Apportionment and evolution of pollution sources in a typical riverside groundwater resource area using PCA-APCS-MLR model.

Research paper by Li L Meng, Rui R Zuo, Jin-Sheng JS Wang, Jie J Yang, Yan-Guo YG Teng, Rong-Tao RT Shi, Yuan-Zheng YZ Zhai

Indexed on: 27 Oct '18Published on: 27 Oct '18Published in: Journal of Contaminant Hydrology


Comparative analysis was performed of changing groundwater quality over ten years (2006-2016) by source apportionment and spatial distribution characteristics. This shallow groundwater in a typical riverside groundwater resource area was studied using principal component analysis (PCA) and factor analysis (FA), coupled with the absolute principal-component-score multiple-linear-regression (APCS-MLR) receptor model. The relationship among land-use types, hydro-chemical composition, and evolution of the quality of groundwater from natural and anthropogenic sources was demonstrated. The results showed that water-rock interaction, agricultural fertilizer, and domestic and industrial wastewater were responsible for the evolution of contamination in the groundwater. The major potential pollution sources that had significant effect on groundwater quality variables were categorized into three groups: heavy metals (iron, manganese), nutrients (ammonia nitrogen, nitrite and nitrates), and organic pollution (chemical oxygen demand). The APCS-MLR model considered the average contribution of each different potential pollution source to these categories separately. The potential pollution sources in the groundwater presented an obvious spatial distribution with an area of high concentration distributed mainly in the western and northwestern areas downstream from the Songhua River. The variation of land use type and evolution of the spatial distribution of the pollution sources in the groundwater showed good consistency. Eventually, PCA /FA coupled with APCS-MLR became a versatile tool for comprehensive source apportionment of groundwater. Copyright © 2018 Elsevier B.V. All rights reserved.