Sentinel 5 average air quality of Slovenia in the years on 1km x 1km grid

This dataset was generated for the project EkoVizija as part of the programming competition Arnes' HackathON 2025.Dataset contains data about air quality and land cover/use for a grid that covers Slovenia. Tiles of the grid are sized 1km x 1km. The file Slovenia_Grid_Coordinates_1km.csv contains the grid with 1km x 1km tiles that covers the slovenia that was used for the other files in the dataset. The columns in the file are grid_id,lon,lat,sw_lon,sw_lat,ne_lon,ne_lat. The format for these columns is the same as other files in the dataset. Files Slovenia_AirQuality_1kmGrid_<year>_withBounds.csv contain yearly average values for air pollutants in Slovenia on a 1km x 1km grid calculated from Sentinel 5 datasets, specifically Sentinel-5P OFFL CO: Offline Carbon Monoxide, Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide, Sentinel-5P OFFL SO2: Offline Sulfur Dioxide, Sentinel-5P OFFL O3: Offline Ozone and Sentinel-5P OFFL CH4: Offline Methane. The calculations were made using Google Earth Engine. The script that was used is available in the attached repository in the folder GEE scripts. Each file in the dataset with the name in the format of Slovenia_AirQuality_1kmGrid_<year>_withBounds.csv contains the data for the <year> in the following format: Name of the column in the file Type Description grid_id string the id of the 1km x 1km tile of the grid lon number longitude of the central point of the tile lat number latitude of the central point of the tile sw_lon number longitude of the sout-west corner of the tile sw_lat number latitude of the sout-west corner of the tile ne_lon number longitude of the north-east corner of the tile ne_lat number latitude of the north-east corner of the tile year number the year the average is calculated for no2_ppb number the average value of nitrogen dioxide in the tile for the year in ppm (parts per million) co_ppb number the average value of carbon monoxide in the tile for the year in ppm (parts per million) so2_ppb number the average value of sulfur dioxide in the tile for the year in ppm (parts per million) o3_ppb number the average value of ozone in the tile for the year in ppm (parts per million) ch4_ppb number the average value of methane in the tile for the year in ppm (parts per million) Files lulc_results_<year>.csv contain the calculation of which land cover/use is in each tile of the grid between <year> and <year>+1 from the data dowlnoaded from Sentinel-2 10-Meter Land Use/Land Cover dataset made by Esri, Impact Observatory, that is distributed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0). The labels/class for land cover/use are the same as in the original data: Value Name Description 1 Water Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains. 2 Trees Any significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath). 4 Flooded vegetation Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture. 5 Crops Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land. 7 Built Area Human made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt. 8 Bare ground Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines. 9 Snow/Ice Large homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10 Clouds No land cover information due to persistent cloud cover. 11 Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants. The lulc_results_<year>.csv files contain the data in following format: Name of the column in the file Type Description grid_id string the id of the 1km x 1km tile of the grid lon number longitude of the central point of the tile lat number latitude of the central point of the tile sw_lon number longitude of the sout-west corner of the tile sw_lat number latitude of the sout-west corner of the tile ne_lon number longitude of the north-east corner of the tile ne_lat number latitude of the north-east corner of the tile LULC_mode number mode (most frequent) class in the tile class_1_percent number percentage of the class 1 (water) in the tile class_2_percent number percentage of the class 2 (trees) in the tile class_3_percent number percentage of the class 3 in the tile class_4_percent number percentage of the class 4 (flooded vegetation) in the tile class_5_percent number percentage of the class 5 (crops) in the tile class_6_percent number percentage of the class 6 in the tile class_7_percent number percentage of the class 7 (built area) in the tile class_8_percent number percentage of the class 8 (bare ground) in the tile class_9_percent number percentage of the class 9 (snow/ice) in the tile class_10_percent number percentage of the class 10 (clouds) in the tile class_11_percent number percentage of the class 11 (range land) in the tile Data all_data.csv and all_data_changed.csv combine the satellite data from SENTINEL 2 and SENTINEL 5, adding a few more columns, such as: Name of the column in the file Type Description population_sum number population density road_length_m number length of roads in meters within the square distance_to_factory number distance to the industry points The following data for all_data.csv was collected from: - Population data (Humanitarian Data Exchange): https://data.humdata.org/dataset/kontur-population-slovenia/resource/574f0189-410f-4dac-b762-f1df1ec0bc78 - Roads data (OpenStreetMap): https://data.humdata.org/dataset/hotosm_svn_roads - Land classification data: Esri Land Cover data Sentinel-2 10-Meter Land Use/Land Cover, The same data that is in lulc_results_<year>.csv - Air pollutants data: Copernicus SENTINEL 5 - Industry data (From European Environment Agency: https://www.eea.europa.eu/data-and-maps/data/member-states-reporting-art-7-under-the-european-pollutant-release-and-transfer-register-e-prtr-regulation-23/european-pollutant-release-and-transfer-register-e-prtr-data-base, permalink: 3578652f4e8e43bba4f0555a4b5933d0)