Source tracing techniques for groundwater contamination in land-sea interaction zone: A critical review
- 1Key Laboratory of Solar Power System Engineering, Jiuquan Vocational Technical University, Jiuquan, 735000, China
- 2State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; University of Chinese Academy of Sciences, Beijing, 100049, China
Abstract
The land-sea interaction zone, as an ecologically sensitive interface linking terrestrial and marine systems, features complex material and energy exchange processes. The identification of contaminant sources in coastal groundwater is challenging due to the dual influence of anthropogenic activities and complex surface water-groundwater interactions. Over the years, various source tracing techniques have been developed from multiple perspectives, such as indicators, analytical methods, and data processing. This paper systematically reviews main contamination source tracing techniques applied in coastal groundwater, basing on chemical, geophysical prospecting, inverse modeling and biological methods. The advantages and limitations of these techniques in terms of tracing principles, applicable scope, and effectiveness are discussed comprehensively. A major challenge is that most existing source tracing techniques require a large number of sampling points, provide only coarse spatial resolution, and lack sufficient accuracy in identifying contamination source types. This study aims to offer technical references for tracing groundwater contamination sources in land-sea interaction zones. Future research should focus on developing more advanced tracing techniques based on information-rich microbial communities and organic matter fingerprints, integrated with machine learning techniques.
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BibTeX
@article{chen2026source,
title = {{Source tracing techniques for groundwater contamination in land-sea interaction zone: A critical review}},
author = {Miao Chen and Feipeng Wang and Dongbin Wei and Qiwen An and Yijia Liu and Yuguo Du and Yuzhou Yin and Zengpeng Li and Zhihua Chen},
journal = {Water Cycle},
year = {2026},
month = oct,
doi = {10.1016/j.watcyc.2026.06.004},
url = {https://doi.org/10.1016/j.watcyc.2026.06.004},
note = {CC BY},
}
RIS
TY - JOUR
TI - Source tracing techniques for groundwater contamination in land-sea interaction zone: A critical review
AU - Miao Chen
AU - Feipeng Wang
AU - Dongbin Wei
AU - Qiwen An
AU - Yijia Liu
AU - Yuguo Du
AU - Yuzhou Yin
AU - Zengpeng Li
AU - Zhihua Chen
PY - 2026
DA - 2026/10/30
JO - Water Cycle
DO - 10.1016/j.watcyc.2026.06.004
UR - https://doi.org/10.1016/j.watcyc.2026.06.004
AB - The land-sea interaction zone, as an ecologically sensitive interface linking terrestrial and marine systems, features complex material and energy exchange processes. The identification of contaminant sources in coastal groundwater is challenging due to the dual influence of anthropogenic activities and complex surface water-groundwater interactions. Over the years, various source tracing techniques have been developed from multiple perspectives, such as indicators, analytical methods, and data processing. This paper systematically reviews main contamination source tracing techniques applied in coastal groundwater, basing on chemical, geophysical prospecting, inverse modeling and biological methods. The advantages and limitations of these techniques in terms of tracing principles, applicable scope, and effectiveness are discussed comprehensively. A major challenge is that most existing source tracing techniques require a large number of sampling points, provide only coarse spatial resolution, and lack sufficient accuracy in identifying contamination source types. This study aims to offer technical references for tracing groundwater contamination sources in land-sea interaction zones. Future research should focus on developing more advanced tracing techniques based on information-rich microbial communities and organic matter fingerprints, integrated with machine learning techniques.
SN - 2666-4453
ER -