Human thermal comfort is linked to multi-sensory attributes and psychological cognition besides the influence of prominent meteorological parameters. To date, even the most advanced thermal comfort models like UTCI consider the interaction between human and external surroundings to be a heat transfer problem applying thermodynamic equations in the derivation behind thermal comfort indices. Machine learning (ML) models offer the advantage of improving the accuracy of predictor variable, but they fail to determine the relationship between the independent variables and data-driven prediction models. Structural Equation Modelling (SEM), an advanced modelling technique, can expose all hidden relationships among variables using latent variables while not being data-driven like ML. This study aimed to develop a structural framework which systematically explains these relationships and quantify the effects of 24 variables on outdoor thermal comfort using SEM due to its ability to represents the response of the dependent variable to a unit change in an explanatory variable. The method employed 635 structured interviews, observations, and wide-ranging micrometeorological measurements conducted concurrently in three Pedestrianized urban alleys in Cairo. The model statistically confirmed the hypothesis that overall thermal comfort in urban spaces is significantly influenced by multiple factors. Of these, background microclimatic conditions had the greatest relevance and certain urban features the least. 1 unit increase microclimate leads to a change in thermal comfort by 0.423 and 0.349 in summer and winter, respectively. In terms of thermal perception, the model shows that individuals are willing to tolerate a wider range if certain urban features are present such as shaded seating opportunities. These outcomes may aid to provide important guidance for urban designers to enhance thermal perception in urban streets.

中文翻译：

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炎热干旱气候下室外城市空间的热感知：结构方程建模 (SEM) 方法

除了重要气象参数的影响外，人体热舒适度还与多感官属性和心理认知有关。迄今为止，即使是最先进的热舒适模型（如 UTCI）也将人类与外部环境之间的相互作用视为传热问题，在热舒适指数的推导中应用热力学方程。机器学习（ML）模型具有提高预测变量准确性的优势，但它们无法确定自变量与数据驱动的预测模型之间的关系。结构方程模型 (SEM) 是一种先进的建模技术，可以使用潜在变量揭示变量之间的所有隐藏关系，同时不像 ML 那样由数据驱动。本研究旨在开发一个结构框架，系统地解释这些关系，并使用 SEM 量化 24 个变量对室外热舒适性的影响，因为它能够表示因变量对解释变量单位变化的响应。该方法采用了 635 次结构化访谈、观察和广泛的​​微气象测量，同时在开罗的三个步行城市小巷进行。该模型从统计上证实了这样的假设：城市空间的整体热舒适度受到多种因素的显着影响。其中，背景小气候条件的相关性最大，而某些城市特征的相关性最小。小气候增加 1 个单位会导致夏季和冬季热舒适度分别变化 0.423 和 0.349。就热感知而言，该模型表明，如果存在某些城市特征（例如阴影座位机会），个人愿意容忍更广泛的范围。这些结果可能有助于为城市设计师增强城市街道的热感知提供重要指导。