Use of an Artificial Sweetener to Identify Sources of Groundwater Nitrate Contamination

The artificial sweetener acesulfame (ACE) is a potentially useful tracer of waste water contamination in groundwater. In this study, ACE concentrations were measured in waste water and impacted groundwater at 12 septic system sites in Ontario, Canada. All samples of septic tank effluent (n = 37) had ACE >6 µg/L, all samples of groundwater from the proximal plume zones (n = 93) had ACE >1 µg/L and, almost all samples from the distal plume zones had ACE >2 µg/L. Mean mass ratios of total inorganic nitrogen/ACE at the 12 sites ranged from 680 to 3500 for the tank and proximal plume samples. At five sites, decreasing ratio values in the distal zones indicated nitrogen attenuation. These ratios were applied to three aquifers in Canada that are nitrate‐stressed and an urban stream where septic systems are present nearby to estimate the amount of waste water nitrate contamination. At the three aquifer locations that are agricultural, low ACE values (<0.02‐0.15 µg/L) indicated that waste water contributed <15% of the nitrate in most samples. In groundwater discharging to the urban stream, much higher ACE values (0.2‐11 µg/L) indicated that waste water was the likely source of >50% of the nitrate in most samples. This study confirms that ACE is a powerful tracer and demonstrates its use as a diagnostic tool for establishing whether waste water is a significant contributor to groundwater contamination or not.     

A COMSOL-PHREEQC Coupled Python Framework for Reactive Transport Modeling in Soil and Groundwater

The CPqPy framework coupling COMSOL and PHREEQC based on Python was developed. This framework can achieve the simulation of diversified situations including multi-physics coupling and geochemical reactions of soil and groundwater. The multi-physics coupling models are calculated in COMSOL, whereas PHREEQC was applied to calculate the geochemical models through the Phreeqpy library in Python. Feasibility and accuracy of CPqPy were verified and applied to two cases, including a solute transport model considering equilibrium reaction and ion exchange as well as a reactive transport model of a variable saturation soil considering kinetic reaction. The results show a high degree of credibility of CPqPy. The framework has the advantages of strong portability, and it can be further used in conjunction with multiple Python calculation libraries, which greatly extends the application of the reactive transport model.     

Stochastic Modeling of Groundwater Fluoride Contamination: Introducing Lazy Learners

While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na⁺ and Ca²⁺ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.     

Use of GIS,Statistics and Machine Learning for Groundwater Quality Management: Application to Nitrate Contamination

Water Resources - Groundwater NO3 contamination (GNC) threatens the drinkability of water in many countries worldwide. It could cause serious health problems and sometimes lead to death. This paper...     

Groundwater Development Stress: Global‐Scale Indices Compared to Regional Modeling

The increased availability of global datasets and technologies such as global hydrologic models and the Gravity Recovery and Climate Experiment (GRACE) satellites have resulted in a growing number of global‐scale assessments of water availability using simple indices of water stress. Developed initially for surface water, such indices are increasingly used to evaluate global groundwater resources. We compare indices of groundwater development stress for three major agricultural areas of the United States to information available from regional water budgets developed from detailed groundwater modeling. These comparisons illustrate the potential value of regional‐scale analyses to supplement global hydrological models and GRACE analyses of groundwater depletion. Regional‐scale analyses allow assessments of water stress that better account for scale effects, the dynamics of groundwater flow systems, the complexities of irrigated agricultural systems, and the laws, regulations, engineering, and socioeconomic factors that govern groundwater use. Strategic use of regional‐scale models with global‐scale analyses would greatly enhance knowledge of the global groundwater depletion problem.     

Sustained Ability of a Natural Microbial Community to Remove Nitrate from Groundwater

Microbial-mediated nitrate removal from groundwater is widely recognized as the predominant mechanism for nitrate attenuation in contaminated aquifers and is largely dependent on the presence of a carbon-bearing electron donor. The repeated exposure of a natural microbial community to an electron donor can result in the sustained ability of the community to remove nitrate; this phenomenon has been clearly demonstrated at the laboratory scale. However, in situ demonstrations of this ability are lacking. For this study, ethanol (electron donor) was repeatedly injected into a groundwater well (treatment) for six consecutive weeks to establish the sustained ability of a microbial community to remove nitrate. A second well (control) located upgradient was not injected with ethanol during this time. The treatment well demonstrated strong evidence of sustained ability as evident by ethanol, nitrate, and subsequent sulfate removal up to 21, 64, and 68%, respectively, as compared to the conservative tracer (bromide) upon consecutive exposures. Both wells were then monitored for six additional weeks under natural (no injection) conditions. During the final week, ethanol was injected into both treatment and control wells. The treatment well demonstrated sustained ability as evident by ethanol and nitrate removal up to 20 and 21%, respectively, as compared to bromide, whereas the control did not show strong evidence of nitrate removal (5% removal). Surprisingly, the treatment well did not indicate a sustained and selective enrichment of a microbial community. These results suggested that the predominant mechanism(s) of sustained ability likely exist at the enzymatic- and/or genetic-levels. The results of this study demonstrated the in situ ability of a microbial community to remove nitrate can be sustained in the prolonged absence of an electron donor.     

Estimating Aquifer Sensitivity to Nitrate Contamination Using Geochemical Information

Methods for predicting aquifer sensitivity to contamination typically ignore geochemical factors that affect the occurrence of contaminants such as nitrate. Use of geochemical information offers a simple and accurate method for estimating aquifer sensitivity to nitrate contamination. We developed a classification method in which nitrate-sensitive aquifers have dissolved oxygen concentrations > 1.0 mg/L, Eh values >250 mV, and either reduced iron concentrations < 0.1 mg/L or total iron concentrations < 0.7 mg/L. We tested the method in four Minnesota aquifer systems having different geochemical and hydrologic conditions. A surficial sand aquifer in central Minnesota exhibited geochemical zonation, with a rapid shift from aerobic to anaerobic conditions 5 m below the water table. A fractured bedrock aquifer in east-central Minnesota remained aerobic to depths of 50 m, except in areas where anaerobic ground water discharged upward from an underlying aquifer. A bedrock aquifer in southeast Minnesota exhibited aerobic conditions when overlain by surficial deposits lacking shale, whereas anaerobic conditions occurred under deposits that contained shale. Surficial sand aquifers in northwest Minnesota contained high concentrations of sulfate and were anaerobic throughout their extent. Nitrate-nitrogen was detected at concentrations exceeding 1 mg/L in 135 of 149 samples classified as sensitive. Nitrate was not detected in any of the 109 samples classified as not sensitive. We observed differences between our estimates of sensitivity and existing sensitivity maps, which are based on methods that do not consider aquifer geochemistry. Because dissolved oxygen, reduced iron, and Eh are readily measured in the field, use of geochemistry provides a quick and accurate way of assessing aquifer sensitivity to nitrate contamination.     

Numerical Modeling of Nitrate Removal in Anoxic Groundwater during River Flooding of Riparian Zones

A numerical study demonstrates the effects of flooding on subsurface hydrological flowpaths and nitrate removal in anoxic groundwater in riparian zones with a top peat layer. A series of two-dimensional numerical simulations with changing conditions for flow (steady state or transient with flooding), hydrogeology, denitrification, and duration of flooding demonstrate how flowpaths, residence times, and nitrate removal are affected. In periods with no flooding groundwater flows horizontally and discharges to the river through the riverbed. During periods with flooding, shallow groundwater is forced upwards as discharge through peat layers that often have more optimal conditions for denitrification caused by the presence of highly reactive organic matter. The contrast in hydraulic conductivity between the sand aquifer and the overlying peat layer, as well as the flooding duration, have a significant role in determining the degree of nitrate removal.     

Using a Cloud to Replenish Parched Groundwater Modeling Efforts

Groundwater models can be improved by introduction of additional parameter flexibility and simultaneous use of soft-knowledge. However, these sophisticated approaches have high computational requirements. Cloud computing provides unprecedented access to computing power via the Internet to facilitate the use of these techniques. A modeler can create, launch, and terminate “virtual” computers as needed, paying by the hour, and save machine images for future use. Such cost-effective and flexible computing power empowers groundwater modelers to routinely perform model calibration and uncertainty analysis in ways not previously possible.     

Monitored Natural Attenuation to Manage PFAS Impacts to Groundwater: Scientific Basis

Sites impacted by per- and polyfluoroalkyl substances (PFAS) pose significant challenges to investigation and remediation, including very low cleanup objectives, limited information on natural PFAS degradation processes in the subsurface, and the apparent mobility and persistence of PFAS. Consequently, monitored natural attenuation (MNA) may be considered less applicable to PFAS compared to biodegradable classes of chemicals such as petroleum hydrocarbons and chlorinated solvents that can completely biodegrade to innocuous end products. However, MNA has proven effective for certain non-degrading metals, metalloids, and radionuclides (e.g., chromium, arsenic, and uranium). To assess the applicability of MNA to PFAS, this paper reviews the fate and transport properties of PFAS in conjunction with the various physiochemical factors that control the subsurface movement of chemicals. This analysis demonstrates that two important retention processes: (1) chemical retention in the form of PFAS precursors, and (2) geochemical retention in the form of sorption and matrix diffusion to mitigate the movement and potential impacts of PFAS in groundwater that may form the scientific basis for applying MNA to PFAS contamination. This paper describes the scientific and regulatory basis for using MNA to manage PFAS-impacted groundwater.     

The Value of Groundwater Modeling to Support a Pump and Treat Design

A wide range of rules, algorithms, and models are available to design an effective pump and treat remediation system. Often, one refers to the effectiveness of the developed pump and treat system to demonstrate how valuable the use of a groundwater model can be. An economic valuation of the groundwater model is usually missing. This study provides a framework that puts the discussion concerning the use of groundwater models in an economic perspective. It is not only demonstrated that a more effective pump and treat system can be designed using a groundwater model, but also the economic implications of using a groundwater model are calculated. A set of economic decision rules is applied to determine the economic value of a groundwater model. It is shown that investing in a groundwater model can be economically worthwhile. The remediation time is reduced, remediation costs are saved and the property can be sold more early. These benefits outweigh the costs of developing a groundwater model, and hence a positive net benefit (NB) is determined.     

Modeling Groundwater Depletion at Regional and Global Scales: Present State and Future Prospects

Except for frozen water in ice and glaciers, groundwater is the world’s largest distributed store of freshwater and has strategic importance to global food and water security. In this paper, the most recent advances quantifying groundwater depletion (GWD) are comprehensively reviewed. This paper critically evaluates the recently advanced modeling approaches estimating GWD at regional and global scales, and the evidence of feedbacks to the Earth system including sea-level rise associated with GWD. Finally, critical challenges and opportunities in the use of groundwater are identified for the adaption to growing food demand and uncertain climate.     

Groundwater Quality Modeling with a Small Data Set

Seventeen groundwater quality variables collected during an 8‐year period (2006 to 2013) in Andimeshk, Iran, were used to implement an artificial neural network (NN) with the purpose of constructing a water quality index (WQI). The method leading to the WQI avoids instabilities and overparameterization, two problems common when working with relatively small data sets. The groundwater quality variables used to construct the WQI were selected based on principal component analysis (PCA) by which the number of variables were decreased to six. To fulfill the goals of this study, the performance of three methods (1) bootstrap aggregation with early stopping; (2) noise injection; and (3) ensemble averaging with early stopping was compared. The criteria used for performance analysis was based on mean squared error (MSE) and coefficient of determination (R²) of the test data set and the correlation coefficients between WQI targets and NN predictions. This study confirmed the importance of PCA for variable selection and dimensionality reduction to reduce the risk of overfitting. Ensemble averaging with early stopping proved to be the best performed method. Owing to its high coefficient of determination (R² = 0.80) and correlation coefficient (r=0.91), we recommended ensemble averaging with early stopping as an accurate NN modeling procedure for water quality prediction in similar studies.     

Application of DARLAM to Regional Haze Modeling

— The CSIRO Division of Atmospheric Research limited area model (DARLAM) is applied to atmospheric transport modeling of haze in southeast Asia. The 1998 haze episode is simulated using an emission inventory derived from hotspot information and adopting removal processes based on SO₂.¶Results show that the model is able to simulate the transport of haze in the region. The model images closely resemble the plumes of NASA Total Ozone Mapping Spectrometer and Meteorological Service Singapore haze maps. Despite the limitation of input data, particularly for haze emissions, the three-month average pattern correlation obtained for the whole episode is 0.61. The model has also been able to reproduce the general features of transboundary air pollution over a long period of time. Predicted total particulate matter concentration also agrees reasonably well with observation.¶The difference in the model results from the satellite images may be attributed to the large uncertainties of emission, simplification of haze deposition and transformation mechanisms and the relatively coarse horizontal and vertical resolution adopted for this particular simulation.     

Identification of Multiple Sources of Groundwater Contamination by Dual Isotopes

Chlorinated solvents are one of the most commonly detected groundwater contaminants in industrial areas. Identification of polluters and allocation of contaminant sources are important concerns in the evaluation of complex subsurface contamination with multiple sources. In recent years, compound‐specific isotope analyses (CSIA) have been employed to discriminate among different contaminant sources and to better understand the fate of contaminants in field‐site studies. In this study, the usefulness of dual isotopes (carbon and chlorine) was shown in assessments of groundwater contamination at an industrial complex in Wonju, Korea, where groundwater contamination with chlorinated solvents such as trichloroethene (TCE) and carbon tetrachloride (CT) was observed. In November 2009, the detected TCE concentrations at the study site ranged between nondetected and 10,066 µg/L, and the CT concentrations ranged between nondetected and 985 µg/L. In the upgradient area, TCE and CT metabolites were detected, whereas only TCE metabolites were detected in the downgradient area. The study revealed the presence of separate small but concentrated TCE pockets in the downgradient area, suggesting the possibility of multiple contaminant sources that created multiple comingling plumes. Furthermore, the variation of the isotopic (δ¹³C and δ³⁷Cl) TCE values between the upgradient and downgradient areas lends support to the idea of multiple contamination sources even in the presence of detectable biodegradation. This case study found it useful to apply a spatial distribution of contaminants coupled with their dual isotopic values for evaluation of the contaminated sites and identification of the presence of multiple sources in the study area.     

Modeling Variably Saturated Multispecies Reactive Groundwater Solute Transport with MODFLOW‐UZF and RT3D

A numerical model was developed that is capable of simulating multispecies reactive solute transport in variably saturated porous media. This model consists of a modified version of the reactive transport model RT3D (Reactive Transport in 3 Dimensions) that is linked to the Unsaturated‐Zone Flow (UZF1) package and MODFLOW. Referred to as UZF‐RT3D, the model is tested against published analytical benchmarks as well as other published contaminant transport models, including HYDRUS‐1D, VS2DT, and SUTRA, and the coupled flow and transport modeling system of CATHY and TRAN3D. Comparisons in one‐dimensional, two‐dimensional, and three‐dimensional variably saturated systems are explored. While several test cases are included to verify the correct implementation of variably saturated transport in UZF‐RT3D, other cases are included to demonstrate the usefulness of the code in terms of model run‐time and handling the reaction kinetics of multiple interacting species in variably saturated subsurface systems. As UZF1 relies on a kinematic‐wave approximation for unsaturated flow that neglects the diffusive terms in Richards equation, UZF‐RT3D can be used for large‐scale aquifer systems for which the UZF1 formulation is reasonable, that is, capillary‐pressure gradients can be neglected and soil parameters can be treated as homogeneous. Decreased model run‐time and the ability to include site‐specific chemical species and chemical reactions make UZF‐RT3D an attractive model for efficient simulation of multispecies reactive transport in variably saturated large‐scale subsurface systems.