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Article

Effects of Coal Mining Activities on the Changes in Microbial Community and Geochemical Characteristics in Different Functional Zones of a Deep Underground Coal Mine

by
Zhimin Xu
1,2,3,
Li Zhang
1,*,
Yating Gao
1,
Xianfeng Tan
3,4,5,
Yajun Sun
1,2 and
Weixiao Chen
1
1
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
2
Fundamental Research Laboratory for Mine Water Hazards Prevention and Controlling Technology, Xuzhou 221116, China
3
Engineering Research Center of Zero-Carbon and Negative-Carbon Technology in Depth of Mining Areas, Ministry of Education, Xuzhou 221116, China
4
Shandong Provincial Lunan Geology and Exploration Institute, Shandong Provincial Bureau of Geology and Mineral Resources No. 2 Geological Brigade, Jining 272100, China
5
Shandong Engineering Research Center of Geothermal Energy Exploration and Development, Jining 272100, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(13), 1836; https://doi.org/10.3390/w16131836
Submission received: 15 May 2024 / Revised: 18 June 2024 / Accepted: 25 June 2024 / Published: 27 June 2024
(This article belongs to the Special Issue Mine Water Safety and Environment)
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Abstract

:
For deep underground coal mining ecosystems, research on microbial communities and geochemical characteristics of sediments in different functional zones is lacking, resulting in the knowledge of zone-level mine water pollution prevention and control being narrow. In this study, we surveyed the geochemical distinctions and microbial communities of five typical functional zones in a representative North China coalfield, Xinjulong coal mine. The data indicated that the geochemical compounds and microbial communities of sediments showed distinguishing features in each zone. The microbial community richness and diversity were ranked as follows: surface water > rock roadways > sumps > coal roadways ≥ goafs. Canonical Correlation Analysis (CCA), Spearman correlation and co-occurrence network analysis demonstrated that microbial communities were sensitive and closely related to hydrochemical processes. The microbial community distribution in the underground mine was closely related not only to nutrient elements (i.e., C, S, P and N), but also to redox-sensitive substances (i.e., Fe and As). When it comes to mine water pollution prevention and control, the central zones are goafs. With the increase in goaf closure time, total nitrogen (TN), total organic carbon (TOC) and total sulfur (TS) decreased, but As, Fe and total phosphorus (TP) gradually increased, and the characteristic pollutant SO42− concentration in water samples decreased. Additionally, the sulfate-reducing bacteria (SRB) had relatively higher proportions in goafs, suggesting goafs were able to purify themselves. In practical engineering, in situ nitrogen injection technology used to expel oxygen and create an anaerobic environment can be implemented to enhance SRB reducing sulfate in goafs. Meanwhile, because coal mine pollution discharge generally only discharges mine water and leaves sediment underground, the pollutants can be transferred to the sediment by strengthening the relevant reactions including the heavy metal solidification and stabilization function of bacteria.

1. Introduction

The influence of coal mining and utilization on the water environment is gradually increasing, especially in China, which is one of the largest coal producers and consumers in the world [1,2,3]. Microorganisms that play an important role in the biogeochemical cycle can catalyze the generation or degradation of contaminants in the water environment, which is significant for environmental protection and human health. An external environmental disturbance may significantly change the structure of a microbial community and even change the material cycle and energy flow process of the entire ecosystem [4]. Coal mining activities can cause changes in the living environment of microorganisms, including dissolved oxygen (DO), total organic carbon (TOC), pH and the concentrations of nutrients and metals [5,6]. Additionally, microbial processes are known to impact biogeochemical processes in the environment [7,8]. Compared to alkaline mine drainage, there has been more research on the evolution of acid mine drainage (AMD) and its impact on the chemical properties and microbial community composition of surface rivers (soils) after discharge. However, the interaction between site microbial communities and survival carriers (water and sediment) in deep underground coal mines has not received much attention [9,10].
For coal mining and transportation, as well as ventilation and drainage, different zones have been built in underground coal mines to meet production needs. Examples include working panels, roadways, sumps and drainage systems. Due to the different design functions of these zones, the provenance and environment of these areas are different. The coal working panel is the first production site of coal and is rich in coal. Coal is mainly composed of C, H, O, N, S, P and other elements, in which organic matter and minerals (e.g., pyrite) can be supplied to microbial metabolic activities [11,12,13,14]. Further, oxygen (O2) inflows into the underground coal mine through roadways can affect the in situ redox conditions of the different zones of the coal mine, enhancing or suppressing different types of microbial metabolic activities. For example, Gao [15] and Zhang [16] screened many aerobic and anaerobic polycyclic aromatic hydrocarbon (PAH)-dominant degradation bacteria from sediments of coal roadways using phenanthrene and catechol as substrates, respectively. PAHs in coal provide a carbon source for these dominant degradation bacteria. After mining is finished for a certain panel, a wall is built to close it for safety; the closed panel is called a goaf with much remaining coal at this time. Then, in the goaf, with the time after goaf closure, the water level increases and the O2 content decreases gradually, leading to the occurrence of continuous physical, chemical and biological changes. A water sump is used to temporarily store underground mine water and sediment of the whole mine, which are collected from rock roadways, coal roadways, goafs, etc. The microbial and geochemical characteristics of a water sump may reflect the comprehensive characteristics of the collected material. Therefore, the microbial communities of mine water and sediments in different zones may be unique, leading to differences in the geochemical characteristics of mine water and sediments.
Microorganisms in coal mines are primarily associated with water and sediments. Our previous study reported that hydrochemical elements and microbial communities of the water samples in a coal mine displayed apparent zone-specific patterns, and the microbial communities corresponded to the redox-sensitive indices’ levels [17]. However, it has been reported that the microbial communities and functions in water and sediment differ to some extent. Generally, compared with suspended microorganisms in water, the community structure of microorganisms attached to sediments is more stable, resulting from the higher richness and diversity [18,19,20]. The microbial community in water is more likely to spread with water flow, while the microbial community in sediment is more stable. Furthermore, the microorganisms in the sediments may not only act as bioindicators for monitoring mining activities, but also potentially immobilize or degrade toxic contaminants. However, for deep underground coal mining ecosystems, research on the microbial communities and functions of sediments in different functional zones is lacking, which limits our knowledge of zone-level mine water pollution prevention and control.
In this study, we collected 18 samples of sediments from different zones (i.e., rock roadway, coal roadway, goaf, water sump, surface water) of a typical deep North China coalfield. These samples were subjected to high-throughput sequencing and geochemical analysis. The purposes of this work are as follows: (1) to analyze the responses of microbial communities and geochemical characteristics of sediments to coal mining disturbances; (2) to reveal the connections between microbial communities and geochemical characteristics; (3) to illustrate the reasons for the variation in microbial communities and functions in different zones and their environmental response characteristics. This study aims to provide a systematic presentation of the microbial community structure and ecological variations in underground coal mines, which will enhance our understanding of microbial treatment technology for mine pollution.

2. Materials and Methods

2.1. Study Sites

Xinjulong coal mine, a typical North China coalfield located in the southwestern part of Shandong Province, China (Figure 1a), is the study site of this research. This mine belongs to the alluvial–diluvial plain of the Yellow River, with an area of 142.29 km2. The ground elevation ranges from +40.01 m to +46.14 m, with an average of 43.26 m. At the time of sampling, the Xinjulong coal mine had been mined for about 12 years. Its annual production capacity is about 6–7.5 million tons, and the mining elevation is about -810 m (Figure S1). The coal seams are based on the Ordovician limestone and composed of Middle Carboniferous and Permian strata. The overlying strata are Neogene and Quaternary. The main coal seam, namely the #3 coal seam, is in direct contact with the Shanxi Formation sandstone fissure aquifer (S3). In addition, roadways pass through the Taiyuan Formation limestone aquifer (L3), which is located on the floor of the #3 coal seam (Figure S1). Therefore, S3 sandstone and L3 limestone aquifers are the main water supply source aquifers in the current coal mining process. Part of the mine water is discharged into the Zhushui River through the main outfall (Figure 1c). Some abbreviations used in this work are as follows: rock roadways (RRs), coal roadways (CRs), goafs (goafs), water sumps (sumps), surface water (SW).

2.2. Sediment Sampling

In total, 18 sediment samples, including 15 underground samples (Figure 1b) and 3 surface water samples (Figure 1c), were collected in May 2021. The mine-affected aquifers discharge groundwater during coal mining. Then, part of the groundwater flows into roadways, tunneling panels or goafs due to water head difference. Meanwhile, most of the mine water is collected into the water sumps along the drainage ditches, followed by being conveyed to the surface. The treated water is discharged to the Zhushui River. The sediments in the five relevant zones shown in Table S1 were sampled so as to research the whole process of mine water occurrence, collection and discharge. The characteristics and sampling design of each zone are as follows:
(1)
The groundwater from water-filling aquifers was the origin of mine water. A rock roadway is a roadway with a rock area higher than 80% in the excavation section and is mainly used for ventilation, transportation equipment and materials, etc. The 4 rock roadway sampling points passed through the L3 limestone aquifer (RRs: RR1, RR2, RR3, RR4). Thus, most of the rock roadway water originated from the L3 limestone groundwater and collected in the drainage ditches of the rock roadways, except for RR2 which collected the drainage of coal roadways and rock roadways.
(2)
A coal roadway is a roadway with a coal area higher than 80% in an excavation section including a panel, which was mainly used for mining and transporting coal. The main water-filling aquifer of coal roadways was the S3 sandstone aquifer, and the sediment contained much coal (CRs: CR1, CR2).
(3)
After mining is finished for a certain panel, a wall is built to close it for safety; the panel is called the goaf at this time. The continuous entry of groundwater gradually raises the water level in the goaf; coupled with the consumption of biochemical reactions, this results in a gradual reduction in the level of O2. At this time, the goaf becomes a key zone where physical, chemical and biological changes occur. Thus, the goafs whose water supply source aquifers were all S3 sandstone groundwater were selected for this study (goafs: G1, G2, G3, G4). Here, we had to substitute space for time in the experimental design (the goafs were closed in 2021, 2012, 2010 and 2009) because the microbial community and geochemical characteristics of goafs closed several years ago were not detected.
(4)
A water sump is used to temporarily store underground mine water and sediment from the whole mine, and it collects the water and sediment from groundwater aquifers, rock roadways, coal roadways, goafs, etc. Five water sump samples (sumps: WS1, WS2, WS3, WS4, WS5) were collected.
(5)
The mine water in the water sumps is conveyed to the surface. Then, some treatment processes should be performed to treat the mine water before it is discharged into the Zhushui River. Three sample points were designed so as to analyze the contribution of mine drainage to the microbial habitat and community in rivers. The sampling points were: the intersection of Zhushui River and mine drainage (SW1), the upstream section of Zhushui River (SW2) and the downstream section of Zhushui River (SW3).
Each sample was actually collected from different subareas within a location, and the samples were bulked. More than 1000 g for each sample was placed into sterile sealed bags. Then, 200 g and 20 g for each sample were separated on a super-clean bench. The 200 g sample was stored at 4 °C for geochemical property detection; the 20 g sample was kept at −80 °C before DNA extraction. Water samples were also taken from each sampling point, but this article only focuses on the analysis of sediment samples.

2.3. Sediment Geochemical Property Determination

Fourteen geochemical properties of sediment samples were measured by laboratory analysis. The determination methods and reference standards are shown in Table 1. The water content in collected samples was not detected. Before element content detection, the sediment samples were dried in the shade. So, the element content presented in this study was standardized to dry weight.

2.4. DNA Extraction and 16S rRNA Gene Sequencing

The total bacterial DNA was extracted from sediment samples using a FastDNA Spin Kit for soil (MP Biomedical, Santa Ana, California, USA) according to the manufacturer’s instructions. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) primer pairs by an ABI GeneAmp® 9700 PCR thermocycler (ABI, Santa Ana, CA, USA). The remaining PCR amplification and Illumina Miseq sequencing steps were similar to those in our previous studies [21,22], as shown in the Supplementary Materials.

2.5. Processing and Statistical Analyses of Sequence Data

Operational Taxonomic Unit (OTU) clustering and species taxonomy analyses were performed after the samples were distinguished. All sequences can be divided into OTUs according to different similarities, and bioinformation statistical analysis is usually performed on OTUs at a 97% similarity level (Uparse, version 7.1). The RDP classifier (version 2.2) was used to obtain each OTU’s corresponding species classification information [15,23]. The Bayesian algorithm was used to perform taxonomic analysis on 97% OTU representative sequences at 97% similarity and count the community species composition of each sample at each taxonomic level. The Silva database was used for comparative analysis using Qiime2 2023.2 software [24].
The richness, coverage and diversity of species in a community can be obtained by alpha diversity index analysis. And the common indices are ACE, Chao1, Shannon, Simpson, coverage, etc., calculated on Mothur 1.30.2 [5]. Principal component analysis (PCA), a typical beta diversity analysis, can reflect the differences and distances between samples by analyzing the composition of different sample communities [24,25]. According to the obtained bacterial abundance data, strict statistical methods were used to test the hypothesis of species among microbial communities in different groups (or samples), so as to evaluate the significance level of species abundance differences and obtain significant differences in species. The Kruskal–Wallis H test applies to multi-group comparisons [24]. Canonical Correlation Analysis (CCA) is one of the most commonly used algorithms for mining data associations. It combines Correspondence Analysis with Multiple Regression Analysis and performs regression with environmental factors at each step of the calculation, also known as Multiple Direct Gradient Analysis. This method is mainly used to reflect the relationship between bacteria and environmental factors [26,27]. The OTU abundances of all sediment samples were ranked, and the information of the 100 most abundant OTUs was processed by R language. This work set method = ”spearman” and adjust = ”fdr” to calculate the correlation coefficient and adjust the p value. Biological interactions were represented as co-occurrence networks on the platform Gephi 0.9.2 [28]. Functional annotation of prokaryotic taxa (FAPROTAX) is an artificially constructed database that maps prokaryotic taxa (e.g., genera or species) to metabolic or other ecologically relevant functions (e.g., nitrification, denitrification) based on artificially cultured representative literature. In addition, FAPROTAX may be more appropriate for functional prediction of the biogeochemical cycles of environmental samples [29]. The FAPROTAX analysis was performed on FAPROTAX (1.2.1).

3. Results and Discussion

3.1. Geochemical Variation Characteristic of Sediments across Five Different Zones

As shown in Figure 2, the geochemical compounds of sediments differed obviously in the samples collected from different functional zones of the coal mine. The pH of river sediments (7.8–8.0) was significantly lower than that of coal mine samples. In underground coal mine samples, the pH values of goafs were the lowest (7.6–8.9), and the pH values of rock roadways were relatively high (8.4–10.4) due to the hydrolysis of the calcium carbonate in L3 limestone. The concentrations of TOC, total sulfur (TS) and total nitrogen (TN), which can provide abundant nutrients for microorganisms, in coal mine sediments were higher than those in river sediments. This suggested that the TOC, TS and TN coming from coal and mining activities could indeed change the living environment of microorganisms. However, the concentrations of TN, TS and TOC in goafs were lower. On the contrary, total phosphorous (TP) was quite different from the above nutrients, with the highest concentration in river sediments, followed by goafs. The contents of As, Fe and TP in different zones of the mine were similar, and the concentration in the goafs was distinctly higher than that in other underground zones. As and P have a strong binding ability with Fe and can easily form coprecipitation or be adsorbed by iron minerals, so the concentration distribution of As, Fe and TP is consistent [30,31,32]. Our previous research reported that the oxidation of pyrite associated with coal led to the highest concentration of SO42− in coal roadway water samples, and SO42− was the characteristic pollutant of the mine water [21,22]. After the cessation of mining, a goaf is formed. The characteristics of the lowest pH and the highest Fe (19.46–429.03 g/kg) in the goafs indicated that in the initial stage of mine water entering the goaf, the water level gradually rose, and the pyrite oxidation reaction became more complete (Equations (S1)–(S3)). However, with increasing goaf closure time, O2 is gradually consumed, and anoxic bacteria may become the dominant bacteria instead of aerobic bacteria, which would affect the geochemical characteristics of a goaf. So, the long-term evolution of biogeochemistry in goafs needed to be clarified in combination with microbial data.
For heavy metals, the contents of Cu, Zn and Pb were higher in rock roadways and sumps but lower in coal roadways and goafs. This phenomenon was related to the elemental composition of sediment sources (Tables S2 and S3). As shown in Table S2, the Cu, Zn and Pb contents of S3 sandstone and L3 limestone were generally higher than those of coal. The highest content of Hg was 0.26–0.41 mg/kg in coal roadways, while the lowest content of Cd was 0.11–0.14 mg/kg. The biggest change in sediment compounds in different positions of the river was found for Cr. The average Cr concentrations in all four zones of the underground mine were lower than those in the river. The Cr concentration in the upstream section of the river was 26% higher than that in the intersection and the downstream section of the river, which may result from the mixture of surface water and the mine drainage (with low concentrations of Cr). Therefore, considering the large amount of heavy metals in these sediments, in order to prevent and control groundwater pollution, it is necessary to reduce the discharge of sediments as much as possible and maintain the stability of these harmful elements in the sediments.
The geochemical characteristics of the sediments in the sumps were closely related to their catchment source. A small part of the mine water collected by the #1 (WS1, WS3) and #2 (WS4) sumps was from the same source, and both sumps were all located at a burial depth of 853 m. Furthermore, the #1 sump collected mainly S3 groundwater, and the #2 sump collected mainly L3 groundwater (>50%). Although the #2 sump was close to the #1 sump and shared part of the drainage pipelines, the concentration of some substances in the sediment of the #2 sump was several times higher than that of the #1 sump and other sumps, including TP (1013 mg/kg), Fe (335.16 g/kg), As (171 mg/kg) and Mn (968 mg/kg). Apparently, this was due to different sources of receiving water. In the half box plot diagrams, there are outliers (Figure 2). It is speculated that there are a lot of Fe and Mn colloids and their adsorbed P and As complexes in the L3 groundwater. The concentrations of TS (10.673 g/kg), TN (6.8 g/kg), TOC (859.8 g/kg) and pH (9.24) in the sediments of the lower sump (WS2) were higher, and this may be related to the fact that the lower sump mainly collects coal mine water of the tunneling panel which contains lots of organic matter. The level 2 sump (WS5) collected mine water from deep unmined roadways, located at a burial depth of 1063 m. The concentrations of TS, TN, TOC and As in the sediments of the level 2 sump were obviously lower than those of other sumps, but the concentration of Cr and pH value were higher. This was due to the coal seam near the level 2 sump not being exploited yet, and the main water source was the groundwater aquifer and rock roadways.
Several geochemical compounds of the Zhushui River sediments were affected by mine drainage. Compared with the sediments of the intersection of Zhushui River and mine drainage (SW1) and the upstream section of Zhushui River (SW2), the concentrations of As and Fe in the downstream section of Zhushui River (SW3) sediments increased slightly, while the concentrations of TS, TOC, Zn, Cu and Cr decreased. At the same time, the drainage (36.5 °C) increased the river temperature from 16 °C in the upstream section to 24.9 °C in the downstream section. For this reason, on the one hand, this effect is due to the leaching effect of mine drainage on the river sediment and the precipitation and adsorption/desorption of the discharged suspended matter. On the other hand, the mixture of river water and mine drainage would affect the living environment and physical–chemical–biological interaction process of microorganisms, finally leading to geochemical parameter changes [8,28].

3.2. Overall Microbial Diversity and Taxonomic Composition Changes in Different Zones

3.2.1. The Alpha Diversity Analysis of Sediment Sample

A total of 1,010,374 valid sequence reads were obtained from 18 sediment samples through high-throughput sequencing, ranging from 38,178 to 73,065 reads per sample, and clustered into 23,739 OTUs. OTU (97% sequence similarity) numbers ranged from 210 to 2441 per sample. The coverage values were from 97.48% to 99.90% (Table 2). This indicated that the community composition of samples could be accurately reflected.
The OTUs, ACE, Chao1, Shannon and Simpson indices of samples are shown in Table 2. The results showed that the microbial community richness of the five zones was ranked as follows: surface water > rock roadways > water sumps > coal roadways > goafs; the microbial community diversity of the five zones was ranked as follows: surface water > rock roadways > water sumps > coal roadways ≈ goafs. Therefore, the microbial community richness and diversity of the five zones were consistent. Moreover, the total core and unique species in coal roadways and goafs were relatively fewer, which was contrary to the data of water samples (Figure 3a). In coal roadways, small molecular organics dissolved from coal were more easily used by microorganisms, and some coal substances were toxic to microorganisms, so microorganisms were more likely to be distributed in the liquid phase. In goafs, the hydrolysis of Fe(III) generated by the oxidation of pyrite produced a large amount of iron oxides, and there was more As in these iron oxides, which might be not conducive to the colonization of microorganisms on the solid phase. The highest numbers of species were found in sumps and rock roadways (Figure 3a), but the microbial community richness and diversity of surface waters were the highest. In addition, the result of PCA showed that compared with other zones of samples, the difference in microbial community structure in coal roadways was higher, while the intra-group similarities of community structure in surface waters and goafs were higher (Figure 3b). The underground sediment samples clustered separately from the surface samples, indicating that the microbial community structure of surface samples was clearly different from that of underground samples.

3.2.2. Microbial Community Characteristics of Sediments in Five Zones

Within five zones of sediment samples, a total of 68 phyla were detected, and the top 17 most abundant phyla accounted for up to 99% of the total microbial community (Table S4). The distribution of the top 17 most abundant microbial phyla within different zones is shown in Figure 4a. Except for the surface water samples, the following bacteria were mainly detected in the four zones of underground sediment samples: Pseudomonadota (25.64–74.50%), Actinomycetota (4.32–18.59%), Chloroflexota (3.24–9.58%), Nitrospirota (0.59–17.71%), Acidobacteriota (1.20–15.54%), Bacillota (2.33–10.15%), Thermodesulfobacteriota (0.25–5.74%). The dominant phyla were similar to those in the water samples [21], and Pseudomonadota was reported to predominate in mining areas, aquifers and rivers [33,34]. The Kruskal–Wallis H test results showed that the abundance of Patescibacteria, which can participate in the N cycle, was significantly different in terms of the five zones (0.01 < p < 0.05) (Figure 4b) [35]. In addition, the proportion of Pseudomonadota, most of which are aerobic heterotrophic, was the highest (74.5%) in coal roadways and the lowest (25.64%) in goafs. The proportion of Nitrospirota in goaf (17.71%) and sump (13.08%) was relatively higher. The proportion of Acidobacteriota and Thermodesulfobacteriota in goafs was 15.54% and 5.74%, respectively, which was much higher than that in other samples. Except for the microorganisms mentioned above, the abundance of d_Bacteria (3.22%), Methylomirabilota (3.62%), Myxococcota (1.86%), DTB120 (1.8%) and Dadabacteria (1.43%) in goafs was higher than that in other zones. In addition, Dadabacteria have the potential to degrade microbially dissolved organic matter [36,37]. With time after goaf closure, complex reactions such as pyrite oxidation, sulfate reduction and organic matter degradation may occur gradually in goafs. Therefore, elucidating the evolution process of mine water quality in goafs is very important for groundwater pollution prevention and control in coal mine areas.
The top 50 most abundant genera in sediment samples of the five zones are shown in Figure 5a. For sediment samples in the underground coal mine, the dominant bacterial genera mainly included Acinetobacter (0.17–35.12%), Pseudomonas (3.65–11.92%), Nitrospira (0.56–12.30%), o_Subgroup_2(0–12.5%), Thiobacillus (0.06–0.97%), f_Rhodobacteraceae (1.27–3.36%), c_Thermodesulfovibrionia (0.02–5.28%), Hydrogenophage (1.02–3.17%) and Dietzia (0.01–5.85%). At the same time, it was obvious that surface water sediments and underground coal mine samples belonged to two branches in the sample clustering, which also indicated that there were great differences in the microbial community composition (Figure 5a). The Kruskal–Wallis H test of distribution of the top 15 most abundant microbial genera showed that Thiobacillus, Dietzia, Acinetobacter and KCM-B-112, which were related to sulfur oxidation and hydrocarbon degradation, had significant differences (0.01 < p < 0.05) among the five zones’ samples (Figure 5b). Thioclava, which could accelerate the oxidation of pyrite, had a higher abundance in coal roadways, leading to the higher SO42− concentration of coal roadways (Figure S2). Nitrospira was mainly present in goafs (12.30%) and sumps (11.91%). Geothermobacter, belonging to Thermodesulfobacteriota, was mainly present in goafs, accounting for 1.39%. In addition, c_Thermodesulfovibrionia is a kind of anaerobic bacteria that can reduce sulfate and produce H2S [39]; it showed the highest proportion in goafs (5.28%) but was not detected in coal roadways. This may be one of the reasons why the SO42− concentration in coal roadways was higher than that in goafs.
The sediments in surface environment had more species than underground coal mine sediments, and there were 157 unique species in surface water sediments (Figure 3a). Chloroflexota, Thermodesulfobacteriota, Bacteroidota and Patescibacteria accounted for 19.22%, 7.25%, 5.67% and 2.37% in river sediments, respectively. Meanwhile, Cyanobacteriota and Spirochaetota, which perform oxygen-producing photosynthesis [40], were only found in surface water sediments. In combination with alpha diversity analysis, the richness and diversity of bacteria in the sediments showed no significant changes from the upstream section (SW2) to the intersection (SW1) and then to the downstream section (SW3) of Zhushui River (Table 2). The bacterial composition differences of the three samples were small, and the community structure was relatively stable. The functional prediction results shown in Figure 6 suggested that the abundances of microbial function in the three samples were generally similar as well. The abundance of functions related to S (e.g., dark oxidation of sulfur compounds, sulfate respiration and dark sulfide oxidation) in the downstream sediment samples was slightly lower than that in the upstream section, but the abundance of functions related to N (e.g., nitrate reduction, nitrate respiration and nitrogen respiration) was similar to that in the upstream section. In other words, the microbial information of sediment samples did not reflect the significant impact of mine drainage on surface water sediments. The microbiological results of the water samples are not similar: after receiving discharge from mine drainage, the community structure of river samples changed significantly [41]. The microbes in river water spread faster. However, we are unable to capture chemical heterogeneity in sediments, and our chemical and microbiological views of the sediments are averages of a variety of micro-environments.

3.3. Correlation between Microbial Communities and Environmental Variables

Based on the microbial community composition and geochemical compounds of sediment samples in five zones, the CCA method was used to analyze the correlation between the species composition and environmental variables. After the deletion of 2 collinearity variables (i.e., Cu and Zn), 12 geochemical components remained in the CCA analysis (Figure 7a), and the CCA result data are shown in Table S5. The distribution of points with different colors and shapes represents the similarity of the microbial communities and environmental responses in different samples. The surface sediment samples clustered separately from the underground samples, and the intra-group similarity of surface sediment samples was the highest. Environmental factors are represented by vectors, and the length of the vectors represents their degree of influence on community composition. Points and vectors located in the same direction indicate that environmental factors are positively correlated with the changes in the sample species community. Those located in opposite directions indicate a negative correlation. Cr was positively correlated with the microbial community structure of the surface sediment samples. Other sediment samples generally clustered according to grouping, but there were also outliers within the groups. For sumps, the landing point of the #2 sump (WS4) was far from other sump points. This was because the #2 sump only collected the L3 aquifer water, and the sediment contained more reddish brown iron manganese oxide or hydroxide, while the other four sumps collected diverse incoming waters containing more suspended coal particles. For goafs, the outlier was the goaf newly closed in 2021 (G1), which had the shortest closure time. This suggested that goaf formation time may have a great influence on microbial community structure and geochemical parameters, and more detail is shown in Section 3.4. The vertical distance from the sample point to the environmental factor vector (and extension line) represents the influence of environmental factors on the samples. Fe (r2 = 0.85, p = 0.001) and As (r2 = 0.83, p = 0.001) had a great influence on the microbial community distribution of sediment samples and were especially positively correlated with the microbial community distribution of goafs (closed in 2009, 2010 and 2012) and the #2 sump. Additionally, the vectors of Fe and As almost coincided. The angle between vectors is related to their correlation (positive: acute angle, negative: obtuse angle, no correlation: right angle). Fe and As had a strong positive correlation, and they had similar significant impact on the microbial community distributions of goafs (closed in 2009, 2010 and 2012) and the #2 sump (WS4). pH, TS, Hg, TN and TOC, which were negatively correlated with Cd, Mn and TP, were positively correlated with the microbial community distributions of rock roadways, coal roadways and water sumps. The coal roadway (CR1) with the highest TOC concentration was an outlier for all the sediment samples, indicating that its community composition was significantly different from that of most samples. In a word, the distribution of microbial communities in the underground mine was closely related not only to nutrient elements such as C, S, P and N, but also to redox-sensitive substances such as Fe and As. This mainly resulted from the new water–coal (rock) reaction driven by coal mining disturbance.
In order to clarify the relationship between bacteria and environmental variables in the underground coal mine, a Spearman correlation analysis was carried out for the top 50 bacteria and environmental variables of the sediment samples (Figure 7b). The greater the absolute value of the correlation coefficient, the darker the color. It was noteworthy that the correlation of most of the top 50 microbial species with Fe and As was opposite to that of pH, TOC, TS and TN, possibly due to the coupling relationship between iron transformation and the nutrient compound cycle. Limnobacter, Acinetobacter, Phenylobacterium, Dietzia, Hydrogenophaga, Paracoccus, Thauera, f_Sphingomonadaceae and Novosphingobium, which can degrade organic pollutants (e.g., PAHs and petroleum hydrocarbons), were significantly positively correlated with pH, TOC, TS and TN but significantly negatively correlated with As, Fe and TP (p ≤ 0.05). Thioclava, which can promote the oxidation of pyrite, was significantly positively correlated with TS and TN (p ≤ 0.05). c_Thermodesulfovibrionia, which can reduce SO42−, and o_Rokubacteriales, which has a generalist metabolic strategy in oligotrophic environments, were both significantly positively correlated with Fe and As (p ≤ 0.05); meanwhile, they were mainly present in goafs. More than half of the bacteria were significantly positively correlated with Cr, Zn, Cd, Cu and Pb (p ≤ 0.05), suggesting these microorganisms may be involved in the transformation of heavy metals [17,42,43].
The changes in microbial community structure are influenced not only by the external environment, but also by the relationships among microorganisms. There are various types of biological interactions, such as synergistic, antagonistic and neutral [8]. The microbial co-occurrence networks of the sediment samples were constructed to visualize the microbe–microbe interactions, as illustrated in Figure 7c. The more connections that pass through a node, the larger the node. The thicker the connections, the larger the absolute value of the correlation coefficient between nodes. A total of 100 nodes and 1552 links were detected, and most of the links exhibited positive correlations (79%). The average degree and average weighted degree values of the sediment samples (31.040 and 21.846, p < 0.01) were higher than those of water samples (11.558 and 8.196, p < 0.05), which indicated that the microbial correlations in the sediment samples were much closer. All the nodes were grouped into four modules with different colors. The interactions in each module may be due to the shared niches or a high level of phylogenetic relatedness [44]. The bacterial genera of module 1 and module 2 had higher connectivity and accounted for 31% and 27% of the top 100 OTUs, respectively, and they may occupy the core of the microbial community in the sediments. Additionally, among the four modules, the average degree value of nodes in module 1 was highest (38.19), and the degree values of 58% of the nodes in module 1 were higher than 40. Several taxa were visualized in similarly dense connections, including organic degrading bacteria (i.e., Novosphingobium, f_Sphingomonadaceae, Thauera, Gordonia, Dietzia, Hyphomonas, etc.), denitrifying bacteria (Rhodobacter, f_Rhodobacteraceae, Hydrogenophaga, Paracoccus, Pseudomona, Gemmobacter, etc.), sulfur-oxidizing bacteria (Thioclava) and sulfate-reducing bacteria (SRB, f_Desulfuromonadaceae). Notably, Defluviimonas, Ilumatobacter and Lautropia were regarded as the biotic interaction hubs for module 2. Defluviimonas can contribute to denitrification performance and low-molecular-weight PAH degradation [45,46]. Ilumatobacter may participate in phosphorus removal and organics degradation [41,47]. Lautropia was discovered in an anammox biofilm system and might participate in nitrogen metabolism. In addition, the nitrate-reducing bacteria from the Lautropia genus were described as anaerobic hydrocarbon-degrading bacteria in soil contaminated with hydrocarbons [48]. Module 3 included several biotic interaction hubs (i.e., f_Hydrogenophilaceae, c_Subgroup_25, norank_Bacteria, c_OM190, o_S085, o_Rokubacteriales, o_RBG-13-54-9 and Nitrospira). Among them, f_Hydrogenophilaceae, c_OM190, o_RBG-13-54-9 and Nitrospira might play an important role in N cycles [49], and c_OM190, o_S085 and o_Rokubacteriales are slow-growing oligotrophs that prefer low-nutrient conditions [50]. In addition, the nodes corresponding to Cavicella, Knoellia, Limnobacter, Thiobacillus and Acidaminobacter were identified as the biotic interaction hubs for module 4. Among them, Cavicella, Knoellia and Limnobacter may degrade hydrocarbon; Thiobacillus can oxidize sulfide; and Cavicella and Acidaminobacter are also involved in N cycles. The bacterial genera associated with elemental cycles (S, N, C, P) not only exhibited interrelationships but also modified geochemical characteristics through their metabolic activities.
In summary, after coal mining activities, the occurrence environment of the coal seam was disturbed, producing new water–coal (rock) interactions and then affecting the geochemical characteristics and microbial communities. In addition, the geochemical characteristics affected the distribution of microbial communities to a certain extent. Microbial communities formed relatively independent response groups in response to environmental changes and at the same time drove changes in geochemical components.

3.4. Mechanism of Microbial Community Variation in Goafs with Increasing Goaf Closure Time

Coal mining activities were mainly carried out in the tunneling panel. During the coal mining, the water from the roof or floor water-filling aquifers flowed into the panel, resulting in new water–coal (rock) reactions with broken coal, gangue and a little rock debris. At the end of the mining activity, a closed wall was built to close the panel, and goafs were formed. After groundwater recovery and roadway drainage, the goafs were filled with water, leading to a long-term dynamic water–coal (rock) reaction, which was a key process for the formation and evolution of mine water quality. In addition, the microbial living environment changed, for example, gradually from the original aerobic environment to anoxic/anaerobic environment, so the original microbial community may have changed. Thus, in this study, representative sampling points were selected to research the characteristics of microbial community changes in the whole process of the coal panel, from mining to closing (Figure 8). According to the sampling conditions of the mining area, the goafs that closed in 2021, 2012, 2010 and 2009 were sampled in the coal mine. The 6305 panel (CR2) was a tunneling panel and the predecessor of a goaf. Here, we had to substitute space for time in the experimental design because the microbial community and geochemical characteristics of goafs closed several years ago were not detected. This may have affected the exactitude of the findings, but the pattern was consistent. Compared with the goafs, the environment of tunneling panel was more complex. The ACE index of the tunneling panel sample was much higher than that of the goaf samples, while the Simpson index was the opposite (Table 2). This suggested that both the species richness and the diversity of the tunneling panel were greater than those in the goaf. Compared to the 6305 panel, nutrients available to microorganisms were gradually consumed, and the environment of the goafs gradually became more uninhabitable. Some bacteria that could not adapt to this new environment gradually died, while some bacteria that could adapt continued to multiply, leading to the decrease in species richness and diversity.
During the mining phase, Thioclava, which can expedite the oxidation of pyrite, had a higher abundance in coal roadway (6305 panel), leading to the higher SO42− concentration of the 6305 panel (Figure S2). Meanwhile, Sphingobium, f_Flavobacteriaceae, Dietzia and f_Sphingomonadaceae, which can degrade organic pollutants, were mainly present in the 6305 panel. Additionally, the FAPROTAX prediction indicated that the functions of dark sulfide oxidation, dark oxidation of sulfur compounds, hydrocarbon degradation and aromatic compound degradation were higher in the 6305 panel and the goaf closed in 2021 (Figure 9). After mining was stopped, with the increase in goaf closure time, the pH, TN, TS and TOC in sediments gradually decreased, while As, Fe and TP gradually increased, and the sediment evolved into reddish brown iron oxide or hydroxide precipitation (Figure 8). It could be inferred that pyrite in the goafs had been oxidized by oxygen to produce H+ and iron oxide (hydroxide). At the same time, sulfur-oxidizing bacteria (e.g., Thioclava and Thiobacillus) may have greatly improved the reaction rate, similar to the widely reported occurrence of acid mine water [28,51,52].
It was noteworthy that compared with the 6305 panel, the bacteria with a sulfate reduction function (i.e., c_Thermodesulfovibrionia, f_Desulfuromonadaceae, p_Desulfobacterota, Desulfoprunum and Desulfomicrobium) in goafs had relatively higher proportions (Figure 8). The functions of sulfate respiration and respiration of sulfur compounds were highest in the goaf closed in 2021 (Figure 9). In addition, with the increase in goaf closure time, the concentration of the characteristic pollutant SO42− in goaf water samples also decreased by 42% within 12 years (Figure S2), suggesting that the closed goafs were able to purify themselves in the long term. With the increase in goaf closure time, the goaf environment gradually became airless, and O2 was gradually decreased, which was conducive to the growth and propagation of SRB. Moreover, the small-molecule organic matter released from coal could also provide a continuous carbon source for SRB [11,12,13,14], thereby improving the self-purification ability of goafs. In order to verify the conclusion of the field research above, indoor simulation batch experiments and three-dimensional box experiments on the evolution of mine water quality in a goaf for 365 days were conducted [53]. The results indicated that the concentration of SO42− increased first (0–30 d), while after 30 days, the concentration of SO42− gradually decreased; by 365 days, the SO42− concentration decreased by 15%. At this time, the microbial sequencing results also suggested that the abundance of SRB was higher, and the reduction of SO42− produced H2S with a rotten egg odor. Comparing the changes in SO42− concentration under different sealing conditions, it could be concluded that a longer sealing time is more beneficial for SRB to reduce SO42−.
In addition, there was a significant difference in function abundance between the newly formed goaf (G1) and the goafs with long closure times (G3, G2, G4). The abundance of functions related to S and N in the goafs with long closure times was much lower than that in the goaf closed in 2021. In addition, bacteria belonging to Acidobacteriota were not detected in the goaf closed in 2021 (i.e., o_Subgroup_2, c_Subgroup_21, c_Subgroup_25), possibly because the Acidobacterial community composition is influenced by higher TOC, TN and pH [54]. Moreover, many genera belonging to Thermodesulfobacteriota (i.e., Geothermobacter) were detected in larger numbers in the goaf closed in 2021, which may be related to the highest concentration of TS and SO42− (as substrate) in the sediments [55,56].
In conclusion, the microbial and geochemical results confirmed that the goafs were able to purify themselves. With the increase in goaf closure time, the concentration of SO42− in water samples decreased, while As and Fe in sediments increased. In practical engineering, according to the mechanism of SO42− generation and degradation, in situ nitrogen injection technology can be implemented to maintain an anaerobic environment in a goaf. This can reduce the oxidation of low-valent sulfides from the source and promote the SO42− reduction of SRB, thereby achieving pollution prevention and control of mine water. However, after a long reaction (in a goaf closed for about 10 years), the SO42− reduction rate may be limited by the lack of carbon sources, so environmentally friendly slow-release carbon sources (e.g., wood chips, leaves, corn cobs) can be added when a goaf is closed. Furthermore, coal mine pollution discharge generally only includes mine water and leaves sediment underground, which is called “drainage without sludge discharge”. Therefore, the pollutants can be transferred to the sediment by strengthening the relevant reactions including the heavy metal solidification and stabilization function of bacteria (e.g., heavy metal adsorption, precipitation and redox).

4. Conclusions

In order to classify the characteristics of the whole process of mine water (occurrence, collection and discharge) in a deep underground coal mine, the sediments in relevant areas were sampled and analyzed. In summary, the conclusions of this study are as follows:
(1)
The geochemical compounds of sediments differed obviously in the samples collected from different functional zones of the coal mine. The concentrations of TOC, TS and TN in the coal mine sediments were higher than those in river sediments, but the concentrations of TN, TS and TOC in goafs were lower. The concentrations of As, Fe and TP in the goafs was distinctly higher than those in other underground zones, and the pH values of goafs were the lowest, indicating that in the initial stage of mine water entering the goaf, the water level gradually rose, and the pyrite oxidation reaction became more complete.
(2)
The microbial community richness and diversity were ranked as follows: surface water > rock roadways > sumps > coal roadways ≥ goafs. The microbial community composition in the different functional zones were eminently different. Surface water sediments and underground coal mine samples belong to two branches in the sample clustering. Thioclava, which can accelerate the oxidation of pyrite, had a higher abundance in coal roadways. Bacteria related to SO42− reduction (i.e., c_Thermodesulfovibrionia, Desulfomicrobium and Geothermobacter) and nitrification (i.e., Nitrospira) accounted for higher proportions in goafs. Cyanobacteriota and Spirochaetota, which perform oxygen-producing photosynthesis, were only found in surface water sediments.
(3)
The relationships between microbial communities and geochemical characteristics were illustrated by CCA, Spearman correlation and co-occurrence network analysis, which demonstrated that microbial communities were sensitive and closely related to hydrochemical processes. The surface sediment samples clustered separately from the underground samples. The distribution of microbial communities in the underground mine was closely related not only to nutrient elements such as C, S, P and N, but also to redox-sensitive substances such as Fe and As. The correlation of most of the top 50 microbial species with Fe and As was opposite to that with pH, TOC, TS and TN, possibly due to the coupling relationship between iron transformation and the nutrient compound cycle. Co-occurrence network analysis, with 79% positive correlations, proved that the bacterial genera associated with elemental cycles (S, N, C, P) exhibit interrelationships and modify geochemical characteristics through their metabolic activities.
(4)
Goafs were the critical zones of mine water pollution prevention and control. Compared with a tunneling panel, the bacteria with a sulfate reduction function (SRB) had relatively higher proportions in goafs, and the functions of sulfate respiration and respiration of sulfur compounds were highest in the goaf closed in 2021. In addition, with the increase in goaf closure time, the concentration of the characteristic pollutant SO42− in water samples decreased, while As and Fe in sediments increased, suggesting that goafs were able to purify themselves. The small-molecule organic matter released from coal could also provide a continuous carbon source for SRB. Therefore, microbial treatment technologies such as artificial enhancement of SRB reducing sulfate could be applied to remediate groundwater pollution in coal mine areas.
In practical engineering, in situ nitrogen injection technology can be implemented to maintain an anaerobic environment in a goaf, and environmentally friendly slow-release carbon sources (e.g., wood chips, leaves, corn cobs) can be added when a goaf is closed. This can not only reduce the oxidation of low-valent sulfides from the source, but also promote the SO42− reduction of SRB. In addition, due to “drainage without sludge discharge”, the pollutants can be transferred to the sediment by strengthening the relevant reactions including the heavy metal solidification and stabilization function of bacteria.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/w16131836/s1: Figure S1: Stratigraphic profile of #3 coal in Xinjulong coal mine; Table S1: The information of sample points, including name, location and zone; PCR amplification and 16S rRNA gene sequencing; Table S2: Content of major elements in rock (coal) samples (ppm); Table S3 Mineral contents in rock (coal) samples (%); Table S4: Microbial community taxa percents of sediment samples in different zones on different levels; Figure S2: Change in SO42− concentration in goaf water samples; Table S5: The envfit environment factors of CCA; Table S6: Comparison table of genus raw classification output and abbreviated names.

Author Contributions

Conceptualization, Z.X., L.Z. and Y.G.; methodology, L.Z., Y.G. and Z.X.; data curation, L.Z. and Y.G.; software, Y.G.; project administration, Y.S., Z.X. and X.T.; formal analysis, Y.G. and X.T.; funding acquisition, Z.X.; investigation, L.Z. and Y.G.; supervision, Y.S.; resources, Z.X., X.T. and Y.S.; visualization, Y.G., W.C. and L.Z.; writing—original draft, L.Z.; writing—review and editing, L.Z., X.T., Z.X. and W.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 42172272 and No. U23B2091), and Engineering Research Center of Zero-carbon and Negative-carbon Technology in Depth of Mining Areas, Ministry of Education (China University of Mining and Technology), Ministry of Education (No. 2023-4).

Data Availability Statement

The raw reads of 16S rRNA gene sequencing were submitted to the NCBI Sequence Read Archive database (accession No. PRJNA901229).

Acknowledgments

The authors would like to acknowledge Weikui Lv and Tianjian Jia (Shandong Energy Xinwen Group Xinjulong Co., Ltd.), for their assistance in field sampling, and Liang Chen (Tianjin University) for his language reviewing of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a) Location of Xinjulong coal mine in China; (b) location map of sampling points in coal mine; (c) location map of sampling points on surface. The colors of the sampling points represent five zones. The yellow dotted line represents the mine drainage direction, and the blue arrow represents the Zhushui River direction.
Figure 1. (a) Location of Xinjulong coal mine in China; (b) location map of sampling points in coal mine; (c) location map of sampling points on surface. The colors of the sampling points represent five zones. The yellow dotted line represents the mine drainage direction, and the blue arrow represents the Zhushui River direction.
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Figure 2. Half box plots of sediment geochemical compounds in five zones. (a) pH; (b) TN; (c) TS; (d) TOC; (e) TP; (f) Fe; (g) As; (h) Mn; (i) Zn; (j) Cr; (k) Pb; (l) Cu; (m) Hg; (n) Cd.
Figure 2. Half box plots of sediment geochemical compounds in five zones. (a) pH; (b) TN; (c) TS; (d) TOC; (e) TP; (f) Fe; (g) As; (h) Mn; (i) Zn; (j) Cr; (k) Pb; (l) Cu; (m) Hg; (n) Cd.
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Figure 3. (a) Venn diagram (the numbers on the overlaps represent the number of shared species, and the bar chart shows the total number of species on the genus level in each zone); (b) PCA on the genus level. The closer two sample points are, the more similar their species composition is.
Figure 3. (a) Venn diagram (the numbers on the overlaps represent the number of shared species, and the bar chart shows the total number of species on the genus level in each zone); (b) PCA on the genus level. The closer two sample points are, the more similar their species composition is.
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Figure 4. (a) Relative abundance of the dominant lineages on the phylum level for different zones’ sediment samples; (b) Kruskal–Wallis H test of the distribution of the top 15 most abundant microbial phyla in different zones’ sediment samples (* 0.01 < p ≤ 0.05). Here, the new accepted names are used for all the phyla [38].
Figure 4. (a) Relative abundance of the dominant lineages on the phylum level for different zones’ sediment samples; (b) Kruskal–Wallis H test of the distribution of the top 15 most abundant microbial phyla in different zones’ sediment samples (* 0.01 < p ≤ 0.05). Here, the new accepted names are used for all the phyla [38].
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Figure 5. (a) Heatmap of the top 50 most abundant genera in sediment samples of the five zones; (b) Kruskal–Wallis H test of the distribution of the top 15 most abundant microbial genera in different zones’ sediment samples (* 0.01 < p ≤ 0.05).
Figure 5. (a) Heatmap of the top 50 most abundant genera in sediment samples of the five zones; (b) Kruskal–Wallis H test of the distribution of the top 15 most abundant microbial genera in different zones’ sediment samples (* 0.01 < p ≤ 0.05).
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Figure 6. Functional prediction based on FAPROTAX of surface water sediment samples.
Figure 6. Functional prediction based on FAPROTAX of surface water sediment samples.
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Figure 7. (a) CCA result of the relations between geochemical variables and microbial communities in sediment samples. (b) Heatmap of Spearman rank correlation coefficients between the geochemical variables and the top 50 microbial phylotypes on the genus level (* 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001). (c) Co-occurrence network of bacterial genera, based on correlation analysis (the screened top 100 most abundant OTUs) (p < 0.01). The comparison of genus raw classification output and abbreviated names is shown in Table S6.
Figure 7. (a) CCA result of the relations between geochemical variables and microbial communities in sediment samples. (b) Heatmap of Spearman rank correlation coefficients between the geochemical variables and the top 50 microbial phylotypes on the genus level (* 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001). (c) Co-occurrence network of bacterial genera, based on correlation analysis (the screened top 100 most abundant OTUs) (p < 0.01). The comparison of genus raw classification output and abbreviated names is shown in Table S6.
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Figure 8. Change characteristics of microbial community (a) and geochemical composition (b) in the 6305 panel and goafs with different closure times.
Figure 8. Change characteristics of microbial community (a) and geochemical composition (b) in the 6305 panel and goafs with different closure times.
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Figure 9. FAPROTAX function prediction result of 6305 panel and goafs with different closure times.
Figure 9. FAPROTAX function prediction result of 6305 panel and goafs with different closure times.
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Table 1. Sediment samples’ geochemical property determination methods and reference standards.
Table 1. Sediment samples’ geochemical property determination methods and reference standards.
ParameterMethodReferenceLimit of
Quantification
TSIgnition iodimetry and EDTA interconnect titration methodLY/T 1255-1999—
TNKjeldahl distillation–volumetric methodDZ/T 0279.29-20160.02 g/kg
TPAlkali fusion Mo-Sb Anti spectrophotometric methodHJ 632-201110 mg/kg
TOCDry combustion methodRock and Mineral Analysis (2011) 84.2.37—
pHPotentiometryGB/T 50123-20190.01
AsAtomic fluorescence spectrometryGB/T 22105.2-20080.01 mg/kg
HgAtomic fluorescence spectrometryGB/T 22105.1-20080.001 mg/kg
Fe, Mn, Gr,
Pb, Cu, Zn		Inductively coupled plasma atomic emission spectrometryDZ/T 0279.2-20166.3, 0.02, 0.2, 0.7, 0.5 and 0.03 mg/kg
CdAtomic absorption spectrophotometryRock and Mineral Analysis (2011) 84.2.6—
Note: TS: total sulfur; TN: total nitrogen; TP: total phosphorus; TOC: total organic carbon. The element content presented in this study was standardized to dry weight.
Table 2. Alpha diversity of microbial community in sediments.
Table 2. Alpha diversity of microbial community in sediments.
Sampling
Zone		Sample NameSequenceOTUsACEChao1ShannonSimpsonCoverage
(%)
Surface waterSW167,14724413111.663118.895.980.0297.57%
SW273,06524003155.003098.215.920.0297.50%
SW368,16124133013.802980.906.050.0197.72%
Coal roadwaysCR155,401210332.24277.001.610.4899.78%
CR260,79216522251.872272.905.700.0198.21%
Rock roadwaysRR150,151633811.11795.903.760.0799.40%
RR259,35516912265.652222.675.390.0298.18%
RR356,09920152821.822730.455.580.0397.70%
RR461,85822123063.542959.395.760.0297.48%
GoafsG138,821395409.32413.604.250.0499.90%
G254,471349442.19455.313.390.0999.69%
G353,805355459.87472.863.220.0999.67%
G458,729455562.46586.103.680.0799.62%
Water sumpsWS155,56320102584.422541.665.940.0198.02%
WS238,178837875.14877.794.560.0699.71%
WS356,68021212949.832836.155.650.0397.61%
WS460,1687851343.551148.833.020.2799.02%
WS541,930765873.83875.613.310.1699.47%
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Xu, Z.; Zhang, L.; Gao, Y.; Tan, X.; Sun, Y.; Chen, W. Effects of Coal Mining Activities on the Changes in Microbial Community and Geochemical Characteristics in Different Functional Zones of a Deep Underground Coal Mine. Water 2024, 16, 1836. https://doi.org/10.3390/w16131836

AMA Style

Xu Z, Zhang L, Gao Y, Tan X, Sun Y, Chen W. Effects of Coal Mining Activities on the Changes in Microbial Community and Geochemical Characteristics in Different Functional Zones of a Deep Underground Coal Mine. Water. 2024; 16(13):1836. https://doi.org/10.3390/w16131836

Chicago/Turabian Style

Xu, Zhimin, Li Zhang, Yating Gao, Xianfeng Tan, Yajun Sun, and Weixiao Chen. 2024. "Effects of Coal Mining Activities on the Changes in Microbial Community and Geochemical Characteristics in Different Functional Zones of a Deep Underground Coal Mine" Water 16, no. 13: 1836. https://doi.org/10.3390/w16131836

APA Style

Xu, Z., Zhang, L., Gao, Y., Tan, X., Sun, Y., & Chen, W. (2024). Effects of Coal Mining Activities on the Changes in Microbial Community and Geochemical Characteristics in Different Functional Zones of a Deep Underground Coal Mine. Water, 16(13), 1836. https://doi.org/10.3390/w16131836

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