Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia

Aerosol Optical Depth (AOD) and Fine Mode Fraction (FMF) are important information for air quality research. Both are mainly obtained from satellite data based on a radiative transfer model, which requires heavy computation and has uncertainties. We proposed machine learning-based models to estimate AOD and FMF directly from Geostationary Ocean Color Imager (GOCI) reflectances over East Asia. Hourly AOD and FMF were estimated for 00-07 UTC at a spatial resolution of 6 km using the GOCI reflectances, their channel differences (with 30-day minimum reflectance), solar and satellite viewing geometry, meteorological data, geographical information, and the Day Of the Year (DOY) as input features. Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) machine learning approaches were applied and evaluated using random, spatial, and temporal 10-fold cross-validation with ground-based observation data. LightGBM (R-2 = 0.89-0.93 and RMSE = 0.071-0.091 for AOD and R-2 = 0.67-0.81 and RMSE = 0.079-0.105 for FMF) and RF (R-2 = 0.88-0.92 and RMSE = 0.080-0.095 for AOD and R-2 = 0.59-0.76 and RMSE = 0.092-0.118 for FMF) agreed well with the in-situ data. The machine learning models showed much smaller errors when compared to GOCI-based Yonsei aerosol retrieval and the Moderate Resolution Imaging Spectroradiometer Dark Target and Deep Blue algorithms. The Shapley Additive exPlanations values (SHAP)-based feature importance result revealed that the 412 nm band (i. e., ch01) contributed most in both AOD and FMF retrievals. Relative humidity and air temperature were also identified as important factors especially for FMF, which suggests that considering meteorological conditions helps improve AOD and FMF estimation. Besides, spatial distribution of AOD and FMF showed that using the channel difference features to indirectly consider surface reflectance was very helpful for AOD retrieval on bright surfaces

Estimating PM2.5 Concentrations Using 3 KM MODIS AOD Products: A Case Study in British Columbia, Canada

PM2.5 refers to fine particles with diameters smaller than 2.5 μm. The rising level of PM2.5 reveals adverse effects on climate change, economic losses, international conflicts, and public health. Exposure to the high level of PM2.5 would increase the risk of premature death, especially for people with weak immune systems, such as children and elder people. The main sources of PM2.5 include combustion of biomass, vehicle and industrial emissions, and wildfire smoke. British Columbia (BC), Canada, with a land area of 944,735 km2 and 27 regional districts, experienced its record-breaking wildfire season in 2017. However, due to the uneven distribution of PM2.5 ground monitoring stations in BC, PM2.5 concentrations in the rural area are difficult to retrieve. Remote sensing techniques and geographical information systems (GIS) could be used as supplementary tools to estimate PM2.5 concentrations. Aerosol Optical Depth (AOD) has been proven to have a strong correlation with PM2.5. Moderate Resolution Imaging Spectroradiometer (MODIS) provides AOD products in both 3 km and 10 km resolutions. The 3 km MODIS AOD products were released in 2013, and have been widely used to estimate PM2.5 concentrations in several studies. This study adopted the 3 km Aqua MODIS AOD products to estimate PM2.5 concentrations in BC in the year of 2017 by combining ground station measurements, meteorological and supplementary data. MODIS AOD products were validated with ground-level AErosol RObotic NETwork (AERONET) AOD data. The Multiple Linear Regression (MLR) model, Geographically Weighted Regression (GWR) model, and a novel theoretical model were then conducted to estimate PM2.5 concentrations by integrating MODIS AOD products, ground-level PM2.5 concentrations, meteorological and supplementary data. After comparing the performance of the three models, the GWR model was used to generate annual, seasonal, and monthly spatial distribution maps of PM2.5. The application feasibility of MODIS AOD products in predicting PM2.5 was also examined. The validation results showed that there was a strong correlation between the MODIS AOD and the AERONET AOD. The GWR model had the best prediction performance, while the MLR generated the worst prediction results. After analyzing the spatial distribution maps of PM2.5 with ground-level PM2.5 distribution maps, it could be concluded that the PM2.5 concentrations estimated by the GWR model almost follow the same trend as ground station measured PM2.5. In addition, PM2.5 concentrations were the highest in summer and August based on the estimation results of seasonal and monthly GWR models. It indicated that the application feasibility of MODIS AOD products in predicting PM2.5 concentrations during BC’s wildfire season was high

EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product

We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50¿km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90¿min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1¿%, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3¿%.Peer ReviewedPostprint (published version

Satellite based methane emission estimation for flaring activities in oil and gas industry: A data-driven approach(SMEEF-OGI)

Klimaendringer, delvis utløst av klimagassutslipp, utgjør en kritisk global utfordring. Metan, en svært potent drivhusgass med et globalt oppvarmings potensial på 80 ganger karbondioksid, er en betydelig bidragsyter til denne krisen. Kilder til metanutslipp inkluderer olje- og gassindustrien, landbruket og avfallshåndteringen, med fakling i olje- og gassindustrien som en betydelig utslippskilde. Fakling, en standard prosess i olje- og gassindustrien, antas ofte å være 98 % effektiv ved omdannelse av metan til mindre skadelig karbondioksid. Nyere forskning fra University of Michigan, Stanford, Environmental Defense Fund og Scientific Aviation indikerer imidlertid at den allment aksepterte effektiviteten på 98 % av fakling ved konvertering av metan til karbondioksid, en mindre skadelig klimagass, kan være unøyaktig. Denne undersøkelsen revurderer fakkelprosessens effektivitet og dens rolle i metankonvertering. Dette arbeidet fokuserer på å lage en metode for uavhengig å beregne metanutslipp fra olje- og gassvirksomhet for å løse dette problemet. Satellittdata, som er et nyttig verktøy for å beregne klimagassutslipp fra ulike kilder, er inkludert i den foreslåtte metodikken. I tillegg til standard overvåkingsteknikker, tilbyr satellittdata en uavhengig, ikke-påtrengende, rimelig og kontinuerlig overvåkingstilnærming. På bakgrunn av dette er problemstillingen for dette arbeidet følgende "Hvordan kan en datadrevet tilnærming utvikles for å forbedre nøyaktigheten og kvaliteten på estimering av metanutslipp fra faklingsaktiviteter i olje- og gassindustrien, ved å bruke satellittdata fra utvalgte plattformer for å oppdage og kvantifisere fremtidige utslipp basert på maskinlæring mer effektivt?" For å oppnå dette ble følgende mål og aktiviteter utført. * Teoretisk rammeverk og sentrale begreper * Teknisk gjennomgang av dagens toppmoderne satellittplattformer og eksisterende litteratur. * Utvikling av et Proof of Concept * Foreslå en evaluering av metoden * Anbefalinger og videre arbeid Dette arbeidet har tatt i bruk en systematisk tilnærming, som starter med et omfattende teoretisk rammeverk for å forstå bruken av fakling, de miljømessige implikasjonene av metan, den nåværende «state-of-the-art» av forskning, og «state-of-the-art» i felt for fjernmåling via satellitter. Basert på rammeverket utviklet i de innledende fasene av dette arbeidet, ble det formulert en datadrevet metodikk, som benytter VIIRS-datasettet for å få geografiske områder av interesse. Hyperspektrale data og metandata ble samlet fra Sentinel-2 og Sentinel-5P satellittdatasettet. Denne informasjonen ble behandlet via en foreslått rørledning, med innledende justering og forbedring. I dette arbeidet ble bildene forbedret ved å beregne den normaliserte brennindeksen. Resultatet var et datasett som inneholdt plasseringen av kjente fakkelsteder, med data fra både Sentinel-2 og Sentinel-5P-satellitten. Resultatene understreker forskjellene i dekningen mellom Sentinel-2- og Sentinel-5P-data, en faktor som potensielt kan påvirke nøyaktigheten av metanutslippsestimater. De anvendte forbehandlingsteknikkene forbedret dataklarheten og brukervennligheten markant, men deres effektivitet kan avhenge av fakkelstedenes spesifikke egenskaper og rådatakvaliteten. Dessuten, til tross for visse begrensninger, ga kombinasjonen av Sentinel-2 og Sentinel-5P-data effektivt et omfattende datasett egnet for videre analyse. Avslutningsvis introduserer dette prosjektet en oppmuntrende metodikk for å estimere metanutslipp fra fakling i olje- og gassindustrien. Den legger et grunnleggende springbrett for fremtidig forskning, og forbedrer kontinuerlig presisjonen og kvaliteten på data for å bekjempe klimaendringer. Denne metodikken kan sees i flytskjemaet nedenfor. Basert på arbeidet som er gjort i dette prosjektet, kan fremtidig arbeid fokusere på å innlemme alternative kilder til metan data, utvide interesseområdene gjennom industrisamarbeid og forsøke å trekke ut ytterligere detaljer gjennom bildesegmenteringsmetoder. Dette prosjektet legger et grunnlag, og baner vei for påfølgende utforskninger å bygge videre på.Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon. Climate change, precipitated in part by greenhouse gas emissions, presents a critical global challenge. Methane, a highly potent greenhouse gas with a global warming potential of 80 times that of carbon dioxide, is a significant contributor to this crisis. Sources of methane emissions include the oil and gas industry, agriculture, and waste management, with flaring in the oil and gas industry constituting a significant emission source. Flaring, a standard process in the Oil and gas industry is often assumed to be 98% efficient when converting methane to less harmful carbon dioxide. However, recent research from the University of Michigan, Stanford, the Environmental Defense Fund, and Scientific Aviation indicates that the widely accepted 98% efficiency of flaring in converting methane to carbon dioxide, a less harmful greenhouse gas, may be inaccurate. This investigation reevaluates the flaring process's efficiency and its role in methane conversion. This work focuses on creating a method to independently calculate methane emissions from oil and gas activities to solve this issue. Satellite data, which is a helpful tool for calculating greenhouse gas emissions from various sources, is included in the suggested methodology. In addition to standard monitoring techniques, satellite data offers an independent, non-intrusive, affordable, and continuous monitoring approach. Based on this, the problem statement for this work is the following “How can a data-driven approach be developed to enhance the accuracy and quality of methane emission estimation from flaring activities in the Oil and Gas industry, using satellite data from selected platforms to detect and quantify future emissions based on Machine learning more effectively?" To achieve this, the following objectives and activities were performed. * Theoretical Framework and key concepts * Technical review of the current state-of-the-art satellite platforms and existing literature. * Development of a Proof of Concept * Proposing an evaluation of the method * Recommendations and further work This work has adopted a systematic approach, starting with a comprehensive theoretical framework to understand the utilization of flaring, the environmental implications of methane, the current state-of-the-art of research, and the state-of-the-art in the field of remote sensing via satellites. Based upon the framework developed during the initial phases of this work, a data-driven methodology was formulated, utilizing the VIIRS dataset to get geographical areas of interest. Hyperspectral and methane data were aggregated from the Sentinel-2 and Sentinel-5P satellite dataset. This information was processed via a proposed pipeline, with initial alignment and enhancement. In this work, the images were enhanced by calculating the Normalized Burn Index. The result was a dataset containing the location of known flare sites, with data from both the Sentinel-2, and the Sentinel-5P satellite. The results underscore the disparities in coverage between Sentinel-2 and Sentinel-5P data, a factor that could potentially influence the precision of methane emission estimates. The applied preprocessing techniques markedly enhanced data clarity and usability, but their efficacy may hinge on the flaring sites' specific characteristics and the raw data quality. Moreover, despite certain limitations, the combination of Sentinel-2 and Sentinel-5P data effectively yielded a comprehensive dataset suitable for further analysis. In conclusion, this project introduces an encouraging methodology for estimating methane emissions from flaring activities within the oil and gas industry. It lays a foundational steppingstone for future research, continually enhancing the precision and quality of data in combating climate change. This methodology can be seen in the flow chart below. Based on the work done in this project, future work could focus on incorporating alternative sources of methane data, broadening the areas of interest through industry collaboration, and attempting to extract further features through image segmentation methods. This project signifies a start, paving the way for subsequent explorations to build upon

Geostatistical modelling of PM10 mass concentrations with satellite imagery from MODIS sensor

Several epidemiological studies suggested that there is an association between incidence and exacerbation of adverse respiratory and cardiovascular health effects and air pollution. Accurate, high resolution maps of ground-level Particulate Matter (PM) are highly awaited for environmental policies and future monitoring stations design. Though the measurements made by the ground stations can ensure a high level of reliability, still they cannot provide full spatial coverage over an area, giving rise among other things to misclassified epidemiological studies. Fine particles are usually categorized by size distribution, known as fractions: PM10 represents the particles with aerodynamic diameter smaller than 10 µm and comprises the thoracic (or coarse) fraction – with diameter in the range 2.5-10 µm – and the smaller inhalable (or fine) fraction. Although including the less dangerous thoracic particles, PM10 measurements are usually far more available and hence lend themselves better for modelling. Spaceborne aerosols products like the ones offered by the polar-orbiting MODerate resolution Imaging Spectrometer (MODIS) are successfully finding practical applications for scientific research studies and, though not previously intended, the Aerosol Optical Thickness (AOT, or simply τ ) from MODIS revealed to have a leading role in the evaluation of surface air quality due to its full spatial (clear-sky constrained) coverage and daily overpasses almost throughout the globe. Despite the “promised land” has not been reached yet, researchers have verified an existing correlation between aerosols and particulate concentrations, rising expectation of air quality models for high-scale environmental characterization. Air quality modelling is generally a challenging application, due to the wide range of sources affecting this variable and the high spatial and temporal variability of the particles, especially over high populated areas with rugged topography and complex meteorological profiles. In this thesis, different variogram-based geostatistical techniques are evaluated to predict the concentrations of PM10, with a focus on the effective advantages brought by AOT from satellites. This work is meant as a guide for students and researchers who are taking their first steps in this specific application, as well as to experts of the field who want to overview geostatistical filling of PM concentrations, and weigh up the usefulness of MODIS imagery. Different areas of study and temporal resolutions will be considered, so as to propose directions and outline conclusions on how this task – still far from being definitively ruled out – should be approached. Aside from modelling, the interactive visualization, extraction and analysis of the model-based predicted maps are also covered, cutting-edge Web-based software architectures based on the Open Geospatial Consortium (OGC) standard services are proposed, giving rise to increased capabilities in the spatio-temporal elaboration of the model results. The availability of spaceborne maps of AOT at an increased nominal resolution of 1×1 km2 has been a unique occasion to experiment their role for air quality issues; the latest algorithmics from leading FOSS-like (Free and Open Source Software) modelling software where learned and used, resulting in several new testing results in a field where variogram-based geostatistics were lacking. Solutions for novel online analysis and visualization capabilities were explored, in order to approach an open and interconnected uncertainty-enabled Web

GEWEX water vapor assessment (G-VAP): final report

Este es un informe dentro del Programa para la Investigación del Clima Mundial (World Climate Research Programme, WCRP) cuya misión es facilitar el análisis y la predicción de la variabilidad de la Tierra para proporcionar un valor añadido a la sociedad a nivel práctica. La WCRP tiene varios proyectos centrales, de los cuales el de Intercambio Global de Energía y Agua (Global Energy and Water Exchanges, GEWEX) es uno de ellos. Este proyecto se centra en estudiar el ciclo hidrológico global y regional, así como sus interacciones a través de la radiación y energía y sus implicaciones en el cambio global. Dentro de GEWEX existe el proyecto de Evaluación del Vapor de Agua (VAP, Water Vapour Assessment) que estudia las medidas de concentraciones de vapor de agua en la atmósfera, sus interacciones radiativas y su repercusión en el cambio climático global.El vapor de agua es, de largo, el gas invernadero más importante que reside en la atmósfera. Es, potencialmente, la causa principal de la amplificación del efecto invernadero causado por emisiones de origen humano (principalmente el CO2). Las medidas precisas de su concentración en la atmósfera son determinantes para cuantificar este efecto de retroalimentación positivo al cambio climático. Actualmente, se está lejos de tener medidas de concentraciones de vapor de agua suficientemente precisas para sacar conclusiones significativas de dicho efecto. El informe del WCRP titulado "GEWEX water vapor assessment. Final Report" detalla el estado actual de las medidas de las concentraciones de vapor de agua en la atmósfera. AEMET ha colaborado en la generación de este informe y tiene a unos de sus miembros, Xavier Calbet, como co-autor de este informe

Convective episodes near the intertropical discontinuity in summertime West Africa: representation in models and implications for dust uplift

This study focusses on the production of mesoscale convective systems (MCSs) close to the intertropical discontinuity (ITD), including their associated dust uplift processes and representation in global and high-resolution, limited-area models. Findings from this work include a detailed description of the synoptic-scale meteorology important for the formation of a large, rare Saharan MCS and the spectacular dust plume which it created in June 2010. Results are presented from a high resolution simulation of this event using the Weather Research and Forecasting (WRF) model. Important dynamical processes which govern the triggering and development of the MCS are discussed as well as the mechanisms for dust uplift with which it is associated. Also highlighted from the simulation is the strong dependence of simulations on initial conditions and the disagreement at particular times between some operational analysis and reanalysis products (referred to here as (re)analysis products). (Re)Analyses are ostensibly representing the atmosphere at the same time and with the same observations as each other. Despite this, disagreement with respect to low-level moisture distribution between (re)analyses is shown to be large at times. Disagreement is as a result of different representations of the West African monsoon (WAM) flow and is greatest during the retreat after a northward excursions. It is also found that extreme disagreement events are linked to the occurrence of rainfall and anomalously high aerosol optical depth (AOD) values north of the zonal-mean ITD (ITD©). The seasonal patterns of rainfall in the Sahara and disagreement between (re)analysis products are shown to be similar, suggesting a link between the occurrence of convective storms and the representation of the West African Monsoon. There is also a spatio-temporal connection between anomalous rainfall events and anomalously high AOD values. Analysis of the synoptic-scale meteorology reveals a statistically significant 925 hPa geopotential dipole present during extreme rainfall events. This has been used to produce a preliminary version of an anomalous rainfall in the Sahara (ARS) index

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Tropospheric BrO plumes in Arctic spring – A comparison of TROPOMI satellite observations and model results

Halogen radicals can drastically alter the polar atmospheric chemistry. This is made evident by a recurrent destruction of boundary layer ozone during polar springs called ozone depletion events (ODEs). ODEs are caused by enhanced concentrations of reactive halogens, in particular bromine monoxide (BrO) radicals. Measurements suggest that there are two distinct sets of environmental conditions favoring the emissions of bromine to the atmosphere, namely cold and stable meteorological conditions on one side and less stable conditions associated with low-pressure systems on the other. This thesis investigates the importance of these differing environmental conditions on a pan-Arctic scale by comparing TROPOMI satellite observations of BrO with the results of an meterology model coupled with atmospheric chemistry (WRF-CHEM) for the Arctic spring of 2019. For the retrieval of tropospheric BrO from satellite measurements an algorithm is developed which allows to assess the tropospheric partial column without reliance on external input. Compared to other retrieval algorithms, it enables the full utilization of TROPOMI’s high spatial resolution (7 × 3.5 km²) while also avoiding biases from the use of model data. Satellite observations are used to validate model assumptions. It was demonstrated that a bromine release mechanism from the snow-pack employed in numerous models is unfit to predict ODEs in early February at high solar zenith angles. Case studies demonstrated that the observed spatial patterns and large BrO columns observed during late polar spring in association with polar cyclones can be explained by the intrusion of bromine into the free troposphere. It was shown that the magnitude of bromine emissions from blowing snow in polar cyclones is likely overestimated. A seasonal dependence in the environmental conditions favoring bromine release was established. Calm meteorological conditions favor the occurrence of ODEs during early polar spring. During late March and April, ozone was identified as limiting factor for BrO formation and results indicated high wind speeds as favorable meteorological parameter for bromine release. A statistical analysis of spatial extent and shape of ODEs was conducted, showing a scale of 40 km to 1000 km for bromine enhanced air masses