Future air pollution in the Shared Socio-economic Pathways

Future air pollution in the Shared Socio-economic Pathways

Shilpa Rao

*, Zbigniew Klimont

, Steven J. Smith

, Rita Van Dingenen

, Frank Dentener

, Lex Bouwman

, Keywan Riahi

, Markus Amann

,

Benjamin Leon Bodirsky

, Detlef P. van Vuuren

, Lara Aleluia Reis

, Katherine Calvin

, Laurent Drouet

, Oliver Fricko

, Shinichiro Fujimori

, David Gernaat

, Petr Havlik

, Mathijs Harmsen

, Tomoko Hasegawa

, Chris Heyes

, Jérôme Hilaire

, Gunnar Luderer

, Toshihiko Masui

, Elke Stehfest

, Jessica Stre fl er

, Sietske van der Sluis

,

Massimo Tavoni

aInternationalInstituteforAppliedSystemsAnalysis,Schlossplatz-1,A-2361,Laxenburg,Austria

bNorwegianInstituteofPublicHealth,POBox4404,Nydalen,0403,Oslo,Norway

cJointGlobalChangeResearchInstitute,PacificNorthwestNationalLaboratory,5825UniversityResearchCourt,Suite3500,CollegePark,MD20740,USA

dDepartmentofAtmosphericandOceanicScience,UniversityofMaryland,CollegePark,MD20742,USA

eJointResearchCentre,InstituteforEnvironmentandSustainability,ViaEnricoFermi2749,I 21027,Ispra(VA),Italy

fPBLNetherlandsEnvironmentalAssessmentAgency,Ant.vanLeeuwenhoeklaan9,3721MA,Bilthoven,TheNetherlands

gDepartmentofEarthSciences,FacultyofGeosciences,UtrechtUniversity,POBox80021,3508TA,Utrecht,TheNetherlands

hGrazUniversityofTechnology,Inffeldgasse,A-8010Graz,Austria

iPotsdamInstituteforClimateImpactResearch(PIK),POBox601203,14412Potsdam,Germany

jCommonwealthScientificandIndustrialResearchOrganization,AgricultureFlagship,StLucia,QLD,4067,Australia

kCopernicusInstituteofSustainableDevelopment,UtrechtUniversity,Heidelberglaan2,3584CSUtrecht,TheNetherlands

lFondazioneEniEnricoMattei(FEEM),CorsoMagenta63,20123Milan,Italy

mCentroEuro-MediterraneoperiCambiamentiClimatici(CMCC),viaAugustoImperatore,16-I-73100Lecce,Italy

nNationalInstituteforEnvironmentalStudies,CenterforSocial&EnvironmentalSystemsresearch,16-2Onogawa,Tsukuba,Ibaraki,305-8506,Japan

oMercatorResearchInstituteonGlobalCommonsandClimateChange(MCC),TorgauerStraße12-1510829Berlin,Germany

pPolitecnicodiMilano,PiazzaLeonardodaVinci,32,20133Milan,Italy

ARTICLE INFO Articlehistory:

Received15December2015 Receivedinrevisedform24May2016 Accepted31May2016

Availableonlinexxx Keywords:

Scenarios Airpollution

Integratedassessmentmodels

ABSTRACT

Emissionsofairpollutantssuchassulfurandnitrogenoxidesandparticulateshavesignificanthealth impactsaswellaseffectsonnaturalandanthropogenicecosystems.Thesesameemissionsalsocan changeatmosphericchemistryandtheplanetaryenergybalance,therebyimpactingglobalandregional climate.Long-termscenariosforairpollutantemissionsareneededasinputstoglobalclimateand chemistrymodels,andforanalysislinkingairpollutantimpactsacrosssectors.Inthispaperwepresent methodologyandresultsforairpollutantemissionsinSharedSocioeconomicPathways(SSP)scenarios.

Wefirstpresentasetofthreeairpollutionnarrativesthatdescribehigh,central,andlowpollution controlambitionsoverthe21stcentury.Thesenarrativesarethentranslatedintoquantitativeguidance foruseinintegratedassessmentmodels.TheresultingpollutantemissiontrajectoriesundertheSSP scenarios cover a wider range than the scenarios used in previous international climate model comparisons. In the SSP3 and SSP4 scenarios, where economic, institutional and technological limitationsslowairqualityimprovements,globalpollutant emissionsoverthe21^stcenturycan be comparabletocurrentlevels.PollutantemissionsintheSSP1scenariosfalltolowlevelsduetothe assumptionoftechnologicaladvancesandsuccessfulglobalactiontocontrolemissions.

ã2016TheAuthors.PublishedbyElsevierLtd.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1.Introduction

Despite efforts tocontrol atmospheric pollutantemissions, ambientairqualityremainsamajorconcerninmanypartsofthe world.Airpollution hassignificantnegativeimpactsonhuman health(Popeetal.,2002;Dockeryetal.,1993;Jerrettetal.,2009).

* Correspondingauthorat:InternationalInstituteforAppliedSystemsAnalysis, Schlossplatz-1,A-2361,Laxenburg,Austria.

E-mailaddress:rao@iiasa.ac.at(S.Rao).

http://dx.doi.org/10.1016/j.gloenvcha.2016.05.012

0959-3780/ã2016TheAuthors.PublishedbyElsevierLtd.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

xxx–xxx ContentslistsavailableatScienceDirect

Global Environmental Change

j o u r n al h o m ep a g e: w w w . el s e v i e r . c o m / l o c at e / g l o e n vc h a

Morethan80%oftheworld’spopulationisexposedtopollutant concentrationsexceedingtheWorldHealthOrganization(WHO) recommendedlevels(Braueretal.,2012)andaround3.6million deathscanbeattributedtoambientairpollutionwithanother4 million from household related sources (Lim et al., 2012).

Moreover,air pollutioncan alter ecosystems, damagebuildings andmonuments,aswellasinfluenceearth’senergybalanceand thereforeclimatechange.

Long-term global scenarios for air pollutantemissions have beenused for atmospheric chemistry and Earthsystem model simulationsintendedtoexaminefuturechangesinclimate,air,and watersystems.Thesescenariosreflectplausiblefutureemissions basedonsocioeconomic,environmental,andtechnologicaltrends.

Thesescenariosaregenerallyproducedbyintegratedassessment models(IAMs)(Mossetal.,2010),whichprojecteconomicgrowth, population,energyconsumption,land-useandagriculturealong withassociated GHGand pollutantemissions.Recentexamples includeinparticular, theRepresentativeConcentrationPathway (RCP)scenarios(vanVuurenetal.,2011a),whichwerethemulti- modelglobalscenariosofgreenhousegasesandairpollutantsused intheCoupledModel IntercomparisonProjectphase5 (CMIP5) (Tayloretal.,2011).TheRCPsweredevelopedtospanarangeof climateforcinglevelsandwerenotassociatedwithspecificsocio- economicnarratives.Thesescenariosreflectedtheprevailingview thatairqualitypolicieswillbesuccessfullyimplementedglobally andthatemissionscontroltechnologywillcontinuetoevolveand asaresultshowsignificantdeclinesinparticulatematter(PM)and ozoneprecursoremissionsoverthe21stcenturyatagloballevel (Amann et al., 2013; van Vuuren et al., 2011b). More recent scenarios have included alternative assumptions on pollution control,inanefforttobetterunderstandtheroleofairpollution controlin termsofreferencescenariodevelopmentandtheco- benefitsfromclimatepolicies(seeforexampleRogeljetal.,2014;

Raoetal.,2013;Westetal.,2013;Chuwahetal.,2013).While providingawiderrangeofpollutionfutures,theassumptionson airpollutioncontrolinthesescenariosare,however,stilllargely independentofunderlyingscenarionarratives.

Itisgenerallyassumedinlong-termscenarios,implicitly,that pollutantconcentrationgoalswillcontinuetobemoreambitious overtime,onceincomesbecomesufficientlylarge.However,the time,stringency,andenforcementsuccessoffuturetargetsfora particularregioncannotgenerallybeknownandmustideallybe treatedasscenariovariable.Inalong-termscenariocontext,itis furthernecessarythat assumptionsonair pollution controlare consistent with the underlying challenges to climate change mitigationandadaptation.Pollutionoutcomesinsuchscenarios can then be expected to be a cumulative result of a range of variables including socio-economic development, technological change,efficiencyimprovementsandpoliciesdirectedatpollution controlaswellasalternativeconcernsincludingclimatechange, energyaccess,andagriculturalproduction.

TheSharedSocioEconomicPathways(SSPs)(Kriegleretal.,2012) areanewgenerationofscenariosandstorylinesprimarilyframed withinthecontextofclimatechangemitigationandadaptation.The SSP narratives (vanVuurenetal.,2014;O’Neill etal.,2014)comprisea textualdescriptionof howthe futuremight unfold,including a descriptionofmajorsocio-economic,demographic,technological, lifestyle,policy, institutionalandothertrends.In thispaper,our overarchinggoalistodevelopplausiblerangesoffutureairpollutant emissiondevelopmentpathways intheSSPscenarios,whichare basedoninternallyconsistentandcoherentassumptionsonthe degreeandimplementationoffutureairpollutioncontrol.Other papersinthisSpecialIssuesummarizeparalleleffortsintermsof elaborationofdevelopmentsintheenergysystem,landuseand greenhousegasemissionsintheSSPscenarios(Baueretal.,2016;

Poppetal.,2016).

Thestructureofthepaperisasfollows.Wefirstdescribethe developmentofasetofalternativeassumptionsonthedegreeand implementationof‘pollutioncontrol’intheSSPscenarios.These assumptions then reflect historical evidence and prevailing attitudesandprogressonpollutioncontrolandpotentialattitudes tothehealth andenvironmentalimpacts ofairpollution inthe future. We further postulate a link between these alternative developmentpathways for pollution control and a specific SSP narrative.Wealsodescribequantitativeguidancewithregardsto implementationoftheseassumptionsinIAMs.Finally,thepaper summarizeskeyresultsfromdifferentIAMinterpretationsofthe SSP scenarios interms of airpollutantemissions andregional ambientairquality.

2.Methodology

In the following sections, we first summarize the overall descriptionoftheSSPscenarios. Wenext describethedevelop- mentofasetofqualitativeassumptionsonpollutioncontrolthat can be linked to the overall SSP narratives and present a quantitative proposalfor implementation of theseassumptions inIAMs.

2.1.DescriptionofSSPscenarios

The SSPs depict five different global futures (SSP1–5) with substantially different socio-economic conditions. Each SSP is describedbyaqualitativenarrative(Kriegleretal.,2012).Fourof thenarratives (SSP1, SSP3, SSP4, and SSP5),are defined by the variouscombinationsofhighorlowsocio-economicchallengesto climatechangeadaptationandmitigation.Afifthnarrative(SSP2) describesmedium challenges of both kinds and is intended to representafutureinwhichdevelopmenttrendsarenotextremein any of the dimensions, but rather follow middle-of-the-road pathways.Aspartofthescenariodevelopmentprocess,consistent andharmonizedquantitativeelaborationsofpopulation;urbani- zationandeconomicdevelopmenthavebeendevelopedforallthe SSPs.ThequantitativeelaborationsoftheSSPnarrativesarethen referredtoas‘baseline’scenarios.

TheSSPnarrativesthemselvesdonotincludeexplicitclimate policies.However,additionalclimatemitigationrunshavebeen developedthatincludeforeachSSPbaseline,additionallong-term radiativeforcingtargetsof2.6,4.5and6.0W/m²in2100.Climate mitigation scenarios in the SSP framework further include a numberofadditionalassumptionsonspecificissuesrelatedtothe level of internationalcooperation;thetiming of themitigation effortovertime;andtheextentoffragmentation(particularlyin the short-to medium-term). These are characterized as shared policyassumptions(SPAs)whichdescribeforeachSSPnarrative, the most relevant characteristics of future climate mitigation policies,consistentwiththeoverallSSPnarrativeaswellastheSSP baselinescenariodevelopments.ThemitigationeffortoftheSSP scenariosisthenafunctionofboththestringencyofthetargetand theunderlyingenergyandcarbonintensitiesinthebaselines.This couldresultin somecasesininfeasibilities intermsofmeeting mitigationtargets(foracompleteoverviewoftheSSPbaselineand climatemitigationscenarios(seeRiahietal.,2016).

AnumberofIAMsrantheelaborationsofSSPscenarios.These include IMAGE (van Vuuren et al., 2016); MESSAGE-GLOBIOM (Frickoetal.,2016);AIM/CGE(Fujimorietal.,2016);GCAM(Calvin etal.,2016);REMIND-MAgPIE(Kriegleretal.,2016);andWITCH- GLOBIOM(Emmerlingetal.,2016).Detailedinformationonthe modelscanbefoundintheSupplementaryInformation(SI).For simplification,foreachofthefiveSSPs,onemarkerIAMhasbeen identified(representativeofaspecificSSPfromasingleIAM).The selection was guided by consideration of internal consistency

xxx–xxx

acrossdifferentSSPinterpretationsaswellastheabilityofamodel torepresentthespecificstorylines.Thishelpedtoensurealsothat thedifferencesbetweenmodelswerewellrepresentedinthefinal setofmarkerSSPs.AdditionalreplicationsoftheSSPsfrom‘non marker’ models then provide insights into possible alternative projectionsofthesamestoryline.Themulti-modelapproachwas importantforunderstandingtherobustnessoftheresultsandthe uncertaintiesassociatedwiththedifferentSSPs.

Table1summarizestheSSPscenarioset.

2.2.PollutioncontrolintheSSPnarratives

Inthissection,wenowdescribethedevelopmentofasetof assumptionsonpollutioncontrolthatcanbeusedtoguidethe interpretationofSSPnarratives.

Whilethereisnouniquerelationshipbetweeneitherpollutant levelsoremissioncontrolsandincome(Stern,2005;Carson,2010;

Smithetal.,2005),acontinuedtighteningofpollutiontargetscan beconsideredaconsequenceofgrowingattentiongiventohealth outcomeswithincreasingincome,orperhapsalsoasaresultof new research that ties additional morbidity and mortality modalitiestoairpollution.Theadverseimpacts ofairpollution are well documented and costs of control technologies have generallydeclinedovertime.Thismeansthatdevelopingcountries can benefit frompast experience and have oftenimplemented pollutioncontrolswellinadvance,relativetoincome,ascompared to historical experience in currently more affluent regions.

Countries have, however, different physical, economic and institutional circumstances that impact both the amount and effort needed to achieve pollution goals. Pollutant emission densitiesinthedevelopingworldaresometimesquitehighand, evenwithmoreadvancedtechnology,reachingpollutiontargets maybemore difficult.The same levelof pollution controlwill resultindifferentconcentrationlevelsindifferentlocations.

Policies to control the adverse impacts of air pollution are numerous and regionally diverse. They are generally aimed at avoidingexceedingspecifiedtargetsforconcentrationlevels(for example,sulfur-di-oxide,ozone,andparticulatematter)butgoals forecosystemprotection(e.g.,fromacidificationandeutrophica- tion)havealsobeenpursuedinseveralregions.Pollutiontargets areperiodicallyrevisedatboththegloballevel(e.g.WHO)andby nationalandregionalbodies.Levelsofpollutioncontrolarealso often different across sectors. Further, in some circumstances, traditional‘end-ofpipe’pollutioncontrolmayhavelessofarolein reducingemissionsthantheeffectsofsocio-economicgrowthand related fuel and technological shifts (Rafaj et al., 2014). Thus

‘pollutioncontrol’itselfcouldrefertoawiderangeofpoliciesand developments.For example, policiesaddressing climatechange

often, as a co-benefit, reduce atmospheric emissions, thus improvingambientairquality(McCollumetal.,2013;vanVuuren et al., 2006; Bollen, 2008) . Conversely, policies targeting air pollutionwillhavealsoclimateimpacts,e.g.,(Carmichael,2008;

Shindelletal.,2012),althoughclimateco-benefitsmaybesmaller thanpreviouslyexpected(SmithandMizrahi,2013;Stohletal., 2015).Technologicalavailabilitycanalsobeakeyinfluenceonthe degreeofpollutioncontrol,especiallyiffeworonlycostlyoptions areavailable.Inpracticedamagesare,eitherimplicitlyorexplicitly, balancedagainsttheeconomiccostsofpollutioncontrol,forwhich technologycharacteristics,particularlycostsofpollutioncontrolor loweremissionalternativesareakeydriver.

We cannot capture all these complexities within current integratedscenarios.Wefirstsimplifyourapproachbyidentifying threecharacteristicsforairpollutionnarratives:

1. Pollutioncontroltargets(e.g.concentrationstandards),which wespecifyrelativetothoseincurrentOECDcountries.

2. Thespeedatwhichdevelopingcountries‘catchup’withthese levelsandeffectivenessofpoliciesincurrentOECDcountries.

3. Thepathwaysforpollutioncontroltechnologies,includingthe technologicalfrontierthatrepresentsbestpracticevaluesata giventime.

Basedonthesecharacteristics,wedevelopedthreealternative assumptions for future pollution controls (strong, medium and weak),whicharefurthermappedtospecificSSPscenarios.This terminologyfollowsthesameconventionasotherstudiesusedto informtheSSPscenariodesignprocess(KCandLutz,2016;Crespo Cuaresma,2016).

Themediumpollutioncontrolscenario(SSP2)envisionsaworld that continuesfollowingcurrent trends.Duetothediffusionof technology and knowledge, there is some ‘catch-up’, where countriesachieve levelsof emission controland policyefficacy inadvance,intermsofincomelevels,ofthehistoricalrecordin currentOECD countries.Pollutionconcentrationtargetsbecome moreambitiousoverthecenturyasincomegrows,thecommit- menttosetandenforcepollutiontargetsbecomingincreasingly effective,and more valueis placed onhealth and environment protection. To reachthesetargets, someregions willultimately require implementation of very efficient technologies, some perhaps requiring advances over current technology levels.

Regions with large population densities or adverse physical conditions (e.g. geographic features that lead to frequent high pollutionepisodes)maynotachievetheirdesiredoutcomes.

Thestrongpollutioncontrolscenarios(SSP1andSSP5)assume that increasing health and environmental concerns result in successful achievement of pollutanttargets substantially lower Table1

Summaryofscenarios.

Identifier Descriptor MarkerIAM Alsocomputedby(non-marker IAMs)

CentralSPAassumptionsforClimateMitigation

SSP1 Sustainability IMAGE(van Vuurenet al., 2016)

All Earlyaccessionwithglobalcollaborationasof2020

SSP2 Middle-of-the- road

MESSAGE- GLOBIOM (Frickoetal.,2016)

All Somedelaysinestablishingglobalactionwithregionstransitioningtoglobal cooperationbetween2020and2040

SSP3 Regional rivalry

AIM/CGE(Fujimori etal.,2016)

IMAGE,GCAM,MESSAGE- GLOBIOM,WITCH-GLOBIOM

Lateaccession higherincomeregionsjoinglobalregimebetween2020and2040, whilelowerincomeregionsfollowbetween2030and2050

SSP4 Inequality GCAM AIM/CGE,WITCH-GLOBIOM SameasSSP1

SSP5 Fossil-fuelled development

REMIND-MAgPIE AIM/CGE,GCAM,WITCH- GLOBIOM

SameasSSP2

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thancurrentlevelsinthemediumtolongterm.Associatedwith this scenario is a faster rate of pollution control technology development,withgreatereffectivenessascompared tocurrent technologies. The ambitious air quality goals in the strong pollution control scenario would require, in some regions, implementationofcurrentbestavailabletechnology(andperhaps evenbeyond)and assureoverallenforcementof environmental lawssupportedbyefficientlyoperatinginstitutions.

Weakpollutioncontrolscenarios(SSP3andSSP4)assumethat the implementation of pollution controls is delayed and less ambitiousinthelong-termcomparedtothemediumscenario.This maybeduetothelargechallengesseveralregionsface,including, highemissiondensitiesindevelopingcountries’megacities,failure to develop adequate air quality monitoring, and/or weaker institutionsresultinginpoorenforcementofrespectivelegislation.

Theproblemsareaggravatedbytheassumptionthatinternational cooperation is weaker resulting in low ambition or slow developmentofinternationallawsthatalsoleadstoslowerrates of technological improvements and trans-boundary pollution contributestohigherbackgroundconcentrationsinmanyregions.

These pollution control storylines are matched to the SSP scenario narratives as shown in Table 2. The strong pollution controlnarrativeisassumedfortheSSP1andSSP5scenariosdueto theirhighlevels of development,focus onhuman capital, and reduced inequality. Conversely, we associate the low pollution controlnarrativewiththeSSP3and SSP4scenariosduetotheir lower levels of development and greater inequality. The SSP2 scenarioismappedtothemediumpollutioncontrolnarrative.The speed and absolutevaluetowhich countrygroups converge is differentiated across the SSPs. While we qualify three sets of assumptionsonpollutioncontrolthataremappedtothefiveSSP scenarios,wenotethatevenwithsimilarassumptionsonpollution control,pollutionoutcomesinspecificSSPscenarioswilldifferdue to varying assumptions on economic and population growth, energyconsumptionpatterns,andotherscenariocharacteristics.

2.3.ImplementationinIAMs

For quantitative interpretation of the storylines, there is a further need to bridge the gap between the complexity in estimatingpollution emissionsand theirimpacts,theability of availablemeasures,suchasemissioncontrols,tomitigatethese impacts, and the need for simplified representations of these processesin IAMs. Given that IAMs do not generallyrepresent explicitairpollutioncontroltechnologiesonadetailedlevel,we detailbelowanapproachwherescenarioparametersarebroadly

representedintermsofchangesinemissionfactorsderivedfroma moredetailedairpollutionmodel.Thisapproachhasbeenusedin a numberofrecent studies(Riahi etal.,2012)and allowsfora relativelysimplisticmethodtorepresentquantitatively,concepts relatedtothespeed anddegreeofimplementationofpollution controldevelopedanddescribedearlier.

We baseour quantitative guidance ona dataset of regional emission factors(i.e.,emissionsperunit of energy)forenergy- relatedcombustionandtransformationsectorsuntil2030based oncurrent policies and technological options derived fromthe GAINSmodel(Amannetal.,2011,Klimontetal.,inPreparation).

Thisdataset includesemission factorsfor 26 world regionsfor sulfurdioxide(SO2), nitrogenoxides(NOx),organiccarbon(OC), blackcarbon(BC),carbon monoxide(CO),non-methanevolatile organiccarbons(NMVOC),andammonia (NH3)fromallenergy combustion and process sources. The detailed emissionsfactor datawasprocessedtoaccommodatetheaggregatestructureand resolution of the IAMs (see supplementary information (SI) Section1forfurtherdetails).Theemissionfactorsusedinclude:

CLE: ‘current legislation’ These emission factors assume efficientimplementationofexistingenvironmentallegislation.

Itthusdescribesascenarioofpollutioncontrolwherecountries implement all plannedlegislation until 2030 withadequate institutional support. The CLE emission factors are “fleet average” valuesthat aretheaggregateemission factorof all agesofequipmentoperatinginthegivenyear.

MTFR: ‘maximum technically feasible reduction’ These emissionfactorsassumefullimplementationof‘bestavailable technology’asitexiststodayby2030independentoftheircosts butconsideringeconomiclifetimeoftechnologiesandselected other constraints that could limit applicability of certain measures in specific regions. While, the full penetration of MTFRmeasuresinthenear-termisnotafeasiblescenario,these values serve rather as ultimately achievable air pollutant emission factors for conventional technologies considered beingavailableatthepresenttime.

Inordertodeveloptrajectoriesforemissionfactorsthatcould beconsistentwiththeSSPstorylines,wedrawonexperienceand results from a number of existing and forthcoming studies including(Raoetal.,2013;Riahietal.,2012)wheresimilarsets ofemissionfactorshavebeenusedinasingleIAMinconjunction withafullscaleatmosphericchemistrymodel,thusprovidingan indicationoftheimplicationofsuchemissionfactordevelopment in terms of resultingatmospheric concentrations of PM2.5and correspondinghealthimpactsin themedium-term.Weidentify twomaincomponentsintermsofemissionfactordevelopment:

Table2

QualitativeframeworkforpollutioncontrolintheSSPs.

Policy strength

Policytargets Technological

innovation

SSP link

KeyrelevantcharacteristicsofSSPs

HighIncomecountries MediumandLowincomecountries Strong Policiesoverthe21stcenturyaimformuch

lowerpollutantlevelsthancurrenttargetsin ordertominimizeadverseeffectson population,vulnerablegroups,and ecosystems.

Comparativelyquickcatch-upwiththe developedworld(relativetoincome)

Pollutioncontrol technologycostsdrop substantiallywith controlperformance increasing.

SSP1, SSP5

Sustainabilitydriven;rapid developmentofhumancapital, economicgrowthand

technologicalprogress;prioritized healthconcerns

Medium Lowerthancurrenttargets Catch-upwiththedevelopedworldat incomelevelslowerthanwhenOECD countriesbegancontrols(butnotas quickasinthestrongcontrolcase).

Continuedmodest technologyadvances.

SSP2 Middleoftheroadscenario

Weak Regionallyvariedpolicies. Tradebarriersand/orinstitutional limitationssubstantiallyslowprogress inpollutioncontrol.

Lowerlevelsof technologicaladvance overall.

SSP3, SSP4

Fragmentation,inequalities xxx–xxx

Until 2030, emission factors assumed in the different SSP scenariosreflectassumptions ontheattitudestohealth and environment and the institutional capacity to implement pollution control in the near-term.They include full imple- mentationofCLEpollutioncontrolmeasuresinthemedium scenariobutallowforpartialandadditionalcontrolintheweak andstrongpollutioncontrolscenarios.

After2030,thetrajectoriesareassumedtodependontheextent towhich economic developmentimplies that lower-income regionscatch-uptoOECD levelsintermsof implementation (e.g.emissionfactorreductions)andtheextentoftechnological change, i.e., the progress towards MTFR levels of emission factors.TheMFTRvaluesareassumedtobestaticthemselves anddonotchangewithtimeandwedonotspeculateabout impact of innovation on further improving the reduction efficiencyof the bestmeasures we included.Thus, while in somesense, we may be conservative for the pathways and regionswithhighpenetrationofMTFRequivalenttechnology, ontheotherhand,giventhatmostMFTRvaluesherearebased oncurrentsmall-scale applications,weassume thattechno- logicalprogressinthescenarioswillmaturethesetechnologies andallowforwideapplicationoverthelonger-term.

Fig.1showsaconceptualrepresentationofthedevelopmentof pollutioncontrolpolicyandassociatedemissionfactorchangein thedifferentSSPs.Amoredetailedillustrationofhowtheemission factorsinthedatasetcanbeusedtoemulatetheaboveguidelines ispresentedinsection1.2oftheSI.

TheIAMsusetheemissionfactordataprovidedandquantita- tiveguidelinesdescribedtoindividuallydeveloptheSSPscenarios.

Theemissionfactorsareimplementedinthebaselinescenarios describing the SSP narratives, while the climate mitigation scenariosthendescribetheadditionalimpactsofclimatepolicies on air pollution emissions and air quality, compared to the baselines.Thus, theclimatemitigationscenariosdo notinclude furtherpoliciesonairpollutioncontrolcomparedtothebaseline scenarios. It isimportant tonotethatthe modelsusedifferent inventoriesforthe2000–2010periods,andarenotbenchmarked toasinglesource.Thedifferencesacrossmodelsinthisperiodthen reflecttheuncertaintyininventorydataandtosomeextent,the regional and sector aggregation of the IAMs. For land-use, international shipping, and other sectors not covered in the emissionfactordataset,additionalassumptionsaremade(seeSI [3943]formoredetailsoninventoriesanddriversforemissions acrosstheIAMs.).Theassumptionsformethane(CH4)fromenergy, wasteandland-usesectorsareseparatelydescribedinBaueretal.

(2016)andPoppetal.(2016)andsummarizedintheSI.

3.Results

Inthissection,wesummarizekeyresultsfortheSSPscenarios in termsof airpollution emissionsandregionalairquality. We describethefullrangeofmarkerandnon-markerrangesforthe SSP scenarios.In termsof climatemitigation,we onlyfocuson centralSPAcaseforeachSSP.

Results are mainly presented at a global scale and further discussedforfiveaggregateregions:

OECD90countriesandnewEUmemberstatesandcandidates (OECD);

reformingeconomiesofEasternEuropeandtheFormerSoviet Union(excludingEUmemberstates)(REF);

countriesoftheMiddleEastandAfrica(MAF);

countriesofLatinAmericaandtheCaribbean(LAM);and Asiancountries(withtheexceptionoftheMiddleEast,Japanand

FormerSovietUnionstates)(ASIA).

3.1.Emissionsofselectedairpollutants

Fig. 2 shows potential emissions futures across the SSP scenariosinthe2005–2100periodforselectedpollutants.Results for remainingpollutants aresummarized in theSI. Weinclude emissionrangesfromtheRCPscenariosetaswellastheentire rangeofscenariosfromtheIPCCFifthAssessmentReport,inorder to place the SSP scenarios in context. Differences in historical emissions between themodels (2000–2010) are due to use of different inventories by IAMs (Table S1 and individual model descriptions) and arewithin uncertainty ranges (Granier et al., 2011;Lamarqueetal.,2010).Forexample,forSO2,historicalglobal emissionsuncertaintyhasbeenestimatedatabout10%,withlarger uncertaintiesforsomeregions(Smithetal.,2010).Uncertaintyis muchlargerforblackcarbonemissions,estimatedtobeafactorof two(Bondetal.,2004).Beyonduncertaintiesinactivitydataand emissions factors, additional aspects include the relatively aggregate representation of sectors in IAMs and the large uncertainties in land-use and land-use change emissions (see Poppetal.,2016forfulldescriptionofland-usesector).

TheSSP3baselineshowsanincreaseinfutureemissionsover theshort-termacrossallpollutantsexaminedhere,duetolarge population growth and relatively slower and heterogeneous economicgrowth.Atagloballevel,emissionscontinueincreasing forthenexttwotothreedecadesandby2100showonlyaslight declinefromcurrentlevels.TheSSP4baseline,whichhasidentical assumptions onpollutantcontrols, showsloweremissionsthan

Fig.1.ProposedPathwaysforAirPollutionPolicyinSSPsovertime.Righthandinsetshowsschematicdevelopmentofemissionfactors.Weusehereidenticaldefinitionsof incomecountrygroups(lowincome(L)countries,middleincome(M)countries,andhighincome(H)countries)asusedintheSSPprocessfordevelopmentofeconomic projections,basedonrecentWorldBankclassifications.https://secure.iiasa.ac.at/web-apps/ene/SspDb/static/download/ssp_suplementary%20text.pdf.

xxx–xxx

SSP3 for all pollutants asa result of differentevolution of the energy system (see text below). The SSP2 shows a consistent declineinallpollutantsthroughoutthecenturywhileSSP1and SSP5exhibit a morerapid decline asa resultof moreeffective pollution control and lower fossil fuel intensities resulting in lowestemissionsinthesecondhalfofthecentury.

Pollutant emissionsin theSSPscenarios spanacrossa much wider range than the RCP scenarios. In general, baseline SSP3 emissionsaresignificantlyhigherthanthelargestRCPvalues,with NOxandBCemissionsintheSSP1baselinecase lowerthanthe lowestRCPvalue.Whilescenariodynamicsand assumptionson transportation and access to clean energy for cooking in developingcountriesaremajordriversofemissionoutcomesof NOx and BC, respectively, anotheraspect is theupdated set of pollutantcontrolassumptionsandtheemissionfactorsusedinthis study.Resultsforremainingpollutantsshowsimilartrends(see SI).

The climatemitigation scenarios (Fig. 2 illustrates4.5W/m² (45)and2.6W/m²(26)cases)resultinmostcasesinco-benefitsin termsoflowerpollutantemissionsthanthebaselines.Thelargest co-benefits from climate policy occur in the weak pollution control,SSP3scenario,whichalsohasthehighestcorresponding baselineemissions,whiletheSSP1/5scenariosshowmorelimited reductionsinairpollutantsfromclimatepolicies.WhileSO2and NOxemissionsshowthelargestreductionsandthemodelranges within the SSPs are much smaller than in baseline cases, BC emissionsdonotdeclineasmuch asaresultofassumptionson fuel-substitution in the residential sector (see discussion in Section3.3).

3.2.Emissionintensities

Fasteconomicgrowthandhighemissionintensities(emissions perunitofenergyused)inmanyAsiancountrieshaveledtosevere pollutionepisodesacrossthecontinent.Inspiteoftheeffortstocut airpollutantemissionsfromkeysources,theintensitiesremain wellabovethose observedinOECDcountries(Fig.3)whereair qualitystandardsarepresentlythehighest.Emissionintensitiesin theOECDarethusalreadylow,andplannedlegislationisexpected toreducetheseevenfurtherby2030.

In the SSP baselines, emission intensities in ASIA decline significantlyby2050inallSSPs.Economicgrowthandtheaverage incomeinASIAin2030differssignificantlyacrossSSPs,withalow valueof10billionUS2005$inSSSP3andahighvalueof28billion US2005billion$ in SSP5 (see also (Crespo Cuaresma, 2016) for detailsoneconomicassumptionsinSSPs).Thus,countriescouldbe expected to adopt pollution controls with varied schedules, dependingonindividualinstitutional,financialandtechnological capacities(seepreviousdiscussioninSection2).

Therelativecontributionofpollutantcontrolmeasuresinterms ofactualreductionsinairpollutionwilldependontheSSPbaseline pathway. Major energy transitions in the SSP scenarios occur graduallyandassumptionsforpollutioncontrolcanbeassumedto be particularly important in the first few decades in terms of reducingemissionintensities.Forexample,coalbasedelectricity evolves relatively similarly until 2050 across the SSPs and consequentlythedifferencesindevelopmentofemissionintensi- tiesinASIAwithinthistimeframeisadirectreflectionofpollution control.

Over the longer term, the scenarios diverge significantly in termsofenergyandfuelstructures.TheSSP1andSSP5baselines Fig.2. EmissionsofSO2,NOXandBCinSSPmarkerbaselines(Ref)and4.5(labeledas45)and2.6(labeledas26)W/m²climatemitigationcases.Shadedareaindicatesrangeof totalemissionsfromRCPscenariorangefrom(vanVuurenetal.,2011a).AssessmentReport(AR5)rangereferstothefullrangeofscenariosreviewedintheFifthAssessment Report(AR5)ofWorkingGroupIIIoftheIntergovernmentalPanelonClimateChange(IPCC)https://tntcat.iiasa.ac.at/AR5DB/;Historicalvaluesarederivedfrom(Lamarque etal.,2010);Coloredbarsindicatetherangeofallmodels(markersandnon-markers)in2100.

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showa transitiontowardslesspollutingfuels andtechnologies, and thusresult in a rapidand sustainedreduction in emission intensities in ASIA. Conversely in the SSP3 and SSP4 worlds, relatively weaker technological change and higher fossil fuel intensitiesintheenergysystemleadtohigherlevelsofpollutant emissions.TheSSP2scenarioshowslarge-scaleelectrification-for example,electrificationinASIAgrowsrapidlyandby2030hasa similar share of final energy as current OECD levels. In the transportationsector,liquid fuels arethe majorfueluntil mid- centuryinallSSPscenarios.TheSSP1showsonlyaslightdeclinein liquids while, SSP5 shows the largest increase. This reflects alternativenarrativesoffuturemobilityresultingfromdifferences inlifestyles,preferencesandtechnology.

Wenotethatfor BCemissionsfromtheresidentialsectorin ASIA,emissionintensitiesremainhighthroughoutthecenturyin theSSP3andSSP4baselinescenariosmainlybecauseofcontinued biomassuse.IntheSSP3scenario,forexample,biomassuseinASIA iscloseto20EJin2100,almostthesameastoday’slevels.Inthe SSP1, the assumption of rapidly increasing access to cleaner cookingfuelsmeansthatBCemissionsdeclinesubstantiallyandby 2030emissionintensitiesconvergetoOECDlevels.

Assumingproperenforcementofairpollutionpoliciesinthe OECDregion,climatepolicieshaveverylittleimpactintermsof pollutantemissionintensities.InASIA,climatepoliciesdecrease emissionintensitiesforSO2andNOx,withmorelimitedimpacton BC,infact,aslightincreaseisindicatedintheSSP3scenario(see discussiononsectorimpactsofclimatepoliciesandco-benefitsin Section3.3).

3.3.Sectoremissions

The SSP scenarios offer a wide diversity of future growth patterns and how they relate to regional energy demand

convergenceand modernizationofenergy use(see Baueretal., 2016fordetails).Inordertounderstandtheimpactsofalternative energy developments, we look at broad developments of pollutantsacrosssectors(Fig.4).

3.3.1.Baselinescenarios

The energy sector emissions are dominated by electricity production,whichcurrentlycontributesamajorshareofSO2and in the developing countriesalsoof NOx. Bothemission control assumptionsandtechnologyassumptions,suchasthoseforclean coalornon-fossiltechnologies,canhaveasubstantialimpacton futureemissions.

The industrial sector remains an important source of SO2

emissions inall SSP baselinesand climatemitigationscenarios throughout the century. Fossil-fueluse in the industrial sector comprisesawiderangeofuses,includingprocessheat,internal combustion engines, and process-specific uses such as steel- makingoverarangeofscales,fromsmallplantsandboilerstolarge manufacturing centers. This sector has significant diversity in regulations on pollutant emissions depending on the type of industry.Experiencesofarhasshownthatindustriallegislation lags behind energy or transportation sector in developed and developing countries. Another factor is that fossil fuels can be difficult to replace in some industrial activities,such as those relatedtohightemperatureprocessheat.Someprocessessuchas steel making require specificfuels likecoking coal, which also differinpollutantintensityascomparedtocoal.IntheSSPbaseline cases,SSP2andSSP3showacontinuouslyincreasingcoalusein thissectorwhileitdeclinesinSSP1andSSP5,especiallytowards theendofthecenturyresultinginstrongreductionofemissionsof SO2andNOx.Coaluseinsmallboilers,cokeandbrickproduction industrycanbesignificantsourcesofBC(Bondetal.,2004).Inthe longterm,atransitiontomoreefficientandcleanertechnologies Fig.3. EmissionsintensitiesformajorpollutantsinASIAandOECDinSSPbaselinesand26and45mitigationscenarios(bothmarkerandnon-markerscenariosincluded).

Emissionintensitiesdefineddifferentlyforpollutants;SO2intensityisinreferencetoenergysupply,NOxandBCinreferencetofinalenergyfromrespectivesectors.

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willresultindeclineinemissions;intheSSP3scenariothissector hasasignificantshareofBCemissionsuntilmid-century.

The transportation sector is a major source of NO_x and BC emissionsthroughatleastmid-centuryinnearlyallSSPscenarios.

Asdiscussedearlier,continueduseofliquidfuelsmeansthatNOX

emissionsfromthetransport sectorremain relatively highand onlydeclineinthesecondhalfofthecentury.Thesedifferencesare broadly reflected at the regional level as well (SI).The end of centurydecreaseintheSSP1isduetothewidespreadadoptionof hydrogen-fueledvehicles.Inthenextdecades,however,NOXand BCemissionsstillremainrelativelyhighevenintheSSP1scenario, mainlyduetothelargeincreaseinliquidfueluseoffsettingthe increasingstringencyoflegislation,particularlyinASIA.

TheresidentialsectorisamajorsourceofBCemissionsaswell asotherproductsofincompletecombustionlikeorganiccarbon (OC)and carbon monoxide (CO). Exceptfor SSP1 and SSP5, BC emissions from this sector remain fairly constant until mid- centuryacrossallSSPsbutthendeclinesubstantiallyinthesecond halfofthecenturyexceptintheSSP3andSSP4scenarios.Thelatter scenariosassumesustaineduseoftraditionalbiomassthroughout thecentury.Thissubstantiatesrecentfindingsthatemissionsfrom thebuildingssectoraredrivenmorebyassumptionsaboutenergy accessthanexplicitpollutioncontrols(Raoetal.,2016).

Emissionsfrominternationalshippingreflectassumptionson thelevelofimplementationofproposedinternationalregulations inthenear-termaswellasspecificassumptionsonthechangesin fueluseinthebaselinesandclimatemitigationscenariosoverthe longer-term(seeSIforassumptions).TheInternationalConvention for thePrevention of Pollution from Shipsor Marine Pollution Convention(MARPOL)AnnexVI(IMO,2006)setslimitsonsulfur contentoffuelsandNOxemissionsfromshipexhaust.While to someextenttherearedifferencesacrossSSPsintermsoflevelsof implementationofsuchprotocols,weseethatemissionsinallthe baselines show a downward trend for SO2 emissions (50–80%

declinecomparedto2005in2030).

The land-usesector (including openbiomass burning) is an importantsourceofBCemissions(closeto30%ofBCemissionsin 2005).TheassumptionsmadebyIAMsforthissectorvaryquite substantially in their level of detail (see SI for details). The developmentofairpollutantemissionsfromthissectordoesnot necessarilyfollowtheassumptionsdrivingtheairpollutionpolicy intheSSPsbutrather,landusepracticesrelatedtodeforestation andsavannahburning.Inmostscenariosemissionsfromlandopen burningchangeonlymarginallyinthemid-termwiththelong- termtendencytodecline,especiallyintheSSP1.

3.3.2.Climatemitigationscenarios

The emission responsestoa carbonpolicy cangenerally be linkedtochangesinfuelconsumptionorchangesinunderlying technologies.SeeSIforprimaryandfinalenergydetailsintheSSP scenarios. The intensityof the climatepolicy target is also an importantfactor;althoughmorestringentmitigationtargetsasin the26scenariodonotnecessarilyalwaysleadtolargerpollutant reductionscomparedthelessstringent45case.

TheaggregateresponseofSO2emissionstoaclimatepolicyis similarinallSSPs.Thisisduelargelytocoalcombustionbeinga common source of both SO₂ and CO₂, and a similar relative responsetoaclimatepolicyintheelectricitygenerationsector.SO2

emissions fall in all modelsas coal-fired electricity production either decreases or shifts tocarbon capture and storage (CCS) technologies.Soforexample,SSP4andSSP2showincreasedshares of gas-firedCCS and nuclear powerbecause of the highsocial acceptancefortheseoptionsinthosestorylines.Reductionsfroma climate policy are larger in the SSP3 and SSP4 scenarios as compared toSSP1. This canpartly beexplained bythe weaker assumptionsonpollutioncontrolintheSSP3/4.Themuchstronger transportationBC emissioncontrols in theSSP1/5scenarioand resulting low emission levels, coupled with substantial use of syntheticfuels,meanthat,inabsoluteterms,thereislessroomfor emissionstofurtherdecreaseasliquidfuelconsumptiondecreases Fig.4.World,Emissionsbysector,BaselinesandClimateMitigationcases.RCPscenariosindicatedforreference.OnlymarkerSSPscenariosrepresented.Valuesfor2005are fromRCP8.5whileerrorbarsshowuncertaintyacrosswholerangeofSSPandRCPscenarios.

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underaclimatepolicy.ThelargerbaselinecaseemissionsinSSP3 resultinapotentialforalargerrelativereductionintheclimate policycase.SO2emissionsfrominternationalshippingdropoffby theendofthecenturyintheclimatemitigationscenarios. This responseismainlyduetotheeffectofhighcarbonpricesinthis sectorandthemovetowardsalternativefuelslikeliquefiednatural gas(LNG)inthissector.

ForNOx emissions,weseethat major reductionsoccuronly mid-century. Before that, relative inertiain the energy system meansthatliquidfuelsremainanimportantpartofthefuelmixin thissector(closetoormorethan90%).Whilepollutantcontrolsin thissectorarerelativelynumerousandstringentinmanyregions, continued oil use in this sector means that emissions do not declinerapidlyevenintheSSP1/5scenarios.NOxemissioncontrols intheenergysectorareusuallylesseffectivethanSO2controlsand asa result, weobservethat NOx emissionsresponse fromthis sectorisless than thatof SO2 (seeSI for summaryof assumed controls).

TheBCemissionsreductioninresponsetoacarbonpolicyis smallerandwefindthatforCO2emissionreductionsofuptoabout 50%,mid-centuryin the45 and 26scenarios, BCemissionsare generally only reduced by 10–20%. The scenarios show a substantial reduction in BC emissions from the transportation sectorduetoreductionsinliquidfuelconsumptionandshiftto electricity,hydrogen,electricity,andbiomass-basedliquids.There isrelativelysmallresponseintheindustrialsectorBCemissionsto climatepolicy,duetothelimitedscopeforreductionsinthissector, thecontinueduseofliquidfuels,andarequirementforsomelevel of carbonaceous fuels. These differences in response in the industrialsectoraredue,inpart,todifferentrepresentationsof industrial fuel demand in these models. Traditional biomass consumptionintheresidentialsectorisonlymildlyimpactedbya climatepolicyinallofthemodels,withmostoftheshiftsalready occurringinthebaselinesduetootherpoliciesandassumptionson energyaccess.Forexample,intheSSP1scenariowithrelatively rapidratesofmodernizationindevelopingcountriesandaswitch tocleanerorlesspollutingsourcesforcooking,climatepolicydoes notbringadditionalreductions.Althoughnotexploredindetail here,wenotethatitispossiblethatclimatepolicymaynegatively impactemissionsfromthissectorasaresultofhighcarbonprices whichmayinsomecasesresultinanincreaseinbiomassusefor cookingindevelopingcountriesintheshort-term(see alsoRao etal.,2016).

4.Rangesforregionalairqualityoutcomes

In order togain an initialunderstandingof the regionalair qualityoutcomes across SSP scenarios, we estimateair quality undertheSSPscenariosusingTM5-FASSTmodel(VanDingenen et al., 2009), a reduced-form globalair quality source-receptor model (AQ-SRM). This allows us to provide an approximate estimateofairqualityoutcomes,althoughasnotedbelow,more detailed analysis, for example in CMIP6, is warranted. This approachoflinkingemissionoutcomesfromIAMstoareduced formair qualitymodel andallows ustocomputemulti-model, multi-scenarioairqualityoutcomes(Raoetal.,2016)(seeSIfor detaileddescriptionoftheFASSTmodelanditsapplicationtothe SSPscenarios).WeestimateannualaveragePM2.5concentrations (fineparticulatematterwithdiameterlessthan2.5

m

six-month average ozone concentrations (Fig. 5). We further provideacomparisonofthefractionofpopulationexposedacross the SSP scenarios to WHO levels defined as recommended maximum exposure level or air quality guideline (AQG) (10

m

m

m

m³)(WHO, 2006). For this purpose, we use hereas a basis, a median population trajectory (Riahi et al., 2012), which is

comparable to the SSP2 and SSP4 population projections in 2050 (see SI for comparison of population across the SSP scenarios).Thus,ourresultsaspresentedheredonotreflectthe diversityin regionalpopulationgrowthacrosstherangeof SSP narratives and only reflect the differences in assumptions on pollution control and underlying energyand land-usedevelop- ment.FutureanalysisusingSSP-specificspatiallyexplicitpopula- tionestimateswillbeusefulinenhancingourunderstandingofin termsofchangeswithinaregionduetomajorshiftsinpopulation distributionpatterns.

We find that the range of PM2.5 and ozone levels for the differentSSPscenariosisconsistentwiththeRCPrange(whichwas estimated using the same model and population basis), but displaysalargervariabilityamongtheSSPvariants.Differencesare largestinparticular inASIA,in linewiththewiderdiversity in growthpatternsreflectedinthepollutantemissiontrends.Inall regions,thefullrangeofmodeloutcomesfortheweakpollution control scenarios (SSP3/4) showsignificantlyhigher concentra- tionscomparedtothosewithstrongpollutioncontrol(SSP1/5).We alsofindthat,exceptforASIAandtheMAF,inallregions,morethan 95%ofthepopulationiscurrentlyunderthe25

m

levelforallscenarios.By2050,OECDcountriesstronglyimprove underallSSPscenarios,reducingconcentrationsfurtherwith80to 95%ofthepopulationexposedtolevelsbelow10

m

MAFregion,mineraldustisresponsibleformostoftheexposure above25

m

havelittleimpactontheexposedpopulation.CurrentlyinASIA, averageconcentrationsarearound25

m

thepopulationisexposed tolevelsabove10

m

levelsabove25

m

differentpartsofASIA,withChinahavinganaverageof32

m

Indiawithanaverageof30

m

PM2.5 concentration below 10

m

populationexposedto10

m

PM2.5concentrationisnear35

m

inPM2.5by2050willbereflectedinpopulationexposuretothis limitlevel.Indeed,thestrongpollutioncontrolscenarios(SSP1and SSP5) decrease the population fraction in the above 35

m

exposure classtoabout15%,whereas thelowpollution control variants(SSP3and SSP4)increase thefractionwith25 and 18%

respectively.

By 2050, climate policy leads to substantial co-benefits on pollutionlevelsinASIA,wherePM2.5levelsdecreaseby5–11

m

m³ relative tothebaseline scenario. For theotherregions, the maximalbenefitisaround2

m

benefitsareobservedinscenariosSSP3/SSP4directairpollution policieswereassumedtobelesseffective,inparticularforASIA (seealsoSI).

Ozoneprecursorsare,ingeneral,moredifficulttocontroland ozonelevelshavealargerimpactfromremotesourcesaswellas increasing methane concentrations. We find that in the SSP scenarios, regional ozonelevels do showclear regional differ- encesby2050.ASIAasawholeisnotabletostabilizeozoneat presentlevelsevenunderstrongairpollutionpolicies(SSP1and SSP5),althoughalsointhiscaselargedifferencesin trendsare foundbetweenindividualcountries.India’sozoneconcentrations areestimatedtoincrease(orstabilize)from63ppbvin2005to 2050valuesof63,70,or80ppbvforthelow,mediumandhigh pollution control variant, respectively, while ozone in China decreasesfrom56ppbvin2005to48,50,or53ppbvrespectively in2050.

5.Discussion

The SSP scenarios were developed to include narratives on futureairpollutioncontrolthatareconsistentwithcurrenttrends

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inairqualitypolicies;experienceincontroltechnologyapplica- tion;andregionaldifferencesinaffluenceanddegreeofcontrol.

Thisnewgenerationofglobalscenariosresultsinamuchwider range of air pollution emission trajectoriesthan the RCPs. The baselinerealizationsofSSP3scenariohaveglobalemissionsator abovethehighestlevel inthe RCPs,while theSSP1 scenariois generally near the lower end or below the RCPs. Pollutant emissionsinclimatemitigationcasesarelowerstill,withsome SSPtrajectoriesbelowtheRCPemissionlevels.TheSSPscenarios, thus,provideawiderangeoffutureemissions,foruseinglobaland regionalstudiesofclimateandsustainability.

The SSP1andSSP5 scenarios,which includeassumptions on globallysuccessfulimplementationofstrongpollution controls, bringthemostsignificantreductionsinairpollutantemissions;by mid-centuryemissionsdeclinegloballyby30–50%inthebaseline

scenariosandupto70%intheclimatemitigationscenarios.The SSP2,middleoftheroadscenario,generallyachievesreductionsby 2100similartoSSP5.IntheSSP3scenario,wherecurrentpollution controlplansarenotfullyachieved,globalpollutantemissionsdo notsubstantially declineand evenslightly increasein themid- term.Inspiteofimprovingemissionintensityinallregions,the improvements in the developing world are too small to offset growthinfossilfueluseandotheremissiondrivers.Evenbythe end of the century when emission intensities in the highest polluting regions decline to the current OECD levels, global emissionsremainhighinSSP3,barelybelowthecurrentlevels.

Exceptforthestrongestclimatepolicycasesconsidered,theair pollutioncontrolpoliciesinSSP3stillresultinrelativelyhigherair pollutantemissions,althoughtherearesignificantreductionsin SO2 and NOX. The emission trajectories for the SSP4 marker Fig.5.Leftpanel:region-populationweightedmeanPM2.5inm^{g/m3(leftaxis)frommarkerscenario(bluecolorbars)andaveragefromthe3RCPscenarios(greybar),} contributionofnaturalPM2.5(hatchedarea)fortheyear2005(leftmostbar)and2050.Green,orangeandredcoloredmarkersindicatethefractionofthepopulationexposed

to<10,<25and<35mg/m³respectively(rightaxis).Rightpanel:meanozoneconcentration(maximal6-monthlymeanofdailymaximumozone).Forthegroupedscenarios

SSP1/5andSSP3/4theconcentrationrepresentsthemeanoftherespectivemarkerscenarios.Errorbarsshowtheconcentrationrange(min/max)ofregionalaveragesfromall modelsinthe(setof)SSPscenariosshown,includingnon-marker.FortheRCPbars,theerrorbarindicatesthemin/maxrangewithinthesetof3RCP2.6,RCP4.5andRCP8.5 scenarios.(Forinterpretationofthereferencestocolourinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)

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