Developing a platform for rapid online DNA monitoring of microbial communities in wastewater treatment systems

该拟议项目是与大温哥华的合作伙伴关系，旨在开发改进的技术，用于检测和减轻其污水处理厂（WWTP）二级处理过程中的问题微生物，以优化处理性能，可靠性，并最终优化环境水质。污水处理厂提供城市水循环的恢复功能，通常包括去除颗粒碳材料的初级处理和去除可溶性有机化合物以及氮和磷等营养物质的二级处理。二级废水处理完全依靠微生物来降解废水中存在的不同形式的污染，并尽量减少对接收沃茨的影响。然而，二级废水处理中的某些微生物可能会因起泡或沉降不良（膨胀）而导致工艺不稳定和混乱。该项目将侧重于开发使用下一代便携式DNA测序技术的协议，以快速分析废水微生物群落。通过积极的微生物监测使用这些新兴的检测方法，它将有可能获得早期预警信号时，有问题的微生物正在增长，并制定相应的战略，调整二级工艺操作，以满足规定的污水标准，并保持生态系统的健康在加拿大的环境接收沃茨。该项目将支持下一代环境工程师的培训，让4名研究生参与应用基因组学、生物信息学和数据科学的创新解决方案。

This proposed project is a partnership with Metro Vancouver to develop improved technologies for the detection and mitigation of problematic microorganisms in secondary treatment processes at their wastewater treatment plants (WWTPs), with a view to optimizing treatment performance, reliability, and ultimately environmental water quality. WWTPs provide the restorative function of the urban water cycle, and typically include primary treatment to remove particulate carbon materials, and secondary treatment to remove soluble organic compounds as well as nutrients like nitrogen and phosphorus. Secondary wastewater treatment is entirely dependent on microorganisms to degrade different forms of pollution present in wastewater, and to minimize impacts on receiving waters. However, certain microorganisms in secondary wastewater treatment can cause process instability and upsets due to foaming or poor-settling (bulking). This project will focus on developing protocols using next-generation portable DNA sequencing technology to rapidly profile wastewater microbial communities. Through active microbial monitoring using these emerging detection methods, it will be possible to gain early warning signals for when problematic microorganisms are growing, and develop strategies to tune secondary process operation accordingly to meet regulated effluent standards and maintain ecosystem health in Canada's environmental receiving waters. This project will support the training of the next-generation of environmental engineers by involving 4 graduate students in innovative solutions applying genomics, bioinformatics, and data science.