探討細懸浮微粒時序走勢之空間分布－以台中地區微型感測器為例

Exploring the Spatial Patterns of the PM2.5 Time Series-A Case Study of Microsensors in Taichung

黃玉潔(Yu-Jie Huang) ； 王舜弘(Shun-Hong Wang) ； 曾聖澧(ShengLi Tzeng)

《中國統計學報》 60卷3期 (2022/09) Pp. 178-194

摘要

細懸浮微粒（PM2.5）對健康的影響極大，近來環境保護署推動建置空污品質微型感測器在各鄉鎮監測PM2.5，但PM2.5微型感測器之監測值易受到儀器異常或受附近環境影響且數量眾多難以逐一探討。本研究以兩階段方法分析相關數據，首先建立同時描述空間和時間訊號的時空模型，並過濾感測器的雜訊，再以函數型主成分分析進行分群。該方法除運算速度快，所得的函數型主成分分析也考慮到空間上的相關性。本文探討區域污染的時間序列走勢，並由分群分析中找出對應不同人口活動和工業區的群集。

關鍵字

函數型主成分分析 ； 細懸浮微粒 ； 時空模型 ； 微型感測器

並列摘要

Air pollutants of PM2.5 are fine particles having a significant impact on human health. Recently, Taiwan Environmental Protection Agency has been promoting the installation of air pollution quality micro-sensors to monitor PM2.5 concentrations at various locations in Taiwan. However, the measurements from PM2.5 micro-sensors are easily influenced by instrument anomalies or the nearby environment, and the number of micro-sensors is too large to be investigated individually. In this study, we propose a two-stage approach to analyze such data. In the first step, a spatio-temporal model was established to describe both the spatial and the temporal signals, and to reduce the noises in the raw observations. The second step then grouped the micro-sensors into clusters based on a functional principal component analysis (FPCA). The main advantage of this two-step method is its computational efficiency. Moreover, the resulting functional principal components take into account the spatial correlations between micro-sensors, which were not considered in a typical FPCA. We investigated the time series of PM2.5 concentrations measured by the micro-sensors, and we identified several regional clusters representing different characteristics of residents' activities or industrial land uses.

並列關鍵字

functional principal component analysis ； PM2.5 ； spatio-temporal model ； microsensor

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