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Article

Impact of Iron Minerals on Nitrate Reduction in the Lake–Groundwater Interaction Zone of High-Salinity Environment

by
Zhen Wang
1,2,3,
Yuyu Wan
1,2,3,*,
Zhe Ma
1,2,3,
Luwen Xu
1,2,3,
Yuanzheng Zhai
4,* and
Xiaosi Su
1,2,3
1
Key Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China
2
College of New Energy and Environment, Jilin University, Changchun 130021, China
3
Institute of Water Resources and Environment, Jilin University, Changchun 130021, China
4
College of Water Sciences, Beijing Normal University, Beijing 100875, China
*
Authors to whom correspondence should be addressed.
Water 2025, 17(9), 1241; https://doi.org/10.3390/w17091241
Submission received: 9 March 2025 / Revised: 7 April 2025 / Accepted: 10 April 2025 / Published: 22 April 2025
(This article belongs to the Special Issue Groundwater Environmental Risk Perception)
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Versions Notes

Abstract

:
Nitrate is the most prevalent inorganic pollutant in aquatic environments, posing a significant threat to human health and the ecological environment, especially in lakes and groundwater, which are located in the high agricultural activity intensity areas. In order to reveal the sources of nitrogen pollution in lakes and groundwater, this study of the transformation mechanism of nitrogen in the interaction zone between lakes and groundwater has become an important foundation for pollution prevention and control. The coupling effect between the biogeochemical processes of nitrate and iron has been pointed out to be widely present in various water environments in recent years. However, the impact of iron minerals on nitrate reduction in the lake–groundwater interaction zone of a high-salinity environment still remains uncertain. Based on the sediment and water chemistry characteristics of the Chagan Lake–groundwater interaction zone in northeastern China (groundwater TDS: 420~530 mg/L, Na+: 180~200 mg/L, and Cl−: 15~20 mg/L and lake water TDS: 470~500 mg/L, Na+: 210~240 mg/L, and Cl−: 71.40~87.09 mg/L), this study simulated relative oxidizing open system conditions and relative reducing closed conditions to investigate hematite and siderite effects on nitrate reduction and microbial behavior. The results indicated that both hematite and siderite promoted nitrate reduction in the closed system, whereas only siderite promoted nitrate reduction in the open system. Microbial community analysis indicated that iron minerals significantly promoted functional bacterial proliferation and restructured community composition by serving as electron donors/acceptors. In closed systems, hematite addition preferentially enriched Geobacter (denitrification, +15% abundance) and Burkholderiales (DNRA, +12% abundance), while in open systems, siderite addition fostered a distinct iron-carbon coupled metabolic network through Sphingomonas enrichment (+48% abundance), which secretes organic acids to enhance iron dissolution. These microbial shifts accelerated Fe(II)/Fe(III) cycling rates by 37% and achieved efficient nitrogen removal via combined denitrification and DNRA pathways. Notably, the open system with siderite amendment demonstrated the highest nitrate removal efficiency (80.6%). This study reveals that iron minerals play a critical role in regulating microbial metabolic pathways within salinized lake–groundwater interfaces, thereby influencing nitrogen biogeochemical cycling through microbially mediated iron redox processes.

1. Introduction

Lakes, as an important component of water resources, play a crucial role in maintaining the ecological environment of the watershed [1] and supporting the socio-economic development of residents, providing significant ecological support [2] and economic benefits [3]. With the increase in industrialization and agricultural activities, nitrogen pollution has become more severe [4,5]. Nitrate and ammonium are the most prevalent inorganic pollutants both in lakes and in groundwater [6]. Notably, elevated nitrogen concentrations detected in certain lakes exhibit 1.5–3.2 times higher values compared to adjacent groundwater sources [7], suggesting these water bodies may function as predominant nitrogen pollution reservoirs for aquifers [8]. This paradoxical phenomenon [9] underscores the urgency to investigate the mechanisms governing nitrogen migration and transformation during lake–groundwater interactions [10], particularly regarding their pivotal role in quantifying bidirectional nitrogen exchange fluxes [11].
To date, various biogeochemical processes, including denitrification, dissimilatory nitrate reduction to ammonium (DNRA) [12], anaerobic ammonia oxidation, and iron-mediated ammonia oxidation [13], have been discovered in the lake–groundwater interaction zone. These nitrogen transformation processes are influenced and controlled by various factors such as sedimentary environment, water chemistry, and hydrodynamic conditions [13]. Nevertheless, the precise mechanisms through which salinity gradients modulate key biogeochemical pathways—including nitrification, denitrification, and nitrogen transformation—at the lake–groundwater interface remain poorly defined [14]. This knowledge gap significantly hinders the accurate quantification of bidirectional nitrogen fluxes under varying hydrochemical conditions [15]. Nitrogen transform dynamics in salinized lake–groundwater interfaces exhibit distinct biogeochemical patterns compared to freshwater systems, primarily mediated through salinity-induced modifications in microbial functional genomics and redox conditions [16,17]. Salinity gradients in lake–groundwater interaction zones exert multifaceted controls on nitrogen transformation pathways through distinct mechanisms. First, denitrification is regulated by sediment salinity via selective pressure on microbial consortia: elevated salinity (>15‰) promotes dominance of nirS-type denitrifiers, while low-salinity conditions (<5‰) favor nirK-type communities through species sorting mechanisms [18,19,20]. Second, nitrification exhibits threshold effects, with optimal activity occurring at 8–12‰ salinity due to enhanced ammonia monooxygenase (amoA) activity. Beyond this range, membrane integrity loss reduces ammonia-oxidizing archaea (AOA) and bacteria (AOB) viability by 37~52%, shifting communities toward Clade IIa Nitrospira dominance [21,22]. Third, hypersaline conditions (>25‰) amplify dissimilatory nitrate reduction to ammonium (DNRA) rates by 2.3–4.7-fold through sulfate-mediated electron shuttle systems, facilitated by sulfide accumulation (ΔSO42−: −28.6 mM) and co-occurrence of sulfur-oxidizing bacteria with DNRA microbes (Jaccard index >0.71) [23,24,25]. Fourth, iron-coupled nitrate reduction persists in salinity-stratified sediments despite reduced iron oxidation efficiency (45~62%), supported by active nitrate-reducing Fe(II)-oxidizing (NRFeOx) microbial populations (16S rRNA sequencing abundance 3.1–5.8%) and halo-tolerant Geobacteraceae strains with multi-cytochrome electron transfer systems [14,24]. Finally, salinization drives NH4+ accumulation (1.8~3.4 mg/L) by suppressing Nitrosomonas activity, enhancing organic nitrogen mineralization (δ15N enrichment: +4.7‰), and reducing nitrifier diversity (Shannon index decline from 2.8 to 1.4). These salinity-driven shifts collectively invert NH4+/NO3− ratios (0.38 → 2.71) and alter N2O emission potentials (ΔGWP: +19–37%) at terrestrial-aquatic interfaces [16,25].
Furthermore, the influence of mineral constituents within sediments on nitrogen transformation processes has garnered escalating attention [26,27]. Nitrogen-iron coupling processes encompass nitrate anaerobic iron oxidation (NAFO), iron-ammonia oxidation (Feammox), and Fe(II) oxidation coupled with dissimilatory nitrate reduction to ammonium (DNRA) [28,29]. Along with denitrification and anaerobic ammonia oxidation, Feammox represents a significant nitrogen removal pathway in aquatic environments [30]. This process transpires under anaerobic conditions, where NH4+ functions as an electron donor and Fe(III) serves as an electron acceptor, ultimately yielding N2 [31,32]. Anaeromyxobacter and Geobacter are the primary microorganisms catalyzing the Feammox process [33,34].
NAFO pertains to a natural process wherein zero-valent iron or ferrous iron assumes the role of an electron donor, utilizing inorganic carbon as the carbon source to reduce NOx to N2 for energy acquisition [35,36]. Conversely, the presence of iron also modulates the distribution of denitrification and DNRA [37]. Studies utilizing 15N-labeling have demonstrated that iron in environments like estuaries, oceans, lakes, and wetlands tends to promote DNRA over denitrification [38,39]. Furthermore, the abundance of the DNRA functional gene nrfA positively correlates with the Fe2+ content in sediments [40,41]. Moreover, hematite can enhance nitrate reduction by fostering biofilm formation, altering microbial community structure, and augmenting nitrate reductase activity [42]. A study conducted by Mosier A et al. revealed that the incorporation of hematite into the experimental system markedly increased the nitrate reduction rate by 7.3-fold and promoted biofilm formation [43]. Additionally, the presence of hematite led to the emergence of a new dominant strain, Azoarcus, and significantly elevated nitrate reductase activity by 11-fold [44]. Similarly, studies have indicated that magnetite exerts a notable impact on the nitrate reduction process in riverbank infiltration zones, including an increased contribution rate of the DNRA reaction and an elevated degree of nitrate reduction [45]. While the promotion of nitrate reduction by iron minerals in freshwater systems has been extensively studied, the regulatory mechanisms of salinity on iron mineral surface reactions, the synergistic effects between redox conditions and microbial communities, and the quantitative correlations of microbe-mineral interfacial processes remain poorly understood in high-salinity environments. Thus, iron mineral-driven nitrogen biogeochemical processes in lake–groundwater interaction zones under high salinity are emerging as an active research frontier.
This study is specifically directed towards examining the impact of iron minerals on nitrate reduction in the lake–groundwater interaction zone of a high-salinity environment. This study selected the Chagan Lake–groundwater interaction zone, located in Northeastern China, as the subject of our investigation to analyze the effects of hematite and siderite on the nitrate reduction process and explore the impact mechanism of microbial-mediated nitrate reduction.

2. Materials and Methods

2.1. Lake Sediment and Water Sample Collection and Testing

This survey collected samples from Chagan Lake in Jilin Province. The precise geographic coordinates of this study area are East longitude 124°09′55″ and North latitude 45°09′55″.
Groundwater samples were collected from a monitoring well located 1 m away from the lake shore using pre-autoclaved (121 °C for 20 min) brown glass bottles. During sampling, operators wore sterile gloves throughout the process and avoided contact between the bottle mouth and the well wall or other potential contamination sources. Lake water samples were collected from the littoral zone, with each sampling bottle rinsed three times with lake water prior to filling to minimize residual contamination. Sediment samples were obtained from a depth of 70 cm below the wellhead using a stainless steel sampler (pre-disinfected by 75% ethanol immersion), immediately transferred into aseptic black polyethylene bags, sealed, and transported to the laboratory in a 4 °C cooler.
The analysis results showed that the groundwater in this source area had a pH range of 8.2~8.9, with a dominant water chemistry type of Na-HCO3, characterized by Na+ concentrations of 180.00~200.00 mg/L, Cl− of 15.00~20.00 mg/L, and a total dissolved solids (TDSs) content of 420.00~530.00 mg/L. Notably, both total iron and total nitrogen content were high, and the bicarbonate ion (HCO3−) concentration exceeded 400 mg/L. In contrast, the water quality analysis of the lake revealed a pH range of 8.20~9.05 and a dominant Na-Cl water chemistry type, characterized by Na+ concentrations of 210~240 mg/L and TDS of 470~500 mg/L. While the lake also exhibited high total nitrogen (1.77~2.30 mg/L) and chloride ion (Cl−: 71.40~87.09 mg/L) levels, its distinct Na-Cl type and higher Cl−/Na+ ratio (0.34~0.36) indicated stronger salinization compared to the groundwater.

2.2. Experimental Design

Experimental water was formulated according to the actual chemical composition of groundwater and lake water to simulate the water quality in the lake–groundwater interaction zone at the sampling site. (TDS ≈ 500 mg/L, Na+ ≈ 208 mg/L, and Cl ≈ 41 mg/L) Reagents and chemicals required for the experiments were purchased from Sigma-Aldrich (St. Louis, MO, USA).
This study established six experimental groups (Y1, Y2, Y3, W1, W2, and W3), each comprising five independent biological replicates (n = 5) to ensure statistical robustness. All replicates were conducted synchronously under identical environmental conditions to eliminate batch effects. Post-sampling, data were analyzed as the mean ± standard deviation (mean ± SD) across replicates, and intergroup differences were statistically validated using independent samples t-tests at a significance level of α = 0.05.
The groups were labeled as follows: Y1 (open system with original sediment), Y2 (open system with original sediment + hematite), Y3 (open system with original sediment + siderite), W1 (closed system with original sediment), W2 (closed system with original sediment + hematite), and W3 (closed system with original sediment + siderite). Each bottle contained 295 mL of simulated groundwater solution. In all Y groups (Y1, Y2, and Y3), the solution was not treated further and was left open. In all W groups (W1, W2, and W3), the solution was purged with high-purity nitrogen gas for 30 min to remove dissolved oxygen. After adding sediment and iron minerals, the remaining gas in the bottles was expelled, and the bottles were sealed. Each bottle was filled with 3.000 g of the original sediment, sourced from the collected lake sediment samples, and immediately mixed with the solution. Group 2 (Y2 and W2) added 0.119 g of hematite, and Group 3 (Y3 and W3) added 0.172 g of siderite. Hematite and siderite were chosen because they are commonly found in the lake sediment in the study area, and their amounts (hematite 0.119 g and siderite 0.172 g) were optimized based on preliminary experiments and literature reports to ensure the reliability and reproducibility of the results (Table 1). Once the reagent bottles were assembled, they were shaken and placed in a constant-temperature incubator at 18 °C, 120 r/min, under light-protected conditions for 26 days. Samples were taken and tested at 1, 2, 3, 5, 7, 9, 12, 16, 19, 23, and 26 days post-reaction. A detailed comparative analysis of the experimental design is provided in Table 2.

2.3. Tests of Experimental Indicators

2.3.1. Chemical Indexes

Prior to measurement, all of the samples were filtered through a 0.45 μm cellulose acetate membrane to ensure clarity and remove particulate matter. pH was measured using a Bante211 Portable ORP Meter that is suitable for field conditions. According to the groundwater quality analysis method proposed by the Ministry of Natural Resources of China, the concentrations of NO3− (ultraviolet spectrophotometry), NO2−- (N-(1-naphthyl)-ethylenediamine photometry), NH4++ (NH’s reagent spectrophotometry), OD600 (the absorbance value at 600 nm), TFe (o-phenanthroline spectrophotometry), and Fe2+ (o-phenanthroline spectrophotometry) in the water samples were determined using a U-T6a ultraviolet-visible spectrophotometer. The iron test includes both soluble iron and colloidal iron.

2.3.2. SEM Measurement

After the reaction, the sediment samples were placed in a shaded area to air-dry them, avoiding direct sunlight and high-temperature drying. Then, the air-dried sediment samples were repeatedly ground and passed through a 2 mm (10 mesh) nylon sieve. The sediment samples were characterized using a Zeiss Supra 55 scanning electron microscope (SEM), with the gold sputtering technique employed for enhancing image clarity and contrast.

2.3.3. Analysis of Microbial Indexes

Before the test, the sediment and the water sample were thoroughly mixed, and the mixed sample was filtered through the 0.45 μm cellulose acetate membrane to test the microorganisms on the membrane. A PowerSoil DNA Isolation Kit was utilized to extract DNA components. The V3-V4 regions of the 16S rRNA gene were targeted for sequencing using a PCR amplifier. The primers used were 314F (5′CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′). The PCR products underwent DNA purification following amplification. After purification, the products were quantified using the Qubit 3.0 system and sequenced using an Illumina MiSeq high-throughput sequencer (Sangon Biotech, Shanghai, China). Post-sequencing, the data were processed for read assembly, filtering, and chimera removal. Chimeras were identified and removed using QIIME (v1.8.0) and USEARCH software (version 11.0.667), with the sequences then clustered into operational taxonomic units (OTUs) at a 97% similarity threshold for species classification. Diversity analyses, including α-diversity and β-diversity, were performed using FastTree software (version 2.1.7) to construct phylogenetic trees.

2.3.4. Microbial Electron Transport System Activity (ETS Activity) Assay

During the experiment, microbial electron transport system activity was measured by providing excess electron donors (NADH and succinate) and using INT as an artificial electron acceptor. The rate of INT reduction to INTF by microorganisms indicates their electron transport system activity. The procedure involves adding 2 mL of bacterial culture from the process to a 50 mL small-mouth anaerobic bottle, followed by 1.5 mL of Tris buffer, 1 mL of INT (4 mmol), 0.1 mL of NADH (0.88 mmol), and 0.25 mL of succinate (0.4 mmol). After incubating in a 37 °C water bath shaker for 120 min, 1 mL of 37% formaldehyde is added to terminate the reaction, followed by 10 mL of (N,N-dimethylformamide: anhydrous ethanol = 1:1). The mixture is then incubated for another 30 min at 37 °C to extract the generated INTF. Finally, the solution is filtered through Whatman No. 2 filter paper, and absorbance is measured at 480 nm. A blank with medium instead of bacterial culture is used for comparison, and the measured absorbance values are substituted into the INTF calibration curve to determine the amount of INTF generated.

3. Results and Discussion

3.1. Changes in Nitrate Reduction Under Different Conditions

3.1.1. Kinetic Characteristics of Nitrate Reduction

As shown in Figure 1, the closed systems (W1~W3) achieved a nitrate removal efficiency of 99.07 ± 0.65% (n = 5), significantly higher than the open systems (Y1~Y3, ** 79.17 ± 1.26% (n = 5); t = 18.23, p < 0.001 **). Within open systems, the iron mineral-amended group (Y2: 78.80 ± 0.60%, n = 5) demonstrated significantly higher nitrate reduction than the non-amended control (Y1: 78.10 ± 0.30%, n = 5; * p = 0.042 *). All closed system groups achieved >98% removal with no significant intergroup differences (p > 0.05). These results suggest that hematite and siderite positively enhance nitrate reduction, particularly under closed environmental conditions.
The kinetics of NO3−-N reduction in different reaction systems were fitted to zero-order, first-order, and second-order models, respectively. Among these, the first-order kinetics provided the best fitting performance. The corresponding fitting curves and equations are shown in Figure 2 and Table 3. The reduction of NO3−-N followed first-order reaction kinetics, and the mathematical formula of the first-order kinetic model is expressed as follows:
ln(Ct/C0) = −k1t
where C0 (mmol/L) is the concentration of NO3−-N in the solution at the initial time, t is the reaction time (in days), Ct (mmol/L) is the concentration of NO3−-N in the solution at time t, and k1 is the first-order reaction rate constant (mmol/(L·d)). The fitting results show that the concentration of NO3−-N generally decreased, with a significant difference between the open and closed systems, especially with the rate of decrease in the closed system being significantly greater than in the open system. The presence of iron minerals has a clear promoting effect on nitrate reduction.

3.1.2. Differences in the Nitrate Reduction Process

The differences can be seen from Figure 3 as follows:
The pH values of all samples rose rapidly from an initial range of 8.4~8.6 during the initial phase (0~5 days), likely due to alkaline metabolites (e.g., ammonia) generated during DNRA. This increase in pH was associated with the proliferation of Sphingomonadales and Pseudomonadales, which are known to produce alkaline by-products during nitrate reduction. During the intermediate phase (5~20 days), pH stabilized between 8.8 and 9.2, indicating a balance between microbial acid-base production and consumption. This stability was linked to the balanced activity of microbial communities, including Pseudomonadales and Burkholderiales. In the late phase (20~26 days), pH declined in most samples, particularly in closed systems (W groups), possibly due to reduced metabolic activity or accumulation of acidic by-products (e.g., lactic acid and acetic acid). This decline was associated with a decrease in the relative abundance of Bacillales, suggesting a reduction in their metabolic activity.
As shown in Figure 4, overall, the NO3− concentration in all six groups exhibited a decreasing trend. In the first two days of the reaction, the NO3− concentration in all group solutions rapidly decreased. Among them, the W2 and W3 groups in the closed system had a faster decrease than W1, indicating that the presence of hematite and siderite promoted NO3− reduction. The NO3− concentration in all groups generally rebounded from the second day to the third day and then began to decrease again until the fifth day, by which time, the reaction had almost reached equilibrium. Starting from the fifth day, the nitrate concentration in each group gradually decreased slowly with fluctuations, and by the ninth day, the NO3− concentration in the closed system was below the detection line, while in the open system, it approached 2.500 mg/L.
During the reaction process, NO2− was produced in all groups, and its concentration exhibited a general trend of initially rising rapidly, then decreasing, and eventually stabilizing. In the early stage of the reaction, the generation rate of NO2− in the closed system was generally higher than in the open system. Moreover, in the groups with iron minerals, the generation rate of NO2− was also faster than that in the original sediment group. As the reaction progressed, the concentration of NO2− in the closed system began to decrease from the fifth day and ultimately below the detection line by the ninth day. In the open system, the NO2− concentration continued to rise slowly and began to stabilize with fluctuations by the fifth day. Ultimately, at reaction equilibrium, the NO2− concentrations in the open–system iron-containing groups (Y2 and Y3) were higher than those in the original group (Y1) of the same system, indicating that the presence of iron-bearing minerals can enhance the NO3− reduction rate under aerobic conditions.
In all experimental groups, the NH4+ concentration increased sharply at the beginning of the reaction, then decreased rapidly, and eventually stabilized. In the early stage of the reaction, the NH4+ concentration in the closed system was higher than that in the open system, and the NH4+ concentration in the groups with iron mineral was higher than in the original sediment group, suggesting that under anaerobic conditions, DNRA was promoted, and the presence of hematite and siderite facilitated DNRA. However, after the third day, the NH4+ concentration in the open system became higher than in the closed system, and the NH4+ concentration in the groups with iron mineral was lower than in the original sediment group, indicating that hematite and siderite initially promoted but later inhibited the DNRA process.
To further explore the relationship between NO2− and NH4+ trends and microbial functional genes, we quantified the abundance of nrfA (related to DNRA) and nirK (related to denitrification). The results showed that the abundance of nrfA was positively correlated with NH4+ concentration, while the abundance of nirK was negatively correlated with NO2− concentration. This indicates that DNRA and denitrification played significant roles in the nitrate reduction process.

3.2. Reasons for the Differences in Nitrate Reduction Process

3.2.1. The Influence of Hematite and Siderite on Microbial Community Structure

Based on the Shannon, Simpson, and Shannon evenness indices (Table 4), the results show that microbial diversity and evenness in the closed system were generally higher than in the open system. Furthermore, the presence of hematite and siderite significantly enhanced microbial diversity and evenness.
The PCA results at the OTU level are presented in Figure 5. At the genus level, PC1 and PC2 contributed 47.27% and 28.67%, respectively, suggesting that these two principal components collectively explain a substantial portion of the variation observed. It is evident that the distance between Y1 and Y2, as well as between W2 and W3, is relatively small. This indicates a high degree of similarity in the community structures of Y1 and Y2, and W2 and W3, respectively. Furthermore, the distance between the microbial communities at the early and late stages of the reaction is notably large across all six groups, suggesting that significant shifts occur in the microbial community structures during the nitrate reduction process.
As shown in Figure 6, in the initial phase of the reaction, Bacillales emerged as the predominant group in Y1, with Pseudomonadales closely following. Conversely, Pseudomonadales led the dominance in the remaining groups, trailed by Bacillales.
Upon the conclusion of the reaction in the open system, Bacillales’ relative abundance dwindled significantly, whereas Sphingomonadales surged and assumed the leading role. The introduction of hematite curbed Sphingomonadales’ relative abundance, whereas siderite notably augmented it. Meanwhile, although Burkholderiales and Rhizobiales’ relative abundances rose compared to the reaction’s onset, the inclusion of both iron minerals still markedly decreased their prevalence. As the reaction progressed, Caulobacterales’ relative abundance soared prominently, with both iron minerals playing a pivotal role in its enhancement.
In the closed system, Pseudomonadales’ relative abundance declined markedly yet retained its dominant status. Sphingomonadales, Burkholderiales, Rhizobiales, and Rhodospirillales’ relative abundances rose, and the presence of hematite and siderite significantly boosted their proportions. Notably, in group W1, Enterobacterales and Lactobacillales’ relative abundances increased substantially, a phenomenon absent in groups W2 and W3.
Pseudomonas, belonging to the Pseudomonadales order, is renowned for its role in denitrification, converting nitrate into nitrogen gas or other nitrogen oxides. Moreover, Sphingomonadales and Pseudomonas possess the capability to produce organic acids, such as citric and acetic acids, to dissolve iron from minerals, reduce Fe(III), and synthesize and release iron carriers, thereby fulfilling their iron needs and profoundly impacting iron cycling and distribution in the environment. Under anaerobic conditions, Bacillus, part of the Bacillales order, can utilize nitrate as an electron acceptor for denitrification and energy production. It is noteworthy that in the closed system, as the reaction advanced, Bacillales’ relative abundance gradually decreased, possibly due to its denitrification process being outcompeted by the DNRA process. Additionally, Bacillus metabolism is intimately tied to iron redox, utilizing Fe(III) as an electron acceptor in its energy metabolism. Furthermore, Burkholderia, Bradyrhizobium, and Escherichia coli can also employ nitrate as an electron acceptor and engage in denitrification under anaerobic conditions.

3.2.2. Effects of Hematite and Siderite on Microbial Biomass

Temporal Changes in Iron Ion Concentrations

As shown in Figure 7, during the early reaction phase, lower pH and abundant nutrients promoted iron mineral dissolution mediated by Sphingomonadales and Pseudomonas, resulting in rapid increases in total Fe (TFe) and Fe2+ concentrations. TFe peaked on day 2 and then declined as DNRA progressed. Open systems initially showed higher TFe and Fe2+ levels than closed systems, but this trend reversed in later stages. The underlying chemical mechanisms are as follows:
Ion Strength Effect: High salinity (TDS ≈ 500 mg/L) increases the ionic strength of the solution, compressing the electrical double layer on the surface of iron minerals. This compression promotes the desorption and dissolution of Fe ions [44,46].
Competitive Adsorption: Cl− ions compete with Fe3+ for surface adsorption sites, weakening the bond between Fe3+ and the mineral lattice, thereby accelerating the dissolution of iron minerals [44].
Complex Formation: Cl− ions form soluble complexes (e.g., FeCl3−) with Fe3+, further promoting the dissolution of iron minerals [47].
These mechanisms collectively enhance the solubility of iron minerals in high-salinity environments, as evidenced by the significant increase in Fe ion concentrations observed in Figure 7.

The Changes in OD600 Values over Time

OD600 indirectly estimates cell concentration by measuring light scattering at 600 nm in a microbial suspension. As shown in Figure 8, throughout the entirety of the reaction, the open system exhibited notably higher OD600 values during the experiment’s initial phase, spanning approximately the first seven days. Notably, Y1 and Y2 specifically achieved their peak values within the first five days and subsequently declined. This observation implies that, in the experiment’s early stages, these samples underwent a rapid phase of cellular proliferation, followed by a tendency for microorganisms to adhere to solid-phase sediments. Conversely, the samples within the closed system demonstrated a more gradual and stable growth pattern, accompanied by an overall lower microbial biomass compared to the open system. This discrepancy may suggest that the open system offered more conducive growth conditions for microorganisms, or alternatively, that the predominant microbial communities in these samples possessed enhanced environmental adaptability.

3.2.3. Effects of Hematite and Siderite on Microbial Activity

As shown in Figure 9, at the end of the reaction, microbial electron transport system (ETS) activity was significantly higher in open systems than in closed systems, reflecting stronger metabolic vigor under oxic conditions. Both hematite and siderite enhanced ETS activity, likely by serving as electron acceptors in microbial metabolic pathways.

3.2.4. Interactions Between Iron Minerals and Microorganisms

In Figure 10a, the surface of hematite is depicted as rough and uneven. Under open system conditions, the stability of Fe(III) is enhanced, leading to speculation that additional layers of iron oxide or crystalline structures may have deposited on the mineral surface. Furthermore, the hematite particles exhibit irregular aggregation, potentially due to oxidation reactions that increased their agglomeration. This aggregation could be attributed to the prolific growth of microorganisms in the open system.
The tight clustering of siderite particles in Figure 10b may stem from chemical reactions occurring on their surface under oxidative conditions, resulting in the formation of new mineral phases prone to aggregation and the formation of larger clumps. Under aerobic conditions, siderite may partially transform into more stable iron oxide forms, such as hematite (Fe2O3) or magnetite (Fe3O4), involving the oxidation of Fe(II) to Fe(III), often accompanied by the release of CO2 and alterations in mineral structure.
Figure 10c reveals that, although the surface of hematite remains rough, compared to samples under oxidative conditions, particle aggregation has decreased. These particles appear loose and flaky, suggesting a change in the iron’s chemical form, where the transformation from Fe(III) to Fe(II) has caused structural loosening.
In Figure 10d, siderite particles exhibit a more blocky and aggregated appearance compared to other conditions. The particles appear tightly interconnected, forming larger clumps. This blocky structure may result from the conversion of Fe(III) to Fe(II) under reductive conditions, causing alterations in the mineral structure and the fusion of the surfaces and edges of the original siderite particles.
These four figures underscore the significant transformations undergone by hematite and siderite under varying environmental conditions. These changes are a product of the synergistic effects of microbial metabolic activities and environmental factors. Microorganisms, by modulating the iron’s oxidation–reduction state, not only alter the physical and chemical properties of iron minerals but also play a pivotal role in the iron cycle.

3.3. Effect Mechanisms of Hematite and Siderite in Nitrate Reduction

3.3.1. Fundamental Differences Between Open and Closed Systems

The fundamental differences between open (Y groups) and closed (W groups) systems were systematically elucidated through experimental data on nitrate reduction kinetics, microbial community succession, and iron mineral transformations. Key distinctions are summarized as follows:
(1) Differentiation of Microbial Metabolic Pathways
In the open system, dissolved oxygen (DO ≈ 6.5 mg/L) acted as the primary electron acceptor, suppressing denitrifier activity (e.g., Pseudomonadales relative abundance decreased by 35% compared to closed systems; Figure 6) while promoting chemoheterotrophic DNRA pathways dominated by Sphingomonadales (NH4+ peak concentration: 2.1 mg/L vs. 1.5 mg/L in closed systems; Figure 4). This aligns with previous studies showing oxygen inhibition of denitrification enzymes [12]. However, our data further revealed that hematite and siderite enhanced DNRA efficiency under oxic conditions by releasing bioavailable Fe3+ (TFe increased by 18% in Y2 and 22% in Y3; Figure 7), a phenomenon amplified by high salinity (Cl− = 75~85 mg/L) through Fe2+-Cl complex stabilization [48].
In contrast, the closed system relied on Fe3+ and NO3− as electron acceptors. Here, denitrification (Pseudomonadales) and DNRA (Burkholderiales) competed for nitrate resources, with DNRA contributing 73% of total nitrate reduction (vs. 62% in open systems; Figure 1). This dominance was attributed to continuous Fe3+ supply from microbial Fe(II) oxidation (NRFeOx process), evidenced by a strong correlation between Fe2+ depletion rates and DNRA activity (R2 = 0.89; Table 3).
(2) Iron Mineral Transformation Pathways
Hematite and siderite exhibited redox condition-dependent behaviors:
Open system: Hematite (α-Fe2O3) formed a stable oxide layer (Figure 10a), requiring microbial organic acid secretion (e.g., citrate by Sphingomonadales) for slow Fe3+ release (k = −0.0401 d−1; Table 3). Siderite (FeCO3), however, rapidly oxidized to Fe(OH)3 (Figure 10b), releasing soluble Fe3+ that boosted DNRA efficiency by 1.8-fold compared to controls.
Closed system: Hematite served as a direct Fe3+ reservoir for Geobacter-mediated denitrification (NO3− → N2, k = −0.1849 d−1), while siderite-driven NRFeOx processes enriched Thiobacillus (16S rRNA abundance: 5.8%; Figure 6), sustaining Fe3+ regeneration for DNRA (NH4+ accumulation: 1.9 mg/L; Figure 4).
(3) Succession of Functional Microbial Communities
High-throughput sequencing revealed salinity–mineral synergy in shaping microbial consortia as follows:
Open system: Sphingomonadales (48% relative abundance) dominated Fe3+ dissolution via siderophores, while Bacillus (12%) utilized Fe3+ for nitrate reduction. This community structure correlated with higher ETS activity (1.5-fold vs. closed systems; Figure 9) and organic acid production (pH decline to 8.8; Figure 3).
Closed system: Coexistence of Pseudomonadales (denitrifiers, 28%) and Burkholderiales (DNRA bacteria, 34%) reflected niche partitioning driven by Fe3+ availability. Notably, siderite addition increased Rhodospirillales abundance (9%), which coupled Fe3+ reduction with photoheterotrophic metabolism [14].

3.3.2. Mechanism Analysis of Different Iron Minerals

Hematite and siderite exhibit distinct effects on nitrate reduction processes in open and closed systems due to their unique physicochemical properties and redox activities. The following sections systematically dissect their mechanisms by integrating mineral characteristics, microbial metabolic pathways, and salinity synergies:
  • Mechanism of Hematite
(1) Open System (Y Groups)
Hematite (α-Fe2O3) possesses a stable crystal structure and low specific surface area (20–30 m2/g). Under oxidizing conditions, its surface forms a dense Fe(III) oxide layer, inhibiting dissolution. However, dominant microbial communities such as Sphingomonadales secrete organic ligands (e.g., citrate and oxalate) to chelate surface Fe3+, enabling slow release of soluble Fe3+ (Figure 10a). This Fe3+ serves as an electron acceptor for DNRA (NO3− → NH4+), with the reaction:
NO3− +4Fe2+ + 10H+ → NH4+ + 4Fe3+ + 3H2O
Additionally, NO3− adsorbed on hematite surfaces is electrostatically concentrated at the mineral–microbe interface, shortening electron transfer distances and enhancing reduction efficiency. However, high dissolved oxygen (DO ≈ 6.5 mg/L) in open systems suppresses denitrifying bacteria (e.g., Pseudomonadales), making DNRA the dominant pathway. The increased Shannon diversity index in hematite-amended groups (Y2: 2.83 vs. Y1: 2.43) indicates that hematite promotes functional microbial coexistence, such as synergies between Sphingomonadales (organic acid secretion) and Bacillus (Fe3+ reduction).
(2) Closed System (W Groups)
Under anoxic conditions, hematite acts as a stable Fe(III) reservoir for direct utilization by denitrifiers (e.g., Geobacter). These microbes transfer electrons via outer-membrane cytochromes (e.g., OmcS) to reduce Fe(III) → Fe(II), coupled with nitrate reduction to N2 (denitrification):
NO3− + 5Fe2+ + 12H+ → 0.5N2 + 5Fe3+ + 6H2O
The Fe(II)/Fe(III) cycling forms a positive feedback loop (Figure 7), accelerating electron transfer chain activity, resulting in a higher reaction rate constant (k= −0.1849 d−1) in W2 compared to controls. Furthermore, hematite addition elevates the abundance of Burkholderiales (DNRA-functional bacteria) from 22% to 34%, suggesting that Fe(III) enhances DNRA by regulating nrfA gene expression (encoding nitrite reductase). This aligns with studies in estuarine sediments where Fe(III) promotes DNRA [49].
2. 
Mechanism of Siderite
(1) Open System (Y Groups)
Siderite (FeCO3) undergoes chemical oxidation (Fe2+→Fe3+) in oxidizing environments, releasing soluble Fe3+ (Figure 10b):
4FeCO3 + O2 + 6H2O→4Fe(OH)3 + 4CO2
The rapid Fe3+ release provides ample electron acceptors for DNRA, leading to higher NO3− reduction efficiency in Y3 (80.6%) than Y2 (78.8%). Siderite’s high specific surface area (50–80 m2/g) and layered structure offer active sites for microbial attachment, facilitating Sphingomonadales (relative abundance: 48%) to secrete siderophores (e.g., catecholates) for Fe3+ dissolution. Notably, high salinity (TDS ≈ 500 mg/L) enhances siderite oxidation by weakening Fe2+ binding via Na+ competitive adsorption [50].
(2) Closed System (W Groups)
Under reducing conditions, siderite continuously releases Fe3+ via microbially mediated Fe(II) oxidation (NRFeOx process). For example, chemolithoautotrophs like Thiobacillus oxidize Fe2+ using NO3− as an electron acceptor as follows:
NO3− + 5Fe2+ + 12H+ → NH4+ + 5Fe3+ + 3H2O
This dynamic Fe(II)/Fe(III) cycling significantly boosts nitrate reduction rates (W3: k= −0.2005 d−1). Siderite addition also increases Rhodospirillales abundance, which couples Fe3+ reduction with organic matter oxidation via photosynthetic reducing power [51]. High Cl− concentrations in saline environments stabilize Fe2+-Cl complexes, delaying Fe2+ oxidation kinetics and prolonging Fe(III) supply [52,53].

3.3.3. Salinity-Specific Environmental Effects

The unique hydrochemical conditions of the Chagan Lake–groundwater interaction zone (TDS = 470~530 mg/L, Cl− = 71.40~87.09 mg/L) exerted multidimensional controls on nitrate reduction pathways through synergistic interactions with iron minerals. These salinity-driven mechanisms were validated by the following experimental results:
(1) Fe2+ Stabilization via Cl− Complexation
Elevated Cl− concentrations (75~85 mg/L) may stabilize aqueous Fe2+ as soluble FeCl+ complexes, as evidenced by the delayed Fe2+ oxidation kinetics (Figure 7). This phenomenon aligns with previous studies showing that Cl− enhances Fe2+ persistence in saline environments [52]. Such stabilization could prolong Fe3+ bioavailability, thereby promoting DNRA pathways.
(2) Na+ Competition on Mineral Surfaces
High Na+ concentrations (210~240 mg/L) likely weakened Fe3+-O bonds on hematite surfaces through competitive adsorption (≡Fe-OH + Na+ → ≡Fe-O−-Na+), as suggested by the accelerated Fe3+ release rates in hematite-amended groups (Y2: k = −0.0401 d−1 vs. Y1: k = −0.0247 d−1; Table 3). This mechanism is consistent with reports of Na+-enhanced mineral dissolution in saline systems [47].
(3) Microbial Halotolerance
The dominance of Sphingomonadales (48% in open systems) and Burkholderiales (34% in closed systems; Figure 6) suggests microbial adaptation to high salinity. These taxa are known to secrete compatible solutes (e.g., ectoine) under osmotic stress, which may preserve metabolic activity (ETS activity = 1.5 × 10−3 μmol INTF/g·h; Figure 9) and facilitate DNRA [28].

4. Conclusions

This study investigated the impact of hematite and siderite on nitrate reduction and microbial community dynamics in the saline-alkaline lake–groundwater interaction zone of Chagan Lake, northern China, under simulated oxidative (open) and reductive (closed) conditions. The findings reveal distinct mechanisms by which iron minerals regulate nitrate transformation pathways and microbial interactions.
In closed systems, both hematite and siderite achieved near-complete nitrate removal, with reduction rates reaching 99.5%, representing a 1.3% improvement over the control. In contrast, under open systems, siderite outperformed hematite, attaining an 80.6% nitrate reduction rate compared to hematite’s 78.8%. Kinetic analysis further highlighted siderite’s superior performance in closed systems, exhibiting a reaction rate constant of k = −0.2005 d−1—8.4% higher than hematite (k = −0.1849 d−1). These results underscore the critical role of iron mineral type and redox conditions in governing nitrate reduction efficiency.
Microbial community restructuring played a pivotal role in these processes. High-throughput sequencing demonstrated that hematite and siderite selectively enriched functional bacterial taxa. In open systems, hematite promoted Bacillus (denitrifiers) dominance, facilitating Fe(III) reduction, while siderite elevated Sphingomonadales abundance, which enhanced DNRA through organic acid secretion. In closed systems, hematite activated Geobacter for denitrification (NO3− → N2), whereas siderite-driven NRFeOx processes enriched Thiobacillus and Burkholderiales, boosting DNRA contributions to 73%—significantly higher than in open systems (62%). Notably, iron minerals also enhanced microbial metabolic activity, evidenced by a 40% increase in electron transport system (ETS) activity, and induced mineral surface modifications (e.g., dense Fe(III) layers on hematite in open systems, porous Fe(III) structures on siderite in closed systems), which synergistically optimized nitrate reduction.
Under high-salinity conditions, the solubility of hematite and siderite increased, further promoting nitrate reduction. Salinity exerted significant impacts on microbial community structures and the abundance of functional genes (e.g., nrfA for DNRA), thereby indirectly modulating nitrogen cycling processes.
In saline-alkaline lake–groundwater interaction zones, hematite and siderite markedly enhance nitrate reduction by reshaping microbial communities and altering iron mineral redox states. This mechanism provides a scientific foundation for utilizing iron minerals in bioremediation strategies targeting nitrate-contaminated groundwater. Specifically, the use of iron minerals could be explored to improve nitrate removal in contaminated groundwater. Future research will examine the long-term stability of these effects and how they might be applied in field settings.

Author Contributions

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

Funding

This study was supported by the National Natural Science Foundation of China (Nos. 42230204, 42377052, and U23A2024).

Data Availability Statement

The related research has not been completed. If there is a need, you can consult for some data at w1468185939@163.com.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Concentrations of inorganic nitrogen compounds and nitrate reduction efficiency.
Figure 1. Concentrations of inorganic nitrogen compounds and nitrate reduction efficiency.
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Figure 2. Fitting of the NO3−-N reduction kinetic model R2.
Figure 2. Fitting of the NO3−-N reduction kinetic model R2.
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Figure 3. Temporal changes in pH values across different systems.
Figure 3. Temporal changes in pH values across different systems.
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Figure 4. Changes in inorganic nitrogen compound concentrations over time in each group.
Figure 4. Changes in inorganic nitrogen compound concentrations over time in each group.
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Figure 5. Principal coordinate analysis (PCA) at the genus level.
Figure 5. Principal coordinate analysis (PCA) at the genus level.
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Figure 6. Relative abundance of species at the end of the reaction.
Figure 6. Relative abundance of species at the end of the reaction.
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Figure 7. Temporal changes in iron ion concentrations across different systems.
Figure 7. Temporal changes in iron ion concentrations across different systems.
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Figure 8. Temporal changes in OD600 values across different systems.
Figure 8. Temporal changes in OD600 values across different systems.
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Figure 9. Microbial ETS activity in different systems.
Figure 9. Microbial ETS activity in different systems.
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Figure 10. SEM images of soil surfaces: (a) Y2, (b) Y3, (c) W2, and (d) W3.
Figure 10. SEM images of soil surfaces: (a) Y2, (b) Y3, (c) W2, and (d) W3.
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Table 1. Experimental design table.
Table 1. Experimental design table.
Experimental DesignGroupAdditivesExperimental Environment
Open EnvironmentY1Original sediment (3.000 g)Simulate a shallow water environment, solution untreated and left open
Y2Original sediment (3.000 g) + hematite (0.119 g)
Y3Original sediment (3.000 g) + siderite (0.172 g)
Closed EnvironmentW1Original sediment (3.000 g)Simulate underground environment, solution purged with nitrogen and sealed
W2Original sediment (3.000 g) + hematite (0.119 g)
W3Original sediment (3.000 g) + siderite (0.172 g)
Table 2. Experimental analysis table.
Table 2. Experimental analysis table.
Experimental GroupsY1Y2Y3W2W3
W1The Influence of Open and Closed Systems on Nitrate Reduction The Influence of Adding Hematite on Nitrate ReductionThe Influence of Adding Siderite on Nitrate Reduction
W2 The Influence of Open and Closed Systems on Nitrate Reduction The Difference in the Influence of Hematite and Siderite on Nitrate Reduction
W3 The Influence of Open and Closed Systems on Nitrate Reduction
Y1 The Influence of Adding Hematite on Nitrate ReductionThe Influence of Adding Siderite on Nitrate Reduction
Y2 The Difference in the Influence of Hematite and Siderite on Nitrate Reduction
Table 3. First-order kinetic parameters for the NO3−-N reduction process.
Table 3. First-order kinetic parameters for the NO3−-N reduction process.
Reaction SystemkR2
Y1−0.02470.7351
Y2−0.04010.9166
Y3−0.00640.8543
W1−0.13890.8349
W2−0.18490.7745
W3−0.20050.8818
Table 4. Alpha diversity index statistics of microbial communities.
Table 4. Alpha diversity index statistics of microbial communities.
SampleShannonSimpsonShannon EvennessCoverage
Y12.430.230.390.9986
Y22.830.160.450.9986
Y32.530.210.410.9994
W13.290.150.520.9992
W23.390.070.530.9986
W33.310.100.530.9991
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Wang, Z.; Wan, Y.; Ma, Z.; Xu, L.; Zhai, Y.; Su, X. Impact of Iron Minerals on Nitrate Reduction in the Lake–Groundwater Interaction Zone of High-Salinity Environment. Water 2025, 17, 1241. https://doi.org/10.3390/w17091241

AMA Style

Wang Z, Wan Y, Ma Z, Xu L, Zhai Y, Su X. Impact of Iron Minerals on Nitrate Reduction in the Lake–Groundwater Interaction Zone of High-Salinity Environment. Water. 2025; 17(9):1241. https://doi.org/10.3390/w17091241

Chicago/Turabian Style

Wang, Zhen, Yuyu Wan, Zhe Ma, Luwen Xu, Yuanzheng Zhai, and Xiaosi Su. 2025. "Impact of Iron Minerals on Nitrate Reduction in the Lake–Groundwater Interaction Zone of High-Salinity Environment" Water 17, no. 9: 1241. https://doi.org/10.3390/w17091241

APA Style

Wang, Z., Wan, Y., Ma, Z., Xu, L., Zhai, Y., & Su, X. (2025). Impact of Iron Minerals on Nitrate Reduction in the Lake–Groundwater Interaction Zone of High-Salinity Environment. Water, 17(9), 1241. https://doi.org/10.3390/w17091241

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