Urban Temperature Research Articles

Investigating the spatial interaction between urban expansion and the regional thermal environment in Guangxi Beibu Gulf urban agglomeration of China

Rapid urbanization and its associated surface modifications play a vital role in generating and intensifying the urban heat island (UHI). Numerous research has investigated the impact of landscape composition and structure on urban land surface temperature (LST), yet the spatial clustering and heterogeneity between urbanization and the regional thermal environment remained largely unexplored. Here, we analyzed land use dynamics and LST variations in the Guangxi Beibu Gulf urban agglomeration (GBG_UA) from 2000 to 2020. The spatial relationships between urban expansion and the regional thermal environment were explored by bivariate spatial autocorrelation and coupling coordination degree analysis. The results demonstrated that the land use in the GBG_UA remained relatively stable, except for the expansion of developed land in urban centers. The area of heat island zones clearly increased, leading to an enhancement of the UHI effect. Urban development and the regional thermal environment exhibited a robust positive spatial autocorrelation. The H–H (High-High) and L-L (Low-Low) types displayed significant aggregation effects. The H–H cluster areas dominated the central urban cores of prefecture-level cities, while the L-L correlation areas were primarily found in mountainous regions. The overall coupling coordination degree was relatively low, with most areas falling into the moderate dissonance level. While it showed a positive shift, with the regions of concordance levels expanding and coupling coordination degree increased. Our study reveals a close spatial correlation between urban sprawl and the UHI effect. The results can offer valuable insights for optimizing the urban green infrastructure to enhance urban heat mitigation and adaptation.

Current and future environmental suitability for bats hosting potential zoonotic pathogens in rural Kenya.

Synanthropic bats live in close proximity to humans and domestic animals, creating opportunities for potential pathogen spillover. We explored environmental correlates of occurrence for a widely distributed synanthropic African bat, Mops pumilus-a species associated with potential zoonotic viruses-and estimated current and future environmental suitability in the Taita Hills region and surrounding plains in Taita-Taveta County in southeast Kenya. To project future environmental suitability, we used four Coupled Model Intercomparison Project Phase 6 general circulation models that capture temperature and precipitation changes for East Africa. The models were parameterized with empirical capture data of M. pumilus collected from 2016 to 2023, combined with satellite-based vegetation, topographic, and climatic data to identify responses to environmental factors. The strongest drivers for current environmental suitability for M. pumilus were short distance to rivers, higher precipitation during the driest months, sparse vegetation-often related to urban areas-and low yearly temperature variation. To predict current and future areas suitable for M. pumilus, we created ensemble niche models, which yielded excellent predictive accuracies. Current suitable environments were located southward from the central and southern Taita Hills and surrounding plains, overlapping with urban centers with the highest human population densities in the area. Future projections for 2050 indicated a moderate increase in suitability range in the southern portion of the region and surrounding plains in human-dominated areas; however, projections for 2090 showed a slight contraction of environmental suitability for M. pumilus, potentially due to the negative impact of increased temperatures. These results show how environmental changes are likely to impact the human exposure risk of bat-borne pathogens and could help public health officials develop strategies to prevent these risks in Taita-Taveta County, Kenya, and other parts of Africa.

Impacts of Changes in Soil Moisture on Urban Heat Islands and Urban Breeze Circulations: Idealized Ensemble Simulations

Soil moisture plays important roles in land surface and hydrological processes, and its changes can greatly affect weather and climate. In this study, we examine how changes in soil moisture impact the urban heat island (UHI) and urban breeze circulation (UBC) through idealized ensemble simulations. As soil moisture increases, the latent heat flux increases considerably in the rural area. Hence, in the rural area, the sensible heat flux and surface temperature decrease, which decreases the rural air temperature. The decrease in rural air temperature leads to increases in UHI intensity and thus UBC intensity. The urban air temperature also decreases with increasing soil moisture since the cooler rural air is advected to the urban area by the enhanced low-level convergent flow of the UBC. However, the decrease in air temperature is smaller in the urban area than in the rural area. As the UBC intensity increases, the sensible heat flux in the urban area increases. The increase in sensible heat flux in the urban area further increases the UHI intensity. The positive feedback between the UHI intensity and the UBC intensity is revealed when soil moisture increases. The decrease in air temperature in both the urban and rural areas leads to the decrease in planetary boundary layer (PBL) height. As a result, the vertical size of the UBC decreases with increasing soil moisture. As the UBC intensity increases with increasing soil moisture, the advection of water vapor from the rural area to the urban area increases. Combined with the decrease in PBL height, this reduces the water vapor deficit or even leads to the water vapor excess in the urban area depending on soil moisture content.

Assessment of universal thermal climate index (UTCI) using the WRF-UCM model over a metropolitan city in India.

Rapid urbanization increases urban air temperature, considerably affecting health, comfort, and the quality of life in urban spaces. The accurate assessment of outdoor thermal comfort is crucial for urban health. In the present study, a high-resolution mesoscale model coupled with a layer Urban Canopy Model (WRF-UCM) is implemented over the city of Hyderabad (17.3850° N, 78.4867° E) to simulate urban meteorological conditions during the summer and winter period of 2009 and 2019. The universal thermal climate index (UTCI) has been estimated using the model-derived atmospheric variables and a human biometeorology parameter to assess the linkages between the outdoor environment and thermal comfort. Results revealed that during summer, the city experiences nearly 50h of very strong thermal stress, whereas about 120h of slight cold stress are experienced during winter. The urban area in Hyderabad expanded from 5 to 15% during the study period, leading to a 2.5℃ (2.8 ℃) increase in land surface temperature, and a 1.2 (1.9 ℃) rise in air temperature at 2m height and 1.5 (2.5 ℃) UTCI during summer (winter) time. The analysis reveals that the maximum UTCI values were noticed over built-up areas compared to other land classes during daytime and nighttime. The results derived from the present study have shown that the performance of WRF-UCM-derived UTCI reasonably portrayed the significant impact of urbanization on thermal comfort over the city and provided useful insights with regard to urban comfort and welfare.

A prospective exploration of the urban exposome in relation to headache in the Dutch population-based Occupational and environmental health cohort study (AMIGO)

ObjectiveHeadache is one of the most prevalent and disabling health conditions globally. We prospectively explored the urban exposome in relation to weekly occurrence of headache episodes using data from the Dutch population-based Occupational and Environmental Health Cohort Study (AMIGO). Material and MethodsParticipants (N = 7,339) completed baseline and follow-up questionnaires in 2011 and 2015, reporting headache frequency. Information on the urban exposome covered 80 exposures across 10 domains, such as air pollution, electromagnetic fields, and lifestyle and socio-demographic characteristics. We first identified all relevant exposures using the Boruta algorithm and then, for each exposure separately, we estimated the average treatment effect (ATE) and related standard error (SE) by training causal forests adjusted for age, depression diagnosis, painkiller use, general health indicator, sleep disturbance index and weekly occurrence of headache episodes at baseline. ResultsOccurrence of weekly headache was 12.5 % at baseline and 11.1 % at follow-up. Boruta selected five air pollutants (NO2, NOX, PM10, silicon in PM10, iron in PM2.5) and one urban temperature measure (heat island effect) as factors contributing to the occurrence of weekly headache episodes at follow-up. The estimated causal effect of each exposure on weekly headache indicated positive associations. NO2 showed the largest effect (ATE = 0.007 per interquartile range (IQR) increase; SE = 0.004), followed by PM10 (ATE = 0.006 per IQR increase; SE = 0.004), heat island effect (ATE = 0.006 per one-degree Celsius increase; SE = 0.007), NOx (ATE = 0.004 per IQR increase; SE = 0.004), iron in PM2.5 (ATE = 0.003 per IQR increase; SE = 0.004), and silicon in PM10 (ATE = 0.003 per IQR increase; SE = 0.004). ConclusionOur results suggested that exposure to air pollution and heat island effects contributed to the reporting of weekly headache episodes in the study population.

Urbanization impact on meteorological condition and O3 concentration under past and future climates scenarios over the Greater Bay Area in Southern China

Cities face twin challenges of changing climate and urban heat island (UHI) effect, with the environmental implications of urbanization still unclear. This study employs the Weather Research and Forecasting (WRF) model to examine the impacts of urbanization in the Greater Bay Area (GBA) during warm and cold seasons in past (2000) and near-future (2030) scenarios [i.e., under shared socioeconomic pathways (SSP): SSP1-26, SSP2-45, and SSP5-85]. Datasets from the World Urban Database and Access Portal Tools (WUDAPT) were incorporated into the WRF model to improve the representation of urbanization effects on weather patterns. Our findings indicate that urban expansion significantly increases urban temperatures and decreases wind speed across selected climate change scenarios. The spatially averaged urban temperatures in the fine resolution domain for 2030 could rise by 0.9 °C (warm season) and 2.4 °C (cold season), respectively, and urban wind speed decreases by ∼ 3 m/s under the SSP5-85 scenario. In cold season, the UHI effect could last over 20 h, while urban area may experience cooler nighttime temperatures than rural areas in warm season. Moreover, the future scenario predicts higher daytime O3 levels due to the warming effects of climate change and urbanization, but lower nighttime levels in warm season, attributed to intensified south-easterly sea breeze and background winds, when compared to the past scenario. This study highlights the importance of incorporating urbanization and climate change in future urban atmospheric environment studies, and underscores that urban climate change adaptation and mitigation should consider extra impacts on built-environment caused by urbanization.

PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping

Abstract. The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping using Sentinel-2 satellite imagery. The Random Forest algorithm is utilized for LCZ classification, with evaluation conducted through spectral separability analysis and accuracy assessment between PRISMA and Sentinel-2 derived LCZ maps as well as with the benchmark LCZ Generator mapping tool. An initial experiment on the effect of PRISMA image pan-sharpening on LCZ spectral separability is also presented. Results obtained for Milan (Northern Italy) demonstrate the potential of hyperspectral imagery in enhancing LCZ identification compared to multispectral data, with promising improvements in LCZ maps overall accuracy. Finally, air temperature patterns within each LCZ class are explored, qualitatively confirming the influence of urban morphology on thermal comfort.

Biophysical Drivers of Seasonal Hysteresis of Urban Heat Islands Across Climates and Urban Landscapes

AbstractThe phenomenon of seasonal hysteresis between urban heat islands (UHIs) and background surface temperature was reported in many studies, but a unifying explanation for the causes of its magnitude, shape and looping direction remained less clear, and the impacts of urban landscapes and background climate on such hysteresis were also the subject of enquiry. Here we performed a systematic study based on a long‐term offline simulation for surface energy budgets, surface air and skin temperature of the urban‐biosphere system in East China. It was carried out with an up‐to‐date land surface model — the CLM5‐LCZs (the Community Land Model version 5 (CLM5) coupled with a newly developed urban canopy model representing diverse urban landscapes with the local climate zones classification scheme). To isolate the causes of UHIs hysteresis, a simplified framework was constructed by combining a mathematical representation of grid/subgrid model outputs, the two‐resistance mechanism method and the time‐lagged cross correlation analysis. The results exhibited distinct hysteresis patterns of UHIs such as twisted, concave‐down, concave‐up and convex‐up curve across diverse background climates and urban landscapes. The basic shapes of hysteresis depict a gradual transition from concave/convex‐up to concave/convex‐down by characterized by a larger winter UHIs from a wetter and warmer climate to a drier and colder climate. Magnitude and seasonality of surface UHIs are separately dominated by the urban‐rural contrast of evapotranspiration and heat storage at daytime and nighttime, whereas across‐climate variations of daytime and nighttime surface UHIs hysteresis are regulated by the phase shift of convection efficiency and anthropogenic heat. Urban landscapes affect the hysteresis mainly by altering the amplitude rather than the shape of looping curves. Our work could provide a fuller picture of how to mitigate urban warming considering inter‐seasonal tradeoffs over a broader region.

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

Machine learning bias correction and downscaling of urban heatwave temperature predictions from kilometre to hectometre scale

AbstractThe urban heat island (UHI) effect exacerbates near‐surface air temperature (T) extremes in cities, with negative impacts for human health, building energy consumption and infrastructure. Using conventional weather models, it is both difficult and computationally expensive to simulate the complex processes controlling neighbourhood‐scale variation of T. We use machine learning (ML) to bias correct and downscale T predictions made by the Met Office operational regional forecast model (UKV) to 100 m horizontal grid length over London, UK. A set of ML models (random forest, XGBoost, multiplayer perceptron) are trained using citizen weather station observations and UKV variables from eight heatwaves, along with high‐resolution land cover data. The ML models improve the T mean absolute error (MAE) by up to 0.12°C (11%) relative to the UKV. They also improve the UHI diurnal and spatial representation, reducing the UHI profile MAE from 0.64°C (UKV) to 0.15°C. A multiple linear regression performs almost as well as the ML models in terms of T MAE, but cannot match the UHI bias correction performance of the ML models, only reducing the UHI profile MAE to 0.49°C. UKV latent heat flux is found to be the most important predictor of T bias. It is demonstrated that including more heatwaves and observation sites in training would reduce overfitting and improve ML model performance.

Examining thermal inequities: Land surface temperature, social vulnerability, and historical redlining in San Antonio, TX

Rising urban temperatures significantly impact human health, with their effects varying across socio-economic groups. With a focus on San Antonio, Texas, during its notably intense summer heat in 2023, this research explores the intricate relationship between urban heat and social vulnerability. Through the analysis of Land Surface Temperature (LST) data from Landsat 8 data, captured during three exceptionally hot days, a positive correlation between LST and the Social Vulnerability Index (SVI) was revealed. This relationship shows that areas with higher SVI, indicative of greater vulnerability, consistently experience higher LSTs. The study further investigated two distinct census tracts: one in a previously redlined area with a high SVI and another in a ‘A-best’ HOLC area with a low SVI. The comparative study between these two tracts revealed significant Normalized Difference Vegetation Index (NDVI) and subsequent thermal variances. It was found that the tract in the historically redlined area consistently experiences higher minimum and mean LST, indicating how past housing policies continue to impact present-day environmental and social conditions. Findings reveal the critical connection between urban heat and social vulnerability, contributing to a broader understanding of the interplay between historical and social factors in shaping thermal inequities in a historically redlined city.

Analysis of urban heat islands combining Sentinel 2 and Landsat 8 satellite images in Hochiminh city

Urbanization in big cities has led to many impacts, the most obvious of which is the increasing of impervious surfaces from urban areas, socio-economic construction, concrete roads and the reduction of green space (vegetation, water surface). The changes land use land cover (LULC) led to the changes in the land surface temperature (LST), the formation of urban temperature islands (UHI), which have changed the local climate. This study combined Landsat 8 and Sentinel 2 images to enhance the process and calculation of LST value, spatial resolution was improved to 10m compared to 30m when calculated by Landsat 8 image. In which, the study also used ArcGIS software to classify 4 land surface covers, namely built-up land, vegetation land, water surface and the other land in order to monitor the urbanization process (with kappa coefficients all over 0.8) and assess the trend of changing LST on each LULC in the period 2015 - 2022. The results showed that when combined with Sentinel 2 images, LULC such as built-up land, roads and vegetation cover were more prominent, helping to improve more accurate results when calculating LST. Besides the trend of urbanization development in Ho Chi Minh city, it also leads to changes in the distribution of UHI over time.

Analysis of the Impact of Urban Green Spaces on Mitigating the Urban Heat Island Effect

The Urban Heat Island (UHI) effect, characterized by higher temperatures in urban areas compared to their rural surroundings, poses significant challenges for energy consumption, air quality, and public health. This study examines the impact of urban green spaces on mitigating the UHI effect in four major Spanish cities: Madrid, Barcelona, Seville, and Valencia. Using high-resolution satellite imagery, ground-based temperature measurements, and advanced GIS and statistical modeling techniques, we quantified the temperature differences between green and non-green urban areas. Our findings indicate that green spaces, including parks, street trees, and green roofs, significantly reduce urban temperatures, with parks exhibiting the highest cooling effects. The spatial analysis revealed that UHI hotspots are concentrated in densely built-up areas, while green spaces effectively mitigate these effects. Seasonal variations show that the cooling benefits of green spaces are more pronounced during the summer months. This study highlights the importance of incorporating diverse and well-distributed green spaces in urban planning to enhance thermal comfort, reduce energy consumption, and improve urban livability. Policy recommendations are provided to guide the integration of green infrastructure in climate adaptation strategies for Spanish cities.

The Heterogeneous Effects of Microscale-Built Environments on Land Surface Temperature Based on Machine Learning and Street View Images

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development.

Assessment of public space potential for climate change adaptation in three Colombian cities: nature-based solutions and urban design guidelines

ABSTRACT Cities need to interact more harmoniously with their environment, especially with regard to the urban water cycle. Currently, public space design standards in Colombia do not include rainwater management for reuse in buildings and irrigation of green spaces, wasting treated water on activities that do not require it. The environmental design of public spaces can also help reduce urban temperatures, especially in dense urban areas. Adapting cities and public spaces to climate change is crucial, especially in geographic contexts such as Colombia, where the El Niño and La Niña climate oscillations will further increase precipitations or droughts in regions of Colombia, making urban and rural populations more vulnerable to flooding, landslides and sea level rise. This study aims to assess the public space potential for the adoption of nature-based solutions (NBSs) in three Colombian cities with different hydrological conditions, climates and urban morphologies: Bogotá, Bucaramanga and Cartagena. The potential for NBSs in urban context was assessed with land suitability analysis and MCDA tools. The results showed that adoption of NBS in public spaces and urban design guidelines can be a valuable strategy to prepare our cities for climate change and provide a better support for urban ecosystem services in Colombia.

Body size responses to urban temperature variations are driven by life history traits in spiders

Abstract Urban ecosystems exhibit altered environmental conditions compared to the rural surroundings, including higher temperatures, the so‐called urban heat island (UHI) effect. Along urbanisation gradients, temperature variations occur at various scales ranging from the landscape to the microhabitat, with possible consequences on living organisms. As ectothermic animals, arthropods are particularly affected by UHIs because warming may directly induce physiological effects on their metabolism. Body size is a commonly used metric to investigate arthropod responses to thermal stress, but the scale‐dependent role of temperature variation as predictor of body size remains unclear. To assess temperature–size relationships across spatial scales and test if a common pattern to several species emerges, we performed morphological measurements on 2283 individuals representing 11 spider species belonging to Hahniidae, Linyphiidae, Lycosidae and Tetragnathidae. Spiders were collected on 36 grasslands distributed along a broad UHI gradient. Intraspecific shifts in body size were related to temperature in the Lycosids Pardosa prativaga and Pardosa pullata at the landscape and local scales, respectively, whereas no clear effects were observed regarding the other examined species. In both species, sex was an important endogenous factor that modulated individual responses; females were more affected than males by temperature. The contrasting amplitudes of the responses observed among Lycodids and the other species indicate that large, low dispersive and univoltine species are under a stronger influence of temperature than small bivotline species that can disperse aerially. Our results on spiders highlight that considering species‐specific life history traits helps to understand contrasting responses among species, and this may be essential for drawing general patterns of phenotypic changes among arthropods under anthropogenic warming. Read the free Plain Language Summary for this article on the Journal blog.

Simulation advances with EnviBatE- A case study on urban heat island mitigation in Singapore

Urban morphology and surface thermal properties are two key factors that determine the local-level urban heat island effect. Among different strategies that help in mitigating this effect, applying high reflective cool coating on urban surfaces is one of the most effective ways for changes in both the daytime and night-time urban air temperatures. In this paper, advances in the EnviBatE microclimate model for district-level numerical simulations are presented and the results are compared against full-scale experimental measurements conducted in Singapore. Key aspects of the EnviBatE tool along with their mathematical models are demonstrated. The experimental site consists of two side-by-side street canyons located at an industrial estate in the west of Singapore: one canyon with conventional and the other with cool surfaces for side-by-side comparison. The results suggest that the drop in street-level canyon air temperature varies from 1 to 4 °C with the application of the cool surface coating on roofs, walls and roads in comparison with conventional canyon under various scenarios. By analysing the effects of the three types of built-up surfaces separately, the cool roads scenario appears to have the greatest impact on canyon air temperature (at 3 m above ground), followed by the cool roofs scenario. The cool walls scenario produces negligible cooling impact. The advantages of the cool coating application on urban surfaces in reducing air temperature are statistically analysed and demonstrated. The pedestrian comfort in terms of the Universal Thermal Climate Index (UTCI) is proposed to be integrated with EnviBatE and discussed using the simulation results.

Anthropogenic heat from buildings in Los Angeles County: A simulation framework and assessment

Anthropogenic heat (AH), i.e., waste heat from buildings to the ambient environment, increases urban air temperature and contributes to the urban heat island effect, which leads to more air-conditioning energy use and higher associated waste heat during summer, forming a positive feedback loop. This study used a bottom-up simulation approach to develop a dataset of the annual hourly AH profiles for 1.7 million buildings in Los Angeles (LA) County for the year 2018 aggregated at three spatial resolutions: 450 m, 12 km, and the census tract. Building AH exhibits strong seasonal and diurnal patterns, as well as large spatial variations across the urban areas. Building AH peaks in May and reaches a maximum of 878 W/m2 within one of several AH hotspots in the region. Among the three major AH components (surface convection, heat rejection from HVAC systems, and zonal air exchange), the surface convection component is the largest, accounting for 78% of the total building AH across LA County. Higher AH is attributed to large building density, a high percentage of industrial buildings, and older building stock. While AH peaks during the day, the resulting ambient temperature increases are much larger during the night. During the July 2018 heatwave in LA County, building AH (excluding the surface component) leads to a daily maximum ambient temperature increase of up to 0.6 °C and a daily minimum ambient temperature increase of up to 2.9 °C. It is recommended that reducing summer building AH should be considered by policy makers in developing mitigation measures for cities to transition to clean energy while improving heat resilience.

SPATIOTEMPORAL ANALYSIS OF DENGUE CASES IN CEBU CITY FROM YEAR 2015 TO 2022

Abstract. Long-term climate changes, including increased temperature, shift in precipitation, wind patterns, and other climate factors, can disrupt the balance of nature and have significant implications in the transmission of dengue fever. This study investigated the spatial and temporal dynamics of dengue cases in Cebu City, a key metropolitan area in the Philippines characterized by a significant rate of urbanization in recent years. Climate Engine (CE), a cloud-based computing and visualization tool, was utilized in this study for database sources of Landsat 8 pre-processed satellite images. Time-series dataset of land surface temperatures (LST) and varying environmental indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) were examined to investigate the effects of increased urban surface temperatures, expanding urban structures, and diminishing vegetation in Cebu City on dengue cases from 2015 to 2022. The spatial distribution of dengue cases was analyzed through GeoDA to identify hotspots within the city. The annual dengue cases in Cebu City exhibit a temporal trend with a peak in 2016 (4637 cases) and a lowest point in 2021 (399 cases), the year when the pandemic struck. Most dengue cases were recorded between June and December, exhibiting a strong seasonal pattern, and primarily concentrated within the wet season. Barangay Guadalupe topped the number of cases (1781) followed by Barangay Lahug (1219 cases), and Barangay Labangon (1128 cases) from 2015 to 2022. These three residential barangays are in proximity to each other, indicating a potential localized clustering of dengue cases in neighboring areas. The equation derived from the linear regression model serves as a predictive tool for estimating dengue cases in Cebu City and is expressed as Dengue cases = −28.436 + 2.137 (LST) − 13.943 (NDVI) + 8.565 (NDWI) − 10.217 (NDBI). The findings of this study will have practical implications for urban planning and the development of local policies aimed at mitigating the rise in dengue cases.

Geographic analysis of the distribution of surface temperature, vegetation cover and its relationship with socioeconomic indicators – Cuiabá/MT

In Brazil and around the world, the problems resulting from urban planning, changes in land cover and the increase in urban infrastructure influence temperature change. The aim of this research is to understand the difference in temperature over the last 36 years (1985-2021) in the city of Cuiabá/MT, and its relationship with vegetation and socioeconomic and racial-ethnic factors. Annual images (36 images) from the Landsat 5 and 8 satellites between 1985 and 2021 were used, and Google Earth Engine was used to obtain this data for the urban area of the city of Cuiabá. Mapping of the spatial distribution of surface temperature, vegetation and changes in land use and cover was carried out using data from the MapBiomas project and socio-economic data from the Instituto Brasileiro de Geografia e Estatística (IBGE, Brazilian Institute of Geography and Statistics in English) 2010 census. The results indicated that over the last 36 years Cuiabá's surface temperature has increased by an average of 13°C, as part of the result of the growth of urban infrastructure and loss of vegetation cover, specifically in the peripheral areas. There is an inequality between the high-income population living in neighborhoods with milder temperatures and the low-income population living in neighborhoods with higher temperatures. In the temperature and skin color data, the white population generally lives in neighborhoods with milder temperatures, unlike the black population, which mostly suffers from high temperatures.