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Journal of Environmental Science and Health, Part A
Toxic/Hazardous Substances and Environmental Engineering
Volume 56, 2021 - Issue 7
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Research Article

Arsenic speciation, the abundance of arsenite-oxidizing bacteria and microbial community structures in groundwater, surface water, and soil from a gold mine

ORCID Icon, , , , ORCID Icon, , & ORCID Icon show all
Pages 769-785 | Received 12 Aug 2020, Accepted 02 May 2021, Published online: 26 May 2021
 

Abstract

The arsenic speciation, the abundance of arsenite-oxidizing bacteria, and microbial community structures in the groundwater, surface water, and soil from a gold mining area were explored using the PHREEQC model, cloning-ddPCR of the aioA gene, and high-throughput sequencing of the 16S rRNA gene, respectively. The analysis of the aioA gene showed that arsenite-oxidizing bacteria retrieved from groundwater, surface water, and soil were associated with Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria. In groundwaters from the mining area, there were relatively high ratios of aioA/total 16S rRNA gene copies and the dominance of As5+, which suggested the presence and activity of arsenite-oxidizing bacteria. Metagenomic analysis revealed that the majority of the soil and surface water microbiomes were Proteobacteria, Actinobacteria, Bacteroidetes, and Chloroflexi, whereas the groundwater microbiomes were dominated exclusively by Betaproteobacteria and Alphaproteobacteria. Geochemical factors influencing the microbial structure in the groundwater were As, residence time, and groundwater flowrate, while those showing a positive correlation to the microbial structure in the surface water were TOC, ORP, and DO. This study provides insights into the groundwater, surface water, and soil microbiomes from a gold mine and expands the current understanding of the diversity and abundance of arsenite-oxidizing bacteria, playing a vital role in global As cycling.

Acknowledgments

We would also like to thank King Mongkut’s University of Technology Thonburi through the KMUTT 55th Anniversary Commemorative Fund for supporting high performance computing.

Additional information

Funding

This work was funded principally by the Thailand Research Fund (TRF) Grant for New Scholar (MRG6180127). This research project was also supported by the Thailand Toray Science Foundation (TTSF) through the Science & Technology Research Grant and by the Faculty of Science, Mahidol University.

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