Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model

Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles...

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Veröffentlicht in:Environment international 2019-03, Vol.124, p.170-179
Hauptverfasser: Stafoggia, Massimo, Bellander, Tom, Bucci, Simone, Davoli, Marina, de Hoogh, Kees, de' Donato, Francesca, Gariazzo, Claudio, Lyapustin, Alexei, Michelozzi, Paola, Renzi, Matteo, Scortichini, Matteo, Shtein, Alexandra, Viegi, Giovanni, Kloog, Itai, Schwartz, Joel
Format: Artikel
Sprache:eng
Schlagworte:
Aerosol optical depth
Exposure assessment
Machine learning
Particulate matter
Random forest
Satellite
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Zusammenfassung:Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM 
ISSN:0160-4120
1873-6750
DOI:10.1016/j.envint.2019.01.016