High resolution monthly NOx emissions

Produced by

KNMI

Data type

Gridded

Horizontal coverage

Netherlands/Ruhr region (west=2° E, east=9° E, south=50° N, north=54° N)

Horizontal resolution

0.05° x 0.05°

Temporal resolution

Monthly

Temporal coverage

2019-2020

Model

DECSO v5.6

File format

Zarr

Download the data

https://data.seedsproject.eu/seeds_hires-monthly-nox-emissions_knmi_20190101-20201231_decso_v1/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

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). High resolution monthly NOx emissions [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10731796

Download instructions

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_hires-monthly-nox-emissions_knmi_20190101-20201231_decso_v1/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')