Spatial distribution, potential risk assessment, and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in sediments of Lake Chaohu, China

Li, Chaocan; Huo, Shouliang; Yu, Zhiqiang; Xi, Beidou; Zeng, Xiangying; Wu, Fengchang

Spatial distribution, potential risk assessment, and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in sediments of Lake Chaohu, China

2014

Twenty-nine sediment samples were collected from Lake Chaohu, a shallow eutrophic lake in Eastern China, and were analyzed for 15 priority polycyclic aromatic hydrocarbons (PAHs) to determine the spatial distribution and exposure risks of PAHs. Three receptor models, the principal component analysis-multiple linear regression (PCA-MLR) model, the positive matrix factorization (PMF) model, and the Unmix model, were used in combination with the PAHs diagnostic ratios to investigate the potential source apportionment of PAHs. A clear gradient in the spatial distribution and the potential toxicity of PAHs was observed from west to east in the sediments of Lake Chaohu. ∑₁₅PAH concentrations and the TEQ were in the range of 80.82-30 365.01 ng g⁻¹ d.w. and 40.77-614.03, respectively. The highest values of the aforementioned variables were attributed to urban–industrial pollution sources in the west lake region, and the levels decreased away from the river inlets. The three different models yielded excellent correlation coefficients between the predicted and measured levels of the 15 PAH compounds. Similarly, source apportionment results were derived from the three receptor models and the PAH diagnostic ratios, suggesting that the highest contribution to the PAHs was from coal combustion and wood combustion, followed by vehicular emissions. The PMF model yielded the following contributions to the PAHs from gasoline combustion, diesel combustion, unburned petroleum emissions, and wood combustion: 34.49, 24.61, 16.11, 13.01, and 11.78 %, respectively. The PMF model produced more detailed source apportionment results for the PAHs than the PCA-MLR and Unmix models.

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