Variations in outdoor thermal comfort in an urban park in the hot-summer and cold-winter region of China

Abstract

• Outdoor thermal comfort is evaluated in an urban park in a hot-summer and cold-winter region of China (Chengdu). • Machine learning techniques are applied to determine the (nonlinear) relationship between thermal sensation vote and meteorological factors. • The effect of landscape spaces on human thermal comfort varies across the season. • NTR of PET is 6.79 °C to 19.90 °C, and NTR of UTCI is 13.27 °C to 27.25 °C. TAR of PET is 2.8 °C to 21.9 °C, and TAR of UTCI is 8.1 °C to 28.3 °C. Global warming and rapid urbanization have exacerbated the urban heat island effect. Urban parks contribute to alleviating such an effect and achieving the “carbon emission peak before 2030” and “carbon neutrality before 2060” goals of China. Their popularity is considerably influenced by human thermal comfort. However, limited thermal comfort studies have been conducted in the hot-summer and cold-winter region of China. This study examines human thermal comfort in different landscapes of an urban park in Chengdu and determines the thermal benchmarks. A machine learning (random forest) analysis shows that human thermal sensation is affected by different meteorological factors in different seasons. In addition, the influences of landscape space on human thermal comfort have considerable differences in different seasons. Residents prefer strong solar radiation in winter but fast wind speed in summer. UTCI (universal thermal climate index) is better than PET (physiological equivalent temperature) for outdoor thermal comfort assessment in the study area. This study serves as a valuable baseline and technical reference, contributing to sustainable urban park design.