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Society For Risk Analysis Annual Meeting 2009

Risk Analysis: The Evolution of a Science

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

T3-D
Symposium: State of Science for Quantifying Human Exposure to Fire Particulate Matter

Room: Salon E   1:30-3:00 PM

Chair(s): Chris Frey



T3-D.1  13:30  Evaluation of Residential Indoor PM2.5 Concentrations Associated with Indoor Emissions and Penetration of Ambient Air . Cao Y, Deshpande B, Frey HC*; North Carolina State University   frey@ncsu.edu

Abstract: The indoor residential microenvironment is typically the location of the highest share of personal exposure to PM2.5 of both ambient and non-ambient origin, compared to other microenvironments. The Stochastic Human Exposure and Dose Simulation (SHEDS-PM) model developed by the U. S. Environmental Protection Agency is a probabilistic tool to estimate the population distribution of PM2.5 exposures. This study provides a critical assessment of the data and methodology used in SHEDS-PM to estimate indoor residential PM2.5 exposure. The SHEDS-PM algorithms are evaluated and compared to best practices. The default input data are reviewed and more recent data are identified in order to represent regional and seasonal variations. A sensitivity analysis of the SHEDS-PM residential indoor model indicates that air exchange rate, deposition rate and penetration factor affect indoor PM2.5 concentration strongly. The mass balance approach in SHEDS-PM for estimating indoor residential PM2.5 concentration is based on the assumption that an entire residence is a single, well-mixed zone. This assumption is evaluated by applying an indoor air quality model, RISK to compare ambient PM2.5 penetration for single-zone and multi-zone scenarios, and to assess differences comparing locations and timing of indoor emissions such as cooking and smoking. The results are similar for single and multi-zone cases for penetration of ambient PM2.5. Indoor concentrations in the zones where indoor emissions occurred were estimated to be significantly higher than predicted by the single-zone case when the emissions event occurred. However, depending on the inter-zonal air exchange rates, the concentration becomes uniform among all zones within a period of time after the indoor emissions event ends. Hence, correction factors are recommended to convert single-zone estimates to more accurate exposure estimates for indoor emission events.

T3-D.2  13:50  Modeling of In-vehicle PM2.5 Exposure Using the Stochastic Human Exposure and Dose Simulation Model. Liu X*, Frey H.C., Cao Y; North Carolina State University   xliu6@ncsu.edu

Abstract: Factors that influence in-vehicle PM2.5 exposure are indentified and assessed. The methodology used in the current version of Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM) for in-vehicle PM2.5 concentration is reviewed, and alternative modeling approaches are identified and evaluated. SHEDS-PM uses a linear regression model to estimate in-vehicle PM2.5 concentration based on ambient PM2.5 concentration, such as from a fixed site monitor (FSM) or a grid cell average concentration estimate from an air quality model. The ratio of in-vehicle to FSM concentration varies substantially with respect to vehicle types and other factors. SHEDS-PM was used to estimate PM2.5 exposure for 1% of people living in Wake County, NC in order to assess the importance of in-vehicle exposures. In-vehicle PM2.5 exposure can be as much as half of the total exposure for some individuals, depending on employment status and the time spent in-vehicle during commuting. An alternative modeling approach is explored based on the use of a dispersion model to estimate near-road PM2.5 concentration based on FSM data and a mass balance air quality model for estimating in-vehicle concentration. Various scenarios of in-vehicle microenvironments have been identified and modeled. Recommendations for updating the input data to the existing model, and implementation of the alternative modeling approach are made.

T3-D.3  14:10  Evaluation of the Modeling of Exposure to Environmental Tobacco Smoke (ETS) in the SHEDS-PM Model. Cao Y*, Frey HC; North Carolina State University   ycao4@ncsu.edu

Abstract: Environmental tobacco smoke (ETS) is estimated to be a major contributor to indoor PM concentration and human exposures to fine particulate matter of 2.5 microns or smaller (PM2.5). The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS-PM) model developed by the US Environmental Protection Agency estimates distributions of outdoor and indoor PM2.5 exposure for a specified population based on ambient concentrations and indoor emissions sources. Because indoor exposures to ETS can be high, a critical assessment was conducted of the methodology and data used in SHEDS-PM for estimation of indoor exposure to ETS. For the residential microenvironment, SHEDS uses a mass-balance approach, for the restaurant and bar microenvironments, SHEDS-PM uses a linear-regression equation, which are comparable to best practices. The default inputs in SHEDS-PM were reviewed and more recent and extensive data sources were identified. Sensitivity analysis was used to determine which inputs should be prioritized for updating. Data regarding the cigarette emission rate was found to be the most important. SHEDS-PM does not currently account for in-vehicle ETS exposure; however, in-vehicle ETS-related PM2.5 levels can exceed those in residential microenvironments by a factor of 10 or more. Therefore, a mass-balance based methodology for estimating in-vehicle ETS PM2.5 concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS-PM model. For the residential microenvironment, updated data resulted in 10 and 35 percent higher mean and standard deviation, respectively, for inter-individual variability in exposures than those based on defaults. Inter-individual and geographical variability of ETS exposure are also evaluated.

T3-D.4  14:30  Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis. Clougherty JE, Houseman EA, Levy JI*; Harvard School of Public Health; The Warren Alpert Medical School of Brown University   jilevy@hsph.harvard.edu

Abstract: Land use regression and factor analysis (FA) approaches have been used to examine source contributions to outdoor fine particulate matter (PM2.5), although few studies have combined these methods to construct and explain latent source effects. Moreover, people spend most of their time indoors, and these methods have not been commonly applied to examine indoor and outdoor source contributions to indoor residential concentrations. We collected 3-4 day samples of nitrogen dioxide and PM2.5 inside and outside of 43 homes in summer and winter, from 2003 to 2005, in and around Boston, Massachusetts. Particle filters were analyzed for elemental carbon, trace element, and water-soluble metal concentrations using reflectometry, X-ray fluorescence, and high-resolution inductively coupled mass spectrometry. We regressed indoor constituent concentrations against outdoor concentrations, modified by a ventilation proxy, to estimate the indoor-attributable fraction. We then applied confirmatory FA, constrained to non-negative loadings, to estimate latent source effects, separately, on total indoor concentrations and the indoor-attributable fractions. Finally, we developed predictive regression models using GIS-based outdoor source terms and questionnaire data for indoor source activities. FA on total constituent concentrations reasonably separated outdoor-dominated factors (long-range transport, fuel oil, road dust, diesel) from indoor-dominated factors (indoor combustion, cleaning, resuspension). These results were supported by factors identified in FA of indoor-outdoor regression residuals. Regression models predicting indoor-dominated factors had limited statistical power, but significant source terms corroborated some factor interpretations. Our approach provides direction for future studies attempting to characterize indoor and outdoor source contributions to indoor PM2.5 concentrations and personal exposures, ultimately informing studies of air pollution health effects.



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