Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO₂ Concentrations Using Measurements Sampled with Google Street View Cars

Abstract

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO₂ on every street in Amsterdam (<i>n</i> = 46.664) and Copenhagen (<i>n</i> = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers (<i>n</i> = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated <i>r</i>_s (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated <i>r</i>_s = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher <i>r</i>_s = 0.65 with the deterministic model predictions compared to the data-only (<i>r</i>_s = 0.50) and LUR model (<i>r</i>_s = 0.61). In Copenhagen, mixed model estimates correlated <i>r</i>_s = 0.51 with external model predictions compared to <i>r</i>_s = 0.45 and <i>r</i>_s = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations (<i>r</i>_s = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.