整合空間資訊技術與土地利用迴歸模式推估高屏空品區細懸浮微粒之時空分布

Integrate Geospaital Information Technologies and Land-use Regression to Estimate the Spatial-temporal Variability of Fine Particulate Matter in Kaohsiung-Pingtung Air Quality Zone

曾芷琳(Chih-Lin Zeng) ； 蕭雅萍(Ya-Ping Hsio) ； 曾于庭(Yu-Ting Zeng) ； 陳穆貞(Mu-Jean Chen) ； 龍世俊(Shih-Chun Candice) ； 吳治達(Chih-Da Wu)

《航測及遙測學刊》 24卷2期 (2019/06) Pp. 79-87

https://doi.org/10.6574/JPRS.201906_24(2).0001

摘要

近年來細懸浮微粒（Fine Particulate Matters, PM_(2.5)）對健康的影響日益受重視。台灣於2006年起將PM_(2.5)列為空氣品質之監測指標之一，然而受限於台灣全島之測站數量稀少且分布不均，加上區域內汙染排放源類型多元且複雜，故如何有效推估污染物濃度之時空變異，實為當前環境流行病學的重要課題之一。基於此，本研究選擇台灣空污問題最為嚴重之高屏空氣品質管理區為試區，利用區內15個環保署空氣品質監測站於2006年至2012年之年平均PM_(2.5)濃度監測資料為材料，透過地理資源系統以及遙感探測等空間資訊技術獲取測站周邊之土地利用資訊，進而建立土地利用迴歸模式（Land Use Regression Model, LUR），以推估高屏空品區PM_(2.5)之時空分布。研究結果指出，最終所建立之土地利用迴歸模式之R^2、校正後R^2以及RMSE分別為0.82、0.82以及3.81μg/m^3；選入之變數包含與工業區之距離、方圓3,000 m內NDVI最小值、氣溫以及方圓1,500 m內稻作面積，其中又以與工業區之距離為最主要因子（partial R^2=0.76）；在模式驗證方面，留一驗證與外部資料驗證之R^2分別為0.81及0.67，整體來說模式之推估能力尚算穩定；最後利用所建模式推估高屏空品區之PM_(2.5)濃度分布，結果顯示，研究期間PM_(2.5)濃度呈略微下降趨勢，並且高濃度地區多分布於高屏沿海之都會區，尤以高雄市區之汙染程度最為嚴重。

關鍵字

細懸浮微粒 ； 空間資訊技術 ； 土地利用迴歸模式 ； 高屏空品區 ； 時空分布

並列摘要

Along with industrialization and urbanization, the exploitation of natural resources makes emissions of air pollutants increases. Fine Particulate Matter (PM_(2.5)) is one of the pollutants which affect human health and has been attracting attention in recent years. Observations from the limited number of monitoring sites are not capable to depict the spatial concentration pattern of PM_(2.5). Diverse culture-specific emission contributors distributed in communities raise the difficulty of emission classification as well. This study selected Kaohsiung-Pingtung Air Quality Zone (Kao-Ping AQZ) as the study area. Annual average PM_(2.5) level was calculated based on the on-site observations from 15 EAP monitoring stations during 2006 to 2012. Land-use allocation surrounding the monitoring sites were obtained from GIS databases. A Land Use Regression (LUR) model then developed to estimate the spatial-temporal variability of PM_(2.5) concentrations of Kao-Ping AQZ. The overall model R^2, adjusted R^2, and RMSE was 0.82, 0.82, and 3.81μg/m^3, respectively. Several land-use and environmental variables were selected as important predictor variables, including distance to the nearest industrial park, minimum of NDVI within 3000m buffer range, temperature, and area of farm within 1500m buffer range. With the partial R^2 of 0.76, distance to the nearest industrial park was the dominant variable in the developed model. The results of model validation again confirmed the model performance while the R^2 from cross validation and external data verification was 0.81 and 0.67, respectively. The predictions of PM_(2.5) variability based on the developed LUR model showed a slightly decreasing trend during the study period while coastal metropolitan areas were always the higher pollutant areas.

並列關鍵字

Fine Particulate Matter (PM_(2.5)) ； Geospaital Information Technologies ； Land Use Regression ； Kaohsiung-Pingtung Air Quality Zone ； Spatial-temporal Variability