Creating a favorable thermal environment in an outdoor space is essential for attracting more occupants to outdoor areas and vitalizing a city. It is possible to study occupants’ needs in an outdoor thermal environment by observing their attendance and behaviors, since people may exhibit certain adaptive measures, such as seeking shade, using parasols, etc., “vote with their feet”, or even leave the space, if they feel uncomfortable. In order to investigate the influence of thermal environment on attendance and adaptive behaviors in outdoor spaces, in this study we carried out field campaigns in a university campus in a cold-climate city. The thermal environment was monitored, while surveys of thermal perceptions and observations of attendance and adaptive behaviors were conducted. Through the data analyses, it was found that the thermal environment had a great impact on the attendance of optional activities, but necessary activities were not influenced. The greatest influence on attendance came from air temperature. The influences of wind and humidity on attendance were found to be coupled with that of air temperature. Adaptive behaviors, such as seeking shade, using parasols, changing clothes, and changing the lengths of stay, were also greatly influenced by air temperature.

Thermal comfort throughout a whole year is complicated because the conditions vary greatly in different seasons. The aim of this paper was to understand the influence of seasonal characteristics on the thermal comfort in classrooms and to propose a method to select the suitable adaptive coefficients for different conditions. Field investigations were conducted in a teaching building in Tianjin during autumn and winter. In the study, multiple environmental parameters including the air temperature, black globe temperature, relative humidity, air velocity, and the vertical temperature difference. The respondents’ thermal sensation was collected by questionnaires. The respondents’ thermal sensation and the influence of the seasonal characteristics on the thermal sensation were analyzed. The results showed that the thermal neutral temperature varied in different seasons, and the deviations between mean thermal sensation vote (MTSV) and predicted mean vote (PMV) were evidently discrepant in different seasons. Furthermore, the adaptive coefficient α for the adaptive thermal comfort prediction model (APMV) were 0.25 and 0.57 for autumn and winter respectively. Based on the above result, a discriminant function (DFA) using the seasonal factors to select the suitable α was obtained.

Recently, health risk assessment and early warning systems for high-temperature events have become critical concerns. However, current high-temperature warning systems primarily focus on temperature alone, which fails to accurately reflect the actual heat exposure levels and associated health risks. Therefore, this paper proposes an improved AHP (analytic hierarchy process) combined with a multi-level gray evaluation method for assessing human health risks during high-temperature conditions. A comprehensive early warning system is developed, incorporating various indicators, including human status, building conditions, and weather forecasts, making it more holistic than traditional temperature-based warning systems. A case study shows that the highest evaluation score for young individuals is 3.41, while elderly males receive the highest score of 2.5. Furthermore, the highest evaluation score for males is 3.41, while for females the highest score of 3.1. The warning results indicate that for young individuals, no alert is issued; for the elderly, a red alert is triggered; and for middle-aged individuals, the system issues orange and yellow alerts based on varying levels of risk. This study can be used to monitor health risk and provide alert message to humans. Based on the proposed early warning system, people can be able to predict health risk in time.