Soil colloids can significantly enhance spreading of polybromodiphenyl ethers in groundwater by serving as an effective carrier.

Polybromodiphenyl ethers (PBDEs), the widely used flame retardants, are common contaminants in surface soils at e-waste recycling sites. The association of PBDEs with soil colloids has been observed, indicating the potential risk to groundwater due to colloid-facilitated transport. However, the extent to which soil colloids may enhance the spreading of PBDEs in groundwater is largely unknown. Herein, we report the co-transport of decabromodiphenyl ester (BDE-209) and soil colloids in saturated porous media. The colloids released from a soil sample collected at an e-waste recycling site in Tianjin, China, contain high concentration of PBDEs, with BDE-209 being the most abundant conger (320 ± 30 mg/kg). The colloids exhibit relatively high mobility in saturated sand columns, under conditions commonly observed in groundwater environments. Notably, under all the tested conditions (i.e., varying flow velocity, pH, ionic species and ionic strength), the mass of eluted BDE-209 correlates linearly with that of eluted soil colloids, even though the mobility of the colloids varies markedly depending on the specific hydrodynamic and solution chemistry conditions involved. Additionally, the mass of BDE-209 retained in the columns also correlates strongly with the mass of retained colloids. Apparently, the PBDEs remain bound to soil colloids during transport in porous media. Findings in this study indicate that soil colloids may significantly promote the transport of PBDEs in groundwater by serving as an effective carrier. This might be the reason why the highly insoluble and adsorptive PBDEs are found in groundwater at some PBDE-contaminated sites.

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.

Natural background levels and driving factors of aluminum in shallow groundwater of an urbanized delta: Insight from eliminating anthropogenic-impacted groundwaters.

Knowledge on natural background levels (NBLs) of aluminum (Al) in groundwater can accurately assess groundwater Al contamination at a regional scale. However, it has received little attention. This study used a combination of preselection and statistic methods consisting of the oxidation capacity and the boxplot iteration methods to evaluate the NBL of shallow groundwater Al in four groundwater units of the Pearl River Delta (PRD) via eliminating anthropogenic-impacted groundwaters and to discuss driving factors controlling high NBLs of Al in groundwater in this area. A total of 280 water samples were collected, and 18 physico-chemical parameters including Redox potential, dissolved oxygen, pH, total dissolved solids, HCO3 -, NH4 +, NO3 -, SO4 2-, Cl-, NO2 -, F-, K+, Na+, Ca2+, Mg2+, Fe, Mn, and Al were analyzed. Results showed that groundwater Al NBLs in groundwater units A-D were 0.11, 0.16, 0.15, and 0.08 mg/L, respectively. The used method in this study is acceptable for the assessment of groundwater Al NBLs in the PRD, because groundwater Al concentrations in various groundwater units in residual datasets were independent of land-use types, but they were opposite in the original datasets. The dissolution of Al-rich minerals in sediments/rocks was the major source for groundwater Al NBLs in the PRD, and the interaction with Al-rich river water was secondary one. The high groundwater Al NBL in groundwater unit B was mainly attributed to the acid precipitation and the organic matter mineralization inducing the release of Al in Quaternary sediments. By contrast, the high groundwater Al NBL in groundwater unit C mainly was ascribed to the release of Al complexes such as fluoroaluminate from rocks/soils into groundwater induced by acid precipitation, but it was limited by the dissolution of Mg minerals (e.g., dolomite) in aquifers. This study provides not only useful groundwater Al NBLs for the evaluation of groundwater Al contamination but also a reference for understanding the natural geochemical factors controlling groundwater Al in urbanized deltas such as the PRD. PRACTITIONER POINTS: The natural background level (NBL) of groundwater aluminum in the Pearl River Delta (PRD) was evaluated. The dissolution of aluminum-rich minerals in sediments/rocks was the major source for groundwater aluminum NBLs in the PRD. The acid precipitation and organic matter mineralization contribute to high groundwater Al NBL in the groundwater unit B. The acid precipitation contributes to high groundwater Al NBL in the groundwater unit C, while dissolution of magnesium minerals limits it.

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.

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.

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.

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).

A novel paraffin-based N/P controlled-release material for biostimulation of phenol biodegradation in groundwater.

To address the problem of the weak natural restoration ability of oligotrophic groundwater environments, a novel N/P controlled-release material (CRM) for biostimulation, prepared by an improved method, was developed. CRMs can encapsulate N and P (N/P) salts for sustained release in aquifers. Paraffin-based CRMs can be used to control N/P release rates by adjusting the particle size of CRMs and the mass ratio of the paraffin. The developed CRMs had a more remarkable adaptability to groundwater than other materials. Specifically, 0.4-cm CRMs released N/P stably and efficiently over a wide temperature range (7-25 â„ƒ), and the release properties of various CRMs were not affected by pH. The release of N/P followed Fickian diffusion, and a dissolution-diffusion model was established to elucidate the mechanism of the controlled release. In contrast to bare N/P, CRMs obviously enhanced the biodegradation rate of phenol and prolonged the effectiveness of supplying N/P. The degradation rate of phenol in the CRM system increased by 20.8 %. The different supply modes of N/P, CRMs and bare N/P, resulted in differences in salinity. Metagenomic analysis showed that this difference changed the proportion of various phenol-degrading genera and thus changed the abundance of genes associated with the phenol degradation pathway.

Research on the relation between hydro-chemical and geological characteristics in karst area: Case study in Zhong Liang Mountain, Southwest China.

Karst groundwater, which is one of most important drinking water sources, is vulnerable to be polluted as its closed hydraulic relation with surface water. Thus, it is very important to identify the groundwater source to control groundwater pollution. The Pearson correlation coefficient among major ions (Na + K+, Ca2+, Mg2+, HCO3 -, SO4 2-, and Cl-) was employed to deduce the groundwater types in Zhong Liang Mountain, Southwest China. Then, the combined method of principal component analysis and cluster analysis were employed to identify the groundwater sources in a typical karst region of southwest China. The results shown that (1) the high positive correlation between cations and anions indicated the water-rock reaction of Ca-HCO3, Ca-SO4, (Na + K)-Cl, and Mg-SO4. (2) The major two principal components that would represent water-rock reaction of CaSO4 and Ca-HCO3 would, respectively, explain 60.41% and 31.80% of groundwater information. (3) Based on the two principal components, 33 groundwater samples were clustered into eight groups through hierarchical clustering, each group has similar water-rock reaction. The findings would be employed to forecast the surge water, that was an important work for tunnel construction and operation. PRACTITIONER POINTS: The components of groundwater was highly correlated with water-rock reaction. The principal component analysis screens the types of groundwater. The cluster analysis identifies the groundwater sources.

Achieving sustainable trichloroethylene removal from nitrate-containing groundwater: Effects of particle size and dosage of microscale zero-valent iron on its synergistic action with anaerobic bacteria.

The coupling of microscale zero-valent iron (mZVI) and anaerobic bacteria (AB) has gained increasing attention due to its ability to enhance dechlorination efficiency by combining the advantages of chemical and microbial reduction. However, the implementation of these coupling technologies at the field scale is challenging in terms of sustainability goals due to the coexistence of various natural electron acceptors in groundwater, which leads to limited electron selectivity and increased secondary risk. Therefore, this study used trichloroethylene (TCE) as a probe contaminant and nitrate (NO3-) as a typical co-occurring natural electron acceptor to optimize the overall sustainable remediation performance of an mZVI/AB coupled system by adjusting the mZVI particle size and dosage. Results revealed that mZVI particles of different sizes exhibit different microorganism activation capabilities. In contrast to its 2 µm and 7 µm counterparts, the 30 µm mZVI/AB system demonstrated a strong dosage-dependency in TCE removal and its product selectivity. Finally, multi-criteria analysis (MCA) methods were established to comprehensively rank the alternatives, and 30 µm mZVI (15 g/L dosage) was determined to be the best remediation strategy with the highest total sustainability score under all studied hydro-chemical conditions when equal weights were applied to technical, environmental, and economic indicators. Our work provides a paradigm for comprehensively assessing the sustainable remediation performance of chlorinated aliphatic hydrocarbons polluted groundwater in practical applications.

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.

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.

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 climatic factors. In this work, XGBoost, a tree-based algorithm, was applied to comprehensively analyze the drivers of groundwater contamination from nitrate, using data on 13 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 the nitrogen loading time (year) 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.

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.

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.

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.

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.

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).