Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London.

ABSTRACT: Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have 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, by up to 65%, 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 nitrogen dioxide (NO₂), 0% to +4% for ozone (O₃), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀), and there was no response for PM_2.5. Using explainable machine le

SUBMITTER: Ma L 

PROVIDER: S-EPMC10666281 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature