Modelling daily PM_2.5 concentrations at high spatio-temporal resolution across Switzerland

Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM_2.5) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM_2.5 monitoring data was supplemented by imputing PM_2.5 concentrations at PM₁₀ sites, using PM_2.5/PM₁₀ ratios at co-located sites. Daily PM_2.5 concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM_2.5 in cells with AOD but without PM_2.5 measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM_2.5 predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM_2.5 concentrations. We present high spatiotemporal models for daily PM_2.5 exposure (2003–2013) across Switzerland at 100 × 100 m, using satellite AOD and supplementing sparse monitoring data with the PM₁₀ network.