Predicting Daily Ozone Air Pollution With Transformers
Sebastian Hickman (University of Cambridge), Paul Griffiths (University of Cambridge), Peer Nowack (University of East Anglia) and Alex Archibald (University of Cambridge)
Abstract
Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Ozone at the surface also has considerable negative impacts on vegetation and crop yields. Ozone concentrations are affected by environmental factors, including temperature, which means that ozone concentrations are likely to change in future climates, posing risks to human health. This effect is known as the ozone climate penalty, and recent work suggests that currently polluted areas are likely to become more polluted by ozone in future climates. In light of recent stricter WHO regulations on surface ozone concentrations, we aim to build a predictive data-driven model for recent ozone concentrations, which could be used to make predictions of ozone concentrations in future climates, better quantifying future risks to human health and gaining insight into the variables driving ozone concentrations. We use observational station data from three European countries to train a transformer-based model to make predictions of daily maximum 8-hour ozone.