Engineering Research Center of Groundwater pollution Control and Remediation, Ministry of Education, College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing 100875, China.
Engineering Research Center of Groundwater pollution Control and Remediation, Ministry of Education, College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing 100875, China; Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing 100875, China.
Sci Total Environ. 2019 Dec 15;696:133972. doi: 10.1016/j.scitotenv.2019.133972. Epub 2019 Aug 19.
Increasing anthropogenic contamination poses a significant threat to groundwater security. Identifying potential contamination sources and apportioning their corresponding contributions are of vital importance for the prevention of contamination and management of groundwater resources. In this study, principal component analysis (PCA), modified grey relational analysis (MGRA), absolute principal component score-multiple linear regression (APCS-MLR), and positive matrix factorization (PMF) receptor modeling technologies were employed to evaluate the groundwater quality and apportion the potential contamination sources in the Lalin river basin, a main grain production district in the northeast of China. The contamination assessment with PCA and MGRA suggested that the groundwater in Lalin river basin was polluted due to human activities. The PCA method identified five and four potential contamination sources in wet and dry seasons, respectively, and the main sources were basically same. The APCS-MLR and PMF methods apportioned the source contributions to each groundwater quality variable. The final results showed that agricultural sources including waste water, agrochemicals and fertilizers were identified as the main sources of groundwater contamination both in wet and dry seasons. In addition, groundwater management strategies learned from the advanced experiences were discussed to protect the groundwater system in that region.
人为污染的增加对地下水安全构成了重大威胁。确定潜在的污染来源并分配它们的相应贡献对于防止污染和管理地下水资源至关重要。在本研究中，采用主成分分析（PCA）、改进的灰色关联分析（MGRA）、绝对主成分得分-多元线性回归（APCS-MLR）和正定矩阵因子（PMF）受体模型技术，评估了中国东北地区主要粮食产区拉林河流域的地下水水质，并分配了潜在的污染来源。PCA 和 MGRA 的污染评估表明，拉林河流域的地下水受到人类活动的污染。PCA 方法分别在湿季和干季识别出了五个和四个潜在的污染来源，主要来源基本相同。APCS-MLR 和 PMF 方法将污染源对每个地下水质量变量的贡献进行了分配。最终结果表明，农业源，包括废水、农药和化肥，被确定为湿季和干季地下水污染的主要来源。此外，还讨论了从先进经验中吸取的地下水管理策略，以保护该地区的地下水系统。
