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University Partnership Agreements
Dr. Daniel A. Vallero,
EPA Principal Investigator (919) 541-3306
Email: Vallero.Daniel@epa.gov
Interagency Agreement DW 8993 0582
Lawrence Berkeley National Laboratory, Environmental Energy
Technologies Division
Indoor Environment Department, 
Exposure and Risk Assessment Group
Thomas E. McKone, Principal Investigator
Cooperative Agreement CCR 831 625
Environmental and Occupational Health Sciences Institute
(EOHSI)
Paul J. Lioy and Panos G. Georgopoulos, Co-Principal Investigators
Exposure Measurement and Assessment
Division and Computational Chemodynamics
Laboratory
Both Agreements teams are associated with universities (LBNL with University of California, Berkeley and Stanford University in Stanford, California and EOHSI with Robert B. Wood Johnson Medical School and Rutgers University) resulting in two consortia which collectively possesses world-class experience in human exposure modeling and risk assessment. Collaboration of several EPA scientists with the University Partners contributes to the Research Program at EPA aimed at:
Reducing Uncertainty in Human Health Risk Assessment by Characterizing Multipathway Human Exposure and Source-to-Dose Relationships Using State-of-the-Art Modeling Methods
Objective: The primary goal of this comprehensive research program is to develop a scientifically-robust, complete multimedia, multipathway Human Exposure Source-to-Dose Modeling Framework software program that can:
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Estimate exposures and doses to both the general population and to identifiable susceptible subpopulations, and
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Predict and diagnose the complex relationships between source and exposure and dose.
Rationale: The overall EPA Exposure and Dose Research
Branch needs to have a sound, scientifically-based research approach for
understanding how people are actually exposed to pollutants and the factors
and pathways influencing exposure and dose. This research program, centered
around these two University Partnerships, addresses the need to integrate
and incorporate human exposure measurements, models, and methods from
all aspects into a comprehensive, scientifically-sound framework for evaluating
human exposures.
Benefits: The modeling framework software and its various components
and modules and other analytical tools developed through these UPAs will
be of great value to researchers engaged in the development, refinement,
and interpretation of human exposure modeling techniques.
In addition, the risk assessment community will have a set of useful modeling
software packages to enable multimedia, multipathway exposures to be estimated
- with explicitly quantified variability and uncertainty - for a wide
variety of subpopulations and environmental contaminants. This capability
will also provide EPA Program Offices with the analytical software tools
and algorithms needed to support the prioritization of pollutants and
pollutant source categories in support of future mitigation strategies
to evaluate human exposure and risk implications of pollutants in air,
soil, food and water.
In designing future measurement studies, this capability will aid in determining
which areas in the source-to-dose human exposure process are associated
with the greatest uncertainties.
Progress to Date: Research has focused on the development of the
conceptual components for a source-to-dose human exposure modeling framework
and software system. Initial testing has been done on the framework for
exposures and dosimetry using the Stochastic Human Exposure and Dose System
(SHEDS) models. Significant progress has been made in the development
and refinement of important framework components such as an improved understanding
of phase, spatial, and temporal distributional characteristics for various
pollutants of interest and the prediction of variability and uncertainty
in modeling outputs.
Selected Publications:
Burke, J. M., Zufall, M. J. and Ozkaynak, H. "A Population Exposure Model for Particulate Matter: Case Study Results for PM2.5 in Philadelphia, PA," Journal of Exposure Analysis & Environmental Epidemiology (2001) 11(6), 470-489.
Zartarian, V.G., H. Ozkaynak, J.M. Burke, M.J. Zufall, M.L.
Rigas, and E.J. Furtaw, Jr. "A modeling framework for estimating
children's residential exposure and dose to chlorpyrifos via dermal residue
contact and nondietary ingestion." Environ. Health Perspect. 108,
6: 505-514 (2000).
Klepeis, N. E., W. C. Nelson, W. R. Ott, J. P. Robinson, A. M. Tsang,
P. Switzer, J. V. Behar, S. C. Hern and W. H. Engelmann, "The National
Human Activity Pattern Survey (NHAPS): A Resource for Assessing Exposure
to Environmental Pollutants," J. Exposure Analysis & Environmental
Epidemiology (2001), 11(3), 231-252.
Ozkaynak, H., Evans, G. F., Pahl, D. A., and Graham, J. A. Overview of
EPA's human exposure and source-to-dose modeling program: HEADSUP. ISEA
2000 Exposure Analysis in the 21st Century: Integrating Science, Policy,
and Quality of Life, Monterey Peninsula, CA, October 24-27, 2000.
Bennett, D. H., T. E. McKone, and W. E. Kastenberg, "Evaluating Chemical
Persistence in a Multimedia Environment: A CART Analysis," J. Environmental
Toxicology and Chemistry (2000), 19, 32-46.
Bennett, D. H., T. E. McKone, and W. E. Kastenberg, "CART Screening
Level Analysis of Characteristic Time - A Case Study," Presented
at Special Session on Persistent Organic Pollutants, 217th ACS National
Meeting and Exposition, March 21-25, 1999, Anaheim, CA. Published in Persistent,
Bioaccumulative and Toxic Chemicals, ACS Symposium Series, 2001, No. 773,
R. L. Lipnick (ed.), 29-41.
Maddalena, R. L., T. E. McKone, D. P. H. Hsieh, and S. Geng, "Influential
Input Classification in Probabilistic Multimedia Models," Stochastic
Environmental Research and Risk Assessment (2001), 15, 1-17.
Bennett, D. H., M. Scheringer, T. E. McKone, and K. Hungerbuhler, "Predicting
Long-Range Transport: A Systematic Evaluation of Two Multimedia Transport
Models," Environmental Science & Technology (2001), 35, 1181-1189.
Switzer, P. and W. R. Ott, "Theory of Exposure Models: Derivation
of an Indoor-Outdoor Averaging Time Model from the Mass Balance Equation,"
Department of Statistics, Stanford University, Stanford, CA, Technical
Report No. 2001-22, September 2001, 67 pp.