Search Results (158)

Groundwater contamination by dissolved metals and metalloids in the Chiang Mai Basin is an important drinking-water concern, yet the coupled depth patterns, hydrogeochemical controls, composite contamination status, and screening-level health implications have not previously been assessed in an integrated basin-scale framework. This study evaluated 120 groundwater samples from alluvial wells classified by depth as shallow (≤30 m, n = 40), intermediate (31–60 m, n = 35), and deep (>60 m, n = 45). Samples were analyzed for nine dissolved metals and metalloids (Fe, Mn, As, Cd, Pb, Cr, Zn, Hg, and Se) together with pH, Eh, and total dissolved solids (TDS). The highest exceedance frequencies were observed for Fe (72.5% of samples, >0.3 mg/L acceptability threshold), Mn (65.0%, >0.08 mg/L), and As (45.8%, >10 μg/L). Fe and Mn increased significantly with depth, whereas As was enriched in deep wells but showed no statistically significant depth dependence. Pearson correlation and principal component analysis consistently identified a dominant redox-associated component in which Fe, Mn, and As covaried negatively with Eh, supporting redox-sensitive co-enrichment in deeper groundwater. Contamination factors calculated relative to selected global groundwater background values were >6 for all seven evaluated metals (Fe, Mn, As, Cd, Pb, Cr, and Zn), and the overall pollution load index (PLI) was 9.11, with the highest depth-specific PLI in deep wells (10.42). These indices are interpreted here as background-relative screening tools rather than stand-alone regulatory measures. A screening-level ingestion risk assessment identified arsenic as the dominant toxicological driver, with hazard quotients (HQ) of 1.97 for adults and 4.60 for children, and an estimated lifetime cancer risk (LCR) of 8.87 × 10^–4. The results support targeted monitoring of deeper wells, routine screening for As and Mn, and treatment strategies that can address the co-occurring Fe–Mn–As assemblage in alluvial groundwater. Full article

Hydrogeochemistry is a practical and low-impact tool for mineral exploration that relies primarily on groundwater as sampling media. It is particularly valuable for blind or deeply buried deposits where surface geochemical methods are ineffective, as groundwater acts as a natural integrator of geochemical signals from depth. This study presents a PRISMA 2020-compliant systematic review of hydrogeochemical exploration practices published between 1946 and 2025, synthesizing 118 empirically screened case studies from diverse geological and climatic settings. The review evaluates the geochemical processes governing aqueous dispersion halos, including sulphide oxidation, water–rock interaction, redox controls, and physicochemical speciation, and assesses how these processes influence pathfinder behaviour and anomaly expression. Quantitative synthesis highlights consistent patterns in hydrogeochemical footprints across major mineral systems and demonstrates the effectiveness of thermodynamically informed and multivariate interpretation strategies over simple concentration-based approaches. Emerging trends identified include the growing application of non-traditional stable isotope fractionation, nanoparticle geochemistry using single-particle ICP-MS, and integration of hydrogeochemical datasets with GIS, geophysics, and machine learning-based prospectivity modelling. Unlike recent narrative reviews, this study provides a fully reproducible, structured evaluation of the global evidence base and formalizes a standardized end-to-end workflow. Full article

Physicochemical and microbiological parameters are important indicators of drinking water quality. This study assessed the quality of groundwater used for drinking in four regions of Kosovo at 16 locations using an integrated assessment framework that combined physicochemical, microbiological, and Water Quality Index (WQI) approaches. The results reveal substantial spatial variability in water quality. While most physicochemical parameters remained within recommended limits, elevated values of total dissolved solids (up to 2792.5 mg/L), electrical conductivity (up to 2768.5 µS/cm), nitrate (up to 60.75 mg/L), and phosphate (up to 0.875 mg/L) were observed at several locations, indicating localized hydrogeochemical and anthropogenic influences. Dissolved oxygen levels were generally low (0.68–5.49 mg/L), reflecting limited aeration conditions in groundwater systems. Microbiological analysis revealed critical contamination, with Escherichia coli concentrations up to 299.9 CFU/100 mL, and all sampling sites exceeded permissible limits, indicating widespread fecal pollution and rendering the groundwater unsafe for direct consumption. WQI assessment further confirmed this condition, where 93.75% of locations were classified as medium quality using the NSF-WQI method, whereas the WA-WQI method categorized 68.75% of samples as poor and 6.25% as very poor. The novelty of this study lies in the integrated evaluation of hydrogeochemical processes and microbiological contamination using dual WQI methods and multivariate statistical analysis, providing a comprehensive understanding of groundwater degradation pathways. The findings are significant for policymakers, environmental managers, and public health authorities, highlighting the urgent need for groundwater treatment, improved sanitation infrastructure, and sustainable water resource management strategies in vulnerable regions. Full article

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article

For the purpose of investigating the hydrochemical signatures and formation processes of mine water at the Feicheng Coal Mine, a total of 61 samples, including fifth limestone water (FLW), old goaf water (OGW), and ordovician limestone water (OLW), were collected and examined via statistical and hydrochemical approaches. The assessment of mine water suitability for irrigation employed sodium percentage (Na%), sodium adsorption ratio (SAR), permeability index (PI), and magnesium hazard ratio (MHR). The mine water proves slightly alkaline, featuring Na⁺ as the leading cation and SO₄²⁻/HCO₃⁻ as the leading anions. By average concentration, cations decrease in the order Na⁺ > Ca²⁺ > Mg²⁺, and anions decrease as SO₄²⁻ > HCO₃⁻ > Cl⁻. The hydrochemical types of OLW and FLW samples were primarily Ca-HCO₃ and Ca-Mg-Cl, whereas the OGW samples were predominantly of the Na-Cl-SO₄ and Na-HCO₃ types. Rock weathering serves as the main control on water chemistry, with hydrochemical components sourced largely from evaporite and carbonate dissolution. The sodium present in the water is likely attributable to silicate mineral dissolution or cation exchange processes. Cation exchange, with forward exchange dominant, is also a key hydrogeochemical process in the study area. SI results reveal that calcite and dolomite have reached saturation, while gypsum and halite remain undersaturated and tend to dissolve further. Irrigation suitability assessments indicate that most of the water quality in the Feicheng Coal Mine is excellent or good. A limited number of samples exhibited relatively high salinity, and most of them can be directly irrigated. To this end, this study proposes targeted treatment solutions, thus facilitating mine water development and utilization. Full article

To identify the sources and driving mechanisms of nitrate contamination in pore water around Nansi Lake, 54 pore water samples were analyzed via hydrogeochemical analysis, Gibbs diagrams, ionic ratios, and principal component analysis (PCA). The pore water is predominantly slightly alkaline, with dominant cations Ca²⁺ and Na⁺, and anions HCO₃⁻ and SO₄²⁻. Nitrate-nitrogen (NO₃⁻-N) concentrations range from 0.82 to 54.31 mg·L⁻¹, with a coefficient of variation of 1.41 and an exceedance rate of 18.52%, indicating significant external inputs. A positive correlation between NO₂⁻ and NO₃⁻ suggests denitrification in some areas. Nitrate concentrations exhibit distinct spatial heterogeneity: high concentrations occur in agricultural/aquaculture lakeside plains and urban areas, low concentrations near coal mining subsidence zones, and transitional zones showing outward diffusion. Nitrate sources are predominantly anthropogenic. High Cl⁻ and low NO₃⁻/Cl⁻ ratios indicate domestic and aquaculture wastewater infiltration, whereas low Cl⁻ and high NO₃⁻/Cl⁻ ratios indicate agricultural fertilizer input. Industrial and natural sources are minor. PCA identified three controlling factors (cumulative variance 69.81%): coal mining and industrial/domestic pollution (39.82%), carbonate rock weathering (19.44%), and agricultural activities (10.55%). Health risk assessment shows no significant risk for adults (hazard quotient (HQ) < 1), but children face localized risks at nine sites (HQs of 1.25–2.26) in intensive farming, urban, and transitional zones. Excessive fertilizer application and sewage leakage are the primary causes, posing methemoglobinemia risks to infants. This study provides a scientific basis for nitrate pollution control and sustainable water management in the Nansi Lake Basin and offers methodological insights for similar lacustrine plain regions. Full article

The hydrogeochemical characterization of shallow volcanic lakes at the Sete Cidades Volcano (São Miguel, Azores) provides new insights into the processes controlling water chemistry in low-depth lacustrine systems within active volcanic environments. Fourteen lakes (0.6–4 m deep) were sampled during two campaigns (winter 2024 and spring/summer 2025), combining in situ physicochemical measurements and major ion analyses along vertical profiles. The lakes are holomictic, cold (11.3–17.6 °C), slightly acidic (pH 5.66–5.95), and weakly mineralized (EC ~65–69 µS/cm), indicating dilute waters of predominantly meteoric origin. Hydrochemical facies are dominated by Na–Cl type, with strong correlations between chloride and conductivity (r = 0.857), supporting a major contribution from marine atmospheric deposition. To move beyond correlation-based interpretation, Gibbs diagrams and saturation indices (PHREEQC) were applied to constrain the dominant geochemical processes. Most samples plot within the precipitation dominance field, while all calculated saturation indices are negative (SI < 0), indicating undersaturation with respect to carbonate, evaporite, and silicate minerals. These results demonstrate that water chemistry is primarily controlled by atmospheric inputs, with only minor contributions from water–rock interaction and negligible influence of evaporation or mineral equilibrium processes. Seasonal increases in HCO₃⁻ and dissolved CO₂ at depth suggest enhanced organic matter decomposition during warmer periods, highlighting the role of biogeochemical processes in modulating carbon dynamics in shallow systems. The absence of a clear hydrothermal signature further distinguishes these lakes from deeper volcanic systems in the Azores. This study provides the first integrated hydrogeochemical framework for shallow volcanic lakes in the region, combining classical hydrochemistry with process-based tools. The results establish a quantitative baseline for assessing environmental change and improve the interpretation of external (atmospheric) versus internal (geochemical and biological) controls in volcanic lake systems. Full article

The Beishan area of Gansu, China, is the primary candidate site for the geological disposal of China’s high-level radioactive waste (HLW). To assess the long-term safety of this repository, the evolutionary patterns of groundwater and the primary migration vector of radionuclides must be understood. Through experiments and hydrogeochemical simulations of snowmelt samples from the Qilian Mountains and deep rock samples from Beishan, we reveal different hydrochemical compositions and types of the snowmelt and deep groundwater. The results show that the hydrochemical type of Qilian Mountain snowmelt is SO₄–Na·Ca, whereas that of the deep groundwater in the Beishan is Cl·SO₄–Na, indicating substantial differences in the hydrochemical characteristics of the two samples. The water–rock interactions between snowmelt and granite are dominated by the dissolution of silicate minerals and the precipitation of carbonate minerals, accompanied by cation exchange and adsorption. After the interaction, the hydrochemical type of the snowmelt becomes SO₄–Na, with total dissolved solids (TDS) consistently maintained at ~500 mg/L, which is distinct from the TDS range of 1540–2045 mg/L observed for the deep groundwater in the Beishan. Under the experimental and simulation conditions set in this study, the water–rock interactions between Qilian Mountain snowmelt and Beishan granite cannot reproduce the hydrochemical characteristics of the deep groundwater in the Beishan. This study provides theoretical support for the hydrogeological safety assessment of HLW geological repositories. Full article

Water accumulation in goafs in Chinese coal mines is a major hidden hazard that can trigger water inrush accidents and may also affect aquifer integrity and regional water security. Reliable delineation of goaf water distribution and identification of water-source types are therefore essential for mine water-hazard control and groundwater protection. This paper reviews the main technical routes for goaf groundwater investigation, including geophysical prospecting, hydrogeochemical and isotopic identification, direct inspection tools, and data-driven intelligent workflows. For geophysical detection, the mechanisms, engineering applicability, and key constraints of the Transient Electromagnetic Method (TEM), Surface Nuclear Magnetic Resonance (NMR), the High-Density Resistivity Method (HDRM), and the Coherent Frequency Component (CFC) electromagnetic wave reflection coherence method are synthesized, with emphasis on interpretation boundaries and uncertainty sources under complex geological conditions. For source identification, conventional hydrochemistry, stable isotopes, and laser-induced fluorescence are summarized, and intelligent recognition models such as neural networks and support vector machines are discussed in terms of workflow positioning and practical performance limits. A unified evaluation rationale is established and a semi-quantitative method–metric matrix is constructed to compare techniques in terms of reliability, deployability, cost level, environmental adaptability, and information value, thereby clarifying their functional roles and complementarities within staged engineering workflows. The synthesis indicates that major bottlenecks include limited deep capability under strong interference, pronounced interpretational non-uniqueness caused by complex geology and irregular goaf geometries, and constrained timeliness and generalization for mixed-source identification. Future directions are summarized as multi-method integration with fusion-driven interpretation, intelligent and quantitative decision support with quality control, and sensor–platform advances enabling more practical three-dimensional investigation, aiming to improve the reliability and engineering usability of goaf groundwater hazard assessment. Full article

Sustainable groundwater management is essential for water security and human health protection. Fluoride contamination is a serious concern for the sustainable drinking water supply in many parts of Pakistan, including Balochistan, where arid climate conditions and geological formations support the enrichment of fluoride. The toxic nature of fluoride contamination has resulted in negative health impacts on the local population. Conventional geostatistical techniques are usually ineffective to delineate the nonlinear relationships that affect the distribution of fluoride. This study aims to develop a machine learning-driven spatial modelling framework for classifying the spatial distribution of fluoride contamination in groundwater across the study area. The model will help to understand the spatial variability of fluoride contamination and its controlling factors, essential for effective mitigation and early warning systems. Physiochemical elements were used as predictive features in this study, utilizing a unified feature importance framework combining hydrogeochemical analysis, spatial distribution assessment, and ensemble SHAP-based interpretation to identify consistent predictors. Model performance was evaluated using a nested cross-validation framework, followed by validation on an independent geology-informed spatial holdout test set to ensure realistic generalization. Among machine learning models, the Logistic Regression (LR), Support Vector Classifier (SVC), XGBoost (XGB), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbours (KNN) were evaluated. Support Vector Classifier (SVC) demonstrated a high predictive performance. On the independent spatial holdout dataset, SVC achieved an overall accuracy of 0.75 and an area under the receiver operating characteristic curve (AUC) of 0.821. In addition to classification, a human health risk assessment was conducted using chronic daily intake (CDI) and hazard quotient (HQ) calculations for children and adults, identifying several high-risk water supply schemes. The prediction maps successfully delineated high-risk fluoride points across specific areas, offering a tool for sustainable groundwater management. This study helps to achieve a Sustainable Development Goal (Clean Water and Sanitation, SDG#6) and promotes long-term sustainable planning in water-stressed areas by integrating spatial machine learning mapping and health risk assessment. Full article

The Jordan Valley is a heavily modified, data-limited transboundary river basin where water availability is constrained by both climate conditions and intensive human intervention. This study applies an integrated hydrological and hydrogeochemical modeling framework using the Soil and Water Assessment Tool (SWAT) to quantify basin-scale water availability and quality and to assess climate change impacts for the period 2000–2021. Results indicate that the basin is strongly evapotranspiration-dominated, with mean annual precipitation of 298.9 mm and precipitation-derived evapotranspiration accounting for 66.3% of rainfall. When externally supplied irrigation water is included, total evapotranspiration increases markedly, highlighting the strong dependence of agriculture on imported surface water and groundwater abstractions. Only a small fraction of total water input contributes to river discharge toward the Dead Sea, indicating a very limited internal water surplus. Hydrological dynamics are largely controlled by upstream dams and transboundary diversions, while nitrate and sediment simulations demonstrate a close coupling between hydrology, land use, and water quality. Climate projections suggest further reductions in water availability during the 21st century, exacerbating existing water scarcity. Overall, the study illustrates how intensive regulation and irrigation dependency constrain water availability in the Jordan Valley and in similar heavily modified transboundary river basins. Full article

Groundwater in Al-Madinah Al-Munawwarah faces considerable challenges from high salinity, elevated TDS, and nitrate contamination, primarily due to urbanization and industrial activities, making ongoing monitoring and management essential for its sustainable use in both drinking water and agriculture. The assessment of groundwater quality was conducted on 44 wells tapping two major aquifers (basaltic and alluvial) in the region, utilizing various geochemical techniques, including ICP-MS, FAAS, and XRF, to evaluate hydrochemical characteristics and identify the primary controlling factors. Key physicochemical parameters, including total dissolved solids (TDSs), electrical conductivity (EC), pH, total hardness (TH), and major ion concentrations, were evaluated. The results indicate that several parameters exceed permissible limits established by Gulf and international standards, reflecting highly saline conditions that could adversely affect drinking water safety and agricultural practices. Elevated nitrate levels and other contaminants indicate a combination of geological processes, including mineral leaching, and anthropogenic activities, such as agricultural runoff. Correlations among various ions reveal complex interactions driven by both natural and human factors. High nitrate and potassium concentrations, particularly in the alluvial aquifer, combined with weak correlations with geogenic ions, indicate anthropogenic inputs. Heavy metals in groundwater were classified into two groups: those within permissible limits (Ag, Ba, Be, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Sb, and U) and those exceeding recommended limits (Zn, Al, As, Se, and Tl). Elevated metal concentrations are primarily attributed to water–rock interactions and the fertilizer use in surrounding agricultural areas. These findings highlight the urgent need for continuous monitoring and proactive groundwater to ensure sustainable and safe use of water resources. Full article

The geothermal system in the Jiaodong Peninsula is situated within a continent–ocean transition zone, where complex interactions among meteoric water, geothermal fluids, and seawater produce diverse hydrogeochemical and isotopic signatures, complicating geothermal resource assessment and sustainable development. To constrain recharge sources and seawater mixing mechanisms, geothermal water samples were systematically collected from 15 geothermal fields and analyzed using integrated hydrogeochemical methods and multi-isotope tracers (δD–δ¹⁸O, δ³⁴S-SO₄²⁻, ⁸⁷Sr/⁸⁶Sr, and ³H). The results show that geothermal waters are predominantly recharged by meteoric precipitation, with δD–δ¹⁸O values distributed along the meteoric water line, while low d-excess values indicate prolonged circulation and significant water–rock interaction. Seawater mixing exhibits marked spatial heterogeneity: only 5 of the 15 fields show detectable marine influence. Chloride-based calculations suggest apparent seawater fractions of up to ~34% in BQ and <4% in DY, whereas the remaining fields show negligible mixing. Sulfur and strontium isotopes indicate contributions from external sulfate sources and continued water–rock interaction rather than simple mixing with modern seawater. Low tritium contents further imply involvement of deeply circulated paleo-seawater. The system is therefore interpreted as a fault-controlled seawater-mixing geothermal system, providing insights into coastal geothermal evolution and resource evaluation. Full article

Fluoride (F⁻) contamination in deep groundwater threatens drinking water security, yet its enrichment is commonly governed by coupled nonlinear hydrogeochemical feedbacks that are difficult to resolve with linear diagnostics alone. Here, we integrate an explainable deep learning framework (HydroAttentionNet + SHAP) with thermodynamic and mass-conservative inverse modeling (PHREEQC) to quantitatively link data-driven thresholds to mineral water processes in a multi-aquifer system. Using 258 deep-well samples, we delineate a robust evolution pathway from background to ultra-high-fluoride (Ultra-High F⁻, ≥1.5 mg/L) waters. HydroAttentionNet achieves strong predictive skill (R² = 0.77) and reveals a clear mechanistic tipping behavior: alkalinity (HCO₃⁻/CO₃²⁻) is the primary trigger for F⁻ activation, while progressive Na+ enrichment and Ca²⁺ depletion act as amplifiers by suppressing a(Ca²⁺) and weakening fluorite precipitation capacity. PHREEQC simulations confirm a coupled “salinization–decalcification–fluoridation” loop in which (i) evaporite dissolution elevates ionic strength (salt effect) and supplies Na⁺ to promote Na–Ca exchange, and (ii) carbonate re-equilibration drives calcite precipitation as an efficient Ca sink, offsetting ~45.8% of Ca²⁺ inputs; together, these processes maintain fluorite undersaturation and sustain net fluorite dissolution, contributing 56.6% of newly added dissolved F⁻ in evolved end-members. Monte Carlo health risk assessment (10,000 iterations) indicates substantial intergenerational inequity: 67.9% of children exceed the non-carcinogenic risk threshold (HQ > 1), compared with 29.3% of adults. Sensitivity analysis identifies source-water fluoride concentration as the dominant driver (Spearman r = 0.93), implying that supply-side interventions (defluoridation, well-screen optimization, and blending with low-F sources) are substantially more effective than behavioral measures. Full article

Monitoring the hydrochemistry of groundwater and the H-O isotopes in the Jingpo Lake volcanic area, China, is fundamental to studying the mechanisms of volcanic and seismic events, as well as the associated hazards. To study the hydrogeochemistry of fluids in the Jingpo Lake volcanic area, water samples from seven sites were tested for hydrogeochemistry, H-O isotopes, and radon (Rn) content. The genesis and evolution of the groundwater system were elucidated through an integrated approach employing Gibbs diagrams, ionic ratio analyses, reservoir temperature estimation (silica–enthalpy method), and inverse geochemical modeling with PHREEQC. The results showed that the dominant water chemistry type was HCO₃⁻, primarily influenced by volcanic rock weathering and deep hydrothermal activity. Spring and well water were influenced by cation exchange, adsorption, and rock weathering dissolution. The H-O isotope composition and radon content indicate that atmospheric precipitation is the main source of supply, while well water is influenced by deep fluids. According to the Na-K-Mg triangle diagram, most of the groundwater was shallow and immature, whereas the well water was partially balanced. The temperature of the geothermal water was controlled by the geothermal gradient, depending on its occurrence and circulation depth. Additionally, the equilibrium temperature of the thermal reservoir was calculated using the silica–enthalpy equation method, with the concentrations of dissolved components in the water taken into account. The temperature of the thermal reservoir of the well water and the depth of groundwater circulation were estimated. The original reservoir temperature in the study area was calculated to range from 108 °C to 156 °C, with a geothermal water-to-shallow groundwater mixing ratio of between 71% and 85%. The estimated shallow temperature ranged from 64.9 °C to 74.9 °C. These hydrogeochemical signatures reflect active water–rock interactions and the contribution of deep-seated geothermal fluids, providing robust evidence for ongoing geothermal activity in the Jingpo Lake volcanic system. The findings enhance our understanding of the recent geological evolution and present-day hydrothermal processes of this potentially active volcanic field, which establishes a crucial hydrogeochemical baseline for future monitoring and hazard assessment studies. Full article