title: Averaging time modeling of exposure simulation with application to the El Camino Real vehicle data.
abstract: When profiles of activity patterns are used to generate time series of simulated exposure, one typically samples from exposure distributions which are microenvironment-specific to each activity. If the simulation time step is short, then independent sampling at each time step, ignoring autocorrelation, will result in aggregates with too little variability from one simulation to another. Autocorrelation can often be modeled with one or two extra parameters and then used in the simulation. Furthermore, one may substantially reduce computation by generating a single averaged exposure for each activity segment whose distribution depends in a simple way on the activity duration and the modeled autocorrelation. The process is illustrated using the El Camino Real commuting exposure study data of Ott, Switzer, and Willits.
authors.affiliation: Department of Statistics, Stanford University, CA 94305.
authors.forename: P
authors.initials: P
authors.lastname: Switzer
authors.forename: W R
authors.initials: WR
authors.lastname: Ott
authors.forename: N H
authors.initials: NH
authors.lastname: Willits
mesh_terms: D002248:Carbon Monoxide / Q000032:analysis; D003198:Computer Simulation; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D004784:Environmental Monitoring; D004785:Environmental Pollutants / Q000032:analysis; D006801:Humans; D013997:Time Factors; D001335:Vehicle Emissions / Q000009:adverse effects*
chemical_list: D004785:Environmental Pollutants; D001335:Vehicle Emissions; D002248:Carbon Monoxide