Characteristics and Driving Factors of PM2.5 Concentration Changes in Central China

Despite nationwide control efforts, central China experiences persistently high annual PM2.5 concentrations (~50 μg/m3), which are particularly severe in January (exceeding 110 μg/m3). This study employs an integrated approach combining a Multiple Linear Regression (MLR) model derived from random forest analysis with the WRF-CMAQ chemical transport modeling system to quantitatively disentangle the driving factors of PM2.5 concentrations in central China. Key findings reveal significant spatiotemporal heterogeneity in anthropogenic contributions, evidenced by consistently higher north–south gradients in regression residuals (reflecting emission impacts), linked to spatially varying industrial and transportation influences. Critically, the reduction in anthropogenic impacts over six years was substantially smaller in winter (January: 27 to 23 μg/m3) compared to summer (15 to −18 μg/m3, July), highlighting the profound role of emissions in driving severe January pollution events. Furthermore, WRF-CMAQ simulations demonstrated that adverse meteorological conditions in January 2020 counteracted emission controls, causing a net increase in PM2.5 of +13 μg/m3 relative to 2016, thereby offsetting ~68% of the reductions achieved through emission abatement (−19 μg/m3). Significant regional transport, especially affecting northern and central Henan, further weakened local control efficacy. These quantitative insights into the mechanisms of PM2.5 pollution, particularly the counteracting effects of meteorology on emission reductions in critical winter periods, provide a vital scientific foundation for designing more effective and targeted air quality management strategies in central China.