河口海岸環境的懸浮顆粒物質絮凝行為受到環境的生物地化過程作用下，隨時空演變進行相當複雜的動力過程。為探討絮凝機制變動過程，本研究以生物-礦物絮凝模式(Maggi, 2009)為基礎，模擬探討不同天氣與季節條件下的絮凝行為。為模擬真實膠羽顆粒的季節性變化，模式將潮波耦合紊流對膠羽動力的影響納入考量，並透過生物影響選擇模式參數。本研究利用比利時近岸高濁度海域(北海南部)實測膠羽粒徑、流速、紊流與懸浮顆粒物質濃度等資料，進行模式的參數化校驗與驗證工作。結果顯示，波浪作用在波-潮所主導的絮凝環境中至為重要。因此，在模式計算的動力條件中，必須納入波潮耦合的紊流動力。此外，本研究亦透過膠羽強度確認生物作用對膠羽發展的季節性影響。
本研究採用實驗設計法針對絮凝模式中的九個模式參數進行分析討論，解析模式參數的敏感度，並尋求符合模擬條件的最優化參數組。經測試分析結果顯示，最具影響力的五個模式參數包括為膠羽初始粒徑、膠羽碎形維度、無因次聚集校驗參數、無因次破碎校驗參數及膠羽強度等五個。其中，聚集參數與膠羽強度會提高膠羽聚集機制，在夏季(四月至九月)較冬季(十月至隔年三月)佔優勢，破碎參數則在冬季較為重要。相對於生物活力較弱的冬季，夏季可觀測到較強的膠羽鍵結強度。，因為在這個季節中，絮凝機制受到旺盛生物生長所分泌的粘性有機物質所影響。在本研究中，膠羽強度根據季節採用不同的變化值，模擬結果重現了真實膠羽顆粒的季節性演變(Fettweis et al., 2014)，顯示生物影響可藉由不同季節膠羽強度的設定納入模式。
本研究探討與時間有關的特徵粒徑(D50)膠羽，故在單峰分布的膠羽模擬效能會比多峰粒徑分布的模擬效能為佳。雖然，採用多峰粒徑分布模式在某些條件下亦可改善模擬效能，但必須耗費更多的計算時間。另一方面，用來量測現場粒徑分布的雷射粒徑分析儀(LISST)，除有量測上的不確定性外，也可能因測量到非膠羽顆粒(如砂粒徑)而造成粒徑分布的偏差，在實測的風暴潮環境下實測的粒徑分布中已可觀察到這些現象。
本研究亦利用現場長期監測資料進行模式驗證。模擬果顯示，膠羽粒徑變化會隨著現場環境(天氣與季節)而變動。為模擬膠羽行為，模式應用上必須將天候條件納入最優化參數最優化的選擇的操作中。此外，與生物作用有關的膠羽強度已根據季節採用不同的變化值，使得生物影響效應參數化為與時間(夏季與冬季)有關的函數，未來應用上建議與光強度、溫度或營養鹽等環境因子進行關聯性分析。

Flocculation of Suspended Particulate Matter (SPM) in estuarine and coastal environments is a complex process that is influenced by physical, biological, and chemical mechanisms. The bio-mineral flocculation model of Maggi (2009) used in this study was adapted to simulate flocculation under various weather conditions and during different seasons. The adaptation incorporated the effect of tide–wave-combined turbulence on floc dynamics and the choice of model parameter in order to simulate biological effects that are responsible for the observed seasonal variation in floc size. The model was calibrated and validated using in situ data of floc size, current velocity, turbulence and SPM concentration from the high turbid Belgian nearshore area (southern North Sea). The results show that tide-wave-combined turbulence needs to be incorporated when simulating flocculation in a tide-wave-dominated environment. Additionally, the results confirmed that floc strength has a seasonal influence on floc development.
The flocculation model has nine parameters that have to be determined. In order to investigate the sensitivity of these parameters on the model output and to find an optimized parameter set for the model, the Design of Experiment method was used. The results have shown that the most sensitive parameters are the primary particle size, fractal dimension, aggregation, breakage and floc strength. The aggregation parameter and the floc strength, which both enhance aggregation, are more dominant in summer than in winter. On the contrary, the breakage parameter is more important in winter. A stronger floc-binding strength was observed in the summer season (April–September), during which flocculation was influenced by abundant sticky organic substances, compared with the weak-biomass winter season (October–March). This seasonal variation in floc size (Fettweis et al., 2014) was reproduced in the model using varying values for various floc strengths in different seasons. The results revealed that the biological effect should be incorporated in the model by using floc strength values optimized for seasons.
The flocculation model uses a single characteristic diameter (i.e. D50) as time-dependent variable. Because of this the model performs better when the Particle Size Distribution (PSD) of the flocs is unimodal. In case of multimodal PSDs the model output is less accurate. Using a multimodal on flocculation model could possibly improve results during certain conditions but may cost computation time. On the other hand, the LISST instrument, which was used to measure the in situ PSD, may, besides of the measuring uncertainty, also capture particles that are not flocs (sand grains) and that can show up as additional modes in PSD during e.g. storm conditions.
The model was further validated using long-term in situ data. The data set shows variations in floc size that depend on weathers and seasons. In order to simulate the floc behavior, season and weather optimized parameter sets need to be included. Currently the biological effects are parameterized as a function of time (summer-winter). This could be refined in future studies by using e.g. light intensity, temperature or the coupling with a biological (nutrient dynamics) model.

目　錄 Contents
論文審定書 i
論文公開授權書 ii
誌謝 iii
摘要 v
Abstract vii
目錄 Contents x
圖次 Figures xiv
表次 Tables xx
第一章 前言 Introduction 1
1.1 河口海岸環境懸浮顆粒物質的重要性 The importance of suspended (particulate matters in estuarine and costal environments) 1
1.2 動機與目的 (Motivation and Objective) 4
1.3 論文大綱 (Outline) 4
第二章 文獻回顧 Literature surveys 5
2.1 懸浮顆粒物質的特性 (The characteristic of suspended particulate matters) 5
2.1.1 膠羽組成 (Floc components) 5
2.1.2 膠羽結構 Floc structure 6
2.1.3 膠羽密度與沉降速度 (Floc density and settling velocity) 9
2.2 懸浮顆粒物質的絮凝機制(SPM Flocculation) 13
2.3 懸浮顆粒物質絮凝機制的影響因素 (The influenced factors of SPM flocculation) 16
2.3.1 紊流影響 (Turbulence) 16
2.3.2 懸浮顆粒物質濃度 (Concentrations of suspended particulate matters ) 17
2.3.3 有機物質含量 (Organic matter contents) 17
2.3.4 鹽度 (Salinity) 18
2.4 絮凝數值模式的發展 (The development of flocculation numerical model) 20
第三章 觀測資料 Observations 22
3.1 研究場址 (Research sites) 22
3.2 觀測儀器 (Instruments) 26
3.3 資料概況 (Data) 27
3.3.1 歷年船測資料 (Vessel data) 29
3.3.2 2008年Tripod資料 (Tripod data collected from 2008) 30
3.3.3 2011年Tripod資料 (Tripod data collected from 2011) 37
3.4 觀測因子間的關係 (The relationships between observed factors) 43
第四章 研究方法 Methodology 46
4.1 絮凝模式 (Flocculation Model) 46
4.1.1 模式原理 (Model description) 46
4.1.2 潮波耦合引致的水動力系統 (Combined Dynamic of Tides and Waves) 49
4.2 模式參數優化分析 (Analysis of the model parameter optimization) 53
4.2.1 實驗設計 (Design of Experiment, DOE) 53
4.2.2 反應曲面法 (Response surface methodology, RSM) 55
4.2.3 因子設計 (Factorial Design) 58
4.2.4 陡升(降)路徑法 (method of steepest ascent/descent) 60
4.2.5 中心混層設計(Central composite design, CCD) 61
4.2.6 模式參數優化的統計檢驗 (Statistical test of model parameter optimization) 62
第五章 絮凝機制模擬 Flocculation simulations 63
5.1 模式參數優化之實驗設計 (Design Experiment for model parameter optimization) 63
5.1.1 資料說明 (Data) 63
5.1.2 模式參數選擇及其範圍設定 (Chosen model parameters and their test ranges ) 65
5.1.3 分析流程 (Analysis procedure) 68
5.2 優化分析結果 (Results of the Optimal analysis) 69
5.2.1 初始模式參數條件下之不同水動力系統模擬 (Simulations with default parameter setting under different hydrodynamic systems) 69
5.2.2 不同天氣條件之優化模式參數 (Optimization settings of model parameters under different weathers) 71
5.2.3 小結 (Summaries) 81
5.3 長期SPM絮凝機制模擬 (Long-term SPM flocculation simulation) 83
5.3.1 冬季模擬 (Winter simulations) 83
5.3.2 夏季模擬 (Summer simulations) 87
5.3.3 小結 (Summaries) 91
第六章 結論與建議 Conclusions and Suggestions 95
6.1 結論 (Conclusions) 95
6.2 建議 (Suggestions) 98
參考文獻 References 99
個人著作 Publications 108
 
圖　次
圖 1-1 懸浮顆粒物質沉積物在河口海岸環境之生物化循環與環境間的關係 (Modified from Wakeham and Lee 1993) 3
圖 2-1 懸浮顆粒物質組成 6
圖 2-2 膠羽的多峰粒徑分布(multimodal PSD)及膠羽聚集物之四個粒徑級別，包括初始粒徑(primary particles)、絮狀物(flocculi)、微膠羽(microflocs)以及大型膠羽(macroflocs) (資料來源:Lee et al., 2012) 8
圖 2-3 碎形幾何概念(資料來源: Verney et al., 2011) 8
圖 2-4 不同類型的膠羽粒徑與其密度的關係(資料來源: Gibbs, 1983) 12
圖 2-5 懸浮顆粒絮凝機制概念圖 14
圖 2-6 懸浮顆粒物質絮凝機制從水中至底床的傳輸過程 (資料來源: Mehta, 1989) 15
圖 3-1 本研究之研究場址(北海南部)與觀測站位置 24
圖 3- 2 中尺度影像光譜儀(Moderate Resolution Imaging Spectroradiometer，MODIS)之2002至2009年的影像所獲得的不同風向之北海南部海域平均表面懸浮顆粒物質(SPM)濃度(mg L-1）分布圖：(A)西南風向、(B)東北風向、(C)西北風向及(D)東南風向。星符號為觀測站Blankenberge (BLA)位置。(資料來源: Fettweis et al., 2014) 24
圖 3-3 冬季(10月至隔年3月，上圖)和夏季(4至9月，下圖)的平均表層懸浮顆粒濃度(mg L-1，左)和葉綠素濃度(Chl，10-3 mg L-l，右)之分布圖。資料來自於MERIS衛星觀測所得，十字符號代表觀測站BLA位置。(資料來源: Fettweis et al., 2014) 25
圖 3-4(a) 2008年2月2－11日BLA觀測站tripod系統的現場資料時序列圖。由上至下之觀測項目分別為膠羽顆粒(D50, μm)、0.2 and 2 mab懸浮顆粒物質濃度(mg L−1)、風速 (m s−1)與風向(度，degree)、示性波高 (Hs, m)、流速與波浪軌跡速度 (m s−1)、紊流剪應率(s−1)及水深(m)。 32
圖 3-4(b) 2008年3月8－27日BLA觀測站tripod系統的現場資料時序列圖。由上至下之觀測項目分別為膠羽顆粒(D50, μm)、0.2 and 2 mab懸浮顆粒物質濃度(mg L−1)、風速 (m s−1)與風向(度，degree)、示性波高 (Hs, m)、流速與波浪軌跡速度 (m s−1)、紊流剪應率(s−1)及水深(m)。 33
圖 3-4(c) 2008年4月20－30日BLA觀測站tripod系統的現場資料時序列圖。由上至下之觀測項目分別為膠羽顆粒(D50, μm)、0.2 and 2 mab懸浮顆粒物質濃度(mg L−1)、風速 (m s−1)與風向(度，degree)、示性波高 (Hs, m)、流速與波浪軌跡速度 (m s−1)、紊流剪應率(s−1)及水深(m)。 34
圖 3-5(a) 2008年期間各觀測組別在一潮週期下之時(hourly)粒徑分布圖。從上至下、由左而右依序為冬季平日之 2月3－4日(單峰粒徑分布)、2月8－11日(多峰粒徑分布)及3月8－10日(多峰粒徑分布)；夏季平日之4月20－25(多峰粒徑分布)日及4月26－30日(單峰粒徑分布)。 35
圖 3-5(b) 2008年期間各觀測組別在一潮週期下之時(hourly)粒徑分布圖。從上至下、由左而右依序為西南風暴潮期間之2月1－2日(單峰粒徑分布)、2月5－7日(單峰粒徑分布)及3月10－13日(單峰粒徑分布)；東北風暴潮期間之3月16－19日(多峰粒徑分布)及3月21－25日(多峰粒徑分布)。 36
圖 3-6(a) 2011年冬季期間(2月10日至3月3日) MOW1觀測站tripod系統的現場資料時序列圖。由上至下之觀測項目分別為膠羽顆粒(D50, μm)、0.2 and 2 mab懸浮顆粒物質濃度(mg L−1)、風速 (m s−1)與風向(度，degree)、示性波高 (Hs, m)、流速與波浪軌跡速度 (m s−1)、紊流剪應率(s−1)及水深(m)。 39
圖 3-6(b) 2011年夏季期間(4月29日至5月11日) MOW1觀測站tripod系統的現場資料時序列圖。由上至下之觀測項目分別為膠羽顆粒(D50, μm)、0.2 and 2 mab懸浮顆粒物質濃度(mg L−1)、風速 (m s−1)與風向(度，degree)、示性波高 (Hs, m)、流速與波浪軌跡速度 (m s−1)、紊流剪應率(s−1)及水深(m)。 40
圖 3-7(a) 2011年冬季期間之觀測組別在一潮週期下之時(hourly)粒徑分布圖。從上至下、由左而右依序為2月10－12日(多峰粒徑分布)、2月13－16日(單峰粒徑分布)、2月17－26日(多峰粒徑分布)及2月27日－3月2日(多峰粒徑分布)。 41
圖 3-7(b) 2011年夏季期間之觀測組別在一潮週期下之時(hourly)粒徑分布圖。由上至下、由左而右依序為4月29日－5月4日(單峰粒徑分布)、5月5－8日(多峰粒徑分布)及5月9－11日(多峰粒徑分布)。 42
圖 3-8 濁度(OBS)與懸浮顆粒濃度(SPM)關係 44
圖 3-9 冬季期間，濁度(OBS)與懸浮顆粒濃度(SPM)關係 44
圖 3-10 夏季期間，濁度(OBS)與懸浮顆粒濃度(SPM)關係 45
圖 3-11 懸浮顆粒濃度(SPM)中的有機物質含量比例() 45
圖 3-12 冬季期間，懸浮顆粒濃度(SPM)中的有機物質含量比例() 45
圖 3-13 夏季期間，懸浮顆粒濃度(SPM)中的有機物質含量比例(v) 45
圖 3-14 平靜天候下，流速(U)與柯莫葛洛夫微小長度尺寸()關係 45
圖 3-15 風暴潮期間，流速(U)與柯莫葛洛夫微小長度尺寸()關係 45
圖 4-1 反應曲面示意圖，假設溫度與壓力式影響產品良率的主要因子，利用反應曲面法找到達到最高良率的因子值(資料來源： Montgomery, 2017)。 56
圖 4-2 反應曲面設計流程圖(資料來源: Box and Wilson, 1951) 57
圖 4-3 因子設計概念圖 59
圖 4-4 陡升路徑示意圖 (資料來源: Box and Wilson 1951) 60
圖 4-5 中心混層設計概念圖，其中紅色點為星點，黑色點為原測試點。 61
圖 5-1 2008年冬季期間絮凝模擬時序列圖。由上至下分別為2月3－4日、2月8－11日、及3月8－10日。圖中標記包括:紅點為實測資料、菱形為純潮汐引致動力系統之初始模式參數值條件的模擬結果、虛線為考慮波潮耦合動力系統之初始模式參數條件的模擬結果、實線為波潮耦合動力系統之優化模式參數組條件的模擬結果。 76
圖 5-2 2008年夏季期間之絮凝模擬時序列圖。由上至下分別為4月20－25日及4月26－30日。 76
圖 5-3 2008年西南風暴潮期間之絮凝模擬時序列圖。由上至下分別為2月1－2日、2月5－8日及3月10－13日。圖中標記包括:紅點為實測資料、菱形為純潮汐引致動力系統之初始模式參數值條件的模擬結果、虛線為考慮波潮耦合動力系統之初始模式參數條件的模擬結果、實線為波潮耦合動力系統之優化模式參數組條件的模擬結果。 77
圖 5-4 2008年東北風暴潮期間之絮凝模擬時序列圖。由上至下分別為3月16－19日及3月21－25日。 77
圖 5-5 不同水動力系統作用下之不同天氣條件的膠羽顆粒實測資料與模擬結果比較圖。上為僅受潮汐作用的水動力系統，下為波潮耦合的水動力系統。由左至右分別為冬季平日、夏季平日、西南風暴潮及東北風暴潮期間。 78
圖 5- 6 僅受潮汐引致之水動力系統作用下，不同天氣條件(由左至右為冬季平日、夏季平日、西南風暴潮及東北風暴潮)的紊流長度(Kolmogorov microscale)與不同體積率比較圖。上排為紊流長度與聚集體積率(dVaggregationVfield)比較圖，下排為與破碎體積率(dVbreakageVfield)的比較圖。 79
圖 5- 7 受波潮耦合水動力系統作用下，不同天氣條件(由左至右為冬季平日、夏季平日、西南風暴潮及東北風暴潮)的紊流長度(Kolmogorov microscale)與不同體積率比較圖。上排為紊流長度與聚集體積率(dVaggregationVfield)比較圖，下排為與破碎體積率(dVbreakageVfield)的比較圖。 80
圖 5-8 2011年冬季觀測時間之絮凝顆粒模擬結果時序圖。其中，紅點為實測資料、虛線為考慮潮汐引致之水動力系統、實線為考慮波潮耦合系統下，使用冬季平日優化參數組設定。 86
圖 5-9 不同時期之2011年冬季絮凝顆粒模擬結果(上圖為2月17－26日模擬結果，下圖為2月27－3月3日模擬結果)時序圖。其中，紅點為實測資料、虛線為考考慮波潮耦合系統下，使用冬季平日優化參數組設定、實線為考慮波潮耦合系統下，使用東北風暴潮優化參數組設定。 86
圖 5-10 2011年夏季絮凝顆粒模擬結果時序圖。其中，紅點為實測資料、虛線為考慮潮汐引致之水動力系統、實線為考慮波潮耦合系統下，使用冬季平日優化參數組設定。 89
圖 5-11 2011年4月29日至5月4日絮凝顆粒模擬結果時序圖。其中，紅點為實測資料、虛線為考慮潮汐引致之水動力系統，使用夏季平日優化參數組設定、實線為考慮波潮耦合系統下，使用東北風暴潮優化參數組設定。 90
 
表　次
表 3-1 本研究所選用之2003-2011年期間於北海南部近岸海域的觀測資料 28
表 3-2 2003-2006年船測資料之分析項目 29
表 3-3 模式參數最適性分析選用之2008年觀測資料的環境資訊 31
表 3-4 季節性絮凝機制模擬使用之2011年觀測資料的環境資訊 38
表 4-1 絮凝模式的變數定義 51
表 4-2 模式參數之初始設定值 52

表 5-1 模式參數優化性分析所選用的組別 64
表 5-2 模式參數優化分析中選用的模式參數及其範圍 67
表 5-3 2008年觀測資料在不同動力系統的模擬結果與觀測資料的均方根誤差比較表 74
表 5-4 2008年期間不同天候條件下之絮凝模擬優化模式參數值 75
表 5-5 冬季(2011年)絮凝機制模擬效能 95
表 5-6 夏季(2011年)絮凝機制模擬效能 99

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