Source Apportionment of Fine Particulate Matter in Wuhan: Application of Rolling Positive Matrix Factorization Under Different Seasons and Episodic Events - Ziye Guo; Qiongqiong Wang; Nan Chen; Bo Zhu; Huang Zheng; Shaofei Kong; Mingjie Xie; Huan Yu (Aerosol and Air Quality Research)
Abstract Introduction Source apportionment of fine particulate matter (PM2.5) from positive matrix factorization (PMF) model with long-term measurements of chemical composition data can be biased due to changes of source profiles across the sampling period. Rolling PMF strategy with source profile constraints has been demonstrated as an effective tool for source apportionment of the non-refractory submicron aerosol, while its application in PM chemical speciation data under different pollution conditions was less studied. Methods Here, we conducted source apportionment of PM2.5 using rolling PMF based on hourly chemical speciation data during winter (November 2021 to January 2022) and summer months (May to July 2023) at a suburban site in Wuhan, China. Rolling PMF runs were conducted with source profile constraints using the a-value method in the Source Finder Professional. Results Rolling PMF runs for both winter and summer data indicated major PM2.5 contributors were secondary sources and biomass burning in Wuhan, contributing to 61.9% and 19.4% in winter and 58.9% and 22.7% in summer, respectively. Larger source profile variabilities were observed for winter data than summer data. The rolling PMF runs effectively reproduced the source contributions of seasonal PMF for most sources, with Pearson Correlation Coefficient (R) of 0.95–0.99 and slopes of 0.9–1.1. Analysis of the short-lasting episodic events further validated the applicability of rolling PMF, which can provide more timely and environmentally interpretable results. Conclusion This study improved our understandings of the seasonal variations of the PM sources and the formation of episodic events, providing valuable insights for formulating promptly effective air pollution control measures. Graphical abstract