Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field

Transcription

1 Relablty Evaluaton of Groundwater Contamnaton Source Characterzaton under Uncertan Flow Feld Mahsa Amrabdollahan and Bthn Datta Abstract Groundwater contamnaton s one of the serous envronmental problems. Effectve remedaton strateges requre accurate characterstcs of contamnaton sources. Contamnaton source dentfcaton approaches need accurate flow and contamnant transport smulaton models. In order to obtan relable solutons, the smulaton models need to be provded wth relable hydrogeologc nformaton. In real lfe scenaros usually sparse and lmted hydrogeologc nformaton s avalable. In ths study two hydraulc conductvty samplng networs are raned based on ther effectveness n dentfyng relable contamnaton source characterstcs. Usng multple realzatons of hydraulc conductvty felds, and the locaton and sze of the contamnant plume at dfferent montorng stages, an ndex of relablty s estmated for each hydraulc conductvty samplng networ. It s demonstrated that the source characterstcs dentfed by utlzng the samplng networ wth hgher ndex of relablty results n more accurate characterzaton of contamnaton sources. Therefore the developed methodology provdes a tool to select an approprate hydrogeologc samplng networ for more effcent characterzng of contamnaton sources. Index Terms Groundwater contamnaton, uncertanty, hydraulc conductvty, relablty. I. INTRODUCTION Groundwater contamnatons can result from napproprate ndustral operatons, waste dsposal, and mnng actvtes. The two most common real-world scenaros whch need to be assessed precsely n contamnated groundwater systems are: 1) exstng groundwater contamnaton from past actvtes, and ) possble contamnaton that may rse from proposed future actvtes [1]. Characterzaton of contamnaton plumes and desgnng effectve remedaton plans requre an accurate characterzaton of contamnaton sources. The (1) locaton, () actvty duraton, and (3) magntude of njected pollutant fluxes are three mportant characterstcs whch enable engneers and managers to delneate and control contamnaton plumes. The contamnaton sources are characterzed usng an optmzaton method whch ams to mnmze the dfference between smulated and measured contamnant concentratons at montorng locatons. The smulaton model should replcate the actual mgraton Manuscrpt receved Aprl 5, 014; revsed July 18, 014. Ths wor was supported n part by the CRC for Contamnaton Assessment and Remedaton of Envronment, Australa, and James Coo Unversty, Australa. The authors are wth the CRC for Contamnaton Assessment and Remedaton of Envronment, Mawson Laes, Australa, and James Coo Unversty, Townsvlle, Australa (e-mal: mahsa.amrabdollahan@my.jcu.edu.au, bthn.datta@jcu.edu.au). of contamnants n a groundwater aqufer. The groundwater hydrogeologc parameter values and boundary condtons are essental nputs to the smulaton model. However, the hydrogeologc parameter values are nherently uncertan and mprecse, and acqurng data s a tme consumng and cost extensve procedure. Therefore, a new methodology s developed to evaluate the relablty of the avalable hydrogeologc nformaton wth respect to ts contrbuton to the accuracy of contamnant source characterzaton process. Amrabdollahan and Datta [] presented an overvew on pollutant source dentfcaton technques and dscussed some of the relevant ssues n ths area. The lned smulaton optmzaton technque characterzes the contamnaton sources by an nternal lnage between flow and contamnant transport smulaton models and the selected optmzaton technque [3]-[5]. The soluton of water flow and transport model requres the nowledge of varous sol hydrogeologc parameters as well as the determnaton of boundary condtons, whch are subjected to dfferent sources of uncertanty. Tedeman and Gorelc [6] studed the model parameter uncertanty (hydraulc conductvty and recharge factor) n the desgn of clean up strategy n a Vnyl Chlorde contamnated system. In a Long Term Montorng (LTM), the uncertanty n the hydrogeologcal condton needs to be addressed to be able to trac successfully the contamnaton plume. Mugunthan and Shoemaer [7] evaluated the effcency of the LTM desgns by ther performance n smultaneously nterpolatng many equally lely plume confguratons that may be possble for a gven set of hydrogeologcal data. Ther model mnmzes the montorng nstallaton, samplng, and analyzng costs. The optmzaton s constraned by the relatve error n the estmaton of total mass over all grd ponts at whch the nterpolaton was performed. Amrabdollahan and Datta [8] studed the effect of hydrogeologc parameter value uncertanty n optmal characterzaton of contamnaton sources. An uncertanty analyss s a vtal component of the contamnant characterzaton whch feeds the rs analyss and the economc targets. In ths study the un-modeled uncertanty assocated wth hydrogeologc parameter value s addressed. The hydraulc conductvty uncertanty has major mpact on the accuracy of the flow and contamnant transport models. A new framewor s developed to evaluate the mpact of the adopted hydraulc conductvty samplng networ on the accuracy of contamnaton source characterzaton model. Therefore, the acqured nformaton about the accuracy of dentfed source characterstcs can be used to desgn low rs remedaton plans. The followng sectons dscuss: the lned smulaton-optmzaton contamnant source dentfcaton methodology; the model DOI: /IJESD.015.V

2 for analyss of relablty of avalable flow feld nformaton (sampled hydraulc conductvty data); and, the performance evaluaton of the proposed methodology for an llustratve study area. It s followed by the dscusson of soluton results and conclusons. II. THE LINKED SIMULATION OPTIMIZATION CONTAMINATION SOURCE IDENTIFICATION The source dentfcaton model conssts of an optmzaton algorthm whch swtches the nverse source characterzaton process to a forward smulaton model. The optmzaton model generates canddate unnown source fluxes (decson varables) whch are utlzed to estmate resultng contamnant concentratons at montorng wells n a forward smulaton model. Fnally the optmal soluton s obtaned by mnmzng the dfferences between observed and smulated values. The objectve functon for the optmzaton problem s defned as follow. n nob ( ob ob) / ( ob ) (1) 1ob1 Mn F Cest Cobs Cobs Subject to: Cest f ( D, K,, x, y, z, q ) 1,..., N () max 0 q q 1,..., N (3) where n, nob and N are the total number of concentraton observaton tme perods, avalable montorng locatons and canddate source locatons, respectvely. Cest ob and Cobs ob are the concentraton estmated by the smulaton model and the observed concentraton at the observaton locaton ob and at the end of tme perod, respectvely. q s the contamnant release flux for the canddate locaton. qmax s the upper bound for contamnant release fluxes. α s a constant and t should be suffcently large so that errors at low concentratons do not domnate the soluton [5]. The objectve functon s constraned by the flow and transport smulaton models (Eq. ()). Eq. (3) lmts the canddate contamnant flux values, at each potental locaton, to an upper bound. The Adaptve Smulated Annealng (ASA) optmzaton algorthm s utlzed n ths study. Smulated Annealng (SA) starts from a feasble soluton and an objectve functon. A new soluton s randomly selected from ts neghbors and the objectve functon s evaluated for the new selected soluton. If the new soluton has a better objectve functon value, the most recent soluton s accepted and the search moves to a new pont and contnues from there. If the new soluton s not better than the current one, the new soluton may or may not be accepted dependng on the acceptance probablty. The acceptance probablty s strongly nfluenced by the choce of a parameter T. ASA s a varant of SA n whch the algorthm parameters that control the temperature schedule and random selecton are automatcally adjusted accordng to the algorthm progress. Ths maes the algorthm more effcent and less senstve to the user defned parameters requred to be estmated n SA [9]. In ths study the avalable spatally sparse hydraulc conductvty parameter values are nterpolated to the whole aqufer usng an nterpolaton method. The flow and transport smulaton models use a set of nterpolated conductvty data to estmate the contamnated concentraton at the montorng locatons. The choce of hydraulc conductvty samplng locatons affects the relablty of flow and transport smulaton models. III. RELIABILITY EVALUATION A. Generaton of Hydraulc Conductvty Feld An nterpolaton algorthm estmates the spatal dstrbuton of parameter values. Usng a lmted avalable number of hydraulc conductvty data ponts, dfferent realzatons of a hydraulc conductvty feld can be estmated. Usually one sngle nterpolaton technque does not wor well for all smulatons. The geostatstcal nterpolaton algorthms provde a framewor for the ncorporaton of the spatal varablty. However, they are computatonally demandng, and also to obtan accurate results they requre a large number of data ponts wth nown values. Mugunthan and Shoemaer [7] compared the effcency of three nterpolaton algorthms: Inverse squared Dstance weghtng (ID); Ordnary Krgng (OrK); and Quantle Krgng (QK). The ID s a smple determnstc method, whereas OrK and QK are non-determnstc geostatstcal methods. They showed that the OrK and ID almost perform equally well. However, the ID method was chosen over OrK due to the ease of computaton. Followng the result of Mugunthan and Shoemaer [7] and consderng the fact that n real lfe usually a lmted number of data ponts wth nown values are avalable, the ID method s utlzed. Note that Krgng needs a carefully selected sample varogram and an approprate log-transformaton of the data. To acqure the accurate statstcal propertes of data, a substantal number of measurements s requred. However, usually n real contamnated stes the number of avalable hydraulc conductvty measurements s lmted (compared to the sze of study area) and not enough to accurately estmate the statstcal propertes of hydraulc conductvty dstrbuton for the entre study area. Usng the ID method, the value of varable Z at the un-sampled locaton x 0, * Z ( x 0 ), s estmated based on the data from the surroundng locatons, Zx ( ), as Eq. (4). n * Z ( x ) w Z( x ) (4) 0 1 where w are the weghts related to the each Zx ( ) value and n s the number of the closest sampled data ponts used for the nterpolaton purpose. The weghts are estmated usng Eq. (5). 513

3 w 1/ d n 1 1/ d where d s the dstance between the estmated pont and the sample. Usually n s selected based on the spatal correlaton between the avalable data ponts. However, to have an accurate estmaton of the statstcal correlaton, a substantal number of measurements s requred. Therefore, usually n actual study areas, the n value s selected based on the experence and judgment of the decson maer. Based on the decson of the n value, dfferent realzatons of the hydraulc conductvty feld can be estmated from a gven set of samplng locatons. In ths study for the evaluaton purpose, four realzatons of the hydraulc conductvty feld s generated for each set of samplng locatons. The ID nterpolaton algorthm nvolves generatons of equally lely flow condtons usng 5, 50, 75, and 100 percent of the actual avalable data ponts as the number of closest neghbors ncluded n the nterpolaton. B. Hydraulc Conductvty Uncertanty Calculaton Contamnants are njected from sources at unnown tmes and are spread over a groundwater aqufer. The accuracy of spatal and temporal estmates of the concentratons depends on the accuracy of the hydraulc conductvty feld. The ndex of spatal uncertanty and varablty of a hydraulc conductvty feld s estmated usng Eq. (6). where, j,, j, and,, j R 1, j, ( ) R 1, j, (5) (6) are the hydraulc conductvty value at locaton, j, and usng the η th realzaton and the average hydraulc conductvty value at locaton, j, and usng all realzatons, respectvely. R s the total number of realzatons., j, s estmated for all cells n a fnte dfference dscretzed study area. C. Contamnaton Plume The contamnaton plume boundares are dentfed usng the avalable contamnant montorng concentratons. The ID method usng all avalable concentraton measurements estmates the spatal concentratons at any gven tme. To ensure realstc spatal estmates, a threshold value s requred to be consdered to defne the plume boundary at montorng tmes. The plume boundary as defned by a threshold concentraton magntude s estmated wth respect to the measured contamnant concentratons at any gven tme. At each montorng tme stage, the measured concentratons are nterpolated throughout the aqufer usng the ID nterpolaton algorthm. Then the lower twenty fve percentle value of all nterpolated or measured concentratons s defned as the threshold for the plume boundary at a gven tme. The fnte dfference numercal method s used to estmate the contamnant concentratons. Any partcle of contamnant starts mgraton from the contamnaton source. At any gven locaton, the uncertanty n the hydraulc conductvty value at that specfc locaton affects the accuracy of the estmated concentraton. The naccuracy generally propagates along the flow drecton due to the transport of the contamnants over the aqufer. In ths way the naccuracy s propagated through the mgraton of the plume over the whole aqufer. D. Relablty Estmaton The ndex of relablty for a selected hydraulc conductvty samplng locatons s estmated usng the characterzed contamnant plumes and ndces of spatal uncertanty and varablty of hydraulc conductvty. The salent steps n the proposed methodology are descrbed below: Step 1: The ndces of spatal uncertanty and varablty of conductvty are estmated for all dscretzed cells usng Eq. (6). Step : The montorng tme stage counter s set to 1 (=1). Step 3: The measured contamnant concentratons at tme stage are nterpolated throughout the study area (C,j,.) Step 4: The lower twenty fve percentle value of the nterpolated or measured concentratons s defned as the plume boundary threshold (λ). Step 5: For all cells, f C, j,, j, 1 otherwse, j, 0 t Step 6: For all t f, j, =1 then, j, =1. Step 7: Increase the tme counter by 1. If n (n n the total number of montorng tme stages), repeat steps 3 to 6 for each montorng tme stage. Step 8: Estmate the relablty ndex (µ) usng Eq. (7). n Nrow Ncol Nlay, j,, j, (7) 1 1 j1 1 1/ ( ( )) where Nrow, Ncol, and Nlay are the number of rows, columns and layers of the fnte dfference dscretzed study area. µ s estmated for any gven set of hydraulc conductvty samplng locatons. The samplng networ wth hgher relablty ndex s expected to result n hgher accuracy n estmaton of contamnant source characterstcs. IV. PERFORMANCE EVALUATION The method for estmaton of the relablty ndex for a gven hydraulc conductvty samplng networ s demonstrated n a three-dmensonal hypothetcal contamnated aqufer. Performance of the developed methodology s evaluated usng a hypothetcal study area and synthetc hydraulc conductvty data. An advantage of usng synthetc data, for the evaluaton purpose, s that the actual source characterstcs used to smulate the aqufer responses and also the hydrogeologc data are nown, whch allows for testng of the developed methodology, ndependent of feld 514

4 data relablty. A. Study Area The study area s 1500 m long, 1000 m wde and 30 m deep. It s dscretzed nto 30 rows, 0 columns and two layers. The plan vew of the study area s llustrated n Fg. 1. The top, bottom and left sde boundares have specfed heads, and the rght hand sde one has varable head boundary condtons. The locaton of actve extracton wells (sns), the canddate contamnaton source locatons, and 9 montorng wells are shown by trangular sgns, square sgns, and numbers, respectvely. The hatched boxes show two hydraulc conductvty samplng networs. Two of the contamnaton sources are actve and actual and one s dummy (not actual source). The contamnant fluxes are specfed constant n every stress perod. The study perod s dvded nto fve stress perods. Table I shows the length of stress perods, and the extracton wells, and contamnant sources propertes. Contamnaton Source Flux (g/day) Extracton well Flow rate (L/day) TABLE I: CHARACTERISTICS OF THE CONTAMINATION SOURCES AND EXTRACTION WELLS Locaton Stress Perod Row Column Layer days 183 days 183 days 183 days 196 days Dummy Source (Constant Head Boundary H=5 m) 1500 m S S S 3 Constant Head Boundary H=39.5 m Extracton Wells; Contamnaton Sources; Montorng 1 Wells; and Hydraulc Conductvty samplng networs A and B, respectvely. Fg. 1. Study area. In the evaluaton process, t s assumed that all the aqufer hydrogeologc parameter values are nown wthout any error except the hydraulc conductvty (K). Table II shows the aqufer parameters and characterstcs. B. Flow Feld The flow feld s analogous to the real hydrogeologc condton n feld. The actual hydraulc conductvty values are generated randomly throughout the study area, consderng a heterogeneous hydraulc conductvty feld. The study area s dvded nto grds each 50 m long, and 50 m wde for the purpose of specfyng hydraulc conductvty values. Consderng a two-layer three-dmensonal model, for each locaton two K values correspondng to the 10 m and 0 m depths are requred. A realstc presentaton of porous medum can nclude a hydraulc conductvty feld dstrbuted as a Log-Normal functon through space [10]. When the K s log-normally dstrbuted and Y=log K, then the parameter Y can be generated from a normal dstrbuton functon wth mean Y and standard devaton Y. A truncated Latn Hypercube Samplng (LHS) s utlzed to produce more effcent estmates than those obtaned from random samplng of the dstrbuton functon. In the LHS the probablty dstrbuton functon s dvded nto non-overlappng, equal-probablty ntervals. The sample s taen from each nterval and permuted n a way that the correlaton of the feld s accurately presented [11]. The samplng s truncated to the values whch are wthn (0.6 Y, 1.4 Y ) range. For the values located at the depth of 10 m, the mean and standard devaton are 0 m/day and 15% of the mean, respectvely. The dstrbuton functon utlzed for the 0 m depth has a mean value of 15 m/day, and the standard devaton s

5 tmes mean value. Usng the Krgng nterpolaton technque, the actual hydraulc conductvty feld for the evaluatons s generated. Fg. shows the K values for the aqufer frst layer. C. Hydraulc Conductvty Samplng Networ Two sets of hydraulc conductvty samplng locatons are consdered. Assumng the budgetary lmtatons, 0 samples are collected n both cases. However, dfferent samplng locatons are selected for each networ. In Fg. 1, samplng networs are shown. The blac boxes located on the boundares show Samplng Networ A, and the red boxes located wthn the study area, show Samplng Networ B. The plan vew shows, 10 locatons for each networ. Note that samples are collected for both layers at two depths (10 m and 0 m). Therefore, n total 0 samples are collected n each case. locatons, then analyzed and correspondng hydraulc conductvty values are obtaned. In ths example, the llustratve study area (Fg. ) s the representatve of the actual feld. Therefore, the hydraulc conductvty measurement values correspondng to the samplng locatons are obtaned usng the hydraulc conductvty feld as descrbed n the secton Hydraulc Conductvty Feld. The flow smulaton model (MODFLOW) requres hydraulc conductvty values at all the fnte dfference dscretzed cells. The ID nterpolaton algorthm s utlzed to generate the entre hydraulc conductvty felds usng data collected at samplng locatons A and B (Fg. 3). As Fg. 3 shows, the nterpolated conductvty felds usng samplng networ A and B are not dentcal. Although, both hydraulc conductvty samplng data are collected from one actual feld, dfferent nterpolated values are generated. The dfferences among Fg., Fg. 3(a) and Fg. 3(b) demonstrate the source of un-modelled uncertanty n flow smulaton model. In real aqufers, precse hydrogeologcal characterstcs are not avalable. Therefore, the only typcal avalable data are as the felds shown n the Fg. 3(a) and Fg. 3(b). Fg.. The actual hydraulc conductvty feld, frst layer (unt s m/day). (a) (a) (b) Fg. 4. Index of spatal uncertanty and varablty of hydraulc conductvty. (, j, ): (a) samplng networ A, (b) samplng networ B. (b) Fg. 3. Generated hydraulc conductvty felds usng the ID nterpolaton algorthm and n=0. (unt s m/day): (a) usng samplng networ A, (b) usng samplng networ B. In real lfe cases, samples are collected at selected TABLE II: AQUIFER PARAMETERS AND CHARACTERISTICS Length (m) 1500 Wdth (m) 1000 Depth (m) 30 Porosty 0.5 Longtudnal Dspersvty (m) 35 Horzontal Dspersvty (m) 3.5 Vertcal Dspersvty (m) 0.35 Specfc Storage (m -1 ) 0. D. Index of Spatal Uncertanty and Varablty of Conductvty At ths stage four realzatons for each samplng networ A 516

6 and B, are generated. The realzatons are obtaned as solutons of the ID nterpolaton algorthm mplementaton usng n= 5, 10, 15 and 0 (Eq. (4)). Then the ndex of uncertanty and varablty of conductvty s estmated usng Eq. (6). Fg. 4 shows the estmated ndex values obtaned usng samplng networ A and B. E. Contamnaton Plume The 9 contamnaton montorng locatons are shown n Fg. 1. The contamnant samples are collected at 16 tme stages (every 6 months). The measured concentratons at each montorng stages are nterpolated usng ID nterpolaton algorthm. As the result, the contamnant concentraton s estmated throughout the aqufer. analyss s for the methodology evaluaton purpose only. Usng the ASA based lned smulaton-optmzaton source dentfcaton algorthm (Eqs. (1-3)) the contamnant source characterstcs are dentfed. The lned smulaton-optmzaton was executed two tmes usng samplng networ A and B one at a tme. Usng both samplng networs, the dentfed fluxes for source at all stress perods s zero. It shows that usng data from both networs, the locatons of actual sources are dentfed accurately. Therefore, both models correctly dentfed the actual source locatons. The Normalzed Absolute Error of Estmaton (NAEE%), computed usng Eq. (8), s utlzed to quantfy the error n the estmated source fluxes. SP 1 q estmate NAEE% 100 SP 1 q q actual actual (8) Fg. 5. Contamnaton plume 183 days after source actvaton. Fg. 5 shows the contamnaton plume 183 after the actvaton of sources. The maxmum estmated concentratons at ths tme stage s 84 (mg/l). The plume threshold s 1 (mg/l). Note that for the purpose of uncertanty quantfcaton, the contamnant source characterstcs are unnown and the only avalable nformaton s the concentratons measured at montorng locatons. F. Relablty ndex The contamnaton plumes at 16 montorng perods are utlzed to estmate, j, for = 1: 30; j=1: 0; =1: and =1: 16. The relablty ndex s estmated usng Eq. (7). Followng the steps n Fg. 1, the estmated uncertanty ndex (µ) for samplng networ A and B are and , respectvely. Therefore, usng hydraulc conductvty samplng networ A wll result n more accurate dentfed source characterstcs. The source characterstcs dentfed usng samplng networ A are more relable for desgnng contamnaton management or remedaton plans n ths study area. V. RESULTS AND DISCUSSION The developed methodology was utlzed to ran two hydraulc conductvty samplng networs wth respect to ther effectveness n dentfy relable contamnant source characterstcs. It was concluded that networ A outperforms networ B. Snce the llustratve study area was utlzed for the performance evaluaton, the actual contamnant source characterstcs are avalable (Table I). Note that ths nformaton s not avalable n real felds and the followng where estmate q and actual q are the estmated and actual source fluxes for stress perod, respectvely. SP s the total number of stress perods. The estmated NAEE% for source 1 and 3 are defned n Table III. The estmated NAEE% values confrm the results obtaned by estmated relablty ndex (µ). The samplng networ A outperforms samplng networ B by 5.66% and 13.64% for source 1 and 3, respectvely. TABLE III: THE NORMALIZED ABSOLUTE ERROR OF ESTIMATION (%) AND INDEX OF UNCERTAINTY Index of Relablty Source 1 Source usng Sample Networ A usng Sample Networ B In ths study, the utlzed contamnant montorng locatons were selected arbtrarly. The proposed methodology has the potental for applcaton to desgn montorng networs dedcated to the contamnaton source dentfcaton. The montorng locatons can be selected n the regons where the level of uncertanty n the flow feld s low. The smultaneous desgn of hydraulc conductvty samplng locatons and montorng networ can be used to decrease the uncertanty n the contamnaton source dentfcaton. VI. CONCLUSION Ths study presents a methodology to ran the relablty of hydraulc conductvty samplng networs n reducng uncertanty n contamnaton source characterzaton. In contamnated groundwater aqufers, the source of polluton s unnown n terms of locaton, actvty duraton, and flux. Moreover, lmted feld hydraulc conductvty nformaton s generally avalable due to the budgetary constrants. In ths study multple realzatons of a hydraulc conductvty feld for dfferent samplng networs s utlzed. Then the ndex of relablty for each selected hydraulc conductvty samplng 517

7 networ s estmated. Ths ndex s shown to be correlated to the accuracy of contamnaton source characterzaton. The contamnaton source dentfcaton model whch utlzed the hydraulc conductvty data wth hgher ndex of relablty s expected to delver more accurate results. The developed methodology provdes the decson maers wth a tool to select an effectve hydraulc conductvty samplng networ to reduce the uncertanty assocated wth lac of adequate hydrogeologc nformaton. The reducton n contamnaton source dentfcaton uncertanty wll eventually decrease the cost of management and remedaton plans, and ncrease the relablty of any decson taen on management of the contamnated aqufer. ACKNOWLEDGMENT We acnowledge the fnancal support for ths wor provded by CRC for Contamnaton Assessment and Remedaton of Envronment (CRC-CARE), Australa, and by James Coo Unversty, Australa. REFERENCES [1] R. A. Freeze, The role of stochastc hydrogeologcal modelng n real-world engneerng applcatons, Stochastc Envronmental Research and Rs Assessment, vol. 18, no. 4, pp , 004. [] M. Amrabdollahan and B. Datta, Identfcaton of contamnant source characterstcs and montorng networ desgn n groundwater aqufers: an overvew, Journal of Envronmental Protecton, vol. 4, no. 5A, pp. 6-41, 013. [3] M. M. Aral, J. Guan, and M. L. Masla, Identfcaton of Contamnant Source Locaton and Release Hstory n Aqufers, Journal of Hydrologc Engneerng, vol. 6, no. 3, pp. 5-34, 001. [4] B. Datta, D. Charabarty, and A. Dhar, Smultaneous Identfcaton of unnown groundwater polluton sources and estmaton of aqufer parameters, Journal of Hydrology, vol. 376, no. 1-, pp , 009. [5] P. S. Mahar and B. Datta, Optmal dentfcaton of ground-water polluton sources and parameter estmaton, Water Resources Plannng and Management-ASCE, vol. 17, no. 1, pp. 0-9, 001. [6] C. Tedeman and S. M. Gorelc, Analyss of uncertanty n optmal groundwater contamnant capture desgn, Water Resources Research, vol. 9, no. 7, pp , [7] P. Mugunthan and C. A. Shoemaer, tme varyng optmzaton for montorng multple contamnants under uncertan hydrogeology, Boremedaton Journal, vol. 8, no. 3-4, pp , 004. [8] M. Amrabdollahan and B. Datta, Identfcaton of pollutant source characterstcs under uncertanty n contamnated water resources systems usng adaptve smulated anealng and fuzzy logc, Int. J. of GEOMATE, vol. 6, no. 1, pp , 014. [9] L. Ingber, Adaptve smulated annealng (ASA): Lessons learned, Control Cybern., vol. 5, no. 1, pp , [10] R. A. Freeze, A stochastc-conceptual analyss of one-dmensonal groundwater flow n nonunform homogeneous meda, Water Resources Research, vol. 11, no. 5, pp , [11] Z. Doou and G. F. Pnder, Optmal search strategy for the defnton of a DNAPL source, Journal of Hydrology, vol. 376, no. 3, pp , 009. Mahsa Amrabdollahan got the M.Sc. degree n cvl engneerng n 007, and M.Sc. degree n water resources management-cvl engneerng n 010. She s currently a PhD student n James Coo Unversty, Townsvlle, Australa. She studes the effect of hydrogeologc uncertanty on groundwater contamnaton source dentfcaton methods. Her major research areas nclude groundwater hydrogeology, groundwater contamnaton, optmzaton technques, and uncertanty analyss. Bthn Datta got the PhD degree from Purdue Unversty, U.S. He has served as a postdoctoral researcher at Unversty of Aransas, U.S.A. Unversty of Washngton, Seattle, U.S.A and Unversty of Calforna, Davs, U.S.A. from 1986 to Now, he wors n the Cvl Engneerng Department at James Coo Unversty. Dr. Datta s nternatonally recognzed n the feld of water resources management. Hs research s n the U.S. durng the early part of hs career and later at I.I.T. Kanpur, Inda. Currently he has been devoted to methodology development for solvng complex and dffcult large scale problems related to water resources management n James Coo Unversty, Australa. He has collaborated wth many researchers from countres le U.S.A., Canada, Denmar, Inda, and Australa. He was been assocated wth the research capacty buldng at CRC-CARE at Unversty of South Australa snce 007 and also served n ts annual revew commttee. He has also contrbuted nternatonally to research gudance and tranng as a vstng professor n a number of Unverstes of repute, ncludng Denmar Techncal Unversty, Dalhouse Unversty, Halfax Canada, and Asan Insttute of Technology, Bango, Thaland. He has also served at the leadng Engneerng Unversty n Inda, I.I.T. Kanpur, as a professor and the head of Cvl Engneerng Department from 004 to