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系統識別號 U0026-0708201910382800
論文名稱(中文) 微生物硝化功能評估生物指標:以實場煉焦廢水生物除氮系統為例
論文名稱(英文) Biological indicators for assessment of the nitrification activity: using a full-scale biological nitrogen removal process for coke wastewater as an example
校院名稱 成功大學
系所名稱(中) 環境工程學系
系所名稱(英) Department of Environmental Engineering
學年度 107
學期 2
出版年 108
研究生(中文) 林如樺
研究生(英文) Ju-Hua Lin
學號 P56064128
學位類別 碩士
語文別 英文
論文頁數 73頁
口試委員 指導教授-黃良銘
口試委員-林財富
口試委員-童心欣
口試委員-林志高
中文關鍵字 硝化活性評估  生物性指標  amoA mRNA  氨氧化細菌  硝化抑制作用  煉焦廢水 
英文關鍵字 Nitrification activity assessment  Biological indicator  amoA mRNA  Ammonia-oxidizing bacteria  Nitrification inhibition  Coke wastewater 
學科別分類
中文摘要 生物廢水處理系統是處理煉焦廢水中一個經濟性的方法,煉焦廢水含有酚類有機物以及非有機物組成包含氰離子、硫氰根離子與氨氮。一旦未經處理的廢水排放至環境中,這些汙染物可能會對生態與河川水質造成破壞。
而分子生物學中的各種生物指標有助於我們測量與評判生物廢水處理系統中的生物功能。生物指標包含基因含量、基因表現與微生物組成等。在硝化反應當中,氨氧化菌可使用氨氮藉由氨氧化反應產生亞硝酸並得到能量。amoA是氨氧化反應中的一個重要基因,它參與了氨氮轉為羥胺的步驟。
為了使用生物處理方法處理煉焦廢水,氮的去除包含硝化、脫硝作用,以及有機物的降解是必要的。然而依賴自營作用的硝化菌生長速率緩慢而使硝化作用在煉焦廢水處理系統中相對敏感且容易被抑制。
為了降低煉焦廢水處理系統因硝化抑制而失常的頻率,本研究目的在於建立分子生物指標以評估實場硝化活性,並藉由實場水質變化檢核其作為生物活性評估指標之適用性。分子生物指標包括使用定量型聚合酶鏈鎖反應分析amoA基因含量、mRNA表現量以及nitrobacter 16S rRNA and nitrospira 16S rRNA的基因含量,以及使用終端限制片段長度多態性與定序分析氨氧化族群組成。
藉由分析實場廢水處理程序中硝化槽的水質,可發現氨氮的去除率會伴隨高氨氮濃度的進流而降低,在不同的進流氨氮濃度下,氨氧化菌群數量和amoA mRNA表現保持穩定,但氨氧化菌族群則會隨之變化。例如具有終端限制片段長度 F491/R491的氨氧化菌 Nitrosomonas eutropha可在高氨氮後期被馴養。由硝化批次試驗則可得知:400 mg/L苯酚會與硝化作用產生競爭性抑制,但隨著苯酚降解而迅速恢復,而175 mg/L喹啉則會引起顯著的氨氧化抑制,主要藉由抑制amoA mRNA基因的表現導致比亞硝生成速率的降低。整體而言,在批次試驗中,amoA mRNA表現的生成曲線與強度能幫助我們了解汙泥活性,並且釐清抑制物對硝化活性的抑制機制,進一步應用於實場中。
英文摘要 Biological wastewater treatment plant (WWTP) is an economic way commonly used for nitrogen removal in coke wastewater which contained phenolic organic compounds and inorganic constituents like cyanides, thiocyanate and ammonia. When untreated wastewater released to environment, these pollutants may hurt to ecology and caused deterioration of water quality in river.
In order to assess the function of biological WWTP, many indicators from molecular techniques could help us to make decisions, including quantity of genes (DNA level), expression of genes (RNA level), the microorganism community, etc. During nitrification, ammonia-oxidizing bacteria (AOB) can obtain energy by oxidizing ammonia to nitrite. As one of the important functional gene for ammonia oxidation, amoA is involved in the reaction converting ammonia to hydroxylamine.
Biological treatment for coke wastewater requires serial nitrogen removal process, e.g., nitrification and denitrification, and organic chemical degradation. Among them, nitrification is relatively sensitive due to the slow growth rate of autotrophic microorganism and can be easily inhibited.
In order to reduce the frequency of abnormalities on nitrification in the coking WWTP, the routine assessment of nitrification activity is necessary. The objective of this research is to develop molecular biological indicators, such as monitoring of amoA mRNA, for nitrification activity in full-scale WWTP and verify its applicability by incorporating water quality. As for the molecular indicators, quantitative PCR (qPCR) was used to quantify the genes of amoA, nitrobacter 16S rRNA and nitrospira 16S rRNA, while terminal restriction fragment length polymorphism (T-RFLP) and sequencing was used to analysis AOB community composition.
By monitoring the water quality in nitrification tank of the full-scale WWTP, it could be found that ammonia removal efficiency decreased from 72% to 13% when increasing ammonia concentration from 38 mg N/L to 104 mg N/L. AOB population and amoA mRNA expression maintained stable but AOB community change along with the elevation of influent ammonia concentration. For example, Nitrosomonas eutropha-like AOB strain with T-RF F491/R491 was domesticated after the adaptation of high concentration of ammonia. Batch results indicates that nitrification might be reduced by 400 mg/L of phenol through competitive inhibition though it could be recovered rapidly after phenol degradation and by 175 mg/L of quinoline due to the suppression on amoA mRNA which leads to the severe decrease in the specific nitrite production rate. In summary, monitoring the expression pattern and strength of amoA mRNA in nitrification batch tests could help us to assess the nitrification activity and clarify the inhibitors and its mechanism, showing that molecular indicator could be a useful tool to evaluate biological activity in the full-scale WWTP.
論文目次 Table of content
摘要 I
Abstract III
Acknowledgements V
List of Tables XI
List of Figures XIII
Chapter 1 Introduction 1
Chapter 2 Literature Review 3
1-1 Biological treatment system 3
1-2 Biological indicators 4
2-2-1 Micro scale and molecular scale 4
2-2-2 Biological indicators assessment on biological WWTP activity and applications 5
2-3 Nitrogen cycle and ammonia oxidation 10
2-3-1 Nitrogen cycle 10
2-3-2 Ammonia oxidation and ammonia-oxidizing bacteria 11
2-4 Ammonia oxidation inhibition 14
2-5 Coke wastewater and problem of treatment 18
Chapter 3 Materials and Methods 20
1-3 Research framework 20
1-4 Full-scale coke wastewater treatment system 21
1-5 Nitrification batch test 22
3-4 Water quality analysis 26
3-4-1 General water quality analysis 26
3-4-1 Instrumental analysis 26
3-5 Biological indicator measurement 27
3-5-1 Sample collection and store 27
3-5-2 DNA extraction 27
3-5-3 RNA extraction and reverse transcription 29
3-5-4 Real-time polymerase chain reaction 31
3-5-5 Terminal restriction fragment length polymorphism 35
3-5-6 Cloning library 36
Chapter 4 Results and Discussion 39
4-1 Comparison between biological indicators and water quality change in full-scale coke WWTP 39
4-1-1 Water quality indicators performance in WWTP 39
4-1-2 Biological indicators performance in WWTP 46
4-2 amoA mRNA expression under different initial ammonia and inhibitory effect in batch test 49
4-2-1 Nitrification batch test 1 49
4-2-2 Nitrification batch test 2 54
4-2-3 Comparing between batch test 1 and batch test 2 57
4-3 AOB community analysis from amoA gene and amoA mRNA expression 61
4-3-1 Community change of AOB in full-scale coke wastewater treatment system 61
4-3-2 amoA mRNA expression from different AOB species in batch test 64
Chapter 5 Conclusions and Suggestions 67
Chapter 6 References 69

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