1. Quantifying uncertainties of OMI NO 2 data Implications for air quality applications Bryan Duncan, Yasuko Yoshida, Lok Lamsal, NASA OMI Retrieval Team NASA Goddard Space Flight Center, Greenbelt, MD AQAST STM, Rice U., Houston, TX, January 15-17, 2014 2005-2007 2009-2011 OMI NO 2 data = proxy for surface NO x levels

2. Policy-Relevance Goal: Use OMI NO 2 satellite data to monitor changes & trends in NO x & NO x emissions, particularly where AQS monitors are sparse or absent. Problem: Data uncertainties are not well quantified for AQ applications. Ozone Season OMI NO 2 ∆OMI NO 2 2005 2012 2005-2012 → as NO x emissions decrease, the signal-to-noise also decreases so that quantification of the uncertainties becomes even more important.

3. OMI NO 2 ∆OMI NO 2 (%) 2005 2012 2005-2012 NO 2 columns (molecules/cm 2 ) > 0.5x10 15 (probably too low) > 1.0x10 15 > 1.5x10 15 (probably too high) (x10 15 molecules/cm 2 ) Just how large do you think the uncertainties are – ballpark estimate?

4. Effort to Better Quantify Uncertainties for AQ Applications While the versions of the OMI NO 2 data have improved substantially over the years, there is still room for improvement. NASA OMI Team’s plans for algorithm development: (1) Improved spectral fitting for NO 2 - is being developed by our group (KNMI's spectral fitting has problem). (2) High resolution surface reflectivity data base (MODIS) (3) High resolution year-specific a-priori NO 2 profile shape (4) Inclusion of aerosols in the retrieval of NO 2 (5) Development of independent cloud product for use in NO 2 retrievals. → I’ll continue to work with the OMI Team to improve the NO 2 data product for AQ applications.

5. Aura Ozone Monitoring Instrument (OMI) How does OMI NO 2 data compare to surface observations? OMI detects pollution in the free troposphere and boundary layer. The AQS surface sites only detect “nose-level” concentrations.

6. The observed response of Ozone Monitoring Instrument (OMI) NO 2 columns to NO x emission controls on power plants in the United States: 2005-2011 Bryan N. Duncan, Yasuko Yoshida, Benjamin de Foy, Lok N. Lamsal, David G. Streets, Zifeng Lu, Kenneth E. Pickering, and Nickolay A. Krotkov Main Conclusions Aura OMI NO 2 data can be used to a) monitor emissions from power plants and b) demonstrate compliance with environmental regulations. BUT, careful interpretation of the data is necessary. How do variations in OMI NO 2 data compare to CEMS data for power plants?

7. How do OMI NO 2 data compare to AQS data? N=20 North East 1 N=23 North East 2 N=6 Chicago N=13 Houston N=51 Southern California N=32 Central Valley AQS data: hourly, use 13-14 PM data (corresponding to OMI overpass time) OMI NO 2 data: daily, gridded at 0.1° latitude x 0.1° longitude Use data if both AQS and OMI are available to compute monthly/annual means

8. Houston Time series of AQS and OMI NO 2 Normalized Anomaly Data are deseasonalized. Change Relative to 2005 (%) AQS OMI

9. Correlation of monthly mean AQS & OMI NO 2 Anomalies Correlation worsens with increasing latitude. North East 1 North East 2 Chicago HoustonSouthern CaliforniaCentral Valley ** Because the data are normalized, there is no bias.

10. North East 1 North East 2 Chicago Time series of AQS and OMI NO 2 Likely issue: Improper filtering of OMI data for snow & ice or lack of statistical significance. Normalized Anomaly Normalized Anomaly Normalized Anomaly Change Relative to 2005 (%) Change Relative to 2005 (%) Change Relative to 2005 (%) N N N AQS OMI

11. Houston S. California Central Valley Time series of AQS and OMI NO 2 Normalized Anomaly Normalized Anomaly Normalized Anomaly Change Relative to 2005 (%) Change Relative to 2005 (%) Change Relative to 2005 (%) AQS OMI

12. N of AQS sites Mean, allMedian, all Mean, no winter* Median, no winter* North East 120 -37.9 -40.2 -40.3 -37.4 -41.4 -31.9 -46.1 -35.4 North East 223 -39.8 -37.3 -38.1 -34.6 -43.1 -39.7 -40.7 -38.4 Chicago6 -28.2 -44.4 -30.2 -41.7 -27.7 -37.1 -31.7 -31.0 Houston13 -31.9 -32.5 -35.0 -27.4 -30.2 -31.5 -32.1 -32.9 S. California51 -38.8 -42.3 -39.8 -37.7 -38.6 -40.0 -38.8 -30.4 Central Valley 32 -27.9 -34.7 -27.3 -31.7 -24.3 -31.3 -23.9 -29.0 ∆NO 2 (%) from 2005 to 2012 Largest decreases in areas with large regional backgrounds. AQS OMI * Used data from April to October only.

13. Extra Slides

14. Effort to Better Quantify Uncertainties for AQ Applications Some issues to investigate: I) Sensitivity tests to understand the impact of assumptions made in the creation of the OMI data product. For instance, the influence of trends in: a) Aerosols, surface reflectivities, and clouds. b) Vertical profile shape as NO 2 continues to decrease. c) Stratospheric and free tropospheric NO 2. d) Etc. II) Coastal cities (e.g., Seattle, San Francisco) “Interpretation of data in coastal locations is difficult due to (1) complex natural variability by stronger wind and (2) errors in retrievals. Auxiliary information on reflectivity and profile shape, both of which affect the retrievals, could be far from the reality.”

15. Regulations of NO x Emissions → Emission controls devices (ECDs) were installed on power plants, reducing emissions (e.g., 90%). 1)Power Plants (~68% decrease since late 1990s) → 1998 NO x State Implementation Plan (SIP) Call 22 eastern states during summer → 2005 Clean Air Interstate Rule (CAIR) 27 eastern states → 2011 Cross-State Air Pollution Rule (CSAPR) 28 eastern states 2) Mobile Source (~43% decrease since late 1990s) → Clean Air Act Amendments (CAAA) of 1990 Tier 1 (phased-in between 1994 and 1997) standards Tier 2 (phased-in between 2004 and 2009) standards