You are here:
TIME SERIES MODEL FOR CIGARETTE SMOKING ACTIVITY PATTERNS: MODEL VALIDATION FOR CARBON MONOXIDE AND RESPIRABLE PARTICLES IN A CHAMBER AND AN AUTOMOBILE
Ott, W., L. Langan, AND P. Switzer. TIME SERIES MODEL FOR CIGARETTE SMOKING ACTIVITY PATTERNS: MODEL VALIDATION FOR CARBON MONOXIDE AND RESPIRABLE PARTICLES IN A CHAMBER AND AN AUTOMOBILE. U.S. Environmental Protection Agency, Washington, D.C., EPA/600/J-93/426 (NTIS PB94101771), 1992.
Human activity pattern-exposure models require accurate submodels for the exposures in microenvironments that people occupy, including those containing environmental tobacco smoke (ETS). his paper describes the Sequential Cigarette Exposure Model (SCEM), a general-purpose mathematical model developed for calculating the pollutant concentration time series in a well-mixed microenvironment of known volume when any cigarette smoking activity pattern occurs. he SCEM is-based on solutions to the mass balance equation that represent smoking emissions for each cigarette as a rectangular input function over time, and this paper evaluates the performance of the SCEM in several experiments. his paper presents theoretical equations for the minimum, maximum, and mean of the pollutant concentration time series for any sequential smoking activity pattern, including a uniform smoking activity pattern input time series (that is, the "habitual smoker"), and these equations agree well with those reported elsewhere in the literature. he model is evaluated for carbon monoxide (CO) and respirable particles in a controlled experiment in a well-mixed chamber with a cigarette smoking machine. n the chamber, values for source strengths and air exchange rates were determined experimentally from the time series response of CO and respirable particles to continuous, uninterrupted smoking, which can be viewed as a "step input" to the model. sing the difference between the exponential decay rates for particles and CO after smoking ceases, the value of the sink term was determined. he time series of concentrations predicted by the model compared well with the observed concentration time series of concentrations. o apply the model to a real microenvironment, an investigator drove an automobile at 20 mph on streets free of traffic while another investigator sat in the passenger seat and smoked a cigarette. he observed time series of CO and particles agreed well with the time series predicted by the model, both for the front and back seats. he equations presented in this paper can be applied to other motor vehicles if the volumes, air exchange rates, and cigarette smoking activity patterns are known, or to other similar microenvironments and pollutants if the source strengths and sink terms are known.