A modelling framework for local thermal comfort assessment related to bicycle helmet use.

Thermal discomfort due to accumulated sweat increasing head skin wettedness may contribute to low wearing rates of bicycle helmets. Using curated data on human head sweating and helmet thermal properties, a modelling framework for the thermal comfort assessment of bicycle helmet use is proposed. Local sweat rates (LSR) at the head were predicted as the ratio to the gross sweat rate (GSR) of the whole body or by sudomotor sensitivity (SUD), the change in LSR per change in body core temperature (Δtre). Combining those local models with Δtre and GSR output from thermoregulation models, we simulated head sweating depending on the characteristics of the thermal environment, clothing, activity, and exposure duration. Local thermal comfort thresholds for head skin wettedness were derived in relation to thermal properties of bicycle helmets. The modelling framework was supplemented by regression equations predicting the wind-related reductions in thermal insulation and evaporative resistance of the headgear and boundary air layer, respectively. Comparing the predictions of local models coupled with different thermoregulation models to LSR measured at the frontal, lateral and medial head under bicycle helmet use revealed a large spread in LSR predictions predominantly determined by the local models and the considered head region. SUD tended to overestimate frontal LSR but performed better for lateral and medial head regions, whereas predictions by LSR/GSR ratios were lower and agreed better with measured frontal LSR. However, even for the best models root mean squared prediction errors exceeded experimental SD by 18-30%. From the high correlation (R > 0.9) of skin wettedness comfort thresholds with local sweating sensitivity reported for different body regions, we derived a threshold value of 0.37 for head skin wettedness. We illustrate the application of the modelling framework using a commuter-cycling scenario, and discuss its potential as well as the needs for further research.

A mathematical model for predicting cardiovascular responses at rest and during exercise in demanding environmental conditions.

The present research describes the development and validation of a cardiovascular model (CVR Model) for use in conjunction with advanced thermophysiological models, where usually only a total cardiac output is estimated. The CVR Model detailed herein estimates cardio-dynamic parameters (changes in cardiac output, stroke volume, and heart rate), regional blood flow, and muscle oxygen extraction, in response to rest and physical workloads, across a range of ages and aerobic fitness levels, as well as during exposure to heat, dehydration, and altitude. The model development strategy was to first establish basic resting and exercise predictions for cardio-dynamic parameters in an "ideal" environment (cool, sea level, and hydrated person). This basic model was then advanced for increasing levels of altitude, heat strain, and dehydration, using meta-analysis and reaggregation of published data. Using the estimated altitude- and heat-induced changes in maximum oxygen extraction and maximum cardiac output, the decline in maximum oxygen consumption at high altitude and in the heat was also modeled. A validation of predicted cardiovascular strain using heart rate was conducted using a dataset of 101 heterogeneous individuals (1,371 data points) during rest and exercise in the heat and at altitude, demonstrating that the CVR Model performs well (R2 = 0.82-0.84) in predicting cardiovascular strain, particularly at a group mean level (R2 = 0.97). The development of the CVR Model is aimed at providing the Fiala thermal Physiology & Comfort (FPC) Model and other complex thermophysiological models with improved estimations of cardiac strain and exercise tolerance, across a range of individuals during acute exposure to environmental stressors.NEW & NOTEWORTHY The present research promotes the adaption of thermophysiological modeling to the estimation of cardiovascular strain in individuals exercising under acute environmental stress. Integration with advanced models of human thermoregulation opens doors for detailed numerical analysis of athletes' performance and physiology during exercise, occupational safety, and individual work tolerability. The research provides a simple-to-validate metric of cardiovascular function (heart rate), as well as a method to evaluate key principles influencing exercise- and thermoregulation in humans.

Estimated work ability in warm outdoor environments depends on the chosen heat stress assessment metric.

With a view to occupational effects of climate change, we performed a simulation study on the influence of different heat stress assessment metrics on estimated workability (WA) of labour in warm outdoor environments. Whole-day shifts with varying workloads were simulated using as input meteorological records for the hottest month from four cities with prevailing hot (Dallas, New Delhi) or warm-humid conditions (Managua, Osaka), respectively. In addition, we considered the effects of adaptive strategies like shielding against solar radiation and different work-rest schedules assuming an acclimated person wearing light work clothes (0.6 clo). We assessed WA according to Wet Bulb Globe Temperature (WBGT) by means of an empirical relation of worker performance from field studies (Hothaps), and as allowed work hours using safety threshold limits proposed by the corresponding standards. Using the physiological models Predicted Heat Strain (PHS) and Universal Thermal Climate Index (UTCI)-Fiala, we calculated WA as the percentage of working hours with body core temperature and cumulated sweat loss below standard limits (38 °C and 7.5% of body weight, respectively) recommended by ISO 7933 and below conservative (38 °C; 3%) and liberal (38.2 °C; 7.5%) limits in comparison. ANOVA results showed that the different metrics, workload, time of day and climate type determined the largest part of WA variance. WBGT-based metrics were highly correlated and indicated slightly more constrained WA for moderate workload, but were less restrictive with high workload and for afternoon work hours compared to PHS and UTCI-Fiala. Though PHS showed unrealistic dynamic responses to rest from work compared to UTCI-Fiala, differences in WA assessed by the physiological models largely depended on the applied limit criteria. In conclusion, our study showed that the choice of the heat stress assessment metric impacts notably on the estimated WA. Whereas PHS and UTCI-Fiala can account for cumulative physiological strain imposed by extended work hours when working heavily under high heat stress, the current WBGT standards do not include this. Advanced thermophysiological models might help developing alternatives, where not only modelling details but also the choice of physiological limit criteria will require attention. There is also an urgent need for suitable empirical data relating workplace heat exposure to workability.

Thermal Indices and Thermophysiological Modeling for Heat Stress.

The assessment of the risk of human exposure to heat is a topic as relevant today as a century ago. The introduction and use of heat stress indices and models to predict and quantify heat stress and heat strain has helped to reduce morbidity and mortality in industrial, military, sports, and leisure activities dramatically. Models used range from simple instruments that attempt to mimic the human-environment heat exchange to complex thermophysiological models that simulate both internal and external heat and mass transfer, including related processes through (protective) clothing. This article discusses the most commonly used indices and models and looks at how these are deployed in the different contexts of industrial, military, and biometeorological applications, with focus on use to predict related thermal sensations, acute risk of heat illness, and epidemiological analysis of morbidity and mortality. A critical assessment is made of tendencies to use simple indices such as WBGT in more complex conditions (e.g., while wearing protective clothing), or when employed in conjunction with inappropriate sensors. Regarding the more complex thermophysiological models, the article discusses more recent developments including model individualization approaches and advanced systems that combine simulation models with (body worn) sensors to provide real-time risk assessment. The models discussed in the article range from historical indices to recent developments in using thermophysiological models in (bio) meteorological applications as an indicator of the combined effect of outdoor weather settings on humans.

Modified wind chill temperatures determined by a whole body thermoregulation model and human-based facial convective coefficients.

Wind chill equivalent temperatures (WCETs) were estimated by a modified Fiala's whole body thermoregulation model of a clothed person. Facial convective heat exchange coefficients applied in the computations concurrently with environmental radiation effects were taken from a recently derived human-based correlation. Apart from these, the analysis followed the methodology used in the derivation of the currently used wind chill charts. WCET values are summarized by the following equation:[Formula: see text]Results indicate consistently lower estimated facial skin temperatures and consequently higher WCETs than those listed in the literature and used by the North American weather services. Calculated dynamic facial skin temperatures were additionally applied in the estimation of probabilities for the occurrence of risks of frostbite. Predicted weather combinations for probabilities of "Practically no risk of frostbite for most people," for less than 5 % risk at wind speeds above 40 km h(-1), were shown to occur at air temperatures above -10 °C compared to the currently published air temperature of -15 °C. At air temperatures below -35 °C, the presently calculated weather combination of 40 km h(-1)/-35 °C, at which the transition for risks to incur a frostbite in less than 2 min, is less conservative than that published: 60 km h(-1)/-40 °C. The present results introduce a fundamentally improved scientific basis for estimating facial skin temperatures, wind chill temperatures and risk probabilities for frostbites over those currently practiced.

The Universal Thermal Climate Index UTCI compared to ergonomics standards for assessing the thermal environment.

The growing need for valid assessment procedures of the outdoor thermal environment in the fields of public weather services, public health systems, urban planning, tourism & recreation and climate impact research raised the idea to develop the Universal Thermal Climate Index UTCI based on the most recent scientific progress both in thermo-physiology and in heat exchange theory. Following extensive validation of accessible models of human thermoregulation, the advanced multi-node 'Fiala' model was selected to form the basis of UTCI. This model was coupled with an adaptive clothing model which considers clothing habits by the general urban population and behavioral changes in clothing insulation related to actual environmental temperature. UTCI was developed conceptually as an equivalent temperature. Thus, for any combination of air temperature, wind, radiation, and humidity, UTCI is defined as the air temperature in the reference condition which would elicit the same dynamic response of the physiological model. This review analyses the sensitivity of UTCI to humidity and radiation in the heat and to wind in the cold and compares the results with observational studies and internationally standardized assessment procedures. The capabilities, restrictions and potential future extensions of UTCI are discussed.

Deriving the operational procedure for the Universal Thermal Climate Index (UTCI).

The Universal Thermal Climate Index (UTCI) aimed for a one-dimensional quantity adequately reflecting the human physiological reaction to the multi-dimensionally defined actual outdoor thermal environment. The human reaction was simulated by the UTCI-Fiala multi-node model of human thermoregulation, which was integrated with an adaptive clothing model. Following the concept of an equivalent temperature, UTCI for a given combination of wind speed, radiation, humidity and air temperature was defined as the air temperature of the reference environment, which according to the model produces an equivalent dynamic physiological response. Operationalising this concept involved (1) the definition of a reference environment with 50% relative humidity (but vapour pressure capped at 20 hPa), with calm air and radiant temperature equalling air temperature and (2) the development of a one-dimensional representation of the multivariate model output at different exposure times. The latter was achieved by principal component analyses showing that the linear combination of 7 parameters of thermophysiological strain (core, mean and facial skin temperatures, sweat production, skin wettedness, skin blood flow, shivering) after 30 and 120 min exposure time accounted for two-thirds of the total variation in the multi-dimensional dynamic physiological response. The operational procedure was completed by a scale categorising UTCI equivalent temperature values in terms of thermal stress, and by providing simplified routines for fast but sufficiently accurate calculation, which included look-up tables of pre-calculated UTCI values for a grid of all relevant combinations of climate parameters and polynomial regression equations predicting UTCI over the same grid. The analyses of the sensitivity of UTCI to humidity, radiation and wind speed showed plausible reactions in the heat as well as in the cold, and indicate that UTCI may in this regard be universally useable in the major areas of research and application in human biometeorology.

Physiological responses to temperature and humidity compared to the assessment by UTCI, WGBT and PHS.

In COST Action 730, a multi-segmental thermophysiological model was used to describe physiological strain reactions for different climatic conditions in order to develop a 'Universal Thermal Climate Index' (UTCI). UTCI predictions for warm climates were compared with empirical data from the laboratory tests. The comparison was performed by means of equivalence lines within a psychrometric chart so that the combined influence of air temperature and humidity on physiological strain may be assessed. Within a reasonable regime of air temperatures and relative humidities (RH), the differences between simulated and measured values were as follows: for rectal temperatures below 0.3°C, for skin temperatures below 1.5°C, for sweat rates below 200 g/h and for heart rates (estimated from relative cardiac output) below 30 min(-1). This characterises the validity of the model with respect to the description of the influence of heat and humidity on physiological strain. The same comparison to physiological data was also conducted for the equivalent temperature calculated for UTCI. In order to compare UTCI with other thermal indices used in occupational health, the physiological data have also been compared to equivalence lines of WBGT (Wet Bulb Globe Temperature) and PHS (Predicted Heat Strain) indices.

UTCI-Fiala multi-node model of human heat transfer and temperature regulation.

The UTCI-Fiala mathematical model of human temperature regulation forms the basis of the new Universal Thermal Climate Index (UTC). Following extensive validation tests, adaptations and extensions, such as the inclusion of an adaptive clothing model, the model was used to predict human temperature and regulatory responses for combinations of the prevailing outdoor climate conditions. This paper provides an overview of the underlying algorithms and methods that constitute the multi-node dynamic UTCI-Fiala model of human thermal physiology and comfort. Treated topics include modelling heat and mass transfer within the body, numerical techniques, modelling environmental heat exchanges, thermoregulatory reactions of the central nervous system, and perceptual responses. Other contributions of this special issue describe the validation of the UTCI-Fiala model against measured data and the development of the adaptive clothing model for outdoor climates.

Predicting urban outdoor thermal comfort by the Universal Thermal Climate Index UTCI--a case study in Southern Brazil.

Recognising that modifications to the physical attributes of urban space are able to promote improved thermal outdoor conditions and thus positively influence the use of open spaces, a survey to define optimal thermal comfort ranges for passers-by in pedestrian streets was conducted in Curitiba, Brazil. We applied general additive models to study the impact of temperature, humidity, and wind, as well as long-wave and short-wave radiant heat fluxes as summarised by the recently developed Universal Thermal Climate Index (UTCI) on the choice of clothing insulation by fitting LOESS smoothers to observations from 944 males and 710 females aged from 13 to 91 years. We further analysed votes of thermal sensation compared to predictions of UTCI. The results showed that females chose less insulating clothing in warm conditions compared to males and that observed values of clothing insulation depended on temperature, but also on season and potentially on solar radiation. The overall pattern of clothing choice was well reflected by UTCI, which also provided for good predictions of thermal sensation votes depending on the meteorological conditions. Analysing subgroups indicated that the goodness-of-fit of the UTCI was independent of gender and age, and with only limited influence of season and body composition as assessed by body mass index. This suggests that UTCI can serve as a suitable planning tool for urban thermal comfort in sub-tropical regions.

The UTCI-clothing model.

The Universal Thermal Climate Index (UTCI) was conceived as a thermal index covering the whole climate range from heat to cold. This would be impossible without considering clothing as the interface between the person (here, the physiological model of thermoregulation) and the environment. It was decided to develop a clothing model for this application in which the following three factors were considered: (1) typical dressing behaviour in different temperatures, as observed in the field, resulting in a model of the distribution of clothing over the different body segments in relation to the ambient temperature, (2) the changes in clothing insulation and vapour resistance caused by wind and body movement, and (3) the change in wind speed in relation to the height above ground. The outcome was a clothing model that defines in detail the effective clothing insulation and vapour resistance for each of the thermo-physiological model's body segments over a wide range of climatic conditions. This paper details this model's conception and documents its definitions.

Validation of the Fiala multi-node thermophysiological model for UTCI application.

The important requirement that COST Action 730 demanded of the physiological model to be used for the Universal Thermal Climate Index (UTCI) was its capability of accurate simulation of human thermophysiological responses across a wide range of relevant environmental conditions, such as conditions corresponding to the selection of all habitable climates and their seasonal changes, and transient conditions representing the temporal variation of outdoor conditions. In the first part of this study, available heat budget/two-node models and multi-node thermophysiological models were evaluated by direct comparison over a wide spectrum of climatic conditions. The UTCI-Fiala model predicted most reliably the average human thermal response, as shown by least deviations from physiologically plausible responses when compared to other models. In the second part of the study, this model was subjected to extensive validation using the results of human subject experiments for a range of relevant (steady-state and transient) environmental conditions. The UTCI-Fiala multi-node model proved its ability to predict adequately the human physiological response for a variety of moderate and extreme conditions represented in the COST 730 database. The mean skin and core temperatures were predicted with average root-mean-square deviations of 1.35 ± 1.00°C and 0.32 ± 0.20°C, respectively.

Physiological modeling for technical, clinical and research applications.

Various and disparate technical disciplines have identified a growing need for tools to predict human thermal and thermoregulatory responses to environmental heating and cooling and other thermal challenges such as anesthesia and non-ionizing radiation. In this contribution, a dynamic simulation model is presented and used to predict human thermophysiological and perceptual responses for different applications and situations. The multi-segmental, multi-layered mathematical model predicts body temperatures, thermoregulatory responses, and components of the environmental heat exchange in cold, moderate, as well as hot stress conditions. The incorporated comfort model uses physiological states of the human body to predict thermal sensation responses to steady state and transient conditions. Different validation studies involving climate-chamber physiological and thermal comfort experiments, exposures to uncontrolled outdoor weather conditions, extreme climatic and radiation asymmetry scenarios revealed the model to predict physiological and perceptual responses typically within the standard deviation of the experimental observations. Applications of the model in biometeorology, clothing research, the car industry, clinical and safety applications are presented and discussed.

Single-sector thermophysiological human simulator.

Thermal sweating manikins are used to analyse the heat and mass transfer phenomena in the skin-clothing-environment system. However, the limiting factor of present thermal manikins is their inability to simulate adequately the human thermal behaviour, which has a significant effect on the clothing microenvironment. A mathematical model of the human physiology was, therefore, incorporated into the system control to simulate human thermoregulatory responses and the perception of thermal comfort over a wide range of environmental and personal conditions. Thereby, the computer model provides the physiological intelligence, while the hardware is used to measure the required calorimetric states relevant to the human heat exchange with the environment. This paper describes the development of a single-sector thermophysiological human simulator, which consists of a sweating heated cylinder 'Torso' coupled with the iesd-Fiala multi-node model of human physiology and thermal comfort. Validation tests conducted for steady-state and, to some extent, transient conditions ranging from cold to hot revealed good agreement with the corresponding experimental results obtained for semi-nude subjects. The new coupled system enables overall physiological and comfort responses, health risk and survival conditions to be predicted for adult humans for various scenarios.

Validation of an individualised model of human thermoregulation for predicting responses to cold air.

Most computer models of human thermoregulation are population based. Here, we individualised the Fiala model [Fiala et al. (2001) Int J Biometeorol 45:143-159] with respect to anthropometrics, body fat, and metabolic rate. The predictions of the adapted multisegmental thermoregulatory model were compared with measured skin temperatures of individuals. Data from two experiments, in which reclining subjects were suddenly exposed to mild to moderate cold environmental conditions, were used to study the effect on dynamic skin temperature responses. Body fat was measured by the three-compartment method combining underwater weighing and deuterium dilution. Metabolic rate was determined by indirect calorimetry. In experiment 1, the bias (mean difference) between predicted and measured mean skin temperature decreased from 1.8 degrees C to -0.15 degrees C during cold exposure. The standard deviation of the mean difference remained of the same magnitude (from 0.7 degrees C to 0.9 degrees C). In experiment 2 the bias of the skin temperature changed from 2.0+/-1.09 degrees C using the standard model to 1.3+/-0.93 degrees C using individual characteristics in the model. The inclusion of individual characteristics thus improved the predictions for an individual and led to a significantly smaller systematic error. However, a large part of the discrepancies in individual response to cold remained unexplained. Possible further improvements to the model accomplished by inclusion of more subject characteristics (i.e. body fat distribution, body shape) and model refinements on the level of (skin) blood perfusion, and control functions, are discussed.