You are here:
VALIDATION OF A METHOD FOR ESTIMATING LONG-TERM EXPOSURES BASED ON SHORT-TERM MEASUREMENTS
Citation:
Wallace, L A. AND R W. Williams. VALIDATION OF A METHOD FOR ESTIMATING LONG-TERM EXPOSURES BASED ON SHORT-TERM MEASUREMENTS. Presented at International Society of Exposure Analysis 14th Annual Conference, Philadelphia, PA, October 17-21, 2004.
Impact/Purpose:
The primary objectives are:
1) to complete the validation and development of exposure databases resulting from the NERL PM panel studies (data produced under TDs 5676 and 3937),
2) to perform analyses on these databases and identify key factors that have the potential of influencing human exposures to PM constituents, and
3) to summarize and report the findings of these additional analyses.
Description:
A method for estimating long-term exposures from short-term measurements is validated using data from a recent EPA study of exposure to fine particles. The method was developed a decade ago but data to validate it did not exist until recently. In this paper, data from repeated visits to 37 persons over one year (up to 28 measurements per person) are used to test the model. Both fine particle mass and elemental concentrations measured indoors, outdoors, and on the person are examined. To provide the most stringent test of the method, only two single-day distributions are randomly selected for each element to predict the long-term distributions. The precision of the method in estimating the long-term geometric mean and geometric standard deviation appears to be on the order of 10%, with no apparent bias. The precision in estimating the 99th percentile ranges from 19-48%, again without obvious bias. It appears likely that the precision can be improved by selecting a number of pairs of single-day distributions instead of just one pair. Occasionally the method fails to provide an estimate for the long-term distribution. In that case, a repeat of the selection procedure using a slightly different basis for the random selection of measurements can provide an estimate. Although the method assumes a log-normal distribution, most of the distributions tested failed the Chi-square test for log-normality. Therefore the method appears suitable for application to distributions that depart from log-normality.
Although this work was reviewed by EPA and approved for publication, it may not reflect official Agency policy.