Predicting Daily PM_2.5 Exposure with Spatially Invariant Accuracy Using Co-Existing Pollutant Concentrations as Predictors

The spatiotemporal variation of PM_2.5 should be accurately estimated for epidemiological studies. However, the accuracy of prediction models may change over geographical space, which is not conducive for proper exposure assessment. In this study, we developed a prediction model to estimat...

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Autors principals:	Shin Araki, Hikari Shimadera, Hideki Hasunuma, Yoshiko Yoda, Masayuki Shima
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2022-05-01
Col·lecció:	Atmosphere
Matèries:	random forests machine learning fine particulate matter Japan
Accés en línia:	https://www.mdpi.com/2073-4433/13/5/782
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