Search Results (2,099)

Surface dust samples were collected from bus stops and parking lots in different functional areas of Binzhou City, Shandong Province, China. This study investigated the contamination characteristics, source apportionment, and potential ecological and health risks of potentially toxic elements (PTEs) in these dust samples. Eight target PTEs, including As, Zn, Pb, Cu, Cd, Cr, Ni, and Mn, were quantitatively analyzed. The results revealed distinct concentration differences in these elements between bus stop dust and parking lot dust. Several PTEs exceeded the corresponding local soil background values, predominantly Zn, Pb, Cu, Cd and Cr. Principal component analysis (PCA) indicated that Zn, Pb, Cu, Cr, Ni, and Mn in bus stop dust were mainly sourced from traffic emissions, whereas As and Cd primarily originated from atmospheric deposition. For parking lot dust, Zn, Pb, Cu, Cd, Cr, and Mn were predominantly attributed to traffic sources, while As and Ni were mainly derived from natural background sources. The geo-accumulation index (I_geo) demonstrated that As, Cr, Ni, and Mn had negligible environmental impact, Pb, Cu, and Cd induced slight pollution, and Zn resulted in moderate pollution. Except for Cd, the average individual potential ecological risk index ($E_r^i$) values for all elements were below 40, suggesting a low ecological risk. Cd posed a moderate ecological hazard, whereas the comprehensive ecological risk index ($E_r^i$) values of all analyzed elements were at an extremely low level. The hazard index (HI) values via different exposure pathways and for all PTEs in both bus stops and parking lots were lower than 1, indicating no significant non-carcinogenic health risk. The carcinogenic risk ranking of elements was Cr > Cd > Ni > As, and their carcinogenic risk values (CR) via inhalation exposure were below 1 × 10⁻⁶, indicating no carcinogenic risk. This study provides a scientific basis for the environmental quality control and risk management of surface dust in urban bus stops and parking lots. Full article

Surface congestion and taxiing delays at high-density airports increasingly constrain aviation sustainability, as ground-phase fuel consumption and emissions constitute a significant share of total airport emissions. Existing studies typically decouple air traffic flow management from ground resource scheduling, hindering coordinated optimization of punctuality, environmental benefits, and resource utilization. This paper proposes a multi-objective optimization method for runway-taxiway systems oriented toward air–ground collaborative decision-making, integrating Calculated Take-Off Time (CTOT) compliance constraints. A tri-objective mixed-integer programming model is formulated to minimize CTOT deviation, total taxiing time, and runway workload imbalance. A hybrid intelligent algorithm, SSA-SCA-NSGA-II, is designed with a bidirectional elite feedback mechanism to address this NP-hard problem. Validation uses real operational data of 58 departure flights during a peak period at Beijing Daxing International Airport. The results demonstrate that the proposed method achieves effective trade-offs on the Pareto front: CTOT compliance rate increased from 77.6% to 89.7–96.6%; total taxiing time decreased from 692 min to 551–635 min; and dual-runway utilization imbalance declined from 5.2% to 1.7–3.8%. These improvements translate into quantifiable sustainability gains: fuel consumption is reduced by 1425–3525 kg and CO₂ emissions by 4503–11,139 kg per peak hour, alongside a 19-percentage point improvement in punctuality that lowers passenger delay costs and reduces controller coordination workload. By simultaneously advancing environmental sustainability (carbon footprint reduction), economic sustainability (fuel and operational cost savings), and social sustainability (service punctuality and labor efficiency), the framework provides a measurable, monitorable, and policy-relevant decision-support tool for green airport surface operations aligned with sustainable development goals (SDGs). Full article

Global climate change poses a critical challenge to sustainable urban development. The construction of low-carbon transportation systems is therefore a core strategy for enhancing the sustainability of mega-city road networks. Combining the characteristics of urban road traffic networks, this paper establishes a method for vehicle trip segmentation and carbon emission estimation based on GPS trajectory data (5699 vehicles, Beijing, September 2019) and the COPERT emission model, analyzing the spatiotemporal distribution characteristics of vehicle emissions. By incorporating the Life Cycle Assessment (LCA) emissions of electric vehicles, this study proposes carbon reduction strategies based on stochastic selection and ranking-based optimization from two dimensions: road-segment and vehicle electrification. Simulation methods are employed to evaluate the effectiveness of different strategies, as well as road network carbon emissions, under four vehicle electrification structures: Pyramid, Inverted Pyramid, Olive, and Dumbbell. Results indicate that carbon emission intensity rises significantly due to traffic congestion during peak hours. Under the LCA framework, Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) show significantly lower emissions than traditional Internal Combustion Engine Vehicles (ICEVs). Under the specified scenario assumptions, the ranking-based optimization scheme is estimated to yield carbon reductions approximately 2 times (segment control) and 3 times (electrification) those of the stochastic selection scheme, respectively. The study concludes that integrating EV promotion policies with precise carbon reduction control strategies can effectively mitigate urban road network carbon emissions. Full article

Medical and pharmaceutical last-mile deliveries are simultaneously expected to be fast, reliable and temperature-safe for patients and to become measurably greener, yet these objectives often pull transport operations in opposite directions. Third-party logistics (3PL) providers are therefore increasingly required not only to report transport CO₂ emissions, but also to learn from them; however, it remains unclear whether the routine operational data they collect are sufficiently informative to enable such emission-based learning in this regulated and service-critical setting. This study examines the predictive limits of machine learning models in estimating CO₂ emissions in medical and pharmaceutical last-mile deliveries performed by a 3PL operator. Using operational data from six customers, we compare global and customer-specific models for the following two dependent variables: total CO₂ emissions per transport operation and CO₂ emissions per pallet. Linear and non-linear models, including linear regression, ElasticNet, Random Forest, HistGradientBoosting and XGBoost, are evaluated using chronological train-test splitting and cross-validation. The results show that global models fail to outperform a naïve benchmark, with negative R² values for both emission measures. Customer-level models reveal substantial heterogeneity as follows: for selected customers, especially those with more regular operational patterns, moderate predictive performance is achieved, while for others, emissions remain largely unpredictable using the available variables. The findings suggest that routine shipment-level data are insufficient for robust emission prediction in 3PL last-mile operations. Emission-based learning requires richer contextual, vehicle, route, traffic and telematics data, as well as customer-sensitive modelling approaches. The study contributes by identifying the data and modelling limits of sustainability intelligence in medical and pharmaceutical last-mile logistics. Full article

Sustainable pavement maintenance increasingly requires coordinated management of infrastructure condition, renewable-energy availability, carbon emissions, financial resources, and operational capacity. This study proposes a renewable-energy resource management framework for low-carbon network-level pavement maintenance using simulation-based pavement-energy modeling and multi-agent deep reinforcement learning. The proposed framework develops an AnyLogic-based pavement-energy simulation environment in which road sections, deterioration states, work zones, maintenance crews, equipment resources, photovoltaic generation, battery storage, grid support, diesel backup, carbon tracking, and budget consumption are represented within one integrated decision environment. To support adaptive maintenance control, pavement sections are modeled as interacting agents, while road connectivity, dispatch dependency, traffic interaction, and maintenance-route relationships are encoded through graph structures. A graph-based multi-agent deep reinforcement learning model, named Graph-MAPPO, is then used as the decision controller. The model integrates multi-head graph attention for spatial dependency learning, GRU-based temporal memory for deterioration-history representation, finite-element-assisted structural-risk indicators for hidden damage characterization, and constraint-aware action masking to prevent infeasible decisions under budget, carbon, energy, crew, and equipment constraints. Two calibrated datasets were generated to support the framework: a pavement network and maintenance dataset containing 4437 records and 55 features, and a renewable energy-carbon-budget dataset containing 9875 records and 38 features. The decision controller jointly selects the pavement section, treatment type, intervention timing, crew, equipment, and energy mode. Results from 20 experimental configurations show that the balanced Graph-MAPPO policy improves average $PCI$ from 69.4 to 78.9, achieves an $RSL$ gain of 6.8 years, reduces emissions to 58.3 tCO₂e, maintains a renewable-energy share of 74.6%, and limits the constraint-violation rate to 1.8%. Under high renewable-energy availability, the framework achieves the best overall performance, with an average $PCI$ of 80.2, renewable-energy share of 84.6%, emissions of 50.8 tCO₂e, and reward of 0.90. These findings demonstrate that integrating pavement-energy simulation, renewable-energy resource allocation, carbon-aware maintenance planning, structural-risk awareness, and multi-agent decision control can support more adaptive, low-carbon, and resource-efficient pavement maintenance management. Full article

Urban soils act as sinks for polycyclic aromatic hydrocarbons (PAHs) indicating the intensity of the anthropogenic load, while potential environmental and human health concerns may arise. In the present study, the concentrations, spatial distribution, source apportionment and potential health risks of 16 priority PAHs were investigated in urban soils from the city of Thessaloniki, Northern Greece. Surface soil samples were collected from 19 locations characterized by different land uses and traffic conditions. The total levels of the 16 PAHs exhibited substantial variability, with a range of 14.09–1565.4 μg kg⁻¹, reflecting heterogeneous contamination patterns across the city. PAH profiles were dominated by high-molecular-weight compounds (4–6 rings) accounting for over 80% of the total PAHs. Diagnostic molecular ratios highlighted pyrogenic sources, verifying that high-temperature combustion processes dominated the PAH inputs in the urban soils from Thessaloniki city. The factor score plot made prominent the presence of localized contamination hotspots in areas characterized by intense and continuous traffic activity, spotlighting vehicular traffic emissions and transport-related activities as primary sources of PAHs in the study area. Carcinogenic risk assessment based on the BaP-EQ approach indicated acceptable risk levels for most of the sampled soils, although limited localized hotspots with elevated carcinogenic risk were identified. This study provides important baseline information for understanding PAH contamination in urban environments and supports the development of targeted pollution mitigation and environmental management strategies. Full article

Brake wear particles are an increasingly relevant source of traffic-related particulate emissions and are addressed by the recently introduced Euro 7 emission regulation. Airborne fractions of brake wear emissions, in particular, have been associated with adverse effects on human health and other organisms. Although several brake particle mitigation strategies have demonstrated promising results under controlled laboratory conditions, their effectiveness under variable open-road driving conditions remains insufficiently understood. This study therefore investigates the transfer of two test-bench-validated mitigation strategies to a fully instrumented passenger vehicle capable of measuring brake particle number (PN) and particulate mass (PM) emissions. The first strategy is a passive approach based on a modified brake pad–disc material pairing, while the second is an active filtration system that extracts particle-laden air directly from the brake friction zone. Both approaches were evaluated during two open-road driving cycles: a real driving emissions (RDE)-compliant cycle and a more dynamic cycle characterized by higher brake stress. Airborne particle emissions were measured over a size range from 300 nm to 10 µm. During the RDE-compliant cycle, the passive approach reduced PN and PM emissions by 44% and 94%, respectively, compared with the reference brake system. Under the higher thermal and mechanical loads of the dynamic cycle, the reductions decreased to 10% for PN and 64% for PM. The active filtration system achieved an increase in PN of 4% in RDE conditions and 11% under high-severity driving. Nevertheless, PM emissions were reduced by 23–97%, depending on its operating mode of the filtration system and the associated airflow and energy demand. For high-severity driving, the PM emissions have been reduced by 40% compared to the reference braking system. These results show that both mitigation approaches hold the potential to reduce brake particle emissions under open-road conditions, although their effectiveness depends strongly on brake load and system operation. The study extends previous laboratory-based investigations by directly comparing passive and active mitigation strategies on the same vehicle under real-world driving conditions. Full article

Polymetallic mining severely disrupts farmland soil ecosystems, yet the vertical migration of heavy metals, interlayer pollution disparities between topsoil and deep soil, and quantitative source apportionment of composite pollutants remain poorly understood in mining–agricultural overlapping zones. Two core hypotheses were accordingly proposed: mining-derived heavy metals can migrate downward and accumulate in deep soil layers, and the coupling of geostatistical analysis and receptor modeling enables reliable differentiation between geogenic and anthropogenic pollution sources. To test these hypotheses, 512 topsoil and 148 deep soil samples were collected from the Fenghuang Mining Area for quantification of eight metals and metalloids (including As). Geostatistical approaches, the single pollution index (Pi), and Nemerow comprehensive pollution index (P_N) were utilized to characterize spatial heterogeneity and evaluate pollution severity, while a coupled PCA–PMF receptor model was adopted for quantitative source identification; vertical comparisons of element concentrations across soil profiles further validated the robustness of source apportionment outputs. The results revealed extensive heavy metal enrichment in both soil layers, with only topsoil Cd exceeding China’s risk screening value for agricultural land. Hg exhibited pronounced spatial variability and prominent anthropogenic fingerprints, and all target metals displayed consistent spatial distribution patterns along vertical soil profiles. Four distinct pollution sources were discriminated: geogenic sources dominating Cu, Zn, Cr, and Ni accumulation, mining-industrial emissions as the major contributor to Hg pollution, mixed industrial–agricultural inputs governing As and Pb enrichment, and traffic activities serving as the primary Cd source. Cd was identified as the priority pollutant threatening local farmland security. Confirmed downward percolation of anthropogenic metals creates persistent latent ecological risks across the study area, where mining and industrial discharges represent the dominant anthropogenic pollution inputs. This work systematically elucidates the geochemical signatures, vertical migration pathways, and quantitative source contributions of heavy metals in mining-disturbed farmlands, delivering solid scientific support for targeted source control, tiered risk management, and soil ecological remediation within the Fenghuang Mining Area. Moreover, the multi-method integrated analytical framework developed herein provides transferable guidance for heavy metal pollution mitigation in global polymetallic mining–agricultural regions with analogous geological and industrial backgrounds. Full article

Multimodal transport plays an irreplaceable role in international trade due to its cost and efficiency advantages. However, optimizing multimodal transport paths that simultaneously consider economic costs, carbon emissions, demand uncertainty, and traffic congestion remains a critical challenge. This paper establishes a scenario-based multi-objective optimization model to minimize total transportation costs and carbon emissions under uncertain demand and road congestion. To address this complex combinatorial problem, we propose LMSSA, an improved multi-objective salp swarm algorithm that integrates Bernoulli chaotic mapping, adaptive parameter adjustment, and a co-directional leader–follower update strategy. These enhancements significantly improve the balance between global exploration and local exploitation, overcoming premature convergence common in traditional salp swarm algorithms. The algorithm’s effectiveness is validated through extensive experiments on 50 instances of varying scales (8 to 100 nodes) and a real-world case study of multimodal transport in northern China. Results demonstrate that LMSSA outperforms the standard multi-objective salp swarm algorithm in convergence speed, solution quality, and robustness, providing enterprises with more economical, low-carbon, and resilient transportation decisions under uncertain and congested conditions. Full article

This study investigates the long-term variability and evolution of particulate matter with an aerodynamic diameter of <2.5 µm (PM_2.5) sources in Vilnius, Lithuania, during the period 2013–2021. Source apportionment was performed using Positive Matrix Factorization (PMF) based on elemental composition data obtained through particle-induced X-ray emission (PIXE) analysis. The results revealed substantial year-to-year variability in the chemical profiles of the identified sources. Crustal/mineral dust was characterized by high contributions of lithogenic elements, including Si, Ca, Ti, and Fe, while soil dust exhibited elevated proportions of Al, Ca, and Fe. Traffic non-exhaust emissions were marked by elevated Cu, Zn, and Pb in 2013–2015, whereas exhaust emissions in 2019–2021 were characterized by sulfur-rich aerosols. Industrial and oil combustion sources showed enhanced contributions of Ni, V, and Cr, particularly in 2016, 2018, and 2020. Biomass/wood burning represented a major seasonal source, reaching peak intensity in 2018–2019 and characterized by elevated K and Zn contributions. A notable long-term trend was the increasing importance of soil-derived particles, as reflected by Al contributions rising to 91.2% by 2021. Overall, the major PM₂.₅ source categories remained relatively stable, while their chemical fingerprints and relative importance exhibited substantial temporal variability. Full article

Heavy-duty transit vehicles (N1–N3) (heavy vehicles) can generate disproportionate environmental and economic impacts in urban transport corridors despite representing a relatively small share of total traffic volume. This study develops an integrated probabilistic framework for assessing the relationships between traffic-flow variability, CO₂e emissions, particulate-matter-derived climate impacts, and external pollution costs associated with transit transport. The methodology combines traffic-flow modeling, emission estimation, PM-to-CO₂e transformation, probabilistic analysis, Monte Carlo simulation, sensitivity analysis, and scenario-based intervention assessment. Separate analyses were conducted for M1 passenger vehicles and heavy vehicles to evaluate differences in emission behavior, uncertainty, and economic impacts. The results indicate substantial structural differences between light-duty and heavy-vehicle regimes. Passenger-car traffic exhibited relatively stable emission distributions, whereas heavy vehicles demonstrated significantly greater variability, uncertainty, and emission intensity. Sensitivity analysis identified heavy-vehicle flow as the dominant factor influencing overall system emissions and pollution costs. Scenario analysis indicated that restrictions targeting heavy-vehicle traffic have the potential to generate considerably larger environmental benefits than generalized traffic-reduction measures. Probabilistic assessment further revealed that heavy vehicles contribute disproportionately to high-emission risk regimes and uncertainty propagation within the system. The proposed framework provides an integrated approach for evaluating climate impacts, uncertainty and economic externalities of transit transport. The results highlight the importance of heavy-vehicle management in reducing emissions and pollution costs while supporting risk-informed transport policy development. Full article

The exponential growth of AI-driven data traffic necessitates the evolution of Data Center Networks toward high bandwidths and sub-microsecond latency. While co-packaged optics (CPO) offer a pathway to reduced energy consumption and increased capacity, they introduce significant challenges in optical chip coupling and packaging complexity. This study explores monolithically integrated metasurfaces as an alternative for optical interfaces, potentially reducing the need for bulky external microlens arrays or extremely precise mechanical alignment. We design an amorphous silicon (a-Si) metasurface on a Silicon-On-Insulator (SOI) platform operating at 1310 nm. By spatially mapping nanopillar radii to satisfy a spherical phase profile, we achieved near-vertical beam emission with an emission angle of 0.88° focused at a focal length of 98.99 μm. Broadband characterization across a 20 nm band confirms stable focusing and a confined spot size with moderate roll-off toward the band edges. The sensitivity of the emission profile of the device to fabrication imperfections in pillar radius, height, and sidewall taper is quantified. The coupling to a polymer-based optical redistribution layer (ORDL) is also studied, and the corresponding modal analysis demonstrates a maximum coupling efficiency of 68.2% into an SU-8 polymer waveguide. Tolerance analysis results reveal deterioration of 0.9 dB and 0.4 dB for $\pm 0.6 \mathsf{\mu }\mathrm{m}$ horizontal and $\pm 1.5 \mathsf{\mu }\mathrm{m}$ vertical misalignment respectively, making the interface compatible with relaxed alignment assembly assumptions, although experimental packaging validation remains required. The methodology is further validated at 1550 nm, demonstrating its applicability across telecom bands. These results suggest that integrated metasurfaces may simplify the packaging stack and enhance density for next-generation CPO links by providing precise, on-chip wavefront manipulation. Full article

This study aims to systematically evaluate the balancing mechanism between road performance, carbon emissions, and economic cost when selecting asphalt materials for severe cold regions, filling the gap in multi-criteria decision-making for composite chemical modifications. To address alternating temperatures, heavy traffic, and modified asphalt transport difficulties, this study presents a novel evaluation framework focusing on the performance–environmental–cost nexus of a desulfurized rubber powder composite SBS-modified asphalt mixture, which provides a clear technological breakthrough for high-ratio scrap tire recycling in seasonal frost zones. Two reference mixtures serve as comparisons: a conventional rubber powder composite SBS (styrene–butadiene–styrene triblock)-modified asphalt mixture (CR-SBS) and an SBS-modified asphalt mixture (SBS). A comparative experiment was conducted between the two materials and the SBS-modified asphalt mixture (ACR-SBS) compounded with desulfurized rubber powder. High-temperature stability was tested by the rutting test, low-temperature crack resistance by the beam bending test, and water stability by the immersion Marshall and freeze–thaw splitting tests. Life cycle carbon emissions and economic costs were quantified from raw material acquisition to construction. The results show that desulfurized rubber powder composite with ACR-SBS delivers the most superior overall road performance. However, it also generates the highest life cycle carbon footprint. Its total carbon emission reaches 162,800 kgCO₂eq, which is 13.7% (19,600 kgCO₂eq) higher than SBS (143,200 kgCO₂eq) and 7.7% (11,600 kgCO₂eq) higher than CR-SBS (151,200 kgCO₂eq). The total cost of ACR-SBS is 391,000 CNY, which is 1.5% (6000 CNY) higher than SBS (385,000 CNY) and 1.3% (5000 CNY) lower than CR-SBS (396,000 CNY). These findings provide a basis for the selection of high-performance, low-carbon, and economical composite-modified asphalt in severe cold regions. Full article

The energy transition in maritime shipping is increasing the importance of alternative fuels and port infrastructure capable of handling them in a safe, regular, and economically justified manner. In this context, LNG remains a transitional fuel with a relatively high level of technological and organizational maturity, particularly in regions characterized by intensive liner, ferry, and RO-RO traffic. This article proposes a universal model for organizing LNG distribution within the port–transport system, based on three interdependent dimensions: demand potential, infrastructure readiness, and operational feasibility. The model structure enables the classification of ports according to their functions within the regional bunkering network and the identification of nodes of the greatest systemic importance. The model was validated using data on vessel calls, the structure of container and RO-RO traffic, LNG infrastructure status, and monthly traffic variability. The analysis demonstrated that the most justified LNG distribution arrangement in the Baltic Sea is polycentric in nature and concentrated in ports, combining a high degree of transport regularity with confirmed LNG readiness. The results indicate that the rationale for LNG infrastructure development is selective in nature and depends on the actual position of a port within the transport network, rather than solely on cargo throughput volume. The proposed model also retains its applicability to other alternative fuels after adjustment of technological, regulatory, and operational parameters. By supporting the selective development of alternative-fuel infrastructure in ports with the highest systemic relevance, the model contributes to sustainable maritime transport planning and to the transition toward lower-emission port–transport systems. Full article

This study analyzed the spatial distribution and seasonal variation characteristics of Volatile Organic Compounds (VOCs) at four sites (GS, GJ, BHS, and JN) representing different emission environments in Seoul and identified potential pollution sources using principal component analysis (PCA). The results showed that VOC concentrations were relatively high at the GS site, which is influenced by both industrial and traffic emissions, and at the JN site, characterized by heavy urban traffic, whereas the BHS site, representing a background area, exhibited the lowest concentrations, indicating clear spatial heterogeneity. Alkanes accounted for the largest proportion of VOCs at all sites, and low-molecular-weight alkanes as well as combustion-related compounds showed elevated concentrations during winter. In contrast, aromatic compounds exhibited site-specific seasonal patterns, with relatively higher concentrations observed during summer or autumn at some locations. The diurnal variation patterns displayed a bimodal distribution with concentration peaks during morning and evening rush hours, indicating the direct influence of traffic emissions. Furthermore, the T/B ratio and PCA results suggested that vehicle emissions and combustion sources were the dominant contributing factors (PC1) to ambient VOCs in Seoul, while non-road emission sources such as solvent use and industrial activities, characterized mainly by aromatic compounds, also contributed significantly (PC2). The findings of this study can serve as fundamental data for future quantitative source apportionment studies and the development of risk-based air quality management strategies for VOCs in Seoul. Full article