Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques

Lead-contaminated groundwater exposes residents to health risks in Makululu, Zambia

Groundwater contamination by lead (Pb) occurs commonly in historically mined regions and presents health risks to exposed residents. Poor documentation of elevated Pb levels in underserved regions prevents the development of environmental contamination policies and interventions. This study investigates Pb contamination and associated carcinogenic and non-carcinogenic health risks in Makululu, a large compound near the former Pb mine in Kabwe City, Zambia. We analyzed 34 drinking groundwater samples from hand pump boreholes (n = 21) and shallow wells (n = 13). Pb concentrations exceeded WHO and USEPA standards for safe drinking in 100% of boreholes (median = 0.15 mg/L) and 77% of shallow wells (median = 0.06 mg/L). Linear regression analysis identified temperature and depth as key predictors of Pb concentrations, with higher temperatures correlating with increased Pb levels (p = 0.008), and deeper boreholes (50-100 m) exhibiting significantly higher Pb concentrations (p = 0.007) than shallow wells (10 m). Health risk assessments revealed that adults consuming borehole water had an average daily dose of 0.005 mg/kg/day of Pb exceeding WHO thresholds, with hazard quotients (HQ > 1) indicating non-carcinogenic risks. The findings highlight the urgent need for policies to mitigate environmental pollution, enforce water quality standards to reduce Pb levels and protect public health in Makululu.

Flood risk susceptibility analysis in Larkana district Pakistan using multi criteria decision analysis and geospatial techniques

Flooding is one of the most devastating natural disasters in worldwide, with significant socioeconomic and environmental impacts. In such a scenario, flood susceptibility analysis is essential for successful risk management and disaster preparedness. The Larkana district located in densely populated area of Sindh province, approximately 1.8 million population with 0.32 million households, is highly susceptible to floods due to its geographical location, river systems, and hot climatic patterns with extremely high temperature in summer and mild winter. Larkana district often faces significant flood risks mainly due to monsoon rains and the overflow of the Indus River which often lead to widespread flooding. The research aims to identify and quantify key flood risk factors, including rainfall pattern, distance to river, topography, land use/land cover, soil texture, vegetation index, and drainage infrastructure. AHP was applied to prioritize these risk factors based on expert opinions and their relative significance in contributing to flood vulnerability. GIS employed for spatial analysis and mapping of risk zones, allowing for detailed visualization of high-risk areas across the district. For this study, the various data sources such as topographic data, land use and landcover information, rainfall and infrastructure data, were used to develop a comprehensive flood susceptibility model. The AHP method was employed to determine the relative weights with consistency ratio (CR) and GIS techniques to generate flood susceptibility maps by considering all nine flood risk factors. Flood risk levels were further classified into five different classes as very low, low, moderate, high and very high. By using AHP, the weights of each parameter were calculated as a percentage, and it was determined that four out of nine parameters had 79% impact on flood hazard. These factors are ranked as the most influential factors in flood hazards for study area, with rainfall, distance to river, slope, and elevation having the greatest influences, while LULC, TWI, NDVI, soil type, and curvature had 21% impacts, much less impact as compared to top ranked parameters. After that, each flood risk parameter maps were reclassified. These maps were superimposed by weighted overlay maps in order to show that 7.65% of the entire area is at very high flood risk, 63.89% is at high risk, 28.38% is at moderate risk, and 0.12% is at low risk. The findings and established complete flood susceptibility model will facilitate policymakers and disaster management authorities by identifying high-risk locations and prioritizing mitigation measures, ultimately reducing the impact of floods on local communities and infrastructure in Larkana district.

Kriging-interpolated mapping and predictive modeling of groundwater F- and NO3- contamination with chemometric and health risk assessments in Ghana's Birimian Province

Increasing global reports of fluoride (F-) and nitrate (NO3-) contamination in groundwater highlight the urgency of identifying pollution hotspots to safeguard public health. This study investigates groundwater quality in two agricultural regions of Ghana's Birimian province, filling a vital research gap. This study utilized a diverse set of tools, including physicochemical analyses, violin plot visualizations, the Pollution Index of Groundwater (PIG), the Water Pollution Index (WPI), health risk assessments, Pearson's correlation analysis, and artificial neural network modeling. These approaches evaluated the key factors affecting groundwater quality, identified contamination sources and hotspots, and assessed associated human health risks. Results revealed predominantly alkaline groundwater (pH 7-9), with F- ranging from 0.0 to 1.5 mg/L and NO3- exceeding 500 mg/L in some areas. The PIG and WPI rated 81.94-94.44% of samples suitable for consumption, with mean scores of 0.54 and 0.51, respectively, highlighting NO3-, pH, and K+ as primary quality influencers. Violin plots showed multimodal distributions in TDS, NO3-, Ca2+, and Mg2+, suggesting complex hydrogeochemical dynamics. Health risk assessments indicated oral exposure risks ranging from low to very high, with NO3- posing a sixfold greater threat than F-. Spatial analysis tied F- contamination in central and southern areas to geological formations, while higher NO3- in the northern part aligned with agricultural activities. Correlation analysis and neural network modeling confirmed the geogenic origin of F- whereas the mixed sources of NO3- strongly tied to anthropogenic inputs. These insights urge targeted remediation and offer a scalable framework for global groundwater challenges.

Health risk assessment of groundwater use for drinking in West Nile Delta, Egypt

Human health is at risk from drinking water contamination, which causes a number of health problems in many parts of the world. The geochemistry of groundwater, its quality, the origins of groundwater pollution, and the associated health risks have all been the subject of substantial research in recent decades. In this study, groundwater in the west Rosetta Nile branch of the Nile Delta Aquifer is examined for drinking potential. Numerous water quality indices were applied, such as water quality index (WQI), synthetic pollution index (SPI) models, and health risk assessment (HRA) method. The limits of the measured parameters are used to test its drinking validity on the basis of WHO recommendations. TDS in the southern regions is within the desirable to allowable limits with percent 25.3% and 29.33%, respectively. Nearly all the study area has desirable value for HCO3, Al and Ba. Ca and Mg have desirable values in the center and south portion of the investigated area, whereas in the north are unsuitable. Na, Cl and SO4 fall within the desired level in the regions of the south but become unsuitable towards the north. Mn and NO3 are inappropriate except in the northwestern part. Fe is within suitable range in the southwestern and northwestern regions. Pb, Zn, Cu, and Cd were undetected in the collected samples. Regarding to WQI the study area is classified into 4 classes good, poor, very poor and unfit for drinking water from south to north. According to SPI model, 20%, 18.7%, 18.7%, 8% and 34.6% of water samples are suitable, slightly, moderately, highly polluted and unfit, respectively from south to north. Based on HRA, Children are the most category endangered with percent 14.7% of the overall samples obtained, followed by females and males with percent 12% and 8%, respectively. This study offers insights into the conservation and management of coastal aquifers' groundwater supplies. These findings have significant implications for developing strategies and executing preventative actions to reduce water resource vulnerability and related health hazards in West Nile Delta, Egypt.

Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils

Machine learning (ML) models for accurately predicting heavy metals with inconsistent outputs have improved owing to dataset outliers, which influence model reliability and accuracy. A comprehensive technique that combines machine learning and advanced statistical methods was applied to assess data outlier's effects on ML models. Ten ML models with three outlier detection methods predicted Cr, Ni, Cd, and Pb in Narayanganj soils. XGBoost with density-based spatial clustering of applications with noise (DBSCAN) improved model efficacy (R2). The R2 of Cr, Ni, Cd, and Pb was considerably enhanced by 11.11 %, 6.33 %, 14.47 %, and 5.68 %, respectively, indicating that outliers affected the model's HM prediction. Soil factors affected Cr (80 %), Ni (72.61 %), Cd (53.35 %), and Pb (63.47 %) concentrations based on feature importance. Contamination factor prediction showed considerable contamination for Cr, Ni, and Cd. LISA revealed Cd (55.4 %), Cr (49.3 %), and Pb (47.3 %) as the significant pollutant (p < 0.05). Moran's I index values for Cr, Ni, Cd, and Pb were 0.65, 0.58, 0.60, and 0.66, respectively, indicating strong positive spatial autocorrelation and clusters with similar contamination. Finally, this work successfully assessed the influence of data outliers on the ML model for soil HM contamination prediction, identifying crucial regions that require rapid conservation measures.

Unravelling integrated groundwater management in pollution-prone agricultural cities: A synergistic approach combining probabilistic risk, source apportionment and artificial intelligence

Groundwater is vital for agricultural cities, but intensive farming and fertilizer use have increased contamination risks, particularly for non-carcinogenic health hazards. This study reveals the sources of contaminants in groundwater, their health impacts, and targeted strategies in such cities. The study analyzed 115 groundwater samples, with the main groundwater chemical type being HCO₃-Na·Ca. Significant exceedances were found in Mg²⁺, HCO₃-, F-, total hardness (TH), and Mn, with HCO₃- and Mg²⁺ surpassing standards in nearly all samples. The average Comprehensive Environmental Water Quality Index (CEWQI) was 100.68, indicating that overall groundwater quality in the study area is good. High-quality water is mainly found near reservoirs and rivers, while urban and eastern regions have relatively poorer water quality. The proportion of groundwater unsuitable for drinking is low. Monte Carlo risk assessments revealed that F- and NO₃- pose non-carcinogenic risks to both adults and children, with NO₃- presenting a higher potential health risk. The Positive Matrix Factorization (PMF) model identified that groundwater pollution primarily results from natural geological processes and human activities, with agriculture being the major anthropogenic factor. AI-based zoning strategies highlighted industrial areas and high-fluoride zones as critical areas requiring enhanced prevention and control measures.

Characterization of potentially toxic elements in leachates from active and closed landfills in Nigeria and their effects on groundwater systems using spatial, indexical, chemometric and health risk techniques

This study examines potentially toxic elements (PTEs) in leachates from three landfills (active and closed sites) to quantify and compare their degree of toxicity and percolation effects on aquifer groundwater based on distance zonation. The finding revealed that the active landfill leachates had higher concentrations (P < 0.05) of PTEs, with EC, TDS, Cd, Cu, Fe, Zn, Pb, and Mn being above the standard limits for wastewater. The leachate pollution index confirmed high toxicity of the active sites. The geospatial maps suggest that the aquifer was influenced by leachate migration effects, with the groundwater situated within 100 m of the landfills exceeding the regulatory limits. Based on the Ficklin-Caboi assessment, the groundwaters were categorized into "low metals-near neutral" and "high metals-near neutral." The contamination degree, heavy metal contamination index, groundwater quality index, and water pollution index denote that over 40% of the groundwaters are "highly to extremely" polluted and are unfit for drinking, while over 50% are "excellently pure." The geospatial maps revealed that all the groundwaters within 100 m of the landfills are polluted, especially those near the active landfills. The prevailing factors impairing the quality of groundwater were Pb > Cd > TDS > Fe > EC > Cr > pH > Mn > Se > Co. The correlation coefficients, principal components, and cluster analyses confirmed the heterogeneous nature of the landfills and that the solid wastes were mainly from industrial, commercial, and household sources. Aside from the migration effect of leachates, other anthropogenic and geological factors are influencing the aquifer systems. The health risk assessment showed that the groundwaters within 100-500 m of the landfills are capable of causing noncarcinogenic and cancer health risks in exposed populations, with children and those within the distance of 100 m being the most vulnerable groups.

Trace elements and heavy metal(loid)s triggering ecological risks in a heavily polluted river-reservoir system of central Mexico: Probabilistic approaches

The contamination of trace elements and heavy metal(loid)s in water bodies has emerged as a global environmental concern due to their high toxicity at low concentrations to both biota and humans. This study aimed to evaluate the ecological risk associated with the occurrence and spatial distribution of Mn, Fe, Co, Cd, Ni, Zn, Sb, As, Tl, Cu, Pb, U, and V in the heavily polluted waters of an important river-reservoir system (Atoyac River Basin) in central Mexico, using two-level tired probabilistic approaches: Risk Quotient based on Species Sensitivity Distribution (RQSSD) and Joint Probability Curves (JPCs). The concentrations of these elements varied widely, ranging from 0.055 μg L-1 to 9200 μg L-1 and from 0.056 μg L-1 to 660 μg L-1, in both total and dissolved fractions, respectively. Although geogenic and anthropogenic sources contribute to the presence of these elements in waters, the discharge of untreated or poorly treated industrial wastewater is the main source of contamination. In this regard, the RQSSD results indicated high ecological risk for Mn, Fe, Co, Ni, Zn, and Sb, and medium or low ecological risk for As, Tl, U, and V at almost all sampling sites. The highest RQSSD values were found downstream of a large industrial corridor for Co, Zn, Tl, Pb, and V, with Tl, Pb, and V escalating to higher risk levels, highlighting the negative impact of industrial contamination on biota. The JPC results for these elements are consistent with the RQSSD approach, indicating an ecological risk to species from Mn, Fe, Co, Ni, Zn, and Sb in waters of the Atoyac River Basin. Therefore, the results of this study offer a thorough assessment of pollution risk, providing valuable insights for legislators on managing and mitigating exposure.

Natural background level, source apportionment and health risk assessment of potentially toxic elements in multi-layer aquifers of arid area in Northwest China

Groundwater contaminated by potentially toxic elements has become an increasing global concern for human health. Therefore, it is crucial to identify the sources and health risks of potentially toxic elements, especially in arid areas. Despite the necessity, there is a notable research gap concerning the sources and risks of these elements within multi-layer aquifers in such regions. To address this gap, 54 phreatic and 24 confined groundwater samples were collected from an arid area in Northwest China. This study aimed to trace the sources and evaluate the human health risks of potentially toxic elements by natural background level (NBL), positive matrix factorization (PMF) model, and health risk model. Findings revealed exceeding levels of potentially toxic elements existed in phreatic and confined aquifers. Source apportionment and NBL results indicated that mineral dissolution, evaporation, redox reactions, and human activities were the main factors for elevated concentrations of potentially toxic elements. High Fe and Mn concentrations were attributed to reduction environments, while F accumulation resulted from slow runoff, and irrigation from the Yellow River. Due to high F levels, more than one-third of groundwater samples (phreatic: 33.14 %, confined: 56.22 %) posed non-carcinogenic health risks to population groups. Adults displayed higher carcinogenic risks (phreatic: 19.47 %, confined: 34.16 %) than infants (phreatic: 0 %, confined: 0 %) and children (phreatic: 1.26 %, confined: 7.97 %) owing to the toxic elements of Cr. The confined aquifer presented greater health risks than the phreatic aquifer. Consequently, controlling the levels of F and Cr in multi-layered aquifers is key to reducing health risks. These findings provide valuable insights into protecting groundwater from contamination by potentially toxic elements in multi-layered aquifers worldwide.

Hydrogeochemical evolution and heavy metal characterization of groundwater from southwestern, Nigeria: An integrated assessment using spatial, indexical, irrigation, chemometric, and health risk models

This study examines the hydrogeochemical and heavy metal parameters of groundwater in Ojo District to determine its suitability for use, potential sources, and human health implications. Ten groundwater samples were assessed, and hydrogeochemical modelling was performed via the Aquachem software. The chemical ions were in the following order: EC > (107.78-448.65 μS/cm) > TDS (182.02-320.77 mg/l) > TH (46.22-182.45 mg/l) > pH (5.55-6.35); HCO3 - (64.13-125.82 mg/l) > Na+ (36.87-96.49 mg/l) > Ca2+ (47.65-58.88 mg/l) > SO4 2- (19.94-53.67) > NO3 - (15.55-44.25 mg/l) > Cl- (20.43-27.16 mg/l) > Mg2+ (11.09-16.87 mg/l) and K+ (2.55-7.86 mg/l). The concentrations of heavy metals in groundwater were in the range of: Fe (0.11-0.27 mg/l) > Mn (0.003-0.16 mg/l) > Ni (0.05-0.12 mg/l) > Zn (0.003-0.05 mg/l) > Pb (0.001-0.03 mg/l) > As (0.001-0.005 mg/l) > Cr (0.002-0.005 mg/l) > Cd (0.001-0.003 mg/l) and Cu (0.001-0.0002 mg/l), with Pb, Mn, and Ni exceeding their allowable limits. The Schoeller and Gibbs plots revealed that the major mechanisms controlling the aquifer groundwater in Ojo region are geological rock weathering and mineralization, with a minimal influence of saltwater intrusion. The piper trilinear diagram also revealed that none of the cation was dominant while the anions were strongly dominated by HCO3 - (weak acids). The hydrogeochemical facies which describes the geochemical characteristics of the groundwater were classified into 3 types; "Ca2+-Mg+-HCO3 - (65 %)", "mixing zones (30 %)", and "Na+-K+-Cl--HCO3 - (5 %)". The hydrogeochemical modelling revealed that the groundwater is characterized by forward cation exchange, while rock-water interactions (silicate dissolution) were heavily involved in the geochemical processes. The single pollution index showed that Pb, Ni, and Mn contributed significantly to contamination, and the multi-pollution indices showed that the groundwater was slightly-moderately polluted. The integrated groundwater quality index revealed that only 10 % were clean, 50 % were poor or moderately unclean, 30 % were highly unclean, and only 10 % were extremely unclean (unfit for utilization). The water pollution index showed that 70 % of the groundwater was good. The irrigation indices suggest that the groundwater would enhance soil quality and support plant growth. Multivariate analysis revealed that the groundwater is being influenced by geogenic factors and anthropogenic activities. The health risk assessment (Hazard Quotient and Hazard Index) showed that exposure of adults to the investigated groundwaters could result in noncarcinogenic adverse effects. The cancer risk values also exceeded the minimum limit (1.0 x 10-6) and thresholds (1.0 x 10-4) for adults, indicating the carcinogenic potential of the groundwater.

Leveraging experimental and computational tools for advancing carbon capture adsorbents research

CO2 emissions have been steadily increasing and have been a major contributor for climate change compelling nations to take decisive action fast. The average global temperature could reach 1.5 °C by 2035 which could cause a significant impact on the environment, if the emissions are left unchecked. Several strategies have been explored of which carbon capture is considered the most suitable for faster deployment. Among different carbon capture solutions, adsorption is considered both practical and sustainable for scale-up. But the development of adsorbents that can exhibit satisfactory performance is typically done through the experimental approach. This hit and trial method is costly and time consuming and often success is not guaranteed. Machine learning (ML) and other computational tools offer an alternate to this approach and is accessible to everyone. Often, the research towards materials focuses on maximizing its performance under simulated conditions. The aim of this study is to present a holistic view on progress in material research for carbon capture and the various tools available in this regard. Thus, in this review, we first present a context on the workflow for carbon capture material development before providing various machine learning and computational tools available to support researchers at each stage of the process. The most popular application of ML models is for predicting material performance and recommends that ML approaches can be utilized wherever possible so that experimentations can be focused on the later stages of the research and development.

A comprehensive evaluation of the contamination scenario and water quality in the gas fields of north-east region, Bangladesh

Gas fields generate a significant volume of produced water, disposed in the vicinity of gas fields in Bangladesh after processing. It may have a variety of effects on ecology and the environment. This study was conducted to assess the contamination scenario and quality of produced and discharged water from gas fields in northeastern Bangladesh. The physicochemical analyses for this study were performed using standard procedures. Based on the outcomes of the analyzed samples, the current research employs a variety of indexing and statistical approaches to investigate the overall status of the studied water. The physiochemical analysis revealed high electrical conductivity (EC), total dissolved solid (TDS), salinity, and Na contents in both produced and discharged water. No severe cases have been identified, certain metals, such as Fe, Ni, and Cd, have been detected at levels high enough to impact specific index values in some cases. The results of the weighted contamination index (WCI) indicated mild to considerable pollution in the gas field region. The average score of potential ecological risk (PER) reflects minimal ecological risk. The heavy metal toxicity load (HTML) reveals negligible metal pollution in the studied water. The agricultural risk indices displayed increased sodium concentrations and EC, resulting in salinity and sodium risks. The magnesium absorption ratio is within the allowable range. In addition, the average heavy metal pollution index (HMPI) value demonstrates that the produced and discharged water is unsuitable for drinking. The entropy-based water quality index (EWQI) is below the threshold limit (<100) for all samples, indicating satisfactory water quality. This study is an early effort to evaluate the quality of wastewater produced and discharged from gas fields in Bangladesh. The findings of this research will provide valuable insights for future researchers and regulators in effectively managing and mitigating pollution from wastewater.

Assessment of potential human health, radiological and ecological risks around mining areas in northeastern Brazil

Mining is responsible for the release of metallic pollutants and radioactive materials into the environment, which have the potential to disrupt ecosystems and pose significant risks to human health. Significant mining activity is concentrated in the municipality of Caetité (northeastern Brazil), where Latin America's only active uranium mine and significant iron ore deposits are located. Although previous studies have shown that the regional soil and water resources are highly contaminated by various toxic elements and that exposure to these elements is known to have adverse effects on human health, the health risks in this mining region have never been assessed. The aim of this unprecedented comprehensive investigation was to assess the health, radiological and ecological risks in this mining region, which is home to nearly 100,000 people. To achieve our goal, soil and water samples were collected in the vicinity of the mines and in the main settlements in the region. Fifteen metallic toxic elements were determined using Instrumental Neutron Activation Analysis and Inductively Coupled Plasma Optical Emission Spectrometry. The HERisk code, which follows the main methodological guidelines for risk assessment, was used to quantify human health, radiological and ecological indices. The average values of the total risk and cancer risk indices indicated that region falls into the moderate risk category (1.0 ≤ HItot < 4.0). However, 63% of the sites had high risk values, with Fe, Co and As being the metals contributing most to total and cancer risk, respectively. Near the mining areas, the potential ecological risk can be considered extreme (PERI ≥ 600). The values of the calculated radiological indices correspond to typical values ​​in natural uranium areas. However, in the communities near the mine, the dose values are slightly above the permissible limit (1 mSv y-1), so they must be continuously monitored, and risk mitigation measures must be taken.

Heavy metal concentrations in drinking water sources in two mining districts in Ghana

- In Ghana, mining of minerals at small-scale and large-scale is widespread across many districts, leading to significant heavy metal pollution in the environment. In this study, the concentrations of iron (Fe), manganese (Mn), arsenic (As), and mercury (Hg) in the different drinking water samples collected from households, institutions, water points and surface water in two mining districts namely the Wassa East and Asutifi North were analyzed. The water types collected included boreholes, wells, piped water into yards, public standpipes, rainwater, sachet water, and surface water. The results indicated that the levels of Fe and Mn were higher than As and Hg in all the drinking water samples. The levels of As and Hg in drinking water from households and institutions were higher in the Wassa East district compared to the Asutifi North district. However, the metal levels at water points were similar in both districts. In surface water, Fe levels were higher in the Wassa East district compared to the Asutifi North district with median values of 1243 μg/L and 860 μg/L for the Wassa East and Asutifi North districts, respectively. In contrast, the Mn levels were higher in the Asutifi North district than the Wassa East district with median values of 9.5 μg/L and 90 μg/L for Wassa East and Asutifi North districts, respectively. All the metals (Mn, As, and Hg) studied except Fe were within the recommended WHO level. The Heavy Metal Pollution Index (HPI) values for the different water types in households, institutions and water points were all below the critical limit of 100. The Water Quality Index (WQI) indicates that the boreholes, piped water into yards, and public standpipes in both districts were classified as excellent or good, making them suitable for drinking. However, the wells and surface water in both districts were classified as very poor and unfit for drinking, respectively.

Toxicological risk assessment using spring water quality indices in plateaus of Giresun Province/Türkiye: a holistic hydrogeochemical data analysis

Water scarcity is a growing concern due to rapid urbanization and population growth. This study assesses spring water quality at 20 stations in Giresun province, Türkiye, focusing on potentially toxic elements and physicochemical parameters. The Water Quality Index rated most samples as "excellent" during the rainy season and "good" during the dry season, except at stations 4 (40° 35' 12″ North/38° 26' 34″ East) and 19 (40° 44' 28″ North/38° 06' 53″ West), indicating "poor" quality. Mean macro-element concentrations (mg/L) were: Ca (34.27), Na (10.36), Mg (8.26), and K (1.48). Mean trace element values (μg/L) were: Al (1093), Zn (110.54), Fe (67.45), Mn (23.03), Cu (9.79), As (3.75), Ni (3.00), Cr (2.84), Pb (2.70), Co (1.93), and Cd (0.76). Health risk assessments showed minimal non-carcinogenic risks, while carcinogenic risk from arsenic slightly exceeded safe limits (CR = 1.75E-04). Higher arsenic concentrations during the rainy season were due to increased recharge, arsenic-laden surface runoff, and human activities. Statistical analyses (PCA, PCC, HCA) suggested that metals and physico-chemical parameters originated from lithogenic, anthropogenic, or mixed sources. Regular monitoring of spring water is recommended to mitigate potential public health risks from waterborne contaminants.

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

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

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review studies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determination coefficients (R, R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.