2008-08-012024-05-18https://scholars.lib.ntu.edu.tw/handle/123456789/698171摘要：本計畫的目標是評估於台北都會區中長期暴露濃度的時空分佈。現今由空氣污染所導致的疾病，大多是長期暴露於一定劑量的污染濃度所造成，因此如何準確推求空氣污染長期暴露，以求得具代表性的暴露平均濃度，對於空氣污染防制或是環境衛生領域方面研究相當重要。而都會區中長期暴露評估的分析會牽涉到許多的不確定性，包含 (1) 有限的觀測資料，(2) 複雜的都市型態，(3) 人群的移動。而本計畫則是希望建置一以時空地理統計為基礎的空間暴露評估模型，去分析台北都會區中長期對空氣懸浮粒子之暴露濃度隨空間時間之變化情形。 此計畫主要分成三個部分，分別利用貝氏最大熵法為架構，（一）建立空氣污染長期暴露空間分佈評估模式: 將討論暴露模式中，由小時間尺度的觀測值，到長期尺度的推估中，時間尺度改變及其不確定性的問題，（二）建立都會區中暴露評估模式: 利用土地利用回歸及鄰近法建立能考慮都會區中土地使用、交通流量等資訊的暴露評估模式，藉以改進僅以少數空氣污染監測資料為基底的暴露評估，（三）整合不同空間尺度下之土地利用回歸模型及空氣污染數值模型所得到的結果，並利用第一年之成果，建立台北都會區長期暴露評估模式，且進行不確定分析，描繪出台北都會區空氣懸浮粒子長期暴露濃度隨空間時間變化之情形。 <br> Abstract: The objective of this project is to develop an exposure model to assess the spatiotemporal distribution of long-term exposure in Taipei city. Since many of chronic diseases are induced by the long-time exposure to the air pollution, the estimation of the magnitude and uncertainty of exposure throughout the area of interest is important to the air pollution regulations or controls and environmental health research. In the intraurban area, the estimation of exposure level involves many elements with high uncertainty, such as (1) limited observations, (2) the complex land-use and emission sources within the city, and (3) the movement of individuals, etc. This project would like to develop a geostatistcal-based exposure model to assess the spatiotemporal distribution of long-term exposure level to particulate matter (PM10 and PM2.5) in Taipei city based on the scarce air pollution observations in space with the consideration of spatial variations of its covariates such as land-use, meteorological conditions. This project is divided into three parts. Based on the Bayesian maximum entropy framework, we would like to (a) to develop the spatial data-oriented long-term exposure model which produces the estimation of long-term (monthly or yearly) exposure level based on the air pollution observations in short-term (daily or hourly temporal scale). Such model considers the uncertainty from the change of scale; (b) to build the intraurban exposure model for Taipei city to consider the land-use and activity patterns within the city to complement the lack of detail spatial information from air pollution observations; and (c) to construct the complete spatial long-term exposure estimation model for Taipei city by the integration of the physical-based air quality model in urban scale, the data-oriented long-term exposure model in local scale, and the temporal upscaling model.貝氏最大熵法暴露評估時空分析Bayesian Maximum entropy methodexposure assessmentspatiotemporal analysis