Online observation of PM chemical composition in coastal city of the Yangtze River Delta: temporal variation and comparative analysis of pollution types.

Aerosol chemical composition significantly impacts air quality, radiation balance, and human health. This study analyzed the temporal variation characteristics of PM chemical composition and its variation characteristics during different pollution episodes (haze, ozone (O), and dust) using online observational data of atmospheric pollutants (pollutant gases and PM components) and meteorological elements in Jiaxing during 2023. The top five annual average concentrations of components were as follows: NO (30.02 %) > POC (14.26 %) > NH (13.92 %) > SO (13.58 %) > SOC (12.52 %). The contribution of NO to PM significantly decreased in summer and increased in winter, while SO exhibited the opposite trend, with its summer percentage nearly double that in winter. The percentage of SOC was significantly higher in summer and autumn compared to that in spring and winter. The percentage of POC notably increased in autumn. The percentage of NH remained stable throughout the year (12.84 %-14.49 %). Water-soluble ions (WSIIs) and carbonaceous aerosols reached their highest concentrations in December (35.73 μg/m and 15.97 μg/m, respectively) and their lowest in July (8.34 μg/m) and April (6.93 μg/m), respectively. Seasonal differences in diurnal variation were minimal. The evolution of PM composition varied under different pollution. During haze pollution, as PM concentration increased, NO percentage increased, while O concentration decreased. Under severe haze pollution, NO percentage increased by 41.51 %, and O concentration decreased by 39.09 %. During O pollution, as O concentration increased, SOC and SO percentages increased (by 55.27 % and 47.82 %, respectively), while the NO percentage decreased (by 42.46 %). During dust pollution, the metals percentage increased sharply by 457.94 % as PM concentrations rose, particularly driven by a 974.30 % increase in Al. The influence of meteorological factors exhibited significant seasonal and component-specific variations, as indicated by Generalized Additive Models (GAMs) and Shapley Additive Explanations (SHAP).