Predictive model for heat stress-related symptoms among steel mill workers in East Java, Indonesia
Introduction: As a tropical country, Indonesia’s climate is hot and humid throughout the year, implicating hot workplace environment and leading to workers’ susceptibility to heat stress exposure. Workers at a steel processing mills exposed to an extremely hot environment are prone to experience hea...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Putra Malaysia
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/109446/1/2023111810021604_2022_1230.pdf http://psasir.upm.edu.my/id/eprint/109446/ https://medic.upm.edu.my/upload/dokumen/2023111810021604_2022_1230.pdf |
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| Summary: | Introduction: As a tropical country, Indonesia’s climate is hot and humid throughout the year, implicating hot workplace environment and leading to workers’ susceptibility to heat stress exposure. Workers at a steel processing mills exposed to an extremely hot environment are prone to experience heat stress-related symptoms caused by occupational heat stress. Methods: The study aimed to build a predictive model of heat stress-related symptoms in steel mill workers based on physiological and environmental parameters. The respondents of this study were 119 operators exposed to a hot workplace in Surabaya, Sidoarjo, and Gresik, East Java, Indonesia. Results: The result as a high correlation (p<0.05) in predictive between the model Wet Bulb Globe Temperature (WBGT), core body temperature, heart rate and heat stress-related symptoms with R-value of 0.78 or 78. In addition, there is a weak correlation between heat stress symptoms and systolic and diastolic blood pressure, as well as humidity factors and heat stress-related symptoms. Heat stress-related symptoms have a linear correlation with the value of WBGT, body core temperature and heart rate, while body core temperature has the highest value of correlation and WBGT is attributed to the lowest of all to the heat stress-related symptoms. Conclusion: With these values in hand, ones can predict whether workers will be exposed to heat stress work environment. Furthermore, with this model, it can predict heat stress-related symptoms in a particular workplace. |
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