Elsevier

Atmospheric Environment

Volume 40, Issue 23, July 2006, Pages 4393-4407
Atmospheric Environment

Modeling residential exposure to secondhand tobacco smoke

https://doi.org/10.1016/j.atmosenv.2006.03.018Get rights and content

Abstract

We apply a simulation model to explore the effect of a house's multicompartment character on a nonsmoker's inhalation exposure to secondhand tobacco smoke (SHS). The model tracks the minute-by-minute movement of people and pollutants among multiple zones of a residence and generates SHS pollutant profiles for each room in response to room-specific smoking patterns. In applying the model, we consider SHS emissions of airborne particles, nicotine, and carbon monoxide in two hypothetical houses, one with a typical four-room layout and one dominated by a single large space. We use scripted patterns of room-to-room occupant movement and a cohort of 5000 activity patterns sampled from a US nationwide survey. The results for scripted and cohort simulation trials indicate that the multicompartment nature of homes, manifested as inter-room differences in pollutant levels and the movement of people among zones, can cause substantial variation in nonsmoker SHS exposure.

Introduction

Secondhand tobacco smoke (SHS) consists of contaminants present in the air owing to the combustion of tobacco products, most commonly cigarettes. SHS is a mixture of exhaled mainstream smoke and sidestream smoke from smoldering tobacco that has been diluted with ambient air, consisting of thousands of organic and inorganic chemical species in both gaseous and particle phases (Jenkins et al., 2000). Nicotine is a major volatile organic constituent of SHS (Daisey et al., 1998, Singer et al., 2003), which has contributed to its extensive use as an SHS tracer. Inhalation exposure to SHS has been associated with many maladies, including sudden infant death syndrome (SIDS), lung cancer, and heart disease mortality (NCI, 1999, CARB, 2005).

Summed over populations, homes are recognized as the predominant locations where people are exposed to SHS (Klepeis et al., 2001). SHS contributes significantly to residential particulate air pollution, increasing concentrations by 10s of μg m-3 on average (Özkaynak et al., 1996, Neas et al., 1994, Spengler et al., 1985). It appears that 30–40% of children in the US are at risk of exposure to SHS in their home (McMillen et al., 2003, Schuster et al., 2002).

We hypothesize that the complex dynamics of SHS pollutants and the complex behavior of human beings in typical multiroom dwellings can result in a large range of nonsmoker exposures to SHS constituents. To the extent that this hypothesis is true, understanding the variation in exposure and the role of influencing factors could be important for epidemiological studies and risk assessments. Knowledge about this variability could also form a basis for developing interventions aimed at reducing SHS exposure.

Löfroth (1993) determined that moderate to substantial differences in concentrations of SHS pollutants can occur between rooms of smoking households when interior doors are left open. Field studies of combustion air pollutants in homes containing gas stoves or heaters have shown that air pollutant concentrations can vary significantly among rooms (Palmes et al., 1977).

Research on modeling human exposure to air pollution, including multizone exposure in residences, has been active for a few decades (Ott et al., 1988, Sparks et al., 1991, Koontz and Nagda, 1991, Wilkes et al., 1992, Burke et al., 2001). However, previous modeling efforts have not precisely characterized the influence of housing characteristics and human activity on SHS exposure. The current work builds on past proven multizone indoor air pollutant or exposure models and applications, particularly those by Nazaroff and Cass (1989), Sparks et al. (1991), Koontz and Nagda (1991), Wilkes et al. (1992), Miller and Nazaroff (2001), and Ott et al. (2003). Our broad goals are to identify and quantify important determinants of residential SHS exposure. Specifically, in this paper we seek to elucidate the influences of contaminant transport and occupant location in residential environments in which room-to-room concentrations can vary.

Section snippets

Methods

We have developed a computerized model that tracks the individual minute-by-minute location of a smoker and a nonsmoker as they move among rooms of a house during a single day. The model incorporates key aspects of the house configuration, including time-dependent door and window positions and the operation of a central air handling system. The model was developed using the R language (R Development Core Team, 2005).

Fig. 1 depicts the conceptual flow of the model. First, the house and occupants

Results and discussion

Using the results of our Phase I scripted and Phase II cohort simulation trials, we calculated 24-h average exposure concentrations for each nonsmoker (time-integrated exposure (μg m-3-min) divided by 1440 min). We also calculated the ratio of 24-h nonsmoker inhaled intake of particle mass to the total mass of particles emitted by cigarettes in the home over the same 24-h period, i.e., the intake fraction, in units of parts per million (mginhaled/mgemitted×106) (Bennett et al., 2002). For

Summary and conclusions

We developed a mechanistic simulation tool to study the complex interplay among spatially and temporally varying factors affecting secondhand tobacco smoke (SHS) exposure in residential environments. This approach enables exploration of how small changes in individual behavior on time scales of minutes or hours can have large impacts on time-averaged exposure concentrations, owing to changes in SHS concentrations in rooms and in the proximity of the nonsmoker to SHS emissions.

During Phase I of

Acknowledgments

This research was funded in part by a University Partnership Agreement (UPA) between the US Environmental Protection Agency (EPA) and Lawrence Berkeley National Laboratory (LBNL) via Interagency Agreement DW-988-38190-01-0 with the US Department of Energy (DOE) under Contract No. DE-AC03-76SF00098. The UPA was led by Halûk Özkaynak (EPA) and Thomas McKone (LBNL). Support for completing this manuscript was also supplied through a grant from the Flight Attendant Medical Research Institute (FAMRI)

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