Abstract
A land remediation project involves the removal of potentially toxic chemicals from a polluted site. Lands abandoned by industry are often contaminated with heavy metals like mercury, lead, chemicals, arsenic, and other toxins like dichlorodiphenyltrichloroethane biphenyls from electronic devices, and volatile organic chemicals (VOCs) from lubricants and chemicals. Risk assessment in environmental settings requires modernized systematic methodologies due to the complexity of today’s environmental problems. When people eat, drink, or work in polluted environments, they put their health at risk and may even get cancer. Integrating geospatial information systems (GIS) with pollutant dispersion models makes environmental risk assessment and early warning possible. This research thus presents a GIS-based ecological risk identification and assessment model (GIS-ERIAM) for assessing risk for efficient land rehabilitation. Environmental cleanup sites’ catalog information is the source of these details. With satellite imagery, GIS makes it simple to keep an eye on the environment and track the abundance of different types of plants and animals The ecological risk assessment (ERA) model can support recognition and prioritize risk management. By integrating direct and indirect environment interactions, the risk conditions of the whole ecology and its elements have been quantified and demonstrated in the study. The numerical outcomes illustrate that the recommended GIS-ERIAM model improves the performance by 98.9%, risk level prediction by 97.3%, risk classification by 96.4%, and detection of soil degradation ratio of 95.6% compared to other existing methods.
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Yibo Wang, conceptualization, data curation, methodology, writing—original draft, data curation, visualization, supervision, editing, writing—review & editing, and software.
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Wang, Y. Ecological risk identification and assessment of land remediation project based on GIS technology. Environ Sci Pollut Res 30, 70493–70505 (2023). https://doi.org/10.1007/s11356-023-27158-z
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DOI: https://doi.org/10.1007/s11356-023-27158-z