TECHNOSCIENCE ARTICLE Evaluation of Aquifer Vulnerability by …1)_Ta03.pdf · Evaluation of...

Evaluation of Aquifer Vulnerability by GIS Application: Case studyfromAsadabad, Hamadan (Western Iran)

Oroji Balal

Department of Environment Science, University of Malayer,Malayer, Hamadan. 6541747387, Iran.

Key words: Nitrate pollution, Modif ied DRASTIC

StudyArea: Asadabad aquifer, Hamadan, IranCoordinates: 37° 07 to 37° 25 N; 48° 07 to 34° 47 E

TECHNOSCIENCE ARTICLE

Published by: National Cave Research and Protection Organization, IndiaVol. 4(1): 38-44

Year 2017

ISSN- 2348 5191 (Print) & 2348 8980 (Electronic)

ambientSCIENCE

Abstract

Introduction:The term vulnerability is used to explain the degree towhich human or environmental systems are likely to getharm due to perturbation or stress, and can be known for adetermined system, hazard, or group of hazards (Popescu

2008). In hydrogeology, vulnerability evaluationtypically describes the effectiveness of a specif ic aquifer tocontamination that could decrease the Groundwaterquality. The idea was f irst of all imposed in France by theend of the 1960s to create awareness about groundwatercontamination (Vrba & Zaporozec, 1994).

No single standardized description for Groundwatervulnerability exists; although, the concept describes therelative ease with which the Groundwater resource couldbe contaminated. The National Research Council (1993)determined it as the tendency or likelihood forcontaminants to reach a specif ied position in theGroundwater system after introduction at some locationabove the uppermost aquifer. Two idioms are used todescribe Groundwater vulnerability: essential andspecif ic. The essential vulnerability is the naturalsusceptibility to contamination based on the physicalcharacteristics of the environment while specif ic

et al.,

vulnerability is determined as an accounting for thetransport properties of a particular contaminant or groupof contaminants through the subsurface (Jessica & Sonia,2009). Theassessmentof vulnerabilitygathering complex,the hydrogeological data must be handled in such a way sothat it can be used by a non–specialist people such asdecis ion–makers. Vulnerabi l i ty evaluat ion ofGroundwater aquifer provides a basis for initial protectivemeasurement for important groundwater resources andwould normally be the f irst step in a groundwaterpollution hazard assessment and quality when its interest(Foster, 1987).

Many approaches have been expanded for assessinggroundwater vulnerability and could be grouped intothree major categories (Tesoriero 1998): a. overlayand index methods; b. methods using process–basedsimulation models; c. statistical methods. In overlay andindex methods, factors which are controlling themovement of pollutants from the ground surface into thesaturate zone (e.g., geology, soil, impact of vadose zone,etc.) are mapped depending on existing and/or deriveddata. Subjective numerical values (rating) are thenassigned to each factor based on its importance on

et al.,

DRASTIC model has been used to map groundwatervulnerability due to pollution in various areas. This methodneeds to be calibrated and reformed for a specif ic aquiferand pollution. The suggested methodology was applied toAsadabad aquifer located in western Iran. In the presentresearch, the rates of DRASTIC parameters have beenreformed so that the vulnerability potential to pollution canbe determined more exactly. The new rates were calculatedusing the relationships between each parameter and thenitrate concentration in the groundwater. The measurednitrate condensation values were used to correlate thepollution potential in the aquifer to DRASTIC index. Theresults showed that the modif ied DRASTIC is better thanthe original method for the nonpointsource of pollutions inagricultural zones. Application of the new rates, a newDRASTIC map was expanded that shows that 2.1% of thearea fall in high vulnerability class. The assessed area was34.2% and 58.19% for moderate class and, for lowvulnerability class, 63.7% and 31.39% respectively, beforeand afterusing the newrates.

Ambient Science, 2017: Vol. 04(1); 38-44DOI:10.21276/ambi.2017.04.1.ta03

http://www.caves.res.in/

controlling pollutants circulation. The rate maps arelinear to produce f inal vulnerability map of a region. Thegroundwater vulnerability assessment by such methods isqualitativeand relative. The main benef itof such methodsis that some of the factors controlling the movement ofpollutants can be assessment over the large area, whichmakes them appropriate for regional scale evaluation(Thapinta & Hudak, 2003). Fortunately, with thedevelopment of GIS digital technology, adoption of suchmethods for creating vulnerability maps is an easy task.Several overlay and index methods have been expanded.The most common one are: the DRASTIC system (Aller

., 1987), the GOD system (Foster, 1987), the AVI ratingsystem (Stempvoort ., 1993), the SINTACS method(Civita, 1994), the German method (Von Hoyer & Sofner,1998), the EPIK (Doerfliger e , 1999), and the Irishperspective (Daly , 2002). Process–based method andstatistical methods are not generally used forvulnerabilityevaluation because they are constrained by data shortage,computational hardness, and experience required forimplementing them.

The DRASTIC system is the widest method used in theassessmentof the intrinsicvulnerability forawide range ofpotential contaminants. It is an overlay and index modeldeliberate to product vulnerability scores by combiningseveral thematic maps. It was originally developed in theUSA under a cooperative agreement between the NationalWater Well Association (NWWA) and the USEnvironmental Protection Agency (EPA) for detailhydrogeology assessment of pollution potential(Rundquist , 1991). The word DRASTIC is an acronymfor most main factors within the hydrogeological settingsw h i c h co n t r o l g r o u n d w a te r p o l l u t i o n . T h ehydrogeological setting is a compound description of allmajor hydrogeological and geologic factors which affectthe groundwater movement into through, and out of thearea. These factors are depth to water, aquifer media, netrecharge, topography (slope), soil media, hydraulicconductivityand impactof thevadose zone. The DRASTICnumeric ranking system contains three major parts:ratings, weights, and ranges. Groundwater vulnerabilitywas appraised using hydrogeological factor that caninfluence the pollutant transport by thevadose zone to thewater bearing strata using GIS–based DRASTIC method(Aller , 1987). The flowchart shows the generaloverview of the research methodology. Figure-1 showsmethodology flowcharts for groundwater vulnerabilityanalysisusing DRASTIC model in GIS.

Weights in each DRASTIC factor is appraised withrespect to each other to determine the relative importanceof each factor. Table-1 shows each factor is specif ied arelative weight range of 1–5 (1). The most substantial factoris allocated f ive; the least substantial is assign one.DRASTIC has two weight classif ications, one for normal

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conditions (standard) and the other for conditions withintense agricultural activity. The last is called pesticideDRASTIC index and represent a special case of DRASTICindex. The difference between the two versions ofDRASTIC is in the assignment of relative weights for theseven DRASTIC factors. All the other parts of the twoindices are identical. The application of the pesticideswhen mixed with sensitive groundwater, it can impose asubstantial impact on the water quality. Agriculturalcontaminants i.e., when pesticides get dissolve in theirrigated waterand permeate through thesoil prof ile.

Despite its amicability, the DRASTIC method doeshave some disadvantages. This method uses sevenparameters in its calculation of “Vulnerability Index” witheachparameterbeing specif ied aspecial weightand ratingvalue as shown in Table 2 (Aller , 1987). The influenceof zonal characteristics is not calculated for in the methodand so the same weights and rating values are used all over.In addition, there is no standard algorithm to test andvalidate the method for an aquifer. Some researchers havetried to correlate the vulnerability index withcontaminantorchemical parameters (Kalinski ., 1994;Rupert, 1999; Maclay 2001). Some other researchershave correlated land use to vulnerability (Secunda, 1998;Worrall & Koplin, 2004), but, they did not use it to right orweightsof the DRASTIC model.The f inal vulnerability index is a weighted sum of theseven factors and can be calculated using the followingformula:

Where D, R, A, S, T, I and C are the seven factors of theDRASTIC method, (w) the weight of the factor, and (r) therating associated. Values of DRASTIC index very from 26to 256 in thecaseof the DRASTIC pesticidesand from 23 to226 in the case of the DRASTIC standard version. Aller

(1987) did not suggest any classif ication for theirDRASTIC results, thus the vulnerability ranges of theDRASTIC index used in this study generally to the mostcommonly used reference in the literature. Table-3 showscriteria of the evaluation of the degrees of vulnerability(Civita & De Regibus, 1995; Corniello 1997).

The objective of this study is to evaluate the intrinsicvulnerabilityof theprincipal aquifer inAsadabad city, Iranfor both general and pesticides contaminants using themost popular DRASTIC overlay and index method.Vulnerability assessments are a key component of theintegrated management of water resources. Theseassessments can help to guide the decision making aboutfuture development and the options available to protectand monitor the groundwater resource. In linking waterplanning and land use planning together, decision makerscanconsiderasetof scenariosand selectapath that is mostin linewith thedesired futureof theircommunity.

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VI = DrDw + RrRw + ArAw + SrSw + TrTw + IrIw + CrCw. ---(1)

Ambient Science (2017) Vol.-04(1):p. 39

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http://www.caves.res.in/Ambient Science (2017) Vol.-04(1):p. 40

Materialsand methods:Based on the collected data and required information, adatabase in the GIS environment was prepared forcalculation of f inal vulnerability index in the methods.The main hydro-geologic factors affecting thetransmission of pollution, including depth to water table,net recharge, aquifer media, soil media, topography,impact of the vadose zone, aquifer hydraulic conductivity,and land use parameters were rated, weighted, andintegrated using GIS 9.3. Finally, the maps of Asadabadplain's unconf ined aquifer vulnerability were prepared. Inorder to calibrate the DRASTIC model, the concentrationwas chosen as the primary contamination parameter.Twenty agricultural wells were chosen for sampling andanalysis. Twosetsof samples in Juneand Septemberof 2014were taken. Figure- 2 shows the location of the samples inthe study area. The precise position of each well wasdetermined using Global Positioning System techniques.The Asadabad aquifer with an area of 962 squarekilometers is situated in western Iran (Fig.-2) in Asadabadbasin. In the region under study, almost 1.5 percent of theirrigation water that is about 4 million cubic meters(MCM) inf iltrates into the groundwater per year. Inaddition part of the municipal wastewater i.e. about 4MCM, from the cities of Chardoli, and Asadabad,percolates into the groundwater annually (Anonymous,2014). These factors have resulted in the groundwater insome parts of the aquifer being polluted, making itnecessary to have an precise plan to prevent more damageto the groundwater resources (Anonymous, 2014). In thepresent study, rates of DRASTIC parameters werecalibrated for the specif ic area. Using nitratemeasurements in the groundwater, statistical analysis wasapplied to correlate nitrate concentration withvulnerability index and calibrate parameters rates.

Asadabad aquifer located in the west of Iran selected as acase study to show the applicability of the proposedmethod. The chosen study region is mostly included ofagricultural lands and the use of fertilizers and pesticidesarecommon inpractices.

For the purposes of this investigation, nitrate waschosen as the primary (contaminant) control parameterused to modify the DRASTIC rates. Nitrate is notgenerallypresent in groundwater under natural conditions, itusually permeates from the surface layer. It can, so, beused as an indicator to show whether the vulnerabilityindexcorrectly represents theactual situation in the studyregion. To use nitrate for optimizing the weights, indicatethat the following conditions should be satisf ied(Panagopoulos , 2006). The source of nitrate must bedue to agricultural activities at the surface. The areadistribution must be relativelyuniform leaching of nitratemust be due to recharges from the surface over a longperiod of time to ensure the correlation between humanand contamination activities the combination of arelatively shallow depth of groundwater i.e. high watertableelevation in the studyareawithagriculture being themain activity satisf ies the necessary condition to usenitrate as a calibration parameter. Nitrate concentrationsmeasured in June 2014 were used tocalibrate the indexandmeasurements in September 2014 were used to computethe correlation factor. The nitrate concentrations weredivided into 5 classes and the mean of every class was usedtocompute the modif ied rateof each DRASTIC parameterbased on the Wilcoxon rank–sum nonparametricstatistical test (SAS Institute Inc. , 1999).

TThe DRASTIC index is generally built into GeographicInformation Systems (GIS)–based maps to facilitateplanning and management of groundwater protection,where each hydrogeological setting is a mappable unitwith common hydrogeological factors. This practice,aimed to indicate areas at low, moderate or high risk ofgroundwater contamination, was applied in Japan,

et al.

Resultsand Discussion:

Figure-2: Location map of the study area in Asadabad basin

Figure-1: Methodology flowchartsfor groundwater vulnerabilityanalysis using DARSTICmodel in GIS.

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Jordan, and New Zealand. The DRASTIC method has beencriticized in the past due to its subjectivity as well as thelack of some important hydrogeological characteristicsand specif ic properties of contaminants. For example,Dixon (2004) indicated that groundwater contaminationpotential maps were more consistent with f ield data whensoil structure was taken into consideration in a studyconducted in Woodruff County in the Mississippi Deltaregion of Arkansas. Sandersen & Jorgensen (2003)explained that, even when aquifers are deep-seated andappear to be well protected, preferential flow paths fordownward transport of contaminated water from shallowaquifers may occur. Melloul & Collin (1998) suggested thatboth vertical and lateral flow play an important role ingroundwatercontamination.

Worrall & Kolpin (2004) found that interactionbetween site and chemical factors represents the mostimportant process in the occurrence of pesticides ingroundwater, based on multi-annual monitoring datasetsfrom the United Kingdom and Mid-Western UnitedStates. They indicated that the best-f it model to predictthe occurrence of herbicides in groundwater of theMid–West United States combined organic carboncontent, percentage sand content and depth to the watertable with molecular descriptors representing molecularsize, molecular branching and functional groupcompositionof the herbicides.

The attribute layers for the seven DRASTIC parameterswere assembled within a Geographic Information Systemformat, the commercially available ArcGIS 9.3 softwarebeing used to perform the necessary calculations in rasterformat. The depths to water table were measured at 30observation wells in June 2014. Figure-2 also showing thelocation of the sampled wells. Using the measurements atthese points, the two–dimensional variation in water tableelevation was constructed. The geostatistical analystextension with Kriging interpolation algorithm in ArcGISwas used to interpolate the points and create the rastermap with a pixel size of 100 m. Kriging has shown a greatsuccess for incorporation in groundwater studies(Gundogdu & Guney, 2007; Kumar, 2007; Theodossiou,1999).

Figure-3 shows depth to water table in Asadabadaquifer. Using the created maps and based on the ratingsystem recommended in the original DRASTIC model, thedepths were divided into different classes. Net recharge inthe study area is the result of rainfall influence, river flow,irrigation return flow and absorption wells. Based on awater balance calculation, the net recharge for the studyareawas noted a 272 MCM peryear. Table 4 shows thewaterbalance in the study area compute by Hamadan WaterAuthorities (Anonymous, 2014). Dispensation ofhydraulic conductivity in the study area was used tocompute the spatial distribution of the net recharge.

Hydraulic conductivity distribution map was developedusing the pumping test results and geo–electrical study inthe area. Areas with a higher hydraulic conductivity havethe higher potential for influence. Three other layers,namely, the soil the vadose zone and the aquifer–mediawere classif ied based on the drilling logs for each well andTable 3. Using the topographic map of the study areaprepared by the National Cartographic Center, a digitalelevation model (DEM) with a pixel size of 100 meters wascreated.

Transmissivity was measured in the pumping wells and,based on these measurements, hydraulic conductivity wascomputed. Geostatistical algorithms were used tointerpolate the hydraulic conductivity and create theraster layer. After creating all the necessary layers, eachpixel was classif ied and rated, then, reproduced by itsweighting factor and the DRASTIC index calculated. Theresulted index was divided into 5 equal groups (Aller .,1987). Small numbers indicate low vulnerability potentialand large numbers are related to those regions that havehigh pollution potential. Figure-4 shows originalvulnerability maps and nitrates concentration for thestudyarea.

et al

Figure-3: Depth to water tablein Asadabad aquifer

Figure-4: Originalvulnerability maps andnitrates concen-tration forstudy area

Figure-5: Modif ied (factorrating) vulnerability mapsand nitratesconcentrations

Classes ofDRASTIC

IndexVulnarability

Low

Medium

High

Depth: Watertable(values)

<10

10-20

20-30

>30

3 4

Classes ofDRASTIC

IndexVulnarability

Low

Medium

High

5



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Show the depth from the ground surface to the water table; deeper water tablelevels imply lesserchance forcontamination tooccur.

Show the amount of water which penetrates the ground surface and reaches thewatertable, rechargewaterrepresents thevehicle fortransporting pollutants.

Refers to the saturated zone material properties, which controls the pollutantattenuationprocesses.

Show the uppermost weathered portion of the unsaturated zone and control theamountof recharge thatcam inf iltratedownward.

Mention to the slope of the land surface, it dictates whether the runoff will remainon thesurface toallowcontaminantpercolationtothesaturated zone.

Is specif ic as the unsaturated zone material, it controls the passage andattenuationof thecontaminated material tothesaturated zone.

Presentation the ability of the aquifer to transmit water, hence determines the rateof flowof contaminant material within thegroundwatersystem.

Depth to water

Net Recharge

Aquifer media

Soil media

Topography

Impact of vadose zone

Hydraulic Conductivity

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A

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Table 1. Weights of the factors in the DARSTIC (Standard and Pesticides) (after Aller et al., 1987).

Factor Description DRASTIC versionRW (St.) Pesti.

Table 2. The original DRASTIC rating and weights systems(Aller et al., 1987).

Range R* Range R* Range R* Range R*

R*-

Aquifer Media Vadose Zone Material Soil Media

R* R* R*

DRASTIC Weight: 3DRASTIC Weight: 5 DRASTIC Weight: 2Pesticide Weight: 3 Pesticide Weight: 3 Pesticide Weight: 5R*-

Depth to Water Recharge Topography Conductivity(m) (mm) (m/D) (Slope %)

DRASTIC Weight: 5 DRASTIC Weight: 4 DRASTIC Weight: 1 DRASTIC Weight: 3Pesticide Weight: 5 Pesticide Weight: 4 Pesticide Weight: 3 Pesticide Weight: 2

(a)

(b)

Range Range Range

0-1.5 10 0-50.8 1 0-2 10 0.04-4.1 11.5-4.6 9 50.8-101.6 3 2-6 9 4.1-12.3 24.6-9.1 7 101.6-177.8 6 6-12 5 12.3-28.7 49.1-15.2 5 177.8-254 8 12-18 3 28.7-41 615.2-22.8 3 41-82 822.8-30.4 2 >254 9 >18 1 >82 10>30.4 1

Rating

Massive Shale Confining Layer Thin or AbsentMetamorphic/ Silt/Clay GravelIgneousWeathered Shale SandMetamorphic IgneousGlacial Till Limestone PeatBedded Sandstone, Sandstone Shrinking ClayLimestoneMassive Sandstone Bedded Limestone, Sandy Loam

SandstoneMassive Limestone Sand and Gravel, Loam

W. SiltSand and Gravel Sand and Gravel Silty LoamBasalt Basalt Clay LoamKarst Limestone Karst Limestone Muck

No shrinking Clay

Rating

2 1 103 3 10

4 3 9

5 3 86 6 7

6 6 6

8 6 5

8 8 49 9 310 10 2

1

Table 3. Criteria of the evaluation of the degrees of vulnerability(Civita and De Regibus, 1995; Corniello , 1997).

Degree of vulnerability DRASTIC index

Table 4. Groundwater balance of Asadabad aquifer in 2014.

Inflow Value Outflow Value

Table 5. Correlation factors between nitrate concentration andoriginal vulnerability index.

Pearson's Corr. Coef. Number of Data Factor

Table 6. The original and modif ied weighting rates based onnitrate concentration for the vaeious hydrogeological parameter

settings (Al-Zabet, 2002 and Aller , 1987).

Factor Range Original Mean NO3 (mgl-1) Modif iedrating concentration rating

et al.

et al.

(MCM/year) (MCN/year)

< 80 Very low80 120 Low120 160 Moderate160 200 High> 200 Very high

Recharge from river 69 Drainage from groundwater 257Recharge from rainfall 58 Discharge from wells 8Underground inflow 43 Underground outflow 3Return flow from 4 Evaporation from 4domestic Wastewater groundwaterReturn flow from 98agriculture wellTotal 272 Total 272

100% 20 Nitrate Concentration63% 20 DARSTIC index

(I)

Depth 0 1.5 10 5.31 10to ground 1.5 4.75 9 1.51 2.3water (m) 4.75 9.14 7 2.19 5

9.14 15.24 5 1.87 6.115.24 22.86 3 2.03 2.922.86 30.48 2 2 1.4>30.48 1 1.65 1.1

RW (St.)- Relative Weight (Standard); Pesti.- Pesticide




(II)

Hydraulic 0.4 4.1 1 1.7 1.23conductivity 4.1 12.3 2 3.2 3.5(m/day) 12.3 28.7 4 4.21 6.3

28.7 - 41 6 4.9 6

(III)

Recharge 0 58.8 1 2 3.1(mm) 50.8 101.6 3 2.81 2.5

101.6 177.8 6 3.2 6.5177.8 254 8 3.51 8.1>254 9 4.7 9

(IV)

Soil type Clay loam 3 2.9 3Silty loam 4 5 4.3loam 5 3.1 5.2Sandy loam 6 2.8 5.4Shrinking 7 2.2 3.1Peat 8 9.41 10

(V)

Impact Silt/clay 3 4 3.1of vadose Silty sand clay 4 6.1 4.9zone Sandstone 5 3.2 2.8

Sand & gravel 6 3.9 4.1w. siltcoarse 8 9.3 8

Pearson's corr. coeff. Number of data Factor100% 20 Nitrate index70% 20 DRASTIC index





Table 7. Correlation factors between nitrate concentration andmodif ied vulnerability index.

Conclusion:

The presented method would be useful for regional scaleevaluation though it suffers from few flaws. The resultsrepresent that there is a major need to managegroundwater resources in a sustainable way to avoidoverexploitation and contamination of groundwater.Also, allowing suff icient recharge to prevent draftexceeding the recharge. This study highlighted areas ofhigh vulnerability and medium vulnerability; wherespecial measuresshould be taken to improve theconditionof the groundwater resources. The sites for artif icialrainwater harvesting to restore the discharged resourcemust be encouraged. In addition, the sites wherecontamination can cause unrepairable damages, that canbe prevented. The highlighted areas must be monitored

extensively for further analysis. The purpose of thisresearch was to assess the vulnerability potential of theAsadabad aquifer using the original and modif iedDRASTIC index. However the DRASTIC method usuallygivessatisfactoryresults in theassessmentof groundwaterintrinsic vulnerability to pollution, it cannot be used foraccurate assessment of the groundwater pollution risk.Hence, it is necessary to calibrate and modify the originalalgorithm in order to obtain more precise results. Resultsof this study represent that nitrate concentration could beused as a modifying parameter with substantialimprovement in the resulting index that could lead tomore realistic management of groundwater quality. Theproposed method could be suggested for the agriculturalareaswhere theextensive useof nitrates iscommon, whereanaccumulationof nitrates in thegroundwater isaroutinephenomenon due to its leaching from the soil surfacelayers.

I would like to thank FallahKarimi of Urmia University for hisinsight and direction on this project, as well as Abbas uroji oftheMining Zarshuranfor his assistance in determining geologiccharacteristicsand processes.

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