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 PM 2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System Hui Li a, Fazlay Faruque a,Worth Williams a, Mohammand Al- Hamdan b, Jeffrey Luvall b a University of Mississippi Medical Center, Jackson, Mississippi 39216 b NASA Marshall Space Flight Center, Huntsville, Alabama 35812

2. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Introduction NASA founded project on developing an DSS for asthma surveillance, intervention, and prevention NASA founded project on developing an DSS for asthma surveillance, intervention, and prevention Real-Time PM 2.5 Estimation System: 3 components Real-Time PM 2.5 Estimation System: 3 components Originally developed NASA Marshall Space Flight Center (MSFC) Originally developed NASA Marshall Space Flight Center (MSFC) –AOD-PM 2.5 linear regression models –A Surface Model to Interpolate AOD-derived and ground PM 2.5 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. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Introduction: continue MODIS AOD shows great promise in improving estimate of PM 2.5 MODIS AOD shows great promise in improving estimate of PM 2.5 –Gupta et al., 2006; Kumar et al., 2007 Challenging on using satellite data in a real-time pollution system Challenging on using satellite data in a real-time pollution system –Affected by many factors –Vary widely in different regions and different seasons

4. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 AOD-PM2.5 Relationship in 2004 AOD-PM2.5 Relationship in 2005

5. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Introduction: continue Two major aspects worth consideration in a real-time air quality system 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. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Goal Goal: identify the optimal temporal scale on determining AOD-PM 2.5 correlation coefficients to improve PM 2.5 estimation using satellite AOD data Goal: identify the optimal temporal scale on determining AOD-PM 2.5 correlation coefficients to improve PM 2.5 estimation using satellite AOD data 08/12/08 Calculated date 08/10/08 Within the last 3 days

7. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Model domain and monitoring stations

8. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Methodology Five different temporal scales on utilizing satellite data and evaluating their impact on the model performance 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 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. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 AOD sample data Within a radius of 0.9 degree inside a station Within a radius of 0.9 degree inside a station Pixel Point Station Range of a station AOD=(AOD1+AOD2+AOD3)/3

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

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

13. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Discussion Impact of Data Integration on the Model Performance 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 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-PM 2.5 regression models on a monthly basis –The conclusion might not be able to apply to other areas considering different atmosphere conditions Areas to Improve Areas to Improve –Incorporate others factors to determine the optimal temporal scale using satellite data

14. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Conclusion The best optimal temporal scale is not the last 3 or 10 days in the solution 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 The temporal scale of the latest 30 days displays the best model performance This conclusion does not consider the confounding impact of weather conditions This conclusion does not consider the confounding impact of weather conditions

15. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Acknowledge Funding Agency Funding Agency –NASA Stennis Space Flight Center

16. Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Questions or Comments?