Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning
This study proposes a novel approach combining machine learning (ML) techniques with meteorological model simulations to evaluate the heat-related mortality reduction potential of a climate change adaptation measure, namely, the installation of energy-saving or temperature-decreasing modifications i...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research: Health |
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| Online Access: | https://doi.org/10.1088/2752-5309/ada96e |
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| author | Yukitaka Ohashi Ko Nakajima Yuya Takane Yukihiro Kikegawa Tomohiko Ihara Kazutaka Oka |
| author_facet | Yukitaka Ohashi Ko Nakajima Yuya Takane Yukihiro Kikegawa Tomohiko Ihara Kazutaka Oka |
| author_sort | Yukitaka Ohashi |
| collection | DOAJ |
| description | This study proposes a novel approach combining machine learning (ML) techniques with meteorological model simulations to evaluate the heat-related mortality reduction potential of a climate change adaptation measure, namely, the installation of energy-saving or temperature-decreasing modifications in an urban area (e.g. greening, high-albedo paints, and photovoltaics). These methods have been used separately to assess the future urban health. The Weather Research and Forecasting–Canopy-Building Energy Model (WRF–CMBEM) was used to simulate spatiotemporal urban meteorological conditions, and ML was applied to predict daily heat-related deaths in the 23 wards of Tokyo during the extremely hot summer of 2018. The urban energy-saving and heat island mitigation scenarios evaluated in this study were ground surface greening, no anthropogenic heat from buildings to the atmosphere, rooftop photovoltaics, and cool roofs. ML accurately predicted heatstroke- and ischemic heart disease (IHD)-related daily deaths using important meteorological factors. After meteorological changes from the control case to four urban modification scenarios were predicted using the WRF–CMBEM, potential reductions in heat-related deaths were estimated using previously successful ML-trained models. The results showed that in July–August 2018, the ground surface greening case effectively decreased the outdoor surface air temperature by 0.28 °C (50-percentile), 0.37 °C (90-percentile), and 0.56 °C (Max) in all grids resolved at 1 km. Temperature changes reduced heatstroke deaths by 43% and IHD deaths by 18% during the peak period of deaths in summer 2018. Cool roofs resulted in temperature decreases of 0.23 °C (50-percentile), 0.31 °C (90-percentile), and 0.36 °C (Max) and 14% and 13% reductions in heatstroke and IHD deaths, respectively. The results suggest that the implementation of urban modifications can effectively reduce heat-related deaths, especially during heatwaves and extremely hot summers. |
| format | Article |
| id | doaj-art-9f63b632786f483b93921585629c9a1a |
| institution | Kabale University |
| issn | 2752-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research: Health |
| spelling | doaj-art-9f63b632786f483b93921585629c9a1a2025-02-07T15:22:55ZengIOP PublishingEnvironmental Research: Health2752-53092025-01-013202500110.1088/2752-5309/ada96ePredicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learningYukitaka Ohashi0https://orcid.org/0000-0003-0257-828XKo Nakajima1https://orcid.org/0000-0003-4332-8738Yuya Takane2https://orcid.org/0000-0002-6259-2748Yukihiro Kikegawa3https://orcid.org/0000-0002-5225-653XTomohiko Ihara4https://orcid.org/0000-0002-9252-0091Kazutaka Oka5https://orcid.org/0000-0002-7711-241XFaculty of Biosphere-Geosphere Science, Okayama University of Science , Kita-ku, Okayama City, JapanEnvironmental Management Research Institute, National Institute of Advanced Industrial Science and Technology (AIST) , Tsukuba City, Ibaraki, JapanEnvironmental Management Research Institute, National Institute of Advanced Industrial Science and Technology (AIST) , Tsukuba City, Ibaraki, JapanSchool of Science and Engineering, Meisei University , Hino City, Tokyo, JapanGraduate School of Frontier Sciences, The University of Tokyo , Kashiwa City, Chiba, JapanCenter for Climate Change Adaptation, National Institute for Environmental Studies (NIES) , Tsukuba City, Ibaraki, JapanThis study proposes a novel approach combining machine learning (ML) techniques with meteorological model simulations to evaluate the heat-related mortality reduction potential of a climate change adaptation measure, namely, the installation of energy-saving or temperature-decreasing modifications in an urban area (e.g. greening, high-albedo paints, and photovoltaics). These methods have been used separately to assess the future urban health. The Weather Research and Forecasting–Canopy-Building Energy Model (WRF–CMBEM) was used to simulate spatiotemporal urban meteorological conditions, and ML was applied to predict daily heat-related deaths in the 23 wards of Tokyo during the extremely hot summer of 2018. The urban energy-saving and heat island mitigation scenarios evaluated in this study were ground surface greening, no anthropogenic heat from buildings to the atmosphere, rooftop photovoltaics, and cool roofs. ML accurately predicted heatstroke- and ischemic heart disease (IHD)-related daily deaths using important meteorological factors. After meteorological changes from the control case to four urban modification scenarios were predicted using the WRF–CMBEM, potential reductions in heat-related deaths were estimated using previously successful ML-trained models. The results showed that in July–August 2018, the ground surface greening case effectively decreased the outdoor surface air temperature by 0.28 °C (50-percentile), 0.37 °C (90-percentile), and 0.56 °C (Max) in all grids resolved at 1 km. Temperature changes reduced heatstroke deaths by 43% and IHD deaths by 18% during the peak period of deaths in summer 2018. Cool roofs resulted in temperature decreases of 0.23 °C (50-percentile), 0.31 °C (90-percentile), and 0.36 °C (Max) and 14% and 13% reductions in heatstroke and IHD deaths, respectively. The results suggest that the implementation of urban modifications can effectively reduce heat-related deaths, especially during heatwaves and extremely hot summers.https://doi.org/10.1088/2752-5309/ada96eheatstrokeischemic heart diseasesmachine learningmeteorological modelTokyourban climate change |
| spellingShingle | Yukitaka Ohashi Ko Nakajima Yuya Takane Yukihiro Kikegawa Tomohiko Ihara Kazutaka Oka Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning Environmental Research: Health heatstroke ischemic heart diseases machine learning meteorological model Tokyo urban climate change |
| title | Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning |
| title_full | Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning |
| title_fullStr | Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning |
| title_full_unstemmed | Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning |
| title_short | Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning |
| title_sort | predicting the reduction in heatstroke and heart disease related mortality under urban modification scenarios using machine learning |
| topic | heatstroke ischemic heart diseases machine learning meteorological model Tokyo urban climate change |
| url | https://doi.org/10.1088/2752-5309/ada96e |
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