Search Results (643)

Globally, intensive groundwater extraction has led to widespread land subsidence, posing severe threats to urban infrastructure, structural safety, and flood control capacity, and resulting in substantial economic losses and ecological degradation. Based on dynamic monitoring data and a poroelastic fluid–solid coupling model developed using COMSOL Multiphysics 6.2, this study systematically investigates the characteristics and evolution of land subsidence in Decheng District before and after the implementation of a groundwater extraction ban. Furthermore, recommendations and strategies for the sustainable management of regional groundwater resources are proposed. The results indicate that after the ban was enforced in 2020, the extraction volumes of deep and shallow groundwater in Decheng District decreased from 830,000 m³/a and 33,070,000 m³/a to 178,000 m³/a and 20,775,000 m³/a, respectively. The ban significantly influenced groundwater levels, with the recovery rate of deep groundwater increasing markedly from approximately 0.5 m/a before the ban to about 5 m/a afterward. Groundwater levels directly govern the rate of land subsidence; their decline increases the effective stress within the strata, leading to aquifer compaction and subsequent subsidence. Following the ban, the subsidence rate in Decheng District decreased significantly, with the annual subsidence volume reduced by more than 80% compared to the pre-ban period. Predictive analysis using the fluid–solid coupling model reveals that extraction from deep confined aquifers is the main driver of regional subsidence, with a time lag of approximately five years between groundwater level changes and subsidence response. After the implementation of the extraction ban, the subsidence rate slowed considerably. Over the long term, the subsiding strata tend to stabilize, although most of the subsidence that has already occurred is irreversible, making it difficult for the strata to return to their original state. In summary, the groundwater extraction ban has effectively facilitated groundwater recovery and mitigated land subsidence in Decheng District, though the response exhibits both temporal lag and spatial variability. Future work should focus on establishing an integrated monitoring and regulation system for land subsidence and groundwater dynamics to ensure the coordinated security of both water resources and the geological environment. These findings provide a scientific basis for informing land subsidence prevention and guiding the rational exploitation of groundwater resources in Decheng District. Full article

To enhance the design and construction efficiency of artificial ground freezing (AGF) in water-rich sandy strata, this study takes the No. 2 cross-passage of Zhengzhou Metro Line 8 as a case study and conducts an integrated analysis combining field monitoring and numerical simulation. During the freezing process, a sensor network was deployed to capture real-time data on temperature distribution and pore water pressure evolution. Based on the collected measurements, a three-dimensional hydrothermal coupled model was developed using COMSOL Multiphysics 6.1 and validated against field data. The results demonstrate a distinct multi-stage evolution in the formation of the frozen curtain, with the highest heat exchange rate observed at the initial phase. Under a 50-day freezing schedule, increasing the average coolant temperature by 4 °C still yielded a frozen wall that meets the design thickness requirement. Additionally, several cost-effective freezing schemes were explored to accommodate varying construction timelines. This study supports sustainable urban infrastructure development by minimizing energy consumption during artificial ground freezing (AGF) processes. Full article

A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities. Full article

The grasslands and shrublands of northern and central Mexico cover nearly 25% of the country and harbor high biodiversity. However, they are increasingly degraded by agriculture, urbanization, infrastructure development, and water overexploitation. To assess the status of medium- and large-sized mammals in these threatened ecosystems, we quantified species richness, relative abundance, and naïve occupancy across vegetation types and seasons. From April 2023 to February 2024, monthly track surveys and camera trapping were performed, and the data were analyzed in R. We documented 16 species representing four orders and nine families, with Carnivora being the most diverse (eight species). The species richness varied by habitat, ranging from 11 in montane forest to 13 in semi-desert grassland, the latter habitat having the highest Shannon and Simpson indices, particularly in the dry season. Odocoileus virginianus and Sylvilagus audubonii were consistently the most abundant species in montane forest and desert scrub, whereas Cynomys mexicanus predominated in semi-desert grasslands, accounting for >90% of detections during the rainy season. Rare species included Lynx rufus, Taxidea taxus, and Ursus americanus, each with isolated detections. Rarefaction and sample coverage curves approached asymptotes (>99%), indicating sufficient sampling effort. Naïve occupancy and encounter rates were highest for O. virginianus (0.82) and S. audubonii (0.68), with a strong positive correlation between the two metrics (r² = 0.92). These findings provide robust baseline information on mammalian diversity, abundance, and habitat associations in semi-arid anthropogenic landscapes, supporting future monitoring and conservation strategies. Full article

Advanced and Urban Air Mobility (AAM and UAM) represent emerging transportation concepts that involve the use of novel aircraft technologies to transport passengers and cargo within urban, regional, and intra-regional environments. These systems may include Electric Vertical Take-off and Landing (eVTOL) aircraft, Short Take-off and Landing (STOL) aircraft, and unmanned aerial vehicles (UAVs), which are being considered for a range of applications including passenger transport, cargo delivery, and other specialized operations. This study introduced a state-specific analytical framework that integrates different methodologies and data to enable a more precise evaluation of AAM viability in the State of Illinois, compared to generic national or global assessments, capturing the state’s unique mobility patterns, infrastructure constraints, and demographic distributions. One of the main goals is to provide a comprehensive evaluation of the potential implications—both challenges and opportunities—associated with AAM and UAM operations. The analysis examines potential impacts on mobility, infrastructure, economic development, and public services, with particular emphasis on identifying key considerations for policy development. The research framework categorizes use cases into two broad types: AAM for the transportation of people and cargo, and AAM for functional applications such as emergency response, agriculture, and infrastructure monitoring. The study provides a detailed quantitative assessment of passenger air taxi services, including demand estimation, business model feasibility analysis, integration effects on existing transportation systems, and infrastructure requirements. For other AAM applications, the analysis identifies operational considerations, regulatory implications, and potential barriers to implementation, establishing a foundation for future detailed evaluation. Full article

This article presents an original, multi-depth soil-temperature monitoring system based on TMP117 digital sensors designed for deployment at several depths. The objective was to evaluate the system’s accuracy and applicability for building-energy performance assessment under contemporary climate conditions. Urban measurements at depths between 1.0 and 2.0 m were compared with ground temperatures derived using PN-EN 16798-5-1:2017-07 with Typical Meteorological Year (TMY) inputs and with observations from the Polish Institute of Meteorology and Water Management (IMWM). Standard inputs underestimated soil temperature on average by 1.1–2.3 °C (TMY) and 2.0–2.8 °C (IMWM), with the bias increasing with depth. For a ground-to-air heat-exchanger (GAHE) assessment, energy benefits estimated from standard inputs were lower in measurements by approximately 30–60% for pre-cooling and 70–86% for pre-heating. Measurements also revealed location-dependent differences between boreholes attributable to underground infrastructure. These findings indicate that non-local or outdated climate datasets can materially overestimate GAHE potential and confirm the need for local, multi-depth ground measurements and periodic updates of standard climate inputs to reflect urbanized conditions and climate change. The presented system constitutes a practical, scalable tool for engineers and designers of HVAC systems relying on ground heat exchange. Full article

Natural threats are becoming increasingly frequent and difficult to anticipate, urging public authorities and stakeholders to adopt sustainable methodologies and tools capable of continuously supplying historical and real-time data on hazards and their impacts. Such tools enable the prompt activation of recovery actions, enhance the resilience of citizens and the built environment, and contribute to the achievement of the Sustainable Development Goals (SDGs). This paper presents an interoperable and multipurpose framework developed within the MULTICLIMACT project (GA n. 101123538), designed to enhance urban smartness and sustainability, and to support and improve resilience in municipal decision-making. The framework integrates heterogeneous data sources into a unified environment, covering infrastructures, buildings, and social systems. It also includes physiological monitoring, which collects physiological parameters from wearable sensors in a privacy-preserving way, and microclimate monitoring, which records indoor air quality in inhabited environments. Simulation-based analyses are applied to capture cascading effects of disruptions, while multidimensional indicators (societal, economic, operational, and health-related) are used to quantify resilience. The approach was implemented in the Italian municipality of Camerino, where hazard monitoring systems, impact assessment tools, and indoor comfort data were integrated and validated in the SCP-MULTICLIMACT platform. The proposed approach offers a replicable model for integrating environmental and health data in support of climate resilience and sustainable urban development. Full article

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data. Full article

Urban trees play a critical role in mitigating climate change by capturing atmospheric CO₂ and providing multiple co-benefits, including cooling urban environments, reducing building energy demand, and enhancing citizens’ physical and psychological well-being. This study presents the Co Carbon Trees Measurement project, a citizen science initiative implemented in the city of Viladecans, Spain, involving 658 students, local administration, and academia, three components of the EU mission’s quadruple helix governance model. Over one year, 1274 urban trees were measured for trunk diameter and height to quantify annual CO₂ sequestration using a direct measurement approach combining field data collection with a mobile application for a height assessment and a flexible measuring tape for diameter. Results indicate that carbon fixation increases with tree size, displaying a parabolic function with larger trees sequestering significantly more CO₂. A range between 10 and 20 kg of CO₂ is sequestered by the urban trees in the period 2024–2025. The study also highlights the broader benefits of urban trees, including shading, mitigation of the urban heat island effect, and positive impacts on mental health and social cohesion. While the total CO₂ captured in Viladecans (≈810 tons/year) is small relative to city emissions (≈170,000 tons/year), the methodology demonstrates a scalable, replicable approach for monitoring progress toward climate neutrality and integrating urban trees into planning and climate action strategies. This approach positions green infrastructure as a central component of sustainable and resilient urban development. Full article

Historic urban landscapes, despite their cultural significance, often face neglect, limiting their potential to increase the value of historical centers. Defined as a complex sociotechnical network that involves a variety of agencies incorporating material, immaterial, natural, and artificial elements, these landscapes present significant challenges for architects because of their layered and diverse components. Actor–network theory (ANT) is used as a methodological and ontological framework to address this complexity. However, a notable research gap exists on the basis of the lack of clear representation and practical application of ANT to address the complexity of these historic urban landscapes. To bridge this gap, this study uses Bey’s palace as a case study to develop a comprehensive framework based on a digital mapping approach rooted in ANT. This framework traces, visualizes, and analyzes historic urban landscapes as intricate systems of agencies, leveraging graph theoretical algorithms and computational analysis tasks from network analysis tools to increase their effectiveness. This investigation is based on two key concepts: the actor/actant and the actor network. The research employed Bruno Latour’s concepts of translation, agency, and the mapping controversies technique grounded in graph-theoretic algorithm tasks to decipher the complexities of Bey’s palace system. The results identify seven clusters as actor networks and highlight the roles of key actors/actants, such as Ahmed Bey, decorative elements, courtyard gardens, and Moorish architecture. This methodological approach provides architects and urban planners with practical tools to better understand, analyze and preserve historic urban landscapes, enriching their cultural and historical value. By transforming contested discourses into measurable networks indicators, this interdisciplinary framework directly supports SDG11 (Sustainable Cities and Communities), especially Target 11.4, in safeguarding cultural heritage by enabling the prioritization, monitoring and governance of cultural, social and infrastructural assets in historic urban landscapes. Full article

Rural tropical regions face escalating threats from zoonotic AIV and dengue virus but lack sewered infrastructure for conventional wastewater surveillance. We implemented surface water-based surveillance (SWBS) in peri-urban Dhaka (Bangladesh) and Ruili (China) from July to November 2023 and coupled it with machine learning-enhanced digital epidemiology. Reverse transcription quantitative PCR (RT-qPCR) was employed to detect the M gene of AIV and to subtype H1, H5, H7, H9, and H10 in surface water. Wild bird feces (n = 40) were collected within 3 km of positive sites to source-track AIV. For the dengue virus, a serogroup-specific RT-qPCR assay targeting the CprM gene was used. Genomic sequencing of AIV and dengue virus was performed to elucidate phylogenetic relationships with local clinical strains. Clinical data related to dengue fever were also collected for correlation analysis. Meanwhile, 13 dengue-related keyword search volumes were harvested daily from Google, Bing and Baidu for four cities to reveal the relationship between dengue epidemics and the web search index. AIV H5 was detected in Dhaka city from week 38, peaking at week 39, while dengue virus was persistently detected from week 29 to week 45, aligning with clinical trends. Time-series cross-correlation analysis revealed that variations in surface water viral load led clinical case reports by approximately two weeks (max CCF = 0.572 at lag −2). In Ruili city, dengue virus was detected from week 32 to week 44. To sharpen sensitivity, 383 weekly web search series for 13 dengue keywords from four countries were screened; random-forest and XGBoost models retained five symptom queries that generated a composite index explaining 79% of variance in dengue RNA levels in an independent Ruili test set (n = 24) and reduced superfluous sampling by 35%. Phylogenetic analysis verified identity between water-derived and patient-derived DENV-2, confirming local transmission. The study demonstrates that AIV SWBS is optimally integrated with wild bird sampling for source attribution, whereas dengue SWBS achieves maximal efficiency when combined with real-time web search monitoring, providing tailored, low-cost early-warning modules for resource-constrained tropical settings. Full article

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

Sustainability in software engineering encompasses environmental, human, social, and economic dimensions, each essential for ensuring software’s positive and lasting impact. This paper presents an innovative Internet of Things (IoT)-based Smart Waste Management (SWM) system. The proposed system addresses key limitations in existing solutions, including lack of real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. While prior studies have investigated IoT applications in SWM, challenges remain in achieving dynamic, integrated, and scalable systems for sustainable urban development. The proposed solution introduces a holistic architecture that enables real-time monitoring of waste bin levels and fire incidents through Waste Bin Level Monitoring Units (BLMUs) equipped with ultrasonic and flame sensors. Data is transmitted via Wi-Fi to a centralized City Command and Control Center (4C), allowing for automated alerts and dynamic route optimization. A dual-platform software suite supports both administrative and operational workflows: a desktop web application and a role-based Android mobile app developed in Flutter, and integrated with Google Cloud Firestore, enabling centralized data management and efficient resource allocation. We validated the system through a working prototype, demonstrating notable contributions including enhanced emergency responsiveness, optimized waste collection routes, and improved stakeholder engagement. This research contributes to the advancement of sustainable urban infrastructure by offering a scalable, data-driven SWM framework grounded in software engineering principles and aligned with smart city objectives. This paper presents an innovative IoT-based Smart Waste Management (SWM) system that addresses key limitations in existing solutions, including insufficient real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. Full article

A well-performing road network is essential for modern society. But any road is nothing without its users—cyclists, drivers, pedestrians. Road network cannot be managed without knowing who the roads serve. The gaps in this knowledge lead to decisions that hinder efficiency, equality, and sustainability. This is why monitoring traffic is imperative for road management. However, traditional short-term traffic counting methods fail to provide full coverage at a reasonable cost. This study assessed the economic feasibility of drone-enabled traffic monitoring systems across Estonian urban environments through comparative spatial and economic analysis. Hexagonal tessellation was applied to 255 urban locations, identifying 47,530 monitoring points across 4077 grid cells. Economic modeling compared traditional counting costs with drone-based systems utilizing ultralight drones and nomadic 5G infrastructure. Monte Carlo simulation evaluated robustness under varying operational intensities from 30 to 180 days annually. Analysis identified an 8-point density threshold for economic viability, substantially lower than previously reported requirements. Operational intensity emerged as the critical determinant: minimal operations (30 days) proved viable for 9.0% of locations, while semi-continuous deployment (180 days) expanded viability to 81.6%. The findings demonstrate that drone-based monitoring achieves 60–80% cost reductions compared to traditional methods while maintaining equivalent accuracy (95–100% detection rates for vehicles, cyclists, and pedestrians), presenting an economically superior alternative for 67% of Estonian urban areas, with viability extending to lower-density locations through increased operational utilization. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article