臺灣博碩士論文加值系統

近年來，世界上的氣懸感染疾病種類逐漸增加且疾病強度有增加趨勢，嚴重威脅人類健康安全。現代社會人於室內活動時間占一整天約90%，當室內發生潛在氣懸疾病感染時，將會造成嚴重感染。為了預測氣懸感染疾病感染風險，已有流行病學學者建立感染數值模型評估氣懸感染疾病風險，Wells-Riley感染傳輸方程式為流行病學學者常用之感染數值模型。Wells-Riley感染傳輸方程式在使用上有兩個重要假設，須符合空氣均勻混和與穩態，但是這兩項限制假設並不與實際狀況相符，因此有學者提出結合計算流體動力學與Wells-Riley方程式來解決空氣混合限制，雖然計算流體動力學能解決前述問題，但是此模型不易進行長時間計算與大範圍不同空間之汙染物濃度分佈，且無法估計室內人員暴露程度與累積劑量，所以亦有學者建議利用多空間氣流模型來分析氣懸感染疾病傳輸並評估感染風險。
本研究目的探討結合多空間氣流模型CONTAM與Wells-Riley感染傳輸方程式進行氣懸感染風險評估之可行性，並提出以二氧化碳暴露量之綜合模型進行感染風險評估。本研究分為兩部分，第一部分為驗證CONTAM模擬汙染物之準確性，在國立宜蘭大學工學院大樓203教室與營建管理研究室進行二氧化碳濃度監測實驗，利用CONTAM進行模擬並與實測值比較，模擬結果決定係數分別為0.96與0.99，表示CONTAM模擬單空間汙染物濃度起伏變化有其準確性。第二部分為模擬過去所發生氣懸感染疾病之文獻，並使用本研究所提出結合CONTAM暴露量與非穩態Wells-Riley方程式之綜合模型進行感染風險評估，並與原文獻評估結果進行比較。第一個小學麻疹感染文獻案例模擬研究結果為第一期實際感染與模擬感染人數分別為28人與21人；第二期實際感染與模擬感染人數分別為31與24人；將兩期感染結合分析實際與模擬感染人數分別為59與46人。誤差率分別為26.2%、24.1%與22.7%。第二個診所麻疹文獻案例利用CONTAM模擬出診所內各受感染病人暴露量並求出最低暴露量結果，表示如其他易感者暴露量高於此值時，將可能受到感染，研究結果模擬出當暴露量高於156.75 min．ppm將可能受到感染。

In recent years, the type and intensity of airborne infection disease is gradually increased in the world, and results in serious threat to human’s health and safety. The duration of people’s indoor activity is beyond 90% of all day. The outbreak of airborne infection will occur if infectors stay in indoor space. In order to predict the risk of airborne infection disease, epidemiology scholars have established infection numerical model to assess the risk of airborne infections. Wells-Riley equation is commonly used to assess risk of airborne infection in research. Wells-Riley equation has two assumptions that indoor air is uniformly mixed and is steady-state. The assumptions are difficult to correspond with reality. Therefore, the integrated model combined computer fluid dynamics (CFD) and Wells-Riley equation to predict the risk of airborne infection is conducted. But the CFD method is difficult to analyze the pollutant concentration in multi-zone space during a long time and the inhaled dose of susceptible persons. The integrated model combined the multi-zone airflow model and the Wells-Riley equation to predict the airborne infection risk is suggested by literature. This study investigate the feasibility of integrated model combined the multi-zone airflow model and the Wells-Riley equation to assess the risk of airborne infection, and propose to assess risk of airborne infection by carbon dioxide exposure. The study includes two parts, part one is to verify the accuracy of simulating pollutant concentration by using CONTAM. The carbon dioxide concentration monitored in Engineering Building 203 and Construction Management Laboratory is compared with the simulated value from CONTAM. The results of the coefficient of determination between measured data and simulated data in the experiments of Engineering Building 203 and Construction Management Laboratory are 0.96 and 0.99 separately. It indicates that the CONTAM has high accuracy to simulate pollutant concentration in indoor space. Part two is to use the integrated model to analyze the risk of airborne infection and compared with the infectors from the actual results of the literature. The first case is an outbreak of measles in elementary school. The results of the first case show that the predicted infectors of simulation and of investigation are 28 and 21 in first generation. The predicted infectors of simulation and of investigation are 31 and 24 in second generation. The predicted infectors of simulation and of investigation are 59 and 46 in combining the first and the second generation. Error rate is 26.2%, 24.1%, and 22.7%in above three scenarios separately. The second case is an outbreak of measles in a pediatric clinic. The simulation of CONTAM results in the criteria exposure of infection according to the reality of infection. The results indicate that a susceptible person will be infected if the exposure is above 156.75 min·ppm.

目錄
摘要 I
Abstract II
謝誌 IV
目錄 V
圖目錄 VII
表目錄 X
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究流程 3
第二章 文獻回顧 5
2.1 室內空氣品質介紹 5
2.1.1 室內汙染物種類 5
2.1.2 國內空氣品質相關法規 7
2.1.3 二氧化碳 9
2.2 氣懸感染疾病介紹 11
2.2.1 氣懸微粒物理性質 11
2.2.2 氣懸感染疾病傳播途徑 12
2.2.3 氣懸疾病調查方式 13
2.3 感染機率風險之數值模型 16
2.3.1 Wells-Riley 方程式 16
2.3.2 量子(quantam) 17
2.3.3 Wells-Riley方程式發展及應用 19
2.4 結合感染機率模式與數值流體模式之綜合模型 23
第三章 數值模擬分析與實驗介紹 34
3.1 CONTAM軟體介紹 34
3.1.1 CONTAM假設與理論 35
3.1.2 建築物元件配置 35
3.1.3 模擬設定 46
3.2 單空間汙染物濃度模擬 47
3.2.1 實驗地點與實驗對象 47
3.2.2 二氧化碳監測與建築通風參數測量 47
3.2.3 濃度模擬流程 52
3.3 感染傳輸模型 54
3.3.1 以二氧化碳為基礎之Wells-Riley方程式 55
3.3.2 結合CONTAM與非穩態Wells-Riley方程式之綜合模型 56
第四章 單空間濃度與文獻案例模擬結果 58
4.1 單空間汙染物濃度模擬 58
4.1.1 工學院大樓203教室 58
4.1.2 營建管理研究室 62
4.2 郊區小學麻疹案例模擬 66
4.2.1 文獻背景 66
4.2.2 建築尺寸與通風參數 68
4.2.3 模擬假設 70
4.2.4 CONTAM模擬結果 71
4.3 小兒科診所案例模擬 80
4.3.1 文獻背景 80
4.3.2 建築尺寸與通風參數 82
4.3.3 模擬假設 83
4.3.4 CONTAM模擬結果 85
第五章 結論與建議 88
5.1 結論 88
5.2 建議 89
參考文獻 90
附錄A CONTAM模擬參數表 94
附錄B 文獻資料 104
附錄C CONTAM汙染物濃度與暴露量模擬結果 106

參考文獻
一、中文文獻
1.行政院環境環保署，2010，「室內空氣品質標準」，行政院環境環保署，環保法規101年11月23號公佈。
2.余雅如，2010，醫院室內空氣品質模式模擬，國立交通大學環境工程研究所碩士論文。
3.林政隆，2007，傳染性疾病居家隔離空間之通風系統對室內氣懸感染機制之影響，國立宜蘭大學土木工程學系碩士論文。
4.陳海曙，1990，「室內空氣品質不佳之案例研究」，中華民國建築學會第三屆建築學術研究發表會論文集，pp. 263-266。
5.彭定吉，1992，集合住宅室內空氣品質(CO2、CO、粉塵)現場量測方法之探討，國立成功大學建築工程研究所碩士論文。
6.董春雲，2008，「負壓隔離病房通風性能與感染風險之研究」，國立台北科技大學博士論文。
二、英文文獻
1.American Thoracic Society. 1990. Environment controls and lung disease. AM. Rev. Respir. Dis., 142-915.
2.ASHARE 62, 2007, Ventilation for Acceptable Indoor Air Quality, American Society of Heating Refrigeration and Air-Conditioning Engineers, Inc.
3.ASHRAE Position Document on Airborne Infectious Diseases, 2014, American Society of Heating Refrigeration and Air-Conditioning Engineers, Inc.
4.Azimi, P., stephens, B., 2013 HVAC filtration for controlling infectious airborne disease transmission in indoor environments: Predicting risk reductions and operational costs, Building and environment 70, pp. 150-160.
5.Beggs, C.B., Shepherd, S.J., Kerr, K.G., 2010, Potential for airborne transmission of infection in the waiting areas of healthcare premises: stochastic analysis using a Monte Carlo model, BMC Infectious Diseases;10(1):247.
6.CONTAM Libraries, www.bfrl.nist.gov/IAQanalysis/CONTAM/table03_res.htm.
7.Costello, T.A., Meador, N.F., Shanklin, M.D., 1984, CO2¬ compared to SF6 as an air infiltration tracer. Transactions of the ASAE, Vol. 27, pp 844-846.
8.Dick, E.C., Jennings, L.C., Mink, K.A., Wartgow, C.D., Inhorn, S.L., 1987, Aerosol transmission of rhinovirus colds, The Journal of Infectious Diseases, Vol.156, No.3, pp. 442-448.
9.Dols, W.S., Polidoro, B.J., 2015, CONTAM User Guild and Program Documentation Version 3.2, NIST, USA.
10.Dutton, S., Shao, L., Riffat, S., 2008, Validation and Parametric Analysis of Energyplus: Air Flow Network Model Using CONTAM, Third National Conference of IBPSA-USA, Berkeley, California.
11.Emmerich, S.J., Heinzerling, D., Choi, J.I., Persily, A.K., 2013, “Multizone modeling of strategies to reduce the spread of airborne infectious agents in healthcare facilities,” Building and Environment, Vol. 60, pp. 105-115.
12.Gustafson, T.L., Lavely, G.B., Brawner, E.R., Hutcheson, R.H., Wright, P.F., Schaffner, W., 1982, An outbreak of airborne nosocomial Varicella. Pediatrics, Vol. 70, NO. 4, pp. 550-556.
13.Haas, A., Weber, A., Dorer, V., Keilholz, W., and Pelletret, R., 2002, COMIS v3.1 simulation environment for multizone air flow and pollutant transport modelling. Energy and Buildings, 34, pp. 873-882.
14.Hinds, W.C., 1999, Aerosol technology. 2nd edition. John Wiley & Sons, New York.
15.Holmberg, S., Chen, Q., 2003, Air flow and particle control with different ventilation systems in a classroom, Indoor Air, Vol. 13, pp. 200-204.
16.Issarowa C.M., Mulder N., Wood R., 2015, Modelling the risk of airborne infectious disease using exhaled air. Journal of Theoretical Biology Vol. 372, pp. 100-106.
17.Leong, D.K.N., McCammon, C.S., Monteith, L.E., Cheng, M.L. Woebkenberg, J., Macher, J.M., 1998, Air sampling instrument selection guide: indoor air quality. American Conference of Governmental Industrial Hygienists, Inc.
18.Li, F., Liu, J., Pei, J., Lin C.H., Chen, Q.Y., 2013, Experimental study of gaseous and particulate contaminants distribution in an aircraft cabin, Atmospheric Environment, Vol.85, pp. 223-233.
19.Liao, C.M., Chang, C.F., and Liang H.M., 2005, A Probabilistic Transmission Dynamic Model to Assess Indoor Airborne Infection Risks, Risk Analysis, Vol. 25, No. 5., pp. 1097-1107.
20.Liao, C.M., Lin, Y.J., Cheng, Y.H., 2013, Model the impact of control measures on tuberculosis infection in senior care facilities, Building and Environment, Vol. 59, pp. 66-75.
21.Moser, M.R., Bender, T.R., Margolis, H.S., Noble, G.R., Kendal A.P., Ritter, D.G., 1979, An outbreak of influenza aboard a commercial airliner. American Journal of Epidemiology, Vol. 110, pp. 1–6.
22.Nardell, E.A., Keegan, J., Cheney, S.A., Etkind, S.C., 1991, Airborne infection. Theoretical limits of protection achievable by building ventilation. American Review of Respiratory Disease;144(2):302-306.
23.Noakes, C.J., Sleigh, P. A., 2009, Mathematical models for assessing the role of airflow on the risk of airborne infection in hospital wards, J. R. Soc. Interface, Vol. 6, pp. 791-800.
24.Noakes, C.J., Sleigh, P.A., Khan, A., 2012, Appraising healthcare ventilation design from combined infection control and energy perspectives, HVAC&R Research; 18:4, 658-670.
25.Pantelic, J., Tham, K.W., 2012, Assessment of the mixing air delivery system ability to protect occupants from the airborne infectious disease transmission using Wells–Riley approach, HVAC&R Research, Vol. 18, pp. 562-574.
26.Qian, H., Li, Y.G., Nielsen, P.V., Huang, X.H., 2009, Spatial distribution of infection risk of SARS transmission in a hospital ward. Building and Environment, Vol. 44, pp. 1651-1658.
27.Remington, P.L., Hall, W.N., Davis, I.H., Herald, A., Gunn, R.A., 1985, Airborne Transmission of Measles in a Physician’s Office, JAMA, Vol. 253, No. 11, pp. 1574-1577.
28.Richardson, E.T., Morrow, C.D., Kalil, D.B., Bekker, L.G., 2014, Shared air: a renewed focus on ventilation for the prevention of tuberculosis transmission. PlOS One, Vol. 9, Issue 5, e96334.
29.Riley, E.C., Murphy, G., and Riley, R.L., 1978, Airborne spread of measles in a suburban elementary school. American journal of epidemiology Vol. 107, No. 5 by The Johns Hopkins University School of Hygiene and Public Health.
30.Rudnick, S.N., and Milton, D.K., 2003, Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor Air, Vol. 13, pp. 237-245.
31.Sze To, G.N., Chao, C.Y.H., 2010, Review and comparison between the Wells–Riley and dose-response approaches to risk assessment of infectious respiratory diseases, Indoor Air, Vol. 20, pp. 2-16.
32.Tung, Y.C., Hu, S.C., 2008, Infection risk of indoor airborne transmission of deseases in multiple spaces, Architectural Science Review, Vol.51, pp. 14-40.
33.Wang, A.J., Zhang, Y.H., Sun, Y.G., Wang, X.L., 2008, Experimental study of ventilation effectiveness and air velocity distribution in an aircraft cabin mockup, Building and Environment, Vol 43, pp. 337-343.
34.Wells, W.F., 1955, Airborne contagion and air hygiene: an ecological study of droplet infection. Cambridge, MA, Harvard University Press.
35.WHO,WorldHealthOrganization,2016, http://www.who.int/gho/tb/epidemic/cases_deaths/en/
36.Zhang, T.F., Li, P.H., Wang, S.G., 2012, A personal air distribution system with air terminals embedded in chair armrests on commercial airplanes. Building and Environment, Vol 41, pp. 89-99.
37.Zhu, S.W., Srebric, J., Spengler, J.D., Demokritou, P., 2012, An advanced numerical model for the assessment of airborne transmission of influenza in bus microenvironments, Building and Environment, Vol.47, pp. 67-75.