A systematic review of forward simulation-based methods for point-source water pollution localization.

Authors

Litian Zhang, Jingang Huang, Feng Li, Xiaobin Xu

Published in

Journal of contaminant hydrology. Volume 282. Pages 105049. Jul 09, 2026. Epub Jul 09, 2026.

Abstract

Accurate localization of point-source water pollution is essential for mitigating the adverse environmental and health impacts of unauthorized wastewater discharges. However, localization methods relying solely on pollutant characteristics suffer from significant limitations, necessitating a systematic synthesis of forward simulation-based localization techniques. To fill the gap, this review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically analyze 62 publications (2001-2024), focusing on three main methods (simulation-optimization, probability-selection, and surrogate-modeling) in surface water, groundwater, and water supply networks. The analysis reveals a marked methodological shift since 2015 toward surrogate-modeling as the dominant method, identifies positive correlations between water body type and both the modeling software (r = 0.8) and study area scale (r = 0.4), and tracks the evolution of target pollutants from conservative substances to conventional and emerging contaminants. Methodological advances in the three methods have evolved to enhance global search capabilities, improving accuracy and Markov Chain Monte Carlo (MCMC) sampling efficiency, and capturing spatiotemporal dynamics. Overall, this review aims to provide practical guidance for researchers and engineers on method selection and software choice in water pollution localization, while also revealing persistent challenges including model opacity, emerging contaminant identification, and multi-source data integration. Future studies demand explainable AI, and IoT-remote sensing fusion, and interdisciplinary collaboration to achieve more transparent, robust, and reliable systems.

PMID:
42437537
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.

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