Source Of Groundwater Contamination Research Articles

Sorption and Phase Associations of Chromate and Vanadate with Two Contrasting North Carolina Saprolites

Geogenic chromium (Cr) and vanadium (V) contamination of groundwater in the Piedmont region of North Carolina poses threats to public health. These contaminants are naturally derived from saprolite and aquifer materials, but geochemical variability in these materials makes it difficult to predict specific risks of Cr and V in well water. The objectives of this study were to (1) determine host phases of Cr and V in representative subsurface materials; (2) characterize contaminant binding parameters of chemically variable saprolites; and (3) examine the influence of saprolite chemistry on contaminant sorption, speciation, and phase associations. Isotherm experiments revealed that saprolite samples sorbed roughly an order of magnitude more V than Cr. Chemical extractions and synchrotron-based X-ray fluorescence showed that substantial Cr and V were bound with metal oxide/oxyhydroxides in native and Cr-and V-sorbed saprolites; however, electrostatically bound fractions were also present, representing potentially important sources of groundwater contamination. X-ray absorption-near-edge-structure spectroscopy indicated that sorbed Cr was found as reduced Cr(III), whereas sorbed V was dominated by the oxidized V(V) and intermediate V(IV) species. Results from this study could be used to help parameterize mechanistic models and improve prediction of the Cr and V contamination potential of shallow aquifers.

Landfill leachate: An invisible threat to soil quality of temperate Himalayas.

Landfills are the most affordable and popular method for managing waste in many parts of the world, However, in most developing nations, including India, the infiltration of hazardous materials from improperly managed dumping site continues to be a significant environmental problem. Around the world, leachate is a significant point source of contamination in numerous environmental media, including soil, groundwater, and surface water. Soil is an important asset as it is the key factor for food production and has tremendous significance in achieving sustainable development goals (SDGs). The contaminants from soil enter into food chain and ultimately reach humans. So in order to prevent the adverse effects of toxic elements on humans, there is need to maintain the soil quality and to prevent deterioration. Keeping in view the consequences of unscientific management of waste, the goal of the experiment was to determine how landfill leachate from Achan landfill affected the soil quality in the temperate Himalayas. All four seasons of the year, viz Spring, Summer, Autumn, and Winter, at four sites viz, Center of dumping site, inside, Outside and Control were monitored. Among sites center was found to have maximum value of EC (3.04 dS/m), Moisture content (42.51%), N (285.43 mg/kg), P (70.07 mg/kg), K (265.71 mg/kg), Ca (957.67 mg/kg), Mg(402.42 mg/kg), Zn (2.02 mg/kg), Fe (10.56 mg/kg), Cu (2.07 mg/kg), Mn (10.73 mg/kg), Pb (85.02 mg/kg), Cd (4.50 mg/kg), Ni (29.04 mg/kg), Cr (23.37 mg/kg), As (14.10 mg/kg). While as the lowest value of all parameters was reported at control site. From the study it is recommended that the waste generated is mostly organic (65-75%), thus can be segregated and treated at source. The waste can be treated at source using microbial consortium technology in order to transform the waste in to wealth in a sustainable way and to prevent the deterioration of soil quality.

The Effect Characterization of Lens on LNAPL Migration Based on High-Density Resistivity Imaging Technique

Light non-aqueous phase liquids (LNAPLs), which include various petroleum products, are a significant source of groundwater contamination globally. Once introduced into the subsurface, these contaminants tend to accumulate in the vadose zone, causing chronic soil and water pollution. The vadose zone often contains lens-shaped bodies with diverse properties that can significantly influence the migration and distribution of LNAPLs. Understanding the interaction between LNAPLs and these lens-shaped bodies is crucial for developing effective environmental management and remediation strategies. Prior research has primarily focused on LNAPL behavior in homogeneous media, with less emphasis on the impact of heterogeneous conditions introduced by lens-shaped bodies. To investigate the impact of lens-shaped structures on the migration of LNAPLs and to assess the specific effects of different types of lens-shaped structures on the distribution characteristics of LNAPL migration, this study simulates the LNAPL leakage process using an indoor two-dimensional sandbox. Three distinct test groups were conducted: one with no lens-shaped aquifer, one with a low-permeability lens, and one with a high-permeability lens. This study employs a combination of oil front curve mapping and high-density resistivity imaging techniques to systematically evaluate how the presence of lens-shaped structures affects the migration behavior, distribution patterns, and corresponding resistivity anomalies of LNAPLs. The results indicate that the migration rate and distribution characteristics of LNAPLs are influenced by the presence of a lens in the gas band of the envelope. The maximum vertical migration distances of the LNAPL are as follows: high-permeability lens (45 cm), no lens-shaped aquifer (40 cm), and low-permeability lens (35 cm). Horizontally, the maximum migration distances of the LNAPL to the upper part of the lens body decreases in the order of low-permeability lens, high-permeability lens, and no lens-shaped aquifer. The low-permeability lens impedes the vertical migration of the LNAPL, significantly affecting its migration path. It creates a flow around effect, hindering the downward migration of the LNAPL. In contrast, the high-permeability lens has a weaker retention effect and creates preferential flow paths, promoting the downward migration of the LNAPL. Under conditions with no lens-shaped aquifer and a high-permeability lens, the region of positive resistivity change rate is symmetrical around the axis where the injection point is located. Future research should explore the impact of various LNAPL types, lens geometries, and water table fluctuations on migration patterns. Incorporating numerical simulations could provide deeper insights into the mechanisms controlling LNAPL migration in heterogeneous subsurface environments.

Co-tracing of groundwater emerging and conventional contaminants in karst area of Southwest China: Distribution, source, and source-specific health risks

Groundwater pollution, especially the co-contamination of emerging contaminants (ECs) and conventional contaminants, is increasingly recognized as a critical global concern. Karst aquifers are found throughout the world, providing critical water sources and sustaining rivers and ecosystems, and are particularly vulnerable to pollution. In this study, the characteristics of the contamination, potential sources, and source-specific health risks related to ECs and trace elements (TEs) in groundwater were investigated in Guiyang (a typical karst city in southwest China). The distribution of TEs followed a descending pattern from urban to suburban to rural areas. Interestingly, the median concentrations (217 ng/L) of Caffeine (CAF) in rural areas surpassed those in urban areas (145 ng/L) and even exceeded these in densely populated areas. Additionally, ECs and TEs in groundwater are significantly influenced by population density and land use types within a 1000-meter buffer zone around the sampling sites. Hierarchical cluster analysis and positive matrix factorization suggested that five principal sources of contamination were identified: domestic wastewater (29.7 %), industrial production (25.2 %), agricultural activities (16.3 %), natural and industrial sources (14.9 %), and pharmaceuticals (13.9 %). Monte Carlo simulations revealed that the non-carcinogenic risks to children in urban areas are significant, with 30.0 % of these risks exceeding the safety threshold. Source-specific health risk assessments indicated that industrial production is the primary source of risk. Furthermore, this study introduces a co-tracing method to trace the sources of groundwater contamination in karst systems. The co-tracing method based on the ECs and TEs allows for a deeper understanding of contaminant transfer and its fate in karst groundwater.

An ensemble optimizer with a stacking ensemble surrogate model for identification of groundwater contamination source

The application of the simulation-optimization method for groundwater contamination source identification (GCSI) encounters two main challenges: the substantial time cost of calling the simulation model, and the limitations on the accuracy of identification results due to the complexity, nonlinearity, and ill-posed nature of the inverse problem. To address these issues, we have innovatively developed an inversion framework based on ensemble learning strategies. This framework comprises a stacking ensemble model (SEM), which integrates three distinct machine learning models (Extremely Randomized Trees, Adaptive Boosting, and Bidirectional Gated Recurrent Unit), and an ensemble optimizer (E-GKSEEFO), which combines two newly proposed swarm intelligence optimizers (Genghis Khan Shark Optimizer and Electric Eel Foraging Optimizer). Specifically, the SEM serves as a surrogate model for the groundwater numerical simulation model. Compared to the original simulation model, it significantly reduces time cost while maintaining accuracy. The E-GKSEEFO, functioning as the search strategy for the optimization model, greatly enhances the accuracy of the optimization results. We have verified the performance of the SEM-E-GKSEEFO ensemble inversion framework through two hypothetical scenarios derived from an actual coal gangue pile. The results are as follows. (1) The SEM exhibits improved fitting performance compared to single machine learning models when dealing with high-dimensional nonlinear data from GCSI. (2) The E-GKSEEFO achieves significantly higher accuracy in the identification results of GCSI than individual optimizers. These findings affirm the effectiveness and superiority of the proposed SEM-E-GKSEEFO ensemble inversion framework.

Pollution source characterization and evaluation of groundwater quality utilizing an integrated approach of Water Quality Index, GIS and multivariate statistical analysis

ABSTRACT The Mid-Gangetic Plain, a vital farmland in India, faces increasing groundwater quality deterioration due to anthropogenic activities. This study aimed to assess groundwater quality and contamination sources in the region utilizing statistical methods. A total of 78 groundwater samples were collected and analyzed using standard methods. The hydrochemistry analysis of samples revealed that several parameters such as Ca2+, Mg2+, HCO3−, NO3−, F− and PO43− surpassed the limits prescribed by the Bureau of Indian Standards (BIS). The principal component analysis yielded three significant factors, explaining 68.96% variation, highlighting geogenic and anthropogenic influences on groundwater chemistry. Hierarchical cluster analysis categorized groundwater into three clusters based on the parameters with similar trends of variation. Furthermore, discriminant analysis identified four significant variables (Mg2+, F−, Cl− and NO3−) responsible for creating the distinction among the identified clusters. Hydrogeochemical categorization and multivariate statistical analyses indicated that rock–water interaction, weathering, leaching and anthropogenic activities collectively influenced groundwater quality throughout the studied region. The Water Quality Index reveals that 59% of samples have good water quality, while 41% exhibit poor quality predominantly concentrated in the south-western, south-eastern and central regions. This study demonstrates the efficacy of statistical techniques to interpret complex datasets and grasp water quality dynamics, enhancing groundwater management.

Simultaneous identification of groundwater contamination source information, model parameters, and boundary conditions under an unknown boundary mode

Simultaneous identification of groundwater contamination source information, model parameters, and boundary conditions under an unknown boundary mode

Highly efficient remediation of Sb-contaminated mine drainage using nano-calcium peroxide induced co-precipitation treatment

Antimony (Sb) mines provide an important metalloid to support the electronic industry but also leave extensive leakage into the aqueous environment. The toxic and carcinogenic heavy metal Sb from mine drainage constitutes a major source of groundwater and soil contamination, indicating intense demands for the environmental-friendly and cost-effective technology to achieve effective remediation. The emerging nanotechnology represents a new technological prospect. In this study, nano-calcium peroxide (nano-CaO2, 8–24 nm) was successfully fabricated by an optimized pH-regulated chemical precipitation method and was used for the lab-based experiments of nano-remediation, with the Sb-contaminated mine drainage collected on-site from an abandoned mine cavern in the Qingjiagou Antimony Mine, Henan Province, China. It is demonstrated that the specifically prepared nanoscale CaO2 has better solubility and faster reaction rates than the commercial CaO2, resulting in promising effect for the nano-remediation of Sb-contaminated groundwater. 97.8 % of an initial 2.8 mg/L Sb contamination was removed in 5 min under the optimum conditions, consisting of 0.5 mg/mL CaO2, 1.0 mg/mL polymeric ferric sulfate (PFS), an oscillation frequency of 120 rpm and a temperature of 298 K. The batch experiments revealed that the initial pH, oscillation frequency, temperature and illumination, as well as hydro-chemical competing ions (i.e., K+, Na+, Ca2+, Mg2+, Mn2+, SO42−, NO3− and Cl−) of the real groundwater all hardly affected the removal efficiency. However, the existence of HCO3− could lead to an obvious impact on the treatment as it would react with the generated OH.

Exploring waterborne viruses in groundwater: Quantification and Virome characterization via passive sampling and targeted enrichment sequencing

Aquifers, which provide drinking water for nearly half the world's population, face significant challenges from microbial contamination, particularly from waterborne viruses such as human adenovirus (HAdV), norovirus (NoV) and enterovirus (EV). This study, conducted as part of the UPWATER project, investigates the sources of urban groundwater contamination using viral passive sampling (VPS) and target enrichment sequencing (TES). We assessed the abundance of eight viral pathogens (HAdV, EV, NoV genogroup I and II, rotavirus, influenza A virus, hepatitis E virus and SARS-CoV-2) and investigated the virome diversity of groundwater in the aquifer of the Besòs River Delta in Catalonia. Over a period of 7 months, we collected 114 samples from the aquifer using nylon and nitrocellulose membranes to adsorb viruses over a 10-day period. Human faecal contamination was detected in nearly 50 % of the groundwater samples, with mean HAdV total counts ranging from 1.23E+02 to 3.66E+03 GC, and occasional detections of EV and NoV GI and GII. In addition, deep sequencing revealed a diverse virome in the aquifer, with detection of human pathogens, including adenovirus, astrovirus, calicivirus, enterovirus, herpesvirus, papillomavirus and rotavirus. Time-integrated sampling using VPS increases the likelihood of virus detection and, when combined with TES, can provide a deeper understanding of virus prevalence in this important water compartment. This approach is expected to streamline long-term monitoring efforts and enable small communities or water managers with limited resources to effectively manage their groundwater reservoirs.

Simulation of coupled groundwater flow and contaminant transport using quintic B-spline collocation method

PurposeThe purpose of this study is to introduce a new numerical scheme with no stability condition and high-order accuracy for the solution of two-dimensional coupled groundwater flow and transport simulation problems with regular and irregular geometries and compare the results with widely acceptable programs such as Modular Three-Dimensional Finite-Difference Ground-Water Flow Model (MODFLOW) and Modular Three-Dimensional Multispecies Transport Model (MT3DMS).Design/methodology/approachThe newly proposed numerical scheme is based on the method of lines (MOL) approach and uses high-order approximations both in space and time. Quintic B-spline (QBS) functions are used in space to transform partial differential equations, representing the relevant physical phenomena in the system of ordinary differential equations. Then this system is solved with the DOPRI5 algorithm that requires no stability condition. The obtained results are compared with the results of the MODFLOW and MT3DMS programs to verify the accuracy of the proposed scheme.FindingsThe results indicate that the proposed numerical scheme can successfully simulate the two-dimensional coupled groundwater flow and transport problems with complex geometry and parameter structures. All the results are in good agreement with the reference solutions.Originality/valueTo the best of the authors' knowledge, the QBS-DOPRI5 method is used for the first time for solving two-dimensional coupled groundwater flow and transport problems with complex geometries and can be extended to high-dimensional problems. In the future, considering the success of the proposed numerical scheme, it can be used successfully for the identification of groundwater contaminant source characteristics.

Breaking the mold of simulation-optimization: Direct forward machine learning methods for groundwater contaminant source identification

Groundwater Contaminant Source Identification (GCSI) is important for addressing environmental concerns. Currently, it is widely achieved using the Simulation/Optimization (S/O) method. However, the utilization of optimization techniques may cause high computation costs and parameter equifinality issues. We introduce two innovative GCSI methods, Direct Forward Machine Learning (DFML) and One-Hot Machine Learning (OHML), utilizing the classical Artificial Neuro Network (ANN) model in the machine learning field. Both new methods eliminate the need for an optimization algorithm in GCSI, thus reducing the construction effort and improving efficiency. The first method, DFML can directly estimate eight parameters, providing valuable insights into the contaminant location, release history, and aquifer properties. The second method, OHML can estimate the spatial probability distribution of the contaminant location through one-hot encoding, addressing uncertainties in the contaminant source location estimations realized by DFML.Evaluations demonstrate that both methods exhibit satisfying performances. DFML can estimate contaminant location and aquifer properties with high accuracy; and estimate the release history information with moderate accuracy. The OHML correctly assigns the higher contaminant probabilities to regions containing true contaminant locations. The combination of DFML and OHML offers a comprehensive framework. This study contributes valid methodologies to the GCSI field.

Source differentiation of BTEX compounds in groundwater contaminated due to refinery activities

This study aims to identify sources of groundwater contamination in a refinery area using integrated compound-specific stable isotope analysis (CSIA), oil fingerprinting techniques, hydrogeological data, and distillation analysis. The investigations focused on determination of the origin of benzene, toluene, ethylbenzene, and xylenes (BTEX), and aliphatic hydrocarbons as well. Groundwater and floating oil samples were collected from extraction wells for analysis. Results indicate presence of active leaks in both the northern and southern zones. In the northern zone, toluene was found to primarily originate from oil products like aviation turbine kerosene (ATK or aviation fuel), kerosene, regular gasoline, and diesel fuel. Additionally, stable isotope ratios of carbon and hydrogen for ethylbenzene, o-xylene (ortho xylene) and p-xylene (para xylene) in zone A suggested the pollution originated from gasoline within the northern zone. The origin of super gasoline (with higher octane) identified in southern zone using δ13C and δ2H values of toluene in the floating oil and groundwater samples. Further, biodegradation of toluene likely occurred in southern zone according to δ13C and δ2H. The findings underscore the critical importance of integrating CSIA and fingerprinting techniques to effectively address the challenges of source identification and relying solely on each method independently is insufficient. Accordingly, comparing the GC-MS results of floating oil samples with ATK and jet fuel (JP4) standards can be effectively utilized for source differentiation. However, this method showed no practical application to distinguish different types of diesel or gasoline. The accuracy and reliability of source identification of BTEX compounds may significantly improve when hydrogeological data incorporates with stable isotopes analysis. Additionally, the results of this study will elevate the procedures for fuel-related contaminants source identification of the polluted groundwater that is crucial to develop effective remediation strategies.

Groundwater Contamination Source Recognition Based on a Two-Stage Inversion Framework with a Deep Learning Surrogate

Groundwater contamination source recognition is an important prerequisite for subsequent remediation efforts. To overcome the limitations of single inversion methods, this study proposed a two-stage inversion framework by integrating two primary inversion approaches—simulation-optimization and simulation-data assimilation—thereby enhancing inversion accuracy. In the first stage, the ensemble smoother with multiple data assimilation method (a type of simulation-data assimilation) conducted a global broad search to provide better initial values and ranges for the second stage. In the subsequent stage, a collective decision optimization algorithm (a type of simulation-optimization) was used for a refined deep search, further enhancing the final inversion accuracy. Additionally, a deep learning method, the multilayer perceptron, was utilized to establish a surrogate of the simulation model, reducing computational costs. These theories and methods were applied and validated in a hypothetical scenario for the synchronous identification of the contamination source and boundary conditions. The results demonstrated that the proposed two-stage inversion framework significantly improved search accuracy compared to single inversion methods, with a mean relative error and mean absolute error of just 4.95% and 0.1756, respectively. Moreover, the multilayer perceptron surrogate model offered greater approximation accuracy to the simulation model than the traditional shallow learning surrogate model. Specifically, the coefficient of determination, mean relative error, mean absolute error, and root mean square error were 0.9860, 9.72%, 0.1727, and 0.47, respectively, highlighting its significant advantages. The findings of this study can provide more reliable technical support for practical case applications and improve subsequent remediation efficiency.

Identification of hydraulic conductivity and groundwater contamination sources with an Unscented Kalman Smoother

Identification of hydraulic conductivity and groundwater contamination sources with an Unscented Kalman Smoother

Determination of contamination sources and geochemical reactions in groundwater of a mine area using Cu, Zn, and S–O isotopes

To determine contamination sources and pathways, the use of multiple isotopes, including metal isotopes, can increase the reliability of environmental forensic techniques. This study differentiated contamination sources in groundwater of a mine area and elucidated geochemical processes using Cu, Zn, S–O, and O–H isotopes. Sulfate reduction and sulfide precipitation were elucidated using concentrations of dissolved sulfides, δ34SSO4, δ18OSO4, and δ66Zn. The overlying contaminated soil was possibly responsible for the contamination of groundwater at <5 mbgl, which was suggested by low δ65Cu values (0.419–1.120‰) reflecting those of soil (0.279–1.115‰). The existence of dissolved Cu as Cu(I) may prevent the increase in δ65Cu during leaching of contaminated soil in the sulfate-reducing environment. In contrast, the groundwater at >5 mbgl seemed to be highly affected by the contamination plume from the adit water, which was suggested by high SO42− concentrations (407–447 mg L−1) and δ65Cu (0.252–2.275‰) and δ66Zn (−0.105‰–0.362‰) values at a multilevel sampler approaching those of the adit seepages. Additionally, the O–H isotopic ratios were distinguished between <5 mbgl and >5 mbgl. Using δ65Cu and δ66Zn to support the determination of groundwater contamination sources may be encouraged, particularly where the isotopic signatures are distinct for each source.

Application of a Bayesian-Based Integrated Approach for Groundwater Contamination Sources Parameter Identification Considering Observation Error

Groundwater contamination source (GCS) parameter identification can help with controlling groundwater contamination. It is proverbial that groundwater contamination concentration observation errors have a significant impact on identification results, but few studies have adequately quantified the specific impact of the errors in contamination concentration observations on identification results. For this reason, this study developed a Bayesian-based integrated approach, which integrated Markov chain Monte Carlo (MCMC), relative entropy (RE), Multi-Layer Perceptron (MLP), and the surrogate model, to identify the unknown GCS parameters while quantifying the specific impact of the observation errors on identification results. Firstly, different contamination concentration observation error situations were set for subsequent research. Then, the Bayesian inversion approach based on MCMC was used for GCS parameter identification for different error situations. Finally, RE was applied to quantify the differences in the identification results of each GCS parameter under different error situations. Meanwhile, MLP was utilized to build a surrogate model to replace the original groundwater numerical simulation model in the GCS parameter identification processes of these error situations, which was to reduce the computational time and load. The developed approach was applied to two hypothetical numerical case studies involving homogeneous and heterogeneous cases. The results showed that RE could effectively quantify the differences caused by contamination concentration observation errors, and the changing trends of the RE values for GCS parameters were directly related to their sensitivity. The established MLP surrogate model could significantly reduce the computational load and time for GCS parameter identification. Overall, this study highlights that the developed approach represents a promising solution for GCS parameter identification considering observation errors.

Seasonal Variation and Antibiotic Susceptibility Patterns of Bacteriological Parameters in Groundwater Sources in Oyi LGA, Anambra State, Nigeria

Potable water is essential for health and survival of all life forms. Contamination of groundwater sources by microbes, organic and inorganic matter has continued to negatively affect the well-being of most people in developing countries such as Nigeria. Thus, this study aimed to determine the seasonal variation and antibiotic susceptibility patterns of bacteriological parameters in groundwater sources in Oyi LGA, Anambra State, Nigeria. Hand-dug well and borehole water sites were randomly selected in five communities of Oyi LGA, and water samples were aseptically collected and analyzed using standard microbiological procedures. The total heterotrophic counts of bacteria isolated from groundwater samples during both seasons were between 1.3×102 and 2.87×104 CFU/mL, total coliform counts ranged from 2 to 165 MPN/100mL and faecal coliform ranged from 0 to 32 MPN/100mL. Probable bacterial isolates in groundwater samples were Escherichia coli, Klebsiella aerogenes, Shigella flexneri, Enterococcus faecalis and Enterobacter sp. Seasonal variations showed there were more bacterial loads in sampled water during wet season than dry season. Antibiotic susceptibility test revealed that all the bacterial isolates were sensitive to Levofloxacin and Ofloxacin, and are recommended for treatment of diseases caused by some of these pathogens. Shigella flexneri exhibited highest susceptibility (60%) to the antibiotics, while Enterobacter faecalis showed highest resistance (70%). Escherichia coli exhibited 50% susceptibility and 50% resistance. The sampled water sources did not meet the WHO and NSDWQ guidelines for drinking water, and therefore not fit for human consumption without adequate treatment.

Development of a multi-level factorial data assimilation approach for groundwater contaminant sources identification

The ensemble Kalman filter (EnKF) is recognized as a widely used approach for groundwater contamination sources (GCSs) parameters identification. The identification accuracy is affected by the quantification of sources of uncertainty (such as random disturbances on model inputs and measurements) in the assimilation process, but few studies have revealed the contributions of different sources of uncertainty on identification accuracy. To this end, this study developed an innovative multi-level factorial data assimilation (MFDA) approach, which integrates ensemble Kalman filtering and multi-level factorial analysis, to comprehensively reveal the impact of sources of uncertainty on the GCSs parameters identification accuracy for the first time. Not only did it explore the direct impact of individual sources of uncertainty on the GCSs parameters identification accuracy, more importantly, it also revealed the composite ones from multi-parameter interactions among these sources of uncertainty. The developed MFDA approach quantified the contributions of multiple sources of uncertainty, including model input data error (i.e. precipitation), model parameter error (i.e. hydraulic conductivity), contaminant concentration measurements error, and ensemble size. And the feasibility of the developed approach was verified by two hypothetical numerical case studies. The results indicated that: i) it was critically important to reduce the contamination concentration measurement errors to improve GCSs parameter identification accuracy. ii) model parameter and model input data were not sensitive to the identification accuracy. iii) the interaction between sources of uncertainty significantly affects the identification accuracy, which illustrated the interaction cannot be ignored in the identification process. Overall, this study highlights that the developed MFDA approach represents a promising solution for improving GCSs parameters identification accuracy.

Arsenic contamination of groundwater sources and a comparative study of its remediation by various conventional and recent approaches in Nepal

AbstractGroundwater is a major source of drinking water for millions of people across the globe. Most of these sources are reported to contain elevated levels of arsenic. Human health and ecosystems are adversely affected by arsenic‐contaminated drinking water. So, arsenic‐contaminated groundwater has become a major global health concern. This paper reviews groundwater contamination sources and gives an outline of arsenic contamination in different places in Nepal. It also critically reviews the health hazards associated with arsenic poisoning. A comparative study has been made regarding different aspects of conventional and recent technologies, and the one that is the most suitable for remediation of contaminated water has been recommended.

A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity

AbstractAccurate identification of groundwater contamination sources is important for designing efficacious site remediation strategies. Currently, the methods for identifying contamination sources mainly fall into three distinct categories: simulation optimization, Bayesian inference, and data assimilation. Each method has its own advantages and disadvantages under specific site conditions. To evaluate the applicability of these methods, we chose one representative inversion algorithm from each category, namely the Improved Butterfly Optimization Algorithm (IBOA) for simulation optimization, the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) for data assimilation, and the DiffeRential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM(ZS)) for Bayesian inference. We conducted a comprehensive evaluation of these methods' performance under different model complexities, employing a surrogate model as a substitute for the complex forward model. By addressing two distinct problems involving conservative pollutant transport and Light Non‐Aqueous Phase Liquid (LNAPL) transport with biodegradation, we employed four criteria (elapsed time, result accuracy, posterior probability distribution, and noise resistance) for evaluation. The findings unequivocally indicate that DREAM(ZS) outperforms others in terms of result accuracy and posterior probability distribution. It also adeptly navigates the interrelations among disparate unknown variables. The strength of ES‐MDA lies in its efficiency. It achieves relatively satisfactory results with a reduced computational burden. In contrast, IBOA underperforms in both test problems. In terms of resistance to noise, both DREAM(ZS) and ES‐MDA perform better than IBOA does.