Designing an optimal multivariate geostatistical groundwater quality monitoring

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IntroductionInenvironmentalmonitoringsuchasgroundwaterqualityinvestigations,thecollecteddatamayharborsignificantuncertainty,includingcomplexorextremelycomplicatedvariationsintheobservedvaluesofmea-surablecharacteristicsoftheinvestigatedmediumorpollutionsourcesintimeandspace.Giventhehighcostandrisksassociatedwithsuchinvestigations,develop-

mentofefficientproceduresfordesigningandadjustinginformation-effectivemonitoringnetworksisanessen-tialtaskformoreaccuratelyunderstandingthespatialdistributionorvariationsofmonitoringvariables.Therefore,theinformationgeneratedbysuchoptimalmonitoringnetworksshouldprovidesufficient,butnotredundantinformationtofullyunderstandthespatialphenomenaofmonitoringvariablesortheirvariations.Thesenetworkscanbeusedtocharacterizenaturalre-

Ming-ShengYehYu-PinLinLiang-ChengChang

Designinganoptimalmultivariate

geostatisticalgroundwaterqualitymonitoringnetworkusingfactorialkrigingandgeneticalgorithms

Received:19September2005Accepted:10January2006Publishedonline:18March2006ÓSpringer-Verlag2006

AbstractTheoptimalselectionofmonitoringwellsisamajortaskindesigninganinformation-effectivegroundwaterqualitymonitoringnetworkwhichcanprovidesufficientandnotredundantinformationofmonitoringvariablesfordelineatingspatialdistributionorvariationsofmonitoringvariables.Thisstudydevelopsadesignapproachforanoptimalmultivariategeostatisticalgroundwaterqualitynetworkbyproposinganetworksystemtoidentifygroundwaterqualityspatialvariationsbyusingfactorialkrigingwithgeneticalgorithm.Thepro-posedapproachisappliedindesigningagroundwaterqualitymonitoringnetworkforninevari-ables(EC,TDS,Cl),Na,Ca,Mg,

SO4

2),MnandFe)inthePingtung

PlaininTaiwan.Thespatialstruc-tureresultsshowthatthevario-gramsandcross-variogramsoftheninevariablescanbemodeledintwospatialstructures:aGaussianmodelwithranges28.5kmandaspherical

modelwith40kmforshortandlongspatialscalevariations,respectively.Moreover,theninevariablescanbegroupedintotwomajorcomponentsforbothshortandlongscales.Theproposedoptimalmonitoringdesignmodelsuccessfullyobtainsdifferentoptimalnetworksystemsfordelin-eatingspatialvariationsoftheninegroundwaterqualityvariablesbyusing20,25and30monitoringwellsinbothshortscale(28.5km)andlongscale(40km).Finally,thestudyconfirmsthattheproposedmodelcandesignanoptimalgroundwatermonitoringnetworkthatnotonlyconsidersmultiplegroundwaterqualityvariablesbutalsomonitorsvariationsofmoni-toringvariablesatvariousspatialscalesinthestudyarea.

KeywordsGroundwaterqualityÆMonitoringnetworkdesignÆFactorialkrigingÆOptimizationÆSpatialVariationÆPingtungplainÆTaiwan

EnvironGeol(2006)50:101–121DOI10.1007/s00254-006-0190-8ORIGINALARTICLE

M.-S.YehÆL.-C.ChangDepartmentofCivilEngineering,NationalChiaoTungUniversity,Hsinchu30010,Taiwan

Y.-P.Lin(&)DepartmentofBioenvironmentalSystemsEngineering,NationalTaiwanUniversity,1Section4RooseveltRoad,Taipei10617,TaiwanE-mail:yplin@ntu.edu.twTel.:+886-2-23686980Fax:+886-2-23635854sourcesforthemanagementofresourcesortodelineatepollutedareaandvariationforremediationandriskassessment.Geostatistics,aspatialstatisticaltechniqueusedinenvironmentalmonitoring,iswidelyappliedtoanalyzeandmapdistributionsofconcentrationsandvariationsinspaceandtime.Geostatisticsusesvariogramstocharacterizeandquantifyspatialvariability,performrationalinterpolation,andestimatethevarianceintheinterpolatedvalues.Avariogramquantifiesthecom-monlyobservedrelationshipbetweenthevaluesofdata,pertainingtothesamples,andthesamples‘proximity.Kriging,ageostatisticalmethod,isalinearinterpolationprocedurethatprovidesabestlinearunbiasedestimator(BLUE)forquantitiesthatvaryspatially.Recently,kriginghasbeenwidelyusedtoanalyzeandmapthespatialvariabilityanddistributionofinvestigateddatainmanyfields.Multivariategeostatisticalmethods,suchasfactorialkriging,combinetheadvantagesofgeosta-tisticaltechniquesandmultivariateanalysis,whileincorporatingspatialortemporalcorrelationsandmultivariaterelationshipstodetectandmapdifferentsourcesofspatialvariationondifferentscales(Lin2002).Factorialkrigingisavariantofkrigingwhichaimsatestimatingandmappingthedifferentsourcesofspatialvariabilityidentifiedontheexperimentalvario-gram(Goovaerts1992and1998).ExamplesoffactorialkrigingstudiesincludeGoovaerts(1994),GoovaertsandWebster(1994),Dobermannandothers(1995),EinaxandSoldt(1998),Jimenez-EspinosaandChica-Olmo(1999),Bocchietal.(2000),Castrignanoetal.(2000a,b),Batistaandothers(2001)andLin(2002).Inmonitoringnetworkdesignstudies,manyresearchershaveconsideredgeostatisticalapproachestodesigningoradjustingenvironmentalmonitoringsys-temsandquantifyingtheinformationalvalueofmoni-toringdataandtheirvariations,forexample,Rouhani(1985),RouhaniandHall(1988),ChristakosandOlea(1988),Loaiciga(1989),HudakandLoaiciga(1993),Benjemaaetal.(1994),Pestietal.(1994)andWangandQi(1998).Recently,Brusetal.(1999)usedageostatis-ticalsamplingschemetodiscusssamplingsizeandpointsforestimatingthemeanextractablephosphoruscon-centrationoffields.VanGroenigenetal.(1999)extendedspatialsimulatedannealingwiththekrigingmethodtooptimizespatialsamplingschemesforobtainingtheminimalkrigingestimationvariance.Lark(2000)usedfuzzyandkrigingmethodstodefineasamplingschemefordesigningsamplinggridsfromimpreciseinformationofsoilvariability.PrakashandSingh(2000)appliedkrigingvariancereductiontodesignagroundwatermonitoringnetwork,aswellaslocationsofadditionalwellsfrompredefinedlocations.Basedonthevariancereductionmethod,LinandRouhani(2001)havedevel-opedamultiple-pointvarianceanalysis(MPV),whichutilizesboththemultiple-pointvariancereduction