Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment

Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment

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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

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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

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region, groundwater is now becoming progressively polluted by contamination with poisonous

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substances like arsenic. Intensive irrigation with arsenic contaminated ground water in dry spell

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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

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Ganga basin. Water quality was investigated through determination pH, EC, TDS, salinity, Na,

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K, Ca, Mg, SAR, SSP, CO3, HCO3, RSC, Cl, As, Fe, Zn, Mn and Cu, etc. Results pointed severe

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As contamination in ground water of three sites of the study area. ARC GIS software is now able

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to process maps along with tabular data and compare them well, to provide the spatial

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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

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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

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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

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times as abundant as fresh water found in lakes and streams and has a strong influence on river and

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wetland habitats for plants and animals. The groundwater is not directly visible from surface and its

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quantification is tedious when we consider total amount of water of the world. By protecting against

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pollution and managing its use with caution, our ecosystem will be maintained and future will be

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safeguarded (Mullen et al., 2018).

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The quality of the deep aquifers also differs from place to place, however, it is generally

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considered suitable for common use (Choudhury and Rakshit, 2012; Mishra and Desai, 2006). In

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present scenario, analysis and assessment of groundwater quality parameters have been critically

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considered under research (Ning and Chang, 2002). Groundwater is not only used for drinking,

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cooking and washing but in the dry season, it is used in significant quantities to irrigate crops. Pyrite

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may get oxidized to iron oxides under aerobic conditions and release arsenic, sulfate and various trace

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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

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al., 2003). Theories about the source of arsenic in Gangetic Plains are as below:

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(i)

of the Rajmahal trap area arsenic contamination reached as high as 200 ppm. (Saha, 1991).

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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

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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

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consuming. Thus prediction method using hydrogeochemical data is a good alternative for very costly

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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

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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

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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

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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

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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

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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

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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

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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.,

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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

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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

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Arsenic intake exposure to drinking water was predicted by measuring a daily intake of arsenic (DIA) by using the following equation

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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).

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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

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oxides/hydroxides of iron that contain arsenic (Islam et al., 2004; Campbell et al., 2006; Tufano et al.,

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2008) and oxidation of iron sulfides (Mcarthur, 1999; Carraro et al., 2015) both are the leading

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geochemical processes which cause arsenic accumulation in groundwater. Iron showed significant

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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

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with iron (r = -0.214), potassium (r = -0.247), calcium (r = -0.435) and magnesium (r = -0.408) but in

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case of copper (r = +0.296) there exist significant positive correlation with sodium (Table 2). Thus

8

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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

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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

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(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

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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,

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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-

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significant relationship with arsenic content in groundwater of this area, they still had great impact

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over total variation in data (Fig. 2). Chloride ions function as a conservative tracer, and it can be used

9

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to monitor the effects of underground flow of hydrochemistry and to use it as an underground arsenic

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concentration indicator (Cao et al., 2018). In a reduced environment, the dissolution and reduction of

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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.

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Principle component analysis is also helpful in the prediction of groundwater arsenic concentration and

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thereby provide improved assessment tools for Southeast Asian countries (Cho et al., 2011). In this

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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.

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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

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is clear from spatial variability map of pH (Fig. 3a.) that pH goes higher in the bank of river Ganga and

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decreases in the areas which are far apart. The Geostatistics specially uses the semivariogram method

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to measure the spatial variability of a regional variable, and provides input parameters for the spatial

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interpolation of kriging (Yang et al., 2009). The inherently elevated pH of the groundwater increases

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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

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(Smedley et al., 2002; Park et al., 2006). Exponential model and Gaussian model were best suited for

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the spatial variability map of residual sodium carbonate and sodium adsorption ratio of water samples

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(Table 4). It can be concluded from the maps (Fig. 3b.) that at the meandering position of river Ganga

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the deposition of sodium was high showing high residual sodium carbonate and sodium adsorption

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ratio. J Bessel and spherical models suit well for the spatial variability map of phosphorus and arsenic,

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whereas variability map of iron and chlorine followed Rational Quadratic Model. Phosphorus content

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was low in the turning of Ganga (Fig. 3c.) but arsenic content were high specially in the meandering

10

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position (Fig. 3d.) indicating the negative relationship between them. Spatial variability map of arsenic

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evidently shows that three places of study area were potentially arsenic affected which may become

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severe threat in near future. Small natural ponds and meandering position of rivers were also

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considered as characteristic geomorphic features for arsenic accumulation by Shrestha et al. (2004),

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because in this position fine grain sediment deposition is very high and in fine-grained sediments, high

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concentrations of arsenic is naturally recorded (Diwakar et al., 2015). Fine grained primary and

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secondary minerals are well known for adsorbing arsenic competently (Stanger, 2005; Chakraborty et

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al., 2007; Uddin, 2017) and side by side can release a considerable amount of arsenic whenever the

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conditions favour. Naturally new holocene alluvium is un-oxidized and comprises of very highly

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organic-rich primary soil particles like sand silt and clay (Acharyya and Shah, 2007). Acharyya and

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Shah (2007) also reported that especially the recent alluvial deposit from flood plain and entrenched

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channels that are located at the meandering position are prone to arsenic contamination. Arsenic is

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released from associated Holocene sediments in groundwater that were probably deposited from the

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surrounding Tertiary Barail hill range. Pollution in groundwater was previously reported from the

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various states of middle Gangetic plain like Uttar Pradesh, Bihar, Jharkhand of India (Chakraborti et al.

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2003; Shah, 2010).

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Chloride content (Fig. 3e.) was higher in places apart from river Ganga whereas iron content

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(Fig. 3f.) was higher in the meandering position of river Ganga. The nugget to sill ratio specifies the

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quantity of random components to the spatial heterogeneity of the system (Odoi et al., 2011). The ratio

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of <0.25, 0.25–0.75, and >0.75 might be used to label the strong, moderate, and weak spatial

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autocorrelation, respectively (Cambardella et al., 1994). In our study we found that, the nugget : sill

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ratio for pH, arsenic and iron were 0.321, 0.270 and 0.704 respectively indicating moderate spatial

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autocorrelation. Spatial autocorrelation for residual sodium carbonate and sodium adsorption ration

11

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were 0.012 and 0.146 that fall under strong category, and for phosphorus and chlorine these showed

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weak spatial autocorrelation. In general, extrinsic factors can be attributed to weak spatial dependence

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of parameters and intrinsic factors can be attributed to strong spatial dependence (Cambardella et al.,

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1994). As this Indo Gangetic Plain is well known for intensive agricultural practice with indiscriminate

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use of agrochemicals, there may be some possibility of chlorine and phosphorus contamination which

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causes weak spatial autocorrelation.

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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

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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

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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

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carcinogenic effects of arsenic on humans (WHO 2004; ATSDR 2007; Brammer and Ravenscroft

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2009). Nguyen et al. (2009) conclude that the health of nearly 42% people of Vietnam might be

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affected by arsenic where its life time cancer risk reaches as high as 5.0×10-3.The mean life time

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cancer risk from arsenic through dermal contact and ingestion pathway was recorded as 7.89×10-6 and

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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

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potential risk of arsenic exposure to the people. Alam et al. (2016) stated that areas with high cancer

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risk will face more serious health problems (i.e. lung cancer, liver cancer and urinary cancer).

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Mazumder (2008) also reported that the long-term arsenic exposure cause lung and skin cancers.

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3. Conclusions

12

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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

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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

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levels of iron, as well as of potassium increase the arsenic content of the water samples tested. Alkali

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desorption and anionic competition are the dominant reasons for arsenic contamination in this area.

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Three sites adjoining to Ganga river exceeded the safe level of human cancer risk from arsenic

271

exposure.

272

Acknowledgements

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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

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Salinity Research Institute for providing toposheet and facilities regarding ARC-GIS software.

277

Conflict of interest

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The authors declare that they do not have any conflict of interest.

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Reference

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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.