Dynamic site-dependent Life Cycle Assessment for assessing impact of human toxicity of a double glazed PVC window

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Dynamic site-dependent Life Cycle Assessment for assessing impact of human toxicity of a double glazed

PVC window

Patrice Megange, Pierre Ngae, Amir-Ali Feiz, Thien-Phu Le

To cite this version:

Patrice Megange, Pierre Ngae, Amir-Ali Feiz, Thien-Phu Le. Dynamic site-dependent Life Cycle Assessment for assessing impact of human toxicity of a double glazed PVC window. Procedia CIRP, ELSEVIER, 2020, 27th CIRP Life Cycle Engineering Conference (LCE2020)Advancing Life Cycle Engineering : from technological eco-efficiency to technology that supports a world that meets the development goals and the absolute sustainability, 90, pp.316-321. �10.1016/j.procir.2020.02.056�.

�hal-02915108�

ContentslistsavailableatScienceDirect

Procedia CIRP

journalhomepage:www.elsevier.com/locate/procir

27thCIRP LifeCycleEngineering (LCE)Conference

Dynamic site-dependent Life Cycle Assessment for assessing impact of human toxicity of a double glazed PVC window

Patrice Megange

, Pierre Ngae, Amir-Ali Feiz, Thien-Phu Le

LMEE, Univ Evry, Université Paris-Saclay, 91020 Evry cedex, France

a rt i c l e i nf o

Keywords:

human toxicity by inhalation traditionnal Life Cycle Assessment spatial-temporal specificities dynamic LCA

coupling

a b s t ra c t

Currentlevel ofmorbidity generated byindustrialairpollutionrequires effectivetoolscombiningpol- lutantratesandtheirrisksonhumanhealth.TraditionalLifeCycleAssessment(LCA)appearstobethe mostappropriatemethodforassessingimpactofhumantoxicitybyinhalation.However,itsinterestis limitedduetolackofspatial-temporalspecificitiesofemissions.Tosolvethisproblem,thisresearchwork showsthefeasibilityofasite-dependentdynamicLCA,obtainedbyacouplingbetweenatraditionalLCA, amethodoftemporalandspatialdisaggregationofpollutionratesandamodelofnumericaldispersion.

© 2020TheAuthor(s).PublishedbyElsevierB.V.

ThisisanopenaccessarticleundertheCCBY-NC-NDlicense.

(http://creativecommons.org/licenses/by-nc-nd/4.0/)

Introduction

Air pollution is considered to be greatest health risk related toenvironment(WorldHealthOrganizationReport2019).Inorder tocontrol thresholdofstigmatizedparticulates(PM10andPM2.5) andgases(sulfurdioxide(SO₂)andnitrogenoxides(NO_X)),many countriesadoptmajor regulations anduse efficient andsophisti- catedtechnologies tomeasure their rates (Crunaire and Spinelle, 2018). Inview ofhealth emergency,communicationofthesecol- lecteddatamustbeallpartofhealthimpactstudy(Merlin,2015).

Therefore,priorityofmanufacturersistouseeffectivesystemsfor preventingandevaluating impact ofhumantoxicity produced by airpollutants.Variousenvironmentalimpactassessmenttoolsare effectivesuchasCarbonFootprintortraditionalLifeCycleAssess- ment (LCA).The latterbenefits from its skill to assess global is- sues (resourceconsumption) and numerous impacts (greenhouse effect,eutrophication,ozonedepletion,etc.).Nevertheless,itshigh degree of abstraction (Marchand et al., 2013) and its static ap- proach make it inappropriate to accurately assess local impacts suchashumantoxicity(Pottingetal.,1999,Causseetal.,2016).A waytoresolvethisproblemisthedynamicLCAwithitsabilityto integrate temporaldimension ofprocesses andphysical phenom- ena(Negishi etal., 2018,Shimakoetal., 2017, Beloin-Saint-Pierre etal., 2014). Moreover, the need to get data on many sites and

✩Peer-review under responsibility of the scientific committee of the 27th CIRP Life Cycle Engineering (LCE) Conference.

∗ Corresponding author.

E-mail address: p.megange@iut.univ-evry.fr (P. Megange).

the importance of local specificities in pollutant emissions can’t be neglected(Aissani,2008). Indeed,somecharacteristics suchas emissionheight,topography,meteorology,architectureofbuildings modifyprogressionofthesepollutantsinatmosphereandsotheir impactonenvironment.Thus,numericalmodelingprovesveryin- terestingasthechemical equationsandatmosphericdispersionof pollutantsaresolved intheentiredomain(Michelotetal.,march 2015).Duration ofemission and its latencywhich modify nature ofthetoxiceffects(Querini andRousseaux,2012)arealsoimpor- tant informationtoreduce uncertaintiesandincrease accuracyof assessmentofhumanhealtheffects(Couillet,2002).Inthispaper, theproposedmethodprovidesamuchmorerobustdeviceinorder toassesstheimpact ofhumantoxicityby inhalationofairpollu- tants.Itsprincipleisbasedonthecouplingofinterestingtools:

• The dynamicLCA with the Enhanced Structural Path Analysis (ESPA)method(Beloin-Saint-Pierreetal.,2014)

• Thenumericalmodelingofthedispersionofpollutantswitha Gaussianmodel

• The contextualized computational philosophy of the USEtox model(Rosenbaumetal.,2011).

StateoftheartofLifeCycleAssessment

LCAisoneofthemostusedtechniquestoidentifyandquantify effects ofemissionsof toxicsubstances onenvironment. It is as- sociatedwithallstagesofaproduct(fromrawmaterialextraction toendoflife,recyclingorreuse) andisgovernedbyISO(Interna- tionalOrganizationforStandardization)14040-14044,illustratedin Fig.1,whichdescribesitsprinciplesandframework.

https://doi.org/10.1016/j.procir.2020.02.056

2212-8271/© 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Fig. 1. Synoptic view of ISO 14040-14044

Nevertheless, in order to simplify calculation, traditional LCA doesnottakeintoaccountsingularitiesofsites.Itleadstolossof informationconcerningterritorialspecificitiesforsystemmodeling andassessment oflocalimpacts such ashuman toxicity (Aissani, 2008, Boize et al., 2008). Therefore, to improve its results tradi- tional LCA uses a consensual model: USEtox (Rosenbaum et al., 2011). Its effectiveness lies in the fact that USEtox offers many typesofscenariosdependingonthecompartment(air,water,soil, etc.) andthe affectedarea (urban,continental,global) while tak- ingintoaccountcomplexitiesrelatedtodispersionofatmospheric pollutants(advection,diffusion,etc.).However,coupledwithtradi- tional LCA,USEtoxremains a staticmodelwhile relevance ofdy- namicapproachissuggestedinmanystudies[(Negishietal.,2018, Shimakoetal.,2017),and(Beloin-Saint-Pierreetal.,2014)].

Dynamic LCA is defined asintroduction of time parameter in traditional LCA (Collingeet al., 2013). In thisway, aggregation of flowswhichareemittedseparatelyisavoided(Aissani,2008)anda morerealisticanalysisofgeneratedimpactsisobtained(Collingeet al.,2013).TemporaldimensioncanbeintroducedduringLifeCycle Inventory(LCI)phase[(Shimakoetal.,2017,Beloin-Saint-Pierreet al.,2014,Aissani,2008),and(Beloin-Saint-Pierre,2012)]aswellas duringtheLifeCycleImpactAssessment(LCIA)phase[(Negishi et al.,2018,Shimakoetal.,2017,Levasseuretal.,2011),and(Kirkinen etal.,2010)].Unfortunately,mostofthesedynamicLCAsseemtoo oftenspecificforone typeofindicatorandlackconceptualization (Negishietal.,2018).Nevertheless,twoapproachesstandout:

• DynamicprocessofLCA(DyPLCA)(Negishietal., 2018,Beloin- Saint-Pierre,2012). Its maingoal isto develop amethodology andtoolsto addresstimedependencyinLCA,withafocuson developinganintegratedmodelingsolutionforbothLifeCycle Inventory(LCI),inforegroundandbackground,andforLifeCy- cleImpactAssessment(LCIA).Althoughitisaturnkeytool,ac- cordingto us, it minimizestherole played by fatefactor(FF) (Shimakoetal.,2017):acrucialfactortoconsiderspatialspeci- ficitieswhilecalculatingimpactofhumanto

• EnhancedStructural Path Analysis(ESPA) method.This imple- mentation of a dynamic LCI development methodology iden- tifies technological fields and proposes a two-axis approach:

spatialandtemporal.Thisapproachguidedourchoice forthis method.

MethodologyofDynamicLCAsite-dependent

When determining human toxicity, resulting from the inhala- tionoftoxicsubstances,itisnecessarytoevaluateforeachtypeof substancethedoseinhaledperday,foranindividualorforapop- ulation,accordingto thesourcenearorfarfromtheplace ofex-

posure.Thepresentedmethodologyinthispapermakesitpossible tocomputethedistributionintimeandspaceofthepollutingsub- stancesemittedusingESPAmethod.Thetransportanddiffusionof thesesubstancesto thepopulations’ exposuresites iscarriedout usingaGaussian solutionofthetransport-diffusionequation.The dailydoseinhaledby thepopulations(USEtox,etc.)isthen deter- minedbyspatial-temporalintegration.

By coupling the ESPA method, the Gaussian atmospheric dis- persionmodelandtheUSEtoxmodel,humantoxicityisthusesti- mated.

1.1. ESPAmethod

This chapter explains how the ESPA method, described in (Beloin-Saint-Pierreetal., 2014) and(Shimakoetal., 2017), isad- justedto obtain temporally andspatially differentiated LCI.Thus, for a given pollutant substance "x", the temporal distribution of emittedmassisobtainedatdifferentsites"k",m_x,k (t).Theinven- torydistributionvector(ofthespatialandtemporalemittedmass distribution)isgivenbythefollowingequations(1)and(2):

v

I+T(^t)^+T2(^t)^+...+Tp(^t)

∗r(^t) ⁽¹⁾

where:

• "∗"representsthematrixconvolutionproduct

• E_x(t)isanl×ninterventiondistributionmatrixofemittedpol- lutant flow(fe_i) byeach process.l isthetotalnumberofsites andnisthetotalnumberofelementaryflows

• Iisann×nidentitydistributionmatrix

• T(t) is a n × n technological distribution matrix ofeconomic flow(fp_i,jissuppliedtoprocessPiandmanufacturedbyprocess Pj)

• T^p,p-timesconvolutiondistributionmatrixproduct

• r(t)isan×1referencedistributionvectorofthefinal process flow(fp₀)

• vx(t)isanl×1inventorydistributionvector

v

mx,1(^t)^mx,2(^t)^mx,3(^t) ^{... m}x,l(^t)

(2) 3.2.Gaussianmodelofatmosphericdispersion

Gaussianplume dispersion modelis usedinthisstudyforlo- cal assessment of air pollutant concentrations (Equation 3). This papertakes intoaccount numerous hypotheses such asunidirec- tionalwindfieldinspace,andsteadystate.Thelastassumptionis justifiedbecauseitisconsideredthatthedistributiontimestepis large comparedto the transport-diffusiontime in thedomain. In thisstudy,theplume dispersion isjustan alternativeto thepuff dispersion.Hereweareonlyinterestedinthedispersionofpollu- tantsduetoatmosphericturbulenceandthespatial-temporalvari- ationofmeteorologicalparameters.

C_x,k(^x,y,z)⁼₂

π

σ

σ

−(^y−y0)²

2σy²

e ⁻

(z−H)² 2σz²

+e ⁻

(z+H)² 2σz²

(3)

where:

• C_x,k (x,y,z) (kg/m³): concentration ofthe substance "x" in site

"k"atacoordinatepoint(x,y,z)

• Qx,_k (kg/s):substance"x"flowrateemittedfromthestackorthe extractunitisgivenbyequation(4):

Q_x,k=

mx,k(^t)

nnz × ^{t (4)}

where:nnzisanumberofnon-zeroelementofm_x,k (t).^tisthe timestepofmx,_k(t)

• U_k(m/s):averagelocalwindspeed

• σ^y

σ^z}(^m)^{: standard deviationsof theGaussian} distribution from itslocationdeterminedbytheempiricaldispersioncoefficients ofBriggsinurbanareas.

3.3.USEtoxmodelofcalculationofhumantoxicityimpacts

USEtox is the consensus model for impact assessment of hu- mantoxicity and eco-toxicity. By integratingtemporal parameter into coefficient m_x,k(t) and taking into account spatial specifici- tiesofemission andreceptionzone ofpollutants retainedduring calculation of characterization factor, an impact score of spatial- temporalhumantoxicitybyinhalation(IS_hk(t))isthereforedeter- mined(Equation5):

IS_hk(^t)⁼

x

CF_x,k×m_x,k(^t) ⁽⁵⁾

where CF_x,k is the Characterization Factor (Comparative Toxic Unit/kgemitted)ofthesubstance"x"determinedinasite"k".CF_x,k isgivenby:

CF_x,k=

IF_x,k

FF_x,k×XF_x,k×EF (6)

where:

• FF_x,k:FateFactor(day)ofsubstance"x",inasite"k"

• XF_x,k:Exposurefactor(day⁻¹)ormassfraction(orvolumefrac- tion)ofasubstance"x"absorbed(directlyorindirectly)bylocal populationperday

XF_x,k is determined using Equation (7), considering, for this study, only one environmental medium (outside air) and one singleabsorptionprocess(inhalation):

XF_x,k=IR_air×N_k

V_k (7)

where:

• IR_air (m³/(person.day)):Individualhuman consumption rateof air;byaverageIR_air =13m³/(person.day)

• N_k(persons):numberofpersonsinselectedarea"k"

• V_k= Area ofthe location × height ofthe urban area (240m highaccordingtoUSEtox):volume(m³)ofairinthesite"k"

• EF:toxicologicalEffectFactorofachemicalonhumans

• IF_x,k:IntakeFractionofemittedmasscomingintocontactbyair withthelocalhumanpopulation.

• FF_x,kisdeterminedbytheEquation(8): FF_x,k= M_x,k

Q_x,k (8)

where:

• M_x,k(kg):isthemassofsubstance"x"availableinsite"k",cal- culatedusingEquation(9):

Mx,k=

Cx,k

×Vk (9)

In order to carry out a site-dependent dynamic LCA, the methodologyusedinthisstudyisbasedontwostepsillustratedin Fig.2),asacomplementtophasesimposedby ISO14040-14044;

botharecarriedoutbytwosoftwares,OpenLCAandMATLAB:

• Step 1: making a spatialand temporal disaggregationof sub- stanceemission ratesduringLCIphase:couplingbetweentra- ditionalLCAandESPAmethod.

• Step2:usinganumericalmodelofdispersion(Gaussianmodel forthisstudy)inordertointroducespatialspecificitiesforthe computationofFateFactor(FF)andIntake Fraction(IF)during LCIAphase:couplingbetweenUSEtoxmodelandGaussiannu- mericalmodel.

Fig. 2. Methodology of Dynamic LCA site-dependent Resultsanddiscussions

Thisresearchworkpresentsamethodintroducingthetemporal dimensiontoperformadynamicsite-dependentLCAintheinven- toryphase ofthetraditionalLCA(phase2)ofISO14040-14044.It helpscomparingtheuseofasite-dependentdynamicLCAtoatra- ditionalLCAtocarryouttheassessmentofhumantoxicityimpact inaneco-designapproach.

Theproduct studiedis adoubleglazedwindow andthefunc- tional unit, inspiredby the Technical Sheet of the Environmental andHealthDeclaration(FDES),isdefinedby:tocloseapermanent opening of 1 m²in an exterior wall, while allowing the passage oflight,theopening/manualclosing,thermalinsulation,sealing, windresistance,airpermeabilityandacousticinsulation,inaccor- dancewithgoodpractice.

Thegeographicalboundary chosenisFrance.Theresourcecra- dle respects the processes given by the Ecoinvent 3.4 database, usedforthisstudy,andthemanufacturingprocessesillustratedin Fig.3.ThegraveoftheoutputsisconsidereddependingonEcoin- vent system modelCut-Off (the point of cut-off isat the endof the activityproducing therecyclable material). The substance"x"

selectedforthisstudyisnitrogenoxide(NO_X).

Regarding the manufacturing phase, the window components notselectedforthisstudyare:

• Thethermalinsulationgasbetweenthetwopanes

• Packagingelementssuchasplasticfilm,cardboard,polystyrene, andsoon

• Installationelementssuchasfixinglugs,seals,andsoon A first prospection work made it possible to define the vari- ous manufacturingprocesses,presentedonfig.3,aswell astheir geographicalsite,fig.4.Therefore,themaincitiesselectedandim- pactedbypollutantemissionsareParisinIle-de-France;Strasbourg intheGrandEst region; LilleintheHautsde FranceandLyonin

Fig. 3. Manufacturing process of a double glazed PVC window

Fig. 4. Manufacturing regions of a double glazed PVC window Table 1

Temporal distribution of the process flows

double gazed PVC window (day “0” : end of assembly)

day -38 … -18 … -4 … 0

fp 0(t) (m ²) 0 … 0 … 0 … 1

fp 0,1(t) (kg) 0 … 0 … 11.45 … 0

fp 0,2(t) (kg) 0 … 0 … 1.32 … 0

fp 0,3(t) (kg) 0 … 0 … 0.23 … 0

fp 0,4(t) (kg) 0 … 0 … 0.21 … 0

fp 0,5(t) (kg) 0 … 0 … 0.08 … 0

fp 0,6(t) (kg) 0 … 0 … 20.24 … 0

fp 1,7(t) (kg) 0 … 1 … 0 … 0

fp 2,8(t) (kg) 0 … 1 … 0 … 0

fp 3,9a(t) (kg) 0 … 1 … 0 … 0

fp 4,9b(t) (kg) 0 … 1 … 0 … 0

fp 5,10(t) (kg) 0 … 1 … 0 … 0

fp 6,11(t) (kg) 0 … 1 … 0 … 0

theAuvergne-Rhône-Alpesregion.Thecolorindicationusedforthe processisassociatedwiththatofitsregion.

ToresolvetheStep1(fig.2),Table1givesthevaluesofprocess distributionflows,fp_i,j(t)(Fig.3),includedinT(t).T(t)ispresented inEquation(10).

T(^t)⁼

⎡

⎢ ⎢

⎢ ⎢

⎢ ⎢

⎢ ⎢

⎢ ⎢

⎢ ⎢

⎢ ⎢

⎢ ⎢

⎣

0000000000000 fp0,1(^t)^000000000000 fp0,2(^t)^000000000000 fp0,3(^t)^000000000000 fp0,4(^t)^000000000000 fp_0,5(^t)^000000000000 fp0,6(^t)^000000000000 0fp1,7(^t)^00000000000 00fp_2,8(^t)^0000000000 000fp3,9a(^t)^000000000 0000fp4,9b(^t)^00000000 00000fp_5,10(^t)^0000000 000000fp6,11(^t)⁰⁰⁰⁰⁰⁰

⎤

⎥ ⎥

⎥ ⎥

⎥ ⎥

⎥ ⎥

⎥ ⎥

⎥ ⎥

⎥ ⎥

⎥ ⎥

⎦

(10)

Table 2

Temporal distribution of the elementary flows of NO X

double gazed PVC window (day “0” : end of assembly)

day -38 … -18 … -4 … 0

f e 0(t) (kg) 0 … 0 … 0 … 3,27

f e 1( t) (kg) 0 … 0 … 5.50E-03 … 0

f e 2(t) (kg) 0 … 0 … 1.54E + 00 … 0

f e 3(t) (kg) 0 … 0 … 2.68E-01 … 0

f e 4(t) (kg) 0 … 0 … 2.45E-01 … 0

f e 5(t) (kg) 0 … 0 … 8.49E-02 … 0

f e 6(t) (kg) 0 … 0 … 1.65E-03 … 0

f e 7(t) (kg) 0 … 3.95E-03 … 0 … 0

f e 8(t) (kg) 0 … 1.04E-02 … 0 … 0

f e 9a(t) (kg) 0 … 3.12E-04 … 0 … 0

f e 9b(t) (kg) 0 … 2.88E-04 … 0 … 0

f e 10(t) (kg) 0 … 3.06E-04 … 0 … 0

f e 11(t) (kg) 0 … 8.90E-02 … 0 … 0

Fig. 5. .

The elementary distribution flows fe_i(t),see Table 2, are built usingOpenLCAdatabase,Ecoinvent3.4.

The ESPA method uses convolution product on temporal and spatialdimensionbetweendifferentelementsof equation(1). An algorithm written underMATLAB performs calculations. Thus, all elements of matrices and vectors are "discretized" in time and space.Figures 5a, 5b,5c and5d show the results obtainedwith DynamicLCA.Emissionratesaremuchlowerthanthesinglevalue obtainedwith a traditional LCA (Fig. 6). Indeed, the huge differ- enceisduetothefactthattraditionalLCAperformsthesumofthe emissionratesgeneratedbymanufacturingprocesseswhichdonot takeplaceinoneplace(Fig.3andFig.4)andinasingleday.Con- sequently,the dynamic LCA usedin this studygives much more realisticemission ratesthanthoseofthetraditionalLCA. Further- more,whenit’sknownthat thealertthresholdforasubstanceis measuredinmicrograms/m³ asanhourlyaverage,thepresenceof thetimescaleintheDynamicLCAisinteresting.

PollutantratesindicatedinFigures5a,5b,5cand5dbeingspa- tialized,they therefore relate to the processeslocated in each of theregions:

Fig. 6. NO Xemission with traditional LCA (kg) Table 3a

Results F FNOXksite-dependent

NO X Paris Strasbourg Lille Lyon F FNOXk(day) 1.10-3 4.60.10-4 1.19.10-3 6.19.10-4

Table 3b

Results I F NOXk site-dependent

NO X Paris Strasbourg Lille Lyon

I F NOXk [kg intake/kg emitted] 1.10E-06 2.14E-07 3.16E-07 4.33E-07

• P0+P1+P2+P3+P4+P5:region1

• P6:region2

• P7:region3

• P8+P9+P10+P11:region4

Thecolorindicationusedinthesefiguresrespectsthatoffig.4. Tables 3a and3bshow results concerning Fate Factor(FF_NOX

k) andIntake Factor(IF_NOX

k) ofNO_X.Theyareobtainedusingstep 2 (Fig. 2) ofthe methodologydeveloped in thisarticle.To perform thecalculations,analgorithmwrittenunderMATLABisused.

The advantage of the last couplingis to estimate the concen- trationofpollutantsinanareadownstreamofanemissionsource.

Thus,theuseofacouplingwiththedispersionmodeloftheGaus- sian plume guarantees to take into account the parameters that govern the dispersion of pollutants in the atmosphere and spe- cificweather conditions(wind direction, etc.). The calculation of thetwofactors,theFFandtheIF,isthereforecontextualized.

ThevalueofIF_NOX

Paris,relativetothatoftheotherselectedcities, indicatesthatParis,withitspopulation,willbethemostimpacted in human toxicity during the manufacture of the double glazed window.Incomparison,thetraditionalUSEtoxmodel,afterarough calculation,proposes foran intra-urban intake fraction(IF)of an

"average"world cityofanemittedpollutantanIFvalueoftheor- derof24ppm(Humbertetal.,2014).Therefore,onesinglevalue wouldbeproposedwithtraditionalLCA,whereasinourstudythe differentvaluesareconsistentwiththefactthatspatialandmete- orologicalcontextsdiffer.

ConclusionandOutlook

Nowadays, criticallevels of morbidityreached by atmospheric pollutionmakeitimportantto provideeffectiveforesighttoolsto stemgrowthofthephenomenon.

A state ofthe arthas pointedout that systemictool exist for modelingimpactsofhumantoxicity:traditionalLCA.Itisstillcrit- icized because of results which do not take into account spatial specificitiesandtemporal parameters. This indigence createsloss ofinformationforarigorousassessmentoflocalimpacts.Afunc- tional and physical conceptual method is therefore proposed in thisresearch work. It is based on a multi-coupling. It is carried outbetweena traditionalLCAsoftware,OpenLCA, atemporaland spatialdispersionmethod,ESPA,andadigitalpollutantdispersion

model,theGaussian model.The ESPAmethoduses aconvolution productwhichgeneralizestheideaofslidingaverage.Itappliesto temporalandspatialdata.Despitea distributionofrandomemis- sionrates,thevaluesbeingtemporallyandspatiallydisaggregated, becomemorerealisticthanthoseproposedbytraditionalLCA.

Pollutant flows and their dependence on spatial specificities (meteorology, topography, etc.)make it interesting to usea digi- taldispersionmodeltoassesstheconcentrationsofpollutantsthat infectthegeographicareasobserved.AlthoughUSEtoxalreadyin- cludes urban, regionalandglobal environments,it doesnot have detailedspatialresolution.Inourstudy,ourmulti-couplingmakes itpossibletocalculateasite-dependentFateFactor(FF)andasite- dependentIntakeFraction(IF).TheIFrepresentsthefractionofthe emitted masswhich entersthe humanpopulation. The finalpur- poseofthisworkwillbetocalculateamorerobustandmorere- alisticspatial-temporalimpactofhumantoxicitybythanthatpro- posedbyconventionalLCA.

LCA is the best suited tool to bring the environment back to thecenterofdesignforacompany.Itmustthenmakeitpossible to assesstherisks ingreater compliance andalsogreater perfor- mance. Looking at the results, it is more judicious to use a dy- namicsite-dependentLCAthan atraditionalLCAto carryout the assessmentoftheimpactoftoxicity forhumans inaneco-design approach.Thefirstone thereforeleadstoan eco-designapproach inthe contextofcontinuousimprovement.The organizationcon- cernedcanthen proposemoreeffectivesolutions topreventhigh levelsofanthropogenicairpollutants.

Theintrinsiccomplexityofurban pollution,whichdependson bothurbanplanningand“immission” -theresultofemissionafter modificationbyurbanmorphologyandmeteorologicalparameters - leads to think about improving the Gaussian dispersion model chosen in this research work. Hence the outlook of considering the useof a weather modeling systemandadvanced air quality:

CALPUFF.The CALPUFF modelisdesignedto simulatethedisper- sion offloating point sources, puffsorcontinuousas well asthe dispersion of floating continuous linear sources. The model also includes algorithms to manage the effect of washout by nearby buildingsonthewaytotheplumesofpollution.Consequently,in anunstablestate,

CALPUFF associated withESPAand theOpenLCA software can improvetheresultsandenablemorecomplex studies.Inthispa- per,thecontextualizationofourstudywaslimitedto4regions.

Thus,CALPUFF willallow ittobe extendedmoreconveniently toalargernumber.

TheuseofregionalgeographicdatainGISsoftwareisenvisaged inorder tocross-reference precisegeographicinformationon the locationofemissionsourcesandreceptionareas.

Creditauthorstatement

Patrice MEGANGE: Conceptualization, Methodology, Writing, Software,Contextualizing,Computation,Investigation

PierreNGAE:Methodology,Software,Supervision,Computation AmirFEIZ: Visualization,Supervision

ThienPhuLE:Supervision,Writing-Reviewing

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