Population identification and functional characterization of microbiomes in biological nitrogen removal system for landfill leachate

다국어 초록 (Multilingual Abstract)

In the case of landfill leachate, the amount generated is also increasing along with the annual amount of landfill waste, and legal regulations are being strengthened as well. As a result, the treatment of landfill leachate containing high concentrations of ammonia nitrogen, heavy metals, and non-degradable organic matter is becoming more important, and the treatment of high concentrations of nitrogen and non-degradable organic matter should be carried out using active sludge in the biological treatment process. At this time, since nitrogen in leachate is removed by denitrification and nitrification of ammonium oxide and nitrite oxide bacteria, microbiological research is essential, and recalcitrant organic substances are known to affect the denitrification process, so research is needed to confirm the stable performance of the nitrogen removal process.
Therefore, this study analyzed the relative abundace and metabolic capabilities of nitrogen removal-related microorganisms and the effect on the denitrification of humic substances whether biological nitrogen removal in the current treatment plant is being carried out stably. To this end, a microbiological study was conducted through Metagenome shotgun analysis, which can analyze the entire microbial community and functional genes in the sample by collecting raw leachate and biological process leachate from the treatment basin in the Sudokwon landfill.
As a result of metabolic function analysis in all samples, metabolism was the most dominant function, and Nitrogen Metabolism analysis indicated that at least 20% of the denitrification capacity was always maintained in the denitrification basin, indicating a constant denitrification function in the biological treatment basin. Through microbial community analysis, it was also possible to identify the correlation between COD, NH4-N, and T-N removal efficiencies and the relative abundance of denitrifying and nitrifying microorganisms. Correlations between narG and nosZ relative abundance were difficult to determine, however. In the case of humic materials, there was a difference in the amount of occurrence by period due to the changing temperature and rainfall, and it increased the most in summer. Based on microbial community analysis and genetic analysis, denitrification efficiency improves with an increase in humic substances in the biological treatment basin, and even during the summer when nitrogen removal efficiency decreases most, it appears to maintain the denitrification efficiency. By examining microbiological studies, these results confirm that the biological treatment process for nitrogen removal in leachate is stable regardless of time-varying factors.

국문 초록 (Abstract)

매립지에서 발생하는 침출수의 경우, 매립 폐기물의 연간 발생량 증가와 더불어 침출수 발생량 또한 함께 증가하는 추세를 보이고 있으며 법적 규제 또한 강화되고 있다. 이에 따라, 고농도의 암모니아성 질소, 중금속, 난분해성 유기물 등을 포함한 채로 처리장으로 유입되는 매립지 침출수의 처리는 더욱 중요해지고 있으며, 생물학적 처리 공정 내 활성 슬러지를 이용하여 고농도의 질소와 난분해성 유기물질 등을 처리함으로써 더욱 강화되고 있는 법적 규제를 충족시켜 방류해야 한다. 이 때, 침출수 내 질소는 암모늄 산화균과 아질산염 산화균의 탈질 및 질산화에 의해 제거되기 때문에 미생물학적 연구가 필수적이며, 난분해성 유기물질은 탈질 과정에 영향을 미치는 것으로 알려져 있어 질소 제거 공정의 안정적 수행 여부 확인을 위해 연구가 필요하다고 보았다.
따라서 본 연구에서는 질소 제거 관련 미생물의 군집 및 대사 능력 분석 그리고 난분해성 물질에 해당하는 휴믹 물질의 거동 및 탈질에 대한 영향을 분석하여 현 처리장 내 생물학적 질소 제거가 원활히 이루어지고 있는지 살펴보고자 하였다. 이를 위해 수도권매립지 침출수 처리장의 원수 및 생물학적 공정 처리수를 채취하여 시료 내 전체 미생물 군집 및 기능유전자 분석이 가능한 Metagenome shotgun analysis를 통해 분자미생물학적 연구를 수행하였다.
연구 결과, 대사 기능 분석을 통해 Metabolism은 모든 시료에서 가장 우세한 기능이였으며, 그 중 Nitrogen Metabolism을 분석한 결과 탈질-질산화조에서 탈질 능력이 항상 약 20% 이상 유지됨을 확인하여 해당 생물학적 처리조 내의 탈질 기능은 일정하게 유지되고 있음을 추정할 수 있었다. 또한, 미생물 군집 분석을 통해 얻은 탈질 및 질산화 미생물의 상대적 풍부도와 COD, NH4-N, T-N 제거 효율 간의 상관관계 분석을 통해 관련 미생물 규명이 가능하였으나 탈질유전자 narG와 nosZ의 상대적 풍부도와의 상관관계는 확인하기 어려운 것으로 밝혀졌다. 휴믹 물질의 경우, 변화하는 기온과 강우량에 의해 시기별 발생량 차이가 나타났으며 여름철에 가장 증가하였다. 미생물 군집 및 유전자 분석 결과 휴믹 물질 증가에 따라 생물학적 반응조 내 탈질 성능 또한 향상되었으며, 이에 따라 질소 제거 효율이 가장 감소하는 여름철에도 탈질 효율이 유지되는 것으로 보인다. 최종적으로 본 연구는 침출수 내 질소 제거를 위한 생물학적 처리 공정이 시기별로 변동하는 인자와 상관없이 안정적으로 수행되고 있음을 미생물학적 연구를 통해 확인하였다.

목차 (Table of Contents)

• TABLE OF CONTENTS························································· i
• LIST OF FIGURES································································ iii
• LIST OF TABLES································································· v
• ABSTRACT········································································· vi
• Chapter 1 Introduction··························································· 1
• 1.1 Research background······················································· 1
• 1.2 Research objectives························································· 7
• Chapter 2 Materials and methods ············································· 8
• 2.1 Leachate and activated sludge sampling ······························· 8
• 2.2 Analytical methods························································ 9
• 2.3 DNA extraction and Metagenome shotgun sequencing ·············· 11
• 2.4 Bioinformatic analysis···················································· 12
• 2.5 Statistical analysis ························································· 13
• Chapter 3 Results and discussion ············································ 14
• 3.1 Microbial population identification ······································· 14
• 3.2 Metabolic function analysis ················································ 18
• 3.3 Nitrogen metabolism-related genes analysis ····························· 22
• 3.4 Correlation between biological removal efficiency and denitrifying genes 23
• 3.5 Identification of the taxanomy using denitrifying genes ··············· 26
• 3.6 Correlation between biological removal efficiency and microbiomes 27
• 3.7 Influence of humic substances on denitrification ······················· 29
• Chapter 4 Conclusions ··························································· 37
• References ··········································································· 40
• Abstract in Korean ································································ 49