Search Results (741)

Water is one of the most valuable and essential resources for human life, yet its scarcity has become a pressing global issue exacerbated by climate change and population growth. To address the increasing demand for water driven by urbanization, industrial expansion, tourism, and agricultural needs, many countries are turning to desalination as a viable solution. This study investigates the integration of renewable energy sources (RES) with desalination technologies to enhance both sustainability and efficiency. A comprehensive review of major desalination methods has been conducted, with a particular focus on the application of solar and wind energy. Additionally, the challenges associated with renewable energy-powered desalination, including the need for effective energy storage systems and the inherent volatility of power supply, were explored. Our findings indicate that coupling renewable energy with desalination not only significantly reduces carbon emissions but also enhances the sustainability of water supply systems. The study also emphasizes the importance of emerging technologies, such as hybrid energy storage systems (HESS) and machine learning (ML), in optimizing RES powered desalination processes. Ultimately, this study aims to guide future research and development initiatives, promoting the global adoption of desalination systems powered by renewable energy. Full article

Historical urban environments are frequently abandoned with the rise in expansion. An example is Kendari, a city that is over two centuries old with long historical colonialism, such as the Dutch East Indies and Japan. The city is presently eroded due to modern development and demographic pressure. Therefore, this research aimed to identify how the urban layout of Kendari was used to define long-term preservation procedures. Conzen’s school of urban morphology methodology, utilized for the examination of the historical evolution of the urban landscape and the interaction with present urban development processes, was used to conduct this research. Historical cartographic data and changes in land use were used to perform a detailed examination of the evolution of the street structure, land distribution, and architectural layout. The result showed how historical, cultural, and economic aspects shaped the formation of Kendari old town. In conclusion, this research improved the understanding of Kendari’s historical urban structure, and supplied useful empirical data for planning the future development and conservation of the districts. Full article

Traffic flow prediction can guide the rational layout of land use. Accurate traffic flow prediction can provide an important basis for urban expansion planning. This paper introduces a personalized lightweight federated learning framework (PLFL) for traffic flow prediction. This framework has been improved and enhanced to better accommodate traffic flow data. It is capable of collaboratively training a unified global traffic flow prediction model without compromising the privacy of individual datasets. Specifically, a spatiotemporal fusion graph convolutional network (MGTGCN) is established as the initial model for federated learning. Subsequently, a shared parameter mechanism of federated learning is employed for model training. Customized weights are allocated to each client model based on their data features to enhance personalization during this process. In order to improve the communication efficiency of federated learning, dynamic model pruning (DMP) is introduced on the client side to reduce the number of parameters that need to be communicated. Finally, the PLFL framework proposed in this paper is experimentally validated using LPR data from Changsha city. The results demonstrate that the framework can still achieve favorable prediction outcomes even when certain clients lack data. Moreover, the communication efficiency of federated learning under this framework has been enhanced while preserving the distinct characteristics of each client, without significant interference from other clients. Full article

Rapid human development has altered land use types, significantly impacting carbon stock, and poor land use will lead to an increase in carbon emissions and exacerbate climate change. Understanding the relationship between land use changes and carbon storage is critical for developing sustainable land management strategies that support carbon sequestration and climate change mitigation. In this study, we analyzed and processed the land use transition changes from 1990 to 2020 and calculated the corresponding carbon storage. Based on the patterns of change and influencing factors (elevation, slope, soil type, GDP, population density, etc.), we predicted the future changes in land use and carbon storage in the Yiluo River Basin under different social development scenarios. It was found that due to the severe impact of natural factors, from 1990 to 2020, the area of cultivated land and grassland decreased by 1150.04 km² and 936.66 km², respectively, and the area of forested land and built-up area expanded by 1087.84 km² and 969.26 km², respectively. Carbon stocks in the region decreased between 1990 and 2010, followed by a modest recovery from 2010 to 2020, resulting in a total reduction of approximately 2.188 × 10⁶ t. Spatially, carbon stocks diminished in the eastern part but increased in the western part. To assess the long-term sustainability implications, the study simulated four future development scenarios for human society: natural development, urban development, ecological protection, and water conservation. The results showed that in the urban expansion scenario, the proportion of construction land increased significantly, while the ecological protection scenario led to a substantial expansion of forested areas. Notably, carbon stocks showed a significant increase only under the ecological protection scenario, whereas they exhibited a declining trend in all other scenarios. Full article

The continuous expansion at the urban scale has produced a lot of construction waste, which has created increasingly serious problems in the environmental, social, and economic realms. Reuse of this waste can address these problems and is critical for sustainable development. In recent years, construction waste has been extensively recycled and transformed into highly sustainable construction materials called controlled low-strength materials (CLSMs) in backfilling projects, pile foundation treatment, roadbed cushion layers, and other applications. However, CLSMs often experience shrinkage and cracking due to water loss influenced by climatic temperature factors, which can pose safety and stability risks in various infrastructures. The purpose of this paper was to study the mechanism of crack formation and strength degradation in a CLSM in a dry environment and to analyze the deterioration process of the CLSM at the macro- and micro-scales by using image analysis techniques and scanning electron microscopy (SEM). The test results show that with the drying time, the CLSM samples had different degrees of cracks and unconfined compressive strength (UCS) decreases, and increasing the content of ordinary Portland cement (OPC) reduced the number of cracks. The addition of bentonite with the same OPC content also slowed down the crack development and reduced the loss of UCS. The development of macroscopic cracks and UCS is caused by the microscopic scale, and the weak areas are formed due to water loss in dry environments and the decomposition of gel products, and the integrity of the microstructure is weakened, which is manifested as strength deterioration. This research provides a novel methodology for the reuse of construction waste, thereby offering a novel trajectory for the sustainable progression of construction projects. Full article

The assessment of ecological resilience in arid regions is crucial for understanding and mitigating the impacts of climate change and human activities, ensuring the sustainable management of these vulnerable ecosystems. Taking the Economic Belt on the Northern Slope of the Tianshan Mountains (EBNSTM) as the research area, a multi-dimensional evaluation model coupling vulnerability, health, and connectivity was used to explore the spatiotemporal variation and driving forces of ecological resilience. Firstly, a sub-item evaluation of ecological resilience was conducted from three aspects, including ecological vulnerability evaluation based on the CRITIC and AHP models, ecological health evaluation based on the InVEST model, and landscape connectivity evaluation based on the MSPA method. Then, the sequence polygon method was utilized to conduct a comprehensive multi-dimensional assessment of ecological resilience based on the aforementioned three evaluation results. Finally, the geographical detector model was utilized to identify the driving factors behind the spatial heterogeneity of ecological resilience. The results show the following: (1) From 2000 to 2020, the overall ecological resilience showed an upward trend and significant spatial heterogeneity. The overall distribution pattern exhibited a spatial feature of south higher, north lower, where the southern region displayed a clear high-high clustering characteristic, exerting a positive and radiating influence on surrounding areas. (2) The main driving factors of the spatial heterogeneity are DEM, precipitation, NPP, GDP, and PM2.5. And among different factors, the dual-factor enhancement effect is greater than the nonlinear enhancement of a single factor. (3) Human activities are important influencing factor, and the impact of urban expansion and economic growth on ecological resilience is becoming increasingly significant. Therefore, in the process of economic development, full consideration should be given to the self-repairing and adaptive capabilities of the ecosystem. Full article

Rapid urban expansion and chaotic urban land-use patterns cause many socio-economic and environmental issues, e.g., traffic congestion and urban heat islands; thus, scientific planning considering land-use trade-offs and layout optimization is highly required for resolving these issues, especially in the urban renewal stage. However, previous spatial optimization methods were weak in processing land-use patches and ignored their neighborhood dependency, leading to fragmented and inapplicable optimization results. Accordingly, this study proposes a patch-level and neighborhood-dependency spatial optimization method (PNO) to adjust urban land-use patterns considering multiple optimization targets (i.e., improving population and economy but controlling land surface temperature). The PNO represents land-use patterns in a graph structure, quantifies land-use patterns’ impacts on the population, economy, and land surface temperature, defines the spatiotemporal constraints of land-use optimization considering neighborhood-dependency and optimization sequences, and finally optimizes land uses and their spatial layouts based on a multi-objective genetic algorithm. Experiments were conducted in the urban area of Beijing, and the results suggested that, after optimization, the population and GDP can be improved by 667,323 people (4.72%) and USD 10.69 billion in products (2.75%) in the study area; meanwhile, the land surface temperature can be reduced by 0.12 °C (−0.32%). Through comparison, the proposed PNO outperforms previous spatial optimization methods, e.g., NSGA-II, in processing land-use patches as well as their neighborhoods. Taking the land-use map in 2022 as a reference, the PNO optimization results are more consistent with actual land-use changes (consistency of 25%), compared to the existing spatial optimization results (consistency of 10.6%). Thus, PNO is more applicable to land-use planning in urban renewal circumstances. Full article

Spatiotemporal vegetation changes serve as a key indicator of regional ecological environmental quality and provide crucial guidance for developing strategies for regional ecological protection and sustainable development. Currently, vegetation change studies in the Yangtze River Basin primarily rely on the Normalized Difference Vegetation Index (NDVI). However, the NDVI is susceptible to atmospheric and soil conditions and exhibits saturation phenomena in areas with high vegetation coverage. In contrast, the kernel NDVI (kNDVI) demonstrates significant advantages in suppressing background noise and improving saturation thresholds through nonlinear kernel transformation, thereby enhancing sensitivity to vegetation changes. To elucidate the spatiotemporal characteristics and driving mechanisms of vegetation changes in the Yangtze River Basin, this study constructed a temporal kNDVI using MOD09GA data from 2000 to 2022. Considering sectional heterogeneity, rather than analyzing the entire region as a whole as in previous studies, this research examined spatiotemporal evolution characteristics by sections using four statistical metrics. Subsequently, Partial Least Squares Path Modeling (PLSPM) was innovatively introduced to quantitatively analyze the influence mechanisms of topographic, climatic, pedological, and socioeconomic factors. Compared to traditional correlation analysis and the geographical detector method, PLSPM, as a theoretically driven statistical method, can simultaneously process path relationships among multiple latent variables, effectively revealing the intensity and pathways of driving factors’ influences, while providing more credible and interpretable explanations for kNDVI variation mechanisms. Results indicate that the overall kNDVI in the Yangtze River Basin exhibited an upward trend, with the midstream demonstrating the most significant improvement with minimal interannual fluctuations, the upstream displaying an east-increasing and west-stable spatial pattern, and the downstream demonstrating coexisting improvement and degradation characteristics, with these trends expected to persist. Driving mechanism analysis reveals that the upstream was predominantly influenced by the climatic factor, the midstream was dominated by terrain, and the downstream displayed terrain–soil coupling effects. Based on these findings, it is recommended that the upstream focus on enhancing vegetation adaptation management to climate change, the midstream need to coordinate the relationship between topography and human activities, and the downstream should concentrate on controlling the negative impacts of urban expansion on vegetation. Full article

Urban growth, a pivotal characteristic of economic development, brings many environmental and ecological challenges. Modeling urban growth is essential for understanding its spatial dynamics and projecting future trends, providing insights for effective urban planning and sustainable development. This study aims to assess the spatiotemporal patterns of urban growth and morphological evolution in mainland Shanghai from 2016 to 2060 using the SLEUTH model under multiple growth scenarios based on the Shanghai Urban Master Plan (2017–2035). A comprehensive set of urban growth metrics and quadrant analysis were employed to quantify the magnitude, rate, intensity, and direction of urban growth, as well as morphological evolution, over time. We found that (1) significant urban growth was observed across most scenarios, with the exception of stringent land protection. The most substantial growth occurred prior to 2045 with an obvious north–south disparity, where southern regions demonstrated more pronounced increases in urban land area and urbanization rates. (2) The spatiotemporal patterns of the rate and intensity of urban growth exhibited similar characteristics. The spatial pattern followed a “concave shape” pattern and displayed anisotropic behavior, with the high values for these indicators primarily observed before 2025. (3) The urban form followed a diffusion–coalescence process, with patch areas dominated by the infilling mode and patch numbers dominated by the edge-expansion mode. This resulted in significant alternating urban growth models in the infilling, edge-expansion, and leapfrog modes over time, influenced by varying protection intensities. These findings provide valuable insights for forward-looking urban planning, land use optimization, and the support of sustainable urban development. Full article

In recent years, the accelerated urbanization process in China has led to increased land resource constraints and unregulated expansion, imposing significant pressure on ecosystems and the environment. As a critical node along the Silk Road Economic Belt, the Turpan–Hami region has experienced rapid urban development under policy support but faces challenges in resource utilization efficiency and sustainable development. To address these challenges, this study innovatively combines nighttime light remote sensing data to quantify urban economic development intensity and integrates socioeconomic and natural environment indicators based on previous research. Four tree-based ensemble learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)—were employed to predict potential urban economic development suitability zones and their suitability intensity. The results show that the CatBoost model performed the best in suitability prediction, revealing significant spatial disparities: high-suitability areas are concentrated in regions with superior resource conditions and well-developed infrastructure, whereas areas with terrain constraints and inadequate infrastructure exhibit lower suitability. An analysis of changes over historical periods (2010, 2015, and 2020) demonstrates a gradual expansion of high-suitability regions over time. Full article

Green areas, thanks to their relatively unified natural systems, play several key roles. They contribute to the proper functioning and sustainable development of cities and also determine the quality of life for their inhabitants. As a result, urban planners and policy-makers frequently aim to maximize the benefits of green spaces by creating various programs and strategies focused on green infrastructure development, such as the Green City initiative. One of the objectives of this program is to create new urban parks. This research focuses on developing a new method for selecting sites for urban parks, taking into account factors related to the environment, accessibility, and human activity. The research was carried out for the area of Ciechanów city. To make the city areas more attractive to residents, the authorities aim to increase green spaces and also revitalize the existing greenery. The combination of the Fuzzy AHP method and fuzzy set theory (selecting appropriate fuzzy membership for each factor), along with the use of large and diverse geospatial datasets, minimized subjectivity in prioritizing criteria and allowed for a fully automated analysis process. Among the factors analyzed, land use emerged as the most significant, followed by the normalized difference vegetation index (NDVI) and proximity to surface water. The results indicated that 16% of the area was deemed highly suitable for urban park development, while 15% was considered unsuitable. One-at-a-time (OAT) sensitivity analysis, based on changes in the weight of the land-use factor, revealed that a 75% reduction in weight resulted in a nearly 57.2% decrease in unsuitable areas, while a 75% increase in weight led to a 40% expansion of the most suitable locations. The potential park locations were compared with a heat map of urban activity in the city. The developed method contributes to the discourse on the transparency of location decisions and the validity of the criteria used, to promote sustainable urban development that provides residents with access to active recreation. Full article

The relationship between the urbanization process and the ecological environment is key to regional development. As a typical Chinese city undergoing rapid urban development, Zhengzhou is an important representative of the urbanization process and the changes in the ecological environment. In this study, we explored the response relationship between urban development and the ecological environment in Zhengzhou, using night light data, Landsat satellite imagery, and population data from this city. The analysis of the NTL data showed that there were three stages of development in Zhengzhou from 2000 to 2021: the slow expansion stage from 2000 to 2003, the steady expansion stage from 2004 to 2011, and the rapid expansion stage from 2012 to 2021. The multi-year average RSEI value of Zhengzhou was less than 0.4, and it showed a trend of first increasing and then decreasing, indicating that the quality of the city’s ecological environment was poor and indirectly indicating that the urbanization degree of the region was significant. The changes in the NTL and RSEI indicate that urban development has significantly reduced the quality of the city’s ecological environment, particularly after Zhengzhou entered the stage of rapid expansion. The coupling degree (C) and coupling coordination degree (D) between urbanization and the ecological environment showed a decreasing trend, and the average value was lower than 0.3. This indicates that the ecological environment in Zhengzhou has been seriously affected by the process of urbanization, and the natural ecology has been strongly impacted by human activity. C and D also showed a decreasing trend from 2000 to 2015 but increased from 2016 to 2021, indicating that the ecological environment in Zhengzhou has gradually improved. The degree of coordination D between urbanization and the ecological environment in Zhengzhou had a strong negative correlation with the population size and growth rate but a positive correlation with the Moran value, indicating that an increase in the population increases the burden on the ecological environment. However, a reasonable spatial population distribution is conducive to improving regional urban–ecological coordination. Full article

This paper explores the feasibility of floating urban development in Italy, given its extensive coastline and inland hydrographic network. The key drivers for floating urban development, as an adaptive approach in low-lying waterfront areas, include the increasing threats posed by rising sea levels and flooding and the shortage of land for urban expansion. However, as not all waterfront areas are suitable for floating urban development, a geographical analysis based on a thorough evaluation of multiple factors, including urban–economic parameters and climate-related variables, led to the identification of a specific area of the Lazio coast, the river Tiber Delta. A comprehensive urban mapping process provided a multifaceted geo-referenced information layer, including several climatic, urban, anthropic, and environmental parameters. Within the GIS environment, it is possible to extract and perform statistical analyses crucial for assessing the impact of flood and sea-level rise hazards, particularly regarding buildings and land cover. This process provides a robust framework for understanding the spatial dimensions of flood and sea-level rise impacts and supporting informed design-making. A research-by-design phase follows the simulation research and mapping process. Several design scenarios are developed aimed at regenerating this vulnerable area. These scenarios seek to transform its susceptibility to flooding into a resilient, adaptive, urban identity, offering climate-resilient housing solutions for a population currently residing in unauthorized, substandard housing within high flood-risk zones. This paper proposes a comprehensive analytical methodology for supporting the design process of floating urban development, given the highly determinant role of site-specificity in such a challenging and new urban development approach. Full article

Lomami National Park, located in the Democratic Republic of the Congo (DR Congo), is renowned for the integrity of its forest ecosystems, safeguarded by the absence of agricultural activities and limited road access. However, these ecosystems remain under-researched, particularly in terms of forest cover dynamics. This research gap poses a significant challenge to establishing rigorous monitoring systems, which are essential for ensuring the long-term preservation of these valuable ecosystems. This study utilized Google Earth Engine to preprocess Landsat images from 2008, 2016, and 2024, employing techniques such as atmospheric correction and cloud masking. Random Forest classification was applied to analyze land cover changes, using training datasets curated through ground-truthing and region-of-interest selection. The classification accuracy was evaluated using metrics such as overall accuracy, producer’s accuracy, and user’s accuracy. To assess landscape configuration, metrics such as class area, patch number, largest patch index, disturbance index, aggregation index, and edge density were calculated, distinguishing between the park’s core and peripheral zones. Spatial transformation processes were analyzed using a decision tree approach. The results revealed a striking contrast in forest cover stability between Lomami National Park and its surrounding periphery. Within the park, forest cover has been preserved and even showed a modest increase, rising from 92.60% in 2008 to 92.75% in 2024. In contrast, the peripheral zone experienced a significant decline in forest cover, decreasing from 79.32% to 70.48% during the same period. This stability within the park extends beyond maintaining forested areas; it includes preserving and enhancing the spatial structure of forest ecosystems. For example, edge density, a key indicator of forest edge compactness, remained stable in the park, fluctuating between 8 m/ha and 9 m/ha. Conversely, edge density in the peripheral zone exceeded 35 m/ha, indicating that forest edges within the park are considerably more cohesive and intact than those in the surrounding areas. The spatial transformation processes also underscored these contrasting dynamics. In the park, the primary process was the aggregation of primary forest patches, reflecting a trend toward continuous and connected forest landscapes. By contrast, the peripheral zone exhibited dissection, indicating fragmentation and the breakdown of forest patches. These findings highlight the park’s critical role in maintaining both the extent and structural integrity of forest ecosystems, setting it apart from the more degraded periphery. They underscore the resilience of forest ecosystems in the face of limited anthropogenic pressures and the crucial importance of effective land management and rigorous conservation strategies in addressing the challenges posed by urbanization and rural expansion. Additionally, the results emphasize that well-adapted conservation measures, combined with specific demographic and socio-economic conditions, can play a pivotal role in achieving long-term forest preservation and ecological stability. Full article

This study aims to estimate the shear wave velocity and identify the depth of the bedrock and the engineering site characterization utilizing the multichannel analysis of surface waves (MASW) method for sustainable urban development in the Al-Madinah Al-Munawarah area. Twenty-seven MASW profiles were carried out using Geode digital seismographs with a 24-geophone array of 4.5 Hz in the urban expansion area of Al-Madinah Al-Munawarah. The methodology entailed rigorous calibration during data collection, processing, and inversion to ensure precise shear velocity measurements. Results reflect subsurface conditions accurately where shear wave velocity (Vs) varies between 180 m/s and 1200 m/s across three main layers: alluvium deposits, which transfer laterally in some areas into vesicular basalt with Vs ranges from 180 to 360 m/s; fractured basalt where Vs varies between 360 and 760 m/s; and weathered basaltic rock with Vs that spans from 760 to 1200 m/s. Moreover, the average shear wave velocity of up to 30 m depth (Vs30) and ranging from 180–480 m/s indicate Site Class D (stiff soil) and Class C (soft rock and dense soil) according to National Earthquake Hazards Reduction Program (NEHRP). Furthermore, the depth of bedrock varies between 18 and 29 mm indicating the great thickness of soil deposits throughout the study area. These results provide civil engineers and urban planners with vital data about soil deposits characterization and geotechnical conditions in the area where alluvium deposits and vesicular basalt represent weak zones that require more attention during urban construction. Results will contribute as well, to a great extent, in achieving the sustainable development plans of Saudi Vision 2030. Full article