Yasuyuki Ishida

With global warming and the increasing frequency of extreme heat events, heat health risk assessment (HHRA) has become a critical topic in climate change studies. However, the study themes, methods, and governance orientation of HHRA vary significantly across spatial scales, limiting the comparability and practical integration of assessment outcomes. This study conducts a review of the HHRA literature from 2007 to 2025, analyzing publication trends and evolving research paradigms. The results indicate the following: (1) rapid growth in the field with a notable shift from identifying static vulnerabilities to adopting “Hazard–Exposure–Vulnerability–Adaptability” (HEVA) frameworks, particularly at the micro-scale; (2) a clear scale-dependent hierarchy in assessment focus, where macro-scale studies identify regional trends, meso-scale research targets urban spatial heterogeneity and green–blue infrastructure, and micro-scale assessments emphasize housing conditions and individual perceptions; and (3) machine learning has been widely applied to capture complex nonlinear mechanisms and threshold effects. Finally, this study further emphasizes the importance of establishing a full-process feedback mechanism from macro-level early warning to meso-scale planning and micro-scale intervention, bridging the gap between regional policy and community-level action and providing a theoretical foundation for building climate-resilient cities.

OpenFOAM, one of the widely used Computational Fluid Dynamics (CFD) solvers, requires that the static pressure in all cells at the outlet boundary of the computational domain to be fixed at a constant value due to its specifications. This specification causes excessive static pressure fluctuations that do not occur in real phenomena when turbulent velocity fluctuations with fluctuating flow rates are imposed as the inflow boundary condition. In this study, the excessive static pressure fluctuations were suppressed by maintaining a constant inflow rate. The influence of the excessive pressure fluctuations on the prediction accuracy of Large-Eddy Simulation (LES) results of wind pressure acting on cube walls was then investigated. It was confirmed that suppressing the excessive static pressure fluctuations improved prediction accuracy. Although this study targets the wind pressure acting on the cube’s walls, the findings and methodology in this study provide valuable insights for the LES of wind pressure on building walls and flow fields around buildings using OpenFOAM.

Urban heat during summer has recently become a serious issue. Although urban trees have a cooling effect, this effect must be evaluated quantitatively for effective landscape and streetscape design. However, models capable of predicting hourly changes in typical tree transpiration under summer conditions at the whole‐tree scale rather than the individual‐leaf or forest scale are lacking. In addition, leaf surface temperatures in the lower canopy, which significantly affect the radiative environment of pedestrians, must be accurately predicted. Nevertheless, the prediction accuracy of heat balance changes on leaf surfaces due to transpiration has not been adequately validated. Such models must also consider plant physiological responses, which vary with the surrounding physical environment, to ensure high accuracy. In this study, a literature review was performed to identify transpiration and stomatal conductance models that can predict typical transpiration and heat balance properties. Subsequently, hourly transpiration rate changes of the whole tree and spatial distribution of leaf surface temperatures in the lower canopy were measured. The parameters of the stomatal conductance model were also estimated to enable predictions using both models combined. The present model is novel because it reproduces whole‐tree transpiration rate characteristics and leaf surface temperature trends in the lower canopy.

Longwave radiation is a crucial factor affecting human thermal comfort and thermal stress, especially in outdoor spaces in summer, owing to the vast effect of longwave radiation emitted from high-heated asphalt roads, building walls, and automobiles. Although controlling the longwave radiation environment to improve thermal comfort in summer is crucial, the prediction of the longwave radiation environment is frequently conducted only at the assessment stage of the final proposal because of the high computational cost of radiation calculations and unsteady heat balance analysis considering multiple reflections. This is a significant constraint for the design of urban and architectural environments. A previous study proposed a method to rapidly estimate the longwave radiation environment based on a point-by-point method with longwave radiation intensity distributions of the heat sources. To use this method, 3D models of the geographical objects in urban areas, such as buildings and trees, must be accurately generated, and these models should have information on the longwave radiation emitted in each direction from each object. However, no specific examples of a 3D model and longwave radiation intensity distribution have been presented. In this study, a 3D modeling method for geographical objects in urban areas with longwave radiation information based on drones and photogrammetric techniques was utilized. Moreover, a 3D model of a small-scale building was generated. A longwave radiation intensity distribution was produced for the building. Based on the distribution data, the directional characteristics of longwave radiation were discussed, and the availability of the proposed method was assessed.