State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China.
State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China.
J Environ Manage. 2022 Nov 1;321:115925. doi: 10.1016/j.jenvman.2022.115925. Epub 2022 Aug 18.
The identification and apportionment of the multiple pollution sources are essential and crucial for improving the effectiveness of surface water resources management. In this study, the surface water samples were collected from Taihu Lake Basin, and the optimal water quality parameters for the receptor models were selected firstly with multivariate statistical analyses. In order to identify the potential pollution sources in surface water, dissolved organic matter (DOM) was analyzed with the excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Through the Pearson correlation analysis of water quality parameters and DOM components, the pollution sources were further verified, i.e., agricultural activities, domestic sewage, phytoplankton growth/terrestrial input and industrial sources. In addition, principal component analysis (PCA) combined with the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were employed to quantify pollution sources. Compared with PCA-APCS-MLR model, PMF model resulted in higher performance on evaluation statistics and lower proportion of unexplained variability, thus showed more realistic and robust representation. The results of PMF showed that agricultural activities (42.08%) and domestic sewage (21.16%) were identified as the dominant pollution sources of surface water in the study area. This study highlights the effectiveness of EEM-PARAFAC in identifying the pollution sources, and the applicability of PMF in apportioning the contributions of each potential pollution source in surface water.
识别和分配多种污染源对于提高地表水管理的效率至关重要。本研究采集了太湖流域的地表水样本，首先通过多元统计分析选择了最优的受体模型水质参数。为了识别地表水的潜在污染源，对溶解有机物（DOM）进行了分析，采用激发-发射矩阵耦合平行因子分析（EEM-PARAFAC）。通过水质参数和 DOM 成分的 Pearson 相关分析，进一步验证了污染源，即农业活动、生活污水、浮游植物生长/陆地输入和工业源。此外，还采用主成分分析（PCA）结合绝对主成分得分-多元线性回归（APCS-MLR）和正定矩阵因子分解（PMF）模型对污染源进行定量分析。与 PCA-APCS-MLR 模型相比，PMF 模型在评估统计数据上表现更好，未解释的可变性比例更低，因此更能真实和稳健地反映情况。PMF 模型的结果表明，农业活动（42.08%）和生活污水（21.16%）是研究区域地表水的主要污染源。本研究突出了 EEM-PARAFAC 在识别污染源方面的有效性，以及 PMF 在分配地表水各潜在污染源贡献方面的适用性。
