Distinguishing the contributions of different smelting emissions to the spatial risk footprints of toxic elements in soil using PMF, Bayesian isotope mixing models, and distance-based regression

Richmond Anaman; Chi Peng; Zhichao Jiang; Charles Amanze; Bridget Ataa Fosua

doi:10.1016/j.scitotenv.2024.173153

Distinguishing the contributions of different smelting emissions to the spatial risk footprints of toxic elements in soil using PMF, Bayesian isotope mixing models, and distance-based regression

Sci Total Environ. 2024 May 10:933:173153. doi: 10.1016/j.scitotenv.2024.173153. Online ahead of print.

Authors

Richmond Anaman¹, Chi Peng², Zhichao Jiang¹, Charles Amanze³, Bridget Ataa Fosua¹

Affiliations

¹ Institute of Environmental Engineering, School of Metallurgy and Environment, Central South University, Changsha 410083, China.
² Institute of Environmental Engineering, School of Metallurgy and Environment, Central South University, Changsha 410083, China. Electronic address: chipeng@csu.edu.cn.
³ School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China.

PMID: 38735332
DOI: 10.1016/j.scitotenv.2024.173153

Abstract

Toxic element pollution of soils emanating from smelting operations is an escalating global concern due to its severe impact on ecosystems and human health. In this study, soil samples were collected and analyzed to quantify the risk contributions and delineate the spatial risk footprints from smelting emissions for 8 toxic elements. A comprehensive health risk contribution and delineation framework was utilized, consisting of Positive matrix factorization (PMF), spatial interpolation, an advanced Bayesian isotope mixing model via Mixing Stable Isotope Analysis in R (MixSIAR), and distance-based regression. The results showed that the mean concentrations of As, Cd, Cu, Hg, Pb, and Zn exceeded the background levels, indicating substantial contamination. Three sources were identified using the PMF model and confirmed by spatial interpolation and MixSIAR, with contributions ranked as follows: industrial wastewater discharge and slag runoff from the smelter site (48.9 %) > natural geogenic inputs from soil parent materials (26.7 %) > atmospheric deposition of dust particles from smelting operations (24.5 %). Among the identified sources, smelter runoff posed the most significant risk, accounting for 97.9 % of the non-carcinogenic risk (NCR) and 59.9 % of the carcinogenic risk (CR). Runoff also drove NCR and CR exceedances at 7.8 % and 4.7 % of sites near the smelter, respectively. However, atmospheric deposition from smelting emissions affected soils across a larger 0.8 km radius. Although it posed lower risks, contributing just 1.1 % to NCR and 22.6 % to CR due to the limited elevation of toxic elements, deposition reached more distant soils. Spatial interpolation and distance-based regression delineated high NCR and CR exposure hotspots within 1.4 km for runoff and 0.8 km for deposition, with exponentially diminishing risks at further distances. These findings highlight the need for pathway-specific interventions that prioritize localized wastewater containment and drainage controls near the smelter while implementing broader regional air pollution mitigation measures.

Keywords: Emissions; MixSIAR; Positive matrix factorization; Risk footprints; Spatial interpolation.