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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
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Emission Sources of Ozone and PM2.5
SO2 NOx VOC PM NH3
Ozone PM2.5
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Traditional Framework for Developing State Implementation Plan (SIP)
Cohan et al., 2007
Air Quality Modeling
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Objective
Development of optimal (cost-minimized) control strategies for:
- achieving ozone and PM2.5 targets - at multiple locations simultaneously.
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Ozone IsoplethsUnit: ppb
.,....),,(][ 3 metVOCNOxfO
http://www-personal.umich.edu/~sillman/ozone.htm
Current
Target
Multiple choices for control strategies.
VOC-sensitive
NOx-sensitive
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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?
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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
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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
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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
,,,, ..
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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)
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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)
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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
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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
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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
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• 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]
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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 -
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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.