More than surface temperature: mitigating thermal exposure in hyper-local land system

Turner, V. Kelly; Rogers, Morgan L.; Zhang, Yujia; Middel, Ariane; Schneider, Florian Arwed; Ocón, Jonathan P.; Seeley, Megs; Dialesandro, John

doi:10.1080/1747423x.2021.2015003

Cited by 27 publications

(14 citation statements)

References 63 publications

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“…Due to differences between urbanized areas and metropolitan area geographic boundaries, SUHI values are not available for a small fraction (7.5%) of the census tracts in our full sample. Since SUHI is based upon differences in urban-rural land surface temperatures ( T s ), we acknowledge that it is an imperfect measure of heat exposure [the relevance of remotely-sensed land surface temperatures as a measure of urban heat stress is discussed in detail elsewhere ( 65 , 66 , 76 )]. Briefly, the relationship between air temperatures ( T a) and T s is complex, especially at fine spatial (intra-urban) and temporal (hourly, daily) scales.…”

Section: Methodsmentioning

confidence: 99%

Inequality in the availability of residential air conditioning across 115 US metropolitan areas

et al. 2022

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Continued climate change is increasing the frequency, severity, and duration of populations' high temperature exposures. Indoor cooling is a key adaptation, especially in urban areas, where heat extremes are intensified—the urban heat island effect (UHI)—making residential air conditioning (AC) availability critical to protecting human health. In the United States (U.S.), the differences in residential AC prevalence from one metropolitan area to another is well understood, but its intra-urban variation is poorly characterized, obscuring neighborhood-scale variability in populations’ heat vulnerability and adaptive capacity. We address this gap by constructing empirically derived probabilities of residential AC for 45,995 census tracts across 115 metropolitan areas. Within cities, AC is unequally distributed, with census tracts in the urban ‘core’ exhibiting systematically lower prevalence than their suburban counterparts. Moreover, this disparity correlates strongly with multiple indicators of social vulnerability and summer daytime surface UHI intensity, highlighting the challenges that vulnerable urban populations face in adapting to climate-change driven heat stress amplification.

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Section: Methodsmentioning

confidence: 99%

Inequality in the availability of residential air conditioning across 115 US metropolitan areas

et al. 2022

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“…In addition, LST measurements can be affected by the urban canyon effect 41 , 42 , and errors can arise due to the complex dependence of emissivity on urban materials and vegetation 43 . Recent literature has highlighted the need to assess thermal exposure at a hyper-local level, considering factors such as exposure to the sun, wind, and relative humidity 44 . For these reasons, in this research, we do not focus on the population exposure to heat but on urban areas with high values of LST.…”

Section: Resultsmentioning

confidence: 99%

Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes

et al. 2023

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The population experiencing high temperatures in cities is rising due to anthropogenic climate change, settlement expansion, and population growth. Yet, efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes are still lacking. Here, we implement a spatial regression model based on remote sensing data that is able to assess the population exposure to LST extremes in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies. We define exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population exposed, in person ⋅ day. Our findings reveal that urban vegetation plays a considerable role in decreasing the exposure of the urban population to LST extremes. We show that targeting high-exposure areas reduces vegetation needed for the same decrease in exposure compared to uniform treatment.

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“…Moreover, it is also one of the first to use these methods to predict the spatial variability of RH, and thus estimates of ambient moist heat stress. We find that, when combined with other ancillary information, satellite-derived LST can be a strong predictor of ambient air temperature, even though using LST directly as a proxy for urban heat exposure may be misleading (Chakraborty et al 2022, Turner et al 2022. Overall, since our ML model identified LST from both the Landsat two-month average and the closest time period as being the variables contributing the most predictive power to our model, this means that the air temperature variability is embedded within LST variability, as reflected in the feature importance scores (figure 2).…”

Section: Discussionmentioning

confidence: 92%

Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress

Wang,

Hsu,

Chakraborty

2023

Environ. Res.: Infrastruct. Sustain.

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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 meters). 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.

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More than surface temperature: mitigating thermal exposure in hyper-local land system

Cited by 27 publications

References 63 publications

Inequality in the availability of residential air conditioning across 115 US metropolitan areas

Inequality in the availability of residential air conditioning across 115 US metropolitan areas

Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes

Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress

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