Despite decades of regulatory progress, air pollution, continues to pose a major public health burden in European cities. The transport sector remains a dominant contributor to urban air pollution (particularly to NO₂), yet its impacts under real-world driving conditions are still insufficiently quantified. Attribution modeling studies form an important pillar for improving this understanding. However, commonly used approaches most often still rely on static emission inventories, which fail to take the spatial and temporal variability of true traffic dynamics into account. For this reason, modeling errors are often dominated by the emission uncertainties even at street-scale resolution.

This study presents a digital twin framework for urban air quality modeling that integrates the microscopic traffic simulation model SUMO with the urban microscale dispersion model CAIRDIO. In SUMO each vehicle is modeled explicitly on individual interactively managed routes, enabling to dynamically represent real-world traffic conditions, such as congestion patterns leading to emission spikes. Traffic flows and speeds in the network are continuously adjusted at calibration points with available real-time traffic measurements. Emissions are derived directly from simulated traffic data and imported into the CAIRDIO model, which computes urban flow, dispersion and air chemistry based on realistically evolving boundary conditions for meteorological and air composition.

We showcase an application study on the city of Leipzig, for which real-city weather conditions and NOx dynamics are modeled at the neighborhood scale over a period of one week. Based on the results, we assess the contribution of traffic-related emissions to ambient NO₂ levels in different urban micro environments (high traffic sites, residential areas) and discuss the impact of variable atmospheric conditions on dispersion and chemical evolution characteristics. Representation of diurnal peak NO₂ timing and magnitude in the data-driven emission modeling approach is further evaluated against a conventional modeling approach using available static emissions. Finally, the tool’s capability for scenario-based assessment of, e.g., traffic rerouting impacts is demonstrated, which can quantitatively support the implementation of traffic management strategies, infrastructure modifications, and urban air quality mitigation measures.

How to cite: Weger, M., Houben, T., Trabert, T., Sohr, A., Brockfeld, E., and Bumberger, J.: Urban emission and air quality modeling informed by microscopic traffic data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21816, https://doi.org/10.5194/egusphere-egu26-21816, 2026.

EGU26-21816

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Vehicular non-exhaust emissions have become a dominant source of particulate pollution in urban areas. However, the dynamic physicochemical evolution transforming solid friction materials into aerosols under extreme braking conditions remains elusive, which significantly hinders accurate source apportionment. To bridge this gap, we established a comprehensive macro-to-micro analysis framework, integrating transient emission kinetics, multi-scale chemical fingerprinting, and machine learning techniques to decipher the formation mechanism of brake wear particles (BWPs).

By combining laboratory chassis dynamometer experiments (under WLTC and extreme AMS cycles) with high-resolution online monitoring (EEPS/APS), we captured the real-time formation dynamics. Crucially, at friction interface temperatures exceeding 300°C, we observed distinct "banana-shaped" particle size distribution evolution, directly indicative of rapid particle nucleation and subsequent growth events. However, connecting these macro-kinetics to micro-composition revealed a striking physicochemical paradox. Nanoscale single-particle elemental mapping indicated that ultrafine particles were predominantly composed of metallic elements (Fe/Cu) with negligible carbon signals. In sharp contrast, bulk surface spectroscopy (XPS/FTIR) of collected PM_2.5 samples revealed a composition overwhelmingly dominated by organic carbon functional groups derived from resin binders.

To reconcile this discrepancy, we pioneered a novel hybrid machine learning methodology. This approach uniquely couples Deep Residual Networks (ResNet) for texture extraction with XGBoost for geometric decision-making. This intelligent analysis allowed for high-throughput quantification of single-particle morphology, revealing that these burst-phase nanoparticles exhibit near-perfect sphericity, ruling out mechanical abrasion. Consequently, we propose a mechanism of metal-vaporization induced heterogeneous nucleation, whereby trace metallic components vaporize to form high-density condensation nuclei. These nuclei subsequently trigger the heterogeneous condensation and coating of semi-volatile organic vapors, thereby forming particles with a distinctive metal-core/organic-shell architecture. Our results redefine the braking process as an active high-temperature physicochemical reactor, providing a robust, data-driven foundation for understanding the complex formation mechanisms of non-exhaust emissions.

How to cite: Zhang, F., Peng, J., Zhang, J., Qi, F., Zhang, Q., and Mao, H.: Friction-Induced Heterogeneous Nucleation: Unravelling the Formation Mechanism of Brake Wear Particles via a Hybrid Machine Learning Framework and Multi-Dimensional Characterization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21685, https://doi.org/10.5194/egusphere-egu26-21685, 2026.

EGU26-21685

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PM10 and PM2.5 are major sources of air pollution in Korea, particularly during the winter, necessitating systematic management. South Korea operates an automated air quality monitoring network to manage atmospheric conditions, with Cheongju City currently maintaining eight such monitoring stations. In this study, we analyzed hourly measurements from these eight stations in 2024 to understand the characteristics of PM10 and PM2.5 in Cheongju.
Cheongju City, covering an area of 941 km², is a basin-type city located in central Korea. Surrounded by mountains, the city's topographical structure contributes to higher PM10 and PM2.5 concentrations compared to the Korean national average, despite residential areas accounting for less than 30% of the total area.
First, we conducted a Pearson correlation analysis for each monitoring station to identify common contributing pollutants to PM10 and PM2.5 concentrations. For PM10, the correlation between the eight stations ranged from 0.88 to 0.99, while for PM2.5, it ranged from 0.74 to 0.91. These high correlations suggest that the current monitoring network, which measures at hourly intervals, may need to adopt shorter measurement intervals for more precise data.
Seasonal trends indicated that PM10 and PM2.5 concentrations are high in spring and winter but low in summer. The elevated concentrations during spring and winter are attributed to long-range transport from China, as well as domestic emissions from vehicles and heating. Regarding diurnal trends, PM10 concentrations peak during commuting hours due to vehicular traffic, while PM2.5 levels reach their daily minimum between 3:00 PM and 5:00 PM. These findings can be utilized to establish systematic management plans for PM10 and PM2.5 concentrations in Cheongju City.

Acknowledgment:This research was supported by the Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute (KEITI), funded by the Ministry of Environment (MOE).

How to cite: Lee, S. and Kim, Y.: A Study on the Management Plan of Automatic Measurement Networks Based on Seasonal and Time-of-Day Characteristics of PM10 and PM2.5 in Cheongju City, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2666, https://doi.org/10.5194/egusphere-egu26-2666, 2026.

EGU26-2666

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Road dust acts as both a vector and a source of multiple urban pollutants, including polycyclic aromatic hydrocarbons (PAHs), which can be transported into surface waters via runoff, contributing to widespread environmental contamination. This study integrates chemical analyses, magnetic measurements, Artificial Neural Network modeling, source tracking, and ecological risk assessment to evaluate PAHs contamination in Warsaw’s urban road dust and its potential ecological and human health risks. A total of 206 road dust samples were collected across diverse urban locations, encompassing variations in traffic intensity, building height, urban layout, and municipal heating activity. Samples were characterized by particle size fractions and magnetic properties, including magnetic susceptibility, saturation magnetization, and remanent magnetization. A subset of 57 samples focusing on the fine fraction (<0.2 millimeters) was analyzed for sixteen priority PAHs compounds, markers of combustion-derived pollution.

Total PAHs concentrations (∑16PAHs) in the fine fraction ranged from below the limit of quantification to twelve milligrams per kilogram, with an average of 3.5 milligrams per kilogram. The most abundant compounds were acenaphthene, fluorene, and phenanthrene, while high molecular weight PAHs accounted for approximately fifty-five percent of total PAHs. Diagnostic isomer ratios, including indeno[1,2,3-cd]pyrene to the sum of indeno[1,2,3-cd]pyrene and benzo[ghi]perylene, indicated traffic-related pyrogenic sources dominated. Magnetic susceptibility normalized to fine particle proportion (χWN) correlated strongly with total and high molecular weight PAHs (r = 0.79, R² ≈ 0.63), confirming its utility as a rapid, non-destructive proxy for organic contamination. Multivariate analyses revealed distinct pollution patterns, grouping PAHs by shared sources and physicochemical behavior. Elevated PAHs levels occurred in the city center, while peripheral areas had lower concentrations.

Artificial Neural Network models predicted PAHs concentrations from magnetic properties, particle size fractions, traffic intensity, building height, urban layout, and municipal heating patterns. A consolidated model across all samples achieved moderate performance (correlation coefficients ~0.45–0.57), whereas models stratified by municipal heating activity performed significantly better. Neural networks for periods of inactive heating yielded high correlation coefficients (0.91–0.94) and low root mean square errors, indicating strong predictive capability and stability. Sensitivity analysis identified building height and heating-related factors as most influential. Combining neural network predictions with isomer ratio diagnostics allowed source tracking of PAHs, distinguishing contributions from traffic, combustion heating, and urban structural influences. Magnetic proxies, particle size, and urban parameters efficiently identified PAH pollution hotspots in dense urban areas.

Ecological risk assessment using MERM-Q showed most samples fell into the low-risk category, with highest values in the city center. These results provide quantitative insights into potential ecological and human health risks posed by traffic-related PAHs, highlighting road dust as both a local pollutant and a vector transporting contaminants into broader urban environments and surface waters. This methodology enables rapid identification of pollution hotspots, supports targeted mitigation strategies, and informs urban planning, traffic management, and municipal heating policies to reduce environmental and health hazards. By combining predictive modeling with source apportionment, this study offers a robust framework for monitoring and managing hazardous organic pollutants in cities.

This research was funded in whole by the National Science Centre, Poland under grant number 2021/43/D/ST10/00996.

How to cite: Dytłow, S. K., Kida, M., Pochwat, K., Karasiński, G., and Karasiński, J.: "Another One Bites the Dust" : Artificial Neural Networks Application and Source Tracking of Polycyclic Aromatic Hydrocarbons and Ecological Risk Posed by Urban Road Dust, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4043, https://doi.org/10.5194/egusphere-egu26-4043, 2026.

EGU26-4043

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Air pollution remains a significant global issue. Understanding the drivers of air pollution and reducing associated premature deaths have become one of the United Nations’ Sustainable Development Goals. In recent years, traffic emissions have become the main source of urban air pollution in the UK and globally. In the EU, road transport has been identified as the primary source of total NO_x (comprising NO₂ and NO), contributing 39% of total emissions. In densely populated regions, large-scale events and daily congestion further aggravated the problems. Although traffic flow and temporal character are proven to play an important part in the high-resolution NO_x prediction, a systematic component of NO_x variability remains unexplained. Thus, current policies based solely on traffic flow control and fleet turnover can hardly reduce the whole network emission effectively in real-world. The observed persistence and spatial coherence of the residual errors also suggest that missing information should not be identified as random noise, but comes from the network dynamics at the microscale driven by individual driving behaviour.

Our study addresses this gap by examining network-level driving behaviour as a candidate mechanism underlying this unexplained variability, and quantifying its impact on urban NO_x emissions. By developing a driving style–based urban digital twin emission simulation framework, we investigate how behavioural heterogeneity influences the spatial and temporal distribution of NO_x emissions across the Greater Manchester road network. The empirical driving behaviour parameters were localized from 16,897,293 records. Apart from that, realistic hourly traffic flows from Department for Transport (DfT) were employed as constraints in the HBEFA-linked emission modelling within SUMO for spatial analysis based on a ring-based segmentation. Results show that behavioural heterogeneity not only affects the magnitude of NO_x emissions, but also the spatial distribution.Contrary to the widely held belief that aggressive driving uniformly increases emissions across the network, we find that aggressive driving reshapes emission patterns by relocating hotspots rather than simply amplifying network-wide totals.

Therefore, emission mitigation benefits are highly context-dependent: policies promoting smoother driving are likely to be most effective in suburban and inter-ring corridors, while targeted restrictions on aggressive driving may be necessary in high-density urban cores during vulnerable periods. Moreover, behavioural interventions aligned with travel demand patterns may outperform static, network-wide emission policies. By constructing a scalable network-level digital twin, this study establishes driving style as a controllable and policy-relevant parameter. The developed framework supports scenario testing, spatial sensitivity evaluation, and behavioural–emission inference at the network scale, contributing to more effective and spatially equitable transport emission management.

How to cite: Zeng, Y., Topping, D., and Zhang, S.: Driving Behaviour as a Missing Control Lever in Urban NOx Mitigation: A Network-Level Digital Twin of Spatial–Temporal Hotspot Migration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7290, https://doi.org/10.5194/egusphere-egu26-7290, 2026.

EGU26-7290

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Port environments are characterized by complex and highly variable emissions from shipping, industrial and traffic activities, which results in distinct spatial and temporal heterogeneity in air pollutant concentrations. Fuel cells used in port activities, whether stationary or mobile, can be adversely affected by elevated pollutant concentrations. Therefore, we investigate the concentrations of volatile organic compounds (VOCs), trace gases, and ultrafine particles in the Port of Rotterdam as part of the KaLiBer joint project. MOBILAB serves as the mobile measuring platform equipped with state-of-the-art instrumentation for particle and gas-phase analysis, enabling both mobile transects and stationary monitoring. VOCs were measured using proton-transfer-reaction mass spectrometry (PTR-MS), capturing a wide range of oxygenated and non-oxygenated species, alongside simultaneous observations of CO, CO₂, NOₓ, SO₂, NH₃, and ultrafine particle (UFP) number concentrations. The mobile measurements were conducted over 10 days along selected routes in the port area and subsequent 30 days of stationary monitoring during summer. PTR-MS measurements covered approximately 130 masses, allowing a detailed characterization of both oxygenated and non-oxygenated VOCs.

The mobile measurements capture distinct spatial gradients and short-lived concentration spikes linked to local emission plumes. In contrast, the stationary data set reveals long-term variability and background conditions with additional plumes from ships passing by. Average concentrations and prominent diurnal patterns suggest distinct emission sources from shipping, heavy-duty traffic, and industrial activities. Spatial analysis reveals elevated VOC concentrations accompanied by high UFP and CO₂ levels, along port roadways. This suggests a dominant contribution from traffic-related sources. In the urban background, mixing ratios decreased due to chemical transformation and atmospheric dilution. The aromatic-to-oxygenated VOC (OVOC) ratios reveal distinct differences between fresh emissions along traffic-affected routes and more chemically aged air masses at the stationary site. This emphasizes the dynamic interplay between primary emissions and atmospheric processing. Although toluene/benzene ratios were comparable during mobile and stationary periods, they reflect mixed contributions from traffic, shipping, and industrial sources.

The combined mobile–stationary dataset demonstrates the significance of high-time-resolution measurements for capturing emission variability, chemical processing, and source contributions in complex port environments. Such insights are vital for quantifying transport-sector contributions to urban air pollution. They are essential for assessing potential health impacts, and effective emission mitigation and air quality management. Beyond atmospheric characterization, the observed concentration ranges, variability, and occurrence of short-lived pollutant peaks are important for designing and optimizing air filtration systems that protect fuel cell technology. In particular, understanding the temporal occurrence of VOCs and acidic gases is essential for minimizing catalyst poisoning, membrane degradation, and performance losses. This, in turn, improves fuel cell durability and operational lifetime under real-world port conditions.

This work is funded by the Federal Ministry for Economic Affairs and Energy based on a resolution of the German Bundestag under funding code 03EN5043D.

How to cite: Bhowmik, H. S., Dubus, R., Adam, M. G., Klemp, D., Busse, M., Grill, A., Lang, T., Leites, K., Misz, U., Peters, J., Runge, M., Schweiger, F., and Wegener, R.: High-Time-Resolution Analysis of Organic and Inorganic Trace Gases and Particles in a Major European Port Using Mobile and Stationary Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7448, https://doi.org/10.5194/egusphere-egu26-7448, 2026.

EGU26-7448

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Vehicle cabins represent a confined transport microenvironment where emissions from interior materials can contribute substantially to occupant exposure to volatile organic compounds (VOCs), particularly during early use and under low-air-exchange conditions. We compared VOC emissions from two widely used seat upholstery materials, genuine leather and microfiber leather, using a 1 m³ emission chamber. Test pieces with an exposed area of 0.40 m² (40 × 100 cm) were placed in the chamber, and air samples were collected at 2 h, 8 h and 24 h to capture early-use conditions, within-day build-up and a full-day exposure window. VOCs were quantified by preconcentration GC–MS targeting 116 compounds with internal and external standard calibration. The sampled aliquot per injection (20 µL) was negligible relative to the chamber volume, supporting quasi-static conditions.

The two materials exhibited clear differences in both total quantified VOC burden and chemical composition over 2–24 h. Genuine leather showed higher concentrations at later time points, increasing from 117 ppb at 8 h to 171 ppb at 24 h. The profile was dominated by oxygenated VOCs with monotonic increases over time; acetone and isopropyl alcohol each approached 75 ppb by 24 h, consistent with sustained release and accumulation under sealed conditions. In contrast, microfiber leather maintained a lower but comparatively stable total VOC level (64–73 ppb across 2–24 h) while showing an aromatic-dominated feature: toluene remained consistently elevated (18–20 ppb) throughout the period, substantially higher than in genuine leather (3–6 ppb). Time-series behavior further differentiated compound classes, with oxygenated species generally exhibiting accumulation-type trends, whereas selected ester-like compounds displayed faster decay consistent with short-lived volatilization.

These results highlight that compound-resolved, time-resolved measurements can distinguish material-specific emission fingerprints and identify exposure-relevant windows that are not apparent from bulk VOC metrics alone. Ongoing experiments extend the same framework to heat-loading (solar-load) and ventilation-representative conditions to quantify temperature and air-exchange sensitivity of in-cabin VOC exposure.

How to cite: Gao, J., Zheng, J., Wu, T., and Zhao, W.: VOCs from vehicle interior materials in a transport microenvironment: time-resolved emissions, exposure windows, and mitigation relevance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9500, https://doi.org/10.5194/egusphere-egu26-9500, 2026.

EGU26-9500

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Inland shipping is potentially an important contributor to local air pollution. Long-lasting diesel engines of inland waterway vessels operate at high temperatures and emit NO_x (NO + NO₂), which have negative impacts on human health.  Emissions from inland ships are concentrated near waterways, making their effect on air quality particularly relevant in densely populated regions located along intensively used waterways, such as the Rhine River. Monitoring and quantifying these emissions is necessary for assessing the importance of inland shipping on local air quality besides other emission sources, such as car traffic.

We use MAX-DOAS (Multi AXis-Differential Optical Absorption Spectroscopy) measurements to quantify NO_x emissions from inland ships on the Rhine River in Koblenz, Germany. This remote sensing technique captures ship exhaust plumes from a riverbank while vessels pass the line of sight of the instrument. NO₂ column densities measured at different elevation angles provide information about the vertical NO₂ distribution in and around the plume. Here we present cross-sections of average ship exhaust plumes of NO₂ for different ship types and sizes and for different operation conditions (upstream and downstream) derived from a long-term dataset collected over a period of more than one year. From the combination of the NO₂ plumes and wind data, the corresponding NO_x emissions are estimated.

How to cite: Ripperger-Lukošiūnaitė, S., Ziegler, S., Eger, P., Beirle, S., Donner, S., Hoor, P., and Wagner, T.: Quantification of average NOx emissions from inland ships for different ship types and operation conditions derived from MAX-DOAS measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10990, https://doi.org/10.5194/egusphere-egu26-10990, 2026.

EGU26-10990

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The missing mechanisms of atmospheric sulfate formation remain a challenging issue for urban haze mitigation worldwide. Over the past decades, with the significant reduction of exhaust emissions and the electrification of vehicle fleets, non-exhaust emissions from vehicle braking have become a major source of aerosol particles in urban environments. Here, we demonstrate that brake wear particles (BWPs), an emerging urban aerosol source, possess exceptional catalytic efficiency for SO₂ oxidation and sulfate production under dark ambient conditions. Their SO₂ uptake coefficient (up to 1.5 × 10^-5) is orders of magnitude higher than those of mineral dust or soot. This remarkable reactivity originates from a self-sustained synergy between Fe₂O₃ and carbonaceous components: oxygen vacancies in Fe₂O₃ continuously activate atmospheric O₂ and H₂O to generate reactive oxygen species and Fe-OH for SO₂ oxidation, while organics and elemental carbon promote H₂O dissociation through proton abstraction and enhance SO₂ adsorption at carbon defects, respectively. Together, these processes sustain cyclic catalysis and mitigate site deactivation. Our findings establish BWPs as a previously overlooked class of reactive aerosols, with broad implications for multiphase chemistry, atmospheric modeling, and air quality management.

How to cite: Qi, F., Peng, J., and Mao, H.: Unexpectedly rapid sulfate formation on the surface of vehicular brake wear particles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15668, https://doi.org/10.5194/egusphere-egu26-15668, 2026.

EGU26-15668

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In the pilot region of Leipzig, the Helmholtz Centre for Environmental Research (UFZ) is implementing a sensor network comprising 25 mid-cost measuring devices for the continuous monitoring of air quality parameters PM₂.₅, PM₁₀, O₃, and NOₓ. The initiative is conducted within the project of AIAMO (Artificial Intelligence and Mobility), which aims to expand the existing digital infrastructure for an environmental digital twin which supports data-driven decision-making in sustainable urban development. 
The sensor network focuses on a defined study area (6 km x 6 km) within the city. In close cooperation with various municipal authorities – including the Environment Agency and the Transport and Civil Engineering Agency – traffic-related scenarios are being developed to facilitate the effects of upcoming urban air quality limit changes at an early stage. 

The selection of locations was based on existing or potential air quality hot spots where exceedances of limit values are either observed or expected. Both areas with high traffic volumes and urban background areas, such as parks, were considered. The locations were selected iteratively with all relevant authorities from the city of Leipzig. To this end, various inner-city scenarios have been defined. 
Within these scenarios, continuous air quality measurements are integrated with traffic data and model simulations to produce a consistent, spatially and temporally resolved representation of local emission sources, dispersion dynamics, and possible mitigation strategies. Based on long-term measurement series, targeted measures are identified, tested within virtual environments, and iteratively evaluated in cooperation with municipal stakeholders of Leipzig. The overarching goal is to ensure compliance with air quality standards and to promote environmentally sensitive traffic management strategies.
The project illustrates how regionally anchored urban sensor networks, data-driven analyses, and scenario-based modelling approaches can contribute to the development of sustainable and urban air quality services in contemporary city contexts.

How to cite: Trabert, T., Houben, T., Sohr, A., Brockfeld, E., and Bumberger, J.: Urban air quality monitoring for environmentally sensitive traffic management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16591, https://doi.org/10.5194/egusphere-egu26-16591, 2026.

EGU26-16591

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Exposure to unabated organic aerosol emissions is known to induce pulmonary inflammation and exacerbate respiratory symptoms, primarily through oxidative stress and direct toxic injury. However, the mechanisms by which specific compounds within the primary emissions from road transport, that can also act as secondary organic aerosol precursors, impact health require further characterization. An in vitro safety assessment of selected intermediate-/semi-volatile organic compounds (I/SVOCs) was conducted using human alveolar epithelial A549 cells, murine macrophage RAW 264.7 cells, and rat alveolar macrophage NR8383 cells under both conventional submerged monolayer cultures and advanced air–liquid interface (ALI) exposure systems. The first stage of the biological evaluation comprised a series of experiments using selected I/SVOCs commonly found in road transport-derived aerosols, namely polycyclic aromatic hydrocarbons (e.g., naphthalene and pyrene) and alkanes (e.g., dodecane, tetradecane, and docosane). Furthermore, actual gasoline internal combustion engine exhaust was collected on polytetrafluoroethylene (PTFE) membranes and subsequently subjected to biological evaluation.

The in vitro assessment of the I/SVOCs demonstrated significant cytotoxicity, oxidative stress, and inflammatory responses in all tested cell lines. Traditional submerged cultures revealed concentration-dependent effects, including reactive oxygen species (ROS) generation, glutathione depletion, apoptosis, G₂/M cell cycle arrest, and increased levels of pro-inflammatory cytokines. Moreover, advanced human ALI organotypic airway tissue models exposed via the VITROCELL® Essentials ALI exposure system (VITROCELL SYSTEMS GmbH, Waldkirch, Germany) were employed to more accurately replicate real-world respiratory exposure conditions. The ALI model represents an advanced in vitro airway system in which differentiated primary airway cells, cultured on microporous membrane scaffolds, are directly exposed to aerosols and gases at the air–liquid interface. Unlike submerged monolayer cultures, these differentiated primary cells exhibit transcriptional profiles that more closely resemble the in vivo human airway epithelium. Consequently, ALI airway models more accurately reproduce in vivo airway architecture, including barrier integrity and metabolic activity, while enabling exposure scenarios that better reflect real-world human inhalation. In addition, the integration of the ALI exposure system with a volatile organic compound (VOC) generator and a real-time multi-gas analyser, connected via a bypass sampling line downstream of a mixing chamber, enhanced the exposure precision, flexibility, and physiological relevance. Collectively, the findings are expected to provide a robust basis for the classification and prioritization of I/SVOCs according to their potential health risks, thereby supporting informed decision-making in air quality regulation and public health protection.

Acknowledgement: This research was funded by the European Union’s Horizon Europe research and innovation programme within the AEROSOLS project under grant agreement No. 101096912 and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant numbers 10092043 and 10100997].

How to cite: Sideratou, Z., Mavroidi, B., Katsaros, F., and Zeraati-Rezaei, S.: In vitro toxicological assessment of secondary organic aerosol precursors from road transport , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19211, https://doi.org/10.5194/egusphere-egu26-19211, 2026.

EGU26-19211

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Predicting the carcinogenic potential of emerging pollutants is vital to safeguarding public health and the environment. Approaches relying on high-fidelity chemical models incur substantial computational costs, while approaches solely relying on mathematical methods often lack robust predictive performance. In recent years, to address these limitations, Quantitative Structure–Activity Relationship (QSAR) models integrating chemical structure with mathematical methods have been developed. QSAR facilitates the implementation of the three principles of Replacement, Reduction, and Refinement (3Rs) in the context of green and sustainable chemistry for carcinogenicity prediction. Nevertheless, the prediction accuracy of existing models requires enhancement, as it is currently limited due to uncertainties in chemical classification databases, limited feature selection, and the complexity of carcinogenic mechanisms.

This study developed a tailored machine learning QSAR platform to predict the carcinogenic potential of Polycyclic Aromatic Hydrocarbons (PAHs), potentially reducing reliance on in vivo testing. The platform employs the Random Forest machine learning method, an ensemble of decision trees, based on molecular structure features (i.e., descriptors including constitutional, topological, geometrical, etc.) and carcinogenicity classification data. A total of 66 PAHs were selected based on available evidence of their presence in emissions from transport. PAH carcinogenicity classification data were extracted primarily from the International Agency for Research on Cancer (IARC), the Integrated Risk Information System (IRIS), and the European Chemicals Agency (ECHA) databases. PAHs were subsequently classified into carcinogenic (+1) and non-carcinogenic (−1) categories. Of the 66 PAHs, 56 were used for model training and 10 for evaluation using machine learning validation criteria, including accuracy, precision, sensitivity (i.e., recall), and the harmonic mean of precision and recall (i.e., F1 score). The optimal combination of model hyperparameters was selected based on the lowest average prediction error (i.e., out-of-bag error). Molecular descriptors were calculated using PaDEL-descriptor software, yielding 1,875 descriptors for each compound. Constant and highly correlated molecular descriptors (>0.96) were removed, reducing the descriptors to 291.

The results indicate that feature importance analysis successfully reduced the molecular descriptors to a final set of 12. This reduction is critical for preventing overfitting, given the limited transport-derived PAH carcinogenicity data available. The platform demonstrated robustness regarding uncertainties in the initial categorisation of compounds. Furthermore, it captured the most influential molecular characteristics for predicting PAH carcinogenicity. Its high accuracy is evidenced by F1 scores of 0.95 and 0.83 for the training and evaluation sets, respectively. Consequently, this study demonstrates that integrating QSAR with Random Forest can facilitate cost-effective and accurate prediction of the carcinogenic potential of unclassified PAHs, supporting the transition toward New Approach Methodologies (NAMs) by reducing the need for costly in vitro and in vivo testing.

Acknowledgement: This research was funded by the European Union’s Horizon Europe research and innovation programme within the AEROSOLS project under grant agreement number 101096912 and the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant numbers 10092043 and 10100997].

How to cite: Najafpour, N., Herreros, J. M., Tsolakis, A., Sideratou, Z., Katsaros, F., and Zeraati-Rezaei, S.: Development of a Machine Learning QSAR Platform to Predict Carcinogenicity Potential of Transport-derived PAHs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18990, https://doi.org/10.5194/egusphere-egu26-18990, 2026.

EGU26-18990

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Fugitive road dust (FRD) is a major contributor to urban particulate matter (PM) in Chinese cities, accounting for an estimated 25%–90%^1,2 of total PM emissions and creating substantial air-quality and health burdens. Despite this relevance, policy and research have focused primarily on exhaust emissions, while FRD remains comparatively under-characterized and weakly regulated³. Importantly, non-exhaust PM from FRD persists under fleet electrification; recent evidence indicates that the monetized impacts of PM associated with battery electric vehicles can be comparable to, or higher than, those from internal combustion engine vehicles due to continued non-exhaust sources⁴.

The U.S. EPA AP-42 approach is widely used to estimate road dust resuspension and is also embedded in China’s technical guidance (HJ/T 393-2007). Within this framework, the silt load (sL, mass of particles <75 μm per meter squre, g.m^-2) is the critical input governing emission intensity. However, conventional sL sampling (e.g., gravimetric sampling or mobile vacuum-based surveys) is labor-intensive and difficult to scale to national wide inventories with representative spatial coverage.

Here, we compile a national database of in situ sL measurements from 28 Chinese cities (>300 roads) and develop interpretable machine-learning models to predict sL by road class and city context. We fuse these predictions with open-source traffic data (200 cities; 20-min resolution; 8 months of records) and apply the AP-42 framework to construct a link-level FRD PM_2.5 emission inventory for urban China. Multiple algorithms (XGBoost, support vector regression, and ensembles) are evaluated, and SHAP (SHapley Additive exPlanations) is used to quantify feature contributions and diagnose non-linear effects.

The best-performing models achieve strong generalization (test R² > 0.7). SHAP results identify road class, precipitation, ambient PM₁₀ concentration, cleaning-vehicle density, longitude, traffic volume, and heavy-duty vehicle share as key drivers of sL, with pronounced non-linear decrease in sL as vehicle speed rises. FRD emission’s contribution to the traffic PM_2.5 emission were estimated in city-level, range from 25% to ~80%. Overall, this work first to infer silt load nationally using ML and translate it into a link-level inventory using open traffic data, provides a scalable pathway to high-resolution FRD emission estimation and supports targeted mitigation and urban transport planning.

• 1.Wang, L. et al.Environmental challenges in electrification: Traffic-induced non-exhaust PM2.5 emissions in Cangzhou, China. Transp. Res. Part Transp. Environ.151, 105137 (2026).
• 2.Chen, S. et al.Fugitive Road Dust PM2.5 Emissions and Their Potential Health Impacts. Environ. Sci. Technol.53, 8455–8465 (2019).
• 3.Harrison, R. M. et al.Non-exhaust vehicle emissions of particulate matter and VOC from road traffic: A review. Atmos. Environ.262, 118592 (2021).
• 4.Liu, Y. et al.Exhaust and non-exhaust emissions from conventional and electric vehicles: A comparison of monetary impact values. J. Clean. Prod.331, 129965 (2022).

How to cite: Chu, M. and Shen, H.: Link-Level Mapping of Fugitive Road-Dust Emissions in Urban China Using Explainable Machine Learning and Open Traffic Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6623, https://doi.org/10.5194/egusphere-egu26-6623, 2026.

EGU26-6623

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Air pollution growth significantly impacts human health with different sources, i.e., industrial emission, vehicle emission, biomass burning, and urban dust. This analysis focuses on the recent events of the gas tanker and LPG blast on 20 December 2024 and 07 October 2025 at the Jaipur-Ajmer Highway in Rajasthan, India. The study measured air pollutants pre-, during, and post-event from the Central Pollution Control Board (CPCB) and satellite data analysis of the concentration of AOD, black carbon, and active fire to confirm smoke plume and meteorological parameters to explain the pollutant dispersion during the event. The result found that PM_2.5, PM₁₀, and CO concentrations increase suddenly when PM_2.5 crosses the AQI limit of baseline levels. The concentration of fire data confirmed active thermal anomalies during the event sites, comparing with air pollutant spikes. This observation found that high explosions during the event can significantly degrade air quality post-event, depending on meteorological conditions.

How to cite: Bhati, V. S.: Analysis of air pollution during the Gas tanker burning over the semi-arid eastern plain zone of Rajasthan, India, i.e., Jaipur, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1305, https://doi.org/10.5194/egusphere-egu26-1305, 2026.

EGU26-1305

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Nitrogen dioxide (NO₂) is an established indicator of air quality, as it is produced in every process in which fossil fuels are burnt. Air quality is closely linked to economic growth if this is predominantly based on fossil fuels.

It is produced in all the overarching sectors that make up the gross domestic product, namely agriculture, known as the "primary sector", energy and industrial production, known as the "secondary sector", and finally the "tertiary sector", which includes all forms of transport. NO₂ in the troposphere has a lifetime of only about 1-2 days. Therefore, the NO₂ distribution can be used to draw relatively good conclusions about the location of the emission sources and economic activity can be tracked.

We focus in our study on the influence of remote working practices on tropospheric NO₂ vertical column densities over northern Italy, including the Milan metropolitan area. The analysis is based on daily observations from the Ozone Monitoring Instrument (OMI) onboard the Aura satellite, covering the period from 2004 to 2023.

A comparison between gross domestic product (GDP) trends and changes in NO₂ variability indicates that periods of reduced economic activity—most notably after 2019 during the COVID-19 pandemic—are characterized by a weakening of shorter-term oscillations (2–5 days) relative to lower-frequency variability at weekly (7-day) timescales. This shift can be partly attributed to changes in commuting behavior associated with reduced working hours and the increased prevalence of remote work. Widespread lockdown measures forced much of the global workforce to transition to remote work. In many regions, these practices remained more prevalent than before the pandemic.

Overall, our findings demonstrate that anthropogenic NO₂ pollution responds sensitively to changes in commuting patterns, with implications for air quality, public health, and ecosystem health.

How to cite: Engert, R., Bichler, R., and Bittner, M.: Investigating the influence of remote working conditions on tropospheric NO2 vertical column density over northern Italy observed by Aura/OMI, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10027, https://doi.org/10.5194/egusphere-egu26-10027, 2026.

EGU26-10027

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Air pollution has intensified globally with the acceleration of industrialization and urbanization, posing significant threats to human health and ecosystem stability. In particular, particulate matter (PM) is a major hazardous component that has been strongly linked to the development and worsening of respiratory and cardiovascular diseases. PM concentrations exhibit substantial spatial variability even over short distances due to differences in emission sources, meteorological conditions, and land-use characteristics. Therefore, high-resolution spatial monitoring is essential for accurate exposure assessment, particularly in densely populated and environmentally vulnerable areas. However, existing ground-based monitoring networks are spatially unevenly distributed, thereby constraining their capacity to accurately represent fine-scale spatial variability and localized high-concentration events in urban environments. To address these limitations, this study aims to estimate PM concentrations at a spatial resolution of 20 m using high-resolution satellite imagery, thereby complementing ground-based observations and enabling detailed characterization of local PM variability and hotspots. Sentinel-2 satellite data were integrated with multiple ancillary datasets, including meteorological variables, a digital elevation model, and land cover information, to estimate PM concentrations. Multiple machine learning and deep learning algorithms were implemented and systematically compared, and XGBoost was identified as the optimal model. Model performance was evaluated using multiple statistical metrics. The results demonstrated high predictive performance for both PM10 and PM2.5 concentrations. For PM10, the model achieved a mean absolute error (MAE) of 6.684㎍/㎥, a root mean square error (RMSE) of 11.132㎍/㎥, and a determination of coefficient (R²) of 0.887. Similarly, PM2.5 estimation yielded an MAE of 4.094㎍/㎥, an RMSE of 6.648㎍/㎥, and an R² of 0.841. These findings confirm the feasibility and effectiveness of generating high-resolution PM concentration maps using Sentinel-2 satellite data. This study provides a robust framework for detailed assessment of urban-scale PM spatial distributions and offers valuable baseline data for population exposure assessment and the development of targeted air quality management policies.

How to cite: Koh, M., Park, S., and Park, S.: High-Resolution Estimation of Particulate Matter Concentrations based on Sentinel-2 Satellite Imagery Using Machine Learning and Deep Learning Approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15982, https://doi.org/10.5194/egusphere-egu26-15982, 2026.

EGU26-15982

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Air pollution is recognised as one of the leading environmental risks to global health, contributing to severe respiratory and cardiovascular diseases. In urban environments, air pollutant concentrations exhibit strong spatial variability at very fine scales, which cannot be adequately resolved by regional air quality models. The recently adopted Directive (EU) 2024/2881 establishes stricter regulatory standards, including a new annual mean limit of 20 μg/m³ for NO₂, which is frequently exceeded at specific urban locations, highlighting the need for high-resolution data fusion approaches that integrate air quality models with observational data to support exposure assessment and regulatory compliance.

Current state-of-the-art methods increasingly fuse Sentinel-5P (TROPOMI) tropospheric NO₂ columns with high-resolution geospatial proxies and modelled data to refine regional air quality outputs for urban-scale applications. However, high-resolution uncertainty quantification is largely absent from these products, limiting their interpretability. Establishing a clear methodology to provide this information is essential for effective air quality management, as stakeholders require reliable confidence metrics alongside best estimates to design robust mitigation strategies.

In this work, we present a statistical downscaling framework applied to CALIOPE, an operational air quality system integrating meteorological, emission, and photochemical models within a three-level nested configuration covering Europe, the Iberian Peninsula, and Catalonia. In the innermost domain, CALIOPE provides hourly forecasts at a 1 km × 1 km resolution. The proposed methodology bridges the gap between regional and urban scales by integrating high-resolution geospatial covariates (including traffic intensity networks, CORINE land-use data, terrain elevation, and distances to industrial sources) to produce concentration maps at the target high resolution. In particular, the proposed downscaling procedure operates on a non-uniform spatial mesh, achieving resolutions of up to 25 m near emission sources, with dense sampling along the road network and progressively coarser resolution towards the regional background. Sentinel-5P (TROPOMI) tropospheric NO₂ column data are interpolated to the final grid and incorporated as a spatially continuous covariate, ensuring regional consistency, particularly in areas lacking ground-based monitoring. The modelling strategy follows a source-oriented stratified approach inspired by area-oriented Kriging principles, separating traffic-influenced and background environments. Deterministic concentration trends are estimated using non-linear machine learning algorithms, including Random Forest and Gradient Boosting, while spatially correlated residuals are interpolated using ordinary kriging. Crucially, uncertainty quantification is explicitly integrated by propagating both model and spatial interpolation uncertainties, resulting in an uncertainty-aware product that provides local confidence estimates alongside predicted concentrations.

The framework is applied to estimate annual mean NO₂ concentrations for 2024 in Catalonia, Spain, serving as a high-resolution diagnosis for the regional government. Beyond standard concentration maps, the system provides stakeholders with probability of exceedance maps, relative to the regulatory thresholds, and pixel-level uncertainty metrics. Statistical performance evaluated through Leave-One-Out Cross-Validation demonstrates significant improvements over raw regional outputs, achieving an R² of 0.87 and reducing the Root Mean Square Error by 35% to 3.4 μg/m³. These results highlight the potential of the proposed approach to resolve complex urban patterns at regional scales for multiple cities, while supporting targeted public health interventions and evidence-based policy-making.

How to cite: Julian-Izquierdo, A., Campos, C., Criado, A., Carnerero, C., Soret, A., C Vossepoel, F., and M. Armengol, J.: Uncertainty-aware downscaling of NO2 surface levels in urban environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17954, https://doi.org/10.5194/egusphere-egu26-17954, 2026.

EGU26-17954

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Transport-related emissions remain a primary driver of ambient air quality degradation in the European Union, especially in urban environments. The Net4Cities project focuses on establishing an integrated network of air quality and noise measurements across 11 cities in 10 European countries. This initiative aligns with the recently adopted air quality directive (EU/2024/2281), which mandates stricter regulation for target pollutants such as volatile organic compounds (VOCs), thereby supporting the European Union’s Zero Pollution Action Plan.

From March to September 2025, ambient air samples were collected once a month between 08:00 and 09:00 AM local time across all partner cities—Tbilisi, Antwerp, Zurich, Berlin, Duesseldorf, Limassol, Heraklion, Rotterdam, Oslo, Barcelona, and Southampton, using evacuated silco-steel canisters. Once sampled, the canisters are analyzed in GC-MS/FID at Forschungszentrum Juelich for quantification of a broad range of VOCs. Overall, the VOC concentration is dominated by alcohols, aldehydes, alkenes, and ketones. Individual species like ethanol, methanol, and acetaldehyde also played a major role in the overall VOC mixing ratio, followed by alkenes (e.g., ethene and propene) and acetone. But the atmospheric impact of VOCs is determined by their OH reactivity, which reflects the turnover rate of the individual VOCs with OH radicals and is calculated as the product of the VOC concentration and its reaction rate constant with OH. OH-reactivity characterizes the local ozone production for VOC mixtures of different compositions into a single parameter. While species like ethanol and methanol dominate the concentration, species like monoterpenes (such as limonene, α-pinene, myrcene, δ3-carene, isoprene, and β-ocimene) and aromatics (such as toluene and xylenes) dominate the OH reactivity despite their lower mixing ratios. Along with VOCs, carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), nitrogen oxides (NO_x) and nitrous oxide (N₂O) were also quantified for each city. Furthermore, the observed variability in ∑VOC/NOx ratios reflect a high degree of heterogeneity in local emission profiles across cities. Overall, the results suggest that while oxygenated species dominate the total VOCs concentration, the OH reactivity across these cities is mainly governed by highly reactive monoterpenes and aromatics.

This work is co-funded by the European Union under Project 101138405—Net4Cities, the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (grant no. 10107404), and the Swiss Secretariat for Education, Research and Innovation (SERI) (grant no. 23.00622).

How to cite: Rana, A., Wegener, R., Adam, M. G., Dubus, R., Kesper, L., Klemp, D., Rohrer, F., Schmitz, S., Drossaart van Dusseldorp, S., Garcia, P., Jibuti, G., Kalivitis, N., Domingues, F., Oftedal Barrault, S., van der Gaag, E., Pikridas, M., Baalbaki, R., Van Poppel, M., and von Schneidemesser, E.: Characterizing urban traffic emissions across Europe: Insights from canister sampling in Net4Cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9231, https://doi.org/10.5194/egusphere-egu26-9231, 2026.

EGU26-9231

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Motor vehicles remain a significant contributor to urban air pollution, emitting compounds in both the gas and particulate phases. While emissions under hot driving conditions have decreased due to improvements in exhaust after-treatment systems, cold starts continue to contribute disproportionately to total vehicle emissions. Furthermore, gas-phase organic compounds emitted during cold starts can be oxidized in the atmosphere, leading to the formation of secondary organic aerosol (SOA).
To enhance our understanding of cold-start emissions and their potential to form SOA, measurements of approximately 21,000 cold starts were carried out inside an underground parking garage from November 29 to December 24, 2024. The garage consists of 250 parking spaces and is located beneath a shopping mall in Patras, Greece. 
The particle phase was measured by a scanning mobility particle sizer (SMPS) and further characterized using a high-resolution aerosol mass spectrometer (HR-ToF-AMS) and a high-resolution proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) coupled to a CHARON inlet. Black carbon was quantified by an Aethalometer (AE33). Gas-phase composition was measured with the PTR-MS, while inorganic trace gases including NO, NO₂, CO, CO₂, O₃, and SO₂ were continuously monitored. Tenax tubes were also collected from the garage ambient air for offline GC-MS analysis. Additionally, the traffic flow was recorded at both the entrance and exit of the garage. The SOA formation from vehicle emissions was investigated using an oxidation flow reactor (OFR) under controlled OH exposures by varying combinations of the reactor’s UV lamps.
The measurements of the traffic and the concentrations were combined to derive average emissions of gas and particulate phase pollutants. The estimated cold start emission factors were then compared to those reported in the Dutch Εmission Inventory. Within the OFR, organic aerosol concentrations increased from four to eleven times depending on OH exposure, with the production of highly oxygenated organic compounds. Aromatic compounds were identified as the dominant precursors of SOA.

How to cite: Kaltsonoudis, C., Pavlidis, D., Androulakis, S., Matrali, A., Vasilakopoulou, C., Apostolopoulos, I., Argyropoulou, G., Christopoulou, C., Seitanidi, K., Kuenen, J., el Malki, M., Chen, Y., Prevot, A., and Pandis, S.: Measurements of Real-World Cold Start Emissions and Secondary Organic Aerosol Formation in a Parking Garage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5501, https://doi.org/10.5194/egusphere-egu26-5501, 2026.

EGU26-5501

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The Cyprus Institute recently developed, tested, validated, and deployed a dual multi-sensor uncrewed aerial systems (UAS) platform for high-resolution (1-second), in-situ monitoring of the chemical composition of stack emissions. The first UAS consists of a custom-built multicopter equipped with a commercial optical particle counter POPS (Handix Scientific Inc.) and a micro-aethalometer (AethLabs). These instruments allow for three-dimensional measurements of particulate matter (PM2.5) and black carbon (BC), two key air quality parameters regulated across the European Union. The second UAS integrates on a custom-calibrated SO₂–CO₂ sensor suite to quantify the sulfur content of ship emissions (in compliance with the EU directive (EU) 2016/802).

Together, these UAS platforms enable precise and flexible four-dimensional profiling of aerosols and trace gases within dynamic pollution plumes, in complex and regulated environments. The platforms show great potential for monitoring air quality in urban areas, coastal shipping corridors, and industrial zones, providing high-resolution data on primary emissions and their rapid atmospheric processing within the dispersion of the plume. Results from these novel UAS systems from recent field campaigns, part of the Edu4ClimAte Horizon Europe program, are presented here. Additional results from future campaigns will be made available.

How to cite: Kezoudi, M., Papaconstantinou, R., Papetta, A., Marenco, F., Quehe, P.-Y., Konatzii, R., Sciare, J., and Team, U.: Advancing UAS-Based Monitoring of Stack Emission of Air Pollutants, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1104, https://doi.org/10.5194/egusphere-egu26-1104, 2026.

EGU26-1104

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Black Carbon (BC) is a key component of fine particulate matter that impacts air quality, climate, and public health. Understanding its sources is essential for effective mitigation strategies. This study analyses 5+ years of continuous BC observations in Berlin using Aethalometer AE33 measurements, alongside co-located Particulate Matter (PM₂.₅, PM₁₀), Nitrogen Oxides (NOx), Carbon Monoxide (CO) concentrations, and ultrafine particle (UFP) data. BC source contributions are assessed, with NOx and CO serving as traffic-combustion markers, while biomass burning contributions are examined through seasonalBC variability and its relative contribution is validated with levoglucosan, potassium K+ and/or Elemental Carbon/Organic Carbon (EC/OC) measurements. This study is part of the Net4Cities project, contributing to a broader understanding of urban air pollution dynamics and policy interventions, with a focus on transport sources. Aethalometer wavelength-dependent absorption analysis will be used to to apportion relative contribution of liquid fuel and solid fuel BC, with NOx and CO correlations used to evaluate liquid fuel BC estimates. Multiple assumptions and approaches for source apportionment will be tested to quantify uncertainties and the implications evaluated. Relationships between BC and UFP data are investigated for the final year of data to link BC emissions and particle number concentrations in an urban environment.

This work is co-funded by the European Union under Project: 101138405 — Net4Cities, the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (grant no. 10107404), and the Swiss Secretariat for Education, Research and Innovation (SERI) (grant no. 23.00622). 

How to cite: von Schneidemesser, E., Setia, H., Kanakidou, M., Pajunoja, A., Papoutsidaki, K., Pikridas, M., Savadkoohi, M., and Schmitz, S.: Black Carbon Trends and Source Apportionment in Berlin: A Multi-Year Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17372, https://doi.org/10.5194/egusphere-egu26-17372, 2026.

EGU26-17372

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Citizen science is increasingly recognised as a critical component of environmental monitoring, particularly in contexts where conventional observation systems lack spatial density, social reach, or local relevance. However, scaling citizen science and integrating its outputs into research and decision-making require robust infrastructures, inclusive engagement models, and tools that make complex data accessible to diverse audiences.

This contribution presents an integrated citizen science infrastructure approach that combines participatory sensing, data integration, and artificial intelligence to support healthy, sustainable, resilient and inclusive cities. Drawing on experiences from CitiObs, we demonstrate how AI-driven tools, including large language models (LLMs), are used to navigate and contextualise complex citizen science resources—such as toolkits and documentation—and to support the interpretation and communication of citizen-generated environmental data. Beyond AI, we highlight innovative, user-centred design approaches, including the structured use of hashtags to curate and connect documentation, which enhance discoverability, accessibility, and knowledge reuse across projects and communities.

We also show how artistic and creative approaches can support community-led action and more inclusive forms of environmental communication. In one CitiObs case, residents of a noise-affected neighbourhood in Barcelona deployed environmental monitors and, in collaboration with local creatives, co-designed Rut, an interactive AI chatbot that reflected community voices and experiences. Posters with QR codes placed in public space invited passers-by to engage with Rut via Telegram, where it answered noise-related questions and shared residents’ stories, helping translate monitoring data into relatable narratives.

CitiObs has worked with 35 European Citizen Observatories and, in its final year, is engaging with 50 Citizen Observatory Fellows worldwide. These cases illustrate how citizen science infrastructures, AI-supported tools, and participatory methodologies can be adapted for low- and middle-income countries (LMICs) and underserved urban communities. We emphasise that direct collaboration with communities not only strengthens social inclusion, but also plays a key role in validating methods, improving data quality, and ensuring policy relevance.

By linking technological innovation and creative practices with community-centred approaches, this work highlights pathways for embedding citizen science more effectively into urban environmental management and evidence-based policy.

How to cite: Castell, N., Crystal, T., Tekes, S., Guy, J., Calvo Juarez, M., and Gonzalez, O.: Translating Citizen Data into Urban Action: AI and Creative Approaches for Inclusive Environmental Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17358, https://doi.org/10.5194/egusphere-egu26-17358, 2026.

EGU26-17358

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Black carbon (BC) is a product of incomplete combustion, is climate relevant and has negative impacts on human health. It absorbs radiation as an aerosol in the atmosphere, but also changes the albedo of snow covered surfaces and leads to earlier melting. The origins are either anthropogenic or natural, with different annual cycles. While anthropogenic sources like domestic burning peak in the winter, natural sources like wild fires and agricultural burning peak during spring and summer. Due to its short lifetime are the global atmospheric concentrations highly variable.

We use ground based observational data from different global networks, and the atmospheric transport model FLEXPART driven by ERA5 meteorological analysis, emission inventories and the inversion framework FLEXINVERT. By minimizing the mismatch modelled and observed BC concentration we improve the emission inventories for natural emissions (GFAS) and anthropogenic emissions (LRTAP). For every year up to 50 stations are used and each observation is matched with a 50 day FLEXPART backward calculation.

We discuss the distribution and sources of global BC aerosols over the period 2015 to 2022 and compare existing emission inventories with the improved constraints of the global BC emissions derived with the FLEXINVERT inversion framework.

How to cite: Eckhardt, S., Thompson, R. L., and Evangeliou, N.: Global black carbon emissions from 2015-2022 constrained by observations and transport modelling , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12808, https://doi.org/10.5194/egusphere-egu26-12808, 2026.

EGU26-12808

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Central Asian urban centers face persistent air quality challenges characterized by elevated fine particulate matter (PM_2.5) concentrations stemming from rapid urbanization, intensive industrial activity, and a heavy reliance on coal-fired central heating plants. Despite the known hazardous nature of polycyclic aromatic hydrocarbons (PAHs), which are carcinogenic and mutagenic, a critical paucity of longitudinal observational data exists for this region [1-3]. This study presents the first systematic, year-long assessment of 14 PM_2.5-bound PAHs in Almaty and Astana, Kazakhstan, to characterize their environmental behavior and public health implications.

Results indicated that the annual average concentrations of total PAHs were 144.9±109.1 ng/m³ in Almaty and 25.6±19.9 ng/m³ in Astana, with the most severe pollution recorded during the heating season. Notably, annual benzo[a]pyrene (BaP) concentrations exceeded international guideline values by 5 to 20 times. Almaty’s higher pollution burden is attributed to its coal-intensive heating and mountainous basin topography, which facilitates frequent temperature inversions and atmospheric stagnation, thereby trapping pollutants. Conversely, Astana’s open steppe landscape promotes better ventilation, though it remains susceptible to episodic accumulation during Siberian high-pressure events.

A significant seasonal compositional shift was observed: while naphthalene was the dominant compound year-round, both cities exhibited a substantial increase in high-molecular-weight (4–6 rings) species during winter, driven by increased residential heating combustion. Source identification using diagnostic ratios and principal component analysis confirmed that coal and biomass combustion are the primary contributors to PM_2.5-bound PAH levels during the heating season. In the non-heating season, the relative influence of traffic-related emissions and liquid-fuel combustion increased, especially in Astana.

A stochastic human health risk assessment implemented via a Monte Carlo framework revealed alarming inhalation cancer risks. Under the WHO-recommended risk metrics, the probability of exceeding the 10^-4 inhalation cancer risk threshold was 100% in Almaty and 77.8% in Astana. BaP and dibenz[a,h]anthracene were identified as the most consequential contributors to this risk. These findings emphasize the urgent need for annual regulatory standards and a transition toward cleaner energy sources to mitigate the severe health risks associated with wintertime air pollution in Central Asia.

Acknowledgments

This research was funded by the Science Committee of the Ministry of Higher Education and Science of the Republic of Kazakhstan (Grant No. AP27510649, 2025-2027).

References

[1] Tursumbayeva et al. Cities of Central Asia: New hotspots of air pollution in the world. Atmospheric Environment. 309  (2023) 119901.

[2] A. Omarova et al. Emerging threats in Сentral Asia: Comparative characterization of organic and elemental carbon in ambient PM_2.5 in urban cities of Kazakhstan, Chemosphere. 370 (2025) 143968.

[3] Mukhtarov et al. An episode-based assessment for the adverse effects of air mass trajectories on PM_2.5 levels in Astana and Almaty, Kazakhstan, Urban Clim. 49 (2023) 101541.

How to cite: Omarova, A., Kashtanov, A., Sovetova, D., and Baimatova, N.: Spatiotemporal PM2.5-bound Polycyclic Aromatic Hydrocarbons Dynamics and Stochastic Cancer Risks in the Metropolitan Hubs of Central Asia  , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10277, https://doi.org/10.5194/egusphere-egu26-10277, 2026.

EGU26-10277

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Introduction and background

Roadside environments in opencast coal mining regions represent some of the most emission-intensive transport corridors in India, characterised by heavy-duty diesel operated vehicles and strong contributions from both exhaust and non-exhaust sources. Despite their importance for health risk and climate impacts, real-world characterisation of fine particulate matter (PM2.5) emissions and mitigation scenarios for mining-related transport activities remains limited.  

Methodology

PM_2.5 sampling campaigns were conducted at roadside locations along an active coal haul road and an urban road in Easter Maharashtra, India. Collected PM samples were analysed for elemental and organic carbon (EC & OC) through thermo-gravimetric analysis, total metals through ICP-MS, ions using ion chromatography, and PAHs through GC-MS.

Results and conclusions

High PM_2.5 (~500 ± 190 µg/m³) levels were observed near coal-haul road and city road. Carbonaceous species and ions dominated the PM mass, and elevated OC and EC and both roads, with coal road showing a strong EC signature (30 ± 15 µg/m³), reflecting combustion and vehicular sources. High molecular weight PAHs, particularly BaP, dominated the PAHs fraction, indicating a stronger influence from vehicular sources alongside pyrogenic sources. The ionic fraction was characterised by Cl^-(20-30%), SO₄^2-(5-10%), NH₄⁺ (10-25%), K⁺, and NO₃^- (~10%), indicating secondary formation and coal burning. Roadside PM2.5 showed elevated concentration of crustal and non-exhaust metals (Ca, Mg, Fe, Cr, Ni, Pb) and trace metals linked to mining-allied activities (Cu, Co, Se, As). APCS-MLR attributed PM2.5 around the mining region to non-exhaust, crustal dust, and coal combustion (52%), vehicular emissions and coal combustion (32%), and biomass burning and secondary inorganic aerosols (20%). Traffic characterisation revealed that super emitters (overloaded, poorly maintained diesel trucks with visible plumes) were significantly correlated with both fine and coarse PM levels (r>0.6, p<0.05). Building on the source-resolved PM_2.5 profiles, various mitigation scenarios were examined, including baseline, targeted reductions in vehicular exhaust, control of non-exhaust and resuspended dust sources under current and future mining growth conditions. The scenario analysis strongly indicated that transport-focussed interventions, particularly reduction in diesel exhaust and non-exhaust emissions from mining fleet, can yield substantial reductions in fine PM and its associated toxic component levels. Therefore, this study highlights the critical role of real-world chemical characterisation and source measurements in mitigation of transport related emission in complex industrial settings with mixed-sources. The findings underscore the need for targeted mitigation strategies such as regulation of super emitters, fleet upgradation, and control of non-exhaust emissions to improve air quality in diesel transportation dominated mining corridors.  

How to cite: Gupta, K., Chang, V., Yellishetty, M., and Phuleria, H. C.: From sources to solutions: Real world characterization of PM2.5 emissions in mining transport corridors and future mitigation scenarios , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18894, https://doi.org/10.5194/egusphere-egu26-18894, 2026.

EGU26-18894

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The atmospheric pollutants forecast is mainly limited by high uncertainties in emissions, meteorology and chemo-physical processes. Although static monitoring networks have expanded significantly in recent years, large-scale  comprehensive mobile observation campaigns remain scare, particularly in high emission region. To address this need,  we executed a on-road mobile campaign covering over 5000 km accross the North China Plain in June 2025  utilizing a zero-emission comprehensive mobile platform equipped with numbers of high-precision instruments (SPAMS, SP2, SMPS, VOCUS-PTR, PM_2.5/O₃/NO_x analyzer etc.). The campaign is guided by atmospheric pollution sensitivity analysis. The observations revealed complex evolutionary characteristics and high spatiotemporal heterogeneity of pollutants within identified sensitive hotspots, and the data were assimilated into the Nested Air Quality Prediction Modeling System (NAQPMS) using a three-dimensional variational (3D-Var) assimilation system.  Compared to the control run, the model performance for atmospheric chemical components along the mobile path improved, validating the effectiveness of the "sensitivity identification–mobile observation–data assimilation" closed-loop system.

How to cite: Pan, X., Yao, W., Liu, H., Ye, J., and Wang, Z.: Unveiling pollution emission heterogeneity in atmospheric sensitivity hotspots: insights from a ~6000-km comprehensive road-based campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4389, https://doi.org/10.5194/egusphere-egu26-4389, 2026.

EGU26-4389

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Nitrogen oxides (NOx) play a key role in atmospheric chemistry by regulating ozone formation in conjunction with non-methane volatile organic compounds (NMVOC). In addition, nitrogen dioxide (NO₂) poses significant health risks at elevated concentrations. European air-quality legislation limits annual mean NO₂ to 40 µg m⁻³, while recent World Health Organization guidelines recommend a substantially lower annual mean of 10 µg m⁻³, highlighting the need for accurate urban emission estimates. Here we present nearly a decade of direct eddy-covariance flux measurements of NOx, NMVOC, and CO₂ in a European urban area exposed to persistently high NO₂ levels.

We show that NOx emissions from older policy-relevant projection models underestimated traffic-related emissions by up to a factor of two. Although updated inventories predict higher emissions, substantial scenario-dependent discrepancies remain when compared with direct flux observations. Long-term measurements reveal a sustained decline in traffic-related NOx and NMVOC emissions, with the strongest reductions observed in 2020 during COVID-19 mobility restrictions. However, the NOx/CO₂ traffic emission flux ratio remained largely unchanged during this period, indicating that short-term reductions in traffic activity did not alter fleet-average emission characteristics. The observed long-term decline is instead consistent with a progressive technological shift towards cleaner, lower-NOx emitting vehicle fleets.

Trends in NMVOC emissions are more complex: traffic-related NMVOC decline in parallel with NOx, while oxygenated VOCs exhibit both increasing and decreasing trends, reflecting changes in source composition. Together, these results demonstrate the value of long-term direct flux measurements for evaluating emission inventories and policy scenarios, and provide robust observational evidence of structural changes in European urban traffic emissions.

How to cite: Karl, T., Jud, W., Lamprecht, C., Stichaner, M., Peron, A., Graus, M., and Yuan, B.: Sustained reductions in European traffic air-pollution emissions revealed by long-term eddy-covariance fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2303, https://doi.org/10.5194/egusphere-egu26-2303, 2026.

EGU26-2303

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Civil aviation and airports have been shown to be important sources of both Ultrafine particles (UFPs)  and Volatile Organic Compounds (VOCs) in urban areas¹. UFPs are a major air quality concern because their small diameter (< 100 nm) allows them to reach the lungs’ alveolar regions causing adverse health effects. The aviation emission profile from the USA’s Environmental Protection Agency includes 15 hazardous VOCs, such as benzene and numerous carcinogenic Polycyclic Aromatic Hydrocarbons (PAHs)². To assess the impact of UFPs and VOCs emissions from aviation on nearby air quality, two intensive one-month measurement campaigns of gaseous and particulate matter were performed in November 2022 and August 2024, 1 km downwind of Zürich Airport. The results indicate that high UFP number concentrations up to 300 000 cm⁻³ originate solely from aircraft operations, as shown by the similar diurnal profiles between air traffic movements and UFPs concentrations in Fig. 1a. These emissions are either advected downwind of the airport or mixed downward during aircraft landing overpasses. Using Positive Matrix Factorisation (PMF) on the VOCUS Proton Transfer Reaction Mass Spectrometer (PTR-MS) data, a factor containing naphthalene species and several alkanes with m/z > 100 (Fig. 1 c) has been attributed to VOCs aviation-related emissions. This is further supported by the co-increase of its time series with UFPs temporal evolution (Fig 1.b). However, when the site is not downwind and under the influence of landing overpasses, only UFPs concentrations increased, rather than the VOCs aviation-related factor (Fig. 1a), highlighting landing overpasses as a major source of UFPs but not of VOCs. This contrast likely results from lower engine thrust during taxiing at the airport than during landing overpass, which produces more VOCs due to reduced combustion efficiency³. At this stage, we cannot exclude a contribution of VOC emissions from engine refuelling. Future work will investigate the formation and evolution of VOCs in aviation plumes and their potential role in UFPs formation and growth. The widespread presence of UFPs and the co- emission of VOCs poses health concerns for communities near airports that regulators should address.

Figure 1: 10-minutes averaged a) Diurnal cycle of air traffic at Zürich airport, UFPs number concentration n_totalPM , and VOCs aviation emissions when the measurement site was downwind of Zürich airport during the fall 2022 measurement campaign and b) 10-days time series of the same variables. C) Factor profile of the VOCs aviation emissions determined by a source apportionment on the VOCUS PTR-MS data.

This work was supported by the Swiss Federal Office of Civil Aviation (SFLV 2020-080). We acknowledge the support from ZHAW, EMPA, Frithjof Siegerist (SRTechnics), and the City of Kloten.

(1) Masiol, M.; Harrison, R. M. Aircraft Engine Exhaust Emissions and Other Airport-Related Contributions to Ambient Air Pollution: A Review. Atmos. Environ. 2014, 95, 409–455. https://doi.org/10.1016/j.atmosenv.2014.05.070.

(2) US EPA, O. Organic Gas Speciation Profile for Aircraft. https://www.epa.gov/regulations-emissions-vehicles-and-engines/organic-gas-speciation-profile-aircraft (accessed 2026-01-12).

(3) Anderson, B. E.; Chen, G.; Blake, D. R. Hydrocarbon Emissions from a Modern Commercial Airliner. Atmos. Environ. 2006, 40 (19), 3601–3612. https://doi.org/10.1016/j.atmosenv.2005.09.072.

How to cite: Tinorua, S., Bauer, M., Brem, B., Decker, Z., Slowik, J., Prévôt, A., Mishra, S., Götsch, M., Sintermann, J., and Gysel-Beer, M.: Influence of Airports on Nearby Air Quality Through Emissions of Ultrafine Particles and Volatile Organic Compounds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17614, https://doi.org/10.5194/egusphere-egu26-17614, 2026.

EGU26-17614

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Black carbon (BC) is a key indicator of combustion-related urban air pollution, strongly associated with road traffic, industrial emissions and residential heating, and is addressed within the European Ambient Air Quality Directive (EU) 2024/2881 as a parameter for air quality assessment. Reliable BC measurements at low ambient concentrations are therefore required for air quality studies and regulatory monitoring. The Aethalometer is currently the instrument of choice for monitoring ambient air quality and for scientific studies of BC. One issue with the Aethalometer is that it does not use a direct absorption measurement principle; it measures light attenuation and calculates black carbon (BC) absorption using several empirically determined factors. Photoacoustic spectroscopy (PAS) is a filter-free measurement principle that directly measures the absorption of BC. The PAS-based AVL Micro Soot Sensor (MSS) was originally developed for exhaust soot measurements in the automotive sector. In this study, the MSS was adapted for ambient air monitoring by modifying it to improve sensitivity and accuracy, resulting in the AVL Black Carbon Monitor. In the laboratory,  a first benchmark was performed to compare the sensitivity, stability and applicability for air quality measurements of different PAS-based BC instruments against the Aethalometer. The results demonstrate that the AVL Black Carbon Monitor enables time-resolved black carbon (BC) measurements in the nanogram-per-cubic-meter range, making it suitable for long-term urban use. Field measurements were carried out at multiple urban monitoring sites in Graz, Austria, representing locations with varying traffic exposure and residential surroundings. The observed BC time series show characteristic diurnal variability associated with traffic activity. Spatial differences between sites reflect varying local influences on emissions. The study illustrates the applicability of PAS-based BC monitoring for urban air quality assessment and transport-related emission characterization.

How to cite: Knoll, M., Reingruber, H., Arndt, M., Kotnik, P., Murg, J., and Bergmann, A.: Ambient black carbon monitoring in urban environments using photoacoustic spectroscopy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20627, https://doi.org/10.5194/egusphere-egu26-20627, 2026.

EGU26-20627

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Traffic emissions remain a critical source of air pollutants, and electric vehicles (EVs) related policies have been proposed recently to mitigate the adverse impacts on urban air quality. In this study, a scenario-driven Random Forest model is developed to conduct a policy-oriented assessment of EV impacts on air pollution mitigation in Guangdong Province, China. Results show that traffic-affected air pollution concentrations have a significant decreasing trend, especially for NO₂ and PM_2.5. Additionally, real-world measurements of station-based EV charging consumptions and the EV charging station distribution are involved to quantify the future changes in air pollution concentrations of PM₂.₅, NO₂, SO₂, and CO, responding to varying EV policy implementation intensities. It reveals that a further decline of air pollutant concentrations can be achieved with the increase of EV implementation intensity. Compared to the average values in 2023, mean further reductions of 0.46 µg/m³, 0.37 µg/m³, 0.048 µg/m³, and 0.0043 mg/m³ for PM_2.5, NO₂, SO₂ and CO are presented when there is a 30% increase in the number of EV charging stations and charging demands. This study conducts a fact-based analysis for evaluating the traffic-affected air pollution benefits from EVs adoption, which also provides a scientific basis for formulating the air pollution mitigation policies.

How to cite: Yu, X. and Wong, M. S.: Quantifying the air quality benefits of electric vehicles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15825, https://doi.org/10.5194/egusphere-egu26-15825, 2026.

EGU26-15825

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