Mr. Thilo Erbertseder Profile

Thilo Erbertseder

Research Scientist at Deutsches Zentrum für Luft- und Raumfahrt (DLR)

SPIE Involvement:

Conference Chair | Editor | Author

Area of Expertise:

satellite remote sensing , air quality and health , air pollution , atmospheric composition , climate change

Websites:

Company Website

Thilo Erbertseder is an atmospheric scientist and project manager at DLR (www.dlr.de). He has 22 years of experience in atmospheric composition research, the topic air quality and health as well as in managing large international consortia; His research focus is on combining satellite observations, in-situ data and numerical models with a strong interest in application development.

Currently he is the DLR PI of the projects e-shape (EU H2020, GEO flagship, pilot on air quality and health), NextGEOSS (EU H2020, pilot on air pollution in megacities), “Urban Climate Under Change” (German Federal Ministry of Education and Research, satellite- and aircraft-based evaluation of the urban climate model PALM-4U) and “Environmental Stressors and Health” (DLR). He was the scientific coordinator of PASODOBLE - the FP7 Copernicus Downstream Service Project for regional and local air quality with 21 partners and over 50 institutional users (2010-2014), ESA Sentinel-4/-5 Mission Advisory Group member (2010-2013) and coordinator of a project on satellite-based air quality monitoring and forecasting for Saudi Arabia (2009-2010);

He was External Expert for the European Commission / Joint Research Center at the Institute for Energy and Transport (2014-2017) and EUMETSAT for the topic air quality (2013-2015). He has participated in over 50 international and national research projects; PI of several projects, author of more than 25 peer-reviewed publications and 3 patents.

Proceedings Article | 20 October 2023 Presentation

Regional mobility patterns and exposure to environmental stressors: effects on the health risk assessments due to air pollution

Lorenza Gilardi, Mattia Marconcini, Annekatrin Metz-Marconcini, Thomas Esch, Hannes Taubenboeck, Thilo Erbertseder

Proceedings Volume 12735, 127350G (2023) https://doi.org/10.1117/12.3000174

KEYWORDS: Risk assessment, Data modeling, Air contamination, Satellites, Nitrogen dioxide, Sensors, Pollution, Ozone, Model-based design

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Proceedings Article | 23 September 2021 Presentation + Paper

Comparing the aggregated health risk from air pollution calculated from different Earth observation resources

M. Houdayer, E. Khorsandi, J. Handschuh, F. Baier, T. Erbertseder

Proceedings Volume 11864, 118640A (2021) https://doi.org/10.1117/12.2599929

KEYWORDS: Data modeling, Satellites, MODIS, Data processing, Atmospheric modeling, Ozone, Statistical modeling

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Ambient air quality (AQ) is a recurrent issue in cities, exacerbated in low- and middle-income countries. Global AQ impacts on health can be assessed using an aggregated health risk indicator (ƩRIs), derived from in-situ stations, chemical transport models (CTMs) or satellite data. AQ monitoring is well covered in the city of Munich (Germany): in-situ stations, POLYPHEMUS CTM with three domains, CAMS-Reanalysis of the regional model ensemble and satellite data from MODIS. From these data sets, the ƩRIs was calculated considering four major air pollutants (NO2, PM10, PM2.5, O3), using their respective relative risk for the mortality-all causes health end-point. Then, the ƩRIs from the models and the satellite data were compared to the ƩRIs in-situ by means of basic statistics, time series and violin plots and the mean relative difference (MRD). Using the ƩRIs allows to further observe the contribution of individual pollutants to the index. For the mortality all causes health end point between 2017 and 2018, ground observations and CTMs show an increase of ca. 12-13% when exposed to ambient air pollution. The difference between traffic and background stations can be observed: ƩRIs in situ mean is higher at the traffic station than at the background stations. This order is however reversed when considering ƩRIs mean from the models. The four CTMs simulate the ƩRIs well and its seasonality is also represented. Most of the data are spread around the mean and the median for all data sets and stations with an overall distribution skewed towards high values. With 0.5<r2<0.6, POLYPHEMUS/DLR yields medium correlation, regardless the domain, while CAMS-Reanalysisreturns high correlation (r2 ≈ 0.8) for all the studied stations. The MRD indicates an underestimation of the ƩRIs by CAMS-Reanalysis, while POLYPHEMUS tends to overestimate it for the larger domains (positive MRD). The difference in the r2 between the two CTMs is due to their singularities: POLYPHEMUS/DLR uses free runs while CAMS-Reg uses a data assimilation process with station measurements (among them the two studied background stations). The overestimation of Johanneskirchen by POLYPHEMUS/DLR comes from its location nearby a power plant and the wind direction. Finally, the very high values in early 2017 can be explained by fireworks, which are not reproduced by models. It is show in this study that estimating a global health risk from air pollution is possible using in-situ measurements, models and satellite. Finally, satellite data can be helpful to assess the ƩRIs worldwide

Proceedings Article | 23 September 2021 Presentation + Paper

Urban air pollution exposure: an assessment exploiting world settlement footprint and land use data

L. Gilardi, A. Metz-Marconcini, M. Marconcini, T. Erbertseder

Proceedings Volume 11864, 1186406 (2021) https://doi.org/10.1117/12.2600414

KEYWORDS: Air contamination, Pollution, Satellites, Remote sensing, Data modeling, Spatial resolution, Roads

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Proceedings Article | 9 October 2020 Presentation + Paper

Deriving ground-level PM2.5 concentrations over Germany from satellite column AOD for implementation in a regional air quality model

Jana Handschuh, Frank Baier, Thilo Erbertseder, Martijn Schaap

Proceedings Volume 11531, 1153104 (2020) https://doi.org/10.1117/12.2574020

KEYWORDS: Satellites, Atmospheric modeling, Sensors, Data modeling, Performance modeling, MODIS, Pollution control, Air contamination, Pollution, Associative arrays

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Fine particulate matter (PM2.5) has strong and adverse effects on the environment and human health. To estimate health risks and environmental impacts it is very important to know about current and prospective amounts of ground-level PM2.5 concentrations on regional scales. The in-situ station network in Germany is well developed but still provides only selective spatial information on air pollution. For a gapless monitoring additional data sets are required. Satellite data provides area-wide measurements of air pollutants and can depict their synoptic distribution in an adequate way. Chemical-transport models are moreover able to predict the amount and dispersion of aerosols in a very high temporal and spatial resolution, which makes them the key tool in monitoring air quality on regional and local scales. Modelling of aerosols is still very uncertain due to the complexity of accurately including aerosol properties and transfer processes, but also because of inaccurate emission data bases. In our study we use satellite data to produce detailed maps of PM2.5 distributions for Germany with the objective of using them as input for the air quality forecast system POLYPHEMUS/DLR. We want to improve the general performance of the model by adjusting the PM2.5 amounts in the model with observation data from satellites in terms of data assimilation. PM2.5 concentrations cannot be measured directly by satellites. This paper presents a semi-empirical linear regression approach to estimate ground-level PM2.5 concentrations using satellite observations of aerosol optical depth (AOD). The method was applied to different satellite sensor products, namely MODIS and SLSTR. For both sensors the resulting PM2.5 concentrations showed good correlations with in-situ station measurements with R-values of 0.83 for MODIS and 0.81 for SLSTR for the considered year 2018. Differences in the spatial coverage of the two satellite sensors induced us to combine the data sets to an ensemble product. We found major benefits using this ensemble, primarily regarding the data amount for the calculation of local mean values of PM2.5 concentrations. We could produce detailed maps of ground-level PM2.5 concentrations which can be used for the identification of high polluted areas, the monitoring of transnational pollution patterns and the localization of specific emission sources. The assimilation of the produced datasets into the air quality model POLYPHEMUS/DLR will be the next step in our study.

Proceedings Article | 22 September 2020 Presentation

Satellite-based remote sensing of the urban atmosphere: examples from German cities

Thilo Erbertseder

Proceedings Volume 11535, 1153507 (2020) https://doi.org/10.1117/12.2577126

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Proceedings Article | 20 September 2020 Presentation

Opening Remarks by Conference Chairs

Thilo Erbertseder, Ying Zhang, Nektarios Chrysoulakis

Proceedings Volume 11535, 1153502 (2020) https://doi.org/10.1117/12.2584139

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Proceedings Article | 26 October 2018 Presentation + Paper

Air quality monitoring and simulation on urban scale over Munich

E. Khorsandi, F. Baier, T. Erbertseder, M. Bittner

Proceedings Volume 10793, 1079303 (2018) https://doi.org/10.1117/12.2503969

KEYWORDS: Atmospheric modeling, Roads, Satellites, Chemistry, Light, Meteorology

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Proceedings Article | 20 October 2006 Paper

The UV service of the ESA-GSE Project PROMOTE

Ralf Meerkötter, Thilo Erbertseder, Jens Kammann, Ralf Blumenthal, Fabrizio Flore, Emilio Simeone, Gaetano Licitra, Aapo Tanskanen

Proceedings Volume 6362, 63621B (2006) https://doi.org/10.1117/12.689833

KEYWORDS: Ultraviolet radiation, Ozone, Clouds, Atmospheric modeling, Sun, Satellites, Skin, Algorithm development, Internet, Data modeling

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Proceedings Article | 19 October 2006 Paper

Requirements for the spatial resolution, temporal resolution, and measuring uncertainties of total ozone measurements to calculate the erythemally effective UV radiation with a pre-selected accuracy

Alois Schmalwieser, Günther Schauberger, Thilo Erbertseder, Michal Janouch, Gerrie Coetzee, Philipp Weihs

Proceedings Volume 6362, 63621C (2006) https://doi.org/10.1117/12.689565

KEYWORDS: Radiation effects, Ultraviolet radiation, Ozone, Atmospheric modeling, Spatial resolution, Spectrophotometry, Temporal resolution, Sun, Satellites, Clouds

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