博碩士論文 105621007 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:10 、訪客IP:3.80.85.76
姓名 柯立晉(Li-Jin Ke)  查詢紙本館藏   畢業系所 大氣科學學系
論文名稱 開發適用於大氣邊界層觀測的無人機系統
(Development of a UAV system for Atmospheric Boundary Layer Measurement)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 大氣邊界層(Planetary Boundary Layer, PBL)位於地球大氣最底層,其結構發展除了與太陽輻射加熱、人類經濟活動及植被分布密切相關外,日夜的演變也影響許多小尺度天氣現象與整體空氣品質。但過去研究中,大氣邊界層的資料受限於觀測技術,缺乏高解度的時間與空間資料,且不論是氣象塔、探空氣球、繫留氣球或是飛機觀測都有其優缺點與使用限制,故考量到人力和觀測成本,許多觀測手段是無法做到整天或每天的常態觀測,目前台灣大多以探空資料或地面光達遙測來取得大氣邊界層熱力結構與空氣污染物的垂直分布。無人機是近年來新興的觀測平台之一,成本低廉,所需的人力也相對簡便,因此本研究旨在建構一套適用於大氣邊界層的無人機觀測系統,並以此來探討邊界層垂直發展結構與空氣污染物的變化,希望有助於邊界層機制參數化的建立與改善氣象及空污模式的預報。
本系統搭配三種無人機酬載氣象及氣膠光學儀器,以獲取大氣邊界層溫度、相對濕度、壓力、風向、風速、氣膠粒子濃度與粒徑分布的垂直分布資料。為了減少所使用的儀器於量測上的誤差,先後進行了室內與室外的平行比對,室內溫濕度與壓力平行比對的均方根誤差(Root-Mean-Square Error, RMSE)為0.30℃、1.84%、0.11 hPa,室外日間與夜間3 km內溫濕壓平行比對的RMSE則分別為0.74℃、3.54%、0.72 hPa及0.21℃、3.34%、0.14 hPa,誤差皆於合理範圍且資料具高度相關性。實際觀測上,從2017年開始針對境外傳輸、本地污染及夏日北台灣邊界層發展結構等案例進行密集觀測,並整合探空氣象、剖風儀雷達風場與光達氣膠垂直分布等資料以驗證本系統實用與可靠性。結果分析顯示,本系統於0-3 km內的氣象探測結果與氣象局例行施放的探空觀測具良好的一致性,於邊界層高度的偵測與光達消偏振反演幾乎吻合。氣膠量測結果比較光達背向散射的連續觀測資料之中,垂直分布不連續處與本系統觀測的逆溫位置一致,氣膠垂直分布也與本系統所得類似。相較於與剖風儀雷達反演的風場,無人機於離地100 m後所量測的風向與風速RMSE分別為19.02°與1.91 m s-1,故仍有誤差待評估與修正。整體而言,本研究驗證本團隊所自行開發的無人機觀測系統及技術,可以有效應用於大氣邊界層內觀測,解釋大氣邊界層發展與空氣污染垂直分布間之關係,並可作為地面遙測觀測的驗證工具。
摘要(英) The Planetary Boundary Layer is located at the bottom of the Earth′s atmosphere. Its structural development is closely related to solar radiation heating, human economic activities, and vegetation distribution. The evolution of day and night also affects small-scale weather and air quality. However, in the past studies, the data of the atmospheric boundary layer was limited by observation techniques, lacking high-resolution time and space data. Whether meteorological tower, radiosonde, tethered balloon or aircraft observation, both have advantages, disadvantages and use restrictions. Considering the cost of manpower and usage, many observation methods are unable to observe normal or daily normal observations. At present, the vertical distribution of the thermal structure of the atmospheric boundary layer and air pollutants is mostly detected by sounding data or ground-based remote sensing in Taiwan. Unmanned aerial vehicle (UAV) is one of the new observing platforms in recent years. The cost is much cheaper and the manpower needed is relatively simple. Therefore, the purpose of this study is to construct the UAV observation system for the atmospheric boundary layer and to explore the vertical structure of the boundary layer and the change of the air pollutants. It will help to establish the parameterized boundary layer mechanism and improve the prediction of the weather and air pollution model.
The system is equipped with meteorological and aerosol optical instruments on three kinds of drones to obtain the vertical distribution data of atmospheric boundary layer temperature, humidity, pressure, wind direction, wind speed, aerosol particles concentration and size distribution. In order to reduce the error of the instrument used in the measurement, the parallel comparison between indoor and outdoor has been carried out. The root-mean-square error (RMSE) of indoor temperature, humidity, and the pressure is 0.30 °C, 1.84%, 0.11 hPa, respectively. The RMSE of outdoor temperature and humidity within 3 km during outdoor day and night are 0.74 °C, 3.54% and 0.21 °C, 3.34%, respectively. The error is within a reasonable range and the data are highly correlated in actual observation, since 2017, this study focuses on the case for long-range transport, local pollution and the development structure of northern Taiwan boundary layer in summer, and integrates the data of sounding, wind profiler and the vertical distribution of the aerosol which is inversion by lidar to verify the practicality and reliability of the system. The results show that the meteorological observation results of the system within 0-3 km are in good agreement with the sounding observations by the central weather bureau. The detection of the boundary layer height is almost consistent with the polarization depolarization data. The main error source is the radiant heating and the response time of the sensor. The aerosol measurement results compare the continuous observation data of the backscattering signal. In addition, the aerosol results compared with the continuous observation data of the light backscattering. The vertical distribution of the aerosol distribution is similar to that of the system, except that the vertical distribution discontinuity is consistent with the temperature inversion observed by the system. Compared with the wind field inversion with the wind profiler, the wind direction and wind speed RMSE measured by the drone after 100 m from the ground are 19.02° and 1.91 m s-1 respectively, so there are still errors to be evaluated and corrected. As a whole, this study proves that the unmanned aerial vehicle (UAV) system and technology developed by our team can be applied to the atmospheric boundary layer to explain the relationship between the development of atmospheric boundary layer and the vertical distribution of air pollution, and can be used as a verification tool for remote sensing.
關鍵字(中) ★ 無人機
★ 行星邊界層
關鍵字(英) ★ Unmanned Aerial Vehicle
★ Planetary Boundary Layer
論文目次 摘要 i
ABSTRACT iii
誌謝 v
目錄 vi
圖目錄 viii
表目錄 xi
一、 前言 1
1-1 研究動機 1
1-2 研究目的 3
二、 文獻回顧 4
2-1 無人機 4
2-1-1 無人機定義 4
2-1-2 無人機分類 5
2-2 大氣邊界層 6
2-3 無人機觀測應用回顧 7
三、 研究方法 12
3-1 無人機與酬載設備 12
3-2 無人機系統架構及觀測策略 16
3-3 旋翼機之風向風速估算原理 18
3-4 地面遙測儀器: 剖風儀雷達與光達 21
3-5 邊界層高度估算 22
四、 結果與討論 25
4-1 酬載儀器比對 25
4-1-1 室內平行比對 25
4-1-2 室外平行比對 26
4-1-3 無人機酬載與探空比對 28
4-1-4 風場平行比對 30
4-2 量測誤差評估與討論 31
4-3 觀測大氣邊界層發展個案分析 32
4-4 監測大氣污染物個案分析 38
4-4-1 境外傳輸個案 38
4-4-2 本地污染個案 40
4-4-3 垂直氣膠粒徑與濃度特徵分析 43
五、 總結與未來展望 45
5-1 結論 45
5-2 未來展望 47
參考文獻 48
表 54
圖 63
參考文獻 Altstadter, B., A. Platis, B. Wehner, A. Scholtz, N. Wildmann, M. Hermann, R. Kathner, H. Baars, J. Bange, and A. Lampert (2015), ALADINA – an unmanned research aircraft for observing vertical and horizontal distributions of ultrafine particles within the atmospheric boundary layer, Atmospheric Measurement Techniques, 8(4), 1627-1639.
Andrews, A. E., J. D. Kofler, M. E. Trudeau, J. C. Williams, D. H. Neff, K. A. Masarie, D. Y. Chao, D. R. Kitzis, P. C. Novelli, C. L. Zhao, E. J. Dlugokencky, P. M. Lang, M. J. Crotwell, M. L. Fischer, M. J. Parker, J. T. Lee, D. D. Baumann, A. R. Desai, C. O. Stanier, S. F. J. De Wekker, D. E. Wolfe, J. W. Munger, and P. P. Tans (2014), CO2, CO, and CH4 measurements from tall towers in the NOAA Earth System Research Laboratory′s Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts, Atmospheric Measurement Techniques, 7(2), 647-687.
Alvarado, M., F. Gonzalez, A. Fletcher, and A. Doshi (2015), Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites, Sensors (Basel), 15(8), 19667-19687.
Alvarado, M., F. Gonzalez, P. Erskine, D. Cliff, and D. Heuff (2017), A Methodology to Monitor Airborne PM10 Dust Particles Using a Small Unmanned Aerial Vehicle, Sensors (Basel), 17(2), 343.
Anenberg, S. C., J. J. West, A. M. Fiore, D. A. Jaffe, M. J. Prather, D. Bergmann, and P. Hess (2009), Intercontinental impacts of ozone pollution on human mortality, Environ. Sci. Technol., 43, 6482–6487.
Amici, S., M. Turci, F. Giulietti, S. Giammanco, M. F. Buongiorno, A. La Spina, and L. Spampinato (2013), Volcanic environments monitoring by drones mud volcano case study. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5-10.
Balsley, B., M. Jensen, and R. Frehlich (1998), The use of state-of-the-art kites for profiling the lower atmosphere, Boundary-Layer Meteorology, 87(1), 1-25.
Banta, R. M., Y. L. Pichugina, N. D. Kelley, R. M. Hardesty, and W. A. Brewer (2013), Wind Energy Meteorology: Insight into Wind Properties in the Turbine-Rotor Layer of the Atmosphere from High-Resolution Doppler Lidar, Bulletin of the American Meteorological Society, 94(6), 883-902.
Baserud, L., J. Reuder, M. O. Jonassen, S. T. Kral, M. B. Paskyabi, and M. Lothon (2016), Proof of concept for turbulence measurements with the RPAS SUMO during the BLLAST campaign, Atmospheric Measurement Techniques, 9(10), 4901-4913.
Baars, H., T. Kanitz, R. Engelmann, D. Althausen, B. Heese, M. Komppula, J. Preisler, M. Tesche, A. Ansmann, U. Wandinger, J.-H. Lim, J. Y. Ahn, I. S. Stachlewska, V. Amiridis, E. Marinou, P. Seifert, J. Hofer, A. Skupin, F. Schneider, S. Bohlmann, A. Foth, S. Bley, A. Pfuller, E. Giannakaki, H. Lihavainen, Y. Viisanen, R. K. Hooda, S. N. Pereira, D. Bortoli, F. Wagner, I. Mattis, L. Janicka, K. M. Markowicz, P. Achtert, P. Artaxo, T. Pauliquevis, R. A. F. Souza, V. P. Sharma, P. G. van Zyl, J. P. Beukes, J. Sun, E. G. Rohwer, R. Deng, R.-E. Mamouri, and F. Zamorano (2016), An overview of the first decade of PollyNET: an emerging network of automated Raman-polarization lidars for continuous aerosol profiling, Atmospheric Chemistry and Physics, 16(8), 5111-5137.
Bianco, L., and J. M. Wilczak (2002), Convective boundary layer depth: improved measurement by Doppler radar wind profiler using fuzzy logic methods, Journal of Atmospheric and Oceanic Technology, 19(11), 1745–1758.
Brady, J. M., M. D. Stokes, J. Bonnardel, and T. H. Bertram (2016), Characterization of a Quadrotor Unmanned Aircraft System for Aerosol-Particle-Concentration Measurements, Environ Sci Technol, 50(3), 1376-1383.
Brosy, C., K. Krampf, M. Zeeman, B. Wolf, W. Junkermann, K. Schafer, S. Emeis, and H. Kunstmann (2017), Simultaneous multicopter-based air sampling and sensing of meteorological variables, Atmospheric Measurement Techniques, 10(8), 2773-2784.
Chang, C. C., J. L. Wang, C. Y. Chang, M. C. Liang, and M. R. Lin (2016), Development of a multicopter-carried whole air sampling apparatus and its applications in environmental studies, Chemosphere, 144, 484-492.
Corrigan, C. E., G. C. Roberts, M. V. Ramana, D. Kim, and V. Ramanathan (2008), Capturing vertical profiles of aerosols and black carbon over the Indian Ocean using autonomous unmanned aerial vehicles, Atmos. Chem. Phys., 8(3), 737-747.
Dai, C., Q. Wang, J. A. Kalogiros, D. H. Lenschow, Z. Gao, and M. Zhou (2014), Determining Boundary-Layer Height from Aircraft Measurements, Boundary-Layer Meteorology, 152(3), 277-302.
Dabberdt, W. F., G. L. Frederick, R. M. Hardesty, W. C. Lee, and K. Underwood (2004), Advances in meteorological instrumentation for air quality and emergency response, Meteorology and Atmospheric Physics, 87(3), 57–88.
de Boer, G., S. Palo, B. Argrow, G. LoDolce, J. Mack, R.-S. Gao, H. Telg, C. Trussel, J. Fromm, C. N. Long, G. Bland, J. Maslanik, B. Schmid, and T. Hock (2016), The Pilatus unmanned aircraft system for lower atmospheric research, Atmospheric Measurement Techniques, 9(4), 1845-1857.
de Boer, G., S. Palo, B. Argrow, G. LoDolce, N. Curry, D. Weibel, W. Finnamore, P. D′Amore, S. Borenstein, T. Nichols, J. Elston, M. Ivey, A. Bendure, B. Schmid, C. Long, H. Telg, R. Gao, T. Hock, and G. Bland (2017), ERASMUS Field Campaign Report, ARM Climate Research Facility. DOE/SC-ARM-17-009.
Dupont, E., L. Menut, B. Carissimo, J. Pelon, and P. Flamant (1999), Comparison between the atmospheric boundary layer in Paris and its rural suburbs during the ECLAP experiment. Atmos. Environ., 33(6), 979-994.
Egger, J., S. Bajrachaya, R. Heinrich, P. Kolb, S. Lammlein, M. Mech, J. Reuder, W. Schaper, P. Shakya, J. Schween, and H. Wendt (2002), Diurnal winds in the Himalayan Kali Gandaki valley. Part III: Remotely piloted aircraft soundings, Mon. Weather Rev., 130(8), 2042–2058.
Emeis, S., K. Schafer, and C. Munkel (2009), Observation of the structure of the urban boundary layer with different ceilometers and validation by RASS data, Meteorologische Zeitschrift, 18(2), 149-154.
Gao, R. S., A. E. Perring, T. D. Thornberry, A. W. Rollins, J. P. Schwarz, S. J. Ciciora, and D. W. Fahey (2013), A High-Sensitivity Low-Cost Optical Particle Counter Design, Aerosol Science and Technology, 47(2), 137-145.
Gao, R. S., H. Telg, R. J. McLaughlin, S. J. Ciciora, L. A. Watts, M. S. Richardson, J. P. Schwarz, A. E. Perring, T. D. Thornberry, A. W. Rollins, M. Z. Markovic, T. S. Bates, J. E. Johnson, and D. W. Fahey (2015), A light-weight, high-sensitivity particle spectrometer for PM2.5 aerosol measurements, Aerosol Science and Technology, 50(1), 88-99.
Giebel, G., U. S. Paulsen, J. Bange, A. la Cour-Harbo, J. Reuder, S. Mayer, A. van der Kroonenberg, and J. Molgaard (2012), Autonomous Aerial Sensors for Wind Power Meteorology-A Pre-Project.
Greenberg, J., A. Guenther, and A. Turnipseed (2009), Tethered balloon-based soundings of ozone, aerosols, and solar radiation near Mexico City during MIRAGE-MEX, Atmospheric Environment, 43(16), 2672-2677.
Guo, J., Y. Miao, Y. Zhang, H. Liu, Z. Li, W. Zhang, J. He, M. Lou, Y. Yan, L. Bian, and P. Zhai (2016), The climatology of planetary boundary layer height in China derived from radiosonde and reanalysis data, Atmospheric Chemistry and Physics, 16(20), 13309-13319.
Hammann, E., A. Behrendt, F. Le Mounier, and V. Wulfmeyer (2015), Temperature profiling of the atmospheric boundary layer with rotational Raman lidar during the HD(CP)2 Observational Prototype Experiment, Atmospheric Chemistry and Physics, 15(5), 2867-2881.
Hu, X.-M., J. W. Nielsen-Gammon, and F. Zhang (2010), Evaluation of Three Planetary Boundary Layer Schemes in the WRF Model, Journal of Applied Meteorology and Climatology, 49(9), 1831-1844.
Holzworth, G. C. (1964), Estimates of mean maximum mixing depths in the contiguous United States, Mon. Weather Rev, 92(5), 235-242.
Hill, M., T. Konrad, J. Meyer, and J. Rowland (1970), A small, radio-controlled aircraft as a platform for meteorological sensors.
Korhonen, K., E. Giannakaki, T. Mielonen, A. Pfuller, L. Laakso, V. Vakkari, H. Baars, R. Engelmann, J. P. Beukes, P. G. Van Zyl, A. Ramandh, L. Ntsangwane, M. Josipovic, P. Tiitta, G. Fourie, I. Ngwana, K. Chiloane, and M. Komppula (2014), Atmospheric boundary layer top height in South Africa: measurements with lidar and radiosonde compared to three atmospheric models, Atmospheric Chemistry and Physics, 14(8), 4263-4278.
Kunz, M., J. V. Lavric, C. Gerbig, P. Tans, D. Neff, C. Hummelgard, H. Martin, H. Rodjegard, B. Wrenger, and M. Heimann (2018), COCAP: a carbon dioxide analyser for small unmanned aircraft systems, Atmospheric Measurement Techniques, 11(3), 1833-1849, doi:10.5194/amt-11-1833-2018.
Lenschow, D. H., M. Zhou, X. Zeng, L. Chen, and X. Xu (2000), Measurements of fine-scale structure at the top of marine stratocumulus, Boundary-layer meteorology, 97(2), 331-357.
Li, J., Q. Fu, J. Huo, D. Wang, W. Yang, Q. Bian, Y. Duan, Y. Zhang, J. Pan, Y. Lin, K. Huang, Z. Bai, S.-H. Wang, J. S. Fu, and P. K. K. Louie (2015), Tethered balloon-based black carbon profiles within the lower troposphere of Shanghai in the 2013 East China smog, Atmospheric Environment, 123, 327-338.
Liu, S., and X.-Z. Liang (2010), Observed Diurnal Cycle Climatology of Planetary Boundary Layer Height, Journal of Climate, 23(21), 5790-5809.
Mahrt, L., R. Heald, D. Lenschow, B. Stankov, and I. Troen (1979), An observational study of the structure of the nocturnal boundary layer, Boundary-Layer Meteorology, 17(2), 247-264.
Martin, S., J. Bange, and F. Beyrich (2011), Meteorological profiling of the lower troposphere using the research UAV "M2AV Carolo", Atmospheric Measurement Techniques, 4(4), 705-716.
Martucci, G., R. Matthey, V. Mitev, and H. Richner (2007), Comparison between Backscatter Lidar and Radiosonde Measurements of the Diurnal and Nocturnal Stratification in the Lower Troposphere, Journal of Atmospheric and Oceanic Technology, 24(7), 1231-1244.
Meng, Z. Y., G. A. Ding, X. B. Xu, X. D. Xu, H. Q. Yu, and S. F. Wang (2008), Vertical distributions of SO2 and NO2 in the lower atmosphere in Beijing urban areas, China, Sci Total Environ, 390(2-3), 456-465.
McKee, D. (1993), Tropospheric ozone: human health and agricultural impacts, CRC Press.
Nathan, B. J., L. M. Golston, A. S. O′Brien, K. Ross, W. A. Harrison, L. Tao, D. J. Lary, D. R. Johnson, A. N. Covington, N. N. Clark, and M. A. Zondlo (2015), Near-Field Characterization of Methane Emission Variability from a Compressor Station Using a Model Aircraft, Environ Sci Technol, 49(13), 7896-7903.
Neumann, P. P., and M. Bartholmai (2015), Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit, Sensors and Actuators A: Physical, 235, 300-310.
Van den Kroonenberg, A., T. Martin, M. Buschmann, J. Bange, and P. Vorsmann (2008), Measuring the Wind Vector Using the Autonomous Mini Aerial Vehicle M2AV, Journal of Atmospheric and Oceanic Technology, 25(11), 1969-1982.
Villa, T. F., F. Gonzalez, B. Miljievic, Z. D. Ristovski, and L. Morawska (2016), An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives, Sensors (Basel), 16(7), 1072.
Ramana, M. V., V. Ramanathan, D. Kim, G. C. Roberts, and C. E. Corrigan (2007), Albedo, atmospheric solar absorption and heating rate measurements with stacked UAVs, Quarterly Journal of the Royal Meteorological Society, 133(629), 1913-1931.
Ramanathan, V., F. Li, M. V. Ramana, P. S. Praveen, D. Kim, C. E. Corrigan, H. Nguyen, E. A. Stone, J. J. Schauer, G. R. Carmichael, B. Adhikary, and S. C. Yoon (2007), Atmospheric brown clouds: Hemispherical and regional variations in long-range transport, absorption, and radiative forcing, Journal of Geophysical Research, 112(D22).
Ramanathan, V., M. V. Ramana, G. Roberts, D. Kim, C. Corrigan, C. Chung, and D. Winker (2007b), Warming trends in Asia amplified by brown cloud solar absorption, Nature, 448(7153), 575-578.
Rappengluck, B., C. Forster, G. Jakobi, and M. Pesch (2004), Unusually high levels of PAN and ozone over Berlin, Germany, during nighttime on August 7, 1998, Atmospheric Environment, 38(36), 6125-6134.
Renno, N. O., and E. R. Williams (1995), Quasi-Lagrangian measurements in convective boundary layer plumes and their implications for the calculation of CAPE, Monthly weather review, 123(9), 2733-2742.
Roberts, G., M. Ramana, C. Corrigan, D. Kim, and V. Ramanathan (2008), Simultaneous observations of aerosol–cloud–albedo interactions with three stacked unmanned aerial vehicles, Proceedings of the National Academy of Sciences, 105(21), 7370-7375.
Sasakawa, M., K. Shimoyama, T. Machida, N. Tsuda, H. Suto, M. Arshinov, D. Davydov, A. Fofonov, O. Krasnov, and T. Saeki (2010), Continuous measurements of methane from a tower network over Siberia, Tellus B: Chemical and Physical Meteorology, 62(5), 403-416.
Seibert, P., F. Beyrich, S.-E. Gryning, S. Joffre, A. Rasmussen, and P. Tercier (2000), Review and intercomparison of operational methods for the determination of the mixing height, Atmospheric environment, 34(7), 1001-1027.
Seidel, D. J., C. O. Ao, and K. Li (2010), Estimating climatological planetary boundary layer heights from radiosonde observations: Comparison of methods and uncertainty analysis, Journal of Geophysical Research, 115(D16), 113.
Soddell, J. R., K. McGuffie, and G. J. Holland (2004), Intercomparison of atmospheric soundings from the aerosonde and radiosonde, Journal of Applied Meteorology, 43(9), 1260-1269.
Spiess, T., J. Bange, M. Buschmann, and P. Vorsmann (2007), First application of the meteorological Mini-UAV′M2AV′, Meteorologische Zeitschrift, 16(2), 159-169.
Stull, R. B. (1988), An introduction to boundary layer meteorology, Atmospheric sciences.
Villa, T. F., F. Gonzalez, B. Miljievic, Z. D. Ristovski, and L. Morawska (2016), An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives, Sensors (Basel), 16(7), 1072.
Wang, Q., and S. Wang (2004), Turbulent and thermodynamic structure of the autumnal arctic boundary layer due to embedded clouds, Boundary-layer meteorology, 113(2), 225-247.
Wang, R., X. Xu, S. Jia, R. Ma, L. Ran, Z. Deng, W. Lin, Y. Wang, and Z. Ma (2017), Lower tropospheric distributions of O3 and aerosol over Raoyang, a rural site in the North China Plain, Atmospheric Chemistry and Physics, 17(6), 3891-3903.
Wang, J. M., J. G. Murphy, J. A. Geddes, C. L. Winsborough, N. Basiliko, and S. C. Thomas (2013), Methane fluxes measured by eddy covariance and static chamber techniques at a temperate forest in central Ontario, Canada, Biogeosciences, 10(6), 4371-4382.
Wilcox, E. M., R. M. Thomas, P. S. Praveen, K. Pistone, F. A. M. Bender, and V. Ramanathan (2016), Black carbon solar absorption suppresses turbulence in the atmospheric boundary layer, Proceedings of the National Academy of Sciences, 113(42), 11794-11799.
Yue, X., and N. Unger (2014), Ozone vegetation damage effects on gross primary productivity in the United States, Atmospheric Chemistry and Physics, 14(17), 9137-9153.
Zeng, X., M. A. Brunke, M. Zhou, C. Fairall, N. A. Bond, and D. H. Lenschow (2004), Marine atmospheric boundary layer height over the eastern Pacific: Data analysis and model evaluation, Journal of Climate, 17(21), 4159-4170.
指導教授 王聖翔 鄭芳怡(Sheng-Hsiang Wang Fang-Yi Cheng) 審核日期 2018-8-20
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明