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Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: A case study of PM2.5 during the COVID-19 outbreak in Hubei Province, China.
Liu, Hongwei; Yue, Fange; Xie, Zhouqing.
  • Liu H; Department of Environmental Science and Engineering, Institute of Polar Environment & Anhui Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Yue F; Department of Environmental Science and Engineering, Institute of Polar Environment & Anhui Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Xie Z; Department of Environmental Science and Engineering, Institute of Polar Environment & Anhui Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei, Anhui, 230026, China; Center for Excellence in Urban Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian, 361021, China. Electronic address: zqxie@ustc.edu.cn.
Environ Pollut ; 300: 118932, 2022 May 01.
Article in English | MEDLINE | ID: covidwho-1664904
ABSTRACT
Air pollution is becoming serious in developing country, and how to quantify the role of local emission and/or meteorological factors is very important for government to implement policy to control pollution. Here, we use a random forest model, a machine learning (ML) approach, combined with a de-weather method to analyze the PM2.5 level during the COVID-19 outbreak in Hubei Province. The results show that changes in anthropogenic emissions have reduced PM2.5 concentrations in February and March 2020 by about 33.3% compared to the same period in 2019, while changes in meteorological conditions have increased PM2.5 concentrations by about 8.8%. Moreover, the impact of meteorological conditions is more significant in the central region, which is likely to be related to regional transport. After excluding the contribution of meteorological conditions, the PM2.5 concentration in Hubei Province in February and March 2020 is lower than the secondary standard of China (35 µ g/m3). Our estimates also indicate that under similar meteorological conditions as in February and March 2019, an emission reduction intensity equivalent to about 48% of the emission reduction intensity during the lockdown may bring the annual average PM2.5 concentration to the standard (35 µ g/m3). Our study shows that machine learning is a powerful tool to quantify the influencing factors of PM2.5, and the results further emphasize the need for scientific emission reduction as well as joint regional control measures in future.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollutants / Air Pollution / COVID-19 Type of study: Case report / Observational study / Randomized controlled trials Limits: Humans Country/Region as subject: Asia Language: English Journal: Environ Pollut Journal subject: Environmental Health Year: 2022 Document Type: Article Affiliation country: J.envpol.2022.118932

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollutants / Air Pollution / COVID-19 Type of study: Case report / Observational study / Randomized controlled trials Limits: Humans Country/Region as subject: Asia Language: English Journal: Environ Pollut Journal subject: Environmental Health Year: 2022 Document Type: Article Affiliation country: J.envpol.2022.118932