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()

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 related to regional transport. After excluding the contribution of meteorological conditions, the PM(2.5) concentration in Hubei Province in February and March 2020 is lower than the secondary standard of China (35 [Formula: see text] g/m(3)). 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 PM(2.5) concentration to the standard (35 [Formula: see text] g/m(3)). Our study shows that machine learning is a powerful tool to quantify the influencing factors of PM(2.5), and the results further emphasize the need for scientific emission reduction as well as joint regional control measures in future.