This session brings together data-driven approaches for analysing atmospheric composition, combining recent advances in source apportionment methodologies with developments in atmospheric measurements. Contributions addressing receptor-oriented models, multivariate analysis techniques, and novel or improved approaches for source attribution of air pollutants and greenhouse gases are welcomed, alongside studies that focus on measurement quality, calibration, uncertainty quantification, and harmonisation across observational platforms and monitoring networks.
The session encourages case studies, methodological developments, and intercomparison exercises that include high-quality in situ, mobile, airborne, and emerging measurement platforms (e.g. low-cost sensors, UAVs, mobile laboratories), receptor modelling, satellite observations, and supporting model simulations. Of particular interest are studies that improve the consistency, interpretability, and comparability of observational datasets across temporal and spatial scales, new methodologies on source apportionment of air pollution, application of receptor models and studies that strengthen the link between atmospheric composition measurements, emissions attribution, and science–policy applications. By fostering dialogue between observational and modelling communities, the session aims to advance a more integrated understanding of atmospheric composition relevant to both air quality management and climate mitigation.
Orals: Thu, 7 May, 14:00–15:45 | Room 1.85/86
Reliable and traceable measurements of greenhouse gases (GHGs) and related tracers are essential for detecting trends, understanding sources and sinks, and supporting climate policy. The World Meteorological Organization’s (WMO) Global Atmosphere Watch (GAW) programme provides a Quality Management Framework to ensure that observations meet strict requirements for compatibility and traceability. As part of this initiative, Empa is operating the World Calibration Centre for Surface Ozone, Carbon Monoxide, Methane, Carbon Dioxide, and Nitrous Oxide (WCC-Empa) since 1996, providing independent verification of measurement quality across the GAW network.
A core activity of WCC-Empa is to conduct on-site system and performance audits at GAW stations. These audits include station visits, comparisons with travelling standards and extended parallel measurements, which are used to assess traceability to GAW reference scales. Over the past 30 years, WCC-Empa has performed over 120 audits worldwide, enhancing the quality and availability of data, particularly in regions with limited resources. Operator training and capacity building are also key components, ensuring sustainable improvements in measurement practices.
Recent audits demonstrate substantial progress in CH₄ and CO₂ measurements, driven by advancements in laser spectroscopy. The adoption of cavity-enhanced laser spectroscopy has led to a significant increase in compliance with WMO/GAW network compatibility goals. Over 80% of CH₄ and more than 50% of CO₂ comparisons now meet the respective targets (2 nmol mol⁻¹ for CH₄; 0.1 µmol mol⁻¹ for CO₂). This represents a roughly twofold improvement for CH₄ and a threefold improvement for CO₂ compared to previous gas chromatography (GC) and non-dispersive infrared (NDIR) techniques.
However, challenges remain for CO due to frequent amount fraction drifts in calibration standards, and for N₂O due to the limited availability of high-accuracy reference materials. These challenges result in uncertainties that exceed the network compatibility goals. Currently, only around 20% of CO audits and less than 10% of N₂O audits meet their respective goals (2 nmol mol⁻¹ for CO and 0.1 nmol mol⁻¹ for N₂O). Strengthening traceability chains for these gases is critical for harmonisation across networks.
This contribution will present traceability concepts within GAW, summarise WCC-Empa audit results and lessons learned, and outline ongoing efforts to improve the internal consistency and cross-network comparability of GHG and tracer measurements.
How to cite: Zellweger, C., Steinbacher, M., and Emmenegger, L.: Traceability and Internal Consistency of Greenhouse Gases and Related Tracers within the Global Atmosphere Watch Programme, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7688, https://doi.org/10.5194/egusphere-egu26-7688, 2026.
Methane (CH₄) is an important greenhouse gas with multiple natural and anthropogenic sources. Precise δ¹³CH₄ measurements are essential for distinguishing these sources, understanding biogeochemical cycles, and improving climate models. While in-situ CRDS (Cavity Ring-Down Spectroscopy) measurements may have limited absolute precision, well-calibrated continuous measurements of δ13CH4 provide high-temporal-resolution data that are essential for reliably attributing atmospheric CH4 sources.
Here, we present an instrumental characterization, determination of cross-sensitivities and an improved calibration strategy for high-precision δ13CH4 measurements in ambient air using a Picarro G2201-i CRDS analyzer. This approach combines the determination of internal correction parameters from regular measurements (every 5-6 hours) of a single calibration gas at atmospheric concentration with annual multi-point calibrations using reference gases at 10 ppm CH4 spanning an isotopic range of -60 to -37 ‰ in δ13CH4. This strategy corrects the non-linearity in δ13CH4 with changing CH4 mole fraction, which can reach up to 3 ‰ in δ13CH4 over the 2-10 ppm CH4 range.
Applying the Keeling-plot method to nightly CH₄ enhancements in Heidelberg, Germany, the new calibration leads to δ13CH4 source signatures for individual events differing up to 4.6 ‰ to the previous one-point δ-calibration. Using this new calibration scheme, the mean δ13CH4 source signature for 2021-2025 was (-52.3 ± 0.3) ‰, slightly more enriched compared to 2014-2020 ((-53.9 ± 0.3) ‰, presented by Hoheisel and Schmidt, 2024). Only a careful instrument characterization combined with an adapted calibration strategy can ensure the high precision required for δ¹³CH₄ data suitable for quantitative atmospheric studies.
How to cite: Feuerle, M., Wietzel, J. B., and Schmidt, M.: Optimizing a calibration strategy for precise δ13CH4 measurements of ambient air using a CRDS analyzer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11367, https://doi.org/10.5194/egusphere-egu26-11367, 2026.
The precise measurement of the nitrous oxide (N2O) isotope ratio in the atmosphere is required to understand global emission trends. Currently, however no internationally accepted reference materials for atmospheric amount fraction N2O exist with characterised isotope ratio including uncertainties. Therefore, there is an urgent need for the development of reference materials to fill this traceability gap and meet the requirements to underpin global atmospheric measurements. Reference materials are typically traceable to internationally recognised stable isotope ratio scales: AIR-N2 and VSMOW (Vienna Standard Mean Ocean Water) for δ15N and δ18O respectively. The linearly asymmetric structure of the N2O molecule introduces an additional challenge in the measurement of the position specific δ15Nα (central N) and δ15Nβ (terminal N). Reference materials are required to span the ranges of δ15Nα and δ15Nβ expected across samples from global atmospheric measurements in addition to bulk δ15N.
We will present progress towards the development of atmospheric amount fraction N2O in synthetic air reference materials with characterised isotope ratios suitable for calibration of optical isotope ratio spectrometers (OIRS). The reference materials span a wide range of δ values. The pure N2O used to prepare the N2O in synthetic air reference materials was prepared at Empa (Switzerland) and has traceability to the primary AIR-N2 and VSMOW scales. The N2O in synthetic air reference materials were certified for N2O amount fraction and isotope ratio using OIRS against traceable reference materials. We will present on certification of N2O isotope ratios based on two approaches: direct calibration of delta values; and calibration of isotopocule amount fractions. A comparison of the two approaches to certification was performed considering the sensitivities, uncertainty contributions, traceability and potential biases of each approach.
The sensitivities in the measured isotope ratios to commonly occurring synthetic air matrix impurities (e.g. trace N2O), and the sensitivity of delta value certification to N2O amount fractions have been assessed to provide a comprehensive uncertainty budget for the certification of N2O in synthetic air reference materials using each approach.
Reproducibility within 0.3 ‰ has been demonstrated across five measurements over a 6-month period using delta and isotopocule amount fraction approaches. Standard measurement uncertainties (k=1) within 1.4 ‰ for δ15Nα, δ15Nβ and 0.5 ‰ for δ15N, δ18O were achieved for certification based on a delta value approach. Standard measurement uncertainties (k=1) within 1.5 ‰ for δ15Nα, δ15Nβ, δ15Nand 0.7 ‰ for δ18O were achieved for certification based on an isotopocule amount fraction approach.
How to cite: Hillier, A., Wilson, F., Hill-Pearce, R., Gardiner, T., Nehrbass-Ahles, C., Fisher, R., Lowry, D., Mohn, J., and Brewer, P.: First preparation of isotopic nitrous oxide in synthetic air reference materials for underpinning measurements of δ15N-N2O, δ15N-N2OSP and δ18O-N2O, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10104, https://doi.org/10.5194/egusphere-egu26-10104, 2026.
Advances in spectroscopic techniques for measuring radiocarbon (14C) in carbon dioxide (CO2) allow near-real-time analyses of atmospheric CO2 and the characterization of the fossil fuel fraction of CO2 emissions on sub-hourly timescales. Calibration and drift correction of these measurements require high-purity CO2 with near-modern (atmospheric) radiocarbon and stable isotopic (δ13C) signatures. Most commercially available compressed CO2 gases are fossil fuel-derived (14C-dead), while biogenic CO2 remains a niche product with limited purity specifications. Both total gas purity and the presence of ppbv-level impurities of nitrous oxide (N2O) can adversely impact the spectroscopic Δ14C-CO2 measurements.
We present results on purity and isotopic characterization (δ13C, Δ14C) of different CO2 gas sources as part of ongoing work to develop CO2 standard gases with modern Δ14C and δ13C signatures. We tested CO2 from distinct biogenic sources, including brewery, ethanol production, and biogas production. We also characterize several high-purity fossil CO2 sources. Gases were tested for Δ14C-CO2 and N2O impurities by saturated-absorption cavity ring-down spectroscopy (SCAR) using a commercial instrument (ppqSense). These measurements were calibrated against spectra from CO2 released from a NIST oxalic acid standard (SRM 4990C). We also characterized Δ14C-CO2 by accelerator mass spectrometry, δ13C-CO2 using isotope ratio mass spectrometry, and the presence of trace impurities using different techniques. Preliminary results show the N2O impurities from three tested biogenic gas sources vary by a factor of 103, the 14C content varies by almost 30%, and the δ13C values vary by over 25 ‰. These results will be presented in the larger context of supporting semi-automated SCAR Δ14C-CO2 measurements in Dübendorf, Switzerland.
How to cite: Whitehill, A., Siegwolf, P., Hammer, S., Preunkert, S., Hill-Pearce, R., Channell, S., Lee, S., Eun, H., Hong, K., Emmenegger, L., Tuzson, B., and Mohn, J.: Comparison of carbon dioxide sources for use as spectroscopic radiocarbon (Δ14CO2) standards, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1700, https://doi.org/10.5194/egusphere-egu26-1700, 2026.
Due to its relatively short half-life, radon (²²²Rn) is considered a tracer of boundary layer mixing processes. This property is exploited in the Radon Tracer Method (RTM), a top-down technique for estimating local-to-regional emissions of trace gases. However, its applicability has been shown to be site-specific and to require experimental measurement of soil radon exhalation. Here, we apply the RTM to estimate regional methane emissions in the footprint of the ICOS (Integrated Carbon Observation System) Ispra 100m tall tower in northern Italy. We present methane and radon concentrations from the tall tower, alongside measurements of soil radon exhalation rate and soil water content conducted at two sites near the tower between 2023 and 2025. Methane fluxes are calculated using two types of radon flux as input to the RTM: 1) radon fluxes measured in situ, and 2) modeled radon fluxes obtained from the traceRadon project. We find that both approaches yield comparable methane flux estimates, supporting the use of modeled radon fluxes as an alternative to direct measurements. Furthermore, the resulting methane fluxes are in good agreement with anthropogenic emissions reported by the EDGAR inventory.
How to cite: Segato, D., Arriga, N., Mancini, S., Mainardi, A., Santarelli, S., Minchillo, G., and Manca, G.: Estimating regional methane emissions using the Radon Tracer Method – a case study from North Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14715, https://doi.org/10.5194/egusphere-egu26-14715, 2026.
Air-quality concentration standards inherently do not specify which emissions controls are necessary to achieve them. Such standards set up a planning challenge that is fundamentally underdetermined, since many distinct emissions pathways can achieve the standard. Forward scenario testing rarely reveals which control levers are truly required versus merely sufficient, and does not necessarily identify optimal approaches. Here, we present a planning-oriented receptor modeling framework that inverts the traditional source apportionment approach. Instead of attributing observed concentrations to sources, we apportion the emissions reductions required for attainment to specific locations, precursors, and sectors, conditional on receptor-based concentration constraints.
We couple a source–receptor sensitivity matrix (mapping emissions changes to downwind concentration responses at receptors) with a constrained Bayesian inverse problem that infers the minimal, spatially explicit emissions changes needed to meet a fine particulate matter (PM2.5) concentration target everywhere (or within a specified attainment definition). An emissions prior regularizes solutions toward a baseline inventory, while constraints enforce physical and policy realism (e.g., non-negativity, sectoral controllability, optional caps/targets by precursor or region). This yields a transparent “control apportionment” output dictating how much each source category must change and where, in order to satisfy receptor targets. In addition, the model estimates uncertainty-aware diagnostics of which receptors bind and which sources dominate the required controls.
In application across the contiguous United States, we show that strategies with comparable economy-wide reductions (~10%) can produce dramatically different attainment outcomes depending on spatial allocation, ranging from near-universal compliance to minimal improvements in population exposure. By systematically exploring the feasible solution space, we quantify a compliance penalty for misallocation: the additional emissions reductions required when controls are applied non-optimally. Together, the framework bridges receptor modeling and attainment planning by producing source-resolved, defensible control requirements and actionable diagnostics that help agencies benchmark, compare, and stress-test attainment strategies.
How to cite: Manchanda, C., Koolik, L. H., Ünal, A., Fung, I. Y., Marshall, J. D., Morello-Frosch, R., Turner, A. J., Harley, R. A., and Apte, J. S.: Planning-Oriented Receptor Modeling: Apportioning Emissions Reductions Required for PM2.5 Attainment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13426, https://doi.org/10.5194/egusphere-egu26-13426, 2026.
The aim of the work was to determine the seasonal variability of particulate matter (PM) sources at the summit of Mt. Sonnblick in the Austrian Alps (3106 m asl, 12°57’E, 47°03’N) based on the chemical analysis of the PM10 fraction. The Sonnblick Observatory is situated on the main alpine ridge and represents a high-altitude remote sampling site. The observatory is part of the Global Atmosphere Watch (GAW) and Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) monitoring networks. Results obtained at remote monitoring stations enable seeing long-range air mass influences and could be treated as a reference source for other studies. A total of 244 weekly PM10 samples were collected from 17 January 2019 to 28 December 2023.
Comprehensive chemical analyses were conducted to obtain concentrations of (i) carbohydrates and their derivatives and selected ions (by ion chromatography), (ii) organic and elemental carbon (by thermal-optical analysis) and (iii) selected elements (by inductively coupled plasma optical emission spectroscopy). In total, 36 components of PM in collected samples were measured. The obtained results were then used to identify the emission sources during the study period and to determine their seasonal variability and contributions. For this purpose, Positive Matrix Factorization (PMF) by U.S. Environmental Protection Agency was used. The analysis resolved four major source factors connected with: 1) Saharan dust events, 2) biomass burning, 3) anthropogenic-related emissions, and 4) sugars and sugar-related compounds. Each emission source was characterized with individual temporal pattern through the whole measurement period, e.g. the strong seasonal pattern was confirmed for sugars and sugar-related factor, with increased concentration during the vegetation season. Additionally, more than 15 Saharan dust events were identified and confirmed by numerical models and backward trajectory analyses. The overall results revealed that the Saharan dust events were confirmed as the dominant factor during the study period, with an approximately 40 % contribution to the PMF-related mass, while the average contribution of sugars and sugar-related compounds as well as that of the factor connected with anthropogenic-related emissions was around 25 %. The smallest contribution was found for the factor related to biomass burning ~10 %.
Acknowledgements
This work was financially supported by the Excellence Initiative – Research University program at the AGH University of Krakow (ID: 13958).
How to cite: Skiba, A., Kau, D., Riedelberger, T., Hochwartner, C., Vukicevic, A., Schauer, G., Scherllin-Pirscher, B., and Kasper-Giebl, A.: Chemical characterization and source apportionment of aerosols at a high-altitude site (3106 m asl) in the Austrian Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12583, https://doi.org/10.5194/egusphere-egu26-12583, 2026.
Ambient particulate matter (PM) in industrially influenced environments often contains a complex chemical composition, reflecting interactions among local emissions, regional transport, and natural background sources. When chemically-speciated datasets are available, receptor modeling provides a powerful framework for attributing observed concentrations of emissions—such as metals and metalloids—to their contributing sources, particularly in settings where industrial signatures are chemically distinct from urban PM and locally resuspended dust.
The Horne Smelter in Rouyn-Noranda, Quebec, Canada, is the only remaining copper smelter in the country and the largest processor of metals from electronic scrap in North America. In recent years, emissions from the facility have been subject to heightened scrutiny due to elevated concentrations of metals and metalloids measured at surrounding monitoring stations. Robust PM source attribution is therefore critical for interpreting long-term monitoring data and informing emission reduction strategies. In particular, there is a need to quantify the relative contributions of different smelter-related activities to elemental concentrations measured at locations throughout the town of Rouyn-Noranda, which directly borders the facility.
This study applies Positive Matrix Factorization (PMF) to multi-year datasets of chemically speciated PM10 (PM <10µm) and total suspended particles (TSP) samples from multiple monitoring stations in the vicinity of the Horne Smelter—alongside metrological data including wind speed and direction—to reveal the dominant sources of metals and metalloids to ambient air and their emission dynamics. PMF is a widely used receptor modeling technique that resolves diverse multi-species datasets into an optimized number of factors, or chemical sources. Analysis focused on trace metals and metalloids sources and concentrations, including arsenic, lead, and chromium. The PMF analysis resolved seven (7) consistent source factors, five of which are associated with distinct materials and processes related to smelter operations (e.g., Bath Smelting, Feedstock, E-waste, Primary Furnace off gas), while the remaining two factors are crustal & road dust and vehicle emission sources which may come from the town, distance sources, or the smelter. By leveraging long-term datasets, temporal patterns of source contributions are revealed, with road dust and fugitive ore-related sources decreasing during winter months when snow cover is prevalent, while smokestack-related smelting sources show no consistent seasonal patterns. These trends confirm the identification of the sources based on their chemical profiles.
Additionally, we analyze the differences in source profiles between PM10 and TSP datasets and stations with varying time resolution (hourly vs 1-3 day). This secondary analysis explores how representative multi-day average samples are in describing PM in comparison to high resolution measurements, especially for industrial emissions. These findings demonstrate the value of long-term, multi-site PMF analyses for improving source attribution of metal and metalloid-rich PM in industrial regions and provide insights relevant to emission reduction efforts.
How to cite: Norris, E. and Hayes, P.: Resolving Metal-Rich Industrial Fingerprints: Multi-Site PMF Insights from PM10 and TSP at a Canadian Copper Smelter, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7951, https://doi.org/10.5194/egusphere-egu26-7951, 2026.
Fine particular matter (PM) pollution still occurs frequently in China. Source apportionment of pollutants is a necessary prerequisite for proposing pollution prevention and control policies, and most studies have been conducted using receptor or air quality models. This work combined the composition of PM2.5 and top-down gas pollutant emission (calculated by a Mass-Conserving inversion estimate framework based on daily TROPOMI NO2 and CO columns and ground observation), and jointly analyzed the results using an Empirical Orthogonal Functions Principal Components Analysis (EOF) approach at 0.05°×0.05° over the Taiyuan Basin, an urban, economic, and industrial aera of Shanxi Province. This area presents an important study region wherein atmospheric pollution is relatively severe, with diverse pollutant sources and challenging topography. This method used is both flexible and demonstrates the details and correct days when the pollutant events happened. Various pollution sources have been detected, including dust and haze, emissions from industrial enterprises of different scales, and diverse combustion-related sources. In addition to standard source types, there are some more pronounced pollution events that can be found, such as during the Spring Festival of each year, there is a significant increase in CO/NOx implying increased residential combustion sources. The same signal is even more obvious in rural areas during 2020 when the COVID-19 induced lockdowns occurred. At the same time in 2021, we demonstrate both further reduction of pollution from large enterprises and poorer control of small, scattered sources and residential combustion sources, including a further increase in CO/NOx. The opposite result with a decrease in emission intensity and decrease CO/NOx ratio in 2022 is observed, inferring a close relationship with strict control during the Winter Olympics. Diagnosis of pollution events by NOx and CO Emissions calculated by Mass-Conserving Inversion method and compositions of PM2.5 was successfully attempted, and it is hoped that it can form a basis for other rapidly changing regions found in topographically challenging regions throughout the Global South.
How to cite: Li, X., Cohen, J., Qin, K., and Tiwari, P.: Quantifying Pollution Events using Gas and Aerosol Observations via Flexible Receptor Framework over Urban Shanxi, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11174, https://doi.org/10.5194/egusphere-egu26-11174, 2026.
Posters on site: Thu, 7 May, 16:15–18:00 | Hall X5
As a major methane hotspot, East Asia urgently requires high-resolution, reliable emission maps to support mitigation strategies. We applied and evaluated a regional inversion framework by coupling the CarbonTracker Data Assimilation Shell (CTDAS) with WRF-Chem. This framework was used to perform inversions across East Asia (13.3°N–49.3°N, 99.8°E–150.2°E) for 2022 based on ground-based atmospheric methane observations, using a 9 km nested grid focused on the Yangtze River Delta (YRD) in eastern China. Pseudo-observation tests show robust recovery of the prescribed “true” emissions across three well-constrained regions: the YRD, Korea, and northern Japan (≥37°N). Among eight tested parameters and input datasets, the number of ensemble member accounts for most of the uncertainty reduction. In the YRD sectoral inversion, major sources (fuel exploitation, waste, natural, and agricultural emissions) are effectively corrected, whereas minor contributors (<6%, livestock and others) remain weakly constrained. Based on the in-situ inversion, we find that prior methane emissions over East Asia are generally overestimated. In the YRD, posterior annual emissions are reduced by 8.4%–22.4% (0.13–0.35 Tg a⁻¹) relative to the prior across the four provinces. The prior appears to underestimate emissions in summer, whereas it overestimates emissions in the other seasons. The strongest seasonal adjustment occurs in winter, with reductions of 27.3%–39.6%. In other Asian regions, inversion output shows that EDGARv8 underestimates northern Japan by 8.3% and overestimates Korea by 8.6%. This study provides the first benchmark of the CTDAS–WRF-Chem system for East Asia. Future works will assimilate multiple observation types over a longer period to deliver more comprehensive reference for mitigation planning, especially in China.
How to cite: Lu, M., Chen, H., Houweling, S., and Hubacek, K.: A high-resolution regional CTDAS-WRF-Chem framework for constraining methane emissions over East Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20231, https://doi.org/10.5194/egusphere-egu26-20231, 2026.
Atmospheric carbon dioxide (CO2) is the most significant anthropogenic greenhouse gas, driving changes in Earth’s radiative balance and climate. The Indo-Gangetic Plain (IGP) is a global hotspot of CO2 emissions due to dense population, intensive energy use, and widespread industrial activity; however, continuous and high-precision CO2 observations across the region remain sparse. To address this gap, we conducted the first long-term in situ measurements of atmospheric CO2 at a suburban site in Sonipat, located upwind of the Delhi National Capital Region. Using a state-of-the-art laser-based cavity ring-down spectrometer, continuous CO2 observations were collected from February 2023 to February 2024, revealing an annual mean concentration of 422.3 ± 26.52 ppm. The seasonal cycle shows lower concentrations during the monsoon (404.9 ± 25.95 ppm) and elevated values in the post-monsoon season (438.8 ± 27.73 ppm), driven primarily by changes in boundary layer dynamics, regional emissions, and biospheric fluxes. Consistent diurnal patterns further highlight the influence of convective mixing, rush-hour traffic, and local industrial activity.
To quantify fossil fuel CO2 (CO2ff) contributions, we present the first radiocarbon (14C) measurements in CO2 from Sonipat for April 2024 to March 2025. These data provide direct constraints on CO2ff and enable separation of fossil and biospheric carbon components. By combining 14C-derived CO2ff with collocated high-frequency carbon monoxide (CO) observations, we derive a constant CO–CO2ff enhancement ratio (RCO). This ratio is then applied to reconstruct a continuous, high-resolution CO2ff time series, capturing the strong seasonality linked to local emissions and meteorology. Subtracting fossil fuel and background contributions allows the isolation of regional biospheric CO2 signals.
Together, these integrated measurements demonstrate the value of continuous CO2, CO, and 14C observations for improving carbon budget assessments over the IGP and highlight the critical role of multi-species atmospheric monitoring in constraining regional carbon fluxes.
Keywords: In-situ measurements, Indo-Gangetic Plain, Fossil fuel CO2, Radiocarbon
How to cite: Vazhathara, V. J., Kunchala, R. K., Philip, S., and Sharma, R.: Integrating radiocarbon measurements for CO2 source attribution at a suburban site upwind of the Delhi NCR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-748, https://doi.org/10.5194/egusphere-egu26-748, 2026.
Recent advances in laser spectroscopy have substantially improved the detection of atmospheric trace gases, accelerating progress in environmental monitoring. The number of commercially available analyzers has grown, offering measurements for an expanding range of species. Manufacturers are increasingly prioritizing reduced power consumption, compact design, and cost-effectiveness, while aiming to maintain high sensitivity and selectivity. This presentation highlights recent instruments developed for monitoring atmospheric nitrous oxide (N₂O), the third most important long-lived greenhouse gas and the largest single contributor to stratospheric ozone depletion. Despite its global relevance, N₂O remains insufficiently observed, in part due to the high cost of established measurement technologies. The emergence of more economical, energy-efficient, and compact instruments presents an opportunity to strengthen the global N₂O monitoring network.
We assessed eight commercial analyzers at Empa using four spectroscopic techniques: (i) mid-infrared tunable diode laser absorption spectroscopy (TDLAS) with Interband Cascade Lasers (ICLs) and Quantum Cascade Lasers (QCLs), (ii) optical-feedback cavity-enhanced absorption spectroscopy (OF‑CEAS), (iii) off-axis integrated cavity output spectroscopy (OA‑ICOS), and (iv) cavity ring-down spectroscopy (CRDS). The tests revealed significant performance differences across techniques and considerable variability among instruments, particularly for mid-IR TDLAS systems using ICLs. The latter showed pronounced performance drift over time and, thus, were found unsuitable for sustained monitoring.
Overall, OA‑ICOS and CRDS analyzers remain the most robust solutions, consistent with their widespread adoption in World Meteorological Organization's Global Atmosphere Watch (GAW) program and the European Integrated Carbon Observation System Research Infrastructure (ICOS‑RI). While lower-cost alternatives exist, they usually involve trade-offs in precision and accuracy. Instrument selection should therefore balance cost, size, and performance requirements for the intended application.
How to cite: Steinbacher, M., Zellweger, C., and Emmenegger, L.: Assessment of eight laser spectrometers for atmospheric nitrous oxide analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12044, https://doi.org/10.5194/egusphere-egu26-12044, 2026.
Accurate characterization of greenhouse gas (CH4, N2O) and atmospheric pollutant (NH3) plumes is essential for quantifying point-source emission rates and understanding regional carbon-nitrogen cycles. However, current plume analysis workflows face significant bottlenecks. At the algorithmic level, plume identification, background subtraction, and feature extraction rely heavily on subjective manual expertise, hindering standardized and high-throughput outputs. At the data fidelity level, conventional closed-path systems suffer from signal desynchronization caused by the sorption kinetics of polar molecules (e.g., NH3) within sampling line, creating uncertainty in multi-species correlation analysis and source apportionment and resulting real-time decision-making during field campaigns.
To overcome these limitations, this study proposes an automated multi-species plume analysis framework driven by Generative AI. The innovation lies in an end-to-end mapping architecture that autonomously transforms multi-dimensional raw observation sequences into structured scientific insights. It integrates advanced recognition algorithms for plume signal stripping and high-precision emission rate inversion by fusing synchronized 3D wind fields, geospatial coordinates, and solar radiation data. Analytical performance is further enabled by high-fidelity input data acquired from a self-developed open-path quantum cascade laser (OP-QCL) spectrometer, which delivers inherently synchronized 10 Hz multi-species signals with inherent physical synchronization. This work eliminates the need for complex pre-processing of sampling artifacts at the hardware level, thereby increasing the efficiency of high-level feature extraction.
Field validation demonstrates that this AI-driven workflow achieves a paradigm shift in processing efficiency, reducing data interpretation time from hours to minutes. In industrial and wastewater treatment scenarios, the system captured transient fluctuations of CH4 (up to 7539 ppb) and NH3 (background increments of ~37 ppb). Leveraging the high temporal coherence between species (r = 0.62, p < 0.01; lag,±1 s), the AI successfully extracted representative source fingerprints (CH4/NH3 ratio ≈10), with inversion robustness verified through controlled release experiments. Notably, the real-time feedback supports an adaptive sampling strategy, enabling dynamic path adjustments during mobile monitoring to ensure high-fidelity capture of stochastic emission events. This integrated, intelligent framework fills a critical gap in real-time plume capturing and provides a robust digital toolset for industrial emission regulation and the realization of carbon-neutral goals.
How to cite: Zhao, Y., Shen, W., Jiang, R., Lin, T.-J., and Wang, Y.: AI-Driven Toolset for High-Efficiency N2O/CH4/NH3 Open-path Gas Analyzer Plume Data Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16702, https://doi.org/10.5194/egusphere-egu26-16702, 2026.
There are still no commutable reference materials available at scale to support and harmonise the measurement of stable isotope ratios of greenhouse gases, and while calibration good practice guides exist, awareness and implementation remain limited. Together, these gaps are hampering progress toward global comparability of carbon (CO₂) and methane (CH₄) isotope data, amid growing expectations for accurate, traceable measurements that can stand as defensible evidence in regulatory and legal contexts. Under the initiative of the CCQM Task Group on Greenhouse Gas Isotope Ratio Metrology, the international community has taken coordinated steps to address these challenges and strengthen metrological support.
A first milestone was the Global Workshop on Harmonisation of Optical Isotope Ratio Analyser Calibration Practices, held in September 2025, which gathered over 100 experts from metrology institutes, atmospheric monitoring networks, and instrument manufacturers. The workshop addressed critical calibration and data harmonisation challenges and produced a list of user-driven recommendations for instrument manufacturers, encouraging implementation of new functionality to improve metrological traceability.
A second milestone was the launch of a global survey to map current CH₄ isotope measurement capabilities. Conducted under the CCQM GAWG/IRWG Task Group framework, the survey collected data from laboratories worldwide on their ability to measure and calibrate δ¹³C-CH₄ and δ²H-CH₄ in air and pure methane.
This presentation will share the outcomes of both activities. It will summarise the workshop recommendations for instrument manufacturers to enable transparent, traceable calibration workflows, and present the results of the global survey. These include examples such as strong consensus on the urgent need for traceable CH₄ reference materials at atmospheric amount fractions, and significant variation in calibration workflows, underscoring the need for harmonisation.
These findings provide the first global evidence base for prioritising development of isotopic reference materials and harmonised calibration guidelines. They also inform future work on establishing Calibration and Measurement Capabilities (CMCs) for CO2 and CH₄ isotopes at NMIs and DIs. By presenting these results, we aim to engage stakeholders in shaping the intended outcome of this international effort: building a robust metrological infrastructure to support accurate, comparable CO2 and CH₄ stable isotope measurements for science, policy, and legal accountability.
How to cite: Nehrbass-Ahles, C. and Srivastava, A.: Advancing Global Harmonisation of Carbon and Methane Stable Isotope Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19615, https://doi.org/10.5194/egusphere-egu26-19615, 2026.
Methane (CH4) is an attractive target for emissions reduction due to its short atmospheric lifetime and emission from distinct source sectors. The primary UK emissions are from the fossil fuel, agricultural and waste landfill sectors that have different δ13C(CH4) and δ2H(CH4) isotopic signatures. Top-down estimates of total emissions are already made using continuous measurements of CH4 amount fraction using a tall tower network and inversion modelling but there are limited observations for isotope ratio. Continuous measurements of the isotope ratio in atmospheric CH4 provide an additional observable to disaggregate the relative emissions by source sector.
NPL have developed Boreas, an automated cryogenic preconcentrator coupled to an optical isotope ratio spectrometer (OIRS). Boreas purifies CH4 from a ~5 L ambient air, removing air matrix gases and delivers a sample of CH4 in N2 at ~550 ppm to the spectrometer, improving measurement precision. The OIRS is calibrated using mixtures prepared gravimetrically from a single high-purity CH4 parent that has been characterised for δ13C and δ2H by mass spectrometry, and the measurements are referenced to a whole air working standard that is sampled in sequence with the air.
Boreas has been deployed to an atmospheric monitoring station and makes simultaneous measurements of δ13C(CH4) and δ2H(CH4) at hourly intervals, with a repeatability of 0.07‰ for δ13C(CH4) and 0.9‰ for δ2H(CH4). We will show results from four years of continuous measurements of δ13C(CH4) and δ2H(CH4) at a GAW regional station in the Southeast of England, and a new deployment in Scotland.
How to cite: Rennick, C., Safi, E., Yeo, C., Wilson, F., Kikaj, D., Chung, E., and Gardiner, T.: Continuous methane isotope ratio δ13C(CH4) δ2H(CH4) at UK tall tower sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20182, https://doi.org/10.5194/egusphere-egu26-20182, 2026.
Radon (222Rn) is an ideal tracer for studying atmospheric mixing and evaluating atmospheric transport models because its lifetime is comparable to the timescale of atmospheric ventilation. A persistent challenge for atmospheric transport models is accurately representing vertical mixing especially under stable boundary layer conditions. Errors in this representation directly propagate into biases in greenhouse gas flux estimates derived from inverse modelling. Here, we demonstrate the potential of consistent, harmonized atmospheric 222Rn observations to assess transport model performance and improve methane (CH4) emission estimates using a joint CH4-222Rn inversion framework.
To this end, we compiled and harmonized 222Rn activity concentration measurements – alongside concurrent CH4 observations – from multiple atmospheric sites across central Europe. Using the Stochastic Time-Inverted Lagrangian Transport (STILT) model and prior flux estimates, we calculated the differences between observed and modelled concentrations (the so-called model-data mismatches, MDMs) for both 222Rn and CH4. We found significant correlations between the MDMs of 222Rn and CH4, indicating shared errors in their simulations, which primarily originate from the transport model’s inadequate representation of vertical mixing. To exploit this information, we conducted a dual-tracer CH4-222Rn inversion using the CarboScope-Regional inversion framework. We present the latest CH4 flux estimates from this dual-tracer approach and compare them with results from a single-tracer CH4-only inversion without additional 222Rn information. Finally, we assess how biases and uncertainties in 222Rn observations and 222Rn flux maps propagate into the dual-tracer inversion and affect the derived CH4 flux estimates. Our findings highlight the critical need for harmonized, spatially and temporally extensive 222Rn data, as well as accurate 222Rn flux maps, to fully leverage the dual-tracer approach and improve the reliability of CH4 flux estimates.
How to cite: Maier, F., Rödenbeck, C., Karstens, U., Koch, F.-T., Gachkivskyi, M., Smerald, A., and Gerbig, C.: Using harmonized radon (222Rn) observations in a dual-tracer inversion to estimate CH4 emissions , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4933, https://doi.org/10.5194/egusphere-egu26-4933, 2026.
Accurate measurement of atmospheric greenhouse gases is essential for tracking emissions and assessing the effectiveness of mitigation strategies. Methane (CH4), a potent greenhouse gas with diverse sources, requires isotope analysis to determine its origin (e.g. biogenic, anthropogenic, thermogenic, etc.) in the atmosphere. Precise and consistent measurements of atmospheric δ13C-CH4 are critical for the source attribution and understanding global methane budget.
Cavity ring-down spectroscopy (CRDS) is a versatile technique for monitoring atmospheric δ13C in CH4, providing rapid, continuous measurements with high precision (better than 0.8 ‰). CRDS isotope ratio analyzers like the Picarro G2201 offer flexibility for both laboratory and field applications. However, δ13Cmeasurements can be affected by instrument biases, drift, concentration dependence and matrix gas effects. These challenges highlight the need for a careful and traceable calibration strategy to ensure accurate, reproducible, and comparable results.
There are two approaches to calibration laser spectroscopic isotope analyzers: (a) Isotope-ratio (δ-based) approach: Reference gases with assigned δ13C values, traceable to e.g. the Vienna Pee Dee Belemnite (VPDB) scale, are used to directly calibrate δ13C-CH4 (or CO2). Samples are measured between two reference gases in a bracketing sequence, which allows correction for short-term instrumental drift and assignment of δ values. This approach is most effective when sample and reference CH4 amount fractions (concentrations) are close. (b) Isotopologue approach: The amount fractions of individual isotopologue (¹²CH4 and ¹³CH4) are calibrated separately. δ13C-CH4 is then calculated from their ratio using the conventional delta notation relative to e.g. VPDB. This approach is well suited for measurements over a wider range of CH4 amount fractions.
In this work, we carefully evaluate the capabilities of both calibration approaches for δ13C measurement in CH4 (also for CO2), targeting field site application. We compare results from both approaches, assess their relative accuracy, precision and suitability for long term in situ atmospheric δ13C monitoring. Metrological data qualities, such as traceability of the results and Guide to the expression of uncertainty in measurement (GUM) complaint uncertainties evaluation, are addressed. The use of accurate and reliable reference gases traceable to the VPDB scale ensure that the isotope ratio measurements for CH4 (and CO2) are metrologically reliable to ensure comparability across instruments and laboratories.
References
- 19ENV05 STELLAR; D5: Good practice guide for specification and application of OIRS for atmospheric measurements, including sample handling protocol, optimised analytical procedures, traceability to the international standards and target uncertainties (0.05 ‰ for δ13C-CO2 and δ18O-CO2).
- Srivastava, A., ... & Nwaboh, J. (2025). Developing calibration and measurement capabilities for atmospheric CH4 stable isotope ratios at NMIs/DIs: metrology for global comparability. Metrologia, 62(3), 032001.
Acknowledgement
This work was carried out within project 24GRD03-MetHIR, which has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.
How to cite: Yadav, K., Abbasi, H., and Nwaboh, J.: Metrological calibration approaches for atmospheric measurement of δ13C-CH4 employing CRDS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20685, https://doi.org/10.5194/egusphere-egu26-20685, 2026.
Mitigating methane emissions is widely regarded as one of the most cost-effective strategies for combating climate change. Achieving this goal requires a complementary and flexible combination of source attribution and quantification technologies with various minimum detection limits (MDLs) and spatial-temporal resolutions. Ground-based mobile monitoring (MOMO) offers advantages such as high temporal resolution, lower MDLs, and great source attribution capability in mixed-source environments. However, the lack of guidance on appropriate methodological choices have limited its integration into the broader “space–air–ground” methane monitoring framework, particularly for sources with diverse emission characteristics. Here, we present a comprehensive evaluation of MOMO techniques, including their advantages, limitations, quantification uncertainties, and MDLs. Building on this assessment, we propose a “Plus MOMO” strategy to address monitoring needs ranging from regional-scale source identification to source-level localization, while enabling discrimination between fossil-fuel and biogenic methane emissions. To support this approach, we developed a MOMO data integration and visualization platform designed to facilitate multi-source data fusion and interpretation. The “Plus MOMO” strategy has been applied in several in situ case studies, including methane leaks from rural natural gas usage in Beijing, emissions from high- and low-gas coal mines, and abandoned coal mines. Based on these applications, we advocate the development of standardized MOMO protocols and a “MOMO Plus” multi-source data integration framework to improve the accuracy and robustness of methane emission attribution and quantification.
How to cite: Lu, X. and Gao, L.: Methane Emissions Attribution and Quantification Based on “Plus Mobile Monitoring” Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22280, https://doi.org/10.5194/egusphere-egu26-22280, 2026.
Accurate source characterization of particulate matter requires the simultaneous analysis of chemical composition and the ability to distinguish between continuous background emissions and transient episodic events. Traditional source apportionment methods, such as Positive Matrix Factorization (PMF), often smooth out short-term spikes when applied to long-term datasets, effectively "averaging" high-intensity episodes into stable source profiles. This limitation poses a significant challenge in complex Asian urban atmospheres, where specific pollution events can dominate short-term air quality deterioration yet remain obscured in annual averages.
This study utilizes high-time-resolution measurements of PM2.5 chemical composition collected during the 2024–2025 ASIA-AQ campaign in Southern Taiwan. We applied a dynamic source apportionment approach to resolve episodic sources that are typically difficult to identify using conventional long-term analysis.
Our analysis identifies distinct "firework-related emissions" factors that are strictly episodic. These factors were characterized by high loadings of Bismuth (Bi) and specific trace metal signatures. Results indicate that while these sources contribute minimally to the annual average PM2.5 mass, they are the dominant contributors (> 50%) during specific pollution episodes. Failing to isolate these episodic factors leads to the misattribution of pollution mass to other continuous sources, such as traffic or industrial emissions.
This study demonstrates that relying solely on long-term average source apportionment may underestimate the health risks associated with acute exposure events. The proposed event-driven analysis framework provides a more accurate scientific basis for targeted control strategies in highly polluted environments.
How to cite: Wu, J.-X., Hsiao, T.-C., Zhang, R., and Adachi, K.: Revealing Hidden Episodic Sources in Complex Urban Atmospheres: A Case Study of Firework Events during the ASIA-AQ Campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21547, https://doi.org/10.5194/egusphere-egu26-21547, 2026.
Pollutants released from open biomass burning (OBB) considerably impact air quality, human health, and ecosystems. Only a few studies have used geostationary satellites to monitor OBB emissions in China. Therefore, we construct the China Open Biomass Burning Emissions Inventory (COBBEI) from 2020 to 2023. This dataset included eight pollutants with a temporal resolution of 1 hour and a spatial resolution of 2 km. The COBBEI integrated multi-satellite data, including MODIS, NPP, and Fengyun-4A (FY-4A). The Fire Radiation Power (FRP) data were reconstructed to the FRP cycle, and we integrated the curves to obtain the hourly biomass burned. We also developed five filtering rules based on FRP, considering fire point frequency, radiation values, timing, and variation. These rules were applied to correct the land cover maps, and their validity was verified. The annual average emissions of CO2, CO, CH4, NOx, SO2, PM2.5, K, and LG were 46530, 2262, 132, 82, 25, 247, 11, and 12 Gg, respectively. The spatial distribution characteristics of all eight pollutants were generally consistent. Northeast China served as a major center of pollutant emissions. Different types of fires exhibited various spatial distributions. Emission peaks from cropland and grassland fires typically occurred between 11:00 and 13:00, with a smaller peak at midnight. The number of SFs significantly increased, indicating a rise in the extent and decentralization of OBB, particularly in Tibet, Qinghai, and Sichuan. By validating the method and comparing it with other databases, it was confirmed that COBBEI reduced uncertainty in the OBB emission inventory by providing more information on fire points and effectively screening out fires that were not from OBB. The dataset could offer essential data for air quality modeling, environmental policy development, and fire emergency response strategies.
How to cite: Shi, J. and Ji, Y.: China Open Biomass Burning Emissions Inventory (COBBEI) from 2020 to 2023: A Fusion Approach with Fengyun-4A and Polar Orbit Satellites Data Based on Filtering Rules, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1921, https://doi.org/10.5194/egusphere-egu26-1921, 2026.
The main causes and trends of air pollution in the Czech Republic
Radim Seibert1, Vladimíra Volná1, Daniel Hladký1
1Czech Hydrometeorological Institute, Air Quality Department, K Myslivně 3/2182, 708 00 Ostrava, Czech Republic
Air pollution in the Czech Republic shows significant regional differences resulting from the specific historical, natural, and socioeconomic conditions of individual areas. The main causes include historical industrial development, mining and related energy production, individual household heating, and major transport hubs, whose impact is amplified by the Czech Republic's central location within Europe. The spatial distribution of pollution is further influenced by varying orographic and meteorological conditions, which determine the degree of dispersion or accumulation of pollutants.
Detailed knowledge of the main causes of pollution is essential for the effective proposal and targeting of measures to improve air quality, both at regional and national level. Since 2018, the Czech Hydrometeorological Institute has been addressing this issue systematically, through projects and in cooperation with other specialised institutions (Seibert et al., 2025; Seibert et al., 2024; Volná et al., 2024).
Source apportionment carried out between 2018 and 2024 using receptor models as part of several research projects has highlighted a significant spatial gradient of pollution from individual domestic heating with solid fuels. While the overall share of domestic heating in the Czech Republic in PM concentrations decreases from north to south, the share of emissions from biomass heating in this direction is increasing. This is probably a consequence of the higher share of coal in the fuel mix in the vicinity of historic coal regions.
In terms of the temporal development of air pollution, the concentrations and mutual ratios of organic markers reveal a declining share of PM originating from biomass combustion in individual domestic heating, accompanied by an improvement in the quality of combustion processes. Although overall PM concentrations showed a decreasing trend, there was a notable increase in certain elements associated with coal combustion between 2018 and 2024. This could have been caused by the dramatic increase in energy prices after 2019 and the related transition to more affordable fuels.
References:
- Seibert, R., Pokorná, P., Vodička, P., Volná, V., Lhotka, R., Zíková, N., Ondráček, J., Windell, L.C., Schwarz, J., Ždímal, V. (2025). Multi-site vs. supersite aerosol source apportionment in a lignite basin area. Environmental Pollution 386, 127226, https://doi.org/10.1016/j.envpol.2025.127226.
- Seibert, R., Kotlík, B., Kazmarová, H., Dombek, V., Volná, V., Hladký, D., Krejčí, B. (2024). Regional and seasonal drivers of metals and PAHs concentrations in road dust and their health implications in the Czech Republic. Heliyon 10, e40725, https://doi.org/10.1016/j.heliyon.2024.e40725.
- Volná, V.; Seibert, R.; Hladký, D.; Krejčí, B.(2024). Identification of Causes of Air Pollution in a Specific Industrial Part of the Czech City of Ostrava in Central Europe. Atmosphere 2024, 15, 177. https://doi.org/10.3390/atmos15020177.
How to cite: Volná, V., Seibert, R., and Hladký, D.: The main causes and trends of air pollution in the Czech Republic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6770, https://doi.org/10.5194/egusphere-egu26-6770, 2026.
Accurate source apportionment of particulate pollution is essential for timely emission control strategies to improve air quality and mitigate its health impacts. Traditional receptor models face challenges including extensive data dependency, technical complexities, and computational burden. Here we develop a machine learning (ML) based source apportionment model that leverages diverse aerosol composition measurements across a wide range of spatiotemporal scales. Particularly in the near-real time fashion, the model enables swift PM2.5 source apportionment using high temporal resolution online measurements on certain sites, supporting agile policy responses. On the larger spatial and temporal scales, the model also accurately quantifies PM2.5 sources for two decades in the Pearl River Delta region in China and the state of California in U.S., exhibiting strong generalization capability. For the megacities, the ML model results reveal that Shenzhen, China, experienced a significant decline in PM2.5 over the past decade due to the successful control of anthropogenic sources, while Los Angeles, U.S., witnessed a flattened PM2.5 trend under the joint effects of the reduced coal combustion and the exacerbated climate-driven wildfire pollution. This study highlights the potential of ML in air pollution research and policy-making toward environmental sustainability.
How to cite: Peng, X. and Feng, N.: Machine learning-based source apportionment of particulate pollution aids real-time predictions and long-term emission regulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8639, https://doi.org/10.5194/egusphere-egu26-8639, 2026.
Shipping emissions are an important contributor to PM2.5 in coastal regions. Globally, the Bohai Sea is recognized as the most aerosol-polluted inland sea. Here, we quantify the influence of shipping-related PM2.5 from the Bohai Sea along the western coastal region using synchronous observations across coastal and inland areas. A coordinated monitoring network was established with three sites located approximately 9, 30, and 50 km from the coastline, providing continuous measurements of PM2.5 mass and chemical composition from April 2023 to February 2024. On average, PMF-resolved shipping emissions contributed 8.4%, 1.6%, and 1.2% of PM2.5 at sites located ~9, ~30, and ~50 km from the coast, respectively, indicating a clear decrease with increasing distance inland. Seasonally, shipping contributions at the coastal site were 5.9–13.0 times higher than those observed at ~50 km inland, with the strongest spatial gradients occurring in summer and the weakest in winter. These results provide direct observational evidence that shipping emissions can measurably influence urban PM2.5 concentrations up to ~50 km inland. Our findings underscore the importance of explicitly accounting for marine emission sources in coastal air quality management, particularly for semi-enclosed seas such as the Bohai Sea.
How to cite: Li, L.: Distance-dependent contributions of shipping emissions to PM2.5 along the western Bohai coast: insights from multiple site PMF modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12305, https://doi.org/10.5194/egusphere-egu26-12305, 2026.
Comprehensive untargeted analysis of atmospheric organic aerosol filter samples can provide detailed insight into the history of air masses and atmospheric conditions. Due to the large amount of data, automatic analysis must be used in order to interpret and connect individual datapoints across multiple measurements in a given dataset.
This poster presents results of untargeted atmospheric organic aerosol analysis with LC/HRMS. The samples were acquired during the ACROSS campaign in summer of 2022 in the Rambouillet forest in central France (south-west of Paris). Through automated analysis, several individual compounds and compound classes could be identified as tracers or markers for certain sources and events.
In order to identify anthropogenic marker compounds, the influence of wind direction has been investigated in this work. Samples were divided into two different subsets. Samples where the primary wind direction was coming over central Paris and the Seine river were in subset A (higher anthropogenic influence estimated), and samples where the primary wind direction was coming over rural France were in subset B (lower anthropogenic influence estimated). By applying K-Means clustering, a total of 30 individual compounds were found to be indicative of samples from subset A. As expected, the compounds were mainly nitrophenols and a whole compound class containing one sulphur atom (CnH2nSO5). Those compounds have also been found in earlier studies in anthropogenically influenced aerosol samples (Wang et al. 2021). Here, no information about the structure could be provided, so in this study, an inhouse developed algorithm was used to identify fragments of the individual compounds from FullMS/AIF measurements to yield structural information. That information indicated that the compounds do not possess a sulphate or sulfonate group but are rather a thiol. The identified compounds can be used as tracers for anthropogenic influences in future studies.
The influence of the diurnal cycle on the concentration of individual compounds was also studied, and by applying the same methods, compounds specific for night and day could be identified. Note that other factors (e.g. the diurnal cycle) can just as easily be investigated, completely depending on the desired information.
The presented analysis methods hugely benefit from the availability of large, continuous and high-quality datasets with accurate and detailed metadata. This ensures that smaller contributing factors can be identified with statistical significance. Although the provided dataset that was acquired during the ACROSS campaign is comparatively large, datasets acquired at different locations, during different seasons and with an increased time resolution might be beneficial for identifying more contributing factors with a higher statistical significance. We therefore aim to set up an easy-to-operate and low maintenance aerosol measurement station. This station will also be used to allow students to get hands on experience in atmospheric aerosol analysis and analytical techniques in general.
This poster presents the results and findings of the analysis of the ACROSS samples regarding aerosol marker classes for specific sources and atmospheric events. We thank Vincent Michoud and Chris Cantrellas co-organizers of the ACROSS 2022 campaign.
How to cite: Karbach, N., Pouyes-Artiguenave, P., Perraudin, E., Villenave, E., and Hoffmann, T.: Identification of anthropogenic marker compounds through LC/HRMS analysis of atmospheric organic aerosol from the ACROSS dataset: Development and method validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16659, https://doi.org/10.5194/egusphere-egu26-16659, 2026.
Reliable source apportionment of ambient air pollutants is essential for effective air quality management. Positive Matrix Factorization (PMF) has been the most widely applied receptor-based method for ambient particulate matter (PM) (Hopke et al., 2020). Although EPA PMF v5 incorporates approaches to evaluate uncertainties in source profiles due to measurement error and rotational ambiguity (Paatero et al., 2014), it does not provide quantitative estimates of uncertainties in source contributions. Previous attempts to address this issue have been limited to sensitivity tests rather than rigorous uncertainty analyses. Here we introduce a novel approach to quantify uncertainties in source contributions (G matrix) within PMF solutions. By combining PMF-derived factor profiles (F matrix) with observed concentration data, we employ an Effective Variance Least Squares (EVLS) reverse-calculation framework to estimate the standard deviation of each source contribution, yielding a more rigorous and quantitative assessment of PMF uncertainties. A total of 837 valid daily filter-based speciation samples, collected from May 2013 to February 2019 in Tianjin, China (Dai et al., 2023), were used for PMF modeling and methodological testing. Compared with conventional PMF analysis, the PMF-EVLS approach yields both source contribution concentrations and their associated standard deviations. These uncertainties, expressed as standard deviations, reflect the combined influence of various error sources (e.g., model assumptions, measurement errors). The proposed PMF–EVLS method was demonstrated to effectively estimate the uncertainty of source-specific PM2.5 concentrations, thereby enhancing the reliability of source-specific health effect assessments and supporting air quality management decisions.
Refs:
1. Dai, Q., Chen, J., Wang, X., Tian, Y., et al. (2023). Environ. Pollut. 325:121344.
2. Hopke, P.K., Dai, Q., Li L., Feng Y. (2020). Sci. Total Environ. 740, 140091.
3. Paatero, P., Eberly, S., Brown, S.G., Norris, G.A. (2014). Atmos. Meas. Tech. 7, 781–797.
How to cite: Dai, Q., Chen, J., Zhang, X., Tian, Y., Feng, Y., and Hopke, P.: PMF-resolved source contribution uncertainty estimation via effective variance least squares, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10765, https://doi.org/10.5194/egusphere-egu26-10765, 2026.
Despite decades of EU air-quality policies and sector-specific emission controls, Northern Italy remains a European hotspot for PM pollution. With the forthcoming revision of the Ambient Air Quality Directive expected to tighten limit values, regions already close to (or exceeding) current thresholds will face an even greater compliance challenge. This study characterises the chemical composition and major contributing sources of PM2.5 at two sites in NE Italy: Belluno (Alpine valley) and Conegliano (Po Valley foothills).
A total 266 daily samples were collected during 2023. Comprehensive chemical speciation was performed, including elemental and organic carbon, major inorganic ions, major and trace elements, carboxylic acids, monosaccharides, alcohol-sugars, anhydrosugars, and benzothiazoles. These compounds serve as tracers of specific urban and regional emission sources, such as secondary aerosol formation, biomass burning, biogenic emissions, and traffic-related sources. In addition, a suite of organophosphate flame retardants (OFRs) was quantified.
PM2.5 mass closure was achieved, supporting the robustness of the chemical dataset. Source contributions were resolved through positive matrix factorization (PMF), complemented by post-processing analyses to better investigate local and regional influences. Results highlight differences between the two sites, reflecting their distinct geographical and meteorological settings. At Belluno, residential biomass burning emerges as a dominant PM2.5 source during the cold season, with pronounced wintertime increases associated with persistent thermal inversions and limited atmospheric dispersion. In Conegliano, PM2.5 shows a strong contribution from secondary aerosol formation and regional transport consistent with Po Valley influence. Traffic, biogenic aerosol, and resuspended dust contribute to a lesser yet non-negligible extent at both locations. An OFRs-related factor grouping most flame retardants was identified but having negligible influence on PM2.5 mass. Overall, the study provides insight into the role of local versus regional sources and meteorological controls on PM2.5 pollution in different sites across NE Italy, offering valuable information to support targeted mitigation strategies in both mountain and lowland environments.
Funding: European Union - NextGenerationEU, in the framework of the iNEST - Interconnected Nord-Est Innovation Ecosystem (iNEST ECS_00000043 – CUP H43C22000540006).
How to cite: Masiol, M., Salvini, M., D’Amico, M., Visin, F., Gambaro, A., Feltracco, M., Favaro, E., Mazzi, G., Radaelli, M., Formenton, G., Silvestri, P., Crivellaro, G., Paloschi, A., Nadal, T., and Hopke, P. K.: Extended chemical speciation and source apportionment of PM2.5 at two sites in NE Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13510, https://doi.org/10.5194/egusphere-egu26-13510, 2026.
