1. ^aAlion Science and Technology Inc., Research Triangle Park, North Carolina, USA
2. ^bNational Exposure Research Laboratory, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA
3. ^cOffice of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, North Carolina, USA

Correspondence: Dr. G. Glen, Alion Science and Technology, PO Box 12313, Research Triangle Park, NC 27709, USA. Tel.: +919 406 2157; Fax: +919 549 4665; E-mail: gglen@alionscience.com

Received 6 December 2006; Accepted 6 March 2007; Published online 5 September 2007.

Abstract

Human exposure time-series modeling requires longitudinal time–activity diaries to evaluate the sequence of concentrations encountered, and hence, pollutant exposure for the simulated individuals. However, most of the available data on human activities are from cross-sectional surveys that typically sample 1 day per person. A procedure is needed for combining cross-sectional activity data into multiple-day (longitudinal) sequences that can capture day-to-day variability in human exposures. Properly accounting for intra- and interindividual variability in these sequences can have a significant effect on exposure estimates and on the resulting health risk assessments. This paper describes a new method of developing such longitudinal sequences, based on ranking 1-day activity diaries with respect to a user-chosen key variable. Two statistics, "D" and "A", are targeted. The D statistic reflects the relative importance of within- and between-person variance with respect to the key variable. The A statistic quantifies the day-to-day (lag-one) autocorrelation. The user selects appropriate target values for both D and A. The new method then stochastically assembles longitudinal diaries that collectively meet these targets. On the basis of numerous simulations, the D and A targets are closely attained for exposure analysis periods >30 days in duration, and reasonably well for shorter simulation periods. Longitudinal diary data from a field study suggest that D and A are stable over time, and perhaps over cohorts as well. The new method can be used with any cohort definitions and diary pool assignments, making it easily adaptable to most exposure models. Implementation of the new method in its basic form is described, and various extensions beyond the basic form are discussed.