Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

The extensive use of pesticides for increasing the agricultural production is affecting the quality of groundwater. The objectives of this article are to (i) develop pesticide relative leaching ranks for well sites, (ii) develop maps for human health risks due to pesticide applications, and (iii) identify the most significant parameters in pesticide simulations for groundwater vulnerability assessment. The methods include (i) development of acifluorfen relative leaching ranks for 25 well sites using ArcPRZM‐3, (ii) development of health risk maps using model simulated maximum dissolved bentazon concentrations on the basis of USA drinking water quality guidelines, (iii) sensitivity analysis for 14 ArcPRZM‐3 input parameters using the Plackett–Burman method. ArcPRZM‐3 is a user‐friendly system for spatial modeling of pesticide leaching from surface to groundwater. Thirteen acifluorfen relative leaching potential ranks were developed in which the pesticide leaching decrease from 1 to 13. The model predicted ranks for well 34 and well 9 were 2nd and 3rd, respectively, and acifluorfen was detected in both wells during the physical monitoring. The percentages of high health risks in the agricultural areas were 48.38 and 72.72% for Randolph and Independence Counties, respectively. The most significant parameters were thickness of horizon compartment, runoff curve number of antecedent moisture condition II for cropping, soil bulk density, and total application of pesticide. The irrigation, soil permeability, and numerical dispersion could impact the pesticide leaching in soils toward groundwater. The ArcPRZM‐3 system could be efficiently applied for spatial modeling and mapping of pesticide concentrations for groundwater vulnerability assessment on a large scale.     

Limitations of Current Approaches for Predicting Groundwater Vulnerability from PFAS Contamination in the Vadose Zone

Published literature for reported sorption coefficients (K_d) of eight anionic per- and polyfluoroalkyl substances (PFAS) in soil was reviewed. K_d values spanned three to five log units indicating that no single value would be appropriate for use in estimating PFAS impacts to groundwater using existing soil-water partition equations. Regression analysis was used to determine if the soil or solution parameters might be used to predict K_d values. None of the 15 experimental parameters collected could individually explain variability in reported K_d values. Significant associations between K_d and soil calcium and sodium content were found for many of the selected PFAS, suggesting that soil cation content may be critical to PFAS sorption, as previously noted in sources like Higgins and Luthy (2006), while organic carbon content was significant only at elevated levels (>5%). Unexplained discrepancies between the results from studies where PFAS were introduced to soil and desorbed in the laboratory and those that used material from PFAS-impacted sites suggest that laboratory experiments may be overlooking some aspects critical to PFAS sorption. Future studies would benefit from the development and use of standardized analytical methods to improve data quality and the establishment of soil parameters appropriate for collection to produce more complete data sets for predictive analysis.     

Vulnerability evaluation of a coastal aquifer via GALDIT model and comparison with DRASTIC index using quality parameters

In recent years, environmental assessments of groundwater resources have resulted in the development of models that help identify the vulnerable zones. An aquifer is investigated using both GALDIT and DRASTIC indices. The GALDIT model is developed to determine the vulnerability of coastal aquifers in terms of saltwater intrusion whereas the DRASTIC model is generally applicable to all aquifers. Having compared the results of both the GALDIT and DRASTIC models with quality parameters, the salinity model proved to be more appropriate in identifying the vulnerability of coastal aquifers. The results show a Pearson correlation coefficient between TDS and the GALDIT vulnerability map of 0.58 while the corresponding value for the DRASTIC index is 0.48.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR A. Fiori     

The Influence of Clay Zones on Land Subsidence from Groundwater Pumping

The objective of this article is to analyze the influence of clay zones on subsidence from groundwater pumping. Finite element analyses were conducted on a sand‐only aquifer and a sand aquifer with two clay zones located at different distances from the well face. A model that accounts for recoverable and nonrecoverable strains was used to simulate the sand and clay. This model couples the groundwater flow with the stress‐deformation response of the aquifer materials. Each aquifer was pumped from a single well for a period of 6 months, and then the groundwater level was lowered gradually to an elevation below the elevation of the clay zones and kept there for 10 years. The groundwater level was then raised gradually back to the original elevation over a period of 10 years. The results of the analyses show that the ground surface subsidence profile is strongly influenced by the presence of the clays zones. The ground surface sags where these clay zones are present resulting in a wavy ground surface profile. Subsidence continued when pumping is stopped, albeit at a much slower rate than during pumping, and when the groundwater level is below the elevation of the clay zones. Clay zones further away from the well face lag the subsidence of clay zones nearer the well face because of lower changes in hydrostatic head. Sags in ground surface subsidence profile from groundwater pumping are indicators of the presence of low hydraulic conductive geological materials.     

A Comparison of Data‐Driven Groundwater Vulnerability Assessment Methods

Increasing availability of geo‐environmental data has promoted the use of statistical methods to assess groundwater vulnerability. Nitrate is a widespread anthropogenic contaminant in groundwater and its occurrence can be used to identify aquifer settings vulnerable to contamination. In this study, multivariate Weights of Evidence (WofE) and Logistic Regression (LR) methods, where the response variable is binary, were used to evaluate the role and importance of a number of explanatory variables associated with nitrate sources and occurrence in groundwater in the Milan District (central part of the Po Plain, Italy). The results of these models have been used to map the spatial variation of groundwater vulnerability to nitrate in the region, and we compare the similarities and differences of their spatial patterns and associated explanatory variables. We modify the standard WofE method used in previous groundwater vulnerability studies to a form analogous to that used in LR; this provides a framework to compare the results of both models and reduces the effect of sampling bias on the results of the standard WofE model. In addition, a nonlinear Generalized Additive Model has been used to extend the LR analysis. Both approaches improved discrimination of the standard WofE and LR models, as measured by the c‐statistic. Groundwater vulnerability probability outputs, based on rank‐order classification of the respective model results, were similar in spatial patterns and identified similar strong explanatory variables associated with nitrate source (population density as a proxy for sewage systems and septic sources) and nitrate occurrence (groundwater depth).     

A Hierarchical Bayesian Model Averaging Framework for Groundwater Prediction under Uncertainty

Groundwater prediction models are subjected to various sources of uncertainty. This study introduces a hierarchical Bayesian model averaging (HBMA) method to segregate and prioritize sources of uncertainty in a hierarchical structure and conduct BMA for concentration prediction. A BMA tree of models is developed to understand the impact of individual sources of uncertainty and uncertainty propagation to model predictions. HBMA evaluates the relative importance of different modeling propositions at each level in the BMA tree of model weights. The HBMA method is applied to chloride concentration prediction for the “1,500‐foot” sand of the Baton Rouge area, Louisiana from 2005 to 2029. The groundwater head data from 1990 to 2004 is used for model calibration. Four sources of uncertainty are considered and resulted in 180 flow and transport models for concentration prediction. The results show that prediction variances of concentration from uncertain model elements are much higher than the prediction variance from uncertain model parameters. The HBMA method is able to quantify the contributions of individual sources of uncertainty to the total uncertainty.     

A Permanent Multilevel Monitoring and Sampling System in the Coastal Groundwater Mixing Zones

To study the spatial and temporal variability of water dynamics and chemical reactions within the coastal groundwater mixing zones (CGMZs), high‐resolution periodical and spatial groundwater sampling within CGMZs is needed. However, current samplers and sampling systems may require heavy driving machines to install. There is also possible contamination from the metal materials for current samplers and sampling systems. Here, a permanent multilevel sampling system is designed to sample coastal groundwater within CGMZs. This cost‐effective system consists of metal‐free materials and can be installed easily. The system is tested in Po Sam Pai and Tingkok, Tolo Harbor and Hong Kong. Major ions, nutrients, stable isotopes and radium and radon isotopes were analyzed and the data provided scientific information to study the fresh‐saltwater interface fluctuations, and temporal variations and spatial heterogeneity of geochemical processes occurred within CGMZs. The reliable spatial and temporal data from the sampling system demonstrate that the system functions well and can provide scientific data for coastal aquifer studies.     

Wetland-Scale Mapping of Preferential Fresh Groundwater Discharge to the Colorado River

Quantitative evaluation of groundwater/surface water exchange dynamics is universally challenging in large river systems, because existing methodology often does not yield spatially-distributed data and is difficult to apply in deeper water. Here we apply a combined near-surface geophysical and direct groundwater chemical toolkit to refine fresh groundwater discharge estimates to the Colorado River through a 4-km² wetland that borders the town of Moab, Utah, USA. Preliminary characterization of raw electromagnetic imaging (EMI) data, collected by kayak and by walking, was used to guide additional direct-contact electrical measurements and installation of new monitoring wells. Chemical data from the wells strongly supported the EMI spatial characterization of preferential fresh groundwater discharge embedded in natural brine groundwaters and weighted to the southern wetland section. Inversion of the EMI data revealed sub-meter scale detail regarding bulk electrical conductivity zonation across approximately 15.5 km of transects, collected in only 3 days. This electrical detail indicates processes such as salinization of the unsaturated zone and direct discharge through the Colorado River sediments and a tributary creek bed. Overall, the study contributed to a substantial reduction in fresh groundwater discharge estimates previously made using sparse existing well data and a simplified assumption of diffuse fresh groundwater discharge below the entire wetland. EMI will likely become a widely used tool in systems with natural electrical contrast as groundwater/surface water hydrogeologists continue to recognize the prevalence of preferential groundwater discharge processes.     

Regional Estimation of Fresh Groundwater Vulnerability: Methodological Aspects and Practical Applications

Two independent methods for estimating groundwater vulnerability to pollution are described. The methods are used for regions with different geological and hydrogeological conditions. A new combined approach taking into account the physicochemical processes that take place in all parts of the protected zone is proposed.     

Mapping Groundwater Potential Through an Ensemble of Big Data Methods

Groundwater resources are crucial to safe drinking supplies in sub-Saharan Africa, and will be increasingly relied upon in a context of climate change. The need to better understand groundwater calls for innovative approaches to make the best out of the existing information. A methodology to map groundwater potential based on an ensemble of machine learning classifiers is presented. A large borehole database (n = 1848) was integrated into a Geographic Information Systems (GIS) environment and used to train, validate and test 12 machine learning algorithms. Each classifier predicts a binary target (positive or negative borehole) based on the minimum flow rate required for communal domestic supplies. Classification is based on a number of explanatory variables, including landforms, lineaments, soil, vegetation, geology and slope, among others. Correlations between the target and explanatory variables were then generalized to develop groundwater potential maps. Most algorithms attained success rates between 80% and 96% in terms of test score, which suggests that the outcomes provide an accurate picture of field conditions. Statistical learners were observed to perform better than most other algorithms, excepting random forests and support vector machines. Furthermore, it is concluded that the ensemble approach provides added value by incorporating a measure of uncertainty to the results. This technique may be used to rapidly map groundwater potential for rural supply or humanitarian emergencies in areas where there is sufficient historical data but where comprehensive field work is unfeasible.     

Assessment of Groundwater Vulnerability to Contamination by Organochlorine Pesticides in the Area of Caucasian Mineral Water

The procedure of evaluating pesticide travel time through the aeration zone to the groundwater table is presented. It is found that the time of pollutant travel through rocks of the aeration zone depends on the thickness and composition of rocks of this zone, the porosity of rocks, distribution factor, volumetric humidity, and the infiltration recharge. As shown, the most vulnerable are the valleys of principal rivers of the area of Caucasian Mineral Water, where the Recent and Middle Quaternary aquifers confined to these valleys are intensely used for domestic and drinking needs.     

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.     

The Basic Principles and Recommendations for the Assessment and Mapping of the Degree of Groundwater Protection against Pollution

It is shown that the interaction between subsurface hydrosphere and other environmental components and the possible extent of its pollution through these components can be characterized by the degree of groundwater protection against pollution and can be represented on appropriate maps based on simple model estimates of pollutant movement through the lithosphere serving as a protection zone. The principles and methods used in evaluating and different-scale mapping of groundwater protection degree and their vulnerability with respect to pollutants with different toxicity are discussed.     

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.     

Application of Groundwater Vulnerability Overlay and Index Methods to the Jijel Plain Area (Algeria)

Today, scientists are deeply concerned by the vulnerability of groundwater reservoirs to pollution. Relatively simple overlay and index methods can be used to produce groundwater vulnerability maps in geographic information system. In addition, this study deals with contamination from nonpoint sources. In this study, two such models, DRASTIC and GOD, were applied in the Jijel Plain area of northeast Algeria and compared with measured groundwater nitrate concentrations. This showed that results from DRASTIC were better than GOD, 69% correlation with nitrate compared to 56%. DRASTIC was better able to identify vulnerable zones along the river valleys. The DRASTIC model was then modified using the nitrate concentrations to optimize the rating score given within each parameter range and sensitivity analysis to change the weighting given for each parameter. These combined changes gave a final Pearson's correlation of 83% with nitrate. This showed that recharge, aquifer type, and topography were the key factors in controlling vulnerability to nitrate pollution.     

A field demonstration of the entropy-weighted fuzzy DRASTIC method for groundwater vulnerability assessment

Abstract

Groundwater vulnerability assessment based on the DRASTIC index has been widely used since the 1980s to map potential risks of groundwater contamination. However, its applicability and usefulness are affected by two uncertain and subjective factors. One is the discretization of continuous input variables and the other is the assignment of different weights to the index variables. In this study, an entropy-weighted fuzzy-optimization approach was developed to augment and improve the classic DRASTIC method by reducing the uncertainties associated with variable discretization and weight assignment. The modified DRASTIC method was applied to a study site in Shandong, north China. The entropy-weighted fuzzy-optimization approach is shown to provide a more rigorous delineation of the relative vulnerability distribution. Meanwhile, the new approach does not require the use of more parameters. The results suggest that this approach significantly improves and enhances the ability of the classic DRASTIC method in a more systematic and rigorous way.

Editor D. Koutsoyiannis

Citation Yu, C., Zhang, B.X., Yao, Y.Y., Meng, F.H., and Zheng, C.M., 2012. A field demonstration of the entropy-weighted fuzzy DRASTIC method for groundwater vulnerability assessment. Hydrological Sciences Journal, 57 (7), 1420–1432.     

Investigation of Groundwater Potential Zones in Hard Rock Terrain,Wadi Na'man,Saudi Arabia

In arid and hyper-arid zones, groundwater exploration is one of the most significant ways to locate potential new water supplies. Geophysical prospecting is currently the most successfully used method for locating new supplies, but it is rather costly. Satellite remote sensing (RS) detection, however, with its integration of Geographic Information Systems (GIS) provides the best chance for identifying and initially evaluating water-bearing formations. In the western part of Saudi Arabia, Wadi Na'man has for centuries been one of the major water sources for the city of Makkah Al-Mukarramah. It is therefore very important to find appropriate groundwater potential (GP) zones in this wadi for water supply. This study utilizes RS and GIS techniques, and also studies the hydrogeological, geological, and geomorphological characteristics that have significant impact on groundwater occurrence in Wadi Na'man. Representative layers are generated for each component and each given a weight ratio that depends on the level of influence. The overlay and integration of these thematic layers was used to produce a map that shows the most promising potential groundwater areas and classifies local potentials as either low, medium, or high. The results also reveal that the areas overall-rated as “promising” (i.e., classified as medium or high) represent approximately 17-25% of the total basin area and consist mainly of Quaternary sediments and connected fractured rock areas.     

Applying a Regional Transport Modeling Framework to Manage Nitrate Contamination of Groundwater

Regional nitrate contamination in groundwater is a management challenge involving multisector benefits. There is always conflict between restricting anthropogenic activities to protect groundwater quality and prioritizing economic development, especially in productive agriculture dominated areas. To mitigate the nitrate contamination in groundwater, it is necessary to develop management alternatives that simultaneously support environmental protection and sustainable economic development. A regional transport modeling framework is applied to evaluate nitrate fate and transport in the Dagu Aquifer, a shallow sandy aquifer that supplies drinking water and irrigation water for a thriving agricultural economy in Shandong Province in east coastal China. The aquifer supports intensive high-value vegetable farms and nitrate contamination is extensive. Detailed land-use information and fertilizer use data were compiled and statistical approaches were employed to analyze nitrogen source loadings and the spatiotemporal distribution of nitrate in groundwater to support model construction and calibration. The evaluations reveal that the spatial distribution and temporal trends of nitrate contamination in the Dagu Aquifer are driven by intensive fertilization and vertical water exchange, the dominant flow pattern derived from intensive agricultural pumping and irrigation. The modeling framework is employed to assess the effectiveness of potentially applicable management alternatives. The predictive results provide quantitative comparisons for the trend and extent of groundwater quality mitigation under each scenario. Recommendations are made for measures that can both improve groundwater quality and sustain productive agricultural development.