Variability of environmental exposures to volatile organic compounds

The relationships between levels of volatile organic compounds (VOCs) in blood and air have not been well characterized in the general population where exposure concentrations are generally at parts per billion levels. This study investigates relationships between the levels of nine VOCs, namely, benzene, chloroform, 1,4-dichlorobenzene, ethylbenzene, methyl tert-butyl ether (MTBE), tetrachloroethene, toluene, and m-/p-and o-xylene, in blood and air from a stratified random sample of the general US population. We used data collected from 354 participants, including 89 smokers and 265 nonsmokers, aged 20-59 years, who provided samples of blood and air in the National Health and Nutrition Examination Survey (NHANES) 1999-2000. Demographic and physiological characteristics were obtained from self-reported information; smoking status was determined from levels of serum cotinine. Multiple linear regression models were used to investigate the relationships between VOC levels in air and blood, while adjusting for effects of smoking and demographic factors. Although levels of VOCs in blood were positively correlated with the corresponding air levels, the strength of association (R 2 ) varied from 0.02 (ethylbenzene) to 0.68 (1,4-DCB). Also the blood-air relationships of benzene, toluene, ethylbenzene, and the xylenes (BTEX) were influenced by smoking, exposure-smoking interactions, and by gender, age, and BMI, whereas those of the other VOCs were not. Interestingly, the particular exposure-smoking interaction for benzene was different from those for toluene, ethylbenzene, and the xylenes. Whereas smokers retained more benzene in their blood at increasing exposure levels, they retained less toluene, ethylbenzene, and xylenes at increasing exposure levels. Investigators should consider interaction effects of exposure levels and smoking when exploring the blood-air relationships of the BTEX compounds in the general population.

Section: Discussionmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Relationships between levels of volatile organic compounds in air and blood from the general population

Lin

Egeghy

Rappaport

2007

“…Comparable analyses could be conducted using VOC data from the EPA's total exposure methodology (TEAM) studies conducted from 1980 through 1984 (Wallace, 1987) by treating cities as pseudo-PSUs. Rappaport and Kupper (2004) Variance components then were estimated using SAS Proc VARCOMP. Maximum likelihood estimation was used because it always produces non-negative estimates of variance components; moreover, the estimates usually were comparable to those produced by the other methods.…”

Section: Variance Componentsmentioning

confidence: 99%

Cost/variance optimization for human exposure assessment studies

Whitmore

Pellizzari

Zelon

et al. 2005

The National Human Exposure Assessment Survey (NHEXAS) field study in EPA Region V (one of three NHEXAS field studies) provides extensive exposure data on a representative sample of 249 residents of the Great Lakes states. Concentration data were obtained for both metals and volatile organic compounds (VOCs) from multiple environmental media and from human biomarkers. A variance model for the logarithms of concentration measurements is used to define intraclass correlations between observations within primary sampling units (PSUs) (nominally counties) and within secondary sampling units (SSUs) (nominally Census blocks). A model for the total cost of the study is developed in terms of fixed costs and variable costs per PSU, SSU, and participant. Intraclass correlations are estimated for media and analytes with sufficient sample sizes. We demonstrate how the intraclass correlations and variable cost components can be used to determine the sample allocation that minimizes cost while achieving pre-specified precision constraints for future studies that monitor environmental concentrations and human exposures for metals and VOCs.

“…However, only a few studies of environmental exposures to various air pollutants also reported between-person and within-person variance components for the study groups (e.g., Lee et al, 2004;Rappaport and Kupper, 2004;Egeghy et al, 2005;Lin et al, 2005;Sorensen et al, 2005;Lanki et al, 2007;Sarnat et al, 2009), two of these studies investigated exposure to PM 2.5 and BS (Sorensen et al, 2005;Lanki et al, 2007). These variance components have been more commonly estimated with personal exposure data collected from occupational settings (Kromhout et al, 1993;Preller et al, 1995;Rappaport et al, 1999;Burstyn et al, 2000;Liljelind et al, 2001;Symanski et al, 2001;Peretz et al, 2002;Hagstrom et al, 2008;Spaan et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Variability of environmental exposure to fine particles, black smoke, and trace elements among a Swedish population

Johannesson

Rappaport

Sällsten

2011

Self Cite

Mixed-effects models were used to estimate within-person and between-person variance components, and some determinants of environmental exposure to particulate matter (PM 2.5 ), black smoke (BS) and trace elements (Cl, K, Ca, Ti, Fe, Ni, Cu, Zn, and Pb) for personal measurements from 30 adult subjects in Gothenburg, Sweden. The within-person variance component dominated the total variability for all investigated compounds except for PM 2.5 and Zn (in which the variance components were about equal). Expressed as fold ranges containing 95% of the underlying distributions, the within-person variance component ranged between 5-fold and 39-fold (median: sixfold), whereas the between-person variance component was always osixfold (median: threefold). The relatively large within-person variance components can lead to attenuation bias in exposure-response relationships and point to the importance of obtaining repeated samples of PM exposure from study subjects in epidemiological investigations of urban air pollution. On the basis of the variance components estimated for the various particulate species, between 3 and 39 repeated measurements per subject would be required to limit attenuation bias to 20%. Significant determinants for personal exposure levels were urban background air concentrations (PM 2.5 , BS, Cl, Zn, and Pb), cigarette smoking (PM 2.5 , BS, K, and Ti), season (PM 2.5 , Fe, and Pb), and the time spent outdoors or in traffic (Fe).