Suitability Evaluation of CCME-WQI and GWQI for the Modeling of Groundwater and Human Health Risk Assessment of Heavy Metals-Eastern India


 The present study assessed the suitability of groundwater by using the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) and the Groundwater Water Quality Index (GWQI) Model. Six heavy metals viz. arsenic (As), Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), and Nickel (Ni) were investigated in the groundwater from 65 locations of Ranchi city by the Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The spatial distribution of WQI was established by Inverse Distance Weighted (IDW) interpolation technique using ArcGIS 10.3. The mystery of hydrogeochemical evolution in groundwater was elucidated by plotting the Piper trilinear diagram of major cations (Ca2+, Na+, Mg2+, K+) and anions (HCO3-, Cl-, SO42-, F-). Significant fluctuations in the water level during PRM (7.38mbgl to 10.5 mbgl) and POM (4.3- 6.4 mbgl) season were observed in the central part of the study area. Performance evaluation of WQI models indicated that the CCMEWQI performed better than GWQI for assessing the quality index of groundwater with a comparatively higher coefficient value (R2 0.97) and less NMSE (4.34) RMSE (27.38), MAPE (0.357). The health risk of heavy metals via the oral route was investigated by calculating hazard quotient (HQ) and hazard index (HI). The HI value was observed maximum for As followed by Mn >Pb > Ni >Fe >Cu for adults and children. The spatial distribution map of HI indicated that most of the studies area are at a non-carcinogenic risk of heavy metals. The study provides immense help for water authorities and public health decision-makers to prevent the community's health risk.


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Globally, groundwater is deliberated as the safest reservoir and a good source of essential elements for life preservatives. It is 34 unevenly distributed below the earth's surface, mainly dependent upon geographical location, the permeability of rocks, rainfall,   application as NSFWQI (Brown et al. 1970). However, the various water quality indices were already reviewed globally till 47 1970 (Steinhart et al. 1981). Afterward, in 1995, the Canadian Council of Ministers of the Environment (CCME) has developed 48 another WQI (CCME-WQI) model based on the British Columbia Water quality Index (BCWQI) to assess and simplify the 49 water quality data (Rocchini and Swain 1995;CCME 2001).

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The Indian interest in WQI was received in 1983 when Bhargava (1983) used this tool to classify and zone the river  application of CCME-WQI to assess the quality of groundwater (Venkatramanan et al. 2016;Wagh et al. 2017). In addition, it 57 was also noted that no work had been carried out to compare the effectiveness of GWQI and CCME-WQI model to evaluate 58 the suitability of groundwater for public consumption.

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Hence the present study was undertaken with the objective of (i) physicochemical characterization, including 60 correlation analysis, ion balancing, and piper diagram with special reference to the groundwater of Ranchi city (ii) the effect   For this study, two WQI models viz. GWQI and CCME-WQI were applied to categories the groundwater quality for drinking

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The computation of CCME-WQI involves three general steps viz. (i) choosing variables, (ii) selecting guidelines, and 95 (iii) calculation of index scores. Further, the model was divided into three factors [Factor 1 (Scope), Factor 2 (Frequency), and 96 Factor 3 (Amplitude)] to produce a single unit less value that indicates the overall quality of water.

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Factor 1 (Scope) assess the proportion through which the variables deviate from their objectives and can be expressed

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The CCME-WQI is calculated as (Eq. 7): The constant divisor (1.732) normalizes the resultant values in the ranges from 0 to 100. 0 represents the worst and 100

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represents the best quality of water quality, and the value between 0 to 100 was categorized into marginal, fair, and good.

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The standards for drinking water recommended by the Bureau of Indian Standard (BIS 2012) was used for the 123 computation of GWQI, involving three steps:

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In the very first step, each of the 12 variables assigned a weight ( wi) according to their relative importance (ranging from 1 125 to 5) in the overall quality of water for drinking purposes.

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In the second step, the relative weight ( Wi) is computed by using Eq.:

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Where Wi and wi represent the relative weight and weight of each parameter, respectively, and n is the number of parameters.

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In the third step of this method, the quality rating scale (qi ) of each parameter was calculated by dividing its concentration for 130 each sample of water with its respective standard according to the guidelines of BIS and then is multiplied by 100 (Eq 9):

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Where qi and Ci are the quality rating and concentration of each chemical parameter in each water sample in mg/L, respectively,

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Si is taken from the guideline of BIS (2012).

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Hence, for computing the GWQI, SIi index of each parameter is calculated initially (Eq. 10), which is then used to determine Where SIi the is the sub-index of i th parameter, qi is the rating based on the concentration of the ith parameter, and n is the 140 number of parameters.

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The spatial distribution map of GWQI and CCMEWQI models was plotted using the interpolation technique (IDW)

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The RfD values for all the elements were based on USEPA (2011).

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The HI is usually a multiple substance/ single-exposure pathway ratio and can be express as sum of all metal's HQ (Eq.17) Suitable quality affirmation methodology and safeguard were carried out to ensure reliability, and samples were carefully handled to avoid contamination. Glassware was appropriately cleaned, and analytical grade reagents were used. Milli Q water

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The descriptive statistics of physicochemical quality of groundwater for PRM and POM season are listed in the (Table 1). All

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In order to investigate the correlation of major cation (Ca 2+ , Na + , Mg 2+, K + ) and anions (HCO3 -, Cl -, SO4 2-, F -) with 188 other water quality parameters the Pearson correlation matrix was established for both the season (Table. 2a cation, whereas the right for anions ( Fig. 2a-b). In addition, it was also noted that the cation -Ca 2+ was found to be dominated

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The other factors like geological formation, permeability of rocks, infiltration rate, type of aquifers, etc. also greatly affect the 231 level of water (Gopinath and Seralathan, 2006;Panaskar et al. 2016). In overall the western part was found to be more prone 232 to water level fluctuations than other part of study area for all the season (Fig. 3c). The occurred reason behind this phenomenon The application of WQI modeling is a worthy technique for the assessment of drinking water suitability. In the present study,

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Based on GWQI modeling, the class of water was again classified into five categories, but the index value (50 to 300) 248 of this approach was higher than CCMEWQI (Table. 3b). The spatial distribution of GWQI for PRM and POM season was  (Table 3b). The overall WQI of water was found comparatively upright 253 in POM season by both the modeling approach.

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The research results and sensitivity analysis revealed that CCMEWQI performed better than GWQI for assessing the

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All these mechanisms made the CCMEWQI model performed better than GWQI.     eastern, and some portions of western regions (Fig. 6 b). The distribution of Fe in the groundwater of this area may be attributed The health risk of heavy metals in groundwater via the oral route was investigated for all 65 locations. The value of HQ, which 295 expresses the effects of non-carcinogenic risk, was observed maximum for As followed by Mn >Pb > Ni >Fe >Cu for adults 296 and children (Fig. 7a-f). The HQ of each metal except As was observed less than unity, indicating no significant health risk on 297 humans from the intake of these metals. Moreover, the elevated HQ of As may be due to its high concentration range in the

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Conclusive evidence of sensitivity analysis revealed that CCMEWQI (R 2 0.97) performed better than GWQI (R 2 318 0.95) for assessing the quality index of groundwater. The calculated non-carcinogenic risks of heavy metals 319 indicated that most of the study areas are at a non-carcinogenic risk, except the northern region. Continuous 320 monitoring and treatment are essential to reduce health risks in the study area. Data collected and analyzed in this study are available from the corresponding author upon request.