Assessment of Groundwater Quality and Mapping

Assessment of Groundwater Quality and Mapping Human Health Risk in Central Ganga Alluvial Plain, Northern India Ashwani Raju & Anjali Singh

Environmental Processes An International Journal ISSN 2198-7491 Environ. Process. DOI 10.1007/s40710-017-0232-0

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Author's personal copy Environ. Process. DOI 10.1007/s40710-017-0232-0 O R I G I N A L A RT I C L E

Assessment of Groundwater Quality and Mapping Human Health Risk in Central Ganga Alluvial Plain, Northern India Ashwani Raju 1 & Anjali Singh 2

Received: 6 February 2017 / Accepted: 24 April 2017 # Springer International Publishing Switzerland 2017

Abstract The present paper deals with the assessment of hydrogeochemical processes controlling groundwater quality in Lucknow urban area located in an alluvial plain of Ganga-Gomti interfluve, India. Physico-chemical data were derived from 42 groundwater samples, and were further used to map human health risk in Lucknow monitoring area. The analysis reveals that the carbonate and silicate weathering along with reverse ion exchange, cation exchange, sulphide oxidation, dissociation of residual halides are the major solute processes controlling the ionic concentration of groundwater in Lucknow. Groundwater is predominantly of Na+-Ca2+-HCO3− type having all cations and anions within prescribed WHO limits except for Fe2+ and NO3−. Ionic concentrations of groundwater were combined to derive a Water Quality Index (WQI) using spatial overlay in Geographic Information Systems (GIS). WQI reveals that about 47.8% of the total 133.52 km2 area covering Cis-Gomti localities of the city has nearly poor water quality for drinking use which is possibly due to high Fe2+ and NO3− influx in shallow unconfined aquifers. WQI is integrated with urban population density index data to map spatial distribution of human health risk due to consumption of poor water quality. Results reveal that the sites located in and around the vicinity of Alambagh, Kaiserbagh and Hazratganj, covering 9.81% of the total area, are potential ‘hot spot’ at moderate to high health risk, and require substantial remediation measures to control the anthropogenic influx on a long-term basis.

* Ashwani Raju [email protected] Anjali Singh [email protected]

1

Geological Survey of India, Jabalpur -482003 Madhya Pradesh, India

2

School of Earth Sciences, Banasthali University, P.O. Banasthali Vidyapith-304022, Rajasthan, India

Author's personal copy Raju A., Singh A.

Keywords Lucknow . Groundwater quality . Alluvium-water interaction . Water quality index (WQI) . Human health risk

1 Introduction The chemical parameters of groundwater play a fundamental role in classifying and precisely understanding the hydrochemical system in any area. In recent years, several hydrogeochemical investigations are concerned with the geogenic and anthropogenic impacts on groundwater in different watersheds as well as in urban areas (Umar et al. 2006; Raju and Reddy 2007; Singh et al. 2008; Jain et al. 2010; Raju et al. 2011; Oinam et al. 2012; Rajmohan and Prathapar 2016). Groundwater extraction in response to the increasing demand for water has recently been surpassed groundwater replenishment, triggering a continuously declining water table (Tiwari et al. 2009). Shallow aquifers are more susceptible to contamination which may also deteriorate the deep groundwater due to vertical flow (Lee et al. 2008; Nandimandalam 2012; Jang et al. 2012; Zhai et al. 2013). Vadose zone in aquifers act as a natural filter for the contaminants. Confined aquifers have many interlayer impermeable clay layers that prevent groundwater contamination (Conboy and Goss 2000). Unconfined aquifers are more vulnerable to contamination in the vadose zone due to high soil permeability and porosity (Jiang et al. 2009). Hence, water chemistry is not homogenous in both unconfined and confined aquifers within the same region (Rajmohan and Prathapar 2016). To investigate the compositional differences between surface and groundwater samples, and to determine spatial variations in groundwater composition under the influence of natural and anthropogenic factors, water quality datasets are significantly analyzed by using multivariate statistical techniques, such as principal component analyses, cluster analysis, discriminant analysis or partial least square technique (Singh et al. 2005; Singh et al. 2004). In the past decades, Geographical Information Systems (GIS) are widely used as an effective tool to analyze the widespread data for precise regional representation of the variables from multiple sources at multiple locales. GIS methods illustrate spatial prediction at unknown points and can be used for diverse mapping applications for groundwater related studies, assessment of ‘spatial risk’ in health science, geochemistry, pollution modelling, climatic phenomena, etc. The outcome of GIS provides invaluable information about potential hot spots or spatial distribution of risk for exploratory analysis in a regional geographic space (Oliver et al. 1992; Berke 2004). Recently, health risk assessments via contaminated water consumption have been previously studied worldwide (Törnqvist et al. 2011; Mohmand et al. 2015; Bhowmik et al. 2015). GIS based spatial interpolation techniques and prediction models when integrated with other parameters (like geochemical data, population data etc.), provide meaningful regional scale visuals of the area and human population count at risk. India is the largest groundwater consumer country, with an estimated annual extraction exceeding 230 km3 from a total 396 km3 utilizable dynamic fresh groundwater resources (Singh et al. 2012). Groundwater has substantial contribution towards India’s socio-economic development, as about 60% of irrigated agriculture, 85% of drinking water, and 50% of industrial and urban water requirements are reliant on it (Central Ground Water Board 2009). Over the last few decades, exponential increases in the population and rapid urban expansions have been observed in some major Indian cities. The uncontrolled urbanization puts immense pressure on groundwater resources to cater the demands of users. According to the Central Water Commission (Central Water Commission 2005), the per capita water availability in India has

Author's personal copy Assessment of Groundwater Quality and Mapping Human Health...

declined from 5176 m3 in 1951 to 1703 m3 in 2005 due to the progressive growth rate of infrastructure and the country’s economic development. New urban expansions, intense industrial and agricultural activities, global and regional climate change have stressed the hydrological cycle, and thus, water supplies are getting gradually chaotic in the cities. As a result, groundwater resources are getting overexploited and depleted day by day. Anthropogenic activities, such as land disposal of wastes and sewage, leaching of fertilizers and pesticides also contaminate the groundwater resources occurring beneath. Thus, for the protection of groundwater resources, the areas or ‘hot spots’ which are liable to contamination by anthropogenic activities need to be delineated, which can be best achieved through assessment of groundwater geochemistry. The Lucknow city, a state capital of one of the largest states (Uttar Pradesh) of India, has witnessed an uncontrolled increase in urbanization in the past few decades. The urban area of Lucknow has increased at a rate of 30.29% per year from 1955 to 1973, which further increased at a rate of 62.47% per year from 1973 to 2010 (Singh et al. 2015). Groundwater is the main source of drinking and municipal use in Lucknow, and is undergoing severe quality deterioration due to urbanization. To address the mentioned issue, groundwater samples have been collected from the Lucknow city and analyzed for various chemical parameters to: (1) understand the hydrogeochemical processes controlling groundwater quality and ascertain its suitability for drinking purposes; and (2) map human health risk from exposure to contaminated drinking water. The present study could be useful in providing suggestions to policy makers and land use planners to consider precautionary measures for water users.

2 Study Area Lucknow district spreads over a spatial coverage of about 2500 km2 between 26°30’to 27°10′ N latitudes and 80°30’to 81°13′E longitudes with altitude varying from 103 m to 130 m above mean sea level (Fig. 1). The Gomti River divides the study area into two parts: Trans-Gomti and Cis-Gomti area occupying the northern and southern parts of the city, respectively. The Lucknow urban area forms an integral part of Sai-Gomti basin of Central Ganga Plains, characteristically having fluvial terrigenous clastic depositional system which is composed of older and newer alluvial sediments of Quaternary age. The Newer alluvium occupies the active floodplains of river Gomti in lowlands and is composed of micaceous grey sands, silt and clay of recent age. Older alluvium occurring on highlands is composed of unconsolidated alluvial sediments comprising interlayered 1-2 m thick fine sand and silty mud deposits with patches of ‘kankar or calcrete’ horizons (concretionary type impure carbonate) of Upper to Middle Pleistocene age. Kankar are widespread as discrete patches in higher plateau surface (T2) and have significant 0.5–1.0 m thick layers in the top few meters of central alluvial plain (Agarwal et al. 1992).

3 Materials and Methodology Overview 3.1 Sampling Technique and Analytical Procedures A total of 42 groundwater samples were collected from shallow (27 samples from India MarkII hand pumps) and deep water (15 samples from tube wells) aquifers from various locations in Lucknow city (Fig. 1). The sample locations were chosen in such a way that the analysis of


Fig. 1 Sampling location map of the study area

physicochemical parameters provide best representation of entire Lucknow urban area. The groundwater samples were collected in dried, pre-cleaned one-liter polyethylene bottles from each of the location after pumping and discarding the initial released water. Parameters like, pH and Electrical Conductivity (EC) were measured in the field. Total hardness (TH) was estimated by Ethylene Diamine Tetra Acetic acid (EDTA) titrimetric method. Total dissolved solids (TDS) were estimated by calculation method. Alkalinity was analyzed using the Metrohm Autotitrator with 0.01 N HCl as a titrant following the inflection point titration method. Subsequently, all samples were filtered using 0.45 μm cellulose nitrate membrane filters and analyzed for major cations such as Sodium (Na+ mg/L), Potassium (K+ mg/L), Calcium (Ca2+ mg/L), Magnesium (Mg2+ mg/L) and major anions such as Fluorine (F− mg/L), Chlorine (Cl− mg/L), Nitrate (NO32− mg/L), Sulphate (SO42− mg/L), Bicarbonate (HCO3− mg/ L) by Ion Chromatography (Metrohm 792 Basic), which showed a precision of ±2%. The system was calibrated using multielement cation and anion standards. The accuracy of the chemical analysis was verified by calculating the ion-mass balance, which was observed to be within an acceptable limit (±5%).

3.2 Geochemical Analysis The derived physico-chemical parameters (Table 1) were compiled and analyzed using correlation analysis (Table 2) for ion chemistry and qualitative assessment of groundwater. Correlation analysis describes the statistical relation between two or more physico-chemical parameters (variables) such that systematic changes in the value of one are accompanied by

Author's personal copy Assessment of Groundwater Quality and Mapping Human Health... Table 1 Range of physico-chemical parameters and their comparison with the prescribed WHO limits for drinking water Chemical parameters

Min.

Max.

Mean

Permissible limits WHO (2011)

Percentage of the samples exceeding WHO permissible limit

pH EC, μS/cm TDS, mg/L TH, mg/L HCO3−, mg/L Cl−, mg/L NO3−, mg/L SO42−, mg/L F−, mg/L Ca2+, mg/L Mg2+, mg/L Na+, mg/L K+, mg/L Fe2+, mg/L

7.9 482.0 308.5 153.5 316.1 1.5 9.3 6.6 0.2 20.8 23.3 14.3 3.8 0.3

8.9 1399.0 895.4 574.1 570.3 183.7 173.8 152.3 1.3 124.8 91.5 205.4 18.2 2.2

8.4 871.2 557.6 357.0 450.5 68.6 65.0 58.1 0.7 61.4 49.6 96.2 10.6 0.5

7.0–9.2 -500–1500 100–500 300–600 250–600 50.00 200–600 0.9–1.5 75–200 30–150 50–200 10–12 0.3

0.0 0.0 0.0 4.8 0.0 0.0 57.1 0.0 0.0 0.0 0.0 4.8 35.7 100.0

systematic changes in the other. The result of the correlation analysis statistically represents how closely two variables co-vary; it can vary from −1 (perfect negative correlation) to +1 (perfect positive correlation). Correlation analysis provides the information about the origin and source of variables and their evaluation pathway (Rajmohan and Prathapar 2016). Parameters showing r2 > 0.7 are considered to be strongly correlated whereas r2 between 0.5–0.7 shows moderate correlation (Oinam et al. 2012). Based on correlation analysis, various graphical plots were observed between individual or combinations of physico-chemical parameters to evaluate the hydrogeochemical processes controlling groundwater geochemistry. Gibbs (1970) diagram has widely been used in previous studies to assess the relationship between chemical components of groundwater and their respective aquifer lithologies (Narany et al. 2014; Oinam et al. 2012; Raju et al. 2011). The diagram constitutes three distinct field namely, precipitation dominance, evaporation dominance and rock (weathering) dominance, wherein TDS values were plotted against (Na++K+)/(Na++K++Ca2+) for cations Table 2 Correlation matrix of the physico-chemical parameters

pH EC TDS TH HCO3− Cl− NO3− SO42− F− Ca2+ Mg2+ Na+ K+ Fe2+

pH

EC

TDS TH

HCO3− Cl−

NO3− SO42− F−

Ca2+

Mg2+ Na+ K+

1.00 0.01 0.01 -0.01 0.05 −0.02 -0.07 0.10 0.31 0.07 −0.11 0.01 -0.01 −0.32

1.00 1.00 0.43 0.37 0.84 0.52 0.68 0.25 0.25 0.35 0.82 0.62 0.22

1.00 0.43 0.37 0.84 0.52 0.68 0.25 0.25 0.35 0.82 0.62 0.22

1.00 0.07 −0.20 0.16 0.23 −0.13 0.60 0.28 0.20 0.19

1.00 0.52 −0.04 0.44 0.15 0.38 0.24 0.26

1.00 −0.07 −0.10 −0.11 −0.25

1.00 0.14 0.30 0.34

1.00 0.30 0.39 0.45 0.60 −0.14 0.74 0.62 0.01 0.11 0.03

1.00 0.55 0.59 0.28 0.31 0.22 0.82 0.53 0.15

1.00 0.14 0.49 0.31 0.53 0.44 0.12

1.00 −0.07 −0.12 0.46 0.23 −0.21

Fe2+

1.00 0.59 1.00 0.26 0.11 1.00


(Gibbs ratio 1) and against (Cl−)/(Cl− + HCO3−) for anions (Gibbs ratio 2) to illustrate the natural mechanism controlling groundwater chemistry. Likewise, other graphical plots such as Na+/Cl−; HCO3−/(HCO3−+SO42−); Ca2+/Mg2+; (Ca2++Mg2+)/HCO3−; (Na++K+)/(Ca2++ Mg2+); (Na++K+)/Cl− and (Ca2++Mg2+)/(SO42−+HCO3−) and mineral stability plots were also discussed to study the mechanism controlling groundwater geochemistry of Lucknow monitoring area.

3.3 Water Quality Index (WQI) The cumulative influences of individual hydrogeochemical parameters on groundwater quality were further derived using spatial overlay in GIS platform. GIS computes the spatial distribution of an individual parameter using various interpolation methods and can use together to assess the suitability of groundwater for human consumption. Weighted overlay analysis involves calculating the relative weight (Wi) of the individual parameter depending upon its strength to affect the suitability of groundwater for drinking purpose (Eq. 1). The maximum weight of 5 has been assigned to NO3−, Fe2+ and F− due to their highest influence on human health while HCO3− is given the minimum weight of 1 as it plays an insignificant role in the water quality assessment (Table 3; Raju et al. 2015). Wi ðRelative weightÞ ¼

wi ∑ni¼1 wi

ð1Þ

where wi is the assigned weight, and n is the number of parameters used in the analysis. The relative rate (Ri) to each parameter reflects the scale of risk by drinking water, and it is computed as a function of ionic concentration of individual parameter (ri) above WHO drinking water threshold limit (WHOi) using Eq. (2): Ri ðRelative rateÞ ¼

ri x100 WHOi

ð2Þ

Table 3 Relative weights of physico-chemical parameters (Raju et al. 2015) Physico-chemical parameter

Prescribed WHO (2011) limits for drinking water (mg/L)

Assigned Weight (w)

Relative Weight (W)

TDS TH HCO3− Cl− NO3− SO42− F− Ca2+ Mg2+ Na+ K+ Fe2+ ∑=

1500 500 600 600 50 600 1.5 200 150 200 12 0.3

3 4 1 3 5 3 5 3 3 4 2 5 41

0.073 0.098 0.024 0.073 0.122 0.073 0.122 0.073 0.073 0.098 0.049 0.122 1.000


The Standard Index (SIi) is calculated for individual parameters, which are summed up to calculate the WQI of the study area using Eqs. (3) and (4), respectively: SIi ðStandard IndexÞ ¼ Wi x Ri

ð3Þ

WQI ðWater Qulaity IndexÞ ¼ ∑ðSIiÞ

ð4Þ

3.4 Human Health Risk Index The spatial distribution of drinking water of poor quality represented by WQI indicates health risk to the urban population (Bhowmik et al. 2015). To present a rough estimate of health risk, a remote sensing and GIS based approach has been used for quantitative assessment of percentage of the area and urban population affected by exposure to poor quality water in the study area. For this, first an urban population density index is prepared from the Defence Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) and University of Columbia’s World Gridded Population Density (GPWv4) datasets for Lucknow (Palanichamy 2014; Singh et al. 2015). GPWv4 is a gridded data product of globally-integrated national population data and Housing Censuses. The gridded data sets are constructed from national or sub-national input administrative units with an output resolution of 30 arc-seconds (Center for International Earth Science Information NetworkCIESIN 2016). In an urban scenario, road and street lights, parks, secretariat and other subordinate government work places, cantonment area etc. have negligible population density during night. Therefore, night-light datasets were merged together with the population density data to obtain a qualitative representation of urban population density (Singh et al. 2015). The derived Urban Population Density Index (UPDI) was further used with WQI (representing area of poor water quality) to map the spatial distribution of population density at health risk in the study area using Eq. (5): Population Density Health Risk ¼

ðU rban Population DensityÞ x ðArea at Risk Þ ð5Þ T otal Area

4 Results and Discussion 4.1 Qualitative Assessment of Groundwater Geochemistry The quantities of dissolved constituents are the results of interaction of groundwater with aquifer material (geological formations) or discharge of anthropogenic wastes. Depletion of groundwater resources due to overexploitation and increased evaporation results in enhancement of dissolved constituents. To access the groundwater quality, physico-chemical parameters for 42 samples have been analyzed (Table 1). In general, pH values show slight alkaline nature. Electrical conductivity (EC) and Total Dissolved Solids (TDS as calculated from ECx0.64) represents concentration of dissolved ionic constituents and dissolved inorganic salts with small quantities of organic matter, respectively. The pH, EC and TDS values of all the collected samples were well within the WHO permissible limit. The total hardness (TH) to


a large extent is the result of dissolved Ca2+ and Mg2+ ion in groundwater. About 23.8% and 76.2% samples were classified under hard and very hard water category, respectively (Sawyer and McCarty 1967). Very high concentrations of TH above WHO prescribed limit were observed in two shallow well samples (Hazratganj and Triveni Nagar).

4.1.1 Cation Chemistry Among the cations, Na+ is the dominant ion which ranges from 14.3–205.4 mg/L (mean, 96.2 mg/L). Groundwater contained 44% of Na+ ion among all cations and only two samples had Na+ concentration above the WHO prescribed limit. Cation ascendancy was represented by Na+ followed by Ca2+(range: 20.8–124.8 mg/L; mean: 61.4 mg/L), Mg2+(range: 23.3–91.5 mg/ L; mean: 49.6 mg/L) and K+(range: 3.8–18.2 mg/L; mean: 10.6 mg/L) (Table 1). About 35% samples had high K+ concentration above the prescribed limit and were observed in Hazratganj, Aminabad, Alambagh, Cantt., Aliganj, Triveni Nagar, Rajajipuram and Kaiserbagh areas of the city. Ca2+ and Mg2+ were within WHO prescribed limits for drinking water. Iron (Fe2+) concentration ranged from 0.3 to 2.2 mg/L(mean, 0.3 mg/L) which exceeds the permissible limit in all samples. The cation chemistry indicates that about 74% of the samples are of Na+ dominating Na+ > Ca2+ > Mg2+ > K+ or Na+ > Mg2+ > Ca2+ > K+ type, followed by Ca2+ > Na+ > Mg2+ > K+ (14%) and Ca2+ > Mg2+ > Na+ > K+ (12%) type.

4.1.2 Anion Chemistry HCO3− is the major anion that ranges from 316.1–570.3 mg/L (mean: 450.5 mg/L) followed by Cl− ranging from 1.5–183.8 mg/L (mean: 68.6 mg/L), NO3− ranging from 9.3–173.8 mg/L (mean: 65 mg/L) and SO42− ranging from 6.6–152.3 mg/L (mean: 58.1 mg/L) (Table 1). Among all anions, 70% of the total anionic concentration is contributed by HCO3−. The anion chemistry indicates that all samples are of HCO3− dominating of which about 39% of the groundwater samples are of HCO3−>NO3−>Cl−>SO42− or HCO3−>NO3−>SO42−>Cl− followed by HCO3−>Cl−>NO3−>SO42− or HCO3−>Cl−>SO42−>NO3− (32%) and HCO3−>SO42 − >Cl−>NO3− or HCO3−>SO42−>NO3−>Cl− (29%) type. Besides major anions, F− in groundwater samples ranged from 0.2–1.2 mg/L (mean: 0.7 mg/L). All anions except NO3− are well within WHO prescribed limit for drinking water. About 55% of the groundwater samples have NO3− concentration higher than the permissible limit of 50 mg/L.

4.2 Hydrogeochemical Facies and Classification of Groundwater The hydrogeochemical facies reflect the chemical processes that occur between the groundwater and subsurface strata. These facies are a function of lithology, solution kinetics and flow pattern in the aquifer (Raju et al. 2011). To understand the geochemical evolution of groundwater, Piper trilinear (Piper 1944) and Chadha rectangular diagram (Chadha 1999) have widely been used. In Piper diagram, major cations (Ca2+, Mg2+ and Na++K+) and major anions (SO42−, Cl− and HCO3−+CO32−) are plotted in the two base triangles as a percentage of their sum. The Piper plot (Fig. 2a) showed that the majority of groundwater samples were of Ca2+-HCO32− type and few of mixed Ca2+-Na+-HCO32− types, suggesting that silicate weathering is the major process controlling chemistry of groundwater. The ternary cation diagram indicated the dominance of alkaline earth metals (Ca2++Mg2+) over alkali metals (Na++K+) in the shallow wells, suggesting ‘kankar’ dissolution or reverse ion exchange as the


Fig. 2 a Piper trilinear diagram; (b) Chadha’s diagram showing chemical character of groundwater

dominant processes. However, some deep-water wells have shown dominance of alkali metals (Na++K+) over alkaline earth metals (Ca2++Mg2+), suggesting cation exchange process in the alluvium-water interactions. The ternary anion diagram (Fig. 2a) relates the SO42−, Cl− and HCO3−+CO32− species, suggesting the dominance of HCO3− as a substantial by-product of silicate weathering in almost all groundwater samples. In Chadha rectangular diagram, the difference between alkaline earth (Ca2++Mg2+) and alkali metals (Na++K+), and difference between weak acidic anions (HCO3−+CO32−) and strong acidic anions (Cl−+SO42−) as a percentage of their sum were plotted on x- and y-axis, respectively. Chadha rectangular plot (Fig. 2b) shows that about 76% of groundwater samples are of Ca2+-Mg2+-HCO3− type (Field 5), which suggests that calcrete dissolution, reverse ion exchange and silicate weathering are the dominant processes. Besides, few deep-water aquifer samples are of Na+-HCO3− type (Field 8), which suggests silicate weathering as the major process controlling groundwater chemistry in the deeper water.

4.3 Correlation Analysis TDS of groundwater samples is strongly correlated with the EC (r 2 = 1; p 0.2 indicate K+ in solution from dissolution of K-feldspar. The graph between (Na++K+) and (Ca2++Mg2+) reflects the dominance of (Na++K+) over (Ca2++Mg2+) in deep confined aquifer and vice-versa in shallow unconfined aquifer (Fig. 4e). Relatively high concentration of (Na++K+) in the deep confined aquifer is due to the silicate weathering of Na-feldspar and K-feldspar. In deep water aquifers, (Ca2++Mg2+) may be derived from the weathering of Ca2+-clay and Mg2+-clay layers but in relatively less quantities due to depletion of (Ca2++Mg2+) during cation exchange with (Na++ K+). The low concentration of (Na++K+) in shallow unconfined aquifer was depicted by (Na++K+)/Cl− graph (Fig. 4f). The (Na++K+)/Cl− graph reflects that >90% of the groundwater samples lie over the 1:1 equilibrium line [(Na++K+)/Cl−>1)], which indicates (Na++K+) release as a function of silicate weathering in both deep confined and shallow unconfined aquifers. However, very few groundwater samples fall below the 1:1 equilibrium line [(Na++K+)/Cl−