环境评价的模糊方法在大气质量评价上的运用.docx

环境评价的模糊方法在大气质量评价上的运用.docx

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环境评价的模糊方法在大气质量评价上的运用

英文参考文献

Fuzzyapproachestoenvironmentaldecisions:

applicationtoairquality

BernardE.A.Fisher

abstract

Thispaperconsidersflexibleapproachestodecisionsdesignedtoimproveenvironmentalqualityhavingregardtouncertainty.Theperformanceofsimpleandcomplexmodels,forforecastingairqualityarereviewed,andbothtypesareshowntoinvolveconsiderableuncertaintyregardedastypicalofenvironmentalsystems.Thismeansthatdecisionsusuallydependoncombiningtwoormorequiteuncertainenvironmentalcriteria,anditisshownthatthiscanbeapproachedsystematicallyifafuzzylogicframeworkisadopted.Fuzzysetaggregationincludes,asspecialcases,otherdecision-makingframeworks,suchasmulti-criteriaanalysisandconventionalprobabilitybasedmethods.Examplesarepresentedofhowitcanbeappliedtosituationsinvolvingmodelsandusedtoincorporatebroaderfactorsinvolvingrisk,andsocio-economicconsiderations.

1.Theproblem

Ideallyenvironmentalmodelsshouldcontainthebestknownscience,betestedagainstmeasurements,andthenusedforpredictionanddecision-makingiftheyaresupportedbythenecessaryinputdataandperformwellagainstmeasurements.Thishasledinrecentyearstothedevelopmentofmorecomplex,fundamentalmodels,inwhicheverypartoftheenvironmentalsystemisdescribedinasmuchdetailaspossible,theso-called‘reductionist’approach.Ifthedevelopmentinmodellingproducedbetterpredictionsthiswouldbethewaytoproceed.Fromexamplesinthefieldofairpollution,itisarguedinthispaperthatlimitationsinprocessdescription,andthelackofdetaileddataonconcentrations,depositionoremissionsetc.,meanthatthisapproachhasnotproducedusefullybetterpredictions.Eventhemostadvancedmodelsarestillassociatedwithlargeuncertainties（Hunt,2000;FuntowiczandRavetz,2005;Saloranta,2001）.

Thealternativeapproachtoforecastingistoconsiderthedecisionthatislikelytobringenvironmentalimprovementsandtoconsidermodelswhichallowdecisionstobemade,so-called‘fitforpurpose’models.Thesemodelsmayinvolvegreateruncertaintythanmorecomplexmodels,buttheyfacilitatemorereadilythetreatmentofuncertainty,throughsensitivityanalysis.Moreover,thedatarequirementstorunthemarelessstringent.Theymayalsoallowbroaderfactorstobeincludedintothedecision-makingprocess,incorporatingrisk,andoptimisingsocialandeconomicfactors,etc.

Anenvironmentaldecisionoughttodependonacriterionmeetinganumericalobjectivewithuncertaintytreatedexplicitly（RoyalCommissiononEnvironmentalPollution,1998）.Onewayofincludingtheuncertaintyistoassumethatthecriterioninvolvesthemembershipofafuzzyset.Thisisanobviousapplicationoffuzzylogictoenvironmentaldecision-making.Insomecases,theenvironmentalcriterionisvagueorimprecise,suchaswhendealingwiththe‘qualityoflife’（MendesandMotizuki,2001）,therankingofecosystemsintermsofenvironmentalconditionsandimpacts（Tranetal.,2002）,orenvironmentalimpactassessment（EneaandSalemi,2001）.

Afuzzysetisageneralisationofanormalsetforwhichthereisnotasharpboundarybetweenbelongingandnotbelongingtotheset.AfuzzysetisdefinedbyitsmembershipfunctionandtwoexamplesareshowninFig.1,inwhichtwohypotheticalenvironmentalqualitycriteria（relatingtohealthandnoise）alongacross-sectionthoughacityareshown,wherexisthedistancefromthecitycentre.Inbothcasesnearthecitycentre（atsmallvaluesofx）,theenvironmentalqualityispoorer,andthereforethereisagreaterpossibilitythatenvironmentalqualityobjectivesareexceeded.Thisisshownbythehighermembershipfunctionsatshortdistancesfromthecitycentreforthefuzzysetsdescribingunsatisfactoryhealthandnoise.Iftheenvironmentalqualityweredescribedbyanormalset,themembershipfunctionwouldequal1outtothedistanceatwhichtheenvironmentalqualitywasjudgedsatisfactoryandthemembershipfunctionwouldbesetequalto0atgreaterdistances.

Qualitativefactors,involvingexpertjudgementofthepedigreeofamodel,shouldbepartofanassessmentofmodelperformance,goingbeyondtraditionalscientificapproaches,suchassensitivityanalysisandmodelvalidation（VanderSluijsetal.,2003）.Widerjudgementsarerequiredwhenreviewingtheformulationofamodelordecidingwhetheritisappropriatetouseanestablishedmodelinanovelapplication.Becauseofitshistoryoftestingmodelperformance,andtheuseofobjectivesinairqualitymanagement,airqualityprovidesagoodtestcaseoffuzzydecision-making.However,fuzzydecision-makingiscloselyrelatedtootherenvironmentaldecision-makingframeworks,suchasmulti-criteriadecisionanalysisforflooddefence（Tung,2002）,ortheweightedutilityapproachforabruptclimatechange（Perrings,2003）.Itisnotproposedthatfuzzydecision-makingissuperiortotheseothermethods,butitdoesprovideastructuredframeworkwithinwhichothermethodsmaybeincorporated.

Inthispaper,weconsiderhowconventionalapproachestoairqualitymodelscanbeextendedwithinafuzzylogicframeworktoincludewiderconsiderations,notjustthoseconcernedwiththeaccuracyofthemodel.Thesewider,generallypolicyrelated,considerationsarerelevanttohowthemodellingresultswillbeused,andhenceshouldalsobeanintegralpartofthedecision.

2.Performanceofairqualitymodels:

simpleandcomplex

2.1.Short-rangemodels

Anumberofassessmentsofdispersionmodels’performancetopredictconcentrationsovershortdistancesoutto30kmhavebeenmade.SomeofthesehavebeensummarisedbyIreland（2003）,andforurbanareasbyFisher（2005a）.Theyshowthatvarioussummarystatisticscanbeusedtojudgemodelperformance.Modelperformancealsovariesaccordingtothewayresultsareinterpreted.Forexample,forpointsourcesmuchoftheerrorcanarisefromerrorsinthepredictionofthedirectionoftheplumecentreline.Theerrorismuchsmallerifpredictedandmeasuredmaximumgroundlevelconcentrationsarecomparedwithoutregardtothelocationofthemaxima.Generally,studiessuggestthatmostpredictionsarelikelytobewithinafactoroftwoofthepredictions,asimple,usefulmeasureofperformance,butperformanceisnotconsistentlybetterthanafactoroftwo.Theuncertaintyinmodelpredictionscouldbebasedonotherestimators,suchastheresidualsumofsquares.Thefactoroftwoiseasytoexplaintodecisionmakersandwouldbeinagreementwiththeuncertaintyassociatedwithsettingtheboundaryofairqualitymanagementareas,recommendedininformalguidanceproducedbyNationalSocietyforCleanAir（2003）.

Theuncertaintyassociatedwitherrorsinmodelformulation,orwhetherthemodelisappropriatetoanapplicationaremoredifficulttodealwith.Inpart,thismaybeaddressedbyusingmorethanoneairqualitydispersionmodelinpredictions（Fisher,2003;Fisheretal.,2002）.Theoveralluncertaintycanonlybeaddressedbyrecoursetocomparisonswithmeasurementsofsuitablequalityandquantity.Thepedigreeofthemodelcanhelp（VanderSluijsetal.,2003）andwouldbringinconsiderationsastowhetherthemodelhadbeenusedforsimilarapplicationsandiftheformulationofthemodelisreadilyavailableforreviewbypotentialusers.Whenthestructureoftheenvironmentalmodelisverycomplex,whichisoftenthecaseinregionalairqualitymodels,sensitivityanalysistochoosethebestvaluesforparameters,isnotusuallyundertaken,becauseeithertheruntimesaretoolong,orthedatasetsofobservationsaretoolimitedtoevaluateeachcomponentindetail.

2.2.Regionalmodels

Thepredictionofconcentrationsanddepositionoverlongandshortdistancesusingairqualitymodelsdependsinpartontheuncertaintyintheinputvaluesfortheparametersinthemodel.Thiscanbeassessedinarelativelystraightforwardway,ifpossibleerrorsinmodelformulationareignored.Tworecentstudieshavedeterminedtheuncertaintyinmodelpredictionsforaciddeposition（Abbottetal.,2003）anddispersioncalculations（Halletal.,2000a,b）basedonsensitivitystudies,Monte-Carlosimulationandscenarioanalysis.Thedegreeofuncertaintyisagainbroadlywithinafactoroftwoi.e.changinginputvalueswithintheirboundsofuncertaintygenerallyleadstopredictionswhichliewithinarangeoftwoofacentralvalue,butpredictionsbetterthanafactoroftwocannotbeobtained.Abbottetal.（2003）havealsoshownthatthecommonlyappliedmethodsofderivingcriticalloadshaveuncertaintiesofcomparablemagnitude.

Anexampleofacomplexintegratedatmosphericmodeltocalculatelong-termannualaverageatmosphericconcentrationsanddepositionsoveraregionsuchasEurope,istheuseofModels-3.Recentresearch（Cocksetal.,2004）hasdemonstratedthatmeso-scalepollutiontransportmodels,suchasModels-3,canberuntoproducehourlytime-seriesofpollutionconcentrationanddepositionfieldsoverlongperiods,suchasayear,providingdirectcomparisonswithpercentileexceedencesofshort-termairqualityobjectives.Long-termaveragesderivedfromtime-seriesalsoprovideabenchmarkforcomparisonwitholderestablishedstatisticalaciddepositionmodels,whichmayonlybeusedtocalculatelong-termaverages.IssuesregardingthepracticaluseofModels-3forregulatoryassessmentpurposesremain,butthedemonstrationsupportstheviabilityoftheconceptofamodularassessmentframeworkincorporatingcomplexinteractions.Asanintegrated,‘reductionist’model,theresultsprovideextrainformationonquantities,suchassecondaryparticulatematter.

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