Search Results (473)

Vehicular traffic is a major source of air pollution; however, the contribution of remotely acquired traffic information to local machine-learning (ML) air-pollution models remains insufficiently characterised. This study evaluates four interpretable tree-based ML models (Random Forest, Extra Trees, LightGBM, and XGBoost) under six predictor scenarios combining progressively larger predictor sets, ranging from remotely acquired traffic, meteorological, and temporal variables alone to the inclusion of measurements from one and four neighbouring monitoring stations, to estimate NO₂, PM₁₀, PM_2.5, and O₃ concentrations across several sites in London. ML model performance was compared with a ridge linear regression model as a baseline, with spatial interpolation methods and with a cross-site validation experiment. When modelling without data from neighbouring stations, the RMSE for NO₂ ranged from 9.73 to 11.66 μg/m³ without traffic information, compared with 8.72 to 11.52 μg/m³ when traffic information was included. Additionally, for NO₂, SHAP analyses indicate that traffic-related variables can contribute at levels comparable to pollutant measurements from neighbouring monitoring stations in traffic-dominated environments. 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

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

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

Rainfall–runoff pollution poses a major challenge to urban water quality management, particularly in rapidly developing regions. However, its spatial variability and source characteristics remain inadequately understood. This study investigates the types, concentrations, and sources of pollutants in rainfall runoff across different urban land-use settings, with the aim of providing insights for more effective water management strategies. By quantifying pollutant occurrence frequencies, comparing reported event mean concentrations, and summarizing literature-reported pollution sources, we evaluated the spatial heterogeneity of runoff contamination from a descriptive perspective. The compiled literature data showed descriptive differences in reported pollution levels among land-use types, with relatively high pollutant concentrations frequently reported in residential and traffic areas. These differences should be interpreted as functional-area-based patterns rather than continuous geographic spatial distributions. Pollutants such as chemical oxygen demand (COD), suspended solids (SS), and total nitrogen (TN) frequently exceeded China’s Class V surface water quality standards. Atmospheric deposition and surface litter were the most frequently reported pollution sources, while traffic-related activities were frequently associated with elevated heavy metal concentrations in the reviewed studies. These findings underscore the urgent need for targeted, land-use-specific pollution control strategies that not only reduce runoff pollution but also improve source-control efficiency for sustainable urban water management. This study offers valuable insights that may be transferable to other urban environments worldwide, with important implications for policy development and urban resilience in the face of increasing environmental pressures. Full article

To clarify the pollution characteristics and source composition of heavy metals in the paddy soils of the Yellow River irrigation district of Ningxia, a total of 515 surface soil samples were collected, and the concentrations of As, Hg, Pb, Cd, and Cr were measured. Regional-scale pollution assessment and source apportionment were conducted using the geo-accumulation index, spatial interpolation analysis, and the Positive matrix factorization (PMF) model. Based on the regional pollution assessment and spatial distribution patterns, a representative area with relatively elevated Cd accumulation and more pronounced anthropogenic influence was selected for local-scale isotope investigation. Eight paddy soil samples and potential end-member samples were collected, and, combined with literature-based end-member data, Pb and Cd isotopes were analyzed using the MixSIAR Bayesian (version 3.1.12) mixing model to further constrain the sources of Pb and Cd in village soils. The results showed that some sampling points in the study area exceeded the soil background values, but none of the points surpassed the screening values for agricultural soil pollution risk, indicating that the overall risk of paddy soils in the study area remained low. Geo-accumulation index results indicated that As, Pb, and Cr were predominantly classified as unpolluted, whereas Hg and Cd showed more pronounced accumulation, with most sampling points reaching unpolluted to moderately polluted or higher. PMF results revealed that heavy metals in the study area primarily originated from natural sources, agricultural activities, coal combustion-related sources, and industrial–traffic mixed sources. Cd was mainly influenced by agricultural sources, Hg was primarily affected by coal combustion and related industrial activities, and Pb exhibited a mixture of multiple sources. Local isotope analysis in the representative area further indicated that industrial and agricultural sources were the main contributors to soil Cd, accounting for 34.3% and 33.1%, respectively. Pb was primarily derived from agricultural activities (38.1%), while coal emissions, industrial sources, natural sources, and traffic contributed 19.2%, 18.3%, 16.9%, and 7.5%, respectively, indicating a complex mixture of agricultural, industrial, coal-combustion, natural, and traffic-related inputs. The combined application of PMF and Pb/Cd isotopes allowed for constraints on heavy metal sources at both regional and local scales, providing a scientific basis for pollution control and agricultural safety management in paddy soils of the Yellow River irrigation district. Full article

Short-term traffic flow forecasting becomes especially difficult when incomplete observations, within-window frequency variation, and state-dependent sensor relations occur together. Missing readings can affect both node features and the spatial dependencies inferred from them, yet these issues are commonly modeled separately. We therefore propose RIFT-STGNN, a Robust Interleaved Frequency–Trend Spatio-Temporal Graph Neural Network for multi-step traffic flow forecasting. RIFT-STGNN follows a coordinated information flow: observation status is retained during temporal–frequency encoding, the frequency representation supports both node features and graph construction, and four graph sources are fused before each Graph-GRU update. Trend-aware temporal attention then produces direct multi-step forecasts. Experiments on PEMS03, PEMS04, PEMS07, and PEMS08 show competitive numerical performance relative to selected literature-reported baselines under the 12-step setting. On PEMS04 and PEMS08, the three-run mean MAE values are 17.73 and 13.14, respectively. These values are numerically 4.63% and 9.00% lower than the corresponding literature-reported sAMDGCN values. Component ablations, state-dependent graph analysis, and controlled missing-rate experiments support the roles of dynamic graph learning, within-window frequency encoding, and mask-aware input handling under the evaluated settings. Full article

Oxidative stress is a central mechanism underlying the toxicity of fine particulate matter (PM_2.5); however, the source-specific chemical drivers of particle-associated oxidative potential remain incompletely understood. In this study, the oxidative potential (OP) of ambient PM_2.5 in Xi’an was investigated during winter and summer using the dithiothreitol (DTT) assay, with particular emphasis on the toxicological roles of water-soluble metals and emission sources. PM_2.5 exhibited significantly higher volume-normalized OP (DTT_v) in winter, indicating an enhanced particle-associated oxidative stress burden during the heating period. Notably, although water-soluble metals accounted for only a minor fraction of PM_2.5 mass, interpretable machine learning analysis (XGBoost–SHAP) identified potassium and manganese as dominant contributors to OP, highlighting the importance of biomass burning tracers and redox-active transition metals in particle-mediated reactive oxygen species generation. Source apportionment further revealed pronounced seasonal contrasts: dust sources contributed substantially to wintertime OP primarily due to their large mass loading, whereas traffic-related emissions dominated OP in summer owing to their high intrinsic oxidative toxicity. Overall, these findings suggest that variations in PM_2.5 oxidative potential are more closely associated with chemical composition and source-specific oxidative activity than with particle mass alone, providing additional insight into the factors influencing PM-related health risks. Full article

Disentangling the relative roles of emissions and atmospheric processes in controlling air-pollutant concentrations remains a central challenge in air-quality management. Although machine-learning (ML) models can accurately predict pollutant concentrations, the temporal evolution of the importance of individual drivers is often difficult to interpret. Here, we introduce a framework for reconstructing time-resolved feature importance (FI) in ML air-quality models. Hourly NO₂ and PM_2.5 concentrations were simulated across clusters of observations, defined along concentration trajectories in a state–space spanned by source intensity and meteorological variables. Within each cluster, predictor importance is quantified and mapped back onto the corresponding time points, yielding continuous FI time series for all predictors. The framework is demonstrated using observations from the nationwide air-quality network in Israel, together with traffic-related source indicators and meteorological parameters. The dominant drivers differ markedly between the two pollutants: NO₂ variability is primarily associated with local emissions, mechanical transport, and turbulent mixing, whereas PM_2.5 variability reflects predictors that are related to nucleation, coagulation, hygroscopic growth, long-range transport, and chemical transformation. The feature importance exhibits pronounced seasonal, regional, and diurnal variability, including modulation around traffic rush hours. These results demonstrate the value of time-resolved interpretability for diagnosing drivers of air-pollutant variability and improving the representation of processes in statistical air-quality models. Full article

5G electromagnetic field (EMF) measurement is more challenging than measurement in previous cellular generations because 5G New Radio uses time-division duplexing, flexible bandwidths, beam sweeping, massive MIMO, and user-specific traffic beams. As a result, the measured synchronisation signal block (SSB) or PBCH-DMRS level may not directly represent the maximum exposure produced by data transmission. This motivates a combined tutorial and structured survey of existing 5G EMF measurement studies and procedures. This paper reviews the literature on 5G EMF measurement by classifying existing methods into frequency-selective measurement, code-selective measurement, actual exposure assessment, maximum-exposure extrapolation, and network-counter-based assessment. Representative field studies, public measurement reports, and network-data-based studies are compared according to their measurement scenarios, exposure objectives, and limitations. The paper further discusses key uncertainty sources, including beam/gain offset, TDD duty cycle, bandwidth extrapolation, traffic variation, spatial sampling, and equipment-related uncertainty. Finally, open challenges related to FR2 millimetre-wave measurements and reconfigurable propagation environments are discussed. By combining tutorial background with a structured survey, this paper clarifies 5G EMF measurement procedures, maximum-exposure extrapolation, uncertainty sources, and FR2 millimetre-wave measurement challenges. Full article

The aim of this article is to demonstrate the significance of Iceland’s Ring Road as a key route providing access to geotourism attractions and to discuss its role in shaping visitor traffic in the context of tourist feedback and the principles of sustainable tourism development. The study is based on an analysis of 223 online reviews concerning the Ring Road, supplemented by the author’s own field observations from travelling around Iceland. Opinions relating to natural and anthropogenic assets, tourist infrastructure, transport accessibility, travel safety, visitor concentration and the interpretation of geological heritage were analysed. The results indicate that the Ring Road is highly rated by tourists, primarily because of the exceptional natural assets located along and near the route. At the same time, the analysis revealed challenges related to traffic concentration at the most recognisable attractions, the uneven quality of tourist infrastructure, high costs, travel safety and the limited use of the educational potential of geosites. The findings show that online reviews, when combined with field observations, can serve as a useful source of knowledge about the practical conditions for the sustainable use of geotourism attractions in popular natural destinations. Full article

Highway corridors are increasingly being discussed not only as zones of ecological disturbance but also as components of regional green infrastructure with potential carbon sequestration functions, yet their long-term evolutionary characteristics and multi-scale associated factors remain insufficiently understood. Using multi-source time-series data from 2000 to 2023, we developed an analytical framework integrating the CASA model, Random Forest, and geographically weighted regression (GWR). To ensure methodological rigor, we implemented a Spatial K-fold Cross-Validation strategy and incorporated Partial Dependence Analysis (PDA) to identify non-linear thresholds. The results indicate that: (1) Vegetation carbon sequestration within Shandong’s highway corridors increased significantly, with total sequestration rising from 5.54 × 10⁶ t in 2000 to 1.55 × 10⁷ t in 2023, representing an average annual growth rate of approximately 5.0%. This growth transitioned from a relatively stable phase to a more rapid growth phase. (2) A clear distance-related ecological pattern was observed. Statistical tests (Kruskal–Wallis H test) confirmed that vegetation carbon sequestration exhibited a significant non-monotonic gradient ($p<0.05$), with a stable peak zone observed 50–100 m from the roadbed. This peak zone is associated with a spatial “trade-off” pattern between the attenuation of traffic-related stressors and roadside ecological management. (3) The observed spatial pattern was associated with a nonlinear coupling of natural background conditions and human disturbance. Precipitation and temperature were the dominant associated factors, while PDA further identified a critical precipitation threshold (~750 mm) and localized tipping points for human interference, with a distinct road-disturbance-sensitive zone evident within 200–500 m. The results suggest that high-standard ecological design and active restoration measures are associated with lower ecological disturbance and higher vegetation carbon sequestration performance in some highway corridors. However, these relationships should be interpreted cautiously, as they may also be influenced by differences in climate background, topography, land-use context, and road construction history. These findings provide empirical evidence to inform differentiated ecological restoration and low-carbon management of traffic corridors. Full article

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation. Full article

The present study investigates the presence, sources, and impacts of microplastics (MPs) in different soil types, including agricultural, urban, and forest areas, through a synthesis of results of published scientific papers. MPs originate from a variety of human activities, such as the widespread use of plastic mulch in agriculture and the application of organic fertilizers and treated sewage sludge, as well as from vehicle tire wear, industrial processes, and the gradual degradation of plastic products in the environment. In urban soils, the main sources of MPs are related to road traffic, industrial activity, and landfills, while in forest soils, concentrations are generally lower. However, MPs in forest areas are thought to be carried there by the air, by runoff, or from nearby areas with human activity. Available data show that larger MP particles tend to remain in the surface layers of the soil, while smaller particles can penetrate deeper soil layers, increasing their bioavailability and the likelihood of interaction with microorganisms and plant root systems. In terms of their chemical composition, polyethylene (PE) and polypropylene (PP) polymers dominate in agricultural soils, which is directly linked to agricultural practices, while polystyrene (PS) and polyvinyl chloride (PVC) are more frequently detected in urban soils. The morphological types of MPs include fragments, fibers, and films, while their color characteristics provide clues to possible sources of origin, such as plastic ground covers, tire wear, and packaging materials. Overall, the study’s results underscore the growing environmental significance of MP soil pollution and highlight the need for more effective management and recycling of plastic materials, as well as for further interdisciplinary research aimed at understanding the mechanisms of transport, accumulation, and long-term ecological effects of microplastics in terrestrial ecosystems. Full article

Vehicle-to-Vehicle (V2V) cooperative object detection enhances environmental perception capabilities in complex traffic scenarios by sharing sensory information among vehicles, but limited transmission bandwidth and wireless channel noise can significantly affect the reliable transmission of cross-vehicle semantic features and lead to a degradation in detection performance at the receiver. Although existing semantic communication methods based on DeepJSCC can alleviate the cliff effect of traditional separated source–channel coding under low signal-to-noise ratio conditions, they typically rely on additional external autoencoder structures, which increase model complexity and the deployment burden on vehicular edge computing platforms. Meanwhile, under high compression ratios, these methods struggle to adequately preserve detection-related fine-grained information, such as object boundaries, spatial locations, and local structures. Motivated by these challenges, we develop Mamba-KGSC as a lightweight knowledge-guided semantic communication framework for robust V2V cooperative object detection. At the transmitter, Mamba-KGSC utilizes the internal time-scale parameters of the Mamba-YOLO-T backbone network to generate spatial semantic masks, realizing the sparse encoding and transmission of task-relevant features while avoiding the introduction of complex external codec networks. At the receiver, a multi-source knowledge base constraint verification module is constructed to refine the initial detection results by combining physical consistency screening with visual–physical spatial joint redundancy suppression, thereby suppressing physically inconsistent misdetections and repeated detections induced by channel noise. The experimental evaluation indicates that, under a 50% compression ratio, multiple SNR settings, and different channel models, the front-end semantic communication branch of Mamba-KGSC improves mAP@0.5:0.95 by an average of 1.90 percentage points over the DeepJSCC baseline. The multi-source knowledge base constraint verification module further reduces abnormal and duplicate candidate bounding boxes. Overall, Mamba-KGSC provides a balanced solution in terms of transmission cost, detection accuracy, model complexity, and physical consistency, offering a lightweight implementation scheme for robust V2V cooperative detection in challenging communication environments. Full article