| 研究生: |
朱進宏 Ju, Jin-hong |
|---|---|
| 論文名稱: |
煉油廠排放有機性有害空氣污染物對民眾健康風險評估影響因素研究 Study on Relevant Parameters in Health Risk Assessment of Organic Air Toxics Emissions from Refinery |
| 指導教授: |
蔡俊鴻
Tsai, Jiung-hong |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 環境工程學系 Department of Environmental Engineering |
| 論文出版年: | 2009 |
| 畢業學年度: | 97 |
| 語文別: | 中文 |
| 論文頁數: | 265 |
| 中文關鍵詞: | 煉油製程 、致癌風險機率分佈 、排放量推估 、有機性有害空氣污染物 、不確定性 |
| 外文關鍵詞: | Refinery Process, Cancer Risk Stochastic Distribution, Uncertainty Parameter, Emission Estimates Approach, Organic Air Toxics |
| 相關次數: | 點閱:118 下載:0 |
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本研究主要探討煉油製程排放有機性有害空氣污染物所致鄰近地區居民健康風險評估程序之不確定性因素,依循美國加州空氣資源局及環境健康危害評估處所建置方法,援用熱點分析及報告軟體(HARP)做為評估工具,以國內煉油製程六種污染源(排放管道、設備元件、儲槽、廢氣燃燒塔、裝載場及廢水廠)為案例,探討應用國外評估程序於國內進行健康風險評估之影響因素,整體研究架構係以較保守極端條件為基準。
研究煉油製程六種污染源排放VOC量推估結果差異性顯示,排放管道排放量造成差異最小(佔總排放量1%),設備元件排放量推估差異最大(佔總排放量33%)。
參考WebFIRE、SPECIATE 4.0及加州AB2588有害空氣污染物年度報告所列HAP排放係數,試算HAP排放量,挑選差異較大案例進行比較,結果顯示排放特徵成份分類(Speciate)選用差異造成污染源及污染物管制優先性改變,毒性及危害性亦明顯變化;案例分析結果顯示,致癌效應設施優先性指數差異可達202倍。
四種有害空氣污染物(苯、乙苯、甲苯及二甲苯) 年平均及小時濃度與監測站數據比對分析結果顯示,模擬值皆較監測值為低,年平均模擬值與監測值差異約11~17倍。
執行研究案例健康風險評估結果顯示,總致癌風險最高值為10600×10-6,發生於石化工業區,鄰近地區總致癌風險大多落於10-5~10-6,達到SCAQMD需告知民眾(Notification Level)位準(10×10-6),亦達到需進行風險削減(Risk Reduction Audit & Plan)位準 (25×10-6) ;慢性健康總危害指數最高值為5.2,發生於石化工業區場區,鄰近地區慢性健康總危害指數大多落於0.01~0.001,未達到需告知民眾位準,亦未達到需進行風險削減位準。
整體而言,排放量推估造成健康風險評估結果差異十分明顯,潛在原因包括:VOC排放係數選用、AP-42經驗公式參數選用、HAP排放特徵選用、控制效率合理性等均造成健康風險差異。排放管道VOC排放量推估造成致癌風險機率分布中位數差異最小(0.1×10-6),儲槽排放推估造成差異低小;不同VOC排放推估方法造成致癌風險分佈中位數最高相差3×10-6。排放特徵選用所造成差異最大,選取案例造成整體致癌風險機率分布中位數最高差異78倍。
This study investigates the uncertainty and the relevant parameters of health risk assessment procedure for organic air toxics released from refinery. The assessment methodology has been developed by California Air Resources Board and Office of Environmental Health Hazard Assessment which was part of Hot Spots Analysis and Reporting Program (HARP). Six emission source categories, including stack, equipment component, storage tank, flare, loading/unloading, and wastewater treatment plant, in petroleum refinery were discussed in the case study.
The results indicate that the greatest variance of VOC emission estimation among different approaches was found in the category of equipment leakage (33%). The variance of stack emission was small.
Applying WebFIRE, SPECIATE 4.0 and the California AB2588 air toxics annual report to calculate HAP emission in two scenarios. The result indicated that the priority of facility with high cancer risk was changed due to the selection of process SCC and target pollutant in SPECIATE 4.0 database.
In this study, the ambient concentrations of four organic air toxics (benzene, ethyl benzene, toluene, and xylene) in monitoring station were used to compare to the simulation output from air dispersion modeling. The result shows that the field monitoring data were higher than the model simulation output with 11-17 folds. The maximum value of total cancer risk for target air toxics is 10,600×10-6 for case study. The value achieves the notification level according to SCAQMD regulation (10×10-6) which also means the reduction of risk base on the regulation (25×10-6) of the Risk Reduction Audit & Plan. The maximum chronic hazard index (HI) for non-carcinogenic toxics is 5.2 for this case study. The HI values are 0.01~0.001 which means there is not critical risk in this case.
In brief, selection of VOC emission factor, AP-42 empirical formula parameter, HAP emission profiles, and reasonable control efficiency strongly influence the result of health risk assessment. The HAP emission profile selection causes the greatest difference on risk assessment among those factors (up to 78 times). Otherwise, the air dispersion model may also effect on health risk assessment attribute to the model algorithm, the parameter assumed, the meteorological data to select, the emissions elevation, the photochemical reaction, the breathing rate, the contaminants deposit.
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