博碩士論文 105326018 詳細資訊




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姓名 黃柏竣(Po-Chun Huang)  查詢紙本館藏   畢業系所 環境工程研究所
論文名稱 以無人飛行載具(UAV)平台探討空氣污染物之垂直分佈特徵及搭載之氣膠儀器性能評估
(Investigating the vertical distribution of air pollutants by UAV platform and performance evaluation of aerosol instruments)
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摘要(中) 本研究選擇適合搭載於UAV的微型氣膠儀器(混和式冷凝微粒計數器、攜帶型光學微粒計數器、PM sensor、氣膠吸收光譜儀、微型平板電移動度分析儀)進行性能評估。考慮到UAV在飛行時,環境條件(溫度、相對濕度、壓力)的變化可能會對儀器量測造成影響,因此於實驗室中,設計不同環境參數和流量評估儀器性能,試圖獲得各條件下儀器的校正曲線。結果顯示大多數所選儀器的性能均會受環境條件、量測流量、環境濃度變化而影響。表明若UAV量測數據沒有以不同條件作為考量進行修正,則量測結果會有明顯誤差。
並於2018年11月15日及12月6日,利用DJI 600Pro六軸旋翼機搭載經評估後的微型儀器於台北都會區進行環境量測。期間觀測到大氣處於穩定及不穩定兩種案例。結果顯示各項污染物濃度於高空均有明顯分層的情況,可能因為風向不同所導致。當大氣轉為不穩定狀態時,發現邊界層有向上發展的現象,各污染物開始在較高的高度出現。粒徑分佈及體積分佈的結果顯示大氣中粒徑140 nm的微粒於數量上占多數,但非PM2.5的主要貢獻來源。且有發現黑碳在高空中累積,透過黑碳的光吸收指數(absorption Ångström exponent, AAE)顯示在不同高度處的碳成分不同,表明其來源可能不同,也說明大氣整體在垂直方向上為非均勻的狀態。
摘要(英) This study presents the helicopter unmanned aerial vehicle (UAV) equipped with different sensors for investigating the vertical distribution of atmospheric aerosols. High temporal and spatial resolution measurements within the first 500 m of atmosphere are presented. Five minimized instruments, including Mixing condensation particle counter, Portable optical particle counter, PM sensor, Aethalometer, parallel-plate differential mobility analyzer, were tested. The performance is calibrated under different environmental conditions. Four flights were conducted on November 15, 2018, from 11:37 pm to 12:10 pm (GMT+8) and December 6, 2018, from 09:48 pm to 10:32 pm (GMT+8). The first two flights fly up to 200 m, and the other two are up to 500 m. The cabinet with aerosol instruments was mounted at the bottom of UAV. Vertical profiles of aerosol particle number concentration, particle size distribution, black carbon mass concentration, PM2.5 were shown.
關鍵字(中) ★ 地球輻射收支
★ 大氣邊界層
★ 無人飛行載具
★ 環境量測
★ 微型氣膠儀器
★ 儀器性能評估
關鍵字(英) ★ Atmospheric boundary layer
★ Unmanned aerial vehicle
★ Environmental aerosol
★ Minimize aerosol instrument
論文目次 摘要 vi
Abstract vii
誌謝 viii
目錄 ix
圖目錄 xi
表目錄 xiii
第一章 前言 1
1.1研究動機 1
1.2研究目的 2
第二章 文獻回顧 4
2.1 UAV環境監測現況 4
2.1.1 UAV類型及應用範圍 4
2.1.2 UAV量測環境氣象資訊 6
2.1.3 UAV量測環境氣膠及氣體 8
2.2 攜帶型氣膠量測儀器 11
2.2.1 混和式冷凝微粒計數器 (Mixing Condensation particle counter, MCPC) 11
2.2.2 攜帶式光學微粒光譜儀 (Portable optical particle spectrometer , POPS) 14
2.2.3 氣膠吸收光譜儀(Aethalomater) 17
2.2.4 微型平板電移動度粒徑分析儀 (mini-plate defferential mobility analyzer) 18
第三章 研究方法 29
3.1 MCPC計數能力驗證 29
3.2 POPS粒徑分佈性能評估系統 32
3.3 MA350性能評估 35
3.4 PM sensor性能評估系統 36
3.5 mini-plate DMA之性能評估系統 38
3.5 UAV搭載微型氣膠儀器量測高空環境氣膠 41
第四章 結果與討論 45
4.1 MCPC計數性能驗證 45
4.2 POPS性能評估結果 50
4.2.1 POPS控制氣膠流與鞘流比下之粒徑分佈分析 50
4.2.2 POPS不控制氣膠流與鞘流比下之粒徑分佈分析 61
4.3 MA350性能評估結果 76
4.4 PM Sensor性能評估結果 78
4.5 mini-plate DMA性能評估結果 81
4.5.1 mini-plate DMA於不同氣膠流與鞘流比下選徑結果 82
4.5.2 mini-plate DMA於不同環境條件下選徑結果 85
4.5.3 mini-plate DMA電壓掃描結果 93
4.5.4 mini-plate DMA於不同環境條件下之掃描結果 101
4.6 UAV搭載微型氣膠儀器之高空量測結果 105
第五章 結論 112
參考文獻 114
口試委員意見回覆 125
參考文獻 1. Samet, J.M., et al., Fine particulate air pollution and mortality in 20 US cities, 1987–1994. New England journal of medicine, 2000. 343(24): p. 1742-1749.
2. Hoek, G., et al., Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study. The lancet, 2002. 360(9341): p. 1203-1209.
3. Delfino, R.J., C. Sioutas, and S. Malik, Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health. Environmental health perspectives, 2005. 113(8): p. 934.
4. Bräuner, E.V., et al., Exposure to ultrafine particles from ambient air and oxidative stress–induced DNA damage. Environmental health perspectives, 2007. 115(8): p. 1177.
5. Patel, M.M. and R.L. Miller, Air pollution and childhood asthma: recent advances and future directions. Current opinion in pediatrics, 2009. 21(2): p. 235.
6. Smijs, T.G. and S. Pavel, Titanium dioxide and zinc oxide nanoparticles in sunscreens: focus on their safety and effectiveness. Nanotechnology, science and applications, 2011. 4: p. 95.
7. Lohmann, U. and J. Feichter, Global indirect aerosol effects: a review. Atmospheric Chemistry and Physics, 2005. 5(3): p. 715-737.
8. Roeckner, E., et al., Impact of carbonaceous aerosol emissions on regional climate change. Climate dynamics, 2006. 27(6): p. 553-571.
9. Schulz, M., et al., Radiative forcing by aerosols as derived from the AeroCom present-day and pre-industrial simulations. Atmospheric Chemistry and Physics, 2006. 6(12): p. 5225-5246.
10. Koch, D. and A. Del Genio, Black carbon semi-direct effects on cloud cover: review and synthesis. Atmospheric Chemistry and Physics, 2010. 10(16): p. 7685-7696.
11. Myhre, G., et al., Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations. Atmospheric Chemistry and Physics, 2013. 13(4): p. 1853.
12. Stull, R.B., An introduction to boundary layer meteorology. Vol. 13. 2012: Springer Science & Business Media.
13. Düsing, S., et al., Helicopter-borne observations of the continental background aerosol in combination with remote sensing and ground-based measurements. Atmospheric Chemistry and Physics, 2018. 18(2): p. 1263.
14. Birmili, W., et al., Long-term observations of tropospheric particle number size distributions and equivalent black carbon mass concentrations in the German Ultrafine Aerosol Network (GUAN). 2016.
15. Liu, Q. and D.-R. Chen, Experimental evaluation of miniature plate DMAs (mini-plate DMAs) for future ultrafine particle (UFP) sensor network. Aerosol Science and Technology, 2016. 50(3): p. 297-307.
16. Ramanathan, V., et al., Indian Ocean Experiment: An integrated analysis of the climate forcing and effects of the great Indo‐Asian haze. Journal of Geophysical Research: Atmospheres, 2001. 106(D22): p. 28371-28398.
17. Siebert, H., et al., Probing finescale dynamics and microphysics of clouds with helicopter-borne measurements. Bulletin of the American Meteorological Society, 2006. 87(12): p. 1727-1738.
18. Jacob, D.J., et al., The Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) mission: design, execution, and first results. Atmospheric Chemistry and Physics, 2010. 10(11): p. 5191-5212.
19. Toon, O.B., et al., Planning, implementation, and first results of the Tropical Composition, Cloud and Climate Coupling Experiment (TC4). Journal of Geophysical Research: Atmospheres, 2010. 115(D10).
20. Seinfeld, J.H. and S.N. Pandis, Atmospheric chemistry and physics: from air pollution to climate change. 2012: John Wiley & Sons.
21. Karion, A., et al., Long-term greenhouse gas measurements from aircraft. Atmospheric Measurement Techniques, 2013. 6(3): p. 511-526.
22. Chang, Y.-H., 地球同步衛星 (Himawari-8) 在逐時大氣氣膠光學厚度之反演與分析. 2017, National Central University.
23. Ramanathan, V., et al., Warming trends in Asia amplified by brown cloud solar absorption. Nature, 2007. 448(7153): p. 575.
24. Rango, A., et al., Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 2009. 3(1): p. 033542.
25. Quan, J., et al., Analysis of the formation of fog and haze in North China Plain (NCP). Atmospheric Chemistry and Physics, 2011. 11(15): p. 8205-8214.
26. Kipling, Z., et al., What controls the vertical distribution of aerosol? Relationships between process sensitivity in HadGEM3–UKCA and inter-model variation from AeroCom Phase II. Atmospheric Chemistry and Physics, 2016. 16(4): p. 2221-2241.
27. Villa, T.F., et al., An overview of small unmanned aerial vehicles for air quality measurements: Present applications and future prospectives. Sensors, 2016. 16(7): p. 1072.
28. Ozdemir, U., et al., Design of a commercial hybrid VTOL UAV system. Journal of Intelligent & Robotic Systems, 2014. 74(1-2): p. 371-393.
29. Everaerts, J., The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008. 37(2008): p. 1187-1192.
30. Marshall, D.M., et al., Introduction to unmanned aircraft systems. 2016: Crc Press.
31. Cai, G., B.M. Chen, and T.H. Lee, Unmanned rotorcraft systems. 2011: Springer Science & Business Media.
32. Van den Kroonenberg, A., et al., Measuring the wind vector using the autonomous mini aerial vehicle M2AV. Journal of Atmospheric and Oceanic Technology, 2008. 25(11): p. 1969-1982.
33. Spiess, T., et al., First application of the meteorological Mini-UAV′M2AV′. Meteorologische Zeitschrift, 2007. 16(2): p. 159-169.
34. Martin, S., J. Bange, and F. Beyrich, Meteorological profiling of the lower troposphere using the research UAV" M 2 AV Carolo". Atmospheric Measurement Techniques, 2011. 4(4): p. 705-716.
35. Allen, G., et al., Feasibility of aerial measurements of methane emissions from landfills. Environmental Agency: Rotherham, UK, 2014.
36. Mayer, S., et al., Atmospheric profiling with the UAS SUMO: a new perspective for the evaluation of fine-scale atmospheric models. Meteorology and Atmospheric Physics, 2012. 116(1-2): p. 15-26.
37. Reuder, J., M. Jonassen, and H. Ólafsson, The Small Unmanned Meteorological Observer SUMO: Recent developments and applications of a micro-UAS for atmospheric boundary layer research. Acta Geophysica, 2012. 60(5): p. 1454-1473.
38. Bates, T.S., et al., Measurements of atmospheric aerosol vertical distributions above Svalbard, Norway, using unmanned aerial systems (UAS). 2013.
39. Lawrence, D.A. and B.B. Balsley, High-resolution atmospheric sensing of multiple atmospheric variables using the DataHawk small airborne measurement system. Journal of Atmospheric and Oceanic Technology, 2013. 30(10): p. 2352-2366.
40. Sobester, A., et al. High altitude unmanned air system for atmospheric science missions. in 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, including the AIAA Balloon Systems Conference and 19th AIAA Lighter-Than. 2011.
41. Corrigan, C., et al., Capturing vertical profiles of aerosols and black carbon over the Indian Ocean using autonomous unmanned aerial vehicles. Atmospheric Chemistry and Physics, 2008. 8(3): p. 737-747.
42. Ramana, M., et al., Albedo, atmospheric solar absorption and heating rate measurements with stacked UAVs. Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 2007. 133(629): p. 1913-1931.
43. Finn, A. and S. Franklin, UAV-based atmospheric tomography. 2011.
44. Berman, E.S., et al., Greenhouse gas analyzer for measurements of carbon dioxide, methane, and water vapor aboard an unmanned aerial vehicle. Sensors and Actuators B: Chemical, 2012. 169: p. 128-135.
45. Paul, J.B., L. Lapson, and J.G. Anderson, Ultrasensitive absorption spectroscopy with a high-finesse optical cavity and off-axis alignment. Applied optics, 2001. 40(27): p. 4904-4910.
46. Baer, D.S., et al., Sensitive absorption measurements in the near-infrared region using off-axis integrated-cavity-output spectroscopy. Applied Physics B, 2002. 75(2-3): p. 261-265.
47. Khan, M.A., M.A. Zondlo, and D.J. Lary. Open-path greenhouse gas sensor for UAV applications. in Quantum Electronics and Laser Science Conference. 2012. Optical Society of America.
48. Watai, T., et al., A lightweight observation system for atmospheric carbon dioxide concentration using a small unmanned aerial vehicle. Journal of Atmospheric and Oceanic Technology, 2006. 23(5): p. 700-710.
49. Lin, P.-h. and W.-n. Chen. The study of aerosol and ozone measurements in lower boundary layer with UAV helicopter platform. in EGU General Assembly Conference Abstracts. 2013.
50. Illingworth, S., et al., Measurement of boundary layer ozone concentrations on‐board a Skywalker unmanned aerial vehicle. Atmospheric Science Letters, 2014. 15(4): p. 252-258.
51. Rossi, M., et al. Gas-drone: Portable gas sensing system on UAVs for gas leakage localization. in SENSORS, 2014 IEEE. 2014. IEEE.
52. Gallego, V., M. Rossi, and D. Brunelli. Unmanned aerial gas leakage localization and mapping using microdrones. in Sensors Applications Symposium (SAS), 2015 IEEE. 2015. IEEE.
53. Gerhardt, N., et al., Analysis of inlet flow structures for the integration of a remote gas sensor on a multi-rotor unmanned aircraft system. Proceedings of the ACUS, 2014.
54. Malaver Rojas, J.A., et al. Development of a gas nanosensor node powered by solar cells. in Solar2011, the 49th AuSES Annual Conference. 2011. Australian Solar Energy Society.
55. Malaver Rojas, J.A., et al., Towards the development of a gas sensor system for monitoring pollutant gases in the low troposphere using small unmanned aerial vehicles. 2012.
56. Malaver, A., et al., Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases. Sensors, 2015. 15(2): p. 4072-4096.
57. Malaver Rojas, A.J., et al., Design and flight testing of an integrated solar powered UAV and WSN for remote gas sensing. 2015.
58. McGonigle, A., et al., Unmanned aerial vehicle measurements of volcanic carbon dioxide fluxes. Geophysical research letters, 2008. 35(6).
59. Diaz, J.A., et al., Unmanned aerial mass spectrometer systems for in-situ volcanic plume analysis. Journal of the American Society for Mass Spectrometry, 2015. 26(2): p. 292-304.
60. Yang, X., et al., Aircraft measurement over the Gulf of Tonkin capturing aloft transport of biomass burning. Atmospheric Environment, 2018. 182: p. 41-50.
61. Jian, Y. and T.-M. Fu, Injection heights of springtime biomass-burning plumes over peninsular Southeast Asia and their impacts on long-range pollutant transport. Atmospheric Chemistry and Physics, 2014. 14(8): p. 3977-3989.
62. Altstädter, B., et al., ALADINA–an unmanned research aircraft for observing vertical and horizontal distributions of ultrafine particles within the atmospheric boundary layer. Atmospheric Measurement Techniques, 2015. 8(4): p. 1627-1639.
63. Harrison, W.A., et al., Using remote control aerial vehicles to study variability of airborne particulates. Air, Soil and Water Research, 2015. 8: p. ASWR. S30774.
64. Brady, J.M., et al., Characterization of a quadrotor unmanned aircraft system for aerosol-particle-concentration measurements. Environmental science & technology, 2016. 50(3): p. 1376-1383.
65. Gao, R., et al., A light-weight, high-sensitivity particle spectrometer for PM2. 5 aerosol measurements. Aerosol Science and Technology, 2016. 50(1): p. 88-99.
66. Change, I.P.O.C., Climate change 2007: The physical science basis. Agenda, 2007. 6(07): p. 333.
67. Myhre, G., et al., Anthropogenic and natural radiative forcing. Climate change, 2013. 423: p. 658-740.
68. Pope III, C.A. and D.W. Dockery, Health effects of fine particulate air pollution: lines that connect. Journal of the air & waste management association, 2006. 56(6): p. 709-742.
69. Mustajbegovic, J., et al., Respiratory findings in chemical workers exposed to low concentrations of organic and inorganic air pollutants. American journal of industrial medicine, 2000. 38(4): p. 431-440.
70. Wilcox, E.M., et al., Black carbon solar absorption suppresses turbulence in the atmospheric boundary layer. Proceedings of the National Academy of Sciences, 2016. 113(42): p. 11794-11799.
71. Babu, S.S., et al., Free tropospheric black carbon aerosol measurements using high altitude balloon: Do BC layers build “their own homes” up in the atmosphere? Geophysical Research Letters, 2011. 38(8).
72. Bhugwant, C., et al., Impact of traffic on black carbon aerosol concentration at la Reunion Island (Southern Indian Ocean). Atmospheric Environment, 2000. 34(20): p. 3463-3473.
73. Sioutas, C., R.J. Delfino, and M. Singh, Exposure assessment for atmospheric ultrafine particles (UFPs) and implications in epidemiologic research. Environmental health perspectives, 2005. 113(8): p. 947.
74. Flagan, R.C., History of electrical aerosol measurements. Aerosol Science and Technology, 1998. 28(4): p. 301-380.
75. Hewitt, G., The charging of small particles for electrostatic precipitation. Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics, 1957. 76(3): p. 300-306.
76. Knutson, E. and K. Whitby, Aerosol classification by electric mobility: apparatus, theory, and applications. Journal of Aerosol Science, 1975. 6(6): p. 443-451.
77. Alonso, M. and Y. Endo, Dispersion of aerosol particles undergoing Brownian motion. Journal of Physics A: Mathematical and General, 2001. 34(49): p. 10745.
78. Ranjan, M. and S. Dhaniyala, A new miniature electrical aerosol spectrometer (MEAS): Experimental characterization. Journal of Aerosol Science, 2008. 39(8): p. 710-722.
79. Alsharifi, T. and D.-R. Chen, On the design of miniature parallel-plate differential mobility classifiers. Journal of Aerosol Science, 2018. 121: p. 1-11.
80. Stratmann, F., et al., Differential electrical mobility analysis: A theoretical study. Aerosol Science and Technology, 1997. 26(4): p. 368-383.
81. Birmili, W., et al., Determination of differential mobility analyzer transfer functions using identical instruments in series. Aerosol Science and Technology, 1997. 27(2): p. 215-223.
82. Biskos, G., K. Reavell, and N. Collings, Unipolar diffusion charging of aerosol particles in the transition regime. Journal of Aerosol Science, 2005. 36(2): p. 247-265.
83. Gysel, M., G. McFiggans, and H. Coe, Inversion of tandem differential mobility analyser (TDMA) measurements. Journal of Aerosol Science, 2009. 40(2): p. 134-151.
84. Jiang, J., et al., Transfer functions and penetrations of five differential mobility analyzers for sub-2 nm particle classification. Aerosol science and technology, 2011. 45(4): p. 480-492.
85. Martinsson, B.G., M.N. Karlsson, and G. Frank, Methodology to estimate the transfer function of individual differential mobility analyzers. Aerosol Science & Technology, 2001. 35(4): p. 815-823.
86. Hummes, D., et al., Experimental determination of the transfer function of a differential mobility analyzer (DMA) in the nanometer size range. Particle & particle systems characterization, 1996. 13(5): p. 327-332.
87. Stolzenburg, M.R., An ultrafine aerosol size distribution measuring system. Ph. D. Thesis, Department of Mechanical Engineering, University of Minnesota, 1988.
88. Li, W., L. Li, and D.-R. Chen, A new deconvolution scheme for the retrieval of true DMA transfer function from tandem DMA data. Aerosol science and technology, 2006. 40(12): p. 1052-1057.
89. Kim, J.H., et al., Slip correction measurements of certified PSL nanoparticles using a nanometer differential mobility analyzer (Nano-DMA) for Knudsen number from 0.5 to 83. Journal of Research of the National Institute of Standards and technology, 2005. 110(1): p. 31.
90. Allen, M. and O. Raabe, Re-evaluation of Millikan′s oil drop data for the motion of small particles in air. Journal of Aerosol Science, 1982. 13(6): p. 537-547.
91. Willeke, K., Temperature dependence of particle slip in a gaseous medium. Journal of Aerosol Science, 1976. 7(5): p. 381-387.
92. Jennings, S., The mean free path in air. Journal of Aerosol Science, 1988. 19(2): p. 159-166.
93. Zhao, H., P.H. Brown, and P. Schuck, On the distribution of protein refractive index increments. Biophysical journal, 2011. 100(9): p. 2309-2317.
94. Bezantakos, S., F. Schmidt-Ott, and G. Biskos, Performance evaluation of the cost-effective and lightweight Alphasense optical particle counter for use onboard unmanned aerial vehicles. Aerosol Science and Technology, 2018. 52(4): p. 385-392.
指導教授 蕭大智 江康鈺 審核日期 2019-6-18
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