1. Collection 6 Aerosol Products Becoming Available
C6 Aerosol Product Includes:
MYD04_L2
Dark Target AOD at 10 km2
Deep Blue AOD at 10 km2
Deep-Dark Merged AOD
MYD04_3K
Dark Target AOD at 3 km2

2. Urban Aerosol Retrieval in MODIS Dark Target Algorithm: Implications to Air Quality Monitoring
Pawan Gupta1,2 , Rob Levy2, Shana Mattoo2,3, and Leigh Munchak2,3
1GESTAR Universities Space Research Associations
2 NASA Goddard Space Flight Center, Greenbelt, MD, USA
3SSAI
Air Quality Applied Sciences Team 6th Semi-Annual Meeting (AQAST 6)
January 15-17, 2014

3. Motivation: Why Urban?
PM2.5 pollution levels in many mega cities exceeds the WHO standards by 5 to 10 times.
Satellite observed aerosol information has been increasingly in use for air quality monitoring efforts at local to regional to global scales.
MODIS Dark Target AOD validation studies have shown a bias over urban areas due to surface assumptions (Oo et al., 2010; Castanho et al., 2007, Munchak et al., 2013, Gupta et al., 2013)
Urban areas comprise 0.5% of the Earth’s surface, but will contain 2/3 of the Earth’s population by 2025, thus addressing urban bias in AOD is critical for obtaining accurate air quality information from space.

4. City Center Appears as ‘HOT SPOT’ in MODIS DT AOD
Distance (deg)
MODIS AOD
New Delhi, India
Gupta et al., 2012

5. Aerosol and Pollution in Mega Cities
Lets see how the spatial distribution of AOD among these cities looks ---here you are looking on the 10 year mean AOD maps for area of about 200x200 square km around selected mega cities. Here please don’t compare the cities in terms of their AOD values as color scales are not same for all the cities. But the one thing I want you to notice about all these cities are city centers …they appeared as hot spot over all most all cities. The data gaps are due to either water bodies or retrieval restrictions due to complex surface properties.
Mega cities appeared as hot-spots in MODIS AOD images with high gradient from center to outside the city area.

6. Mean bias (MODIS 3 km - AERONET) averaged over the campaign duration at each AERONET location
Percent of pixels identified as urban in same 15 km box around AERONET station
Munchak et al., 2013
Land identified as urban by MODIS land cover product at 500 m resolution

7. Surface Characterization in MODIS Dark Target (MDT) Retrieval
MDT assumes a relationship between the visible (VIS) and shortwave-IR (SWIR) surface reflectance, based on statistics of dark-target (primarily vegetated) surfaces.
Levy et al., 2007, 2013
RVIS = f (RSWIR, Angles, NDVISWIR)
Over brighter and more variable surfaces (e.g. urban), the assumed VIS/SWIR relationship breaks down (Oo et al., 2010; Castanho et al., 2007)

8. Accounting for Urban Bias
Here, we use MODIS Land surface product (“MOD09”, Vermote et al.) to derive a new VIS/SWIR surface relationship for urban areas where urban % > 20%.
VIS/SWIR ratio versus Urban%
RVIS = f (RSWIR, Angles, NDVISWIR, Urban%)

9. Inter-Comparison with AERONET

10. DISCOVER-AQ, Houston
3 km2
10 km2

13. C6 vs C6_Urban – Aqua, April 18, 2010
Chicago
(0.41±0.14, 0.26±0.09)
Washington DC
(0.21±0.02, 0.17±0.01)
Atlanta
(0.18±0.04, 0.15±0.03)
C6, C6_Urban
C6 vs C6_Urban – Aqua, April 18, 2010

14. Spring Time Reduction in AOD over Urban Regions

15. Philadelphia / New York
Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA
Spring 2010
Philadelphia / New York

16. Washington DC / Baltimore
Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA
Spring 2010
Washington DC / Baltimore

17. Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA
Spring 2010
Atlanta

18. Implication to Surface PM Air Quality
Ancillary Data
MODEL
Satellite AOD
Surface PM
Does high AODs in these cities implies high level of surface particulate matter pollution? The answer is yes and no both ….AOD represents optical attenuation in entire column of the atmosphere –from surface to the top of the atmosphere it depends on aerosols mass loading but it also depends on other environmental parameters whereas surface air quality or surface level PM2.5 mass concentration is measured at surface and heavily depends on local meteorology. Therefore, in last one decade more than one hundred research studies have been published to demonstrate the application of satellite AOD in deriving surface air quality. 90% of these studies have utilized some kind of statistical function to fit between AOD and PM while some studies also used meteorological variables. The success of these models varies in different part of the world and depends on accuracies of AOD retrieval as well as knowledge of vertical distribution of aerosols. The fundamental assumption in AOD to PM2.5 conversion is that the most of the aerosols are with in the boundary layer and boundary layer is well mixed. If there is aerosols layer above the boundary layer then these linear relationships break. The map here shows one of such estimation of PM2.5 derived air quality. The height of bar shows population whereas bars are color coded according to air quality categories based on PM2.5 mass concentration as obtained from AOD values. Based on available ground measurements in these cities – the annual mean PM2.5 derived from AODs are within 20-30% of those obtained from the ground for annual mean values. AOD or aerosols is also an important and one of the most uncertain component in climate change research.
Driving surface PM from column AOD measurements is challenging problem, having more reliable AOD over urban areas will improve PM estimation skills of statistical/physical models

19. Summary
MODIS land surface reflectance and land cover classification data sets have been used to define a VIS/SWIR surface reflectance relationship to be used over urban surfaces (urban percentage > 20%). The standard C6 MODIS Dark-Target surface reflectance relationship was replaced.
Reduced AOD is seen over urban areas. Compared to AERONET observations, these new retrievals remove some of the high bias normally seen over large urban areas.
Ongoing/Future Work
Evaluating the urban surface relationship over global cities, testing over longer time series.
Evaluating Aerosol Models used over Urban Areas in the DTA.
Implementing into the MODIS Dark Target Land algorithm?
Exploring impacts of new AOD retrieval on regional and global studies of air quality, PM2.5 and health