
Natascha Kljun
Professor

Including the Urban Canopy Layer in a Lagrangian Particle Dispersion Model
Författare
Summary, in English
In this study we introduce a novel extension of an existing Lagrangian particle dispersion model for application over urban areas by explicitly taking into account the urban canopy layer. As commonly done, the original model uses the zero-plane displacement as a lower boundary condition, while the extension reaches to the ground. To achieve this, spatially-averaged parametrizations of flow and turbulence characteristics are created by fitting functions to observational and numerical data. The extended model is verified with respect to basic model assumptions (well-mixed condition) and its behaviour is investigated for unstable/neutral/stable atmospheric stabilities. A sensitivity study shows that the newly introduced model parameters characterizing the canopy turbulence impact the model output less than previously existing model parameters. Comparing concentration predictions to the Basel Urban Boundary Layer Experiment—where concentrations were measured near roof level—shows that the modified model performs slightly better than the original model. More importantly, the extended model can also be used to explicitly treat surface sources (traffic) and assess concentrations within the urban canopy and near the surface (pedestrian level). The small improvement with respect to roof level concentrations suggests that the parametrized canopy profiles for flow and turbulence characteristics realistically represent the dispersion environment on average.
Avdelning/ar
- Centrum för miljö- och klimatvetenskap (CEC)
Publiceringsår
2022
Språk
Engelska
Sidor
1-34
Publikation/Tidskrift/Serie
Boundary-Layer Meteorology
Volym
185
Issue
1
Dokumenttyp
Artikel i tidskrift
Förlag
Springer
Ämne
- Earth and Related Environmental Sciences
Nyckelord
- Global sensitivity analysis
- Pedestrian level
- Turbulence parametrization
- Urban air pollution
- Well-mixed condition
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 0006-8314