Groundwater Contamination Risk Assessment Research Articles

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

Regional Assessment of Groundwater Contamination Risk from Crude Oil Spillages in the Niger Delta: A Novel Application of the Source-Pathway-Receptor Model

AbstractOnshore oil spills are known for their disastrous environmental impacts and potential to cause lasting damage to underlying groundwater. The Niger Delta is particularly vulnerable to widespread spillages linked to extensive oil exploration, transportation, and theft-related incidents. This research employed a geospatial approach in formulating risk equations, based on the source-pathway-receptor (S-P-R) model using multiple openly available data sets, to assess groundwater contamination risk in the Niger Delta Region (NDR), Nigeria. To develop the overall risk equation, the study combined fourteen thematic data layers including the volume of oil spilled, type of spill, slope, elevation, proximity to spill site, pipeline, oil wells and streams, drainage density, mean annual precipitation and population density. These layers were integrated into source potency, pathway transmissivity, and receptor susceptibility. The NDR was systematically categorized into low, moderate, and high groundwater risk zones. The delineation revealed that high-risk zones predominantly span the central areas, extending from southeast to northwest, effectively encircled by regions of low to medium risk located in both the northern and southern extents of the delta. The efficacy of the risk model was corroborated by existing knowledge. Moderate to high-risk zones were found to be in about 16% of the NDR, revealing previously unknown areas of risk. This spatial configuration underscores a significant gradient in contamination risk across the NDR, with the central corridor emerging as a critical focus for groundwater protection and remediation efforts. In line with UN Sustainable Development Goal (SDG) #6, this study recommends targeted strategies to ensure clean water provision in these identified high-risk areas. By leveraging the S-P-R model within this complex and sensitive ecological area, this research both advances environmental risk assessment and sets a precedent for future large-scale environmental risk assessments utilizing open-source data.

Risk assessment and natural attenuation study of groundwater contamination in an oil reservoir in Fuxin

Risk assessment and natural attenuation study of groundwater contamination in an oil reservoir in Fuxin

A framework for risk assessment of groundwater contamination integrating hydrochemical, hydrogeological, and electrical resistivity tomography method.

Groundwater contamination have been widely concerned. To reliably conduct risk assessment, it is essential to accurately delineate the contaminant distribution and hydrogeological condition. Electrical resistivity tomography (ERT) has become a powerful tool because of its high sensitivity to hydrochemical parameters, as well as its advantages of non-invasiveness, spatial continuity, and cost-effectiveness. However, it is still difficult to integrate hydrochemical, hydrogeological, and ERT datasets for risk assessment. In this study, we develop a general framework for risk assessment by sequentially jointing hydrochemical, hydrogeological, and ERT surveys, while establishing petrophysical relationships among these data. This framework can be used in groundwater-contaminated site and help to delineate the distribution of contaminants. In this study, it was applied to a nitrogen-contaminated site where field ERT survey and borehole information provided valuable measurement data for validating the consistency of contamination and hydrogeological condition. Risk assessment was conducted based on the refined results by the establishment of relationship between conductivity and contaminants concentration with . The contamination source was identified and the transport direction was predicted with the good agreement of = 0.965 between simulated and observed groundwater head, which can help to propose measures for anti-seepage and monitoring. This study thus enhances the reliability of risk assessment and prediction through a thought-provoking innovation in the realm of groundwater environmental assessment.

Groundwater contamination & health risk assessment with special reference to As in different tehsils of Bhakkar district

The present study was conducted to evaluate the concentration of arsenic in groundwater in Bhakkar district, Western Punjab Province, Pakistan. To analyze hydrochemical characteristics, 68 groundwater samples from various water schemes in four tehsils were taken. While most of the samples' concentrations of major cations and anions were within the WHO's maximum allowed range, almost 44% had higher arsenic levels than what was considered acceptable for drinking water. Gibbs diagrams were used to show how groundwater chemistry is affected by the dominance of rocks and evaporation crystallization. An investigation of the Indus River's lateral distribution and upstream to downstream locations' arsenic concentrations showed variability. According to the Health Risk Assessment (HRA), samples from Bhakkar tehsil (73.3%), Darya Khan tehsil (60%), and Kalur Kot tehsil (13.3%) all surpassed the hazard quotient level of “1,” whereas all samples from Mankera tehsil were within acceptable bounds. The hazard quotient numbers displayed a range of 0.038–5.127, with an average of 1.149 – suggesting that a considerable portion of people consuming water laden with high levels of arsenic may suffer from non-cancerous health complications. Moreover, the carcinogenic risk values were as high as 2.31 × 10−3, implying a heightened danger of cancer over the long term due to arsenic-contaminated water. It was determined that Bhakkar tehsil had the highest quantity of arsenic among the tehsils in the Bhakkar district, making the groundwater unfit for drinking.

A DFN-based framework for probabilistic assessment of groundwater contamination in fractured aquifers

It is challenging to conduct groundwater contamination risk assessment in fractured aquifers containing a large number of complex fractures, especially in a situation where the uncertainty of massive fractures and fluid-rock interactions is inevitable. In this study, a novel probabilistic assessment framework based on discrete fracture network (DFN) modeling is proposed to assess the uncertainty of groundwater contamination in fractured aquifers. The Monte Carlo simulation technique is employed to quantify the uncertainty of fracture geometry, and the environmental and health risks of the contaminated site are probabilistically analyzed in conjunction with the water quality index (WQI) and hazard index (HI). The results show that the contaminant transport behavior in fractured aquifers can be strongly affected by the distribution of the fracture network. The proposed framework of groundwater contamination risk assessment is capable of practically accounting for the uncertainties involved in the mass transport process and effectively assessing the contamination risk of fractured aquifers.

Risk Assessment of Groundwater Contamination in the Gala, Tenguel, and Siete River Basins, Ponce Enriquez Mining Area—Ecuador

Groundwater is a strategic resource, which is experiencing a growing threat of contamination worldwide. This study aimed to assess the vulnerability of the groundwaters in the basins of the rivers Gala, Tenguel, and Siete in the Ponce Enriquez mining area, considering as a hypothesis that anthropogenic activities (mainly mining and agricultural) conducted in the area generate a high risk of contamination. Vulnerability to contamination was quantified using the DRASTIC and GOD methods. In addition, the risk of contamination (Rc) was calculated considering the vulnerability index (IV) and the danger index (IP). Geographic information system (GIS) environment was used for the spatial analysis and the generation of vulnerability maps. The DRASTIC method showed that the negligible-to-low vulnerability category was predominant (50.7% of the area), followed by moderate vulnerability (25.6%), and high-to-extreme vulnerability (23.7%). Regarding the GOD method, the low and medium vulnerability categories were predominant in 41.4% and 27.5% of the area, respectively. Both methods agree that the center area, where the mining activities are located, has a vulnerability ranging from negligible to moderate. The IP was low in the eastern zone (58.7 km2) and ranged from moderate (426.2 km2) to high in the central and western zones (371.1 km2), where mining activities predominate. The RC revealed that 20% of the study area corresponded to a high contamination index; from which, 87% comes from agricultural activities, 9% from mining activities, and 4% due to shrimp farms and urban areas. This is the first study that evaluates the vulnerability to the contamination of groundwater in a mining area of Ecuador. The results of this research can serve as a baseline for future research, since the methodology used provides information on the priority areas for the protection of aquifers, considering the current land use in the study area.

Groundwater Contamination Risk Assessment Based on Groundwater Vulnerability and Pollution Loading: A Case Study of Typical Karst Areas in China

Groundwater contamination risk assessment is an important basis to support the protection of the groundwater ecological environment. In this research, the groundwater contamination risk in typical karst areas in China was evaluated by PLEIK model (P: protective cover; L: land use; E: epikarst development; I: infiltration conditions; K: karst development) and classification and discrimination methods were used to assess groundwater vulnerability and pollution source load; the water quality index method was used to assess the status of groundwater contamination in the research area. The results show that groundwater vulnerability values in the research area range from 3.04 to 7.79, and most areas have low groundwater vulnerability. Groundwater pollution loading indexes, in the region of the pollution sources, gathered numerical up-water quality status evaluation that shows that most of the regional groundwater quality in the research area has good groundwater at present. The groundwater contamination risk assessment results show that the groundwater pollution risk is mainly at a very low level in most areas of the study area, but the groundwater pollution risk is higher in the areas where the pollution sources gather. The result reveals that the regional groundwater contamination risk level for regional groundwater ecological environment protection provides a theoretical basis for policy making.

An Integrated Bayesian and Machine Learning Approach Application to Identification of Groundwater Contamination Source Parameters

The identification of groundwater contamination source parameters is an important prerequisite for the control and risk assessment of groundwater contamination. This study developed an innovative approach for the optimal design of observation well locations and the high-precision identification of groundwater contamination source parameters. The approach involves Bayesian theory and integrates Markov Chain Monte Carlo, Bayesian design, information entropy, machine learning, and surrogate modeling. The optimal observation well locations are determined by information entropy, which is adopted to mine valuable information about unknown groundwater contamination source parameters from measurements of contaminant concentration according to Bayesian design. After determining the optimal observation well locations, the identification of groundwater contamination source parameters is implemented through a Bayesian-based Differential Evolution Adaptive Metropolis with Discrete Sampling–Markov Chain Monte Carlo approach. However, the processes of both determination and identification are time-consuming because the original simulation model (that is, the contaminant transport model) needs to be invoked multiple times. To overcome this challenge, a machine learning approach, that is, Multi-layer Perceptron, is used to build a surrogate model for the original simulation model, which can greatly accelerate the determination and identification processes. Finally, two hypothetical numerical case studies involving homogeneous and heterogeneous cases are used to verify the performance of the proposed approach. The results show that the optimal design of observation well locations and high-precision identification of groundwater contamination source parameters can be implemented accurately and effectively by using the proposed approach. In summary, this study highlights that the integrated Bayesian and machine learning approach provides a promising solution for high-precision identification of groundwater contamination source parameters.

Synoptic Risk Assessment of Groundwater Contamination from Landfills

Waste management in Europe has improved in recent years, reducing the amount of waste disposed at landfills. However, there are still many landfills in the countries. It is well known that landfills that do not have measures in place to control leachate entering groundwater can contaminate groundwater long after the landfill is closed. Collecting monitoring results from all landfills allows permitting and management agencies to improve action plans. This relies on a synoptic risk assessment that allows prioritization and milestones to be set for required actions. The developed method of synoptic risk assessment is based on a conceptual model of the landfill and the results of chemical groundwater monitoring tested at 69 landfills in Slovenia. The study confirms that most landfills have a direct or indirect impact on groundwater quality. All landfills were classified into three priority classes on the basis of the synoptic risk assessment. The results show that a total of 24 landfills have a clearly pronounced impact on groundwater. A total of 31 landfills have a less pronounced impact due to the favorable natural attenuation capacity of the soil or the technically appropriate design of the landfill itself. A total of 14 landfills have a less pronounced or negligible impact on groundwater.

Groundwater contamination and human health risk assessment in the main karst areas of Romania

Long-term monitoring reveals the temporal evolution of groundwater chemistry and the potential human health risk posed by the use of contaminated waters (Giri and Singh 2015). Groundwater aquifers are an important source of drinking water in Romania. This study was conducted to appraise the groundwater chemistry and the potential non-carcinogenic risks for human health associated with the groundwater consumption through oral and dermal pathways. In order to achieve this aim, 193 samples were collected from 29 groundwater sources in the main karst areas of Romania during 2019-2021. A total of 15 parameters were analyzed (F-, Cl-, SO42-, NO3, Ca, Na, K, Mg, Fe, Mn, Cu, Zn, Ni, Cr, and As) and compared with the World Health Organization standards for drinking water (WHO 2017). The heavy metals pollution index showed that no groundwater samples had detectable levels of metal contamination, while the heavy metal evaluation index revealed that 4 out of 29 groundwater sources were classified as contaminated: 3 sources with a medium level of pollution and 1 source with a high level of pollution, indicating a potential risk for human health. The human health risk for oral exposure indicated a potential non-carcinogenic risk only in the karst area from South Dobrogea. The non-carcinogenic risk posed by nitrates is higher than that posed by metals in the aquifers from South Dobrogea. Therefore, control and remedial actions should be taken to ensure appropriate water quality for the locals using the contaminated water sources.

Assessment of Groundwater Contamination Risk in Oilfield Drilling Sites Based on Groundwater Vulnerability, Pollution Source Hazard, and Groundwater Value Function in Yitong County

Oilfield drilling sites are the potential dispersive pollution source of groundwater, especially to shallow groundwater. The pollution risk assessment in these areas is an important reference for effective groundwater management and protection. The vulnerability assessment alone is not sufficient for groundwater contamination risk assessment. In this study, we developed a comprehensive groundwater pollution risk assessment method for oilfield drilling sites that combine groundwater vulnerability, pollution source hazard, and groundwater value function to produce a more comprehensive result. Consider the oilfield drilling area in Yitong County of Jilin Province, China, as an example. Thematic maps of the three aspects (groundwater vulnerability, pollution source hazard, and groundwater value function) were generated in ArcGIS environment to assess the contamination risk of groundwater in quaternary pore unconfined aquifer. The results show that 9.92% of the study area is characterized as being at high risk. These areas are mainly distributed around the center position of the oil drilling site, floodplains, and the reservoir. The moderate risk area accounts for 21.04% of the total area. It is distributed in the first-level terrace, mainly because of the high function value of groundwater. The remaining 69.04% of the study area is characterized as none and mild risk, mainly distributed in the valleys and terraces. This integrated groundwater contamination risk assessment method is suited for comparative assessment of multiple-point sources of contamination at a regional scale. Finally, the groundwater contamination risk grade distributed in this area provides a reference for effective protection and sustainable supply of groundwater in the oilfield drilling area.

Impacts of changing watershed conditions in the assessment of future groundwater contamination risk

Rural regions, which are commonly reliant on groundwater for water supply, often face groundwater quality challenges related to agricultural activity. To sustainably manage and protect groundwater resources, it is necessary to have a clear understanding of inherent groundwater vulnerability and the contamination risk posed by pollutant loading. Index-based methods typically assess groundwater contamination risk in a static manner and commonly represent agricultural land as a single land use class despite the complexity of land management practices that may be present. This paper illustrates modification of the conventional DRASTIC-LU methodology, through a newly developed DRASTIC-AgLU-CC approach, to better understand how groundwater contamination risk may change by mid-century while simultaneously examining the potential influence of land use scheme complexity on model performance and prediction. Recharge, depth to water table, and land use are treated as dynamic factors through the integration of downscaled global climate model data, empirically estimated water table fluctuation and recharge, and trend-based land change modelling. The Upper Parkhill watershed in southwestern Ontario, Canada serves as a case study for method application. Study results indicate that more detailed agricultural land use representation, specifically the inclusion of crop rotation and tile drainage data, has the potential to improve model predictive ability; furthermore, predicted future changes in groundwater contamination risk may vary depending on the manner in which land use is represented. Within the Upper Parkhill watershed, mid-century land use and climate conditions may increase groundwater contamination risk, although minimal change is anticipated. The presented approach provides a valuable screening tool to better understand the potential for changing groundwater contamination risk in rural regions and can be used to guide groundwater monitoring programs and water supply development.

Groundwater contamination risk assessment based on advection-dispersion equation

The consequences of contaminated groundwater can seriously affect sustainable development; present and future generations being seriously affected by inadequate drinking water quality, loss of water supply, degraded surface water systems, high remediation costs, more expenses for other water supplies, and likely health issues. Therefore, an effective way to protect groundwater resources is by assessing the risk of groundwater contamination. An assessment of groundwater pollution should be performed to determine the level of risk posed by soil and groundwater contamination and establish if remediation strategies are required to protect controlled waters from site-derived contamination. Furthermore, if remediation is deemed necessary, site-specific remedial targets should be derived. A case study is presented, where a Conceptual Site Model was derived based on a “Source-Pathway-Receptor” exposure mechanism using historical information. Primary sources of contamination at the site are residual contamination within the soil and groundwater, and samples were collected from the site and tested in the laboratory; the concentration of water samples was compared to Romanian Drinking Water Standards. The following potential migration pathways have been identified: Leaching from soil and Migration of contaminated groundwater. The Detailed Quantitative Risk Assessment (DQRA) has modelled the leaching of contaminants from the site via infiltration and vertical migration to the groundwater and subsequent lateral groundwater migration, with dilution and attenuation process active, to the compliance point, using Ogata-Banks equation. The results of this assessment indicate that the concentration of contaminants does not represent a significant risk to controlled waters.

Groundwater contamination risk assessment using a modified DRATICL model and pollution loading: A case study in the Guanzhong Basin of China

Groundwater contamination risk assessment is not only the basis for groundwater management, but also an effective tool for groundwater pollution control and prevention. However, only groundwater vulnerability assessment is not enough to prove the risk of groundwater contamination. Therefore, this study describes an evaluation method combining aquifer intrinsic vulnerability and pollution source loading to evaluate groundwater contamination risk in Guanzhong Basin on a macro scale. A modified DRATICL model was introduced to evaluate the intrinsic vulnerability, and the analytic hierarchy process (AHP) and the entropy weight method were combined to determine the weight of each evaluation factor. Pollution loading was evaluated by quantifying the characteristics of potential pollution sources, mainly including pollutant toxicity, pollutant release possibility and potential pollutant release amount. Finally, total iron, Cl−, SO42−, F−, COD (Chemical Oxygen Demand), NO3−, NO2− and TDS (Total Dissolved Solids) are used to calculate the water quality index and verify the model results. The results showed that industries were the most harmful potential pollution sources in the study area, followed by landfills. Very high vulnerability areas were mainly situated around Huazhou District, Huayin and Dali County, as well as the low terraces around Zhouzhi County and Hu County, which are mainly caused by shallow groundwater depth and high net recharge. The final groundwater contamination risk results showed the high groundwater contamination risks are detected around Xi'an City, Xianyang City, Hancheng City and Dali County. Both high vulnerability and high pollution loading were present at the Jingwei District in the north of Xi'an City, where a priority attention should be given.

Groundwater contamination and human health risk assessment in Ikire community, Osun State, Nigeria

Groundwater contamination and human health risk assessment in Ikire community, Osun State, Nigeria

Socio-Institutional Drivers of Groundwater Contamination Hazards: The Case of On-Site Sanitation in the Bwaise Informal Settlement, Kampala, Uganda

Socio-institutional factors are poorly addressed in the risk assessment of groundwater contamination. This paper contributes to the development of a socio-institutional assessment framework based on a case study of contamination by on-site sanitation (OSS) in an informal settlement of Bwaise (Kampala, Uganda). We conducted a snapshot survey of the recent extent of groundwater contamination by OSS using microbial and hydro-chemical indicators. Through transition arenas and key informant interviews, we investigated the socio-institutional drivers of the contamination. Overall, 14 out of the 17 sampled groundwater sources tested positive for Escherichia coli during the wet season. Nitrate concentrations at four sources exceeded the World Health Organization guideline value (50 mg/L), attributed to OSS. Despite the high contamination, the community highly valued groundwater as an alternative to the intermittent municipal water supply. We deduced six drivers of groundwater contamination, including land-use management, user attributes, governance, infrastructure management, groundwater valuation, and the operating environment (“LUGIVE”). Qualitative indicators for each of the drivers were also construed, and their interlinkages presented in a causal loop diagram, representing a socio-institutional assessment framework. The framework can help policymakers and the community to analyze various socio-institutional control levers to reduce the risk of groundwater contamination by OSS in informal settlements.

Groundwater contamination and health risk assessment across an urban gradient: Case study of Onitcha metropolis, south-eastern Nigeria

Urban population growth in south-Eastern Nigeria and associated proliferation of built-up areas predispose groundwater to contaminants of anthropogenic origin. This study compared the groundwater quality across an urban gradient, in Onitsha, a notable urbanized region and beehive of commercial activity in southeastern Nigeria. Groundwater samples (n = 120) from sixty boreholes tapping into aquifers spanning major settlement clusters at upper Iweka, Mgbuka and Nkpor, were sampled over 5-months from August to December 2019. Faecal coliforms, water-soluble metals, and nutrients (nitrates and phosphates) were estimated in groundwater samples. Noncarcinogenic risks of heavy metals in ground water samples was estimated for Average daily dose (ADD) and Total Hazard Quotient (THQ) using both deterministic and probabilistic approaches. Concentrations of mercury and heavy metals (Fe, Pb, and Cd) estimated in water samples revealed a general exceedance of World Health Organization (2009) standards for drinking water. However, significantly higher Hg, Pb and faecal coliform levels (0.122 ± 0.03 mg/L, 0.115 ± 0.014 mg/L, 2.3 × 106 ± 300 MPN respectively) in groundwater of the more built-up areas of upper Iweka highlight increased chances of metals and pathogens impacts on water quality with increased urbanization. High incidence of nutrients (nitrates: 258.40 ± 62.1 and phosphorus: 33.05 ± 4.06 mg/L) in groundwater of less urbanized areas suggests impacts of fertilizer applications from greater agricultural activity. Probabilistic estimates for heavy metals in ground water from Onitsha via ingestion route, showed that Upper Iweka had the highest HQ (3.262) followed by Mgbuka (2.1233) and Nkpor (2.0187). On the other hand, estimates showed that Mgbuka had the highest HQ (0.0241) followed by Nkpor (0.016) and Upper Iweka (0.014) for dermal exposure routes. Strategic mitigation measures including urban planning and infrastructural upgrade are recommended to protect the water resources, prevent further groundwater contamination and reduce population health risks emanating from poor drinking water sources.

Groundwater vulnerability assessment using GIS-based DRASTIC model in Nangasai River Basin, India with special emphasis on agricultural contamination

Nangasai basin is a semi-arid watershed where agriculture is the main source of economy. In present day, increasing population demands increase in food productivity which leads to increase use of fertilizers and chemical pesticides in agriculture. These fertilizers on the other hand mix up with the groundwater and increase the pollution, which affects human health adversely. So, for controlling the groundwater contamination risk proper water resource management and assessment of groundwater vulnerability is extremely important. Total 7 hydrogeological parameters have been considered for this study, and the final groundwater vulnerability map has been prepared by overlay weighted method with the help of DRASTIC index, which is classified into 5 vulnerable classes (very high, high, moderate, low, and very low). In the south and south-eastern regions of the basin namely Deghi, Bankada, Baram, Macha, Katin, Tilabani high groundwater contamination is been observed. For validating the model, the water quality parameters-nitrate and TDS have been used with the accuracy of 89% and 86% respectively. Using effective as well as scientifically approved methods, the anthropogenic and agricultural contamination can be controlled and managed which will lower the risk of contamination. This map can be further utilized as a base map for management of groundwater pollution and its planning.

Cancer risk assessment and modeling of groundwater contamination near industrial estate, Lahore, Pakistan

Abstract More than 60% of the population of Pakistan has no access to safe drinking water. Industrial zones near populated areas make conditions more severe due to continuous contamination. The aim of this study was to use statistical tools for correlation and source identification and health risk assessment of contamination due to Sundar Industrial Estate (SIE), Lahore, Pakistan. Drinking and wastewater samples were collected from SIE and analyzed for physical, chemical, microbial, and heavy metals analysis. Results showed that heavy metals and microbial contamination were beyond the National Drinking Water Quality Standards of Pakistan while high values of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) wastewater were responsible for contamination of drinking water through seepage. There was a medium to strong correlation among parameters of all samples as indicated by Pearson correlation and analysis of variance. Principal component analysis and cluster analysis indicated sources of contamination, i.e., refuse leachate and untreated effluent discharges as main source of pollutants for drinking water. Health risk assessment showed a high intake of heavy metals through drinking water. Hazard quotient and hazard index indicated high probability of non-carcinogenic risk while cancer risk assessment suggested that out of every 100 of the population 93 people may suffer carcinogenic effects.