High-time-resolution PM2.5 source apportionment based on multi-model with organic tracers in Beijing during haze episodes

Abstract Fine particulate matter (PM2.5) is a prominent atmospheric pollutant that poses serious adverse effects on air quality and human health. PM2.5 source apportionment based on receptor model suggests that Beijing is polluted by mixed emission sources, but the model is limited by a lack of organic tracers and an inability to distinguish between contributions from local and regional transport. In this study, positive matrix factorization (PMF) model with organic tracers was employed to analyze refined PM2.5 pollution sources at 1-h time resolution, and the contribution of regional transport was quantified using Particulate source apportionment technology (PSAT) in the Comprehensive Air Quality Model with Extensions (CAMx). The results identified nine source types using PMF model based on offline data for PM2.5 concentrations, organic carbon, elemental carbon, water-soluble ions, trace elements and organic species. Gasoline and diesel exhausts were distinguished by adding polycyclic aromatic hydrocarbons (PAHs), C19-C24 n-alkanes as key organic tracers. In addition, levoglucosan and hexadecanoic acid are important additions for identifying biomass burning and cooking, respectively. Furthermore, the contribution of specific sources and source regions, from the formation to dissipation of two typical haze episodes (EP1 and EP2) in Beijing, was quantitatively analyzed. EP1 was primarily caused by local emissions with an average contribution rate of 67.5%, characterized by secondary source, gasoline and diesel exhausts, as well as industrial source. EP2 was dominated by secondary source from regional transport contributing approximately 50%. Short-range transport from Baoding (9.1%) and Langfang (5.8%) in Hebei Province was the largest external contributor, and long-range transport contributed 20% of the PM2.5 concentration. This study suggests that combining receptor model-based source apportionment with air quality model has practical significance for understanding the causes of haze episodes, setting city-specific emission reduction measures and improving air quality in the Beijing-Tianjin-Hebei (BTH) region.