Analysis and comparison of PAHs, PFASs and OCPs sources in lake sediments by receptor model

Receptor models were typically used in air pollution studies and few publications are available for receptor models that consider the details of parameters and procedures in evaluating the trace organic pollutants in sediments. PMF, Unmix and PCA-MLR are three receptor modes which have been widely used in source apportionment analysis in air pollution study. The sediment samples were collected from seven lakes in Jiangsu and Anhui provinces. Three models were applied to analyze the source apportionment of different semi volatile organic pollutants, polycyclic aromatic hydrocarbons (PAHs), perfluoroalkyl substances (PFASs) and organochlorine pesticides (OCPs). The aims of this study were to explore the applications of the three receptor models in the sediment pollution, based on a comparison of their modeling results. The three models exhibit consistent results, and the simulated values and measured values fit well, indicating that these models are well applied in the source apportionment of sediment pollutants. The results show that PAHs in the sediment in the study area are mainly from three sources, including vehicle exhaust, coal combustion and kerosene combustion. There are three main sources of PFASs in the sediment, including fluoropolymer processing aid, fluororesin coating and metal plating, textile treatments, precious metals and coatings industry, and food paper packing. The source of OCPs is mainly pesticide residues, including DDTs, hexachlorocyclohexane(BHC), and some endrin aldehyde, chlordane and sulfudan. Among the three receptor models, PMF can give corresponding weight to the data based on the uncertainty of the input data, Unmix has advantage of selecting indicators associated to specific sources into the model, while PCA-MLR can quantify the results of cluster analysis. Using multiple models to analyze the source apportionment of sediment pollutant can make the models complementary and the results more accurate, avoiding the limitation of a single model.