Heterogeneous Urban Environment Research Articles

Terrain's steepness governs sensitivity of urban oak forests to climate variability

Urban forests, vital for providing ecosystem services in cities, face challenges from climatic factors, particularly in heterogeneous urban environments. Kyiv, one of Europe’s largest and most populated cities, boasts a varied topography, with about 55 % of its area covered by forests, where Quercus robur predominates. In this study we investigate the sensitivity of Q. robur trees to climatic factors within Kyiv's urban forest, utilizing tree ring widths as a proxy. Employing dendrochronological approaches, we built nine site-mean ring-width chronologies and identified the growth pointer years and their frequency. Using moving windows in daily correlation analysis, we examined Q. robur response to climatic variations over the last 135 years. Additionally, linear mixed-effects models (LMMs) were applied to the site-specific characteristics such as slope steepness, altitude a.s.l., soil type, soil mechanical composition, soil pH, forest type and coefficient of soil saturated hydraulic conductivity, to identify which of them exert a significant influence on studied trees’ sensitivity to precipitation and temperature. Our results showed that the frequency of negative or positive pointer years in Q. robur growth coincides with extremely dry or wet years. The precipitation amount falling from the dormant period to the early growing season emerges as the key factor for Q. robur trees' radial growth. The trees' sensitivity to this factor was found to be significantly influenced by the slope steepness. Our findings contribute valuable insights into the complex interplay between urban heterogeneity, climatic factors, and the climatic sensitivity of Q. robur in urban areas. This research contributes to a deeper understanding of urban forests’ dynamics, proposing management strategies for slope stability, soil water retention, and natural tree regeneration.

Adaptive shading: How microclimates and surface types amplify tree cooling effects?

Shading is the most critical process in tree cooling. Although the influence of tree features on shade supply has been extensively studied, the efficiency of shade performance in diverse environments remains poorly understood. The latter is crucial, as urban areas are highly heterogeneous, and local microclimates and land cover heterogeneity are among the most perceptible and observable aspects. We randomly selected several typical tree patches in Beijing to explore whether and how tree-shade efficiency varies with these factors. We used temporal variations as a proxy to study the impact of microclimatic heterogeneity on tree shade efficiency by comparing sunny and cloudy conditions, as well as different times of the day. In addition, we investigated the differences in shading efficiency over various surfaces by comparing pairs of regular urban surfaces (asphalt, concrete, permeable brick, and grass). We found that tree shade significantly improved the local thermal environment, but its effectiveness varied with the environmental conditions. Tree shading resulted in a higher cooling efficiency under greater external thermal stress. In addition, the cooling efficiency was greater on surfaces with higher temperatures. We also identified a notable characteristic of tree shading for cooling: the thermal environment beneath the tree shade tends to stabilize at a relatively constant value across various external conditions. These results provide practical guidelines for optimizing tree layouts in highly heterogeneous urban environments and enhancing limited tree resources for more effective and economical improvement of urban thermal conditions.

Exploring dispersion modelling for resuspended pollen particles in a heterogeneous urban environment

ABSTRACT Urban air quality assessment in regard to biogenic emissions such as airborne pollen grains (PGs) is indispensable in the urban environment which significantly affects human health, climates, ecosystems, and energy production. Dispersion and deposition of PGs because of resuspension events (ReSE) are investigated using computational fluid dynamics (CFD). The mechanisms in PGs movement are achieved through the Lagrangian formulation-based discrete-phase model (DPM). The observed pollen grains distribution pattern is highly spatially dependent on heterogeneous urban obstacles as it induces modified characteristics of the wind flow field. Wind-urban structure interplays in PGs concentration variation. The simulated results identify the pollen hot-spot zones, where vortical flow features and low wind speed dominate. This approach offers a detailed understanding of the dispersion patterns of airborne PGs in urban environments because of ReSE with high resolution.

Towards urban wind utilization: The spatial characteristics of wind energy in urban areas

Urban wind energy is gaining increasing recognition for its environmentally friendly nature, particularly in the context of energy shortages. Predicting wind speed in urban environments is challenging due to the varying roughness and drag caused by obstacles on the ground. The complexity of urban morphology significantly disrupts the wind field and hampers the utilization of wind energy. This study aimed to investigate the diverse impacts of highly heterogeneous urban environment on the urban wind energy by innovatively establishing spatially varying power law wind profiles. Firstly, long-term LIDAR observations were conducted to measure the vertical wind profile in the study area. Then, a high-resolution LCZ map was created and integrated into the Weather Research and Forecasting (WRF) model to reproduce the urban wind field, which was validated using the observational data. Finally, the spatial characteristics of the urban wind field and wind energy were analyzed based on 3 representative points and the urban LCZ categories. The results show that the average power law exponents of each LCZ classification ranges from 0.29 to 0.75, with the average power law exponent of the high-volume building area being larger than 0.6. The highest wind power density in the study area can reach 343.3W/m2 at 200m height. Further, significant negative correlation coefficients were found between wind power density and building height, as well as building density, with values of approximately −0.6 and −0.35, respectively. Overall, the high-volume-ratio built-up areas with higher roughness had a notable impact on the wind speed by increasing the power law exponents. These spatially varying wind profiles are expected to provide technical support for the utilization of urban wind energy.

Determinants of Aedes mosquito larval ecology in a heterogeneous urban environment- a longitudinal study in Bengaluru, India.

Aedes-borne disease risk is associated with contemporary urbanization practices where city developing structures function as a catalyst for creating mosquito breeding habitats. We lack better understanding on how the links between landscape ecology and urban geography contribute to the prevalence and abundance of mosquito and pathogen spread. An outdoor longitudinal study in Bengaluru (Karnataka, India) was conducted between February 2021 and June 2022 to examine the effects of macrohabitat types on the diversity and distribution of larval habitats, mosquito species composition, and body size to quantify the risk of dengue outbreak in the landscape context. A total of 8,717 container breeding sites were inspected, of these 1,316 were wet breeding habitats. A total of 1,619 mosquito larvae representing 16 species from six macrohabitats and nine microhabitats were collected. Aedes aegypti and Aedes albopictus were the dominant species and significantly higher in artificial habitats than in natural habitats. Breeding preference ratio for Aedes species was high in grinding stones and storage containers. The Aedes infestation indices were higher than the WHO threshold and showed significant linear increase from Barren habitat to High density areas. We found Ae. albopictus breeding in sympatry with Ae. aegypti had shorter wing length. A large proportion of larval habitats were man-made artificial containers. Landscape ecology drives mosquito diversity and abundance even at a small spatial scale which could be affecting the localized outbreaks. Our findings showed that sampling strategies for mosquito surveillance must include urban environments with non-residential locations and dengue transmission reduction programmes should focus on 'neighbourhood surveillance' as well to prevent and control the rising threat of Aedes-borne diseases.

Integrating Machine Intelligence to Estimate PM2.5 Concentration for Sustainable Urban Air Quality Management

Air quality degradation, particularly the proliferation of fine particulate matter (PM2.5), poses a critical threat to environmental sustainability and public health. This paper introduces a comprehensive machine learning (ML) framework designed to predict PM2.5 concentrations, addressing the complexities inherent in heterogeneous urban environments. Drawing from a review of existing literature encompassing diverse ML methodologies applied to PM2.5 prediction, this study proposes an innovative approach amalgamating various data sources, including meteorological, geographical, and anthropogenic factors. Leveraging ensemble learning techniques and novel algorithmic models, our framework aims to surpass limitations encountered in current predictive models, enabling accurate and localized PM2.5 predictions. The significance of this research lies in its potential to offer a robust tool for environmental policymakers and urban planners, facilitating informed decisions towards mitigating PM2.5 pollution and fostering sustainable environments. Through evaluation of multiple ML algorithms, this paper contributes a novel predictive model crucial for enhancing air quality management.

Rising Coastal Groundwater as a Result of Sea‐Level Rise Will Influence Contaminated Coastal Sites and Underground Infrastructure

AbstractSea‐level rise (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much land area as inundation from SLR, and 5,282 additional state‐managed sites of contamination may be vulnerable to inundation from GWR in a 1.0 m SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the saltwater interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are disproportionately exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area.

Investigation of the ENVI-met model sensitivity to different wind direction forcing data in a heterogeneous urban environment

Abstract. As the frequency of extreme heat events in cities is significantly increasing due to climate change, the implementation of adaptation measures is important for urban planning. Microclimate modelling approaches enable scenario analyses and evaluations of adaptation potentials. An ENVI-met microclimate model was setup for a heterogeneous urban study area in Cologne/Germany characterized by closed building structures in the eastern part and an urban park area in the western part. The goal of this paper is to evaluate the model sensitivity and performance to different wind direction forcing and demonstrate the importance of accurate wind forcing data for precise microclimate modelling evaluated with sensor measurements. To this end, we compared simulated air temperatures at 3 m height level using measured wind direction forcing data with simulated air temperatures using constant wind direction forcing from west, north, east and south direction. All other forcing data like wind speed were kept exactly the same as in the reference run. This sensitivity study was performed for a warm summer day in 2022. The model results of all five model runs (reference plus four scenarios) were compared to microclimatological measurements derived from one station of a dense meteorological sensor network located in the study area using the simulated mean air temperatures. We found significant temperature differences between the four sensitivity tests and the reference run as well as to the sensor measurements. Temperature differences between the reference run and the measurements were small and a high statistical model fit could be determined (Nash Sutcliffe Model Efficiency Coefficient/NSE = 0.91). The four model runs with constant wind directions showed significantly larger differences to measurement data and a worse statistical correlation between simulated and observed data (NSE between 0.62 and 0.15). For constant west winds, cooler air temperatures and higher wind speeds were found in the urban park and in the streets and courtyards east of the park. Constant east wind causes warmer air temperatures in the urban park area and lower wind speeds in the street canyons and inner courtyards. This shows that cooling effects in adjacent building blocks due to the greened urban park largely depend on the wind direction. The sensitivity tests show that the wind direction effect can result in local air temperature differences of up to 4 K on average. These results shows that even on summer days with low wind speeds, accurate wind direction data is highly relevant for accurate air temperature simulation. This finding can have important implications for urban planning and the design of green infrastructures in cities, e. g. for the design of fresh air corridors.

Mapping invasive alien plant species with very high spatial resolution and multi-date satellite imagery using object-based and machine learning techniques: A comparative study

ABSTRACT Invasive alien plant species (IAPS) have negative impacts on ecosystems, including the loss of biodiversity and the alteration of ecosystem functions. The strategy for mitigating these impacts requires knowledge of these species’ spatial distribution and level of infestation. In situ inventories or aerial photo interpretation can be used to collect these data but they are labor-intensive, time-consuming, and incomplete, especially when dealing with large or inaccessible areas. Remote sensing may be an effective method of mapping IAPS for a better management strategy. Several studies using remote sensing to map IAPS have focused on single species detection and were conducted in relatively homogeneous natural environments, while other common, more heterogeneous environments, such as urban areas, are often invaded by multiple IAPS, posing management challenges. The main objective of this study was to develop a mapping method for three major IAPS observed in the urban agglomeration of Quebec City (Canada), namely Japanese knotweed (Fallopia japonica); giant hogweed (Heracleum mantegazzianum); and phragmites (Phragmites australis). Mono-date and multi-date classification approaches were used with WorldView-3 and SPOT-7 satellite imagery, acquired in the summer of 2020 and in the autumn of 2019, respectively. To estimate presence probability, object-based image analysis (OBIA) and nonparametric classifiers such as Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were used. Overall, multi-date classification using WorldView-3 and SPOT-7 images produced the best results, with a Kappa coefficient of 0.85 and an overall accuracy of 91% using RF. For XGBoost, the Kappa coefficient was 0.81 with an overall accuracy of 89%, whereas the Kappa coefficient and overall accuracy were 0.80 and 88% for SVM classifier, respectively. Individual class performances based on F1-score revealed that Japanese knotweed had the highest maximum value (0.95), followed by giant hogweed (0.91), and phragmites (0.87). These results confirmed the potential of remote sensing to accurately map and simultaneously monitor the main IAPS in a heterogeneous urban environment using a multi-date approach. Although the approach is limited by image and reference data availability, it provides new tools to managers for IAPS invasion control.

Morphometric characters of Platanus × acerifolia (Aiton) Willd. leaves under the heterogeneous urban environments in the Republic of Srpska

Urban environments are often polluted with numerous contaminants originating from extensive traffic, industry and heating systems. London planetree (Platanus × acerifolia (Aiton) Willd.) is considered as a tree species resistant to air pollutants and climate changes in urban environments. Its resistance is based on the traits of adaptation to dry and warm conditions, bioindication of the degree of urban pollution and plant species with an important role in the nature-inspired solutions for urban areas sustainability. Therefore, this study aims to determine the differences in the morphometric traits of the leaf blade of London planetree, regarding the urban conditions of several cities in the Republic of Srpska. The study was carried out during May-June 2019. Trees for leaf sampling were selected in urban parks and alleys surrounded by high-traffic density streets. At the morphological level, the following leaf blade traits were analyzed: leaf blade length (L), maximum width of the axis of the midrib (W), distance from the midrib to the right margin (X), distance from the midrib to the left margin (Y), distance from the right lateral vein to the main vein (WR), distance from the left lateral vein to the main vein (WL), length of the right lateral vein (RL) and length of the left lateral vein (LL). The results showed that foliar comparisons have been useful in the determination of morphological differences of urban trees influenced by contrasting urban environments. Our results indicate that, based on leaf blade morphometric characters, Bijeljina was distinguished as a separate urban area, Doboj and Prijedor were grouped, while Banja Luka and Trebinje formed the second group. The results indicate the importance of applied analysis in the biological monitoring of the pollution level of urban environments.

Microbial biomarkers as indication of dynamic and heterogeneous urban water environments.

Water samples for the 16S rRNA gene and water quality analyses were collected from around 155km of river segments surrounding the urban areas in Xi'an, China. Multiple statistical analyses showed that the dynamic shifts of microbial communities in the Chan, Ba, and Feng Rivers from the spring to the summer seasons were apparent but little in the Zao River. The heterogeneity of microbial distributions was more due to the influence of hydrologic conditions and various sources of inflows in the rivers. The LEfSe analysis showed that the Chan and Zao Rivers, both more impacted by the sewage effluents, were more differentially abundant with bacteria related to polluted water, but the Ba and Feng Rivers, both on the outer side of the city, were more abundant with microbial communities in soil and freshwater environments in the summer. Multiple statistical analyses indicated that environmental variables had significant impacts on microbial communities. The geographical information system-based spatial analysis showed heterogeneity of microbial community distributions along the rivers. This study showed that the high-throughput sequencing analysis could identify some pathogenic bacteria that would significantly threaten public health and eco-environments in urban rivers.

Categorizing urban space based on visitor density and diversity: A view through social media data

Analyses of urban spaces have often stressed the importance of both the density and diversity of the people they attract. However, the diversity of people is a challenging concept to operationalize within the context of urban spaces, which is why many evaluations of urban space have relied primarily on density-based measures. We argue that a focus on only one of the two aspects misses important aspects of the variety of urban spaces in our cities. To address this, we design a methodology that evaluates both the density and diversity of human behavior in urban spaces based on geosocial media data. We operationalize density as the frequency of tweets from visitors to a particular location and diversity as the variety of the home neighborhoods of those visitors. Taking Singapore as a test case, we identify networks between the home neighborhoods of 28k Twitter users based on 2.2 million geolocated tweets collected between 2012 and 2016. Based on these data, we categorize the urban landscape of Singapore into four “performance” categories, namely High-Density/High-Diversity, High-Density/Low-Diversity, Low-Density/High-Diversity, and Low-Density/Low-Diversity. Our findings illustrate that this combined indicator provides useful nuance compared to differentiation between well and less performing spaces based on density alone. By enabling a categorization of urban spaces that fits closer to the diversity of human behavior in these spaces, human mobility data sets, such as the social media data we use, open the door to a practical evaluation of the design and planning of our heterogeneous urban environment.

Integrating geographical information systems, remote sensing, and machine learning techniques to monitor urban expansion: an application to Luanda, Angola

ABSTRACT According to many previous studies, application of remote sensing for the complex and heterogeneous urban environments in Sub-Saharan African countries is challenging due to the spectral confusion among features caused by diversity of construction materials. Resorting to classification based on spectral indices that are expected to better highlight features of interest and to be prone to unsupervised classification, this study aims (1) to evaluate the effectiveness of index-based classification for Land Use Land Cover (LULC) using an unsupervised machine learning algorithm Product Quantized K-means (PQk-means); and (2) to monitor the urban expansion of Luanda, the capital city of Angola in a Logistic Regression Model (LRM). Comparison with state-of-the-art algorithms shows that unsupervised classification by means of spectral indices is effective for the study area and can be used for further studies. The built-up area of Luanda has increased from 94.5 km2 in 2000 to 198.3 km2 in 2008 and to 468.4 km2 in 2018, mainly driven by the proximity to the already established residential areas and to the main roads as confirmed by the logistic regression analysis. The generated probability maps show high probability of urban growth in the areas where government had defined housing programs.

Going Mobile to Address Emerging Climate Equity Needs in the Heterogeneous Urban Environment

Abstract The Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step toward this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments. This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties, including wind profiles throughout the atmospheric column. The mobile observatory captured unique observations during its first few intensive observation periods. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY, reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY, offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods. Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort.

Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques.

Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapidly growing urban area within a short time is a prime factor for administrators for better environmental assessment and sustainable development of urban areas. Traditional survey approaches, on the other hand, are unable to meet the demand for rapid urban land management development, and there is a pressing need to develop new methods to address the limitations of existing ones. This study compares various urban spectral indices to other existing approaches in order to determine which index provides a better representation of the impervious area in the urban watershed. Landsat 8 OLI (Operational Land Imager) satellite images acquired on 15 March 2014 and 31 March 2020 are used in the present study. Indices, namely Modified Built-up Index (MBUI), SwiRed Index (SwiRed), and Enhanced Built-up and Bareness Index (EBBI), were utilized to extract impervious areas. Thresholding of indices is done by comparing them with 1000 reference points taken from Google Earth imagery of the respective years. The accuracy of the urban indices is assessed by comparing the results with high-resolution Google Earth Satellite Images. The impervious area is extracted from spectral indices and other remote sensing techniques such as maximum likelihood classification and support vector machine classification techniques. The overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed for the 2014 dataset was found to be 95.1%, 90.8%, 83.9%, 78.9%, and 87.3%, respectively, and the overall accuracy of SVM, MLC, MBUI, EBBI, and SwiRed was found to be 96%, 89.2%, 89.1%, 85.5%, and 92.6%, respectively. Impervious areas in the heterogeneous urban environment can be monitored in a better way and within lesser time by using spectral indices generated using Landsat 8 OLI (Operational Land Imager) satellite data.

A Suggestion-Based Hierarchical Energy Efficient Control Framework for Connected and Automated Vehicles in Heterogeneous Urban Environments

A Suggestion-Based Hierarchical Energy Efficient Control Framework for Connected and Automated Vehicles in Heterogeneous Urban Environments

ИССЛЕДОВАНИЕ ВЛИЯНИЯ НЕОДНОРОДНОЙ ГОРОДСКОЙ СРЕДЫ НА КАДАСТРОВУЮ СТОИМОСТЬ ЗЕМЕЛЬНЫХ УЧАСТКОВ

One of the most informative indicators of a land plot in the USRER is the cadastral value, periodi-cally determined by a special method by certified specialists from budgetary institutions that have re-cently been part of the state corporation Roscadastre. One of the problems of calculating the cadastral value is the collection and accounting of up-to-date information about the land plot and its environ-ment, followed by the correct implementation of the valuation algorithm. In this regard, the purpose of this work is to study the impact of a heterogeneous urban environment, constantly replenished with new infrastructure facilities as a result of its development and requiring their consideration in the cadastral valuation procedure. At the same time, it was found that the modern practice of assessment does not always use the presence of even existing objects, which served as the basis for this study. Thus, the object of the study was the cadastral value of land plots obtained as a result of the official cadastral valuation in Novosibirsk (land plots from segment 13 “Gardening and horticulture, low-rise residential development”) and tak-ing into account the impact of heterogeneities of the urban environment. Analytical selection and cor-relation-regression analysis of price-forming factors from the list determined by methodological guide-lines was performed. As a result, a regression model was obtained that confirmed the hypothesis of a disproportion in the calculation of the cost of land plots in relation to the level of development of the infrastructure of a settlement. In particular, a significant factor is the distance to the center of local ed-ucation and the existing kindergarten. The main result of the work was a list of qualitative and quanti-tative characteristics of land plots, the use of which makes it possible to increase the reliability of the results of the state cadastral assessment. The reasons preventing the qualitative determination of the cadastral value are indicated.

Targeted implementation of cool roofs for equitable urban adaptation to extreme heat

Cities are facing the twin pressures of greenhouse gas driven climatic warming and locally induced urban heating. These pressures are threatening populations that are sensitive to extreme heat due to sociodemographic factors including economic means. Heat-reducing infrastructure adaptation measures such as reflective “cool” materials can reduce urban temperatures. Here we examine the needs-based equity implications associated with heat-reducing cool roofing in Maricopa County, Arizona through application of high-resolution urban-atmospheric simulations. We simulate heatwave conditions and evaluate the air temperature reduction arising from uniform cool roof implementation (i.e., the entire urbanized county), and contrast results against simulated cooling impacts of needs-based targeted cool roof implementation in sociodemographically heat sensitive areas. We find that installing cool roofs uniformly, rather than in a targeted fashion, provides on average 0.66 °C reduction in the highest heat sensitivity area and 0.39 °C temperature reduction in the lowest heat sensitivity area due in part to a higher roof area density in the heat sensitive area. Targeting cool roof implementation yields 0.45 °C cooling in the most sensitive areas compared to 0.22 °C cooling in the least sensitive areas, meaning that needs-based targeted cool roofs in high sensitivity areas provide more relief than cool roofs targeted at low sensitivity areas, thus providing more cooling where it is most needed. Needs-based targeted implementation has the dual benefits of concurrently producing more than twice as much cooling and reducing heat exposure for the largest absolute number of individuals in the densely populated, highly heat sensitive areas. Targeting cool roof implementation to high heat sensitivity areas, however, does not achieve thermally equal temperatures in Maricopa County because the high sensitivity areas were substantially warmer than low sensitivity areas prior to implementation. This study illustrates the utility of a new “Targeted Urban Heat Adaptation” (TUHA) framework to assess needs-based equity implications of heat-reducing strategies and underscores its importance by examining the impacts of cooling interventions across sociodemographically heterogeneous urban environments.

Seismic vulnerability index method for hybrid timber–masonry structures. Numerical calibration and application to the city of Valparaíso, Chile

This paper presents a procedure for the extension of the Vulnerability Index Method (VIM) to the seismic assessment of hybrid timber–masonry buildings. The research is based on the execution of a detailed numerical study of this peculiar structural typology to identify the classes and calibrate the scores assigned to influential vulnerability parameters, namely the irregularities of the structural system, the type of floor, and the state of conservation of the building. A sensitivity analysis of the seismic capacity is developed by performing pushover analyses with several models corresponding to realistic variations of each structural or material parameter. The seismic performance of the buildings is analysed by the N2 Method. This methodology leads to the proposal of a new calibrated vulnerability index form that, together with those existing in literature for masonry and reinforced concrete structures, can be used for the seismic assessment of heterogeneous urban environments. The proposed VIM is applied to the assessment of 111 buildings located in the historical centre of the city of Valparaíso, Chile. The computation of the vulnerability index allows a fast evaluation of the seismic vulnerability of the urban buildings. The results plotted on vulnerability maps built within a GIS environment allows the assessment of the impact of potential collapses on the serviceability of two emergency evacuation routes included in the civil protection plans of the city.

Adaptation and validation of a voxel based energy transport model for conifer species

A spatially explicit energy balance model previously developed for deciduous trees was extended to account for physical differences in conifer species. These differences include the emission and scattering of radiation in needles compared to leaves as well as the boundary-layer conductance for needles. To validate the model, an array of sensors was placed in and around two separate isolated Picea pungens trees in a heterogeneous urban environment. Model validation examined components of the local-volume averaged needle energy budget utilizing radiative and turbulent fluxes as the forcing inputs. Analysis focused on the model's ability to reproduce integrated upward shortwave and longwave radiation, local radiation attenuation through the tree crown, and local needle surface temperature. The model performed quite well with R2 values above 0.9 for calculated versus measured integrated upward longwave and shortwave radiation and above 0.87 for needle surface temperature. The high level of agreement with experimental measurements was only possible with the modifications made to the boundary layer conductance for needles.