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Observing U.S. urban NO x emissions from Ozone Monitoring Instrument (OMI) satellite retrievals Zifeng Lu, David G. Streets Decision and Information Sciences.

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Presentation on theme: "Observing U.S. urban NO x emissions from Ozone Monitoring Instrument (OMI) satellite retrievals Zifeng Lu, David G. Streets Decision and Information Sciences."— Presentation transcript:

1 Observing U.S. urban NO x emissions from Ozone Monitoring Instrument (OMI) satellite retrievals Zifeng Lu, David G. Streets Decision and Information Sciences Division Argonne National Laboratory Presented at AQAST 6 Meeting Rice University, Houston, TX January 15-17, 2014

2 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 Introduction 2 Our previous efforts on emissions estimation from satellite retrievals –Power plants in China NO x emissions Zhang et al., 2009; Wang et al., 2010; Wang et al., 2012 SO 2 emissions Li et al., 2010 –Power plants in India NO x emissions Lu and Streets, 2012 SO 2 emissions Lu et al., 2013 –Power plants in US NO x emissions Duncan et al., 2013 Good agreement between satellite observations and bottom-up emissions for areas dominated by power plant emissions This work NO x emissions from US urban areas Problem to be solved Is it possible to use OMI NO 2 retrievals to estimate the NO x emissions from US urban areas? If so, how accurate are the estimates? Problem to be solved Is it possible to use OMI NO 2 retrievals to estimate the NO x emissions from US urban areas? If so, how accurate are the estimates?

3 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 Processing OMI NO 2 Level 2 Data OMI NO 2 tropospheric vertical columns –Berkeley High-Resolution (BEHR) retrievals v2.0A (2005-2011) Russell et al., 2011, 2012 –NASA Standard Product (NASA SP) v2.1 collection 3 (2005-2013) Boersma et al., 2011; Bucsela et al., 2013 Filters –Solar zenith angle < 70 degree –Cloud fraction < 0.2 –Terrain reflectivity < 0.3 –Cross track positions 11-50 (1-based) –Dynamically filter OMI anomaly pixels and error pixels using XtrackQualityFlags and VCDQualityFlags Additional –Only summertime data (i.e., May to August) –Oversampling to a 2 km x 2 km grid 3

4 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 Decrease of OMI NO 2 over US 4 BEHR Summer 2005 BEHR Summer 2011 NASA SP Summer 2005 NASA SP Summer 2013 Summertime BEHR OMI NO 2 (2005 vs. 2011) Summertime NASA SP OMI NO 2 (2005 vs. 2013)

5 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 Selection of Urban Areas 5 –Examine the top 80 urban areas on the basis of population –Combine the adjacent urban areas sharing the same NO 2 hotspot –Exclude some urban areas, the NO 2 signals of which are not isolated 51 urban areas –~40% of the total NO x emissions in the US NASA SP Summer 2005-2013

6 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 Fitting OMI NO 2 hot spots with 2-D Gaussian function Fioletov et al., GRL, 2011; Lu et al., EST, 2013 Since, the parameter  physically means the total number of NO 2 molecules observed (or the observed NO 2 burden). Unit of  is molecules, mass units 6

7 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 Example: Chicago 7 Urban AreaOMI BEHR 20052-D Gaussian Fit% DifferenceNO x Emissions Emission inventory Power plants CEMS Biomass burning GFED3.1 Other Xing et al., 2013

8 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 Example: Houston 8 Urban AreaOMI NASA SP 20052-D Gaussian Fit% DifferenceNO x Emissions

9 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 NO x emissions vs. OMI NO 2 burden 9 Good agreement between NO x emissions and OMI NO 2 observations Berkeley retrievals are ~30% higher than NASA retrievals over urban areas The 95% CI of the summertime NO 2 lifetime in US urban areas –Berkeley retrievals 2.1~5.6 hNASA retrievals 1.4~4.6 h Uncertainties of urban NO x emissions estimated from OMI NO 2 observations –Berkeley retrievals ±45%NASA retrievals ±57% Each point represents a yearly fitted result for an urban area Error bars are the 95% CIs of fitted  BEHRNASA SP 95% CI:  45%95% CI:  57%

10 Zifeng Lu, Observing US urban NO x emissions from OMI satellite retrievals AQAST 6 Meeting, Rice University, Houston, TX, January 15, 2014 Interannual trend of the sum of fitted OMI NO 2 burden for all selected urban areas 10 From 2005 to 2011 From 2005 to 2013 Total amount of NO 2 observed by the OMI over selected urban areas 24% decrease 36% decrease Total NO x emissions from selected urban areas 26% decrease33% decrease Averages of annual mean NO 2 concentrations in selected urban areas 25% decrease30% decrease BEHRNASA SP

11 Conclusions NASA Air Quality Applied Sciences Team (AQAST) program Thank you for your attention! Questions? Contact Zifeng Lu zlu@anl.gov & David G. Streets dstreets@anl.govzlu@anl.govdstreets@anl.gov Acknowledgements  OMI NO 2 retrievals can be used to constrain the trends and estimate the amounts of NO x emissions from urban areas with reasonable accuracy  For a single urban area, the 95% CI of the estimated NO x emission is ±45% for the Berkeley retrievals and ±57% for the NASA retrievals  The total OMI NO 2 burden over major US urban areas decreased by >30% from 2005 to 2013, in good agreement with decreases in bottom-up emissions and ground-based measurements


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