Spatiotemporal distribution of different forms of sulfur in acid mine drainage and their relationships with environmental factors

doi:10.21203/rs.3.rs-3967490/v1

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Spatiotemporal distribution of different forms of sulfur in acid mine drainage and their relationships with environmental factors

https://doi.org/10.21203/rs.3.rs-3967490/v1

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The production of acid mine drainage (AMD) involves oxidation of FeS₂ to SO₄²⁻, during which a variety of intermediate sulfur forms (S₂O₃²⁻, S⁰, SnO₆²⁻, SO₃²⁻) are generated. This study aimed to characterize the spatiotemporal distributions of different forms of these intermediates and their relationships to environmental factors, focusing on an abandoned pyrite mine area. Samples were collected from different water stages and the physicochemical factors were determined on site. High performance liquid chromatography, ion chromatography, and Illumina high-throughput sequencing were used to determine the distributions of iron and sulfur forms and the microbial community structure at each site. Pearson and Spearman correlation were used to analyze the relationships between the distributions of different forms of sulfur and environmental factors during the formation and migration of AMD. The results suggested that SO₄²⁻ mainly originated from gypsum dissolution and oxidation of the coal mine and FeS₂. The dry season was associated with lower water pH and temperature and higher DO and ORP. The maximum correlation coefficient between TFe and SO₄²⁻ decay was 0.9308, which could be attributed to the formation of sulfate secondary iron-containing minerals. SO₄²⁻ pollution decreased with increasing migration distance of AMD and showed seasonal variation closely related to precipitation and groundwater flow. The abundance and diversity of microbial community decreased with the production of AMD, mainly acidophilus and sulfur/iron-oxidizing bacteria. Ferrovum occupied an absolute dominant position in weakly acidic samples, and Acidibacter and Sphingomonas were not polluted. Neutral samples include Lachnospiraceae NK4A136 group, Ralstonia, Sinomonas, etc. pH and SO₄²⁻ showed negative correlations with DO, temperature, and ORP, whereas the dominant strain Acidithiobacillus was positively correlated with SO₄²⁻. Increases in water temperature and ORP promoted the transformation of sulfur. The regulation of sulfur conversion to acid is key for developing strategies for preventing and reversing AMD pollution.

Acid mine drainage

FeS2

Migration and Transformation of Surfur

Environmental Factors

Acid mine drainage (AMD) is an environmental pollutant that is typically produced in large volumes from diffuse sources and persists in the environment. AMD can lead to water body acidification and toxic metal pollution, thereby posing a risk to environmental and human health (Wang et al., 2021b; Ding et al., 2019). Consequently, AMD has received widespread attention globally. AMD is mainly produced through the oxidation of metal sulfide minerals, particularly pyrite (FeS₂). The main pollutants of AMD include metal ions and SO₄²⁻ (Xin et al., 2021; Johnson and Hallberg, 2005). Most previous studies on AMD have focused on the environmental effects of the migration and transformation of toxic metals (Luo et al., 2022; Ying et al., 2022). In contrast, relatively few studies have examined the migration and transformation of sulfur forms in AMD.

AMD pollution of water resources is more difficult to rehabilitate than that of the atmosphere (Zak et al., 2021) due to the great impact on the natural sulfur cycle (Nordstrom et al., 2015). AMD pollution leads to high concentrations of SO₄²⁻ and other sulfides in lakes, rivers, and other surface waters, which pose risks to aquatic ecosystems (Jiao et al., 2023). Sulfate accumulation in the surrounding soil also leads to environmental challengers, such as soil hardening and acidification, which can in turn negatively affect vegetation growth and crops (Lear et al., 2009; Yuan et al., 2022) as well as the health of the environment and humans (Ferreira et al., 2021; Gao et al., 2019; Neamtiu et al., 2017).

Pyrite oxidation follows the thiosulfate and sulfur cycle pathways (Tu et al., 2017). S₂O₃² produced by FeS₂ oxidation is unstable under acidic conditions, thereby leading to the conversion of SO₃²⁻ to S⁰, which in turn is oxidized to SO₃²⁻, S₄O₆²⁻, and SO₄²⁻ (Schippers et al., 1996; Xu and Schoonen., 1995). Tu et al. (2017) studied the intermediate sulfur species of pyrite oxidation in the presence and absence of microorganisms; Huang (2011) analyzed the form of sulfur in hot spring water; Li et al. (2021) explored the distributions of sulfur forms in river water in a mining area. The sulfur transformation process is affected by environmental factors and the composition of AMD. Further study is needed on the relationships between different sulfur forms and environmental factors.

The aim of the present study was to characterize the spatiotemporal distributions of different forms of sulfur in AMD and their relationships to environmental factors, focusing on a waste pyrite mining area in Luzhou City. The objectives of the present study were to characterize: (1) the spatiotemporal distributions of different sulfur forms in AMD; (2) the migration and transformation of sulfur forms and the relationships between them and environmental factors; (3) the main factors regulating AMD production. The results of the present study can contribute to management and rehabilitation of AMD in the environment.

2.1 Study area

The study area of the present study is in Luobu Town, Xuyong County, Sichuan Province (105° 27′ 04′′–105° 29′ 53′′ E, 28° 03′ 16′′–28° 07′ 16′′ N). The study area falls into a subtropical humid monsoon climate zone with a temperature ranging from − 8.5 to 41.9°C. Rainfall is seasonal, with 70% of annual rainfall in summer and autumn. Surface water is mainly supplied by seasonal streams, gullies, and karst caves.

The terrain of the study area decreases from south to north and is highly variable. Strata of the Triassic Feixianguan Formation (T₁f) form a band on one side of the mountain; the Permian Longtan Formation (P₂l) stratum is in an inclined trough valley; carbonate formations, such as the Permian Maokou and the Qixia formations (P₁m + q), manifest as karst landforms. The study area has well-developed karst landforms, including caves, funnels, hills, dissolution depressions, and troughs. The underground river pipeline system of the study area is also complex. Underground tunnel mining is the main form of pyrite mining in the study area. The mining process has disrupted the original strata and water flow channels and has led to exposure of nodular siderite and pyrite to the atmosphere. Groundwater in the aquifer tends to converge in abandoned mines, with this water reacting with pyrite and leading to leaching of iron and sulfur.

2.2 Sample collection and preservation

The present study collected 63 groundwater samples from 7 sampling points: D1 as a control point, D2, D3, and D5 as points of FeS₂ mine water inflow, D4 to represent coal mine water, and D6 and D7 as AMD inflow points. Samples were collected in the wet season, normal season, and dry season (Fig. 1) pH, dissolved oxygen (DO), oxidation-reduction potential (ORP), and total dissolved solids (TDS) were measured on site using a portable water quality meter. Sample collection, transportation, storage, and analysis were strictly in accordance with Chinese groundwater quality standards (GB/T14848-2017). Samples were taken at a depth of 0.2 m. Water samples were filtered through a 0.22-um filter membrane, after which samples were decanted in bottles to overflowing to prevent air bubbles, and stored at a temperature of 4°C. Samples were transported back to the laboratory and tested within 48 h. Sample fixation on site was needed to stabilize unstable forms of sulfur. SO₃²⁻, S₂O₃²⁻, and S₄O₆²⁻ were added to phosphate buffer and the pH of the solution was adjusted to 9.0 using sodium hydroxide. S²⁻ was added to zinc acetate and sodium hydroxide was added to the solution to a pH that would facilitate precipitation. Microbial samples were collected by centrifuge tube after high temperature sterilization at 120°C and stored at 4°C. Microbial samples were transported back to the laboratory and stored at − 80°C. Sample microbial characterization was completed within 48 h.

2.3 Testing samples and analysis

The present study measured pH, DO, water temperature (T), and ORP of water samples. Sulfur forms of the water samples changed in response to changes in the physical and chemical properties of the water environment. Metal cations in the samples were determined by flame atomic spectrophotometry and ultraviolet spectrophotometry. Anions were determined by ion chromatography. The intermediate forms of sulfur was determined by high performance liquid chromatography (HPLC). S²⁻ was determined by p-aminodimethylaniline spectrophotometry. Methods used to characterize the microbial community of the samples included sample DNA purification, polymerase chain reaction (PCR) amplification, sample quantification, library construction, and sequencing. Sequencing of samples was completed by Luoning Biological Company. The present study used analytically pure chemical reagents to maintain quality assurance and quality control (QA/QC). The standard deviation of the test results was less than 5% and the sample recovery rate was between 90–110%.

Microbial Alpha and Beta diversity were analyzed using R. Heat map analysis and the corr.test () function in the R psych package were used to conduct correlation analysis. PHREEQC software was used to analyze the SI-Gypsum saturation index and to simulate the distributions of Fe species and ion activity under different pH conditions. Grapher software was used to construct Piper three-line diagrams.

3.1 Hydro-chemical characteristics

The Piper trilinear diagram allows a general reflection of the chemical characteristics and hydro-chemical types of sample water and is widely used in groundwater hydro-chemical analysis and evaluation (Gao et al., 2021). The diamond component of the Piper diagram illustrates the hydro-chemical types of a groundwater sample for the dry, normal, and wet seasons (Fig. 2). The results showed that groundwater of the background point (D1) was of the HCO₃-Ca-Mg type. The groundwater type of the remaining sites was SO₄·Cl-Ca·Mg. All groundwater samples were concentrated in the Piper diagram F area, indicating a sulfate type and a bicarbonate background water type. The diagram suggested that the high concentration of SO₄²⁻ in AMD in the study area may be mainly derived from FeS₂.

Ions in water reflect the weathering process affecting the chemistry of water and material transport of minerals in a basin. Therefore, water ions are a direct reflection of hydro-geochemical processes regulating water quality (Bajjali et al., 1997). The TDS content of the background point was between 293.6–324.1 mg/L; that of the FeS₂ gushing points were 4,924.9–11,476.6 mg/L. SO₄²⁻ and TDS in coal mine gushing water were much lower than those of FeS₂ acid gushing water at 730.9–930.9 mg/L and 1,877.4–2,038.4 mg/L, respectively. FeS₂ acid mine water generally showed high TDS and SO₄²⁻ and low pH. Therefore, TDS and SO₄²⁻ were negatively correlated with pH across all the seasons (Fig. 3a, Fig. 3b). TDS in the dry season exceeded that in the normal season and the wet season. TDS and SO₄²⁻ were highly correlated over all the seasons, indicating that SO₄²⁻was the main salt contributing to TDS in the study (Fig. 3c).

As shown in Fig. 4a, samples of gushing mine water and AMD were mainly distributed above the 1:1 line, indicating various sources of Ca²⁺ in the samples. Samples of the background point were distributed on the 1:1 line (Fig. 4b), indicating that these samples were mainly impacted by gypsum dissolution (Mao et al., 2023). The results indicated that oxidation of the coal mine was the source of SO₄²⁻ in coal mine water point D4, whereas oxidation of FeS₂ was the main contributor to the hydrochemistry of the remaining points. The SI values of all points were less than 0.5 (Fig. 4c), whereas that of the background point was less than − 0.5. This result could be attributed to the good quality of the water sample due to high elevation of the background point. The SI values of the remaining points ranged from − 0.5 to 0.5, with all falling into a water-rock equilibrium state without excess SO₄²⁻ precipitation. The results of the correlation between Ca²⁺ and SO₄²⁻ and the characterization of the geological background indicated that the gypsum dissolution dominated water chemistry and the background point, whereas oxidation leaching of FeS₂s contributed to the high content of SO₄²⁻ of the remaining points.

3.2 Spatiotemporal distributions of physicochemical factors

AMD pollution resulted in a difference in the pH of groundwater (Fig. 5a). The pH of the background point ranged between 7.28 and 7.4, indicating overall neutral to weakly alkaline water; the pH of FeS₂ water inflow points ranged between 2.09 and 3.10, indicating highly acidic water; that of coal mine water inflow (D4) was between 6.84 and 7.00. The high concentration of HCO₃⁻ (> 470 mg/L) effectively neutralized acidity of samples resulting from AMD. The buffering effect was achieved by HCO₃⁻ counteracting SO₄²⁻, resulting in persistence of neutral conditions (Majzlan et al., 2018). Outflow of acidic water resulted in erosion of D6 and D7 to their hydraulically related periphery and a pH of 3.15–3.5 and 3.55–4.17, respectively. There was clear seasonal variation in pH, with the lowest and highest pH in the dry season and wet season, respectively. Strong buffering was provided by the high concentration of Fe³⁺ in the water body, where hydro-lyzation resulted in the release of a large quantity of H⁺. This effectively offset the surface runoff dilution effects in the wet season, resulting in consistently lower pH of the water body all the year round.

DO in water samples was closely related to the distributions of sulfur and iron forms at different sites (Fig. 5b). There was minor variation in DO at the background point; those at D2 and D3 were relatively low due to the large quantity of oxygen consumed during the oxidation of FeS₂ to produce AMD; DO was higher at D4 and D5 due to effective aeration and oxygenation. Migration of AMD resulted in its continuous contact with the atmosphere and the subsequent increases in DO at D6 and D7 from 4.37 to 5.16 mg/L and from 4.42 to 5.39 mg/L, respectively. DO in the dry season exceeded that in the wet season.

The water temperature in the wet season exceeded those of the normal and dry seasons. Water temperature differed among the different sampling points (Fig. 5d) due to the minimal effect of outside meteorological conditions on Groundwater. The integration of ORP with other water quality indicators can reflect the ecological environment. ORP was different among the different sampling points with no clear seasonal variation (Fig. 5c). The ORP values at D2, D3, and D5 were between 441.3 and 549.6 mV, with that at D5 the highest. This site was also shown to be highly oxidizing. Gushing out of mine water contributed to a downward trend in ORP along the direction of groundwater flow. D4 remained in a flooded state due to long-term blocking off of the mine, in which ORP ranging from − 120.9 to − 99.3 mV, indicating the high reducibility of the environment at this point.

3.3 Spatiotemporal distributions of sulfur species

3.3.1 Spatiotemporal distribution of SO₄²⁻

The background point showed a low concentration of SO₄²⁻ of 33.94 to 55.15 mg/L. The mine FeS₂ drainage point had a relatively high FeS₂ concentration, reaching 10,873.93 mg/L in the dry season. Coal mine water had a SO₄²⁻ exceeding 800 mg L all the year round. There was an attenuation in SO₄²⁻ along the direction of groundwater flow. The migration of SO₄²⁻ showed non-conservative behavior closely related to the formation and adsorption of secondary high-iron minerals. In addition, suspended solids and sediment surfaces in AMD were positively charged, thereby facilitating further adsorption of SO₄²⁻ (Chen et al., 2015). The dry-season SO₄²⁻ concentration exceeded that in the normal and wet seasons. The results of T-test analysis showed that the decreases in dry and wet season SO₄²⁻ concentrations were closely related to seasonal precipitation and groundwater flow. These processes resulted in the formation of various complexes with SO₄²⁻ in aqueous solution to form Fe-SO₄. The simulattioned by the PHREEQC software (Fig. 6b) showed that the rank of different sulfate forms in the study area according to concentration was SO₄²⁻ > FeSO₄⁰ > HSO₄⁻ > FeHSO₄⁻ > FeSO₄⁺ > Fe(SO₄)₂⁻ > FeHSO₄²⁺. The proportion of SO₄²⁻ gradually increased with increasing pH. SO₄²⁻ in the study area mainly existed in the form of SO₄²⁻.

3.3.2 Spatiotemporal distribution of metastable sulfur

The main sulfur form in AMD was + 6 valence SO₄²⁻, whereas the accumulations of metastable sulfur forms (SO₃²⁻, S₂O₃²⁻, S₄O₆²⁻, and S²⁻) were minor (Fig. 7). SO₃²⁻, S₂O₃²⁻, and S₄O₆²⁻ of the water gushing point (D2, D3, D5) were incompletely oxidized to SO₄²⁻, with a small quantity of accumulation. The rank of the different sampling points in terms of quantities of sulfur forms was D3 > D5 > D4 > D2 (Fig. 7a, 7b, 7c). S²⁻ can accumulate in trace quantities during the redox of sulfur. D4 showed the highest accumulation of S²⁻ (Fig. 7d). This result can be attributed to the redox potential of the water environment. Environments with strong reducibility tend to show a greater accumulation of S²⁻ (Sun et al., 2014). Migration of AMD results in rapid oxidation of the metastable sulfur. Some metastable sulfur forms showed clear seasonal variation. Wet season SO₃²⁻ exceeded those in the normal and dry seasons. S²⁻ was also highest in the dry season. The main form of metastable sulfur in AMD was SO₃²⁻, followed by S₄O₆²⁻ and S₂O₃²⁻, whereas the content of S²⁻was very low. These results can be attributed to the conversion of S₄O₆²⁻ and S₂O₃²⁻ to + 4 valence SO₃²⁻ before conversion to SO₄²⁻. Consequently, the accumulation of SO₃²⁻ exceeded those of other forms of metastable sulfur. Since S²⁻ is typically present in a reducing environment, the strong oxidizing environment of AMD restricts the accumulation of S²⁻ to a low level.

3.4 Distributions of iron forms

Fe valence states of AMD were + 2 and + 3 and there were low contents of Fe³⁺ and Fe²⁺ at the background point D1 (Fig. 8a). The average total iron concentrations of D2, D3, and D5 were 1,780.35 mg/L, 977.98 mg/L, and 902.51 mg/L, respectively. Fe of coal mine water (D4) was low (3.22 mg/L) and mainly in the form of Fe³⁺. The evolution of Fe was regulated by changing redox conditions during water flow. There was a significant positive correlation between total iron (TFe) and SO₄²⁻ (Fig. 8b; R² = 0.9308), which could be attributed to input from tributaries and the generation of many secondary iron minerals (Bao et al., 2018). Seasonal variation in of TFe was consistent with that of SO₄²⁻, with stepwise downward trends across all three seasons, indicating that Fe was also affected by precipitation and groundwater flow.

Fe in AMD can form various complexes with SO₄²⁻, and pH significantly changes the morphology and activity of each Fe-S complex. PHREEQC simulation of the distribution of Fe (II) speciation (Fig. 8c) showed that large quantities of Fe²⁺ and SO₄²⁻ formed an FeSO₄⁰ complex in acidic water (pH < 2.5) due to the high concentration of SO₄²⁻, with the content of this complex slightly exceeding that of free Fe²⁺. The concentration of SO₄²⁻ gradually decreased with increasing pH, and the proportion of free Fe²⁺ in the water gradually increased. Under an acidic environment (2.5 < pH < 5.1), the rank of Fe (II) forms according to concentration was: Fe²⁺ > FeSO₄⁰ > FeHSO₄⁺ > FeOH⁺ >> Fe(OH)₂⁰ > > Fe(OH)₃⁻. At a pH > 5.1, the ionic activity of FeOH⁺ exceeded that of FeHSO₄⁺. Complexation of Fe (III) exceeded that of Fe (II), with the former forming various complexes (Fig. 8d). Fe (III) in an acidic environment (pH < 3.9) mainly existed in the FeSO₄⁺, Fe(SO₄)₂⁻, and FeHSO₄²⁺ forms. Under an acidic environment (3.9 < pH < 4.9), Fe(OH)₂⁺ and Fe(OH)²⁺ in addition to FeSO₄⁺ were the main complex states. With increasing pH above 4.9, there was a rapid increase in the ion activity of Fe(OH)₃, becoming the most important complex state in neutral water, whereas the contents of other Fe (III) forms gradually decreased.

The redox potential and pH of AMD are comprehensive reflections of changes in Fe and S in the water environment. Figure 9 shows the boundary conditions in Fe and S morphological stability (Sun et al., 2014). Redox conditions regulate changes in S and Fe species. Iron mainly exists in the form of Fe²⁺ at a pH < 3. Fe³⁺ dominated the iron forms in AMD pollution sites due to the influences of pH, redox potential, and DO. Fe(OH)₃ was the dominant Fe form at the background point. The distribution of sulfur forms indicated a negative oxidation-reduction potential of D4 coal mine water, with the sample distributed on the boundary between H₂S(aq) and SO₄²⁻. This point accumulated S²⁻, whereas oxidation affected the remaining points, leading to the dominance of positive hexavalent SO₄²⁻.

3.5 Spatial characteristics of microbial community structure

3.5.1 Microbial community diversity

The α-diversity index of samples reflects the abundance, uniformity, and diversity of microbial communities. Table 1 shows the operational taxonomic units (OTUs) and Chao 1, Shannon, and Simpson indices of each sample. The present study achieved a > 98% coverage of all 7 samples, indicating that the present study comprehensively characterized the microbial community of environmental samples. Abundance of the microbial community is reflected by the OTUs and Chao 1 index. The background point (D1) and point D2 showed the highest and lowest numbers of OTUs at 509 and 125, respectively. The neutral D1 and D4 samples showed the highest Chao 1 index values of 510.5000 and 472.4839, respectively, whereas D2 under the lowest pH had the lowest Chao 1 index of 158.4615. The Chao 1 index values of the remaining samples were low, at an average of 348.1074. The Simpson and Shannon index values of the samples reflected the diversity of the microbial community and were highest for neutral samples at D1 and D4 at 5.15 and 0.98, respectively; those of samples affected by lower pH (D2, D3, D5, D6, D7) were at an average of 2.47 and 0.60, respectively. These results indicated that microbial community abundance and diversity decreased with decreasing pH. The present study conducted Principal Co-ordinates Analysis (PCoA) to further analyze differences between samples and the possible driving factors (Fig. 10). Principal coordinates 1 and 2 (PCo1 and PCo2) explained the most observed variation in the seven samples of 47% and 25%, respectively. In the PCoA plot, D2 (pH < 2.2) was in the second quadrant; the remaining mine water gushing points and AMD pollution points (2.2 < pH < 4) were gathered at the junction of the first and fourth quadrants; points D1 and D4 (neutral pH) were gathered in the third quadrant. These results showed significant differences in microbial community among samples under different pH conditions.

Table 1

The α diversity index of the microbial community in the study area
Sample	OTUs	Chao 1	Shannon	Simpson	Coverage
D1	509	510.5000	5.016988	0.972192	0.998961
D2	125	158.4615	2.857449	0.569117	0.995548
D3	300	343.9091	2.313590	0.584752	0.989611
D4	462	472.4839	5.284899	0.988506	0.996141
D5	311	363.7143	2.010801	0.491940	0.987830
D6	286	332.1220	2.608647	0.652277	0.990798
D7	276	352.6842	2.538871	0.697020	0.986049

3.5.2 Microbial community composition

(1) Phylum-level analysis of microbial community composition

There were significant phylum-level differences in microbial community structure of samples under different levels of AMD pollution (Fig. 11). Proteobacteria has been shown to dominate environments affected by AMD (Bao et al., 2017). The results of the present study observed a similar trend, with this phylum dominant in the seven samples in the study area, and particularly in samples under an acidic environment (D2, D3, D5, D6, D7), reaching a proportion of 83%. The average proportion of Proteobacteria in neutral water samples (D1, D4) was 36.01%. Firmicutes, Bacteroidetes, and Actinobacteria were also widely distributed in the study area, particularly in coal mine water gushing water (D4), reaching 23.57%, 17.88%, and 8.92%, respectively. Acidobacteria was relatively abundant in D1. The highest abundance of Euryarchaeota was in D2 (8.67%) characterized by the highest concentrations of SO₄²⁻ and TFe and lowest pH, indicating that this phylum is found in areas of serious AMD pollution. AMD pollution and mineral composition appeared to significantly change the composition of microbial species.

(2) Genus-level analysis of microbial community composition

Dominant bacteria at the genus level at point D1 included Acidibacter, Sphingomonas, MND1, and RB41; at D4 they included Lachnospiraceae NK4A136 group, Ralstonia, Enterobacter, Pelomonas, and Sinomonas. Buffering by bicarbonate resulted in a close to neutral pH at D4 and this site had low metal ions concentrations. Consequently, microbial diversity at D4 was relatively high, whereas the proportion of sulfur-oxidizing microorganisms was relatively small. The relative abundance of Ferrovum among sampling points exceeded 60%, except at D2 in which it was 9.36%. Ferrovum is an iron-oxidizing bacterium that can fix carbon through the Calvin-Benson-Bassham cycle and is widely distributed in iron-containing acidic water (Hua et al., 2015). Acidithiobacillus was the dominant genus in D2 at 27.75%. Acidithiobacillus has been shown to tolerate acidic (pH < 2.5) and warm (30°C) water. The results showed that the distribution of microbial community structure was closely related to water pH and temperature. At the same time, the concentrations of SO₄²⁻and Fe in D2 exceeded those at other sites. This result indicated that Acidithiobacillus may have stronger acid production capacity. Acidithiobacillus ferrooxidans (At.f) was the only member of Acidithiobacillus at D2. This species can oxidize Fe²⁺ to Fe³⁺. The genus Metallibacterium was the third-most abundant in the study area and dominated D2 (21.21%). Microbes of genus Metallibacterium are tolerant to a wide range of pH environments, and similar to Acidithiobacillus, can obtain energy through oxidation-reduction of ferrous and sulfur compounds (Bartsch et al., 2017). In addition, D2 showed relatively high proportions of Acidibacter and Ferrithrix. Both genera represent thermophilic bacteria which have a tolerance for high temperatures (± 30°C).

These results suggest that AMD pollution significantly changes the diversity and community structure of microorganisms in the water environment, with serious AMD pollution significantly reducing microbial diversity and resulting in the dominance of acidophilic and sulfur/iron oxidizing bacteria. Acidophilic and sulfur/iron oxidizing bacteria further lead to water acidification. Oxidation facilitated by At.f and Metallibacterium results in the rapid acceleration of the cyclic conversion rate of sulfur in AMD, with metastable sulfur rapidly converted to SO₄²⁻. Abundance of Acidithiobacillus and Metallibacterium in D2 far exceeded that in D3, D4, and D5.Therefore, metastable sulfur in mine water (D2) was lower than that in other mine water samples.

3.6 Analysis of relationships between sulfur and Fe forms

3.6.1 Physical and chemical factors regulating sulfur and Fe forms

The present study examined the correlations between sulfur and iron forms and the regulating impacts of environmental factors. The proportions of different sulfur and iron forms were used to determine correlations. Data for environmental factors were assumed to follow normal distributions. Pearson correlation analysis was used to calculate correlation coefficients (Fig. 13). The results showed that pH was negatively and positively correlated with total sulfur (TS) and S²⁻, respectively; water temperature and ORP were positively correlated with TS and TFe. SO₄²⁻ accounted for > 99% of TS at each point, indicating that SO₄²⁻ content in AMD was proportional to acidity. Increases in water temperature and ORP promoted the conversion of sulfur, thereby generating SO₄²⁻. The results showed that DO significantly affected the valence states of iron and sulfur. DO showed negative correlations with Fe²⁺ and metastable sulfur, indicating that DO promoted the conversion of Fe²⁺ and metastable sulfur to Fe³⁺ and SO₄²⁻.

3.6.2 Microbial species abundance

The present study examined correlations between microbial species abundance and environmental factors. Spearman analysis was used to identify the 10 most abundant species at the genus level for analyzing correlations with sulfur forms (Fig. 14). The abundance of At.f showed significant positive correlations with SO₄²⁻ and TFe and a significant negative correlation with pH. At.f also showed positive correlations with metastable sulfur forms S²⁻, SO₃²⁻, S₂O₃²⁻, and S₄O₆²⁻. This result indicated that At.f promotes the transformation of iron and sulfur. At.f dominated the microbial community at D2 and SO₄²⁻ and iron at this sampling point far exceeded those at other sites. Ferrovum is an iron-oxidizing bacterium and dominated the microbial community at the remaining acidic water sites. Ferrovum mainly oxidizes Fe²⁺ rather than sulfur. Therefore, the dominance of Ferrovum was dependent on the presence of sufficient Fe³⁺ and O₂ to oxidize FeS₂. The ability of Ferrovum to produce acid is weaker than that of Acidithiobacillus. Metallibacterium also showed significant positive correlations with SO₄²⁻ and TFe and a significant negative correlation with pH. Ralstonia showed significant positive correlations with metastable sulfur forms S²⁻, SO₃²⁻, S₂O₃²⁻, and S₄O₆²⁻, indicating that Ralstonia has a certain reducing capacity.

The present study aimed to characterize the spatiotemporal distributions of different forms of sulfur in AMD and their relationships to environmental factors, focusing on a waste pyrite mining area in Luzhou City. The main conclusions of the present study are summarized below:

(1) Along the AMD runoff direction, the hydro-chemical type changed from HCO₃-Na type to SO₄·Cl-Ca·Mg type. The point − 0.5 < SI < 0.5 is in the water-rock equilibrium state, and presents the characteristics of high TDS, high SO₄²⁻and low pH. SO₄²⁻is mainly derived from gypsum dissolution, coal mine oxidation and FeS₂ oxidation. The physical and chemical factors showed obvious spatial and temporal changes. In the dry season, the pH and temperature of the water body were lower, and the DO and ORP were higher.

(2) The sulfur form in AMD in the study area is mainly SO₄²⁻, and the metastable sulfur SO₃²⁻、S₂O₃²⁻、S₄O₆²⁻ and S²⁻ have only a small amount of accumulation in the cyclic transformation of sulfur. With the migration of AMD, they are gradually oxidized to SO₄²⁻. The concentrations of SO₄²⁻and S²⁻in the dry season were higher than those in the normal season and the wet season, and the contents of SO₃²⁻, S₂O₃²⁻and S₄O₆²⁻were the highest in the wet season, indicating that the distribution of sulfur forms was affected by seasonal changes and was closely related to precipitation and groundwater flow.

(3) In AMD, Fe (II) is mainly in the form of free Fe²⁺ and complex FeSO₄⁰. Fe (III) mainly exists in the form of complex state, and the activity of each complex state changes with the change of pH and the attenuation of SO₄²⁻. Fe²⁺ and Fe³⁺ coexisted in the original environment, and were oxidized to Fe³⁺ with the increase of DO and pH. The correlation coefficient between TFe and SO₄²⁻ decay is as high as 0.9308, which is closely related to the formation of sulfate secondary iron-bearing minerals.

(4) AMD decreased the abundance and diversity of microbial communities and resulted in the dominance of acidophilus and sulfur/iron-oxidizing bacteria, including Acidithiobacillus and Metallibacterium. Dominant genera at sites not impacted by AMD included Acidibacter and Sphingomonas; those at sites impacted by AMD but with neutral pH included Lachnospiraceae NK4A136 group, Ralstonia, and Sinomonas.

(5) Environmental factors regulated the distribution of sulfur forms. There was a negative correlation between pH and SO₄²⁻, whereas SO₄²⁻ was positively correlated with DO, temperature, ORP, total iron, and Acidithiobacillus. SO₄²⁻ promoted acidity. Increases in water temperature and ORP facilitated the conversion of sulfur. At.f promoted the conversion of iron and sulfur, indicating that environmental factors regulate the conversion of sulfur.

Author Contribution

Man Gao wrote the main manuscript text ,Guo Liu analysis. All authors reviewed the manuscript.

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No competing interests reported.

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Spatiotemporal distribution of different forms of sulfur in acid mine drainage and their relationships with environmental factors

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Study area

2.2 Sample collection and preservation

2.3 Testing samples and analysis

3. Results and discussion

3.1 Hydro-chemical characteristics

3.2 Spatiotemporal distributions of physicochemical factors

3.3 Spatiotemporal distributions of sulfur species

3.3.1 Spatiotemporal distribution of SO₄²⁻

3.3.2 Spatiotemporal distribution of metastable sulfur

3.4 Distributions of iron forms

3.5 Spatial characteristics of microbial community structure

3.5.1 Microbial community diversity

3.5.2 Microbial community composition

3.6 Analysis of relationships between sulfur and Fe forms

3.6.1 Physical and chemical factors regulating sulfur and Fe forms

3.6.2 Microbial species abundance

4. Conclusions

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1

Spatiotemporal distribution of different forms of sulfur in acid mine drainage and their relationships with environmental factors

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Study area

2.2 Sample collection and preservation

2.3 Testing samples and analysis

3. Results and discussion

3.1 Hydro-chemical characteristics

3.2 Spatiotemporal distributions of physicochemical factors

3.3 Spatiotemporal distributions of sulfur species

3.3.1 Spatiotemporal distribution of SO42−

3.3.2 Spatiotemporal distribution of metastable sulfur

3.4 Distributions of iron forms

3.5 Spatial characteristics of microbial community structure

3.5.1 Microbial community diversity

3.5.2 Microbial community composition

3.6 Analysis of relationships between sulfur and Fe forms

3.6.1 Physical and chemical factors regulating sulfur and Fe forms

3.6.2 Microbial species abundance

4. Conclusions

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1

3.3.1 Spatiotemporal distribution of SO₄²⁻