Low cost Internet of things based sensor networks for air quality in cities

Air pollution is a major public health concern, with over 7 million deaths globally attributed to it annually, as stated by the World Health Organization (WHO) in 2018. Existing real-time Air Quality (AQ) monitoring stations are expensive to install and maintain; therefore, such air quality monitoring networks are sparsely deployed and lack the measurement density to develop high-resolution spatiotemporal air pollutant monitoring. The data generated also lacks accuracy, but still, they have great potential to complement the existing air quality assessment framework. Therefore, this thesis aims to propose a comprehensive architecture for utilizing low-cost sensors in air pollution monitoring. The thesis presents a novel approach to deploy a low-cost sensor network in a city and use a hybrid convolutional-long short-term memory (Conv-LSTM) model for spatiotemporal prediction of air pollution. This approach utilizes both convolutional layers to capture spatial patterns in the sensor data and LSTM layers to capture temporal dependencies. The use of a hybrid model allows for the simultaneous capture of both spatial and temporal patterns in the data, resulting in more accurate predictions compared to models that only utilize one or the other. The research also explores the use of statistical models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) and Nonlinear Autoregressive with exogenous inputs (NARX) models for air quality forecasting, presenting a comparison of the proposed hybrid model with other such state-of-the-art statistical and machine learning models. The results show that the proposed Conv-LSTM model outperforms these approaches in terms of prediction accuracy and robustness and, therefore, is a promising approach for spatiotemporal prediction of air pollution using low-cost sensor data. Additionally, the thesis proposes a general solution to analyze how the noise level of measurements and hyperparameters of a Gaussian process model affect the prediction accuracy and uncertainty of low-cost sensor data. The thesis further presents an extensive evaluation of the proposed hybrid model using real-world data from the low-cost sensor network deployed in Sheffield, and the results demonstrate the effectiveness of the proposed approach. Finally, the real-world studies present the integration of low-cost sensor data into a decision-making system, social and behavioural changes driven by such sensors and the impact of these results on driving policy changes to achieve the World Health Organization’s (WHO) 2021 target for air quality

Advanced Air Quality Management with Machine Learning

Air pollution has been a significant health risk factor at a regional and global scale. Although the present method can provide assessment indices like exposure risks or air pollutant concentrations for air quality management, the modeling estimations still remain non-negligible bias which could deviate from reality and limit the effectiveness of emission control strategies to reduce air pollution and derive health benefits. The current development in air quality management is still impeded by two major obstacles: (1) biased air quality concentrations from air quality models and (2) inaccurate exposure risk estimations Inspired by more available and overwhelming data, machine learning techniques provide promising opportunities to solve the above-mentioned obstacles and bridge the gap between model results and reality. This dissertation illustrates three machine learning applications to strengthen air quality management: (1) identifying heterogeneous exposure risk to air pollutants among diverse urbanization levels, (2) correcting modeled air pollutant concentrations and quantifying the bias of sources from model inputs, and (3) examine nonlinear air pollutant responses to local emissions. This dissertation uses Taiwan as a case study, due to its well-established hospital data, emission inventory, and air quality monitoring network. In conclusion, although ML models have become common in atmospheric and environmental health science in recent years, the modeling processes and output interpretation should rely on interdisciplinary professions and judgment. Except for meeting the basic modeling performance, future ML applications in atmospheric and environmental health science should provide interpretability and explainability in terms of human-environment interactions and interpretable physical/chemical mechanisms. Such applications are expected to feedback to traditional methods and deepen our understanding of environmental science

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

Earth Observation in the EMMENA Region: Scoping Review of Current Applications and Knowledge Gaps

Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services