Comparison of NO X emissions and NO 2 concentrations from a regional scale air quality model (CMAQ-DDM/3D) with satellite NO 2 retrievals (SCIAMACHY) over the continental U.S. Burcak Kaynak 1, Yongtao Hu 1, Randall V. Martin 2,3 and Armistead G. Russell 1 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, th Annual CMAS Conference Chapel Hill

O Overview Objective: 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 NO 2 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/cm 2 ) SCIAMACHY satellite retrievals from Martin et al., 2006* * Martin, R. V., et al. (2006), J. Geophys. Res.-Atmos., 111(D15308) SCIAMACHY

MM5: meteorology 34 vertical levels, Four- Dimensional Data Assimilation (FDDA) SMOKE: emissions [VISTAS 2002 inventory + Emission projection use growth factors from the EGAS Version CEM] CMAQ v4.5 with DDM-3D: concentrations & sensitivities SAPRC 99 Chemical Mechanism 13 vertical layers (up to ~ 15km) Modeling Approach

Model Simulations domain: North America resolution: 36km x 36km episode: July-August 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 NO x emissions Lightning case Lightning increased NOx emissions around South East especially in Florida, Mid-West and over the Atlantic Ocean. Base case

PAN Photolysis CMAQ – ICARTT comparison [consistent overestimation and high variability of PAN in CMAQ] CMAQ – SCIAMACHY comparison [lower NO 2 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 NO 2 comparison Altitude (km)

CMAQ vs. SCI Domain-wide CMAQ higher simulated levels in urban areas lower in the surrounding areas Possible reasons: the pixel size of SCIAMACHY having a smoothing effect chemistry or transport problems in the model, e.g. NO 2 oxidizing faster than actual. SCIAMACHY consistently higher around LA higher from NY to ocean Lightning reduced some discrepancy in mid-east, south, but put too much NO 2 around Toronto-Illinois in Aug04

CMAQ vs. SCI [Domain-wide] West: high correlation, low slopeEast: low correlation, higher slope R 2 = , Slope = R 2 = , Slope = CA has the highest correlation according to both months (R 2 > 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)

Los Angeles CMAQ vs. SCI [Land type] “Urban” SCIAMACHY Los Angeles is high Houston, Chicago & Phoenix is low. “Rural” SCIAMACHY NV, WA very high ID, OR low (similar to emissions) “Rural-Point” have some outliers, But overall correlation is good.

NO 2, scia/NO 2, 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 Jul 04 Aug 04 Eastern, North-eastern U.S.

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 Observations

 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 NO 2 from the model, but high correlation.  Eastern U.S. has comparable NO 2 levels, but correlation is lower.  On a state-by-state basis, most western states and a few eastern states have simulated NO 2 columns lower than observed. Summary

 NO 2 total columns from satellite correlate well with simulated NO 2 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 NO 2 out of the small scan area –probably minor-  insufficient time for conversion of NO to NO 2 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 Work Inverse modeling using NO 2 columns w/ FDDA

Acknowledgements  NASA Project SV (NNG04GE15G), and EPA grants (RD , RD and RD )  Russell Group, Georgia Institute of Technology  Randall Martin for SCIAMACHY NO 2 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.