Ms. Jana Handschuh Profile

Jana Handschuh

at Deutsches Zentrum für Luft- und Raumfahrt eV

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Publications (2)

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 | 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.

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