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

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 PM 2.5 , PM 10 , and O 3 , in that order. PM 2.5 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 realtime 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 PM 2.5 pollution conditions: (1) the Z-value of PM 2.5 daily average concentration is higher than 1.65 (Scenario I), and (2) the daily average concentration of PM 2.5 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 PM 2.5 in Scenarios I and II. The results of this study are consistent with previously relevant findings in Taiwan.

Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region

“…One in eight deaths in India is said to be caused by air pollution, according to a report co-authored by the Indian Council of Medical Research [1]. Particulate matter (PM) includes inhalable PM (PM 10 , aerodynamic diameter ≤ 10 µm) and finer PM (PM 2.5 , aerodynamic diameter ≤ 2.5 µm), and PM-based air pollution is said to be the leading cause of deaths from ambient air pollution [2][3][4]. The annual average PM 10 and PM 2.5 concentrations in Indian cities were found to be 106.4 and 58.6 µg·m −3 , respectively, with every 10 µg·m −3 increase in PM 2.5 increasing all-cause mortality by between 3% and 26%, chances of childhood asthma by 16%, chances of lung cancer by 36%, and heart attacks by 44%.…”

Analysis of the spatio-temporal network of air pollution in the Yangtze River Delta urban agglomeration, China

“…SNA is a quantitative analysis method that combines graph theory with mathematical methods, it can effectively measure and analysis the network characteristics [ 30 ]. SNA has been used to describe the characteristics of the air pollution network [ 31 , 32 ]. In our paper, the 27 core cities in the YRDUA are used as the network nodes, and the spatial correlation of air pollution identified above is used as paths to build social network.…”