台灣自2005年起將細懸浮微粒（Particulate Matter, PM2.5）列為空氣品質監測指標之一，由於監測站數量有限且分佈不均，加上區域內污染源類型多樣且複雜，進而造成估算上的誤差，有效估算污染物濃度時空變化已成為當前環境流行病學的重要議題。因此，本研究選擇空氣污染問題嚴重的高雄地區作為研究區域。
本研究利用高雄地區12個空氣品質監測站2005年至2021年的監測數據，結合地理資訊系統（GIS），取得監測站周邊的土地利用資訊，建立土地利用迴歸模型（Land Use Regression, LUR），研究針對不同季節分析PM2.5濃度的主要影響因子，並將氣象因子納入相關性分析，以探討PM2.5合格日與超標日時的氣象條件及颱風事件對PM2.5濃度的影響。
結果顯示，氣象因子和土地利用特徵對PM2.5濃度有顯著影響，冬季PM2.5濃度最高，夏季最低，各測站的PM2.5濃度顯示出明顯的空間異質性和季節變化特徵。風向和PM10濃度在各季節皆為顯著影響因子，商業住宅距離和道路距離對PM2.5濃度也有顯著影響並且通過比較高雄地區測站與萬里測站的數據，研究量化境內污染源對PM2.5濃度的貢獻比例分別為春季36%、夏季42%、秋季51%、冬季50%。
交通和工業排放對PM2.5濃度的影響，反映人類活動的強度及其空間分布對空氣品質的重要影響。

Since 2005, Taiwan has included fine particulate matter (PM2.5) as an air quality monitoring indicator. Due to the limited number and uneven distribution of monitoring stations, coupled with the diverse and complex types of pollution sources within regions, estimation errors arise, making the effective estimation of spatiotemporal variations in pollutant concentrations a significant issue in contemporary environmental epidemiology. Therefore, this study focuses on the Kaohsiung area, where air pollution is a serious problem.
This study utilizes monitoring data from 12 air quality monitoring stations in Kaohsiung from 2005 to 2021, combined with Geographic Information System (GIS) technology, to obtain land use information around the monitoring stations. Land Use Regression (LUR) models were established to analyze the main influencing factors of PM2.5concentrations in different seasons. Meteorological factors were incorporated into the correlation analysis to investigate the meteorological conditions on days with PM2.5 levels meeting standards and exceeding standards, as well as the impact of typhoon events on PM2.5concentrations.
The results show that meteorological factors and land use characteristics significantly affect PM2.5 concentrations, with the highest concentrations in winter and the lowest in summer. Each monitoring station exhibited significant spatial heterogeneity and seasonal variation in PM2.5 concentrations. Wind direction and PM10 concentration were significant influencing factors across all seasons. Additionally, the distances to commercial residential areas and roads significantly impacted PM2.5 concentrations. By comparing data from Kaohsiung area stations with those from the Wanli station, the study quantified the contribution of domestic pollution sources to PM2.5concentrations, with 36% in spring, 42% in summer, 51% in autumn, and 50% in winter. The impact of traffic and industrial emissions on PM2.5concentrations highlight the significant influence of human activity intensity and spatial distribution on air quality.

1. Beelen, R., Hoek, G., van den Brandt, P. A., Goldbohm, R. A., Fischer, P., Schouten, L. J., Brunekreef, B. (2013). Long-term exposure to traffic-related air pollution and lung cancer risk. Epidemiology, 24(5), 731-741.
2. Bell, M. L., Ebisu, K., Peng, R. D., Samet, J. M., & Dominici, F. (2011). Hospital admissions and chemical composition of fine particle air pollution. American Journal of Respiratory and Critical Care Medicine, 183(1), 73-78.
3. Bell, M. L., McDermott, A., Zeger, S. L., Samet, J. M., & Dominici, F. (2007). Ozone and short-term mortality in 95 US urban communities, 1987-2000. JAMA, 292(19), 2372-2378.
4. Briggs, D. J., Collins, S., Elliott, P., Fischer, P., Kingham, S., Lebret, E., & Elliott, P. (1997). Mapping urban air pollution using GIS: A regression-based approach. International Journal of Geographical Information Science, 11(7), 699-718.
5. Charlson, R. J., Schwartz, S. E., Hales, J. M., Cess, R. D., Coakley, J. A., Hansen, J. E., & Hofmann, D. J. (1992). Climate forcing by anthropogenic aerosols. Science, 255(5043), 423-430.
6. Chen, J. N., Li, Z. X., & Lin, Y. J. (2018). Application of GIS and geographically weighted regression model in studying PM2.5 concentration in Kaohsiung City. Environmental Science, 39(5), 201-213.
7. Chen, W. H., Huang, W. Y., Huang, C. H., & Ho, H. C. (2014). Spatial and temporal variations of PM2.5 and PM10 concentrations in Taiwan during 1994–2010. Atmospheric Environment, 85, 376-386.
8. Chen, Y., Wu, S., & Chen, H. (2020). High-resolution PM2.5 land use regression models combining local sources and urban morphology in Taiwan. Environmental Research, 183, 109194.
9. Cheng, C. Y., Lee, Y. T., Chen, S. Y., & Huang, L. K. (2015). Characteristics of PM2.5 and PM10 concentrations and metal elements at different functional areas in Kaohsiung, Taiwan. Aerosol and Air Quality Research, 15(6), 2279-2291.
10. Deng, Z., Liu, J., Wang, Y., Zhang, Q., & Hu, B. (2019). Impact of typhoon on air quality: A case study of PM2.5 in Shanghai, China. Environmental Pollution, 252, 1683-1691.
11. Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., & Speizer, F. E. (1993). An association between air pollution and mortality in six US cities. New England Journal of Medicine, 329(24), 1753-1759.
12. Harrison, R. M., Yin, J., & Tilling, R. (2001). Chemical composition of PM10 and PM2.5 particles in the atmosphere of Birmingham (UK). Atmospheric Environment, 35(36), 5913-5925.
13. Hoek, G., Beelen, R., de Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., & Brunekreef, B. (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment, 42(33), 7561-7578.
14. Hsieh, M. H., Wang, Z. W., & Lin, K. H. (2006). Application of GIS and land use regression model to estimate PM10 concentration in Taipei City. Journal of Environmental Information Science, 14(2), 100-110.
15. Hsu, S. C., Liu, S. C., Huang, Y. T., Lung, S. C. C., Tsai, F., & Chen, W. C. (2016). Seasonal and diurnal variations of PM2.5 composition and source apportionment in urban Taiwan. Atmospheric Environment, 142, 396-408.
16. Huang, C., Li, L., & Cheng, Z. (2015). Predicting PM2.5 concentrations in Beijing with land use regression and meteorological data. Environmental Research, 144(Part A), 91-100.
17. Huang, L. (2020). Characteristics and influencing factors of PM2.5 pollution in Xi'an from 2014 to 2017. Arid Meteorology, 38(3).
18. Huang, W., Wang, G., Lu, S., Xu, H., & Gao, S. (2014). Chemical composition of PM2.5 in the atmosphere of Beijing, China, and seasonal variations. Chemosphere, 114, 1-8.
19. Intergovernmental Panel on Climate Change (IPCC). (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC.
20. Jacobson, M. Z. (2012). Air pollution and global warming: history, science, and solutions. Cambridge University Press.
21. Jerrett, M., Burnett, R. T., Ma, R., Pope, C. A., Krewski, D., Newbold, K. B., & Thun, M. J. (2005). Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology, 16(6), 727-736.
22. Kuo, C. H., Chien, L. C., & Tsai, W. L. (2021). Using dynamic factor analysis to identify key factors affecting PM2.5 concentration in southern Taiwan. Environmental Science and Technology, 55(7), 1203-1215.
23. Lee, Y. T., Huang, Z. H., & Zhang, Z. C. (2017). Combining GIS and numerical models to simulate air quality in Taoyuan City. Environmental Simulation and Assessment, 25(4), 250-262.
24. Lin, C. Y., Wang, Z., Chang, F. J., & Yang, D. C. (2018). The impact of transboundary air pollution from China on surface ozone in Taiwan. Atmospheric Chemistry and Physics, 18(11), 7443-7454.
25. Lin, Y. C., Chen, J. H., & Zhang, S. F. (2011). Using GIS and air quality monitoring station data to analyze PM2.5 distribution in central and southern Taiwan. Air Pollution Control, 21(1), 85-97.
26. Liu, J., Han, Y., Tang, X., Zhu, J., & Zhu, T. (2009). Estimating PM2.5 concentration using PM10 data and meteorological conditions. Atmospheric Environment, 43(32), 5477-5485.
27. Marshall, J. D., Nethery, E., & Brauer, M. (2008). Within-urban variability in ambient air pollution: comparison of estimation methods. Atmospheric Environment, 42(6), 1359-1369.
28. Pope, C. A., Ezzati, M., & Dockery, D. W. (2009). Fine-particulate air pollution and life expectancy in the United States. New England Journal of Medicine, 360(4), 376-386.
29. Putaud, J. P., Raes, F., Van Dingenen, R., Bruggemann, E., Facchini, M. C., Decesari, S.,& Dell’Acqua, A. (2004). A European aerosol phenomenology-2: chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe. Atmospheric Environment, 38(16), 2579-2595.
30. Schwartz, J. (1995). Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. Thorax, 50(5), 531-538.
31. Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric chemistry and physics: From air pollution to climate change. John Wiley & Sons.
32. Tsai, P. J., Wang, H. Y., & Chen, M. T. (2019). Using GIS and machine learning techniques to establish a PM2.5 concentration prediction model in Tainan City. Air Pollution Control, 29(3), 150-162.
33. Tsai, Y. I., Chen, C. L., & Tsai, H. H. (2012). PM2.5 in suburban and rural sites of central Taiwan. Atmospheric Research, 118, 52-63.
34. Tseng, Y. T., Wu, C. T., & Lung, S. J. (2018). Application of land use regression model to estimate the spatiotemporal distribution of fine particulate matter in the northern air quality zone. Journal of Photogrammetry and Remote Sensing, 23(3), 191-204.
35. Wang, Q., Wang, Y., Zhang, X., Xu, Y., & Jiang, Q. (2010). Effect of Typhoon Hagupit (2008) on particulate matter pollution in Pearl River Delta region. Journal of Environmental Sciences, 22(10), 1453-1460.
36. Wang, Z. W., Li, M. H., & Zhang, J. X. (2014). Analyzing PM2.5 concentration changes in Taichung City using GIS and air quality monitoring data. Environmental Monitoring and Assessment, 186(12), 8711-8722.
37. Wei, J., Huang, W., Zhang, J., Schauer, J. J., & Wang, T. (2016). Characterization of PM2.5-bound nitrated phenols in Beijing, China: seasonal variation, sources, and risk assessment. Environmental Pollution, 212, 161-168.
38. World Health Organization (WHO). (2013). Review of evidence on health aspects of air pollution – REVIHAAP project. Technical Report.
39. Wu, C. D., Zeng, Y. T., Lung, S. C., & Su, H. J. (2017). PM2.5 and cardiovascular admissions in a subtropical island: A case-crossover study in Taiwan. Journal of Exposure Science & Environmental Epidemiology, 27(1), 13-19.
40. Wu, C. Y., Wu, C. T., Chen, Y. Z., Hsu, C. Y., & Chen, M. J. (2020). Application of kriging/land use regression hybrid model to estimate the spatiotemporal distribution of particulate matter in the Lin-Yuan coastal petrochemical industrial area. Journal of Photogrammetry and Remote Sensing, 25(1), 11-23.
41. Wu, Y., Hao, Y., & Yu, D. (2017). Spatiotemporal dynamics of PM2.5 in China: A structural equation modeling analysis. Journal of Cleaner Production, 166, 1071-1081.
42. Zheng, S., Cheng, K., Wang, L., Sun, Z., & Hu, J. (2017). Changes of PM2.5 concentrations in China from 2000 to 2015. Atmospheric Environment, 159, 186-198.
43. Zhu, Y., Hinds, W. C., Kim, S., Shen, S., & Sioutas, C. (2018). Study of ultrafine particles near a major highway with heavy-duty diesel traffic. Atmospheric Environment, 36(27), 4323-4335.
44. 交通部運輸研究所(2020)，路網數值圖，https://www.iot.gov.tw/
45. 內政部(2016)，20公尺網格數值地形模型資料，政府資料開放平臺，https://data.gov.tw/
46. 內政部國土測繪中心(2007)，國土測繪圖資服務，https://maps.nlsc.gov.tw/
47. 台灣電力公司(2023)台灣電力公司https://www.taipower.com.tw/tc/news_noclassify_info.aspx?id=2878&chk=cab6caa1-7787-473b-a3bf-fe9b6c460b81&mid=334&param=pn%3D1%26mid%3D334%26key%3D
48. 曾于庭(2016)，應用土地利用迴歸模式推估台灣全島細懸浮微粒之時空分布，國立嘉義大學森林暨自然資源學系研究所碩士論文
49. 曾妤婷(2020)，利用HYSPLIT模式與氣象因子探討臺灣中部地區PM2.5高污染成因，國立中興大學環境工程學系所碩士論文
50. 林宜穎(2006)，颱風或熱帶低壓與中部空品區空氣品質之相關探討，國立中央大學大氣物理研究所碩士論文
51. 林淑鈴(2016)，台灣空氣品質影響因素探討-以細懸浮微粒(PM2.5)為例，國立政治大學行政管理碩士學程碩士論文。
52. 王詩雅(2022)，應用衛星遙測資料於土地利用迴歸模型推估細懸浮微粒之時空分布，國立臺灣大學環境工程學研究所碩士論文
53. 環境部(2005-2021)，環境部空氣品質監測網，https://airtw.moenv.gov.tw/
54. 環境部(2023)，環境部焚化廠基本資料環境部，https://data.moenv.gov.tw/dataset/detail/FAC_S_01
55. 高雄市政府(2016)，高雄市政府資料開放平台，https://data.kcg.gov.tw/dataset/industrial-park
56. 高雄市政府環境保護局(2021)，高雄市空氣污染防制計畫 (109年至112年) 核定版，高雄市政府環境保護局