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Receptor models are a useful tool for identifying sources of polycyclic aromatic hydrocarbons (PAHs) in multiple environmental media. In this study, three different receptor models (including the principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix models) were used to apportion the sources of 16 priority PAHs in a sediment core of Lake Dagze Co. The ∑PAHs (sum of all 16 measured PAHs) concentrations ranged from 51.89 to 132.82 ng/g with an average of 80.39 ng/g. The ∑PAHs were dominated by 2-3 ring PAHs, accounting for 80.12% on average, thereby indicating that they mainly originated from biomass and coal combustion and/or from long-range atmospheric transportation. The three models produced consistent source apportionment results. The greatest contributor to ∑PAHs was biomass combustion, followed by coal combustion, vehicle emissions, and petrogenic sources. Moreover, the temporal variation of the common sources was well-correlated among models. The multi-method comparison and evaluation results showed that all three models were useful tools for source apportionment of PAHs, with the PMF model providing better results than the PCA-MLR and Unmix models. The temporal trends of factor contributions were verified by PAHs with different ring numbers. Significant correlations were found between the simulated concentrations of each source factor and the PAHs with different ring numbers (P0.01), except for the petrogenic source identified by the Unmix model (P0.05). This study can provide useful information for further investigation of source apportionment of PAHs in the sediment cores. |
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