Efficient Partial Denitrification-Anammox Process Enabled by a Novel Denitrifier with Truncated Nitrite Reduction Pathway
ENVIRONMENTAL TECHNOLOGY & INNOVATION(2024)
Chinese Acad Sci
Abstract
Partial denitrification coupled with anaerobic ammonium oxidation (PD-anammox) is a promising technology for cost-effective nitrogen removal from wastewater. Nitrite availability is crucial to anammox performance but often limited by the slow partial denitrification process. Here we report an efficient PD-anammox system driven by the novel denitrifier Bacillus velezensis C1-3 with truncated nitrite reduction pathway. Whole-genome sequencing analysis revealed that the lack of nitrite reductase genes nirS/nirK and norBC in strain C1-3 enabled nitrite accumulation without the need for precise control of carbon dosage. By coupling it with anammox sludge, over 79% total nitrogen (TN) removal was stably achieved, under a TN loading rate of 660mg/L/d and a carbon/nitrogen ratio below 1.0. Mechanism explorations indicate that the niche differentiation of C1-3 and anammox bacteria facilitated their mutualism while avoiding nitrite competition. This study demonstrates a novel strategy for establishing efficient PD-anammox process by harnessing the unique metabolic deficiency of denitrifiers, shedding light on the development of stable and sustainable biological nitrogen removal technologies with minimal carbon footprint.
MoreTranslated text
Key words
Partial denitrification,Bacillus velezensis,Anammox,Whole genome sequencing
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined