Prediction of Groundwater Arsenic Hazard Employing Geostatistical Modelling for the Ganga Basin, India
2. Materials and Methods
2.1. Study Area
- Data procurement and preparation (including GIS-based database);
- Covariate analysis and Geostatistical Modeling for developing the Arsenic Hazard Index (AHI);
- Generation of groundwater arsenic hazard maps and validation.
2.2.1. Data Procurement and Preparation
2.2.2. Variable Analysis and Geostatistical Modelling for Developing the Arsenic Hazard Index (AHI)
Assessment of Association of Variables with Arsenic
Analytical Hierarchy Process (AHP) Model
- The unstructured problem was defined.
- The hierarchical framework was developed based on the selected variables.
- The pair-wise comparison matrix between all variables was constructed.
- The ranking for pair-wise comparisons was performed based on the reviews from literature to obtain the relative importance of the alternatives.
- Furthermore, consistency verification was performed using the following equations after estimation of the eigenvalue for each row:
- The consistency index (CI) was calculated as below,n = number of groundwater affecting variables.
- The consistency ratio (CR) was calculated as below:where RCI = Random Consistency Index (Table 3).
- All the steps from 3 to 6 were repeated for each hierarchy level. It was ensured that the CR values must be less than 0.1 .
- Finally, an overall priority ranking was developed for selecting the best alternative.
Frequency Ratio (FR)
- where FR = frequency ratio of a class for the covariate,
- A = the number of pixels with arsenic hazard for each covariate,
- A′= the total number of pixels with arsenic hazard,
- B = the number of pixels in the class area of the covariate, and
- B′= the number of total pixels in the study area.
- where PR = the prediction rate,
- RF = the relative frequency,
- Max = the maximum value of RF, and
- Min = minimum value of the RF.
Arsenic Hazard Index (AHI)
- where P = the individual covariate,
- r = rating (AHP) and prediction rates (FR), and
- w= weightage (AHP) and relative frequency (FR).
2.2.3. Generation of Groundwater Arsenic Hazard Maps and Validation
- By AUROC: The area under the Receiver Operating Characteristics (AUROC) curve was plotted between the sensitivity (true positive rate, TPR) and specificity (1–FPR). TPR is the fraction of interpretations that were correctly predicted to be positive out of all positive interpretations (TP/(TP + FN)), while the false-positive rate (FPR) is the fraction of interpretations that are incorrectly predicted to be positive out of all negative interpretations (FP/(TN + FP)). It tells us the accuracy of the model applied. This is the independent cut-off metric that has wide application in hydrological studies. AUROC was performed using the ArcSDM tool in GIS, which required a final output map and testing datasets (25% of total arsenic samples). AUROC may vary from 0 to 1; the higher the value, the higher the reliability [57,58].
- Through Current Scenario: 201 groundwater samples were physically collected from three districts each of Bihar and Uttar Pradesh to validate the predicted arsenic hazard with the ground situation (sampling was performed in Dec 2019 (pre-COVID) and March 2021 (post-COVID), respectively). The selection of states and districts was wholly based on the recent groundwater arsenic hotspot declarations . The standard APHA procedures were followed during the collection of samples and field measurements . Groundwater samples were collected from the handpumps at the locations as mentioned in the secondary arsenic datasets in pre-washed plastic bottles. Before sampling, each hand pump was purged for about 10–15 min. Water temperature, pH, EC, oxidation-reduction potential (ORP), and As concentration were measured at the site using portable analysis kits (HACH HQ40d, MQuant Arsenic Test Kit), whereas other ionic species and heavy metals analysis were performed with the instruments (Metrohm Ion Chromatograph, Agilent 8900 Triple Quadrupole ICP-MS) available in the institute laboratories.
3. Results and Discussion
3.1. Assessment of Association of Arsenic with Variables
3.1.1. Depth to Water Level (DTW)
3.1.4. Types of Aquifers
3.1.5. Soil Types
3.1.6. Land Use Land Cover (LULC)
3.1.8. Groundwater Abstraction
3.1.9. Groundwater Quality
- Dissolved SilicaWeathering of hard rock aquifers results in the release of silicates in groundwater, which is available in the form of dissolved silica (SiO2) . As per the data, silica concentration ranged from 1 to 170 mg/L (Figure 3i). Higher silica was reported in the central basin states such as Uttar Pradesh, Madhya Pradesh, and northern Jharkhand. However, the highest concentration of arsenic was reported in Bihar, Uttar Pradesh, and West Bengal (>0.05 mg/L). These districts also lie in alluvial plains, where arsenic becomes trapped and creates reducing conditions, followed by the reductive dissolution mechanism to release arsenic into groundwater . Silicate minerals from the bulk of sediments marked the largest arsenic reservoir with 75% arsenic, whereas Fe–Mn oxyhydroxides, the minor elements, made the second-largest arsenic reservoir with 16% arsenic . This study observed a high percentage of arsenic occurrence in the regions of dissolved silica from 23 to 34 mg/L, suggesting that high silica promote high pH and low redox conditions accompanying silicate mineral dissolution, which in turn releases arsenic from silicate minerals.
- BicarbonatesGroundwater bicarbonate concentration ranged from 61 to 860 mg/L. Higher bicarbonate concentration was reported in the states of Bihar, Rajasthan, Uttar Pradesh, and West Bengal, whereas a similar pattern was observed with arsenic (>0.05 mg/L) (Figure 3j). The shallow alluvial stratigraphy enriched with organic carbon permits a part of it to infiltrate into deep aquifers with the percolating water. This organic carbon promotes microbial respiration and reductive dissolution resulting in the leaching of arsenic and HCO3− into groundwater . This study observed a high percentage of arsenic occurrence in the regions with bicarbonate concentrations ranging from 323 to 499 mg/L, suggesting that bicarbonates have the ability of complex formation with the iron and manganese oxyhydroxide, which results in substituting bicarbonates for arsenic in the minerals and sediments and releases the arsenic into the groundwater.
- IronGroundwater iron concentration ranges from 0.05 to 12.51 mg/L. Higher iron concentration was reported in southern Haryana, West Bengal; northern Chhattisgarh, Jharkhand; northeastern parts of Madhya Pradesh; and distributed uniformly in Bihar. The majority of the area with iron concentration 0.88–2.88 mg/L had a high concentration of arsenic (>0.05 mg/L) (Figure 3k). During the dry season, the chemical weathering of clayey sediments loses Na and K; hence, less mobile elements such as Fe and Al remain enriched. Due to the strong affinity of arsenic with pyrites, the weathering of pyrite releases arsenic into the groundwater. This study showed a high percentage of arsenic occurrence in the regions with iron concentrations ranging from 0.89 to 2.88 mg/L, suggesting that reductive dissolution of Fe–Mn oxyhydroxides release arsenic in groundwater .
- Electrical Conductivity (EC)Electrical conductivity is one of the groundwater quality parameters dealing with charged particles and dissolved solids in the groundwater. High conductance attributes of Na+, K+, Mg2+, Ca2+, Cl−, SO42−, and HCO3− ions in groundwater have a high binding affinity towards arsenic and hence release less or no arsenic in groundwater . As most of the study area comprises alluvium, high EC in some parts could be because of ion exchange and solubilization with agricultural runoff . In the present study, EC values ranged from 141 to 3060 μS/cm with values predominately ranging from 141 to 851 μS/cm (Figure 3l). The study represents low EC values in the pre-monsoon season with elevated arsenic, suggesting less binding affinity of ions towards arsenic, resulting in the release into the groundwater.
- HardnessAs per the data, total hardness concentration ranged from 64 to 2779 mg/L. Elevated hardness concentration was reported in 0.3 % of the area, including Haryana, Rajasthan, Uttar Pradesh, and West Bengal, which had the least arsenic testing data points. Hardness ranging from 256 to 405 mg/L occupied more area (approx. 45%), and most of the data points in these locations also represented a high concentration of arsenic (>0.05 mg/L) (Figure 3m). The results showed that low hardness (Ca2+ and Mg2+) increases dissolved iron concentration, undergoes reductive dissolution, and leaches arsenic into the groundwater.Conversely, it is stated that high hardness enhances the binding capacity of Ca and Mg ions to form arsenate complexes, resulting in less mobilization of arsenic [85,86]. A finding stated that sorption of Ca and Mg ions increase positive surface charge, increasing its valency and increasing anion sorption such as arsenate ions onto iron oxides through charge neutralization [87,88]. Hence, in the current study, low hardness is observed accompanied by low adsorption and increased Fe hydroxide flocs, which boost co-precipitation and release of arsenic .
- SulphateSulphate concentration ranged from 2 to 745 mg/L. Higher sulphate concentration was reported in the states of Haryana, northern Himachal Pradesh, northern parts of Madhya Pradesh, Rajasthan, southern Uttar Pradesh, central Jharkhand, and western West Bengal. Most of the data points at the locations with concentrations of 14 to 29 mg/L displayed a high concentration of arsenic (>0.05 mg/L) (Figure 3n). Low sulphate with high arsenic reflects low redox potential (more contamination), promoting sulphate reduction and inhibiting oxidation. Low sulphate accelerates reactive iron concentration, which in association with microbes results in the reductive dissolution of arsenic-rich iron oxyhydroxide [90,91]. This reaction is accompanied by biogenic pyrite formation, increased arsenic mobility, and its release into groundwater [92,93]. This association supports the negative correlation of arsenic with sulphate.
- ArsenicTraining datasets (75%) of arsenic concentration were used to generate the thematic layers and for geostatistical modelling. As the arsenic concentration ranges from 0 to 1 mg/L, the training and testing datasets were distributed in three classes, as per the permissible limits: <0.01, 0.01–0.05, and >0.05 mg/L. Training datasets showed higher arsenic in Bihar, Jharkhand, and West Bengal. Most of the data points at these locations matched the high concentration of arsenic in the testing datasets (>0.05 mg/L) (Figure 3o). The mechanism involved in arsenic release apparently includes the oxidation of pyrite, the reductive dissolution of iron oxyhydroxide (bacteria-mediated reductive dissolution), desorption/absorption, sulphide oxidation, and competitive ion exchange [68,94,95,96].
3.2. Groundwater Arsenic Hazard Mapping
- Assessment of weights and ratings and evaluation of prediction rates:Based on the AHP model, the weights and ratings for all the variables were evaluated. The assessment was wholly based on the correlation coefficient values of arsenic with each covariate and within each covariate class. Computation modalities of weights and the rating for the selected variables are explained in Table S1. Pair-wise comparison specified that water depth, geomorphology, hydrogeology, and land-use patterns had the highest weightage. The overall consistency ratio for the data used was 0.08 (<1), reflecting reasonable consistency for the present study.Similarly, FR values were calculated with each thematic layer’s prediction rate, based on the relationship of each layer with arsenic testing datasets. The tabular format for the same is provided in Table S2.
- Computation of Arsenic Hazard Indexes and generation of maps:After assigning weightage and rating to the variables based on the AHP, all the thematic layers were combined into a single layer using the arsenic hazard index and overlay analysis in ArcGIS. The AHI was computed as the sum of the product of weights and ratings assigned to each covariate considered for the study . The generated final arsenic hazard map correctly visualizes the likelihood of arsenic hazard zones. The index values are divided into four categories in the order of increasing degree of hazard likelihood, viz, very low, low, moderate, and high [97,98]. The final map is presented with natural the Jenks classification method in ArcGIS, based on the natural groupings inherent in the data  and shown in (Figure 4a). The map represents a discrete pattern. The highest index value has been displayed around and along the Ganges River, which lies in the channel of the alluvium plains. The majority of Uttar Pradesh, Bihar, parts of Rajasthan, Chhattisgarh, Jharkhand, Madhya Pradesh, and eastern and western regions of West Bengal show a high arsenic contamination of approx. 35% (Table 5, Figure 5). These highly contaminated zones require more attention towards better land and water resources management in order not to further raise arsenic pollution and toxicity among the inhabitants.
3.3.1. By AUROC Curve
3.3.2. Comparison with Actual Field Data
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|1.||Depth to Water level (DTW)||Water Resource Information System (WRIS) ||Tabular||2015|
|2.||Slope||United States Geological Survey (USGS) ||Raster||2015|
|3.||Geomorphology||Geological Survey of India (GSI) ||Map||2007|
|4.||Types of aquifers||Central Ground Water Board (CGWB) ||Tabular||2010|
|5.||Soil||The Food and Agriculture Organization of the United Nations (FAO) ||Raster||2007|
|6.||Land Use Land Cover (LULC)||United States Geological Survey (USGS) ||Landsat Imagery||2015|
|7.||Rainfall||Centre for Hydrometeorology and Remote Sensing (CHRS) ||Raster||2015|
|8.||Groundwater Abstraction||Central Ground Water Board (CGWB) ||Tabular||2013|
|9.||Groundwater Quality Parameters||Central Ground Water Board (CGWB) ||Tabular||2015|
|Intensity of Importance||Definition||Explanation|
|1||Equal importance||Two activities contribute equally to the objective|
|3||Weak importance of one over another||Experience and judgment slightly favour one activity over another|
|5||Essential or strong importance||Experience and judgment strongly favour one activity over another|
|7||Demonstrated importance||Activity is strongly favoured, and its dominance is demonstrated in practice|
|9||Absolute importance||The evidence favouring one activity over another is of the highest possible order of affirmation|
|2,4,6,8||Intermediate values between the two adjacent judgements||When compromise is needed|
of Rows (n)
|S.No.||Hazard Likelihood||Area (AHP)||% Area||Area (FR)||% Area|
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Dhamija, S.; Joshi, H. Prediction of Groundwater Arsenic Hazard Employing Geostatistical Modelling for the Ganga Basin, India. Water 2022, 14, 2440. https://doi.org/10.3390/w14152440
Dhamija S, Joshi H. Prediction of Groundwater Arsenic Hazard Employing Geostatistical Modelling for the Ganga Basin, India. Water. 2022; 14(15):2440. https://doi.org/10.3390/w14152440Chicago/Turabian Style
Dhamija, Sana, and Himanshu Joshi. 2022. "Prediction of Groundwater Arsenic Hazard Employing Geostatistical Modelling for the Ganga Basin, India" Water 14, no. 15: 2440. https://doi.org/10.3390/w14152440