1. 1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan Gupta and Yang Liu Salt Lake City, April 22-25, 2012

2. Estimation of PM2.5 mass concentration at surface (µgm -3 ) while utilizing satellite derived Aerosol Optical Depth (AOD – unit less quantity) at visible wavelength OBJECTIVE

3. Sensitive groups should avoid all physical activity outdoors; everyone else should avoid prolonged or heavy exertion Sensitive groups should avoid prolonged or heavy exertion; everyone else should reduce prolonged or heavy exertion Sensitive groups should reduce prolonged or heavy exertion Unusually sensitive people should consider reducing prolonged or heavy exertion None Cautionary Statements 201-300 151-200 101-150 51-100 0-50 Index Values PM 10 (ug/m 3 ) PM 2.5 (ug/m 3 ) Category 355- 424 255- 354 155- 254 55-154 0-54 150.5- 250.4 65.5-150.4 40.5-65.4 15.5-40.4 0-15.4Good Very Unhealthy Unhealthy Unhealthy for Sensitive Groups Moderate What are we looking for ?

4. Surface Aerosol Rayleigh Scattering Water vapor + other gases (absorption) Ozone 10km Satellite Sun Column Satellite Measurement Particle size Composition Water uptake Vertical Distribution Satellite retrieval issues - inversion (e.g. aerosol model, background). Seven MODIS bands are utilized to derive aerosol properties 0.47, 0.55, 0.65, 0.86, 1.24, 1.64, and 2.13 µm 10X10 km 2 Res. What Satellite Provides?

5. Measurement Technique AOD – Column integrated value (top of the atmosphere to surface) - Optical measurement of aerosol loading – unit less. AOD is function of shape, size, type and number concentration of aerosols PM2.5 – Mass per unit volume of aerosol particles less than 2.5 µm in aerodynamic diameter at surface (measurement height) level

6. What satellite provides and how to get it?

7. DRAFT May 11, 2007 May 12, 2007 May 13, 2007 May 14, 2007 May 15, 2007 May 16, 2007 MODIS-Terra True Color Images

8. DRAFT May 11, 2007 May 12, 2007 May 13, 2007 May 14, 2007 May 15, 2007 May 16, 2007 MODIS-Terra Aerosol Optical Thickness

9. Data Quality – MODIS DT Product (Levy et al. 2010) Expected error: ±(0.05+0.15) 9

10. AOT to PM

11. AOD – PM Relation   – particle density  Q – extinction coefficient  r e – effective radius  f PBL – % AOD in PBL  H PBL – mixing height Composition Size distribution Vertical profile

12. Support for AOD-PM 2.5 Linkage  Current satellite AOD is sensitive to PM 2.5 (Kahn et al. 1998)  Polar-orbiting satellites can represent at least daytime average aerosol loadings (Kaufman et al., 2000)  Missing data due to cloud cover appear random in general (Christopher et al., 2010 ) 2 4 6 8 10 12 12

13. AOT-PM Chu et al., 2003 Wang et al., 2003

14. Gupta, 2008 AOT-PM2.5 Relationship

15. Questions to Ask: Issues How accurate these estimations are ? Is PM2.5-AOT relationship is always linear? How does uncertainty in AOT retrieval impact estimation of air quality Does this relationship changes in space and time? Does this relationship changes with change in aerosol type? How meteorology drive this relationship? How about vertical distribution of aerosols in the atmosphere?

16. Assumption for Quantitative Analysis When most particles are concentrated and well mixed in the boundary layer, satellite AOD contains a strong signal of ground-level particle concentrations.

17. Modeling the Association of AOD With PM 2.5  The relationship between AOD and PM 2.5 depends on parameters hard to measure: Vertical profile Size distribution and composition Diurnal variability  We develop statistical models with variables to represent these parameters Model simulated vertical profile Meteorological & other surrogates Average of multiple AOD measurements No textbook solution!

18. Methods Developed So Far  Statistical models Correlation & simple linear regression Multiple linear regression with effect modifiers Linear mixed effects models Geographically weighted regression Generalized additive models Hierarchical models combining the above Bayesian models Artificial neural network  Data fusion models Combining satellite data with model simulations  Deterministic models Improving model simulation with satellite data

19. Limitation: Vertical Distribution of Aerosols

20. Vertical Distribution Engel-Cox et al., 2006 Al-Saadi et al., 2008

21. Aug 17 Aug 18 Aug 19 Aug 20 Aug 21 Aug 22 Aug 23 Aug 25 Aug 28 5 km (courtesy of Dave Winker, P.I. CALIPSO) What Satellites can provide for vertical information? - CALIPSO

22. Observed vs Estimated (AOD only) Advantages of using reanalysis meteorology along with satellite Observed vs Estimated (AOD + Meteorology) Linear Correlation Coefficients

23. Combination of model and satellite observations van Donkelaar et al., 2010

24. Suggested Reading