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. 2022 Dec 18;10(3):358-378.
doi: 10.1080/23328940.2022.2149024. eCollection 2023.

Beyond heat exposure - new methods to quantify and link personal heat exposure, stress, and strain in diverse populations and climates: The journal Temperature toolbox

Gisel Guzman-Echavarria  1 , Ariane Middel  2   3 , Jennifer Vanos  4
Affiliations

Beyond heat exposure - new methods to quantify and link personal heat exposure, stress, and strain in diverse populations and climates: The journal Temperature toolbox

Gisel Guzman-Echavarria et al. Temperature (Austin). .

Abstract

Fine-scale personal heat exposure (PHE) information can help prevent or minimize weather-related deaths, illnesses, and reduced work productivity. Common methods to estimate heat risk do not simultaneously account for the intensity, frequency, and duration of thermal exposures, nor do they include inter-individual factors that modify physiological response. This study demonstrates new whole-body net thermal load estimations to link PHE to heat stress and strain over time. We apply a human-environment heat exchange model to examine how time-varying net thermal loads differ across climate contexts, personal attributes, and spatiotemporal scales. First, we investigate summertime climatic PHE impacts for three US cities: Phoenix, Miami, and New York. Second, we model body morphology and acclimatization for three profiles (middle-aged male/female; female >65 years). Finally, we quantify model sensitivity using representative data at synoptic and micro-scales. For all cases, we compare required and potential evaporative heat losses that can lead to dangerous thermal exposures based on (un)compensable heat stress. Results reveal misclassifications in heat stress or strain due to incomplete environmental data and assumed equivalent physiology and activities between people. Heat strain is most poorly represented by PHE alone for the elderly, non-acclimatized, those engaged in strenuous activities, and when negating solar radiation. Moreover, humid versus dry heat across climates elicits distinct thermal responses from the body. We outline criteria for inclusive PHE evaluations connecting heat exposure, stress, and strain while using physiological-based methods to avoid misclassifications. This work underlines the value of moving from "one-size-fits-all" thermal indices to "fit-for-purpose" approaches using personalized information.

Keywords: Personal heat exposure; extreme heat; human heat balance; mean radiant temperature; partitional calorimetry; situated knowledge; thermal strain; thermal stress; thermal stress index.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Environmental and inter-individual factors influencing a) personal heat exposure (PHE), b) heat stress, and c) heat strain. See Table 1 for definitions. To determine heat strain in individuals over time (t, dt) we must know the spatio-temporal evolution of temperature (Tair), humidity (H), wind speed (Ws), radiation (r), atmospheric pressure (p) of the surrounding environment, their metabolic rate (M), clothing insolation (Iclo), skin temperature Tsk, skin wettedness (ω), and sweat rate (SR). Relevant definitions are listed in Table 1.
Figure 2.
Figure 2.
Relationships between the need for heat loss (positive values) or heat gain (negative values) to reach thermal equilibrium (Ereq, x-axis) against the potential evaporative heat losses given a set of environmental conditions and clothing insulation (Emax, y-axis). The ratio of Ereq-to- Emax relates the degree of thermal stress to heat exerted on the body (required skin wettedness, ωreq). Critical values of ωreq are represented with black lines: 0.5 for low-efficient heat loss (thick line) and 1 to limit compensable/uncompensable heat stress (thin line). The purple lines represent values of Emax_sweat for the young/acclimatized and elderly/non-acclimatized when the liquid sweat loss is non-replenished. The vertical gray line at Ereq = 0 indicates thermal equilibrium. Therefore, the numbered zones correspond to 1) cold stress, 2) compensable heat stress (CHS), 3) CHS with low sweating efficiency (CHS + Low Seff), 4) uncompensable heat stress (UHS), 5) CHS with limited sweat rate (CSH + LS), and 6) CHS + Low Seff+ LS.
Figure 4.
Figure 4.
Simulated MRT hourly data for the hottest day in the TMY file from Phoenix (July 16th, 2022). Control corresponds to indoor conditions e.g. MRT equal to air temperature.
Figure 5.
Figure 5.
Case 1 scenarios – Two-dimensional histograms of the rate of required whole-body heat loss, Ereq (x-axis), and the maximum potential evaporative rate, Emax (y-axis,) for three personal profiles (columns) – Middle-aged female; Middle-aged male; Elderly female––while walking in the summertime in Phoenix, Miami, and New York (rows). Black diagonal lines: ωreq = 0.5 (thick) and 1 (thin). Purple lines: values of Emax_sweat for the young/acclimatized and elderly/non-acclimatized (lighter shade) when liquid sweat loss is not replenished. Vertical gray line at Ereq = 0 indicates thermal equilibrium.
Figure 3.
Figure 3.
The MaRTy human-biometeorological station located in a single-family home backyard in an open low-rise Local Climate Zone (LCZ 6, SVF: 0.909) neighborhood in Phoenix, Arizona. The portable station monitors net radiation in three directions to provide highly accurate MRT values, air temperature, relative humidity, and wind velocity.
Figure 6.
Figure 6.
Case 2 scenarios – Two-dimensional histogram for the rate of required whole-body heat loss, Ereq (x-axis), and the maximum potential evaporative rate, Emax (y-axis), for the middle-aged female profile performing different activities (blue =resting, grey =walking, pink =jogging). The model was run using hourly TMY summertime data for Phoenix, Miami, and New York. Black diagonal lines: ωreq = 0.5 (thick) and 1 (thin). Purple lines: values of Emax_sweat for the young/acclimatized when the liquid sweat loss is non-replenished. Vertical gray line at Ereq = 0 indicates thermal equilibrium.
Figure 7.
Figure 7.
Case 3 scenarios – Two-dimensional histogram for the rate of required whole-body heat loss, Ereq (x-axis), and the maximum potential evaporative rate, Emax (y-axis), for the middle-aged female personal profile walking while facing different constant wind speeds at human height (gray = calm winds (0.5 ms−[1]), light blue = light breeze (3 ms−[1]), dark blue = moderate breeze (7 ms−[1]). The model was run using hourly TMY summertime data from Phoenix, Miami, and New York. Black diagonal lines: ωreq = 0.5 (thick) and 1 (thin). Purple lines: values of Emax_sweat for the young/acclimatized when the liquid sweat loss is non-replenished. Vertical gray line at Ereq = 0 indicates thermal equilibrium.
Figure 8.
Figure 8.
Case 4 scenarios – Two-dimensional histogram for the rate of required whole-body heat loss, Ereq (x-axis), and the maximum potential evaporative rate, Emax (y-axis), for the middle-aged female personal profile walking in indoor, partly cloudy, and clear sky conditions (histogram colors). The model was forced with hourly TMY summertime data from Phoenix, Miami, and New York. Black, gray, and purple lines as in .Figure 2
Figure 9.
Figure 9.
Case 4 – Hourly ωreq estimates for the hottest day in the TMY file from Phoenix (July 16th). Results for the middle-aged female profile. Each color corresponds to ωreq for model 1 (Control), 2, 3, and 4. The horizontal lines delimite ω: 0.5 and 1.
Figure 10.
Figure 10.
Case 5 – From top to bottom: (a,b) modeled ωreq (or HSI) for the three personal profiles (Middle-aged Female/Male andElderly female, all assumed to be walking); (c, d) air temperature and mean radiant temperature; (e, f) relative humidity; and (g, h) wind speed. The left panel corresponds to 1-minute microclimate data during a June 9–11th, 2022 heatwave in Phoenix, AZ, and the right panel to the hourly data for the hottest day in the TMY in Phoenix, AZ (July 16th). Graph a represent ωreq as 20-minute rolling means. In (a, b), the horizontal lines delimitωreq: 0.5 and 1, and the yellow line in b) corresponds to the control run (gray line) in Figure 9.
Figure 11.
Figure 11.
Case 5 – Two-dimensional histogram for the rate of required whole-body heat loss, Ereq (x-axis), and the maximum potential evaporative rate, Emax (y-axis), for the three personal profiles performing different activities (histogram colors) during a heat wave (June 9–11th of 2022) in Phoenix, Arizona. The model was run using the 1-minute average of data collected with MaRTy in the backyard of a single-family home. Black diagonal lines: ωreq = 0.5 (thick) and 1 (thin). Purple line: values of Emax_sweat for the young/acclimatized when the liquid sweat loss is not replenished. Vertical gray line at Ereq = 0 indicates thermal equilibrium.
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Grants and funding

GEG and JKV acknowledge funding from the National Science Foundation Award #CMMI-2045663; AM also acknowledges funding from NSF Award #CMMI-1942805. During this time, GEG was also funded by an ASU graduate college Graduate College, Arizona State University Interdisciplinary Enrichment Research Fellowship.

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