A high-spatial-resolution dataset of human thermal stress indices over South and East Asia

Cardiovascular morbidity risk attributable to thermal stress: analysis of emergency ambulance dispatch data from Shenzhen, China

BACKGROUND

Climate change has raised scientific interest in examining the associations of weather conditions with adverse health effects, while most studies determined human thermal stress using ambient air temperature rather than the thermophysiological index.

OBJECTIVES

To evaluate the association between emergency ambulance dispatches (EADs) related to cardiovascular causes and heat/cold stress in Shenzhen, a city in southern China, with the aim of providing new insights for local policymakers.

METHODS

A time series analysis using ambulance dispatch data of cardiovascular diseases in Shenzhen, China (2013-2019) was conducted. A quasi-Poisson nonlinear distributed lag model was applied to explore the relationship between emergency ambulance dispatches (EADs) due to cardiovascular causes and thermal stress (determined by Universal Thermal Climate Index, UTCI). Attributable fractions were estimated to identify which UTCI ranges have a greater health impact.

RESULTS

The relationship between UTCI and EADs due to cardiovascular diseases exhibits a reverse J-shaped curve. The effects of cold stress were immediate and long-lasting, whereas the effects of heat stress were non-significant. Compared with the optimal equivalent temperature (71st percentile of UTCI, 29.22 °C), the relative risks for cumulative (0-21 days) exposures to cold stress (1st percentile, - 0.13 °C; 5th percentile, 7.68 °C) were 1.55 (95%CI:1.28,1.88) and 1.44 (95%CI:1.22,1.69), respectively. Thermal (cold and heat) stress was responsible for 10.81% (95%eCI: 5.67%,15.43%) of EADs for cardiovascular diseases, with 9.46% (95%eCI: 3.98%,14.40%) attributed to moderate cold stress (2.5th ~ 71st percentile). Greater susceptibility to cold stress was observed for males and the elderly. Heat stress showed harmful effects in the warm season.

CONCLUSIONS

Our results demonstrated that cold exposure elevates the risk of EADs for cardiovascular causes in Shenzhen, and moderate cold stress cause the highest burden of ambulance dispatches. Health authorities should consider effective adaptation strategies and interventions responding to cold stress to reduce the morbidity of cardiovascular diseases.

A high temporal resolution global gridded dataset of human thermal stress metrics

The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.

Spatiotemporal link between El Niño Southern Oscillation (ENSO), extreme heat, and thermal stress in the Asia-Pacific region

Climate change is closely monitored and numerous studies reports increasing air temperature and weather extremes across the globe. As a direct consequence of the increase of global temperature, the increased heat stress is becoming a global threat to public health. While most climate change and epidemiological studies focus on air temperature to explain the increasing risks, heat strain can be predicted using comprehensive indices such as Universal Thermal Climate Index (UTCI). The Asia-Pacific region is prone to thermal stress and the high population densities in the region impose high health risk. This study evaluated the air temperature and UTCI trends between 1990 and 2019 and found significant increasing trends for air temperature for the whole region while the increases of UTCI are not as pronounced and mainly found in the northern part of the region. These results indicate that even though air temperature is increasing, the risks of heat stress when assessed using UTCI may be alleviated by other factors. The associations between El Niño Southern Oscillation (ENSO) and heat stress was evaluated on a seasonal level and the strongest regional responses were found during December-January (DJF) and March-May (MAM).

Augmented human thermal discomfort in urban centers of the Arabian Peninsula

Anthropogenic climate change has amplified human thermal discomfort in urban environments. Despite the considerable risks posed to public health, there is a lack of comprehensive research, evaluating the spatiotemporal changes in human thermal discomfort and its characteristics in hot-hyper arid regions, such as the Arabian Peninsula (AP). The current study analyzes spatiotemporal changes in human thermal discomfort categories and their characteristics in AP, using the newly developed high-resolution gridded ERA5-HEAT (Human thErmAl comforT) dataset for the period 1979-2022. In addition, the study assesses the interplay between the Universal Thermal Climate Index (UTCI) and El Niño-Southern Oscillation (ENSO) indices for the study period. The results reveal a significant increase in human thermal discomfort and its characteristics, with higher spatial variability in the AP region. The major urban centers in the southwestern, central, and southeastern parts of AP have experienced significant increases in human thermal discomfort (0.4-0.8 °C), with higher frequency and intensity of thermal stress during the study period. The temporal distribution demonstrates a linear increase in UTCI indices and their frequencies and intensities, particularly from 1998 onward, signifying a transition towards a hotter climate characterized by frequent, intense, and prolonged heat stress conditions. Moreover, the UTCI and ENSO indices exhibit a dipole pattern of correlation with a positive (negative) pattern in the southwestern (eastern parts) of AP. The study's findings suggest that policymakers and urban planners need to prioritize public health and well-being in AP's urban areas, especially for vulnerable groups, by implementing climate change adaptation and mitigation strategies, and carefully designing future cities to mitigate the effects of heat stress.

Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece

Existing assessments of the thermal-related impact of the environment on humans are often limited by the use of data that are not representative of the population exposure and/or not consider a human centred approach. Here, we combine high resolution regional retrospective analysis (reanalysis), population data and human energy balance modelling, in order to produce a human thermal bioclimate dataset capable of addressing the above limitations. The dataset consists of hourly, population-weighted values of an advanced human-biometeorological index, namely the modified physiologically equivalent temperature (mPET), at fine-scale administrative level and for 10 different population groups. It also includes the main environmental drivers of mPET at the same spatiotemporal resolution, covering the period from 1991 to 2020. The study area is Greece, but the provided code allows for the ease replication of the dataset in countries included in the domains of the climate reanalysis and population data, which focus over Europe. Thus, the presented data and code can be exploited for human-biometeorological and environmental epidemiological studies in the European continent.

Analysis of heatwaves based on the universal thermal climate index and apparent temperature over mainland Southeast Asia

Heatwaves have caused significant damage to human health, infrastructure, and economies in recent decades, and the occurrences of heatwaves are becoming more frequent and severe across the globe under climate change. The previous studies on heatwaves have primarily focused on air temperature, neglecting other variables like wind speed, relative humidity, and radiation, which could lead to a serious underestimation of the adverse effects of heatwaves. To address this issue, this study proposed to the use of more sophisticated thermal indices, such as universal thermal climate index (UTCI) and apparent temperature (AT), to define heatwaves and carry out a comprehensive heatwave assessment over mainland southeast Asia (MSEA) from 1961 to 2020. The traditional temperature-based method was also compared. The results of the study demonstrate that the annual maximum temperature in heatwave days (HWA) and the annual average temperature in heatwave days (HWM) are significantly underestimated if only air temperature is considered. However, UTCI and AT tend to predict a lower frequency of yearly heatwave occurrences and shorter durations. Trend analysis indicates a general increase in heatwave occurrences across MSEA under all thermal indices in the past six decades, particularly in the last 30 years. This study's approach and findings provide a holistic view of heatwave characteristics based on thermal indices and highlight the risk of intensified heat stress during heatwaves in MSEA.

Modelling variations of emergency attendances using data on community mobility, climate and air pollution

Air pollution is associated with morbidity and mortality worldwide. We investigated the impact of improved air quality during the economic lockdown during the SARS-Cov2 pandemic on emergency room (ER) admissions in Germany. Weekly aggregated clinical data from 33 hospitals were collected in 2019 and 2020. Hourly concentrations of nitrogen and sulfur dioxide (NO2, SO2), carbon and nitrogen monoxide (CO, NO), ozone (O3) and particulate matter (PM10, PM2.5) measured by ground stations and meteorological data (ERA5) were selected from a 30 km radius around the corresponding ED. Mobility was assessed using aggregated cell phone data. A linear stepwise multiple regression model was used to predict ER admissions. The average weekly emergency numbers vary from 200 to over 1600 cases (total n = 2,216,217). The mean maximum decrease in caseload was 5 standard deviations. With the enforcement of the shutdown in March, the mobility index dropped by almost 40%. Of all air pollutants, NO2 has the strongest correlation with ER visits when averaged across all departments. Using a linear stepwise multiple regression model, 63% of the variation in ER visits is explained by the mobility index, but still 6% of the variation is explained by air quality and climate change.

A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020

Human-perceived temperature (HPT) describes the joint effects of multiple climatic factors such as temperature and humidity. Extreme HPT events may reduce labor capacity and cause thermal discomfort and even mortality. These events are becoming more frequent and more intense under global warming, posing severe threats to human and natural systems worldwide, particularly in populated areas with intensive human activities, e.g., the North China Plain (NCP). Therefore, a fine-scale HPT dataset in both spatial and temporal dimensions is urgently needed. Here we construct a daily high-resolution (~1 km) human thermal index collection over NCP from 2003 to 2020 (HiTIC-NCP). This dataset contains 12 HPT indices and has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. Moreover, it exhibits high spatiotemporal consistency with ground-level observations. The dataset provides a reference for human thermal environment and could facilitate studies such as natural hazards, regional climate change, and urban planning.

CMIP6 models informed summer human thermal discomfort conditions in Indian regional hotspot

The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022-2059) and far (2060-2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.

Image Recommendation System Based on Environmental and Human Face Information

With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user's current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user's current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system's ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users' emotional responses, resulting in a positive mood change for most users. Moreover, the system's scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system's ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human-computer interaction, psychology, and social sciences.

Reconstruction of the hourly fine-resolution apparent temperature (Humidex) with the aerodynamic parameters

Apparent temperature is the preferred measure of hotness or coldness expressed to depict the human sense. Spatially explicit measurement of the hourly apparent temperature is essential for capturing the threats to bioclimatic comfort and preventing potential mortality/morbidity risk from heat or cold. However, existing apparent temperature products only provide daily observations at the spatial resolution of several dozen kilometers, resulting in some substantial underestimations for some life-threatening thermal stresses highly localized in space and time. Furthermore, some data-driven models lack mechanical constraints on the turbulent exchange between the surface and the atmosphere, making some unsatisfactory accuracy. Here, we propose Humidex reconstruction model incorporating atmospheric dynamics theory and aerodynamic parameters (i.e., heat and momentum roughness lengths for natural surfaces and three urban canopy geometry parameters for artificial surfaces), capable of developing an hourly dataset at fine-grained spatial resolution (0.01° × 0.01°). In this study, a total of 2952 h in four seasons were selected to test the seasonal performance of this model, taking the Yangtze River Delta as an example. The results show that the Humidex products from this model generally outperform the existing comparable products, with the hourly population root mean square error (RMSE) ranging from 1 to 2 °C in winter and autumn and 2-3 °C in spring and summer. Moreover, the constraint of aerodynamic parameters can reduce RMSE with a significant margin for each season, up to 2 °C, especially in areas with dense woodlands or buildings. In addition, the results demonstrate the excellent performance of this model in capturing short-lived thermal health threats, which are easily overlooked when observed data only provides a daily variation. This indicates that the model can allow researchers and practitioners investigate the fine-grained spatial and temporal evolution of thermal stress and its impact on public health, tourism, learning, and work performance.

Assessment of Summer Regional Outdoor Heat Stress and Regional Comfort in the Beijing-Tianjin-Hebei Agglomeration Over the Last 40 Years

Outdoor thermal comfort (OTC) is critical for public health, labor productivity, and human life. Growing extreme heat events caused by climate change have a serious impact on OTCs, especially in urban areas. Quantitatively characterizing and evaluating the spatiotemporal changes in OTCs are essential, and more applications are needed in urban agglomerations. Therefore, taking the Beijing-Tianjin-Hebei (BTH) urban agglomeration as the study area, this study aimed to quantitatively assess the summer regional OTC from 1981 to 2020. First, the Universal Thermal Climate Index (UTCI) was used as the indicator of daily thermal stress, and then a Composite Thermal Comfort Score was proposed to evaluate the long-term, summertime, regional OTC considering the extent, duration, and intensity of daytime and nighttime thermal stress. The results showed that (a) the increase in UTCI (0.32°C/10a at daytime and 0.21°C/10a at nighttime) and heat stress frequency (0.88 at daytime and 0.39 d/10a at nighttime) were manifested over BTH, indicating a worse OTC. Spatial and temporal heterogeneity was also demonstrated. (b) The general OTC showed a decreasing north-south gradient pattern. At daytime, the northern mountainous zone presented the best OTC, the southern plain zone, especially Hengshui, Langfang, and Cangzhou, showed the worst. At nighttime, the mountain-plain transition zone showed the best OTC, the northern mountainous zone showed the worst since more cold stress occurred. Our findings will be useful in informing climate change adaptation strategies to ensure urban resilience as extreme heat increases in the context of climate change.

Effects of mask wearing duration and relative humidity on thermal perception in the summer outdoor built environment

During the pandemic, face masks are one of the most significant self-protection necessities, but they also cause heat stress. By using the ERA5 (ECMWF Reanalysis 5th Generation) database and the local weather bureau data, the effect of mask wearing on outdoor thermal sensation has been investigated by a survey conducted in the hot summer and cold winter region of eastern China in the summer of 2020. Results show that wearing a face mask for a longer period result in a higher level of discomfort, and the primary source of discomfort is hot and stuffy feelings. The effect of relative humidity is crucial for mask wearers in warm-biased thermal environments, as mean thermal sensation vote (TSV) peaks when environmental relative humidity reaches the range of 70% to 80% and decreases after this range due to the evaporation within the microclimate created by a face mask. Meanwhile, prolonged mask wearing increases participants' hot feelings, especially in warm environments. Specifically, participants wearing face masks for less than 30 min feel hot at a physiological equivalent temperature (PET) value of 34.4 °C, but those who wear them for over 60 min express hot feelings even at a PET value of 24.7 °C. The participants who wear a face mask while walking slowly outdoors have similar thermal sensations to those who do not wear a mask, but are in a higher activity level. The findings demonstrate that mask wearing has a crucial impact on outdoor thermal comfort assessment in a warm-biased outdoor thermal environment.

Integration of GIS and remote sensing to derive spatially continuous thermal comfort and degree days across the populated areas in Jordan

The widespread availability of high-resolution digital elevation data and high computational capabilities, along with GIS tools, has revolutionized big data processing, management, and interpolation. The present investigation generates high spatial resolution maps of thermal comfort levels, heating (HDD), and cooling (CDD) degree days across the populated areas in Jordan. Results show that areas having indoor apparent temperature (IAT) of 26 °C or above, which represents warm/hot conditions on this thermal index, cover a large portion of the study area during July and August. This thermal zone encompasses a large cluster of the major urban centers in the country. For instance, Amman, Zarqa, and Irbid, which host more than 80% of the population of the country, experience 13, 14, and 19 h of warm to very warm conditions during July and August, demonstrating that cooling needs are required to bring about thermal comfort for dwellings and office buildings. Heavy cooling loads, 1700-2000 CDDs, are restricted to the Jordan Rift Valley (JRV) and other small, low-level urban centers. With the exception of the JRV, the populated areas in the country experience cold to very cold conditions during the three coldest months, December through February. Very cold conditions in winter, IAT ≤ 8 °C, span more than 13-14 h of the diurnal cycle in most urban centers. The HDD range from values close to zero along the JRV to ⁓ 1900 in the southern mountains. Heating loads for dwellings and office buildings are very demanding and represent a pressing financial challenge to bring about thermal comfort to homes and public buildings during winter. The present procedure can be integrated with auxiliary data within a GIS environment to investigate numerous climatological, environmental, and site suitability issues. The present procedure can be used for operational purposes over territorial or regional scales for a wide range of applications.

Wet-Bulb Globe Temperature, Universal Thermal Climate Index, and Other Heat Metrics for US Counties, 2000-2020

Epidemiologic research on extreme heat consistently finds significant impacts on human morbidity and mortality. However, most of these analyses do not use spatially explicit measures of heat (typically assessing exposures at major cities using the nearest weather station), and they frequently consider only ambient temperature or heat index. The field is moving toward more expansive analyses that use spatially resolved gridded meteorological datasets and alternative assessments of heat, such as wet-bulb globe temperature (WBGT) and universal thermal climate index (UTCI), both of which require technical geoscientific skills that may be inaccessible to many public health researchers. To facilitate research in this domain, we created a database of population-weighted, spatially explicit daily heat metrics - including WBGT, UTCI, heat index, dewpoint temperature, net effective temperature, and humidex - for counties in the conterminous United States derived from the ERA5-Land gridded data set and using previously validated equations and algorithms. We also provide an R package to calculate these metrics, including gold-standard algorithms for estimating WBGT and UTCI, to facilitate replication.