Estimating marine PM_2.5 concentrations and exploring the drivers over the eastern China seas using multi-source data...

Fine particulate matter (PM_2.5) plays an important role in marine ecosystems and regional climate processes. However, accurate monitoring of PM_2.5 over marine environments remains challenging due to sparse ground observations and the complex interactions among meteorological conditions, atmospheric chemistry, and pollutant transport. To address this issue, a multi-source data framework for estimating PM_2.5 concentrations over the eastern China seas was developed by integrating aerosol optical depth (AOD), meteorological variables, and atmospheric chemical parameters (NO₂, CO, O₃, and SO₂). Three machine learning algorithms, including Back Propagation Neural Network (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were evaluated using sample-based, spatial-based, and temporal-based validation strategies. Results show that XGBoost demonstrated competitive performance among the three models across the different validation strategies, suggesting that the proposed framework is applicable to marine PM_2.5 estimation. Based on the XGBoost-derived PM_2.5 estimates, the spatio-temporal variations and dominant influencing factors of marine PM_2.5 from 2019 to 2024 were further examined. The estimated PM_2.5 concentrations showed a clear decreasing gradient from west to east and from north to south, with high-value areas mainly located in Bohai Bay and the Shandong coastal regions, indicating a strong influence from terrestrial emissions and coastal pollutant transport. Temporally, PM_2.5 showed a fluctuating downward trend and a stable seasonal cycle, with winter maxima and summer minima. The marked decline in 2020 further reflected the response of marine PM_2.5 to changes in inland anthropogenic emissions. SHapley Additive exPlanations (SHAP) analysis showed that AOD dominated PM_2.5 variations in spring, summer, and autumn, whereas reduced boundary layer height, enhanced northwesterly transport, and increased pollution-related gases such as NO₂ and CO jointly promoted wintertime PM_2.5 accumulation. These findings demonstrate the value of integrating atmospheric chemical parameters into marine PM_2.5 estimation and provide insights into air pollution mechanisms over the eastern China seas.