細懸浮微粒資料精確度校正探討：以臺北地區空氣盒子為例

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

2013年國際癌症研究機構正式將空氣污染列為環境致癌物，該報告揭示了一項重要的訊息，即空氣污染物的控制已經刻不容緩。在各項空氣污染物中，最有損人體健康的是懸浮微粒，它能滲透人體呼吸道並深入肺部，若人們長期暴露於懸浮微粒污染的環境中，則會提高肺癌罹患之風險。故本研究的主要目一，在於探索空氣盒子監測數據以及官方測站監測數據之間的空間相關性與差異性。目的二，利用資料探勘技術與樣條插值函數建立空間內插模型，來表示研究區內的細懸浮微粒污染分布。目的三，透過地理加權迴歸建立空間迴歸模型，藉此校正空氣盒子數據。研究結果顯示，兩種資料集之間確實存在著高度的空間相關性，而地理加權迴歸的殘差分布具有空間群聚趨勢，最後利用Getis-Ord's Gi熱區分析得知，殘差的分布顯著聚集於萬華區與中正區交界處，以及大同區鄰近中山區一帶，然而此誤差群聚分布之現象，其背後必定隱含著某種特殊的土地利用型態和交通模式。綜納上述各項結果，本研究證實了空氣盒子資料可以藉由空間內插法與地理加權迴歸模型進行校正，儘管殘差的顯著空間聚集現象原因仍有待進一步探究。

In 2013, the International Agency for Research on Cancer formally classified air pollution as an environmental carcinogen. This report highlights the urgency that hazardous air pollutants should be controlled. Among the air pollutants, particulate matter (PM) is most detrimental to human health. It can penetrate the respiratory tract deep into the lungs, and stay in the body. If people are exposed to particle pollution for a long time, they have a much higher chance of contracting lung cancer than those who are not exposed 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 to develop a spatial interpolation formula and model to show the distribution of PM2.5 in 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 (GWR). The results show that a very high spatial correlation between the two data sets exists, and residual from GWR displays a spatial clustering pattern. Based on Getis-Ord's Gi*, the hotspots of residuals are located in Wanhua and Tatung districts. These districts have certain unique land use types and traffic patterns. All of these results show that the original research purposes have been achieved, and the spatial interpolation and regression models can be used to calibrate AirBox data. It is recommended that the causes of the high spatial cluster pattern are further investigated in future studies.