Assessing the Fate of Benzophenone-Type UV Filters and Transformation Products during Soil Aquifer Treatment: The Biofilm Compartment as Bioaccumulator and Biodegrader in Porous Media.

The fate of selected UV filters (UVFs) was investigated in two soil aquifer treatment (SAT) systems, one supplemented with a reactive barrier containing clay and vegetable compost and the other as a traditional SAT reference system. We monitored benzophenone-3 (BP-3) and its transformation products (TPs), including benzophenone-1 (BP-1), 4,4'-dihydroxybenzophenone (4DHB), 4-hydroxybenzophenone (4HB), and 2,2'-dihydroxy-4-methoxybenzophenone (DHMB), along with benzophenone-4 (BP-4) and avobenzone (AVO) in all involved compartments (water, aquifer sediments, and biofilm). The reactive barrier, which enhances biochemical activity and biofilm development, improved the removal of all detected UVFs in water samples. Among monitored UVFs, only 4HB, BP-4, and AVO were detected in sediment and biofilm samples. But the overall retained amounts were several orders of magnitude larger than those dissolved. These amounts were quantitatively reproduced with a specifically developed simple analytical model that consists of a mobile compartment and an immobile compartment. Retention and degradation are restricted to the immobile water compartment, where biofilm absorption was simulated with well-known compound-specific Kow values. The fact that the model reproduced observations, including metabolites detected in the biofilm but not in the (mobile) water samples, supports its validity. The results imply that accumulation ensures significant biodegradation even if the degradation rates are very low and suggest that our experimental findings for UVFs and TPs can be extended to other hydrophobic compounds. Biofilms act as accumulators and biodegraders of hydrophobic compounds.

Process-Based and Probabilistic Quantification of Co and Ni Mobilization Risks Induced by Managed Aquifer Recharge.

Managed aquifer recharge (MAR) is an increasingly used water management technique that enhances water availability while commonly generating water quality benefits. However, MAR activities may also trigger adverse geochemical reactions, especially during the injection of oxidant-enriched waters into reducing aquifers. Where this occurs, the environmental risks and the viability of mitigating them must be well understood. Here, we develop a rigorous approach for assessing and managing the risks from MAR-induced metal mobilization. First, we develop a process-based reactive transport model to identify and quantify the main hydrogeochemical drivers that control the release of metals and their mobility. We then apply a probabilistic framework to interrogate the inherent uncertainty associated with adjustable model parameters and consider this uncertainty (i) in long-term predictions of groundwater quality changes and (ii) in scenarios that investigate the effectiveness of modifications in the water treatment process to mitigate metal release and mobility. The results suggested that Co, Ni, Zn, and Mn were comobilized during pyrite oxidation and that metal mobility was controlled (i) by the sediment pH buffering capacity and (ii) by the sorption capacity of the native aquifer sediments. Both tested mitigation strategies were shown to be effective at reducing the risk of elevated metal concentrations.

Co-occurrence of arsenic and fluoride in groundwater of Guide basin in China: Genesis, mobility and enrichment mechanism.

Endemic arsenic poisoning and fluorosis caused by primary high arsenic (As) and high fluoride (F-) groundwater have become one of the most serious environmental geological problems faced by the international society. High As and high F- groundwater exists in Neogene confined aquifers in Guide basin, with concentrations of 355 µg/L and 5.67 mg/L, respectively, and showing a co-occurrence phenomenon of As and F- in the groundwater. This poses a double threat to the health of tens of thousands of local residents. In this study, based on the systematic collection of groundwater and borehole sediment samples, analysis of hydrochemistry and isotope indexes, combined with laboratory tests, purpose of this study is to reveal the migration rule and co-enrichment mechanism of As and F- in aquifers, and finally establish a hydrogeochemical conceptual model of the enrichment process of As and F-. The main conclusions are as follows: hydrochemical type of unconfined and confined groundwater in Guide basin is Ca-Na-HCO3 and Na-Cl-HCO3 type, respectively. Main minerals in sediments are quartz and plagioclase. Concentrations of As and F- are lower in unconfined groundwater, but higher in confined groundwater, and which show a gradual increasing trend along the groundwater flow path. The mineralization of natural organic matter in confined aquifer causes iron and manganese oxide minerals containing As to dissolve gradually, which leads to the gradual release of As into groundwater. Large amount of HCO3- produced by mineralization of organic matter precipitate with Ca2+ in groundwater, resulting in reduction of Ca2+ content, promoting the dissolution of fluoride-containing minerals such as fluorite (CaF2), and continuously releasing F- into groundwater. Meanwhile, competitive adsorption reactions in confined aquifers causes more As and F- to be released from mineral surface into groundwater, which gradually migrate and accumulate along groundwater flow. Finally, it is established that a conceptual model for the formation of high As and F- groundwater in the confined aquifer of Guide basin. The research results not only help to improve our understanding of the formation and evolution of groundwater with high As and F- with similar geological background, but also provide scientific basis for rational development and utilization of groundwater, and prevention and control of chronic As and F- poisoning in local and similar areas.

Integrated approach to understand the multiple natural and anthropogenic stresses on intensively irrigated coastal aquifer in the Mediterranean region.

Understanding the major factors influencing groundwater chemistry and its evolution in irrigation areas is crucial for efficient irrigation management. Major ions and isotopes (δD-H2O together with δ18O-H2O) were used to identify the natural and anthropogenic factors contributing to groundwater salinization in the shallow aquifer of the Wadi Guenniche Plain (WGP) in the Mediterranean region of Tunisia. A comprehensive geochemical investigation of groundwater was conducted during both the low irrigation season (L-IR) and the high irrigation season (H-IR). The results show that the variation range and average concentrations of almost all the ions in both the L-IR and H-IR seasons are high. The groundwater in both seasons is characterized by high electrical conductivity and CaMgCl/SO4 and NaCl types. The dissolution of halite and gypsum, the precipitation of calcite and dolomite, and Na-Ca exchange are the main chemical reactions in the geochemical evolution of groundwater in the Wadi Guenniche Shallow Aquifer (WGSA). Stable isotopes of hydrogen and oxygen (δ18O-H2O and δD-H2O) indicate that groundwater in WGSA originated from local precipitation. In the H-IR season, the δ18O-H2O and δD-H2O values indicate that the groundwater experienced noticeable evaporation. The enriched isotopic signatures reveal that the WGSA's groundwater was influenced by irrigation return flow and seawater intrusion. The proportions of mixing with seawater were found to vary between 0.12% and 5.95%, and between 0.13% and 8.42% during the L-IR and H-IR seasons, respectively. Irrigation return flow and the associated evaporation increase the dissolved solids content in groundwater during the irrigation season. The long-term human activities (fertilization, irrigation, and septic waste infiltration) are the main drives of the high nitrate-N concentrations in groundwater. In coastal irrigation areas suffering from water scarcity, these results can help planners and policy makers understand the complexities of groundwater salinization to enable more sustainable management and development.

Metagenomic analysis revealed highly diverse carbon fixation microorganisms in a petroleum-hydrocarbon-contaminated aquifer.

As one of the ultimate products of hydrocarbon biodegradation, inorganic carbon always be used to evaluate hydrocarbon biodegradation rates in petroleum-hydrocarbon-contaminated (PHC) aquifers. The evaluation method was challenged because of the existence of carbon fixation microorganisms, which may uptake inorganic carbons and consequently cause the biodegradation rates to be underestimated. We wonder if there are carbon fixation microorganisms in PHC aquifers. Although an extremely limited number of carbon fixation microorganisms in PHC sites have been studied in previous studies, the vast majority of microorganisms that participate in carbon fixation have not been systematically identified. To systematically reveal carbon fixation microorganisms and their survival environmental conditions, high-throughput metagenomic sequencing technologies, which are characterized by culture-independent, unbiased, and comprehensive methods for the detection and taxonomic characterization of microorganisms, were introduced to analyze the groundwater samples collected from a PHC aquifer. Results showed that 1041 genera were annotated as carbon fixation microorganisms, which accounted for 49% of the total number of genera in the PHC aquifer. Carbon fixation genes involved in Calvin-Benson-Bassham (CBB), 3-hydroxy propionate (3HP), reductive tricarboxylic acid (rTCA), and Wood-Ljungdahl (WL) cycles accounted for 2%, 41%, 34%, and 23% of the total carbon fixation genes, respectively, and 3HP, rTCA, and WL can be deemed as the dominant carbon fixation pathways. Most of the identified carbon fixation microorganisms are potential hydrocarbon biodegraders, and the most abundant carbon fixation microorganisms, such as Microbacterium, Novosphingobium, and Reyranella, were just the most abundant microorganisms in the aquifer system. It's deduced that most of the microorganisms in the aquifer were facultative autotrophic, and undertaking the dual responsibilities of degrading hydrocarbons to inorganic carbon and uptaking inorganic carbon to biomass.

NO3- as an electron acceptor elevates antibiotic resistance gene and human bacterial pathogen risks in managed aquifer recharge (MAR): A comparison with O2.

Managed aquifer recharge (MAR) stands out as a promising strategy for ensuring water resource sustainability. This study delves into the comparative impact of nitrate (NO3-) and oxygen (O2) as electron acceptors in MAR on water quality and safety. Notably, NO3-, acting as an electron acceptor, has the potential to enrich denitrifying bacteria, serving as hosts for antibiotic resistance genes (ARGs) and enriching human bacterial pathogens (HBPs) compared to O2. However, a direct comparison between NO3- and O2 remains unexplored. This study assessed risks in MAR effluent induced by NO3- and O2, alongside the presence of the typical refractory antibiotic sulfamethoxazole. Key findings reveal that NO3- as an electron acceptor resulted in a 2 times reduction in dissolved organic carbon content compared to O2, primarily due to a decrease in soluble microbial product production. Furthermore, NO3- significantly enriched denitrifying bacteria, the primary hosts of major ARGs, by 747%, resulting in a 66% increase in the overall abundance of ARGs in the effluent of NO3- MAR compared to O2. This escalation was predominantly attributed to horizontal gene transfer mechanisms, as evidenced by a notable 78% increase in the relative abundance of mobile ARGs, alongside a minor 27% rise in chromosomal ARGs. Additionally, the numerous denitrifying bacteria enriched under NO3- influence also belong to the HBP category, resulting in a significant 114% increase in the abundance of all HBPs. The co-occurrence of ARGs and HBPs was also observed to intensify under NO3- influence. Thus, NO3- as an electron acceptor in MAR elevates ARG and HBP risks compared to O2, potentially compromising groundwater quality and safety.

A Novel 3D Reconstruction Sensor Using a Diving Lamp and a Camera for Underwater Cave Exploration.

Aquifer karstic structures, due to their complex nature, present significant challenges in accurately mapping their intricate features. Traditional methods often rely on invasive techniques or sophisticated equipment, limiting accessibility and feasibility. In this paper, a new approach is proposed for a non-invasive, low-cost 3D reconstruction using a camera that observes the light projection of a simple diving lamp. The method capitalizes on the principles of structured light, leveraging the projection of light contours onto the karstic surfaces. By capturing the resultant light patterns with a camera, three-dimensional representations of the structures are reconstructed. The simplicity and portability of the equipment required make this method highly versatile, enabling deployment in diverse underwater environments. This approach is validated through extensive field experiments conducted in various aquifer karstic settings. The results demonstrate the efficacy of this method in accurately delineating intricate karstic features with remarkable detail and resolution. Furthermore, the non-destructive nature of this technique minimizes disturbance to delicate aquatic ecosystems while providing valuable insights into the subterranean landscape. This innovative methodology not only offers a cost-effective and non-invasive means of mapping aquifer karstic structures but also opens avenues for comprehensive environmental monitoring and resource management. Its potential applications span hydrogeological studies, environmental conservation efforts, and sustainable water resource management practices in karstic terrains worldwide.

Developing managed aquifer recharge (MAR) to augment irrigation water resources in the sand and gravel (Crag) aquifer of coastal Suffolk, UK.

Managed aquifer recharge (MAR) offers a potential innovative solution for addressing groundwater resource issues, enabling excess surface water to be stored underground for later abstraction. Given its favourable hydrogeological properties, the Pliocene sand and gravel (Crag) aquifer in Suffolk, UK, was selected for a demonstration MAR scheme, with the goal of supplying additional summer irrigation water. The recharge source was a 4.6 km drainage channel that discharges to the River Deben estuary. Trialling the scheme in June 2022, 12,262 m3 of source water were recharged to the aquifer over 12 days via a lagoon and an array of 565 m of buried slotted pipes. Groundwater levels were raised by 0.3 m at the centre of the recharge mound with an approximate radius of 250 m, with no detrimental impact on local water features observed. The source water quality remained stable during the trial with a mean chloride concentration (133 mg L-1) below the regulatory requirement (165 mg L-1). The fraction of recharge water mixing with the groundwater ranged from 69% close to the centre and 5% at the boundary of the recharge mound, leading to a reduction in nitrate-N concentration of 23.6 mg L-1 at the centre of the mound. During July-September 2022, 12,301 m3 of recharge water were abstracted from two, 18 m boreholes to supplement surface irrigation reservoirs during drought conditions. However, the hydraulic conductivity of the Crag aquifer (∼10 m day-1) restricted the yield and thereby reduced the economic viability of the scheme. Construction costs for the MAR system were comparatively low but the high costs of data collection and securing regulatory permits brought the overall capital costs to within 18% of an equivalent surface storage reservoir, demonstrating that market-based mechanisms and more streamlined regulatory processes are required to incentivise similar MAR schemes.

Multi-Facet analysis of analytical and numerical models to resolve sustainable artificial recharge rates in unconfined aquifers.

Managed aquifer recharge (MAR) has emerged as a potential solution to resolve water insecurity, globally. However, integrated studies quantifying the surplus source water, suitable recharge sites and safe recharge capacity is limited. In this study, a novel methodology is presented to quantify transient injection rates in unconfined aquifers and generate MAR suitability maps based on estimated surplus water and permissible aquifer recharge capacity (PARC). Subbasin scale monthly surplus surface runoff was estimated at 75% dependability using a SWAT model. A linear regression model based on numerical solution was used to capture the aquifer response to injection and to calculate PARC values at subbasin level. The available surplus runoff and PARC values was then used to determine the suitable site and recharge rate during MAR operation. The developed methodology was applied in the semi-arid region of Lower Betwa River Basin (LBRB), India. The estimated surplus runoff was generally confined to the monsoon months of June to September and exhibited spatial heterogeneity with an average runoff rate of 5000 m3/d in 85% of the LBRB. Analysis of the PARC results revealed that thick alluvial aquifers had large permissible storage capacity and about 50% of the LBRB was capable of storing over 3500 m3/d of water. This study revealed that sufficient surplus runoff was generated in the LBRB, but it lacked the adequate safe aquifer storage capacity to conserve it. A total 65 subbasins was identified as the best suited sites for MAR which had enough surplus water and storage capacity to suffice 20% of the total water demand in the LBRB. The developed methodology was computationally efficient, could augment the field problem of determining scheduled recharge rates and could be used as a decision-making tool in artificial recharge projects.

Uncertainty-based saltwater intrusion prediction using integrated Bayesian machine learning modeling (IBMLM) in a deep aquifer.

Data-driven machine learning approaches are promising to substitute physically based groundwater numerical models and capture input-output relationships for reducing computational burden. But the performance and reliability are strongly influenced by different sources of uncertainty. Conventional researches generally rely on a stand-alone machine learning surrogate approach and fail to account for errors in model outputs resulting from structural deficiencies. To overcome this issue, this study proposes a flexible integrated Bayesian machine learning modeling (IBMLM) method to explicitly quantify uncertainties originating from structures and parameters of machine learning surrogate models. An Expectation-Maximization (EM) algorithm is combined with Bayesian model averaging (BMA) to find out maximum likelihood and construct posterior predictive distribution. Three machine learning approaches representing different model complexity are incorporated in the framework, including artificial neural network (ANN), support vector machine (SVM) and random forest (RF). The proposed IBMLM method is demonstrated in a field-scale real-world "1500-foot" sand aquifer, Baton Rouge, USA, where overexploitation caused serious saltwater intrusion (SWI) issues. This study adds to the understanding of how chloride concentration transport responds to multi-dimensional extraction-injection remediation strategies in a sophisticated saltwater intrusion model. Results show that most IBMLM exhibit r values above 0.98 and NSE values above 0.93, both slightly higher than individual machine learning, confirming that the IBMLM is well established to provide better model predictions than individual machine learning models, while maintaining the advantage of high computing efficiency. The IBMLM is found useful to predict saltwater intrusion without running the physically based numerical simulation model. We conclude that an explicit consideration of machine learning model structure uncertainty along with parameters improves accuracy and reliability of predictions, and also corrects uncertainty bounds. The applicability of the IBMLM framework can be extended in regions where a physical hydrogeologic model is difficult to build due to lack of subsurface information.

Management zones in transboundary aquifers: A review of delineation methods under a new framework of cross-border groundwater impacts.

Attention on the use of transboundary aquifers (TBAs) and their cross-border impacts is growing as countries become increasingly concerned about their long-term water security. Cross-border impacts, in groundwater quality and quantity, tend to concentrate in specific parts of TBAs, as they largely depend on the transboundary flow dynamics where anthropogenic actions operate. Thus, there is a growing consensus that strategies intended to prevent or mitigate such impacts should be implemented in strategic zones rather than in the whole TBA. These transboundary groundwater management zones (TGMZs) are relatively recent but have become a prominent topic in TBA management. However, until now, limited effort has been put into exploring the concept of TGMZs and the methods for their delineation. This research aims to fill these gaps and provide a basis for the delineation of TGMZs, thus helping neighbouring countries meet international responsibilities regarding the right to use and enjoy groundwater in TBAs. By reviewing academic and grey literature accessible from public sources, we present an overview of the concept and terminology of TGMZs, the approaches proposed for their delineation, and current operating examples. Additionally, we build a conceptual framework for assessing cross-border groundwater impacts by identifying their typologies and causal factors. We then apply our framework to evaluate and compare three reported methods which identify and delineate TGMZs from distinct perspectives, thereby gaining insights into their principles, performances, and limitations. Finally, we provide recommendations for further research towards optimising methods for delineating TGMZs.

Spatiotemporal assessment of groundwater quality and quantity using geostatistical and ensemble artificial intelligence tools.

The study investigated the spatiotemporal relationship between surface hydrological variables and groundwater quality/quantity using geostatistical and AI tools. AI models were developed to estimate groundwater quality from ground-based measurements and remote sensing images, reducing reliance on laboratory testing. Different Kriging techniques were employed to map ground-based measurements and fill data gaps. The methodology was applied to analyze the Maragheh aquifer in northwest Iran, revealing declining groundwater quality due to industrial. discharges and over-extraction. Spatiotemporal analysis indicated a relationship between groundwater depth/quality, precipitation, and temperature. The Root Mean Square Scaled Error (RMSSE) values for all variables ranged from 0.8508 to 1.1688, indicating acceptable performance of the semivariogram models in predicting the variables. Three AI models, namely Feed-Forward Neural Networks (FFNNs), Support Vector Regression (SVR), and Adaptive Neural Fuzzy Inference System (ANFIS), predicted groundwater quality for wet (June) and dry (October) months using input variables such as groundwater depth, temperature, precipitation, Normalized Difference Vegetation Index (NDVI), and Digital Elevation Model (DEM), with Groundwater Quality Index (GWQI) as the target variable. Ensemble methods were employed to combine the outputs of these models, enhancing performance. Results showed strong predictive capabilities, with coefficient of determination values of 0.88 and 0.84 for wet and dry seasons. Ensemble models improved performance by up to 6% and 12% for wet and dry seasons, respectively, potentially advancing groundwater quality modeling in the future.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy.

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.

Effect of seasonal variations in sea level on residual saltwater removal upstream of subsurface dam in coastal layered heterogeneity aquifers.

Subsurface dams have been recognized as one of the most effective measures for preventing saltwater intrusion. However, it may result in large amounts of residual saltwater being trapped upstream of the dam and take years to decades to remove, which may limit the utilization of fresh groundwater in coastal areas. In this study, field-scale numerical simulations were used to investigate the mechanisms of residual saltwater removal from a typical stratified aquifer, where an intermediate low-permeability layer (LPL) exists between two high-permeability layers, under the effect of seasonal sea level fluctuations. The study quantifies and compares the time of residual saltwater removal (Tre) for constant sea level (CSL) and seasonally varying sea level (FSL) scenarios. The modelling results indicate that, in most cases, seasonal fluctuations in sea level facilitate the dilution of residual saltwater and thus accelerate residual saltwater removal compared to a static sea level scenario. However, accounting for seasonal sea level variations may increase the required critical dam height (the minimum dam height required to achieve complete residual saltwater removal). Sensitivity analyses show that Tre decreases with increasing height of subsurface dam (Hd) under CSL or weaker sea level fluctuation scenarios; however, when the magnitude of sea level fluctuation is large, Tre changes non-monotonically with Hd. Tre decreases with increasing distance between subsurface dam and ocean for both CSL and FSL scenarios. We also found that stratification model had a significant effect on Tre. The increase in LPL thickness for both CSL and FSL scenarios leads to a decrease in Tre and critical dam height. Tre generally shows a non-monotonically decreasing trend as LPL elevation increases. These quantitative analyses provide valuable insights into the design of subsurface dams in complex situations.

Identification of groundwater nitrate sources in an urban aquifer (Alborz Province, Iran) using a multi-parameter approach.

High concentrations of NO3̄ in water resources are detrimental to both human health and aquatic ecosystems. Identification of NO3̄ sources and biogeochemical processes is a crucial step in managing and controlling NO3̄ pollution. In this study, land use, hydrochemical data, dual stable isotopic ratios and Bayesian Stable Isotope Mixing Models (BSIMM) were integrated to identify NO3̄ sources and estimate their proportional contributions to the contamination of the Karaj Urban Aquifer (Iran). Elevated NO3̄ concentrations indicated a severe NO3̄ pollution, with 39 and 52% of groundwater (GW) samples displaying the concentrations of NO3̄ in exceedance of the World Health Organization (WHO) standard of 50 mg NO3̄ L-1 in the rainy and dry seasons, respectively. Dual stable isotopes inferred that urban sewage is the main NO3̄ source in the Karaj Plain. The diagram of NO3̄/Cl‾ versus Cl‾ confirmed that municipal sewage is the major source of NO3̄. Results also showed that biogeochemical nitrogen dynamics are mainly influenced by nitrification, while denitrification is minimal. The BSIMM model suggested that NO3̄ originated predominantly from urban sewage (78.2%), followed by soil organic nitrogen (12.2%), and chemical fertilizer (9.5%) in the dry season. In the wet season, the relative contributions of urban sewage, soil nitrogen and chemical fertilizer were 87.5, 6.7, and 5.5%, respectively. The sensitivity analysis for the BSIMM modeling indicates that the isotopic signatures of sewage had the major impact on the overall GW NO3̄ source apportionment. The findings provide important insights for local authorities to support effective and sustainable GW resources management in the Karaj Urban Aquifer. It also demonstrates that employing Bayesian models combined with multi-parameters can improve the accuracy of NO3̄ source identification.

Groundwater quality evaluation of the weathering mantle in crystalline basement aquifer system, southern Brazil.

Groundwater is the main source of water for more than 2 billion people worldwide. In southern Brazil, the Crystalline Basement Aquifer System is composed of strategic groundwater reservoirs. Groundwater is mostly taken from shallow wells, and it is often used without any treatment, which poses a risk to public health. The present study aims to evaluate shallow groundwater quality and the geochemistry of shallow and deep groundwater located in the municipality of Canguçu, southern Brazil. The physicochemical and microbiological parameters of groundwater samples collected from shallow wells were monitored and analyzed using ANOVA variance analysis and water quality index (CCME WQI) approaches. Also, the results were compared with secondary data from deep wells. The monitored shallow wells had thermotolerant coliforms, Escherichia coli, pH, potassium, manganese, iron, and nitrate in disagreement with the guidelines of the World Health Organization. Moreover, variance analysis showed that the parameters temperature, dissolved oxygen, pH, chloride, and magnesium were the most influenced by seasonal variations. According to the CCME WQI, most samples had good quality (60%), 28% had fair quality, and 12% had poor quality. In addition, the field campaigns with higher precipitation rates also presented fair quality. Therefore, most of the shallow groundwater quality is affected by surface pollutants from the urban area, aggravated in rainy periods. Whereas deep groundwater is influenced by geochemistry mechanisms. The results revealed the risk of water consumption for public health and the urgent need for better maintenance of these wells and water treatment implementation.

Management of saltwater intrusion using 3D numerical modelling: a first for Pacific Island country of Vanuatu.

Small island countries like Vanuatu are facing the brunt of climate change, sea level rise (SLR), tropical cyclones, and limited or declining access to freshwater. The Tagabe coastal aquifer in Port Vila (the capital of Vanuatu) shows the presence of salinity, indicating saltwater intrusion (SWI). This study aims to develop and evaluate effective SWI management strategies for Tagabe coastal aquifer. To manage SWI, the numerical simulation model for the study area was developed using the SEAWAT code. The flow model was developed using MODFLOW and the transport model was developed using MT3DMS. Whereby SEAWAT solved flow and transport equations simultaneously. The model was calibrated, and different scenarios were evaluated for the management of SWI. The SLR was also considered in the model simulations. The results indicated that increased population, pumping rates, and SLR affect the SWI rates. To manage the SWI, we introduced hydraulic barriers like barrier wells and injection wells which effectively managed SWI in Tagabe coastal aquifer. The results from this study are significantly important whereby, the water managers, site owners, and governing bodies can use the management strategies presented in this study to create policies and regulations for managing SWI rates in Port Vila. Additionally, the water industry, private businesses, and investors who wish to extract groundwater from the Tagabe can use this study as a reference for daily or yearly freshwater production rates without the risk of SWI.

Determination of aquifer hydraulic parameters and groundwater protective capacity in parts of Nsukwa clan, Nigeria.

The goal of this study is to determine aquifer parameters and groundwater protective capacity in parts of the Nsukwa clan using geoelectric and pumping test methods. Seventeen vertical electrical soundings were acquired to determine the geoelectric properties, while two wells were drilled to determine the lithology and the aquifer parameters of the area. The result showed that the lithology comprised lateritic topsoil and sand, fine sand, medium sand, and coarse sand, respectively. Geoelectric data interpretation using Win Resist software revealed a close correlation with the well record. Geoelectric data analysis indicated that prolific aquifer can be sourced within the third and fourth layers, located within 24.2-43.8 m and comprised medium to coarse sand. The aquifer resistivity ranged from 703.1 to 26,367.7 Ωm. The Dar Zarrouk parameters, such as transverse resistance (R) and longitudinal conductance (S), were applied to determine the aquifer transmissivity (T) and hydraulic conductivity (K). The computed T and K from geoelectric sounding ranged from 11.37 to 34.79 m2/day, with a mean value of 18.51 m2/day and 0.8243 m/day, respectively, while the T and K values from the pumping test are 18.58 m2/day and 0.8251 m/day, respectively. S and R values ranged from 0.001179 to 0.0131619 Ω-1 and 2434 to 102,090 Ωm2, respectively, revealing a poor aquifer protective capacity and moderate yield. The storativity and storage coefficient of the aquifer values of 0.0023 and 0.072 m2/min, respectively, revealed a confined aquifer capable of providing sufficient water to the people. These findings showed moderate aquifer potential with poor protective capacity; thus, adequate aquifer protective strategies are recommended.

Improving groundwater vulnerability assessment in structurally controlled hard rock aquifer: insight from lineament density and land use/land cover pattern.

A comprehensive seasonal assessment of groundwater vulnerability was conducted in the weathered hard rock aquifer of the upper Swarnrekha watershed in Ranchi district, India. Lineament density (Ld) and land use/land cover (LULC) were integrated into the conventional DRASTIC and Pesticide DRASTIC (P-DRASTIC) models and were extensively compared with six modified models, viz. DRASTIC-Ld, DRASTIC-Lu, DRASTIC-LdLu, P-DRASTIC-Ld, P-DRASTIC-Lu, and P-DRASTIC-LdLu, to identify the most optimal model for vulnerability mapping in hard rock terrain of the region. Findings were geochemically validated using NO3- concentrations of 68 wells during pre-monsoon (Pre-M) and post-monsoon (Post-M) 2022. Irrespective of the applied model, groundwater vulnerability shows significant seasonal variation, with > 45% of the region classified as high to very high vulnerability in the pre-M, increasing to Ì´67% in post-M season, highlighting the importance of seasonal vulnerability assessments. Agriculture and industries' dominant southern region showed higher vulnerability, followed by regions with high Ld and thin weathered zone. Incorporating Ld and LULC parameters into DRASTIC-LdLu and P-DRASTIC-LdLu models increases the 'Very High' vulnerability zones to 17.4% and 17.6% for pre-M and 29.4% and 27.9% for post-M, respectively. Similarly, 'High' vulnerable zones increase from 32.5% and 25% in pre-M to 33.8% and 35.3% in post-M for respective models. Model output comparisons suggest that modified DRASTIC-LdLu and P-DRASTIC-LdLu perform better, with accurate estimations of 83.8% and 89.7% for pre-M and post-M, respectively. However, results of geochemical validation suggest that among all the applied modified models, DRASTIC-LdLu performs best, with accurate estimations of 34.4% and 20.6% for pre-M and post-M, respectively.

Microbial biogeography of the eastern Yucatán carbonate aquifer.

IMPORTANCE: The extensive Yucatán carbonate aquifer, located primarily in southeastern Mexico, is pockmarked by numerous sinkholes (cenotes) that lead to a complex web of underwater caves. The aquifer hosts a diverse yet understudied microbiome throughout its highly stratified water column, which is marked by a meteoric lens floating on intruding seawater owing to the coastal proximity and high permeability of the Yucatán carbonate platform. Here, we present a biogeographic survey of bacterial and archaeal communities from the eastern Yucatán aquifer. We apply a novel network analysis software that models ecological niche space from microbial taxonomic abundance data. Our analysis reveals that the aquifer community is composed of several distinct niches that follow broader regional and hydrological patterns. This work lays the groundwork for future investigations to characterize the biogeochemical potential of the entire aquifer with other systems biology approaches.