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研究生:張世穎
研究生(外文):Shih-Ying Chang
論文名稱:運用傅立葉轉換紅外線光譜儀及輻射形光徑煙流分布重建法定位污染源:重建演算法之評估
論文名稱(外文):Using 2-D Radial Plume Mapping Technique with OP-FTIR for Source Localization: Evaluation of Reconstruction Algorithms
指導教授:吳章甫吳章甫引用關係
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:環境衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2008
畢業學年度:97
語文別:英文
論文頁數:129
中文關鍵詞:定位空氣污染開徑式傅立葉轉換紅外線光譜儀輻射形光徑煙流重建法光學遙測逸散源
外文關鍵詞:source localizationplume reconstructionoptical remote sensingOP-FTIRair pollutantRPMCT
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輻射形光徑煙流重建法為使用光學遙測儀器進行污染物分布重建及污染源定位之技術。在此技術中,兩種主要之重建演算法被用來進行污染物分布重建-Sooth basis function minimization (SBFM)和Non-negative least square (NNLS)。本研究之目的在比較此兩種主要重建法對於污染源定位及污染物分布重建之表現,此兩種演算法之相同處為重建出與測量得到之PIC相同的PIC,差異處為SBFM使用預選之基本方程式描述污染物之分布,而NNLS則是直接重建逸散區域污染物之濃度。除此之外,在SBFM重建中,我們使用兩種不同之基本方程式(對稱和非對稱)來描述污染源。
本實驗由兩部份構成,第一部份為電腦模擬實驗,在電腦模擬中,首先產生450個基本分布並使用輻射形光徑煙流重建法重建污染物之分布及污染源。結果顯示SBFM使用非對稱基本方程式(bivariate lognormal distribution)做為基本方程式可以重建出完整之污染物;而當污染源位置接近OP-FTIR時,SBFM使用對稱之基本方程式(bivariate Gaussian distribution)做為基本方程式時可以相當精確地重建出污染源之位置,但此時NNLS無法重建出正確之汙染源。而當污染物遠離OP-FTIR時,使用NNLS進行重建會得到較好的結果而SBFM使用對稱之基本方程式無法定位污染源。
本研究之第二部份為實地實驗,使用開徑式傅立葉轉換紅外線光譜儀及輻射形光徑煙流重建法實地進行人為釋放之污染源定位。實地實驗之結果與電腦模擬之結果相似,當污染源遠離OP-FTIR時,使用NNLS重建演算法可以得到相當精確之污染源評估,而當污染源靠近OP-FTIR時,使用SBFM則可以得到較好的結果。此外,由三種重建方法所重建之污染源可以指出真實污染源之方向,佐以重建之污染源附近之較短之測線,可以加以判斷由何者重建出之污染源最接近真實污染源;當附近之短光徑沒有偵測到污染物時,可以選擇NNLS做為重建演算法,而當附近之短光徑測量到污染源時,則可使用SBFM做為重建演算法。
The OP-FTIR measurement combining the RPM technique is able to reconstruct the plume and thus localize the emission source. In this thesis, both the computational simulation and the field experiment are implemented. Two major kinds of the reconstruction algorithm used in RPM technique are evaluated. The first one is the smooth basis function minimization (SBFM) algorithm and the second one is the non-negative least square(NNLS) algorithm. The two algorithms are both implemented by fitting the reconstructed path integrated concentration (PIC) to the measured PIC. The differences are that the SBFM superimposes a basis function to describe the plum while the NNLS directly estimate the concentration value in the emission domain. In addition, two different kind of basis functions (symmetric and skewed) are used to describe the plume in SBFM reconstruction.
In the simulation analysis, 450 test distributions are generated to be localized by the RPM technique with different reconstruction algorithms. The result shows that the SBFM algorithm using the bivariate lognormal distribution as basis function gives the best result in both the aspects of plume reconstruction and source localization. Furthermore, when the plume is near the OP-FTIR, the SBFM reconstruction using bivariate Gaussian distribution as basis function may yield better result in the aspect of the source reconstruction comparing to the NNLS reconstruction. However, when the plume is far from the OP-FTIR, the NNLS reconstruction is able to localize the emission source more accurately than the SBFM using bivariate Gaussian distribution as basis function.
In the field experiment, four experiments with four pairs of different source locations are conducted to be localized by the RPM technique. The result shows that the reconstructed source locations by the three methods are able to point out the correct direction towards the real source. Furthermore, judging by the peripheral short monitoring lines, the reconstructed source location that is closest to the real source location can be chosen and gives the best estimation of the emission source location.
中文摘要 I
Abstract II
Chapter1. Introduction 1
1.1 Fourier transformed infrared spectrometer (FTIR) 1
1.2 Traditional methods for source localization 4
1.2.1 The area sampling array method 4
1.2.2 The computed tomographic (CT) method 6
1.2.3 The application of the CT technique 9
1.3 Radial plume mapping (RPM) technique 10
1.3.1 RPM with SBFM reconstruction algorithm 10
1.3.2 The RPM with “grid based” reconstruction algorithm 12
1.4 Study design and objectives 15
Chapter2. Materials and Methods 23
2.1 Data collection 23
2.2 Data analysis 27
2.2.1 The computational simulation 27
2.2.2 The field experiment 45
3.1 Computational simulation results 53
3.1.1 The plume reconstruction 53
3.1.2 The reliability of source localization 60
3.1.3 The prior screening process 63
3.1.4 The uncertainty analysis of SBFM reconstruction 66
3.2 Field experiment results 71
3.2.1 The spectrum quantification 71
3.2.2 The source localization 72
3.2.3 The reliability of the reconstruction result 80
Chapter4. Conclusions and suggestions 116
4.1 The simulation experiment 116
4.2 The field study 117
4.3 Suggestions 117
4.4 Limitations 119
References 120
1.Smith, B.C., Fundamentals of Fourier Transform Infrared Spectroscopy. Boca Raton: CRC Press, 1996.
2.Kristine, C. and J.D. Tate, Monitor ambient air with optical sensing system. Chemical Engineering, 1997. 104(6): p. 110.
3.Freddie, E.H., Jr., Case study: Environmental monitoring using remote optical sensing (OP-FTIR) technology at the Oklahoma City Air Logistics Center industrial wastewater treatment facility. Federal Facilities Environmental Journal, 2004. 15(1): p. 21-37.
4.Michel, G. and F.-J. Edgar. Air pollution monitoring with two optical remote sensing techniques in Mexico City. 2004: SPIE.
5.Lin, C., L. Naiwei, C. Pao-Erh, Y. Jen-Chin, and S. Endy, Fugitive coke oven gas emission profile by continuous line averaged open-path Fourier transform infrared monitoring. Journal of the Air and Waste Management Association (1995), 2007. 57(4): p. 472-9.
6.Wu, C.F., M.G. Yost, J. Varr, and R.A. Hashmonay, Applying open-path FTIR with a bi-beam strategy to evaluate personal exposure in indoor environments: experimental results of a validation study. AIHA J (Fairfax, Va), 2003. 64(2): p. 181-8.
7.Wu, C.F., M.G. Yost, R.A. Hashmonay, and T.V. Larson, Applying open-path FTIR with computed tomography to evaluate personal exposures. Part 2: experimental studies. Ann Occup Hyg, 2005. 49(1): p. 73-83.
8.Chan, C.-C., S. Ruei-Hao, C. Ta-Yuan, and T. Dai-Hua, Workers'' exposures and potential health risks to air toxics in a petrochemical complex assessed by improved methodology. International Archives of Occupational and Environmental Health, 2006. 79(2): p. 135-42.
9.Hashmonay, R.A., D.F. Natschke, K. Wagoner, D.B. Harris, E.L. Thompson, and M.G. Yost, Field evaluation of a method for estimating gaseous fluxes from area sources using open-path Fourier transform infrared. Environmental Science & Technology, 2001. 35(11): p. 2309-13.
10.Todd, L.A., M. Ramanathan, K. Mottus, R. Katz, A. Dodson, and G. Mihlan, Measuring chemical emissions using open-path Fourier transform infrared (OP-FTIR) spectroscopy and computer-assisted tomography. Atmospheric Environment, 2001. 35(11): p. 1937-1947.
11.Todd, L. and D. Leith, Remote Sensing and Computed Tomography in Industrial Hygiene. American Industrial Hygiene Association Journal, 1990. 51(4): p. 224-233.
12.Todd, L. and G. Ramachandran, Evaluation of algorithms for tomographic reconstruction of chemical concentrations in indoor air. American Industrial Hygiene Association Journal (AIHAJ), 1994. 55(5): p. 403-17.
13.Yost, M.G., A.J. Gadgil, A.C. Drescher, Y. Zhou, M.A. Simonds, and S.P. Levine, Imaging indoor tracer-gas concentrations with computed tomography: experimental results with a remote sensing FTIR system. American Industrial Hygiene Association Journal (AIHAJ), 1994. 55(5): p. 395-402.
14.Park, D.Y. and M.G. Yost, Evaluation of virtual source beam configurations for rapid tomographic reconstruction of gas and. Journal of the Air & Waste Management Association (1995), 1997. 47(5): p. 582.
15.Hashmonay, R.A. and M.G. Yost, Localizing gaseous fugitive emission sources by combining real-time optical remote sensing and wind data. J Air Waste Manag Assoc, 1999. 49(11): p. 1374-9.
16.Hashmonay, R.A., M.G. Yost, and C.F. Wu, Computed tomography of air pollutants using radial scanning path-integrated optical remote sensing. Atmospheric Environment, 1999. 33: p. 267-274.
17.Piper, A.R., L.A. Todd, and K. Mottus, A field study using open-path FTIR spectroscopy to measure and map air emissions from volume sources. Field Analytical Chemistry & Technology, 1999. 3(2): p. 69.
18.Wu, C.F., M.G. Yost, A.H. R, and D.Y. Park, Experimental evaluation of a radial beam geometry for mapping air pollutants using optical remote sensing and computed tomography. Atmospheric Environment, 1999. 33: p. 4709-4716.
19.Hashmonay, R.A., K. Wagoner, D.F. Natschke, D.B. Harris, and E. Thompson, Radial Computed Tomography Of Air Contaminants Using Optical Remote senisng. The Air & Waste Management Association''s 95th Annual Conference & Exhibition, 2002.
20.Compendium Method TO-16: Long-Path Open-Path Fourier Transform Infrared Monitoring of Atmospheric Gases, 2nd edition. . U.S EPA, 1999.
21.Chen, C.-L., F. Hung Yuan, and S. Chi-Min, Source location and characterization of volatile organic compound emissions at a petrochemical plant in Kaohsiung, Taiwan. Journal of the Air and Waste Management Association (1995), 2005. 55(10): p. 1487-97.
22.Byer, R.L., Two-dimensional remote air-pollution monitoring via tomography. Optics Letters, 1979. 4: p. 75.
23.Todd, L.A. and R. Bhattacharyya, Tomographic reconstruction of air pollutants: evaluation of measurement geometries. Applied Optics-OT, 1997. 36(30): p. 7678.
24.Verkruysse, W., Improved method "grid translation" for mapping environmental pollutants using a two-dimensional CAT scanning system. Atmospheric Environment, 2004. 38(12): p. 1801.
25.Verkruysse, W. and A.T. Lori, Novel algorithm for tomographic reconstruction of atmospheric chemicals with sparse sampling. Environmental Science & Technology, 2005. 39(7): p. 2247-54.
26.Samanta, A. and L.A. Todd, Mapping chemicals in air using an environmental CAT scanning system: evaluation of algorithms. Atmospheric Environment, 2000. 34(5): p. 699-709.
27.Gordon, R., R. Bender, and G.T. Herman, Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. Journal of Theoretical Biology, 1970. 29(3): p. 471-81.
28.Tsui, B.M.W., Comparison between ML-EM and WLS-CG algorithms for SPECT imagereconstruction. IEEE Transactions on Nuclear Science, 1991. 38(6): p. 1766.
29.Reis, M.L., Maximum entropy algorithms for image reconstruction from projections. Inverse Problems, 1992. 8(4): p. 623.
30.Drescher, A.C., A.J. Gadgil, P.N. Price, and W.W. Nazaroff, Novel approach for tomographic reconstruction of gas concentration distributions in air: Use of smooth basis functions and simulated annealing. Atmospheric Environment, 1996. 30(6): p. 929-940.
31.Tsai, M.Y., M.G. Yost, C.F. Wu, R.A. Hashmonay, and T.V. Larson, Line profile reconstruction: validation and comparison of reconstruction methods. Atmospheric Environment, 2001. 35: p. 4791-4799.
32.陳靜慧, 運用開徑式傅立葉轉換紅外光光譜儀定位大氣環境中之逸散源. 台灣大學, 2008.
33.Price, P.N., Pollutant tomography using integrated concentration data from non-intersecting optical paths. Atmospheric Environment, 1999. 33(2): p. 275.
34.Ram, A.H., G.Y. Michael, D.B. Harris, and Edgar L. Thompson, Jr. Simulation study for gaseous fluxes from an area source using computed tomography and optical remote sensing. 1999: SPIE.
35.Other Test Method OTM10: Optical Remote Sensing for Emission Characterization from Non-Point Sources. U.S EPA, 2006.
36.Hashmonay, R.A., R.M. Varma, M.T. Modrak, R.H. Kagann, R.R. Segall, and P.D. Sullivan, Radial Plume Mapping: A US EPA Test Method for Area and Fugitive Source Emission Monitoring Using Optical Remote Sensing. Advanced Environmental Monitoring, 2007. Springer: p. 21-36.
37.Hashmonay, R.A., Theoretical evaluation of a method for locating gaseous emission hot spots. Journal of the Air and Waste Management Association (1995), 2008. 58(8): p. 1100-6.
38.Aitchison J, B.J., The Lognormal Distribution. 1957.
39.Lin, L.I., A concordance correlation coefficient to evaluate reproducibility. Biometrics, 1989. 45(1): p. 255-68.
40.Herman, G.T., A. Lent, and S.W. Rowland, ART: mathematics and applications. A report on the mathematical foundations and on the applicability to real data of the algebraic reconstruction techniques. Journal of Theoretical Biology, 1973. 42(1): p. 1-32.
41.Lawson, C.L. and R.J. Janson, Solving Least Squares Problems. Society for Industrial and Applied Mathematics: Philadelphia, 1995. Chapter 23: p. 158-165.
42.Nielsen, H.B., S.N. Lophaven, and J. Søndergaard, The Kriging Toolbox for MATLAB. http://www2.imm.dtu.dk/~hbn/dace/, 2002.
43.Stockwell, W.R., P. Middleton, J.S. Chang, and X. Tang, The second generation regional acid deposition model chemical mechanism for regional air quality modeling. Journal of Geophysical Research ; Vol/Issue: 95:D10, 1990: p. Pages: 16,343-16,367.
44.Tsoukala, V.K. and C.I. Moutzouris, Gas transfer under breaking waves: experiments and an improved vorticity-based model. Annales Geophysicae, 2008. 26(8): p. 2131-2142.
45.Misra, P.K. and A. Chtcherbakov, Study of atmospheric dispersion of pollutant plumes from elevated stacks assuming a finite limit to the rate of vertical dispersion. Atmospheric Environment, 2008. 42(19): p. 4601-4610.
46.Gupta, A.K., G. Gonzalez-Farias, and J.A. Dominguez-Molina, A multivariate skew normal distribution. Journal of Multivariate Analysis, 2004. 89(1): p. 181-190.
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