Search Results (590)

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

The Yangtze River Delta urban agglomeration, characterized by high population density, an advanced transportation system, and a concentration of industrial activity, is one of the regions severely affected by O₃ pollution in central and eastern China. Using data collected from 251 monitoring stations between 2015 and 2025, this paper analyzed the spatio-temporal variation of 8 h O₃ concentrations and instances of exceedance. On the basis of exploring the influence of meteorological factors on regional 8 h O₃ concentration, the potential source contribution areas of pollutants under the exceedance condition were investigated using the HYSPLIT model. The results indicate a rapid increase in the 8 h O₃ concentration at a rate of 0.91 ± 0.98 μg·m⁻³·a⁻¹, with the average number of days exceeding concentration standards reaching 41.05 in the Yangtze River Delta urban agglomeration. Spatially, the 8 h O₃ concentrations were higher in coastal areas and lower in inland regions, as well as elevated in plains compared to hilly terrains. This distribution was significantly distinct from the concentration growth trend characterized by higher levels in the northwest and lower levels in the southeast. Furthermore, it diverged from the spatial characteristics where exceedances primarily occurred in the heavily industrialized northeastern region and the lightly industrialized central region, indicating that the growth and exceedance of 8 h O₃ concentrations were influenced by disparate factors. Local human activities have intensified the emissions of ozone precursor substances, which could be the key driving factor for the significant increase in regional 8 h O₃ concentrations. In the context of high temperatures and low humidity, this has contributed to elevated levels of 8 h O₃ concentrations. When wind speeds were below 2.5 m·s⁻¹, the proportion of 8 h O₃ concentrations exceeding the standards was nearly 0 under almost calm wind conditions, and it showed an increasing trend with rising wind speeds, indicating that the potential precursor sources that caused high O₃ concentrations originated occasionally from inland regions, with very limited presence within the study area. This observation implies that the main cause of exceedances was the transport effect of pollution from outside the region. Therefore, it is recommended that the Yangtze River Delta urban agglomeration adopt economic and technological compensation mechanisms within and between regions to reduce the emission intensity of precursor substances in potential source areas, thereby effectively controlling O₃ concentrations and improving public living conditions and quality of life. Full article

Modern software solutions have a multiplicative effect on enhancing quality of life across various urban sectors, including the environment, education, public health, security, transportation, time efficiency, employment, and other key aspects of city living. This article addresses a specific issue concerning the organisation of leisure activities for both local residents and tourists, using the Chechen Republic as a case study. In response, the study aimed to develop a digital solution to address this challenge, with potential for integration into the Republic’s unified digital ecosystem. By employing system analysis methods, the authors identified the key objects and stakeholders involved in the problem domain. They also defined the software product’s functionality and classified user categories. Using Unified Modelling Language methods, a use case diagram was developed to illustrate the conceptual operation of the system. Furthermore, object-oriented design methods were applied to create a user interface prototype for the software product. As a result, a digital service was developed that enables users to create personalised leisure routes, taking into account individual goals, time constraints, traffic conditions, and the real-time status of urban infrastructure. The resulting software solution is both customisable and scalable. The article also presents selected examples of project development. Full article

Accurate bus travel time prediction is crucial for improving travel experience, especially in transfer journeys. This study introduces a novel multi-route bus travel time prediction system-based PMLNet, a partition and combination prediction framework, addressing the gap in accurate prediction models by incorporating macro and local impact factors. The system employs a pre-processing algorithm for constructing travel chains, partitions travel time into four components, utilizes LSTM along with the newly proposed MDARNN model for predicting each component, and applies four real-time traffic impact factors to calibrate the predictions of each component. Experimental validation on four bus routes demonstrates PMLNet’s superior performance, achieving mean absolute percentage errors (MAPE) as low as 2.91% and mean absolute errors (MAE) below 1.45 min, outperforming traditional models and various partitioned combination frameworks. These findings underscore PMLNet’s potential to significantly improve public transportation services by providing more accurate travel time predictions, ultimately enhancing the user experience in intelligent transportation systems. Full article

Generation Z (Gen Z; born 1997–2012) is reshaping global tourism through digital fluency, ethical awareness, and a desire for authentic, sustainable travel experiences. This study surveys 413 Gen Z travelers across France, Portugal, Romania, and the USA to map their booking behaviors, information sources, transport modes, accommodations, dining practices, and leisure activities. The findings reveal a strong preference for independent online bookings and social-media-influenced destination choices (Instagram, TikTok), with air and car travel being used for long-distance journeys and walking/public transit being used for local journeys. Accommodation spans commercial hotels and private rentals, while informal, local dining and nature- or culture-centered leisure prevail. Chi-square tests were performed to identify differences between countries. To reveal distinct traveler segments and their country’s modulations towards sustainability, a hierarchical cluster analysis was performed. The results uncover four segments: “Tech-Active, Nature-Oriented Minimalists” (32.3% in France); “Moderate Digital Planners” (most frequent across all countries, particularly dominant among Romanian respondents); “Disengaged and Indecisive Travelers” (overrepresented in the USA); and “Culturally Inclined, Selective Sustainability Seekers” (>30% in France/Portugal). Although sustainability is widely valued, only some segments of the studied population consistently act on these values. The results suggest that engaging Gen Z requires targeted, value-driven digital strategies that align platform design with the cohort’s diverse sustainability commitments. Full article

Accurately identifying the sources of fine particulate matter (PM_2.5) pollution is crucial for pollution control and public health protection. Taking the PM_2.5 pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM_2.5, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM_2.5 surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO₃⁻, contributing up to 49% during peak pollution), with calcium ion (Ca²⁺) and sulfate ion (SO₄²⁻) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM_2.5 concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM_2.5 accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts. Full article

Urban mobility resilience plays a central role in sustainable urban planning discussions, especially considering the challenges of extreme events, climate change, and the increasing scarcity of fossil fuels. This study evaluates urban mobility resilience in Petrópolis (RJ), incorporating socio-spatial heterogeneity and energy vulnerability. This research fills methodological gaps in the literature by proposing a composite resilience index that integrates technical, socioeconomic, and fossil fuel dependency variables within a robust multicriteria framework. We selected eleven variables relevant to urban mobility and organized them into inference blocks. We normalized the variables using Gaussian functions, respecting their maximization or minimization characteristics. We applied the Analytic Hierarchy Process (AHP) to assign weights to the criteria and then aggregated and ranked the results using multicriteria analysis. The final index represents the adaptive capacity of urban territories facing the energy crisis, and we applied it spatially to the neighborhoods of Petrópolis. The analysis identified a significant concentration of neighborhoods with low resilience, particularly in quadrants, combining deficiencies in public transportation, high dependence on fossil fuels, and socioeconomic constraints. Factors such as limited pedestrian access, insufficient motorized public transport coverage, and a high proportion of elderly residents emerged as significant constraints on urban resilience. Intervention strategies that promote active mobility, improve accessibility, and diversify transportation modes proved essential for strengthening local resilience. The results emphasize the urgent need for public policies to reduce energy vulnerability, foster active mobility, and promote equity in access to transportation infrastructure. Full article

Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime. 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

This article summarizes the scope and content of the Special Issue (SI) entitled “Geographic Information Systems (GIS) and Cartography for a Sustainable World” and its contribution to the global discourse regarding sustainability concerns. At the heart of the discussion in this SI lies: (i) GIS, a valuable tool and a means for modeling, designing, and analyzing (spatial) data and processes related to the pursuance of sustainability objectives at both local and global scales; and (ii) Cartography as a discipline, which through maps and visualizations can convey the present state. The latter can play a vital role in educating, empowering, and raising public awareness with regard to sustainability concerns on the one hand, and can form a basis for policy-makers, scientists, and citizens for articulating effective sustainability strategies on the other. The fulfillment of the SI goals is attained through a collection of 26 papers that delve into and attempt to visualize sustainability achievements or concerns on a variety of themes in different parts of the world. More specifically, the content of this collection of papers can be categorized into the following sustainability-related themes: Urbanization, Transportation, Carbon Emissions Management, Infrastructure, Rural Development, and Climate Change. The main conclusion is that planning and implementing sustainability policies is a challenging and multi-level task, and must be carried out within a fully dynamic decision environment. Although some progress has already been made, more intensive and collective efforts from scientists, governments, the entrepreneurial community, and citizens are needed in order for the ambitious goals of Agenda 2030 to be reached. Full article

Introduction: The interface between T cells and the tumor microenvironment, termed the ‘immunological synapse’, consists of multiple checkpoint protein pairs co-expressed on both sides of the synapse. mir-16, a microRNA from a widely known tumor-suppressor family of miRNAs, was previously shown by us to be downregulated in melanoma. As other miRNAs from this family have been shown to directly target checkpoint proteins, here we investigated whether miR-16 influences the expression patterns of checkpoint proteins in melanoma. Methods: Single-cell gene expression data from the melanoma microenvironment were retrieved from a public database. Melanoma cell lines were established from metastatic lesions and transiently transfected with an hsa-miR-16-5p-mimic RNA or a mir-16-expressing plasmid. The mRNA expression profiles were analyzed using an Affymetrix microarray. Direct targets of miR-16 were identified by luciferase reporter assays. Protein levels were assessed by Western blotting. Results: Bioinformatic analysis revealed that the expression levels of eight checkpoint mRNAs, known to be present on the melanoma side of the immunological synapse, were highly correlated. Four of these mRNAs contained putative binding sites for the miR-15/16 family. miR-16 expression was significantly reduced in melanoma cells, compared to normal melanocytes. Luciferase reporter assays demonstrated that miR-16 directly targets the 3′ untranslated regions (3′UTRs) of CD40, CD80. The mRNAs downregulated following miR-16 overexpression were highly enriched for genes involved in autophagy, vesicle-mediated transport, and the regulation of protein membrane localization. Among these, VTI1B and SMPD1 were confirmed to be direct targets of miR-16. Transient overexpression of miR-16 resulted in a significant reduction in SMPD1 and VTI1B levels in melanoma cell lines. Conclusions: Our findings suggest that miR-16 potentially modulates melanoma tumorigenesis, metastasis and immunogenicity by altering the composition of checkpoint proteins at the immunological synapse and by regulating cellular pathways associated with intracellular trafficking and transmembrane protein presentation. Full article

Rivers are recognized as significant pathways and transportation for microplastics (MPs), an emerging contaminant, to aquatic environments. However, there is limited evidence on how riverine reservoirs influence MPs transport. To fill this gap and provide baseline empirical data and insights to South African context, the current study assessed the seasonal variation in MP densities from sediments collected upstream, within the reservoir, and downstream of the Vondo Reservoir along the Mutshindudi River. We hypothesised that MP densities would be highest within the reservoir, due to the lack of constant flow that would otherwise transport accumulated particles downriver. Additionally, we expected the cool–dry season to be associated with the highest MP densities. As expected, high MP densities were observed within the reservoir (117.38–277.46 particles kg⁻¹ dwt) when compared to the downstream (72.63–141.50 particles kg⁻¹ dwt) and upstream (28.81–91.63 particles kg⁻¹ dwt) sites of the reservoir. The cool–dry season (91.63–277.46 particles kg⁻¹ dwt) exhibited the highest MP densities compared to the hot–wet season (28.81–141.50 particles kg⁻¹ dwt). However, MP densities downstream the reservoir were higher during the hot–wet season (141.50 ± 24.34 particles kg⁻¹ dwt) compared to the cool–dry season (72.63 ± 48.85 particles kg⁻¹ dwt). The most dominant MP particles identified were white, transparent, and black fibres/filaments composed primarily of polypropylene (PP) and polyethylene (PE). This suggests diverse sources of MP particles. No significant correlations were found between water parameters and MP densities across sampling sites and seasons, indicating a widespread and context-independent presence of MPs. These findings contribute to MP studies in freshwater environments and further reinforce the role of sediments as sink for MPs and suggest that riverine reservoirs similar to dams can trap MPs, which may then be remobilized downstream during high-flow periods. Importantly, the results of this study can support local municipalities in implementing targeted plastic pollution mitigation strategies and public awareness campaigns, particularly because the Vondo Reservoir serves as a critical water resource for surrounding communities. Full article

Arsenic (As) contamination in groundwater represents a major global public health threat, particularly in alluvial aquifer systems where redox-sensitive geochemical processes facilitate the mobilization of naturally occurring trace elements. This study investigates groundwater quality, particularly focusing on the origin of arsenic contamination in shallow and deep alluvial aquifers of the Lower Sakarya River Basin, which are crucial for drinking, domestic, and agricultural uses. Groundwater samples were collected from 34 wells—7 tapping the shallow aquifer (<60 m) and 27 tapping the deep aquifer (>60 m)—during wet and dry seasons for the hydrogeochemical characterization of groundwater. Environmental isotope analysis (δ¹⁸O, δ²H, ³H) was conducted to characterize origin and groundwater residence times, and the possible hydraulic connection between shallow and deep alluvial aquifers. Mineralogical and geochemical characterization of the sediment core samples were carried out using X-ray diffraction and acid digestion analyses to identify mineralogical sources of As and other metals. Pearson correlation coefficient analyses were also applied to the results of the chemical analyses to determine the origin of metal enrichments observed in the groundwater, as well as related geochemical processes. The results reveal that 33–41% of deep groundwater samples contain arsenic concentrations exceeding the WHO and Turkish drinking water standard of 10 µg/L, with maximum values reaching 373 µg/L. Manganese concentrations exceeded the 50 µg/L limit in up to 44% of deep aquifer samples, reaching 1230 µg/L. On the other hand, iron concentrations were consistently low, remaining below the detection limit in nearly all samples. The co-occurrence of As and Mn above their maximum contaminant levels was observed in 30–33% of the wells, exhibiting extremely low sulfate concentrations (0.2–2 mg/L), notably low dissolved oxygen concentration (1.45–3.3 mg/L) alongside high bicarbonate concentrations (450–1429 mg/L), indicating localized varying reducing conditions in the deep alluvial aquifer. The correlations between molybdenum and As (rdry = 0.46, rwet = 0.64) also indicate reducing conditions, where Mo typically mobilizes with As. Arsenic concentrations also showed significant correlations with bicarbonate (HCO₃⁻) (rdry = 0.66, rwet = 0.80), indicating that alkaline or reducing conditions are promoting arsenic mobilization from aquifer materials. All these correlations between elements indicate that coexistence of As with Mn above their MCLs in deep alluvial aquifer groundwater result from reductive dissolution of Mn/Fe(?) oxides, which are primary arsenic hosts, thereby releasing arsenic into groundwater under reducing conditions. In contrast, the shallow aquifer system—although affected by elevated nitrate, sulfate, and chloride levels from agricultural and domestic sources—exhibited consistently low arsenic concentrations below the maximum contaminant level. Seasonal redox fluctuations in the shallow zone influence manganese concentrations, but the aquifer’s more dynamic recharge regime and oxic conditions suppress widespread As mobilization. Mineralogical analysis identified that serpentinite, schist, and other ophiolitic/metamorphic detritus transported by river processes into basin sediments were identified as the main natural sources of arsenic and manganese in groundwater of deep alluvium aquifer. Full article

To advance evidence-based urban design prioritizing non-motorized mobility, this study proposes a street view-enabled explainable machine learning framework that systematically links built environment semantics to non-motorized transportation vitality optimization. By integrating Baidu Street View images with deep learning-based object detection (Faster R-CNN), we quantify fine-grained human-powered and mechanically assisted mobility vitality. These features are fused with multi-source geospatial data encompassing 23 built environment variables into an interpretable machine learning pipeline using SHAP-optimized random forest models. The key findings reveal distinct nonlinear response patterns between HP and MA modes to built environment factors; for instance, a notable promotion in mechanically assisted NMT vitality is observed as enterprise density increases beyond 0.2 facilities per ha. Emergent synergistic and threshold effects are evident from variable interactions requiring multidimensional planning consideration, as demonstrated in phenomena such as the peaking of human-powered NMT vitality occurring at public facility densities of 0.2–0.8 facilities per ha, enterprise densities of 0.6–1 facilities per ha, and spatial heterogeneity patterns identified through Bivariate Local Moran’s I clustering. This research contributes an innovative technical framework combining street view image recognition with explainable AI, while practically informing urban planning through evidence-based mobility zone classification and targeted strategy formulation, enabling more precise optimization of pedestrian-/cyclist-oriented urban spaces. Full article

As urbanization has accelerated, rail transit has evolved from being a mere means of transportation to a public area that houses the city’s cultural memory and serves as a crucial portal for the public to understand the culture of the city. As an urban public space with huge passenger flow, the metro (or subway) cultural space has also become a public cultural space, serving communal welfare and representing the image of the city. It is currently attracting more and more attention from the academic community. Wuhan, located in central China, has many subway lines and its engineering construction has set several national firsts, which is a typical sample of urban subway development in China. In this study, we use Python 3.13.0 crawler technology to capture the public’s comments on cultural space of Wuhan metro in social media and adopt SnowNLP sentiment score and LDA thematic clustering analysis to explore the overall quality, distinct characteristics, and deficiencies of Wuhan metro cultural space construction, and propose targeted design optimization strategies based on this study. The main findings are as follows: (1) The metro cultural space is an important window for the public to perceive the city culture, and the public in general shows positive perception of emotions: among the 16,316 data samples, 47.7% are positive comments, 17.8% are neutral comments, and 34.5% are negative comments. (2) Based on the frequency of content in the sample data for metro station exit and entrance space, metro train space, metro concourse and platform space, they are ranked as weak cultural spaces (18%), medium cultural spaces (33%), and strong cultural spaces (49%) in terms of the public’s perception of urban culture. (3) At present, there are certain deficiencies in Wuhan metro cultural space: the circulation paths in concourses and platforms are overly dominant, leaving little space for rest or interaction; the cultural symbols of metro train space are fragmented; the way of articulation between cultural and functional space in the metro station exit and entrance space is weak, and the space is single in form. (4) Wuhan metro cultural space needs to be based on locality landscape expression, functional zoning reorganization, innovative scene creation to optimize the visual symbol system and behavioral symbol system in the space, to establish a good image of the space, and to strengthen the public’s cultural identity and emotional resonance. Full article