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1 w w m y wz Vol. 13, No. 4, pp. 40~53, 2008 œ w d : û 1,2 Á»k 1 Áš 1 Áš 1 *Á y 2 1w w 2 œ w w w y w A Study of Improvement for the Prediction of Groundwater Pollution in Rural Area: Application in Keumsan, Korea Beom-Keun Cheong 1,2 ÁGi-Tak Chae 1 ÁDong-Chan Koh 1 ÁKyung-Seok Ko 1 *ÁMin-Ho Koo 2 1 Korea Institute of Geoscience & Mineral Resources (KIGAM), Groundwater Environmental Group 2 Department of Geoenvironmental Sciences, Kongju National University ABSTRACT Groundwater pollution prediction methods have been developed to plan the sustainable groundwater usage and protection from potential pollution in many countries. DRASTIC established by US EPA is the most widely used groundwater vulnerability mapping method. However, the DRASTIC showed limitation in predicting the groundwater contamination because the DRASTIC method is designed to embrace only hydrogeologic factors. Therefore, in this study, three different methods were applied to improve a groundwater pollution prediction method: US EPA DRASTIC, Modified-DRASTIC suggested by Panagopoulos et al. (2006), and LSDG (Land use, Soil drainage, Depth to water, Geology) proposed by Rupert (1999). The Modified-DRASTIC is the modified version of the DRASTIC in terms of the rating scales and the weighting coefficients. The rating scales of each factor were calculated by the statistical comparison of nitrate concentrations in each class using the Wilcoxon rank-sum test; while the weighting coefficients were modified by the statistical correlation of each parameter to nitrate concentrations using the Spearman's rho test. The LSDG is a simple rating method using four factors such as Land use, Soil drainage, Depth to water, and Geology. Classes in each factor are compared by the Wilcoxon rank-sum test which gives a different rating to each class if the nitrate concentration in the class is significantly different. A database of nitrate concentrations in groundwaters from 149 wells was built in Keumsan area. Application of three different methods for assessing the groundwater pollution potential resulted that the prediction which was represented by a correlation (r) between each index and nitrate was improved from the EPA DRASTIC (r = 0.058) to the modified rating (r = 0.245), to the modified rating and weights (r = 0.400), and to the LSDG (r = 0.415), respectively. The LSDG seemed appropriate to predict the groundwater pollution in that it contained land use as a factor of the groundwater pollution sources and the rating of each class was defined by a real pollution nitrate concentration. Key word : Groundwater pollution prediction, Vulnerability, Nitrate, Rural area, GIS w d» w y (U.S. EPA) w sƒ DRASTIC (Aller et al., 1987), Panagopoulos et al.(2006)ƒ w M-DRASTIC, Rupert(1999)ƒ w LSDG û w. û w w m p w, x, m s ùkü w d» w š. DRASTIC sƒ w 149 d w ƒ w, w ƒ d l mw d z sƒw. EPA *Corresponding author : kyungsok@kigam.re.kr š : : : m : ¾ 40

2 w d : û 41 DRASTIC w,, d, m, x, sy, w w w sƒw, w w ƒ s w w dw z., 150 m DRASTIC w û ùkû. wr, M-DRASTIC DRASTIC w ù ƒ e l w. w 0.245, ƒ e w w d. LSDG m (Land use), m (Soil drainage), w (Depth to water), (Geology) p wš w s³ m w w», w ƒ LSDG w EPA DRASTIC ƒ j. M-DRASTIC LSDG d ƒw, ƒ e x xy» ƒ û dw». LSDG d ƒ LSDG m sw» q. : w d,,,, GIS 1. w ƒw w š. w ƒ sƒw š. ù w w e ƒ wš, w d» w ƒ dw. w ƒ sƒw» w š, ƒ t y (U.S. EPA) DRASTIC (Aller et al., 1987). DRASTIC y w» wù ¾ w ü w ( Á½, 1996; Á w, 1997;, 1999; y, 2001; Á y, 2004; w, 2004; Rupert, 1999; Baalousha, 2006; Panagopoulos et al., 2006; Antonakos and Lambrakis, 2007; Wang et al., 2007; Jamrah et al., 2007). (1999) y (2001), y (2004)» DRASTIC m ƒw DRASTIC w š, Rupert (1999) Panagopoulos et al.(2006) d w ƒ e w w w, Wang et al.(2007) d Ì p w s ƒ w w w. EPA DRASTIC w w sƒw» dw»., DRASTIC d w ƒ d š p w sƒw. DRASTIC w ƒ sw» DRASTIC (index) w dw. (2000) w d w sƒ w, w d s sww w. ù w w ( w 6, w w 7 2w 2) y 4 3w ³ w w w y z 2. w xy d w sw g w. š ³ š. w d w s w w w w w sƒw DRASTIC w» w d w w., DRASTIC w ƒ e w mw w DRASTIC w w ƒ w, w (Delphi method) w» w ƒ w. DRASTIC ƒ e w l w û w š (Panagopoulos et al., 2006; Rupert, 1999; Antonakos and Lambrakis, 2007),» m sww

3 42 Á»kÁš Áš Á y w d wš w ƒ w š. EPA DRASTIC Panagopoulos et al.(2006) Rupert(1999) w w d» û wš, l ü ww w d w., EPA DRASTIC w xy w d š, M-DRASTIC LSDG mw w ü ww d w. 2. w d» EPA DRASTIC, Panagopoulos et al.(2006) w M-DRASTIC, Rupert(1999) w LSDG w» w GIS vp w l, ww k sƒw. w. x m (9.7%), (10.0%), (72.9%), (4.1%) ƒ w,, / ƒ swš. w ü 5 (2002~2006 ) s k l s³ 15.6 ton s³(17.5 ton/km 2 ) û p ùkü (, 2007;, 2007) DRASTIC w ƒ sƒ w GIS œ» w DRASTIC w. DRASTIC w w ƒ (Groundwater Pollution Potential) sƒw w w eywš, w ww sƒw. DRASTIC 1987 y (U.S. EPA) yxz(nwwa) w.» DRASTIC d yw,, n (transmissivity), p (tortuosity),» sw sƒ ù, x w ƒ wš, w ƒ w e 7ƒ w k (Aller et al., 1987). DRASTIC w w ƒ w w (Depth to water), (Net recharge), d (Aquifer media), m (Soil media), x(topography), sy w(impact of vadose zone media), d (Hydraulic conductivity of the aquifer). w t w ¾ ¾, w ƒ ƒw t ƒ û» û š. t mw en sy w, ƒ t d š ƒ» ƒw š sƒ. d d, n j š. m y y w sy ty t l 1.83 m ü d m d Ëš, j»ƒ û š sƒ. x t, x j t f w enw ƒ û» û š. sy w d sy ù sy d, d É ƒ j» û š sƒ. d d k ƒ š (Aller et al., 1987). w w w sƒ w» w, ƒ (factor) (range) (rating) wš ƒ e(weight) w DRASTIC (DRASTIC index: w Di) w. Di w w s ƒw. Di ( 1). Di = r D w D +r R w R +r A w A +r S w S +r T w T +r I w I +r C w C ( 1)», r a ƒ, w a ƒ ƒ e. Diƒ w š, Diƒ û w û š sƒ. DRASTIC t l, wì d š,

4 w d : û 43 Table 1. Basic assumption of DRASTIC (after Aller et al., 1987) 1. The contaminant is introduced at the ground surface. 2. The contaminant is flushed into the groundwater by precipitation. 3. The contaminant has the mobility of water. 4. The area evaluated using DRASTIC is 100 acre(0.4 km 2 ) or larger. w w, 0.4 km 2 w» ƒ k w (Table 1).» ƒ w ƒ sƒ ù, ƒ j w w sƒ w q w w w y w š ƒ w ( ûe, 1994) M-DRASTIC M-DRASTIC Panagopoulos et al.(2006) w DRASTIC ƒ e w. (DRASTIC model, modified factor ratings: w MRD) (DRASTIC model, modified factor ratings and weight: w MRWD) r M(modified)- DRASTIC ew. EPA DRASTIC ƒ w, ƒ s w w w. ƒ, DRASTIC d w, d w s³ ƒ mg/l š, ƒ, d 10 w. y s³ ƒ mg/lƒ ù, d w 8.9 w (8.9 = 23.13/ ). ƒ e w, ƒ (Spearman's rho) mw ƒ m w sƒ wš ù w. Spearman's rho y d w m, ƒ ƒ w ù ³ s» w š, 0( ) l ± 1¾ ƒ, Spearman (rank correlation coefficient) š w ( k, 2001). m SPSS(Chicago, IL, USA) m v w w. m w ƒ ƒ ƒ e 5 w. ƒ e 5 EPA DRASTIC ƒ e ƒ. ù w mw ƒ e w. ƒ, m ƒ ƒ, m w ƒ e 5 w. w, x ƒ ù, ƒ e 4.9 w (4.9 = 0.183/ ). ƒ e m w (p value < 0.05) sww w. w EPA DRASTIC x w EPA DRASTIC w d š š (Panagopoulos et al., 2006) LSDG LSDG Rupert(1999) w w sƒ m (Land use), m (Soil drainage), w (Depth to water), (Geology) sƒ w. Rupert (1999) w e ƒ š ew ù, r LSDG š e w w. LSDG 7 DRASTIC w ƒ wš, w m ( m w), w, m w. Rupert(1999)ƒ m w t w w» w ƒ, m w e w m w. ù m t ùkü LSDG sƒ» d ƒà š. m w. m (urban), (irrigated agriculture), (rangeland), (dryland agriculture), (forest) ù. wš œ (industry) sww 4 w. m m œw 1:25,000 m xy w. wr, m y(excessive), y(well), m(moderate), (poor) w. w» w

5 44 Á»kÁš Áš Á y m m» m y (excessive), y(well), (Moderately poor) w. w, Rupert(1999) w 0~100, 101~300, 301~600, 601~900, 900 v p 5 ù. w š w 0~5, 5~10, 10~20, 20 m 4 w. Rupert(1999) m, m, w w ù, (Geology) p ƒw, z, d, y, y,, 6 w. LSDG w s³ m w ƒ wš ƒ e w w. Rupert(1999) s³ w m g w (Wilcoxon rank sum test) w. g w g w l w t j» w, m w ƒ w (Nonparametric test). d e w š d e w m w, w ƒ ù» ƒ ³ s ù m» ù ùkü ( z, 2004). m SPSS (Chicago, IL, USA)m v w ww. ƒ w ƒ m w ù wš, w ùkü w w. Rupert(1999) Idaho w sƒ ww š, 3 ƒ ƒ w ùkû LSDG sƒ w ùkü sƒ» w 3ƒ sƒ w» w buffer overlay analysis( w BOA ) w (Alley, 1993). BOA l buffer e xwš, w w. mw yw w e ü DRASTIC, M-DRASTIC, LSDG ƒ ƒƒ w w w. buffer j» w» w 50, 100, 150, 200, 250, 350, 650, 750, 850, 1000 m buffer w w, ƒ ùkù 150 m buffer w w. w 149 yw mw w. š sw, 50 m w d ww. w» ƒ w ¾ w z x d ww. x, ph,» (EC), (DO) d w, ph DO d» (calibration) ww. e y w d w,, ph k sx w - HCO 3 w. (Na +, K +, Ca 2+, Mg 2+, HCO 3, Cl, SO 2 4, NO 3, F ) w w y IC (Dionex ICS-1500) w w, ICP-AES w w. w³x(charge balance) m w w š, w t w t (CRM: Certified Reference Material) w ww û 50% w wš w sƒ w d w ƒ (, 2007; y, 2008). ( / ) r, ( sw) 98.2%, (,, sw) 67.9%, 40.3%, 38.4%, 22.6%, 12.2% 50% š. y w wš. w d w w sƒ w ƒ v w. w wš s ƒ š sw, sƒ w d ww. 127 o 38' 03" ~ 127 o 19' 19", 35 o 58' 32" ~ 36 o 16' 06" ew, 576 km 2. x p w š 904 m š (878 m),» (537 m), (537 m)

, s³ w š 250 m (Fig. 1). s³» 11.8 o C š, s³ 1,300 mm. d y, e» r, d,» y,» n,» y, 4» d (Fig.

6 w d : û 45 Fig. 1. Relief and geographic map of the study area. Fig. 2. Geology map of the Keumsan area (modified from Hong and Choi, 1978; Hong et al., 1980)., s³ w š 250 m (Fig. 1). s³» 11.8 o C š, s³ 1,300 mm. d y, e» r, d,» y,» n,» y, 4» d (Fig. 2, y yá, 1978; y y, 1980). e» r r, d z, y z,, z.» y y,» y, z,, ³ (y yá, 1978; y y, 1980). «wš,,, 4 «. y, ü,, š,, w x. 3. š 3.1. DRASTIC w w x d w d ƒ w w

7 46 Á»kÁš Áš Á y Fig. 3. DRASTIC map of Keumsan area: (a) Depth to water, (b) Aquifer media, (c) Soil media, (d) Topography, (e) Impact of vadose zone, (f) hydraulic Conductivity. w w. ArcGIS œ» ƒ e(inverse Distance Weight, IDW) w ü w w w w š(fig. 3a), w

8 w d : û 47 s d ûš, ¾ p ùkþ. w 4, ƒ Table (1999) w (water balance analysis) w,»»» d d 30 (1971~2000 ) s w. w» w v w Turc w š, Thornthwaite x w., ü w 254 mm 9 ùkû d d w 1 : 50,000 w. d j /y, z, d, k 3 w t w (Fig. 3b). ƒ Table 2. d z w y» /y w m m w 1 : 25,000 m ( œ: ) w ww. m m 54, m m y 117 m m. m 7 š,, s sw ùkû (Fig. 3c), ƒ Table x x ArcGIS vp w 1: 25,000 e x š w ³e ƒ (Triangulated Irregular Network, TIN) w etš (Digital Elevation Model, DEM) w z, (%) w (Fig. 3d). 66%ƒ 18% ùkþ, p w š w. k 5 w, ƒ Table 2. Table 2. Original rating, areal percent, and ranges of DRASTIC factors of the study area Depth to water (m) Factors Rating Area(%) Ranges < < 5 Net Recharge (mm/yr) more than 254 Aquifer Media Soil Media Topography (%) Impact of Vadose zone media Hydraulic Conductivity of the aquifer (GPD/ft 2 ) Metamorphic/Igneous Limestone Alluvial Rivers/Reservoir Clayey Soils Clay Loam Silty/Silty Clay Loam Sandy Loam Sand Gravel more than Metamorphic/Igneous Limestone Alluvial sy w ù m d sw» w w s y w ( m, 2005). j /y, z, d 3 š(fig. 3e), ƒ Table 2. sy s d s w ùkû d d w p w (Freeze and Cherry, 1979). 3 š (Fig. 3f), ƒ Table 2. sy ƒ d s s.

48 Á»kÁš Áš Á y 3.1.8. s s³ 23.3 mg/l, 0.04 mg/l 113.6 mg/l ù kü.» (44.3 mg/l) w 21 14% w w (Fig. 4), 0.8% 156.5% ùkþš, ew š. 3.2. DRASTIC sƒ ƒ ƒ e w z, œ s 50 50 m w DRASTIC w.

9 48 Á»kÁš Áš Á y s s³ 23.3 mg/l, 0.04 mg/l mg/l ù kü.» (44.3 mg/l) w 21 14% w w (Fig. 4), 0.8% 156.5% ùkþš, ew š DRASTIC sƒ ƒ ƒ e w z, œ s m w DRASTIC w. Di s³ 133, 130, ùkü (Fig. 5a). d z Diƒ ùkû, w ù s³ w. d z p y sƒ» w ƒ, DRASTIC 7 Fig. 4. Distribution of nitrates concentrations of alluvial groundwater from Keumsan area. Fig. 5. Result of index map: (a) EPA DRASTIC, (b) MRD, (c) MRWD, (d) LSDG..

10 w d : û 49 Table 3. Original and modified ratings for DRASTIC factors of the study area Factors Ranges Original rating Mean NO 3 (mg/l) Modified rating Depth to water (m) 30 < < net Recharge (mm/yr) more than Aquifer Media Soil Media Topography (%) Impact of Vadose zone media Hydraulic Conductivity of the aquifer (GPD/ft 2 ) Metamorphic/Igneous Limestone Alluvial Rivers/Reservoir 0 0 Clayey Soils Clay Loam Silty/Silty Clay Loam Sandy Loam Sand Gravel more than Metamorphic/Igneous Limestone Alluvial d, sy w p» ùkü š. wr, û Diƒ ùkù z p Di ƒ, w ü z ùkùš. w BOA ( 150 m) w Di w. ƒ û ùkû.» w EPA DRASTIC w k Diƒ sƒ» BOA û ùkü M-DRASTIC M-DRASTIC MRD MRWD ƒ, w Table 3 w., w w ù w DRASTIC ƒ ùkû. ƒ d sy, DRASTIC p d, z, y / w ù, y / s³ (23.1 mg/l) z (16.9 mg/l) ùkù M-DRASTIC d, y /, z ë š,. 50 m ü d w DRASTIC w» d, sy, d p ùkü d p, q j w. wr m x»

11 50 Á»kÁš Áš Á y DRASTIC ùküš, ƒ œ s, m sp» w. w w MRD (Modified rating DRASTIC index: w MRDi) s³ 190.4, 193.2, ùkþ (Fig. 5b). MRDi Di s³, w ƒ 10 EPA DRASTIC. EPA DRASTIC ƒ d MDRi, z û MRDi. z y / û. w, ƒ s³ MDRi s ƒ m p, ƒ». EPA DRASTIC ƒ BOA w ww š, ƒ EPA DRASTIC MRWD (Modified rating and weight DRASTIC index: w MRWDi) s³ 49.8, ùkü (Fig. 5c). MRDi j ƒ e» w, m (ƒ e 5.0) x (ƒ e 4.9) š ù sƒ». ƒ e Table 4 š, ( 2). MRWDi = 5S + 4.9T ( 2)», S soil media, T topography ƒƒ. MRDi ƒ d MRWDi š, û MRWDi (Fig. 5c). ƒ û ( 5) 18% w». BOA w, EPA DRASTIC š, MRDi Panagopoulos et al. Table 4. Original and modified weight of the DRASTIC factors and correlation coefficients between DRASTIC factors and nitrates concentration DRASTIC factors Original weight Spearman's rho Modified factor coefficient weight Depth to groundwater Recharge 4 Aquifer type Soil type * 5.0 Topography * 4.9 Impact of the vadose zone Hydraulic conductivity *: p < 0.05 where p is the statistical significance level Table 5. Calibrated ratings, areal percent, and range of LSDG factors of study area Factors Rating Area(%) Ranges Landuse Depth to water (m) Geology Forest Dryland agriculture Irrigated agriculture Urban < < Phyllite (Og2) Metasedimentary rock (Og1) Quartz porphyry (Qp) Granite (Gr) Alluvial (Qa) Limestone (Ls) (2006) m w ƒ m w 5 ƒ sƒ, ƒ» ƒ ùkû š q. w w e w ƒ LSDG LSDG w ƒ w Fig. 6, Table 5. ƒ m w ƒ w š, j. ƒ, m g w s³

12 w d : û 51 Fig. 6. Box-Whisker diagram of correlations between concentrations of nitrate and each factors : (a) Landuse, (b) Soil drainage, (c) Depth to water, (d) Geology. Table 6. Correlation coefficients between indices of EPA DRASTIC, M-DRASTIC, and LSDG within 150 m buffer and log NO 3 concentration Model definition Correlation coefficient Step correlation improvement Improvement from EPA DRASTIC EPA DRASTIC MRD MRWD LSDG p % ƒ ùk ûš,, p ƒƒ % ƒ ùk û. 1 w š, ù ƒ š, ƒ ùkù 2 w. m w ƒ œ m w» w ù ƒ sƒ š š, ( 3). LSDGi(LSDG index) = r L w L +r D w D +r G w G ( 3)», r a ƒ (a), w a ƒ ƒ e. 3 w, LSDGi s³ 17, 5 24 (Fig. 5d). LSDGi skw d ùkù,

13 52 Á»kÁš Áš Á y Table 7. Summary of advantage and disadvantage of each method Methods Advantage Disadvantage DRASTIC MRD MRWD LSDG Low cost for collecting data sets This method can be applied to wide area The interrelationships among the parameters decreases the possibility of ignoring some important parameter Statistically accurate because of reducing error while it calculate index using many parameters This method can be applied to complicated geology Rating includes the pollution status Relatively simple and perspicuous statistics are applied Pollution prediction improved from DRASTIC Weighting includes the pollution status Relatively simple and perspicuous statistics are applied Pollution prediction improved from DRASTIC Rating includes the pollution status Relatively simple and perspicuous statistics are applied Potential pollution sources (land use) are included Pollution prediction improved from DRASTIC Less parameters reduces error and difficulties Too many parameters Difficult to applied to particular hydrologic setting which is controled by specific parameter The selection of parameters is based on qualitative judgement Some parameters affecting contaminant transport can be disregarded This method can overestimate the vulnerability in alluvial groundwater Difficult to evaluate by pollution status The pollution prediction can be ineffective because it includes only hydraulic parameters Few case studies This method can over and under-estimate the pollution prediction where lack of groundwater quality data There will be differences between theoretical rating (DRASTIC) and realistic rating (MRD) Rating cannot be applied generally Few case studies Important parameters affecting contaminant transport can be disregarded Oversimplified Few case studies Oversimplified Rating cannot be applied generally /, w 10 m ü, y, z, d sw». ƒ w BOA w LSDGi, w DRASTIC w 0.357, MRD w 0.170, MRWD w 0.015ƒ (Table 6). 4. w ƒ d ƒ» EPA DRASTIC š wš w» w Panagopoulos et al.(2006) w M-DRASTIC(MRD MRWD ), Rupert(1999) w LSDG û w w dw EPA DRASTIC l wš w. ƒ ƒ d w z, w ƒ ƒ BOA mw ww š, EPA DRASTIC» ù ƒ y w. DRASTIC LSDG ƒ š, MRD 0.245, MRWD ƒƒ 0.187, EPA DRASTIC w š, M-DRASTIC LSDG ƒ ƒ, m k k w w d ùkû. w d w M-DRASTIC ù LSDG xy w ù m sw DRASTIC w ww. ù xy w w» w ƒ ƒv wš ƒ e ƒ eƒ. M-DRASTIC LSDG w» w mw ƒ e w t yƒ v w., ü w ƒ

14 w d : û 53 ü v w. ƒ Table 7 w. w sƒù w d w w,, sy, w m z y.» ƒ w M-DRASTIC ù LSDG w x k w w dw w. ù yw w y w w, w xy ƒ w. p M-DRASTIC LSDG ƒ w ƒ w. m w ü ww ã w, yw» y w w ƒ y w. w» w y» ( ) y w. m w» œ. š x m, w œ,» œ, 2005, ½w w» š., 2007, m 2007., 2000, w d» w,, p. 320., 2007, m 2007., ½ y,, ½, 1999, w w», w w y wz, 6(2), k, 2001, x» m w w, w,, p y, 2001, GIS w» yû2 w d w, w wz, 4, ûe, 1994, w ƒ sƒ» DRASTIC, y wz, 27, , ½, 1996, DRASTIC SYSTEM w w ƒ x, w GISwz, 4, 1-11., w, 1997, GIS» w Ÿ w sƒ, w w y wz, 4, z, 2004, z m w,,, p. 526.,, ûe,, 1999, GIS w» s k w ƒ sƒ, w w y wz, 6, , y, 2004, w d w GIS y, w wz, 7, w,, ½, ½, yw, 2004, DRASTIC w w w sƒ, y, 37, y y,, ½, 1980, w (1 : 50,000) s,. y y,, 1978, w (1 : 50,000) s,. y, 2008, 2006 m ( ), y w,, p Aller, L., Bennett, T., Lehr, J.H., Petty, R.J., and Hackett, G., 1987, DRASTIC: A standardized system for evaluating ground water pollution potential using hydrogeology settings, USEPA, USEPA Document, EPA-600/ Alley, W.M., 1993, Regional groundwater qaulity, Van Nostrand Reinhold, New York. Antonakos, A.K. and Lambrakis, N.J., 2007, Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates, based on the drastic model, an example from NE Korinthia, Greece, Journal of Hydrology, 333, Baalousha, 2006, Vulnerability assessment for the Gaza Strip, Palestine using DRASTIC, Environ Geol, 50, Jamrah, A., Al-Futaisi, A., Rajmohan, N., and Al-Yaroubi S., 2007, Assessment of groundwater vulnerability inthe coastal region of Oman using DRASTIC index method in GIS environment, Environ Monit Assess, DOI /s Panagopoulos, G.P., Antonakos, A.K., and Lambrakis, N.J., 2006, Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical methods and GIS, Hydrogeol. J., 14, Rupert, M.G., 1999, Improvements to the DRASTIC Ground- Water Vulnerability Mapping Method, USGS, USGS Fact Sheet, FS Wang, Y., Merkel, B.J., Li, Y., Ye, H., Fu, S., and Ihm, D., 2007, Vulnerability of groundwater in Quaternary aquifers to organic contaminants; a case study in Wuhan City, China, Environ. Geol., 53,

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