應用土地利用迴歸模式推估北部空品區細懸浮微粒之時空分布

Spatial-Temporal Variability of Fine Particulate Matter in Northern Air Quality Zone of Taiwan Using Land Use Regression

曾于庭(Yu-Ting Zeng) ； 吳治達(Chih-Da Wu) ； 龍世俊(Shih-Chun Candice Lung)

《航測及遙測學刊》 23卷3期 (2018/09) Pp. 191-204

https://doi.org/10.6574/JPRS.201809_23(3).0004

摘要

細懸浮微粒（Fine Particulate Matter,PM_(2.5)）對人體健康之衝擊與危害為近日倍受關注的議題。受限於空氣品質監測站數目上之分布、加上多元且複雜的區域污染排放源，如多元餐廳類型、工業區等，因此土地利用迴歸模式（Land Use Regression LUR）在國際間越來越被廣泛運用，然而目前國內相關應用案例仍不多見。本研究針對細懸浮微粒以及土地利用迴歸模式之相關文獻進行系統性回顧後，利用環保署於北部空品區所設立之24個空氣品質監測站於2006-2011年六年間之所有歷史監測資料，分別建立逐年、逐月北部空品區PM_(2.5)之土地利用迴歸模式；而在變數選取部分，納入前人研究鮮少考量的常態化差異植生指標（Normalized Difference Vegetation Index, NDVI)衛星影像來代表環境植生狀況，以及中式餐飲、寺廟等亞洲特有汙染源進行分析。結果顯示，中式餐飲、寺廟等亞洲特有汙染源都有包含在最後模式當中，所建立的月以及年模式之R^2為0.75、0.85，經過多種模式驗證方法亦確認，本研究所建模式穩定且可信。最後利用建立之模式推估北部空品區污染濃度之時空分布發現，都會地區常年均為濃度較高處，隨著年份增加細懸浮微粒濃度有略微下降之趨勢。

關鍵字

細懸浮微粒 ； 地理資訊系統 ； 土地利用迴歸模式 ； 亞洲特有污染源

並列摘要

Fine Particulate Matter (PM_(2.5)) is one of the pollutants which affects human health and has been attracting attention in recent years. Due to the uneven distribution and limited number of monitoring sites, observations from the monitoring stations are not capable of depicting the variability of PM_(2.5) over intra-urban areas in Taiwan. Land Use Regression (LUR) is one of the solution to deal with this challenge and has been widely applied in western cities recently. However, there are limited cases related to LUR in Taiwan. In this work, 6-year observations of 24 air quality monitoring stations located in the Northern Air Quality Zone of Taiwan operated by Environmental Protection Agency were collected. Compared with previous studies, several culture-specific emission sources such as temples, and Chinese restaurants, and a long-term satellite-based vegetation index were further considered to develop the LUR model at monthly and yearly resolutions for estimating the spatial-temporal variability of PM_(2.5). The R^2 of the resultant models are 0.75 and 0.85 for monthly and yearly resolutions, respectively. Moreover, temples, and Chinese restaurants were both selected as important predictors in the developed models. The results of external data verification confirmed the robustness of the model performance. Finally, spatial-temporal variability of fine particle estimated using the developed models show a slightly decreasing during the study period over high polluted areas clustered in the urban cores of Taipei and Taoyuan cities.

並列關鍵字

Fine Particulate Matter ； Geographic Information System ； Land Use Regression ； Culture-specific Emission Source