Near Real-Time Monitoring of Air Pollution Over US Using ... - Capita

Near Real-Time Monitoring of Air Pollution Over US
Using EOS Terra/Aqua MODIS Direct Broadcasting
A proposal submitted to
NRA-03-OES-02
EOS NRA
NASA Peer Review Services, Code Y
500 E Street SW, Suite 200
Washington, DC 20024-2760
Tel: 202-479-9030
PI Co-I
D. Allen Chu, Senior Scientist/Project Manger Lorraine Remer
Science Systems and Applications, Inc. Climate and Radiation Branch
NASA GSFC, Code 913 NASA Goddard Space Flight Center
Greenbelt, MD 20706, USA Greenbelt, MD 20771, USA
301-614-6237(voice), 614-6307 (fax) 301-614-6 (voice), 614-6307 (fax)
e-mai: achu@climate.gsfc.nasa.gov e-mail: remer@climate.gsfc.nasa.gov
Co-Is
Jim Szykman J. A. Al-Saadi
Emissions Monitoring & Analysis Division Chemistry & Dynamics Branch
Office of Air Quality Planning and Standards Atmospheric Sciences Competency
US EPA, c/o NASA Langley Research Center NASA Langley Research Center
Hampton, VA 23681, USA Hampton, VA 23681, USA
757- 864-2709 (voice), 864-8461 (fax) 757- 864-5164 (voice), 864-6326 (fax)
e-mail: szykman.jim@epa.gov e-mail: j.a.al-saadi@larc.nasa.gov
Bryan Baum
NASA Langley Research Center, c/o University of Wisconsin-Madison
Madison, WI 53706, USA
608-263-3898 (voice), 608-262-5974 (fax)
e-mail: bryan.baum@ssec.wisc.edu
Collaborators
Judd Welton, NASA Goddard Space Flight Center, e-mail: welton@virl.gsfc.nasa.gov
Eric Vermote, University of Maryland at College Park, e-mail: eric@kratmos.gsfc.nasa.gov
Fred Dimmick, EPA, Office of Air Quality Planning and Standards, e-mail: di mick.fred@epa.gov
Richard Wayland, EPA, Office of Air Quality Planning and Standards, e-mail: wayland.richard@epa.gov
David Williams, EPA, Office of Research and Development, e-mail: williams.davidj@epa.gov
Date Submitted: April 15, 2003
Starting Date: October 1, 2003
Performance Period: October 1, 2003- September 30, 2006
Total amount in three years: $ 737,400 K

TABLE OF CONTENTS
ABSTRACT………...……………………………………………………….…………….i
PROJECT DESCRIPTION …………………………………………………………… ...1
1. INTRODUCTION…………………………………………………………………… .1
1.1 Aerosols and Air Quality Needs………………………………………………....1
1.2 Satellite Remote Sensing of Aerosol Over Land………………………………..1
2. PRELIMINARY STUDY……….……………..............................................................4
2.1 Northern Italy (Ideal Case)……………………………….....................................4
2.2 Eastern United States………………………………...............................................5
2.3 Long Range Dust Transport Over US…………………………… .....................10
3. SCIENTIFIC OBJECTIVES…………………………………………….…………....10
4. SCIENTIFIC APPROACH…………………………………………….…………… 11
4.1 Available Data …………………… .......................................................................11
4.2 Timeliness of Data………..…………….………….……………….…………….12
4.3 Proposed Work…………………………………………………………………...13
Task 1. Study the relationship of MODIS-derived aerosol optical depth and
PM2.5 concentration measured at surface.................................................13
Task 2. Examine aerosol transport pattern in urban and rural areas………. ...14
Task 3. Evaluate high spatial resolution of MODIS aerosol for better characterizing
aerosol spatial distribution………………….…………………. 15
Task 4. Prototype a combined MODIS and PM2.5 product for air quality forecast………………………………………………………………………….
16
5. NRA PROGRAM RELEVANCE…………………………………………………...18
6. MANAGEMENT PLAN…………………………………………………….…… ...19
6.1 Timeline…………………………………………………………….…………......19
6.2 Personnel And Responsibilities……………………………….… ......................20
7. BUDGET AND MANPOWER REQUIREMENTS…………………………….….21
7.1 Budget Explanation …………………………………………………………… ..21
7.2 Current/Pending Support and Relationship to Other Submitted Proposals………………………………………..................................................................22
8. CONCLUSIONS………………………………………………………………….….23
9. REFERENCES…………………………………….………………………………….23
APPENDIX A -SSAI BUDGET SUMMARY ………………………………………...25
APPENDIX B - LaRC BUDGET SUMMARY……………………………………… ..29
APPENDIX C - EPA IN-KIND BUDGET SUMMARY…………………………… ..33
APPENDIX D - TOTAL BUDGET SUMMARY……………………………..............37
APPENDIX E -VITA ………………………………………………… ...........................41
a. D. Allen Chu
b. Lorraine Remer
c. Jim Szykman
d. J. A. Al-Saadi
e. Bryan Baum

Near Real-Time Monitoring of Air Pollution Over US Using EOS
Terra/Aqua MODIS Direct Broadcasting
ABSTRACT
MODIS (Moderate Resolution Imaging Spectroradiometer) measurements provide us with
new insights into global aerosol characteristics. MODIS retrieves not only aerosol optical depth
(τa) but also the fraction of fine-mode particles. Its ability to derive aerosol over land has greatly
advanced our understanding of aerosol into the source region. Extensive MODIS validation with
AERONET (Aerosol Robotic Network) measurements from 2000 to 2002 shows that more than
two-thirds of MODIS retrievals (~6,000) fall within the expected range of retrieval errors of
∆τa=±0.05±0.2τa (e.g., 25% for τa = 1); outliers are primarily due to sub-pixel cloud, water, and
snow/ice contaminations. Other validations with AATS (Ames Airborne Tracking Sunphotometer),
shadowband, or Microtops measurements acquired in SAFARI (Southern African Regional
Science Initiative) 2000 and ACE-Asia (Aerosol Characterization Experiment – Asia) field experiments
conform to the findings as described above.
The MODIS capabilities and accuracy of the standard 10 km aerosol product enable us to
answer questions concerning aerosol effects on the global energy balance and hydrological systems.
In addition, the standard MODIS aerosol optical depth data can be used as an important
tool for monitoring air pollution and air quality. The preliminary analysis shows promising results
of correlating MODIS-derived columnar aerosol optical depth with PM2.5 (particulate matter
with diameter < 2.5 µm) mass concentration measured at the surface, with correlation coefficients
found as high as 0.8-0.9 in many metropolitan areas (e.g., Houston, Chicago, Wilmington,
etc.). With direct broadcasting (data processing < 1 hour), near real-time monitoring of air pollution
can be possible in any place of the US. Also, using both MODIS sensors from EOS-Terra
(10:30 a.m. equator-crossing time) and EOS-Aqua (1:30 p.m. equator-crossing time) satellites
enables us to study aerosol diurnal variation especially in the source region. Such information
will be an important Decision Support Tool for air quality managers at the EPA and other U.S.
and international agencies. Given aerosol vertical profiles obtained from Micropulse Lidar or
GLAS (Geoscience Laser Altimeter System; launched in January 2003), it is possible to derive
PM2.5 concentration from MODIS. In this proposal, we will develop the necessary scientific understanding
between MODIS-derived τa and in-situ PM2.5 concentration. We accomplish this
through the study of the relationship of MODIS-derived τa and in-situ sampled PM2.5 concentration
in detail and at as many locations as possible. We will also examine regional aerosol transport
patterns over the E. US in both urban and rural areas to help better understand the evolution
and demise of PM episodes (e.g., biomass burning from forest fire or other weather driven
events), and evaluate whether higher resolutions (e.g., 5×5, 3×3, or 1×1 km 2 ) of MODIS aerosol
retrievals will better characterize aerosol spatial distribution than the standard product, especially
in the metropolitan areas. Finally, we will use these scientific results to develop a combined
product utilizing near real-time MODIS aerosol optical depth data and EPA PM2.5 mass concentration
measurements for air quality assessment and forecast.
i

PROJECT DESCRIPTION
1. INTRODUCTION
1.1 Aerosols and Air Quality Needs
Aerosols in the atmosphere arise from both natural sources: biomass burning, wind-blown
dust, and sea salts and anthropogenic sources: fossil fuel combustion, industrial processes, and
transportation. In the lower boundary layer, aerosol, also known as particulate matter (PM), is a
human health hazard and also can reduce visibility significantly. Based upon the US EPA report,
though significant improvements have been done in the last 30 years, over 80 million people in
the United States still live in counties with PM concentrations above the level of the ambient air
quality standards (EPA, 2001), for example, for PM2.5 (particles < 2.5 µm in diameter) concentration
~65 µg/m 3 in 24 hours (or a annual mean of 15 µg/m 3 ) under the Clean Air Act (CAA).
These standards are purported to prevent approximately 15,000 premature deaths when fully implemented
(EPA, 1997a; EPA, 1999a).
Currently, the EPA, along with Regional Planning Organizations, and state and local governments
use a multitude of decision support tools to assess, control and report the nature of air
pollution related to PM. The existing air quality decision processes rely solely on urban scale
models and ground based measurement networks, which do not have the capability of synoptic
view of aerosol movement and distribution. The MODIS direct broadcast is a unique NASA asset,
which can be brought to bear on these decision-making processes of air quality (processing
time < 1 hour of satellite overpass). Though this proposal we will build upon existing collaborative
efforts between NASA and EPA to improve the science behind the assessment, prediction,
and real-time decision-making tools for the air quality management community.
1.2 Satellite Remote Sensing of Aerosol Over Land
Satellite remote sensing has established long records of aerosol measurements since the late
1970s. However, most of the aerosol properties were derived over ocean (e.g., Advanced Very
High Resolution Radiometer, AVHRR) until the discovery of TOMS (Total Ozone Mapping
Spectrometer) new capability in detecting absorbing aerosols over land and ocean using ultraviolet
spectrum (0.34 and 0.38 µm) [Hsu et al., 1996; Herman et al., 1997]. Neither of the instruments
was designed to retrieve aerosols partly because coarse resolution (4 km for AVHRR and
50 km for TOMS) makes it difficult to avoid cloud contamination. In addition, the restriction of
spectral ranges to only 2 channels in the solar spectrum is difficult for AVHRR to separate the
observed signal into atmospheric and land components. Although TOMS does not have the same
problem since the earth's surface is uniformly dark in the ultraviolet spectrum, the strong sensitivity
of TOMS’s detection of absorbing aerosols to aerosol height limits its applications. The
MODIS (Moderate Resolution Imaging Spectroradiometer) sensors on EOS-Terra MODIS
launched in 1999 and on EOS-Aqua in 2002 were designed to systematically retrieve aerosol
properties over both land and ocean [Kaufman et al., 1997; Tanre et al., 1997]. The success of
MODIS aerosol retrieval is due to a unique set of seven well-calibrated channels (0.47, 0.55,
0.67, 0.87,1.24, 1.64, 2.1 µm) from visible to shortwave infrared wavelength. MODIS ability to
derive aerosol optical depth (τa) and the fraction of fine mode particles over land and ocean has
drastically broadened our understanding of global aerosols in both source and downwind regions.
Figures 1(a) and (b) show, respectively, the seasonal means of MODIS-derived τa and corresponding
fine-mode τa for spring, summer, autumn, and winter from December 2000 to November
2001.
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Figure 1(a). Seasonal means of MODIS-derived aerosol optical depth for spring (March-May 2001),
summer (June-August 2001), autumn (September-November 2001), and winter (December 2000-
February 2001) [Chu et al., 2003].
Figure 1(b). Seasonal means of MODIS-derived fine-mode aerosol optical depth for spring
(March-May 2001), summer (June-August 2001), autumn (September-November 2001), and winter
(December 2000-February 2001).
From the global perspective, it is clear to see the seasonal events such as the springtime
Asian dust spreading around the N. hemisphere, the African dust outflow shifting with latitude in
different seasons, and the dry-season biomass burning from Southern Africa and South America.
2

The dry-season biomass burning in Southern Africa and South America are the most pronounced
feature, followed by the springtime dust outbreaks in the East Asia and summertime urban/industrial
pollution in the eastern China, India, eastern US, and western Europe. The extensive
validation of MODIS aerosol retrievals against the observations of 100 AERONET sunphotometers
between 2000 and 2002 depicts that more than two-thirds of MODIS retrievals (~6,000)
fall within the expected range of retrieval errors ∆τa=±0.05±0.2τa (e.g., 25% for τa = 1); outliers
are primarily due to sub-pixel cloud, water, and snow/ice contaminations [Remer et al., 2003].
Shown in Figure 2 are co-located MODIS and AERONET points sorted by AEROENT τa values
and averaged over every 300 points. The slightly low biases for τa > 0.6 are primarily caused by
the underestimation of black carbon content in southern African biomass burning.
The regional validation in the eastern US and southern America [Chu et al., 2002] showed
expected results from MODIS as compared to those obtained from MODIS airborne simulator
(MAS) in the pre-launch SCAR-A (Sulfate, Clouds, And Radiation-Atlantic) and SCAR-B
(Smoke, Clouds, And Radiation, Brazil) field experiments [Kaufman et al., 1997; Chu et al.,
1998]. This consistency reflects that aerosol models used in the retrievals properly characterize
aerosol properties for urban/industrial aerosol (single scattering albedo, ω ~0.96) in the eastern
US and biomass burning aerosol ( ~0.9) in South America. Furthermore, the assumptions of
aerosol models in regions with the occurrence of urban/industrial and biomass-burning aerosols
based upon the aerosol models derived SCAR-A and SCAR-B have achieved satisfactory success
with only slight refinements needed in China (ωο ~0.85-0.90) and Southern Africa (ωο
~0.85) to account for the higher black carbon content in those regions.
Figure 2. Comparison of MODIS and AERONET-derived τa at 550 nm. The 5,906 co-located points
are sorted by AEROENT τa and averaged over every 300 points. At high τa where data is sparser, few
points are included in the average as indicated. Error bars represent standard deviation in each bin.
The dash lines represent pre-launch expected retrieval error. The regression and correlation coefficient
are calculated before binning and averaging.
MODIS aerosol algorithm is performing within expectations over land on both global and
regional scales. However, can the standard products developed to answer important questions
concerning climate change be applied to regional or local air pollution? The three case studies in
the next section will demonstrate MODIS capability with promising results of applying MODIS
aerosol products to air pollution monitoring as well as issues that require further investigations.
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2.0 PRELIMINARY STUDY
2.1 Northern Italy (IDEAL CASE)
Preliminary analysis of MODIS standard aerosol product suggests that aerosol optical depth
derived at 10 km × 10 km resolution can be applied to regional air pollution. Shown in Figure 3
is the case study of heavy air pollution observed by MODIS in Northern Italy on September 26,
2000 (10:15 UTC) [Chu et al., 2003]. Heavy aerosol loading can be seen extending from the
foothills of the Alps to the Venice Lagoon. The MODIS-derived τa around Ispra (~1.2) is found
in good agreement with AERONET observations (~1.0), which is more than double the September
monthly mean of ~0.43 (or triple the annual mean ~0.32). In the region near Venice Lagoon,
the MODIS-derived τa (~0.45) and AERONET sun photometer measurements (~0.4) are more
comparable with the September monthly mean ~0.34 (or annual mean ~0.30). Under stagnant
condition, accumulated aerosol abundance can reach τa > 1 (at 0.55 µm) before being removed
by wind or precipitation. The good agreement between MODIS and AERONET-derived τa at
Ispra (source) and Venice (downwind) and the 2 nd order polynomial fit derived between Ispra
and Venice depict the accuracy and spatial sensitivity of MODIS aerosol retrievals in a regional
pollution episode.
Figure 3. Left column: MODIS RGB image (top) and spatial gradient of 2
4
nd order polynomial fit
(bottom); right column geographic and topographic maps (top and middle) and MODIS-derived
aerosol optical depth (bottom).

The above agreement between MODIS and AERONET-derived τa does not answer the
question of air quality in entirety. Both MODIS and AERONET measure total column quantities,
while air quality is mostly concerned with concentration of particulate matter (PM) at the surface.
To prove MODIS's ability, we must show that aerosol optical depth is correlated with PM
concentration measured at the surface. Because of diurnal variability, exact match-ups in time
between the measured PM and the MODIS overpass is important. In the absence of hourly PM
measurements at Ispra, the relationship of aerosol optical depth and PM10 concentration (µg/m 3 )
measured at surface is studied by matching 24-hour PM10 concentration with daily-averaged
AERONET τa (as a surrogate for MODIS) from August to October 2000. A correlation coefficient
of 0.82 and an intercept of 8 are derived (see Figure 4). Stable air masses and fixed pollution
sources are believed to contribute to such good correlation with a very small intercept. This
case study is unique to show the application of MODIS aerosol retrievals to air pollution research
in part because the topography in Northern Italy allows pollutants to accumulate under
stagnant condition without mixing with other types of aerosol and in part because the existence
of two AERONET stations can be used to compare with MODIS aerosol retrievals in both source
and downwind regions. In less idealized conditions, there are factors that can hinder the correlation,
which are to be discussed in the following case studies focused in the United States.
Figure 4. Correlation of 24-hour PM10 concentration and daily average AERONET τa (as a surrogate
for MODIS) at Ispra, Italy from August to October 2000
2.2 Eastern United States
The US EPA (Environmental Protection Agency) Air Quality Monitoring Network provides
an extensive coverage of PM measurements (see Figure 5). A major component of the proposed
research is to understand the relationship between the MODIS aerosol products and the extensive
PM2.5 measurements by the EPA. Where, when, and why do they agree or disagree? In this case
study, we will focus on the summer haze in June 2002.
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It is well known that summer haze is responsible for producing bad air quality in the eastern
US. According to the 3-year average of PM2.5 concentration, the maximal concentrations (20-30
µg/m 3 ) extend from Ohio and Pennsylvania southward (to Tennessee) and southeastward (to
Maryland/Delaware) in the month of June (see Figure 6). The pattern is changed slightly in July
and August as high-pressure systems increasingly dominate the climatology of the region.
Figure5. The Distribution of EPA PM monitoring stations over US. PM fine: PM2.5 (particulate matter
with diameter < 2.5 µm); PM10 (particulate matter with diameter < 10 µm).
3-12 12-15 15-17 17-20 20-30
Figure6. 3-year average of PM2.5 (µm/m 3 ) observed by 1100 FRM monitors over US.
Shown in Figure 7 are the composites of MODIS-derived aerosol optical depth (color) and
cloud optical depth (black-white) from June 23 to 26, 2002, with wind speed/direction (at 850
mb) superimposed to the images. High aerosol loading air masses can be seen moving in concert
with the winds from the upper mid-west to the northeast, over the Mid-Atlantic States, then out
to the Atlantic Ocean. On June 25, PM2.5 concentrations spiked in Maryland and Washington
DC, with code-red air quality alerts being issued for the areas corresponding to elevated τa ≥ 1.
6

MODIS aerosol optical depth is shown to provide the pseudo-synoptic view of aerosol events
across North America unlike any other measurements, including the EPA’s current network of
isolated stations. Note MODIS’s ability to separate aerosol from clouds. However, aerosol optical
depth cannot be retrieved in cloud covered regions, resulting in spatial gaps in the synoptic
view. Nevertheless, the minor deficiency does not undermine MODIS capability for use in monitoring
regional air pollution.
Figure 7. The composites of MODIS-derived aerosol optical depth (color) and cloud optical depth (black-white)
with superimposed winds (red arrow) from June 23 to 26, 2002.
The relationship of MODIS-derived τa and surface PM2.5 concentrations was examined over
the upper mid-west and eastern US (including 11 states and Washington DC) during June 2002.
Time series of hourly PM2.5 measurements and temporally co-located MODIS τa observations
are shown in Figure 8 for 4 representative ground sites. There is a high degree of correlation between
the surface and column-integrated quantities at these sites with correlation coefficients
ranging from 0.75 to 0.9. These high correlations are typical for other sites throughout the region
during June. The time series depicts generally in-phase variations at Chicago (Illinois), Lansing-
East Lansing (Michigan), and Canton-Massillon (Ohio), indicating similar regional pollution
sources and air mass control in the region. At these three sites, episodic maximums in PM2.5 concentrations
occurred from June 9-11 and June 20-26. Farther to the east, at Wilmington-Newark
(Delaware), similar maximums occurred, although at later dates (June 10-13 and 24-27).
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(a)
(b)
(c)
(d)
Figure 8. Time series of PM2.5 mass concentration observed at (a) Chicago, Illinois, (b) Lansing, Michigan,
(c) Canton-Massillon, Ohio, and (d) Wilmington-Newark, Delaware-Maryland in June 2002 (solid
line: hourly PM2.5 ; dotted line: 24-hour running average of PM2.5 ; red dots: MODIS τa plotted in a ratio
of 1:70 with respect to PM2.5)
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Regional correlations between MODIS τa and surface PM2.5 were calculated for June 22-27
by including all ground sites within EPA-defined Region 3 (Illinois, Indiana, Michigan, Minnesota,
Ohio, and Wisconsin) and 5 (Washington DC, Delaware, Maryland, Pennsylvania, Virginia,
and West Virginia) (Figure 9). Correlation coefficients of 0.72 and 0.82 are found, demonstrating
that the correlations do not break down over the large areas and that MODIS aerosol retrievals
have sufficient accuracy for applying to regional air pollution.
Figure 9. Correlation of MODIS-derived τa and PM2.5 mass concentration in Region 3 (Illinois, Indiana,
Michigan, Minnesota, Ohio, and Wisconsin) and Region 5 (Washington DC, Delaware, Maryland,
Pennsylvania, Virginia, and West Virginia).
The scatter observed in Figure 9 may be introduced by factors such as aerosol vertical distribution,
type, and properties (e.g., hygroscopicity). In addition, the grid size of aerosol retrievals
may potentially degrade the correlation, because 10 × 10 km 2 may be too coarse to resolve
any finer scale variability. Shown in Figure 10 are the time series of PM2.5 concentrations, measured
at two EPA monitoring sites less than 20 km apart around Pittsburgh. The PM2.5 measurements
from these two EPA sites show very different hourly peak concentrations, partially associated
with an apparent diurnal variability that is larger at one site. MODIS aerosol retrievals appear
to behave better at tracking PM2.5 concentration at one station (top panel, correlation ~0.75)
than the other (bottom, correlation ~0.68). Higher spatially resolved MODIS aerosol optical
depth data (e.g., 5 × 5, 3 × 3, or 1 × 1 km 2 ) are expected to provide better sensitivity to large
variation of PM at locations near the metropolitan area.
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Figure 10. Time series of PM2.5 mass concentration observed at two monitoring sites less than 20 km
apart around Pittsburgh (solid line: hourly PM2.5 measurements; dotted line: 24-hour running average
of PM2.5 ; red dots: MODIS τa plotted in a ratio of 1:70 with respect to PM2.5 mass concentration).
2.3 Long-Range Dust Transport Over US
We have shown some examples of good correlation between MODIS-derived τa and surface
PM2.5 measurements. However, it is not the case in less idealized conditions. Aerosol vertical
distribution, type, and properties as well as spatial resolution of MODIS aerosol retrieval can
hinder the correlation between MODIS-derived columnar τa and surface PM concentrations. In
addition, long-range transport also plays an important role. Here we show a case study to illustrate
such sensitivities.
In early April 2001, a large dust storm developed over Gobi desert in Northern China due to
cold air outbreak. Huge amounts of dust were transported across the Pacific within a week, and
over North America in mid-late April. An assessment on the impact of the Gobi desert dust on
ground level PM concentrations across the United States (Szykman et al., 2002) indicates poor
correlations between aerosol loading (shown by TOMS Aerosol Index, AI) and ground level PM
concentrations cross entire US, which was caused by the dust, once lofted into the free atmosphere,
being transported within the free atmosphere (500-700 mb) as it passed over the United
States. Figure 11 illustrates this scenario on April 16, 2001 with high AI over the eastern US and
low AI over the western US, while the PM2.5 (soil) ground concentrations show an opposite distribution.
We expected similar results between MODIS derived optical depth and in-situ PM
concentrations. For this type of event, lidar measurements can provide aerosol vertical distribution
as a proof and can also be used quantitatively to derive aerosol loading in the lower atmosphere
below the dust layer.
Figure 11 Left: is a false color image of TOMAI for April 16, 2001; right: PM2.5 soil mass concentrations
across the US for April 16, 2001. These figures combined visually illustrate how dust detected by
the TOMS AI does not correlated well with ground level PM concentrations for the same time period.
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3. SCIENTIFIC OBJECTIVES
The overall goal is to use MODIS to improve the science of useful aerosol information to
the Air Quality community, and to provide air quality managers with a well-understood Decision
Support Tool to enable them to make better decisions in terms of issuing air quality warnings. In
order to achieve the goal, we propose to
(1) study the relationship of MODIS-derived aerosol optical depths and PM concentrations
measured at the surface from EPA Air Quality Monitoring Network at different locations,
(2) examine regional aerosol transport patterns in both urban and rural areas,
(3) evaluate the higher resolutions (e.g., 5×5, 3×3, or 1×1 km 2 ) of MODIS aerosol retrievals
for better characterizing aerosol spatial distribution, and
(4) prototype the development of a combined product utilizing near real-time MODIS aerosol
optical depth and EPA PM2.5 mass concentration measurements.
4. SCIENTIFIC APPROACH
The near real-time monitoring of air pollution requires not only the sound scientific objectives
but also timely measurements. Timing is crucial for the decision-making air quality managers.
Therefore we will address first the availability and timeliness of the data sets to be used in
the proposal, followed by the tasks.
4.1 Available Data
• MODIS L1B true color images and L2 aerosol products (http://modis-atmos.gsfc.nasa.gov)
MODIS L1B (level 1-B) calibrated radiance data are used to generate true color image of
the view of entire US. The true color browse images are a useful tool to identify clear and
cloudy scenes, similar to our own eyes viewing from the surface. The width of MODIS swath
of 2,330 km shows the advantage over any other surface measurements. As a result, the L2
(level 2) MODIS aerosol products represent such good pseudo-synoptic views of aerosol
events. The two MODIS sensors onboard EOS-Terra (10:30 a.m. equator-crossing time) and
EOS-Aqua (1:30 p.m. equator-crossing time) provide the twice-a-day measurements of the
Earth atmosphere allowing investigation of the diurnal variation of aerosol. The MODIS aerosol
product contains aerosol optical depth derived at a spatial resolution of 10×10 km 2 at nadir.
The MODIS data to be used in this proposal are versions 3 and 4 data sets. The version 3 data
covers from November 2000 to October 2002. The version 4 data is currently being produced
to cover more than 3 years of MODIS measurements from February 26, 2000 to October 31,
2003 (to be finished by November 2003).
• EPA PM2.5 mass concentration measurements (http://www.epa.gov/airnow/today/ ;
http://www.airnowdata.org/pmfine/hourly.html)
The primary in-situ EPA PM2.5 data to be used for this proposal are collected continuous
(hourly) at approximately 250 sites using R&P TEOM (or equivalent) Ambient Particle Monitor.
Additional in-situ PM measurements that EPA will make available for this work include
(1) data from the Federal Reference Method (FRM) network implemented in support of the
PM2.5 National Ambient Air Quality Standards (NAAQS) (EPA, 1997b), (2) data from the National
Ambient Monitoring Stations (NAMS) PM2.5 chemical speciation (TRENDS) network,
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and (3) the Interagency Monitoring of PROtected Visual Environmental (IMPROVE) visibility
network (Eldred et al., 1988) which both provide data on chemical composition of PM2.5.
These data sets are not available in near real-time, like the TEOM data, but are available in the
AQS central data archive approximately 3 months from sample collection. Both the PM2.5
TRENDS and IMPROVE networks measure mass concentrations in addition to significant
PM2.5 chemical constituents; sulfate, nitrate, sodium, potassium, ammonium, and carbon and
will be valuable data sets to determine the chemical compositions of PM2.5 in helping to understand
variability between MODIS aerosol optical depth and in-situ PM2.5 mass concentrations.
The US EPA maintains a central data archive for all PM measurements, known as the Air
Quality System (AQS) (EPA, 2002). This archive contains ambient air pollution data collected
by EPA, state, local, and tribal air pollution control agencies. This data archive is maintained
and managed by US EPA’s Office of Air Quality Planning and Standards. An overview of this
data archive is available at http://www.epa.gov/ttn/airs/airsaqs/sysoverview.htm.
• Micropulse Lidar (MPL) and GLAS measurements (http://mplnet.gsfc.nasa.gov)
The MPL is a single channel (0.523 µm), autonomous, and eye-safe lidar system. The MPL
is used to determine the vertical structure of clouds and aerosols. The MPL data can be analyzed
to produce optical properties such as extinction and optical depth profiles of the clouds
and aerosols. The primary goal of MPLNET (MPL Network) is to provide long-term data sets
of cloud and aerosol vertical distributions at key sites around the world. This data set will be
used to validate and help improve global and regional climate models, and also serve as
ground-truth sites for NASA/EOS satellite programs such as the Geoscience Laser Altimeter
System (GLAS) on the ICESat (Ice, Cloud, and Land Elevation Satellite) spacecraft (launched
on January 12, 2003). MPLNET measurements are currently only available from GSFC and
ARM Cart sites but there are plans to expand to, for example, Monterey and San Diego, California
in the near future.
• NASA DAO (Data Assimilation Office) and NCEP (National Centers for Environmental Prediction)
assimilated wind, temperature, and humidity profiles (http://dao.gsfc.nasa.gov) or
(http://www.ncep.noaa.gov)
The assimilated wind, temperature, and humidity profiles provide necessary information of
meteorological condition associated with aerosol events. NASA DAO products are currently
being produced for “First Look” and “Late Look” with GEOS4-DAS (or fvDAS) since October
1, 2002. The data are output every 6 hours including 48 vertical layers (surface-0.01 hPa) at
1°× 1.25° spatial resolution. There is a plan to increase the spatial resolution to 0.5° × 0.625° in
the near future (2004). The GEOS4-DAS products will be compared episodically with NCEP
assimilated products for consistency.
4.2 Timeliness of Data
MODIS data is processed to obtain aerosol products at MODAPS (MODIS Data Processing
System) and then archived at the Goddard DAAC. Generally the global aerosol products become
available to the public within a few days of satellite observation. Sometimes there is a delay and
obtaining aerosol products can take longer. A lag in availability of a few days is acceptable for
climate studies. However, in terms of a Decision Support Tool for air quality forecasting and
management, a lag of a few days or even one day is not acceptable. In order to make the MODIS
aerosol product useful to air quality managers the first order of business is to implement the
standard aerosol algorithms into a direct broadcast system for near real-time accessibility.
Fortunately, NASA has funded the project “International MODIS/AIRS Processing Package
12

(IMAPP) For EOS Direct Broadcast Data” through 2006. In that proposal, CIMSS/SSEC (The
Cooperative Institute for Meteorological Satellite Studies/Space Science and Engineering Center)
at University of Wisconsin-Madison has offered to implement MODIS aerosol algorithm
into IMAPP by June 2004 and produce MODIS aerosol products (MOD04) in HDF format without
any extra cost. If using IMAPP, MOD04 aerosol products can be generated within 1 hour of
EOS-Terra and EOS-Aqua overpasses, which suits the need for near real-time monitoring of air
pollution.
The continuous (hourly) PM2.5 mass concentration data is collected by state and local air
agencies and then transferred to a central data archive maintained by US EPA/OAOPS. This data
is available within 1-hour collection time from 252 stations. US EPA is currently prototyping
near real-time reporting of these data for use in forecasting their Air Quality Index for PM2.5.
MPL measurements can be viewed from the near real-time browse image, which then can be
used to study the first order aerosol vertical distribution. The image is generated at a rate of 60
seconds sampling time. Final aerosol extinction profiles are derived based upon the 30-minute
average of the measurements.
NASA DAO (or NCEP) data are generated every 6 hours (0600, 1200, 1800, 2400Z) daily.
The DAO “First Look” product is especially designed for field experiment. It suits our needs for
near real-time analysis of the meteorological fields.
4.3 Proposed Work
The preliminary analysis in the eastern US shows how MODIS aerosol products can be used
effectively to track the synoptic scale movement of pollutants over the United States. The preliminary
analysis has also provided very promising results of the correlation between MODIS
aerosol optical depth and PM2.5 mass concentration. However, it also shows situations in which
the correlation degrades. These situations when the MODIS column measurements do not agree
well with surface measurements need to be better understood. Only then will MODIS’s potential
as a Decision Support Tool in the Air Quality community be realized. There are many detailed
analyses to be done in order to achieve this goal. Tasks 1-3 are to achieve scientific in nature. In
Task 4, we propose development of data fusion product with twice-a-day MODIS aerosol optical
depth data and hourly EPA PM2.5 mass concentration measurements for the continental US.
Task 1: Study the relationship of MODIS-derived aerosol optical depths and PM2.5 mass concentrations
measured at the surface
The correlation of MODIS-derived aerosol optical depths and PM2.5 mass concentrations
measured at the surface is dependent upon several factors associated with aerosols: aerosol vertical
distribution, type, and properties. To understand the interplay between these factors in determining
the relationship between MODIS-derived aerosol optical depth and ground-based PM2.5
measurements, we need to utilize a suite of data sets (no matter how limited). These include
MPL measurements (currently only available at GSFC and the ARM Cart site in the continental
US), and assimilated data (e.g., wind, temperature, relative humidity, etc.) from NASA DAO or
NCEP. The GLAS instrument onboard ICESat recently launched in January 2003 can provide
aerosol vertical profiles with a broader spatial coverage, which will be used when the data become
available. In addition to MPL sites (e.g., GSFC, ARM Cart site), we will also use assimilated
temperature profile data to search for temperature inversion layers. The data collected will
be grouped into separate populations depending on the accuracy of the MODIS aerosol optical
depths as a predictor of surface PM2.5 concentrations. The separate populations will then be
evaluated for the similarities of vertical profiles of aerosol, temperature, humidity, and wind
speed.
13

However, The MODIS aerosol retrievals (twice a day), EPA PM2.5 mass concentrations
(hourly), MPL aerosol vertical profiles, and NASA DAO (or NCEP) assimilated data (every 6
hours) do not provide coherent measurements at the same place at the same time. The fully
equipped SMART-COMMIT (Surface Measurements And Radiative Transfer – Chemical, Optical,
Microphysical Measurements of In-situ Troposphere) portable platform with the R&P
TEOM Ambient Particle Monitor and Radiance Research Nephelometers, when integrated with
the Micropulse Lidar and AERONET Sun photometer at NASA/GSFC can provide simultaneous
measurements of PM2.5 mass concentration, particle scattering coefficient (useful for distinguishing
different aerosol types), aerosol vertical distribution, and aerosol optical depth (a surrogate
for MODIS). Its mobility is value-added to the super site approach. The super site approach suits
well to the objectives of long-term atmospheric characterization at NASA GSFC and Earth Observation
Systems (EOS) validation field experiment of the proposal “SMART-COMMIT Investigation
to Enrich NASA/ESE Earth Observing Systems Research” led by PI Dr. Si-Chee Tsay.
Figure 12 shows instrumentation and configuration of SMART-COMMIT. When not deployed
in the field experiment, SMART-COMMIT will be taking measurements at GSFC.
TEOM PM2.5 Nephelometer Micropulse Lidar Sunphotometer
Figure 12 SMART-COMMIT instrumentation and configuration in mobile 20 ft sea container for
housing remote sensing instruments and camper trailer for housing in-situ instruments.
Task 2: Examine regional aerosol transport patterns in both urban and rural areas
Urban sources are easily identified and monitored by surface-based instrumentation. However,
it is not the case in remote areas. For example, forests can burst into flames by lighting or
human negligence, generating significant amounts of smoke without warning. Under the proper
meteorological condition, it can be spread over a wide area or transported far away from the
source region. As shown in Figure 7, MODIS aerosol optical depth data provide an excellent
pseudo-synoptic view of the movement of aerosol. This task is to build the database of aerosol
transport patterns using both Terra and Aqua MODIS-derived aerosol optical depths along with
NASA DAO (or NCEP) assimilated winds data. In addition, a forward trajectory model will be
14

used to compare the forecast with MODIS-derived aerosol optical depth and a back-trajectory
model will be used to verify the location of the source and the altitude of transport. Figure 13
shows a 3-D view of simulated air parcel tracks superimposed on MODIS aerosol optical depths
and PM2.5 mass concentrations. Based upon this analysis, EPA can determine the location of new
PM2.5 monitoring stations, especially in some key pathways of transport, in the future.
Figure 13. Same as the left panel of Figure 11 except with a superimposed 3-D view of air parcel simulation
(pink) on MODIS-derived τa and PM2.5 mass concentration. MODIS-derived τa is shown as 2-D color
image and PM2.5 mass concentration the vertical color bar.
Task 3: Evaluate the higher resolutions (e.g., 5×5, 3×3, or 1×1 km 2 ) of MODIS aerosol retrievals
for better characterizing aerosol spatial distribution
The MODIS aerosol algorithm was designed with global climate issues in mind to produce a
10×10 km 2 product in a timely fashion (

etrievals at different resolutions and to determine the spatial scale of urban aerosol variability.
Figure 14. MODIS-derived τa in metropolitan areas around Great Lakes (e.g., Milwaukee, Chicago, Detroit,
Cleveland, Toronto) on April 26, 2001.
A higher resolution aerosol optical depth product is required to accurately estimate the loadings
on a regional scale. From an air quality assessment perspective, the network of ground
measurements may not accurately reflect the geospatial variability of a region. The higher resolution
product may be able to provide the information needed to bridge the gaps between ground
sensors with more accuracy and confidence than geostatistics such as kriging. The highresolution
aerosol product will be developed and evaluated as a prototype for the Northeast region
of the United States. This will be done as a multi-scale comparison with the 10km standard
product to determine the benefits as well as the computational costs associated with production
of the higher resolution dataset.
Task 4: Prototype the development of a combined near real-time product utilizing MODIS aerosol
optical depth and EPA PM2.5 mass concentration measurements
In this task we will prototype the development of the near-real time product(s) primarily for
use by EPA and state/local air agencies in determining next day PM2.5 air quality levels within
major populated regions of the US. Our goal in this task is to develop a product that will help
improve the accuracy of next day air quality forecast related to US EPA’s PM2.5 Air Quality Index
(AQI) (EPA,1999b). Because of the original nature of this product, we will develop it
through an evolutionary approach. This will allow for necessary adjustments of the prototype as
it is developed, based upon user feedback, so we can produce a final product that meets expectations
of the user community.
As part of the currently funded IMAPP project, the standard MODIS aerosol products
(MOD04) will be produced from direct broadcast data for every view of the US. This near realtime
data is the minimum product necessary to be useful to air quality managers in the US. However,
the MODIS data is only one of the necessary data streams to generate a truly useful product
for air quality managers and forecasters. The addition of near real-time ground level PM2.5 mass
concentration measurements will provide a truly powerful tool to support air quality forecast and
management decisions. To generate this prototype product, we will bring together the MODIS
direct broadcast data and EPA PM2.5 mass concentration data within 1-2 hours of data availability.
In addition, we will also bring in meteorological data to help determine synoptic scale
weather patterns that affect the formation, transport, and demise of PM2.5.
We plan to use the results of the analysis from Tasks 1-3 to determine the optimum suite of
MODIS-derived aerosol products for the air quality community. The satellite component of this
combined product will be produced at the optimum spatial resolution determined by Task 3 with
16

well-understood error bars. Depending on the results of Task 1, the product may include pertinent
auxiliary information to help interpret and translate the satellite-derived column values to
important surface-based quantities.
All near real-time data will be brought to one central location, NASA-LaRC AtSC, for processing
and dissemination. Figure 15 shows the proposed data responsibility and flow for the generation
of the combined near real-time product. To generate a useful product for use in air quality
forecasting, we will combine the multivariate data in time and space. For this we anticipate using
data fusion software, such as VGeo (Virtual Global Explorer and Observatory). VGeo is an interactive
data fusion and visual analysis tool for the construction of time varying, threedimensional
data products involving large, multi-variate datasets.
Data Responsibility and Flow for Prototype Near-Real Time Combined MODIS/PM2.5
Product
CIMMS/SSEC
IMAPP
(International; MODIS/AIRS
Processing Package (IMAPP)
for EOS Direct Broadcast Data)
Chemical
DAS
[Assimilation]
RAQMS
MODIS Prototyping Data
10:30 & 1:30
local time
NASA DAO FvDAS
(Data Assimilation Office
Finite vol-
Data Assimilation umeSystem)Reanaly[Assimilasistion] Meteorological
Dat
a
NASA LaRC AtSC
(Prototype DAO near-real time combined
MODIS/PM2.5/Met Off-line data product for air
quality forecasters and managers – produced
within 1 hour of receipt of
MODIS data.)
EPA PM2.5 AIRNow
(Air Quality Index
Forecast Submittal System)
US EPA/state/local air agencies
dissemination of next day PM2.5
Air Quality Index through
established communication channels
Figure 15. Data responsibility and flow for the generation of the combined near real-time product.
17
EPA AQS/AIRNow
Data Archive
(Central Data Archive for
near-real time data reported
by State/Local Chemical Air Agencies)
DAS
[Assimilation]
PM Hourly PM2.5
Data
User Feedback
For improvements

Figure 16. Left panel is a top down view of a 3-D false color image of MODIS-derived τa and cloud optical
thickness, PM2.5 mass concentration, and NCEP 850mb winds. Right panel is 3-D false color iamge of
MODIS-derived τa and cloud optical thickness, hourly PM2.5 mass concentration (vertical color bars), and
hourly Hysplit air parcel simulation (pink cubes).
Figure 16 shows picture files extracted from VGeo from previous analysis of MODIS aerosol
optical depth and cloud optical depth, hourly PM2.5 TEOM monitors, NCEP reanalysis wind
fields, and Hysplit (Draxler et al., 1997; Draxler et al., 1998) trajectory results for September 10,
2002. In addition to combining these data stream visually, we will also provide near real-time
analysis and results. This will allow for analytical interpretation of the visual results, analogous
to that of weather forecasting, where visual output is combined with summary site statistics. This
analysis will likely be a subset of the analysis conducted under Task 1. The output of this analysis
will be combined with the visual output of the data fusion product and pushed to a central
EPA site on the WWW used by air quality forecasters.
The prototype Air Quality Decision Support Tool is envisioned to have multiple analytic
components beyond aerosol optical depth retrievals and AQI. The user will be able to interactively
query and display data, run simple data mining algorithms such as time series analysis on
the archive, and overlay other geospatial datasets on the aerosol optical depth products to perform
correlative analysis. At the conclusion of Task 4 we expect to have a final prototype Decision
Support Tool evaluated by the U.S. EPA and other Air Quality management organizations
for near real-time air quality forecasting within the United States and possibly internationally.
5. NRA PROGRAM RELEVANCE
The NRA states, “It is likely that some of the EOS data will have immediate applications
relevant to U.S. decision makers, forecast/warning services…. A limited number of Science Applications
Projects will be considered for funding to enable real-time deliverables for the decision-support
systems and for shore-term evaluation of satellite-observable, environmental conditions
like fire locations, air pollution and aerosol coverage and evolution….”
The MODIS aerosol products from Terra and Aqua have immediate applications for U.S. air
quality managers who need to make decisions and order air quality warnings for the general public.
These managers could use the existing MOD04 (Terra MODIS)/MYDO4 (Aqua MODIS)
products in a near-real-time manner. As a first step we intend to provide the standard product
within the required time frame, at minimal cost to this proposal. However, the value of this
product to the Air Quality community needs scientific evaluation, and a more appropriate product,
based on the same physics as the standard product, can be developed, implemented and
18

tested within the 3-year duration of this current NRA. The proposed research fits the letter and
spirit of the Science Applications section of the present NRA.
6. MANAGEMENT PLAN
To accomplish the objectives requires a dedicated team to each task with at least one NASA
and one EPA representative on each team. The primary teams are the MODIS Aerosol Team (PI
Chu and Co-I Remer), the CIMMS/SSEC Direct Broadcast Team (Co-I Bryan Baum), the
NASA-LaRC/US EPA Air Quality Applications Team (Co-Is, Szkyman and Al-Saddi), and the
US EPA Air Quality Index Team (Co-I Szkyman and EPA Collaborators). The Principal Investigator
will oversee and provide guidance for all tasks, with a particular focus on Tasks 1 and 3.
The US EPA Co-Investigator will provide the overall guidance for Tasks 2 and 4. Each team has
its responsibilities headed by PI or Co-I(s). In addition to emails, the effective coordination with
other teams will require monthly teleconferences and quarterly meetings held alternately between
GSFC, CIMMS/SSEC, and EPA. The detailed project schedule and responsibilities are
shown in the next two subsections.
6.1 Timeline
Table 1. Project Schedule
Task 1
Task 2
Task 3
Task 4
Study the relationship of MODIS-derived aerosol optical depth and
PM2.5 mass concentration measured at the surface
19
Start
Date
End
Date
Summarize initial findings of MODIS/PM2.5 relationship. 10/01/03 12/31/03
Create central database of historical MODIS/PM2.5/meterological data to support analyses
under Task 1 and 2.
10/01/03 12/31/03
Develop detailed methodology for handling multiple data sets and analyses, and develop
computer code.
10/01/03 12/31/03
Process data for co-incident MODIS V3 aerosol and hourly PM2.5 concentrations. 12/01/03 02/28/04
Interpret results and develop necessary statistical model that explain multi-variant relationships
of MODIS/PM2.5/meterological data.
03/01/04 06/30/04
Analyze correlation of MODIS (V3 & V4) aerosol optical depth and PM2.5 concentration
measurements over entire US in four different seasons
10/01/03 09/30/04
Collect and study coherent measurements acquired at GSFC super site and in field deployments
Examine regional aerosol transport pattern in both urban and rural
areas
10/01/03 09/30/05
Review MODIS (V3 and V4) and PM2.5 data to identify suspect PM2.5 regional transport
events
12/01/03 01/31/04
Use MODIS aerosol optical depth (V3 and V4) to develop and run air parcel trajectories for
transport analysis
02/01/04 02/28/04
Characterize synoptic scale weather patterns using NASA DOA (or NCEP) reanalysis data 03/01/04 04/01/04
Interpret results and provide an overall summary of regional transport patterns based on
developed data
Evaluate higher resolution (e.g., 5x5, 3x3, or 1x1 km
04/01/04 06/30/04
2 ) of MODIS
aerosol retrievals for better characterizing aerosol spatial distribution
Implement necessary changes to MODIS aerosol algorithm for generating high-resolution
aerosol product
10/01/03 12/31/03
Error analysis of aerosol optical depths retrieved at different resolutions 01/01/04 03/31/04
Validate the high-resolution retrievals with fine-resolution sunphotometer network
(CLAMS, Baltimore County, etc.)
04/01/04 06/30/04
Apply to the analysis of Task 1 in metropolitan areas 07/01/04 09/30/04
Prototype the development of a combined near-real time product
utilizing MODIS aerosol optical depth and EPA PM2.5 mass concentration
measurements
Discuss user requirements and data definitions. Design, Develop, and Test software for
the receipt of near-real time data flow into NASA-LaRC AtsC
07/01/04 09/30/04

Design, Develop, and Test interface with Data Fusion Software 10/01/04 01/01/05
Design, Develop, Integrate, and Test Initial Combined Product 12/01/04 03/01/05
Test Prototype of Near Real-Time Data Fusion and Output 03/01/05 09/30/05
Validate Data Products 06/01/05 12/31/05
Deploy in Operational Test Mode 01/01/06 09/30/06
Operationalize for Transfer to AQ User Community 06/01/06 09/30/06
Note: the start and end dates are based upon the funding beginning on October 1, 2003
6.2 Personnel And Responsibilities
PI Allen Chu has been responsible for the development, implementation, and validation of
the MODIS aerosol retrieval over land for the past 7 years. He led quality assessment and assurance
of MODIS atmosphere products in planning and implementation. For this proposal he will
be responsible for overseeing the entire management of the proposed research. He will concentrate
on Task 3 and later on Task 4, while working closely with co-I Szykman on Task 1.
Co-I Lorraine Remer is an established expert in the remote sensing of aerosols from satellite,
and assuming that her maintenance/refinement proposal is accepted will be the MODIS team
member responsible for the standard MODIS aerosol algorithm. In terms of this proposal she
will be involved with all scientific decisions concerning the MODIS products and air quality, and
will provide advice, as needed.
Co-I James Sykzman is a senior analyst within US EPA air program. Over the past four
year, he has worked directly with NASA personnel to provide EPA’s input into NASA code YO
Application Program for Air Quality. He will coordinate involvement for EPA collaborators on
all tasks and concentrate on Tasks 1, 2, and 4.
Co-I J. A. Al-Saadi and LaRC colleagues conducted the preliminary comparisons of
MODIS aerosol optical depth and EPA surface PM2.5 measurements shown herein. He will lead
task 4 working closely with the PI and Co-I Szykman, and will be responsible for the administration
of the LaRC portion of the proposal. He will also assist Co-I Szykman in conducting Tasks 1
and 2.
Co-I Bryan Baum is a respected expert in remote sensing and the processing of remote sensing
data. He will work with the personnel at CIMSS of University of Wisconsin-Madison to help
integrate the aerosol products (MOD04) into the MODIS Direct Broadcast data stream. He will
provide technical expertise to users of the aerosol products and provide scientific expertise for
studies involving the aerosol products.
Collaborator E. Judd Welton is a PI of MPLnet Project, a global network of Micropulse lidar
and is involved in the development and analysis of GLAS aerosol data. He will be help in the
use and interpretation of the MPL data from GSFC and the ARM Cart site.
Collaborator Eric Vermote is an expert in atmospheric correction and radiative transfer. He
is the MODIS science team member who developed the internal 1 km aerosol product for the
Land team’s atmospheric correction process. In terms of this proposal, he will offer advice on
the development of fine resolution aerosol products.
Collaborator Fred Dimmick leads US EPA’s Air Quality Trends Analysis Group and is recognized
expert within the air quality community for developing and providing time relevant integrate
analysis of air quality data for all of US EPA National Ambient Air Quality Standard. He
will off advice on the development of our integrate data analysis and will be one of EPA’s primary
user for the output of Tasks 1-4.
Collaborator Richard Wayland is a recognized expert in the field of Air Quality Forecasting,
and leads US EPA’s group responsible for the AIRNow Ozone and PM2.5 Air Quality Forecast-
20

ing. He will work closely with PI and Co-I’s Szykman and Al-Saadi to prototype the development
of a combined near real-time product under Task 4 for the air quality forecast community.
Collaborator David Williams is a respected expert in remote sensing and data processing. In
terms of this proposal, he will offer assistance on the development and validation of fine resolution
aerosol products, Task 3, and implementation and evaluation of the prototype Air Quality
Decision Support Tool at US EPA, Task 4.
7. BUDGET AND MANPOWER REQUIREMENTS
7.1 Budget Explanation
• Direct labor represents civil servant salary and benefits contributed by GSFC. The Co-I,
Dr. Lorraine Remer will devote 0.1 FTE (full time equivalent) on this proposal.
• Budget is requested for direct labor charges plus benefit for: one FTE senior researcher
(PI, Dr. D. Allen Chu). (See details in Appendix A)
• The pass-thru grant to the LaRC is tabulated in detail in Appendix B. NASA-LaRC indirect
cost are request for Jim Szykman, US EPA civil servant located on-site at NASA-LaRC.
The associated salary costs for Jim Szykman are reflected in Appendix C of US EPA budget
summaries (EPA In-kind-resource).
• PI travel includes one domestic trip to AMS or AGU in each year, plus one trip for collaborative
discussion between the PI and co-Is each year. We do not anticipate purchasing hardware
or software for this study. Cost for supplies include small purchases and publication costs.
• Civil servant travel includes one domestic trip to AGU or other important domestic meeting
in each year.
• Facility and administrative costs are for GSFC indirect charges (see percentages in worksheet)
• Other applicable costs include branch, division and directorate assessments
7.2 Current/Pending Support and Relationship to Other Submitted Proposals
As of December 2003, neither PI Chu nor Co-I Remer will have any confirmed continuing
support. Co-I Al-Saadi has the continuing supports listed in the current support below
Current support:
Proposal Title: Infusing Satellite Data into Environmental Air Quality Applications
PI: D. O. Neil; Co-I: Al-Saadi; Source of Support: NASA Earth Science Applications Office
(NASA Code YO)
Amount of Support: 0.50 FTE during FY03-FY05
Proposal Title: Global 3D Model Studies of Polar Processes and Radionuclides, with Application
to the Global Modeling Initiative PI: David Considine; Co-I: Al-Saadi; Source of Support:
NASA; Amount: 0.25 FTE during FY03
Proposal Title: Analysis of Aircraft and Satellite Measurements of Lower Stratospheric Trace
Gases and Lagrangian Photochemical Modeling; PI: R. Bradley Pierce, Co-I: Al-Saadi; Source
of Support: NASA; Amount: 0.25 FTE during FY03
Proposals pending, submitted in response to NOAA/NASA JCSDA:
Proposal Title: Assimilation of Satellite and Surface Aerosol Measurements into Operational Data
Systems
21

PI: D. Allen Chu; Source of Support: NOAA; Amount: $157,000 for two years 2003 – 2005
Proposals pending, submitted to NRA-02-OES-02
Proposal Title: ENSO Climate Variability and its Impact on Stratospheric Ozone; PI: David
Considine, Co-I: J. A. Al-Saadi; Source of Support: NASA; Amount: 0.25 FTE
Proposal Title: Assimilation of satellite chemical measurements using a hierarchy of trajectory,
regional, and global photochemical models; PI: R. Bradley Pierce, Co-I: J. A. Al-Saadi; Source
of Support: NASA; Amount: 0.25 FTE
Proposals pending, submitted in response to NRA-02-OES-06 include:
Proposal Title: Combined Active and Passive Measurements for Improved Understanding of
Aerosol and Cloud Characteristics
PI: Matthew McGill, Co-I: D. Allen Chu; Source of Support: NASA; Amount:$700,000 annually
Proposal Title: Integrated Satellite and Cloud Modeling Study of Aerosol Effects on Radiation,
Cloud Microphysics and Precipitation in the Boreal Regions
PI: Lorraine Remer; Source of Support: NASA; Amount: $167,400 annually
Proposal Title: "IDS proposal: The Global Aerosol System and its direct and indirect forcing of
climate."
PI: Yoram Kaufman, co-I Lorraine Remer; Source of Support: NASA; Amount: $700,000 annually
Proposal Title: Measurement of the First Aerosol Indirect Effect at Southern Great Plains using
Ground-based Remote Sensing, Satellite Remote Sensing and Modeling.
PI: Graham Feingold, Co-I Lorraine Remer; Source of Support: NASA; Amount: $145,000 annually
Proposals submitted concurrently in response to NRA-03-OES-02:
Proposal Title: Using Terra Data to Examine Tropospheric Aerosol and Trace Gas Distribution
PI: David Edwards,Co-I: D. Allen Chu; Source of Support: NASA; Amount: $ 734,632 annually
Proposal Title: Maintenance and refinement of the global MODIS aerosol products from Terra
and Aqua (MOD04/MYD04)
PI: Lorraine Remer; Source of Support: NASA; Amount: $456,600 in FY04
Proposal Title: Measurement based evaluation of the aerosol radiative forcing of climate
PI: Yoram Kaufman, co-I Lorraine Remer; Source of Support: NASA; Amount: $300,000 annually
Relationship to other proposals submitted to this NRA (NRA-02-OES-02):
The Remer proposal submitted above is a maintenance/refinement proposal of the existing/standard
MODIS aerosol product. It does not compete with this proposal, which is a science
proposal geared toward understanding the MODIS aerosol products in terms of regional and local
air quality applications. The other proposals listed above, submitted to this NRA (Kaufman)
address large-scale global change issues that the standard product was developed to explore.
Thus there is no overlap.
8. CONCLUSIONS
22

The assembled team for this proposal representing both those at NASA responsible for developing
and evaluating the MODIS aerosol algorithm and those at the EPA representing the
concerns of the Air Quality community provide a unique fusion of experience that can accelerate
the useful application of Terra and Aqua MODIS aerosol products for immediate needs in the
U.S. and internationally. The standard MODIS aerosol products provide accurate total column
aerosol information over land and can be used with confidence in global change studies. We
show some preliminary results that suggest that the products are also appropriate on the local and
regional scale for air quality management applications. Although very encouraging, these preliminary
results also show us that scientific uncertainty needs to be narrowed in order to produce
a high quality Decision Support Tool.
9. REFERENCES
Chu, D. A., Y. J. Kaufman, L. A. Remer et al., Remote sensing of smoke from MODIS airborne
simulator during SCAR-B experiment, J. Geophys. Res., 103, 31,979-31,987, 1998.
Chu, D. A., Y. J. Kaufman, C. Ichoku et al., Validation of MODIS aerosol optical depth retrieval
over land, Geophys. Res. Lett., 29(12), DOI 10.1029/2001GL013205, 2002.
Chu, D. A., Y. J. Kaufman, J-D Chern, J-M Mao, C. Li, H. B. Holben, Global Monitoring of Air
Pollution over Land from EOS-Terra MODIS, J. Geophys. Res., submitted, 2003.
Draxler, R.R.; Hess, G.D. Description of the HYSPLIT_4 modeling system, NOAA Technical
Memorandum ERL ARL-224; Dec.1997, pp.24.
Draxler, R.R.; Hess, G.D. An overview of the HYSPLIT_4 modeling system for trajectories, dispersion
and deposition; Aust. Met. Mag. 1998, 47, pp. 295-308.
Eldred, R. A.; Cahill, T.A.; Pitchford, M.; Malm, W.C. IMPROVE – a new remote area particulate
monitoring system for visibility studies; Proc. APCA (Air Pollution Control Assoc.) Annual
Meeting Pittsburgh, PA. 1988, 81, 1-16, 1988.
Hsu, N. C., J. R. Herman, P. K. Bhartia, C. J. Seftor et al., Detection of biomass burning smoke
from TOMS measurements, Geophys. Res. Lett., 23, 745-748, 1996.
Herman, J. R., P. K. Bhartia, O. Torres, N. C. Hsu, C. J. Seftor, and E. Celarier, Global distribution
of UV-absorbing aerosols from Nimbus-7/TOMS data, J. Geophys. Res., 102, 16,911-
16,922, 1997.
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