Spatio-temporal Bayesian Quantile Regression for High Air-Pollution Concentrations

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 (NO ₂ , PM ₁₀ , and PM _2.5 ) 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 NO ₂ ; 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 while preserving same extreme spots. These quantile-based exposure surfaces may support decision making and subsequent epidemiological studies.