Metagenomic insights into microbial diversity in a groundwater basin impacted by a variety of anthropogenic activities

Metagenomic landscape of sediments of river Ganga reveals microbial diversity, potential plastic and xenobiotic degradation enzymes

The Ganga is the largest river in India, serves as a lifeline for agriculture, drinking water, and religious rites. However, it became highly polluted due to the influx of industrial wastes and untreated sewages, leading to the decline of aquatic biodiversity. This study investigated the microbial diversity and plastic-xenobiotic degrading enzymes of six sediment metagenomes of river Ganga at Prayagraj (RDG, TSG, SDG) and Devprayag (KRG, BNG, BRG). The water quality parameters, higher values of BOD (1.8-3.7 ppm), COD (23-29.2 ppm) and organic carbon (0.18-0.51%) were recorded at Prayagraj. Comparative analysis of microbial community structure between Prayagraj and Devprayag revealed significant differences between Bacteroidetes and Firmicutes, which emerging as the predominant bacterial phyla across six sediment samples. Notably, their prevalence was highest in the BRG samples. Furthermore, 25 OTUs at genus level were consistent across all six samples. Alpha diversity exhibited minimal variation among samples, while beta diversity indicated an inverse relationship between species richness and diversity. Co-occurrence network analysis established that genera from the same and different groups of phyla show positive co-relations with each other. Thirteen plastic degrading enzymes, including Laccase, Alkane-1 monooxygenase and Alkane monooxygenase, were identified from six sediment metagenomes of river Ganga, which can degrade non-biodegradable plastic viz. Polyethylene, Polystyrene and Low-density Polyethelene. Further, 18 xenobiotic degradation enzymes were identified for the degradation of Bisphenol, Xylene, Toluene, Polycyclic aromatic hydrocarbon, Styrene, Atrazene and Dioxin etc. This is the first report on the identification of non-biodegradable plastic degrading enzymes from sediment metagenomes of river Ganga, India. The findings of this study would help in pollution abatement and sustainable management of riverine ecosystem.

Effects of environmental parameters on spatial and temporal distribution of marine microbial communities in the southern Black Sea

The Black Sea is a unique environment with strong and permanent vertical stratification, with a thin layer of oxic zone above and a permanent anoxic zone below. Few high-throughput genomic surveys have been conducted to examine microbiota in the Black Sea. Yet, there is no study on the seasonal and vertical variation in microbial community compositions, driving forces and mechanisms of community assembly. In this study, seasonal, vertical, and spatial microbial assemblages were studied in terms of diversity, abundance, and community structure using 16S rRNA metabarcoding. 16S rRNA metabarcoding confirmed seasonal changes in microbial communities and the presence of distinct microbial groups among different water layers. Taxa belonging to Cyanobiaceae contributed a large fraction of the total biomass and were the most abundant autotrophic bacteria found across the whole water column, including hydrogen sulfide-containing anoxic zone. Temperature, salinity, water density, conductivity, light, chlorophyll-a, O2, NO3, NH3, PO4, Si, and H2S had a significant influence on the vertical bacterial community assemblages. The copper mine discharge system at 180 m did not affect microbial community structure and composition. Temperature seemed to be a primary factor in the variance between shallow depths. In conclusion, the lack of light, low dissolved oxygen levels, and low temperature do not restrict microbial diversity, as proven by the higher diversity observed in deeper zones. Wastewater in Black Sea region may be discharged into the Black Sea to depth of 180 m or deeper without impacting microbial ecology.

Microbial community composition analysis to decipher the possible role of inherent bacteria for in-situ arsenic (As) bioremediation

Biogeochemical reduction and mobilization of sediment-bound arsenic (As) is the major concern for widespread groundwater As contamination in the middle Gangetic plains. The present work examines a microcosm based bio-stimulation study and substrate amendments over 45 days to analyze the bacterial community structure and distribution to indicate the possible in-situ bioremediation strategy in the area. Initially, Bacterial phyla Proteobacteria was predominantly present in all the samples, followed by Actinobacteria, Bacteroidetes, and Firmicutes whereas Cyanobacteria was noted as the minor group. In genus level, Delftia, Acinetobacter, Lysobacter, Bacillus, and Pseudomonas were the major groups of bacteria in the As-rich aquifer system, while Planctomycetes dominated the bio-stimulated samples, followed by a minute portion of Proteobacteria. Alpha diversity and Chaol curve further determined the species richness in the samples with an As tolerant capacity of 152.28 ppb. The presence of γ-Proteobacteria as the dominating member in high As-content water indicated their predominant role in As mobilization, whereas, dominance of α-Proteobacterial members in low As-content water indicated their involvement in As detoxification. The complete change in microbial community structure within the bio-stimulated conditions indicated the extensive role of arsenite-oxidizing microbial communities within different levels of As-contaminated areas in Bihar that will enlighten the significant role of these communities in As-biogeochemical cycle.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03612-0.

The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach

The rapid expansion of urbanization has resulted in an insufficient of groundwater resource. In order to use groundwater more efficiently, a risk assessment of groundwater pollution should be proposed. The present study used machine learning with three algorithms consisting of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to locate risk areas of arsenic contamination in Rayong coastal aquifers, Thailand and selected the suitable model based on model performance and uncertainty for risk assessment. The parameters of 653 groundwater wells (Deep=236, Shallow=417) were selected based on the correlation of each hydrochemical parameters with arsenic concentration in deep and shallow aquifer environments. The models were validated with arsenic concentration collected from 27 well data in the field. The model's performance indicated that the RF algorithm has the highest performance as compared to those of SVM and ANN in both deep and shallow aquifers (Deep: AUC=0.72, Recall=0.61, F1 =0.69; Shallow: AUC=0.81, Recall=0.79, F1 =0.68). In addition, the uncertainty from the quantile regression of each model confirmed that the RF algorithm has the lowest uncertainty (Deep: PICP=0.20; Shallow: PICP=0.34). The result of the risk map obtained from the RF reveals that the deep aquifer, in the northern part of the Rayong basin has a higher risk for people to expose to As. In contrast, the shallow aquifer revealed that the southern part of the basin has a higher risk, which is also supported by the location of the landfill and industrial estates in the area. Therefore, health surveillance is important in monitoring the toxic effects on the residents who use groundwater from these contaminated wells. The outcome of this study can help policymakers in regions to manage the quality of groundwater resources and enhance the sustainable use of groundwater resources. The novelty process of this research can be used to further study other groundwater aquifers contaminated and increase the effectiveness of groundwater quality management.

Community structure and function of microbiomes in polluted stretches of river Yamuna in New Delhi, India, using shotgun metagenomics

The large population residing in the northern region of India surrounding Delhi mostly depends on water of River Yamuna, a tributary of mighty Ganga for agriculture, drinking and various religious activities. However, continuous anthropogenic activities mostly due to pollution mediated by rapid urbanization and industrialization have profoundly affected river microflora and their function thus its health. In this study, potential of whole-genome metagenomics was exploited to unravel the novel consortia of microbiome and their functional potential in the polluted sediments of the river at Delhi. Analysis of high-quality metagenome data from Illumina NextSeq500 revealed substantial differences in composition of microbiota at different sites dominated by Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria and Chloroflexi phyla. The presence of highly dominant anaerobic bacteria like Dechloromonas aromatica (benzene reducing and denitrifying), Rhodopseudomonas palustris (organic matter reducing), Syntrophus aciditrophicus (fatty acid reducing) and Syntrophobacter fumaroxidans (sulphate reducing) in the polluted river Yamuna signifies the impact of unchecked pollution in declining health of the river ecosystem. A decline in abundance of phages was also noticed along the downstream river Yamuna. Mining of mycobiome reads uncovered plethora of fungal communities (i.e. Nakaseomyces, Aspergillus, Schizosaccharomyces and Lodderomyces) in the polluted stretches due to the availability of higher organic carbon and total nitrogen (%) could be decoded as promising bioindicators of river trophic status. Pathway analysis through KEGG revealed higher abundance of genes involved in energy metabolism (nitrogen and sulphur), methane metabolism, degradation of xenobiotics (Nitrotoluene, Benzoate and Atrazine), two-component system (atoB, cusA and silA) and membrane transport (ABC transporters). Catalase-peroxidase and 4-hydroxybenzoate 3-monooxygenase were the most enriched pollution degrading enzymes in the polluted study sites of river Yamuna. Overall, our results provide crucial insights into microbial dynamics and their function in response to high pollution and could be insightful to the ongoing remediation strategies to clean river Yamuna.

Selective pressure on microbial communities in a drinking water aquifer - Geochemical parameters vs. micropollutants

Groundwater quality is crucial for drinking water production, but groundwater resources are increasingly threatened by contamination with pesticides. As pesticides often occur at micropollutant concentrations, they are unattractive carbon sources for microorganisms and typically remain recalcitrant. Exploring microbial communities in aquifers used for drinking water production is an essential first step towards understanding the fate of micropollutants in groundwater. In this study, we investigated the interaction between groundwater geochemistry, pesticide presence, and microbial communities in an aquifer used for drinking water production. Two groundwater monitoring wells in The Netherlands were sampled in 2014, 2015, and 2016. In both wells, water was sampled from five discrete depths ranging from 13 to 54 m and was analyzed for geochemical parameters, pesticide concentrations and microbial community composition using 16S rRNA gene sequencing and qPCR. Groundwater geochemistry was stable throughout the study period and pesticides were heterogeneously distributed at low concentrations (μg L^-1 range). Microbial community composition was also stable throughout the sampling period. Integration of a unique dataset of chemical and microbial data showed that geochemical parameters and to a lesser extent pesticides exerted selective pressure on microbial communities. Microbial communities in both wells showed similar composition in the deeper aquifer, where pumping results in horizontal flow. This study provides insight into groundwater parameters that shape microbial community composition. This information can contribute to the future implementation of remediation technologies to guarantee safe drinking water production.

Toxicity assessment and microbial response to soil antibiotic exposure: differences between individual and mixed antibiotics

Increasing amounts of antibiotics are introduced into soils, raising great concerns on their ecotoxicological impacts on the soil environment. This work investigated the individual and joint toxicity of three antibiotics, tetracycline (TC), sulfonamide (SD) and erythromycin (EM) via a whole-cell bioreporter assay. TC, SD and EM in aqueous solution demonstrated cytotoxicity, whilst soil exposure showed genotoxicity, indicating that soil particles possibly affected the bioavailability of antibiotics. Toxicity of soils exposed to TC, SD and EM changed over time, demonstrating cytotoxic effects within 14-d exposure and genotoxic effects after 30 days. Joint toxicity of TC, SD and EM in soils instead showed cytotoxicity, suggesting a synergetic effect. High-throughput sequencing suggested that the soil microbial response to individual antibiotics and their mixtures showed a different pattern. Soil microbial community composition was more sensitive to TC, in which the abundance of Pseudomonas, Pirellula, Subdivision3_genera_incertae_sedis and Gemmata varied significantly. Microbial community functions were significantly shifted by EM amendments, including signal transduction mechanisms, cytoskeleton, cell wall/membrane/envelope biogenesis, transcription, chromatin structure and dynamics, and carbohydrate transport and metabolism. This work contributes to a better understanding of the ecological effects and potential risks of individual and joint antibiotics on the soil environment.

Spatial distribution and health risk assessment of As and Pb contamination in the groundwater of Rayong Province, Thailand

This study investigates the presence of arsenic (As) and lead (Pb) in groundwater and their spatial distribution in Ban Khai District, Rayong Province, Thailand. Forty groundwater samples were collected at different locations in the dry and wet seasons during March and August of 2019, respectively. The hydrochemical facies illustrate that the major groundwater types in both seasons mainly consisted of Ca-Na-HCO_3, Ca-HCO₃-Cl and Na-HCO₃ types. The concentration of As ranged from <0.300 to 183.00 μg/L, accounting for 22% (18 of 80 samples), exceeding the WHO guidelines of 10 μg/L. The spatial distribution of As was distinctly predominant as a hot spot in some areas during the wet season. The wells may have been contaminated from human activity and thus constituted a point source in the adjacent area. For Pb, its concentration in all the wells were not exceeded 10 μg/L of the WHO guidelines, appearing as a background concentration in this area. Most of the wells were shown to be in an oxidation state, supporting As^V mobility. Moreover, the area also had a nearly neutral pH that promoted As^V desorption, while the presence of undissolved Pb in the aquifers tended to increase. Furthermore, chemical applications to agricultural processes could release the As composition into the groundwater. The health risk resulting from oral consumption was at a higher risk level than dermal contact. The non-carcinogenic risk affecting the adult population exceeded the threshold level by approximately 27.5% of the wells, while for the children group, the risk level was within the limit. Total cancer risk (TCR) of adult residents exceeded the acceptable risk level (1 × 10^-6) in all wells, causing carcinogenic health effects. Therefore, health surveillance is important in monitoring the toxic effects on the local residents who use groundwater from these contaminated wells. Furthermore, a sanitation service and an alternative treatment of the water supply will be needed, especially in wells with high As levels.

Acid mine drainage potential of waste rocks in a gold mine (Thailand): application of a weathering cell test and multivariate statistical analysis

In the process of gold mining, large amounts of broken waste rocks are produced and left at the surface under atmospheric conditions, which may generate acid mine drainage (AMD). This study aimed to predict the AMD generation potential and determine the concentrations of potentially toxic metals at three dump sites for a gold mine in Thailand. The AMD generation potentials of waste rock samples collected from the oxide, transition and sulfide dump sites was determined using the weathering cell test. The kinetic test had a 7-d cycle and was run for ~ 21 cycles; the effluent pH, conductivity, redox potential and levels of sulfate, and major and trace metals (i.e., As, Co, Cu, Fe, Mn, Pb and Zn) present in each cycle were measured. Some samples generated significant amounts of AMD, especially the massive sulfide samples from the transition and sulfide dump sites. The effluent water pH in the oxide and sulfide dump sites was neutral to slightly alkaline (pH ~ 6-9), while it was acidic to neutral (pH ~ 3-7) in the transition dump site. The transition dump site samples generated significantly higher acidity and sulfate levels than those from the oxide and sulfide dump sites. Furthermore, some waste rock samples, including the massive sulfide from the transition dump site, released relatively high amounts of heavy metals; in addition, sulfate reached levels (9.48 mg kg^-1 of waste rock) high enough to pose a risk to ecosystems. The long-term acid generation suggested that some waste rock samples from sulfide dump site and transition dump site will continue to generate acid for long periods. Based on data from the weathering cell test and multivariate statistical analysis, the transition dump site potentially generates a lower pH leachate than other waste rock dumps.

Identification of groundwater microbial communities and their connection to the hydrochemical environment in southern Laizhou Bay, China

The microbial community plays an important role in the biogeochemical cycle in coastal groundwater ecosystems. However, the composition and controlling factors of the microbial community in coastal closed groundwater systems (CCGSs) with high salinity have rarely been studied. Here, we investigated and analyzed the hydrochemical characteristics and microbial community composition of seven brine samples with high total dissolved solid (TDS) values ranging from 74.5 to 132.3 g/L within and across three coastal saltworks (Yangkou, Hanting, and Changyi) in southern Laizhou Bay (SLB). The bacterial diversity was independent of salinity. Compared with those of low-salinity groundwater, the diversity of the microbial community in brine was lower, but the richness was slightly higher. There was a significant correlation between the microbial community diversity and groundwater sources, which indicated that the microbial communities were affected by groundwater sources. A comparison of the microbial community compositions of the three saltworks showed that the Hanting and Changyi saltworks had similar microbial communities due to their similar sampling depths. In addition, the main force shaping the differences in the microbial communities in both coastal open groundwater systems (COGSs) and CCGSs was identified as the hydraulic connection with the seawater controlled by hydrogeological conditions formed throughout geological history. This study can help to elucidate the biogeochemical processes in coastal aquifers.

Human biomarkers associated with low concentrations of arsenic (As) and lead (Pb) in groundwater in agricultural areas of Thailand

Human biomarkers were used to evaluate the lead (Pb) and arsenic (As) exposure of local people who lived in an agricultural area with intense agrochemical usage and who consumed groundwater. Although the heavy metals/metalloids in the groundwater were at low concentrations, they could cause adverse effects due to a high daily water intake rate over the long term. Biomarkers (hair, fingernails and urine) were collected from 100 subjects along with the local shallow groundwater and tap water, which is the treated deep groundwater, and investigated for the concentrations of As and Pb. Shallow groundwater had an average pH of 5.21 ± 1.90, ranging from 3.77 to 8.34, with average concentrations of As and Pb of 1.311 µg/L and 6.882 µg/L, respectively. Tap water had an average pH of 5.24 ± 1.63, ranging from 3.86 to 8.89, with the average concentrations of As and Pb of 0.77 µg/L and 0.004 µg/L, respectively. The levels of both As and Pb in the hair, fingernails and urine of shallow groundwater-consuming residents were greater than those in the hair, fingernails and urine of tap water-consuming residents. Interestingly, the As level in urine showed a linear relationship with the As concentration in groundwater (R2 = 0.91). The average water consumption rate was approximately two-fold higher than the standard; thus, its consumption posed a health risk even at the low As and Pb levels in the groundwater. The hazard index (HI) ranged from 0.01 to 16.34 (average of 1.20 ± 2.50), which was higher than the acceptable level. Finally, the concomitant factors for As and Pb in the urine, hair and nails from both binary logistic regression and odds ratio (OR) analysis indicated that groundwater consumption was the major concomitant risk factor. This study suggested that direct consumption of this groundwater should be avoided and that the groundwater should be treated, especially before consumption. In conclusion, urine is suggested to be a biomarker of daily exposure to As and Pb, while for long-term exposure to these metals, fingernails are suggested as a better biomarker than hair.

Exploring the allochthonous pollution influence on bacterial community and co-occurrence dynamics of River Ganga water through 16S rRNA-tagged amplicon metagenome

River Ganga is one of the largest and most sacred rivers of India. This river is largely affected by anthropogenic activities causing significant increase in water pollution. The impact of drains discharging polluted water on the bacterial community dynamics in the river remains unexplored. To elucidate this, the targeted 16S rRNA V3-V4 variable region amplicon sequencing and bioinformatic analysis were performed using water from upstream, drain, and downstream of river Ganga. Analysis revealed significant difference in relative abundances of bacterial communities. The increase in bacterial abundance and alpha diversity was detected in the downstream compared to the upstream. Environmental factors were found significantly different between upstream and downstream water. At the phyla level, highly abundant taxa such as Proteobacteria, Actinobacteria, Planctomycetes, Bacteroidetes, and Verrucomicrobia were observed. Bacterial genera like Prevotella, Bacteroides, Blautia, and Faecalibacterium (fecal indicator) had higher abundance in the downstream site. Network co-occurrence revealed that bacterial communities have a modular profile with reduced interaction in drain and downstream water. The network of co-occurring bacterial communities consists of 283 nodes with edge connectivity of 6900, 7074, and 5294 in upstream, drain, and downstream samples, respectively. Upstream communities exhibited the highest positive interaction followed by the drain and the downstream sites. Additionally, highly abundant pathogenic species such as Acinetobacter baumannii and Prevotella copri were also detected in all samples. This study suggests the drain to be allochthonous pollution vector that significantly contributes to bacterial community enrichment. From the results of this study, it is apparent that the lotic water may be used as the ecological reference to understand and monitor the variations in the bacterial communities and their co-occurrence dynamics in the fresh water ecosystems.

Arsenic speciation, the abundance of arsenite-oxidizing bacteria and microbial community structures in groundwater, surface water, and soil from a gold mine

The arsenic speciation, the abundance of arsenite-oxidizing bacteria, and microbial community structures in the groundwater, surface water, and soil from a gold mining area were explored using the PHREEQC model, cloning-ddPCR of the aioA gene, and high-throughput sequencing of the 16S rRNA gene, respectively. The analysis of the aioA gene showed that arsenite-oxidizing bacteria retrieved from groundwater, surface water, and soil were associated with Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria. In groundwaters from the mining area, there were relatively high ratios of aioA/total 16S rRNA gene copies and the dominance of As⁵⁺, which suggested the presence and activity of arsenite-oxidizing bacteria. Metagenomic analysis revealed that the majority of the soil and surface water microbiomes were Proteobacteria, Actinobacteria, Bacteroidetes, and Chloroflexi, whereas the groundwater microbiomes were dominated exclusively by Betaproteobacteria and Alphaproteobacteria. Geochemical factors influencing the microbial structure in the groundwater were As, residence time, and groundwater flowrate, while those showing a positive correlation to the microbial structure in the surface water were TOC, ORP, and DO. This study provides insights into the groundwater, surface water, and soil microbiomes from a gold mine and expands the current understanding of the diversity and abundance of arsenite-oxidizing bacteria, playing a vital role in global As cycling.

Side Effects of Pesticides and Metabolites in Groundwater: Impact on Denitrification

The impact of two pesticides (S-metolachlor and propiconazole) and their respective main metabolites (ESA-metolachlor and 1,2,4-triazole) on bacterial denitrification in groundwater was studied. For this, the denitrification activity and the bacterial diversity of a microbial community sampled from a nitrate-contaminated groundwater were monitored during 20 days in lab experiments in the presence or absence of pesticides or metabolites at 2 or 10 μg/L. The kinetics of nitrate reduction along with nitrite and N₂O production all suggested that S-metolachlor had no or only little impact, whereas its metabolite ESA-metolachlor inhibited denitrification by 65% at 10 μg/L. Propiconazole and 1,2,4-triazole also inhibited denitrification at both concentrations, but to a lesser extent (29–38%) than ESA-metolachlor. When inhibition occurred, pesticides affected the reduction of nitrate into nitrite step. However, no significant differences were detected on the abundance of nitrate reductase narG and napA genes, suggesting an impact of pesticides/metabolites at the protein level rather than on denitrifying bacteria abundance. 16S rRNA gene Illumina sequencing indicated no major modification of bacterial diversity in the presence or absence of pesticides/metabolites, except for ESA-metolachlor and propiconazole at 10 μg/L that tended to increase or decrease Shannon and InvSimpson indices, respectively. General growth parameters suggested no impact of pesticides, except for propiconazole at 10 μg/L that partially inhibited acetate uptake and induced a decrease in microbial biomass. In conclusion, pesticides and metabolites can have side effects at environmental concentrations on microbial denitrification in groundwater and may thus affect ecosystem services based on microbial activities.

Next generation sequencing approaches to evaluate water and wastewater quality

The emergence of next generation sequencing (NGS) is revolutionizing the potential to address complex microbiological challenges in the water industry. NGS technologies can provide holistic insight into microbial communities and their functional capacities in water and wastewater systems, thus eliminating the need to develop a new assay for each target organism or gene. However, several barriers have hampered wide-scale adoption of NGS by the water industry, including cost, need for specialized expertise and equipment, challenges with data analysis and interpretation, lack of standardized methods, and the rapid pace of development of new technologies. In this critical review, we provide an overview of the current state of the science of NGS technologies as they apply to water, wastewater, and recycled water. In addition, a systematic literature review was conducted in which we identified over 600 peer-reviewed journal articles on this topic and summarized their contributions to six key areas relevant to the water and wastewater fields: taxonomic classification and pathogen detection, functional and catabolic gene characterization, antimicrobial resistance (AMR) profiling, bacterial toxicity characterization, Cyanobacteria and harmful algal bloom identification, and virus characterization. For each application, we have presented key trends, noteworthy advancements, and proposed future directions. Finally, key needs to advance NGS technologies for broader application in water and wastewater fields are assessed.

Effects of Arsenic and Iron on the Community and Abundance of Arsenite-Oxidizing Bacteria in an Arsenic-Affected Groundwater Aquifer

Arsenic (As) contamination of groundwater aquifers is a global environmental problem, especially in South and Southeast Asian regions, and poses a risk to human health. Arsenite-oxidizing bacteria that transform As(III) to less toxic As(V) can be potentially used as a groundwater As remediation strategy. This study aimed to examine the community and abundance of arsenite-oxidizing bacteria in groundwater with various As concentrations from Rayong Province, Thailand using PCR-cloning-sequencing and quantitative PCR (qPCR) of catalytic subunit of arsenite oxidase gene (aioA). Key factors influencing their community and abundance were also identified. The results demonstrated that arsenite-oxidizing bacteria retrieved from groundwater were phylogenetically related to Betaproteobacteria and Alphaproteobacteria. The aioA gene abundances ranged from 8.6 × 10¹ to 1.1 × 10⁴ copies per ng of genomic DNA, accounting for 0.16-1.37% of the total 16S rRNA bacterial gene copies. Although the abundance of arsenite-oxidizing bacteria in groundwater was low, groundwater with As(III) dominance likely promoted their abundance which possibly played an important role in chemolithoautotrophic oxidation of As(III) to As(V). Fe and As(III) were the major environmental factors influencing the community and abundance of arsenite-oxidizing bacteria. The knowledge gained from this study can be used to further contribute to the development of bioremediation strategies for As removal from groundwater resources.

Microbial community structure in aquifers associated with arsenic: analysis of 16S rRNA and arsenite oxidase genes

The microbiomes of deep and shallow aquifers located in an agricultural area, impacted by an old tin mine, were explored to understand spatial variation in microbial community structures and identify environmental factors influencing microbial distribution patterns through the analysis of 16S rRNA and aioA genes. Although Proteobacteria, Cyanobacteria, Actinobacteria, Patescibacteria, Bacteroidetes, and Epsilonbacteraeota were widespread across the analyzed aquifers, the dominant taxa found in each aquifer were unique. The co-dominance of Burkholderiaceae and Gallionellaceae potentially controlled arsenic immobilization in the aquifers. Analysis of the aioA gene suggested that arsenite-oxidizing bacteria phylogenetically associated with Alpha-, Beta-, and Gamma proteobacteria were present at low abundance (0.85 to 37.13%) and were more prevalent in shallow aquifers and surface water. The concentrations of dissolved oxygen and total phosphorus significantly governed the microbiomes analyzed in this study, while the combination of NO₃ ^--N concentration and oxidation-reduction potential significantly influenced the diversity and abundance of arsenite-oxidizing bacteria in the aquifers. The knowledge of microbial community structures and functions in relation to deep and shallow aquifers is required for further development of sustainable aquifer management.

Mechanisms of arsenic contamination associated with hydrochemical characteristics in coastal alluvial aquifers using multivariate statistical technique and hydrogeochemical modeling: a case study in Rayong province, eastern Thailand

The rapid development of Rayong Province has resulted in increased demands on groundwater usage. This has potentially induced the release of contaminants such as arsenic (As), among others (i.e., NO₃^-, PO₄^3-) from various land use types-especially in intensive agricultural areas and heavy industrial areas, including landfill sites. The objectives of this research are to investigate the As speciation and groundwater chemistry occurring due to different hydrogeological settings and the influence of human activities and to explain the mechanism of As release in the coastal alluvial aquifers in Rayong Province using multivariate statistical techniques and hydrogeochemical modeling (PHREEQC). Six major water facies, mainly consisting of Ca-Na-HCO₃-Cl and Ca-Na-Cl, were included in the hydrochemical analysis. Arsenic levels were inversely correlated with NO₃^-, SO₄^2-, DO, and ORP, confirming the reducing environment in the groundwater system. The results from the PHREEQC model show that most wells were strongly under-supersaturated with respect to arsenorite, scorodite, and arsenic pentoxide. Arsenic (As) is probably derived from the dissolution of Fe oxide and hydroxide (i.e., Fe(OH)₃, goethite, maghemite, and magnetite). The multivariate statistical techniques revealed that the As species mainly consisted of As(III), governed by the reducing environment, while As(V) may be desorbed from Fe oxide and hydroxide as the pH increases. Anthropogenic inputs and intensive pumping may enhance the reducing environment, facilitating the release of As(III) into the groundwater. The knowledge gained from this study helps to better understand the mechanisms of As contamination in coastal groundwater aquifers, which is useful for groundwater management, including the optimum pumping rate and long-term monitoring of groundwater quality.

Testing Different Membrane Filters for 16S rRNA Gene-Based Metabarcoding in Karstic Springs

Introduction: Karstic springs are used worldwide by rural communities as sources of fresh water for humans and livestock. In Romania, one-third of the population has no direct access to a public water supply. The present study is part of a country-wide project to develop simple, quick and cheap methods for seasonal environmental and microbiological monitoring of karstic springs used as drinking water by rural populations. Critical steps for monitoring workflow consist of evaluating water quality and selecting suitable membrane filters to efficiently capture environmental DNA for further microbial diversity estimation using 16S rRNA gene-based metabarcoding. Methods: Several commercial membrane filters of different compositions and pore sizes were tested on the water sampled from three karstic springs in Romania, followed by water chemistry and whole community 16S rRNA gene-based metabarcoding analysis. Results: We found that different types of applied membrane filters provide varying recovery in diversity and abundance of both overall and pathogenic bacteria. Conclusions: The result of the experiment with different filters shows that mixed cellulose ester, cellulose acetate, and nitrate membranes of 0.20 and 0.22 µm are the best for amplicon-based metabarcoding monitoring of karst springs.

Characterization of antibiotic resistance genes and bacterial community in selected municipal and industrial sewage treatment plants beside Poyang Lake

Sewage treatment plants (STPs) are significant reservoirs of antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB). Municipal STPs (MSTPs) and industrial STPs (ISTPs) are the two most important STP types in cities. In this study, the ARGs, mobile genetic elements (MGEs), and bacterial communities of selected STPs, including two MSTPs and one ISTP, in the vicinity of Poyang Lake were comprehensively investigated through high-throughput qPCR and high-throughput Illumina sequencing. The results showed that the profiles of ARGs, MGEs and bacteria differed between the ISTP and the two MSTPs, most likely due to differences in influent water quality, such as the Pb that characterized in the ISTP's influent. The longer hydraulic retention times (HRTs) of the two MSTPs than of the ISTP may also have accounted for the different profiles. Thus, a prolonged HRT in the CASS process seems to allow a more extensive removal of ARGs and bacteria in ISTPs with similar treatment process. By providing comprehensive insights into the characteristics of ARGs, MGEs and the bacterial communities of the selected MSTPs and ISTP, our study provides a scientific basis for controlling the propagation and diffusion of ARGs and ARB in different types of STPs.

Multivariate Statistical Analysis of Hydrochemical Data and Stable Isotopes of Groundwater Contaminated with Nitrate at Huay Sai Royal Development Study Center and Adjacent Areas in Phetchaburi Province, Thailand

Due to the continuous expansion in agriculture production and industry for many years, groundwater usage has been increasing, with a decrease in groundwater levels in many cases. In addition, in some areas, groundwater quality has degraded due to agrochemical contamination from agricultural areas. The aims of this research pertains to aquifers as follows: (1) to evaluate hydrochemical characteristics of groundwater using multivariate statistical analysis, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), and (2) to integrate the stable isotopes 18O and 2H with hydrochemical data to evaluate the origin of the groundwater and indirectly identify the pollution sources of groundwater contaminated with nitrate (NO3). Water samples were collected from 60 groundwater wells with different hydrogeological characteristics and land use types in both the rainy season (in October) and the summer seasons (in February) in the Cha Am district of Phetchaburi Province. The groundwater was separated into 3 types: Ca-Na-Cl, Ca-Na-HCO3-Cl, and Na-Cl. Two groundwater wells (no. 19 and 41), which were located southeast and southwest of the study area, had relatively high NO3− concentrations (47 mg/L NO3 and 50 mg/L NO3, respectively) that were higher than the groundwater quality standards. These two wells corresponded to the second group that was exposed by HCA. The PCA results revealed the influence of seawater intrusion. Furthermore, multivariate statistical analysis (PC 2) revealed that the NO3− that is mainly released from potassium nitrate (KNO3), for example, during pineapple cultivation, directly contaminated the groundwater system.