PHOTOCHEMICAL MODELING PHOTOCHEMICAL MODELING OF THE OZARKS ISOPRENE OF THE OZARKS ISOPRENE EXPERIMENT (OZIE): EXPERIMENT (OZIE): COMPARISON OF MEGAN AND COMPARISON OF MEGAN AND BEISBEIS
Kirk Baker and Annmarie CarltonKirk Baker and Annmarie Carlton
U.S. Environmental Protection U.S. Environmental Protection AgencyAgency
October 12, 2010October 12, 2010
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Biogenic VOCs
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Biogenic VOC comprise approximately 75-80% of the North American annual 2005 NEI
Biogenic emissions largely a function of plant type, leaf area index, temperature, and solar radiation (PAR)
Biogenic emissions for regional and global scale photochemical are typically developed from either MEGAN or BEIS Model of Emissions of Gases and Aerosols from Nature
(MEGAN), currently developed by NCAR Biogenics Emission Inventory System (BEIS), currently
developed by US EPA
July 1998 OZarks Isoprene Experiment (OZIE) field study designed for evaluation of biogenic models
MOTIVATION
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Compare MEGAN and BEIS emission estimates to surface and upper air measurements from OZIE field study
Evaluate sensitivity of CMAQ predictions when using alternative biogenic emissions models for Primary gas phase species: e.g., isoprene, monoterpenes,
sesquiterpenes secondary species: e.g., O3, PM2.5, SOA Primary and secondary: formaldehyde (HCHO)
Predictions of BVOCs are highly dependent on meteorology e.g., temperature, solar radiation What is the bias in met model predictions of these variables? How do predictions differ when alternative source of solar
radiation are employed, e.g., how photosensitive are MEGAN and BEIS emission estimates?
Ozarks Isoprene Experiment: July 1998
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OZIE Project Details
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Surface TNMOC, isoprene, a-pinene, b-pinene, HCHO, O3 Solar radiation, soil temperature, wind speed, wind direction,
temperature, relative humidity Aloft
Balloon: Isoprene, a-pinene, b-pinene Aircraft (7 flights):, O3, isoprene, TNMOC, HCHO
Other Available Observation Data: AIRS (1-hr avg)
Ozone, NO2/NOX, PM10, CO CASTNET/IMPROVE (24-hr avg)
Speciated PM2.5
FSL RAOB Springfield, MO Temperature, wind vector
U.S. Airways (DS472) (Hourly reported) Temperature, mixing ratio, wind speed, wind direction
CMAQ Modeling Overview
Modeled June 15-July 31, 1998 episode
CMAQ v4.7.1 (N2a) CB05 & AERO5
WRF v3.1 MCIP v3.4.1.1
BEIS v3.14 MEGAN v2.04 2001 v2 based
anthropogenic emissions SMOKE inline emissions
36 km – continental US 12 km (blue)
34 and 14 vertical layers
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Biogenic Modeling
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BEIS v3.14 and MEGAN v2.04 WRF 2m Temp. and shortwave downward radiation (98a) WRF 2m Temp. and satellite estimated photosynthetically
activated radiation (PAR) (98b) PAR is the visible light fraction of shortwave downward
radiation
Satellite PAR estimates taken from GEWEX Continental Scale International Project (GCIP) and Americas Prediction Project (GAPP) Surface Radiation Budget (SRB) [http://www.atmos.umd.edu/~srb/gcip/cgi-bin/historic.cgi] Satellite PAR resolution 0.5◦ x 0.5◦ and covers the
continental U.S. Missing hours and days replaced by hourly monthly average
satellite estimated PAR
Monthly domain (12OZIE1) total emissions
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Mol
es/D
ay/1
0000
Mol
es/D
ay/1
0000
98a 98b 98a 98a98b 98b
BEIS BEISMEGAN MEGAN98a 98b
98a: WRF PAR98b: satellite PAR
Isoprene Emissions
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BEIS: Isoprene
MEGAN: Isoprene
Hour of the day (LST)
Em
issi
ons
(mol
es/s
ec)
Monoterpene Emissions
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MEGAN: Monoterpenes
Hour of the day (LST)
Em
issi
ons
(mol
es/s
ec)
Sesquiterpene Emissions
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MEGAN: Sesquiterpenes
Hour of the day (LST)
Em
issi
ons
(mol
es/s
ec)
Temperature & PAR at OZIE Sites
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Warm bias over all hours & days Morning and early afternoon PAR underestimated Satellite PAR estimates compare better to ground observations
Hour of the day (LST) Hour of the day (LST)
Temperature Bias: OZIE sites PAR Bias: OZIE sites
Te
mp
era
ture
(C
)
Wa
tts/
m2
Isoprene Performance12km grid, 34 vertical layers
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Measured (ppbC) Measured (ppbC)
Mod
eled
(p
pbC
)
Mod
eled
(p
pbC
)
Isoprene Bias: July Episode
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ppbC
BEIS: Isoprene
MEGAN: Isoprene
Monoterpene* Performance15
α-pinene + β-pinene are approx. 50-70% of total monoterpenes (Sakulyanontvittaya et al, 2008). There should be an overprediction.
α-pinene + β-pinene
Measured (ppbC)
Mod
eled
“T
ER
P”
(ppb
C)
α-pinene + β-pinene
Measured (ppbC)
Mod
eled
“T
ER
P”
(ppb
C)
Surface Formaldehyde Performance
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Measured (ppbC)
Mod
eled
(p
pbC
)
Balloon Measurements: Isoprene
Balloon: isoprene (right)
Model estimates (using satellite PAR) compared to vertical balloon and aircraft measurements
Measurements made in an open field
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Mod
el L
ayer
Concentration (ppbC)
Aircraft Measurements
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Isoprene Formaldehyde Ozone
Concentration (ppbC) Concentration (ppb) Concentration (ppb)
Mod
el L
ayer
•measurements made over tree canopy
Model Performance: Ozone
MEGAN predicts slightly higher regional O3
Satellite PAR MEGAN estimates result in slightly less O3 than using WRF PAR
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Bia
s (p
pb)
Observed Ozone Bin (ppb)
Model Performance: speciated PM2.5
Similar PM2.5 performance using BEIS and MEGAN at rural speciated monitors
Sulfate performance related to SO2 controls between 1998 and 2001
More OC using MEGAN emissions
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98a 98b 98a 98b
BEIS MEGANOBS
Con
cent
ratio
n (μ
g/m
3)
Daily CMAQ PM2.5 SOC Estimates21
*semi-emipirical EC tracer method to estimate SOC (Yu et al 2007)
µg
/m3
µg
/m3
Conclusions and Future Directions
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MEGAN BVOC estimates are substantially higher and more photosensitive than BEIS
CMAQ O3 and PM2.5 estimates are similar with BEIS or MEGAN Accept either for SIP modeling based on current state of chemical
mechanisms Future modeling study to investigate if control strategies would
differ Simulations with process analysis to investigate importance of
VOCs, oVOCs Test with different chemical mechanisms, e.g, low NOx isoprene
chemistry
SOC predictions much higher with MEGAN but rural SOC still substantially underpredicted
Field Study to re-visit OZIE Have BVOC emissions/ambient mixing ratios changed in response
to changes in emissions and/or climate? Measure SOA tracers to investigate which individual
precursors/pathways contribute most to the SOC underprediction
Acknowledgements
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Allan Beidler, James Beidler, and Chris Allen (CSC) for BEIS, MEGAN, and SMOKE emissions modeling
Lara Reynolds (CSC) for WRF application Rob Gilliam (US EPA/ORD) for assistance and
troubleshooting for 4 km WRF application Marc Houyoux and Rich Mason (US EPA/OAQPS) for
assistance with anthropogenic emissions inventory Christine Wiedenmyer (NCAR) for observation data Mike Koerber (LADCO) for observation data George Pouliot and Tom Pierce (US EPA/ORD)
END
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Future Field Study Directions
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What/where would a new “OZIE” experiment include? Ozarks Surface and aloft measurements Isoprene, monoterpenes, and sesquiterpenes SOC tracer measurements OH radical Speciated PM2.5; Ozarks get a lot of acidic aerosol in
the summer from the Ohio Valley region Carbon dioxide, NOX (is this a low NOX environment?) Summer and non-summer periods like
winter/fall/spring? Roving measurements to different land use types like
crops and grasslands? Urban biogenic assessment?
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MEGAN: Broadleaf Trees
BELD3: Oak+Others
BELD3: USGS Deciduous Trees
*Others = Sycamore+Sweetgum+Willow+Populus
Landuse Information
•MEGAN uses MODIS landuse and plant species information to make gridded emission factors
•BEIS uses BELD3, a combination of plant species information and USGS landuse
•Different isoprene emission factors for high emitting species (top right) and USGS deciduous tree category (bottom right) in BEIS
•Difficult to differentiate plant coverage and emissions with MEGAN’s gridded emission factor product (one plant functional type shown below)
Nitric Oxide Emissions
Biogenic nitric oxide (NO) estimated with BEIS3.12 comprises approximately 9% of the total continental United States 2005 annual NO emission inventory
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MEGAN: Nitric Oxide
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k ( MM5) height press Depth
34 15676 100 200433 13672 145 158532 12087 190 132231 10765 235 113930 9626 280 100429 8622 325 90028 7721 370 81727 6904 415 75026 6154 460 69325 5461 505 64524 4816 550 60423 4213 595 56822 3645 640 53621 3109 685 50820 2601 730 38819 2212 766 28218 1931 793 27417 1657 820 17816 1478 838 17515 1303 856 17214 1131 874 16913 961 892 16712 795 910 8211 712 919 8210 631 928 81
9 550 937 808 469 946 807 390 955 796 310 964 795 232 973 784 154 982 393 115 986.5 392 77 991 381 38 995.5 380 0 1000 38
MM5 Vertical Layers
