Elsevier

Atmospheric Environment

Volume 71, June 2013, Pages 399-407
Atmospheric Environment

Identifying and quantifying secondhand smoke in multiunit homes with tobacco smoke odor complaints

https://doi.org/10.1016/j.atmosenv.2013.02.018Get rights and content

Abstract

Accurate identification and quantification of the secondhand tobacco smoke (SHS) that drifts between multiunit homes (MUHs) is essential for assessing resident exposure and health risk. We collected 24 gaseous and particle measurements over 6–9 day monitoring periods in five nonsmoking MUHs with reported SHS intrusion problems. Nicotine tracer sampling showed evidence of SHS intrusion in all five homes during the monitoring period; logistic regression and chemical mass balance (CMB) analysis enabled identification and quantification of some of the precise periods of SHS entry. Logistic regression models identified SHS in eight periods when residents complained of SHS odor, and CMB provided estimates of SHS magnitude in six of these eight periods. Both approaches properly identified or apportioned all six cooking periods used as no-SHS controls. Finally, both approaches enabled identification and/or apportionment of suspected SHS in five additional periods when residents did not report smelling smoke. The time resolution of this methodology goes beyond sampling methods involving single tracers (such as nicotine), enabling the precise identification of the magnitude and duration of SHS intrusion, which is essential for accurate assessment of human exposure.

Highlights

► We monitored 5 multiunit homes with secondhand smoke (SHS) odor complaints. ► We measured particle size, particle composition, and VOCs. ► Logistic regression models correctly identified SHS in 8 smoke odor periods. ► Chemical mass balance analysis produced estimates of SHS in 6 of the 8 periods. ► Identification and quantification of SHS at precise timescales is feasible.

Introduction

Secondhand tobacco smoke (SHS) contains >170 toxic substances that contribute to a wide range of both chronic and acute health problems (Repace, 2007; Flouris et al., 2010). In 2006, the U.S. Surgeon General concluded that (a) SHS exposure increases children's risk of sudden infant death syndrome, acute respiratory infections, ear problems, and severe asthma; (b) adults exposed to SHS experience immediate adverse cardiovascular effects, as well as coronary heart disease and lung cancer; and (c) there is no safe level of exposure to SHS.

The intrusion of SHS from one multiunit home (MUH) to another has recently garnered increased attention (Wickinoff et al., 2010; Wilson et al., 2011). A large number of people throughout the world live in MUHs, and they typically spend much of their time at home (69% for U.S. adults, according to Klepeis et al., 2001). Recent studies from the U.S. illustrate this point. Over one quarter of the U.S. population, or 79 million Americans live in MUHs (King et al., 2012), and 44% of MUH residents with smoke-free home rules experienced SHS intrusion (Licht et al., 2012).

Accurate quantification of SHS intrusion is critical for assessing resident exposure. This requires identifying emissions as SHS, and quantifying them via a measure such as PM2.5 (particles ≤2.5 μm in diameter). In MUHs, where SHS becomes diluted or mixed with emissions from other sources (e.g. cooking), this can be a considerable challenge.

Two prior studies used nicotine tracer measurements in public housing units around Boston (Kraev et al., 2009) or in Minneapolis apartments (Bohac et al., 2011) to positively identify SHS intrusion; however, since nicotine sorbs strongly and unpredictably to surfaces in spaces where smoking does not normally occur (Van Loy et al., 1998), its concentration may not correlate well with PM2.5 concentrations from SHS in receptor residences. In addition, nicotine typically requires long sampling periods if SHS concentrations are low.

A third study in Buffalo, NY used real-time PM2.5 measurements, coupled with resident logs of particle-generating activities, to quantify SHS intrusion (King et al., 2010). However, the study incorporated no other way of positively identifying the measured PM2.5 as SHS, aside from the resident activity reports. Thus, this quantification method requires participation of the smoker, and would not be accurate if SHS and other emissions (e.g., cooking) were generated simultaneously.

To overcome these challenges, Dacunto et al. (submitted for publication) used statistical models and chemical mass balance (CMB) model analysis based on multiple measurements to identify and quantify SHS in source and receptor rooms under controlled conditions. These measurements focused on three distinguishing features of SHS simultaneously – particle size, particle composition, and volatile organic compounds (VOCs). In contrast with other common indoor sources, SHS has few particles above 1 μm, a relatively high amount of UV-absorbing material (such as polycyclic aromatic hydrocarbons, or PAHs) compared with black carbon, and a distinct VOC source signature (Dacunto et al., submitted for publication). In both source and receptor rooms, the study successfully predicted the presence of SHS 80–100% of the time with logistic regression models, and quantified all true sources 69% of the time using CMB. However, the study did not apply the approach to real MUHs with SHS intrusion problems.

We hypothesize that identification and quantification of SHS intrusion in MUHs can be accomplished using logistic regression and CMB. This exploratory study applies these analysis techniques to data from five MUHs in the San Francisco Bay Area. We test the modeling approach by analyzing periods with reported smoke odor, cooking activity, or other sources, and evaluating model agreement regarding the presence of specific sources.

Section snippets

Selection of homes

The homes selected for the study were a convenience sample of nonsmoking units with SHS intrusion problems reported by residents (i.e., reported SHS odor). We obtained contact information for residents of these units through county public health departments (which the residents had contacted regarding their SHS problem), or from word-of-mouth. We prescreened using a questionnaire or interview to ensure that (a) the smoke was regular and likely strong enough to be measured, (b) the participants

Homes A & B

Homes A and B were adjacent to one another in San Jose, CA (Fig. 1a); for these homes we analyzed four reported and three suspected SHS intrusions (Table 2). Their common neighbor would smoke in a garage immediately below the master bathroom of each. The smoke apparently entered Home A through the master bathroom exhaust fan in the 6.3 m3 toilet and shower area, and Home B around pipe fittings in the small cabinet under the master bathroom sink. We placed a full suite of instruments under the

Conclusion

We have demonstrated that accurate identification and quantification of SHS intrusions at precise time resolution is feasible in real multiunit homes using measurements of particle size, particle composition, and VOCs. Logistic regression models correctly identified SHS in eight periods when residents smelled tobacco smoke, and CMB produced estimates of the quantity of SHS in six of the eight. In addition, both approaches properly identified or apportioned all six cooking periods used as

Acknowledgments

This research was supported by funds from the California Tobacco-Related Disease Research Program, Grant Number 19CA0123. We thank Francis Capili of the Santa Clara County of Public Health, and Derek Smith of the San Mateo County Health System, for their assistance in finding study participants. Finally, we thank ClearWay Minnesota for the loan of the aethalometers, and Martha Hewett, David Bohac, and Josh Novacheck of the Center for Energy and Environment for their extensive aethalometer

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