Assessment of heavy metal contamination in the surface sediments, seawater and organisms of the Pearl River Estuary, South China Sea

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 950, P. 175266 - 175266

Published: Aug. 3, 2024

Language: Английский

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Metal contamination in the Ashtamudi Wetland ecosystem: Source identification, toxicological risk assessment of Ni, Cd, Cr, and Pb and remediation strategies

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 212, P. 117534 - 117534

Published: Jan. 15, 2025

Language: Английский

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Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 934, P. 173284 - 173284

Published: May 18, 2024

Language: Английский

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Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 369, P. 122322 - 122322

Published: Aug. 31, 2024

Language: Английский

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Evaluation of the Heavy Metal Pollution Induced by Sand Mining in Poyang Lake Based on the Fuzzy PERI Model

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 124 - 124

Published: Jan. 4, 2025

Sand mining significantly impacts heavy metal pollution in aquatic ecosystems. However, uncertainties measured concentrations sediments caused by sand activities are unavoidable. To address this, a fuzzy potential ecological risk index (PERI) model was developed based on the triangular number (TFN) theory. The incorporates TFN of individual metals, comprehensive TFN, and transitional PERI model. This approach applied to regions Poyang Lake, with following results: (i) In Jiujiang region, TFNs Cu, Pb, Cd before were {11.84, 16.61, 19.45}, {8.58, 11.73, 14.46}, {32.80, 34.80, 37.20}, respectively, all categorized as “low” grade. (ii) Before mining, vectors for Shangrao {0.000, 1.000, 0.000, 0.000} 0.344, 0.656, 0.000}, whereas after they changed {0.184, 0.816, 0.195, 0.805, respectively. (iii) After probabilities exceeding 0 0.566, 0.549, 0.952, metals showing probability 0.626 region. (iv) Compared conventional model, more effectively evaluates risks, including cumulative effects. It reflects variations induced offers insights assessment other ecologically sensitive areas.

Language: Английский

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Chemometric Assessment of Anthropogenically Impacted Ancient Artificial Wetland, Bhojtal, India, for its Drinking Water Suitability and Hydrogeochemical Classification

Water Air & Soil Pollution, Journal Year: 2025, Volume and Issue: 236(2)

Published: Jan. 25, 2025

Language: Английский

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Potential of biochar derived from sewage sludge and calcium sulfate as an amendment for acidic soils：milk Chinese cabbage cultivation

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 115641 - 115641

Published: Jan. 1, 2025

Language: Английский

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Soil heavy metals assessment of the Zhoukou riparian zone base of Shaying river basin, China: spatial distribution, source analysis and ecological risk

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(3)

Published: Feb. 17, 2025

Language: Английский

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Inversion of heavy metal elements in characteristic agricultural areas of Shanxi Province: Application of the airborne multimodular imaging spectrometer

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113393 - 113393

Published: March 29, 2025

Language: Английский

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Crop rotation and green manure type enhance organic carbon fractions and reduce soil arsenic content

Agriculture Ecosystems & Environment, Journal Year: 2024, Volume and Issue: 378, P. 109287 - 109287

Published: Sept. 9, 2024

Language: Английский

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