Assessing the role of spatial aggregation schemes with varying campaign durations of mobile measurements on land use regression models for estimating nitrogen dioxide

Abstract

Mobile air pollution measurements are typically aggregated by varying road segment lengths, grid cell sizes, and time intervals. How these spatiotemporal aggregation schemas affect the modeling performance of land use regression models has seldom been assessed. We used 5.7 million mobile nitrogen dioxide (NO2) measurements collected over 160 days in Amsterdam (The Netherlands) and subsampled them into five campaign durations (10–70 days). We aggregated the measurements from each campaign duration onto road segments and grid cells with five spatial scales (25–200 m). A stepwise linear regression (SLRs) and random forests (RFs) were trained for each aggregated dataset to predict NO2 concentrations. The model accuracies were validated using a 30% hold-out sample of mobile measurements and external Palmes long-term stationary measurements (n = 105). At increased spatial scales, the prediction accuracy decreased for RFs but increased for SLRs when validated against mobile measurements. Using long-term stationary measurements, prediction accuracy varied across scales without any clear pattern. Regardless of cells or road segments, the models performed similarly at small scales (i.e., 25 m and 50 m). Models based on road segments were less sensitive to spatial scales than those based on cells in mobile and long-term external validations. Longer campaign durations increased the prediction accuracies of long-term NO2 concentrations, though the gain in accuracy diminished after 50 days. In conclusion, our results suggest that road segments are preferred when the aggregation scale gets larger as this approach likely reduces scale-dependent influences. The campaign duration plays a more important role in long-term NO2 prediction than spatial scales.