathing rate: volume of air breathed per time; e.g., Bennett & Zeman, 2004), DF is the deposition fraction of aerosol particles in the respiratory tracts (e.g., Holmes, 1994; ICRP, 1994), and nN0 = dN/dlog(Dp) is the lognormal particle number size distribution. f is a dose metric such as particle 3  surface area  D p2 or particle mass  D p  p  , where ρp is the particle density. 6  The double integral is evaluated for an exposure time period Δt = t2 – t1. It is noticeable that the deposition fraction (DF) is different for different parts of the respiratory system (head/throat, tracheobronchial, and pulmonary/alveolar). It is also dependent on the physiology, status, and activity of the subject (Löndahl et  al., 2007; ICRP, 1994; MPPD). A summary about VE and DF is presented in Table 2.1 and Fig. 2.1.





6 Exposure Scenarios during an SDS 6.1 Exposure Outdoors Dust particles have a bulk density of about 2.6 g/cm−3. During an SDS episode, they have a particle number size distribution that can be characterized by two modes: accumulation and coarse. The geometric mean diameters are 0.5 and 2.5 μm, respectively. The corresponding particle number concentration is within the range of 100–1000 cm−3 and 10–100 cm−3, respectively. A person (adult male or female) may have common activities as sitting, standing, walking, running, and yard work. Sitting and standing can be considered as being at Table 2.1  Minute ventilation (volume of air breathed, VE [m3/h]), for an adult male and female (Holmes, 1994). The last column indicates the corresponding status (rest or exercise) assumed for deposition fraction (DF, Fig. 2.1) curve used in the inhaled deposited dose rate Activity Yard work Running (8 km/h) Walking (4.0 km/h) Standing Sitting

Female 1.08 3.03 1.20 0.48 0.42

Male 1.74 3.48 1.38 0.66 0.54

DF curve type Exercise Exercise Exercise At rest At rest

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Fig. 2.1  Size-resolved deposition fraction (DF) curves in the respiratory tracts for adults (Löndahl et al., 2007; ICRP, MMPD): (a) males exercising, (b) males at rest, (c) females exercising, and (d) females at rest Table 2.2  Regional inhaled deposited dose rate [mg/h] calculated for adult males and females during an SDS scenarios in the outdoor air

Male Yard work Running 8.0 km/h Walking 4.0 km/h Standing Sitting Female Yard work Running 8.0 km/h Walking 4.0 km/h Standing Sitting

Minimum exposure level Head TB Alv Total

Maximum exposure level Head TB Alv

Total

1.6 3.2 1.3 2.3 1.9

2.5 5.0 2.0 0.3 0.2

3.6 7.1 2.8 0.9 0.7

7.7 15.4 6.1 3.5 2.8

16.1 32.2 12.8 23.0 18.8

25.2 50.4 20.0 2.8 2.3

35.7 71.4 28.3 8.9 7.2

77.0 154.0 61.1 34.6 28.4

1.1 2.9 1.2 1.6 1.4

1.5 4.3 1.7 0.2 0.2

2.2 6.3 2.5 0.6 0.5

4.8 13.5 5.3 2.4 2.1

10.5 29.4 11.7 16.2 14.2

15.2 42.5 16.8 2.2 1.9

22.3 62.5 24.8 5.8 5.1

47.9 134.5 53.3 24.2 21.2

rest, whereas walking, running, and yard work can be considered as exercising. The corresponding VE and DF are according to Table 2.1 and Fig. 2.1. Using Eq. (2.1) to calculate the dose during 1 h (i.e., dose rate) reveals enormous numbers (Table 2.2). The minimum exposure level corresponds to the lowest concentrations and the maximum exposure level corresponds to the highest concentrations.

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The total dose rate for an adult male varies within 3–15 and 28–154 mg/h during the minimum exposure level and the maximum exposure level, respectively. The high-dose rates correspond to the extreme exercising activity (i.e., running with 8 km/h) and the low-dose rates correspond to being at rest (i.e., sitting). According to the regional dose rate, the largest fraction (~46%) is deposited in the alveolar region during exercising and in the head region during resting (~66%). As for females, the dose rate is lower than that for males but it showed a similar feature. It varies within 2–14 and 21–135 mg/h during the minimum and maximum exposure levels, respectively. Compared to conditions without an SDS, the dose rate can be one order of magnitude lower than that obtained during an SDS. Keeping in mind the chemical composition and toxicity of dust particles, the excessive amount of accumulated in the respiratory tracts during an SDS exposure can impose serious health effects.

7 Exposure Indoors Personal exposure to dust particles may occur outdoors and indoors. During SDS, dust particles may penetrate indoors via the air exchange processes across the building shell or the ventilation system. Although filtration technique is used in mechanical ventilation system aerosols may find their ways into the indoor air. As the main source of mineral dust particles is from outdoor origin, their concentrations in the indoor air are expected to be lower. For tightly closed buildings with minimum leak pathways, the penetration of coarse particles is minimum. However, the fraction of the dust particles within the accumulation mode has high-penetration probability into the indoor air. Therefore, fine dust particles are likely to be found indoors even when the building is well sealed. In order to quantify the exposure to dust particles indoors, a simplified indoor aerosol model (IAM) can be utilized to predict the exposure level inside an indoor environment. A simplified IAM has several important applications, where one is human exposure assessment providing information about real-time exposure (Hussein et al., 2013, 2015). The indoor aerosols can be of an indoor or outdoor origin. However, in the case of mineral dust particles, the outdoor air is the main source. Eventually, dust particles in the indoor air are either deposited onto surfaces or removed from the indoor air via air cleaners or ventilation. This dynamic behavior of indoor aerosols can be described by the mass balance equation



dI  PO     d  I  Sin , dt (2.2)

where t is the time; I and O are the indoor and outdoor concentrations of the dust particles, respectively; P is the penetration factor of aerosol particles while being transported from the outdoor air into the indoor air; λ is the ventilation rate; λd is the deposition rate of aerosol particles onto available indoor surfaces; and Sin represents

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the emission rates from an indoor source. Well-mixed indoor air is a key assumption for the mass balance equation to be valid. In the case of dust particles during an SDS, the emission rate Sin is zero because the main source is from the outdoor air. Assuming constant values for O, P, λ, and λd, the mass balance equation (Eq. 2.2) can be solved analytically I t   I0e

    d  t





PO     t 1  e  d  ,    d 

(2.3)

where I(t) is the number concentration of the dust particles indoors and I0 is their initial concentration at t = 0. For mineral dust particles originating from the outdoor air, I0 is zero. Therefore, the analytical solution presented in Eq. (2.3) can be further simplified to I t  

PO     t 1 e  d  .     d

(2.4)

A constructive sensitivity analysis for such simple IAM can be utilized to illustrate the role and contribution of the model parameters (O, P, λ, and λd) on the time evolution of the dust particles’ concentrations indoors. The most important part is the time needed to reach a steady-state concentration (Isteady) in the indoor air. It is also valuable to explore the indoor-to-outdoor concentration (IO) ratio when relating the time evolution of the indoor dust particles concentrations to those outdoors, where O is constant. The sensitivity analysis can be performed for a single room (4 × 4 × 3 m3) with the following assumptions: empty room, well-mixed indoor air, and constant model parameters (O, P, λ, and λd). At first, the role of the penetration factor (P) was assessed. The steady state value of the IO ratio (i.e., IOsteady) is achieved after a certain period of time (Fig. 2.2a). The higher the value of P, the higher the IOsteady value will be. The time at which IOsteady is achieved depends on the ventilation rate (Fig. 2.2b). The higher the λ was, the higher the IOsteady was, and the less time was needed to achieve the IOsteady value. Interestingly, when λ > > λd, the IOsteady = P, which usually occurs for dust particles with diameters within the accumulation mode diameter range of 0.1–1 μm. The role of the deposition rate (λd) is illustrated in Fig. 2.2c; the higher the λd is, the lower the IOsteady is, and the shorter the time needed to achieve the IOsteady value. As clearly seen from this sensitivity analysis, the indoor exposure level to dust particles during an SDS episode can be reduced (i.e., lowering the value of IOsteady) by improving the filtration efficiency of the building shell or the filter grade class used in the mechanical ventilations system (i.e., reducing the value of P). Alternatively, the ventilation rate (λ) could be reduced in order to achieve a lower value for IOsteady. In addition, reducing λ yields a delay in reaching the steady-state condition IOsteady. Ideally, increasing the deposition rate (λd) yields to lowering the value of IOsteady. Practically, using an air purifier is equivalent to increasing the deposition rate (λd).

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Fig. 2.2  Sensitivity analysis for a simplified indoor aerosol model illustrating the role of (a) the penetration factor (P), (b) the ventilation rate (λ), and (c) the deposition rate (λd)

8 Recommendations This chapter presented a valuable quantification for the exposure to mineral dust particles during an SDS episode. It is clearly illustrated here that reducing the health effects of dust particles exposure is achieved by reducing the exposure to these harmful aerosols. It is highly recommended to avoid being outdoors during an SDS episode. While being indoors, it is important to apply the following settings for the indoor environment conditions: improve the filtration efficiency, lower the air exchange rate between the indoor and the outdoor air, and use an air purifier.

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Chapter 3

Sources, Drivers, and Impacts of Sand and Dust Storms: A Global View Ali Darvishi Boloorani, Masoud Soleimani, Ramin Papi, Najmeh Neysani Samany, Pari Teymouri, and Zahra Soleimani

Abstract  The occurrence of sand and dust storms (SDS) has become a common phenomenon at a global scale. SDS is one of the most threatening geo-­environmental hazards that impact various components of the Earth system, including geomorphological evolution, biogeochemical cycles, climate, human health, environment, and desertification. In this chapter, the most important natural and anthropogenic factors affecting the occurrence of SDS as well as their consequences are categorized and investigated. Numerous natural and unnatural environmental factors that cause the occurrence of SDS are classified into four main domains: climatic characteristics, soil and sediment, land cover, and socio-economic. Also, different dimensions of the effects of SDS on different components of land ecosystems are presented. Focusing on the growing contribution of human activities to natural resources, the A. Darvishi Boloorani (*) · M. Soleimani · N. Neysani Samany (*) Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran e-mail: [email protected]; [email protected]; [email protected] R. Papi Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran Department of GIS and SDI, National Cartographic Center (NCC), Tehran, Iran e-mail: [email protected] P. Teymouri Health and Environment Research Center, Tabriz University of Medical Sciences, Tabriz, Iran Department of Environmental Health Engineering, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran e-mail: [email protected] Z. Soleimani Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran Department of Environmental Health engineering, Semnan University of Medical Sciences, Semnan, Iran e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Al-Dousari, M. Z. Hashmi (eds.), Dust and Health, Emerging Contaminants and Associated Treatment Technologies, https://doi.org/10.1007/978-3-031-21209-3_3

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main unnatural cause of SDS is undoubtedly the direct and indirect impacts of human activities, which can be seen in many parts of the world following the overexploitation of natural resources. An overall picture of global sources and atmospheric transport paths of SDS is presented. The main global sources of SDS including desert areas such as Africa’s Sahara, the Middle East, Central and East Asia, and parts of South Africa, Australia, and the Americas are presented. Depending on the type of physicochemical characteristics of soil at the source of emission, SDS can have different effects on terrestrial and marine ecosystems in areas far beyond their origin, due to the long-distance transportability of aeolian sediments through dominant global atmospheric pathways. Besides identifying the chemical composition of the SDS sources and the type of its effects on the various components of the Earth system, this study argues that it is necessary to apply management measures both at the source and SDS-affected areas. Keywords  Sand and dust storms · SDS drivers · SDS impacts

1 Background on Sand and Dust Storms Sand and dust storms (SDS) are wind erosion events derived from natural and unnatural factors, which commonly occur in arid and semi-arid regions of the world. Although SDS can occur in areas under a wide range of environmental conditions, their effects are often experienced outside these source regions as well. SDS generally occur when severe, turbulent winds interact with dry soil surfaces that contain scarce or no vegetation. Dust particles are of significant importance as they can be carried to higher altitudes via weather turbulences, transported over vast distances via winds, and finally deposited in aquatic and or terrestrial environments. The majority of particles transported more than 100 km from their origin have a diameter smaller than 20 μm. However, particles larger than 60 μm have also been observed thousands of kilometers from their emission sources (Shepherd et  al., 2016). Fig. 3.1 represents a schematic diagram of the process of wind erosion (SDS formation) and detachment of particles of different sizes from the soil’s surface through three mechanisms, i.e., creep (500  to 2000  μm  in  diameter), saltation (100 to 500 μm), and suspension (less than 100 μm). Assuming that all environmental conditions are the same, the mechanism of wind collision with the soil’s surface and transport of particles is a function of soil particle size. Therefore, smaller particles are suspended for a longer time and can travel longer distances before deposition (sometimes thousands of kilometers) in view of their weight and the effect of gravity. Generally, the sand and dust suspension periods in the atmosphere depend on their altitude, with aerosols in the upper troposphere suspended for a longer time and transported greater distances than those in the lower troposphere (Bolin et al., 1974). Nevertheless, all aerosols eventually fall out of the atmosphere through dry deposition due to gravity and turbulent fluxes (Shao, 2008) or through wet deposition. The latter occurs through two processes: (1) in-cloud (via cloud droplets) and

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Fig. 3.1  Formation of SDS as a function of soil particle size and wind speed

(2) below-cloud (via rain drop scavenging of atmospheric dust particles). Coarsegrained dust particles (larger than 2 μm) are generally removed from the atmosphere through dry deposition, whereas fine-grained particles are scavenged by wet deposition. The rate of dry and wet deposition is a function of season and geography, with the former predominantly seen in the hot months (high SDS and low precipitation) and the latter more common in areas with high precipitation rates (Gonçalves et al., 2002; Choobari et al., 2014). According to estimations, approximately two billion tons of dust particles enter the atmosphere annually, 75% of which is deposited to the land and 25% to the oceans (Shao et al., 2011). There are no precise distinctions made between sand storms and dust storms. A storm carries a collection of particles of different sizes, including clay (smaller than 2  μm), silt (2–50  μm), and sand (50–2000  μm). The World Meteorological Organization (WMO) defines SDS as a natural hazard resulting from surface winds that lift and transport large volumes of dust particles, reducing visibility to less than 1000  m and ~0  in extreme events. Synoptic codes specified by WMO based on horizontal visibility have been used for creating a series of standard international definitions for SDS events (SYNOP WW codes: 09, 07, 06, 08) (Middleton et al., 2019).

2 Drivers of SDS Wind erosion is generally a function of weather conditions in conjunction with the characteristics of the Earth’s surface, the effects of which are determined through two major environmental factors, i.e., wind erosivity and soil erodibility. As a major driver of broad-scale wind erosion, wind erosivity is the wind’s ability to transport

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Fig. 3.2  Factors contributing to SDS formation

fine-grained dry soil particles, while the latter is the property of dry, particulate soil which governs its entrainment by the wind (Sivakumar, 2005). Surface stability or wind erosion resistance depends on the erodibility of the surface material and the extent of surface cover by non-erosive materials such as rock, vegetation, and ice/ snow. A large portion of dust particles originate from surfaces that lack soil formation processes, especially lands corresponding to dried-up ephemeral water bodies, where soil particles are easily mobilized by light wind conditions. Therefore, SDS are caused by the direct and indirect consequences of a set of natural, and occasionally man-made factors. The main physical and chemical parameters affecting wind erosion and consequently the formation of SDS can be divided into four catagories including climatic characteristics (such as drought, precipitation, air temperature, humidity, evapotranspiration, and wind speed), soil (texture,  sediment  type, organic matter, and moisture), land cover (vegetation type and density), and socio-­ economic (agriculture, water control/usage, and land use) Fig. 3.2. It is worth mentioning that  the mechanism through which each factor affects SDS is different (Shepherd et al., 2016).

2.1 Wind Regime and Intensity Wind and air instability are among the main factors giving rise to SDS. Aside from the other necessary environmental conditions, in order to be considered as a factor contributing to SDS, the wind speed must exceed a certain erosivity threshold (Darvishi Boloorani et al., 2022a), which varies for different regions [for example, in Central Asia 6.5 m s−1 (Xi & Sokolik, 2016), West Asia 6 m s−1 (Najafi et al., 2014),

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and Saharan desert in Africa 5 m s−1 (Kok et al., 2012)]. Therefore, wind speed is a determining factor in the formation of SDS events. The higher the wind speed, the more the region’s susceptibility to SDS formation (Lee & Sohn, 2009).

2.2 Moisture The presence of air moisture in the atmosphere is also a key condition, where unstable weather with enough moisture results in precipitation that can limit SDS formation. Moisture  due to increasing the adhesion of topsoil particles reduces SDS emission rate. Weather instability in hot regions is one of the key factors contributing to SDS, and during periods of reduced instability and turbulences there will be fewer and less severe SDS events.

2.3 Vegetation Vegetation density and structure are two other contributing factors, with SDS being a consequence of vegetation cover loss, which can be resulted from the natural processes and anthropogenic activities. Vegetation can reduce wind speed and wind erosivity, acting as a mechanical barrier to prevent soil particles from being lifted and transported. The threshold for wind speed erosivity is known to increase exponentially with higher vegetation density and coverage (Shi et al., 2004).

2.4 Anthropogenic Drivers There is still a great deal of uncertainty regarding the extent of the effects of human activities on SDS. However, the influences of human activities on natural ecosystems will likely rise in the coming decades. Human interventions have led to climate changes and the transformation of the earth’s natural ecosystems with irreversible consequences such as desertification, land degradation and the formation of SDS resources. Human activities can have different influences on SDS occurrence coupled with natural and inherent features of every ecosystem, for instance the role of precipitation in providing soil moisture, increasing particle adhesion, and vegetation growth,  which ultimately reduces  soil erosion and SDS emission  (Gao et al., 2012). A plethora of studies has investigated the factors contributing to SDS and their effects during the past three decades, adding to the body of knowledge in this field. Despite these efforts, there are still numerous unexplored aspects about the effects of natural and unnatural factors contributing to SDS. In view of the existing body of

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knowledge, and the assumption that future SDS events are the result of past processes, it is possible to take measures to identify and manage SDS sources by considering main SDS drivers.

3 Impacts of SDS SDS are environmental hazards with mostly negative effects on different components of the affected ecosystems such as geomorphological evolution, biogeochemical cycles, climate, and human, agricultural and wildlife health risks (Goudie & Middleton, 2006). Evidence suggests that aeolian processes interact with different types of ecosystems at different scales and contribute to important biophysical processes between the Earth’s living and non-living components. Although water is the main cause of soil erosion on a global scale, wind is the predominant erosive mechanism in dry lands, i.e., 40% of the Earth’s surface, in which wind processes, either alone or in conjunction with hydrological processes, are the main drivers of ecosystem processes (Ravi et al., 2011). Aeolian processes lead to diverse and significant consequences, involving loss of nutrient-rich topsoil layer through SDS on the local scale (Zobeck & Fryrear, 1986). Meanwhile, dust emission can result in damage to developing plants via abrasion (Armbrust, 2000), air pollution, and respiratory problems in humans (Griffin et al., 2001). On the regional scale, through the deposition of dust particles and its effects on the global weather biogeochemical cycles (Mahowald et  al., 2008), the transport of dust particles can affect the balance of atmospheric radiation energy (Kaufman et al., 2002). According to the literature, the major effects of SDS on the Earth system and environment are outlined in Figs. 3.3 and 3.4.

3.1 Vegetation Cover Aeolian processes also affect soil texture and hydrological processes by redistributing soil particles and nutritious elements, which can then affect vegetation composition, efficiency, and spatial patterns (Schlesinger et al., 1990). Containing vital plant nutrients such as potassium and phosphorus, topsoil is the most fertile part of the soil that can be transported by wind over time, thus increasing soil erodibility and accelerating land degradation and desertification (Stefanski & Sivakumar, 2009). When combined with raindrops and air humidity, dust particles create a thick layer on the surfaces of plant leaves that are not easy to clean and require an external force to be removed. This can prevent light from reaching the chlorophyll responsible for photosynthesis and disturb the CO2 exchange with the atmosphere, which can ultimately hinder plant growth by reducing or even completely shutting off photosynthesis. Therefore, SDS can negatively affect plant efficiency (e.g., natural vegetation and agricultural products) and destroy them (Farmer, 1993). On a large scale, SDS impacts on vegetation cover can pave the way for desertification and the ensuing

3  Sources, Drivers, and Impacts of Sand and Dust Storms: A Global View

Fig. 3.3  The major effects of SDSs on different components of the Earth system

Fig. 3.4  Schematic representation of SDS impacts on the environment

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SDS formation. On this basis, the effects of SDS in one region can therefore lead to the formation of active SDS sources even in distant areas.

3.2 Animals The indirect effects of dust particles on plants and animals is the consumption of dust-contaminated plants by animals, which can introduce detrimental chemicals to the animal’s body. This can create a chain of problems as some of these herbivores, such as cows, sheep, and camel, are consumed by humans. In other words, dust contaminations can enter plant, animal, and human life cycles. The contamination of plants by dust can therefore affect entire natural/artificial living ecosystems.

3.3 Human One of the main concerns with SDS are the effects on human health, particularly respiratory problems, which are associated with  exposure to particulate matter (PM). SDS directly contribute to air pollution by increasing the concentration of PM in the atmosphere and negatively affecting the air quality on local and global scales. With the doubling of the rate of air pollution-induced mortality, recent studies have shown that the effect of SDS on human health is significantly higher than previously estimated, resulting in the classification of air pollution as the greatest environmental threat to human health (Milford et al., 2020). Meanwhile, mineral dust is of particular importance as it originates from deserts and dry lands, which are responsible for the emission of 30–35% of PM every year (Bouet et  al., 2019). Mineral dust covers a large spectrum of particles of different sizes (0.1–50 μm) with extensive effects on human health. Particles larger than 10 μm cannot be inhaled, but still can cause skin and eye related illnesses, while smaller particles (including PM10, PM2.5 and smaller particles) are generally trapped inside the upper respiratory tract, with the tiniest particles causing respiratory problems such as asthma, tracheitis, and pneumonia. Microorganisms (bacteria, fungi, protozoa, and viruses) and other organic materials such as pollen spores can survive long range transportation at global scales (Goudie, 2014). Besides, SDS are also associated with non-­ respiratory mortality and injuries due to reduced visibility that can result in transportation accidents.

3.4 Marine Ecosystems Dust particles are one of the external sources of nutrients and metals deposited in the oceans, which somehow are vital for life, and their supply by the atmosphere can control the primary production of the oceans. For example, the iron content of SDS

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can affect the marine biogeochemical cycles and consequently the perception of the carbon cycle in the oceans (Jickells et al., 2005). Their effect on marine primary productivity is mainly through the effects of dust-borne nutrients on single-celled photosynthetic organisms called phytoplankton, which are the primary producers of new organic materials in seas and oceans. The deposition of atmospheric dust particles in certain parts of the ocean leads to the growth of phytoplankton, further nitrogen fixation, and changes in the composition of phytoplankton species. This input can also catalyze carbon sequestration through the biological pump, thus affecting the emission of ocean dimethyl sulfide and consequently cloud albedo. Changes in the ocean biogeochemical cycles can influence the atmospheric CO2 concentration, resulting in climate feedbacks. In this way, the input of nutrients via dust can stimulate primary production by phytoplankton in the sunlit upper surface using CO2 from the atmosphere for photosynthesis (UNEP, 2020).

3.5 Terrestrial Ecosystems (Phosphorus Cycle) SDS sources are generally located in regions with high soil phosphorus concentrations in macro-scales in dry lands and deserts, but water restrictions due to climatic conditions affect phosphorus cycles in these ecosystems. On the other hand, semi-­ arid regions on the edges of deserts receive a significant amount of aeolian phosphorus from the neighboring desert areas due to aeolian processes and are most likely affected by phosphorus deposition. This is generally the main reason for the long-­ term maintenance of productivity in these regions. For instance, studies on soil chronosequences in Hawaii revealed that phosphorus originated from Asian SDS sources contributed to the maintenance of productivity in Hawaiian ecosystems on million-year time scales (Chadwick et al., 1999). Therefore, to maintain their long-term productivity, some ecosystems may be dependent on phosphorus inputs originating from either distant ecosystems or the weathering of phosphorus-­ rich minerals from their own soil substrates. Studies show that this is true especially in semi-arid steppes in Africa and Eurasia (Okin et al., 2004).

3.6 Atmosphere and Hydrological Cycles The fine-grained PM in the atmosphere plays a key role in the balance of atmospheric radiation and the hydrological cycles through their use as condensation nuclei for the formation of clouds and precipitation and on radiation energy. SDS can affect climate and cloud microphysical processes in several ways, including direct (absorption and scattering of short- and longwave radiation)  (Darvishi Boloorani et  al., 2022b), semi-direct (changing of the atmospheric temperature structure and burning off of cloud droplets via radiation), and indirect (affecting the optical properties and cloud lifespan and reducing/increasing of precipitation processes). There is also evidence of the dust-induced modification of the cloud

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radiative forcing, which can reduce the cooling effect of clouds on ground surfaces and the atmosphere. On the other hand, variability in dust composition can affect the physical, chemical, and optical properties of particles, which can lead to positive or negative radiative forcing by dust particles. For instance, small changes in the dust iron content can severely affect the short- and longwave radiation. A rise in the iron content can result in an increasing atmospheric temperature, while a reduction can increase the back-scattering of solar radiation to the atmosphere, and consequently a reduction in the solar irradiance (energy) received by the Earth’s surface. A process whose output affects the input of another system is called feedback, creating a chain cycle of actions or reactions. There is a bilateral  (feedback) relationship between atmospheric processes  and dust particles, so while the atmosphere can affect SDS and its spatial–temporal distribution, SDS can affect the atmosphere and its dynamics, through radiative forcing. This can cause a change in vertical temperature profiles and wind speed in the atmosphere, exerting a feedback effect on dust emission. An example is the effect of Saharan dust-radiation on Monsoon winds in Asia and West Africa (Choobari et al., 2014).

3.7 Aquatic Ecosystem Surface water resources are highly sensitive to contamination by different pollutants, introduced via inlets, drainages, runoff, and atmospheric deposition, the latter being one of the main pollutants of surface water (Hillery et al., 1998). Atmospheric pollutants including dust particles are transported by wind, which may be mixed together and deposited in surface water bodies via wet or dry deposition. As mentioned earlier, particles may contain nutrients and metals, and get deposited in aquatic ecosystems (Burton et al., 2013). For instance, deposition of sulfate- and nitrate-rich dust can result in the acidity of surface water bodies (Ding et al., 2014). Aquatic plants and animals typically live and flourish under specific environmental conditions, and so their health may be threatened by the deposition of heavy metals and other pollutants that interfere with the natural characteristics of surface water ecosystems. For instance, lake fish can survive up to a certain level of water acidity or temperature, changes in which, through dust deposition, can gravely endanger their life. Aquatic plants are no exception and will react to changes in environmental parameters. Therefore, the life of living species can be affected by water contamination or interference in its biogeochemical cycles. Meanwhile, many farmlands are usually fed by lakes and rivers, therefore the introduction of pollutants by dust deposition on these water resources can ultimately affect crop  productivity. In extreme cases, contamination can destroy the natural vegetation of an ecosystem and induce desertification and, consequently, trigger a feedback effect on dust emission. A dynamic aquatic ecosystem in its normal condition can be negatively affected by SDS-associated pollutants originating from distant areas, which can gradually alter and/or destroy the target ecosystem.

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3.8 Global Sources of SDS Identification of SDS sources coupled with local environmental knowledge can contribute to  understand their characteristics and mitigate the ensuing hazards. Considering the global extent of SDS, remote sensing has been recognized as the best tool for studying its long-term spatial–temporal patterns (Darvishi Boloorani et al., 2023). Satellite images have helped to draw a general picture of global SDS sources based on atmospheric aerosols detected via Total Ozone Mapping Spectrometer (TOMS)/Nimbus 7 (Prospero et al., 2002; Washington et al., 2003) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua (Ginoux et al., 2012). The major sources of high-aerosol loading, due to natural and unnatural causes, are located in the Northern Hemisphere, mainly over an extensive dust belt extending from the western coast of North Africa, the Middle East, and Central and South Asia to East Asia and China. There are very fewer aeolian processes outside the global belt, including relatively smaller sources in the southern hemisphere deserts of southern Africa (Botswana and Namibia), South America (Atacama), and Australia. The total amount of dust emission in the southern hemisphere accounts for only 6.7% of the total global SDS emission at an estimated 138 million tons per year, in comparison to the northern hemisphere total estimate of 1935 million tons per year (Ginoux et al., 2004). In terms of aerosol concentrations in the atmosphere, the most active sources are the Sahara Desert and deserts in Asia. Africa’s Sahara is the world’s main source of SDS, accounting for half of all aeolian desert sediments deposited in the oceans (Goudie and Middleton, 200). The main global sources and transport pathways of SDS have been identified using satellite and meteorological data, as shown in Fig. 3.5.

3.9 SDS Sources Classification The main sources of global SDS are located in dry lands in topographic depressions, consisting of deep alluvial deposits from intermittent floods during the Quaternary and Holocene periods (Prospero et al., 2002). Natural SDS originate primarily from deserts, semi-arid regions, and beds of ephemeral and/or dried-up water bodies, and secondarily from areas with low soil moisture content and vegetation cover, which are directly related to low precipitation. Examples of the latter are SDS sources in the West Sahara and the Sistan Basin in Iran. Besides, anthropogenic sources play a role in dust emission due to degradation of ecosystems and contribute to climate change. A prime example of this can be seen in the Sahel desertification in North Africa and the ensuing rise in the transport of dust particles across the Atlantic Ocean (Choobari et  al., 2014). In general, and aside from anthropogenic/natural characteristics and the drivers of SDS, the global overview of the main SDS sources revealed that they can be categorized into four types as follows:

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I. Deserts and sand dunes. II. Dried-out water bodies (River/Lake/Wetland). III. Degraded lands (Farmland/Rangeland/Pasture). IV. Coastal areas.

3.10 Composition Contents of Global SDS After being emitted from their sources, dust particles can travel very long distances via atmospheric wind systems such as the trade winds. Recently, there has been an advancement due to scientific investigations/remote sensing in our understanding of SDS trajectories from global source regions, as presented in Fig. 3.5. As discussed in Sect. 3.3, the mineralogical composition of dust particles can have various effects on different components of Earth’s marine and terrestrial ecosystems. The chemical properties of the particles originating from the main global sources of SDS, as measured in previous studies, are presented in Fig. 3.6. Where SDS predominantly contain elements such as quartz or silicon dioxide (SiO2), they may also contain significant concentrations of aluminum oxide (Al2O3), iron oxide (Fe2O3), calcium oxide (CaO), and magnesium oxide (MgO). Many SDS source’s topsoil also contain a variety of salts, organic matter, pathogenic microorganisms (e.g., bacteria, fungi, protozoa, and viruses) and anthropogenic pollutants (e.g., pesticides, herbicides, and pharmaceuticals) (Middleton & Kang, 2017).

Fig. 3.5  Major sources of global SDS and their atmospheric transport paths based on prevailing wind patterns (Middleton, 2020)

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Fig. 3.6  Mineral compositions of the main global SDS sources; the mean percentage of elements measured in previous studies (Wang, 2015)

3.11 Feedback Impacts of Human Activities and SDS Before widespread technological advances, industrialization, and rapid population growth, SDS were considered natural-meteorological hazards or rather a special feature of natural ecosystems in semi-arid and desert regions of the world. Today, regardless of sustainable development, human activities in natural ecosystems have led to many destructive and irreparable consequences. As a result, some parts of the world are now experiencing the expansion of active SDS sources. Given the growing population in many countries and the lack of enough resources to meet vital human needs, there is no doubt that human activities that are detrimental to ecosystems will continue to expand. Coupled with natural phenomenon such as climate

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change and drought, living conditions for humans in the future will likely be more problematic. Therefore, the occurrence of SDS is likely to intensify as one of the consequences of these conditions. The anthropological aspect of SDS is significant in terms of both human effects on the SDS formation and vice versa.

3.12 Human Impacts on SDS Most SDS  emission from global desert sources are natural, while anthropogenic factors are less involved. The highest emission rate and concentration of fine-grained particles are related to deserts and semi-arid regions with highly scattered and low population centers (Fig. 3.7b and c), as observed in areas such as the Sahara Desert. Meanwhile, there is high-population density in some SDS sources such as the Persian Gulf countries, especially Mesopotamia within the Tigris and Euphrates Basin, suggesting that anthropogenic factors and population densities can contribute to high-dust emissions. A case in point is the expansion of SDS sources in Iraq and Syria over the past decades. Studies show that since the 1970s, there has been a fall in water inflow to Tharthar, Habbaniyah, Razzaza, Najaf Sea, and Hammar lakes and central, Hawizeh and Hammar wetlands in Mesopotamia, with dam construction projects being launched in Iraq, Turkey, and Syria (Darvishi Boloorani et al., 2021a). This has led to the gradual drying of these water bodies, whose dried beds are now becoming a major emission sources due to their erodible fine-grained sediments. At the same time, the rapid growth of the population, and consequently the need for water (for agricultural and drinking), has led to a serious water shortage crisis in Iraq and Syria. This was also aggravated by two periods of severe drought, i.e., 2000–2004 and 2008–2012. All of these factors have triggered the formation of SDS originating from these dried lake beds and wetlands. About 904 SDS events have been detected in the Tigris and Euphrates Basin (especially in Iraq and Syria) over the past two decades (2000–2020) using satellite images, 61% of which are associated with dried beds of the lakes and wetlands (Darvishi Boloorani et al., 2021a). This confirms the significant effects of anthropogenic activities on dust emissions. SDS in this region are created by monsoon winds called Shamal. These winds are most active in the summer and cause severe SDS events. Therefore, they have a seasonal origin and pattern. Shamal winds originate from the northwest of the Middle East and are channelized to the Persian Gulf by the highlands of Zagros mountains. These mainly affect the Persian Gulf countries, especially Iran, Kuwait, the UAE, and Bahrain (Parajuli et  al., 2014). There is still a great deal of uncertainty Fig. 3.7  SDS conditions in global sources based on the long-term average values of MERRA-2 reanalysis data (1980–2020). Part figures a, b, c, and d, respectively, show the global average concentration of fine-grained dust particles in the atmospheric column, the concentration of fine-­ grained dust particles on the Earth’s surface, dust emitted from sources, and dry and wet deposition for a 41-year period (from 1980 to 2020)

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associated with the exact share of anthropological activities in causing global dust sources expansion estimated to a range from less than 10% to a maximum of 50%. The most likely number is estimated at 25% (Shepherd et al., 2016).

3.13 SDS Effects on Humans Areas with a high-population density are more vulnerable to SDS (Darvishi Boloorani et al., 2021b), where socio-economic damages are much higher due to the larger population, which naturally entails more human facilities and infrastructures exposed to SDS. The importance of the issue is highlighted in Fig. 3.7d, where areas with high-dust deposition rates often have a higher population density, such as the east and southwest regions of the Sahara Desert and the Persian Gulf countries. In addition to the effects of SDS on the environment and human health, as described in Sect. 3.3, a high rate of dust deposition can ultimately lead to feedback effects on dust and the intensification or formation of new SDS sources. The southwestern Sahara region does not contribute much to dust emissions (Fig. 3.7c), whereas the deposition rates are very high (Fig. 3.7d). This is a function of the dust transport paths based on the prevailing atmospheric wind flows and conditions may contribute to regional desertification.

4 Conclusion There are a multitude of studies on SDS from around the world, generating an invaluable wealth of knowledge and information. Comprehensive review and categorization of the produced information can help to enrich the specialized knowledge in this field. To achieve such knowledge, in the present study, the most important natural and anthropogenic factors affecting the occurrence of SDS were investigated and categorized. In addition, various effects of SDS on different components of terrestrial and aquatic ecosystems are presented. Focusing on the growing effects of SDS to the Earth’s ecosystems, an overall picture of global sources and atmospheric transport paths of SDS are presented. Assuming that human pressure on the environment is greater in areas with a high-population density, the impact of anthropogenic factors on geo-environmental hazards like SDS is also important and calls for further investigations. Due to anthropogenic effects on SDS in densely populated areas, the socio-economic consequences of the SDS occurrence in such areas will be high. Several studies have shown that the effects of dust particles originating from a source region can be observed thousands of kilometers away. It is possible to identify the areas affected by SDS worldwide by studying SDS sources and their main atmospheric transport pathways. Accordingly, it is possible to determine the type of impact on dust deposition areas based on the physical and chemical compositions of surface soils in source areas.

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Recommendations  It is strongly recommended that research to be undertaken to enhance our understanding of vulnerable ecosystems/populations in both SDS emission and deposition areas. The main research areas are: • As the first step in mitigating SDS effects, it is necessary to identify SDS emission sources based on the interaction between various environmental factors including atmospheric conditions and land surface characteristics. • It is essential to focus on SDS sources that affect larger populations (in terms of management and mitigation measures). • Bio-aerosols and mineral particles in SDS can have different effects on human health and other living organisms, vegetation, and, in general, terrestrial and aquatic ecosystems. Therefore, characterizing the soil’s physical and chemical compositions  in  SDS sources and studying the types of effects of these compounds on the Earth’s ecosystems is needed. • Vulnerability/risk assessment and mapping in the framework of disaster risk reduction is a recommended task which enable us to  effectively mitigate the harmful effects of SDS. • SDS mitigation measures are not limited to the emission sources and there is a need for studies in areas of SDS deposition that are routinely impacted. Acknowledgments  The authors gratefully acknowledge the assistance of Dr. Dale Griffin (https://www.usgs.gov/staff-­profiles/dale-­griffin), who reviewed manuscripts.

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Darvishi Boloorani, A., Najafi, M. S., Soleimani, M., Papi, R., & Torabi, O. (2022b). Influence of Hamoun Lakes’ dry conditions on dust emission and radiative forcing over Sistan plain Iran. Atmospheric Research, 272, 106152. https://doi.org/10.1016/j.atmosres.2022.106152 Darvishi Boloorani, A., Papi, R., Soleimani, M., Al-Hemoud, A., Amiri, F., Karami, L., Neysani Samany, N., Bakhtiari, M., & Mirzaei, S. (2023). Visual interpretation of satellite imagery for hotspot dust sources identification. Remote Sensing Applications: Society and Environment, 29, 100888. https://doi.org/10.1016/j.rsase.2022.100888 Ding, J., et al. (2014). Identifying diffused nitrate sources in a stream in an agricultural field using a dual isotopic approach. The Science of the Total Environment., 484, 10–18. Farmer, A.  M. (1993). The effects of dust on vegetation—a review. Environmental Pollution, 79(1), 63–75. Gao, T., Han, J., Wang, Y., Pei, H., & Lu, S. (2012). Impacts of climate abnormality on remarkable dust storm increase of the Hunshdak Sandy Lands in northern China during 2001–2008. Meteorological Applications, 19(3), 265–278. Ginoux, P., Prospero, J. M., Torres, O., & Chin, M. (2004). Long-term simulation of global dust distribution with the GOCART model: Correlation with North Atlantic oscillation. Environmental Modelling and Software, 19(2), 113–128. Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., & Zhao, M. (2012). Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Reviews of Geophysics, 50(3), 3005. Gonçalves, F. L. T., Ramos, A. M., Freitas, S., Dias, M. A. S., & Massambani, O. (2002). In-cloud and below-cloud numerical simulation of scavenging processes at Serra Do Mar region, SE Brazil. Atmospheric Environment, 36(33), 5245–5255. Goudie, A. S. (2014). Desert dust and human health disorders. Environmental International, 63, 101–113. Goudie, A. S., & Middleton, N. J. (2006). Desert dust in the global system. Springer Science & Business Media. Griffin, D. W., Kellogg, C. A., & Shinn, E. A. (2001). Dust in the wind: long range transport of dust in the atmosphere and its implications for global public and ecosystem health. Global Change and Human Health, 2(1), 20–33. Hillery, B. R., et al. (1998). Atmospheric deposition of toxic pollutants to the Great Lakes as measured by the Integrated Atmospheric Deposition Network. Environmental Science Technology, 32(15), 2216–2221. Jickells, T. D., et al. (2005). Global iron connections between desert dust, ocean biogeochemistry, and climate. Science, 308(5718), 67–71. Kaufman, Y. J., Tanré, D., & Boucher, O. (2002). A satellite view of aerosols in the climate system. Nature, 419(6903), 215–223. Kok, J. F., Parteli, E. J. R., Michaels, T. I., & Karam, D. B. (2012). The physics of wind-blown sand and dust. Reports on Progress in Physics, 75): 10, 106901. Lee, E., & Sohn, B. (2009). Examining the impact of wind and surface vegetation on the Asian dust occurrence over three classified source regions. Journal of Geophysical Research: Atmosphere, 114, D6. Mahowald, N. M., et al. (2008). Atmospheric iron deposition: Global distribution, variability, and human perturbations. Annual Review of Marine Science, 1, 245–278. Middleton, N. (2020). Health in dust belt cities and beyond—An essay by Nick Middleton. British Medical Journal, 371, m3089. Middleton, N., & Kang, U. (2017). Sand and dust storms: Impact mitigation. Sustainability, 9(6), 1053. Middleton, N., Tozer, P., & Tozer, B. (2019). Sand and dust storms: Underrated natural hazards. Disasters, 43(2), 390–409. Milford, C., et al. (2020). Impacts of desert dust outbreaks on air quality in urban areas. Atmosphere (Basel), 11(1), 23.

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Najafi, M. S., Khoshakhllagh, F., Zamanzadeh, S. M., Shirazi, M. H., Samadi, M., & Hajikhani, S. (2014). Characteristics of TSP loads during the Middle East Springtime Dust Storm (MESDS) in Western Iran. Arabian Journal Geosciences, 7(12), 5367–5381. Okin, G. S., Mahowald, N., Chadwick, O. A., & Artaxo, P. (2004). Impact of desert dust on the biogeochemistry of phosphorus in terrestrial ecosystems. Global Biogeochemical Cycles, 18(2). Parajuli, S. P., Yang, Z., & Kocurek, G. (2014). Mapping erodibility in dust source regions based on geomorphology, meteorology, and remote sensing. Journal of Geophysical Research. Earth Surface, 119(9), 1977–1994. Prospero, J.  M., Ginoux, P., Torres, O., Nicholson, S.  E., & Gill, T.  E. (2002). Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Reviews of Geophysics, 40(1), 1–2. Ravi, S., et al. (2011). Aeolian processes and the biosphere. Reviews of Geophysics, 49(3), 2011. Schlesinger, W. H., et al. (1990). Biological feedbacks in global desertification. Science, 247(4946), 1043–1048. Shao, Y. (2008). Physics and modelling of wind erosion (Vol. 37). Springer Science & Business Media. Shao, Y., et  al. (2011). Dust cycle: An emerging core theme in Earth system science. Aeolian Research, 2(4), 181–204. Shepherd, G., Terradellas, E., Baklanov, A., & Kang, U. (2016). Global assessment of sand and dust storms. Nairobi, Kenya. Shi, P., Yan, P., Yuan, Y., & Nearing, M. A. (2004). Wind erosion research in China: Past, present and future. Progress in Physical Geography, 28(3), 366–386. Sivakumar MVK. (2005). Impacts of sand storms/dust storms on agriculture, Natural disasters and extreme events in agriculture, Springer, pp. 159–177. Stefanski, R., & Sivakumar, M.  V. K. (2009). Impacts of sand and dust storms on agriculture and potential agricultural applications of a SDSWS. IOP Conference Series: Earth and Environmental Science, 7(1), 12016. UNEP. (2020). Impacts of sand and dust storms on oceans: A scientific environmental assessment for policy makers. United Nations Environment Programme. Wang, J. X. L. (2015). Mapping the global dust storm records: Review of dust data sources in supporting modeling/climate study. Current Pollution Reports, 1(2), 82–94. Washington, R., Todd, M., Middleton, N.  J., & Goudie, A.  S. (2003). Dust-storm source areas determined by the total ozone monitoring spectrometer and surface observations. Annal of the Association of American Geographers, 93(2), 297–313. Xi, X., & Sokolik, I. N. (2016). Quantifying the anthropogenic dust emission from agricultural land use and desiccation of the Aral Sea in Central Asia. Journal of Geophysical Research: Atmosphere, 121(20), 12–270. Zobeck, T. M., & Fryrear, D. W. (1986). p89 Chemical and physical characteristics of windblown sediment II. Chemical characteristics and total soil and nutrient discharge. Transactions of the ASAE, 29(4), 1037–1041.

Chapter 4

Exposure of Dust Storms and Air Pollution (PM10, PM2.5) and Associated Health Risk in the Arid Region Ali Al-Hemoud

1 Arid and Semi-Arid According to the United Nations Convention to Combat Desertification (UNCCD) arid, semi-arid and dry sub-humid areas are defined as areas, other than polar and sub-polar regions, in which the ratio of annual precipitation to potential evapotranspiration falls within the range from 0.05 to 0.65. Dust sources are commonly associated with topographical lows located in the arid regions with annual rainfall under 200–250 mm (Prospero et al., 2002). There are nine potential dust source regions (Arabian Peninsula, Central Asia, eastern and western China, Australia, North and South Africa, and North and South America). The Sahara desert in North Africa accounts for 58% of the total global dust emissions (Tanaka & Chiba, 2006). Numerous factors over the arid and semi-arid region, including wind regime and soil conditions, are important drivers of dust emission over the dust belt (Shi et al., 2021). The dust belt stretches from the Sahara Desert through the Middle East and the Arabian Peninsula, Iran, and Central Asia to the Gobi Desert of China and Mongolia. The Middle East and North Africa (MENA) region, which neighbors the Sahara desert, is considered the dustiest region in the World (WorldBank, 2019). Out of 151 countries directly affected by sand and dust storms, 45 (23%) are classified as dust source areas, of which 85% (38 out of 45) are in Africa and Asia. The string of deserts and semi-desert areas that stretch from the Sahara and Middle East through central Asia are depicted in Fig. 4.1 (Middleton, 2020).

A. Al-Hemoud (*) Environment and Life Sciences Research Center, Kuwait Institute for Scientific Research, Safat, Kuwait e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Al-Dousari, M. Z. Hashmi (eds.), Dust and Health, Emerging Contaminants and Associated Treatment Technologies, https://doi.org/10.1007/978-3-031-21209-3_4

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Fig. 4.1 Major global sources of desert dust and transport pathways. (Adapted from Middleton (2020))

1.1 Dust Storms The World Meteorological Organization (WMO) defines dust storms as ensemble of particles lifted to great heights by strong and turbulent winds and reduce visibility at eye level (1.8 m) to less than 1000 m (McTainsh & Pitblado, 1987); however, when the visibility at eye level is reduced but not to less than 1000 m, it is defined as blowing dust, while dust haze resides in the atmosphere from a previous dust storm (UNEP, 2016). Dust storms are also classified based on visibility, wind speed (WS), and particulate matters with aerodynamic diameters less than 10 μm (PM10). For instance, a dust storm is defined when PM10 is in the range 500–2000, visibility is below 1000 m, and wind speed is above 17 m/s (Hoffmann, Funk, Wieland, et al., 2008b; b). In Kuwait, aeolian dust has been defined based on two factors, wind speed and visibility (Al-Hemoud et al., 2020). Wind speed is classified according to the Beaufort wind force scale. The adopted wind speed scale is modified from the original Beaufort 12-point unit values. Accordingly, dust events are classified based on visibility and wind speed using the following three categories: (1) dust storms: visibility ≤1000 m and WS ≥ 8 m/s; (2) rising or blowing dust: visibility ≥1000 m and WS ≥ 8 m/s; and (3) suspended or dust haze: visibility ≤5000 m and WS ≤ 8 m/s. Various definitions of dust events are shown in Table 4.1.

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Table 4.1  Dust Event Definitions

Dusty air (DA) Light dust storm (DS1) Dust storm (DS2) Strong dust storm (DS3) Serious strong DS (DS4) Dust storm

Blowing dust Dust haze

Visibility (m) > 2000

Wind Speed (m/s) –

< 2000

–

< 1000

> 17

< 200

> 20

< 50

> 25

PM10 (μg/ m3) 50– 200 200– 500 500– 2000 2000– 5000 > 5000

The result of turbulent winds raising large quantities of dust into the air and reducing visibility to less than 1000 m. Raised by winds to moderate heights above the ground reducing visibility at eye level (1.8 m), but not to less than 1000 m. Produced by dust particles in suspended transport which have been raised from the ground by a dust storm prior to the time of observation. An ensemble of particles of dust or sand energetically lifted to great heights by a strong and turbulent wind.

Dust storm or sandstorm Drifting and An ensemble of particles of dust or sand raised, at or near blowing the observation site, from the ground to small or dust or sand moderate heights by a sufficiently strong and turbulent wind. Dust haze A suspension in the air of dust or small sand particles, raised from the ground prior to the time of observation by a dust storm or sandstorm. The dust storm or sandstorm may have occurred either at or near the observation site or far from it.

Reference (Hoffmann, Funk, Wieland, et al., 2008b, b)

(McTainsh & Pitblado, 1987)

World International Organizational (WMO) - International Cloud Atlas. https://cloudatlas. wmo.int/en/drifting-­ and-­blowing-dust-or-­­ sand.html

1.2 PM10 and PM2.5 The long-range airborne transport of dust particles significantly impacts air quality. The duration of dust storm events varies considerably; some dust storms last for only a few hours, while others can extend to several days. Dust outbreak intensity is commonly expressed in terms of the ambient air levels of PM10 (particles with aerodynamic diameter of less than 10 μm). The influence of total suspended particles (TSP) and PM2.5 (particles with aerodynamic diameter of less than 2.5 μm) is also expressed in desert dust episodes. Studies have shown sharp increase in particle concentrations during dust storm events. The ranges (lowest-highest) of maxima values reported in the Middle East during desert dust outbreaks for PM10 and PM2.5

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were 700–5619 and 42–1368  μg/m3, respectively (Goudie, 2014; Querol et  al., 2019). In general, PM10 is highly affected by dust outbreaks, and the dust grain size modes between 3–7 μm are considered representative of PM10 mass (Querol et al., 2019). There is a moderate amount of dust affecting PM2.5. For instance, at a Sahara site 3% of TSP desert dust is attributed to PM10, and  300 μg/m3

(continued)

Main findings Dust events with a lag of 3 days were significantly associated with total respiratory hospitalization for males and females, with RRs of 1.14 (95% CI: 1.01–1.29) and 1.18 (95% CI: 1.00–1.41); dust events with a lag of 4 days were significantly associated with upper respiratory tract infection in males (RR 1.28, 95% CI 1.04–1.59), and dust events with a lag of 6 days were significantly associated with pneumonia in males, with an RR of 1.17 (95% CI 1.00–1.38).

4  Exposure of Dust Storms and Air Pollution (PM10, PM2.5) and Associated Health… 59

7

6

Study period, location 2005, Mongolia

(Wiggs et al.. 2001–2001, 2003) Karakalpakstan (Uzbekistan)

Reference (Pan et al., 2006)

Table 4.2 (continued) Health outcomes Respiratory health, peak expiratory flow rate (PEFR) values of the lungs

Respiratory health

Target population Schoolchildren

Children (7–11)

Respiratory health Questionnaire

Dust deposition using simple dust deposition Traps, and total suspended dust in the PM10 range

Method Dust event definition Daily PM10 and PM2.5 Time-series analysis and multiple regression model

Main findings Respiratory symptoms were positively associated with the concentrations of PM10 and PM2.5 (P  16 years

All ages

Health outcomes Cerebral ischemic attack, epilepsy, headache

All ages

Target population All ages

Daily PM10

AOD > 0.5

Daily PM10

Spearman correlation

Generalized additive regression models

WHO AirQ2.2.3

Method Dust event definition Pearson correlation Daily PM10

Main findings Hospital admissions for patients with cerebral ischemic attack, epilepsy, and headaches on dusty days increased significantly compared to hospital admissions on clean days. Significant correlation between bronchial asthma and PM10 (r = 0.292, p ≤ 0.05) Cognitive cardiac failure hospitalization 2.209 (CI: 2.069–2.359), acute coronary syndrome hospitalization 1.304 (CI: 1.273–1.336). No significant association between dust events and total mortality. 4.7% (95% CI: 3.2–6.7%) of respiratory diseases and 4.2% (95% CI: 2.6–5.8%) of cardiovascular diseases, were attributed to PM10 concentrations above 10 μg/m3.

62 A. Al-Hemoud

(Alangari et al., 2015)

9

10 (Ebenstein et al., 2015)

(Geravandi et al., 2017)

8

7

Reference (Khaniabadi, Daryanoosh, et al., 2017a)

2007–2009, Israel

2/2012–3/2012, Riyadh (Saudi Arabia)

2010–2012, Ahvaz (Iran)

Hospital admissions due to respiratory conditions

Asthma

Children (2–12)

All ages

Respiratory diseases

Health outcomes COPD and respiratory mortality

All ages

Study period, location Target population 2015–2016, Ilam All ages (Iran)

PM10 > 1000 μg/m3

PM10 > 50 μg/m3

Dust event definition Daily PM10

Poisson regression Daily PM10 models

Spearman correlation

Spearman correlation

Method WHO AirQ2.2.3

(continued)

Main findings 4.9% of hospital visits for COPD (95% CI: 3.0–6.8%) and 7.3% of respiratory mortality (CI: 4.9–19.5%) were attributed to PM10 concentrations above 10 μg/m3, respectively. Significant correlation between PM10 and respiratory diseases (r = 0.64, p ≤ 0.007). No correlation between the average daily PM10 levels and children with acute asthma between days with PM10 > 1000 μg/m3, representing major sand storms, plus the following 5 days and other days with PM10  200 μg/m3 Pearson correlation Daily PM10

Method Additive Poisson regression models

Significant correlation between PM10 and cardiovascular patient emergency visits (r = 0.48, p ≤ 0.05), and insignificant correlation between PM10 and respiratory patient emergency visits (r = 0.19, p > 0.05).

Main findings At a lag of 1 day dust storm days were associated with ACS hospitalization 1.007 (95% CI 1.002–1.012). Hospitalization for COPD exacerbation 1.16 95%CI: 1.08–1.24; p  200 μg/m3 No significant association between dust storm events and same-day respiratory mortality (RR = 0.96; 95%CI 0.88–1.04), cardiovascular mortality (RR = 0.98; 95%CI 0.96–1.012), or all-cause mortality (RR = 0.99; 95%CI 0.97–1.00). PM10 > 200 μg/m3 Significant association with an increased risk of same-day asthma and respiratory admission, adjusted relative risk of 1.07 (95% CI: 1.02–1.12) and 1.06 (95% CI: 1.04–1.08), respectively.

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2

(Stafoggia et al., 2016)

Reference Mediterranean 1 (Lorentzou et al., 2019a, b)

2001–2010, 13 European cities of the Mediterranean basin

9/2018, Crete (Greece)

Study period, location

Table 4.2 (continued)

Emergency visits for allergies, COPD exacerbations, dyspnea

Mortality: All-cause, respiratory, CVD. Hospital admissions: respiratory and cardiovascular

Cardiovascular and respiratory admissions for patients ≥15 years old and respiratory admissions in the age group 0–14 years

Health outcomes

All ages

Target population

Dust event definition

Main findings

A significant correlation between PM10 and: (a) ED visits for dyspnea (r = 0.929, p  125 μg/m3 lasting for at least 3 h at the background monitoring station PM10 ratios PM10 remote ≥ 0.5 × PM10 urban PM10 remote/PM10 urban > Annual median PM10/NO2 > 0.6 Horizontal visibility 0.105/km (heavy) and 0.066–0.105/km (moderate) Dust extinction coefficient >0.1/km Aerosol optical >0.4 depth (AOD) High AOD values that corroborate PM10 being above the 90th percentile for a given year Satellite images and Dust presence as shown by the corrected aerosol maps reflectance images Backward-­ trajectories Public weather databases

Large air masses from the Sahara Emergency management, law enforcement, sky warn spotters, damage surveys, media reports, and the general public

Examples from epidemiological studies Chan et al. (2008) Hong et al. (2010) Al-Taiar and Thalib (2014) Yitshak-Sade et al. (2015) Yang et al. (2005a, b) and Chang et al. (2006) Perez et al. (2008) Samoli et al. (2011a, b) Mallone et al. (2011) Li et al. (2018) and Ma et al. (2016) Ogi et al. (2014) Achilleos et al. (2019) Nakamura et al. (2015, 2016) Ueda et al. (2012) Watanabe et al. (2015) Achilleos et al. (2019) Samoli et al. (2011a, b)

Achilleos et al. (2019) and Tobías and Stafoggia (2020) Tobías et al. (2011a, b) and Zauli Sajani et al. (2011) Crooks et al. (2016)

3.1 Dust as a Binary Variable There are different ways in which days can be dichotomized into “dust event” and “non-dust event” days (Table 6.1). Some studies in the Middle East relied on a certain PM10 threshold, in which “dust event” days are those that exceed a pre-specified value such as two standard deviations above the background average concentration of PM10 or simply an arbitrary cut-off of PM10  >  200  μg/m3 (Al-Taiar & Thalib, 2014; Thalib & Al-Taiar, 2012; Yitshak-Sade et  al., 2015). A study in Italy used

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PM10:NO2 ratio >0.6 to separate non-dust days from days with higher ground-level PM10 concentration than would be expected from traffic-related air pollution only (Mallone et al., 2011). One can also utilize the presence of remote (or background) and urban monitoring stations to derive dust event days, given that remote stations are affected mainly by natural sources, and in some cases by transported pollution, such as dust storms (Samoli et al., 2011a, b; Perez et al., 2008). Others relied on days with poor visibility metrics to assign dust days. Horizontal visibility is defined as the maximum distance at which an observer can see and identify an object in a horizontal plane (in km). It was used as an indicator of poor air quality when the hourly distribution of visibility dropped below the 25th percentile (Achilleos et al., 2019). Investigators also used optical remote sensing technologies such as the polarization Light Detection and Ranging (LIDAR) system that can measure real-time laser reflection from ground sites to estimate presence of dust particles (Nakamura et al., 2015). Another remote sensing measure is aerosol optical depth (AOD), which can measure high aerosol loadings in the air based on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. A study in Kuwait used dust presence shown on corrected reflectance images from MODIS and aerosol map images from forecasting models developed by the Barcelona Supercomputing Center (BSC) (Achilleos et al., 2019). Air mass back-­ trajectory analyses calculated using means of the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model can be used to identify days in which large air masses from desert regions (e.g., Sahara) were transported to the geographical area of interest (Tobías et al., 2011a, b). In fact, one common approach is to use a combination of tools including meteorological products, aerosol maps, air masses back-trajectories, and other satellite images (Tobías & Stafoggia, 2020). One study in the United States identified dust storms from a weather service database that is derived from different and less objective sources such as sky warn spotters, damage surveys, law enforcement, media reports, and even reports from the general public (Crooks et al., 2016).

3.2 Dust as a Continuous Variable Apart from identifying dust days and non-dust days, there are more complex ways in which dust can be quantified and then used to estimate adverse health effects. First, there is the European Union (EU) reference method, which follows a multi-­ stage approach using PM10 levels from monitoring sites (Tobías & Stafoggia, 2020; Escudero et al., 2007). Regional daily PM10 in non-dust days is used to calculate a 30-day moving 40th percentile for each day of the series; this will serve as the “expected” PM10 values in the absence of dust. Then, the difference between the observed PM10 in dust days and the expected PM10 will be the dust contribution or the “desert PM10.” Another way to quantify dust is using the LIDAR dust extinction

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coefficient, a method that has been commonly applied in Eastern Asia for Asian dust (Kashima et al., 2012). LIDAR can recognize the distinct shape of the Asian dust particles, especially when the lower atmosphere is well mixed. An extinction coefficient of 0.1/km in some Eastern Asian countries can be translated to about 100 μg/ m3 of Asian dust (Kashima et al., 2012; Sugimoto et al., 2003). Other multi-model and reanalysis products can be utilized to quantify dust across different regions. For example, the MERRA-2 products are used to develop a global reanalysis of surface-­ level dust (Randles et al., 2017). In Northern Africa, the Middle East, and Europe, the World Meteorological Organization (WMO) provides a multi-model product that is able to estimate dust (Huneeus et al., 2016). Furthermore, PM2.5 and PM10 samples can be collected on filters using impactor samplers. These filters can then be weighed for gravimetric mass analysis and analyzed using X-ray fluorescence (XRF) to measure trace elements (Brown et  al., 2008; Alahmad et al., 2021). Once these speciated samples are obtained, receptor models such as positive matrix factorization (PMF) are applied to identify major sources of PM and also characterize the magnitude of these sources and their contribution to the total mass (Kim et al., 2003). Dust source factors are identified from the presence of crustal elements (Alolayan et al., 2013). The daily concentration (in μg/m3) of dust factors isolated from the total PM as estimated by the PMF model can be used for epidemiologic investigations.

4 Epidemiological Modeling of Dust Exposure Conventionally, the effects of dust on health are estimated in the “short-term” rather than “long-term.” For example, if today was a dust storm day, what are the immediate effects on mortality in a given population on that same day or over a couple of days. As for long-term effects, suppose we are interested in assessing the relationship between dust storms and lung cancer, then a prospective cohort study would be suitable to compare the relative risk of developing lung cancer among individuals who live in areas affected more by dust storms to those who are not. These designs are not straightforward, and we are not aware of any similar studies conducted by observational epidemiologists, although there have been some in vivo attempts to understand the cellular carcinogenic response on human cells from dust particles (Ardon-Dryer et  al., 2020). Exposure assessment here would be even harder to assign with a large possibility for measurement error or misclassification (differential and non-differential). For the purposes of this chapter, we will focus on two common designs in environmental epidemiology that examine the “short-term” effects of dust storms on health: time-series and case-crossover studies. For simplicity, the illustrative examples provided in this section will assume a binary exposure variable for dust storms (dust day vs. non-dust day).

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4.1 Study Designs 4.1.1 Time-Series Studies This ecologic design is often employed by investigators who want to quantify the short-term association of dust storms and mortality (all-cause and cause-specific), myocardial infarction, stroke or other acute disease-specific hospital admissions (Bhaskaran et al., 2013). Time-series regression is a common approach in econometrics in which hourly or daily series of stock market prices, for example, are used to forecast their movements (Granger, 1969). When applied to environmental epidemiology, investigators start with count data in a sequence form recorded at regular time intervals, usually day to day. If we take all-cause non-accidental mortality (International Classification of Diseases-10; A00-R99) as the outcome of interest, then daily counts of this mortality series merged with some column that assign these days as either dust storm or non-dust storm day will suffice to construct the needed dataset for a time-series regression analysis (Table 6.2). The dataset may also contain daily measures of potential confounders such as temperature (confounding is discussed later in this chapter). The question that can be addressed is therefore “Is there an association between day-to-day variation in dust storm days and daily risk of death?” Because the dependent variable here is counts of death, a Poisson distribution would be suitable to run the regression. Yet, under a Poisson distribution, the variance of outcome counts must equal the mean. This assumption is usually not met in real-life settings and the variance is found to be greater than the mean; the data are then “over-dispersed.” Alternatively, a quasi-Poisson or a negative binomial distribution can relax this assumption; therefore, we do not have to worry about over-­ dispersion of the count data (Imai & Hashizume, 2015; Alahmad et al., 2019). The raw count data are likely to be dominated by seasonality and long-term trends. For example, we know that more people die in the winter compared to warm months. We may also see a general trend in the rate of mortality over a series that has many years (mostly downward in many developed countries). It is, therefore, necessary to effectively separate these cyclical and long-term patterns from the short-term relationship between dust storm days and daily mortality counts. This Table 6.2  Example dataset in any given city that is suitable for time-series analysis

Date 1-Jan-­ 2019 2-Jan-­ 2019 3-Jan-­ 2019 …

Mortality count 43

Dust storm day (1; dust storm, 0; non-dust storm) 1

Temperature (C) 6.4

Other time-varying confounders …

38

0

4.0

…

32

1

8.9

…

…

…

…

…

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can be efficiently achieved by fitting a flexible spline function of time. Splines are vectors of mathematical smoothing parameters that allow curves to take a cyclical shape. Splines are governed by the number of knots (or degrees of freedom) that can be sufficient to remove the effect of seasonality. Too few knots can result in under-­ fitting that do not capture the main cyclical and long-term patterns, and too many knots can result in over-fitting making the curve too wiggly and capturing noise in the data. Fitting natural cubic splines with 4 to 7 degrees of freedom per year is commonly applied in environmental epidemiology time-series studies. Additionally, we may also want to control for differences in dust and mortality in different days of the week; i.e., there could be less pollution and less mortality during weekend days vs. weekdays (Bhaskaran et al., 2013). Generally, the statistical models for time-series analyses can be as follows: log  E Yi   Intercept  Dust i  ns  datei , df  4  k 

 Day of the week i  Ci

(6.1)

where E[Yi] is the expected death count for day i, ns are natural splines, k are number of years in the series, and C′ represents time-varying confounders such as weather variables. Dust in day i is the exposure of interest modeled as an indicator variable (1: dust storm days, and 0: non-dust storm days). The interpretation of the dust variable would be: “the mortality risk ratio comparing dust days to non-dust days.” Here, we account for seasonality and long-term trends by fitting natural splines with 4 degrees of freedom per year. Day of the week is a categorical variable (7 dummy variables from Monday to Sunday). These models estimate the effects of dust storm on the same day. However, delayed or lagged effects are commonly seen. That is, yesterday’s dust may be a better predictor of today’s mortality rather than today’s dust. Actually, these lagged effects can stretch for a number of days rather than just yesterday. Lagged exposure up to 6  days (lag 0–5  days) is commonly examined in the literature (Achilleos et al., 2019). It is important to note here that the dust–mortality relationship is typically heterogeneous between populations, and it is relatively less generalizable over different regions (Hashizume et al., 2020; Karanasiou et al., 2012). People living in desert environments/cities tend to have certain behavioral and adaptation responses such as staying indoors, keeping tight homes during dust events, use of filter or household air conditioning, and other factors that differ from one place to another. 4.1.2 Case-Crossover Studies The case-crossover design is a special case of case-control studies that was initially developed in the 1990s to study short-term or “transient” effects of certain exposures on acute myocardial infarctions (Maclure, 1991). Since then, it has become a dominant design in air pollution epidemiology (Jaakkola, 2003). Case-crossover

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designs consider all study subjects to be cases; these are individuals who experienced an adverse health episode that occurred in a defined point of time. The idea is that each subject serve as his or her own control and the inference is based on comparing the exposure distribution. In estimating the effects of dust storms on the risk of dying, similar to time-series studies, the question of interest is still the short-term association between dust and death. We start with dates in which death happened for our study population, then we choose a number of other days (control days) in which we know the subjects did not die on. After that, the exposure distribution (i.e., dust storm) is compared between the case day and the control days. The selection of control days can effectively remove the seasonal effect by “matching” since we can choose days of in the same month. Even better, we can choose control days of the same day of the week to account for patterns during the week (weekends vs. weekdays) and any potential serial correlation between days that are in a row (Fig. 6.1). Additionally, although it is unintuitive, selecting control days that are after the event offer an attractive option; they allow us to account for long-term trends in the outcome and the exposure. In other words, if we only select control days before the case day, on average, the control days could have higher exposures since overall air quality is improving over time, and our exposure might appear protective when in fact this observation is biased by the long-term declining pattern of exposure. Generally, the statistical models for case-crossover analyses are estimated using a conditional logistic regression:

January 2019 Sun

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Fig. 6.1  Selection of control days (O) for a person who died (X) on January 16, 2019, in a case-­ crossover design

6  Epidemiology of Dust Effects: Review and Challenges



 pij log   1 p ij 

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    Intercept i  Dust ij  Cij  (6.2)

where pij is the probability of dying in a stratum i (a matched set for one subject) for a case (j = 1) or a control (j = 0) day; C′ represents time-varying confounders, and dust is the exposure of interest modeled as an indicator variable (1: dust storm days, and 0: non-dust storm days). The interpretation of the dust variable would be: “the odds ratio of death comparing dust days to non-dust days.” Here, we account for seasonality, day of the week, and long-term trends by matching. And similar to time-series analyses, these models can incorporate lagged effects of the exposure. More attractive alternatives such as the conditional Poisson models are found to be simpler to implement and quicker to run as compared to conditional logistic models (Armstrong et al., 2014).

4.2 Confounders In conventional epidemiology, classic confounders are usually individual risk factors like age, sex, body mass index (BMI), alcohol, and smoking. But in a time-­ series design, these factors cannot confound the relationship between dust storms and mortality for a number of reasons. First, the time-series structure of the dataset is based on counts per day (or any time interval of choice) and not individuals. With that in mind, only variables that vary from day to day (or according to the unit of analysis) can be considered as potential confounders Bhaskaran et al., 2013). The classic variables mentioned above are unlikely to meet this criterion. Second, any variable that changes from day to day needs to be associated with the fluctuations of the environmental exposure in order to confound the relationship. For example, one’s age or smoking habits cannot be related to dust storms occurrence or variation. For case-crossover studies, recall that each case is matched to the same person at a nearby day in which he or she did not have the event. Therefore, by design (i.e., matching), potential confounding by these time-invariant factors is effectively controlled for (Jaakkola, 2003). Nevertheless, whether it is time-series or case-­crossover analysis, age, or smoking status may account as effect measure modifiers in this exposure-response relationship, but not as confounders. So, what could confound the relationship then? Confounders must be associated with both the exposure and the outcome and must not be on the causal pathway between the exposure and the outcome. We have to think of variables that are associated with day-to-day fluctuations in dust storms and are also associated with increased mortality. Take temperature as an example here. Maximum hot ambient temperatures coincide with dust storm days during summer, and many papers reported higher temperature and ozone levels during Sahara dust days in Europe (Mallone et al., 2011; Tobías et al., 2011a, b; Jiménez et al., 2010). Such temperatures and heatwaves are also associated with increased short-term risk of mortality

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(Gasparrini et al., 2015). Other confounders that are commonly considered in the dust-mortality studies include the following: relative humidity, atmospheric pressure, flu epidemic days, and public holidays (Karanasiou et al., 2012). Some investigators considered correlation and potential confounding effects from other pollutants such as SO2, NO2, or O3 and have adjusted for them in statistical models (Samoli et al., 2011a, b; Middleton et al., 2008).

4.3 Question of Interest In the previous section, we were investigating dust storms as the exposure of interest in the association with mortality. There are, however, other epidemiological questions that can be explored using the same study designs. Namely, dust being a confounder or an effect measure modifier in the overall air pollution and mortality relationship (Table 6.3). 4.3.1 Dust as a Confounder Let us take the relationship between PM10 and mortality in a time-series analysis as an example. Here, dust storm days significantly increase the concentrations of PM10, and dust storms are associated with daily mortality. This put “dust storm days” as a potential confounder. If we therefore adjust for dust storm days in the model equation, the coefficient for PM10 can be interpreted as the increase in mortality rate for every 1 μg/m3 increase of PM10, independent of dust storms. Conceptually, this can be achieved by adding PM10 as an additional independent variable as in the following equation:

Table 6.3  What is the causal question of interest when we explore the role of dust in the overall air quality (particulate matter) and mortality relationship? “Dust” modeled as: Exposure of interest Confounder

The question being answered Tobías and Stafoggia (2020): “Is mortality higher on dust days compared to non-dust days?” “Is there an association between daily PM concentrations and mortality, independent of dust advections?”

Effect measure modifier

“Is the association between daily PM and mortality different on dust versus non-dust days?”

DAGa

Adopted from Tobías and Stafoggia (2020) a Directed acyclic graph (DAG) with the causal pathway of interest being depicted by the dotted arrow

6  Epidemiology of Dust Effects: Review and Challenges



log  E Yi   Model 1  PM10i

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(6.3)



where E[Yi] is the expected death count for day i, and model (1) includes the adjusted model described previously with dust storm as an indicator variable. In the case where a certain city lacks PM10 measurements from remote monitoring stations, and cannot separate urban pollutants (e.g., traffic pollution) from dust, applying this model can tease out the potential relative increase in mortality per unit increment of PM10 in urban stations alone that are not related to dust. Yet, this approach is far from perfect given that particulate matter composition differs from one location to another. 4.3.2 Dust as an Effect Modifier If we assume that the PM10 composition is different on dust storm vs. non-dust storm days, then we might as well consider that the associated PM10 mortality differs by presence of dust. This is a classic condition of effect measure modification by dust storms. To reiterate, we are now asking the following question: “Is the association between daily PM and mortality different on dust versus non-dust days?” (Tobías & Stafoggia, 2020). This can be achieved by including an interaction term between PM10 and the dust variable as shown in this model equation:

log  E Yi   Model 1  PM10i  Dust i  PM10i



(6.4)

where E[Yi] is the expected death count for day i, and model (Zhang et al., 2016) includes the adjusted model described previously with dust storm as an indicator variable. The effect of PM10 on mortality during dust storm days would be the sum of the coefficients for PM10i and the interaction term Dusti × PM10i. Meanwhile, on non-dust storm days (dust = 0), the relative increase in mortality for every 1 μg/m3 increase of PM10 is represented by the PM10i coefficient alone. The additional benefit of this model is examining whether the interaction between PM10 and dust is statistically significant; that is when the p-value (from Wald test) for the interaction term is 44 μg/ m3)

PM10 66.93 ± 25.11 μg/m3

Pollutant PM2.5 44 μg/m3

(continued)

Gender, age, family income, maternal education, smoking in the house, infant body weight at birth, number of siblings, open/close windows, and air purifier was on or off

Confounder control Meteorological parameters (including temperature and rainfall), birth order, child sex, multiple birth, maternal age at birth, maternal age at birth squared, education, mother’s religion, safe water, DPT immunization, maternal mortality, child anemia, out-of-pocket %, health expenditure, tractors/area, % GDP agriculture, poverty gap, and GNI Meteorological parameters (temperature, rainfall, and wind speed), NOx, O3, CO, and SO2

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Taiwan

Asian dust 1997– Respiratory 2007 system

Yu et al. (2012)

Japan

Asian dust 2010– Respiratory 2013 system

Taiwan

Studied area Japan

SDS origin Period Interest of study Asian dust 2005– Respiratory 2009 system

Wang et al. Asian dust 2000– Respiratory (2014) 2009 system

Nakamura et al. (2016)

References Kanatani et al. (2010)

Table 7.2 (continued) Outcome Increasing in the crude (ORs) of asthma hospitalization with the value of 1.88 (95% CI:1.04–3.41, P = 0.037) was reported per 0.1 mg/m3 increase in daily mineral dust concentrations Increasing in the adjusted ORs with the values of 1.244 (95% CI, 1.128– 1.373), 1.314 (95% CI, 1.189–1.452), and 0.273 (95% CI, 1.152–1.408) were reported in emergency visits due to respiratory illnesses at the lag day 0, lag day 1, and lag day 2 Among preschool children, hospital admissions compared to nondusty days increased by 5.66% and 6.37% at lag day one and lag day three, respectively The strongest correlation was observed at the lag day 3 with the value of 2.19% (95% CI = 1.95–2.43, P = 0.0001)

Meteorological parameters (temperature and relative humidity), NO2, SO2, and photochemical oxidants [Ox]

SPM (>50 μg/ m3)

PM10 90.64 μg/m3

Meteorological parameters (temperature) and day-of-week effects

PM Meteorological parameters (short-term levels (temperature), SO2, CO, and O3 ≥100 μg/m3 & long-term average≥ 50 μg/m3)

Confounder control Meteorological parameters (temperature, air pressure, relative humidity, wind speed), NO2, SO2, photochemical oxidants [Ox], nonmineral dust particles, pollen, age, and gender

Pollutant PM (>0.1 mg/m3)

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Taiwan

Chien et al. Asian dust 1997– Respiratory (2012) 2007 system

South Korea

Taiwan

Asian dust 2000– Infant mortality 2011

Jia and Ku (2019)

Studied area Taiwan

Chien et al. Asian dust 2002– Conjunctivitis (2014) 2007 morbidity

SDS origin Period Interest of study Asian dust 1998– Respiratory 2007 system

References Yu et al. (2013)

Outcome The relative ratio of preschool children’s respiratory clinic visits was statistically elevated across the studied areas ranging from 1.01 to 1.08 during post-ADS periods compared to non-ADS periods The infant mortality was increased by 0.414 per 100,000 by the occurrence of Asian dust events alongside China’s general pollution (one SD increase in mean AQI) During ADS events, an acute increase was observed in the relative rate of conjunctivitis clinic visits with the value of 1.48% (95% CI = 0.79–2.17) Increasing the PM10 levels by 10.0 μg/m3 was linked to the increase of 0.99% (95% CI = 0.98–1.01) in preschool clinic visits.

Confounder control Meteorological parameters (temperature), day-of-week effects, NOx, O3, and SO2

Meteorological parameters (temperature), and day of- the-week

PM10 (>100 μg/m3)

(continued)

Meteorological parameters (temperature), day of-the-week, NO2, O3, CO, and SO2

PM10 (>100 μg/m3)

PM10 Meteorological parameters 66.93 ± 25.11 μg/m3 (temperature, rainfall, and wind speed), NOx, O3, CO, and SO2

Pollutant PM10 (>100 μg/m3)

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Australia

Asian dust 2000– Respiratory 2009 system

Australian 2004– Respiratory and dust 2009 asthma-related morbidity

Merrifield et al. (2013)

USA

Studied area Kuwait

Kang et al. (2012)

2000– Respiratory 2003 system

Period Interest of study 1996– Mortality risk 2000 due to respiratory, cardiovascular, and all-causes

American dust

SDS origin ME dust

Grineski et al. (2011)

References Al-Taiar and Thalib (2013)

Table 7.2 (continued) Outcome Among children (0–15 years old), a spike was noticed in the risk of same-day respiratory and all-cause mortality, which was not statistically significant An increased risk of hospitalization for asthma and acute bronchitis with the values of 1.19 (95% CI = 1.00–1.41) with 3 days lag after a low wind episode and 1.33 (95% CI = 1.01– 1.75) with 1 day lag after a dust episode, respectively Not statistically significant increase in pneumonia admissions among children (0–6 years old) during dusty days relative to nondusty days The relative rates of respiratory and asthma admissions with the values of 1.286 (95% CI = 1.192– 1.392, p 100 μg/m3)

PM10 (>11,000 μg/m3)

Horizontal visibility Meteorological parameters (temperature), NO2, PM2.5 reduced to below 10 km

Pollutant PM10 (>200 μg/m3)

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Thalib and Al-Taiar (2012)

References Ueda et al. (2010)

ME dust

1996– Respiratory 2000 system

SDS origin Period Interest of study Asian dust 2001– Respiratory 2007 system

Kuwait

Studied area Japan Outcome No statistically significant association between Asian dust outbreaks and asthma hospital admissions A statistically significant increase in the risk of asthma hospital admission on the same day was observed compared to nondusty days PM10 (>200 μg/m3)

Meteorological parameters (temperature and relative humidity), and day of- the-week

Pollutant Confounder control Horizontal visibility Meteorological parameters reduction (temperature and relative humidity), NO2, Ox, CO, and SO2

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and advisories and increment of infants’ birth weight by 13.6 and 4.4  g, respectively. The observed associations show the effectiveness of additional pollution alerts in enhancing public health. Each additional declared Yellow Dust Event during pregnancy leads to an increment of birth weight by about 8.6 g. Advisories and warnings were linked with a reduction in the possibilities of premature birth and its lethal growth and an increase in the infant’s gestational age. Also, the number of declared Yellow Dust Events, warnings, and advisories had a significant positive effect on birth weight in any trimester, and there were no statistically notable changes from each other among the three trimesters (Altindag et al., 2017). The linkage between infant mortality (including cardiovascular and respiratory deaths) and Asian dust outbreaks in the presence of China’s general pollution was investigated during 2000–2011 (Jia & Ku, 2019). Asian dust events, alongside China’s general pollution (one SD increase in mean AQI), could increase the infant mortality by 0.414 per 100,000 (about 1.09% of the mean level) in South Korea (Jia & Ku, 2019).

9.3 United States of America The plausible linkage between birth outcomes and dust storm events was addressed by Jones (2020) across the United States of America between 2010 and 2017 (Jones, 2020) (Table 7.2). Dust storm occurrence during pregnancy increased the possibility of prematurity and low birth weight in new borns by 1.8 and 1.4 percentage points, respectively. Dust events within the mother’s gestation period decreased the gestation length by 0.157 weeks (about a day) and birth weight by 30.49 g, compared to bases of 38.52 weeks and 3279 g, respectively. The study showed that dust storms pose the most significant negative impacts in the third trimester, whereas the magnitude of adverse effects was four to five times larger than other trimesters. The coincidence of dust episodes in the third trimester resulted in reduced gestation length and birth weight by 0.701 weeks (about 5 days) and 135.32 g, respectively (Jones, 2020). The third trimester is an important phase of fetal weight gain, easily interrupted by pollution exposure. Moreover, stress and infections in the third trimester can cause premature delivery. Contrary, since there was no statistically significant impact during the second trimester, cognitive brain development pathways were ruled out. The frequency of dust events can also impact the pregnancy. Mothers experiencing six or more episodes gave birth to babies with low birth weight by 5.7 (8.7) percentage points, while those experiencing one event had babies with low birth weight by 0.9 (1.1) percentage points. Interestingly, each NWS (National Weather Service) dust warnings could decrease the adverse impact of dust on prematurity (by 0.8 percentage points) and low birth weight (by 0.9 percentage points) (Jones, 2020).

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9.4 Caribbean Region In the Caribbean region (Guadeloupe archipelago), the impact of Saharan dust episodes on preterm births was investigated, including 909 pregnant women during 2004–2007 in the Guadeloupe archipelago (Viel et al., 2019) (Table 7.2). Increased adjusted Odd Ratios (ORs) for the risk of preterm birth were witnessed for both the average PM10 levels and the proportion of intense Saharan dust intrusions in all births with the values of 1.40 per SD changes (95% CI = 1.08 to 1.81) and 1.54 per SD changes (95% CI = 1.21 to 1.98), respectively. Similar ORs were produced for spontaneous preterm births with more comprehensive 95% CIs, while lower ORs were produced for induced preterm births (Viel et al., 2019). In a parallel study, the impact of Saharan dust outbreaks on severe SGA (small for gestational age) births was investigated, including 909 pregnant women during 2004–2007  in the Guadeloupe archipelago (Caribbean region) (Viel et  al., 2020) (Table 7.2). An increased adjusted Odds Ratio (OR) was observed for SGA for birth weight with the value of 1.85 (95% CI = 1.22–2.82, P = 0.004), while significant changes from unity were not shown in ORs of SGA for length and SGA for head circumference with values of 1.03 (95% CI = 0.75–1.41, P = 0.84) and 0.93 (95% CI  =  0.70–1.25, P  =  0.65), respectively (Viel et  al., 2020). Considering different subtypes of SGA weight, symmetrical SGA had an 18% prevalence and showed the highest adjusted Odds Ratios (ORs) with the value of 3.28 (95% CI = 1.08–10.02, P = 0.04). A significant increase was not observed in ORs value of asymmetric SGA (OR = 1.52, 95% CI = 0.97–2.40, P = 0.07). Since symmetric SGA usually starts in the first trimester of pregnancy, the observed high OR suggests that Saharan dust outbreaks impact pregnancy through severe placental insufficiency in the early stages (Viel et al., 2020).

9.5 Spain On the other hand, a conducted study in Barcelona, Spain, examined with 3565 pregnant women to figure out the adverse impacts of Saharan dust events on pregnancy complications, including bacteriuria and preeclampsia, as well as pregnancy outcomes such as gestational age at delivery and birth weight during 2003–2005 (Dadvand et al., 2011) (Table 7.2). There was no statistically notable linkage among Saharan dust outbreaks and pregnancy complications (bacteriuria and preeclampsia) and outcomes (birth weight) in their study. It could be because of the small and limited sample size making the research vulnerable to type II errors. There was only a small but statistically notable rise in gestational age at delivery linked to several dust events during the whole pregnancy and third trimester with the values of 0.5 and 0.8 days, respectively (Dadvand et al., 2011).

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10 Geographical Differences on Respiratory System 10.1 Japan The impact of Asian dust on Japanese new-borns’ developed respiratory/nose/eye was investigated during 2014–2016. An increased adjusted OR (for both nonwheezers and wheezers) with the value of 1.04 (95% CI = 1.01–1.07) was reported per 10  μg/m3 increase in PM2.5. Nose symptoms were reported among wheezers after a latent period of 4 and 5 days, followed by respiratory symptoms (Itazawa et al., 2019). Another conducted study in Japan studied the impact of Asian dust on Asthma hospitalization during 2005–2009. The crude Odds Ratio (OR) of asthma hospitalization increased by the value of 1.88 (95% CI = 1.04–3.41, P = 0.037) per 0.1 mg/m3 increase in daily mineral dust concentrations (heavy dust episodes). The increased risk in infants was observed later in the week (Kanatani et al., 2010). Kanatani et al. (2014) observed the linkage among Asian dust outbreaks and the development of allergic, asthma, and respiratory illnesses in Japanese infants during 2011–2014 (Kanatani et al., 2014). Preschool children including infants were examined to study the association among Asian dust on respiratory illnesses including common cold, bronchitis, bronchial asthma, and pharyngitis during 2010–2013. An increased adjusted OR with the values of 1.244 (95% CI  =  1.128–1.373), 1.314 (95% CI = 1.189–1.452), and 0.273 (95% CI = 1.152–1.408) were reported in emergency visits due to respiratory illnesses at the lag day 0, lag day 1, and lag day 2, respectively (Nakamura et al., 2016). On the other hand, a study on the connection among Asian dust outbreaks and asthma hospital admissions in Fukuoka during 2001–2007 did not reveal a statistically significant association (Ueda et al., 2010).

10.2 Taiwan Taiwanese preschool children (including infants) participated in a study conducted by Wang et  al. (2014) to examine the impact of Asian dust on asthma hospital admissions during 2000–2009. AD episodes significantly affected preschool (0–6 years old) as well other age groups. Among preschool children, hospital admissions compared to nondusty days increased by 5.66% and 6.37% at lag day one and lag day three, respectively. However, the number of asthma admission was not significantly affected on the day of the AD episode (Wang et al., 2014). Yu et al. (2012) studied the connection between respiratory clinic visits (including acute respiratory infections, allergic rhinitis, pneumonia and influenza, extrinsic allergic, alveolitis, bronchiectasis, asthma, and other diseases of the upper respiratory tract) and AD outbreaks during 1997–2007 in Taiwan (Yu et al., 2012). The correlation between clinic visits and AD events was negative until the second day after the dust storm. After the latent period of 2 days, the correlation became positive and lasted until the

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seventh day lag (except for lag day 6). The relative ratio was increased by 22.53% (95% CI = 22.69–22.36, P = 0.0001) for preschool children. Among preschool children (including infants), the strongest correlation was observed at the lag day 3 with the value of 2.19% (95% CI = 1.95–2.43, P = 0.0001) (Yu et al., 2012). The changes in the relative ratios of preschool children’s (including infants) respiratory clinic visits (including acute respiratory infections, pneumonia and influenza, allergic rhinitis, bronchiectasis, asthma, extrinsic allergic alveolitis, and other diseases of the upper respiratory tract) due to the Asian dust events in Taiwan during 1988–2007 were explored (Yu et al., 2013). The relative ratio of preschool children was statistically elevated across the studied areas ranging from 1.01 to 1.08 during post-ADS periods compared to non-ADS periods (Yu et al., 2013). In another conducted study, the relative rates of clinic visits for respiratory illnesses were increased during post-ADS intrusions among preschool children (including infants) with the value of 2.54% (95% CI = 2.43–2.66) compared to pre-ADS periods during 1997–2007 (Chien et al., 2012). Contrary, the RR of clinic visits was notably lower during ADS episodes by −1.62% (95% CI = −1.71–1.52) compared to pre-ADS periods. It was estimated that any 10.0  μg/m3 in PM10 levels were linked to the increase of 0.99% (95% CI = 0.98–1.01) in preschool clinic visits (Chien et al., 2012). On the other hand, a 10-year study in Taiwan evaluated the connection among Asian dust outbreaks and the daily number of pneumonia admissions among children (0–6 years old). The number of admissions increased during dust events compared to nondusty days, but it was not statistically significant (p  >  0.05) (Kang et al., 2012).

10.3 USA During 2000–2003, children (1–17 years old) were examined to explore the impacts of local dust events on acute bronchitis and asthma hospital admissions in Texas, USA (Grineski et al., 2011). An increased risk of hospitalization for acute bronchitis and asthma with the values of 1.19 (95% CI = 1.00–1.41) with 3 days lag after a low wind episode and 1.33 (95% CI = 1.01–1.75) with 1 day lag after a dust episode, respectively, was noticed. Boys were less sensitive to acute bronchitis hospital admissions than girls (1.83, 95% CI  =  1.09–3.08) after a dust episode but more venerable to acute bronchitis hospital admissions than girls after low wind events (Grineski et al., 2011).

10.4 Australia Children aged between 0 and 5 were examined to assess the impact of dust storms on respiratory and asthma hospital admission during 2004–2009 (Merrifield et al., 2013). The relative rates of respiratory and asthma admissions were 1.286 (95%

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CI = 1.192–1.392, p  0.05). Multicollinearity among the pollen types was also identified. Multicollinearity is an important feature to determine the correlations among the predictors (pollen types) for use in regression. Spearman’s rho non-parametric correlations between pollen types with cut-off scores of 0.7 were regarded as highly correlated. A multiple regression prediction was used to predict allergy and asthma patient admissions from pollen types. The dependent (response) variable was identified by the “number of allergy patients” and “number of asthma patients,” while the predictors (explanatory) variables were identified as the “pollen types” with the highest mean pollen counts. The equation for multiple regression is as follows:

Yˆ  b0  b1 X1  b2 X 2    bp X p



(11.1)

where Yˆ is the predicted value of the dependent variable (i.e., number of allergy patients), X1 through Xp are p distinct predictor variables (i.e., pollen types), b0 is the value of Y when all of the predictors are equal to zero (the intercept), and b1 through

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bp is the estimated regression coefficients. A regression model was identified as significant if p ≤ 0.005. To determine whether there are linear or non-linear relationships between the dependent variables (i.e., number of allergy patients) and the predictors (i.e., pollen types), we applied the “Best-fit” model to test for five relationships: linear, loess, mean of Y, cubic, and quadratic, and calculate the respective r2 value for each.

3 Results 3.1 Prevalence of Pollen in Kuwait Pollen data were collected in Kuwait from 28 sites for 3  years starting from fall 2009 until summer 2011. There were a total of 10 pollen types with mean counts ≥1.0. The highest mean counts were recorded for four pollen types: Chenopodiaceae (31.21), Cyperaceae (23.42), Leguminosae (15.54), and Gramineae/Poaceae (15.10) (Table 11.1 and Fig. 11.4).

Table 11.1  Summary counts for 21 pollens in Kuwait (fall 2009–summer 2011) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Pollen type Chenopodiaceae Cyperaceae Leguminosae Gramineae/Poaceae Plantaginaceae Malvaceae Pinaceae Brassicaceae Compositae Ephedraceae Zygophyllaceae Euphorbiaceae Trilete spore Liliaceae Convolvulaceae Juncaceae Acanthaceae Plumbaginaceae Geraniaceae Cicatricose Tamaricaceae

n 224 224 224 224 224 224 224 224 224 224 224 224 224 224 224 224 224 224 224 224 112

Pollen types with mean counts ≥1.0

a

Min 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Max 340 350 189 130 43 43 15 10 12 7 7 6 4 5 5 8 3 5 3 4 4

Sum 6990 5247 3481 3382 987 639 322 322 300 272 89 62 84 42 37 29 13 54 21 25 23

Mean 31.21a 23.42a 15.54a 15.10a 4.41a 2.85a 1.44a 1.44a 1.34a 1.21a 0.40 0.28 0.38 0.19 0.17 0.13 0.06 0.24 0.09 0.11 0.11

SD 44.401 41.300 20.337 18.853 6.290 4.884 2.815 2.214 2.267 1.461 1.329 0.659 0.799 0.657 0.772 0.818 0.343 0.823 0.449 0.569 0.563

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Fig. 11.4  Mean pollen counts for 21 pollens–Kuwait (fall 2009–summer 2011)

The variation of mean pollen counts by season for 10 pollens (top ten with the highest mean counts) is presented in Fig. 11.5. The box plots for each pollen type are presented in Fig. 11.6. The Kruskal–Wallis one-way ANOVA showed that the pollen type counts differed by season. The highest mean ranks were recorded in the spring (April and May) for the following 5 pollen types: Chenopodiaceae, Gramineae_P, Cyperaceae, Plantaginaceae, and Euphorbiaceae. High pollen counts were recorded during the summer (July and August) for 2 pollen types: Leguminosae and Brassicaceae. Fall (October and November) and winter (January and February) had only one high pollen type, Malvaceae and Pinaceae, respectively. Compositae pollen showed similar counts in all four seasons (no significant variations).

3.2 Multicollinearity Among Pollen Types Testing for normality using Shapiro–Wilk tests showed that the 10 pollen types were not normally distributed (p  ≤  0.001). Multicollinearity is identified when two variables are highly correlated. Testing for multicollinearity showed that

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Fig. 11.5  Mean pollen counts for 10 pollens classified by seasons–Kuwait (fall 2009–summer 2011)

Gramineae_P is moderately and highly correlated with Leguminosae (rs = 0.708) and Cyperaceae (rs = 0.901), respectively (Table 11.2). 3.2.1 Association Between Pollen Types and Health A multiple regression prediction was used to predict allergy patient admissions from pollen types. The dependent (response) variable was the “number of allergy patients,” while the predictors (explanatory) variables were the three “pollen types” with the highest mean counts (Chenopodiaceae, Cyperaceae, and Leguminosae). The reason why Gramineae_P was excluded from the regression model even though it had a high mean pollen count (15.10) was because it was significantly correlated with Cyperaceae and Leguminosae. From Table 11.3, it is apparent that the regression is highly significant (p ≤ 0.005) and 95% (r2 = 0.950) of the variance in allergy patient admissions can be explained from the set of the three predictors (the three pollen types). However, because the linear relationship does not exist between the dependent variable and the predictors, we concluded that allergy patient admissions can be explained very well by only the high pollen counts of Chenopodiaceae

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Fig. 11.6  Box plots representing mean pollen counts for 10 pollens classified by seasons–Kuwait (fall 2009–summer 2011)

Chenopodiac Leguminosae Gramineae_P Cyperaceae Plantaginac Brassicacea Malvaceae Compositae Pinaceae Ephedraceae

Chenopodia 1.00 0.646 0.621 0.471 0.585 0.330 0.544 0.287 0.361 0.251

1.00 0.708 0.674 0.565 0.294 0.410 0.160 0.193 0.489

Leguminos

1.00 0.901 0.667 0.211 0.299 0.106 0.153 0.630

Gramin_P

1.00 0.623 0.187 0.209 0.064 0.036 0.737

Cyperacea

1.00 0.410 0.327 0.144 0.271 0.513

Plantagina

Table 11.2  Spearman’s rho non-parametric correlations between pollen types

1.00 0.399 0.450 0.294 0.281

Brassicac

1.00 0.445 0.140 0.125

Malvace

1.00 0.061 0.028

Composita

1.00 −0.010

Pinaceae

1.00

Ephedrac

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Table 11.3  Regression model with the dependent variable (number of allergy patients) and three predictors (pollen types) Model summarya Change statistics R Adjusted R Std. error of R Square F Sig. F Model R square square the estimate change change df1 df2 change 0.975b 0.950 0.913 690.792 0.950 25.527 3 4 0.005 1 a Dependent variable: allergy patients b Predictors: (constant), Cyperaceae, Leguminosae, Chenopodiaceae Coefficientsa Unstandardized coefficients Model B 1 (constant) 10809.947 Chenopodiaceae 7.840 Leguminosae −3.654 Cyperaceae −2.089 a Dependent variable: allergy patients

Std. error 800.003 0.969 1.102 0.495

Standardized coefficients Beta 1.144 −0.440 −0.659

t 13.512 8.090 −3.317 −4.221

Sig. 0.000 0.001 0.029 0.013

Table 11.4  Regression model with the dependent variable (number of asthma patients) and three predictors (pollen types) Model Summarya Change statistics R Adjusted R Std. error of R square F Sig. F Model R square square the estimate change change df1 df2 change 0.734b 0.538 0.192 808.638 0.538 1.553 3 4 0.332 1 a Dependent variable: asthma patients b Predictors: (constant), Cyperaceae, Leguminosae, Chenopodiaceae Coefficientsa Unstandardized coefficients Model B Std. error 1 (Constant) 3568.437 936.480 Chenopodiaceae 0.956 1.134 Leguminosae −2.106 1.290 Cyperaceae −0.331 0.579 a Dependent variable: asthma patients

Standardized coefficients Beta 0.363 −0.661 −0.272

t 3.810 0.842 −1.633 −0.571

Sig. 0.019 0.447 0.178 0.598

(beta = 1.144, p ≤ 0.001). Running the prediction model again, but this time with the “number of asthma patients” as the dependent (response) variable showed that “asthma patient admissions” cannot be predicted from pollen types (Sig. 0.332) (Table 11.4).

11  Pollen Prevalence and Health Impact in Kuwait Fig. 11.7  Best-fit models between “allergy patients” and pollens (Chenopodiaceae, Leguminosae, Cyperaceae)

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3.3 Best-Fit Model between Allergy Patients and Pollens To model the relationship between the “number of allergy patients” and pollens (Chenopodiaceae, Leguminosae, Cyperaceae), we used the best-fit model approach. A linear relationship between the allergy patients and pollens is considered weak as shown in Fig. 11.7 (r2 = 0.331, 0.138, 0.038) for (Chenopodiaceae, Leguminosae, Cyperaceae), respectively; therefore, a non-linear relationship exists between the response variable “allergy patients” and the predictors “pollen types.” There appears to be a curvature in terms of the relationship between allergy patients and pollen types. The best-fit model that determines this relationship was shown to follow the “cubic” function. From the “cubic” model, we can account for 74.2%, 27.6%, and 52.8% in the variation of allergy patient admissions and Chenopodiaceae, Leguminosae, and Cyperaceae, respectively. The “Quadratic” mirrors the “Cubic” model for Cyperaceae (r2 = 0.528), and as a result, we can confirm that the “Cubic” model is the best fit that determines the relationship between allergy patient’s admissions and pollens.

References Ahmed, M., & Al-Dousari, A.  M. (2013). Geomorphological characteristics of the Um-Rimam depression in northern Kuwait. Kuwait Journal of Science, 40(1), 165–178. Al-Awadhi, A. A. (1973). The study of allergy in Kuwait. Part I. Prevalence and the role of the environmental factors. Report, Ministry of Public Health, Kuwait. Al-Awadhi, J. (2005). Dust fallout characteristics in Kuwait: A case study. Kuwait Journal of Science, 32, 135–152. Al-Awadhi, J., & Al-Dousari, A. (2012). Dust fallout in northern Kuwait, major sources and characteristics. Kuwait Journal of Science, 39(2A), 171–187. Al-Dousari, A.  M. (2009). Recent studies on dust fallout within preserved and open areas in Kuwait. In N. Bhat et al. (Eds.), Desertification in arid lands: Causes, consequences and mitigation. Rodel Dela Costa Publisher. Al-Dousari, A. M., Ibrahim, M. I., Al-Dousari, N., Ahmed, M., & Al-Awadhi, S. (2018). Pollen in aeolian dust with relation to allergy and asthma in Kuwait. Aerobiologia, 34(3), 325–336. https://doi.org/10.1007/s10453-­018-­9516-­8 Al-Dousari, A. M., Alsaleh, A., Ahmed, M., Misak, R., Al-Dousari, N., Al-Shatti, F., Elrawi, M., & William, T. (2019). Off-road vehicle tracks and grazing points in relation to soil compaction and land degradation. Earth Systems and Environment, 3(3), 471–482. https://doi.org/10.1007/ s41748-­019-­00115-­y Al-Dousari, A., Pye, K., Al-Hazza, A.  A., Al-Shatti, F., Ahmed, M., Al-Dousari, N., & Rajab, M. (2020). Nanosize inclusions as a fingerprint for aeolian sediments. Journal of Nanoparticle Research, 22, 94. (2020). https://doi.org/10.1007/s11051-­020-­04825-­7 Al-Dowaisan, A., Fakim, N., Khan, M., Arifhodzic, N., Panicker, R., Hanoon, A., & Khan, I. (2004). Salsola pollen as a predominant cause of respiratory allergies in Kuwait. Annals of Allergy, Asthma Immunology, 92(2), 262–267. https://doi.org/10.1016/S1081-­1206(10)61558-­X Al-Kulaib, A. A. (1990). The climate of Arabian Gulf (p. 87). That Alsalasil Press. (In Arabic). Davies, R. R. (1969). Spore concentrations in the atmosphere at Ahmadi, a new town in Kuwait. Journal of General Microbiology., 55, 425–432. https://doi.org/10.1099/00221287-­55-­3-­425

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Halwagy, M.  H. (1988). Concentration of airborne pollen at three sites in Kuwait. Grana, 27, 53–62. https://doi.org/10.1080/00173138809427732 Halwagy, M. H. (1994). Airborne pollen of Kuwait City, Kuwait 1975–1987. Grana, 33, 333–339. https://doi.org/10.1080/00173139409429021 Halwagy, R., & Halwagy, M. (1984). Seasonal distribution of pollen particles in the atmosphere of Kuwait. Pollen et Spore, 26(2), 201–216. Kellogg, C.  A., & Griffin, D.  W. (2006). Aerobiology and the global transport of desert dust. Trends in Ecology & Evolution, 21(11), 638–644. https://doi.org/10.1016/j.tree.2006.07.004 Latif, A. A., Al-Kazimi, K. A., Mohamed, D. E., & Al-Omany, M. (1989). Evaluation of sensitivity and specificity of skin prick test in correlation with specific age for nine inhalant allergens in Kuwait. Abstract, 8th Medical Conf. Kuwait Medical Assoc. 124. Ministry of Public Health, Kuwait. Mahaney, W.  C., Vortisch, W.  B., & Spence, J.  R. (1988). Pollen study and scanning electron microscopy of aeolian grains in a compound paleosol in the Mutonga drainage, Mount Kenya. Journal of African Earth Sciences (and the Middle East), 7(7–8), 895–902. https://doi. org/10.1016/0899-­5362(88)90004-­8 McTainsh, G., & Strong, C. (2007). The role of Aeolian dust in ecosystems. Geomorphology, 89(1–2), 39–54. https://doi.org/10.1016/j.geomorph.2006.07.028 Middleton, N., & Kang, U. (2017). Sand and dust storms: Impact mitigation. Sustainability, 9(6), 1053. https://doi.org/10.3390/su9061053 Mohamed, D., Abdul, H. M., & Kazmy, K. (1983). Descriptive analysis of asthma and other allergic diseases in Kuwait. Proc. 18th Middle East Reg. Conf. Int. Un. Ag. Tubercul. Ministry of Public Health, Kuwait, pp. 272–279. Omar, S. A. S. (2000). Vegetation of Kuwait – A comprehensive illustrative guide to the flora and ecology of the desert of Kuwait (p. 159). Kuwait Institute for Scientific Research. First edition. Reheis, M., & Kihl, R. (1995). Dust deposition in southern Nevada and California (1984–1989); relations to climate, source area and source lithology. Journal of Geographical Research, 100(D5), 8893–8918. https://doi.org/10.1029/94JD03245 Rommerskirchen, F., Eglinton, G., Dupont, L., Güntner, U., Wenzel, C., & Rullkötter, J. (2003). A north to south transect of Holocene Southeast Atlantic continental margin sediments: Relationship between aerosol transport and compound-specific δ13C land plant biomarker and pollen records. Geochemistry, Geophysics, Geosystems, 4(12). https://doi. org/10.1029/2003GC000541 Safar, M. I. (1980). Frequency of dust in day-time summer in Kuwait. Meteorological Department, Directorate General of Civil Aviation. (In Arabic). Traverse, A. (2007). Paleopalynology. Topics in geobiology (2nd ed.). Springer. Wilkinson, W. M. (1964). Development of allergy in the desert. Journal of Tropical Medicine and Hygiene, 67, 16–18. Zhou, C. H., Gong, S. L., Zhang, X. Y., Wang, Y. Q., Niu, T., Liu, H. L., Zhao, T. L., Yang, Y. Q., & Hou, Q. (2007). Development and evaluation of an operational SDS forecasting system for East Asia: CUACE/Dust. Atmospheric Chemistry and Physics Discussions, 7, 7987–8015.

Chapter 12

Dust and Health: Control Methods and Strategies Ali Al-Dousari, Modi Ahmed, Abdulaziz Alshareeda, Noor Al-Dousari, Salem Alateeqi, and Abeer Alsaleh

Abstract  The wind, as the most active process in the desert ecosystem, can blow sand, silt, and clay-sized particles for long distance over continents and oceans. Silica dust can cause health risk problems, namely lung disease, eye infection, cardiovascular morbidity, and mortality and affect infants and pregnancy. Therefore, this chapter passes through three success stories majorly using biological fixation for sand and dust storms (SDS) source areas (hotspots). The mobile sand and dust rates were reduced by 94% and 64.5%, respectively, in the sand and dust traps located downwind of a cultivated area with native vegetation compared to upwind. The massive plantation of native vegetation as a biological fixation method is a proper solution for the mobile sand and dust in the hotspot areas in the Middle East. Therefore, the biological fixation using native plants for two SDS major hotspot areas located in southern Iraq could reduce SDS and dust by at least 40%. Also, a proper dust and air quality control strategic plan was designed for indoor and outdoor air. Keywords  Dust · Health · Air quality · Sand and dust storms · Native plants · The Middle East

1 Introduction The wind is the most effective factor in the desert ecosystem. It can blow sand, silt, and clay-sized particles over long distances as dust and sandstorms (Al-Dousari, 2009). A sand and dust storm (SDS) is associated with low visibility of fewer than 1000 m. (Al-Kulaib, 1990). The Middle East has one of the greatest levels of dust A. Al-Dousari (*) · M. Ahmed · A. Alshareeda · N. Al-Dousari · S. Alateeqi · A. Alsaleh Environment & Life Sciences Research Center, Kuwait Institute for Scientific Research, Kuwait City, Kuwait e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Al-Dousari, M. Z. Hashmi (eds.), Dust and Health, Emerging Contaminants and Associated Treatment Technologies, https://doi.org/10.1007/978-3-031-21209-3_12

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deposition in the world (Al-Dousari, 2009; Al-Dousari et al., 2004, 2008, 2016). Dust occurs all months round in the northern Arabian Gulf, as they do in many other Arab Nations (Al-Hemoud et  al., 2019; Al-Dousari  et  al., 2019a). Kuwait for instance has on average 255.4 dusty days (sand and dust storms, rising dust, suspended dust, and haze) per year (Safar, 1980). A disaster can be identified as “a massive upheaval of the life of the community or a society at any size caused by hazardous events associated with exposure conditions, susceptibility, and capacity, leading to either one of the following losses and consequences: human, material, economic, and environmental.” Sand and dust storms (SDS) are the most dangerous natural threat that happens frequently in the Middle East ecosystem and is regarded as a distinctive aspect of the environment in desert regions. The transmission of dust presents a series of threats to human society (Middleton et al., 2021). SDS are prevalent in Kuwait, and they have serious socioeconomic consequences, such incidents have a wide range of adverse implications, particularly as it pertains to the economic disruptions totaling up to USD 9.36 million per year and the major health repercussions for the inhabitants (Al-Hemoud et al., 2019).

1.1 Health and Dust Dust serves as a carrier for bioactive components including fungi, bacteria, and endotoxins to spread through the atmosphere (Kellogg & Griffin, 2006). Some bacteria (meninges) are recognized as disease predecessors, and their ability to be conveyed by dust has been demonstrated. Endotoxins, for example, may play a role in asthmatic symptoms and inflammation (Kirkhorn & Garry, 2000). Despite the fact that dust events have occurred for millennia, there is concern that climate change would exacerbate desertification and the frequency and volume of dust created. Arid environmental conditions cause serious human health issues including respiratory ailments, heart diseases, skin diseases, stomach and lung cancer, and others, all of which are potentially lethal. Fine solid matter particles make up dust; these dust particles are a variety of contaminants that originate from both natural and anthropogenic sources, and they have a multitude of environmental and public health consequences. Dust is produced by both natural and man-made processes, such as fossil fuel burning, grinding, overexploitation and depletion of natural resources, and so on, and it can linger in the atmosphere for hours or even days. Dust exposure increases toxicity due to its non-biodegradability and cancerous qualities, as well as heavy metals in the dust. The impact of dust on public health varies depending on the geographical region and dust type (Knippertz & Stuut, 2014).

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1.2 Effects on the Respiratory System Silica dust can cause health risk problems, namely lung disease. Several research studies have confirmed a strong connection between dust storms and severe asthma attacks (adults as well as children); these results are in accordance across countries such as Saudi Arabia, Kuwait, Australia, Greece, and the United States (Meo et al., 2013; Thalib & Al-Taiar, 2012; Samoli et al., 2011; Grineski et al., 2011; Rutherford et  al., 1999). Worsening of chronic obstructive pulmonary disease (COPD) and allergic rhinitis have comparable patterns (Tam et al., 2012; Meltzer et al., 2012). Asthma affects almost 235 million people in the world (WHO, 2018), and estimates imply that asthma frequency rises every decade by 50%, with children experiencing the greatest increases (Braman, 2006). Asthma is a complicated inflammatory illness marked by episodes of coughing, wheezing, and shortness of breath caused by reversible airway restrictions. Asthma costs Europe around 21 billion dollars in direct medical and indirect expenditures (Braman, 2006). Asthma-related costs in the United States (USA) totaled 56 billion US dollars in 2007 (CDC, 2011). During sandstorms, 17-year research in Kuwait discovered a greater risk of fatality than on non-sandstorm days (2000–2016). Patients with allergies, asthma, or pulmonary difficulties, according to Achilleos et al. (2019), may be affected by sand and dust storms. Symptoms might range from mucosal irritation to mortality in extreme situations. Dust storms in the Middle East have been associated with a rise in same-day asthma and hospitalization for respiratory illnesses, particularly in children (Al-Taiar & Thalib, 2014). Additionally, exposure for a long time to PM2.5 has been connected with an enlarged hazard of death over a 30-year period (Al-Hemoud et al., 2019). Furthermore, in the Middle East, exposure to PM10 was observed in hospital records to exacerbate bronchial asthma attacks (Al-Hemoud et al., 2019). Hashizume et al. (2020) discovered a 9% rise in respiratory disease hospitalizations 3  days following an Asian dust event. In addition, asthma and pneumonia admissions rose by 14.5 and 8.5%, respectively. Dust particles trigger an inflammatory reaction and induction of oxidative stress in respiratory epithelial cells, resulting in genetic damage and poor respiratory health (Meng & Zhang, 2007). After dust inhalation, many dust particles are conveyed to the airways, and airway epithelial cells detect dust particles, which activate macrophages, dendritic cells, and innate immune cells, prompting responses in a variety of immune cell groups with varied pathologic implications on the lungs in diverse respiratory illnesses.

1.3 Effects on the Cardiovascular System A relationship has been discovered in several research between cardiovascular morbidity and mortality and desert dust exposure. Generally, during Asian dusty days in Taiwan, Chan et  al. (2008) discovered a 26, 35, and 20% surge in emergency appointments for total cardiovascular diseases, ischemic heart diseases, and

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cerebrovascular accidents. A significant 3.65% increase in heart failure admissions was found in Taiwan following dust storm episodes (Yang et al., 2009). In Cyprus, Middleton et al. (2008) identified a 10.4% rise in cardiovascular hospitalization on SDS days. A higher incidence of stroke (both ischemic and hemorrhagic) has been associated with dust storms in several research studies (Yang et al., 2005; Kang et al., 2013; Kamouchi et al., 2012). The dust impacts on cardiovascular health are the result of a complicated process that includes a dust-induced cascade of inflammatory reactions, which causes a rise in blood pressure and heart rate, and even a reduction in the contractility of myocytes (Brook, 2008).

1.4 Effects on Eyes The dust is hazardous to one’s eye health. These effects might range from minor to severe, based on the number of dust particles that the eye has been potentially exposed to (Gupta & Muthukumar, 2018). Dust is a prevalent component in contaminated air, and it can lead to serious eye diseases. The eye is a delicate organ with built-in safety shields that protect it from airborne toxins and pollution. The main components of these shields are the eyelids, lashes, and tear glands. If such layers are missing, the eyes are more prone to infection (Angayarkanni et  al., 2016). Irritation and discomfort are relatively modest side effects whereas dry eyes, retinal hemorrhage, periocular eyelid dermatitis, and conjunctivitis are all significant eye issues (Latka et  al., 2018). Also, severe eye disorders include meibomian gland dysfunction, glaucoma, and atopic dermatitis. Ocular dehydration, which renders the eyes more susceptible to infections, is a common symptom of all of these disorders. In certain cases, the infection might progress to the point of vision loss (Latka et al., 2018).

1.5 Effects on Infants Chemicals, minerals, and biological characteristics in the dust can be hazardous to human health. They can have a severe deleterious impact on neonates and children due to a combination of physiological, environmental, and behavioral variables. Infants are extremely susceptible during prenatal development when their organs, brains, and lungs are still developing where they can breathe more contaminated air than adults because they breathe faster. Potential disease systems have more opportunities to grow and affect people’s health as their average lifespan increases. Children’s bodies age quickly, especially their lungs, making them more susceptible to inflammation and other air pollution impacts. Even when pregnant, they are sensitive to maternal vulnerability to environmental contamination. Getting subjected to airborne pollutants before pregnancy can endanger the infant. Any type of exposure, whether ingestion, breathing, or during pregnancy, long time effects are

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possible. (Abadi et al., 2019; Landrigan et al., 2019; Mahapatra et al., 2020; PayneSturges et al., 2019; WHO, 2018). Thus, this study aims to focus on success stories that deal with dust and air quality and also to design a dust and air quality control proper strategic plan.

2 Success Stories in Controlling Dust Using Native Plantations 2.1 Native Plants as Control Measures for SDS In Kuwait, there are 15 main native desert species that were chosen based on the last vegetation maps (2000). These dominant native plants are Cyperus conglomeratus, Haloxylon salicornicum, Rhanterium epapposum, Astragalus spinosus, Lycium shawii, Citrullus colocynthis, Panicum turgidum, Calligonum polygonoides, Nitraria retusa, Tamarix aucheriana, Halocnemum strobilaceum, Salicornia europaea, Heliotropium bacciferum, Arnebia decumbens, and Convolvulus oxyphyllus. Green belts and natural vegetation in Kuwait were tested for their potential to capture mobile sand and dust, the wild desert vegetation Calligonum, Haloxylon, Lycium, and Nitraria, in specific creating the largest volumes of nabkha sand body of all native plants, with the volume of 13.3  m3, 14.5  m3, 15.5  m3, and 21.9  m3, respectively (Fig. 12.1). In addition, as compared to upwind, establishing a green belt with four or more lines is proven to be efficient in enhancing soil characteristics and limiting drifting sand by 93%. The main restoration aspect for the degraded areas of the Liyah Protected Area (LPA) is the re-implantation of native flora. Between April 2011 and May 2015, about 110 thousand native vegetation were massively planted to help rehabilitate the devastated areas of the LPA, with a focus on critically threatened and most effective species in controlling mobile sand and dust, such as haloxylon, lycium, and calligonum in the Liyah preserved area (LPA), and the plantation was spreading in the form of six vegetative islands placed near together (Fig. 12.2). The vicinity of 3-m spacing among native plants causes aeolian sediments to be trapped at a rate of 30.5 cm above the ground across the entire agricultural island areas. These islands were discovered to have a beneficial effect, capturing roughly 115 tons of aeolian sediments, saving $6,112,764 USD in anticipated disposal costs surrounding human populations. In May 2011, traps for sand and dust were positioned up to monitor aeolian activity phenomena, and the outcomes revealed that the amount of deposited dust and trapped sand grow up during May 2012, causing the dieback of some plants. The sand and dust fluxes reduce by 94% and 64.5%, respectively, downwind of the site with 110 thousand planted native vegetation in comparison to upwind (Figs. 12.3 and 12.4). Therefore, the Liyah rehabilitation project using massive native plantations is expected to reduce the negative effect on Mutlaa City located downwind of the Liyah Preserved Area.

Controlled mobile sand around plants (m3)

125.00

10.00

1.00

25.00 5.00 1.00

0.10

0.01

0.20 0.04

The economic cost saved by plants (USD)

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Fig. 12.1  The cost saved by a single plant and its trapping capability for mobile sand and dust for 15 main native desert species in Kuwait

Fig. 12.2  The Liyah Preserved area upwind of the Mutla urban area in Kuwait to reduce the effect of mobile sand and dust (left) and part of the 6 vegetation islands with larger nabkha deposits around the Nitraria (right)

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25000 Annual dust fallout (tons\km2)

Control area 20000

Upwind Downwind

15000

10000

5000

0 2011-2012

2012-2013

2013-2014

2014-2015

Fig. 12.3  Dust fallout monitoring in the Liyah in northern Kuwait

Annual drifted sand (gm.cm-1.m-1)

2500 Control area 2000

Upwind Downwind

1500

1000

500

0

2011-2012

2012-2013

2013-2014

Fig. 12.4  Sand monitoring in the Liyah in northern Kuwait

2014-2015

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Fig. 12.5  Arial photos of the Gudhi area before (right) and after (left) massive plantation in the area

Kuwait Institute for Scientific Research intends to establish research facilities in the Gudhi area (Fig.  12.5), which is located in the north of Kuwait Bay, with 653,000 m2 as a total area, and the area is ecologically and environmentally very sensitive. This coastline strip is characterized by diverse fauna and vegetation, with vast nabkhas of Nitraria retusa and Lycium shawii drawing numerous biodiversity researchers. The abundance of nabkhas in a given location is a reliable predictor of fluvial and aeolian activity. The construction of facilities in this region is expected to have a substantial negative impact on biodiversity. Furthermore, any potential infrastructure development in the region needs to address the following hazards: . Sand encroachment is a concern due to its location along a high-wind passage. 1 2. The flooding incidents during flood seasons, because it is positioned within the major drainage network. 3. The erosion is caused by waves along a 1200 m stretch of the coast. The wind played a major part in the accumulation of sand and dust on the northeastern and southwestern sides, according to the quantitative data analysis. Samples were taken and analyzed on a regular time basis. The analyses revealed that sand buildup and dust deposition increased over time. In addition to the negative effect of camping and overgrazing near the research area, the volume of sediment deposited has been steadily increased, posing a potential hazard to the area Accordingly, a strategic approach was devised to address the three challenges mentioned above, by diverting aerodynamically of the main wind direction (the northwest wind). Hence, an architectonic plan was developed to shift mobile sand and dust far away from the intended facilities (Fig.  12.6). Drones were used to explore the vegetation area. Approximately 20,000 (10,000 Nitraria retusa and 10,000 Lycium shawii) native plants were produced and transplanted. Furthermore, 200 mangroves were cultivated. The findings revealed the area’s inappropriateness for mangrove production in its present condition, with an 88% success rate for Nitraria retusa and Lycium

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Fig. 12.6  Design for native plants for an aerodynamic shift of mobile sand and dust away from an installation that was implemented in the Gudhi area in Kuwait

shawii plants which are the most efficient sand and dust traps among Kuwait‘s native plants. After 12 months of cultivation, the amount of sand and dust blowing in the direction of the wind dropped by 90% and 30%, respectively, when compared to the outflow of the wind. Birds and crawling animals were collected in pitfall traps in four different locations and quantitative biological evaluations were done. The area’s biodiversity index is 0.15%, indicating a healthy and thriving, diverse ecology. Throughout this research, over thirty-six animal species were registered, namely Ten species of arachnids, five species of reptiles, fifteen species of beetles, and insects (sixteen species); In the Gudhi area, sixty-four birds were recognized, accounting for 20% of all birds observed in Kuwait.

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3 Future Studies 3.1 Controlling Regional Source Areas of Sand and Dust Storms (SDS) To control SDS incidents in the Middle East, it is critical to control and mitigate land degradation in areas where sand and dust storms arise, such as southern Iraq. Similarly, in Kuwait, steps should be taken to minimize the effect of sand and dust storms, as well as the other complications they cause. SDS is driven by meteorological conditions and soil properties in source areas, both local and worldwide (Al-Dabbas et al., 2012; Saxena et al., 2003; UNISDR, 2017; UN, 2018). For each season, accurate wind trajectories were determined, and the biggest SDS contributor to the Arabian/Persian Gulf, which occurs between February and April, was identified to be the predominant northern winds from southern Iraq and Syria (Doronzo et al., 2016) with 470 tons per km2 as mean fallen dust per year arriving the northern portion of the Persian/Arabian Gulf (Al-Dousari et al., 2020). Sand particles (>0.063 mm) represent around 37% of the total weight of dust deposits (Al-Dousari & Al-Hazza, 2013; Al-Dousari, 2005; Al-Dousari et  al., 2009, 2017, 2019a, b). The dominant component of dust is silt and clay (63%); this elevates dust concentrations in the ambient air over many days (Al-Dousari et al., 2019c, 2020, 2022). SDS trajectories from Iraq to Kuwait, including the Diwaniya area (“Middle belt”), and Samawa and Nasiriya areas (“Southwest belt”), both of which SDS affecting Kuwait (Fig. 12.7). Figure 12.6 depicts these hotspot areas from a recent satellite image: the “Middle belt” is area 1 and the “Southwest belt” is area 2. Plant cover is indicated by the red portions, whilst the white areas are devoid of flora, revealing the sand sheets previously mentioned. According to estimates, this region contributes up to 40% of the sand and dust transferred to the Arabian/Persian Gulf, and the Diwaniya field (“Middle belt”) rests are the major hotspot area. Severe SDS jets pass from this exact area to downstream countries, traveling internationally and encompassing the entire northern part of the Arabian/Persian Gulf, as well as extending all the way to Qatar and Emirates. Therefore, the major hotspots will be stabilized within the coming 4 years with the intention to limit the SDS and dust events in the region by least 40% in close future. Such above project will enhance the air quality for around 40 million people in southern Iraq, Ahvaz (Iran), Kuwait, and the eastern portion of the Persian/Arabian Gulf region.

3.2 Molecular Biology as a Future Solution Due to the breakthroughs in molecular biology, researchers should concentrate on determining the molecular mechanism of action of aeolian dust particles and attempting to detect genetic defects produced by road dust direct exposure for a

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Fig. 12.7  Illustration of “Southwest belt” and “Middle belt” containing sand dunes and sand sheets affecting Kuwait (Al-Dousari et al., 2022). Hotspot areas outlined as area 1 and area 2, using MODIS Aqua satellite image a recent dust storm jet from the source area (28 July 2018)

lengthy period of time. Given the paucity of information on the effects of road dust at different stages of pregnancy, the effect of possible fetal abnormalities due to dust particles on pregnant women is an attractive topic of research. Researchers should also focus on developing methods to reduce the impact of dust on the workplace and urban surroundings. As a result, when implementing control measures, the worst-­ case scenarios that have impacted urban areas should be considered such as settlements in the southern Sahara of Africa or cities on the southern Mesopotamian Flood Plain of the Middle East.

4 Conclusions and Recommendations It is critical to establish a decent index for air quality (IAQ) in metropolitan areas that contain (schools, universities, institutions, ministries, and hospitals) since they house vulnerable members of society at threat of infection. IAQ of Building should keep mindful of the growing worry about antibiotic resistance to properly address the goals of future concerns. The growing concern about antibiotic resistance should be factored into the IAQ to properly meet the objectives of future issues. As a corollary, there is an increasing need to use ventilation as an efficient means of infection prevention and control in hospitals. Though, simultaneously, large-scale energy

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uses in hospital buildings and a concerted effort to preserve energy and minimize the carbon footprint of hospital facilities cannot be overlooked. The spread of airborne germs in hospitals can be caused by poorly vented, constructed, or controlled healthcare facilities. Patients’ immune systems are compromised, making them more vulnerable to infection. Vocational illnesses have previously been linked to excessive amounts of hazardous chemicals exposure. Because of precautionary measures, such as the use of heating, ventilation, and air conditioning (HVAC) systems, the levels have since reduced. Only if good ventilation rates are used on a continuous basis, will they be efficient. Chemical pollution monitoring and management in health care facilities must be guided by the principles of vocational promoting health, as well as the suitable and sufficient usage of HVAC systems. Several countries have long been concerned about IAP exposure and the use of sufficient ventilation. Because of the possible risks to patients and employees, this issue is particularly prevalent in healthcare facilities. These threats necessitate the use of high-quality ventilation systems to remove pollutants. In hospitals, good ventilation systems can lower the incidence of microbial (bacterial and fungal) infections among hospital workers and patients. Microorganisms found in the indoor air cause a significant infection risk and diseases. This chapter includes study results on ventilation systems for health indoors working environment, as well as strategies to improve IAQ in the workplace. In relation to suitable ventilation systems, this chapter provides important information for both healthcare workers and mechanical engineers to decrease the risk of microbiological pathogens (bacteria and fungi) in medical environments. Natural ventilation is sometimes overlooked when developing ventilation methods since there are no standards to guide designers in creating natural ventilation holes. There is a scarcity of studies on the effectiveness of naturally air-conditioned buildings, particularly in terms of reduced energy consumption. Despite the aforementioned advantages, natural ventilation design that can be managed should be included in HVAC and other ventilation systems mainly in hospitals and schools and scientific laboratories whenever possible, both to reduce reliance on fossil fuels as much as possible and a considerable reduction the cost of installing and operating air conditioning systems. It is vital to have a very high natural ventilation band so that the degree of heat exchange between the outside and the inside can be adjusted in light of the circumstances. Pollution, air movement, and temperature are all controlled by determining ventilation rates. The building’s heat capacity and ventilation with heat recovery must be taken into account. The dust and air quality control strategic plan design (Fig. 12.8) is needed to be implemented for indoor and outdoor air. Also, some recommended points should be taken into consideration, as follows: 1. Public awareness and standard operating procedures for successfully disseminating warnings based on the impact-based forecasting technique may be included in warning procedures. 2. When there is a dust storm, stay indoors. If you have to go out, always wear a face mask (even when driving).

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Organic Pollutants

Dust & Air pollutants control strategies

Formaldehydes Acetaldehydes Naphthalene PAHs C6H6 Trichloroethylene Tetrachloroethylene Irritants

Inorganic Pollutantss

Microorganisms

NOx, Cox, Sox, PM (Particulate Matter) Radon Pollens Bacteria Viruses Fungi

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-Seal the chemical containers properly. -Indoor Plants employment. -Insecticides, sprays & fresheners for the air should not utilisation. -Use water-based paint instead of solventbased paint.

-The building's design and ventilation. -Natural ventilation should be planned properly. -Summer and winter ventilation management. -Combination of natural & artificial Ventilation. -Air purifiers utilisation ( Filters). -Negative air pressure utilisation. -Dispose of infectious garbage properly by incinerating it. -Dispose of syringes and other medical waste properly. -Disinfectants utilisation to kill bacteria.

Fig. 12.8  Dust and air quality control strategic plan design

3. Closing the house’s windows and use a moist towel to cover any apertures. 4. The therapy of any simple infection should not be disregarded. 5. Immediately as soon after arriving home, having a bath, as the breathing system would be less susceptible to precipitated pollutants in the human body and outerwear. 6. For the allergic and asthmatic, cleaning the house and replacing the bed coverings is done with a moist towel rather than a brush which generates dust in the air. 7. Fully commit to using the preventative spray as well as any other treatments recommended by your doctor. 8. Periodically rinse the nose with water or a lotion containing water and salt. 9. For those who are infected with throttling, a runny nose, and a lot of secretions, a nasal spray with a tiny dose of cortisone is used. 10. The air conditioner filter in your home should be properly cleaned on a regular basis. 11. Avoid irritants, particularly smoke and incense in the ambient air. 12. Asthmatics should always have an emergency inhaler on them and seek the nearest hospital if the symptoms do not improve after taking the inhaler.

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Index

A Aeolian, 18, 36, 39, 41, 52, 60, 216, 217, 219, 235, 238, 240 Airborne microbes, 187–211 Airborne pathogen, 147 Air quality, 18, 19, 38, 53, 54, 94, 98, 102, 104, 235, 240–243 Allergy, 55, 60, 66, 81, 107, 116, 118, 142, 166, 179, 180, 216, 217, 220, 221, 223, 226–228, 233 Arabian Gulf Region, 188–211, 240 Assessment, 3, 4, 18, 21, 24, 47, 95–99, 188–211 Asthma, 2, 7, 38, 55, 62, 63, 65, 68–72, 95, 107, 115, 116, 118, 124, 126, 127, 130, 131, 142, 166, 178, 217, 220, 226, 233 B Bioaerosols, 47, 139–145, 147–150, 159–162, 164–168, 173, 180 Birth outcome, 119–129 C Chronic obstructive pulmonary disease (COPD), 55, 63, 64, 66, 72, 95, 107, 116, 233 Conjunctivitis, 8, 55, 73, 82, 83, 107, 116, 125, 174, 178, 234

D Desert dust, 5, 52–71, 74, 94–96, 106–108, 115, 116, 138, 147, 149, 233 Dust, 2–11, 17–22, 24–26, 32–34, 36, 38–42, 44, 46, 51–57, 59–62, 67, 70–74, 79–88, 94–99, 101–107, 114–132, 138–150, 158, 160, 168, 171, 172, 188–211, 216, 217, 219, 231–243 effects, 2–11, 94, 106 outbreaks, 19, 53, 54, 115, 127–131 storms, 3, 5, 19, 33, 51–74, 85, 94–96, 98–106, 114–132, 138, 165, 180, 189, 205, 210, 233, 234, 241, 242 E Epidemiology, 94–108 H Health, 2, 18, 36, 54, 81, 94, 114, 138, 159, 191, 216, 232 Heavy metals, 2–5, 40, 80, 81, 86, 87, 115, 167, 232 Human health, 2–11, 18–20, 38, 46, 47, 54–56, 94, 106, 108, 116, 138, 140, 142, 158–180, 205, 211, 217, 232, 234

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Al-Dousari, M. Z. Hashmi (eds.), Dust and Health, Emerging Contaminants and Associated Treatment Technologies, https://doi.org/10.1007/978-3-031-21209-3

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248 I Infection, 20, 59, 62, 80, 82–85, 96, 117, 118, 128, 130, 131, 143–147, 150, 173–175, 177–180, 188, 208, 234, 241–243 Inhaled deposited dose, 17, 22, 23 K Kuwait, 17, 44, 52, 54, 61, 62, 65, 72, 94, 95, 98, 107, 118, 126, 127, 159, 169, 171, 188–211, 216–224, 232, 233, 235–241 M Microbiology, 158–180 Microorganisms, 6, 38, 42, 81, 82, 96, 115, 139–142, 146, 147, 150, 159–163, 165–168, 180, 188–193, 196, 197, 206, 208–210, 216, 242 The Middle East, v, vii, 19, 20, 32, 41, 44, 51, 53, 54, 56, 61, 74, 97, 99, 107, 116, 158, 159, 166, 168, 169, 180, 188, 231–233, 240, 241 Mortality, 7, 8, 10, 38, 54, 56, 61–63, 65–67, 70–72, 74, 94, 95, 99–101, 103–105, 107, 114, 117–120, 122, 123, 125, 126, 128, 143, 174, 233 N Native plants, 235–239

Index P Parental exposure, 117, 118, 120 Particulate matter, 3, 8, 38, 52, 56, 57, 70, 73, 80, 83, 85, 94, 104–106, 114–118, 143, 147 Persian Gulf countries, 44, 46, 157–180 Pollen, 8, 38, 94, 96, 115, 124, 159, 160, 180, 216–228 Q Quantification, 17–26, 106, 191, 192, 196, 197 R Respiratory diseases, 7, 55, 62, 63, 95, 114, 142, 233 Review and challenges, 93–108 S Sand and dust storms (SDS), vii, viii, 17–26, 31–47, 51, 114, 115, 118, 119, 121, 122, 124, 126, 158–180, 231–240 drivers, 31–47 impacts, 31–47 W Wind speed, 33–35, 40, 52, 53, 123–125, 138, 162, 165, 180