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

ACS ES&T WATER(2023)

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摘要
Urbanrivers are subjected to potentially toxic element pollution,which is concentrated in river sediments and poses a risk to hydrobiosisand human health. Reducing the accumulation of potentially toxic elementsin sediments is a sustainable way to mitigate this risk. In this study,the field sampling of river sediments in Tongxiang, China, to characterizetheir eutrophication indicators and As, Cd, and Pb concentrationswas conducted to assess the potential exposure risks. A combinationof statistical analysis and machine learning techniques was employedto identify the drivers of potentially toxic element accumulationin the sediments. Results indicate that eutrophication is the keyfactor contributing to potentially toxic element accumulation in sediments.The random forest model, with R (2) valuesof 0.434, 0.790, and 0.798 for As, Cd, and Pb, respectively, providedthe best prediction of potentially toxic element concentration. TP,TOC, and TN had the most significant impact on As, Cd, and Pb accumulationin sediments, respectively. The scenario analysis revealed that a20% and 50% decrease in the eutrophication index of sediments resultedin an average reduction of 7.50% and 7.88% in As content, 18.92% and45.95% in Cd content, and 14.67% and 35.21% in Pb content, respectively.Our study provides valuable information for policymakers to developstrategies for preventing and controlling potentially toxic elementpollution in urban areas. No researchexists on a nutrient-toxic element accumulationlink in river sediments. This study reports alleviating eutrophicationcan mitigate toxic element accumulation in sediments, informing effectivepollution prevention strategies for policymakers.
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关键词
Machine learning, River sediments, Potentiallytoxic elements, Eutrophication, Public health
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