Spatial distribution and risk assessment of potentially toxic elements in coastal mangrove floodplain Top-Soils of Bangladesh

Environmental Nanotechnology Monitoring & Management, Journal Year: 2025, Volume and Issue: unknown, P. 101044 - 101044

Published: Jan. 1, 2025

Language: Английский

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Alleviating Health Risks for Water Safety: A Systematic Review on Artificial Intelligence-Assisted Modelling of Proximity-Dependent Emerging Pollutants in Aquatic Systems

Published: Feb. 21, 2025

Emerging pollutants such as pharmaceuticals, industrial chemicals, heavy metals, and microplastics are a growing ecological risk affecting water soil resources. Another challenge in current wastewater treatments includes tracking treating these pollutants, which can be costly. As concern, emerging do not have lower limit levels detrimental to aquatic resources minuscule amounts. Thus, the assessment of multiple community-based sources surface groundwater is prioritized area study for resource management. It provides basis health management arising diseases cancer dengue caused by unsafe sources. Accordingly, utilizing artificial intelligence, wide-range data-driven insights synthesized assist propose solution pathways without need exhaustive experimentation. This systematic review examines intelligence-assisted modelling notably machine learning deep models, with proximity dependence correlated synergistic effects both humans life. underscores increasing accumulation their toxicological on community how utilized addressing research gaps related treatment methods pollutants.

Language: Английский

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Impact of anthropogenic activities and the associated heavy metal pollution in Sundarbans waterways: threats to commercial fish and human health

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(12)

Published: Nov. 21, 2024

Language: Английский

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A Comprehensive Study of Spatial Distribution, Pollution Risk Assessment, and Source Apportionment of Topsoil Heavy Metals and Arsenic

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2151 - 2151

Published: Dec. 10, 2024

Accurately identifying pollution risks and sources is crucial for regional land resource management. This study takes a certain coastal county in eastern China as the object to explore spatial distribution, risk, source apportionment of heavy metals topsoil. A total 633 samples were collected from topsoil with depth ranging 0 20 cm, which came different topographical use types (e.g., farmland, industrial areas, mining areas), concentrations HMs As measured by using atomic fluorescence spectrometry inductively coupled plasma mass spectrometry. Firstly, distribution soil (Cd, Cr, Hg, Ni, Pb) arsenic (As) was predicted incorporating environmental variables strongly affecting formation into geostatistical methods machine learning approaches. Then, various indicators employed conduct evaluations, potential ecological risk assessments implemented based on generated map. Finally, conducted random forest (RF), absolute principal component score–multiple linear regression (APCS-MLR), correlation analysis, As. Findings this research reveal that RF approach yielded best prediction performance (0.59 ≤ R2 0.73). The Nemerow geoaccumulation indices suggest levels exist area. average As, Ni are 7.233 mg/kg, 0.051 27.43 mg/kg respectively, being 1.14 times, 1.27 1.15 times higher than background levels, respectively. central–northern region presented slight Hg Cd identified primary factors. Natural, agricultural, transportation, activities main sources. These findings will assist design targeted policies reduce urban offer useful guidelines similar regions.

Language: Английский

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A systematic evaluation of advanced machine learning models for nickel contamination management in soil using spectral data

Journal of Hazardous Materials Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100576 - 100576

Published: Dec. 1, 2024

Language: Английский

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