Close
Help





JOURNAL

Environmental Health Insights

Insights Into the Morphology of the East Asia PM Annual Cycle Provided by Machine Learning

Submit a Paper


Environmental Health Insights 2017:11 1178630217699611

Original Research

Published on 29 Mar 2017

DOI: 10.1177/1178630217699611


Further metadata provided in PDF



Sign up for email alerts to receive notifications of new articles published in Environmental Health Insights

Abstract

The abundance of airborne particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less (PM2.5) is a significant environmental and health issue. Many tools have been used to examine the relationship between PM2.5 abundance and meteorological variables, but some of the relationships are nonlinear, non-Gaussian, and even unknown. Machine learning provides a broad range of practical algorithms to help examine this issue. 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.



Downloads

PDF  (1.31 MB PDF FORMAT)

RIS citation   (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)

XML   (61.85 KB XML FORMAT)

BibTex citation   (BIBDESK, LATEX)





Quick Links


New article and journal news notification services