臺灣博碩士論文加值系統

由於近年來空氣污染的問題日益嚴重，對於人們的日常生活以及健康造成了許多負面的影響，本研究的目的是探討造成空氣汙染的影響因子，並對於台灣空氣污染政策與美國空氣污染政策進行比較與分析，探討台美兩國對於這些造成空氣汙染嚴重的因子制定哪些相關政策。
研究結果顯示，台美兩國政策上，在固定燃燒源分類中，美國較著重在露天燃燒，台灣政策較無提到這部分，本研究認為未來台灣可以著重露天燃燒政策的制定；工業排放、汽機車排放、人口密度及GDP是四個影響空氣汙染的人為因素，台美兩國對於工業排放與汽機車排放這兩個因子都有相關政策規範，但對於人口密度和GDP這兩個因子尚未制定相關政策，本研究認為是由於經濟發展的考慮，因此這兩個因子較難被改善。

In the recent years, as the problem of air pollution has become more and more serious, it has caused many negative impacts on our daily life and health. The purpose of this research is to explore the factors that exacerbate air pollution to compare Taiwan's air pollution policies with the US air pollution policies, and then to explore the relevant policies that Taiwan and the United States have formulated for these factors that cause serious air pollution.
The research results show that the United States’ policies focus on open burning, and Taiwan’s policies don’t mention this part in the classification of fixed combustion sources. The research results suggest that Taiwan may focus on the formulation of open burning policies in the future. Industrial emissions, Automobile and motorcycle emissions, population density and GDP are the four human factors that affect air pollution, Taiwan and the United States have formulated relevant policies and regulations on the two factors of industrial emissions and automobile and motorcycle emissions, but the two countries have not formulated relevant policies on two factors of population density and GDP, and the research considers that these two factors are difficult to improve due to economic development considerations.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
第一章、緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
第二章、文獻回顧 5
2.1 影響因素 5
2.1.1 空氣汙染影響因子 5
2.1.2 PM2.5影響因子 9
2.1.3 臭氧影響因子 11
2.2 過去預測使用的方法 16
2.2.1 過去預測空氣汙染的方法 16
2.2.2 SVR的優勢 18
第三章、 研究方法 23
3.1 研究設計 23
3.2 個案研究法 24
第四章、 研究結果 27
4.1 個案背景與介紹 27
4.2 個案研究結果 29
4.2.1 台灣政策 29
4.2.2 美國政策 32
4.2.3 分析與比較 40
第五章、 結果與未來建議 47
英文參考文獻 49
中文參考文獻 57

Bell, M. L., Goldberg, R., Hogrefe, C., Kinney, P. L., Knowlton, K., Lynn, B., Rosenthal,J., Rosenzweig,C., & Patz,J.A. (2007). Climate change, ambient ozone, and health in 50 US cities. Climatic Change, 82(1-2), 61–76. doi: 10.1007/s10584-006-9166-7
Bloomer, B. J., Stehr, J. W., Piety, C. A., Salawitch, R. J., & Dickerson, R. R. (2009). Observed relationships of ozone air pollution with temperature and emissions. Geophysical Research Letters, 36(9). doi: 10.1029/2009gl037308
Brauer,M., Amann,M., Burnett,R.T., Cohen,A., Dentener,F., Ezzati,M., Henderson,S.B., Krzyzanowski,M., Martin,R.V., Dingenen,R.V., Donkelaar,A.V.,& Thurston,.G.D.(2012). Exposure Assessment for Estimation of the Global Burden of Disease Attributable to Outdoor Air Pollution. Environmental Science & Technology 2012, 46, 652−660. doi: 10.1021/es2025752
Brook, R. D., Brook, J. R., Urch, B., Vincent, R., Rajagopalan, S., & Silverman, F. (2002). Inhalation of Fine Particulate Air Pollution and Ozone Causes Acute Arterial Vasoconstriction in Healthy Adults. Circulation, 105(13), 1534–1536. doi: 10.1161/01.cir.0000013838.94747.64
Castro-Neto, M., Jeong, Y.-S., Jeong, M.-K., & Han, L. D. (2009). Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 36(3), 6164–6173. doi: 10.1016/j.eswa.2008.07.069
Chan, K. Y., & Jian, L. (2013). Identification of significant factors for air pollution levels using a neural network based knowledge discovery system. Neurocomputing, 99, 564–569. doi: 10.1016/j.neucom.2012.06.003
Chang, S.-C., & Lee, C.-T. (2006). Assessment of PM10 Enhancement by Yellow Sand on the Air Quality of Taipei, Taiwan in 2001. Environmental Monitoring and Assessment, 132(1-3), 297–309. doi: 10.1007/s10661-006-9534-9
Chau, K. W., & Wu, C. L. (2010). A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. Journal of Hydroinformatics, 12(4), 458–473. doi: 10.2166/hydro.2010.032
Chen, K.-S., Lin, C., & Chou, Y.-M. (2001). Determination of Source Contributions to Ambient PM2.5in Kaohsiung, Taiwan, Using a Receptor Model. Journal of the Air & Waste Management Association, 51(4), 489–498. doi: 10.1080/10473289.2001.10464287
Chen, Z., Cai, J., Gao, B., Xu, B., Dai, S., He, B., & Xie, X. (2017). Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region. Scientific reports, 7, 40735. doi: 10.1038/srep40735
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 and Air Quality Research, 10(5), 456–462. doi: 10.4209/aaqr.2010.04.0025
Cobourn, W. G., Dolcine, L., French, M., & Hubbard, M. C. (2000). A Comparison of Nonlinear Regression and Neural Network Models for Ground-Level Ozone Forecasting. Journal of the Air & Waste Management Association, 50(11), 1999–2009. doi: 10.1080/10473289.2000.10464228
Elattar, E. E., Goulermas, J., & Wu, Q. H. (2010). Electric Load Forecasting Based on Locally Weighted Support Vector Regression. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(4), 438–447. doi: 10.1109/tsmcc.2010.2040176
Fann, N., Lamson, A. D., Anenberg, S. C., Wesson, K., Risley, D., & Hubbell, B. J. (2011). Estimating the National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone. Risk Analysis, 32(1), 81–95. doi: 10.1111/j.1539-6924.2011.01630.x
Finlayson-Pitts,B.J.,& James, N.P.J.(1997). Tropospheric Air Pollution: Ozone, Airborne Toxics, Polycyclic Aromatic Hydrocarbons, and Particles. Science 16 May 1997:Vol. 276, Issue 5315, 1045-1051.doi: 10.1126.
Gong,J.P., Hu,Y.M., Liu,M., Bu,R.C.,Chang,Y., Li,C.L., &Wu,W.(2015). Characterization of Air Pollution Index and Its Affecting Factors in Industrial Urban Areas in Northeastern China. Polish Journal of Environmental Studies Vol. 24, No. 4 (2015), 1579-1592.doi:10.15244/pjoes/37757
Guo, G., Fu, Y., Dyer, C., & Huang, T. (2008). Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression. IEEE Transactions on Image Processing, 17(7), 1178–1188. doi: 10.1109/tip.2008.924280
Huang, F., Li, X., Wang, C., Xu, Q., Wang, W., Luo, Y., … Guo, X. (2015). PM2.5 Spatiotemporal Variations and the Relationship with Meteorological Factors during 2013-2014 in Beijing, China. Plos One, 10(11). doi: 10.1371/journal.pone.0141642
Hung,L.J., Tsai,S.S., Chen,P.S., Yang,Y.H., Liou,S.H., Wu,T.N., & Yang,C.Y.(2012). Traffic Air Pollution and Risk of Death from Breast Cancer in Taiwan: Fine Particulate Matter (PM2.5) as a Proxy Marker. Aerosol and Air Quality Research, 12: 275–282, 2012 .doi: 10.4209/aaqr.2011.09.0155
Jacob, Daniel J., & Darrel A. Winner. (2009). Effect of climate change on air quality. Atmospheric Environment 43(1): 51-63. doi: 10.1016/j.atmosenv.2008.09.051
Jerrett, M., Burnett, R. T., Pope, C. A., Ito, K., Thurston, G., Krewski, D., Shi,Y., Calle,E.,&Thun, M. (2009). Long-Term Ozone Exposure and Mortality. New England Journal of Medicine, 360(11), 1085–1095. doi: 10.1056/nejmoa0803894
Ji, X., Yao, Y., & Long, X. (2018). What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective. Energy Policy, 119, 458–472. doi: 10.1016/j.enpol.2018.04.040
Kim, Y. P., & Lee, G. (2018). Trend of Air Quality in Seoul: Policy and Science. Aerosol and Air Quality Research, 18(9), 2141–2156. doi: 10.4209/aaqr.2018.03.0081
Kuklinska, K., Wolska, L., & Namiesnik, J. (2015). Air quality policy in the U.S. and the EU – a review. Atmospheric Pollution Research, 6(1), 129–137. doi: 10.5094/apr.2015.015
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., & Pozzer, A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569), 367–371. doi: 10.1038/nature15371
Li, X., Feng, Y. J., & Liang, H. Y. (2017). The Impact of Meteorological Factors on PM2.5 Variations in Hong Kong. IOP Conference Series: Earth and Environmental Science, 78, 012003. doi: 10.1088/1755-1315/78/1/012003
Lin, G., Fu, J., Jiang, D., Hu, W., Dong, D., Huang, Y., & Zhao, M. (2013). Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China. International Journal of Environmental Research and Public Health, 11(1), 173–186. doi: 10.3390/ijerph110100173
Lin, Y., Zou, J., Yang, W., & Li, C. Q. (2018). A Review of Recent Advances in Research on PM2.5 in China. International journal of environmental research and public health, 15(3), 438. doi:10.3390/ijerph15030438
Lu, C.-J., Lee, T.-S., & Chiu, C.-C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125. doi: 10.1016/j.dss.2009.02.001
McConnell,R., Berhane,K., Gilliland,F., London, S., Islam,T., Gauderman,W., Avol,E., Margolis,H.,& Peters,J.(2002). Asthma in exercising children exposed to ozone: a cohort study. Lancet. 359. 386-391. doi: 10.1016/S0140-6736(02)07597-9.
McKendry,I.G.(2002). Evaluation of Artificial Neural Networks for Fine Particulate Pollution (PM10 and PM2.5) Forecasting. Journal of the Air & Waste Management Association, 52:9, 1096-1101.doi: 10.1080/10473289.2002.10470836.
Nieto, P. G., Combarro, E., Díaz, J. D. C., & Montañés, E. (2013). A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study. Applied Mathematics and Computation, 219(17), 8923–8937. doi: 10.1016/j.amc.2013.03.018
Nieto, P. G., Combarro, E., Díaz, J. D. C., & Montañés, E. (2013). A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study. Applied Mathematics and Computation, 219(17), 8923–8937. doi: 10.1016/j.amc.2013.03.018
Patton, M. and Cocharn, M. (2002) .A Guide to Using Qualitative Research Methodology. Médecins Sans Frontières, Paris. https://evaluation.msf.org/sites/evaluation/files/a_guide_to_using_qualitative_research_methodology.pdf
Shaban, K. B., Kadri, A., & Rezk, E. (2016). Urban Air Pollution Monitoring System With Forecasting Models. IEEE Sensors Journal, 16(8), 2598–2606. doi: 10.1109/jsen.2016.2514378
Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223–1240. doi: 10.1016/j.rser.2011.08.014
Tsai,S.S.,Chang ,C.C.,Liou,S.H., & Yang,C.Y.(2014). The Effects of Fine Particulate Air Pollution on Daily Mortality: A Case-Crossover Study in a Subtropical City, Taipei, Taiwan. International Journal of Environmental Research and Public Health 2014, 11, 5081-5093.doi:10.3390/ijerph110505081.
United Nations Environment Programme(2015). United States of America Air Quality Catalogue.
United States Environmental Protection Agency. Reducing Emissions of Hazardous Air Pollutants.
V Lu,H.Y., Wu,Y.L., Mutuku,J.K., Chang,K.H.(2019). Various Sources of PM2.5 and their Impact on the Air Quality in Tainan City, Taiwan. Aerosol and Air Quality Research, 19: 601–619, 2019. doi: 10.4209/aaqr.2019.01.0024
Vinagre, E., Pinto, T., Ramos, S., Vale, Z., & Corchado, J. M. (2016). Electrical Energy Consumption Forecast Using Support Vector Machines. 2016 27th International Workshop on Database and Expert Systems Applications (DEXA). doi: 10.1109/dexa.2016.046
Vingarzan, R. (2004). A review of surface ozone background levels and trends. Atmospheric Environment, 38(21), 3431–3442. doi: 10.1016/j.atmosenv.2004.03.030
Wang, J., & Ogawa, S. (2015). Effects of Meteorological Conditions on PM2.5 Concentrations in Nagasaki, Japan. International Journal of Environmental Research and Public Health, 12(8), 9089–9101. doi: 10.3390/ijerph120809089
Wang, L., Zhang, F., Pilot, E., Yu, J., Nie, C., Holdaway, J., … Krafft, T. (2018). Taking Action on Air Pollution Control in the Beijing-Tianjin-Hebei (BTH) Region: Progress, Challenges and Opportunities. International Journal of Environmental Research and Public Health, 15(2), 306. doi: 10.3390/ijerph15020306
Weizhen, H., Zhengqiang, L., Yuhuan, Z., Hua, X., Ying, Z., Kaitao, L.,Donghui,L., Peng,W.,& Yan, M. (2014). Using support vector regression to predict PM10 and PM2.5. IOP Conference Series: Earth and Environmental Science, 17, 012268. doi: 10.1088/1755-1315/17/1/012268
Weizhen,H., Zhengqiang,L., Yuhuan,Z., Hua,X., Ying,Z., Kaitao ,L., Donghui,L., Peng,W.,& Yan,M.(2014). Using support vector regression to predict PM10 and PM2.5. IOP Conf. Series: Earth and Environmental Science 17 (2014) 012268 doi:10.1088/1755-1315/17/1/012268
Yang, H., Huang, K., King, I., & Lyu, M. R. (2009). Localized support vector regression for time series prediction. Neurocomputing, 72(10-12), 2659–2669. doi: 10.1016/j.neucom.2008.09.014
Yang, Y., Li, J., Zhu, G., & Yuan, Q. (2019). Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998–2016. International Journal of Environmental Research and Public Health, 16(7), 1149. doi: 10.3390/ijerph16071149
Yan-Song, X., Le, Y., & Pengwei, X. (2008). Analysis of PM10 Concentration Time Series and Climiate Factor in Hangzhou 2004. 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing. doi: 10.1109/ettandgrs.2008.115
Yeh, H.-L., Hsu, S.-W., Chang, Y.-C., Chan, T.-C., Tsou, H.-C., Chang, Y.-C., & Chiang, P.-H. (2017). Spatial Analysis of Ambient PM2.5 Exposure and Bladder Cancer Mortality in Taiwan. International Journal of Environmental Research and Public Health, 14(5), 508. doi: 10.3390/ijerph14050508
Yun, G., He, Y., Jiang, Y., Dou, P., & Dai, S. (2019). PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015. Atmosphere, 10(2), 55. doi: 10.3390/atmos10020055
Zhan, D., Kwan, M.-P., Zhang, W., Wang, S., & Yu, J. (2017). Spatiotemporal Variations and Driving Factors of Air Pollution in China. International Journal of Environmental Research and Public Health, 14(12), 1538. doi: 10.3390/ijerph14121538
Zhan, D., Kwan, M.-P., Zhang, W., Yu, X., Meng, B., & Liu, Q. (2018). The driving factors of air quality index in China. Journal of Cleaner Production, 197, 1342–1351. doi: 10.1016/j.jclepro.2018.06.108
Zhang, D., Liu, J., & Li, B. (2014). Tackling Air Pollution in China—What do We Learn from the Great Smog of 1950s in LONDON. Sustainability, 6(8), 5322–5338. doi: 10.3390/su6085322
Zhang, D., Liu, J., & Li, B. (2014). Tackling Air Pollution in China—What do We Learn from the Great Smog of 1950s in LONDON. Sustainability, 6(8), 5322–5338. doi: 10.3390/su6085322
Zhao, H., Guo, S., & Zhao, H. (2019). Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM2.5 Concentration: A Provincial Panel Data Model Analysis of China. International Journal of Environmental Research and Public Health, 16(16), 2926. doi: 10.3390/ijerph16162926
Zhao, H., Guo, S., & Zhao, H. (2019). Quantifying the Impacts of Economic Progress, Economic Structure, Urbanization Process, and Number of Vehicles on PM2.5 Concentration: A Provincial Panel Data Model Analysis of China. International Journal of Environmental Research and Public Health, 16(16), 2926. doi: 10.3390/ijerph16162926
Zhou, C., Chen, J., & Wang, S. (2018). Examining the effects of socioeconomic development on fine particulate matter (PM2.5) in Chinas cities using spatial regression and the geographical detector technique. Science of The Total Environment, 619-620, 436–445. doi: 10.1016/j.scitotenv.2017.11.124
中文參考文獻
台灣行政院環境保護署（107）。107年版「環境白皮書」。