Society For Risk Analysis Annual Meeting 2012
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.
|Chair(s): Louis Cox|
Sponsored by EASG
W3-K.1 13:30 Overview and Evaluation of Alternative Air Quality Exposure Metrics used in Air Pollution Epidemiological Studies . Ozkaynak H*; USEPA firstname.lastname@example.org|
Abstract: Epidemiological studies of air pollution have traditionally relied upon surrogates of personal exposures, such as ambient concentration measurements from central-site monitoring stations. However, this approach may introduce exposure prediction errors and misclassification of exposures for certain spatially heterogeneous pollutants, such as those associated with traffic emissions. We review alternative air quality and human exposure metrics applied in recent air pollution health effects studies in the United States. In particular, exposure metrics for particulate and gaseous pollutants are evaluated in the context of different epidemiological studies recently conducted in the U.S by EPA, Rutgers/Rochester/LBNL, Emory/Georgia Tech and New York State Department of Health and North Carolina State University researchers. Alternative exposure metrics considered, include: central site or interpolated monitoring data, regional pollution levels based on measurements or models (CMAQ) and local scale (AERMOD) air quality models, hybrid models, statistically blended modeling and measurement data, concentrations adjusted by home infiltration rates, and the population human exposure (SHEDS and APEX) model predictions. Health data sets used in the epidemiological applications, include: daily mortality and respiratory hospital admissions in New York City, daily hospital emergency department visits in Atlanta, daily myocardial infarctions and adverse birth outcomes in New Jersey. Applications of different exposure metrics are compared in their ability to characterize the spatial and temporal variations of multiple ambient air pollutants across the study areas. These metrics were then used to estimate associations between ambient air pollution and acute morbidity and mortality. Results show both pollutant and metric specific differences in predicted relative risk estimates. Application-specific enhancements to air quality exposure indicators for future air pollution health studies are recommended
W3-K.2 13:50 Using dose assessment results to optimize environmental monitoring plans. Perona R*, Ryti RT, Tiller B; Neptune and Company, Inc.; Environmental Assessment Services, Inc. email@example.com|
Abstract: In the United States, large Federal facilities have responsibilities for conducting environmental monitoring of a variety of media. At Department of Energy (DOE) facilities there are requirements to assess radiological dose to offsite members of the public from DOE sources. Dose assessments must address all reasonably complete exposure pathways, including relevant agricultural pathways, and are typically conducted using a combination of measured and modeled environmental radionuclide concentrations. Using dose assessment results and environmental monitoring plans for the Hanford Site in eastern Washington state as a case study we show how one can use dose assessment results to optimize such plans for abiotic and biotic media. Conversely, we show how one may also compare radionuclide concentrations predicted by environmental modeling with those measured in the environment to refine transport models. Issues related to detection limits, background concentrations of radionuclides, and dose thresholds that constrain this type of analysis are evaluated.
W3-K.3 14:10 Evaluation of a wildfire smoke forecast system for public health risk assessment. Yao J*, Brauer M, Henderson SB; YJ: University of British Columbia, Vancouver, BC, Canada; BM: University of British Columbia, Vancouver, BC, Canada; HSB: British Columbia Centre for Disease Control, Vancouver, BC, Canada firstname.lastname@example.org|
Abstract: Wildfire smoke is a major contributor to extreme particulate matter air pollution events and has been associated with cardiopulmonary health effects. With climate change, more wildfires are predicted and their impact on public health will likely increase. Existing exposure assessment tools are limited for assessing human risk. Air quality monitoring networks have inadequate spatial coverage, and remote sensing platforms measure smoke in the total atmospheric column instead of on the ground. A system that can supplement these tools and predict smoke concentrations in advance will be a valuable tool to better manage this public health risk. We evaluated the performance of a smoke forecasting system (Western Canada BlueSky) that predicts PM2.5 from wildfires up to 60 hours in advance. Our analysis compared forecasting output with ground-level measurements and remote sensing images, using several model evaluation statistics to test temporal and spatial agreement. In addition, we used Poisson regression to assess associations between BlueSky predictions and respiratory health indicators, including counts of prescriptions dispensed to relieve symptoms of obstructive lung diseases and outpatient physician visits for respiratory diseases. Results suggest inconsistent agreement (correlations range from -0.37 to 0.93) between BlueSky predictions, monitoring measurements, and remote sensing images. The best agreement was observed in areas heavily impacted by wildfire smoke. A 30 ug/m3 increase in BlueSky prediction was associated with 7% increase in prescription dispensation (RR = 1.07; 95%CI = 1.06-1.08). This risk level is comparable to those estimated with monitoring measurements (RR = 1.17 [1.15-1.20]) and remote sensing data (RR =1.08[1.05-1.11]). The results of this study suggest that BlueSky predictions may be a good supplement to the traditional exposure assessment tools for more timely and comprehensive assessment of public health risk related to wildfire smoke.
W3-K.4 14:30 Warmer is healthier: Effects on mortality rates of changes in average fine particulate matter (PM2.5) concentrations and temperatures in 100 U.S. cities. Cox LA*; Cox Associates and University of Colorado email@example.com|
Abstract: Important recent studies have projected that reducing particulate pollution would substantially reduce average daily mortality rates and prolong lives, especially among the elderly (age greater than 75). These risk reduction benefits are projected by statistical models of significant positive associations between levels of fine particulate matter (PM2.5) levels and daily mortality rates. We examine the empirical correspondence between changes in average PM2.5 levels and temperatures from 1999 and 2000, and changes in average daily mortality rates, in each of 100 U.S. cities in the National Mortality and Morbidity Air Pollution Study (NMMAPS) data base, which has extensive PM2.5, temperature, and mortality data for those two years. Increases in average daily temperatures appear to significantly reduce average daily mortality rates, as expected from previous research. Unexpectedly, reductions in PM2.5 do not appear to cause any reductions in mortality rates. PM2.5 and mortality rates are both elevated on cold winter days, creating a significant positive statistical relation between their levels, but we find no evidence that reductions in PM2.5 concentrations cause reductions in mortality rates. For risk analysts and policy makers, it is crucial to use causal relations, rather than statistical associations, to project the changes in human health risks that are likely to be caused by interventions such as reductions in particulate air pollution or temperatures.
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