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

ABSTRACT: 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 NO₂ decreased significantly in each city (∼30%-50%), whereas accompanied by a notable increase in O₃. The diurnal patterns show that th

SUBMITTER: Lv Y 

PROVIDER: S-EPMC9106379 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature