| 金属硫化物矿山水系统中微生物群落组成及多样性 |
| 摘要点击 1443 全文点击 399 投稿时间：2022-09-02 修订日期：2022-10-31 |
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| 中文关键词 金属硫化物矿山 矿山废水 重金属污染 微生物群落组成 高通量测序 |
| 英文关键词 metal sulfide mine mine wastewater heavy metal pollution microbial community composition high-throughput sequencing |
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| 中文摘要 |
| 为探索矿山水系统中因采矿活动引起的重金属污染特征和理化性质,以及微生物群落对不同污染水平生境的响应, 以中国安徽铜陵狮子山矿区水系统(采矿区废水、堆矿区渗流水、选矿区废水、矿山渗透水和生活池塘水等)为研究对象,通过对水体的理化性质及重金属污染特征分析,并利用Illumina HiSeq 2500测序技术对不同类型废水中微生物群落组成丰度及结构多样性进行了研究,探讨了矿山废水污染特征与微生物群落结构和多样性的相关性.结果表明,水体pH在采矿区(MW1、MW2)、堆矿区(HW)和选矿区(DW)呈强酸性,矿山水体污染主要来自于采矿活动.不同功能类型废水中微生物群落结构差异显著,其中重金属污染最重的DW的微生物群落多样性和丰度均比其他4个区域弱.高通量测序得到门水平上15门细菌和3门古菌,其中优势细菌群为Proteobacteria、Bacteroidetes、Nitrospirae和OD1,优势古菌为Euryarchaeota.PCoA分析表明相似的水体类型的样本聚类相似.相关性热图和典型相关分析(CCA)得到该区域矿山废水中的微生物群落主要受到了pH、电导率(EC)、SO2-4、化学需氧量(COD)和重金属的综合影响.通过全面了解矿山水系统中水体理化性质及污染特征,并探索极端环境中的功能菌株,可为矿山废水的管理、排放和生物修复提供菌种以及理论依据. |
| 英文摘要 |
| To understand the characteristics of heavy metal pollution and physicochemical properties caused by mining activities in mine water systems and the response of the microbial community to habitats with different contamination levels, this study selected different types of water (mining area wastewater, spoil heap area wastewater, dressing area wastewater, mine seepage water, and pond water) as the variables related to the mining activities in the water system of the Shizishan mining area in Tongling, Anhui Province. The pollution characteristics and physicochemical properties were compared, and the relationship between environmental factors and the microbial communities were analyzed. The results showed that the content of heavy metals, the physicochemical properties, and the structure and diversity of the microbial community of different types of water were significantly different in different mine areas, among which the most seriously polluted areas were the mining area, the spoil heap area, and the dressing area. There were significant differences in microbial community structure among different functional types of wastewaters, and the diversity and abundance of the microbial community in DW with the heaviest heavy metal pollution were weaker than those in the other four regions. PcoA analysis showed that samples of similar water types had similar clustering. Spearman correlation heat map analysis and canonical correlation analysis (CCA) indicated that heavy metal pollution, pH, electrical conductivity (EC), SO2-4, and chemical oxygen demand (COD) had the greatest effect on the microbial communities in the mine water systems. Moreover, this study found that Proteobacteria, Euryarchaeota, and Bacteroidetes dominated in mine water systems, and their potential use could be explored in the future. Our results provide a better understanding of the different types of water pollution characteristics in mine water systems and the key factors that determine the microbial community structure. |
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