The temporal evolution of PM2.5 pollution events in taiwan: Clustering and the association with synoptic weather
Journal
Atmosphere
Journal Volume
11
Journal Issue
11
Pages
1-14
Date Issued
2020
Author(s)
Abstract
This study conducted a cluster analysis on the fine particulate matter (PM2.5) data over Taiwan from 2006 to 2015 and diagnosed their association with the synoptic weather patterns. Five clusters are identified via a hierarchical clustering algorithm; three of them correspond to severe events, each with a distinct pattern of temporal evolution within the 240-h window. The occurrence of the different clusters exhibits strong seasonal variation. Two of the polluted clusters are more frequently associated with weak synoptic weather, while the other one is related to northeasterly winds and fronts. Detailed case studies show that the weather patterns’ temporal evolutions clearly modulate the transition among various pollution clusters by influencing the changes in local circulation and atmospheric stability. In winter, the clusters characterizing severe PM2.5 pollution events occur when Taiwan is influenced by persistent weak synoptic condition, while in autumn, the long-range transport by strong northerly winds leads to the occurrence of severe PM2.5 pollution. The current results shed light on the potential of combining the data-driven approach and the numerical weather forecasting model to provide extended range forecasts of local air pollution forecasts. ? 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Clustering algorithms; Hierarchical clustering; Pollution; Atmospheric stability; Data-driven approach; Fine particulate matter (PM2.5); Local air pollutions; Long range transport; Numerical weather forecasting; Synoptic conditions; Synoptic weather patterns; Weather forecasting; atmospheric pollution; cluster analysis; long range transport; particulate matter; synoptic meteorology; temporal evolution; weather; Taiwan
Type
journal article