This study assessed heat-stress using heat-stress indices for seven Korean cities (Seoul, Incheon, Daejeon, Gwangju, Daegu, Ulsan, and Busan) from 2013 to 2015 during the summer, and analyzed the correlation between heat-stress and heat-related illnesses. A heat-stress response (H-SR) function was d...
This study assessed heat-stress using heat-stress indices for seven Korean cities (Seoul, Incheon, Daejeon, Gwangju, Daegu, Ulsan, and Busan) from 2013 to 2015 during the summer, and analyzed the correlation between heat-stress and heat-related illnesses. A heat-stress response (H-SR) function was developed for each heat-stress index and the associated heat-related illness rate.
To estimate the future rate of heat-related illness, projected urban climate data was used to simulate the future heat environment, and then the calculated heat-stress indices were applied to the HS-R function to determine the rate of future heat-related illness for each index and region. The heat-stress indices that were used include daily maximum temperature, heat index, wet bulb globe temperature (WBGT) and predicted heat strain (PhS). PhS was calculated using the Man-ENvironment heat EXchange bio-climate model (MENEX). Meteorological data (temperature, relative humidity, wind speed, cloud cover, and pressure) used for calculating present day (2013-2015) indices were obtained from the automatic synoptic observing system (ASOS). Future (2028-2030) heat-stress data was determined from WRF model output using HadGEM2-AO lateral boundary conditions, which followed the RCP6.0 scenario. Heat-related illness data were obtained from the Health Insurance Review and Assessment Service.
Results showed that the levels of heat-stress varied with the heat-stress index. It was therefore necessary to use a heat index that combined an indirect index that considered human heat balance, and a direct index that utilized both temperature and additional meteorological factors.
The heat indices in each city differed based on the characteristics of the region (inland, coastal, central, southern, and so on). For example, Seoul, Incheon, Daejeon, and Gwangju were expected to experience increased heat stress based on model projections. An assessment of the estimated health impacts showed that social and environmental characteristics also varied regionally. The correlation between heat-stress indices and the rate of heat-related illness was high, with an adjusted R2 over the 0.9 in all regions except Seoul and Daejeon. This confirms that heat-stress indices explain the rate of heat-related illness well. Using the HS-R function, when daily maximum temperature was applied, the rate of future heat-related illness for 7 cities increased by 12% as compared to the present day. However, when PhS was applied, the rate of future heat-related illness decreased by 10% as compared to present day. Further research to understand additional confounding factors (air pollution, age, social-environmental factors etc.) and increase the study period is required. Notwithstanding, the results of this study contribute to basic research regarding managing the effects of heat-related illness arising from the high temperature environment.
This study assessed heat-stress using heat-stress indices for seven Korean cities (Seoul, Incheon, Daejeon, Gwangju, Daegu, Ulsan, and Busan) from 2013 to 2015 during the summer, and analyzed the correlation between heat-stress and heat-related illnesses. A heat-stress response (H-SR) function was developed for each heat-stress index and the associated heat-related illness rate.
To estimate the future rate of heat-related illness, projected urban climate data was used to simulate the future heat environment, and then the calculated heat-stress indices were applied to the HS-R function to determine the rate of future heat-related illness for each index and region. The heat-stress indices that were used include daily maximum temperature, heat index, wet bulb globe temperature (WBGT) and predicted heat strain (PhS). PhS was calculated using the Man-ENvironment heat EXchange bio-climate model (MENEX). Meteorological data (temperature, relative humidity, wind speed, cloud cover, and pressure) used for calculating present day (2013-2015) indices were obtained from the automatic synoptic observing system (ASOS). Future (2028-2030) heat-stress data was determined from WRF model output using HadGEM2-AO lateral boundary conditions, which followed the RCP6.0 scenario. Heat-related illness data were obtained from the Health Insurance Review and Assessment Service.
Results showed that the levels of heat-stress varied with the heat-stress index. It was therefore necessary to use a heat index that combined an indirect index that considered human heat balance, and a direct index that utilized both temperature and additional meteorological factors.
The heat indices in each city differed based on the characteristics of the region (inland, coastal, central, southern, and so on). For example, Seoul, Incheon, Daejeon, and Gwangju were expected to experience increased heat stress based on model projections. An assessment of the estimated health impacts showed that social and environmental characteristics also varied regionally. The correlation between heat-stress indices and the rate of heat-related illness was high, with an adjusted R2 over the 0.9 in all regions except Seoul and Daejeon. This confirms that heat-stress indices explain the rate of heat-related illness well. Using the HS-R function, when daily maximum temperature was applied, the rate of future heat-related illness for 7 cities increased by 12% as compared to the present day. However, when PhS was applied, the rate of future heat-related illness decreased by 10% as compared to present day. Further research to understand additional confounding factors (air pollution, age, social-environmental factors etc.) and increase the study period is required. Notwithstanding, the results of this study contribute to basic research regarding managing the effects of heat-related illness arising from the high temperature environment.
주제어
#기후변화
※ AI-Helper는 부적절한 답변을 할 수 있습니다.