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Mapping near-surface NO2 concentrations in urban areas by combining in situ and remote sens-ing observations with city-scale transport simulations
NO2 is a primary air pollutant with a high spatial and temporal variability. Therefore, high-resolution maps are critical for linking NO2 exposure and health impact. In the Munich NO2 imaging campaign, NO2 was measured with airborne and ground-based in-situ and remote sensing instruments. In this project, you will analyse the MuNIC dataset and develop an algorithm to convert APEX NO2 maps to maps of near-surface NO2 concentrations.
Keywords: air pollution, remote sensing, cities
NO2 is a primary air pollutant with a high spatial and temporal variability. Therefore, high-resolution maps are critical for linking NO2 exposure and health impact. In the Munich NO2 imaging campaign, NO2 was measured with airborne and ground-based in-situ and remote sensing instruments. In this project, you will analyse the MuNIC dataset and develop an algorithm to convert APEX NO2 maps to maps of near-surface NO2 concentrations.
**Key questions:**
- Can we develop a simple model for mapping NO2 columns to surface concentrations?
- How accurate do we need to measure NO2 with airborne remote sensing to obtain sufficiently accurate NO2 maps?
**Key tasks:**
- Simulate the 3D NO2 distribution in Munich using the GRAMM/GRAL modelling system.
- Validate the model using the rich dataset collected during the MuNIC campaign.
- Develop an algorithm to map airborne NO2 columns to near-surface concentrations.
**More information:**
Kuhlmann, G., Chan, K. L., Donner, S., Zhu, Y., Schwaerzel, M., Dörner, S., Chen, J., Hueni, A., Nguyen, D. H., Damm, A., Schütt, A., Dietrich, F., Brunner, D., Liu, C., Buchmann, B., Wagner, T., and Wenig, M.: Map-ping the spatial distribution of NO2 with in situ and remote sensing instruments during the Munich NO2 imaging campaign, Atmos. Meas. Tech., 15, 1609–1629, https://doi.org/10.5194/amt-15-1609-2022, 2022.
NO2 is a primary air pollutant with a high spatial and temporal variability. Therefore, high-resolution maps are critical for linking NO2 exposure and health impact. In the Munich NO2 imaging campaign, NO2 was measured with airborne and ground-based in-situ and remote sensing instruments. In this project, you will analyse the MuNIC dataset and develop an algorithm to convert APEX NO2 maps to maps of near-surface NO2 concentrations.
**Key questions:**
- Can we develop a simple model for mapping NO2 columns to surface concentrations? - How accurate do we need to measure NO2 with airborne remote sensing to obtain sufficiently accurate NO2 maps?
**Key tasks:**
- Simulate the 3D NO2 distribution in Munich using the GRAMM/GRAL modelling system. - Validate the model using the rich dataset collected during the MuNIC campaign. - Develop an algorithm to map airborne NO2 columns to near-surface concentrations.
**More information:**
Kuhlmann, G., Chan, K. L., Donner, S., Zhu, Y., Schwaerzel, M., Dörner, S., Chen, J., Hueni, A., Nguyen, D. H., Damm, A., Schütt, A., Dietrich, F., Brunner, D., Liu, C., Buchmann, B., Wagner, T., and Wenig, M.: Map-ping the spatial distribution of NO2 with in situ and remote sensing instruments during the Munich NO2 imaging campaign, Atmos. Meas. Tech., 15, 1609–1629, https://doi.org/10.5194/amt-15-1609-2022, 2022.
Not specified
Gerrit Kuhlmann (gerrit.kuhlmann@empa.ch)
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Note: Applications are only possible for students from Swiss universities. Internships are only possible within the framework of a compulsory internship during studies.
Gerrit Kuhlmann (gerrit.kuhlmann@empa.ch)
--
Note: Applications are only possible for students from Swiss universities. Internships are only possible within the framework of a compulsory internship during studies.