Search Results (2,513)

With the increasingly widespread application of unmanned aerial vehicle (UAV) systems in disaster monitoring, urban management, logistics transportation, and reconnaissance, efficient dynamic task allocation has become a key issue in improving task execution efficiency. To address the challenges posed by dynamic changes in task objectives and resource constraints that traditional task allocation methods struggle with in complex environments, this paper proposes a multi-objective particle swarm optimization algorithm, DCMPSO, for UAV dynamic reconnaissance task allocation. First, the framework of DCMPSO is constructed, dividing the optimization of dynamic problems into three parts: environment change detection, environment change response, and actual optimization, with the designed strategy of range prediction strategy based on centroid translation. Then, simulation experiments are conducted to verify the effectiveness of the algorithm mechanisms through ablation experiments and to demonstrate the superiority of DCMPSO in convergence and distribution compared to DNSGA-II and SGEA through comparative experiments. Finally, a multi-UAV dynamic task allocation model is established and optimized, proving that DCMPSO can correctly solve the UAV dynamic multi-objective allocation problem and effectively find its optimal solution, providing an effective solution for practical applications. Full article

With the rapid development of the social economy, human activities have increasingly disrupted water environments, and the continuous input of pollutants poses significant challenges for water environment management. Taking the Xiaoxingkai Lake basin as the study area, this paper develops a social–economic–water environment model based on the system dynamics methodology, incorporating subsystems for population, agriculture, and water pollution. The model focuses on four key indicators of pollution severity, namely, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and ammonia nitrogen (NH₃-N), and simulates the changes in pollutant loads entering the river under five different scenarios from 2020 to 2030. The results show that agricultural non-point sources are the primary contributors to TN (79.5%) and TP (73.7%), while COD primarily originates from domestic sources (64.2%). NH₃-N is mainly influenced by urban domestic activities (44.7%) and agricultural cultivation (41.2%). Under the status quo development scenario, pollutant loads continue to rise, with more pronounced increases under the economic development scenario, thus posing significant sustainability risks. The pollution control enhancement scenario is most effective in controlling pollutants, but it does not promote socio-economic development and has high implementation costs, failing to achieve coordinated socio-economic and environmental development in the region. The dual-reinforcement scenario and moderate-reinforcement scenario achieve a balance between pollution control and economic development, with the moderate-reinforcement scenario being more suitable for long-term regional development. The findings can provide a scientific basis for water resource management and planning in the Xiaoxingkai Lake basin. Full article

To evaluate water pollution status and sustainable development potential in the Huai River Basin, this study focused on the spatiotemporal evolution and influencing factors of the grey water footprint (GWF) across 35 cities in the basin from 2005 to 2020. This study quantifies the GWF from agricultural, industrial, and domestic perspectives and analyzes its spatial disparities by incorporating spatial autocorrelation analysis. The Tapio decoupling model was applied to explore the relationship between pollution and economic growth, and geographic detectors along with the STIRPAT model were utilized to identify driving factors. The results revealed no significant global spatial clustering of GWF in the basin, but a pattern of “high in the east and west, low in the north and south” emerged, with high-value areas concentrated in southern Henan and northern Jiangsu. By 2020, 85.7% of cities achieved strong decoupling, indicating improved coordination between the environment and economy. Key driving factors included primary industry output, crop sown area, and grey water footprint intensity, with a notable interaction between agricultural output and grey water footprint intensity. The quantitative analysis based on the STIRPAT model demonstrated that seven factors, including grey water footprint intensity and total crop sown area, exhibited significant contributions to influencing variations. Ranked by importance, these factors were grey water footprint intensity > total crop sown area > urbanization rate > population size > secondary industry output > primary industry output > industrial wastewater discharge, collectively explaining 90.2% of the variability in GWF. The study provides a robust scientific basis for water pollution control and differentiated management in the river basin and holds significant importance for promoting sustainable development of the basin. Full article

Urban wetlands play a crucial role in maintaining ecological balance, carbon sequestration, and water purification. Sediments are key carriers for wetlands to store elements such as carbon, nitrogen, and phosphorus in the aquatic environment. This study analyzed different sediment layers of seven wetlands in Yangzhou, aiming to explore the relationship between physicochemical factors and microbial communities in wetland sediments, as well as to predict the functions of microbial communities. Functional prediction of microbial communities was conducted based on amplicon sequencing analysis, and the neutral community model was used to determine the formation and evolution process of microbial communities. The results showed that in three wetlands, namely Zhuyu Bay (ZYW), Luyang Lake (LYH), and Runyang Wetland (RYSD), the contents of carbon components (total carbon, total soluble carbon, microbial biomass carbon) in the 0–20 cm sediment layer were higher, while the carbon component contents in Baoying Lake (BYH) showed the opposite trend. Among them, the contents of total nitrogen, alkali-hydrolyzable nitrogen, total phosphorus, available phosphorus, total potassium, and available potassium in the 0–20 cm sediment layer of Runyang Wetland (RYSD) were significantly the highest. This indicates that in Runyang Wetland (RYSD), the 0–20 cm layer has more abundant carbon components and mineral nutrients compared to the 20–40 cm layer. Among the seven wetlands, it was found that the content of total potassium was all greater than 10 g/kg, which was much higher than the contents of total phosphorus and total nitrogen. Analysis of microbial communities revealed that the dominant archaeal phyla were Thaumarchaeota and Euryarchaeota, and the dominant bacterial phyla were Proteobacteria and Acidobacteria. The distribution of functional genes was mainly concentrated in Zhuyu Bay (ZYW) and Luyang Lake (LYH). Zhuyu Bay Wetland (ZYW) had potential advantages in light utilization function, and Luyang Lake (LYH) had potential advantages in carbon and nitrogen cycle functions. The assembly process of the archaeal community was mainly affected by stochastic processes, while the bacterial community was mainly affected by deterministic processes. However, water content, total phosphorus, and available potassium all had strong correlations with both archaeal and bacterial communities. The research results preliminarily reveal the connections between the physicochemical properties of sediments, microbial communities, and their potential functions in Yangzhou urban wetlands, providing an important scientific basis for the protection and management of wetland ecosystems. Full article

The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R² of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R² of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO₂ emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. Full article

Metro carriages, as enclosed transport microenvironments, have been understudied regarding pollution characteristics and health risks from ACs, especially during high-temperature summers that amplify exposure. This study applied NTS techniques for the first time across three major Chengdu metro lines, systematically identifying sixteen ACs, including hazardous species such as acetophenone, benzonitrile, and benzoic acid that are often overlooked in conventional BTEX-focused monitoring. The TAC concentration reached 41.40 ± 5.20 µg/m³, with half of the compounds exhibiting significant increases during peak commuting periods. Source apportionment using diagnostic ratios and PMF identified five major contributors: carriage material emissions (36.62%), human sources (22.50%), traffic exhaust infiltration (16.67%), organic solvents (16.55%), and industrial emissions (7.66%). Although both non-cancer (HI) and cancer (TCR) risks for all population groups were below international thresholds, summer tourists experienced higher exposure than daily commuters. Notably, child tourists showed the greatest vulnerability, with a TCR of 5.83 × 10⁻⁷, far exceeding that of commuting children (1.88 × 10⁻⁷). Benzene was the dominant contributor, accounting for over 50% of HI and 70% of TCR. This study presents the first integrated NTS and quantitative risk assessment to characterise ACs in summer metro environments, revealing a broader range of hazardous compounds beyond BTEX. It quantifies population-specific risks, highlights children’s heightened vulnerability. The findings fill critical gaps in ACs exposure and provide a scientific basis for improved air quality management and pollution mitigation strategies in urban rail transit systems. Full article

Coupled grey and green infrastructure (CGGI) offers a promising pathway toward sustainable stormwater management in historic urban environments. This study compares CGGI and conventional grey infrastructure (GREI)-only strategies across four degrees of layout centralization (0%, 33.3%, 66.7%, and 100%) in the Quanzhou West Street Historic Reserve, China. Using a multi-objective optimization framework integrating SWMM simulations, life-cycle cost (LCC) modeling, and resilience metrics, we found that the decentralized CGGI layouts reduced the total LCC by up to 29.6% and required 60.7% less green infrastructure (GI) area than centralized schemes. Under nine extreme rainfall scenarios, the GREI-only systems showed slightly higher technical resilience (Tech-R: max 99.6%) than CGGI (Tech-R: max 99.1%). However, the CGGI systems outperformed GREI in operational resilience (Oper-R), reducing overflow volume by up to 22.6% under 50% network failure. These findings demonstrate that decentralized CGGI provides a more resilient and cost-effective drainage solution, well-suited for heritage districts with spatial and cultural constraints. Full article

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. Full article

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, and facility location and allocation. Real-world case studies from cities like Braga, Lisbon, Uppsala, and Cyprus demonstrate how MOEAs can enhance operational efficiency, boost energy recovery, and reduce environmental impacts. While these algorithms offer significant advantages, challenges remain in computational complexity, adapting to dynamic environments, and integrating with emerging technologies. Future research directions highlight the potential of combining MOEAs with machine learning and real-time data to create more flexible and responsive waste management strategies. By leveraging these advancements, MOEAs can play a pivotal role in developing sustainable, efficient, and adaptive waste disposal systems capable of meeting the growing demands of urbanization and stricter environmental regulations. Full article

This study investigated the nuanced influence of urban morphology on the thermal performance of nine mass housing blocks (21–26, 28–30) in New Belgrade’s Central Zone. These blocks, showcasing diverse structures, provided a robust basis for evaluating the design parameters. ENVI-met simulations were used to assess two scenarios: an “asphalt-only” environment, isolating the urban structure’s impact, and a “real-world” scenario, including green infrastructure (GI). Overall, the findings emphasize that while GI offers mitigation, the inherent urban built structure fundamentally determines thermal outcomes. An urban block’s thermal performance, it turns out, is a complex interplay between morphological factors and local climate. Crucially, simple metrics like Green Area Percentage (GAP) and Building Coverage Ratio (BCR) proved unreliable predictors of thermal performance. This highlights the critical need for urban planning regulations to evolve beyond basic surface indicators and embrace sophisticated, context-sensitive design principles for effective heat mitigation. Optimal performance arises from morphologies that actively manage heat accumulation and facilitate its dissipation, a characteristic exemplified by Block 22’s integrated design. However, even the best-performing Block 22 remains warmer compared to denser central areas, suggesting that urban densification can be a strategy for heat mitigation. Given New Belgrade’s blocks are protected heritage, targeted GI reinforcements remain the only viable approach for improving the outdoor thermal comfort. Full article

Smart cities are technologically advanced urban environments where interconnected systems and data-driven technologies enhance public service delivery and quality of life. These cities rely on information and communication technologies, the Internet of Things, big data, cloud computing, and other Industry 4.0 tools to support efficient city management and foster citizen engagement. Often referred to as digital cities, they integrate intelligent infrastructures and real-time data analytics to improve mobility, security, and sustainability. Ubiquitous sensors, paired with Artificial Intelligence, enable cities to monitor infrastructure, respond to residents’ needs, and optimize urban conditions dynamically. Given the increasing significance of Industry 4.0 in urban development, this study adopts a bibliometric approach to systematically review the application of these technologies within smart cities. Utilizing major academic databases such as Scopus and Web of Science the research aims to identify the primary Industry 4.0 technologies implemented in smart cities, assess their impact on infrastructure, economic systems, and urban communities, and explore the challenges and benefits associated with their integration. The bibliometric analysis included publications from 2016 to 2023, since the emergence of urban researchers’ interest in the technologies of the new industrial revolution. The task is to contribute to a deeper understanding of how smart cities evolve through the adoption of advanced technological frameworks. Research indicates that IoT and AI are the most commonly used tools in urban spaces, particularly in smart mobility and smart environments. Full article

With the continuous increase in urban population, the subway is the main way to alleviate traffic congestion. However, the construction environment of subway stations is complex, and the safety risks are extremely high. Therefore, it is of great practical significance to scientifically and systematically evaluate the safety vulnerability of subway station construction. This paper takes the Chengdu subway project as an example, and establishes a metro station construction safety vulnerability evaluation index system based on the driving forces–pressures–state–impacts–responses (DPSIR) theory with 5 first-level indexes and 23 second-level indexes, and adopts the fuzzy hierarchical analysis method (FAHP) to calculate the subjective weights, and the improved Harris Hawks optimization–projection pursuit method (HHO-PPM) to determine the objective weights, combined with game theory to calculate the comprehensive weights of the indicators, and finally uses the improved cloud model of Bayesian feedback to determine the vulnerability level of subway station construction safety. The study found that the combined empowerment–improvement cloud model assessment method is reliable, and the case study verifies that the vulnerability level of the project is “very low risk”, and the investigations of safety hazards and the pressure of surrounding traffic are the key influencing factors, allowing for the proposal of more scientific and effective management strategies for the construction of subway stations. Full article

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article

The ongoing advancement of urbanization has significantly amplified its impacts on the water environment. Understanding the coupling relationships between urbanization and the water environment (UAWE) is crucial for Chinese policymakers aiming to promote sustainable urban development. In this study, a comprehensive UAWE evaluation model was developed to examine the development trajectories in Neijiang City from 2012 to 2022. Methodologically, a comprehensive evaluation approach was applied to assess urbanization and water resource trends over this period, followed by the development of a Coupling Coordination Degree Model (CCDM) to quantify their synergistic relationship. The results showed that the coupling between the comprehensive urbanization index and the water environment system evolved over time, as reflected in the following key findings: (1) Neijiang underwent three distinct stages from 2012 to 2022 in terms of coupling and coordination between urbanization and the water environment: Basic Coordination (2012–2015), Good Coordination (2016–2020), and Excellent Coordination (2020–2022). (2) Urbanization exerted varying impacts on subsystems of the water environment, with the pressure-response subsystems exhibiting marked volatility from 2012 to 2022. The impact intensity followed the order spatial urbanization > economic urbanization > social urbanization > population urbanization. These findings offer valuable theoretical and practical insights for aligning urban sustainability goals with effective water environment protection measures. This study provides essential guidance for policymakers in Neijiang and similar regions, enabling the development of tailored strategies for sustainable urbanization and enhanced water management. Full article

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article