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Use of Thermoregulatory Models to Evaluate Heat Stress in Industrial Environments

Author

Listed:
  • Irena I. Yermakova

    (International Scientific-Training Centre for Information Technologies and Systems, UNESCO, National Academy of Sciences, 03187 Kyiv, Ukraine)

  • Adam W. Potter

    (Thermal and Mountain Medicine Division, U.S. Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Bldg 42, Natick, MA 01760, USA)

  • António M. Raimundo

    (Department of Mechanical Engineering, ADAI-LAETA, University of Coimbra, Pólo II da Universidade de Coimbra, 3030-788 Coimbra, Portugal)

  • Xiaojiang Xu

    (Thermal and Mountain Medicine Division, U.S. Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Bldg 42, Natick, MA 01760, USA)

  • Jason W. Hancock

    (Thermal and Mountain Medicine Division, U.S. Army Research Institute of Environmental Medicine, 10 General Greene Avenue, Bldg 42, Natick, MA 01760, USA
    Oak Ridge Institute for Science and Education (ORISE), 1299 Bethel Valley Rd., Oak Ridge, TN 37830, USA)

  • A. Virgilio M. Oliveira

    (Department of Mechanical Engineering, ADAI-LAETA, University of Coimbra, Pólo II da Universidade de Coimbra, 3030-788 Coimbra, Portugal
    Coimbra Polytechnic-ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal)

Abstract

Heat stress in many industrial workplaces imposes significant risk of injury to individuals. As a means of quantifying these risks, a comparison of four rationally developed thermoregulatory models was conducted. The health-risk prediction (HRP) model, the human thermal regulation model (HuTheReg), the SCENARIO model, and the six-cylinder thermoregulatory model (SCTM) each used the same inputs for an individual, clothing, activity rates, and environment based on previously observed conditions within the Portuguese glass industry. An analysis of model correlations was conducted for predicted temperatures (°C) of brain ( T Brain ), skin ( T Skin ), core body ( T Core ), as well as sweat evaporation rate ( ER ; Watts). Close agreement was observed between each model (0.81–0.98). Predicted mean ± SD of active phases of exposure for both moderate ( T Brain 37.8 ± 0.25, T Skin 36.7 ± 0.49, T Core 37.8 ± 0.45 °C, and ER 207.7 ± 60.4 W) and extreme heat ( T Brain 39.1 ± 0.58, T Skin , 38.6 ± 0.71, T Core 38.7 ± 0.65 °C, and ER 468.2 ± 80.2 W) were assessed. This analysis quantifies these heat-risk conditions and provides a platform for comparison of methods to more fully predict heat stress during exposures to hot environments.

Suggested Citation

  • Irena I. Yermakova & Adam W. Potter & António M. Raimundo & Xiaojiang Xu & Jason W. Hancock & A. Virgilio M. Oliveira, 2022. "Use of Thermoregulatory Models to Evaluate Heat Stress in Industrial Environments," IJERPH, MDPI, vol. 19(13), pages 1-17, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:7950-:d:851406
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    References listed on IDEAS

    as
    1. Jakob Petersson & Kalev Kuklane & Chuansi Gao, 2019. "Is There a Need to Integrate Human Thermal Models with Weather Forecasts to Predict Thermal Stress?," IJERPH, MDPI, vol. 16(22), pages 1-18, November.
    2. B. R. M. Kingma & H. Steenhoff & J. Toftum & H. A. M. Daanen & M. A. Folkerts & N. Gerrett & C. Gao & K. Kuklane & J. Petersson & A. Halder & M. Zuurbier & S. W. Garland & L. Nybo, 2021. "ClimApp—Integrating Personal Factors with Weather Forecasts for Individualised Warning and Guidance on Thermal Stress," IJERPH, MDPI, vol. 18(21), pages 1-26, October.
    3. Karin Lundgren Kownacki & Chuansi Gao & Kalev Kuklane & Aneta Wierzbicka, 2019. "Heat Stress in Indoor Environments of Scandinavian Urban Areas: A Literature Review," IJERPH, MDPI, vol. 16(4), pages 1-18, February.
    4. Tjaša Pogačar & Zala Žnidaršič & Lučka Kajfež Bogataj & Andreas D. Flouris & Konstantina Poulianiti & Zalika Črepinšek, 2019. "Heat Waves Occurrence and Outdoor Workers’ Self-assessment of Heat Stress in Slovenia and Greece," IJERPH, MDPI, vol. 16(4), pages 1-12, February.
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