Total Exposure Assessment Methodology Study Research Articles

Source apportionment of exposures to volatile organic compounds: II. Application of receptor models to TEAM study data

Four receptor-oriented source apportionment models were applied to personal exposure measurements for toxic volatile organic compounds (VOCs). The measurements are from the total exposure assessment methodology studies conducted from 1980 to 1984 in New Jersey (NJ) and California (CA) and the 1987–1990 CA Indoor Exposure study. The receptor models applied are the Chemical Mass Balance model, Principal Component Analysis/Absolute Principal Component Scores, Positive Matrix Factorization, and Graphical Ratio Analysis for Composition Estimates/Source Apportionment by Factors with Explicit Restriction. Major sources of personal exposure to toxic VOCs appear to have been aromatic sources resembling automobile exhaust, gasoline vapor, or environmental tobacco smoke, and a 1,1,1-trichloroethane-dominated source that may be associated with solvent or pesticide use. Drycleaning chemicals, deodorizers or mothballs, and building materials or carpet emissions also appear to have been significant sources of exposure. Source apportionment results from the four models agreed reasonably well for the NJ data. The performance of the models was generally poorer for the CA data, and the corresponding source apportionment results were less consistent across the models.

Source apportionment of exposure to toxic volatile organic compounds using positive matrix factorization.

Data from the Total Exposure Assessment Methodology studies, conducted from 1980 to 1987 in New Jersey (NJ) and California (CA), and the 1990 California Indoor Exposure study were analyzed using positive matrix factorization, a receptor-oriented source apportionment model. Personal exposure and outdoor concentrations of 14 and 17 toxic volatile organic compounds (VOCs) were studied from the NJ and CA data, respectively. Analyzing both the personal exposure and outdoor concentrations made it possible to compare toxic VOCs in outdoor air and exposure resulting from personal activities. Regression analyses of the measured concentrations versus the factor scores were performed to determine the relative contribution of each factor to total exposure concentrations. Activity patterns of the NJ and CA participants were examined to determine whether reported exposures to specific sources correspond to higher estimated contributions from the factor identified with that source. For a subset of VOCs, a preliminary analysis to determine irritancy-based contributions of factors to exposures was carried out. Major source types of toxic VOCs in both NJ and CA appear to be aromatic sources resembling automobile exhaust, gasoline vapor, or environmental tobacco smoke for personal exposures, and automobile exhaust or gasoline vapors for outdoor concentrations.

Exposure Measurement Error in Time-Series Studies of Air Pollution: Concepts and Consequences

Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them.

Exposure measurement error in time-series studies of air pollution: concepts and consequences.

Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them.

Breath measurements as volatile organic compound biomarkers.

A brief review of the uses of breath analysis in studies of environmental exposure to volatile organic compounds (VOCs) is provided. The U.S. Environmental Protection Agency's large-scale Total Exposure Assessment Methodology Studies have measured concentrations of 32 target VOCs in the exhaled breath of about 800 residents of various U.S. cities. Since the previous 12-hr integrated personal air exposures to the same chemicals were also measured, the relation between exposure and body burden is illuminated. Another major use of the breath measurements has been to detect unmeasured pathways of exposure; the major impact of active smoking on exposure to benzene and styrene was detected in this way. Following the earlier field studies, a series of chamber studies have provided estimates of several important physiological parameters. Among these are the fraction, f, of the inhaled chemical that is exhaled under steady-state conditions and the residence times. tau i in several body compartments, which may be associated with the blood (or liver), organs, muscle, and fat. Most of the targeted VOCs appear to have similar residence times of a few minutes, 30 min, several hours, and several days in the respective tissue groups. Knowledge of these parameters can be helpful in estimating body burden from exposure or vice versa and in planning environmental studies, particularly in setting times to monitor breath in studies of the variation with time of body burden. Improvements in breath methods have made it possible to study short-term peak exposure situations such as filling a gas tank or taking a shower in contaminated water.