Combining Information from Multiple Sources: Global Modelling of Air Pollution

Abstract

Air pollution constitutes the highest environmental risk factor in relation to health. In order to provide the evidence-base required to perform health impact analyses, to inform policy and to develop potential mitigation strategies, comprehensive information is required on the state of global air pollution. Traditionally, the primary source of information on air pollution has come from ground monitoring networks but these may not always be able to provide the spatial (and/or temporal) coverage that is required and may need to be supplemented with information from other sources such as chemical transport models, satellite remote sensing and land use information.

In this thesis, a series of models are presented for integrating data from multiple sources, enabling accurate estimation of global exposures to ambient fine particulate matter (PM$_{2.5}$). Using a Bayesian hierarchical modelling framework, we estimate exposures to PM$_{2.5}$, together with associated measures of uncertainty, at high geographical resolution addressing many of the issues encountered with previous approaches. These modelling frameworks and the resulting estimates are used to answer a series of substantial questions related to spatial variation in air pollution and the changes over time. Specifically, it is used to assess the global burden of disease attributable to PM$_{2.5}$, to assess the trends in population exposures to PM$_{2.5}$ over time and to estimate the health co-benefits of climate change legislation aiming to reduce greenhouse gasses.

Date of Award	1 May 2020
Original language	English
Awarding Institution	University of Bath
Supervisor	Gavin Shaddick (Supervisor) & Melina Freitag (Supervisor)