Mohajeri, NahidWalch, AlinaGudmundsson, AgustHeaviside, ClareAskari, SadafWilkinson, PaulDavies, Michael2024-05-162024-05-162024-05-162021-01-0110.5334/bc.124https://infoscience.epfl.ch/handle/20.500.14299/207972WOS:001208589600010In 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 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 decline in NO2-attributable mortality. These positive effects of the mobility restrictions public health can be used to evaluate policies for improved outdoor air quality.TechnologyAir PollutionAir QualityCitiesCovid-19Environmental HealthLockdownMachine LearningMobilityNo2Pm2.5Public HealthTransportVehiclestext::journal::journal article::research article