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

摘要

The contribution of meteorology and emissions to long-term PM 2.5 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 PM 2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM 2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM 2.5 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 PM 2.5 : a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM 2.5 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 PM 2.5 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 PM 2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM 2.5 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 PM 2.5 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 PM 2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM 2.5 and gas precursors in these years. Although emissions dominated the long-term PM 2.5 trends, the meteorology-driven anomalies also contributed ?3.9 ?% to 2.8?% of the annual mean PM 2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., ? 6.3?% to 4.9?% of the annual mean PM 2.5 concentrations in the Beijing-Tianjin-Hebei region, ? 5.1?% to 4.3?% in the Fenwei Plain, ? 4.8?% to 4.3?% in the Yangtze River Delta, and ? 25.6?% to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM 2.5 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.