The urban heat island and its impact on heat waves and human health in Shanghai

Fierce Heat and Players’ Health: Examining the View on Japan High School Baseball

A summer high school baseball tournament is held every mid-summer in Koshien Stadium. “Koshien Baseball” is very popular in Japan; however, it faces the problem of extremely high temperatures during games. Thus, high school players are threatened by harsh environmental conditions. For this reason, two Internet surveys were distributed to the same individuals. Then, their views regarding the Koshien tournament before and after the provision of information regarding environmental change in Japan were gathered. Using these data, this study examined how their views changed after being introduced to the information. Compared with their previous views, it was found that (1) respondents were more likely to agree that the management rules of the Koshien tournament should be altered to protect players’ health, and (2) the impact of providing information is greater for female respondents, young respondents, and highly educated respondents. This study provides evidence that the effect of information provision varies according to gender, age, and educational background. However, the mechanism causing this difference has not yet been analyzed. It would be valuable to consider this mechanism in future research.

Effects of Urban Landscape and Sociodemographic Characteristics on Heat-Related Health Using Emergency Medical Service Incidents

It is well known that extremely hot weather causes heat-related health issues. Health problems, especially in urban areas, are becoming increasingly important due to urban heat island effect. Understanding the impact of neighborhood characteristics is important for research into the relationship between thermal environment and human health. The objectives of this study were to explore the urban landscape and sociodemographic characteristics affecting heat-related health and identify spatial inequalities for vulnerable groups. A total of 27,807 heat-related EMS incidents were used at the census block group level (N = 285). We used land cover database and Landsat satellite images for urban landscape variables and used 2019 U.S. Census data for sociodemographic variables. Negative binomial regression was used to identify the neighborhood variables associated with the heat-related EMS incidents in each block group. Heat-related health has been alleviated in block groups with high green areas. However, the negative effects of thermal environments on human health were higher in areas with a high percentage of impervious surface, over 65 years, non-white people, no high school diploma, or unemployment. The results indicate that heat-related health problems can be addressed through prevention strategies for block group variables. Local intervention efforts to solve health issues should be targeted at more vulnerable areas and groups.

On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.

Current and future threats to human health in the Anthropocene

It has been widely recognised that the threats to human health from global environmental changes (GECs) are increasing in the Anthropocene epoch, and urgent actions are required to tackle these pressing challenges. A scoping review was conducted to provide an overview of the nine planetary boundaries and the threats to population health posed by human activities that are exceeding these boundaries in the Anthropocene. The research progress and key knowledge gaps were identified in this emerging field. Over the past three decades, there has been a great deal of research progress on health risks from climate change, land-use change and urbanisation, biodiversity loss and other GECs. However, several significant challenges remain, including the misperception of the relationship between human and nature; assessment of the compounding risks of GECs; strategies to reduce and prevent the potential health impacts of GECs; and uncertainties in fulfilling the commitments to the Paris Agreement. Confronting these challenges will require rigorous scientific research that is well-coordinated across different disciplines and various sectors. It is imperative for the international community to work together to develop informed policies to avert crises and ensure a safe and sustainable planet for the present and future generations.

Estimating Heat-Related Exposures and Urban Heat Island Impacts: A Case Study for the 2012 Chicago Heatwave

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi-layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2-3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat-related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat-related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat-related health impacts.

Rethinking the urban physical environment for century-long lives: from age-friendly to longevity-ready cities

In response to increasing life expectancies and urbanization, initiatives for age-friendly cities seek to facilitate active and healthy aging by strengthening supports and services for older people. While laudable, these efforts typically neglect early-life exposures that influence long-term well-being. With a focus on the urban physical environment, we argue that longevity-ready cities can accomplish more than initiatives focused solely on old age. We review features of cities that cumulatively influence healthy aging and longevity, discuss the need for proactive interventions in a changing climate, and highlight inequities in the ambient physical environment, especially those encountered at early ages, that powerfully contribute to disparities in later life stages. Compared with strategies aimed largely at accommodating older populations, longevity-ready cities would aim to reduce the sources of disadvantages across the life course and simultaneously improve the well-being of older people.

Long-term trend of heat waves and potential effects on phytoplankton blooms in Lake Qiandaohu, a key drinking water reservoir

Global warming is increasing the frequency and duration of heat waves, which is defined as when air temperature exceeds a threshold for more than specific consecutive days. Ecosystem around the globe will be impaired by heat waves just like the exposures to dangerously high temperatures as a public health threat to human. However, the knowledge of the response of lake and reservoir ecosystem to heat waves is largely unknown although it has been argued that climate warming may increase the incidence of harmful algal blooms. We examined the long-term trend of heat waves and how the variability of phytoplankton biomass responds to lake heat waves on a deep reservoir (Lake Qiandaohu). Long-term (1980-2020) meteorological observation in the lake watershed showed a significant warming trend of 0.36 °C per decade for the yearly average of daily average air temperature and the yearly average of daily maximum air temperature of 18.32 °C was observed in 2016. Meanwhile, a significant increasing number of heat wave events lasting longer was observed, and Lake Qiandaohu suffered an unusually severe lake heat wave in summer 2016. Significant correlations were found between the yearly average of daily maximum air temperature and heat days, heat wave events, and heat wave days. Nuisance phytoplankton bloom was found in Lake Qiandaohu by high frequency observation and remote sensing monitoring in summer 2016. Remote sensing estimation from two Landsat 8 Operational Land Imager (OLI) images showed that the average chlorophyll a (Chla) was 7.45 ± 4.89 μg/L on July 18 before heat wave and 18.96 ± 0.98 μg/L on August 19 during the heat wave. Two heat wave events lasting from July 20 to August 2 and August 11 to 26 with average surface water temperature of 29.93 and 31.99 °C promoted two marked phytoplankton blooms with average Chla concentrations of 11.75 ± 4.08 and 10.53 ± 1.65 μg/L in the central lake region, respectively, as evidenced by high-frequency buoy data. These findings suggest that heat waves are likely to yield an increased threat of harmful algal bloom in freshwater ecosystems. With lake heat waves projected to increase in frequency, duration, and spatial extent with global climate change, more studies are needed to improve our understanding of lake heat waves and their potential effects on the species, communities, frequency of phytoplankton bloom, and also help providing advanced schemes of water quality management.

Spatial and Temporal Analysis of Surface Urban Heat Island and Thermal Comfort Using Landsat Satellite Images between 1989 and 2019: A Case Study in Tehran

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.

Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression

The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers.

Urban heat: an increasing threat to global health

Shilu Tong and colleagues describe the health consequences of extreme urban heat and the priorities for action and research to mitigate the harms

Impact of heat waves and cold spells on cause-specific mortality in the city of São Paulo, Brazil

The impact of heat waves and cold spells on mortality has become a major public health problem worldwide, especially among older adults living in low-to middle-income countries. This study aimed to investigate the effects of heat waves and cold spells under different definitions on cause-specific mortality among people aged ≥65 years in São Paulo from 2006 to 2015. A quasi-Poisson generalized linear model with a distributed lag model was used to investigate the association between cause-specific mortality and extreme air temperature events. To evaluate the effects of the intensity under different durations, we considered twelve heat wave and nine cold spell definitions. Our results showed an increase in cause-specific deaths related to heat waves and cold spells under several definitions. The highest risk of death related to heat waves was identified mostly at higher temperature thresholds with longer events. We verified that men were more vulnerable to die from cerebrovascular diseases and ischemic stroke on cold spells and heat waves days than women, while women presented a higher risk of dying from ischemic heart diseases during cold spells and tended to have a higher risk of chronic obstructive pulmonary disease than men during heat waves. Identification of heat wave- and cold spell-related mortality is important for the development and promotion of public health measures.

Beyond intensity of urban heat island effect: A continental scale analysis on land surface temperature in major Chinese cities

Intensity of urban heat island (UHII) is a key indicator to quantify the magnitude of UHI effect. Change in UHII, however, can be affected by change in urban or rural temperature, or both. Numerous studies have investigated the change of UHII, but how it relates to change in urban and rural temperature is poorly understood. Here we present a continental scale analysis on the change in UHII from 2000 to 2015 in 31 major cities in China, and further explored whether such change was caused by urban warming. We used time series analysis based on MODIS land surface temperature product (MOD11A2) in summer. We found: (1) A majority of the cities (25 out of 31) had an increasing trend in UHII from 2000 to 2015, among which 44% were significant, suggesting a significantly intensified UHI effects. (2) Intensified UHII does not necessarily mean deterioration of urban thermal environment, and vice versa. For the 25 cities with an increasing trend in UHII, 11 cities had a decreasing trend of LST in both urban and rural areas. Meanwhile, for the 6 cities with a decreasing trend in UHII, three had warming trend in both urban and rural areas. (3) The attribution of changes in UHII to that of urban and rural LST indicated that it is necessary to quantify the change of LST in both urban and rural areas to fully quantify the impact of urbanization on UHI effect.

An urban climate-based empirical model to predict present and future patterns of the Urban Thermal Signal

Air temperature is a key aspect of urban environmental health, especially considering population and climate change prospects. While the urban heat island (UHI) effect may aggravate thermal exposure, city-level UHI regression studies are generally restricted to temporal-aggregated intensities (e.g., seasonal), as a function of time-fixed factors (e.g., urban density). Hence, such approaches do not disclose daily urban-rural air temperature changes, such as during heatwaves (HW). Here, summer data from Lisbon's air temperature urban network (June to September 2005-2014), is used to develop a linear mixed-effects model (LMM) to predict the daily median and maximum Urban Thermal Signal (UTS) intensities, as a response to the interactions between the time-varying background weather variables (i.e., the regional/non-urban air temperature, 2-hours air temperature change, and wind speed), and time-fixed urban and geographic factors (local climate zones and directional topographic exposure). Results show that, in Lisbon, greatest temperatures and UTS intensities are found in 'Compact' areas of the city are proportional to the background air temperature change. In leeward locations, the UTS can be enhanced by the topographic shelter effect, depending on wind speed - i.e., as wind speed augments, the UTS intensity increases in leeward sites, even where sparsely built. The UTS response to a future urban densification scenario, considering climate change HW conditions (RCP8.5, 2081-2100 period), was also assessed, its results showing an UTS increase of circa 1.0 °C, in critical areas of the city, despite their upwind location. This LMM empirical approach provides a straightforward tool for local authorities to: (i) identify the short-term critical areas of the city, to prioritise public health measures, especially during HW events; and (ii) test the urban thermal performance, in response to climate change and urban planning scenarios. While the model coefficient estimates are case-specific, the approach can be efficiently replicated in other locations with similar biogeographic conditions.

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus and Web of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.

Effects of Land Use on Land Surface Temperature: A Case Study of Wuhan, China

In this study, we aim to understand the impact of land use on the urban heat island (UHI) effect across an urban area. Considering the case study of Wuhan, China, land use factors and land surface temperatures (LSTs) of 589 planning management units were quantified in order to identify the spatial autocorrelation of LST, which indicated that a traditional regression would be invalid. By investigating the relationships between land use factors and the LST in summer, based on spatial regression models including the spatial lag model and the spatial error model, four conclusions were derived. First, the spatial error model effectively explains the relationships between LST and land use factors. Second, the impact on LST of the percentage of industrial areas is significant even though the impacts of land cover and building-group morphology indicators are combined, indicating that anthropogenic heat emission of industrial production contributes to high LSTs. Third, the relationship between the percentage of commercial area and LST is significant in the Pearson correlation analysis and traditional regression models, while not significant in spatial error model, suggesting that the urban heat environment of a commercial area is determined by the land use factors of the surrounding area. Fourth, the UHI effect in industrial and commercial areas could be precisely mitigated by not locating industrial areas beside residential areas, and setting up buffer zones between commercial areas and surrounding traditional residential areas. Overall, the results of this study innovatively deepen the understanding of the impact of the percentage of different urban land use types on the urban heat environment at the scale of planning management units, which is conducive to formulating precise regulation measures for mitigating UHI effects and improving public health.

Effect of non-optimum ambient temperature on cognitive function of elderly women in Germany

Non-optimum ambient temperature has been associated with a variety of health outcomes in the elderly population. However, few studies have examined its adverse effects on neurocognitive function. In this study, we explored the temperature-cognition association in elderly women. We investigated 777 elderly women from the German SALIA cohort during the 2007-2010 follow-up. Cognitive function was evaluated using the CERAD-Plus test battery. Modelled data on daily weather conditions were assigned to the residential addresses. The temperature-cognition association over lag 0-10 days was estimated using multivariable regression with distributed lag non-linear model. The daily mean temperature ranged between -6.7 and 26.0 °C during the study period for the 777 participants. We observed an inverse U-shaped association in elderly women, with the optimum temperature (15.3 °C) located at the 68th percentile of the temperature range. The average z-score of global cognitive function declined by -0.31 (95%CI: 0.73, 0.11) for extreme cold (the 2.5th percentile of temperature range) and -0.92 (95%CI: 1.50, -0.33) for extreme heat (the 97.5th percentile of temperature range), in comparison to the optimum temperature. Episodic memory was more sensitive to heat exposure, while semantic memory and executive function were the two cognitive domains sensitive to cold exposure. Individuals living in an urban area and those with a low educational level were particularly sensitive to extreme heat. In summary, non-optimum temperature was inversely associated with cognitive function in elderly women, with the effect size for heat exposure particularly substantial. The strength of association varied by cognitive domains and individual characteristics.

Comparative Analysis of Variations and Patterns between Surface Urban Heat Island Intensity and Frequency across 305 Chinese Cities

Urban heat island (UHI), referring to higher temperatures in urban extents than its surrounding rural regions, is widely reported in terms of negative effects to both the ecological environment and human health. To propose effective mitigation measurements, spatiotemporal variations and control machines of surface UHI (SUHI) have been widely investigated, in particular based on the indicator of SUHI intensity (SUHII). However, studies on SUHI frequency (SUHIF), an important temporal indicator, are challenged by a large number of missing data in daily land surface temperature (LST). Whether there is any city with strong SUHII and low SUHIF remains unclear. Thanks to the publication of daily seamless all-weather LST, this paper is proposed to investigate spatiotemporal variations of SUHIF, to compare SUHII and SUHIF, to conduct a pattern classification, and to further explore their driving factors across 305 Chinese cities. Four main findings are summarized below: (1) SUHIF is found to be higher in the south during the day, while it is higher in the north at night. Cities within the latitude from 20° N and 40° N indicate strong intensity and high frequency at day. Climate zone-based variations of SUHII and SUHIF are different, in particular at nighttime. (2) SUHIF are observed in great diurnal and seasonal variations. Summer daytime with 3.01 K of SUHII and 80 of SUHIF, possibly coupling with heat waves, increases the risk of heat-related diseases. (3) K-means clustering is employed to conduct pattern classification of the selected cities. SUHIF is found possibly to be consistent to its SUHII in the same city, while they provide quantitative and temporal characters respectively. (4) Controls for SUHIF and SUHII are found in significant variations among temporal scales and different patterns. This paper first conducts a comparison between SUHII and SUHIF, and provides pattern classification for further research and practice on mitigation measurements.

Evolution of the minimum mortality temperature (1983-2018): Is Spain adapting to heat?

The objective of this study was to analyze at the level of Spain's 52 provinces province level the temporal evolution of minimum mortality temperatures (MMT) from 1983 to 2018, in order to determine whether the increase in MMT would be sufficient to compensate for the increase in environmental temperatures in Spain for the period. It also aimed to analyze whether the rate of evolution of MMT would be sufficient, were it to remain constant, to compensate for the predicted increase in temperatures in an unfavorable (RCP 8.5) emissions scenario for the time horizon 2051-2100. The independent variable was made up of maximum daily temperature data (Tmax) for the summer months in the reference observatories of each province for the 1983-2018 period. The dependent variable was daily mortality rate due to natural causes (ICD 10: A00-R99). For each year and province, MMT was determined using a quadratic or cubic fit (p < 0.05). Based on the annual MMT values, a linear fit was carried out that allowed for determining the time evolution of MMT. These values were compared with the evolution of Tmax registered in each observatory during the 1983-2018 analyzed period and with the predicted values of Tmax obtained for an RCP8.5 scenario for the period 2051-2100. The rate of global variance in Tmax in the summer months in Spain during the 1983-2018 period was 0.41 °C/decade, while MMT across the whole country increased at a rate of 0.64 °C/decade. Variations in the provinces were heterogeneous. For the 2051-2100 time horizon, there was predicted increase in Tmax values of 0.66 °C/decade, with marked geographical differences. Although at the global level it is possible to speak of adaptation, the heterogeneities among the provinces suggest that the local level measures are needed in order to facilitate adaptation in those areas where it is not occurring.

Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology

Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Environmental heat-related health symptoms among community in a tropical city

Due to the changing climate, more frequent and prolonged heatwaves are expected to have a catastrophic consequence on urban human settlement. In tropical cities such as Kuala Lumpur (KL), the quality of the urban environment is made worse by urban heat island (UHI) phenomena due to poor urban planning practices. The prolonged exposure to urban heat is hypothesized to influence human health and well-being, especially in tropical urban areas with high population density. Therefore, a study was conducted to understand the association of urban heat stress with physical, psychosomatic and psychological (PPP) health symptoms within a tropical urban setting. Continuous urban microclimate monitoring is conducted using an automated weather station to define the level of heat stress in the study area expressed as Physiological Equivalent Temperature (PET). A cross-sectional approach is used to identify heat-related health symptoms experienced by the urban population. Through exploratory factor analysis, a total of 38 PPP health symptoms are reduced into 8 heat-related health clusters which are sensory organ pain, heat-related illnesses, cardiopulmonary, pain, fatigue, anxiety, somatization, and depression-related symptoms. Heat stress was found to significantly affect psychosomatic pain (p = 0.016) as well as psychological anxiety (p = 0.022) and somatization (p = 0.041) related symptoms. Other health clusters were not significantly associated with heat stress. More studies are needed to unravel the influence of confounding factors and the long-term impact of urban heat on the health and well-being of the urban population in a tropical city.

Impact of Land Cover Composition and Structure on Air Temperature Based on the Local Climate Zone Scheme in Hangzhou, China

At present, conflicts between urban development and the climate environment are becoming increasingly apparent under rapid urbanization in China. Revealing the dynamic mechanism and controlling factors of the urban outdoor thermal environment is the necessary theoretical preparation for regulating and improving the urban climate environment. Taking Hangzhou as an example and based on the local climate zones classification system, we investigated the effects of land cover composition and structure on temperature variability at the local scale. The measurement campaign was conducted within four local climate zones (LCZ 2, 4, 5, and LCZ 9) during 7 days in the summer of 2018. The results showed that the temperature difference within the respective LCZ was always below 1.1 °C and the mean temperature difference between LCZs caused by different surface physical properties was as high as 1.6 °C at night. Among four LCZs, LCZ 2 was always the hottest, and LCZ 9 was the coolest at night. In particular, the percentage of pervious surface was the most important land cover feature in explaining the air temperature difference. For both daytime and nighttime, increasing the percentage of pervious surface as well as decreasing the percentage of impervious surface and the percentage of building surface could lower the local temperature, with the strongest influence radius range from 120 m to 150 m. Besides, the temperature increased with the SVF increased at day and opposite at night.

A Mobile Vehicle-Based Methodology for Dynamic Microclimate Analysis

Accurate monitoring is critical for better understanding cities' microclimate conditions and safeguard citizens' health. Previous studies have performed microclimate analyses of urbanized areas by implementing data from either stable meteorological stations or satellites, or by developing mobile stations. Here, we present a vehicle-based monitoring campaign capable of monitoring both the scalar and directionally dependent variables that regulate the canopy layer environment. Under this framework, we performed a monitoring campaign within an Italian city, and measured simultaneously air temperature (±  0.3 ∘ C @ 20 ∘ C ), relative humidity (±  2% @ 20 ∘ C ), directional shortwave radiation (calibration uncertainty: < 1.8%), CO 2 (±  50 ppm +2%) and PM10 (< 1%) concentration, wind speed (±  3% @ 40 m/s) and direction (±  3 ∘ @ 40 m/s), and specific location (latitude, longitude and elevation). The presented assessment can be carried out within almost any area that motorized vehicles are allowed to access (e.g. through public transportation vehicles). Its application together with other mobile stations that can specifically assess also pedestrian areas, such as footpaths, urban parks, sidewalks and bike paths, as well as fixed meteorological stations and remote sensing techniques can contribute to a better understanding of microclimate patterns and hence to more efficient urban planning and risk assessments.

Spatial Modeling and Analysis of Heat-Related Morbidity in Maricopa County, Arizona

The objective of the present study was to examine the effects of a confluence of demographic, socioeconomic, housing, and environmental factors that systematically contribute to heat-related morbidity in Maricopa County, Arizona, from theoretical, empirical, and spatial perspectives. The present study utilized ordinary least squares (OLS) regression and multiscale geographically weighted regression (MGWR) to analyze health data, U.S. census data, and remotely sensed data. The results suggested that the MGWR model showed a significant improvement in goodness of fit over the OLS regression model, which implies that spatial heterogeneity is an essential factor that influences the relationship between these factors. Populations of people aged 65+, Hispanic people, disabled people, people who do not own vehicles, and housing occupancy rate have much stronger local effects than other variables. These findings can be used to inform and educate local residents, communities, stakeholders, city managers, and urban planners in their ongoing and extensive efforts to mitigate the negative impacts of extreme heat on human health in Maricopa County.

Technological opportunities for sensing of the health effects of weather and climate change: a state-of-the-art-review

Sensing and measuring meteorological and physiological parameters of humans, animals, and plants are necessary to understand the complex interactions that occur between atmospheric processes and the health of the living organisms. Advanced sensing technologies have provided both meteorological and biological data across increasingly vast spatial, spectral, temporal, and thematic scales. Information and communication technologies have reduced barriers to data dissemination, enabling the circulation of information across different jurisdictions and disciplines. Due to the advancement and rapid dissemination of these technologies, a review of the opportunities for sensing the health effects of weather and climate change is necessary. This paper provides such an overview by focusing on existing and emerging technologies and their opportunities and challenges for studying the health effects of weather and climate change on humans, animals, and plants.

Disproportionate exposure to urban heat island intensity across major US cities

Urban heat stress poses a major risk to public health. Case studies of individual cities suggest that heat exposure, like other environmental stressors, may be unequally distributed across income groups. There is little evidence, however, as to whether such disparities are pervasive. We combine surface urban heat island (SUHI) data, a proxy for isolating the urban contribution to additional heat exposure in built environments, with census tract-level demographic data to answer these questions for summer days, when heat exposure is likely to be at a maximum. We find that the average person of color lives in a census tract with higher SUHI intensity than non-Hispanic whites in all but 6 of the 175 largest urbanized areas in the continental United States. A similar pattern emerges for people living in households below the poverty line relative to those at more than two times the poverty line.

Urban Heat Island Effects on Megacities in Desert Environments Using Spatial Network Analysis and Remote Sensing Data: A Case Study from Western Saudi Arabia

Contemporary cities continue to face significant geoenvironmental challenges due to constant rapid urbanization. Furthermore, the governments of cities worldwide are considering the green cities approach to convert their cities’ weaknesses into opportunities. The 2030 Saudi vision supports smart growth concepts, with a vision of speeding up economic growth while ensuring that natural assets strengthen the country’s foundations. The urban heat island (UHI) effect is a threatening phenomenon that increases the required cooling loads and negatively affects urban communities and the quality of life, especially in arid environments. This study integrates remote sensing and spatial network analysis to investigate the UHI using the distribution of land surface temperatures (LST) extracted from satellite data during both winter and summer seasons in Makkah city. We investigated and compared the UHIs in two districts, Al-Sharashef and AlEskan, representing the organic and deformed iron-grid with fragmented paralleled street networks, respectively. The spatial analysis of different LST maps, which were derived from Landsat-8 images revealed significant differences between the two case studies. The mean temperature for the AlEskan district was 1–1.5 °C higher than that of the Al-Sharshaf district. This difference can be attributed to the different urban fabrics between the two districts. Moreover, the zones that are currently under construction show relatively higher LST compared to residential zones. The research revealed that the organic/compact urban fabric is better than the deformed iron-grid urban fabric in mitigating the UHI. However, these results are specific to the test site; however, they emphasize the role of integration of remote sensing and spatial network analysis in urban planning. In light of these findings, we recommend integrating remote sensing-based LST analysis with spatial analysis of urban fabrics to better understand the causal effects of UHI, especially in cities located in desert environments. This can help mitigate the impact of projected global warming and contribute to improving the quality of urban life.

A human-centred assessment framework to prioritise heat mitigation efforts for active travel at city scale

Hot weather not only impacts upon human physical comfort and health, but also impacts the way that people access and experience active travel options such as walking and cycling. By evaluating the street thermal environment of a city alongside an assessment of those communities that are the most vulnerable to the effects of heat, we can prioritise areas in which heat mitigation interventions are most needed. In this paper, we propose a new approach for policy makers to determine where to delegate limited resources for heat mitigation with most effective outcomes for the communities. We use eye-level street panorama images and community profiles to provide a bottom-up, human-centred perspective of the city scale assessment, highlighting the situation of urban tree shade provision throughout the streets in comparison with environmental and social-economic status. The approach leverages multiple sources of spatial data including satellite thermal images, Google street view (GSV) images, land use and demographic census data. A deep learning model was developed to automate the classification of streetscape types and percentages at the street- and eye-view level. The methodology is metrics based and scalable which provides a data driven assessment of heat-related vulnerability. The findings of this study first contribute to sustainable development by developing a method to identify geographical areas or neighbourhoods that require heat mitigation; and enforce policies improving tree shade on routes, as a heat adaptation strategy, which will lead to increasing active travel and produce significant health benefits for residents. The approach can be also used to guide post COVID-19 city planning and design.

Spatiotemporal variation in urban overheating magnitude and its association with synoptic air-masses in a coastal city

Urban overheating (UO) may interact with synoptic-scale weather conditions. The association between meteorological parameters and UO has already been a subject of considerable research, however, the impact of synoptic-scale weather conditions on UO magnitude, particularly in a coastal city that is also near the desert landmass (Sydney) has never been investigated before. The present research examines the influence of synoptic-scale weather conditions on UO magnitude in Sydney by utilizing the newly developed gridded weather typing classification (GWTC). The diurnal, and seasonal variations in suburban-urban temperature contrast (ΔT) in association with synoptic-scale weather conditions, and ΔT response to synoptic air-masses during extreme heat events are investigated in three zones of Sydney. Generally, an exacerbation in UO magnitude was reported at daytime over the years, whereas the nocturnal UO magnitude was alleviated over time. The humid warm (HW), and warm (W) air-masses were found primarily responsible for exacerbated daytime UO during extreme heat events and in all other seasons, raising the mean daily maximum ΔT to 8-10.5 °C in Western Sydney, and 5-6.5 °C in inner Sydney. The dry warm (DW), and W conditions were mainly responsible for urban cooling (UC) at nighttime, bringing down the mean daily minimum ΔT to - 7.5 to - 10 °C in Western Sydney, and - 6 to - 7.5 °C in inner Sydney. The appropriate mitigation technologies can be planned based on this study to alleviate the higher daytime temperatures in the Sydney suburbs.

Synergetic interaction between spatial land cover dynamics and expanding urban heat islands

Inherent statistics of the surface temperature pattern were used to categorize urban heat islands (UHIs) for a tropical mega city and its satellite towns. Application of flexible threshold values for UHI zoning made this procedure independent of seasonal or locational influences. UHI zones for the years 1999, 2009, and 2019 were mapped from Landsat thermal bands by applying the mono-window algorithm. The parameters affecting the UHI intensity were rigorously investigated. Dynamics of land use land cover patterns provided in-depth insight into the spatiotemporal variability of UHI. The abrupt rises in localized surface temperature for every decade were recognized and thoroughly explained with the fall in fractional vegetation cover index and increase in normalized difference impervious surface index. The temporal nature of urban agglomeration and fragmentation of vegetation cover was quantified through landscape metric algorithms. The vegetation pattern and associated surface temperature fall were further used to evaluate the weakening of UHI intensity around major recreational zones. Substantial cooling by 0.938 °C was noted on daytime, from urban built-up at a 50-m distance to the green parks. Differential rates of urbanization and associated magnification of UHIs were looked into separately for central urban and satellite town areas. The characteristics of built-up density and proximity to green areas were employed to strategize mitigatory measures for the constantly growing UHI problem. Urgent needs for sustainable policies and green landscaping were highlighted through multi-criterion analysis.

Heatwaves and Summer Urban Heat Islands: A Daily Cycle Approach to Unveil the Urban Thermal Signal Changes in Lisbon, Portugal

Lisbon is a European Mediterranean city, greatly exposed to heatwaves (HW), according to recent trends and climate change prospects. Considering the Atlantic influence, air temperature observations from Lisbon’s mesoscale network are used to investigate the interactions between background weather and the urban thermal signal (UTS) in summer. Days are classified according to the prevailing regional wind direction, and hourly UTS is compared between HW and non-HW conditions. Northern-wind days predominate, revealing greater maximum air temperatures (up to 40 °C) and greater thermal amplitudes (approximately 10 °C), and account for 37 out of 49 HW days; southern-wind days have milder temperatures, and no HWs occur. Results show that the wind direction groups are significantly different. While southern-wind days have minor UTS variations, northern-wind days have a consistent UTS daily cycle: a diurnal urban cooling island (UCI) (often lower than –1.0 °C), a late afternoon peak urban heat island (UHI) (occasionally surpassing 4.0 °C), and a stable nocturnal UHI (1.5 °C median intensity). UHI/UCI intensities are not significantly different between HW and non-HW conditions, although the synoptic influence is noted. Results indicate that, in Lisbon, the UHI intensity does not increase during HW events, although it is significantly affected by wind. As such, local climate change adaptation strategies must be based on scenarios that account for the synergies between potential changes in regional air temperature and wind.

A Comparative Analysis of Different Future Weather Data for Building Energy Performance Simulation

The building energy performance pattern is predicted to be shifted in the future due to climate change. To analyze this phenomenon, there is an urgent need for reliable and robust future weather datasets. Several ways for estimating future climate projection and creating weather files exist. This paper attempts to comparatively analyze three tools for generating future weather datasets based on statistical downscaling (WeatherShift, Meteonorm, and CCWorldWeatherGen) with one based on dynamical downscaling (a future-typical meteorological year, created using a high-quality reginal climate model). Four weather datasets for the city of Rome are generated and applied to the energy simulation of a mono family house and an apartment block as representative building types of Italian residential building stock. The results show that morphed weather files have a relatively similar operation in predicting the future comfort and energy performance of the buildings. In addition, discrepancy between them and the dynamical downscaled weather file is revealed. The analysis shows that this comes not only from using different approaches for creating future weather datasets but also by the building type. Therefore, for finding climate resilient solutions for buildings, care should be taken in using different methods for developing future weather datasets, and regional and localized analysis becomes vital.

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.

Quantitative Analysis of Spatial Heterogeneity and Driving Forces of the Thermal Environment in Urban Built-up Areas: A Case Study in Xi’an, China

Clarifying the spatial heterogeneity of urban heat island (UHI) effect is of great significance for promoting sustainable urban development. A GeoDetector was used to detect the influential natural and society factors. Natural factors (normalized difference vegetation index (NDVI), soil-regulating vegetation index (SAVI), normalized building index (NDBI), and modified normalized difference water index (MNDWI)) as well as society factors (road density (RDD), and population density (POPD)) were selected as driving factors to be tested for their explanatory power for land surface temperature (LST). Results indicated that the Moran’s I index value for the LST of the built-up area is 0.778. The top three factors influencing the LST were NDBI, NDVI, and SAVI, the explanatory power of which was 0.7593, 0.6356, and 0.6356, respectively. The interactive explanatory power for NDBI and MNDWI was 0.8108 and for NDBI and RDD was 0.8002, these two interactions are double enhanced interaction relationships. The results of this study play a guiding role in the development of urban thermal environment regulation schemes and ecological environment planning.

Evaluating the Urban Canopy Scheme TERRA_URB in the COSMO Model for Selected European Cities

The increase in built surfaces constitutes the main reason for the formation of the Urban Heat Island (UHI), that is a metropolitan area significantly warmer than its surrounding rural areas. The urban heat islands and other urban-induced climate feedbacks may amplify heat stress and urban flooding under climate change and therefore to predict them correctly has become essential. Currently in the COSMO model, cities are represented by natural land surfaces with an increased surface roughness length and a reduced vegetation cover, but this approach is unable to correctly reproduce the UHI effect. By increasing the model resolution, a representation of the main physical processes that characterize the urban local meteorology should be addressed, in order to better forecast temperature, moisture and precipitation in urban environments. Within the COSMO Consortium a bulk parameterization scheme (TERRA_URB or TU) has been developed. It parametrizes the effects of buildings, streets and other man-made impervious surfaces on energy, moist and momentum exchanges between the surface and atmosphere, and additionally accounts for the anthropogenic heat flux as a heat source from the surface to the atmosphere. TU implements an impervious water-storage parameterization, and the Semi-empirical Urban canopy parametrization (SURY) that translates 3D urban canopy into bulk parameters. This paper presents evaluation results of the TU scheme in high-resolution simulations with a recent COSMO model version for selected European cities, namely Turin, Naples and Moscow. The key conclusion of the work is that the TU scheme in the COSMO model reasonably reproduces UHI effect and improves air temperature forecasts for all the investigated urban areas, despite each city has very different morphological characteristics. Our results highlight potential benefits of a new turbulence scheme and the representation of skin-layer temperature (for vegetation) in the model performance. Our model framework provides perspectives for enhancing urban climate modelling, although further investigations in improving model parametrizations, calibration and the use of more realistic urban canopy parameters are needed.

Global Population Exposed to Extreme Events in the 150 Most Populated Cities of the World: Implications for Public Health

Climate change driven increases in the frequency of extreme heat events (EHE) and extreme precipitation events (EPE) are contributing to both infectious and non-infectious disease burden, particularly in urban city centers. While the share of urban populations continues to grow, a comprehensive assessment of populations impacted by these threats is lacking. Using data from weather stations, climate models, and urban population growth during 1980–2017, here, we show that the concurrent rise in the frequency of EHE, EPE, and urban populations has resulted in over 500% increases in individuals exposed to EHE and EPE in the 150 most populated cities of the world. Since most of the population increases over the next several decades are projected to take place in city centers within low- and middle-income countries, skillful early warnings and community specific response strategies are urgently needed to minimize public health impacts and associated costs to the global economy.

Localized synergies between heat waves and urban heat islands: Implications on human thermal comfort and urban heat management

Heat waves (HWs) and urban heat islands (UHIs) can potentially interact. The mechanisms behind their synergy are not fully disclosed. Starting from the localized UHI phenomenon, this study aims i) to reveal their associated impacts on human thermal comfort through three different definitions of HW events, based on air temperature (airT), wet-bulb globe temperature (WBGT) and human-perceived temperature (AppT) respectively, and ii) to understand the role of air moisture and wind. The analysis was conducted in four districts (NH, JD, MH and XJH) with different urban development patterns and geographic conditions, in the megacity of Shanghai with a subtropical humid climate. Results evidenced the localized interplay between HWs and UHIs. The results indicate that less urbanized districts were generally more sensitive to the synergies. JD district recorded the highest urban heat island intensity (UHII) amplification, regardless of the specific HW definition. Notably, during AppT-HWs, the increment was observed in terms of maximum (1.3 °C), daily average (0.8 °C), diurnal (0.4 °C) and nocturnal UHII (1.0 °C). Nevertheless, localized synergies between HWs and UHIs at different stations also exhibited some commonalities. Under airT-HW, the UHII was amplified throughout the day at all stations. Under WBGT-HW, diurnal UHII (especially at 11:00-17:00 LST) was consistently amplified at all stations. Under AppT-HW conditions, the nocturnal UHII was slightly amplified at all stations. Air moisture and wind alleviated the synergistic heat exacerbation to the benefit of thermal comfort. The extent depended on geographic condition, diurnal and nocturnal scenarios, temperature type and HW/normal conditions. Stronger HW-UHI synergies indicate the necessity to develop specific urban heat emergency response plans, able to capture and intervene on the underlying mechanisms. This study paves to way to their identification.

Impact of environmental changes on Dermatology

Urbanization, pollution and the modification of natural landscapes are characteristics of modern society, where the change in human relations with the environment and the impact on biodiversity are environmental determinants that affect the health-disease relationship. The skin is an organ that has a strong interface with the environment and, therefore, the prevalence patterns of dermatoses may reflect these environmental changes. In this article, aspects related to deforestation, fires, urbanization, large-scale agriculture, extensive livestock farming, pollution and climatic changes are discussed regarding their influence on the epidemiology of skin diseases. It is important that dermatologists be aware of their social responsibility in order to promote sustainable practices in their community, in addition to identifying the impacts of environmental imbalances on different dermatoses, which is essential for the prevention and treatment of these diseases.

A Regression Analysis of the Carbon Footprint of Megacities

Urbanization and climate change are two major issues that humanity faces in the 21st century. Megacities are large urban agglomerations with more than 10 million inhabitants that emerged in the 20th century. The world’s top 100 economies include many North and South American megacities, such as New York, Los Angeles, Mexico City, Sao Paulo and Buenos Aires; European cities such as London and Paris; and Asian cities such as Tokyo, Osaka, Seoul, Beijing and Mumbai. This paper addresses a dearth of megacity energy metabolism models in the literature. Cross-sectional data for 36 global megacities were collected from many literature and Internet sources. Variables included megacity name, country and region; population; area; population density; (per capita) GDP; income inequality measures; (per capita) energy consumption; household electricity prices; (per capita) carbon and ecological footprint; degree days; average urban heat island intensity; and temperature and precipitation. A descriptive comparison of the characteristics of megacities was followed by ordinary least squares with heteroskedasticity-robust standard errors that were used to estimate four alternative multiple regression models. The per-capita carbon footprint of megacities was positively associated with the megacity GDP per capita, and the megacity ecological footprint; and negatively associated with country income inequality, a low-income country dummy, the country household electricity price, and the megacity annual precipitation. Targeted policies are needed, but more policy autonomy should be left to megacities. Collecting longitudinal data for megacities is very challenging but should be a next step to overcome misspecification and bias issues that plague cross-sectional approaches.

Adherence of Healthcare Workers to Saudi Management Guidelines of Heat-Related Illnesses during Hajj Pilgrimage

Heat-related illnesses (HRIs), such as heatstroke (HS) and heat exhaustion (HE), are common complications during Hajj pilgrims. The Saudi Ministry of Health (MoH) developed guidelines on the management of HRIs to ensure the safety of all pilgrims. This study aimed to assess healthcare workers' (HCWs) adherence to the updated national guidelines regarding pre-hospital and in-hospital management of HRIs. This was a cross-sectional study using a questionnaire based on the updated HRI management interim guidelines for the Hajj season. Overall, compliance with HE guidelines scored 5.5 out of 10 for basic management and 4.7 out of 10 for advanced management. Medical staff showed an average to above average adherence to pre-hospital HS management, including pre-hospital considerations (7.2), recognition of HS (8.1), case assessment (7.7), stabilizing airway, breathing, and circulation (8.7), and cooling (5). The overall compliance to in-hospital guidelines for HS management were all above average, except for special conditions (4.3). In conclusion, this survey may facilitate the evaluation of the adherence to Saudi HRIs guidelines by comparing annual levels of compliance. These survey results may serve as a tool for the Saudi MoH to develop further recommendations and actions.

Seasonal Variations of Fine Particulate Matter and Mortality Rate in Seoul, Korea with a Focus on the Short-Term Impact of Meteorological Extremes on Human Health

Rapid industrialization of Korea’s economy has brought with it environmental pollution that threatens human health. Among various other pollutants, ambient fine particulate matter known to endanger human health often exceeds air quality standards in Seoul, South Korea’s capital. The goal of this research is to find the impact of meteorological extremes and particle levels on human health. The analysis was conducted using hourly air pollutant concentrations, meteorological variables, and the daily mortality from cerebrovascular disease. Results show that the effect of fine particulate matter on mortality from cerebrovascular disease was more noticeable during meteorological extremes. The linkage between extreme weather conditions and mortality was more apparent in winter than in summer. Comprehensive studies of various causes of diseases should be continued to more accurately analyze the effects of fine particulate matter on human health and meteorological extremes, and to further minimize the public health impact of air pollution and meteorological conditions.

Performance evaluation and microbial community analysis of the biofilter for removing grease and volatile organic compounds in the kitchen exhaust fume

Corncob-based activated carbon has very good adsorption performance and can provide a favourable growing environment for microorganisms. In this study, a biofilter packed with corncob-based activated carbon was constructed to remove grease and total volatile organic compounds (TVOCs) in kitchen exhaust fume. Results show that the biofilter was suitable for the biodegradation of grease and VOCs, and the maximum elimination capacities (EC_max) were 112 and 235 g/(m³·h) at an empty bed residence time of 3.24 s, respectively. When the pH of the filler dropped to 5.0 ± 0.2, the removal efficiencies (RE) of grease and TVOCs in the biofilter decreased to the minimum values (75% and 77%, respectively). The REmax were respectively 88 ± 4% (for TVOC) at 70% filler moisture content and 90 ± 3% (for grease) at 76% filler moisture content. Molecular characterization results showed Thermobacillus sp. as dominating microbial group in the packing media.

Green Infrastructure as an Urban Heat Island Mitigation Strategy—A Review

Research on urban heat mitigation has been growing in recent years with many of the studies focusing on green infrastructure (GI) as a strategy to mitigate the adverse effects of an urban heat island (UHI). This paper aims at presenting a review of the range of findings from GI research for urban heat mitigation through a review of scientific articles published during the years 2009–2020. This research includes a review of the different types of GI and its contribution for urban heat mitigation and human thermal comfort. In addition to analysing different mitigation strategies, numerical simulation tools that are commonly used are also reviewed. It is seen that ENVI-met is one of the modelling tools that is considered as a reliable to simulate different mitigation strategies and hence has been widely used in the recent past. Considering its popularity in urban microclimate studies, this article also provides a review of ENVI-met simulation results that were reported in the reviewed papers. It was observed that the majority of the research was conducted on a limited spatial scale and focused on temperature and human thermal comfort.

Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics

The effects of heat waves (HW) are more pronounced in urban areas than in rural areas due to the additive effect of the urban heat island (UHI) phenomenon. However, the synergies between UHI and HW are still an open scientific question and have only been quantified for a few metropolitan cities. In the current study, we explore the synergies between UHI and HW in Seoul city. We consider summertime data from two non-consecutive years (i.e., 2012 and 2016) and ten automatic weather stations. Our results show that UHI is more intense during HW periods than non-heat wave (NHW) periods (i.e., normal summer background conditions), with a maximum UHI difference of 3.30°C and 4.50°C, between HW and NHW periods, in 2012 and 2016 respectively. Our results also show substantial variations in the synergies between UHI and HW due to land use characteristics and synoptic weather conditions; the synergies were relatively more intense in densely built areas and under low wind speed conditions. Our results contribute to our understanding of thermal risks posed by HW in urban areas and, subsequently, the health risks on urban populations. Moreover, they are of significant importance to emergency relief providers as a resource allocation guideline, for instance, regarding which areas and time of the day to prioritize during HW periods in Seoul.

Assessing Inequitable Urban Heat Islands and Air Pollution Disparities with Low-Cost Sensors in Richmond, Virginia

Air pollution and the urban heat island effect are consistently linked to numerous respiratory and heat-related illnesses. Additionally, these stressors disproportionately impact low-income and historically marginalized communities due to their proximity to emissions sources, lack of access to green space, and exposure to other adverse environmental conditions. Here, we use relatively low-cost stationary sensors to analyze PM2.5 and temperature data throughout the city of Richmond, Virginia, on the ten hottest days of 2019. For both hourly means within the ten hottest days of 2019 and daily means for the entire record for the year, the temperature was found to exhibit a positive correlation with PM2.5. Analysis of hourly means on the ten hottest days yielded a diurnal pattern in which PM2.5 levels peaked in the early morning and reached their minima in the mid-afternoon. Spatially, sites exhibiting higher temperatures consistently had higher PM2.5 readings, with vulnerable communities in the east end and more intensely developed parts of the city experiencing significantly higher temperatures and PM2.5 concentrations than the suburban neighborhoods in the west end. These findings suggest an uneven distribution of air pollution in Richmond during extreme heat events that are similar in pattern but less pronounced than the temperature differences during these events, although further investigation is required to verify the extent of this relationship. As other studies have found both of these environmental stressors to correlate with the distribution of green space and other land-use factors in cities, innovative and sustainable planning decisions are crucial to the mitigation of these issues of inequity going forward.

Nocturnal Surface Urban Heat Island over Greater Cairo: Spatial Morphology, Temporal Trends and Links to Land-Atmosphere Influences

This study assesses the spatial and temporal characteristics of nighttime surface urban heat island (SUHI) effects over Greater Cairo: the largest metropolitan area in Africa. This study employed nighttime land surface temperature (LST) data at 1 km resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua sensor for the period 2003–2019. We presented a new spatial anomaly algorithm, which allowed to define SUHI using the most anomalous hotspot and cold spot of LST for each time step over Greater Cairo between 2003 and 2019. Results demonstrate that although there is a significant increase in the spatial extent of SUHI over the past two decades, a significant decrease in the mean and maximum intensities of SUHI was noted. Moreover, we examined the dependency between SUHI characteristics and related factors that influence energy and heat fluxes between atmosphere and land in urban environments (e.g., surface albedo, vegetation cover, climate variability, and land cover/use changes). Results demonstrate that the decrease in the intensity of SUHI was mainly guided by a stronger warming in daytime and nighttime LST in the neighborhood of urban localities. This warming was accompanied by a decrease in surface albedo and diurnal temperature range (DTR) over these areas. Results of this study can provide guidance to local urban planners and decision-makers to adopt more effective mitigation strategies to diminish the negative impacts of urban warming on natural and human environments.

Influence of Heat Reflective Coating on the Cooling and Pavement Performance of Large Void Asphalt Pavement

To reduce the temperature of asphalt pavement in summer, and alleviate the urban heat island effect, a comprehensive method of combining a heat reflective coating and large void asphalt pavement was proposed. Using the developed coating cooling test equipment, the cooling effect of the coating on a large void asphalt mixture was studied in six different proportions, four different colors, and four different dosages, and the durability of the coating was verified by abrasion tests. Finally, the best dosage of the coating was recommended through an adhesion test of the coating, and a water permeability and anti-skid performance test of the pavement. The results show that the reflectivity of the coating can be improved by adding functional fillers, of titanium dioxide and floating beads, into the coating. The order by reflectivity and cooling effect of the four color coatings was green > red > gray > blue, and the maximum cooling value of the green coating reached 9.7 ℃. The cooling performance of the coating decreased with the increase of wear time, and the rate of decrease was fast, then slow, and finally tended to be stable after 20,000 times wear. The coating reduced the anti-skid performance and the water permeability coefficient of large void asphalt pavement, but still maintained a high level. The green coating with 15% titanium dioxide and 10% floating beads is recommended as the cooling coating for large void asphalt pavement, and its dosage should be controlled at about 0.4–0.8 kg/m2.

Surface Urban Heat Island in Middle City: Spatial and Temporal Characteristics

Currently, cities have aroused the interest of researchers due the local climate change caused by the surface urban heat island (SUHI) effect. The impact of anthropogenic land use and cover changes has led to more frequent intense SUHI, with direct consequences on urban quality of life. Therefore, this research aims at analyzing the influences of natural and anthropogenic variables on the seasonality and spatial SUHI intensity in a Brazilian city, using remote sensing data and analysis of several physical parameters. Results show that the city of São Carlos has an SUHI mosaic and surface urban freshness island (SUFI). On average, 86% of the urban area presented a SUHI, whilst most SUFIs are located near watercourses, parks, slopes and valley bottoms, revealing the effects of green areas and relief on creation of microclimates. The SUHI showed significant seasonal variability.

Influence of Urban Scale and Urban Expansion on the Urban Heat Island Effect in Metropolitan Areas: Case Study of Beijing–Tianjin–Hebei Urban Agglomeration

Global large-scale urbanization has a deep impact on climate change and has brought great challenges to sustainable development, especially in urban agglomerations. At present, there is still a lack of research on the quantitative assessment of the relationship between urban scale and urban expansion and the degree of the urban heat island (UHI) effect, as well as a discussion on mitigation and adaptation of the UHI effect from the perspective of planning. This paper analyzes the regional urbanization process, average surface temperature variation characteristics, surface urban heat island (SUHI), which reflects the intensity of UHI, and the relationship between urban expansion, urban scale, and the UHI in the Beijing–Tianjin–Hebei (BTH) urban agglomeration using multi-source analysis of data from 2000, 2005, 2010, and 2015. The results show that the UHI effect in the study area was significant. The average surface temperature of central areas was the highest, and decreased from central areas to suburbs in the order of central areas > expanding areas > rural residential areas. From the perspective of spatial distribution, in Beijing, the southern part of the study area, the junction of Tianjin, Langfang, and Cangzhou are areas with intense SUHI. The scale and pace of expansion of urban land in Beijing were more than in other cities, the influencing range of SUHI in Beijing increased obviously, and the SUHI of central areas was most intense. The results indicate that due to the larger urban scale of the BTH urban agglomeration, it will face a greater UHI effect. The UHI effect was also more significant in areas of dense distribution in cities within the urban agglomeration. Based on results and existing research, planning suggestions are proposed for central areas with regard to expanding urban areas and suburbs to alleviate the urban heat island effect and improve the resilience of cities to climate change.

Socioeconomic level and associations between heat exposure and all-cause and cause-specific hospitalization in 1,814 Brazilian cities: A nationwide case-crossover study

BACKGROUND

Heat exposure, which will increase with global warming, has been linked to increased risk of a range of types of cause-specific hospitalizations. However, little is known about socioeconomic disparities in vulnerability to heat. We aimed to evaluate whether there were socioeconomic disparities in vulnerability to heat-related all-cause and cause-specific hospitalization among Brazilian cities.

METHODS AND FINDINGS

We collected daily hospitalization and weather data in the hot season (city-specific 4 adjacent hottest months each year) during 2000-2015 from 1,814 Brazilian cities covering 78.4% of the Brazilian population. A time-stratified case-crossover design modeled by quasi-Poisson regression and a distributed lag model was used to estimate city-specific heat-hospitalization association. Then meta-analysis was used to synthesize city-specific estimates according to different socioeconomic quartiles or levels. We included 49 million hospitalizations (58.5% female; median [interquartile range] age: 33.3 [19.8-55.7] years). For cities of lower middle income (LMI), upper middle income (UMI), and high income (HI) according to the World Bank's classification, every 5°C increase in daily mean temperature during the hot season was associated with a 5.1% (95% CI 4.4%-5.7%, P < 0.001), 3.7% (3.3%-4.0%, P < 0.001), and 2.6% (1.7%-3.4%, P < 0.001) increase in all-cause hospitalization, respectively. The inter-city socioeconomic disparities in the association were strongest for children and adolescents (0-19 years) (increased all-cause hospitalization risk with every 5°C increase [95% CI]: 9.9% [8.7%-11.1%], P < 0.001, in LMI cities versus 5.2% [4.1%-6.3%], P < 0.001, in HI cities). The disparities were particularly evident for hospitalization due to certain diseases, including ischemic heart disease (increase in cause-specific hospitalization risk with every 5°C increase [95% CI]: 5.6% [-0.2% to 11.8%], P = 0.060, in LMI cities versus 0.5% [-2.1% to 3.1%], P = 0.717, in HI cities), asthma (3.7% [0.3%-7.1%], P = 0.031, versus -6.4% [-12.1% to -0.3%], P = 0.041), pneumonia (8.0% [5.6%-10.4%], P < 0.001, versus 3.8% [1.1%-6.5%], P = 0.005), renal diseases (9.6% [6.2%-13.1%], P < 0.001, versus 4.9% [1.8%-8.0%], P = 0.002), mental health conditions (17.2% [8.4%-26.8%], P < 0.001, versus 5.5% [-1.4% to 13.0%], P = 0.121), and neoplasms (3.1% [0.7%-5.5%], P = 0.011, versus -0.1% [-2.1% to 2.0%], P = 0.939). The disparities were similar when stratifying the cities by other socioeconomic indicators (urbanization rate, literacy rate, and household income). The main limitations were lack of data on personal exposure to temperature, and that our city-level analysis did not assess intra-city or individual-level socioeconomic disparities and could not exclude confounding effects of some unmeasured variables.

CONCLUSIONS

Less developed cities displayed stronger associations between heat exposure and all-cause hospitalizations and certain types of cause-specific hospitalizations in Brazil. This may exacerbate the existing geographical health and socioeconomic inequalities under a changing climate.