Western european land use regression incorporating satellite- and ground-based measurements of NO2 and PM10

Danielle Vienneau, Kees De Hoogh, Matthew J. Bechle, Rob Beelen, Aaron Van Donkelaar, Randall V. Martin, Dylan B. Millet, Gerard Hoek, Julian D. Marshall

Research output: Contribution to journalArticlepeer-review

126 Scopus citations

Abstract

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 R2) is 0.48-0.58 for NO2 and 0.22-0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R2) 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.

Original languageEnglish (US)
Pages (from-to)13555-13564
Number of pages10
JournalEnvironmental Science and Technology
Volume47
Issue number23
DOIs
StatePublished - Dec 3 2013

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