Collection 6 Aerosol ProductsBecoming 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

Urban Aerosol Retrieval in MODIS Dark Target Algorithm: Implications

to Air Quality Monitoring

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

Pawan Gupta1,2 , Rob Levy2, Shana Mattoo2,3, and Leigh Munchak2,3

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.

City Center Appears as ‘HOT SPOT’ in MODIS DT AOD

New Delhi, IndiaGupta et al., 2012

Distance (deg)

MO

DIS

AO

D

Mega cities appeared as hot-spots in MODIS AOD images with high gradient from center to outside the city area.

Aerosol and Pollution in Mega Cities

Land identified as urban by MODIS land cover product at 500 m resolution

Percent of pixels identified as urban in same 15 km box around AERONET station

Mean bias (MODIS 3 km - AERONET) averaged over the

campaign duration at each AERONET location

Munchak et al., 2013

Levy et al., 2007, 2013

• MDT assumes a relationship between

the visible (VIS) and shortwave-IR

(SWIR) surface reflectance, based on

statistics of dark-target (primarily

vegetated) surfaces.

Surface Characterization in MODIS Dark Target (MDT) Retrieval

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)

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%)

Inter-Comparison with AERONET

DISCOVER-AQ, Houston

10 km2 3 km2

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 v

s C

6_U

rban

– A

qu

a,

Ap

ril 18,

2010

C6, C6_Urban

Spring Time Reduction in AOD over Urban Regions

Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA

Spring 2010

Philadelphia / New York

Washington DC / Baltimore

Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA

Spring 2010

Atlanta

Aerosol Retrieval Improvements over Large Urban Corridors of Eastern USA

Spring 2010

Implication to Surface PM Air Quality

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

MODEL SurfacePM

Satellite AOD

Ancillary Data

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

Summary