Enhancing Evacuation Planning and Management through Vehicular Communication

Abstract

Activity changes during the COVID-19 lockdown brought an unprecedented opportunity to understand the likely effectiveness of prospective air quality management policies on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for NO2, 0% to +4% for O3, -5% to +0% for PM10 and there was no response for PM2.5. Using explainable machine learning, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city centre), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.