現代人每天約有90%的時間待在不同之室內空間，顯示室內溫度才是較有可能影響居住人群的健康；因此，為能更精確且快速地的定義出區域民眾實際之溫度暴露範圍，本研究將透過代表性建築物之實場量測評估，建立一以室外之大氣測站溫度來預測室內溫度之統計預測模式，並考量各種建築物特性、氣候資料及室內人員的調適行為，如能源的消耗使用（冷暖氣機調節室內溫度）等影響室內溫度之因素。
本研究選取占國人室內居處比例最高 (73%) 的建築類別-住宅，在全臺11個縣市包含臺南、嘉義、臺中、新竹、臺北、基隆、宜蘭、花蓮、臺東、恆春及高雄共30個室內空間中設置長期連續性之溫溼度監測系統，記錄每小時溫度變化，同步搜集建築物特性、室內人員活動情形(包含電力指標)以及室外大氣監測資料。最後利用混合效應模型 (Mixed effect model) 進行各項指標因素在模式中影響係數的推估，並以實場量測值做模式的反覆驗證。
研究住宅之平均屋齡為22.3年，67%屬於5層樓以下，平均地坪為36.6坪。2012年9月至2013年7月的監測結果顯示，室內外的平均溫度分別為25.2°C （範圍14.0-35.0°C）與23.4°C （範圍8.0-36.3°C），兩者相關性0.84 (p for Spearman rank correlation < 0.001)，隨月份 (0.4-0.68) 或城市 (0.55-0.87) 不同有極大變異。進一步探討其他影響室內溫度之因子，顯示大氣測站資料包括溫度、濕度、氣壓、風速、日照時數及風向皆顯著影響。另也發現四個小時前的室外溫度對於室內溫度之影響性大於當下室外溫度。建築物特性的部分，包含調查空間的窗戶數及樓層皆有顯著之影響。能源消耗指標（電力）及受試者使用行為部分，推測受限於調查月份及樣本數不足等因素，未能發現與室內溫度的變化有關係。本研究以上述篩選出的顯著影響因子作為模型建立之優先考量，並固定納入｢調查月份｣、｢城市｣及｢室外溫度×月份｣等變項。以全臺灣為例，四小時前的室外溫度可解釋36.2% (R2) 室內溫度的變動情形，進一步加入了建築物特性等變項，則發現每增加一扇窗，室內溫度會增加0.22°C (R2 = 36.8%)。為使模型的預測能力及其應用更加完整，我們進一步依緯度分別建立北、中及南部地區之模型，以解決地理區域有關之氣候條件上的差異；北區模型（新竹、臺北、基隆及宜蘭）之結果顯示，研究空間每增加一個樓層，室內溫度會增加0.30°C；中區部分（嘉義、臺中及花蓮）及南區部分（台南、高雄、恆春及臺東）並未發現建築物特性與室內溫度變化有關係。整體來看，北區模型擁有最高之解釋力 (R2 = 44%)，其次則為中區 (36.4%) 及南區 (33.8%) 模型。研究最後進行交叉驗證 (cross-validation) 以確認模型的預測能力，分別就在全臺灣、北部、中部及南部地區所建立之四個模式計算其預測與實際值間的相關性，為0.73、0.88、0.75及0.70，平均值的差距分別為-1.85、-1.14、-1.53及-2.15°C；研究發現，在5至7月及9至11月間，模式的預測能力是較差的，推測可能與室內人員之使用行為有關，像是冷氣空調的使用增加了室內外的溫差。
在臺灣地區，室外溫度對室內溫度之影響有明顯之延遲效應，且其相關性會受到建材蓄熱性及室內人員使用行為的影響。本研究之模型預測能力最高能解釋44%的室內溫度變化，因地區的不同其預測能力範圍介於約35-45%，且在12至4月間會有較準確的推估。此模式的建立將可提供流行病學研究更精準地估算區域的溫度暴露指標，對於釐清氣候變遷導致臺灣民眾的健康風險等評估研究將能有較確實的結果與討論；此外亦可進一步作為災變預警系統建置之重要基礎，透過室內溫度值的預測，預防長期居處室內之易感性族群因溫度瞬變而導致健康風險。

As people spend most of their time in different buildings, indoor temperature is thought to be better associated with human health performance compared to ambient levels. Current study is aimed to establish the statistical model to predict indoor temperature based on outdoor information from atmospheric monitoring stations. Several factors including building characteristics, outdoor climate data and adaptive behavior of occupants, such as energy consumption through using air conditioner, are further evaluated.
Thirty homes, where people spend about 73% of their daily time, were selected from 11 cities in Taiwan. Real-time monitors for temperature were set inside the rooms where occupants spent most of their time when inside the house. Building characteristics, human activities and electricity consumption data were recorded weekly, and corresponding outdoor climate data from local station was collected accordingly. Mixed effect model has been conducted for model establishment after adjusting for above-mentioned determinants. The model is further cross-validated afterwards.
Study buildings are with average age and area of 22.3 years and 121 m2, respectively, and 67% of them are below five-floor-high. During the period of investigation from September 2012 to July 2013, average indoor temperature was 25.2°C (range from 14.0 to 35.0°C) while outdoors was 23.4°C (range from 8.0 to 36.3°C). Correlation coefficient between hourly indoor and outdoor temperatures is 0.84 (p< 0.001) and varies by month (range from 0.4 to 0.68) and city (range from 0.55 to 0.87). Four-hour lag of outdoor temperature is found to be with the best estimation compared to the in-time and other lag ones. Information collected from ambient station including temperature, relative humidity, atmospheric pressure, wind speed, sunshine hours and wind direction are significant predictors associated with the variation of indoor temperature. Building characteristics including number of windows and floors were related to the outcome of interest as well. Factors such as energy consumption of electricity and behavior records of occupants were yet to result in the significant effects on the change of indoor temperature which might due to the short study period and limit sample size. Above-mentioned variables with significant effects were prioritized to consider in the establishment of model fixed with the variables of "month of investigation", "city" and interaction term of "outdoor temperature × month of investigation". As to the model for whole Taiwan, the 4-hour-lag outdoor temperature explains 36.2% (R2) of the variance for the in-time indoor temperature. When taking building characteristics into account, it’s found that every increase of number of window is associated with an increase of 0.22°C for indoor temperature (R2 = 36.8%). In order to enhance the predictive ability and availability of the model, we further separated the dataset into northern, central and southern region defined by latitudes which reflected geographical climatic differences. Results of the northern model (Hsinchu, Taipei, Keelung and Yilan) show that every increment of one floor number is associated with an increase of 0.30°C for indoor temperature. As to the model for central region (Chiayi, Taichung, and Hualien) and southern region (Tainan, Kaohsiung, Hengchuen/Pingtung and Taitung) were yet to result in the significant effects on the change of indoor temperature. Overall, the model established in northern Taiwan shown with the highest ability to explain the variation of indoor temperature (R2=44%), followed by models in central (R2=36.4%) and south area (R2=33.8%). Cross-validation is conducted afterward to confirm the predictive ability of models. Correlations between predicted and observed values of whole island, and northern, central and southern area are 0.73, 0.88, 0.75 and 0.70, respectively, while average difference are -1.85,-1.14,-1.53 and -2.15°C, respectively. Lower predictive ability during May to July and September to November might be related to frequently adaptive behavior of occupants, such as using air conditioner, which resulted into higher level of difference between indoor and outdoor temperature.
In Taiwan, the effect of outdoor temperature on the indoors seems to present apparent delay, and their associations are affected by the heat retention of building materials as well as occupants’ adaptive behaviors. In this study, at most, a total of 44% of the variance of indoor temperature can be explained by our model, depending on the region of interest, and the prediction will be more accurate during the period of December to April. The model can be used as a means of mapping city- or country-wide indoor temperatures under various scenarios of outdoor environments, to provide a better representation of individuals’ exposure levels in ecological studies examining the diversity of regional impaction between global warming and health effects. Furthermore, the model will be considered as the critical basis for the early warning system, through the prediction of indoor temperature in advance, to prevent potential health risk of susceptible group due to the sudden changes of the temperature.

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