Integrated Observations and Models for
Air Quality Management and Science
Dr. Rudolf B. Husar, Washington University, PI
Table of Contents
Introduction ......................................................................................................................................... 2
Aerosol and Land Surface Retrieval............................................................................................. 3
Aerosol characterization ................................................................................................................. 7
End-user-driven application of satellite observations ......................................................... 9
Husar AQ Applied Science Group ............................................................................................... 14
References and Citations .............................................................................................................. 15
Curriculum Vitae .............................................................................................................................. 17
Rudolf Husar ................................................................................................................................................. 17
Stefan Falke ................................................................................................................................................... 19
Current/Pending Support............................................................................................................. 21
Budget Justification......................................................................................................................... 22
1
Introduction
Air Quality Background and Megatrends. Air pollution is a persistent, but constantly
changing result of human activities. Over the past decades major changes have occurred
that fundamentally transformed the nature of air pollution, its consequences and its
mitigation. There was a shift from primary pollutants SO2, soot and TSP to the secondary
pollutants, PM2.5 and ozone, formed in the atmosphere from precursor gases. Unlike the
short-lived, primary pollutants, PM2.5 and ozone have atmospheric lifetimes of multiple
days, which result in "long-range transport" across state and international boundaries.
These air quality megatrends and other factors have resulted in major shifts and
challenges for air quality analysts, managers and policy makers. Establishing sourcereceptor relationships and the tracking of emission trends requires more detailed analyses
and higher demand on data and the data distribution system. There was also a shift from a
command and control management style to a ‘weight of evidence” style, which strives to
include all stakeholders (federal, state, local, industry, international) and encourages
market-based resource allocations.
The first part of the 21st century is an occasion for a transition toward a healthy and
sustainable air quality characterized by another set of major shifts in local, regional and
global air pollution. Earth observations will be even more essential as inputs for
characterizing changes in air quality, estimating the damage to human health and welfare
and assessing the effectiveness of the control strategies.
Satellite Air Quality Observations. Fortunately, the air quality sensing and monitoring
revolution that started in the 1990's offers significant new opportunities for progress in
these areas. In particular, satellite remote sensing of aerosols, ozone, and their precursors
now offer 1-30 kilometer-resolution, column observations daily throughout the world.
Satellite aerosol measurements have also shown great potential to contribute to Earth
science and to the detection and management of some disasters. New information
technologies, effective data management, and analysis tools allow more detailed and
agile data analyses. Finally, the stakeholders now increasingly recognize the need and the
benefits of data sharing, collaboration and coordination.
Remotely sensed observations can complement existing observational platforms and
support the air quality assessment processes in multiple ways by (Scheffe et. al., 2009):
1. Providing direct observational evidence of regional and long range transport;
2. Source detection and emission inventory improvements;
3. Evaluation of AQ models;
4. Tracking emissions trends (accountability);
5. Complementing surface networks through filling of spatial-temporal gaps;
6. Forecasting pollutant levels.
The quantitative measurement of aerosol properties has proven to be particularly
challenging since the beginning of the satellite era in the 1960s. This is no surprise, since
satellite aerosol measurement techniques are among the most challenging and complex
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aerosol measurements. Thus, before satellite aerosol observations can reach the
‘promised land’ (Hoff and Christopher, 2009), it will be necessary to better understand
their measurement limitations (Hidy et al., 2009) and to develop science-based methods
of integrating satellite data with surface observations, emission inventories and chemical
transport models (NRC 2010).
As a member of the NASA Air Quality Applied Science Team (AQAST), R. Husar
intends to champion solutions to three specific challenges to the above problems:
1. Co-Retrieval of aerosols and land surface for improved satellite aerosol and surface
measurements.
2. Aerosol characterization of the aerosol system through science-based integration of
multi-sensory data.
3. End-user-driven application of satellite observations.
The solution to these challenges would allow the creation of integrated, relevant AQ
observations to be placed in the hands of air quality analysts. These three specific topics
were selected because of the perceived strong need and the extensive experience of R.
Husar and collaborators in these topics.
Aerosol and Land Surface Retrieval
Below we describe a set of collaborative development approaches for the improvement of
satellite aerosol retrieval with simultaneous detection of land surface color. This activity
will contribute to the development of algorithms that simultaneously retrieve the
changing surface characteristics and the aerosol properties and will support the AQAST
objectives to:
 Deliver proofs-of-concept for possible applications;
 Deliver applied research studies that scope and enable applications of Earth
science products in air quality decision making
 Scope and deliver data services specifically for air quality data users and
applications-oriented community
In principle, atmospheric aerosols are well suited for satellite remote sensing. The sun
provides a stable light source, the aerosol scattering covers the entire solar spectrum and
the radiation is easily detectable by high-resolution sensors. The difficulties for
quantitative aerosol measurements include: (a) Aerosol scattering is just one of the four
components of the detectable radiation and separation of the weak aerosol signal form the
other signal components is error-prone; (b) Associating the angular back-scattering by
aerosols with intrinsic aerosol properties is burdened with ambiguity; (c) The downwardlooking satellite sensors measure the total back-scattering, i.e. the integral over the entire
atmospheric column. A more detailed description is found in the book chapter ‘Satellite
Measurements of Aerosols’ (Husar, 2010).
The very same physical laws that govern satellite aerosol detection are applied to
atmospheric visibility studies. In fact, satellite aerosol detection and day-time vision by
the human eye operate on the same general principle of passive remote sensing.
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However, the two fields have different objectives and evolved rather independently from
each other. The objective of visibility studies is to measure and model the ambient
aerosol microphysics and chemistry and to evaluate the resulting effects on the optical
environment. Satellite aerosol remote sensing on the other hand intends to solve the much
more difficult inverse problem of deriving the properties of atmospheric aerosols from
the measured optical effects at the top of the atmosphere.
The inherent problem is that the retrieval of the unknown aerosol properties requires
knowledge of those same properties. The current practice for resolving the inversion
paradox is to prepare a discrete set of ‘aerosol models’, and to choose the model that best
fits the satellite optical data in form of spectral or angular scattering. The aerosol models
and the surface reflectance models are typically tailored to the characteristics of the
particular sensor. Much of the knowledge on ‘aerosol models’, including aerosol size
distribution dynamics, chemical composition and optical properties has been generated
through atmospheric aerosol and visibility research and it is directly applicable to the
improvement of satellite aerosol detection. Conversely, satellite aerosol monitoring can
significantly enhance the understanding of atmospheric aerosols, including emissions,
transport and spatio-temporal pattern. Thus, dynamic models for different aerosol types
could be co-developed by aerosol scientists and remote sensing specialists. More robust
and continually updated surface reflectance models could help aerosol retrieval as well as
surface characterization. Iterative co-retrieval of aerosol and surface properties could be
one possible approach.
The co-retrieval method of satellite aerosol detection specifically addresses the problem
of land surface reflectance as interference on aerosol detection. The method was
developed and used by Husar’s group starting in 1998, since the routine availability of
the 8 band SeaWiFS sensor data. The work of three graduate students and postdoctoral
fellows contributed throughout the past decade but the development is not complete and
would benefit from further collaborative research. The method (Raffuse, 2003) seeks to
detect simultaneously the true color of the changing land surfaces as well as the proper
integral aerosol properties. The separation of the aerosol signal from the surface
reflectance was performed by the solution of the appropriate radiation transfer equation
as derived in (Husar and White, 1976). The resulting relationship between the surface
reflectance and aerosol optical parameters is shown in Figure 1a.
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Figure 1 a. Radiative interactions of aerosols and surfaces b. surface reflectance for a typical farmland derived from daily SEAWiFS
data.
The method is iterative which begins by initially detecting the surface reflectance on
cloud and haze free days. The initial surface reflectance is still contaminated with
residue haze. The next step is to estimate the aerosol reflectance as the excess radiance,
i.e. the difference between the total minus the surface reflectance. Based on the surface
and excess reflectance, an initial size distribution is estimated using the eight wavelength
between 0.41 and 0.67 mm. In the second iteration the residue aerosol signal is removed
to yield a ‘cleaner’ surface reflectance, which in turn is used to provide a ‘better’ estimate
of the aerosol burden. The angular, scattering angle-dependence of surface reflectance
(BDRF) for each 1 kilometer-sized pixel is also derived from observations.
From the perspective of aerosol detection, the highly textured and colorful land reflection
from the Earth’s surface constitutes a significant background signal that needs to be dealt
with. Over land, the angular dependence of the reflected radiation depends both on the
angle of the incoming radiation as well as the angle of reflected radiation and is defined
by the Bidirectional Diffuse Reflectance Function (BDRF). The spectral reflectance of
many surfaces varies with season. For example, Figure 1b shows that the derived
seasonal variation of farmland reflectance at blue, green and red wavelengths exceeds a
factor of two.
Figure 1b provides a graphic illustration of the key steps in satellite aerosol-surface coretrieval. The hazy SeaWiFS image (Fig. 2a) shows the composite signal received by the
sensor on July 16, 2009. The image was synthesized from the blue (0.412 μm), green
(0.555 μm), and red (0.67 μm) channels. The aerosol-free surface reflectance (Fig. 2b)
was derived from time series data. The aerosol optical depth (Fig. 2c) was calculated
from the excess radiance, i.e. the difference between the hazy and haze-free reflectances
at the blue (412 nm) wavelength. The resulting aerosol pattern shows qualitatively the
hazy patches (red) over the ocean as well as in the vicinity of cloud systems. The black
areas are blocked out by the cloud mask
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Figure 2 a. True-color composite of SeaWiFS satellite data for July 15, 1999. b. Surface reflectance. c. Derived aerosol optical depth,
AOD. d, e, f. Measured apparent reflectance at 412, 670 and 870 nm wavelength respectively showing increasing atmospheric
transparency at higher wavelengths.Figure 2b. Surface reflectance in the absence of aerosols and clouds. 2c. Derived aerosol optical
depth, AOD. Figures 2 (Husar, 2010)
Within Husar’s group this activity will include the project collaborator, Professor
Virendra Sethi and graduate student, Ratish Menon from IIT-Bombay, India. Informal
conversations with remote sensing specialists (Dr. L. Remer) indicated interest in
participation.
A further opportunity for collaboration pertains to the aerosol layering. It is known that
aerosols tend to reside in distinct layers of the atmosphere, as illustrated schematically in
Figure 3. Figure 3b and 3c also illustrate the layering and mixing processes in the
atmosphere. The vertical distribution of aerosols can be subdivided into three layers with
each layer having different content, aerosol lifetime and mixing characteristics. The
stratospheric or Junge layer (Fig. 3a) is composed primarily of volcanic sulfuric acid
aerosols that circle the Earth at 10-15 km elevation for a year or two after volcanic
eruptions. Below the stratosphere is the tropospheric layer which is the carrier of dust,
smoke or occasional industrial haze ejected from the boundary layer.
Each aerosol type shown in Figure 3a has its own sources, transport and optical
characteristics and therefore deserves to be treated as a separate aerosol type with specific
aerosol model parameterization. The aerosol types that are in separate boxes are
externally mixed and their respective radiative effects are additive. A group of aerosol
6
types in the boundary layer that are in adjacent boxes represent components that are
generally internally mixed and their physical, chemical and optical properties and
behaviors need to be considered jointly. It also offers physically-based classification of
aerosol types. The classification is also compatible with chemical transport models, and
allows a tighter coupling of Earth observation and atmospheric modeling systems.
Clearly, the formulation of the specific sub-models for each type requires considerable
joint effort by both the aerosol and remote sensing specialists.
Figure 3. a. Schematic representation of the aerosol vertical distribution. b. Astronaut photo of stratospheric aerosol layers. c. Complex
physical and radiative interaction between aerosols and clouds. The bottom layer is the planetary boundary layer (PBL) that contains
all the sources and sinks of atmospheric aerosols. Precipitation and other removal mechanisms restricts the aerosol lifetime in the PBL
to 3-5 days. The
The outcome of the above collaborative approach is an improved understanding of
atmospheric aerosol optics and an algorithm to simultaneously retrieve the changing
surface characteristics and the aerosol properties.
Aerosol characterization
The second area to be championed is aerosol characterization, which refers to the full
multi-dimensional description of the atmospheric aerosol system. The challenges of this
topic are both conceptual and practical. This activity will support the AQAST objectives
to:
 Use multiple satellite observations (especially multi-sensor products) to better
connect research knowledge and results with air quality monitoring and decision
making activities of other Government agencies, businesses, and other
organizations;
 Scope and deliver data services specifically for air quality data users and
applications-oriented community;
 Deliver applied research studies that scope and enable applications of Earth
science products in air quality decision making;
While the characterization of gaseous species requires only the physical dimensions of
space and time (x,y,z,t), the full characterization of an aerosol system requires at least
two additional dimensions, particle size, D, and aerosol composition, C (x, y, z, t, D, C).
A conceptual framework for aerosol characterization was first formulated by Friedlander
(1971) and expanded by McMurry (2000). However the framework needs to be extended
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to include the spatial dimensions (x,y,z). We present an aerosol characterization approach
below.
Full characterization essentially involves filling the 6-dimensional data space, while the
available anchor points from observations is a very sparse subset of the overall data
space. In principle, comprehensive chemical transport models could fill in the nonmeasured, missing values. However, such approach would require raising the capabilities
of data assimilation techniques to a new and much higher level.
It is evident that a full characterization of the aerosol system requires multiple sensors
and a diverse set of approaches. Surface observations are necessary to estimate the
population exposure to air pollutants but these observations characterize only a thin
horizontal slice of the AQ system. Most pollutant mass resides and virtually all the
atmospheric processes are happening aloft. Vertical column and profile observations are
key to a complete characterization of the AQ system. Satellite sensors, (e.g. AOD, O3,
CO, CO2, CH4, SO2, NO2, C2H2) have high spatial resolution (x,y), intermittent sampling
(t’) and they detect the optical effect of a vertical column, i.e. the integral over three
dimensions, height z, particle size D and composition, C, (z, D, C).
Integration opportunities can be viewed from a number of perspectives, including:
Horizontal blending by linking similar pollutants horizontally by blending urban and
rural based networks; Vertical blending by blending of satellite data, combination of total
integrated column values, and surface; Species blending by collocating a variety of
different species measurements to yield multiple pollutant characterizations and Effects
blending by combining precipitation and ambient/dry observation networks to develop
deposition fields. (Scheffe, et. al., 2009).
A comprehensive ‘blending’ pertain to the linking and reconciliation air quality
emissions, observations and models as developed conceptually by this proposer for
EPA’s Hemispheric Transport of Air Pollutants Program (HTAP, 2007). The importance
of this reconciliation is that currently, there is no ‘closure’ between emissions,
observations and models. In other words, since the emissions are inputs to air quality
models and they do not correspond to reality, the models cannot represent reality.
Consequently, the models cannot be evaluated against observations. This lack of closure
significantly hampers progress. The objective of this activity is to iteratively improve the
emissions, models and observations, i.e. until a reasonably robust closure of the three
components is achieved.
The connecting arrows (blue in Figure 4a) represent the major connections and operations
that link observations, emission inventories, and modeling. Observations are an essential
foundation, providing the data for model evaluation as well as for data assimilation into
the models. Through inverse modeling, observations also allow estimation of emissions.
Improving our assessment of air quality will require an integrated approach where the
best available knowledge from observations, emissions, and models is combined.
Furthermore, reconciliation of the three areas or aspects (observations, emission
inventories, models) requires considerable iteration until the deviations and
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inconsistencies are minimized.
An alternative representation of the pollutant characterization process and steps is shown
in Figure 4b based on Scheffe et. al, (2009). The map to the right indicates that the
optimized air composition arises from the proper incorporation of all available
observations into a model through assimilation or other means. It also shows that such an
‘optimized’ pollutant characterization would be applicable to many different aspects of
air quality management. Because observations can serve multiple objectives, and
disparate organizations share the common need of characterizing the environment, an
intrinsic connectivity exists among a variety of measurement programs. Consequently, a
comprehensive multiple organization view of observational systems is necessary to take
advantage of existing data elements and identify important strengthen their suitability for
integration.
The general problem of reconciling emissions models and observations is a research area
that has been identified as a high priority by both international atmospheric chemistry
work groups (IGAC, 2004) as well as more recently in the U.S. (NRC, 2010).
Figure 4 a. Conceptual diagram of the interrelationship between emissions, observations and models. 4 b. Conceptual representation of
integrated observation-modeling system (Scheffe et. al. 2009).
A comprehensive, tested and applied pollutant characterization framework does not yet
exist. Collectively developing and applying such a framework would be a major
achievement for the Air Quality Applied Science Program. This topic falls into the longterm, persistent activity. The product of such an approach would be a contiguous sixdimensional reconciled aerosol dataset (x, y, z, t, D, C) that can serve multiple air quality
applications in a manner similar to the manner in which data assimilation and re-analysis
has served the weather analysis and forecast community. This activity on the emissionobservation-model closure will be conducted primarily by R. Husar in collaboration with
other members of the AQAST, in particular with Professor Ray Hoff, a named
collaborator in this proposal.
End-user-driven application of satellite observations
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The third area to be championed is the empowerment of air quality analyst-practitioners
with remote sensing observations and associated tools and methods. This set of AQanalysts-users would complement the remote sensing professionals who have developed
and illustrated many of the satellite air quality applications. Practicing AQ analysts are
close to the air quality management/decision making process, they have deep expertise in
pollution sources, transport and other characteristics and they are well equipped to select
the proper satellite data products to the application of interest. This activity supports the
AQAST objectives to:
 Integrate expertise on natural sciences, engineering, data systems, social sciences,
and human factors to enable applications to organizational decision making.
 Provide technical feedback on NASA data products from the applicationsoriented perspective and end users in the Air Quality community;
 Use multiple satellite observations (especially multi-sensor products) to better
connect research knowledge and results with air quality monitoring and decision
making activities of other Government agencies, businesses, and other
organizations;
 Articulate specific applied research needs and actual or possible societal benefits
stemming from the research and applications;
Traditionally, air quality analysis was a slow, deliberate investigative process occurring
months or years after the monitoring data had been collected. Satellites, real-time
pollution detection and the World Wide Web have changed all that. Analysts can now
observe air pollution events as they unfold. They can ‘congregate’ through the Internet in
ad hoc virtual work-groups to share their observations and collectively create the insights
needed to elucidate the observed phenomena. Air quality analysis has become much more
agile and responsive to the needs of air quality managers, the public and the scientific
community. The new information technologies now allow real-time, ‘just-in-time’ data
analysis for the characterization and partial explanation of the major air pollution events
as well as more in-depth post-analysis (Husar and Poirot, 2005).
For the users of satellite data the new developments introduced a new set of problems.
The ‘data deluge’ problem is especially acute for analysts interested in aerosol pollution,
since the aerosol processes are inherently complex, the relevant data range from detailed
surface-based chemical measurements to extensive satellite remote sensing products and
the integration of these requires the use of sophisticated data integration and modeling
science methods. Unfortunately, neither the current science, nor available tools and
methods are adequate for such sophisticated data integration. As a consequence, satellitederived, Earth observations are severely under-utilized in making societal decisions. A
remedy is anticipated from the Global Earth Observation System of Systems (GEOSS),
(GEO, 2005) an emerging public information infrastructure for finding, accessing and
applying diverse Earth observations that are useful for science and decision makers in air
quality and elsewhere.
Facilitation of seamless access to satellite and related observations. Accessing and
manipulating observational datasets presents challenges to data user groups accessing
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single systems. Information technology solutions to harmonize datasets could reduce the
burden on analysts in accessing, reducing, understanding, and manipulating a spectrum of
disparate datasets inherent in integrated assessments. Consistent database formats need to
be established and criteria defined with sufficient variability to enable the efficient
inclusion of new methods. Collecting data rarely are complemented by adequate
resources to process and analyze information (Scheffe, et. al., 2009). Demands on data
processing elements will necessarily increase as integration expands the breadth of
assessments. The federated data system, DataFed (Husar and Poirot, 2005) is a registered
component of GEOSS which provides an architecture to facilitate interoperability of data
systems from diverse organizations (Figure 5) and links surface-based air quality data
integration systems, such as VIEWS with satellite and modeling systems. DataFed
expands the range of environmental characterization relevant to comprehensive
integrated environmental assessments. These emerging integrated systems offer a vision
for addressing information technology facets of comprehensive assessments but will
require investments and engagement from participating user communities (Scheffe, et.
al., 2009).
Figure 5. DataFed architecture for federating and harmonizing distributed datasets using standards-based services.
The capabilities provided in DataFed serve the interface between data providers and data
consumers. In serving this intermediary role, DataFed relies on its connections and
relationships with other data systems and decision support systems. For example, in
addressing the air quality science topics proposed above, co-retrieval of aerosols and land
surfaces and aerosol characterization, DataFed will provide connections with data sources
for accessing the satellite observations, surface monitoring and model outputs that are
used as inputs into the algorithms and analyses. DataFed will also provide user-oriented
tools in order to effectively disseminate the results of the research to researchers, analysts
and decision makers. In executing the air quality science aspects of the proposal, the
Husar team will continue to establish trusted relationships with individuals and
organizations as well as create interoperable connections with related information
systems. Some of the community-oriented groups with which we will work in applying
and implementing DataFed services and tools are:
The Committee on Earth Observation Satellites (CEOS) Atmospheric Composition
Constellation (ACC) group, in particular the Atmospheric Composition Portal (AC
Portal) effort that is being coordinated in conjunction with the CEOS Workgroup on
Information Services and Systems (WGISS). The AC Portal aims to be a collaboration
among CEOS members and the broader AC community in supporting interoperability
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among the atmospheric composition research and applications communities by providing
access, tools, and contextual guidance to scientists and value-adding organizations in
using remotely sensed atmospheric composition data, information, and services. (Lindsay
et al, 2009).
The Community Initiative for Emissions Research and Applications (CIERA) is a new
group with the goal to facilitate research on AQ and climate emissions by improving
emissions data access and ease-of-use and supporting more systematic emission data
evaluations and impact studies. CIERA is envisioned to be a research and regulatory
partnership that produces assessed emission databases, increases interactions between
emissions research and development, and informs improved emission inventories.
The Hemispheric Transport of Air Pollutants (HTAP) Program is also supported by
DataFed along with other nodes of the HTAP Data Network in the US and Europe. The
HTAP Data network is a connectivity infrastructure, as well as a set of user-centric tools
to empower the collaborating analysts. It embraces and leverages the shared resources
through an open, collaborative federation philosophy. The NASA-and EPA-supported
emerging Exceptional Event (EE) Network connects nodes participating in the analysis of
air pollution events relevant to the EE Rule.
Each of these networks is in the service of specific projects with clear, autonomous
mandate, resources and governance. However, since the nodes are exposing data through
standard service interfaces and communication protocols, in principle, all the resources
can be registered and shared through the GEOSS Common Infrastructure (GCI). Husar
and his team will continue championing and facilitate the registration and use of shared
data through GEOSS. The named collaborators for the end-user-driven application of
satellite observations are R. Poirot and W. White, both members of EPA’s Clean Air
Science Advisory Committee.
Below are illustrative examples of the application of satellite observations to air quality
using DataFed.
Exceptional Events Analysis. In the past, air quality regulations for aerosols relied on
surface-based monitoring networks to enforce the air quality standards. More recently
EPA’s Exceptional Event Rule (EE) (Federal Register, 2008) explicitly encourages the
use of satellites for the documentation of aerosol contributions, which originate outside
the jurisdiction of air quality control agencies. In fact, compelling satellite observations
of smoke and dust events (Fig.6) have prompted the introduction of the EE Rule itself.
The combination of multi-sensory satellite observations along with surface monitoring
data and diagnostic transport models provides the evidence that the Mexican Smoke
pollution event was due to uncontrollable, extra-jurisdictional causes and not from urbanindustrial sources. Similar integration of satellite and surface observations and models
have elucidated the intercontinental transport of wind-blown dust from the East Asian
Gobi Desert and its impact on the West Coast of North America (Husar et al., 2001).
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Figure 5 Surface reflectance derived from the SeaWiFS satellite data for May 14-17 1998. The spectral reflectance data were rendered
as a "true color" digital image by combining the blue (0.412 mm), green (0.550 mm), and red (0.670 mm) channels. The TOMS
absorbing aerosol index (green, levels 12 and 30) and the visibility-derived extinction coefficients are superimposed as red contours
(red, levels 0.2. and 0.4 m-1) According to the visibility data the most severe Mexican smoke impact occurred on the surface of the
Midwestern U.S. (Husar, et al, 2000)
Emission Characterization. A feature of satellite observations is that the measurement,
aerosol optical thickness (AOT) is a vertical columnar integral over. Hence, a secondary
integration over space provides an estimate of the pollutant mass in the atmosphere. This
feature makes satellites uniquely suitable to estimate pollutant emissions. As an
illustration, the columnar NO2 concentration from the OMI satellite sensor is shown in
Figures 6 a and b, representing the NO2 on aggregated for Friday and Sunday of each
week. From the numerical integration it is evident that on Fridays the NO2 emissions are
about 25% higher than on Sundays. Similar analysis is also use to estimate the emissions
from episodic fires, dust sources etc.
Figure 6. NO2 column concentration a. Friday average b. Sunday Average based on OMI sensor data.
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Husar AQ Applied Science Group
Rudolf Husar, Washington University in St. Louis, PI and director of the Center for Air
Pollution Impact and Trend Analysis (CAPITA). His qualifications for the above arises
from decades of experience as an atmospheric scientist, air pollution specialist and an
active promoter and practitioner of collaborative research and air quality problem solving
through community building.
Stefan Falke, Washington University in St. Louis and Northrop Grumman Information
Systems, co-I will primarily be involved in the ‘End-user-driven application of satellite
observations’ activity and will serve a liaison role with other groups, such as the CEOS
ACC and CIERA, in order to ensure effective communication, information exchange, and
interoperability.
Collaborator
Raymond Hoff
Richard Poirot
Virendra Sethi
Warren White
Proposal Focus Area
Aerosol Characterization
End-user-driven application of satellite observations
Aerosol and Land Surface Color Retrieval
End-user-driven application of satellite observations
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References and Citations
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IGACO, 2004, The Integrated Global Atmospheric Chemistry Observation, for
Monitoring of the Environment from Space and from Earth, The Changing
Atmosphere, An integrated Global Atmospheric Chemistry Observation Theme for
IGOS Partnership. ESA SP-1282, September 2004Report GAW No. 159 (WMO TD
No. 1235), September.
Lindsay, F.; Lynnes, C.; Leptoukh, G.; Falke, S. R.; Robinson, E. M.; Hildenbrand, B.;
Goussev, O.; Sommer, P., Interoperability in an Atmospheric Composition Portal,
American Geophysical Union, Fall Meeting 2009.
15
McMurry, P.H., 2000. A Review of Atmospheric Aerosol Measurements, Atmospheric
Environment, 34: 1959-1999.
NRC, 2010. Global Sources of Local Pollution: An Assessment of Long-Range
Transport of Key Air Pollutants to and from the United States, National Research
Council, National Academy Press.
Peppler, R.A., C. P. Bahrmann, J. C. Barnard, J. R. Campbell, M.-D. Cheng, R. A.
Ferrare, R. N. Halthore, L. A. Heilman, D. L. Hlavka, N. S. Laulainen, C.-J. Lin, J. A.
Ogren, M. R. Poellot, L. A. Remer, K. Sassen, J. D. Spinhirne, M. E. Splitt, and D. D.
Turner, 2000. Southern Great Plains Site Observations for the Smoke Pall Associated
with the 1998 Central American Fires; Bull. Am. Meteor. Soc., 81: 2563-2591.
Raffuse, S. M. 2003. Estimation of daily surface reflectance over the United States from
the SEAWifS sensor. Washington University
Scheffe, R.D., P.A. Solomon, R. ,Husar,, T. Hanley, M. Schmidt, M. Koerber, M. Gilroy,
J. Hemby, N. Watkins, M. Papp, J. Rice, Joann, J.Tikvart, R., Valentinetti, 2009. The
National Ambient Air Monitoring Strategy:Rethinking the Role of National Networks J.
Air Waste Manage. Assoc., 59: 579-590.
Wayland, R.A.; T.S. Dye, 2005. AIRNOW: America’s Resource for Real-Time and
Forecasted Air Quality Information; Environmental Manager 2005, September, 19-27.
16
Curriculum Vitae
Rudolf Husar
Center for Air Pollution Impact and Trend Analysis (CAPITA), Campus Box 1124,
Washington University, St. Louis, MO 63130-4899.
Phone: (314) 935-6099 Fax: (314) 935-7211, e-mail: rhusar@wustl.edu
Professional Preparation
1962–66
1966-71
1971-73
Dipl. Ing. Mechanical Engineering, Technical University, Berlin, FRG.
Ph.D. Mechanical Engineering, U. of Minnesota, Minneapolis, MN, US.
Post-Doctoral Fellow, California Institute of Technology, Pasadena, CA, US.
Appointments
2007-Present
1979-Present
1976-2007
1976-77
1973-76
Professor, Energy, Environmental and Chemical Eng., Washington U. St. Louis,
Director, Center CAPITA, Washington University St. Louis, MO
Professor, Mechanical Engineering, Washington U. St. Louis, MO
Visiting Professor, Meteorological Institute, Stockholm U., Sweden
Associate Professor, Mechanical Engineering, Washington U. St. Louis, MO
Activities
Rudolf Husar is currently a Professor of Energy, Environment and Chemical Engineering at
Washington University St. Louis, MO. For over three decades Husar has been an active
researcher, advisor and teacher of air pollution, with special interest in atmospheric aerosols. He
has made pioneering contributions to the size distribution and composition of atmospheric
aerosol, regional and global scale distribution transport of atmospheric aerosols, and to long-term
air pollution pattern and trend analysis. Husar’s research has contributed to development of
NAAQS for PM2.5 and the associated Exceptional Event Rule. Since the 1970’s Husar has
demonstrated the application of Earth observing satellites to air quality management. Currently,
he is participating in the Global Earth Observing System of Systems (GEOSS) as a data architect
and systems designer. He is working on aerosol data accessibility, interoperability and sharing.
His particular interests are satellite and surface observations data, multi-sensory data integration.
He was a contributor to the Air Quality Criteria for Particulate Matter documents, served on
various National Science Foundation committees and EPA Clean Air Scientific Advisory
Committee (CASAC) committees. Professionally, Husar works on the combination of
atmospheric science and environmental informatics. He is the developer of the Federated Data
System, DataFed hosted at Washington University. He is the co-leader of the Earth Science
Information Partners (ESIP) Air Quality Workgroup. Lead air quality analyst for the Group on
Earth Observations (GEO) Task US-09-01a and lead of NASA Data Systems Web Services subgroup. Husar has promoted openness and inclusiveness in his activities as part of the
Hemispheric Transport of Air Pollution (HTAP) and International Global Atmospheric Chemistry
(IGAC) program. He demonstrates and encourages teamwork and the use of the web as a
communication/data-sharing medium.
Member, Hungarian Academy of Sciences, 1998
Associate Editor, Atmospheric Systems, The Scientific World, 2001-present
Member of Editorial Board, Environmental Monitoring and Assessment, 2000-present
Chair WMO Panel on Global Aerosol Data System, Chair, 1991, 2006
WMO Panel on Space Observations of Tropospheric Aerosols, Group Leader, 1990
Collaborators: Dr. Shen Dingli, Director of the Center for American Studies at Fudan
University; Dr. Virendra Sethi, Head of the Center for Environmental Science and
Engineering, India Institute of Technology-Mumbai
17
Selected Publications
Scheffe, R.D., P.A. Solomon, R. ,Husar,, T. Hanley, M. Schmidt, M. Koerber, M. Gilroy, J. Hemby, N.
Watkins, M. Papp, J. Rice, Joann, J.Tikvart, R., Valentinetti.The National Ambient Air Monitoring
Strategy:Rethinking the Role of National Networks J. Air Waste Manage. Assoc., 59, 579-590, 2009.
Hidy, G. M., J.R. Brook, J.C. Chow, M. Green, R.B. Husar, C. Lee, R.D. Scheffe, A.Swanson, Aaron, J.G.
Watson, Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land, J.
Air Waste Manage. Assoc., 59, 1130-1139, 2009.
Husar, R.B., Hoijarvi, K., Falke, S.R., Robinson, E.M., Percivall, G.S., DataFed: An Architecture for
Federating Atmospheric Data for GEOSS, IEEE Systems Journal, 2, 366-373, 2008.
Husar, R.B., Poirot R.L., DataFed and FASTNET: Tools for Agile Air Quality Analysis, Environmental
Managers, Air & Waste Management Association, September 2005, 39-41, 2005.
Husar, R.B., D. M. Tratt, B. A. Schichtel, S. R. Falke, F. Li D. Jaffe, S. Gassó, T. Gill, N. S. Laulainen, F.
Lu, M.C. Reheis, Y. Chun, D. Westphal, B. N. Holben, C. Gueymard 1 I. McKendry, N. Kuring, G. C.
Feldman, C. McClain, R. J. Frouin, J.Merrill, D. DuBois, F. Vignola, T. Murayama, S. Nickovic, W.E.
Wilson, K.Sassen, N. Sugimoto, and W.C. Malm. The Asian Dust Events of April 1998. J. Geophys. Res.,
106(D16), 18317-18330, 2001.
Husar R.B., J.M. Prospero and L.L. Stowe, Characterization of Tropospheric Aerosols over the Oceans
with the NOAA Advanced Very High Resolution Radiometer Optical Thickness Operational Product, J.
Geophys. Res. 102, D14, 16889-16909 (1997).
Chapters in Books
1 . Husar R.B. Satellite Measurements of Atmospheric Aerosols. In: Aerosol Measurement: Principles,
Techniques, and Applications Baron P.A., K. Willeke, P. Kulkarni (Eds.) Wiley-Interscience, New York.
In press.
2 . Husar, R.B. The Emergence of the Bimodal Distribution Concept. In: History and Review of Aerosol
Science, G.J. Sem, D. Boulaud, P. Brimblecombe, D.S. Ensor, J.W. Gentry, J.C.M. Marijnissen, and O.
Preining (Eds.) American Association for Aerosol Research, 2005.
3. Husar, R.B., Intercontinental Transport of Dust - a Historical and Recent Observational Evidence. In:
Intercontinental Transport of Air Pollution, A. Stohl, (Ed.) The Handbook of Environmental Chemistry,
4G, Springer Verlag Berlin, Heidelberg, New York , 2004.
4. Heintzenberg, J., F. Raes, S.E. Schwartz, I. Ackermann, P. Artaxo, T.S. Bates, C. Benkovitz, K. Bigg, T.
Bond, J.L. Brenguier, F.L. Eisele, J. Feichter, A.I. Flossmann, S. Fuzzi, H.F. Graf, J.M. Hales, H.
Herrmann, T. Hoffmann, B. Huebert, R.B. Husar, R. Jaenicke, B. Kärcher, Y. Kaufman, G.S. Kent, M.
Kulmala, C. Leck, C. Liousse, U. Lohmann. Tropospheric Aerosols. In: Atmospheric Chemistry in a
Changing World, The IGBP Series, G.P Brasseur, R.G. Prinn, A.A.P. Pszenny, (Eds.) Springer, 2003.
5. Husar R.B. Atmospheric Aerosol Science before 1900. In: History of Aerosol Science, Preining, O. and
Davis, E. J. (Eds.) Verlag der Oesterreichischen Akademie der Wissenschaften,Vienna, 2000.
6. Husar, R.B., Sulfur and Nitrogen Emission Trends for the U.S. In: Industrial Metabolism: Restructuring
for Sustainable Development, R.U. Ayres and U.E. Simonis (Eds.), United Nations University Press,
Tokyo, 1994.
7. Husar R.B. Historical Trends in Atmospheric Sulfur Deposition and Methods for Assessing Long-Term
Trends in Surface Water Chemistry, with T.J. Sullivan, D.F. Charles. In: Acid Deposition and Aquatic
Ecosystems: Regional Case Studies, D.F. Charles (Ed.) Springer-Verlag, New York, 1991.
8. Husar R.B. and J.D. Husar. Sulfur. In The Earth as Transformed by Human Action, B.L. Turner et al.
(Eds.) Cambridge University Press with Clark University, Cambridge, 1990.
9. Husar, R.B. Emissions of Sulfur Dioxide and Nitrogen Oxides and Trends for Eastern North America.
In: Acid deposition: Long-Term trends, National Research Council (U.S.). Committee on Monitoring and
Assessment of Trends in Acid Deposition, 1986.
10. Husar, R.B. and J.Holloway, The Properties and Climate of Atmoshperic Haze. In: Hygroscopic
Aerosols, L.H. Ruhnke and A. Deepak, (Eds) A. Deepak Publishing, 1984.
11. Husar R.B., J.P. Lodge, D.J. Moore (Eds.). Sulfur in the Atmosphere, Pergamon Press, 1978.
18
Stefan Falke
Department of Energy, Environmental, and Chemical Engineering
Washington University in St. Louis
Campus Box 1180, One Brookings Drive, St. Louis, MO 63130
e-mail: stefan@wustl.edu
Northrop Grumman Information Systems
Suite 1740, 1010 Market Street, St. Louis, MO 63101
Phone: (314) 259-7908, e-mail: stefan.falke@ngc.com
Professional Appointments
2002–
Research Assistant Professor, Department of Energy, Environmental and
Chemical Engineering, Washington University
2005–
Manager, Geospatial Information Services for Energy & Environment, Northrop
Grumman Corporation Information Systems, St. Louis, MO
2000–2002
American Association for the Advancement of Science (AAAS) Science and
Technology Policy Fellow, US Environmental Protection Agency, Office of
Environmental Information
1999–2000 Research Associate, Center for Air Pollution Impact and Trend Analysis,
Washington University
Education
1999
1993
1992
D.Sc. in Environmental Engineering, Washington University
M.S. in Engineering and Policy, Washington University
B.A. in Physics, Lehigh University
Active Research Projects
9/2006–8/2010
PI, Sensor-Analysis-Model Interoperability Technology Suite, NASA
Earth Science Technology Office, $1,231,000
9/2008-12/2009
PI, GEOSS Pilot, Northrop Grumman Independent R&D
9/2008-9/2009
PI, Advancing an Interoperable Air Quality Community Network for
NOx, NASA
11/2004–10/2009
PI, Application of Earth Science Enterprise Data and Tools to
Particulate Air Quality Management
Professional Service
Lead, Committee on Earth Observation Satellites (CEOS), Working Group on Information
Systems & Services (WGISS), Atmospheric Science Interest Group
Co-lead, Air Quality and Health Working Group, Global Earth Observation System of Systems
(GEOSS) Architecture Implementation Pilot, Phase 2
Co-chair, Earth Science Information Partners Federation (ESIP) Air Quality Workgroup
Co-chair, Open Geospatial Consortium Earth Systems Science Workgroup
Co-chair Decision Support Systems, Air & Waste Management Association Aerosol and
Atmospheric Optics: Visual Air Quality and Radiation Balance Conference, 2008
Co-chair Web Based Information Systems, EPA International Emission Inventory Conference,
2006
19
Co-chair Decision Support Systems for Wildland Fire Management, EastFire Conference, 2005
Proposal Reviewer for the National Science Foundation, National Aeronautics and Space
Administration, US Environmental Protection Agency
Manuscript Reviewer for Atmospheric Environment, Journal of the Air & Waste Management
Association, Journal of Applied Meteorology, Journal of Geophysical Research
Selected Publications
Fairgrieve, S. , Makuch, J., and Falke, S. (2009), “PULSENet: An Implementation of Sensor
Web Standards,” 2009 International Symposium on Collaborative Technologies and Systems,
Baltimore, MD, May 18-22, 2009.
McCabe, D.C., P.G. Dickerson Jr., R.B. Husar, T.J. Keating, F.E. Lindsay, E.M. Robinson, S.R.
Falke, (2009), “The GEO Air Quality Community of Practice: a Call to Participate,” 33rd
International Symposium on Remote Sensing of Environment (ISRSE), Stresa, Italy, May 3-8,
2009.
Falke, S.R., and Fialkowski, E., (2009), “Your Output is My Input: Collaborative Portals,” 18th
International Emission Inventory Conference: Comprehensive Inventories - Leveraging
Technology and Resources, Baltimore, MD, April 14-16, 2009.
Husar, R.B., Hoijarvi, K., Falke, S.R., Robinson, E.M., and Percivall, G.S. (2008), “DataFed:
An Architecture for Federating Atmospheric Data for GEOSS” IEEE Systems Journal, Vol. 2,
No. 3, pp. 366-371.
Falke, S., and Sullivan, D., (2008), “Processing Services in Earth Observation Sensor Web
Information Architectures: Using Sensor Webs for Air Quality Science and Applications
Today, Tomorrow and Yesterday,” Earth Science Technology Conference, University of
Maryland, June 24-26, 2008.
Falke, S., and Husar R., (2008) “Web Resources for Sharing and Analyzing Information about
Smoke from the October 2007 Southern California Wildfires,” Air & Waste Management
Association Aerosol and Atmospheric Optics: Visual Air Quality and Radiation Balance
Conference, Moab Utah, April 28-May 2, 2008.
Falke, S., G. Stella, and T. Keating, (2007) "Cyberinfrastructure for Emissions Data and Tools,"
in proceedings, 15th International Emission Inventory Conference: Reinventing Inventories New Ideas in New Orleans, US EPA, May 15-18, 2006.
Falke, S., E. Dorner, and Dunn B. (2007) “Sensor Observation Interoperability and Open
Geospatial Consortium (OGC) Specifications” in proceedings, Data Sharing and
Interoperability on the World-wide Sensor Web Workshop, IEEE, Boston, MA, April 22-24,
2007
Yuan Z., Ramaswami B., Casaletto D., Falke S., Angenent L. T. and Giammar D. E. (2007)
“Evaluation of chemical indicators for tracking and apportionment of phosphorus sources to
Table Rock Lake in Southwest Missouri” Water Research, Vol. 41, No. 7, pp. 1525-1533.
Falke, S., G. Stella, T. Keating, and B. Hemming, (2006) "Cyberinfrastructure for Emissions
Data and Tools," in proceedings, 15th International Emission Inventory Conference:
Reinventing Inventories - New Ideas in New Orleans, US EPA, May 15-18, 2006.
20
Current/Pending Support
Investigator: Rudolf Husar, Washington University
Support: Current
Funding Source: NASA NNX09AT53G
Project: NASA + NAAPS Products for AQ Decision-Making
Duration: 10/2009–09/2012
Work-Months/Year Committed in 2010: 3
PI: Rudolf Husar
Total Award: $493,000
Support: Current
Funding Source: Northrop Grumman
Project: Support to Northrop Grumman re NASA ROSES contract
Duration: 09/2006–09/2010
Work-Months/Year Committed in 10-12/2010: 0
PI: Rudolf Husar
Total Award: $128,000
Support: Proposed
Funding Source: NASA NNH09ZDA00IN-AQAST
Project: Integrated Observations and Models for Air Quality Management and Science
Duration: 10/2010–09/2015
Work-Months/Year Committed in 10-12/2010: 0
PI: Rudolf Husar
Total Award: $128,000
CoI Stefan R. Falke
Investigator: Stefan Falke, Northrop Grumman
Support: Current
Funding Source: NASA
Project: Sensor Analysis-Modeling Interoperability Technology Suite (SAMITS)
Duration: 09/2006–09/2010
Work-Months/Year Committed in 2010: 2
PI: Stefan Falke
Total Award: $1,218,000
Support: Pending
Funding Source: US EPA (via STI subcontract)
Project: Cyberinfrastructure for Air Quality Management (CyAir)
Duration: 04/2010-10/2010
Work-Months/Year Committed in 2010: 2.5
PI: Tim Dye, Sonoma Technology, Inc.
Total Award: $150,000
Co-PI: Stefan Falke, Rudolf Husar
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Budget Justification
Personnel
The PI, Rudolf B. Husar, PhD, will be responsible for the research described in
this proposal. Rudolf B. Husar is Professor of Energy, Environmental and
Chemical Engineering and Director of the Center of Air Pollution and Trend
Analysis (CAPITA) at Washington University in St. Louis. Husar’s annual
effort devoted to this project is quantified in the budget detail.
The Co-Investigator for this project is Stefan R. Falke, PhD, Research Assistant
Professor in Energy, Environmental and Chemical Engineering and also Manager,
Geospatial Information Services for Energy & Environment, at Northrop
Grumman Corporation Information Systems, St. Louis, MO.
Kari Hoijarvi is the developer of the federated data system, DataFed. He will be
the programmer and developer for the Air Quality Data Finder.
A annual increases are budgeted for faculty salaries, three percent for the PI, CoI
and Sr. Computer Scientist are consistent with the University Policy.
Funds are requested to provide wages for a Graduate Research Assistant. The
GRA support and annual increases are structured to offer competitive rate
necessary to attract qualified students and are consistent with the University
Policy.
Fringe Benefits
The PI, CoI, and Sr. Computer specialist qualify for University benefits which
include contributions to FICA, 403B retirement plan, health, and disability. The
GRA and undergraduate students are not eligible for University benefits.
Equipment:
Year 1: To cover the cost of a data processing computer with additional memory
and disk space.
Year3. Upgrade to the data processing computer.
Travel
Year 1
Washington, DC or equivalent
Airfare
$350
2 days + travel days
$702*
*As per Department of State Domestic Travel Guidelines
$1,052
Washington DC or equivalent, three trips
$3,156
TBA symposium
Airfare
4 days +travel days
Registration
$430
$1,314*
$600
22
$2,344
Supplies/Other
$2,840 is requested each year computer network support as well as publication
charges.
Indirect Cost
The Indirect Cost rate used for this proposal is 52.0% MTDC, approved
06/07/2005 by the DHHS. The MTDC for this proposal is $849,381 and
corresponding indirect cost is $435,438.
23