1. 1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET-AQ Applied Remote SEnsing Training A project of NASA Applied Sciences Pawan Gupta Originally presented at LADCO, March 12-15, 2012

2. Estimation of PM2.5 mass concentration at surface (µg m -3 ) while utilizing satellite derived Aerosol Optical Depth (AOD – unitless quantity) at a 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 Do Satellites Provides?

5. Measurement Techniques AOD – Column integrated value (top of the atmosphere to surface) - Optical measurement of aerosol loading – unitless. AOD is a 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 satellites provide 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. AOT to PM

10. Hoff and Christopher, 2009 AOD to PM2.5 - Theoretical

11. PM2.5 Estimation: Popular Methods Two Variable Method Multi- Variable Method Artificial Neural Network MSC AOT PM2.5 Y=mX + c 5D + 2D + 1 1D + and Empirical Methods, Data Assimilation etc. are under utilized

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

13. Gupta, 2008 AOT-PM2.5 Relationship

14. Limitation: Major Unsolved Issue All three Statistical models (TVM, MVM, ANN) underestimate high PM2.5 loadings

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

16. 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

17. Questions to Ask: Issues How accurate are these estimates ? Is the PM2.5-AOT relationship always linear? How does uncertainty in AOT retrievals affect estimation of air quality Does this relationship change in space and time? Does this relationship change with change in aerosol type? How does meteorology drive this relationship? How does vertical distribution of aerosols in the atmosphere affect these estimates?

19. TVM MVM Advantages of using reanalysis meteorology along with satellite

20. TVM MVM ANN Spatial Comparison MODIS-Terra, July 1, 2007 Satellite-derived PM2.5 fills the gap in surface measurements All three methods underestimate the higher PM2.5 concentrations. Gupta et al., 2010, to be submitted

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