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Groundwater Pollution Source Identification and Apportionment Using PMF and PCA-APCS-MLR Receptor Models in Tongchuan
Wenqu Li1,2, Jianhua Wu3,4, Changjing Zhou5,6
1School of Water and Environment, Chang'an University, No. 126 Yanta Road, Xi'an, 710054, Shaanxi, China.
Archives of Environmental Contamination and Toxicology
|August 3, 2021
Summary
Natural processes are the primary driver of groundwater pollution in Tongchuan City, China, with the coal industry, agriculture, and urbanization also contributing. The Positive Matrix Factorization (PMF) model proved more accurate than PCA-APCS-MLR for source apportionment.
Area of Science:
- Environmental Science
- Hydrogeology
- Water Quality Assessment
Background:
- Groundwater contamination poses a significant threat to water resources.
- Identifying and quantifying pollution sources is crucial for effective management.
- Tongchuan City faces potential groundwater pollution from various anthropogenic and natural activities.
Purpose of the Study:
- To identify and quantitatively evaluate potential groundwater pollution sources in Tongchuan City.
- To assess the applicability of Positive Matrix Factorization (PMF) and Principal Component Analysis-Absolute Principal Component Scores-Multiple Linear Regression (PCA-APCS-MLR) models for groundwater pollution source apportionment.
- To understand the spatial distribution of pollution sources and their impact on groundwater quality.
Main Methods:
- Collection and analysis of 59 groundwater samples using 14 key water quality indicators.
- Application of PMF and PCA-APCS-MLR models for source identification and contribution analysis.
- Comparison of model performance and accuracy based on R-squared values.
Main Results:
- Both PMF and PCA-APCS-MLR models identified four main groundwater contamination sources: natural evolution, coal industry, agriculture, and urbanization.
- Natural evolution was identified as the predominant source of groundwater pollution.
- Spatial analysis revealed distinct pollution patterns: sewage discharge in the east, coal industry impact in the west, and agricultural pollution in the north.
- The PMF model demonstrated higher accuracy (R²: 0.4440-0.9991) compared to the PCA-APCS-MLR model (R²: 0.5180-0.9530).
Conclusions:
- Natural processes are the primary contributors to groundwater pollution in Tongchuan City.
- The coal industry, agriculture, and urbanization significantly impact local groundwater quality.
- The PMF model is a more accurate and reliable tool for groundwater pollution source apportionment in this region.
- Understanding source-specific impacts is essential for targeted groundwater protection and remediation strategies.
