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Georgia Institute of Technology
Air Pollutant Transport, Control and Modeling Issues in the
Eastern United States
Ted RussellAir Resources Engineering Center
Georgia Tech
August 26, 2003
Georgia Institute of Technology
Acknowledgements
• GIT Colleagues– T. Odman, J. Boylan, A. Hakami, M. Bergin, D. Cohan,
Y. Hu, A. Unal, D. Tian, M. Kahn, H. Park, A. Marmur, J. Wilkinson, M. Chang, et al.
• SAMI– Financial support and, particularly, intellectual
contributions and guidance
• RFF Colleagues– J-S. Shih and A. Krupnick
• NSF and EPA
Georgia Institute of Technology
Issues• Ozone modeling morphing in to multi-pollutant
models– Confidence for ozone (?)– How about other species?
• What are the major uncertainties, and what is being done?
• Long range and near-field impacts of air pollutants – Is it me or my next door neighbor, or the guy down the
street?
• Georgia EAC Modeling– Issues– Preliminary results
• Other bits of interest– Reactivity, VOC vs. NOx (it never dies)
Georgia Institute of Technology
AQM State of the Science: Where are We?
• “One atmosphere”/“3rd generation” urban-to-regional/continental models are at the forefront– Combined
gas/aerosol/deposition & nested/multiscale/adaptive (?)
– Some built in diagnostic features
• Process analysis• Sensitivity analysis
• Dominant PM models– REMSAD (simplified)– Models 3/CMAQ– CAMX-PM (less application for
PM)
Regional Multiscale Model
Georgia Institute of Technology
Grids
Nested
Multiscale
Adaptive
Georgia Institute of Technology
How Good Are They?
• All evidence suggests that they describe the processes most affecting the evolution of ozone and (if equipped) particulate matter (o.k., many components of PM) after pollutant emission
Science (chemistry/physics)
Mathematics
Computational implementation
Evaluation
Uncertainties
Limitations & uncertainties
Limitations
Uncertainties, lack of confidence
Holes lead to:
Application Uncertainties, poor results
Georgia Institute of Technology
Confidence?• Should go in as a skeptic, however• Current models rather accurately (not perfectly) simulate
ozone and (most) associated species– Multiple, very different areas:
• Urban (L.A., Mexico City, NY, Tokyo, …)• Regional (Eastern U.S., NW, Europe, Asia, …)
– Different meteorologies• Across seasons, high, mid, low ozone
• Explain observed trends– 10 year trend in NOx, VOC, CO and ozone in Switzerland
• Doesn’t come easy:– 60% emissions, 30% meteorology, 5% model development, 5%
model application/evaluation • Emissions still most uncertain
• Expect “rapid” advances in PM– Supersites, RPO modeling, EPA-funded research
Georgia Institute of Technology
Performance (Good)Surface O3 in faqs4
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
Observed
Sim
ua
lte
d
y=0.56*x+0.030R2=0.49
Surface Ozone (40ppb cutoff)
05
10152025303540
Episode Day
MN
E(%
)
faqs12
faqs4
35%
Ozone
Sulfate
Georgia Institute of Technology
Performance (Not so good)• PM Performance (Seignuer et
al., 2003)– Errors from recent studies
using CMAQ, REMSAD• Organic carbon: 50-140%
error• Nitrate: 50-2000% error
– Understand the reason for much of the error in nitrate
• Deposition, heterogeneous reaction
• Ammonia emissions still rather uncertain
– OC more difficult• Understand part (most?)• More complex mixture• Primary/precursor emissions
uncertain Organic Carbon
Georgia Institute of Technology
Long vs. Short Range Impacts
• What is the relative contribution of local vs. nearby vs. distant emission sources?
• Approach– Apply air quality model with sensitivity analysis to
develop a matrix of interstate and intrastate impacts of NOx and SO2 emissions on ozone and PM2.5, respectively.
• NOx emissions had minimal impact on PM2.5 because the aerosol is relatively acidic (for now)
• Periods– RFF
» May, 1995: typical levels of ozone and sulfate» July, 1995: High levels of sulfate and ozone
– Southern Appalachians Mountains Initiative: Year average
Sources: Shih et al., (2003) and Boylan et al., (2003)
Georgia Institute of Technology
J uly
12
July
14
Ground Level ElevatedExample: Impact of Michigan NOx emissions downwind:
Georgia Institute of Technology
8-hour Ozone change for 30% Elevated Source NOx Reduction
Georgia Institute of Technology
g/m3/1000 tons/dayMay-July episodes
Sulfate Sensitivity to SO2 Emissions
Georgia Institute of Technology
GR
SM
LIG
O
SH
RO
JO
KM
CO
HU
SIP
S
JE
FF
OT
RC
SH
EN
DO
SO
Ozone W126 to Elevated NOxOzone W126 to Ground NOx
Wet Sulfate to SO2
Aerosol Sulfate to SO2
-3.000
-2.000
-1.000
0.000
Se
ns
itiv
itie
s (
%)
Sensitivities to 10% Emission Reductions in TN
Ozone W126 to Elevated NOx Ozone W126 to Ground NOx
Wet Sulfate to SO2 Aerosol Sulfate to SO2
Georgia Institute of Technology
Long Range vs. Near Field
• While long-range transport is important, near field impacts are major– Both SO2/sulfate and NOx/ozone
• Interstate transport very episode specific– SAMI looked at annual average, even stronger
conclusion about importance of local (near-field) controls
Georgia Institute of Technology
Georgia EAC Modeling
• Cities/Episodes– Augusta, North Georgia– Primary: August 2000 (episode selection suggests
representative)– Coming: August 1999, July 2001
• Issues– Modification of inventory (NET96NEI99)– Default vertical diffusivity
• Performance– Ozone– Precursors
• Preliminary 2007
Georgia Institute of Technology
36-km
4-km
12-km
Georgia Institute of Technology
Problem 1: Minimum vertical eddy diffusivity
• CMAQ Defaul of 1.0 m2/s
Plot of Simulated and Observed Surface Ozone Concentrations in Columbus, GA, Using a minimum vertical eddy diffusivity of 1.0m2/s.
Georgia Institute of Technology
Figure 6.11 Midnight Surface Ozone Concentrations on August 17th, 2000 in the FAQS 12-km Grid Using the Minimum Vertical Eddy Diffusivity of 1.0m2/s.
Still very high at midnight in most urban and suburban areas, over 30ppb and 60ppb
Georgia Institute of Technology
Simulated and Observed Surface NO Concentrations in the FAQS 12-km Grid for the Episode of August 11th - 20th,2000, the Minimum Vertical Eddy Diffusivity of 1.0m2/s was used in CMAQ.
Underestimate at night
Georgia Institute of Technology
Figure 6.14 Midnight Surface Ozone Concentrations on August 17th, 2000 in the FAQS 12-km Grid Using the Minimum Vertical Eddy Diffusivity of 10 -4m2/s.
• Minimum diffusivity reset to 10-4 m2/s (McNider and Pielke, 1981)
Corrected !
Georgia Institute of Technology
Time Series Plot of Simulated and Observed Surface Ozone Concentrations in Columbus, GA,the Minimum Vertical Eddy Difusivity of 10-4m2/s was used in CMAQ.
Georgia Institute of Technology
Figure 6.17 Simulated and Observed Surface NO Concentrations in the FAQS 12-km Grid for the Episode of August 11th - 20th,2000, the Minimum Vertical Eddy Difusivity of 10-4m2/s was used in CMAQ.
Corrected !
Georgia Institute of Technology
Late Afternoon Surface Ozone Concentrations on August 17th, 2000 in the FAQS 12-km Grid Using the Minimum Vertical Eddy Diffusivity of 10-4m2/s.
Isolated strange hot spots?
Georgia Institute of Technology
Time Series Plot of Simulated and Observed Surface Ozone Concentrations in Santa Rosa County, FL, the Minimum Vertical Eddy Difusivity of 10-4m2/s was used in CMAQ.
Extremely high ozone happened in the grid cells over mixed land-use where water is the majority
Georgia Institute of Technology
Problem 2: Atmosphere too stable over cells with mixed
water/other landuses• Stability determined by land-use with largest
fraction• Cell vertical diffusion went very stable over
lakes and coastal sites• Used 9-point averaging in such cells
Georgia Institute of Technology
Late Afternoon Surface Ozone Concentrations on August 17th, 2000 in the FAQS 12-km Grid Using the Minimum Vertical Eddy Diffusivity of 10-4m2/s and a 9-point averaging method.
• A 9-point averaging method was used to smooth …
Georgia Institute of Technology
Time Series Plot of Simulated and Observed Surface Ozone Concentrations in Santa Rosa County, FL, the Minimum Vertical Eddy Diffusivity of 10-4m2/s and a 9-point averaging method
Georgia Institute of Technology
Surface Ozone Daily MNB during the Episode of August 11th -20th, 2000 for the 106 Stations in the 12-km Grid or the 25 Stations in the 4-km Grid
Surface Ozone Daily MNE during the Episode of August 11th -20th, 2000 for the 106 Stations in the 12-km Grid or the 25 Stations in the 4-km Grid
Daily Bias and Errors of Ozone
Georgia Institute of Technology
Time Series Plot of Simulated and Observed Surface NO Concentrations at PAMS Station
NO
Diurnal Plots
Time Series Plot of Simulated and Observed Surface NOZ Concentrations at PAMS Station
NOy
Georgia Institute of Technology
Time Series Plot of Simulated and Observed Surface ARO1 Concentrations at PAMS Station
ARO1
Time Series Plot of Simulated and Observed Surface ALK1 Concentrations at PAMS
Station
ALK1
Georgia Institute of Technology
Estimated Change in Daily Maximum Ozone Concentrations in the 12km grid (on the left) and the 4-km grid (on the right) from August 17th 2000 to 2007 under the existed Federal Control
Strategies.
Difference of Daily Maximum Ozone: 2007-2000
Reductions typically 8-14 ppb
Georgia Institute of Technology
NOx vs. VOC: Did the Pendulum Swing Too Far?
• Are VOC controls beneficial, and where?
• Is attainment dependent upon NOx controls?
• What are the additional issues?
Georgia Institute of Technology
Chemical RegimesRadical Limited: Abundant NO2 removes OH, inhibittingoxidation of VOCs and HO2/RO2 formation: Low utilization of NOx emissions
Volatile Organic Compounds (VOCs)
Nitr
oge
n O
xide
s (
NO
x)
Low O3
Transport
NOx Limited: Lack of NOx limits ozoneformation via photolysis, increased destruction of HO2/RO2: High utilizationof NOx emissions
High O3
Georgia Institute of Technology
Influence of Biogenics
Volatile Organic Compounds (VOCs)
Nitr
oge
n O
xide
s (
NO
x)
SEHot, sunny
SETypicalNE
Typical
Georgia Institute of Technology
Biogenic Emissions
(a)
(b)
(c)
Day 1 Day 2 Day 3
Biogenic emissions vary significantly by location and day
Source: Hanna et al., 2003
Georgia Institute of Technology
VOC SensitivityAnthropogenic VOC Isoprene
Georgia Institute of Technology
VOCVOCNOXNOXM
ob
ilM
ob
ilee
No
n-
No
n-
Mo
bile
Mo
bile
Georgia:
Even in Atlanta, VOC controls can be effective & some local NOx inhibition (seen in observations, too)
Georgia Institute of Technology
VOC vs. NOx
• VOC controls effective for controlling ozone, even on high ozone days– Can be relatively more effective on cooler, lower
ozone days– Additional benefits: lower carbonaceous PM, toxics
• NOx controls key to attainment in high biogenic areas
• Controlling VOC reactivity may be even more cost-effective– Coming ANPR (California leading)
Georgia Institute of Technology
Organic Reactivity Assessment
• Organics behave differently – need a way to quantify the impact each has on the ozone production.– Impact on ozone per mass of VOC emitted can vary by orders of
magnitude.• More cost effective management possible if one accounts for ozone
formation from emissions, not just VOC mass – Allows manufacturers flexibility in formulation– Controls perverse choices
• Less, but much more reactive VOC
• Pioneered in California for fuels– Moved to consumer products– Recent studies have shown applicability in eastern US, EPA moving forward
• Conducted large scale (LACAeastern US) 3-D simulations to assess reactivity– Variability, scales, approaches– Applied URM using SAPRC99.
Georgia Institute of Technology
Reactivity of organic compounds
• Reactivity of an organic compound, as a measure of its ozone formation potential can be defined as (Carter, 1994):
• By definition, reactivity assessment is sensitivity analysis.
• Assessed reactivities for various organics using URM & DDM-3D– Carter used CAMX and DDM
ii E
O
E
O
33
Georgia Institute of Technology
Relative Reactivity Metrics, MIR-3D
MIR-3D-1hr
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
2MBT
BUTD
PRPE
ISOP
XYLM
ETHE
HCHO
ACRO
CCHO
APIN
TOLU
MCPT
ETOH
N_C5
N_C4
C6H6
MTBE
MEOH
IPOH
DODC
C2H6
BALD
Jul-95
Jul-10
May-95
May-10
Box mode MIR
Rel
ativ
e R
eact
ivit
y
Georgia Institute of Technology
SOS
• SOS activities moving towards Texas– Many SOS-affiliated teams applying expertise in the
Houston area– UTexas taking lead role
• State-of-Science document underway• No new EPA money for traditional SOS
– Significant “individual” investigator work– Funding for Texas studies
Georgia Institute of Technology
Summary• PM models coming of age
– Uncertainties, particularly in emissions, organics
• While interstate transport can be significant…– Local impacts typically large(st)
• Preliminary EAC modeling for Georgia suggests a 8-14 ppb reduction in ozone regionally– For August 2000 episode– 2001, 1999 episodes underway
• Reactivity appears to work in the East– Some differences than in the West
• VOC controls effective in cities, if not beyond– Even in Atlanta with all of its isoprene– NOx scavenging found in Atlanta as well
• Observed and modeled
Georgia Institute of Technology
FAQS Episodes
• August 11, 2000 – August 20, 2000– Primary FAQS period– High ozone statewide– FAQS measurements, Houston Supersite period– Col 8-hr, Mac. 8-hr, Atl&Mac&Col&Aug 8-hr, All 8-hr, Atl. 1-hr
• August 10, 1999 – August 21, 1999– High ozone and PM– Atlanta Supersite period– Mac 8-hr, Atl&Mac&Col&Aug 8-hr (part), All 8-hr (part), Aug
8-hr• July 5, 2001 – July 20, 2001
– High ozone and PM– FAQS measurements, ESP01 period– Atl 8-hr, Atl 1&8-hr, Atl&Mac&Col&Aug 8-hr, All 8-hr, Aug 8-
hr