title: Mathematical models for predicting indoor air quality from smoking activity.
abstract: Much progress has been made over four decades in developing, testing, and evaluating the performance of mathematical models for predicting pollutant concentrations from smoking in indoor settings. Although largely overlooked by the regulatory community, these models provide regulators and risk assessors with practical tools for quantitatively estimating the exposure level that people receive indoors for a given level of smoking activity. This article reviews the development of the mass balance model and its application to predicting indoor pollutant concentrations from cigarette smoke and derives the time-averaged version of the model from the basic laws of conservation of mass. A simple table is provided of computed respirable particulate concentrations for any indoor location for which the active smoking count, volume, and concentration decay rate (deposition rate combined with air exchange rate) are known. Using the indoor ventilatory air exchange rate causes slightly higher indoor concentrations and therefore errs on the side of protecting health, since it excludes particle deposition effects, whereas using the observed particle decay rate gives a more accurate prediction of indoor concentrations. This table permits easy comparisons of indoor concentrations with air quality guidelines and indoor standards for different combinations of active smoking counts and air exchange rates. The published literature on mathematical models of environmental tobacco smoke also is reviewed and indicates that these models generally give good agreement between predicted concentrations and actual indoor measurements.
authors.affiliation: Stanford University, Stanford, California 94305-4065, USA. wayne@stat.stanford.edu
authors.forename: W R
authors.initials: WR
authors.lastname: Ott
mesh_terms: D016902:Air Pollution, Indoor / Q000032:analysis*; D004058:Diffusion; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D010316:Particle Size; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018570:Risk Assessment; D014028:Tobacco Smoke Pollution / Q000032:analysis*; D014691:Ventilation
chemical_list: D014028:Tobacco Smoke Pollution