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    Title: 焚化爐戴奧辛煙道採樣指紋分析統計方法探討
    Other Titles: Statistical methodology on dioxin fingerprint analysis of incinerator stack samples
    Authors: 史詒君;Shih, Yi-chun
    Contributors: 淡江大學數學學系碩士班
    陳主智;Chen, Chu-chih
    Keywords: 指紋分析;戴奧辛;焚化爐;Dirichlet分布;Bootstrap;fingerprint analysis;dioxin;incinerator;Dirichlet distribution;Bootstrap
    Date: 2008
    Issue Date: 2010-01-11 02:57:08 (UTC+8)
    Abstract: 目前台灣以焚化爐焚燒來處理垃圾的比率愈來愈高,但是焚化爐煙道所排出的戴奧辛卻變成為環境的一大問題。目前判斷戴奧辛煙道排放來源的主要方法之一是利用指紋分析比對,但這種方法過於主觀而且只要所分析的化合物種類太多圖譜便會複雜,所以必須利用統計方法分析,才能有正確客觀的科學依據。
    本文將先針對目前13座分散在台灣的焚化爐煙道的戴奧辛採樣樣本化合物含量比率進行指紋分析,再利用Dirichlet分布來進行統計檢定。將採樣樣本當成大樣本進行概似比檢定比較焚化爐間化合物的差異,並佐以Boostrap法進行驗證。結果顯示檢定的結果與指紋分析的結果吻合,但還是建議以增加樣本數才會讓結果更為精確。
    In Taiwan, municipal solid waste incinerator (MSWI) is the dominant municipal waste treatment method, which is known to be source of dioxin release in the environment. The dioxin compounds consist of 17 2,3,7,8-PCDD/Fs different chemicals with the histogram of relative percentages form a fingerprint. By analyzing the fingerprint of a dioxin sample one may identify possible pollutant sources of collected dioxin samples. Up to date there has been lacking a formal statistical method for fingerprint analysis. The aims of this thesis is to develop a solid statistical method to compare fingerprints of dioxin samples collected at different sites to identify whether they have the same pattern. The dataset consists dioxin samples collected from 13 incinerator stacks across Taiwan with sample sizes ranging from 3 to 20. The Dirichlet distribution was applied to characterize the fingerprint of samples collected from the same stack. Statistical hypothesis testing is then to test whether the vector of parameters of the corresponding Dirichlet distributions from two different stack samples are the same or not using likelihood ratio test statistic. Asymptotic result as well as bootstrap method were applied and compared for the fingerprint comparisons. The results showed that the proposed method conformed to the preliminary analysis using Pearson correlation coefficients.
    Appears in Collections:[數學學系暨研究所] 學位論文

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