Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. (March 2019)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Environment international. Volume 124(2019)
• Page Start: 170
• Page End: 179
• Publication Date: 2019-03
• Subjects: Aerosol optical depth -- Exposure assessment -- Machine learning -- Particulate matter -- Random forest -- Satellite; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2019.01.016
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: 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 < 10 μm), fine (PM < 2.5 μm, PM2.5 ) and coarse particles (PM between 2.5 and 10 μm, PM2.5–10 ) at 1-km 2 grid for 2013–2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5–10 concentrations in monitors where only PM10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km 2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R 2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5–10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects. Highlights: Estimates of fine and coarse particles at fine spatiotemporal scale are lacking in Italy We applied a multistage random forest model combining PM data with satellite, land-use and meteorology We imputed missing satellite AOD data using ensemble atmospheric models We estimated daily PM10, PM2.5 and PM2.5-10 at a 1-km 2 grid over Italy for the years 2013-2015 Our model displayed good CV fitting (R 2 =0.75 for PM10, R 2 =0.80 for PM2.5, R 2 =0.64 for PM2.5-10 ) and negligible bias