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Characterization of heat index experienced by individuals residing in urban and rural settings

Abstract

Heatwave warning systems rely on forecasts made for fixed-point weather stations (WS), which do not reflect variation in temperature and humidity experienced by individuals moving through indoor and outdoor locations. We examined whether neighborhood measurement improved the prediction of individually experienced heat index in addition to nearest WS in an urban and rural location. Participants (residents of Birmingham, Alabama [N = 89] and Wilcox County, Alabama [N = 88]) wore thermometers clipped to their shoe for 7 days. Shielded thermometers/hygrometers were placed outdoors within participant’s neighborhoods (N = 43). Nearest WS and neighborhood thermometers were matched to participant’s home address. Heat index (HI) was estimated from participant thermometer temperature and WS humidity per person-hour (HI[individual]), or WS temperature and humidity, or neighborhood temperature and humidity. We found that neighborhood HI improved the prediction of individually experienced HI in addition to WS HI in the rural location, and neighborhood heat index alone served as a better predictor in the urban location, after accounting for individual-level factors. Overall, a 1 °C increase in HI[neighborhood] was associated with 0.20 °C [95% CI (0.19, 0.21)] increase in HI[individual]. After adjusting for ambient condition differences, we found higher HI[individual] in the rural location, and increased HI[individual] during non-rest time (5 a.m. to midnight) and on weekdays.

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Fig. 1: Diurnal pattern of maximum and mean HI[individual] (yellow triangle) compared to HI[neighborhood] (gray dot) and HI[WS] (blue square) in rural residents (participant N = 88, neighborhood iButton N = 13, WS N = 4), urban residents (participant N = 57, neighborhood iButton N = 18, WS N = 2), and urban OutWor (participant N = 32, neighborhood iButton N = 11, WS N = 2).
Fig. 2: Date pattern of maximum and mean HI[individual] (yellow triangle) compared to the matched HI[neighborhood] (gray dot) and HI[WS] (blue square) in rural residents (participant N = 88, neighborhood iButton N = 13, WS N = 4), urban residents (participant N = 57, neighborhood iButton N = 18, WS N = 2), and urban OutWor participants (OutWor) (participant N = 32, neighborhood iButton N = 11, WS N = 2).
Fig. 3: Mean frequency % of risk classification based on heat index in rural residents (N = 88), urban residents (N = 57), and urban OutWor (N = 32).

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Acknowledgements

We gratefully acknowledge collaboration with Sheila Tyson, Keisha Brown, and Nakeia Pullman (Friends of West End), and Sheryl Threadgill-Mathews and Ethel Johnson (West Central Alabama Community Health Improvement League), for their aid in recruitment and implementation of the research. Thanks to Mary Evans, Anna Scott, Michael Milazzo, Pranavi Ghugare, Kaya Bryant, and Claudiu Lungu for help with the data collection.

Funding

This project was funded through a grant from the National Institute of Environmental Health Sciences (R01ES023029).

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Correspondence to Julia M. Gohlke.

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Wang, S., Wu, C.Y.H., Richardson, M.B. et al. Characterization of heat index experienced by individuals residing in urban and rural settings. J Expo Sci Environ Epidemiol 31, 641–653 (2021). https://doi.org/10.1038/s41370-021-00303-x

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Keywords

  • Exposure assessment
  • Exposure sensors
  • Environmental health policy

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