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研究生:楊永盛
研究生(外文):Yang, Yung-Sheng
論文名稱:以公開資料與光散傳感器為基礎探究交通流量與 PM2.5 之關聯性
論文名稱(外文):Using Open Data and Light Scattering Infrared Sensors to Explore the Relationship between Traffic Flow and PM2.5
指導教授:李素箱李素箱引用關係鄭江宇鄭江宇引用關係
指導教授(外文):Lee, Su-ShiangCheng, Chiang-Yu
口試委員:龔昶元楊錫賢鄭正宗林志偉
口試委員(外文):Kung, Chaang-YungYang, Hsi-HsienCheng, Cheng-ChungLin, Chih-Wei
口試日期:2018-04-27
學位類別:博士
校院名稱:朝陽科技大學
系所名稱:企業管理系台灣產業策略發展博士班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:84
中文關鍵詞:PM2.5開放資料交通流量
外文關鍵詞:PM2.5open datathe number of vehicles
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空氣污染問題近年來越來越受到各界矚目,特別是大氣中肉眼所無法看見之 PM2.5污染。雖然過去許多研究已經證實 PM2.5對於人體所帶來之傷害,然而對於 PM2.5成因仍眾說紛紜。一般認知機動車輛所排放之廢氣可能是 PM2.5濃度升高的原因之一,而截至目前為止仍少有研究針對此項目進行驗證。有鑑於此,本研究以開放資料為基礎,探討兩者 PM2.5 濃度與車流量之間是否確實存有顯著關聯性。除此之外,為彌補開放資料中的廣域 PM2.5 濃度值無法正確對應區域 PM2.5 濃度值之缺憾,本研究額外以自行架設之 PM2.5傳感器來收集區域 PM2.5 濃度數據,此舉將有助於交叉驗證廣域資料分析之發現。研究結果顯示,PM2.5 濃度與交通流量之間確實存在一定程度之顯著關聯性,然而卻有時呈現兩者無顯著相關、甚至是兩者負相關之情況,可能原因在於 PM2.5污染源濃度,尚有可能受風速、風向、濕度等其他天候因素影響而改變。在研究貢獻方面,本研究使用開放資料來探討 PM2.5污染與車流量關聯性,由於前述所使用的資料兼具「時空特性」以及「廣域與區域」,故研究結果可供政府空污防制政策運用參考。
In recent years, the issue of air pollution, especially invisible particulate matter (so-called PM2.5), receives highly attention from all sectors of society. Although previous research has validated that PM2.5 damages human health, divergent claims about reasons behind the PM2.5 are upheld in academia. Vehicle exhaust emissions are generally believed to be one of the reasons that cause high level of PM2.5; however, this postulation so far requires academic validation. To this end, this study applies the open data of PM2.5 value to explore the significant relationship between the level of PM2.5 and the number of stir vehicles on the road. In addition, the current study deployed self-monitored PM2.5 micro sensors not only to collect localized PM2.5 value, but also to overcome the limitation of the open data, a wide-area dataset that cannot accurately correspond to the local value of PM2.5. This could help to further validate the analysis outcome of wide-area PM2.5 data. The research findings indicate that the level of PM2.5 and the number of vehicles are significantly and positively correlated with each other to a certain extent, but that these two values sometimes show no or negative correlation. The explanation for this is that wind speed, wind directions, humidity, and other meteorological factors — all may vary the level of PM2.5. The findings of this study could serve as a reference when the government is developing anti-air pollution policies, because this study takes into account temporal and spatial variations by encompassing both wide-area and local PM2.5 value from the open data and self-collected data respectively.
摘要.................................... I
目錄.................................... VI
表目錄.........................................VIII
圖目錄..................................... IX
第一章 緒論............................. 1
第一節 研究背景............................. 1
第二節 研究動機............................. 2
第三節 研究目的............................. 7
第二章 文獻探討............................. 11
第一節 細懸浮微粒............................. 11
第二節 細懸浮微粒檢測方法.........................20
第三節 開放資料 (Open Data)......................28
第四節 交通流量............................. 38
第三章 研究方法與步驟.............................40
第一節 研究資料取得............................. 40
第二節 資料關聯性確認.............................49
第三節 研究步驟............................. 50
第四章 資料收集與分析.............................52
第一節 廣域關聯性分析.............................54
第二節 區域關聯性分析.............................62
第五章 結論與建議.............................70
第一節 研究結論.............................70
第二節 研究貢獻.............................73
第三節 後續研究建議.............................75
參考文獻.............................76
中文文獻.............................76
英文文獻.............................78

表目錄

表1-1 細懸浮微粒相關研究………………………………………………………………….6
表2-1 開放資料相關研究………………………………………..………………………….37

圖目錄

圖1-1 宏碁電腦歷年溫室氣體排放及減量目標………………………………..….….…….2
圖1-2 2012-2016國內能源消費總量(部門別)…………………………..….………………..3
圖1-3 台電系統歷年發購電分佈……………………………………….......………………..4
圖1-4 我國機動汽車登記數及密度…………………………………….………..…………..5
圖1-5 我國汽車車齡結構…………………………………………….……………..………..5
圖1-6 能源相關二氧化碳排放量回顧與預測……………………….…………..…………..8
圖2-1 細懸浮微粒攻擊模式………………………………………………….……..……....15
圖2-2 細懸浮微粒Beta射線衰減方法……………………………………….……..……....21
圖2-3 細懸浮微粒重量檢測法流程……………………………………………..…….…....23
圖2-4 細懸浮微粒重量法檢測濾紙與秤重器……………………………....…..…….…....24
圖2-5 細懸浮微粒微量震盪天秤檢測法儀器…..………..………………....…..…….…....24
圖2-6 米氏散射示意…..………………………………..…………………....…..…….…....25
圖2-7 夏普SHARP粉塵傳感器GP2Y1010AU0F….………………………………..…....27
圖2-8 夏普SHARP粉塵傳感器運作原理…………..………………………………..…....27
圖2-9 谷歌Google圖片搜尋資料使用權………..…..………………………………..…....29
圖2-10 知識螺旋………………………….……..…..………………………………..……..32
圖2-11 中央政府資料開放平臺…………………….………………………………..……..35
圖2-12 台北市政府資料開放平臺………………….………………………………..……..35
圖2-13 阿里指數資料開放平臺…………………….………………………………..……..36
圖3-1 行政院環保署環境資源資料庫.…………….……………………………..…….......41
圖3-2 新北市三重區細懸浮微粒觀測資料…………….…………………………..……....42
圖3-3 交通部公路總局車輛偵測器開放資料…………….…………………………..…....43
圖3-4 車輛偵測器開放資料 (2017/12)…………….……….………………………….......44
圖3-5 粉塵傳感器Arduino驅動程式碼 (1)……….……….………………………….......45
圖3-6 粉塵傳感器Arduino驅動程式碼 (2)……….……….………………………….......45
圖3-7 粉塵傳感器Arduino驅動程式碼 (3)……….……….………………………….......46
圖3-8 粉塵傳感器運作偵測值……….……………………...………………………….......46
圖3-9 夏普粉塵傳感器電壓值與微粒濃度相關性……………………………...….….......47
圖3-10 夏普粉塵傳感器電壓值與微粒濃度迴歸式……………………………......….......47
圖3-11 夏普粉塵傳感器組裝與測試……………………………………………......…...........48
圖3-12 研究流程…………………………………………………………………......….................50
圖4-1 三重區重陽路二段細懸浮微粒PM2.5資料 (每小時值)………………………......52
圖4-2 三重區重陽路二段車流量資料 (每五分鐘值) …………………………………........53
圖4-3廣域關聯性分析結果……………………………………………………….....................56
圖4-4細懸浮微粒與車流量迴歸分析 (廣域) …………………………………..............56
圖4-5區域關聯性分析結果……….……………………...………………………….................64
圖4-6細懸浮微粒與車流量迴歸分析 (區域) …………………………………..............64
圖5-1 微型PM2.5採集裝置於實務上應用案例…………………………………..............71
圖5-2 環保署三重測站空氣品質相關數據………………………………….................72


中文文獻
1.經濟部能源局統計月報,https://www.moeaboe.gov.tw/ecw/populace/web_book/WebReports.aspx?book=M_CH&menu_id=142。
2.立法院 (2018),空氣污染防制法修正草案,https://www.ly.gov.tw/Pages/Detail.aspx?nodeid=6588&pid=166957 (last accessed: 2018/04/12)。
3.自由時報 (2018),工廠勒令停工偷啟爐空氣盒子逮到,http://news.ltn.com.tw/news/local/paper/1188199 (last accessed: 2018/03/30)。
4.交通部統計查詢網 (2016),我國汽車車齡結構,https://stat.motc.gov.tw/mocdb/stmain.jsp?sys=100 (last accessed: 2018/01/24)。
5.宏碁電腦 (2016),集團溫室氣體排放及減量目標,https://www.acer-group.com/ag/zh/TW/content/energy-climate-change (last accessed: 2018/01/23)。
6.英國廣播電台BBC中文網 (2013),倫敦1952「大煙霧」禍兮福兮?,http://www.bbc.com/zhongwen/trad/uk/2013/01/130114_uk_london_fog.shtml (last accessed: 2018/01/23)。
7.陳則皓 (2017)。台灣不同程度PM2.5污染事件區域性來源之模擬分析。碩士論文,國立雲林科技大學環境與安全衛生工程系,雲林。
8.張又文 (2017)。台灣中部地區細懸浮微粒變化趨勢及前驅物分析。碩士論文,國立臺灣海洋大學河海工程學系,基隆。
9.張政凱 (2017)。以直讀式儀器探討大眾交通工具PM2.5之個人暴露情況。碩士論文,國立聯合大學環境與安全衛生工程學系碩士班,苗栗。
10.張展維 (2017)。車流量與空氣細懸浮微粒相關性分析。碩士論文,國立暨南大資訊管理學系碩士班,南投。
11.楊映茹 (2017)。臺灣大氣細懸浮微粒管制策略分析與改善有效性評估。碩士論文,國立成功大學環境工程學系,台南。
12.廖勇柏 (2016),台灣癌症地圖,http://taiwancancermap.csmu-liawyp.tw/ (last accessed: 2018/01/23)。
13.廖健捷 (2017)。利用移動式平台量測台灣北部都會區道路上細懸浮微粒在時間與空間上的變化。碩士論文,國立中央大學環境工程研究所,中壢。
14.蔡沛君 (2017)。台灣特徵人為活動細懸浮微粒PM2.5 體外毒性測試方法建立。碩士論文,國立陽明大學環境與職業衛生研究所,台北。
15.蔡明勳 (2017)。2006-2012 年工業區及住宅區細懸浮微粒濃度特性探討-以高雄市為例。碩士論文,國立屏東科技大學環境工程與科學系所,屏東。
16.蔡佩憲 (2008)。台北都會區不同交通流量地區大氣懸浮微粒及重金屬濃度研究。碩士論文,國立陽明大學環境與職業衛生研究所,台北。
17.蔡鴻德 (2017)。臺灣空氣品質現況與防制策略,行政院環境保護署會議簡報。https://www.epa.gov.tw/public/Data/77148555471.pdf (last accessed: 2018/02/04)。
18.環境資源資料庫 (2017),機動車輛登記數及密度,https://erdb.epa.gov.tw/DataRepository/ReportAndStatistics/StatSceMotors.aspx (last accessed: 2018/01/25)。
19.2011年臺灣公路容量手冊,交通部運輸研究所,民國100年10月。
20.交通工程規範,交通部,民國104年12月。
21.陳威東(2014)。氣候影響國道抗滑值與交維方案之研究。碩士論文,國立中央大學土木工程研究所研究所,桃園。


英文文獻
1.Ackoff, R. L. (1989). From data to wisdom. Journal of applied systems analysis, 16(1), 3-9.
2.Ahlgren, B., Hidell, M., & Ngai, E. C. H. (2016). Internet of things for smart cities: Interoperability and open data. IEEE Internet Computing, 20(6), 52-56.
3.Almeida, S. M., Pio, C. A., Freitas, M. C., Reis, M. A., & Trancoso, M. A. (2005). Source apportionment of fine and coarse particulate matter in a sub-urban area at the Western European Coast. Atmospheric Environment, 39(17), 3127-3138.
4.Arzberger, P., Schroeder, P., Beaulieu, A., Bowker, G., Casey, K., Laaksonen, L., ... & Wouters, P. (2004). An international framework to promote access to data.
5.Besaratinia, A., & Pfeifer, G. P. (2008). Second-hand smoke and human lung cancer. The lancet oncology, 9(7), 657-666.
6.Charron, A., Harrison, R. M., Moorcroft, S., & Booker, J. (2004). Quantitative interpretation of divergence between PM10 and PM2. 5 mass measurement by TEOM and gravimetric (Partisol) instruments. Atmospheric Environment, 38(3), 415-423.
7.Charron, A., & Harrison, R. M. (2005). Fine (PM2. 5) and coarse (PM2. 5-10) particulate matter on a heavily trafficked London highway: sources and processes. Environmental science & technology, 39(20), 7768-7776.
8.Chen, L. W. A., Chow, J. C., Doddridge, B. G., Dickerson, R. R., Ryan, W. F., & Mueller, P. K. (2003). Analysis of a summertime PM2. 5 and haze episode in the mid-Atlantic region. Journal of the Air & Waste Management Association, 53(8), 946-956.
9.Cheng, Y. H., & Li, Y. S. (2010). Influences of traffic emissions and meteorological conditions on ambient PM10 and PM2. 5 levels at a highway toll station. Aerosol Air Qual. Res, 10, 456-462.
10.Chow, J. C., Watson, J. G., Lowenthal, D. H., Solomon, P. A., Magliano, K. L., Ziman, S. D., & Richards, L. W. (1993). PM10 and PM2. 5 compositions in California's San Joaquin Valley. Aerosol Science and Technology, 18(2), 105-128.
11.Cyrys, J., Heinrich, J., Hoek, G., Meliefste, K., Lewné, M., Gehring, U., ... & Wichmann, H. E. (2003). Comparison between different traffic-related particle indicators: elemental carbon (EC), PM2. 5 mass, and absorbance. Journal of Exposure Science and Environmental Epidemiology, 13(2), 134-143.
12.Dias, G. M., Bellalta, B., & Oechsner, S. (2015, November). Predicting occupancy trends in Barcelona's bicycle service stations using open data. In SAI Intelligent Systems Conference (IntelliSys), 2015 (pp. 439-445). IEEE.
13.EIA, Energy Information Administration (2016). International Energy Outlook, Chapter 9. https://www.eia.gov/outlooks/ieo/pdf/emissions.pdf. Last accessed: 2018/01/21.
14.Enge Solutions (2016). The Equipment of Particulate Matter 2.5 (PM2.5) Using Gravimetric Analysis. http://engesolutions.com.br/en/servicos/analise-de-sujidade/analise-gravimetrica/. Last accessed: 2018/02/11.
15.Ezzine, H., Bouziane, A., & Ouazar, D. (2014). Seasonal comparisons of meteorological and agricultural drought indices in Morocco using open short time-series data. International Journal of Applied Earth Observation and Geoinformation, 26, 36-48.
16.Farhek.com (2016). Mie Scattering of Growing Molecular Contaminants Optical: The Incident Wave at a Given Angle On. http://farhek.com/jd/3y1b831/scattering-of/813pj8/. Last accessed: 2018/02/12.
17.Fonken, L. K., Xu, X., Weil, Z. M., Chen, G., Sun, Q., Rajagopalan, S., & Nelson, R. J. (2011). Air pollution impairs cognition, provokes depressive-like behaviors and alters hippocampal cytokine expression and morphology. Molecular psychiatry, 16(10), 987.
18.Genc, S., Zadeoglulari, Z., Fuss, S. H., & Genc, K. (2012). The adverse effects of air pollution on the nervous system. Journal of toxicology, 2012.
19.Gobeli, D., Schloesser, H., & Pottberg, T. (2008, January). Met one instruments BAM-1020 beta attenuation mass monitor US-EPA PM2. 5 federal equivalent method field test results. In The Air & Waste Management Association (A&WMA) Conference, Kansas City, MO, January.
20.Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis 6th ed. Uppersaddle River: Pearson Prentice Hall.
21.Henderson, S. B., Beckerman, B., Jerrett, M., & Brauer, M. (2007). Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environmental science & technology, 41(7), 2422-2428.
22.Holmes, R. M., & Dingle, A. N. (1965). The relationship between the macro-and microclimate. Agricultural Meteorology, 2(2), 127-133.
23.Huang, W., Cao, J., Tao, Y., Dai, L., Lu, S. E., Hou, B., ... & Zhu, T. (2012). Seasonal variation of chemical species associated with short-term mortality effects of PM2. 5 in Xi’an, a central city in China. American journal of epidemiology, 175(6), 556-566.
24.Huang, N. H., Qin, W. A. N. G., & Dong-Qun, X. U. (2008). Immunological Effect of PM2. 5 on Cytokine Production in Female Wistar Rats1. Biomedical and Environmental Sciences, 21(1), 63-68.
25.Janssen, K. (2011). The influence of the PSI directive on open government data: An overview of recent developments. Government Information Quarterly, 28(4), 446-456.
26.Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data and open government. Information systems management, 29(4), 258-268.
27.Johnson, P., & Robinson, P. (2014). Civic hackathons: Innovation, procurement, or civic engagement?. Review of Policy Research, 31(4), 349-357.
28.Kittelson, D. B., Watts, W. F., & Johnson, J. P. (2004). Nanoparticle emissions on Minnesota highways. Atmospheric Environment, 38(1), 9-19.
29.Lonati, G., Ozgen, S., & Giugliano, M. (2007). Primary and secondary carbonaceous species in PM2. 5 samples in Milan (Italy). Atmospheric Environment, 41(22), 4599-4610.
30.Meymandpour, R., & Davis, J. G. (2015, January). Enhancing recommender systems using linked open data-based semantic analysis of items. In Proceedings of the 3rd australasian web conference (AWC 2015) (Vol. 27, p. 30).
31.Mie, G. (1976). Contributions to the optics of turbid media, particularly of colloidal metal solutions. Contributions to the optics of turbid media, particularly of colloidal metal solutions Transl. into ENGLISH from Ann. Phys.(Leipzig), v. 25, no. 3, 1908 p 377-445.
32.Noble, C. A., Vanderpool, R. W., Peters, T. M., McElroy, F. F., Gemmill, D. B., & Wiener, R. W. (2001). Federal reference and equivalent methods for measuring fine particulate matter. Aerosol Science & Technology, 34(5), 457-464.
33.Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and leadership: a unified model of dynamic knowledge creation. Long range planning, 33(1), 5-34.
34.Pan, W. C., Wu, C. D., Chen, M. J., Huang, Y. T., Chen, C. J., Su, H. J., & Yang, H. I. (2016). Fine particle pollution, alanine transaminase, and liver cancer: a Taiwanese prospective cohort study (REVEAL-HBV). JNCI: Journal of the National Cancer Institute, 108(3).
35.Paulin, L., & Hansel, N. (2016). Particulate air pollution and impaired lung function. F1000Research, 5.
36.Pastuszka, J. S., Konieczyński, J., & Talik, E. (2014). Surface Properties of Particles Emitted from Selected Coal-Fired Heating Plants and Electric Power Stations in Poland: Preliminary Results. Archives of environmental protection, 40(3), 13-27.
37.Pedersen, M., Andersen, Z. J., Stafoggia, M., Weinmayr, G., Galassi, C., Sørensen, M., ... & Nagel, G. (2017). Ambient air pollution and primary liver cancer incidence in four European cohorts within the ESCAPE project. Environmental research, 154, 226-233.
38.Pérez, N., Pey, J., Cusack, M., Reche, C., Querol, X., Alastuey, A., & Viana, M. (2010). Variability of particle number, black carbon, and PM10, PM2. 5, and PM1 levels and speciation: influence of road traffic emissions on urban air quality. Aerosol Science and Technology, 44(7), 487-499.
39.PICO (2017). TEOM Continuous Particulate Monitor TEOM1405 Series. http://www.pico.co.th/?names=product&files=detail&pid=337. Last accessed: 2018/02/11.
40.Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002). Data quality assessment. Communications of the ACM, 45(4), 211-218.
41.Rogge, W. F., Mazurek, M. A., Hildemann, L. M., Cass, G. R., & Simoneit, B. R. (1993). Quantification of urban organic aerosols at a molecular level: identification, abundance and seasonal variation. Atmospheric Environment. Part A. General Topics, 27(8), 1309-1330.
42.Schroeter, J. D., Musante, C. J., Hwang, D., Burton, R., Guilmette, R., & Martonen, T. B. (2001). Hygroscopic growth and deposition of inhaled secondary cigarette smoke in human nasal pathways. Aerosol Science & Technology, 34(1), 137-143.
43.Sharp-world (2017). Application Note of Sharp Dust Sensor GP2Y1010AU0F. http://www.sharp-world.com/products/device/lineup/data/pdf/datasheet/gp2y1010au_appl_e.pdf. Last accessed: 2018/02/13.
44.Shen, S., Jaques, P. A., Zhu, Y., Geller, M. D., & Sioutas, C. (2002). Evaluation of the SMPS–APS system as a continuous monitor for measuring PM2. 5, PM10 and coarse (PM2. 5− 10) concentrations. Atmospheric Environment, 36(24), 3939-3950.
45.Sigaud, S., Goldsmith, C. A. W., Zhou, H., Yang, Z., Fedulov, A., Imrich, A., & Kobzik, L. (2007). Air pollution particles diminish bacterial clearance in the primed lungs of mice. Toxicology and applied pharmacology, 223(1), 1-9.
46.Sioutas, C., Kim, S., Chang, M., Terrell, L. L., & Gong Jr, H. (2000). Field evaluation of a modified DataRAM MIE scattering monitor for real-time PM2. 5 mass concentration measurements. Atmospheric Environment, 34(28), 4829-4838.
47.Solomon, S., Ivy, D. J., Kinnison, D., Mills, M. J., Neely, R. R., & Schmidt, A. (2016). Emergence of healing in the Antarctic ozone layer. Science, 353(6296), 269-274.
48.Sun, Q., Wang, A., Jin, X., Natanzon, A., Duquaine, D., Brook, R. D., ... & Chen, L. C. (2005). Long-term air pollution exposure and acceleration of atherosclerosis and vascular inflammation in an animal model. Jama, 294(23), 3003-3010.
49.Tao, J., Zhang, L., Ho, K., Zhang, R., Lin, Z., Zhang, Z., ... & Wang, G. (2014). Impact of PM2. 5 chemical compositions on aerosol light scattering in Guangzhou—the largest megacity in South China. Atmospheric Research, 135, 48-58.
50.Themetoneinstrumentsmonitor (2016). The Met One Instruments Inc Monitor. http://themetoneinstrumentsmonitor.blogspot.tw/2016/11/american-geophysical-union-meeting-at.html. Last accessed: 2018/02/10.
51.Turpin, B. J., & Lim, H. J. (2001). Species contributions to PM2. 5 mass concentrations: Revisiting common assumptions for estimating organic mass. Aerosol Science & Technology, 35(1), 602-610.
52.Tuti, T., Bitok, M., Paton, C., Makone, B., Malla, L., Muinga, N., ... & English, M. (2015). Innovating to enhance clinical data management using non-commercial and open source solutions across a multi-center network supporting inpatient pediatric care and research in Kenya. Journal of the American Medical Informatics Association, 23(1), 184-192.
53.Underwood, E. (2017). THE POLLUTED BRAIN:Evidence builds that dirty air causes Alzheimer’s, dementia. Last accessed: 2018/02/01.
54.Watson, J. G., Zhu, T., Chow, J. C., Engelbrecht, J., Fujita, E. M., & Wilson, W. E. (2002). Receptor modeling application framework for particle source apportionment. Chemosphere, 49(9), 1093-1136.
55.WHO (2013). Health effects of particulate matter, World Health Organization. http://www.euro.who.int/__data/assets/pdf_file/0006/189051/Health-effects-of-particulate-matter-final-Eng.pdf. Last accessed: 2018/01/31.
56.Williams, M. L., Burnap, P., & Sloan, L. (2017). Crime sensing with big data: The affordances and limitations of using open-source communications to estimate crime patterns. The British Journal of Criminology, 57(2), 320-340.
57.Yeatts, K., Svendsen, E., Creason, J., Alexis, N., Herbst, M., Scott, J., ... & Devlin, R. B. (2007). Coarse particulate matter (PM2. 5–10) affects heart rate variability, blood lipids, and circulating eosinophils in adults with asthma. Environmental health perspectives, 115(5), 709.
58.Zehr, S. C. (1994). Accounting for the ozone hole. The Sociological Quarterly, 35(4), 603-619.
59.Zheng, M., Fang, M., Wang, F., & To, K. L. (2000). Characterization of the solvent extractable organic compounds in PM2. 5 aerosols in Hong Kong. Atmospheric Environment, 34(17), 2691-2702.

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