Air Quality Research Using Remote Sensing

Edited by
December 2022
190 pages
  • ISBN978-3-0365-5893-6 (Hardback)
  • ISBN978-3-0365-5894-3 (PDF)

This book is a reprint of the Special Issue Air Quality Research Using Remote Sensing that was published in

Environmental & Earth Sciences

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.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
tropospheric NO2 concentrations; nitrogen dioxide; OMI; spatio-temporal trends; DBEST; PolyTrend; time-series analysis; breakpoint detection; air pollution; TROPOMI; COVID; nitrogen oxides; satellite-based; NO2; land use regression; exposure assessment; carbon monoxide; COVID-19; China; surface concentration; TROPOMI; IASI; drone; UAV; gas sensors; odour; air pollution; industrial emissions; mapping; environmental monitoring; aerosol optical depth; CAMS; COVID-19; machine learning; MODIS; urban form; PM2.5; landscape metrics; geographically weighted regression; Yunnan Plateau; biomass burning; cross-border transport; PM2.5; WRF-Chem; formaldehyde; trend; OMI; satellite; monitor; annual; seasonal; temperature; meteorology; PM2.5; AOD; PM2.5; AOD; machine learning; Europe; open data; n/a

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