2026-07-16T23:58:01Z https://b2share.fz-juelich.de/api/oai2d

oai:b2share.fz-juelich.de:b2rec/be5cde68c6ba4ab08b6687b17adbb3b6 2019-02-24T20:08:56Z e9b9792e-79fb-4b07-b6b4-b9c2bd06d095

EUDAT https://b2share.fz-juelich.de/records/be5cde68c6ba4ab08b6687b17adbb3b6 EUDAT B2SHARE 2019 Schultz, Martin G. This file has been obtained from the Tropospheric Ozone Assessment Report database described by Schultz, M.G. et al., Elementa Sci. Anthrop., 2017, doi:http://doi.org/10.1525/elementa.244. It contains 6 years of annual NO2 concentration percentiles at German measurement sites and corresponding station metadata. The intended use of these data is to demonstrate the set-up and training of a simple feed forward neural network, which shall attempt to predict the NO2 statistics based on the station characterisation from the metadata information. The data are stored as csv file (comma delimited) with 7 header lines plus column headings. Column headings are: year,id,station_id,station_type,station_type_of_area,station_nightlight_1km,station_wheat_production,station_nox_emissions,station_omi_no2_column,station_max_population_density_5km,perc75,perc98. station_id, station_type, and station_type_of_area are string variables, all other columns are numeric. year, id, and station_id should be ignored for the machine learning. perc75 and perc98 are 75%-iles and 98%-iles, respectively and given in units of nmol per mol (equivalent to ppbv). Creative Commons Attribution (CC-BY) info:eu-repo/semantics/openAccess 3.2.4 → Chemistry → Atmospheric chemistry NO2, air pollution, machine learning, TOAR csv m.schultz@fz-juelich.de 215.9 kB 1 file