Heat impacts on school sports club activities in Japan under climate change and the effectiveness of countermeasures

doi:10.21203/rs.3.rs-5103411/v1

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Heat impacts on school sports club activities in Japan under climate change and the effectiveness of countermeasures

https://doi.org/10.21203/rs.3.rs-5103411/v1

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Millions of students in Japan participate in school sports club activities, where thousands of heat illness cases occur every year. With future climate change, there is concern about the increased health risks posed to students in sports club activities by the worsening heat environment. However, few quantitative evaluations of the impact of heat on sports club activities and countermeasures under climate change have been conducted. Here, we evaluated the impact of heat and effectiveness of countermeasures based on hourly wet-bulb globe temperature (WBGT) projections under multiple climate scenarios. For the 2060s to 2080s, even under the scenario with strict control of greenhouse gas (GHG) emissions (SSP1-1.9), strenuous exercise will be restricted for one to four months in five out of eight WBGT categories defined in this study for Japan and for one to six months in six categories under the scenario with little control of GHG emissions (SSP5-8.5). In three of the four scenarios, unlike in the past, all physical activity should be stopped at least once a week in one or more categories. Countermeasures such as early morning exercise and reducing the frequency of outdoor activities are effective in reducing the risk of heat illness. However, under the most pessimistic scenario, there would be one to four months of restricted periods of strenuous exercise in the warmer regions of Japan, even with such countermeasures. Although heat illness countermeasures are still necessary for sports club activities at present, further countermeasures and behavioral changes are inevitable in the future.

Health Policy

Climate Analysis and Modeling

heat illness

school sports clubs

climate change

wet-bulb globe temperature

XGBoost.

Millions of students in Japan participate in school sports clubs, which spend several hours a week exercising. Regular moderate-to-vigorous physical activity for an hour or more is recommended for school-age youth because it can improve cardiorespiratory function, reduce stress, and improve social skills (Strong et al., 2005). In addition to short-term benefits for skeletal, mental, and cardiorespiratory health, adolescent physical activity positively affects long-term physical activity levels and health (Hallal et al., 2006; Peralta et al., 2020). Thus, school sports club activities contribute to enhancing the well-being of student athletes in the short and long term. Nevertheless, an inherent risk of orthopedic injuries (e.g., fractures, sprains) and illnesses (e.g., heat illness) exists in sports participation, and these injuries and illnesses should be addressed to maximize the benefits of physical activity. Exertional heat illness is a common injury that affects thousands of student athletes in Japan (Japan Sport Council, n.d.). The surface air temperature in Japan, like the global surface temperature (IPCC, 2021), is increasing at a rate of 1.35°C per century (Japan Meteorological Agency, 2024), and the frequency and intensity of extreme temperatures are projected to increase in the future (MEXT & JMA, 2020). This makes it essential to study the impact of climate change on school sports club activities and assess the effectiveness of potential countermeasures. However, research in this area is limited, primarily due to the lack of hourly heat projection data at a practical scale (i.e., city). The outputs of global climate models (GCMs), which are generally used to project climate change impacts, often do not include diurnal variability, and the horizontal resolution is typically 50 km or coarser. Data of such a resolution may be useful for national or prefectural level assessments but are not suitable for assessing activities in specific cities and times of day, such as school club activities. The wet-bulb globe temperature (WBGT) is a composite thermal index that considers factors such as the air temperature, humidity, radiant heat, and wind speed (Yaglou & Minard, 1957) and is widely referenced to prevent heat illness. In previous studies, daily GCM data were used to project hourly WBGTs for evaluating global outdoor workers’ exposure to heat (Takakura et al., 2018) and feasible host cities of the Summer Olympic Games (Oyama et al., 2022) under climate change. However, there are no such hourly data for assessing cities across Japan. Furthermore, previous studies are based on GCM outputs from the Coupled Model Intercomparison Project Phase (CMIP) 5 (Taylor et al., 2012), which is one generation old. Therefore, in this study, the aim is to evaluate the heat impact of future climate change on school sports club activities in Japan and the effectiveness of countermeasures via city-specific hourly WBGT projections derived from high-resolution climate scenarios on the basis of the latest CMIP6 (O’Neill et al., 2016) GCM outputs (Oyama & Takakura, 2024).

Building an hourly WBGT prediction model from daily weather data

Historical hourly WBGT data

As an objective variable for building a machine learning model for prediction of the hourly WBGT, a 12-year (2010–2021) historical hourly WBGT dataset in 842 cities in Japan was obtained from a website of the Ministry of the Environment in Japan (MOEJ). Eleven cities in Japan provide measured WBGT values, whereas others provide estimates only based on a formula based on air temperature, relative humidity, solar radiation, and wind speed (Ono & Tonouchi, 2014); thus, estimated data were used to ensure consistency across all cities. A validation using three years of WBGT data from six cities in various regions in Japan revealed that the determination coefficient between the WBGT estimates and observations is very high (determination coefficient (R²) = 0.998), and 97.6% of the estimation errors are within ± 1°C, making the estimates applicable to the summer WBGTs throughout Japan (Ono & Tonouchi, 2014). We utilized the WBGT data to reconstruct and evaluate the results.

Historical daily weather data

As explanatory variables for building a machine learning model for predicting the hourly WBGT, daily mean/maximum/minimum air temperatures (T_a/T_a,max/T_a,min) and daily mean wind speed (WS) were obtained from the Automated Meteorological Data Acquisition System (AMeDAS) of the Japan Meteorological Agency (JMA). The daily mean relative humidity (RH) and total solar radiation (SR) were obtained from the corresponding 1 km grid of agro-meteorological grid square data from the National Agriculture and Food Research Organization in Japan (NARO).

Building an hourly WBGT prediction model from daily weather data

To predict the hourly resolution of the WBGT, a machine learning technique called extreme gradient boosting (XGBoost) (Chen & Guestrin, 2016) was applied to build a model to predict the hourly WBGT using the above daily weather indices as explanatory variables. XGBoost was noted to have high WBGT prediction accuracy (Niwa & Manabe, 2024). The modeling concept is expressed in Eq. (1).

$$\:{WBGT}_{s,d,hh}=\:{f}_{hh}\left({x}_{c,d-1}\:,{x}_{c,d}\:,\:{x}_{c,d+1}\:,\:{cos\theta\:}_{c,d}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$

Here, x_c,d = (T_a,c,d, T_a(max),c,d, T_a(min),c,d, RH_c,d, WS_c,d, SR_c,d), where T_a,c,d …, SR_c,d are the T_a, T_a(max), T_a(min), RH, WS, and SR in city c on day d, respectively, and d − 1 and d + 1 represent the days before and after d, respectively. In addition, cosθ_c,d = (cosθ_c,d,00, cosθ_c,d,01, …, cosθ_c,d,23), where θ_c,d,hh is the solar zenith angle at time hh of day d in city c (when cosθ_c,d,hh < 0, cosθ_c,d,hh = 0). These indicators were selected because many of them are useful for predicting hourly WBGT via daily weather indicators (Takakura et al., 2018, 2019). A model was then constructed using these 42 variables as explanatory variables and hourly WBGT as the objective variable. To improve the prediction accuracy and versatility of XGBoost, hyperparameters (HPs) were optimized for each hour via training and validation data. For HP tuning, Optuna (Akiba et al., 2019), an open source HP optimization framework, was used.

The results of the model evaluation demonstrated that the model perform well across different hours of the day (Fig. 1, Fig. 2). The mean absolute error (MAE) remains low throughout the day, with values ranging from 0.55 to 0.95. Although there are variations, with peaks at approximately 9:00 and 15:00, the overall values indicate that the model's predictions are consistently close to the actual values. This range of MAEs is sufficiently low considering that Class 1, the highest accuracy standard for electronic instruments for WBGT measurement defined in JIS B 7922:2023 (Japanese Standards Association & Japan Electric Measuring Instruments Manufacturers’ Association, 2023), which is one of the Japanese Industrial Standards (JIS), allows an error of ± 1°C. The root mean squared error (RMSE) values vary between 0.8 and 1.3, peaking at 9:00 and 15:00. R² consistently remains high, with values ranging from 0.96 to 0.99, with low peaks at 9:00 and 15:00. The bias score fluctuates near zero, with values ranging from − 0.005 to 0.002. Therefore, a highly accurate prediction model could be built via this method, and the final model was built with all the data used for training and validation.

Future projections

Projection of future hourly WBGTs on the basis of climate scenario data across Japan

The bias-corrected daily climate scenario data over Japan (NIES2020) (Ishizaki, 2021) were used as input data to project future hourly WBGTs for the grid corresponding to the target cities. NIES2020 data are based on the outputs of five global climate models (GCMs: MIROC6, MRI-ESM2-0, ACCESS-CM2, IPSL-CM6A-LR, and MPI-ESM1-2-HR) and four GHG emission pathways (shared socioeconomic pathway (SSP)1-1.9, SSP1-2.6, SSP2-4.5, and SSP5-8.5). However, SSP1-1.9 data include three GCMs only (MIROC6, MRI-ESM2-0 and IPSL-CM6A-LR). We used the data for the 2030s to 2050s and 2060s to 2080s to make projections for the middle and end of the 21st century.

To reproduce city-specific WBGTs, bias correction parameters were identified between the hourly WBGT values predicted from daily weather variables in the agro-meteorological grid square data and the hourly WBGT values in the target cities. This correction addresses the bias between average WBGTs per grid and city-specific WBGTs. As mentioned in the Discussion later in the manuscript, with WBGT measurements becoming more commonly recorded at schools, in this study, a method to correct the bias of grid data by using data from specific points (AMeDAS observation points for this study) is presented. This method would enable the evaluation of WBGTs and heat impacts under climate change for each school in the future. For the validation of the bias correction results, the hold-out method was employed to avoid overlearning the data. For each city, for years with more than 100 data points, bias correction parameters were identified from the data of all years, except the final year; bias correction was applied to the data of the final year; and the results were compared with the MOEJ-provided hourly WBGT. As a result, the MAE for the 842 cities decreased from 0.62 to 0.54, the RMSE decreased from 0.79 to 0.71, and the bias decreased from 0.19 to 0.01 after bias correction. The MAE, RMSE, and bias for each city are shown in Fig. 3. In most cities, errors and biases improved significantly. Then, bias correction using the identified parameters was performed on the future gridded hourly WBGT data to project the hourly WBGT for the target cities.

The overall process of the projection of WBGT data on target cities on the basis of climate scenario data across Japan is presented in Fig. 4.

Projection of heat impacts on school sports club activities

School sports club activities in Japan are held mainly after school from 3 to 6 p.m. Daily activity time is advised to be no more than 2 hours on weekdays and 3 hours on weekends, with at least 2 rest days per week, and should be as short and reasonable as possible (Japan Sports Agency & Agency for Cultural Affairs Japan, 2022). A guideline for heat illness prevention in sports activities in Japan, including school club activities, is widely referenced and includes the following: stop strenuous exercise when the WBGT is between 28°C and 31°C (28 ≦ WBGT < 31°C) and stop all exercise when the WBGT is 31°C or higher (31°C ≦ WBGT) (Japan Sport Association, 2019). In Japanese sports club activities, it has been reported that the number of heat illness cases correlates well with outdoor WBGTs that range up to 31°C, regardless of whether the activity takes place indoors or outdoors (Iwashita, 2018). We therefore evaluated whether school sports club activities for two hours per day within 3 to 6 p.m., five days a week, would be limited by the WBGT under future climate change. The 71st percentile values of the WBGT from 3 to 6 p.m. (corresponding to the highest value during the 5 relatively cool days of the 7-day period) were evaluated as follows: heat level 1 (stop strenuous exercise) when the value reaches 28 ≤ WBGT < 31°C for 2 hours or more, heat level 2 (stop all exercise) when the value reaches 31°C ≤ WBGT for 2 hours or more, and level 0 otherwise. A schematic diagram of the evaluation of the heat level is shown in Fig. 5.

In addition, as a sensitivity analysis, considering the existing findings (Oyama et al., 2024) that lower heat criteria should be used in cooler regions, where the average WBGT from May to October is less than 18°C, the projected heat impact for the heat level 1 criterion is 25 < WBGT ≤ 28°C instead of 28 < WBGT ≤ 31°C, and the heat level 2 criterion is 28°C < WBGT instead of 31°C < WBGT.

Assessment of the effectiveness of countermeasures

As countermeasures against heat, three measures were set, as they are both feasible in school sports club activities and reproducible in this study: A: including early mornings (7 to 9 a.m.) in activity times; B: replacing two of the five outdoor activity sessions with indoor activities (e.g., indoor training, strategy meetings, team building); and C: implementing both A and B. We assessed to what extent these measures reduce the projected heat impact. As in the previous section, a sensitivity analysis was also carried out to evaluate a 3°C reduction in the criteria for heat levels 1 and 2. A schematic diagram of the evaluation of the heat level with such countermeasures is shown in Fig. 6.

Programming environment used in the study

The analysis, modeling, and visualization for this study were conducted via the Python and R programming environments, which were used in Microsoft Windows 11 Pro. To construct an hourly WBGT prediction model from daily weather data and the future projection of WBGTs in target cities, we utilized Python version 3.12 (October 2023, https://www.python.org/downloads/release/python-3120/). For the projection of heat impacts on school sports club activity and assessment of countermeasures’ effectiveness, we utilized R version 4.3.2 (October 2023, https://cran.r-project.org/bin/windows/base/old/4.3.2/).

Future projections of the WBGT

Although we projected future hourly WBGTs for 842 cities, here, we present WBGT values based on eight categories (WBGT categories) according to the average WBGT from April to October for the historical period (1980–2014). The distribution of the categories across Japan is shown in Fig. 7. Figure 8 shows the 71st percentile predicted hourly WBGTs for the 2060s to 2080s, averaged for each of the WBGT categories. In all the WBGT categories, the WBGTs were greater in the SSP scenarios than in the historical scenario, and among the SSP scenarios, the WBGTs increased significantly in the scenarios with higher GHG emissions. Cooler categories tended to have greater WBGT increases than warmer categories did. In the historical scenario, the WBGT reached 28°C in five categories. In the SSP1-1.9 scenario, with significantly reduced GHG emissions, six categories reached 28°C, and in the SSP5-8.5 scenario, with little GHG emission control, all categories except the coolest category reached 28°C. In the SSP5-8.5 scenario, the WBGT reached 31°C in five categories. The projections for each city are presented in Supplementary Fig. 1, those for each region are presented in Supplementary Fig. 2, and those for the 2030s to 2050s are presented in Supplementary Figs. 3 to 5.

Projection of heat impacts on school sports club activities

The April–October heat levels for each of the eight categories from the 2060s to the 2080s, which were evaluated on the basis of the projected WBGT from 3 to 6 p.m., are shown in Fig. 9a. In the SSP1-1.9 scenario, heat level 1 was reached for the five categories, and only the warmest category reached heat level 2, whereas in the SSP5-8.5 scenario, heat level 1 was reached for the six categories, and heat level 2 was reached in four categories. Restriction of club activities at heat level 1 or higher were necessary in 12 different month-category combinations (one month in one category, two months in two categories, three months in one category, and four months in one category) for SSP1-1.9, and in 19 month-category combinations (one month in one category, two months in one category, three months in two categories, four months in one category, and six months in one category) for SSP5-8.5. When the heat level criterion was adjusted down by 3°C when the average WBGT for May-October in each region was 18°C or lower, a change from heat level 0 to 1 occurred in August in the two categories under the SSP1-1.9, SSP1-2.6, and SSP2-4.5 scenarios (Fig. 9b). The duration of restricted club activities did not change on the basis of the adjustment of the heat level criterion for SSP5-8.5. In both Fig. 9a and Fig. 9b, the scenarios with higher GHG emissions tended to show greater differences among GCMs. The projections for each city are presented in Supplementary Fig. 6, those for each region are presented in Supplementary Fig. 7, and those for the 2030s to 2050s are presented in Supplementary Figs. 8 to 10.

Effectiveness of countermeasures

The effectiveness of the three countermeasures set out in this study for the 2060s to 2080s in the SSP5-8.5 scenario, where the heat impact is the most significant, is shown in Fig. 10. Countermeasure A, which incorporates the early morning hours (7 to 9 a.m.) into practice times, eliminates the heat level 2 categories where all activities are difficult in any month, but there are still five categories in July and six categories in August for heat level 1, where strenuous exercise is prohibited. Restricted club activities at heat level 1 or higher were necessary in 14 different month-category combinations (one month in one category, two months in one category, three months in one category, and four months in two categories) when countermeasure A was used, and the combinations were reduced by five. Countermeasure B, which replaces two of the five outdoor activity sessions with indoor activities, eliminated categories with heat level 2, except the warmest category in July and August, but four categories in July and five categories in August still reached heat level 1. Restricted club activities at heat level 1 or higher were necessary in 12 different month-category combinations (one month in one category, two months in two categories, three months in one category, and four months in one category) when countermeasure B was used, and the combinations were reduced by seven. Countermeasure C, which takes countermeasures A and B, had the greatest effect, with three categories still at heat level 1 in July and four in August, although there were no categories at heat level 2. Restricted club activities at heat level 1 or higher were necessary in 10 different month-category combinations (one month in one category, two months in one category, three months in one category, and four months in one category) when countermeasure C was used, and the combinations were reduced by nine. When the heat level criterion was lowered by 3°C when the average WBGT for May–October in each region was below 18°C, there was no difference in the results compared with when the level was not lowered. The projections for the 2030s to 2050s are presented in Supplementary Fig. 11.

Study findings

In this study, we projected future heat impacts and the effectiveness of countermeasures for school sports club activities in which millions of Japanese youths participate on the basis of city-specific hourly WBGT projections derived from climate scenarios.

For the 2060s to 2080s, even under the scenario with strict control of GHG emissions (SSP1-1.9), strenuous exercise is projected to need to be restricted for one to four months in five out of eight categories and for one to six months in six categories under the scenario with little control of GHG emissions (SSP5-8.5) (Fig. 9a). The results also show that in three of the four scenarios, unlike in the historical scenario, heat level 2, where all exercise should be stopped, is reached in one or more categories. Although the increase in the WBGT due to climate change tends to be greater in the cooler categories than in the warmer categories (Fig. 8), the period of restricted activity is projected to be longer in the warmer categories (Fig. 9a). Especially in the four warmest categories (where the average WBGT from April to October for the historical period (1980-2014) is more than 18°C), the period of restriction is expected to last up to 3 to 6 months, which might require significant changes in the annual activity schedule. The overall trend did not differ when the criteria for heat levels were lower in the cooler categories (Fig. 9b). Although no previous studies have assessed the heat impact of climate change on school club activities, the above results are consistent with the prediction that it will be difficult to host the Summer Olympic Games in many Asian cities at the end of the 21st century under high GHG emission scenarios (Oyama et al., 2022, 2023; Smith et al., 2016). The heat impact, however, can persist for several months in regular activities, such as school sports club activities, unlike short-term events, such as the Summer Olympic Games.

The three countermeasures set up in this study were more effective in reducing the combinations of the months and categories of activity restriction (heat level 1 or 2) in the order of C, B, and A (Fig. 10). Even in the SSP5-8.5 scenario, where the WBGT increases the most, all the measures would be effective, as they would almost eliminate or completely eliminate regions reaching heat level 2, where all the exercise is prohibited. However, even with the most effective countermeasure C, the SSP5-8.5 scenario would result in a period of one to four months of restricted strenuous exercise in the warmer categories. The countermeasures adopted in the study of Summer Olympic Games, such as activities in late night/early morning or in other cities/regions (Oyama et al., 2022, 2023), are not adopted in this study because they may interfere with other school activities. However, temporarily relocating activities to cooler cities (i.e., cities in the Hokkaido and Tohoku regions), such as during summer vacations, may be effective under climate change conditions.

Limitations

With respect to the countermeasures adopted in this study, several points should be considered. Countermeasures A and C, which include early morning practice, have the risk of leading to sleep deprivation due to scheduling conflicts with classes and the relatively high prevalence of a delayed circadian system among high school students (Youngstedt, 2024). In Japan, a study of junior high schools in Nagano Prefecture indicated that early morning practice not only reduced heat exposure but also had the advantage of providing a sense of fulfillment and exhilaration; however, it may also have a negative impact on sleep time, homework, and classroom activities (Nishijima & Aoyagi, 2018). In addition, Japanese schoolteachers already work very long hours, with a study finding that approximately 60% of teachers work more than 11 hours a day and that longer hours are significantly associated with greater stress compared to teachers who work less than 9 hours a day (Matsushita & Yamamura, 2022). Therefore, while early morning practice is effective in mitigating heat stress, the potential negative effects on students and teachers need to be considered and addressed. For example, the recent transition of school club activities to local communities (including municipalities and companies) may encourage teachers to outsource the supervision of school club activities, increasing the likelihood of the implementation of early morning practice. On the other hand, reducing the frequency of outdoor activities, which is included in countermeasures B and C, is desirable from the point of view of reducing the physical workload of teachers and staff, but care should be taken to ensure that the benefits of sports club activities are maintained. School students are recommended to participate in 60 minutes or more of moderate to vigorous physical activity per day, which is expected to have positive effects on self-concept, anxiety, depressive symptoms, and academic performance, in addition to physical benefits on musculoskeletal and cardiovascular health, adiposity, and blood pressure (Strong et al., 2005). Since sports club activities are the primary physical activity among many students, care should be taken to ensure that students are still provided with sufficient exercise opportunities, such as indoor training, physical education classes, and school events, with strategical scheduling. Since there are disparities among schools in terms of the equipment and expenses required for indoor training and the human capital and knowledge required for tactical meetings and team building, it is important to take measures to prevent inequalities among schools.

The WBGT projections used in this study (Oyama & Takakura, 2024) are corrected on the basis of data from meteorological stations in each city, and microenvironmental differences in the WBGT between individual schools were not evaluated in the current study. Since each school has different surrounding land uses, sunlight exposures, elevations, etc., local measurements are recommended when considering actual countermeasures. Once such observational data are accumulated, the WBGT and heat effects of each school can be more accurately evaluated via the methodology of this study. For example, in one prefecture in Japan, approximately 90% of junior high schools measure WBGT, and 64.1% act on the results of the measurements (Kubo & Akaogi, 2023). For schools and other facilities without access to on-site WBGT measurements, developing WBGT data on a grid basis while considering surrounding factors, such as land use (Hsu et al., 2023), may be more effective than relying on station-by-station data. Notably, the heat risk evaluation of this research is also based on the existing heat criteria of the Japan Sports Association. Even during the period covered by the historical scenario (1980 to 2014), when heat levels 1 and 2 were the not likely to be reached, several thousand cases of heat illness occurred annually in schools, so the risk of heat illness exists even at heat level 0. Furthermore, the WBGT, which is the focus of this study, is an index of environmental conditions and is only one of many risk factors for heat illness, such as the type of exercise and activity style. Therefore, the results of this study suggest that further countermeasures and behavioral changes are needed in the future, although countermeasures against heat illness are still currently needed.

MAE

mean absolute error

determination coefficient

WBGT

wet-bulb globe temperature.

Data availability

Data generated and analyzed during the study will be made available upon reasonable request after review of the submitted paper by the authors.

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The authors declare no competing interests.

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Heat impacts on school sports club activities in Japan under climate change and the effectiveness of countermeasures

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Abstract

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Introduction

Materials and methods

Building an hourly WBGT prediction model from daily weather data

Future projections

Results

Future projections of the WBGT

Projection of heat impacts on school sports club activities

Effectiveness of countermeasures

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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