The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques

JS Wijnands; KA Nice; S Seneviratne; J Thompson; M Stevenson

Journal article

The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques

JS Wijnands, KA Nice, S Seneviratne, J Thompson, M Stevenson

Atmospheric Pollution Research | TURKISH NATL COMMITTEE AIR POLLUTION RES & CONTROL-TUNCAP | Published : 2022

DOI: 10.1016/j.apr.2022.101438

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Abstract

In response to the COVID-19 pandemic, most countries implemented public health ordinances that resulted in restricted mobility and a resultant change in air quality. This has provided an opportunity to quantify the extent to which carbon-based transport and industrial activity affect air quality. However, quantification of these complex effects has proven to be difficult, depending on the stringency of restrictions, country-specific emission source profiles, long-term trends and meteorological effects on atmospheric chemistry, emission levels and in-flow from nearby countries. In this study, confounding factors were disentangled for a direct comparison of pandemic-related reductions in absol..

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University of Melbourne Researchers

Kerry Nice Author

Sachith Seneviratne Author

Jason Thompson Author

Mark Stevenson Author

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Grants

Awarded by National Health and Medical Research Council

Funding Acknowledgements

This work was supported by the Melbourne Energy Institute. J.T. is supported by an Australian Research Council Discovery Early Career Research Award [grant number DE180101411] . M.S. is supported by a National Health and Medical Research Council (Australia) Fellowship [grant number APP1136250] . The funding sources had no involvement in study design, analysis, writing, and the decision to submit the article for publication. The authors would like to acknowledge the three anonymous reviewers for their valuable feedback, which helped improve the quality of the original manuscript.