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Using human activity data in exposure models: Analysis of discriminating factors

Journal of Exposure Science & Environmental Epidemiology volume 13, pages 294–317 (2003)Cite this article

Abstract

This paper tests factors thought to be important in explaining the choices people make in where they spend time. Three aggregate locations are analyzed: outdoors, indoors, and in-vehicles for two different sample groups: a year-long (longitudinal) sample of one individual and a cross-sectional sample of 169 individuals from the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD). The cross-sectional sample consists of persons similar to the longitudinal subject in terms of age, work status, education, and residential type. The sample groups are remarkably similar in the time spent per day in the tested locations, although there are differences in participation rates: the percentage of days frequenting a particular location. Time spent outdoors exhibits the most relative variability of any location tested, with in-vehicle time being the next. The factors found to be most important in explaining daily time usage in both sample groups are: season of the year, season/temperature combinations, precipitation levels, and day-type (work/nonwork is the most distinct, but weekday/weekend is also significant). Season, season/temperature, and day-type are also important for explaining time spent indoors. None of the variables tested are consistent in explaining in-vehicle time in either the cross-sectional or longitudinal samples. Given these findings, we recommend that exposure modelers subdivide their population activity data into at least season/temperature, precipitation, and day-type “cohorts” as these factors are important discriminating variables affecting where people spend their time.

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Notes

  1. The indoor codes were: residence; other's residence; “work” (all locations); offices; malls/stores; restaurants/bars; and “all others”. Outdoor locations were: residential yard; sidewalks/parking lots; parks/natural areas; gas stations; and “all others”.

  2. Physical activity (PA) is a broad term in the exercise physiology/nutrition literatures. It is any movement of the body produced by skeletal muscles that results in an expenditure of energy (Kohl et al., 1988). Exercise is one component of physical activity. PA basically is everything that a human does, including all energy expended except that needed for sleeping, basal metabolism, digestion, or growth (retained energy) and reproduction. A marker of daily PA is the physical activity index (PAI). It basically is the total energy expended for a day divided by the person's basal metabolism. See McCurdy (2000) for more information on these subjects.

  3. Other educational categories used in CHAD are: none; some elementary; elementary school graduate; some high school; high school graduate; some college; missing; and “any,” which includes all categories, including missing.

  4. A categorization of the activity day into four day-types: “working” (income-producing) weekday; nonworking weekday; Saturday, and Sunday. Nine different day-types are used for the longitudinal data set, as described below, but the finer distinctions could not be made for the C/S data. Day-type subsequently was reduced to two categories: working weekdays and all-other days.

  5. Precipitation is in inches of water measured; there was no distinction made in the data available to us between rain and snow events.

  6. These are all OAQPS models. APEX stands for Air Pollution Exposure Model; HAPEM is the Hazardous Air Pollution Exposure Model; pNEM is the Probabilistic NAAQS Exposure Model.

  7. The standard deviation (SD) of the sample is shown by the ± symbol. Subsequent citations to the range drop the word “range” and only show it as a set of figures separated by a dash; X̄=Mean.

  8. A dimensional assessment of the maximum possible impact on the results predicted by Eq. (1) indicates that non-normally distributed variables account for 20–26% of the variance in ln(OUT). The actual impact would be much less than that in practice.

  9. The subject lost a diary about 20.00 h on day #358, February 14, 2000, in snow covering an airport parking lot as he was loading luggage into a car. The “penalty” for this noncompliance was carrying the diary for 3 additional days, which is why n=368 rather than 365.

  10. Actually, the H0 is that the data fit the stated distribution using a χ2 approximation to a large-sample K–S statistic at =0.05.

  11. With α=0.05, you could expect one out of 20 correlations to be significant by chance, even if there is no actual correlation.

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Acknowledgements

We thank the two anonymous reviewers of this paper for helpful suggestions regarding its content and format. The authors also thank two of our EPA colleagues, Dr.s Janet Burke and Alan Huber for a detailed review. Responding to all of the issues and comments that arose hopefully improved the clarity, presentation, and applicability of this paper.

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  1. Human Exposure and Atmospheric Sciences Division, Exposure Modeling Research Branch, National Exposure Research Laboratory/Office of Research and Development, US Environmental Protection Agency, North Carolina, USA

    Thomas Mccurdy & Stephen E Graham

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  1. Thomas Mccurdy
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  2. Stephen E Graham
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Correspondence to Thomas Mccurdy.

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The United States Environmental Protection Agency through its Office of Research and Development funded and managed the research described here. It has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute an endorsement or recommendation for use.

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Table 11
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Table A2

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Mccurdy, T., Graham, S. Using human activity data in exposure models: Analysis of discriminating factors. J Expo Sci Environ Epidemiol 13, 294–317 (2003). https://doi.org/10.1038/sj.jea.7500281

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