Environmental Health Criteria 214

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HUMAN EXPOSURE ASSESSMENT investigation, especially during the design and data analysis stages. Sample size determination is an important use of statistics during the planning of an exposure assessment study. Numerical and graphical descriptive statistics can be used to summarize exposure data and perform preliminary analyses of relationships between and among exposure variables. In many cases, exposure data are approximately normally or lognormally distributed and can thus be readily incorporated into standard parametric statistical inference methods such as estimation and hypothesis testing. In addition, other parametric statistical models such as ANOVA, linear regression and logistic regression can be used to quantify associations among exposure measures. In situations where the number of observations is small or the data cannot be transformed to an approximately normal distribution, non-parametric methods such as the sign, Mann-Whitney and Kruskal-Wallis tests can be used to test hypotheses. 5. HUMAN TIME-USE PATTERNS AND EXPOSURE ASSESSMENT 5.1 Introduction Methods for the collection and application of time-use data in exposure studies are critically reviewed in this chapter. All methods have their limitations. With appropriate quality assurance, however, information on time use and activity patterns collected by questionnaire, diary, interview, observation or technical means can be very valuable for interpreting and modelling exposures. Although the methodologies of time-activity data collection are universal, they need to be applied and their vocabularies selected keeping in mind the population and culture of concern and objectives of the study. Accurately and reliably documenting the time-activity patterns of the general and target populations are important components of understanding and mitigating human exposure (see Table 15). People's activity patterns, eating and drinking habits, and lifestyle characteristics must be superimposed over concentrations in environmental media before it is possible to derive realistic estimates of actual human exposure. Too often in the past, pollutant concentrations in a particular medium have been assumed to represent exposure, only for it to be found later that they did not provide an accurate picture owing to modifying factors such as the time people spend indoors rather than outdoors, food preparation and cooking, and use of bottled water instead of tap water. Experience has shown that exclusive reliance on central monitoring sites (e.g., urban air pollution monitoring sites, samples from drinking-water reservoirs) and bulk sampling procedures (e.g., spot checks for pesticides in food) for determining human exposures may be insufficient in many cases. In an exposure context, data about human time use and activity patterns (often referred to as time-activity data) have four related purposes. 1. Knowledge of the activities performed while a study participant carried a personal monitor can aid in identifying the determinants of exposure, i.e., "What did this person do that led her/him to have such a high exposure?" and "To what extent can exposure be explained the amount of time spent in specific activities or locations?" For instance, several studies in which activity pattern data were collected in conjunction with monitoring data have shown that indicators such as commuter status, work status, cooking fuel type, http://www.inchem.org/documents/ehc/ehc/ehc<strong>214</strong>.htm Page 78 of 284 6/1/2007
HUMAN EXPOSURE ASSESSMENT season, residential location and day of week are important in differentiating exposure to carbon monoxide and nitrogen dioxide (Akland et al., 1985; Ryan et al., 1990; Schwab et al., 1990; Berglund et al., 1994a). Investigations of VOC exposure have found that people who reported engaging in auto-related activities (e.g., exposure to vehicle exhausts, pumping gasoline and visiting a service station) were associated with statistically significant increases in breath and personal exposure levels of several aromatic and aliphatic compounds; Table 15. Features of time-activity studies aimed at exposure assessment Location Pollutant Participant characteristics Survey characterist Cincinnati, No pollutant 487 people under age 70; March and August 19 Ohio, USA representative; includes diary; minute resol children; oversample 3-day sample; time asthmatics; data on age, year; day of week; gender, race, income, of day work status, health status California, No pollutant 1780 people over age 11; October 1987-July 1 USA representative of recall and question English-speaking households; time of year; day o stratified by region; data time of day on demographics and socio-economic status California, No pollutant 1200 children under age April 1989-March 19 USA 12; representative of recall and question English-speaking households; time of year; day o stratified by region; data time of day on demographic and socio-economic status Kanawha No pollutant 90 children aged 9-11; July and September Valley, West longitudinal (4 weeks); diaries; 30-min res Virginia, USA stratified by gender, except travel (15 m respiratory health; data on time of year; day o demographic, socio-economic time of day status and health variables Table 15. (continued) Page 79 of 284 Location Pollutant Participant characteristics Survey characterist New York, No pollutant 1000 children aged 5-12; Mid-1990 to mid-199 New Jersey; stratified by state, 24-h recall of chil Pennsylvania, weekday/weekend and season; activities by adult Oregon; data on demographic, caregiver; 30-min r Washington; socio-economic status and questionnaire; time California, community type year; day of week; USA of day http://www.inchem.org/documents/ehc/ehc/ehc<strong>214</strong>.htm 6/1/2007
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