Satellite NO2 data improve national land use regression models for ambient NO2 in a small densely populated country

Land use regression (LUR) modelling has increasingly been applied to model fine scale spatial variation of outdoor air pollutants including nitrogen dioxide (NO2). Satellite observations of tropospheric NO2 improved LUR model in very large study areas, including Canada, United States and Australia. The aim of our study was to assess the value of satellite observations of NO2 in modelling the spatial variation of annual average NO2 concentrations in a small densely populated country. We used surface level annual average NO2 concentration and geographic information system data from 144 monitoring sites spread over the Netherlands: 26 regional background, 78 urban background and 40 traffic sites for developing land use regression models. For the 144 monitoring sites we obtained the annual average tropospheric NO2 concentration for 2007 from the Ozone Monitoring Instrument (OMI) satellite sensor. These OMI data reflect a spatial scale of about 10 × 10 km. We calculated the correlation between satellite and surface level NO2 concentrations for all sites and for background sites only. We next evaluated whether adding satellite observations improved land use regression models. Annual average satellite observations of tropospheric NO2 correlated well spatially with annual average urban plus regional background (R = 0.74, n = 104 sites) and especially regional background NO2 concentrations (R = 0.88, n = 26). The correlation was moderate for all sites, including traffic locations (R = 0.51, n = 144). A LUR model including satellite NO2 observations performed better (overall R2 = 0.84) than LUR models including geographical coordinates or indicator variables (overall R2 65–74%) in modeling concentrations at the 104 background sites across the Netherlands. Satellite NO2 observations agreed well with measured surface concentrations at background locations and improved land use regression models, even in a small densely populated country