Search Results (163)

Green roofs are increasingly being adopted as sustainable, nature-based solutions for managing urban stormwater, mitigating the urban heat island effect, and saving energy in buildings. However, the long-term performance of their individual components—particularly filter geotextiles—remains understudied, despite their critical role in maintaining system functionality. The filter layer, responsible for preventing clogging of the drainage layer with fine substrate particles, directly affects the hydrological performance and service life of green roofs. While most existing studies focus on the initial material properties, there is a clear gap in understanding how geotextile filters behave after prolonged exposure to real-world environmental conditions. This study addresses this gap by assessing the mechanical and structural integrity of geotextile filters after five years of use in both extensive and intensive green roof systems. By analyzing changes in surface morphology, microstructure, and porosity through tensile strength tests, digital imaging, and scanning electron microscopy, this research offers new insights into the long-term performance of geotextiles. Results showed significant retention of tensile strength, particularly in the machine direction (MD), and a 56% reduction in porosity, which may affect filtration efficiency. Although material degradation occurs, some geotextiles retain their structural integrity over time, highlighting their potential for long-term use in green infrastructure applications. This research emphasizes the importance of material selection, long-term monitoring, and standardized evaluation techniques to ensure the ecological and functional resilience of green roofs. Furthermore, the findings contribute to advancing knowledge on the durability and life-cycle performance of filter materials, promoting sustainability and longevity in urban green infrastructure. Full article

The article presents an innovative methodology supporting sustainable urban development through the strategic expansion of green infrastructure in Warsaw, based on the 3-30-300 concept. The proposed approach integrates a multi-criteria Fuzzy Analytic Hierarchy Process (F-AHP) with Geographic Information System (GIS) tools, enabling objective and precise identification of suitable locations for new parks of at least 1 hectare in size. The analysis considers five key factors: distance from populated areas, land cover and use, surface temperature, proximity to nuisance facilities, and an NDVI index value. The study results demonstrated a significant increase in green space accessibility across the city. In all districts of Warsaw, the number of residential buildings meeting the criterion of a maximum 300 m distance to a park or forest increased—from 2% in Rembertów to 32% in Wilanów. The districts of Ursynów and Wilanów exceeded the 30% green space coverage threshold, while Białołęka reached 29%. These results indicate the real potential to achieve the goals of the 3-30-300 concept, contributing simultaneously to sustainable urban development, improved quality of life, mitigation of the urban heat island effect, increased biodiversity, and enhanced climate change adaptation. Spatial limitations related to high-density development were also identified—many districts lack available space for large parks. A viable solution supporting balanced development may lie in implementing smaller green forms, such as green squares or micro-parks, particularly in areas of planned development. The proposed methodology serves as a practical tool to support land-use management and sustainable spatial planning, addressing contemporary environmental, social, and urban challenges. Full article

An urban heat island (UHI) refers to urban areas that experience higher temperatures due to heat absorption and retention by impervious surfaces compared to the surrounding rural areas. Urban wetlands are crucial in mitigating the UHI effect and improving climate resilience via their cooling effect. This study examines Colombo, Sri Lanka, the RAMSAR-accredited wetland city in South Asia, to assess the cooling effect of urban wetlands based on 2023 dry season data for effective sustainable management. We used Landsat 8 and 9 data to create Land Use/Cover (LUC), Land Surface Temperature (LST), and surface-reflectance-based maps using the Google Earth Engine (GEE). The Enhanced Vegetation Index (EVI), Modified Normalized Difference Water Index (mNDWI), topographic wetness, elevation, slope, and impervious surface percentage were identified as the influencing variables. The results show that urban wetlands in Colombo face tremendous pressure due to rapid urban expansion. The cooling intensity positively correlates with wetland size. The threshold value of efficiency (TVoE) of urban wetlands in Colombo was 1.42 ha. Larger and more connected wetlands showed higher cooling effects. Vegetation- and water-based wetlands play an important role in <10 km urban areas, while more complex shape configuration wetlands provide better cooling effects in urban and peri-urban areas due to edge effects. Urban planners should prioritize protecting wetland areas and ensuring hydrological connectivity and interconnected wetland clusters to maximize the cooling effect and sustain ecosystem services in rapidly urbanizing coastal cities. Full article

In this research, we present the development and validation of a compact, resource-efficient (low-cost, low-energy), distributed, real-time traffic and air quality monitoring system. Deployed since November 2023 in a small town that relies on burning various fuels and waste for winter heating, the system comprises three IoT units that integrate image processing and environmental sensing for sustainable urban and environmental management. Each unit uses an embedded camera and sensors to process live data locally, which are then transmitted to a central database. The image processing algorithm counts vehicles by type with over 95% daylight accuracy, while air quality sensors measure pollutants including particulate matter (PM), equivalent carbon dioxide (eCO₂), and total volatile organic compounds (TVOCs). Data analysis revealed fluctuations in pollutant concentrations across monitored areas, correlating with traffic variations and enabling the identification of pollution sources and their relative impacts. Recorded PM10 daily average levels even reached eight times above the safe 24 h limits in winter, when traffic values were low, indicating a strong link to household heating. This work provides a scalable, cost-effective approach to traffic and air quality monitoring, offering actionable insights for urban planning and sustainable development. Full article

Rapid urbanization in Karachi, Pakistan, has resulted in increased impervious surfaces, leading to significant challenges, such as frequent flooding, urban heat islands, and loss of vegetation. These issues pose challenges to urban resilience, livability, and sustainability, which further demand solutions that incorporate urban greening and effective water management. This research uses remote sensing technologies and Geographic Information Systems (GISs), to analyze current surface treatments and their relationship to Karachi’s blue-green infrastructure. By following this approach, we evaluate flood risk and identify key flood-conditioning factors, including elevation, slope, rainfall distribution, drainage density, and land use/land cover changes. By utilizing the Analytical Hierarchy Process (AHP), we develop a flood risk assessment framework and a comprehensive flood risk map. Additionally, this research proposes an innovative Sponge City (SC) framework that integrates nature-based solutions (NBS) into urban planning, especially advocating for the establishment of green infrastructure, such as green roofs, rain gardens, and vegetated parks, to enhance water retention and drainage capacity. The findings highlight the urgent need for targeted policies and stakeholder engagement strategies to implement sustainable urban greening practices that address flooding and enhance the livability of Karachi. This work not only advances the theoretical understanding of Sponge Cities but also provides practical insights for policymakers, urban planners, and local communities facing similar sustainability challenges. Full article

Understanding building energy demand is critical for addressing climate uncertainty challenges and ensuring sustainable urban growth. This study develops a building energy demand (BED) model to explore how climate variation and urban expansion affect residential and commercial space heating and cooling demands in Morocco for three scenarios, namely, 2005, 2018, and 2018 + 1.5 °C. The results show that coastal cities have lower heating and cooling needs due to the oceanic influence, while interior cities require significantly higher heating demand per-unit-floorspace. Between 2005 and 2018, urban growth increased total heating and cooling demand by 218.8 GWh, particularly in northern and coastal regions, despite per-unit-floorspace reductions in milder climates and improved building efficiency in 2018. Residential heating remains the dominant energy use, though commercial demand is significant in urban centers. Under the 2018 + 1.5 °C hypothetical scenario, heating demand across Morocco declines by 335.8 GWh compared to 2018, with urban areas amplifying this trend. Meanwhile, cooling demand increases slightly by 44.4 GWh, with major cities experiencing relative increases of up to 50%. These findings highlight a trade-off where reduced winter heating needs are partly offset by increased summer cooling demands in densely urbanized areas. The study identifies key urban hotspots for targeted interventions, emphasizing the need for energy-efficient building designs, climate-adaptive urban planning, and resilient energy management strategies to sustainably address shifting seasonal energy patterns. Full article

This literature review examines the relationship between the urban environment and human health, focusing on five key parameters: air pollution, extreme temperatures, noise, green spaces, and urban mobility. A systematic review was conducted using indexed scientific databases (Scopus, Web of Science, and PubMed) and technical reports, following predefined search terms and exclusion criteria. A total of 131 publications were selected and analyzed. The study highlights the negative health effects of air pollution, heat, and noise—particularly on the respiratory, cardiovascular, nervous, and reproductive systems—especially in vulnerable populations including older adults, children, pregnant women, individuals with chronic illnesses, and those living in socioeconomically disadvantaged areas. In contrast, green spaces and sustainable mobility have shown beneficial impacts, including improvements in mental health, increased physical activity, and indirect benefits as they contribute to reducing air pollution, urban heat, and noise. Among all parameters, air pollution emerges as the most extensively studied and regulated, while significant research gaps persist in the fields of urban mobility and noise pollution. Furthermore, regulatory development remains limited across all parameters analyzed, highlighting the need for more comprehensive and consistent policy frameworks. Based on the evidence, three key urban strategies are proposed: renaturalizing cities, promoting sustainable mobility, and implementing data-driven management and educational tools. These actions are essential to create healthier, more resilient, and sustainable urban environments. Full article

Brazil has made progress in Municipal Solid Waste (MSW) management through national legislation focused on integrated waste handling. However, challenges persist, particularly regarding MSW overproduction. A sustainable alternative is Refuse-Derived Fuel (RDF), generated from MSW with or without biomass addition. To be viable for combustion, RDF must meet established energy and environmental quality standards. In this context, a mathematical model based on fuzzy logic was developed to classify RDF quality and support decision-making. Five RDF samples were tested, evaluating their Lower Heating Value (LHV), chlorine, and mercury contents using calorimetry, atomic absorption, and X-ray fluorescence. Results indicate that RDF produced solely from MSW tends to have inadequate LHV, necessitating drying pretreatment. Even with the addition of peanut shells, the highest classification achieved was “Regular”, suggesting limited suitability for combustion in furnaces or boilers without pretreatment. Since the general composition of MSW in Brazil is consistent with the characteristics analyzed, RDF may remain unviable for energy recovery under similar conditions. Economic feasibility studies on drying are recommended, especially in urban centers with limited landfill space. Full article

In rapidly urbanizing regions, encroachment on native green spaces has exacerbated ecological issues such as urban heat islands and flooding. Accurate mapping of tree species distribution is therefore vital for sustainable urban management. However, the high heterogeneity of urban landscapes, resulting from the coexistence of diverse land covers, built infrastructure, and anthropogenic activities, often leads to reduced robustness and transferability of remote sensing classification methods across different images and regions. In this study, we used very high–resolution Pléiades imagery and field-verified samples of eight common urban trees and background land covers. By employing transfer learning with advanced segmentation networks, we evaluated each model’s accuracy, robustness, and efficiency. The best-performing network delivered markedly superior classification consistency and required substantially less training time than a model trained from scratch. These findings offer concise, practical guidance for selecting and deploying deep learning methods in urban tree species mapping, supporting improved ecological monitoring and planning. Full article

Extreme climate change is today’s world’s most pressing and challenging problem. Increases in greenhouse gas emissions, the warming of the atmosphere and ocean, increased precipitation, rising sea levels, and temperature rise are the major effects of climate change that significantly affect urban infrastructure. Green Infrastructure (GI) is an increasingly acknowledged tool for climate change adaptation that contributes to sustainable urban and rural development. This study reviewed 111 research articles to identify and summarize the research findings about the role of GI in climate change adaptation. Furthermore, the research articles are grouped into three sectors with the most benefits of green infrastructure in climate change adaptation: mitigating urban heat islands, increasing ecosystem resilience, and flood risk management. The literature was further divided according to the developed or utilized strategies and techniques. The findings suggest that the topic of GI’s role in climate change adaptation is very current and it has been studied frequently in the last five years. Full article

The continued expansion of cities in economic, population and geographical terms leads to significant environmental and infrastructural pressures, including the need for efficient municipal solid waste (MSW) management. This research focuses on the characterization of MSW generated in the city of Skopje and the investigation of its thermo-physical properties and energy utilization potential. The analyses cover physical and chemical properties, including density, moisture content, volatile matter, ash, and higher heating value, using adequate testing methods. The results indicate that MSW has a relatively high gross calorific value, surpassing typical MSW ranges comparable to those of solid fossil fuels. With approximately 79.42% volatile matter and a low ash content of 7.76%, the considered MSW demonstrates excellent combustibility. Chemical analysis reveals high carbon (53.12%) and hydrogen (7.69%) levels, supporting high energy value, while low nitrogen (0.84%) and sulfur (0.26%) levels ensure minimal NO_x and SO_x emissions. These characteristics position MSW as a suitable feedstock for energy production in incineration facilities. However, the heterogeneous composition of MSW presents challenges to process stability, necessitating prior waste preparation. The research concludes that harnessing waste energy potential could contribute to sustainability, reduce reliance on fossil fuels, and improve the environmental conditions in large urban areas. Full article

The rapid urbanization processes across the world can be considered one of the most influential factors in climate change, particularly in metropolitan areas. In South America, the growing population and recurrent non-sustainable or controlled urban land management plans are even increasing the negative consequences of urban heat islands. As a representative case study, Soledad in northern Colombia is an area with recurrent strong wind events, which have caused significant damage to property and human lives, conditioning urban plans. This research aimed to assess the micrometeorological conditions in areas of Soledad, where cyclonic events are highly frequent, to gather essential data on urban planning to understand microclimate changes. We conducted in situ measurements of air temperature, surface temperature, wind speed, relative humidity, and atmospheric pressure across different Local Climate Zones (LCZs). Data were analyzed to assess the impact of urban form, vegetation, and sky openness on microclimatic variations. Our results demonstrated that urban morphology, vegetation cover, and sky openness significantly influenced local microclimates, with lower Sky View Factor (SVF) and higher Leaf Area Index (LAI) values contributing to reduced temperatures and improved airflow. Areas with denser urban canyons exhibited higher temperatures and lower wind speeds, emphasizing the need for strategic urban planning to mitigate heat stress and enhance ventilation. Full article

Space heating consumption prediction is critical for energy management and efficiency, directly impacting sustainability and efforts to reduce greenhouse gas emissions. Accurate models enable better demand forecasting, promote the use of green energy, and support decarbonization goals. However, existing models often lack precision due to limited feature sets, suboptimal algorithm choices, and limited access to weather data, which reduces generalizability. This study addresses these gaps by evaluating various Machine Learning and Deep Learning models, including K-Nearest Neighbors, Support Vector Regression, Decision Trees, Linear Regression, XGBoost, Random Forest, Gradient Boosting, AdaBoost, Long Short-Term Memory, and Gated Recurrent Units. We utilized space heating consumption data from the European Central Bank Headquarters office as a case study. We employed a methodology that involved splitting the features into three categories based on the correlation and evaluating model performance using Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and R-squared metrics. Results indicate that XGBoost consistently outperformed other models, particularly when utilizing all available features, achieving an R² value of 0.966 using the weather data from the building weather station. This model’s superior performance underscores the importance of comprehensive feature sets for accurate predictions. The significance of this study lies in its contribution to sustainable energy management practices. By improving the accuracy of space heating consumption forecasts, our approach supports the efficient use of green energy resources, aiding in the global efforts towards decarbonization and reducing carbon footprints in urban environments. Full article

Green belts, consisting mainly of natural forests, woodlands, and agricultural areas surrounding major cities, play an essential role in regulating urban development and controlling the expansion of metropolitan areas. Although this concept has been extensively studied in the world’s major metropolitan areas, it remains relatively unknown in many countries, particularly in Africa. There is a great need for research to better understand urban vegetation cover on the continent. This article proposes a systematic review of African publications on green cover for the period 2010 to 2024. A descriptive and thematic analysis of the selected scientific papers was carried out using a database established to examine the state of existing research and understanding of the management of these plant formations in Africa. The results of these analyses highlight several major challenges facing urban forestry, including increasing anthropogenic pressures, lack of urban planning that integrates urban forestry, and shortcomings in the management of existing forest landscapes. The thematic analysis has also helped to identify the topics addressed by African researchers, identify gaps in research, and suggest directions for future studies. Three priority areas emerge from this analysis: the conservation of natural or artificial green belts around cities, the impact of these forest landscapes on urban heat islands (climate impact), and the sustainability of ecosystem management in the context of sustainable urbanization. These guidelines will enable a better understanding and valorization of green belts in Africa, thus contributing to the construction of more sustainable cities and the efficient management of forest landscapes. Full article

The frequent occurrence of urban floods (UFs) poses significant threats to public safety and the national economy. Accurate estimation of urban flood susceptibility (UFS) and the identification of potential hotspots are critical for effective UF management. However, existing UFS studies often fall short due to a limited understanding of UFs’ nature, frequently relying on disaster factors analogous to those used for natural floods while neglecting key urban characteristics, limiting the accuracy of UFS estimates. To address these challenges, we propose a novel framework for UFS assessment. Unlike those studies that focus primarily on topographic and surface characteristics, our approach integrates urban-specific factors that capture the distinctive attributes of the urban environment, including Urban Heat Island Intensity, Urban Rain Island Intensity, Urban Resilience Index, and Impervious Surface Percentage. Guangzhou was selected as the study area, where machine learning methods were employed to calculate UFS, and Shapley Additive Explanation was utilized to quantify the contributions of employed factors. We evaluated the significance of urban factors from three perspectives: classifier performance, map accuracy, and factor importance. The results indicate that (1) urban factors hold significantly greater importance compared to other factors, and (2) the incorporation of urban factors markedly enhances both the performance of the trained classifier and the accuracy of the UFS map. These findings underscore the value of integrating urban factors into UFS assessments, thereby contributing to more precise UF management and supporting sustainable urban development. Full article