Using machine learning to understand the temporal morphology of the PM2.5 annual cycle in East Asia
2019
Wu, Daji | Lary, David J. | Zewdie, Gebreab K. | Liu, Xun
PM₂.₅ 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 PM₂.₅ concentration. However, even this enhanced observational network leaves many gaps in characterizing the PM₂.₅ 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 PM₂.₅ concentration. This study used a PM₂.₅ 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 PM₂.₅ concentration. These variables were then examined via multiple linear regression models to provide physical mechanistic insights into the morphology of the PM₂.₅ annual cycles.
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