簡易檢索 / 詳目顯示

研究生: 吳亘卓
Wu, Hsuan-Cho
論文名稱: 支援城市移動的微型細懸浮微粒感測研究
Research of PM2.5 Micro-Sensing Under Urban Mobility
指導教授: 陳伶志
Chen, Ling-Jyh
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2016
畢業學年度: 104
語文別: 中文
論文頁數: 38
中文關鍵詞: 懸浮微粒細懸浮微粒微型感測器城市移動力
英文關鍵詞: Particulate Matter, PM2.5, Urban Mobility, MQTT
DOI URL: https://doi.org/10.6345/NTNU202204924
論文種類: 學術論文
相關次數: 點閱:65下載:32
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 隨著科技的進步,越來越多的產業誕生,但是相對的環境汙染也日趨嚴重,當中懸浮微粒(Particulate Matter, PM)是逐漸受到大家重視的一個議題,懸浮微粒對人類的健康有所影響,輕微的影響是引發過敏反應,嚴重的可能會導致肺癌、肺炎,因此在Urban Mobility的情況下透過microsensor能即時、準確地得知目前所在位置的懸浮微粒濃度與否,並判斷目前所在環境是否適合人類活動是一項非常重要的問題。
    本論文主旨在Urban mobility 下PM2.5 微型感測器的選取、使用水流量感測器找出與風速之間的影響程度和推測PM2.5濃度與風速之間的關係。利用Arduino UNO、LinkIt One開發板來實作,在有網路的環境下使用MQTT將資料上傳至Database,並將量測裝置安裝於定點位置與機車上,利用本論文在室內實驗找出的相對應關係反推在戶外實地測試的實驗結果,希望藉由分析結果能夠得知汽機車族目前所在位置的PM2.5濃度多寡,便能判斷此地點是否適合逗留或是必須盡快離開以免吸入過多的PM2.5導致身體不適。

    第1章、緒論 1 第2章、相關研究討論 5 第3章、PM2.5 微型感測器選取與穩定性 7 3-1 PM2.5 微型感測器選取 7 3-2 Sharp-D與G3準確度比較 9 3-2-1 開發環境與開發板介紹 9 3-2-2 PM2.5準確度實驗設置 10 3-2-3 PM2.5準確度實驗結果 11 3-3 G3穩定性 12 3-3-1 開發環境與開發板介紹 12 3-3-2 MQTT 14 3-3-3 穩定性實驗設置 15 3-3-4 穩定性實驗結果 16 第4章、PM2.5與風速的關係 18 4-1 實驗設備介紹 19 4-2 風速影響實驗設置 20 4-3 風速影響實驗結果 21 第5章、風速測量 23 5-1 風速測量實驗設置 23 5-2 風速測量實驗結果 25 第6章、戶外實驗 26 6-1 戶外實驗設置 26 6-2 戶外實驗結果 28 第7章、討論 31 第8章、結論與未來工作 34

    [1] Hedgecock, W. , P. Volgyesi, A. Ledeczi, and X. Koutsoukos, "Dissemination and Presentation of High Resolution Air Pollution Data from Mobile Sensor Nodes," Annual Southeast Regional Conference, 2010.
    [2] Srinivas Devarakonda, Parveen Sevusu, Hongzhang Liu,Ruilin Liu, Liviu Iftode, Badri Nath, "Real-time air quality monitoring through mobile sensing in metropolitan areas," ACM SIGKDD International Workshop on Urban Computing, 2013.
    [3] Al-Ali, A.R. , Zualkernan, I. ; Aloul, F. "A Mobile GPRS-Sensors Array for Air
    Pollution Monitoring," IEEE, SENSORS JOURNAL, 2010.
    [4] Sørensen M1, Daneshvar B, Hansen M, Dragsted LO, Hertel O, Knudsen L, Loft
    S. "Personal PM2.5 exposure and markers of oxidative stress in blood,"
    Environmental Health Perspectives, February 2003.
    [5] Michelle L. Bell, Francesca Dominici, Keita Ebisu, Scott L. Zeger and Jonathan M.
    Samet, " Spatial and Temporal Variation in PM2.5 Chemical Composition in the
    United States for Health Effects Studies," Environmental Health Perspectives,
    July 2007, Vol. 115, No. 7, pp.989-995.
    [6] Janssen, NA; Hoek, G; Simic-Lawson, M; Fischer, P; van Bree, L; ten Brink,
    H; Keuken, M; Atkinson, RW; Anderson, HR;Brunekreef, B; et al., "Black
    carbon as an additional indicator of the adverse health effects of airborne particles
    compared with PM10 and PM2.5," Environmental Health Perspectives, December
    2011, pp.1691-1699.
    [7] Matthias Budde, Rayan El Masri, Till Riedel, Michael Beigl, "Enabling
    Low-Cost particulate Matter easurement for Participatory Sensing Scenarios," 12th
    International Conference on Mobile and Ubiquitous Multimedia, 2013.
    [8] Matthias Budde, Michael Beigl, "Investigating the use of commodity dust sensors
    for the embedded measurement of particulate matter," Ninth International
    Conference on Networked Sensing Systems (INSS), 2012.
    [9] Yun Cheng, Xiucheng Li, Zhijun Li, Shouxu Jiang, Yilong Li, Ji Jia,
    Xiaofan Jiang, "AirCloud : A Cloud-based Air-Quality Monitoring System for
    Everyone," 12th ACM Conference on Embedded Network Sensor Systems,2014,
    pp.251-265.
    [10] Edmund Y. W. Seto, Annarita Giani, Victor Shiai, Curtis Wang, Posu Yan, Allen
    Y. Yang, Michael Jerrett, "A Wireless Body Sensor Network for the Prevention
    and Managment if Asthma," IEEE International Symposium on Industrial
    EmbeddedSystems(SIES), pp.120-123, july 2009.
    [11] Jason Jingshi Li, Boi Faltings, Olga Saukh, David Hasenfratz, and Jan Beutel,
    "Sensing the Air We Breathe - The OpenSense Zurich Dataset, "AAAI
    Conference on Artificial Intelligence, 2012.
    [12] Xuxu Chen, Yu Zheng, Yubiao Chen, Qiwei Jin, Weiwei Sun, Eric Chang,
    Wei-Ying Ma, "Indoor Air Quality Monitoring System for Smart
    Buildings," ACM International Joint Conference on Pervasive and Ubiquitous
    Computing, pp.471-475, 2014.
    [13] Asha B. Chelani, "Statistical Characteristics of Ambient PM2.5 Concentration at a
    Traffic Site in Delhi: Source Identification Using Persistence Analysis and
    Nonparametric Wind Regression," Aerosol and Air Quality Research,
    pp.1768-1778, 2013.
    [14] Burcu Onat, Baktygul Stakeeva, "Personal exposure of commuters in public
    transport to PM2.5 and fineparticle counts," Atmospheric Pollution Research 4,
    pp.329-335, 2013.
    [15] Jianhua Wang, Susumu Ogawa. "Effects of Meteorological Conditions on
    PM2.5 Concentrations in Nagasaki, Japan." nt. J. Environ. Res. Public
    Health, pp.9089-9101, December 2015.
    [16] N. Nikzad, N. Verma, C. Ziftci, E. Bales, N. Quick, P. Zappi, K. Patrick, S.
    Dasgupta, I. Krueger, T. S. Rosing, and W. G. Griswold. "CitiSense: Improving
    Geospatial Environmental Assessment of Air Quality using a Wireless Personal
    Exposure Monitoring System." In Proceedings of the ACM International
    Workshop on Wireless Health, 2012.
    [17] S.-C. Hu, Y.-C. Wang, C.-Y. Huang, and Y.-C. Tseng. "A Vehicular Wireless
    Sensor Network for CO2 Monitoring." In Proceedings of the IEEE Sensors,
    2009.
    [18] K. Weekly, D. Rim, L. Zhang, A. M. Bayen, W. W. Nazaroff, and C. J. Spanos.
    "Low-cost coarse airborne particulate matter sensing for indoor occupancy
    detection." In Proceedings of the IEEE International Conference on Automation
    Science and Engineering (CASE), 2013.

    [19] D. S. Tudose, T. A. Patrascu, A. Voinescu, R. Tataroiu, and N. Tapus. "Mobile
    Sensors in Air Pollution Measurement." In Proceedings of the IEEE International
    Workshop on Positioning Navigation and Communication (WPNC), 2011.
    [20] Y. Xiang, R. Piedrahita, R. P. Dick, M. Hannigan, Q. Lv, and L. Shang. "A
    Hybrid Sensor System for Indoor Air Quality Monitoring." In Proceedings of the
    IEEE International Conference on Distributed Computing in Sensor Systems
    (DCOSS), 2013.

    下載圖示
    QR CODE