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- W4384377310 abstract "Excessive concentrations of metals in surface waters may impose risks to both human health and biota. Several processes, such as advection, dispersion, and sorption, determine the transport and fate of these elements in rivers. This study models the transport and fate of 19 metals across 107 km of the Parauapebas river, in the state of Pará, Brazil. The main goal is to evaluate the risk associated with threshold exceedances in the river until the city of Parauapebas, where the river is a source of drinking water. The Hec-Ras software is used to simulate river flow during dry and rainy seasons, and the results are integrated into the SihQual model. This tool incorporates a module for metal changes, considering the dissolved/particulate dynamics, and other relevant processes. The model's accuracy is evaluated using simulated and observed data at 10 monitoring points. The modeled results fall within a 20% range of the observed data variation, particularly for concentration intervals that pose concern for human health and ecological safety. The results indicate iron, manganese, and aluminum as the main substances of concern for surface water quality. Although the risks for the adult health indicated by sampling measurements from 2017 are low, simulated scenarios of increased metal levels indicate higher risks of threshold exceedances. The study implies that reducing metal contents in water for human consumption near Parauapebas city is necessary. Point sources have a larger impact on water quality during the dry season, while diffuse contributions have a larger impact on metal concentrations during the rainy season, especially in deforested areas. The modeling framework can guide pollution control and remediation efforts for stakeholders in the public or industrial sectors." @default.
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- W4384377310 date "2023-09-01" @default.
- W4384377310 modified "2023-10-14" @default.
- W4384377310 title "Modeling transport and fate of metals for risk assessment in the Parauapebas river" @default.
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- W4384377310 doi "https://doi.org/10.1016/j.eiar.2023.107209" @default.
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