In 2020, Covid-19-related mobility restrictions resulted in the most extensive human-made air-quality changes ever recorded. The changes in mobility are quantified in terms of outdoor air pollution (concentrations of PM2.5 and NO2) and the associated health impacts in four UK cities (Greater London, Cardiff, Edinburgh and Belfast). After applying a weather-corrected machine learning (ML) technique, all four cities show NO2 and PM2.5 concentration anomalies in 2020 when compared with the ML-predicted values for that year. The NO2 anomalies are -21% for Greater London, -19% for Cardiff, -27% for Belfast and -41% for Edinburgh. The PM2.5 anomalies are 7% for Greater London, -1% for Cardiff, -15% for Edinburgh, -14% for Belfast. All the negative anomalies, which indicate air pollution at a lower level than expected from the weather conditions, are attributable to the mobility restrictions imposed by the Covid-19 lockdowns. Spearman rank-order correlations show a significant correlation between the lowering of NO2 levels and reduction in public transport (p < 0.05) and driving (p < 0.05), which is associated with a decline in NO2-attributable mortality. These positive effects of the mobility restrictions on public health can be used to evaluate policies for improved outdoor air quality.
Policy relevance: Finding the means to curb air pollution is very important for public health. Empirical evidence at a city scale reveals significant correlations between the reduction in vehicular transport and in ambient NO2 concentrations. The results provide justification for city-level initiatives to reduce vehicular traffic. Well-designed and effective policy interventions (e.g. the promotion of walking and cycling, remote working, local availability of services) can substantially reduce long-term air pollution and have positive health impacts.
Keywords: Covid-19; NO2; PM2.5; air pollution; air quality; cities; environmental health; lockdown; machine learning; mobility; public health; transport; vehicles.