Contaminant Transport Research Articles

Stochastic inversion of discrete fracture networks using genetic algorithms

Reservoir management and contaminant transport simulations rely on accurate modeling of the subsurface. This task becomes more challenging when the reservoir of interest also has a large amount of fractures. Inverse modeling of these fractured media involves first performing multiple field tests such as pumping tests, tracer tests, or seismic surveys. Inverse modeling methods then are used to find potential geologic models that produce simulated results that match field observations. Popular methods include the Ensemble Kalman Filter, Markov Chain Monte Carlo, and simulated annealing. A common challenge these methods face is that inverse modeling of fractured media is inherently a multiobjective optimization task. The inverse modeling method must find a discrete fracture model that produces the same flow characteristics as observed in field pumping or tracer tests. But it also must find a discrete fracture model with a fracture network that matches the fracture parameter distribution observed by the field measurements such as seismic surveys. This challenge can be approached in two steps. The first step is producing discrete fracture network models with parameter distributions that match field observations from seismic surveys. This study focused on the second step, involving the development of a method that can take a population of discrete fracture networks and generate new fracture networks in a way that preserves the fracture parameter distribution. This was done using a genetic algorithm modified to apply to the domain of discrete fracture networks. During genetic mixing, the fractures of the child model are generated by randomly copying over fractures from the parent models. This process ensures the child model adopts the fracture parameter distribution of the parent models. This study tests the effectiveness of this genetic algorithm on a synthetic example. The results of the experiment show the genetic algorithm is able to effectively produce a population of discrete fracture models with breakthrough curves that match the curves of the reference model.

Partitioning behavior and mechanism of polyhalogenated carbazoles in water and suspended particulate matter

Polyhalogenated carbazoles (PHCZs), known as new dioxin-like compounds, are a new class of emerging environmental contaminants that have received increasing attention in recent years due to their wide distribution and dioxin-like toxicity. This study investigated the partition characteristics and adsorption mechanisms of eight PHCZs in the aqueous phase and suspended particulate matter (SPM). The competitive impact of humic acid (HA) on the adsorption of PHCZs was revealed when the effects of various environmental conditions (HA, temperature, perturbation disturbance, and pH) behavior were explored. The key finding of this study is that SPM, which is its effective vector, could adsorb 70.7 % of ΣPHCZs. The equilibrium adsorption amount is ranked as 3,6-ICZ > 3,6-BCZ > 2,7-BCZ > 3,6-CCZ > 1,3,6,8-BCZ > 3-BCZ > 2-BCZ > CZ. The halogen species, the degree of halogenation, and the substitution position of the PHCZs influence the amount of adsorption, where the log Kow values, the steric effect (Es), and the density contribute the most to the amount of adsorption, and the specific adsorption mechanisms are van der Waals force, π-π, hydrogen bonding, and nonspecific hydrophobic interactions. The adsorption reaction of PHCZs by SPM is endothermic, and the amount of adsorption increases with increasing temperature, oscillation velocity, and decrease in pH. HA may reduce the adsorption sites of hydroxyl, carbonyl, amide groups, and π-π bonds on SPM for PHCZs, while the SPM@HA conjugates can provide new sites for the adsorption of PHCZs. According to the experimental findings of this study, SPM plays a significant role in contaminant transport. As a result, when conducting environmental investigations of PHCZs and even other hydrophobic contaminants, we must fully consider the level of contaminants present in SPM to reveal the ecological risks accurately.

Heavy metal pollution in Mongolian-Manchurian grassland soil and effect of long-range dust transport by wind

Grasslands provide a range of valuable ecosystem services, but they are also particularly fragile ecosystems easily threatened by human activities, such as long-term open-pit mining and related industrial activities. In grassland area, dust containing heavy metal(loid)s generated by mines may further migrate to remote places, but few studies have focused on the long-range transport of contaminants as an important pollution source. In the present study, one of the largest and most intact grassland ecosystems, the Mongolian-Manchurian steppe, was selected to investigate its pollution status and track potential sources. A total of 150 soil samples were collected to explore reginal distribution of nine heavy metal(loid)s that has potential risk in grassland. We conducted a combined multi-variant analysis of positive matrix factorization (PMF) and machine learning, which foregrounded the source of long-range transport of contaminants and inspired the hypothesis of a novel stochastic model to describe contaminants distribution. Results showed four different sources accounting for 44.44% (parent material), 20.28% (atmospheric deposition), 20.39% (farming), and 14.89% (transportation) of the total concentration, respectively. Factor 2 indicated that coal surface mining lead to a significant enrichment of As and Se with their concentration far above the global average level, which was different from other reported grassland areas. Machine learning results further confirmed that atmospheric and topographic features were their contamination controlling factors. The model results proposed that As, Se and Cu released by surface mining will be transported over long distance under prevailing monsoon, until finally deposited in the windward slope of mountain due to terrain obstruction. The long-range transport by wind and deposition of contaminants may be a prevailing phenomenon in temperate grassland, making it a pollution source that cannot be ignored. Evidence from this study reveals the urgency of precautions for fragile grassland ecosystems around industrial areas and provides a basis for its management and risk control policies.

Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method.

Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modeling. A general Bayesian framework is proposed that combines the CA model with observed data to identify unknown sources of river pollution. To reduce the computational burden of the Bayesian inference, a CA contaminant transport model is developed to efficiently simulate pollutant concentration values in the river. These simulated concentration values are then used to calculate the likelihood function of available measurements. The Markov chain Monte Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters, which is a sampling-based method that enables the estimation of complex posterior distributions. The suggested methodology is applied to a real case study of the Fen River in Yuncheng City, Shanxi Province, Northern China, and it estimates the release time, release mass, and source location with relative errors below 19%. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources.

Reaction: Harnessing reactive oxygen species (ROS) for water purification

Shishu Zhu received his PhD from the Harbin Institute of Technology in 2019 and became an associate professor at the South China University of Technology (SCUT) in 2023. His research fields include in situ chemical oxidation processes, the transport and deposition of contaminants, and organic geochemistry. Wenxiao Zheng is a PhD candidate at SCUT. His research focuses on identifying and controlling toxic byproducts in reactive-species-initiated wastewater treatment. Weijian Duan is a research fellow at SCUT. His research focuses on developing cutting-edge electrochemical strategies for nitrogen and carbon removal from wastewater. Chunhua Feng received his PhD from Hong Kong University of Science and Technology in 2007 and then joined SCUT as an assistant professor. He is now a professor at SCUT. His research interests are environmental electrochemistry, reactive-species-based geochemistry, and wastewater treatment. Shishu Zhu received his PhD from the Harbin Institute of Technology in 2019 and became an associate professor at the South China University of Technology (SCUT) in 2023. His research fields include in situ chemical oxidation processes, the transport and deposition of contaminants, and organic geochemistry. Wenxiao Zheng is a PhD candidate at SCUT. His research focuses on identifying and controlling toxic byproducts in reactive-species-initiated wastewater treatment. Weijian Duan is a research fellow at SCUT. His research focuses on developing cutting-edge electrochemical strategies for nitrogen and carbon removal from wastewater. Chunhua Feng received his PhD from Hong Kong University of Science and Technology in 2007 and then joined SCUT as an assistant professor. He is now a professor at SCUT. His research interests are environmental electrochemistry, reactive-species-based geochemistry, and wastewater treatment. Reaction: Avoid fairy dust when engineering water-purification technologiesPaul WesterhoffChemJune 08, 2023In BriefThis Catalysis article discusses the risks of using nanomaterials for water purification and alternative strategies, painting a fuller picture of research in clean water. Full-Text PDF Catalyst: The roles of chemistry in clean water for allYoung-Shin JunChemJune 08, 2023In BriefUnderstanding nanoscale interfacial reactions unlocks the chemistry controls that are critical for generating clean water. This catalysis article discusses three important roles of chemistry in clean water: understanding and controlling evolving interfaces induced by nucleation, deciphering and utilizing hidden interfaces in nanoconfined spaces, and harnessing interfaces with functionalized surfaces. Chemically guided developments of new materials and technologies for purifying clean water can bring all water resources back to one H2O, which supports life for all people. Full-Text PDF

Modeling Contamination Transport (Nitrate) in Central Basin Erbil, Kurdistan Region, Iraq with Support of MODFLOW Software

Groundwater is one of the essential water resources in the Kurdistan Region and Erbil City. The development leads to depletion in the quality and quantity of groundwater. This study aimed to model groundwater flow in the study area within the central basin and simulate the nitrate transport from the Erbil landfill site for 10, 20, 30, 40, and 50 years using the Groundwater Modeling System (GMS) version 10.6.2. The study area covers 579.72 km2; data about the groundwater observation head was obtained from the general direction of groundwater. A sample of wastewater was taken from landfill leachate to check the its properties. Landfill leachate is considered a point source of contamination. The GMS is a powerful software for modeling groundwater flow and contamination transport. The results show a good correlation between the observed and predicted heads, and the R2 of the model equals 0.9917. The plume of nitrate contamination spread horizontally and vertically to about 1.5 km downstream landfill and approximately 1.5km upstream landfill. The nitrate reaches the third layer to a depth of more than 325 m after 50 years.

Modeling the Effect of Hyporheic Flow on Solute Residence Time Distributions in Surface Water

Understanding the dynamics of hyporheic flow is important for managing water resources, since this interfacial flow exchange affects the fate and transport of contaminants in rivers. This study numerically quantifies the effect of hyporheic exchange on solute residence times in surface water systems by simulating solute transport in unified turbulent open-channel and hyporheic zone systems. Interfacial hyporheic fluxes (qint) increase with increased Reynolds number (Re) that produces an enhanced bottom pressure gradient over the ripple bed. Heavy-tailed breakthrough curves emerge when hyporheic flow is considered in transport simulation. This reveals that hyporheic flow is a dominant driver of non-Fickian transport in surface water as this interfacial flow exchange delays solute transport with slow porewater flows. Furthermore, the increase in Re extends the longitudinal spreading of solute tracers because a higher surface flow velocity intensifies the magnitude of hyporheic flow and associated storage effects. This can be confirmed by the ratio of the maximum residence time to the peak arrival time that increases with the increase in Re, following a power-law relationship with both Re and qint.

Interface-induced dispersion in the unsaturated porous media: A pore-scale perspective

Although the fluid invasion process impacts the transport and dispersion of chemicals and contaminants in the resident phase of natural, manufactured, and biological unsaturated porous media, these effects remain poorly understood. In this study, we investigate the role of hydrodynamic dispersion in different invasion patterns within the resident phase. While nonidealities in pore structures contribute to mechanical dispersion, our findings demonstrate that the interface front's morphology and the invading phase's distribution significantly influence mechanical dispersion within the resident phase.Our results show that the complex distribution of the invading phase under unstable displacement greatly affects mechanical dispersion measures, such as velocity distribution, variance, and mean square displacement obtained through particle tracking. The interface also alters hydrodynamic dispersion measures, such as the local Peclet number, and generates extensive diffusion-dominated regions across the domain. This ultimately leads to solute trapping between pores that cannot reach the outlet. However, under stable invasion, mechanical and hydrodynamic dispersion do not deviate significantly from the reference saturated condition. In this scenario, dispersion anomalies occur within a short distance ahead of the moving interface, beyond which variations become similar to the reference media.

Integration of groundwater vulnerability with contaminants transport modeling in unsaturated zone, case study El-Sharqia, Egypt

Nowadays, irrigation uses large amount of marginal wastewater due to continuous decline in fresh water supply. As a consequence, using this wastewater for different purposes can cause some adverse environmental impacts. Anthropogenic activities such as septic tanks, sewage ponds, and polluted drains have large influence on deterioration of shallow groundwater aquifers. So, construction of many wastewater treatment plants in these areas is mandatory to control and mitigate this deterioration. Groundwater vulnerability assessment maps and contamination simulation in unsaturated zone can be beneficial in understanding contaminants pathways and groundwater quality evolution. This work is mainly focused on aquifer vulnerability assessment to pollution and the role of vadose zone in attenuation of contaminants transport through it prior to groundwater seepage. Therefore, about 56 drainage and groundwater samples were collected and analyzed for potentially toxic elements. The most vulnerable sector was determined using GOD method revealing that central parts of the study area are the most threatened zones with some scattered sporadic zone of sensitivity to pollution and this was verified through the zonation of Pb, Fe, and Mn spatial concentrations. The leakage of these elements through the unsaturated zone was further simulated using HYDRUS-1D model for the next 10-year period to determine the extent of the pollution plumes and maximum concentration of these elements that percolate to the groundwater directly. The concentration of Fe, Pb, and Mn at the end of the simulation reached low concentrations at the bottom layer of the unsaturated zone.

Transport and removal of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns

Green infrastructure drainage systems are innovative treatment units that capture and treat stormwater. Unfortunately, highly polar contaminants remain challenging to remove in conventional biofilters. To overcome treatment limitations, we assessed the transport and removal of stormwater vehicle-related organic contaminants with persistent, mobile, and toxic (in short: PMTs) properties, such as 1H-benzotriazole, NN'-diphenylguanidine, and hexamethoxymethylmelamine (PMT precursor), using batch experiments and continuous-flow sand columns amended with pyrogenic carbonaceous materials, like granulated activated carbon (GAC) or wheat-straw derived biochar. Our results indicated that all investigated contaminants were subjected to nonequilibrium interactions in sand-only and geomedia-amended columns, with kinetic effects upon transport. Experimental breakthrough curves could be well described by a one-site kinetic transport model assuming saturation of sorption sites, which we inferred could occur due to dissolved organic matter fouling. Furthermore, from both batch and column experiments, we found that GAC could remove contaminants significantly better than biochar with higher sorption capacity and faster sorption kinetics. Hexamethoxymethylmelamine, with the lowest organic carbon-water partition coefficient (KOC) and largest molecular volume among target chemicals, exhibited the lowest affinity in both carbonaceous adsorbents based on estimated sorption parameters. Results suggest that sorption of investigated PMTs was likely driven by steric and hydrophobic effects, and coulombic and other weak intermolecular forces (e.g., London–van der Waals, H-bonding). Results from extrapolating our data to a 1-m depth geomedia-amended sand filter suggested that GAC and biochar could enhance the removal of organic contaminants in biofilters and last for more than one decade. Overall, our work is the first to study treatment alternatives for NN'-diphenylguanidine and hexamethoxymethylmelamine, and contributes to better PMT contaminant removal strategies in environmental applications.

Transport of heavy metal contaminants in a composite liner under non-isothermal condition

A mathematical model for heavy metal contaminants (HMCs) transport in a triple-layer composite liner with defective geomembrane under the non-isothermal condition is developed in this study, where the GMB/GCL/CCL (geomembrane, geosynthetic clay liner, and compacted clay liner) composite liner is adopted as an example and the Langmuir adsorption model is incorporated. The proposed model is solved by the finite difference approach, and its correctness is validated by comparison with the experimental results, an existing analytical solution, and another numerical method. Later, the transport behaviors of HMCs are explored with the established model. Compared with the isothermal condition, the non-isothermal condition enlarges the transport flux, but also reduces the concentration of HMCs. The relative concentration based on the Langmuir adsorption model is higher than that based on the linear adsorption model, which is related to the decrease of the retardation factor under the Langmuir adsorption model. Furthermore, the parametric study shows that when the leachate head h t is between 1.0 and 3.0 m, the defined breakthrough time t b increases by about 1.57 a with the increase of GCL thickness l g by 1 cm, and the t b increases by about 9.07 a with the increase of CCL thickness l c by 0.1 m.

Numerical estimation of the fractional advection–dispersion equation under the modified Atangana–Baleanu–Caputo derivative

The transport of contaminants is a crucial environmental issue, and accurate modeling of this phenomenon is vital for developing effective strategies for its management. In this study, We introduce a non-integer model of the advection–dispersion problem arising in the transport of contaminants. The used derivative is described in the modified Atangana–Baleanu–Caputo (MABC) sense which is a new definition based on an extension of the Atangana and Baleanu derivatives. We employ discrete Chebyshev polynomials to gain the numerical solution of the considered equation. First, we generate a new operational matrix through discrete Chebyshev polynomials properties and proposed derivative. Next, via discrete Chebyshev polynomials and the operational matrix, we gain an algebraic system whose solutions are easily obtained. Finally, we solve some examples and compare the results with those obtained from other numerical methods to confirm the practicality and accuracy of the suggested scheme.

Effect of retardation and capillarity on organic contaminant diffusion through an unsaturated composite liner: An analytical approach

AbstractIn unsaturated composite liners, the heterogeneous distribution of water content and capillarity are common phenomena with remarkable influence on the transport parameters. This study presents an analytical solution to assess the effect of unsaturation‐dependent retardation and capillarity on the organic contaminant diffusion through an unsaturated composite liner. The solution is obtained by the generalized integral transform technique and is verified against other analytical and numerical solutions. Particular attention is paid to the potential errors in predicting organic contaminant transport caused by unsaturation‐independent retardation factor assumption. The results show that neglecting saturation‐dependent retardation may cause considerable errors. The considerations of capillary fringe and degradation also have significant effects on the errors. In addition, a simplified solution based on a new averaged retardation factor is proposed and its performance is evaluated.

Multi-Dimensional Machine Learning Analysis of Polyaniline Films Using Stitched Hyperspectral ToF-SIMS Data

The self-organizing map with relational perspective mapping (SOM-RPM) is an unsupervised machine learning method that can be used to visualize and interpret high-dimensional hyperspectral data. We have previously used SOM-RPM for the analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) hyperspectral images and three-dimensional (3D) depth profiles. This provides insightful visualization of features and trends of 3D depth profile data, using a slice-by-slice view, which can be useful for highlighting structural flaws including molecular characteristics and transport of contaminants to a buried interface and characterization of spectra. Here, we apply SOM-RPM to stitched ToF-SIMS data sets, whereby the stitched data are used to train the same model to provide a direct comparison in both 2D and 3D. We conduct an analysis of spin-coated polyaniline (PANI) films on indium tin oxide-coated glass slides that were subjected to heat treatment under atmospheric conditions to model PANI as a conformal aerospace industry coating. Replicates were shown to be precisely equivalent, both spatially and by composition, indicating a clear threshold for annealing of the film. Quantitative assessment was performed on the chemical breakdown trends accompanying annealing based on peak ratios, while spectral analysis alone shows only very subtle differences which are difficult to evaluate quantitatively. The SOM-RPM method considers data sets in their totality and highlights subtle differences between samples often simply differences in peak intensity ratios.

Efficient Calibration of Groundwater Contaminant Transport Models Using Bayesian Optimization.

Numerical modeling is a significant tool to understand the dynamic characteristics of contaminants transport in groundwater. The automatic calibration of highly parametrized and computationally intensive numerical models for the simulation of contaminant transport in the groundwater flow system is a challenging task. While existing methods use general optimization techniques to achieve automatic calibration, the large numbers of numerical model evaluations required in the calibration process lead to high computing overhead and limit the efficiency of model calibration. This paper presents a Bayesian optimization (BO) method for efficient calibration of numerical models of groundwater contaminant transport. A Bayes model is built to fully represent calibration criteria and derive the objective function for model calibration. The efficiency of model calibration is made possible by the probabilistic surrogate model and the expected improvement acquisition function in BO. The probabilistic surrogate model approximates the computationally expensive objective function with a closed-form expression that can be computed efficiently, while the expected improvement acquisition function proposes the most promising model parameters to improve the fitness to the calibration criteria and reduce the uncertainty of the surrogate model. These schemes allow us to find the optimized model parameters effectively by using a small number of numerical model evaluations. Two case studies for the calibration of the Cr(VI) transport model demonstrate that the BO method is effective and efficient in the inversion of hypothetical model parameters, the minimization of the objective function, and the adaptation of different model calibration criteria. Specifically, this promising performance is achieved within 200 numerical model evaluations, which substantially reduces the computing budget for model calibration.

A stable RBF-FD method for solving two-dimensional variable-order time fractional advection-diffusion equation

The meshless method is used in this paper to provide a numerical solution for a two-dimensional variable-order time fractional advection-diffusion equation. The radial basis function and the finite difference scheme on two-dimensional (2-D) arbitrary domains are the foundations of this accurate and reliable meshless approach. The name of this approach is radial basis function-generated finite differences (RBF-FD). The finite difference technique and the RBF-FD approximation are both employed for discrete time and space domains, respectively. The stability and convergence related to the time discretization of this approach are then discussed and theoretically proven. The advantage of the RBF-FD method is that it has high-order convergence rates and its ability to manage sparse node layouts in arbitrary domains. The use of polyharmonic splines (PHS) as a RBF and the absence of the shape parameter are also a positive points for the mentioned method. The effectiveness and precision of the proposed method are examined with three distinct examples, incorporating two model examples and a real-world application of contamination transportation phenomena.

Analytical modeling of contaminant transport along sloping coastal beaches in presence of tidal waves and exponential rainfall infiltration

Coastal beaches are the most important part of the marine environment and are highly influenced by the interactions taking place between groundwater and surface water of any form. Generally, the purity index of the groundwater is based on the concentration of Total Dissolved Solids (TDS) in it. Analytical solutions are the best tools for groundwater flow and transport modeling. However, contaminant transport along the sloping coastal beaches with complex boundaries cannot be addressed with available analytical solutions. In the present study, new analytical models are developed for groundwater flow and contaminant transport for sloping coastal beaches with continuity and tidal boundary conditions. To assess the performance of the new analytical solution and to validate the range of aquifer parameters, numerical simulation is performed using Du-Fort Frankel (DFF) Scheme. Numerical experimentation is carried out using the Tchebycheff and L2 norms. It is observed that the new analytical solution for the contaminant transport gives acceptable results over the wide range of the aquifer parameters. To show the effectiveness of the developed models, two case studies from Indian coastal aquifers namely, Kalpakkam, Tamil Nadu, and Bhavnagar, Gujarat are considered. The profiles of the contaminant concentration are obtained to study the TDS behavior along these complex coastal beaches in the spatiotemporal directions. The results are compared with numerical model results and found to be satisfactory. The combined effect of the initial rainfall and rainfall decay constants showed a significant impact on the concentration of TDS. TDS concentrations are observed to be varying highly with the variation in the tidal constituents and bed slope.

Effect of Salinity and Fractionation Size of Cattle Manure on the Distribution and Transport of Bacterial Contamination in Sandy Soil

Effect of Salinity and Fractionation Size of Cattle Manure on the Distribution and Transport of Bacterial Contamination in Sandy Soil

Simulation of Contaminants Transport from a Dumpsite into Shallow Aquifer of the Upper Deltaic Plain Deposits, Western Niger Delta

Dumpsites are a copious point source of groundwater pollution in the Niger Delta. The objective of this study seeks to assess contaminants transported from a dumpsite around Ekpan into groundwater. Dumpsite leachate and groundwater samples were collected for analysis, and the Modular 3D Solute Transport Model (MT3D) was used to evaluate the impact of leachate on the quality and flow direction of groundwater. The chemical analyses of leachate samples showed significant potential to contaminate groundwater. The pH of greater than 7 and BOD5/COD ratio of less than 0.1 indicated that the dumpsite is old with stabilized leachate. Groundwater models showed that the dumpsite has the propensity for Na and TDS increasing to 1500 and 3700 mg/L respectively after 10 years. The contaminants infiltrate into successive layers beneath the dumpsite and spread laterally in the direction of groundwater flow (southwestern direction). This study revealed that groundwater near the dumpsite did show some significant contamination. A pattern of decreasing concentration of contaminants with distance from the dumpsite was observed due to natural attenuation.

Groundwater contaminant transport modeling using MODFLOW and MT3DMS: a case study in Rajshahi City

Abstract Rapidly growing urbanization and industrialization processes including man-made activities result in groundwater contamination that becomes unsafe for human use. In this study, the groundwater flow and contaminant migration through aquifers in Rajshahi City were modeled using MODFLOW and MT3DMS codes. ModelMuse, a graphical user interface (GUI), is used to run the codes and the hydrological and geological data of the region are used as the input parameters for the model. The travel distance of five selected contaminants such as chromium (Cr), copper (Cu), manganese (Mn), lead (Pb), and zinc (Zn), from the source (e.g. landfill site), were simulated corresponding to travel times of 1, 3, 5, 10, 15, 20, and 50 years. The study results showed that the migration distance of the contaminants increases over time and follows a logarithmic trend. Among the contaminants, the model-predicted results show that the concentration of Cr and Pb in the groundwater varies more than 90% from their standards over the period of 50 years, which suggests that these two pollutants are the prime contaminants polluting groundwater in the coming future. This model can be used as an effective decision-making tool for the monitoring of groundwater contaminant transport for a specific location.