Regional metal pollution risk assessment based on a big data framework: A case study of the eastern Tianshan mining area, China. (December 2022)

• Record Type: Journal Article
• Authors: Cheng, Yinyi; Zhou, Kefa; Wang, Jinlin; Cui, Shichao; Yan, Jining; De Maeyer, Philippe; Van de Voorde, Tim
• Is Part Of: Ecological indicators. Volume 145(2023)
• Publication Date: 2022-12
• Subjects: Big data technology -- Heavy metal pollution -- Risk assessment -- Index evaluation system -- Eastern Tianshan; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2022.109585
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Big data technology and multisource data were used for high-risk area identification. A framework linking heavy metals in soils with their sources was established. A heavy metal pollution assessment result was obtained with good performance. Mineral exploration affected the distribution of heavy metal pollutants in soil. Abstract: With the increasing maturity of big data technology, its application in the field of environmental assessment has become an important issue. The mining of mineral resource can affect the balance in the ecological environment surrounding mining areas and the normal life activities of humans through groundwater. To solve this problem, big data-based methods were used to evaluate the pollution risk in mining areas in this article. Based on a spatial big data management framework, in this paper, high-risk areas in eastern Tianshan were quantitatively analyzed and delineated via a new comprehensive heavy metal pollution assessment system. A distributed storage environment, unstructured management method and spatial index coding were used to uniformly manage spatial data in vector and raster formats retrieved from different sources. A system involving 18 geological environment indicators was used to evaluate the risk level in the study area and in delineated areas with a high risk of heavy metal pollution. The results indicated that the proposed framework could efficiently store and process spatial data and realize environmental pollution risk assessment in a big data environment. In addition, some of the high-pollution risk areas identified based on the assessment results were consistent with actual mine locations. Overall, the results show that integrating multi-source geological characteristics through a comprehensive evaluation system based on big data can have a positive effect on improving the accuracy of heavy metal pollution risk assessment. Heavy metal pollution assessment can provide a reference for environmental monitoring and governance of the eastern Tianshan region and offer new solutions to large-scale heavy metal pollution assessment.