Search Results (51)

Gasoline evaporation is a significant source of urban volatile organic compounds (VOCs). In this study, we selected Nanjing, a major city in the Yangtze River Delta of China, and developed a high-resolution (1 km × 1 km) gridded VOC species emission inventory for gas stations based on measured VOC emission characteristics and statistical data on gasoline and diesel sales. The results show that VOC emissions from gas stations were correlated with population density and road networks, and were mainly concentrated in the downtown area. The emitted VOCs were dominated by alkanes (58%) and oxygenated VOCs (19%), with i-pentane, n-butane, and methyl tert-butyl ether (MTBE) as the major components. C4–C5 alkenes were identified as the key contributors to ozone (O₃) formation, while aromatics contributed most to secondary organic aerosol (SOA) formation. Health risk assessment indicates that, for gas station workers, both carcinogenic and non-carcinogenic risks associated with gasoline and diesel VOC evaporation exceed acceptable thresholds. Benzene, 1,2-dichloroethane, and 1,2-dibromoethane posed the highest carcinogenic risks, whereas acrolein, benzene, and 1,3-butadiene contributed most to non-carcinogenic risks. For urban residents, the health risks from gas station VOC emissions were generally within acceptable levels; however, under unfavorable meteorological conditions, residents living near gas stations may still face elevated health risks. This study highlights the significant impacts of gas station-related VOC emissions on air quality and human health, and informs targeted control and mitigation strategies for gasoline evaporation. Full article

The construction industry remains among the most hazardous, with workers in horizontal transportation infrastructure facing additional risks from dynamic work zones, live traffic exposure, and variable environmental conditions. Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) offer new approaches to accident analysis and prevention, yet their applications toward improving occupational safety in transportation construction have not been comprehensively reviewed. This paper presents a systematic review of 54 studies published between 2016 and 2025 collected from two online databases (Transportation Research International Documentation and Web of Science). This review synthesizes how immersive technologies contribute to occupational risk assessment, safety training, and real-time hazard monitoring in the construction of roads, bridges, tunnels, and work zones. Each study is classified across two dimensions: the immersive medium (VR, AR, etc.) and the operational context within the construction lifecycle (onsite tools, offsite monitoring and planning, simulation-based analysis, and workforce education). This dual classification is the first to systematically map immersive technology applications for occupational safety, specifically within horizontal transportation infrastructure. The findings of this review demonstrate the unique use cases of each immersive medium, revealing that VR is primarily used for controlled experimentation and full-immersion remote analysis, whereas AR and handheld devices are preferred for field-deployed applications. Despite these promising capabilities, widespread adoption remains limited by hardware constraints, challenging field conditions, and organizational resistance. This suggests that future work should focus on safety systems tested in real-world settings and rigorously evaluated by domain experts to enable their integration into standard workplace risk management practices. Full article

Historic linear corridors in living-heritage settings concentrate identity, everyday mobility, and visitor experience. Balancing authenticity, adaptability, and publicness therefore benefits from evidence that jointly characterizes long-term physical change, network accessibility, and eye-level interface conditions. Existing assessments often focus on façades or single time slices, leaving limited evidence that relates decades of built-fabric reconfiguration (changes in building footprints, street edges, and open-space fragmentation) to multi-scale accessibility and pedestrian-facing qualities. We propose an integrated and interpretable workflow for the Beishan Street corridor in the West Lake World Heritage core (Hangzhou) over 1929–2024. Scale-sensitive morphological metrics, multi-radius network measures (integration and centrality), and street-view semantic segmentation are aligned at corridor-segment resolution and examined together with segment-level functional intensity derived from POIs using transparent linear models. The results indicate a long-term shift from a lakeshore-led to a road-led spatial logic, followed by post-2000 stabilization near saturation. Average integration increases, while the high-integration tail becomes thinner. In connector-removal scenarios, the eastern segment shows a relative accessibility decline, and a central hinge node emerges as a vulnerability hotspot (bottleneck) where through-movement concentrates. Eye-level profiles differ by segment: the west exhibits maximal canopy and lower sky visibility, the center shows stronger continuous walls around compounds with intermittent forecourt openings, and the east is characterized by compact residential heritage frontage with low vegetation. Segment-level associations suggest that address and wayfinding density tends to co-occur with clearer frontages, wider sky cones, and stronger tree cover. Transportation-related and access/passage facilities tend to co-occur with higher ground-plane legibility, measured as wider and more continuous road and sidewalk surfaces. Medical and government clusters tend to co-occur with lower sky openness. Recommended actions include the following: (1) mesh-aware protection of key connectors and the hinge, (2) segment-specific targets for façade share and ground cues with planned punctuations, (3) tailored interface standards for institutional clusters, (4) scalable address and wayfinding systems, and (5) event staging that preserves effective roadway and sidewalk capacity. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

Due to intensive urbanization, global warming, and increasing energy demands, the impact of urban heat islands is becoming more significant. This study investigates the contribution of vehicular emissions to air pollution and its effects on urban heat island intensity in a selected area of Belgrade, Serbia, between March and September 2015, using a combination of experimental measurements and numerical simulations. Furthermore, this study presents the results of the research on the impact of assessment of traffic-induced air pollution on the appearance of thermal islands in the urban environment, as well as the characterization of thermal islands and their quantification. This study quantifies the effects of traffic-related emissions and urban meteorological parameters on the intensity of the urban heat island by combining field measurements with a validated three-dimensional numerical model and shows that higher traffic density increases pollutant concentrations and cooling energy demand in buildings. The study includes experimental measurements of traffic intensity and modeling of gas emissions from major roads. Using long-term and short-term field measurements, concentrations of carbon dioxide and other pollutants were analyzed with meteorological parameters and their cumulative impact to assess their impact on local air quality. A three-dimensional numerical model for simulating the dispersion of pollutants has been developed, confirmed and validated by experimental data. The results highlight a direct correlation between traffic density and pollutant concentrations, emphasizing the need for strategic urban planning and sustainable transport policies to mitigate the effects of air pollution. A validated numerical model was used to simulate dynamic changes in temperature fields and carbon dioxide concentrations caused by vehicular emissions. The findings reveal that the Urban Heat Island Intensity (UHII) for the selected area in Belgrade reached peaks of up to 12 °C during the summer measurement period, with typical values in July ranging from 5 °C to 9 °C. Furthermore, the validated numerical model demonstrated that the removal of urban trees would lead to a local air temperature increase of 1.5 °C to 3 °C, quantifying the significant cooling potential of green infrastructure. These results highlight a direct correlation between traffic density, pollutant concentrations, and the intensification of urban heat islands, emphasizing the need for strategic urban planning. Furthermore, the findings reveal that increased traffic not only elevates air pollutant levels but also enhances the intensity of urban heat islands, leading to higher cooling energy demands in buildings. These insights are vital for developing effective mitigation strategies to improve the sustainability of urban environments and living conditions. These findings provide a clear directive for urban planners: the integration and preservation of green infrastructure is a highly effective UHI mitigation strategy, capable of reducing local temperatures by 1.5–3 °C. Furthermore, the results strongly support the implementation of targeted traffic management policies in dense urban cores as a dual strategy to improve air quality and reduce local thermal loads. Full article

The rapid urban growth in Saudi Arabia has intensified challenges in sustainable solid waste management, particularly in selecting suitable landfill sites that minimize environmental risks and protect public health. Tabuk Province, located in the northwest of the Kingdom, represents a region where arid climatic conditions, fragile ecosystems, and increasing urbanization make landfill sitting highly complex. Traditional decision-making approaches often struggle to capture uncertainties in expert opinions and spatial data, leading to less reliable outcomes. While Geographic Information Systems and Multicriteria Decision-Making have been applied to this field, the explicit integration of fuzzy logic remains limited, especially in arid regions. This study addresses this gap by combining the Fuzzy Analytic Hierarchy Process with Geographic Information Systems to establish a more robust framework for landfill site selection in Tabuk. Seven critical criteria were considered, including distance from major roads, airports, urban centers, coastlines, wetlands, and protected areas, with expert assessments analyzed through fuzzy reasoning to improve decision reliability. The results generated a spatial suitability map highlighting priority zones for landfill development, particularly in the western and southwestern areas of the province, where environmental sensitivity is lower and accessibility to infrastructure is greater. The findings emphasize that proximity to urban areas and road networks are dominant factors influencing suitability. The novelty of this study lies in its methodological integration, which enhances transparency, adaptability, and objectivity in landfill sitting. By promoting environmentally responsible waste management, the framework directly supports the Sustainable Development Goal of Good Health and Well-Being and the Sustainable Development Goal of Sustainable Cities and Communities, ensuring safer urban development and healthier living conditions. Moreover, the approach is transferable to other arid and semi-arid regions, offering valuable insights for countries facing similar challenges in sustainable urban planning. Full article

The complex geological environment in western Sichuan, China, leads to frequent debris flow disasters, posing significant threats to the lives and property of local residents. In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The models were trained with data in Songpan County and used for debris flow susceptibility prediction in Mao County, using small watersheds as assessment units. Seventeen key feature factors based on multi-source remote sensing data encompassing topography and geomorphology, geological structures, environmental elements, and human activities were selected as input parameters after assessment with Pearson correlation analysis. Model performance was rigorously evaluated through ten-fold cross-validation, and hyperparameter optimization was employed to enhance predictive accuracy. To assess the models’ robustness, the trained models were applied to the neighboring Mao County for cross-regional validation. The results consistently indicate that elevation, seismic nucleation density, population density, and distance to roads are the primary controlling factors influencing susceptibility. Comparative analysis between the Songpan and Mao County reveals that the RF model significantly outperforms SVM and XGBoost in accuracy and robustness. Therefore, the RF model is better suited for debris flow susceptibility assessment in western Sichuan. Although the effectiveness of this model may be limited by the relatively small sample size of debris flow events in the dataset and potential variations in environmental conditions across different regions, it still holds promise for providing a scientific basis and decision-making support for disaster mitigation in comparable areas of western Sichuan. Full article

The end of the pandemic has been officially declared; however, the requirement to ensure hygienic living conditions in tourist accommodations remains a top priority for all hotel establishments and a prerequisite for every customer. Our systematic review studied the level of effectiveness of existing technological means and practices in order to limit COVID-19 infections and to protect customers from other factors aggravating their health, focusing on hotel restaurants. The PRISMA-S method was used. Database research (ABI/INFORM, ProQuest, Scopus EBSCO Business Source Premier, CBCA Business, Pubmed, and Embase) was undertaken between 6/2020 and 4/2024 with keywords comprising “hotels restaurants”, “health and safety”, “effectiveness/efficacy”, “primary analysis”, secondary analysis”, etc. In total, 1110 articles were initially identified, but eventually, 20 papers were selected comprising customer-level questionnaires, systematic reviews, and expert opinions/surveys. Different criteria were used for study assessment according to the type of study. So far, only a very limited number of studies have focused on the effectiveness of different health and safety measures in hotel restaurants. Even though the studies focusing on AI, robotics, and further technological means for enhancing customer satisfaction and the overall level of cleanliness are quite limited, the constant investment of hotels and restaurants in new technologies appears to be a one-way road. Full article

Despite being long-lived and massive, giant sequoias (Sequoiadendron giganteum (Lindl.) J. Bucholz) are susceptible to erosion given their relatively shallow root structure. Human-caused soil compaction and vegetation loss through social trails are primary drivers of erosion in giant sequoia groves, particularly for trees that are near formal trails and access roads. We develop a method to observe and quantify the near-tree impacts from park visitors and to relate the overall amount of use with ground cover impact parameters to assess whether the desired conditions of each grove are being met for the park to maintain a spectrum of recreational opportunities. We collected data on visitation, ground cover, soil compaction, and social trailing using a combination of targeted surveys and observations at the three giant sequoia groves in Yosemite National Park. The Mariposa Grove receives the most visitation, and use levels among groves were consistent with relative size and facilities available. Selected parameters for ground cover data were analyzed by comparing values within undisturbed versus trampling-disturbed subplots at both 0–2 m and 2–8 m. Exposed soil cover and compaction were generally higher in anthropogenically disturbed subplots versus undisturbed subplots, and vegetation cover was reduced in some disturbed subplots. Each grove had one surveyed tree where average soil compaction was ≥2.2 kg/cm², which may limit root growth and impact seedling regeneration. Each of the three groves had some trees with social trail presence, yet less than 7% of mature trees within any grove were impacted by social trails, and most social trails were rated as having low impairment. Coupling soil compaction measurements and estimates of trampling-disturbed areas with mapping of social trail conditions within groves provides a general assessment of visitor-associated impacts to sequoia groves and can facilitate a relatively rapid way to track hotspot (i.e., increasingly impacted) trees over time. Full article

Microbiome studies targeting hypervariable regions of the 16S rRNA gene are suitable for understanding interactions between animals and their associated bacteria. While many studies focus on the gut microbiome, assessments of blood microbiota remain scarce despite the prevalence of blood-borne pathogens in vertebrates. This study aimed to investigate the bacterial community in blood samples from 79 living and 7 road-killed lowland tapirs (Tapirus terrestris), a vulnerable species, sampled in two biomes in midwestern Brazil: Pantanal and Cerrado. Animals were categorized by condition (living or road-killed), sex, age, and biome. V3–V4 16S rRNA fragments were obtained from 86 blood samples and 4 negative controls. After filtering contaminants, 13,742,198 sequences representing 2146 ASVs were analyzed. Alpha diversity significantly differed by condition, while beta diversity differed by condition, site, and age (adults vs. sub-adults). For living animals (79/86 samples), alpha diversity showed no significant differences, but beta diversity differed by age. Different vector-borne bacterial pathogens, including Anaplasmataceae, Bartonella, and Borrelia spp., were detected. Additionally, evidence of transient translocation of microbial communities from other body regions to the bloodstream was observed. Amplification of bacterial 16S rRNA from blood samples of wild T. terrestris provided novel information about the diversity of blood-borne microbiota of lowland tapirs, members of a poorly studied mammalian family. Next-generation sequencing proved to be a valuable tool for screening potential vector-borne pathogens in this host. Full article

Assessment of landslide hazards often depends on the ability to track possible changes in natural slopes. To that end, historical air photos can be useful, particularly when slope stability is compromised by visible cracking. Undocumented landsliding rejuvenates a paleo-landslide on a busy motorway connecting Jerusalem to a small Jewish settlement. Recently, a plan for broadening the motorway was approved, and we were asked to study the hazards of the road by Israeli NGOs and Palestinian residents of the area. We captured high-resolution topography around the unstable slope using drone-borne photogrammetry and LiDAR surveys. The modern data allow us to analyze historic air photos and topo maps to assess the level of sliding prior to and during modern landscaping. Our results indicate horizontal offsets of ~0.9–1.8 m and vertical offsets of 1.54–2.95 m at selected sites. We next assess the possible role of anthropogenic versus natural factors in compromising slope stability. We analyze monthly rain records together with seismic catalogs spanning several decades. Shortly after the motorway construction in 1995, a January 1996 rainstorm triggered a massive rockfall. The rockfall blocked traffic with up to 4 m-diameter boulders. We found that while a certain level of rain is a necessary condition for mobilizing the rock mass, it is the anthropogenic intervention that caused the rockfall in this site. We conclude that the recent plan for broadening the motorway jeopardizes the lives of vehicle passengers and the lives of future residents should the development materialize. Full article

Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, and Salp Swarm Algorithm—SSA). The modeling phase was initiated by using a database comprising 160 landslide occurrences derived from Google Earth imagery; field surveys; and eight conditioning factors (lithology, slope, elevation, distance to stream, land cover, precipitation, slope aspect, and distance to road). Afterward, the Gamma Test (GT) method was used to optimize the selection of input variables. Subsequently, the optimal inputs were modeled using hybrid metaheuristic ANFIS techniques and their performance evaluated using four relevant statistical indicators. The comparative assessment demonstrated the superior predictive capabilities of the ANFIS-HHO model compared to the other models. These results facilitated the creation of an accurate susceptibility map, aiding land use managers and decision-makers in effectively mitigating landslide hazards in the study region and other similar ones across the world. Full article

Flooding, a pervasive and severe natural disaster, significantly damages environments and infrastructure and endangers human lives. In affected regions, disruptions to transportation networks often lead to critical shortages of essential supplies, such as food and water. The swift and adaptable delivery of relief goods via vehicle is vital to sustain life and facilitate community recovery. This paper introduces a novel model, the Vehicle Routing Problem for Relief Item Distribution under Flood Uncertainty (VRP-RIDFU), which focuses on optimizing the speed of route generation and minimizing waiting times for aid delivery in flood conditions. The Genetic Algorithm (GA) is employed because it effectively handles the uncertainties typical of NP-Hard problems. This model features a dual-population strategy: random and enhanced populations, with the latter specifically designed to manage uncertainties through anticipated route performance evaluations, incorporating factors like waiting times and flood risks. The Population Sizing Module (PSM) is implemented to dynamically adjust the population size based on the dispersion of affected nodes, using standard deviation assessments. Introducing the Complete Subtour Order Crossover (CSOX) method improves solution quality and accelerates convergence. The model’s efficacy is validated through simulated flood scenarios that emulate various degrees of uncertainty in road conditions, affirming its practicality for real-life rescue operations. Focusing on prioritizing waiting times over travel times in routing decisions has proven effective. The model has been tested using standard CVRP problems with 20 distinct sets, each with varying node numbers and patterns, demonstrating superior performance and efficiency in generating vehicle routing plans compared to the shortest routes, which serve as the benchmark for optimal solutions. The results highlight the model’s capability to deliver high-quality solutions more rapidly across all tested scenarios. Full article

The Hindukush and Himalaya regions of Pakistan are chronically prone to several geological hazards such as landslides. Studying landslides in these regions is crucial for risk assessment and disaster management, as well as for determining the effects of adverse climatic conditions, infrastructure management, and increasing anthropogenic activities. High-relief mountains in these regions face severe challenges because of frequently occurring landslides and other natural hazards, especially during intensive rainfall seasons and seismic activity, which destroy infrastructure and cause injuries and deaths. Landslides in the Alpuri Valley (Hindukush) and the Neelum Valley (Himalaya) have been activated through high magnitude earthquakes, intensive rainfalls, snowfall, floods, and man-made activities. Landslide susceptibility mapping in these areas is essential for sustainable development as it enables proactive risk management, up-to-date decision-making, and effective responses to landslide hazards, ultimately safeguarding human lives, property, and the environment. In this study, the relative effect method was applied for landslide susceptibility modeling in both study areas to determine the capability to reduce the effects of landslides, and to improve the prediction accuracy of the method. The relative effect is a statistical model that has only been used for very limited time for landslide susceptibility with effective results. A total of 368 (Neelum Valley) and 89 (Alpuri Valley) landslide locations were identified, which were utilized to prepare the reliable landslide inventory using GIS. In order to evaluate the areas at risk for future landslides activities and determine their spatial relationship with landslide occurrences, the landslide inventory was developed with 17 landslide causative factors. These factors include slope gradient, slope aspect, geology, plan curvature, general curvature, profile curvature, elevation, stream power index, drainage density, terrain roughness index, distance from the roads, distance from the streams, distance from fault lines, normalized difference wetness index, land-use/land-cover, rainfall, and normalized difference vegetation index. Finally, the performance of the relative effect method was validated using the success and prediction curve rate. The AUC-validated result of the success rate curve in the Alpuri Valley is 74.75%, and 82.15% in the Neelum Valley, whereas, the AUC-validated result of the prediction rate curve of the model is 87.87% in the Alpuri Valley and 82.73% in the Neelum Valley. These results indicate the reliability of the model to produce a landslide susceptibility map, and apply it to other landslide areas. The model demonstrated a more effective result in the Alpuri Valley, having a smaller area. However, the results are also desirable and favorable in Neelum Valley, with it being a large area. It will assist in general landslide hazard management and mitigation, and further research studies related to future landslide susceptibility assessments in other parts of the region. Full article

The urban ambient environment is directly responsible for the health conditions of millions of people. Comfortable living space is a significant aspect that urban policymakers need to address for sustainable planning. There is still a notable lack of studies that link the spatial profile of urban climate with city-specific built-up settings while assessing the vulnerability of the city population. Geospatial approaches can be beneficial in evaluating patterns of thermal discomfort and strategizing its mitigation. This study attempts to provide a thorough remote sensing framework to analyze the summer magnitude of thermal discomfort for a city in a tropical hot and humid climate. Spatial profiles of dry bulb temperature, wet bulb temperature and relative humidity were prepared for this purpose. A simultaneous assessment of various discomfort indices indicated the presence of moderate to strong heat stress to a vast extent within the study area. The central business district (CBD) of the city indicated a ‘danger’ level of heat disorder for outdoor exposure cases. Nearly 0.69 million people were vulnerable to a moderate threat from humid heat stress, and around 0.21 million citizens faced strong heat stress. Combing city morphology in the study showed that mid-rise buildings had the maximum contribution in terms of thermal discomfort. City areas with built-up cover of more than 68%, along with building height between 5.8 m and 9.3 m, created the worst outdoor discomfort situations. Better land management prospects were also investigated through a multicriteria approach using morphological settlement zones, wind direction, pavement watering, building regulations and future landscaping plans. East–west-aligned road segments of a total 38.44 km length were delineated for water spray cooling and greener pavements. This study is likely to provide solutions for enhancing ambient urban health. Full article