Abstract
Although studies of occupational exposure to volatile organic compounds (VOCs) often partition variability across groups, and between and within persons, those of environmental exposure to VOCs have not involved such partitioning. Using data from the Environmental Protection Agency's total exposure assessment methodology (TEAM) studies, we partitioned exposure variability across cities, and between and within persons for nine VOCs. The estimated variance components decreased in the order: within-person > between-person > across city. Despite their smaller magnitudes, estimates of between-person and across-city variance components were sufficiently large to provide reasonable contrast for informative epidemiology studies of most VOCs. Estimates of between-person variance components for environmental VOCs were similar to those published for occupational VOCs (groups defined by job and factory). However, estimates of within-person variance components were much greater for environmental VOCs, probably due to the greater diversity of locations (including the workplace) visited by the general public over time. For benzene and perchloroethylene, we used a simple model to calculate numbers of personal measurements required to relate the exposure level to health outcome statistically. About 10 times more personal measurements would be required to investigate perchloroethylene exposure as compared to benzene exposure; this disparity reflects the greater within-subject variability of perchloroethylene data compared to benzene data. We conclude that variability should be partitioned for environmental VOC exposures in much the same manner as for occupational exposures. There should be sufficient variability in the levels of most VOCs across cities and between subjects to provide reasonable contrast for informative epidemiology studies, as we illustrate for exposures to benzene. Yet, epidemiologists should be wary of investigating environmental VOCs without preliminary data with which to estimate the variance structure of exposure variables.
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Notes
The following VOCs were investigated: benzene, chloroform, ethylbenzene, methylchloroform, p-dichlorobenzene, perchloroethylene, styrene, trichloroethylene, and o-xylene.
The following VOCs were reported by Kromhout et al.: benzene, diphenyl, diphenylether, ethanal, formaldehyde, an unspecified organic vapor, perchloroethylene, styrene, toluene, total solvents, trichloroethane, and xylene.
The VOCs routinely measured above detection limits were 1,3-butadiene, benzene, toluene, ethylbenzene, p-, m-, and o-xylene, 1,3,5-trimethylbenzene, styrene, p-isopropyltoluene, 1,2,4-trimethylbenzene, p-dichlorobenzene, and naphthalene. Sampled locations included homes, offices, restaurants, pubs, department stores, cinemas, perfume shops, libraries, laboratories, train stations, trafficked roads, cars, trains, and buses.
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Acknowledgements
This work was supported by the National Institute for Environmental Health Sciences through Grants P42ES05948 and P30ES10126. The authors appreciate the assistance of Dr. Peter Egeghy in downloading data from EPA's online database.
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Appendices
Appendix A
Samples of data obtained from the TEAM database, providing personal and outdoor air concentrations, day and night, for nine VOCs are summarized in Table A1
Appendix B
Derivation of a relationship to predict sample sizes
Assume exposure Model (1) with kh=k subjects in the hth city (h=1, 2, …, H>3) and nhi=n measurements/subject), and assume that each person is assigned his/her city's observed mean (log-scale) exposure
(since μyhi is unobservable in Model (4)), where
is the observed (log-scale) mean exposure for the ith person in the hth city. Then the unweighted least-squares estimator of β1 in Model (4), using a group-based analysis, is
where
and
Using relationships given by Tielemans et al. (1998), we can show that the expected value of θ̂*1 is E(θ̂*1)=cβ1, where
and where σα2, σB2, and σW2 are defined under Model (1). Also the standard error of θ̂*1 has the form:
Using the above relationships for E(θ̂1*) and SE(θ̂1*) and assuming the variance components σ2e, σ2α, σ2B, and σ2W are known (a standard assumption for such sample size derivations), a 100(1−α)% large-sample approximate confidence interval for θ1 is
Assuming θ1>0 (indicating an adverse effect of exposure on the health outcome), we seek choices for H(>3), k, and n (≥2) satisfying the following inequality:
where (1−β) is the smallest probability we would accept for the event that the lower limit of the confidence interval exceeds zero. For large samples, we equivalently have
where Z is approximately a standard normal deviate. Hence, we require
leading to
After substituting for c and SE(β̂*1), we obtain
After some manipulation, this leads to Eq. (5) in the text.
Appendix C
Statistics for VOC concentrations (μg/m3) from 24-h personal measurements in five US cities (1981–1987) are summarized in Table C1.
Appendix D
Statistics for VOC concentrations (μg/m3) from 24-h outdoor measurements in five US cities (1981–1987) are summarized in Table D1.
Appendix E
Spearman's correlation coefficients for 24-h personal measurements of nine VOCs in five US cities are summarized in Table E1. See Appendix A for nominal sample sizes
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Rappaport, S., Kupper, L. Variability of environmental exposures to volatile organic compounds. J Expo Sci Environ Epidemiol 14, 92–107 (2004). https://doi.org/10.1038/sj.jea.7500309
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DOI: https://doi.org/10.1038/sj.jea.7500309
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