Table of Contents 1 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




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Integrated Observations and Models for
Air Quality Management and Science
Dr. Rudolf B. Husar, Washington University, PI

Table of Contents


Table of Contents 1

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


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 source-receptor 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 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 downward-looking 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. 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.

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 co-retrieval. 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

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 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 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 non-measured, 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 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 long-term, persistent activity. The product of such an approach would be a contiguous six- dimensional 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 emission-observation-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

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 AQ-analysts-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 applications-oriented 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, satellite-derived, 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 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 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 urban-industrial 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).


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.



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

Proposal Focus Area

Raymond Hoff

Aerosol Characterization

Richard Poirot

End-user-driven application of satellite observations

Virendra Sethi

Aerosol and Land Surface Color Retrieval

Warren White

End-user-driven application of satellite observations

References and Citations


Federal Register. 2008. Rules: Revised Exceptional Event Data Flagging Submittal and Documentation Schedule to Federal Register [FR Doc E8-29747] [40 CFR Part 50]. http://www.thefederalregister.com/d.p/2008-12-16-E8-29747.

Friedlander, S.K. 1971. The characterization of aerosols distributed with respect to size and chemical composition—II. Classification and design of aerosol measuring devices. Aerosol Science 2: 331–340.

GEO 2005. Group on Earth Observations | Home. http://www.earthobservations.org/., 2005

Hidy, G. M., J.R. Brook, J.C. Chow, M. Green, R.B. Husar, C. Lee, R.D. Scheffe, A.Swanson, J.G. Watson. 2009. Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land, J. Air Waste Manage. Assoc., 59: 1130-1139.

Hoff R.M. and S.A. Christopher. 2009. Remote Sensing of Particulate Pollution from Space: Have we Reached the Promised Land? J. Air & Waste Manage. Assoc. 59: 645–675.

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.

Husar, R.B., B.A. Schichtel, S.R. Falke, F. Li, W.E. Wilson, J. Pinto, W.C. Malm, D.G. Fox, G. C. Feldman, C. McClain, N. Kuring, B.N. Holben, E.F. Vermote, J.R. Herman, C.D. Elvidge, 2000. The Impact of the 1998 Central American Smoke on the Atmospheric Environment of Eastern North America, AWMA Specialty Conference, on PM: Particulate Matter and Health, Charleston, SC, January 24–28, Paper 10AS-P5; http://capita.wustl.edu/Central-America/reports/JGR/SmokeImpactJGRDoc.doc.

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 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. 2001. Asian dust events of April 1998. Journal of Geophysical Research 106, D16: 18317-18330.  

Husar, R. B., and R. L. Poirot. 2005. DataFed and FASTNET: Tools for Agile Air Quality Analysis. Environmental Manager (September): 39-41.

Husar R.B and W.H White. 1976. On the Color of the Los Angeles Smog. Atmospheric Environment, 10: 199-204.

HTAP, Hemispheric Transport of Air Pollution, 2007. United Nations Economic Commission for Europe, Air Pollution Studies No.16. United Nations, New York and Geneva.

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.
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.


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 Dipl. Ing. Mechanical Engineering, Technical University, Berlin, FRG.

1966-71 Ph.D. Mechanical Engineering, U. of Minnesota, Minneapolis, MN, US.

1971-73 Post-Doctoral Fellow, California Institute of Technology, Pasadena, CA, US.



Appointments

2007-Present Professor, Energy, Environmental and Chemical Eng., Washington U. St. Louis,

1979-Present Director, Center CAPITA, Washington University St. Louis, MO

1976-2007 Professor, Mechanical Engineering, Washington U. St. Louis, MO

1976-77 Visiting Professor, Meteorological Institute, Stockholm U., Sweden

1973-76 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 sub-group. 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
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.


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 D.Sc. in Environmental Engineering, Washington University

1993 M.S. in Engineering and Policy, Washington University

1992 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

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.

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






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 $430

4 days +travel days $1,314*

Registration $600

$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.








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