Heavy Metal Contamination Alters Soil Bacterial Community Structure Around Cement Factories

Abstract

AbstractThe cement industry has been a cornerstone of economic development since the Industrial Revolution. However, cement production releases heavy metals that can alter the composition and function of microbial communities. Despite the potential environmental risks, few studies have examined the impact of cement production on nearby microbiomes. In this study, we collected topsoil samples from areas surrounding four cement factories in Korea to assess the contamination levels of heavy metals—arsenic, cadmium, lead, nickel, and mercury—and investigate the response of the bacterial community to these heavy metals. Most soil samples showed severe heavy metal contamination, raising concerns regarding environmental hazards and the urgent need for remediation. Heavy metal contamination altered the composition of bacterial communities. Arsenic, cadmium, and nickel were identified as the primary heavy metals responsible for shifts in bacterial community structures. At the genus level, Ramlibacter, Pseudolabrys, Candidatus Xiphinematobacter, Solirubrobacter, Bacillus, Blastococcus, Steroidobacter, and Skermanella served as biomarkers distinguishing severely contaminated soils from uncontaminated ones. Bacterial communities clustered into modules based on their tolerance to heavy metals. Each heavy metal was associated with a unique set of amplicon sequence variants in the network. To adapt to heavy metal accumulation, bacterial communities in heavily polluted soils showed increased enrichment of resistance-related pathways, such as the pentose phosphate pathway and tetrapyrrole biosynthesis, compared with those in uncontaminated soils. Overall, our findings suggest that heavy metal contamination in soils near cement factories is closely associated with distinct bacterial communities that may contribute to bioremediation and promote plant growth.

Keywords: Heavy metals, Bacterial community, microbiome, cement, co-occurrence network analysis, microbial biomarkers