運用土地利用迴歸模式評估臺北都會區懸浮微粒之空間變異性

Abstract

在歐洲空氣汙染世代研究(European Study of Cohorts for Air Polluton Effects, ESCAPE)中，交通強度(traffic intensity)、道路長度、及距離道路遠近等變項為其發展之懸浮微粒土地利用迴歸(Land use regression, LUR)模式中常見的交通指標，本研究於2009年9月開始至2010年10月為止，在台北都會區內20個家戶監測點進行了三次為期二周的PM2.5、PM2.5吸收度、PM10、和PMcoarse(PM10－PM2.5)長期環境監測，同時收集整個研究地區和汙染排放有關的交通及土地利用資料，並利用ESCAPE計畫所使用的模式發展架構，建立台北地區PM2.5、PM2.5吸收度、PM10、和PMcoarse的LUR模式。同時也進一步探討在道路密集、工商業活動繁忙、基礎建設高度發展下的台北都會區中，有哪些當地的土地利用變項可以改善LUR模式的表現。本研究發現在研究期間內，台北都會區各懸浮微粒的年平均濃度分別為PM2.5 26.0 ± 5.6 μg m-3、PM10 48.6 ± 5.9 μg m-3、PMcoarse 23.3 ± 3.1 μg m-3、而PM2.5吸收度為2.0 ± 0.4 × 10-5 m-1。在各懸浮微粒的LUR模式表現上，模式解釋力R2分別為PM2.5的0.95、PM2.5吸收度的0.96、PM10的0.87、以及PMcoarse的0.65。模式中PM2.5的濃度會受到交通、工業、興建中工地、及居住面積的增加而上升，但隨著河流面積而下降；PM2.5吸收度受到交通、工業、商業面積的增加而上升；PM10模式類似PM2.5，濃度受到交通、工業、商業、及興建中工地面積增加而上升；PMcoarse濃度則和高架道路的長度增加而會有上升的效應。道路面積相較於道路長度分別在PM2.5及PM10模式可以多解釋0.27及0.06(調整後R2，adjusted R2)的空間變異。在PM2.5吸收度模式中，道路面積和交通設施相較於道路長度可多解釋0.29空間變異。在PMcoarse模式中，工業和其他當地土地利用變項相較於道路長度，使模式的adjust R2從0.39改進至0.60。本研究結論出道路面積相較於道路長度有較好的空間變異解釋性，同時也和車流量有較好的相關性，因此在缺少車流量資料的情況下，台北都會區的PM LUR模式在結合道路面積及其他新增的變項後可以獲得改善。由於LUR模式相對於傳統空氣擴散模式有較易使用及所需輸入資料量較少等特點，本研究建議可以透過LUR模式的應用，作為在環境控制、環境政策管理、及健康衝擊效應上，一個有效的評估及預測工具。

Traffic intensity, length of road, and proximity to roads are the most common traffic indicators in the land use regression (LUR) models for particulate matter in ESCAPE study areas in Europe. This study conducted three 14-day measurments at 20 monitoring sites in Taipei metropolis and explored what local variables can improve the performance of LUR models in an Asian metropolis with high densities of roads and strong activities of industry, commerce and construction. By following the ESCAPE procedure, we derived LUR models of PM2.5, PM2.5 absorbance, PM10, and PMcoarse (PM10－PM2.5) in Taipei. The overall annual average concentrations of PM2.5, PM10, and PMcoarse were 26.0 ± 5.6, 48.6 ± 5.9, and 23.3 ± 3.1 μg m-3, respectively, and the absorption coefficient of PM2.5 was 2.0 ± 0.4 × 10-5 m-1. Our LUR models yielded R2 values of 0. 95, 0.96, 0.87, and 0.65 for PM2.5, PM2.5 absorbance, PM10, and PMcoarse, respectively. PM2.5 levels were increased by local traffic variables, industrial, construction, and residential land-use variables and decreased by rivers; while PM2.5 absorbance levels were increased by local traffic variables, industrial, and commercial land-use variables in the models. PM10 levels were similar to PM2.5, increased by local traffic variables, industrial, commercial, and construction land-use variables. PMcoarse levels were increased by elevated highways. Road area explained more variance than road length by increasing the incremental value of 0.27 and 0.06 adjusted R2 for PM2.5 and PM10 models, respectively. In the PM2.5 absorbance model, road area and transportation facility explain 0.29 more variance than road length. In the PMcoarse model, industrial and new local variables instead of road length improved the incremental value of adjusted R2 from 0.39 to 0.60. We concluded that road area can better explain the spatial distribution of PM2.5 and PM2.5 absorbance concentrations than road length. By incorporating road area and other new local variables, the performance of each PM LUR model was improved. The results suggest that LUR model can be applicated as a useful approach for assessing and predicting exposures in the context of environmental health impact assessment or environmental policy control.