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Applied Geography 28 (2008) 32–53

www.elsevier.com/locate/apgeog

A GIS based DRASTIC model for assessing groundwatervulnerability in shallow aquifer in Aligarh, India

Atiqur Rahman�

Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia University, Jamia Nagar, New Delhi 110 025, India

Abstract

Surface water quality can be determined by hydrological responses that vary geographically. The sub-surface hydrologic

environment, however, has a primary influence on groundwater movement and hence pollutant migration to the sub-

surface water. Maps of aquifer vulnerability to pollution are becoming more in demand because on the one hand

groundwater represents the main source of drinking water, and on the other hand high concentrations of human/economic

activities, e.g. industrial, agricultural, and household represent real or potential sources of groundwater contamination.

There is a need to conduct studies on groundwater pollution. The main objective of this paper is to find out the

groundwater vulnerable zones in shallow aquifers in Aligarh and its surrounding areas, which is one of the fastest growing

big cities of north India, using the DRASTIC model in a Geographical Information System (GIS) environment. This

model is based on the seven data layers that provide the input to the modeling. It corresponds to the initials of seven layers

i.e. Depth of water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone and hydraulic

Conductivity. ILWIS 3.0 (Integrated Land and Water Information System) and Arcview 3.2a GIS software were used to

find out the water vulnerable zones in shallow aquifers. The study shows that more than 80% of the city’s groundwater is

under medium to high vulnerability to water pollution which is the main cause of concern for nearly 0.8 million people

living in Aligarh. The GIS technique has provided an efficient tool for assessing and analyzing the vulnerability to

groundwater pollution. The study suggests that this model can be an effective tool for local authorities who are responsible

for managing groundwater resources.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Groundwater vulnerability; Shallow aquifers; DRASTIC model; GIS technique; Aligarh-India

Introduction

Environmental concerns related to groundwater (GW) generally focus on the impact of pollution andquality degradation in relation to human uses, particularly domestic supply. Due to high population growthand industrialization, greater amounts of domestic and industrial effluents are discharged that lead to thepollution of GW in shallow aquifers. Water pollution is a serious problem in India as almost 70% of itssurface water resources and a great number of its GW reserves are already contaminated by biological,organic, and inorganic pollutants (Rao & Mamatha, 2004). The environmental concern related to the GW

e front matter r 2007 Elsevier Ltd. All rights reserved.

geog.2007.07.008

1 26981717x3312; fax: +91 11 26980229.

esses: ateeqgeog@yahoo.co.in, r_ateeq@lycos.com (A. Rahman).

ARTICLE IN PRESSA. Rahman / Applied Geography 28 (2008) 32–53 33

quality generally focuses on the impact of pollution and quality degradation on human health. Nearly two-third of all ailments in India, such as jaundice, cholera, diarrhea and dysentery, typhoid, etc. are caused by theconsumption of polluted water and these water-borne diseases claim nearly 1.5 million lives annually in thecountry, which means three persons die every 10 minutes due to contaminated water (Ghazali, 1992).

Even today more than 90% of our rural population is primarily dependent on GW (Chandrashekhar,Adiga, Lakshminarayana, Jagdeesha, & Nataraju, 1999). The quality of GW is as important as that ofquantity because GW is the only source of drinking water in most of urban areas of India. The drinking waterquality in Indian cities has been deteriorating in recent years mainly due to the high growth of population,unplanned growth of cities, mixed land use patterns, no proper sewage system, and poor disposal of thewastewater both from household as well as industrial activities. This has led to the pollution of shallowaquifers in and around Indian cities in general and Aligarh in particular (Rahman, 2003).

GW pollution is nothing but artificially induced degradation of natural GW quality. In contrast withsurface water pollution, sub-surface pollution is difficult to detect, is even more difficult to control, and maypersist for years, decades, or even centuries (Todd, 1980). GW vulnerability is a function of the geologic settingof an area, as this largely controls the amount of time, i.e. the residence time of the GW that has passed sincethe water fell as rain, infiltrated through the soil, reached the water table, and began flowing to its presentlocation (Prior, Boekhoff, Howes, Libra, & VanDorpe, 2003). In any given area, the GW within an aquifer, orthe GW produced by a well, has some vulnerability to contamination from human activities. This conceptexists since the 1960s, yet there is no standard definition of aquifer vulnerability. The most common definitioncomes from Vrba and Zaporotec (1994), who described aquifer vulnerability as a concept representing theintrinsic properties of aquifer systems as a function of their sensitivity to human and natural activities.Vulnerability mapping is defined as a technique for quantifying the sensitivity of the resource to itsenvironment, and as a practical visualization tool for decision-making. GW vulnerability is also defined as thetendency and likelihood for general contaminants to reach the water table after introduction at the groundsurface (SNIFFER, 2004). In fact the term ‘‘vulnerability of GW to contamination’’ was first used by Margat(1968). ‘‘GW vulnerability’’ is used in the opposite sense to the term natural protection against contamination.GW vulnerability to contamination was defined by the National Research Council (1993) as ‘‘the tendency orlikelihood for contaminants to reach a specified position in the GW system after introduction at some locationabove the uppermost aquifer.’’

Vowinkel, Clawges, Buxton, Stedfast, and Louis (1996) defined vulnerability as sensitivity plus intensity,where ‘‘intensity’’ is a measure of the source of contamination. Clearly, GW vulnerability is a function notonly of the properties of the GW flow system (intrinsic susceptibility) but also of the proximity of contaminantsources, characteristics of the contaminant, and other factors that could potentially increase loads of specifiedcontaminants to the aquifer and (or) their eventual delivery to a GW resource (Michael, Thomas, Michael, &Dennis, 2005). As per US General Accounting Office (GAO) (1991) hydro-geologic vulnerability is ‘‘afunction of geologic factors such as soil texture and depth to GW.’’ Vulnerability is ‘‘a function of thesehydro-geologic factors, as well as the pesticide use factors that influence the site’s susceptibility,’’ whereas risk‘‘incorporates the size of the population at risk from potential pesticide contamination, i.e. the number ofpeople who obtain their drinking water from GW in the area.’’ Vulnerability is distinct from pollution risk;pollution risk depends not only on vulnerability but also on the existence of significant pollutant loadingentering the sub-surface environment (Margane, 2003). It is possible to have high aquifer vulnerability but norisk of pollution, if there is no significant pollutant loading; and to have high pollution risk in spite of lowvulnerability, if the pollutant loading is exceptional. It is important to make clear the distinction betweenvulnerability and risk. Leaching potential refers to the risk that soluble pesticides will be transmitted throughthe soil to the GW reservoir (Huddleston, 1996). Leaching potential depends on the soil permeability, watertable conditions, and hydraulic loading. A vulnerability assessment defines the risk to an aquifer based on thephysical characteristics of the vadose zone and aquifer and the presence of potential contaminant sources.These quotes illustrate the diversity in terminology. Foster (1987) aquifer pollution vulnerability is ‘‘theintrinsic characteristics which determine the sensitivity of various parts of an aquifer to being adverselyaffected by an imposed contaminant load.’’ GW pollution risk is ‘‘the interaction between the naturalvulnerability of the aquifer and the pollution loading that is, or will be, applied on the sub-surfaceenvironment as a result of human activity.’’

ARTICLE IN PRESSA. Rahman / Applied Geography 28 (2008) 32–5334

With the growing recognition of the importance of underground water resources, efforts are increasing toprevent, reduce, and eliminate GW pollution. The DRASTIC model can be a valuable tool for identifying GWsupplies that are vulnerable to contamination using basic hydro-geologic variables believed to influencecontaminant transport from surface sources to GW (Kalinski, Kelly, Bogardi, Ehrman, & Yamamoto, 1994).In India much work has been done to test underground water for various trace and major elements. So farvery few integrated approaches have been used to assess vulnerability of water using a GeographicalInformation System (GIS) approach and remotely sensed data. The first project involving the partialautomation of DRASTIC using GIS concepts was done at University of Kansas where Merchant,Whittemore, Whistler, McElwee, and Wood (1987) and Merchant (1994) applied a commercially available‘‘Erdas’’ software package to data compiled for Harvey Country, Kansas. Kaplan, Meinhold, Anidu, andHauptmann (1986) developed a GIS aimed at GW management for Nassau and Suffolk counties on LongIsland, New York. Hendrix and Buckley (1986) used GIS technology for the study of water supply affected bynaturally occurring radon contamination in dolomite aquifers with a high probability of pollution of GW bysurface activity. DRASTIC consists of several components, the first of which is the designation of mappablehydro-geologic parameters (Aller, Bennet, Lehr, & Petty, 1987).

The main objective of this paper is to assess GW vulnerability to pollution in a shallow aquifer using theDRASTIC GIS model in combination with hydro-geological data layers i.e. Depth of water, net Recharge,Aquifer media, Soil media, Topography, Impact of vadose zone and hydraulic Conductivity.

Conceptual framework and theoretical background

Various techniques and methodologies have been developed to evaluate environmental impacts associatedwith GW pollution and the concept of aquifer vulnerability is one of them. Maps are produced from a set ofdecisional criteria linked to a number of physical parameters but the choice depends on the model used for it.Vulnerability maps can be calculated with the aid of a GIS. GIS allows spatial data gathering and, at the sametime, gives a means for data processing, such as geo-referencing, integration, aggregation, or spatial analysis(Burrough and McDonnell, 1998). The National Water Well Association (NWWA, Aller, Bennet, Lehr, Petty,& Hacket, 1985) developed the DRASTIC model to assess aquifer sensitivity by combining data sets thatdescribe the depth-to-GW, recharge rates, aquifer material, soil composition, land slope, vadose zonematerials, and saturated hydraulic conductivity. DRASTIC has been the most commonly used aquifersensitivity assessment method; however, it is not intended to predict the occurrence of GW contamination (USEPA, 1985). Recent work has further improved upon this method, evolving the method beyond a simple ratingof sensitivity, to a descriptive approach identifying areas with similar hydro-geologic characteristics (i.e.hydrologic setting) and assessing individually these areas for GW susceptibility to potential contamination(Hearne, Wireman, Campbell, Turner, & Ingersall, 1992). This type of analysis using the data gathered atappropriate scales and with sufficient map accuracy is much more useful for local decision makers. TheDRASTIC model was used for the assessment of GW pollution potential in Anekal Taluk 9n semi-arid area ofBangalore district by Chandrashekhar et al. (1999). The output maps produced using this methodology can beused as a screening tool to ascertain whether the area is more or less vulnerable to GW pollution. GWvulnerability map has also been prepared in and around Ranchi, Jharkhand using the DRASTIC method(Jha and Sebastian, 2005). The DRASTIC index is computed for the Musi sub-basin from Amberpet toNallacheruvu in Hyderabad, where there is high DRASTIC index, TDS is high, and hence the GW pollution isgreater (Anwar, Prem, & Rao, 2003).

The DRASTIC method has been used for vulnerability mapping projects in the United States and discussedas a possible tool for such assessments (Atkinson and Thomlinson, 1994; Hearne et al., 1992; Kalinski et al.,1994). As per EPA (2003) overlay and index-based screening tool such as DRASTIC developed by Aller et al.(1987) provides a viable option. The advantage of the overlay and index approach such as that used byDRASTIC is that modifications can be readily made (EPA, 2003) and can be used for larger scale studies(Smith, Scott, & Fugitt, 1994). DRASTIC has been used as a screening tool to investigate broad geographicareas for susceptibility to GW contamination by pesticides using existing hydro-geologic parameters in GISs(Fritch, McKnight, Yelderman, & Arnold, 2000; Kim & Hamm, 1999; Rosen, 1994). Some applications ofDRASTIC to predict vulnerability of GW to pesticides were successful and some were not (Lowe and Butler,

ARTICLE IN PRESSA. Rahman / Applied Geography 28 (2008) 32–53 35

2003). Assessment of vulnerability or delineation of vulnerable zone is not an easy task due to complexinteractions of the parameters involved. Studies in recent years have used DRASTIC in a Fuzzy logic-basedenvironment for pesticide modeling to account for uncertainty (Chen and Kao, 1997; Dixon, Scott, Dixon, &Steele, 2002). Fuzzy rule-based models provide comparable results with less input data as well as improvedvulnerability prediction when DRASTIC factors are used (Dixon, 2001, 2004, 2005; Dixon and Scott, 1998;Dixon et al, 2002). Incorporation of fuzzy rules and Neural Network (NN) with DRASTIC variablesimproved vulnerability prediction for pesticides. A GW vulnerability mapping methodology that requires lessextensive site-specific data, and at the same time, is robust when data are uncertain and incomplete will be auseful screening tool.

The DRASTIC model was used for vulnerability assessment in Portugal using hydro-geological parameters,aquifer recharge, and the final map of DRASTIC aquifer vulnerability for Portugal at 1:500,000 scale wasdeveloped in ARC/INFO GIS software (Lobo-Ferreira & Oliveira, 1997). Alternatively, vulnerability mappingcould be performed in relation to groups of polluting activities (Foster et al., 2003), such as sewage disposal,agriculture, and particular groups of industries. Rundquist et al. (1991) have produced statewide GW vulnerabilityassessment in Nabraska using DRASTIC/GIS model and identified the areas vulnerable to GW pollution andconcluded that DRASTIC methodology can be executed successfully with minimal training and experience.Various parameters for GW quality like pH, EC, TDS, Cl, Na, K, total hardness (TH), etc. were tested andthereafter ILWIS GIS software was used for mapping the spatial variations of these parameters and also saltaffected areas were demarcated (Durbude, Naradrajan, & Purandara, 2003). GW pollution vulnerability usingDRASTIC/GIS was carried out for Midnapur, West Bengal. DRASTIC index for both generic and industrial-municipal and pesticide pollutants were derived and vulnerability maps for both were prepared (Shahid, 2000).

The study therefore, utilized a procedure similar to the DRASTIC (Aller et al., 1985) model andenhancements made to it by EPA and USGS (Hearne et al., 1992), for evaluating GW vulnerability in shallowaquifers.

Study area: Aligarh

Aligarh was taken up for this study. It is a medium-size fast growing urban center of north India, which liesin the shadow of the country’s capital New Delhi (Fig. 1). It has an area of about 40 km2. The city’spopulation has increased from 0.25 millions in 1971 (Census of India, 1991) to 0.8 million in 2001 (AligarhDistrict Census Handbook, 2001). Topographically the district of Aligarh represents a shallow trough,saucepan shape in appearance, with the River Ganga in the northeast and River Yamuna in the northwestforming the highland peripheries (Rahman, 1998). Aligarh lies in this center of a low-lying tract (Fig 1).Aligarh was taken for vulnerability assessment because in this city there is lack of natural drainage, rainwaterand wastewater collects in the low-lying areas of the city, and there is no outlet for this water. The city not onlygets submerged during the rainy season from mid June to mid September (average annual rainfall is about77 cm), but also some parts remain submerged round the year because of imperfect natural drainage (Singhand Rahman, 2001). Mean monthly temperature is 38 1C and the highest temperature (48 1C) is in May–June,whereas the lowest (3–4 1C) is recorded in December.

Data base and methodology

In the present study the DRASTIC method, a standard system for evaluating GW pollution potential isused. The DRASTIC model is used in many countries because the input information required for itsapplication is either readily available or can easily be obtained from various Government agencies. This modelwas developed for the purpose of GW protection in the United States of America and its methodology isreferred as ‘‘DRASTIC.’’ This methodology developed as a result of a cooperative agreement between theNWWA and the US Environmental Protection Agency (EPA). It was designed to provide systematicevaluation of GW pollution potential. The DRASTIC model is based on seven parameters, corresponding toseven layers to be used as input parameters for modeling, whose required information were obtained fromvarious Government and semi-Government agencies at a desired scale (Table 1). The acronym DRASTICcorresponds to the initials of the seven base maps:

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Fig. 1. Locational aspects of Aligarh city.

A. Rahman / Applied Geography 28 (2008) 32–5336

�

Depth to water � Net Recharge � Aquifer media � Soil media � Topography � Impact of the vadose zone � Hydraulic Conductivity

Each of the above seven DRASTIC parameters is mapped and classified either into ranges or intosignificant media types, which have an impact on pollution potential. Each factor or parameter is assigned asubjective rating. Weight multipliers are then used for each factor to balance and enhance its importance. Thefinal vulnerability map is based on the DRASTIC index (Di) which is computed as the weighted sum overlayof the seven layers using the following equation:

DRASTIC Index ðDiÞ ¼ DrDw þ RrRw þ ArAw

þ SrSw þ TrTw þ IrIw þ CrCw, ð1Þ

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Table 1

Data used for creation of hydro-geological parameters for DRASTIC model

Sl. no. Data type Sources Format Scale of

map

prepared

Date Output layer

1 Borehole data (water table

level)

II Construction Div., UP Jal

Nigam, Aligarh

Table 1:50,000 1996 Depth of water (D)

2 Average annual rainfall Meteorological Dept.,

Mausam Bhavan, New Delhi

Table 1:50,000 1981–2002 Recharge (R)

3 Geology map Dept. of Geology, AMU,

Aligarh

Map 1:50,000 1970 Aquifer (A)

4 Soil map National Soil & Land Use

Board, New Delhi

Map 1:50,000 1971 Soil (S)

5 Topographical sheets Survey of India (SoI),

New Delhi

Map sheet 1:50,000 1973 Topography (T)

6 Geological profile Dept. of Geology, AMU,

Aligarh

Map 1:50,000 1971 Impact of vadose zone

(I)

7 Hydraulic conductivity National Ground Water

Board, Faridabad

Table 1:50,000 2000 Hydraulic

conductivity (C)

A. Rahman / Applied Geography 28 (2008) 32–53 37

where D, R, A, S, T, I, and C are the seven parameters, r is the rating value, and w the weight assigned to eachparameter.

Every parameter in the model has a fixed weight indicating the relative influence of the parameter intransporting contaminants to the GW. The parameter ratings are variable, which allow the user to calibratethe model to suit a given region (Dixon, 2005). The factors D, R, S, T, and C have been assigned one value perrange. But factors A and I have been assigned a ‘‘typical’’ rating and a variable rating, respectively. Ratingsand weights of each parameter are shown in Table 3, which vary from 1 to 10, with higher values describinggreater pollution potential. A higher rating of 8 was assigned to higher recharge areas, e.g. water bodies andcoarse sand aquifer. This is because in these areas the pollutants can easily reach to the GW and there isgreater pollution vulnerability.

Furthermore, the numerical weights and ratings, which were established using the Delphi technique (Aller etal., 1987), are well defined and have been used worldwide (Al-Adamat, Foster, & Baban, 2003; Anwar et al.,2003; Babiker, Mohammed, Hiyama, & Kato, 2005; Chandrashekhar et al., 1999; Dixon, 2005; Margane,2003; Napolitano, 1995; Rundquist et al., 1991; Shahid, 2000). The Delphi technique utilizes the practical andresearch experiences of professionals in the area of interest to assess levels of risk. Typically, the experts areasked to rate the risk level of certain activities under a set of initial conditions. The activities and conditionspresented are general in nature and not site specific. A scale of risk is established prior to start of the project(e.g. a scale of 1–5 with 5 being the highest possible risk). The highest weight 5 has been assigned to twoparameters. The depth to water and impact of the vadose zones because, lower the water table higher will bethe chance of pollutants to reach to GW. Discussion was done with the expert from the field of hydro-geologyand geology and a consensus on the risk level of the activity was reached that suits the area of this study,Aligarh. The information gained from the experts is recorded and can be used to analyze in specificcircumstances and sites (Lull, Tindall, & Potts, 1995).

These parameter ratings are used for all evaluations. Weights are assigned for various hydrological settings,which ranges from 1 to 5, with higher weights representing greater pollution potential (Table 2). These sevensets of data layers were digitized and were converted to raster data sets that were processed using IntegratedLand and Water Information System (ILWIS) 3.0 developed by the International Institute of Geo-Information Science and Earth Observation (ITC), The Netherlands and Arcview 3.2a GIS of ESRI, the USA.

Once the DRASTIC Index has been computed, it is possible to identify areas, which are more likely to besusceptible to GW contamination relative to others. The higher the DRASTIC Index, the greater is the GWpollution potential. The flow chart of the methodology is shown in Fig. 2.

The DRASTIC model rates relative sensitivity of land units by integrating information on depth to GW,impact of vadose zone, soils, recharge, hydraulic conductivity, topography (slope), and aquifer media in

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Table 2

Assigned weight for DRASTIC parameters

Factors/hydro-

logical settings

Description Relative

weights

Depth to water It is depth from ground to water table, deeper the water table lesser will be the chances of

pollutants to interact with ground water

5

Net Recharge It is the amount of water/unit area of land that penetrates the ground surface and reaches the water

table, it is the reporting agents for pollutants to the ground water

4

Aquifer media It is the potential area for water storage, the contaminant attenuation of aquifer depends on the

amount and sorting of fine grains, lower the grain size higher the attenuation capacity of aquifer

media

3

Soil media Soil media is the uppermost and weathered part of the ground, soil cover characteristics influence

the surface and downward movement of contaminants

2

Topography It refers to slope or steepness, areas with low slope tend to retain water for longer, this allows a

greater infiltration of recharge of water and a greater potential for contaminant migration and

vulnerable to ground water contamination and vice versa

1

Impact of vadose

zone

It is the ground portion found between the aquifer and the soil cover in which pores or joints are

unsaturated, its influence on aquifer pollution potential similar to that of soil cover, depending on

its permeability, and on the attenuation characteristics of the media

5

Hydraulic

conductivity

It refers to the ability of the aquifer formation to transmit water; an aquifer with high conductivity

is vulnerable to substantial contamination as a plume of contamination can move easily through

the aquifer

3

Raw data Parameter maps Processing Final product

Topography Water table

Rainfall landform information

Well logs published reports

Soil data

Topography

Well logs published reports

Transmissivity saturated thickness

Depth of water

Net Recharge

Aquifer media

Soil media

Slope

Impact of vadose zone

Hydraulic conductivity

GISDRASTIC

MAP

Fig. 2. Flow chart of methodology for ground water vulnerability analysis using DRASTIC model in GIS.

A. Rahman / Applied Geography 28 (2008) 32–5338

ARTICLE IN PRESSA. Rahman / Applied Geography 28 (2008) 32–53 39

determining a ranking of GW sensitivity. It was designed to allow flexibility so that the local hydro-geologicalsetting and its parameters could be weighted appropriately. The hydro-geological setting is defined by thespatial representation of designated mappable units. The mappable units incorporate the major hydro-geological factors, which affect and often control GW movement. Thus, the modifications to the DRASTICwere incorporated in both the selection and weighting of these factors.

Sensitivity analysis

Some scientists argue that GW contamination vulnerability may be worked out without using all theparameters of the DRASTIC model (Barber, Bates, Barron, & Allison, 1993; Merchant, 1994). Some others(Napolitano and Fabbri, 1996) have argued that weights and ratings used in the model are subjective and thereis no reason not to doubt the accuracy of the vulnerability index thus worked out. Still some may doubt theresults of this model in the absence of supporting experimental evidence. In order to remove all these doubts,sensitivity analysis of the model and GW contamination analysis are carried out.

In the first instance the rated parameters of the model have been examined for interdependence andvariability as a high degree of interdependence of the parameters may lead to the risk of misadjustment(Babiker et al., 2005; Rosen, 1994). There carried out two sensitivity tests: the map removal sensitivity analysisand the single parameter sensitivity analysis. The first test was for the first time carried out by Lodwick,Monson, and Svoboda (1990) and the second test was introduced by Napolitano and Fabbri (1996). These twotests are also used by the Babiker et al. (2005).

The first test identifies the sensitivity of vulnerability map by removing one or more layer maps and isworked out using the following equation:

S ¼ ðjV=N � V 0=nj=V Þ � 100, (2)

where S is the sensitivity measure, V and V0 are the unperturbed and the perturbed vulnerability indices,respectively, and N and n are the number of data layers used to compute V and V0. The unperturbedvulnerability index is the actual index obtained by using all seven parameters and the perturbed vulnerabilityindex was computed using a lower number of parameters.

The single parameter sensitivity test was carried out to assess the influence of each of the seven parametersof the model on the vulnerability measure. In this analysis real or ‘‘effective’’ weight of each parameter wascompared with its assigned or ‘‘theoretical’’ weight. The effective weight of a parameter in a sub-area wascalculated by using following equation:

W ¼ ðPr Pw=V Þ � 100, (3)

where W refers to the ‘‘effective’’ weight of a parameter in a polygon, Pr and Pw are the respective rating andweight of that parameter, respectively, and V is the overall vulnerability index of that polygon.

The GW contamination analysis was carried out by taking samples of water from 60 wells during May–June2004. The sample water was tested for trace elements, e.g. Cu, Zn, and Pb. The tests were carried out as perstandard methods for the examination of water and wastewater as carried out by the American Public HealthAssociation (APHA, 1995). The analysis was carried out to compare the experimental results with thecontamination vulnerability levels as shown by overall vulnerability map prepared by using the DRASTICmodel.

Results and discussion

Depth to water

Depth to water is one of the most important factors because it determines the thickness of material throughwhich infiltrating water must travel before reaching the aquifer-saturated zone. Depth to water consequentlyimpacts on the degree of interaction between the percolating contaminant and sub-surface materials (air,minerals, water) and, therefore, on the degree, extent, physical and chemical attenuation, and degradationprocesses. In general, the aquifer potential protection increases with depth to water. The bore well data wascollected from the Jal Nigam Department, Aligarh. These point data were contoured by interpolating and

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<5 mts.

5-15 mts.

> 15mt..

Kilometers

Fig. 3. Depth to water of Aligarh.

A. Rahman / Applied Geography 28 (2008) 32–5340

divided into three categories i.e. o5m, 5–10m, and 410m (Fig. 3). Thereafter it was converted into grid tomake it raster data for GIS operation. The depth-to-water table interval range, DRASTIC rating, weight, andresulting index are shown in Table 3. Areas with high water tables are vulnerable because pollutants haveshort distances to travel before contacting the GW. So, the deeper the GW smaller the rating value.

Net recharge

The net recharge is the amount of water from precipitation and artificial sources available to migrate downto the GW. Recharge water is, therefore, a significant vehicle for percolating and transporting contaminantswithin the vadose zone to the saturated zone. It carries the solid and liquid contaminants to the water tableand also increases the water table. Aligarh is in a depression as described earlier. It lies between the Yamunaon the left and the Ganga on the right (Fig. 1). Aligarh district is a part of fertile upper Ganga Yamuna doab.Therefore, the land around Aligarh city is intensively cultivated. Aligarh district was also chosen as one of thedistrict for the implementation of the Green Revolution Program in 1967 which led to the use of high yieldingvariety (HYV) of seeds and associated technology of intensive irrigation, chemical fertilizers, pesticides etc.Recently the sprawling city of Aligarh has greatly encroached upon the surrounding fertile land (Fazal, 2005).As a result Aligarh water gets recharged by both the rivers and also from irrigation water that contributesmore to the GW pollution because of fertilizer and pesticides used in agricultural activities. Potential netrecharge for non-irrigated land is considered to be between 0 and 2 in/unit area, because Aligarh is an urbanarea so the net recharge varies from o3 to 44 in (Fig. 4) and, from water bodies net recharge is considered tobe in excess of 10 in/unit area (Frenzel and Kaehler, 1990). Urban and built-up areas, open plots, and barrenland were assigned a rating of 1 (Table 3) because it is the zone of highest runoff due to impervious surfaceareas. Water bodies and wetlands were assigned a rating of 8. An area with high GW recharge is at high riskbecause of the permeable pathway from the surface to the water table. So high recharge rates were assigned tothese areas. Net recharge was then calculated using the following equation:

Net recharge ¼ ðRainfall� EvaporationÞ �Recharge rate: (4)

Aquifer media

GW flow, contaminant fate, and transport modeling are important components of most aquifer remediationstudies. The aquifer media refers to the consolidated and unconsolidated rock, which serves as a storage of

ARTIC

LEIN

PRES

S

Table 3

DRASTIC parameters

1. Depth to water 2. Net recharge 3. Aquifer media 4. Soil media

Ranges

(m)

Ratings

(Dr)

Index

(D)

Land use classes Ranges

(in)

Ratings

(Dr)

Index

(D)

Aquifer Ratings

(Dr)

Index

(R)

Ranges Ratings

(Dr)

Index

(R)

o5 10 50 Built-up o3 1 4 Fine to medium

sand

5 15 Clay loam 7 14

5–15 9 45 Barren lands/open plots with

vegetation

3–4 3 12 Sand & kankar 5 15 Loam 6 12

415 7 35 Water bodies/wet lands 44 8 32 Coarse sand 8 24 Sandy

loam

5 10

Weight: 5 Weight: 4 Weight: 3 Weight: 2

5. Topography 6. Impact of the vadose zone 7. Hydraulic conductivity

Slope Ranges (m) Ratings (Dr) Index (D) Ranges Ratings (Dr) Index (D) Ranges (1000G.P.D./ft2) Ratings (Dr) Index (R)

o5 10 10 Hard clay 5 25 o20 2 6

5–10 5 5 Surface clay 8 40 20–40 4 12

410 1 1 Clay kankar 6 30 40–60 6 18

Sandy clay 9 45 60–80 8 24

Fine sand 6 30 80–100 10 30

Medium sand 8 40

Weight: 1 Weight: 5 Weight: 3

A.

Ra

hm

an

/A

pp

liedG

eog

rap

hy

28

(2

00

8)

32

–5

341

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< 3 inches

3-4 inches

> 4 inchesKilometers

Fig. 4. Net recharge of Aligarh.

Sand & Kaankar

Fine-medium sand

Coarse sand

Kilometers

Fig. 5. Aquifer media of Aligarh.

A. Rahman / Applied Geography 28 (2008) 32–5342

water (Chandrashekhar et al., 1999). The aquifer is defined as a rock formation which will yield sufficientquantities of water for use (Anwar et al., 2003). The shallow aquifers occur within a depth of 50m from theearth’s surface. An aquifer media map was prepared from the well log data and topographical maps. Thealluvium is composed primarily of floodplain deposits consisting of fine to medium sand, sand and kankar,and coarse sand (Fig. 5). Aquifer ratings were assigned weight of 5 for both fine to medium sand, sand andkankar, and 8 for coarse sand (Table 3). The contaminant attenuation of the aquifer depends on the amountand sorting of fine grains. In general, larger the grain-size and the more fractures or openings within theaquifers, the higher the permeability and lower the attenuation capacity; consequently the greater thepollution potential (Anwar et al., 2003). So the coarse (saturated or unsaturated) media was assigned a highrating value compared to the fine media.

Soil media

In general, soil pollution potential is affected largely by the type and amount of clay present, the shrink/swell potential (controlling the development of macro-pores and other secondary permeability features), andthe soil grain size. The DRASTIC index includes soil ratings appropriate for the pollution potential associated

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Clay loam

Loam

Sandy loamkilometers

Fig. 6. Soil media of Aligarh.

A. Rahman / Applied Geography 28 (2008) 32–53 43

with the development of secondary permeability. The characteristics of soil influence the amount of rechargeinfiltrating the ground surface, the amount of potential dispersion, the purifying process of contaminants, etc.Soil cover characteristics influence the surface and downward movement of contaminants. The presence of finegrain size materials, such as clay, peat, or silt, and the percentage of organic matter within the soil cover candecrease intrinsic permeability, and retard or prevent contaminant migration via physico-chemical processes,i.e., absorption, ionic exchange, oxidation, and biodegradation. A soil map was prepared from the district soilmap of National Bureau of Soil Survey and Land Use Planning (1991) and weightages and ratings wereassigned (Table 3). Soil does not vary much in the study area, it is either clay loam or loam with some sandyloam (Fig. 6). Coarse soil media have high rates in comparison to fine soil media.

Topography

Topography refers to slope of an area. Areas with low slope tend to retain water for longer period of time.This allows a greater infiltration or recharge of water and a greater potential for contaminant migration. Areaswith steep slopes, having large amounts of runoff and smaller amounts of infiltration are less vulnerable toGW contamination. Regions of consistently low topography are vulnerable as water can pool and infiltrateinto the sub-surface in these areas. The topographic map was prepared from the Survey of India (SoI)toposheet of 1971 on the scale of 1:50,000. Contours of 20m were digitized and then digital elevation model(DEM) was prepared in ArcView GIS software. Thereafter slope was extracted from the DEM and it wasdivided into three classes (Table 3). Topography will give an indication on whether a pollutant will run off orremain on the surface long enough to infiltrate into the GW (Lynch, Reynders, & Schulze, 1994). Slopevariation in the study area is very small, i.e. o5m, 5–10m, and 410m (Fig. 7). This is because the study areais part of the Great North Indian plains. Flat areas were assigned high rates because in flat areas the runoffrate is less, so more percolation of contaminants to the GW.

Impact of the vadose zone

The vadose zone’s influence on aquifer pollution potential is essentially similar to that of soil cover,depending on its permeability, and on the attenuation characteristics of the media. The impact of vadose zoneis a complex phenomenon, combining aquifer media and topographic characteristics. Movement of waterwithin the vadose zone is studied in hydro-geology, and is of importance to contaminant transport. Impact ofvadose zone was prepared from the lithological cross-sections obtained from the geophysical data. The vadose

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< 5%

5-10%

> 10%Kilometers

Fig. 7. Topography of Aligarh.

Hard clay

Surface Clay

Clay kankad

Sandy clayFine sand

Medium sandKilometers

Fig. 8. Impact of vadose zone of Aligarh.

A. Rahman / Applied Geography 28 (2008) 32–5344

zone has a high impact on water movement if it is composed of a permeable material. The weights and ratingsfor the vadose zone are shown in Table 3. Vadose zones have been mapped as shown in Fig. 8.

Hydraulic conductivity

Aquifer hydraulic conductivity is the ability of the aquifer formation to transmit water. It depends on theintrinsic permeability of the material and on the degree of saturation. This critical factor controls thecontaminant migration and dispersion from the injection point within the saturated zone and, consequentlythe plume concentration in the aquifer. Hydraulic conductivity values were calculated after calculatingtransmissibility from pumping test data and have been mapped as shown in Fig. 9. An aquifer with high

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20-40

60-80

< 20

40-60

80-100

Kilometers

Fig. 9. Hydraulic conductivity of aquifer of Aligarh.

Table 4

Area under vulnerability to groundwater pollution in Aligarh

Sl. no. DRASTIC Index value Area (in hect.) Area (in % age) Vulnerability zones

1 49–85 780.94 19.57 Low

2 86–104 957.88 24.00 Moderate

3 105–141 2252.34 56.43 High

Total 3991.06 100.00

A. Rahman / Applied Geography 28 (2008) 32–53 45

conductivity is vulnerable to substantial contamination as a plume of contamination can move easily throughthe aquifer (Table 3). This is different from an aquifer media as an aquifer with an impermeable media can stillconduct water in the presence of fractures (Fritch et al., 2000).

The final vulnerability map was obtained by running the model in the ILWIS 3.2 GIS environment by usingthe seven hydro-geological data layers. The DRASTIC scores obtained from the model vary from 49 to 141.These values were reclassified into three classes using the quantile classification scheme i.e., low vulnerablezones, moderate vulnerable zones and high vulnerable zones (Table 4 and Fig. 10).

The results of this study shows that of the total 3991.06 hectares an area of about 780.94 hectare is in thelow vulnerable zone with a DRASTIC index range between 49 and 85, about 957.58 hectares are in themoderately vulnerable zone with a DRASTIC index ranging between 86 and 104, and about 2252.34 hectaresare in the high vulnerability zone with a DRASTIC index ranging between 105 and 141. This means that morethan half of the Aligarh city’s GW is at high risk in terms of pollution potential. These areas are mainly in thesouth-west and central parts of the city where the physical factors like gentle slope and high water table arevery well supporting the chances of getting shallow aquifer water polluted. The areas, which are under the highvulnerable pollution, are mainly the old parts i.e. central and south and south west of Aligarh like Sarai Man

Singh, Rasal Ganj, Delhi Gate, Ghas Ki Mandi, Tan Tan Para, Jaiganj, etc. The areas having moderatevulnerability are in the north and eastern parts of Aligarh and include Nagal Dodhpur, Vikas Puri, Railwaycolony, Mamu Bhanja, etc. In the northern area of the city very few areas are under high vulnerable zone,

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namely Jamalpur, Bhamola Mafi, Qila Road, and Nagla Jamalpur, which is due to lesser slope, high recharge,and coarse sand aquifer media that has high pollution potential rating.

Wastewater discharge from household and small-scale industries remains there for many months and evenyears in low lying areas and hence the GW recharged by this wastewater leads to the high vulnerable zones.The DRASTIC map produced can be applied to any pollutant which interacts with water at the surface but itdoes not give a clear picture of the pollution potential of individual chemical constituents as to how much theycontribute to the pollution of GW. This is one of the important limitations of this study.

Sensitivity analysis of the DRASTIC model

Table 5 presents statistical summary of the seven rated parameters of the DRASTIC model to workoutvulnerability of GW in Aligarh. An examination of the means of the parameters reveals that the highestcontribution to the vulnerability index is made by net recharge (mean ¼ 10) closely followed by depth to water(mean ¼ 9). The third and significant place is occupied by topography (mean ¼ 7.3). Aquifer media andhydraulic conductivity both having mean value of 4 contribute lowest to the contamination of GW, while soilmedia and impact of vadose zone (mean value 5 and 6, respectively) contribute moderately to the vulnerabilityindex. The coefficient of variations indicate that a high contribution to the variation of vulnerability index ismade by aquifer media (77.5%), and hydraulic conductivity (75%), moderate contribution is made by impactof vadose zone (56.7%), topography (56.2), soil media (50.0%), and depth to water (40.0%), while the lowestcontribution is made by net recharge (23.0%).

In order to examine interdependence of rated seven parameters of the DRASTIC model, n(n�1)/2 (where n

is the number of parameters ¼ 7), i.e. 21 Spearman’s rank order correlations are computed. Out of these 21correlations only four (Table 6) are significant at or more than 95% level of confidence. Even thesecorrelations may be due to chance. It means that parameters are largely independent and there is very littlerisk of misadjustment in the final index.

Map removal sensitivity analysis

Tables 7 and 8 show map removal sensitivity analysis that was performed by removing one or more datalayer. This had been done taking into account all 369 sub-areas because the sub-areas larger in size, more than100m2, are considered. The study shows that there is a clear variation in vulnerability index because ofremoving a layer at a time. The map removal analysis shows that variation of GW vulnerability index is to beexpected highest upon the removal of net recharge layer from the computation because the mean variation

High vulnerable

Low vulnerable

Moderate vulnerable

Kilometers

Fig. 10. Groundwater vulnerable zones of Aligarh.

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Table 6

Summary of rank-order correlation analysis result between seven DRASTIC parameters

Correlated parameters Correlation coefficient r Significance level P

Depth to water and soil media 0.91 o0.0001

Net recharge and hydraulic conductivity 0.81 o0.005

Soil media and topography 0.93 o0.0001

Depth to water and topography 0.87 o0.005

Note: Only statistically significant (confidence level at or more than 95%) inter-correlations area shown.

Table 5

Statistical summary of the DRASTIC parameters map

D R A S T I C

Min. 2 7 5 5 1 5 2

Max. 18 11 8 7 7 9 10

Mean 9 10 4 5 7.3 6 4

SD 3.6 2.3 3.1 2.5 4.1 3.4 3

CV (%) 40.0 23.0 77.5 50.0 56.2 56.7 75.0

SD—Standard deviation and CV—Coefficient of variation.

Table 7

Statistics of map removal sensitivity analysis

Parameters removed Variation index (in percent)

Min Max Mean SD

D 0 18.4 11.3 5.3

R 0 35.2 13.7 4.8

A 2 7.8 3.4 2.8

S 8 15.2 6.6 4.2

T 1 20.3 10.1 1.2

I 1.2 17.3 5.8 2.3

C 0 19.2 8.1 2.5

Analysis has been undertaken for 369 sub-areas, SD is standard deviation.

Table 8

Statistics of map removal sensitivity analysis

Parameters used Variation index (in per cent)

Min Max Mean SD

D, R, S, T, I, C 2 7.8 3.4 2.8

D, R, S, T, C 1.0 15.2 5.6 2.1

D, R, T, C 0 16.8 12.7 3.7

D, R, T 0 24.1 6.1 4.2

D, R 0 52.0 17.3 8.4

R 5 40.6 14.4 5.3

A. Rahman / Applied Geography 28 (2008) 32–53 47

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Table 9

Statistics of single parameter sensitivity analysis

Parameters Theoretical weight Theoretical weight (%) Effective weight (%)

Mean Min Max SD

D 5 21.7 23.6 5.3 46.3 9.7

R 4 17.4 25.3 12.5 41.0 4.7

A 3 13.0 8.9 3.2 14.6 9.1

S 2 8.7 4.5 3.9 20.4 2.3

T 1 4.3 8.2 1.4 10.1 2.7

I 5 21.7 13.4 5.7 26.4 7.5

C 3 13.0 16.1 5.3 33.8 6.7

Analysis has been undertaken for 369 sub-areas, SD is standard deviation.

A. Rahman / Applied Geography 28 (2008) 32–5348

index is (13.7%). This can be because of high theoretical weight assigned to net recharge parameter.Furthermore, the vulnerability index seems to be sensitive to the removal of depth to water layer as meanvariation index is 11.3%. Removal of topography layer also bears considerably on the variation ofvulnerability index to its high mean variation index (10.1). Removal of the hydraulic conductivity and soilmedia (respective mean variation indices are 8.1% and 6.6%) have also contributed significantly to thevariation of GW vulnerability index. The least sensitive parameter is aquifer media, the mean variation indexbeing 3.4%.

Table 8 shows variation of vulnerability index due to the removal of one or more data layer at a time fromthe DRASTIC model computation. The removal of maps was based on the earlier map removal sensitivitymeasure (Table 7). The layers, which compel less variation in the final vulnerability index, were removed firstand then next lesser one and so on. The least average variation index is found by removing the aquifer medialayer (3.4%). The average variation index increases when data layer of impact of vadose zone is removed withaquifer media. No consistency in the trend of mean variation index is observed when more layers are removed.This trend shows that almost all the parameters are necessary to work out vulnerability index.

Single parameter sensitivity analysis

The significance of seven parameters for the assessment of the DRASTC GW vulnerability index from theprevious map removal analysis is already established. The single parameter sensitivity analysis is to comparetheir ‘‘theoretical’’ weights with that of ‘‘effective’’ weights. The effective weight is a function of value of thesingle parameter with regard to the other six parameters as well as the weight assigned to it by the DRASTICmodel (Babiker et al., 2005). The effective weights of the DRASTIC parameters exhibited some deviation fromthat of the theoretical weights. Table 9 reveals that net recharge and depth to water layer tend to be the mosteffective parameters in the vulnerability assessment because their mean effective weights, 25.3% and 23.6%,respectively, are higher than their respective theoretical weights. The hydraulic conductivity (16.1%) alsoshows a higher effective weight compared to its theoretical weight. Topography also shows much highereffective weight (8.2%) than its theoretical weight (4.3%). All other layers show lower effective weights whencompared with their theoretical weights. This shows the importance of net recharge, depth to water, hydraulicconductivity, and topography layers in the DRASTIC model and to get accurate and detailed information onthese three specific factors.

GW contamination analysis

The purpose of this analysis is to assess the relationship between GW vulnerability map and trace elementsconcentration in GW. The concentrations of trace elements copper (Cu), lead (Pb), and cadmium (Cd) havebeen shown in Figs. 11–13, respectively. It is found out that Cu 43%, Pb 45%, and Cd 43% are beyond theirrespective permissible limits (Rahman, 2003). These sampled wells map was overlaid on the vulnerability map

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0

0.5

1

1.5

2

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Sampled Wells

(mg

/l)

Fig. 11. Copper concentrations in different wells.

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Sampled Wells

0

0.2

0.4

0.6

0.8

1

1.2

(mg

/l)

Fig. 12. Zinc concentrations in different wells.

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

Sampled Wells

(mg

/l)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Fig. 13. Lead concentrations in different wells.

A. Rahman / Applied Geography 28 (2008) 32–53 49

(Fig. 14) in order to see how many wells with high concentration of trace elements are found in differentvulnerable zones. Out of 60 wells about 27 wells which have high concentration of trace elements are located inthe high vulnerable zones, 9 wells which have a high concentration of trace elements are located in themoderate vulnerable zones, 3 wells which have a high concentration of trace elements are located in the lowvulnerable zones, and the rest (21 wells) have lower concentration of trace elements. This supplements theresults obtained by the DRASTIC model.

Summary and conclusions

This study employed a GIS model to determine the vulnerability of GW to contamination in Aligarh cityand its surrounding areas. This was accomplished using the DRASTIC model. The output map was obtained

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Fig. 14. Vulnerable zones and well location of Aligarh city.

A. Rahman / Applied Geography 28 (2008) 32–5350

to determine the vulnerability of GW pollution in shallow aquifers. The results reveal that GW in and aroundAligarh in larger part are under moderate to high pollution vulnerable zones. The vulnerable zones wereclassified into three, i.e. low, moderate, and high zones. The study shows that 56.43% of the total study area isunder the high vulnerable zone, which is mainly as a result of the topography of Aligarh and its surroundingareas. About 20% of the area is under low vulnerable zone. The most of city area comes under high vulnerablezone because various chemicals are discharged into open drains from the lock, brass, and other household anda few bigger industries, which has led to the pollution of shallow aquifers in Aligarh. This is the major cause ofconcern for nearly one million populations living in Aligarh. The study shows that with the help of GISsoftware once the different data layers are prepared and overlaid the water vulnerable zones can easily beidentified.

This study produced a very valuable tool for those who are in management position because it gives a verycomprehensive indication of vulnerability to GW contamination. The high vulnerability of GWcontamination makes it absolutely necessary to local authorities for managing GW resources, monitoringthis problem closely and to act accordingly. Infiltration of storm water runoff with respect to GW protectionshowed that the risk of GW pollution can only be minimized by using infiltration systems with a passagethrough the top soil and planning, construction, and operation of infiltration systems.

The DRASTIC model, which is used for preparing the pollution potential map, can be used as a screeningtool to see whether a particular area is more or less vulnerable to GW pollution. This delineation allows cityplanners and administrators to direct their resources to those vulnerable areas, which are most critical to GWpollution, thereby making most of the limited resources available to them. Apart from GW vulnerabilityassessment, the DRASTIC model can be used for a wide range of applications. It can also be used inprioritization of areas for monitoring purposes. It can help the planners and policy makers while selecting theareas for waste disposal and industrial sites etc.

High vulnerable water zones are usually difficult to monitor, as it requires the drilling of many monitoringwells, which is very expensive. The vulnerability map produced in this study gives a decision maker a verycomprehensive idea of areas that need to be closely monitored, as well as those areas which are less likely tobecome contaminated and require less intensive monitoring. The mapping technique used for this analysis is aqualitative method of describing the occurrence and distribution of GW pollution. The assessment criteria and

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guidelines are based on the US EPA’s DRASTIC model (Aller, et al., 1985). Although originally developed asa means of evaluating GW pollution potential, the DRASTIC model also provides a qualitative assessmentwith respect to GW availability.

The study suggests that the DRASTIC model can be used for prioritization of vulnerable areas in order toprevent the further pollution to already more polluted areas. There is a need to develop a system that can beused to identify areas where attention or protection effort is required. There should be a detailed and frequentmonitoring in high and very high vulnerable zones in order to monitor the changing level of pollutants. Thereis an urgent need for a large scale planning for solving the drainage problem due to the typical topography ofAligarh and that will go a long way in solving the problem of GW pollution in and around the Aligarh. Waterdistribution pipelines are very old and there is an urgent need for replacement and proper laying out of newpipelines to ensure safe and good water to the resident population of Aligarh and its surrounding area. Areaunder high GW vulnerable zone in shallow aquifer will keep on increasing until regular draining out ofwastewater is not done.

Acknowledgments

The author is thankful to Department of Science and Technology (DST), Ministry of Science andTechnology, Government of India, New Delhi for funding this project under Fast Track Scheme for YoungScientist (Ref. no. SR/FTP/ES-42/2000). I pay my special thanks to Prof. Mohd. Firoz Khan, Jamia MilliaIslamia, New Delhi for his valuable suggestions and help in revising the paper. Thanks are also due to Dr. S.Fazal, AMU, Aligarh, and Mr. S. Maithani, IIRS, Dehradun. Thanks are also extended to Water WorksDepartment, Aligarh, II Construction Division, UP Jal Nigam, Aligarh for providing data.

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