Comparison of NOX emissions and NO2

concentrations from a regional scale air quality model (CMAQ-DDM/3D) with

satellite NO2 retrievals (SCIAMACHY) over

the continental U.S.

Burcak Kaynak1, Yongtao Hu1, Randall V. Martin2,3 and Armistead G. Russell1

 1 School of Civil and Environmental Engineering, Georgia Institute of Technology

2 Department of Physics and Atmospheric Science, Dalhousie University 3 Harvard-Smithsonian Center for Astrophysics

October 7, 2008

7th Annual CMAS ConferenceChapel Hill

OOverviewObjective:Improve the understanding of atmospheric chemistry & emissions Regional air quality models Ground-based & aircraft observations Satellite retrievals

Why ?Improve emission estimates & our understanding of atmospheric

processes Understand strengths & weaknesses of the satellite retrievals, get

ideas to improve the quality of the retrievals for further use in the tropospheric air pollution research

How ? Extensive comparison of observations & model results Model advancements Assimilation of the observations within the model by an inverse

modeling technique

Scanning Imaging Absorption Spectrometer for Atmospheric Chartography

onboard the ENVISAT which was launched in March 2002 into a sun-synchronous orbit

global measurement of atmospheric NO2 columns through nadir observation of global backscatter

typical spatial resolution: 30km x 60km

global coverage: over 6 days

scans through U.S.in the mornings (~ 10:30 local time)

units in tot trop. columns(molecules/cm2)

SCIAMACHY satellite retrievals from Martin et al., 2006*

* Martin, R. V., et al. (2006), J. Geophys. Res.-Atmos., 111(D15308)

SCIAMACHY

MM5: meteorology34 vertical levels, Four-Dimensional Data Assimilation (FDDA)

SMOKE: emissions [VISTAS 2002 inventory + Emission projection use growth factors from the EGAS Version 4.0 + CEM]

CMAQ v4.5 with DDM-3D: concentrations & sensitivitiesSAPRC 99 Chemical Mechanism13 vertical layers (up to ~ 15km)

Modeling Approach

Model Simulations

domain: North Americaresolution: 36km x 36kmepisode: July-August 2004

3 region types selected:“urban”: 7 cities“rural”: 11 rural areas w/o

any urban area or large scale EGUs

“rural-point”: 116 large scale EGUs w/o urban areas

3 Simulations: Base case Lightning case * PAN photolysis case

* Kaynak, B., et al. (2008), ACP

Lightning NOx emissions

Lightning case

Lightning increased NOx emissions around South East especially in Florida, Mid-West and over the Atlantic Ocean.

Base case

d) Aug04

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PAN (pptv)

Altitude

Lightning case

Base case

ICARTT

c) Jul04

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PAN (pptv)

Altitude

PAN PhotolysisCMAQ – ICARTT comparison [consistent overestimation and high variability of PAN in CMAQ]

CMAQ – SCIAMACHY comparison [lower NO2

columns from CMAQ, especially rural regions even after lightning emissions]

PAN Photolysis included in CMAQ:

Resulted minor improvement in CMAQ – ICARTT PAN comparison with similar vertical profile (improvement up to 5% MNE and MNB for individual flights)

No significant change obtained in CMAQ – SCIAMACHY NO2 comparison

Altitu

de

(km

)

CMAQ vs. SCIDomain-wideCMAQ higher simulated levels in urban areaslower in the surrounding areas

Possible reasons:the pixel size of SCIAMACHY having a smoothing effect chemistry or transport problems in the model, e.g. NO2 oxidizing faster than actual.

SCIAMACHY consistently higher around LAhigher from NY to ocean

Lightning reduced some discrepancy in mid-east, south,but put too much NO2 around Toronto-Illinois in Aug04

CMAQ vs. SCI [Domain-wide]West: high correlation, low slope East: low correlation, higher slopeR2=0.58-0.62, Slope =0.74-0.80 R2=0.35-0.41, Slope =0.87-1.02

CA has the highest correlation according to both months (R2 > 0.70).WA and GA also have good correlation.

CMAQ vs. SCI [State-wide]

State averages for July & August 2004

inconsistencies between two months (OR, ID, MT)OR: possible overestimation of the fire emissions for July 2004

CMAQ lower in west (CA, NV, AZ, UT, NM, CO, WY) & in a few northeastern states (ME, NH)

y = 0.35x + 2E+15

R2 = 0.45

y = 0.84x + 3E+13

R2 = 0.88

y = 0.99x + 3E+12

R2 = 0.46

y = 0.33x + 2E+15

R2 = 0.40

y = 1.07x - 4E+13

R2 = 0.90

y = 1.08x + 1E+14

R2 = 0.44

0.E+00

2.E+15

4.E+15

6.E+15

8.E+15

1.E+16

0.E+00 2.E+15 4.E+15 6.E+15 8.E+15 1.E+16

SCIAMACHY NO2 (molecules/cm2)

CM

AQ

NO2 (m

ole

cu

les

/cm2 )

.

rural-point base

rural-point light

rural base

rural light

urban base

urban light

Los Angeles

CMAQ vs. SCI [Land type]

“Urban”SCIAMACHYLos Angeles is high Houston, Chicago & Phoenix is low. “Rural”SCIAMACHYNV, WA very highID, OR low(similar to emissions)

“Rural-Point”have some outliers,But overall correlationis good.

NO2, scia/NO2, cmaq

(averaged for 2 months)

Red: SCIAMACHY is higher

(Los Angeles, NV, WA)

Green: CMAQ is higher

(ID, OR, Houston)

Yellow: comparable

[Land type]

ICARTT Intex-NA

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NO2 [ppbv]A

ltit

ud

e [k

m]

y = 0.916x + 0.197

R2 = 0.218

y = 0.898x + 0.180

R2 = 0.215

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0.0 5.0 10.0 15.0 20.0 25.0CMAQ [ppbv]

ICA

RTT

[ppb

v]

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NO2 [ppbv]

Alt

itu

de

[km

]

Jul 04

Aug 04

y = 0.66x + 0.32

R2 = 0.29

y = 0.66x + 0.33

R2 = 0.29

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CMAQ [ppbv]

ICA

RT

T [p

pb

v]

Base NO2

Lightning NO2

Eastern,

North-eastern U.S.

DATE MNB MNE MNB MNE(% ) (% ) (% ) (% )

Boston at night 11-J ul-04 87.47 136.51 90.13 137.02Balloon Intercept 15-J ul-04 -38.88 77.05 -6.28 88.32

NYC Part 1 20-J ul-04 -6.32 55.95 2.91 56.60NYC Part 2 21-J ul-04 -23.68 45.47 -11.05 47.49NYC Part 3 22-J ul-04 -0.20 51.76 10.33 54.13

Montour Power Plant et al 25-J ul-04 276.47 317.53 276.48 316.46WCB Part 1: Thunderstorm 27-J ul-04 393.95 451.98 421.24 464.76

WCB Part 2: Fires 28-J ul-04 -51.17 92.69 -31.23 95.27NYC at night 31-J ul-04 51.64 88.89 58.87 89.29

J ul 87.08 149.94 99.61 153.16New England at dawn 3-Aug-04 70.39 109.34 77.76 110.82

Ohio Valley Power Plants 6-Aug-04 -49.95 65.46 -49.94 65.45NYC, Boston at night 7-Aug-04 4.45 77.06 4.67 76.92

Ohio Valley, NYC at night 9-Aug-04 12.60 73.17 13.15 73.24NYC Plume night into day 11-Aug-04 67.20 83.61 72.50 88.45

Clouds 14-Aug-04 135.35 160.12 149.64 171.61Transit to Florida via Atlanta 15-Aug-04 -18.46 84.10 -13.18 79.60

Aug 27.91 90.94 32.16 92.81All Flights 60.09 123.03 68.84 125.63

Base case Lightning case

No negative bias in Los Angeles (21 AIRS stations), on the contrary CMAQ has a positive bias indicating overestimation.

Atlanta is overestimated which is not observed in satellite.

Houston, Chicago & Phoenix are overestimated, similar to satellite.

Ground ObservationsDATE MNB MNE MNB MNE

(% ) (% ) (% ) (% )J ul Atlanta 296.04 322.99 310.88 336.65

Chicago 74.88 129.87 79.25 133.35Houston 172.52 207.37 185.57 218.25LosAngeles 83.26 150.15 84.50 151.35NewYork 22.02 70.98 23.81 72.03Phoenix 57.07 115.97 63.53 120.27Seattle -5.60 86.21 -5.60 86.21

J ul urban 45.96 100.84 48.82 102.80daytime all -3.30 89.13 -0.29 90.07

10:00 AM -26.49 80.17 -24.00 80.73Aug Atlanta 430.87 455.45 430.87 455.45

Chicago 65.74 122.75 65.74 122.75Houston 137.53 179.41 137.53 179.41LosAngeles 86.95 151.39 86.95 151.39NewYork 23.72 69.86 23.72 69.86Phoenix 58.70 116.28 58.70 116.28Seattle -32.16 65.21 -32.16 65.21

Aug urban 49.77 97.90 52.51 100.00daytime all -0.52 91.90 2.09 92.92

10:00 AM -25.44 81.08 -23.22 81.73

Base case Lightning case

Lightning emissions resulted in minor improvements for some regions, but overall correlation did not improve.

CMAQ usually is higher than the SCIAMACHY observations in urban centers, but lower in surrounding areas. Possible reasons:

the pixel size of SCIAMACHY having a smoothing effect, diagnostic biases in the SCIAMACHY retrieval analyses, biases in the emissions estimates, chemistry, transport problems in the model.

Western U.S. has lower NO2 from the model, but high correlation.

Eastern U.S. has comparable NO2 levels, but correlation is lower.

On a state-by-state basis, most western states and a few eastern

states have simulated NO2 columns lower than observed.

Summary

NO2 total columns from satellite correlate well with simulated NO2 for

“rural” regions but less so with “urban” & “rural-point” (even though power plant emissions are well known)

Los Angeles is the major outlier between simulated and observed abundances in “urban” regions. This may indicate

a retrieval/analysis error, a bias in emission estimates specific to that region (or, conversely

biases in the other regions), modeling issues specific to that area.

The potential reasons for lower correlation of “rural-point” could be the transport of NO2 out of the small scan area –probably minor-

insufficient time for conversion of NO to NO2 in power point plumes.

High correlation of “rural” regions is helpful for using the satellite retrievals to obtain emission estimates for area sources that low in amounts and are sparse which is hard to capture otherwise.

Summary

Specifically “rural” areas in NV, WA may have more and ID, OR may have less emissions than inventories show.

Emission estimates from uncertain sources like lightning and fire can be improved using satellite retrievals.

Using satellite observations is still problematic but comparisons are promising; even though the uncertainties are high, using satellite retrievals for data assimilation can give more insightful information and quantitative results for improving emission inventories of some states which showed significant discrepancies from the satellite retrievals. More studies like this and with other models, inter-method measurement comparisons are needed.

Summary

Future WorkInverse modeling using NO2 columns w/ FDDA

Acknowledgements NASA Project SV6-76007 (NNG04GE15G), and EPA grants

(RD83096001, RD83107601 and RD83215901)

Russell Group, Georgia Institute of Technology

Randall Martin for SCIAMACHY NO2 retrievals

Global Hydrology Resource Center (GHRC) for providing the NLDN flash data

Kenneth Pickering for suggestions on vertical allocation of lightning NOx

Bill Carter for suggestions for PAN photolysis

Thanks for your time.