Search Results (324)

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

Digital infrastructures are increasingly exposed to diverse and evolving cyber threats, highlighting the need for robust intrusion detection systems (IDSs). Although machine learning (ML)-based IDSs have achieved strong performance, most existing frameworks are still developed and evaluated mainly under intra-dataset settings, providing limited evidence of their ability to generalize across unseen environments. Moreover, few studies go beyond simply reporting cross-dataset performance to propose dedicated mechanisms for improving generalization. To address this limitation, we propose BHM-IDS, a three-stage hierarchical intrusion detection framework that combines behavior-driven hierarchy with multi-dataset training to improve generalization. The first stage performs binary detection of benign versus malicious traffic, while the second stage classifies malicious traffic into two behaviorally distinct groups: the first corresponding to flood and exhaustion attacks and the second to infiltration and exploitation attacks. The final stage performs fine-grained attack classification through two specialized multi-class classifiers. To expose the framework to more diverse attacks, CIC-IDS2017 is enriched with CIC-DDoS2019 during training, while CSE-CIC-IDS2018 is used as an external test dataset to evaluate generalization. The cross-dataset validation results yielded stage-wise accuracies of 0.93, 0.96, and 0.99, respectively, while the complete end-to-end framework achieved a weighted recall of 0.93. Recall values ranging from 0.76 to 1.00 were obtained for several major classes, including benign traffic, Patator, DoS, and DDoS, although limitations remained for certain attack categories, particularly Web Attack. Overall, the proposed framework demonstrated promising and competitive performance compared with simpler frameworks and existing state-of-the-art approaches. These findings highlight the potential of combining behavior-driven hierarchical classification with multi-dataset training to improve cross-dataset generalization in IDSs. Full article

Software-defined Networking (SDN) is a promising networking architecture that separates the control and data planes to allow flexible network management. However, the SDN architecture makes networks vulnerable to various security threats, such as Distributed Denial-of-Service (DDoS) attacks. A DDoS attack is one of the most common SDN threats, aiming to exhaust a network’s computational and bandwidth resources. Self-similarity is a statistical property of time series in which data patterns repeat at different time scales. Several studies have shown that network traffic exhibits increased self-similarity during DDoS attacks, making it a promising tool for DDoS detection. Despite the effectiveness of statistical methods for detecting DDoS, some methods, such as self-similarity, are discarded due to their high computational cost, leading to detection delays. This paper proposes a lightweight Rescaled Range ($R/S$)-based scheme for effective real-time DDoS attack detection in SDN. The scheme employs the Welford online algorithm to compute statistical parameters of the $R/S$ scheme. Experimental results demonstrate that the proposed scheme efficiently captures changes in self-similarity and detects TCP/UDP DDoS attacks in real time. Moreover, it achieves high detection performance compared to other $R/S$ methods, with a False Positive Rate (FPR) below 0.5% and an average computation time of 0.047 ms. Full article

The rapid transition toward electric mobility is widely recognized as a key strategy for improving urban environmental quality. However, while electric vehicles eliminate tailpipe emissions, they continue to contribute to environmental pressures through non-exhaust sources such as tire wear, road surface abrasion, and particle resuspension. This study develops a composite index framework for assessing road-segment-level operational environmental burdens associated with electric traffic, focusing on traffic operations, electric vehicle load characteristics, non-exhaust emission potential, and meteorological dispersion conditions. The framework does not constitute a life-cycle assessment and does not include battery production, electricity-generation mix, or other upstream environmental impacts. The framework combines four dimensions of influence: traffic operations, electric vehicle characteristics, non-exhaust emission processes, and meteorological dispersion conditions. Indicator selection was performed using the Delphi method, while indicator weights were determined through the Analytic Hierarchy Process (AHP). The methodological contribution lies not in the individual methods applied, but in their integration into a road-segment-level assessment framework specifically designed to identify and prioritize environmentally sensitive locations under traffic electrification scenarios. The resulting model incorporates sixteen indicators aggregated into a single environmental impact index that enables the ranking, classification, and prioritization of urban road segments according to their environmental burden. A case study conducted on selected urban streets demonstrates that non-exhaust emission indicators, particularly tire wear and particle resuspension, represent the most influential factors in the assessment process. Within the illustrative five-segment case study, the relative road-segment ranking remained unchanged under the electrified-traffic scenario, while the structure of the assessed burden shifted toward non-exhaust processes. The proposed framework provides a practical decision-support tool for urban planners and transport authorities by enabling the identification of environmentally sensitive locations, prioritization of infrastructure interventions, and support for sustainable mobility strategies in increasingly electrified urban transport systems. 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 PM_2.5 source categories remained relatively stable, while their chemical fingerprints and relative importance exhibited substantial temporal variability. Full article

Tire and road wear particles (TRWPs) are major sources of non-exhaust traffic emissions. However, a limited understanding of their generation mechanisms and the lack of efficient collection methods under realistic driving conditions hinder accurate assessment. This study addresses these challenges by developing a vehicle-based methodology for the controlled recovery and characterization of TRWPs in the near-field region, rather than for direct quantification of real-world emissions. An autonomous electric vehicle was employed to ensure stable driving conditions and eliminate exhaust interference. Near-field distribution of TRWPs was visualized using a high-sensitivity optical scattering system. Based on this, a sealed tire enclosure with a high-power on-vehicle vacuum collection system was designed to enhance particle containment and recovery. Controlled circular driving tests were conducted on a dedicated outdoor test track under well-defined and repeatable conditions to enable system-level evaluation of TRWP generation and collection relative to measured tire wear. Particles were analyzed by thermogravimetric analysis, microscopy, scanning electron microscopy–energy-dispersive X-ray spectroscopy, and particle imaging. The results demonstrated stable, reproducible TRWP generation with ~60% collection efficiency relative to tire mass loss. These values are reported as system-dependent recovery indicators rather than precise emission estimates. Additional tests with an expanded recovery protocol indicated that collection efficiency can increase to ~81% (range: 73–91%), highlighting the influence of collection coverage. The collected TRWPs exhibited heterogeneous morphology, bimodal size distribution, and a mixed rubber–mineral composition in the 10–100 μm range. Spatial analysis revealed that TRWPs predominantly accumulated within a narrow zone around the driving lane. While the controlled experimental configuration enables reproducible particle generation and high-efficiency recovery, it represents a simplified driving scenario and may not fully capture the variability of real-world traffic conditions, including straight-line driving and transient maneuvers. Overall, this study demonstrates a technical framework for reproducible and comparative recovery of tire-associated particles under identical, well-defined conditions. The approach is intended to support controlled characterization studies while explicitly acknowledging limitations related to representativeness, particle origin attribution, and quantitative emission relevance, rather than to establish emission factors or mechanistic descriptions of TRWP generation. Full article

Non-exhaust emissions (NEEs), particularly brake wear particles (BWPs), have become a dominant source of traffic-related particulate matter (PM), accounting for approximately 77% of PM10 and 60% of PM2.5 emissions. Accurate quantification of these emissions is essential under increasingly stringent regulations such as Euro 7. However, measurement reliability is strongly influenced by particle transport and sampling losses. This review provides a state-of-the-art analysis of laboratory-scale methodologies for investigating BWP emissions, focusing on pin-on-disc (PoD) tribometers and inertia dynamometer systems. Particular attention is given to chamber design, airflow management, sampling configurations, and the mechanisms governing particle transport efficiency. The literature indicates that PoD systems are often affected by complex and non-uniform flow fields, leading to incomplete particle capture and reduced representativeness, whereas inertia dynamometers, especially when coupled with constant volume sampling (CVS), provide more controlled and reproducible conditions. Key loss mechanisms, including inertial deposition, diffusion, gravitational settling, and non-isokinetic sampling effects, are major contributors to uncertainty. The reviewed studies highlight that aerodynamic limitations in PoD systems, particularly box-shaped chambers, promote flow recirculation and particle losses. Advanced optimization approaches that combine artificial neural networks (ANNs) with computational fluid dynamics (CFD) simulations show strong potential to improve system design and measurement reliability. Full article

We examine the growth of Internet Protocol version 6 (IPv6) routing state from 2010 to 2025. The global IPv4 address space has been exhausted, and the transition to IPv6 is ongoing. Using publicly accessible data from the RIPE Route Collectors (RRCs), we show that growth in the number of globally visible IPv6 routing prefixes follows different models over time, reflecting different growth patterns: exponential, power-law, and stretched-exponential. In addition to building models using publicly available RIPE data, we use this data source to demonstrate that our analysis holds across different Internet Exchange Points (IXPs) around the world and has predictive value. We provide in-depth analyses of IPv6 routing state growth, and we believe these are the first such analyses. Additionally, we highlight previous similar analyses of other aspects of network characteristics (such as topology and network traffic), and show that our analyses provide new insights. Specifically, we show the following: (1) previous models that have worked well for other network characteristics do not work well for routing state; (2) growth patterns for IPv6 routing state have changed significantly over time; (3) growth patterns cannot be described by a single model, and need to be analysed in a piecewise fashion; (4) fitting of previous data might not necessarily result in good predictive quality, and we identify the factors that may affect the predictive quality of a model and the predictive models that are suitable at the current time. Our analyses include metrics for assessing model fit. Overall, we observe a decrease in the rate of growth of IPv6 routing state, while the overall use of IPv6 continues to grow. We provide a critical evaluation of our approach, and also discuss possible factors affecting the growth of global IPv6 routing state. Full article

Ship exhaust emissions have become an increasingly prominent global atmospheric environmental issue, triggering a series of ecological disturbances and adverse public health consequences. However, comprehensive analyses of the research progress and evolution trends in this field remain scarce. This study systematically retrieved 1346 scholarly publications in the ship exhaust emissions field for the period 2011–2025 from the Web of Science Core Collection and carried out a bibliometric analysis encompassing publication outputs, contributing countries/regions, and keyword characteristics. The findings reveal a sustained and robust growth trajectory in global research output, with annual publications increasing nearly fivefold over the 15-year study period. Notably, academic interest in this field has increased significantly since 2020 due to the implementation of the global sulfur cap regulation. Core thematic clusters (mean silhouette S = 0.7205) in this field include source apportionment, numerical modeling analysis, atmospheric criteria pollutants, and technological emission reduction strategies. The geographical distribution of research output shows a significant positive correlation with the importance of regional maritime economies. China, the United States, and Germany are the leading contributors in terms of publication outputs, while frequent research collaborations have been observed among European countries. Since 2021, the emergence of Automatic Identification System data as a keyword with high burst strength (intensity = 3.60) marks a paradigm shift toward a “big data-enabled refined management” framework. Concurrently, the sustained burst activity of keywords including nitrogen oxides, volatile organic compounds, and traffic-related emissions from 2023 to 2025 indicates rapidly growing scholarly attention to secondary aerosol precursors from shipping, and the critical need for coordinated multi-pollutant control strategies. Future research directions for ship exhaust emissions are expected to transition from fundamental characterization research to big data-driven monitoring and estimation methods, as well as advanced emission reduction technologies. The bibliometric insights derived from this study provide a valuable reference framework for subsequent in-depth studies on ship exhaust emissions. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. About 98 percent of large businesses will use cloud computing services in 2025 to enable remote working. The highly distributed structures of cloud computing are prone to attacks starting from weakened access control to data breaches. The sources making cloud systems vulnerable to attacks are public accessibility, auto scaling, and shared form of network architecture. Distributed Denial of Service (DDoS) is one of the most serious forms of attacks where multiple botnets get created simultaneously and flood massive requests for the cloud services. If the DDoS attack is not identified early it leads to the unavailability of cloud services, increased cost of migration, exhaustion of resources, and frequent violations of Service Level Agreements (SLAs). Hence, there is a need to detect DDoS at an early stage. Traditional machine learning models demand high computational power and larger memory capacity which make it unsuitable for a real-time cloud environment. This limitation is overcome by presenting a novel Quantum-Enhanced Knowledge Distillation framework (QKD) to detect DDoS attacks in cloud systems. QKD is a highly potential form of architecture which uses quantum computing to enhance the knowledge transfer between teacher and student models. The knowledge is extracted from the teacher model and quantum encoding of knowledge is performed. The complex correlation between the features of the traffic is extracted by applying the entanglement gates. The student model is trained considering the distillation loss and optimized until convergence. The simulation of the QKD is performed using DynamicCloudSim 3.0.3 simulator considering benchmark dataset CIC-DDoS2019and the performance is further validated using expected value analysis methodology. The performance of QKD is found to be promising toward performance metrics such as packet loss rate, attack detection time, attack recovery ratio, bandwidth utilization, and response time. Full article

Non-exhaust emissions represent a growing share of traffic-related particulate matter and are increasingly addressed by regulatory frameworks. This study presents a comparison of two brake emission test cycles, the California Brake Dynamometer Cycle (CBDC) and Worldwide Harmonized Light Vehicles Test Procedure Braking Cycle (WLTP-BC), the latter being formally embedded in current regulations. Firstly, we present a detailed comparison of WLTP-BC and CBDC in terms of parameters that are shown to affect or may affect braking control and particle emissions (braking torque, vehicle speed, acceleration, friction work, disc temperature, etc.). Secondly, we present a way to evaluate test system torque control quality, supplementing the friction work-based method present in United Nations Regulation (UNR) No. 179, and quantitatively assess the control quality between the cycles. CBDC was found to be the more challenging cycle to execute. However, the testbench control architecture was found to be sufficient to execute it with high fidelity. Thirdly, we present the emission results obtained from the cycles, both per kilometre driven, as well as per friction work done. We argue that the latter is better for comparing the emission results obtained from cycles with different braking profiles. Driving the CBDC resulted in higher particle mass (PM) emissions but similar particle number (PN) emissions. In light of this dataset, friction work seems to be a better predictor for PN than for PM. While this study encompasses only a single friction pair, and more tests with different brakes would be required to generalize the findings, the results highlight the importance of cycle selection in emission research, both in terms of quantifying the emissions and demands for the test system. Full article

Receptor modeling of ambient pollutant concentrations plays a central role in urban air quality assessments. This study proposes an integrated framework combining Self-Organizing Maps (SOM), Positive Matrix Factorization (PMF), and Time-Series Analysis (TSA) for a comprehensive evaluation of urban air pollution patterns and source dynamics. The methodology was applied to multi-annual air quality and meteorological datasets (2009–2018) from two major Bulgarian cities, Plovdiv and Varna. The SOM was used for assessing the overall parameter patterns of the cities, leading to a clear clustering of the site samples on the map. Thus, PMF was run separately for the two sites, identifying a different number of sources (three and four, respectively). Traffic-related and sulfur-rich combustion sources were identified in both cities, while a crustal/resuspended dust factor was observed only in Varna. TSA revealed distinct temporal behaviors among source types. Traffic-related aerosol contributions decreased in both cities (−5.14% yr⁻¹ in Plovdiv; −9.30% yr⁻¹ in Varna), whereas sulfur-rich combustion factors showed increasing trends (+4.64% yr⁻¹ and +2.97% yr⁻¹, respectively). Traffic fresh exhaust factors exhibited pronounced seasonal variability and significant weekday–weekend differences in both cities. The integrated SOM–PMF–TSA framework enhanced source interpretability and temporal characterization, providing a robust approach for urban air quality assessment and supporting targeted air pollution management strategies. Full article

Industrial control systems (ICSs) increasingly rely on legacy communication protocols such as ModbusTCP, which lack built-in security mechanisms and remain widely exposed to network-based attacks. This paper investigates the security limitations of authentication mechanisms in ModbusTCP-enabled programmable logic controllers (PLCs) and demonstrates how plaintext credential transmission and limited connection handling capabilities can be exploited to perform brute-force and denial-of-service (DoS) attacks. An experimental testbed based on two industrial Delta PLC families (DVP-13SE and DVP-311SV3) was developed to systematically evaluate these vulnerabilities under realistic conditions. The results show that authentication credentials can be easily captured through network sniffing, while the PLC communication stack supports a maximum of 16 concurrent connections and can process up to approximately 8600 Modbus operations per second, making it susceptible to resource exhaustion and performance degradation under distributed attack scenarios. To address these limitations, this paper proposes a lightweight deception-based protection mechanism, termed the PLC misleading algorithm (PMA), which is implemented directly within the PLC ladder logic. Unlike traditional network-level defenses, PMA operates at the device level and dynamically misleads attackers by generating controlled randomized responses while preserving consistent behavior for legitimate clients. Experimental results demonstrate that PMA significantly mitigates brute-force effectiveness by preventing reliable password extraction while introducing minimal overhead (2.2% memory usage) and maintaining acceptable communication latency. Additionally, the proposed approach significantly reduces observable attack traffic, with only 0.246 Modbus operations per second observed during the attack phase, thereby limiting the effectiveness of automated exploitation tools. These findings highlight the potential of in-device deception mechanisms as a practical and deployable security layer for legacy industrial systems, and provide new insights into the resilience of PLC-based infrastructures against network-level attacks. This work bridges the gap between lightweight PLC-level protections and the growing need for robust cybersecurity mechanisms in industrial IoT environments. Full article

With the progressive decline of tailpipe emissions, non-exhaust sources such as brake wear are becoming an increasingly important contributor to traffic-related particulate matter in urban environments. In this context, improving real-world characterization of brake wear particles is essential for air-pollution assessment, source apportionment, and the development of cleaner and more sustainable road transport systems. Here, we investigated the emissions levels, particle size distribution and elemental composition of particles released during harsh real-world braking events by a single light-duty vehicle braking system equipped with an original manufacturer (OEM) non-asbestos organic (NAO) pad formulation. Using a direct on-vehicle sampling system combined with real-time particle sizing and high-resolution microscopy, we observed that particle emissions remained close to background levels at speeds up to 100 km/h, but rose sharply at 120 km/h, reaching 3.7 × 10⁷ #/cm³ in the 8–10 nm size range. This increase suggests that higher speeds are associated with elevated particle emissions, likely due to the higher braking temperatures reached at increased vehicle speeds. The emitted particles were mainly spherical agglomerates rich in iron, titanium, barium, zirconium, and sulphur, consistent with NAO pad formulations. Our results show that the investigated NAO pad system can deteriorate under thermal stress, potentially leading to higher levels of nanoparticle emissions compared to low-metallic or semi-metallic pads investigated under similar conditions. These findings provide real-world evidence relevant to urban air quality research, support the refinement of non-exhaust emissions inventories, and highlight the importance of thermally resilient friction-material formulations for mitigating residual particulate emissions in increasingly cleaner transport systems. Full article

Non-exhaust particulate emissions are expected to remain a relevant source of traffic-related air pollution, including an increase in electrified vehicle fleets. Particle formation results from tribological interactions and is influenced by both operating conditions and friction material system. This study presents an extended measurement methodology under application-relevant tribological conditions for the reproducible quantification of PM₁₀ and PM_2.5 emissions from dry-running friction systems and applies it to a systematic investigation of operating parameter and friction pairing effects. A dry inertial brake test bench with an enclosed friction chamber and integrated aerosol measurement chain was used under controlled tribologically relevant conditions. Specific friction work and specific friction power were varied by adjusting sliding velocity, contact pressure, and inertial load. Six friction pairings, comprising four representative friction lining types combined with either C45 cast steel or GGG40 gray cast iron, were examined. In situ PM₁₀ and PM_2.5 measurements were complemented by gravimetric wear and microstructural analyses. The results show that specific friction work has a direct influence on PM₁₀ and PM_2.5 emissions, whereas the independent effect of contact pressure is secondary. Friction power exhibits material-dependent effects. Emissions also vary strongly with friction pairing, indicating that operating conditions and material system must be considered jointly when assessing low-emission brake systems. Full article