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標題: 空氣品質變化對水鳥生態環境衝擊之評估研究
Evaluation Study of Air Quality Change Impacts on Ecological Management of Waterfowl Conservation
作者: Meng-Jung Chou
關鍵字: Time series analysis;multivariate statistics;collinearity;estuary;wetland;waterfowl;air pollution;ecology;thermal power plant;時間序列分析;多變量分析;共線性;海岸;濕地;海岸;空氣污染;生態;火力發電廠
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The Taichung plant of Taiwan power company (Tai-power), the biggest thermal power plant in far-east Asia, was constructed in one of the most important waterfowl habitats (Dadu estuary). The Air pollution drew a great attention to us, affecting the biological environment and impacting on the waterfowl species number in the estuary. The objective of this study is to analyze the relationship between air pollution and waterfowl population by statistical models.
The multiplicative decomposition method has been adapted to determine the species variations, including seasonal (S), long-term (T), circular (C), and irregular (I). The results indicated: (1) There were two seasonal high peaks in April and November each year with the lowest trough in June. Furthermore, since the winter visitors had the dominant numbers in total species, the seasonal changes were mainly depended on the winter bird migration. (2) The long-term trend decreased with time from 1989 to 2012; (3) The waterfowl was gradually restored back from lowest point in 1996, but the difference between 1989 and 2003 indicated the irreversible effect existed. (4) The irregular variation was proved as a random distribution. Moreover, statistically from simple liner regressions, there were 7 significant variables ranking as follows: PM10 (avg) >NOx (avg) >NO2 (avg) >NO2 (max) >O3 (max) >NOx (max) >SO2 (avg). The Whole model from the multiple liner regressions included the 12 variables: NO (avg), NO2 (avg), NOx (avg), SO2 (avg), PM10 (avg), O3 (avg), NO (max), NO2(max), NOx (max),SO2(max), PM10 (max) and O3(max) could achieve the largest explanatory variation percentage of the air pollution effectiveness. Furthermore, model selection analysis indicated the same result (from the backward, stepwise and Cp selection) to form the best efficient model of the air pollution effectiveness in this estuary. Finally, after a series of collinearity analysis, this study tried to explore the relationship between waterfowl species number and air pollutants compositions. The Backward, Stepwise and Cp selection only picked up the vital variables (NOx (avg), NO (avg), SO2 (avg) & PM10 (avg)) within the model showed the lowest collinearity among all the models.
In conclusion, all the results indicated that the air quality had crucial impacts on the waterfowl population; chronic effects from air pollution were essential for the species number change. We hope this study not only could help us to understand the avian succession but also to evaluate the anthropogenic effects to contribute sustainable development of global life support system.

研究中運用相乘性時間序列模式來了解水鳥種數的時序變化,分析包括季節性、長期性、循環性與不規則性等時序分解因子。分析結果顯示以下重點:(1) 一年之中有4月與11月兩個種數高峰,而最低峰則落於6月。(2) 在1989到2012這個研究區間,長期趨勢顯示出水鳥種數不斷減少。(3) 於1996年水鳥種數達到最低峰,隨後雖然有逐漸回升的現象,但計算結果顯示已出現不可回復的現象,無法恢復至原有的種類數量。(4) 不規則性因子在本研究利用多項統計方法驗證後,首次證實其不規則性質。除此之外,本研究運用單迴歸分析篩選出7個影響水鳥種數統計顯著的空氣品質因子,依照其模式解釋度排序如下:懸浮微粒(平均值)、氮氧化物(平均值)、二氧化氮(平均值)、二氧化氮(最大值)、臭氧(最大值)、氮氧化物(最大值)、二氧化硫(最大值)。若以複迴歸全模式來詮釋水鳥種數變化,則可使用以下12個變數構成最大解釋模式:一氧化氮(平均值)、二氧化氮(平均值)、氮氧化物(平均值)、二氧化硫(平均值)、懸浮微粒(平均值)、臭氧(平均值)、一氧化氮(最大值)、二氧化氮(最大值)、氮氧化物(最大值)、二氧化硫(最大值)、懸浮微粒(平均值)、臭氧(最大值)。另外,迴歸模式選擇分析法以向後選擇、逐步選擇與Cp選擇法得到同一個最佳模式,以最佳模式解釋效率來選用空氣品質變數解釋溪口濕地水鳥變化。最後經過一系列的共線性分析的驗證,以確認最佳模式之共線程度,結果顯示由氮氧化物(平均值)、一氧化氮(平均值)、二氧化硫(平均值)、懸浮微粒(平均值)所建構之最佳模式具有最低共線性與解釋效率。
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