Quantifying contribution of weather patterns to PM2.5 concentrations based on spatial effects and health risk assessment

Urban air pollution, known to seriously affect residents' health, is a growing concern globally. In the past decade, the central-eastern region of China has become one of the most polluted areas in the world. In this study, we used data on PM2.5 and the exposed population, conducted an in-depth analysis using the ERA5 reanalysis dataset and analysis methods (including weather patterns clustering, and Moran's I, exposure-response relationship). Aiming to identify regional pollution characteristics, meteorological impact mechanisms and the health concerns attributable to PM2.5 in central and eastern China. Through clustering technology, the weather during the study period was clustered into four weather patterns, denoted as T1-T4; among the four weather patterns, T2 and T4 were the main weather patterns in winter. Moreover, we discussed the interannual contribution of changing weather patterns (and their synergy with spatial effects) to PM2.5. The results indicated that, after considering spatial effects, there was a slight increase in the contribution rate of weather patterns to the interannual variations in PM2.5 (maximum increase of 4.1%). The results of health risk assessment revealed that the annual changes in the number of cases of acute and chronic bronchitis and PM2.5 concentrations in each city were correlated. Notably, these findings can provide a reliable reference for promoting optimal air quality in cities, by strengthening mitigation strategies and supporting policymakers to ensure the prevention and control of regional pollutants.