Environmental Risk Assessment of Trace Metal Pollution: A Statistical Perspective DOI Creative Commons
Matthew Chidozie Ogwu, Sylvester Chibueze Izah,

Wisdom Ebiye Sawyer

et al.

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(4)

Published: Feb. 28, 2025

Abstract Trace metal pollution is primarily driven by industrial, agricultural, and mining activities presents complex environmental challenges with significant implications for ecological human health. Traditional methods of risk assessment (ERA) often fall short in addressing the intricate dynamics trace metals, necessitating adoption advanced statistical techniques. This review focuses on integrating contemporary methods, such as Bayesian modeling, machine learning, geostatistics, into ERA frameworks to improve precision, reliability, interpretability. Using these innovative approaches, either alone or preferably combination, provides a better understanding mechanisms transport, bioavailability, their impacts can be achieved while also predicting future contamination patterns. The use spatial temporal analysis, coupled uncertainty quantification, enhances hotspots associated risks. Integrating models ecotoxicology further strengthens ability evaluate health risks, providing broad framework managing pollution. As new contaminants emerge existing pollutants evolve behavior, need adaptable, data-driven methodologies becomes ever more pressing. advancement tools interdisciplinary collaboration will essential developing effective management strategies informing policy decisions. Ultimately, lies diverse data sources, analytical techniques, stakeholder engagement, ensuring resilient approach mitigating protecting public

Language: Английский

Environmental Risk Assessment of Trace Metal Pollution: A Statistical Perspective DOI Creative Commons
Matthew Chidozie Ogwu, Sylvester Chibueze Izah,

Wisdom Ebiye Sawyer

et al.

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(4)

Published: Feb. 28, 2025

Abstract Trace metal pollution is primarily driven by industrial, agricultural, and mining activities presents complex environmental challenges with significant implications for ecological human health. Traditional methods of risk assessment (ERA) often fall short in addressing the intricate dynamics trace metals, necessitating adoption advanced statistical techniques. This review focuses on integrating contemporary methods, such as Bayesian modeling, machine learning, geostatistics, into ERA frameworks to improve precision, reliability, interpretability. Using these innovative approaches, either alone or preferably combination, provides a better understanding mechanisms transport, bioavailability, their impacts can be achieved while also predicting future contamination patterns. The use spatial temporal analysis, coupled uncertainty quantification, enhances hotspots associated risks. Integrating models ecotoxicology further strengthens ability evaluate health risks, providing broad framework managing pollution. As new contaminants emerge existing pollutants evolve behavior, need adaptable, data-driven methodologies becomes ever more pressing. advancement tools interdisciplinary collaboration will essential developing effective management strategies informing policy decisions. Ultimately, lies diverse data sources, analytical techniques, stakeholder engagement, ensuring resilient approach mitigating protecting public

Language: Английский

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