Pollutants often exist in tailings and surrounding areas as complex mixtures, and the resulting combined effects make it difficult to identify the primary target pollutants, particularly common inorganic anions. To address this, high-throughput 16S rRNA gene sequencing was used to characterize the microbial community structure in groundwater around rare earth tailing ponds, and multivariate statistical analyses were applied to link community patterns to specific environmental variables. A total of 14 groundwater samples were collected from seven sites (two spatial replicates per site) along a contamination gradient. The results showed distinct differences in microbial community composition between the control site and the tailing-pond-impacted sites. Nitrosomonas was the dominant genus at highly contaminated sites, while halotolerant genera such as Seohaeicola, Pusillimonas, and Oceanibaculum also showed elevated relative abundances. Redundancy analysis (RDA) with forward selection identified the co-occurring elevated concentrations of NH4+ and SO42− (originating from tailing pond leachate) as the environmental variables most strongly associated with microbial community structure (p < 0.05). In contrast, the microbial community at the control site WLJ-5, located farthest from the tailing pond, was markedly different. These findings suggest that shifts in microbial community composition and the prevalence of specific microorganisms may serve as potential bioindicators to assist in identifying the dominant contaminant types in groundwater around rare earth tailing ponds.
Chen et al. (2026) studied this question.