Research Topics

'Dynamic outdoor thermal comfort'

Most previous thermal comfort evaluations have generally used the steady-state method, which assumes that the human body and the surrounding environment are in thermal equilibrium. The issue with the steady-state method, however, is that it cannot take into account dynamic changing human thermal adaptation . To understand the physiological, psychological, and behavioral effect of people in outdoor spaces in the future, it is important to identify the dynamic thermal comfort provided by urban-plannings and adaptation methods (e.g. fan-attached jacket). Accordance with this, I developed a wearable device to measure the physiological responses and have been conducting the experiment in various countries. Additionally, I proposed a new dynamic thermal comfort index.

'Thermal exposure at the population level'

Climate-related risks have increasingly raised concerns about public health and social vulnerability. In this context, assessing thermal environments in relation to the presence and dynamics of people has become essential for developing effective adaptation strategies. However, few studies have integrated dynamic population changes with comprehensive thermal indices. To address this gap, this study proposes the Dynamic Population Thermal Exposure (DPTE), which combines the Universal Thermal Climate Index (UTCI) with Mobile Spatial Statistics (MSS) datasets. DPTE enables the identification of temporal patterns in population-level thermal risks, thereby offering a new perspective on climate impacts from the viewpoint of people and society.

'Long-term change in thermal comfort estimated from weather stations and machine leaning model'

There is growing concern about the health risks posed by global warming and the heat island effect. Despite the fact that a human body is affected by not only air temperature but also other weather indicators such as radiation, humidity and wind speed, many discussions consider it in terms of only the change in air temperature. In order to take measures, it is important to estimate and evaluate human sensation i.e. thermal comfort. This study evaluate the long term change in thermal comfort.

Additionally, I propose a machine-learning approach in order to downscale the coarse-resolution Mean Radiant Temperature (MRT) obtained from the ERA-5 to match the surroundings of the observations. This proposal could help researches to estimate local-scale MRT in order to evaluate the thermal environment without high-performance computers or fine-scale weather database.