DOI: 10.5281/zenodo.15745794
title: One region, one model - explainable hybrid spatiotemporal machine learning to uncover air pollution drivers in Styria, Austria

description: This dataset is intended for a research paper and contains two main folders. One folder includes ERA5 satellite and local meteorological data for each monitoring station, while the other folder stores pollution data for the same stations. In addition, there are two separate files: one contains Land Use Regression (LUR) features for each station, and the other provides temporal features.

The dataset is organized as follows:

1. Pollution Data Columns





Timestamp




SO2 µg/m³




NO µg/m³




NO2 µg/m³




O3 µg/m³




STBK 10K µg/m³



2. ERA5 Satellite & Local Meteorological Data Columns





timestamp




Bockberg_10 metre U wind component




Bockberg_10 metre V wind component




Bockberg_2 metre dewpoint temperature




Bockberg_2 metre temperature




Bockberg_Skin temperature




Bockberg_Leaf area index, high vegetation




Bockberg_Leaf area index, low vegetation




Bockberg_Surface net long-wave (thermal) radiation




Bockberg_Surface latent heat flux




Bockberg_Evaporation




Lufttemperatur (LUTE)




Relative Luftfeuchte (LUFE)




u-component




v-component



3. Land Use Regression (LUR) Feature Columns





Residential & Accommodation




Commercial & Retail




Transportation & Infrastructure




Industrial & Manufacturing




Natural & Agricultural




Public & Civic Services




Healthcare & Wellness




Educational & Religious




Food & Hospitality




Miscellaneous & Other Structures



4. Temporal Feature Columns





Timestamp




Julian_Date




Day_in_year




Month_in_year




Year




Holiday



Land Use Regression (LUR) features were created using OpenStreetMap data. The extracted elements were categorized into columns based on their characteristics. Below are the categories and their corresponding OpenStreetMap (OSM) classes:

Category – OSM Classes

Residential & Accommodationapartments, house, dormitory, semi-detached house, bungalow, detached, allotment house, group home, nursing home, assisted living, backyard

Commercial & Retailsupermarket, mall, variety store, second-hand, bakery, convenience, ice cream, books, jewelry, shoes, electronics, tobacco, bookmaker, stationer

Transportation & Infrastructuretrain station, bus, railway, platform, station, parking, parking aisle, footway, cycleway, pedestrian, bridge, corridor, building passage, driveway, steps, road, trunk, secondary, primary, tertiary, secondary link, primary link, alley, path, track

Industrial & Manufacturingmanufacture, industrial, warehouse, car parts, car repair, car wash, carport, garage, garages, weighing scales, recycling, transformer tower, generator, substation, electrical, greenfield, brownfield, construction

Natural & Agriculturalforest, wood, farmland, grass, meadow, grassland, scrub, heath, cliff, mountain range, plain, river, stream, basin, vineyard, greenhouse, greenhouse horticulture, farmyard

Public & Civic Servicescourthouse, town hall, civic, military, police, fire station, community centre, social facility, funeral directors, rehabilitation, clubhouse, public, shelter, recreation ground, events venue, casino

Healthcare & Wellnesshospital, doctor, doctors, health centre, pharmacy, psychotherapist, rehabilitation

Educational & Religiousuniversity, college, school, kindergarten, chapel, church, mosque, place of worship

Food & Hospitalityrestaurant, cafe, fast food, biergarten, pub, kitchen

Miscellaneous & Other Structuresoffice, service, civic, building, roof, detached, outbuilding, shed, hut, terrace, fountain, station, tree row