Data Entry: Please note that the research database will be replaced by UNIverse by the end of October 2023. Please enter your data into the system https://universe-intern.unibas.ch. Thanks
Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant measurements, support fine-scale LUR modeling for larger areas. Here, we describe NO2 and PM10 LUR models for Western Europe (years: 2005-2007) based on <1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 km to 10 km, altitude, and distance to sea. We explore models with and without satellite-based NO2 and PM2.5 as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted R(2)) is 0.48-0.58 for NO2 and 0.22-0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R(2)) by, on average, 0.05 for NO2 and 0.11 for PM10. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available.
