Heavy metal habitat: A novel framework for mapping heavy metal contamination over large-scale catchment with a species distribution model. (1st November 2022)

• Record Type: Journal Article
• Authors: Li, Jianguo; Xie, Zunyi; Qiu, Xiaocong; Yu, Qiang; Bu, Jianwei; Sun, Ziyong; Long, Ruijun; Brandis, Kate J.; He, Jie; Feng, Qi; Ramp, Daniel
• Is Part Of: Water research. Volume 226(2022)
• Publication Date: 2022-11-01
• Subjects: Heavy metals -- Species distribution model -- Catchment -- Carriers -- Environmental variables -- Human variables; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2022.119310
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: Species distribution model was introduced to study heavy metal(loid) contamination. Heavy metal(loid)s were ecologicalized as biological species to map their habitats. The output maps of heavy metals correspond well with their determinant variables. SDM was proved to offer new insights in the field of water pollution. Abstract: Heavy metal(loid)s (HMs) have been consistently entering the food chain, imposing great harm on environment and public health. However, previous studies on the spatial dynamics and transport mechanism of HMs have been profoundly limited by the field sampling issues, such as the uneven observations of individual carriers and their spatial mismatch, especially over large-scale catchments with complex environment. In this study, a novel methodological framework for mapping HMs at catchment scale was proposed and applied, combining a species distribution model (SDM) with physical environment and human variables. Based on the field observations, we ecologicalized HMs in different carriers as different species. This enabled the proposed framework to model the 'enrichment area' of individual HMs in the geographic space (termed as the HM 'habitat') and identify their 'hotspots' (peak value points) within the catchment. Results showed the output maps of HM habitats from secondary carriers (soil, sediment, and wet deposition) well agreed with the influence of industry contaminants, hydraulic sorting, and precipitation washout process respectively, indicating the potential of SDM in modeling the spatial distributions of the HM. The derived maps of HMs from secondary carriers, along with the human and environmental variables were then input as explanatory variables in SDM to predict the spatial patterns of the final HM accumulation in river water, which was observed to have largely improved the prediction quality. These results confirmed the value of our framework to leverage SDMs from ecology perspective to study HM contamination transport at catchment scale, offering new insights not only to map the spatial HM habitats but also help locate the HM transport chains among different carriers. Graphical abstract: Image, graphical abstract