Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013–2020. (August 2022)

• Record Type: Journal Article
• Authors: Guo, Yong; Li, Kangwei; Zhao, Bin; Shen, Jiandong; Bloss, William J.; Azzi, Merched; Zhang, Yinping
• Is Part Of: Chemosphere. Volume 300(2022)
• Publication Date: 2022-08
• Subjects: Air quality -- Clean air action -- Weather normalization -- Random forest model -- Meteorological influence; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2022.134608
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: China has implemented two national clean air actions in 2013–2017 and 2018–2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013–2020. The observed concentrations of all criteria pollutants except O3 showed significant declines from 2013 to 2020, with PM2.5 annual decline rates of 6–9% in most cities. In contrast, O3 concentrations increased with annual growth rates of 1–9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O3 to NO2 concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O3 pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O3 during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis. Graphical abstract: Image 1 Highlights: We used a random forest method to remove meteorological effect for air quality data. The deweathered results show smoothed but similar variation in annual trends. The meteorology can play positive or negative role depending on specific conditions. Beijing shows positive meteorology in reducing pollutants for two clean air actions.