Source apportionment of PM2.5 during haze episodes in Shanghai by the PMF model with PAHs

Abstract Positive matrix factorization (PMF) is a commonly used receptor model for PM2.5 source apportionment, relying on tracers to identify emission sources. Adding organic tracers such as polycyclic aromatic hydrocarbons (PAHs) to PMF model could enhance the performance of constraining emission sources. In this study, the PMF with PAHs was applied to 112 PM2.5 samples, including full-year and haze episodes during 2015–2016 in Shanghai. Compared to the original PMF, the PMF with PAHs newly identified the industry source and revised of the contributions of biomass burning and coal combustion. The PMF with PAHs were used to reconstruct the source apportionment of PM2.5 in four winter haze episodes. Taking the first two haze episodes (defined as P1and P2) as examples of frequent air mass changes, the P1 accumulation stage was successively affected by local and northwest sources. Vehicle (35.3%) and secondary particles (25.7%) were the primary contributors to PM2.5, due to local accumulation. Industry sources obtained a 25.9% increment under the effect from the northwest sources. P2 was successively controlled by the southwest and northwest sources from the clean stage to the dissipation stage. Significantly increased contributions of biomass burning and industry sources to PM2.5 were observed. Compared to other episodes, the P2 accumulation stage had the highest coal-combustion proportion (5.6%) under the influence of the northwest sources from Shaanxi, Shanxi, and Henan. Moreover, the toxicities of different emission sources were evaluated based on PAHs and BaPeq. Coal combustion, biomass burning, and vehicle sources were the three major contributors, especially coal combustion, contributing 17.1–66.8% to PAHs and 22.6–66.4% to BaPeq but less than 5.6% to PM2.5. We believe that these results will help policymakers by providing insights into the reduction in emission sources for regulation in urban environments and the mitigation of health risks.