Groundwater Quality Data Research Articles

Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km2) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO4 and NH4. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO4, Fe, PO4, and NH4. In contrast, SK performed worst for the species except for SO4. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling.

Assessing groundwater quality and its association with child undernutrition in India

Background and objectivesGroundwater contamination poses a significant health challenge in India, particularly impacting children. Despite its importance, limited research has explored the nexus between groundwater quality and child nutrition outcomes. This study addresses this gap, examining the association between groundwater quality and child undernutrition, offering pertinent insights for policymakers. Data and methodsThe study uses data from the fifth round of the National Family Health Survey (NFHS) and the Central Groundwater Board (CGWB) to analyze the association between groundwater quality and child nutritional status. The groundwater quality data were collected by nationwide monitoring stations programmed by CGWB, and the child undernutrition data were obtained from the NFHS-5, 2019–21. The analysis included descriptive and logistic regression model. The study also considers various demographic and socio-economic factors as potential moderators of the relationship between groundwater quality and child undernutrition. FindingsSignificant variation in groundwater quality was observed across India, with numerous regions displaying poor performance. Approximately 26.53 % of geographical areas were deemed unfit for consuming groundwater. Environmental factors such as high temperatures, low precipitation, and arid, alluvial, laterite-type soils are linked to poorer groundwater quality. Unfit-for-consumption groundwater quality increased the odds of undernutrition, revealing a 35 %, 38 %, and 11 % higher likelihood of stunting, underweight, and wasting in children, with higher pH, Magnesium, Sulphate, Nitrate, Total Dissolved Solids, and Arsenic, levels associated with increased odds of stunting, underweight, and wasting. Higher temperatures (>25 °C), high elevations (>1000 m), and proximity to cultivated or industrial areas all contribute to heightened risks of child undernutrition. Children consuming groundwater, lacking access to improved toilets, or living in rural areas are more likely to be undernourished, while females, higher-income households, and those consuming dairy, vegetables, and fruits daily exhibit lower odds of undernutrition. Policy implicationsPolicy implications highlight the urgent need for investment in piped water supply systems. Additionally, focused efforts are required to monitor and improve groundwater quality in regions with poor water quality. Policies should emphasize safe sanitation practices and enhance public awareness about the critical role of safe drinking water in improving child health.

Groundwater quality in coastal area: A case study in Parangtritis Village, Bantul, Yogyakarta, Indonesia

Groundwater in coastal areas is one of the natural resources that is vulnerable to quality degradation due to population activities in coastal areas. This is also the case in Parangtritis Village, a coastal area with various potential regions for the population's welfare, ranging from tourism to agriculture, animal husbandry, and fisheries. Therefore, this study explores groundwater quality in Parangtritis Village, Kretek District, Bantul Regency, Yogyakarta. Groundwater quality data was collected through field surveys based on land use, with water quality parameters including odor, color, nitrate, nitrite, and E. coli. The Minister of Health Regulation Document Number 32 of 2017 was adopted as a benchmark for groundwater quality in the research area. Further, by using the gathered data, the groundwater quality was classified based on limiting parameters. Groundwater quality is distributed based on limiting parameters such as odor, color, nitrite, and E. coli bacteria. Odor and color limitations are found in agricultural areas, tourism areas, and fish farms. Nitrite limitations are found in residential and livestock areas. E. coli bacteria limitations are found in all land use areas.

Emerging nitrate contamination in groundwater: Changing phase in a fast-growing state of India

The consumption of nitrate-contaminated groundwater is often associated with potential health risks, particularly in children. This study aimed to assess the hydrochemistry and nitrate contamination in groundwater of Kerala state, India for the years 2010 and 2018 and evaluate the potential human health risks due to nitrate exposure in adults, and children through oral ingestion and dermal contact pathways. Nitrate-contaminated zones were identified by spatial mapping of nitrate concentration based on groundwater quality data of 324 wells. Groundwater is typically acidic to slightly alkaline, and the electrical conductivity (EC) varied from 33 to 1180 μS/cm in 2010 and 34.6–2500 mg/L in 2018 indicating a noticeable increase over the years. Most samples fall within low salt enrichment category. The nitrate concentration in groundwater varied from 0 to 173 mg/L with a mean of 15.4 mg/L during 2010 and 0 to 244 with a mean of 20.3 mg/L during 2018. Though nitrate concentrations show uneven spatial distributions due to both natural and anthropogenic sources, the spatial clustering of higher concentrations remains almost same in both periods. In 2010, non-carcinogenic risk, as measured by Health Index Total (HITotal) values in groundwater for the investigated region, ranged from 0.005 to 4.170 (mean of 0.349) for males, 0.005 to 4.928 (mean of 0.413) for females, and 0.008 to 7.243 (mean of 0.607) for children, while in 2018, the corresponding values varied from 0.001 to 5.881 (mean of 0.501) for males, 0.002 to 6.950 (mean of 0.592) for females, and 0.003 to 10.215 (mean of 0.870) for children, indicating a substantial increase in risk, for females and children. Greater health risk is observed in children during both the periods. The findings emphasize the need for proper water quality management, especially in regions with higher vulnerability to nitrate pollution, to safeguard human health and well-being.

Characterization of groundwater potability and irrigation potential in Uttar Pradesh, India using water quality index and multivariate statistics

Abstract This study includes groundwater quality data from 290 monitoring sites from 69 districts of Uttar Pradesh, India. The analysis of the data showed that 1.3, 75.52, 47.93, and 31.03% of groundwater samples had concentrations of electrical conductivity (EC), total hardness (TH), Mg2+, and HCO3−, respectively, higher than the maximum permissible limit. Groundwater quality index (GWQI) was calculated for these 290 monitoring sites which revealed that 21 sites (7.24%) had inappropriate GWQI for drinking water, and 18 sites (7.24%) had an unsuitable index for irrigation. Most of the sampling sites (98.97%) showed high EC contents in groundwater with a mean value of 999.33 μS/cm. Fluoride content was found within the permissible limits in 95.52% of the samples, while 4.48% had high concentrations. The use of hierarchical cluster analysis differentiated all the sites into two clusters: one with high pollution and the other with low pollution. Significant correlations exist between physicochemical and irrigation indicators in the correlation matrix. High loadings of EC, TH, Ca2+, Mg2+, Na+, Cl−, and SO42− were identified in the first principal component, which are thought to be pollution-controlled processes from anthropogenic sources. According to the Chadha diagram, CaHCO3 and Ca–Mg–HCl were the two most prevalent chemicals in the water.

Prediction of ground water quality in western regions of Tamilnadu using LSTM network

Assessing and safeguarding groundwater quality is critical for sustaining life in water-scarce regions like western Tamil Nadu. The motivation behind this study stems from the pressing need to address water quality challenges in a region grappling with scarcity. Despite existing efforts, a notable research gap exists in predictive tools that comprehensively capture the nuanced temporal variations and trends in groundwater quality. This is where the LSTM network steps in, showcasing exceptional accuracy in short-term predictions and discerning long-term trends. This research uses Long Short-Term Memory (LSTM) networks, a variant of recurrent neural networks, to predict groundwater quality in South Indian Regions, especially in Tamil Nadu. Extensive data, encompassing parameters such as pH, dissolved oxygen, turbidity, and various chemical constituents, were gathered over an extended timeframe. The LSTM model was then trained on this historical dataset, factoring in temporal dependencies and seasonality inherent in groundwater quality data. The validation process rigorously tests the LSTM model against actual groundwater quality measurements. The results were impressive, as the model demonstrated a remarkable ability to unravel the complex variations in groundwater quality.

Issues of Bias in Groundwater Quality Data Sets in an Irrigated Floodplain Aquifer of Variable Salinity

In arid regions characterized by large variations in groundwater salinity, the data derived from irrigation and domestic water supply wells may exhibit bias, reflecting an overall lower salinity than the true aquifer distribution. This bias stems from the decommissioning, non-use, or disrepair of wells that are frequently sources of higher salinity readings, rendering them unavailable for sampling. Baseflow-fed streams, agricultural drains, seeps, springs issuing into agricultural drains, and randomly located test hole samples tend to manifest higher averages and ranges of salinity when compared to supply wells. Agricultural drain flows, springs, and test holes, if sampled following recommended guidelines, are less susceptible to such bias. This study presents a case of groundwater bias identified through an initial water well sampling program in El Paso (Texas, USA). Subsequent rounds of sampling, incorporating drain samples, spring samples, and test hole samples, revealed a more comprehensive understanding of the salinity dynamics. The dataset not only highlights the existence of bias but also provides evidence for a combined geological and agricultural origin of salinity. Additionally, it demonstrates that drain sampling in an earlier study did not accurately depict a primary salinity source due to incomplete analysis of the data. Recommendations are outlined to mitigate bias, emphasizing the importance of sample control from baseflow-fed drains, springs, water wells, and test hole samples. The study also infers the upwelling of saline groundwater from deeper formations in the study area, contributing to a more comprehensive understanding of groundwater salinity dynamics.

Delineation of Potential Groundwater Zones and Assessment of Their Vulnerability to Pollution from Cemeteries Using GIS and AHP Approaches Based on the DRASTIC Index and Specific DRASTIC

The risk of aquifer contamination is determined by the interaction between the pollutant load and the vulnerability of an aquifer. Owing to the decomposition of bodies and degradation of artefacts, cemeteries may have a negative impact on groundwater quality and suitability for use due to the leaching of organic compounds (e.g., biodegradable organics, pharmaceuticals, and formaldehyde), inorganic compounds (e.g., nitrate and heavy metals), pathogenic bacteria, and viruses. Factors such as burial and soil type, rainfall amount, and groundwater depth may increase aquifer vulnerability to pollutants generated in cemeteries. The potential for groundwater contamination was investigated in two cemeteries of the Soure region in Portugal (Samuel–UC9 and Vinha da Rainha–UC10), using the classic DRASTIC model, followed by some adjustments, depending on the particularities of the locations, resulting in a Final Classification considered as Specific DRASTIC. By combining Remote Sensing (RS), Geographic Information System (GIS), and Analytical Hierarchy Process (AHP), groundwater potential zones (GWPZs) were identified, and aquifer vulnerability was assessed, which included the elaboration of thematic maps using GIS operation tools. The maps allowed for the identification of areas with different susceptibilities to contamination: from “Low” to “Very high” for the DRASTIC index and from “Very Low” to “Very high” for the Specific DRASTIC index. Although the difference between the UC9 and UC10 cemeteries is negligible, UC10 is more vulnerable because of its proximity to the community and critically important mineral water resources (such as Bicanho Medical Spa). The Specific model seems better-suited for describing vulnerability to cemeteries. Although there is limited groundwater quality data for the area, the development of vulnerability maps can identify areas that can be sensitive spots for groundwater contamination and establish procedures for pollution prevention.

Comparative Study of Geospatial Techniques for Interpolating Groundwater Quality Data in Agricultural Areas of Punjab, Pakistan

Groundwater Arsenic (As) data are often sparse and location-specific, making them insufficient to represent the heterogeneity in groundwater quality status at unsampled locations. Interpolation techniques have been used to map groundwater As data at unsampled locations. However, the results obtained from these techniques are affected by various inherent and external factors, which lead to uncertainties in the interpolated data. This study was designed to determine the best technique to interpolate groundwater As data. We selected ten interpolation techniques to predict the As concentration in the groundwater resources of Punjab, Pakistan. Two external factors, the spatial extent of the study area and data density, were considered to assess their impact on the performance of interpolation techniques. Our results show that the Inverse Distance Weighting (IDW) and Spline interpolation techniques demonstrate the highest accuracy with the lowest RMSE (13.5 ppb and 16.7 ppb) and MAE (87.8 ppb and 89.5 ppb), respectively, while the Natural Neighbor technique shows the lowest accuracy with the highest RMSE (2508.7 ppb) and MAE (712.1 ppb) to interpolate groundwater As data. When the study area’s extent was modified, IDW showed the best performance, with errors within ±1.5 ppb for 95% of the wells across the study area. While data density has a positive correlation with interpolation accuracy among all techniques, the IDW remained the best method for interpolation. It is therefore concluded that IDW should be used to interpolate groundwater quality data when observed data are sparse and randomly distributed. The utilization of IDW can be useful for As monitoring and management in groundwater resources.

Identification of Saltwater Intrusion Distribution in North Padang Cermin Area, Lampung, Indonesia

The residential areas of North Padang Cermin are mainly located near the coastal area with a high level of groundwater use. Continuous groundwater use could potentially raise saltwater intrusion and lead to a decrease in groundwater quality. The research is conducted to identify the saltwater intrusion spread based on groundwater quality data. Geological observations were carried out to determine the geological conditions of the research area, followed by qualitative and quantitative hydrogeological observations by measuring TDS (Total Dissolved Solid), EC (Electrical Conductivity), pH, temperature, color, and taste. The Herzberg method was used to identify freshwater thickness against seawater intrusion. Hydrogeologically, the research area has four aquifer systems: aquifers with fissure and intergranular flow, aquifers with intergranular flow and wide-distribution productivity, aquifers with fissure and intergranular flow and local distribution, and aquifers with rare groundwater. Groundwater flow has a radial pattern with the most extensive hydraulic gradient with a value of 0.16 m towards the eastern part of the research area. The indications of seawater intrusion were found in the water samples measurement located on the east of the research area with water type of brackish–salty with a TDS value of 1,443 – 3,790 ppm and an EC of 3,000 – 7,580 (μS). Based on the Herzberg method, the distribution of seawater intrusion is estimated to occur at a depth of 36.8 m.

Modeling the spatial dependence of quality data using distance-based inputs and a data-driven model

In the current study, which aimed to model groundwater quality, a method has been proposed to account for the spatial structure of the data in the modeling process. This method incorporates the distance between observation points and the estimation point as one of the model inputs. The Multilayer Perceptron (MLP) model was utilized, and the models were trained and tested using groundwater quality data obtained from 153 observation wells in a region in northwestern Iran. Two distinct datasets were used. The mean error (ME), R-squared, and the Nash-Sutcliffe efficiency coefficient (NSE) values for models with eight or more neighboring wells indicated satisfactory performance. For the first dataset and training phase, the models achieved ME values less than 15 dS/m, R-squared values ranging from 0.57 to 0.73, and NSE values ranging from 0.57 to 0.72. In the testing phase, the ME for the best-trained models was −0.2, highlighting the absence of underestimation or overestimation in the results. Additionally, the R-squared and NSE for the best model were calculated as 0.54 and 0.53, respectively. Similar results were achieved for the second dataset. It was also observed that incorporating the distance to the observation wells improves the outcomes compared to using only electrical conductivity (EC) as an input, and additionally, considering more neighboring wells positively affected the performance of the models and allowed for more precise salinity estimation. For the first dataset and based on the calculated metrics, models using 8, 16, or 20 neighboring wells, with EC and distance as inputs, demonstrated comparable performance. For the second dataset, the model with 12 neighboring wells was selected as the best model. The findings suggest that models with a specific number of neighboring wells and appropriate input variables can accurately estimate EC in groundwater systems.

Assessment of effectiveness of leachate control measures adopted by the GHMC at the Jawaharnagar dump yard, Hyderabad.

To address the problems posed by hitherto un-scientific method of dumping un-segregated solid waste at the Jawaharnagar dump yard (JDY) recently Greater Hyderabad Municipal Corporation (GHMC) adopted certain measures to scientifically process the waste at the JDU; this involves extraction of RDF and manure at various stages besides obtaining mostly inert material for final land filling. The leachate obtained during the process is separately collected and treated at the site. This study aims to check the effectiveness of these measures in controlling the leachate to the surrounding aquifer as groundwater plays a significant role in sustainable use of water resources. Groundwater samples are collected from the surrounding region and analyzed for TDS, TH, chlorides, fluorides besides PH once before the monsoon and once after the monsoon. From the results it can be shown that leachate pollution of groundwater is still a problem. When compared with the groundwater quality data in the literature taken before the recent GHMC measures, the latest data shows marginal improvement as the legacy leachate from the previous landfill nearby continues to pollute the groundwater in the vicinity of the dump yard.

A machine learning-based approach to predict groundwater nitrate susceptibility using field measurements and hydrogeological variables in the Nonsan Stream Watershed, South Korea

Identifying and predicting the nitrate inflow and distribution characteristics of groundwater is critical for groundwater contamination control and management in rural mixed-land-use areas. Several groundwater nitrate prediction models have been developed; in particular, a nitrate concentration model that uses dissolved ions in groundwater as an input variable can produce accurate results. However, obtaining sufficient chemical data from a target area remains challenging. We tested whether machine learning models can effectively determine nitrate contamination using field-measured data (pH, electrical conductivity, water temperature, dissolved oxygen, and redox potential) and existing geographic information system (GIS) data (lithology, land cover, and hydrogeological properties) from the Nonsan Stream Watershed in South Korea, an area where nitrate contamination occurs owing to intensive agricultural activities. In total, 183 groundwater samples from different wells, mixed municipal sites, and agricultural activities were used. The results indicated that among the four machine learning models (artificial neural network (ANN), classification and regression tree (CART), random forest (RF), and support vector machine (SVM)), the RF (R2: 0.74; RMSE: 3.5) and SVM (R2: 0.80; RMSE: 2.8) achieved the highest prediction accuracy and smallest error in all groundwater parameter estimates. Land cover, aquifer type, and soil drainage were the primary RF and SVM model input variables, representing agricultural activity-related and hydrogeological infiltration effects. Our research found that in rural areas with limited hydro-chemical data, RF and SVM models could be used to identify areas at high risk of nitrate contamination using spatial variability, GIS-aided visualization, and easily accessible field-measured groundwater quality data.

Water quality index, ecotoxicology and human health risk modelling of surface and groundwater along illegal crude oil refining sites in a developing economy

Water quality index, ecotoxicology and human health risk models were applied to surface and groundwater samples along illegal crude oil refining sites in Rivers State, Nigeria. Eight (8) surface water and four (4) groundwater sampling points were identified along illegal refining sites. Thirty-six (36) samples in triplicates were collected monthly from each of the twelve (12) sampling points over a three (3) month period. Water samples were collected and analyzed using standard methods as prescribed by the American Public Health Association. The mean pH for surface and groundwater ranged from 5.61 ± 0.15 to 7.34 ± 0.10 and 5.80 ± 0.10 to 6.39 ± 0.13, respectively. Turbidity, TDS, and BOD data for surface water samples exceeded the WHO guideline values. The ionic dominance pattern of anions for both surface and groundwater water samples were the same and in the order Cl− > SO42− > NO3− > PO42−. Mean heavy metal concentration was in the order Pb > Ni > Fe > Cd > Mn > Cu for surface water and Pb > Cd > Fe > Mn > Ni > Cu for groundwater. Cd and Pb concentrations in both sources were generally high, with Cd exceeding the WHO guideline value (GV). The CCME water quality index model ranked 62.5% of surface water as marginal, 12.5% as good, 12.5% as poor, and 12.5% as fair. The impact of heavy metals on public health was in the order Pb > Cd > Ni > Fe > Mn, with 83% of samples seriously affected by Pb pollution. The potential ecological risk index ranged from 1.61 × 103 to 2.64 × 103 for surface water and 8.10 × 102 to 2.21 × 103 for groundwater. Heavy metal contamination was very high, and the ecological risk effect was extremely high. The health risk through oral ingestion was in the order of adults > infants > children. Two principal components, PC1 and PC2, explained 50.51% and 16.00% of the variations in surface water quality, respectively. For groundwater quality data, three principal components explained the observed variations in water quality data, of which 51.39% is attributed to PC1, 26.29% to PC2, and 16.58% to PC3.

Environmental risks and opportunities of orphaned oil and gas wells in the United States

Hundreds of thousands of documented and undocumented orphaned oil and gas wells exist in the United States (U.S.). These wells have the potential to contaminate water supplies, degrade ecosystems, and emit methane and other air pollutants. Thus, orphaned wells present risks to climate stability and to environmental and human health, which can be reduced by plugging. To quantify environmental risks and opportunities of well plugging at the national level, we analyze data on 81 857 documented orphaned wells across the U.S. We find that 4.6 million people live within 1 km of a documented orphaned well. 35% of the documented orphaned wells are located within 1 km of a domestic groundwater well, yet only 8% of the wells have groundwater quality data within a 1 km radius. Methane emissions from the documented orphaned wells represent approximately 3%–6% of total U.S. methane emissions from abandoned oil and gas wells, but this estimate is based on measurements at 0.03% of U.S. abandoned wells. 91% of the documented orphaned wells overlie formations favorable for geologic storage of carbon dioxide and hydrogen, meaning that orphaned well plugging can reduce leakage risks from future storage projects. Finally, we estimate plugging costs for documented orphaned wells to exceed the $4.7 billion federal funding by 30%–80%, emphasizing the importance of prioritizing federal spending on wells with large remediation benefits. Overall, environmental monitoring data are not extensive enough to quantify risks, especially those related to air and water quality and human health. Plugging orphaned wells can provide opportunities for geologic storage of carbon dioxide and hydrogen and geothermal energy development, thereby facilitating efforts to transition to net-zero energy systems. Our analysis on environmental risks and opportunities of orphaned wells provides a framework that can be used to manage the millions of documented and undocumented orphaned wells in the U.S. and abroad.

Spatial Optimization of the Groundwater Quality Monitoring Network in the Kingdom of Bahrain

Groundwater resource development, management, and planning essentially rely on their quantitative and qualitative monitoring. The information obtained from the monitoring networks of a given groundwater resource is used as a significant indicator for the status of that resource, and management schemes are subsequently made in order to develop and utilize this resource on a sustainable basis. In this study, the performance of the existing monitoring network of the quality of groundwater in Bahrain was evaluated and was spatially optimized using the geostatistical method of kriging. The estimation variance was used as a criterium in the design process, and variance reduction was used to measure the network performance. The process resulted in an increase in the number of observation points from 15 currently monitored wells to 91 wells, with 74% of these being augmented industrial and commercial wells that were to be self-reported to internalize the cost of groundwater management in their users. It is recommended that temporal optimization procedures for the groundwater level are conducted and that monitored groundwater quality data are stored in a dedicated groundwater management information system (MIS) along with the monitored data of groundwater levels and abstraction to effectively support the process of decision making for groundwater planning and management.

Groundwater vulnerability assessment for drinking water suitability using Fuzzy Shannon Entropy model in a semi-arid river basin

Groundwater protection against geogenic and anthropogenic diffuse pollutants are of preeminent importance for convoying groundwater-dependent hydro-aquatic ecosystems and safeguarding human health by securing a safe and sustainable water security plan. Mapping groundwater vulnerability to pollution represents a topical area of research where traditional models suffer from inherent prejudices. A novel approach to identify groundwater vulnerable zones is thus presented in this study by combining Fuzzy Shannon Entropy (FSE) with a decision support algorithm to replace DRASTIC, GOD and other related methods. We proposed a contemporary “FSE-GVI″ framework for assessing aquifer vulnerability, which for the first time expands and redesigns the seven original geological and hydrogeological parameters with geochemical and anthropogenic factors. Applying such technique weights of 13 geo-environmental and hydrogeological factors (slope, soil, pH, recharge), and anthropogenic factors (land use land cover, population density, groundwater based schemes for irrigation) has been optimized. The computed map has been categorized into very high (10.82%), high (23.20%), moderate (29.29%), low (25.02%) and very low (11.64%).The groundwater of the river basin is rated on a scale of excellent, good, poor, very poor and unfit for consumption. The model is compared to 74 groundwater quality data, and the findings demonstrate an excellent correlation between water quality index (r = 0.84), Total dissolve solids (70%), iron (72%), hardness (85%) and alkalinity levels (75%).The findings of the research reveals 27.3 % of the sampling sites (villages) under poor water quality and 23.21% of very high vulnerable zone which requires competent planning, development, managerial technique to safeguard groundwater resources.

Hydrochemistry and quality appraisal of groundwater in Birr River Catchment, Central Blue Nile River Basin, using multivariate techniques and water quality indices

Due to the continuous population growth and the expansion of industry and agriculture in Ethiopia, groundwater demand has been increasing to supplement the erratic surface water. Therefore, the availability of sufficient and clean groundwater should be appraised and tracked regularly to secure its multi-purposes. This work aims to assess the appropriateness of groundwater for drinking, and irrigation uses and identifies the key factors controlling groundwater quality in the Birr River Catchment (BRC), Blue Nile River Basin, Ethiopia. For this purpose, a total of 79 groundwater samples were assessed for physicochemical parameters. Major ion analysis, multivariate techniques (MCA, HCA, and PCA), and multi-hydrochemical indices wereapplied in the analysis of groundwater quality data. Hydrochemical analysis indicated that the principal cation and anion were Ca2+ and HCO3-, respectively. The spatial analysis of the major ions revealed a positive trend for Mg2+, Na+, K+, HCO3-, Cl-, and SO42- along the groundwater flow path from the upland to the Birr river valley. Conversely, Ca2+ shows a deleterious tendency along the groundwater flow direction. The aquifer has three principal hydrochemical facies: Ca-HCO3, Ca/Mg-HCO3, and Na-HCO3. The water quality analysis indicates that with the exception of TDS, Ca2+, Mg2+, and HCO3- in a few locations, most of the parameters analyzed are within the WHO allowable limits and are thus considered suitable for drinking water. The combined use of Gibbs and ionic ratio plots confirmed that silicate weathering was invariably prevailing in the region. The Chloro-Alkali Indices (CAIs) have indicated that cation exchange occurs in more than 85% of groundwater samples. However, there were indications of the influence of reverse ion exchange in the rest of the data. The MCA, PCA, and HCA disclosed that geo-genic sources accompanied by human activities mainly control the groundwater quality of the catchment. However, water quality assessment indices show that groundwater in the highest proportion of the catchment is suitable for human consumption and agricultural use.

Chemical Speciation and Leaching of Trace Metals in Groundwater from the Depleted Landfills, India

Abstract The poor groundwater quality through the leaching of contaminants from depleted landfill is a concern to the scientific community. Therefore, the role of landfills on groundwater quality cannot be neglected in an urban area. The factors influencing the leaching of trace metals in groundwater are soil profile, geochemical and environmental condition of disposed of refuse materials, groundwater-table depth, and climatic factors. This research work delineates landfill role in contaminating groundwater through the chemical speciation of trace metals. Analyzed groundwater quality data indicate most of the samples were classified under Ca2+-Na+ type cation and Cl- type of anion hydrogeochemical facies. The investigations of the mineral equilibrium indicate equilibrium with silicate minerals, which favors kaolinite formation. Saturation index indicates that hematite, goethite, chrysotile, dolomite, ferric-hydroxide, hydro-xyapatite, jarosite-K, cerussite, vivianite, and willemite are reactive minerals in the aquifer water and control their hydrogeochemistry. The study of chemical speciation of trace metals indicates the high possibility of oxidation-reduction, ion-exchange, and chemical-weathering reaction mechanism, which causes the release of trace metal ions and further contaminated aquifer water through leaching. It also justifies through study of contaminant movement in vertical profile of the soil. The chemical speciation of trace metals indicates a reducing atmosphere in the aquifer due to the dominance of Fe2+, Mn2+, Zn2+, Pb2+, and Cu2+ ions in aquifer water. Mn2+ and Zn2+ concentration decreases with depth, while Fe2+ and Pb2+ ion concentration low in the middle layer of the aquifer indicate the contribution through anthropogenic input since it is not available in geology of study area.

Changing correlations: a flexible definition of non-Gaussian multivariate dependence

Dependencies between variables are often very complex, and may for high values, be different from that of the low values. As the normal distribution and the corresponding copula behave symmetrically for low and high values the frequent application of the normal copula for the description of the dependence may be inappropriate. In this contribution a new way of defining high dimensional multivariate distributions with changing correlations is presented. The method can also be used for a flexible definition of tail dependence. Examples of copulas with linear changing correlations illustrate the methodology. Parameter estimation methods and simulation procedures are discussed. A five dimensional example using groundwater quality data and another four dimensional one using air pollution data, are used to illustrate the methodology.