The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM₁₀, PM_2.5) from January 2020 to September 2021. Measurements of PM₁₀ and PM_2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM₁₀ and PM_2.5 were reduced by 9–10 μg/m³ below typical peak levels experienced in recent years, mean daily PM₁₀ and PM_2.5 concentrations were only ∼1 μg/m³ lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM₁₀ and PM_2.5 levels to within WHO health-based guidelines and to achieve compliance with PM_2.5 targets developed under the Environment Act 2021.