Meteorology-normalized variations of air quality during the COVID-19 lockdown in three Chinese megacities

Atmospheric Pollution Research

article

Yunqian Lv¹  Hezhong Tian¹  Lining Luo¹  Shuhan Liu¹  Xiaoxuan Bai¹  Hongyan Zhao¹  Shumin Lin¹  Shuang Zhao¹  Zhihui Guo¹  Yifei Xiao¹  Junqi Yang¹ 

[1] State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University;Center for Atmospheric Environmental Studies, Beijing Normal University

关键词: COVID-19;    Air quality;    Random forest model;    Meteorological impacts;    Road traffic;    Emission control strategy;   

DOI  :  10.1016/j.apr.2022.101452

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in air quality. Here, we applied a machine learning algorithm (random forest model) to eliminate meteorological effects and characterize the high-resolution variation characteristics of air quality induced by COVID-19 in Beijing, Wuhan, and Urumqi. Our RF model estimates showed that the highest decrease in deweathered PM 2.5 in Wuhan (−43.6%) and Beijing (−14.0%) was at traffic stations during lockdown period (February 1- March 15, 2020), while it was at industry stations in Urumqi (−54.2%). Deweathered NO 2 decreased significantly in each city (∼30%–50%), whereas accompanied by a notable increase in O 3 . The diurnal patterns show that the morning peaks of traffic-related NO 2 and CO almost disappeared. Additionally, our results suggested that meteorological effects offset some of the reduction in pollutant concentrations . Adverse meteorological conditions played a leading role in the variation in PM 2.5 concentration in Beijing, which contributed to +33.5%. The true effect of lockdown reduced the PM 2.5 concentrations in Wuhan, Beijing, and Urumqi by approximately 14.6%, 17.0%, and 34.0%, respectively. In summary, lockdown is the most important driver of the decline in pollutant concentrations, but the reduction of SO 2 and CO is limited and they are mainly influenced by changing trends. This study provides insights into quantifying variations in air quality due to the lockdown by considering meteorological variability, which varies greatly from city to city, and provides a reference for changes in city scale pollutant concentrations during the lockdown.

【 授权许可】

CC BY