Hui Li a , Fazlay Faruque a ,Worth Williams a , Mohammand Al-Hamdan b , Jeffrey Luvall b

1. 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 Measurements of PM2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System Hui Lia, Fazlay Faruquea ,Worth Williamsa, Mohammand Al-Hamdanb, Jeffrey Luvallb aUniversity of Mississippi Medical Center, Jackson, Mississippi 39216 bNASA Marshall Space Flight Center, Huntsville, Alabama 35812

2. Introduction • NASA founded project on developing an DSS for asthma surveillance, intervention, and prevention • Real-Time PM2.5 Estimation System: 3 components Originally developed NASA Marshall Space Flight Center (MSFC) • AOD-PM2.5 linear regression models • A Surface Model to Interpolate AOD-derived and ground PM2.5 to continue surfaces • Approach to integrate the above two interpolated surfaces into a final output surface based on the weight (90% for ground surface via 10% for AOD-derived surface)

3. Introduction: continue • MODIS AOD shows great promise in improving estimate of PM2.5 • Gupta et al., 2006; Kumar et al., 2007 • Challenging on using satellite data in a real-time pollution system • Affected by many factors • Vary widely in different regions and different seasons

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

5. Introduction: continue • Two major aspects worth consideration in a real-time air quality system • Approach to integrate satellite data with ground data for the pollution estimation • Identification of an optimal temporal scale for calculating the correlations of AOD and ground data

6. Goal • Goal: identify the optimal temporal scale on determining AOD-PM2.5 correlation coefficients to improve PM2.5 estimation using satellite AOD data Within the last 3 days 08/12/08 Calculated date 08/10/08

7. Model domain and monitoring stations

8. Methodology • Five different temporal scales on utilizing satellite data and evaluating their impact on the model performance • Within the last 3 days • Within the last 10 days • Within the last 30 days • Within the last 90 days • Time period with the highest correlation in a year • Statistics for performance evaluation • Mean Bias (MB) • Normalized Mean Bias (NMB) • Root Mean Square Error (RMSE) • Normalized Mean Error (MNE) • Index of Agreement (IOA)

9. AOD sample data • Within a radius of 0.9 degree inside a station Pixel Point Station Range of a station AOD=(AOD1+AOD2+AOD3)/3

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

13. Discussion • Impact of Data Integration on the Model Performance • Model performance show only slight difference among the five selected temporal scales for the correlation of AOD and ground data • The weight of satellite data should be dependent on their relationship with ground data • Optimal Temporal Scale for the Correlation of AOD and Ground data • The optimal temporal scale: within the latest 30 days suggests that it might be a good strategy to build AOD-PM2.5 regression models on a monthly basis • The conclusion might not be able to apply to other areas considering different atmosphere conditions • Areas to Improve • Incorporate others factors to determine the optimal temporal scale using satellite data

14. Conclusion • The best optimal temporal scale is not the last 3 or 10 days in the solution • The temporal scale of the latest 30 days displays the best model performance • This conclusion does not consider the confounding impact of weather conditions

15. Acknowledge • Funding Agency • NASA Stennis Space Flight Center

16. Questions or Comments?