Improved Retrieval of PM 2.5 From Satellite Data Using Non-Linear Methods

1. Improved Retrieval of PM2.5From Satellite Data Using Non-Linear Methods Meytar Sorek-Hamer David Broday May 21, 2013 IAAR

2. Data Current operational Resolution 10kmx10km Satellite observations Ground monitoring data • Ground monitors of PM2.5 are point measurements and have limited spatial information.

3. What has been done around the world ? (Hoff & Christopher. AWMA. 2009)

4. What about West USA and Israel?

5. What about West USA and Israel? • Possible reasons: • Differences in surface reflectance • Differences in the relative humidity profile • Geographical differences in PM composition (Engel Cox et al. AtmEnv. 2004 ; Zhang et al. AWMA. 2009)

6. San Joaquin Valley (SJV), CA • Located in central California (approx. four million residents). • SJV air quality PM monitoring network includes 6 stations that measure continuously and are spread over a 60,840 km2area. (CARB, 2008)

7. Linear Relationship R-square=0.17 R-square=0.2 SJV ,CA ISRAEL

8. From Linear Relationship To Non-Linear Relationship • A regression in which observational data are modeled by a combination of nonlinear functions of the model parameters.

9. Generalized Additive Model (GAM) • The linear relationship between the parameters and the response variable is replaced by a functional relationship • Each function can be different, ( f1 can be linear, f2 can be a natural spline, etc.) Satellite parameters (e.g. AOD, DB_AOD, OMI_NO2) Daily PM2.5 Smoothing functions

10. Adding a Boosting procedure Choosing variables that would optimize the model Results of running the boosting procedure with ten variables. Each bar represents the relative influence (y-axis) of a certain variable (x-axis) on the response variable (PM2.5 concentration) with interactions among variables.

11. 2 Adding a Boosting procedure • Sensitivity analysis of applying the hierarchical boosting results with the GAM. • The black bar (case 4) represents the best result.

12. Generalized Additive Model (GAM) SJV ,CA ISRAEL

13. Multivariate Adaptive Regression Spline (MARS) Piecewise linear basis functions when x>t when x<t otherwise zero Daily PM2.5 At each step, the model selects the knot (t) and its corresponding basis functions that give the largest decrease in the residual sum of squares.

14. GAM vs. MARS MARS GAM  (Leathwicket al. Ecological Modeling. 2006)

15. Multivariate Adaptive Regression Spline (MARS)

16. Israel-Summary of Results Linear Regression GAM MARS

17. SJV-Summary of Results Linear Regression GAM R-square=0.17 MARS

18. Conclusions • Non-Linear regression methods improve AOD-PM2.5 relationship over SJV and Israel. • MARS – more efficient, showed better results: >300% improvement (R2=0.71 ; Linear R2=0.17) • Further work to improve current results taking into account daily changes in the AOD-PM2.5 relationships.

19. Acknowledgments Data MOE IEC Ashdod/Ashkelon municipal association NASA Consulting NASA Ames Research Center, CA – Dr. Anthony Strawa, Dr. Robert Chatfield, Robert Esswein Prof. Ayala Cohen, Head of Statistical Lab, Technion Funding EHF Doctoral Fellowship Israel Council for Higher Education THANK YOU