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Journal of Aerosol Science
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Steady-state measurement of the effective particle density of cigarette smoke
Tyler J. Johnson a, Jason S. Olferta'*, Ross Cabotb, Conor Treacyb,
Caner U. Yurterib, Colin Dickens b, John McAugheyb, Jonathan P.R. Symonds'
a Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta T6G 2G8, Canada b British American Tobacco, Group Research & Development, Southampton SO15 8TL, UK c Cambustion Ltd., J6 The Paddocks, 347 Cherry Hinton Road, Cambridge CB1 8DH, UK
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ARTICLE INFO
Article history:
Received 28 November 2013 Received in revised form 13 April 2014 Accepted 18 April 2014 Available online 24 April 2014
Keywords: Cigarette smoke Tobacco
Effective density CPMA
ABSTRACT
The steady-state effective particle density of mainstream smoke from a University of Kentucky 3R4F reference cigarette was determined using a Differential Mobility Analyser (DMA) and Centrifugal Particle Mass Analyser (CPMA). The cigarette smoke was generated using a smoking machine under ISO puffing parameters (35 ml puff of 2 s duration, every 60 s) and collected in a Tedlar® bag. This smoke generation process resulted in the first puff of the smoking cycle aging approximately 7 min longer than the last puff. The effective particle density (measured immediately upon completion of the last puff) was found to be independent of the particle mobility size, indicating the particles have a spherical morphology, with an average density of 1180 + 113 kg/m3. Particle coagulation was also found to occur within the Tedlar® bag by comparing particle mobility size distributions, measured with a Scanning Mobility Particle Sizer (SMPS), against an analytical model. This model showed that particle coagulation dominated the particle number concentration decay within the Tedlar® bag compared to particle diffusion or settling losses. Therefore cigarette smoke particles must have a liquid component to maintain a constant effective particle density function in the presence of coagulation. After the 7 min filling process, the effects of particle aging time and initial particle number concentration in the Tedlar® bag on the effective particle density were found to be small and indistinguishable within the bias uncertainty of the measurement system.
© 2014 Published by Elsevier Ltd.
1. Introduction
Tobacco smoking is a recognised risk to health; such risk being dose-related (Doll et al., 2004; International Agency for Research on Cancer, 2004; U.S. Department of Health and Human Services, 2010). These risks can be assumed to result from repeated and prolonged exposure to a range of tobacco smoke toxicants, which leads to a chronic insult of the respiratory and cardiovascular systems (e.g. through mechanisms of inflammation or oxidative stress), or to exposure to individual or classes of toxicants that exert toxic effects through more specific mechanisms (Stratton et al., 2001).
* Corresponding author. Tel.: +1 780 492 2341; fax: +1 780 492 2200. E-mail address: jolfert@ualberta.ca (J.S. Olfert).
http://dx.doi.org/10.1016/joaerosci.2014.04.006 0021-8502/© 2014 Published by Elsevier Ltd.
Tobacco smoke is a complex mixture of many thousands of individual substances (Rodgman & Perfetti, 2013), up to approximately 150 of which have been identified as toxicants or otherwise named as Harmful and Potentially Harmful Constituents (Fowles & Dybing, 2003; U.S. Department of Health and Human Services, Food and Drug Administration, 2012). Various toxicological approaches have been applied to try to identify which toxicants are the most important to the various diseases caused by smoking (Fowles & Dybing, 2003; Rodgman & Green, 2003; Cunningham et al., 2011).
Local and systemic doses of smoke and its individual constituents are an important part of the toxicological assessment, and deposition and retention mechanisms will vary with the physical and chemical form of each compound (Baker & Dixon, 2006; St. Charles et al., 2013). The deposition of soluble, vapour phase constituents such as carbonyl compounds will be driven mainly by diffusion and both measured and modelled deposition efficiency will be near 100% (Moldoveanu et al., 2007; Corley et al., 2012). Semi-volatile constituents, for example nicotine, will be associated with the particle phase in fresh smoke, but will evaporate and deposit as vapour phase on inhalation; again with near 100% efficiency as measured (Baker & Dixon, 2006) or modelled (Ingebrethsen, 2006). In contrast, the deposition of particle (or droplet) bound species will be driven by inhalation behaviour and properties such as particle size and particle density (International Commission for Radiological Protection, 1994; Baker & Dixon, 2006; St. Charles et al., 2013).
Numerous cigarette smoke studies have been completed collating dynamic smoke measurements, such as smoke chemical composition (Adam et al., 2007; Bodnar et al., 2012), particle mobility size distributions (Adam et al., 2009), ventilation/filter effects (Shin et al., 2009; Adam et al., 2010) and lung deposition (Dickens et al., 2009). However, few studies have measured the effective density of cigarette smoke. Furthermore, to the knowledge of the authors, no studies have determined the size-resolved or time-resolved effective particle density of cigarette smoke.
Effective particle density (pef ), as described by McMurry et al. (2002), is determined by dividing the particle mass (mp) by its mobility equivalent volume:
Peff = —f 0)
where dm is the particle mobility diameter. This aerosol property affects a particle's aerodynamic behaviour, thus influencing particle impaction and settling losses. Therefore effective particle density must be known to accurately model human lung deposition (Zhang et al., 2012). Previous deposition models of cigarette smoke (Saber & Bazargan, 2011; Pichelstorfer et al. 2013) have used the effective density determined by Lipowicz (1988).
Lipowicz (1988) measured an average effective density of 1120 + 40 kg/m3 (95% confidence interval) for particles from a University of Kentucky (2013) 1R3F reference cigarette (15.0 mg ISO tar as Nicotine Free Dry Particulate Matter (NFDPM)), using a Millikan Cell with a holding time of 1 h and a 1000 times dilution. However this study only measured particles larger than 1 mm and therefore did not investigate the effective particle density of cigarette smoke at its mobility size distribution peak of approximately 180-280 nm (Adam et al., 2009). Chen et al. (1990), using a Walton smoking machine and University of Kentucky 2R1F reference cigarettes (23.4 mg ISO tar as NFDPM), also calculated an average effective particle density of 1120 kg/m3 through the relationship of a particle's mobility diameter to its aerodynamic diameter as outlined by DeCarlo et al. (2004). However the value determined by Chen et al. (1990) is the mass-weighted average effective particle density. Therefore this value is dominated by the properties of the larger particles within the distribution.
If the effective particle density is known as a function of the particle mobility size it is possible to convert the particle mobility size distribution to a mass distribution. Previous cigarette smoke studies have assumed unit density (Adam et al., 2009; Alderman & Ingebrethsen, 2011) to calculate the mass delivery of different cigarettes from their mobility size distributions.
Therefore this paper determines the size-resolved steady-state effective particle density of smoke from a University of Kentucky 3R4F reference cigarette (9.4 mg ISO tar as NFDPM) using a Differential Mobility Analyser-Centrifugal Particle Mass Analyser (DMA-CPMA) system. These effective particle density measurements will allow future studies to more accurately convert particle mobility size distributions to mass distributions and model the deposition of cigarette smoke particles in the human airway. Combined with Scanning Mobility Particle Sizer (SMPS) measurements and simple aerosol modelling, these effective density measurements also allow conclusions to be drawn regarding particle morphology and composition.
2. Experimental set-up
The experimental set-up used to measure the steady-state effective particle density of cigarette smoke is shown in Fig. 1. Smoking is inherently a transient process due to the changes in puffing flow rates within a puff and changes in transit time of the smoke through the rod during the consumption of one cigarette. University of Kentucky (2013) 3R4F reference cigarettes were smoked under ISO puffing parameters (35 ml sinusoidal puff of 2 s duration, every 60 s: (ISO3308:2012, 2012)) into a Tedlar® bag using a RM20D smoking machine (Borgwaldt KC, Hamburg, Germany). The smoking machine is capable of smoking 20 cigarettes in a single sequence under ISO parameters. The particle number concentration in the Tedlar® bag was controlled by changing the initial volume of HEPA filtered, dilution air placed in the Tedlar® bag and the number of cigarettes in the smoking machine.
Once the smoking machine completed the smoking sequence, the Tedlar® bag was detached from the smoking machine and attached to the inlet of a TSI (Minnesota, United States) DMA: Model 3080. A DMA selects particles based on their electrical mobility equivalent diameter by classifying particles by their drag to electrostatic force ratio (Knutson & Whitby, 1975).
Cigarette: 3R4F Smoking Routine: ISO3308:2012 Puff Inhalation Volume: 35 ml Puff Duration: 2 s Ventilation: Unblocked Number of Puffs: 8 Interval Between Puffs: 60 s
('10 LA 0.6 LPM Tedlar®->
\Bag y
Fig. 1. Experimental set-up of the DMA-CPMA system used to measure the steady-state effective particle density of smoke from a University of Kentucky 3R4F reference cigarette, where Qsh is the sheath flow rate used in the DMA classifier.
The mobility-selected particles were then further classified using a Cambustion (Cambridge, United Kingdom) CPMA. A CPMA classifies particles by their mass-to-charge ratio by balancing opposing centrifugal and electrostatic forces (Olfert & Collings, 2005). The particle number concentration of the mobility-selected and mass-to-charge classified particles was then measured using a TSI Condensation Particle Counter (CPC: Model 3022A) placed downstream of the CPMA. A CPC condenses a liquid such as butanol or water on the particles until they are large enough to be detected and counted by an optical sensor (Agarwal & Sem, 1980). The CPC upstream of the CPMA was used to normalise the measured number concentration of the classified particles, thus accounting for variations in the unclassified particle number concentration over time.
The CPMA mass-to-charge setpoint is controlled by its cylinders' rotational speeds and the voltage potential placed between them (Olfert et al., 2006). The mass-space of the mobility-selected particles is scanned by stepping through CPMA mass-to-charge setpoints and recording the classified particle number concentration as a function of these setpoints. This scanning aspect limits the DMA-CPMA system to steady state measurements.
If the effective particle density is constant across the particle mobility size distribution and multiply-charged particles are not present, the peak of the classified particle number concentration identifies the average mass of the mobility size-selected particles. If multiply-charged particles are present, the average mass of each charge state can still be distinguished as separate maxima if the mass-to-charge ratio of each charge state at a common electrical mobility are sufficiently different and the DMA and CPMA are operated at sufficient resolutions or the fraction of multiply-charged particles is small. Uncharged particles are removed by the DMA and therefore do not affect the results.
In past studies, the mass distribution of particles classified by a mass analyser1 has been fitted using many different functions to identify the peak, such as an asymmetric normal distribution or lognormal distribution (Tajima et al., 2011), or a convolution of the DMA and mass analyser transfer functions (Barone et al., 2011). The CPMA data, generated during this research, were fitted using a lognormal function through chi-squared minimisation as described by Tajima et al. (2011) and Johnson et al. (2013).
A TSI SMPS was also used to measure the particle mobility size distribution of the cigarette smoke in the Tedlar® bag. A SMPS is essentially a DMA and CPC in series, where the DMA scans through selected particle mobility sizes and the CPC measures the particle number concentration of each particle mobility size bin (Wang & Flagan, 1990).
3. Results and discussion
The bias uncertainty of the effective particle density measured by the DMA-CPMA system with a 95% confidence interval was determined to be 9.4% by propagating2 the 3% DMA uncertainty in particle mobility size (Kinney et al., 1991) with the 2.8% CPMA uncertainty in mass-to-charge setpoint (Symonds et al., 2013). This uncertainty in particle mobility size and effective density applies to all of the results shown herein.
The effective particle density function of the "un-aged" cigarette smoke was determined from the initial scan of the DMA-CPMA system of each Tedlar® bag as shown in Fig. 2. The smoke was produced using one University of Kentucky 3R4F reference cigarette in the smoking machine and 9 L of initial dilution air in the Tedlar® bag. This work refers to "un-aged" smoke as smoke that was measured immediately after the completion of the puff cycle on the smoking machine. Therefore
1 Mass analyser is a generic term referring to either a CPMA or an Aerosol Particle Mass Analyser (APM, Ehara et al., 1996). Both instruments use the same classifying principles, but the CPMA's inner cylinder rotates slightly faster than the outer cylinder, generating a stable system of forces and improving the transfer efficiency of the CPMA compared to an APM (Olfert & Collings, 2005).
2 Derived from Eq. (1) the uncertainty (u) is determined by uPeff /peff — ^(um/m)2 + 9(udm /dm)2
there is a difference of approximately 7 min aging time in the Tedlar® bag between the first and last puff of the smoking cycle. This difference in aging times was unavoidable due to the scanning nature of the DMA-CPMA system.
The effective particle density was found to be independent of particle mobility size indicating the particles have a spherical morphology (DeCarlo et al., 2004). The average effective particle density was 1180 + 113 kg/m3, considering both the bias (111 kg/m3) and precision (24 kg/m3) uncertainties, and agrees within error with the value determined by Lipowicz (1988) of 1120 + 40 kg/m3. Lipowicz measured particles greater than 1 mm, produced from a University of Kentucky 1R3F reference cigarette, using a Millikan Cell with a holding time of 1 h and a 1000 times dilution.
Consecutive particle mobility size distributions from the same Tedlar® bag were also measured using a SMPS as shown in Fig. 3. The SMPS measured particles with mobility diameters from 14.6 nm to 685.4 nm. The SMPS uncertainty was estimated to be 3% in terms of mobility particle diameter (as determined by Kinney et al. (1991) for a DMA) and 10% in terms of particle number concentration due to the uncertainty of the CPC (TSI, 1999). These values represent the minimum uncertainty of the SMPS system and do not account for further uncertainty introduced by the data inversion. To account for particles larger than this range the SMPS data was fitted with a lognormal curve using chi-squared minimisation. As expected the particle number concentration (N) decreased with time. This decrease was thought to be due to diffusion and settling losses within the Tedlar® bag, as well as coagulation effects. The count median diameter (CMD) increased with time, likely indicating the present of both diffusion losses and coagulation effects. Finally the geometric standard deviation (GSD) increased with time, a possible indication of coagulation. If diffusion and settling losses occurred, but coagulation was not present, the GSD would decrease or the particle size distribution would become narrower due to the preferential losses of smaller particles to diffusion and larger particles to settling. These hypotheses were validated by comparing the SMPS
1500 1400 1300 1200 1100 1000 900 800 700 600 500 400 300 200 100 0
.........
Average Effective Particle Density ' (Ave= 1180 ± 113 kg/m3)
Particle Mobility Diameter,d (nm)
Fig. 2. Effective particle density function of un-aged cigarette smoke produced by smoking one University of Kentucky 3R4F reference cigarette into 9 L of dilution air and using the initial DMA-CPMA system scan from multiple Tedlar® bags, where the error bars represent the bias uncertainty.
5.5 5 4.5 4
— 3.5
1 3 ■o
1 2 "O
- 0 s (CMD=228 nm, GSD=1.51, N=1.57e+06 cm-3) 263 s (CMD=233 nm, GSD=1.53, N=1.24e+06 cm-3)
- 474 s (CMD=236 nm, GSD=1.56, N=1.11e+06 cm-3)
- 722 s (CMD=240 nm, GSD=1.57, N=9.84e+05 cm-3)
101 102 Particle Mobility Diameter,dm (nm)
Fig. 3. Effects of particle aging time on the cigarette smoke particle mobility size distribution, produced from smoking one University of Kentucky 3R4F reference cigarette into 9 L of dilution air and taking consecutive SMPS scans from the same Tedlar® bag.
results against the modelled effects of particle diffusion, settling and coagulation as shown in Fig. 4.
The diffusion and settling losses were modelled as outlined by Hinds (1999) and were found to be negligible in terms of particle number concentration over the time period considered. This method also predicted the upper limit of diffusion and settling losses as it assumed a constant aerosol concentration was maintained outside of the losses gradient (Hinds, 1999). The effects of coagulation were modelled using the lognormal distributed aerosol coagulation rate in the gas-slip regime determined by Lee & Chen (1984) with the equation for the particle number concentration outlined by Lee (1983). This simple analytic solution shows coagulation dominated the particle number concentration decay within the Tedlar® bag. Coagulation has also been found to occur in other cigarette smoke studies, such as Robinson & Yu (1999) and Ingebrethsen et al. (2011). Therefore cigarette smoke particles must have no internal voids and a liquid component to fill the interstitial spacing formed between the particles during coagulation and maintain an effective density independent of the particle mobility size. This liquid component has been observed in other cigarette smoke studies as well (Carter & Hasegawa, 1975; Borgerding & Klus, 2005; Rodgman & Perfetti, 2013). It has also been determined that approximately 0.9 mg of water is produced from each University of Kentucky 3R4F reference cigarette smoked following the ISO puffing parameters (University of Kentucky, 2013).
The effects of initial particle number concentration in the Tedlar® bag on effective particle density was also determined, as shown in Fig. 5, by varying the number of cigarettes used with the smoking machine at once. One to four 3R4F cigarettes were smoked into a Tedlar® bag with 9 L of initial dilution air. It was determined that effective particle density of the "un-aged"
t ra Q_
> SMPS Data
----------Coagulation
---Diffusion
-----Settling
100 200 300 400 500 600 700 Time from first SMPS measurement, t (s)
Fig. 4. Comparison of the particle number concentration in the Tedlar® bag measured using a SMPS against a model accounting for coagulation effects, diffusion losses or setting losses, where the error bars represent the minimum bias uncertainty.
1500 ^ 1400 3 1300
ro 1200 1100 1000 900 800 700 600 500 400 300 200 100 0
to c œ Q
1.40e+06
3 £ 3 £ 3 £ 3 £
ro ro ro ro
II i: g « CN II §7
+l C +1 C +1 C +l C
00 CO o CO o to CO o
5.42e+06
1.18e+07
2.05e+07 -3\
Initial Particle Concentration, N (cm 3)
Fig. 5. Effects of initial Tedlar® bag particle number concentration on the effective particle density of 250 nm cigarette smoke particles produced by varying the number of University of Kentucky 3R4F reference cigarettes (1 to 4 cigarettes) smoked into 9 L of dilution air, where the values shown are the average effective density plus/minus the total uncertainty of each case and n is the number of samples measured for each case.
250 nm particles decreased slightly as initial particle number concentration in the Tedlar® bag increased. However this trend is small and within the total uncertainty of the measurement system. The precision uncertainty (95% confidence interval) of the tests were determined to be 92 kg/m3, 45 kg/m3, 175 kg/m3 and 49 kg/m3 for increasing initial particle concentration; respectively.
The effects of particle aging time on effective density was also determined, as shown in Fig. 6, by taking consecutive scans with the DMA-CPMA system from the same Tedlar® bag. The smoke was produced using one 3R4F cigarette in the smoking machine and 9 L of initial dilution air in the Tedlar® bag. It was determined that as the 250 nm particles aged, the effective particle density increased slightly. This small increase could be due to the lighter components of the particles evaporating over time. However this trend is once again small and within the bias uncertainty of the measurement system.
To investigate the possibility of evaporation the suspended particle mass concentration was calculated from the SMPS data and compared against the conservation of mass within the system as shown in Fig. 7. As expected the suspended particle mass concentration decreased over time in both the experimental data and model due to particle settling and diffusion losses. While the SMPS data shows more mass is lost than predicted by the model (assuming conservation of mass), an indication of evaporation, the values still agree within the bias uncertainty. The uncertainty shown was determined to be 16.4% by propagating the 9.4% effective density uncertainty, 3% mobility size uncertainty and 10% particle number concentration uncertainty (TSI, 1999). This value actually underestimates the overall mass uncertainty, as it does not account for the uncertainty introduced by the SMPS inversion.
Previous studies have observed losses from the evaporation of cigarette smoke particles, specifically fresh smoke (Chen et al., 1990; Kane et al., 2010) or when sufficiently diluted, such as side stream smoke (Ingebrethsen & Sears, 1989; Baker & Proctor, 1990).
it= HI
1500 1400 1300 1200 1100 1000 900 800 700 600 500 400 300 200 100 0
0 200 400 600 800 1000 1200 1400 1600 1800 Time from first DMA-CPMA measurement, t (s)
Fig. 6. Effects of particle aging time on the effective particle density of 250 nm cigarette smoke particles produced by smoking one University of Kentucky 3R4F reference cigarette into 9 L of dilution air and taking consecutive CPMA scans from the same Tedlar® bag, where the error bars represent the bias uncertainty.
p ff = 4.26e-02 t + 1219
ro □ SMPS Data ^ .......Total Particle Mass Conserved
0 100 200 300 400 500 600 700 Time from first SMPS measurement, t (s)
Fig. 7. Comparison of the suspended particle mass concentration in the Tedlar® bag measured using a SMPS against conservation of mass within the system, accounting for diffusion and setting losses, where the error bars represent the minimum bias uncertainty.
Lipowicz (1988) and references therein noted that the evaporation of cigarette smoke particles in air largely occurs within the first minute. Therefore the absence of significant evaporation in this study was expected, as the smoke was aged at least 7 min in the Tedlar® bag and diluted by 9 L of HEPA filtered ambient air.
4. Conclusions and summary
The steady-state effective particle density of smoke from a University of Kentucky 3R4F reference cigarette was determined to be independent of the particle mobility size with an average of 1180 + 113 kg/m3. This constant effective density function indicates the particles have a spherical morphology. The influence of particle coagulation within the Tedlar® bag was also verified by comparing consecutive particle mobility size distributions, measured using an SMPS, to a model accounting for coagulation effects, diffusional losses or settling losses over time. Therefore cigarette smoke particles must have a liquid component to maintain an effective density independent of particle mobility size with coagulation occurring. The effective particle density increased slightly with aging time and decreased slightly as the initial particle number concentration in the Tedlar® bag increased. These effects were small and within the bias uncertainty of the measurement system.
Acknowledgements
This research was funded by British American Tobacco (Investments) Ltd. References
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