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TEPA (Taiwan Environmental Protection Administration) currently has regulated six types of air pollutants based on the AQI. Among these, the three items most prone to exceeding the standard are PM2.5, PM10, and O3, in that order. PM2.5 pollution episodes in Taiwan mainly occur in winter and spring when the northeast monsoon prevails. In addition to local pollution sources, transboundary air pollution affects Taiwan. Obviously, the existing AQ monitoring data analyzed by the BD-oriented perspective not only simplifies the simulation calculation and verification resources of the AQ model but also assists in real-time insight into the causal relationships between the AQ and important parameters of meteorology, pollution sources, and regions. This study integrates the BD-oriented Social Network Analysis (SNA) approach and data visualization tools to analyze the event co-occurrence and spatial correlation characteristics of two pollution scenarios for AQ monitoring stations based on two severe PM2.5 pollution conditions: (1) the Z-value of PM2.5 daily average concentration is higher than 1.65 (Scenario I), and (2) the daily average concentration of PM2.5 exceeds TEPA’s regulation on the AQ standard (Scenario II), to identify the regional leading nodes suitable for different pollution scenarios. Furthermore, Principal Component Analysis (PCA) and time series data are employed to verify the spatial-temporal representation of these leading nodes, which can be regarded as means to the real-time AQ management decision-making as well as instant transboundary pollution precaution in the future. This study contributes to the application of the discrete data-driven approach (SNA) and the continuous data-driven approach (PCA) in an ambient AQ monitoring network, which can clearly explain and analyze the regional high pollution characteristics of PM2.5 in Scenarios I and II. The results of this study are consistent with previously relevant findings in Taiwan.

Identifying Leading Nodes of PM2.5 Monitoring Network in Taiwan with Big Data-oriented Social Network Analysis