Contaminant Transport Model Research Articles

Time‐lapse inversion of self‐potential data through particle filtering

AbstractIn environmental sciences, comprehending the movement of subsurface contaminants is crucial for formulating effective remediation measures. The self‐potential (SP) method has become a common tool for delineating landfill contamination plumes. Contaminant diffusion or migration represents dynamic processes, with corresponding SP responses evolving over time. However, conventional SP interpretation approaches have predominantly relied on static single‐frame inversion, overlooking the temporal correlation in time‐series SP data and resulting in cumulative errors. To tackle this challenge, we introduce a novel method for time‐lapse inversion of SP data leveraging particle filtering. This approach recursively refines the priori state model through posteriori observations to achieve precise estimations of dynamic models. Specifically, a spherical polarization model is deployed to establish the state equations of underground contaminant diffusion and transport models, whereas the observation model is derived through forward modeling. The proposed method is validated using two synthetic examples and one lab‐measured dataset. The findings demonstrate the efficacy of the time‐lapse inversion algorithm in precisely estimating dynamic models, outperforming static single‐frame inversion based on the particle swarm optimization algorithm. The posteriori distribution of particles approximates a bell‐shaped distribution, with the true state closely positioned near the peak probability. Therefore, we affirm that conducting time‐lapse inversion of time‐series SP data through particle filtering is an effective and dependable approach for accurately estimating dynamic model states.

Unraveling the complex dynamics of temporal moments for multispecies contaminant transport in saturated porous media: A global sensitivity analysis approach

In this study, we focus on the application of global sensitivity analysis (GSA) to gain insight into the influence of model parameters on the outputs of multispecies contaminant transport model. In this context, employing GSA methods (Morris and FAST) allows us to comprehend the relative significance of each input parameter in the model. We specifically analyzed the effects of model parameters on the concentration breakthrough curves and associated temporal moments of concentration of contaminant species. The spatial variability of sensitivity indexes from Morris and FAST methods for parent and daughter contaminant species is also investigated. We represent uncertainties of longitudinal dispersivity, porosity, first-order degradation constants of parent and daughter contaminant species, and sorption distribution coefficient of parent species into the coupled multispecies contaminant transport model and GSA framework. Our results show a complex interplay between different model parameters and their varying influences on different output metrics. The ranking of parameters based on GSA shows that (i) the contaminant mass recovery of final daughter species of three species chain reaction is governed by the first-order degradation coefficient of parent and first daughter species; (ii) dispersivity and porosity are critical parameters that govern the mean residence time and variance of breakthrough curve of final daughter species; (iii) the coefficient of skewness is more sensitive to dispersivity, sorption distribution coefficient, and first-order degradation coefficient of parent species as compared to other model parameters. The variation in the sensitivity indices among contaminant species and with travel distance is observed. This study highlights the significance of global sensitivity analysis when simulating the multiple species contaminants in saturated porous media.

Microbial community storm dynamics signal sources of "old" stream water.

Accurate characterization of the movement of water through catchments, particularly during precipitation event response, is critical for hydrological efforts such as contaminant transport modeling or prediction of extreme flows. Abiotic hydrogeochemical tracers are commonly used to track sources and ages of surface waters but provide limited details about transit pathways or the spatial dynamics of water storage and release. Alternatively, biotic material in streams is derived from thousands of taxa originating from a variety of environments within watersheds, including groundwater, sediment, and upslope terrestrial environments, and this material can be characterized with genetic sequencing and bioinformatics. We analyzed the stable water isotopes (δ18O and δ2H) and microbiome composition (16S rRNA gene amplicon sequencing) of the Marys River of western Oregon, USA during an early season storm to describe the processes, storage, and flowpaths that shape surface water hydrology. Stable water isotopes (δ18O and δ2H) typified an event response in which stream water is composed largely of 'old' water introduced to the catchment before the storm, a common though not well understood phenomenon. In contrast, microbial biodiversity spiked during the storm, consisting of early- and late-event communities clearly distinguishable from pre-event communities. We applied concentration-discharge (cQ) analysis to individual microbial taxa and found that most Alphaproteobacteria sequences were positively correlated (i.e., were mobilized) with discharge, whereas most sequences from phyla Gammaproteobacteria and Bacteroidota were negatively correlated with discharge (i.e., were diluted). Source predictions using the prokaryote habitat preference database ProkAtlas found that freshwater-associated microbes composed a smaller fraction of the microbial community during the stream rise and a larger fraction during the recession, while soil and biofilm-associated microbes increased during the storm and remained high during recession. This suggests that the "old" water discharged during the storm was likely stored and released from, or passed through, soil- and biofilm-rich environments, demonstrating that this approach adds new, biologically derived tracer information about the hydrologic pathways active during and after this event. Overall, this study demonstrates an approach for integrating information-rich DNA into water resource investigations, incorporating tools from both hydrology and microbiology to demonstrate that microbial DNA is useful not only as an indicator of biodiversity but also functions as an innovative hydrologic tracer.

Robust estimation of hydrogeological parameters from wireline logs usingsemi-supervised deep neural networks assisted with global optimization-based regression methods

Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AE-NN), assisted with global optimization methods for estimating hydrogeological parameters in the Quaternary aquifer system in the Debrecen area, Hungary. Traditional methods for estimating aquifer parameters typically depend on field experiments and laboratory analyses, which are both costly and time-consuming, and often fail to account for the heterogeneity of groundwater formations. In this study, deep AE-NN models are trained to extract latent space (LS) representations that capture key features from the available well logs, including spontaneous potential (SP), natural gamma ray (NGR), shallow resistivity (RS), and deep resistivity (RD). The LS log is then correlated with shale volume and hydraulic conductivity, as determined by the Larionov and Csókás methods, respectively. Regression analysis revealed a Gaussian relationship between the LS log and shale volume and a negative nonlinear relationship with hydraulic conductivity. Global optimization methods, including simulated annealing (SA) and particle swarm optimization (PSO), were used to refine the regression parameters, enhancing the predictive capabilities of the models. The results demonstrated that AE-NN assisted with global optimization methods can be effectively used to estimate shale volume and hydraulic conductivity, proposing a novel and independent approach for estimating hydrogeological parameters critical to groundwater flow and contaminant transport modeling.

Sequential model identification with reversible jump ensemble data assimilation method

In data assimilation (DA) schemes, the form representing the processes in the evolution models are pre-determined except some parameters to be estimated. In some applications, such as the contaminant solute transport model and the gas reservoir model, the modes in the equations within the evolution model cannot be predetermined from the outset and may change with the time. We propose a framework of sequential DA method named Reversible Jump Ensemble Filter (RJEnF) to identify the governing modes of the evolution model over time. The main idea is to introduce the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method to the DA schemes to fit the situation where the modes of the evolution model are unknown and the dimension of the parameters is changing. Our framework allows us to identify the modes in the evolution model and their changes, as well as estimate the parameters and states of the dynamic system. Numerical experiments are conducted and the results show that our framework can effectively identify the underlying evolution models and increase the predictive accuracy of DA methods.

Finite difference solution for contaminant transport through a GCL composite cutoff wall-aquifer system considering semipermeable membrane effect

Semipermeable membrane effect is an important factor affecting the contaminant transport behavior through the bentonite-based barriers for waste containment. However, this effect has been ignored in majority of the existing analytical and numerical studies on contaminant transport, although it has been widely demonstrated in laboratory and field tests. Taking into account the semipermeable membrane effect, this study presents a numerical model for one-dimensional contaminant transport through a geosynthetic clay liner (GCL) composite cutoff wall-aquifer system consisting of a bentonite-based wall, a GCL and an aquifer. The finite difference method is used to obtain the solution. The numerical solution is validated against the laboratory experimental data, the existing analytical solution and COMSOL software simulations. A series of parametric analyses are conducted using the proposed numerical solution. The results indicate that the semipermeable membrane effect has a significant impact on the contaminant transport behavior and ignoring this effect can greatly underestimate the contaminant breakthrough time and overestimate contaminant outflow. For the conditions examined in this paper, the inclusion of semipermeable membrane effect results in a 7.1%∼78.6% increase in breakthrough time and a 5.3%∼40.7% decrease in mass flux, and the semipermeable membrane effect in bentonite-based wall, relative to that in the GCL, has greater impact on contaminant transport. The concentration-dependent semipermeable membrane efficiency coefficient ω more significantly slows down the contaminant transport relative to the constant ω, particularly under the conditions of low hydraulic conductivity, low source concentration and low hydraulic gradient. Moreover, it’s important to avoid simultaneous deterioration of both the bentonite-based wall and the GCL, otherwise the breakthrough time of the GCL composite cutoff wall can decrease significantly.

Forward and inverse river contaminant transport modeling using group preserving scheme

Environmental concerns have necessitated the development of computational models predicting pollutant dispersion in natural water systems. Due to the ill-posed nature of the inverse contaminant transport equation, solving this equation using all stable and convergent inverse methods is impossible. Factors such as river geometry, unsteady and non-uniform flow, and tidal influences add to the complexity of the inverse problem. These factors have prompted the evaluation of Group Preserving Scheme for environmental applications. The inverse solution method derives a general equation for solving ordinary differential equations by addressing a dynamical system at negative time steps, ensuring convergence. Three test cases have been presented to evaluate Backward Group Preserving Scheme (BGPS). These have included validation using observational data from Missouri River, inverse simulation in a tidal river, and sensitivity analysis of parameters such as pollutant patterns, advection, dispersion, and decay coefficients. The dataset includes calibrated data from Missouri River, which demonstrated high accuracy, with Mean Relative Error (MRE) ranging from 2.8% to 5.0% for the inverse model. Under tidal conditions, accuracy decreases over time but remains robust, with a Nash–Sutcliffe efficiency of 0.77–0.96 and an MRE of 0.9%–5.9%. Sensitivity analysis revealed optimal model performance for Péclet numbers greater than 500. The model performed best with gradual, wide-peaked pollutant patterns and moderate decay rates (Damköhler number between 5 and 10). BGPS proves effective for transport simulation and concentration history reconstruction in complex rivers, including those with tidal influences, offering a robust tool for water contamination analysis in various flow conditions.

Analytical model for two-dimensional contaminant transport in a cut-off wall and aquitard system

The cut-off wall is an effective countermeasure to prevent contaminant transport from a contaminated site. However, the aquitard beneath the cut-off wall may serve as a preferential path, leading to an earlier breakthrough of contaminants in the cut-off wall system. In this study, we present a 2-D analytical model for characterizing the contaminant transport in the cut-off wall and aquitard system. The Green function method and discretization method are used to investigate contaminant migration in a semi-infinite domain. We show that the one-dimensional cut-off wall model may be insufficient to predict contaminant transport when the aquitard hydraulic conductivity (k2) exceeds 5 × 10-10 m/s. The contaminant mass outflow from the aquitard for the case with k2 = 2 × 10-9 m/s may account for 53 % of the total contaminant mass outflow. Increasing k2 from 5 × 10-10 m/s to 5 × 10-9 m/s leads to a 2.9-fold increase of cumulative mass outflow. An approximate fitting formula is proposed to calculate the required minimum thickness of cut-off wall under different type of aquitards. The thicker cut-off wall is required to ensure the cumulative mass outflow will not exceed the allowable value when k2 is relatively high (e.g., 1 × 10-9 m/s).

Modeling Contaminant Transport from a Red Mud Pond – A Case Study from South India

The present study focused on impact assessment of redmud pond on groundwater using numerical groundwater flow and contaminant transport modeling using pertinent data including water quality and 2D electrical resistivity imaging data. The observed groundwater depths range from 1.2 to 25.8 meters below ground level with deeper levels observed in upstream and higher elevated regions in the study area. Elevated pH and electrical conductivity (>3000 μS/cm) characterize the Redmud ponds, while other groundwater samples meet BIS drinking water standards. Major ion concentrations contouring indicates high total dissolved solids (TDS) around Redmud ponds due to leakage and in the downstream due to domestic sewage pollution. Most locations adhere to BIS drinking water limits except for one downstream site. The resistivity data indicated hard formations (>18 m depth) with resistivity >500 Ohm.m, weathered basalt (12-18 m, 60-150 Ohm.m), and saturated water (12 m, 1-5 Ohm.m). Contaminated aquifers (<1 Ohm.m) are detected up to 3 m depth, and noticed accumulating contaminated water (27 m depth) in the NW corner of the Redmud pond from seepage. Predominant groundwater flow from the Redmud pond towards public water bodies and streams is highlighted by simulated contaminant transport model. Collaboration and comprehensive management are recommended to protect the watershed’s environmental integrity and public health.

Determination of Contaminant Transport Parameters for a Local Aquifer by Numerical Modeling of Two Plumes: Trichloroethylene and Hexavalent Chromium

The municipal wellfield in Collierville, Tennessee, is contaminated with trichloroethylene (TCE) and hexavalent chromium (Cr (VI)) due to industrial operations dating back to the 1970s and 1980s. This study aims to elucidate the aquifer’s contaminant transport mechanisms by determining longitudinal and transverse dispersivities through inverse modeling. Utilizing MT3DMS for contaminant transport simulation, based on a well-calibrated groundwater flow model, and leveraging Python’s multiprocessing library for efficiency, the study employs a trial-and-error methodology. Key findings reveal that longitudinal dispersivity values range from 5.5 m near the source to 20.5 m further away, with horizontal and vertical transverse dispersivities between 0.28 m and 3.88 m and between 0.03 m and 0.08 m, respectively. These insights into the aquifer’s dispersivity coefficients, which reflect the scale-dependent nature of longitudinal dispersivity, are crucial for optimizing remediation strategies and achieving cleanup goals. This study underscores the importance of accurate parameter estimation in contaminant transport modeling and contributes to a better understanding of contaminant dynamics in the Collierville wellfield.

Contaminant Transport Modeling and Source Attribution With Attention‐Based Graph Neural Network

AbstractGroundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution.

Hydrogeological assessment and contaminant transport modelling of Enyimba landfill site in Aba, Nigeria

ABSTRACT Commercial and industrial growth in cities generates hazardous waste, often disposed of in open dump sites, contaminating groundwater. Understanding pollutant movement and numerical models are crucial for effective water resource management. This study assessed hydrogeological dynamics and pollutant transfer in the Enyimba landfill site, Nigeria, using field surveys and contaminant modelling to understand groundwater quality and arsenic contamination spread. The research technique included field and laboratory experiments, as well as water sampling during both dry and wet seasons, to evaluate groundwater quality. The geochemical examination of groundwater samples revealed the amounts of several metals using atomic absorption spectrophotometry. The study used contaminant transport modelling with Visual MODFLOW Flex 6.1 and MT3DMS software to simulate groundwater flow and solute transport within the aquifer layers. Predictive simulations over a 20-year timeframe demonstrated the progressive spread of arsenic pollution from the landfill, resulting in an ellipsoid-shaped plume stretching from its source. Arsenic levels have gradually increased, posing a serious threat to groundwater quality and human health. The research highlights the need for remedial measures to reduce groundwater pollution, including waste recovery and recycling facilities and geo-membrane use. It emphasizes proactive actions to protect groundwater supplies and promote environmental sustainability in the Enyimba landfill site.

Arsenic contamination in groundwater of moribund delta of Bengal basin: Quantitative assessment through adsorption kinetics and contaminant transport modelling

Arsenic contamination in groundwater of moribund delta of Bengal basin: Quantitative assessment through adsorption kinetics and contaminant transport modelling

Physicochemical and Heavy Metal Analysis of Industrial Pollution Model Calibration and Validation Using Field Measurements

This study assessed industrial pollution impacts on rivers near Port Harcourt, Nigeria using physicochemical analysis, heavy metal data, and contaminant transport modeling. Key findings show the rivers are severely degraded with extreme levels of heavy metals, low dissolved oxygen, high conductivity, nutrients, and fecal bacteria exceeding water quality guidelines. Uncontrolled effluent discharges from industries are primarily responsible. A first-order advection-diffusion model reliably predicted the rapid initial dilution and slower downstream attenuation of metals like magnesium and cadmium. The model was successfully calibrated and validated using field measurements to determine key transport parameters including dispersion coefficients. The severe contamination indicates current effluent treatment and regulations are inadequate and require urgent strengthening to control sources, expand monitoring, remediate contamination, restore habitats, and protect ecosystem and public health. Sustained engagement of government, industry, communities and researchers is essential to devise integrated solutions that improve water quality. The modeling provides quantitative guidance on pollution impacts and mitigation needs. Further work should refine predictions, establish ecological thresholds, and validate model results with biomonitoring to support evidence-based, adaptive management.

Contaminant transport modelling of heavy metal pollutants in soil and groundwater: An example at a non-ferrous smelter site

Contaminant transport modelling of heavy metal pollutants in soil and groundwater: An example at a non-ferrous smelter site

Evaluation of an Impulse-Response Emulator for Groundwater Contaminant Transport Modeling.

There is a significant need to develop decision support tools capable of delivering accurate representations of environmental conditions, such as ground and surface water solute concentrations, in a timely and computationally efficient manner. Such tools can be leveraged to assess a large number of potential management strategies for mitigating non-point source pollutants. Here, we assess the effectiveness of the impulse-response emulation approach to approximate process-based groundwater model estimates of solute transport from MODFLOW and MT3D over a wide range of model inputs and parameters, with the goal of assessing where in parameter space the assumptions underlying this emulation approach are valid. The impulse-response emulator was developed using the sensitivity analysis utilities in the PEST++ software suite and is capable of approximating MODFLOW/MT3D estimates of solute transport over a large portion of the parameter space tested, except in cases where the Courant number is above 0.5. Across all runs tested, the highest percent errors were at the plume fronts. These results suggest that the impulse-response approach may be suitable for emulation of solute transport models for a wide range of cases, except when high-resolution outputs are needed, or when very low concentrations at plume edges are of particular interest.

Lagrangian modelling of reactive contaminant transport in the multi-component marine medium

The particle-tracking method for transporting radionuclides in multicomponent ocean medium (water and multifractional suspended and deposited sediments) is considered using a probabilistic approach for simulating interaction processes between several states of radioactivity. The state transformations as a result of reactions of the first order were described using the master equation for the probability of the particle being in the given state. Transition probabilities between all possible states can be obtained from the numerical solution of the matrix master equation that is derived in the paper. In the first approximation, the Euler method was used to obtain a solution for the next time step. This approach can be applied to any linear system of equations describing phase transitions with any number of states, but it requires small values of the transition probabilities to ensure only a single-phase change during one time step. The paper also focuses on deriving the Lagrangian interface conditions between the water column and bottom deposition. To apply the probabilistic approach, the boundary conditions were considered as the reaction terms in a thin near-bottom interface layer in which boundary conditions were converted into source terms. For this layer, the corresponding master equation was derived to obtain transitional probabilities for particle states. The developed approaches were tested on numerical and analytical solutions of two test cases. It was found that the optimal thickness of the interface layer must be larger than the maximum vertical displacement of the particle during the one-time step, but it must be small enough to approximate the condition of uniform distribution of concentration in this layer.

Advanced discretization techniques for groundwater flow and contaminant transport modeling : Precision analysis and numerical methodologies

This comprehensive study delves into the complex interactions of groundwater flow and contaminant transport, meticulously addressing nonlinearities stemming from porosity variations and concentration-dependent reactions. Leveraging state-of-the-art discretization techniques alongside the Newton-Raphson method, our research provides a highly accurate numerical solution, adept at capturing the intricate interplay between flow dynamics and contaminant migration in realistic environmental scenarios. The comparative analysis spans a spectrum of critical aspects, including convergence rates, the efficacy of discretization techniques, sensitivity analysis, benchmark validation, and a distinctive comparison with Linearized models. Our findings yield valuable insights into hydraulic head distribution patterns, the evolution of contaminant concentrations over time, the nuanced impact on flow velocities, and the temporal evolution of contaminant plumes. Furthermore, we conduct a meticulous analysis of nonlinear source/sink contributions, shedding light on their significance in the coupled groundwater system. The outcomes of this study contribute significantly to the precision and advancement of groundwater modeling methodologies. By unraveling the intricacies of coupled flow and transport with nonlinearities, we aim to provide the scientific community with robust tools for addressing real-world groundwater challenges. This research not only enhances our understanding of complex hydro geological systems but also paves the way for more accurate and reliable predictions in groundwater flow and contaminant transport modeling.

Exposure risk assessment of representative phthalate acid esters and associated plastic debris under the agricultural land use in typical Chinese regions

Phthalate acid esters (PAEs) are frequently detected in the global environment and can cause potential health hazards. In this study, quantitative exposure risk assessment was undertaken to derive soil generic assessment criteria (GAC) for six representative PAEs under the agricultural land use in the evaluated Chinese regions, which coupled multi-media transport and human exposure models based on multiple exposure pathways including vegetables consumption, dermal absorption, ingestion of soil and dust, and the exposure from non-soil sources. It is identified that the PAEs in agricultural soil are dominated by DEHP and DnBP representing 72–96% of the total PAEs. The GAC for BBP and DEHP, calculated on the basis of region-specific exposure parameters and soil properties in various locations, are stringent, signifying greater potential health risks from exposure to them, warranting more rigorous contamination management. The proposed soil GAC for plastic debris are 100, 107, 73 and 88 mg kg−1 for Heilongjiang Province, Beijing City, Jiangsu and Guangdong Provinces respectively. Additionally, the potential risks of 1.68 × 10−6 and 7 × 10−6 are identified for BBP and DEHP in Guangdong Province as indicated by the exceedance of target risk level of 1 × 10−6, with the consumption of vegetables being the dominant contributor to the total estimated PAEs exposure. Overall, this methodology based on the coupled contaminant transport and exposure models incorporating region-specific data provides a technical framework to derive science-based soil GAC for representative PAEs for maintaining and assessing soil quality and food safety under the agricultural land use.

A Semi-Analytical Model of Contaminant Transport in Barrier Systems with Arbitrary Numbers of Layers

In regions with sanitary landfills, unsuitable liner designs can result in significant soil and groundwater contamination, leading to substantial environmental remediation costs. Addressing this challenge, we propose a semi-analytical model for solute transport that uses the advection–dispersion–reaction equation in a multi-layered liner system. A distinctive feature of our model is its ability to account for infiltration velocity, arbitrary numbers of layers, thin layers such as geomembranes, and mass flow. We validated our model against existing published models and applied it to a case study of a real sanitary landfill in the capital of Brazil. Through parametric analyses, we simulated contaminant transport across various layers, including the geomembrane (GM), geosynthetic clay liner (GCL), soil liner (SL), and compacted clay liner (CCL). The analyses showed the importance of choosing the most appropriate construction system based on the location and availability of materials. Considering toluene contamination, a GM molecular diffusion coefficient (DGM) greater than 10−13 m2 s−1 exhibited similar efficiency when compared with CCL (60 cm thick). In addition, the results showed that the liner system may have the same efficiency in changing SL (60 cm thick) for a GCL (1 cm thick).