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Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment
Journal Pre-proof Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment Arghya Chattopadhyay, Anand Prakash Singh, Satish Kumar Singh, Arijit Barman, Abhik Patra, Bhabani Prasad Mondal, Koushik Banerjee PII:
S0045-6535(19)31847-8
DOI:
https://doi.org/10.1016/j.chemosphere.2019.124623
Reference:
CHEM 124623
To appear in:
ECSN
Received Date: 20 May 2019 Revised Date:
14 August 2019
Accepted Date: 18 August 2019
Please cite this article as: Chattopadhyay, A., Singh, A.P., Singh, S.K., Barman, A., Patra, A., Mondal, B.P., Banerjee, K., Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment, Chemosphere (2019), doi: https://doi.org/10.1016/j.chemosphere.2019.124623. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
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Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk
2
assessment
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Arghya Chattopadhyaya*, Anand Prakash Singha, Satish Kumar Singha, Arijit Barmanb, Abhik
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Patraa, Bhabani Prasad Mondalc and Koushik Banerjeec
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a
Department of Soil Science and Agricultural Chemistry, Institute of Agricultural Science, Banaras Hindu University, Varanasi-221005, Uttar Pradesh, India
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b
Division of Soil and Crop Management, Central Soil Salinity Research Institute, Karnal, Haryana-132001, India
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c
Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi- 110012, India
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*Corresponding author:
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Arghya Chattopadhyay
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Department of Soil Science and Agricultural Chemistry,
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Institute of Agricultural Science,
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Banaras Hindu University,
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Varanasi-221005, Uttar Pradesh, India
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E-mail address: [email protected]
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Tel.: +91-7052546501
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Abstract
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The Indo-Gangetic alluvium is prime region for intensive agricultural. In some areas of this
3
region, groundwater is now becoming progressively polluted by contamination with poisonous
4
substances like arsenic. Intensive irrigation with arsenic contaminated ground water in dry spell
5
results in the formation of As(III) which is more toxic. Thus groundwater quality assessment of
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Gangetic basin has become essential for its safer use. Therefore we under took study on the
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spatial variability of arsenic by collecting georeferred groundwater samples on grid basis from
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various water sources like dug well, bore and hand pumps covering the river bank region of
9
Ganga basin. Water quality was investigated through determination pH, EC, TDS, salinity, Na,
10
K, Ca, Mg, SAR, SSP, CO3, HCO3, RSC, Cl, As, Fe, Zn, Mn and Cu, etc. Results pointed severe
11
As contamination in ground water of three sites of the study area. ARC GIS software is now able
12
to process maps along with tabular data and compare them well, to provide the spatial
13
visualization of information and using this tool, the Geographical Information System (GIS) of
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arsenic was developed. It was noticed from spatial maps that concentration of arsenic was more
15
near the meandering points of Ganga.
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Key words: Arsenic; ARC GIS; Groundwater quality; Risk assessment; Spatial variability map;
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Varanasi
1
1. Introduction
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Groundwater makes up about 98 per cent of all usable fresh water on the planet and is about 60
3
times as abundant as fresh water found in lakes and streams and has a strong influence on river and
4
wetland habitats for plants and animals. The groundwater is not directly visible from surface and its
5
quantification is tedious when we consider total amount of water of the world. By protecting against
6
pollution and managing its use with caution, our ecosystem will be maintained and future will be
7
safeguarded (Mullen et al., 2018).
8
The quality of the deep aquifers also differs from place to place, however, it is generally
9
considered suitable for common use (Choudhury and Rakshit, 2012; Mishra and Desai, 2006). In
10
present scenario, analysis and assessment of groundwater quality parameters have been critically
11
considered under research (Ning and Chang, 2002). Groundwater is not only used for drinking,
12
cooking and washing but in the dry season, it is used in significant quantities to irrigate crops. Pyrite
13
may get oxidized to iron oxides under aerobic conditions and release arsenic, sulfate and various trace
14
elements, so human activities surround coal mining areas often are responsible for arsenic problems in
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coal mine sites (Pal, 2015). In June of 2002, arsenic contamination was discovered in Bihar on the
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plains of Central Ganga in the middle of Uttar Pradesh and the upper Ganges plains. (Chakraborti et
17
al., 2003). Theories about the source of arsenic in Gangetic Plains are as below:
18
(i)
of the Rajmahal trap area arsenic contamination reached as high as 200 ppm. (Saha, 1991).
19 20 21 22 23
The Ganges and its tributaries transported arsenic from the Gondwana coal, especially in the west
(ii)
In the Eastern Himalayas, Arsenic is transported from the Gurbathan base-metal deposits by the tributaries of Bhagirathi and Padma rivers (Roy, 1999).
(iii) Arsenic moved from the Himalayas to the different areas through fluvial sediments and is currently the most accepted theory (McArthur et al., 2004). 1
24
25
WHO (1993) mentioned 10 ppb as the upper limit of arsenic in drinking water. Karagas et al.,
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in 2001 reported that risk of skin cancer from arsenic exposure began, when concentration of arsenic in
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drinking water goes beyond >170 µg/L. Arsenic contamination in groundwater specially from the
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Ganga river’s Holocene alluvium goes beyond 200 times of the safe limit (10 ppb) as guided by WHO,
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and about 25% of the wells go beyond 50 ppb and affect at least 25 million people (Ravenscroft et al.,
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2005). Roberts et al. (2007) estimated that 1000 tons of arsenic can be transferred to the cultivated land
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worldwide in every year with arsenic contaminated under groundwater, which creates a lot of risk for
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the upcoming food production. Heikens et al. (2007) noted that concentration of arsenic in soil is
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increasing due to irrigation, but it is difficult to measure risk.
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Efforts for measuring spatial density of pollution for soil and water are both costly and time
35
consuming. Thus prediction method using hydrogeochemical data is a good alternative for very costly
36
and labor-intensive monitoring schemes (Cao et al., 2018). A geostatistical approach based on kriging
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procedures, with Variograms evaluation, is being used for spatial characterization of soil and water
38
quality parameters as well as for the regional threat of health risk (Goovaerts, 1997). There are many
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reports for using ordinary kriging (OK) methods to evaluate the spatial distribution of water quality
40
parameters (Diodato and Ceccarelli, 2004; Antunes and Albuquerque, 2013; Carraro et al., 2013; Dalla
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Libera et al., 2017; Dalla Libera et al., 2018; Boente et al., 2018). To evaluate the spatial distribution
42
of water quality parameters, Liang et al. (2016) found the Ordinary Kriging (OK) method satisfactory.
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Thus for finding out the potentially arsenic affected site, geostatistical methods, such as kriging will be
44
the best option as it provides a linear un-biased prediction for the unsampled grid nodes. GIS has been
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extensively used to understand the contaminant’s spatial distribution like As in groundwater and soils
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(Orlando et al., 2005; Chakraborti et al., 2017). Predictions for unsampled points include the weighted 2
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amount of adjacent sample specimen variables. Weights are calculated from the spatial structures of
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experimental variograms and are selected by reducing the estimation variance (Cressie, 1990;
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Wackernagel, 1995). Spatial relations between the variables, was suitable for studying the process of
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arsenic release in various places in under groundwater. Principal component analysis also helps to
51
check the relationships between variables, and ultimately the maps of spatial variations of the principal
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components and that found to be significant parameter were prepared.
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1. Material and method
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1.1. Study area
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Georeferenced water samples were collected from sixty spot adjoined to river Ganga in the
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Varanasi region on grid basis (Fig. 1.) at 500 m intervals having 1: 50,000 scale, covering the area of
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N 25° 30’ 26.4”, E 83° 10’ 7.4” to N 25° 12’ 28.9”, E 82° 54’ 8.0”.
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1.2. Sampling technique
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Water samples were collected in one liter plastic bottle after starting of the pump about half an
60
hour and were preserved by acidification with 5 mL of nitric acid (14 M) in each 1 liter of water
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sample to bring the pH <2, and placed in a refrigerator keeping the temperature below 4°C. Before
62
analysis all samples were filtered through Whatman 42 filter paper.
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1.3. Chemical analysis
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Various water quality parameters were determined by the standard procedures as described by
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APHA (2005) and Maiti (2001). Analysis of metal ions were performed on Atomic Absorption
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Spectrophotometer (AAS) and arsenic content of water was determined by VGA-AAS (Vapour
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Generation Atomic Absorption Spectrometry (Agilent Technologies VGA 77 AA spectrophotometer,
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Australia, Serial no. MY16020008) according to the method defined by Van Herreweghe et al. (2003).
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A cathode lamp holding 193.7 nm wave length is used as a light source. 0.5% sodium borohydrate in 3
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0.5% sodium hydroxide solution used as reductant, and 10% hydrochloric acid was used as acide
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solution. Combination of these two solution act as carrier solution in VGA and help to reduce the
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analyte in hydride form. In order to determine the total arsenic of samples, 10 mL water sample was
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taken in a volumetric flask of 100 mL, then 1 mL of 5% of potassium iodide, 1 mL of 5% of ascorbic
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acid and 10 mL hydrochloric acid were added to it and kept it for 45 minutes. The final volume of all
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the samples were made up to 100 mL and analysis was done using vapour generation method (VGA-
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AAS). All the reagents were GR (Guaranteed Reagent) grade, procured from E. Merck (India) Ltd.,
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Mumbai, India, and the solutions were prepared freshly during analysis.
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1.4. Statistical analysis
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The descriptive statistic was done by using statistical package of SPSS version 16.0 (SPSS Inc.,
80
USA) (Norusis, 2000) and Geostatistic was done by using ARC GIS 10.4 software.
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1.5. Geostatistical analysis
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The geostatistical approach is good for evaluation of the attributes in which observed values is
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the weighted average of every estimate the in the neighborhood. The weights mainly depend on fitting
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the variogram to the measured points. Variogram synthetize the relationship between couples of data
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distant h. In order to determine the nugget effect (C0), the sill (C0 +C1) and the range (a), the
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experimental variogram is matched with a theoretical one γ(h), which may be spherical, exponential or
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Gaussian or other less used models. The common equation for measuring estimate value is:
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Ẑ(S0) = ∑ iZ(Si)
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where, Ẑ(S0) = prediction value of location (S0)
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N = number of calculated sample points neighboring the prediction location;
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λi = the weight factor gained from fitted variogram
4
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Z(Si) = observed value of location (Si)
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Half the average square difference between data pairs Z(si) and Z(si+h) at xi and si+h locations
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is the semivariogram γ(h). The following equation provides an estimate of the semivariogram with
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N(h) of the number of sampling pairs detached by a distance of h(lag)
1 { − + ℎ } ℎ = 2ℎ
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1.6. Risk assessment
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Present investigation shows that arsenic content of some areas cross the safe limit (10 ppb) and were as
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high as 22 ppb, thus if an adult can take 3 L of water per day then net accumulation of arsenic in his
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body will be 66 ppb. European Food Safety Authority Panel on Contaminants in the Food Chain
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(EFSA CONTAM) established a Benchmark dose lower confidence limit (BMDL) of 8 µg/kg body
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weight per day (EFSA CONTAM Panel, 2009). WHO's (WHO, 2001) current guideline value for
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arsenic in drinking water (10 µg L-1) is based on the assumption that average daily intake of water for
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an adult having 60 kg body weight is 2 liter. In view of adverse health effects of arsenic, WHO
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proposed to reduce the current safe limit from 10 ppb (WHO, 2001). National standard of Australia has
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been lowered to 7 ppb viewing negative impact of arsenic. USEPA (USEPA-IRIS, 1998) describes
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various health risk assessment models to evaluate adverse non carcinogenic and carcinogenic effect of
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arsenic. In this study we go for hazard quotient and life time cancer risk assessment which given below
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1.6.1. Ingestion pathway
109 110
Arsenic intake exposure to drinking water was predicted by measuring a daily intake of arsenic (DIA) by using the following equation
5
DIA =
× #$ × # × !" %&
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where, DIA of arsenic indicating daily intake of arsenic (mg L-1 day), C is the arsenic concentration in
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drinking water (µ L-1), BW is body weight (kg), DI stands for average daily intake rate (L day-1), AT is
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the averaging time (d), ED means exposure duration (yr) and EF is the exposure frequency (d yr-1).
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In this equation we used DI = 2 L, BW = 60 kg (Chakraborti et al., 2017) EF = 365 days, AT =
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21900 days and ED = 60 years (Chakraborti et al., 2017).
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1.6.2. Dermal contact pathway
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This is calculated by following equation (Li et al., 2018) ADD = C × Ki × '% ×
()×(*×(+×,) -.×/0
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Where, ADD stands for average daily dose through dermal contact pathway (mg kg-1 day-1), Ki is the
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parameter related to dermal adsorption (0.001 cm h-1), CF is a unit conversion factor (0.002 L cm-3),
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SA stands for surface areas of the body (16,600 cm2), EV stands for bathing frequency of local
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residents in Varanasi (1 times day-1).
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1.6.3. Noncarcinogenic health risk assessment
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The non-carcinogenic health risk due to exposure of arsenic is done by calculating the hazard
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quotient (HQ), recommended by the USEPA, where HQ is calculated as the ratio of DIA (daily intake
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of arsenic), to the RfD (oral reference dose) below which there are no expected adverse effects; and
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this is estimated by 12 =
6
% 34
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The value of RfD varies according to different pathway. RfD = 0.0003 mg kg-1 body weight day-1
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ingestion pathway and 0.000123 mg kg-1 body weight day-1 for dermal contact pathways, respectively
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(USEPA-IRIS, 1998).
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No adverse non-carcinogenic health effects happen when the calculated HQ value is < 1, and
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adverse non-carcinogenic health effects are likely to occur when hazard quotient (HQ) value is > 1.
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The HQ is expressed as the summing up of HQ ingestion and HQ dermal in order to evaluate the over-
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all non-carcinogenic effects from more than one exposure pathway, and it is expressed as HQ =
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HQingestion + HQdermal
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1.6.4. Lifetime cancer risk assessment The lifetime risk of arsenic-related cancer (CR) has been calculated using
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3 = % × '$ 138
Whereas, SF is the slope factor of the arsenic (mg L-1 d-1) derived from the associated Risk related
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information (USEPA, 2012) and SF standard is 1.5 and 3.66 (mg kg-1 day-1) respectively for ingestion
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and dermal contact pathways.
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2. Result and Discussion
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2.1.
Descriptive statistic of different groundwater quality parameters
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The mean pH value of all the samples was 8.00 and negatively skewed (Table 1). At higher pH
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(> 8), arsenic desorption is reported to occur from the surfaces of metal oxides, especially in iron and
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manganese containing metal oxides which Can result in high levels of arsenic in groundwater
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(Smedley and Kinniburgh, 2002).
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Mean E.C. value of the samples was 447 µS cm-1 that falls under low category indicating low
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salt content. All water samples collected from adjoining areas of river Ganges, were high in total
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dissolved solids and sodium content having mean values of 289 ppm and 2.42 me L-1 respectively. 7
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Calcium+magnesium content of those water sample was low (5.50 me L-1) thus indicating high sodium
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adsorption ratio of 1.6 (Table 1). Low carbonate (2.35 me L-1) and bicarbonate (4.92 me L-1) content
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resulted in high residual sodium carbonate (1.78) content (Table 1). Zinc, copper, manganese, chlorine
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and iron contents of all the samples were in low category but phosphorus contents were in medium to
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high range (Table 1). Mean Arsenic content of the samples was 8.5 ppb which is below safe limit, but
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some points cross the limit where arsenic content attains value as high as 22 ppb.
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2.2.
Correlation of arsenic with major cations and anions present in groundwater
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Metalloids in natural water system usually have interactions among themselves and also with
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other ions. Pearson correlation study revealed the actual interdependence among various water
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parameters. There occurred high degree of interdependence between arsenic and iron and between
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arsenic and potassium content in the water samples. Among the various cations, arsenic is positively
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correlated with iron (r = +0.207) and potassium (r = +0.174) content of the water sample at 5% level of
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significance and negatively correlated with sodium (r = -0.178) and copper (r = -0.277) (Table 2).
163
Bhattacharya et al. (2003) also observed a positive correlation (r = +0.77) between arsenic and iron and
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Mueller and Hug (2018) found a positive correlation (r=+ 0.35) between potassium concentrations and
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arsenic in the groundwater of Nepal. It is generally accepted that abiotic and biotic reduction of
166
oxides/hydroxides of iron that contain arsenic (Islam et al., 2004; Campbell et al., 2006; Tufano et al.,
167
2008) and oxidation of iron sulfides (Mcarthur, 1999; Carraro et al., 2015) both are the leading
168
geochemical processes which cause arsenic accumulation in groundwater. Iron showed significant
169
positive correlation with calcium (r = +0.249) and magnesium (r = +0.249) and potassium proved
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significant positive correlation with zinc (r = +0.466). Sodium showed significant negative correlation
171
with iron (r = -0.214), potassium (r = -0.247), calcium (r = -0.435) and magnesium (r = -0.408) but in
172
case of copper (r = +0.296) there exist significant positive correlation with sodium (Table 2). Thus
8
173
from correlation study it is clear that whenever there was an increment in sodium content, other metal
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ions like iron, potassium, calcium and magnesium decreased significantly. Among the various major
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anions phosphorus shows significant negative correlation (r = -0.237) with arsenic at 1% level of
176
significance and strong positive correlation with carbonate (r = +0.441) (Table 2). Gurung (2005) and
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Bhowmick et al. (2013) reported that high phosphate and bicarbonate concentrations in ground favour
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arsenic mobilization in the aquifer. Competitive absorption between arsenic and bicarbonate also
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allowed arsenic to dissociate from surfaces of various Fe oxyhydroxides and many minerals such as
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illite, montmorillonite, kaolinite etc. (Appelo et al., 2002; Radu et al., 2005; Guo et al., 2011). Arsenic
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desorption from mineral surfaces inside the aquifers also helps to arsenic accumulation in groundwater
182
(Appelo et al., 2002; Smedley et al., 2003) and being an anion phosphorus is a well-known competitor
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of arsenic.
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2.3.
Principle component analysis for significant variables of groundwater parameters
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Principle component analysis helps us to identify the major variables which are responsible for
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main variation in the total data set (Table 3). Among various component analyzed, zinc and manganese
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act as major variables in principle component 1 and principle component 2 and responsible for 73.51%
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and 15.03% of the total variation, respectively. Arsenic became dominant component in both principle
189
component 3 and principle component 4 sharing 5.57% and 2.84% of the total variation shown in the
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table 3. Those components which were responsible for less than one per cent of variation were
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neglected. Comparison between principle component 2 and 4 revealed that high levels of chlorine,
192
Sodium Adsorption Ratio (SAR) and Residual Sodium Carbonate (RSC) were responsible for lower
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level of arsenic in groundwater for this study area (Fig. 2). Although zinc and manganese showed non-
194
significant relationship with arsenic content in groundwater of this area, they still had great impact
195
over total variation in data (Fig. 2). Chloride ions function as a conservative tracer, and it can be used
9
196
to monitor the effects of underground flow of hydrochemistry and to use it as an underground arsenic
197
concentration indicator (Cao et al., 2018). In a reduced environment, the dissolution and reduction of
198
iron or manganese oxydroxides favors the release of both ferrous and arsenic (Nickson et al., 1998;
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Berg et al., 2001; Smedley et al., 2002; Islam et al., 2004; Wang et al., 2010) to the groundwater.
200
Principle component analysis is also helpful in the prediction of groundwater arsenic concentration and
201
thereby provide improved assessment tools for Southeast Asian countries (Cho et al., 2011). In this
202
study, arsenic occupied component 3 and 4, so it may cause dangerous impact in groundwater of
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Varanasi region in upcoming era.
204
2.4.
Geostatistical analysis of principal components
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Spatial variability analysis of pH was best suited under Rational Quadratic Model (Table 4). It
206
is clear from spatial variability map of pH (Fig. 3a.) that pH goes higher in the bank of river Ganga and
207
decreases in the areas which are far apart. The Geostatistics specially uses the semivariogram method
208
to measure the spatial variability of a regional variable, and provides input parameters for the spatial
209
interpolation of kriging (Yang et al., 2009). The inherently elevated pH of the groundwater increases
210
the total pH dependent negative charges of the minerals and colloidal surfaces that in turn increases
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ionic repulsion and stimulates arsenic desorption from the surface of sediments into aqueous phase
212
(Smedley et al., 2002; Park et al., 2006). Exponential model and Gaussian model were best suited for
213
the spatial variability map of residual sodium carbonate and sodium adsorption ratio of water samples
214
(Table 4). It can be concluded from the maps (Fig. 3b.) that at the meandering position of river Ganga
215
the deposition of sodium was high showing high residual sodium carbonate and sodium adsorption
216
ratio. J Bessel and spherical models suit well for the spatial variability map of phosphorus and arsenic,
217
whereas variability map of iron and chlorine followed Rational Quadratic Model. Phosphorus content
218
was low in the turning of Ganga (Fig. 3c.) but arsenic content were high specially in the meandering
10
219
position (Fig. 3d.) indicating the negative relationship between them. Spatial variability map of arsenic
220
evidently shows that three places of study area were potentially arsenic affected which may become
221
severe threat in near future. Small natural ponds and meandering position of rivers were also
222
considered as characteristic geomorphic features for arsenic accumulation by Shrestha et al. (2004),
223
because in this position fine grain sediment deposition is very high and in fine-grained sediments, high
224
concentrations of arsenic is naturally recorded (Diwakar et al., 2015). Fine grained primary and
225
secondary minerals are well known for adsorbing arsenic competently (Stanger, 2005; Chakraborty et
226
al., 2007; Uddin, 2017) and side by side can release a considerable amount of arsenic whenever the
227
conditions favour. Naturally new holocene alluvium is un-oxidized and comprises of very highly
228
organic-rich primary soil particles like sand silt and clay (Acharyya and Shah, 2007). Acharyya and
229
Shah (2007) also reported that especially the recent alluvial deposit from flood plain and entrenched
230
channels that are located at the meandering position are prone to arsenic contamination. Arsenic is
231
released from associated Holocene sediments in groundwater that were probably deposited from the
232
surrounding Tertiary Barail hill range. Pollution in groundwater was previously reported from the
233
various states of middle Gangetic plain like Uttar Pradesh, Bihar, Jharkhand of India (Chakraborti et al.
234
2003; Shah, 2010).
235
Chloride content (Fig. 3e.) was higher in places apart from river Ganga whereas iron content
236
(Fig. 3f.) was higher in the meandering position of river Ganga. The nugget to sill ratio specifies the
237
quantity of random components to the spatial heterogeneity of the system (Odoi et al., 2011). The ratio
238
of <0.25, 0.25–0.75, and >0.75 might be used to label the strong, moderate, and weak spatial
239
autocorrelation, respectively (Cambardella et al., 1994). In our study we found that, the nugget : sill
240
ratio for pH, arsenic and iron were 0.321, 0.270 and 0.704 respectively indicating moderate spatial
241
autocorrelation. Spatial autocorrelation for residual sodium carbonate and sodium adsorption ration
11
242
were 0.012 and 0.146 that fall under strong category, and for phosphorus and chlorine these showed
243
weak spatial autocorrelation. In general, extrinsic factors can be attributed to weak spatial dependence
244
of parameters and intrinsic factors can be attributed to strong spatial dependence (Cambardella et al.,
245
1994). As this Indo Gangetic Plain is well known for intensive agricultural practice with indiscriminate
246
use of agrochemicals, there may be some possibility of chlorine and phosphorus contamination which
247
causes weak spatial autocorrelation.
248
2.5.
Risk assessment from arsenic ingestion through drinking water
249
The hazard quotient (HQ) is used as the index for non-carcinogenic arsenic health risk
250
assessment. In our study area, many places exceed the safe limit of HQ (i.e. ≤ 1) and its mean value
251
reaches as high as 1.056 for ingestion pathway and 2.578 for dermal contact pathway (Table 5). Mean
252
life time cancer risks (CR) for arsenic was 2.71×10-5 (Table 5), which was 27.1 times higher than that
253
of the negligible risk level (1.00×10-6). There have been global reports of non-carcinogenic or
254
carcinogenic effects of arsenic on humans (WHO 2004; ATSDR 2007; Brammer and Ravenscroft
255
2009). Nguyen et al. (2009) conclude that the health of nearly 42% people of Vietnam might be
256
affected by arsenic where its life time cancer risk reaches as high as 5.0×10-3.The mean life time
257
cancer risk from arsenic through dermal contact and ingestion pathway was recorded as 7.89×10-6 and
258
1.93×10-5 (Table 5). In respect to the USEPA threshold value (>10-6 is unacceptable), the maximum
259
risk value obtained in the Varanasi region, exceeded the threshold value by ten times, indicating a clear
260
potential risk of arsenic exposure to the people. Alam et al. (2016) stated that areas with high cancer
261
risk will face more serious health problems (i.e. lung cancer, liver cancer and urinary cancer).
262
Mazumder (2008) also reported that the long-term arsenic exposure cause lung and skin cancers.
263
3. Conclusions
12
264
Finally it can be concluded that arsenic content of the adjoining villages of river Ganga is high
265
as compared to the other villages located far apart from the Ganga, and it crosses the safe limit at the
266
meandering position of the river. All water samples were alkaline in nature and inherently contained
267
high amount of sodium. High phosphorus content significantly decreases the arsenic content but higher
268
levels of iron, as well as of potassium increase the arsenic content of the water samples tested. Alkali
269
desorption and anionic competition are the dominant reasons for arsenic contamination in this area.
270
Three sites adjoining to Ganga river exceeded the safe level of human cancer risk from arsenic
271
exposure.
272
Acknowledgements
273
The authors want to thank the Department of Science and Technology, Government of India for
274
financial support, the Department of Soil Science and Agricultural Chemistry for extending laboratory
275
facilities and Indian Agricultural Research Institute for various software analyses and Central Soil
276
Salinity Research Institute for providing toposheet and facilities regarding ARC-GIS software.
277
Conflict of interest
278
The authors declare that they do not have any conflict of interest.
279
Reference
280
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440
21
1
Figure captions
2
Fig. 1. Map of middle Gangetic basin of Varanasi region
3
Fig. 2. Ordination diagram of principal component analysis showing effects of various
4 5
groundwater quality parameters on arsenic content Fig. 3. Spatial variability map of different ground water quality parameters
Table 1 Descriptive statistic of different ground water quality parameters pH
EC
Na+
(µS cm-1)
(me L-1)
SAR
RSC
PO43-
Cl-
(ppm)
(me L- (III) 1
)
As
Fe2+
Zn2+
Mn2+
Cu2+
(ppm)
(ppb)
(ppb)
(ppb)
(ppb)
Mean
8.00
447
2.42
1.6
1.78
3.74
2.82
8.5
4.60
27.5
4.46
3.99
Standard
0.47
126.42
3.03
2.18
1.74
2.32
0.60
3.27
4.60
12.27
15.30
9.38
Variance
0.22
15982
9.19
4.75
3.03
5.39
0.36
10.72
21.19
150.58 234.06 88.00
Skewness
-0.41
1.13
1.98
2.02
1.18
2.49
0.27
0.58
2.05
0.71
5.39
4.04
Kurtosis
-1.19
1.49
3.78
3.85
0.54
8.79
-0.82
-0.47
4.92
0.16
31.86
16.12
deviation
Table 2 Pearson correlation study of arsenic with major cations and anions present in ground water Cations 2+
2+
2+
As
Fe
Zn
Mn
Cu+
Na+
K+
Ca2+
Mg2+
(ppb)
(ppm)
(ppb)
(ppb)
(ppb)
(ppm)
(ppm)
(ppm)
(ppm)
As (ppb)
1.000
Fe2+ (ppm)
0.207*
1.000
Zn2+ (ppb)
0.025
0.071
1.000
Mn2+ (ppb)
-0.155
-0.088
-0.150
1.000
Cu+ (ppb)
-0.277**
-0.091
-0.327**
0.133
1.000
Na+ (ppm)
-0.178*
-0.214**
0.017
0.090
0.296**
1.000
K+ (ppm)
0.174*
-0.016
0.466**
0.041
-0.081
-0.247**
1.000
Ca2+ (ppm)
0.018
0.249**
0.050
-0.111
-0.214**
-0.435**
0.140
1.000
Mg2+ (ppm)
-0.001
0.249**
0.027
-0.119
-0.141
-0.408**
0.133
0.994**
Anions As (ppb) CO32- (me L-1) -
-1
As
CO32-
HCO3-
PO43-
Cl-
(ppb)
(me L-1)
(me L-1)
(ppm)
(me L-1)
1.000
HCO3 (me L )
-0.150
1.000
PO43- (ppm)
0.094
-0.019
1.000
Cl- (me L-1)
-0.237**
0.441**
0.126
1.000
As (ppb)
-0.068
0.118
-0.346**
0.035
* Significant at 5%, ** Significant at 1%
1.000
1.000
Table 3 Principle component analysis for significant variables of ground water parameters Statistic or Variable
PC 1
PC 2
PC 3
PC 4
Eigen value
5137.38
1050.57
389.254
198.6
Percentage of variance
73.514
15.033
5.5701
2.84
PO43- (ppm)
-16.4
-14.1
-5.55
-7.66
Cl- (me/L)
-30.8
-14.2
-4.87
-8.49
As (ppb)
17.5
-12.3
-13.9
32.665
Mn (ppb)
-39.3
89.8
-4.06
1.79
Cu (ppb)
-34.5
-7.96
53.9
6.55
RSC
-39.1
-14.6
-7.42
-15.77
SAR
-43.7
-8.19
-7.35
-11.3
pH
4.0
-8.62
-1.71
1.04
Fe (ppm)
-13.4
-18.4
-13.6
9.67
Zn (ppb)
194.7
8.49
4.69
-8.44
Eigen vectors
Table 4 Geostatistical analysis of principal components Ground water parameters
No. of
Best-fit model
Nugget
Range
Partial sill
observation
Nugget to
Root Mean
sill ratio
Square error
pH
60
Rational quadratic
0.072
0.233
0.224
0.321
0.308
Residual Sodium
60
Exponential
0.051
0.243
4.113
0.012
1.101
Sodium Adsorption Ratio
60
Gaussian
0.997
0.239
6.807
0.146
1.862
Phosphorus
60
J Bessel
3.722
0.064
3.306
1.126
2.139
Arsenic
60
Spherical
4.654
0.010
17.25
0.270
4.590
Chlorine
60
Rational quadratic
0.266
0.022
0.181
1.470
0.632
Iron
60
Rational quadratic
13.209
0.0636
18.75
0.704
4.401
Carbonate
Table 5 Risk assessment from arsenic ingestion through drinking water
DIA Mean Median Mini. Safe limit
Adverse non-carcinogenic risk
Adverse carcinogenic risk
Hazard Quotient (HQ) HQi HQd
(Carcinogenic Risk) CRi CRd
HQ
Average Daily Dose
CR
0.317
1.056
2.578
3.634
5.26×10-6
7.89×10-6
1.93×10-5
2.71×10-5
0.290
0.967
2.359
3.326
4.82×10-6
7.23×10-6
1.76×10-5
2.48×10-5
0.01
0.033
0.081
0.115
1.66×10-7
2.49×10-7
6.08×10-7
8.56×10-7
<1
i = ingestion path way, d = dermal pathway
<10-6
Map of Uttar Pradesh
Map of Ganga basin in Varanasi
Map of India
Total sampling sites: 60 Starting location: N 25° 30’ 26.4”, E 83° 10’ 7.4” Final location: N 25° 12’ 28.9”, E 82° 54’ 8.0”
1 2
Fig. 1.
1
0.75 Zn2+
RSC
0.5 pH PO43-
Component 2
0.25
0 As
Fe2+ -0.25
ClSAR Mn2+ Cu2+
-0.5
-0.75
-1 -1
-0.75
-0.5
-0.25
0 Component 1
3 4
Fig. 2.
0.25
0.5
0.75
1
5 6
Fig. 3.
a
b
c
d
e
f
1
Highlights:
2
Arsenic toxicity was high at some of the meandering positions of the Ganga river.
3
Alkali desorption was the dominant reasons for arsenic release.
4
Ground water samples were highly alkaline.
S0045-6535(19)31847-8
DOI:
https://doi.org/10.1016/j.chemosphere.2019.124623
Reference:
CHEM 124623
To appear in:
ECSN
Received Date: 20 May 2019 Revised Date:
14 August 2019
Accepted Date: 18 August 2019
Please cite this article as: Chattopadhyay, A., Singh, A.P., Singh, S.K., Barman, A., Patra, A., Mondal, B.P., Banerjee, K., Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment, Chemosphere (2019), doi: https://doi.org/10.1016/j.chemosphere.2019.124623. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
1
Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk
2
assessment
3
Arghya Chattopadhyaya*, Anand Prakash Singha, Satish Kumar Singha, Arijit Barmanb, Abhik
4
Patraa, Bhabani Prasad Mondalc and Koushik Banerjeec
5 6
a
Department of Soil Science and Agricultural Chemistry, Institute of Agricultural Science, Banaras Hindu University, Varanasi-221005, Uttar Pradesh, India
7 8
b
Division of Soil and Crop Management, Central Soil Salinity Research Institute, Karnal, Haryana-132001, India
9 10
c
Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi- 110012, India
11 12
*Corresponding author:
13
Arghya Chattopadhyay
14
Department of Soil Science and Agricultural Chemistry,
15
Institute of Agricultural Science,
16
Banaras Hindu University,
17
Varanasi-221005, Uttar Pradesh, India
18
E-mail address: [email protected]
19
Tel.: +91-7052546501
1
Abstract
2
The Indo-Gangetic alluvium is prime region for intensive agricultural. In some areas of this
3
region, groundwater is now becoming progressively polluted by contamination with poisonous
4
substances like arsenic. Intensive irrigation with arsenic contaminated ground water in dry spell
5
results in the formation of As(III) which is more toxic. Thus groundwater quality assessment of
6
Gangetic basin has become essential for its safer use. Therefore we under took study on the
7
spatial variability of arsenic by collecting georeferred groundwater samples on grid basis from
8
various water sources like dug well, bore and hand pumps covering the river bank region of
9
Ganga basin. Water quality was investigated through determination pH, EC, TDS, salinity, Na,
10
K, Ca, Mg, SAR, SSP, CO3, HCO3, RSC, Cl, As, Fe, Zn, Mn and Cu, etc. Results pointed severe
11
As contamination in ground water of three sites of the study area. ARC GIS software is now able
12
to process maps along with tabular data and compare them well, to provide the spatial
13
visualization of information and using this tool, the Geographical Information System (GIS) of
14
arsenic was developed. It was noticed from spatial maps that concentration of arsenic was more
15
near the meandering points of Ganga.
16
Key words: Arsenic; ARC GIS; Groundwater quality; Risk assessment; Spatial variability map;
17
Varanasi
1
1. Introduction
2
Groundwater makes up about 98 per cent of all usable fresh water on the planet and is about 60
3
times as abundant as fresh water found in lakes and streams and has a strong influence on river and
4
wetland habitats for plants and animals. The groundwater is not directly visible from surface and its
5
quantification is tedious when we consider total amount of water of the world. By protecting against
6
pollution and managing its use with caution, our ecosystem will be maintained and future will be
7
safeguarded (Mullen et al., 2018).
8
The quality of the deep aquifers also differs from place to place, however, it is generally
9
considered suitable for common use (Choudhury and Rakshit, 2012; Mishra and Desai, 2006). In
10
present scenario, analysis and assessment of groundwater quality parameters have been critically
11
considered under research (Ning and Chang, 2002). Groundwater is not only used for drinking,
12
cooking and washing but in the dry season, it is used in significant quantities to irrigate crops. Pyrite
13
may get oxidized to iron oxides under aerobic conditions and release arsenic, sulfate and various trace
14
elements, so human activities surround coal mining areas often are responsible for arsenic problems in
15
coal mine sites (Pal, 2015). In June of 2002, arsenic contamination was discovered in Bihar on the
16
plains of Central Ganga in the middle of Uttar Pradesh and the upper Ganges plains. (Chakraborti et
17
al., 2003). Theories about the source of arsenic in Gangetic Plains are as below:
18
(i)
of the Rajmahal trap area arsenic contamination reached as high as 200 ppm. (Saha, 1991).
19 20 21 22 23
The Ganges and its tributaries transported arsenic from the Gondwana coal, especially in the west
(ii)
In the Eastern Himalayas, Arsenic is transported from the Gurbathan base-metal deposits by the tributaries of Bhagirathi and Padma rivers (Roy, 1999).
(iii) Arsenic moved from the Himalayas to the different areas through fluvial sediments and is currently the most accepted theory (McArthur et al., 2004). 1
24
25
WHO (1993) mentioned 10 ppb as the upper limit of arsenic in drinking water. Karagas et al.,
26
in 2001 reported that risk of skin cancer from arsenic exposure began, when concentration of arsenic in
27
drinking water goes beyond >170 µg/L. Arsenic contamination in groundwater specially from the
28
Ganga river’s Holocene alluvium goes beyond 200 times of the safe limit (10 ppb) as guided by WHO,
29
and about 25% of the wells go beyond 50 ppb and affect at least 25 million people (Ravenscroft et al.,
30
2005). Roberts et al. (2007) estimated that 1000 tons of arsenic can be transferred to the cultivated land
31
worldwide in every year with arsenic contaminated under groundwater, which creates a lot of risk for
32
the upcoming food production. Heikens et al. (2007) noted that concentration of arsenic in soil is
33
increasing due to irrigation, but it is difficult to measure risk.
34
Efforts for measuring spatial density of pollution for soil and water are both costly and time
35
consuming. Thus prediction method using hydrogeochemical data is a good alternative for very costly
36
and labor-intensive monitoring schemes (Cao et al., 2018). A geostatistical approach based on kriging
37
procedures, with Variograms evaluation, is being used for spatial characterization of soil and water
38
quality parameters as well as for the regional threat of health risk (Goovaerts, 1997). There are many
39
reports for using ordinary kriging (OK) methods to evaluate the spatial distribution of water quality
40
parameters (Diodato and Ceccarelli, 2004; Antunes and Albuquerque, 2013; Carraro et al., 2013; Dalla
41
Libera et al., 2017; Dalla Libera et al., 2018; Boente et al., 2018). To evaluate the spatial distribution
42
of water quality parameters, Liang et al. (2016) found the Ordinary Kriging (OK) method satisfactory.
43
Thus for finding out the potentially arsenic affected site, geostatistical methods, such as kriging will be
44
the best option as it provides a linear un-biased prediction for the unsampled grid nodes. GIS has been
45
extensively used to understand the contaminant’s spatial distribution like As in groundwater and soils
46
(Orlando et al., 2005; Chakraborti et al., 2017). Predictions for unsampled points include the weighted 2
47
amount of adjacent sample specimen variables. Weights are calculated from the spatial structures of
48
experimental variograms and are selected by reducing the estimation variance (Cressie, 1990;
49
Wackernagel, 1995). Spatial relations between the variables, was suitable for studying the process of
50
arsenic release in various places in under groundwater. Principal component analysis also helps to
51
check the relationships between variables, and ultimately the maps of spatial variations of the principal
52
components and that found to be significant parameter were prepared.
53
1. Material and method
54
1.1. Study area
55
Georeferenced water samples were collected from sixty spot adjoined to river Ganga in the
56
Varanasi region on grid basis (Fig. 1.) at 500 m intervals having 1: 50,000 scale, covering the area of
57
N 25° 30’ 26.4”, E 83° 10’ 7.4” to N 25° 12’ 28.9”, E 82° 54’ 8.0”.
58
1.2. Sampling technique
59
Water samples were collected in one liter plastic bottle after starting of the pump about half an
60
hour and were preserved by acidification with 5 mL of nitric acid (14 M) in each 1 liter of water
61
sample to bring the pH <2, and placed in a refrigerator keeping the temperature below 4°C. Before
62
analysis all samples were filtered through Whatman 42 filter paper.
63
1.3. Chemical analysis
64
Various water quality parameters were determined by the standard procedures as described by
65
APHA (2005) and Maiti (2001). Analysis of metal ions were performed on Atomic Absorption
66
Spectrophotometer (AAS) and arsenic content of water was determined by VGA-AAS (Vapour
67
Generation Atomic Absorption Spectrometry (Agilent Technologies VGA 77 AA spectrophotometer,
68
Australia, Serial no. MY16020008) according to the method defined by Van Herreweghe et al. (2003).
69
A cathode lamp holding 193.7 nm wave length is used as a light source. 0.5% sodium borohydrate in 3
70
0.5% sodium hydroxide solution used as reductant, and 10% hydrochloric acid was used as acide
71
solution. Combination of these two solution act as carrier solution in VGA and help to reduce the
72
analyte in hydride form. In order to determine the total arsenic of samples, 10 mL water sample was
73
taken in a volumetric flask of 100 mL, then 1 mL of 5% of potassium iodide, 1 mL of 5% of ascorbic
74
acid and 10 mL hydrochloric acid were added to it and kept it for 45 minutes. The final volume of all
75
the samples were made up to 100 mL and analysis was done using vapour generation method (VGA-
76
AAS). All the reagents were GR (Guaranteed Reagent) grade, procured from E. Merck (India) Ltd.,
77
Mumbai, India, and the solutions were prepared freshly during analysis.
78
1.4. Statistical analysis
79
The descriptive statistic was done by using statistical package of SPSS version 16.0 (SPSS Inc.,
80
USA) (Norusis, 2000) and Geostatistic was done by using ARC GIS 10.4 software.
81
1.5. Geostatistical analysis
82
The geostatistical approach is good for evaluation of the attributes in which observed values is
83
the weighted average of every estimate the in the neighborhood. The weights mainly depend on fitting
84
the variogram to the measured points. Variogram synthetize the relationship between couples of data
85
distant h. In order to determine the nugget effect (C0), the sill (C0 +C1) and the range (a), the
86
experimental variogram is matched with a theoretical one γ(h), which may be spherical, exponential or
87
Gaussian or other less used models. The common equation for measuring estimate value is:
88
Ẑ(S0) = ∑ iZ(Si)
89
where, Ẑ(S0) = prediction value of location (S0)
90
N = number of calculated sample points neighboring the prediction location;
91
λi = the weight factor gained from fitted variogram
4
92
Z(Si) = observed value of location (Si)
93
Half the average square difference between data pairs Z(si) and Z(si+h) at xi and si+h locations
94
is the semivariogram γ(h). The following equation provides an estimate of the semivariogram with
95
N(h) of the number of sampling pairs detached by a distance of h(lag)
1 { − + ℎ } ℎ = 2ℎ
96
1.6. Risk assessment
97
Present investigation shows that arsenic content of some areas cross the safe limit (10 ppb) and were as
98
high as 22 ppb, thus if an adult can take 3 L of water per day then net accumulation of arsenic in his
99
body will be 66 ppb. European Food Safety Authority Panel on Contaminants in the Food Chain
100
(EFSA CONTAM) established a Benchmark dose lower confidence limit (BMDL) of 8 µg/kg body
101
weight per day (EFSA CONTAM Panel, 2009). WHO's (WHO, 2001) current guideline value for
102
arsenic in drinking water (10 µg L-1) is based on the assumption that average daily intake of water for
103
an adult having 60 kg body weight is 2 liter. In view of adverse health effects of arsenic, WHO
104
proposed to reduce the current safe limit from 10 ppb (WHO, 2001). National standard of Australia has
105
been lowered to 7 ppb viewing negative impact of arsenic. USEPA (USEPA-IRIS, 1998) describes
106
various health risk assessment models to evaluate adverse non carcinogenic and carcinogenic effect of
107
arsenic. In this study we go for hazard quotient and life time cancer risk assessment which given below
108
1.6.1. Ingestion pathway
109 110
Arsenic intake exposure to drinking water was predicted by measuring a daily intake of arsenic (DIA) by using the following equation
5
DIA =
× #$ × # × !" %&
111
where, DIA of arsenic indicating daily intake of arsenic (mg L-1 day), C is the arsenic concentration in
112
drinking water (µ L-1), BW is body weight (kg), DI stands for average daily intake rate (L day-1), AT is
113
the averaging time (d), ED means exposure duration (yr) and EF is the exposure frequency (d yr-1).
114
In this equation we used DI = 2 L, BW = 60 kg (Chakraborti et al., 2017) EF = 365 days, AT =
115
21900 days and ED = 60 years (Chakraborti et al., 2017).
116
1.6.2. Dermal contact pathway
117
118
This is calculated by following equation (Li et al., 2018) ADD = C × Ki × '% ×
()×(*×(+×,) -.×/0
119
Where, ADD stands for average daily dose through dermal contact pathway (mg kg-1 day-1), Ki is the
120
parameter related to dermal adsorption (0.001 cm h-1), CF is a unit conversion factor (0.002 L cm-3),
121
SA stands for surface areas of the body (16,600 cm2), EV stands for bathing frequency of local
122
residents in Varanasi (1 times day-1).
123
1.6.3. Noncarcinogenic health risk assessment
124
The non-carcinogenic health risk due to exposure of arsenic is done by calculating the hazard
125
quotient (HQ), recommended by the USEPA, where HQ is calculated as the ratio of DIA (daily intake
126
of arsenic), to the RfD (oral reference dose) below which there are no expected adverse effects; and
127
this is estimated by 12 =
6
% 34
128
The value of RfD varies according to different pathway. RfD = 0.0003 mg kg-1 body weight day-1
129
ingestion pathway and 0.000123 mg kg-1 body weight day-1 for dermal contact pathways, respectively
130
(USEPA-IRIS, 1998).
131
No adverse non-carcinogenic health effects happen when the calculated HQ value is < 1, and
132
adverse non-carcinogenic health effects are likely to occur when hazard quotient (HQ) value is > 1.
133
The HQ is expressed as the summing up of HQ ingestion and HQ dermal in order to evaluate the over-
134
all non-carcinogenic effects from more than one exposure pathway, and it is expressed as HQ =
135
HQingestion + HQdermal
136
1.6.4. Lifetime cancer risk assessment The lifetime risk of arsenic-related cancer (CR) has been calculated using
137
3 = % × '$ 138
Whereas, SF is the slope factor of the arsenic (mg L-1 d-1) derived from the associated Risk related
139
information (USEPA, 2012) and SF standard is 1.5 and 3.66 (mg kg-1 day-1) respectively for ingestion
140
and dermal contact pathways.
141
2. Result and Discussion
142
2.1.
Descriptive statistic of different groundwater quality parameters
143
The mean pH value of all the samples was 8.00 and negatively skewed (Table 1). At higher pH
144
(> 8), arsenic desorption is reported to occur from the surfaces of metal oxides, especially in iron and
145
manganese containing metal oxides which Can result in high levels of arsenic in groundwater
146
(Smedley and Kinniburgh, 2002).
147
Mean E.C. value of the samples was 447 µS cm-1 that falls under low category indicating low
148
salt content. All water samples collected from adjoining areas of river Ganges, were high in total
149
dissolved solids and sodium content having mean values of 289 ppm and 2.42 me L-1 respectively. 7
150
Calcium+magnesium content of those water sample was low (5.50 me L-1) thus indicating high sodium
151
adsorption ratio of 1.6 (Table 1). Low carbonate (2.35 me L-1) and bicarbonate (4.92 me L-1) content
152
resulted in high residual sodium carbonate (1.78) content (Table 1). Zinc, copper, manganese, chlorine
153
and iron contents of all the samples were in low category but phosphorus contents were in medium to
154
high range (Table 1). Mean Arsenic content of the samples was 8.5 ppb which is below safe limit, but
155
some points cross the limit where arsenic content attains value as high as 22 ppb.
156
2.2.
Correlation of arsenic with major cations and anions present in groundwater
157
Metalloids in natural water system usually have interactions among themselves and also with
158
other ions. Pearson correlation study revealed the actual interdependence among various water
159
parameters. There occurred high degree of interdependence between arsenic and iron and between
160
arsenic and potassium content in the water samples. Among the various cations, arsenic is positively
161
correlated with iron (r = +0.207) and potassium (r = +0.174) content of the water sample at 5% level of
162
significance and negatively correlated with sodium (r = -0.178) and copper (r = -0.277) (Table 2).
163
Bhattacharya et al. (2003) also observed a positive correlation (r = +0.77) between arsenic and iron and
164
Mueller and Hug (2018) found a positive correlation (r=+ 0.35) between potassium concentrations and
165
arsenic in the groundwater of Nepal. It is generally accepted that abiotic and biotic reduction of
166
oxides/hydroxides of iron that contain arsenic (Islam et al., 2004; Campbell et al., 2006; Tufano et al.,
167
2008) and oxidation of iron sulfides (Mcarthur, 1999; Carraro et al., 2015) both are the leading
168
geochemical processes which cause arsenic accumulation in groundwater. Iron showed significant
169
positive correlation with calcium (r = +0.249) and magnesium (r = +0.249) and potassium proved
170
significant positive correlation with zinc (r = +0.466). Sodium showed significant negative correlation
171
with iron (r = -0.214), potassium (r = -0.247), calcium (r = -0.435) and magnesium (r = -0.408) but in
172
case of copper (r = +0.296) there exist significant positive correlation with sodium (Table 2). Thus
8
173
from correlation study it is clear that whenever there was an increment in sodium content, other metal
174
ions like iron, potassium, calcium and magnesium decreased significantly. Among the various major
175
anions phosphorus shows significant negative correlation (r = -0.237) with arsenic at 1% level of
176
significance and strong positive correlation with carbonate (r = +0.441) (Table 2). Gurung (2005) and
177
Bhowmick et al. (2013) reported that high phosphate and bicarbonate concentrations in ground favour
178
arsenic mobilization in the aquifer. Competitive absorption between arsenic and bicarbonate also
179
allowed arsenic to dissociate from surfaces of various Fe oxyhydroxides and many minerals such as
180
illite, montmorillonite, kaolinite etc. (Appelo et al., 2002; Radu et al., 2005; Guo et al., 2011). Arsenic
181
desorption from mineral surfaces inside the aquifers also helps to arsenic accumulation in groundwater
182
(Appelo et al., 2002; Smedley et al., 2003) and being an anion phosphorus is a well-known competitor
183
of arsenic.
184
2.3.
Principle component analysis for significant variables of groundwater parameters
185
Principle component analysis helps us to identify the major variables which are responsible for
186
main variation in the total data set (Table 3). Among various component analyzed, zinc and manganese
187
act as major variables in principle component 1 and principle component 2 and responsible for 73.51%
188
and 15.03% of the total variation, respectively. Arsenic became dominant component in both principle
189
component 3 and principle component 4 sharing 5.57% and 2.84% of the total variation shown in the
190
table 3. Those components which were responsible for less than one per cent of variation were
191
neglected. Comparison between principle component 2 and 4 revealed that high levels of chlorine,
192
Sodium Adsorption Ratio (SAR) and Residual Sodium Carbonate (RSC) were responsible for lower
193
level of arsenic in groundwater for this study area (Fig. 2). Although zinc and manganese showed non-
194
significant relationship with arsenic content in groundwater of this area, they still had great impact
195
over total variation in data (Fig. 2). Chloride ions function as a conservative tracer, and it can be used
9
196
to monitor the effects of underground flow of hydrochemistry and to use it as an underground arsenic
197
concentration indicator (Cao et al., 2018). In a reduced environment, the dissolution and reduction of
198
iron or manganese oxydroxides favors the release of both ferrous and arsenic (Nickson et al., 1998;
199
Berg et al., 2001; Smedley et al., 2002; Islam et al., 2004; Wang et al., 2010) to the groundwater.
200
Principle component analysis is also helpful in the prediction of groundwater arsenic concentration and
201
thereby provide improved assessment tools for Southeast Asian countries (Cho et al., 2011). In this
202
study, arsenic occupied component 3 and 4, so it may cause dangerous impact in groundwater of
203
Varanasi region in upcoming era.
204
2.4.
Geostatistical analysis of principal components
205
Spatial variability analysis of pH was best suited under Rational Quadratic Model (Table 4). It
206
is clear from spatial variability map of pH (Fig. 3a.) that pH goes higher in the bank of river Ganga and
207
decreases in the areas which are far apart. The Geostatistics specially uses the semivariogram method
208
to measure the spatial variability of a regional variable, and provides input parameters for the spatial
209
interpolation of kriging (Yang et al., 2009). The inherently elevated pH of the groundwater increases
210
the total pH dependent negative charges of the minerals and colloidal surfaces that in turn increases
211
ionic repulsion and stimulates arsenic desorption from the surface of sediments into aqueous phase
212
(Smedley et al., 2002; Park et al., 2006). Exponential model and Gaussian model were best suited for
213
the spatial variability map of residual sodium carbonate and sodium adsorption ratio of water samples
214
(Table 4). It can be concluded from the maps (Fig. 3b.) that at the meandering position of river Ganga
215
the deposition of sodium was high showing high residual sodium carbonate and sodium adsorption
216
ratio. J Bessel and spherical models suit well for the spatial variability map of phosphorus and arsenic,
217
whereas variability map of iron and chlorine followed Rational Quadratic Model. Phosphorus content
218
was low in the turning of Ganga (Fig. 3c.) but arsenic content were high specially in the meandering
10
219
position (Fig. 3d.) indicating the negative relationship between them. Spatial variability map of arsenic
220
evidently shows that three places of study area were potentially arsenic affected which may become
221
severe threat in near future. Small natural ponds and meandering position of rivers were also
222
considered as characteristic geomorphic features for arsenic accumulation by Shrestha et al. (2004),
223
because in this position fine grain sediment deposition is very high and in fine-grained sediments, high
224
concentrations of arsenic is naturally recorded (Diwakar et al., 2015). Fine grained primary and
225
secondary minerals are well known for adsorbing arsenic competently (Stanger, 2005; Chakraborty et
226
al., 2007; Uddin, 2017) and side by side can release a considerable amount of arsenic whenever the
227
conditions favour. Naturally new holocene alluvium is un-oxidized and comprises of very highly
228
organic-rich primary soil particles like sand silt and clay (Acharyya and Shah, 2007). Acharyya and
229
Shah (2007) also reported that especially the recent alluvial deposit from flood plain and entrenched
230
channels that are located at the meandering position are prone to arsenic contamination. Arsenic is
231
released from associated Holocene sediments in groundwater that were probably deposited from the
232
surrounding Tertiary Barail hill range. Pollution in groundwater was previously reported from the
233
various states of middle Gangetic plain like Uttar Pradesh, Bihar, Jharkhand of India (Chakraborti et al.
234
2003; Shah, 2010).
235
Chloride content (Fig. 3e.) was higher in places apart from river Ganga whereas iron content
236
(Fig. 3f.) was higher in the meandering position of river Ganga. The nugget to sill ratio specifies the
237
quantity of random components to the spatial heterogeneity of the system (Odoi et al., 2011). The ratio
238
of <0.25, 0.25–0.75, and >0.75 might be used to label the strong, moderate, and weak spatial
239
autocorrelation, respectively (Cambardella et al., 1994). In our study we found that, the nugget : sill
240
ratio for pH, arsenic and iron were 0.321, 0.270 and 0.704 respectively indicating moderate spatial
241
autocorrelation. Spatial autocorrelation for residual sodium carbonate and sodium adsorption ration
11
242
were 0.012 and 0.146 that fall under strong category, and for phosphorus and chlorine these showed
243
weak spatial autocorrelation. In general, extrinsic factors can be attributed to weak spatial dependence
244
of parameters and intrinsic factors can be attributed to strong spatial dependence (Cambardella et al.,
245
1994). As this Indo Gangetic Plain is well known for intensive agricultural practice with indiscriminate
246
use of agrochemicals, there may be some possibility of chlorine and phosphorus contamination which
247
causes weak spatial autocorrelation.
248
2.5.
Risk assessment from arsenic ingestion through drinking water
249
The hazard quotient (HQ) is used as the index for non-carcinogenic arsenic health risk
250
assessment. In our study area, many places exceed the safe limit of HQ (i.e. ≤ 1) and its mean value
251
reaches as high as 1.056 for ingestion pathway and 2.578 for dermal contact pathway (Table 5). Mean
252
life time cancer risks (CR) for arsenic was 2.71×10-5 (Table 5), which was 27.1 times higher than that
253
of the negligible risk level (1.00×10-6). There have been global reports of non-carcinogenic or
254
carcinogenic effects of arsenic on humans (WHO 2004; ATSDR 2007; Brammer and Ravenscroft
255
2009). Nguyen et al. (2009) conclude that the health of nearly 42% people of Vietnam might be
256
affected by arsenic where its life time cancer risk reaches as high as 5.0×10-3.The mean life time
257
cancer risk from arsenic through dermal contact and ingestion pathway was recorded as 7.89×10-6 and
258
1.93×10-5 (Table 5). In respect to the USEPA threshold value (>10-6 is unacceptable), the maximum
259
risk value obtained in the Varanasi region, exceeded the threshold value by ten times, indicating a clear
260
potential risk of arsenic exposure to the people. Alam et al. (2016) stated that areas with high cancer
261
risk will face more serious health problems (i.e. lung cancer, liver cancer and urinary cancer).
262
Mazumder (2008) also reported that the long-term arsenic exposure cause lung and skin cancers.
263
3. Conclusions
12
264
Finally it can be concluded that arsenic content of the adjoining villages of river Ganga is high
265
as compared to the other villages located far apart from the Ganga, and it crosses the safe limit at the
266
meandering position of the river. All water samples were alkaline in nature and inherently contained
267
high amount of sodium. High phosphorus content significantly decreases the arsenic content but higher
268
levels of iron, as well as of potassium increase the arsenic content of the water samples tested. Alkali
269
desorption and anionic competition are the dominant reasons for arsenic contamination in this area.
270
Three sites adjoining to Ganga river exceeded the safe level of human cancer risk from arsenic
271
exposure.
272
Acknowledgements
273
The authors want to thank the Department of Science and Technology, Government of India for
274
financial support, the Department of Soil Science and Agricultural Chemistry for extending laboratory
275
facilities and Indian Agricultural Research Institute for various software analyses and Central Soil
276
Salinity Research Institute for providing toposheet and facilities regarding ARC-GIS software.
277
Conflict of interest
278
The authors declare that they do not have any conflict of interest.
279
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21
1
Figure captions
2
Fig. 1. Map of middle Gangetic basin of Varanasi region
3
Fig. 2. Ordination diagram of principal component analysis showing effects of various
4 5
groundwater quality parameters on arsenic content Fig. 3. Spatial variability map of different ground water quality parameters
Table 1 Descriptive statistic of different ground water quality parameters pH
EC
Na+
(µS cm-1)
(me L-1)
SAR
RSC
PO43-
Cl-
(ppm)
(me L- (III) 1
)
As
Fe2+
Zn2+
Mn2+
Cu2+
(ppm)
(ppb)
(ppb)
(ppb)
(ppb)
Mean
8.00
447
2.42
1.6
1.78
3.74
2.82
8.5
4.60
27.5
4.46
3.99
Standard
0.47
126.42
3.03
2.18
1.74
2.32
0.60
3.27
4.60
12.27
15.30
9.38
Variance
0.22
15982
9.19
4.75
3.03
5.39
0.36
10.72
21.19
150.58 234.06 88.00
Skewness
-0.41
1.13
1.98
2.02
1.18
2.49
0.27
0.58
2.05
0.71
5.39
4.04
Kurtosis
-1.19
1.49
3.78
3.85
0.54
8.79
-0.82
-0.47
4.92
0.16
31.86
16.12
deviation
Table 2 Pearson correlation study of arsenic with major cations and anions present in ground water Cations 2+
2+
2+
As
Fe
Zn
Mn
Cu+
Na+
K+
Ca2+
Mg2+
(ppb)
(ppm)
(ppb)
(ppb)
(ppb)
(ppm)
(ppm)
(ppm)
(ppm)
As (ppb)
1.000
Fe2+ (ppm)
0.207*
1.000
Zn2+ (ppb)
0.025
0.071
1.000
Mn2+ (ppb)
-0.155
-0.088
-0.150
1.000
Cu+ (ppb)
-0.277**
-0.091
-0.327**
0.133
1.000
Na+ (ppm)
-0.178*
-0.214**
0.017
0.090
0.296**
1.000
K+ (ppm)
0.174*
-0.016
0.466**
0.041
-0.081
-0.247**
1.000
Ca2+ (ppm)
0.018
0.249**
0.050
-0.111
-0.214**
-0.435**
0.140
1.000
Mg2+ (ppm)
-0.001
0.249**
0.027
-0.119
-0.141
-0.408**
0.133
0.994**
Anions As (ppb) CO32- (me L-1) -
-1
As
CO32-
HCO3-
PO43-
Cl-
(ppb)
(me L-1)
(me L-1)
(ppm)
(me L-1)
1.000
HCO3 (me L )
-0.150
1.000
PO43- (ppm)
0.094
-0.019
1.000
Cl- (me L-1)
-0.237**
0.441**
0.126
1.000
As (ppb)
-0.068
0.118
-0.346**
0.035
* Significant at 5%, ** Significant at 1%
1.000
1.000
Table 3 Principle component analysis for significant variables of ground water parameters Statistic or Variable
PC 1
PC 2
PC 3
PC 4
Eigen value
5137.38
1050.57
389.254
198.6
Percentage of variance
73.514
15.033
5.5701
2.84
PO43- (ppm)
-16.4
-14.1
-5.55
-7.66
Cl- (me/L)
-30.8
-14.2
-4.87
-8.49
As (ppb)
17.5
-12.3
-13.9
32.665
Mn (ppb)
-39.3
89.8
-4.06
1.79
Cu (ppb)
-34.5
-7.96
53.9
6.55
RSC
-39.1
-14.6
-7.42
-15.77
SAR
-43.7
-8.19
-7.35
-11.3
pH
4.0
-8.62
-1.71
1.04
Fe (ppm)
-13.4
-18.4
-13.6
9.67
Zn (ppb)
194.7
8.49
4.69
-8.44
Eigen vectors
Table 4 Geostatistical analysis of principal components Ground water parameters
No. of
Best-fit model
Nugget
Range
Partial sill
observation
Nugget to
Root Mean
sill ratio
Square error
pH
60
Rational quadratic
0.072
0.233
0.224
0.321
0.308
Residual Sodium
60
Exponential
0.051
0.243
4.113
0.012
1.101
Sodium Adsorption Ratio
60
Gaussian
0.997
0.239
6.807
0.146
1.862
Phosphorus
60
J Bessel
3.722
0.064
3.306
1.126
2.139
Arsenic
60
Spherical
4.654
0.010
17.25
0.270
4.590
Chlorine
60
Rational quadratic
0.266
0.022
0.181
1.470
0.632
Iron
60
Rational quadratic
13.209
0.0636
18.75
0.704
4.401
Carbonate
Table 5 Risk assessment from arsenic ingestion through drinking water
DIA Mean Median Mini. Safe limit
Adverse non-carcinogenic risk
Adverse carcinogenic risk
Hazard Quotient (HQ) HQi HQd
(Carcinogenic Risk) CRi CRd
HQ
Average Daily Dose
CR
0.317
1.056
2.578
3.634
5.26×10-6
7.89×10-6
1.93×10-5
2.71×10-5
0.290
0.967
2.359
3.326
4.82×10-6
7.23×10-6
1.76×10-5
2.48×10-5
0.01
0.033
0.081
0.115
1.66×10-7
2.49×10-7
6.08×10-7
8.56×10-7
<1
i = ingestion path way, d = dermal pathway
<10-6
Map of Uttar Pradesh
Map of Ganga basin in Varanasi
Map of India
Total sampling sites: 60 Starting location: N 25° 30’ 26.4”, E 83° 10’ 7.4” Final location: N 25° 12’ 28.9”, E 82° 54’ 8.0”
1 2
Fig. 1.
1
0.75 Zn2+
RSC
0.5 pH PO43-
Component 2
0.25
0 As
Fe2+ -0.25
ClSAR Mn2+ Cu2+
-0.5
-0.75
-1 -1
-0.75
-0.5
-0.25
0 Component 1
3 4
Fig. 2.
0.25
0.5
0.75
1
5 6
Fig. 3.
a
b
c
d
e
f
1
Highlights:
2
Arsenic toxicity was high at some of the meandering positions of the Ganga river.
3
Alkali desorption was the dominant reasons for arsenic release.
4
Ground water samples were highly alkaline.