Abstract
The novel coronavirus (COVID-19), first identified at the end of December 2019, has significant impacts on all aspects of human society. In this study, we aimed to assess the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta (YRD) region using a random forest (RF) model. To estimate the accuracy of the model, the cross-validation (CV), determination coefficient R(2), root mean squared error (RMSE) and mean absolute error (MAE) were used. The results demonstrate that the RF model achieved the best performance in the prediction of PM(10) (R(2) = 0.78, RMSE = 8.81 μg/m(3)), PM(2.5) (R(2) = 0.76, RMSE = 6.16 μg/m(3)), SO(2) (R(2) = 0.76, RMSE = 0.70 μg/m(3)), NO(2) (R(2) = 0.75, RMSE = 4.25 μg/m(3)), CO (R(2) = 0.81, RMSE = 0.4 μg/m(3)) and O(3) (R(2) = 0.79, RMSE = 6.24 μg/m(3)) concentrations in the YRD region. Compared with the prior two years (2018-19), significant reductions were recorded in air pollutants, such as SO(2) (-36.37%), followed by PM(10) (-33.95%), PM(2.5) (-32.86%), NO(2) (-32.65%) and CO (-20.48%), while an increase in O(3) was observed (6.70%) during the COVID-19 period (first phase). Moreover, the YRD experienced rising trends in the concentrations of PM(10), PM(2.5), NO(2) and CO, while SO(2) and O(3) levels decreased in 2021-22 (second phase). These findings provide credible outcomes and encourage the efforts to mitigate air pollution problems in the future.