The air quality in an airliner cabin is related to several factors including pollutant sources inside the aircraft, outdoor pollutants, the volume of the airliner cabin, ventilation rates, and air mixing within the cabin. To assess the air quality in the airliner cabin environment in this study, pollutants were selected for monitoring that (1) had known or suspected sources in the aircraft and (2) could be monitored or sampled in occupied airliner cabins with small, unobtrusive instrumentation that would not concern passengers or alert the flight crew to the sampling activity which could cause them to take steps to alter ventilation rates.

The parameters selected for measurement in this study are listed in Table 2-1. The pollutants measured included components of ETS (nicotine, respirable particles, and carbon monoxide), carbon dioxide, ozone, and microbial aerosols. The rationale for selection of these parameters is given below.

Environmental tobacco smoke consists of a complex mixture of air contaminants in both the gaseous and particulate phases — more than 3,800 compounds have been identified in cigarette smoke. To assess the health risks due to exposure to ETS, it is necessary to accurately quantify ETS. Because it is not possible to measure all ETS contaminants, marker or tracer contaminants must be used as indicators of exposure to ETS. The tracers to be measured should have the following characteristics:

  • Be unique to tobacco smoke
  • Occur in sufficient quantities in ETS to facilitate accurate detection and quantification
  • Have similar emission rates across a variety of tobacco products
  • Occur in a consistent ratio to other contaminants in ETS.

Of the 3,800 compounds identified, and the 300 to 400 compounds that have been measured in ETS, there are numerous vapor-phase organic compounds, particles, particulate phase organics, nitrogen oxides, and some tobacco-specific nitrosamines. Most of these compounds, however, have not been adequately studied to permit their use as ETS tracers. Some, such as N-nitrosonornicotine, meet some of the criteria as a tracer, but the current measurement technologies are inadequate for accurate quantification at the low levels present in indoor environments, even with heavy smoking.

Nicotine meets most of the criteria as an ETS tracer. It is unique to ETS; in most environments, tobacco smoke is the only source of nicotine. Nicotine is the major constituent in ETS, after water, and sensitive analytical methods are available to quantify it, even in environments with low levels. Nicotine exists primarily in the vapor phase. Data from Hamnond et al. (1987) and Murnmatsu et al. (1984) suggest that nicotine/particulate matter ratios are more constant than those previously measured in studies that used smoking machines to generate ETS. Nicotine also serves as a good tracer because nicotine in sidestream smoke does not vary substantially for different brands of cigarettes (Rickert et al. I984).

Carbon monoxide has been measured in numerous studies to represent ETS. In areas with heavy smoking or where other sources of CO do not exist, CO provides a measure of ETS exposure.

Respirable particles (RSP) are a major component of ETS. In numerous studies summarized by Repace (1987), tobacco smoke has been shown to play a predominant role in the concentration of RSP indoors. As a result of these studies, RSP is currently the most extensive database for modeling ETS in indoor environments and is considered to be among the best tracers for ETS and associated human exposure (NRC 1986).

Ozone was selected for measurement in this study because it has been demonstrated to be a pollutant of concern in aircraft cabins. Data collected in the GASP program (Nastrom and Holdeman 1980) have shown that ozone concentrations increase with increasing latitude, are maximal during spring, and vary with weather systems. The importance of ozone is obvious from the fact that standards of 0.25 ppm of peak concentrations and 0.1 ppm for 3-hour intervals have been established by the FAA. The data on ozone concentrations in occupied airliner cabins are, however, limited and not current. Therefore, collection of ozone data in this study was warranted.

Pollutant/Measurement Parameters
ETS Contaminants
Respirable Particles
Carbon Monoxide
Other Pollutants
Carbon Dioxide
Microbial Aerosols
Other Parameters
Relative Humidity
Cabin Pressure
Air Exchange Rate

The sources of carbon dioxide (C02) in the airliner cabin are the passengers. Because of the high density of passengers on some flights, it is important to measure C02. Current guidelines for exposure to carbon dioxide (C02) include the ACGIH time-weighted average (TWA) limit of 5,000 ppm, and ASHRAE’s guideline of 1,000 ppm (ASHRAE 1989). The ASHRAE guideline of 1,000 ppm, recommended to satisfy comfort (odor) criteria, is widely used as an indicator of the adequacy of ventilation in indoor environments. Carbon dioxide measurements were performed on each flight for comparison to the relevant standards and guidelines and as an indicator of air quality and ventilation.

Airborne microbial aerosols have been quantified in a variety of indoor environments. Concentrations of biological aerosols in aircraft cabins, however, have not been measured. The aircraft cabin represents a unique environment with its high density of occupants and specialized ventilation system. Although ventilation air during flight may contain very few biological particles, these particles may infiltrate the cabin during ground activities, be carried on by passengers, and most importantly, may be generated from passengers by skin shedding or coughing, sneezing, and talking.

In this study, fungi and bacteria were sampled on each aircraft. The sampled organisms were cultured and quantified to determine the three to five most prevalent genera of bacteria and fungi on each flight. Additionally, Staphylococcus aureus and Streptococcus pyogenes, two organisms that can be directly related to dispersion from passengers, were quantified in bacterial samples.

In addition to pollutant measurements. temperature relative humidity, and cabin air pressure were measured at each sampling location. Temperature and pressure are required parameters for calculating volumetric sampling rates, and relative humidity is recognized as an important parameter in airliner cabins.

Air exchange rates were also measured on each flight. Data on air exchange rates are important for use in interpretation of pollutant measurements, modeling, and development of mitigation strategies.

Cosmic radiation was also included as a parameter for which a risk assessment would be performed in this study. However, measurements of cosmic radiation were not made on the flights during the monitoring program. A decision was made not to perform measurements after a review of currently available data in draft and final reports (FAA 1989) and the UNSCEAR reports (1982, 1986, and 1988). The evaluation of this information indicated that the available data was adequate to perform the risk assessment.


Determination of an appropriate sample size (i.e., number of flights to be monitored) was based primarily on study needs relating to risk assessment for exposure to ETS contaminants. In the context of risk assessment, two types of potential health effects of ETS exposure are of primary concern:

  • Chronic health effects related to average ETS concentrations encountered by airline passengers or flight attendants
  • Acute health effects related to occasions on which the peak concentrations encountered are sufficiently high to trigger human health responses.

Thus, the sample size required for the study was one that would enable estimation of both average ETS concentrations and the proportion of flights where certain concentration levels were exceeded with a reasonable degree of precision. Each of these perspectives for estimation of sample size is discussed in greater detail below.

2.2.1 Estimating an Average Concentration

To properly support a risk assessment for chronic effects of ETS exposure, the average concentration of ETS contaminants on both smoking and nonsmoking flights needs to be estimated as precisely as possible. A common estimation goal is to have a 95 percent confidence that the average measured concentration differs from its true, but unknown, value for specific sampling conditions by a relatively small margin of error. The formula for the sample size (n) necessary to meet this objective is as follows (Cochran 1963)

t2 * s2
n=————- (1)


– represents the number of standard deviations (approximately two) that account for the central 95 percent of the area under a normal curve
– is the estimated standard deviation for the ETS contaminant
d – is the margin of error (expressed as a fraction of the average) that can be tolerated in estimating the average concentration of the ETS contaminant.

In practice, it is difficult to obtain estimates for the value of s that can be expected, as this quantity depends both on the mean concentration and the extent of variation about the mean. A more stable quantity is the coefficient of variation (CV), or ratio of the standard deviation to the mean, which often lies in the range from 0.5 to 2.0 for environmental measurements. It the margin of error in equation (1) is expressed as a fraction of the mean, (i.e., d = f ╖ x) and the standard deviation is also expressed relative to the mean (i.e., s = CV ╖ x), then the above equation can be restated as:

t2 * CV2
n=————- (2)

or, solving for f,

f= t * CV /√n (3)

Equation (3) expresses the precision with which the mean concentration can be estimated as a function of the CV and sample size. For example, assuming a CV of 1.0 and a sample size of 100 flights, the associated value of f is 0.2, meaning that there is a 95 percent confidence of estimating the average concentration within +_20 percent.

Some estimated values of f for different values of the CV and sample size are given in Table 2-2. When the sample size initially is fairly small, relatively rapid improvement in precision can be achieved with modest increases in sample size (e.g., from 20 to 40 or 40 to 60). The marginal gain in precision drops off rapidly as the sample size exceeds 100. For example, for a CV of 1.0, the precision improves by 6 percentage points (from +_32 percent to +_26 percent) when the sample size is increased from 40 to 60 but improves by only one percentage point (from +_15.5 percent to +_14.6 percent) when the sample size is increased from 160 to 180 flights.

Coefficient of Variation**
Sample Size* 0.6 0.8 1.0 1.2 1.4 1.6
20 28.0% 37.3% 46.7% 56.0% 65.4% 74.7%
40 19.1 25.5 31.9 38.3 44.7 51.1
60 15.4 20.6 25.8 30.9 36.1 41.3
80 13.4 17.8 22.3 26.8 31.3 35.7
100 11.9 15.9 19.9 23.8 27.8 31.8
120 10.8 14.4 18.0 21.6 25.3 28.9
140 10.0 13.3 16.7 20.0 23.4 26.7
160 9.3 12.4 15.5 18.6 21.8 24.9
180 8.7 11.6 14.6 17.5 20.4 23.3
* Number of flights to be monitored.
** Ratio of standard deviation to mean concentration for contaminant to be monitored.

Thus, as indicated above and illustrated in Figure 2-1, the optimal sample size appears to lie in the range between 60 and 120 flights. A further consideration is the magnitude of the CV that can be expected. If RSP measurements in residential environments (e.g., Spengler et al. 1985) are indicative, then a CV on the order of 0.8 could be expected for smoking flights, meaning that the average concentration could be estimated within f25 percent through measurements on 40 to 50 flights. However, recent data collected by Oldaker and Conrad (1987) in the airline cabin environment indicate that a CV on the order of 1.3 can be expected for nicotine measurements in smoking sections of aircraft. In this case, approximately 80 flights would be required to estimate the nicotine average with a reasonable degree of precision (e.g., +_30 percent); if the CV turned out to be as high as 1.5, then 100 flights would be needed to achieve this degree of precision.


2.2.2 Estimating the Proportion of Flights for Which a Concentration is Exceeded

To properly support a risk assessment for acute effects of ETS exposure, the proportion of flights for which the peak concentration exceeds some level of concern (e.g., the concentration at which sensitive individuals may have reactions such as respiratory or eye irritation) needs to be estimated as precisely as possible. The formula for the sample size necessary to estimate a proportion (p) within a certain margin of error (d) is similar to equation (1); substituting the variance (p x q, where q = 1 – p) about an estimated proportion for s2 in that equation, the following relationship is obtained:

t2 * p *q
n =————-(4)

or, solving for d,

d = t * √pq/n

(5) Some estimated values of d for different values of p and n are given in Table 2-3. As in the case of estimating the mean concentration, the greatest marginal gain in precision is made at relatively low sample sizes. For example, for an estimated average proportion of 0.5, the margin of error would be reduced by 0.03 (from 0.16 to 0.13) if the sample size were to be increased from 40 to 60 flights, whereas the error would be reduced by only 0.005 (from 0.078 to 0.073) if the sample size were to be increased from 160 to 180 flights.


Estimated Proportion of Flights
Sample Size* 0.50 0.25 0.10 0.05
20 0.234 0.202 0.140 0.102
40 0.160 0.138 0.096 0.070
60 0.129 0.112 0.077 0.056
80 0.112 0.097 0.067 0.049
100 0.100 0.086 0.060 0.043
120 0.090 0.078 0.054 0.039
140 0.084 0.072 0.050 0.036
160 0.078 0.067 0.047 0.034
180 0.073 0.063 0.044 0.032
200 0.069 0.060 0.042 0.030

The interpretation of the table entries can be illustrated as follows: if the measured proportion is 0.5 and the sample size is 100 flights, then the estimated error is 0.1; thus, there is a 95 percent confidence that the true proportion is in the interval from 0.4 to 0.6.

As few as 60 flights could be adequate to estimate a proportion such as 0.5 (interval from 0.37 to 0.63) or 0.25 (interval from 0.14 to 0.36), but this number would not be adequate for estimating smaller proportions. For Example, if the estimated proportion were 0.05 and the number of sampled flights were 60, then the interval surrounding this estimate would have a lower bound below zero, meaning that the estimated proportion could not be statistically distinguished from zero. On the other hand, it may not be necessary to estimate relatively small proportions with any great certainty; it is probably sufficient to know that the proportion is relatively small.

2.2.3 Target Sample Size

Whether viewed from the perspective of estimating means or estimating proportions, a sample size in the range of 60 to 120 smoking flights was considered to be sufficient to meet the needs of the study. Resources adequate to obtain this range of sample size were requested and received for the study. The exact number of flights that could be monitored with these resources could not be determined at the outset of the study, due to fluctuations in air fares and some uncertainty in the amount of technician time required for monitoring flights together with pre- and post-flight activities.

Nonsmoking flights were also to be monitored by study design. Although the primary emphasis of the study was on smoking flights, nonsmoking flights needed to be monitored to provide a benchmark for comparison with smoking flights and to verify the assumption that levels of ETS contaminants were relatively low on such flights. Because the coefficient of variation for nonsmoking flights was expected to be about one half to two-thirds that of smoking flights, the same relative precision could be obtained with one-half to one-quarter the number of smoking flights. Thus, a sample size in the range of 20 to 40 nonsmoking flights was considered to be sufficient.

In selecting flights to be monitored (see Section 2.4), smoking and nonsmoking flights were sampled independently, subject to the overall constraint that 75 percent of all monitored flights be smoking flights and the remaining 25 percent be nonsmoking flights. This approach was consistent with the target sample sizes of 60 to 120 smoking flights and 20 to 40 nonsmoking flights.


To meet the objectives of the study, while performing the monitoring under the constraints associated with an occupied airliner cabin environment, the instrumentation package used in the cabin had to meet the following criteria:

  • Produces data that meet requirements for risk assessment
  • Unobtrusive and small size — all instruments and sensors fit in a single, compact carry-on bag
  • Lightweight
  • No requirement for external power
  • Quiet operation
  • Compliance with FAA regulations — will not interfere with the aircraft navigation or communication systems
  • Compliance with DOT regulations relating to the carriage of hazardous materials
  • Will not cause concern to passengers during use.

The monitoring package configured for the study consisted of instruments and sensors for measurement of time-varying concentrations of contaminants in addition to samplers for collection of integrated samples. It also included a data acquisition system for recording outputs from the continuous monitors. The instrumentation was packaged in a single, com-

pact carry-on bag typical of that carried by other airline passengers. Details of configuration of the instrumentation package are provided below.

2.3.1 Description of Measurement Methods and Instrumentation

The measurement parameters of the study, sample collection methods, analysis methods, and relevant references are summarized in Table 2-4.


Sample CollectionAnalysis
ETS Contaminants
carbon monoxideContinuous monitorSolid polymer electrolyteNagda and Koontz, 1985
NicotineSodium-bisulphate treated filterGas chromatography -- nitrogen selective detectorHammond et al., 1987
Respirable particles (integrated)Filtration with cyclone separatorGravimetryHammond et al., 1987
Respirable particles (continuous)Continuous monitorNephelometryIngebrethsen et al., 1988
Microbial Aerosols

Burge et al., 1987

BacteriaImpactionCulture/microscopyBurge et al., 1987
OzoneMBTH-coated filter*SpectrophotometryLambert et al., 1989
Carbon dioxideDetector tubeLength of stainLynch, 1981
Other Parameters
TemperatureContinuousPlatinum RTDASHRAE, 1985
Relative humidityContinuous

Thin film dielectric sensor

ASHRAE, 1985
Barometric pressureContinuousPiezoresistanceASHRAE, 1985
Air exchange (passive)Sorbent tubeGas chromatography of perfluorocarbon tracerDietz and Cote, 1982

The requirement that the instrumentation be small , unobtrusive and battery-powered placed a major constraint on the selection of instrumentation; some compromises had to be made to accommodate smaller sampling devices that could be used in the airliner cabin. Because of the accelerated schedule of the project and resource constraints, new instrumentation designed specifically for this study could not be developed. Measurement methods and instruments were those with accepted performance in past studies and commercial availability.

Carbon monoxide was monitored continuously on the aircraft with a General Electric (GE) Model l5ECS3CO3 Carbon Monoxide Detector. The detector uses a solid polymer electrolyte technology for measurement of C0. The detector has been used extensively in field monitoring programs conducted by the U.S. Environmental Protection Agency (Akland et al. 1985) and by GEOMET (Nagda and Koontz 1985).

Like other portable CO monitors, the GE CO detector has a lower detectable limit of 1 ppm, but its resolution of p.l ppm is better than many other detectors. The manufacturer specifies an accuracy of 1+_0 percent. In a GEOMET field survey (Nagda and Koontz 1985) measurement error at 4.5 ppm was shown to be less than 9 percent, and the precision was +_10 percent or better. Interferences with the detector have been well characterized and are effectively eliminated by use of a solid chemical filter (Ott et al. 1986).

Carbon monoxide was measured at all monitoring locations in the airliner cabin as described in Section 2.5. The analog output signal of the detector was scanned every 10 seconds and 1-minute averages were recorded by the data acquisition system (DAS) in the instrumentation package.

Nicotine was measured with the filtration method described by Hammond et al. (1987). The method involves collecting RSP on a pre-filter and vapor phase nicotine on a second filter treated with sodium bisulphate. This sampling method was selected because it has a number of advantages over the use of other solid Sorbent methods such as the NIOSH (1977) method that uses XAD-2 resin and the method of Muramatsu et al. (1984) that uses Uniport-S coated with 10 percent silicon OV-17. With the method developed by Hammond, a single pump and sampler can be used for efficient collection of both RSP and vapor phase nicotine. With Sorbent tubes, the 1.7 1/min flow rate required for separation by the cyclone can generate excessively high pressure drops adversely affecting sampler pump performance and noise levels. The performance of the nicotine collection method has been demonstrated in environmental chamber tests by Hammond et al. (1987). The collection efficiency of the filter method has been shown to be greater than 99 percent. Recovery of nicotine from the filter has been shown to be greater than 98 percent. The pumps used in this study had built-in pressure compensation to maintain constant flow rates at +_5 percent of the set point. The limit of detection for the method is 0.1 ug/m3 for a 2-hour sample. Nicotine analysis was performed by gas chromatography (GC) with a nitrogen selective detector (Hammond et al.1987).

Respirable particles (RSP) were measured during each flight by two complementary methods — a Gravimetric method for the measurement of the integrated average respirable particle mass during the smoking period and an optical method for real-time measurement of peak and time-varying RSP concentrations for the entire period between departure and arrival at the airport gates. A 10-mm nylon cyclone (MSA Inc.) was used as a pre-separator to remove particles larger than 3.5 um diameter for both methods. Use of the 10-mm cyclone in the instrumentation package was desirable because it could be used as a pre-separator for both the MINIRAM and the filter cassette used for Gravimetric determinations, thereby providing comparable particle size distributions for each method. The compact size of the cyclone made its use more unobtrusive than larger impactors that are available and that would need to be exposed above the instrument bag. The lower airflow rates needed for the cyclone limited the volume of air that could be sampled, and therefore the amount of particle mass that could be collected, particularly on short flights. However, the lower airflow rate and pressure drop placed less of a load on the sampling pumps, enabling their use on battery power for extended flight durations and multiple flights during a day. Integrated average RSP measurements were performed by standard methods of collection on preconditioned, tared filters. Filters were weighed under controlled temperature and relative humidity conditions on a microbalance with a resolution of 1.0 ug. Lower limits of detection with the analytical system were approximately 15 ug of mass (absolute) on a filter, considering the combined errors of the two weightings required (tare weight and final weight) for the Gravimetric analysis.

A MINIRAM Model PDM-3 (MIE, Bedford, MA) was used to provide the time varying (1-minute average) and peak concentrations of respirable particle mass during each flight. The MINIRAM is a compact, light-scattering aerosol monitor that was configured with a pump and a cyclone pre-separator for measurement of RSP, rather than total suspended particles.

Concentrations of RSP were recorded automatically every minute with the “package” DAS. RSP measurements were performed at each sampling location in the cabin.

Prior to use in aircraft, the accuracy of the MINIRAM was validated by calibration in an environmental chamber, described by Leaderer et al. (1984), at the John B. Pierce Foundation Laboratory. The monitors were calibrated dynamically during exposure to ETS-RSP generated by occupants in the chamber, as described in Section 2.3.3. RSP concentrations with the MINIRAM were compared to measurements with a piezoelectric microbalance and with Gravimetric methods to enhance the comparability of data from this study with previous studies of ETS-RSP (e.g., Repace and Lowrey 1980, 1982).

Microbial aerosols were sampled on each flight with a portable, battery-powered sieve plate sampler, the Surface Air System (SAS) compact air sampler. Selection of the SAS compact sampler represented a compromise between collection efficiency, sampler size, and logistical constraints in the airliner cabin.

The Bioaerosols Committee of the American Conference of Government Industrial Hygienists has stated that slit to agar samplers and All-Glass Impingers most efficiently collect viable bioaerosols (Burge et al. 1987). The slit to agar sampler, however, is bulky and requires AC power. The All-Glass Impingers require use of a liquid solution for collection making it difficult to use unobtrusively on an aircraft. Viable aerosols have also been collected on filter cassettes. But, loss of organisms due to desiccation can be highly variable and would be a critical problem in this study because of the low relative humidity on aircraft and the need to store samples between flight legs. A large model of the SAS that samples at 180 1/min and has higher collection efficiency was also considered. But the size of the instrument precluded its use.

Two types of media, R2 agar (R2A) and Tryptic Soy Agar (TSA) were used for collection of microbial aerosols. The R2A supported both saprophytic bacteria and fungi. The TSA was included to ensure that human pathogens such as Staphylococcus aureus and Streptococcus pyogenes were efficiently recovered.

To ensure that representative samples were collected and plates were not underexposed or overexposed, time-bracketing exposure was done at 40, 60, 80, 120 and 180 seconds per collection site, at a flow rate of 90 1/min. Microbial aerosol samples were collected at two locations in the coach section of aircraft on smoking flights and at one site (centre of coach) on nonsmoking flights. Samples were collected near the end of the flight, prior to descent.

Ozone was measured by collecting it on treated filters, with subsequent laboratory analysis by a spectrophotometric method. A number of alternative methods were evaluated for unobtrusive measurements of ozone during flights. Commercially available ozone monitors for real-time measurements of ozone did not meet the criteria for sampling because they are large, bulky instruments that require A.C. power, require ethylene for reaction with ozone, use liquid dyes for reaction with ozone, or have inadequate sensitivity for ambient air measurements. Length-of-stain detector tubes for measurement of short-term (grab sample) concentrations were also considered. However, detector tubes have poor accuracy and precision at low concentrations and the applicability of grab samples for the assessment of ozone concentration for flights of extended duration would be limited.

The method selected for this study was based on work by Lambert et al. (1989) on solid Sorbents for measurement of ozone. Glass-fiber filters were treated with 3-methyl-2-benzothiazolinone-acetone azine and 2-phenylphenol in 1:4 molar solid mixture prepared according to the method of Lambert et al. (1989). The coated filters were placed in opaque 37-mm filter cassette holders. Samples were collected by drawing air across the filter at a rate of approximately 1 1/min. Because aircraft altitude could not be measured in this study, a standardized protocol was implemented that involved sampling during the period from 15 minutes after takeoff until 30 minutes prior to the scheduled arrival. Collection efficiency and recovery efficiency of each lot of samplers was addressed by exposing a subset of each lot of filters to known ozone concentrations at low (approximately 10 percent) relative humidity. Both spiked and blank filters were included with field samples to address storage and handling effects.

Carbon dioxide was measured during each flight with length-of- stain diffusion detector tubes. The diffusion tubes, Draeger Carbon Dioxide 500/a-D, allow for integrated measurements of C02 over periods from less than an hour to 8 hours. The tubes had a range from 500 to 20,000 ppm-hour, making them suitable for the flight durations encountered in this study. Although real-time monitoring of C02 concentrations would have been preferable, the non-dispersive infrared analyzers currently available with well-documented performance characteristics were too large to be used in the unobtrusive instrument package.

The detector tubes used in this study were opened after becoming airborne (no-smoking light off). The sample collection was terminated when the no-smoking light was illuminated, at which time the length-of- stain was recorded. Resolution of the reading was approximately 125 ppm.

Air exchange was measured on all flights with a .passive perfluorocarbon tracer (PFT) method (Dietz and Cote 1982). The method employs miniature PFT sources for constant release of tracer gas and capillary adsorption tubes (CATs) for sample collection by passive diffusion; no pumps are required.

PFT sources were carried by half of the members of each flight’s technician team. The samplers were carried and used by the other half of the team, facilitating release and sampling at distinctly different locations in the aircraft. On nonsmoking flights, a single tracer gas was released by the technician sitting near the centre of the plane. The CAT sampler was deployed by the technician near the rear of the aircraft. On smoking flights, samples were collected at two locations in the coach section, in the centre of the nonsmoking section, and in the boundary section. On these fights two different types of perfluorocarbon tracers were released in the smoking and nonsmoking sections. Use of the two tracers enabled assessment of the transport of air from the smoking section to the nonsmoking section of the airliner cabin.

In addition to instrumentation for measurement of the ETS contaminants and other pollutants described above, the monitoring package also included a thermohygrometer for measurement of temperature and relative humidity and an analog barometer for cabin pressure.

The thermohygrometer (Solomat Model 455) was a thin film dielectric sensor for measurement of relative humidity (RH) over the range from 0 to 100 percent. The accuracy of the sensor is +_2 percent with a resolution of 0.1 percent RH. Temperature was measured with a platinum RTD having an accuracy of +_0.5 C (0.9 F) and a resolution of 0.1 C.

Cabin air pressure was recorded with a genthe measure (Model 7105-A) analog output barometer. The device has a piezoresistive diaphragm sensor for measurements over a range from 600 to 1100 mbar with an accuracy of 0.88 mbar.

A Metrosonics DL-714 data logger was used in the instrumentation package to record outputs from the CO detector, MINIRAM RSP monitor, thermohygrometer and barometer. All channels were scanned every 10 seconds and 1-minute averages were recorded. The data logger was downloaded each evening with a personal computer and data were recorded on diskettes.

2.3.2 Configuration of the Monitoring Instrumentation Package.

All instruments selected for use in this study were compact and lightweight, so that they could be readily configured into an unobtrusive monitoring package in the form of a single carry-on piece of baggage. An example of one of the instrumentation packages is depicted in Figure 2-2.

The basic instrument package included two continuous monitors (CQ and MINIRAM); three low-volume pumps for sample collection; temperature, relative humidity, and pressure sensors; and the data logger. The instrument bag was approximately 18 inches long, 9 inches wide, and 9 inches high, and conformed to regulations for carry-on baggage. The total weight of the bag with instruments was less than 10 pounds. It was typical of bags carried by many airline passengers. Probes were inconspicuously located along the edge of the bag near the handles and zippers for intake of air. The package was designed with external switches such that it did not need to be opened at any time during a flight.

2.3.3 Instrumentation Testing

The measurement methods used in this study were standard or accepted methods, the performance of which have been documented in scientific literature. The monitoring instruments, such as the CO and the RSP monitors, were commercially produced with well-documented performance specifications from previous field monitoring programs by GEOMET and other researchers, as indicated by the references included previously in Table 2-1.2-21

For this study, it was necessary to perform electromagnetic compatibility tests on all of the devices to be used on the aircraft to ensure that they did not interfere with the aircraft navigation or communication systems. These tests were performed by the Federal Aviation Administration (FAA) Technical Center’s Communication, Navigation and Spectrum Engineering Branch, ACN-210.

Emission measurements were conducted with the instrumentation package located one meter from the receiver antenna. A calibrated antenna and a spectrum analyzer were used to receive the radiated emissions and a plotter was used to record the data. These emission measurements were conducted over a frequency range of 10-kilohertz (kHz) to 1 gigahertz (GHz). Results of the tests showed that even the worst-case emission levels measured would not be of sufficient magnitude to interfere with aircraft operations.

Also included in the preparation and calibration of instrumentation for the monitoring program were exposures of the MINIRAM optical particle monitors to ETS-generated RSP to derive calibration equations specific to ETS-generated RSP. A series of three exposures was performed in a controlled environment test chamber with relatively constant ETS-RSP concentrations generated by human smokers at low, moderate, and high smoking rates. A second set of tests was conducted in a closed office, where ETS-RSP was generated intermittently to obtain varying RSP concentrations during the measurement period. The MINIRAMs, fitted with the 10-mm cyclone to remove particles larger than 3.5-nm diameter, were collocated with a TSI Model 8510 piezobalance and a triplicate set of Gravimetric filter samplers during each of the five tests. Measurements were made approximately once every 10 minutes with the piezobalance over each 3- to 4-hour test period for comparison to the MINIRAM readings. Results of the piezobalance and MINIRAM measurements were also integrated over the 3-hour period for comparison to the integrated Gravimetric sample.

Test NumberGravimetric*Piezobalance**MINIRAM***
1169.3 ±57.0191.1162.0 ±21.8
2126.4 ±22.4140.689.5 ±17.3
356.8 ±20.386.762.8 ±15.4
4170.2 ±39.5214.2176.1 ±20.2
5149.4 ±16.1261.3206.0 ±17.5
* Average ±standard deviation for triplicate samples collected during test
** Integrated average concentration over the duration of the test
*** Integrated average ±standard deviation for multiple instruments

As shown in Table 2-5, the integrated average concentrations of RSP measured with the piezobalance over the duration of each test were higher than both the MINIRAM and Gravimetric measurements in all five tests. The MINIRAM average readings ranged from 64 to 86 percent of the average readings with the piezobalance. The integrated average MINIRAM concentrations d1d not exhibit a bias with respect to the Gravimetric measurements, with the MINIRAM measurements being higher in one case, lower in two cases, and nearly the same in the other two cases.

A linear regression was performed of the MINIRAM measurements against the piezobalance measurements to derive the calibration equations for the real-time optical measurements with each of the eight MINIRAMs used in the study. Piezobalance measurements were used (1) to maximize the number of observations and measurement range underlying the regression equation and (2) for comparability to other ETS fled studies in which piezobalances were used for near real-time measurements (Repace 1987).

For this regression procedure, the measurement obtained by the piezobalance was treated as the independent variable and the MINIRAM measurement as the dependent variable. For the eight units, the calibration equations for the MINIRAM (after rearranging algebraically to predict MINIRAM concentrations relative to the piezobalance as the reference device) had slopes that ranged from 1.08 to 1.33 and the intercept ranged from 0 to 12 ug/m3. The R-squared value for all eight equations was greater than 0.95. The specific equation for each unit was used during data processing to calculate RSP concentrations measured continuously during each flight. As noted by Repace (1987), the piezobalance method may overestimate particle mass at low aerosol concentrations due to artifact formations in the Corona discharge.

Consequently, MINIRAM mass estimates were referenced to the Gravimetric method by multiplying the calibrated results by 0.75, the ratio of Gravimetric to piezobalance results from the chamber tests (Table 2-5).


2.4.1 Alternative Approaches

Alternative approaches to selecting flights can vary according to features such as (1) completeness of the sampling frame (i.e., set of flights from which the sample is to be selected), (2) degree of stratification of flights (i.e., placement into categories) prior to selection, (3) extent to which randomization is used 1n selecting flights, and (4) associated costs and logistics. Three basic approaches covering the range of alternatives were considered for the study:

  • Sample of flights to and from a fixed location
  • Stratified sample of flights
  • Sample of flights selected with equal probabilities.

All three approaches included the notion of randomization. For example, for the approach involving flights to and from a fixed location such as Washington, D.C., the other locations (airports) could be selected at random. Thus, this set of flights would involve round trips to and from Washington, D.C. The main advantages of the approach would be lower fares associated with round trips and relatively simple field logistics.

Because each trip would begin and end in Washington, D.C., the costs associated with hotel accommodations and time between flights could also be minimized. Despite these attractions, this approach was dismissed because of the possibility that the relatively narrow sampling frame could result in substantial biases. For example, flights departing from or arriving at Washington, D.C., could have different smoking rates, levels of biological contamination, or ozone levels than flights involving other points of departure or arrival.

A stratified sample of flights would involve grouping flights by major factors expected to cause variations in concentrations before selecting flights within each group at random. Such factors would include type of aircraft (reflecting differences in cabin volume, passenger capacity, air exchange rates, and extent of air recirculation) and geographic area (reflecting different flight paths and possibly differences in ground-level biological contamination or passenger smoking rates).

Major advantages of this approach would be (1) the ability to represent various types of flights and (2) greater control over potential factors affecting measured concentrations.

The stratified sampling approach would essentially involve defining strata representing different types of aircraft (e.g., narrow body and wide body) and different points of departure (e.g., four geographic regions). For an initial subset of flights, each stratum would be represented either equally or in proportion to the number of departing flights. Based on a review of the initial results, the strata with the largest variances could be represented more heavily 1n the next subset of flights to achieve a more efficient sampling design. The ultimate sample of flights chosen in this manner would have known but unequal selection probabilities.

The stratified sampling approach was also rejected, primarily because the need to review initial results would jeopardize the study schedule. Due to time lags associated with laboratory analysis of samples, at least one to two months would be required after monitoring the initial subset of flights for receipt of laboratory results, analysis of these results, and corresponding adjustments to the sampling design. Because some of the field technicians were hired and trained specifically for this project and the study had an extremely tight time schedule, such a hiatus in the field effort could not be entertained.

The approach chosen for this study was to randomly sample flights with equal probabilities of selection. This approach involved developing a list of all flights originating in the United States and selecting flights at random from this list. Through reliance on randomization, this approach has a high likelihood of representing various types of flights. Through use of quota sampling (described later), constraints can also be introduced to guarantee that different types of aircraft are represented. Further advantages of this approach are (1) that development of parameter estimates (e.g., mean concentration, variance about the mean, or proportion of flights with a peak concentration above a certain level) is very straightforward and (2) any modifications to the overall sample size needed to accommodate resource constraints can be accomplished by expanding or contracting the set of flights selected for monitoring, without invalidating the overall sampling design.

2.4.2 Implementation of the Chosen Approach

One possible drawback of the chosen approach (and of the stratified approach as well) is potential inefficiencies in linking together the flights selected for monitoring. For example, if the first flight selected were from New York to Dallas and the second flight selected were from Denver to Atlanta, then additional resources would be required to transport the field team from Dallas to Denver for monitoring of the second flight. This interim flight could not be legitimately monitored because it was not part of the random sample of flights selected for monitoring. The approach described below was designed to reduce this type of inefficiency yet constitute an equal-probability-of-selection method (EPSEM) (Kish 1965).

With recognition that each flight involving a U.S. airport 1s uniquely associated with a specific airport of departure, a random sample of flights can be selected in a different yet virtually equivalent manner. For example, if the number of flights scheduled for a given month is 100,000 and the number of flights to be monitored is 100, then the probability of selection any flight is 1/1,000. If an airport is first selected at random with a probability proportional to the number of flights (n) departing from this airport and a specific flight departing from the airport is then chosen at random as one of the 100 flights to be monitored, then the probability of selection (p) for that flight can be expressed as follows:

p = 100 x (n/100,000) x (1/n) = 1l1,000

With this approach, the probability of selection (1/1,000) is still the same for any flight, regardless of the airport of departure. However, the approach offers the added advantage that all airports of departure can be randomly chosen at the outset, after which individual flights can be randomly selected. By imposing the further constraint that the flights chosen for monitoring link the randomly selected airports of departure, the efficiency of the sample can be greatly increased while maintaining a randomized procedure for flight, selection.

Operationally, this procedure required the following steps:

  • A set of airports of departure was chosen at random with probabilities proportional to the number of flights departing from each airport; this step was performed separately for 120 airports for smoking flights and 40 airports for nonsmoking flights; sampling was performed with replacement, such that any airport could be chosen more than once.
  • Chains of smoking and nonsmoking flights were randomly constructed by initially choosing an airport at random from the set as the starting point, then choosing a second airport of departure from the set at random; for smoking flights, the second airport was chosen subject to the constraint that the flight from the first to the second airport be of sufficient duration to be a smoking flight; for nonsmoking flights, the second airport was chosen subject to the constraint that the flight be of shorter duration (i.e., less than two hours); this process was continued by applying similar constraints in selecting the third airport, and so on.

Chains of flights lasting approximately six days were constructed in the manner described above. Some of the chains consisted of a series of smoking flights followed by a series of nonsmoking flights; this approach was taken so that a team of four technicians responsible for monitoring smoking flights could later split into two teams of two technicians for monitoring nonsmoking flights (see Section 2.6). By design, some of the smoking flights involved international destinations; in these cases, the entire chain involved only smoking flights. Further details on selection of airports and construction of chains are provided below.

Selection of Airports. In constructing chains of flights, difficulties would be encountered if relatively small airports were included, because (1) the number of other airports with which smaller airports connect is limited and (2) the distances flown from smaller airports are generally short, making it difficult to find smoking flights involving such airports. Consequently, candidate airports for selection were restricted to those located in large and medium air traffic hubs (i.e., communities accounting for at least 0.25 percent of the total enplaned passengers in all services and operations in the United States).

According to airport activity statistics compiled by the U.S. Department of Transportation (1987), these hubs collectively accounted for more than 90 percent of all passenger enplanements in the United States during the 12-month period ending December 31, 1987. Within these hubs, the sampling frame was further restricted to 70 individual airports that individually accounted for at least 0.25 percent of 1987 U.S. enplanements. These 70 airports collectively accounted for slightly less than 90 percent of 1987 U.S. enplanements.

For smoking flights, a total of 120 points of departure were selected–102 departure points for domestic flights and 18 points of departure or arrival for international flights. A magnetic tape containing records for all flights scheduled to depart from U.S. airports during January 1989 was obtained from the U.S. Department of Transportation and used to tabulate departures from each airport for domestic smoking flights, domestic nonsmoking flights, and international flights.

Domestic smoking flights were defined as follows:

  • Flights of greater than two hours duration for all carriers except United and Northwest Airlines
  • Flights for United Airlines of greater than 1,000 miles distance
  • Flights for Northwest Airlines involving an airport in H_11 as the port of arrival or departure and an airport in the continental United States as the other port.These definitions are generally consistent with smoking/nonsmoking designations made by major U.S. airlines. International flights were readily identifiable from a special code provided in the database. All remaining flights (i.e., those that were not domestic smoking flights or not international flights) were defined to be domestic nonsmoking flights.

The 102 points of departure for domestic smoking flights were chosen in accordance with the proportion of smoking flights for which each airport accounted, as tabulated from the data base provided by DOT; that is, the proportion was multiplied by 102 and rounded to the nearest whole number to determine the number of times that the airport should appear in the sample as a point of departure. Thus, apart from differences due to rounding, the sample of 102 points of departure to be used for domestic smoking flights in this study represented airports in virtually the same proportion as these airports were represented by domestic flights departing during January 1989.

Table 2-6. Airports of Departure Chosen for Domestic Flights
Airport (City)Number of FlightsAirport (City)Number of Flights
Smoking Flights  
DFW a Jas9BDL (Hartford) 
ORD (Chicago)6BNA (Nashville) 
DEN (Denver)5BWI (Baltimore) 
LAX (Los Angeles)5CLE (Cleveland) 
ATL (Atlanta)4CLT (Charlotte) 
EWR (Newark)4CVG (Cincinnati) 
LGA (New York)4DAY (Dayton) 
BOS (Boston)3DTW (Petroit) 
IAH (Houston)3HNL (Honolulu) 
JFK (New York)3HOU (Houston) 
MCO (Orlando)3IAD (Washington, DC) 
MIA (Miami)3IND (Indianapolis) 
PHL (Philadelphia)3LAS (Las Vegas) 
PHX (Phoenix)3MCI (Kansas City) 
SEA (Seattle)3MDW (Chicago) 
SFO (San Francisco)3MSP (Minneapolis) 
STL (St. Louis)3MSY (New Orleans) 
DCA (Washington, OC)2ONT (Los Angeles) 
FLL (Ft. Lauderdale)2PBI (West Palm Beach) 
PIT (Pittsburgh)2PDX (Portland) 
SLC (Salt Lake City)2RDU (Raleigh) 
TPA (Tampa)2RSW (Ft. Myers) 
SAN (San Diego) SJC (San Jose) 
  SNA (Los Angeles) 
Nonsmoking Flights  
ATL Atlanta3DEN (Denver) 
ORD (Chicago)3EWR (Newark) 
DFW (Dallas)2H4U (Houston) 
DTW (Detroit)2IAD (Washington, DC) 
LAX (Los Angeles)2LAS (Las Vegas) 
MSP (Minneapolis)2LGA (New York) 
PIT (Pittsburgh)2MCI (Kansas City) 
SFO (San Francisco)2MCO (Orlando) 
BNA (Nashville)1MEM (Memphis) 
BOS (Boston)1PHL (Philadelphia) 
BWI (Baltimore)1PHX (Phoenix) 
CLE (Cleveland)1RDU (Raleigh) 
CLT (Charlotte)1SAN (San Diego) 
CVG (Cincinnati)1SLC (Salt Lake City) 
DCA (Washington, _)1STL (St. Louis) 

In total, 47 airports were selected as departure points (see Table 2-6); of these, 25 airports appeared once in the sample, five appeared twice, 10 appeared three times, three appeared four times, and four appeared five or more times. Dallas-Ft. Worth (DFW) international airport appeared the most times (nine) because its location in the southern central part of the country resulted in many flights of sufficient duration to allow smoking, including flights to the east and west coasts as well as to locations in the northeast and northwest regions of the country. In some cases, individual cities were represented by more than one airport (e.g., Los Angeles by LAX, ONT, and SNA).

International flights were included in the sample to provide flights of greater duration, and possibly with different smoking rates than domestic smoking flights. As summarized in Table 2-7, fewer than 10 percent of the domestic smoking flights were of a 5-hour or greater duration, whereas more than a third of the international flights were of this duration.

Expressing international flights of a 5-hour or greater duration (approximately 10,000) as a ratio to all domestic smoking flights (approximately 122,000) indicates that nine international flight: should be monitored (compared to 102 domestic smoking flights) to preserve this ratio in the study sample. However, in recognition that the statistics in Table 2-7 represent only international flights departing from the United States (i.e., excluding the arriving flights), the number of international flights to be monitored was doubled to 18, yielding a total sample of 120 smoking flights to be monitored.

U.S. airports of departure/arrival for international flights were chosen in proportion to their relative frequencies during January 1989 for such flights, as determined from analysis of the data file provided by the Department of Transportation. International destinations were then chosen from the most frequent destinations for the chosen U.S. airports. As with the domestic smoking flights, some airports were chosen more than once.

The chosen U.S. airports and associated international destinations are summarized in Table 2-8. The only constraint in choosing the international destinations was that each destination be used an even number of times (i.e., once to serve as an airport of arrival and once to serve as an airport of departure). The international arrival/departure points included London for six flights, Frankfurt and Tokyo for four flights each, and Paris and Rio de Janeiro for two flights each.

Points of departure for nonsmoking flights were determined in the same manner as for smoking flights — by (1) calculating the proportion of nonsmoking flights represented by each airport of departure, as tabulated from the data base provided by DOT and (2) multiplying this proportion by 40 and rounding to the nearest whole number. In total, 30 airports were selected as departure points (see Table 2-6); of these, 22 airports appeared once in the sample, six appeared twice, and two appeared three times.

Construction of Chains. As mentioned previously, two types of chains were developed:

  • Chains involving domestic smoking flights and international lights
  • Chains involving domestic smoking and nonsmoking flights

Six chains were initially developed using a subset of airports drawn from the randomly selected pool of 102 airports of departure for domestic smoking flights, 18 airports of departure/arrival for international flights, and 40 airports of departure for domestic nonsmoking flights.

One-third of the airports (i.e., 34 for smoking flights, 6 for international flights, and 13 for nonsmoking flights) were chosen at random from the larger pool as a basis for constructing these six initial chains.

Percentage of Flights
Duration of Flight
Domestic SmokingInternational
2.0 - 2.49348
2.5 - 2.992710
3.0 - 3.992216
4.0 - 4.99106

Based on the costs incurred in monitoring this initial subset of flights, it would then be possible to determine the number of additional flights that could be monitored with the remaining resources.

The distribution of flights (i.e., domestic smoking, international, nonsmoking) for each of the initial six chains is summarized in Table 2-9. All chains included domestic smoking flights; three of the chains also included international flights and the other three chains also including nonsmoking flights. Each chain began with an airport of departure for a smoking flight.

An example chain that included international flights is shown in Table 2-10. The type of flight is indicated in the first column as S (domestic smoking), I (international) or P (positioning). Positioning flights were needed to transport field technicians from Washington, OC to the first airport of departure for the chain and from the final airport of arrival back to Washington; these flights were not monitored. Boston was randomly selected as the first airport of departure for this chain, requiring an initial positioning flight from Washington to Boston. The only other constraint in constructing the chain was that the last smoking flight end at an airport of departure for the first international flight; this airport was randomly selected from the two (JFK and ORD) associated with the international destination (Frankfurt) that was randomly chosen for this chain. A final positioning flight was required to transport the field team from the last arrival point (Chicago) to Washington.


Type of Flight*

Day of Monitoring

Airport of Departure

Airport of Arrival

Local Time of Departure

Local Time of Arrival

Duration (Hours)

In most cases, two domestic smoking flights could be monitored per day (the first day was an exception because of the need for a positioning flight). The domestic smoking flights for this chain ranged in duration from 2.2 to 3.6 hours. By comparison, both international flights were close to eight hours in duration, meaning that only one such flight could be monitored per day. In addition, due to the relatively long flight duration coupled with required pre- and post-flight duties, the technicians remained at the international destination for a day before monitoring the return flight.

An example chain that included nonsmoking flights is shown in Table 2-11. Six smoking flights and five nonsmoking flights were monitored for this chain. A positioning flight was required to get the technicians from Washington to the startling point for the chain (La Guardia airport in New York). The last smoking flight was constrained to arrive at an airport of departure (San Francisco) for a nonsmoking flight.

Because the team of four technicians split into two teams of two technicians (designated A and B in the table) and San Francisco could be used as a departure point for only one flight, a positioning flight was required to transport the B team to Kansas City (MCI), the other randomly selected starting point. The B team’s last monitored flight ended in Washington but the A team’s last monitored flight ended in Denver, requiring a positioning flight to return them to Washington. The smoking flights had durations ranging from 2.1 to 4.2 hours and the nonsmoking flights ranged in duration from 0.7 to 2.2 hours. Thus, the longest nonsmoking flight exceeded two hours, but the carrier (United) has a nonsmoking policy for flights of fewer than 1,000 miles.

In monitoring the first six chains, it was found that the resources required for the field team were nearly double those anticipated, due to (1) fare increases, (2) the resources required for positioning flights, (3) flight delays that generally increased layover times when multiple flights were monitored on a single day, and (4) technician activities at the end of each monitoring day and at the end of each chain.


Type of Flight*

Day of Monitoring

Airport of Departure

Airport of Arrival

Local Time of Departure

Local Time of Arrival

Duration (Hours)

*P = positioning flight (not monitored);
S = domestic smoking flight;
N = domestic nonsmoking flight;
A and B indicate teams of two technicians each from the starting team of four technicians.

It was determined that the remaining resources enabled monitoring of 39 additional flights; these flights were divided among four chains, as summarized in Table 2-12. In total, 92 flights were monitored–69 smoking flights (including eight international flights) and 23 nonsmoking flights.


2.5.1 Monitoring Locations

During the program, teams of four technicians performed air quality monitoring on smoking flights. Teams of two technicians performed the monitoring on nonsmoking flights.

Air quality monitoring was performed by each technician at an assigned seat. Technicians could not move about the aircraft to perform any measurement activities. The four monitoring locations selected on each smoking flight included the following:

  • Coach smoking section
  • Nonsmoking section — boundary (within three nonsmoking rows of the coach smoking section)
  • Nonsmoking section — middle
  • Nonsmoking section — remote (i.e., most remote rows from the coach smoking section, except on international flights, on which seat was in business class).

Examples of the target monitoring locations for three different types of aircraft are depicted in Figure 2-3. Some aircraft, such as the Boeing 747 and DC10, sometimes have the coach smoking section in the front of the coach nonsmoking section. As shown in the figure, the monitoring location in the smoking section was generally near the rear of the section to facilitate accurate counting by the technician of smoking during the flight. The target boundary monitoring location was within three rows of the smoking section. Although technicians in the boundary section were assigned seats in advance of the flight, they were instructed to change seats if the size of the smoking section was modified at the time of passenger check-in in order to stay within three rows of the smoking section. Technicians were not assigned to the first-class section, but the remote location in the coach section was to be within two to four rows of first class. Technicians could not sit in the first row of the coach section or at any bulkhead sections because the instrument package needed to be stored under n sent in front of them for takeoff and landing.

G6 5
H6 5

On international flights, which were all smoking flights, one technician was located in the nonsmoking portion of the Business class section. This location was used instead of the nonsmoking random location. The size of the business class section on international flights is significant and it usually has multiple rows allocated to smoking. The number of smokers and their close proximity to nonsmokers warranted monitoring in this section.

On nonsmoking flights two locations were monitored. Those locations corresponded to the locations depicted in Figure 2-3, labeled as (1) nonsmoking section — middle and (2) smoking section.

Within each assigned section, the seat was selected randomly so that middle, aisle, and window seats would each be represented during the study.

During the flight, the monitoring instrumentation package was placed on the technician’s lap or the seat in front, resulting in measurements at a height within approximately 12 inches of the technicians breathing zone. The technician was allowed to place the monitoring package on an adjacent unoccupied seat to facilitate trips to the lavatory or eating on longer flights. The instrument package was stored under the seat during takeoff and landing. However, as described in a following subsection, this period did not include the period of integrated measurements of nicotine and RSP.

ETS contaminants and the physical parameters were measured at all locations on each flight. However, the other pollutants were measured at a subset of locations, as summarized in Table 2-13.

2.5.2 Monitoring Schedule

Field monitoring activities for this study were initiated in March 1989, by conducting a pretest that included four flights over a 3-day period. Details of the pretest are described in Section 2.6.

The formal monitoring program was initiated on April 4, 1989. Two teams of four technicians each performed monitoring on ten chains of flights. Each chain covered periods of 5 to 8 days with 7 to l2 flights per chain. International flights were included in some chains.

Monitoring continued during May and was completed in June 1989. A total of 92 flights were monitored over a period of approximately ten weeks.

Chains were started on each of the seven days of the week to provide full temporal coverage on a weekly basis. Chains also varied in duration, such that the technician’s day of return to the Washington, DC area also spanned the range of the seven days of the week.

Temporal representation of the time of day for flights was achieved in the study by scheduling departures over a complete range of times from early morning to early evening.

2.5.3 Field Monitoring Protocols

Field monitoring protocols were developed to ensure uniform operational procedures by the technicians during the performance of the monitoring program. Conformance to these protocols was documented in “Daily Log” documentation forms completed by each technician on each day of monitoring.


Page 2 of 3 Date: / / lech: (3) MINIRAM Checkout( ] Press TIME and MEAS _o get C.GO
[ ] Turn on pump (Switch 3)
( ] Check that pump is operating
( ] Data logger to CH1
( ] Wait 2 minutes
( ] CH1 Readings: mV, mV, mV
( ] Previous night’s zero reading was: mV
( ) Check pump battery (If light does not come on or
Low Batt displayed, replace battery.)
Battery OK? ( ) Yes ( ) No
If no, battery replaced? ( ) Yes ( ] No
( ] Turn pump OFF
(4) CO Detector Checkout
( ] Turn ON
( ) Battery OK?
( ] Data logger to CH2
( ] Wait 2 minutes
[ ] CH2 readings: mY, mV, mY
( ) Detector panel meter reading: ppm
[ ] Turn detector OFF
(5) Solomat Checkout
( ] Solomat ON (Switch 1)
[ ] Data logger to CH3
[ ] CH3 readings: F, F, F
( ] Press NEXT on data logger for CH4
[ ] CH4 readings: rh, rh, rh
[ ] Turn Solomat OFFSDLO2 (3/29/89)


The “Daily Log” used for documenting field activities was divided into the following five sections that were bound into a single booklet:

  • Start of Day Documentation Log
  • Flight Documentation Log (1st Flight)
  • Pre-Flight
  • 1st Flight
  • Post-Flight
  • Flight Documentation Log (2nd Flight)
  • Flight Documentation Log (3rd Flight)
  • End of Day Documentation Log.

The following summary of the operational protocol for the field monitoring activity includes exchanges of pages from the log to describe the operational procedures.

The daily activities for the monitoring program can be broken into these categories:

  • Start of Day preparations
  • Monitoring of flights
  • End of Day calibrations, instrumentation checkout, sampler handling, and chain of custody procedures.

Figure 2-4 depicts a page from the Start of Day Documentation Log that shows the types of checkout activities that occurred at the start of each day. These activities included the following:

  • Programming of the data logger
  • Checkout and zero reading of the MINIRAM
  • Operational checkout of the CO monitor
  • Operational checkout of the temperature, relative humidity, and pressure sensors
  • Operational checkout of all pumps
  • Operational checkout of the microbial aerosol sampler
  • Inventory of samplers for the day
  • Final preparations for the day’s flights.All “Start of Day” preparations were performed at the technician’s hotel just prior to departure for the airport.
FLIGHT DOCUMENTATION LOG (Pre-flight Page 1 of 9(1st Flight)Airline: Flight No.:
Date: / / tech:
Prepare New Samplers
(1) Nicotine Cassette number:
( ) Bottom of cassette faces up
( ) Cyclone assembly locked in place
( ] All sampling lines connected (Inlet_cyclone_pump)
( ) Sample line inlet capped
(2) C02 Diffusion Tube number:
(3) Ozone Cassette number:
(4) CAT Sampler number:
(5) PFT Sources with this package:
( ] None
( ] Silver (NS + S sections)
( ] Blue (NS only)
[ ] Lime (S only)
[ Turn _N _sensors_ Time:
[ ] Solomat ON (Switch 1)
( ] Pressure sensor ON (Switch 2)
[ ] CO detector ON
[ ] Is the data logger collecting data (displays L)?
( ] Yes ( ] No
( ] If no, reprogrammed to start at:
[ ] Is battery OK? (Change if Low Battery is displayedComments:FDLO2 (3/29/89)

After arrival at the airport, check-in of luggage, and passage through security, the technician proceeded to the boarding area to perform pre-flight activities. Pre-flight activities, summarized on page 1 of the FLIGHT DOCUMENTATION LOG (Pre-Flight), depicted in Figure 2-5, included the following:

  • Sampler identification numbers were recorded on the log
  • The nicotine/RSP-sampling cassette was loaded on the cyclone assembly
  • The ozone cassette was installed
  • PFT sources and samplers were logged, as appropriate
  • The temperature, relative humidity, and pressure sensors were turned on
  • The MINIRAM and CO detectors were turned on

The operational status of the data logger was verified. As part of the pre-flight activities, the technicians in the boundary and smoking sections also checked their seat locations at the gate in case the size of smoking section was changed during gate check-in.

Technicians boarded the planes as regular passengers, with no special pre-boarding requirements. After taking their seats, the technicians began sampler deployment, monitoring and documentation activities.

The operational protocol for each flight is summarized in Table 2-14. The activities summarized in the table were documented on pages 2 through 9 of the Flight Documentation Log. Page 3, depicted in Figure 2-6, for example, was used to record activities related to the start of sample collection.

The technician assigned to the smoking section was responsible for a series of activities related to smoking. As shown in Figure 2-7, this technician completed n section on snaking information and also made counts at 15-minutes intervals of the number of cigarettes being smoked.


Time Period ActivityPost-boarding – Technician in smoking section checks that:
– ashtrays are empty
– PFT sources deployed
– Temperature/RH sensor exposed
– Sampling lines exposed and uncapped
– MINIRAM pump turned on
– Instrument bag placed under set for takeoff
– Documentation log entries made
Depart gate – Record time
Takeoff – Record time
Airborne: No smoking light – Start nicotine/RSP pump turned off
– Open C02 diffusion tube
– Uncap CAT (PFT) sampler
– Make log entries
Cruise altitude (15 minutes – Turn ozone pump on after no smoking light – Make log entry turned off)
Smoking period – Technician in smoking section records number of smokers on 15-minute intervals
Pre-descent – Perform microbial aerosol sampling
Cruise descent (30 minutes – Turn off ozone pump before scheduled arrival)
No smoking flight on – Turn off nicotine/RSP sampler
– Cap CAT (PFT) sampler
– Read CO2 diffusion tube
– Stow bag under seat for landing
– Make log entries
Gate arrival – Turn off MINIRAM pump
– Cap sampling lines
– Collect cigarette butts
– Collect information on passenger load and previous flight
– Deplane

At the end of the flight this technician also collected cigarette butts from ashtrays. These were then counted in the airport to obtain an accurate count of cigarettes smoked. It the butts could not bc collected from all seats in the smoking section due to time constraints, the number of seats of collection was recorded.

After deplaning, the technician performed a series of procedures in the airport that included turning off various sensors, removing sampling media, and documenting sampler IOs. These activities were recorded on page 9 of the Flight Documentation Log.

The On-fly Log contained identical but color-coded sections for up to three flights a day. On days with multiple flights, the pre-flight, flight, and post-flight protocols described above were repeated and documented.

The final section of the Dafly Log was the END OF DAY DOCUMENTATION LOG used to record instrument checkout and calibrations following the last flight of each day. These activities, summarized on the 8 pages of this section of the log, included the following:

  • Downloading, verification, and backup of data to diskette
  • Checkout of tarperztsre/relative humidity sensor
  • Checkout of pressure sensor
  • CO detector checkout and maintenance
Page 3 of 9 Airline: Flight:Date: ! / Tech: [ Board_ng 1 Time:( ] PFT Sources deployed:
( ) Sampling Lines and Temp%Ri_Sensor Exposed
( ] Uncap sampling lines
( ] MINIRA_I pump ON (Switch 3): _ _ _ _ [Depar Gate Time: a to Time: _ [ Airborne: _I- g Time:
( ] Nicotine pump ON (Switch 4):
( ] CO diffusion tube opened:
( ] CA_ sampler uncapped: _ _ _ _ [ Cruise due Time:
( ] Ozone pump ON: _ _
(15 minutes after N-S light OFF)Comments:FDLO2 (3/29/89)
Page 4 of 9 Airline: Flight No.:
Date: / / Tech:_Smoking section Information
Ashtrays empty at start of flight: ( ] Yes ( ) No
Smoking rows: to
Number of passengers n smoking section:
Number of passengers in boundary section:
(Three rows nearest to smoking section)
— A -minute intervals
beginning on first 5-minute block after N-S flight of
Time Count Time CountFDLO2 (3l29/89)
  • Zero and span of the CO detector
  • Zero reading of MINIRAM
  • Calibration of MINIRAM pump
  • Calibration of nicotine/RSP sampling pump
  • Calibration of ozone sampling pump
  • Calibration of duplicate sampling pumps
  • Archival of all samplers
  • Shipment of microbial aerosol samples
  • Completion of logs
  • Chain of custody procedures.2.5.4 Quality Assurance and Quality Control Procedures

Quality assurance (QA) 1s an important element of field monitoring program. For this study, a QA program was developed that included appropriate quality control (QC) procedures to ensure that monitoring instrumentation was performing properly in the field and that precision and accuracy of the measurement results conformed to QA objectives.

QC procedures during the monitoring program are summarized in Table 2-15 and briefly described below.

Quality control procedures for integrated samples, including nicotine, RSP, and ozone, consisted of measurements of sampler pump flow rates in the field on a daily basis, submission of field blanks and duplicates to the analytical laboratory, and standard laboratory QC procedures.

Sampling pump airflow rates were measured with Matheson precision rotseters calibrated 1n GEOMET’s laboratory against an NBS-traceable Teledyne-Hastings mass flowmeter. The airflow rates of sampling pumps were measured at the end of each day and were adjusted and recalibrated if the flow differed by more than 5 percent of the target flow rate.


ParametersQC ProceduresNumber of QC SamplesTotal Number of SamplesPercent QC Samples
NicotineField blanks203226
 Field duplicates3532211
 Analytical blanks1 per session
 Analytical spikes3 per session
 Duplicate injections322322100X
RSP (Gravimetric)Field blanks203226
 Field duplicates3532211
 Control filter1 per session
OzoneField blanks2112317
 Field duplicates81236
 Analytical spikes5 per session
 Analytical blanks3 per session
Carbon dioxideField blanksN/A1619
 Field duplicates141616
Carbon monoxideIero check (field) -- 2 to 3 times/week*
 Span check (field)-- 2 to 3 times/week*
 multipoint calibrations-- twice weekly
RSP (optical)Zero check (field)-- twice daily
Microbial aerosolsSampler flow checks-- weekly
Sampler pump airflow ratesCalibration with precision rotameters-- daily
Sampler transfersChain-of-custody procedures 
*Dependent on duration of each chain

Over ten percent of the total number of nicotine, RSP and ozone samplers were dedicated as quality assurance samples, as shown in Table 2-15. These were submitted to the analyst as routine samples. In the laboratory, the QC procedures included analytical blanks, analytical spikes, multipoint calibrations of the gas chromatograph or spectrophotometer and control filters for RSP.

Multipoint calibrations of the CO detectors using certified calibration gases were performed at the GEOMET Indoor Air Laboratory at the beginning and end of each chain. Additionally, the performance of the CO detectors was assessed in the field by means of zero and span checks.

Zero air and calibration gas at a concentration of _.65 ppm of CO were carried by each team of technicians in gas sampling bags. Air was drawn from the bags by the detectors during the End of Day activities to obtain zero and span check rending.

Chain-of-custody procedures were implemented throughout the field monitoring program to document transfers of sampler media and documentation logs. An example of the chain-of-custody log 1s depicted in Figure 2-8. As shown in the figure, every transfer of sampler media required the signature of the recipient, who then assumed responsibility for that sampler. Similar forms were used to document shipments to the analytical laboratories.


2.6.1 Pretest Protocol

A pretest was performed prior to the formal field monitoring program. Activities in the pretest mimicked, to the fullest extent possible, the field monitoring program. The pretest provided a final shakedown of instrumentation, measurement methods, and operational protocols; results of the pretest were used to refine operational protocols and documentation procedures.

The pretest for the monitoring program was performed in March 1989. It consisted of monitoring on four commercial airline flights over a three-day period. The flights were selected and developed into a2-57 chain that originated and terminated 1n Washington, DC, to mimic the chaining procedure that would be used in the formal monitoring program. Aircraft represented in the four flights included a 767, DC-10, and two 737-300s.

The four flights monitored were smoking flights, with durations of 4 to 5.5 hours. Flights of longer duration were selected for the pretest because one objective was to assess spatial variation of nicotine and RSP concentrations. To address this objective, eight locations were selected in each aircraft to examine horizontal variations. At four of the eight locations, a vertical array was configured to sample nicotine and RSP at 25 cm (10 inches) and 150 cm (59 inches) above the floor, in addition to the breathing-height sample. Integrated samples were collected throughout the “smoking” period.

The pretest was also used to assess various methods for obtaining information on smoking during the flight. Three different approaches to counting smokers were used:

  • Counting smokers at 15-minute intervals
  • Counting smokers at 10-minute intervals
  • Counting smokers during visits to the lavatory at fixed intervals.

These counts were compared to counts of smokers made on n continual basis by one or two technicians seated in the smoking section. The results of the various counting methods were also compared to the number of cigarette butts collected from the ashtrays at the end of the flight.

The pretest provided an opportunity to test procedures for measurement of air exchange rates with the PFT method. PFT deployment and sampler placement methods were tested at all eight locations in the airliner cabin to determine the appropriate sites for placement of sources and samplers.

In addition to the shakedown of methodologies and instrumentation, the pretest conducted on commercial flights provided the opportunity to assess logistical problems related to airport security clearance;

pre-flight and post-flight activities in airport waiting areas; start-of-day and end-of-day preparation, maintenance and calibration activities; and passenger and flight attendant reaction to technician activities.

2.7 Pretest Results

Nicotine Concentration (ug/m3)
Height*Flight 1Flight 2Flight 3Flight 4

High0 00
Low0.2 00

Middle0  0


Boundary-1High   0
Middle 900
Boundary-3High  **0
Middle  **0
Low  05.9
* Samples placed at "high" were 150 cm above the floor, at "medium rare near breathing height, and at "low" were 25 cm above the floor. Samples were collected at the three heights at four of eight locations. At the other four locations, samples were collected only at the "middle" height.
** Samples invalid

The four pretest flights provided a good range of smoking rates, with cigarette butt counts ranging from a low of 33 on the second flight (Boeing 737-300 aircraft) to a high of 166 cigarette butts collected on the first flight (Boeing 767).

On the four flights, nicotine concentrations ranged from non-detectable to 67.6 ug/m3, as shown in Table 2-16. Concentrations of nicotine 1n samples collected in the smoking and boundary sections were highly variable. There were no clear biases in concentration related to sampler height, with three of five sample sets collected in smoking sections having the highest nicotine concentration (in the vertical plane) located near the floor and the other two having highest concentrations at 60 inches (i.e., above breathing height).

RSP concentrations on the four flights ranged from 8 to 317 Ng/m3 (Table 2-17). Concentrations were generally lowest in the nonsmoking sections, highest in smoking sections, and intermediate in the boundary section. There was often substantial vertical variation. For the sample sets, the highest concentrations were measured near the floor, whereas three sample sets had the highest concentration at the 150-cm height.

Results of nicotine and RSP measurements confirmed that selection of the four target locations for monitoring (smoking, boundary, nonsmoking middle, and nonsmoking remote) would be appropriate and required for data interpretation. The measurements performed at the three heights above the floor did indicate substantial differences in concentrations at the three heights. Although the data base for the four flights was too small to determine the significance of the differences, the data suggested that measurements in the formal monitoring program should be performed with the instrument package on the technician’s lap or lap tray to obtain measurements of contaminants most representative of the passenger breathing level.

Correct placement of the PFT sources and samplers in the airliner cabin was essential to the performance of the measurement system. Because the number of technicians during the monitoring program would be limited to one on smoking flights and two on nonsmoking flights, tests were performed during the pretest flights to determine how source and sampler locations could be optimized. For example, during the pretest some technicians carried both sources and samplers to determine how far the source needed to be from the sampler.

Results of air exchange measurements during the pretest are presented in Table 2-18 and compared to nominal air exchange rates for the four flights. For three cases where technicians sat within one row of one another measurements with the samplers agreed within 6 percent of each other. Air exchange rates were underestimated by as much as 80 percent, if the samplers were located at the same seat location as the sources, but separation of sources and samplers by as little as one row of seats yielded acceptable measurement results. Based on the results, deployment of sources by technicians in the nonsmoking (remote) and smoking sections and samplers at the other two seats was used in the study.

Seat Location (section)Seat RSP concentration (ug/nr3)
Height*Flight 1Flight 2Flight 3Flight 4
Nonsmoking (Remote)-1High36 5955
Low46 5658
Nonsmoking (Remote)-2High    
Middle44  61
Nonsmoking (Middle)-1High34811767
Middle32 7067
Nonsmoking (Middle)-2High    
Boundary-1High   0
Low   127
Boundary-3High  **59
Middle  **132
Low  63145
* Samples placed at "high" were 150 cm above the floor, at "medium rare near breathing height, and at "low" were 25 cm above the floor. Samples were collected at the three heights at four of eight locations. At the other four locations, samples were collected only at the "middle" height.
** Samples invalid

During the pretest flights, two different counting methods and three different estimation methods were used to estimate the number of cigarettes smoked during a flight. The counting methods consisted of (1) counting or collection of cigarette butts f ran ashtrays at the end of the flight and (2) recording of every smoking event independently by two technicians. The estimation methods included (1) recording the count of smoking events observed during a one-minute interval every 10 minutes, (2) recording the count of smoking events observed during a one-minute interval every 15 minutes, and (3) recording the count of cigarettes being smoked during a trip to the lavatory every 30 minutes.

Results of the counting and estimation tests are shown in Table 2-19. Compared to the counting of butts, the most definitive method 1n the pretest because of airline cooperation” the 15-minute interval counts appeared to be the most appropriate method for estimation of smoking events. Both 10-minute interval and l.5-minute interval counts gave reasonable estimates on some of the fights, but 10-minute intervals did not improve the accuracy of this estimation method. The major factor affecting the accuracy of smoking counts was seat location. The ability to see smokers in front of the technician most strongly affected counting accuracy, and technicians seated toward the front of the smoking section tended to underestimate smoking rates. Therefore, seat locations near the rear of the smoking section were to be selected for the formal monitoring program. Technician trips to the lavatory as a method to count smokers were not logistically feasible due to food and beverage service and resulted 1n highly inaccurate counts on two of the four flights.

During the pretest flights, attempts were made initially to count the number of cigarette butts in the ashtrays on the aircraft at the end of the flight. This was generally difficult. Collection of cigarette butts in bags at the end of the flight for subsequent counting in the airport proved to be a better approach, particularly on flights requiring a fast turnaround. This method was used in the normal monitoring program.2-65


  1. Akland, G.G., T.O. Hartwell, T.R. Johnson, and R.W. Whitmore, 1985. ‘Measuring Human Exposure to Carbon Monoxide in Washington, D.C. and Denver, Colorado, During the Winter of 1982-83.” Environ. Sci. Tech. 19:911-918.
  2. ASHRAE, 1989. “Ventilation for Acceptable Indoor A1r Quality” ANSI/ASHRAE 62-1989. American Society of Heating, Refrigerating, and Air Conditioning Engineers.
  3. ASHRAE, 1985. ASHRAE Handbook — 1985 Fundamentals. American Society of Heating, Refrigerating, and Air conditioning engineers, Atlanta, GA.
  4. Burge, H.A., M. Chatigny, J. Feeley, K. Kreiss, P. Morey, J. Otten, and K. Peterson, 1987. ‘Bioaerosols–Guidelines for Assessment and Sampling of Saprophytic Aerosols in the Indoor Environment” Appl. Indust. Hya. 2(5):R-10 to R-16.
  5. Cochran, W.C., 1963. Sampling Techniques. John Wiley and Sons. New York, NY.
  6. Dietz, R.N. and E.A. Cote, 1982. ‘Air Infiltration Measurements in a Home Using a Convenient Perfluorocarbon Tracer Technique.” Environ. Intern. 8:419-433.
  7. Federal Aviation Administration (FAA). 1989. Radiation Exposure of Air Carrier Crewmembers. AAM-624. FAA, U.S. Department o transportation.
  8. Hammond, S.K., B.P. Leaderer, A.C. Roche, and M. Schenker, 1987.
    ‘Collection and Analysis of Nicotine As Marker for Environmental Tobacco Smoke.” Atmos. Environ.. 21(2):457-462.
  9. Ingebrethsen, B.J., D.L. Heavner, A.L. Angel, J.M. Conner, T.J. Steichen, and C.R. Green, 1988. “A Comparative Study of Environmental Tobacco Smoke Particulate Mass Measurements for an Environmental Chamber.” J. Air Poll. Control Assoc. 38:413-417.
  10. Klsh, L., 1965. Survey Sampling. John Wiley & Sons, Inc., New York, NY.
  11. Langbert, J.L., J.Y. Paukstelis, and Y.C. Chiang, 1989. “3-Methyl-2-benzothlazolinone Acetone Azine with 2-Phenylphenol as a Solid Passive Monitoring Reagent for Ozone.’ Environ. Sci. Technol. 23:241-243.
  12. Leaderer, B.P., Cain, W.S., Isseroff, R. and Berglund, L.G., 1984. “Ventilation Requirements in Buildlngs — II. Particulate Matter and Carbon Monoxide From Cigarette Smoking.” Atmos. Environ. 18(1):99-106.
  13. Lynch, A.L., 1981. Evaluation of Ambient Air quality Personnel Monitoring. CRC Press, Inc. Boca Raton,
  14. L. Muramatsu, M., S. Umemura, T. Okada, and H. Tomita, 1984. ‘Estimation of Personal Exposure to Tobacco Smoke with a Newly Developed Nicotine Personal Monitor.” Environ. Res. 35:218-227.
  15. Nagda, N.L. and M.D. Koontz, 1985. “Microenvironmental and Total Exposures to Carbon Monoxide for Three Population Subgroups.” J. Air Poll. Control Assoc. 35(2):134-137.
  16. Nastrom, G.D. and J.D. Holdeman, 1980. T Date Administration, Washington, D.C.
  17. National Research Council, 1986. Environmental Tobacco Smoke: re Exposures And Assessing Health Effects. Academy Press. Washington, D.C.
  18. NIOSH, 1977. NIOSH Manual of Analytical Methods, Vol. 3, 2nd edition, Publication No. -1 – , National Institute of Occupational Safety and Health, U.S. Department of Health, Education and Welfare.
  19. Oldaker, G.B. III and F.C. Conrad, Jr., 1987. ‘Estimation of Effect of Environmental Tobacco Smoke on Air Quality Within Passenger Cabins of Commercial Aircraft.” Environ. Sci. Technol. 21(10):994-999.
  20. Ott, W.R., C. Williams, C.E. Rodes, R.J. Drago, and F.J. Burman, 1986. “Automated Data-Logging Personal Exposure Monitors for Carbon Monoxide” J. Air Poll. Control Assoc. 36(8):893-887.
  21. Repace, J.L., 1987. “Indoor Concentrations of Environmental Tobacco Smoke: Field Surveys,” in Environmental Carcinogens Methods of Analysis and Exposure Measurement Volume –Passive smoking, International Agency for Prevention of cancer scientific Publications No. 91, Lyon, France.
  22. Repace, J.L., and A.H. Lowry, 1982. “Tobacco Smoke, Ventilation and Indoor A1r Quality.” American Society of Heating, Refrigeration and Air Conditioning Engineers transactions . 4- 14.
  23. Repace, J.L., and A.H. Lowry, 1980. “Indoor Air Pollution, Tobacco Smoke and Public Health.” Science 208:464-472.
  24. Rlckert, W.S., J.C. Robinson, and N. Collishaw, 1984. “Yields of Tar, Nicotine, and Carbon Monoxide in the Sidestream Smoke From 15 Brands of Canadlan Cigarettes.” Am. J. Public Health 74-228-231.
  25. Spengler, J.D., R.D. Treitman, T.D. Tosteson, D.T. Mage, and M.L. Soxzek, 1985. Personal Exposures to Respirable Particulates and Implications for Air Pollution Epidemiology.” Environ. Sci. Technol. 19:700-707.
  26. United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). 1982. Ionizing Radiation: Sources and Biological Effects. Report to the General Assembly. Annex B. Paragraph 1 . UN Publication Sales No. E.82.IX.8. United Nations. New York. As cited in United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). 1988. Sources, Effects and Risks of Ionizing Radiation. Report to the General Assembly.
  27. United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). 1986. Genetic and Somatic Effects of Ionizing Radiation. Report to the General Assembly. Annex Biological effects of pre-natal irradiation. UN Publication Sales No. E.B6.IX.9. United Nations. New York.
  28. United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). 1988. Sources, Effects and Risks of Ionizin9 Radiation. Report to the General Assembly. Annex F: Radiation carcinogenesis in man. UN Publication Sales No. E.B8.IX.7. United Nations. New York.
  29. U.S. Department of Transportation, Federal Aviation Administration, Research and Special Programs Administration, 1987. Air ort Activity Statistics of Certified Route Air Carriers: 12 Months ending December 31 19 7. U. S. Government Pricing Office, Washington, D.C.

Download poster


  • "Es ist schwieriger, eine vorgefaßte Meinung zu zertrümmern als ein Atom."
    (Het is moeilijker een vooroordeel aan flarden te schieten dan een atoom.)
    Albert Einstein

  • "Als je alles zou laten dat slecht is voor je gezondheid, dan ging je kapot"
    Anonieme arts

  • "The effects of other people smoking in my presence is so small it doesn't worry me."
    Sir Richard Doll, 2001

  • "Een leugen wordt de waarheid als hij maar vaak genoeg wordt herhaald"
    Joseph Goebbels, Minister van Propaganda, Nazi Duitsland

  • "First they ignore you, then they laugh at you, then they fight you, then you win."
    Mahatma Gandhi

  • "There''s no such thing as perfect air. If there was, God wouldn''t have put bristles in our noses"
    Coun. Bill Clement

  • "Better a smoking freedom than a non-smoking tyranny"
    Antonio Martino, Italiaanse Minister van Defensie

  • "If smoking cigars is not permitted in heaven, I won't go."
    Mark Twain

  • I've alllllllways said that asking smokers "do you want to quit?" and reporting the results of that question, as is, is horribly misleading. It's a TWO part question. After asking if one wants to quit it must be followed up with "Why?" Ask why and the majority of the answers will be "because I'm supposed to" (victims of guilt and propaganda), not "because I want to."
    Audrey Silk, NYCCLASH