Separating emission and meteorological contributions to long-term PM 2.5 trends over eastern China during 2000–2018

Xiao, Qingyang; Zheng, Yixuan; Geng, Guannan; Chen, Cuihong; Huang, Xiaomeng; Che, Huizheng; Zhang, Xiaoye; He, Kebin; Zhang, Qiang

page9476The contribution of meteorology and emissions to long-term PMinline-formula2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PMinline-formula2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PMinline-formula2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PMinline-formula2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PMinline-formula2.5: a generalized additive model (GAM) driven by the satellite-based full-coverage daily PMinline-formula2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PMinline-formula2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PMinline-formula2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PMinline-formula2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PMinline-formula2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PMinline-formula2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PMinline-formula2.5 and gas precursors in these years. Although emissions dominated the long-term PMinline-formula2.5 trends, the meteorology-driven anomalies also contributed inline-formula−3.9 % to 2.8 % of the annual mean PMinline-formula2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., inline-formula−6.3 % to 4.9 % of the annual mean PMinline-formula2.5 concentrations in the Beijing-Tianjin-Hebei region, inline-formula−5.1 % to 4.3 % in the Fenwei Plain, inline-formula−4.8 % to 4.3 % in the Yangtze River Delta, and inline-formula−25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PMinline-formula2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.



Xiao, Qingyang / Zheng, Yixuan / Geng, Guannan / et al: Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000–2018. 2021. Copernicus Publications.


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