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• W4385755756 startingPage "035003" @default.
• W4385755756 abstract "Abstract Through conversion of land cover to more built-up, impervious surfaces, cities create hotter environments than their surroundings for urban residents, with large differences expected between different parts of the city. Existing measurements of ambient air temperature and heat stress, however, are often insufficient to capture the intra-urban variability in heat exposure. This study provides a replicable method for modeling air temperature, humidity, and moist heat stress over the urban area of Chapel Hill while engaging citizens to collect high-temporal and spatially-resolved air temperature and humidity measurements. We use low-cost, consumer-grade sensors combined with satellite remote sensing data and machine learning to map urban air temperature and relative humidity over various land-cover classes to understand intra-urban spatial variability of ambient heat exposure at a relatively high resolution (10 m). Our findings show that individuals may be exposed to higher levels of air temperature and moist heat stress than weather station data suggest, and that the ambient heat exposure varies according to land cover type, with tree-covered land the coolest and built-up areas the warmest, and time of day, with higher air temperatures observed during the early afternoon. Combining our resulting dataset with sociodemographic data, policymakers and urban planners in Chapel Hill can use data output from this method to identify areas exposed to high temperature and moist heat stress as a first step to design effective mitigation measures." @default.
• W4385755756 created "2023-08-12" @default.
• W4385755756 creator A5039626546 @default.
• W4385755756 creator A5041572932 @default.
• W4385755756 creator A5055995988 @default.
• W4385755756 date "2023-08-25" @default.
• W4385755756 modified "2023-09-27" @default.
• W4385755756 title "Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress" @default.
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• W4385755756 doi "https://doi.org/10.1088/2634-4505/acef57" @default.
• W4385755756 hasPublicationYear "2023" @default.
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