Modelling air pollution concentrations via spatio-temporal Bayesian quantile regression

Exposure to high air pollution levels, especially in urban contexts, is a major risk factor for human health. Most models in literature, however, focus on the bulk of the distribution, and only few address its extremes, such as the right tail. In this work, we apply a Bayesian spatio-temporal quantile regression (QR) framework to daily air pollution data (NO2, PM10, and PM2.5) in the Rome (Italy) municipality between 2011 and 2022. The model specification includes temporal, spatial, and spatio-temporal predictors, and a spatial Gaussian process (GP) to adjust intercept levels and capture spatial variability between monitoring sites. Models were evaluated through temporal and spatio-temporal cross-validation (CV), and sensitivity analyses were performed. Results highlighted that the majority of variability was captured by the GP. Spatial variability was captured especially for NO2; the same pollutant, however, was also the most difficult to predict in spatial CV. All pollutants showed good temporal CV results and proper in-sample calibration. Exposure surfaces for 2011 and 2022 highlighted an overall decreasing trend whilst preserving the same high-concentration hotspots. These quantile-based exposure surfaces may support decision-making and subsequent epidemiological studies.