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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

:

"" .

74

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

8

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