Groundwater vulnerability to pesticide pollution assessment in the alluvial aquifer of Western Bengal basin, India using overlay and index method

Journal Pre-proof Groundwater vulnerability to pesticide pollution assessment in the alluvial aquifer of Western Bengal basin, India using overlay and index method Srimanti Duttagupta, Abhijit Mukherjee, Kousik Das, Avishek Dutta, Animesh Bhattacharya, Jayanta Bhattacharya

PII:

S0009-2819(19)30048-0

DOI:

https://doi.org/10.1016/j.chemer.2020.125601

Reference:

CHEMER 125601

To appear in:

Geochemistry

Received Date:

15 August 2019

Revised Date:

6 January 2020

Accepted Date:

12 January 2020

Please cite this article as: Duttagupta S, Mukherjee A, Das K, Dutta A, Bhattacharya A, Bhattacharya J, Groundwater vulnerability to pesticide pollution assessment in the alluvial aquifer of Western Bengal basin, India using overlay and index method, Geochemistry (2020), doi: https://doi.org/10.1016/j.chemer.2020.125601

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Groundwater vulnerability to pesticide pollution assessment in the alluvial aquifer of Western Bengal basin, India using overlay and index method

Srimanti Duttagupta1*, Abhijit Mukherjee*1, 2, Kousik Das1, Avishek Dutta3, 4, Animesh Bhattacharya1, 5, Jayanta Bhattacharya1, 6

School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur,

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1.

Kharagpur 721302, West Bengal, India 2.

Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, Kharagpur

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721302, West Bengal, India

School of Bioscience, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal,

Integrative Oceanographic Division, Scripps Institute of Oceanography, University of California, San Diego, CA, 92093, USA

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4.

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India

Public Health Engineering Department, Government of West Bengal, India

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Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302,

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5.

West Bengal, India

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*Corresponding author: Srimanti Duttagupta ([email protected]; contact:

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+1-858-336-3490)

Abhijit Mukherjee ([email protected]; contact: +91- 9007228876)

Abstract

The groundwater of the Western Bengal basin is found to be polluted by various non-point sourced contaminants. A significant increase in anthropogenic activities subsequently affects water quality. This study is the first attempt to evaluate the groundwater vulnerability to 1

pesticide pollution due to anthropogenic activities across the Western Bengal basin. Pesticide concentration of 141 wells for three consecutive years (2012 – 2014) was collected and data were acquired from West Bengal Pollution Control Board (WBPCB). Based on seven hydrogeological parameters groundwater specific vulnerability was assessed. The vulnerability to pesticide pollution across the Western Bengal basin was again validated with analyzed pesticide concentration for 235 wells for 2014 – 2016. The agricultural prone region of the study area is highly susceptible to pesticide pollution. The spatiotemporal trend of different

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pesticides suggests the basin is more vulnerable to insecticides such as malathion than that of herbicides. The application of pesticides influences mostly the spatio-temporal variability of pesticide concentration in groundwater of shallow aquifer across Western Bengal basin;

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however, the natural hydrogeological setting of this area is one of the most influential parameters which impact the pesticide infiltration onto aquifer. Depth to the water table is the

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most sensitive parameter which showed a significant impact on pesticide concentration in

improve water quality.

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groundwater. Vulnerability assessment across different land-uses helps the decision-makers to

1. Introduction

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Keywords: Vulnerability; Risk assessment; Pesticide; Aquifer; Western Bengal basin

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Over the last five decades, unprecedented development of groundwater resources has been

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observed. Approximately 2 billion people rely on groundwater as their drinking water resources across the world (Oki and Kanae, 2006). In India, an estimated groundwater resource is 396 km3/year, which accounts for about 80% of domestic water requirement. Previous studies have shown that 85% of rural areas of India rely on groundwater as their drinking water source (Lal et al., 2016). The estimate also shows that more than 70% of drinking water is sourced to groundwater (Margat and Van der Gun, 2013). Deterioration of groundwater quality by

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geogenic and anthropogenic activities has been previously studied across India (Güler et al., 2012) . Such activities constitute a significant challenges for the sustainable groundwater resource management. One of the well-known facts regarding arsenic pollution in Western Bengal basin (WBB) aquifer evidence that hydrogeological setup and land use are primary controlling factors affecting groundwater based drinking water resources (Mazumder et al., 1988; Mukherjee et al., 2007). Hence, vulnerability assessment of the aquifer system and settings have been evaluated to

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mitigate such concern (Burgess et al., 2010). Western Bengal basin is considered to be the world’s largest densely populated area occupying 21,000 km2 land area. Unlike geogenic pollution, the occurrence of anthropogenic pollutants in groundwater is rarely explored.

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Agricultural regions of WBB occupy more than 70% of the total land area (Saha and Alam,

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2014). The fertile soil of the area helps to leach organic pesticides and fertilizers into the sedimentary aquifer (Duttagupta et al., 2018). The groundwater from these aquifers is being

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used as a drinking water resource. Vulnerability to different geogenic inorganic pollution has been well studied (Mukherjee et al., 2007, 2011). There is a need to evaluate the potential risks

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of pesticide usage that may lead to contamination of water resources, both for regulatory agencies, extension services and water managers. The present document attempts to elucidate

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the groundwater vulnerability assessment using overlay/index techniques for pesticide concentration in groundwater. The purpose behind the distinct groundwater vulnerability to the

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polluted area is the variations in hydro-geological settings across WBB. The overlay/index system has been primarily the most widely adopted process for tremendous scale aquifer sensitivity and groundwater vulnerability assessments. The DRASTIC (Aller et al. 1987) is one of the most extensively used overlay and index procedure to verify intrinsic groundwater vulnerability to contamination (Al-Adamat et al. 2003; Babiker et al. 2005; Tirkey et al. 2013; Shirazi et al. 2013). DRASTIC evaluate pollution potential based on the seven hydrogeological 3

parameters (Aller et al. 1987). Every parameter is assigned a weight based on its relative value concerning the contamination potential. This study helps to understand the pesticide vulnerability of shallow groundwater and its temporal variability in known severely arsenic affected blocks of parts of the Western Bengal basin. Since 2012, the Central Groundwater Board (CGWB) and West Bengal Pollution Control Board (WBPCB) have started an assessment of groundwater quality and quantity. The present study deals with a detailed understanding of vulnerability pesticide pollution

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across the Western Bengal basin based on the available data acquired from WBPCB for 2012 - 2014. The vulnerability assessment was also validated using primary data collected from shallow groundwater wells from 2014 to 2016. The depth to water table of shallow wells are

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mostly within 25 m. Pesticide annual data for 2012 to 2014, collected by WBPCB, were

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considered for the assessment of vulnerability. Primary data were collected from 235 groundwater wells during 2014–2016 on seasonal basis following the standard method of

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pesticide extraction and analyses. We conjecture vulnerability dominating factors based on hydrogeological parameters to identify and express the groundwater specific vulnerability to

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pesticide pollution across WBB. 2. Materials and Methods

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2.1 Study Area

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The study area is located in the southern part of the state of West Bengal, India, occupying about 21,000 km2 area. Groundwater flow across the basin is strongly influenced by the aid of the rainfall brought about by using the southeast monsoon wind. There are special dry seasons (October/November to May/June) and moist seasons (June/July to September/October). Annual rainfall lies within <1,200 mm to 1,600 mm (CGWB 1994). It marks the presence of single, semi-confined, primary aquifer (Sonar Bangla aquifer), having a thickness from ~80 m

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in the north to ~300 m bMSL (below mean sea level) toward the south (Bay of Bengal) and east (Bengal basin), and superimposing a basinal-scale basal clay aquitard (Mukherjee et al., 2007). Very few confined, isolated aquifers are present within the basal aquitard. Extensive, intermediate-depth aquitard, which may constrain advective-dispersive mixing of shallow, known arsenic-contaminated and deeper, uncontaminated groundwater, are only found south of ~22.75°N. The natural flow in the area is dominated by topographically-driven, regionalscale, southward flow at horizontal and vertical hydraulic gradients of 0.09 m/km and 0.06-

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0.08 m/km, respectively. Simulations based on present and projected irrigation pumping rates developed composite cones of depression as large as 20 km in diameter, having a vertical gradient up to ~0.25 m/km at present. Calculated present-day vertical fluxes range 119 from

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~9 × 10 m³/d inflow at the land surface to ~2 × 10 m³ 120 /d outflow at depths of 200 m bls (below the land surface), entailing downward groundwater movement and mixing in areas

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lacking intermediate-depth aquitards. Although these aquitards have generally been able to

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limit the mixing of contaminated and uncontaminated groundwater, stable isotopic and major solute compositions of the groundwater above and below the aquitards are similar, indicating similar pathways of recharge and hydrochemical evolution (Mukherjee et al., 2007, 2011).

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areas (Figure 1).

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WBB consists of mostly three different land-uses namely urban, peri-urban and agricultural

2.2 Data Acquisition and Management

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Based on available data, three insecticides (malathion, chlorpyrifos, and lindane) and

two herbicides (alachlor and atrazine) frequently detected across the study area were considered for this study. WBPCB also identified these insecticides and herbicides from 141 wells during 2012-2014. Figure 1 consists location of 235 wells used for temporal variability and 141 wells used by WBPCB for pesticide quantification. WBPCB analyzed pesticides in

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groundwater following the established USEPA method 1699 as mentioned in Woudneh et al., (2009). 2.3 Assessment of Vulnerability The locations of the reference points across the study area were measured using GARMIN® to detect the actual latitude and longitude (datum WGS 1984). The study was designed to produce a scientifically defensible vulnerability map of the study area in the

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ArcGIS® v. 9.2 platform. To lower the distortion for discretization of the grids of the lithologic and groundwater models, the location data had been projected to Universal Transverse Mercator (UTM 1984), Zone 45 (important meridian 87, spheroid GRS 80, reference latitude

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0, scale element of 0.9996, false easting 500,000, false northing 0). The coordinates of the area, of the demonstrated field area for each kind of modeling, were easting 600,000, northing

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2,380,000. Lithological information for 141 wells collected from WBPCB were collected from secondary sources mentioned in Mukherjee et al., (2007). This study was developed to a depth

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of 50 m below mean sea level (MSL), and topographic elevation was defined by the SRTM 90

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DEM grid.

Information regarding depth to water table was collected from the Central Groundwater Board, Govt. of India. Net rainfall recharge data were gathered from India Meteorological

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Department (IMD), Ministry of Earth Sciences, Govt. of India. Aquifer media and hydraulic

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conductivity data were collected as mentioned in Mukherjee et al., 2007. IMD climatological services provided information regarding the soil media. The Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Database (DEM) was used to collect the topographical data. A two-tier numerical ranking system is implemented in a DRASTIC method for the entire study region. The final vulnerability index is obtained by the weighted sum of the 6

parameters. Each of the parameters is assigned a weight based on the importance (most important as 5 and least as 1). Depending upon the relative prominent role in impacting pollution potential, a factor score is given for each parameter with a rating for different ranges of the values. The typical ratings range from 1–10. Data for pesticide concentrations from primary analyses and WBPCB have been taken from DRASTIC vulnerable index and was calculated as per equation 1 which is given below: DRASTIC index = ∑𝒎 𝒋=𝟏(𝐖𝐣𝐑𝐣)

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(1)

Here, Wj and Rj are the weights and ratings of the jth hydrogeological factors (Aller et al., 1987). The final vulnerability map is established on the DRASTIC Index (DI) which is

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calculated as the weighted sum overlay of the seven layers making use of the following equation 2:

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DI=DrDw + RrRw + ArAw + SrSw + TrTw + IrIw + CrCw

(2)

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Here, D, R, A, S, T, I and C represents the seven hydrological parameters, e.g., D – Depth to water table; R – Net rainfall recharge; A – Aquifer media; S – Soil type; T –

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Topography; I – Impact of vadose zone and C – Hydraulic conductivity. Assigned rates and weights to the parameters were indicated as r and w respectively. The above-described system

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was processed under the ArcGIS ® platform version 9.2, as it is a platform for the thematic layer preparation and mathematical calculations. The relative assigned weight(s) of DRASTIC

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model parameters and their descriptions were accumulated in table 1 and 2. The DRASTIC vulnerability index was then divided into three zones with respect to their scoring value. A high DRASTIC index indicates high vulnerability. The vulnerability map produced by the model was validated by pesticide concentration data collected from primary field works and also data collected from WBPCB. The final vulnerability map is indicated as pesticide DRASTIC (1) for pesticide concentrations analyzed from 235 wells (primary data) and pesticide DRASTIC (2) 7

for pesticide concentration collected from 141 wells of WBPCB. DRASTIC (1) and (2) contain accumulative concentrations of previously mentioned five pesticides. 2.4 Statistical analyses 2.4.1 Sensitivity analyses A sensitivity analysis was carried out to show the relationship between the effective and theoretical weight of the DRASTIC parameters. The study helped to avoid the subjectivity

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to nature for vulnerability assessment which provided essential information to assign the weighting and rating ranges of the parameters. The single parameter removal sensitivity analysis test indicated the influence of each parameter on final vulnerability measurement.

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The effective weight has been worked out using the following relation:

(3)

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W = [(Pr/Pw)/v] x 100

Here, W refers to the effective weight, Pr and Pw are the respective ratings and weight of each

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parameter and v denotes overall vulnerability index. Sensitivity analyses were conducted from both the DRASTIC (1) and (2) model.

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2.4.2 Multivariate statistical analyses

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Multivariate statistics using hierarchical cluster analyses (HCA) was conducted based on the pesticide concentration used in pesticide DRASTIC (1) (Hair et al., 1998). The HCA

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dendogram was constructed by Ward’s method with squared Euclidean distance (Szekely and Rizzo, 2005). HCA was used to investigate relationships between the locations. HCA was analyzed by E-views (v. 9.5) statistical software. 2.4.3 Discriminant analysis Discriminant analysis (DA) is used to categorize the cases into categorical-dependent values, usually, a dichotomy using R × 64 v 3.3.1 software. The DA is executed to find out the 8

seasonal effect. The present study includes pesticide concentration primary data used in DRASTIC (1) on a seasonal basis. The DA follows the following equation F(Gi) = ki +Ʃn j=1 wijpij

(4)

Here, i is the number of groups (G), ki is the constant inherent to each group, n is the number of parameters used to classify a set of data into a given group, wj is the weight coefficient, assigned by DA to a given selected parameter (pj). DA was performed on the data set based on

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three different modes i.e. standard mode (STD), forward mode (FWD), and backward mode (BAK) to construct the best discriminant functions to confirm the significance of the seasonal variation.

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3. Results and Discussion 3.1 Extent of Organics and Vulnerability

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Seven thematic layers have been primed representing every parameter on the GIS

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platform based on the data produced through field survey and analysis and also collected from 235 wells and WBPCB data for 141 wells. The overlay index method was individually for 2012

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to 2014 and 2014 to 2016. Interpolated maps on the GIS platform have been constructed to achieve the correlation between the spatial distributions of pesticide correlation. . The vulnerability maps were prepared by overlaying the layers in a GIS environment, where the

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indices were calculated for each cell of 50 × 50 m. The pesticide DRASTIC (1 and 2) scores

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range from 0 to 210. Based on the classification by Engle et al. (2007), the DRASTIC exhibited a range of vulnerability. According to these vulnerable ranges, it has been categorized into three interval classes for the study: low, medium and high based on the vulnerability index (Table 3, Figure 2). Parameter wise salient statistics of the scores of the cells minimum, maximum, mean, standard deviation and coefficient of variation for DRASTIC are produced.

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All the seven layers generated in the ArcGIS 10.2 combine to assign the relative weight value (1 – 5) to compute the vulnerability index. 3.1.1 Depth to water table Depth to water table considered to be significant parameter which may influence the infiltration of contaminants into aquifer, followed by the net recharge (Nobre et al., 2007). Depth to the water table is an essential primary parameter that determines the natural

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attenuation (i.e., sorption, dispersion, biodegradation). The average depth to the water table is estimated from the collected data from different points across the study area during 2012 – 2014 and 2014 – 2016. A rasterized point map is assigned a rating according to the DRASTIC

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method and an interpolated point map has been generated (Figure S1). The depth of the water table in the western Bengal basin is shallow mostly within 25m, decreasing gradually north to

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south. The map suggests that the northern part of the western Bengal basin more susceptible to

3.1.2 Net recharge

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contamination according to pesticide DRASTIC assumptions.

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The amount of water per unit area of land is considered as net recharge, which penetrates the subsurface and reaches the water table. Net recharge is one of the important

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parameters since the infiltration water is a pollutant transport vector. The study area is characterized by an average rainfall of 587 mm/year, the net recharge to the groundwater

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aquifer is mainly controlled by land use/ land cover on the surface (Figure S1). Less recharge rate is associated with urban land use due to roofs and pavements which prevents percolation of water downward. 3.1.3 Aquifer media

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Aquifer media exerts key control over the hydraulic conductivity, groundwater flow and gradient. Aquifer media have been identified from the lithological data of different wells mentioned in Mukherjee et al. (2007). The study area is dominated through a major singleaquifer process, which is traditionally unconfined/semi-confined except within the southernmost part (Figure S1). In several localities, this primary aquifer has been locally divided into more than one layers by the presence of discontinuous aquitards (with a lateral range of one to a few kilometers), but excluding southernmost phase (southern portion of North

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24 Parganas and South 24 Parganas), the chemistry is typically equivalent in the course of (Mukherjee 2006; Mukherjee and Fryar 2007; Mukherjee et al. 2007), supporting the interpretation of a single hydrostratigraphic unit. The deeper, isolated aquifers are

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differentiable from the semi-confined portions of this principal aquifer on the base of

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groundwater chemistry (Mukherjee 2006). 3.1.4 Impact of vadose zone

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The vadose or unsaturated zone governs the attenuation appearances of the pollutants between the soil and the water table. According to the rating values, a thematic map has been

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prepared. Aquifer media and soil type involve the physical phenomenon e.g. sorption which influences the aquifer contamination. As per the lithological information of WBB, the aquifer

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is primarily covered by clayey sediment with patches of sand and silt. In the impact of the vadose zone layer (Figure S1), sand and gravel are assigned a high rating value of 8, the sandy

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clay is assigned a moderate score 6, while the low rating values 3 is assigned to clay. From a basic hydrogeologic point of view, these sediments have been categorized as aquifer and aquitard. 3.1.5 Soil media

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A part of an unsaturated or vadose zone characterized by significant biogeochemical activities is considered as soil media. The different soil types were assigned rates according to their permeability (depending on the texture). A high score clay loam is assigned to the immature soil and the lowest score was assigned to the soil in the sandy clay loam (Figure S1). 3.1.6 Topography The slope of the land surface is considered as topography. The larger the slope, the

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higher the runoff, and the lower the infiltration. Lower infiltration signifies the chance of percolation of contaminants is much less (Babiker et al., 2005). The topography layer displayed a gentle slope (0~1%) over most of the study area (Figure S1). These areas with steep slopes

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are typically assigned a low rating score representing their insignificant effect on the aquifer vulnerability. As WBB does not involve such topographical elevation, therefore the influence

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3.1.7 Hydraulic conductivity

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of topography and hydraulic conductivity were the least significant

Although the much less permeable sediments like clay transmit some groundwater, they

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separate the overlying aquifer(s) from decrease aquifer(s) by using hydraulic conductivity (k) difference. Within the study area, the extent, thickness and hydraulic conductivity of these clay or aquitard layers are very most important as they govern the 3-dimensional glide of

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groundwater on the regional scale. The average hydraulic conductivity was once premeditated

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from the transmissivity and modelled aquifer thickness for the four districts, and was once located to range between 19.8 m/day and 65.0 m/day, with a mean of 42.1 m/day (Mukherjee et al., 2007) (Figure S1). . The pesticide DRASTIC (1) and (2) vulnerability map revealed high vulnerability covering 30 % of the area, clustering in two patches, (i) parts of Nadia, and (ii) parts of South 24 Parganas including Kolkata. Small patches of higher vulnerability is also observed in parts

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of North 24 Parganas. The DRASTIC (1) vulnerability map showed that 9.36 % of the area belongs to higher vulnerable zone and DRASTIC (2) also reflected similar kind of results for highly vulnerable area (8.52 %) (Table 3). In the pesticide DRASTIC model (1 and 2), depth to water table was the primary influencing parameters followed by net recharge due to rainfall. Pesticide DRASTIC model 2 depicts the spatial distribution of five different pesticides on three consecutive years since 2012 for 141 wells. However, pesticide DRASTIC 1 represents similar kinds of spatio-temporal

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trends during 2014–2016 (Figures 3 and 4). 3.2 Single parameter sensitivity

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Statistical analysis was performed to analyze and display the results of each DRASTIC parameter. Table S1 and S2 show the theoretical weight assigned by the method, the same

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weight normalized to 100, the average real weight computed on the 417 sub-areas, the standard deviation, the minimum and the maximum values. As per the attribute values of the effective

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weight of each parameter, distinctive condition subareas have been reclassified. The real percentage weights were then grouped into classes of 5%. Those computations were used to

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obtain seven maps representing the effective weight of each area. Table S1 and S2 show that the depth to water table (D) indicating the highest effective weight.

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3.3 Spatial similarity and site grouping HCA dendogram helps us to detect the similarity between the sites. Pesticide

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concentration and vulnerability mapping showed distinct differences between land-uses. HCA showed three different clusters which showed the differences of land-uses. Pesticide use during 2014 – 2016 in cluster 1 suggested the area mostly belongs to rural land-use with higher agricultural activities (Figure S2). Cluster 2 primarily suggests peri-urban areas, where urbanization and agricultural activities both can be observed. However, cluster 3 depicts the distinct differences between the other two clusters. Consumption of pesticides is one of the 13

most important parameters which influenced by land-uses. Moreover, the influence of hydrogeological settings supports to infiltrate the contaminant into the shallow aquifer. 3.4 Temporal variation The pre-monsoonal application of pesticides was higher than that of post-monsoon. During crop harvesting, applications of herbicides were observed to be less than other insecticides. The discriminate analyses for five different pesticides were considered to be significant for malathion the most, followed by alachlor (Table S3). Across the study area,

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malathion was applied in higher quantity as it is a widely used organophosphate insecticide and it has been used successfully to eradicate fruit flies in bait sprays where the low concentration of insecticide is mixed with attractants (Miles and Takashima, 1991). The

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application rate of organophosphate insecticides have been increased by 28.2 % since 2010

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(Cordell et al., 2009). The use of malathion is higher in this area; however, the application of herbicides such as atrazine is higher in the pre-monsoonal season. Crop-harvesting during post-

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monsoon season decreases the application of herbicides (Devi et al., 2017). To determine the spatial variation of among 235 different wells used in DRASTIC (1), DA

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was employed and it was performed using the primary data of five pesticide concentrations. Pesticide parameters were dependent variables, while pre and post-monsoon season were

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treated as independent variables. DA was carried out via standard mode, forward stepwise and backward stepwise modes, and the accuracy of seasonal classification using standard, forward

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and backward modes discriminate functions were accumulated in Table S4. The temporal DA results suggest that Malathion, Alachlor, Lindane are the most significant parameters to discriminate between the two seasons which means that these three parameters account for most of the expected temporal variations in the groundwater quality. Atrazine is primarily used for rice, wheat and maize cultivation across WBB. Atrazine are one of the extensively used pre-emergence herbicides applied to prevent the weed seed during summer (Biswas et al., 14

2018). However, during post-monsoon winter season application of atrazine reduces due to the harvesting of crops. However, alachlor is also used for rice cultivation. But, across WBB, alachlor is extensively used for soybeans. Insecticide use does not vary with the season as insecticide e.g. malathion is generally used to combat mosquitoes and fruit flies. Irrespective of land-use, the application of malathion is more extensive than other pesticides. The concentration of individual pesticides decreased in the post-monsoon season. It can be attributed to the dilution due to the recharge of the aquifer during the monsoon.

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4. Conclusion

In this study, an attempt has been made to evaluate the groundwater vulnerability of Western Bengal basin (WBB), based on the DRASTIC method. Seven hydrological parameters

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were used to represent the natural hydrogeological settings of the aquifer. The DRASTIC

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vulnerability index was computed, and the values were reclassified into three classes highlighted as low, medium and high vulnerable zones. Pesticide concentration of groundwater

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aquifer of 141 wells for three consecutive years collected from WBPCB suggests that its hydrogeological setting influences the area susceptible to pesticide contamination. The

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hypothesis was again validated with analyzed pesticide concentration for 235 wells for 2014– 2016. The agricultural prone area of WBB is highly susceptible to pesticide pollution. The

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spatiotemporal trend of different pesticides suggests WBB is more vulnerable to insecticides such as malathion than that of herbicides. Application of herbicide is restricted to a particular

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season; however, there is no such restriction for insecticide application. The agricultural herbicides such as atrazine are extensively used for paddy, wheat and maize cultivation and mostly to combat the weed seed during pre-monsoon. The Agricultural rural areas of Nadia district of WBB can be considered as hotspot for pesticide vulnerability. Approximately, 69% of the sampling sites consists of two or more pesticide in groundwater, 12% had four or more, and 2% had all the five pesticides in groundwater. The application of pesticides primarily 15

influences the spatiotemporal variability of pesticide concentration in groundwater shallow aquifer across WBB; however, natural hydrogeological setting of this area is one of the most influential parameters which may impact on pesticide infiltration onto aquifer. Depth to the water table is the most sensitive parameter which showed a significant impact on pesticide concentration in groundwater. Vulnerability assessment across different land-uses of WBB helps the decision-makers to identify priorities to improve water quality that has deteriorated due to pollution from various anthropogenic activities.

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Acknowledgement

Authors would like to thank Water Supply and Sanitation Organization, Public Health

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Engineering Department of Govt. of West Bengal, for providing hydrogeological and lithological information of the Western Bengal basin. The authors would also like to thank

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West Bengal Pollution Control Board for providing the data and other information of 141 wells across the study area. Srimanti Dutta Gupta would like to express her gratitude to Soumyajit

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Sarkar for his support in statistical analyses. Srimanti Dutta Gupta would like to thank

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Tables Table 1: The assigned weight(s) of vulnerability model parameters and their description Description

Depth to water table

It is the depth from ground surface to the top of groundwater table, deeper the

Assigned relative weight 5

e-

pr

Parameters

Pr

water level is, the lesser the chance of contamination Net rainfall recharge

It is an amount of water which recharges the

4

Aquifer media

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aquifer, high amount of recharge carries more contaminant It represents the property which defines the aquifer matrix like discharge,

3

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high discharge constitutes to high contamination

Soil media

Topography

It is the controlling parameter of infiltration, which represents the soil type,

2

cohesive soils retain the contaminants than noncohesive soils

It represents the slope of land surface, gentle undulations sustain the water in a place forcing water to percolate into the ground

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1

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It is the unsaturated part of earth between ground surface and top of the

5

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Impact of vadose zone

phreatic zone, lesser the soil thickness is higher chance of contaminant

Hydraulic conductivity

pr

interaction with the water table

It is the ability of aquifer to transmit water, high transmissivity injects more

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Pr

e-

contaminants into the aquifer

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3

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Table 2: The assigned rating(s) of vulnerability model parameters and their ranges

Parameters for Vulnerability Assessment using DRASTIC Net Recharge

Aquifer Media

Soil Media

Table Ratings

Ranges

Ratings

Ranges

Ratings

Classes

(Dr)

Classes

(Rr)

Classes

(Ar)

4-Feb

10

<0.1

1

0.1-0.2

8

0.2-0.3

>8

7

0.3-0.4

4

permeability Average aquifer permeability Low aquifer permeability

6

aquifer Aquitard

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8-Jun

2

Ratings

Classes Silty

Pr

9

10

8

Clay Silty

Impact of

Hydraulic

Vadose Zone

Conductivity

Ranges

Ratings

Ranges

Ratings

Ranges

Ratings

(Sr)

Classes

(Tr)

Classes

(Ir)

Classes

(Cr)

1

<0.5

10

Clay

2

<25

2

2

0.5-1

9

Gravel

3

25-50

4

6

Clay Clay Loam Loam Sandy

3

>1

8

Sand

4

50-75

6

4

Clay

4

5

75-100

8

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6-Apr

High

Ranges

e-

Ranges

Topography

pr

Depth of Water

Sandy Clay Loam

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Table 3: DRASTIC Index values in western Bengal basin and area percentage Area (%)

Vulnerability

value

zone

DRASTIC

DRASTIC (1)

DRASTIC (2)

0-70 71-140 141-210

Low Moderate High

59.806 32.045 8.149

62.31 28.33 9.36

64.41 27.07 8.52

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DRASTIC Index

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Figures Legends Figure 1: Map of the Western Bengal basin (a) showing 235 groundwater sample collection sites for primary analyses and 141 groundwater sampling sites for secondary data from WBPCB; (b) land use/land cover across the Western Bengal basin using IRS LISS II data; (c) Modelled cross-section of primary aquifer (Sonar Bangla aquifer) of the study area (modified from Mukherjee et al., 2007)

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Figure 2: Flow chart elaborating the methodology for vulnerability map Figure 3: Vulnerability map including the groundwater pesticide concentration from 235 wells across WBB during 2014 – 2016

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Figure 4: Vulnerability map including the groundwater pesticide concentration from 141 wells

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collected by WBPCB across WBB during 2012 – 2014

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