臺灣博碩士論文加值系統

高光譜遙測技術於環境品質檢測之應用廣泛，不僅節省時間與人力成本，並可以少破壞樣品造成二次污染，可達到綠色化學與水資源永續利用之優勢。但該技術於水中重金屬監測之相關研究有限。本研究旨於探討利用高光譜遙測技術於水體中重金屬溶液監測之可行性，並嘗試探討該方法是否會受到以葉綠素與濁度為主環境基質之影響。研究中將利用多元線性回歸建立重金屬預測模型，並透過數據預處理，包含一階微分與對數轉換，以及利用離群值分析和篩選適合濃度範圍，進行重金屬預測模型優化。遙測實驗於室外進行(太陽能量需大於22k lux)，量測距離為30 cm，水樣為實驗室配置水樣，分別含有銅、鉛、鋅等重金屬，濃度範圍為100 to 2000 mg/L。實驗所獲得之高光譜數據，首先透過相關性分析以擷取各金屬溶液之特徵波長，分別為銅975、980、1016、1023、1041、1057、1065 nm、鉛979、1013、1027、1035、1052、1060、1068 nm和鋅943、965、999、1036、1041、1057、1065 nm。在高光譜數據之預處理，銅以原始數據為最佳，鉛跟鋅以對數轉換為最佳。研究中發現，當銅、鉛和鋅濃度範圍為100-2000 mg/L時，其決定係數(coefficient of determination, adjusted R2)分別為0.78、0.62、0.65，正規化均方根誤差(normalized root mean square error, NRMSE)分別為0.15、0.19、0.18，只有銅金屬之預測模型判斷係數與預測能力良好，其他兩種金屬決定係數與預測能力為普通；當進一步進行模式優化後，當銅溶液模型於100-1700 mg/L、鉛溶液模型於900-1900 mg/L、鋅溶液模型於700-1300 mg/L時，優化重金屬預測模型的adjusted R2與NRMSE達到高度預測能力，銅、鉛和鋅R2分別為0.84、0.87、0.93，NRMSE分別為0.12、0.17、0.10。這些研究結果顯示高光譜遙測方法於銅金屬溶液預測性佳，可歸因於溶液本身的藍色性值，然而，研究結果顯示，此研究方法可能有上限值，在高濃度濃度時，吸收與反射能量達到飽和狀態，進而造成預測結果不佳。在重金屬溶液添加環境基質(葉綠素濃度為0.22-0.88 μg/L與濁度濃度範圍51-204 NTU)之試驗中，明顯發現部分環境基質的特徵波長與重金屬圖譜相疊，但對於重金屬特徵波長之影響較小。

Hyperspectral remote sensing is widely used in environmental monitoring, which not only saves labor time for sampling and analysis, but also reduces use of chemicals for sample pretreatment. These advantages signify the concept of green chemistry and water sustainability. The demand for good quality, realtime, non-destructive and high resolution water quality information therefore has been growing in research areas of environmental monitoring. This study aims to investigate the feasibility of using hyperspectral remote sensing technique by visible-near infrared spectroradiometer (VNIR, FieldSpec HandHeld 2) for rapid water monitoring of heavy metals, followed by establishment of their prediction models. Heavy metals of lead (Pb), zinc (Zn) and copper (Cu) in the range of concentration between 100 to 2000 mg/L were selected as the target samples in this study. The influences of environmental parameters such as chlorophyll a (Chl-a) or turbidity on the spectral data were also investigated. The sensitive bands for the target metals were characterized in the range from 800 nm to 1075 nm, based on the reflectance spectral data. Spectral data for developing of the quantitative predictive model was first preprocessed with first derivative and logarithm transformation, followed by establishing of the prediction model using multivariate linear regression (MLR). The models were then optimized by removing outliers and screening of suitable concentration ranges. Logarithm transformation was determined as the best method for predicting Pb and Zn, whereas raw data can be used for predicting Cu. The optimized prediction model for Cu was found to have the highest coefficient of determination (adjusted R2) of 0.84 and least normalized root mean square error (NRMSE) of 0.12, while using the outputs of 7 sensitive bands (975, 980, 1016, 1023, 1041, 1057, 1065 nm) at range of 100-1700 mg/L. This result could be attributed to the blue color characteristic of the solution, whereas the others remain clear. In addition, the addition of environmental substances of Chl-a (0.22-0.88 μg/L) and turbidity (51-204 NTU) was found to have little intervention to the prediction models. All the information would be useful for future practical applications for identification of pollution sources of the study metals.

誌謝
摘要
ABSTRACT
目錄
圖目錄
表目錄
第一章 前言
1.1 研究背景
1.2 研究動機與目的
第二章 文獻回顧
2.1 重金屬之檢測
2.1.1 傳統方法
2.1.2 非破壞性光譜儀分析法
2.1.3 攜帶式高光譜儀(Hyperspectral Spectrometer)
2.2 遙測應用
2.2.1 衛星遙測應用
2.2.2 無人飛機(UAV)
2.3 水質參數經驗公式
2.4 環境基質對金屬之影響
2.5 金屬對環境與人體的影響
第三章 研究方法
3.1 研究內容
3.2 水樣配製
3.2.1 標準溶液配製方法
3.3 實驗儀器與分析方法
3.3.1 高光譜儀
3.3.2 ICP-AES分析儀
3.4 現地量測之光譜架設
3.5 特徵光譜篩選與數據處理
3.6 建立多元線性回歸預測模型
第四章 研究結果與分析
4.1 水體重金屬與環境基質(濁度與葉綠素)原始光譜圖
4.1.1 三種重金屬(銅、鋅、鉛)溶液反射率差異性
4.1.2 不同重金屬(銅、鋅和鉛)濃度變化
4.1.3 葉綠素溶液光譜圖
4.1.4 濁度溶液光譜圖
4.2 重金屬和環境基質(濁度與葉綠素)特徵波長之相關性分析
4.2.1 重金屬(銅、鋅、鉛)
4.2.2 葉綠素
4.2.3 濁度
4.3 重金屬(銅、鉛和鋅)添加環境基質(濁度與葉綠素)之特徵光譜影響
4.3.1 重金屬溶液添加葉綠素
4.3.2 重金屬溶液添加濁度
4.4 建立多元回歸預測模型
4.4.1 水中重金屬預測模式
4.4.2 優化水中重金屬預測模式
4.4.2.1 離群值分析與模型優化
4.4.2.2 適用濃度範圍篩選與模型優化
4.4.3 環境基質水質預測模式之建立
4.4.3.1 濁度
4.4.3.2 葉綠素
4.4.4 優化環境基質水質預測模式
4.5 優化水質預測模型之適用性探討
4.5.1 濁度對重金屬預測之影響
4.5.2 重葉綠素對重金屬預測的影響
4.5.3 重金屬對濁度預測之影響
4.5.4 重金屬對葉綠素預測之影響
第五章 結論與建議
5.1 結論
5.2 未來建議
第六章 參考文獻
附錄目錄
附錄一 重金屬水質預測模式-方程式
附錄二 葉綠素檢測方法

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