Controlled experiments measuring personal exposure to PM2.5 in close proximity to cigarette smoking.

Few measurements of exposure to secondhand smoke (SHS) in close proximity to a smoker are available. Recent health studies have demonstrated an association between acute (<2 h) exposures to high concentrations of SHS and increased risk of cardiovascular and respiratory disease. We performed 15 experiments inside naturally ventilated homes and 16 in outdoor locations, each with 2-4 non-smokers sitting near a cigarette smoker. The smoker's and non-smokers' real-time exposures to PM2.5 from SHS were measured by using TSI SidePak monitors to sample their breathing zones. In 87% of the residential indoor experiments, the smoker received the highest average exposure to SHS, with PM2.5 concentrations ranging from 50-630 µg/m(3) . During the active smoking period, individual non-smokers sitting within approximately 1 m of a smoker had average SHS exposures ranging from negligible up to >160 µg/m(3) of PM2.5 . The average incremental exposure of the non-smokers was higher indoors (42 µg/m(3) , n = 35) than outdoors (29 µg/m(3) , n = 47), but the overall indoor and outdoor frequency distributions were similar. The 10-s PM2.5 averages during the smoking periods showed great variability, with multiple high concentrations of short duration (microplumes) both indoors and outdoors.

Identifying and quantifying secondhand smoke in source and receptor rooms: logistic regression and chemical mass balance approaches.

Identifying and quantifying secondhand tobacco smoke (SHS) that drifts between multiunit homes is critical to assessing exposure. Twenty-three different gaseous and particulate measurements were taken during controlled emissions from smoked cigarettes and six other common indoor source types in 60 single-room and 13 two-room experiments. We used measurements from the 60 single-room experiments for (i) the fitting of logistic regression models to predict the likelihood of SHS and (ii) the creation of source profiles for chemical mass balance (CMB) analysis to estimate source apportionment. We then applied these regression models and source profiles to the independent data set of 13 two-room experiments. Several 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 of the source room provided significant PM2.5 concentration estimates of all true sources in 9 of 13 experiments and was half-correct (i.e., included an erroneous source or missed a true source) in the remaining four. In the receptor room, CMB provided significant estimates of all true sources in 9 of 13 experiments and was half-correct in another two.