文摘
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 NOb>2b> and PMb>10b> LUR models for Western Europe (years: 2005鈥?007) 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 NOb>2b> and PMb>2.5b> as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted R2) is 0.48鈥?.58 for NOb>2b> and 0.22鈥?.50 for PMb>10b>. Inclusion of satellite data improved model performance (adjusted R2) by, on average, 0.05 for NOb>2b> and 0.11 for PMb>10b>. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available.