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

Daji Wu; Gebreab K Zewdie; Xun Liu; Melanie Anne Kneen; David John Lary

doi:10.1177/1178630217699611

Insights Into the Morphology of the East Asia PM_2.5 Annual Cycle Provided by Machine Learning

Environ Health Insights. 2017 Mar 29:11:1178630217699611. doi: 10.1177/1178630217699611. eCollection 2017.

Authors

Daji Wu¹, Gebreab K Zewdie¹, Xun Liu¹, Melanie Anne Kneen², David John Lary¹

Affiliations

¹ William B. Hanson Center for Space Sciences, The University of Texas at Dallas, Richardson, TX, USA.
² Department of Environmental Science, Collin College, Plano, TX, USA.

Abstract

The abundance of airborne particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less (PM_2.5) is a significant environmental and health issue. Many tools have been used to examine the relationship between PM_2.5 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 PM_2.5 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.

Keywords: Air pollution; PM2.5; SOM; annual cycles; random forests.