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A random forest approach to estimate daily particulate matter, nitrogen dioxide, and ozone at fine spatial resolution in Sweden

Stafoggia, M. and Johansson, C. and Glantz, P. and Renzi, M. and Shtein, A. and de Hoogh , K. and Kloog, I. and Davoli, M. and Michelozzi, P. and Bellander, T.. (2020) A random forest approach to estimate daily particulate matter, nitrogen dioxide, and ozone at fine spatial resolution in Sweden. Atmosphere, 11 (3). p. 239.

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Abstract

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 particulate matter (PM) have been developed in several countries, estimates of daily nitrogen dioxide (NO 2 ) and ozone (O 3 ) concentrations at high spatial resolution are lacking, and no such models have been developed in Sweden. We collected data on daily air pollutant concentrations from routine monitoring networks over the period 2005-2016 and matched them with satellite data, dispersion models, meteorological parameters, and land-use variables. We developed a machine-learning approach, the random forest (RF), to estimate daily concentrations of PM 10 (PM<10 microns), PM 2.5 (PM<2.5 microns), PM 2.5-10 (PM between 2.5 and 10 microns), NO 2 , and O 3 for each squared kilometer of Sweden over the period 2005-2016. Our models were able to describe between 64% (PM 10 ) and 78% (O 3 ) of air pollutant variability in held-out observations, and between 37% (NO 2 ) and 61% (O 3 ) in held-out monitors, with no major differences across years and seasons and better performance in larger cities such as Stockholm. These estimates will allow to investigate air pollution effects across the whole of Sweden, including suburban and rural areas, previously neglected by epidemiological investigations
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Environmental Exposures and Health > Physical Hazards and Health (Röösli)
UniBasel Contributors:de Hoogh, Kees
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:MDPI
e-ISSN:2073-4433
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
  • : 10.1136/bmjopen-2018-026449
edoc DOI:
Last Modified:07 Apr 2020 12:27
Deposited On:07 Apr 2020 12:24

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