Quantifying the driving factors of particulate matter variabilities in the Beijing-Tianjin-Hebei and Yangtze River Delta regions from 2015 to 2022 by machine learning approach

Abstract. Accurately quantifying the relative roles of anthropogenic emissions and meteorological conditions is essential for understanding long term changes in particulate matter (PM). Using ground observations from 40 cities, GEOS-FP meteorology, CEDS emissions, and monthly LightGBM models, this study assesses the drivers of PM2.5 and PM10 across the Beijing–Tianjin–Hebei (BTH) and Yangtze River Delta (YRD) regions during 2015–2022. The models demonstrate strong predictive skill (R/R2=0.82/0.67 for PM2.5 and 0.81/0.65 for PM10), with consistently high performance across cities. Both pollutants exhibit significant decreasing trends over the study period. Counterfactual experiments show that emission reductions overwhelmingly dominate these improvements. PM2.5 emission driven changes intensify from −9.1 µg m−3 in 2016 to −31.4 µg m−3 in 2022, while PM10 reductions strengthen from −9.8 to −42.9 µg m−3. Meteorology driven contributions appear as positive net anomalies at the interannual scale (approximately +2–4 µg m−3 for PM2.5 and +0.5–3 µg m−3 for PM10), indicating that air quality improvements were achieved despite year to year meteorological influences. SHAP attribution highlights 2 m air temperature (T2M), humidity (QV2M), and key precursors as dominant predictors. Interaction diagnostics further indicate that meteorological conditions modulate the effectiveness of precursor emissions, without implying direct causal mechanisms. These results provide a comprehensive data driven assessment of the factors shaping PM evolution in two major urban clusters of China.