Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Identifying Optimal Temporal Scale for the Correlation of AOD and Ground

Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008

Identifying Optimal Temporal Scale for the Correlation of Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PMAOD and Ground Measurements of PM2.52.5 to Improve to Improve

the Model Performance in a Real-time Air Quality the Model Performance in a Real-time Air Quality Estimation SystemEstimation System

Hui LiHui Liaa, Fazlay Faruque, Fazlay Faruqueaa ,Worth Williams ,Worth Williamsaa, Mohammand Al-, Mohammand Al-HamdanHamdanbb, Jeffrey Luvall, Jeffrey LuvallbbaaUniversity of Mississippi Medical Center, Jackson, Mississippi University of Mississippi Medical Center, Jackson, Mississippi 3921639216bbNASA Marshall Space Flight Center, Huntsville, Alabama 35812NASA Marshall Space Flight Center, Huntsville, Alabama 35812


IntroductionIntroduction

NASA founded project on developing an DSS for asthma NASA founded project on developing an DSS for asthma surveillance, intervention, and preventionsurveillance, intervention, and prevention

Real-Time PMReal-Time PM2.52.5 Estimation System: 3 components Estimation System: 3 components Originally developed NASA Marshall Space Flight Center (MSFC)Originally developed NASA Marshall Space Flight Center (MSFC)

– AOD-PMAOD-PM2.52.5 linear regression models linear regression models– A Surface Model to Interpolate AOD-derived and A Surface Model to Interpolate AOD-derived and

ground PMground PM2.52.5 to continue surfaces to continue surfaces– Approach to integrate the above two interpolated Approach to integrate the above two interpolated

surfaces into a final output surface based on the surfaces into a final output surface based on the weight (90% for ground surface via 10% for weight (90% for ground surface via 10% for AOD-derived surface)AOD-derived surface)


Introduction: continueIntroduction: continue

MODIS AOD shows great promise in MODIS AOD shows great promise in improving estimate of PMimproving estimate of PM2.5 2.5

– Gupta et al., 2006; Kumar et al., 2007Gupta et al., 2006; Kumar et al., 2007 Challenging on using satellite data in a Challenging on using satellite data in a

real-time pollution systemreal-time pollution system– Affected by many factorsAffected by many factors– Vary widely in different regions and Vary widely in different regions and

different seasonsdifferent seasons


AOD-PM2.5 Relationship in 2004 AOD-PM2.5 Relationship in 2005


Introduction: continueIntroduction: continue

Two major aspects worth Two major aspects worth consideration in a real-time air quality consideration in a real-time air quality systemsystem– Approach to integrate satellite data with Approach to integrate satellite data with

ground data for the pollution estimationground data for the pollution estimation– Identification of an optimal temporal scale Identification of an optimal temporal scale

for calculating the correlations of AOD for calculating the correlations of AOD and ground dataand ground data


GoalGoal

Goal: identify the optimal temporal Goal: identify the optimal temporal scale on determining AOD-PMscale on determining AOD-PM2.52.5

correlation coefficients to improve correlation coefficients to improve PMPM2.52.5 estimation using satellite AOD estimation using satellite AOD

datadata

08/12/08Calculated date

08/10/08

Within the last 3 days


Model Model domain domain and and monitoring monitoring stationsstations


MethodologyMethodology

Five different temporal scales on utilizing satellite data and Five different temporal scales on utilizing satellite data and evaluating their impact on the model performanceevaluating their impact on the model performance– Within the last 3 daysWithin the last 3 days– Within the last 10 daysWithin the last 10 days– Within the last 30 daysWithin the last 30 days– Within the last 90 daysWithin the last 90 days– Time period with the highest correlation in a yearTime period with the highest correlation in a year

Statistics for performance evaluationStatistics for performance evaluation– Mean Bias (MB)Mean Bias (MB)– Normalized Mean Bias (NMB)Normalized Mean Bias (NMB)– Root Mean Square Error (RMSE)Root Mean Square Error (RMSE)– Normalized Mean Error (MNE)Normalized Mean Error (MNE)– Index of Agreement (IOA)Index of Agreement (IOA)


AOD sample dataAOD sample data

Within a radius of 0.9 degree inside a Within a radius of 0.9 degree inside a stationstation

Pixel Point

Station

Range of a station

AOD=(AOD1+AOD2+AOD3)/3


2004 2005

Mean Bias

-0.15

-0.10

-0.05

0.00 3 days10 days30 days90 daysWarm/Cold

2004 2005

Normalized Mean Bias

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2


2004 2005

Roor Mean Square Error

0.0

0.5

1.0

1.5

2.0

2.5

3.0


2004 2005

Normalized Mean Error

0

5

10

15

3 days10 days30 days90 daysWarm/Cold

Temporal scale (3 days)

R Squared (2004)

Fre

quen

cy0.0 0.2 0.4 0.6 0.8 1.0

050

100150200250

40 36 24 7 1


R Squared (2004)

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

100150200250

138

6131 15 0


R Squared (2004)

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

100150200250

174

9840

0 0


R Squared (2004)

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

100150200250 209

10156

0 0


R Squared (2005)

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

100150200

250

29 48 41 15 4


R Squared (2005)

Fre

quen

cy0.0 0.2 0.4 0.6 0.8 1.0

050

100150200

250

12369 43 18 0


R Squared (2005)

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

100

150200250

13293

42 23 0


R Squared (2005)

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

050

100

150200250

160

90 114

0 0

Distribution of R-Squared values across different temporal scales in 2004 and 2005


DiscussionDiscussion

Impact of Data Integration on the Model PerformanceImpact of Data Integration on the Model Performance – Model performance show only slight difference among the five Model performance show only slight difference among the five

selected temporal scales for the correlation of AOD and ground selected temporal scales for the correlation of AOD and ground datadata

– The weight of satellite data should be dependent on their The weight of satellite data should be dependent on their relationship with ground datarelationship with ground data

Optimal Temporal Scale for the Correlation of AOD and Optimal Temporal Scale for the Correlation of AOD and Ground dataGround data– The optimal temporal scale: within the latest 30 days The optimal temporal scale: within the latest 30 days

suggests that it might be a good strategy to build AOD-PMsuggests that it might be a good strategy to build AOD-PM2.52.5 regression models on a monthly basisregression models on a monthly basis

– The conclusion might not be able to apply to other areas The conclusion might not be able to apply to other areas considering different atmosphere conditions considering different atmosphere conditions

Areas to Improve Areas to Improve – Incorporate others factors to determine the optimal temporal Incorporate others factors to determine the optimal temporal

scale using satellite datascale using satellite data


ConclusionConclusion

The best optimal temporal scale is not The best optimal temporal scale is not the last 3 or 10 days in the solutionthe last 3 or 10 days in the solution

The temporal scale of the latest 30 The temporal scale of the latest 30 days displays the best model days displays the best model performanceperformance

This conclusion does not consider the This conclusion does not consider the confounding impact of weather confounding impact of weather conditionsconditions


AcknowledgeAcknowledge

Funding AgencyFunding Agency– NASA Stennis Space Flight CenterNASA Stennis Space Flight Center


Questions or Comments?Questions or Comments?

Documents

Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Identifying Optimal Temporal Scale for the Correlation of AOD and Ground