Evaluating high-resolution mean radiant temperature within an urban street canopy: Resolving spatiotemporal variations with LiDAR/thermal infrared scanning and data-driven simulation

Abstract

Mean radiant temperature ($T_{mrt}$) is crucial for assessing human thermal exposure, because it quantifies the largest sources of spatial variability in pedestrian-perceived thermal stress and comfort in complicated urban environments. Despite the availability of existing methods for evaluating $T_{mrt}$, the lack of detailed urban three-dimensional (3D) models and spatially resolved pedestrian-level irradiance from urban surfaces poses a significant challenge in obtaining high-resolution $T_{mrt}$. This paper introduces a methodology that combines light detection and ranging with thermal infrared (TIR) scanning with data-driven simulations. The approach was applied to a street canyon segment in Salt Lake City during the summer, to construct a complex 3D street model that enables assessment of high spatial resolution (0.3 $m^2$) shortwave and longwave radiant fluxes of urban surfaces. Based on the refined 3D radiation field, pedestrian-received irradiance at different locations were sampled and then a high-spatial-resolution field (0.5 $m^2$) of pedestrian-level sampled $T_{mrt}$ ($T_{mrt\_sampled}$) was generated. Such a method for calculating $T_{mrt\_sampled}$ is efficient, requiring only 30 s for each round. Direct solar exposure of ground and wall materials, together with their substantial thermal inertia, raised $T_{mrt\_sampled}$ to peak values of 59.17 ∼ 65 °C by 17:00. Due to differences in mesh and mechanism for quantifying $T_{mrt}$, $T_{mrt}$ simulated via the solar and longwave environmental irradiance geometry model typically were 4 ∼ 6 °C higher than $T_{mrt\_sampled}$ over sunlit surfaces, and their root mean square error (mean bias error) reached 4.71 (3.19) °C when the solar elevation was high and ground shadows were minimal.