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, Environmental Science, Environment
Keywords: Fine Particulate Matter, Accuracy Calibration, Citizen Science, Internet of Things, Air Quality Model Simulation
Session Type: Paper
Day: 4/14/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Napoleon B1, Sheraton 3rd Floor
Presentation File: No File Uploaded

In 2013, the International Agency for Research on Cancer formally classified air pollution as an environmental carcinogen. 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, and deposited in the body. Our government has gradually revised air pollution control regulations and incorporate the fine particulate matter (PM_2.5) into monitoring programs. With the rise of citizen science, civil society developed a participatory urban sensing framework for PM_2.5 monitoring named “AirBox.” It can automatically measure the PM_2.5 concentration, air temperature and humidity. The system combines wireless network architecture, enabling data to be stored in the cloud, publicly displayed and shared. People can easily set up a sensing system around their homes, so AirBox quickly laid in every corner of the city. AirBox compensated for the insufficient spatial density problem of official monitoring station, but its data accuracy is worse than the official. In addition, the suitability of the location selection also affects the monitoring data so the validity of the data remains to be discussed. In this study, the Environmental Protection Administration's effective monitoring data is used to estimate the pollution concentration and carry out error analysis with the monitoring data of the AirBox. We expect to find out the error between the two cases, and to propose an correction reference guide to ensure the quality of the data.

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