Groundwater Contamination Vulnerability Research Articles

Baseline of the outcropping Guarani Aquifer System in Rivera and Tacuarembó, Uruguay: Piezometry and Vulnerability

The piezometric, water table and vulnerability baseline of the outcropping Guaraní Aquifer System (GAS) in the departments of Rivera and Tacuarembó (Uruguay) is presented. The flow network and chemical composition are essential to understanding underground dynamics and identifying recharge and discharge zones, preferential flow directions, and determining hydraulic gradients. It is essential to analyze its evolution in time and space from the baseline, for the management and protection of the resource. Vulnerability was determined by applying the GOD and EKv methods, obtaining similar results, with areas of high and moderate vulnerability in most of the studied area. The risk of groundwater contamination was determined using the method of Foster & Hirata(34), obtaining high risk for potentially polluting loads from lack of sanitation, solid household waste, irregular settlements, and gas stations, in areas of moderate and high vulnerability. Industrial and mining activity and a cemetery represent a moderate risk in most cases. Having maps of vulnerability and risk of groundwater contamination is essential to identify the most vulnerable areas and implement detailed study, control, and protection tools aimed at conserving and mitigating impacts on GAS.

Groundwater vulnerability assessment using SINTACS model and GIS: A case study in Raipur city

The purpose of this study is to evaluate groundwater contamination vulnerability in the Raipur region using a Geographic Information System (GIS) based model. Researchers have worked on this method in the past, but none attempted to apply this method to the Raipur region. Various hydrogeological layers of the model i.e., water table depth, unsaturated conditions, soil media, net recharge, topographic slope, aquifer media, and hydraulic conductivity have been weighted as well as classified, integrated into the GIS environment to obtain groundwater contamination vulnerability zones (GVI). The (VI) estimated ranges between 84 to 215. Furthermore, the outcome shows that 5.42% of the region shows very low vulnerability and 14.26% of the region indicates very high vulnerability. Groundwater samples of ten locations were obtained from the delineated groundwater vulnerability zones to validate the model outcomes. The nitrate concentration was observed in the low groundwater vulnerable zone is < 15 mg/l, whereas it ranges from 15-40 mg/l in the moderate groundwater vulnerable zone and more than 40mg/l in the highly vulnerable zone of groundwater. The model approach SINTACS vulnerability index (SVI) is found to be well correlated with the nitrate concentration of the region and is appropriate for GVA of Raipur city. This model approach implemented was therefore found to be efficient for the area’s prevailing hydrogeological settings.

Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination

The accurate evaluation of groundwater contamination vulnerability is essential for the management and prevention of groundwater contamination in the watershed. In this study, advanced multiple machine learning (ML) models of Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR), and ensemble Random Forest Regression (RFR) were applied to determine the most accurate performance for the evaluation of groundwater contamination vulnerability. Eight vulnerability factors of DRASTIC-L were rated based on the modified DRASTIC model (MDM) and were used as input data. The adjusted vulnerability index (AVI) with nitrate values was used as output data for the modeling process. The performance of three models was verified using the statistical performance criteria of MAE, RMSE, r2, and ROC/AUC values. The ensemble RFR model showed the highest performance in comparison with standalone SVR and RBNN models. Specifically, ensemble RFR kept all promising solutions during the model performance due to its flexibility and robustness, and the vulnerability map obtained by the RFR model was more accurate for predicting the most vulnerable areas to contamination. It was concluded that ensemble RFR was a robust tool to enhance the evaluation of groundwater contamination vulnerability, and that it could contribute to environmental safety against groundwater contamination.

Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms.

A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination. To overcome the drawbacks of the ODM, and to maximize the accuracy of the final contamination vulnerability index, two levels of modeling strategy were proposed. The first modeling strategy used particle swarm optimization (PSO) and differential evolution (DE) algorithms to determine the effective weights of DRASTIC parameters and to produce new indices of ODVI-PSO and ODVI-DE based on the ODM formula. For strategy-2, a deep learning neural networks (DLNN) model used two indices resulting from strategy-1 as the input data. The adjusted vulnerability index in strategy-2 using the DLNN model showed more superior performance compared to the other index models when it was validated for nitrate values. Study results affirmed the capability of the DLNN model in strategy-2 to extract the further information from ODVI-PSO and ODVI-DE indices. This research concluded that strategy-2 provided higher accuracy for modeling the aquifer contamination vulnerability in the study area and established the efficient applicability for the aquifer contamination vulnerability modeling.

ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area

The enhanced assessment of groundwater contamination vulnerability is necessary for the management and conservation of groundwater resources because groundwater contamination has been much increased continuously in the world by anthropogenic origin. The purpose of this study is to determine the best model among three ANFIS-MOA models (the adaptive neuro-fuzzy inference system (ANFIS) combined with metaheuristic optimization algorithms (MOAs) such as genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization (PSO)) in assessing groundwater contamination vulnerability at a nitrate contaminated area. The Miryang City of South Korea was selected as the study area because the nitrate contamination was widespread in the city with two functions of urban and rural activities. Eight parameters (depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and landuse) were classified into the numerical ratings on basis of modified DRASTIC method (MDM) for the input variables of ANFIS-MOA models. The Original ANFIS, and 3 combined models of ANFIS-PSO, ANFIS-DE and, ANFIS-GA used 95 adjusted vulnerability indices (AVI) as the target data of training (70% data) and testing (30% data) processing. The performance of 4 models was evaluated by mean absolute errors (MAE), root mean square errors (RMSE), correlation coefficients (R), ROC/AUC curves and predicted AVI (PAVI) maps. The statistical results, spatial vulnerability maps and correlation coefficients between PAVIs and nitrate concentrations revealed that the order of model excellence was ANFIS-PSO, ANFIS-DE, ANFIS-GA, and Original ANFIS, and that ANFIS-PSO showed the highest performance in training and testing processing. The performance rates of ANFIS-MOA models were also compared with 10 recent popular worldwide models using the correlation coefficients between PVI and nitrate concentrations, and they were superior to other recent popular models. ANFIS-MOA models were also useful for resolving the subjectivity of physical and hydrogeological parameters in original DRASTIC method (ODM) and MDM. It is expected that ANFIS-PSO models will produce the excellent results in assessing groundwater contamination vulnerability and that they can greatly contribute to the groundwater security in other areas of the world as well as Miryang City of South Korea.

Groundwater contamination vulnerability assessment using DRASTIC method, GSA, and uncertainty analysis

Groundwater is one of the most important water resources in arid and semi-arid regions, whose quality and quantity are variable. In this study, the vulnerability of Ardabil aquifer was investigated using a classic and modified DRASTIC model. The weights of the parameters used in the DRASTIC model are universal; however, according to the characteristics of each region, these weights can be variable and associated with uncertainty. Sensitivity analysis is an important tool in vulnerability analysis to identify influential parameters. Global sensitivity indices are the mathematical models that can be computed by Monte Carlo methods and used for estimating the influence of individual variables or groups of variables on the model output. In this study, the global sensitivity analysis (GSA) was used to determine the weights of the layers of DRASTIC model based on the regional data in ArcGIS software. Using new weights extracted with GSA, the modified DRASTIC map was obtained for 2011 and 2016. The classic DRASTIC model classified the study area into medium to very high vulnerability, while the modified model presented very low to moderate vulnerability. Based on the new weights of DRASTIC model, the vulnerability increased due to the rise in precipitation in 2016 compared with 2011. To compare the results, the uncertainty map was analyzed using the Monte Carlo method of both classic and modified models. The results showed that the modified DRASTIC model was in a better condition, and this map was classified into 3 classes including the ranges of 0–1, 1–2, and 2–3. In both models, the groundwater level and the topography layers had the highest and lowest weights, respectively. Generally, in the case of the uncertainty map, it is better to determine the new weight for each region separately.

Quantification of macropore flow in Danish soils using near-saturated hydraulic properties

Larger pores allowing macropore flow can rapidly transport contaminants as well as nutrients from the soil surface to the subsoil and groundwater. Modeling of the involved macropore flow is complex due to large number of required parameters. One way to deal with this difficulty is the precise evaluation of the soil hydraulic conductivity function using robust pedotransfer functions for the near-saturated part. Aiming to develop pedotransfer functions to estimate saturated and unsaturated hydraulic conductivities [Ks and K(h)] as well as water contents at specific pressure heads, we used Gaussian Process Regression (GPR), a non-parametric machine learning model, to obtain van Genuchten (1980) retention and conductivity parameters for Danish soils based on soil texture and organic matter. We defined K10 as K(h) at − 10 cm pressure head. The difference between the logarithm of Ks and K10 [log(Ks) − log(K10)] was denominated log(Kjump) and was assumed to convey the potential degree of macropore flow considering only soil hydraulic properties. Macropore flow in Denmark was evaluated through the developed PTFs for log(Ks) and log(K10) with an average RMSE of 0.635 and 0.594 (for K in cm d−1) under a confidence interval obtained by bootstrapping. Dynamics of pressure head in an initially saturated soil column with a zero-flux top boundary condition and a 2 m deep groundwater level were simulated using the Hydrus-1D model. The result of this modeling showed that soils in the eastern part of Denmark had a higher likelihood to experience macropore flow. Finally, using the spatial distribution of meteorological data, a macropore flow map of Denmark was produced. Precipitation was a dominating factor on macropore flow in sandy clay loam and sandy loam soils when compared to sandy and loamy sand soils. Finally, maps of relative risk classes of macropore flow qualitatively differentiated land areas in Denmark for vulnerability of nutrient loss and groundwater contamination.

Groundwater vulnerability assessment of Hoshangabad and Budni industrial area, Madhya Pradesh, India, using geospatial techniques

The quality of groundwater can be determined by hydrogeological formations which vary geographically. Subsurface geology has significant role in governing the movement and quality of groundwater. The present study aims assessment of groundwater contamination vulnerability in Hoshangabad and Budni industrial area using the DRASTIC model approach. The model is hybridization of the seven parameters that provides input to the model. In the model, rating and weightage to each parameter were assigned as per the relative significance of the parameter in groundwater contamination. Groundwater contamination vulnerability index (GWVI) has been computed by integrating of these data layers in ArcGIS environment. The obtained GWVI in the area varies from 66 to 170, which was further divided into five zones, i.e. (1) very low GW contamination zone, (2) low GW contamination zone, (3) moderate GW contamination zone, (4) high GW contamination zone and (5) very high GW contamination zone. Further, the model has been validated by analysing the sulphide concentration in groundwater of the delineated GW contamination vulnerable zones. The model has been found effective for the prevailing hydrogeological settings of the area. The model can serve as an effective tool for the concerned authority, social workers and government/non-organizations for the management of groundwater resources in the area. Further, application of the GIS technique has been found useful in preparing the database of each variable of the model.

GIS modeling approach for assessment of groundwater vulnerability in parts of Tawa river catchment area, Hoshangabad, Madhya Pradesh, India

Assessment of the groundwater contamination vulnerability was carried using a GIS based DRASTIC model in Tawa river catchment, Hoshangabad, Madhya Pradesh, India. The study aimed at demarcation of the susceptible zones of groundwater contamination by integration of the various hydrogeological layers of the model in GIS environment. The input model variables i.e. depth of water table, net recharge, aquifer media, soil type, topography, impact of vadose zone and hydraulic conductivity were evaluated from different sources incorporated in the model and groundwater contamination vulnerability map was prepared. The output groundwater vulnerability map shows that 34.84% of the area is in high risk zone of groundwater contamination. The high probability of contamination in the zone is due to rigorous farming and encouraging recharge facilities along the river valleys. An extensive part covering 54.84% area towards the eastern and western side of Tawa River is falling in moderate zone of groundwater contamination. A small area covering 10.32% towards southern Satpura hill slopes with low contamination chances lies in low risk zone of groundwater contamination. Further, for validation of the model results 30 groundwater samples were collected from the delineated groundwater vulnerability zones. These samples were processed for evaluation of nitrate concentration. In low vulnerable zone, nitrate was present in <5 ppm, in moderate vulnerable zone nitrate varies from 5-20 ppm, whereas in high contamination zone nitrate varies from 20-76 ppm. Thus, the applied model approach has been found effective for the prevailing hydrogeological settings of the area. The model can be used as an efficient tool for the assessment and management of groundwater resources.

Decentralized Wastewater Treatment System Impact Assessment on Groundwater Resources: Case Study Dana Biosphere Reserve/Jordan

Jordan is one of the world&amp;rsquo;s most water-scarce countries. Almost all of the water supply systems in Jordan depend on groundwater and springs which are highly depleted. While only 60 percent of households in Jordan are connected to the sewage systems. Hence, there is a significant, untapped potential for decentralized approaches for wastewater management. DWWTS are environmentally sound and sustainable technology can be used for suburban and rural communities such the ones found in the Dana Biosphere Reserve area. Groundwater contamination risk is the critical point when implementing wastewater treatment systems including DWWTS. DRASTIC Model, an inexpensive method for evaluating the vulnerability of groundwater resources to pollution based on hydrogeologic settings, was applied to assess the groundwater contamination vulnerability in the study area. The DRASTIC index value indicates that the potential for polluting groundwater is low. This study recommends implementing DWWTS to serve communities in Dana Biosphere Reserve area emphasizing the development of groundwater monitoring program during the operation of the facility.&amp;nbsp;&amp;nbsp;

Groundwater pollution potential evaluation in Khorramabad-Lorestan Plain, western Iran

Assessing the vulnerability of groundwater contamination is an important issue in water resource management. Currently, due to the increased activity in agriculture and the use of chemical fertilizers, contamination potential assessment in the Khoraramabad-Lorestan Plain has become very critical. In the present study, groundwater pollution potential is evaluated using two frameworks, namely DRASTIC and SINTACS. For this purpose, seven hydrogeological factors (i.e., groundwater depth, recharge, aquifer media, soil media, topography, unsaturated zone impact, and hydraulic conductivity) are imported to DRASTIC and SINTACS models. Then, using the overlapping method, these seven parameters are combined to prepare a final map. According to these methods, most of the high pollution potential areas are in the north and northwest plains covering an area of 50.49 km2 in the DRASTIC framework and 65.49 km2 in SINTACS model. Zones with less pollution potential are in the southern part of the plains. To verify the results of the two models, a vegetation map of the area was used, which shows that the area with the greatest contamination potential corresponds to the agricultural irrigated lands where chemical fertilizers are commonly applied.

Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio (GFR) and Optimized-DRASTIC Methods

Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess nitrogen fertilizer leaching down into the aquifer. The percolation of nitrate depends on several hydrogeological conditions of the valley. Groundwater contamination vulnerability mapping uses hydrogeologic conditions to predict vulnerable areas. This paper presents a new Geodetector-based Frequency Ratio (GFR) method and an optimized-DRASTIC method to generate nitrate vulnerability index values for the CV. The optimized-DRASTIC method combined the individual weights and rating values for Depth to water, Recharge rate, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity. The GFR method incorporated the Frequency-Ratio (FR) method to derive rating values and the Geodetector method to derive relative Power of Determinant (PD) values as weights to generate nitrate susceptibility index map. The optimized-DRASTIC method generated very-high to high index values in the eastern part of the CV. The GFR method showed very-high index values in most part of the San Joaquin and Tulare basin. The quantitatively derived rating values and weights in the GFR method improved the vulnerability index and showed better consistency with the observed nitrate contamination pattern than optimized-DRASTIC index, suggesting that GFR is a better method for groundwater contamination vulnerability mapping in the CV aquifer.

An Easy-to-Use Method for Assessing Nitrate Contamination Susceptibility in Groundwater

This research presents a methodology for assessing nitrate contamination susceptibility in groundwater using thematic maps, derived mainly from the land use map and from statistical data available at national/regional institutes of statistics (especially demographic and environmental data). The methodology was applied in a large area of southern Italy encompassing 4 alluvial and volcanic groundwater bodies, with high concentrations of NO3. The Potential Nitrate Contamination is believed to derive from three sources: agricultural, urban, and periurban. The first one is related to the use of fertilizers. For this reason the land use map was reclassified on the basis of the crop requirements in terms of fertilizers to obtain the Agricultural Potential Nitrate Contamination (APNC) map. The urban source considers leakages from the sewage network and, consequently, it depends on the anthropogenic pressure, expressed by the population density, particularly concentrated in the urbanized areas (Urban Potential Nitrate Contamination (UPNC) map). The periurban sources include unsewered areas, especially present in the periurban context, where illegal sewage connections coexist with on-site sewage disposal (cesspools, septic tanks, and pit latrines) (Periurban Potential Nitrate Contamination (PuPNC) map). The Potential Nitrate Contamination (PNC) map is produced by overlaying the APNC, UPNC, and PuPNC maps. The map combination process is straightforward, being an algebraic combination: the output values are the arithmetic average of the input values. The final pollution susceptibility (RISK) map is obtained by combining the PNC map with the groundwater contamination vulnerability (GwVu) map. The methodology, successfully applied in the study area with a relatively good correlation between the nitrate contamination susceptibility map and the nitrate distribution in groundwater, appears to be effective and have a significant potential for being applied worldwide.

Assessment of intrinsic vulnerability to the contamination of karst aquifer using the COP method in the Carste Lagoa Santa Environmental Protection Unit, Brazil

This study addresses the intrinsic groundwater vulnerability by using the COP method index and GIS technology on the karst-fissured aquifer of the Carste Lagoa Santa Protection Unit, located in the state of Minas Gerais, Brazil. This area has a great importance due to its rich paleontology, archeology and speleology. The region is groundwater dependent, and it has been suffering pressure by economic development. Therefore, it is important to identify vulnerable areas in which infiltration of contaminants is likely to happen. The main goal of this study was to create an intrinsic groundwater contamination vulnerability map (scale 1:50.000), which will be used to assist the implantation of groundwater monitoring net project in the area. The index vulnerability was evaluated by analyzing COP model parameters through geoprocessing techniques. However, in order to adapt the method to the studied area conditions, changes in frame and values of input parameter weights (lithology and slope/vegetation) were necessary. The outcomes showed five categories of groundwater contamination vulnerable zones: high to the highest vulnerability level (10.95%), moderated (58.77%) and low to the lowest vulnerability (30.28%). Despite the low percentage of high to the highest vulnerably level area, they must be considered by environmental managers due to strong relationship between superficial and groundwater in the area.

Numerical modelling on fate and transport of coupled adsorption and biodegradation of pesticides in an unsaturated porous medium

The vertical transport of pesticides from a surface application through an unsaturated porous medium system is of major concern to assess the vulnerability of groundwater contamination. In order to better understand the transport of pesticides in the groundwater, it is essential to understand the flow and predict the pesticide concentration in the unsaturated zone. A one-dimensional numerical model is developed to simulate the transport of pesticides in an unsaturated porous medium, considering the effect of adsorption and microbial degradation with inhibition in addition to conventional advective and dispersive transport. The effect of unsaturated soil parameters on the water flow dynamics and the factors governing the pesticide transport in an unsaturated porous medium is also numerically investigated. The numerical results suggest that the adsorption and biodegradation is an important sink in contaminant removal. In addition, the microbial growth inhibition due to pesticide restricts the potential for biodegradation at low inhibition constant.

Transport of ammonium and nitrate in saturated porous media incorporating physiobiotransformations and bioclogging

ABSTRACTThe vertical transport of nitrates from fertilizer application and wastewater irrigation through the subsurface and saturated zone is of major concern to assess the vulnerability of groundwater contamination. The present study addresses the transport of nitrogenous fertilizers such as ammonium and nitrate in the presence of organic carbon (acetate) in a one-dimensional soil column under saturated conditions, considering the effect of adsorption and biotransformation. The soil had a neutral pH range and was classified as loamy sand, with a 0.89% organic carbon content. Batch studies revealed that sorption occurred in the order of ammonium > acetate > nitrate following a Freundlich isotherm model. Mixed heterotrophic native soil bacteria for aerobic nitrification and anoxic denitrification were developed, and the growth kinetic parameters were simulated using a Haldane inhibition model for nitrification and a Monod inhibition model for denitrification. Results from biotransformation studies suggested that denitrification was the predominant process, with significant bacterial growth and clogging of pores occurring monotonously reaching a stationary phase by 12 days. Pore-clogging phenomenon not only reduces the permeability of the soil by 5 orders of magnitude but also increases the contact time of the contaminant with the soil microbe and thereby delays the transport process and decreases the effluent ammonium and nitrate concentrations. A tailing breakthrough in a leaching study illustrates that water flux variation (0.153 and 0.509 cm/min) did not influence the transport of solutes, rather irreversible chemical bonding retains more ammonium than nitrate in the soil matrix.

Experimental and Simulation Studies on Nitrogen Dynamics in Unsaturated and Saturated Soil Using HYDRUS-2D

Effective irrigation practices have become an optimal means of providing water and nutrients to crops as well as preventing the vulnerability of ground water contamination. This could be achieved by understanding the fate and transport processes of nitrogen compounds in the subsurface. However, nitrogen dynamics in the plant rhizosphere is very complex, which depends on many factors such as soil temperature, pH, water content, soil microbes, soil type and plant characteristics and cannot be easily quantified. Using state-of-the-art modelling techniques, an attempt was made to evaluate the reactive transport of ammonium nitrogen under continuous and alternate wetting and drying mode (AWD) of irrigation in soil columns using a HYDRUS 2D model. The model quantifies the soil sorption, microbial transformations such as nitrification and denitrification, leaching, and final release to aquifer for ammonium and nitrate input fluxes. This quantification helped in designing an optimal fertigation and irrigation schedule. Soil column study was done with variable saturation and in a combined unsaturated (45cm) and saturated (5cm) representing vadose and aquifer. Drip irrigation with wastewater containing 100mg/L of ammonium and 500mg/L of organic carbon (acetate) was applied based on the recommended total quantity of nutrients in continuous and pulse modes to the column. The soil parameters, initial and boundary conditions used in the model were obtained through experimental studies. The HYDRUS-2D model was developed, calibrated and validated with experimental results. The model performed could predict well the experimental data. Under continuous irrigation, nitrification (0.23/d) was the predominant process whereas both nitrification and denitrification occurs simultaneously in AWD with the overall nitrate removal efficiency of 60%. Consequently, the scenario prediction using this model for optimal fertigation schedule was done for groundnut crop. Further this model could be extended for various scenario predictions for designing optimal irrigation-fertigation schedules for sustainable agricultural practices.

Experimental and numerical investigations on nitrogen species transport in unsaturated soil during various irrigation patterns

The transport of nitrogen coming from wastewater applied agricultural field is a major problem in assessing the vulnerability of groundwater contamination. In this study, laboratory column experiments are conducted in order to simulate the paddy, groundnut and wheat irrigation with wastewater. The experiments are carried out with high clay content (≈35%) soil from Kancheepuram, Tamilnadu and low clay (≈9%) soil from Ludhiana, Punjab, India. Furthermore, a numerical model and HYDRUS-1D model are developed to simulate the experimental results. The experimental results show that there is no effluent collected at the bottom of the column during groundnut irrigation in Kancheepuram soil and effluent collected except during first irrigation in the case of wheat irrigation in Ludhiana soil. The experimental and numerical results illustrate that when 50 mg/l of ammonium and 20 mg/l of nitrate nitrogen applied during paddy irrigation, the peak nitrate nitrogen concentration of 50 mg/l is arrived after 10 days in Kancheepuram soil due to low permeability and relatively less background soil nitrogen. But in the case of Ludhiana soil with 94 mg/l of total nitrogen applied during paddy irrigation, the peak nitrate nitrogen concentration of 1,620 mg/l is observed at first day due to high permeability and high soil background nitrogen concentration. Additionally, the model results show that the application of high nitrogen content wastewater for irrigation in Ludhiana soil will affect the groundwater quality even when the groundwater table is deep as compared with Kancheepuram soil.

An Assessment of Groundwater Contamination Vulnerability and Priority Areas for Groundwater Management Using GIS and Analytic Hierarchy Process

The purpose of this study is to improve the previous groundwater contamination vulnerability assessment method, apply it to the study area, and select priority areas for groundwater management based on the quantitative analysis of groundwater contamination vulnerability. For this purpose, first, the previous 'potential contamination' based on groundwater contamination vulnerability assessment method was upgraded to the methodology considering 'adaptation capacity' which reduced contamination. Second, the weight of groundwater contamination vulnerability assessment factors was calculated based on the analytical hierarchy process(AHP) and the result of survey targeting groundwater experts. Third, Gyeonggi-do was selected as the study area and the improved methodology and weight were implemented with GIS and actual groundwater contamination vulnerability assessment was carried out. Fourth, the priority area for groundwater contamination management was selected based on the quantitative groundwater contamination vulnerability assessment diagram. The improved detailed groundwater contamination vulnerability assessment factors in this study were a total of 15 factors, and 15 factors were analyzed as new and improved weight with higher 'adaptation capacity' than the assessment factor corresponding to the previous 'potential contamination' in the weight calculation result using AHP. Also, the result of groundwater contamination vulnerability assessment in Gyeonggi Province using GIS showed that Goyang and Gwangmyeong which were adjacent to Seoul had a high groundwater contamination vulnerability and Pocheon and Yangpyeong County had a relatively low groundwater contamination vulnerability. In this study, the previous groundwater contamination vulnerability assessment was improved and applied to study areas actually. The result of this study can be utilized both directly and indirectly for the groundwater management master plan at national and local government level in the future.

Numerical modelling on fate and transport of petroleum hydrocarbons in an unsaturated subsurface system for varying source scenario

The vertical transport of petroleum hydrocarbons from a surface spill through an unsaturated subsurface system is of major concern in assessing the vulnerability of groundwater contamination. A realistic representation on fate and transport of volatile organic compounds at different periods after spill is quite challenging due to the variation in the source behaviour at the surface of spill as well as the variation in the hydrodynamic parameters and the associated inter-phase partitioning coefficients within the subsurface. In the present study, a one dimensional numerical model is developed to simulate the transport of benzene in an unsaturated subsurface system considering the effect of volatilization, dissolution, adsorption and microbial degradation of benzene for (i) constant continuous source, (ii) continuous decaying source, and (iii) residual source. The numerical results suggest that volatilization is the important sink for contaminant removal considering the soil air migration within the unsaturated zone. It is also observed that the coupled effect of dissolution and volatilization is important for the decaying source at the surface immediately after the spill, whereas rate-limited dissolution from residually entrapped source is responsible for the extended contamination towards later period.