Related Experiment Videos
Using human activity data in exposure models: analysis of discriminating factors
Thomas McCurdy1, Stephen E Graham
1Exposure Modeling Research Branch, Human Exposure and Atmospheric Sciences Division, National Exposure Research Laboratory/Office of Research and Development, US Environmental Protection Agency, North Carolina 27711, USA. mccurdy.thomas@epa.gov
Journal of Exposure Analysis and Environmental Epidemiology
|August 19, 2003
Summary
Understanding where people spend time is key. Season, weather, and day type significantly influence daily location choices, impacting exposure modeling.
Area of Science:
- Environmental Health
- Human Activity Patterns
- Exposure Science
Background:
- Understanding human activity patterns is crucial for accurate exposure assessment.
- Previous studies have analyzed time-activity patterns, but factors influencing location choices require further investigation.
Purpose of the Study:
- To identify key factors influencing where individuals spend their time (outdoors, indoors, in-vehicles).
- To analyze these factors using both longitudinal and cross-sectional data.
Main Methods:
- Analysis of two sample groups: a year-long individual study and a cross-sectional sample (169 individuals) from the US EPA's Consolidated Human Activity Database (CHAD).
- Statistical analysis to determine the importance of factors like season, temperature, precipitation, and day-type on time allocation.
Main Results:
- Season, season/temperature combinations, precipitation, and day-type (work/nonwork, weekday/weekend) were significant predictors of time spent outdoors and indoors.
- Time spent outdoors showed the most relative variability, followed by in-vehicle time.
- No tested variables consistently explained in-vehicle time allocation.
Conclusions:
- Environmental and temporal factors, including season, temperature, precipitation, and day-type, are critical determinants of human time-location patterns.
- Exposure modelers should segment population activity data by these identified "cohorts" for improved accuracy.
- In-vehicle time remains a complex behavior with less predictable influencing factors based on current analysis.