A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK

TL;DR

This study uses a large dataset and Gaussian process modeling to quantify the impact of the UK COVID-19 lockdown on NO2 air pollution levels, providing both numerical estimates and a practical software framework.

Contribution

It introduces a spatiotemporal Gaussian process model with covariates to assess lockdown effects on air quality and offers a software tool for similar analyses.

Findings

NO2 levels dropped 36.8% within two weeks of lockdown

NO2 levels were 29-38% lower than expected without lockdown

The model accurately captures complex spatiotemporal NO2 dynamics

Abstract

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.