Improving the Accuracy of Daily Satellite-Derived Ground-Level Fine Aerosol Concentration Estimates

for North America

Aaron van Donkelaar, Randall V. Martin, Adam N. Pasch, James J. Szykman, Lin Zhang, Yuxuan Wang and Dan Chen

As part of a broader project involvingJohn E. White, Philip Dickerson, Shobha Kondragunta, and Tim Dye

Fall AGUSan Francisco

3 Dec 2012

More than 36 Million Americans (~40% of area) not Covered by Monitoring Networks of Fine Aerosol (PM2.5)

AirNow Operational Map(airnow.gov)

Without satellite data, no contouring is possible in the hatched areas

“I am sick with an environmental illness and have to check the air quality every day. The only site in Mississippi is the Gulf Coast. Will any other areas in North Mississippi be added in the future?”

“HELP! I don’t live in a city, & your site does not help me know air quality except in cities. I have COPD & am beginning to suffer. I need to know what to expect here – not 60 or 70 miles West”

Quotes

Develop Satellite Remote Sensing for Use by AirNow

AirNow is the national framework for acquiring and distributing air quality information

• Collects, quality assures, and transfers real-time and forecasted air quality information to the public

• Gathers data provided by 130 federal, state, and local air quality agencies

• Issues weather/air quality news stories• Partners with national media and other agencies• Provides air quality education and outreach

America’s “go to” resource for current and forecasted air quality information

Begin by Inferring PM2.5 from Satellite Aerosol Optical Depth (AOD) and Simulated η (PM2.5/AOD)

Excluded regions with biased AOD (>0.1 or 20%) as identified with AERONET

Estimated PM2.5 = η· AOD

GEOS-ChemChemical Transport Model

vertical structure ▪aerosol properties▪

meteorological effects ▪

4

MISR- Multi-angle- 4 bands- 6-9 day

coverage

MODIS- Single

viewpoint- 36 bands- daily coverage

Significant Long-term Mean Agreement of Satellite-Derived PM2.5 with In Situ Measurements

SatelliteDerived

In-situ

Sat

ellit

e-D

eriv

ed [μ

g/m

3]

In-situ PM2.5 [μg/m3]

Ann

ual M

ean

PM

2.5 [

μg/m

3 ] (2

001-

2006

)

r

MODIS AOD 0.39

MISR AOD 0.39

Combined AOD 0.61

Combined PM2.5 0.77

van Donkelaar et al., EHP, 2010

But Original Estimates Have Limited Daily Skill

• Large daily error (67%)• Driven by bias and noise in AOD

& AOD/PM2.5

• Practical means to address?

• Also included additional filters from Hyer et al. (2010)

Daily Error in Original (2004, 2006, 2008)

van Donkelaar et al., ES&T, 2012

Original for specific day (Jun 27, 2005)

Climatological Bias Correction Informed by In Situ Observations from Training Dataset (2005, 2007, 2009)

• Regress 90-day running comparisons for 2005, 2007, and 2009 to identify bias

• Make correction surface using spatial interpolation of average

• Reduces mean daily error by ~10%

Bias Correction Improves Daily Accuracy

Error in Original (2004, 2006, 2008)

Error in Bias-corrected (2004, 2006, 2008)

Used PM2.5 monitors from training dataset to identify seasonal bias

van Donkelaar et al., ES&T, 2012

Bias Corrected for Jun 27, 2005

• Regional mean daily error drops another 10% (41.9%)

Spatial Smoothing Improves Skill FurtherSmoothed Ratio vs Climatology

Error in Original (2004, 2006, 2008)

Error in Smoothed & Bias-corrected (2004, 2006, 2008)

van Donkelaar et al., ES&T, 2012

Bias Corrected & Smoothed, Jun 27, 2005

Near-real-time MISR AOD Would Improve Coverage in Southwest

MODIS&MISR

MODIS

van Donkelaar et al., ES&T, 2012

Mean Number of Annual Observations

Insignificant Change in Error (46.7% 46.4%)

No Penalty from Using AOD/PM2.5 from Different YearsEnables Offline Calculation of AOD/PM2.5

Daily AOD/PM2.5 for specific year Climatological daily AOD/PM2.5 from training dataset (2005, 2007, 2009)

Error vs validation dataset (2004, 2006, 2008)

van Donkelaar et al., ES&T, 2012

Similar Performance for Extreme EventsUsing Offline Calculation of AOD/PM2.5

Error vs validation dataset (2004, 2006, 2008) during extreme events (90th percentile)

van Donkelaar et al., ES&T, 2012

Implementation and Outreach

• NOAA NESDIS receiving MODIS AOD & calculating AOD/PM2.5

• Data feed Sonoma Tech Data fusion AirNow

• Created videos to describe technique

• Created a committee to test, evaluate, and share findings using regional case studies.

Relevant Talk: White et al., GC13D-04 – EPA AirNow Satellite Data Processor (ASDP) for improved AQI

Relevant Posters:Pasch et al., A21C-0065 – Performance of the AirNow Satellite Data Processor

Conclusions

• Practical near-real-time technique to infer PM2.5 from satellite AOD

• Bias correction and smoothing significantly reduce daily error

• Would benefit from further improvements to AOD retrieval and AOD/PM2.5 calculation

• Collocated AOD/PM2.5 measurements would provide valuable information to evaluate simulation

Acknowledgements:NASA, EPA, NOAA, Environment Canada

Next steps in next talk by Szykman et al.