Search Results (166)

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

High-quality green gardens can markedly enhance the quality of life and mental well-being of their users. However, health and lifestyle constraints make it difficult for people to enjoy urban gardens, and traditional methods struggle to offer the high-fidelity experiences they need. This study introduces a 3D scene reconstruction and rendering strategy based on implicit neural representation through the efficient and removable neural radiation fields model (NeRF-RE). Leveraging neural radiance fields (NeRF), the model incorporates a multi-resolution hash grid and proposal network to improve training efficiency and modeling accuracy, while integrating a segment-anything model to safeguard public privacy. Take the crabapple tree, extensively utilized in urban garden design across temperate regions of the Northern Hemisphere. A dataset comprising 660 images of crabapple trees exhibiting three distinct geometric forms is collected to assess the NeRF-RE model’s performance. The results demonstrated that the ‘harvest gold’ crabapple scene had the highest reconstruction accuracy, with PSNR, LPIPS and SSIM of 24.80 dB, 0.34 and 0.74, respectively. Compared to the Mip-NeRF 360 model, the NeRF-RE model not only showed an up to 21-fold increase in training efficiency for three types of crabapple trees, but also exhibited a less pronounced impact of dataset size on reconstruction accuracy. This study reconstructs real scenes with high fidelity using virtual reality technology. It not only facilitates people’s personal enjoyment of the beauty of natural gardens at home, but also makes certain contributions to the publicity and promotion of urban landscapes. Full article

The objective was to evaluate the quality of an instrument to measure the experience of household water insecurity (WI) and the factors associated with the prevalence of WI in an urban area in a municipality in the Western Brazilian Amazon. A cross-sectional, population-based study was conducted to investigate 983 urban households. The Household Water Insecurity Experiences (HWISE) scale was used to measure the psychometric properties of reliability and validity. An exploratory factor analysis was conducted, and the prevalence ratio (PR, 95% CI) was calculated, considering WI as the dependent variable and the other household variables as independent variables. WI affected 46.2% (95% CI: 43.0–49.4%) of the households, independently associated with: head of the family as parent/other and presence of a child in the household. The instrument exhibited unidimensionality in the factor analyses and was considered to be both reliable and valid, as indicated by a Cronbach’s α coefficient of 0.958. Household WI is a serious public health problem in the Amazon in correlation with both social vulnerability and a lack of public services. As a preliminary approach, the scale proved to be valid and reliable. However, considering the Amazonian context, misunderstandings about some issues by respondents were identified, and further validation studies are needed to improve the intelligibility of these questions. Full article

Single-image super-resolution (SISR) plays a critical role in enhancing visual quality for real-world applications, including industrial inspection and embedded vision systems. While deep learning-based approaches have made significant progress in SR, existing lightweight SR models often fail to accurately reconstruct high-frequency textures, especially under complex degradation scenarios, resulting in blurry edges and structural artifacts. To address this challenge, we propose a Dense Residual Fused Attention Network (DRFAN), a novel lightweight hybrid architecture designed to enhance high-frequency texture recovery in challenging degradation conditions. Moreover, by coupling convolutional layers and attention mechanisms through gated interaction modules, the DRFAN enhances local details and global dependencies with linear computational complexity, enabling the efficient utilization of multi-level spatial information while effectively alleviating the loss of high-frequency texture details. To evaluate its effectiveness, we conducted ×4 super-resolution experiments on five public benchmarks. The DRFAN achieves the best performance among all compared lightweight models. Visual comparisons show that the DRFAN restores more accurate geometric structures, with up to +1.2 dB/+0.0281 SSIM gain over SwinIR-S on Urban100 samples. Additionally, on a domain-specific rice grain dataset, the DRFAN outperforms SwinIR-S by +0.19 dB in PSNR and +0.0015 in SSIM, restoring clearer textures and grain boundaries essential for industrial quality inspection. The proposed method provides a compelling balance between model complexity and image reconstruction fidelity, making it well-suited for deployment in resource-constrained visual systems and industrial applications. Full article

In remote sensing building detection tasks, data acquisition remains a critical bottleneck that limits both model performance and large-scale deployment. Due to the high cost of manual annotation, limited geographic coverage, and constraints of image acquisition conditions, obtaining large-scale, high-quality labeled datasets remains a significant challenge. To address this issue, this study proposes an automatic semantic labeling framework for remote sensing imagery. The framework leverages geospatial vector data provided by OpenStreetMap, precisely aligns it with high-resolution satellite imagery from Bing Maps through projection transformation, and incorporates a quality-aware sample filtering strategy to automatically generate accurate annotations for building detection. The resulting dataset comprises 36,647 samples, covering buildings in both urban and suburban areas across multiple cities. To evaluate its effectiveness, we selected three publicly available datasets—WHU, INRIA, and DZU—and conducted three types of experiments using the following four representative object detection models: SSD, Faster R-CNN, DETR, and YOLOv11s. The experiments include benchmark performance evaluation, input perturbation robustness testing, and cross-dataset generalization analysis. Results show that our dataset achieved a mAP at 0.5 intersection over union of up to 93.2%, with a precision of 89.4% and a recall of 90.6%, outperforming the open-source benchmarks across all four models. Furthermore, when simulating real-world noise in satellite image acquisition—such as motion blur and brightness variation—our dataset maintained a mean average precision of 90.4% under the most severe perturbation, indicating strong robustness. In addition, it demonstrated superior cross-dataset stability compared to the benchmarks. Finally, comparative experiments conducted on public test areas further validated the effectiveness and reliability of the proposed annotation framework. Full article

Urban parks are an important component of public urban spaces, which directly impact the living experiences of residents and the urban image. High-quality urban parks are crucial for enhancing the well-being of residents. This study selected Fukuoka, Japan, as the study site. Five urban parks were chosen to evaluate landscape visual quality by using the Scenic Beauty Estimation (SBE) method. The Semantic Differential (SD) method was used to get sample subjective landscape features. Meanwhile, sample objective landscape features were obtained by using semantic segmentation techniques in deep learning and combined with spatial analysis to understand their distribution. A regression model was established, which used the SBE values as the dependent variable and subjective landscape features as the independent variables to analyze the relationship between urban park landscape visual quality and subjective landscape features. The regression analysis revealed that sense of layering, harmony, interestingness, sense of order, and vitality were the core factors influencing visual quality. All five features had a significant positive impact on landscape visual quality. The sense of order was the most influential factor, which would be the key to enhancing the landscape perception experience. Moreover, the XGBoost model and SHAP value from machine learning were used to reveal the nonlinear relationships and significant threshold effects between urban park visual quality and five objective landscape features: openness, greenness, enclosure, vegetation diversity, and Shannon–Wiener diversity index. This study showed that when openness exceeded 0.27, the positive effect was significant. The optimal threshold for the greenness was 0.38. Vegetation diversity and enclosure had to be below 0.82 and 0.58, respectively, to have a positive impact. Meanwhile, the positive influence of the Shannon–Wiener diversity index reached its maximum at a value of 1.37. This study not only establishes a systematic method for diagnosing landscape problems and evaluating landscape visual quality but also provides both theoretical support and practical guidance for urban park landscape optimization. Full article

Sewer pipelines are essential urban infrastructure that play a key role in sanitation and disaster prevention. Regular condition assessments are necessary to detect defects early and determine optimal maintenance timing. However, traditional visual inspection using closed-circuit television (CCTV) footage is time-consuming, labor-intensive, and dependent on subjective human judgment. To address these limitations, this study develops a convolutional neural network (CNN)-based sewer defect classification model and analyzes how data quality—such as mislabeled or redundant images—affects model accuracy. A large-scale public dataset of approximately 470,000 sewer images was used for training. The model was designed to classify non-defect and three major defect categories. Based on the ResNet50 architecture, the model incorporated dropout and L2 regularization to prevent overfitting. Experimental results showed the highest accuracy of 92.75% at a dropout rate of 0.2 and a regularization coefficient of 0.01. Further analysis revealed that mislabeled, redundant, or obscured images within the dataset negatively impacted model performance. Additional experiments quantified the impact of data quality on accuracy, emphasizing the importance of proper dataset curation. This study provides practical insights into optimizing data-driven approaches for automated sewer defect detection and high-performance model development. Full article

Several studies in the literature have emphasized that public open spaces, such as recreational parks in residential neighborhoods, play a crucial role in enhancing social interaction among residents. As such, access to these parks is a key factor that may influence their use and, in turn, affect the quality of social engagement within the community. Traditional approaches to park accessibility, which focus solely on physical distance, have notable limitations, as proximity alone does not reliably predict park usage. Therefore, physical accessibility should be complemented by assessments of perceived or psychological accessibility. This study is designed to propose a framework for enhancing social interaction through improved access to recreational parks in the residential neighborhoods of the Dammam Metropolitan Area (DMA). It employs a mixed-methods approach comprising two primary methodologies: (1) observational behavioral mapping to identify key influencing factors based on user activities within the selected case study areas, and (2) an end-user questionnaire survey analyzed through inferential statistics, specifically Analysis of Variance (ANOVA), to assess residents’ perceptions of park accessibility and social interaction. The results indicate that adequate park maintenance significantly improves physical accessibility, while elements such as safety are essential for fostering psychological comfort. The ANOVA results yielded an F-value of 4.72 and a p-value of 0.00, confirming a statistically significant effect of the park’s physical features on facilitating social contact among local residents. The study presents a framework that integrates key demographic and social factors influencing park usage, advocating for infrastructure improvements aligned with user perceptions to foster greater community engagement. It highlights that addressing psychological barriers is just as important as making physical enhancements to achieve effective park accessibility. By combining physical design, demographic insights, and user experiences, the framework serves as a practical guide for planning inclusive and socially responsive public spaces. This research contributes to the fields of urban planning, social sustainability, and environmental psychology by offering localized insights and practical tools for implementation. Future research is recommended to further refine urban strategies that promote equitable access to recreational parks, particularly by addressing demographic-specific needs and psychological barriers that influence social interaction in open spaces. Full article

Urban green spaces play a critical role in promoting health, well-being and social inclusion. However, many such spaces remain underutilized by women due to perceived safety risks and inadequate infrastructure. The aim of this study is to understand the level of accessibility of these areas for women of different ages, considering their diverse needs related to physical activity and the sense of safety in public space. This research investigates the behavioural experiences of women in Zielona Góra, Poland, focusing on municipal parks and forests. A mixed-methods approach was applied, including on-site observations, in-depth interviews, online surveys and scenario modelling using Fuzzy Cognitive Maps (FCMs), involving 204 women aged 15–85. The results show that 48% of respondents avoid green areas due to barriers such as poor lighting, fear of wild animals or unpredictable individuals and insufficient infrastructure. Women preferred afternoon visits and valued the presence of others for increased safety. The five most frequented parks were identified based on accessibility and infrastructure quality. Scenario simulations confirmed that even single targeted interventions could improve perceived safety and increase usage. This study highlights the need for inclusive urban design that addresses the specific experiences and requirements of women in public green spaces. Full article

Artificial intelligence (AI) technology not only promotes rapid economic development but also plays an irreplaceable role in improving environmental quality. Based on the quasi-natural experiment of the National Artificial Intelligence Innovation Comprehensive Experimental Zone, this paper empirically studies the effect and mechanism of AI on urban air quality (AQ) using the multi-time difference-in-difference model. The research results showed that AI improved the AQ of cities. The mechanism analysis results indicated that there was a positive mediating effect of government environmental attention on the relationship between AI and AQ improvement. Public environmental attention can further enhance the role of AI in improving urban AQ. Further analysis revealed that the improvement effect of AI on urban AQ was mainly reflected in eastern cities and non-resource-based cities. The research conclusion of this study provides reliable empirical evidence for leveraging AI to empower urban green development and assist in air pollution prevention practices. Full article

Soundscape is the sonic environment that a living being, like a human or animal, experiences in a certain setting. It affects how a space functions and how the being perceives its quality. Consequently, the soundscape is crucial in ecosystems globally. In recent decades, researchers have explored soundscapes using various methodologies across different disciplines. This study aims to provide a brief overview of the soundscape research history, pinpoint key authors, institutions, and collaboration networks, and identify trends and main themes through a bibliometric analysis. A search in the Scopus database on 26 February 2025 found 5825 articles, reviews, and conference papers on soundscape published from 1985 to 2024. The analysis indicated a significant increase in soundscape publications, rising from 1 in 1985 to 19 in 2002, and reaching 586 in 2024. J. Kang was the most prolific author with 265 publications, while University College London emerged as the most productive institution. Co-citation analysis revealed three research groups: one focused on urban soundscapes, another on aquatic soundscapes, and a third on soundscapes in landscape ecology. The keyword co-occurrence analysis identified three themes: “soundscape(s), acoustic environment, and urban planning”, “noise, animal(s), bioacoustics, biodiversity, passive acoustic monitoring, fish, and bird(s)”, and “human(s), sound, perception, and physiology”. Full article

The unprecedented development of coastal cities in West Africa is marked by anarchic urbanization accompanied by ineffective environmental management, leading to water pollution. This study is conducted in the southern districts of Lomé, Togo, an area built on sandbars where inappropriate attitudes, behaviors, and inadequate hygiene and sanitation practices prevail. The objective of this study is to characterize the quality of groundwater in the study area. Bacteriological and physicochemical analyses were carried out on 11 wells in 10 districts in the southern districts during the four seasons of the year. The analysis shows that the groundwater is polluted in all seasons. Nitrate concentrations exceed 50 mg/L in 65% of the samples, while chloride levels surpassed 250 mg/L in 18% of the cases. Regardless of the season, the dominant facies is sodium chloride and potassium chloride. In all districts, the analysis of microbiological parameters including total germs (30 °C, 100/mL), total coliforms (30 °C, 0/mL), Escherichia coli (44 °C, 2/250 mL), fecal streptococci (0/100 mL), and anaerobic sulfite reducers (44 °C, 2/20 mL) reveals values exceeding the European Union standards (2007). Groundwater contamination is facilitated by the sandy nature of the soil, which increases its vulnerability to various pollutants. Togo continues to experience cholera outbreaks, aggravated by poor sanitation infrastructure and limited vaccination coverage. Public health efforts are directed toward improving sanitation and raising awareness about waterborne and non-communicable diseases. Full article

Communities are fundamental to national development, functioning as essential social units where local cultural identity and public participation play a crucial role. However, rapid urbanization has led to a decline in interpersonal interactions, weakened community bonds, and increased social divides, which in turn reduce residents’ engagement in public affairs. This study aims to explore the relationship between cultural identity and community revitalization to promote sustainable community development. We will achieve this by analyzing the implementation experiences of two rural Taiwanese communities: Huanan Community in Gukeng, Yunlin, and Chenggong Community in Dadou, Taichung City. Using exploratory factor analysis and regression analysis as our methodologies, we seek to understand how cultural identity fosters cohesion, enhances participation, and supports sustainable development in community revitalization. Our research findings indicate that cultural identity is composed of cultural engagement, cultural belonging, and cultural integration. Conversely, community revitalization encompasses aspects of daily life, life experiences, personal economic evaluation, community industry development, and residents’ environmental awareness. The overall research framework demonstrates that cultural identity has a strong influence on community revitalization, identifying strategies to improve residents’ quality of life and foster vibrant communities. Full article

The evaluation of electric buses used in public transportation operations encompasses several critical factors that directly influence the operational efficiency, as well as the economic viability, environmental advantages, and user experience. Energy consumption is a critical metric for assessing the energy efficiency of electric buses. It facilitates a better understanding of vehicle performance across varying road conditions and advances the implementation of energy-saving solutions. The passenger demand model is a tool used to assess the quality and experience of electric buses, with the assessment being based on real usage. The operational mileage is defined as the driving distance of electric buses on a single charge. This parameter has a significant impact on both urban coverage and route optimization. The article under consideration identifies evaluation indicators for electric buses. These indicators are derived from a set of 100 questionnaire responses, which were collected in Győr, Hungary. The classification of the indicators into three segments—mechanical, operational and bus transportation system—is proposed, with the underlying rationale and significance of each indicator’s selection being elucidated. The findings indicate that this component is essential for developing a comprehensive evaluation system for electric buses and serves as a solid foundation for more intricate future studies. Full article

Accurate and adaptive bus dispatching is vital for medium-sized urban centers, where static schedules often fail to accommodate fluctuating passenger demand. In this work, we propose a dynamic heuristic that integrates machine learning-based demand forecasts into a discrete-time planning horizon, thereby enabling real-time adjustments to dispatch decisions. Additionally, we introduce a tailored mathematical model—grounded in mixed-integer linear programming and space-time flows—that serves as a benchmark to evaluate our heuristic’s performance under the operational constraints typical of traditional public transportation systems in Colombian mid-sized cities. A key contribution of this research lies in combining predictive modeling (using Prophet for passenger demand) with operational optimization, ensuring that dispatch frequencies adapt promptly to varying ridership levels. We validated our approach using a real-world case study in Montería (Colombia), covering eight representative routes over a full day (5:00–21:00). Numerical experiments show that: 1. Our heuristic matches or surpasses 95% of the optimal solution’s operational utility on most routes, with an average gap of 4.7%, relative to the benchmark mathematical model. 2. It maintains high service levels—above 90% demand coverage on demanding corridors—and robust bus utilization, without incurring excessive operating costs. 3. It reduces computation times by up to 98% compared to the optimization model, making it practically viable for daily scheduling where solving large-scale models exactly can be prohibitively time-consuming. Overall, these results underscore the heuristic’s practical effectiveness in boosting profitability, optimizing resource use, and rapidly adapting to demand fluctuations. The proposed framework thus serves as a scalable and implementable tool for transportation operators seeking data-driven dispatch solutions that balance operational efficiency and service quality. Full article