A Random Forest Approach to Estimate Daily Particulate Matter, Nitrogen Dioxide, and Ozone at Fine Spatial Resolution in Sweden

A Random Forest Approach to Estimate Daily Particulate Matter, Nitrogen Dioxide, and Ozone at Fine Spatial Resolution in Sweden

Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for pa...

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Bibliographic Details
Main Authors: Massimo Stafoggia, Christer Johansson, Paul Glantz, Matteo Renzi, Alexandra Shtein, Kees de Hoogh, Itai Kloog, Marina Davoli, Paola Michelozzi, Tom Bellander
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Atmosphere
Subjects:
air pollution
epidemiology
machine learning
nitrogen dioxide
ozone
particulate matter
random forest
Online Access:https://www.mdpi.com/2073-4433/11/3/239

Internet

https://www.mdpi.com/2073-4433/11/3/239

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