Shuai Zhang

Working in hyperthermal environments can lead to heat-related illnesses. Evaluating and predicting high-temperature environments can effectively reduce heat risks and hazards. However, there is still a lack of corresponding high-temperature environment assessment methods and indicators in existing research. Moreover, traditional evaluation indicators and prediction methods have shortcomings in objectivity, accuracy, and practicality. To fill these gaps, a climate chamber was constructed to simulate different environmental conditions, and human labor experiments with 98 subjects were conducted. The ambient temperatures were set to 34 °C, 36 °C, 38 °C, and 40 °C, and the relative humidity was set to 60%, 70%, 80%, and 90%, respectively. During the experiments, the subjects’ oral temperatures, heart rates, skin temperatures, and subjective perceptions were recorded. Based on the obtained parameters of the subjects, two principal components with an explained variance of 92.131% were extracted by principal component analysis, and with the determination of weightings, a comprehensive evaluation index (F) was established and the F-score was calculated. According to the F-score, 16 different hyperthermal environments were classified into three categories through hierarchical clustering analysis and discriminant analysis, with the corresponding F-score ranges of 28.14–39.76, 39.17–45.21, and 44.13–52.39. An analysis was conducted on the value of physiological and subjective indicators to test the nature of classification.

Hyperthermal environments are encountered in many situations, and significant heat stress can exacerbate the fatigue perception of individuals and potentially threaten their safety. Heat acclimation (HA) interventions have many benefits in preventing the risk of incidents. However, whether HA interventions in specific environments can cope with other different hyperthermal environments remains uncertain. In this study, forty-three young male participants were heat-acclimated over 10 days of training on a motorized treadmill in a fixed hyperthermal environment, and they were tested in different hyperthermal environments. Physiological indices (rectal temperature (T_r), heart rate (HR), skin temperature (T_sk), and total sweat loss (M_sl)) and subjective perception (rating of perceived exertion (RPE) and thermal sensation votes (TSVs)) were measured during both the heat stress test (HST) sessions and HA training sessions. The results show that HR and T_sk significantly differed between pre- and post-heat acclimation (p < 0.05 for all) following the acclimation program. However, after heat acclimation training, the reduction in T_r (ΔT_r) was more notable in lower-ET* environments, and M_sl showed distinct changes in different ET* environments. The RPE and TSV decreased after HA interventions, although the difference was not significant. The results indicate that HA can effectively reduce the peak of physiological parameters. However, when subjected to stronger heat stress, the improvement effects of heat acclimation on human responses will be affected. In addition, HA can alleviate physiological thermal strain, thereby reducing the adverse effects on mobility, but it has no effect on the supervisor’s ability to perceive the environment. This study suggests that additional HA training can reduce the risk of activities in high-temperature environments but exhibits different effects under different environmental conditions, indicating that hot acclimation suits have selective effects on the environment. This study provides recommendations for additional HA training before high-temperature activities.

A kitchen is an important part of a residence, and air quality in kitchens is a major factor affecting residents’ health and performance. Its influence is especially more evident in kitchens with typical Chinese cooking. Thus, it is important to understand distribution characteristics of oil fume particles in Chinese kitchens, and research on typical Chinese kitchen environments and movement characteristics of soot particles should be emphasized. Therefore, to understand the air pollution in kitchen environments, a full-scale experimental platform was built based on common Chinese-style residential kitchens to monitor the soot particles, explore the space–time distribution characteristics of the soot particles, and analyze the variation characteristics and distribution laws of number concentration (NC) and mass concentration (MC). The results indicated that NC_0.3μm value fluctuated slightly during the whole cooking process, while NC_{0.3μm~0.5μm} and NC_{0.5μm~1.0μm} fluctuated sharply. During the peak period, NC_{0.3μm~0.5μm} and NC_{0.5μm~1.0μm} were significantly higher than those at the beginning and end of cooking (p < 0.05), and there was no significant difference between values of NC_{0.3μm~0.5μm} and NC_{0.5μm~1.0μm} at the beginning and end of cooking (p > 0.05 for both). NC_0.3μm and NC_{0.3μm~0.5μm} were significantly different between each position (p < 0.05 for all). There were also significant differences in NC_0.3μm between pure meat dishes, pure vegetable dishes, and mixed dishes (p < 0.001). The proportion of cooking meat will significantly affect the composition and concentration of lampblack particles. Type of dishes is an important influencing factor for the emission rates of particulate pollutants. Small-sized lampblack particles were more likely to diffuse to the surroundings and affected the air quality of the whole room. Particulate matter in the kitchen space during the frying process is more harmful to the health of personnel.

With the rapid increase in demand for the construction and development of island cities in the South China Sea, the urban heat island phenomenon in such cities should become a key factor to be considered in future urban planning. This paper took Sanya, China as a typical case, and long-term field experiments were conducted in the Mangrove Bay Area in summer and winter. An innovative urban green space cooling model was proposed, using the “green space cooling index” to quantitatively characterize the green space cooling effect, and aiming to minimize the intensity of urban heat islands. This paper studied the optimization method of green space planning and layout under the constraint of a centralized green space total area. Moreover, a genetic algorithm was adopted to optimize the calculation and the layout of the urban green space. The experimental results showed that the urban heat island intensity was more significant at night and was less effective in the daytime during summer. In winter, the urban heat island intensity had a greater effect in the daytime and was less influential at night. Finally, optimization results indicated that the average urban heat island intensity in summer was reduced by 8.8% under the optimal layout urban green space of 0.025 km². The maximum reduction in heat island intensity occurred at 7:00 am (0.48 °C). When 0.0625 km² urban green space was planned, the average urban heat island intensity index in summer was reduced to 0.27 °C, with a decrease of 20.5%.