Introduction This dataset is a value-added product based on 'Up-to-date air quality station measurements', administered by the European Environmental Agency (EEA) and collected by its member states. The original hourly measurement data (NO2, SO2, O3, PM10, PM2.5 in µg/m³) was reshaped, gapfilled and aggregated to different temporal resolutions, making it ready to use in time series analysis or spatial interpolation tasks. Reproducible code for accessing and processing this data and notebooks for demonstration can be found on Github. Accessing and pre-processing hourly data Hourly data was retrieved through the API of the EEA Air Quality Download Service. Measurements (single files per station and pollutant) were joined to create a single time series per station with observations for multiple pollutants. As PM2.5 data is sparse but correlates well with PM10, gapfilling was performed according to methods described in Horálek et al., 2023¹. Validity and verification flags from the original data were passed on for quality filtering. Reproducible computational notebooks using the R programming language are available for the data access and the gapfilling procedure. Temporal aggregates Data was aggregated to three coarser temporal resolutions: day, month, and year. Coverage (ratio of non-missing value) was calculated for each pollutant and temporal increment. A threshold of 75% was applied to generate reliable aggregates. All pollutants were aggregated by their aritmethic mean. Additionally, two pollutants were aggregated using a percentile method, which has shown to be more appropriate for mapping applications. PM10 was summarized using the 90.41th percentile. Daily O3 was further summarized as the maximum of the 8-hour running mean. Based thereon, monthly and annual O3 was aggregated using the 93.15th percentile of the daily maxima. For more details refer to the reproducible computational notebook on temporal aggregation. Data columns column hourly daily monthly annual description Air.Quality.Station.EoI.Code x x x x Unique station ID Countrycode x x x x Two-letter ISO country code Start x Start time of (hourly) measurement period <Pollutant> x x x x One of NO2; SO2; O3; O3_max8h_93.15; PM10; PM10_90.41; PM2.5 in µg/m³ Validity_<Pollutant> x Validity flag of the respective pollutant Verification_<Pollutant> x Verification flag of the respective pollutant filled_PM2.5 x Flag indicating if PM2.5 value is measured or supplemented through gapfilling (boolean) year x x x Year (2015-2023) cov.year_<Pollutant> x x Data coverage throughout the year (0-1) month x x Month (1-12) cov.month_<Pollutant> x x Data coverage throughout the month (0-1) doy x Day of year (0-366) cov.day_<Pollutant> x Data coverage throughout the day (0-1) Station meta data The below table lists relevant meta data on the station level, including type and area of measurement stations, as well as their coordinates. It is, hence, static and does not vary over time. These data are directly included in daily, monthly, and annual aggregates. To optimize file size for hourly data, it is stored seperately (in a file named "EEA_stations_meta_table.parquet") and can be joined. column description Air.Quality.Station.EoI.Code Unique station ID (required for join) Countrycode Two-letter ISO country code Station.Type One of "background", "industrial", or "traffic" Station.Area One of "urban", "suburban", "rural", "rural-nearcity", "rural-regional", "rural-remote" Longitude & Latitude Geographic coordinates of the station Parquet file format This dataset is shipped in Parquet files. Parquet is a relatively new and very memory-efficient format, that differs from traditional tabular file formats (e.g. CSV) in the sense that it is binary and cannot be opened and displayed by common tabular software (e.g. MS Excel, Libre Office, etc.). Daily, monthly and annual data files are small (> 200Mb) and stored in a single file each. They are written in GeoParquet format, making them ready to use in e.g. GIS (via download) or cloud environment (via URL). Hourly data is much larger (3.7Gb) and is therefore partitioned by `Countrycode` (one file per country) to enable reading smaller subsets. Users should use an Apache Arrow implementation to open the files, for example in Python, R, C++, or another scripting language. Reading the data there is straight forward (see the code samples below). Hourly data can be downloaded in single files or all together in a zipped archive. R code: # required librarieslibrary(arrow)library(sf) # read air quality and meta dataaq = read_parquet("airquality.no2.o3.so2.pm10.pm2p5_4.annual_pnt_20150101_20231231_eu_epsg.3035_v20240718.parquet") aq = st_as_sf(aq) |> st_set_crs(4326) Python code: # required libraryimport geopandas as gpd # read air qualityaq = gpd.read_parquet("airquality.no2.o3.so2.pm10.pm2p5_4.annual_pnt_20150101_20231231_eu_epsg.3035_v20240718.parquet")