Search Results (10)

Liquefied petroleum gas (LPG), composed mainly of propane and butane, is widely used as an energy source in residential, commercial, and industrial sectors; however, its high flammability poses a critical risk in the event of accidental leaks. In Peru, where LPG constitutes the main domestic energy source, leakage emergencies affect thousands of households each year. This pattern is replicated in developing countries with limited energy infrastructure. Early quantitative detection of propane, the predominant component of Peruvian LPG (~60%), is essential to prevent explosions, poisoning, and greenhouse gas emissions that hinder climate change mitigation strategies. This study presents PropNet-R, a convolutional neural network (CNN) designed to estimate propane concentrations (ppm) from thermal images. A dataset of 3574 thermal images synchronized with concentration measurements was collected under controlled conditions. PropNet-R, composed of four progressive convolutional blocks, was compared with SqueezeNet, VGG19, and ResNet50, all fine-tuned for regression tasks. On the test set, PropNet-R achieved MSE = 0.240, R² = 0.614, MAE = 0.333, and Pearson’s r = 0.786, outperforming SqueezeNet (MSE = 0.374, R² = 0.397), VGG19 (MSE = 0.447, R² = 0.280), and ResNet50 (MSE = 0.474, R² = 0.236). These findings provide empirical evidence that task-specific CNN architectures outperform generic transfer learning models in thermal image-based regression. By enabling continuous and quantitative monitoring of gas leaks, PropNet-R enhances safety in industrial and urban environments, complementing conventional chemical sensors. The proposed model contributes to the development of sustainable infrastructure by reducing gas-related risks, promoting energy security, and strengthening resilient, safe, and environmentally responsible urban systems. Full article

In recent decades, China has witnessed remarkable growth in housing construction, yet housing-related complaints have not declined significantly, highlighting the gap between housing quality and public expectations. Against this background, this study analyzes 32,277 national surveys to unpack residential satisfaction with green-livable communities in China. Entropy and standard-deviation weighting identified 16 priority indicators; artificial neural networks revealed weak direct influence of basic demographics on satisfaction, highlighting non-linear demand patterns. While 65–75% of respondents are satisfied with most attributes, significant city-level gaps persist—Beijing peaks near 90%, Chongqing falls below 50%. Dissatisfaction converges on three domains: infrastructure (parking, barrier-free access), building performance (leakage, noise, thermal defects) and smart systems (security, energy, health monitoring). Residents’ improvement priorities have shifted from basic shelter to health safety, smart technology, humanistic care and ecological amenities. A “basic-security + quality-upgrade” strategy is proposed: short-term repairs of common defects, medium-term smart-sustainable upgrades and long-term participatory governance. The findings not only enrich the theoretical framework of community satisfaction research but also provide practical guidance for enhancing community quality and meeting residents’ expectations in the context of China’s rapid urbanization and housing development. Full article

The assessment of ecosystem service (ES) supply–demand relationships is critical for addressing regional sustainable development challenges, yet systematic studies integrating spatial drivers analysis and multiscenario forecasting in rapidly urbanizing mountainous regions remain scarce. This study focuses on Chongqing as a representative case to investigate spatial patterns, driving mechanisms, and future trajectories of ES supply–demand dynamics. Through spatial quantification of four key ES (food provision, water retention, soil conservation, carbon fixation) and statistical analysis of socioeconomic datasets from 2010 to 2020, geographical weighted regression modeling was employed to identify spatially heterogeneous drivers. Long-term projections (2030–2060) were developed using climate–economy integrated scenarios reflecting different global development pathways. The results demonstrate three principal findings: First, while regional ecosystem quality maintains stable with an improved supply–demand ratio (0.260 to 0.320), persistent deficits in carbon fixation capacity require urgent attention. Second, spatial mismatches exhibit intensifying polarization, with expanding deficit zones concentrated in metropolitan cores and their periurban peripheries. Third, thermal-hydrological factors (aridity index, temperature) coupled with land intensification pressures emerge as dominant constraints on ES supply capacity. Scenario projections suggest coordinated climate mitigation and sustainable development strategies could maintain the supply–demand ratio at 0.189 by 2060, outperforming conventional development pathways by 23.5–41.2%. These findings provide spatial decision support frameworks for balancing ecological security and economic growth in mountainous megacities, with methodological implications for cross-scale ES governance in developing regions. Full article

The impact of urban morphology characteristics on regional thermal environments is a crucial topic in urban planning and climate adaptation research. However, existing studies are often limited to a single dimension and fail to fully reveal the spatiotemporal impact mechanisms of multi-dimensional urban morphology on thermal environments and their connection to regional planning policies. This study focuses on the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), combining quantitative data from landscape pattern indices, land use expansion patterns, and local climate zones (LCZs) derived from 2000 to 2020. By using geographically weighted regression and spatial autocorrelation analysis, we systematically explore the spatiotemporal effects and mechanisms of multi-dimensional urban morphology characteristics on regional thermal effects. We found the following points. (1) Built-up land patch density is significantly positively correlated with LST, with the urban heat island (UHI) effect spreading from core areas to the periphery; this corroborates the thermal environment differentiation features under the “multi-center, networked” spatial planning pattern of the GBA. (2) Outlying expansion mitigates local LST rise through an ecological isolation effect, and infill expansion significantly exacerbates the UHI effect due to high-intensity development, reflecting the differentiated impacts of various expansion patterns on the thermal environment. (3) LCZ spatial distribution aligns closely with regional planning, with the solar radiation shading effect of high-rise buildings significantly cooling daytime LSTs, whereas the thermal storage properties of traditional building materials and human heat sources cause nighttime LST increases; this reveals the deep influence of urban morphology mechanisms, building materials, and human activities on thermal environments. The findings provide scientific support for achieving a win–win goal of high-quality development and ecological security in the GBA while also offering a theoretical basis and practical insights for thermal environment regulation in high-density urban clusters worldwide. Full article

The acceleration of urbanization has significantly impacted the changing regional thermal environment, leading to a series of ecological and environment-related problems. A scientific evaluation of the urban thermal security pattern (TSPurban) strongly benefits the planning and layout of sustainable development and the construction of comfortable human settlements. To analyze the characteristics of the TSPurban under cross-regional differences and provide targeted solutions to mitigate the urban heat island effect in later stages, the logical system research framework of the TSPurban based on the “construction–evaluation–optimization” model was explored using reverse thinking. This study selected the Wuhan metropolitan area in China as the research object. First, a morphological spatial pattern analysis (MSPA) model was used to extract the top 30 core heat island patches, and Conefor 2.6 software was used for connection analysis to evaluate their importance. Second, based on the characteristics of various land cover types, the friction (cost) map of surface urban heat island (SUHI) diffusion was simulated. The spatial attributes of the heat island resistance surface were examined using a standard deviation ellipse and hot spot analysis. Finally, this paper used circuit theory to find 56 low-cost heat island links (corridors) and circuit scape software to find widely distributed vital nodes. The optimization of the TSPurban network was then investigated using a reverse thinking process. Heat island patches, corridors, and vital nodes are among the crucial components of the TSPurban. By obstructing corridor links and disturbing important nodes, it is possible to appropriately and proficiently reduce the TSPurban network’s connection efficiency and stability, which will have a positive influence on regional climate mitigation and the heat island effect. Full article

Identifying the spatiotemporal coupling characteristics of cropland-grain production-population is essential for the rational utilization of cropland and the evaluation of national and regional food security. Based on the grain production statistical data, GlobeLand30, and WorldPop data in the years 2000, 2010, and 2020, the spatiotemporal changes in China’s cropland area, grain production, and population and their coupling characteristics over the past two decades were detected at the grid level using the models of barycenter fitting and coupled dynamic analysis. The results showed that spatial change of cropland area in China was roughly characterized by the increase in the northwest and the decrease in the southeast; while grain production was characterized by an increase in the north and a decrease in the south, and population was roughly characterized by an increase in urban areas of the southeast coastal regions and a decrease in traditional agricultural areas. The barycenter of cropland area and that of grain production moved toward the northwest and the northeast, respectively, which mismatch the spatial pattern of hydro-thermal conditions of cropland resources in China and thus result in the increased risk of the national grain production system. Meanwhile, the barycenter of grain production and that of population continued to move in opposite directions overall, and the distances between their barycenters increased from 119.65 km in 2000 to 455.16 km in 2020, indicating that the phenomenon of ‘north-to-south grain diversion’ is intensifying. Our results highlight that the spatial mismatch of cropland-grain production-population in China has increased over the past two decades. Full article

Urbanization and population growth have led to urban areas with a substantial concrete surface compared to adjacent rural areas, creating challenges regarding fresh food, water security, and the need for agricultural land. Climate change affects the rainfall pattern and ground water in urban areas, so the gradual growth of urban rooftop agriculture (URTA) is an increasing trend for the owners of residential buildings. URTA is increasing in the form of private initiatives, but without consideration of efficient water management techniques and application of other related inputs. URTA differs substantially from traditional agriculture in terms of sunshine, thermal regime, the moisture dynamics of a concrete roof top, etc. Considering these aspects of URTA, an effective, efficient, science-based and economically viable irrigation method is necessary to popularize this approach and consequently increase the productivity of crops. With this in mind, the drip irrigation method is considered for the cultivation and determination of water productivity for selected species of plants such as the Bottle Gourd, Tomato, Chili, and Brinjal in the URTA, which was also compared to the traditional irrigation approach. This is why groundwater and green (grey and rain) water were considered as the source of irrigation during the dry season, based on the daily crop evapotranspiration and moisture content of the plant growing medium. For this reason, ET₀ of the selected crops was measured using the CROPWAT 8.0 model. The results of this study revealed that the optimum irrigation water requirement of any crop in URTA is around 54% access (ETc), and 46–64% of access irrigation water is used by the traditional method compared to the drip irrigation method. The study reported that with drip irrigation with potable water, the yield was increased by 21.43–22.40% and rain and grey-water also increased yield by 31.87–33.33% compared to container and traditional pipe irrigation. It was also found that the water qualities of mixed water (grey and rainwater) are in an acceptable range limit for irrigation. As a result, urban planners, city dwellers, and researchers can formulate appropriate plans to cultivate different species of plants through this water saving irrigation method using green water, and should explore the concept of water-smart URTA technologies as organic inventions embedded in these results. Full article

Salinization in arid or semiarid regions with water logging limits cropland yield, threatening food security. The highest level of farmland salinization, that is, abandoned salinized farmland, is a tradeoff between inadequate drainage facilities and sustainable farming. The evolution of abandoned salinized farmlands is closely related to the development of cropping systems. However, detecting abandoned salinized farmland using time-series remote sensing data has not been investigated well by previous studies. In this study, a novel approach was proposed to detect the dynamics of abandoned salinized farmland using time-series multispectral and thermal imagery. Thirty-two years of temporal Landsat imagery (from 1988 to 2019) was used to assess the evolution of salinization in Hetao, a two-thousand-year-old irrigation district in northern China. As intermediate variables of the proposed method, the crop-specific planting area was retrieved via its unique temporal vegetation index (VI) pattern, in which the shape-model-fitting technology and the K-means cluster algorithm were used. The desert area was stripped from the clustered non-vegetative area using its distinct features in the thermal band. Subsequently, the abandoned salinized farmland was distinguished from the urban area by the threshold-based saline index (SI). In addition, a regression model between electrical conductance (EC) and SI was established, and the spatial saline degree was evaluated by the SI map in uncropped and unfrozen seasons. The results show that the cropland has constantly been expanding in recent decades (from 4.7 × 10⁵ ha to 7.1 × 10⁵ ha), while the planting area of maize and sunflower has grown and the area of wheat has decreased. Significant desalinization progress was observed in Hetao, where both the area of salt-affected land (salt-free area increased approximately 4 × 10⁵ ha) and the abandoned salinized farmland decreased (reduced from 0.45 × 10⁵ ha to 0.19 × 10⁵ ha). This could be mainly attributed to three reasons: the popularization of water-saving irrigation technology, the construction of artificial drainage facilities, and a shift in cropping patterns. The decrease in irrigation and the increase in drainage have deepened the groundwater table in Hetao, which weakens the salt collection capacity of the abandoned salinized farmland. The results demonstrate the promising possibility of reutilizing abandoned salinized farmland via a leaching campaign where the groundwater table is sufficiently deep to stop salinization. Full article

The energy demand of the district heating system (DHS) occupies an important part in urban energy consumption, which has a great impact on the energy security and environmental protection of a city. With the gradual improvement of people’s economic conditions, different groups of people now have different demands for thermal energy for their comfort. Hence, heat metering has emerged as an imperative for billing purposes and sustainable management of energy consumption. Therefore, forecasting the heat load of buildings with heat metering on the demand side is an important management strategy for DHSs to meet end-users’ needs and maintain energy-saving regulations and safe operation. However, the non-linear and non-stationary characteristics of buildings’ heat load make it difficult to predict consumption patterns accurately, thereby limiting the capacity of the DHS to deliver on its statutory functions satisfactorily. A novel ensemble prediction model is proposed to resolve this problem, which integrates the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and support vector regression (SVR), called CEEMDAN-SVR in this paper. The proposed CEEMDAN-SVR algorithm is designed to automatically decompose the intrinsic mode according to the characteristics of heat load data to ensure an accurate representation of heat load patterns on multiple time scales. It will also be useful for developing an accurate prediction model for the buildings’ heat load. In formulating the CEEMDAN-SVR model, the heat load data of three different buildings in Xingtai City were acquired during the heating season of 2019–2020 and employed to conduct detailed comparative analysis with modern algorithms, such as extreme tree regression (ETR), forest tree regression (FTR), gradient boosting regression (GBR), support vector regression (SVR, with linear, poly, radial basis function (RBF) kernel), multi-layer perception (MLP) and EMD-SVR. Experimental results reveal that the performance of the proposed CEEMDAN-SVR model is better than the existing modern algorithms and it is, therefore, more suitable for modeling heat load forecasting. Full article

The built environment contributes around half of total greenhouse gas emissions and with 87% of residential buildings that we will have by 2050 already built, it is vital to adopt sustainable retrofitting practices. The question is: what are the viable solutions? One answer may be green roof retrofitting. The environmental benefits include reduced operational carbon emissions, reduced urban heat island effect, increased bio-diversity, housing temperature attenuation and reduced stormwater run-off. The economic benefits are the reduced maintenance costs and lower running costs. The social gain is the creation of spaces where people have access to green areas. However, the barriers to retrofitting include the perceptions of structural adequacy, the risk of water damage, high installation and maintenance costs, as well as access and security issues. Many Australian and Brazilian residential buildings have metal sheet roofs, a lightweight material with poor thermal performance. During the summer, temperatures in Sydney and Rio de Janeiro reach 45 degrees Celsius, and in both cities, rainfall patterns are changing, with more intense downpours. Furthermore, many residential buildings are leased, and currently, tenants are restricted by the modifications that they can perform to reduce running costs and carbon emissions. This research reports on an experiment on two small-scale metal roofs in Sydney and Rio de Janeiro to assess the thermal performance of portable small-scale modules. The findings are that considerable variation in temperature was found in both countries, indicating that green roof retrofitting could lower the cooling energy demand considerably. Full article