Shortened description of MAX-DOAS, spectral imaging and other techniques:
This session aims to present research activities and instrument developments in the field of atmospheric remote sensing, particularly emphasising Multi-AXis (MAX-) DOAS and (hyper-) spectral imaging techniques which use scattered sunlight as a light source. Contributions from other passive and active DOAS applications are also welcome.
MAX-DOAS and spectral trace gas imaging techniques provide an essential link between in-situ measurements of trace gas concentrations or reported point source emissions and column-integrated measurements from satellites. They play a key role in satellite validation and are found to be a valuable addition to global measurement networks.
Shortened description of the use of uncrewed aircraft systems (UAS):
Uncrewed Aircraft Systems (UAS) are revolutionizing atmospheric and climate sciences by significantly expanding observational capabilities. Driven by rapid platform development and sensor miniaturization, these systems now provide critical datasets linking hydrology and ecology with applied fields like wind energy and pollutant transport.
This session invites abstracts on scientific contributions utilizing fixed-wing UAS, multicopters, and tethered balloon systems. We welcome presentations on novel instrumentation, recent measurement efforts, and the use of UAS datasets to improve numerical modeling, data assimilation, and weather prediction. Contributions highlighting recent field campaigns are especially encouraged to foster a deeper understanding of atmospheric processes.
Orals: Thu, 7 May, 10:45–12:30 | Room 1.85/86
Between 21 May and 24 June 2024, the third Cabauw Intercomparison of UV-Vis DOAS Instruments (CINDI-3) took place. In addition to conventional PANDORA and MAX-DOAS instruments, three imaging instruments participated: the IMPACT instrument from the University of Bremen, and SEMPAS and the NO2 camera from the Royal Belgian Institute for Space Aeronomy. While the first two instruments sweep a linear array of fibers to construct a hypercube of the scene, the NO2 camera is a native imager which builds a hypercube by scanning in the spectral domain. Still, these three instruments pursue the same objective of improving both the temporal and spatial resolution of NO2 measurements, enabling new applications such as monitoring ship emissions and urban pollution at a street-level scale.
From 14 June until the conclusion of the CINDI-3 campaign, the three imagers were operated synchronously, thereby capturing both the temporal and spatial dynamics of the NO2 field in the direction of Rotterdam. This coordinated operation allows for a comparison between the differential slant column densities of the three imagers. On 17 June 2024, during the afternoon, an NO2 plume originating from the Port of Rotterdam was observed by all three imagers. The measurements acquired both prior to and during the plume event show good correlation among the instruments.
Throughout the CINDI-3 campaign, daily NO2 forecasts were provided based on the E-PRTR emission database using the FLEXPART-WRF dispersion model based on dynamically-downscaled GFS forecast data. To provide further insight for the plume event on 17 June, an ensemble of simulations using different planetary boundary layer schemes was carried out based on downscaled GFS and ERA5 meteorological data. The simulated plume location and the horizontal SCDs show good agreement with the observational results from the imaging instruments.
This contribution will highlight how a fortuitous event (the blowing of industrial pollution in the direction of the CINDI-3 campaign site) became an excellent test case for intercomparing non-conventional DOAS instruments, and how a plume dispersion model could both confirm the hypothesis of the distant plume origin and be validated by remote sensing instruments.
How to cite: Busschots, C., Gramme, P., Dekemper, E., Mettepenningen, G., Van Roozendael, M., Haveresch, H., Richter, A., Schönhardt, A., Mainika, A., Bittner, S., Poulidis, A.-P., and Vrekoussis, M.: Observation of a Rotterdam plume event during CINDI-3, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11607, https://doi.org/10.5194/egusphere-egu26-11607, 2026.
Nitrogen oxides (NOx= NO + NO2) are air pollutants of relevance for human health and atmospheric chemistry. As part of the DiNO campaign (DOAS measurements to investigate NO-to-NO2 conversion in power plants), NO2 was measured in the exhaust plumes of the Niederaußem and Neurath lignite power plants in the western part of Germany. Motivated by the rapid chemical conversion of emitted NO to NO2 and the subsequent evolution towards an NO-NO2 equilibrium as the plume ages, three stationary MAX-DOAS instruments (Multi-AXis Differential Optical Absorption Spectroscopy) scanned the plumes up to a distance of 3.8 km from the source. A mobile DOAS instrument complemented the stationary observations and provided real-time mapping of horizontal NO2 distributions. This combination captured temporal and spatial variabilities in the NO2 concentrations under changing wind conditions.
These measurements also enable the investigation of plume dispersion under different atmospheric stability conditions. In particular, the influence of atmospheric stability on plume spread and the entrainment of ozone-rich surrounding air is examined, as these processes impact the rate of NO-to-NO2 conversion. Furthermore, the potential effect of spectral saturation of the NO2 absorption in DOAS retrievals during the early plume development was investigated.
How to cite: Reischmann, L., Ziegler, S., Beirle, S., Donner, S., Lauster, B., Radloff, N., and Wagner, T.: Investigating NO-to-NO2 conversion in power plant plumes using combined stationary and mobile DOAS measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11140, https://doi.org/10.5194/egusphere-egu26-11140, 2026.
Nitrous acid (HONO) is a key precursor of hydroxyl radicals (OH·), exerting an important influence on regional atmospheric oxidation capacity and the formation of ozone (O₃) and secondary aerosols. However, its multiple sources and complex formation pathways lead to substantial uncertainties in source apportionment and process constraints. In agricultural regions in particular, the contributions of soil microbial emissions and heterogeneous conversion on soil/aerosol surfaces remain poorly constrained by long-term observations, resulting in systematic underestimation in models.
To this end, leveraging our self-developed 2D MAX-DOAS remote-sensing observation network spanning typical regions across China, we conducted a two-year continuous campaign (2022–2023) at an agricultural site in Shouxian County, Anhui Province (32.44 °N,116.79 °E). Vertical profiles of HONO, NO₂, and aerosols were retrieved with the PriAM algorithm. Data consistency and instrumental stability were evaluated via dual-instrument intercomparison, enabling an investigation of HONO spatiotemporal variability, formation mechanisms, and estimated emission fluxes in agricultural environments.
The two systems showed excellent agreement for HONO, NO₂, and aerosols, with R² up to 0.90, demonstrating robust long-term stability. HONO exhibited pronounced near-surface accumulation, being mainly confined below 0.5 km and decreasing exponentially with altitude. Diurnal variations displayed a clear morning–evening bimodal pattern in spring, autumn, and winter, typically peaking at 09:00 and 16:00 Beijing time (BJT). In summer, this bimodality weakened due to enhanced photolysis and dilution associated with a deeper boundary layer, leading to a much smaller diurnal amplitude.
Seasonally, HONO emission fluxes showed a pronounced winter maximum and summer minimum. Winter accumulation was promoted by low temperature, high humidity, a shallow boundary layer, and sustained NO₂ supply. Autumn was mainly influenced by residual nitrogen inputs during harvest and straw burning, whereas spring enhancements were closely linked to increased soil emissions following fertilization during wheat regreening. In summer, stronger photolysis and more efficient vertical mixing inhibited accumulation. High-HONO events predominantly occurred under RH>70 % and T<10 °C, indicating that moist reactive interfaces and stable stratification jointly favor HONO formation and accumulation. Within ±2 weeks of fertilization, near-surface HONO, NO₂, and aerosol concentrations increased synchronously, with maximum enhancements of 1500 %, 200 %, and 700 %, respectively. The HONO/NO₂ ratio increased markedly after fertilization and decreased with altitude, suggesting direct HONO release from reactive nitrogen in soils via microbial processes, with additional contributions from heterogeneous NO₂ reactions on soil and aerosol surfaces. Potential Source Contribution Function (PSCF) analysis further indicated that elevated HONO during the spring fertilization period was dominated by local sources, with limited influence from long-range transport.
This study provides key vertically resolved observational evidence to quantitatively constrain the magnitude and spatiotemporal evolution of agriculturally driven HONO sources, thereby supporting improved HONO parameterization in regional chemical models and assessments of its impact on atmospheric oxidation capacity.
How to cite: Hu, W., Li, A., and Hu, Z.: Spatiotemporal distribution and formation mechanisms of HONO in agricultural areas based on long-term 2D MAX-DOAS observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4353, https://doi.org/10.5194/egusphere-egu26-4353, 2026.
Nitrogen dioxide (NO₂), ozone (O₃), chlorine dioxide (OClO), and bromine monoxide (BrO) are key constituents in stratospheric ozone chemistry and have been routinely observed for several decades using Differential Optical Absorption Spectroscopy (DOAS). To convert DOAS differential slant column densities (dSCDs) into geometry-independent vertical column densities (VCDs), accurate Air Mass Factors (AMFs) are required.
In this study, stratospheric AMFs for these four trace gases were calculated with the fully spherical radiative transfer model MYSTIC [Mayer, 2009] for the Antarctic stations Belgrano (78°S) and Marambio (64°S). Twilight photochemical effects were taken into account through a photochemical box model coupled to the TOMCAT/SLIMCAT three-dimensional model [Chipperfield et al., 2006]. Vertical concentration profiles generated by this model, were averaged along the corresponding light paths using a ray-tracing approach and subsequently implemented in the spherical radiative transfer calculations.
For validation, the derived AMFs and SCDs were evaluated against results from a pseudo-spherical radiative transfer model and against observed slant column densities of NO₂, O₃, OClO, and BrO measured by INTA MAX-DOAS instruments at both Antarctic sites. The modelled SCDs successfully reproduce the measurements within the error bars for NO₂, O₃ and OClO. In the case of BrO, its tropospheric contribution, not considered in the photochemical model, has to be taken into account to find a good agreement.
References
Mayer, B.: Radiative transfer code in the cloudy atmosphere, European Phys. J. Conferences, 1, 75–99, doi: 10.1140/epjconf/e2009-00912-1, 2009.
Chipperfield, M. P.: New version of the TOMCAT/SLIMCAT offline chemical transport model: intercomparison of stratospheric tracer experiments, Q. J. Roy. Meteor. Soc., 132, 1179–1203, doi:10.1256/qj.05.51, 2006.
How to cite: Gómez Martín, L., Prados Roman, C., Chipperfield, M. P., van Roozendael, M., Puentedura, O., Navarro-Comas, M., Ochoa, H., and Yela, M.: UV/Vis Stratospheric Air Mass Factors considering photochemistry at Marambio and Belgrano Antarctic stations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11135, https://doi.org/10.5194/egusphere-egu26-11135, 2026.
Tropospheric and stratospheric ozone variability plays a critical role in controlling the polar radiation budget. Although the factors influencing stratospheric ozone variation are quite well researched, tropospheric and near-surface ozone are subject to both dynamical and photochemical constraints that are still poorly understood. Measurements from a Thermo Scientific TEI-49i in-situ ozone analyzer at the Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Nunavut, Canada (80.05°N, 86.42°W) show that surface ozone in the Arctic is characterized by two major annual depletion events: (1) a primary depletion which occurs during the springtime following polar sunrise, and has been attributed to enhancements of BrO (bromine explosion events), as well as (2) a secondary depletion which occurs in the late summer and early fall period. The PEARL MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) ultraviolet (UV)-visible Ground-Based Spectrometer (PEARL-GBS) results show tropospheric BrO enhancements correlated with periods of reduced ozone concentrations. Additionally, we also use several UV-visible Pandora spectrometers affiliated with the Pandonia Global Network (PGN) to evaluate the feasibility of retrieving BrO and IO, which are not currently among the standard PGN products. We present here preliminary results of polar Pandora BrO and IO retrievals and investigate the potential link between summertime IO and late summer ozone depletions detected at Eureka. For this investigation, we use the MAX-DOAS measurements made by three Pandora instruments: Pandora #144 which was located at the PEARL Ridge Lab from 2019-2023, Pandora #280 located at the Eureka 0-Altitude PEARL Auxiliary Laboratory (0PAL) since 2024, and Pandora #152 located at the Norwegian Polar Institute Sverdrup in Ny-Ålesund, Svalbard (78.92°N, 11.93°E) since 2019. We retrieve BrO and IO from the Pandora spectra and find good agreement between Pandora and PEARL-GBS BrO dSCDs (Differential Slant Column Densities). Although measured enhancements of BrO from the Eureka instruments coincide with periods of springtime reduced surface ozone, this is not as evident for the IO results in the context of the summer ozone depletions. We demonstrate the capability of using the Pandora instruments to retrieve these halogen species with MAX-DOAS measurements. These Pandora retrieval methods for BrO and IO will allow for further study of the role of these trace gases in the polar tropospheric ozone cycle.
How to cite: Joshy, K., Bates, D., Alwarda, R., Fjæraa, A. M., Griffin, D., Tiefengraber, M., Effertz, P., Fogal, P., Zhao, X., and Strong, K.: Investigating Polar Tropospheric Ozone with Novel BrO and IO Retrievals from Pandoras, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15864, https://doi.org/10.5194/egusphere-egu26-15864, 2026.
Two important challenges with UAS-based measurements of greenhouse gases (GHGs) are large baseline drifts of gas sensors and source identification when there are several sources distributed within a footprint. Ambient conditions, such as temperature and humidity, known to influence the accuracy of gas sensors, change fast during flight at different altitudes and speeds. Big such drifts limit UAS-based measurements to high and often anthropogenic emissions as they cause strong gradients in concentration levels. Emissions across landscapes often generate much weaker concentration gradients and are also more extended, making fluxes and source areas more challenging to constrain.
We present a newly developed approach to reduce this instrument drift significantly, enabling flux measurements in natural environments. We have also developed the approach further to allow the matching of gas structures on vertical wall flight paths to sources and sinks in the footprint using an independent tracer of air movements across the scene. The new method produces drift-corrected simultaneous measurements of multiple GHGs (CO2, CH4, N2O) and wind data. We will present results using different flight strategies (e.g. single wall, two-wall, and rectangular walls) in both anthropogenic and natural environments.
How to cite: Gålfalk, M. and Bastviken, D.: A drone-based approach for measurements of multiple greenhouse gases with minimized gas sensor drift for increased sensitivity and improved source area identification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15107, https://doi.org/10.5194/egusphere-egu26-15107, 2026.
Accurate quantification of carbon dioxide (CO2) emissions in forest ecosystems remains challenging because of pronounced spatial heterogeneity, complex terrain, and the coexistence of vertical and horizontal transport processes in the lower atmosphere. Conventional approaches, including eddy covariance (EC) towers, satellite remote sensing, and manned aircraft measurements, are typically limited to two-dimensional or spatially constrained observations and therefore cannot fully resolve three-dimensional CO2 exchange. In this study, a UAV-based observational platform is developed and evaluated to quantify CO2 transport in both horizontal and vertical directions over forest ecosystems. The system integrates a high-precision closed-path CO2 analyzer with a calibrated ultrasonic anemometer and applies complementary box-pattern and profile-pattern flight strategies within a mass-balance framework. Field experiments were conducted at the Qianyanzhou Experimental Station in southern China between 2023 and 2024, and UAV-derived fluxes were compared with long-term EC tower measurements. Vertical CO2 fluxes derived from profile-pattern measurements using the gradient method show strong agreement with EC observations across all seasons, demonstrating the capability of the platform to capture canopy-scale turbulent exchange. Box-pattern measurements further enable direct estimation of horizontal CO2 transport and reveal pronounced diurnal contrasts, with lateral advection dominating during morning and evening periods and vertical uptake prevailing under well-mixed midday conditions. Sensitivity analyses using multiple box sizes indicate that area-normalized net CO2 emissions are robust with respect to control-volume dimensions. Overall, this study demonstrates that UAV-based measurements provide a reliable and flexible approach for resolving three-dimensional CO2 transport in forest ecosystems, offering a valuable complement to conventional flux-tower observations, particularly in heterogeneous and complex terrains.
How to cite: Li, S., Sun, H., Chen, K., and Zhang, K.: UAV-based method for measuring CO2 emissions in forest ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4644, https://doi.org/10.5194/egusphere-egu26-4644, 2026.
Forests are dynamic sources and sinks of primary biological aerosol particles (PBAPs) that influence ecosystem health, atmospheric chemistry, and climate. However, the spatial and temporal distribution of these particles in and above forest canopies remains poorly understood. In this study, we deployed two complementary UAV platforms to characterize bioaerosol concentrations, composition, and environmental drivers in a spruce forest in Wienerwald, Lower Austria.
Sampling campaigns in June and July 2024 utilized a suite of sensors on the UAVs, including portable optical particle counters (POPS, 0.16 – 3.37 µm), impingers, and PM-, VOC & meteorological sensors. These aerial measurements were referenced against a ground station including a Wideband Integrated Bioaerosol Sensor (WIBS-5/NEO, 0.5 – 30 µm) and a Grimm 11-D (0.25 – 35.15 µm) aerosol spectrometer, and the same set of PM-, VOC & meteorological sensors. Two additional sampling days were conducted in June 2025 with additional UAV-based aerosol- and VOC-sampling.
We observed higher overall particle number concentrations at near-canopy altitudes (<5 m above canopy) compared to higher altitudes, with concentrations negatively correlated with total-VOC trends. In addition, we saw elevated overall particle concentrations above the canopy during morning hours, followed by a midday decrease that coincided with rising temperatures and falling relative humidity. The fluorescence data from the ground-based WIBS indicated that a substantial fraction of supermicron (>2.5 μm) particles were biological. Their fluorescence signature and elevated concentration at high relative humidities suggest a large contribution of fungal spores, which was confirmed by microscopy of samples from ground-based and, in 2025, UAV-mounted cascade impactor sampling. PBAP concentrations generally increased with high relative humidity, consistent with well-documented humidity-driven enhancements in biological particle release.
This multi-platform UAV approach provides a robust framework for resolving forest-atmosphere exchange processes, yielding critical data to improve atmospheric models and our understanding of ecosystem-climate feedback loops.
How to cite: Wieland, F., Rupprecht, M., Cameron, J., Farell, L., Gratzl, J., Hanlon, R., Horral, J., Lane, J., Langer, P., Lavey, J., Peller, G., Smedley, C., Sterlich, P., Wlasits, P. J., Schmale III, D., and Grothe, H.: Aerial Mapping of (Bio)aerosols Using Dual UAV Platforms in an Austrian Spruce Forest., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6647, https://doi.org/10.5194/egusphere-egu26-6647, 2026.
Many natural ecosystems are composed of heterogeneous patches differentiated by e.g. topography, wetness levels, or vegetation composition, leading to strong small-scale variability in surface–atmosphere exchange fluxes. Quantifying this variability remains challenging, as traditional approaches rely either on episodic point-scale measurements (e.g. chambers) or on eddy-covariance (EC) observations that integrate fluxes over large and spatially mixed footprints. Unmanned Aerial Vehicles (UAV) offer a unique observational capability to bridge this scale gap by providing flexible, high-resolution measurements of atmospheric trace gas distributions.
Here, we present a case study based in Stordalen Mire in subarctic Sweden, where we set-up a site-level inversion method to differentiate the flux rate signatures from different patch types. We used the LES-model EULAG (EUlerian LAGrangian) to simulate high-resolution flow patterns and benchmark the spatial variability of modelled concentrations with data from UAV-based grid surveys of CO2 and CH4 mixing ratio. Model evaluation showed an R2 exceeding 0.60, with model uncertainties mostly related to the transport model uncertainty and the UAV sampling footprint that does not evenly sample landcover types. The inversion fluxes were subsequently compared to patch-level chamber measurements of carbon fluxes from palsa, bog, and fen, and showed a good agreement in flux patterns across those patch types dominating the UAV-sampled footprint. To reduce the computational requirements and make the workflow more efficient, bLSmodelR, a backward Lagrangian stochastic (bLS) dispersion model, was added as an alternative transport model to inform the inversion. Results based on bLS transport in a standard setup showed comparable results to the LES model, while the reduced computation time allowed more degrees of freedom for refining the optimization.
Our results demonstrate the potential of UAV-based atmospheric measurements, combined with transport modelling, to resolve surface–atmosphere exchange heterogeneity within complex landscapes. Ongoing efforts aim to derive patch-level fluxes over the mire by integrating UAV-measured mixing ratios with eddy-covariance and chamber measurements collected within nested footprints during the STORDALENX25 campaign in summer 2025.
How to cite: Yazbeck, T., Bolek, A., Schlutow, M., Ivanova, K., Oxley, L., Triches, N. Y., Eves, N. J., Wahl, E., Kanakkassery, S., Vogt, J., Pratt, E., Heimann, M., and Göckede, M.: Quantifying landcover-specific fluxes over a heterogeneous landscape through coupling UAV-measured mixing ratios with transport models and eddy-covariance measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9448, https://doi.org/10.5194/egusphere-egu26-9448, 2026.
Single multirotor uncrewed aerial systems (UASs) are rapidly moving towards operational profiling in the atmospheric boundary layer (ABL) because of their low cost and ease of operation. At the same time, these features allow researchers to deploy such systems for advanced campaign-based sampling strategies. Using a fleet of UASs allows the flexibility to sample the spatiotemporal structure of turbulence in the ABL. Such observations are particularly useful in complex terrain where it is difficult to sample with classical approaches. During the TEAMx campaign, the DLR SWUF-3D fleet of UASs was operated in a remote Alpine valley. Each SWUF-3D UAS is outfitted with a rapid response temperature sensor and can determine the 3D wind field at 5 Hz; these measurements are calibrated in-field against a sonic anemometer. The 3D box-pattern configuration of a UAS fleet hovers up to 18 min at fixed-position across the valley and allows spatial gradients to be calculated. In July 2025, 88 box-pattern fleet flights were completed across a range of weather conditions. Valley heating mechanisms are unique due to contributions in all three dimensions but rarely have the observations to characterize the volume effects. The flexibility of UAS deployment provides the opportunity to analyze the heating budget terms with in-situ observations. In valleys, the horizontal heat flux contribution is no longer negligible and varies with proximity to valley walls. After verifying the UAS fleet observations of heat flux against a sonic anemometer, the range of uncertainty is demonstrated. Then the heterogeneity of heat flux observations will be related to atmospheric stability using vertical UAS profiles throughout the diurnal cycle.
How to cite: Lappin, F. M., Alexa, A., Wiech, A., and Wildmann, N.: Using the SWUF-3D UAS fleet to determine heat flux characteristics in an Alpine valley, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10718, https://doi.org/10.5194/egusphere-egu26-10718, 2026.
Accurate prediction of wind turbine performance and structural loads relies on understanding the complex, three-dimensional nature of wind farm flows. This encompasses not only atmospheric boundary layer turbulence in the inflow but also the wake dynamics critical for turbines operating in array configurations.
We present results from the SWUF-3D drone fleet, a novel measurement system deployed at the WiValdi research park in Krummendeich. Following validation against wind tunnel tests and meteorological masts, the drone swarm captured synchronized, multi-point measurements of the flow field surrounding operational turbines. The dataset reveals detailed profiles of mean wind speed deficits and wake turbulence, while also resolving the distinct signatures of tip vortex decay. These results highlight the potential of drone swarms to serve as flexible, high-precision references for validating wake models. Furthermore, they provide a crucial validation tool for Doppler wind lidar retrievals, bridging the gap between intensive in situ campaigns and continuous, long-term remote sensing.
How to cite: Wildmann, N. and Kistner, J.: The SWUF-3D Drone Fleet: A Tool for High-Resolution In Situ Measurements of Wind Farm Aerodynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4782, https://doi.org/10.5194/egusphere-egu26-4782, 2026.
Quantifying methane emissions from diffuse sources, including landfills and agricultural systems, is essential for improving emission inventories and assessing the effectiveness of mitigation measures in near-real time. Unmanned aerial vehicles (UAVs) provide a flexible and cost-efficient platform for atmospheric methane measurements, particularly in complex or difficult-to-access environments. However, confidence in UAV-derived emission estimates depends on robust validation and transparent uncertainty characterization. Despite the growing use of UAV-based quantification methods, systematic validation remains limited, and the lack of standardized validation procedures and consistent uncertainty reporting continues to hinder comparability across studies and limits confidence in reported emission rates.
Here we evaluate a UAV-based mass balance approach for methane emission quantification using controlled-release experiments operated by the National Physical Laboratory at two UK sites: an isolated aerodrome providing an idealised test environment, and an operational agricultural facility with measurement transects positioned downwind of the controlled release to avoid interference from background sources. Controlled methane releases spanned a wide range of emission rates (0.02–40 kg h⁻¹) and included both point and extended source configurations representative of agricultural (manure) and landfill emission scenarios. Release rates were blind to the researchers prior to flux calculation. Methane concentrations were measured in situ using a Los Gatos Research GLA-133 analyser mounted on a DJI M600 UAV, with emissions quantified using downwind horizontal transects within a mass balance framework. We also present wind measurements from an onboard 2D sonic anemometer, which were compared with an on-site high-precision anemometer mast after accounting for UAV motion/orientation and compass calibration. Together, these data were used in a mass balance framework to assess the accuracy and operational robustness of the approach. Overall, comparison between known and estimated fluxes showed very good agreement (slope = 0.998; Pearson’s r = 0.98), with a mean bias of −24.5%.
This study supports the development and validation of UAV-based techniques for methane monitoring and highlights their potential for use in regulatory contexts and emission inventory verification. We further examine how environmental conditions, source geometry, and release characteristics influence agreement between estimated and controlled emission rates.
How to cite: Tsivlidou, M., McQuilkin, J., Ricketts, H., and Allen, G.: Controlled-release validation of a UAV-based mass balance approach for quantifying methane emissions at two sites., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5557, https://doi.org/10.5194/egusphere-egu26-5557, 2026.
Uncrewed aerial vehicles (UAVs) are becoming an essential tool to monitor greenhouse gases (GHGs) such as carbon dioxide (CO2) and methane (CH4), particularly over the known sources such as landfills and industrial sites. UAV-based flux quantification over these sources is generally practiced by flying vertical curtain patterns at certain downwind distances to constrain the whole plume originating from the source and applying either mass balance or Gaussian plume inversion techniques for constraining the source strength. Extending this technique over natural ecosystems, however, requires attribution of multiple sources and sinks that contribute to the observed GHG mixing ratios, as opposed to the single well-defined sources typical for single-plume applications.
The objective of this study is to develop a UAV-based approach for CO2 and CH4 flux estimations over natural ecosystems. In the context of the STORDALENX25 campaign, we conducted multiple vertical curtain pattern flights over a sub-Arctic wetland. Using a backward Langrangian stochastic model (bLSmodelR), we estimated the areal extent (i.e. footprint) of all vertical flux curtains. UAV-based CO2 and CH4 fluxes calculated by the mass balance technique were then normalized using the estimated footprint area to obtain flux values per square meter. A comparison was made against eddy-covariance-tower-based flux reference calculations whenever both platforms footprints were approximately overlapping. Subsequently, calculated areal fluxes were aggregated together with land cover classes using random forest regression to estimate surface fluxes across the mire. Overall, this study demonstrates a pathway towards UAV-based surface flux estimations over natural ecosystems, resolving patch-level variability and thus reducing uncertainties in flux upscaling to the ecosystem level.
How to cite: Bolek, A., Yazbeck, T., Wahl, E., Vogt, J., Triches, N. Y., Schlutow, M., Pratt, E., Oxley, L., Ivanova, K., Eves, N., Kanakkassery, S. B., Heimann, M., and Göckede, M.: Integrating UAV-based CO2 and CH4 fluxes into an atmospheric transport model for surface flux attribution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7123, https://doi.org/10.5194/egusphere-egu26-7123, 2026.
Uncrewed Aircraft Systems (UAS) are rapidly becoming essential tools for high-resolution air quality monitoring. However, integrating low-weight and low-power sensors on UAS platforms introduces specific challenges that can impact data integrity. This study addresses a critical measurement artifact observed using Alphasense OPC-N3 optical particle counters mounted on a DJI Matrice 300. Immediately upon take-off, a reproducible reduction in Particle Number Concentration (PNC) of up to 60 % was detected. Through systematic experimentation, we isolated the source of this error, investigating both rotor downwash and platform-induced vibrations. Contrary to common assumptions regarding downwash effects, our results conclusively identify high-frequency propulsion vibrations as the primary cause of the significant underestimation of particle concentrations. We demonstrate that implementing mechanical decoupling measures successfully eliminates this artifact, restoring measurement accuracy during flight. These findings underscore the necessity for rigorous sensor characterization and integration strategies to ensure reliable mobile air quality data.
How to cite: zum Berge, K., Schön, M., Bange, J., and Platis, A.: Analysis of Vibration-Induced Errors in UAV-Mounted Optical Particle Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10250, https://doi.org/10.5194/egusphere-egu26-10250, 2026.
Drained peatlands can act as significant sources of greenhouse gases (GHG), in contrast to undisturbed peatlands which act as long-term carbon sinks. Rewetting measures can help to reduce the GHG emissions, however, obtaining accurate GHG flux estimates to evaluate the effectiveness of these measures is complicated by the spatial heterogeneity of many peatland sites.
To improve the spatial coverage of GHG flux estimates in these sensitive ecosystems, a new lightweight laser-based sensor is being developed capable of measuring concentrations of CO2, CH4, and N2O plus water vapor down to the ppb level. This sensor has a low power consumption and a total mass of less than 1 kg, allowing it to be mounted on a steerable balloon-drone (lighter than air unmanned aerial vehicle, LTA-UAV).
This study focuses on the development and evaluation of a measurement and analysis framework that will be applied to the LTA-UAV-borne sensor observations. Surface-atmosphere GHG fluxes will be estimated using two approaches: a vertical flux-gradient method and a mass continuity method, both based on concentration profiles acquired along vertical and horizontal flight paths. Profile data for testing these approaches are obtained using a tower-based gas analyser at an eddy-covariance instrumented peatland site. These measurements are used to assess the methodologies and the potential of the LTA-UAV system for GHG flux estimation at heterogeneous peatland sites.
How to cite: Hogan, P., Helbig, M., and Sachs, T.: A lightweight laser-based gas sensor and balloon UAV for spatial quantification of greenhouse gas fluxes in peatlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10813, https://doi.org/10.5194/egusphere-egu26-10813, 2026.
Accurate quantification of the Surface Energy Balance (SEB) in complex terrains remains an open challenge due to high spatial heterogeneity and scale mismatches between surface fluxes and atmospheric observations. Eddy Covariance Towers (ECT) provide continuous flux monitoring, but their spatial representativeness is limited in environments where atmospheric conditions vary over short distances, particularly in Alpine regions. Uncrewed Aircraft Vehicles (UAV) represent a complementary observation strategy by enabling distributed measurements of the near-surface variables that affect turbulent and radiative exchanges in sub-mesoscale ranges.
This work reports on the development, validation, and field deployment of a UAV-based measurement platform and data aggregation workflow for the spatial acquisition of wind, air temperature, relative humidity, and shortwave radiation in proximity to ECTs, to estimate the role of local circulations and advection on the SEB closure.
At first, computational fluid dynamics (CFD) simulations using the k–ε turbulence model were performed to assess propeller-induced aerodynamic distortions and to guide sensor onboarding on a multirotor UAV. Then, wind-tunnel experiments were executed at the WindShape facility (Switzerland), using a dedicated UAV testing setup comprising a 6-degree-of-freedom robotic arm for controlling UAV attitude and orientation. Lastly, a field campaign including repeated flights and hovering stops at predefined locations was carried out in Mezzolombardo (Italy) within the framework of the TEAMx international research programme.
By comparing ECT and UAV data, some general remarks can be made. In the case of temperature (T) and relative humidity (RH) measurements, sensors with fast response time are crucial for exploiting at best the limited UAV flight endurance. Our field tests highlighted T and RH maximum differences between the ECT HMP155A Vaisala sensor and UAV Galltech PM15P sensor measurements of 1.0 °C and 7%, respectively in a range of 100 m from the tower. However, this data is very fragmented due to the long acquisition time needed to get to steady-state values. In the case of wind measurements, no issues with sensor response time were noticed. Horizontal wind gusts up to 3 m/s were recorded by a TriSonica Mini LI-550 mounted on the UAV, while wind gusts up to 2 m/s were recorded by the Gill HS-100 anemometer of the ECT. Our preliminary results aim at demonstrating that multirotor UAV platforms have the capability of capturing information that ECT observations alone cannot resolve, provided that high-res. / high-freq. sensors are onboarded and conditioned according to the aforementioned procedure. This strategy (possibly empowered by UAV swarms) is expected to greatly contribute to the interpretation of flux footprints and assessment of horizontal heterogeneity in ongoing and future SEB closure studies.
How to cite: Soltaninezhad, M., Tondini, S., Mendicino, R., Scuri, G., Carpentari, S., Vendrame, N., Zardi, D., Giovannini, L., and Monsorno, R.: Multirotor UAV observations of wind, thermodynamics, and shortwave radiation in proximity to eddy covariance towers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18418, https://doi.org/10.5194/egusphere-egu26-18418, 2026.
Traditional in-situ and remote sensing methods leave observational gaps for the high-resolution 3D wind vector in the atmospheric boundary layer. We present PARASITE (Portable Aircraft Rucksack for Atmospheric Sensing and In-Situ Turbulence Estimation) a sensor and logging system to estimate the 3D wind vector using robust, off-the-shelf uncrewed aircraft systems (UAS) from internal avionics, independent of external flow sensors or calibration infrastructure. The approach combines a physics-based model combined with a neural network for residual error correction, both calibrated in a standalone process without requiring a reference sensor or wind tunnel. Validation campaigns took place at the German Meteorological Service (DWD) observatory in Falkenberg, Germany (Winter 2025) and Forschungszentrum Jülich, Germany (Summer 2024). The dataset includes 19 radiosonde ascents up to 2000 m above sea level and 8 flight hours adjacent to ultrasonic anemometers on a 99 m mast. Conditions ranged from 0.3 to 11 m s−1 under thermally stable stratification for the sonic anemometer comparison, and convective conditions with wind speeds ranging from 0.0 to 11 m s−1 for the radiosonde profiles. For 1 min averages compared to ultrasonic anemometer data, the UAS measurements show excellent correlation. Horizontal wind speed errors are low, with a root mean squared error (RMSE) of 0.30 m s−1 and a mean error (ME) of 0.01 m s−1. Wind direction shows an RMSE of 4◦ and ME of 0.5◦. Analysis of raw 10 Hz vertical wind data yields an ME of−0.04 m s−1 and RMSE of 0.44 m s−1. Analysis of ensemble averaged power spectra and structure functions confirms the method resolves turbulence following the Kolmogorov −5/3 law up to ∼2 Hz, comparable to reference instrumentation. Furthermore, comparisons with radiosonde profiles indicate the measurement is independent of air density.
How to cite: Schoen, M., Büchau, Y.-G., zum Berge, K., Bange, J., and Platis, A.: A meteorological PARASITE: High-resolution 3D wind vector from off-the-shelf UAS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19937, https://doi.org/10.5194/egusphere-egu26-19937, 2026.
How to cite: Graßmel, L., Schindewolf, J., Voss, P., and Pithan, F.: Bias quantification and correction for meteorological sensors of an air-mass following drifting balloon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20037, https://doi.org/10.5194/egusphere-egu26-20037, 2026.
Azerbaijan hosts one of the largest global concentrations of mud volcanoes, which are natural geological formations that erupt mud, water, and gases, with methane (CH4) being the dominant component. Here, we present results from the recent METHANE-To-Go Azerbaijan field campaign, which aimed to quantify CH4 emissions from mud volcanoes using in-situ fixed-wing drone measurements.
The campaign was carried out mainly in two field phases in May and October 2025, supplemented by additional flights throughout the year, resulting in a total of 36 flight hours. Measurements were performed using a fixed-wing MAS DIHA 350 VTOL UAV equipped with a lightweight, high precision CH4 analyzer, along with wind measurements derived from pitot tube data. A dedicated measurement strategy was developed and optimized specifically for mud volcanoes in the Azerbaijani environment. The approach follows a mass balance framework based on Gauss’s divergence theorem, whereby the drone flies concentric circles around the emission source at multiple altitudes. Fluxes of CH4 are derived from upwind and downwind measurements, enabling the quantification of the total emission enclosed by the cylindrical flight pattern. To validate the applied methodology, five controlled release experiments were conducted, during which CH4 from bottles was released at a known rate. Additionally, two dedicated flights were conducted to calibrate and evaluate the in-situ wind measurements using the onboard pitot tube. In total, 38 measurement flights were conducted at five continuously emitting mud volcanoes, allowing for quantitative emission estimates for these sites. In addition to continuously emitting sources, the method was applied to an explosive eruption of the Otmanbozdagh mud volcano on 11 October 2025, which was surveyed shortly after the event.
In parallel to the flight measurements, ground based air samples were collected and analyzed for gas composition and CH4 carbon stable isotopes, supporting source attribution and the discrimination of mud volcano emissions from potential anthropogenic sources.
The dataset provides the first high resolution in-situ CH4 observations shortly after a mud volcano eruption, as well as quantitative emissions from active mud volcanoes using mass balance approach, indicating source dependent emission rates on the order of several tens to approximately one hundred kilograms per hour. These results contribute to a more robust assessment of the role of geological CH4 sources in the global climate system and demonstrate the potential of fixed-wing drone platforms for quantitative measurements of greenhouse gas fluxes.
How to cite: Moser, M., Fiehn, A., Förster, E., Al-Hinaai, H., Zoller, L., Feyzullayev, A., Abbasov, O., Etiope, G., Moga, R., Menoud, M., and Roiger, A.: Quantifying methane emissions from mud volcanoes using in-situ fixed-wing drone measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20366, https://doi.org/10.5194/egusphere-egu26-20366, 2026.
Since methane contributes significantly to global warming, the accurate monitoring and quantification of its emissions are both essential and a scientific challenge. Addressing this challenge requires an interdisciplinary approach that integrates multiple scientific fields.
Unmanned aerial vehicles (UAVs) have clearly become particularly ideal tools for monitoring and measuring methane emissions. However, to harness the potential and versatility of these tools, carefully structured and coupled procedures are required that contribute to the central goal of minimizing uncertainty in emission estimates. Therefore, this research presents 8lind-Date, a strategic scheme designed to ensure the necessary conditions for accurate and reliable methane emission estimation using UAVs.
The 8lind-Date strategic scheme provides a sequential integration of procedures that optimize both data preprocessing and postprocessing. For example, the sampling window dimensions are maximized and oriented, as much as possible, perpendicular to the main point source of emissions, considering altitude constraints and physical obstacles, all within the available volume. Furthermore, flight path planning is based on an initial diagnostic flight and supported by an automated Gaussian regression system. The learning mechanism of this system leverages a specialized subset of points derived from Lissajous-Bowditch curves, which also serve as optimal flight patterns.
Unlike conventional raster-based flight paths, the Lissajous-Bowditch paths proposed by 8lind-Date provide effective and efficient spatial and temporal coverage of the sampling window. This strategic approach enables the appropriate detection of methane concentrations in industrial facilities, agricultural areas, and other areas with limited access.
The 8lind-Date strategy offers substantial improvements over traditional UAV-based methane monitoring and measurement approaches. Key advantages include reduced flight time (maximizing battery life), reduced data processing time, and maximized extraction of information from measurements (observations). The strategic approach enables the automatic estimation of emissions with low uncertainty without the need for complex systems and models. Furthermore, it offers real-time processing and accurate estimates even in scenarios where the conventional assumption (that the entire gas column is contained within the sampling window) does not hold.
How to cite: Matajira-Rueda, D., Abdallah, C., and Lauvaux, T.: Strategic scheme for optimal and automatic methane monitoring using UAVs (8lind-Date), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21396, https://doi.org/10.5194/egusphere-egu26-21396, 2026.
Understanding how aerosol chemical composition varies with height in the planetary boundary layer (PBL) is essential for interpreting aerosol sources, transformation pathways, and removal processes. Yet, observational constraints on vertically resolved particle-phase molecular composition are still limited, largely due to the lack of flexible sampling approaches. In this study, we introduce an unmanned aerial vehicle (UAV)–based aerosol filter sampling system that enables altitude-resolved particle collection within the PBL, accompanied by in situ measurements of key meteorological parameters, including temperature, relative humidity, and wind speed and direction. Collected aerosol samples are analysed using a chemical ionization time-of-flight mass spectrometer equipped with a Filter Inlet for Gases and AEROsols (FIGAERO-CIMS), allowing detailed characterization of particle-phase molecular composition and volatility.
The platform was tested during an urban deployment, where UAV-based meteorological observations were cross-validated against tower measurements, and aerosol collection performance was assessed through comparison with a co-located ground-based filter sampler. Despite low ambient particle mass loadings (PM₂.₅ ≈ 2 µg m⁻³), the UAV system achieved reliable particle collection, with sampling efficiencies comparable to ground-based measurements and no observable artefacts associated with flight operation. Consistent thermal desorption behaviour between airborne and ground-based samples further demonstrates the robustness of the approach.
We present first results revealing vertical gradients in aerosol molecular composition during PBL evolution, with a particular focus on night-time conditions. Observed compositional differences between altitudes highlight the influence of nocturnal stratification and limited mixing on aerosol chemical structure. Overall, this UAV-based filter sampling strategy expands the observational capability for aerosol chemical measurements and provides a new avenue for investigating PBL dynamics and aerosol processing in the lower atmosphere.
How to cite: Wu, C., Håkansson, L., Tsiligiannis, E., and Hallquist, M.: A drone-based sampling platform for vertically resolved chemical characterization of aerosol particles using chemical ionization mass spectrometry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22217, https://doi.org/10.5194/egusphere-egu26-22217, 2026.
UV–visible limb-scattered observations provide key information on the vertical distributions of trace gases in the middle atmosphere. As Earth-observing satellites progressively acquire large off-nadir limb-imaging capability, imaging spectroscopy is expected to further enhance the spatiotemporal information content of limb observations. However, existing models typically focus on single line-of-sight spectral radiance calculations and do not explicitly incorporate the instrument imaging chain, which hinders end-to-end assessment and broader application of limb-imaging spectrometers. In this study, starting from atmospheric composition and including selected instrument parameters, we implement UV–visible Earth limb-imaging spectral simulations and perform validation with quantitative error analysis.
The proposed approach builds a forward-simulation framework for UV–visible limb-imaging spectroscopy based on the SASKTRAN-HR radiative transfer engine, generating physics-based limb images and hyperspectral three-dimensional data cubes over 300–800 nm and 6–97 km. A three-dimensional atmospheric scene is constructed using CAMS reanalysis data (deriving air number density from pressure and temperature and incorporating ozone and sulfate aerosols, among others), while the upper-atmospheric background state is extended using the CIRA-86 model. The instrument imaging chain is further coupled, including field of view (FOV), spectral response function (SRF), and point spread function (PSF), to represent pixel-level spectral–spatial coupling effects.
Validation is conducted in the spectral domain. Simulated radiances are convolved to the effective spectral resolution of OSIRIS, and a height-by-wavelength evaluation is performed against a single OSIRIS limb scan (scan No. 54300035). Over 300–800 nm and 6–97 km, the simulations exhibit an overall systematic underestimation, with a mean absolute relative error (MARE) of 31.5% (median 25.1%). The dominant error source is attributed to discrepancies between the constructed atmospheric scene and the actual atmospheric state. Within the 20–55 km altitude range commonly used for trace-gas profile retrievals, the MARE is 10.9% (median 8.5%). Errors increase substantially above 80 km (MARE = 63.1%), likely related to stray light and reduced signal-to-noise ratio due to weak scattering signals. In addition, the simulated results are converted into pseudo-color imagery using the CIE 1931 color matching functions to enable a qualitative consistency check of limb radiance gradients and chromaticity variations.
This imaging-spectroscopy simulation framework provides a testbed for limb-imaging instrument design, development and evaluation of trace-gas retrieval algorithms, and satellite validation activities for current and future limb-imaging missions (e.g., ALTIUS).
How to cite: Zhao, Y., Jia, G., Li, Z., Ma, X., Xu, J., and Zhao, H.: Simulation and Validation of UV–Visible Limb-Scattered Imaging Spectroscopy Based on SASKTRAN-HR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3334, https://doi.org/10.5194/egusphere-egu26-3334, 2026.
We present a new Bayesian retrieval algorithm for tomographic reconstruction of nitrogen dioxide (NO2) distributions in urban environments using scanning LP-DOAS instruments. The approach utilizes crossing light paths from multiple DOAS instruments scanning different retroreflectors, enabling the retrieval of three-dimensional trace gas fields in an urban setting. Rather than directly inverting measurements to obtain a single concentration field, the method formulates the problem probabilistically and estimates the full posterior distribution of the NO₂ concentration field given the observations. Bayesian inference is employed to combine measurement information with prior knowledge on spatial structures. The posterior probability is derived from a likelihood function describing the statistical properties of the DOAS measurements and a prior probability encoding assumptions about spatial correlations, using Bayes’ theorem.
Prior knowledge on the NO2 field is parameterized through correlation lengths represented in Fourier space by a power-law power spectrum. The field realizations are generated from latent Gaussian variables and transformed into real space via inverse Fourier transform, ensuring physically plausible spatial smoothness while retaining flexibility to resolve sharp gradients typical for urban pollution sources. The forward model links these concentration fields to line-integrated DOAS observations along the intersecting measurement paths.
How to cite: Wenig, M., Henning, M., and Ye, S.: Tomographic DOAS retrieval of NO2 distributions in an urban setup, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10756, https://doi.org/10.5194/egusphere-egu26-10756, 2026.
Ground-based MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) measurements are widely used to monitor atmospheric pollutants like e.g. NO2, HCHO or SO2 (nitrogen dioxide, formaldehyde or sulfur dioxide). However, measurements of SO2 in plumes from strong emission sources like coal-fired power plants remain challenging due to difficulties in choosing the optimum spectral range for the data analysis. Fit windows at short wavelengths cover the strongest SO2 absorption bands, but suffer from low intensity signals and spectral interference with the strong O3 absorption. Alternative fit windows have higher intensity signals, but cover weaker SO2 absorptions. This study presents a systematic investigation of the SO2 data analysis in different spectral ranges for MAX-DOAS measurements performed close to power plant plumes. In the early plume, the SO2 concentrations can vary strongly and can reach extremely high values close to the stack. The focus lies on improving the quality of the resulting SO2 dSCDs (differential slant column densities) through an optimized selection of spectral fitting windows for SO2 and NO2 under highly polluted conditions.
How to cite: Radloff, N., Reischmann, L., Ziegler, S., and Wagner, T.: Optimizing the spectral analysis of SO2 MAX-DOAS measurements near coal-fired power plants, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10970, https://doi.org/10.5194/egusphere-egu26-10970, 2026.
The institute for the atmospheric and climate science of the Italian national research council (CNR-ISAC) operates a Multi AXis – Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument, capable to measure diffuse solar spectra in the Visible (VIS) and Ultra-Violet (UV) spectral ranges. This instrument, a SkySpec-2D system (https://airyx.de/item/skyspec/), has been installed at the Giorgio Fea observatory in San Pietro Capofiume (SPC), in the middle of the Po Valley, since 1st October 2021. It represents the first Italian MAX-DOAS instrument compliant with the Fiducial Reference Measurements for ground-based DOAS (FRM4DOAS) requirements.
Its spectra are routinely provided to the FRM4DOAS central facility, where are processed using the reference retrieval codes Mexican MAX-DOAS Fit (MMF) and the MAinz Profile Algorithm (MAPA) to derive aerosol extinction and NO2 vertical profiles.
In the last couple of years, in the frame of a European Space Agency (ESA) funded project, we developed an independent retrieval code, the DOAS optimal Estimation Atmospheric Profile (DEAP), for the retrieval of aerosol extinction and trace gases profiles from MAX-DOAS spectra. The DEAP performances have been assessed through the comparisons with the MMF and MAPA reference codes. The MAX-DOAS VIS spectra acquired at SPC are routinely processed by the DEAP code to retrieve aerosol extinction at 477 nm and NO2tropospheric profiles. Both the profiles can be vertically integrated to obtain the tropospheric Aerosol Optical Depth (AOD) and NO2 Vertical Column Density (VCD).
Since 2023, the Giorgio Fea observatory has also been equipped with a Cimel CE-318-T Sun-Sky Lunar Multispectral photometer. Being part of the AErosol RObotic NETwork (AERONET), its measurements are centrally processed to derive aerosol information including AOD, with a higher accuracy compared to the MAX-DOAS ones. A comparison between the AERONET (Cimel) and DEAP (SkySpec-2D) AOD revealed a systematic underestimation of MAX-DOAS AOD, especially during the days with a high aerosol load.
In this work, we present updates to the DEAP code aimed at improving the AOD products from MAX-DOAS spectra, investigating the sources of the observed discrepancy with respect to the AERONET products.
How to cite: Achilli, A., Pettinari, P., Castelli, E., Papandrea, E., Di Liberto, L., Lupi, A., and Massimo, V.: Retrieval of AOD Products from MAX-DOAS Measurements: Improvements and Sinergy with CIMEL, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11071, https://doi.org/10.5194/egusphere-egu26-11071, 2026.
Ground-based MAX-DOAS, FTIR and Pandora remote sensing techniques provide complementary information on formaldehyde (HCHO) vertical columns and profiles. However, differences in vertical sensitivity and retrieval strategies can lead to systematic inconsistencies that complicate intercomparisons and satellite validation. The centralized FRM4DOAS processing framework offers a harmonized approach for MAX-DOAS retrievals currently providing non-official HCHO datasets. However recent results at the Xianghe site (China) revealed a systematic underestimation of about 20 % relative to HCHO direct-sun measurements in the UV and IR, which themselves show excellent mutual agreement. This discrepancy largely disappears when accounting for differences in a priori profile assumptions and vertical sensitivities between MAX-DOAS and FTIR measurements. It is primarily attributed to the limited sensitivity of MAX-DOAS measurements above 2–4 km altitude combined to the use of a priori profiles that neglect the free-tropospheric HCHO contribution. Based on these findings, the use of model-based a priori profiles was recommended as an input for (MMF) optimal estimation retrievals.
In this work, we propose to test this approach at sites hosting co-located MAX-DOAS, FTIR and Pandora instruments. Target candidates are the Bremen, Toronto, Lauder and Ny-Ålesund stations. Where possible, selected Pandora data sets will be processed using the FRM4DOAS system and results will be compared with operationally produced column and profile data from the Pandonia Global Network (PGN). The aim is to report on the consistency between MAX-DOAS and PGN retrievals and investigate possible differences. Such investigations are crucial for robust satellite bias assessment and network interoperability in the context of current and upcoming satellite missions such as TROPOMI, TEMPO, GEMS, Sentinel-4 and Sentinel-5.
How to cite: Pinardi, G., Van Roozendael, M., Friedrich, M. M., Fayt, C., Langerock, B., Vigouroux, C., De Smedt, I., De Mazière, M., Beirle, S., Wagner, T., Tiefengraber, M., and Cede, A.: Intercomparison of HCHO column and profile retrievals from ground-based MAX-DOAS, FTIR and Pandora instruments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13108, https://doi.org/10.5194/egusphere-egu26-13108, 2026.
A multitude of different processes (e.g. biogenic, anthropogenic, pyrogenic) couple the Earth’s surface and the atmosphere by releasing a large variety of trace species. These emissions can lead to air quality degradation and climate forcing amongst others. As the full atmospheric state is hard to capture because of the large number of different species, proxies are of particular interest in atmospheric science.
Two important species in atmospheric chemistry are formaldehyde (HCHO) and glyoxal (CHOCHO), both belonging to the family of volatile organic compounds (VOC). Their sources (direct emissions, secondary production from oxidation of other VOC) and their sinks (photolysis, oxidation by the hydroxyl radical) are similar but differ in importance. These marginally different yields of CHOCHO and HCHO in the individual emission processes can be utilized to discriminate between sources. It was proposed to use their ratio (RGF) as a proxy of the origin of VOC emissions. Multiple publications investigated this hypothesis and resulted in contradictory conclusions for various different conditions.
To gain additional insights into the drivers and limits of the RGF ratio, MAX-DOAS data from four stations with systematically different conditions (Orléans France, Athens Greece, Incheon South-Korea, ATTO-Tower Brazil) is analysed with the focus on the ratio and meteorological conditions.
We observe a consistent decrease of RGF with increasing temperature at all four sites. Accounting for the temperature relationship substantially reduces the annual variability of RGF and removes the influence of relative humidity on RGF while the diurnal variability of RGF remains largely unaffected. In contrast, changing shortwave radiation, boundary-layer height, and wind speed impact RGF only marginally.
How to cite: Bittner, S., Richter, A., Zilker, B., Donner, S., Wagner, T., Alvarado, L. M. A., and Vrekoussis, M.: Investigating the meteorological influence on the Glyoxal-to-Formaldehyde Ratio (RGF), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13855, https://doi.org/10.5194/egusphere-egu26-13855, 2026.
Ships contribute significantly to global NOx emissions. Especially in coastal cities they can become a relevant source of air pollution. Established approaches of monitoring ship emissions often rely on in-situ or LP-DOAS measurements and are subject to different limitations like spatial coverage. Therefore, emission estimates derived from such measurements are typically based on simplified transport models and do not fully account for the actual shape and movement of exhaust plumes.
Remote sensing techniques, such as imaging DOAS (iDOAS) measurements, can help to overcome these limitations. In this study, we present several months of iDOAS measurements of NO2 (nitrogen dioxide) plumes from individual ships at a major shipping lane near the harbor of Hamburg, using the instrument IMPACT (Imaging MaPper for AtmospheriC observaTions, Peters et al., 2019). The measurements were carried out within the framework of the SEICOR measurement campaign (Ship Emission Inspection with Calibration-free Optical Remote sensing), which started in April 2025.
By supplementing the measurements with Automatic Identification System (AIS) data containing information on passing ships, the NO2-column enhancements within the emission plume are detected reliably and calculated from the measured dSCDs in several hundred cases. The high spatial and temporal resolution of the dataset nicely enables a detailed view on plume structure, transport, and dispersion under varying meteorological conditions. The large number of observed plumes allows us to systematically relate plume shape and evolution to key meteorologic parameters (e.g. stability and boundary layer height). The dataset demonstrates that Gaussian plume modeling of single measurements typically is not sufficient to describe the development and emission strength of ship plumes accurately. At the same time, we show that in many cases a mass-balance approach can be used to quantify ship NOx emissions, which are in good agreement with previous studies.
How to cite: Haveresch, H., Schönhardt, A., Richter, A., Wittrock, F., Bittner, S., Poulidis, A. P., Weigelt, A., Schmitt, S., Pöhler, D., Vrekoussis, M., and Bösch, H.: Tracking the Dynamics of Individual Ship Plumes Using Ground-Based Imaging DOAS, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14421, https://doi.org/10.5194/egusphere-egu26-14421, 2026.
Montevideo is a coastal city on the Río de la Plata in South America, characterized by frequent and relatively intense winds, high relative humidity, and moderate urban emissions. Furthermore, the city experiences four seasons with well-defined temperature variations.
Nitrogen dioxide (NO2) and formaldehyde (HCHO) are atmospheric trace gases whose presence exhibit well-defined diurnal and seasonal variations. The joint analysis of NO2 and HCHO allows for the characterization of anthropogenic source influence, as well as the evaluation of the seasonal variability of the atmosphere.
We present an initial assessment of NO2 and HCHO detection in Montevideo in 2024. In this first step, we focused on the differential slant column densities (dSCDs) of both gases under clear skies conditions. The detection was performed using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) in the UV-Vis spectral range. To obtain the NO2 dSCDs we analyzed the spectral window 411–445 nm, and HCHO dSCDs determination in the range of 324.5–359 nm using the software QDOAS [1].
Spectral data were available for 312 of the 366 days in 2024. According to the color index, clear skies were identified on 103 of these days [2]. In approximately 85% of the samples, NO2 dSCDs showed typical behavior at sunrise and sunset, with its minima recorded around noon. In contrast, HCHO dSCDs showed a progressive increase, reaching their maximum around midday or in the early afternoon for all elevation angles (5°, 10°, 30°, 60°, and 90°). The remaining 15% of dSCDs exhibited pronounced behavior in one or both gases; these variations are the subject of the second stage of our investigation. Finally, we examine the obtained results and their relationship with wind direction.
[1]. Danckaert, T., Fayt, C., Roozendael, M. V., Smedt, I. D., Letocart, V., Merlaud, A., and Pinardi, G.: QDOAS Software user manual, http://uv-vis.aeronomie.be/software/QDOAS, 2017.
[2]. Wagner, T., Apituley, A., Beirle, S., Dörner, S., Friess, U., Remmers, J., and Shaiganfar, R.: Cloud detection an classification based on MAX-DOAS observations, Atmos. Meas. Tech., 7, 1289–1320, https://doi.org/10.5194/amt-7-1289-2014, 2014.
How to cite: Silva, A., Agesta, A., Osorio, M., Casaballe, N., and Frins, E.: Characterization of NO2 and HCHO trace gases in Montevideo during 2024 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15564, https://doi.org/10.5194/egusphere-egu26-15564, 2026.
Correspondence to: Pinhua Xie (phxie@aiofm.ac.cn), Xin Tian (xtian@aiofm.ac.cn)
Boundary layer processes detected by Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) often occur at fine vertical scales, which necessitates high-resolution profile retrieval. This study introduces a Markov Chain Monte Carlo (MCMC) approach that uses an Ensemble Sampler with dynamically adjusted parallel sampling chains to ensure effective mixing in high-dimensional spaces. MCMC globally explores the parameter space, directly evaluates the full nonlinear forward model, and generates complete posterior probability distributions. Meanwhile, the study uses a two-stage decoupling strategy based on O4 observations. In the first stage, to avoid under-constraint and overfitting issues, MCMC retrieves aerosol extinction profiles at 100-meter resolution from O4 slant column densities. In the second stage, the posterior aerosol field is used as a fixed constraint to retrieve trace gas concentration profiles at 50-meter resolution. This approach reduces the high-dimensional joint optimization to sequential low-dimensional subproblems, which decreases parameter correlations and enhances MCMC sampling efficiency. The two-stage decoupling also allows MCMC to adopt optimal parallel sampling chain configurations for each stage’s dimensional state vector, ensuring stable retrieval of high-dimensional posterior distributions at fine vertical grids. Comparison with CALIPSO satellite data shows that MCMC-retrieved aerosol profiles achieve an R² of 0.875 and an RMSE of 0.11 km-1. Validation against sun photometer CE318 observations reveals that the MCMC-retrieved aerosol optical depth (AOD) achieves an R² of 0.935 with a relative bias of 10.9%, confirming the algorithm's accuracy. Compared to algorithm model data from AIOFM, AUTH, and Suwon obtained from the CINDI-3 comparison activity, NO2 vertical profiles achieve an R2 of 0.9 with a relative bias of 11%. Further validation with ground-based near-surface NO2 concentration measurements reveals that MCMC-retrieved NO2 concentrations at 50-meter trace gas resolution result in an R2 of 0.912 and an RMSE of 2.95 ppb, compared to an R2 of 0.825 and an RMSE of 3.85 ppb at 200-meter resolution. Increasing the vertical resolution improves the NO2 correlation by 11% and reduces the RMSE by 23%. Therefore, MCMC effectively addresses challenges associated with nonlinearity, non-Gaussian posterior distributions, and high-dimensional sampling, leading to improved vertical resolution in MAX-DOAS profile retrieval.
How to cite: Yu, Z., Xie, P., Tian, X., Xu, J., and Wang, Z.: A High-Resolution MCMC Method for Aerosol and Trace Gas Profile Retrieval from MAX-DOAS Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15678, https://doi.org/10.5194/egusphere-egu26-15678, 2026.
In February 2026, a field campaign called the Geostationary Environment Monitoring Spectrometer - Air Quality (GEMS-AQ) was conducted in Bangkok, Thailand, to investigate the causes of worsening air quality in Southeast Asia. This campaign was part of a broader effort to combine satellite, aerial, and ground-based observations to better understand air pollution dynamics in the Bangkok megacity. One of our research objectives was to validate the diurnal variations of NO2 vertical profiles retrieved from ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) using the JAMSTEC MAX-DOAS network algorithm (JMNet1) and Pandora official products by comparing them with in-situ measurements. To achieve this, we deployed a tethered balloon equipped with a Portable cavity-enhanced Absorption of Nitrogen Dioxide Analyzer (PANDA) based on incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS), which is lightweight and suitable for vertical profiling measurements. One of the advantages of the tethered balloon system is its ability to capture the temporal evolution of the NO2 layer during daytime up to approximately 1.3 km, close to the planetary boundary layer (PBL) height. These results will help improve the accuracy of NO2 tropospheric vertical column density estimates from GEMS by updating a priori NO2 vertical profiles to better reflect real-world conditions.
How to cite: Choi, Y., Lee, G., Chang, L.-S., Ahn, S., Kanaya, Y., Hanisco, T., Park, J., Place, B., Pandey, A., Choi, H., and Kim, M.: Validation of NO2 vertical profiles from PGN and MAX-DOAS using tethered-balloon measurements in Bangkok during the GEMS-AQ campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15831, https://doi.org/10.5194/egusphere-egu26-15831, 2026.
MAX-DOAS techniques provide reliable ground-based remote sensing of trace gas abundances by exploiting the spectral absorption signatures of atmospheric compounds in the UV and visible spectral ranges. From the integrated concentration along light paths corresponding to different viewing directions at a single observation site, vertical profiles can be retrieved. When measurements from multiple ground-based locations are combined, tomographic reconstruction techniques can be applied to estimate the spatial distribution of gas concentrations, accounting for horizontal inhomogeneities.
In this work, we combine measurements from two ground-based instruments scanning a vertical plane across a large urban region. Building on previous studies, we apply inversion algorithms that explicitly account for the sparse and inhomogeneous sampling of the vertical plane, allowing retrieval of the best-estimate concentration distribution consistent with the observations, with an effective horizontal resolution on the order of 50 m. At this spatial scale, previously used simplifying assumptions are no longer justified. Instead, we model the gas distribution as the superposition of a smoothly varying vertical background profile and localized fluctuations within the region of interest. Preliminary results are presented for the reconstruction of NO2 over Montevideo, Uruguay, covering several kilometres in a vertical plane over the city, based on measurements of scattered sunlight acquired between July 2024 and March 2025.
How to cite: Casaballe, N., Frins, E., Barragán, R., Seidel, S., Mejía, A. S., Agesta, A., Osorio, M., Velasco, L., and Coronato, M.: Tomographic reconstruction of NO2 over Montevideo using ground-based MAX-DOAS observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16593, https://doi.org/10.5194/egusphere-egu26-16593, 2026.
Corresponding authors: Pinhua Xie (phxie@aiofm.ac.cn), Jin Xu (jxu@aiofm.ac.cn); Xin Tian (xtian@ahu.edu.cn);
Long-term observations of the vertical distribution of atmospheric pollutants provide critical insights into the temporal evolution and vertical structure of atmospheric pollutants, yet such datasets remain scarce. In this study, continuous MAX-DOAS measurements conducted from 2014 to 2025 (excluding 2019) at the Science Island site in Hefei, China provide information on the vertical distribution of aerosols and concentrations of NO2, HCHO, HONO, and SO2.
Long-term observations reveal pronounced seasonal and interannual variability across all species. Aerosol optical depth (AOD) from MAX-DOAS shows overall consistency with collocated CE318 sun photometer, while near-surface NO2 and SO2 concentrations are consistent with measurements from nearby national air quality monitoring stations, supporting the reliability of the MAX-DOAS retrievals. NO2 and SO2 consistently exhibit wintertime maxima, reflecting enhanced emissions combined with suppressed atmospheric dispersion under stable boundary-layer conditions, whereas HCHO shows pronounced summertime maxima driven by intensified photochemical production. Over the study period, SO2 displays a persistent long-term decline, consistent with sustained reductions in coal combustion and industrial emissions, while NO2 decreases until 2022 and rebounds in 2023, likely associated with the recovery of traffic and industrial activities following COVID-19 lockdowns. Distinct seasonal characteristics are evident in the vertical distributions of different pollutants. NO2 and SO2 exhibit strong near-surface gradients, particularly in winter, indicating the dominant influence of local emissions and limited vertical mixing. In contrast, HCHO shows weaker vertical gradients and enhanced concentrations aloft during summer, highlighting the importance of secondary formation and vertical transport processes. Across all species, strong exponential decreases in concentrations within the lowest 1 km emphasize the combined control of surface emissions and boundary-layer mixing on pollutant distributions in the lower troposphere.
To identify the dominant processes influencing pollutants at different altitudes, these datasets of the vertical distribution of multi-species were jointly analyzed using Positive Matrix Factorization (PMF). The results indicate that pollutant variability near the surface is mainly controlled by local primary emissions and surface-related chemical processes, whereas secondary formation and regional influences play an increasingly important role at elevated levels. Interannual PMF analysis further reveals a systematic shift around 2017-2019, with declining contributions from near-surface emission-related processes and a strengthened influence of secondary and regional processes, reflecting long-term changes in dominant pollution drivers.
Overall, these results demonstrate that long-term MAX-DOAS observations provide valuable insights on both the temporal evolution and vertical structure of key atmospheric pollutants, revealing distinct controlling mechanisms and long-term trends associated with changes in emissions reflected in the vertical distribution of atmospheric pollutants.
How to cite: Wang, Z., Xie, P., Xu, J., Tian, X., Lyu, Y., Li, Y., Yu, Z., Sun, J., Huang, Z., and Huang, Y.: Long-Term Evolution of the Vertical Distribution of Key Atmospheric Pollutants Revealed by MAX-DOAS Observations in Hefei, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18071, https://doi.org/10.5194/egusphere-egu26-18071, 2026.
