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. 2025 Jul 1;15:20776. doi: 10.1038/s41598-025-05799-z

Effects of acid mine drainage on microbial community development and physicochemical properties of mine contaminated sites in Southwest China

Shihong Zhang 1, Pan Wu 1,2,3,✉, Jian Zhang 2, Qiang Li 1, Chuan Luo 1
PMCID: PMC12218272  PMID: 40594805

Abstract

Investigations of the microbial community structures, potential functions and physicochemical property are useful for risk assessments, microbial monitoring, and the biogeochemical behaviour of contained environment by Acid Mine Drainage (AMD). In this study, nine sediment sampling sites were selected at Panjiaozhuang Town, in Guizhou, China to analyze the pollution conditions and their influences on microorganisms. The physicochemical property results showed significant differences in sediment and water physico chemical properties at different group. Compared to the DS group, further studies revealed that US group (severely affected areas) showed strong acidity and high concentrations of heavy metals and salts. The community structure analysis indicated that AMD might enhance the functional bacteria, such as Thiomonas and Ferrovum (increases of 1.2 and 8.1 percent, respectively), and significantly increased the concentrations of Fe and sulfate through the oxidation of pyrite. The KEGG enrichment analysis demonstrated showed that the AMD promoted the migration of sulfur and Fe into water by enhancing bacterial metabolic pathways, such as dark oxidation of sulfur compounds and dark iron oxidation. This article is of great significance for understanding the transformation of pollutants by AMD and provides reference for subsequent bioremediation.

Keywords: Acid mine drainage, Community structure, Thiomonas, Metabolic pathways

Subject terms: Biogeochemistry, Environmental sciences

Introduction

Acid Mine Drainage (AMD) is a critical environmental issue worldwide, particularly in resource-rich but ecologically vulnerable regions, such as the karst areas of Southwest China1–4. AMD occurs primarily due to the oxidation of sulfide minerals (pyrite), when they are exposed to air and water during mining activities5. The sulfuric acid from this chemical reaction, in turn, releases iron (Fe), manganese (Mn), and arsenic (As) and other metals (classes) from the ore into the environment, resulting in the generation of wastewater containing large quantities of heavy metals that migrate to the surrounding area5–7. These acidic waters has a negative impact on the ecosystem and human survival ultimately8,9.

The widespread distribution of carbonate rocks is a unique feature of karst landforms, which is closely related to the genesis and spatial distribution of polymetallic sulfide deposits4. In those typical karst-dominated watershed, carbonate rocks and dilution effect would relieve the presure of acidification induced by AMD, thereby naturally immobilizing the many toxic heavy metals10,11. This nature-based mitigation strategy has restricted the contamination in a limited area but also has altered the sediment enrichment of heavy metals, Fe oxides and associated nutrients11. Meanwhile, many studies have reported that these biogeochemical effects at AMD regions could influence community structure by adjusting water physicochemical properties12. Researchers have found that many decommissioned mine sites can evolve into contaminated sites due to a variety of factors. Such as strongly acidic sediments, pollutant accumulation and enrichment of functional bacteria10,11,13. However, it is rare to study how microbial community evolution, metabolic pathway expression, and changes in physicochemical properties affect the ecosystems of these areas.

The aim of this study was to evaluate the ecological impacts of human mining activities on decommissioned coal mining areas. The conditions of groundwater and sediment contamination at different locations in the area were assessed by physicochemical characterization. The metagenomic approaches (16 s rRNA) were then used to sequence and determine the taxonomic and functional gene components of the samples, as well as to assess microbial community structure, metabolic pathway expression and correlation with environmental factors. This study will help to gain a deeper understanding of the characteristics of pollution in coal mining areas and the changes in microbial community structure and potential functions after human activity.

Study area and methods

Study area

The study was conducted in the Caizitian coal mining area of Panjiaozhuang Town, Xingren City, Guizhou Province. Permian carbonate strata is the main and typical structure in this area14,15. Panjiaozhuang Town features unique geological conditions, such as the extensive distribution of coal-bearing strata and abundant pyrite resources3,14,16,17, many publications have reported on the environmental impacts of AMD18,19. These geological features allow groundwater to flow through cracks in coal seams, leading to AMD infiltration into low-lying areas20,21. In addition to the acid-driven release of heavy metals, the pyrite is also enriched with As17. The study focuses on four main pollution points within the mining area: Huashiban, Liujiagou, Chenigu and Yanjiaotian. Because they are direct discharge points for abandoned mine wastewater, a large amount of AMD continues to infiltrate the environment. Due to lack of proper treatment, these mine effluents have migrated to the surrounding regions and pose a serious threat to the environment. Previous study reported environmental hazards of AMD in mines from Guizhou province19.

The Fig. 1 displays the key pollution points within the Caizitian coal mining area. Based on the AMD discharge flow direction, sampling points are categorized as upstream (US, W1-W3) near the discharge point, middlestream (MS, W4-W6) slightly downstream, and seepage points (DS, C1-C3). Rainwater infiltrates from higher areas of the site through fractures into the groundwater. This process leads to the dissolution and oxidation of pyrite in the shallow coal seam, producing acidic mine drainage. The acidic water eventually seeps out from lower-lying areas downstream, resulting in elevated levels of Fe, Mn, and S in the surrounding soil of the Huashiban and Liujiagou contaminated sites. The soil in these areas is severely compacted, with extremely low organic matter content, making it unsuitable for normal crop growth. The water flowing from the site appears yellow and is marked by rust deposits (Fig. 1). The polluted stream has caused serious damage to the surrounding groundwater and soil, severely affecting the natural landscape of downstream streams22.

Fig. 1.

Fig. 1

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The location figure of the Study Area. This map is based on the standard map No.GS(2020)4632 downloaded from the standard map service website of National Administration of Surveying, Mapping and Geoinformation of China (http://bzdt.ch.mnr.gov.cn/) with no modification to the base map.

Nucleotide sequence accession numbers

The sequence dataset has been deposited in Figshare. (DOI: 10.6084/m9.figshare.28604402; https://doi.org/10.6084/m9.figshare.28604402.v1).

Sediments sampling

Sediment samples points were selected approximately 0.5 m from the shore in areas where the water flow was relatively slow. A sediment scoop was used to collect the surface sediment layer (0-5 cm) underwater. For each sampling point, 2 kg of sediment was collected, and approximately 1 kg was retained using the quartering method, resulting in a total of 9 sediment samples. The water samples were also collected from these areas. To preserve the integrity of the water samples, it was stored in polyethylene drums and filled with nitrogen to create an anaerobic environment. Meanwhile, all the sediment samples were carefully vacuum-sealed in airtight bags to maintain their original state. These water and sediment samples were then transported to the laboratory for further analysis. Some of the samples were stored in refrigerators at 4 ◦C or − 86 ◦C for long-term preservation, while the remaining samples were promptly used for chemical analysis.

Physicochemical analysis

Solid samples were freeze-dried, ground, and sieved through 2 mm mesh for pH measurement and 0.15 mm mesh for other measurements. Physicochemical properties measured included: pH, soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), metal concentrations (Fe, Mn, Zn, Cu, As, Cd, Cr, and Pb). pH was measured using a pH meter (Hach HQ40d, Hach Company, USA). SOM was determined using the loss-on-ignition method (Ball 1964). TN was measured using the Kjeldahl method (Bremner 1960). TP was analyzed using the ascorbic acid method (Murphy and Riley 1962). TK was extracted from soil samples using dilute nitric acid and measured using flame photometry (Hunter and Pratt 1957). Metal concentrations were determined using Inductively Coupled Plasma Spectrometry (ICP-MS; PerkinElmer Optima 8000, PerkinElmer, USA). Solid samples were digested with a mixture of concentrated nitric acid and hydrochloric acid.

Structural and functional analysis of the community

The growth of the microbial populations under different conditions was estimated using quantitative real-time polymerase chain reaction (qPCR). The microbes at the end of enrichment and leaching experiments were analyzed using 16 S rRNA gene high throughput sequencing. The details has been provided in the Supplementary Information (SI).

Statistical analysis

Statistical analyses were conducted using the IBM SPSS statistics software package (version 21.0). An analysis of variance (ANOVA) was used to test the significance of the results and p < 0.05 was considered to be statistically significant.

Results

Physicochemical properties analysis

The physicochemical parameters of sediment samples from the mining area and watershed showed a patchy pattern of variation over a small spatial scale, with rapid changes in parameters such as pH and nutrient levels (Table S2). All US and MS sediment samples exhibited strongly acidic pH values (3.03–3.48), reflecting severe impacts from AMD. In contrast, DS sediments had neutral pH levels (6.44–6.59), suggesting reduced AMD influence due to water inflows.

Among the environmental factors (total phosphorus (TP), total potassium (TK), total Fe content (TF), dissolved Oxygen (OD), total nitrogen (TN),oxidation reduction potential (ORP) and soil organic matter (SOM)), the ANOVA results indicated that only TK was significantly different between the US, MS, and DS regions (P < 0.05). As shown in Table S1, the contents of TK significantly increased from US to DS, and DS having the highest concentration as the inlet point. SOM was the most abundant factors in the MS region, although it decreased at DS sites but still remaining higher than in most US points. The proximity of all sampling sites to arable lands suggested that agricultural runoff may influence sediment nutrient levels, particularly for TN and TP.

Metal(loid) contamination in sediments and water

The concentrations of metal (loid)s in the sediments and water showed distinct obivious variations. The most severe contamination area occurred in US and MS regions, especially near active AMD discharge points (Table S1). The pH and redox reactions of metals are key factors controlling these contamination patterns, influencing the process of metals accumulate or migrate from downstream23.

The spatial distribution of metal (loid)s in the study area is strongly influenced by pH and metal redox kinetics. At sites closer to these discharge points, highly acidic conditions promoted the release of metals and metalloids from soil minerals into the water, facilitating their downstream migration. In the US region, the acidic environment lead to lower metal accumulation in the sediments but higher concentrations in the water. For instance, dissolved Fe (II) remained mobile in acidic conditions but is rapidly oxidized to the less soluble Fe(III) as pH increased. This oxidation process causes Fe accumulation in the MS sediments, particularly near the discharge points. Arsenic (As) often precipitates along with Fe, resulting in a well-documented Fe-As coupling effect in AMD-impacted environments, which can be supported by previous studies24.

In contrast, Mn often appears slowly in the process of oxidation and precipitation25, leading to a abundant accumulationof Mn in DS sediments. When pH approached to neutral levels, metals migrating from US and MS are typically precipitated quickly. This process results in higher concentrations of heavy metals (such as Mn, Cd, Cu, Zn and Cr in DS sediments), while the contents of A and B were relatively low (Table S1). With redox reactions and pH variations, these patterns illustrated the distinct geochemical processes regulated the distribution of metal (loid)s across the watershed. The DS region, in particular, serves as a precipitation area for metals precipitation as they were transported from severely contaminated areas fron upstream.

Microbial diversity analysis in sediments

Alpha diversity metrics, such as the Chao1 and Shannon indices, highlight these differences and are crucial for understanding ecosystem stability and functional capacity. The results revealed significant spatial variations in microbial richness and diversity, with both richness and diversity notably higher in DS sediments, compared to US and MS sediments. For example, the Chao1 index and observed species count were significantly lower in the US and MS sediments compared to the DS sediments (P < 0.05), indicating a reduction in microbial richness in the US and MS regions (Fig. 2). However, no significant difference was found between the US and MS groups (P > 0.05). Interestingly, the microbial diversity measured by Shannon and Simpson indices was significantly lower in the US and MS groups compared to the DS group (P < 0.05). On the contrary, there was no significant difference between the US and MS group (P > 0.05). The higher richness and diversity observed in DS sediments likely result from relatively neutral pH conditions and elevated metal (loid) concentrations. Based on these findings, it can be seen that the microbial communities in DS sediments are more diverse and robust, while the microbial populations diversity in acidic US and MS areas are lower, which may limit the ecosystem function of these areas.

Fig. 2.

Fig. 2

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Comparative analysis of microbial alpha diversity index in US, MS, and DS sediments.

Beta diversity analysis is a vital tool for understanding how microbial communities are structured in response to changing environmental factors. The microbial community structure in sediments from US, MS, and DS regions was analyzed using principal coordinate analysis (PCoA) based on weighted and unweighted UniFrac distances (Fig. 3a). Compared with groups A and B, the results showed that the position of group C in PCoA is significantly different. Combined with the physical and chemical properties of water (Table S2). The PCoA results demonstrated that the water samples were clustered separately and could be divided into two categories: normal site (DS) and contaminated site (US and MS). Further analysis demonstrated that the principal components, PC1 and PC2, showed a cumulative variance of 46.21% after abundance normalization, which showed a great difference in microbial structure among DS, US and UM groups26,27.

Fig. 3.

Fig. 3

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PCA (a) and RDA (b) principal component analysis of each classification unit.

Microbial community structure in sediments

Analysis of changes in microbial community structure is important for assessing the ecological impact of AMD. At phyla level, the bacterial communities were primarily composed of Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, WPS-2, Cyanobacteria, Firmicutes, Nitrospirae and Gemmatimonadetes in the DS, US and MS group (Fig. 4). At the genes level, we analyzed the differences between the top ten species in the US, DS and MS groups (Fig. 5a). The results showed that there was a significant difference among the top 10 species in different groups. For US group, Metallibacterium, Ferrovum, Acidibacter, WPS-2_genera_incertae_sedis, Acidocella, Acidobacteriaceae, Thiomonas, mine, Acidicapsa, Acidiphilium were the top 10 species. Due to the lower water pH and higher concentration in the US group, we further analyzed the proportion of these species in DS and US in comparison to the US group (Fig. 5b). The results showed that, in addition to Acidiphilium, Metallibacterium, Ferrovum, Acidibacter, WPS-2_genera_incertae_sedis, Acidocella, Acidobacteriaceae, Thiomonas, mine, Acidicapsa in DS group existed significantly different from the US group ratio (lower than 1.2–29.0%, P < 0.05). Thiomonas and Ferrovum are typical sulfur-oxidizing and Fe-oxidizing bacteria, which could explain the high concentrations of SO42- and Fe in US group. In Fig. 5c, nine significant differences were found in the top ten species, only Acidicapsa was significantly different in the MS group (1.4%, P < 0.05).

Fig. 4.

Fig. 4

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Relative abundance of microbial community in each group (at phyla level).

Fig. 5.

Fig. 5

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(a) Relative abundance of microbial community in each group (at genes level). (b, c) Comparisons the rate of dominant species between DS&US Group and MS&US group (at genes level).

Further, we compared the metabolic pathway condition in different sediment samples. The top 10 metabolic pathway expressions of the US, DS and MS groups are shown in Fig. 6a. The results showed that the top 10 species also differed significantly among the different groups. For US group, chemoheterotrophy, aerobic chemoheterotrophy, chloroplasts, nitrification, dark iron oxidation, aerobic nitrite oxidation, dark oxidation of sulfur compounds, dark sulfide oxidation, dark sulfur oxidation and dark thiosulfate oxidation were the major metabolic pathways. The expression of these metabolic pathways was further compared with MS and DS group. For DS group, the results showed that the proportions of chemoheterotrophy, aerobic chemoheterotrophy, chloroplasts, dark iron oxidation, dark oxidation of sulfur compounds, dark sulfide oxidation, dark sulfur oxidation and dark thiosulfate oxidation metabolic pathways were lower than those in US group (Fig. 6b), in which dark oxidation of sulfur compounds, dark iron oxidation and chloroplasts were significantly different in the US group (lower than 4.3–24.9%, P < 0.05). The reduced ratio of dark oxidation of sulfur compounds and dark iron oxidation in the DS group indicated that the metabolic capacity of bacteria for sulfide and Fe oxidation was lower than that of the US group, which could further explain the high sulfate and Fe concentrations in the US group. On the contrary, the aerobic nitrite oxidation and nitrification pathways were higher than in Us group, which is consistent with the results of higher TN contents in the sediments of the DS group than the US group (Table S1). In Fig. 6c, only the aerobic nitrite oxidation and nitrification pathways showed significantly different between MS and US group, the similarity of physicochemical properties of water samples in the US and MS groups may be caused by the similarity of bacterial community structure and metabolic pathways. Previous study has reported that ecological restoration and management strategies in regions could be affected by AMD28.

Fig. 6.

Fig. 6

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(a) Relative abundance of metabolic pathway in each group. (b, c) Comparisons the rate of metabolic pathway between DS&US group and MS&US group.

In order to understand the relationship between sediments microbial community and environmental factors (physicochemical parameters, metalloid concentration), we conducted the redundancy analysis (RDA). The results showed that physicochemical parameters and metalloid concentration (e.g., pH, TN, TK and Mn) were the major factors to explain the microbial community variation in DS, US and MS group (Fig. 3b). Moreover, there was a strong correlation between the environmental factors of pH, TK, Cd, and Mn. Among them, TK, pH and Mn factors were obiviously correlated with the microbial community structure in DS group, while TN was significantly correlated with US and MS group. Meanwhile, further analysis revealed that different physicochemical parameters and metalloid concentration had different effects on bacteria. For example, TN had positive correlations with Ferrovum and Thiomonas, and negative to Devosia, Rhodoplanesand and Nitrospira, while TK, pH and Mn showed opposite trends. These results demonstrated that each environmental factor is correlated and had different impacts on the microorganisms in DS, US and MS group.

Correlation and functional analysis

Correlation analysis was conducted to explore the relationships among dominant microbial genera (Fig. S1). Most dominant genera exhibited positive correlations, with the genus Mine showing significant positive correlations with all other dominant genera (P < 0.05). Similarly, Acidobacteriaceae and Ferrovum displayed significant positive correlations with the majority of dominant genera (P < 0.05). Additionally, we examined the correlations between biochemical indicators in sediments and water samples with sediment microorganisms (Fig. 7). A strong consistency was observed between these indicators and the microbial communities (P < 0.05). Specifically, the pH of water samples demonstrated a significant negative correlation with nearly all dominant genera (P < 0.05), while iron (Fe), zinc (Zn), and sulfate (SO42-) exhibited significant positive correlations with the dominant genera (P < 0.05). Further analysis revealed significant negative correlations between pH and differential heavy metals with the majority of dominant species (P < 0.05), alongside significant positive correlations between Fe and antimony (Sb) with the dominant genera (P < 0.05).

Fig. 7.

Fig. 7

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Correlation analysis between dominant species and environmental factors.

Understanding the relationship between microbial community functions and pollutant metabolism is crucial in environmental microbiology. Functional predictions of microbial biogeochemical cycling processes, particularly for carbon, hydrogen, nitrogen, phosphorus, and sulfur cycles, were conducted using Faprotax software (Fig. S2). Due to the presence of species capable of degrading various pollutants, targeted microbial communities typically exhibit stronger metabolic potential. Previous analyses indicated that microbial richness and diversity were significantly higher in downstream sediments compared to upstream sediments. Microorganisms in downstream sediments have stronger metabolic potential, mainly concentrated in processes such as aromatic hydrocarbon degradation, anaerobic ammonia oxidation, and chlorate reduction. In contrast, upstream sediments demonstrate a relatively weaker metabolic potential. Microorganisms in midstream sediments are primarily involved in nitrification, aerobic nitrite oxidation, and chemoheterotrophy, while upstream microorganisms mainly engage in sulfide oxidation and dark sulfur processes. The correlation between microbial diversity and biochemical indicators underscores the significance of environmental conditions in shaping microbial communities and their functional capabilities.

Mechanisms

Redox reactions are often accompanied by proton exchanges25. The aerobic oxidation of sulfide minerals, such as pyrite, results in amounts of protons (H+) and acidification. This process typically produces H+, sulfates (SO42-), dissolved Fe2+ and Mn2+29. The generated H+ further affected the chemical equilibrium between various substances, changing the solubility and bioavailability of metals in the environment30–32. The conceptual mechanism of AMD’s impact on the environment is shown in Fig. 8. AMD can affect the environment through the following three mechanisms:

Fig. 8.

Fig. 8

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Conceptual mechanism of how AMD affects environments.

Oxidation of sulfides and acid neutralization

In karst regions, the acid produced by AMD is partially neutralized by carbonate minerals present in the soil and groundwater. Carbonate minerals, such as calcite and dolomite, react with the acid to produce carbon dioxide and water, thus mitigating the acidity to some extent. However, excess acid continues to dissolve soil minerals, particularly iron and manganese minerals, releasing adsorbed metals and non-metals, such as As. These released elements are transported downstream, contributing to the contamination of water, sediment, and soil.

Acid neutralization and metal precipitation

During the downstream migration, the continuous accumulation and neutralization of acid lead to the rapid precipitation of dissolved Fe2+. This precipitation often includes associated anions, such as arsenate (AsO43-) and phosphate (PO43-). However, the oxidation and precipitation of Mn2+ occur at a slower rate, resulting in the continued movement of some heavy metals and Mn2+ further downstream. This dynamic affects water quality and can have implications for downstream ecosystems.

Downstream metal precipitation

Further downstream, where the pH pproaches neutral conditions, many metals rapidly precipitate due to their low solubility. This results in elevated concentrations of heavy metals in sediments.

Discussion

Extensive research utilizing marker gene sequencing to characterize soil bacterial communities has identified the major phyla that tend to dominate in various soils33. Our works continues to expand regarding how factors such as soil properties (e.g., pH and oxidation reduction dynamics)34–36, climate37, vegetation types38, and nutrient availability39 influenced the composition of soil bacterial communities globally. These factors play crucial roles in shaping microbial diversity and distribution across different ecosystems.

Influence of AMD on environmental chemistry and ecosystem health

The AMD severely alters the chemical balance of aquatic systems, as evidenced by the consistently low pH values observed in certain regions of the study area. These acidic conditions result in enhanced solubility of metal (loid)s, leading to elevated concentrations of contaminants such as Fe and As40. This underscores the pervasive impact of AMD on environmental systems, as previously documented in global studies of mining-impacted watersheds32. However, the neutralization of pH downstream suggested that natural processes, possibly including dilution and the buffering capacity of the receiving environment, mitigated AMD’s immediate effects, offering some resilience. This chemical resilience has ecological implications: as pH improves downstream, conditions become more conducive for biological activity, suggesting that ecosystem recovery is possible over short spatial scales. While neutralization may reduce some immediate threats, the persistent presence of metals highlights that it does not entirely eliminate the transport of metal contaminants, such as Mn in downstream. This raises questions about the long-term accumulation of these metals in sediments and the potential for delayed ecological impacts, including bioaccumulation and toxicity in downstream ecosystems. Therefore, it is the central focus of the remediation strategy to overcome the problems of pH and metal immobilization40.

Potential correlations between elevated SO42- levels and As / Fe concentrations

Among the water physicochemical properties at different sites, the concentrations of As, Fe, and SO42- in DS group were lower than those in US and UM groups, whereas pH was much higher than that in US and UM groups, which may be due to the oxidation of pyrite by sulfur-oxidizing bacteria in the US and UM groups (Thiomonas and Ferrovum), leading to the release of Fe and SO42-, While As in the sediment were also released due to high acidity and bacterial erosion, which resulted in elevated As concentrations in the wastewater of the US and UM groups. At the same time, the dominance of sulfur-oxidizing bacteria may have been further enhanced when As was released into the water, accelerating sulfur oxidation. Previous studies have shown that the presence of As increases the proportionate number of sulfur-oxidizing bacteria41.

Broader implications for remediation and ecosystem restoration

The spatial heterogeneity of AMD impacts observed in this study underscores the need for site-specific remediation strategies42. Efforts to neutralize acidity upstream may be insufficient if downstream metal accumulation continues unchecked. Integrated management strategies that address both the chemical and biological aspects of AMD-affected environments are essential for achieving sustainable remediation outcomes43. Furthermore, the findings point to the importance of considering the cumulative impacts of AMD on both water quality and sediment contamination. Remediation strategies should not only focus on improving water chemistry, but also on stabilizing or removing metal contaminants from sediments, which may act as long-term reservoirs of pollution40. In this context, bioremediation approaches that utilize metal-tolerant or metal-transforming microorganisms may be a promising avenue for reducing the bioavailability of contaminants in sediments43. Finally, the restoration of downstream microbial diversity and activity suggests that ecosystem restoration is possible if the effects of acidity and metal toxicity are effectively minimized9. This provides a hopeful outlook for the recovery of AMD-impacted environments, particularly if proactive remediation efforts are undertaken to prevent further degradation and enhance natural attenuation processes40.

Study limitations

In this study, we selected samples from only nine locations near the mine for analysis of sediment–water physicochemical properties, microbial structure and function due to topographic complexity. However, there is a marked difference in the distribution of vegetation in the area (more plants near the DS area), and the distribution of human activities is not homogeneous (the human living area is mainly concentrated near the US and the MS) from (Fig. 1); therefore, it is essential to collect a large number of samples in the vicinity of the mine that are both large in number and distributed. Therefore, collecting a large number of evenly distributed samples in the vicinity of the mine is necessary to improve the accuracy of the data. Meanwhile, the direction of runoff and groundwater seepage in the area are not yet known, which is an important reference for analyzing the ecological and environmental impacts of sulfur bio-oxidation in the mineIn addition, the interval of our analysis of microorganisms was in V3-V4 part, and further study of archaea and fungi is necessary. We will treat these issues with caution in our subsequent research.

Conclusions

This study explores the impacts of AMD on the surrounding ecosystem with a sample of coal mining areas in Guizhou. The impacts of AMD on the area were assessed by physicochemical properties, community structure and metabolic pathway analysis. On the one hand, the results showed that AMD affects the physicochemical properties of water through migration and dissolution. On the other hand, it promotes the oxidation of pyrite by promoting specific functional bacteria (like Thiomonas and Ferrovum), which affects the concentration of heavy metals and salt in water. The study providing valuable insights into microbial adaptations and the environmental challenges posed by AMD.

Supplementary Information

Supplementary Information. (4.7MB, docx)

Author contributions

S.Z.: data curation, writing—original draft, visualization, validation, and methodology. P.W.: writing—review and editing, project administration, funding acquisition, resources, and supervision. J.Z.: conceptualization and methodology. Q.L. and C.L.: experimental operation and sample collection. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Project of Science and Technology Department of Guizhou Province (GCC[2023]045), Guizhou Provincial Science and Technology Programme Project (Qian Ke He [2023] 006), and the Scientific and Technological Achievements Transformation Project of Guizhou Province (Qian Ke he [2024] 029).

Data availability

Sequence data that support the findings of this study have been deposited in the Figshare repository with the persistent DOI: 10.6084/m9.figshare.28604402; https://doi.org/10.6084/m9.figshare.28604402.v1.

Declarations

Competing interests

The authors declare no competing interests.

Consent for publication

All authors agree to publish this research (including any individual details, images or videos) in Scientific Reports.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-05799-z.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information. (4.7MB, docx)

Data Availability Statement

Sequence data that support the findings of this study have been deposited in the Figshare repository with the persistent DOI: 10.6084/m9.figshare.28604402; https://doi.org/10.6084/m9.figshare.28604402.v1.

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