Post on 04-Jan-2016
Estimating anthropogenic NOx emissions over the US using OMI satellite observations and
WRF-Chem
Anne Boynard
Gabriele Pfister
David Edwards
AQAST June 2012
National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Motivation
Better quantify anthropogenic NOx emissions, which can have large uncertainty [e.g. Street et al., 2003]
NO2 satellite observations are a perfect source of information to constrain NOx emission estimates:
• Global coverage• Good spatial resolution• Sensitivity towards the surface• Short lifetime of NOx => short transport scale
Top-down Approach
ENOX
α
NO2_model NO2_satellite
= Adjustment of the emissions with satellite observations to reduce the disagreement between model and observation.
Assuming that horizontal transport of NOx is negligible, a posteriori emissions can be derived as following: α = ENOX_apriori / NO2_model
=> ENOX_aposteriori = α x NO2_satellite
Martin et al. [2003, 2006] Lamsal et al. [2011]
ENOX = anthropogenic NOx emissionsNO2_model= Modeled NO2 Tropospheric ColumnNO2_satellite= Satellite NO2 Tropospheric Column
Model & Data
A priori anthropogenic NOx emissionsUS EPA 2005 NEI
Satellite NO2 Tropospheric ColumnOMI DOMINO data
[Boersma et al., 2007]
• Average over 9 grid boxes (72km x 72km horizontal resolution)• α is applied only for grid boxes where anthropogenic NOx emissions > 90% Total emissions
Modeled NO2 Tropospheric ColumnWRF-Chem
24km x 24km 10 June – 10 July 2008
We acknowledge the free use of tropospheric NO2 column data from the OMI sensor from www.temis.nl
Larger discrepancy over cities
10 June – 24 June 2008
High polluted regions
Mean Bias Correlation
14±34% 0.84
OMI/WRF-chem (w/ 2005 NEI) comparison
Bias has significantly decreased but we still see large differences locally (e.g. in California)
10 June – 24 June 2008
High polluted regions
Mean Bias Correlation
-7±16% 0.86
OMI/WRF-chem (w/ a posteriori emissions) comparison
A priori versus A posteriori Emissions
Over the CONUS: reduction in anthropogenic emissions of ~7.5%=> Reduction in NOx emissions consistent with EPA Trend data & EDGAR database
A priori emission A posteriori emission
2.3Tg N / year 2.1 Tg N / year
10 June – 10 July 2008
Change in surface Ozone (20UTC)
Over most of the cities, when NOx emissions decrease, O3 increases=>This might have important policy implications for urban areas where NOx emissions are controlled
O3 w/ a posteriori – O3 w/ a priori
New Top-Down Approach: XNOX Method
Using the "total NO2”, we attribute the entire NO2 column to anthropogenic sources while it includes other sources (e.g. fire, biogenic sources)
Anthropogenic NO2 (XNO2) is tagged in our WRF-Chem simulation (chemically active species)
Idea: Using modeled anthropogenic NO2 Trop. Column instead of modeled total NO2 Trop. Column to estimate a posteriori emissions This method allows to get a well defined linear relation between
anthropogenic NOx emissions and anthropogenic NO2 Trop. Column
Question: How things change?
XNO2 contribution to NO2
XNO2 > 0.8 * NO2
10 June – 10 July 2008
When XNOX > 0.8 * NO2 : cities but also power plants show
This method indicates regions where the NOx emission constraint can be applied with high confidence
Martin et al. method versus XNOX method
Increase of NOx emissions of ~10 to 30% with the XNOX method
Anthropogenic NOx emissions Difference between XNOX and Martin et al. method
Summary
Anthropogenic NOx emissions were estimated over the US during summer 2008 using WRF-Chem and OMI satellite data
EPA 2005 NEI was constrained using a top-down approach Bias between model and observations was reduced by 8% using the
adjusted emission inventory The results indicated that EPA 2005 NEI might overpredict NOx
emissions over cities (up to 50%) – large impact on surface O3 Anthropogenic NO2 tracers indicated that this method really only
works well over high emission hot spots (cities – power plants)
THANK YOU!
Anne Boynard
Gabriele Pfister
David Edwards
AQAST June 2012
National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA