Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model

Ambient particulate matter (PM 2.5 and PM 10 ), has been extensively monitored in numerous urban areas across the globe. Over the past decade, there has been a significant improvement in PM 2.5 air quality, while improvements in PM 10 levels have been comparatively modest, primarily due to the limited reduction in coarse particle (PM 2.5 – 10 ) pollution. Unlike PM 2.5, PM 2.5 – 10 predominantly originates from local emissions and is often characterized by pronounced spatial heterogeneity. In this study, we utilized over one million data points on PM concentrations, collected from >100 monitoring sites within a Chinese megacity, to perform spatial source apportionment of PM 2.5 – 10. Despite the widespread availability of such data, it has rarely been employed for this purpose. We employed an enhanced positive matrix factorization approach, capable of handling large datasets, in conjunction with a Bayesian multivariate receptor model to deduce spatial source impacts. Four primary sources were successfully identified and interpreted, including residential burning, industrial processes, road dust, and meteorology-related sources. This interpretation was supported by a considerable body of prior knowledge concerning emission sources, which is usually unavailable in most cases. The methodology proposed in this study demonstrates significant potential for generalization to other regions, thereby contributing to the development of air quality management strategies.