A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature (Humidex), for the greater Vancouver area

Chasing the heat: Unraveling urban hyperlocal air temperature mapping with mobile sensing and machine learning

Many cities face unprecedented high temperatures with increasing extreme events. Heatwaves pose significant health risks, including cardiovascular diseases, heatstroke, and dehydration. Mapping urban near-surface air temperature (Tair) is crucial for understanding thermal exposure and addressing climate change. Previous studies relied on satellite-derived land surface temperature (LST) and stationary monitoring, but high spaio-temporal Tair mapping is still a challenge. This study optimized a mobile sensing scheme using an electric bicycle platform with environmental and image sensors, and deep learning captured local-scale urban factors. A spatio-temporal data fusion model that consisted of three parts, temporal trend extraction, locality analysis, and neighborhood effect analysis, generated hyperlocal Tair maps. The Results from Beijing demonstrated the effectiveness of the framework, achieving the lowest MAE of 0.02 °C. Optimized data collection and the new model achieved accurate temperature predictions and thermal exposure assessment. Efficiency enhanced sensing strategy was also proposed. The study highlights local-scale factors and spatio-temporal dependencies in addressing heatwaves and climate change impacts in urban areas.

The association between humidex and tuberculosis: a two-stage modelling nationwide study in China

BACKGROUND

Under a changing climate, the joint effects of temperature and relative humidity on tuberculosis (TB) are poorly understood. To address this research gap, we conducted a time-series study to explore the joint effects of temperature and relative humidity on TB incidence in China, considering potential modifiers.

METHODS

Weekly data on TB cases and meteorological factors in 22 cities across mainland China between 2011 and 2020 were collected. The proxy indicator for the combined exposure levels of temperature and relative humidity, Humidex, was calculated. First, a quasi-Poisson regression with the distributed lag non-linear model (DLNM) was constructed to examine the city-specific associations between humidex and TB incidence. Second, a multivariate meta-regression model was used to pool the city-specific effect estimates, and to explore the potential effect modifiers.

RESULTS

A total of 849,676 TB cases occurred in the 22 cities between 2011 and 2020. Overall, a conspicuous J-shaped relationship between humidex and TB incidence was discerned. Specifically, a decrease in humidex was positively correlated with an increased risk of TB incidence, with a maximum relative risk (RR) of 1.40 (95% CI: 1.11-1.76). The elevated RR of TB incidence associated with low humidex (5th humidex) appeared on week 3 and could persist until week 13, with a peak at approximately week 5 (RR: 1.03, 95% CI: 1.01-1.05). The effects of low humidex on TB incidence vary by Natural Growth Rate (NGR) levels.

CONCLUSION

A J-shaped exposure-response association existed between humidex and TB incidence in China. Humidex may act as a better predictor to forecast TB incidence compared to temperature and relative humidity alone, especially in regions with higher NGRs.

An assessment of WRF-urban schemes in simulating local meteorology for heat stress analysis in a tropical sub-Saharan African city, Lagos, Nigeria

Megacities, such as Lagos, Nigeria, face significant challenges due to rapid urbanization and climate change, resulting in a higher intensity of the urban heat island effect, coupled with high population density, making the city fall under the category of moderate to high heat stress/risk. Despite this, very few studies have analyzed the urban impact on heat stress over the coastal city, albeit with poor resolution data. In this study, we assessed the performance of an integrated high-resolution WRF-urban scheme driven by the readily available urban canopy information of the local climate zone (LCZ) to simulate local meteorological data for analyzing the spatiotemporal pattern of heat stress over the megacity. Our results show that the WRF-BEP scheme outperformed the other evaluated urban schemes, reducing the normalized root mean squared error by 25%. Furthermore, using humidex, we found a generally high incidence of intense discomfort in highly urbanized areas and noted the significant influence of urban morphology on the pattern of heat stress, particularly at night due to the combined effect of urban warming and higher relative humidity. The most socioeconomically disadvantaged urban areas, LCZ7, were most affected, with "hot" heat stress conditions observed over 90% of the time. However, during the afternoon, we found reduced heat stress in the core urban areas which might be due to the shading effect and/or cold air advection. Our findings would be relevant in the development of the urgently needed climate/heat adaptation plans for the city and other sub-Saharan African cities.

Different explanations for surface and canopy urban heat island effects in relation to background climate

The background climatic conditions and urban morphology greatly influence urban heat island effects (UHIs), but one-size-fits-all solutions are frequently employed to mitigate UHIs. Here, attribution models for surface UHIs (SUHIs) and canopy UHIs (CUHIs) were developed to describe UHI formation. The contribution of factors to SUHIs and CUHIs shows similar dependencies on background climate and urban morphology. Furthermore, the factors that mainly contributed to CUHIs were more complex, and anthropogenic heat was the more critical factor. Influence from urban morphology also highlights that there is no one-size-fit-all solution for heat mitigation at the neighborhood. In particular, maintaining a low building density should be prioritized, especially mitigating CUHIs. Moreover, it is more effective to prioritize urban irrigation maintenance over increasing green cover in arid regions but the opposite in humid regions. The work can provide scientific evidence to support developing general and regional guidelines for urban heat mitigation.

Field temperature performances of in-use permeable sidewalks and asphalt vehicle roads and the potential impacts on apparent temperature and land surface temperature

Permeable pavements help reduce surface temperatures and have been widely implemented in urban areas. This study utilized an in-use permeable pavement sidewalk in front of a mass rapid transit station in the Taipei city center of Taiwan to determine the actual pavement surface temperature performance. A neighboring asphalt road and impervious pavement were also monitored. With a full year of continuous monitoring, the results showed that the temperature of permeable pavement was 3.7 °C lower than that of impervious pavement and 4.5 °C lower than that of asphalt pavement in the hot season. The frequent rainfall in spring resulted in the smallest temperature differences between the different pavement types. The cooling effects of permeable pavement differed at the different air temperatures. At air temperatures lower than 15 °C, the differences among pavement surface temperatures were noticeable. However, when the air temperature was higher than 35 °C, the surface temperature of permeable pavement was not different from that of impervious pavement and was greater than 55 °C. Field observations were carried out to determine the effects on the apparent temperature and the future surface temperature of climate change scenarios. The results showed that permeable pavement could reduce the average apparent temperature to near the air temperature, and asphalt pavement could increase the apparent temperature by 1.2 °C, assuming that the pavement temperature completely affects the air temperature. With the good prediction ability of the machine learning approach and 15 environmental factors, the preliminary prediction showed the projected surface temperature change in Taipei city in 2033. In the worst-case scenario, the average impervious pavement temperature is as high as 39.12 °C, whereas the average permeable pavement temperature is 32.50 °C.

Effects of apparent temperature on daily outpatient and inpatient visits for cause-specific respiratory diseases in Ganzhou, China: a time series study

BACKGROUND

Non-optimum temperatures are associated with increased risk of respiratory diseases, but the effects of apparent temperature (AT) on respiratory diseases remain to be investigated.

METHODS

Using daily data from 2016 to 2020 in Ganzhou, a large city in southern China, we analyzed the impact of AT on outpatient and inpatient visits for respiratory diseases. We considered total respiratory diseases and five subtypes (influenza and pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma and chronic obstructive pulmonary disease [COPD]). Our analysis employed a distributed lag nonlinear model (DLNM) combined with a generalized additive model (GAM).

RESULTS

We recorded 94,952 outpatients and 72,410 inpatients for respiratory diseases. We found AT significantly non-linearly associated with daily outpatient and inpatient visits for total respiratory diseases, influenza and pneumonia, and URTI, primarily during comfortable AT levels, while it was exclusively related with daily inpatient visits for LRTI and COPD. Moderate heat (32.1 °C, the 75.0th centile) was observed with a significant effect on both daily outpatient and inpatient visits for total respiratory diseases at a relative risk of 1.561 (1.161, 2.098) and 1.276 (1.027, 1.585), respectively (both P < 0.05), while the results of inpatients became insignificant with the adjustment for CO and O3. The attributable fractions in outpatients and inpatients were as follows: total respiratory diseases (24.43% and 18.69%), influenza and pneumonia (31.54% and 17.33%), URTI (23.03% and 32.91%), LRTI (37.49% and 30.00%), asthma (9.83% and 3.39%), and COPD (30.67% and 10.65%). Stratified analyses showed that children ≤5 years old were more susceptible to moderate heat than older participants.

CONCLUSIONS

In conclusion, our results indicated moderate heat increase the risk of daily outpatient and inpatient visits for respiratory diseases, especially among children under the age of 5.

Land surface and air temperature dynamics: The role of urban form and seasonality

Due to the scarcity of air temperature (Ta) observations, urban heat studies often rely on satellite-derived Land Surface Temperature (LST) to characterise the near-surface thermal environment. However, there remains a lack of a quantitative understanding on how LST differs from Ta within urban areas and what are the controlling factors of their interaction. We use crowdsourced air temperature measurements in Sydney, Australia, combined with urban landscape data, Local Climate Zones (LCZ), high-resolution satellite imagery, and machine learning to explore the influence of urban form and fabric on the interaction between Ta and LST. Results show that LST and Ta have distinct spatiotemporal characteristics, and their relationship differs by season, ecological infrastructure, and building morphology. We found greater seasonal variability in LST compared to Ta, along with more pronounced intra-urban spatial variability in LST, particularly in warmer seasons. We also observed a greater temperature difference between LST and Ta in the built environment compared to the natural LCZs, especially during warm days. Natural LCZs (areas with mostly dense and scattered trees) showed stronger LST-Ta relationships compared to built areas. In particular, we observe that built areas with higher building density (where the heat vulnerability is likely more pronounced) show insignificant or negative relationships between LST- Ta in summer. Our results also indicate that surface cover, distance from the ocean, and seasonality significantly influence the distribution of hot and cold spots for LST and Ta. The spatial distribution for Ta hot spots does not always overlap with LST. We find that relying solely on LST as a direct proxy for the urban thermal environment is inappropriate, particularly in densely built-up areas and during warm seasons. These findings provide new perspectives on the relationship between surface and canopy temperatures and how these relate to urban form and fabric.

Urban Versus Lake Impacts on Heat Stress and Its Disparities in a Shoreline City

Shoreline cities are influenced by both urban-scale processes and land-water interactions, with consequences on heat exposure and its disparities. Heat exposure studies over these cities have focused on air and skin temperature, even though moisture advection from water bodies can also modulate heat stress. Here, using an ensemble of model simulations covering Chicago, we find that Lake Michigan strongly reduces heat exposure (2.75°C reduction in maximum average air temperature in Chicago) and heat stress (maximum average wet bulb globe temperature reduced by 0.86°C) during the day, while urbanization enhances them at night (2.75 and 1.57°C increases in minimum average air and wet bulb globe temperature, respectively). We also demonstrate that urban and lake impacts on temperature (particularly skin temperature), including their extremes, and lake-to-land gradients, are stronger than the corresponding impacts on heat stress, partly due to humidity-related feedback. Likewise, environmental disparities across community areas in Chicago seen for skin temperature are much higher (1.29°C increase for maximum average values per $10,000 higher median income per capita) than disparities in air temperature (0.50°C increase) and wet bulb globe temperature (0.23°C increase). The results call for consistent use of physiologically relevant heat exposure metrics to accurately capture the public health implications of urbanization.

Association between cooling temperature and outcomes of patients with heat stroke

This study explored the relationship between body temperature and adverse outcomes in patients with heat stroke to identify the optimal target body temperature within the first 24 h. This retrospective, multicentre study enrolled 143 patients admitted to the emergency department and diagnosed with heat stroke. The primary outcome was the in-hospital mortality rate, while secondary outcomes included the presence and number of damaged organs and neurological sequelae at discharge. A body temperature curve was built using a generalized additive mixed model, and the association between body temperatures and outcomes was established by logistic regression. The threshold and saturation effects were used to explore the targeted body temperature management. Cases were divided into the surviving and non-surviving groups. The cooling rate within the first 2 h was significantly higher in the survival group than the non-survival group (β: 0.47; 95% confidence interval [CI]: 0.09-0.84; P = 0.014), while the non-survival group exhibited a lower body temperature within 24 h (β: - 0.06; 95% CI: - 0.08 to - 0.03; P ≤ 0.001). Body temperature after 2 h (odds ratio [OR]: 2.27; 95% CI: 1.14-4.50; P = 0.019) and lowest temperature within 24 h (OR: 0.18; 95% CI: 0.06-0.55; P = 0.003) were significantly related to in-hospital mortality rate. When the body temperature at 0.5 h was 38.5-40.0 °C, the number of damaged organs was at its lowest. In patients with heat stroke, both hyperthermia and hypothermia were associated with adverse outcomes. Hence, an accurate body temperature management is required during the early stages of care.

Analysis of long- and short-term biometeorological conditions in the Republic of Serbia

Long- and short-term biometeorological conditions in the Republic of Serbia were analyzed using official meteorological data from numerous weather stations located across the country. Selected biometeorological indices HUMIDEX, Physiologically Equivalent Temperature (PET), and Universal Thermal Climate Index (UTCI) are calculated based on air temperature, relative humidity, wind speed, and cloudiness data from the meteorological stations on annual and summer level as well as during selected heat wave periods during 2000-2020. Application of different biometeorological indices provides similar but somewhat different results. For example, average annual HUMIDEX and UTCI values indicate no thermal stress and no discomfort at all stations, while PET indicates the occurrence of slight to moderate cold stress at all stations. Average summer PET and UTCI indicate the occurrence of slight to moderate heat stress throughout the country, while HUMIDEX indicates no discomfort. Trends of biometeorological indices on annual and summer level show a general increase throughout the country. Furthermore, heat wave analysis indicated that the most populated cities of Serbia are under dangerous and extreme heat stress during these extreme temperature events, which can influence human health and well-being. The obtained biometeorological information can be used for the preparation of climate adaptation strategies that consider the human biometeorological conditions, with a special focus on developing climate-sensitive and comfortable cities.

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.

Is Cold Apparent Temperature Associated With the Hospitalizations for Osteoporotic Fractures in the Central Areas of Wuhan? A Time-Series Study

Osteoporosis is alarming problem due to aggravation of global aging, especially in China. Osteoporotic fracture (OF) is one of the most severe consequents of osteoporosis. Many previous studies found that environmental factors had adverse effects on human health. Cold temperature was associated with OF and bone metabolism in prior observational and experimental researches. However, few studies had been conducted on the acute effect of low temperature and OF. Data on daily meteorological factors and hospitalizations for OF were collected from Wuhan, China, between January 1, 2017 to December 24, 2019. Apparent temperature (AT), comprehensively considered a variety of environmental factors, was calculated by ambient temperature, relative humidity and wind speed. A generalized linear regression model combined with distributed lag non-linear regression model (DLNM) with quasi-Poisson link was used to explore the association between AT and the number of hospitalizations for OF. Subgroup analyses stratified by gender, age and the history of fracture were applied for detecting susceptible people. The exposure-response curve of AT and OF were generally U-shaped with lowest point at 25.8°C. The significant relationship of AT-OF existed only in cold effect (-2.0 vs. 25.8°C) while not in warm effect (37.0 vs. 25.8°C). Statistically significant risks of OF for cold effects were only found in females [RR = 1.12 (95%CI: 1.02, 1.24) at lag 2 day], aged <75 years old [RR = 1.18 (95%CI: 1.04, 1.33) and 1.17 (95%CI: 1.04, 1.33) at lag 2 and 3 days, respectively] and people with history of fracture [RR = 1.39 (95%CI: 1.02, 1.90) and 1.27 (95%CI: 1.05, 1.53) at lag 1 and 2 days, respectively]. The significant associations of AT on OF were only found in cold effect. The females, people aged <75 years and people with history of fracture possibly appeared to be more vulnerable. Public health departments should pay attention to the negative effect of cold AT and take measures in time.

Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions

Urban environments lie at the confluence of social, cultural, and economic activities and have unique biophysical characteristics due to continued infrastructure development that generally replaces natural landscapes with built-up structures. The vast majority of studies on urban perturbation of local weather and climate have been centered on the urban heat island (UHI) effect, referring to the higher temperature in cities compared to their natural surroundings. Besides the UHI effect and heat waves, urbanization also impacts atmospheric moisture, wind, boundary layer structure, cloud formation, dispersion of air pollutants, precipitation, and storms. In this review article, we first introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies. We also highlight the major research gaps and challenges in our understanding of the impacts of urbanization and provide our perspective and recommendations for future research priorities and directions.

The Dynamic Relationship between Air and Land Surface Temperature within the Madison, Wisconsin Urban Heat Island

The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (Tair) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both Tair measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, Tair and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R2 = 0.76, p = 0.0002) that peaked in late July. The seasonal evolution in the relationship between Tair and LST, along with particularly high variability in LST across agricultural land cover suggest that plant phenology contributes to a seasonally varying relationship between Tair and LST measurements of the UHI.

The role of urban trees in reducing land surface temperatures in European cities

Urban trees influence temperatures in cities. However, their effectiveness at mitigating urban heat in different climatic contexts and in comparison to treeless urban green spaces has not yet been sufficiently explored. Here, we use high-resolution satellite land surface temperatures (LSTs) and land-cover data from 293 European cities to infer the potential of urban trees to reduce LSTs. We show that urban trees exhibit lower temperatures than urban fabric across most European cities in summer and during hot extremes. Compared to continuous urban fabric, LSTs observed for urban trees are on average 0-4 K lower in Southern European regions and 8-12 K lower in Central Europe. Treeless urban green spaces are overall less effective in reducing LSTs, and their cooling effect is approximately 2-4 times lower than the cooling induced by urban trees. By revealing continental-scale patterns in the effect of trees and treeless green spaces on urban LST our results highlight the importance of considering and further investigating the climate-dependent effectiveness of heat mitigation measures in cities.

DTEx: A dynamic urban thermal exposure index based on human mobility patterns

BACKGROUND

Extreme heat in light of climate change is increasingly threatening the health and comfort of urban residents. Understanding spatio-temporal patterns of heat exposure is a critical factor in directing mitigation measures. Current heat vulnerability indices provide insight into heat sensitivities within given communities but do not account for the dynamic nature of the human movement as people travel for different activities. Here, we present a new Dynamic urban Thermal Exposure index (DTEx) that captures the varying heat exposure within urban environments.

METHODS

We developed the DTEx to understand human heat exposure patterns in a mid-sized city. This index incorporates the human movement pattern and the heat hazard pattern obtained via novel and advanced techniques. We generated the human movement pattern from large-scale, anonymized smartphone location data. The heat hazard patterns were extrapolated via machine learning models from air temperature data measured through vehicle-mounted sensors. The exposure index was then developed by combining the two parameters using their standard-deviation-classified indices.

RESULTS

Our exposure index varied between 2 and 12, indicating low to high thermal exposures. Several high-temperature spots associated with a large volume of foot traffic are successfully identified through this DTEx. We observed the hottest spots at shopping plazas but not specifically in the urban center. During the selected football gameday, the exposure index surged across most places near the football stadium but was reduced considerably further away.

DISCUSSION

The proposed DTEx is novel because it provides dynamic heat monitoring capability to facilitate heat mitigation strategies at vulnerable locations in urban environments. Combining the mobility data and extensive sensor data generates rich details on the most heat-exposed areas due to human congregation. Such information will be critical for risk communication and urban planning for policymakers. DTEx could also help smart route planning in sustainable cities to avoid heat hazards risks.

Associations between Perceptions and Measures of Weather and Walking, United States-2015

INTRODUCTION

Weather can be a barrier to walking. Understanding how perceptions of weather as a barrier and measured temperature are associated with walking can inform monitoring and promotion strategies. The objective of this study is to examine the association between perceptions of weather as a barrier to walking and measured weather with the volume of leisure and transportation walking.

METHODS

The 2015 National Health Interview Survey (NHIS) assessed participation in and volume of walking (transportation, leisure) in the past week and frequency of reporting weather as a barrier to walking. Data were collected over the entire year. In 2019, we merged month-specific temperature data from the PRISM climate group with individual NHIS records. We examined associations using logistic (participation) and linear regression models (volume).

RESULTS

Participation in walking increased as frequency of reporting weather as a barrier to walking decreased, from 'almost always' (transportation: 23%, leisure: 42%) to 'a little of the time' (transportation: 40%, leisure: 67%). Among adults reporting walking, walking volume increased as frequency of reporting weather as a barrier decreased from 'almost always' (transportation: 51 min/week, leisure: 64 min/week) to 'never' (transportation: 69 min/week, leisure: 98 min/week). Month-specific temperature was significantly associated with leisure walking with lower participation at the lowest and highest temperature quintiles, although the strength of the association differed by frequency of reporting weather as a barrier.

CONCLUSIONS

In general, prevalence and volume of leisure and transportation walking decreased as the perception of weather as a barrier increased. Low and high temperature conditions were also associated with leisure walking participation, particularly among adults with increased perceptions of weather as a barrier. Our findings highlight the importance of including strategies to help adults overcome perceived and actual weather-related barriers in walking promotion efforts.

A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003 to 2019

While weather stations generally capture near-surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta-related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite-based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003-2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite-hybrid mixed-effects model for each year, regressing Ta measurements against land use terms, day-specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10-fold cross-validation at withheld stations. Across all years, the root-mean-square error ranged from 0.92 to 1.92 K and the R 2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high-quality Ta estimates for epidemiology studies in the MCM region.

Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms

The ubiquitous nature of satellite data has led to an explosion of studies on the surface urban heat island (SUHI). Relatively few have simultaneously used air temperature measurements to compare SUHI with the canopy UHI (CUHI), which is more relevant to public health. Using crowdsourced citizen weather stations (>50,000) and satellite data over Europe, we estimate the CUHI and SUHI intensity in 342 urban clusters during the 2019 heat wave. Satellites produce a sixfold overestimate of UHI relative to station measurements (mean SUHI 1.45°C; CUHI 0.26°C), with SUHI exceeding CUHI in 96% of cities during daytime and in 80% at night. Using empirical evidence, we confirm the control of aerodynamic roughness on UHI intensity, but find evaporative cooling to have a stronger overall impact during this time period. Our results support urban greening as an effective UHI mitigation strategy and caution against relying on satellite data for urban heat risk assessments.

The Mortality Effect of Apparent Temperature: A Multi-City Study in Asia

(1) Background: The health effect of temperature has become a rising public health topic. The objective of this study is to assess the association between apparent temperature and non-accidental deaths, and the mortality burden attributed to cold and heat temperature; (2) Methods: The daily data on temperature and deaths were collected from 10 cities in Thailand, Korea and China. We fitted a time-series regression with a distributed lag nonlinear model (DLNM) to derive the health risk of temperature for each city and then pooled them to get the overall cumulative risk by multivariate meta-analysis. Additionally, we calculated the attributable fraction of deaths for heat and cold, which was defined as temperatures above and below minimum-mortality temperature (MMT); (3) Results: There are regional heterogeneities in the minimum mortality percentiles (MMP) and attributable fractions for different countries. The MMP varied from about the 5-10th percentile in Thailand to 63-93rd percentile in China and Korea. The attributable fractions of the total deaths due to short-term exposure to temperature in Asia is 7.62%, of which the cold effect (6.44%) is much higher than the heat effect (1.18%); (4) Conclusions: Our study suggested that apparent temperature was associated with an increase in non-accidental mortality. Most of the temperature-related mortality burden was attributable to cold, except for Thailand.

Monitoring and Evaluating Nature-Based Solutions Implementation in Urban Areas by Means of Earth Observation

Climate change influences the vulnerability of urban populations worldwide. To improve their adaptive capacity, the implementation of nature-based solutions (NBS) in urban areas has been identified as an appropriate action, giving urban planning and development an important role towards climate change adaptation/mitigation and risk management and resilience. However, the importance of extensively applying NBS is still underestimated, especially regarding its potential to induce significantly positive environmental and socioeconomic impacts across cities. Concerning environmental impacts, monitoring and evaluation is an important step of NBS management, where earth observation (EO) can contribute. EO is known for providing valuable disaggregated data to assess the modifications caused by NBS implementation in terms of land cover, whereas the potential of EO to uncover the role of NBS in urban metabolism modifications (e.g., energy, water, and carbon fluxes and balances) still remains underexplored. This study reviews the EO potential in the monitoring and evaluation of NBS implementation in cities, indicating that satellite observations combined with data from complementary sources may provide an evidence-based approach in terms of NBS adaptive management. EO-based tools can be applied to assess NBS’ impacts on urban energy, water, and carbon balances, further improving our understanding of urban systems dynamics and supporting sustainable urbanization.

Heat Stress-Related Symptoms among Bakery Workers in Lebanon: A National Cross-Sectional Study

Heat exposure is linked to a range of heat-related illnesses and injuries. This study assessed the association between workers’ perceptions of the work environment and reports of heat stress-related health symptoms in bakery workers in Lebanon. A national cross-sectional survey of workers was carried out in 504 bakeries in Lebanon. One worker in each bakery was interviewed using questions relating to the workplace environment and heat stress-related health symptoms. Heat and humidity measurements were recorded in bakeries. Descriptive analyses were performed, and logistic regression assessed relationships between the workplace environment, worker perceptions, and reports of heat stress-related health symptoms. In total, 47.2% of workers experienced heat stress-related symptoms, 83% perceived workplace temperatures as hot, and 48% perceived these temperatures as affecting their health. Humidex readings showed that 49% of bakeries had conditions unsafe for routine work tasks. Working under pressure (AOR = 1.65; 95% CI = 1.12-2.43), job dissatisfaction (AOR = 1.76; 95% CI = 1.12-2.79), and perceptions that high temperatures negatively affected health (AOR = 2.73; 95% CI = 1.87-3.99) were all significantly correlated to reports of heat stress-related symptoms. Females were more likely to experience heat stress-related symptoms (AOR = 1.96; 95% CI = 1.13-3.39). Workers who reported low levels of water consumption at work were also more likely to experience heat stress-related health symptoms. We conclude that heat exposure potentially impacts workers’ health in Lebanese bakeries. Improvements in workplace conditions, adequate infrastructure, and workers’ training are key interventions for maintaining workers’ health.

Impact of COVID-19-Related Traffic Slowdown on Urban Heat Characteristics

Governments around the world have implemented measures to slow down the spread of COVID-19, resulting in a substantial decrease in the usage of motorized transportation. The ensuing decrease in the emission of traffic-related heat and pollutants is expected to impact the environment through various pathways, especially near urban areas, where there is a higher concentration of traffic. In this study, we perform high-resolution urban climate simulations to assess the direct impact of the decrease in traffic-related heat emissions due to COVID-19 on urban temperature characteristics. One simulation spans the January–May 2020 period; two additional simulations spanning the April 2019–May 2020 period, with normal and reduced traffic, are used to assess the impacts throughout the year. These simulations are performed for the city of Montreal, the second largest urban centre in Canada. The mechanisms and main findings of this study are likely to be applicable to most large urban centres around the globe. The results show that an 80% reduction in traffic results in a decrease of up to 1 °C in the near-surface temperature for regions with heavy traffic. The magnitude of the temperature decrease varies substantially with the diurnal traffic cycle and also from day to day, being greatest when the near-surface wind speeds are low and there is a temperature inversion in the surface layer. This reduction in near-surface temperature is reflected by an up to 20% reduction in hot hours (when temperature exceeds 30 °C) during the warm season, thus reducing heat stress for vulnerable populations. No substantial changes occur outside of traffic corridors, indicating that potential reductions in traffic would need to be supplemented by additional measures to reduce urban temperatures and associated heat stress, especially in a warming climate, to ensure human health and well-being.

Using Citizen Sensing to Identify Heat-Exposed Neighbourhoods

Rural areas cool off by night but built-up urban areas lack similar relief and may threaten vulnerable people’s health during heat waves. Temperature varies within a city due to the heterogenous nature of urban environments, but official measurement stations are unable to capture local variations, since they use few measurement stations typically set up outside of urban areas. Meteorological measurements may as such be at odds with citizen sensing, where absolute accuracy is sacrificed in pursuit of increased coverage. In this article, we use geographic information processing methodologies and generate 144 hourly apparent temperature surfaces for Rotterdam during a six-day heat wave that took place in July 2019 in The Netherlands. These surfaces are used to generate a humidex degree hours (HDH) composite map. The HDH metric integrates apparent temperature intensity with duration into one spatially explicit value and is used to identify geographical areas in Rotterdam where citizens may experience adverse health effects of prolonged heat exposure. Combining the HDH map with demographic data allows us to identify the most heat-exposed areas with the largest share of vulnerable population. These neighbourhoods may be the locations most in need of adaptation measures.

Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city

The urban heat island (UHI) effect is an increasingly consequential problem that confronts cities. The accurate characterization and quantification of UHI are crucial for sustainable urban development. Few UHI studies, however, compare data source, spatio-temporal variations, and indicators for the same city in parallel. This study uses Changchun, a snow climate city in China, as an example and compares five different indicators of the UHI based on land surface temperature (LST) derived from Landsat 8 TIRS and hourly air temperature (AT) collected from 41 meteorological weather stations to conduct a more comprehensive comparative study of the UHI. The results show the following. (1) The relationships between LST and AT are all statistically significant, and the surface urban heat island (SUHI) intensity characterized by the LST is considerably stronger than that of AT both in summer and winter. (2) The SUHI intensity is significantly stronger in summer (6.83 °C) than in winter (1.55 °C) based on the morning LST, whereas the UHI intensity (0.27 °C in summer and 0.40 °C in winter) that is simultaneously quantified by the AT has an opposite result. The mean whole-day and daytime UHI intensity difference, which is quantified hourly by the AT between summer and winter, is not significant. The difference between nighttime and daytime UHI intensities is evident in both summer (1.26 °C) and winter (0.76 °C). Additionally, the high temperatures for both LST and AT have a more concentrated distribution in winter than in summer. (3) The values of UHI/SUHI intensity considerably vary based on different indicators. The different choices among land covers to represent "urban" and "rural" areas would significantly affect the values of UHI/SUHI intensity. The selection of appropriate indicators and data sources to quantify the UHI remains a problem that has to be resolved in future studies.

A multi-resolution air temperature model for France from MODIS and Landsat thermal data

Understanding and managing the health effects of ambient temperature (Ta) in a warming, urbanizing world requires spatially- and temporally-resolved Ta at high resolutions. This is challenging in a large area like France which includes highly variable topography, rural areas with few weather stations, and heterogeneous urban areas where Ta can vary at fine spatial scales. We have modeled daily Ta from 2000 to 2016 at a base resolution of 1 km2 across continental France and at a 200 × 200 m2 resolution over large urban areas. For each day we predict three Ta measures: minimum (Tmin), mean (Tmean), and maximum (Tmax). We start by using linear mixed models to calibrate daily Ta observations from weather stations with remotely sensed MODIS land surface temperature (LST) and other spatial predictors (e.g. NDVI, elevation) on a 1 km2 grid. We fill gaps where LST is missing (e.g. due to cloud cover) with additional mixed models that capture the relationship between predicted Ta at each location and observed Ta at nearby weather stations. The resulting 1 km Ta models perform very well, with ten-fold cross-validated R2 of 0.92, 0.97, and 0.95, mean absolute error (MAE) of 1.4 °C, 0.9 °C, and 1.4 °C, and root mean square error (RMSE) of 1.9 °C, 1.3 °C, and 1.8 °C (Tmin, Tmean, and Tmax, respectively) for the initial calibration stage. To increase the spatial resolution over large urban areas, we train random forest and extreme gradient boosting models to predict the residuals (R) of the 1 km Ta predictions on a 200 × 200 m2 grid. In this stage we replace MODIS LST and NDVI with composited top-of-atmosphere brightness temperature and NDVI from the Landsat 5, 7, and 8 satellites. We use a generalized additive model to ensemble the random forest and extreme gradient boosting predictions with weights that vary spatially and by the magnitude of the predicted residual. The 200 m models also perform well, with ten-fold cross-validated R2 of 0.79, 0.79, and 0.85, MAE of 0.4, 0.3, and 0.3, and RMSE of 0.6, 0.4, and 0.5 (Rmin, Rmean, and Rmax, respectively). Our model will reduce bias in epidemiological studies in France by improving Ta exposure assessment in both urban and rural areas, and our methodology demonstrates that MODIS and Landsat thermal data can be used to generate gap-free timeseries of daily minimum, maximum, and mean Ta at a 200 × 200 m2 spatial resolution.

Effect of apparent temperature on daily emergency admissions for mental and behavioral disorders in Yancheng, China: a time-series study

BACKGROUND

Very few studies have focused on the relationship between ambient apparent temperature (AT) and admission of mental and behaviour disorders (MDs). Therefore, a time-series study was conducted in Yancheng, China, to explore the effects of AT on the daily emergency admissions of patients with MDs over the period of 2014-17.

METHODS

A quasi-Poisson generalized linear model (GLM) combined with a distributed lag non-linear model (DLNM) was adopted to explore the associations after adjusting for time trend, day of the week, humidity, sunshine duration, rainfall, holidays and air pollutants. In the subgroup analysis, the modification effects of age and sex were also examined.

RESULTS

Overall, 8438 cases of MDs emergency admissions were identified. With the apparent temperature with the minimum number of admissions (- 3.4 °C) serving as a reference, a positive correlation emerged between high AT and daily emergency admissions of patients with MDs in Yancheng, China, with the lagged effect of 1 to 5 days. The subgroup analysis demonstrated a positive relationship between AT and MDs emergency admissions among males and individuals younger than 45 years old, with no lagged effect.

CONCLUSIONS

The results will provide important scientific evidence for mental health policy-makers and practitioners for possible intervention, especially among the vulnerable populations.

Heat waves in South Korea: differences of heat wave characteristics by thermal indices

Heat wave warning systems and related research define heat waves using various indices and there exists no standard definition for a heat wave. Despite various weather forecast services for heat stress in South Korea, it is unclear how different thermal indices affect the designation of heat waves and health effect estimates. We aimed to analyze trends of heat wave characteristics and mortality associations using various criteria for the warm season (June-September) in 2011-5 for the most populated two cities in South Korea, Seoul and Busan. Hourly weather monitoring data and daily mortality data in each city were obtained. The following indices were estimated to define heat waves: air temperature, heat index (HI), humidex, apparent temperature (AT), effective temperature (ET), and wet-bulb globe temperature (WBGT). The thresholds of each index for heat wave definitions were obtained by statistical distribution (95th percentiles) and minimum mortality temperature (MMT). Thermal indices showed clustering of accumulation of excess heat above thresholds for northeast regions in the cities while air temperature showed it for central regions. Compared to 95th percentiles, the MMTs resulted 14 times longer heat wave days for thermal indices except for air temperature. When MMTs were used, nine times larger excess mortality from heat waves occurred for all indices compared to that from heat waves defined by the 95th percentiles. The thermal indices with the highest association between heat and mortality varied between the two cities: air temperature for Seoul and WBGT for Busan. Heat wave warnings should be based on a thorough comparison of how different heat wave criteria will affect the public health impact of heat wave warnings in terms of identifying a heat wave and degree of health impacts due to it.

Characterizing the Hourly Variation of Urban Heat Islands in a Snowy Climate City during Summer

Temporal variation of urban heat island (UHI) intensity is one of the most important themes in UHI studies. However, fine-scale temporal variability of UHI with explicit spatial information is sparse in the literature. Based on the hourly air temperature from 195 meteorological stations during August 2015 in Changchun, China, hourly spatiotemporal patterns of UHI were mapped to explore the temporal variability and the effects of land use on the thermal environment using time series analysis, air temperature profiling, and spatial analysis. The results showed that: (1) high air temperature does not indicate strong UHI intensity. The nighttime UHI intensity (1.51 °C) was much stronger than that in the daytime (0.49 °C). (2) The urban area was the hottest during most of the day except the period from late morning to around 13:00 when there was about a 40% possibility for an “inverse UHI intensity” to appear. Paddy land was the coolest in the daytime, while woodland had the lowest temperature during the nighttime. (3) The rural area had higher warming and cooling rates than the urban area after sunrise and sunset. It appeared that 23 °C was the threshold at which the thermal characteristics of different land use types changed significantly.

Examining the association between apparent temperature and admissions for schizophrenia in Hefei, China, 2005-2014: A time-series analysis

BACKGROUND

Many studies have investigated the association between schizophrenia and daily mean temperature, but the conclusions were not consistent. Apparent temperature (AT), a comprehensive indicator of temperature and humidity, reflected human thermal sensation more objectively than temperature. We aimed to investigate the relationship between high/low AT and schizophrenia hospitalizations in Hefei, China, and further to identify vulnerable subpopulations.

METHODS

Poisson generalized linear regression model combined with distributed lag non-linear model (DLNM) was applied to quantify the relationship between AT and schizophrenia hospitalizations, compared with the AT of minimum admissions (3.3 °C). Stratified analysis was conducted by sex, marital status and age. Separate models for AT and temperature were compared as well.

RESULTS

The high AT (90th, 28.3 °C) exerted the greatest effect at lag0, with RR = 1.062 (95%CI, 1.019-1.106). The effect of the low AT (10th, -4.7 °C) was first observed at lag3 (RR = 1.007, 95%CI: 1.000-1.015), and increased to the maximum at lag7 (RR = 1.018, 95%CI: 1.009-1.027). The male patients, the married patients and patients aged 41-60 years appeared to be more vulnerable to both high and low ATs. The values of Akaike Information Criterion and mean squared error (MSE) in model with AT were smaller than that with temperature.

CONCLUSIONS

Both high and low ATs were associated with increase in schizophrenia hospitalizations. More protective measures should be taken to avoid adverse AT exposure in susceptible groups.

[Assessment of health risks due to heat and fine particles in Germany: an epidemiological study approach]

BACKGROUND

Exposure to heat and particulate matter is a cause of increased mortality. Climate change and increasing climate variability exacerbate these problems. Experts require assessments with which health risks and the success of preventative measures can be estimated. We implemented an ecological study approach to assess these risks at both small and large scales of reference levels (Federal Republic of Germany and territorial authority).

METHODS

We utilised a case-crossover design to investigate the relationship between exposure and mortality. This study design uses a logistic regression model. Analogously to a matched case-control study, the odds ratio maps the effect strength. The study period included the years 2002-2006.

RESULTS

The analysis demonstrated health risks from exposure to heat for the German population (OR 1.1529, 95% CI 1.1517-1.1541; adjusted OR 1.0658). Significant evidence of a health risk was also documented for exposure to particulate matter (PM_10; OR 1.2987, 95% CI 1.2951-1.3024; adjusted OR 1.0128). The risk does not significantly differ for women versus men; the variable age was also not significant at the level of the country-wide analysis, but for a few subordinate units of space. This study approach can be adapted for assessments at varying levels of reference and periods of time as well as for different populations.

DISCUSSION

The methodological approach is useful for a reproducible study design. Nevertheless, other influencing factors such as ozone or PM_2.5 should be incorporated in subsequent analyses to clarify whether these factors skew the results. Further analysis would also be useful to investigate if and to what extent socio-structural and socio-economic factors affect the associated risk.

Reconstruction of high spatial resolution surface air temperature data across China: A new geo-intelligent multisource data-based machine learning technique

Good knowledge of the surface air temperature (SAT) is critical for scientific understanding of ecological environment changes and land-atmosphere thermodynamic interactions. However, sparse and uneven spatial distribution of the temperature gauging stations introduces remarkable uncertainties into analysis of the SAT pattern. From a geo-intelligent perspective, here we proposed a new SAT reconstruction method based on the multisource data and machine learning technique which was developed by considering autocorrelation of the in situ observed SAT in both space and time, or simply STAML, i.e. Geoi-SVM (Geo-Intelligent Support Vector Machine), Geoi-BPNN (Geo-Intelligent Back Propagation Neural Network) and Geoi-RF (Geo-Intelligent Random Forest). The multisource data used in this study include the in situ observed SAT and multisource remotely sensed data such as MODIS land surface temperature, NDVI (Normalized Difference Vegetation Index) data. Intermodel comparisons amidst reconstructed SAT data were done to evaluate reconstructing performance of abovementioned models. Besides, the SAT reconstructed by CART (Classification and Regression Tree) was also included to evaluate the reconstructing performance of the models considered in this study when compared to SAT data by CART algorithm. We found that the estimation error of the reconstructed SAT by the STAML is smaller than 0.5K (Kelvin). In addition, it is interesting to note that the Geoi-RF performs better with Mean Absolute Error (MAE) of lower than 0.25K, and Root Mean Squared Error (RMSE) and Standard Deviation (SD) of lower than 0.5K respectively. Correlation coefficients between the reconstructed SAT by Geoi-RF and the observed SAT are close to 1. Besides, the estimation accuracy of the SAT by the Geoi-RF technique is 18.51-63.17% higher than that by the other techniques considered in this study. This study provides a new idea and technique for reconstruction of SAT over large spatial extent at regional and even global scale.

Ambient Conditions Prior to Tokyo 2020 Olympic and Paralympic Games: Considerations for Acclimation or Acclimatization Strategies

The Tokyo Olympics and Paralympic games in 2020 will be held in hot and humid conditions. Heat acclimation (in a climatic chamber) or heat acclimatization (natural environment) is essential to prepare the (endurance) athletes and reduce the performance loss associated with work in the heat. Based on the 1990-2018 hourly meteorological data of Tokyo and the derived wet bulb globe temperature (WBGT) (Liljegren method), Heat Index and Humidex, it is shown that the circumstances prior to the games are likely not sufficiently hot to fully adapt to the heat. For instance, the WBGT 2 weeks prior to the games at the hottest moment of the day (13:00 h) is 26.4 ± 2.9°C and 28.6 ± 2.8°C during the games. These values include correction for global warming. The daily variation in thermal strain indices during the Tokyo Olympics (WBGT varying by 4°C between the early morning and the early afternoon) implies that the time of day of the event has a considerable impact on heat strain. The Paralympics heat strain is about 1.5°C WBGT lower than the Olympics, but may still impose considerable heat strain since the Paralympic athletes often have a reduced ability to thermoregulate. It is therefore recommended to acclimate about 1 month prior to the Olympics under controlled conditions set to the worst-case Tokyo climate and re-acclimatize in Japan or surroundings just prior to the Olympics.

Use of earth observations for temperature exposure assessment in epidemiological studies

PURPOSE OF REVIEW

Low, high, extreme, and variable temperatures have been linked to multiple adverse health outcomes, particularly among the elderly and children. Recent models incorporating satellite remote sensing data have mitigated several limitations of previous studies, improving exposure assessment. This review focuses on these new temperature exposure models and their application in epidemiological studies.

RECENT FINDINGS

Satellite observations of land surface temperature have been used to model air temperature across large spatial areas at high spatiotemporal resolutions. These models enable exposure assessment of entire populations and have been shown to reduce error in exposure estimates, thus mitigating downward bias in health effect estimates.

SUMMARY

Satellite-based models improve our understanding of spatiotemporal variation in temperature and the associated health effects. Further research should focus on improving the resolution of these models, especially in urban areas, and increasing their use in epidemiological studies of direct temperature exposure and vector-borne diseases.

Spatiotemporal Prediction of Increasing Winter Perceived Temperature across a Sub-Tropical City for Sustainable Planning and Climate Change Mitigation

Climate variability has been documented as being key to influencing human wellbeing across cities as it is linked to mortality and illness due to changes in the perceived weather cycle. Many studies have investigated the impact of summer temperature on human health and have proposed mitigation strategies for summer heat waves. However, sub-tropical cities are still experiencing winter temperature variations. Increasing winter perceived temperature through the decades may soon affect city wellbeing, due to a larger temperature change between normal winter days and extreme cold events, which may cause higher health risk due to lack of adaptation and self-preparedness. Therefore, winter perceived temperature should also be considered and integrated in urban sustainable planning. This study has integrated the increasing winter perceived temperature as a factor for developing spatiotemporal protocols for mitigating the adverse impact of climate change. Land surface temperature (LST) derived from satellite images and building data extracted from aerial photographs were used to simulate the adjusted wind chill equivalent temperature (AWCET) particularly for sub-tropical scenarios between 1990 and 2010 of the Kowloon Peninsula, Hong Kong. Compared with perceived temperature based on the representative station located at the headquarters of the Hong Kong Observatory, the temperature of half the study area in the Kowloon Peninsula has raised by 1.5 °C. The areas with less green space and less public open space in 2010 show higher relative temperatures. Socioeconomically deprived areas (e.g., areas with lower median monthly income) may suffer more from this scenario, but not all types of socioeconomic disparities are associated with poor sustainable planning. Based on our results and the "no-one left behind" guideline from the United Nations, climate change mitigation should be conducted by targeting socioeconomic neighborhoods more than just aging communities.

The impact of a heat wave on mortality in the emergency department

Extreme heat wave increases the number of emergency department (ED) admissions and mortality rates. The purpose of our study is to investigate the effects of the heat wave experienced in Izmir province of Turkey on mortality.During a 9-day period between 17th and 25th June 2016 (study period), air temperature values were higher than the seasonal norms in Izmir, Turkey. In this cross-sectional study, nontraumatic admissions and in-hospital mortality rates were compared this historical interval of the extreme heat wave with the same period of the previous year and the other 21 days of June 2016.The average air temperature between 17th and 25th June 2016, was higher than the average air temperature of the previous year's same period and the average air temperature from the other 21 days of June 2016 (27.8 ± 3.6 °C, (24.5 ± 1.9°C, 24.1 ± 2.1°C, respectively) (P <.01)During the study period, the mean number of ED visits and mortality rates were significantly higher than the previous year's same period (320 ± 30/day vs 269 ± 27/day, [P <.01], and 1.6% vs 0.7%, [P <.01]).Although the admission rate was similar between the study period and the other 21 days of June 2016 (320 ± 30/day vs 310 ± 32/ day, [P = .445]); in-hospital mortality rate was significantly higher during study period (1.6% vs 0.7%, [P <.01]).During the extreme heat waves, ED admissions and in-hospital mortality rates are increased. Precautions should be addressed for adaptation of people to extreme hot weather.

Modelled spatiotemporal variability of outdoor thermal comfort in local climate zones of the city of Brno, Czech Republic

This study uses the MUKLIMO_3 urban climate model (in German, Mikroskaliges Urbanes KLImaMOdell in 3-Dimensionen) and measurements from an urban climate network in order to simulate, validate and analyse the spatiotemporal pattern of human thermal comfort outdoors in the city of Brno (Czech Republic) during a heat-wave period. HUMIDEX, a heat index designed to quantify human heat exposure, was employed to assess thermal comfort, employing air temperature and relative humidity data. The city was divided into local climate zones (LCZs) in order to access differences in intra-urban thermal comfort. Validation of the model results, based on the measurement dates within the urban monitoring network, confirmed that the MUKLIMO_3 micro-scale model had the capacity to simulate the main spatiotemporal patterns of thermal comfort in an urban area and its vicinity. The results suggested that statistically significant differences in outdoor thermal comfort exist in the majority of cases between different LCZs. The most built-up LCZ types (LCZs 2, 3, 5, 8 and 10) were disclosed as the most uncomfortable areas of the city. Hence, conditions of great discomfort (HUMIDEX >40) were recorded in these areas, mainly in the afternoon hours (from 13.00 to 18.00 CEST), while some thermal discomfort continued overnight. In contrast, HUMIDEX values in sparsely built-up LCZ 9 and non-urban LCZs were substantially lower and indicated better thermal conditions for the urban population. Interestingly, the model captured a local increase of HUMIDEX values arising out of air humidity in LCZs with the presence of more vegetation (LCZs A and B) and in the vicinity of larger bodies of water (LCZ G).

Mapping relative humidity, average and extreme temperature in hot summer over China

Air temperature and relative humidity are the key variables in environmental health research. Both of them are difficult to map especially at national scale because of spatial heterogeneity. This paper presents a methodology for mapping relative humidity, average and extreme temperature in hot summer (June to August) over China. Several data as explanatory variables were applied to random forest regression models to predict relative humidity and temperatures, including surface reflectance, land cover, digital elevation model (DEM), enhanced vegetation index (EVI), latitude, nighttime lights (NLs), as well as buffer zones of road, railroad, river system and administration center. Results based on cross-validation reflect acceptable prediction errors in estimating relative humidity (RMSE=7.4%), average temperature (RMSE=2.4°C), average maximum temperature (RMSE=2.5°C), and extreme maximum temperature (RMSE=2.6°C). Despite the strong correlation between average and extreme temperatures, significant differences exist in their spatial distribution along the latitude direction, especially in the areas such as Hebei, Szechwan, Hubei, Henan, Shandong, and Inner Mongolia. Specifically, social economic activity, relative humidity and vegetation tend to affect extreme heat events, and both latitude and DEM (i.e., geographical position) determine the average level of temperature. Compared with interpolation technology and statistical methods, the proposed methodology demonstrates the ability to generate relative humidity and temperature maps with finer gradients in hot summer over China.

Multiple timescale analysis of the urban heat island effect based on the Community Land Model: a case study of the city of Xi'an, China

Urban heat islands (UHIs) are the phenomenon of urban regions usually being warmer than rural regions, which significantly impacts both the regional ecosystem and societal activities. Numerical simulation can provide spatially and temporally continuous datasets for UHI analysis. In this study, a spatially and temporally continuous ground temperature dataset of Xi'an, China was obtained through numerical simulation based on the Community Land Model version 4.5 (CLM4.5), at a temporal resolution of 30 min and a spatial resolution of 0.05^∘× 0.05^∘. Based on the ground temperature, the seasonal average UHI intensity (UHII) was calculated and the seasonal variation of the UHI effect was analyzed. The monthly variation tendency of the urban heat stress was also investigated. Based on the diurnal cycle of ground temperature and the UHI effect in each season, the variation tendencies of the maximum, minimum, and average UHII were analyzed. The results show that the urban heat stress in summer is the strongest among all four seasons. The heat stress in urban areas is very significant in July, and the UHII is the weakest in January. Regarding the diurnal cycle of UHII, the maximum always appears at 06:30 UTC to 07:30 UTC, while the minimum intensity of the UHI effect occurs at different times in the different seasons. The results of this study could provide a reference for policymakers about how to reduce the damage caused by heat stress.

The Heat Exposure Integrated Deprivation Index (HEIDI): A data-driven approach to quantifying neighborhood risk during extreme hot weather

Mortality attributable to extreme hot weather is a growing concern in many urban environments, and spatial heat vulnerability indexes are often used to identify areas at relatively higher and lower risk. Three indexes were developed for greater Vancouver, Canada using a pool of 20 potentially predictive variables categorized to reflect social vulnerability, population density, temperature exposure, and urban form. One variable was chosen from each category: an existing deprivation index, senior population density, apparent temperature, and road density, respectively. The three indexes were constructed from these variables using (1) unweighted, (2) weighted, and (3) data-driven Heat Exposure Integrated Deprivation Index (HEIDI) approaches. The performance of each index was assessed using mortality data from 1998-2014, and the maps were compared with respect to spatial patterns identified. The population-weighted spatial correlation between the three indexes ranged from 0.68-0.89. The HEIDI approach produced a graduated map of vulnerability, whereas the other approaches primarily identified areas of highest risk. All indexes performed best under extreme temperatures, but HEIDI was more useful at lower thresholds. Each of the indexes in isolation provides valuable information for public health protection, but combining the HEIDI approach with unweighted and weighted methods provides richer information about areas most vulnerable to heat.

Characterizing prolonged heat effects on mortality in a sub-tropical high-density city, Hong Kong

Extreme hot weather events are likely to increase under future climate change, and it is exacerbated in urban areas due to the complex urban settings. It causes excess mortality due to prolonged exposure to such extreme heat. However, there is lack of universal definition of prolonged heat or heat wave, which leads to inadequacies of associated risk preparedness. Previous studies focused on estimating temperature-mortality relationship based on temperature thresholds for assessing heat-related health risks but only several studies investigated the association between types of prolonged heat and excess mortality. However, most studies focused on one or a few isolated heat waves, which cannot demonstrate typical scenarios that population has experienced. In addition, there are limited studies on the difference between daytime and nighttime temperature, resulting in insufficiency to conclude the effect of prolonged heat. In sub-tropical high-density cities where prolonged heat is common in summer, it is important to obtain a comprehensive understanding of prolonged heat for a complete assessment of heat-related health risks. In this study, six types of prolonged heat were examined by using a time-stratified analysis. We found that more consecutive hot nights contribute to higher mortality risk while the number of consecutive hot days does not have significant association with excess mortality. For a day after five consecutive hot nights, there were 7.99% [7.64%, 8.35%], 7.74% [6.93%, 8.55%], and 8.14% [7.38%, 8.88%] increases in all-cause, cardiovascular, and respiratory mortality, respectively. Non-consecutive hot days or nights are also found to contribute to short-term mortality risk. For a 7-day-period with at least five non-consecutive hot days and nights, there was 15.61% [14.52%, 16.70%] increase in all-cause mortality at lag 0-1, but only -2.00% [-2.83%, -1.17%] at lag 2-3. Differences in the temperature-mortality relationship caused by hot days and hot nights imply the need to categorize prolonged heat for public health surveillance. Findings also contribute to potential improvement to existing heat-health warning system.

Impact of ambient temperature on morbidity and mortality: An overview of reviews

The objectives were (i) to conduct an overview of systematic reviews to summarize evidence from and evaluate the methodological quality of systematic reviews assessing the impact of ambient temperature on morbidity and mortality; and (ii) to reanalyse meta-analyses of cold-induced cardiovascular morbidity in different age groups. The registration number is PROSPERO-CRD42016047179. PubMed, Embase, the Cochrane Library, Web of Science, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Global Health were systematically searched to identify systematic reviews. Two reviewers independently selected studies for inclusion, extracted data, and assessed quality. The Assessment of Multiple Systematic Reviews (AMSTAR) checklist was used to assess the methodological quality of included systematic reviews. Estimates of morbidity and mortality risk in association with heat exposure, cold exposure, heatwaves, cold spells and diurnal temperature ranges (DTRs) were the primary outcomes. Twenty-eight systematic reviews were included in the overview of systematic reviews. (i) The median (interquartile range) AMSTAR scores were 7 (1.75) for quantitative reviews and 3.5 (1.75) for qualitative reviews. (ii) Heat exposure was identified to be associated with increased risk of cardiovascular, cerebrovascular and respiratory mortality, but was not found to have an impact on cardiovascular or cerebrovascular morbidity. (iii) Reanalysis of the meta-analyses indicated that cold-induced cardiovascular morbidity increased in youth and middle-age (RR=1.009, 95% CI: 1.004-1.015) as well as the elderly (RR=1.013, 95% CI: 1.007-1.018). (iv) The definitions of temperature exposure adopted by different studies included various temperature indicators and thresholds. In conclusion, heat exposure seemed to have an adverse effect on mortality and cold-induced cardiovascular morbidity increased in the elderly. Developing definitions of temperature exposure at the regional level may contribute to more accurate evaluations of the health effects of temperature.

Delineation of Spatial Variability in the Temperature-Mortality Relationship on Extremely Hot Days in Greater Vancouver, Canada

BACKGROUND

Climate change has increased the frequency and intensity of extremely hot weather. The health risks associated with extemely hot weather are not uniform across affected areas owing to variability in heat exposure and social vulnerability, but these differences are challenging to map with precision.

OBJECTIVES

We developed a spatially and temporally stratified case-crossover approach for delineation of areas with higher and lower risks of mortality on extremely hot days and applied this approach in greater Vancouver, Canada.

METHODS

Records of all deaths with an extremely hot day as a case day or a control day were extracted from an administrative vital statistics database spanning the years of 1998-2014. Three heat exposure and 11 social vulnerability variables were assigned at the residential location of each decedent. Conditional logistic regression was used to estimate the odds ratio for a 1°C increase in daily mean temperature at a fixed site with an interaction term for decedents living above and below different values of the spatial variables.

RESULTS

The heat exposure and social vulnerability variables with the strongest spatially stratified results were the apparent temperature and the labor nonparticipation rate, respectively. Areas at higher risk had values ≥ 34.4°C for the maximum apparent temperature and ≥ 60% of the population neither employed nor looking for work. These variables were combined in a composite index to quantify their interaction and to enhance visualization of high-risk areas.

CONCLUSIONS

Our methods provide a data-driven framework for spatial delineation of the temperature--mortality relationship by heat exposure and social vulnerability. The results can be used to map and target the most vulnerable areas for public health intervention. Citation: Ho HC, Knudby A, Walker BB, Henderson SB. 2017. Delineation of spatial variability in the temperature-mortality relationship on extremely hot days in greater Vancouver, Canada. Environ Health Perspect 125:66-75; http://dx.doi.org/10.1289/EHP224.