Doses and lung burdens of environmental tobacco smoke constituents in nonsmoking workplaces.

This paper models nicotine dose and ultraviolet-absorbing particulate matter (UVPM) alveolar lung burden resulting from exposure to environmental tobacco smoke (ETS) for nonsmokers in workplaces where smoking was reported not to occur. Data were obtained from personal monitoring of ETS in 16 U.S. cities [Jenkins R.A., Guerin M.R., Palausky A., Counts R.W., Bayne C.K., and Dindal A.B. Determination of human exposure to environmental tobacco smoke (ETS): a study conducted in 16 U.S. cities. Draft final report by Oak Ridge National Laboratory for Center for Indoor Air Research, Linthicum, MD, 1996a; Jenkins R.A., Palausky A., Counts R.W., Bayne C.K., Dindal A.B., and Guerin M.R. Exposure to environmental tobacco smoke in sixteen cities in the United States as determined by personal breathing zone air sampling. J. Expos. Anal. Environ. Epidemiol. 1996b: 6(4): 473-502.]. This is a continuation of earlier analyses focusing on nonsmokers in smoking workplaces (SWs) [LaKind J.S., Graves C.G., Ginevan M.E., Jenkins R.A., Naiman D.Q., and Tardiff R.G. Exposure to environmental tobacco smoke in the workplace and the impact of away - from - work exposure. Risk Anal. 1999a: 19(3): 349-358; LaKind J.S., Jenkins R.A., Naiman D.Q., Ginevan M.E., Graves C.G., and Tardiff R.G. Use of environmental tobacco smoke constituents as markers for exposure. Risk Anal. 1999b: 19 (3): 359-373; LaKind J.S., Ginevan M.E., Naiman D.Q., James A.C., Jenkins R.A., Dourson M.L., Felter S.P., Graves C.G., and Tardiff R.G. Distribution of exposure concentrations and doses for constituents of environmental tobacco smoke. RiskAnal. 1999c: 19 (3): 375-390.]. Even though study participants characterized their workplaces as nonsmoking, some individuals reported observing cigarettes in the workplace. Individuals observing six or more cigarettes were excluded from the analysis on the grounds that they were in defacto SWs. Exposure to ETS was lower in nonsmoking than SWs, but even with this exclusion, exposure was not zero. Distributions were selected for each model input, and at least 2000 iterations of the model were made for each dose or lung burden characterization (e.g., for females, for males). In these nonsmoking workplaces (NSWs), neither nicotine nor UVPM concentrations were lognormally distributed. Hence, observed concentrations were used directly via bootstrap sampling (nicotine) or a constant number of times (UVPM) as input to the models. As in SWs, individuals from smoking homes (SHs) experienced greater exposure in NSWs to both nicotine and UVPM than did individuals from nonsmoking homes (NSH; P<0.001 ). The distributions of modeled nicotine dose and UVPM lung burden were highly skewed, with most individuals receiving relatively low exposure to ETS in the workplace. Comparing doses from NSWs modeled here to doses from SWs modeled previously, less difference between smoking and NSWs was apparent in UVPM levels than in nicotine levels. For average exposure, UVPM alveolar lung burdens were approximately 10-fold higher in smoking than NSWs, while average nicotine doses were 20-25 times higher in smoking than NSWs. These findings are in the range observed by other investigators and are partly explained by very low denominators in the ratios (i.e., very low levels experienced in NSWs). For upper bound exposure, the nonsmoking-to-smoking ratios remained about the same for UVPM. For nicotine, the upper bound ratios remained the same for people from NSHs but were halved for people from SHs.

Exposure to environmental tobacco smoke in the workplace and the impact of away-from-work exposure.

Concentrating on exposure in workplaces where smoking occurs, we examined environmental tobacco smoke (ETS)-related concentration data from the 16-City Study. This study involved a large population of nonsmokers, used personal monitors, and encompassed a wide selection of ETS-related constituents. This first article in a series of three describes the 16-City Study, considers the impact of demographic variables, and concludes that these variables did not explain differences in exposure to ETS. We compared 16-City Study concentrations obtained in the workplace to previously reported workplace concentrations and determined that data from this study were representative of current ETS exposure in nonmanufacturing workplaces where smoking occurs. Considering factors other than demographic factors, we found that, not surprisingly, the number of cigarettes observed in the workplace had an impact on exposure concentrations. Finally, we compared people from homes where smoking occurs with people from nonsmoking homes and found that people from smoking homes observed more smoking in the workplace and experienced higher concentrations of ETS-related compounds in the workplace, even when they observed the same number of cigarettes being smoked in the workplace. In two subsequent articles in this series, we discuss relationships between various ETS markers and provide estimates of distributions of doses to nonsmoking workers employed in workplaces where smoking occurs.

Use of environmental tobacco smoke constituents as markers for exposure.

The 16-City Study analyzed for gas-phase environmental tobacco smoke (ETS) constituents (nicotine, 3-ethenyl pyridine [3-EP], and myosmine) and for particulate-phase constituents (respirable particulate matter [RSP], ultraviolet-absorbing particulate matter [UVPM], fluorescing particulate matter [FPM], scopoletin, and solanesol). In this second of three articles, we discuss the merits of each constituent as a marker for ETS and report pair-wise comparisons of the markers. Neither nicotine nor UVPM were good predictors for RSP. However, nicotine and UVPM were good qualitative predictors of each other. Nicotine was correlated with other gas-phase constituents. Comparisons between UVPM and other particulate-phase constituents were performed. Its relation with FPM was excellent, with UVPM approximately 1 1/2 times FPM. The correlation between UVPM and solanesol was good, but the relationship between the two was not linear. The relation between UVPM and scopoletin was not good, largely because of noise in the scopoletin measures around its limit of detection. We considered the relation between nicotine and saliva continine, a metabolite of nicotine. The two were highly correlated on the group level. That is, for each cell (smoking home and work, smoking home but nonsmoking work, and so forth), there was high correlation between average continine and 24-hour time-weighted average (TWA) nicotine concentrations. However, on the individual level, the correlations, although significant, were not biologically meaningful. A consideration of cotinine and nicotine or 3-EP on a subset of the study whose only exposure to ETS was exclusively at work or exclusively at home showed that home exposure was a more important source of ETS than work exposure.

Distribution of exposure concentrations and doses for constituents of environmental tobacco smoke.

The ultimate goal of the research reported in this series of three articles is to derive distributions of doses of selected environmental tobacco smoke (ETS)-related chemicals for nonsmoking workers. This analysis uses data from the 16-City Study collected with personal monitors over the course of one workday in workplaces where smoking occurred. In this article, we describe distributions of ETS chemical concentrations and the characteristics of those distributions (e.g., whether the distribution was log normal for a given constituent) for the workplace exposure. Next, we present population parameters relevant for estimating dose distributions and the methods used for estimating those dose distributions. Finally, we derive distributions of doses of selected ETS-related constituents obtained in the workplace for people in smoking work environments. Estimating dose distributions provided information beyond the usual point estimate of dose and showed that the preponderance of individuals exposed to ETS in the workplace were exposed at the low end of the dose distribution curve. The results of this analysis include estimations of hourly maxima and time-weighted average (TWA) doses of nicotine from workplace exposures to ETS (extrapolated from 1 day to 1 week) and doses derived from modeled lung burdens of ultraviolet-absorbing particulate matter (UVPM) and solanesol resulting from workplace exposures to ETS (extrapolated from 1 day to 1 year).

Estimation of dietary exposure to chemicals: a case study illustrating methods of distributional analyses for food consumption data.

There are a number of sources of variability in food consumption patterns and residue levels of a particular chemical (e.g., pesticide, food additive) in commodities that lead to an expected high level of variability in dietary exposures across a population. This paper focuses on examples of consumption pattern survey data for specific commodities, namely that for wine and grape juice, and demonstrates how such data might be analyzed in preparation for performing stochastic analyses of dietary exposure. Data from the NIAAA/NHIS wine consumption survey were subset for gender and age group and, with matched body weight data from the survey database, were used to define empirically-based percentile estimates for wine intake (microliter wine/kg body weight) for the strata of interest. The data for these two subpopulations were analyzed to estimate 14-day consumption distributional statistics and distributions for only those days on which wine was consumed. Data subsets for all wine-consuming adults and wine-consuming females ages 18 through 45, were determined to fit a lognormal distribution (R2 = 0.99 for both datasets). Market share data were incorporated into estimation of chronic exposures to hypothetical chemical residues in imported table wine. As a separate example, treatment of grape juice consumption data for females, ages 18-40, as a simple lognormal distribution resulted in a significant underestimation of intake, and thus exposure, because the actual distribution is a mixture (i.e., multiple subpopulations of grape juice consumers exist in the parent distribution). Thus, deriving dietary intake statistics from food consumption survey data requires careful analysis of the underlying empirical distributions.

Human exposure assessment. I: Understanding the uncertainties.

Exposure estimates produced using predictive exposure assessment methods are associated with a number of uncertainties that relate to the inherent variability of the values for a given input parameter (e.g., body weight, ingestion rate, inhalation rate) and to unknowns concerning the representativeness of the assumptions and methods used. Despite recent or ongoing consensus-building efforts that have made significant strides forward in promoting consistency in methodologies and parameter default values, the potential variability in the output exposure estimates has not been adequately addressed from a quantitative aspect. This is exemplified by remaining tendencies within federal and state agencies to use worst-case approaches for exposure assessment. In this study, range-sensitivity and Monte Carlo analyses were performed on several different exposure scenarios in order to illustrate the impact of the variability in input parameters on the total variability of the exposure output. The results of this study indicate that the variability associated with the example scenarios range up to more than four orders of magnitude when just some of the parameters are allowed to vary. Comparison of exposure estimates obtained using Monte Carlo simulations (in which selected parameters were allowed to vary over their observed ranges) to exposure estimates obtained using standard parameter default assumptions demonstrate that a default value approach can produce an exposure estimate that exceeds the 95th percentile exposure in an exposed population.

Human exposure assessment. II: Quantifying and reducing the uncertainties.

Alternative methods of human exposure assessment that reduce and/or allow quantification of the uncertainties associated with exposure estimates are surveyed and illustrated. These alternative approaches include (1) use of more appropriate exposure parameter default values rather than values that result in extreme exposure estimates; (2) incorporation of time-activity data to better define appropriate exposure duration values; (3) the use of reasonable exposure scenarios rather than the traditional Maximally Exposed Individual (MEI) approach; (4) the use of stochastic approaches such as Monte Carlo-based and information analysis-based methods; (5) use of bivariate analysis to identify the extent to which interdependencies between different exposure parameters affect the distribution of exposure estimates; (6) use of less-than-lifetime exposure and risk assessment; and (7) incorporation of physiological considerations relevant to absorbed dose estimation, including route-specific impacts, use of improved absorption factors, and application of pharmacokinetic models. Other ways to improve the exposure assessment process, including assuring statistical equivalency in comparing different exposure estimates and incorporation of sensitive subpopulation considerations are also discussed, as are key research needs.

Meta-analysis: methods for combining data to improve quantitative risk assessment.

Although individual studies that constitute the data base for a risk assessment are each evaluated quantitatively as well as qualitatively, assessment of the total data base frequently results in selection of data from particular studies, rather than an effort to combine data quantitatively. Meta-analysis, or the analysis of analyses, provides an approach for the joint evaluation of the results of several studies. In this report, two very similar cancer bioassays of trichloroethylene were used to illustrate some simple meta-analytic techniques and to evaluate the validity and value of these procedures. The results demonstrate that concepts such as the upper 95% confidence limit are highly dependent upon assumptions if several data sets are involved. Use of a Monte Carlo procedure resulted in an increase in the 95% upper confidence limit relative to the value determined by EPA, but by determining the variance of the maximum likelihood estimate of the linear coordinate of the estimated dose-response curve, the 95% upper confidence limit of the cancer potency factor was reduced.

Assessing the risks of Rn exposure: the influence of cigarette smoking.

The principal hazard associated with exposure to Rn progeny is lung cancer. However, most lung cancer is caused by smoking, which raises a dual problem of deriving Rn-progeny cancer risk estimates from miner populations who, in large part, are smokers and applying these estimates to the general population whose lung cancer risk, in large part, is determined by smoking habits. We examine current risk estimates for Rn-progeny-induced lung cancer using a cohort life table methodology. Estimates of lifetime probability of dying of lung cancer, average loss in life expectancy due to premature lung cancer death, and loss in life expectancy per premature lung cancer death are calculated for the general population for 1969 and 1978, nonsmokers, and smokers. These calculations demonstrate that such risk estimates are affected by smoking, and by trends in smoking habits, in several ways. Major smoking-related factors in this interaction are the proportion of smokers in the mining population used to derive lung cancer risk estimates, the proportion of smokers in the "general" population, and the assumed interaction (additive or multiplicative) between lung cancer risk, Rn-progeny exposure, and smoking history. At this time the data are not sufficient to recommend one particular modeling approach. However, our evaluation demonstrates that broad statements about Rn-progeny lung cancer risk such as "x cancers/10(6) person working level month," while informative, are incomplete without further specification. Any risk assessment must clearly state the population assumed to be at risk and the risk model assumed to be operating. Finally, the caveats appropriate to these assumptions should also be enunciated.

Ambient air concentration of sulfur dioxide affects flight activity in bees.

Three long-term (16-29 days) low-level (0.14-0.28 ppm) sulfur dioxide fumigations showed that exposure to this gas has deleterious effects on male sweat bees (Lasioglossum zephrum). Although effects on mortality were equivocal, flight activity was definitely reduced. Because flight is necessary for successful mating behavior, the results suggest that sulfur dioxide air pollution could adversely affect this and doubtless other terrestrial insects.