A Comprehensive Model for Estimating Heat Vulnerability of Young Athletes

Proposing adjustments to heat safety thresholds for junior high and high school sports clubs in Japan

With higher temperatures expected in the future due to global climate change, addressing health risks such as heat illness is increasingly important. In Japan, thousands of heat illness cases occur annually during school sports club activities. The risk may vary by sport, location, and region, but how heat safety thresholds (HSTs) should be adjusted to provide effective guidelines remains uncertain. Thus, we conducted a case-crossover study using data of heat illness cases and wet-bulb globe temperature (WBGT) throughout Japan to evaluate the heat illness risk for the current HSTs and propose adjustments. A significant relationship was found between heat illness incidence and WBGT at the time of the incident, as well as the average WBGT one and two days prior. The risk significantly varies with factors such as club, region, location, year, month, and the average WBGT in summer. Therefore, we recommend lowering the current HSTs by one category (3 °C) in the following cases: (1) clubs at high risk (baseball, softball, soccer/futsal, tennis, track and field, kyudo, and other with sustained exercise or thick uniforms); (2) from April to June; (3) in cooler regions (Hokkaido, Tohoku, Hokuriku, or where the average WBGT in summer≦18℃); (4) for outdoor activities; (5) when heat rapidly increases without adequate heat acclimatization. These findings may inform educators, students, sports authorities, and policymakers in adjusting HSTs to reduce the incidence of heat illness, thereby ensuring safer environments for school sports activities.

Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model

BACKGROUND

The increase in global temperature and urban warming has led to the exacerbation of heatwaves, which negatively affect human health and cause long-term loss of work productivity. Therefore, a global assessment in temperature variation is essential.

OBJECTIVE

This paper is the first of its kind to propose land-use based spatial machine learning (LBSM) models for predicting highly spatial-temporal variations of wet-bulb globe temperature (WBGT), which is a heat stress indicator used to assess thermal comfort in indoor and outdoor environments, specifically for the main island of Taiwan.

METHODS

To develop spatiotemporal prediction models for both the working period and noon period, we calculated the WBGT of each weather station from 2001 to 2019 using temperature, humidity, and solar radiation data. These WBGT estimations were then used as the dependent variable for developing the spatiotemporal prediction models. To enhance model performance, we used innovative approaches that combined SHapley Additive exPlanations (SHAP) values for the selection of non-linear variables, along with machine learning algorithms for model development.

RESULTS

When incorporating temperature along with other land-use/land cover predictor variables, the performance of LBSM models was excellent, with an R2 value of up to 0.99. The LBSM models explained 98% and 99% of the spatial-temporal variations in WBGT for the working and noon periods, respectively, within the complete models. In the temperature-excluded models, the explained variances were 94% and 96% for the working and noon periods, respectively.

IMPACT

WBGT is a common method used by many organizations to access the impact of heat stress on human beings. However, limited studies have mentioned the association between WBGT and health impacts due to the absence of spatiotemporal databases. This study develops a new approach using land-use-based spatial machine learning (LBSM) models to better predict the fine spatial-temporal WBGT levels, with a 50-m × 50-m grid resolution for both working time and noontime. Our proposed methodology could be used in future studies aimed at evaluating the potential long-term loss of work productivity due to the effects of global warming or urban heat island.

Field Campaign Evaluation of Sensors Lufft GMX500 and MaxiMet WS100 in Peruvian Central Andes

The research presents the inter-comparison of atmospheric variables measured by 9 automatic weather stations. This set of data was compared with the measurements of other weather stations in order to standardize the values that must be adjusted when taken to different areas. The data of a set of a total of 9 GMX500, which measures conventional meteorological variables, and 10 WS100 sensors, which measures precipitation parameters. The automatic stations were set up at the Huancayo Observatory (Geophysical Institute of Peru) for a period of 5 months. The data set of GMX500 were evaluated comparing with the average of the 9 sensors and the WS100 was compared with an optical disdrometer Parsivel2. The temperature, pressure, relative humidity, wind speed, rainfall rate, and drop size distribution were evaluated. A pair of GMX500 sensors presented high data dispersion; it was found found that the errors came from a bad configuration; once this problem was solved, good agreement was archived, with low RMSE and high correlation. It was found that the WS100 sensors overestimate the precipitation with a percentage bias close to 100% and the differences increase with the greater intensity of rain. The drop size distribution retrieved by WS100 have unrealistic behavior with higher concentrations in diameters of 1 mm and 5 mm, in addition to a flattened curve.

A Low-Cost Sensor Network for Real-Time Thermal Stress Monitoring and Communication in Occupational Contexts

The MoBiMet (Mobile Biometeorology System) is a low-cost device for thermal comfort monitoring, designed for long-term deployment in indoor or semi-outdoor occupational contexts. It measures air temperature, humidity, globe temperature, brightness temperature, light intensity, and wind, and is capable of calculating thermal indices (e.g., physiologically equivalent temperature (PET)) on site. It visualizes its data on an integrated display and sends them continuously to a server, where web-based visualizations are available in real-time. Data from many MoBiMets deployed in real occupational settings were used to demonstrate their suitability for large-scale and continued monitoring of thermal comfort in various contexts (industrial, commercial, offices, agricultural). This article describes the design and the performance of the MoBiMet. Alternative methods to determine mean radiant temperature (Tmrt) using a light intensity sensor and a contactless infrared thermopile were tested next to a custom-made black globe thermometer. Performance was assessed by comparing the MoBiMet to an independent mid-cost thermal comfort sensor. It was demonstrated that networked MoBiMets can detect differences of thermal comfort at different workplaces within the same building, and between workplaces in different companies in the same city. The MoBiMets can capture spatial and temporal differences of thermal comfort over the diurnal cycle, as demonstrated in offices with different stories and with different solar irradiances in a single high-rise building. The strongest sustained heat stress was recorded at industrial workplaces with heavy machinery.

Applicability of Assessment Indices for Hanok-User Sensory Comfort Based on Visual and Tactile Comfort Evaluation Indicators

This study aimed to present an evaluation system to assess comfort that reflects the unique value of vernacular architecture. For this purpose, the background, purpose, evaluation items, and evaluation methods of Korean Standard-Basic Part: 6300-1, 2 (KS A 6300) were analyzed. As a result, the weakness was found in the KS system that there was no method to evaluate the user’s human sensory comfort. To overcome this problem, the contents of KS A 6300-1, 2 were analyzed, and evaluation indices were categorized to assess the comfort of human sensory experience. A comfort assessment index was then derived to evaluate the visual and tactile comfort of the Korean traditional house by analyzing previous research and systems. To assess the possibility of using the Hanok evaluation system for the derived comfort indices, the actual building environmental and user data were collected for one year. Based on the previous step, Predicted Mean Vote (PMV) and Daylight Glare Probability (DGP) were calculated, and at the same time, a comparative analysis was conducted with the collected user data. As a result, DGP showed a tendency of changing user comfort and an error of 7 to 8% regardless of region, while PMV showed a tendency similar to the change in user discomfort response rate. Accordingly, it was concluded that each evaluation index could be used for the comfort evaluation of Hanok.