Impact of COVID-19 lockdown on particulate matter oxidative potential at urban background traffic sites

In Europe, COVID-19 lockdown restrictions were first implemented in March 2020 to control the spread of the disease from the SARS-CoV-2 virus. Many studies have focused on the influence of the applied measures on pollution levels during this period, but very limited information on the oxidative potential (OP), an emerging metric of particulate matter (PM) exposure. Furthermore, most previous studies also commonly used comparative methods with historical datasets, which may not be estimating the real pollution levels without the lockdown restrictions in place. In this study, the OP of PM collected at urban background (Grenoble, France) and traffic (Bern, Switzerland) sites was assessed using dithiothreitol (DTT) and ascorbic acid (AA) assays. These measurements were also compared with PM and black carbon (BC) mass concentrations, including the wood burning and fossil fuel fractions of BC. To obtain a more realistic pollution level, assuming there were no lockdown restrictions in place, a machine learning technique called the Random Forest (RF) regression model was applied to predict a business-as-usual (BAU) level for OP, PM, and BC in both sites. This model provided a good estimate of the BAU levels, allowing a more realistic assessment of the pollution changes during the lockdown period. The results indicate a clear decrease in OP found in the traffic site, while a more modest change in OP was found at the urban background site, likely due to sustained contributions from wood burning sources for residential heating. Overall, this study confirms the major roles of both of these combustion sources in the OP levels in ambient air. During the lockdown period, the oxidative potential of PM decreased in a traffic site but not in an urban site due to sustained contributions from residential heating emissions. Random forest modelling is useful in predicting business-as-usual levels for air quality studies.