Categorical Indicator Kriging for assessing the risk of groundwater nitrate pollution: the case of Vega de Granada aquifer (SE Spain)

Using numerical methods for map the spatiotemporal geogenic and anthropogenic influences on the groundwater in a detrital aquifer in south Spain

The presence of trace elements in water for domestic supply or irrigation could pose a significant toxic risk for health, due to direct consumption or bioaccumulation through the ingestion of vegetables irrigated with this water. This paper studies the presence of 41 trace elements plus nitrate and bromate in groundwater, using a multivariate statistical tool based on Principal Component Analysis and a geostatistical Kriging method to map the results. Principal Component Analysis revealed 11 significant principal components, which account for 82% and 81% of the total variance (information) respectively for the two dates analysed. Ordinary Kriging was applied to draw maps of the trace elements and PC scores. This research breaks new ground in terms of the large number of parameters used and in terms of the analysis of spatiotemporal variations in these parameters. The results obtained indicate that PC1 represents the natural quality of the aquifer (geogenic) and that there is little change in the average PC1 value between the two dates studied (June near the peak recharge point and November at the end of summer). Agriculture is the human activity that causes the greatest variations in the quality of the groundwater due to the use of fertilizers and due to watering crops with wastewater (PC7_J and PC5_N, June and November, respectively). Other elements of industrial origin, which are dangerous for human health, such as Pb, Cu and Cd, are grouped together in other principal components. The results show that the decline, or even complete absence, of natural recharge during the summer months leads to an increase in the TEs produced by human activity. This indicates that a temporary reduction in the natural recharge could worsen the quality of water resources. Based on the interpretation of the estimated maps, a synthetic map was created to show the spatial distribution of the areas affected by geogenic and anthropogenic factors. Studies with a global approach like this one are necessary in that the possible sources of pollution that could alter the quality of the groundwater and the amount of trace elements and other potentially harmful substances could increase as time goes by. The main advantage of the methodology proposed here is that it reduces the number of parameters, so simplifying the results. This makes it easier to interpret the results and manage the quality of the water.

Rapid groundwater decline and some cases of recovery in aquifers globally

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline1-10, resulting in seawater intrusion11, land subsidence12,13, streamflow depletion14-16 and wells running dry17. However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals18. We show that rapid groundwater-level declines (>0.5 m year-1) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world's regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.

Susceptibility Assessment of Groundwater Nitrate Contamination Using an Ensemble Machine Learning Approach

Groundwater pollution susceptibility mapping using parsimonious approaches with limited data is of utmost importance for water resource and health planning, especially in data-scarce regions. Current research assesses groundwater nitrate susceptibility by considering the various combination of explanatory variables. In this study, the novel machine learning models of weighted subspace random forest (WSRF) and generalized additive model using LOESS (GAMLOESS) are applied, and the results are compared with well-known machine learning models of K-nearest neighbors (KKNN) and random forest (RF). The optimum combination of inputs for groundwater nitrate susceptibility mapping is identified using the k-fold cross-validation methodology. Results indicated that the combination of variables of precipitation, groundwater level, and lithology had the best performance among the 16 combinations. Modeling performance using the optimum combination demonstrated that the new ensemble approach, the WSRF model, had superior performance according to the evaluation metrics of accuracy (0.87), kappa (0.73), precision (0.92), false alarm ratio (0.08), and critical success index (0.75). The susceptibility assessment results of this paper can be a useful tool in developing strategies for the prevention and protection of groundwater pollution.

Denitrification in Intrinsic and Specific Groundwater Vulnerability Assessment: A Review

Several groundwater vulnerability methodologies have been implemented throughout the years to face the increasing worldwide groundwater pollution, ranging from simple rating methodologies to complex numerical, statistical, and hybrid methods. Most of these methods have been used to evaluate groundwater vulnerability to nitrate, which is considered the major groundwater contaminant worldwide. Together with dilution, the degradation of nitrate via denitrification has been acknowledged as a process that can reduce reactive nitrogen mass loading rates in both deep and shallow aquifers. Thus, denitrification should be included in groundwater vulnerability studies and integrated into the various methodologies. This work reviewed the way in which denitrification has been considered within the vulnerability assessment methods and how it could increase the reliability of the overall results. Rating and statistical methods often disregard or indirectly incorporate denitrification, while numerical models make use of kinetic reactions that are able to quantify the spatial and temporal variations of denitrification rates. Nevertheless, the rating methods are still the most utilized, due to their linear structures, especially in watershed studies. More efforts should be paid in future studies to implement, calibrate, and validate user-friendly vulnerability assessment methods that are able to deal with denitrification capacity and rates at large spatial and temporal scales.

Quantifying nitrate pollution sources of the drinking water source area using a Bayesian isotope mixing model in the northeastern suburbs of Beijing, China

Nitrate pollution has become an environmental problem of global concern. One effective way for controlling the nitrate pollution of water is to identify the pollution source and reduce the input of nitrate. This study traces and quantifies the sources of nitrate contamination to groundwater and surface water in the northeastern suburbs of Beijing, where an emergency groundwater source zone is located. Nitrogen and oxygen stable isotope analysis, geospatial analysis techniques, principal component analysis, correlation analysis, and a Bayesian isotope mixing model were used to achieve our goals. The results show that the main sources of nitrate pollution in groundwater were manure and sewage (M&S) (42.6 %) > soil nitrogen (SN) (26.6 %) > NH4+ in fertilizer and rain (NHF&R) (24.5 %) > NO3- fertilizer (NOF) (5.0 %) > NO3- in atmospheric deposition (NAD) (1.3 %), and main sources of nitrate in surface water were M&S (28.8 %) > SN (20.4 %) > NAD (19.8%) > NOF (16.5%) > NHF&R (14.5 %). Due to the high permeability of the aquifer in the study area, there was a strong hydraulic connection between groundwater and surface water. The discharge of treated wastewater (reclaimed water) into the mostly dried river channel in the study area might aggravate nitrate pollution in the groundwater.

Identification and uncertainty analysis of high-risk areas of heavy metals in sediments of the Yangtze River estuary, China

In this study, ordinary kriging (OK) and indicator kriging (IK) were used to analyze the uncertainty associated with high-risk areas of seven heavy metals (As, Cd, Cr, Cu, Hg, Pb, and Zn) in sediments of the Yangtze River estuary during four seasons. The OK results showed that the high-risk areas of Cd, Cr, Cu, Hg, and Pb had a high proportion, with the highest corresponding to Cr pollution (up to 60%). Predictions based on IK revealed that the proportion of high-risk areas of Cr, Cd, and Hg pollution were high, especially that of Cr was higher than 90%. However, there were uncertainties between the OK and IK results. The uncertainty results revealed that the uncertainty areas of Cr pollution were relatively large, accounting for about 30%, while those of Cd, Cu, and Hg pollution were lower than 10%.

An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion

Sustainable management of the environment is based on the preservation of natural resources, first of all, freshwater—both surface and groundwater—from exhaustion and contamination. Thus, development of adequate monitoring solutions, including fast and adaptive modelling approaches, are of high importance. Recent progress in machine learning techniques provide an opportunity to improve the prediction accuracy of the spatial distribution of properties of natural objects and to automate all stages of this process to exclude uncertainties caused by handcrafting. We propose a technique to construct the weighted Water Quality Index (WQI) and the spatial prediction map of the WQI in tested area. In particular, WQI is calculated using dimensionality reduction technique (Principal Component Analysis), and spatial map of WQI is constructed using Gaussian Process Regression with automatic kernel structure selection using Bayesian Information Criterion (BIC). We validate our approach on a new dataset for groundwater quality in the New Moscow region, where groundwater is mostly used for drinking purposes. According to estimated WQI values, groundwater quality across the study region is relatively high, with few points, less than 0.5% of all observations, severely contaminated. Estimated WQIs then were used to construct spatial distribution models, GPR-BIC approach was compared with ordinary Kriging (OK), Universal Kriging (UK) with exponential, Gaussian, polynomial and periodic kernels. Quality of models was assessed using cross-validation scheme, according to which BIC-GPR approach showed better performance on average with 15% higher R2 score comparing to other Kriging models. We show that the proposed geospatial interpolation is a potentially powerful and adaptable tool for predicting the spatial distribution of properties of natural resources.

Groundwater quality for potable and irrigation uses and associated health risk in southern part of Gu'an County, North China Plain

The study area is a part of the North China Plain, where groundwater is heavily abstracted for drinking and irrigation purposes. Groundwater quality is adversely affected due to rapid economic development and urbanization. Therefore, the purposes of this study were to evaluate the suitability of groundwater for drinking and irrigation purposes and to quantify the associated human health risks in the southern part of Gu'an County, North China Plain. The matter-element extension method based on entropy weight was used to evaluate the water quality for drinking, while sodium adsorption ratio, sodium percentage (%Na), residual sodium carbonate and magnesium hazard were used to evaluate the water quality for irrigation. Non-carcinogenic and carcinogenic health risks via different exposure ways were evaluated for different age groups. The study found that the quality of both deep and shallow groundwater in this area was generally suitable for drinking. Deep water quality has better quality than the shallow water. However, 8.70% and 73.92% of water samples pose non-carcinogenic health risks on adults and children, respectively. Children and adults are also at cancer risk due to Cr⁶⁺ and As in drinking groundwater in this area. The main responsible parameters for non-carcinogenic risk are Cr⁶⁺, F^- and Fe, and Cr⁶⁺ is also responsible for carcinogenic risk. These toxic elements are mainly from industries. Therefore, deterioration of groundwater quality can be prevented by strengthening the sewage management of various industries.

Uranium content and uranium isotopic disequilibria as a tool to identify hydrogeochemical processes

This paper studies the uranium content and uranium isotopic disequilibria as a tool to identify hydrogeochemical processes from 52 groundwater samples in the province of Granada (Betic Cordillera, southeastern Spain). According to the geological complexity of the zone, three groups of samples have been considered. In Group 1 (thermal waters; longest residence time), the average uranium content was 2.63 ± 0.16 μg/L, and ²³⁴U/²³⁸U activity ratios (AR) were the highest of all samples, averaging 1.92 ± 0.30. In Group 2 (mainly springs from carbonate aquifers; intermediate residence time), dissolved uranium presented an average value of 1.34 ± 0.13 μg/L, while AR average value was 1.38 ± 0.25. Group 3 comes from pumping wells in a highly anthropized alluvial aquifer. In this group, where the residence time of the groundwater is the shortest of the three, average uranium content was 5.28 ± 0.26 μg/L, and average AR is the lowest (1.17 ± 0.12). In addition, the high dissolved uranium value and the low AR brought to light the contribution of fertilizers (Group 3). In the three groups, ²³⁵U/²³⁸U activity ratios were similar to the natural value of 0.046. Therefore, ²³⁵U detected in the samples comes from natural sources. This study is completed with the determination of major ions and physicochemical parameters in the groundwater samples and the statistical analysis of the data by using the Principal Component Analysis. This calculation indicates the correlation between uranium isotopes and bicarbonate and nitrate anions.

An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by kriging and co-kriging models

Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of kriging-based models (ordinary kriging, simple kriging and universal kriging), which are constructed at Level 1 to predict nitrate concentrations; and a Multiple Co-Kriging (MCoK) model is used at Level 2 to enhance the accuracy of the predictions. Strategy 2 is also at two levels but employs Indicator Kriging (IK) at Level 1 as a probabilistic spatial model to predict areas at risk of exceeding two thresholds of 37.5 mg/L and 50 mg/L of nitrate concentration, and Multiple Co-Indicator Kriging (MCoIK) at Level 2 for a better accuracy. The improvements at Level 2 for both strategies are remarkable and hence they are used to gain an insight into inherent problems. The results of a study delineate areas with excessive nitrate concentrations, which are in the vicinity of urban areas and hence reflect poor planning practices since the 1990s. The results further reveal the patterns on sensitivities to seasonal variations driven by aquifer recharge and strong dilution processes in spring times; and on the role of pumpage impacting aquifers giving rise to possible hotspots of nitrate concentrations.

Employing indicator-based geostatistics and quantitative microbial risk analysis to assess the health risks of groundwater use for household demands on the Pingtung Plain, Taiwan

Because of the limited surface water on the Pingtung Plain, Taiwan, the plain's residents frequently extract groundwater to meet their daily household water demands. The residents may experience gastrointestinal infections due to incidental ingestion of groundwater with fecal pollution. This study used indicator kriging (IK) and quantitative microbial risk analysis (QMRA) to assess the health risks of using groundwater for household cleaning and horticultural irrigation on the Pingtung Plain. First, IK was employed to determine the conditional cumulative distribution function (CCDF) of groundwater Escherichia coli (E. coli). Nonparametric Monte Carlo simulation based on established CCDF was then adopted to characterize the distributions and uncertainty of groundwater E. coli. Finally, QMRA was employed to determine health risks of groundwater use for household cleaning and horticultural irrigation, and the 95th percentiles of the risk distributions were calculated to obtain a representative risk. The study results indicated that the health risks of groundwater use ranged from 3.95 × 10^-5 to 2.49 × 10^-2 infections/user/year and exceeded the acceptable level, 1 × 10^-4 infections/user/year, in most of the aquifers. Accordingly, residents of this plain should not directly extract groundwater for use in daily life.

Multi-scaled response of groundwater nitrate contamination to integrated anthropogenic activities in a rapidly urbanizing agricultural catchment

Anthropogenic activities have a significant contribution to groundwater nitrate contamination at multiple spatial scales in urbanizing agricultural catchments, while how to derive the optimal researching scale and explore the relative importance among anthropogenic activities for groundwater nitrate contamination still remains challenging. In this study, 165 perched groundwater and 120 shallow groundwater samples were collected in two urbanizing agricultural catchments, to explore anthropogenic activity effects on groundwater nitrate contamination crossing multiple spatial scales, integrating the probability kriging, multi-scale comparison at spatial scales of 100 to 1900 m with an increment of 200 m at the block scales, and variance partitioning analysis. Probability of perched and shallow groundwater nitrate concentration > 3 mg L^-1 exhibited strong spatial autocorrelation, with effective ranges of 1091 m and 3743 m from semivariogram, respectively. Relationships between perched and shallow groundwater nitrate concentrations were more significant and robust (r = 0.30-0.52, p < 0.001) at the block scale from 300 to 1100 m, indicating that perched groundwater nitrate closely related to shallow groundwater nitrate. The responses of groundwater nitrate contamination on anthropogenic drivers presented strongly scaling correlation and had the highest correlation at the spatial scale of 1100 m, suggesting the optimal scale for exploring anthropogenic activity effects on groundwater nitrate contamination. The three categories of anthropogenic drivers (urbanization, agriculture intensification, and demographic driver) contributed to 31.0-84.0% part of the total variations in groundwater nitrate contamination at the spatial scale of 1100 m. Particularly, agriculture intensification was the most influential driver for groundwater nitrate contamination, while the urbanizing process and population growth played important roles surrounding urban cores. Our findings highlighted the importance of incorporating multi-scale comparisons on regional groundwater quality evaluation, and provided technical support to the groundwater resource management strategy development in urbanizing agricultural regions.

Rice yield estimation based on forecasting the future condition of groundwater salinity in the Caspian coastal strip of Guilan Province, Iran

Irrigation water salinity is one of the factors that reduces agricultural production. Guilan Province is one of the most important rice-producing areas in Iran where groundwater is used for irrigation. The temporal and spatial variations of groundwater salinity were studied in the coastal strip covering 4285 km² of the province using data from 73 wells, as well as its estimated effect on the rice yield. Data on mean electrical conductivity (EC) for each 6-month period of 12 consecutive years, from the second half of 2002 until the end of 2014, were analyzed and resulted in 25 mean ECs. EC maps and maps of the probability of higher salinity areas were obtained by using ordinary kriging (OK) and indicator kriging (IK) in ArcGIS 9.3 software, respectively. Thereby, areas belonging to different salinity classes were outlined and places with higher salinity reducing the rice yield were identified. In addition, the Mann-Kendall test and Sen's slope were used to project future changes. The results indicated that due to the salinity of groundwater in the coastal strip area, the minimum and the maximum rice yields were 80% and 100%, respectively. Using the IK method, higher probability of groundwater salinity reducing the yield was found from the central parts toward the east. The Mann-Kendal test result showed significant temporal trends of the size of areas below the 100% yield (EC < 1 dS/m) and 90-100% yield (1 < EC < 1.34 dS/m) thresholds. The equations given by Sen's slope estimator indicated that the groundwater salinity will not be a limiting factor for achieving 100% rice yields from the year of 2021 onward in all of the Guilan coastal area. The trend of increasing precipitation in the area may be an important cause.

Severe Nitrate Pollution and Health Risks of Coastal Aquifer Simultaneously Influenced by Saltwater Intrusion and Intensive Anthropogenic Activities

Groundwater quality is critical for regional sustainability and human well-beings in coastal regions, because groundwater is an important water resource for these areas facing water scarcity. Anthropogenic activities might induce nitrate pollution, whereas saltwater intrusion could decrease coastal groundwater discharge into sea to subsequently cause the persistent accumulation of pollutants in coastal aquifer. Rare information is available on the nitrate pollution of coastal aquifer under simultaneous influences of saltwater intrusion and intensive anthropogenic activities. This study investigated the distribution, pollution, possible sources, and potential health risks of groundwater nitrate of typical coastal aquifer simultaneously influenced by saltwater intrusion and intensive anthropogenic activities. The average/maximal concentration of groundwater nitrate was 173.70/824.80 mg/L, indicating the severe accumulation of nitrate in the coastal aquifer. Concentrations of nitrate in coastal groundwater were much higher than those in adjacent seawater. Groundwater salinization did not have significant effects on nitrate distribution. Groundwater in 87.6% of sampling sites was not suitable for drinking based on nitrate evaluation criterion. Anthropogenic activities might induce nitrate pollution in approximately 94.7% of sampling sites. Sources, including sewage and manure, soil nitrogen, and ammonium fertilizers, contributed to groundwater nitrate with concentration > 100 mg/L in the study area, whereas sewage and manure were the predominant source affecting groundwater nitrate in 97.5% of sampling sites. Groundwater nitrate exerted unacceptable noncancer health risks for infants, children, teenagers, and adults in more than 87.6% of the study area. Infants and children were the most susceptibly influenced by groundwater nitrate. It is urgent to take effective measures for controlling groundwater nitrate pollution in the study area.

Spatial dynamic assessment of health risks for urban river cruises

River cruising ships move along river courses, and thus health risks to passengers may vary spatially due to the accidental exposure of river fecal pollution. This study performed a spatial dynamic assessment of health risks for river cruises in the highly urbanized Tamsui River Basin. First, the spatial distributions of river Escherichia coli (E. coli) were probabilistically characterized using indicator kriging (IK). Moreover, the current river cruise information was surveyed to obtain cruise routes and transit times. Then, to explore the parametric uncertainty of quantitative microbial risk assessment (QMRA), the ingestion rate (IR) for boating was determined using Monte Carlo simulation (MCS). Moreover, river E. coli distributions were estimated using nonparametric MCS according to multi-threshold IK estimates. Eventually, after combining the distribution of the joint probability of the IR and E. coli in QMRA, the β-Poisson dose-response function was adopted to analyze risks to river cruise passengers at discretized segments of cruise routes. Health risks to river cruise passengers were integrated at the discretized segments to explore suitable recreational strategies for river cruises. The research results indicate that all health risks do not exceed a daily target level of 8 illnesses per 1000 exposures for single-trip cruise routes. However, health risks to passengers can exceed this level for round-trip cruise routes along highly polluted urban river courses.

In-situ study of migration and transformation of nitrogen in groundwater based on continuous observations at a contaminated desert site

The objective of this study was to explore the controlling factors on the migration and transformation of nitrogenous wastes in groundwater using long-term observations from a contaminated site on the southwestern edge of the Tengger Desert in northwestern China. Contamination was caused by wastewater discharge rich in ammonia. Two long-term groundwater monitoring wells (Wells 1# and 2#) were constructed, and 24 water samples were collected. Five key indicators were tested: ammonia, nitrate, nitrite, dissolved oxygen, and manganese. A numerical method was used to simulate the migration process and to determine the migration stage of the main pollutant plume in groundwater. The results showed that at Well 1# the nitrogenous waste migration process had essentially been completed, while at Well 2# ammonia levels were still rising and gradually transitioning to a stable stage. The differences for Well 1# and Well 2# were primarily caused by differences in groundwater flow. The change in ammonia concentration was mainly controlled by the migration of the pollution plume under nitrification in groundwater. The nitrification rate was likely affected by changes in dissolved oxygen and potentially manganese.

Assessment and uncertainty analysis of groundwater risk

Groundwater with relatively stable quantity and quality is commonly used by human being. However, as the over-mining of groundwater, problems such as groundwater funnel, land subsidence and salt water intrusion have emerged. In order to avoid further deterioration of hydrogeological problems in over-mining regions, it is necessary to conduct the assessment of groundwater risk. In this paper, risks of shallow and deep groundwater in the water intake area of the South-to-North Water Transfer Project in Tianjin, China, were evaluated. Firstly, two sets of four-level evaluation index system were constructed based on the different characteristics of shallow and deep groundwater. Secondly, based on the normalized factor values and the synthetic weights, the risk values of shallow and deep groundwater were calculated. Lastly, the uncertainty of groundwater risk assessment was analyzed by indicator kriging method. The results meet the decision maker's demand for risk information, and overcome previous risk assessment results expressed in the form of deterministic point estimations, which ignore the uncertainty of risk assessment.

A methodology for assessing public health risk associated with groundwater nitrate contamination: a case study in an agricultural setting (southern Spain)

Groundwater nitrate contamination from agriculture is of paramount environmental interest. A continuous consumption of polluted water as drinking water or for culinary purposes is by no means a minor hazard for people's health that must be studied. This research presents a new methodology for the spatial analysis of health risk rate from intake of nitrate-polluted groundwater. The method is illustrated through its application to a water quality sampling campaign performed in the south of Spain in 2003. The probability risk model used by the US Environmental Protection Agency has been applied, considering a residential intake framework and three representative population age groups (10, 40 and 65 years).The method was based upon coupling Monte Carlo simulations and geostatistics, which allowed mapping of the health risk coefficient (RC). The maps obtained were interpreted in the framework of water resources management and user's health protection (municipalities). The results showed waterborne health risk caused by nitrate-polluted water is moderately low for the region. The observed risk was larger for the elderly and children, although no significant differences were found among the three age groups (RC average values of 95th percentile for age of 0.37, 0.33 and 0.37, respectively). Significant risk values of RC > 1 were obtained for 10 % of the surface in the NW site of the study area, where the municipalities with the highest contamination thresholds are located (agricultural activity). Nitrate concentration and intake rate stood out as the main explanatory variables of the RC.

Bioavailability and risk assessment of arsenic in surface sediments of the Yangtze River estuary

The bioavailability and risk assessment of As were studied in sediments of the Yangtze River estuary (YRE). Results showed that residual fractions dominated the As partition (>85%), which attenuated overall bioavailability. After the residual fraction, As mainly partitioned into the Fe-Mn oxides fraction (3.16-4.22%). Arsenic bound to Fe-Mn oxides was higher in wet seasons. The carbonate fraction was minimal, which may result from the negative state presence of As in sediments. According to the risk assessment code, the YRE was classified as low risk. Additionally, the reduction of As(V) to As(III) may occur due to the reducing condition in wet seasons. Considering As(III) is more toxic and mobile, As bound to the exchangeable and Fe-Mn oxides fractions may have more potential ecological risk. Thus, the speciation and fraction should be both considered on the ecological risk of As in sediments of the YRE.

Zonal management of multi-purposes groundwater utilization based on water quality and impact on the aquifer

Groundwater is widely used for drinking, irrigation, and aquaculture in the Pingtung Plain, Southwestern Taiwan. The overexploitation and poor quality of groundwater in some areas of the Pingtung Plain pose great challenges for the safe use and sustainable management of groundwater resources. Thus, establishing an effective management plan for multi-purpose groundwater utilization in the Pingtung Plain is imperative. Considerations of the quality of the groundwater and potential impact on the aquifer of groundwater exploitation are paramount to multi-purpose groundwater utilization management. This study proposes a zonal management plan for the multi-purpose use of groundwater in the Pingtung Plain. The zonal management plan is developed by considering the spatial variability of the groundwater quality and the impact on the aquifer, which is defined as the ratio of the actual groundwater extraction rate to transmissivity. A geostatistical Kriging approach is used to spatially delineate the safe zones based on the water quality standards applied in the three groundwater utilization sectors. Suitable zones for the impact on the aquifer are then spatially determined. The evaluation results showing the safe water quality zones for the three types of utilization demands and suitable zones for the impact on aquifer are integrated to create a zonal management map for multi-purpose groundwater utilization which can help government administrators to establish a water resource management strategy for safe and sustainable use of groundwater to meet multi-purpose groundwater utilization requirements in the Pingtung Plain.

Assessment of groundwater quality using DEA and AHP: a case study in the Sereflikochisar region in Turkey

This study investigated the spatial distribution of groundwater quality in Sereflikochisar Basin, in the Central Anatolian region of Turkey using different hydrochemical, statistical, and geostatistical methods. A total of 51 groundwater samples were collected from the observation wells in the study area to evaluate the characteristics of the groundwater quality. As a relatively simple and practical method, a groundwater quality index (GWQI) was developed to evaluate the overall groundwater quality. In this process, complex decision-making techniques such as analytic hierarchy process (AHP) and data envelopment analysis (DEA) were used. Based on these models, two new indices (A-GWQI and D-GWQI) were proposed. According to the D-GWQI score (from 0.6 to 1), water quality was classified in four categories as unsuitable (0.6–0.7), permissible (0.7–0.8), good (0.8–0.9), and excellent (0.9–1). The spatial distribution maps of the groundwater quality were created using the Kriging method. For each map, seven different semivariogram models were tested and the best-fitted model was chosen based on their root mean square standardized error. These maps showed that the areas with high groundwater quality were in the eastern and southern parts of the study area where the D-GWQI scores were greater than 0.8. Depending on the distance from the Salt Lake, the characteristics of groundwater changed from NaCl to NaHCO3 and CaHCO3 facies. This study shows how to determine the spatial distribution of the groundwater quality and identify the impact of salt lakes on the groundwater quality in inland aquifers. The findings of this study can be applied to ensure the quality of groundwater used for drinking and irrigation purposes in the study area.

Geostatistical analysis of tritium, groundwater age and other noble gas derived parameters in California

Key characteristics of California groundwater systems related to aquifer vulnerability, sustainability, recharge locations and mechanisms, and anthropogenic impact on recharge are revealed in a spatial geostatistical analysis of a unique data set of tritium, noble gases and other isotopic analyses unprecedented in size at nearly 4000 samples. The correlation length of key groundwater residence time parameters varies between tens of kilometers ((3)H; age) to the order of a hundred kilometers ((4)Heter; (14)C; (3)Hetrit). The correlation length of parameters related to climate, topography and atmospheric processes is on the order of several hundred kilometers (recharge temperature; δ(18)O). Young groundwater ages that highlight regional recharge areas are located in the eastern San Joaquin Valley, in the southern Santa Clara Valley Basin, in the upper LA basin and along unlined canals carrying Colorado River water, showing that much of the recent recharge in central and southern California is dominated by river recharge and managed aquifer recharge. Modern groundwater is found in wells with the top open intervals below 60 m depth in the southeastern San Joaquin Valley, Santa Clara Valley and Los Angeles basin, as the result of intensive pumping and/or managed aquifer recharge operations.

Dose rate estimates and spatial interpolation maps of outdoor gamma dose rate with geostatistical methods; A case study from Artvin, Turkey

In this study, compliance of geostatistical estimation methods is compared to ensure investigation and imaging natural Fon radiation using the minimum number of data. Artvin province, which has a quite hilly terrain and wide variety of soil and located in the north-east of Turkey, is selected as the study area. Outdoor gamma dose rate (OGDR), which is an important determinant of environmental radioactivity level, is measured in 204 stations. Spatial structure of OGDR is determined by anisotropic, isotropic and residual variograms. Ordinary kriging (OK) and universal kriging (UK) interpolation estimations were calculated with the help of model parameters obtained from these variograms. In OK, although calculations are made based on positions of points where samples are taken, in the UK technique, general soil groups and altitude values directly affecting OGDR are included in the calculations. When two methods are evaluated based on their performances, it has been determined that UK model (r = 0.88, p < 0.001) gives quite better results than OK model (r = 0.64, p < 0.001). In addition, as a result of the maps created at the end of the study, it was illustrated that local changes are better reflected by UK method compared to OK method and its error variance is found to be lower.

Compositional cokriging for mapping the probability risk of groundwater contamination by nitrates

Contamination by nitrates is an important cause of groundwater pollution and represents a potential risk to human health. Management decisions must be made using probability maps that assess the nitrate concentration potential of exceeding regulatory thresholds. However these maps are obtained with only a small number of sparse monitoring locations where the nitrate concentrations have been measured. It is therefore of great interest to have an efficient methodology for obtaining those probability maps. In this paper, we make use of the fact that the discrete probability density function is a compositional variable. The spatial discrete probability density function is estimated by compositional cokriging. There are several advantages in using this approach: (i) problems of classical indicator cokriging, like estimates outside the interval (0,1) and order relations, are avoided; (ii) secondary variables (e.g. aquifer parameters) can be included in the estimation of the probability maps; (iii) uncertainty maps of the probability maps can be obtained; (iv) finally there are modelling advantages because the variograms and cross-variograms of real variables that do not have the restrictions of indicator variograms and indicator cross-variograms. The methodology was applied to the Vega de Granada aquifer in Southern Spain and the advantages of the compositional cokriging approach were demonstrated.

Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain)

Watershed management decisions need robust methods, which allow an accurate predictive modeling of pollutant occurrences. Random Forest (RF) is a powerful machine learning data driven method that is rarely used in water resources studies, and thus has not been evaluated thoroughly in this field, when compared to more conventional pattern recognition techniques key advantages of RF include: its non-parametric nature; high predictive accuracy; and capability to determine variable importance. This last characteristic can be used to better understand the individual role and the combined effect of explanatory variables in both protecting and exposing groundwater from and to a pollutant. In this paper, the performance of the RF regression for predictive modeling of nitrate pollution is explored, based on intrinsic and specific vulnerability assessment of the Vega de Granada aquifer. The applicability of this new machine learning technique is demonstrated in an agriculture-dominated area where nitrate concentrations in groundwater can exceed the trigger value of 50 mg/L, at many locations. A comprehensive GIS database of twenty-four parameters related to intrinsic hydrogeologic proprieties, driving forces, remotely sensed variables and physical-chemical variables measured in "situ", were used as inputs to build different predictive models of nitrate pollution. RF measures of importance were also used to define the most significant predictors of nitrate pollution in groundwater, allowing the establishment of the pollution sources (pressures). The potential of RF for generating a vulnerability map to nitrate pollution is assessed considering multiple criteria related to variations in the algorithm parameters and the accuracy of the maps. The performance of the RF is also evaluated in comparison to the logistic regression (LR) method using different efficiency measures to ensure their generalization ability. Prediction results show the ability of RF to build accurate models with strong predictive capabilities.