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.

Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML\'s superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Groundwater contamination by nitrate and sulfate in mining areas is a significant challenge. Consequently, the inputs sources of these contaminants and their evolution have received considerable attention, with the knowledge gained critical for improved management of water quality. This study integrated data on multiple stable isotopes and water chemistry data and a Bayesian isotope mixing model to investigate the relative contributions of inputs sources of sulfate and nitrate sources to bodies of water in a karst mining area in southwest China. The outcomes indicated that hydrochemical component in the water bodies of the study area is mainly derived from the dissolution of silicate rocks, carbonate rocks and sulfate minerals as well as the oxidation of sulfides. The human and agricultural wastewater, soil nitrogen, and fertilizers were the predominant inputs sources of nitrate to the mine water environment; the predominant inputs sources of sulfide were mineral oxidation, evaporite dissolution, atmospheric deposition, and sewage. Groundwater is mainly recharged from atmospheric precipitation, and surface water is closely hydraulically connected to groundwater. Nitrogen and oxygen isotope composition and water chemistry indicative of nitrification dominate the nitrogen cycle in the study area. The oxidation of pyrite and bacterial sulfate reduction (SRB) had no significant impact on the stable isotopes of groundwater. The results of this study demonstrate the inputs of different sources to nitrate and sulfate in karst mines and associated transformation processes. The results of this study can assist in the conservation of groundwater quality in mining areas and can act as a reference for future related studies.

Northeastern Algeria boasts numerous hot springs, yet these hydrothermal sites remain largely unexplored for their microbial ecology. The present study explores the bacterial abundance and diversity within two distinct Algerian hot springs (Hammam Saïda and Hammam Debagh) and investigates the link between the prevailing bacteria with geochemical parameters. High-throughput 16S rRNA gene sequencing of water and sediment samples revealed a bacterial dominance of 99.85-91.16% compared to Archaea (0.14-0.66%) in both springs. Interestingly, Saïda hot spring, characterized by higher temperatures and sodium content, harbored a community dominated by Pseudomonadota (51.13%), whereas Debagh, a Ca-Cl-SO4 type spring, was primarily populated by Bacillota with 55.33%. Bacteroidota displayed even distribution across both sites. Additional phyla, including Chloroflexota, Deinococcota, Cyanobacteriota, and Chlorobiota, were also present. Environmental factors, particularly temperature, sodium, potassium, and alkalinity, significantly influenced bacterial diversity and composition. These findings shed light on the interplay between distinct microbial communities and their associated geochemical properties, providing valuable insights for future research on biogeochemical processes in these unique ecosystems driven by distinct environmental conditions, including potential applications in bioremediation and enzyme discovery.

Covered by vast eolian landforms, gravel deposits, and playas, the worldwide typical evaporite deposit land, Qaidam Basin, in northwestern China is analogous to early Mars when the aridification process had lasted for millions of years since the end of a wetter climate. This study aims to investigate the chemical and isotopic characteristics of waters in an evaporite-rich environment, as well as the habitable conditions therein, that have undergone a transformation similar to early Mars. In May 2023, a total of 26 water samples were collected across the representative central axis of a longitudinal aridity gradient in the Qaidam Basin, including categories of meteoric water, freshwater, standing water accumulated after precipitation, salty lacustrine water, and hypersaline brines to inspect compounds made up of carbon, nitrogen, phosphorus, sulfur, halogen, and metallic elements. As evaporation intensified, the salt types transformed from HCO3-Ca·Na to Cl·SO4-Na or ClMg. The dominance of carbonate will gradually be replaced by sulfate and chloride, leaving much more dilute and less detectable contents. The presence of trace ClO4-, ClO3-, ClO2-, and BrO3- was confirmed in a few of the sampled Qaidam waters, indicating the preservation of oxyhalides in waters within an arid region and possibly the presence of relevant microbial enzymes. The isotopes of water, carbonaceous, and nitrogenous compounds provide valuable references for either abiogenic or biogenic signatures. With undetectable amount, phosphorus was found to be the limiting nutrient in evaporative aquatic environments but not necessarily antibiosignatures. Overall, these results suggest that the paleo-lacustrine environments on Mars are more likely to preserve biosignatures if they feature the dominance of carbonate minerals, bioavailable nitrate, phosphorus, and organic carbon, the presence of thermodynamically unstable oxyhalides, and isotope ratios that point to the involvement of biological activity.

Uncontrolled coal mining using non-scientific methods has presented a major threat to the quality of environment, particularly the water resources in eastern himalayan sub-region of India. Water bodies in the vicinity of mining areas are contaminated by acid mine drainage (AMD) that is released into streams and rivers. This study attempted to assess the impact of AMD, deciphering hydrogeochemical processes, seasonal fluctuations, and stable isotope features of water bodies flowing through and around coal mining areas. Self-organizing maps (SOMs) used to separate and categorize AMD, AMD-impacted and non-AMD impacted water from the different study locations for two sampling seasons revealed four clusters (C), with C1 and C2 impacted by AMD, C3 and C4 showing negligible to no impact of AMD. AMD impacted water was SO42- - Mg2+- Ca2+ hydrochemical type with sulphide oxidation and evaporation dominating water chemistry, followed by silicate weathering during both the sampling seasons. Water with negligible-to-no AMD-impact was Mg2+- Ca2+- SO42- to Ca2+ - HCO3- to mixed hydrochemical type with rock weathering and dissolution, followed by ion exchange as major factors controlling water chemistry during both the sampling seasons. Most of physicochemical parameters of C1 and C2 exceeded the prescribed limits, whereas in C3 and C4 water samples, parameters were found within the prescribed limits. Stable isotopes of hydrogen (δ2H) and oxygen (δ18O) during post-monsoon (PoM) varied between -41.04 ‰ and -29.98 ‰, and -6.60 ‰ to -3.94 ‰; and during pre-monsoon (PrM) varied between -58.18 ‰ and - 33.76 ‰ and -8.60 ‰ to -5.46 ‰. Deuterium excess (d-excess) ranged between 1.57 ‰ and 12.47 ‰ during PoM and 5.70 ‰ to 15.17 ‰ during PrM season. The stable isotopes analysis revealed that evaporation, mineral dissolution and mixing with rainwater are the key factors in study area.

Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation. Cross-validation addressed data volume issues, and performance metrics were used for model evaluation. The results revealed that our machine learning models outperformed traditional fluid geothermometers. All machine learning models surpassed traditional methods. The XGBoost model, based on the F-3 combination, demonstrated the best prediction accuracy with an R2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R2 of 0.8302. This study highlights the potential of machine learning for accurate reservoir temperature prediction, offering geothermal professionals a reliable tool for model selection and advancing our understanding of geothermal resources.

Groundwater contamination with arsenic (As) is a significant concern in Pakistan\'s Punjab Province. This study analyzed 69 groundwater samples from Faisalabad, Gujranwala, Lahore, and Multan to understand hydrogeochemistry, health impacts, contamination sources, and drinking suitability. Results revealed varying as concentrations across districts, with distinctive cation and anion orders. Faisalabad exhibited Na+ > Mg2+ > Ca2+ > K+ > Fe2+ for cations and SO42- > Cl- > HCO3- > NO3- > F- for anions. Gujranwala showed Na+ > Ca2+ > Mg2+ > K+ for cations and HCO3-  > SO42- > Cl- > NO3-  > F- for anions. In Lahore, demonstrated: Na+ > Ca2+ > Mg2+ > Fe > K+ for cations and HCO3-  > SO42- > Cl- > NO3-  > F- for anions. Multan indicated K+ > Ca2+ > Mg2+ > Na+ > Fe for cations and HCO3-  > SO42- > Cl- > F- > NO3- ) for anions. Hydrochemical facies were identified as CaHCO3 and CaMgCl types. Principal Component Analysis (PCA), highlighted the influence of natural processes and human activities on groundwater pollution. Water Quality Index (WQI) result reveal that most samples met water quality standards. The carcinogenic risk values for children exceeded permissible limits in all districts, emphasizing a significant cancer risk. The study highlights the need for rigorous monitoring to mitigate (As) contamination and protect public health from associated hazards.

The objective of the current research was to examine the water quality of the River Ravi and the River Sutlej, with a specific focus on potentially toxic elements (PTEs). Additionally, we sought to monitor the sources of pollution in these rivers by gathering samples from the primary drains that carry industrial and municipal waste into these water bodies. Furthermore, we aimed to evaluate the impact of PTEs in surface water on groundwater quality by collecting groundwater samples from nearby populated areas. A total of 30 samples were collected from these three sources: rivers (6 samples), drains (9 samples), and groundwater (15 samples). The analysis revealed that the levels of PTEs in the samples from these three resources having a mean value: arsenic (As) 23.5 µg/L, zinc (Zn) 2.35 mg/L, manganese (Mn) 0.51 mg/L, lead (Pb) 6.63 µg/L, and chromium (Cr) 10.9 µg/L, exceeded the recommended values set by the World Health Organization (WHO). Furthermore, PTEs including (As 84%), (Zn 65%), (Mn 69%), (Pb 53%), (Cr 53%), and (Ni 27%), samples were beyond the recommended values of WHO. The results of the Principal Component Analysis indicated that surface water and groundwater exhibited total variability of 83.87% and 85.97%, respectively. This indicates that the aquifers in the study area have been contaminated due to both natural geogenic factors and anthropogenic sources. These sources include the discharge of industrial effluents, wastewater from municipal sources, mining activities, agricultural practices, weathering of rocks, and interactions between rocks and water. Spatial distribution maps clearly illustrated the widespread mobilization of PTEs throughout the study area. Furthermore, a health risk assessment was conducted to evaluate the potential adverse health effects of PTEs through the ingestion of drinking groundwater by both children and adults. Health risk assessment result show the mean carcinogenic values for As, Cr, Pb and Ni in children are calculated to be (1.88E-04), (2.61E-04), (2.16E-02), and (5.74E-05), respectively. Similarly, the mean carcinogenic values for the above mentioned PTEs in adults were recorded to be (2.39E-05), (3.32E-05), (1.19E-03), and (7.29E-06) respectively. The total hazard index values for As, Zn, Cr, Pb, Mn, Cu, and Ni in children were observed to be (9.07E + 00), (9.95E-07), (4.59E-04), (5.75E-04), (4.72E-05), (2.78E-03), and (5.27E-05) respectively. The analysis revealed that As has an adverse effect on the population of the study area as compared to other PTEs investigated in this study.

Understanding the geochemistry and contamination of rivers affected by anthropogenic activities is paramount to water resources management. The Asopos river basin in central Greece is facing environmental quality deterioration threats due to industrial, urban and agricultural activities. Here, the geochemistry of river sediments and adjacent soil in terms of major and trace elements (Al, Ca, Mg, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) and the geochemical composition of surface water in terms of major ions, trace elements and nutrients along the Asopos river basin were determined. In addition, this study characterized potential nitrate sources through the analysis of stable isotope composition of NO3- (δ15Ν-ΝΟ3- and δ18Ο-ΝΟ3-). Results indicated that specific chemical constituents including nutrients (NO2-, NH4+, PO43-) and major ions (Na+, Cl-) were highest in the urban, industrialized and downstream areas. On the other hand, nitrate (NO3-) concentration in river water (median 7.9 mg/L) showed a decreasing trend from the upstream agricultural sites to the urban area and even more in the downstream of the urban area sites. Ionic ratios (NO3-/Cl-) and δ15Ν-ΝΟ3- values (range from +10.2 ‰ to +15.7 ‰), complemented with a Bayesian isotope mixing model, clearly showed the influence of organic wastes from septic systems and industries operating in the urban area on river nitrate geochemistry. The interpretation of geochemical data of soil and river sediment samples demonstrated the strong influence of local geology on Cr, Fe, Mn and Ni content, with isolated samples showing elevated concentrations of Cd, Cu, Pb and Zn, mostly within the industrialized urban environment. The calculation of enrichment factors based on the national background concentrations provided limited insights into the origin of geogenic metals. Overall, this study highlighted the need for a more holistic approach to assess the impact of the geological background and anthropogenic activities on river waters and sediments.