  In March 2020 the United Kingdom (UK) entered a nationwide lockdown period
due to the Covid-19 pandemic. As a result, levels of nitrogen dioxide (NO2) in
the atmosphere dropped. In this work, we use 550,134 NO2 data points from 237
stations in the UK to build a spatiotemporal Gaussian process capable of
predicting NO2 levels across the entire UK. We integrate several covariate
datasets to enhance the model's ability to capture the complex spatiotemporal
dynamics of NO2. Our numerical analyses show that, within two weeks of a UK
lockdown being imposed, UK NO2 levels dropped 36.8%. Further, we show that as a
direct result of lockdown NO2 levels were 29-38% lower than what they would
have been had no lockdown occurred. In accompaniment to these numerical
results, we provide a software framework that allows practitioners to easily
and efficiently fit similar models.
