@article{78a0ec648c51410cadff983fcce62a4b,
title = "Development of a geographic human heat balance equation to support public health analyses: An Arizona urban sun corridor application",
abstract = "Land surface temperature (LST) estimates often serve as urban heat islands maps and to infer human thermal comfort. Parallel to this, physiological heat balance calculations have been well documented to measure changes in body core temperature and measure risk of heat-related illness. However, there is a need for an improved spatially explicit method to assess human thermal comfort. Using spatial climate data measuring temperature, airflow, and humidity, we developed a geographic body heat storage (BHS) model based on heat exchange and evaporative heat loss from the human body. As proof of concept, we used heat-related illness emergency department visits in two Arizona metropolitan areas to demonstrate that BHS can improve LST's shortcomings, with its increased explanatory power of and linear fit to emergency records. The BHS model can support decision making for public health outcomes as heat risk increases with climate change and urban overheating to more closely approximate the human heat experience. BHS allows can be implemented in different climate regions and with investigations of additional physiological and community variables to better describe risk of heat-related illness.",
keywords = "Heat exposure, Heat related illness, Land surface temperature (LST), Public health, Thermal physiology, Urban microclimate",
author = "Chambers, {Samuel N.} and Brown, {Heidi E.} and Ladd Keith and Erika Austhof",
note = "Funding Information: We used the physiological heat balance equation to estimate the rate of body heat storage (BHS) as a measure of heat stress for the Arizona Urban Sun Corridor, including the Phoenix and Tucson metropolitan areas in Maricopa and Pima counties. BHS is a part of the human heat balance equation and is determined by metabolic rate, external work, heat exchange from the skin surface, and evaporative heat loss from the skin, and heat exchange by convection and evaporation. We then used simple linear regression of HRI emergency department visits estimates using BHS and LST by primary care areas (PCAs) as an experimental comparison. Limitations to BHS such as the local effect of specific buildings and individual trees are unaccounted for, but the structure of our model allows for improvements with the addition of site-specific data in future studies. And unlike existing high resolution biometeorological models, BHS can be across larger areas and account for seasonal averages across multiple years. We developed the BHS model to support future Centers for Disease Control and Prevention BRACE (Building Resilience Against Climate Effects) work in an effort to better support public health and planning efforts which address increasing heat risk across the Arizona Urban Sun Corridor.The BHS model provides a biologically-informed mapping of heat risk, building upon the more familiar LST. This holds potential for information heat mitigation and management efforts to improve public health outcomes by avoiding oversimplification and the assumption that UHI are strictly areas where the ground absorbs more heat. The BHS model maps where the individuals of a population are likely to absorb and conduct more heat and lose less heat by evaporation. We created this BHS model of heat exposure to support future Centers for Disease Control and Prevention BRACE (Building Resilience Against Climate Effects) work in an effort to better support public health and planning efforts to address increasing heat risk across the Arizona Urban Sun Corridor.The BHS model shows a better fit in predicting HRI associated emergency department visits than LST in our application in the Arizona Urban Sun Corridor, especially when restricting to urban areas, though both models indicate missing variables. The BHS model can be incorporated into future research and made available to support decision making. Future work can explore additional covariates to see how BHS performs to improve decision making for public health outcomes as heat risk increases due to climate change and urban overheating. Our results suggest a need for further investigation testing related variables such as health, age, activity levels, and the temporal extent of heat exposure. In addition, a finer scale dataset may improve analysis when available. We suggest further analysis for regions with different climate conditions, such as areas of higher humidity where the body is less efficient at cooling by evaporation.This publication was supported by Cooperative Agreement Number 5 NUE1EH001318-03-00, funded by the Centers for Disease Control and Prevention Climate Ready States and Cities Initiative. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services. Funding Information: This publication was supported by Cooperative Agreement Number 5 NUE1EH001318-03-00 , funded by the Centers for Disease Control and Prevention Climate Ready States and Cities Initiative. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services. Publisher Copyright: {\textcopyright} 2023 The Authors",
year = "2023",
month = nov,
doi = "10.1016/j.rsase.2023.101009",
language = "English (US)",
volume = "32",
journal = "Remote Sensing Applications: Society and Environment",
issn = "2352-9385",
publisher = "Elsevier BV",
}