Li Chaocan, Huo Shouliang, Yu Zhiqiang, Xi Beidou, Zeng Xiangying, Wu Fengchang
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China.
Environ Sci Pollut Res Int. 2014 Oct;21(20):12028-39. doi: 10.1007/s11356-014-3137-8. Epub 2014 Jun 13.
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.
从中国东部的浅水富营养化湖泊巢湖采集了29个沉积物样本,分析其中15种优先多环芳烃(PAHs),以确定PAHs的空间分布和暴露风险。结合PAHs诊断比值,使用三种受体模型,即主成分分析-多元线性回归(PCA-MLR)模型、正定矩阵因子分解(PMF)模型和Unmix模型,研究PAHs的潜在源解析。在巢湖沉积物中观察到PAHs的空间分布和潜在毒性从西向东呈明显梯度。∑15PAH浓度和毒性当量(TEQ)分别在80.82 - 30365.01 ng g(-¹)干重和40.77 - 614.03范围内。上述变量的最高值归因于西湖区域的城市工业污染源,且离河流入水口越远,其水平越低。三种不同模型对15种PAH化合物的预测水平和实测水平之间产生了极好的相关系数。同样,三种受体模型和PAH诊断比值得出了源解析结果,表明对PAHs贡献最大的是煤炭燃烧和木材燃烧,其次是车辆排放。PMF模型对汽油燃烧、柴油燃烧、未燃烧石油排放和木材燃烧产生的PAHs贡献分别为34.49%、24.61%、16.11%、13.01%和11.78%。与PCA-MLR模型和Unmix模型相比,PMF模型对PAHs产生了更详细的源解析结果。