Assessment Of Groundwater Pollution Research Articles

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.

Application of isotope as a nuclear technique in groundwater pollution assessment due to human activities

Clean and sustainable groundwater is an important asset for human life and ecosystems. However, the threat of groundwater pollution is increasing as a result of human activities such as industrial, agricultural, and municipal. The objective of this review article is to examine how isotope techniques, as one of the nuclear techniques serve as an effective tool in studying, detecting, and understanding the issue of groundwater pollution stemming from anthropogenic activity on the surface. This article covers the description and discussion of the types of isotopes used in the detection of groundwater pollution and factors that influence the variation in the composition of isotopes in groundwater based on the findings of previous studies. In addition, this article also comments on the study of groundwater pollution using isotopes in the context of groundwater pollution assessment in Malaysia. This article serves as a guide or reference to researchers, environmental professionals, and water resources managers who want to understand and implement nuclear techniques in the monitoring and assessment of groundwater pollution. This article also outlines a holistic view on the resolution of groundwater pollution issues through the application of nuclear techniques and isotopes as a significant tool in ensuring sustainable environmental management.

Spatial Distribution and Health Risk Assessment of Groundwater Pollution in Kotlakhpat Industrial Complex, Lahore

Spatial Distribution and Health Risk Assessment of Groundwater Pollution in Kotlakhpat Industrial Complex, Lahore

Assessment of environmental forensic indicator for anthropogenic groundwater contamination via target/suspect/nontarget analysis using HRMS techniques

This study compared target/suspect/nontarget analysis via liquid chromatography-high-resolution mass spectrometry (LC-HRMS) with traditional environmental forensic methods, specifically nitrate and its stable N isotope, in assessing groundwater pollution from livestock manure and agriculture. Using an in-house database of 1471 target and suspects, 35 contaminants (pesticides, veterinary drugs, surfactants) were identified, some uniquely linked to specific pollution sources, such as sulfamethazine and 4-formylaminoantipyrine in manure-affected areas. Pesticides were widespread, typically showing higher intensity in agricultural zones. On the other hand, the results of stable N isotope analysis (δ15N-NO3: 4.8 to 16.4‰) indicated the influence of human activities such as fertilizers, sewage, and manure in all sampling sites, including the control site far from the pollution sources and cannot differentiate the specific sources. The study underscores LC-HRMS's efficacy in different pollution sources, surpassing the limitations of stable N isotope analysis, and provides valuable insights for polluted groundwater source tracking strategies.

Assessment of nitrogen budget in detailed spatial pattern using high precision modeling approach with constructed accurate agricultural behavior

Changes in the nitrogen cycle due to fertilizer use can cause severe environmental pollution, particularly groundwater pollution, and threaten biosphere integrity. There are many difficulties and limitations in assessing groundwater pollution and a detailed nitrogen budget in an agricultural catchment. Previous methodologies have failed in an accurate assessment of the nitrogen budget in detailed spatial patterns. Herein, we designed a new modeling approach to assess the nitrogen budget using detailed spatial patterns in an agricultural catchment in the Nara Basin. We revised the Soil and Water Assessment Tool file output format, added the results for river nutrient concentrations and ammonia volatilization to the original output file. In this study, we calibrated and validated crop harvests, paddy evapotranspiration, streamflow, and river water concentrations of nitrate–nitrogen and total nitrogen to improve model accuracy as much as possible. Among them, data for evapotranspiration was obtained from a newly released Landsat dataset. The results showed that the amount of nitrogen leaching in rice paddies was 42 kg/ha, accounting for 65 % of total leaching in the study catchment. Cambisols and Fluvic Gleysols were prone to denitrification, and nitrogen leaching or denitrification occurred relatively more readily in low-slope areas. Furthermore, a detailed analysis of nitrogen cycle processes with high spatial precision indicates that areas with severe surface water pollution may also exhibit significant groundwater pollution. Our findings provide new solutions for assessing the nitrogen budget and groundwater pollution in catchments.

A cooperative construction strategy for multi-parameter spatial variant random fields and its application in groundwater pollution risk assessment.

The spatial variability of hydrogeological parameters is a significant source of uncertainty in groundwater numerical modeling and has a certain risk impact on the prediction of pollutant migration and transformation. Current research has focused on the effects of single-parameter spatial variant random fields or utilizing random sampling methods to randomly combine multiple-parameter spatial variant random fields while ignoring the correlation between parameters. This paper proposes an innovative concept of associated random variables to construct multi-parameter synergistic spatial variant random fields, ensuring both the spatial variability and inherent correlation of the parameters. A hypothetical case was constructed, and the Monte Carlo sampling experiment based on computer simulation was used to assess groundwater pollution risks with multiple associated parameters. The results show that hydraulic conductivity and porosity are the main sensitive parameters. The associated random variable allows for the representation of positive correlation, negative correlation, and no correlation between the hydraulic conductivity and porosity. The pollutant mass concentrations in each observation well conform to the generalized extreme value distribution, and the pollution risks of each water well as well as the concentration distribution intervals of pollutants with different probabilities can be obtained. The influence of associated parameters on the cumulative risk of contaminants in observation wells and pollution degree range is only related to their mathematical distribution and is independent of correlations between parameters. This study addresses the issues of spatial variability and inherent correlation of hydrogeological parameters, which are of great significance for groundwater pollution risk assessment and the promotion of sustainable water quality management of groundwater resources.

Analytical model for contaminants transport in triple composite liners with depth-dependent adsorption process

Predictions of contaminant transport in landfill composite liners are essential to assess the risk of groundwater pollution. The amount of soil organic carbon is strongly depth-dependent, which plays a crucial role in the adsorption of organic contaminants and heavy metal ions. The one-dimensional analytical model of contaminant transport in the triple composite liner consisting of geomembrane, geosynthetic clay liner and attenuation liner is presented considering the influence of depth-dependent adsorption coefficients. A specific sigmodal function is introduced to describe the attenuation of adsorption coefficients along the depth. The solution is obtained by the Laplace transformation. The analytical model was validated by experimental and numerical results. The results of the sensitivity analysis show that depth-dependent adsorption coefficients are critical for the risk assessment of groundwater pollution. The breakthrough time of composite liners can be overestimated by a folder of 2.5 with constant adsorption coefficients. The effects of parameters related to the depth-dependent adsorption process on the long-term transport of contaminants are critical for the design of a stable and effective composite liner system The proposed solution can capture the field observed data well and is consistent with the field observation. In addition, the proposed analytical solution may serve as a benchmark for various numerical models.

Groundwater pollution and risk assessment using indexed-based models: a case study of Igbo-Etiti area, eastern Nigeria.

Water is one of the most abundant resources on earth not evenly distributed. Due to the high shortage of water sources in Igbo-Etiti and intensive agricultural practices in the area, assessment of groundwater pollution and its potential risk to contamination is imperative, as its remediation when contaminated is extremely challenging. Vertical electrical sounding (VES) and physicochemical analysis were carried out in twenty (20) locations and five groundwater samples, respectively, with the aim to evaluate the groundwater pollution indices and the ecological and human health risk associated with its pollution. The VES results delineated five geologic layers and ten characteristic curve types. The fourth layer with the aid of the VES results constrained by the borehole lithologic information on the water bearing geologic unit was delineated as the major exploitable aquifer layer. Results of groundwater vulnerability to pollution classified the area into low (15%), moderate (60%), and high (20%) vulnerability. Physicochemical analysis results on the studied groundwater revealed acidic (pH 4.6) to alkaline (pH 7.2) groundwater conditions. Pollution index of groundwater results delineated a moderate to very high pollution index, with cadmium and arsenic consistently identified as the heavy metals contaminating the groundwater. Ecological risk assessment revealed a moderate to very high integrated potential ecological risk, while human health risk results delineated a target hazard index greater than one in all the studied groundwater samples. This study will serve as a guide to the residents and society in sustainable development decision-making that will preserve the quality of the study area groundwater.

Risk Assessment of Groundwater Pollution by Radionuclides Released from A hypothetical Waste Disposal Site Northeast Sinai – Egypt

Risk Assessment of Groundwater Pollution by Radionuclides Released from A hypothetical Waste Disposal Site Northeast Sinai – Egypt

Using restored heavy metal contaminated soil as brick making material: Risk analysis upon different scenarios, considering the completeness of bricks

Restored heavy metal contaminated soil (RHMCS) can be utilized as building material, but the risks of heavy metal dissolution (HMD) under different scenarios are not clear. This study focused on sintered bricks made from RHMCS and assessed the HMD process and utilization risks of whole bricks (WB) and broken bricks (BB) under two simulated utilization scenarios of leaching and freeze-thaw. Part of the studied bricks were crushed, which increased the surface area (SSA) 3.43-fold and exposed the inner heavy metals, increasing the HMD in BB. However, the HMD in sintered bricks did not exceed the “Groundwater Quality Standard” and “Integrated Wastewater Discharge Standard” under different utilization scenarios, although the dissolution processes were different. In the leaching scenario, the release rate of HMs (As, Cr, Pb) changed from fast to slow over time; the maximum concentration was 17% of the standard limits. In the freeze-thaw scenario, there was no significant correlation between the release of HMs and freeze-thaw time, and the HMD of As was the highest, reaching 37% of the standard limits. Further analysis of health risks of bricks in the two scenarios found that the carcinogenic risks (CR) and the non-carcinogenic risks (NCR) were below 9.56 × 10−7 and 3.21 × 10−2, respectively, which are both lower than the Guidelines for Health Risk Assessment of Groundwater Pollution issued by Ministry of Ecology and Environment of China. These findings suggest that the utilization risks of RHMCS sintered bricks analyzed in this study are low in both scenarios, and higher completeness of bricks leads to higher safety in product utilization. The results provide a reference for the engineering utilization and disposal of building materials made from RHMCS.

The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach

The rapid expansion of urbanization has resulted in an insufficient of groundwater resource. In order to use groundwater more efficiently, a risk assessment of groundwater pollution should be proposed. The present study used machine learning with three algorithms consisting of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to locate risk areas of arsenic contamination in Rayong coastal aquifers, Thailand and selected the suitable model based on model performance and uncertainty for risk assessment. The parameters of 653 groundwater wells (Deep=236, Shallow=417) were selected based on the correlation of each hydrochemical parameters with arsenic concentration in deep and shallow aquifer environments. The models were validated with arsenic concentration collected from 27 well data in the field. The model’s performance indicated that the RF algorithm has the highest performance as compared to those of SVM and ANN in both deep and shallow aquifers (Deep: AUC=0.72, Recall=0.61, F1 =0.69; Shallow: AUC=0.81, Recall=0.79, F1 =0.68). In addition, the uncertainty from the quantile regression of each model confirmed that the RF algorithm has the lowest uncertainty (Deep: PICP=0.20; Shallow: PICP=0.34). The result of the risk map obtained from the RF reveals that the deep aquifer, in the northern part of the Rayong basin has a higher risk for people to expose to As. In contrast, the shallow aquifer revealed that the southern part of the basin has a higher risk, which is also supported by the location of the landfill and industrial estates in the area. Therefore, health surveillance is important in monitoring the toxic effects on the residents who use groundwater from these contaminated wells. The outcome of this study can help policymakers in regions to manage the quality of groundwater resources and enhance the sustainable use of groundwater resources. The novelty process of this research can be used to further study other groundwater aquifers contaminated and increase the effectiveness of groundwater quality management.

Assessment of Ground Water Pollution by Heavy Metals in Some Residential Areas in Kurdistan Region of Iraq

Assessment of Ground Water Pollution by Heavy Metals in Some Residential Areas in Kurdistan Region of Iraq

Numerical Analysis of Potential Contaminant Migration from Abandoned In Situ Coal Conversion Reactors

Abstract. In the context of a potential utilisation of coal resources located in the Mecsek mountain area in Southern Hungary, an assessment of groundwater pollution resulting from a potential water-borne contaminant pool remaining in in situ coal conversion reactors after site abandonment has been undertaken in the scope of the present study. The respective contaminants may be of organic and inorganic nature. A sensitivity analysis was carried out by means of numerical simulations of fluid flow as well as contaminant and heat transport including retardation to assess spatial contaminant migration. Hereby, the main uncertainties, e.g., changes in hydraulic gradient and hydraulic contributions of the complex regional and local fault systems in the study area, were assessed in a deterministic way to identify the relevant parameters. Overall 512 simulations of potential groundwater contamination scenarios within a time horizon exceeding the local post-operational monitoring period were performed, based on maximum contaminant concentrations, cumulative mass balances as well as migration distances of the contaminant plume. The simulation results show that regional faults represent the main contaminant migration pathway, and that contamination is unlikely assuming the given reference model parametrisation. However, contamination within a simulation time of 50 years is possible for specific geological conditions, e.g., if the hydraulic conductivity of the regional faults exceeds a maximum value of 1 × 10−5 m s−1. Further, the parameter data analysis shows that freshwater aquifer contamination is highly non-linear and has a bimodal distribution. The bivariate correlation coefficient heatmap shows slightly positive correlations for the pressure difference, the fault permeability and the simulation time, as well as a negative correlation for the retardation coefficient. The results of this sensitivity analysis have been integrated into a specific toolkit for risk assessment for that purpose.

Multivariate and spatio-temporal groundwater pollution risk assessment: A new long-time serial groundwater environmental impact assessment system

Groundwater pollution risk assessment is an important part of environmental assessment. Although it has been developed for many years, there has not yet been a multi-dimensional method that takes into account long time series and spatial factors. We proposed a new method combines the advantages of remote sensing cloud computing, long-term groundwater modeling simulation and GIS technology to solve it efficiently. A coastal industrial park in Hainan was used as the study area. The depth of groundwater level, rainfall, topography and geomorphology, soil moisture, pollution source, pollution toxicity and other more than 10 parameters were used as the indexes. A comprehensive model with remote sensing cloud computing, DRASTIC model and Modflow + MT3DMS was established to assess the pollution risk from 2014 to 2021. The multi-year results indicated that the risk assessment of groundwater pollution was usually on the vertical coastal direction, and the risk increased from far away to near coast. With the discharge of pollutants in the industrial park, the pollution risk in the area 5 km away from the centre increased year by year until it became stable in 2019, and the risk in the centre of the park reached 1 level, covered an area of up to 145400 square metres, accounted for 0.012% of the whole study area. The assessment results in 2020 and 2021 fluctuate slightly compared with those in 2019. Therefore, in terms of groundwater resource protection and resource management, it is necessary to focus on the detection of pollution in the coastal zone and the pollution within 5 km of the centre to strictly control pollution discharge. In this study, the comprehensive assessment includes surface indicators, subsurface indicators, and pollutant indicators. Finally, we achieve a multivariate, spatial and long time series groundwater pollution risk assessment system, which is a new groundwater environmental impact assessment (GEIA) system.

Employing Geophysical Techniques to Detect and Assess Groundwater Pollution at El-Akader Landfill North Jordan

A geophysical study was conducted for the El-Akader landfill to determine its impact on groundwater, which is located along the northern road of Jaber International Road, north Jordan, which covers an area of 0.806km2. it was commissioned in 1981 to be the only municipal landfill at north Jordan and it is considered the second-largest landfill in the country. A geoelectrical survey was conducted in the study area using the Vertical Electrical Sounding and an electromagnetic survey using the Transit Electromagnetic. Upon analyzing the 42 TEM loops and performing geoelectrical cross-sections, landfill leachate is found to be concentrated below the dumping site and its surroundings and would infiltrate down to about 200 m below land surface. The results of the Vertical Electrical Sounding revealed that the depth of groundwater to be around 300 m with a resistivity less than 5-ohm.m. It also indicates that the leachate is moving mainly vertically, which confirms the TEM results. Contaminants are detected in water samples collected from several nearby wells which indicates a horizontal movement of the landfill leachate which would have been penetrated down through inferred faults deep fractures detected in the area.

Leachate Monitoring and an Assessment of Groundwater Pollution from the Tangier Landfill

Leachate from public landfill in the city of Tangier, which is neither collected nor treated, could constitute a probable source of pollution of the groundwater table and of those of the Mlalah and Khandak Bou Hajjar wadis in the east, and the Mghogha and Ghir Boudra wadis to the south.This work aims to analyze the level of contamination at Tangier’s municipal waste disposal. The leachate samples were collected and analyzed during the period from 2016 to 2019 and the physicochemical parameters (humidity, pH, organic matter, etc.) were determined on these samples as well as the contents of five heavy metals (lead, cadmium, iron, chrome and zinc). Analysis of the well water shows the presence of polluting elements in the leachate water and a high concentration of metals (especially iron) that exceeds standards. This makes the effluent extremely toxic and thus presents a permanent threat to the health of the local population and the surrounding environment.

Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System

Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population.

Risk assessment of groundwater pollution during GLDA-assisted phytoremediation of Cd- and Pb-contaminated soil

Using biodegradable chelate agents during phytoremediation of heavy metal contaminated soil is a growing consensus. However, the potential risk of groundwater pollution and the corresponding quantification during biodegradable chelate-assisted phytoremediation is currently ignored. This study provides a risk-assessment framework containing the practical boundaries and the transport model of chelate-metal complexes and metals at the subsoil, to quantify and predict the leaching risk of biodegradable N,N-bis(carboxymethyl)glutamic acid (GLDA)-induced phytoremediation during heavy rainfall. The case of ethylenediaminetetraacetic acid (EDTA)-assisted phytoremediation was also considered. Here, adsorption experiments and percolation experiments were conducted based on the subsoil to obtain adsorption parameters and hydrodynamic parameters. Optimal parameters derived from inverse fitting and the boundary condition originated from pot leaching experiments were used for the framework establishment and Hydrus-1D simulation. Results showed that fewer GLDA-metal complexes were adsorbed on the soil particles compared with metals, inferring their great mobility in the subsoil. However, the chemical and physical non-equilibrium process indicated by the asymmetric breakthrough curves of the percolation experiments would also affect the transport behavior of contaminants in the sub-vadose zone. In addition, adsorption parameters and optimal hydrodynamic parameters aptly described breakthrough curves of the chelate-metal complexes, and could better predict their transport behavior at the subsoil. Opposite to the previous findings, prediction results showed that heavy rainfall during GLDA-assisted phytoextraction resulted in Cd and Pb pollution in shallow groundwater, where the pollution degree was related to properties of the soil horizon, depth of the water table, and types of contaminants. The degradation feature of GLDA-metal complexes would fail to keep groundwater from heavy metal contamination at the sub-vadose zone with high permeability. Thus, we recommend our risk-assessment framework to optimize the operating condition before utilizing the biodegradable chelate-assisted phytoremediation.

Modelling hydrogeological parameters to assess groundwater pollution and vulnerability in Kashan aquifer: Novel calibration-validation of multivariate statistical methods and human health risk considerations

Modeling and Investigation on pollution potential of aquifers is a matter of importance in terms of management, development and land-use allocation as well as quality monitoring, pollution prevention and groundwater protection. The purpose of this study is to calibrate and modeling the methods used for pollution potential assessment, in which the impact and apportionment of hydrogeological parameters on groundwater pollution of an aquifer located in Kashan is considered. To do so, Analytic Hierarchy Process (AHP) and fuzzy-statistical analysis methods are used for weighting, ranking and standardize the parameters based on research awards of experts and Ad-Hoc systems. This was performed in such a way that the level and importance of each class of classification parameters is considered equal to the final model, and is equivalent to the reclassified class of indices of groundwater quality and human health risk to nitrate pollution. After ranking and standardizing the parameters as well as final model by using fuzzy-statistical approach, the process of weighting the parameters is accomplished with aid of AHP and Factor Analysis-weighted Principal Component Analysis (FA-PCA) methods based on their apportion and impact on groundwater pollution in addition to their correlation with nitrate map. In addition to the correlation with the standardized nitrate concentration, techniques of the root mean square error (RMSE) and coefficient of variation (COV) are employed to validate the model. The results illustrated that parameters of net recharge, soil media, impact of vadose zone, hydraulic conductivity and aquifer media have created the highest apportion and impact on groundwater pollution. In addition, it was found that weight of these parameters for calibrated and validated model of GPPI (groundwater pollution potential index) is proved to be 8.5, 3.4, 3.3, 2.6, 2.2, for GPRI (groundwater pollution risk index) as the best model is 4.4, 3.7, 3.1, 2.9, 1.1, and it is 4.8 for the land use layer, respectively. Weighting procedure was conducted by FA-PCA approach and following considerations were used; R = 73, RMSE = 1.08 and COV = 20%. Moreover, based on these models with better calibration-validation than generic model, it was found high pollution potential in western margin, high pollution risk in the central parts to the western margin, while it was observed not to have that very high pollution potential and risk in Kashan aquifer.

Assessment of Groundwater Pollution using Aqueous Geo-Environmental Indices, Baiji Province, Salah Al-Din, Iraq

The study area is located in Baiji province characterized by anthropogenic activities which may affect the groundwater quality. Therefore, the present work was intended for assessing groundwater quality and its suitability for drinking. Groundwater samples were collected from 33 different well, then analyzed to determine 20 parameter which are TDS, Ca, Mg, Na, K, Cl, SO4, NO3, Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Zn, As, B and Se. Nemerow Pollution Index Synthetic Pollution Index, and Pollution Index of Groundwater were used for determining groundwater suitability for human consumption. Nemerow Pollution Index values indicate that groundwater pollution is ranged from seriously to moderately polluted. According to Synthetic Pollution Index results, the water samples were moderately polluted to unsuitable for drinking. Depending on the rating of Pollution Index of Groundwater values, groundwater was polluted.