# Evaluation of heat stress effect on health in dwellings using thermophysiological model

Jean-Marie Alessandrini, Wenjuan Wei, Mohamad El Kadri, Simon Molesin, Thomas Dominati, Jacques Ribéron, Charles Pelé, Corinne Mandin

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Jean-Marie Alessandrini, Wenjuan Wei, Mohamad El Kadri, Simon Molesin, Thomas Dominati, et al.. Evaluation of heat stress effect on health in dwellings using thermophysiological model. Indoor Air 2022, 17th International conference on Indoor air quality and climate, Jun 2022, Kuopio, Finland. hal-05006787

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# Evaluation of heat stress effect on health in dwellings using thermo-physiological model

Jean-Marie Alessandrini<fnref n="1" />,*, Wenjuan Wei<fnref n="1" />, Mohamad El Kadri<fnref n="1" />, Simon Molesin<fnref n="2" />, Thomas Dominati<fnref n="2" />, Jacques Ribéron<fnref n="1" />, Charles Pelé<fnref n="1" />, Corinne Mandin<fnref n="1" />

## SUMMARY

The French National Heatwave Plan (NHP) is based on a statistical analysis of the outdoor temperature-mortality relationship for the French population for heat exposure management at the nationwide scale, which cannot be fine-tuned to the building scale due to the diversity in building characteristics and occupant types. To allow building-based heat exposure management, the present work aims to determine the indoor heat stress thresholds for different types of occupants. The method is based on physiological modelling to establish quantitative correlations between heat stress and heat strain for different individual groups according to their heat vulnerability. Two thermo-physiological models, i.e., HSDA and NHTM, were used to simulate the core body temperature following exposures to different indoor thermal conditions, i.e., air temperature, relative humidity, and air velocity. The calculations showed a group of people whose heat strain threshold is exceeded for a 32°C indoor temperature but is respected at 30°C.

## KEYWORDS

Climate change, building, population, heat strain, temperature

## 1 INTRODUCTION

The building design, refurbishment, and management can be influenced by climate change mitigation measures that aim primarily at reducing the building's energy consumption. The priority is given to bioclimatic solutions that limit heating consumption in winter and avoid using air conditioning in summer. In mainland France, vernacular practices for regulating the indoor thermal environment of dwellings in summer include (1) reducing the internal and solar heat loads, (2) heat storage in the thermal mass, and (3) heat evacuation by natural ventilation. Under the climate change effect, the multiplication of heatwaves and their disastrous consequences on the population have highlighted the limits of these vernacular practices. The 2003 heatwave caused approximately 70000 deaths in Europe (Kovats and Hajat, 2008) and 14800 deaths in France (Hémon and Jougla, 2004), which led to the development of a National Heatwave Plan (NHP) in France in 2004. The NHP uses a Heat Health Watch Warning System (HHWWS) (Laaidi et al., 2013) that adapts actions to be deployed according to the thresholds of specifically developed indicators to protect people. These indicators were calculated using outdoor temperatures and their thresholds were developed based on a retrospective analysis of the correlation between meteorological data and mortality conducted in 14 cities in mainland France to be representative of different climates (Laaidi et al., 2013). The NHP offers useful protection at the nationwide scale, as it led to 4400 fewer deaths than anticipated in France during the heatwave in 2006 (Fouillet et al., 2008). The NHP can reduce but not avoid deaths associated with heat exposure, because it is based, in particular, on climatic indicators at an urban scale and ignores the level of protection provided at the building scale, e.g., housing.

<footnote n="1">¹ Scientific and Technical Centre for Building (CSTB), Marne-la-Vallée, France</footnote>

<footnote n="2">² Régie Immobilière de la Ville de Paris (RIVP), France</footnote>

<footnote>*Corresponding email: Jean-Marie.ALESSANDRINI@cstb.fr</footnote>
Therefore, the NHP indicators and their thresholds cannot be used to assess the performance of buildings during heat waves or their operating limits.

Standards (ISO) and the World Health Organization (WHO) have proposed indoor air temperature and humidity thresholds that should not be exceeded. Although these thermal stress thresholds can be used to manage the built environments, they are established for the entire population without distinguishing different individual groups according to their heat tolerance. However, the disparity in heat tolerance within the population is too great to be represented by these thresholds. Without heat stress thresholds adapted to different populations according to their vulnerability to heat, the municipal authorities organize a local solidarity network to carry out individualized monitoring to avoid hyperthermia in fragile people (local solidarity plan).

This individualized monitoring is not possible in the design or risk anticipation phases. To assess the level of adaptation to climate change in housing, it is therefore essential to know the thermal tolerance ranges of each type of population as Hanna and Tait have suggested (Hanna and Tait, 2015), in particular the threshold for vulnerable people. To help identify the different groups, an age-based approach was proposed by the French National Health Strategy (2017). Infants (less than 1 year of age) and elderly people (more than 65 years of age) were identified as individual groups partially acclimatized under moderate heat and badly acclimatized under intense heat (Pascal et al., 2017). This group, more vulnerable to heat than children and adults, represents 21% of the French population in 2018 (Insee, 2019).

In this context, the objective of the present work is to develop a method for quantifying the heat stress threshold based on the heat tolerance ranges for different population groups. The method links thermal stress, defined by the thermal load in indoor environments and the individual's activity, to thermal strain, i.e., the physiological stresses resulting from thermal stress. It determines heat stress thresholds for different population groups based on thermal strain ranges that must be maintained to assure the individual's health. The method allows quantifying the thermal stress thresholds that are adapted to a population with a short acclimatization period in mainland France, which can help building owners/operators to identify priority situations and to anticipate the health risks either through adapted technical solutions or through management actions.

## 2 MATERIALS/METHODS

The heat tolerance range is wide because of physiological and behavioral thermoregulation and acclimatization, leading to difficulties in determining thermal stress thresholds adapted to different populations according to their vulnerability to heat. To overcome this difficulty, the developed method links thermal stress to thermal strain whose tolerance range is narrow in homeotherms.

### Description of the general method

The method to determine the heat stress for different individual groups consists of six steps (Figure 1): (1) define the heat strain indicators and the thresholds for different individual groups, e.g., 38 °C for the core body temperature; (2) define the physiological characteristics for different individual groups according to their heat vulnerability; (3) define an indoor thermal environmental condition, e.g., 32 °C and 50% relative humidity; (4) calculate the value of the heat strain indicators using thermo-physiological models; (5) compare the calculated heat strain value with the defined thresholds to identify the characteristics of the individual groups that exceed the thresholds; (6) change the indoor thermal environmental condition and repeat steps 3-5 for the individual groups that exceed the thresholds; (7) define the heat stress indicators and calculate its value according to the thermal environmental condition defined in step 3.

Different methods are possible for step 4, and the selected one defines the heat strain indicator (step 1), the physiological parameters to use, and the heat stress indicator. Using a thermo-
physiological model in step 4 was preferred in the present work because it allowed different heat stress thresholds to be possibly determined according to the populations. Furthermore, epidemiological studies (Pascal et al., 2017) showed a sudden increase in mortality as a function of indoor temperatures, suggesting that an indoor temperature threshold also existed to prevent heat-related risks. Using a model would make it possible to scan a wide range of indoor thermal environments for different populations to determine population-dependent thresholds.

<figure><img src="image_2.png" /><figcaption>Figure 1. Method to determine the heat stress according to the heat strain.</figcaption></figure>

## Thermo-physiological models (step 4)

Two different thermo-physiological models, i.e., the Heat Strain Decision Aid HSDA (Potter et al., 2017) and the Neuro Human Thermal Model NHTM (El Kadri et al., 2020), were used for simulating the core body temperature following exposure to certain thermal conditions. The HSDA and NHTM models were chosen since users can change their individualized parameters and obtain different outputs, in other terms, these models count the interindividual differences. The HSDA model calculates the variations in the core temperature based on four ambient variables, i.e., air temperature, relative humidity, air velocity, and the mean radiant temperature. The individual-dependent variables include the height, weight, basal metabolic rate, and the days of acclimatization, which can be varied to calculate the core temperature of multiple subjects exposed to the same ambient conditions. Unfortunately, this model was developed for adults and needed adaptations to a wider range of populations. Potentially, the NHTM model was intended to be used for a wide range of populations, but it must first be calibrated on experimental work for vulnerable people from a physiological point of view. However, it contains a sweat model that can be constrained, like an altered functioning for an elderly person, and thus complements the heat stress threshold with dehydration. The NHTM was developed by El Kadri et al. (2020) to simulate subjects in dynamic heterogeneous conditions. It consists of 2 systems: the passive system and the active system. The passive system that represents the human body segments is based on a model developed by Wissler (2018) for NASA. It consists of 21 cylindrical elements, each of which is discretized in 21 layers and 12 angular sectors. The active system is the set of physiological mechanisms namely the shivering, the skin blood flow, and the sweat rate governed by the hypothalamus (El Kadri et al., 2020). The shivering and the skin blood flow are based on the signals of thermoreceptors and the sweat rate is proportional to the error signals which is the difference between the body temperature and the threshold of sweating.
## Heat strain indicator (step 1)

To verify whether the maximum heat stress indicators given at step 3 can prevent risks for all types of people, the HSDA and the NHTM models were used to calculate the core temperature. The thermo-physiological modeling aimed to verify whether a core temperature of 38 °C, considered as the threshold temperature to prevent health risks (Cheshire, 2016), was exceeded under the threshold to be defined in step 3.

## Physiological characteristics (step 2)

The ranges of the input parameters to account for the interindividual differences using the HSDA model are given in Table 1. Values were chosen to cover a wide range of the population. At each day/night transition of the simulation, the core temperature before the transition was considered as the initial value after the transition. The initial mean skin temperature was equal to 33 °C for all transitions.

<table><caption>Table 1. Parameters used in the HSDA models to calculate the core temperature</caption><thead><tr><th>Parameter</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Height [cm]</td><td>150</td><td>200</td></tr><tr><td>Weight [kg]</td><td>70</td><td>120</td></tr><tr><td>Basal metabolic rate [W]</td><td>70</td><td>120</td></tr><tr><td>Days of acclimatization</td><td>1</td><td>20</td></tr></tbody></table>

The ranges of the input parameters to account for the interindividual differences using the NHTM model are shown in Table 2. The values used were obtained from the in-vivo and in-vitro measurements in the literature (El Kadri et al., 2020).

<table><caption>Table 2. Parameters used in the NHTM model</caption><thead><tr><th>parameter</th><th></th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td rowspan="2">Gains</td><td>G<sub>SKBF</sub></td><td>0.2</td><td>2</td></tr><tr><td>G<sub>sweat</sub></td><td>0.2</td><td>2</td></tr><tr><td>Sweating threshold</td><td>dT<sub>sweat</sub></td><td>0.1</td><td>1</td></tr><tr><td>Basal metabolic rate [W]</td><td></td><td>70</td><td>120</td></tr><tr><td rowspan="10">Thermal conductivity [W.m<sup>-1</sup>k<sup>-1</sup>]</td><td>λ<sub>br</sub></td><td>0.494</td><td>0.51</td></tr><tr><td>λ<sub>mu</sub></td><td>0.235</td><td>0.415</td></tr><tr><td>λ<sub>fa</sub></td><td>0.176</td><td>0.32</td></tr><tr><td>λ<sub>bo</sub></td><td>0.3</td><td>1.1</td></tr><tr><td>λ<sub>vi</sub></td><td>0.487</td><td>0.515</td></tr><tr><td>λ<sub>li</sub></td><td>0.469</td><td>0.505</td></tr><tr><td>λ<sub>sk</sub></td><td>0.24</td><td>0.56</td></tr><tr><td>λ<sub>lu</sub></td><td>0.291</td><td>0.335</td></tr><tr><td>λ<sub>he</sub></td><td>0.47</td><td>0.51</td></tr></tbody></table>

br: brain; mu: muscle; fa: fat; bo: bone; vi: viscera; li: liver; sk: skin; lu: lungs; he: heart
G<sub>SKBF</sub> is the gain of skin blood flow. G<sub>sweat</sub> is the gain of sweat rate. A low value of G<sub>sweat</sub> helps simulate a person with altered functioning of sweating, as in some sensitive people, by limiting the amount of sweat emitted.
## Indoor thermal conditions (step 3)

WHO (2011) determined a maximum air temperature of 32 °C for the daytime and 24 °C for the nighttime to avoid health risks associated with heat exposure. These air temperatures were fixed for the HSDA and NHTM models (Figure 2(a)). The daytime was fixed between 7 am and 10 pm, and the nighttime was fixed between 10 pm and 7 am according to the code of public health (2017). For the NHTM model, the air temperature at the beginning of the simulation was equal to 26.5 °C (Figure 2(b)), to assure that the core temperature was 37 °C at 7 am. For both models, the impact of the night duration is not studied. This impact should be assessed specifically for dwellings in Urban Heat Islands (UHIs) where the decrease in night-time temperatures is delayed and shortened. The air velocity is fixed at 0.1 m/s and the mean radiant temperature is supposed to be equal to the air temperature in this study.

<figure><img src="image_3.png" /><figcaption>Figure 2. Air temperature profiles for (a) the HSDA model and (b) the NHTM model. Steps 5, 6, and 7 are processed with the results.</figcaption></figure>

## 3 RESULTS AND DISCUSSION

Figure 3(a) shows the simulation results using the HSDA model. For a maximum air temperature of 32 °C during day and 24 °C during night, the mean core temperature exceeded the threshold of 38 °C, suggesting that a group of subjects (almost half of the population) was exposed to a risk of hyperthermia. When the maximum air temperature inside buildings was decreased to 30 °C, both mean and standard deviation of the core temperature were below 38 °C, suggesting that almost 3/4 of the tested situations were safe regarding hyperthermia (Figure 3(b)).

<figure><img src="image_4.png" /><figcaption>Figure 3. Mean and standard deviation of core temperature calculated using the HSDA model for a maximum day temperature of (a) 32 °C and (b) 30 °C.</figcaption></figure>

Figure 4(a) shows the mean and the standard deviation of the core temperature calculated using the NHTM model for a maximum air temperature of 32°C. At this temperature, the mean core temperature was lower than 38 °C, but some people had core temperatures higher than 38 °C,
suggesting that a maximum air temperature of 32 °C presented a risk of hyperthermia for some occupants. When the maximum air temperature was decreased to 30 °C, both mean and standard deviation of the core temperature were below 38 °C, suggesting that almost 3/4 of the tested situations were safe regarding hyperthermia (Figure 4(b)). Thus, the simulation results calculated by the HSDA and NHTM models were consistent.

<figure><img src="image_5.png" /><figcaption>Figure 4. Mean and standard deviation of core temperature calculated using NHTM for a maximum day temperature of (a) 32 °C and (b) 30 °C.</figcaption></figure>

<figure><img src="image_6.png" /></figure>

In addition to core temperature, the NHTM model was used to calculate the water loss by transpiration, sweating, and respiration. According to Popkin et al. (2010), the average need for hydration is 3700 ml/day for men and 2700 ml/day for women. In this study, the average of two quantities was used, i.e., 3200 ml/day. Figure 5 shows the mean and the standard deviation of water loss of subjects exposed to a maximum air temperature of 30 and 32 °C. When the maximum ambient temperature was 32 °C, the water loss of some people was above 3200 ml/day, suggesting that these people were exposed to a risk of dehydration. Under a maximum ambient temperature of 30 °C, all people were in the safety zone regarding a dehydration risk. By comparing the subjects at risk of dehydration according to the water loss with the subjects exposed at risk of hyperthermia according to the core temperature, we found that the first group was a subgroup of the second group, suggesting that all subjects at risk of dehydration were exposed at risk of hyperthermia.

<figure><img src="image_7.png" /><figcaption>Figure 5. Mean and standard deviation of water loss for subjects exposed to a maximum air temperature of (a) 32 °C and (b) 30 °C.</figcaption></figure>

<figure><img src="image_8.png" /></figure>

Based on the simulation results, the physiological properties of the population at risk (vulnerable) and the population in the safe zone (normal) between 30 and 32° C are given in Table 3. The vulnerable subjects have a low skin blood flow and a low sweat rate. These properties decrease the dissipation of heat and, by consequence, the increase of core temperature.
<table><caption>Table 3. Physiological parameters of vulnerable subjects</caption><thead><tr><th>Parameter</th><th></th><th>Mean (vulnerable)</th><th>Mean (normal)</th></tr></thead><tbody><tr><td rowspan="2">Gains</td><td>G<sub>SKBF</sub></td><td>0.82</td><td>1.23</td></tr><tr><td>G<sub>sweat</sub></td><td>0.42</td><td>1.32</td></tr><tr><td>Sweating threshold</td><td>dT<sub>sweat</sub></td><td>0.16</td><td>0.02</td></tr><tr><td>Basal metabolic rate [W]</td><td></td><td>90</td><td>86</td></tr><tr><td rowspan="5">Thermal conductivity [W.m<sup>-1</sup>k<sup>-1</sup>]</td><td>λ<sub>mu</sub></td><td>0.32</td><td>0.35</td></tr><tr><td>λ<sub>fa</sub></td><td>0.25</td><td>0.28</td></tr><tr><td>λ<sub>bo</sub></td><td>0.70</td><td>0.97</td></tr><tr><td>λ<sub>sk</sub></td><td>0.39</td><td>0.52</td></tr><tr><td>λ<sub>he</sub></td><td>0.49</td><td>0.50</td></tr></tbody></table>

To be effective for building stakeholders, links between these thermo-physiological parameters and anthropometric descriptions of populations need to be made. For example, Pascal et al. (2017) defines a group of people, consisting of infants and elderly, partially acclimatized to a moderately hot environment. Similarly, the heatwave alert plan provides for a census of people over 65 years old to organize their monitoring during the heatwave. Typically, for the light activity selected, the aim would be of quantifying the range in which the physiological parameters of infants and the elderly belong. In a similar approach, Hanna and Tait (2015) have suggested classifying the population according to its level of acclimatization. They proposed the low, partial, and high levels and a blurring area between two levels. An alternative approach is to consider this value of 30°C as the lower limit of the blurring area of partially acclimatized people as suggested by the acclimatization duration of 1 day used in the HSDA model. Despite all the limitations of the preliminary results in particular the limit of step 4, the method of the present work can help identify the heat stress threshold for each type of population. If the WHO air temperature threshold at 32 °C is used to protect the partial acclimatization group at the heart of the distribution, a lower limit of the blurring area of 2°C lower seems appropriate in the limits of our assumptions and calculation models.

## 4 CONCLUSIONS

A method was developed to determine the heat stress threshold adapted to a group of population according to its vulnerability to heat exposure. The core temperature of subjects was calculated using two thermo-physiological models, i.e., HSDA and NHTM. With both models, compliance with the WHO thermal strain thresholds led to not exceeding the thermal stress threshold for the majority of the modelled population. But the two models showed that a 32 °C maximum day temperature was not safe for a part of the population that was not sufficiently acclimatized to the heat. For the cases studied, both models show that for a standard population whose acclimatization level varies from 1 to 20 days, part of this population is exposed at risk for an indoor temperature of 32°C, but is mostly protected for a temperature of 30°C.

This method calculates the stress as a function of strain, which can help set different thresholds for indoor environments according to the physiological characteristics of the occupants. However, more work is needed for the method to be robust. In particular at step 2, the quantified physiological variables should be combined with an anthropometric description of the population (e.g., age, gender, etc.) to provide useful information to stakeholders. It is also necessary to specify the limits of application of the models to study vulnerable populations. Step 4 would benefit from being completed by experimental feedback from the literature.
The lack of experimental data on large populations with low acclimatization levels calls for caution in calculation of the temperature thresholds. They cannot, and are not intended to replace the monitoring actions implemented in prevention plans. They provide useful information for building owners or managers in the design or risk prevention phase for selecting technical solutions or sizing the means of prevention.

## ACKNOWLEDGEMENT

The studies described in this paper were supported by the CSTB Research Program and the LoPACC project financed by ADEME.

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