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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/20050

    Title: 應用統計工具分析台南地區掩埋場對於地下水質之影響
    其他題名: Application of Statistical Tools to Identify the Influence of Groundwater Quality by Landfill Leachate in Tainan Area
    Authors: 陳冠彣
    Chen, Guan-Wen
    指導教授: 吳庭年
    Ting-Nien Wu
    Keywords: 掩埋場;監測井;地下水水質;主成分分析;群集分析;時間序列分析
    landfill;monitoring well;groundwater quality;principal component analysis;cluster analysis;time series analysis
    Date: 2013
    Issue Date: 2013-10-09 11:33:40 (UTC+8)
    Abstract: SPSS統計分析工具可以將大量且複雜的資料,簡化成相關性因子且容易辨別,並依資料在時間上變化趨勢做預測分析。本研究運用主成分分析與群集分析來解析台南地區掩埋場對於附近區域地下水水質的影響,以期達到加強掩埋場管理及地下水監測井水質預警的功能。本研究資料取自台南地區掩埋場場置性監測井地下水質監測數據,及「全國環境水質監測資訊網」台南地區區域性監測井地下水質監測數據,分析項目包含總有機碳、氨氮、硝酸鹽氮、總硬度、硫酸鹽、氯鹽等,由主成分分析結果得知台南地區影響地下水水質2個主成分因子為鹽化因子及有機污染物因子。僅針對台南地區掩埋場場置性監測井地下水質監測數據進行主成分分析,可將掩埋場影響地下水水質的型態歸納為硫酸鹽、氯鹽、硝酸鹽、總有機碳等4個污染類型。利用時間序列工具進行掩埋場地下水質歷史資料分析,剖析地下水質之時間變化趨勢。以新營四期掩埋場場置性監測井為例,選擇最佳模式預測地下水水質變化趨勢,總有機碳及硝酸鹽以ARMA(1,1)模式最適合,氨氮及氯鹽以ARMA(2,1)模式最適合,硫酸鹽及導電度以ARIMA(1,1,1)模式最適合。以氨氮作為指標,掩埋場滲出水對於地下水水質的影響於11年後漸趨穩定。
    SPSS statistical analysis tools can reduce the complexity of a large data set and simplify them into certain correlation factors for the ease of recognition. Besides, temporal data can be analyzed for prediction based on its temporal trend. In this study, principal component analysis and cluster analysis were employed to anatomize the influence of landfill leachate on groundwater quality in Tainan area. The obtained results are expected to apply to the improvement of landfill management and the pre-warning of groundwater monitoring. The monitoring data of groundwater quality originated from regular monitoring data of landfill monitoring wells by Tainan EPB and groundwater quality monitoring data of "National Water Quality Monitoring Network" by Taiwan EPA. Monitored groundwater quality including total organic carbon, ammonia, nitrate, total hardness, sulfate and chloride were subjected to statistical analysis. The results of principal component analysis (PCA) showed that salinization and organic pollution are two major factors affecting groundwater quality in Tainan area. Based on PCA results of monitoring wells at landfill sites, landfills were classified into four pollution types as sulfate, chloride, nitrate and total organic carbon. Time series analysis tool was utilized to anatomize temporal groundwater data for forecasting. The Sin-Ying fourth landfill was selected as an example. The best models for forecasting groundwater quality are ARMA(1,1) for total organic carbon and nitrate, ARMA(2,1) for ammonia and chloride, and ARIMA(1,1,1) for sulfate and electric conductivity. Based on the temporal trend of ammonia, the influence of landfill leachate on groundwater quality will become much less after 11years.
    Appears in Collections:[環境工程系所] 博碩士論文

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