Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers

Including satellite observations of nitrogen dioxide (NO<sub>2</sub>) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO<sub>2</sub> samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO<sub>2</sub> estimates from existing national satellite-based LUR models (developed with 68 regulatory monitors). The samplers spanned roadside, urban near traffic (≤100 m to a major road), and urban background (>100 m to a major road) locations. We evaluated model performance using <i>R</i><sup>2</sup> (predicted NO<sub>2</sub> regressed on independent measurements of NO<sub>2</sub>), mean-square-error <i>R</i><sup>2</sup> (MSE-R<sup>2</sup>), RMSE, and bias. Our models captured up to 69% of spatial variability in NO<sub>2</sub> at urban near-traffic and urban background locations, and up to 58% of variability at all validation sites, including roadside locations. The absolute agreement of measurements and predictions (measured by MSE-R<sup>2</sup>) was similar to their correlation (measured by <i>R</i><sup>2</sup>). Few previous studies have performed independent evaluations of national satellite-based LUR models, and there is little information on the performance of models developed with a small number of NO<sub>2</sub> monitors. We have demonstrated that such models are a valid approach for estimating NO<sub>2</sub> exposures in Australian cities.