Groundwater Quality Management Research Articles

An Extensive Review of Leaching Models for the Forecasting and Integrated Management of Surface and Groundwater Quality

The present study reviews leachate models useful for proactive and rehab actions to safeguard surface and subsurface soft water, which have become even more scarce. Integrated management plans of water basins are of crucial importance since intensively cultivated areas are adding huge quantities of fertilizers to the soil, affecting surface water basins and groundwater. Aquifers are progressively being nitrified on account of the nitrogen-based fertilizer surplus, rendering water for human consumption not potable. Well-tested solute leaching models, standalone or part of a model package, provide rapid site-specific estimates of the leaching potential of chemical agents, mostly nitrates, below the root zone of crops and the impact of leaching toward groundwater. Most of the models examined were process-based or conceptual approaches. Nonetheless, empirical prediction models, though rather simplistic and therefore not preferrable, demonstrate certain advantages, such as less demanding extensive calibration database information requirements, which in many cases are unavailable, not to mention a stochastic approach and the involvement of artificial intelligence (AI). Models were categorized according to the porous medium and agents to be monitored. Integrated packages of nutrient models are irreplaceable elements for extensive catchments to monitor the terrestrial nitrogen-balanced cycle and to contribute to policy making as regards soft water management.

Diagnostic screening of private well water using gas chromatography with high resolution mass spectrometry to support well water management

In the US, private well users are responsible for their own water quality testing, but local health officials are often uncertain what tests to recommend, particularly for regulated organic chemical contaminants. This study evaluated the utility of suspect and non-target screening (NTS) high resolution mass spectrometry (HRMS) as a tool to identify a wide range of organic chemicals of emerging concern in private well water and to inform well water management decisions. Qualitative NTS, which detects chemicals without providing concentrations, was used to analyze 25 private well water samples from Wake County, North Carolina. Using the NIST 20 mass spectral database (M1), NTS tentatively identified 106 unique organic chemicals across the 25 samples and an average of 11 organic chemicals per sample. At least one USEPA ToxCast chemical was identified in each private well water sample. Private well water users were interviewed prior to and after their sample's NTS results were reported back; four county groundwater managers were interviewed after aggregated results for all 25 water samples were reported back. All but one well user participant chose to participate in the reporting-back post-interview. The 24 private well users found NTS results useful and valued the contextualization of their results using NTS results for other well users and a local municipal water sample. Most private well users (67%) were surprised by their well water results, especially regarding the number of tentatively identified organic chemicals detected. All the groundwater managers believed the NTS results were useful and could help improve their testing recommendations to private well users. Even with qualitative limitations, NTS results can be an effective and valuable tool to inform the public and governance stakeholders in decisions around groundwater quality management.

Hydrogeochemical Evaluation and Multivariate Statistical Analysis of Groundwater for Sustainable Groundwater Quality Management in the Industrial Corridor of Ranipet District, Tamil Nadu, India

Hydrogeochemical Evaluation and Multivariate Statistical Analysis of Groundwater for Sustainable Groundwater Quality Management in the Industrial Corridor of Ranipet District, Tamil Nadu, India

Assessment of Groundwater Vulnerability to Pollution in the Metro Subwatershed Area Using the GOD Method

The assessment of groundwater vulnerability to contamination in the Sub-Watershed (Sub DAS) Metro plays a crucial role in maintaining the sustainability of water resources. As an integral part of a complex river system, Sub DAS Metro faces the risk of groundwater pollution caused by human activities such as agriculture, industry, and domestic. Thus, research was conducted to determine the level of groundwater vulnerability using the GOD method (Groundwater Occurrence, Depth to Water Table, and Aquifer Media). The study identified three vulnerability classes out of the five existing classes, namely very low, low, and moderate. The GOD index indicates very low vulnerability with values ranging from 0.08 to 0.10, low vulnerability ranging from 0.11 to 0.12, and moderate vulnerability with a value of 0.48. Following the vulnerability assessment using the GOD method, mapping was conducted, assigning green for very low vulnerability areas, yellow for low vulnerability, and red for moderate vulnerability. The research findings provide strategic insights for designing effective mitigation and sustainable management of groundwater quality in Sub DAS Metro, playing a crucial role in preserving the sustainability of the groundwater ecosystem in the region

Numerical Verification of Groundwater Suitability for Irrigation Around the Subsurface Dam Area of Miyako Island, Japan

The sustainable management of water resources is essential for agricultural productivity, especially in areas with scarce water availability. This study focused on assessing groundwater quality for irrigation near the subsurface dam area of Miyako Island, Japan. Water samples from three observation points were tested for various parameters, including electrical conductivity (EC), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), residual sodium bicarbonate (RSBC), permeability index (PI), Kelley's ratio (KR), and magnesium adsorption ratio (MAR). EC values ranged from 270 to 800 µS/cm, suggesting water quality ranging from doubtful to good. SAR values between 0.23 and 1.49 suggested excellent quality. SSP ranged from 7.90% to 31.71%, mostly indicating good to excellent quality. RSBC values fluctuated between -1.57 to 1.45 epm, largely within safe limits. PI values varied from 40.34 to 75.83, indicating good permeability. Total hardness (TH) ranged from 105.50 to 326.45 ppm, classifying the water as hard to very hard. MAR values were below 50, suggesting potential soil issues. A numerical model confirmed observed Ca²⁺ concentrations, showing an increasing trend due to enhanced CO₂ emissions and lower pH. The data analysis revealed strong positive relationships between SSP and KR (r = 0.984), SAR and SSP (r = 0.951), and SAR and KR (r = 0.960). Despite generally acceptable values, continuous monitoring is recommended, especially for hardness, to ensure sustainable crop production. This study underscores the need for regular assessment and management of groundwater quality in subsurface dam areas to mitigate potential adverse effects on soil and agricultural productivity.

Prediction Model of Groundwater Sulphate Based on Combined Multi-source Spatio-temporal Data

The accurate prediction of spatial variation trends in groundwater SO42- is of great significance for improving groundwater quality and regional groundwater management level. The multi-source spatio-temporal data such as land cover data, soil parameter data, digital elevation data, and groundwater pH value in the plain area of the Yarkant River Basin in 2011, 2014, 2017, and 2020 were used as characteristic variables to analyze their correlation with groundwater SO42- concentration. To enhance the prediction accuracy, the Bayesian optimization algorithm (BOA) was used to optimize the random forest regression (RFR). Based on the BOA-RFR model, the importance of the characteristic variables was analyzed, the prediction accuracy of the model was evaluated, and the groundwater SO42- prediction map was generated. The results showed that pH value, ground elevation (GE), and percentage of bare land (BAR) in the contribution area were important parameters influencing groundwater hydrochemical composition, which were significantly negatively correlated with groundwater SO42- concentration, and the importance of impact factors for predicting groundwater SO42- concentration exceeded 25 %. The geostatistical interpolation method was used as an auxiliary tool for the predictive modeling of spatial distribution. After adding auxiliary samples, the R2 of groundwater SO42- concentration prediction of the BOA-RFR model was greater than 0.96, and the maximum values of RMSE and MAE were reduced by 4.7 % and 23.8 %, respectively, compared with the minimum values of the model with fewer samples. The SO42- concentration prediction map showed that high SO42- groundwater was enriched in the northeast of the plain area of the Yarkand River Basin, an area that was expanding.

Groundwater health risk assessment of North China Plain based on Monte Carlo model sensitivity analysis and morphological analysis: A case study of Shijiazhuang City.

The rapid development of the social economy and the influence of human activities can lead to aggravated groundwater pollution. Groundwater safety is the premise of residents' health. Therefore, studying the sustainable utilization and health risks of groundwater quality is important. The groundwater quality and potential health risks were evaluated in the Shijiazhuang area, which is located in the North China Plain in this paper. Based on 159 groundwater samples collected in the study area, the potential health risks of As, Cr6+, Ni, Pb, F-, and NO3 - to humans were evaluated from oral and skin contact. Results of the human health risk assessment showed that the average carcinogenic risk and non-carcinogenic risk of children are higher than those of adults. According to the spatial distribution of the total risk value, adults and children in the southwest of the study area face higher risks. Because of the uncertainty of USEPA, Monte Carlo simulation was used to calculate the probability of health risk assessment and prioritization of contaminant treatment. The results of the Monte Carlo simulation showed that the acceptable range for children is 6.82%, and the acceptable range for adults is 18.07%. According to the HRWM model, carcinogenic pollutants mainly include As, Cr6+, and Ni. The most important chemical species of As is HAsO4 2-, followed by H2AsO4 -. Similarly, CrO4 2- and Ni2+ are the main forms of Cr6+ and Ni. The results of this study can provide data support for the protection and management of groundwater quality in the North China Plain. PRACTITIONER POINTS: Children are more susceptible to carcinogenic risk than adults. After calculation, the main influencing elements are Ni and Cr. Metal morphology analysis was carried out, and the results showed that HAsO4 2-, CrO4 2-, and Ni2+ were the main types.

Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index

This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010–2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH4+-N, Cl-, and NO3--N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.

Drinking water quality in Cooch Behar Municipality, West Bengal, India: Assessment using WQI and public perception

AbstractEffective assessment and management of groundwater quality are essential for safeguarding the integrity of freshwater resources. Cooch Behar Municipality of West Bengal has been chosen for the present study due to its reliance primarily on groundwater for drinking water. The study focuses on assessing groundwater quality in the study area, a planned city with a long history of urbanization. The primary objective is to analyze various parameters relevant to drinking water and compute the Water Quality Index (WQI) with the residents' perception. Forty water samples were collected from Cooch Behar Municipality's Public Health Engineering (PHE) department, encompassing all 20 wards. Each ward provided one sample from a Deep Tube Well (DTW) and a Shallow Tube Well (STW). EC, Fe, TH, Mn, pH, TDS, Turbidity, Ca, and Mg levels were determined using standard instruments and methods, and analyzed geospatial techniques and statistical methods were employed. Results revealed concerning levels of manganese (Mn) and total hardness (TH), exceeding permissible limits in 82.5% and 75% of samples, respectively. While some parameters remained within acceptable limits, computed WQI values ranged from 63.28 to 162.67, indicating significant overall water quality concerns. Alarmingly, 45% of samples were unsuitable for drinking, with an additional 40% classified as very poor. Notably, demographic factors such as household income and education level correlated with perceptions of poor water quality. Policy implications suggest the urgent need for a well‐structured management system to address these findings and ensure safe drinking water for residents.

Quantitative characterization of the effect of compaction rate on ammonium release from clayey sediments

Ammonium-rich pore water will release from clayey sediments into adjacent aquifers during aquitards compaction, which can adversely affect groundwater quality. Differences in the rate of groundwater exploitation can lead to differences in the rate of compaction of aquitards, which may, to some extent, affect the release of chemical components of the pore water from the aquitard. However, the effect of the compaction rate of aquitards on the process of ammonium release from clayey sediments is poorly understood. Three groups of indoor compaction experiments were conducted to identify the effect of compaction rate on ammonium release from clayey sediments during compaction. The results showed that the compaction rate and sediment liquid limit exert a clear controlling effect on the release rate and internal change process of ammonium in clayey sediments. The increase in the ammonium release rate was approximately proportional to the increase in the compaction rate. When the moisture content of the clayey sediments exceeds the liquid limit, the ammonium concentration in pore water increases mainly due to the mineralization of organic matter containing nitrogen and the dissolution of the carbonate-bound ammonium. When the moisture content drops to the liquid limit, the ammonium concentration in pore water begins to decrease. Because the increase in the compaction rate weakened sediment mineralization and accelerated pore water release, the main forms of ammonium released from the sediment change from organic sulfide-bound ammonium and carbonate-bound ammonium to carbonate-bound ammonium and ion-exchanged ammonium. The results of this study have important significance for the management of groundwater quality in groundwater exploitation areas.

Effects of organic carbon and subsurface dams on saltwater intrusion and nitrate pollution in sandy coastal aquifers.

This study explores the impact of a novel approach on the levels of SWI (saltwater intrusion) and NO3- (nitrate) contamination. Some numerical simulations were conducted utilizing a coupled model that incorporates variably saturation and density, as well as convection diffusion reaction within a sandy coastal aquifer. We verified the reliability of the model for SWI based on comparison lab experiments and for chemical reactions based on a comparison of previous in situ observations. Cutoff walls and subsurface dams cannot simultaneously control SWI and reduce NO3- contamination. A novel approach that combines subsurface dams and permeable CH2O (organic carbon) walls (PC-Wall) is proposed. Subsurface dams are utilized to prevent SWI, while PC-Walls are employed to mitigate NO3- pollution. Results demonstrate that the construction of a PC-Wall with a concentration of 1.0mM facilitated a transition from nitrification (Ni)-dominated to denitrification (Dn)-dominated. An increase in CH2O concentration to 1.0mM caused a significant 1942.5 % rise in mDn (the mass of NO3- removed through Dn). Increment of the distance between the PC-Wall and the ocean from 35 to 45m could result in a 103.7 % mDn increase and reduce mN (the compound mass of NO3- remaining in the aquifer) by 11.7 %. The study offers a detailed comprehension of the intricate hydrodynamics of SWI and NO3- pollution. In addition, it provides design guidance for engineering to mitigate contamination by NO3- and controlling SWI, thus fostering the management of groundwater quality.

GIS-based hydrochemical assessment of groundwater in the Bakoya Massif, Northern Morocco

This research investigates the long-term and short-term toxicity of groundwater consumed by the population in a specific region, addressing the growing concern over groundwater pollution—particularly with heavy metals—as a significant environmental issue. The study conducts a comprehensive physicochemical analysis of water quality in the Bakoya massif, Al Hoceima, by examining thirty groundwater samples. Parameters such as pH, total dissolved solids (ranging from 559 to 3564 mg/l), and Electrical Conductivity (ranging from 747 to 4650 µS/cm) were measured. The fluoride concentration varied from 0.01 mg/l to 2.6 mg/l, surpassing the maximum allowable level of 1.5 mg/l for drinking water in Morocco in several samples. This study leverages GIS statistical analysis for hydrochemical distribution, providing insights into the mineralization processes affecting water quality. The findings highlight both natural and anthropogenic factors influencing the fluoride concentration, which remains within safe limits in most locations except for five wells. Additionally, arsenic and silicate concentrations were assessed, with arsenic exceeding the safe limit of 0.5 mg/l in two instances. This study underscores the critical need for ongoing monitoring and management of groundwater quality to safeguard public health and the environment.

Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach

Groundwater plays a pivotal role as a global source of drinking water. To meet sustainable development goals, it is crucial to consistently monitor and manage groundwater quality. Despite its significance, there are currently no specific tools available for assessing trace/heavy metal contamination in groundwater. Addressing this gap, our research introduces an innovative approach: the Groundwater Quality Index (GWQI) model, developed and tested in the Savar sub-district of Bangladesh. The GWQI model integrates ten water quality indicators, including six heavy metals, collected from 38 sampling sites in the study area. To enhance the precision of water quality assessment, the study employed six established machine learning (ML) techniques, evaluating the model's performance based on factors such as uncertainty, sensitivity, and reliability. A major advancement of this study is the incorporation of heavy metals into the framework of the water quality index model. To the best of the authors knowledge, this research marks the first initiative to develop a GWQI model encompassing heavy/trace elements. Findings from the GWQI assessment revealed that groundwater quality in the study area ranged from 'good' to 'fair,' indicating that most water quality indicators met the standard limits set by the Bangladesh government and the World Health Organization. In predicting GWQI scores, artificial neural networks (ANN) outperformed the other ML models. Performance metrics, including root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) for training (RMSE = 0.361; MSE = 0.131; MAE = 0.262), testing (RMSE = 0.001; MSE = 0.00; MAE = 0.001), and prediction evaluation statistics (PBIAS = 0.000), demonstrated the superior effectiveness of ANN. Moreover, the GWQI exhibited high sensitivity (R2 = 1.0) and low uncertainty (less than 2%) in rating water quality. These results affirm the reliability of developed novel model for groundwater quality monitoring and management, especially regarding heavy metals.

Field evaluation of carbon injection method for in-situ biological denitrification in groundwater using geochemical and metataxonomic analyses

This study focuses on the bioremediation of nitrate-contaminated groundwater, which has become a significant environmental problem due to the increasing usage of fertilizers and sewage disposal. The nitrate reduction efficiencies of biological denitrification by injection of carbon source in a pilot-scale treatment system setup were investigated at a groundwater contamination site. The field test was conducted using acetate as a carbon source for 22 days to assess the nitrate reduction efficiencies of in-situ treatment. Geochemical parameters and microbial community analysis using next-generation sequencing were performed before and after carbon source injection. After 12 h of reaction time, nitrate concentration decreased from 31.6 to 4.2 mg-N/L at PC-2, and then remained stable at 3.9 mg-N/L. The nitrate reduction rate when acetate was injected was 29.0 mg-N/L/day. Aquabacterium commune, pseudomonas brassicacearum, dechloromonas denitrificans, and Massilia FAOS were dominant species after acetate injection. Predictive metabolic pathway analysis indicated that nitrate reduction metabolisms during injection of acetate were denitrification and assimilatory nitrate reduction to ammonium. The evaluated hazard quotient of nitrate-contaminated groundwater significantly decreased after acetate injection (non-carcinogenic risk decreased from 1.176 to 0.134 for children). This research could provide fundamental information for decision-makers in nitrate-contaminated groundwater quality protection and management.

Prediction of groundwater quality index to assess suitability for drinking purpose using averaged neural network and geospatial analysis

Groundwater quality management is pivotal for ensuring public health and ecological resilience. However, the conventional water quality indices often face challenges related to parameter selection, geographic coverage, and scalability. The integration of machine learning and spatial analysis represents a promising methodological shift, allowing for high accuracy and adaptive management strategies. The Safe Groundwater Project in Unsupplied Areas (2017–2020) employed a comprehensive Groundwater Quality Index (GQI) to evaluate potable groundwater quality across South Korea, utilizing a large dataset comprising 28 water quality parameters and 3552 wells. This study revealed that over 50 % of the evaluated wells (Total 8326 wells) were inappropriate as sources of drinking water, indicating a pressing need for policy revision. The averaged neural network model achieved a high predictive accuracy of approximately 95 % for GQI grades, outperforming other classification models. The introduction of 2D spatial analysis in conjunction with machine learning algorithms notably increased the predictive accuracy for unevenly distributed groundwater samples. Moreover, this combined approach enabled the intuitive visualization of groundwater vulnerability across various regions, which can inform targeted interventions for effective resource allocation and management. This research represents a methodologically robust, interdisciplinary approach that holds significant implications for a framework for future groundwater quality management and vulnerability assessment.

Enhancing groundwater management using aggregated-data analysis and segmented robust regression: A case study on spatiotemporal changes in water quality

Groundwater quality management, crucial for ensuring sustainable water resources and public health, is the scope of this study. Our objective is to demonstrate the significance of secondary data analysis for the spatiotemporal characterization of groundwater quality. To this end, we develop and employ a robust trend analysis method, in tandem with a spatiotemporal data aggregation method, to accurately identify shifts in groundwater quality over time, even in the face of inflection points or breakpoints. The methods and results reveal diverse trends and characteristics in water quality over space and time across the entire dataset from selected regions in South Korea, emphasizing the importance of analyzing aggregated data beyond individual business locations. The conclusions indicate that this study contributes to the development of more reliable and effective groundwater quality management strategies by addressing gaps in traditional monitoring methods and the challenges of limited monitoring resources and uneven data quality. Future research directions include the application of the developed methods to other regions and data sources, opening avenues for further advances in groundwater quality management.

A multiple isotope (S, H, O and C) approach to estimate sulfate increasing mechanism of groundwater in coal mine area

Groundwater in North China type coal mine area is an important source of domestic, industrial and agricultural water. To explore the sulfate increasing mechanism of groundwater in mining area and identify key influencing factors. In this paper, hydrochemistry and multi-isotope tracer techniques such as δ34SSO4, δ18OSO4, δ2HH2O, δ18OH2O and δ13CDIC were used to study the groundwater circulation law and the migration and transformation mechanism of sulfate and carbonate in coal mine area. The results show that: the hydrochemical types of groundwater in the coal mine area are mainly HCO3− and SO42− anions, while the cations are mainly Ca2+ and Mg2+. The sulfate content is significantly increased, and the pH shows weak alkalinity; the relationship between δ18OH2O and δ18HH2O shows that the dynamic field of groundwater changes significantly after coal mining or closure, and limestone water mainly comes from surface water recharge through ‘skylight’ infiltration. The relationships between δ18OSO4 and δ18OH2O, δ34SSO4 and δ18OSO4 show that the sulfate in groundwater of coal mine area is mainly derived from sulfide oxidation. The ∆δ18OSO4-H2O value of groundwater in coal mine area is greater than 8 ‰, and the oxygen content in sulfate is 25 %–75 % from oxygen in water, indicating that coal mining has disturbed the groundwater in the study area from reducing environment to oxidizing environment, promoted sulfide oxidation, and accelerated the dissolution of carbonate minerals. The δ13CDIC value and δ34SSO4 value in the coal mine area are inversely proportional. The δ13CDIC of groundwater in the coal mine area is affected by the δ34SSO4 value to a certain extent. Sulfuric acid participates in the dissolution of carbonate minerals, making the pH value weak and alkaline as a whole. This paper expounds the migration and transformation law of sulfate in groundwater in coal mine area, which has practical significance for groundwater quality management. The research results can provide theoretical support for the rational development and utilization of groundwater resources in coal mine areas.

Cluster and Factor Analyses as Contributions to the Groundwater Quality Monitoring of the Marizal/São Sebastião Aquifer System, Alagoinhas (Bahia, Brazil)

The Marizal/São Sebastião aquifer system is the main water supply of the municipality of Alagoinhas in the state of Bahia. However, anthropic interventions contribute to soil and groundwater pollution, increasing the need for related research. Multivariate statistical analysis is a widely used tool, helping in the investigation of groundwater quality while being capable of simultaneously evaluating diverse variables of a sample set. In this study, factor analysis and multivariate cluster analysis methodologies were applied. Ten of the most influential variables for groundwater quality were selected and then grouped into two factors. The first factor included electrical conductivity, salinity, calcium, chloride, sulfate, manganese, and iron, which are indicators of water salinity. The second factor encompassed pH, bicarbonate, and phosphate, indicating anthropic interventions and alkalinity in the environment. The multivariate cluster analysis was applied to the parameters of both factors, resulting in dendrograms with four clusters. The present study showed that the multivariate statistical analysis is an efficient tool for monitoring and can contribute to the management of groundwater quality.

Reference conditions and threshold values for nitrate-nitrogen in New Zealand groundwaters

ABSTRACT Management of groundwater quality is assisted by an understanding of reference conditions, which describe the concentration ranges expected for key substances in the absence of human impact. This study evaluates reference conditions for NO3–N in New Zealand groundwater based on three complementary methods: hierarchical cluster analysis, relationships to groundwater age, and regression against a measure of land-use impact. The three methods result in very similar national-scale estimates of reference conditions for NO3–N concentration in oxic, minimally impacted groundwater, with the 80th, 90th and 95th percentiles found to be 1.65 ± 0.12, 1.97 ± 0.14 and 2.32 ± 0.14 mg/l, respectively (weighted average ± 95% confidence level), in good general agreement with previous studies from New Zealand and overseas. Anoxic groundwaters were treated separately for definition of reference conditions, with the 80th and 90th percentiles of NO3–N found to be 0.04 ± 0.01 and 0.16 ± 0.01, respectively (the 95th percentile could not be estimated reliably). For both oxic and anoxic groundwater, where a site-specific investigation has not been conducted to estimate reference conditions at a local scale, we suggest that the 80th percentile is an appropriate national-scale default threshold, to match the thresholds used for surface waters under the Australian and New Zealand Guidelines for Fresh and Marine Water Quality.

Temporal groundwater quality, health risks and source point management zonation of multi-aquifers in Jilin Qian'an, Northeastern China.

Jilin Qian'an, located in Northeastern China's Songnen Plain, relies almost exclusively on groundwater for drinking. The quaternary phreatic aquifer (Q3) is distinguished by high geogenic fluoride and arsenic concentrations, which necessitates reliance on quaternary confined (Q1) and neogene confined (N) aquifers (deeper aquifers) as source point management (SPM) alternatives. However, deeper aquifers are contaminated, necessitating temporal monitoring and unique management strategies. Using 165 samples, this study investigated the appropriateness of deeper restricted aquifers as a continuous SPM alternative by assessing the spatiotemporal groundwater quality and human health risk of the multi-aquifers in Jilin Qian'an from the 1980s to the 2010s. In addition, a source point management zonation (SPMZ) was implemented to define the specific intervention necessary in various portions of the study area. Results indicate water quality parameters were within recommended limits for most samples except fluoride, while arsenic was the most significant heavy metal pollutant. Mean groundwater mineralization in all the aquifers increased with time. Deeper aquifers are still a better alternative to the shallow phreatic aquifer as groundwater quality in the study is of the order N > Q1 > Q3 in the respective aquifers. Cancer risk assessment (CR) shows increases from 2001 to the 2010s in all aquifers except in Q3. SPMZ delineated: High As and high F zones; high As and low F zones; high As zones; high F zones; low F zones; and safe zones. Localized intervention based on SPMZ is recommended, along with the use of alternative water sources.