Embed Size (px)
DESCRIPTION
Development of Cost-Minimized Integrated Control Strategies for Regional Ozone and PM 2.5 Reductions. K.J. Liao 1 , Praveen Amar 2 and A.G. Russell 3 1 Texas A&M University-Kingsville 2 NESCAUM 3 Georgia Institute of Technology. Emission Sources of Ozone and PM 2.5. Ozone. PM 2.5. SO 2. - PowerPoint PPT Presentation
Citation preview
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.
