Predicting monthly high-resolution PM_2.5 concentrations with random forest model in the North China Plain.

ABSTRACT: Exposure to fine particulate matter (PM_2.5) remains a worldwide public health issue. However, epidemiological studies on the chronic health impacts of PM_2.5 in the developing countries are hindered by the lack of monitoring data. Despite the recent development of using satellite remote sensing to predict ground-level PM_2.5 concentrations in China, methods for generating reliable historical PM_2.5 exposure, especially prior to the construction of PM_2.5 monitoring network in 2013, are still very rare. In this study, a high-performance machine-learning model was developed directly at monthly level to estimate PM_2.5 levels in North China Plain. We developed a random forest model using the latest Multi-angle implementation of atmo

SUBMITTER: Huang K 

PROVIDER: S-EPMC6690389 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature