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

Partitioning urban forest evapotranspiration based on integrating eddy covariance of water vapor and carbon dioxide fluxes

Partitioning of evapotranspiration (ET) in urban forest lands plays a vital role in mitigating ambient temperature and evaluating the effects of urbanization on the urban hydrological cycle. While ET partitioning has been extensively studied in diverse natural ecosystems, there remains a significant paucity of research on urban ecosystems. The flux variance similarity (FVS) theory is used to partition urban forest ET into soil evaporation (E) and vegetation transpiration (T). This involves measurements from eddy covariance of water vapor and carbon dioxide fluxes, along with an estimated leaf-level water use efficiency (WUE) algorithm. The study compares five WUE algorithms in partitioning the average transpiration fraction (T/ET) and validates the results using two years of oxygen isotope observations. Although all five FVS-based WUE algorithms effectively capture the dynamic changes in hourly scale T and E across the four seasons, the algorithm that assumes a constant ratio of intercellular CO2 concentration (ci) to ambient CO2 concentration (ca) provides the most accurate simulation results for the ratio of T/ET. The performance metrics for this specific algorithm include the RMSE of 0.06, R2 of 0.88, the bias of 0.02, and MAPE of 8.9 %, respectively. Comparing urban forests to natural forests, the T/ET in urban areas is approximately 2.4-25.3 % higher, possibly due to the elevated air temperature (Ta), greater leaf area index (LAI), and increased soil water availability. Correlation analysis reveals that the T/ET dynamic is primarily controlled by Ta, LAI, net radiation, ca, and soil water content at half-hourly, daily, and monthly scales. This research provides valuable insights into the performance and applicability of various WUE algorithms in urban forests, contributing significantly to understanding the impact of urbanization on energy, water, and carbon cycles within ecosystems.

Assessing potential benefits of visits to neighborhoods with higher tree canopy coverage using mobility data: Associations with cardiovascular health outcomes in twenty US metropolitan areas

BACKGROUND

Research on health benefits due to exposure to green space, such as tree canopy coverage, has predominantly focused on canopy coverage in home neighborhoods. Yet exposures to tree canopy coverage in other spaces visited during the week or on weekends outside the home neighborhoods remains largely unexplored.

OBJECTIVES

We examined whether differences in coverage levels of tree canopy in neighborhoods visited compared to home neighborhoods was associated with lower prevalence of coronary heart disease (CHD) and stroke, adjusting for exposure to home canopy coverage. We further investigated if the associations varied across levels of home canopy coverage, and if they were more pronounced on weekdays or weekends.

METHODS

We used 2018 mobile phone data from the twenty largest U.S. Metropolitan Statistical Areas (MSAs). For each home census tract, we derived a weighted tree canopy coverage exposure from all visited tracts based on the proportion of visits to other tracts by home tract residents. We subtracted home canopy coverage from the weighted canopy coverage in each of the visited tracts to calculate tract-specific differences. We evaluated associations between differences in tree canopy coverage and prevalence of CHD and stroke via spatial error models, adjusting for tract-level home canopy coverage, MSA, socioeconomic and built environment characteristics.

RESULTS

For every ten-percentage-point increase in tree canopy coverage in visited tracts relative to home tracts, there was a 0.32-0.34% decrease in stroke prevalence. Association with CHD prevalence was not observed after adjusting for spatial autocorrelation. Variations between weekdays and weekends were minimal. The difference in tree canopy coverage was associated with CHD prevalence only for home tracts with low tree canopy coverage, while the difference was associated with stroke prevalence across home tracts with low, moderate, and high tree canopy coverage, with diminishing effect size.

DISCUSSION

This study identified that greater tree canopy coverage in visited neighborhoods relative to home neighborhoods was associated with lower stroke prevalence, and associations varied across home neighborhoods with different tree canopy coverage levels. It emphasized the need to factor in the neighborhood mobility networks in urban planning initiatives to promote cardiovascular health.

Local climate, air quality and leaf litter cover shape foliar fungal communities on an urban tree

Foliar fungi on urban trees are important for tree health, biodiversity and ecosystem functioning. Yet, we lack insights into how urbanization influences foliar fungal communities. We created detailed maps of Stockholm region's climate and air quality and characterized foliar fungi from mature oaks (Quercus robur) across climatic, air quality and local habitat gradients. Fungal richness was higher in locations with high growing season relative humidity, and fungal community composition was structured by growing season maximum temperature, NO2 concentration and leaf litter cover. The relative abundance of mycoparasites and endophytes increased with temperature. The relative abundance of pathogens was lowest with high concentrations of NO2 and particulate matter (PM2.5), while saprotrophs increased with leaf litter cover. Our findings show that urbanization influences foliar fungi, providing insights for developing management guidelines to promote tree health, prevent disease outbreaks and maintain biodiversity within urban landscapes.

Meteorological, chemical and biological evaluation of the coupled chemistry-climate WRF-Chem model from regional to urban scale. An impact-oriented application for human health

Extreme climatic conditions, like heat waves or cold spells, associated to high concentrations of air pollutants are responsible for a broad range of effects on human health. Consequently, in the recent years, the question on how urban and peri-urban forests may improve both air quality and surface climate conditions at city-scale is receiving growing attention by scientists and policymakers, with previous studies demonstrating how nature-based solutions (NBS) may contribute to reduce the risk of population to be exposed to high pollutant levels and heat stress, preventing, thus, premature mortality. In this study we present a new modeling framework designed to simulate air quality and meteorological conditions from regional to urban scale, allowing thus to assess the impacts of both air pollution and heat stress on human health at urban level. To assess the model reliability, we evaluated the model's performances in reproducing several relevant meteorological, chemical, and biological variables. Results show how our modeling system can reliably reproduce the main meteorological, chemical, and biological variables over our study areas, thus this tool can be used to estimate the impact of air pollution and heat stress on human health. As an example of application, we show how common heat stress and air pollutant indices used for human health protection change when computed from regional to urban scale for the cities of Florence (Italy) and Aix en Provence (France).

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

Site selection for nature-based solutions for stormwater management in urban areas: An approach combining GIS and multi-criteria analysis

In recent years, particularly following the definition of the UN Sustainable Development Goals (SDGs) for 2030, Nature-Based Solutions (NBS) have gained considerable attention, capturing the interest of both the scientific community and policymakers committed to addressing urban environmental issues. However, the need for studies to guide decision-makers in identifying suitable locations for NBS implementation within urban stormwater management is evident. To address this gap, the present study employs a methodological approach grounded in multi-criteria analysis integrated with Geographic Information Systems (GIS) to identify areas with potential for NBS implementation. In this process, ten NBS were proposed and tested in the drainage area of a shallow tropical urban lake in Londrina, southern Brazil. Additionally, the study investigates areas hosting lower-income populations, a relevant aspect for public managers given the diverse economic subsidies required to implement NBS. Furthermore, the study incorporates a preliminary analysis that evaluates the potential ecosystem benefits to determine the most suitable NBS for a specific site. The result shows that all the ten analyzed NBS were deemed suitable for the study area. Rain barrels had the highest percentage coverage in the study area (37.1%), followed by tree pits (27.9%), and rain gardens (25.4%). Despite having the highest distribution in the basin area, rain barrels exhibited only moderate ecosystem benefits, prompting the prioritization of other NBS with more significant ecological advantages in the final integrated map. In summary, the methodology proposed showed to be a robust approach to selecting optimal solutions in densely populated urban areas.

The potential of urban trees to reduce heat-related mortality in London

Increasing temperatures and more frequent heatwave events pose threats to population health, particularly in urban environments due to the urban heat island (UHI) effect. Greening, in particular planting trees, is widely discussed as a means of reducing heat exposure and associated mortality in cities. This study aims to use data from personal weather stations (PWS) across the Greater London Authority to understand how urban temperatures vary according to tree canopy coverage and estimate the heat-health impacts of London's urban trees. Data from Netatmo PWS from 2015-2022 were cleaned, combined with official Met Office temperatures, and spatially linked to tree canopy coverage and built environment data. A generalized additive model was used to predict daily average urban temperatures under different tree canopy coverage scenarios for historical and projected future summers, and subsequent health impacts estimated. Results show areas of London with higher canopy coverage have lower urban temperatures, with average maximum daytime temperatures 0.8 °C and minimum temperatures 2.0 °C lower in the top decile versus bottom decile canopy coverage during the 2022 heatwaves. We estimate that London's urban forest helped avoid 153 heat attributable deaths from 2015-2022 (including 16 excess deaths during the 2022 heatwaves), representing around 16% of UHI-related mortality. Increasing tree coverage 10% in-line with the London strategy would have reduced UHI-related mortality by a further 10%, while a maximal tree coverage would have reduced it 55%. By 2061-2080, under RCP8.5, we estimate that London's current tree planting strategy can help avoid an additional 23 heat-attributable deaths a year, with maximal coverage increasing this to 131. Substantial benefits would also be seen for carbon storage and sequestration. Results of this study support increasing urban tree coverage as part of a wider public health effort to mitigate high urban temperatures.

Exploring the spatial and seasonal heterogeneity of cooling effect of an urban river on a landscape scale

Urban water bodies can effectively mitigate the urban heat island effect and thus enhance the climate resilience of urban areas. The cooling effect of different water bodies varies, however, the cooling heterogeneity of different sections of a single watercourse or river network is rarely considered. Based on various satellite images, geospatial approaches and statistical analyses, our study confirmed the cooling heterogeneity from spatial and seasonal perspectives of the Suzhou Outer-city River in detail in the urban area of Suzhou, China. The cooling effect of the river was observed in the daytime in four seasons, and it is strongest in summer, followed by spring and autumn, and weakest in winter. The combination of the width of the river reach, the width and the NDVI value of the adjacent green space can explain a significant part of the cooling heterogeneity of the different river sections in different seasons. Land surface temperature (LST) variations along the river are more related to the width of the river reach, but the variations of the cooling distance are more related to the adjacent green space. The cooling effect of a river reach could be enhanced if it is accompanied by green spaces. In addition, the cooling effect of a looping river is stronger on the inside area than on the outside. The methodology and results of this study could help orient scientific landscape strategies in urban planning for cooler cities.

Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurement and satellite imagery

A limited number of meteorological stations and sparse data challenge microclimate assessment in urban areas. Therefore, it is necessary to complement these data with additional measurements to achieve a denser spatial coverage, enabling a detailed representation of the city's microclimatic features. In this study, conducted in Zagreb, Croatia, mobile air temperature measurements were utilized and compared with satellite-derived land surface temperature (LST). Here, air temperature measurements were carried out using bicycles and an instrument with a GPS receiver and temperature probe during a heat wave in June 2021, capturing the spatial pattern of air temperature to highlight the city's microclimate characteristics (i.e. urban heat load; UHL) in extremely hot weather conditions. Simultaneously, remotely sensed LST was retrieved from the Landsat-8 satellite. Air temperature measurements were compared to city-specific street type classification, while neighbourhood heat load characteristics were analysed based on local climate zones (LCZ) and LST. Results indicated significant thermal differences between surface types and urban forms and between street types and LCZs. Air temperatures reached up to 35 °C, while LST exceeded 40 °C. City parks, tree-lined streets and areas near blue infrastructure were 1.5-3 °C cooler than densely built areas. Temperature contrasts between LCZs in terms of median LST were more emphasised and reached 9 °C between some classes. These findings highlight the importance of preserving green areas to reduce UHL and enhance urban resilience. Here, exemplified by the city of Zagreb, it has been demonstrated that the use of multiple datasets allows a comprehensive understanding of temperature patterns and their implications for urban climate research.

Cooling Effect of Green Spaces on Urban Heat Island in a Chinese Megacity: Increasing Coverage versus Optimizing Spatial Distribution

Enhancing the cooling effectiveness of green spaces (GSs) is crucial for improving urban thermal environments in the context of global warming. Increasing GS coverage and optimizing its spatial distribution individually proved to be effective urban cooling measures. However, their comparative cooling effectiveness and potential interaction remain unclear. Here, using the moving window approach and random forest algorithm, we established a robust model (R2 = 0.89 ± 0.01) to explore the relationship between GS and land surface temperature (LST) in the Chinese megacity of Guangzhou. Subsequently, the response of LST to varying GS coverage and its spatial distribution was simulated, both individually and in combination. The results indicate that GS with higher coverage and more equitable spatial distribution is conducive to urban heat mitigation. Increasing GS coverage was found to lower the city's average LST by up to 4.73 °C, while optimizing GS spatial distribution led to a decrease of 1.06 °C. Meanwhile, a synergistic cooling effect was observed when combining both measures, resulting in additional cooling benefits (0.034-0.341 °C). These findings provide valuable insights into the cooling potential of GS and crucial guidance for urban green planning aimed at heat mitigation in cities.

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

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

Urban heat mitigation by green and blue infrastructure: Drivers, effectiveness, and future needs

The combination of urbanization and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate change. Yet, the risk of urban overheating can be mitigated by urban green-blue-grey infrastructure (GBGI), such as parks, wetlands, and engineered greening, which have the potential to effectively reduce summer air temperatures. Despite many reviews, the evidence bases on quantified GBGI cooling benefits remains partial and the practical recommendations for implementation are unclear. This systematic literature review synthesizes the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximize their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorized under 10 main divisions. Certain GBGI (green walls, parks, street trees) have been well researched for their urban cooling capabilities. However, several other GBGI have received negligible (zoological garden, golf course, estuary) or minimal (private garden, allotment) attention. The most efficient air cooling was observed in botanical gardens (5.0 ± 3.5°C), wetlands (4.9 ± 3.2°C), green walls (4.1 ± 4.2°C), street trees (3.8 ± 3.1°C), and vegetated balconies (3.8 ± 2.7°C). Under changing climate conditions (2070-2100) with consideration of RCP8.5, there is a shift in climate subtypes, either within the same climate zone (e.g., Dfa to Dfb and Cfb to Cfa) or across other climate zones (e.g., Dfb [continental warm-summer humid] to BSk [dry, cold semi-arid] and Cwa [temperate] to Am [tropical]). These shifts may result in lower efficiency for the current GBGI in the future. Given the importance of multiple services, it is crucial to balance their functionality, cooling performance, and other related co-benefits when planning for the future GBGI. This global GBGI heat mitigation inventory can assist policymakers and urban planners in prioritizing effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience.

Heterogeneous effects of the availability and spatial configuration of urban green spaces on their cooling effects in China

The impacts of the availability and spatial configuration of urban green spaces (UGS) on their cooling effects can vary with background climate conditions. However, large-scale studies that assess the potential heterogeneous relationships of UGS availability and spatial configuration with urban thermal environment are still lacking. In this study, we investigated the impacts of UGS availability and spatial configuration on urban land surface temperature (LST) taking 306 cities in China as a case study covering a multi-biome-scale. We first calculated the availability of surrounding UGS for urban built-up pixels in each city using a distance-weighted approach, and its spatial configuration was quantified through the Gini coefficient. Then, we employed various regression models to explore how the impacts of UGS availability and the Gini coefficient on LST varies across different LST quantiles and between day- and nighttime. The results revealed that UGS availability was negatively associated with both daytime and nighttime LST, while the Gini coefficient showed a positive impact solely on daytime LST, indicating that an adequate and equally distributed UGS contributes to lower environmental temperatures during the daytime. Furthermore, the impact of UGS availability on LST decreased during both day- and nighttime with increased background LST quantiles. Whereas the impact of the Gini coefficient increased only with daytime LST quantile levels, with its effect remaining almost insignificant during the night. Our findings provide new insights into the impacts of UGS on urban thermal environment, offering significant implications for urban green infrastructure planning aiming at lowering the urban heat island.

Scale dependence of urban green space cooling efficiency: A case study in Beijing metropolitan area

Urban Green Space (UGS), providing environmental, social and economic benefits simultaneously, has been regarded as a cost-effective Nature-based Solution (NbS) to combat the effects of urban heat island (UHI). Under the dual pressure of increasing demand for limited land resources and mitigating UHI, how to scientifically and effectively use the limited space to obtain the maximum cooling efficiency (scaling of cooling intensity and UGS size) is an important component of strategic urban green planning. However, the scale dependence of UGS cooling effect has not yet been sufficiently quantified, particularly with respect to involving small and medium size UGS. Here, we explored the size-dependent UGS cooling efficiency in Beijing using 10,003 UGS patches extracted from high-resolution remote sensing images. We found that 5922 UGS (59.20 %) exhibited a "cooling island effect", the cooling service of UGS could reduce land surface temperature by 0.06 ± 0.05 °C to 3.81 ± 1.01 °C, and the cooling intensity enhanced nonlinearly with increasing size and closely related to the complexity of UGS shape and vegetation quality. We further showed that the cooling efficiency of small, medium and large UGS was -0.004 ± 0.03 (n = 2201), 0.79 ± 0.01 (n = 3570), 0.19 ± 0.03 (n = 151), respectively, suggesting that strategic urban greening to combat urban heat should target on increasing medium-sized UGS and managing the layout of green space. These findings emphasize the significance of considering and further exploring the scale dependence of UGS cooling effect in mitigating urban heat.

Water-energy-vegetation nexus explain global geographical variation in surface urban heat island intensity

Surface urban heat island (SUHI) is a key climate risk associated with urbanization. Previous case studies have suggested that precipitation (water), radiation (energy), and vegetation have important effects on urban warming, but there is a lack of research that combines these factors to explain the global geographic variation in SUHI intensity (SUHII). Here, we utilize remotely sensed and gridded datasets to propose a new water-energy-vegetation nexus concept that explains the global geographic variation of SUHII across four climate zones and seven major regions. We found that SUHII and its frequency increase from arid zones (0.36 ± 0.15 °C) to humid zones (2.28 ± 0.10 °C), but become weaker in the extreme humid zones (2.18 ± 0.15 °C). We revealed that from semi-arid/humid to humid zones, high precipitation is often coupled with high incoming solar radiation. The increased solar radiation can directly enhance the energy in the area, leading to higher SUHII and its frequency. Although solar radiation is high in arid zones (mainly in West, Central, and South Asia), water limitation leads to sparse natural vegetation, suppressing the cooling effect in rural areas and resulting in lower SUHII. In extreme humid regions (mainly in tropical areas), incoming solar radiation tends to flatten out, which, coupled with increased vegetation as hydrothermal conditions become more favorable, leads to more latent heat and reduces the intensity of SUHI. Overall, this study offers empirical evidence that the water-energy-vegetation nexus highly explains the global geographic variation of SUHII. The results can be used by urban planners seeking optimal SUHI mitigation strategies and for climate change modeling work.

Improved human greenspace exposure equality during 21st century urbanization

Greenspace plays a crucial role in urban ecosystems and has been recognized as a key factor in promoting sustainable and healthy city development. Recent studies have revealed a growing concern about urban greenspace exposure inequality; however, the extent to which urbanization affects human exposure to greenspace and associated inequalities over time remains unclear. Here, we incorporate a Landsat-based 30-meter time-series greenspace mapping and a population-weighted exposure framework to quantify the changes in human exposure to greenspace and associated equality (rather than equity) for 1028 global cities from 2000 to 2018. Results show a substantial increase in physical greenspace coverage and an improvement in human exposure to urban greenspace, leading to a reduction in greenspace exposure inequality over the past two decades. Nevertheless, we observe a contrast in the rate of reduction in greenspace exposure inequality between cities in the Global South and North, with a faster rate of reduction in the Global South, nearly four times that of the Global North. These findings provide valuable insights into the impact of urbanization on urban nature and environmental inequality change and can help inform future city greening efforts.

Modeling the spatiotemporal heterogeneity of land surface temperature and its relationship with land use land cover using geo-statistical techniques and machine learning algorithms

Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia's cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High-High (H-H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.

Influence of urban forests on residential property values: A systematic review of remote sensing-based studies

Urban forests provide direct and indirect benefits to human well-being that are increasingly captured in residential property values. Remote Sensing (RS) can be used to measure a wide range of forest and vegetation parameters that allows for a more detailed and better understanding of their specific influences on housing prices. Herein, through a systematic literature review approach, we reviewed 89 papers (from 2010 to 2022) from 21 different countries that used RS data to quantify vegetation indices, forest and tree parameters of urban forests and estimated their influence on residential property values. The main aim of this study was to understand and provide insights into how urban forests influence residential property values based on RS studies. Although more studies were conducted in developed (n = 55, 61.7%) than developing countries (n = 34, 38.3%), the results indicated for the most part that increasing tree canopy cover on property and neighborhood level, forest size, type, greenness, and proximity to urban forests increased housing prices. RS studies benefited from spatially explicit repetitive data that offer superior efficiency to quantify vegetation, forest, and tree parameters of urban forests over large areas and longer periods compared to studies that used field inventory data. Through this work, we identify and underscore that urban forest benefits outweigh management costs and have a mostly positive influence on housing prices. Thus, we encourage further discussions about prioritizing reforestation and conservation of urban forests during the urban planning of cities and suburbs, which could support UN Sustainable Development Goals (SDGs) and urban policy reforms.

Copernicus for urban resilience in Europe

The urban community faces a significant obstacle in effectively utilising Earth Observation (EO) intelligence, particularly the Copernicus EO program of the European Union, to address the multifaceted aspects of urban sustainability and bolster urban resilience in the face of climate change challenges. In this context, here we present the efforts of the CURE project, which received funding under the European Union's Horizon 2020 Research and Innovation Framework Programme, to leverage the Copernicus Core Services (CCS) in supporting urban resilience. CURE provides spatially disaggregated environmental intelligence at a local scale, demonstrating that CCS can facilitate urban planning and management strategies to improve the resilience of cities. With a strong emphasis on stakeholder engagement, CURE has identified eleven cross-cutting applications between CCS that correspond to the major dimensions of urban sustainability and align with user needs. These applications have been integrated into a cloud-based platform known as DIAS (Data and Information Access Services), which is capable of delivering reliable, usable and relevant intelligence to support the development of downstream services towards enhancing resilience planning of cities throughout Europe.

Association of greenness with the disease burden of lower respiratory infections and mediation effects of air pollution and heat: a global ecological study

Exposure to greenness is increasingly linked to beneficial health outcomes, but the associations between greenness and the disease burden of lower respiratory infections (LRIs) are unclear. We used the normalized difference vegetation index (NDVI) and the leaf area index (LAI) to measure greenness and incidence, death, and disability-adjusted life years (DALYs) due to LRIs to represent the disease burden of LRIs. We applied a generalized linear mixed model to evaluate the association between greenness and LRI disease burden and performed a stratified analysis, after adjusting for covariates. Additionally, we assessed the potential mediating effects of fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), and heat on the association between greenness and the disease burden of LRIs. In the adjusted model, one 0.1 unit increase of NDVI and 0.5 increase in LAI were significantly inversely associated with incidence, death, and DALYs due to LRIs, respectively. Greenness was negatively correlated with the disease burden of LRIs across 15-65 age group, both sexes, and low SDI groups. PM2.5, O3, and heat mediated the effects of greenness on the disease burden of LRIs. Greenness was significantly negatively associated with the disease burden of LRIs, possibly by reducing exposure to air pollution and heat.

Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes

The population experiencing high temperatures in cities is rising due to anthropogenic climate change, settlement expansion, and population growth. Yet, efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes are still lacking. Here, we implement a spatial regression model based on remote sensing data that is able to assess the population exposure to LST extremes in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies. We define exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population exposed, in person ⋅ day. Our findings reveal that urban vegetation plays a considerable role in decreasing the exposure of the urban population to LST extremes. We show that targeting high-exposure areas reduces vegetation needed for the same decrease in exposure compared to uniform treatment.

Optical Remote Sensing in Provisioning of Ecosystem-Functions Analysis-Review

Keeping natural ecosystems and their functions in the proper condition is necessary. One of the best contactless monitoring methods is remote sensing, especially optical remote sensing, which is used for vegetation applications. In addition to satellite data, data from ground sensors are necessary for validation or training in ecosystem-function quantification. This article focuses on the ecosystem functions associated with aboveground-biomass production and storage. The study contains an overview of the remote-sensing methods used for ecosystem-function monitoring, especially methods for detecting primary variables linked to ecosystem functions. The related studies are summarized in multiple tables. Most studies use freely available Sentinel-2 or Landsat imagery, with Sentinel-2 mostly producing better results at larger scales and in areas with vegetation. The spatial resolution is a key factor that plays a significant role in the accuracy with which ecosystem functions are quantified. However, factors such as spectral bands, algorithm selection, and validation data are also important. In general, optical data are usable even without supplementary data.

The win-win interaction between integrated blue and green space on urban cooling

The contributions of urban blue and green spaces on urban cooling are widely acknowledged. However, the combined cooling effect of integrated blue and green space remains uncertain. In this study, a combination of modelling and observational analyses uncovered a win-win interaction between coexisting blue and green spaces in terms of urban cooling. That is, the integration of water bodies and green spaces can reinforce the mutual cooling potential and provide greater urban cooling than stand-alone water bodies and green spaces. The results indicated that the known influencing factors such as area, shape and planting structure had no impact on the cooling effect of riverside urban green spaces. Instead, the width of the adjacent river reach and the degree of contact with the reach were significantly positively related to the cooling effect of riverside green spaces. The surface/air temperature of a riverside green space can be 4.2 °C/3.7 °C lower in the daytime in summer, and 3.1 °C/2.7 °C lower in spring than a non-riverside green space of similar size. Urban green spaces with water bodies inside could cause about 0.99 °C and 1.45 °C more deduction of land surface temperature (LST) than simple green spaces of similar size in spring and summer, respectively. There were about 1 °C‑2.9 °C more deductions in the air temperature of a river reach when it is accompanied by green spaces. More specifically, complete coverage with vegetated areas within a 30 m buffer on both riverbanks can result in a 3.1 °C and 3.37 °C higher LST deduction compared to no vegetation coverage on the riverbank in the daytime in spring and summer, respectively. The results of this study extend the understanding of the cooling effect of urban blue-green spaces and provide implications for sustainable urban planning.

Cooling cities through urban green infrastructure: a health impact assessment of European cities

BACKGROUND

High ambient temperatures are associated with many health effects, including premature mortality. The combination of global warming due to climate change and the expansion of the global built environment mean that the intensification of urban heat islands (UHIs) is expected, accompanied by adverse effects on population health. Urban green infrastructure can reduce local temperatures. We aimed to estimate the mortality burden that could be attributed to UHIs and the mortality burden that would be prevented by increasing urban tree coverage in 93 European cities.

METHODS

We did a quantitative health impact assessment for summer (June 1-Aug 31), 2015, of the effect of UHIs on all-cause mortality for adults aged 20 years or older in 93 European cities. We also estimated the temperature reductions that would result from increasing tree coverage to 30% for each city and estimated the number of deaths that could be potentially prevented as a result. We did all analyses at a high-resolution grid-cell level (250 × 250 m). We propagated uncertainties in input analyses by using Monte Carlo simulations to obtain point estimates and 95% CIs. We also did sensitivity analyses to test the robustness of our estimates.

FINDINGS

The population-weighted mean city temperature increase due to UHI effects was 1·5°C (SD 0·5; range 0·5-3·0). Overall, 6700 (95% CI 5254-8162) premature deaths could be attributable to the effects of UHIs (corresponding to around 4·33% [95% CI 3·37-5·28] of all summer deaths). We estimated that increasing tree coverage to 30% would cool cities by a mean of 0·4°C (SD 0·2; range 0·0-1·3). We also estimated that 2644 (95% CI 2444-2824) premature deaths could be prevented by increasing city tree coverage to 30%, corresponding to 1·84% (1·69-1·97) of all summer deaths.

INTERPRETATION

Our results showed the deleterious effects of UHIs on mortality and highlighted the health benefits of increasing tree coverage to cool urban environments, which would also result in more sustainable and climate-resilient cities.

FUNDING

GoGreenRoutes, Spanish Ministry of Science and Innovation, Institute for Global Health, UK Medical Research Council, European Union's Horizon 2020 Project Exhaustion.

A meta-analysis on plant volatile organic compound emissions of different plant species and responses to environmental stress

Urban plants are beneficial to residents' physical and mental health, but can also have adverse impacts. One of the remarked examples is the potential contribution of BVOCs released by urban plants to the generation of ground-level ozone and SOA. The choice of urban plant species, therefore, is critical for air quality improvement in cities. Understanding the rates of BVOCs emitted from different urban plants and how they change in response to environmental stressors is a prerequisite to making the right decision on plant species selection. Here, we performed a meta-analysis on the selected 159 studies that include 357 species to address this need. We found: (1) 89% of deciduous trees emit the three major types of BVOCs, isoprene, monoterpene, and sesquiterpene, but only do 53% evergreen ones. (2) The main types of BVOCs emission by broad-leaved and coniferous plants differ. Seventy-eight percent of broad-leaved, but only 48% of coniferous trees emit isoprene, whereas 74% of broad-leaved, but 93% of coniferous plants emit monoterpene. (3) The emission rates of isoprene and monoterpene differ significantly among species. (4) The analysis on the 77 species collected in previous studies indicated that the effect of environmental stressors varies by different compounds, and the combined effect is not precisely the same as that of a single factor. Based on the meta-analysis, we further identified a few key knowledge gaps and research priorities. First, more studies on the BVOCs emission and carbon allocation at the tree species level are needed. Second, the combined effects of multiple environmental stresses, especially long-term ones, on BVOC emissions and the mechanisms warrant further research. Third, it is vital to evaluate BVOC-climate interactions on global change. Furthermore, there is little empirical work on the synergies and tradeoffs between BVOC emissions and ecosystem services provision of urban plants, which warrants future investigation.

Crown dieback and mortality of urban trees linked to heatwaves during extreme drought

Cities have been described as 'heat islands' and 'dry islands' due to hotter, drier air in urban areas, relative to the surrounding landscape. As climate change intensifies, the health of urban trees will be increasingly impacted. Here, we posed the question: Is it possible to predict urban tree species mortality using (1) species climate envelopes and (2) plant functional traits? To answer these, we tracked patterns of crown dieback and recovery for 23 common urban tree and shrub species in Sydney, Australia during the record-breaking austral 2019-2020 summer. We identified 10 heat-tolerant species including five native and five exotic species, which represent climate-resilient options for urban plantings that are likely to continue to thrive for decades. Thirteen species were considered vulnerable to adverse conditions due to their mortality, poor health leading to tree removal, and/or extensive crown dieback. Crown dieback increased with increasing precipitation of the driest month of species climate of origin, suggesting that species from dry climates may be better suited for urban forests in future climates. We effectively grouped species according to their drought strategy (i.e., tolerance versus avoidance) using a simple trait-based framework that was directly linked with species mortality. The seven most climate-vulnerable species used a drought-avoidance strategy, having low wood density and high turgor loss points along with large, thin leaves with low heat tolerance. Overall, plant functional traits were better than species climate envelopes at explaining crown dieback. Recovery after stress required two mild, wet years for most species, resulting in prolonged loss of cooling benefits as well as economic losses due to replacement of dead/damaged trees. Hotter, longer, and more frequent heatwaves will require selection of more climate-resilient species in urban forests, and our results suggest that future research should focus on plant thermal traits to improve prediction models and species selection.

Warming and cooling effects of local climate zones on urban thermal environment

Understanding the thermal characteristics and contribution ranking of local climate zones (LCZs) is essential since they can help in maintaining environmental harmony. However, previous studies only considered independent effects and could not analyze the combined effects of LCZ on land surface temperature (LST). In this study, we propose a new method to establish an interaction model between LCZs. Five first-level grids with different scales from 270 to 990 m were established to calculate the area proportion of LCZ. The area proportion of LCZ was then applied in the stepwise regression model to quantitatively analyze its magnitude and direction of impact on the LST. The results suggest that the LCZ types of the study area with the highest and lowest average LST were LCZ2 (compact middle-rise building, 39.82°C) and LCZG (water body, 34.24°C), respectively. However, on most scales, the warming effect of LCZ2 was lower than that of LCZE (bare rock or paver), and the cooling effect of LCZG was lower than that of LCZD (low plants). The optimum results were obtained at a scale of 810 m. At this scale, the warming effect was in the order: LCZE (0.314) > LCZ2 (0.236) > LCZ3 (compact low-rise building, 0.135) > LCZ5 (open middle-rise, 0.084) > LCZ6 (open low-rise, 0.056); the cooling effect was in the order: LCZD (-0.272) > LCZA (dense trees, -0.104) > LCZG (-0.103). These findings can help to elucidate the unique warming and cooling effects of LCZ on the interaction condition and the construction of an urban human settlement.

Non-linear effects of meteorological variables on cooling efficiency of African urban trees

Urban tree cover is widely regarded as an environmentally-friendly and effective urban cooling approach. Meteorological variables, including air temperature, wind speed, and humidity, have complex impacts on the cooling efficiency (CE) of urban trees (i.e., the negative ratio of the land surface temperature change to the tree cover percentage change). This means that increasing the urban tree cover to alleviate heat stress is not necessarily suitable for cities with different climates. African cities are confronted with larger heat risks but lack considerations for the effectiveness of urban tree cooling in urban planning. In this study, 40 African major cities with population greater than 500,000 in different climatic regions were selected, and 1459 CEs during each city's corresponding warmest 3 consecutive months were calculated combined with the availability of meteorological data. The generalized additive models revealed the non-linear impacts of air temperature/temperature dew point difference on CE, which were more evident in arid cities. The CE of urban trees actually increased and then decreased along with the increase of air temperature/temperature dew point difference, and the turning point were 34 °C/26 °C, respectively. African cities would have different frequencies of warm days with an air temperature over 34 °C under different Shared Socioeconomic Pathways within the next 30 years. Specially, the cities around Sahel would suffer up to 40-60% days over 34 °C, which meant their urban tree CE would decrease along with air temperature increase. This study highlighted that in African cities, especially those with arid climate, it was unadvisable to only count on increasing tree cover to alleviate urban heat stress in the warming future, which called for other combined cooling strategies.

Dynamic and Heterogeneity of Urban Heat Island: A Theoretical Framework in the Context of Urban Ecology

The dynamic and heterogeneity of the urban heat island (UHI) is the result of the interactions between biotic, physical, social, and built components. Urban ecology as a transdisciplinary science can provide a context to understand the complex social–biophysical issues such as the thermal environment in cities. This study aimed at developing a theoretical framework to elucidate the interactions between the social–biophysical patterns and processes mediating UHI. To do it, we conducted a theoretical review to delineate UHI complexity using the concept of dynamic heterogeneity of pattern, process, and function in UHI phenomenon. Furthermore, a hypothetical heterogeneity spiral (i.e., driver-outcome spiral) related to the UHI was conceived as a model template. The adopted theoretical framework can provide a holistic vision of the UHI, contributing to a better understanding of UHI’s spatial variations in long-term studies. Through the developed framework, we can devise appropriate methodological approaches (i.e., statistic-based techniques) to develop prediction models of UHI’s spatial heterogeneity.

Seasonal Differences in Land Surface Temperature under Different Land Use/Land Cover Types from the Perspective of Different Climate Zones

The process of urbanization is accelerating, and land surface temperature (LST) is increasing, seriously threatening human health. Therefore, it is crucial to explore the differences in LST of different land use/land cover (LULC) types. Using MOD11A2 and MCD12Q1 data, this study explored the seasonal differences in LST of each LULC type from the perspective of different climate zones. The results showed that the maximum and minimum LSTs during the day were higher than those at night. During the day, the LSTs of urban and built-up and barren lands were higher than those of forests, grasslands, and water bodies; at night, the LSTs of urban and built-up lands decreased but remained high, while barren lands showed a significant decrease to LSTs even lower than those of water bodies. In addition, the difference in daytime LST of the LU16 type (barren lands) in different climatic zones was the most obvious and was much higher than that of other LULC types in the middle temperate and south temperate zones, but much lower than those in the middle subtropical and north subtropical zones. This comparison of the LST differences of each LULC type under different climate backgrounds provides an important reference for rational urban planning.

Urban landcover differentially drives day and nighttime air temperature across a semi-arid city

Semi-arid urban environments are undergoing an increase in both average air temperatures and in the frequency and intensity of extreme heat events. Within cities, different composition and densities of urban landcovers (ULC) influence local air temperatures, either mitigating or increasing heat. Currently, understanding how combinations of ULC influence air temperature at the block to neighborhood scale is necessary for heat mitigation plans, and yet limited due to the complexities integrating high-resolution ULC with spatial and temporally high-resolution microclimate data. We quantify how ULC influences air temperature at 60 m resolution for day and nighttime climate normals and extreme heat conditions by integrating microclimate sensor data sensor and high-resolution (1 m²) ULC for Denver, Colorado's urban core. We derive ULC drivers of air temperature using a structural equation model, then use a random forest algorithm to predict air temperatures for 30-year climate normals and an extreme heat condition. We find that, in conjunction with other ULC, urban tree canopy reduces daytime air temperatures (-0.026 °C per % cover), and the combination of impervious surfaces and buildings increases daytime air temperature (0.021 °C per % cover). Compared to daytime hours, nighttime irrigated turf temperature cooling effects are increased from being non-significant to -0.022 °C per % cover, while tree canopy effects are reduced from -0.026 °C during the day to -0.016 °C at night. Overall, ULC drives ~17% and 25% of local air temperature during the day and night, respectively. ULC influence on daytime air temperatures is altered in extreme heat events, both depending on the ULC type and time of day. Our findings inform urban planners seeking to identify potential hot and cool spots within a semi-arid city and mitigate high urban air temperatures through using ULC within larger urban climate mitigation strategies.

Residential green space in association with the methylation status in a CpG site within the promoter region of the placental serotonin receptor HTR2A

Green space could influence adult cognition and childhood neurodevelopment , and is hypothesized to be partly driven by epigenetic modifications. However, it remains unknown whether some of these associations are already evident during foetal development. Similar biological signals shape the developmental processes in the foetal brain and placenta.Therefore, we hypothesize that green space can modify epigenetic processes of cognition-related pathways in placental tissue, such as DNA-methylation of the serotonin receptor HTR2A. HTR2A-methylation was determined within 327 placentas from the ENVIRONAGE (ENVIRonmental influence ON early AGEing) birth cohort using bisulphite-PCR-pyrosequencing. Total green space exposure was calculated using high-resolution land cover data derived from the Green Map of Flanders in seven buffers (50 m-3 km) and stratified into low (<3 m) and high (≥3 m) vegetation. Residential nature was calculated using the Land use Map of Flanders. We performed multivariate regression models adjusted for several a priori chosen covariables. For an IQR increment in total green space within a 1,000 m, 2,000 m and 3,000 m buffer the methylation of HTR2A increased with 1.47% (95%CI:0.17;2.78), 1.52% (95%CI:0.21;2.83) and 1.42% (95%CI:0.15;2.69), respectively. Additionally,, we found 3.00% (95%CI:1.09;4.91) and 1.98% (95%CI:0.28;3.68) higher HTR2A-methylation when comparing residences with and without the presence of nature in a 50 m and 100 m buffer, respectively. The methylation status of HTR2A in placental tissue is positively associated with maternal green space exposure. Future research is needed to understand better how these epigenetic changes are related to functional modifications in the placenta and the consequent implications for foetal development.

ECMWF short-term prediction accuracy improvement by deep learning

This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main idea behind this work is to improve short-term (up to 3 days) forecasts delivered by a global numerical weather prediction (NWP) model called ECMWF (European Centre for Medium-Range Weather Forecasts). In comparison to the existing local weather models that typically provide weather forecasts for limited geographic areas (e.g., within one country but they are more accurate), ECMWF offers a prediction of the weather phenomena across the world. Another significant benefit of this global NWP model includes the fact, that by using it in several well-known online applications, forecasts are freely available while local models outputs are often paid. Our proposed ECMWF-enhancing model uses a combination of raw ECMWF data and additional input parameters we have identified as useful for ECMWF error estimation and its subsequent correction. The ground truth data used for the training phase of our model consists of real observations from weather stations located in 10 cities across two European countries. The results obtained from cross-validation indicate that our parametric model outperforms the accuracy of a standard ECMWF prediction and gets closer to the forecast precision of the local NWP models.

The Extreme Heat Wave over Western North America in 2021: An Assessment by Means of Land Surface Temperature

In our current global warming climate, the growth of record-breaking heat waves (HWs) is expected to increase in its frequency and intensity. Consequently, the considerably growing and agglomerated world’s urban population becomes more exposed to serious heat-related health risks. In this context, the study of Surface Urban Heat Island (SUHI) intensity during HWs is of substantial importance due to the potential vulnerability urbanized areas might have to HWs in comparison to their surrounding rural areas. This article discusses Land Surface Temperatures (LST) reached during the extreme HW over Western North America during the boreal summer of 2021 using Thermal InfraRed (TIR) imagery acquired from TIR Sensor (TIRS) (30 m spatial resolution) onboard Landsat-8 platform and Moderate Resolution Imaging Spectroradiometer (MODIS) (1 km spatial resolution) onboard Terra/Aqua platforms. We provide an early assessment of maximum LSTs reached over the affected areas, as well as impacts in terms of SUHI over the main cities and towns. MODIS series of LST from 2000 to 2021 over urbanized areas presented the highest recorded LST values in late June 2021, with maximum values around 50 °C for some cities. High spatial resolution LSTs (Landsat-8) were used to map SUHI intensity as well as to assess the impact of SUHI on thermal comfort conditions at intraurban space by means of a thermal environmental quality indicator, the Urban Field Thermal Variance Index (UFTVI). The same high resolution LSTs were used to verify the existence of clusters and employ a Local Indicator of Spatial Association (LISA) to quantify its degree of strength. We identified the spatial distribution of heat patterns within the intraurban space as well as described its behavior across the thermal landscape by fitting a polynomial regression model. We also qualitatively analyze the relationship between both UFTVI and LST clusters with different land cover types. Findings indicate that average daytime SUHI intensity for the studied cities was typically within 1 to 5 °C, with some exceptional values surpassing 7 °C and 9 °C. During night, the SUHI intensity was reduced to variations within 1–3 °C, with a maximum value of +4 °C. The extreme LSTs recorded indicate no significant influence of HW on SUHI intensity. SUHI intensity maps of the intraurban space evidence hotspots of much higher values located at densely built-up areas, while urban green spaces and dense vegetation show lower values. In the same manner, UTFVI has shown “no” SUHI for densely vegetated regions, water bodies, and low-dense built-up areas with intertwined dense vegetation, while the “strongest” SUHI was observed for non-vegetated dense built-up areas with low albedo material such as concrete and pavement. LST was evidenced as a good marker for assessing the influence of HWs on SUHI and recognizing potential thermal environmental consequences of SUHI intensity. This finding highlights that remote-sensing based LST is particularly suitable as an indicator in the analysis of SUHI intensity patterns during HWs at different spatial resolutions. LST used as an indicator for analyzing and detecting extreme temperature events and its consequences seems to be a promising means for rapid and accurate monitoring and mapping.