K.J. Liao 1 , Praveen Amar 2 and A.G. Russell 3

Development of Cost-Minimized Integrated Control Strategies for

Regional Ozone and PM2.5

Reductions

K.J. Liao1, Praveen Amar2 and A.G. Russell3

1Texas A&M University-Kingsville2NESCAUM

3Georgia Institute of Technology

Emission Sources of Ozone and PM2.5

SO2 NOx VOC PM NH3

Ozone PM2.5

Traditional Framework for Developing State Implementation Plan (SIP)

Cohan et al., 2007

Air Quality Modeling

Objective

Development of optimal (cost-minimized) control strategies for:

- achieving ozone and PM2.5 targets - at multiple locations simultaneously.

Ozone IsoplethsUnit: ppb

.,....),,(][ 3 metVOCNOxfO

http://www-personal.umich.edu/~sillman/ozone.htm

Current

Target

Multiple choices for control strategies.

VOC-sensitive

NOx-sensitive

Air Pollutant 1

Air Pollutant 2

Air Pollutant n

Control Strategy 1

Control Strategy 2

Control Strategy n

Current LevelsAir Quality Targets (e.g. NAAQS)

Air Pollutant 1

Air Pollutant 2

Air Pollutant n

Optimal Air Pollution Control Strategy for Multi-Pollutants and Multi-Locations

Challenge:Challenge:

Air pollutants at different locations have different responses to changes in precursor emissions from common sources

Cost-minimized?

What We Need to Optimize Air Quality Control Strategies?

Emissions

Air Quality

Emission Control Costs

Responses of air pollutants to emission controls

Optimal control strategy:

Least-cost measures for achieving air quality target

-Cost function

-Limit of control efficiency

Air Quality Modeling

Central

Great Lake

Mid - Atlantic

Northeast

Southeast

West

Central

Great Lake

Mid - Atlantic

Northeast

Southeast

West

Central

Great Lake

Mid - Atlantic

Northeast

Southeast

West

-Two pollutants: - ozone - PM2.5

-Regional precursors:-SO2 - NOx - VOC

- Local primary PM2.5

- EPA Models-3: - MM5 - SMOKE - CMAQ-DDM

Six regions

Assumptions

1. First-order sensitivities:

2. Ignore co-benefits of emission reductions for multiple precursors

3. Primary PM2.5 emissions only have local effects on air quality (Napelenok et al., 2006, Kim et al., 2002): Metropolitan Statistical Area (MSA)

j

ijijijpriorinewi

CSTOHSCC

,,,, ..

Ozone Sensitivity (CMAQ-DDM)

4th MDA 8h ozone concentration (ppbv)

115.2 | 126.6 | 120.2 | 114.6 | 91.3

-10.0

0.0

10.0

20.0

30.0

40.0

50.0

Atlanta Chicago Houston Los Angeles New York

Se

ns

itiv

ity

(p

pb

v)

SOUTHEAST_ NOx GREAT_LAKE_NOx CENTRAL_NOx WEST_NOx

NORTHEAST_NOx MID-ATLACTIC_NOx SOUTHEAST_ VOC GREAT_LAKE_VOC

CENTRAL_VOC WEST_VOC NORTHEAST_VOC MID-ATLACTIC_VOC

(July 2001)

Sensitivity of PM2.5 and Primary PM2.5 Concentrations (CMAQ-DDM)

Average Total PM2.5 (µg m-3)

24.7 | 23.8 | 26.3 | 24.1 | 22.6

0.0

5.0

10.0

15.0

20.0

25.0

Atlanta Chicago Houston Los Angeles New York

Pri

ma

ry P

M2.

5 c

on

ce

ntr

ati

on

or

Se

ns

itiv

ity

(µ

g m

-3)

Primary PM2.5 SOUTHEAST_ SO2 GREAT_LAKE_SO2 CENTRAL_SO2

WEST_SO2 NORTHEAST_SO2 MID-ATLACTIC_SO2 SOUTHEAST_ NOx

GREAT_LAKE_NOx CENTRAL_NOx WEST_NOx NORTHEAST_NOx

MID-ATLACTIC_NOx SOUTHEAST_ VOC GREAT_LAKE_VOC CENTRAL_VOC

WEST_VOC NORTHEAST_VOC MID-ATLACTIC_VOC

Obs. 23.5 18.2 9.7 21.0 13.1

(July 2001)

Per-ton Cost of Emission Reduction(EPA AirControlNET)

SOUTHEAST

0

5,000

10,000

15,000

20,000

25,000

30,000

0 20 40 60 80 100Emission Reduction (%)

Co

st

(19

99

$/t

on

)

SO2

NOx

VOC

GREAT_LAKE

0

5,000

10,000

15,000

20,000

25,000

30,000

0 20 40 60 80 100Emission Reduction (%)

Co

st

(19

99

$/t

on

)

SO2

NOx

VOC

CENTRAL

0

5,000

10,000

15,000

20,000

25,000

30,000

0 20 40 60 80 100Emission Reduction (%)

Co

st (

1999

$/to

n)

SO2

NOx

VOC

WEST

0

5,000

10,000

15,000

20,000

25,000

30,000

0 20 40 60 80 100Emission Reduction (%)

Co

st

(19

99

$/t

on

)

SO2

NOx

VOC

NORTHEAST

0

5,000

10,000

15,000

20,000

25,000

30,000

0 20 40 60 80 100

Emission Reduction (%)

Co

st

(19

99

$/t

on

)

SO2

NOx

VOC

MID-ATLANTIC

0

5,000

10,000

15,000

20,000

25,000

30,000

0 20 40 60 80 100Emission Reduction (%)

Co

st

(19

99

$/t

on

)

SO2

NOx

VOC

Per-ton Cost of Primary PM2.5 Emission Reductions

(EPA AirControlNET)

0

20,000

40,000

60,000

80,000

100,000

120,000

0 10 20 30 40 50 60

Emission Reduction (%)

Co

st (

1999

$/to

n)

Atlanta

Chicago

Houston

Los Angeles

New York

OPtimal Integrated Emission Reduction Alternatives (OPERA)

k

kPMprimarykPMprimaryji

jiji CostCost ,5.2_,5.2_,

,,

kgettarOkpriorOji

kjiOji CCS ,,,,,

,,,, 333

kgettarPMkpriorPMkPMprimarykPMprimaryji

kjiPMji CCCS ,,,,,_,_,

,,,, 5.25.25.25.25.2

kPMprimarykPMprimary

jVOCjVOC

jNOxjNOx

jSOjSO

R

R

R

R

,_,_

,,

,,

,,

5.25.2

22

0

0

0

0

Minimize

Subject to:

Constraints for emission control efficiency

Ozone target

PM2.5 target

• the objective function in OPERA: nonlinear and non-convex

• using the Matlab “fmincon” function: Quasi-Newton method and multiple initial points

Solving Method

[Mathworks, 2009]

Atlanta10%

Chicago10%

Houston 10%

Los Angeles10%

New York10%

All Cities 10%

All Cities 15%

All Cities 20%

Anthropogenic SO2 emissions

SOUTHEAST_ SO2~ 0 ~ 0 ~ 0 ~ 0 2.0 5.7 41.3 infeasible

GREAT_LAKE_ SO2~ 0 10.9 ~ 0 ~ 0 ~ 0 10.8 55.3 infeasible

CENTRAL_SO2~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 25.4 Infeasible

WEST_ SO2~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 Infeasible

NORTHEAST_ SO2~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 Infeasible

MID-ATLANTIC_ SO2~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 Infeasible

Anthropogenic NOx emissions

SOUTHEAST_ NOx31.6 23.8 22.4 5.1 11.5 31.9 63.6 Infeasible

GREAT_LAKE_ NOx16.8 62.9 ~ 0 ~ 0 30.3 64.5 72.4 infeasible

CENTRAL_ NOx19.3 34.2 39.7 20.8 21.2 37.9 77.2 Infeasible

WEST_ NOx~ 0 22.4 ~ 0 ~ 0 ~ 0 ~ 0 64.0 Infeasible

NORTHEAST_ NOx~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 Infeasible

MID-ATLANTIC_ NOx~ 0 ~ 0 ~ 0 ~ 0 47.9 41.3 53.7 Infeasible

Anthropogenic VOC emissions

SOUTHEAST_VOC ~ 0 ~ 0 ~ 0 ~ 0 ~ 0 0.8 17.5 Infeasible

GREAT_LAKE_VOC ~ 0 59.6 ~ 0 ~ 0 1.1 57.1 89.1 Infeasible

CENTRAL_VOC ~ 0 3.3 3.1 ~ 0 ~ 0 3.4 28.7 Infeasible

WEST_VOC ~ 0 0.7 ~ 0 46.6 ~ 0 40.2 74.3 Infeasible

NORTHEAST_VOC ~ 0 ~ 0 ~ 0 ~ 0 75.5 66.9 86.7 infeasible

MID-ATLANTIC_VOC ~ 0 ~ 0 ~ 0 ~ 0 60.4 43.0 78.0 Infeasible

Primary PM2.5 emissions

Atlanta_PM2.522.4 ~ 0 ~ 0 ~ 0 ~ 0 15.4 4.5 Infeasible

Chicago_PM2.5~ 0 11.3 ~ 0 ~ 0 ~ 0 12.3 20.0 Infeasible

Houston_PM2.5~ 0 ~ 0 17.4 ~ 0 ~ 0 19.2 23.3 Infeasible

Los Angeles_PM2.5~ 0 ~ 0 ~ 0 42.4 ~ 0 11.8 14.2 infeasible

New York_PM2.5~ 0 ~ 0 ~ 0 ~ 0 38.0 22.0 22.3 Infeasible

Cost (millions of 1999$) 1,766 14,205 2,422 3,649 10,427 23,459 77,887 -

Conclusions

• OPERA needs responses of air pollutants to emission controls, cost functions of emission reductions and emission control efficiencies.

• Responses of air pollutants to emission controls are quantified using CMAQ-DDM.

• Cost functions and emission control efficiencies are developed using AirControlNET.

• OPERA is efficient in developing cost-minimized control strategies for achieving prescribed multi-pollutant targets at multiple locations and could help policy-makers improve their decision-making processes.