{"ob_id":43886,"uuid":"e6a317f0bc544a4e8a45884b82e6ccae","title":"Operationalising Ambient Air Pollution Estimation","abstract":"This doctoral research focuses on developing and implementing machine-learning methodologies to estimate ambient air pollution, introducing scalable machine-learning approaches for estimating hourly pollution levels, aiming to enhance the accuracy and accessibility of air quality data for environmental analysis and policy-making.","keywords":"Ambient Air Quality, England, Nitrogen Dioxide, Nitrogen Oxides, Ozone, Particulate Matter, Sulphur Dioxide, Machine Learning, Air Pollution Scenarios, AI","status":"","publicationState":"published","identifier_set":[],"observationCollection":[],"parentProject":null,"subProject":[],"responsiblepartyinfo_set":["https://catalogue.ceda.ac.uk/api/v2/rpis/209785/?format=json","https://catalogue.ceda.ac.uk/api/v2/rpis/209787/?format=json"]}