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

这个拟议的项目是与大温哥华地区建立伙伴关系，开发改进的技术，用于在其污水处理厂（WWTPs）的二级处理过程中检测和减少有问题的微生物，以优化处理性能、可靠性，并最终改善环境水质。污水处理厂提供了城市水循环的恢复功能，通常包括去除颗粒碳物质的一级处理，以及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赖于微生物降解废水中存在的不同形式的污染，并尽量减少对接收水的影响。然而，二级废水处理中的某些微生物会由于起泡或沉淀不良（膨胀）而导致过程不稳定和紊乱。该项目将重点开发使用下一代便携式DNA测序技术快速分析废水微生物群落的方案。通过使用这些新兴的检测方法对微生物进行主动监测，当有问题的微生物生长时，将有可能获得早期预警信号，并制定相应的策略来调整二级工艺操作，以满足规定的排放标准，并保持加拿大环境接收水域的生态系统健康。该项目将通过4名研究生参与基因组学、生物信息学和数据科学的创新解决方案，支持下一代环境工程师的培训。

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.