2 Column Retrievals with Groundbased Monitors Randall Martin Dalhousie HarvardSmithsonian with contributions from Melanie Hammer Shailesh Kharol Jeff Geddes Dalhousie U TEMPO Science Team Meeting ID: 335456
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Slide1
Simulation of Absorbing Aerosol Index & Understanding the Relation of NO2 Column Retrievals with Ground-based Monitors
Randall Martin (Dalhousie, Harvard-Smithsonian)with contributions fromMelanie Hammer, Shailesh Kharol, Jeff Geddes (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)Slide2
Growing Use of Remote Sensing for Exposure Assessment
Looking backward: Use of (A) remote sensing data to supplement (B) available routine air quality monitoringLooking 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 plume
dispersion model for PM
10
exposure assessment.
Significant Association of Satellite-derived Long-term PM
2.5
Exposure with Cardiovascular Mortality at Low PM2.5 & Associations with Diabetes and HypertensionCrouse 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 Slide3
Develop Assimilation System of Suite of TEMPO Observations to Estimate PM2.5 Composition, Ground-level Ozone, and Ground-level NO2Absorbing Aerosol Index (aerosol composition) NO2 (ozone and aerosol composition)
Aerosol optical depthOzone profileSO2 (aerosol composition)HCHO (ozone and aerosol composition)Vegetation (VOC emissions)Assimilation System Could Also be Useful for AMF CalculationSlide4
Simulation of Absorbing Aerosol Index (AAI)
GEOS-Chem Simulation of Aerosol Composition Coincident with OMI
LIDORT Radiative Transfer Model
Simulated Absorbing Aerosol Index
TOMS 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 StatesSlide5
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%Slide6
General Approach to Estimate Surface ConcentrationS → Surface Concentration
Ω → Tropospheric column Coincident Model (GEOS-Chem) Profile
Daily
OMI NO
2
Column
Concentration
A
ltitude
Also uses OMI to inform
subpixel
variation following
Lamsal
et al. (2008, 2013)Slide7
Bias in Satellite-Derived NO2 Trend (2005-2011)
Kharol
et al., in prepIn Situ
OMI-Derived
Slope with BEHR ~0.5
y = 0.40x
+
0.02
r =
0.73
n
=
102Slide8
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
NO
z
following
Lamsal
et al. (2008, 2010)
Urban areas included
NO
2
Mixing Ratio (
ppbv
)
y = 0.40x + 0.09r = 0.80
n
= 215
In situ sampled at OMI overpass time
Slope with BEHR over US ~0.5Slide9
Use Land Use Regression (LUR) Datasets to Examine Effects of Monitor Placement
Kharol et al., in prep
LUR from
Jerrett
et al. 2009
Toronto
HamiltonSlide10
Monitor Placement Contributes to Bias Versus Area Average
Kharol et al., in prep
LUR NO
2
at Measurement Site
Area Average LUR NO
2
Slide11
Consistent Relative Trends in Ground-level NO2 Indicate Both Observe Changes in Large-Scale Processes
In situOMI
Kharol et al., in prepSlide12
Remote Sensing Offers Observational Estimate of Area-Average Concentrations
& Changes in Surface NO2ΔNO2 (ppbv yr-1)Trend
Shailesh
Kharol
2005
to 2011
Concentration
NO
2
(
ppbv
)
Lamsal
et al. (2013)Slide13
ConclusionsInitial simulation of Absorbing Aerosol IndexSpatial bias in surface NO2 from satellite and in situ monitors partially arises from monitor placementAmbiguity remains about long-term area-average NO2 in urban areasConsider 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 areasAcknowledgements: NSERC, Environment Canada, Health Canada