請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90590完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蕭大智 | zh_TW |
| dc.contributor.advisor | Ta-Chih Hsiao | en |
| dc.contributor.author | 葉俊發 | zh_TW |
| dc.contributor.author | Jun-Fa Yeh | en |
| dc.date.accessioned | 2023-10-03T16:45:54Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-10-03 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-11 | - |
| dc.identifier.citation | Al-Dabbous, A. N., & Kumar, P. (2015). Source apportionment of airborne nanoparticles in a Middle Eastern city using positive matrix factorization. Environmental Science: Processes & Impacts, 17(4), 802-812. https://doi.org/10.1039/c5em00027k
Anttila, P., Paatero, P., Tapper, U., & Järvinen, O. (1995). Source identification of bulk wet deposition in Finland by positive matrix factorization. Atmospheric Environment, 29(14), 1705-1718. https://doi.org/10.1016/1352-2310(94)00367-T Bohren, C. F., & Huffman, D. R. (2008). Absorption and scattering of light by small particles. John Wiley & Sons. Carslaw, D. C., & Ropkins, K. (2012). openair — An R package for air quality data analysis. Environmental Modelling & Software, 27-28, 52-61. https://doi.org/10.1016/j.envsoft.2011.09.008 Cesari, D., Genga, A., Ielpo, P., Siciliano, M., Mascolo, G., Grasso, F. M., & Contini, D. (2014). Source apportionment of PM2.5 in the harbour-industrial area of Brindisi (Italy): identification and estimation of the contribution of in-port ship emissions. Science of The Total Environment, 497-498, 392-400. https://doi.org/10.1016/j.scitotenv.2014.08.007 Chen, D., Zhao, Y., Zhang, J., Yu, H., & Yu, X. (2020). Characterization and source apportionment of aerosol light scattering in a typical polluted city in the Yangtze River Delta, China. Atmospheric Chemistry and Physics, 20(17), 10193-10210. https://doi.org/10.5194/acp-20-10193-2020 Chen, Y., Rich, D. Q., & Hopke, P. K. (2022). Long-term PM2.5 source analyses in New York City from the perspective of dispersion normalized PMF. Atmospheric Environment, 272. https://doi.org/10.1016/j.atmosenv.2022.118949 Chen, Y. C., Shie, R. H., Zhu, J. J., & Hsu, C. Y. (2022). A hybrid methodology to quantitatively identify inorganic aerosol of PM2.5 source contribution. Journal of Hazardous Materials, 428, 128173. https://doi.org/10.1016/j.jhazmat.2021.128173 Cheng, M., Tang, G., Lv, B., Li, X., Wu, X., Wang, Y., & Wang, Y. (2021). Source apportionment of PM2.5 and visibility in Jinan, China. Journal of Environmental Sciences, 102, 207-215. https://doi.org/10.1016/j.jes.2020.09.012 Coe, H. (2020). Airborne particles might grow fast in cities. In: Nature Publishing Group UK London. Dai, Q., Ding, J., Song, C., Liu, B., Bi, X., Wu, J., Zhang, Y., Feng, Y., & Hopke, P. K. (2021). Changes in source contributions to particle number concentrations after the COVID-19 outbreak: Insights from a dispersion normalized PMF. Science of The Total Environment, 759, 143548. https://doi.org/10.1016/j.scitotenv.2020.143548 Dai, Q., Liu, B., Bi, X., Wu, J., Liang, D., Zhang, Y., Feng, Y., & Hopke, P. K. (2020). Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM2.5 after the COVID-19 Outbreak. Environmental Science & Technology, 54(16), 9917-9927. https://doi.org/10.1021/acs.est.0c02776 Ding, X., Kong, L., Du, C., Zhanzakova, A., Fu, H., Tang, X., Wang, L., Yang, X., Chen, J., & Cheng, T. (2017). Characteristics of size-resolved atmospheric inorganic and carbonaceous aerosols in urban Shanghai. Atmospheric Environment, 167, 625-641. https://doi.org/10.1016/j.atmosenv.2017.08.043 Emami, F., & Hopke, P. K. (2017). Effect of adding variables on rotational ambiguity in positive matrix factorization solutions. Chemometrics and Intelligent Laboratory Systems, 162, 198-202. https://doi.org/10.1016/j.chemolab.2017.01.012 Fountoukis, C., & Nenes, A. (2007). ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–NH4+–Na+–SO42−–NO3−–Cl−–H2O aerosols. Atmospheric Chemistry and Physics, 7(17), 4639-4659. https://doi.org/10.5194/acp-7-4639-2007 Galvao, E. S., de Cassia Feroni, R., & D'Azeredo Orlando, M. T. (2021). A review of the main strategies used in the interpretation of similar chemical profiles yielded by receptor models in the source apportionment of particulate matter. Chemosphere, 269, 128746. https://doi.org/10.1016/j.chemosphere.2020.128746 Gani, S., Bhandari, S., Patel, K., Seraj, S., Soni, P., Arub, Z., Habib, G., Hildebrandt Ruiz, L., & Apte, J. S. (2020). Particle number concentrations and size distribution in a polluted megacity: the Delhi Aerosol Supersite study. Atmospheric Chemistry and Physics, 20(14), 8533-8549. Gani, S., Chambliss, S. E., Messier, K. P., Lunden, M. M., & Apte, J. S. (2021). Spatiotemporal profiles of ultrafine particles differ from other traffic-related air pollutants: lessons from long-term measurements at fixed sites and mobile monitoring. Environmental Science: Atmospheres, 1(7), 558-568. https://doi.org/10.1039/d1ea00058f Grange, S. K., & Carslaw, D. C. (2019). Using meteorological normalisation to detect interventions in air quality time series. Science of The Total Environment, 653, 578-588. https://doi.org/10.1016/j.scitotenv.2018.10.344 Guo, H., Otjes, R., Schlag, P., Kiendler-Scharr, A., Nenes, A., & Weber, R. J. (2018). Effectiveness of ammonia reduction on control of fine particle nitrate. Atmospheric Chemistry and Physics, 18(16), 12241-12256. https://doi.org/10.5194/acp-18-12241-2018 Guo, S., Hu, M., Peng, J., Wu, Z., Zamora, M. L., Shang, D., Du, Z., Zheng, J., Fang, X., Tang, R., Wu, Y., Zeng, L., Shuai, S., Zhang, W., Wang, Y., Ji, Y., Li, Y., Zhang, A. L., Wang, W., . . . Zhang, R. (2020). Remarkable nucleation and growth of ultrafine particles from vehicular exhaust. Proceedings of the National Academy of Sciences, 117(7), 3427-3432. https://doi.org/doi:10.1073/pnas.1916366117 Harrison, R. M., Beddows, D. C., & Dall'Osto, M. (2011). PMF analysis of wide-range particle size spectra collected on a major highway. Environmental Science & Technology, 45(13), 5522-5528. https://doi.org/10.1021/es2006622 Hopke, P. K., Dai, Q., Li, L., & Feng, Y. (2020). Global review of recent source apportionments for airborne particulate matter. Science of The Total Environment, 740, 140091. https://doi.org/10.1016/j.scitotenv.2020.140091 Hopke, P. K., Feng, Y., & Dai, Q. (2022). Source apportionment of particle number concentrations: A global review. Science of The Total Environment, 153104. https://doi.org/10.1016/j.scitotenv.2022.153104 Hudda, N., Simon, M., Zamore, W., Brugge, D., & Durant, J. (2016). Aviation emissions impact ambient ultrafine particle concentrations in the greater Boston area. Environmental Science & Technology, 50(16), 8514-8521. https://doi.org/10.1021/acs.est.6b01815 Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken, A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P., Robinson, A. L., Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. A., . . . Worsnop, D. R. (2009). Evolution of Organic Aerosols in the Atmosphere. Science, 326(5959), 1525-1529. https://doi.org/doi:10.1126/science.1180353 Johnson, J. P., Kittelson, D. B., & Watts, W. F. (2005). Source apportionment of diesel and spark ignition exhaust aerosol using on-road data from the Minneapolis metropolitan area. Atmospheric Environment, 39(11), 2111-2121. https://doi.org/10.1016/j.atmosenv.2004.12.018 Kerminen, V.-M., Teinilä, K., Hillamo, R., & Pakkanen, T. (1998). Substitution of chloride in sea-salt particles by inorganic and organic anions. Journal of Aerosol Science, 29(8), 929-942. https://doi.org/10.1016/S0021-8502(98)00002-0 Khlystov, A., Stanier, C., & Pandis, S. N. (2004). An Algorithm for Combining Electrical Mobility and Aerodynamic Size Distributions Data when Measuring Ambient Aerosol Special Issue ofAerosol Science and Technologyon Findings from the Fine Particulate Matter Supersites Program. Aerosol Science and Technology, 38(sup1), 229-238. https://doi.org/10.1080/02786820390229543 Kittelson, D., Khalek, I., McDonald, J., Stevens, J., & Giannelli, R. (2022). Particle emissions from mobile sources: Discussion of ultrafine particle emissions and definition. Journal of Aerosol Science, 159. https://doi.org/10.1016/j.jaerosci.2021.105881 Kittelson, D. B. (1998). Engines and nanoparticles: a review. Journal of Aerosol Science, 29(5-6), 575-588. https://doi.org/10.1016/S0021-8502(97)10037-4 Kwon, H. S., Ryu, M. H., & Carlsten, C. (2020). Ultrafine particles: unique physicochemical properties relevant to health and disease. Experimental & Molecular Medicine, 52(3), 318-328. https://doi.org/10.1038/s12276-020-0405-1 Lan, Z., Zhang, B., Huang, X., Zhu, Q., Yuan, J., Zeng, L., Hu, M., & He, L. (2018). Source apportionment of PM2.5 light extinction in an urban atmosphere in China. Journal of Environmental Sciences, 63, 277-284. https://doi.org/10.1016/j.jes.2017.07.016 Lee, E., Chan, C. K., & Paatero, P. (1999). Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong. Atmospheric Environment, 33(19), 3201-3212. https://doi.org/10.1016/S1352-2310(99)00113-2 Leinonen, V., Kokkola, H., Yli-Juuti, T., Mielonen, T., Kühn, T., Nieminen, T., Heikkinen, S., Miinalainen, T., Bergman, T., & Carslaw, K. (2022). Comparison of particle number size distribution trends in ground measurements and climate models. Atmospheric Chemistry and Physics, 1-50. https://doi.org/10.5194/acp-2022-225 Li, Y., Huang, H. X., Griffith, S. M., Wu, C., Lau, A. K., & Yu, J. Z. (2017). Quantifying the relationship between visibility degradation and PM2.5 constituents at a suburban site in Hong Kong: Differentiating contributions from hydrophilic and hydrophobic organic compounds. Science of The Total Environment, 575, 1571-1581. https://doi.org/10.1016/j.scitotenv.2016.10.082 Lin, Z., Wang, Y., Zheng, F., Zhou, Y., Guo, Y., Feng, Z., Li, C., Zhang, Y., Hakala, S., Chan, T., Yan, C., Daellenbach, K. R., Chu, B., Dada, L., Kangasluoma, J., Yao, L., Fan, X., Du, W., Cai, J., . . . Kulmala, M. (2021). Rapid mass growth and enhanced light extinction of atmospheric aerosols during the heating season haze episodes in Beijing revealed by aerosol–chemistry–radiation–boundary layer interaction. Atmospheric Chemistry and Physics, 21(16), 12173-12187. https://doi.org/10.5194/acp-21-12173-2021 Liu, S., Hu, M., Wu, Z., Wehner, B., Wiedensohler, A., & Cheng, Y. (2008). Aerosol number size distribution and new particle formation at a rural/coastal site in Pearl River Delta (PRD) of China. Atmospheric Environment, 42(25), 6275-6283. https://doi.org/10.1016/j.atmosenv.2008.01.063 Liu, Z., Hu, B., Zhang, J., Yu, Y., & Wang, Y. (2016). Characteristics of aerosol size distributions and chemical compositions during wintertime pollution episodes in Beijing. Atmospheric Research, 168, 1-12. https://doi.org/10.1016/j.atmosres.2015.08.013 Liu, Z. R., Hu, B., Liu, Q., Sun, Y., & Wang, Y. S. (2014). Source apportionment of urban fine particle number concentration during summertime in Beijing. Atmospheric Environment, 96, 359-369. https://doi.org/10.1016/j.atmosenv.2014.06.055 Maguhn, J., Karg, E., Kettrup, A., & Zimmermann, R. (2003). On-line analysis of the size distribution of fine and ultrafine aerosol particles in flue and stack gas of a municipal waste incineration plant: effects of dynamic process control measures and emission reduction devices. Environmental Science & Technology, 37(20), 4761-4770. https://doi.org/10.1021/es020227p Masiol, M., & Harrison, R. M. (2014). Aircraft engine exhaust emissions and other airport-related contributions to ambient air pollution: A review. Atmospheric Environment, 95, 409-455. https://doi.org/10.1016/j.atmosenv.2014.05.070 Masiol, M., Harrison, R. M., Vu, T. V., & Beddows, D. C. S. (2017). Sources of sub-micrometre particles near a major international airport. Atmospheric Chemistry and Physics, 17(20), 12379-12403. https://doi.org/10.5194/acp-17-12379-2017 Nøjgaard, J. K., Nguyen, Q. T., Glasius, M., & Sørensen, L. L. (2012). Nucleation and Aitken mode atmospheric particles in relation to O3 and NOX at semirural background in Denmark. Atmospheric Environment, 49, 275-283. https://doi.org/https://doi.org/10.1016/j.atmosenv.2011.11.040 Ogulei, D., Hopke, P. K., Ferro, A. R., & Jaques, P. A. (2007). Factor analysis of submicron particle size distributions near a major United States–Canada trade bridge. Journal of the Air & Waste Management Association, 57(2), 190-203. https://doi.org/10.1080/10473289.2007.10465316 Park, J., Kim, H., Kim, Y., Heo, J., Kim, S. W., Jeon, K., Yi, S. M., & Hopke, P. K. (2022). Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF. Science of The Total Environment, 833, 155056. https://doi.org/10.1016/j.scitotenv.2022.155056 Peng, J., Hu, M., Shang, D., Wu, Z., Du, Z., Tan, T., Wang, Y., Zhang, F., & Zhang, R. (2021). Explosive Secondary Aerosol Formation during Severe Haze in the North China Plain. Environmental Science & Technology, 55(4), 2189-2207. https://doi.org/10.1021/acs.est.0c07204 Prodi, F., Belosi, F., Contini, D., Santachiara, G., Di Matteo, L., Gambaro, A., Donateo, A., & Cesari, D. (2009). Aerosol fine fraction in the Venice Lagoon: particle composition and sources. Atmospheric Research, 92(2), 141-150. https://doi.org/10.1016/j.atmosres.2008.09.020 Rivas, I., Beddows, D. C. S., Amato, F., Green, D. C., Jarvi, L., Hueglin, C., Reche, C., Timonen, H., Fuller, G. W., Niemi, J. V., Perez, N., Aurela, M., Hopke, P. K., Alastuey, A., Kulmala, M., Harrison, R. M., Querol, X., & Kelly, F. J. (2020). Source apportionment of particle number size distribution in urban background and traffic stations in four European cities. Environment International, 135, 105345. https://doi.org/10.1016/j.envint.2019.105345 Schraufnagel, D. E. (2020). The health effects of ultrafine particles. Experimental & Molecular Medicine, 52(3), 311-317. https://doi.org/10.1038/s12276-020-0403-3 Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric chemistry and physics: from air pollution to climate change. John Wiley & Sons. Shen, C., Zhao, G., Zhao, W., Tian, P., & Zhao, C. (2021). Measurement report: aerosol hygroscopic properties extended to 600 nm in the urban environment. Atmospheric Chemistry and Physics, 21(3), 1375-1388. https://doi.org/10.5194/acp-21-1375-2021 Sowlat, M. H., Hasheminassab, S., & Sioutas, C. (2016). Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF). Atmospheric Chemistry and Physics, 16(8), 4849-4866. https://doi.org/10.5194/acp-16-4849-2016 Stein, A., & Saylor, R. (2012). Sensitivities of sulfate aerosol formation and oxidation pathways on the chemical mechanism employed in simulations. Atmospheric Chemistry and Physics, 12(18), 8567-8574. https://doi.org/10.5194/acp-12-8567-2012 Sumlin, B. J., Heinson, W. R., & Chakrabarty, R. K. (2018). Retrieving the aerosol complex refractive index using PyMieScatt: A Mie computational package with visualization capabilities. Journal of Quantitative Spectroscopy and Radiative Transfer, 205, 127-134. https://doi.org/10.1016/j.jqsrt.2017.10.012 Taiwan EPA. (2021). https://air.epa.gov.tw/EnvTopics/AirQuality_6.aspx Taiwo, A. M., Harrison, R. M., & Shi, Z. (2014). A review of receptor modelling of industrially emitted particulate matter. Atmospheric Environment, 97, 109-120. https://doi.org/10.1016/j.atmosenv.2014.07.051 Tao, J., Zhang, Z., Wu, Y., Zhang, L., Wu, Z., Cheng, P., Li, M., Chen, L., Zhang, R., & Cao, J. (2019). Impact of particle number and mass size distributions of major chemical components on particle mass scattering efficiency in urban Guangzhou in southern China. Atmospheric Chemistry and Physics, 19(13), 8471-8490. https://doi.org/10.5194/acp-19-8471-2019 Thorpe, A., & Harrison, R. M. (2008). Sources and properties of non-exhaust particulate matter from road traffic: a review. Science of The Total Environment, 400(1-3), 270-282. https://doi.org/10.1016/j.scitotenv.2008.06.007 Tie, X., Huang, R.-J., Cao, J., Zhang, Q., Cheng, Y., Su, H., Chang, D., Pöschl, U., Hoffmann, T., & Dusek, U. (2017). Severe pollution in China amplified by atmospheric moisture. Scientific Reports, 7(1), 1-8. https://doi.org/10.1038/s41598-017-15909-1 Tsai, P.-J., Young, L.-H., Hwang, B.-F., Lin, M.-Y., Chen, Y.-C., & Hsu, H.-T. (2020). Source and health risk apportionment for PM2.5 collected in Sha-Lu area, Taiwan. Atmospheric Pollution Research, 11(5), 851-858. https://doi.org/10.1016/j.apr.2020.01.013 Vejahati, F., Xu, Z., & Gupta, R. (2010). Trace elements in coal: Associations with coal and minerals and their behavior during coal utilization–A review. Fuel, 89(4), 904-911. https://doi.org/10.1016/j.fuel.2009.06.013 Vu, T. V., Delgado-Saborit, J. M., & Harrison, R. M. (2015). Review: Particle number size distributions from seven major sources and implications for source apportionment studies. Atmospheric Environment, 122, 114-132. https://doi.org/10.1016/j.atmosenv.2015.09.027 Wang, N., Zhou, L., Feng, M., Song, T., Zhao, Z., Song, D., Tan, Q., & Yang, F. (2023). Progressively narrow the gap of PM2.5 pollution characteristics at urban and suburban sites in a megacity of Sichuan Basin, China. Journal of Environmental Sciences, 126, 708-721. https://doi.org/10.1016/j.jes.2022.05.017 Wang, W., Lin, Y., Yang, H., Ling, W., Liu, L., Zhang, W., Lu, D., Liu, Q., & Jiang, G. (2022). Internal Exposure and Distribution of Airborne Fine Particles in the Human Body: Methodology, Current Understandings, and Research Needs. Environmental Science & Technology. https://doi.org/10.1021/acs.est.1c07051 Wang, W., Liu, M., Wang, T., Song, Y., Zhou, L., Cao, J., Hu, J., Tang, G., Chen, Z., & Li, Z. (2021). Sulfate formation is dominated by manganese-catalyzed oxidation of SO2 on aerosol surfaces during haze events. Nature communications, 12(1), 1993. https://doi.org/10.1038/s41467-021-22091-6 Wang, X., Shen, X., Sun, J., Zhang, X., Wang, Y., Zhang, Y., Wang, P., Xia, C., Qi, X., & Zhong, J. (2018). Size-resolved hygroscopic behavior of atmospheric aerosols during heavy aerosol pollution episodes in Beijing in December 2016. Atmospheric Environment, 194, 188-197. https://doi.org/10.1016/j.atmosenv.2018.09.041 Wu, T., & Boor, B. E. (2021). Urban aerosol size distributions: a global perspective. Atmospheric Chemistry and Physics, 21(11), 8883-8914. https://doi.org/10.5194/acp-21-8883-2021 Xu, X., Zhao, W., Zhang, Q., Wang, S., Fang, B., Chen, W., Venables, D. S., Wang, X., Pu, W., Wang, X., Gao, X., & Zhang, W. (2016). Optical properties of atmospheric fine particles near Beijing during the HOPE-J3A campaign. Atmospheric Chemistry and Physics, 16(10), 6421-6439. https://doi.org/10.5194/acp-16-6421-2016 Young, L.-H., Hsu, C.-S., Hsiao, T.-C., Lin, N.-H., Tsay, S.-C., Lin, T.-H., Lin, W.-Y., & Jung, C.-R. (2022). Sources, transport, and visibility impact of ambient submicrometer particle number size distributions in an urban area of central Taiwan. Science of The Total Environment, 159070. https://doi.org/10.1016/j.scitotenv.2022.159070 Zhang, K., Wan, H., Wang, B., Zhang, M., Feichter, J., & Liu, X. (2010). Tropospheric aerosol size distributions simulated by three online global aerosol models using the M7 microphysics module. Atmospheric Chemistry and Physics, 10(13), 6409-6434. https://doi.org/10.5194/acp-10-6409-2010 Zhang, X., Karl, M., Zhang, L., & Wang, J. (2020). Influence of aviation emission on the particle number concentration near Zurich Airport. Environmental Science & Technology, 54(22), 14161-14171. https://doi.org/10.1021/acs.est.0c02249 Zhao, G., Tan, T., Hu, S., Du, Z., Shang, D., Wu, Z., Guo, S., Zheng, J., Zhu, W., & Li, M. (2022). Mixing state of black carbon at different atmospheres in north and southwest China. Atmospheric Chemistry and Physics, 22(16), 10861-10873. https://doi.org/10.5194/acp-22-10861-2022 Zhao, P., Du, X., Su, J., Ding, J., & Dong, Q. (2020). Aerosol hygroscopicity based on size-resolved chemical compositions in Beijing. Science of The Total Environment, 716, 137074. https://doi.org/10.1016/j.scitotenv.2020.137074 Zou, J., An, J., Cao, Q., Wang, H., Wang, J., & Chen, C. (2021). The effects of physical and chemical characteristics of aerosol number concentration on scattering coefficients in Nanjing, China: Insights from a single particle aerosol mass spectrometer. Atmospheric Research, 250. https://doi.org/10.1016/j.atmosres.2020.105382 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90590 | - |
| dc.description.abstract | 大氣能見度在臺灣一直受到公眾關注，因為能見度是人眼可直接感知空氣品質優劣的方式。氣膠消光係數被用於量化能見度且強烈地受到微粒粒徑分佈 (PSD) 的影響。有鑒於此，本研究在臺灣臺中地區架設IMPACT監測站，以探討在不同的能見度條件下，PSD的特徵及其來源。首先運用簡易去除氣象影響的方法於源解析模式，也就是擴散正歸化正矩陣因子法 (DN-PMF)。將11.8至2,500奈米的微粒數目粒徑分佈 (PNSD) 結合氣狀污染物、化學成分和環境氣膠消光係數 (bext, amb) 應用到源解析模型中，以估算微粒數目、表面積、體積和環境氣膠消光係數的來源貢獻。
DN-PMF一共分析出六個因子，包括新鮮的交通排放 (F1)、交通相關的來源 (F2)、老化的交通排放/工業排放 (F3)、富含硝酸鹽的二次氣膠/燃燒源 (F4)、臭氧相關的二次氣膠 (F5) 和受污染的海洋性氣膠 (F6)。DN-PMF在F4、F5和F6的晝夜變化行為中，呈現出比固有的PMF更明顯的來源特徵，為因子解讀上提供更可靠的證據。平均而言，F1（43.3%）、F2（32.3%）和F3（15.8%）共同主導微粒數目濃度，而F3、F4、F5和F6則在微粒表面積或體積濃度扮演重要角色，暗示F3、F4、F5和F6可能對微粒質量濃度及能見度具有一定程度的影響。此外，環境觀測和源解析的結果都表明積聚模範圍的微粒是能見度劣化的主因。表面積加權幾何平均粒徑的上升、表面積加權幾何標準差的下降及在300-1,000奈米最高的濃度增幅微粒，共同證實微粒傾向於集中在積聚模的粒徑範圍內。F3和F4對積聚模範圍的微粒貢獻最大，且兩者對bext, amb的合計貢獻接近80%。此結果顯示F3和F4是能見度劣化的主要貢獻來源，應優先實施針對這些來源的減量措施以改善能見度。最後，案例分析揭示了高壓東移及高壓迴流的綜觀天氣型態對能見度下降的重要作用，而通風係數 (VC) 的下降可以用來預警可能會發生能見度劣化事件。 | zh_TW |
| dc.description.abstract | Atmospheric visibility has been receiving public attention in Taiwan since it is a perceptible parameter of air quality by human eyes. Aerosol extinction coefficient (bext) is a strong function of the particle size distribution (PSD) and was used to quantify visibility. Given that, the IMPACT monitoring station was erected to investigate the characteristics and sources of PSDs under different visibility conditions from September 4th, 2020, to May 31st, 2021, in Taichung, Taiwan. A simple approach of removing weather influences, namely the Dispersion normalized PMF (DN-PMF), was applied to the particle number size distributions (PNSDs) from 11.8 to 2,500 nm coupling with the gaseous pollutants, chemical compositions, and ambient aerosol extinction coefficient (bext, amb) for estimating the source contributions of particle number, surface area, volume, and bext, amb.
Apportioned factors are comprised of fresh traffic (F1), traffic-related source (F2), aged traffic/industrial emissions (F3), nitrate-rich secondary aerosol/combustion sources (F4), O3-associated secondary aerosols (F5), and contaminated marine aerosols (F6). The DN-PMF showed more distinct diel patterns in F4, F5, and F6 than the PMF, which provides more robust evidence for the factors' interpretation. On average, F1 (43.3%), F2 (32.3%), and F3 (15.8%) together dominated the particle number concentrations, while F3, F4, F5, and F6 play a significant part in either particle volume or surface area concentration. This implied that F3, F4, F5, and F6 might largely impact the particle mass concentrations or visibility. Furthermore, both observation and source apportionment results showed that the accumulation-mode particles were responsible for the visibility degradation. The increased surface area-weighted GMD, decreased surface area-weighted GSD, and highest concentration enhancement in 300-1,000 nm proved that particles tended to concentrate and reside in the accumulation mode size range. F3 and F4 contributed the largest proportion on the accumulation-mode particles and are attributed to nearly 80% of bext, amb. The results indicated that F3 and F4 are the main reason for visibility degradation, and the abatement targeting these sources should be prioritized for implementation. Finally, the case study revealed that the eastward movement of the high-pressure system and the High Pressure Peripheral Circulation (HPPC) type of synoptic weather also play a vital role in visibility degradation, and the decline in the ventilation coefficient (VC) can serve as an indicator of potential deterioration in visibility. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T16:45:54Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-10-03T16:45:54Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 I
摘要 III Abstract IV Content VI List of Figures VII List of Tables IX Chapter 1 Introduction 1 Chapter 2 Methodology 6 2.1 Experimental site and sampling period 6 2.2 Instrumentation and measurements 6 2.2.1 Particle number size distributions 6 2.2.2 Gas precursors and aerosol compositions 7 2.2.3 Aerosol light extinction coefficient of PM2.5 8 2.2.4 Calculation of ambient bext 9 2.2.5 Meteorological parameters 10 2.3 Data merging process 11 2.3.1 Fundamental principles 11 2.3.2 PNSD merging details 12 2.4 Source apportionment: Dispersion normalized PMF 13 2.4.1 Model overview and input settings 13 2.4.2 Selection of factor numbers 16 2.5 Quality assurance and control (QA/QC) 17 Chapter 3 Results and discussion 19 3.1 Overview of the measurement 19 3.1.1 Particle size distributions 19 3.1.2 Diurnal variations 25 3.2 Dispersion Normalized PMF 28 3.3 Comparison of Dispersion Normalized-PMF with Traditional PMF Model 39 3.4 Source contributions on aerosol light extinction 44 3.5 Case study 47 Chapter 4 Conclusions 55 References 58 Supplemental materials 66 口試委員意見回覆 77 | - |
| dc.language.iso | en | - |
| dc.subject | 擴散正歸化正矩陣因子法 | zh_TW |
| dc.subject | 積聚模微粒 | zh_TW |
| dc.subject | 微粒數目粒徑分佈 | zh_TW |
| dc.subject | 氣膠消光係數 | zh_TW |
| dc.subject | 通風係數 | zh_TW |
| dc.subject | particle number size distribution | en |
| dc.subject | accumulation mode particles | en |
| dc.subject | aerosol light extinction coefficient | en |
| dc.subject | Dispersion normalized positive matrix factorization | en |
| dc.subject | ventilation coefficient | en |
| dc.title | 以微粒粒徑分佈之受體模式源解析解構大氣能見度劣化 | zh_TW |
| dc.title | Sources Apportionment of Atmospheric Visibility Degradation in Central Taiwan: from the Perspective of Particle Size Distribution | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林能暉;林文印;楊禮豪;丁育頡 | zh_TW |
| dc.contributor.oralexamcommittee | Neng-Huei Lin;Wen-Yinn Lin ;Li-Hao Young;Yu-Chieh Ting | en |
| dc.subject.keyword | 擴散正歸化正矩陣因子法,微粒數目粒徑分佈,積聚模微粒,氣膠消光係數,通風係數, | zh_TW |
| dc.subject.keyword | Dispersion normalized positive matrix factorization,particle number size distribution,accumulation mode particles,aerosol light extinction coefficient,ventilation coefficient, | en |
| dc.relation.page | 85 | - |
| dc.identifier.doi | 10.6342/NTU202304091 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-13 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 環境工程學研究所 | - |
| dc.date.embargo-lift | 2026-07-06 | - |
| 顯示於系所單位： | 環境工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 未授權公開取用 | 8.59 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。