Predicting natural arsenic contamination of bedrock groundwater for a local region in Korea and its application

A logistic regression model for the probability of arsenic exceeding the drinking water guidelines (10 μg/L) in bedrock groundwater was developed for a selected county in Korea, where arsenic occurrence and release reactions have been investigated. Arsenic was enriched naturally by the oxidation of sulfide minerals in metasedimentary rocks and mineralized zones, and due to high mobility in alkaline pH conditions, concentrations were high in groundwater of the county. When considering these reactions of arsenic release and water quality characteristics, several geological and geochemical factors were selected as influencing variables in the model. In the final logistic regression model, geological units of limestone and metasedimentary rocks, the concentrations of nitrate and sulfate, and distances to closed mines and adjacent granite were retained as statistically significant variables. Predicted areas of high probability agreed well with known spatial contamination patterns in the county. The model was also applied to an adjacent county, where the groundwater has not previously been tested for the presence of arsenic, and a probability map for arsenic contamination was then produced. Through the analysis of arsenic concentrations at the wells of high probability, it was determined that the applied model accurately indicated the arsenic contamination of groundwater. The logistic regression approach of this study can be applied to predict arsenic contamination in areas of similar geological and geochemical conditions to the county used in this model.     

Spatial distribution of nitrate and related factors in the High Plains Aquifer,Texas

Utilizing geographic information systems (GIS) and statistics, objectives of this study were to evaluate: (a) the spatial distribution of nitrate concentrations in groundwater, and (b) associations between nitrate concentrations and: proximity to playa lakes, hydraulic conductivity of soil, well depth, and land use in the High Plains Aquifer, Texas. Data were compiled from wells sampled during 2000–2008. Nitrate concentrations in approximately 9% of wells exceeded the maximum contaminant level for drinking water. Concentrations were generally higher beneath urban and agricultural land, under permeable soil, and in shallow wells (especially in the southern part of the study area). However, concentrations were lower near playa lakes. While playas focus recharge to groundwater, denitrification in reducing environments lower nitrate concentrations beneath them. This study identifies areas vulnerable to nitrate contamination that warrant continued monitoring and mitigation efforts.     

Spatial and temporal study of nitrate concentration in groundwater by means of coregionalization

 Spatial and temporal behavior of hydrochemical parameters in groundwater can be studied using tools provided by geostatistics. The cross-variogram can be used to measure the spatial increments between observations at two given times as a function of distance (spatial structure). Taking into account the existence of such a spatial structure, two different data sets (sampled at two different times), representing concentrations of the same hydrochemical parameter, can be analyzed by cokriging in order to reduce the uncertainty of the estimation. In particular, if one of the two data sets is a subset of the other (that is, an undersampled set), cokriging allows us to study the spatial distribution of the hydrochemical parameter at that time, while also considering the statistical characteristics of the full data set established at a different time. This paper presents an application of cokriging by using temporal subsets to study the spatial distribution of nitrate concentration in the aquifer of the Lucca Plain, central Italy. Three data sets of nitrate concentration in groundwater were collected during three different periods in 1991. The first set was from 47 wells, but the second and the third are undersampled and represent 28 and 27 wells, respectively. Comparing the result of cokriging with ordinary kriging showed an improvement of the uncertainty in terms of reducing the estimation variance. The application of cokriging to the undersampled data sets reduced the uncertainty in estimating nitrate concentration and at the same time decreased the cost of the field sampling and laboratory analysis. Received: 23 July 1997 · Accepted: 31 March 1998     

Statistical models for the assessment of nitrate contamination in urban groundwater using GIS

The purpose of this study is to develop statistical models for groundwater quality assessment in urban areas using Geographic Information Systems (GIS). To develop the models, the concentrations of nitrate (expressed as nitrogen, NO₃-N), which are different according to the type of land use, well depth and distribution of rainfall, were analyzed in the Seoul (the capital of South Korea) area. Data such as land use, location of wells and groundwater quality data for nitrate contamination were collected and a database constructed within GIS. The distribution of NO₃-N concentrations is not normal, and the results of the Mann-Whitney U-test analysis show the difference of NO₃-N concentration by well depth and by distribution of rainfall. In both the shallow and deep wells, the radius of influence is 200 m in the dry season and 250 m in the rainy season, showing the tendency to increase in the rainy season. The results of correlation and regression analysis indicate that mixed residential and business areas and cropped field areas are likely to be the major contributor of increasing NO₃-N concentration. Land uses are better correlated with NO₃-N in deep wells than in shallow wells.     

Evaluation of groundwater quality for irrigation and drinking using GIS and geostatistics in a peri-urban area of Delhi, India

Intensive agriculture by indiscriminate use of agrochemicals, sewage water, and polluted drain water has posed a serious threat to groundwater quality in some peri-urban areas of Delhi like Najafgarh block. The objective of the study was to determine the groundwater quality and to map their spatial variation in terms of suitability for irrigation and drinking purpose. Ordinary kriging method was used for preparation of thematic maps of groundwater quality parameters such as electrical conductivity, sodium adsorption ratio, bicarbonate, magnesium/calcium ratio, total dissolved solids, chloride, nitrate and hardness. Exponential semivariogram model was best fitted for all quality parameters except chloride and hardness, where spherical model fitted best. Pollution level was highest at south and south-eastern part of the study area. Better quality groundwater may be expected at the northern and western part. High salinity was due to high chloride concentration in the groundwater. Nitrate pollution level was found to be very alarming and need immediate interventions. High dissolved solids and hardness made the groundwater unsuitable for drinking. There were negligible sodium and bicarbonate hazard in the study area. The groundwater quality index was devised to analyse the combined impact of different quality parameters on irrigation and drinking purposes. The irrigation water quality index and drinking water quality index distribution maps delineated an area of 47.29 and 6.54 km² suitable for irrigation and drinking, respectively. These safe zones were found as a small strip along the northern boundary and a very small pocket at the western side of the study area.     

Nitrate pollution of ground waters from shallow basaltic aquifers,Deccan Trap Hydrologic Province,India

Analyses of groundwater samples collected from several locations in a small watershed of the Deccan Trap Hydrologic Province, indicated anomalously higher values of nitrate than the background. However, the NO₃ concentrations in water from dug wells under pastureland where the subsurface material consisted of stony waste were minimum. The maximum values were reported for water from dug wells where the principal land use was agricultural. Lowering of NO₃ values under shallow water-table conditions suggests denitrification. Higher concentrations of nitrate determined for samples collected from the wells with a deeper water-table indicate that denitrification process is inactive. The high values of nitrate coinciding with agricultural land use indicate fertilizers as the main source of nitrate pollution of ground-water. Decrease in Cl/NO₃ ratio for agricultural land use confirms this inference.     

Geostatistical approach for identification of transmissivity structure at Dulliu area in Taiwan

 A thorough understanding of the characteristics of transmissivity makes groundwater deterministic models more accurate. These transmissivity data characteristics occasionally possess a complicated spatial variation over an investigated site. This study presents both geostatistical estimation and conditional simulation methods to generate spatial transmissivity maps. The measured transmissivity data from the Dulliu area in Yun-Lin county, Taiwan, is used as the case study. The spatial transmissivity maps are simulated by using sequential Gaussian simulation (SGS), and estimated by using natural log ordinary kriging and ordinary kriging. Estimation and simulation results indicate that SGS can reproduce the spatial structure of the investigated data. Furthermore, displaying a low spatial variability does not allow the ordinary kriging and natural log kriging estimates to fit the spatial structure and small-scale variation for the investigated data. The maps of kriging estimates are smoother than those of other simulations. A SGS with multiple realizations has significant advantages over ordinary kriging and even natural log kriging techniques at a site with a high variation in investigated data. These results are displayed in geographic information systems (GIS) as basic information for further groundwater study. Received: 27 August 1999 · Accepted: 22 February 2000     

Evaluation of aquifers vulnerability to contamination in the Yarmouk River basin, Jordan, based on DRASTIC method

The existing different human activities and planned land uses put the groundwater resources in Jordan at considerable risk. There are evidences suggesting that the quality of groundwater supplies in north Jordan is under threat from a wide variety of point and non-point sources including agricultural, domestic, and industrial. Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. DRASTIC method incorporates the major geological and hydrogeological factors that affect and control groundwater movement: depth to groundwater (D), net recharge (R), lithology of the aquifer (A), soil texture (S), topography (T), lithology of vadose zone (I), and hydraulic conductivity (C). The main goal of this study is to produce vulnerability maps of groundwater resources in the Yarmouk River basin by applying the DRASTIC method to determine areas where groundwater protection or monitoring is critical. ArcGIS 9.2 was used to create the groundwater vulnerability maps by overlaying the available hydrogeological data. The resulting vulnerability maps were then integrated with lineament and land use maps as additional parameters in the DRASTIC model to assess more accurately the potential risk of groundwater to pollution. The general DRASTIC index indicates that the potential for polluting groundwater is low in the whole basin, whereas the resulting pesticide DRASTIC vulnerability map indicates that about 31% of the basin is classified as having moderate vulnerability, which may be attributed to agricultural activities in the area. Although high nitrate concentrations were found in areas of moderate vulnerability, DRASTIC method did not depict accurately the nitrate distribution in the area.     

Groundwater nitrate vulnerability assessment in alluvial aquifer using process-based models and weights-of-evidence method: Lower Savinja Valley case study (Slovenia)

This paper describes the implementation of process-based models reflecting relative groundwater nitrate vulnerability of the shallow alluvial Lower Savinja Valley (LSV) aquifer in Slovenia. A spatially explicit identification of the potentially vulnerable priority areas within groundwater bodies at risk from a chemical point of view is being required for cost-effective measures and monitoring planning. The shallow LSV unconfined aquifer system consists of high-permeable Holocene and middle- to low-permeable Pleistocene gravel and sand, with a maximum thickness of about 30 m, mainly covered by shallow eutric fluvisoils or variously deep eutric cambisoil. The hydrogeological parameters, e.g. the depth to the groundwater, hydrological role of the topographic slope, etc. usually used in different point count schemes are, in the case of the lowland aquifer and shallow groundwater, spatially very uniform with low variability. Furthermore, the parametric point count methods are generally not able to illustrate and analyze important physical processes, and validation of the results is difficult and expensive. Instead of a parametric point count scheme, we experimentally used the Arc-WofE extension for weights-of-evidence (WofE) modelling. All measurement locations with a concentration higher than the value of 20 mg NO₃ ⁻ per litre of groundwater have been considered as training points (173), and the three process-based models generalized output layers of groundwater recharge (GROWA), nitrate leached from the soil profile (SWAT) and groundwater flow velocity (FEFLOW), served as evidential themes. The technique is based on the Bayesian idea of phenomena occurrences probability before (prior probability) and after consideration of any evidential themes (posterior probability), which were measured by positive and negative weights as an indication of the association between a phenomena and a prediction pattern. The response theme values describe the relative probability that a 100 × 100 m spatial unit will have a groundwater nitrate concentration higher than the training points’ limit values with regard to prior probability value. The lowest probability of groundwater nitrate occurrence is in the parts of the LSV aquifer, which are known as anoxic condition areas with very likely denitrification processes. The cross-validation of the dissolved oxygen and dissolved nitrate response theme confirmed the accuracy of the groundwater nitrate prediction. The WofE model results very clearly indicate regional groundwater nitrate distribution and enable spatial prediction of the probability for increased groundwater nitrate concentration in order to plan the groundwater nitrate reduction measures and optimize the programme for monitoring the effects of these measures.     

Integrated geostatistics and GIS techniques for assessing groundwater contamination in Al Arish area, Sinai, Egypt

Sustainable development in El Arish area of North Sinai, Egypt, is retarded by serious environmental problems, where the land-use and land cover of the region is changing over present time. The impact of human activities in the study area is accompanied by the destruction and over-exploitation of the environment. This study applies multivariate statistics (factor and cluster analyses) and GIS techniques to identify both anthropogenic and natural processes affecting the groundwater quality in the Quaternary sands aquifer. The aim of this study was to investigate the impacts on groundwater resources, the potential pollution sources, and to identify the main anthropogenic inputs of both nutrients and trace metal. Since the depth to the water table is shallow especially in the northern part (<4?m), and the aquifer was exposed on the ground surface, it has poor buffering capacity and the pollution risk is very high. Groundwater chemistry in this coastal region has complex contaminant sources, where intensive farming activities and untreated wastes put stress on groundwater quality. Several areal distribution maps were constructed for correlating water quality with possible contributing factors such as location, land-use, and aquifer depth. These maps identified both anthropogenic and natural processes affecting groundwater quality of the studied aquifer. Cluster analysis was used to classify water chemistry and determine the hydrochemical groups, Q-mode dendrogram is interpreted and there are three main clusters. Factor analyses identify the potential contamination sources affecting groundwater hydrochemistry such as: nitrate, sulfate, phosphate and potassium fertilizers, pesticides, sewage pond wastes, and salinization due to circulation of dissolved salts in the irrigation water itself.     

Mapping groundwater contamination risk using GIS and groundwater modelling. A case study from the Gaza Strip, Palestine

Increasing pressure on water resources worldwide has resulted in groundwater contamination, and thus the deterioration of the groundwater resources and a threat to the public health. Risk mapping of groundwater contamination is an important tool for groundwater protection, land use management, and public health. This study presents a new approach for groundwater contamination risk mapping, based on hydrogeological setting, land use, contamination load, and groundwater modelling. The risk map is a product of probability of contamination and impact. This approach was applied on the Gaza Strip area in Palestine as a case study. A spatial analyst tool within Geographical Information System (GIS) was used to interpolate and manipulate data to develop GIS maps of vulnerability, land use, and contamination impact. A groundwater flow model for the area of study was also used to track the flow and to delineate the capture zones of public wells. The results show that areas of highest contamination risk occur in the southern cities of Khan Yunis and Rafah. The majority of public wells are located in an intermediate risk zone and four wells are in a high risk zone.     

Spatial prediction of soil properties in a watershed scale through maximum likelihood approach

Surface map of soil properties plays an important role in various applications in a watershed. Ordinary kriging (OK) and regression kriging (RK) are conventionally used to prepare these surface maps but generally need large number of regularly girded soil samples. In this context, REML-EBLUP (REsidual Maximum Likelihood estimation of semivariogram parameters followed by Empirical Best Linear Unbiased Prediction) shown capable but not fully tested in a watershed scale. In this study, REML-EBLUP approach was applied to prepare surface maps of several soil properties in a hilly watershed of Eastern India and the performance was compared with conventionally used spatial interpolation methods: OK and RK. Evaluation of these three spatial interpolation methods through root-mean-squared residuals (RMSR) and mean squared deviation ratio (MSDR) showed better performance of REML-EBLUP over the other methods. Reduction in sample size through random selection of sampling points from full dataset also resulted in better performance of REML-EBLUP over OK and RK approach. The detailed investigation on effect of sample number on performance of spatial interpolation methods concluded that a minimum sampling density of 4/km² may successfully be adopted for spatial prediction of soil properties in a watershed scale using the REML-EBLUP approach.     

Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body,Democratic Republic of Congo

A multivariate statistical modelling approach was applied to explain the anthropogenic pressure of nitrate pollution on the Kinshasa groundwater body (Democratic Republic of Congo). Multiple regression and regression tree models were compared and used to identify major environmental factors that control the groundwater nitrate concentration in this region. The analyses were made in terms of physical attributes related to the topography, land use, geology and hydrogeology in the capture zone of different groundwater sampling stations. For the nitrate data, groundwater datasets from two different surveys were used. The statistical models identified the topography, the residential area, the service land (cemetery), and the surface-water land-use classes as major factors explaining nitrate occurrence in the groundwater. Also, groundwater nitrate pollution depends not on one single factor but on the combined influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics. The groundwater nitrate pressure was better predicted with the regression tree model than with the multiple regression model. Furthermore, the results elucidated the sensitivity of the model performance towards the method of delineation of the capture zones. For pollution modelling at the monitoring points, therefore, it is better to identify capture-zone shapes based on a conceptual hydrogeological model rather than to adopt arbitrary circular capture zones.     

Climate controls on nitrate concentration variability in the Abbotsford-Sumas aquifer,British Columbia,Canada

Understanding the linkage between temporal climate variability and groundwater nitrate concentration variability in monitoring well records is key to interpreting the impacts of changes in land-use practices and assessing groundwater quality trends. This study explores the coupling of climate variability and groundwater nitrate concentration variability in the Abbotsford-Sumas aquifer. Over the period of 1992–2009, the average groundwater nitrate concentration in the aquifer remained fairly steady at approximately 15 mg/L nitrate-N. Normalized nitrate data for 19 individual monitoring wells were assessed for a range of intrinsic factors including precipitation, depth to water table, depth below water table, and apparent groundwater age. At a broad scale, there is a negative correlation between nitrate concentration and apparent groundwater age. Each dedicated monitoring well shows unique, non-uniform cyclical variability in nitrate concentrations that appears to correspond with seasonal (1 year) cycles in precipitation as well as longer-period cycles (~5 years), possibly due to ENSO (El Niño Southern Oscillation) or the Pacific North American (PNA) pattern. These precipitation cycles appear to influence nitrate concentrations by approximately ±30 % of the critical concentration (10 mg/L NO₃–N). Not all wells show direct correlation due to many complex local-scale factors that influence nitrate leaching including spatially and temporally variable nutrient management practices and soil/crop nitrogen dynamics (anthropogenic and agronomic factors).     

Probabilistic Assessment of Temperature in the Euganean Geothermal Area (Veneto Region, NE Italy)

In the geothermal Euganean area (Veneto region, NE Italy) water temperatures range from 60 to 86°C. The aquifer considered is rocky and the production wells in this study have a depth ranging from 300 to 500 m. For exploitation purposes, it is important to identify zones with a high probability that the temperature is more than 80°C and zones with a high probability that the temperature is less than 70°C. First, variographic analysis was conducted from 186 temperature data of thermal ground waters. This analysis gave results that are consistent with the main regional tectonic structure, the NW-SE trending Schio-Vicenza fault system. Then indicator variograms of the second, fifth, and eighth decile were compared to identify the spatial continuity at different thresholds. The unacceptability of a multigaussian hypothesis of the random function and the necessity to know the cumulative distribution function in any location, suggested the use of a nonparametric geostatistical procedure such as indicator kriging. Thus, indicator variograms at the cutoffs of 65, 70, 73, 75, 78, 80, 82, and 84°C were analyzed, fitted, and used during the indicator kriging procedure. Finally, probability maps were derived from postprocessing indicator kriging results. These maps identified scarcely exploited areas with a high probability of the temperature being higher than 80°C, between 70 and 80°C and areas with high probability of the temperature being below 70°C.     

The origin and distribution of nitrate in groundwater from village wells in Kotagede, Yogyakarta, Indonesia

The causes and nature of nitrate pollution of wells in a village within Kotagede, a subdistrict of the city of Yogyakarta, Indonesia, were investigated in a detailed hydrological study. Nitrate concentrations in groundwater frequently exceeded the WHO recommended limit of 50 mg L^− 1. Groundwater nitrate concentrations were measured over a 19-month period in monitoring wells and in piezometers placed strategically in relation to sewage tanks within the village. Results indicate that the tanks are major sources of nitrate in the groundwater and that the input is markedly dependent on rainfall, resulting in a surge of nitrate into the groundwater at the beginning of each wet season. That the tanks are a major source was confirmed by measuring nitrate in soil cores obtained by augering close to selected tanks. Washrooms, where people wash themselves, are not significant sources of nitrate. Faecal coliform counts in groundwater from a random selection of wells are very high. The results have implications for the siting of wells and toilets within villages in Indonesia. Received, January 1999/Revised, August 1999/Accepted, August 1999     

Interpolation and mapping of probabilities for geochemical variables exhibiting spatial intermittency

In monitoring a minor geochemical element in groundwater or soils, a background population of values below the instrumental detection limit is frequently present. When those values are found in the monitoring process, they are assigned to the detection limit which, in some cases, generates a probability mass in the probability density function of the variable at that value (the minimum value that can be detected). Such background values could distort both the estimation of the variable at nonsampled locations and the inference of the spatial structure of variability of the variable. Two important problems are the delineation of areas where the variable is above the detection limit and the estimation of the magnitude of the variables inside those areas. The importance of these issues in geochemical prospecting or in environmental sciences, in general related with contamination and environmental monitoring, is obvious. In this paper the authors describe the two-step procedure of indicator kriging and ordinary kriging and compare it with empirical maximum likelihood kriging. The first approach consists of using a binary indicator variable for estimating the probability of a location being above the detection limit, plus ordinary kriging conditional to the location being above the detection limit. An estimation variance, however, is not available for that estimator. Empirical maximum likelihood kriging, which was designed to deal with skew distributions, can also deal with an atom at the origin of the distribution. The method uses a Bayesian approach to kriging and gives intermittency in the form of a probability map, its estimates providing a realistic assessment of their estimation variance. The pros and cons of each method are discussed and illustrated using a large dataset of As concentration in groundwater. The results of the two methods are compared by cross-validation.