Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London.
Environ Sci Technol
; 57(46): 18271-18281, 2023 Nov 21.
Article
en En
| MEDLINE
| ID: mdl-37566731
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 PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, 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 center), 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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Contaminantes Atmosféricos
/
Contaminación del Aire
/
COVID-19
Tipo de estudio:
Prognostic_studies
Límite:
Humans
País/Región como asunto:
Europa
Idioma:
En
Año:
2023
Tipo del documento:
Article