Search Results (2,149)

Low-carbon construction of highway projects constitutes a critical pathway toward achieving the carbon peaking target in the transportation sector. Existing studies have been unable to simultaneously address the identification of carbon emission sources across different engineering types and the delineation of responsible entities for implementing management strategies. This study employs the emission factor method to conduct construction-phase carbon emission accounting for a mountainous expressway in Guangdong Province, China, and reveals significant clustering characteristics of highway construction carbon emissions along two dimensions: the proportion of total emissions and the proportion of material-derived carbon emission sources. Based on K-Means cluster analysis, nine engineering categories—temporary works, subgrade works, pavement works, bridge/culvert works, tunnel works, intersection works, traffic engineering works, greening works, and other works—are classified into four types. Accordingly, a dual-factor classification framework is established, comprising “Core–Material Dominant (CMD)”, “Core–Mixed Balanced (CMB)”, “Peripheral–Material Dominant (PMD)”, and “Peripheral–Mixed Balanced (PMB)”. Differentiated carbon abatement strategies are proposed for each engineering type, with explicit definition of implementation stages and primary responsible entities. Application of the proposed framework to the case project achieved a total carbon abatement of 5.2% during the construction phase. This research provides systematic methodological support for differentiated carbon emission mitigation in highway construction. Full article

The introduction of Connected and Autonomous Vehicles (CAVs) into the existing traffic system represents one of the greatest challenges of modern road traffic engineering. Beyond their role as active traffic participants, CAVs can also be regarded as mobile (floating) sensors, effectively turning the vehicle fleet itself into a distributed, city-wide and motorway-wide sensing infrastructure. The transition from fully human-driven vehicles to fully autonomous vehicles will take decades, giving rise to a prolonged mixed-traffic period in which vehicles with different levels of automation share the same road space. This paper analyses the parameters and measures used for evaluating the throughput, environmental impact, and safety of traffic networks at different CAV penetration rates. It further reviews studies that rely exclusively on data collected from CAVs acting as mobile sensors, examining data-aggregation and traffic-state-estimation methods used to reconstruct macroscopic traffic parameters such as flow, density, headway, and speed. Additionally, measures for evaluating specific use cases for CAVs including mobility-on-demand services and their cost comparison with human-driven taxi operations are also addressed. The energy and emissions implications of CAV deployment, including the added burden of sensing hardware and system-level rebound effects, are also examined. Based on the synthesis performed, a set of representative CAVs penetration rates is proposed as a standardised framework for future mixed-traffic flow evaluations. Full article

Surface ozone (O₃) pollution has become an increasingly important constraint on further improvements in urban air quality, particularly in industrial cities where complex terrain, local emissions, and meteorological conditions interact. In this study, hourly air pollutants, meteorological parameters, and high-time-resolution volatile organic compound (VOC) observations collected at a single urban-core site during September from 2021 to 2024 were used to investigate O₃ pollution characteristics, VOC reactivity, source contributions, and driving mechanisms in a resource-based heavy-industrial city in northwestern Henan Province, China. Ozone formation potential (OFP), diagnostic ratios, positive matrix factorization (PMF), meteorological normalization, and extreme gradient boosting combined with Shapley additive explanations (XGBoost-SHAP) were integrated to identify key reactive species, major sources, and meteorological–precursor interactions. The mean maximum daily 8 h average O₃ concentrations were 113.44, 150.32, 123.59, and 154.18 μg·m⁻³ from 2021 to 2024, respectively, with the highest level observed in 2024 despite the lowest nitrogen dioxide (NO₂) and carbon monoxide (CO) concentrations. O₃ was positively correlated with temperature and negatively correlated with relative humidity, indicating the importance of hot and relatively dry conditions. Total VOC OFP first increased and then declined, with alkenes dominating in 2021 and aromatics exceeding alkenes after 2022. Ethene, m/p-xylene, toluene, and vinyl chloride were identified as priority reactive species. PMF results showed that mixed industrial processes and vehicle exhaust were the dominant VOC sources, contributing 32.3% and 23.8%, respectively. Under the original meteorological-normalization specification, represented meteorological features accounted for 64.7% of the modeled O₃ increase during the study period. Sensitivity specifications retained meteorological dominance but showed that the exact share was model dependent. SHAP analysis further identified temperature, short-term temperature variation, relative humidity, alkenes, and NO₂ as key drivers. These results suggest that O₃ pollution in this heavy-industrial city is jointly shaped by favorable meteorological conditions, reactive VOCs, nitrogen oxides (NOx) chemistry, and combined industrial and traffic emissions. Accordingly, industrial processes, vehicle exhaust, and highly reactive VOC species are likely priority targets for mitigation, while the effectiveness of coordinated VOC–NOx control still warrants further regime-specific evaluation. Full article

Trace element contamination represents a persistent environmental issue, particularly in industrialized areas where anthropogenic emissions overlap with natural geochemical backgrounds. This study investigates the atmospheric deposition of trace elements in the Milazzo district (Italy), which is characterized by intense industrial activity. Pinus pinea L. needles were used as biomonitors to assess the spatial distribution and sources of trace elements, combined with lead isotopic analysis for source apportionment. Forty needle samples were analyzed by ICP-OES and ICP-MS for Ca, K, Mg, Na, P, Al, As, Ba, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sb, Ti, V, Zn, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu, while 25 samples were selected for Pb isotope ratio determination (²⁰⁶Pb/²⁰⁷Pb and ²⁰⁸Pb/²⁰⁶Pb). Multivariate statistical analyses identified source groups related to industrial and petrochemical emissions, vehicular traffic, crustal resuspension, and mixed combustion processes. Elevated concentrations of As, Cr, Mo, Ni, Pb, Sb, V, and Zn ranged from 16.6 μg g⁻¹ (Zn) to 0.09 μg g⁻¹ (Sb), with the following order of abundance: Zn > Cr > Ni > Pb > Mo > V > As > Sb; these elements were found near industrial facilities and urban areas. Enrichment Factor calculations indicated strong anthropogenic contributions to Cd, Cu, Mo, Sb, V, and Zn, with EF > 10, ranging from 10 (Cd) to 60 (Zn), whereas Al, Fe, and Ti exhibited EF values between 0.5 and 2, reflecting geogenic origins. Pb isotopic ratios (²⁰⁶Pb/²⁰⁷Pb = 1.153–1.192 and ²⁰⁸Pb/²⁰⁶Pb = 2.063–2.108) revealed mixing between industrial emissions and the local geological background, with limited influence from historical gasoline-derived Pb. This integrated geochemical and isotopic approach can effectively identify contamination sources in complex industrial environments. Full article

High-resolution mapping of urban surface CO₂ is essential for refined carbon monitoring, emission management, and low-carbon urban planning. Mobile monitoring provides dense street-level observations, but raw CO₂ measurements are often affected by transient traffic disturbances, vehicle idling, and localized plume events, which limits their direct use as stable spatial mapping targets. This study developed an integrated framework for predicting, mapping, and interpreting stable surface CO₂ patterns in Shenzhen by combining vehicle mobile observations, CSF processing, multiscale remote sensing predictors, machine learning. A CSF-based lower-envelope filter was used to suppress short-duration positive peaks and extract a more stable CO₂ accumulation signal from mobile observations. Multiscale predictors representing transportation, urban activity, surface environment, and built form were constructed to characterize both local and surrounding urban contexts. Compared with raw CO₂, the CSF-processed target substantially improved prediction performance. The best validation R² across the candidate models increased from 0.59 to 0.90 in April and from 0.62 to 0.93 in November. The predicted maps identified persistent high-CO₂ areas in central and southwestern Shenzhen. SHAP results showed that transport networks and urban activity reinforced surface CO₂ accumulation, whereas vegetation and open-surface contexts weakened accumulation at broader spatial ranges. These findings provide an interpretable framework for high-resolution urban CO₂ mapping and refined low-carbon governance. Full article

Urban transport emissions are a major contributor to climate change and urban air pollution. Although previous studies have demonstrated that traffic demand, fleet electrification, and driving behavior individually influence vehicular emissions, their combined effects under different congestion conditions remain insufficiently understood. This study investigates the interactions among these factors using the microscopic traffic simulator SUMO (Simulation of Urban MObility). A synthetic urban corridor consisting of five signalized intersections was developed to represent arterial roads in medium-sized cities. A full factorial experimental design was implemented by considering three traffic demand levels, three electric vehicle adoption percentage levels, and three driving behavior profiles, resulting in 27 experimental scenarios with 10 stochastic replications per scenario. Traffic performance and pollutant emissions were evaluated to quantify both the individual and interaction effects of the experimental factors. The results indicate that traffic demand is the primary determinant of CO₂ and NO_x emissions, while fleet electrification substantially reduces emissions, particularly under congested conditions. Driving behavior also plays a role by influencing acceleration and deceleration patterns. Furthermore, statistically significant interaction effects among the experimental factors ($p<0.05$) reveal the benefits of fleet electrification considering the traffic demand and the driving behavior. These findings contribute to the understanding of sustainable urban mobility by providing a comprehensive assessment of how traffic demand, fleet electrification, and driving behavior jointly influence urban traffic performance and vehicle emissions, offering valuable insights for the design of integrated transportation and environmental policies. Full article

Urban air pollution driven by road traffic poses a significant public health challenge in cities with high vehicle density and frequent congestion, particularly in topographically constrained environments such as Almaty, Kazakhstan. This study presents a web-based digital twin prototype for the integrated monitoring and analysis of traffic flow and air quality in Almaty, Kazakhstan. The system autonomously collects data from the TomTom Traffic, OpenWeather Air Pollution, and WAQI APIs and official population statistics for five fixed monitoring stations, computing traffic density, vehicles per hour, road congestion, estimated CO₂ emissions, an air pollution index, and a population exposure index, and providing real-time dashboard visualization alongside longitudinal data accumulation. Over a 50-day deployment (26 May–16 July 2026), 4961 real co-located observations across 18 active days were analyzed; records generated by the prototype’s fallback mechanism during API outages were excluded from the scientific analysis. During this summer period, PM2.5 was low (mean ≈ 6 µg/m³) and spatially uniform, and showed no statistically significant association with traffic intensity (r ≈ −0.03). Traffic indicators were instead weakly but significantly correlated with the vehicle-emitted gases NO₂ (r ≈ 0.16) and CO (r ≈ 0.10), which they preceded by up to about one hour. A short-horizon PM2.5 nowcasting task, evaluated across temporal resolutions with time-series cross-validation, was dominated by temporal persistence, with traffic-derived features contributing negligibly. The absence of a summer traffic–PM2.5 association does not preclude such a relationship during the heating season, when particulate levels are higher. The results indicate that the traffic–air-quality relationship in Almaty is season- and pollutant-dependent, and demonstrate a lightweight, reproducible platform suitable for longitudinal monitoring and future heating-season assessment. Full article

The whole pavement life cycle is divided into five phases: raw material production, construction, service use, maintenance and rehabilitation, and end-of-life (EOL). Distinct system boundary definitions (cradle to gate, cradle to site, cradle to grave) are clearly distinguished, and two categories of vehicle-related emissions are strictly differentiated: baseline vehicle operation emissions (excluded) and pavement-induced incremental emissions (included only for full cradle-to-grave accounting). According to cited highway pavement inventory data (functional unit: 1 m² full cross-section composite pavement, cradle-to-gate material-only boundary), cement-related materials account for merely 4.7% of total structural material mass yet contribute over 84.5% of material-phase carbon emissions, while asphalt mixture construction emissions generally make up less than 10% of mixing-stage outputs. In the use phase, pavement deformation, rolling resistance elevation and surface texture loss trigger extra vehicle fuel consumption and associated greenhouse gas increments. Maintenance-stage emissions stem from repair material manufacturing, on-site machinery operation and traffic congestion delays during lane closure; milling, transportation and recycling dominate EOL carbon outputs. This review further classifies all emissions into direct engineering emissions and pavement-derived indirect emissions, compares carbon performance and service-life extension effects of eight mainstream maintenance strategies, and thoroughly decomposes milling, stockpiling, haulage and recycling links of waste asphalt, alongside multiple environmental burden allocation methods for reclaimed asphalt pavement (RAP). A full spectrum of green low-carbon technologies is summarized, including biochar bio-materials, RAP, crumb rubber, industrial byproducts, warm-mix asphalt (WMA), cold recycling and CCUS negative-carbon materials. We also balance their emission reduction benefits against potential deterioration risks to rutting resistance, fatigue life and moisture stability. Combined with a life-cycle cost assessment (LCCA), this study analyzes cost-emission trade-offs of all technical routes, and deeply discusses multi-source uncertainty, sensitive input parameters and universal methodological limitations of pavement LCA. Core takeaways indicate that raw material production and long-term service use are the two dominant carbon emission stages; a medium RAP-WMA combination and cold in-place recycling represent the most economically and environmentally balanced mitigation solutions. Major research gaps and targeted future research directions are proposed, providing standardized theoretical support and dual environmental–economic decision references for low-carbon asphalt pavement design and full-life carbon accounting. Full article

Anthropogenic greenhouse gases (GHGs) and emissions from maritime transport represent a significant challenge for atmospheric monitoring and control. The Istanbul Strait, characterized by its narrow, winding geography and high traffic density, presents a unique chokepoint where these emissions directly impact local air quality. This study proposes a gas-focused integrated framework that combines Sentinel-5 Precursor (Sentinel-5P) TROPOspheric Monitoring Instrument (TROPOMI) satellite observations with Automatic Identification System (AIS) data to analyze atmospheric trace pollutant time series in the Istanbul Strait during 2025. A bottom-up emission methodology based on the IMO 4th GHG Study was employed, yielding annual gaseous pollutant totals of 213,678 tons of carbon dioxide (CO₂), 5970 tons of nitrogen oxides (NO_x), and 686 tons of sulfur oxides (SO_x). Time-series and cross-correlation analyses demonstrated a quantifiable relationship between AIS-derived NO_x estimates and TROPOMI NO₂ tropospheric column densities (r = 0.76, p < 0.05, n = 12), validating the use of satellite sensors for marine atmospheric monitoring. A decision support system (DSS) proof of concept (PoC) was developed to evaluate emission control scenarios through speed optimization. The results indicate that implementing a 10% speed reduction strategy could reduce CO₂ emissions by 18% (38,462 tons) and generate net economic savings of EUR 3.07 million under the European Union Emissions Trading System (EU ETS) carbon pricing framework. Furthermore, a scenario with a 20% speed reduction resulted in a 35% decrease in CO₂ emissions. The findings underscore the potential of integrating satellite-based gas remote sensing with AIS data, thereby facilitating real-time atmospheric monitoring and strengthening emission control policy enforcement in maritime chokepoints. Full article

Air travel induces substantial landside traffic through the movement of passengers, employees, suppliers, and cargo between airports and their surrounding regions. While this airport-induced landside traffic has received growing attention within airport sustainability research, its associated external costs remain insufficiently quantified. This study develops a modular model to estimate traffic volumes and associated external costs of airport-induced landside traffic. It accounts for key behavioral and operational parameters, including modal split, trip distances, occupancy rates, and trip frequencies, differentiated across user groups and transport modes. The model is applied to Germany as a case study using national mobility statistics, airport data, and external cost factors from European transport studies. The assessment covers greenhouse gas emissions, air pollution, accidents, noise, habitat damage, and upstream fuel supply impacts. Results indicate that airport-induced landside traffic generated external costs of approximately EUR 1.43 billion in Germany in 2019, with passengers and airport employees accounting for the largest shares. Accident costs and greenhouse gas emissions dominate the overall impacts. Sensitivity analyses further show that moderate behavioral changes, such as modal shifts toward public transport and increased vehicle occupancy, can significantly reduce external costs. The findings highlight the importance of integrating landside access into environmental assessments and sustainable airport planning. Full article

Road transport is a major contributor to greenhouse gas (GHG) emissions, representing a significant challenge when it comes to achieving climate goals. Although methodologies such as Activity–Structure–Intensity–Fuel (ASIF) are widely used to estimate transport emissions, their application as regulatory instruments remains unexplored. This study addresses this gap by proposing a framework that operationalizes ASIF for managing Scope 3 emissions in toll-road concessions. Using operational and traffic data from a major Brazilian highway concession, baseline emissions are compared with intervention scenarios involving the adoption of initiatives such as free-flow tolling and fleet electrification. The results demonstrate that emission reductions can be associated with specific ASIF components and can be translated into measurable contractual indicators. In the case study, the implementation of free-flow tolling reduced emissions at toll plazas by up to 37%. Fleet electrification, which is now limited by the charging capacity of roads, can be improved and have its effects captured by the framework so that actions resulting from the concessions made to improve availability can enable policies aimed at reducing total user-related emissions. The study is the first, to the best of the authors’ knowledge, to operationalize the ASIF methodology as a governance and contractual instrument for Scope 3 emissions management in highway concessions in Brazil. By bridging emissions estimation and concession governance, it provides a practical framework for incorporating Scope 3 mitigation targets into concession contracts and climate-oriented transport regulation. Full article

Shopping malls have become major retail and lifestyle destinations that influence travel demand, mode choice, traffic congestion, private vehicle dependence, and transport-related environmental impacts. However, research connecting shopping mall visitation with sustainable urban mobility remains fragmented across transport, urban planning, retail, consumer behaviour, and environmental sustainability. This study uses bibliometric analysis to investigate publication patterns, intellectual structures, international collaboration, thematic clusters, and emerging trends in this interdisciplinary field. A total of 567 English-language articles and reviews published between 1 January 2000 and 7 May 2026 were retrieved from Scopus and analysed using descriptive indicators and VOSviewer-based document co-citation, country-level co-authorship, and keyword co-occurrence analyses. The findings indicate that publication activity has grown considerably since 2017, with China emerging as the most productive contributor and as a central hub for international research collaboration. Document co-citation analysis revealed six intellectual clusters related to parking and traffic congestion, online and in-store shopping behaviour, the built environment and land use, consumer value and retail experience, retail attractiveness and sustainable community planning, and retail revitalisation and urban regeneration. The keyword analysis revealed five main themes: urban transport, carbon emission, travel behaviour, shopping mall, and shopping activity. More recently, carbon emissions, sustainable development, online shopping, energy efficiency, and data-driven solutions have attracted increasing research attention. This study synthesises the fragmented knowledge base and suggests future research directions for low-carbon shopping mobility and sustainable access to retail destinations. Full article

As road transport is a major contributor to anthropogenic CO₂ emissions, the importance of sustainable mobility planning and fleet management becomes very clear. Therefore, this article proposes a physics-based mathematical framework for dynamic estimation of CO₂ emissions. The proposed framework is very flexible and enables CO₂ assessment for different types of vehicles (i.e., combustion engine and electric vehicles), traffic and operating conditions. The proposed software prototype is evaluated through representative urban and peri-urban simulation scenarios. These scenarios involve conventional public transport, private vehicles, and demand-responsive ride-hailing services. The simulation results show that vehicle occupancy is one of the main factors impacting specific CO₂ emissions. In this context, in low-passenger-demand and dispersed travel conditions, demand-responsive mobility services can achieve lower emissions per passenger-kilometer than conventional public transport. In contrast, when occupancy levels are sufficiently high, public transport remains the most efficient option. These results indicate that there is no universally optimal transport mode and that emission efficiency is the result of a matching between vehicle capacity and passenger demand. Therefore, the proposed framework delivers a transparent and practical decision-support tool for transport mobility services. Full article

Atmospheric PM₁₀ was investigated across Campania, southern Italy, during August 2024 to assess its elemental composition and probable sources. In total, 132 daily samples were collected at six ARPAC sites representing harbor, traffic, industrial, school, and regional-background conditions. PM₁₀ concentrations ranged from 2 to 72 µg m⁻³, with the highest and lowest values recorded at the traffic and background sites, respectively. Elemental composition was determined by particle-induced X-ray emission and complemented by SEM–EDS. Elemental-based Positive Matrix Factorization (PMF) resolved six profiles, tentatively assigned to S-rich secondary aerosol, Cl-rich marine aerosol, mixed combustion/industrial emissions, Cu-rich traffic emissions, Ca–Sr-rich road dust, and Pb–Zn-rich waste combustion. At the industrial site, the three anthropogenic profiles together represented 74% of the apportioned mass. Traffic-related, marine, and S-rich secondary aerosol represented 49%, 65%, and 30% at the traffic, harbor, and background sites, respectively. SEM–EDS identified representative irregular S–K-rich and Ca-rich particles, crystalline Na–Cl-rich particles, and fine spherical metal-rich particles. Conditional probability function and trajectory analyses indicated local and regional influences, including a possible secondary sulfate contribution from the Mount Etna region. As the PMF analysis relied exclusively on elemental data, these source assignments should be regarded as indicative rather than definitive. Full article

This study investigates the collaborative green vehicle routing problem with time-dependent travel speeds (CGVRP-TD), which integrates horizontal collaboration among multiple depots with time-dependent traffic conditions. The problem jointly optimizes customer allocation, vehicle routing, and departure-time decisions to minimize transportation-related carbon emissions subject to vehicle capacity and customer time-window constraints. We formulate the CGVRP-TD as a mixed-integer programming model and develop a two-phase adaptive large neighborhood search algorithm with embedded departure-time optimization. The first phase explores routing and customer-assignment decisions using problem-specific operators, including two speed-related removal operators, while the second phase applies exact departure-time optimization to fixed routes. Computational experiments show that the proposed algorithm obtains high-quality solutions efficiently and that both departure-time optimization and speed-related operators contribute to emission reduction. The results further demonstrate that combining horizontal collaboration with time-dependent travel-speed information can substantially reduce transportation emissions while preserving on-time service. We also discuss emission-savings allocation mechanisms for sustaining collaboration among participating depots. Full article