Source Of Groundwater Contamination Research Articles

Groundwater contamination source identification using improved differential evolution Markov chain algorithm.

The groundwater contamination source identification (GCSI) can provide important bases for the design of pollution remediation plans. The Bayesian theory is commonly used in the GCSI problem. Usually, we use the Markov chain Monte Carlo (MCMC) method to realize the Bayesian framework. However, due to the ill-posed nature of the GCSI and the system model's complexity, the conventional MCMC algorithm is time-consuming and has low accuracy. In this study, we proposed an adaptive mutation differential evolution Markov chain (AM-DEMC) algorithm. In this algorithm, the Kent mapping chaotic sequence method, combined with differential evolution (DE) algorithm, was used to generate the initial population. In the iteration process, we introduced a hybrid mutation strategy to generate the candidate vectors. Moreover, we adaptively adjust the essential parameter F of the AM-DEMC algorithm according to the individual fitness value. For further improving the efficiency of solving the GCSI problem, the Kriging method was used to establish a surrogate model to avoid the enormous computational load associated with the numerical simulation model. Finally, a hypothetical groundwater contamination case was given to verify the effectiveness of the AM-DEMC algorithm. The results indicated that the proposed AM-DEMC algorithm successfully identified the contamination sources' characteristics and simulation model's parameters. It also exhibited stronger search-ability and higher accuracy than the MCMC and DE-MC algorithms.

Occurrence and distribution of antibiotics in groundwater, surface water, and sediment in Xiong'an New Area, China, and their relationship with antibiotic resistance genes

The emergence and pollution of antibiotics in surface water in various regions have drawn widespread concern because of the harm to aquatic ecosystems and human health. In this study, we aim to first investigate contamination and ecological risks of 39 antibiotics in Xiong'an New Area (XANA), China, and then illuminate relative abundances of antibiotic resistance genes (ARGs) and their correlations with antibiotics. The sum of antibiotic concentrations in the water circulation system, including surface water, groundwater, and sediment was 12.71–260.56 ng/L, ND–196.12 ng/L, and 38.03–406.31 ng/g, respectively. In surface water and sediment, cephalosporins and quinolones were the primary antibiotics, accounting for 45% and 16% of the total antibiotic concentrations in surface water and for 62% and 32% of the total antibiotic concentrations in sediment; this suggests a significant interaction between the two media. The antibiotic concentration was the highest in shallow groundwater at depths of <50 m (mean concentration of 79.22 ± 56.46 ng/L), indicating that surface water was a possible source of antibiotic contamination in groundwater. AMX presented the highest risk in both surface and groundwater and should be controlled as a priority. Moreover, the selection pressure of antibiotics on ARGs was discovered in the sediment in XANA, because the enrichment of sulA was significantly correlated with spiramycin and lincomycin and the enrichment of blaOXA-1 was significantly correlated with roxithromycin, ciprofloxacin, ofloxacin, and sulfapyridine. Thus, our investigation revealed potential antibiotic contamination in multiple environmental media in XANA, which should be addressed to prevent more serious pollution.

Delineation of necroleachate pathways using electrical resistivity tomography (ERT): Case study on a cemetery in Brazil

Cemeteries can be considered as potential sources of soil and groundwater contamination in burial sites where there is no collection and treatment system for necroleachate, an effluent rich in organic matter and inorganic elements produced during the decomposition of corpses. Brazilian cemeteries are mostly located in the interior of cities, where collective or family deposits with the absence of necroleachate collection devices predominates. The tropical climate, the recurrence, and the distribution of corpses, combined with soil permeability, are decisive elements in the percolation and distribution of necroleachate in the subsurface. An important tool for the diagnosis and evaluation of organic and inorganic contamination in soil and shallow aquifers is geophysics, through research methods related to the contrast of physical properties in the presence of this type of contaminant. This research consists of a geophysical investigation in a medium-sized cemetery in the southeastern Brazil, using the DC resistivity method using three different electrodes arrays: Wenner, Schlumberger and Dipole-Dipole. Additionally, results were combined with the history of burials in the 5 years prior to the geophysical study. The 2D inversion models confirm the presence of low resistivity anomalies related to the presence of ionic compounds. 3D visualization models with results of the three acquisition arrays are compared to the amount of burial by compartmentalized areas of the cemetery, which clearly reveals a relationship of areas with high and recent recurrence of burials and areas of low resistivity in depth. The results do not present significant differences between arrays, with similarities in terms of location and dimension of the most significant areas of low resistivity.

Exploring the potential of graphene oxide nanosheets for porous media decontamination from cationic dyes

Graphene oxide (GO) nanosheets, often embedded in nano-composites, have been studied as promising materials for waste water purification, in particular to adsorb heavy metals and cationic organic contaminants. However, a broader range of potential applications of GO is still unexplored. This work investigated the potential applicability of GO for enhanced in-situ soil washing of secondary sources of groundwater contamination (i.e. the controlled recirculation of a washing GO suspension via injection/extraction wells). The laboratory study aimed at quantifying the capability of GO to effectively remove adsorbed methylene blue (MB) from contaminated sand. The tests were conducted in simplified conditions (synthetic groundwater at NaCl concentration of 20 mM, silica sand) to better highlight the key mechanisms under study. The results indicated a maximum sorption capacity of 1.6 mgMB/mgGO in moderately alkaline conditions. Even though the adsorption of MB onto GO slightly reduced the GO mobility in the porous medium, a breakthrough higher than 95% was obtained for MB/GO mass ratios up to 0.5. This suggests that a very high recovery of the injected particles should be also expected in the field.

Improving safe sanitation practices using groundwater transport modelling and water quality monitoring data.

In many developing countries, poorly constructed pit latrines are the primary source of groundwater contamination. Approaches are needed to identify site-specific separation distances between domestic wells and pit latrines. In this study, tracer transport simulations are combined with water quality monitoring data to identify separation distances in peri-urban Dar es Salaam. Bivariate correlation and linear regression models were used to find the relationships between (1) simulated tracer and distances from the wells to the nearest pit latrines (2) simulated tracer and observed contaminants (nitrate, E. coli, and total dissolved solids). The results showed a strong correlation between tracer with nitrate and E. coli, with Pearson coefficient (r) values of 0.80 and 0.79, but a weak correlation with total dissolved solids (TDS) (r = 0.23). A strong correlation between tracer and distance for shallow and deep wells (r = -0.96, -0.76) was found. Based on the soil type and aquifer properties in the area, wells must be placed at least 34 m from a pit latrine to minimize contamination. With recent advances in sensor technologies and the availability of low-cost sensors, linking simulated tracer with observed contaminant levels may provide an alternative first approach to quickly assess human health risks associated with groundwater contamination.

The Use of Phosphate Washing Sludge to Recover by Composting the Leachate from the Controlled Landfill

The percolation of rainwater and runoff water through household waste in the dumpsite generally leads to an overabundance of leachate in Moroccan landfills, which is a source of soil, surface water and groundwater contamination. In order to ecologically solve the problem posed by the leachate in the dump site, to safeguard the environment and to contribute to sustainable development, we have carried out this study which aims to study the possibility of composting leachate with green waste and phosphate washing sludge. Various combinations with five substrates (leachate, green waste, sugar lime sludge, phosphate washing sludge and olive mill wastewater) in different proportions were used to build five windrows. A 24 h contact between the phosphate sludge or sugar lime sludge and the leachate took place prior to the addition of the green waste for the construction of the different windrows. This contact time ensured the absorption of a significant portion of the leachate and the disappearance of bad odor. A significant reduction was obtained with streptococci and mesophilic flora after 24 h of contact. The monitoring of the physicochemical parameters throughout the composting process showed that the temperature of the different windrows followed a good pace presenting all composting phases. Moisture, pH, C/N ratio and the percentage of degradation of the organic matter conformed to the quality standards of the compost. The combinations of the alkaline treatment and the composting process allowed a significant hygienization of the leachate. The results of the humification parameters and the E4/E6 ratio suggest that the composts obtained with phosphate sludge were the most stable and mature and can be used in the agricultural field or green space.

Stochastic Particle Tracking Application in Different Urban Areas in Central Europe: The Milano (IT) and Jaworzno (PL) Case Study to Secure the Drinking Water Resources

Urban areas are typically characterized by the presence of industrial sites, which are often sources of groundwater contamination, posing a serious threat for the groundwater. In such cases, a crucial step is to find a link between the contaminant sources and freshwater supply wells at risk. As a part of the AMIIGA Project, two different stochastic approaches were applied to assess drinking water supply wells vulnerability in Functional Urban Areas in the presence of several chlorinated hydrocarbons sources in an alluvial aquifer in Milano and a pesticide mega site in a complex geological setting in Poland. In the first case study, the innovative Pilot Point Null-Space Monte Carlo forward particle tracking was used, applying a forward solution instead of the classical backtracking, while in the second case was chosen the classical Monte Carlo methodology. Both case studies represent useful application examples, allowing an effective prioritization of expensive remediation actions in order to protect freshwater wells.

A differential evolutionary Markov chain algorithm with ensemble smoother initial point selection for the identification of groundwater contaminant sources

Groundwater contaminant source identification (GCSI) can provide support for the confirmation of responsibility and the remediation of pollution. This study has developed an innovative combination algorithm to recognize source properties and model parameters in groundwater contamination simultaneously. The research idea is based on Bayes theory and combines an ensemble smoother (ES) algorithm, differential evolutionary Markov chain (DEMC) algorithm, and adaptive kriging surrogate model (AKSM). Poor selection of initial estimates for unknown variables will slow down the convergence rate. Therefore, the initial points are not generated completely randomly, but are partially obtained by the ES algorithm. After the initial points have been determined, the DEMC algorithm is used to recognize source properties and model parameters in groundwater contamination. To improve the efficiency of the DEMC algorithm, the updating formula was adjusted by introducing information about the optimal chain into the iteration. However, the inversion process is time-consuming because both the ES and DEMC algorithms need to run the original simulation model frequently. To solve this problem, an AKSM was established for the original simulation model, which greatly accelerated the inversion process. Different hypothetical cases with different complexities were used to illustrate the validity of the combination algorithm. The identification results implied that the combination algorithm had not only faster convergence, but also higher accuracy. These improvements were more evident in the second case and third case. This implies that the proposed method will play a greater role with increasing problem complexity.

Source Identification and Ecological Risk of Polycyclic Aromatic Hydrocarbons in Soils and Groundwater

Abstract Polycyclic aromatic hydrocarbons (PAHs) are formed from anthropogenic activities, i.e. industrial emissions, incomplete combustion of petroleum, coal and other fossil fuels and other industrial and domestic activities. Research areas of this study are four representative locations in the industrial complex, in the city of Banja Luka, Republic of Srpska, Bosnia and Herzegovina. The main objective of the paper is to determine the ecological risk and to assess probable sources of PAHs contamination in soil and groundwater. The results of this study reflect the effects of coal combustion (pyrogenic origin), petrogenic and biomass origin and may provide basic data for the remediation of PAHs in the location. The ecological risk in soil (at depths of 30, 100, 200, 300 and 400 cm) and groundwater is determined. The mean values of ecological risk in soil and groundwater decreased with soil depth. Values of RQ(NCs) for groundwater were found at high ecological risk, for Ant, Chr, DahA, Acy, Pyr, BaA, Phe, Flo, Nap, Ace and Fluo, with values 28.57, 20.59, 300.00, 242.86, 185.71, 1700.0, 76.67, 53.33, 15.83, 100.00 and 57.14, respectively. ∑16PAH indicated high ecological risk for most PAHs, which decreased with soil depth. The value of RQ(NCs) for ΣPAHs in groundwater indicates high ecological risk (ΣPAHs ≥ 800 and RQ(MPCs) ≥ 1). This is the first study on the ecological risk of PAHs in soil and groundwater in industrial soils in Banja Luka and provides baseline information for further studies and additional investigations of this industrial complex.

A new quasi-3-D model with a dual iterative coupling scheme for simulating unsaturated-saturated water flow and solute transport at a regional scale

Soil water in an unsaturated zone is the major source of groundwater recharge, and likewise soil contaminants are the major source of groundwater contamination. It is thus essential to jointly consider soil water movement and solute transport together with groundwater movement and solute transport, which is particularly important when addressing regional-scale problems. This study developed a new quasi-3-D model for simulating regional-scale saturated–unsaturated water movement and solute transport in arid and semi-arid agricultural areas. The unsaturated soil water movement and solute transport processes are simulated by a 1-D soil water and solute transport model referred to as UBMOD, and the saturated groundwater movement and solute transport are simulated by MODFLOW and MT3DMS. In the quasi-3-D model, the soil water movement and groundwater movement modules are iteratively coupled and the soil solute transport and groundwater solute transport modules are iteratively couple. This is referred to as a dual iterative coupling scheme. Strength and weakness of the quasi-3-D model and the coupling scheme were evaluated with two synthetic cases and a real-world application. Results indicate satisfactory performance of the model, which demonstrates the reliability of the dual iterative coupling scheme. Besides, the impacts of spatial and temporal discretization and stress period on model stability and accuracy has been evaluated. And the model is more computationally efficient than the fully numerical methods based on Richards’ equation when the problem is a 2D/3D problem with a scale larger than 10 m. Moreover, the application to the Hetao Irrigation District indicates that the developed model is effective and efficient for the real-world regional-scale agricultural problems. Above all, it is concluded that the new model is more suitable than existing models for simulating regional-scale unsaturated–saturated soil water movement and solute transport in arid and semi-arid agricultural areas.

Characterization, geostatistical modeling and health risk assessment of potentially toxic elements in groundwater resources of northeastern Iran

Study regionNortheastern Iran. Study focusIn northeastern Iran, water needed for municipal and agricultural activities mainly comes from groundwater resources. However, it is subject to substantial anthropogenic and geogenic contamination. We characterize the sources of groundwater contamination by employing an integrated approach that can be applied to the identification of large-scale contamination sources in other regions. An existing dataset of georeferenced water quality parameters from 676 locations in northeast of Iran was analyzed to investigate the geochemical properties of groundwater. Gridding of the parameters graphically illustrates the areas affected by high concentrations of As, Cl−, Cr, Fe, Mg2+, Na+, NO3−, Se, and SO42-. We then identified potential anthropogenic and geogenic contamination sources by employing random forest (RF) regression modeling. New hydrological insights for the regionRandom forest (RF) models show that the major ions, As, Cr, Fe, and Se content of groundwater are mainly determined by geology in the study area. Modeling also links groundwater NO3− contamination with sewage discharge into aquifers as well as the application of nitrogenous and animal-waste fertilizers. Areas of high salinity result from evaporate deposits and irrigation return flow. Medium to high non-carcinogenic health risk is found in areas with high concentrations of geogenic As and Cr in groundwater. Our approach can be applied elsewhere to analyze regional groundwater quality and associated health risks as well as identify potential sources of contamination.

Groundwater Pollution Source Identification and Apportionment Using PMF and PCA-APCS-MLR Receptor Models in Tongchuan City, China.

Potential sources of groundwater pollution in Tongchuan City, China, were qualitatively identified based on 14 key water quality indicators of 59 groundwater samples, and the contribution of each source to groundwater quality was quantitatively evaluated. Groundwater pollution sources were analyzed using PMF and PCA-APCS-MLR models, and their applicability in groundwater pollution assessment in Tongchuan City was tested. Results indicate that both models identified four sources of groundwater contamination. Natural evolution was the main cause of groundwater pollution in the study area, followed by the coal industry, agriculture, and urbanization. Although the spatial distribution of pollution sources in the two models differed, the urbanized area to the east of the study area was more severely affected by sewage discharge, the west was more obviously affected by the coal industry, and the north was mainly polluted by agriculture. Both of the fitting results of the two models are good, but R2 values obtained by the PMF model (0.4440-0.9991) were generally higher than those obtained by the PCA-APCS-MLR model (0.5180-0.9530), indicating that PMF model results were more accurate than the PCA-APCS-MLR model.

Groundwater contamination sources identification based on the Long-Short Term Memory network

When simulation–optimization (S/O) is used for groundwater contamination source identification (GCSI), to reduce the calculation load and time generated by calling the simulation model, a surrogate model is often used instead of the simulation model. However, when the conversion relationship between the simulation model inputs and outputs is complex and the model is highly nonlinear, the commonly used approach of building a surrogate model may lose its advantage. This study use and check a deep learning method with the long-short term memory (LSTM) network which has great potential for characterizing the input–output conversion relationship of complex nonlinear numerical simulations, to a surrogate model of the simulation model. The accuracy of the surrogate model developed by the LSTM method was compared with that of commonly used surrogate models developed by the Kriging method, the radial basis function network method, and the kernel extreme learning machine method. The surrogate model with the highest accuracy was linked to the optimization model, and the optimization model was solved to identify the contamination source information. Results show that compared with the other three methods, the surrogate model constructed by the LSTM method had the best accuracy and generalization performance. LSTM is therefore an effective method of building a surrogate model. Linking the LSTM surrogate model to an optimization model and then solving the optimization model can save approximately 99% of the computing load and time otherwise required.

Spatial targeting of groundwater vulnerability in the Wewe-Oda river watershed in Kumasi, Ghana

It has been widely recognized that groundwater susceptibility to contamination is a function of a complex interplay of factors including land use dynamics, pollutant sources, hydrogeology, climate and biogeochemical reactions within the aquifer. Spatially targeting the risk levels and coverage of groundwater contamination is a challenging task, requiring a spatial multi-criteria and integrative approach to account for the various drivers. This study applied the original DRASTIC (DRASTIC-O) model, where its weightage was further modified using the advanced Analytic Hierarchical Process (AHP) to develop the DRASTIC-AHP approach. The groundwater modelling approach included specific vulnerability using Pollutant Potential Impact (PPI), and incorporation of land use into the DRASTIC model (DRASTIC-LU). A Composite Groundwater Vulnerability Model was developed to simultaneously integrate the DRASTIC-AHP, PPI and DRASTIC-LU, thus accounting for both natural and human sources of groundwater contamination. The approach was illustrated for the Wewe-Oda watershed, an urban catchment in the Oforikrom Municipality of the Ashanti region in Ghana. The findings show that about 34% of the catchment is at high to very high risks of groundwater contamination. The developed composite model is shown to address limitations of the DRASTIC-O model, particularly by incorporating anthropogenic sources of pollution.

Groundwater resources and quality in Syria

The resources of groundwater and its pollution in Syria are identified, and pathways of contaminants to groundwater are described. Then, suggestions for improvement of groundwater quality in Syria are evaluated. Syria is located in a region where water scarcity is dominant. About 87% of all water in Syria is mainly used for irrigation, with almost 60% of this water taken from groundwater resources. The rest is used for domestic and industrial purposes, which account for 9% and 4%, respectively. Due to over-pumping and the increase in the number of unsustainable wells observed in recent decades, groundwater is quantitatively deteriorating. In general, sources of groundwater contamination fall into two main categories: natural and anthropogenic sources. Important sources of natural groundwater pollution include climate effects. About 43% of groundwater has a high concentration of SO4 and/or NaCl. This is specifically dominant in the eastern region of Syria due to the harsh environment where precipitation is relatively low, and evaporation is high. One of the major sources of anthropogenic groundwater contamination is civil war. Most of the damaged regions are located on permeable rocks, which increases the probability of groundwater contamination due to chemical weapons (CW) used. It is vital to manage and control groundwater resources well. With the increase in water contamination and with the absence of poor water management, access to drinking water will be more of a problem than it is now.

Statistical study of Khibiny Alkaline Massif (Kola Peninsula) groundwater quality with respect to elevated aluminum concentrations

ABSTRACT Current study addresses a problem of elevated aluminum concentrations deteriorating Khibiny Alkaline Massif groundwater quality. The application of chemometric methods to the field dataset 1999–2018 allows to quantitatively describe the groundwater quality, reveal variability patterns and potential sources of elevated aluminum level in the groundwater. The field dataset contains almost 40% more observations of 12 physicochemical groundwater quality parameters than the dataset analyzed in our previous studies on Khibiny groundwater quality assessment reported in the literature. The results revealed statistically significant (α-level=0.05) associations between Al and pH, Cl−, , according to the calculated matrix using distance correlation method. The mathematical models developed with the application of multiple regression and factor/principal component analysis elucidate up to 55.5% Al concentration variability and up to 68.3% of total dataset variance. Calculated for the 19-year period the water quality index values, which changed in early 2000s from fair to a marginal category, still belongs to this category reflecting unsatisfactory water quality conditions. Comparing the current study results to the conclusions drawn in our previous publications it is assumed that the main factors determining substandard groundwater quality have remained the same since last groundwater quality assessment reported in the literature. The examined combination of chemometric methods allows to gain insight into the main features of variability patterns of water quality characteristics and the potential sources of groundwater contamination. This approach forms a reliable foundation for enhancing groundwater quality monitoring and control in the Arctic region of interest and other locations experiencing similar problems.

Selected technology-critical elements as indicators of anthropogenic groundwater contamination

Groundwater contamination originating from anthropogenic industrial activities is a global concern, adversely impacting health of living organisms and affecting natural ecosystems. Monitoring contamination in a complex groundwater system is often limited by sparse data and poor hydrogeological delineation, so that numerous indicators (organic, inorganic, isotopic) are frequently used simultaneously to reduce uncertainty. We suggest that selected Technology-Critical Elements (TCEs), which are usually found in very low concentrations in the groundwater environment, might serve as contamination indicators that can be monitored through aquifer systems. Here, we demonstrate the use of selected TCEs (in particular, Y, Rh, Tl, Ga, and Ge) as indicators for monitoring anthropogenic groundwater contamination in two different groundwater systems, near the Dead Sea, Israel. Using these TCEs, we show that the sources of local groundwater contamination are phosphogypsum ponds located adjacent to fertilizer plants in two industrial areas. In addition, we monitored the spatial distribution of the contaminant plume to determine the extent of well and spring contamination in the region. Results show significant contamination of the groundwater beneath both fertilizer plants, leading to contamination of a series of wells and two natural springs. The water in these springs contains elevated concentrations of toxic metals; U and Tl levels, among others, are above the maximum concentration limits for drinking water.

Simultaneous identification of groundwater contamination source and aquifer parameters with a new weighted-average wavelet variable-threshold denoising method.

This paper first proposed a parallel heuristic search strategy for simultaneous identification of groundwater contamination source and aquifer parameters. As identification results are influenced by many factors, such as noisy contamination concentration data, data denoising is necessary. The existing wavelet threshold denoising method has unavoidable shortcomings; therefore, this paper first proposed a new weighted-average wavelet variable-threshold denoising (WWVD) method to improve the denoising effect for concentration data, which further enhanced the subsequent identification accuracy. However, frequent calls to the simulation model could produce high computational cost during likelihood calculation. Hence, single surrogate model of the simulation model was developed to reduce cost; however, it presented limitation. Thus, this paper first developed a differential evolution-tabu search (DE-TS) hybrid algorithm to construct an optimal ensemble surrogate model, which assembled Gaussian process, kernel extreme learning machine, and support vector regression. The first proposed DE-TS algorithm also improved the approximation accuracy of surrogate model to simulation model. This paper first proposed and implemented a parallel heuristic search iterative process for simultaneous identification, and the identification results were obtained when the iteration process terminated. The accuracy and efficiency of these newly proposed approaches were tested through a hypothetical case. Results showed that the WWVD method not only improved the denoising effect for concentration data but also enhanced the subsequent identification accuracy. The OES model using DE-TS hybrid algorithm improved the approximation accuracy of surrogate model to simulation model, and the parallel heuristic search strategy is helpful for simultaneous identification of groundwater contamination source and aquifer parameters.

The sources of groundwater contamination in Sumgaitchay-Shabranchay interfluv area

As a result of researches conducted in Precaspian zone in Sumgaitchay-Shabranchay interfluve area, intensive swamping process has been revealed. Hydrocarbon migration to the groundwater occurs during the oil-gas recovery processes in Siazan area. The geological-ecological conditions are different in various (Chandagar-Zarat, Siazan-Nardaran, Saadan-Amirkhanly, Zaghli-Zeyva) zones. The main factor here is oil. Moreover, it is necessary to consider the geological structure of the area, lithological composition of the rocks, groundwater depth, as well as their dynamics and other parameters. Due to the farm operations in Shabran area the groundwater are contaminated with peptisides. Baku-Novorossiysk northern oil pipeline passing through Sumgaitchay-Shabranchay area still remains main hazard in groundwater contamination. All mentioned above shows that the monitoring works for the groundwater in the area are necessary.

How Does the Periodic Groundwater Table Fluctuation Impact on Chlorinated Vapor Intrusion?

Periodic groundwater table fluctuations are found frequently in natural aquifers due to sea tides or seasonal recharge. However, their impact on the transport of volatile organic compounds in the vadose zone released from a groundwater contaminant source (i.e., vapor intrusion) has not been well known. A 2D numerical model was developed to explore vapor intrusion processes in the sandy vadose zone, subject to a fluctuating groundwater table with a range of fluctuation amplitudes and periods. A carcinogenic compound, Trichloroethylene (TCE), was chosen as the groundwater contaminant of interest in the current study and assumed to transport into the dwelling through a crack at the corner of the basement. Results showed that the resistant effect caused by high soil moisture contents in the thin capillary fringe is weakened by periodic groundwater table fluctuations, resulting in a higher concentration of gaseous TCE at the building foundation crack, in comparison with that under a static groundwater table. The increase of the gaseous TCE concentration was induced by the enhancement of diffusion and advection due to groundwater table fluctuations. Sensitivity analyses indicated that a higher amplitude and frequency of fluctuations lead to a higher TCE concentration at the crack under the dynamic equilibrium condition. Specifically, compared with the static groundwater table condition, the TCE concentration at the crack increased by one order of magnitude under the condition of groundwater table fluctuations with an amplitude of 0.2 m and a period of one day. The results obtained could provide insights into the importance of the amplitude and frequency of groundwater table fluctuations on vapor intrusion.