Land Use Regression Model Predictors for Ultrafine Particles in Augsburg and Regensburg (2014 and 2017)
Description
Predictors for a supervised land use regression (LUR) model were used to estimate particle number concentration as an indicator of ultrafine particles. Input predictors were computed at the monitoring sites, while selected predictors were calculated on a 50-meter INSPIRE-compliant grid covering the areas of Augsburg and Regensburg. The predictors were extracted as close as possible to the two reference years, 2014 and 2017, for the LUR modeling. The stored predictors include imperviousness and forest density.
This work was supported by the Bavarian State Ministry for the Environment and Consumer Protection under the umbrella of the project network “BayUFP – Measurement, Characterization and Evaluation of Ultrafine Particles.” A publication detailing the modeling approach is currently under review at Urban Climate (Elsevier).
Table of contents (English)
| Data source / Variable | Variable name | Variable description | Unit | Method of calculation | Buffer distances | DOI / Link | Output files |
| European Environment Agency (EEA) / Imperviousness | imp | Average imperviousness | % | raster::extract (R) | 25, 50, 100, 300, 500, 1000, 5000 | https://doi.org/10.2909/3bf542bd-eebd-4d73-b53c-a0243f2ed862 | monitor_imperviousness_predictors.csv |
| European Environment Agency (EEA) / Imperviousness | imp_fs | Average imperviousness, derived with focal sum | % | focal (R ) | 25, 50, 100, 300, 500, 1000, 5000 | https://doi.org/10.2909/3bf542bd-eebd-4d73-b53c-a0243f2ed862 | monitor_imperviousness_predictors.csv |
| European Environment Agency (EEA) / Forests | fdens | Forest density derived with buffer | % | raster::extract (R) | 25, 50, 100, 300, 500, 1000, 5000 |
https://doi.org/10.2909/e677441e-fb94-431c-b4f9-304f10e4dfd8 |
monitor_forest_density_predictors.csv grid_augsburg_fdens_fs_1000.csv grid_regensburg_fdens_fs_1000.csv |
| European Environment Agency (EEA) / Forests | fdens_fs | Forest density derived with focal sum | % | focal (R ) | 25, 50, 100, 300, 500, 1000, 5000 |
https://doi.org/10.2909/e677441e-fb94-431c-b4f9-304f10e4dfd8 |
monitor_forest_density_predictors.csv grid_augsburg_fdens_fs_1000.csv grid_regensburg_fdens_fs_1000.csv |
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