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A Study on Source Apportionment and Source Location by Using PMF as Receptor Model
|關鍵字:||Receptor model;受體模式;PMF;CPF;simulation simple;正矩陣因子法;條件機率函數;自製樣本||出版社:||環境工程學系所||引用:||Alleman, L. Y., L. Lamaison, E. Perdrix, A. Robache, and J.-C. Galloo, "PM10 metal concentrations and source identification using positive matrix factorization and wind sectoring in a French industrial zone," Atmospheric Research, vol. In Press, Accepted Manuscript, No. pp. (2010) Buzcu-Guven, B. and M. P. Fraser, "Comparison of VOC emissions inventory data with source apportionment results for Houston, TX," Atmospheric Environment, vol. 42, No. 20, pp. 5032-5043 (2008) Cai, C., F. Geng, X. Tie, Q. Yu, and J. An, "Characteristics and source apportionment of VOCs measured in Shanghai, China," Atmospheric Environment, vol. 44, No. 38, pp. 5005-5014 (2010) Chang, C., C. Wang, D. Mui, M. Cheng, and H. Chiang, "Characteristics of elements in waste ashes from a solid waste incinerator in Taiwan," Journal of hazardous materials, vol. 165, No. 1-3, pp. 766-773 (2009) Chao, M.-R., C.-W. Hu, Y.-L. 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受體模式PMF（Positive Matrix Factorization）可以判斷出各污染源和各污染源的貢獻量，使用條件機率函數CPF（Conditional probability functions）可以找出污染源方位，用以驗證PMF結果是否正確。CPF有三個定向因子，（1）定義貢獻量的臨界值。（2）限制靜風狀況下的樣本。（3）污染源傳輸至受體點為直線傳輸。本研究目的將以模擬樣本來討論CPF對定向污染源之能力，模擬樣本的製作使用高斯擴散模式AERMOD（AMS/EPA REGULATORY MODEL），模擬時間為2005年四月份，30天(每天每小時一筆樣本)，樣本數共720筆。
A receptor model of positive matrix factorization (PMF) was used to identify the emission sources and contribution of sources. The results were compared with the previous source apportionment results. Conditional Probability Function (CPF) plots were developed for each source using local wind data to explore the directionality of local sources. To use CPF had to define three factors of source location (1) to find a threshold criterion value of the contribution. (2) to define calm wind. (3) the air arriving at a receptor site has traveled a relatively straight path from the source. This research purpose will discuss ability of CPF to location sources by simulation aerosol compositional simples. Making of the simulation sample used dispersion model “AERMOD”（AMS/EPA REGULATORY MODEL）. The simulation was carried in April, 2005 with 720 samples.
The result included set up CPF parameter, error reason of CPF plot and how to improve CPF. (1). ability of CPF of location source when the critical value used 25th percentile、50th percentile、75th percentile and 85th percentile that wind sector used 10° and 22.5°. Ability of location source was better than others when critical value used 85th percentile. (2). value of small contribution will be influenced and over-evaluated by value of big contribution to cause inaccurate CPF plot. (3). the clam wind chooses the wind speed to be smaller than 0.3m/s、1.0m/s and 1.5m/s to select samples. Ability of location source was better than others when clam wind used 1.0m/s. (4).the “effective sample” is defined as the sample under the time point of wind direction with small deference. To define “effective sample” can reduce influence that air arriving at a receptor site has traveled a non-straight path from the source. The result can improve ability of CPF of location source.
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