Introduction: Electronic cigarette (e-cigarette) use is increasing worldwide and is highest among both daily and nondaily smokers. E-cigarettes are perceived as a healthier alternative to combustible tobacco products, but their health risk factors have not yet been established, and one of them is lack of data on aerosol size generated by e-cigarettes. Methods: We applied a real-time, high-resolution aerosol differential mobility spectrometer to monitor the evolution of aerosol size and concentration during puff development. Particles generated by e-cigarettes were immediately delivered for analysis with minimal dilution and therefore with minimal sample distortion, which is critically important given the highly dynamic aerosol/ vapor mixture inherent to e-cigarette emissions.Results: E-cigarette aerosols normally exhibit a bimodal particle size distribution: nanoparticles (11-25 nm count median diameter) and submicron particles (96-175 nm count median diameter). Each mode has comparable number concentrations (10 7 -10 8 particles/cm 3 ). "Dry puff" tests conducted with no e-cigarette liquid (e-liquid) present in the e-cigarette tank demonstrated that under these conditions only nanoparticles were generated. Analysis of the bulk aerosol collected on the filter showed that e-cigarette emissions contained a variety of metals.Conclusions: E-cigarette aerosol size distribution is different from that of combustible tobacco smoke. E-cigarettes generate high concentrations of nanoparticles and their chemical content requires further investigation. Despite the small mass of nanoparticles, their toxicological impact could be significant. Toxic chemicals that are attached to the small nanoparticles may have greater adverse health effects than when attached to larger submicron particles. Implications: The e-cigarette aerosol size distribution is different from that of combustible tobacco smoke and typically exhibits a bimodal behavior with comparable number concentrations of nanoparticles and submicron particles. While vaping the e-cigarette, along with submicron particles the user is also inhaling nano-aerosol that consists of nanoparticles with attached chemicals that has not been fully investigated. The presence of high concentrations of nanoparticles requires nanotoxicological consideration in order to assess the potential health impact of e-cigarettes. The toxicological impact of inhaled nanoparticles could be significant, though not necessarily similar to the biomarkers typical of combustible tobacco smoke.

Using a specially developed breath collection technique and computer-assisted gas chromatography/mass spectrometry (GC/MS), we have identified in the exhaled air of lung cancer patients several volatile organic compounds that appear to be associated with the disease. The GC/MS profiles of 12 samples from lung cancer patients and 17 control samples were analyzed by using general computerized statistical procedures to distinguish lung cancer patients from controls. The selected volatile compounds had sufficient diagnostic power in the GC/MS profiles to allow almost complete differentiation between the two groups in a limited patient population.

We used real-time breath measurement technology to investigate the suitability of some volatile organic compounds (VOCs) as breath biomarkers for active and passive smoking and to measure actual exposures and resulting breath concentrations for persons exposed to tobacco smoke. Experiments were conducted with five smoker/nonsmoker pairs. The target VOCs included benzene, 1,3-butadiene, and the cigarette smoke biomarker 2,5-dimethylfuran. This study includes what we believe to be the first measurements of 1,3-butadiene in smokers' and nonsmokers' breath. The 1,3-butadiene and 2,5-dimethylfuran peak levels in the smokers' breath were similar (360 and 376 microg/m(3), respectively); the average benzene peak level was 522 microg/m(3). We found higher peak values of the target chemicals and shorter residence times in the body than previously reported, probably because of the improved time resolution made possible by the continuous breath measurement method. The real-time breath analyzer also showed the presence of the chemicals after exposure in the breath of the nonsmokers, but at greatly reduced levels. Single breath samples collected in evacuated canisters and analyzed independently with gas chromatography/mass spectrometry confirmed the presence of the target compounds in the postexposure breath of the nonsmokers but indicated that there was some contamination of the breath analyzer measurements. This was likely caused by desorption of organics from condensed tar in the analyzer tubing and on the quartz fiber filter used to remove particles. We used the decay data from the smokers to estimate residence times for the target chemicals. A two-compartment exponential model generally gave a better fit to the experimental decay data from the smokers than a single-compartment model. Residence times for benzene, 1,3-butadiene, and 2,5-dimethylfuran ranged from 0.5 (1,3-butadiene) to 0.9 min (benzene) for tau1 and were essentially constant (14 min) for tau2. These findings will be useful in models of environmental tobacco smoke exposure and risk.

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A simple computer-based screening technique has been developed for classifying human expired air components into 16 chemical classes, based on empirical formulas. The sort procedure was developed to simplify the screening of the composition of expired air samples by sorting all components into chemical classes and classifying components at the greater than 75% and greater than 90% occurrence levels. Both occurrence-rate components are then evaluated as diagnostic markers in a discriminant function model for their ability to detect lung cancer. Of the 386 components detected in the gas chromatography/mass spectrometry (GC/MS) data files, 45 components were present at the greater than 75% occurrence level and 28 components at the greater than 90% occurrence level. Thus, this preliminary sort routine, performed by using a simple macro program installed into a standard personal-computer spread-sheet, greatly reduces the amount of data required for statistical treatment. Such a sort routine can also be applied as easily to other complex GC/MS data files for the purpose of data reduction.