Reducing Arsenic, Cadmium, and Lead Exposure in Urban Areas via Limiting Nutrient Discharges into Rivers

Abstract

Urban rivers are subjected to potentially toxic element pollution, which is concentrated in river sediments and poses a risk to hydrobiosis and human health. Reducing the accumulation of potentially toxic elements in sediments is a sustainable way to mitigate this risk. In this study, the field sampling of river sediments in Tongxiang, China, to characterize their eutrophication indicators and As, Cd, and Pb concentrations was conducted to assess the potential exposure risks. A combination of statistical analysis and machine learning techniques was employed to identify the drivers of potentially toxic element accumulation in the sediments. Results indicate that eutrophication is the key factor contributing to potentially toxic element accumulation in sediments. The random forest model, with R 2 values of 0.434, 0.790, and 0.798 for As, Cd, and Pb, respectively, provided the best prediction of potentially toxic element concentration. TP, TOC, and TN had the most significant impact on As, Cd, and Pb accumulation in sediments, respectively. The scenario analysis revealed that a 20% and 50% decrease in the eutrophication index of sediments resulted in an average reduction of 7.50% and 7.88% in As content, 18.92% and 45.95% in Cd content, and 14.67% and 35.21% in Pb content, respectively. Our study provides valuable information for policymakers to develop strategies for preventing and controlling potentially toxic element pollution in urban areas.

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

“…The special issue includes several review articles encompassing a wide spectrum, ranging from a historical perspective of water data to computational modeling in wastewater treatment to ML modeling of environmental chemical reactions, environmental toxicology, heavy metal removal, and cyanobacterial harmful algal blooms (HABs) . One significant application of these innovative tools is ML-assisted environmental monitoring, which can address diverse problems, such as predicting effluent nutrients or influent flow rates and nutrient loads at wastewater treatment plants, , formation of disinfection byproducts, drivers of the accumulation of potentially toxic elements in sediments, greenhouse gas emissions, , occurrence of PFAS, water quality assessment, microplastics, microcystins, and differentiation of landfill leachate and domestic sludge . ML has also been extensively employed to model environmental chemical reactions and processes, including adsorption onto various materials, , biodegradation, photodegradation, and the physicochemical and meteorological variables that affect the seasonal growth and decline of HABs .…”