Template Evaluating NOx Emission Inventories for Regulatory Air Quality Modeling using Satellite and Model Data Greg Yarwood, Sue Kemball-Cook and Jeremiah Johnson ENVIRON International Corporation January 16, 2014

Introduction A high bias for modeled ozone in the southeast may be affecting the TCEQ’s SIP modeling –Confounds ozone transport assessments –May result from biased NOx emissions Purpose of this project: Use satellite and CAMx model data to assess whether bias is present in NOx emission inventories in the TCEQ’s SIP modeling 2 Episode Average Normalized Bias: June 2006

Mass Balance Method for Evaluating NOx Emissions Comparison of satellite-retrieved and CAMx modeled NO 2 columns –Ω are integrated tropospheric NO 2 vertical column densities (VCD) –Method of Leue et al. (2001); Martin et al. (2003)  Used by Boersma et al. (2008); Tang et al. (2013) NO 2 columns from Ozone Monitoring Instrument 3

Necessary Conditions for Mass Balance Method to Provide Constraints on NOx Emissions Model must accurately simulate formation, transport and fate of NO 2 and its reservoir species –Meteorology, chemistry, boundary conditions –Largest uncertainty should be in the emission inventory Satellite column NO 2 retrieval must have error smaller than the perturbation in the NO 2 columns caused by the uncertainty in the emission inventory 4

NO 2 Column Retrievals Used KNMI DOMINO v2.0 and NASA SP2 retrievals –Estimate of uncertainty introduced by the retrieval in the top- down emission estimates Retrievals begin with the same OMI slant columns, differ in: –Method for stratospheric column determination –AMF calculation 5

CAMx Model TCEQ June 2006 modeling platform 36/12/4 km nested grids CAMx v5.41 –CB6r1 chemical mechanism Evaluated model against surface obs (CAMS, CASTNet, SEARCH, AQS), and INTEX-A aircraft data Calculated modeled VCD for June episode 6

Upgrades Required to Simulate Tropospheric NO 2 VCD with TCEQ Modeling Platform Improved simulation of NO 2 sources/sinks in UT –Lightning NOx emission inventory –TCEQ aircraft emission inventory based on detailed flight track data –Day-specific wildfire emissions (FINN) –CAMx simulation of  Transport of NOy and ozone from stratosphere to troposphere  Vertical transport of chemical species via convection –Revised chemical mechanism CAMx showed sufficient agreement with retrieved columns and INTEX-A data for project to proceed –Does not demonstrate that the CAMx NO 2 columns are correct or that they agree with OMI for the right reasons 7

June 2006 Episode Average Retrieved NO 2 VCD Overall patterns of high and low VCD are similar, but there are differences between the retrievals –Southeastern U.S. –Atlanta, New York maxima –Offshore of Carolinas When used together with a single set of CAMx columns, retrievals will give different top-down emissions estimates 8

Episode Average NO 2 VCD Comparison 9 Differences in OMI/CAMx over East Texas, southeastern U.S. and ocean I-35 <0 in DOMINO/CAMx, not SP2/CAMx

No Smoothing: Top Down - Bottom Up NOx EI Using CAMx with Two Retrievals 10 DOMINOSP2 Results differ over East Texas, broad areas of southeast How to use this information to improve TCEQ NOx EI?

Alternative Method: State-Level Comparison CAMx VCD – DOMINO VCD Simple column comparison by state No smoothing of EI required 11 Texas and adjacent SoutheastOhio River Valley North Northeast West

State-Level Comparison using Two Retrievals Results differ near Texas, in southeast, better agreement further north Retrievals / model give contradictory results on TCEQ NOx inventory in southeast 12 CAMx VCD – DOMINO VCD CAMx VCD - SP2 VCD Texas and adjacent Southeast Ohio River Valley North Northeast West Texas and adjacent Southeast Ohio River Valley North Northeast West

Summary Top-down emissions estimates derived with current generation of regional air quality models and retrievals not recommended for Texas –Uncertainty in modeling of NO 2 and reservoir species and in retrievals Use of satellite data is not straightforward –Analyzing multiple retrievals was very useful in this project Satellite data used together with aircraft flight data are powerful tools for evaluating and improving air quality models 13

Acknowledgements We acknowledge the free use of tropospheric NO 2 column data from the OMI sensor from and the use of NASA SP2 retrieval. We wish to thank the University of Wisconsin- Madison for the use and development of the Wisconsin Horizontal Interpolation Program for Satellites (WHIPS). WHIPS was developed by Jacob Oberman, Erica Scotty, Keith Maki and Tracey Holloway, with funding from the NASA Air Quality Applied Science Team (AQAST) and the Wisconsin Space Grant Consortium Undergraduate Award. 14

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