The Data Accuracy Calibration of Fine Particulate Matter – A Case Study of Airboxes in Taipei

Authors: Jia-Huei Chen*, Department of Geography, National Taiwan Normal University, Kuo-Chen Chang, Department of Geography, National Taiwan Normal University
Topics: Spatial Analysis & Modeling, Temporal GIS, Environment
Keywords: Fine Particulate Matter, Geospatial Data Mining, Citizen Science, Geographical Weighted Regression, Hotspot Analysis
Session Type: Poster
Day: 4/5/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download


In 2013, the International Agency for Research on Cancer formally classified air pollution as an environmental carcinogen. This report brings a momentous meaning that controlling hazardous air pollutants is quite urgent. In the air pollutants, the most detrimental to human health is the particulate matter. It can penetrate the respiratory tract and deep into the lungs. If people are exposed to particle pollution for a long time, they may have much higher chance of lung cancer than those who do not expose to high PM 2.5. One of the main purposes of this research is to explore the spatial correlation and variation between data collected by AirBoxes and data collected by EPA monitor stations. The second purpose is the formula a spatial interpolation model to show the distribution of PM2.5 over the study area, based on data mining and spline techniques. The third purpose is to construct a spatial regression model to calibrate data from AirBoxes based on Geographical Weighted Regression. The results show that there does exist a very high spatial correlation between two data set and residual from GWR displays a spatial clustering pattern. Based on Getis-Ord’s Gi*, the hotspot of residuals are located in Wan-Hwa and TaTung districts with certain unique land use types and traffic patterns. All these show that the original purposes have been achieved and the spatial interpolation and regression models can be used to calibrate AirBox data, though the causes of the high spatial cluster pattern of residual require further study.

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