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Article

Seasonal Distribution of Microbial Community and n-Alkane Functional Genes in Diesel-Contaminated Groundwater: Influence of Water Table Fluctuation

by
Xuefeng Xia
1,*,
Wenjuan Jia
2,
Kai Wang
3 and
Aizhong Ding
2,*
1
School of Business, Linyi University, Linyi 276000, China
2
Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education of China, Beijing Normal University, Beijing 100875, China
3
School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
*
Authors to whom correspondence should be addressed.
Water 2025, 17(11), 1710; https://doi.org/10.3390/w17111710
Submission received: 27 April 2025 / Revised: 31 May 2025 / Accepted: 2 June 2025 / Published: 4 June 2025
(This article belongs to the Special Issue Application of Bioremediation in Groundwater and Soil Pollution)
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Abstract

:
Water table fluctuation alters environment properties and n-alkane transformation, leading to shifts in the groundwater microbial community and functions. A diesel-contaminated aquifer column experiment of seasonal water table fluctuation was designed to explore the mechanisms. Temporal changes in geochemical parameters, n-alkane concentration, bacterial community and functional gene composition were investigated. The results showed that water table fluctuation accelerated the depletion of the diesel n-alkane leakage point. Owing to the variations in the water table, the electron donors (dissolved organic carbon) and electron acceptors (dissolved oxygen, nitrate and sulfate) underwent regular changes, and the bacterial community structure was altered. Dissolved oxygen was the major parameter correlating with the abundance of aerobic functional genes (the sum of the alk_A, alk_R and alk_P) and was beneficial for enhancing the aerobic biodegradation function potential of n-alkanes. However, the static retention of the water table at the highest level inducing water saturation and hypoxia was the critical factor influencing the abundance of anaerobic functional genes (the sum of assA and mcrA) and was favorable for the anaerobic biodegradation function potential of n-alkane. Overall, this study links seasonal water table dynamics to n-alkane biodegradation function potential in aquifers, and suggests that the quality of recharge water, which impacts microbial community assembly and function, should be considered.

1. Introduction

Petroleum hydrocarbons are dominated by crude oil and petroleum products (e.g., oil, diesel or gasoline fuel), which always enter the groundwater through direct natural or anthropogenic leakage [1] and cause potential threats to human beings surrounding the polluted aquifer [2]. After petroleum hydrocarbons enter the aquatic environment, the pollutants’ environmental behaviors of dissolution, dispersion, transport (migration) and transformation (biodegradation, photooxidation, etc.) naturally occur, thereby leading to the attenuation of petroleum hydrocarbons.
Water table fluctuations can drive vertical redistribution of petroleum hydrocarbon pollutants [3], change the profiles of soil water saturation and oxygen contents [4], and correspondingly cause shifts in microbial populations [5]. The biodegradation of petroleum hydrocarbons is mediated by microbial communities, the pathway selection of which is governed by ecological function succession and site-specific geochemical conditions [6]. For the largest component of petroleum hydrocarbons (n-alkanes; [7]), bacterial communities harboring alkane monooxygenase genes (alkB) can use the available dissolved oxygen to mediate the alkane single terminal oxidation [8,9,10]. There are many alkB gene variants (such as alk_A, alk_R and alk_P genes) which share homologous sequences, exist in multiple common bacterial phyla and could degrade n-alkanes with different carbon chain lengths [11]. Then, microbial communities use other available electron acceptors (nitrate, nitrite, sulfate, ferric iron, organic matter and so on) to finish the anoxic biodegradation of n-alkanes, resulting in an anaerobic environment. Methanogenic degradation is an important pathway for n-alkane biodegradation under anaerobic conditions. Bacterial communities harboring alkylsuccinate synthase genes (assA) can mediate a fumarate addition reaction [12], which is believed to be the first step of methanogenesis. Archaeal communities harboring methyl-coenzyme M reductase genes (mcrA) can catalyze the final step of methanogenesis [13].
Regarding the fluctuation of various types of aquifers, there have been many studies on the responses of microbial community composition and abundances of several individual n-alkane functional genes [14,15,16]. However, these studies mainly focused on microbial and functional responses before and after the water table fluctuation occurs and provided only partial information regarding aerobic or anaerobic conditions. A more comprehensive characterization of microbial and functional responses is needed. In addition, monitoring geochemical variables is also necessary to advance insights into the interconnections among biogeochemical parameters, biodegradation processes and microbial functional potential.
In this study, the attenuation of n-alkane in fluctuating diesel-contaminated groundwater was assessed. The main objectives were to (1) investigate the changes in n-alkane concentration and geochemical properties; (2) reveal the variations in bacterial community structure and n-alkane biodegradation functional gene composition; (3) identify the correlation among n-alkane, geochemical properties, bacterial communities and functional genes. Results from this study can enrich our understanding of the succession of n-alkane biodegradation potential throughout the entire water table fluctuation process and its influencing factors.

2. Materials and Methods

2.1. Diesel Transient Leakage and Seasonal Water Table Fluctuation Simulation

Two pilot-scale aquifer columns packed with fine-grained river sediment were used in this study. The length and internal diameter of the columns were 120 cm and 24 cm, respectively (see Figure 1a). The fine-grained river sediment was filled to a height of 110 cm using a wet-packing procedure [17], then the coarse-grained river sediment was filled to a height of 114 cm, and 6 cm of clear headspace was left. The background levels of n-alkanes in the fine-grained river sediment are reported in Figure S1.
Following the filling process, all porous water was naturally discharged through the bottom outlet; the effective porosity was 0.35. The water level was raised to a vertical elevation of 40 cm above the bottom by injecting groundwater (prepared by N2 sparging) from the bottom and remained static for 10 days. Then, a transient leakage from an underground diesel storage tank was simulated by injecting 90 mL of diesel into the water level of each column. For the control column, the water level was maintained stable during the later stage of the experiment.
For the fluctuating column, the water level remained stable for 10 days, followed by a seasonal water table fluctuation. In the first stage, the water level was raised to 60 cm above the bottom by injecting groundwater from the bottom at a flow rate of 1.06 mL/min, maintained for ~16 days, and then was raised to 80 cm above the bottom at the same flow rate. Secondly, the water level was kept stable for ~50 days. Thirdly, the water level was reduced to 60 cm above the bottom by pumping groundwater through the outlet at a flow rate of 1.06 mL/min, maintained for ~16 days, and then was lowered to 40 cm above the bottom at the same flow rate. Finally, the water level was kept stable for 10 days. The experimental timeline in the fluctuating column is shown in Figure 1b. It has similar characteristics with seasonal hydrological variations in most agricultural regions in China, which covers the summer replenishment and winter drainage. The rate at which the water table was raised (~0.04 cm/h) was observed in Central Hebei during irrigation. The fluctuating column exhibited three hydrologic zones: the saturated zone (0–40 cm), fluctuating zone (40–80 cm), and vadose zone (80–110 cm).

2.2. Groundwater Sampling

Groundwater samples were collected after 10 (sampling followed by the injection of diesel), 20, 40, 90, 110 and 120 days using valves (VICI, New York, NY, USA) 40 cm above the bottom (Figure 1). These were at the nodes of the diesel leakage, water table variations and the end of the experiments. The samples were stored at 4 °C for total petroleum hydrocarbon (TPH), n-alkane, geochemical and molecular analysis.

2.3. Chemical and Geochemical Analysis

The TPH was extracted by carbon tetrachloride and measured using a JLBG-126U infrared spectrometer oil detector (Jilin Beiguang, Jilin, China). The n-alkane was extracted by solid-phase extraction (SPE) by an Oasis HLB cartridge and analyzed using GC-MS (Agilent, Santa Clara, CA, USA). The details of the pretreatment and measurement of TPH and n-alkane can be found in the Supplementary Materials.
The temperature (T) was recorded using vertically installed TDR315L probes (Acclima, Los Angeles, CA, USA) positioned within the 30–50 cm depth range above the bottom, which were managed by a data collector CR300 (Campbell, Santa Clara, CA, USA). The pH was detected by an FE 28 Desktop pH meter (Mettler Toledo, Zurich, Switzerland). The dissolved organic carbon (DOC) was measured using a Vario TOC system (Elementar, Jena, Germany). The dissolved oxygen (DO) was measured using DP-PSt3 probes (PreSens, Regensburg, Germany) positioned at a 40 cm depth above the bottom, with data acquisition interfaced through an OXY-10 trace SMA monitoring system (PreSens, Regensburg, Germany). The quantification of NO3− and SO42− anions was performed using ion chromatography (Dionex, Sunnyvale, CA, USA).

2.4. Molecular Analysis

Total genomic DNA was extracted using TIANamp Micro DNA Kits (TIANGEN, Beijing, China) according to the manufacturer’s instructions [18]. The extracted DNA was sent to Allwegene Technology Co., Ltd. (Beijing, China) in China for high-throughput sequencing using the Illumina MiSeq platform. The high-quality sequencing reads were clustered into operational defined taxonomic units (OTUs) under the threshold of 97% similarity [19,20]. The OTUs were assigned to taxonomic groups using the Silva128 16S rRNA database [21,22]. Alpha diversity was calculated with the Mothur package [23], including rarefaction, Good’s coverage and Shannon index.
Quantification for five genes (alk_A, alk_R, alk_P, assA and mcrA) was conducted via quantitative PCR (qPCR) with 2× Taq MasterMix (CWBio, Taizhou, China). For each target gene, specific primer sets were designed and employed for amplification (Table S3). The reaction mixture, PCR protocol, post-amplification thermal ramping protocol and establishment of standard curves were obtained from previous studies [24]; details can be found in the Supplementary Materials. Redundancy analysis (RDA) and multiple correlation analysis were performed to explore the relationships among bacterial communities, functional genes and environmental variables.

3. Results

3.1. n-Alkane Concentration

The n-alkane (accounted for ~80% of the TPH; Figure 2) was significantly positively correlated with the TPH (Table S6; p < 0.01). After the contamination of diesel, the n-alkane concentrations were 46.3 mg/L and 44.0 mg/L in the control and fluctuating columns, respectively. Then, the n-alkane concentration in the control column progressively decreased to 11.3 mg/L by the end of the experiment.
In the fluctuating column, the n-alkane concentration sharply decreased from 44.0 mg/L to 22.5 mg/L when the water table was raised. When the water table was kept static at the highest level, the n-alkane concentration slowly decreased to 18.7 mg/L. Interestingly, the n-alkane concentration also decreased when the water table was dropped. At the end of the experiment, the n-alkane concentration had continuously decreased to 1.8 mg/L.

3.2. Geochemical Properties

3.2.1. Temperature (T), pH

The value of T in the fluctuating column was nearly overlain by that in the control column, which showed a slight upward gradient in both the control and fluctuating columns. The pH value in the fluctuating column was relatively lower and higher than that in the control column before and after day 90, respectively (Figure 3a).

3.2.2. Dissolved Organic Carbon (DOC)

The concentration of DOC first increased to 4.9 mg/L and 4.8 mg/L on day 20 and decreased to 4.3 mg/L and 3.7 mg/L on day 40 in the control and fluctuating columns, respectively. Then, the concentration of DOC in the control column gradually decreased to 3.7 mg/L on day 90, while the concentration of DOC in the fluctuating column gradually increased to 4.0 mg/L on day 90. Finally, the concentration of DOC in the fluctuating column was relatively higher than that in the control column (Figure 3b).

3.2.3. Dissolved Oxygen (DO), NO3− and SO42−

In the control column, the DO gradually decreased throughout the entire experiment (Figure 3c). In the fluctuating column, the DO decreased from 7.7 mg/L to nearly 0 when the water level was raised to its highest value and maintained up to day 110, then increased to 6.5 mg/L by the end of the experiment (Figure 3c). The concentration of NO3− in the fluctuating column was relatively lower and higher than that in the control column before and after day 90, respectively (Figure 3c).
The concentration of SO42− in the control and fluctuating columns increased to 83.0 mg/L and 86.4 mg/L on day 20 and decreased to 62.5 mg/L and 51.7 mg/L on day 40, respectively (Figure 3d). Then, the concentration of SO42− in the control column gradually decreased to 54.4 mg/L on day 90, while the concentration of SO42− in the fluctuating column gradually increased to 93.7 mg/L on day 90. Finally, the SO42− concentration in the fluctuating column was relatively higher than that in the control column (Figure 3d).

3.3. Bacterial Community Structure

The dominant bacterial phyla in the control column were Proteobacteria (17–89%) and Parcubacteria (4–61%), and the proportion of Parcubacteria sharply increased on day 40 and then was relatively stable until the end of the experiment (Figure 4a). However, Proteobacteria (60.7–91.9%) dominated the communities over the experiment in the fluctuating column (Figure 4a).
Core taxa are those temporally shared by multiple samples. Observing at the genus level, a total of 12 genera were present in every groundwater sample, representing the ‘core’ taxa (Figure 4b). The proportion of Novosphingobium showed the highest level in both the control (57.6%) and fluctuating (66.7%) columns on day 20. After the water table was raised, the bacterial community of the fluctuating column differed from that in the control column at equivalent time points. Parcubacteria group (1.4–53.9%) and Unclassified bacterioplankton (0.9–8.6%) generally showed relatively higher abundances in the control column. In the fluctuating column, Novosphingobium (6.2–17.8%), Reyranella (6.6–59.7%) and Aquabacterium (0.4–9.0%) were the three most abundant genera until the end of the experiment. Moreover, it is interesting to note that 20% of the community were assigned to Caulobacter (7.2%) and Sphingobium (12.8%) on day 40, while ~12% were assigned to Sulfuritalea (5.4%) and Ramlibacter (6.5%) on day 110.

3.4. n-Alkane Biodegradation Gene Abundance

The abundances of aerobic functional genes (the sum of alk_A, alk_R and alk_P) and anaerobic functional genes (the sum of assA and mcrA) showed distinctively different temporal variations in these two columns (Figure S3). The abundance of aerobic functional genes in the control column gradually increased to 2.06 × 1013 copies/g DNA on day 90, and then gradually decreased to 5.69 × 1012 copies/g DNA at the end (Figure 5a). In the fluctuating column, the abundance of aerobic functional genes increased to 1.70 × 1013 copies/g DNA, then obviously decreased to 9.65 × 1011 copies/g DNA immediately after the water table had been raised (on day 40). Continuously, it decreased to 3.10 × 1011 copies/g DNA immediately before the water table was lowered (on day 90), and then gradually increased to 5.78 × 1012 copies/g DNA at the end (Figure 5a).
The abundance of anaerobic functional genes in the control column was generally very low over the experiments (Figure 5b). In the fluctuating column, the abundance of anaerobic functional genes was generally very low and there was a very high abundance (4.56 × 1012 copies/g DNA) immediately before the water table was lowered (on day 90) (Figure 5b).

3.5. Correlation Among Environmental Factors, Bacterial Communities and Functional Genes

Redundancy analysis (RDA) was performed to elucidate the effects of the geochemical parameters on the bacterial community structure (Figure 6). At the phylum level, the NO3− (p = 0.038) and SO42− (p = 0.006) concentrations were major factors influencing the bacterial phyla (Table S5a). At the genus level, the pH (p = 0.034), DOC (p = 0.008) and SO42− (p = 0.033) concentrations were the driving factors (Table S5b).
The DO concentration showed a significantly higher positive correlation coefficient with the abundance of aerobic functional gene (p < 0.05). However, the correlation between the selected geochemical parameters and the abundance of anaerobic functional genes was not significant (Table S6).

4. Discussion

4.1. n-Alkane Attenuation of the Leakage Point During Water Table Fluctuation

Initially, the n-alkane concentrations represented the baseline levels in the groundwater, which were much less than that in the medium (Figure S1). The sharply elevated n-alkane concentrations were the result of contamination with diesel. As a kind of light non-aqueous phase and non-wetting liquid, the injected diesel will have formed an oil layer above the water table, and it is expected that the capillary rise can be ignored.
In the control column, the n-alkane concentration then gradually decreased due to microbially mediated processes, transformation or even evaporation. The n-alkane concentration in the fluctuating column obviously decreased when the water table was raised. This is because n-alkanes are non-polar liquids that are readily displaced by water from mineral surfaces and migrate upwards as water table rises [25]. Meanwhile, a small amount of n-alkane may have remained, because of the interactions between the n-alkane and the organic matter or the trapment by fine pores [26]. Surprisingly, the n-alkane concentration did not increase when the water table was dropped. This indicates that the consumption of n-alkane within the continuously saturated zone exceeded the downward migration of n-alkane with the water table due to the entrapment and consumption in the upper fluctuating zone.
At the end point, the n-alkane concentration in the fluctuating column exhibited a relatively lower value, suggesting that water table fluctuation promoted the natural attenuation of n-alkane in the groundwater of the leakage point.

4.2. Geochemical Footprint of the Leakage Point During Water Table Fluctuation

The change in the value of temperature (T) in both the control and fluctuating columns was very close to the room temperature in the laboratory. Therefore, the value of T was not a confounding factor. The value of pH in the groundwater was mostly alkaline in both the control and fluctuating columns, with values ranging from 8.3 to 7.6. Since some complex biogeochemical processes produce protons (H+), the value of pH might decrease with the progress of biogeochemical reactions regarding microbial activity [27].
Microorganisms utilize the dissolved organic carbon (DOC) and n-alkane as carbon sources and electron donors for redox reactions, consuming dissolved oxygen (DO), NO3− and SO42− as electron acceptors. This process leads to the biodegradation of DOC and n-alkane, denitrification, and sulfate reduction, and these biogeochemical indicators were used to verify redox reactions. The notable difference in DOC concentration variations between these two columns occurred when the water remained at the highest level in the fluctuating column. The variation in DOC concentration is related to microbial activity [28], as DOC is released through desorption, dissolution and biogeochemical reactions of soil organic matter [29]. High moisture can provide more carbon sources for microorganisms, which leads to the gradual increase in DOC concentration [30].
The DO concentration in the fluctuating column was below the detection limit when the water level was maintained at its maximum value. This suggests that the consumption of dissolved oxygen by biogeochemical reactions exceeded the recovery of oxygen from above, under the given water saturation conditions [31]. The NO3− concentration showed a slight downward gradient, while the SO42− concentration had a differing variation pattern. The increase in the SO42− concentration was most likely due to the dissolution and desorption processes of inorganic S in the fluctuating zone, although the mineralization of organic S might also be a contributing factor [32]. Moreover, there was a spike in the DOC, NO3− and SO42− concentrations after the water table was dropped, suggesting that a fluctuating water table could provide nutrients for n-alkane degradation [33].

4.3. Temporal Distribution of Bacterial Community Structure

Our results suggested that water table fluctuation altered groundwater bacterial communities at the phylum and genus level, in agreement with previous studies [34,35]. At the phylum level, the main bacteria in the control column were Proteobacteria and Parcubacteria. Proteobacteria can use n-alkane as carbon and energy sources for growth [36]. Parcubacteria (OD1) has been regarded as an aerotolerant bacterium [37], which might participate in n-alkane oxidation under aerobic conditions [38]. In the fluctuating column, Proteobacteria was the only abundant phylum over the entire experiment. Therefore, Proteobacteria was able to survive in both oxic and anoxic environments [39] and played a dominant role in petroleum biodegradation [40].
After the contamination of diesel, Novosphingobium was the most abundant genus in both the control and fluctuating columns on day 20. Novosphingobium is tolerant to various petroleum hydrocarbons and can degrade aliphatic and aromatic hydrocarbons [41]. Furthermore, the differences in core taxa at the genus level between these two columns were due to the rapidly developing redox conditions in the fluctuating column. Parcubacteria group and unclassified bacterioplankton were the important bacterial genera up until the end in the control column. Parcubacteria group might be the genera affiliated with hydrocarbon degradation. Unclassified bacterioplankton belongs to the order Rickettsiales. Neethu et al. (2019) found that the presence of Rickettsiales in oil-contaminated water was spill-specific [42]. The dominant bacterial genera endemic to the fluctuating column were Novosphingobium, Aquabacterium, Reyranella, Caulobacter, Sphingobium, Sulfuritalea and Ramlibacter. Interestingly, Novosphingobium can grow under both aerobic and anoxic conditions [43,44]. Aquabacterium and Reyranella are commonly found in hydrocarbon-contaminated sites and are active in the removal of these pollutants [45,46,47]. Caulobacter and Sphingobium can adapt to a low-oxygen habitat and play critical roles in the biodegradation of n-alkanes [48]. Sulfuritalea and Ramlibacter can degrade various petroleum hydrocarbons under nitrate-reducing conditions [49,50,51]. Their increase might explain the removal efficiency of n-alkanes in anaerobic scenarios, potentially providing insights for n-alkane anaerobic biodegradation. Evidence from core taxa suggested that the n-alkane biodegradation pathway shifted as the water table fluctuated [52].

4.4. Variations in Composition of Functional Genes in Response to Surrounding Environment

Although water table fluctuation was reported to enhance the removal of n-alkane [53], there was less evidence comparing the aerobic and anaerobic degradation potential of n-alkane. Representative aerobic (alkB gene) and anaerobic (assA and mcrA genes) functional genes were selected to infer potential biodegradation pathways. Denitrification, sulfate reduction, etc., are also anaerobic biodegradation functional pathways, but the related functional genes were not analyzed. Our study visualized the biodegradation functional profiles of n-alkane under fluctuating conditions. The detected functional genes demonstrated that the co-existing aerobic and anaerobic microorganisms could orderly utilize n-alkane as a substrate in different micro-niches.
For the aerobic functional gene, three variants of the alkB gene that can survive in aerobic or fluctuating aerobic conditions [17,54] were tracked in the experiments. In the control column, the abundance of aerobic functional genes increased from day 10 to day 90, and subsequently decreased. The initial increase was because of the introduction of diesel when the dissolved oxygen was sufficient [55], while the subsequent decrease was most likely due to a certain degree of reduction in the n-alkane concentration [56]. In the fluctuating column, the abundance of aerobic functional genes was very low during the period when the water table was kept at the highest level. This is due to the anaerobic condition rapidly induced by water table fluctuation, and the oxygen entrainment in such an open system was insufficient to facilitate the aerobic biotransformation.
For the anaerobic functional gene, the pattern of changes in the gene abundances in the control column was not clear. Given the oxic condition induced by sufficient dissolved oxygen, these might be the background abundances of anaerobic functional genes in the microorganisms. Without a spike immediately before the water table was declined, the anaerobic functional gene abundance in the fluctuating column was very similar to that in the control column. The increase in anaerobic functional gene abundance is most likely associated with the long-term water saturation and hypoxia condition [57]. Several studies reported that the raised water table would displace dissolved oxygen and enrich certain anaerobic functional genotypes such as methanogenesis (mcrA); this study corroborated those previous findings [58].

4.5. Identifying Main Factors Driving Succession of Functional Potential

As indicated in the RDA biplot, the NO3− and SO42− concentrations were critical factors for bacterial community structure at the phylum level. DOC, which was an energy source for microbial growth and metabolism [59], had a significant impact on the core taxa structure at the genus level. Moreover, the DO and SO42− concentrations were critical to the structure of core taxa at the genus level. This suggests that the primary factors shaping the overall bacterial community structure were the DO, NO3−, SO42− and DOC concentrations. Therefore, the electron donors and acceptors can be used to explain the bacterial variability, as microbes were active under fluctuating redox conditions [60]. Especially, the n-alkane degraders can utilize DOC and its degradation by-products to enhance the biodegradation functional potential of n-alkanes in shallow aquifers [61]. The high concentration of DOC is conducive to bacterial community diversity, which is consistent with the earlier findings [62].
The DO concentration was significantly correlated with the abundance of aerobic functional genes, strongly hinting that the sufficient oxygen condition could stimulate different bacteria with alkB genes, and alkB-encoding alkane monooxygenase were responsible for the biodegradation of n-alkanes [63]. The correlation between the abundance of anaerobic functional genes and the geochemical parameters was not significant. However, the spike immediately before the water table was dropped suggests that the water saturation and hypoxia conditions were conducive to the enhancement of the anaerobic biodegradation potential of n-alkanes. Nevertheless, the transcript levels of these functional genes are unknown, which precludes a solid validation on biodegradation activity.

5. Conclusions

This study shed light on temporal variations in bacterial community and functional gene composition in simulated diesel-fuel-contaminated groundwater during seasonal water table fluctuation. The results revealed that the water table fluctuation accelerated the depletion of the diesel n-alkane leakage point, and influenced the temporal changes in groundwater bacterial community and microbial functional gene composition. We demonstrated that sufficient oxygen conditions were beneficial for enhancing the ecosystem potential relevant for the aerobic biodegradation of n-alkane. The long-term static retention of the water table at the highest level caused a spike in anaerobic functional gene abundance, suggesting hypoxia conditions could increase the anaerobic biodegradation function potential of n-alkane. Accordingly, water table fluctuation contributed to the aerobic and anaerobic degradation potential switch for n-alkanes. It was found using RDA that the DO, NO3−, SO42− and DOC concentrations were the drivers of temporal variation in bacterial community structure. This proposes a new perspective on studying the biodegradation functional potential succession, particularly when considering the quality of recharge water.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17111710/s1, Table S1: Size distribution of the fine sediment obtained from a Malvern particle sizer; Figure S1: Concentration of n-alkanes with different carbon chain lengths in fine sediment medium; Table S2: External standard calibration curve; Table S3: Primers of qPCR for alk_A, alk_P and alk_R genes; Figure S2: Rarefaction curve of OTUs in the control and fluctuating columns. C represents the control column, F represents the fluctuating column; 10, 20, 40, 90, 110 and 120 represent the samples collected on days 10, 20, 40, 90, 110 and 120, respectively; Table S4: Variation in the alpha-diversity indices of the bacterial communities; Figure S3: Variation in abundances of (a) alk_A, (b) alk_R, (c) alk_P, (d) assA and (e) mcrA genes in the control and fluctuating columns. 10, 20, 40, 90, 110 and 120 represent the samples collected on days 10, 20, 40, 90, 110 and 120, respectively; Table S5(a): Contributions of each factor on variations in bacterial community structure at the phylum level in RDA; Table S5(b): Contributions of each factor on variations in bacterial community structure at the genus level in RDA; Table S6: Multiple correlation analysis among dominant phyla, core taxa at the genus level and different environmental factors analyzed. References [64,65,66,67,68] are cited in Supplementary Materials file.

Author Contributions

Conceptualization, X.X. and A.D.; Methodology, X.X. and A.D.; Software, K.W.; Validation, K.W.; Formal analysis, X.X.; Investigation, X.X.; Data curation, X.X.; Writing—original draft, X.X.; Writing—review and editing, W.J.; Supervision, A.D.; Funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Shandong Provincial Natural Science Foundation (ZR2024QD088) and the Open Project Program of Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education of China (GW202405).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Water 17 01710 g001
Figure 1. (a) Schematic representation of the experimental system. (b) Schematic representation of the experimental timelines showing the sampling points. The blue (1) and red (1) and (2) shading represents the groundwater body in the control and fluctuating columns when the groundwater samples were collected; the orange lines represent the intended pattern of the water table in the columns.
Figure 1. (a) Schematic representation of the experimental system. (b) Schematic representation of the experimental timelines showing the sampling points. The blue (1) and red (1) and (2) shading represents the groundwater body in the control and fluctuating columns when the groundwater samples were collected; the orange lines represent the intended pattern of the water table in the columns.
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Figure 2. Changes in (a) total petroleum hydrocarbon (TPH) concentration and (b) n-alkane concentration in the control and fluctuating columns. The error bars represent the standard deviations of the mean values from triplicate measurements.
Figure 2. Changes in (a) total petroleum hydrocarbon (TPH) concentration and (b) n-alkane concentration in the control and fluctuating columns. The error bars represent the standard deviations of the mean values from triplicate measurements.
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Figure 3. Changes in (a) temperature (T) and pH, (b) dissolved organic carbon (DOC) concentration, (c) dissolved oxygen (DO) and NO3− concentrations and (d) SO42− concentration in the control and fluctuating columns. The error bars represent the standard deviations of the mean values from triplicate measurements.
Figure 3. Changes in (a) temperature (T) and pH, (b) dissolved organic carbon (DOC) concentration, (c) dissolved oxygen (DO) and NO3− concentrations and (d) SO42− concentration in the control and fluctuating columns. The error bars represent the standard deviations of the mean values from triplicate measurements.
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Figure 4. Variation in relative abundance of (a) dominant phyla and (b) core taxa at the genus level in the control and fluctuating columns. C represents the control column, F represents the fluctuating column; 10, 20, 40, 90, 110 and 120 represent the days of sample collection.
Figure 4. Variation in relative abundance of (a) dominant phyla and (b) core taxa at the genus level in the control and fluctuating columns. C represents the control column, F represents the fluctuating column; 10, 20, 40, 90, 110 and 120 represent the days of sample collection.
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Figure 5. Variation in abundances of (a) aerobic functional genes (the sum of alk_A, alk_R and alk_P) and (b) anaerobic functional genes (the sum of assA and mcrA) in the control and fluctuating columns. 10, 20, 40, 90, 110 and 120 represent the days of sample collection.
Figure 5. Variation in abundances of (a) aerobic functional genes (the sum of alk_A, alk_R and alk_P) and (b) anaerobic functional genes (the sum of assA and mcrA) in the control and fluctuating columns. 10, 20, 40, 90, 110 and 120 represent the days of sample collection.
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Figure 6. Redundancy analysis (RDA) between samples, environmental factors and bacterial communities at the (a) phylum and (b) genus level. Blue diamonds and red circles represent the control and fluctuating columns, respectively. C represents the control column, F represents the fluctuating column; 10, 20, 40, 90, 110 and 120 represent the days of sample collection. Factors which significantly impacted bacterial community structure were marked with * (p < 0.05) and ** (p < 0.01).
Figure 6. Redundancy analysis (RDA) between samples, environmental factors and bacterial communities at the (a) phylum and (b) genus level. Blue diamonds and red circles represent the control and fluctuating columns, respectively. C represents the control column, F represents the fluctuating column; 10, 20, 40, 90, 110 and 120 represent the days of sample collection. Factors which significantly impacted bacterial community structure were marked with * (p < 0.05) and ** (p < 0.01).
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Xia, X.; Jia, W.; Wang, K.; Ding, A. Seasonal Distribution of Microbial Community and n-Alkane Functional Genes in Diesel-Contaminated Groundwater: Influence of Water Table Fluctuation. Water 2025, 17, 1710. https://doi.org/10.3390/w17111710

AMA Style

Xia X, Jia W, Wang K, Ding A. Seasonal Distribution of Microbial Community and n-Alkane Functional Genes in Diesel-Contaminated Groundwater: Influence of Water Table Fluctuation. Water. 2025; 17(11):1710. https://doi.org/10.3390/w17111710

Chicago/Turabian Style

Xia, Xuefeng, Wenjuan Jia, Kai Wang, and Aizhong Ding. 2025. "Seasonal Distribution of Microbial Community and n-Alkane Functional Genes in Diesel-Contaminated Groundwater: Influence of Water Table Fluctuation" Water 17, no. 11: 1710. https://doi.org/10.3390/w17111710

APA Style

Xia, X., Jia, W., Wang, K., & Ding, A. (2025). Seasonal Distribution of Microbial Community and n-Alkane Functional Genes in Diesel-Contaminated Groundwater: Influence of Water Table Fluctuation. Water, 17(11), 1710. https://doi.org/10.3390/w17111710

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