Causal prior-embedded physics-informed neural networks and a case study on metformin transport in porous media.

This study introduces a novel approach to transport modelling by integrating experimentally derived causal priors into neural networks. We illustrate this paradigm using a case study of metformin, a ubiquitous pharmaceutical emerging pollutant, and its transport behaviour in sandy media. Specifically, data from metformin's sandy column transport experiment was used to estimate unobservable parameters through a physics-based model Hydrus-1D, followed by a data augmentation to produce a more comprehensive dataset. A causal graph incorporating key variables was constructed, aiding in identifying impactful variables and estimating their causal dynamics or "causal prior." The causal priors extracted from the augmented dataset included underexplored system parameters such as the type-1 sorption fraction F, first-order reaction rate coefficient α, and transport system scale. Their moderate impact on the transport process has been quantitatively evaluated (normalized causal effect 0.0423, -0.1447 and -0.0351, respectively) with adequate confounders considered for the first time. The prior was later embedded into multilayer neural networks via two methods: causal weight initialization and causal prior regularization. Based on the results from AutoML hyperparameter tuning experiments, using two embedding methods simultaneously emerged as a more advantageous practice since our proposed causal weight initialization technique can enhance model stability, particularly when used in conjunction with causal prior regularization. amongst those experiments utilizing both techniques, the R-squared values peaked at 0.881. This study demonstrates a balanced approach between expert knowledge and data-driven methods, providing enhanced interpretability in black-box models such as neural networks for environmental modelling.

Effects and driving mechanisms of bioremediation on groundwater after the neutral in situ leaching of uranium.

The remediation of groundwater subject to in situ leaching (ISL) for uranium mining has raised extensive concerns in uranium mill and milling. This study conducted bioremediation through biostimulation and bioaugmentation to the groundwater in an area in northern China that was contaminated due to uranium mining using the CO2 + O2 neutral ISL (NISL) technology. It identified the dominant controlling factors and mechanisms driving bioremediation. Findings indicate that microorganisms can reduce the uranium concentration in groundwater subject to NISL uranium mining to its normal level. After 120 days of bioaugmentation, the uranium concentration in the contaminated groundwater fell to 0.36 mg/L, achieving a remediation efficiency of 91.26 %. Compared with biostimulation, bioaugmentation shortened the remediation timeframe by 30 to 60 days while maintaining roughly the same remediation efficiency. For groundwater remediation using indigenous microbial inoculants, initial uranium concentration and low temperatures (below 15 °C) emerge as the dominant factors influencing the bioremediation performance and duration. In settings with high carbonate concentrations, bioremediation involved the coupling of multiple processes including bioreduction, biotransformation, biomineralization, and biosorption, with bioreduction assuming a predominant role. Post-bioremediation, the relative abundances of reducing microbes Desulfosporosinus and Sulfurospirillum in groundwater increased significantly by 10.56 % and 6.91 %, respectively, offering a sustainable, stable biological foundation for further bioremediation of groundwater.

Metagenomic and FT-ICR MS insights into the mechanism for the arsenic biogeochemical cycling in groundwater.

Arsenic (As) is a groundwater contaminant of global concern. The degradation of dissolved organic matter (DOM) can provide a reducing environment for As release. However, the interaction of DOM with local microbial communities and how different sources and types of DOM influence the biotransformation of As in aquifers is uncertain. This study used optical spectroscopy, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), metagenomics, and structural equation modeling (SEM) to demonstrate the how the biotransformation of As in aquifers is promoted. The results indicated that the DOM in high-As groundwater is dominated by highly unsaturated low-oxygen(O) compounds that are quite humic and stable. Metagenomics analysis indicated Acinetobacter, Pseudoxanthomonas, and Pseudomonas predominate in high-As environments; these genera all contain As detoxification genes and are members of the same phylum (Proteobacteria). SEM analyses indicated the presence of Proteobacteria is positively related to highly unsaturated low-O compounds in the groundwater and conditions that promote arsenite release. The results illustrate how the biogeochemical transformation of As in groundwater systems is affected by DOM from different sources and with different characteristics.

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks.

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data's abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.

Assessment of the migration characteristics and source-oriented health risks of heavy metals in the soil and groundwater of a legacy contaminated by the chlor-alkali industry in central China.

The chlor-alkali industry (CAI) is crucial for global chemical production; however, its operation has led to widespread heavy metal (HM) contamination at numerous sites, which has not been thoroughly investigated. This study analysed 122 soil and groundwater samples from a typical CAI site in Kaifeng, China. Our aim was to assess the ecological and health risks, identify the sources, and examine the migration characteristics of HMs at this site using Monte Carlo simulation, absolute principal component score-multiple linear regression (APCS-MLR), and the potential environmental risk index (Ei). Our findings revealed that the exceedance rates for Cd, Pb, Hg, and Ni were 71.96%, 45.79%, 49.59%, and 65.42%, respectively. Mercury (Hg) displayed the greatest coefficient of variation across all the soil layers, indicating a significant anthropogenic influence. Cd and Hg were identified as having high and extremely high potential environmental risk levels, respectively. The spatial distributions of the improved Nemerow index (INI), total ecological risk (Ri), and HM content varied considerably, with the most contaminated areas typically associated with the storage of raw and auxiliary materials. Surface aggregation and significant vertical transport were noted for HMs; As and Ni showed substantial accumulation in subsoil layers, severely contaminating the groundwater. Self-organizing maps categorized the samples into two different groups, showing strong positive correlations between Cd, Pb, and Hg. The APCS-MLR model suggested that industrial emissions were the main contributors, accounting for 60.3% of the total HM input. Elevated hazard quotient values for Hg posed significant noncarcinogenic risks, whereas acceptable levels of carcinogenic risk were observed for both adults (96.60%) and children (97.83%). This study significantly enhances historical CAI pollution data and offers valuable insights into ongoing environmental and health challenges.

Evaluation of potential human health risks arising from nitrate and fluoride in the groundwater of Aurangabad, Bihar using GIS and chemometric analysis.

This study employed the groundwater pollution index to assess the appropriateness of groundwater for human consumption. Additionally, the hazard index was utilized to evaluate the potential non-carcinogenic risks associated with fluoride and nitrate exposure among children, women, and men in the study region. A total of 103 samples were collected from the Aurangabad district of Bihar. The analyzed samples were assessed using several physicochemical parameters. Major cations in the groundwater are Ca2+ > Mg2+ and major anions are HCO3- > Cl- > SO42- > NO3- > F- > PO43-. Around 17% of the collected groundwater samples surpassed the allowable BIS concentration limits for Nitrate, while approximately 11% surpassed the allowed limits for fluoride concentration. Principal component analysis was utilized for its efficacy and efficiency in the analytical procedure. Four principal components were recovered that explained 69.06% of the total variance. The Hazard Quotient (HQ) of nitrate varies between 0.03-1.74, 0.02-1.47, and 0.03-1.99 for females, males, and children, respectively. The HQ of fluoride varies between 0.04-1.59, 0.04-1.34, and 0.05-1.82 for females, males, and children, respectively. The central part of the district was at high risk according to the spatial distribution maps of the total hazard index (THI). Noncarcinogenic risks due to THI are 47%, 37%, and 28% for children, females, and males, respectively. According to the human health risk assessment, children are more prone to getting affected by polluted water than adults. The groundwater pollution index (GPI) value ranges from 0.46 to 2.27 in the study area. Seventy-five percent of the samples fell under minor pollution and only one fell under high pollution. The spatial distribution of GPI in the research area shows that the central region is highly affected, which means that this water is unsuitable for drinking purposes.

Identifying the genetic mechanism of medium-low temperature fluoride-enriched geothermal groundwater by the self-organizing map and evaluating health risk in the Wugongshan area, southeast China.

Fluoride-enriched groundwater is a serious threat for groundwater supply around the world. The medium-low temperature fluoride-enriched geothermal groundwater resource is widely distributed in the circum-Wugongshan area. And the fluoride concentration of all geothermal samples exceeds the WHO permissible limit of 1.5 mg/L. The Self-Organizing Map method, hydrochemical and isotopic analysis are used to decipher the driving factors and genetic mechanism of fluoride-enriched geothermal groundwater. A total of 19 samples collected from the circum-Wugongshan geothermal belt are divided into four clusters by the self-organizing map. Cluster I, Cluster II, Cluster III, and Cluster IV represent the geothermal groundwater with the different degree of fluoride concentration pollution, the different hydrochemical type, and the physicochemical characteristic. The high F- concentration geothermal groundwater is characterized by HCO3-Na with alkalinity environment. The δD and δ18O values indicate that the geothermal groundwater origins from the atmospheric precipitation with the recharge elevation of 1000-2100 m. The dissolution of fluoride-bearing minerals is the main source of fluoride ions in geothermal water. Moreover, groundwater fluoride enrichment is also facilitated by water-rock interaction, cation exchange and alkaline environment. Additionally, the health risk assessment result reveals that the fluorine-enriched geothermal groundwater in the western part of Wugongshan area poses a more serious threat to human health than that of eastern part. The fluoride health risks of geothermal groundwater for different group show differentiation, 100% for children, 94.74% for adult females, and 68.42% for adult males, respectively. Compared with adult females and adult males, children faced the greatest health risks. The results of this study provide scientific evaluation for the utilization of geothermal groundwater and the protection of human health around the Wugongshan area.

Enhancing Florida red tilapia aquaculture: biofloc optimization improves water quality, pathogen bacterial control, fish health, immune response, and organ histopathology across varied groundwater salinities.

Freshwater scarcity poses challenges to aquaculture worldwide, including countries like Egypt. In this study, we investigate the feasibility of integrating underground saline water (USW) with varying salinities into a Biofloc (BFT) system for desert mariculture of Florida red tilapia (FRT) and its impacts on water quality, fish performance and health. Four BFT treatments (C/N ratio = 15) were examined in triplicate using four salinity levels 0 ppt, 12 ppt, 24 ppt and 36 ppt, expressed as S0, S12, S24 and S36, respectively. For 75 days, a total of 12 fiberglass tanks (each 250 L-1 water) were used to store FRT fry (average weight of 1.73 ± 0.01 g/fish). The fish were fed an experimental diet (protein/fat = 30/5) and an additional carbon source of rice bran. The results revealed that group S12 showed better growth indicators, higher survival rate, lower FCR, and lower ammonia levels, while group S0 exhibited lower growth indicators (final weight, weight gain, and specific growth rate) than all groups. The serum kidney, liver, and antioxidant indices performed better in the S12 group. At 12 ppt, the immune-related parameter (IgM) increased by 22.5%, while the stress parameter (cortisol) decreased by 40.8% compared to the S0 group. The liver and intestinal histopathological results revealed that the S12 and S24 groups performed better. Pathogenic bacterial load counts favored the S24 group, which had the lowest number among the groups studied. The recommended salinity for FRT cultivation in USW and BFT is 19.94-20 ppt, determined by polynomial regression of FW and FCR.

Mapping and assessment of groundwater pollution risks in the main aquifer of the Mostaganem plateau (Northwest Algeria): utilizing the novel vulnerability index and decision tree model.

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels.

Occurrence and fate of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in an urban aquifer located at the Besòs River Delta (Spain).

Urban aquifers are at risk of contamination from persistent and mobile organic compounds (PMOCs), especially per- and polyfluoroalkyl substances (PFAS), which are artificial organic substances widely used across various industrial sectors. PFAS are considered toxic, mobile and persistent, and have therefore gained significant attention in environmental chemistry. Moreover, precursors could transform into more recalcitrant products under natural conditions. However, there is limited information about the processes which affect their behaviour in groundwater at the field-scale. In this context, the aim of this study is to assess the presence of PFAS in an urban aquifer in Barcelona, and identify processes that control their evolution along the groundwater flow. 21 groundwater and 6 river samples were collected revealing the presence of 16 PFAS products and 3 novel PFAS. Short and ultra-short chain PFAS were found to be ubiquitous, with the highest concentrations detected for perfluorobutanesulfonic acid (PFBS), trifluoroacetic acid (TFA) and trifluoromethanesulfonic acid (TFSA). Long chain PFAS and novel PFAS were found to be present in very low concentrations (<50 ng/L). It was observed that redox conditions influence the behaviour of a number of PFAS controlling their attenuation or recalcitrant behaviour. Most substances showed accumulation, possibly explained by sorption/desorption processes or transformation processes, highlighting the challenges associated with PFAS remediation. In addition, the removal processes of different intensities for three PFAS were revealed. Our results help to establish the principles of the evolution of PFAS along the groundwater flow, which are important for the development of conceptual models used to plan and adopt site specific groundwater management activities (e.g., Managed Aquifer Recharge).

Assessment of spatiotemporal risks for nationwide groundwater nitrate contamination.

National assessments of groundwater contamination risks are crucial for sustaining high-quality groundwater supplies. However, traditional methods often treat groundwater contamination risk as a steady-state indicator without considering spatiotemporal variation in risk-both geographically and over time- caused by anthropogenic and climate influences. In this work, XGBoost, a tree-based algorithm, was applied to comprehensively analyze the drivers of groundwater contamination from nitrate, using data on13 physical features (as used by the index-based ranking method DRASTIC) and 30 anthropogenic features from 1985 to 2010 in the contiguous United States (CONUS). The results indicate that physical features controlling the transport processes, particularly those affecting contaminant travel time from land surface to groundwater (depth to water table and transmissivity), were the dominant factors for nitrate contamination in groundwater. This was followed by features representing the potential nitrogen loading. Positive correlations between most features and nitrogen loading years were found, suggesting their growing influence on contamination risk. Based on the drivers identified for nitrate concentrations exceeding 10 mg/L in groundwater and their varying temporal contributions, this study proposes a reformulated index-based method for contamination risk assessment. With this method an overall accuracy of around 70 % was achieved based on the validation data set. The predicted high-risk areas are mainly intensive irrigation regions, such as the High Plains, northern Midwest, and Central Valley. This new approach contributes to a more accurate and effective assessment of the contamination risks of groundwater on a regional and national scale under temporally varying environmental conditions.

Shallow groundwater table fluctuations promote the accumulation and loss of phosphorus from surface soil to deeper soil in croplands around plateau lakes in Southwest China.

The continuous excessive application of phosphorus (P) fertilizers in intensive agricultural production leads to a large accumulation of P in surface soils, increasing the risk of soil P loss by runoff and leaching. However, there are few studies on the accumulation and loss of P from surface soil to deep soil profiles driven by shallow groundwater table (SGT) fluctuations. This study used the intensive cropland around 7 plateau lakes in Yunnan Province as an example and conducted in situ monitoring of P storage in the soil profile and SGT during the rainy season (RS) and dry season (DS) as well as simulation experiments on soil P loss. The aim was to study the spatiotemporal variation in P accumulation in the soil profile of cropland driven by SGT fluctuations in the RS and DS and estimate the P loss in the soil profile driven by SGT fluctuations. The results showed that fluctuations in the SGT promoted P accumulation from the surface soil to deeper soil. The proportions of P stored in various forms in the 30-60 cm and 60-100 cm soil layers in the RS were greater than those in the DS, while the average proportion in the 0-30 cm soil layer in the DS was as high as 48%. Compared with those in the DS, the maximum decreases in the proportion of P stored as TP and Olsen-P in the 0-100 cm soil layer in the RS were 16% and 58%, respectively, due to the rise in the SGT (SGT <30 cm), while the soil TP storage decreased by only 1% when the SGT was maintained at 60-100 cm. The critical thresholds for soil Olsen-P and TP gradually decreased with increasing soil depth, and the risk of P loss in deeper soil increased. The loss of soil P was increased by fluctuations in the SGT. Based on the cropland area around the 7 plateau lakes, P storage, and SGT fluctuations, the average loss intensity and loss amount of TP in the 0-100 cm soil layer around the 7 plateau lakes were estimated to be 25 kg/ha and 56 t, respectively. Therefore, reducing exogenous P inputs, improving soil endogenous P utilization efficiency and maintaining deep soil P retention are the basic strategies for preventing and controlling P accumulation and loss in deep soil caused by SGT fluctuations.

U(VI) mitigation via forward osmosis: elucidation of retention mechanisms and co-ion effects.

Uranium (U) is a chemical and radioactive toxic contaminant affecting many groundwater systems. The focus of this study was to evaluate the suitability of forward osmosis (FO) for uranium rejection from contaminated groundwater under field-relevant conditions. Laboratory experiments with aqueous solution containing uranium were performed with FO membrane to understand the uranium rejection mechanism under varied pH, draw solution concentration, and presence of co-ions. Further, experiments were performed with U-contaminated field groundwater. Results of the hydrogeochemcial modelling using PHREEQC indicated that the rejection mechanism of uranium was highly dependent on aqueous speciation. Uranium rejection was maximum at alkaline pH with ca. 99% rejection due to charge-based interactions between membrane and dominant uranyl complexes. The results of the co-ion study indicated that nitrate and phosphate ions decrease uranium rejection. Whereas, bicarbonates, calcium, and magnesium ions concentrated uranium in feed solution. Further, the uranium adsorption onto the membrane surface primarily depended on pH of the aqueous solution with maximum adsorption at pH 5.5. Our results show that the World Health Organization's drinking water guideline value of 30 µgL-1 for U could be achieved via FO process in field groundwater containing low dissolved solids.

Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling.

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO4), nitrate (NO3), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO3), phosphate (PO4), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously.

Enhancing hydrological insight: isotopic methods revealing groundwater-surface water interactions in the Lower Quang Tri River Group, Vietnam.

The Lower Quang Tri River Group, situated in central Vietnam, faces a myriad of challenges, notably the decline in groundwater levels and the salinisation of both groundwater and surface water, significantly impacting water availability for domestic, agricultural, and industrial purposes. To address these pressing concerns, this study adopts a comprehensive methodology integrating hydrogeological measurements, isotopic techniques, and chemical analyses of various water sources, including local precipitation, surface water bodies, reservoirs, and groundwater samples. Utilising the deuterium and oxygen-18 signatures (δ2H and δ18O) in water molecules as environmental tracers for the assessment of base flow and water sources enables a nuanced understanding of the intricate interaction between surface water and groundwater. Research findings elucidate that during the dry season, groundwater recharge primarily stems from water in the reservoirs over approximately seven months. Base flow contributes between 80 and 85 % of streamflow during the rainy season, escalating to 100 % during the dry season. The mean travelling time of the base flow is estimated at 120 ± 10 days using the sine curve model developed by Rodgers et al. The insights gleaned from this study are poised to play a pivotal role in guiding the local water resources managers in licensing for the exploitation of a right quantities of groundwater as sustainable management strategies in the region.

Enrichment of different taxa of the enigmatic candidate phyla radiation bacteria using a novel picolitre droplet technique.

The candidate phyla radiation (CPR) represents a distinct monophyletic clade and constitutes a major portion of the tree of life. Extensive efforts have focused on deciphering the functional diversity of its members, primarily using sequencing-based techniques. However, cultivation success remains scarce, presenting a significant challenge, particularly in CPR-dominated groundwater microbiomes characterized by low biomass. Here, we employ an advanced high-throughput droplet microfluidics technique to enrich CPR taxa from groundwater. Utilizing a low-volume filtration approach, we successfully harvested a microbiome resembling the original groundwater microbial community. We assessed CPR enrichment in droplet and aqueous bulk cultivation for 30 days using a novel CPR-specific primer to rapidly track the CPR fraction through the cultivation attempts. The combination of soil extract and microbial-derived necromass provided the most supportive conditions for CPR enrichment. Employing these supplemented conditions, droplet cultivation proved superior to bulk cultivation, resulting in up to a 13-fold CPR enrichment compared to a 1- to 2-fold increase in bulk cultivation. Amplicon sequencing revealed 10 significantly enriched CPR orders. The highest enrichment in CPRs was observed for some unknown members of the Parcubacteria order, Cand. Jorgensenbacteria, and unclassified UBA9983. Furthermore, we identified co-enriched putative host taxa, which may guide more targeted CPR isolation approaches in subsequent investigations.

A proposed methodology to evaluate the influence of climate change on drinking water treatments and costs.

Drinking water (DW) production treatments can be affected by climate change, in particular intense rainfall events, having an impact on the availability and quality of the water source. The current study proposes a methodology for the evaluation of the costs of the different treatment steps for surface water (SW) and groundwater (GW), through the analysis and quantification of the main cost items. It provides the details to count for strong variations in the key quality parameters of inlet water following severe rainfalls (namely turbidity, iron, manganese, and E. coli). This methodology is then applied to a large drinking water treatment plant (DWTP) in Italy, which treats both SW, around 70 %, and GW, around 30%. It discusses the overall DW production costs (from 7.60 c€/m3 to 10.43 c€/m3) during the period 2019-2021 and analyzes the contributions of the different treatment steps in water and sludge trains. Then it focuses on the effects on the treatments of significant variations in SW turbidity (up to 1863 NTU) due to intense rainfalls, and on the daily costs of DW with respect to the average (baseline) costs evaluated on the annual basis. It emerges that, when SW has low turbidity levels, the energy-based steps have the biggest contribution on the costs (final pumping 22 % for SW and 10 % for GW, withdrawal 15 % and 14 %, respectively), whereas at very high turbidity levels, sludge greatly increases, and its treatment and disposal costs become significant (up to 14 % and 50 %). Efforts are being made to adopt the best strategies for the management of DWTPs in these adverse conditions, with the aim to guarantee potable water and optimize water production costs. A mitigation measure consists of increasing GW withdrawal up to the authorized flow rate, thus reducing SW withdrawal. In this context, the study is completed by discussing the potential upgrading of the DWTP by only treating GW withdrawn from riverbank filtration. The DW production cost would be 7.76 c€/m3, which is lower than that seen for the same year (2021) with the current plant configuration (8.32 c€/m3).

Chemical and isotopic tracers combined with mixing models for tracking nitrate contamination in the Pampa de Pocho aquifer, Argentina.

In recent years, it has become evident that human activities have significantly disrupted the nitrogen cycle surpassing acceptable environmental thresholds. In this study, chemical and isotopic tracers were combined with a mathematical mass balance model (EMMA), PHREEQC inverse mixing model, and statistical analyses to evaluate groundwater quality, across an area experiencing substantial human activities, with a specific focus on tracing the origin of nitrate (NO3-) with potential water mixing processes. This multi-technique approach was applied to an unconfined aquifer underlying an agricultural area setting in an inter-mountain depression (i.e., the "Pampa de Pocho Plain" in Argentina). Here, the primary identified geochemical processes occurring in the investigated groundwater system include the dissolution of carbonate salts, cation exchange, and hydrolysis of alumino-silicates along with incorporating ions from precipitation. It was observed that the chemistry of groundwater, predominantly of sodium bicarbonate with sulfate water types, is controlled by the area's geology, recharge from precipitation, and stream water infiltration originating from the surrounding hills. Chemical results reveal that 60% of groundwater samples have NO3- concentrations exceeding the regional natural background level, confirming the impact of human activities on groundwater quality. The dual plot of δ15NNO3 versus δ18ONO3 values indicates that groundwater is affected by NO3- sources overlapping manure/sewage with organic-rich soil. The mathematical EMMA model and PHREEQC inverse modeling, suggest organic-rich soil as an important source of nitrogen in the aquifer. Here, 64 % of samples exhibit a main mixture of organic-rich soil with manure, whereas 36 % of samples are affected mainly by a mixture of manure and fertilizer. This study demonstrates the utility of combining isotope tracers with mathematical modeling and statistical analyses for a better understanding of groundwater quality deterioration in situations where isotopic signatures of contamination sources overlap.

Development of a "big data" groundwater microbial contamination index and spatial comparisons with enteric infection rates in southern Ontario.

Big data have become increasingly important for policymakers and scientists but have yet to be employed for the development of spatially specific groundwater contamination indices or protecting human and environmental health. The current study sought to develop a series of indices via analyses of three variables: Non-E. coli coliform (NEC) concentration, E. coli concentration, and the calculated NEC:E. coli concentration ratio. A large microbial water quality dataset comprising 1,104,094 samples collected from 292,638 Ontarian wells between 2010 and 2021 was used. Getis-Ord Gi* (Gi*), Local Moran's I (LMI), and space-time scanning were employed for index development based on identified cluster recurrence. Gi* and LMI identify hot and cold spots, i.e., spatially proximal subregions with similarly high or low contamination magnitudes. Indices were statistically compared with mapped well density and age-adjusted enteric infection rates (i.e., campylobacteriosis, cryptosporidiosis, giardiasis, verotoxigenic E. coli (VTEC) enteritis) at a subregional (N = 298) resolution for evaluation and final index selection. Findings suggest that index development via Gi* represented the most efficacious approach. Developed Gi* indices exhibited no correlation with well density, implying that indices are not biased by rural population density. Gi* indices exhibited positive correlations with mapped infection rates, and were particularly associated with higher bacterial (Campylobacter, VTEC) infection rates among younger sub-populations (p < 0.05). Conversely, no association was found between developed indices and giardiasis rates, an infection not typically associated with private groundwater contamination. Findings suggest that a notable proportion of bacterial infections are associated with groundwater and that the developed Gi* index represents an appropriate spatiotemporal reflection of long-term groundwater quality. Bacterial infection correlations with the NEC:E. coli ratio index (p < 0.001) were markedly different compared to correlations with the E. coli index, implying that the ratio may supplement E. coli monitoring as a groundwater assessment metric capable of elucidating contamination mechanisms. This study may serve as a methodological blueprint for the development of big data-based groundwater contamination indices across the globe.

Modeling groundwater redox conditions at national scale through integration of sediment color and water chemistry in a machine learning framework.

Redox conditions play a crucial role in determining the fate of many contaminants in groundwater, impacting ecosystem services vital for both the aquatic environment and human water supply. Geospatial machine learning has previously successfully modelled large-scale redox conditions. This study is the first to consolidate the complementary information provided by sediment color and water chemistry to enhance our understanding of redox conditions in Denmark. In the first step, the depth to the first redox interface is modelled using sediment color from 27,042 boreholes. In the second step, the depth of the first redox interface is compared against water chemistry data at 22,198 wells to classify redox complexity. The absence of nitrate containing water below the first redox interface is referred to as continuous redox conditions. In contrast, discontinuous redox conditions are identified by the presence of nitrate below the first redox interface. Both models are built using 20 covariate maps, encompassing diverse hydrologically relevant information. The first redox interface is modelled with a mean error of 0.0 m and a root-mean-squared error of 8.0 m. The redox complexity model attains an accuracy of 69.8 %. Results indicate a mean depth to the first redox interface of 8.6 m and a standard deviation of 6.5 m. 60 % of Denmark is classified as discontinuous, indicating complex redox conditions, predominantly collocated in clay rich glacial landscapes. Both maps, i.e., first redox interface and redox complexity are largely driven by the water table and hydrogeology. The developed maps contribute to our understanding of subsurface redox processes, supporting national-scale land-use and water management.