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Model Report

Modeling Environment for Total Risk-1A

Last Revision Date: 08/31/2009 View as PDF
General Information Back to Top
Model Abbreviated Name:

MENTOR-1A
Model Extended Name:

Modeling Environment for Total Risk-1A
Model Overview/Abstract:
MENTOR-1A uses an integrated, mechanistically consistent source-to-dose modeling framework to quantify inhalation exposure and dose for individuals and/or populations due to co-occurring air pollutants. It uses the "One Atmosphere" concept to characterize simultaneous exposures to multiple atmospheric contaminants taking into account their physical and chemical interactions. MENTOR-1A uses, as one of the options, the USEPA’s Stochastic Human Exposure and Dose Simulation (SHEDS) approach.
MENTOR-1A characterizes cumulative exposures to co-occurring air pollutants, and calculates exposure and dose profiles, while providing the ability to focus on mechanism-relevant time scales and subpopulations of interest. This is achieved by combining information, as per the needs of a specific application, on: demographic characteristics of the population under study, outdoor concentration distributions, indoor/outdoor air exchange rates, indoor sources, time-activity diaries, and biologically based dosimetry. It uses the two dimensional Monte-Carlo methodology to quantify variability and uncertainty in model inputs and outputs.
MENTOR-1A can be used for both “Individual Based Exposure Modeling” (IBEM) and “Population Based Exposure Modeling” (PBEM) approaches. Both these approaches employ a “Person Oriented Modeling” (POM) formulation, i.e. they are driven by the attributes and activities of the exposed “real” and/or “virtual” individual(s). While IBEM implementations utilize the information relevant to “actual” individuals (and produce exposure and dose estimates specific to each one of them), the PBEM implementations focus on the statistical characterization of the exposures and doses of selected populations (at the census tract, county, or state etc. level). Thus, the questions posed by any particular environmental health problem can be tailored to small sets of individuals potentially at risk or to larger populations or subpopulations of interest.
MENTOR-1A has been applied to date to simulate (a) regional potential population exposures and (b) urban population exposures and doses to co-occurring gas phase pollutants and particulate matter. Specifically it has been applied to criteria pollutants such as ozone, and PM2.5, and to air toxics such as benzene, formaldehyde and xylene. Additionally, MENTOR-1A has been applied to simulate longer term (year long) exposures to reactive and relatively inert air pollutants, and has also been applied to estimate study-specific exposures and doses to selected air toxics in multiple cities.
Keywords: MENTOR, inhalation exposure, source-to-dose modeling, uncertainty, variability, air toxics, criteria pollutants, particulate matter, individual based exposure modeling, population based exposure modeling
Model Technical Contact Information:
Agency Contact
Dan Vallero
USEPA, NERL
Vallero.Daniel@epamail.epa.gov
919-541-3306

Developer Contact
Dr. Panos Georgopoulos
panosg@fidelio.rutgers.edu
732-445-0159

Dr. Shengwei Wang
shengwei@fidelio.rutgers.edu
732-445-0393

Dr. Sastry Isukapalli
sastry@fidelio.rutgers.edu
732-445-0171

Computational Chemodynamics Laboratory
www.ccl.rutgers.edu
EOHSI
A Joint Institute of UMDNJ - R.W. Johnson Medical School and Rutgers University
170 Frelinghuysen Rd.
Piscataway, NJ 08854

Model Homepage: http://www.ccl.rutgers.edu/mentor/ Exiting the EPA Site
Substantive Changes from Prior Version: Incorporation of modules for multiple air toxics, longer-term modeling, and individual-based exposure modeling.
Plans for further model development: Refinements in the characterization of indoor sources and their contributions for different chemicals, including volatile organics.

User Information Back to Top
Technical Requirements
Computer Hardware
Operational on multiple, alternative hardware platforms: Intel/AMD based PCs, Sun Workstations (e.g. Sparc), Linux servers or clusters, and Apple Macintosh computers. Minimum requirements: 10 GB free hard drive space, 512 MB RAM, and 500 MHz processor.
Compatible Operating Systems
Any of the following: Windows 2000/XP/Vista, Linux, Solaris, Mac OS X.
Other Software Required to Run the Model
Matlab version 6.5 or higher for Windows/Linux/Solaris/Mac OS X (Matlab statistical toolbox needed). Local or remote installation of Access or MySQL databases are needed. Depending on the application, SAS 8.0, C, or Fortran compilers may be needed. ArcGIS 8 is required for visualization.
Download Information
The model is available to be downloaded. exit EPA
Using the Model
Basic Model Inputs
  • Environmental concentrations of pollutants: these are typically obtained via spatio-temporal interpolation of either the outputs from models such as Models-3 CMAQ (Community Multiscale Air Quality), or measurements at monitoring sites.
  • Demographic characteristics of the population of interest.
  • Time-location-activity diaries, other activity information (e.g. METS values) from the US EPA's Consolidated Human Activity Database (CHAD),
  • Indoor/outdoor air exchange rates, uptake and deposition coefficients.
Basic Model Outputs
Output options include individual/subpopulation temporal exposure and dose profiles (for relevant time scales), summary statistics tables, cumulative internal doses of the contaminants, sensitivity and uncertainty analysis for calculated exposures and doses.
User Support
User's Guide Available?
Not currently available.

Technical documents on the formulation and applications of MENTOR/SHEDS-1A are available, click here. exit EPA

Other User Documents
Selected publications describing the system and applications:

Isukapalli, S.S. and Georgopoulos, P.G. (2001). Computational Methods for Sensitivity and Uncertainty Analysis for Environmental and Biological Models (EPA/600/R-01-068). Research Triangle Park, NC. US EPA, National Exposure Research Laboratory (145 pp.)

Foley, G., Georgopoulos, P.G. and Lioy, P.J. (2003). Examining Accountability for Changes in Population Exposures to 8-Hour Ozone Standard with Implementation of Different Control Strategies. Environmental Science and Technology 37(21): 302A-309A

Georgopoulos P.G., Wang S.W., Vyas V.M., Sun Q., Burke J., Vedantham R., McCurdy T. and Ozkaynak H. (2005). A source-to-dose assessment of population exposures to fine PM and ozone in Philadelphia, PA, during a summer 1999 episode. Journal of Exposure Analysis and Environmental Epidemiology 15: 439−457.

Georgopoulos P.G. and Lioy P.J. (2006). From theoretical aspects of human exposure and dose assessment to computational model implementation: The modeling environment for total risk studies (MENTOR). Journal of Toxicology and Environmental Health - Part B, Critical Reviews 9(6): 457-483.

Georgopoulos P. (2008). A multiscale approach for assessing the interactions of environmental and biological systems in a holistic health risk assessment framework. Water, Air, and Soil Pollution: Focus 8(1): 3-21.

Zhu X., Fan Z., Wu X., Meng Q., Wang S.-W., Tang X., Ohman-Strickland P., Georgopoulos P., Zhang J., Bonanno L., Held J. and Lioy P. (2008). Spatial variation of volatile organic compounds in a "Hot Spot" for air pollution. Atmospheric Environment 42 (32): 7329-7338.

Wang S.-W., Tang X., Fan Z., Lioy P.J. and Georgopoulos P.G. (2009). Modeling personal exposures from ambient air toxics in Camden, New Jersey: An evaluation study. Journal of the Air and Waste Management Association 59: 733-746.

User Qualifications
Bachelor’s degree in Environmental Science/Engineering, Chemical Engineering, Exposure Science, Meteorology, or related fields.

Model Science Back to Top
Problem Identification
There is a need for reducing uncertainty in Human Health Risk Assessment, and for interpreting exposure/dose information relevant to human health studies. This requires development and application of computational models that are capable of representing these relationships at spatial scales ranging from geographic regions to personal and residential microenvironments.
The MENTOR toolbox is an open and “flexible” system that provides components for performing either simple (screening) or detailed (comprehensive) simulations at various scales and levels of detail. MENTOR-1A provides modules for assessing individual and population exposures and doses to multiple, co-occurring chemicals.
The approach of MENTOR has been to develop, apply and evaluate state-of-the-art modeling methods for a wide range of environmental applications, that utilize existing models, when available, or provide new approaches to “fill gaps” in the source-to-dose sequence. MENTOR links state-of-the art predictive models of environmental fate/transport and of human exposure and dose; these models are coupled with up-to-date national, regional, and local databases of environmental, microenvironmental, biological, physiological, demographic, etc. parameters. Thus MENTOR is not a “new model”; it is an evolving open computational toolbox, containing both “pre-existing” and new tools, intended to facilitate consistent multiscale source-to-dose modeling of exposures to multiple contaminants, for individuals and populations.
Summary of Model Structure and Methods
MENTOR/SHEDS-1A consists of the following seven steps for calculating exposures and doses:

1) Estimation of background levels of air pollutants by using appropriate air quality monitoring databases or emissions-based regional air quality modeling; the "default" option consists of Models-3/CMAQ.

2) Estimation of local outdoor pollutant levels that characterize the ambient air for groups of individuals (populations) within appropriate demographic units (e.g., census tracts within the domain of the study).

3) Use of appropriate local scale databases and models, in parallel with steps 1 and 2, to estimate levels and temporal profiles of pollutants in various microenvironments (streets, residences, offices, restaurants, vehicles, etc.).

4) Identification of relevant attributes of the selected populations (age, gender, race, income, etc.) and development of a sample population that represents each administrative unit in the domain of interest.

5) Development of activity or exposure event sequences for each member of the sample populations.

6) Calculation of the appropriate inhalation rates for the members of the sample population by combining the physiological attributes of the simulated individuals and the activities pursued during the individual exposure events.

7) Combination of the intake rates and physiological attributes with microenvironmental or personal sample concentrations for each activity event to assess exposures and doses for the sample population and subsequent statistical extrapolation to the population of interest.

Furthermore, the diagnostic tools in the MENTOR system, such as the Stochastic Response Surface Method (SRSM) and the High Dimensional Model Representation (HDMR) methods, can be used to perform comprehensive sensitivity and uncertainty analyses.

Model Evaluation
MENTOR-1A has been applied to study regional scale “potential” population exposures to ozone in order to evaluate emissions control strategies. It has also been applied to assess “actual” population exposures and biologically effective doses to fine Particulate Matter (PM2.5), Ozone (O3), and air toxics (such as formaldehyde) for urban Philadelphia, PA, and Camden County, NJ. Both the Philadelphia and Camden studies focused on a two-week episode for July 11-24, 1999.

Peer reviewed journal publications and technical reports presenting the application of MENTOR-1A are available.

Key Limitations to Model Scope
The temporal extent of MENTOR-1A ranges from a few minutes to up to a year. Though it can be applied to longer-term simulations, the underlying data needs (e.g. emissions inventories, meteorology, etc.) can become prohibitive. The spatial resolution is currently up to a census block, and the extent can be up to the entire United States
Case Studies
Georgopoulos P.G. and Lioy P.J. (2006). From theoretical aspects of human exposure and dose assessment to computational model implementation: The modeling environment for total risk studies (MENTOR). Journal of Toxicology and Environmental Health - Part B, Critical Reviews 9(6): 457-483.

Georgopoulos P.G., Wang S.W., Vyas V.M., Sun Q., Burke J., Vedantham R., McCurdy T. and Ozkaynak H. (2005). A source-to-dose assessment of population exposures to fine PM and ozone in Philadelphia, PA, during a summer 1999 episode. Journal of Exposure Analysis and Environmental Epidemiology 15: 439−457.

Foley, G., Georgopoulos, P.G. and Lioy, P.J. (2003). Examining Accountability for Changes in Population Exposures to 8-Hour Ozone Standard with Implementation of Different Control Strategies. Environmental Science and Technology 37(21): 302A-309A

Georgopoulos, P.G., Wang, S.W., Vyas, V.M., Sun, Q., Chandrasekar, A., Shade, P., Vedantham, R., Burke, J., McCurdy, T. and Özkaynak, H., 2004. A Source-to-Dose Assessment of Population Exposures to Fine PM, Ozone and Air Toxics in Philadelphia, PA, During a 1999 Summer Episode. Technical Report CERM:2003-01. Prepared for USEPA (Revised draft submitted for agency review)

Zhu X., Fan Z., Wu X., Meng Q., Wang S.-W., Tang X., Ohman-Strickland P., Georgopoulos P., Zhang J., Bonanno L., Held J. and Lioy P. (2008). Spatial variation of volatile organic compounds in a "Hot Spot" for air pollution. Atmospheric Environment 42 (32): 7329-7338.

Wang S.-W., Tang X., Fan Z., Lioy P.J. and Georgopoulos P.G. (2009). Modeling personal exposures from ambient air toxics in Camden, New Jersey: An evaluation study. Journal of the Air and Waste Management Association 59: 733-746.


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