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. The contribution of meteorology and emissions tolong-term PM2.5 trends is critical for air quality management but hasnot yet been fully analyzed. Here, we used the combination of a machine learningmodel, statistical method, and chemical transport model to quantify themeteorological impacts on PM2.5 pollution during 2000–2018.Specifically, we first developed a two-stage machine learning PM2.5prediction model with a synthetic minority oversampling technique to improvethe satellite-based PM2.5 estimates over highly polluted days, thusallowing us to better characterize the meteorological effects on hazeevents. Then we used two methods to examine the meteorological contribution toPM2.5: a generalized additive model (GAM) drivenby the satellite-based full-coverage daily PM2.5 retrievals andthe Weather Research and Forecasting/Community Multiscale Air Quality(WRF/CMAQ) modeling system. We found good agreements between GAM estimations andthe CMAQ model estimations of the meteorological contribution to PM2.5 ona monthly scale (correlation coefficient between 0.53–0.72). Both methodsrevealed the dominant role of emission changes in the long-term trend ofPM2.5 concentration in China during 2000–2018, with notable influencefrom the meteorological condition. The interannual variabilities inmeteorology-associated PM2.5 were dominated by the fall and wintermeteorological conditions, when regional stagnant and stable conditions weremore likely to happen and when haze events frequently occurred. From 2000 to2018, the meteorological contribution became more unfavorable to PM2.5pollution across the North China Plain and central China but were morebeneficial to pollution control across the southern part, e.g., the YangtzeRiver Delta. The meteorology-adjusted PM2.5 over eastern China (denoted East China in figures) peaked in2006 and 2011, mainly driven by the emission peaks in primary PM2.5 and gas precursors in these years. Although emissions dominated thelong-term PM2.5 trends, the meteorology-driven anomalies alsocontributed −3.9 % to 2.8 % of the annual mean PM2.5concentrations in eastern China estimated from the GAM. Themeteorological contributions were even higher regionally, e.g., −6.3 %to 4.9 % of the annual mean PM2.5 concentrations in theBeijing-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 meteorologicaleffects on PM2.5 and the possible worsening trend of meteorologicalconditions in the northern part of China where air pollution is severe andpopulation is clustered, stricter clean air actions are needed to avoid hazeevents in the future.

atmospheric chemistry and physics

Separating emission and meteorological contributions to long-term PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; trends over eastern China during 2000–2018