Project 5: Projecting and Quantifying Future Changes in Socioeconomic Drivers of Air Pollution and its Health-Related ImpactsNoelle E. SelinAssociate ProfessorInstitute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of TechnologyAssociate Director, Technology and Policy Programselin@mit.eduhttp://mit.edu/selin http://mit.edu/selingroupTwitter: @noelleselin

Co-Is:SusanSolomon(MITEAPS),StevenR.H.Barrett(MITAero/Astro),JohnReilly(MITSloan)

ScienceAdvisoryBoard16May2017

Objectives• Objective 1. Improving methods and tools

– Further develop and enhance methods and tools for understanding and assessing the relative importance of global change, technologies, and policies to air quality, relative to other uncertainties.

• Objectives 2 and 3: Air pollution and health implications of policies and technologies

– Quantify the future implications of modifiable factors such as technologies and efficiency improvements in the energy and transportation sectors on regional differences in air pollution impacts.

– Characterize state- and regional-level carbon policy implementation measures with respect to their air pollution health co-benefits.

• Objective 4. Air toxics – Assess how human exposure and impacts from different pollutants and mixtures

may shift over time, and identify potential strategies for regions to shift to less toxic mixtures.

• Objective 5. Influence of Climate– Identify the importance of climate (e.g., temperature, meteorological) change to

the formulation of robust strategies for mitigating health and environmental impacts.

Outline• Introduce Objectives• Initial Results:

– Climate variability, change, and air quality (Objective 1, Objective 5)

– Cross-state pollutant transport (Objective 1, Objective 2-3)

•1

Policies, strategies, technologies

Economic activity linked to emissions

Atmospheric chemistry

and transport

Health outcomes and economic

estimates

Objective 1: Improving methods and toolsMIT Integrated Economy-Air Quality-Health Assessment Framework

Economic and sector modeling

SMOKEpreprocessor;emissionscaling

CAMx,GEOS-Chem

BenMAP

USREP,ReEDS

Objective 2/3: Air pollution and health implications of policies and technologiesPotential for co-benefits from CO2 policy at national scale

Co-benefits exceed costs for cap-and-trade, clean energy policies at national scale

– Each line: a different economic assumption

– Vertical error bars: 95% CI for benefits

Formoreinformation:Thompson,T.M.,S.Rausch,R.K.Saari,andN.E.Selin.2014."ASystemsApproachtoEvaluatingtheAirQualityCo-BenefitsofU.S.CarbonPolicies."NatureClimateChange4,917-923.

Formoreinformation:Thompson,T.M.,S.Rausch,R.K.Saari,andN.E.Selin.2016.”AirQualityCo-BenefitsofSubnationalClimatePolicies.”JournaloftheAirandWasteManagementAssociation

Co-benefits for Northeast clean energy, cap-and-trade policies• Regional

benefits exceed costs

• Some areas of potential disbenefit

Objective 2/3: Air pollution and health implications of policies and technologiesRegional policies can have nation-wide impacts

Formoreinformation:seeEmilDimantchev,MeiYuan

Objective 2/3: Air pollution and health implications of policies and technologiesNew work: What are the costs and (air pollution health co-)benefits of state-level Renewable Portfolio Standards?

Policies-RPSrepealed-RPSunchanged-RPSexpanded

USRegionalEnergyPolicyCGEModel

SMOKE

Reduced-formairpollutioncostmodel(EASIUR,InMAP…)

Futurewelfarecost

FutureVSL-basedbenefits

Formoreinformation:A.Giang,L.C.Stokes,D.G.Streets,E.S.Corbitt,andN.E.Selin.2015."ImpactsoftheMinamata Conventiononmercuryemissionsandglobaldepositionfromcoal-firedpowergenerationinAsia."EnvironmentalScienceandTechnology49,5326-5335.

Morestringentmercury(endofpipe)regulations

Energytransformation(e.g.climatepolicy)Projectedem

issionsin2050

Objective 4: Air toxicsCO2 controls can also have benefits for mercury emissions

Formoreinformation:P.J.Wolfe,A.Giang,A.Ashok,N.E.SelinandS.R.H.Barrett.2016.“CostsofIQLossfromLeadedAviationGasolineEmissions.”EnvironmentalScienceandTechnology,50(17):9026–9033

LeademissionsfromgeneralaviationaircraftovertheU.S.canleadto$1billionindamagesfromlifetimeearningsreductions(duetoIQloss),plusanadditional$0.5billionduetolostproductivity

Objective 4: Air toxicsSmall sources (Pb from general aviation) can have large impacts

Dailymax.8hrO3 PM2.5

US-averagepopulation-weightedannualconcentrations:

3.2± 0.3

0.8± 0.3

2.9± 0.3 1.5± 0.1

0.5 ± 0.1

1.2± 0.1

Formoreinformation:F.Garcia-Menendez,R.K.Saari,E.Monier,andN.E.Selin.2015.“U.S.airqualityandhealthbenefitsfromavoidedclimatechangeundergreenhousegasmitigation.”EnvironmentalScienceandTechnology,49,7580–7588.

Objective 5: ClimateCarbon policy can have direct benefit to U.S. air pollution

Policycost&mortalitybenefits(VSL-based)asfractionofREFscenarioU.S.GDP:

ClimatepolicyrelativetoReferencescenario:

Modeledreductions:(U.S.population-weighted)>1µgm-3 and2.5ppb by2100

AvoidedU.S.deaths:2050:>10,000(4,000- 22,000)2100:>50,000(19,000- 95,000)

Garcia-Menendezetal.,ES&T,2015

Objective 5: ClimateClimate policy health benefits and costs

Objective 5: Climate and air qualityMulti-model Framework

Climate Chemistry

GEOS-Chem

CESM/CAM

Objective 5: Climate and air quality Dimensions of Uncertainty

ChangeinUS-averagedtemperaturesimulatedbyCAM-Chemensembleforreferenceclimatesensitivity(3.0deg C)andpolicycase

ChangeinUS-averagedtemperaturesimulatedbyCAM-Chemensembleforreferenceclimatesensitivity(3.0deg C)andpolicycase+alternativeclimatesensitivities

Objective 5: Climate and air quality Dimensions of Uncertainty

ChangeinUS-averagedtemperaturesimulatedbyCAM-Chemensembleforreferenceclimatesensitivity(3.0deg C)andpolicycase+alternativeemissionspathways

Objective 5: ClimateDimensions of Uncertainty

ChangeinUS-averagedtemperaturesimulatedbyCAM-Chemensembleforreferenceclimatesensitivity(3.0deg C)andpolicycasewithoverlappingperiodssimulatedbyGEOS-Chem

Objectiv 5 : Climate and air quality Dimensions of Uncertainty

2100ReferencescenarioU.S.-averageO3 “climatepenalty”estimatedusing5modelinitializations:

Averaging period (years)

F.Garcia-Menendezetal.,GRL,2017

Results 1: Climate and air quality Natural variability can affect estimates of the “climate penalty”

Results 1: Climate and air quality Large changes in summer O3/PM2.5 detectable by 2100 in reference case

Signal/NoiseRatio

O3 PM2.5

GEOS-Ch

emCESM

/CAM

-Che

m

Seeposter:D.Rothenberg

Results 1: Climate and air quality It may be difficult to detect climate-forced changes in O3/PM2.5under policy scenarios

Signal/NoiseRatio– SummertimeSfc Ozone

NoPolicy

ModeratePolicy

AmbitiousPolicy

2035-2065 2085-2115

Results 1: Climate and air quality It may be difficult to detect climate-forced changes in O3/PM2.5under policy scenarios

Signal/NoiseRatio– SummertimeSfc PM2.5

NoPolicy

ModeratePolicy

AmbitiousPolicy

2035-2065 2085-2115

Pollution transport

• Air pollution does not consider political boundaries

• 8 states (46 counties) do not meet the 24-hour PM2.5 standards*

• 4 states (20 counties ) do not meet the annual PM2.5 standards*

• Clean Air Act (CAA) section 110(a)(2)(D)(i)(I) “[prohibits] any

source or other type of emissions activity within the State from

emitting any air pollutant in amounts which will […] contribute

significantly to nonattainment in, or interfere with maintenance by,

any other State with respect to any such national primary or

secondary ambient air quality standard”

*Source: https://www.epa.gov/green-book

Results 2: Pollution Transport

Transport rule I

• May 2005: Clean Air Interstate Rule (CAIR)• 28 states and the District of Columbia to reduce SO2 and/or

NOx emissions• July 2008: US Court of Appeals for the DC Circuit remanded CAIR

to the Agency• July 2011: Cross State Air Pollution Rule (CSAPR)

• 23 states to reduce annual SO2 and NOX emissions to helpdownwind areas attain the 24-Hour and/or Annual PM2.5

NAAQS• CSAPR implementation in 2015 and 2017

Pollution transportResults 2: Pollution TransportTransport Rule I

• Objective: Assess the cross-state impacts in the US

• For every sector

• For every species

• For 2005 and 2011 (and 2018)

• Other work:

• Specific sectors [Greco et al.(2007), Bastien et al. (2015)]

• Specific locations/regions [Menut et al. (2000)]

• Specific species [Turner et al. (2015), Zhu et al. (2015)]

• Older inventories

• Simplified models

Results 2: Pollution TransportThis work

Modeling challenge

• Computationally expensive chemistry transport modelssimulate chemistry, transport and deposition

Population exposure to

PM2.5

Emissions

Results 2: Pollution TransportModeling Challenge

Modeling challenge

• Computationally expensive chemistry transport modelssimulate chemistry, transport and deposition

• ‘Forward’ modeling: resolution in the output (impacts)

Population exposure to

PM2.5

Emissions

~X

T

EPM2.5

Results 2: Pollution TransportModeling Challenge

Modeling challenge

• Computationally expensive chemistry transport modelssimulate chemistry, transport and deposition

• ‘Forward’ modeling: resolution in the output• Adjoint modeling: resolution in the input (sources)

- allows us to quantify contribution to a quantity ofinterest (objective function) from control parameters

Population exposure to

PM2.5

Emissions

Ew

~x

t

Control parameters

Objective function

Results 2: Pollution TransportModeling Challenge

@J

@Ew(i, j, k, t)

• The adjoint method is a computationally efficient way ofcalculating sensitivities of a (scalar) quantity of interest to avariety of parameters (e.g. emissions)

• It provides high resolution in the inputs (temporal, spatial and interms of species), and it thus allows to quantify what parametersdrive the aggregated quantity of interest (objective function)

objective function (scalar)

3D grid cell

timeEmissions species

Results 2: Pollution TransportAdjoint Motivation

• Mostly scientific (focusing on specific processes and/or speciesand/or optimizing the model)

• In the past few years applications have also started movingtoward policy:- Koo et al. (2013) on intercontinental aircraft pollution

transport- Dedoussi and Barrett (2014) on origins of PM2.5 mortalities in

the US- Turner et al. (2015) on BC source apportionment (CMAQ

adjoint)- Lee at al. (2015) on global PM2.5 mortalities’ origins- …

Results 2: Pollution TransportAdjoint applications so far

@J

@Ew(i, j, k, t)

• Can quantify exchange of air pollution impacts (PM2.5 exposure) between the states

• High number of inputs that drive impacts

PM2.5 exposure in every state

for each emissions species

origin of impacts (states)

time of emission

Results 2: Pollution TransportCross-state pollution in the U.S.

• Using:－ Adjoint of GEOS-Chem CTM [Henze et al. (2007)]

－ EPA NEI 2005 and 2011[US EPA (2008, 2013)]

－ EPA derived Concentration Response Function [US EPA (2011)]

• Sectors defined: [Caiazzo et al. (2013); Dedoussi and Barrett (2014)]

: = Change in population exposure

Electric power generation Road transportationCommercial/residential Marine transportation

Industry Rail transportation

• Impacts estimated:

@J

@Ew(i, j, k, t)

@J

@Ew(i, j, k, t)

Results 2: Pollution TransportCross-state: approach summary

Preliminary results – Please do not cite or quote

• Source-receptor matrix produced for allstates, sectors and species

• Impacts expressed in prematuremortalities

Bureau of Economic Analysis regions

Results 2: Pollution TransportRegional Trans-boundary results -- 2005

Preliminary results – Please do not cite or quote

Results 2: Pollution TransportCross-state results -- 2005

Preliminary results – Please do not cite or quote

Results 2: Pollution TransportCross-state results -- 2005

Preliminary results – Please do not cite or quote

Results 2: Pollution TransportCross-state results -- 2005

Preliminary results – Please do not cite or quote

Results 2: Pollution TransportCross-state results -- 2005

IN 4,690KY 2,750WV 2,530PA 2,030AL 1,520

CT -1,000GA -1,420MA -2,110NC -2,490NY -10,400

MO 60NV 50VT 20AR -2CA -80

Net exchange = Total deaths exported – total deaths imported

+ve: exporter states --ve: importer states~ 0: neutral states

Results 2: Pollution TransportNet Mortalities

• Net flux of impacts between states, shown for the top 10 net fluxes• Expressed in premature deaths

Results 2: Pollution TransportTop 10 net fluxes

Acknowledgments• Funding:

– EPA ACE Center– Other work shown here: U.S. EPA Science to Achieve Results (STAR) Program, U.S.

EPA Climate Change Division; MIT's Leading Technology and Policy Initiative; U.S. National Science Foundation Atmospheric Chemistry, Coupled Natural and Human Systems, and Arctic Natural Sciences programs; MIT Joint Program on Science and Policy of Global Change (and its industrial and foundation sponsors); MIT Research Support Committee Wade Fund; MIT Environmental Solutions Initiative; MIT Center for Environmental Health Sciences/National Institutes of Health (NIEHS)