過去研究證實，室內PM2.5的暴露會增加人體的健康危害風險，因此量測室內PM2.5濃度變化及其影響因子，有助於找出室內PM2.5的污染來源與評估適當的改善策略，進而降低室內人員的暴露風險。然而，室內PM2.5濃度資料不易取得，且近年研究發展的室內PM2.5濃度推估模式，都是以溫帶國家的建築特性、人為活動特性和污染源種類為基礎，但不同氣候帶的建築結構、室內人員活動特性和PM2.5來源均不同，使得不同參數對室內PM2.5濃度的影響程度亦不相同；再者，過去研究多是建立日平均室內PM2.5濃度推估模式，不利於室內PM2.5污染之短期預警系統的發展。
有鑒於此，本研究利用2008年8月至2009年9月間，在台南地區蒐集的93戶一般住宅的室內人員活動特性與建築結構、每小時的室內外溫濕度、每小時的室內外空氣污染物濃度和土地使用型態等資料，並以多變項線性迴歸模式建立室內PM2.5濃度的推估模式，並探討影響室內PM2.5濃度的因子。前述研究住家之每小時室外PM2.5濃度，來自Kriging空間內插法 推估研究住家鄰近的環保署空氣品質監測站量測得的PM2.5濃度；土地使用型態的資料包含Points of Interest (POI) 資料庫和交通部運輸研究所路網數值圖等圖檔，並以ArcGIS觀察環域 (Buffer) 範圍內的土地利用情況。
結果顯示模式的解釋力為74%，室外PM2.5濃度與穿透因子為影響室內PM2.5濃度的重要參數。內部資料驗證和外部資料驗證結果顯示，解釋力分別落在60%-82%與36%-85%。本研究建議未來發展室內PM2.5濃度的推估模式時，應將上述因素納入模式中，以提高模式的推估能力；此外，本研究建立的推估模式可推求每小時的室內PM2.5濃度，有助於做為未來PM2.5污染問題之短期預警系統的發展。

Long-term PM2.5 exposures may contribute to elevated incidence of multiple organs’ damage. However, people spend approximately 90% of their time staying indoors, over- or under-estimates of health risks might be caused due to the difference of PM2.5 level between indoors and outdoors. Therefore, it is necessary to clarify the relationship between indoor and outdoor PM2.5 concentrations; especially, this study using preliminary database to clarify the relationship between indoor and outdoor PM2.5 concentrations. From August 2008 to September 2009, study households contained 93 preschool children's bedroom were selected from Tainan. Research material involved indoor human actives, building characteristic, meteorological data, land use variables and indoor outdoor PM2.5 concentrations. The ambient PM2.5 concentrations were collected from air quality monitoring station and interpolated by Kriging method to be outdoor PM2.5 concentrations. Finally, the Multiple Linear Regression model was used to establish a predictive model for indoor PM2.5 concentrations. And model validation was conducted by using cross validation and external validation to ensure the accuracy of the model. After establishing the predict model, we can find that outdoor PM2.5 concentrations can explain most of the indoor PM2.5 concentrations. In addition, the remaining predictive power is regarding to the infiltration rate. So, if any research wants to establish a model to find the correlation between indoor and outdoor environment in the future, these variables cannot be ignored.

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