I-Cheng Chang 


Department of Environmental Engineering, National Ilan University, Yilan 26047, Taiwan


Received: November 2, 2019
Revised: November 23, 2019
Accepted: November 24, 2019
Download Citation: ||https://doi.org/10.4209/aaqr.2019.11.0554 


Cite this article:
Chang, I.C (2019). Identifying Leading Nodes of PM2.5 Monitoring Network in Taiwan with Big Data-oriented Social Network Analysis. Aerosol Air Qual. Res. 19: 2844-2864. https://doi.org/10.4209/aaqr.2019.11.0554


Highlights

  • The application of SNA represents an innovative research in the field of AQM.
  • SNA has been less used in AQM research in Taiwan.
  • The results of this study are consistent with previously relevant findings in Taiwan.
  • The SNA & PCA can clearly analyze the regional high PM2.5 pollution characteristics.
  • The SNA approach is a reasonable means for determining PM2.5 leading nodes in TAQMN.

 

ABSTRACT


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.


Keywords: PM2.5 pollution scenarios; Transboundary air pollution episodes; Social Network Analysis; Betweennesscentrality.




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