Estimation of daily PM 10 and PM 2.5 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 exposure in nonurban settings. The objective of this study is to estimate daily PM ₁₀ (PM < 10 μm), fine (PM < 2.5 μm, PM _2.5 ) and coarse particles (PM between 2.5 and 10 μm, PM _2.5-10 ) at 1-km ² grid for 2013-2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM _2.5 and PM _2.5-10 concentrations in monitors where only PM ₁₀ 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 PM ₁₀ and PM _2.5 , respectively. Model fitting was less optimal for PM _2.5-10 , 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.
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