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Real‐time PM 2.5 mapping and anomaly detection from AirBoxes in Taiwan
Author(s) -
Huang G.,
Chen L.J.,
Hwang W.H.,
Tzeng S.,
Huang H.C.
Publication year - 2018
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2537
Subject(s) - anomaly detection , kriging , range (aeronautics) , anomaly (physics) , computer science , chart , environmental science , data mining , statistics , machine learning , engineering , mathematics , physics , condensed matter physics , aerospace engineering
Fine particulate matter (PM 2.5 ) has gained increasing attention due to its adverse health effects to human. In Taiwan, it was conventionally monitored by large environmental monitoring stations of the Environmental Protection Administration. However, only a small number of 77 monitoring stations are currently established. Recently, a project using a large number of small devices, called AirBoxes, was launched in March 2016 to monitor PM 2.5 concentrations. Although thousands of AirBoxes have been deployed across Taiwan to give a broader coverage, they are mostly located in big cities and their measurements are less accurate. In this paper, we apply a robust kriging method that provides a smoothly varied real‐time PM 2.5 concentration map and its associated standard error map. In addition, we develop a novel spatio‐temporal control chart that monitors anomalous measurements by utilizing neighboring AirBox information. Our method automatically adapts to different neighboring structures at different AirBox locations without the need to specify a neighborhood range. The proposed method has abilities to detect potential emission sources, malfunctioned AirBoxes, and AirBoxes that are wrongly put indoors.