A soil microbial indicator framework for ecological effect assessment of heavy metal risks in red mud stockpiles: A meta-analysis of available evidence.
Abstract
Red mud stockpiles may pose long-term heavy-metal-related risks to surrounding soils, yet microbial indicators for ecological effect assessment remain poorly defined. Because direct microbial evidence from red mud stockpiles is limited, published studies on heavy metal contaminated soils were synthesized to screen candidate indicators for this scenario. This meta-analysis integrated 116 studies comprising 763 observations and quantified responses of microbial biomass, alpha diversity, community structure, beta diversity, and enzyme activities to cadmium, mercury, arsenic, lead, chromium, copper, zinc, and nickel. Subgroup analyses, climatic meta-regressions, and precision-weighted random forest models evaluated associations between microbial responses and concentration-toxicity category, soil properties, climatic background, land use, and experimental setting. Heavy metal contamination was associated with significant decreases in microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) by 25.8% and 39.6%, respectively, lower bacterial richness and Shannon diversity, and significant shifts in bacterial community structure. Eight enzyme activities showed significant inhibition of 16.8%-41.8%, with dehydrogenase and urease showing high sensitivity and ecological relevance to microbial metabolic activity and nitrogen cycling. Urease, dehydrogenase, and MBC showed the clearest responses across potential ecological risk index-derived categories, while soil pH, soil organic carbon, soil texture, climatic background, land use, and experimental setting were associated with indicator-specific variation among studies. Overall, MBC, MBN, bacterial community structure, dehydrogenase, and urease were identified as candidate core indicators, supporting an evidence-based microbial framework for ecological effect assessment and risk management in red mud stockpile scenarios.