1. Quantifying uncertainties of OMI NO2 data
Quantifying uncertainties of OMI NO2 data Implications for air quality applications
Bryan Duncan, Yasuko Yoshida, Lok Lamsal, NASA OMI Retrieval Team
NASA Goddard Space Flight Center, Greenbelt, MD
OMI NO2 data =
proxy for surface
NOx levels
AQAST STM, Rice U., Houston, TX, January 15-17, 2014

2. Policy-Relevance
Goal: Use OMI NO2 satellite data to monitor changes & trends in NOx & NOx emissions, particularly where AQS monitors are sparse or absent.
Problem: Data uncertainties are not well quantified for AQ applications.
∆OMI NO2
OMI NO2
2005
2012
Ozone Season
→ as NOx emissions decrease, the signal-to-noise also decreases so that quantification of the uncertainties becomes even more important.

3. Just how large do you think the uncertainties are – ballpark estimate?
∆OMI NO2 (%)
Just how large do you think the uncertainties are – ballpark estimate?
NO2 columns
(molecules/cm2)
> 0.5x1015
(probably too low)
> 1.0x1015
> 1.5x1015
(probably too high)
OMI NO2
2005
2012
(x1015 molecules/cm2)

4. Effort to Better Quantify Uncertainties for AQ Applications
While the versions of the OMI NO2 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 NO2 - 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 NO2 profile shape
(4) Inclusion of aerosols in the retrieval of NO2
(5) Development of independent cloud product for use in NO2 retrievals.
→ I’ll continue to work with the OMI Team to improve the NO2 data product for AQ applications.

5. Aura Ozone Monitoring Instrument (OMI)
How does OMI NO2 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. How do variations in OMI NO2 data compare to CEMS data for power plants?
The observed response of Ozone Monitoring Instrument (OMI) NO2 columns to NOx emission controls on power plants in the United States:
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 NO2 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.

7. How do OMI NO2 data compare to AQS data?
North East 1
North East 2
Chicago
N=20
N=23
N=6
Houston
Southern California
Central Valley
N=13
N=51
N=32
AQS data: hourly, use PM data (corresponding to OMI overpass time)
OMI NO2 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. Time series of AQS and OMI NO2
Houston
Data are deseasonalized.
Normalized
Anomaly
Change Relative to 2005 (%)
Error bars are standard error of the mean.

9. Correlation of monthly mean AQS & OMI NO2 Anomalies
Correlation worsens with increasing latitude.
North East 1
North East 2
Chicago
Houston
Southern California
Central Valley
** Because the data are normalized, there is no bias.

10. Time series of AQS and OMI NO2
North East 1
AQS
OMI
Normalized
Anomaly
Relative to
Change
2005 (%) N
North East 2
Normalized
Anomaly
Relative to
Change
2005 (%) N
Chicago
Likely issue:
Improper filtering of OMI data for snow & ice or lack of statistical significance.
Normalized
Anomaly
Relative to
Change
2005 (%) N

11. Time series of AQS and OMI NO2
Houston
Normalized
Anomaly
Relative to
Change
2005 (%)
S. California
Normalized
Anomaly
Relative to
Change
2005 (%)
Central Valley
Normalized
Anomaly
Relative to
Change
2005 (%)

12. Largest decreases in areas with large regional backgrounds.
∆NO2 (%) from 2005 to 2012
Largest decreases in areas with large regional backgrounds.
N of AQS sites
Mean, all
Median, all
Mean, no winter*
Median, no winter*
North East 1 20
-37.9
-40.2
-40.3
-37.4
-41.4
-31.9
-46.1
-35.4
North East 2 23
-39.8
-37.3
-38.1
-34.6
-43.1
-39.7
-40.7
-38.4
Chicago 6
-28.2
-44.4
-30.2
-41.7
-27.7
-37.1
-31.7
-31.0
Houston 13
-32.5
-35.0
-27.4
-31.5
-32.1
-32.9
S. California 51
-38.8
-42.3
-37.7
-38.6
-40.0
-30.4
Central Valley 32
-27.9
-34.7
-27.3
-24.3
-31.3
-23.9
-29.0
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 NO2 continues to decrease.
c) Stratospheric and free tropospheric NO2.
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 NOx Emissions
Power Plants (~68% decrease since late 1990s)
→ 1998 NOx 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
→ Emission controls devices (ECDs) were installed on power plants, reducing emissions (e.g., 90%).
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