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A Fractional AOD Approach to Derive PM2.5 A Fractional AOD Approach to Derive PM2.5 Information Using MISR Data Coupled with Information Using MISR Data Coupled with GEOS-CHEM Aerosol Simulation ResultsGEOS-CHEM Aerosol Simulation Results
Yang Liu, Ralph Kahn, Solene Turquety, Robert M. Yantosca, and Petros Koutrakis
with thanks to Lyatt Jaegle and Rynda Hudman
April 11, 2007
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Satellite retrieved AOD can improve PM2.5 concentration
estimates Valuable in pollution health effect
studies (spatial and temporal coverage)
MISR reports aerosol microphysical properties (e.g., particle size, shape and darkness) May provide much needed PM2.5 speciation and size information in health studies
Can we get more aerosol information Can we get more aerosol information from satellites in addition to AOD?from satellites in addition to AOD?
However, total mass is unlikely the only cause of PM2.5 toxicity
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The PlanThe Plan
1. Develop MISR fractional AODs that utilize MISR AOD and aerosol mixture information
2. Build models using them as predictors to estimate the concentrations of PM2.5 constituents
3. Compare model performance with total AOD models
4. Estimating size distributions of PM2.5 constituents using model results
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Main Take-Home MessagesMain Take-Home Messages
Regression models developed with MISR fractional AODs as major predictors are more flexible, and have significantly higher predicting powers than the total-AOD models
Much more aerosol information in additional to column AOD is hidden in MISR data. Our approach can be used to extract it
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MISR fractional AODs break the total AOD MISR fractional AODs break the total AOD into contributions of individual componentsinto contributions of individual components
otherwise. 0mixture; " successful" a is j mixture if 1
:
"" .
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1 j mixture
Where
MixturesSuccessfulofNo
FractionAOD
AODFractionalj
jmixtureinicomponent
i
8 aerosol components
74 aerosol mixtures
Up to 3 in each mixture + physical
considerations
RT model
AOD for each mixture + success flag
LUT for TOA reflection
Compare with Obs + statistical selection
criteria
Total column AOD = sum of all fractional AODs
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GC aerosol simulations scale column GC aerosol simulations scale column AODs to surface AOD valuesAODs to surface AOD values
14. and 8, 6, 3, 2, 1, components for AODsfractional the to refer AODsnondustMISR :Where
AODnondustMISR AODnondust column CHEM-GEOS
AODnondust surface CHEM-GEOS
AODnondust surface MISR
21. and 19 components for AODsfractional the to refer AODsdustMISR :Where
AODdustMISR AODdust column CHEM-GEOS
AODdust surface CHEM-GEOS
AODdust surface MISR
Note: currently difficult to match more precisely between MISR and GC due to MISR component definitions
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Regression models link fractional AODs Regression models link fractional AODs with particle concentrationswith particle concentrations
Compared with total AOD model
Individual components can have different regression coefficients, or even be insignificant
Each component may assume different growth pattern with increasing RH
Have the potential to estimate major PM2.5 constituents
Indicator alGeographic Indicator Seasonal
factor correction RH AODfractional surface
ionConcentrat tConstituen PM
109
i i
8
10
2.5
i
i
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MISR data contains particle size distributionsMISR data contains particle size distributions
i component tsignifican of tconstituen PM a of 2.5 PDFfPDF i
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A Case StudyA Case Study
1. MISR 2005 aerosol data (version 17)
2. EPA STN database (~200 sites, 24-hr concentrations of PM2.5, SO4, NO3, OC, EC)
3. GEOS-CHEM simulated aerosol profiles (V7-02-04)
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Model Performance – Fractional vs. Total Model Performance – Fractional vs. Total AOD (Eastern US)AOD (Eastern US)
Response N Adj. R2
Significant Predictors N Adj. R2
Significant Predictors
PM2.5 203 0.56
Intercept, AOD1,
AOD2, AOD3,
AOD8, AOD14,
Wet Season 207 0.42
Intercept, AODtotal,
Wet Season
SO4 206 0.62
AOD1, AOD2,
AOD3, AOD8,
AOD21,
Wet Season 206 0.43
AODtotal,
Wet Season
NO3 206 0.13
Intercept, AOD2,
AOD8, AOD14,
Wet Season 204 0.11
AODtotal,
Wet Season
OC 206 0.19
Intercept, AOD1,
AOD2, AOD8,
Wet Season 206 0.15
Intercept, AODtotal,
Wet Season
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Model Performance – Fractional vs. Total Model Performance – Fractional vs. Total AOD (Western US)AOD (Western US)
Response N Adj. R2
Significant Predictors N Adj. R2
Significant Predictors
PM2.5 53 0.56
AOD2, AOD3,
AOD6, AOD8,
AOD21, 54 0.21
AODtotal, Wet
Season
SO4 54 0.40
AOD1, AOD2,
AOD3, AOD8,
AOD21, 54 0.12
AODtotal, Wet
Season
NO3 54 0.55
AOD3, AOD6,
AOD19, Wet
Season 54 0.24
Intercept, AODtotal,
OC 56 0.28
Intercept, AOD2,
AOD3, AOD8,
Wet Season 54 0.11
Intercept, AODtotal, Wet
SeasonNote: sample size too small, changes in adj. R2 are only qualitative indication of improvement
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PM2.5 Size Distribution can be estimated PM2.5 Size Distribution can be estimated using regression coefficientsusing regression coefficients
MISR AERONET
East:Model PM2.5 Mode Diameter = 0.19 mAERONET Mode Diameter = 0.29 m
West:Model PM2.5 Mode Diameter = 0.22 mAERONET Mode Diameter = 0.25 m
Difference: MISR Sampling bias?
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ConclusionsConclusions
Fractional AOD values can be calculated using MISR retrieved aerosol microphysical properties – unique to MISR
Regression models using fractional AODs as predictors perform much better than the total AOD models
Additional PM2.5 information such as composition and size distribution can be obtained using this method
Longer MISR data time series are needed to get robust parameter estimates