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Article

Microbial Community Dynamics in Groundwater of a Petrochemical Refinery: Influence of BTEX and Dichloroethane Contamination

by
Zhengwei Liu
1,2,
Xiaoyu Lin
1,2,
Mingbo Sun
1,2,
Shici Ma
1,2,
Jingru Liu
1,2 and
Shucai Zhang
1,2,*
1
State Key Laboratory of Chemical Safety, Qingdao 266300, China
2
SINOPEC Research Institute of Safety Engineering Co., Ltd., Qingdao 266300, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(22), 3275; https://doi.org/10.3390/w16223275
Submission received: 16 October 2024 / Revised: 11 November 2024 / Accepted: 13 November 2024 / Published: 14 November 2024
(This article belongs to the Topic Soil and Water Pollution Process and Remediation Technologies, 2nd Volume)
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Abstract

:
This study aimed to explore the microbial communities present in aquifer groundwater at a petrochemical refinery and their relationship with groundwater quality parameters, with a focus on common contaminants such as benzene, toluene, ethylbenzene, and dichloroethane (DCA). Groundwater samples were collected from both the source and plume regions to analyze the spatial diversity of the microbial communities utilizing 16S rRNA analysis. The study demonstrated substantial variations in microbial diversity and composition across the sampled sites. The data showed that the operational taxonomic unit count, Shannon index, and Simpson index initially rose before declining with escalating contaminant concentration, suggesting that the level of contaminants significantly influences the abundance and diversity of microbial communities in the phreatic groundwater. Moreover, through SPSS analysis, the study quantitatively established the correlation between the physiochemical characteristics of the groundwater and the microbial community structure. The study disclosed that geochemical parameters, including total alkalinity, ferrous content, and DCA, play a role in shaping the abundance and diversity of microbial communities at the phylum, class, and genus levels. This research contributes to our comprehension of the intricate interplay between microbial communities, particularly those implicated in the biotransformation of benzene and DCA, and their surrounding physiochemical milieu within contaminated zones.

1. Introduction

Groundwater is a critical component of water resources within the crevices of underground rocks and soils [1]. However, over the past several decades, the rapid expansion of the petrochemical industry has led to the release of a multitude of organic contaminants into the environment, including benzene, toluene, ethylbenzene and xylenes (BTEX), chlorinated hydrocarbons, and others. These substances pose a risk to human health. Specifically, BTEX contamination poses a significant threat to the groundwater ecosystem, with the US Environmental Protection Agency classifying these compounds as priority toxic contaminants due to their high toxicity and propensity to migrate into groundwater [2,3,4,5,6,7,8,9,10].
In the natural environment, microbes, the planet’s most abundant and diverse life forms, can utilize various electron acceptors such as dissolved oxygen (DO), nitrate (NO3−), sulfate (SO42−), Fe3+, and Mn4+ to degrade organic contaminants under specific conditions. However, microbes are highly sensitive to natural environmental conditions and are influenced by numerous factors, including the physicochemical properties of the contaminants themselves [11], as well as broader environmental conditions [12]. In other words, changes in these environmental factors—such as ORP, DO, NO3−, SO42−, Fe2+, Mn2+ concentrations, as well as variations in organic contaminant levels—can have direct impacts on the microbial diversity, structure, and spatial distribution of the microbial communities [13,14,15]. Thus far, the structure and function of indigenous microbial communities in contaminated environments have been extensively studied in laboratory settings to understand how they are influenced by environmental factors [16,17,18,19,20]. Winn-Jung Huang et al. explored the effects of environmental factors on the formation of disinfection by-products (DBPs) and arsenic species, as well as the influence of these factors on biotoxicity in a coastal region of central Taiwan [21]. Meng reported that the richness and diversity of the microbial communities in the unsaturated zone (0–5 m) were significantly higher than those in the saturated zone (5–50 m) [22]. RDA results indicated that critical physiochemical parameters that had a significant influence on the composition of microbial communities were TOC, electrical conductivity, NO3−, and Mn2+.
In contrast, microbial communities within actual phreatic groundwater at petrochemical refinery plants will inevitably undergo changes in response to varying environmental conditions [23]; however, the specific microbial groups that become enriched as a result of these shifts remain largely uninvestigated. This is particularly true in the context of BTEX-contaminated plumes, where the presence of DCA is detected in some monitoring wells.
Thus, this study focuses on a BTEX-contaminated plume in southern China, where DCA was detected in select wells. The primary objectives are: (1) To investigate the diversity and composition of the microbial communities using 16S rRNA analysis; (2) to uncover spatial abundance variations of microbial communities affected by the types and concentrations of contaminant; (3) to assess the correlation between physiochemical parameters and the microbial communities through SPSS analysis. The findings of this study will enhance our understanding of the relationship between the structure of microbial communities (with particular emphasis on bacteria involved in the biotransformation of benzene and DCA) and physiochemical parameters, providing valuable insights for future research, protection, and contamination control efforts in groundwater environments.

2. Materials and Methods

2.1. Field Description and Sampling Procedure

The research area is situated within a petrochemical complex in China that has been operational for more than three decades. Figure S1 illustrates the layout of the contaminated site along with the positioning of the wells. According to statistics from the local meteorological office, the annual mean temperature is 21.6 °C. Annually, there is an average precipitation of 1865 mm, coupled with an annual mean evaporation rate of 1875 mm. The aquifer medium primarily consists of sandy clay in the examined depth range. Predominantly, the groundwater type found here is Quaternary phreatic groundwater from a depth of 3 m to 15 m beneath the surface. The elevation of the water table for this phreatic groundwater varies from 8.5 to 12.5 m, largely influenced by the topographical differences in Figure S1. The natural flow direction of the groundwater flows from the northwest towards the southeast. The monitoring system comprises thirteen sampling wells arranged in a quadrilateral configuration, measuring 330 m in length from north to south and 140 m across from east to west.
Groundwater samples were collected from October 10th from the thirteen monitoring wells. The most recent rainfall event took place ten days prior to the sampling. Among these samples, W08 serves as a background reference well, positioned in the upgradient region away from the source area. W07, on the other hand, is situated in close proximity to the source area. Seven sampling wells—namely W06, W05, W16, W4, W15, W12, and W13—are strategically placed along the central axis of the contaminant plume. Three additional wells, W02, W09, and W17, function as flank area monitoring wells, capturing potential lateral spread of contaminants. Lastly, W14 represents a control point in the monitoring wells. All of these sampling points draw from the phreatic groundwater layer. The groundwater samples were meticulously collected at a depth of −1 m beneath the shallow groundwater table using a MicroPurge low-flow groundwater sampling system (SamplePRO, QED Environmental Systems Limited Inc., Dexter, MI, USA) after the construction of the wells, ensuring accurate representation of the subsurface conditions.
For inorganic elements, 1 L groundwater samples were subjected to filtration through a 0.45 μm filter membrane to eliminate any suspended solids or particulate matter that could interfere with the analysis. The filtrate groundwater was then gathered in a 1 L water-collection bottle. In parallel, for the determination of volatile organic compounds (VOCs), 20 mL groundwater samples were gathered in headspace bottles. These samples were promptly refrigerated on ice to minimize the potential for VOC loss and were transported to the laboratory within a day to ensure sample integrity. For biological studies, 3 L groundwater was filtered through a pre-sterilized 0.22 μm pore-size filter to capture biomass representative of the microbial community present. This filtrate containing biomass was then immediately placed on dry ice in an aseptic sampling bag to maintain its low temperature. Upon reaching the laboratory, the captured biomass was stored in an ultralow-temperature refrigerator maintained at −80 °C to preserve the microbial DNA and other biomolecules for subsequent microbial community analysis.

2.2. Analysis of the Groundwater Physiochemical Parameters

Geochemical characterization was conducted through a combination of in situ testing and laboratory analyses. On-site parameters such as pH, DO, and ORP were measured using precise portable instruments. Cations, particularly Fe2+ and Mn2+, were quantified using a portable HACH DR3900 analyzer following the standard methods 8146 (1,10-phenanthroline photometric method) and 8034 (periodate method), respectively. Anion analysis for SO42− and NO3− ions was executed using a Dionex Aquion Ion Chromatography System. Total alkalinity was determined through acid–base titration. The total organic carbon (TOC) content was quantified via the combustion oxidation technique followed by non-dispersive infrared absorption, employing a TOC-L CSH analyzer manufactured by Shimadzu, Japan. Lastly, the content of VOCs, mainly consisting of benzene, toluene, ethylbenzene, xylene, and DCA, was assessed using a purge–trap method combined with gas chromatography–mass spectrometry (Agilent 7890B 5977B-Atomx XYZ Analytical Instruments, Agilent, Santa Clara, CA, USA).
The physicochemical characteristics and the concentrations of the primary contaminants in the groundwater samples were systematically measured in triplicate. The arithmetic mean of these measurements was adopted as the final result, thereby minimizing potential errors and variability inherent in single-point analyses.

2.3. Analysis of Microbial Communities

Total genomic DNA was extracted using the MagPure Soil DNA LQ Kit (Magen Biotechnology, Guangzhou, China) following the manufacturer’s instructions. The concentration of DNA was verified with a NanoDrop and agarose gel and is shown in Table S1. The genomic DNA was used as a template for PCR amplification with the barcoded primers and Tks Gflex DNA Polymerase (Takara, Kyoto, Japan). For microbial diversity analysis, V3–V4 variable regions of 16S rRNA genes were amplified with universal primers 343F (5′-TACGGRAGGCAGCAG-3′) and 798R (5′-AGGGTATCTAATCCT-3′).
Amplicon quality was visualized using gel electrophoresis, and they were purified with AMPure XP beads (Agencourt, CA, USA) and amplified for another round of PCR. After purified with the AMPure XP beads again, the final amplicon was quantified using a Qubit dsDNA assay kit (Waltham, MA, USA). Equal amounts of purified amplicon were pooled for subsequent sequencing.
Raw sequencing data were in FASTQ format. Paired-end reads were then preprocessed using Trimmomatic software (Version 0.35) to detect and cut off ambiguous bases (N). It also cut off low-quality sequences with an average quality score below 20 using the sliding window trimming approach. After trimming, paired-end reads were assembled using FLASH software (Version 1.2.11). Parameters of assembly were: 10 bp of minimal overlapping, 200 bp of maximum overlapping, and a 20% maximum mismatch rate. Sequences underwent further denoising as follows: reads with ambiguous, homologous sequences or below 200 bp were abandoned. Reads with 75% of bases above Q20 were retained. Then, reads with chimerism were detected and removed. These two steps were achieved using QIIME software (1.8.0).
Clean reads were subjected to primer sequence removal and clustering to generate operational taxonomic units (OTUs) using Vsearch software (Version 2.4.2) with a 97% similarity cutoff. The representative read of each OTU was selected using the QIIME package (Version 1.8.0). All representative reads were annotated and blasted against Silva database Version 138 using RDP classifier (confidence threshold was 70%). All representative reads were annotated and blasted against the Unite database (ITSs rDNA) using BLAST.

2.4. Statistical Analysis

To assess the microbial community diversity within the groundwater samples, alpha diversity indices were computed. Specifically, the ACE index was utilized to estimate the species richness, reflecting the number of distinct microbial species present in the community. Meanwhile, the Shannon and Simpson diversity indices were employed to quantify the overall diversity within the communities. These alpha diversity calculations were performed using Mothur software version 1.30.1. Furthermore, to investigate the relationships between the dominant microbes’ abundance and the physicochemical properties as well as the contaminant levels of the groundwater, statistical correlations were analyzed using the IBM SPSS Statistics 21 software. This helped in understanding how the microbial populations might be influenced by or contribute to the environmental conditions and contamination status of the studied groundwater samples.

3. Results

3.1. Groundwater Physicochemical Parameters and Main Contaminants in Study’s Wells

The groundwater quality data collected from the thirteen wells have been summarized and presented in Table 1, alongside a statistical analysis detailed in Table S2.
Microbes can only perform biodegradation within a neutral pH range. The pH of the phreatic groundwater is slightly acidic, regarding the pH values observed in the study, ranging from 5.87 to 6.49. ORP is another significant parameter that reflects the electron-accepting or -donating capacity of the groundwater and plays a crucial role in influencing biodegradation processes. ORP values varied from −132 to 91 mV across the samples. This wide range suggests substantial differences in redox conditions within the phreatic groundwater system. An interesting finding is that areas with relatively higher ORP (more oxidative conditions) tend to be located around the perimeter of the contaminant plume, whereas areas with relatively lower ORP (more reducing conditions) coincide with zones having higher contaminant concentrations within the plume. This observation highlights the relationship between ORP conditions and the distribution of contaminants in the groundwater environment.
Total alkalinity in groundwater typically arises from the presence of hydroxides, carbonates, and bicarbonates derived from elements such as calcium, sodium, potassium, or ammonia. This property plays a critical role in maintaining the pH stability of groundwater, acting as a buffer against changes caused by the generation of acids during organic contaminant degradation processes. In general, contaminated areas generally display higher levels of total alkalinity compared to background wells. This elevated alkalinity can be attributed to the release of alkaline compounds during the biodegradation process or due to the interaction between hydrocarbons and minerals in the aquifer matrix. The buffering capacity provided by total alkalinity helps stabilize the pH and support ongoing biodegradation activities.
DO is a key factor in aerobic biodegradation processes, serving as the most energetically favorable electron acceptor for the breakdown of contaminants. DO concentrations ranged from 0.39 to 2.97 mg/L. Typically, obligate anaerobic bacteria do not thrive in environments where DO levels exceed approximately 0.5 mg/L [24]. NO3− can act as an alternative electron acceptor for anaerobic biodegradation processes, especially through denitrification. However, the NO3− concentrations in the study’s samples were found to vary from 0 to 1.09 mg/L, suggesting that denitrification effects on contaminant degradation are likely minimal given the low NO3− content. SO42− is another important ion that can indicate anaerobic degradation of fuel compounds; its concentration in the groundwater samples spanned from 1.03 to 27.5 mg/L. Fe2+ and Mn2+ concentrations were also measured, ranging from 0.06 to 31.10 mg/L and 0.08 to 16.90 mg/L, respectively.
It is noteworthy that the concentrations of benzene, toluene, ethylbenzene, xylene, phenol, and DCA in the groundwater samples were normalized, with the highest benzene concentration being assigned a value of 1. The normalized contents in the groundwater showed that benzene, toluene, ethylbenzene, xylene, phenol, and DCA ranged from 0 to 1, 0 to 0.05356, 0 to 0.00796, 0 to 0.00334, 0 to 0.00284, and 0 to 0.02104, respectively. These findings reveal that the phreatic groundwater is indeed influenced by anthropogenic activities, particularly since the study area is situated downstream from a production zone within the petrochemical plant where these organic compounds are typical contaminants.
TOC serves as a comprehensive measure of the organic matter content within the aquifer. As detailed in Table S3, the Spearman correlation coefficients reveal a positive correlation between TOC and benzene concentration, indicating that generally an increase in benzene levels is accompanied by a rise in TOC. It can also be seen that the concentration of benzene contamination is the highest, with other contaminant concentrations being relatively lower. Additionally, TOC displays negative correlations with both ORP and DO, implying that as the quantity of organic matter increases, the availability of oxidizing agents, as represented by ORP and DO, tends to decline. ORP and DO are directly proportional. Moreover, there exists a negative correlation between DO and TOC, benzene, toluene, and ethylbenzene.
This suggests that elevated contaminant levels can give rise to TOC and more reducing conditions, as represented by ORP and DO, in groundwater. In accordance with the principles of natural attenuation, it is logical to deduce that the higher the concentration of organic contaminants, the lower the DO concentrations will be, and correspondingly, the lower the ORP will tend to be.

3.2. Characteristics of Microbial Community Structure in Study’s Wells

Thirteen groundwater samples were collected from the phreatic groundwater layer as illustrated in Figure S1, at a depth of 1 m beneath the groundwater table surface. After primer sequence removal and clustering to generate OTUs with a 97% similarity cutoff, the total high-quality sequences and OTU results at different wells in the study area were compiled and are presented in Table 2.
The OTU data serve as a foundation for subsequent comparative and statistical analyses that aim to correlate microbial community composition with the physicochemical parameters and contaminant concentrations in the groundwater [25]. As shown in Figure 1, there exists an inverse V-shaped relationship on the whole, meaning that the OTU number initially increases and then decreases as the contaminant concentration rises. Wells W06, W15, W04, and W05 have the lowest OTU numbers, which might suggest that the diversity of this population diminishes with increasing contamination. Similarly, the abundance-based coverage estimator (ACE) metric, which estimates the total richness of the community, also exhibits a comparable trend to the OTUs. Wells W06, W15, W04, and W05 have the smallest ACE numbers, reinforcing the notion that the variety of microbial species declines with heightened levels of benzene contamination. Excessive contaminant levels can exert toxic effects on microbial growth, which can lead to selection pressures that favor the survival and proliferation of certain microbial populations better adapted to tolerate or degrade the contaminants [26]. This information contributes to a deeper understanding of how microbial communities respond to varying degrees of contamination in the groundwater environment.
Based on the α-diversity analysis, the Shannon index values—a measure of the overall microbial diversity—in the phreatic groundwater of the thirteen sampled wells varied from 2.56 to 6.54. There was no evident geographical pattern in terms of diversity, as the Shannon values did not consistently increase or decrease along a particular direction or gradient. When correlating the Shannon index with the contaminant concentration, Figure 1 illustrates an inverse V-shaped relationship. Wells W05, W15, and W04, which have the highest contaminant levels, also display the lowest Shannon diversity values. Conversely, wells W12, W16, W02, and W07, with intermediate contaminant concentrations, have the highest Shannon values. This suggests that as contaminant levels increase beyond a certain point, the microbial diversity starts to decline. The perimeter wells W08, W09, and W17, with nearly no contaminants, exhibit relatively higher Shannon values, indicating a natural and more diverse microbial community. Parallel to the Shannon index, the Simpson index—a measure of community evenness—followed a similar trend. The Simpson values were lowest in wells W05, W15, and W04, corresponding to the sites with the highest contamination. Meanwhile, wells W02, W12, W16, and W07 had the highest Simpson values, again pointing to a higher biodiversity in areas with medium contaminant concentrations. The perimeter wells W08, W09, and W17 maintained relatively high Simpson values, consistent with their low to negligible contamination levels. These findings collectively suggest that high levels of contaminants in groundwater negatively impact the overall diversity and evenness of the microbial community, while lower contamination supports a more diverse and balanced ecosystem.
In summary, the relationship between the microbial community and organic contaminants in the phreatic groundwater revealed that the OTU count, as well as the Shannon and Simpson diversity indices, exhibited an inverse V-shaped relationship with the contaminant concentration. This implies that microbial diversity reaches its peak when the contaminant concentration is at a moderate level. In these cases, a balance may exist where the contaminants are enough to sustain diverse microbial populations without causing extreme stress or overgrowth of a few tolerant species. In essence, the concentration of contaminants exerts a profound impact on the diversity of microbial communities in the phreatic groundwater. This phenomenon aligns with previous studies, as documented in reference [22], where a comparison of α-diversity results across multiple sampling points along the groundwater flow path reinforced the idea that contaminant concentration is a key determinant of the structure and dynamics of these communities.
The microbial phyla composition in the phreatic groundwater from the thirteen wells was comprehensively analyzed, as depicted in Figure 2. Among the prevalent phyla, Proteobacteria, Bacteroidota, Firmicutes, Patescibacteria, and Spirochaetota emerged as the dominant groups, collectively accounting for a cumulative relative abundance ranging from 72.4% to 99.3% across all samples.
Proteobacteria was the most abundant microbial phylum, dominating the phreatic groundwater with relative abundances ranging from 53.7% to a staggering 94.1% in most wells, except for wells W7 and W16 where the proportions were significantly lower at 13.6% and 7.6%, respectively. Known for its versatility, Proteobacteria includes facultative or obligate anaerobic species capable of thriving in wastewater and groundwater environments and has been linked to the degradation of BTEX compounds [1]. Bacteroidota, with a relative abundance ranging from 1.9% to 10.2%, represents aerobic and nitrifying bacteria that play a crucial role in breaking down organic substances in the groundwater [27,28,29]. Firmicutes had a more variable abundance, ranging from 0.9% to 11.8% across all samples, but reached a notably higher level of 39.3% in well W7. Patescibacteria were particularly abundant in wells W06, W07, and W16, contributing 17.1%, 4.0%, and 28.6%, respectively. These bacteria have been shown to encode a variety of enzymes that can break down complex organic matter into simpler molecules [29,30]. Spirochaetota were primarily concentrated in wells W07 and W16, with respective abundances of 34.7% and 19.9%.
At the class and genus level, the taxa with relative abundances exceeding 1% were designated as “core taxa” [31], which are considered as the main contributors to the overall functionality and resilience of the microbial community. Figure 2 illustrates the dominance of the top 15 core taxa in the phreatic groundwater, as they cumulatively represent a wide range of relative abundances from 76.2% to 99.7% at the class level. This extensive coverage signifies their critical involvement in adapting to and transforming the groundwater environment’s conditions.
The microbial community in the phreatic groundwater was predominantly composed of eight classes: Gammaproteobacteria, Alphaproteobacteria, Bacteroidia, Spirochaetia, Parcubacteria, Actinobacteria, Clostridia, and Desulfitobacteriia. At the class level, these core taxa contributed a collective relative abundance ranging from 62.5% to 99.5% of the total microbial community. Among these, Gammaproteobacteria was the most abundant, with relative abundances varying from 7.2% to a remarkable 93.2% in Figure S2. Alphaproteobacteria and Bacteroidia also played significant roles, showing relative abundances from 0.4% to 26.4% and 1.9% to 10.2%, respectively. When combined, these three classes accounted for a cumulative relative abundance of 58.8% to 98.1% across all samples, except in wells W16 and W7, where the cumulative abundance was notably lower at 13.1% and 18.8%, respectively. Other classes like Spirochaetia, Parcubacteria, Actinobacteria, Clostridia, and Desulfitobacteriia also made notable contributions to the microbial community structure, with their relative abundances ranging from 0% to 34.7%, 0% to 25.8%, 0% to 14.9%, 0% to 13.6%, and 0% to 20.5%, respectively.
The significant presence of Gammaproteobacteria and Alphaproteobacteria in the phreatic groundwater reinforces earlier conclusions that these subclasses of Proteobacteria are the typical dominant microbial groups in the study’s wells. This finding aligns with previous research where Gammaproteobacteria and Alphaproteobacteria were identified as the primary classes in BTEX-contaminated groundwater ecosystems [1]. Bacteroidia, with relative abundances from 1.9% to 10.2%, is also crucial in the phreatic groundwater as it aids in the decomposition of complex organic substances into simpler compounds [32]. Maryam’s study reports that Gammaproteobacteria, Bacteroidia, and Alphaproteobacteria were the main taxa during the removal of phenol facilitated by the use of CaO2 [33]. However, wells W7 and W16 displayed distinct microbial compositions. Instead of the aforementioned dominant groups, they were characterized by the prevalence of Spirochaetia, Parcubacteria, Clostridia, and Desulfitobacteriia. Of note, Clostridia encompasses several fermentative bacteria known to degrade toluene and TPHs under anoxic conditions [1,34]. Consequently, while there is a common set of core microbial communities shared among the majority of the sampling wells, the composition in wells W7 and W16 deviates significantly from this norm as shown in Figure 2.
The identification of Ralstonia, Acinetobacter, Methylobacter, Aquabacterium, Novosphingobium, Hydrogenophaga, Curvibacter, Pseudomonas, Limnobacter, Diaphorobacter, Rhodanobacter, and Sphingomonas as common core taxa at the genus level in the phreatic groundwater emphasizes the diversity of microbial genera within this environment. Their relative abundances spanned a broad range from 0.2% to 63.9% for Ralstonia, 0% to 11.6% for Acinetobacter, 0% to 47.4% for Methylobacter, and so forth, up to 0% to 24.4% for Rhodanobacter and 0% to 9.6% for Sphingomonas. This wide disparity in relative abundances highlights the substantial variation in the microbial community structure across the phreatic groundwater samples, as visually depicted in Figure 2.
The genus Ralstonia has been isolated from oil-contaminated soil and polyethylene, demonstrating its potential for surviving and metabolizing hydrocarbons [35,36]. Acinetobacter, another genus present in the phreatic groundwater, has been employed to degrade petroleum hydrocarbons and shown to enhance the proliferation of native bacteria that break down oil [37]. Methylobacter has also been identified as a petroleum-degrading bacterium [38]. Pseudomonas, a ubiquitous genus in phreatic groundwater, is well-known for its capability to remediate toluene and benzene, playing a pivotal role in the biotransformation of NO3−, Fe2+, and Mn2+ [39,40]. Studies have also suggested that high concentrations of hydrocarbon contaminants might foster the growth of potentially pathogenic strains of Pseudomonas [41,42]. Sphingomonas, a genus that thrives in the presence of contaminants and utilizes them as energy and growth substrates, is adept at degrading organic contaminants [43]. Notably, Sphingomonas was found in many of the studied wells, affirming its importance in the degradation processes within the phreatic groundwater. Desulfitobacterium, specifically detected in wells W7 and W16, is another genus of interest due to its potential to reduce sulfur compounds and participate in anaerobic degradation pathways.
Indeed, it is quite evident that there exists a substantial variation in the abundance and structure of microbial communities across the different wells. Each well harbors a unique combination of microbial genera, with some genera being dominant in certain wells while less prevalent or absent in others. This observation underscores the complexity and heterogeneity of the phreatic groundwater ecosystem, influenced by factors such as the local geochemistry, contaminant profiles, and ORP conditions. The detection of genera like Desulfitobacterium in specific wells suggests that anaerobic degradation processes are also taking place in those areas.

3.3. Relationships Between Microbial Community Characteristics and Groundwater Physiochemical Parameters

Indeed, numerous studies have established that environmental factors play a pivotal role in shaping the composition and diversity of microbial communities in groundwater systems. Key factors include ORP, contaminant concentrations, TOC, and pH, among others. For instance, pH has been identified as a critical parameter affecting the microbial community structure [44]. Another study pointed out that the addition of zero valent iron (ZVI), often used for groundwater remediation, significantly affects both pH and DO levels. This alteration can lead to substantial changes in the composition of subsurface microbial communities. ZVI reactions can create reducing conditions, favoring certain anaerobic microbes and shifting the community away from aerobic ones [45]. Understanding the intricate interplay between these environmental variables and the resident microbial communities is crucial for effective bioremediation strategies and predicting the long-term ecological health of contaminated groundwater environments.
To gain deeper insights into the factors governing the microbial community composition and diversity in the phreatic groundwater, a Spearman correlation analysis was performed to explore the potential relationships between the microbial abundance (the relative abundance of specific taxa) and the organic contaminants (like benzene, toluene, DCA, and phenol), as well as various natural attenuation parameters, including DO, NO3−, SO42−, Fe2+, Mn2+, pH, and TOC. By conducting this analysis, we aim to identify which of these environmental factors have a significant impact on the microbial community structure and function and whether they positively or negatively correlate with microbial abundance and diversity. This information can help elucidate the underlying mechanisms driving the distribution and activity of specific microbial taxa.
Based on the Spearman correlation analysis at the phylum level, several noteworthy relationships were identified in Table S3:
  • There is a positive correlation between the presence of Fe2⁺ and the abundance of Acidobacteriota, Desulfobacterota, and Nitrospirota. This suggests that these microbial phyla might thrive in iron-rich environments, possibly involving iron-reducing or -oxidizing processes.
  • An intriguing finding is the positive correlation between DCA and both Firmicutes and Spirochaetota. This implies that DCA could potentially act as a growth promoter for these two phyla, enhancing their numbers or activity in the groundwater.
  • The presence of M,P-xylene shows a positive correlation with Actinobacteriota. This indicates that Actinobacteria might be involved in the degradation or metabolism of this particular contaminant.
  • Proteobacteria showed a negative correlation with Patescibacteria, Firmicutes, and Spirochaetota. This suggests that the proliferation of Proteobacteria might be hindered when these other phyla are dominant, possibly due to competition for resources or differing tolerances to contaminant type.
From these correlations, it can be inferred that DCA may indeed promote the growth and activity of Firmicutes and Spirochaetota, making them potentially useful indicators or drivers in bioremediation processes. Conversely, it seems to negatively impact the growth of Proteobacteria, indicating the presence of DCA can significantly shape the structure and dynamics of microbial communities in phreatic groundwater ecosystems.
At the class level, the correlation analysis reveals additional insights into how various environmental factors and contaminants influence microbial classes:
  • Fe2⁺ shows a positive correlation with Thermodesulfovibrionia, Thermoanaerobaculia, and Ignavibacteria, indicating that these classes may preferentially thrive in iron-rich environments, potentially participating in iron-related metabolic processes.
  • A positive correlation was found between total alkalinity and the classes Spirochaetia, Clostridia, and Desulfitobacteriia in Table S4. This suggests that more alkaline conditions might encourage the growth and activity of these microbial classes in the groundwater.
  • A strong positive link between phenol concentration and the class Holophagae points towards the ability of these bacteria to adapt and survive in environments with high phenol content, perhaps even utilizing phenolic compounds as a carbon source.
  • Actinobacteria is positively associated with ethylbenzene and M,P-xylene, suggesting a role in the degradation of these aromatic hydrocarbons. Studies by Balachandran et al. [46] and Baoune et al. [47] confirm the capacity of Actinobacteria to degrade various hydrocarbons including naphthalene, phenanthrene, diesel, gasoline, kerosene, benzene, toluene, xylene, and cyclohexane, supporting their involvement in hydrocarbon degradation in contaminated soils and potentially in groundwater as well.
The most intriguing finding from the Spearman correlation analysis at the class level is the positive relationship between DCA concentration and the classes Desulfitobacteriia, Clostridia, and Spirochaetia. Desulfitobacteriia are known for their sulfate-reducing abilities and can play a role in the detoxification of chlorinated compounds, which might explain their positive correlation with DCA. Clostridia are diverse anaerobic bacteria capable of fermentative and reductive processes, and some species can degrade various organic contaminants. Spirochaetia are also anaerobic bacteria that may have adapted to live in environments rich in organohalide compounds or other electron acceptors, which could include DCA under certain conditions. Thus, the presence of DCA might create favorable conditions for these microbial classes, potentially implicating them in DCA metabolism or transformation within the groundwater environment.
The data presented suggest a clear pattern where the presence of DCA in wells W7 and W16 has led to a shift in the dominant microbial communities. Unlike other wells, these two wells are characterized by a dominance of Spirochaetia, Clostridia, and Desulfitobacteriia, which are positively correlated with each other, indicating a possible synergistic interaction or shared metabolic pathways relevant to DCA degradation. The positive correlation between DCA concentration and these microbial classes implies that the presence of DCA might stimulate their growth and proliferation, suggesting their active participation in the biodegradation of DCA. This hypothesis is supported by the observation that the relative abundance of Gammaproteobacteria and Alphaproteobacteria decreases significantly in these wells, which contrasts with their dominance in other wells without DCA levels. Furthermore, the negative correlation between Gammaproteobacteria and the DCA-responsive classes (Spirochaetia, Clostridia, and Desulfitobacteriia) suggests that DCA might exert a detrimental effect on the growth of Gammaproteobacteria, potentially leading to competitive exclusion or inhibition under DCA-enriched conditions. In conclusion, the findings suggest that DCA acts as a selective pressure in the groundwater environment, favoring the growth of Spirochaetia, Clostridia, and Desulfitobacteriia while hindering that of Gammaproteobacteria and Alphaproteobacteria. Understanding these dynamics is crucial for devising appropriate bioremediation strategies targeting DCA-contaminated groundwater and for predicting the potential effects of DCA on the indigenous microbial communities in similar ecosystems.
At the genus level, the analysis in Table S5 revealed several key correlations:
  • pH had a positive correlation with Acinetobacter, Limnobacter, and Rhodanobacter. This indicates that a higher-pH environment may be more conducive to the proliferation of these microbial genera, potentially making them better suited for bioremediation.
  • DO was positively correlated with Novosphingobium and Hydrogenophaga, both of which are known aerobic microbes. Wells W02 and W17, with the highest DO levels, had the highest proportions of these genera.
  • Ralstonia, Sphingomonas, and Sediminibacterium exhibited a positive correlation with TOC, benzene, and ortho-xylene. This suggests that these microbial genera may play a role in the degradation of BTEX contaminants in the groundwater. The positive correlation among these genera hints at a cooperative or synergistic mechanism in breaking down these contaminants.
  • DCA concentration showed a strong positive link with Desulfitobacterium. Intriguingly, Desulfitobacterium was only detected in the phreatic groundwaters of wells W7 and W16, which are contaminated with DCA. Desulfitobacterium has been recognized as a specific microbial species capable of degrading chlorinated hydrocarbons [24]. This points to the potential of Desulfitobacterium in metabolizing or degrading DCA, providing insight into how the presence of this contaminant influences the composition and functional potential of the microbial community in these specific wells.
In summary, the Spearman correlation analysis has illuminated that key physicochemical parameters such as total alkalinity, Fe2+, DCA, xylene, and DO exhibit significant correlations with various microbial taxa at the phylum, class, and genus levels. The identified relationships, such as the positive correlation between DCA and certain microbial classes (Desulfitobacteriia, Clostridia, and Spirochaetia), indicate that these microbial taxa may be actively involved in the biodegradation and transformation of contaminants in the groundwater. This understanding is crucial for assessing the role of indigenous microbes in the natural attenuation of contaminants and can guide the design of more effective and sustainable bioremediation strategies for contaminated aquifers.

4. Conclusions

This study thoroughly examined the relationship between groundwater parameters and the composition and diversity of microbial communities within a petrochemical refinery in southern China. Sampling locations were strategically chosen at the source, plume, and periphery of the contamination zone to capture a comprehensive range of contaminant levels. The results confirmed that elevated contaminant levels can give rise to TOC and more reducing conditions, as represented by ORP and DO, in groundwater. An interesting finding was that the OTU number, along with Shannon and Simpson diversity indices, generally followed an inverse V-shaped relationship with contaminant concentration, peaking at intermediate levels. This suggests that moderate contaminant concentrations foster the highest microbial diversity in the phreatic groundwater. A balance may exist where the contaminants are enough to sustain diverse microbial populations without causing extreme stress. In samples contaminated exclusively with BTEX compounds, Gammaproteobacteria, Alphaproteobacteria, and Bacteroidia accounted for a cumulative relative abundance of 58.8% to 98.1%. And a notable difference in microbial community structure was observed in wells W16 and W7 due to the specific contaminant DCA. The detection of DCA, even at trace amounts within the BTEX-contaminated plume, seemingly had an inhibitory effect on Proteobacteria populations while fostering the proliferation of Clostridia, Desulfitobacteriia, and Spirochaetia. This shift in microbial community structure was clearly evident in the distinct microbial assemblages observed in wells W07 and W16, substantiating the obvious influence of DCA on the resident microbial ecology. Moreover, the study also revealed that geochemical parameters such as total alkalinity, Fe2⁺, xylene, and DO affect the microbial community abundance and diversity across multiple taxonomic ranks, underscoring the significance of these environmental factors in shaping the microbial landscape. Ultimately, this investigation enhanced our comprehension of the intricate interplay between groundwater parameters and the structure of microbial communities in a BTEX-contaminated area.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16223275/s1, Figure S1: Layout of contaminated site showing well location. The arrow shows the flow direction of the groundwater. The red line in (a) depicts the topographic elevation and the red line in (b) depicts the shallow groundwater table elevation; Figure S2: The relative microbial abundance of the Gammaproteobacteria, Alphaproteobacteria, Bacteroidia and the total of these three classes; Table S1: Polymerase chain reaction (PCR) system and reaction conditions; Table S2: The statistical value of the groundwater physicochemical and contaminant concentration data from the study’s fourteen sampling wells; Table S3: The Spearman correlation between the geochemical parameter, contaminant parameter, and the microbiological parameter at the phylum level; Table S4: The Spearman correlation between the geochemical parameter, contaminant parameter, and the microbiological parameter at the class level; Table S5: The Spearman correlation between the geochemical parameter, contaminant parameter, and the microbiological parameter at the genus level.

Author Contributions

All authors contributed to the study conception and design. Z.L. (First Author): conceptualization, methodology, investigation, formal analysis, writing—original draft; X.L.: conceptualization, methodology; M.S.: data curation, validation, writing—original draft preparation; S.M.: visualization, formal analysis, software; J.L.: writing—review and editing, supervision; S.Z.: funding acquisition, resources, supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This project was funded by the Technology Development Program of SINOPEC, China (Grant No. 323018).

Data Availability Statement

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

Conflicts of Interest

Author Zhengwei Liu, Xiaoyu Lin, Mingbo Sun, Shici Ma, Jingru Liu and Shucai Zhang was employed by the company SINOPEC Research Institute of Safety Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Water 16 03275 g001
Figure 1. (A) The OUT number, (B) Simpson value, and (C) Shannon value change with the benzene concentration.
Figure 1. (A) The OUT number, (B) Simpson value, and (C) Shannon value change with the benzene concentration.
Water 16 03275 g001
Water 16 03275 g002
Figure 2. Microbial communities in different groundwater wells in the study area at phylum level (top), class level (middle), and genus level (bottom).
Figure 2. Microbial communities in different groundwater wells in the study area at phylum level (top), class level (middle), and genus level (bottom).
Water 16 03275 g002
Table 1. Average values of the groundwater physicochemical and contaminant concentration data from the study’s thirteen sampling wells. ND means not detected.
Table 1. Average values of the groundwater physicochemical and contaminant concentration data from the study’s thirteen sampling wells. ND means not detected.
pHORP (mV)Total Alkalinity (mg/L)DO (mg/L)NO3− (mg/L)SO42− (mg/L)Fe2+ (mg/L)Mn2+ (mg/L)TOC (mg/L)BenzeneTolueneEthylbenzeneM,P-XyleneO-XylenePhenolDichloroethane
W085.932440.02.881.09027.500.070.7449.8NDNDNDNDNDNDND
W076.08−105332.01.200.0585.601.021.48163.00.155450.013180.00533NDND0.000620.021043
W065.89−8660.00.750.7972.828.842.65128.00.523700.026780.007960.003340.00045NDND
W056.12−10244.80.390.0421.031.6411.80573.01.000000.053560.002000.000920.000270.00039ND
W166.04−78183.01.100.38215.400.060.1079.00.094316.87 × 10−5NDNDNDND7.58 × 10−5
W046.13−9561.30.830.1342.291.580.08682.00.822280.001560.003790.000940.002256.4 × 10−5ND
W156.02−13268.80.970.2524.840.840.18707.00.850710.004830.004550.000340.000250.00027ND
W125.92−7232.01.800.05413.7031.100.0847.20.061370.00564ND0.000448.06 × 10−50.00015ND
W135.879130.62.060.10016.1010.600.624.70.01265NDNDNDND0.00284ND
W026.23−35121.02.49ND4.301.0016.9026.20.019570.001865.21 × 10−5NDND9.48 × 10−5ND
W096.493875.22.400.70221.608.083.046.3ND1.42 × 10−59.48 × 10−62.13 × 10−5NDNDND
W176.15−30204.02.970.04124.551.6616.808.4NDNDNDNDNDNDND
W146.105445.82.280.0436.670.110.715.9NDNDNDNDNDNDND
Table 2. The richness, diversity estimator, and alpha diversity estimator of microbial community in the thirteen sampling wells.
Table 2. The richness, diversity estimator, and alpha diversity estimator of microbial community in the thirteen sampling wells.
Sampling PointValid TagsOTUsACESimpsonShannonChao1Goods Coverage
W0263,353117016140.925.901547.440.9926
W0472,24389413350.663.101223.720.9934
W0569,68488613620.632.561400.740.9928
W0670,73590612110.785.291229.740.9941
W0767,803127215150.945.651512.980.9931
W0855,684108320170.854.671908.770.9914
W0955,644100814160.904.891379.930.9933
W1259,492155019330.966.541897.610.9913
W1364,999114115170.734.141497.530.9926
W1457,903115917200.774.611642.490.9921
W1572,55391112820.623.151303.910.9933
W1667,907147317040.976.491712.610.9923
W1756,13796913780.865.571392.230.9931
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Liu, Z.; Lin, X.; Sun, M.; Ma, S.; Liu, J.; Zhang, S. Microbial Community Dynamics in Groundwater of a Petrochemical Refinery: Influence of BTEX and Dichloroethane Contamination. Water 2024, 16, 3275. https://doi.org/10.3390/w16223275

AMA Style

Liu Z, Lin X, Sun M, Ma S, Liu J, Zhang S. Microbial Community Dynamics in Groundwater of a Petrochemical Refinery: Influence of BTEX and Dichloroethane Contamination. Water. 2024; 16(22):3275. https://doi.org/10.3390/w16223275

Chicago/Turabian Style

Liu, Zhengwei, Xiaoyu Lin, Mingbo Sun, Shici Ma, Jingru Liu, and Shucai Zhang. 2024. "Microbial Community Dynamics in Groundwater of a Petrochemical Refinery: Influence of BTEX and Dichloroethane Contamination" Water 16, no. 22: 3275. https://doi.org/10.3390/w16223275

APA Style

Liu, Z., Lin, X., Sun, M., Ma, S., Liu, J., & Zhang, S. (2024). Microbial Community Dynamics in Groundwater of a Petrochemical Refinery: Influence of BTEX and Dichloroethane Contamination. Water, 16(22), 3275. https://doi.org/10.3390/w16223275

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