Outdoor thermal comfort in urban and rural open spaces: A comparative study in China?s cold region

URBAN CLIMATE(2023)

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Abstract
To elucidate the differences in thermal perceptions between urban and rural residents in outdoor open spaces, we determined factors influencing outdoor thermal comfort (OTC) of urban and rural residents in Xi'an, China by meteorological measures and questionnaires. Physiological equivalent temperature (PET) was used to determine thermal benchmarks. Factors (i.e., physical, social, individual and psychological) that influenced urban and rural residents' thermal percep-tions were analyzed and compared. We found that: 1) The neutral PET (NPET) and preferred PET of rural residents (NPET: 18.83 degrees C; preferred PET: 27.6 degrees C) were both higher than those of urban residents (NPET: 16.82 degrees C; preferred PET: 22.1 degrees C). The lower limit of the neutral PET range (NPETR) and the thermal acceptable range (TAR) for rural residents (NPETR: 8.97-28.70 degrees C; TAR: -2.48-36.33 degrees C) were both lower than these of urban residents (NPETR: 9.28-24.36 degrees C; TAR: -1.75-34.58 degrees C), and the upper limit was both higher than these of urban residents. 2) In winter and summer, urban and rural residents were mainly influenced by physical factors. In spring, individual and psychological factors were primary influencers of urban residents' thermal perceptions, while rural residents were mainly influenced by physical factors. 3) A mathematical model comparing thermal sensation and environmental factors was built using multiple linear regressions, proportions of influences of different factors were determined, and the models per-formed well. Our results could provide a theoretical basis for determining thermal benchmarks between urban and rural residents, and provide technical guidance for designs of urban and rural open spaces concerning thermal comfort.
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Key words
Outdoor thermal comfort (OTC), Thermal benchmarks, Behavior record, Ordinal logistic regression, Physiological equivalent temperature (PET), Predictive model
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