Integrating IoT and AI for Indoor Air Quality Assessment (Internet of Things) 303096485X, 9783030964856

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Internet of Things

Jagriti Saini Maitreyee Dutta Gonçalo Marques Malka N. Halgamuge   Editors

Integrating IoT and AI for Indoor Air Quality Assessment

Internet of Things Technology, Communications and Computing Series Editors Giancarlo Fortino, Rende (CS), Italy Antonio Liotta, Edinburgh Napier University, School of Computing Edinburgh, UK

The series Internet of Things  - Technologies, Communications and Computing publishes new developments and advances in the various areas of the different facets of the Internet of Things. The intent is to cover technology (smart devices, wireless sensors, systems), communications (networks and protocols) and computing (theory, middleware and applications) of the Internet of Things, as embedded in the fields of engineering, computer science, life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in the Internet of Things research and development area, spanning the areas of wireless sensor networks, autonomic networking, network protocol, agent-based computing, artificial intelligence, self organizing systems, multi-sensor data fusion, smart objects, and hybrid intelligent systems. Indexing: Internet of Things is covered by Scopus and Ei-Compendex ** More information about this series at https://link.springer.com/bookseries/11636

Jagriti Saini  •  Maitreyee Dutta Gonçalo Marques  •  Malka N. Halgamuge Editors

Integrating IoT and AI for Indoor Air Quality Assessment

Editors Jagriti Saini Department of Electronics and Communication Engineering National Institute of Technical Teacher’s Training and Research Chandigarh, India Gonçalo Marques Rua General Santos Costa Polytechnic of Coimbra, ESTGOH Oliveira do Hospital, Portugal

Maitreyee Dutta Department of Information Management and Emerging Engineering National Institute of Technical Teacher’s Training and Research Chandigarh, India Malka N. Halgamuge Department of Electrical and Electronic Engineering The University of Melbourne Melbourne, VIC, Australia

ISSN 2199-1073     ISSN 2199-1081 (electronic) Internet of Things ISBN 978-3-030-96485-6    ISBN 978-3-030-96486-3 (eBook) https://doi.org/10.1007/978-3-030-96486-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, Corrected Publication 2022 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

Indoor air pollution has been a significant problem in both developing and developed countries in recent years. Due to prolonged exposure to biomass burning, rural populations are facing typical situations. Urban inhabitants, on the other hand, are subjected to indoor air pollution as a result of contemporary air-tight structures that lack natural ventilation. In this scenario, active researchers worldwide must provide a method for assessing and controlling indoor air quality. This book aims to highlight the problems, limitations, and possibilities in this field of work, as well as the potential of future technologies like the Internet of Things and artificial intelligence to improve the built environment. Only 11 of the 15 writers that submitted abstracts for this edited volume were approved, while the remaining 4 were rejected owing to their lack of connection to the primary topic of study. Only 9 of the 11 full-length chapters were accepted for publication after further assessment. Out of these, 44% of the published research were undertaken in Asian nations, while 55% originated in Europe. In these contributed chapters, various field experts examined various aspects of the IAQ problem domain, including insights into the IAP problem, challenges, health risk assessment, harmful pollutants affecting IAQ levels, modern solutions for air quality management, and development of real-time monitoring systems with AI.  Moreover, Chap. 1 is included to provide an introduction to the book’s scope. To be more precise about the book’s content, the nine chapters shed light on IAQ evaluation and control while also building a relationship with technology improvements in order to solve problems in this field of work. In his chapter “A Comprehensive Review on the Indoor Air Pollution Problem, Challenges, and Critical Viewpoints,” David Galán Madruga provided an indoor air quality benchmark in terms of potential emission sources, concentrations, health effects, and methodologies for measuring air pollutants, with an eye towards indoor air quality managers, control technicians, and potential students. Kraiwuth Kallawicha and Hsing Jasmine Chao covered IAQ monitoring and evaluation systems. For bioaerosol monitoring, the authors focused more on IoT, AI, and real-time sensor monitoring in their chapter “Bioaerosols: An Unavoidable Indoor Air Pollutant that Deteriorates Indoor Air Quality.” In addition, some effective control measures for reducing bioaerosol v

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pollution inside are described. Marco Gola and colleagues emphasize the notion of user-centrality while exploring best practices for ensuring optimal indoor space performance in their chapter “No Impacts on Users’ Health: How Indoor Air Quality Assessments Can Promote Health and Prevent Diseases.” They also shed light on the critical function of modern technology in indoor air evaluations and how users can grasp the hazards. Jozef Švajlenka addressed issues about the internal building environment while identifying elements impacting occupant comfort in his chapter “Aspects of the Internal Environment Buildings in the Context of IoT.” This chapter’s major contribution is to synthesize current information while raising awareness about how IoT might be used to address IAQ-related concerns. Kraiwuth Kallawicha et  al. introduced the notion of IAQ management in residential and commercial buildings utilizing sophisticated methodologies in their chapter “Modern Solutions for Indoor Air Quality Management in Commercial and Residential Spaces.” The management technologies have been outlined, with solutions ranging from basic, user-friendly solutions to complex approaches that require tech-savvy specialists to operate and handle. Nazgol Hafizi and Sadiye Mujdem Vural, in their chapter “An IoT-Based Framework of Indoor Air Quality Monitoring for Climate Adaptive Building Shells,” conducted a thorough review of the literature to address issues about occupant comfort and satisfaction in Climate Adaptive Building Shells. The chapter also outlines a preliminary architecture for IoT-based building monitoring. Deniz Balta et  al. presented experimental research on IAQ and thermal comfort evaluation using Distributed Sensor Based Fuzzy Decision Tree Model in their chapter “Online Monitoring of Indoor Air Quality and Thermal Comfort Using a Distributed Sensor-Based Fuzzy Decision Tree Model.” The suggested model’s findings were compared to those of numerous other methodologies, and a survey-based analysis was also presented, demonstrating the proposed system’s effectiveness. I. M. Dovlatov et al., in their chapter “Appliance for Air Quality Improvement in Premises,” investigated and found the safest and most effective methods of premise disinfection. This experimental study analyzed UV radiation and chemical aerosol based methods to inactivate pathogenic microorganisms, and many comparative measurements were made to evaluate the effectiveness of the applied procedures. Finally, Tahmeena Khan and Alfred J. Lawrence presented a state-of-the-art review of various technological interventions and advancements to address health risks related to air pollution in their chapter “Health Risk Assessment Associated with Air Pollution Through Technological Interventions: A Futuristic Approach.” This chapter takes a futuristic approach to address the difficulties of IAQ evaluation and control by utilizing technology improvements. Finally, the chapters in this book have emphasized the key difficulties, challenges, gaps, possibilities, and technology solutions in the field of indoor air quality evaluation. This book primarily addresses a few key issues, such as how indoor air quality affects human health, what measures can be used to assess building environmental conditions, what influential steps can be taken to address IAQ management concerns, and how new and emerging technologies can be useful in this problem space. However, there are a number of difficult research problems that need to be addressed in the future in order to find new ways to deal with the public health implications of this sector.

Preface

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Without the generous contributions of several outstanding writers, expert reviewers, and Springer’s supporting editorial team, this book would not have been finished. We congratulate all writers for their honest efforts in submitting chapters and updating them as needed in response to reviewers’ comments, recommendations, and feedback. Finally, we would like to extend our gratitude to Mary James, editor at Springer, for her unwavering support throughout this process. Chandigarh, India Chandigarh, India Coimbra, Portugal Melbourne, Australia

Jagriti Saini Maitreyee Dutta Gonçalo Marques Malka N. Halgamuge

Contents

1 Indoor Air Quality: An Emerging Problem Domain����������������������������    1 Jagriti Saini, Maitreyee Dutta, and Gonçalo Marques 2 A Comprehensive Review on the Indoor Air Pollution Problem, Challenges, and Critical Viewpoints������������������������������������������������������    9 David Galán Madruga 3 Bioaerosols: An Unavoidable Indoor Air Pollutant That Deteriorates Indoor Air Quality����������������������������������������������������   27 Kraiwuth Kallawicha and Hsing Jasmine Chao 4 No Impacts on Users’ Health: How Indoor Air Quality Assessments Can Promote Health and Prevent Disease����������������������   43 Marco Gola, Gaetano Settimo, and Stefano Capolongo 5 Aspects of the Internal Environment Buildings in the Context of IoT��������������������������������������������������������������������������������   55 Jozef Švajlenka 6 Modern Solutions for Indoor Air Quality Management in Commercial and Residential Spaces��������������������������������������������������   73 Kraiwuth Kallawicha, Pokkate Wongsasuluk, and Hsing Jasmine Chao 7 An IoT-Based Framework of Indoor Air Quality Monitoring for Climate Adaptive Building Shells ����������������������������������������������������   89 Nazgol Hafizi and Sadiye Mujdem Vural 8 Online Monitoring of Indoor Air Quality and Thermal Comfort Using a Distributed Sensor-Based Fuzzy Decision Tree Model ����������  111 Deniz Balta, Nesibe Yalçın, Musa Balta, and Ahmet Özmen

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9 Appliance for Air Quality Improvement in Premises ��������������������������  135 Igor Mamedyarevich Dovlatov, Leonid Yuryevich Yuferev, and Dmitry Yuryevich Pavkin 10 Health Risk Assessment Associated with Air Pollution Through Technological Interventions: A Futuristic Approach������������  149 Tahmeena Khan and Alfred J. Lawrence Correction to: Indoor Air Quality: An Emerging Problem Domain������������ C1 Index������������������������������������������������������������������������������������������������������������������  169

The original version of this book was revised. The correction is available at https://doi.org/10.1007 /978-3-030-96486-3_11

About the Editors

Jagriti Saini  was born in Himachal Pradesh, district Mandi, in 1992. She holds a diploma in electronics and communication engineering (2010) from GPW Kandaghat and completed her BTech in electronics and communication engineering (2013) from HPU. She received a master’s degree in electronics and communication engineering from the National Institute of Technical Teacher’s Training and Research (NITTTR), Chandigarh (Panjab University), India (2017). She was awarded a gold medal for securing the highest percentile in the entire university during her master’s course. Jagriti is currently pursuing her PhD in electronics and communication engineering from the National Institute of Technical Teacher’s Training and Research (NITTTR), Chandigarh (Panjab University). She has also received an INSPIRE fellowship from the Department of Science and Technology (DST), India, for carrying out her research work. Her current research interests include artificial intelligence, Internet of Things, environmental monitoring, indoor air quality monitoring and prediction, healthcare systems, e-health, and autonomous systems. Her PhD thesis, entitled “Design and Development of Intelligent Indoor Air Quality Monitoring and Prediction System – Vayuveda,” is mainly focused on developing cost-effective real-time monitoring and prediction system for indoor air quality management. She has published more than 25 papers in reputed peer-­ reviewed international journals and conferences. Other than this, she is a frequent reviewer of journals and international conferences and is also working on several edited book projects. Maitreyee Dutta  was born in Guwahati, India. She received the BE degree in electronics and communication engineering in 1993 from Guwahati University and was gold medalist in the same year. She obtained her ME degree in electronics and communication engineering and PhD degree from the Faculty of Engineering at Panjab University. She is currently Professor and Head of Information Management and Emerging Engineering and joint professor in the Computer Science and Engineering Department, National Institute of Technical Teachers’ Training and Research, Chandigarh, India. She has more than 22 years of teaching experience. Her research interests include the Internet of Things, security of data, IP networks, the Internet, xi

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authorization, data privacy, public key encryption, pattern clustering, cloud computing, and data compression. She has more than 100 research publications in reputed journals and conferences. She has completed two sponsored research projects, the establishment of a cyber security lab, funded by the Ministry of IT, Government of India, New Delhi, amounting to Rs. 45.65 lac, and establishment of an advanced cyber security lab, sponsored by MeitY, New Delhi, amounting to 62  lacs. One sponsored project, Securing Billion of Things-SEBOT, funded by All India Council of Technical Education, New Delhi, amounting to Rs. 14.98 lacs is in progress. Gonçalo Marques  holds a PhD in computer science engineering and is a member of the Portuguese Engineering Association (Ordem dos Engenheiros). He is currently working as assistant professor, lecturing courses on programming, multimedia, and database systems. Furthermore, he worked as a software engineer in the Innovation and Development unit of Groupe PSA automotive industry from 2016 to 2017 and in the IBM group from 2018 to 2019. His current research interests include the Internet of Things, enhanced living environments, machine learning, e-health, telemedicine, medical and healthcare systems, indoor air quality monitoring and assessment, and wireless sensor networks. He has more than 80 publications in international journals and conferences, is a frequent reviewer of journals and international conferences, and is also involved in several edited books projects. Malka N. Halgamuge  is a researcher in the Department of Electrical and Electronic Engineering at the University of Melbourne. She obtained her PhD from the same department in 2007. She was awarded the Chinese Academy of Sciences President’s International Fellowship (2017); Incoming Leaders Fellowship from Australia India Institute at Delhi (2016); Next Step Initiative Fellowship (2015); Australia-China Young Scientist Fellowship (2014); Dyason Fellowship at the University of California (UCLA), Los Angeles, USA (2013); Early Career Researcher (ECR) Award from Alexander von Humboldt Foundation (2013); and Solander Fellowships at Lund University (2007 and 2008). She is the recipient of the Vice-Chancellor’s Engagement Award (2010) and Vice-Chancellor’s Knowledge Transfer Award (2008) for her research at the University of Melbourne. She is passionate about research and teaching university students (emerging technologies, Internet of Things (IoT), blockchain, machine learning solutions, and bioelectromagnetics).

List of Reviewers

Editors want to extend special thanks to all the reviewers who participated in the double-blind review process for this book: • Deep Chakraborty Department of Environmental Health Engineering, Faculty of Public Health, Sri Ramachandra Institute of Higher Education and Research (DU), Chennai-­600116, India. • Krishnendu Mukhopadhyay Department of Environmental Health Engineering, Faculty of Public Health, Sri Ramachandra Institute of Higher Education and Research (DU), Chennai-­600116, India. • Nesibe Yalçın Erciyes University, Faculty of Engineering, Department of Computer Engineering, Kayseri, Turkey. • Jozef Švajlenka Department of Construction Technology, Economy and Management, Faculty of Civil Engineering, Technical University of Košice, 042 00 Košice, Slovakia. • David Galán Madruga Department of Atmospheric Pollution (National Reference Laboratory for Air Quality in Spain), National Center for Environmental Health (Health Institute Carlos III), Madrid, Spain. • Alfred Lawrence Department of Chemistry, Isabella Thoburn College, Lucknow-226007, U.P., India. • Tahmeena Khan Department of Chemistry, Integral University, Lucknow-226026, U.P., India.

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Chapter 1

Indoor Air Quality: An Emerging Problem Domain Jagriti Saini

, Maitreyee Dutta

, and Gonçalo Marques

Abstract  Indoor air quality is a major concern for both developed and developing countries. The current populations spend most of their routine time indoors, either at home or office; which makes them susceptible to repeated exposure to hazardous pollutants in the indoor environment. Therefore, it is crucial to find trustworthy ways to monitoring and assessment of these harmful pollutant concentration levels. This chapter provides insights to the potential technologies that can be utilized to design realtime monitoring and assessment systems for indoor environments. Furthermore, it also presents the integration of different technologies to achieve enhanced outcomes. Keywords Indoor air quality · Pollutants · Public health · Internet of things · Artificial intelligence

1.1  Introduction Humans spend over 80–90% of their time indoors, according to several studies [1]. Repeated exposure to hazardous indoor air pollution raises their chances of getting harmed. The majority of air pollution legislation in developed and developing nations focuses on outdoor pollutants and hazardous pollutant emissions, while the negative impacts of interior air pollution are frequently overlooked. Numerous previous medical health investigations [2–4] have shown a strong association between several chronic health disorders and deteriorated indoor air quality (IAQ). The original version of this chapter was revised. The correction to this chapter is available at https://doi.org/10.1007/978-3-030-96486-3_11

J. Saini (*) · M. Dutta National Institute of Technical Teacher’s Training and Research, Chandigarh, India G. Marques Rua General Santos Costa, Polytechnic of Coimbra, ESTGOH, Oliveira do Hospital, Portugal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, Corrected Publication 2022 J. Saini et al. (eds.), Integrating IoT and AI for Indoor Air Quality Assessment, Internet of Things, https://doi.org/10.1007/978-3-030-96486-3_1

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Fatigue, dizziness, headaches, throat, nose, and eye irritations, heart disease, lung illness, and cancer are only a few of the health impacts connected to indoor air pollution [5–8]. Cleaning agents, personal care items, and construction materials that emit toxic pollutants are only a few of the causes of pollution in the interior environment [9–11]. As a result, following certain activities such as cooking, heating, and cleaning, the level of indoor pollutant concentrations can often exceed that of outside pollution [12, 13]. On the one hand, traditional heating and cooling techniques including the burning of wood, coal, and animal dung expose living spaces in rural regions to pollution [14, 15]. On the other hand, ventilation problems in modern airtight structures occur in metropolitan areas, resulting in low air exchange rates and a significant reduction in air quality [16, 17]. Additionally, dangerous particles from the outside environment enter the building environment through open areas, windows, and doors. Because industrial and transportation operations are the primary sources of pollution in outdoor regions, the dangerous pollutants produced by these activities also enter building premises, become trapped in airtight/poorly ventilated structures, and cause major impairment to occupants’ health and well-being. As a result, as compared to working outside, those working indoors are more likely to absorb a larger amount of chemical compounds and particle contaminants. Due to a lack of understanding and resources, the challenges are more prevalent among citizens in underdeveloped nations [18]. Indoor air pollution (IAP) is becoming more of a problem every day, owing to changing lifestyles in which people spend the majority of their time indoors, either for office work or household activities. For newborns, the elderly, those with disabilities, and people who are already unwell, the risk factor is particularly high [19]. Pollutant exposure monitoring, evaluation, and resolution have become critical in this situation. Government agencies, health professionals, politicians, and academics from all over the world are working hard to comprehend the complexities of IAQ management [20, 21]. Advances in technology have opened doors to monitor indoor air contaminants and to get clear insights into personal exposure levels. Internet of Things (IoT) technology offers a potential solution for the development of IAP exposure monitoring [22]. It encourages the use of some miniaturized sensor modules to design inexpensive systems that can assist in exposure estimation due to harmful pollutant concentrations. Furthermore, Artificial Intelligence (AI) is another important domain of interest that could assist in the real-time assessment of IAQ levels with the ability to provide advance alerts to the building occupants regarding critical exposure consequences. Considering the importance of the IAQ assessment and the use of the latest technologies in this context, this chapter provides an introduction to challenges, opportunities, and gaps in the domain. It mainly focuses on the major problem domain – Indoor Air Quality management while putting light on the importance of IoT and AI to solve challenges associated with this field.

1.2  Emerging Technologies: IoT and AI The current generation is fascinated by the word – “smart” or “intelligent.” Although the scientific world is far from being as smart as humans, the field experts have made several efforts to automate things in every sphere. The emerging technologies

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IoT and AI play a considerable role in building smart things. At one side where IoT is all about connecting physical world to perform certain actions and share information, AI opens doors to make computers behave like human brains. These inventions and developments have accelerated the digital transformations, control, and automated assessment in various industries. It is now possible to connect machines, animals, humans, plants, soil, appliances, lakes, stones, buildings, or anything to the network, and this connectivity further leads to smart decision-making. The detailed aspects of both these emerging technologies are explained in the subsections below.

1.2.1  Internet of Things The concept of IoT was given by Kevin Ashton in the year 1999 through his vision of attaching RFID tags to the lipsticks in the store shelves so that they could communicate automatically through radio receivers [23]. This was the first step towards real-time field data collection, and it opened doors to smart communications between devices through the Internet. The concept was further explored by several researchers and field experts to make new advancements in the world. The virtual and physical things connected through IoT display unique attributes and identities, and they are integrated to an information network using intelligent interfaces [24]. The critical requirement in the IoT systems is that there must be an inter-­ connection between things within the network. The system architecture should guarantee uninterrupted operations between things while bridging the gap between the virtual and physical world. The typical design of IoT networks involves several factors such as communication, networking, security, processes, and business models. While designing IoT systems, the developers need to pay more attention to scalability, extensibility, and interoperability among various heterogenous devices [25]. The typical architecture of the IoT system is provided in Fig. 1.1.

Fig. 1.1  General architecture of internet of things

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The system architecture treats every element of this complex system as a well-­ defined subsystem or simple object. Those subsystems and objects can be maintained and reused individually. Therefore, the hardware and software components of IoT can be upgraded and handled efficiently. The physical world defines the sensing layer of the system where all the end objects or things are present. These objects are tied to smart sensors to provide continuous updates about relevant information. The network layer further supports wired or wireless connections among various physical objects. Middleware plays an essential role in managing and analyzing field sensor data. Ultimately, the application layer consists of the interaction options with the users, and it varies as per the goal of the system design.

1.2.2  Artificial Intelligence The concept of AI is influenced by the general structure and behavior of human beings. The human brain works through a network of millions of neurons that share information with each other to make relevant decisions. These neurons are termed as information processing units, and they follow data-based reasoning and experience from past learnings to draw conclusions in typical situations. In a similar manner, AI systems are capable enough to detect patterns, can do automated reasoning, and adapt to new circumstances as well. There are several subbranches of AI that contribute to intelligent system design to address various application domains: machine learning, neural networks, deep learning, natural language processing, fuzzy logic, computer vision, and cognitive computing [26]. AI is all about training computers to do reasoning like human beings. There are plenty of applications in which AI is playing a centralized role in terms of smart decision-making, big data assessment, and risk handling. The AI-based systems have reduced the manual efforts of human beings to do repetitive tasks while increasing processing speed by a considerable level. The interdisciplinary nature of this technology is showing great potential in various domains, including psychology, sociology, physics, biology, statistics, mathematics, philosophy, and computer science [27]. The ability to make intelligent decisions come from the data generated in the respective domain. The IoT systems these days have offered a potential solution to data collection, and it is now possible to receive domain-related information in huge volumes to analyze and assess field conditions. The advanced AI algorithms make it easier to process a massive amount of field data while offering storage scalability as well. AI-based systems learning from past behaviors presented by data, and based on the existing knowledge, can make relevant decisions for future actions. The efficient algorithms can further change machine operations based on the complexity, velocity, volume, and variety of field data. AI research is also contributing to the development of highly efficient methods that can deal with incomplete and uncertain information by utilizing the concepts of economics and probability. Knowledge engineering and knowledge representation allow AI programs to provide intelligent answers to the problem under consideration [26].

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1.3  Integrating IoT and AI for IAQ Assessment The potential of IoT technology to collect a massive amount of field data and the efficiency of AI to process data opens opportunities for insightful inventions. IoT-­ based networks can detect several unusual conditions from the target locations. They are widely preferred for monitoring smart buildings, automated healthcare systems, and other remote applications [28, 29]. Moreover, IoT is rated as a low-­ cost and time-saving solution to address large automation-related problems. In simple terms, this technology can enhance the quality of life in all spheres. In a similar manner, AI contributes to smart resource utilization with enhanced productivity levels. It is possible to use AI algorithms to provide forecasts, analyze trends, and recognize patterns in the application domain. AI has the potential to solve complex problems while minimizing the chances of errors in repetitive tasks. The integration of IoT and AI plays a crucial role in the domain of IAQ assessment and control. The potential of IoT can be utilized for field measurements and monitoring of pollutants from the indoor environment. The manufacturers have already developed several sensor units that can measure a variety of pollutants for extended durations. The IoT-based sensor units can be deployed in residential and commercial spaces to gather real-time data through the Internet [30]. Furthermore, this data can be utilized to enhance the effectiveness of smart building management. The building premises can have several unique pollutants depending upon routine life activities, heating, and cooking arrangements. The existing studies in the literature present several important aspects of IAQ monitoring [31, 32]. They have worked on several pollutants, including PM2.5, PM10, CO, CO2, VOC, and NOx, along with thermal comfort parameters [33]. Furthermore, several advanced machine learning methods have been employed by existing researchers to assess IAQ in the building environment [34]. It is possible to use wired, wireless, or mobile sensor units for remote monitoring, and the received data can be further processed using intelligent AI algorithms. The AI-based methods can understand trends of IAQ and learn about the factors influencing change in pollutant concentration levels. Using the existing knowledge obtained from field data, AI systems can provide relevant forecasts for future IAQ conditions. These alerts or notifications can be utilized for adequate ventilation arrangements in the target indoor environment. Therefore, the integration of IoT and AI for IAQ assessment opens doors to better healthcare management and enhanced comfort of building occupants. Furthermore, these systems are easy to operate, cost-effective, and flexible to deploy. Hence, they appear a trustworthy choice for urban as well as rural IAQ assessment.

1.4  Conclusion IoT and AI have gained popularity within the past few years to handle a variety of field applications. This chapter presented a general introduction to the problem domain, emerging technologies, and the potential of integrating these advancements

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into the indoor environment. There are several sensors that can be deployed in the target environment to gather data about IAQ trends, and this information can be further processed and analyzed through AI-based approaches. However, future researchers need to go through several challenges, gaps, and scopes in the literature before entering the application domain. Only after getting insights about the current state of the art for integrated environmental monitoring, the researchers can identify new opportunities for monitoring and forecasting. Adequate integration can help to design standalone systems with feature-rich designs, and they can be implemented conveniently in the field environment. Conflicts of Interest  The authors declare no conflict of interest.

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13. C. Zhang, S.R. Kuppannagari, R. Kannan, V.K. Prasanna, Building HVAC scheduling using reinforcement learning via neural network based model approximation, in Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, (ACM, New York, NY, USA, 2019), pp. 287–296 14. J. Chen, C. Li, Z. Ristovski, et al., A review of biomass burning: Emissions and impacts on air quality, health and climate in China. Sci. Total Environ. 579, 1000–1034 (2017). https://doi. org/10.1016/j.scitotenv.2016.11.025 15. H.W. de Koning, K.R.  Smith, J.M.  Last, Biomass fuel combustion and health. Bull. World Health Organ. 63, 11–26 (1985) 16. Y. Li, G.M. Leung, J.W. Tang, et al., Role of ventilation in airborne transmission of infectious agents in the built environment ? A multidisciplinary systematic review. Indoor Air 17, 2–18 (2007). https://doi.org/10.1111/j.1600-­0668.2006.00445.x 17. A. Persily, Challenges in developing ventilation and indoor air quality standards: The story of ASHRAE standard 62. Build. Environ. 91, 61–69 (2015). https://doi.org/10.1016/j. buildenv.2015.02.026 18. N. Bruce, R. Perez-Padilla, R. Albalak, Indoor air pollution in developing countries: A major environmental and public health challenge. Bull. World Health Organ. 78, 1078–1092 (2000) 19. V. Mishra, Effect of indoor air pollution from biomass combustion on prevalence of asthma in the elderly. Environ. Health Perspect. 111, 71–78 (2003). https://doi.org/10.1289/ehp.5559 20. K. Kuklinska, L. Wolska, J. Namiesnik, Air quality policy in the U.S. and the EU – A review. Atmos. Pollut. Res. 6, 129–137 (2015). https://doi.org/10.5094/APR.2015.015 21. M.A. Faizan, R. Thakur, Measuring the impact of household energy consumption on respiratory diseases in India. Glob. Health Res. Policy 4, 10 (2019). https://doi.org/10.1186/ s41256-­019-­0101-­7 22. F. Salamone, L. Belussi, C. Currò, et al., Application of IoT and machine learning techniques for the assessment of thermal comfort perception. Energy Procedia 148, 798–805 (2018). https://doi.org/10.1016/j.egypro.2018.08.130 23. P.V. Dudhe, N.V. Kadam, R.M. Hushangabade, M.S. Deshmukh, Internet of things (IOT): An overview and its applications, in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), (IEEE, Chennai, 2017), pp. 2650–2653 24. P. Gokhale, O. Bhat, S. Bhat, Introduction to IOT. Int. Adv. Res. J. Sci. Eng. Technol. 5, 41–44 (2018). https://doi.org/10.17148/IARJSET.2018.517 25. A.J. Jara Valera, M.A. Zamora, A.F.G. Skarmeta, An architecture based on internet of things to support mobility and security in medical environments, in 2010 7th IEEE Consumer Communications and Networking Conference, (2010), pp. 1–5 26. A. Ghosh, D. Chakraborty, A. Law, Artificial intelligence in internet of things. CAAI Trans. Intell. Technol. 3, 208–218 (2018). https://doi.org/10.1049/trit.2018.1008 27. I.M.  Cockburn, R.  Henderson, S.  Stern, The Impact of Artificial Intelligence on Innovation (National Bureau of Economic Research, 2018), pp. 1–40 28. S.B.  Baker, W.  Xiang, I.  Atkinson, Internet of things for smart healthcare: Technologies, challenges, and opportunities. IEEE Access 5, 26521–26544 (2017). https://doi.org/10.1109/ ACCESS.2017.2775180 29. J. Vcelak, A. Vodicka, M. Maska, J. Mrna, Smart building monitoring from structure to indoor environment, in 2017 Smart City Symposium Prague (SCSP), (Prague, Czech Republic, IEEE, 2017), pp. 1–5 30. T.W.  Ayele, R.  Mehta, Air pollution monitoring and prediction using IoT, in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), (IEEE, Coimbatore, 2018), pp. 1741–1745 31. A.A.  Hapsari, A.I.  Hajamydeen, M.I.I.  Abdullah, A review on indoor air quality monitoring using IoT at campus environment. Int. J.  Eng. Technol. 7, 55–60 (2018). https://doi. org/10.14419/ijet.v7i4.22.22191 32. J. Saini, M. Dutta, G. Marques, A comprehensive review on indoor air quality monitoring systems for enhanced public health. Sustain. Environ. Res. 30, 6 (2020). https://doi.org/10.1186/ s42834-­020-­0047-­y

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Chapter 2

A Comprehensive Review on the Indoor Air Pollution Problem, Challenges, and Critical Viewpoints David Galán Madruga

Abstract Over the last decades, environmental pollution has become the main environmental risk to human being health due to the rise regarding waste production, in particular toward the air matrix. At the legislative level, European Directives set air quality objectives to prevent and protect human being health. Nevertheless, the European legislation only applies to outdoor environments, despite people pass ~90% of their time in inside spaces. It exists scientific studies sustain the presence of higher air pollutant levels in indoor than outdoor locations. For this reason, research studies for enlarging knowledge on indoor air quality result priority. Within the previous frame, this chapter aims to provide an indoor air quality benchmark, in terms of potential emission focuses, concentrations, impact on health, and methodologies for measuring air pollutants, focused on indoor air quality managers, control technicians, and potential students. The impact of indoor air quality should be considered at the global level due to several factors, such as indoor pollution is particular for each location, indoor-outdoor air inter-change, and atmospheric pollution is cross-border. The application of new computer tools (IoT and AI) on current and novel measuring air pollution technologies offers a unique chance for inside air quality management. Keywords  Indoor air quality · Background · Polluting focuses and levels · Legislation status · Future challenges

2.1  Introduction In order to offer a starting point to the potential readers, it is necessary to point out that human health may be influenced by external agents, such as environmental factors, among others. In this sense, growth over the last decades, in terms of D. G. Madruga (*) Department of Atmospheric Pollution, National Center for Environmental Health, Carlos III Health Institute, Madrid, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Saini et al. (eds.), Integrating IoT and AI for Indoor Air Quality Assessment, Internet of Things, https://doi.org/10.1007/978-3-030-96486-3_2

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industrialization and urbanization, is translated into a rise of the volume of the wastes toward the different environmental compartments, with special emphasis on the pollutant release into the atmosphere from industrial activities, transport networks, elevated road traffic load, and domestic heating, as dominant emission focuses [1]. Based on the previous development, atmospheric pollution is stated as a remarkable environmental risk for human health globally [2]. Numerous research studies establish linkages between the emergence of harmful effects on human beings and the exposition to air pollutants [3, 4]. Although the damaging effects derived from human being exposure to air-polluting compounds depend on both exposure time and target air pollutant’s concentration, generally speaking, the impact of these compounds encompasses cardiovascular and respiratory disorder, eyes and skin chafe, and chronic sickness, among others [5]. In this context, the 68th World Health Assembly reported that a total of 3.7 million deaths each year derive from exposure to air ambient pollutants (at the outdoor level), increasing to 4.3 million in the case of indoor environments [6]. Therefore, the preventive action plans focusing on the control and abatement of the polluting emissions into the atmosphere should be considered as primordial proceedings, in order to upgrade the air pollution state, and consequently, protect human beings’ health and enhance human well-being. So, a direct relationship between Public Health and atmospheric pollution is sustained. Atmospheric pollution involves a large variety of polluting compounds, which can be found both in the gaseous and particulate phases. Associated with the gaseous phase, the most representative polluting compounds, in terms of monitoring in the atmosphere, mainly in urban environments, drive to ozone, nitrogen oxides (nitrogen monoxide and dioxide), sulfur oxides, volatile organic compounds (benzene, toluene, and xylenes, mainly), and carbon monoxide. Relative to the particulate phase, the measurement of PM10 and PM2.5 particles (with an aerodynamic diameter equal to or less than 10 μm and 2.5 μm, respectively) is prioritized by the European Union. In order to guard human being health against air pollutants exposure, the European Union develops Directives laying down air quality objectives (AQO) for reducing their potential damaging impacts [7]. In this sense, limit and target values for air pollutants are set by air quality standards expressed as average values in an hour, 24 h, calendar year, among other time average (see https://ec.europa.eu/environment/air/quality/standards.htm, accessed June 25, 2021). Within this same context, the World Health Organization (WHO) establishes more restrictive air quality guidelines in comparison to those laid down by European Legislation. As an example, WHO sets limit values of 20 and 10 μg/m3 annual mean for PM10 and PM2.5 particles. The evaluation of air pollutants is a mandatory subject for each Member State, within the European Union, to verify the conformity of those AQO. The emergence of polluting species in the atmosphere is in agreement with the presence of emission focuses. The release of emissions modulates the air pollutant levels. Each environment, external and internal, have its emission sources. Nevertheless, outdoor emission sources may influence indoor air quality and vice-versa because

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of the outdoor-indoor exchange (gases and particles infiltration). Although the atmospheric pollution monitoring should embrace both environments (ambient and indoor, either simultaneously or separately way), a bibliographic revision conducted on one scientific character database (https://www.sciencedirect.com/) proves a higher number of air quality studies at ambient than indoor locations (keywords: ambient air quality levels and indoor air quality levels). A total of 197,542 and 62,634 results, ambient and indoor air quality, respectively, were found under the keywords employed (a search carried out June 28, 2021). This fact sustains the necessity of addressing indoor air quality studies on a larger scale. Based on the previous development, this chapter aims to offer to potential readers an indoor air quality overview, providing relevant aspects within this subject such as types of emission sources, air pollutant levels found in indoor different environments, indoor air pollution influence on human wellness, measurement techniques of inside pollution, and current legislation. Given that the improvement and maintenance of a good indoor air quality is a concern at the level global, the proposed approach pretends to serve as a benchmark for air quality managers, in terms of indoor environments, and control technicians working in this context, as well as those general students or readers interested and worried in this environmental subject. As a remarkable contribution, this chapter features future lines of work, which must be addressed in order to enhance indoor air quality and, therefore, protect human being health. In this sense, the IoT and AI integration on indoor air quality supposes a relevant chance in terms of management.

2.2  Indoor Pollution Background Information Air quality in indoor environments is a well-known issue since the 1970s [8]. In the last years, a rising body of scientific findings has evidenced higher levels of air pollutants in indoor than outdoor environments. In this context, the impact on human wellness might be more significant because of air pollution exposure indoors than outdoors. The indoor air-polluting compounds make up a pollutants mixture coming from outdoor ambient air pollution and indoor-generated pollution. These mixtures contain a notable variety of compounds. They can be ranked as either biological or chemical contaminants [9]. Within the first group are included those pollutants cited by Rosário et al. [10], and to the second group correspondents the tobacco smoke, volatile organic compounds (VOCs), nitrogen oxides (NOx) [11], lead, radon, asbestos, carbon monoxide [12], and synthetic chemicals (https://apps.who.int/iris/ handle/10665/66537, accessed July 2, 2021). It further exists agents influencing indoor air quality, such as temperature, relative humidity, and ventilation, among others. The US Environmental Protection Agency urges to understand the indoor air quality as a term of air quality within and around buildings and structures, especially as it relates to the health and comfort of building occupants (https://search.

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epa.gov/epasearch/?querytext=indoor+air+quality&areaname=&areacontacts=&ar easearchurl=&typeofsearch=epa&result_template=2col.ftl#/, accessed June 29, 2021).

2.2.1  Polluting Emission Sources in Indoor Air The identification of emission focuses of air pollutants in indoor environments is fundamental acting to control and mitigate the release of polluting compounds into indoor air and protect human health. In this sense, the main emission sources of air pollutants at indoor locations can be classified as primary and secondary sources. Primary emission internal sources generate direct releases of pollutants into the indoor atmosphere, while that the occurrence in situ of pollutants yielded by secondary emission indoor sources depends on both the chemical reactions between their precursors (named as primary pollutants) and meteorological conditions, given that these last ones modulate those reactions. Among the potential sources of indoor air emissions, they are worth highlighting: • Tobacco-derived products: The mixture of the smoke issued by the burning of tobacco products is regarded as secondhand smoke or environmental tobacco smoke (ETS). Among products emitting tobacco smoke, they highlight cigarettes and cigars or pipes, although the smoke breathed out by smokers is also included. ETS is one of the most important indoor air pollution sources as well as a considerable threat to human health [13]. Scientific evidence sustains associations between tobacco smoke and tuberculosis. So, a study addressed by Obore et al. [14] concluded that exposure to inside air pollution and secondhand tobacco smoke rises the contracting tuberculosis risk. • Fuel-burning combustion appliances: Within this category are included, among others, fireplaces, gas heating, gas cooktops, gas water heaters, and woodstoves. As fuel used for burning, they highlight fossil fuels, wood, or other biomass types. In particular, biomass and coal are typical solid fuels widely employed, as primary energy sources, in developing regions worldwide, mainly for residential heating and cooking [15]. • Building materials: The chemicals emission into the indoor atmospheres coming from the materials used in the buildings construction results being a dominant factor in the indoor air, which can involve serious public health problems, so the Scientific Community has addressed studies in this sense. Nguyễn-Van et  al. conducted a radiation study in indoor air in traditional homes on Ðồng Van Karst Plateau, northern Vietnam, due to soil building materials. They concluded that the thoron (220Rn) safety threshold was often exceeded in indoor air, especially in dry soils without a treat and on the floors of classical households built with mud [16]. Similarly, Syuryavin et  al. evidenced radon and thoron emissions from

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Korean and Indonesian brick [17]. Aspects such as the type and location of tenement, year of building, and ventilation systems are indivisible features regarding the indoor air quality. In this sense, a study concluded that the concentration of formaldehyde was higher in new than older dwellings (30% approximately). Similarly, this study reported the formaldehyde levels were favored with mechanical ventilation and in concrete constructions [18]. Within the building materials, insulation materials are components growing used in construction, and although their major function is to gain thermal efficiency by playing down the internal and external thermal exchange, they can release polluting compounds into the inside atmosphere [19]. The emanation of polluting species in inside air coming from the materials employed in the building’s construction might negatively reverberate on both human health [20] and human well-being, for example, a poor odor sensation [21]. • Furnishings: It can act as an emission focus capable of producing polluting releases towards internal air. They exist evidence concerning directly or supplemented generation of indoor air pollution from furnishings. Compounds such as acetone, 2-butanone, ethyl acetate, and tert-butyl alcohol have been identified in this sense. Hernández et al. observed total COV concentrations in a magnitude order of up to three times greater, in airtight rooms (absence of mechanical ventilation [22]). • Products for household cleaning and maintenance or personal care: The use of those products has become a serious issue for human health, because of the emissions into indoor air generating this type of products, reaching generate nearly 20% of internal pollution [23]. The personal care products (PCPs) were alluded to for the first time in 1,999, considering those compounds as emerging environmental contaminants [24]. Currently, in the PCPs fabrication, such as preservatives, UV filters, anticorrosion agents, and antimicrobials, among others, a notable number of ingredients are used capable of generating damaging effects on the human being. They exist studies evidencing the presence of polluting species in the indoor environments coming from PCPs [25, 26]. As an example within this section, average benzene concentrations of 32.40 ± 26.38 μg/m3 were determined in 50 beauty salons in Ardabil (Iran) [27]. • Heating and cooling systems: Although it is not considered as an internal emission source, the ventilation processes discharge a primordial role within the indoor air quality frame. Deficient or inadequate ventilation processes carry a diminution or lack of pollutant dilution, which is translated into a deterioration indoor air quality status. This fact would explain the highest indoor pollution levels in winter against other year seasons [28]. At the level of indoor, the inadequate use of potential emission focuses, as a gas stove, can raise the number of pollutants emitted into the atmosphere. The relative weight of each emission source on the total air pollutant concentration in indoor sites depends on whether the pollutant release is continuous (such as is the case of building materials, furnishings, or fresheners) or intermittent (activities as it can be smoking, cleaning, or redecorating).

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The presence of air pollutants in indoor environments does not depend only on internal emission focuses, but also they are a function of outdoor emission sources, due to the internal vs external air inter-exchange (via mechanical ventilation, natural ventilation, or infiltration through the building envelope).

2.2.2  Indoor Air Pollutant Levels in Urban and Rural Sites Once detailed the major emission sources of air pollutants into indoor environments, the occurred levels range in terms of the seasonal period, thermal comfort, heating and ventilation systems type (natural or mechanic), building characteristics (antiquity, isolation, among others), and influence of the circulating outdoor environment (ambient air pollution and meteorological conditions). In order to offer an overview, serving to potential readers as a benchmark regarding air pollutant levels observed in indoor environments, a description in concordance to published scientific studies is addressed. At the residential urban level, a relevant research Spanish group led by X. Querol quantified indoor and outdoor air pollutants levels in 39 schools, 36 of them distributed in Barcelona City and the rest located in Sant Cugat del Vallès (Spain). The sampling points were placed in a classroom with pupils and in the playground (indoor and outdoor sites, respectively). The sampling was conducted along school hours and carried out simultaneously in both points [29]. Broadly, they found high levels of pollutants road-traffic related (PM2.5, NO2, equivalent black carbon, ultrafine particle number concentration, and trace metals) in school playgrounds and indoor environments. In particular, the determination of indoor PM2.5 components (32 species evaluated) exhibited mean concentrations oscillating between 0.14 ng/ m3 for cadmium and 10 μg/m3 for organic carbon and mineral mater. A study conducted on Greece homes for evaluating indoor benzene exposure, given that this one is a tried carcinogen, reported benzene levels of 15.3 ± 8.0 μg/ m3. Higher mean concentrations were determined in the winter period. They proved that mean benzene levels were higher in smokers than in non-smokers homes [30]. Another study was carried out in Valencia (Spain) and in 352 houses located in surrounding villages and medium-sized towns displayed near to 2.5 times higher average benzene concentrations in inside houses than outdoor (2.7 vs 1.2 μg/m3 at an indoor and outdoor site, respectively) [31]. At the commercial urban level, polluting species presence depends on the sampling location surroundings as well as likely emission focuses. In this context, while benzene levels of 39.81 ± 63.30 μg/m3 were monitored in gasoline shops in Belo Horizonte City (Brazil) [32], lower levels were averaged in other commercial establishments, such as theater (30.95 μg/m3), bar (27.18 μg/m3), and restaurants (2.58 μg/ m3) [33]. A specific chance when it comes to blurring indoor air pollution leads to the internal-external ventilation exchange. At the rural level, the criteria air pollutant

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levels (NOx, CO, SO2, C6H6, and particulate matter) normally are lower than those evaluated in urban environments, as a consequence of the influence of important emission focuses, such as traffic load (here it includes the land transport networks) and industrial focuses. Nevertheless, more specific polluting focuses within the rural environments, as it can be the use of biomass fuel, as an energy source, for cooking foods and heating houses, genre important indoor polluting levels. In this sense, Bhargava et  al. quantified high PAH levels in indoor air during cow-dung cake (CDC) combustion in homemade clay-stoves at rural locations in India, against the liquid petroleum gas (LPG) combustion or the non-cooking period [34]. They found higher PAH levels in winter than summer period, reaching total PAH concentrations of 33.53 ± 8.70 μg/m3 and 16.12 ± 5.33 μg/m3 (winter and summer, respectively, using CDC combustion) and 4.18 ± 1.06 μg/m3 and 1.83 ± 0.42 μg/m3 (winter and summer, respectively, using LPG combustion). Among all PAH congeners, the chrysene was the more polluting individual PAH with interval levels of 9.56 ± 3.11 μg/m3 using CDC combustion and benzo(k)fluoranthene with concentrations of 0.93  ±  0.4  μg/m3 employing LPG combustion. Similarly, the highest individual PAH levels in the summer driven to chrysene, independently of used combustion type, attaining levels of 6.52  ±  2.57  μg/m3 (CDC combustion) and 0.60 ± 0.16 μg/m3 (LPG combustion). Similarly, Zhang et al. monitored pollutants in the atmosphere of the rural areas in Northern China derived from using biomass burnings [35]. They evaluated the impact of fuel types, in concordance to the burning status, in terms of PM2.5 levels and main harmful component. So, while PM2.5 concentrations of 210, 220 and 175 μg/m3 were associated with corn stalks, firewood or mixed coal, and wood pellets, respectively, pollutants as CO, NO, NO2, and SO2 were considered the main harmful species when using corn stalks as fuel for heating, CO, NOx, and SO2 teamed to firewood or mixed coal and wood pellets. Within this context, levels of PM2.5 particles and CO were measured in the 40 households’ kitchen in several areas in Indonesia [36]. The used fuel for cooking was fuelwood in both environments. Broadly, the highest pollutants levels were found in the mountainous area against the coastal area. The presence of prevailing emission sources at indoor locations drives to higher air pollutant concentrations indoors than outdoors. An ambitious scientific study conducted in Spain evaluated a wide range of polluting species in the inside and ambient atmosphere of 6 rural primary schools located in Alcolea de Calatrava, Valverde, Picón, Las Casas, Poblete, and Corral de Calatrava [37]. A total of 13 VOCs exhibited indoor/outdoor ratios higher than 1, especially to formaldehyde (30.9), hexanal (40.8), and α-pinene (25.4), which is evidence that dominant sources in indoor environments can sustain a higher impact on indoor VOCs concentrations, and by extension, a higher risk for human being health about outdoor emission focuses.

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2.3  Microbiological Pollution Indoor air quality does not only encompass chemical polluting compounds but also microbiological species, mainly airborne bacteria and fungi. As an example of indoor microbiological pollution, Baurès et  al. assessed indoor microbiological concentrations within locations of special relevance (two French hospitals, University Hospital of Rennes and Nancy, respectively) [38]. They conducted two sampling campaigns, in the winter and summer periods. They quantified mean bacteria concentrations of 213 and 175 GU/m3 (it expressed as genome unit/m3) for Escherichia coli and 902 and  C ) = ∫ ∫ fLC ( L,C ) dC dL



(10.1)



PF represents the quantified risk, and fLC is associated probability density function. If random number C can be defined by local environmental guidelines (i.e., if C = C0), then the risk can be quantified as Eq. (10.2): ∞



PF = P ( L > C0 ) = ∫ fL ( L ) dL c0

(10.2)

Proximity Models The proximity mode is used to measure the closeness of a subject from the source of pollution. It works on the assumption that proximity to the emission source may lead to greater exposure in the exposed population. Literature has suggested that higher traffic density near the residence may aggravate asthma symptoms [44]. A study done in Hamilton showed that women aged between 20 and 44 years of age reported asthma symptoms and were at greater risk if they resided within 50 m of a major road [45]. The proximity model is useful for the prediction of long-term exposure; however, a limited number of covariates are used which could probably act as confounders while establishing the relationship between air pollution and health. For an instance, exposure to vehicular exhaust at places other than homes, the workplace often goes ignored [46], leading to biased risk estimates. Nevertheless,

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the proximity model is still very much in use for environmental epidemiological studies and health effects assessment at a formative stage. Interpolation Models Interpolation models work based on deterministic and stochastic techniques. Concentrations of the chosen pollutants are acquired from the monitoring stations located in the study area. The information can be used for the estimation of pollutants other than the monitoring site. Generally, these estimates are obtained at the center of the grid imposed over the study area, so that a continuous surface of pollution concentration can be established. Kriging models are based on geostatistical techniques [47] employed to develop the continuous surface of pollution. For the prediction of concentrations of SO2 over a small area, geostatistical modelling has been used [48]. Spatial and temporal distributions of ozone in Atlanta have been assessed using the Kriging model [49] to estimate pollutants’ concentration in the chosen area of study. The modelled concentrations have been associated with respiratory health effects and mortality. Long-term consequences on respiratory health in children were examined using the ambient concentrations of SO2. Ambient SO2 concentrations were found to be associated with instances of wheezing and asthma [50]. On a similar pattern modelled ozone and its relationship with pediatric asthma was also studied previously [51]. The interpolation techniques have a distinct advantage of using real pollution data during the computational calculation of exposure estimates. They can quantify the exposure and enable to perform computational calculations for the establishment of the dose-response relationship. However, the model has a disadvantage in that it needs monitoring data from a large number of sampling sites. Approximately data from 10–100 sites is required for an urban area which may vary as dependent upon the topography of the area, meteorological factors, local emitting sources, and probable errors in estimates. Integrated Exposure-Response Functions Air pollution may induce both short- and long-term health impacts to lower respiratory infections causing mortality to young children [52]. Oxidative stress due to the accumulation of particulate matter in the respiratory tract may be the main factor responsible for enhanced susceptibility to infection [53]. According to the Global Burden of Disease (GBD), LRIs are one of the leading causes of death the world over and the fifth-leading cause of mortality. Although it is not viable to count the accurate deaths from environmental pollution, yet the increase in the number of deaths over a while may be estimated and determined statistically in terms of the number of years of life lost (YLLs), and reduction in life expectancy. The GBD method works on the Eqs. (10.3) and (10.4):

M ( age,lat,lon,year ) = γ 0 × AF × Pop



(10.3)

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where M is the attributable mortality, which is a function of age, geographical latitude and longitude, and the year for which air pollution concentrations and population distributions are being considered.

R ( t ) = 1 + α 1 − expexp {−γ ( PM 2.5 − X 0 ) δ }

(10.4)



Equation 10.4 depicts the calculation of relative risk R(t) associated with the exposure to PM2.5 through an integrated exposure-response (IER) function. α, γ, and δ are the parameters that describe the shape of IERs. X0 is the minimum risk exposure having a range of 2.4–5.9 μg/m3 [54]. To study the change in mortality due to ambient pollution-induced LRIs, calculations based on the IER functions were performed for the years 2010–2015 using the global concentration distribution. The results obtained by the model matched accurately with the satellite data [55]. The modelling results showed that in 2015, approximately four million excess deaths were reported worldwide due to ambient air pollution and 727,000 were associated with AAP-LRIs. The revised annual exposure range for PM2.5 is 2.4–5.9 μg/m3 as compared to 5.8–8.8 μg/m3 used for the assessment in 2010 [56]. Generalized Additive Model (GAM) for Mental Health Assessment The negative impact of air pollution and temperature has been also reported [57] including Alzheimer’s and Parkinson’s diseases [58]. Epidemiological studies suggest the long and short term effects of air pollution on mental health [59]. Air pollution caused by vehicular exhaust and dementia incidences has been explored in Sweden [60]. The short-term effects of environmental conditions including key pollutants, temperature, and relative humidity have been assessed in Brazil through a semiparametric generalized model (GAM) combined with a distributed lag non-­ linear model (DLNM) for different age groups and lag time of 0–7 days to create a modelling framework to show non-linear exposure-response relationship and effects caused after a certain period. The model predicts values of the effect of the event with N-day lag, and the combined effect measurement during the period [61]. The semiparametric model is given by Eq. (10.5) [62]: 6



( µi ) =∝ +∑s j ( x ji ) + β1dayi + β 2 holy i + β3 time, j =1

i = 1,…, n,

(10.5)

where E(Yi) = μi, g(.) depicts the logarithmic link function, α is the intercept, s(.) depicts the natural cubic spline for non-linear predictor variables, xji represents the meteorological variables and pollutant concentrations, time refers to the effect of the temporal trend, day represents the day of the week, and holy stands for holidays. The cumulative short-term health effects owing to the exposure of air pollution, temperature, and relative humidity as related to the number of hospitalizations were

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also assessed for both sexes. A noteworthy impact of the environmental variables was found to be associated with the mental and behavioral disorders which varied with the sex and difference in age groups. At lag 0 the relative risk was higher for men than women.

10.3.3  GIS and Modelling The integration of modelling and geographic information system approach has been done for enhanced accuracy of health risk assessment and quantification of mortality associated with air pollution exposure. The integration produces quick and consistent assessment results. In a case study reported from Haiphong city in Vietnam, the traffic scenarios and values of particulate matter were simulated based on geo-­ referenced data. The findings showed an exceeding health burden owing to the exposure of particulate matter. The model included three sub-models in a GIS framework which were applied to estimate the health consequences arising in different scenarios, including emission from different sources. The procedure involves a transport model to predict traffic flow. The obtained results are integrated with an emission model to calculate emission. In the next step, a dispersion model based on the GIS tool is used to calculate air pollutants’ concentration and project on a concentration map which can also estimate the human exposure when superimposed with the population density map. Health effects are assessed by the dose-response functions using the quantified exposures and relative risks. In a study to assess the exposure of PM10, the concentrations were modelled for four different traffic scenarios in Haiphong city. The worst scenario included the maximum concentration for 24 h and 1 year, respectively. The concentrations were mapped using GIS and superimposed with the city maps. The third scenario included sources like bicycles and motorbikes, and the fourth scenario depicted private cars as a source. No significant difference in the PM concentrations was obtained between scenarios 3 and 4. It was anticipated that changes would occur at higher PM concentrations between 20 and 25 μg/m3. An estimated 1288 people died in 2007, as a result of exposure to particulate matter that originated from the vehicular exhaust. It was also anticipated that an increase of 30% in the vehicular load would cause a doubling in the number of extra deaths. It was also predicted that by 2020, a 30% rise in traffic is expected, and the PM10 concentration is expected to reach 24.44 μg/m3 [63] and would also contribute to an increase in the admissions related to COPD. Several other quantitative HRA tools provide air pollution exposure and related health effects based on population distribution, concentration-response relationship, and emission sources and differ in their complexity and exposure information source [64]. Some of these tools and models are summarized in Table 10.1:

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Table 10.1  Some health assessment models [34] S. no. Model 1 BenMap-CE

2

3

4

5

6

7

Application Estimates health impacts at the reduced ozone and PM2.5 levels. The model also enables to quantify health and economic well-being with improved air quality using population data, concentration-response criteria HAPIT Used for the assessment of health benefits associated with low PM2.5 exposure indoors. The tool can also predict the health-associated cost for different scenarios through the available exposure-response data COBRA Used for the assessment of the strategies devised for low emissions and their health and economic impact. The model also suggests high health benefit options which cost-effectively reduce health risks SIM-air The Simple Interactive Model for better Air Quality assesses the impact of policy interventions to curb air pollution in an urban environment. The model works in conjugation with GIS to show local emission sources and generated pollution data to evaluate different scenarios AirQ+software Calculates health impact in improved air quality conditions and evaluates the short- and long-term health effects associated with exposure to different pollutants. Also measures the health impact associated with household pollutants and calculates the premature deaths and diseases using the Health Impact Function (HIF) EcoSense Atmospheric dispersion and air pollution exposure assessment model estimates the long-term health effects on health, through chemical transformation and dispersion of pollutants. The model integrates the local and regional dispersion models to compute the impact of high levels of pollutants GAINS The model recognizes cost-effective policies to reduce pollution. The model estimates the health risks associated with PM and O3 [65]. The impact of primary pollutants including SO2, VOCs, NOx, and NH3 can be quantified through the model [34]

10.4  C  hallenges, Uncertainties, and Opportunities for Sophisticated Technological Intervention The health risk assessment through technological interventions has multiple advantages, and they help policymakers by predicting important information to make policies related to the concentration reduction of air pollutants. The usage of these assessment methods has increased during the past decades due to the abundance of epidemiological studies offering quantification of air pollutants and concentration-­ response relationship which can be of immense help to decision-makers to create awareness in public about the importance of good air quality [66]. However, each model or tool used has its limitations and weaknesses. The simulated concentrations may not represent the actual exposure, thereby posing a challenge in the accurate outcomes for policymakers. It is important to use accurate data for the health assessment. The modelling data and exposure predictions are in reality surrogate as often

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other exposure factors like human activity patterns and human occupancy are not explicitly dealt with. Usually in the case of urban modelling houses located in areas farther from the main road experience lower air pollutant concentration, whereas models generalize the concentrations. Overestimation of the health effects is another limitation because future scenarios are predicted generally based on a total increase in vehicular load without taking into account the accurate vehicular distribution in future as new or better roads may scatter the traffic leading to a reduction in pollution in the future. In case the modelling is done to depict the health risk assessment associated with traffic-related pollution, then temporal disparity of traffic should also be considered along with public transit. Traffic flow occurring due to freight movement must also be incorporated for accurate estimation. Better air quality monitoring and accurate data is also required to enhance the authenticity of the modelled predictions. The pollution maps can be of help to identify potential monitoring locations. Data science approaches that can deal with large, dynamic, and multi-level data are also one of the biggest challenges in the estimation of long-term health effects of air pollutants. Accurate estimation of personal exposure is also very important and requires complex and powerful processing approaches. Advancements in the field of general-purpose computing on graphics processing units (GPGPU) hardware and software have drastically removed the barriers to access and process large data sets. Online platforms like Google Earth Engine can be used for a petabytescale analysis of global satellite data [34]. Data processing scripts can also be shared as open-source code platforms like GitHub which can help in the identification, evaluation, reproduction, and validation of successful methods.

10.5  Conclusion Air pollution has been a great threat to public health since the beginning of the industrial age. Extensive technological interventions have contributed to valuable predictions to curb air pollution at different levels. The health risk assessment associated with air pollution exposure is an important aspect to focus upon for ensuring better health in the future. For futuristic predictions, computational tools and models have emerged as newer and reliable sources which can predict accurate results in a time and cost-effective manner. For decision-makers to come up with effective policy intervention, high-quality data is required which is acquired in different settings and countries. The use of technological interventions like modelling clubbed with different techniques like GIS may give a deeper insight into the association between air quality and health outcomes and may influence the decision-making process and better pollution management. Besides, inequalities in exposure and vulnerability to population must also be explored simultaneously for effective tackling of the air pollution-related health risk assessment.

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Correction to: Indoor Air Quality: An Emerging Problem Domain Jagriti Saini

, Maitreyee Dutta

, and Gonçalo Marques

Correction to: Chapter 1 in: J. Saini et al. (eds.), Integrating IoT and AI for Indoor Air Quality Assessment, Internet of Things, https://doi.org/10.1007/978-3-030-96486-3_1 This book was inadvertently published with the wrong affiliation of Dr. Gonçalo Marques. The correct affiliation is: Gonçalo Marques Rua General Santos Costa Polytechnic of Coimbra, ESTGOH Oliveira do Hospital, Portugal The book was also inadvertently published without abstracts and keywords for chapter 1. These has to be included in the chapter opening as: Abstract: Indoor air quality is a major concern for both developed and developing countries. The current populations spend most of their routine time indoors, either at home or office, which makes them susceptible to repeated exposure to hazardous pollutants in the indoor environment. Therefore, it is crucial to find trustworthy ways to monitoring and assessment of these harmful pollutant concentration levels. This chapter provides insights to the potential technologies that can be utilized to design real-time monitoring and assessment systems for indoor environments. Furthermore, it also presents the integration of different technologies to achieve enhanced outcomes. Keywords: Indoor air quality, Pollutants, Public health, Internet of Things, Artificial intelligence The updated online version of this chapter can be found at https://doi.org/10.1007/978-3-030-96486-3_1 © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Saini et al. (eds.), Integrating IoT and AI for Indoor Air Quality Assessment, Internet of Things, https://doi.org/10.1007/978-3-030-96486-3_11

C1

Index

A Abovementioned methods, 33 Aerosol spraying, 137 Air conditioner (AC), 36 Air conditioning systems, 80 Air disinfection, 137, 138, 141, 142, 147 Air filtration technologies, 79, 80 Air pollution, 150 challenges, 162 dispersion models, 153 emission, 75 exposure assessment, 152 health assessment tools GAM, 160, 161 GIS, 161, 162 HEM, 157 IFSM, 158 integrated exposure-response function, 159 interpolation, 159 proximity mode, 158 health risk, 154, 156, 157 land use regression models, 153 long-term exposure, 156 models, 152 monitoring, 150 sensors, 151 short-term exposure, 156 smartphone, 152 Air purifier units, 79 Air quality measurements, 126 Air quality objectives (AQO), 10 Air ventilation, 78

American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE), 96 Application server, 118 Artificial intelligence (AI), 2–6, 22, 32, 83 Artificial Neural Networks (ANNs), 129 Assessment into the health-related risk (AP-HRA), 155 B Benefits Mapping and Analysis Program (BenMAP-CE), 154 Bioaerosols assessment, 36 contamination prevention, 34 definition, 28, 36 environmental characteristics, 37 health, 28 health effects, 30 IoT definition, 32 modeling techniques, 33 real-time sensor, 34 monitoring and assessment techniques culture-based method, 31 detection methods, 31 non-culture-based method, 32 result method, 30 reducing concentration air filtration, 36 ventilation, 36

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 J. Saini et al. (eds.), Integrating IoT and AI for Indoor Air Quality Assessment, Internet of Things, https://doi.org/10.1007/978-3-030-96486-3

169

Index

170 Bioaerosols (cont.) source removal, 35 sources of indoor, 28, 29 Building-related symptoms (BRS), 75 C Carbon monoxide sensor, 67 Chemical aerosols, 139, 147 Clean air delivery rate (CADR), 79 Climate adaptive building shells (CABSs), 91, 92 characteristics, 93 component, 93 definition, 92 façade system, 93 materials, 93 physical domain, 94–96 Combined irradiators, 147 Commercial spaces, 74 Concentration Response Functions (CRFs), 155 CO2 sensors, 66 COVID 19 pandemic, 52 Cow-dung cake (CDC), 15 D Database server, 118 Data packets, 122 Data processing, 117, 122 Data science, 163 Decision tree algorithm, 125 Disability-adjusted life years (DALYs), 156 Disinfection physical methods, 138 Dispersion models, 153 Distributed lag non-linear model (DLNM), 160 E Environmental tobacco smoke (ETS), 12 F Fog computing, 113 Formaldehyde, 97 Fuzzy decision tree model, 124 G Gas mixture sensor, 66 General-purpose computing on graphics processing units (GPGPU), 163 Geographic information systems (GIS), 150

Global Burden of Disease (GBD), 159 Global Model of Ambient Particulates model (GMAPS), 154 H Heated metal oxide semiconductor, 66 Heating, ventilation and air conditioning (HVAC) systems, 50, 77 High-efficiency particulate air (HEPA) filters, 36, 76 Human exposure model (HEM), 157 I Indoor air pollutants, 97 Indoor air pollution (IAP), 2 action plans, 10 AQO, 10 atmospheric pollution, 10 background, 11 environmental compartments, 10 future challenges, 20–22 health, 16 improvement and maintenance, 11 legislation/methodology level, 22 measurement methodology European level sets, 20 non- normalized monitoring methods, 19 normalized monitoring methods, 17, 18 microbiological pollution, 16 PCA, 21 pollutant levels, urban/rural sites, 14, 15 polluting emission sources, 12, 13 pollution levels, 23 Indoor air quality (IAQ), 1–6, 28, 74, 112 AI, 4, 6 air filtration, 79, 80 architecture, 118 comfort parameters, 115 concrete application, 48 contaminant concentrations, 45 data collection, 121 data cycle, 121 data fusion, 122 data processing, 122 definition, 44 disinfection technology, 80 chemical air sterilizing, 81 house plants, 82 smart indoor air quality, 83 UVGI, 81

Index epidemiological effects, 44 experimental study, 119 framework, 117 gases/vapors, 96 health, 74 heating and cooling techniques, 2 humans, 1 IoT, 3, 5 management, 75, 76 monitoring protocols, 46 monitoring systems, 112 negative impacts, 98 parameter, 47 particles, 96 real-time, 114 sampling methods, 45, 46 sensors, 113, 120 service delivery, 126 symptom, 97 technical service, 78 technology, 76 thermal comfort, 116 user health, reducing impacts assessments, 49 health promoters, 50–52 improve outdoor air quality, 49 VOC, 96 web interface, 119 well-being components, 97 Indoor air quality monitoring (IAQM), 90, 91, 98 CABS data storage, 105 gateway, 103 initial framework, 105, 106 sensors, 100, 103 IoT, 100–102 WSN, 99 Indoor environmental quality (IEQ), 113 Indoor plants, 82 Integrated exposure-response (IER) function, 160 Integrated fuzzy-stochastic modelling (IFSM), 158 Integrated risk information system (IRIS), 158 Internet of Things (IoT), 2–6, 22, 32, 83, 90, 99 buildings, 56 context of construction monitoring internal environment, 66 sensor networks, 64 smart home, 65 definition, 67 indoor air quality, 56

171 indoor environment, buildings, 57 carbon dioxide, 57 Interpolation models, 159 Interpolation techniques, 159 L Leadership in Energy and Environmental Design (LEED), 96 Liquid petroleum gas (LPG), 15 M Machine-learning models, 22 Maximum permissible concentration (MPC), 136 Mean relative error (MRE), 19 Mechanical ventilation, 76, 77 Microbial volatile organic compounds (MVOCs), 28 Microclimatic conditions, 51 Microcomputer, 119 Microorganisms number (MCO), 136 Middleware software, 123 N Natural ventilation, 76, 77 Network Time Protocol (NTP), 118 New fuzzy decision tree model, 113 Nitrogen oxides (NOx), 11 Nondispersive infrared sensor (NDIR), 66 O Objective and evaluations measurements techniques fuzzy model-based, 128, 129 results, 129–131 P Particulate matters (PMs), 46 Passive methodology, 18 PCA-multiple linear regression (PCA-MLR), 21 PCE-423 Hotwire Anemometer, 119 Pollutants, 1, 2, 5 Portable air cleaner, 80 Poultry industry, 136 aerosol, 144 aerosol spraying, 137 bactericidal units, 146 controller, 144

Index

172 Poultry industry (cont.) irradiation installations, 142, 143 methods, 138 microorganisms, 138 radiant flux, 140 re-circulator, 142 results, 139 RNA/DNA, 140 sanitary goal, 137 UV, 137 UV-radiation, 146 vaccination, 136 ventilation, 136 Predicted Mean Vote (PMV), 115 Principal component analysis (PCA), 21 Public health, 9, 12, 22, 23, 41, 43, 61, 88, 160 R Radio frequency identifiers (RFID), 65 Radon, 96 Residential spaces, 74, 77 S Sensors, 100, 103 Service delivery, 126 Sick building syndrome (SBS) categories, 59 definition, 58, 59 factors-related to environment, 60 factors-related to individuals, 60 physical factors, 60 biological pollutants, 61 chemical polluters, 61 electromagnetic radiation, 62 insufficient ventilation, 62 lighting/acoustics, 62 moisture/mold formation, 60

prevention/solution, 63, 64 symptoms, 59 types, 62 Smartphones, 152 Source-receptor (SR), 154 Split-type air conditioner, 80 Subjective evaluation, 132 T Thermal comfort, 115, 116 Total volatile organic compounds (TVOCs), 19 Triptych Soy Agar (TSA), 19 U Ultraviolet (UV), 79 Ultraviolet germicidal irradiation (UVGI), 35, 81 Ultraviolet radiation, 139 Unhealthy building syndrome, 58, 63 V Ventilation systems, 56 Visual comfort, 116 Volatile organic compounds (VOCs), 11, 46, 66, 74, 96 Volatile organic compound (VOC) sensors, 33 W Web server, 117 Wireless sensor networks (WSNs), 98, 99, 114 World Health Organization (WHO), 10, 44 Y Years of life lost (YLL), 156, 159