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.

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 PM_2.5 level during the COVID-19 outbreak in Hubei Province. The results show that changes in anthropogenic emissions have reduced PM_2.5 concentrations in February and March 2020 by about 33.3% compared to the same period in 2019, while changes in meteorological conditions have increased PM_2.5 concentrations by about 8.8%. Moreover, the impact of meteorological conditions is more significant in the central region, which is likely to be

SUBMITTER: Liu H 

PROVIDER: S-EPMC9756305 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature