Quantifying security ratings for firefighting protective clothing wearers within the human body–clothing–environment system

Abstract

A timely and comprehensive evaluation of human thermal safety is critical for reducing the risk of accidents during fire emergency response operations. Previous studies on thermal safety assessment have primarily considered physiological factors but overlooked environmental factors or personal protective equipment. Here, we propose a multilevel early warning assessment framework based on the human body–clothing–environment interaction system using catastrophe theory. First, the catastrophe progression method was used to construct an evaluation index system for thermal safety from three dimensions: physiological factors (core temperature, skin temperature, heart rate, systolic blood pressure), clothing factors (thermal resistance, evaporative resistance), and environmental factors (temperature, relative humidity, and noise). Second, weights were determined using the entropy weight method and range analysis. Next, we established a five-level thermal safety classification system (level I–V: no, light, medium, serious, and critical warning) based on catastrophe theory, representing risk gradients from negligible to critical conditions. Quantitative safety ratings were then obtained using multilevel catastrophe progression judgment. To validate the method, nine experimental scenarios representing varying firefighting conditions were tested for human thermal safety assessment. The results showed that the application of catastrophe models revealed critical thresholds where small parameter changes triggered abrupt shifts in human thermal safety, with the swallowtail model proving effective for analyzing system stability. This method provided a comprehensive assessment of thermal safety ratings in firefighters, with risk levels varying substantially across experimental scenarios and identifying high-risk conditions. This approach can provide timely warnings of thermal strain and improve risk-based decision-making for appropriate preventive practices.