New clothing resistance scheme for estimating outdoor environmental thermal load

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Human thermal climate in the Hungarian lowland is investigated by using a clothing resistance‐operative temperature model performing individual, local, and long‐term concurrent observations of weather and human thermal perception. Human thermal load is characterized in terms of clothing resistance rcl and operative temperature To. The model is also used as a tool to analyse the relationship between rcl and the structural parameters of the body (relative fat mass index (fatBMI) and relative muscle mass index (muscleBMI)). This analysis works with data collected on more than 1,000 occasions of weather and thermal perception observation and uses body structure data of more than 3,000 adults and children. By analysing the data, the following human thermal climate characteristics have been established. (a) To of about 80°C can be considered as the upper limit of heat stress in the Hungarian lowland, similarly, To of about −35 to −37°C can be taken as the lower limit of cold stress. (b) Interpersonal thermal load variations increase with increasing of cold stress; these variations are the smallest within the “thermoneutral” zone. (c) In cold stress situations, there is a significant sexual dimorphism in the relationship between rcl and fatBMI. (d) In night time period of the day, To can vary between 25°C and the lower limit of cold stress. In this range of To, thermal perception types “neutral,” “cool,” “cold,” and “very cold” occurred. Based on the results, it can be seen that interpersonal thermal load variations cannot be neglected in extreme cold weather conditions, and that interpersonal variations of fatBMI index are determinant in the formation of individual clothing resistance.

Section: Methodsmentioning

confidence: 99%

Individual local human thermal climates in the Hungarian lowland: Estimations by a simple clothing resistance‐operative temperature model

Ács

Kristóf

Zsákai

2022

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“…Using energy balance equations for the human skin–clothing interface and for the clothing–air environment interface, neglecting the storage effect, we can obtain the clothing resistance parameter (Ács et al ., 2019)

r_{cl} = ρ ∙ c_{p} ∙ \frac{T_{S} - T_{a}}{M - λ E_{sd} - λ E_{r} - W} - r_{Hr} ∙ ([], \frac{R_{ni}}{M - λ E_{sd} - λ E_{r} - W} + 1),

where ρ is air density (kg·m −3 ), c p is specific heat at constant pressure (Jkg −1 ·C −1 ), r Hr is the combined resistance for expressing the thermal radiative and convective heat exchanges (sm −1 ), T S is skin temperature (°C), T a is air temperature (°C), R ni is isothermal net radiation flux density (Wm −2 ), M is metabolic heat flux density (Wm −2 ), λE sd is the latent heat flux density of dry skin (Wm −2 ), λE r is respiratory latent heat flux density (Wm −2 ) and W is mechanical work flux density (Wm −2 ), which refers to the activity under consideration. r cl is calculated for a walking human in outdoor conditions whose speed is 1.1 ms −1 (4 km∙hr −1 ) and skin temperature is 34°C.…”

Section: Methods and Datamentioning

confidence: 99%

“…Using energy balance equations for the human skinclothing interface and for the clothing-air environment interface, neglecting the storage effect, we can obtain the clothing resistance parameter ( Acs et al, 2019)…”

Section: The Clothing Resistance Modelmentioning

confidence: 99%

“…In this treatment environmental thermal load is represented via operative temperature. Recently, Acs et al (2019) investigated the sensitivity of r cl to wind speed variations and to interperson variations of metabolic heat flux density. They compared these two effects, but did not draw definite conclusions regarding the magnitude of their impacts.…”

Section: Introductionmentioning

confidence: 99%

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Clothing resistance and potential evapotranspiration as thermal climate indicators—The example of the Carpathian region

Ács

Zsákai

Kristóf

et al. 2021

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Clothing resistance parameter rcl and potential evapotranspiration (PET), a major component of Thornthwaite type climate classifications, are used as thermal climate indicators for characterizing the thermal climate of the Carpathian region. rcl is simulated by a model based on clothed human body energy balance considerations. rcl refers to a walking human in outdoor conditions, whose somatotype can differ. Somatotype shapes are determined by applying the Heath–Carter somatotype method. PET is estimated using only air temperature and latitude as inputs. In addition rcl is linked to PET. The annual mean of rcl is statistically interconnected with annual sum of PET, and the annual fluctuation of rcl (drcl = rclmax − rclmin) with the annual fluctuation of PET (dPET = PETmax − PETmin). The Carpathian region's thermal climate is analysed by comparing PET results with rcl model results and rcl results obtained by statistical link. We showed that rcl model results are strongly sensitive to human body somatotype variations. It is also shown that the spatial heterogeneity of thermal climates is the lowest in the lowlands and the highest in the mountains. The spatial heterogeneity of rcl and drcl values obtained by statistical link is comparable to the spatial heterogeneity of PET, but is lower than that obtained from the rcl model. Similarly to rcl model results, rcl results obtained by statistical link are also sensitive to human body somatotype variations. All these results suggest that statistical connections between rcl and PET and drcl and dPET can be used as subunits in Thornthwaite type climate classifications to obtain human thermal climate information. Lastly, areas with the largest thermal contrast are reproduced in terms of both the annual sum of PET and the rcl, which is obtained by both the model and the statistical link.

“…Holmér (1988, 2005) developed the IREQ index, which was used and evaluated in the studies of Griefahn and Forsthoff (1997), Gavhed and Holmér (1998) and Rissanen et al (2000). Based on the research of Campbell and Norman (1997), the clothing resistance index ( rcl index) was developed (Ács et al, 2019) and applied to estimate the human thermal climate of the Carpathian Basin (Ács et al, 2020, 2021b) using the CarpatClim dataset (Spinoni et al, 2015). By definition, these indices must be positive and can only be used in cold conditions, when thermal equilibrium can be achieved by eliminating the heat deficit of the body with clothing.…”

Section: Introductionmentioning

confidence: 99%

Comparison of CMIP6 model performance in estimating human thermal load in Europe in the winter season

Szalkai,

Kristóf,

Zsákai

et al. 2024

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In this work, historical simulations of CMIP6 GCMs are evaluated with respect to the ERA5 reanalysis dataset, in order to examine their ability to assess human thermal load in Europe in the winter season. The period of 1981–2010 is chosen for the analysis, and thermal load is expressed via the clothing resistance index (rcl index; expressed in clo). It is found that the GCMs are able to reproduce the areal differences of thermal load satisfactorily, the spatial correlation with the reanalysis is greater than 0.95 in all cases. The effects of the main geographical constraints (latitude, continentality and elevation) are shown by all GCM simulations, as rcl index values are greater at higher latitudes, away from the ocean and in mountainous areas, although GCMs only capture major mountains (the Caucasus, the Armenian Highlands, the Scandinavian Mountains, the Alps). The root‐mean‐square error (RMSE) is around 0.2 clo in all cases, GCMs generally perform better in homogenous lowland areas, while results are less accurate in highlands and mountains owing to the coarse horizontal resolution of GCMs (~1°). The smallest errors occur over central and western Europe and the Mediterranean region, while results tend to be less accurate over the northeastern part of Europe. Biases in the estimation of heat deficit can mainly be attributed to biases in temperature, but biases in wind speed and atmospheric downward radiation seem to be important factors as well.