Simulation of Absorbing Aerosol Index & Understanding the Relation of NO2 Column Retrievals
with Ground-based Monitors
Randall Martin (Dalhousie, Harvard-Smithsonian)
with contributions from
Melanie Hammer, Shailesh Kharol, Jeff Geddes, Aaron van Donkelaar (Dalhousie U)
TEMPO Science Team Meeting22 May 2014
Michael Brauer (UBC), Dan Crouse (Health Canada), Greg Evans (U Toronto), Mike Jerrett (Berkeley), Lok Lamsal (NASA), Rob Spurr (RT Solutions),
Yushan Su (Ontario MoE), Omar Torres (NASA)
Growing Use of Remote Sensing for Exposure AssessmentLooking backward: Use of (A) remote sensing data to supplement (B) available routine air quality monitoring
Looking forward: Use of (B) available routine air quality monitoring to supplement (A) remote sensing data
Wu J, et al (2006). Exposure assessment of PM air pollution before,during, and after the 2003 Southern California wildfires.
Henderson SB, et al (2008). Use of MODIS products to simplify and evaluate a forest fire plumedispersion model for PM10 exposure assessment.
Significant Association of Satellite-derived Long-term PM2.5 Exposure with Cardiovascular Mortality at Low PM2.5 & Associations with Diabetes and Hypertension
Crouse et al., EHP, 2012; Brook et al., Diabetes Care, 2013; Chen et al., EHP, 2013; Chen et al., Circulation, 2013
Some Groups Using Remote Sensing for Exposure Assessment: WHO, World Bank, OECD, Environmental Performance Index, Global Burden of Disease
Develop Assimilation System of Suite of TEMPO Observations to Estimate PM2.5 Composition, Ground-level
Ozone, and Ground-level NO2
• Absorbing Aerosol Index (aerosol composition) • NO2 (ozone and aerosol composition)• Aerosol optical depth• Ozone profile• SO2 (aerosol composition)• HCHO (ozone and aerosol composition)• Vegetation (VOC emissions)
Assimilation System Could Also be Useful for AMF Calculation
Simulation of Absorbing Aerosol Index (AAI)
GEOS-Chem Simulation of Aerosol Composition Coincident with OMI
LIDORT Radiative Transfer Model
Simulated Absorbing Aerosol IndexTOMS UV Surface
Reflectance (from Omar Torres)
OMI Viewing Geometry
A measure of the aerosol-induced spectral dependence of back-scattered UV
Example observed AAI showing a smoke plume over the United States
354 35410 10
388 388
AAI=-100 log logRayleigh aerosol Rayleigh
I II I
Initial GEOS-Chem & LIDORT Simulation of OMI Absorbing Aerosol Index (July 2008)
Will be Useful to Interpret AAI from TEMPO
Melanie Hammer
OMI
GEOS-Chem & LIDORT
-2.5 -1.5 -0.5 0 0.5 1.5 2.5
OMI Cloud Fraction < 5%
General Approach to Estimate Surface Concentration
S → Surface Concentration
Ω → Tropospheric column
Coincident Model (GEOS-Chem) Profile
OM
MO S S
Daily OMI NO2 Column
Concentration
Alti
tude
Also uses OMI to inform subpixel variation following Lamsal et al. (2008, 2013)
Bias in Satellite-Derived NO2 Trend (2005-2011)
Kharol et al., in prep
In Situ OMI-Derived
Slope with BEHR ~0.5
y = 0.40x + 0.02
r = 0.73n = 102
Why is Satellite-Derived Surface NO2 Biased vs In Situ?
Kharol et al., in prep
In situ (2005-2011) OMI NASA V2.1 (2005-2011)
Molybdenum converter measurements corrected for NOz following Lamsal et al. (2008, 2010)
Urban areas included
NO
2 Mix
ing
Rat
io (p
pbv)
y = 0.40x + 0.09r = 0.80n = 215
In situ sampled at OMI overpass time
Slope with BEHR over US ~0.5
Use Land Use Regression (LUR) Datasets to Examine Effects of Monitor Placement
Kharol et al., in prepLUR from Jerrett et al. 2009
Toronto Hamilton
Monitor Placement Contributes to Bias Versus Area Average
Kharol et al., in prep
LUR
NO
2 at M
easu
rem
ent S
ite
A
rea
Aver
age
LUR
NO
2
Consistent Relative Trends in Ground-level NO2 Indicate Both Observe Changes in Large-Scale Processes
In situOMI
Kharol et al., in prep
Remote Sensing Offers Observational Estimate of Area-Average Concentrations & Changes in Surface NO2
ΔNO2 (ppbv yr-1)
Trend
Shailesh Kharol
2005 to 2011
Concentration
NO2 (ppbv)
Lamsal et al. (2013)
Conclusions
• Initial simulation of Absorbing Aerosol Index
• Spatial bias in surface NO2 from satellite and in situ monitors partially arises from monitor placement
• Ambiguity remains about long-term area-average NO2 in urban areas
• Consider for TEMPO validation a dense collection (>10) of long-term monitors of ground-level NO2 and column NO2 within a TEMPO footprint for multiple urban areas
Acknowledgements: NSERC, Environment Canada, Health Canada