Identifying Winter PM2.5 Sources in an Urban Environment Using Receptor Modeling and Nonparametric Trajectory Analysis

Air quality is closely related to weather patterns. Regional factors such as wind direction, wind speed, and dispersion conditions directly affect fine particulate matter (PM 2.5 ) concentrations. Therefore, categorizing weather patterns aids in the analysis of the corresponding contributions of pollution sources. This study employed three online monitoring instruments to conduct assessments of the chemical species in PM 2.5 during the winter season of 2020 and 2021. The measured data were analyzed using a positive matrix factorization (PMF) model to identify pollution sources and their contributions in Taipei City. Additionally, weather patterns with PM 2.5 concentrations exceeding 25 µg/m 3 were classified using clustering techniques to investigate the relationship between weather patterns and pollution sources. The results revealed that high-concentration weather patterns could be classified into three groups, each showing distinct associations with pollution sources: (1) During periods of low wind speed and poor dispersion, traffic-related pollution was significantly higher compared to other weather patterns, reaching 7.4 µg/m 3 ; (2) Strong northeast monsoon patterns exhibited relatively low averaged PM 2.5 concentration owing to limited pollutant accumulation within the basin compared to the other two weather patterns; and (3) Weather patterns predominantly influenced by northwest winds showed significant contributions from pollution sources related to coal/fuel combustion and industry mixed with secondary aerosols. The PM 2.5 concentration level in northwest wind patterns could reach as high as 56 µg/m 3 . Furthermore, this study represents the first instance in which source contributions obtained through individual receptor site calculations were used in conjunction with nonparametric trajectory analysis (NTA). During the three selected high-pollution events, the results demonstrated that the contributions from different pollution sources were associated with unique spatial distribution patterns. Receptor modeling coupled with weather cluster analysis and NTA can serve as a valuable reference for policymakers to formulate effective PM 2.5 control strategies. Graphical abstract illustrating the relationships among weather patterns, PM₂.₅ source contributions, and transport characteristics in Taipei City