Society For Risk Analysis Annual Meeting 2013

Session Schedule & Abstracts


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Common abbreviations

M3-E
Fine Particulates: New Measurements and Questions Answered

Room: Key Ballroom 5   1:30 PM - 3:00 PM

Chair(s): Chris Frey

Sponsored by EASG



M3-E.1  13:30  Comparison of predicted exposures versus ambient fine particulate matter concentrations. Jiao W*, Frey HC; North Carolina State University   wjiao@ncsu.edu

Abstract: Persons 65 and older are particularly susceptible to adverse effects from PM2.5 exposure. Using the Stochastic Human Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM), distributions of inter-individual variability in daily PM2.5 exposures are estimated for Bronx, Queens and New York Counties in the New York City area for the years 2002 to 2006 based on ambient concentration, air exchange rate, penetration factor, deposition rate, indoor emission sources, census data, and activity diary data. Three research questions are addressed: (1) how much is variability in estimated daily average exposure to ambient air pollution influenced by variability in ambient concentration compared to other exposure factors?; (2) what is the inter-annual variation in daily average exposure?; and (3) what key factors and values of these key factors lead to high exposure? In comparison with CMAQ input ambient concentrations, daily average exposure estimates have more variability. Variation in estimated exposure to pollutants ambient origin is mostly affected by variation in ambient concentration, air exchange rate, and human activity patterns. Estimated daily average exposure to ambient PM2.5 is about 30 to 40 percent less than the ambient concentration. Seasonal differences in estimated exposure are mainly caused by seasonal variation in ACH. There was relatively little estimated inter-annual variation in the daily average exposure to concentration ratio (Ea/C), since factors affecting exposure such as ACH, housing type and activity patterns were assumed to be relatively stable across years. The distribution of inter-individual variability in the Ea/C ratio can be used to identify highly exposed subpopulations to help inform risk management strategies and to provide advisory information to the public.

M3-E.2  13:50  Measurement and Comparison of PM2.5 AND CO Microenvironmental Exposure Concentrations for Selected Transportation Modes. Jiao W, Frey HC*; North Carolina State University   frey@ncsu.edu

Abstract: Daily commutes may contribute disproportionately to overall daily exposure to urban air pollutants such as fine particulate matter (PM2.5) and carbon monoxide (CO). The on-road and near-road microenvironments are of concern because of proximity to on-road traffic emissions. A field data collection study design was developed based on factors that may affect variability in in-transit concentration, including transportation mode, time of day, traffic volume, weather, vehicle ventilation conditions, road geometry and traffic control, traffic vehicle mix, and proximity to intersections. PM2.5 and CO concentrations were measured and compared across pedestrian, bus, and car modes during lunchtime and afternoon rush hour within a three-week time period on pre-selected round trip routes in Raleigh, NC. Variability in the transportation mode concentration ratios of PM2.5 and CO is quantified. Factors affecting variability in PM2.5 and CO concentrations are identified. The average pedestrian concentration is compared with fixed site monitor (FSM) data to determine if FSM is an appropriate surrogate for near-road concentration. Preliminary results indicate that on-road or near-road microenvironmental concentrations are sensitive to transportation mode, traffic volume, and proximity to onroad emission sources. In general, pedestrians experienced the highest PM2.5 concentrations among all measured transportation modes. Peaks in pedestrian PM2.5 concentration are typically associated with a passing truck. In comparison, the average PM2.5 concentration in-car is the lowest because the selected ventilation conditions helped to prevent ingress of particles. A positive association was found between traffic counts and average CO concentrations. Field studies such as this are needed to develop data for input to population-based stochastic exposure simulation models to more accurately predict transportation mode exposure concentrations.

M3-E.3  14:10  Sensitivity of estimated children PM2.5 exposure to activity patterns, and geographic and seasonal variations . Che WW*, Frey HC, Lau AKH; The Hong Kong University of Science & Technology North Carolina State University   wenweicherren@gmail.com

Abstract: Children’s exposure to ambient Particulate Matter (PM) depends on activity patterns and ventilation in micro-environments, which in turn can depend on climate zone and season. Distribution of inter-individual variability in daily PM2.5 exposure for school age children (5-18) were estimated using the U.S. Environmental Protection Agency’s Air Pollutants Exposure model (APEX). The key inputs to APEX include ambient concentration, air exchange rate, penetration factor, deposition rate, census data and activity diary data. Inter-individual variability in exposure of elementary school (6-11), middle school (12-14) and high school (15-18) children were estimated and compared for selected regions in North Carolina (NC), New York City (NYC) and Texas (TX) for four seasons (spring, summer, fall and winter). Home and school are the most important microenvironments for children’s exposure on school days; while home and outdoors are predominant on non-school days. The ratio of ambient exposure to ambient PM2.5 concentration (E/C) is significantly different (P<0.05) on school days (mean=0.51; 90th percentile = 0.57) and on non-school days (mean=0.64; 90th percentile = 0.74) for all simulated children. Daily maximum temperature influences the distribution of high school children exposure. Inter-individual variability in estimated daily average E/C varies by a factor of 2 to 3 over a 95% frequency range. The difference in average daily E/C between the selected regions is 3% to 6% in spring and 22% to 29% in fall. The difference in average daily E/C among seasons ranges from 0%-12% in TX and 15% to 33% in NYC. Thus, population average E/C for school children is sensitive to day type, climate zone and season. The latter two affect residential air exchange rate. These factors lead to inter-city differences in exposure and can introduce error in epidemiological studies if ambient concentration is used as surrogate for exposure.

M3-E.4  14:30  Mortality risk from personal exposure to PM2,5 and UFP in different transportation modes: travel by bus, drive a car, take the metro or ride a bicycle? . Aguila I.E., Jimenez R.B.*, Ruiz P.; Universidad Andres Bello   ignacio.aguila@gmail.com

Abstract: Strong evidence from epidemiological studies suggest that exposure to transport-related pollution increases the risk of premature death. Recent studies have found significant differences between air pollution concentrations in different transportation modes. So far no study has addressed the increase in personal health risks attributable to air pollution exposure in transport environments in Chile. To fill this gap, the main goal of this study was to assess changes in personal health risks from exposure to traffic-related pollutants for commuters in different transportation modes in Santiago, Chile. We estimated premature mortality impacts for the attributable proportion of pollutant exposure to PM2,5 and ultrafine particles while commuting in four transportation modes: car, bicycle, bus and walking. Estimations of increased risk attributable to pollutant exposure were calculated based on previous studies (Jimenez and Bronfman, 2012; Dhondt, et al., 2012). We assessed personal and social risk of overall exposure and transport mode specific exposure to UFP and PM2,5. Changes in exposure concentrations associated with each transport mode were obtained from measurements of direct contributions from traffic emissions and background concentrations to personal exposures in previous studies (Ruiz et al., 2012). Scenario analyses and Montecarlo simulations were preformed in order to assess major sources of uncertainty in health impact estimations, mainly related to C-R functions for UFP, time spent in traffic and mobility patterns. Our preliminary results reveal high levels of risk from exposure to traffic related-pollution for all commuters and especially for cyclists. This information must be taken into account in the development of transportations policies, especially those aiming at stimulating cycling as a viable alternative to car driving in urban areas. Limitations of the study and further implications for transport policies and regulators will be discussed.



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