Di Tian , Georgia Department of Natural Resources Daniel Cohan , Rice University
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Transcript of Di Tian , Georgia Department of Natural Resources Daniel Cohan , Rice University
Georgia Environmental Protection Division
Uncertainty Analysis of Ozone Formation and Emission Control Responses using High-order Sensitivities
Di Tian, Georgia Department of Natural Resources
Daniel Cohan, Rice University
Sergey Napelenok, Atmospheric Sciences Modeling Division, NOAA In partnership with the U.S. EPA
Yongtao Hu, Michael Chang, Armistead Russell, Georgia Institute of Technology
October 2, 2007
6th Annual CMAS Conference, October 1-3, 2007
Georgia Environmental Protection Division
Overview
Source oriented air quality modeling (AQM) Uncertainty analysis – Monte Carlo method Reduced form AQM based on first and high-order
sensitivities Case study
Projected air quality during 2007 in the southeastern U.S. Three base episodes: 8/1 – 8/15/1999, 8/11 – 8/19/2000, 7/5
– 7/17/2001 Uncertainties in simulated ozone concentrations Uncertainties in simulated ozone reduction from emission
controls
Georgia Environmental Protection Division
Source-oriented Air Quality Modeling
Meteorology (MM5): Surface, PBL, Cumulus, Explicit Moisture Scheme, one-way nesting, FDDA, etc.
Emission processing (SMOKE v2.1): Temporal, spatial, speciation Air quality model (CMAQ v4.3): Advection, diffusion, chemistry,
cloud, deposition, etc
Meteorology
Emissions
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Air Quality Model
Chemistry
Georgia Environmental Protection Division
How reliable is air quality modeling?
Stationary Point NOX
Mobile NOX
Biogenic VOC
Anthropogenic VOC
Ozone (grid, time-step)Emissions Concentrations
SourceOriented
AQMN Runs
Simulation 1Simulation 2Simulation 3...Simulation N
Probability distributionmean,std,cov=std/mean,C97.5, C50, C2.5
Uncertainty Analysis – Monte Carlo Method
Quantify uncertainties and provide information to policy makers
Computationally Expensive!!!
If one AQM run takes 10hr, 1000 runs x 10hr/run = 10,000hrs
For 10 types of emissions controls, 10,000hrs x 10 = 100,000hrs
Georgia Environmental Protection Division
Reduced-Form Ozone AQM (RFAQM)
Ozone sensitivities to different emission sources Provide detailed insight into complicated responses
• First and second-order sensitivities (Hakami, 2004 and Cohan, 2005) Vary in Space and time CMAQ-DDM: Decoupled Direct Method Calculate sensitivities/responses of gas and aerosol phase concentrations to
emission changes together with concentrations Computationally efficient Source apportionment and control strategy development
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Nonlinear ozone response to emissions
Taylor expansions:
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Air Quality Modeling - FAQS
Episode N MOC (ppbv) MB (ppbv) RMSE (ppbv) MNB (%) MNE (%)
1999 26032 67.6 -3.10 17.2 -2.69 20.3
2000 14104 63.6 -1.10 14.9 -0.51 19.1
2001 15783 59.1 1.40 12.6 4.19 17.4
Model Performance Statistics
Fall Line Air Quality Studyhttp://cure.eas.gatech.edu/faqs/index.html
Three Episodes: based on CART analysis8/1 – 8/15/19998/11 – 8/19/20007/5 – 7/17/2001
Georgia Environmental Protection Division
Domain-wide daily NOX and VOC emissions during 2007 (tons per day)
1999 2007_1999
Projected Air Quality in 2007
NOX VOC
Sources 1999 2000 2001 1999 2000 2001
Stationary Point 2590 2190 2206 930
Stationary Area 468 3300
Mobile Onroad 2060 2000 2040 1260 1230 1270
Mobile Nonroad 1100 645
Biogenic 368 353 330 39900 37400 32000
Emissions in 2007:
•Growth factor: EGAS
•Controls: NOX SIP call, VOC RACT and MACT, etc
Georgia Environmental Protection Division
Emission Uncertainties
Expert elicitation (Hanna, 2001) Log-normal distributions 95% CI: (nominal / factor, nominal x factor)
• Point source: Factor of 1.5
• Other sources: Factor of 2
Non-road mobile emission uncertainties (Chi, 2004) NOX emissions: Factor of 1.6 VOC emissions: Factor of 1.5
Biogenic emission uncertainties using BEIS3 (Hanna, 2005)
Qualitative uncertainties NARSTO emission inventory assessment, 2005
E 2EE/2
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40
60
80
100
120
140
160
8/3 8/4 8/5 8/6 8/7 8/8 8/9 8/10 8/11 8/12 8/13 8/14 8/15Date
Ozo
ne
con
cen
trat
ion
(p
pb
v)
Daily peak 8-hour ozone concentrations (ppb) and 95% CI
Downtown Atlanta, Georgia, base year 1999
Emission uncertainties, 95% CI
Uncertainties in Ozone Simulations (1)
Stationary point NOX emissions: factor of 1.5Non-point NOX emissions (onroad and nonroad mobile, area): factor of 2Biogenic VOC: factor of 2Anthropogenic VOC: factor of 2
Georgia Environmental Protection Division
Cut-off = 40 ppbv, error bar refers to variability in such ratios, 95% range
Uncertainties in Ozone Simulations (2)
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
C50/norminal C97.5/C50 C50/C2.5
rati
o
1999 2000 2001
-20
0
20
40
60
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120
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180
40 60 80 100 120 140 160 180
nominal concentrations (ppbv)
conc
entr
atio
n pe
rcen
tiles
(pp
bv)
2.5th 50th 97.5th
1999
Summary by different base yearsScatter plots for 95% CI
Georgia Environmental Protection Division
Emission Control Responses
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Ozone concentrations when emissions are reduced by a factor femis
Ozone reduction (ppb)
Control efficiency (%)
Nonlinear ozone response to emissions
Ozone reduction per unit emissions (ppt/tons per day)
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Georgia Environmental Protection Division
Uncertainties in Emission Control Responses (1)
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jjiji
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iii SSCC
1 1
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1
Nonlinear ozone response to emissions Random emissions Pj
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,1 ~~
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jemisfj
j
jj
P
Pf
P
P
Ozone responses to controls of Atlanta point source emissions
0
0.5
11.5
2
2.5
3
3.54
4.5
5
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1emission reduction
ozo
ne
re
du
ctio
n (
pp
b)
nominal
50th
0
2.5
5
7.5
10
12.5
15
17.5
20
22.5
25
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1emission reduction
ozon
e re
duct
ion
(ppt
/tpd
)
nominal
50th
0
0.5
1
1.5
2
2.5
3
3.5
4
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1emission reduction
con
tro
l eff
icie
ncy
(%
)
nominal
50th
Ozone reduction (ppb) Ozone reduction (ppt/tpd) Control efficiency (%)
Peak 8-hr ozone, base year 1999, Downtown Atlanta, Georgia
Georgia Environmental Protection Division
Uncertainties in Emission Control Responses (2)
N
i
N
jjiji
N
iii SSCC
1 1
)2(,
1
)1(0 2
1
Nonlinear ozone response to emissions Random emissions Pj
1)1(
,1 ~~
j
jemisfj
j
jj
P
Pf
P
P
Ozone responses to controls of Atlanta onroad mobile source emissions
Ozone reduction (ppb) Ozone reduction (ppt/tpd) Control efficiency (%)
Peak 8-hr ozone, base year 1999, Downtown Atlanta, Georgia
-10
-5
0
5
10
15
20
25
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
emission reduction
ozo
ne
re
du
ctio
n (
pp
b)
nominal
50th
-20
-100
10
20
3040
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7080
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
emission reduction
ozon
e re
duct
ion
(ppt
/tpd
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nominal
50th
-10
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
emission reduction
con
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l eff
icie
ncy
(%
)
nominal
50th
Georgia Environmental Protection Division
Ozone Reduction (ppb) base year 1999, cutoff = 80ppb
-2
-1.5
-1
-0.5
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1.5
2
2.5
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ozone concentration (ppb)
ozon
e re
duct
ion
(ppb
)
97.5th 50th 2.5th 0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
80 90 100 110 120 130 140 150ozone concentration (ppb)
ozon
e re
duct
ion
(ppb
)
97.5th 50th 2.5th
-5
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-3
-2
-1
0
1
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80 90 100 110 120 130 140 150
ozone concentration (ppb)
ozon
e re
duct
ion
(ppb
)
97.5th 50th 2.5th-5
-4
-3
-2
-1
0
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3
80 90 100 110 120 130 140 150
ozone concentration (ppb)
ozon
e re
duct
ion
(ppb
)
97.5th 50th 2.5th
Atlanta Point
Atlanta Mobile OnroadAtlanta Mobile Nonroad
Outside Atlanta Point
Georgia Environmental Protection Division
Summary of Uncertainties in Emission Control Responses: base year 1999
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
0.1 0.2 0.3 0.4 0.5emission reduction
ozon
e re
duct
ion
(ppb
)Atlanta pointoutside Atlanta pointAtlanta mobile onroadAtlanta mobile nonroad
0.0
5.0
10.0
15.0
20.0
25.0
0.1 0.2 0.3 0.4 0.5emission reduction
ozon
e re
duct
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(ppb
/tpd)
Atlanta pointoutside Atlanta pointAtlanta mobile onroadAtlanta mobile nonroad
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3.0
4.0
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7.0
0.1 0.2 0.3 0.4 0.5emission reduction
Con
trol
effi
cien
cy (
%)
Atlanta pointoutside Atlanta pointAtlanta mobile onroadAtlanta mobile nonroad
Ozone reduction (ppb) Ozone reduction (ppt/tpd)
Control efficiency (%)
0
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10
12
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0.1 0.2 0.3 0.4 0.5emission reduction
Neg
ativ
e re
duct
ion
(%)
Percents of 95% CI overlapping 0
Georgia Environmental Protection Division
Summary
RFAQM developed using first and second order ozone sensitivities
Computationally efficient for detailed uncertainty analysis Uncertainties in ozone simulations
Easily redo for different emission uncertainties Uncertainties in emission control responses
Don’t need to rerun AQM for different emission controls Large nonlinear relationships of ozone to mobile source emissions Emission controls can lead to increased ozone concentrations
Future work Incorporate cost-benefit analysis $$$, evaluate their associated
uncertainties