School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China.
Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China.
Environ Sci Pollut Res Int. 2017 Nov;24(33):25899-25911. doi: 10.1007/s11356-017-0185-x. Epub 2017 Sep 22.
The spatial distribution of polycyclic aromatic hydrocarbons (PAHs) and their source contributions employing receptor models has been widely reported. However, the temporal distribution of PAH source contributions is less studied. Thus, in this paper, three receptor models including principle component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix were used to PAH source apportionment study in a sediment core from Honghu Lake, China. Sixteen USEPA priority PAHs in 37 sliced sediment layers (1-cm interval) were measured, with the concentrations of ∑PAH (sum of 16 PAHs) ranging from 93.0 to 431 ng g. The source apportionment results derived from three receptor models were similar, with three common sources: mixed sources of biomass burning and coal combustion (31.0-41.4% on average), petroleum combustion (31.8-45.5%), and oil leakage (13.1-21.3%). The PMF model segregated an additional source: domestic coal combustion (contributed 20.9% to the ∑PAHs). Four aspects including intra-comparison, inter-comparison, source numbers and compositions, and source contributions were considered in comparison study. The results indicated that the PMF model was most reasonable in PAH source apportionment research in this study.
多环芳烃(PAHs)的空间分布及其采用受体模型的来源贡献已被广泛报道。然而,PAH 来源贡献的时间分布研究较少。因此,本文采用主成分分析-多元线性回归(PCA-MLR)、正矩阵因子分解(PMF)和 Unmix 三种受体模型,对中国洪湖湖底沉积物中的 PAH 源分配进行了研究。在 37 个切片沉积物层(1 厘米间隔)中测量了 16 种美国环保署优先 PAHs,∑PAH(16 种 PAHs 的总和)浓度范围为 93.0-431ng/g。三种受体模型的源分配结果相似,有三个共同的来源:生物质燃烧和煤炭燃烧的混合源(平均占 31.0-41.4%)、石油燃烧(31.8-45.5%)和漏油(13.1-21.3%)。PMF 模型还分离出了一个额外的来源:国内煤炭燃烧(对∑PAHs 的贡献率为 20.9%)。在比较研究中,考虑了四个方面:内部比较、外部比较、源的数量和组成以及源的贡献。结果表明,在本研究的 PAH 源分配研究中,PMF 模型最为合理。