To thoroughly investigate the impact of meteorological conditions and emission changes on winter PM_2.5 variation in the Yangtze River Delta (YRD) from 2015 to 2019, we leveraged advanced modeling techniques, namely, the Weather Research and Forecasting (WRF) model and the Nested Air Quality Prediction Model System (NAQPMS). The results revealed that a notable trend of high-PM_2.5-concentration regions shifted from coastal areas towards to the inland regions. While emission reduction can effectively reduce the concentration of PM_2.5, meteorological changes exert a significant impact on PM_2.5 concentration. Unfavorable meteorological changes in 2018 and 2019 emerged as crucial factors driving PM_2.5 pollution in the region (up 0~50 µg·m⁻³). Our findings also shed light on the potential sources and transport pathways of PM_2.5 pollution in key cities within the YRD, indicating that the coastal channel of Hebei–Shandong–Jiangsu and the inland channel bordering Hebei, Henan, Shandong, and Anhui serve as major contributors. Light and moderate pollution was predominantly influenced by the medium-distance coastal channel (48~70%). Remarkably, short-distance inland (19~54%) and coastal transportation (33~53%) channels emerged as the primary causes of severe PM_2.5 pollution in the YRD. To effectively combat this issue, it is imperative to bolster key control and prevention measures in these regions.

In this study, we introduced a novel Trajectory-Channel Transport Model (TCTM) to unravel spatiotemporal dynamics of PM_2.5 pollution. By integrating high-resolution simulations from the Weather Research and Forecasting (WRF) model with the Nested Air-Quality Prediction Modeling System (WRF-NAQPMS) and 72 h backward-trajectory analysis, TCTM enables the precise identification of source regions, the delineation of key transport corridors, and a quantitative assessment of regional contributions to receptor sites. Focusing on four Yangtze River Delta cities (Hangzhou, Shanghai, Nanjing, Hefei) during a January 2020 pollution event, the results demonstrate that TCTM’s Weighted Concentration Source (WCS) and Source Pollution Characteristic Index (SPCI) outperform traditional PSCF and CWT methods in source-attribution accuracy and resolution. Unlike receptor-based statistical approaches, TCTM reconstructs pollutant transport processes, quantifies spatial decay, and assigns contributions via physically interpretable metrics. This innovative framework offers actionable insights for targeted air-quality management strategies, highlighting its potential as a robust tool for pollution mitigation planning.