Search Results (371)

Transit systems play a vital role in urban mobility, yet predicting individual travel behavior within these systems remains a complex challenge. Traditional machine learning approaches struggle with transit trip data because each trip may consist of a variable number of transit legs, leading to missing data and inconsistencies when using fixed-length tabular representations. To address this issue, we propose a transformer-based dynamic-sequence approach that models transit trips as variable-length sequences, allowing for flexible representation while leveraging the power of attention mechanisms. Our methodology constructs trip sequences by encoding each transit leg as a token, incorporating travel time, mode of transport, and a 2D positional encoding based on grid-based spatial coordinates. By dynamically skipping missing legs instead of imputing artificial values, our approach maintains data integrity and prevents bias. The transformer model then processes these sequences using self-attention, effectively capturing relationships across different trip segments and spatial patterns. To evaluate the effectiveness of our approach, we train the model on a dataset of urban transit trips and predict first-mile and last-mile travel times. We assess performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Experimental results demonstrate that our dynamic-sequence method yields up to a 30.96% improvement in accuracy compared to non-dynamic methods while preserving the underlying structure of transit trips. This study contributes to intelligent transportation systems by presenting a robust, adaptable framework for modeling real-world transit data. Our findings highlight the advantages of self-attention-based architectures for handling irregular trip structures, offering a novel perspective on a data-driven understanding of individual travel behavior. Full article

Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. This study assesses the environmental performance of a prospective IVF system located on a university campus in Portugal, focusing on the integration of photovoltaic (PV) energy as an alternative to the conventional electricity grid (GM). A Life Cycle Assessment (LCA) was conducted using the Environmental Footprint (EF) method and the LANCA model to account for land use and soil-related impacts. The PV-powered system demonstrated lower overall environmental impacts, with notable reductions across most impact categories, but important trade-offs with decreased soil quality. The LANCA results highlighted cultivation and packaging as key contributors to land occupation and transformation, while also revealing trade-offs associated with upstream material demands. By combining EF and LANCA, the study shows that IVF systems that are not soil-based can still impact soil quality indirectly. These findings contribute to a broader understanding of sustainability in urban farming and underscore the importance of multi-dimensional assessment approaches when evaluating emerging agricultural technologies. Full article

The development of urban underground space is a necessary way to realize the sustainable development of the city, and it is also an essential means to solve urban environmental problems such as traffic congestion and resource shortage. Scientific suitability evaluation is the prerequisite for the rational planning and development of underground space. Previous studies have encountered problems such as an imperfect index system, a single weighting method, and loss of membership degrees in fuzzy evaluation, which have led to unreasonable evaluation results. Taking the northern coastal cities of Weifang as the research area, the evaluation index system is established, and the index weights are calculated by the improved structural CRITIC. An improved fuzzy mathematical evaluation model based on the weighted summation method is proposed to carry out the suitability evaluation of underground space development in the research area. The results show that: (1) The proposed method of combination weight and improved fuzzy mathematics evaluation takes into account the scientific weight and avoids the subjective bias, and also corrects the issue of membership degree loss in the membership matrix of comprehensive evaluation. (2) When the area of the grid unit is 0.02% of the area of the research area, the size of the evaluation unit is more reasonable. (3) The area that is very suitable for underground space development accounts for 8.69%, and the more suitable area accounts for 25.55%, mainly located in the northwest and central–southern regions of the research area. It can provide a reference for the suitability evaluation of underground space development. Full article

As climate change intensifies the frequency and severity of extreme weather events, the urgency for resilient and sustainable urban development becomes increasingly critical. This study investigates the role of digital twins in advancing climate-responsive urban strategies, with a focus on their integration into zero-energy buildings (ZEBs) and smart city frameworks. A systematic literature review was conducted following PRISMA guidelines, covering 1000 articles initially retrieved from Scopus and Web of Science between 2014 and 2024. After applying inclusion and exclusion criteria, 70 full-text articles were analyzed. Bibliometric analysis using VOSviewer revealed five key application areas of digital twins: energy efficiency optimization, renewable energy integration, design and retrofitting, real-time monitoring and control, and predictive maintenance. The findings suggest that digital twins can contribute to up to 30–40% improvement in building energy efficiency through enhanced performance monitoring and predictive modeling. This review synthesizes trends, identifies research gaps, and contextualizes the findings within the Middle Eastern urban landscape, where climate action and smart infrastructure development are strategic priorities. While offering strategic guidance for urban planners and policymakers, the study also acknowledges limitations, including the regional focus, lack of primary field data, and potential publication bias. Overall, this work contributes to advancing digital twin applications in climate-resilient, zero-energy urban development. Full article

Rat hepatitis E virus (rat HEV) is an emerging zoonotic virus detected in rodents worldwide, with increasing evidence of presence in environmental sources such as surface water, wastewater and bivalves. This systematic review compiles and analyzes all the published research on rat HEV contamination in these matrices, as well as its implications for human health. A comprehensive literature search was conducted using databases such as PubMed, Scopus, Web of Science, and Mendeley, including studies published up until 27 May 2025. Studies were included if they evaluated rat HEV in water- or food-related matrices using molecular detection. The risk of bias was not assessed. The certainty of evidence was not formally evaluated. Limitations include reliance on PCR methods without infectivity confirmation. Following PRISMA inclusion and exclusion criteria, eight eligible studies were analyzed. The results show high detection rates of rat HEV RNA in influent wastewater samples from several high-income European countries, namely Sweden, France, Italy, Spain and Portugal. Lower detection rates were found in effluent wastewater and surface waters in Sweden. In bivalve mollusks sampled in Brazil, rat HEV RNA was detected in 2.2% of samples. These findings show the widespread environmental presence of rat HEV, particularly in urban wastewater systems. While human infections by rat HEV have been documented, the true extent of rat HEV zoonotic potential remains unclear. Given the risks associated with this environmental rat HEV contamination, enhanced surveillance, standardized detection methods, and targeted monitoring programs in food production and water management systems are essential to mitigate potential public health threats. Establishing such programs will be crucial for understanding the impact of rat HEV on human health. Full article

Peri-urban areas in the Global South are rapidly transforming due to urban expansion, land commodification, and institutional change. Although diverse theoretical perspectives address these dynamics, existing scholarship remains fragmented. This study systematically reviews how various theoretical frameworks deepen our understanding of peri-urban land transformation, focusing on conceptual and institutional dimensions. Following PRISMA 2020 guidelines, a systematic review was conducted on 120 studies published between 1996 and 2024, sourced from Scopus, Web of Science, ProQuest, and additional unindexed repositories. Eligible studies explicitly addressed peri-urban land issues in the Global South and applied theoretical approaches. Data extraction involved detailed coding of study characteristics, theoretical orientations, and thematic insights. Using open and selective coding, 19 thematic codes were identified. Three overarching themes emerged: (1) conceptualizing peri-urban spaces through territorial, functional, and transitional lenses; (2) institutionalization of place; and (3) theoretical interpretations of land transformation grounded in neoclassical, modernization, neo-Marxist, dependency, structuration, institutionalist, and urban political ecology frameworks. Studies were appraised for theoretical rigor, relevance, and potential conceptual bias. Limitations include the exclusion of non-English studies. Findings highlight the need for pluralistic, context-sensitive frameworks, with political ecology offering a particularly integrative analytical lens to examine global–local power dynamics and socio-natural transformations. This review was funded by the Alexander von Humboldt Foundation (Georg Forster Fellowship, grant no. 1233452). Full article

As part of its modernisation and economic diversification policies, Saudi Arabia is building smart, sustainable cities intended to improve quality of life and meet environmental goals. However, involving the public in urban planning remains complex, with traditional methods often proving expensive, time-consuming, and inaccessible to many groups. Integrating artificial intelligence (AI) into public participation may help to address these limitations. This study explores whether Saudi residents are ready to engage with AI-driven tools in urban planning, how they prefer to interact with them, and what ethical concerns may arise. Using a quantitative, survey-based approach, the study collected data from 232 Saudi residents using non-probability stratified sampling. The survey assessed demographic influences on AI readiness, preferred engagement methods, and perceptions of ethical risks. The results showed a strong willingness among participants (200 respondents, 86%)—especially younger and university-educated respondents—to engage through AI platforms. Visual tools such as image and video analysis were the most preferred (96 respondents, 41%), while chatbots were less favoured (16 respondents, 17%). However, concerns were raised about privacy (76 respondents, 33%), bias (52 respondents, 22%), and over-reliance on technology (84 respondents, 36%). By exploring the intersection of generative AI and participatory urban governance, this study contributes directly to the discourse on inclusive smart city development. The research also offers insights into how AI-driven public engagement tools can be integrated into urban planning workflows to enhance the design, governance, and performance of the built environment. The findings suggest that AI has the potential to improve inclusivity and responsiveness in urban planning, but that its success depends on public trust, ethical safeguards, and the thoughtful design of accessible, user-friendly engagement platforms. Full article

The rapid expansion of the urban rail network has increased concerns regarding stray current generated by the DC traction power supply system. This stray current, which arises from inadequate insulation between the rail and the ground, can cause electrochemical corrosion and operational challenges to nearby buried metallic infrastructures. A portion of stray current entering utility transformers may induce DC bias risk, thereby affecting the stability and reliability of distribution networks. This review studies the trends in utility transformer-related DC bias caused by metro stray current. Various modeling approaches and suppression measures are discussed, with an emphasis on comprehensively understanding stray current distribution behavior, the DC bias coupling loop, and its impacts. This review underscores the need for a thorough evaluation of existing DC bias suppression measures, and more effective and efficient measures must be developed to enhance the resilience of distribution networks. The gaps in current research are highlighted, and further studies are advocated, particularly those focusing on dynamic metro conditions, supported by advanced modeling, field applications, and interdisciplinary collaboration, to address the challenges of DC bias in urban rail environments. Full article

Bias and fairness issues in artificial intelligence (AI) algorithms are major concerns, as people do not want to use software they cannot trust. Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. These AI models were analyzed to detect biases they may carry with respect to three variables chosen to represent sensitive populations: gender, race, and first-generation college students. We then describe a method for bias mitigation that uses a combination of machine learning and user interaction. (4) Results and Discussion: We use three scenarios to demonstrate that this interactive bias mitigation approach can successfully decrease the biases towards sensitive populations. (5) Conclusion: Our approach allows the user to examine a model and then iteratively and incrementally adjust bias and fairness metrics to change the training dataset and generate a modified AI model that is more fair, according to the user’s own determination of fairness. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

Background: Trauma-related injuries are among the most common reasons for paediatric hospital presentations and represent a substantial component of orthopaedic care. Their management poses unique challenges due to ongoing skeletal development in children. While most reported fractures occur at home or during sports, prior studies have primarily used data from urban European populations, limiting the relevance of their findings for rural and regional settings. Urban-centred research often informs public healthcare guidelines, treatment algorithms, and infrastructure planning, introducing a bias when findings are generalised outside of metropolitan populations. This study addresses that gap by analysing fracture data from two rural trauma centres in New South Wales, Australia. This study assesses paediatric fractures resulting from severe injury mechanisms in rural areas, identifying common fracture types, underlying mechanisms, and treatment approaches to highlight differences in demographics. These findings aim to cast a light on healthcare challenges that regional areas face and to improve the overall cultural safety of children who live and grow up outside of the metropolitan trauma networks. Methods: We analysed data from two major rural referral hospitals in New South Wales (NSW) for paediatric injuries presenting between 1 January 2018 and 31 December 2022. This study included 150 patients presenting with fractures following severe mechanisms of injury, triaged into Australasian Triage Scale (ATS) categories 1 and 2 upon initial presentation. Results: A total of 150 severe fractures were identified, primarily affecting the upper and lower limbs. Males presented more frequently than females, and children aged 10–14 years old were most commonly affected. High-energy trauma from motorcycle (dirt bike) accidents was the leading mechanism of injury among all patients, and accounted for >50% of injuries among 10–14-year-old patients. The most common fractures sustained in these events were upper limb fractures, notably of the clavicle (n = 26, 17.3%) and combined radius/ulna fractures (n = 26, 17.3%). Conclusions: Paediatric trauma in regional Australia presents a unique and under-reported challenge, with high-energy injuries frequently linked to unregulated underage dirt bike use. Unlike urban centres where low-energy mechanisms dominate, rural areas require targeted prevention strategies. While most cases were appropriately managed locally, some were transferred to tertiary centres. These findings lay the groundwork for multi-centre research, and support the need for region-specific policy reform in the form of improved formal injury surveillance, injury prevention initiatives, and the regulation of under-aged off-road vehicular usage. Full article

The ecological environment of Inner Mongolia constitutes a critical component of China’s ecological civilization construction. To comprehensively assess and monitor ecological quality dynamics in this region, this study employed MODIS remote sensing data products (2000–2020) and derived four key indicators, —vegetation index (NDVI), wetness index (WET), build-up and soil index (NDBSI), and land surface temperature (LST)—via the Google Earth Engine (GEE) platform. A Remote Sensing-based Ecological Index (RSEI) was constructed using principal component analysis (PCA) to establish an annual long-term time series, thereby eliminating subjective bias from artificial weight assignment. Integrated methodologies—including Theil–Sen Median and Mann–Kendall trend analysis, Hurst exponent, and geographical detector—were applied to investigate the spatiotemporal evolution of ecological quality in Inner Mongolia and its responses to climatic and anthropogenic drivers. This study proposes a novel framework for large-scale ecological quality assessment using remote sensing. Key findings include the following: The mean RSEI value of 0.41 (2000–2020) indicates an overall improving trend in ecological quality. Areas with ecological improvement and degradation accounted for 76.06% and 23.84% of the region, respectively, exhibiting a spatial pattern of “northwestern improvement versus southeastern degradation.” Pronounced regional disparities were observed: optimal ecological conditions prevailed in the Greater Khingan Range (northeast), while the Alxa League (southwest) exhibited the poorest conditions. Northwestern improvement was primarily driven by increased precipitation, rising temperatures, and conservation policies, whereas southeastern degradation correlated with rapid urbanization and intensified socioeconomic activities. Our results demonstrate that MODIS-derived RSEI effectively enables large-scale ecological monitoring, providing a scientific basis for regional green development strategies. Full article

Background: Antimicrobial resistance (AMR) is a growing global health concern, driven in part by the environmental release of antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs). Aquatic systems, particularly those exposed to urban, agricultural, and industrial activity, are recognized as hotspots for AMR evolution and transmission. In Chile, the Aconcagua River—subject to multiple anthropogenic pressures—offers a representative model for studying the environmental dimensions of AMR. Methods: Thirteen surface water samples were collected along the Aconcagua River basin in a single-day campaign to avoid temporal bias. Samples were filtered through 0.22 μm membranes and cultured on MacConkey agar, either unsupplemented or supplemented with ceftazidime (CAZ) or ciprofloxacin (CIP). Isolates were purified and identified using MALDI-TOF mass spectrometry. Antibiotic susceptibility was evaluated using the Kirby–Bauer disk diffusion method in accordance with CLSI guidelines. Carbapenemase activity was assessed using the Blue-Carba test, and PCR was employed for the detection of the bla_VIM, bla_KPC, bla_NDM, and bla_IMP genes. Results: A total of 104 bacterial morphotypes were isolated; 80 were identified at the species level, 5 were identified at the genus level, and 19 could not be taxonomically assigned using MALDI-TOF. Pseudomonas (40 isolates) and Aeromonas (25) were the predominant genera. No growth was observed on CIP plates, while 24 isolates were recovered from CAZ-supplemented media, 87.5% of which were resistant to aztreonam. Five isolates exhibited resistance to carbapenems; two tested positive for carbapenemase activity and carried the bla_VIM gene. Conclusions: Our results confirm the presence of clinically significant resistance mechanisms, including bla_VIM, in environmental Pseudomonas spp. from the Aconcagua River. These findings highlight the need for environmental AMR surveillance and reinforce the importance of adopting a One Health approach to antimicrobial stewardship and wastewater regulation. Full article

Air quality (AQ) modeling is at the forefront of estimating pollution levels in areas where the spatial representativity is low. Large metropolitan areas in Asia such as Beijing face significant pollution issues due to rapid industrialization and urbanization. AQ nowcasting, especially in dense urban centers like Beijing, is crucial for public health and safety. One of the most popular and accurate modeling methodologies relies on black-box models that fail to explain the phenomena in an interpretable way. This study investigates the performance and interpretability of Explainable AI (XAI) applied with the eXtreme Gradient Boosting (XGBoost) algorithm employing the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-Agnostic Explanations (LIME) for PM_2.5 nowcasting. Using a SHAP-based technique for dimensionality reduction, we identified the features responsible for 95% of the target variance, allowing us to perform an effective feature selection with minimal impact on accuracy. In addition, the findings show that SHAP and LIME supported orthogonal insights: SHAP provided a view of the model performance at a high level, identifying interaction effects that are often overlooked using gain-based metrics such as feature importance; while LIME presented an enhanced overlook by justifying its local explanation, providing low-bias estimates of the environmental data values that affect predictions. Our evaluation set included 12 monitoring stations using temporal split methods with or without lagged-feature engineering approaches. Moreover, the evaluation showed that models retained a substantial degree of predictive power (R² > 0.93) even in a reduced complexity size. The findings provide evidence for deploying interpretable and performant AQ modeling tools where policy interventions cannot solely depend on predictive analytics tools. Overall, the findings demonstrate the large potential of directly incorporating explainability methods during model development for equal and more transparent modeling processes. Full article

Land use/land cover (LULC) changes significantly affect hydrological processes in watersheds. In this study, the Soil and Water Assessment Tool (SWAT+) model was employed to investigate the hydrological response to LULC changes in the İznik Lake Watershed, a region of significant environmental and social importance in the Marmara Region of Türkiye. This study provides a novel understanding of water balance dynamics of the İznik Lake Watershed through hydrological modeling. The SWAT+ model was calibrated and validated against observed monthly flow data from two gauging stations using three objective functions: Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and the percent bias (PBIAS). The model was utilized to evaluate the impacts of LULC change on water balance components such as surface runoff, percolation, lateral flow, water yield, and evapotranspiration. The results revealed that the expansion of urban areas and reduction in forest land have led to an increase in surface runoff and a decrease in lateral flow and percolation, which in turn have impacted the overall water yield of the watershed. The findings of this study can inform land use planning and management decisions to mitigate the negative impacts of LULC changes on water resources in the İznik Lake Watershed and similar regions. Full article