ClimApp-Integrating Personal Factors with Weather Forecasts for Individualised Warning and Guidance on Thermal Stress

From Public Forecasts to Occupational Heat Safety Management: Evaluating the Effectiveness of the National Weather Service's Operational Heat Risk Forecasts

Heat poses a major environmental risk to occupational safety, necessitating timely insights into associated risks to safeguard workers. In June 2022, the National Weather Service (NWS) initiated operational wet bulb globe temperature (WBGT) forecasts, offering valuable information for heat risk management. This study evaluates the effectiveness of NWS WBGT forecasts, aiming to identify potential areas of caution and improvements for their application for occupational safety management. To this end, the study examines 1.3 million hourly historical NWS WBGT forecast data, comparing it with observed data from 252 weather stations across the US during the summer of 2023. The results offer key insights, revealing that: (1) the accuracy of NWS WBGT forecasts is influenced more by the times of interest than by the forecast horizons; (2) NWS WBGT forecast accuracy varies across different climates in the US, with air temperature bias being the most influential factor in this inaccuracy; and (3) while NWS WBGT forecasts accurately identify the lowest heat risks (i.e. no heat risk), their performance decreases at higher risk levels, emphasizing the importance of careful interpretation in safety management. These insights offer guidance for more cautious interpretations of NWS WBGT forecasts and lay the foundation for future studies on leveraging operational weather forecasting services in effective heat mitigation strategies.

Improving the operational forecasts of outdoor Universal Thermal Climate Index with post-processing

The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated as part of the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6∘ C to almost zero, while the daily mean absolute error decreased from 5∘ C to 3∘ C for the neural network and 3.5∘ C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day.

The Slip and Fall Index: Assessing the risk of slipping and falling on ice

OBJECTIVES

Canadians are at an increased risk of outdoor slip and fall accidents during periods of ice and snow. The aim of this study was to create an index to alert the public of slippery outdoor conditions and promote pedestrian safety.

METHODS

Emergency department (ED) presentations from the four adult hospitals in Calgary, Alberta, Canada, over an 11-year period (January 2008‒December 2018) were extracted and filtered using the ICD-10 code W00 (fall due to ice and snow). Multivariable dispersion-corrected Poisson regression models were used to determine the variables most predictive of these presentations. Month of year, the presence of ice, snow on ground (per 10 cm), and interactions between ice and snow, all up to 3 days prior, were used to create the Slip and Fall Index (SFI).

RESULTS

The dataset included 14,977 slip and fall on ice/snow ED presentations. Females (57.36%, n = 8591) accounted for more presentations than males (42.64%, n = 6386). All months had a significant effect, either being predictive or protective of slip and falls on ice/snow. Current-day ice, snow on ground, and ice up to 3 days prior were predictive of increased presentations. Month and measurements of ice and snow can be input into the SFI, which generates the level of daily risk.

CONCLUSION

The SFI is the first Canadian index with the purpose of measuring the risk of having a slip and fall accident on ice/snow.

Work-rest regimens for work in hot environments: A scoping review

BACKGROUND

To limit exposures to occupational heat stress, leading occupational health and safety organizations recommend work-rest regimens to prevent core temperature from exceeding 38°C or increasing by ≥1°C. This scoping review aims to map existing knowledge of the effects of work-rest regimens in hot environments and to propose recommendations for future research based on identified gaps.

METHODS

We performed a search of 10 databases to retrieve studies focused on work-rest regimens under hot conditions.

RESULTS

Forty-nine articles were included, of which 35 were experimental studies. Most studies were conducted in laboratory settings, in North America (71%), on healthy young adults, with 94% of the 642 participants being males. Most studies (66%) employed a protocol duration ≤240 min (222 ± 162 min, range: 37-660) and the time-weighted average wet-bulb globe temperature was 27 ± 4°C (range: 18-34). The work-rest regimens implemented were those proposed by the American Conference of Governmental and Industrial Hygiene (20%), National Institute of Occupational Safety and Health (11%), or the Australian Army (3%). The remaining studies (66%) did not mention how the work-rest regimens were derived. Most studies (89%) focused on physical tasks only. Most studies (94%) reported core temperature, whereas only 22% reported physical and/or mental performance outcomes, respectively. Of the 35 experimental studies included, 77% indicated that core temperature exceeded 38°C.

CONCLUSIONS

Although work-rest regimens are widely used, few studies have investigated their physiological effectiveness. These studies were mainly short in duration, involved mostly healthy young males, and rarely considered the effect of work-rest regimens beyond heat strain during physical exertion.

Thermal stress information as a tourism-oriented climate product: Performance analysis for selected urban destinations in Romania and Italy

The study addresses the characteristics of a climate service targeting tourists and discusses the evaluation of its products with a particular focus on the thermal stress information. Furthermore, an assessment of the impact of input data on the accuracy and relevance of the thermal stress product is presented. The thermal stress is expressed through UTCI (Universal Thermal Climate Index) and it is computed from UERRA regional reanalysis and E-OBS gridded dataset, for summer season during 2011-2018. The analysis targets 10 cities with different characteristics located in Romania and Italy. It focuses on the impact of three temperature-related input data (instantaneous temperature at 12:00 UTC, daily maximum and daily mean temperature) on the thermal stress intensity. The results show that differences up to 4 days in the pronounced thermal stress category may appear when employing daily maximum temperature compared to the use 12:00 UTC instantaneous temperature, while the use of daily mean temperature leads to strong underestimation of thermal stress in this category. The findings are of interest in defining the technical choices of products to be incorporated in a climate service for tourism in order to assure a good user uptake.

Validating an advanced smartphone application for thermal advising in cold environments

The ClimApp smartphone application was developed to merge meteorological forecast data with personal information for individualized and improved thermal warning during heat and cold stress and for indoor comfort in buildings. For cold environments, ClimApp predicts the personal thermal stress and strain by the use of the Insulation REQuired model that combines weather and personal physiological data with additional consideration of the Wind Chill index based on the local weather forecast. In this study, we validated the individualized ClimApp index relative to measurements and compared it with the Universal Temperature Climate Index (UTCI). To this aim, 55 participants (27 females) were exposed to at least 1 h in an outdoor environment of 10 °C or below (average 1.4 °C air temperature, 74.9% relative humidity, and 4.7 m/s air velocity) inputting their activity level and clothing insulation as instructed by ClimApp. The UTCI and ClimApp indices were calculated and compared to the participants' perceived thermal sensation. The ClimApp index root mean square deviation (RMSD) was below the standard deviation of the perceived thermal sensation which indicates a valid prediction and the UTCI RMSD was higher than the standard deviation which indicates an invalid prediction. The correlation of ClimApp and UTCI to the perceived thermal sensation was statistically significant for both models.

Three dimensional models of human thermoregulation: A review

Numerous human thermoregulatory models have been developed and widely used in various applications such as aerospace, medicine, public health, and physiology research. This paper is a review of three dimensional (3D) models for human thermoregulation. This review begins with a short introduction of thermoregulatory model development followed by key principles for mathematical description of human thermoregulation systems. Different representations of 3D human bodies are discussed with respect to their detail and prediction capability. The human body was divided into fifteen layered cylinders in early 3D models (cylinder model). Recent 3D models have utilized medical image datasets to develop geometrically correct human models (realistic geometry model). The finite element method is mostly used to solve the governing equations and get numerical solutions. The realistic geometry models provide a high degree of anatomical realism and predict whole-body thermoregulatory responses at high resolution and at organ and tissue levels. Thus, 3D models extend to a wide range of applications where temperature distribution is critical, such as hypothermia/hyperthermia therapy and physiology research. The development of thermoregulatory models will continue with the growth in computational power, advancement in numerical methods and simulation software, advances in modern imaging techniques, and progress in the basic science of thermal physiology.

Finite element model of female thermoregulation with geometry based on medical images

INTRODUCTION

this study describes the development of a female finite element thermoregulatory model (FETM) METHOD: the female body model was developed from medical image datasets of a median U.S. female and was constructed to be anatomically correct. The body model preserves the geometric shapes of 13 organs and tissues, including skin, muscles, fat, bones, heart, lungs, brain, bladder, intestines, stomach, kidneys, liver, and eyes. Heat balance within the body is described by the bio-heat transfer equation. Heat exchange at the skin surface includes conduction, convection, radiation, and sweat evaporation. Vasodilation, vasoconstriction, sweating, and shivering are controlled by afferent and efferent signals to and from the skin and hypothalamus.

RESULTS

the model was validated with measured physiological data during exercise and rest in thermoneutral, hot, and cold conditions. Validations show the model predicted the core temperature (rectal and tympanic temperatures) and mean skin temperatures with acceptable accuracy (within 0.5 °C and 1.6 °C, respectively) CONCLUSION: this female FETM predicted high spatial resolution temperature distribution across the female body, which provides quantitative insights into human thermoregulatory responses in females to non-uniform and transient environmental exposure.

Biomarkers for warfighter safety and performance in hot and cold environments

Exposure to extreme environmental heat or cold during military activities can impose severe thermal strain, leading to impairments in task performance and increasing the risk of exertional heat (including heat stroke) and cold injuries that can be life-threatening. Substantial individual variability in physiological tolerance to thermal stress necessitates an individualized approach to mitigate the deleterious effects of thermal stress, such as physiological monitoring of individual thermal strain. During heat exposure, measurements of deep-body (T_c) and skin temperatures and heart rate can provide some indication of thermal strain. Combining these physiological variables with biomechanical markers of gait (in)stability may provide further insight on central nervous system dysfunction - the key criterion of exertional heat stroke (EHS). Thermal strain in cold environments can be monitored with skin temperature (peripheral and proximal), shivering thermogenesis and T_c. Non-invasive methods for real-time estimation of T_c have been developed and some appear to be promising but require further validation. Decision-support tools provide useful information for planning activities and biomarkers can be used to improve their predictions, thus maximizing safety and performance during hot- and cold-weather operations. With better understanding on the etiology and pathophysiology of EHS, the microbiome and markers of the inflammatory responses have been identified as novel biomarkers of heat intolerance. This review aims to (i) discuss selected physiological and biomechanical markers of heat or cold strain, (ii) how biomarkers may be used to ensure operational readiness in hot and cold environments, and (iii) present novel molecular biomarkers (e.g., microbiome, inflammatory cytokines) for preventing EHS.

A Database of Static Thermal Insulation and Evaporative Resistance Values of Dutch Firefighter Clothing Items and Ensembles

The rescue operations' environment can impair firefighters' performance and increase the risk of injuries, e.g., burns and hyperthermia. The bulk and carried weight of heavy protection contributes to lower physical performance, higher metabolic load and internal body heat production. For recommending optimal protection for the tasks and incident scenarios, knowledge of clothing thermal properties is needed. However, detailed data on firefighter protective clothing systems are not available. The aim of the study was to provide scientific background and a dataset that would allow for validation of thermo-physiological models for task-specific conditions of rescue work. Thermal insulation of 37 single items and their variations and 25 realistic protective clothing ensembles were measured on a thermal manikin. Twelve (12) ensembles that evenly covered the whole insulation range were selected for evaporative resistance testing. The equations for summing up individual item's insulation to ensemble insulation and calculating clothing area factor were derived from the dataset. The database of a firefighter clothing system was created. In addition, the local and regional thermal properties of the clothing ensembles were provided for use in future validation of advanced thermo-physiological models for rescue worker exposure predictions and for designing decision aid tools.

Use of Thermoregulatory Models to Evaluate Heat Stress in Industrial Environments

Heat stress in many industrial workplaces imposes significant risk of injury to individuals. As a means of quantifying these risks, a comparison of four rationally developed thermoregulatory models was conducted. The health-risk prediction (HRP) model, the human thermal regulation model (HuTheReg), the SCENARIO model, and the six-cylinder thermoregulatory model (SCTM) each used the same inputs for an individual, clothing, activity rates, and environment based on previously observed conditions within the Portuguese glass industry. An analysis of model correlations was conducted for predicted temperatures (°C) of brain (TBrain), skin (TSkin), core body (TCore), as well as sweat evaporation rate (ER; Watts). Close agreement was observed between each model (0.81-0.98). Predicted mean ± SD of active phases of exposure for both moderate (TBrain 37.8 ± 0.25, TSkin 36.7 ± 0.49, TCore 37.8 ± 0.45 °C, and ER 207.7 ± 60.4 W) and extreme heat (TBrain 39.1 ± 0.58, TSkin, 38.6 ± 0.71, TCore 38.7 ± 0.65 °C, and ER 468.2 ± 80.2 W) were assessed. This analysis quantifies these heat-risk conditions and provides a platform for comparison of methods to more fully predict heat stress during exposures to hot environments.