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USING HUMAN ACTIVITY DATA IN EXPOSURE MODELS: ANALYSIS OF DISCRIMINATING FACTORS
Citation:
McCurdy, T R. AND S E. Graham. USING HUMAN ACTIVITY DATA IN EXPOSURE MODELS: ANALYSIS OF DISCRIMINATING FACTORS. JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY 13(4):294-317, (2003).
Impact/Purpose:
The two main objectives of this research are (1) to improve and update and (2) to analyze the CHAD database.
For objective 1, we will
* Reconfigure the CHAD program into a completely modularized Oracle database.
* Redesign User Interface for effcient utilization of the program's capability.
* Obtain dates for those surveys that did not provide them to us, so that we can obtain associated meteorological/climatic inputs for the person-days of information without them.
* Revise the upper and lower bound delimiters in the energy expenditure distributions used for activity-specific estimates.
For objective 2, we will
* Evaluate data quality.
* Evaluate trends and activities for various subgroups.
* Identify temporal patterns for longitudinal data.
* Characterize resolution required for output for exposure and dose models.
Description:
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/non-work 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.
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.