RESUMO

Water pollution originating from land use and land cover (LULC) can disrupt river ecosystems, posing a threat to public health, safety, and socioeconomic sustainability. Although the interactions between terrestrial and aquatic systems have been investigated for decades, the scale at which land use practices, whether in the entire basin or separately in parts, significantly impact water quality still needs to be determined. In this research, we used multitemporal data (field measurements, Sentinel 2 images, and elevation data) to investigate how the LULC composition in the catchment area (CA) of each water pollution measurement station located in the river course of the Los Perros Basin affects water pollution indicators (WPIs). We examined whether the CAs form a sequential runoff aggregation system for certain pollutants from the highest to the lowest part of the basin. Our research applied statistical (correlation, time series analysis, and canonical correspondence analysis) and geo-visual analyses to identify relationships at the CA level between satellite-based LULC composition and WPI concentrations. We observed that pollutants such as nitrogen, phosphorus, coliforms, and water temperature form a sequential runoff aggregation system from the highest to the lowest part of the basin. We concluded that the observed decrease in natural cover and increase in built-up and agricultural cover in the upper CAs of the study basin between the study period (2016 to 2020) are related to elevated WPI values for suspended solids and coliforms, which exceeded the allowed limits on all CAs and measured dates.

Assuntos

Monitoramento Ambiental , Fósforo , Rios , Poluentes Químicos da Água , México , Rios/química , Poluentes Químicos da Água/análise , Fósforo/análise , Agricultura , Nitrogênio/análise , Poluição da Água/estatística & dados numéricos

RESUMO

Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. This comprehensive review explores the evolution of ML technologies and their integration into environmental science, assessing 230 papers to understand the advancements and challenges in groundwater quality research. It reveals that a substantial portion of the research neglects critical preprocessing steps, crucial for model accuracy, with 83 % of the studies overlooking this phase. Furthermore, while model optimization is more commonly addressed, being implemented in 65 % of the papers, there is a noticeable gap in model interpretability, with only 15 % of the research providing explanations for model outcomes. Comparative evaluation of ML algorithms and careful selection of evaluation metrics are deemed essential for determining model fitness and reliability. The review underscores the need for interdisciplinary collaboration, methodological rigor, and continuous innovation to advance ML in groundwater management. By addressing these challenges and implementing solutions, the full potential of ML can be harnessed to tackle complex environmental issues and ensure sustainable groundwater management. This comprehensive and critical review paper can serve as a guiding framework to establish minimum standards for developing ML in groundwater quality studies.