Collaborative effects of antimony-arsenic contaminations on microbial communities in the typical antimony mining areas of Southwest China

Abstract

Antimony (Sb) and arsenic (As) co-contamination is prevalent in Sb mining areas and poses significant risks to the surrounding ecological environment. However, the extent of this co-contamination and the impact of key environmental variables and long-term exposure on the microbial communities remain poorly understood. Therefore, this study assessed Sb-As levels in three representative antimony mining areas in Southwest China and explored the relationships between microorganisms and environmental variables. The results indicated that the concentrations of soil Sb ranged from 6.90 to 50,794.07 mg/kg and As from 4.56 to 8798.86 mg/kg. The potential ecological risk index (RI) in mining and smelting areas surpassed 260, indicating a significantly high risk level. Sb-As predominantly exist as residual fractions. pH, electrical conductivity (EC), and interactions between Sb-As are critical factors influencing the transformation of their chemical fractions. Sb-As exposure altered the microbial community structure and diversity, with positive correlations dominating the co-network. Spearman correlation, redundancy analysis (RDA), canonical correspondence analysis (CCA), and random forest analysis (RF) indicated that the total concentration of Sb-As, the bioavailable fractions of Sb-As, pH, oxidation-reduction potential (Eh), and EC were the main variables affecting the microbial community. Variation partition analysis (VPA) indicated that Sb-As and their chemical fractions explained more microbial community variation than the physicochemical properties. Moreover, the bioavailable fractions were an even more significant variable influencing the microbial communities than the total concentrations of Sb-As. In-depth research on the ecological impact of Sb-As on microbial communities provides valuable insights for environmental monitoring and management.

Keywords: Antimony-arsenic co-contamination; Chemical fractions; Co-occurrence network; High throughput sequencing; Microbial communities; Potential ecological hazard assessment.