Monthly NOx emissions

Produced by
KNMI
Data type
Gridded
Horizontal coverage
Europe (west=9.9° W, east=29.9° E, south=35.1° N, north=54.9° N)
Horizontal resolution
0.2° x 0.2°
Temporal resolution
Monthly
Temporal coverage
2019-2022
Model
DECSO v6.3
File format
Zarr

Download the data

https://data.seedsproject.eu/seeds_monthly-nox-emissions_knmi_20190101-20221231_decso_v2/dataset.zarr
How to download the data?

Variables

Click on a variable to see it on the viewer.

Total NOx emissions derived from TROPOMI
nox
Total NOx emissions derived from TROPOMI (alt)
nox_alt
Anthropogenic NOx emissions derived from TROPOMI
ant_nox
Anthropogenic NOx emissions derived from TROPOMI (alt)
ant_nox_alt
Biogenic NOx emissions derived from TROPOMI
bio_nox
Biogenic NOx emissions derived from TROPOMI (alt)
bio_nox_alt

Description

NOx emissions have both strong anthropogenic and biogenic sources, while satellite observations see the total resulting NO2 concentrations. The inversion algorithm DECSO (DAily Emissions Constraint by Satellite Observations) is capable to derive the NOx emissions from the sources based on these satellite observations. In this task the DECSO algorithm is further developed to distinguish between anthropogenic and biogenic emissions.

Citation

KNMI. (2024). Monthly NOx emissions [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10731794

Download instructions

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This sample Python code shows how to download this dataset, create subsets of it and save them as NetCDF, using the Xarray library.

import xarray as xr import fsspec # Open remote dataset ds = xr.open_zarr( store=fsspec.get_mapper("https://data.seedsproject.eu/seeds_monthly-nox-emissions_knmi_20190101-20221231_decso_v2/dataset.zarr") ) # Subset a time range, save as NetCDF time_subset = ds.nox.sel(time=slice('2019-06-01', '2019-07-01')) time_subset.to_netcdf('./nox_time_subset.nc') # Subset a time and geographical range, save as NetCDF time_geo_subset = ds.nox.sel(time=slice('2019-06-01', '2019-07-01'), latitude=slice(40, 43), longitude=slice(0,3.5)) time_geo_subset.to_netcdf('./nox_time_geo_subset.nc')