Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: A case study of PM_(2.5) during the COVID-19 outbreak in Hubei Province, China

Liu Hongwei; Yue Fange; Xie Zhouqing

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Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: A case study of PM_(2.5) during the COVID-19 outbreak in Hubei Province, China

机译：Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: A case study of PM_(2.5) during the COVID-19 outbreak in Hubei Province, China

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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 mu 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 mu 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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《Environmental Pollution》 |2022年第5期|118932.1-118932.9|共9页
作者
Liu Hongwei; Yue Fange; Xie Zhouqing;
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作者单位

Univ Sci & Technol China, Inst Polar Environm, Dept Environm Sci & Engn, Hefei 230026, Anhui, Peoples R China|Univ Sci & Technol China, Anhui Key Lab Polar Environm & Global Change, Hefei 230026, Anhui, Peoples R China;

Univ Sci & Technol China, Inst Polar Environm, Dept Environm Sci & Engn, Hefei 230026, Anhui, Peoples R China|Univ Sci & Technol China, Anhui Key Lab Polar Environm & Global Change, Hefei 230026, Anhui, Peoples R China|Chinese Acad Sci, Ctr Excellence Urb;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
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关键词
De-weather method; Random forest model; Emission reduction; Meteorology; Regional transport;

Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: A case study of PM_(2.5) during the COVID-19 outbreak in Hubei Province, China

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