Hierarchical Bayesian Modelling of Spatio-Temporal Air Pollution Patterns in Athens, Greece

Abstract

Air pollution is one of the biggest health risks of our time, mainly due to industrial development worldwide. Persistent exposure to air pollution causes many severe diseases, including lung cancer. It also is responsible for millions of deaths annually worldwide. As a result, various researchers have been developing models to estimate air pollution levels throughout the past decades, aiming to better understand their dynamics. These models can also be used to forecast air pollution levels. This forecasted information is crucial for enabling relevant authorities to adopt precautionary measures and to alert the public in advance, especially when the levels are very harmful to human health. Up to date, modelling air pollution is a challenging task due to the various factors that affect its behaviour. One advanced field that enables the modelling of air pollution levels is spatio-temporal modelling.

The main focus of this dissertation is to develop a spatio-temporal model to estimate parameters and forecast air pollution levels across different locations in Athens, Greece. Among various pollutants, this work focuses on exploring the ozone (O3) pollutant, along with three meteorological variables, to examine their influence on O3 levels. Two statistical approaches were developed for modelling and forecasting monthly concentrations of O3 in Athens, Greece. The first approach is based on time series modelling using seasonal autoregressive integrated moving average (SARIMA) and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models. The second approach is based on spatio-temporal modelling using hierarchical Bayesian spatiotemporal Gaussian process (GP) models. This model has the ability to capture the complex dynamic of O3 levels, which vary based on several factors, including geographic location and meteorology. This model also has the ability to provide predictions for both space and time.

Both SARIMA and SARIMAX models showed a reasonable fit of the data. The SARIMAX outperformed SARIMA models in both training and testing tests in most stations. The hierarchical Bayesian spatio-temporal GP model fits the data well and shows reasonable performance in both forecasting and prediction abilities, with a spatial decay parameter ϕ of 0.13 corresponding to an effective range of 23 km. All statistical data analysis in this dissertation was implemented using the statistical software R.