Identifying and quantifying secondhand tobacco smoke in multiunit Homes
- Identifying and quantifying secondhand tobacco smoke (SHS) that transfers between multiunit homes (MUHs) is essential for accurately assessing resident exposure. Multiple measures of real-time particle size, real-time particle composition, and volatile organic compounds (VOCs) combined together in (a) logistic regression models, and (b) chemical mass balance (CMB) analysis can enable accurate identification and quantification of SHS with precise time resolution. This is possible even in receptor rooms adjacent to the source location. In a controlled study of 13 two-room experiments, logistic regression models correctly predicted the presence of cigarette smoke more than 80% of the time in both source and receptor rooms, with one model correct in 100% of applicable cases. CMB analysis provided PM2.5 concentration estimates of all true sources 9 of 13 times in both source and receptor rooms. Furthermore, in a field study of five MUHs with tobacco smoke odor complaints, logistic regression models identified SHS in eight periods when residents smelled smoke, and CMB provided estimates of SHS magnitude in six of those eight periods. In addition, both approaches enabled identification and/or apportionment of SHS in five additional periods when residents did not report smelling smoke, and properly identified and apportioned all six cooking events used as no-SHS controls. This methodology can augment sampling for single tracers, such as nicotine; while the tracer provides definitive proof of SHS entry at some point during a monitoring period, these approaches enable precise identification of the magnitude and duration of the SHS intrusion. Such precision is essential for an accurate assessment of resident exposure.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Dacunto, Philip J
|Stanford University, Department of Civil and Environmental Engineering.
|Taylor, Jonathan E
|Taylor, Jonathan E
|Statement of responsibility
|Philip J. Dacunto.
|Submitted to the Department of Civil and Environmental Engineering.
|Ph.D. Stanford University 2013
- © 2013 by Philip Joseph Dacunto
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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