Methods: This study enrolls an anticipated 25 commercial roofing workers in the greater Seattle area. Data are collected during summer of 2016, when the greatest anticipated heat exposure and roofing activities are anticipated to occur. Using a repeated measures design, each study subject participates in full shift sampling for at least two days, one cooler and one hotter day, during typical work activities. Personal- and area-level measurements are collected continuously using Thermochron iButtons affixed to works’ waists (personal) and a 3M QuesTemp 36 Heat Stress Monitor and Inspeed Vortex Wind Sensor (area) positioned on the roofs near study participants. Regional-level measurements are collected from nearby weather stations at 15-minute, hourly, and daily intervals. Temperature is recorded at all levels as the dry temperature and apparent temperature. Additional factors influencing exposure, including job task, roofing materials, and proximity to weather stations are collected through researcher observations and participant surveys. The relationship between heat exposure time-series data from the different monitoring strategies (regional, area, personal) are explored. The degree to which each monitoring approach alters the assessment of heat health risk using standard guideline input parameters, including work-shift maximums and minimums, time-weighted averages, and time spent above relevant thresholds, is also assessed.
Results: This study reports the relationship between regional-, area-, and personal-level measures of ambient dry and apparent temperature, taking into account factors that influence individual exposures, in a sample of roofers in the greater Seattle area during a range of warm-month weather conditions.
Conclusions: This study provides insight into the relationship between different monitoring strategies for occupational heat exposure in a working population exposed to both ambient and task-specific sources of heat. Potential advantages and disadvantages of heat exposure data collection at the individual and area level, compared to the regional level, are discussed. Improvement in the understanding of the variability and potential bias of different monitoring strategies is not only informative for future heat-health research, but also for heat management practices and targeted heat related illness prevention efforts.