Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology

被引:0
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作者
Ram Kumar Singh
Martin Drews
Manuel De la Sen
Prashant Kumar Srivastava
Bambang H. Trisasongko
Manoj Kumar
Manish Kumar Pandey
Akash Anand
S. S. Singh
A. K. Pandey
Manmohan Dobriyal
Meenu Rani
Pavan Kumar
机构
[1] TERI School of Advanced Studies,Department of Natural Resources
[2] Technical University of Denmark,Department of Technology, Management and Economics
[3] University of the Basque Country,Department of Electricity and Electronics, Institute of Research and Development of Processes IIDP
[4] Banaras Hindu University,Remote Sensing Laboratory, Institute of Environment and Sustainable Development
[5] Banaras Hindu University,DST
[6] Bogor Agricultural University,Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development
[7] Forest Research Institute (FRI),Department of Soil Science and Land Resource and Geospatial Information and Technologies for the Integrative and Intelligent Agriculture (GITIIA)
[8] Rani Lakshmi Bai Central Agricultural University,GIS Centre
[9] Rani Lakshmi Bai Central Agricultural University,Directorate of Extension Education
[10] Kumaun University,College of Horticulture and Forestry
来源
Scientific Reports | / 11卷
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摘要
The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.
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