Advancing methodologies for assessing the impact of land use changes on water quality: a comprehensive review and recommendations

• Published in: Environmental geochemistry and health Vol. 47; no. 4; p. 101
• Main Authors: Su, Silin, Ma, Kai, Zhou, Tianhong, Yao, Yuting, Xin, Huijuan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2025; Springer Nature B.V
• Subjects: Algorithms; automation; Buffer zones; Catchment scale; Catchments; Climate change; Data collection; Deep learning; Delineation; Earth and Environmental Science; Ecological models; Environment; Environmental Chemistry; Environmental Health; Environmental Monitoring - methods; Geochemistry; Heterogeneity; Land use; Machine learning; Monitoring; Monitoring systems; Patchiness; Public Health; Quality assessment; Real time; Regression analysis; Remote sensing; Research methodology; Research methods; Review Paper; Soil Science & Conservation; Spatial heterogeneity; spatial variation; Spatial variations; Standardization; Terrestrial Pollution; Water analysis; Water Quality; Water quality assessments; Watersheds; Spatial heterogeneity; Basic water quality; Integrated ecological model; Method consistency; Land use and water quality analysis
• ISSN: 0269-4042; 1573-2983; 1573-2983
• DOI: 10.1007/s10653-025-02413-z

With increasing scholarly focus on the ramifications of land use changes on water quality, although substantial research has been undertaken, the findings demonstrate pronounced spatial variability and the heterogeneity of research methodologies. To address this critical gap, this review offers a rigorous evaluation of the strengths and limitations of current research methodologies, providing targeted recommendations for refinement. It systematically assesses the existing body of literature concerning the influence of land use changes on water quality, with particular emphasis on the spatial heterogeneity of research results and the uniformity of employed methodologies. Despite variations in geographical contexts and research subjects, the methodological paradigms remain largely consistent, typically encompassing the acquisition and analysis of water quality and land use data, the delineation of buffer zones, and the application of correlation and regression analyses. However, these approaches encounter limitations in addressing regional disparities, nonlinear interactions, and real-time monitoring complexities. The review advocates for methodological advancements, such as the integration of automated monitoring systems and IoT technologies, alongside the fusion of deep learning algorithms with remote sensing techniques, to enhance both the precision and efficiency of data collection. Furthermore, it recommends the standardization of buffer zone delineation, the reinforcement of foundational water quality assessments, and the utilization of catchment-scale analyses to more accurately capture the influence of land use changes on water quality. Future inquiries should prioritize the development of interdisciplinary ecological models to elucidate the interaction and feedback mechanisms between land use, water quality, and climate change.