Emission Inventories for Regulatory Air Quality Modeling using Satellite and Model Data Greg Yarwood Sue KemballCook and Jeremiah Johnson ENVIRON International Corporation ID: 620018
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Slide1
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, 2014Slide2
Introduction
A
high
bias for modeled ozone in the
southeast may
be affecting the TCEQ’s
SIP modeling
Confounds ozone transport assessmentsMay 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 2006Slide3
Mass Balance Method for Evaluating NOx EmissionsComparison of satellite-retrieved and CAMx modeled NO2 columnsΩ are integrated tropospheric NO2 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
3Slide4
Necessary Conditions for Mass Balance Method to Provide Constraints on NOx EmissionsModel must accurately simulate formation, transport and fate of NO2 and its reservoir speciesMeteorology, chemistry, boundary conditionsLargest uncertainty should be in the emission inventorySatellite column NO2 retrieval must have error smaller than the perturbation in the NO
2
columns caused by the uncertainty in the emission inventory
4Slide5
NO2 Column RetrievalsUsed KNMI DOMINO v2.0 and NASA SP2 retrievals Estimate of uncertainty introduced by the retrieval in the top-down emission estimatesRetrievals begin with the same OMI slant columns, differ in:Method for stratospheric column determination
AMF calculation
5Slide6
CAMx ModelTCEQ June 2006 modeling platform36/12/4 km nested gridsCAMx v5.41CB6r1 chemical mechanismEvaluated model against surface obs (CAMS, CASTNet, SEARCH, AQS), and INTEX-A aircraft dataCalculated modeled VCD for June episode
6Slide7
Upgrades Required to Simulate Tropospheric NO2 VCD with TCEQ Modeling PlatformImproved simulation of NO2 sources/sinks in UTLightning NOx emission inventoryTCEQ aircraft emission inventory based on detailed flight track data
Day-specific wildfire emissions (FINN)
CAMx simulation of
T
ransport of NOy and ozone from stratosphere to troposphereVertical transport of chemical species via convection
Revised chemical mechanismCAMx showed sufficient agreement with retrieved columns and INTEX-A data for project to proceedDoes not demonstrate that the CAMx NO
2 columns are correct or that they agree with OMI for the right
reasons7Slide8
June 2006 Episode Average Retrieved NO2 VCDOverall patterns of high and low VCD are similar, but there are differences between the retrievalsSoutheastern U.S. Atlanta, New York maximaOffshore of CarolinasWhen used together with a single set of CAMx columns, retrievals will give different top-down emissions estimates
8Slide9
Episode Average NO2 VCD Comparison
9
D
ifferences in OMI/CAMx over East Texas, southeastern U.S. and ocean
I-35 <0 in DOMINO/CAMx, not SP2/CAMxSlide10
No Smoothing: Top Down - Bottom Up NOx EIUsing CAMx with Two Retrievals10
DOMINO
SP2
R
esults differ over East Texas, broad areas of southeast
How to use this information to improve TCEQ NOx EI?Slide11
Alternative Method: State-Level ComparisonCAMx VCD – DOMINO VCDSimple column comparison by stateNo smoothing of EI required11
Texas
and
adjacent
Southeast
Ohio River
Valley
North
Northeast
WestSlide12
State-Level Comparison using Two RetrievalsResults differ near Texas, in southeast, better agreement further northRetrievals / model give contradictory results on TCEQ NOx inventory in southeast12
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
WestSlide13
SummaryTop-down emissions estimates derived with current generation of regional air quality models and retrievals not recommended for TexasUncertainty in modeling of NO2
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
13Slide14
AcknowledgementsWe acknowledge the free use of tropospheric NO2 column data from the OMI sensor from www.temis.nl 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.
14Slide15
End15