QUANTIFYING THE IMPACT OF COVID-19 RESTRICTIONS ON EMISSIONS USING INVERSE MODELLING AND MEASUREMENTS

ABSTRACT

An inversion system that uses a Bayesian approach to combine measurements and ADMS-Urban modelled data by adjusting individual source emissions, subject to estimated uncertainty in the measurements and emissions, has previously been applied to optimising road traffic emissions in Cambridge. In this study the system has been applied specifically to the impact of interventions, in particular the impact of COVID-19 lockdowns on NOX emissions from road traffic and other sources in London. The ADMS-Urban model was used to calculate a priori hourly NOX concentrations at 195 receptors in London representing 115 reference monitors and 80 Breathe London Network AQMesh sensors. Input data included hourly meteorological measurements from Heathrow Airport, hourly NOX concentrations from 4 rural background monitoring sites and buildings road centreline data from Ordnance Survey. A priori emissions were obtained from the London Atmospheric Emissions Inventory (LAEI) for 35 point sources, approximately 70,000 major road sources and 2,500 1km grid cells representing minor road, heating and other sources. The analysis period was 1 January 2020 to 30 April 2021. Estimated uncertainties of 4 and 12 µg/m3 were applied to reference and sensor measurements respectively, while emissions uncertainties of 100%, 50%, 20% were applied to road traffic, fuels and other emissions respectively. Road traffic emissions were assumed to have error covariance of 40% of their emissions uncertainty. Measured NOX concentrations in London reduced significantly during lockdown, with the greatest reduction (around 60%) at kerbside and roadside sites in Central London. However, poor dispersal conditions led to increased concentrations at times when restrictions were tightest. In contrast, inversion system results demonstrate that NOX emissions from road traffic dropped by around 60% in London compared with pre-lockdown levels and that this reduction occurred when the strictest lockdown measures were in force. The results also show that NOX road traffic emissions were still approximately 30% lower than pre-lockdown levels at the end of April 2021. This analysis demonstrates that lower cost sensors such as AQMesh can provide valuable insight into the effects of policy measures (in this case lockdown restrictions), if their increased uncertainty compared with reference monitors is accounted for. © British Crown Copyright (2022)