Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers. - Knibbs, L.; Coorey, C.; Bechle, M.; Cowie, C.; Dirgawati, M.; Heyworth, J.; Marks, G.; Marshall, J.; Morawska, L.; Pereira, Gavin; Hewson, M. (American Chemical Society, 2016)
Including satellite observations of nitrogen dioxide (NO2) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO2 samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO2 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 R(2) (predicted NO2 regressed on independent measurements of NO2), mean-square-error R(2) (MSE-R(2)), RMSE, and bias. Our models captured up to 69% of spatial variability in NO2 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(2)) was similar to their correlation (measured by R(2)). 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 NO2 monitors. We have demonstrated that such models are a valid approach for estimating NO2 exposures in Australian cities.