Insights Into the Morphology of the East Asia PM2.5 Annual Cycle Provided by Machine Learning.

In this study, we use machine learning to classify the morphology of PM2.5 seasonal cycles in East Asia. Machine learning is able to objectively classify the seasonal cycles and, without a priori assumption, is able to clearly distinguish between urban and rural areas. We show an example of this in the Sichuan Basin of China. Furthermore, machine learning is also able to provide physical insights by identifying the key factors associated with each distinct shape of the seasonal cycle, such as highlighting the key role played by the topography and the built environment. PMID: 28469447 [PubMed - in process]
Source: Environmental Health Insights - Category: Environmental Health Authors: Tags: Environ Health Insights Source Type: research