Data · dataset · 2024
Aerosol composition, air quality, and boundary layer dynamics in the urban background of Stuttgart in winter
Listed in RADAR and RADAR4Memory — shown once because both records carry DOI 10.35097/vbjzahy9ej4c1b69
Aerosol distributions are of great relevance for air quality especially for cities like Stuttgart with limited air exchange due to its location in a basin.
Description
We collected a comprehensive set of data from remote sensing, in-situ methods including radiosondes for the urban background of downtown Stuttgart to determine the impact of boundary layer mixing processes on local air quality and to evaluate the simulation results of the high-resolution large eddy simulation (LES) model PALM-4U at 10 m grid spacing.
Stagnant meteorological conditions caused accumulation of aerosols and chemical composition analysis shows that ammonium nitrate (37% ± 9%) and organic aerosol (OA, 34% ± 9%) dominated during this winter study. Case studies show that clouds during previous nights can weaken temperature inversion and accelerate boundary layer mixing after sunrise by up to 3 hours. This is important for ground-level aerosol dilution during morning rush hours.
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Furthermore, our observations validate results of the LES model PALM-4U in terms of boundary layer heights and aerosol mixing for 48 hours. The simulated aerosol concentrations follow the trend of our observations but are still underestimated by a factor of 4.5 ± 2.1 due to missing secondary aerosol formation processes, uncertainties of emissions and boundary conditions in the model. This paper firstly evaluates the PALM-4U model performance in simulating aerosol spatio-temporal distributions, which can help to improve the LES model and to better understand sources and sinks for air pollution as well as the role of horizontal and vertical transport.
Links
Where it is published
- DOI doi.org/10.35097/vbjzahy9ej4c1b69 ↗
DOI / persistent id · from radar service eu de
Catalogue records · 1
- OAI-PMH record radar-service.eu/oai/OAIHandler?verb=GetRecord&metadataPrefix=oai_dc&identifier… ↗
metadata API · from radar service eu de
Topics
- From keywords
- Earth & Environmental Science · Earth & Environmental Science
- Inferred from text
- Physical geography and environmental geoscience 74% · Satellite remote sensing 65% · Simulation 75%
Provenance · 2 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| RADAR | 10.35097/vbjzahy9ej4c1b69 | 9 d ago | JSON v1 |
| RADAR4Memory | 10.35097/vbjzahy9ej4c1b69 | 8 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · radar service eu de | connector:radar_service_eu_de@1.0.0 | |
| concepts[field].anzsrc:group:3709 | enrichment · radar service eu de | taxonomy-embedding@1.1.0 | title+keywords+description (74%) |
| concepts[field].local:field:earth-environmental | mapping · radar service eu de | connector:radar_service_eu_de@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · radar4memory radar service eu | connector:radar4memory_radar_service_eu@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · radar service eu de | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:remote-sensing | enrichment · radar service eu de | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · radar service eu de | connector:radar_service_eu_de@1.0.0 | /metadata/dc/description |
| license | source · radar service eu de | connector:radar_service_eu_de@1.0.0 | /metadata/dc/rights |
| publication_date | source · radar service eu de | connector:radar_service_eu_de@1.0.0 | |
| title | source · radar service eu de | connector:radar_service_eu_de@1.0.0 | /metadata/dc/title |