Using machine learning to understand the temporal morphology of the PM annual cycle in East Asia.
Journal:
Environmental monitoring and assessment
PMID:
31254074
Abstract
PM air pollution is a significant issue for human health all over the world, especially in East Asia. A large number of ground-based measurement sites have been established over the last decade to monitor real-time PM concentration. However, even this enhanced observational network leaves many gaps in characterizing the PM spatial distribution. Machine learning provides a variety of algorithms to help deal with these large spatial gaps-combining both remotely sensed and in situ observation data to estimate the global PM concentration. This study used a PM data product of six regions from the results of an unsupervised self-organizing map (SOM) with optimized ensemble learning approaches to highlight the most important meteorological and surface variables associated with PM concentration. These variables were then examined via multiple linear regression models to provide physical mechanistic insights into the morphology of the PM annual cycles.