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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳章甫 | zh_TW |
| dc.contributor.advisor | Chang-Fu Wu | en |
| dc.contributor.author | 黃淳聖 | zh_TW |
| dc.contributor.author | Chun-Sheng Huang | en |
| dc.date.accessioned | 2024-02-26T16:14:08Z | - |
| dc.date.available | 2024-02-27 | - |
| dc.date.copyright | 2024-02-26 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-12-24 | - |
| dc.identifier.citation | Al-Naiema, I. M., Hettiyadura, A. P. S., Wallace, H. W., Sanchez, N. P., Madler, C. J., Cevik, B. K., Bui, A. A. T., Kettler, J., Griffin, R. J., and Stone, E. A. (2018). Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols. Atmospheric Chemistry and Physics, 18(21), 15601-15622.
Al-Naiema, I. M., and Stone, E. A. (2017). Evaluation of anthropogenic secondary organic aerosol tracers from aromatic hydrocarbons. Atmospheric Chemistry and Physics, 17(3), 2053-2065. Albinet, A., Lanzafame, G. M., Srivastava, D., Bonnaire, N., Nalin, F., and Wise, S. A. (2019). Analysis and determination of secondary organic aerosol (SOA) tracers (markers) in particulate matter standard reference material (SRM 1649b, urban dust). Analytical and Bioanalytical Chemistry, 411(23), 5975-5983. Altuwayjiri, A., Pirhadi, M., Taghvaee, S., and Sioutas, C. (2021). Long-term trends in the contribution of PM2.5 sources to organic carbon (OC) in the Los Angeles basin and the effect of PM emission regulations. Faraday Discussions, 226(0), 74-99. Anttila, P., Paatero, P., Tapper, U., and Järvinen, O. (1995). Source identification of bulk wet deposition in Finland by positive matrix factorization. Atmospheric Environment, 29(14), 1705-1718. Atkinson, R. W., Kang, S., Anderson, H. R., Mills, I. C., and Walton, H. A. (2014). Epidemiological time series studies of PM2.5 and daily mortality and hospital admissions: a systematic review and meta-analysis. Thorax, 69(7), 660-665. Behera, S. N., and Balasubramanian, R. (2014). Influence of Biomass Burning on Temporal and Diurnal Variations of Acidic Gases, Particulate Nitrate, and Sulfate in a Tropical Urban Atmosphere. Advances in Meteorology, 2014, 828491. Belis, C. A., Pernigotti, D., Karagulian, F., Pirovano, G., Larsen, B. R., Gerboles, M., and Hopke, P. K. (2015). A new methodology to assess the performance and uncertainty of source apportionment models in intercomparison exercises. Atmospheric Environment, 119, 35-44. Bhattarai, H., Saikawa, E., Wan, X., Zhu, H., Ram, K., Gao, S., Kang, S., Zhang, Q., Zhang, Y., Wu, G., Wang, X., Kawamura, K., Fu, P., and Cong, Z. (2019). Levoglucosan as a tracer of biomass burning: Recent progress and perspectives. Atmospheric Research, 220, 20-33. Brown, S. G., Frankel, A., and Hafner, H. R. (2007). Source apportionment of VOCs in the Los Angeles area using positive matrix factorization. Atmospheric Environment, 41(2), 227-237. Callén, M. S., Iturmendi, A., and López, J. M. (2014). Source apportionment of atmospheric PM2.5-bound polycyclic aromatic hydrocarbons by a PMF receptor model. Assessment of potential risk for human health. Environmental Pollution, 195, 167-177. Canonaco, F., Tobler, A., Chen, G., Sosedova, Y., Slowik, J. G., Bozzetti, C., Daellenbach, K. R., El Haddad, I., Crippa, M., Huang, R. J., Furger, M., Baltensperger, U., and Prévôt, A. S. H. (2021). A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data. Atmospheric Measurement Techniques, 14(2), 923-943. Cao, J. J., Lee, S. C., Ho, K. F., Zhang, X. Y., Zou, S. C., Fung, K., Chow, J. C., and Watson, J. G. (2003). Characteristics of carbonaceous aerosol in Pearl River Delta Region, China during 2001 winter period. Atmospheric Environment, 37(11), 1451-1460. Chan, C.-C., Chio, C.-P., and Chen, S.-Y. (2016). Air pollution and health in Taiwan. Hazardous Air Pollutants: Case Studies from Asia; Shin, DC, Ed, 47-64. Chan, C.-C., Chuang, K.-J., Chen, W.-J., Chang, W.-T., Lee, C.-T., and Peng, C.-M. (2008). Increasing cardiopulmonary emergency visits by long-range transported Asian dust storms in Taiwan. Environmental Research, 106(3), 393-400. Chan, C.-C., Chuang, K.-J., Chien, L.-C., Chen, W.-J., and Chang, W.-T. (2006). Urban air pollution and emergency admissions for cerebrovascular diseases in Taipei, Taiwan. European Heart Journal, 27(10), 1238-1244. Chan, C.-C., and Ng, H.-C. (2011). A case-crossover analysis of Asian dust storms and mortality in the downwind areas using 14-year data in Taipei. Science of The Total Environment, 410-411, 47-52. Chazeau, B., El Haddad, I., Canonaco, F., Temime-Roussel, B., D'Anna, B., Gille, G., Mesbah, B., Prévôt, A. S. H., Wortham, H., and Marchand, N. (2022). Organic aerosol source apportionment by using rolling positive matrix factorization: Application to a Mediterranean coastal city. Atmospheric Environment: X, 14, 100176. Chen, G., Sosedova, Y., Canonaco, F., Fröhlich, R., Tobler, A., Vlachou, A., Daellenbach, K. R., Bozzetti, C., Hueglin, C., Graf, P., Baltensperger, U., Slowik, J. G., El Haddad, I., and Prévôt, A. S. H. (2021). Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window. Atmospheric Chemistry and Physics, 21(19), 15081-15101. Chen, L. W. A., Watson, J. G., Chow, J. C., Green, M. C., Inouye, D., and Dick, K. (2012). Wintertime particulate pollution episodes in an urban valley of the Western US: a case study. Atmospheric Chemistry and Physics, 12(21), 10051-10064. Chen, Y.-C., Shie, R.-H., Zhu, J.-J., and Hsu, C.-Y. (2022). A hybrid methodology to quantitatively identify inorganic aerosol of PM2.5 source contribution. Journal of Hazardous Materials, 428, 128173. Cheng, M.-T., Horng, C.-L., Su, Y.-R., Lin, L.-K., Lin, Y.-C., and Chou, C. C. K. (2009). Particulate matter characteristics during agricultural waste burning in Taichung City, Taiwan. Journal of Hazardous Materials, 165(1), 187-192. Chi, R., Li, H., Wang, Q., Zhai, Q., Wang, D., Wu, M., Liu, Q., Wu, S., Ma, Q., Deng, F., and Guo, X. (2019). Association of emergency room visits for respiratory diseases with sources of ambient PM2.5. Journal of Environmental Sciences, 86, 154-163. Chou, C. C. K., Hsu, W. C., Chang, S. Y., Chen, W. N., Chen, M. J., Huang, W. R., Huang, S. H., Tsai, C. Y., Chang, S. C., Lee, C. T., and Liu, S. C. (2017). Seasonality of the mass concentration and chemical composition of aerosols around an urbanized basin in East Asia. Journal of Geophysical Research: Atmospheres, 122(3), 2026-2042. Chow, J. C., Lowenthal, D. H., Chen, L. W. A., Wang, X., and Watson, J. G. (2015). Mass reconstruction methods for PM2.5: a review. Air Quality, Atmosphere & Health, 8(3), 243-263. Chow, J. C., Watson, J. G., Lowenthal, D. H., Chen, L.-W. A., and Motallebi, N. (2010). Black and Organic Carbon Emission Inventories: Review and Application to California. Journal of the Air & Waste Management Association, 60(4), 497-507. Chuang, M.-T., Chen, Y.-C., Lee, C.-T., Cheng, C.-H., Tsai, Y.-J., Chang, S.-Y., and Su, Z.-S. (2016). Apportionment of the sources of high fine particulate matter concentration events in a developing aerotropolis in Taoyuan, Taiwan. Environmental Pollution, 214, 273-281. Chuang, M.-T., Wu, C.-F., Lin, C.-Y., Lin, W.-C., Chou, C. C. K., Lee, C.-T., Lin, T.-H., Fu, J. S., and Kong, S. S.-K. (2022). Simulating nitrate formation mechanisms during PM2.5 events in Taiwan and their implications for the controlling direction. Atmospheric Environment, 269, 118856. Dai, Q., Liu, B., Bi, X., Wu, J., Liang, D., Zhang, Y., Feng, Y., and 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. Eeftens, M., Beelen, R., de Hoogh, K., Bellander, T., Cesaroni, G., Cirach, M., Declercq, C., Dėdelė, A., Dons, E., de Nazelle, A., Dimakopoulou, K., Eriksen, K., Falq, G., Fischer, P., Galassi, C., Gražulevičienė, R., Heinrich, J., Hoffmann, B., Jerrett, M., Keidel, D., Korek, M., Lanki, T., Lindley, S., Madsen, C., Mölter, A., Nádor, G., Nieuwenhuijsen, M., Nonnemacher, M., Pedeli, X., Raaschou-Nielsen, O., Patelarou, E., Quass, U., Ranzi, A., Schindler, C., Stempfelet, M., Stephanou, E., Sugiri, D., Tsai, M.-Y., Yli-Tuomi, T., Varró, M. J., Vienneau, D., Klot, S. v., Wolf, K., Brunekreef, B., and Hoek, G. (2012). Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project. Environmental Science & Technology, 46(20), 11195-11205. ESCAPE. (2010). ESCAPE Exposure Assessment Manual. Retrieved from http://www.escapeproject.eu/manuals/ESCAPE_Exposure-manualv9.pdf (accessed on July 3, 2023) Ezcurra, A., Ortiz de Zárate, I., Dhin, P. V., and Lacaux, J. P. (2001). Cereal waste burning pollution observed in the town of Vitoria (northern Spain). Atmospheric Environment, 35(8), 1377-1386. Feng, J., Chan, C. K., Fang, M., Hu, M., He, L., and Tang, X. (2006). Characteristics of organic matter in PM2.5 in Shanghai. Chemosphere, 64(8), 1393-1400. Feng, J., Li, M., Zhang, P., Gong, S., Zhong, M., Wu, M., Zheng, M., Chen, C., Wang, H., and Lou, S. (2013). Investigation of the sources and seasonal variations of secondary organic aerosols in PM2.5 in Shanghai with organic tracers. Atmospheric Environment, 79, 614-622. Galvão, E. S., de Cassia Feroni, R., and 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. Gao, J., Peng, X., Chen, G., Xu, J., Shi, G.-L., Zhang, Y.-C., and Feng, Y.-C. (2016). Insights into the chemical characterization and sources of PM2.5 in Beijing at a 1-h time resolution. Science of The Total Environment, 542, 162-171. Gugamsetty, B., Wei, H., Liu, C.-N., Awasthi, A., Hsu, S.-C., Tsai, C.-J., Roam, G.-D., Wu, Y.-C., and Chen, C.-F. (2012). Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization. Aerosol and Air Quality Research, 12(4), 476-491. Guo, H., Kota, S. H., Sahu, S. K., Hu, J., Ying, Q., Gao, A., and Zhang, H. (2017). Source apportionment of PM2.5 in North India using source-oriented air quality models. Environmental Pollution, 231, 426-436. Guo, H., Wang, T., and Louie, P. K. K. (2004). Source apportionment of ambient non-methane hydrocarbons in Hong Kong: Application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model. Environmental Pollution, 129(3), 489-498. Hallquist, M., Wenger, J. C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N. M., George, C., Goldstein, A. H., Hamilton, J. F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M. E., Jimenez, J. L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, T. F., Monod, A., Prévôt, A. S. H., Seinfeld, J. H., Surratt, J. D., Szmigielski, R., and Wildt, J. (2009). The formation, properties and impact of secondary organic aerosol: current and emerging issues. Atmospheric Chemistry and Physics, 9(14), 5155-5236. Han, J. S., Moon, K. J., Lee, S. J., Kim, Y. J., Ryu, S. Y., Cliff, S. S., and Yi, S. M. (2006). Size-resolved source apportionment of ambient particles by positive matrix factorization at Gosan background site in East Asia. Atmospheric Chemistry and Physics, 6(1), 211-223. Hedberg, E., Gidhagen, L., and Johansson, C. (2005). Source contributions to PM10 and arsenic concentrations in Central Chile using positive matrix factorization. Atmospheric Environment, 39(3), 549-561. Henry, R. C. (2003). Multivariate receptor modeling by N-dimensional edge detection. Chemometrics and Intelligent Laboratory Systems, 65(2), 179-189. Heo, J. B., Hopke, P. K., and Yi, S. M. (2009). Source apportionment of PM2.5 in Seoul, Korea. Atmospheric Chemistry and Physics, 9(14), 4957-4971. Hettiyadura, A. P. S., Xu, L., Jayarathne, T., Skog, K., Guo, H., Weber, R. J., Nenes, A., Keutsch, F. N., Ng, N. L., and Stone, E. A. (2018). Source apportionment of organic carbon in Centreville, AL using organosulfates in organic tracer-based positive matrix factorization. Atmospheric Environment, 186, 74-88. Ho, W.-Y., Tseng, K.-H., Liou, M.-L., Chan, C.-C., and Wang, C.-h. (2018). Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City. International Journal of Environmental Research and Public Health, 15(7). Hoek, G., Beelen, R., De Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., and Briggs, D. (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment, 42(33), 7561-7578. Hopke, P. K. (2016). Review of receptor modeling methods for source apportionment. Journal of the Air & Waste Management Association, 66(3), 237-259. Hopke, P. K., Chen, Y., Rich, D. Q., Mooibroek, D., and Sofowote, U. M. (2023). The application of positive matrix factorization with diagnostics to BIG DATA. Chemometrics and Intelligent Laboratory Systems, 240, 104885. Hopke, P. K., Dai, Q., Li, L., and Feng, Y. (2020). Global review of recent source apportionments for airborne particulate matter. Science of The Total Environment, 740, 140091. Hsu, C.-Y., Chiang, H.-C., Chen, M.-J., Chuang, C.-Y., Tsen, C.-M., Fang, G.-C., Tsai, Y.-I., Chen, N.-T., Lin, T.-Y., Lin, S.-L., and Chen, Y.-C. (2017). Ambient PM2.5 in the residential area near industrial complexes: Spatiotemporal variation, source apportionment, and health impact. Science of The Total Environment, 590-591, 204-214. Hu, D., Bian, Q., Lau, A. K. H., and Yu, J. Z. (2010). Source apportioning of primary and secondary organic carbon in summer PM2.5 in Hong Kong using positive matrix factorization of secondary and primary organic tracer data. Journal of Geophysical Research: Atmospheres, 115(D16). Hulskotte, J. H. J., Roskam, G. D., and Denier van der Gon, H. A. C. (2014). Elemental composition of current automotive braking materials and derived air emission factors. Atmospheric Environment, 99, 436-445. Ito, K., Christensen, W. F., Eatough, D. J., Henry, R. C., Kim, E., Laden, F., Lall, R., Larson, T. V., Neas, L., Hopke, P. K., and Thurston, G. D. (2006). PM source apportionment and health effects: 2. An investigation of intermethod variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC. Journal of Exposure Science & Environmental Epidemiology, 16(4), 300-310. Jalaludin, B., Mannes, T., Morgan, G., Lincoln, D., Sheppeard, V., and Corbett, S. (2007). Impact of ambient air pollution on gestational age is modified by season in Sydney, Australia. Environmental Health, 6(1), 16. Jia, C., and Batterman, S. (2010). A Critical Review of Naphthalene Sources and Exposures Relevant to Indoor and Outdoor Air. International Journal of Environmental Research and Public Health, 7(7), 2903-2939. Jorquera, H., and Barraza, F. (2012). Source apportionment of ambient PM2.5 in Santiago, Chile: 1999 and 2004 results. Science of The Total Environment, 435-436, 418-429. Journel, A. G., and Huijbregts, C. J. (1978). Mining geostatistics. London ; New York: Academic Press. Kampa, M., and Castanas, E. (2008). Human health effects of air pollution. Environmental Pollution, 151(2), 362-367. Karnae, S., and John, K. (2011). Source apportionment of fine particulate matter measured in an industrialized coastal urban area of South Texas. Atmospheric Environment, 45(23), 3769-3776. Kim, E., and Hopke, P. K. (2004). Comparison between conditional probability function and nonparametric regression for fine particle source directions. Atmospheric Environment, 38(28), 4667-4673. Kim, E., Hopke, P. K., and Edgerton, E. S. (2004). Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization. Atmospheric Environment, 38(20), 3349-3362. Kim, K.-H., Jahan, S. A., and Kabir, E. (2013). A review on human health perspective of air pollution with respect to allergies and asthma. Environment international, 59, 41-52. Kiser, D., Elhanan, G., Metcalf, W. J., Schnieder, B., and Grzymski, J. J. (2021). SARS-CoV-2 test positivity rate in Reno, Nevada: association with PM2.5 during the 2020 wildfire smoke events in the western United States. Journal of Exposure Science & Environmental Epidemiology, 31(5), 797-803. Kleindienst, T. E., Jaoui, M., Lewandowski, M., Offenberg, J. H., Lewis, C. W., Bhave, P. V., and Edney, E. O. (2007). Estimates of the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern US location. Atmospheric Environment, 41(37), 8288-8300. Kuo, C.-P., Liao, H.-T., Chou, C. C. K., and Wu, C.-F. (2014). Source apportionment of particulate matter and selected volatile organic compounds with multiple time resolution data. Science of The Total Environment, 472, 880-887. Laiman, V., Hsiao, T.-C., Wang, Y.-H., Young, L.-H., Chao, H.-R., Lin, T.-H., Heriyanto, D. S., and Chuang, H.-C. (2022). Contributions of acidic ions in secondary aerosol to PM2.5 bioreactivity in an urban area. Atmospheric Environment, 275, 119001. Lall, R., Ito, K., and Thurston George, D. (2011). Distributed Lag Analyses of Daily Hospital Admissions and Source-Apportioned Fine Particle Air Pollution. Environmental Health Perspectives, 119(4), 455-460. Lanzafame, G. M., Srivastava, D., Favez, O., Bandowe, B. A. M., Shahpoury, P., Lammel, G., Bonnaire, N., Alleman, L. Y., Couvidat, F., Bessagnet, B., and Albinet, A. (2021). One-year measurements of secondary organic aerosol (SOA) markers in the Paris region (France): Concentrations, gas/particle partitioning and SOA source apportionment. Science of The Total Environment, 757, 143921. Larson, T. V., Covert, D. S., Kim, E., Elleman, R., Schreuder, A. B., and Lumley, T. (2006). Combining size distribution and chemical species measurements into a multivariate receptor model of PM2.5. Journal of Geophysical Research: Atmospheres, 111(D10). Lee, E., Chan, C. K., and Paatero, P. (1999). Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong. Atmospheric Environment, 33(19), 3201-3212. Lee, J.-H., Wu, C.-F., Hoek, G., de Hoogh, K., Beelen, R., Brunekreef, B., and Chan, C.-C. (2014). Land use regression models for estimating individual NOx and NO2 exposures in a metropolis with a high density of traffic roads and population. Science of The Total Environment, 472, 1163-1171. Lee, J. H., and Hopke, P. K. (2006). Apportioning sources of PM2.5 in St. Louis, MO using speciation trends network data. Atmospheric Environment, 40, 360-377. Lee, Y.-Y., Lin, S.-L., Aniza, R., and Yuan, C.-S. (2017). Reduction of Atmospheric PM2.5 Level by Restricting the Idling Operation of Buses in a Busy Station. Aerosol and Air Quality Research, 17(10), 2424-2437. Li, J. L., Zhai, X., Zhang, H. H., and Yang, G. P. (2018). Temporal variations in the distribution and sea-to-air flux of marine isoprene in the East China Sea. Atmospheric Environment, 187, 131-143. Li, L., Tan, Q., Zhang, Y., Feng, M., Qu, Y., An, J., and Liu, X. (2017a). Characteristics and source apportionment of PM2.5 during persistent extreme haze events in Chengdu, southwest China. Environmental Pollution, 230, 718-729. Li, N., Chen, J.-P., Tsai, I. C., He, Q., Chi, S.-Y., Lin, Y.-C., and Fu, T.-M. (2016a). Potential impacts of electric vehicles on air quality in Taiwan. Science of The Total Environment, 566-567, 919-928. Li, T.-C., Yuan, C.-S., Huang, H.-C., Lee, C.-L., Wu, S.-P., and Tong, C. (2016b). Inter-comparison of Seasonal Variation, Chemical Characteristics, and Source Identification of Atmospheric Fine Particles on Both Sides of the Taiwan Strait. Scientific Reports, 6(1), 22956. Li, T.-C., Yuan, C.-S., Huang, H.-C., Lee, C.-L., Wu, S.-P., and Tong, C. (2017b). Clustered long-range transport routes and potential sources of PM2.5 and their chemical characteristics around the Taiwan Strait. Atmospheric Environment, 148, 152-166. Li, T., Li, J., Jiang, H., Chen, D., Zong, Z., Tian, C., and Zhang, G. (2020a). Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon. Atmosphere, 11(5), 512. Li, Y., Liu, B., Xue, Z., Zhang, Y., Sun, X., Song, C., Dai, Q., Fu, R., Tai, Y., Gao, J., Zheng, Y., and Feng, Y. (2020b). Chemical characteristics and source apportionment of PM2.5 using PMF modelling coupled with 1-hr resolution online air pollutant dataset for Linfen, China. Environmental Pollution, 263, 114532. Liang, C.-S., Duan, F.-K., He, K.-B., and Ma, Y.-L. (2016). Review on recent progress in observations, source identifications and countermeasures of PM2.5. Environment international, 86, 150-170. Liao, H.-T., Chang, J.-C., Tsai, T.-T., Tsai, S.-W., Chou, C. C. K., and Wu, C.-F. (2020). Vertical distribution of source apportioned PM2.5 using particulate-bound elements and polycyclic aromatic hydrocarbons in an urban area. Journal of Exposure Science & Environmental Epidemiology, 30(4), 659-669. Liao, H.-T., Chou, C. C. K., Chow, J. C., Watson, J. G., Hopke, P. K., and Wu, C.-F. (2015). Source and risk apportionment of selected VOCs and PM2.5 species using partially constrained receptor models with multiple time resolution data. Environmental Pollution, 205, 121-130. Liao, H.-T., Hsieh, P.-Y., Hopke, P. K., and Wu, C.-F. (2022). Development and evaluation of an integrated method using distance- and probability-based profile matching approaches in receptor modeling. Atmospheric Pollution Research, 13(6), 101423. Liao, H.-T., Kuo, C.-P., Hopke, P. K., and Wu, C.-F. (2013). Evaluation of a Modified Receptor Model for Solving Multiple Time Resolution Equations: A Simulation Study. Aerosol and Air Quality Research, 13(4), 1253-1262. Liao, H.-T., and Wu, C.-F. (2020). Trajectory-Assisted Source Apportionment of Winter-Time Aerosol Using Semi-continuous Measurements. Archives of Environmental Contamination and Toxicology, 78(3), 430-438. Liao, H.-T., Yau, Y.-C., Huang, C.-S., Chen, N., Chow, J. C., Watson, J. G., Tsai, S.-W., Chou, C. C. K., and Wu, C.-F. (2017). Source apportionment of urban air pollutants using constrained receptor models with a priori profile information. Environmental Pollution, 227, 323-333. Lin, J. J. (2002). Characterization of the major chemical species in PM2.5 in the Kaohsiung City, Taiwan. Atmospheric Environment, 36(12), 1911-1920. Lin, Y.-C., Li, Y.-C., Amesho, K. T. T., Chou, F.-C., and Cheng, P.-C. (2019). Characterization and quantification of PM2.5 emissions and PAHs concentration in PM2.5 from the exhausts of diesel vehicles with various accumulated mileages. Science of The Total Environment, 660, 188-198. Liu, W., Wang, Y., Russell, A., and Edgerton, E. S. (2005). Atmospheric aerosol over two urban–rural pairs in the southeastern United States: Chemical composition and possible sources. Atmospheric Environment, 39(25), 4453-4470. Liu, W., Wang, Y., Russell, A., and Edgerton, E. S. (2006). Enhanced source identification of southeast aerosols using temperature-resolved carbon fractions and gas phase components. Atmospheric Environment, 40, 445-466. Liu, Y., Austin, E., Xiang, J., Gould, T., Larson, T., and Seto, E. (2021). Health Impact Assessment of the 2020 Washington State Wildfire Smoke Episode: Excess Health Burden Attributable to Increased PM2.5 Exposures and Potential Exposure Reductions. GeoHealth, 5(5), e2020GH000359. Lu, F., Xu, D., Cheng, Y., Dong, S., Guo, C., Jiang, X., and Zheng, X. (2015). Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population. Environmental Research, 136, 196-204. Lu, Z., Liu, Q., Xiong, Y., Huang, F., Zhou, J., and Schauer, J. J. (2018). A hybrid source apportionment strategy using positive matrix factorization (PMF) and molecular marker chemical mass balance (MM-CMB) models. Environmental Pollution, 238, 39-51. Mancilla, Y., Herckes, P., Fraser, M. P., and Mendoza, A. (2015). Secondary organic aerosol contributions to PM2.5 in Monterrey, Mexico: Temporal and seasonal variation. Atmospheric Research, 153, 348-359. Manousakas, M., Diapouli, E., Papaefthymiou, H., Migliori, A., Karydas, A. G., Padilla-Alvarez, R., Bogovac, M., Kaiser, R. B., Jaksic, M., Bogdanovic-Radovic, I., and Eleftheriadis, K. (2015). Source apportionment by PMF on elemental concentrations obtained by PIXE analysis of PM10 samples collected at the vicinity of lignite power plants and mines in Megalopolis, Greece. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 349, 114-124. Manousakas, M., Papaefthymiou, H., Diapouli, E., Migliori, A., Karydas, A. G., Bogdanovic-Radovic, I., and Eleftheriadis, K. (2017). Assessment of PM2.5 sources and their corresponding level of uncertainty in a coastal urban area using EPA PMF 5.0 enhanced diagnostics. Science of The Total Environment, 574, 155-164. Meng, J., Martin, R. V., Li, C., van Donkelaar, A., Tzompa-Sosa, Z. A., Yue, X., Xu, J.-W., Weagle, C. L., and Burnett, R. T. (2019). Source Contributions to Ambient Fine Particulate Matter for Canada. Environmental Science & Technology, 53(17), 10269-10278. Minguillón, M. C., Cirach, M., Hoek, G., Brunekreef, B., Tsai, M., de Hoogh, K., Jedynska, A., Kooter, I. M., Nieuwenhuijsen, M., and Querol, X. (2014). Spatial variability of trace elements and sources for improved exposure assessment in Barcelona. Atmospheric Environment, 89, 268-281. Nabizadeh, R., Yousefian, F., Moghadam, V. K., and Hadei, M. (2019). Characteristics of cohort studies of long-term exposure to PM2.5: a systematic review. Environmental Science and Pollution Research, 26(30), 30755-30771. Ni, P., Wang, X., and Li, H. (2020). A review on regulations, current status, effects and reduction strategies of emissions for marine diesel engines. Fuel, 279, 118477. Norris, G., Duvall, R., Brown, S., and Bai, S. (2014). Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide. U.S. Environmental Protection Agency, Washington DC. Nunes, R. A. O., Alvim-Ferraz, M. C. M., Martins, F. G., and Sousa, S. I. V. (2017). Assessment of shipping emissions on four ports of Portugal. Environmental Pollution, 231, 1370-1379. Oduber, F., Calvo, A. I., Castro, A., Blanco-Alegre, C., Alves, C., Calzolai, G., Nava, S., Lucarelli, F., Nunes, T., Barata, J., and Fraile, R. (2021). Characterization of aerosol sources in León (Spain) using Positive Matrix Factorization and weather types. Science of The Total Environment, 754, 142045. Ogulei, D., Hopke, P. K., Zhou, L., Paatero, P., Park, S. S., and Ondov, J. M. (2005). Receptor modeling for multiple time resolved species: The Baltimore supersite. Atmospheric Environment, 39(20), 3751-3762. Olea, R. A. (2006). A six-step practical approach to semivariogram modeling. Stochastic Environmental Research and Risk Assessment, 20(5), 307-318. Paatero, P., Eberly, S., Brown, S. G., and Norris, G. A. (2014). Methods for estimating uncertainty in factor analytic solutions. Atmospheric Measurement Techniques, 7(3), 781-797. Paatero, P., and Hopke, P. K. (2003). Discarding or downweighting high-noise variables in factor analytic models. Analytica Chimica Acta, 490(1), 277-289. Paatero, P., and Hopke, P. K. (2009). Rotational tools for factor analytic models. Journal of Chemometrics, 23(2), 91-100. Paatero, P., and Tapper, U. (1994). Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5(2), 111-126. Park, M.-B., Lee, T.-J., Lee, E.-S., and Kim, D.-S. (2019). Enhancing source identification of hourly PM2.5 data in Seoul based on a dataset segmentation scheme by positive matrix factorization (PMF). Atmospheric Pollution Research, 10(4), 1042-1059. Parworth, C., Fast, J., Mei, F., Shippert, T., Sivaraman, C., Tilp, A., Watson, T., and Zhang, Q. (2015). Long-term measurements of submicrometer aerosol chemistry at the Southern Great Plains (SGP) using an Aerosol Chemical Speciation Monitor (ACSM). Atmospheric Environment, 106, 43-55. Percy, Z., DeFranco, E., Xu, F., Hall, E. S., Haynes, E. N., Jones, D., Muglia, L. J., and Chen, A. (2019). Trimester specific PM2.5 exposure and fetal growth in Ohio, 2007–2010. Environmental Research, 171, 111-118. Pernigotti, D., and Belis, C. A. (2018). DeltaSA tool for source apportionment benchmarking, description and sensitivity analysis. Atmospheric Environment, 180, 138-148. Pokorná, P., Hovorka, J., and Hopke, P. K. (2016). Elemental composition and source identification of very fine aerosol particles in a European air pollution hot-spot. Atmospheric Pollution Research, 7(4), 671-679. Qiao, T., Zhao, M., Xiu, G., and Yu, J. (2016). Simultaneous monitoring and compositions analysis of PM1 and PM2.5 in Shanghai: Implications for characterization of haze pollution and source apportionment. Science of The Total Environment, 557-558, 386-394. Querol, X., Viana, M., Alastuey, A., Amato, F., Moreno, T., Castillo, S., Pey, J., de la Rosa, J., Sánchez de la Campa, A., Artíñano, B., Salvador, P., García Dos Santos, S., Fernández-Patier, R., Moreno-Grau, S., Negral, L., Minguillón, M. C., Monfort, E., Gil, J. I., Inza, A., Ortega, L. A., Santamaría, J. M., and Zabalza, J. (2007). Source origin of trace elements in PM from regional background, urban and industrial sites of Spain. Atmospheric Environment, 41(34), 7219-7231. Ramadan, Z., Eickhout, B., Song, X.-H., Buydens, L. M. C., and Hopke, P. K. (2003). Comparison of Positive Matrix Factorization and Multilinear Engine for the source apportionment of particulate pollutants. Chemometrics and Intelligent Laboratory Systems, 66(1), 15-28. Reff, A., Eberly, S. I., and Bhave, P. V. (2007). Receptor Modeling of Ambient Particulate Matter Data Using Positive Matrix Factorization: Review of Existing Methods. Journal of the Air & Waste Management Association, 57(2), 146-154. Robinson, E. S., Shah, R. U., Messier, K., Gu, P., Li, H. Z., Apte, J. S., Robinson, A. L., and Presto, A. A. (2019). Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling. Environmental Science & Technology, 53(15), 8925-8937. Savouré, M., Lequy, É., Bousquet, J., Chen, J., de Hoogh, K., Goldberg, M., Vienneau, D., Zins, M., Nadif, R., and Jacquemin, B. (2021). Long-term exposures to PM2.5, black carbon and NO2 and prevalence of current rhinitis in French adults: The Constances Cohort. Environment international, 157, 106839. Sharma, S. K., Mandal, T. K., Jain, S., Saraswati, Sharma, A., and Saxena, M. (2016). Source Apportionment of PM2.5 in Delhi, India Using PMF Model. Bulletin of Environmental Contamination and Toxicology, 97(2), 286-293. Singh, R., Kulshrestha, M. J., Kumar, B., and Chandra, S. (2016). Impact of anthropogenic emissions and open biomass burning on carbonaceous aerosols in urban and rural environments of Indo-Gangetic Plain. Air Quality, Atmosphere & Health, 9(7), 809-822. Siudek, P., and Ruczyńska, W. (2021). Simultaneous Measurements of PM2.5- and PM10-bound Benzo(a)pyrene in a Coastal Urban Atmosphere in Poland: Seasonality of Dry Deposition Fluxes and Influence of Atmospheric Transport. Aerosol and Air Quality Research, 21(10), 210044. Sofowote, U. M., Su, Y., Dabek-Zlotorzynska, E., Rastogi, A. K., Brook, J., and Hopke, P. K. (2015). Constraining the factor analytical solutions obtained from multiple-year receptor modeling of ambient PM2.5 data from five speciation sites in Ontario, Canada. Atmospheric Environment, 108, 151-157. Son, S.-C., Yu, G.-H., Park, S., and Lee, S. (2021). Comprehensive characterization of PM2.5 using chemical, optical, and spectroscopic methods during pollution episodes at an urban site in Gwangju, Korea. Atmospheric Pollution Research, 12(10), 101199. Song, L., Dai, Q., Feng, Y., and Hopke, P. K. (2021). Estimating uncertainties of source contributions to PM2.5 using moving window evolving dispersion normalized PMF. Environmental Pollution, 286, 117576. Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D., and Ngan, F. (2015). NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System. Bulletin of the American Meteorological Society, 96(12), 2059-2077. Streets, D. G., Gupta, S., Waldhoff, S. T., Wang, M. Q., Bond, T. C., and Yiyun, B. (2001). Black carbon emissions in China. Atmospheric Environment, 35(25), 4281-4296. Sturtz, T. M., Schichtel, B. A., and Larson, T. V. (2014). Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to Two IMPROVE Sites Impacted by Wildfires. Environmental Science & Technology, 48(19), 11389-11396. Sugiyama, T., Ueda, K., Seposo, X. T., Nakashima, A., Kinoshita, M., Matsumoto, H., Ikemori, F., Honda, A., Takano, H., Michikawa, T., and Nitta, H. (2020). Health effects of PM2.5 sources on children's allergic and respiratory symptoms in Fukuoka, Japan. Science of The Total Environment, 709, 136023. Sun, X., Wang, K., Ma, L., Tang, B., Sun, S., Meng, F., Jiang, P., and Qi, H. (2022). Source-specific health risks induced by PM2.5-bound metallic species under different pollution scenarios in a cold megacity of Northeast China. Urban Climate, 44, 101205. Taghvaee, S., Sowlat, M. H., Mousavi, A., Hassanvand, M. S., Yunesian, M., Naddafi, K., and Sioutas, C. (2018). Source apportionment of ambient PM2.5 in two locations in central Tehran using the Positive Matrix Factorization (PMF) model. Science of The Total Environment, 628-629, 672-686. Ting, Y.-C., Young, L.-H., Lin, T.-H., Tsay, S.-C., Chang, K.-E., and Hsiao, T.-C. (2022). Quantifying the impacts of PM2.5 constituents and relative humidity on visibility impairment in a suburban area of eastern Asia using long-term in-situ measurements. Science of The Total Environment, 818, 151759. Titos, G., del Águila, A., Cazorla, A., Lyamani, H., Casquero-Vera, J. A., Colombi, C., Cuccia, E., Gianelle, V., Močnik, G., Alastuey, A., Olmo, F. J., and Alados-Arboledas, L. (2017). Spatial and temporal variability of carbonaceous aerosols: Assessing the impact of biomass burning in the urban environment. Science of The Total Environment, 578, 613-625. Tobler, A. K., Skiba, A., Canonaco, F., Močnik, G., Rai, P., Chen, G., Bartyzel, J., Zimnoch, M., Styszko, K., Nęcki, J., Furger, M., Różański, K., Baltensperger, U., Slowik, J. G., and Prevot, A. S. H. (2021). Characterization of non-refractory (NR) PM1 and source apportionment of organic aerosol in Kraków, Poland. Atmospheric Chemistry and Physics, 21(19), 14893-14906. Tucker, W. G. (2000). An overview of PM2.5 sources and control strategies. Fuel Processing Technology, 65-66, 379-392. Turpin, B. J., and Lim, H.-J. (2001). Species Contributions to PM2.5 Mass Concentrations: Revisiting Common Assumptions for Estimating Organic Mass. Aerosol Science and Technology, 35(1), 602-610. Urban, R. C., Lima-Souza, M., Caetano-Silva, L., Queiroz, M. E. C., Nogueira, R. F. P., Allen, A. G., Cardoso, A. A., Held, G., and Campos, M. L. A. M. (2012). Use of levoglucosan, potassium, and water-soluble organic carbon to characterize the origins of biomass-burning aerosols. Atmospheric Environment, 61, 562-569. USEPA. (2023). What are hazardous air pollutants? Retrieved from https://www.epa.gov/haps/what-are-hazardous-air-pollutants (accessed on September 18, 2023) Vallius, M., Janssen, N. A. H., Heinrich, J., Hoek, G., Ruuskanen, J., Cyrys, J., Van Grieken, R., de Hartog, J. J., Kreyling, W. G., and Pekkanen, J. (2005). Sources and elemental composition of ambient PM2.5 in three European cities. Science of The Total Environment, 337(1), 147-162. Vallius, M., Lanki, T., Tiittanen, P., Koistinen, K., Ruuskanen, J., and Pekkanen, J. (2003). Source apportionment of urban ambient PM2.5 in two successive measurement campaigns in Helsinki, Finland. Atmospheric Environment, 37(5), 615-623. Var, F., Narita, Y., and Tanaka, S. (2000). The concentration, trend and seasonal variation of metals in the atmosphere in 16 Japanese cities shown by the results of National Air Surveillance Network (NASN) from 1974 to 1996. Atmospheric Environment, 34(17), 2755-2770. Viana, M., Hammingh, P., Colette, A., Querol, X., Degraeuwe, B., Vlieger, I. d., and van Aardenne, J. (2014). Impact of maritime transport emissions on coastal air quality in Europe. Atmospheric Environment, 90, 96-105. Wang, D., Hu, J., Xu, Y., Lv, D., Xie, X., Kleeman, M., Xing, J., Zhang, H., and Ying, Q. (2014). Source contributions to primary and secondary inorganic particulate matter during a severe wintertime PM2.5 pollution episode in Xi'an, China. Atmospheric Environment, 97, 182-194. Wang, J., Ho, S. S. H., Ma, S., Cao, J., Dai, W., Liu, S., Shen, Z., Huang, R., Wang, G., and Han, Y. (2016). Characterization of PM2.5 in Guangzhou, China: uses of organic markers for supporting source apportionment. Science of The Total Environment, 550, 961-971. Wang, Q., He, X., Huang, X. H. H., Griffith, S. M., Feng, Y., Zhang, T., Zhang, Q., Wu, D., and Yu, J. Z. (2017). Impact of Secondary Organic Aerosol Tracers on Tracer-Based Source Apportionment of Organic Carbon and PM2.5: A Case Study in the Pearl River Delta, China. ACS Earth and Space Chemistry, 1(9), 562-571. Wang, Q., Qiao, L., Zhou, M., Zhu, S., Griffith, S., Li, L., and Yu, J. Z. (2018). Source Apportionment of PM2.5 Using Hourly Measurements of Elemental Tracers and Major Constituents in an Urban Environment: Investigation of Time-Resolution Influence. Journal of Geophysical Research: Atmospheres, 123(10), 5284-5300. Wang, Y.-S., Chang, L.-C., and Chang, F.-J. (2021). Explore Regional PM2.5 Features and Compositions Causing Health Effects in Taiwan. Environmental Management, 67(1), 176-191. Wang, Y., Hopke, P. K., Xia, X., Rattigan, O. V., Chalupa, D. C., and Utell, M. J. (2012). Source apportionment of airborne particulate matter using inorganic and organic species as tracers. Atmospheric Environment, 55, 525-532. Wang, Y., Zhuang, G., Tang, A., Yuan, H., Sun, Y., Chen, S., and Zheng, A. (2005). The ion chemistry and the source of PM2.5 aerosol in Beijing. Atmospheric Environment, 39(21), 3771-3784. Watson, J. G., Zhu, T., Chow, J. C., Engelbrecht, J., Fujita, E. M., and Wilson, W. E. (2002). Receptor modeling application framework for particle source apportionment. Chemosphere, 49(9), 1093-1136. Weagle, C. L., Snider, G., Li, C., van Donkelaar, A., Philip, S., Bissonnette, P., Burke, J., Jackson, J., Latimer, R., Stone, E., Abboud, I., Akoshile, C., Anh, N. X., Brook, J. R., Cohen, A., Dong, J., Gibson, M. D., Griffith, D., He, K. B., Holben, B. N., Kahn, R., Keller, C. A., Kim, J. S., Lagrosas, N., Lestari, P., Khian, Y. L., Liu, Y., Marais, E. A., Martins, J. V., Misra, A., Muliane, U., Pratiwi, R., Quel, E. J., Salam, A., Segev, L., Tripathi, S. N., Wang, C., Zhang, Q., Brauer, M., Rudich, Y., and Martin, R. V. (2018). Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model. Environmental Science & Technology, 52(20), 11670-11681. Wu, C.-f., Larson, T. V., Wu, S.-y., Williamson, J., Westberg, H. H., and Liu, L. J. S. (2007). Source apportionment of PM2.5 and selected hazardous air pollutants in Seattle. Science of The Total Environment, 386(1), 42-52. Wu, C., and Yu, J. Z. (2016). Determination of primary combustion source organic carbon-to-elemental carbon (OC/EC) ratio using ambient OC and EC measurements: secondary OC-EC correlation minimization method. Atmospheric Chemistry and Physics, 16(8), 5453-5465. Xiao, Q., Li, M., Liu, H., Fu, M., Deng, F., Lv, Z., Man, H., Jin, X., Liu, S., and He, K. (2018). Characteristics of marine shipping emissions at berth: profiles for particulate matter and volatile organic compounds. Atmospheric Chemistry and Physics, 18(13), 9527-9545. Xu, J., Wang, Q., Deng, C., McNeill, V. F., Fankhauser, A., Wang, F., Zheng, X., Shen, J., Huang, K., and Zhuang, G. (2018). Insights into the characteristics and sources of primary and secondary organic carbon: High time resolution observation in urban Shanghai. Environmental Pollution, 233, 1177-1187. Yang, X., Zheng, M., Liu, Y., Yan, C., Liu, J., Liu, J., and Cheng, Y. (2022). Exploring sources and health risks of metals in Beijing PM2.5: Insights from long-term online measurements. Science of The Total Environment, 814, 151954. Yatkin, S., Gerboles, M., Belis, C. A., Karagulian, F., Lagler, F., Barbiere, M., and Borowiak, A. (2020). Representativeness of an air quality monitoring station for PM2.5 and source apportionment over a small urban domain. Atmospheric Pollution Research, 11(2), 225-233. Young, L.-H., Hsiao, T.-C., Griffith, S. M., Huang, Y.-H., Hsieh, H.-G., Lin, T.-H., Tsay, S.-C., Lin, Y.-J., Lai, K.-L., Lin, N.-H., and Lin, W.-Y. (2022). Secondary inorganic aerosol chemistry and its impact on atmospheric visibility over an ammonia-rich urban area in Central Taiwan. Environmental Pollution, 312, 119951. Yu, L., Wang, G., Zhang, R., Zhang, L., Song, Y., Wu, B., Li, X., An, K., and Chu, J. (2013). Characterization and Source Apportionment of PM2.5 in an Urban Environment in Beijing. Aerosol and Air Quality Research, 13(2), 574-583. Yu, Q., Chen, J., Qin, W., Cheng, S., Zhang, Y., Sun, Y., Xin, K., and Ahmad, M. (2021). Characteristics, primary sources and secondary formation of water-soluble organic aerosols in downtown Beijing. Atmospheric Chemistry and Physics, 21(3), 1775-1796. Yuan, L., Zhang, Y., Gao, Y., and Tian, Y. (2019). Maternal fine particulate matter (PM2.5) exposure and adverse birth outcomes: an updated systematic review based on cohort studies. Environmental Science and Pollution Research, 26(14), 13963-13983. Zang, Z., Li, Y., Li, H., Guo, Z., and Zhang, R. (2020). Spatiotemporal Variation and Pollution Assessment of Pb/Zn from Smelting Activities in China. International Journal of Environmental Research and Public Health, 17(6), 1968. Zhang, B., Zhou, T., Liu, Y., Yan, C., Li, X., Yu, J., Wang, S., Liu, B., and Zheng, M. (2019). Comparison of water-soluble inorganic ions and trace metals in PM2.5 between online and offline measurements in Beijing during winter. Atmospheric Pollution Research, 10(6), 1755-1765. Zhang, Y., Obrist, D., Zielinska, B., and Gertler, A. (2013). Particulate emissions from different types of biomass burning. Atmospheric Environment, 72, 27-35. Zhang, Y., Sheesley, R. J., Bae, M.-S., and Schauer, J. J. (2009a). Sensitivity of a molecular marker based positive matrix factorization model to the number of receptor observations. Atmospheric Environment, 43(32), 4951-4958. Zhang, Y., Sheesley, R. J., Schauer, J. J., Lewandowski, M., Jaoui, M., Offenberg, J. H., Kleindienst, T. E., and Edney, E. O. (2009b). Source apportionment of primary and secondary organic aerosols using positive matrix factorization (PMF) of molecular markers. Atmospheric Environment, 43(34), 5567-5574. Zhao, M., Xu, Z., Guo, Q., Gan, Y., Wang, Q., and Liu, J.-a. (2022). Association between long-term exposure to PM2.5 and hypertension: A systematic review and meta-analysis of observational studies. Environmental Research, 204, 112352. Zhou, L., Hopke, P. K., Paatero, P., Ondov, J. M., Pancras, J. P., Pekney, N. J., and Davidson, C. I. (2004). Advanced factor analysis for multiple time resolution aerosol composition data. Atmospheric Environment, 38(29), 4909-4920. Zhou, S., Davy, P. K., Huang, M., Duan, J., Wang, X., Fan, Q., Chang, M., Liu, Y., Chen, W., Xie, S., Ancelet, T., and Trompetter, W. J. (2018). High-resolution sampling and analysis of ambient particulate matter in the Pearl River Delta region of southern China: source apportionment and health risk implications. Atmospheric Chemistry and Physics, 18(3), 2049-2064. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91875 | - |
| dc.description.abstract | 受體模式之正矩陣因子法（Positive Matrix Factorization，PMF）廣泛應用於解析細懸浮微粒（PM2.5，氣動粒徑等於或小於2.5微米的微粒）的污染來源。然而，受到輸入數據特性（如：採樣解析度）和其既有模式原理（如：使用物種的共線性問題）的影響，傳統PMF對於PM2.5在時間和空間角度的模式解析上具有一定限制，這些限制也影響了模式結果後續在暴露評估和空氣品質管理上的應用。為改善上述限制，在本論文中，將三種數學方法與PMF模式結合以改善PM2.5的來源解析。
第一個研究為從時間的角度上探討空氣污染事件議題。受到低時間解析度數據，以及應用長期輸入數據集的影響，使得PMF無法解析出導致短期PM2.5污染事件的主要貢獻因子。本研究應用了逐時解析度之在線式微粒成分監測數據，以及移動式數據集技術（或稱「移動窗格技術」；Moving window technique），並結合限縮型PMF（Constrained Positive Matrix Factorization），針對臺灣一PM2.5污染事件案例進行解析。結果顯示，於事件發生期間，在2020年12月12日對PM2.5濃度影響最大的是額外分離之「區域傳輸源」（PM2.5日平均占比 = 61%），而在12月13日則是常態污染之「工業混和硫酸鹽源」（43%）。由於12月13日的PM2.5事件主要是由常態污染源所導致，因此，管理境內的工業排放源對於降低高PM2.5濃度十分重要。 第二個研究的主軸，為量化原生性污染源對PM2.5二次氣膠在不同時間尺度上的貢獻。由於PMF經常解析出獨立之二次氣膠特徵因子，因此，此研究提出了兩階段PMF建模方法，並應用穩健迴歸模式（Robust regression）來重新分配PMF因子內的二次氣膠。模式結果顯示，3小時內的二次氣膠主要由「油類燃燒源」貢獻（二次氣膠相關物種總和 = 2.67 μg/m3）。而在24小時內，二次氣膠的最大貢獻來源是「工業源」（1.65 μg/m3）。研究結果凸顯出需依據不同時間尺度，針對二次氣膠相關物種進行管控的重要性，也為控制原生性污染源和抑制二次氣膠的形成提供了有價值的資訊。 第三個研究為從空間的角度上，針對本地排放源的污染熱點區域進行辨識，這是典型PMF模式的限制之一。研究應用地理統計模型－普通克利金法（Ordinary kriging）來估算PM2.5質量濃度的空間分布，並將其放入限縮型PMF中，以解析和分配污染源貢獻PM2.5的空間變異。本研究透過土地利用特徵評估PMF解析之「道路揚塵/土木建設」在空間分布的合理性。結果顯示，該污染源所貢獻的PM2.5空間分布與「主要道路長度」和「建築工地數量」呈現正相關（相關係數 ≥ 0.40）。土地利用迴歸模式之交叉驗證判定係數（cross-validation R2）為0.48。本研究提出的方法能夠辨識本地排放源的污染熱點區域，並為當地政府提供空氣污染源之管理標的。 考量到PMF具有區分空氣污染來源特徵的優點，建議未來可進一步探討結合PMF與各種數學方法，以提升其未來發展和應用。透過結合各類方法有助於改善PMF的既有限制，並提升其在暴露評估和空氣污染研究中的成效。 | zh_TW |
| dc.description.abstract | The receptor model of Positive Matrix Factorization (PMF) is widely employed for source apportionment of fine particulate matter (PM2.5, particulate matter with an aerodynamic diameter of equal to or less than 2.5 μm). However, due to the properties of input data (e.g., sampling time resolution) and inherent modeling principles (e.g., collinearity of utilized species), conventional PMF has limitations in time- and space-dependent source apportionments of PM2.5, affecting the subsequent application in exposure assessment and air quality management. To address these issues, in this dissertation, three mathematical approaches were integrated with PMF modeling for improvement of PM2.5 source apportionments.
In the first study, the issue of air pollution episode from the temporal perspective was explored. Owing to the use of low time resolution data and an extended input dataset, the major contributor to the short-term PM2.5 episode cannot be identified by PMF. Here, the online particle composition measurements with hourly resolution were applied, and the moving window technique were incorporated with a constrained PMF to resolve a PM2.5 episode case in Taiwan. The results showed that the most significant contributor to the PM2.5 episode on 12/12/2020 was an additionally differentiated factor of regional transport (daily average PM2.5 contribution = 61%), while on 12/13 was the regular pollution of industry/ammonium sulfate related (43%). Since the PM2.5 episode on 12/13 was mainly caused by the regular pollution, managing local industrial emission sources is crucial to reduce elevated PM2.5 levels. In the second study, the temporally-resolved contributions of PM2.5 secondary aerosols from primary emission sources were quantified. As an individual secondary feature factor was commonly resolved from PMF, a two-stage PMF modeling approach was proposed, and a robust regression model was applied to re-apportion the secondary aerosols in retrieved PMF factors. The results showed that the majority of secondary aerosols (sum of secondary aerosol-related species = 2.67 μg/m3) within three hours were mainly contributed by oil combustion, while the largest contributor of secondary aerosols (1.65 μg/m3) over 24 hours was industry. This highlights the importance of regulating secondary aerosol-related species with various time spans as control targets, providing valuable information for controlling primary emission sources and inhibiting the formation of secondary aerosols. The third study focused on characterizing the hotspot regions of a local emission source from the spatial perspective, which cannot be achieved by typical PMF analysis. A geostatistical model of ordinary kriging was introduced to estimate spatially distributed PM2.5 mass concentrations, which were then applied in the constrained PMF for apportionment of source-specific PM2.5 with spatial variation. The spatial distribution of road dust/civil construction retrieved from PMF was evaluated using land use features. The positive Pearson correlations (coefficients ≥ 0.40) were found between the spatially distributed source-specific PM2.5 and land use characteristics of major road length and the number of construction sites. A leave-one-out cross-validation R2 of 0.48 was achieved using the land use regression model. The proposed approach identifies the hotspot regions of local emission sources, offering targets for the management of air pollution sources by local city governments. Given the advantages of PMF in distinguishing features of air pollution sources, it is recommended to further explore the integration of PMF with mathematical approaches to enhance its future development and applications. The integration with multiple approaches can assist in overcoming PMF's inherent limitations and strengthen its effectiveness in exposure assessments and air pollution studies. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-26T16:14:07Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-02-26T16:14:08Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | List of Figures IX
List of Tables XI Chapter 1 Introduction 1 1.1. Fine particulate matter and Positive Matrix Factorization 1 1.2. Knowledge gaps of PMF in current studies 4 1.2.1. Identification and quantification of air pollution episode 4 1.2.2. Characterization of temporally-resolved secondary aerosols 6 1.2.3. Identification of hotspots of the local emission source 8 1.3. Purpose and objectives 10 Chapter 2 Materials and methods 12 2.1. Hybrid approach 1: identification and quantification of air pollution episode 12 2.1.1. Case identification and PM2.5 data collections 12 2.1.2. Procedure for the identification and quantification of air pollution episode 15 2.1.3. PMF model descriptions 18 2.1.4. Comparison of profile similarity 21 2.2. Hybrid approach 2: characterization of temporally-resolved secondary aerosols 22 2.2.1. PM2.5 data collections 22 2.2.2. Chemical analysis for carbonaceous components 25 2.2.3. Estimation of primary and secondary organic carbons 28 2.2.4. Procedure for source apportionment of secondary aerosols 29 2.2.5. PMF modeling with multiple time resolution data and constraints 30 2.2.6. Robust regression modeling with PMF-retrieved factors 32 2.3. Hybrid approach 3: identification of hotspots of the local emission source 33 2.3.1. PM2.5 data collections 33 2.3.2. Procedure for the spatialization of source-specific PM2.5 35 2.3.3. Interpolation of PM2.5 mass concentrations using ordinary kriging 36 Chapter 3 Results and discussion 38 3.1. Hybrid approach 1: identification and quantification of air pollution episode 38 3.1.1. Summary statistics 38 3.1.2. Factor identification of start model PMF result 40 3.1.3. Moving window PMF results 44 3.1.3.1. Profile comparison of the sixth factor among moving windows 44 3.1.3.2. Profile interpretation of the sixth factor 46 3.1.3.3. Factor-specific PM2.5 contribution estimation 49 3.1.4. Sensitivity analyses of model settings for moving windows 51 3.1.4.1. Sensitivity analysis of model constraint for moving windows 51 3.1.4.2. Window size evaluation 53 3.2. Hybrid approach 2: characterization of temporally-resolved secondary aerosols 55 3.2.1. Data pretreatment and summary statistics 55 3.2.2. Interpretation of factor profiles obtained from two-stage PMF 56 3.2.3. Investigation of PMF factor associations using robust regression model 63 3.2.4. Factor contributions to PM2.5 and secondary aerosol-related species 65 3.2.5. Comparison with results using one-stage PMF modeling 69 3.3. Hybrid approach 3: identification of hotspots of the local emission source 74 3.3.1. Summary statistics of collected measurements and interpolated PM2.5 74 3.3.2. PMF source identification and results evaluation 76 3.3.3. Spatial distribution of source-specific PM2.5 of road dust/civil construction 82 Chapter 4 Conclusion 86 4.1. Summary and limitations 86 4.1.1. Hybrid approach 1: identification and quantification of air pollution episode 86 4.1.2. Hybrid approach 2: characterization of temporally-resolved secondary aerosols 87 4.1.3. Hybrid approach 3: identification of hotspots of the local emission source 89 4.2. Overall conclusion and future prospects 91 Acknowledgments 93 References 94 Appendices 111 | - |
| dc.language.iso | en | - |
| dc.subject | 空氣污染熱點 | zh_TW |
| dc.subject | 二次氣膠 | zh_TW |
| dc.subject | 空氣污染事件 | zh_TW |
| dc.subject | 在線式微粒成分數據 | zh_TW |
| dc.subject | 暴露評估 | zh_TW |
| dc.subject | 受體模式 | zh_TW |
| dc.subject | receptor model | en |
| dc.subject | exposure assessment | en |
| dc.subject | online particle composition measurements | en |
| dc.subject | air pollution episode | en |
| dc.subject | secondary aerosol | en |
| dc.subject | air pollution hotspot | en |
| dc.title | 應用混合正矩陣因子法改良細懸浮微粒於時間與空間相依性之來源解析 | zh_TW |
| dc.title | Improving time- and space-dependent source apportionments of PM2.5 using hybrid Positive Matrix Factorization modeling approaches | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 陳家揚;詹長權;楊禮豪;蔡詩偉;蕭大智 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Yang Chen;Chang-Chuan Chan;Li-Hao Young;Shih-Wei Tsai;Ta-Chih Hsiao | en |
| dc.subject.keyword | 受體模式,暴露評估,在線式微粒成分數據,空氣污染事件,二次氣膠,空氣污染熱點, | zh_TW |
| dc.subject.keyword | receptor model,exposure assessment,online particle composition measurements,air pollution episode,secondary aerosol,air pollution hotspot, | en |
| dc.relation.page | 114 | - |
| dc.identifier.doi | 10.6342/NTU202304557 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-12-25 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 環境與職業健康科學研究所 | - |
| dc.date.embargo-lift | 2028-12-24 | - |
| 顯示於系所單位： | 環境與職業健康科學研究所 | |
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