Assessment of Groundwater Quality Using APCS-MLR Model: A Case Study in the Pilot Promoter Region of Yangtze River Delta Integration Demonstration Zone, China

• Published in: Water (Basel) 2023-01, Vol.15 (2), p.225
• Main Authors: Chen, Zi, Zhou, Quanping, Lv, Jinsong, Jiang, Yuehua, Yang, Hai, Yang, Hui, Mei, Shijia, Jia, Zhengyang, Zhang, Hong, Jin, Yang, Liu, Lin, Shen, Rujia
• Format: Article
• Language: English
• Subjects: Agricultural pollution; Anthropogenic factors; Apportionment; Aquifers; Case studies; Chemical evolution; Contaminants; Contamination; Groundwater; Groundwater management; Groundwater pollution; Groundwater quality; Household wastes; Industrial areas; Industrial effluents; Industrial pollution; Industrial wastes; Industrial wastewater; Management; Methods; Nitrogen; Nonpoint source pollution; Phosphorus; Point source pollution; Pollutants; Pollution; Pollution sources; Principal components analysis; Quality assessment; Regression analysis; Rivers; Sediments; Sewage; Sewage effluents; Surface water; Water; Water quality; Water sampling; Water, Underground
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w15020225

Groundwater contaminant source identification is an endeavor task in highly developed areas that have been impacted by diverse natural processes and anthropogenic activities. In this study, groundwater samples from 84 wells in the pilot promoter region of the Yangtze River Delta integration demonstration zone in eastern China were collected and then analyzed for 17 groundwater quality parameters. The principal component analysis (PCA) method was utilized to recognize the natural and anthropogenic aspects impacting the groundwater quality; furthermore, the absolute principal component score-multiple linear regression (APCS-MLR) model was employed to quantify the contribution of potential sources to each groundwater quality parameter. The results demonstrated that natural hydro-chemical evolution, agricultural activities, domestic sewage, textile industrial effluent and other industrial activities were responsible for the status of groundwater quality in the study area. Meanwhile, the contribution of these five sources obtained by the APCS-MLR model were ranked as natural hydro-chemical evolution (18.89%) > textile industrial effluent (18.18%) > non-point source pollution from agricultural activities (17.08%) > other industrial activities (15.09%) > domestic sewage (4.19%). It is believed that this contaminant source apportionment result could provide a reliable basis to the local authorities for groundwater pollution management.