A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Monitoring Air Pollution in the Urban Environment by Remote Sensing
Information on spatial variability of aerosol concentrations is important for understanding pollution transport and for assessing human exposure to the pollutants in health‐related studies. Particle and gaseous concentrations are traditionally measured from ground monitoring sites, which are often limited in the spatial coverage. In contrast, satellite imagery provides continuous measurements of terrestrial and atmospheric components over a large coverage. The purpose of this chapter is to discuss the fundamental considerations in transforming from satellite‐derived aerosol optical depth (AOD) retrievals into particulate matter (PM) concentrations estimations at the ground level and per pixel. Specifically, we firstly review the complexity of air pollution monitoring from space and discuss a promising conceptual framework and appropriate research questions. We provide basic definitions of commonly used concepts in the field and show the difference in data sampling and coverage when using different satellite AOD retrievals varying in spatial resolutions and retrieval assumptions. We then compare between satellite‐retrieved AOD and ground‐based PM2.5 concentrations using several examples and discuss different approaches to model this relationship. We further present different covariates being commonly used in various statistical models, select the most important ones, and show the importance of including different data sets in the models. Finally, we discuss current and future efforts as well as new promising technologies that can advance urban air quality research. Given the complexity of air quality monitoring, we believe that only a comprehensive approach combined with advanced technological development can help shed light on different pollution sources and possible factors controlling their variability .
Monitoring Air Pollution in the Urban Environment by Remote Sensing
Information on spatial variability of aerosol concentrations is important for understanding pollution transport and for assessing human exposure to the pollutants in health‐related studies. Particle and gaseous concentrations are traditionally measured from ground monitoring sites, which are often limited in the spatial coverage. In contrast, satellite imagery provides continuous measurements of terrestrial and atmospheric components over a large coverage. The purpose of this chapter is to discuss the fundamental considerations in transforming from satellite‐derived aerosol optical depth (AOD) retrievals into particulate matter (PM) concentrations estimations at the ground level and per pixel. Specifically, we firstly review the complexity of air pollution monitoring from space and discuss a promising conceptual framework and appropriate research questions. We provide basic definitions of commonly used concepts in the field and show the difference in data sampling and coverage when using different satellite AOD retrievals varying in spatial resolutions and retrieval assumptions. We then compare between satellite‐retrieved AOD and ground‐based PM2.5 concentrations using several examples and discuss different approaches to model this relationship. We further present different covariates being commonly used in various statistical models, select the most important ones, and show the importance of including different data sets in the models. Finally, we discuss current and future efforts as well as new promising technologies that can advance urban air quality research. Given the complexity of air quality monitoring, we believe that only a comprehensive approach combined with advanced technological development can help shed light on different pollution sources and possible factors controlling their variability .
Monitoring Air Pollution in the Urban Environment by Remote Sensing
Yang, Xiaojun (editor) / Chudnovsky, Alexandra A. (author)
Urban Remote Sensing ; 391-422
2021-09-30
32 pages
Article/Chapter (Book)
Electronic Resource
English