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標題: 2012-2016年高雄市PM2.5污染分布暨建構PM2.5之預測模型
The Distribution of PM2.5 in Kaohsiung City during 2012-2016 and the Establishment of PM2.5 Prediction Models
作者: 傅文閤
Wen-He Fu
關鍵字: PM2.5
Meteorological factor
Multiple linear regression model
引用: 1. 行政院環境保護署.(2016). 中華民國空氣品質監測報告104年年報. 台北市:行政院環境保護署. 2. 陳順宇, & 鄭碧娥. (2004). 統計學四版. 台北市:華泰書局. 3. 陳正平, 李清勝, 俞家忠, 王天胤, 羅思懿, 陳鴻仁, ...&林彥穎. (2001). 導致台灣地區高污染之氣象分析與預報(二)(期末報告)(行政院環保署EPA-90-U1L1-02-106). 台北市:國立台灣大學大氣科學系. 4. 鄭尊仁, 吳焜裕, 陳保中, 郭育良, 吳章甫, 林先和, ...&陳美君. (2011). 空氣品質標準檢討評估、細懸浮微粒空氣品質標準研訂計畫(環保署/國科會空污防制科研合作計畫NSC99-EPA-M-001-001). 台北市:國立台灣大學公共衛生學院職業醫學與工業衛生研究所. 5. 吳家安. (2013). 高雄地區大氣中細懸浮微粒之監測分析及管制策略. (碩士), 國立中山大學, 高雄市. 6. 林建宏. (2008). 大氣懸浮微粒濃度及化學組成與氣象因子變異關聯性研究. (碩士), 國立成功大學, 台南市. 7. 劉山豪. (2000). 高雄都會區消光係數與能見度量測及細微粒污染源貢獻量解析. (碩士), 國立中山大學, 高雄市. 8. 行政院環保署,空氣品質監測網,,2018.05. 9. 行政院環保署,環境資源資料庫,機動車輛登記數及密度,,2018.05. 10. Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., . . . Di Carlo, P. (2017). Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmospheric Pollution Research, 8(4), 652-659. 11. Cheng, Z., Wang, S., Jiang, J., Fu, Q., Chen, C., Xu, B., . . . Hao, J. (2013). Long-term trend of haze pollution and impact of particulate matter in the Yangtze River Delta, China. Environ Pollut, 182, 101-110. 12. Gebhart, K. A., Kreidenweis, S. M., & Malm, W. C. (2001). Back-trajectory analyses of fine particulate matter measured at Big Bend National Park in the historical database and the 1996 scoping study. Science of The Total Environment, 276(1), 185-204. 13. McMurry, P. H., Shepherd, M. F., & Vickery, J. S. (2004). Particulate matter science for policy makers: A NARSTO assessment: Cambridge University Press. 14. Ni, X. Y., Huang, H., & Du, W. P. (2017). Relevance analysis and short-term prediction of PM 2.5 concentrations in Beijing based on multi-source data. Atmospheric Environment, 150, 146-161. 15. Olvera Alvarez, H. A., Myers, O. B., Weigel, M., & Armijos, R. X. (2018). The value of using seasonality and meteorological variables to model intra-urban PM 2.5 variation. Atmospheric Environment, 182, 1-8. 16. Ordieres, J. B., Vergara, E. P., Capuz, R. S., & Salazar, R. E. (2005). Neural network prediction model for fine particulate matter (PM2.5) on the US–Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua). Environmental Modelling & Software, 20(5), 547-559. 17. Perez, P., & Gramsch, E. (2016). Forecasting hourly PM2.5 in Santiago de Chile with emphasis on night episodes. Atmospheric Environment, 124, 22-27. 18. Pope III, C. A., & Dockery, D. W. (2012). Health Effects of Fine Particulate Air Pollution: Lines that Connect. Journal of the Air & Waste Management Association, 56(6), 709-742. 19. Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G. D. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA, 287(9), 1132-1141. 20. R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL 21. Saide, P. E., Mena-Carrasco, M., Tolvett, S., Hernandez, P., & Carmichael, G. R. (2016). Air quality forecasting for winter-time PM2.5 episodes occurring in multiple cities in central and southern Chile. Journal of Geophysical Research: Atmospheres, 121(1), 558-575. 22. Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric chemistry and physics: from air pollution to climate change: John Wiley & Sons. 23. Tai, A. P. K., Mickley, L. J., & Jacob, D. J. (2010). Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change. Atmospheric Environment, 44(32), 3976-3984. 24. Tsai, J.-H., Lai, W.-F., & Chiang, H.-L. (2012). Characteristics of particulate constituents and gas precursors during the episode and non-episode periods. Journal of the Air & Waste Management Association, 63(1), 27-40. 25. Yáñez, M. A., Baettig, R., Cornejo, J., Zamudio, F., Guajardo, J., & Fica, R. (2017). Urban airborne matter in central and southern Chile: Effects of meteorological conditions on fine and coarse particulate matter. Atmospheric Environment, 161, 221-234. 26. Yang, H.-Y., Tseng, Y.-L., Chuang, H.-L., Li, T.-C., Yuan, C.-S., & Lee, J. J. (2017). Chemical Fingerprint and Source Identification of Atmospheric Fine Particles Sampled at Three Environments at the Tip of Southern Taiwan. Aerosol and Air Quality Research, 17(2), 529-542. 27. Yuan, C.-S., Lee, C.-G., Liu, S.-H., Chang, J.-c., Yuan, C., & Yang, H.-Y. (2006). Correlation of atmospheric visibility with chemical composition of Kaohsiung aerosols. Atmospheric Research, 82(3-4), 663-679.
摘要: 本研究彙整2012-2016年高雄市一般空氣品質監測站資料,分析PM2.5濃度分布及其變化,結果顯示冬季為PM2.5污染及能見度問題最為嚴重之季節,夏季污染及其問題則最為輕微。以PM2.5日均值15、35、50及70 μg/m3作為標準,劃分為五個等級之濃度區間,結果顯示各測站事件日(超過35 μg/m3)日數有逐年減少的趨勢,且事件日逐漸以35~50 μg/m3為主,2016年該等級日數佔總事件日日數之比例達60.3~79.5%。探討各測站冬季時,氣象因子對PM2.5分布的影響,並比較事件日發生的機率。結果顯示不同氣溫下,各測站事件日發生的機率差異小。各測站於低相對濕度下或無雨時發生事件日的機率較高。除小港及林園測站以外,其他測站於低風速下事件日機率較高。另外,除了林園外,其他測站PM2.5事件日的發生與靜風時數有關。無降雨的條件下,PM2.5污染有逐年改善趨勢,能見度問題則未獲得明顯改善。對各測站分季建立複線性迴歸模型,以預測次日PM2.5日均值,利用2016年資料作為測試資料,驗證模型性能,結果顯示春季模型預測性能最佳,R2值達0.65~0.80;就預測濃度區間而言,夏季模型的預測準確率最高,達98%以上,次高為春、秋季的模型,準確率分別達71.1~83.1%及67.1~89.9%,最低為冬季,準確率僅達50.0~64.4%。
In this study, the data were collected from general air quality monitoring stations in Kaohsiung City during the period from 2012 to 2016, and been analyzed for the changing of PM2.5 concentration distribution. The pollution of PM2.5 and visibility problem are the most serious in winter, and it are the most trifling in summer. Based on the daily average of 15, 35, 50 and 70 μg/m3, divided into five levels. The number of PM2.5 pollution episodes at each stations have decreased year by year, and the main of the daily average is about 35~50 μg/m3 gradually, the ratio of the episodes to the total episodes was 60.3~79.5% in 2016. This study investigated into the influence of meteorological factors on the distribution of PM2.5, and the difference of probability of the episode. The results show that the difference of probability of the episode is small at different temperatures, and the higher probability of occurrence of the episode at low relative humidity or no rain at each stations. Besides Siaogang and Linyuan, the higher probability of occurrence of the episode at low wind speed at each stations. In addition, the occurrence of the episode in relation to the hours of calm winds except Linyuan. Under the condition of no rainfall, the pollution of PM2.5 has been improving year by year, but the problem of visibility has not been improved significantly. This study established the multiple regression model for predicting the next day average PM2.5 concentration, and using data in 2016 as testing data to verify model performance. The results show that the performance of the spring models are the best, R2=0.65~0.80. In terms of the predicted concentration interval, the accuracy of the summer models are the highest, above 98%. The predictive performances of the spring and the autumn models are also good, 71.1~83.1% and 67.1~89.9% respectively. However, the accuracy of the winter models are the lowest, only 50.0~64.4%.
文章公開時間: 2018-08-06
Appears in Collections:環境工程學系所



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