Risk assessment of nitrate groundwater contamination using GIS-based machine learning methods: A case study in the northern Anhui plain, China

Kai Chen; Qimeng Liu; Tingting Yang; Qiding Ju; Mingfei Zhu

doi:10.1016/j.jconhyd.2024.104300

Risk assessment of nitrate groundwater contamination using GIS-based machine learning methods: A case study in the northern Anhui plain, China

J Contam Hydrol. 2024 Feb:261:104300. doi: 10.1016/j.jconhyd.2024.104300. Epub 2024 Jan 14.

Authors

Kai Chen¹, Qimeng Liu², Tingting Yang³, Qiding Ju³, Mingfei Zhu³

Affiliations

¹ School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China; State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science & Technology, Huainan 232001, China.
² School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China. Electronic address: qmliu@aust.edu.cn.
³ School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China.

PMID: 38242063
DOI: 10.1016/j.jconhyd.2024.104300

Abstract

Long-term agricultural activities have affected the sustainable development of groundwater in the Northern Anhui Plain, East China. It is, therefore, important to identify areas at high groundwater pollution risk in the Northern Anhui Plain to ensure effective protection of regional water resources. In this study, 60 groundwater samples were collected from the shallow aquifer of the plain and analyzed for nitrate (NO₃^-) concentrations. In addition, 10 environmental and geological factors including the elevations, distances-to-rivers, slope angles, orientations of slopes, land cover types, topographic wetness index (TWI), geomorphology, lithology, soil types, and precipitation amounts in the study area were selected as input layers. The light gradient boosting machine (LightGBM) and random forest (RF) algorithms, combined with the geographic information system (GIS), were performed to generate the groundwater pollution occurrence probability maps. The descriptive statistics showed that the NO₃^- concentrations in the shallow groundwater ranged from 4.3 to 73.6 mg/L. Most sampling wells exhibited NO₃^- concentrations above the threshold of 18.3 mg/L. The prediction results of the LightGBM and RF algorithms indicated a high groundwater NO₃^- pollution risk in the southern part of the plain. However, the LightGBM algorithm had a better prediction performance than RF, with a higher Kappa value of 0.84. Moreover, the frequency ratio method revealed that the precipitation amounts contributed to the groundwater NO₃^- pollution risk in the study area by 38.14%, followed by the elevations, slope angles, TWI, land cover types, and slope aspects, with contributions of 21.4, 13.02, 8.37, 7.44, and 6.51%, respectively. In the future, sampling of additional wells and further anthropogenic factors shall be considered for the development of more effective groundwater nitrate pollution prevention strategies provided to decision makers.

Keywords: Groundwater pollution; Light gradient boosting machine; Machine learning; North Anhui plain; Random forest.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

China
Environmental Monitoring / methods
Geographic Information Systems
Groundwater*
Machine Learning
Nitrates / analysis
Risk Assessment
Water Pollutants, Chemical* / analysis

Substances

Nitrates
Water Pollutants, Chemical