A Holistic Approach to Microenvironmental Exposure Assessment: Home, Work, and Commute Personal Exposures to Particulate Air Pollution

摘要

Background: Air pollutant concentrations vary temporally and spatially and, as such, regional outdoor air monitors do a poor job of representing personal exposures indoors and outdoors. Because particles from different sources may have different toxicity in terms of human health, the ability to apportion human exposure to different sources of air pollutants is desired. Aims: The goal of this study is to assess within- and between- person variations of microenvironmental exposures in Fort Collins, Colorado. We are examining home, work, and commute personal exposures for each participant for a total of 200 sampling days. Methods: Participants of the Fort Collins Commuter Study were outfitted with a backpack containing direct-reading monitors to estimate concentrations of fine particulate matter (PM2.5 mass), PM2.5 black carbon, and ultrafine particle number concentration for 24 hours. Participants filled out daily surveys about job classification, time spent cooking, passive smoke exposure, and whether windows/doors were open or air conditioning was operating in their workplace or home. By pairing personal monitors with a GPS receiver, we are able to link exposures with microenvironment, sources and activities. A Bayesian framework was used to describe the distributions of personal exposures and the factors that contribute to within- and between- person variability. Results: Commute and home exposures accounted for approximately 60% and 25% of total PM2.5 black carbon exposures and 40% and 50% of total PM2.5 mass exposures, respectively. We will present model results that describe variability within- and between- participants for all microenvironments, as well as the factors that influence variability. Conclusions: We developed a time-resolved personal exposure dataset characterizing the magnitude and variability of indoor and commute exposures to particulate air pollution. Using this dataset we developed an innovative modeling system for estimation of personal pollutant exposures, incorporating multiple types of data (monitoring and questionnaire).