Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach. (February 2023)

• Record Type: Journal Article
• Authors: Hasnain, Ahmad; Sheng, Yehua; Hashmi, Muhammad Zaffar; Bhatti, Uzair Aslam; Ahmed, Zulkifl; Zha, Yong
• Is Part Of: Chemosphere. Volume 314(2023)
• Publication Date: 2023-02
• Subjects: Air quality -- Random forest model -- COVID-19 -- Air pollution -- Yangtze river delta; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2022.137638
• Languages: English
• ISSNs: 0045-6535

Abstract

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 PM10 (R 2 = 0.78, RMSE = 8.81 μg/m 3 ), PM2.5 (R 2 = 0.76, RMSE = 6.16 μg/m 3 ), SO2 (R 2 = 0.76, RMSE = 0.70 μg/m 3 ), NO2 (R 2 = 0.75, RMSE = 4.25 μg/m 3 ), CO (R 2 = 0.81, RMSE = 0.4 μg/m 3 ) and O3 (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 SO2 (−36.37%), followed by PM10 (−33.95%), PM2.5 (−32.86%), NO2 (−32.65%) and CO (−20.48%), while an increase in O3 was observed (6.70%) during the COVID-19 period (first phase). Moreover, the YRD experienced rising trends in the concentrations of PM10, PM2.5, NO2 and CO, while SO2 and O3 levels decreased in 2021–22 (second phase). These findings provide credible outcomes and encourage the efforts to mitigate air pollution problems in the future. Graphical abstract: Image 1 Highlights: Our random forest (RF) model showed high performance in the prediction of air pollutants in the Yangtze River Delta. Significant reductions were recorded in air pollutants during the COVID-19 phase. The Yangtze River Delta experienced rising trends in air pollutant concentrations in 2021–22. Meteorological parameters indicated mixed behavior during the study period.