Groundwater Nitrate Research Articles

Integrated Simulation of Groundwater Flow and Nitrate Transport in an Alluvial Aquifer Using MODFLOW and MT3D: Insights into Pollution Dynamics and Management Strategies

This study employs an integrated numerical modeling approach using MODFLOW and MT3D to simulate groundwater flow and nitrate transport in the alluvial aquifer of Hennaya plain. The groundwater flow model was calibrated and validated against observed hydraulic heads, showing excellent agreement in both steady-state and transient conditions, with a correlation coefficients (R2) of 0.99 and 0.987, respectively. Meticulous calibration yielded adjusted hydraulic conductivity values between 10−1 and 10−11 m/s, with effective porosity ranging from 0.03 to 0.34 and total porosity values varying from 0.29 to 0.38 across the aquifer. Water budget analysis revealed that the aquifer’s primary recharge occurs from the southern side. Nitrate transport modeling indicated that advection is the dominant process, with contaminants migration predominantly occurring from south to north, following the groundwater flow direction. Pollution levels were found to decrease gradually with distance from sources, confirming agricultural activities and sewage disposal as primary contributors to nitrate contamination. Predictive scenarios over a 40-year period explored various management strategies, which suggest that maintaining current nitrogen input rates will lead to continued increases in nitrate pollution, while a 50% reduction in agricultural inputs could significantly improve groundwater quality. However, even with substantial reductions, nitrate concentrations are not expected to reach levels safe for drinking within the simulation timeframe. This study underscores the need for immediate and sustained action to address nitrate pollution in the Hennaya Plain aquifer, emphasizing the importance of stringent nitrogen management practices, particularly in the agricultural sector.

Analysis of Groundwater Chemical Characteristics and Health Risk Assessment: A Case Study of Water Sources in the Tailai Basin

Groundwater nitrate pollution is a pressing threat to public health. To analyze the chemical properties of groundwater across various sources, samples from 15 groundwater outlets within the Tailai Basin were collected during both the dry and wet seasons. An assessment of human health risks based on drinking water habits and skin contact with various water sources was conducted to provide a foundation for the rational development and utilization of groundwater. The results revealed that the main source of chemical components in the groundwater in the study area was evaporite dissolution. Rock weathering and cation exchange predominantly affected the chemical composition of groundwater. The groundwater samples primarily contained Ca2+ cations and HCO3− anions. The concentration of groundwater nitrate at each water source was relatively high. The single-factor water quality evaluation demonstrated that SO42−, NO3−, and TDS exceeded the standard, where NO3− severely exceeded the standard. The water supply and dry seasons were more than 10 times higher than the standard. The HI exceeded 1 for most water sources, and the non-carcinogenic risk index for children significantly surpassed that for adults. This high hazard quotient suggests that groundwater from the majority of sources in the Tailai Basin may adversely affect residents’ health. Water resource management authorities should address groundwater nitrate pollution to ensure the residents’ health and safety. Meanwhile, efforts should focus on preventing and controlling nitrogen pollution in groundwater to support its sustainable development.

Understanding the role of oxidants for enhanced remediation of groundwater nitrate: An insight into surface modification

Rapid urbanization and excessive agricultural runoff caused nitrate levels in groundwater to exceed permissible limits, making drinking water toxic and unsafe for consumption. Numerous physicochemical and biological treatment methods have been explored to remove nitrate from water; however, adsorption, a surface phenomenon, has a wide range of applications because of its rapid rate of reaction, recyclability, and environmentally friendly synthesis method. In this direction, this study aimed to unravel the role of oxidants in the facile synthesis of a competent zirconium-based adsorbent to remove nitrate from groundwater. The wet oxidative method, a facile chemical synthesis process requires an oxidant, which adds the oxidative functionalities on the surface of the adsorbent, which offers excessive anchorage to the transition metal on the surface of the adsorbent during synthesis. The batch experiments were conducted to evaluate the effectiveness of the adsorbents in treating nitrate. The maximum adsorption capacity of competent adsorbent was 113.97 mg/g at 300 K, which is considerably high compared to 62.83 mg/g of raw activated carbon and other existing adsorbents. The sorption capacity is reduced with an increase in temperature, which favors adsorption at lower temperatures. Adsorbents were successfully applied to test the groundwater samples showing increased removal efficiency. About 97 % desorption was obtained using 0.5 M NaOH, which facilitates the adsorbents for reuse. The developed adsorbent shows promising effectiveness in removing nitrate and could be used at a large scale.

Hydrogeochemical characterization and human health risks associated with groundwater nitrate concentrations in the Saiss basin, Morocco

Groundwater has been facing not only prolonged climate change-induced groundwater shortage but also severe deterioration of groundwater quality from natural and/or anthropogenic sources. This study aims to determine the hydrogeochemical processes controlling groundwater chemistry in the Saiss basin, Morocco, and to assess seasonal changes in groundwater quality using the entropy-weighted water quality index (EWQI). In addition, human health risks associated with groundwater nitrate (NO3−) contamination/pollution were investigated using the hazard quotient (HQ). Groundwater chemistry in the Saiss basin was mainly controlled by mineral dissolution/precipitation and direct cation exchange, with slight seasonal variations in the saturation indices (SIs). The EWQI suggested slight groundwater quality deterioration in the dry season due to increased NO3− concentrations. In fact, children were over two times exposed to non-carcinogenic issues associated with groundwater NO3− concentrations when compared with adults. The current study provides further insights into the hydrogeochemical characteristics of groundwater to ensure safe regional water supplies.

Groundwater Nitrate Modeling in Tehran Metropolis Using Artificial Neural Network and Kriging Methods

This study examined the relationship between groundwater quality and land use in Tehran. For this purpose, the possible relationship between the types of land uses and the concentration of nitrate in groundwater parameters was modelled using a Multi-Layer Perceptron (MLP) artificial neural network in geographic information system (GIS). The optimal network model was selected based on the mean root mean square error (RMSE) and correlation coefficient. Interpolation through Kriging was also performed to compare its results with those of the predicted model derived from an artificial neural network. The results showed that the neural network has a high capability for predicting and modelling groundwater nitrate concentration compared to the Kriging method. The high accuracy (RMSE: 0.003) of the neural network makes it a useful tool in relevant management issues. Our results of network sensitivity analysis were similar to scientific findings regarding the factors influencing the formation of nitrate in groundwater. Model outputs in the form of maps, tables, and graphs allowed the study of the role of each variable and the extent of its impact on groundwater quality. Performing various simulations and modelling of groundwater pollution provides an effective benchmark towards optimizing the management, control, planning, and decision-making in urban areas and can lead to economic and environmental savings.

Groundwater Nitrate Modeling in Tehran Metropolis Using Artificial Neural Network and Kriging Methods

This study examined the relationship between groundwater quality and land use in Tehran. For this purpose, the possible relationship between the types of land uses and the concentration of nitrate in groundwater parameters was modelled using a Multi-Layer Perceptron (MLP) artificial neural network in geographic information system (GIS). The optimal network model was selected based on the mean root mean square error (RMSE) and correlation coefficient. Interpolation through Kriging was also performed to compare its results with those of the predicted model derived from an artificial neural network. The results showed that the neural network has a high capability for predicting and modelling groundwater nitrate concentration compared to the Kriging method. The high accuracy (RMSE: 0.003) of the neural network makes it a useful tool in relevant management issues. Our results of network sensitivity analysis were similar to scientific findings regarding the factors influencing the formation of nitrate in groundwater. Model outputs in the form of maps, tables, and graphs allowed the study of the role of each variable and the extent of its impact on groundwater quality. Performing various simulations and modelling of groundwater pollution provides an effective benchmark towards optimizing the management, control, planning, and decision-making in urban areas and can lead to economic and environmental savings.

Source, transport, and fate of nitrate in shallow groundwater in the eastern Niger Delta

The eastern Niger Delta region in Nigeria is a hotspot for reactive nitrogen pollution due to extensive animal husbandry, pit latrine usage, and agricultural practices. Despite the high level of human activity, the sources and processes affecting nitrogen in groundwater remain understudied. Groundwater nitrate (NO3−) concentrations are highly variable, with some areas recording values well above the safe drinking water threshold of 50 mg/L. This is particularly true near municipal sewage systems. Elevated nitrite (NO2−) and ammonium (NH4+) concentrations were also detected in the study area. Sewage analysis revealed NO3− concentrations ranging from 1 to 145 mg/L, NO2− from 0.2 to 2 mg/L, and notably high NH4+ concentrations. A comparison of major ions indicated that 71%, 90%, 87%, and 92% of groundwater samples surpassed reference site levels for calcium (Ca2+), sodium (Na+), potassium (K+), and chloride (Cl−), respectively, pointing to sewage as a likely source of contamination. The NO3−/Cl− ratios at several sites suggested that most groundwater NO3− originates from human waste. Stable isotope analysis of NO3− showed a general enrichment in 15N and, in some cases, a depletion in 18O, indicating that the NO3− originates from sewage-derived NH4+ nitrification. Although denitrification, a process that reduces NO3−, is present, the high dissolved oxygen (DO) and NO3− levels in the groundwater suggest that denitrification is insufficient to fully mitigate NO3− pollution. Consequently, there is a risk of NO3− leaching from shallow aquifers into the Niger Delta’s surface waters and ultimately into the coastal ocean.

Sources and Transformation of Nitrate in Shallow Groundwater in the Three Gorges Reservoir Area: Hydrogeochemistry and Isotopes

Nitrate is among the most widely occurring contaminants in groundwater on a global scale, posing a serious threat to drinking water supplies. With the advancement of urbanization and mountainous agriculture, the nitrate in the groundwater of Wanzhou District in the Three Gorges Reservoir Area has formed a complex combination of pollution sources. To more accurately identify the sources of nitrate in groundwater, this study integrates hydrochemical methods and environmental isotope techniques to analyze the sources and transformation processes in shallow groundwater nitrate under different land-use types. Furthermore, the Bayesian isotope mixing model (MixSAIR) is employed to calculate the contribution rates in various nitrate sources. The results indicate that nitrate is the primary form of inorganic nitrogen in shallow groundwater within the study area, with nitrate concentrations in cultivated groundwater generally higher than those in construction land and forest land. The transformation process of nitrate is predominantly nitrification, with little to no denitrification observed. In cultivated shallow groundwater, nitrate mainly originates from chemical fertilizers (36.3%), sewage and manure (35.4%), and soil organic nitrogen (24.7%); in forested areas, nitrate primarily comes from atmospheric precipitation (35.3%), chemical fertilizers (31.3%), and soil organic nitrogen (22.1%); while in constructed areas, nitrate mainly derives from chemical fertilizers (46.0%) and sewage and manure (32.2%). These results establish a scientific foundation for formulating groundwater pollution control and management strategies in the region and serve as a reference for identifying nitrate sources in groundwater in regions with comparable hydrogeological features and pollution profiles.

Occurrence, migration, and assessment of human health and ecological risks of PFASs and EDCs in groundwater of Northeast China

Northeast China as an important base of grain production in China, has been suffering from potential groundwater pollution due to the excessive and prolonged application of fertilizers and pesticides. However, exploration of emerging contaminants pollution in groundwater and assessment of human health and ecological risks caused by large-scale agricultural activities have been relatively scarce. This study collected groundwater samples from typical agricultural areas in Northeast China to investigate the extent of contamination by nitrate, per- and polyfluoroalkyl substances (PFASs) and endocrine-disrupting compounds (EDCs), and then compared the levels of these pollutants with those in other regions of China. Groundwater nitrate pollution caused by strong agricultural activity is widespread in Northeast China, with nitrate-nitrogen (NO3N) concentrations exceeding 10 mg/L in as many as 40.3 % of 429 samples. 8 types of PFASs (3.7–7.1 ng/L) and 11 types of EDCs (18,114.0–62,029.8 ng/L) were detected in the collected groundwater samples. Using the Risk Quotient (RQ) method, this study assessed ecological risk and found that the risk level of perfluorooctane sulfonate (PFOS) was higher than that of other PFASs. The groundwater EDCs risks in Northeast China was higher compared to other regions in China, with dibutyl phthalate (DBP), Di-(2-ethylhexyl) phthalate (DEHP), Bisphenol A (BPA) having high ecological risk levels. Nitrate, PFASs and EDCs have been detected in deep groundwater (70–100 m depth), indicating that the deeper aquifers could be significantly threatened by pollutants due to human activities. Fertilizers, pesticides, domestic wastewater, and industrial discharges are major sources of groundwater pollutants in the agricultural regions. Industrial-sourced EDCs were widely detected in groundwater of agricultural area, suggesting that the transport of these pollutants is very active in groundwater system. Groundwater monitoring and pollution prevention are extremely urgent, especially for emerging contaminants. This study can provide important warnings and water resource management references for other agricultural areas affected by intensively agricultural activities in the world.

Identification of groundwater nitrate sources and its human health risks in a typical agriculture-dominated watershed, North China.

Identifying nitrate sources and migratory pathways is crucial for controlling groundwater nitrate pollution in agricultural watersheds. This study collected 35 shallow groundwater samples in the Nansi Lake Basin (NLB) to identify groundwater nitrate sources and potential health risks. Results showed that NO3- concentration in 62.9% of groundwater samples exceeded the drinking water standard (50 mg/L). Hierarchical cluster analysis (HCA) was used to classify the sampling points into three groups based on hydrochemical and isotopic data. Groups A and C were situated in the eastern recharge and discharge regions of Nansi Lake, while Group B was located in the Yellow River floodplain west of the lake. Hydrochemical data and nitrate stable isotopes (δ15N-NO3- and δ18O-NO3-) indicated that elevated NO3- primarily originated from soil organic nitrogen (SON) in Group A, while manure and sewage (M&S) were the primary sources in Groups B and C samples. Microbial nitrification was identified as the primary nitrogen transformation process across all groups. The source apportionment results indicated that SON contributed approximately 40.1% in Group A, while M&S contributed about 53.9% and 81.2% in Groups B and C, respectively. The Human Health Risk Assessment (HHRA) model indicated significant non-carcinogenic risks for residents east of Nansi Lake, primarily through the oral pathway, with NO3- concentration identified as the most influential factor by sensitivity analysis. These findings provide new perspectives on identifying and handling groundwater nitrogen pollution in agriculture-dominated NLB and similar basins that require enhanced nitrogen contamination management.

Assessment of Groundwater Quality and Nitrate Related Risks to Human Health: A Case of Angads Plain, Morocco

Assessment of Groundwater Quality and Nitrate Related Risks to Human Health: A Case of Angads Plain, Morocco

Assessing Nitrate Leaching During Drought and Extreme Precipitation: Exploring Deep Vadose‐Zone Monitoring, Groundwater Observations, and Field Mass Balance

AbstractThe increasing concern over agricultural practices' impact on groundwater quality necessitates comprehensive studies to evaluate and compare monitoring strategies for nitrate leaching. This work addresses this imperative by examining three methodologies: deep vadose‐zone monitoring, shallow groundwater intensive monitoring, and field‐level mass balance. The primary objective of the study was to assess nitrate leaching from an intensively cropped processing tomato rotation field using three different methods. Additionally, this study focuses on contrasting conditions between the growing season (characterized by drought in some years) and the winter/rainy season (characterized by extreme precipitation in some years). Results indicate varying degrees of nitrate leaching across methods, with all approaches detecting leaching events during the growing season and off‐season precipitation. Despite uncertainties inherent in field‐level mass balance estimates, they align reasonably with intensive in‐situ monitoring results using the deep Vadose Monitoring System (VMS). Throughout two growing seasons and corresponding fall‐winter rainy periods, the VMS effectively tracked seasonal nitrogen leaching below the root zone, correlating with observed groundwater nitrate concentrations increases following extreme precipitation events. Nitrate leaching increased during heavy rainfall in the winter following dry summer periods observed across the deep vadose zone using two VMS systems. This underscores the importance of continuous monitoring and assessment in understanding nitrate dynamics and groundwater contamination risks. In conclusion, this study contributes to knowledge and ongoing research by providing insights into effective monitoring strategies for nitrate leaching into groundwater from intensive cropping systems.

Identifying the spatial pattern and driving factors of nitrate in groundwater using a novel framework of interpretable stacking ensemble learning

Groundwater nitrate contamination poses a potential threat to human health and environmental safety globally. This study proposes an interpretable stacking ensemble learning (SEL) framework for enhancing and interpreting groundwater nitrate spatial predictions by integrating the two-level heterogeneous SEL model and SHapley Additive exPlanations (SHAP). In the SEL model, five commonly used machine learning models were utilized as base models (gradient boosting decision tree, extreme gradient boosting, random forest, extremely randomized trees, and k-nearest neighbor), whose outputs were taken as input data for the meta-model. When applied to the agricultural intensive area, the Eden Valley in the UK, the SEL model outperformed the individual models in predictive performance and generalization ability. It reveals a mean groundwater nitrate level of 2.22 mg/L-N, with 2.46% of sandstone aquifers exceeding the drinking standard of 11.3 mg/L-N. Alarmingly, 8.74% of areas with high groundwater nitrate remain outside the designated nitrate vulnerable zones. Moreover, SHAP identified that transmissivity, baseflow index, hydraulic conductivity, the percentage of arable land, and the C:N ratio in the soil were the top five key driving factors of groundwater nitrate. With nitrate threatening groundwater globally, this study presents a high-accuracy, interpretable, and flexible modeling framework that enhances our understanding of the mechanisms behind groundwater nitrate contamination. It implies that the interpretable SEL framework has great promise for providing valuable evidence for environmental management, water resource protection, and sustainable development, particularly in the data-scarce area.

Study on the remediation of groundwater nitrate pollution by pretreated wheat straw and woodchips

Groundwater nitrate contamination poses a threat to both the ecological environment and human health. This study investigated the potential of using saturated Ca(OH)2 to pretreat wheat straw and woodchips, aiming to enhance their efficacy as carbon sources for denitrification. The optimization of pretreatment conditions, and the elucidation of underlying mechanisms were explored. The pretreatment process involved the dissolution of lignin and hemicellulose, exposure of the cellulose structure, reduction of hydrogen bonds within cellulose, hydrolysis of polymerized cellulose, and the formation of cracks and hierarchical structures on the surface of the carbon source. These alterations improved the attachment and utilization of microorganisms. The maximum enzymatic reducing sugar yields for wheat straw and woodchips were achieved at solid-liquid ratios of 1:40 and soaking times of 5 and 2 days, respectively. The response surface predicted the optimal pretreatment conditions for wheat straw to be a solid-liquid ratio of 1:88.1 and a soaking time of 8.2 h. Alkaline treatment increased the denitrification rate of woodchips by fivefold and prevented the initial organic matter leaching rate of wheat straw, thereby reducing the risk of secondary pollution. The predominant microbial communities in all samples exhibited functions related to lignocellulose degradation and denitrification. The community composition of solid-phase carbon sources was found to be richer than that of liquid-phase carbon sources, and the pretreatment increased the abundance of lignocellulose degradation and denitrification functional microorganisms. The pretreatment liquid of wheat straw achieved the highest denitrification rate constant (0.43 h−1). Our result validated the feasibility of using the pretreatment liquid as a denitrification carbon source and presenting a novel approach for waste resource utilization.

Evaluation of some anions in groundwater in Riyadh, Saudi Arabia, and human health risk assessment of nitrate and fluoride.

Groundwater is a vital source of water for human and agricultural use in many parts of the world. The purpose of this research was to establish the quality of groundwater in Riyadh, Saudi Arabia, as well as the human health concerns associated with it. We collected and examined groundwater samples for pH, EC, TDS, CaCO3, fluoride (F-), chloride (Cl-), sulfate (SO42-), and nitrate (NO3-). The ion chromatography conductometric detection method was constructed to determine fluoride, chloride, sulfate, and nitrate in groundwater. The suggested method worked well for the anions that were being studied; it had a high coefficient of determination (r2 > 0.998) and average recoveries for all analytes that were between 97.5% and 99.0%, with a range of error of 0.77 to 2.37%. Fluoride concentrations were detected between 0.001 and 0.14mg/L, which are within the acceptable limit by several organizations. Chloride was measured in the range of 17.1 to 966.5mg/L, with some samples above the limits. The influence on sulfate ranged from 2.0 to 1136.0mg/L, with several samples exceeding the limits. In contrast, with nitrate levels ranging from 1.4 to 5.0mg/L, the majority of the samples fall within the acceptable range. The overall intake of fluoride, chloride, sulfate, and nitrate is 0.00605, 138.911, 65.515, and 1.19, respectively, which is lower than the recommended daily consumption except for chloride. The groundwater sample contains fluoride and nitrate with HQ values less than one: 0.000064-0.0641 and 0.033654-0.120192. Humans in Riyadh, Saudi Arabia, do not pose a health risk when digesting or absorbing groundwater fluoride or nitrate.

Unraveling the synergistic interplay of sulfur and wheat straw in heterotrophic-autotrophic denitrification for sustainable groundwater nitrate remediation

Nitrate pollution in groundwater is a global environmental issue that poses significant threats to human health and ecological security. This study focuses on elucidating the mechanisms of heterotrophic-autotrophic cooperative denitrification (HAD) by employing wheat straw and elemental sulfur as electron donors in varying proportions. The research initially underscores that heterotrophic denitrification (HD) accelerates the denitrification process due to its high-energy metabolism. However, as readily degradable organic matter diminished, reliance on more complex substrates such as lignocellulose posed a challenge to HD. This marks a pivotal transition towards autotrophic denitrification (AD), which, despite a slower initial rate, exhibits a more sustained denitrification performance. A low proportion of heterotrophic denitrification layer (e.g., 3:1) at the bottom facilitating efficient and sustainable denitrification. HD is capable of simultaneous removal of nitrates and nitrites, whereas AD demonstrates a higher affinity for nitrates, with nitrite accumulation reaching 100% at high influent nitrate concentrations (100 mg/L). HD not only provides the necessary alkaline environment for AD but also reduces sulfate production, whereas AD utilizes the residual organic carbon and ammonia produced by HD. The heterotrophic layer is characterized by a diverse community, whereas the autotrophic layer is predominantly composed of Thiobacillus. By delineating the interactive mechanisms and characteristics of HAD, this study highlights the importance of balancing heterotrophic and autotrophic activities for the effective remediation of groundwater nitrates.

Source identification of nitrate in groundwater of an agro-pastoral ecotone in a semi-arid zone, northern China: Coupled evidences from MixSIAR model and DOM fluorescence

In the extensive agro-pastoral ecotone of northern China, groundwater is a valuable resource, but it suffers from severe nitrate pollution. However, the extent of the contributions of agricultural and pastoral activities to nitrate accumulation in the groundwater of the region remains unclear. This study aimed to identify the main sources of groundwater nitrate in Chahannur Basin, a typical agro-pastoral ecotone in the semi-arid zone of northern China, using the MixSIAR model based on dual stable isotopes and dissolved organic matter (DOM) fluorescence. The hydrochemical and isotopic results showed that the groundwater has high concentrations of nitrate (up to 208.19 mg/L) with weak denitrification, and nitrate accumulation is mainly driven by mixed input from different sources. The MixSIAR model results indicated that the largest contributors to groundwater nitrate accumulation are manure & sewage, followed by soil nitrogen, chemical fertilizers, and atmospheric deposition. According to the DOM fluorescence characteristics, the groundwater is strongly affected by livestock and poultry waste, followed by soil humic substances. Moreover, the DOM fluorescence results further supported the MixSIAR model results based on the significant correlation between the contributions of nitrate sources and the percentages of fluorescent components, jointly confirming that the main source of nitrate in groundwater is manure and sewage, followed by soil nitrogen. These findings indicate that coupled evidences from the MixSIAR model and DOM fluorescence could be applied to identify the sources of nitrate in groundwater, and this coupling can provide valuable information for local authorities to achieve sustainable groundwater management.

Assessing impacts of land use/land cover patterns to shallow groundwater nitrate pollution in an agricultural-dominant area in northwest China using random forest

Assessing impacts of land use/land cover patterns to shallow groundwater nitrate pollution in an agricultural-dominant area in northwest China using random forest

Groundwater Nitrate Pollution Source Apportionment Under Varying Land Use/Land Cover Patterns

Groundwater Nitrate Pollution Source Apportionment Under Varying Land Use/Land Cover Patterns

Unveiling nitrate contamination and health risks: Insights from groundwater quality assessment and Monte Carlo simulation along the Southern Caspian Sea Coasts

Groundwater resources are at great risk of contamination due to increased industrial and agricultural activities, population growth and urban expansion. This study investigated factors controlling spatio-temporal variability in groundwater quality and nitrate concentration at the southern coast of Caspian Sea, Iran to provide public health risk assessment. Na-Cl (44.8%) and Ca-HCO3 (58.6%) types water were the dominant hydrogeochemical facies in dry and wet seasons, respectively. Most of the examined groundwater samples were found unfit for drinking but appropriate for agricultural irrigation. The chemistry of groundwater predominantly influenced by combination of local lithology and ion exchange in aquifer as well as seawater intrsuin. Nitrate concentration varied from 0.05 to 200 mg/L with a mean value of 33.1 mg/L in which 13.7% and 27.5% of samples showed concentration higher than WHO's recommended value in dry and wet seasons, respectively. The highest nitrate concentrations were observed at locations in proximity to human settlements including cities, villages as well as agricultural lands. The identified pollution hotspots confirm nitrate contributions from un-treated wastewater effluents and agricultural practices with minimum contribution from industrial activities. The result of Monte Carlo simulation revealed that children were at highest risk from drinking of groundwater containing nitrate. This study highlights the urgent need for action to address the growing threat to groundwater quality and public health posed by contamination from various sources in the southern coasts of Caspian Sea.