Quantifying uncertainties of OMI NO2 data Implications for air quality applications

Bryan Duncan, Yasuko Yoshida, Lok Lamsal, NASA OMI Retrieval TeamNASA Goddard Space Flight Center, Greenbelt, MD

AQAST STM, Rice U., Houston, TX, January 15-17, 2014

2005-2007 2009-2011

OMI NO2 data = proxy for surface NOx levels

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.

Ozo

ne S

easo

n

OMI NO2∆OMI NO2

2005 2012 2005-2012

→ as NOx emissions decrease, the signal-to-noise also decreases so that quantification of the uncertainties becomes even more important.

OMI NO2

∆OMI NO2 (%)

2005 2012

2005-2012

NO2 columns(molecules/cm2)

> 0.5x1015

(probably too low)

> 1.0x1015

> 1.5x1015

(probably too high)

(x1015 molecules/cm2)

Just how large do you think the uncertainties are – ballpark estimate?

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.

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.

The observed response of Ozone Monitoring Instrument (OMI) NO2 columns to NOx 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 ConclusionsAura 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.

How do variations in OMI NO2 data compare to CEMS data for power plants?

How do OMI NO2 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 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

Houston

Time series of AQS and OMI NO2

Nor

mal

ized

Anom

aly Data are deseasonalized.

Chan

ge

Rela

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to

2005

(%)

AQSOMI

Correlation of monthly mean AQS & OMI NO2 AnomaliesCorrelation 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.

North East 1

North East 2

Chicago

Time series of AQS and OMI NO2

Likely issue: Improper filtering of OMI data for snow & ice or lack of statistical significance.

Nor

mal

ized

Anom

aly

Nor

mal

ized

Anom

aly

Nor

mal

ized

Anom

aly

Chan

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Rela

tive

to 2

005

(%)

Chan

ge

Rela

tive

to

2005

(%)

Chan

ge

Rela

tive

to

2005

(%)

N

N

N

AQSOMI

Houston

S. California

Central Valley

Time series of AQS and OMI NO2

Nor

mal

ized

Anom

aly

Nor

mal

ized

Anom

aly

Nor

mal

ized

Anom

aly

Chan

ge

Rela

tive

to 2

005

(%)

Chan

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2005

(%)

Chan

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2005

(%)

AQSOMI

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 -31.9-32.5

-35.0-27.4

-30.2-31.5

-32.1-32.9

S. California 51 -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

∆NO2 (%) from 2005 to 2012Largest decreases in areas with large regional backgrounds.

AQSOMI

* Used data from April to October only.

Extra Slides

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.”

Regulations of NOx Emissions

→ Emission controls devices (ECDs) were installed on power plants, reducing emissions (e.g., 90%).

1)Power Plants (~68% decrease since late 1990s)

→ 1998 NOx State Implementation Plan (SIP) Call22 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 1990Tier 1 (phased-in between 1994 and 1997) standardsTier 2 (phased-in between 2004 and 2009) standards