The use of principal component analysis (PCA) in determining factors related to heat stress related symptoms among steel mill workers in hot tropical countries

Heat stress related symptoms are commonplace workers experience heat strain due to heat stress occurring at workplaces. Steel mill workplaces have an extremely high operating temperature around 1800oC, thus operators are most likely to be exposed to hot environments. The study aimed to apply principal component analysis (PCA) in predicting the heat stress symptom model among steel mill workers. Data including environmental variables (WBGT, relative humidity, air temperature; related symptoms), physiological changes (blood pressure of systolic and diastolic, heart rate, and body core temperature) at three steel mills located in East Java, Indonesia, where operators might experience were used in PCA. Based on the principal component analysis (PCA) result, there are three variables that have a strong correlation (> 0.5) with factor 1, namely WBGT, relative humidity and body core temperature. The three variables are then grouped into factor 1; Furthermore, the other two variables have a strong correlation with factor 2, namely blood pressure systolic and diastolic. In conclusion, PCA is able to determine the prediction of heat stress symptoms and is simplified to be used by the steel mill industries.

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