Occupational heat stress threatens worker health and productivity under climate warming, yet common environmental indices cannot fully represent person-specific physiological and perceptual strain. This study developed an integrated heat strain index (IHSI) using high-resolution data from 71 young male participants exposed to nine controlled temperature-speed conditions in an environmental chamber. Environmental, physiological, subjective, and individual variables were integrated through logistic-regression-based scoring, and participant-level validation was used to reduce information leakage from repeated time-series measurements. Results show significant phase-dependent physiological and perceptual changes (p < 0.001). Mean skin temperature continues to rise during recovery, reaching 37.02 ± 0.62 ℃, while subjective discomfort partly decreases, indicating a potential blind spot in self-perception. Partial pressure of end tidal carbon dioxide decreases from 4.45 kPa during preparation to 3.66 kPa during recovery, suggesting that respiratory responses provide additional information beyond thermal and cardiovascular indicators. The IHSI achieves AUC (area under the curve) = 0.8872, average precision = 0.9499, and overall accuracy = 80.6% in participant-level validation. For one-minute-ahead forecasting, CNN-GRU (convolutional neural networks-gated recurrent units) is identified as the best model according to validation-set RMSE, achieving R² = 0.8808, RMSE = 4.9040, and MAPE = 10.5980% using only heat index, heart rate, age, and body mass index. These findings support the feasibility of a continuous, interpretable, and non-invasive framework for individualized heat-strain monitoring and short-term occupational warning.

Cities around the world are exposed to increasing heat stress as a result of the heat island effect exacerbated by extreme weather events, population growth and increased urbanization. To promote outdoor activities and protect human health, there is an urgent need for a tool to assess the outdoor thermal environment. While the air temperature (T_a) and relative humidity (RH) are usually available from weather stations, but the mean radiant temperature (MRT) cannot. The MRT is a key variable in determining outdoor thermal comfort. Therefore, it is necessary to develop a simple and accurate method for calculating MRT, in order to compute the corresponding thermal indices, which can be used to assess the thermal environment more accurately. This study was conducted at a university in Guangzhou, China. Four different ground-cover surfaces (concrete, asphalt, lawn, and granite) were selected for study. The T_a, black-globe temperature (T_g), long-wave (L), and short-wave (K) radiation in six directions, and wind speeds (V_a) were measured under unshaded conditions. The MRT obtained by the six-direction integral method was used as the base value and regression analyses were performed with T_a, K↓ (downward short-wave), and V_a. In addition, the six indices—significance (P), linear regression coefficient (R²), consistency index (d), root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE)—were used for quantitative analysis to explore the feasibility of this method. This study compared five calculation models on MRT: MRT_SDIM (six-direction integral method), MRT_{BGTM-Va-0.1Hz} (black-globe thermometer method, 0.1 Hz wind speed measurement frequency), MRT_BGTM-Va-10Hz (black-globe thermometer method, 10 Hz wind speed measurement frequency), MRT_BGTM-P (polynomial regression based on black-globe thermometer method), and MRT_SDIM-P (polynomial regression based on six-direction integral method). The MRT_SDIM was used as a baseline value, and the values obtained by other four methods were discussed and validated separately. The results show that the calculation model about MRT_SDIM-P has a relatively minimal error with the values ≤ 5 ℃, which satisfies the accuracy requirements for thermal stress. Finally, the data from each area weather station were substituted into the model and compared with the actual measurement days, and the monthly mean and maximum universal thermal climate index (UTCI) were calculated for the entire Guangzhou daytime in 2022. The results obtained from the weather station data calculations fit well with the measured data. Consequently, this study provides a simple method of calculation for outdoor mean radiant temperature. Further, based on the calculated thermal indices, recommendations can be provided for outdoor work and activities.