Regional Air Quality Modeling:From Source Identification to

Health ImpactsAmit Marmur, … , many great students

and senior researchers, and Armistead (Ted) Russell

Georgia Institute of TechnologyAtlanta, Georgia USA

With Special Thanks to:• Paige Tolbert and the Emory crew

– As part of ARIES, SOPHIA, and follow on studies

• NIEHS, US EPA, FHWA, Southern Company, SAMI– Financial assistance

• JGSEE of Thailand• And more…

Issues• Approximately 799,000 excess deaths per year occur per year due

to air pollution – 487,000 in Asia (S, SE and W. Pacific)

• Variety of health impacts in Thailand tied to air pollutants

– Primarily due to:• PM2.5: small particles, Range of health impacts, visibility impairment, …• Ozone • PAPA Studies show strong associations with PM and ozone in Asia

• Most of PM2.5 burden comes from combustion to transform energy– Primary and secondary emissions

• Need reliable approaches to identify how energy sources impact air quality: Source Apportionment– Air quality management– Health impact assessment

Ozone Formation

h (sunlight)

O3

NOx

oxides of nitrogen(NO + NO2)

VOCs Volatile organic compounds

EEVOCVOC

EENOxNOx

Low OLow O33

High OHigh O33

Ozone Isopleth

PM Formation

h (sunlight)

PMNOx

VOCs,OC & EC

SO2

Sulfur dioxide

Particulate Matter

• Complex mixture of solid and liquid particles suspended in the ambient air

• Size classifications– “super-coarse” > 10μm– “coarse” (PM10) < 10μm– “fine” (PM2.5) < 2.5μm– “ultrafine” < 0.1μm

• Many sources• Many chemical species:

BRIG, New Jersey (measured)SulfateNitrateAmmoniumOrganic Carbon

Elemental CarbonSoils and crustals

Fine Particles: Why should we care ?

Airway Inflammation

Effects on Lung Function

RESPIRATORY EFFECTS CARDIOVASCULAR EFFECTS

Effects on Cardiovascular Function

Vascular Inflammation

Image courtesy of the U.S. EPA

Outline• Atmospheric modeling

– Types– Basics– Approaches

• Advanced approaches

• Applications– Source impacts– health impact assessment

PM (Source Apportionment) Models

(those capable of providing some type of information as to how specific sources impact air

quality)PM Models

Emissions-Based

Receptor

Lag. Eulerian (grid)CMB FA

PMF

UNMIXMolec. Mark. Norm.

“Mixed PM”SourceSpecific*

Hybrid

First-principle Statistical

Role of Atmospheric Modeling In Air Quality Assessment

Emissions

Air Quality/Health Impacts

Controls

Pollutant Distributions

Air Quality Model

Air Quality Goals

or

n

jjjii SfC

1,

Receptor Models

n

jjjii SfC

1,

ObsservedAir Quality

Ci(t)

Source Impacts

Sj(t)

Ci - ambient concentration of specie i (g/m3)

fi,j - fraction of specie i in emissions from source j

Sj - contribution (source-strength) of source j (g/m3)

Receptor Models• Strengths

– Results tied to observed air quality– Less resource intensive (provided data is

available)• Weaknesses

– Data dependent (accuracy, availability, quantity, etc.)• Monitor• Source characteristics

– Not apparent how to calculate uncertainties– Do not add “coverage” directly

Emissions-based Air Quality Model

• Representation of physical and chemical processes – Numerical integration

routines• Scientifically most sound

method to link future emissions changes to air quality

ct

Lx, tc fx, t

ct2t Lxt Ly t Lcz2t Lyt Lxt ct

ComputationalPlanes

5-20

50-200

Air Quality Model

200 species x 10000 hor. grids x 20 layers= 40 million coupled, stiff non-linear differential equations

c

tc c R Si

i i i i ( ) ( )u K

Atmospheric Diffusion Equation

Discretize

Operator splitting

50-100

Emissions

Chemistry

Meteorology

NumericsC=AxB+E

Air Quality Model

Air Quality

Temperature Radiation

CloudCover Wind

Emissions1. Anthropogenic2. Geogenic3. Biogenic

Sources

TransportedPollutants

Sources (E,S, BCs)

GeographicalFeatures

Transport(U, K, Vd)

Turbulence

NumericalSolution

Techniques

SurfaceDeposition Sink Processes

Topography &Land use

PhotochemicalReactions

ThermochemicalReactions

HomogeneousProcesses

HeterogeneousProcesses

Computed Concentrations

Meteorology

Aerosol Dynamics

ChemicalProcesses

(R)

Chemistry and Aerosol Dynamics

Atmospheric Modeling Process

Foundation

Pollutant DistributionsEvolving: Sensitivities Uncertainties

EmissionsModel

Meteorological InputsHistorical 2- or 3-D winds; Ground level T, RH; Mixing height, Land use Evolving: 3-D Winds, Diffusivities, Temp., RH, , Solar Insolation (UV & total solar)...

Chemical MechanismHistorical: SpecifiedEvolving: Compiler

NumericalRoutinesHistorical: Advection Chem. Kinet. Evolving Sens. Anal. Proc. Integ. Unc. Anal.

Air QualityModel

EmissionsInputsHistorical: NO, NO2, HONO Lumped VOCs CO, SO2

Evolving: PM, NH3, Detailed VOCs, Adv. Biogenics

Inputs:Emissions InventoryPopulationRoadsLand UseIndustryMeteorology

ModelParameter Calculation

Temperature, Solar Insolation

Chemical Mechanism

Specification

Chemical Mechanism

Specification

Air Quality Data Analysis and ProcessingAir Quality Data Analysis and Processing

Meteorological Model(Diagnostic or Prognostic)

Meteorological Model(Diagnostic or Prognostic)

Model Evaluation

Air Quality Observations

Air Quality Observations

Meteorological Observations

Meteorological Observations

Emissions, Industry and

Human Activity Data

Emissions, Industry and

Human Activity Data

TopographicalData

TopographicalData

Emissions Inventory

Development

Emissions Inventory

Development

Grids

Nested

Multiscale(Odman et al.)

Adaptive(Odman et al.)

About 15 verticalLayers up to 15 km (many in first 1 km)

AIRS Station 47-037-0011; Nashville, Davidson Co, TN (urban)

0

20

40

60

80

100

120

0 24 48 72 96 120 144 168 192 216

Time (starting July 11, 1995)

Ozo

ne (p

pb)

AIRS Station 47-099-0101; Look Rock, Blount Co, TN(high elevation)

020406080

100120

0 24 48 72 96 120 144 168 192 216

Time (starting July 11, 1995)

Ozo

ne (p

pb)

a) Observed

12%

3%

28%

4%

37%

16%

Average PM 2.5 concentration

28.4 g/m3

b) Simulated

11%

3%

34%

3%

28%

21%

Ammonium

Nitrate

Sulfate

EC

OC

Other

Average PM 2.5 concentration

31.6 g/m3

How well do they work?*

*Performance relies on quality of inputs. US has spent decades on emissions inventory development. Meteorological modeling also contributes significantly to errors

Source-based Models

• Strengths– Direct link between sources and air

quality– Provides spatial, temporal and chemical

coverage

• Weaknesses– Result accuracy limited by input data

accuracy (meteorology, emissions…)– Resource intensive

Hybrid: Inverse Model Approach*

Emissions (Eij(x,t)) Ci(x,t), Fij(x,t),

& Sj(x,t)Air Quality

Model +DDM-3D

Receptor Model Observations takenfrom routine measurement

networks or specialfield studies

New emissions:Eij(x,t)

Other Inputs

INPUTS

Main assumption in the formulation:

A major source for the discrepancy between predictions and observations are the emission estimates

What’s next?• Emissions-based air quality models work pretty well, how

might we use them:– Identify, quantitatively, how specific sources impact air quality.– Develop and test control strategies

• Decoupled direct method (implemented in CIT, URM, MAQSIP, CMAQ, CAMX)– Dunker: initial applications– Yang et al.: large scale application, comp. efficient (CIT, URM)– Hakami et al. ,Cohan et al: Higher order, with applications

(MAQSIP, CMAQ)– Napelenok eet al., : PM

• Control strategy assessment– Least cost approach to attainment for Macon, GA (Cohan et al.)

• Assessing impacts of individual sources• Area of Influence analysis (AOI) (similar information as

developing the adjoint)– Or AOPI (potential influence)

• Application to health assessment

Emissions reductions lead to about a 12 ppb ozone reduction:Atlanta and Macon do not attain ozone standard (Macon by 6ppb)

Example Results : Impact of Planned Controls: 2000 vs. 2007

Sensitivity analysis

• Given a system, find how the state (concentrations) responds to incremental changes in the input and model parameters:

Inputs (P)

ModelParameters

(P)

Model

Sensitivity Parameters:

State Variables:

C x, t

S C

Piji

jx, t

If Pj are emissions, Sij are the sensitivities/responses to emission changes, e.g.., the sensitivity of ozone to Atlanta NOx emissions

• Define first order sensitivities as

• Take derivatives of

• Solve sensitivity equations simultaneously

jiij ECS /)1(

Sensitivity Analysis with Decoupled Direct Method (DDM):

The Power of the Derivative

iiiii ERCC

t

C K u

)()(

Advection Diffusion Chemistry Emissions

ijijijij

ESSt

Sij )( )(

JSKu

3-DAir

QualityModel

NOo

NO2o

VOCio

...TKu, v, wEi

ki

BCi

...

O3(t,x,y,z)NO(t,x,y,z)NO2(t,x,y,z)VOCi(t,x,y,z)...

DDM-3DSensitivityAnalysis

s (t)c (t)

piji

j

DDM-3D

J

decoupled

R

ki

j

DDM compared to Brute Force

Emissions of SO2

Sul

fate

j

iij

CS

EB EA

CB

CA

bA

BAij

CCS

C

E

Consistency of first-order sensitivities

Brute Force (20% change) DDM-3D

R2 > 0.99Low bias & error

Advantages of DDM-3D

• Computes sensitivities of all modeled species to many different parameters in one simulation– Can “tell” model to give sensitivities to 10s of

parameters in the same run

• Captures small perturbations in input parameters– Strangely wonderful

• Avoids numerical errors sometimes present in sensitivities calculated with Brute Force

• Lowers the requirement for computational resources

Evidence of Numerical Errors in

BF

NH4 sensitivity to domain-wide SO2 reductions

NOx reductions at a point

Efficiency of DDM-3D

Control Strategy Development

• Macon out of attainment by 6 ppb in 2007

• Want to identify least cost control strategy

• Process:– Identify possible controls and

costs ($/ton of VOC or NOx)– Simulate response to controls

([O3]/ton VOC or NOx)– Calculate control

effectiveness([O3]/$)– Choose most effective controls

until get 6 ppb– Test strategy

Sources of Sources of Macon’s Macon’s ozoneozone

Macon Scherer

Atlanta Branch

8-hr ozone, Aug. 17, 2000(2007 emissions)

MM

AA

SS BB

Sensitivity of 8-hr Ozone in Macon

-0.005

0.000

0.005

0.010

0.015

0.020

0.025

0.030

12-Aug 13-Aug 14-Aug 15-Aug 16-Aug 17-Aug 18-Aug 19-Aug

Atlanta NOX

Branch

Scherer

Macon Non-mobile

Macon Mobile

0

150

300

450

600

750

PlantScherer

Rest ofMacon

"MaconBuffer"

Atlanta(20 cnty)

PlantBranch

Rest ofGA

Yea

r 20

07 N

Ox

Em

issi

on

s (t

pd

) Area

Point

Non-Road

On-Road

NOx emission rates(tpd)

Macon ozone sensitivity (ppt/tpd)

2007 Emissions 2007 Emissions and Sensitivitiesand Sensitivities

0

50

100

150

200

250

300

PlantScherer

Rest ofMacon

MaconBuffer

Atlanta(20 cnty)

PlantBranch

Rest ofGA

S(1

) Mac

on

8-h

r o

zon

e (p

pt/

tpd

)

Source-Receptor Response

Marginal Abatement Costs by Region

$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

0% 10% 20% 30% 40%

Percentage emission reduction in Bibb County

Ma

rgin

al c

ost p

er to

n (Y

ear

20

00$) NOx

VOC

Cost-optimizationCost-optimizationChoose options with

leastmarginal $/impact until: (1) attain a.q. goal, or (2) reach budget

constraint

Cost

Impa

ct

Strategies for Macon attainment (need 6.5 ppb)

$0

$20

$40

$60

$80

$100

$120

$140

$160

$180

$200

0 2 4 6 8 10

Reduction in 8-hour ozone near Macon monitor (ppb)

An

nu

al C

os

t (Y

ea

r 2

00

0$

, in

mill

ion

s)

Macon only

All Georgia

Key Measures• Zero-cost options (PRB coal, burning ban, ...): 1.72 ppb, $0

• Bibb industrial NOx: 0.82 ppb, $2.6 million

• Locomotive controls: 0.77 ppb, $7.3 million

• SCRs at Scherer: 1.63 ppb, $20.9 million

• Vehicle I&M in Bibb: 0.25 ppb, $4.9 million

Provide a technique to evaluate the impacts from a single large emissions source on regional air quality, incorporating non-linear processes and multi-day effects in estimating pollutant responses to relatively small emissions perturbations.

Single-Source Impact Analysis (Bergin et al.)

Motivation and Application

• The ability to evaluate regional secondary pollution impacts from large single sources would provide a valuable tool for more effective air quality management practices, such as refining programs (e.g. emissions trading, regional planning), and supporting more effective compliance enforcement.

• Typical modeling approach (removing the emissions from a single source) has numerical errors.

• Court case led to need to assess impact of a single power plant (Sammis) in Ohio on downwind areas (a distance of up to about 1000 km)

Average Day Elevated NOx Emissions

W. H. Sammis Power Plant(court estimated emissions)

0

500

1000

1500

2000

2500

May-95 Jul-95 Aug-00 OhioElevated EGU

Jul-95Model Inventory

NO

x E

mis

sio

ns

(avg

to

ns/

day

)

excess

allowable

Court Estimated from W.H. Sammis Plant

ApproachTwo air quality models and grids, three ozone episodes, and three sensitivity techniques (brute-force, DDM, higher order DDM)

CMAQ, 36x36 kmAug. 12-20, 20002-ord. DDM

URM, multiscale from 24x24 km2

July 11-19 and May 24-29, 1995DDM

Maximum increase in 1-hr avg O3

Comparison of the maximum increase in hourly-averaged ozone concentrations due to excess NOx emissions from the Sammis plant.

(a) July 11-19, 1995 (b) May 24-29, 1995 (c) August 12-20, 2000

URM with DDMCMAQ with 2nd order DDM

When O3 > 0.060 ppm

1-hr O3 cell responses to excess emissionsAll hours

Max. increases

Max.decreases

maximum = 2.2 maximum = 2.2

minimum = -3.6 minimum = -1.2

CMAQ, 2nd ord DDM, August

Conclusions

• Single-source simulation results agree with past field experiments, indicating that appropriate modeling techniques are available for quantifying single-source regional air quality impacts.

Air Quality Models and Health Impact Assessment

• (How) Can we use “air quality models” to help identify associations between ozone PM sources and health impacts?– Species vs. sources– Very different than

for traditional air quality management

Epidemiology• Identify associations between air quality

metrics and health endpoints:

Sulfate

0

2

4

6

8

10

g / m

3

SDK

FTM

TUC

JST

YG

Sulfate

Health endpoints

StatisticalAnalysis

Association

Epidemiologic Analysis

log{E(CVD)} = + [PM2.5] + covariate terms

Covariates: time trend (mo. knots), day-of-week, holidays, hospital entry/exit, temperature, dew point

70

35

0

140

70

01998 2001 2004 1998 2001 2004

Exposure: daily PM2.5 (g/m3); lag 0, 1, 2 Outcome: daily ED visit counts for CVD

Association between CVD Visits and Air Quality

(Tolbert et al., 2004)

Issues• May not be measuring the species primarily impacting

health– Observations limited to subset of compounds present

• Many species are correlated– Inhibits correctly isolating impacts of a species/primary actors

• Inhibits identifying the important source(s)

• Observations have errors– Traditional: Measurement is not perfect– Representativeness (is this an error? Yes, in an epi-sense)

• Observations are sparse– Limited spatially and temporally

• Multiple pollutants may combine to impact health– Statistical models can have trouble identifying such phenomena

• Ultimately want how a source impacts health– We control sources

Use AQ Models to Address Issues: Link Sources to Impacts

Data

Air Quality Model

SourceImpactsS(x,t)

Health Endpoints

StatisticalAnalysis

Association between Source Impact

and Health Endpoints

Source Impacts on Air Quality(Nov 1998 – Aug 2000)

CMB Source Impacts

8%

9%

2%

6%

1%

38%

3%

9%

19%

5%

LDGV

HDDV

SDUST

BURN

CFPP

AMSULF

AMBSLF

AMNITR

Other OM

UnSpec

Power-plant derived SO4-2

0.0

4.0

8.0

12.0

16.0

20.0

1/1/00 1/31/00 3/1/00 3/31/00 4/30/00 5/30/00 6/29/00 7/29/00 8/28/00 9/27/00 10/27/00 11/26/00 12/26/00 1/25/01 2/24/01 3/26/01 4/25/01 5/25/01 6/24/01 7/24/01 8/23/01 9/22/01 10/22/01 11/21/01 12/21/01

ug

/m3

JST FTM SD TU CMAQ

Diesel Elemental Carbon Particulate Matter

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Au

g-9

9

Se

p-9

9

Oct

-99

No

v-9

9

De

c-9

9

Jan

-00

Fe

b-0

0

Ma

r-0

0

Ap

r-0

0

Ma

y-0

0

Jun

-00

Jul-

00

Au

g-0

0

Se

p-0

0

Oct

-00

No

v-0

0

De

c-0

0

Jan

-01

Fe

b-0

1

Ma

r-0

1

Ap

r-0

1

Ma

y-0

1

Jun

-01

Jul-

01

Au

g-0

1

ug

/m3

CMAQ36km JST FTM TU SD

All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)

0.90

0.95

1.00

1.05

1.10

Wood - PMF Wood -CMB-LGO

K OC PM2.5 Wood - PMF Wood -CMB-LGO

K OC PM2.5

Source-specific RRs: Wood burning

RR

RR

95% CI

All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)

RR significant if CI does not cross unity (RR=1.0)

All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)

0.90

0.95

1.00

1.05

1.10

Wood - PMF Wood -CMB-LGO

K OC PM2.5 Wood - PMF Wood -CMB-LGO

K OC PM2.5

Source-specific RRs: Wood burning

RR

All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)

Wood-PMF

K

Wood-CMB-LGO

OC PM2.5

All respiratory (262 daily ED visits) Upper Respiratory Infection (161 daily ED visits)

0.90

0.95

1.00

1.05

1.10

Diesel

- PM

F

Diesel

- CM

B-LGO

Gasoli

ne -

PMF

Gasoli

ne -

CMB-L

GO

Mob

ile -

PMF

Mob

ile -

CMB-L

GO Zn FeEC

PM2.

5CO

Diesel

- PM

F

Diesel

- CM

B-LGO

Gasoli

ne -

PMF

Gasoli

ne -

CMB-L

GO

Mob

ile -

PMF

Mob

ile -

CMB-L

GO Zn FeEC

PM2.

5CO

Source-specific RRs: Mobile sources

All respiratory (263 daily ED visits) All cardiovascular (86 daily ED visits)

Diesel-PMF,CMB-LGO

Mobile-PMF

PM2.5CO

EC

Fe

Gas-PMF

CO

Source-specific RRs: Soil dust

All resp. (263 ED visits) Asthma/Wheeze (54 ED visits) All CVD (86 ED visits)

0.90

0.95

1.00

1.05

1.10

Soil -PMF

Soil -CMB-LGO

Si CoarsePM

PM2.5 Soil -PMF

Soil -CMB-LGO

Si CoarsePM

PM2.5 Soil -PMF

Soil -CMB-LGO

Si CoarsePM

PM2.5

Soil- CMB-LGO

All resp. (263 ED visits) All CVD (86 ED visits)Asthma (54 ED visits)

Soil-PMF

Si

Soil- CMB-LGO

PM2.5

Source-specific RRs: “Other” OC

0.90

0.95

1.00

1.05

1.10

1.15

1.20

Other

OC OC

PM2.

5

Gasoli

ne -

PMF

Other

OC OC

PM2.

5

Gasoli

ne -

PMF

Other

OC OC

PM2.

5

Gasoli

ne -

PMF

Other

OC OC

PM2.

5

Gasoli

ne -

PMF

Other

OC OC

PM2.

5

Gasoli

ne -

PMF

Other

OC OC

PM2.

5

Gasoli

ne -

PMF

Asthma/ Wheeze (54)

COPD (13) URI (161) Pneumonia(34)

All respiratory(263)

All CVD (86)

“Other” OC

“Other” OC

“Other” OC

“Other” OC

“Oth

er”

OC OC

PM 2.5

Summary• Air quality models provide powerful tools to link how

energy conversion and utilization impact air quality, health and the environment.– Emissions-based (First principles)– Receptor (statistical) models

• Advanced techniques provide means to efficiently assess impacts from individual sources and non-linear interactions– DDM

• Application of PM Source apportionment models in health studies more demanding than traditional modeling– Provide additional power and insight to identifying which

sources impact health, not just which species• Particularly important for organic carbon that comes from many

sources

Thanks… Questions?