Developing an Event Prediction and Correction Framework for Microbial Management in Drinking Water Systems.

开发饮用水系统微生物管理的事件预测和纠正框架。

• 批准号: EP/K035886/1
• 负责人: Ameet Pinto
• 金额: $ 12.31万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK035886%2F1
• 关键词: Developing; Event; Prediction; Correction; Framework

Drinking water is teeming with microbial life. In fact, drinking water can contain anywhere from millions to hundreds of millions of microbial cells per litre, all with extremely different evolutionary histories and abilities. For example, microbes can (1) affect human health by causing diseases, (2) corrode infrastructure, and (3) also deteriorate the aesthetic quality of water. In an effort to limit these detrimental scenarios, drinking water companies invest significant amounts of labour, energy, and money towards limiting microbial presence through the use of disinfection. Though disinfection approaches have been effective in reducing the incidence of waterborne diseases, they are not 100% successful and microbial communities persist. As a result, drinking water companies also engage in microbial management by implementing rigorous sampling programs with the goal of early detection of microbial contamination events. These early detection programs are reactive in nature and can only detect a problem once it has occurred and are limited to informing strategies that try to mitigate the imminent risk posed to consumers. Further, they also typically focus only on pathogenic microorganisms and ignore all other microbial impacts (e.g. corrosion causing bacteria that deteriorate water supply pipes). These inefficiencies in microbial management can be remedied by transitioning from the existing Early Event Detection and Mitigation approach to an Event Prediction and Correction (EPC) framework in the drinking water industry.An EPC framework would enable the drinking water companies to predict the risk presented by an array of detrimental microbes (disease/corrosion/odour/taste causers) over operationally relevant time-scales and allow for the initiation of measured and proactive corrective action strategies to eliminate this risk before it is manifested. The key towards developing a robust EPC framework would be to (1) identify key locations in the drinking water system that can serve as predictive indicators, (2) quantify the temporal dynamics of these locations and how it correlates with the whole drinking water system, and (3) develop statistical models informed by microbial community data to predict contamination events. In this study, we will engage in an extensive effort to characterize the bacterial, protozoal, and fungal communities at multiple drinking water systems in Scotland. This will be followed by a long-term sampling campaign at one representative drinking water system to quantify the spatial and temporal dynamics of the drinking water microbiome. By tapping into the on-going nucleic acid revolution, we will be able to describe the drinking water microbial communities at an unprecedented level of detail. This detailed quantitative insight will be used to parameterize and shape a statistical model that describes assembly of complex microbial communities and predicts their fate in the drinking water system.This project has several anticipated benefits over a range of time-scales. In the short-term, this project will substantially improve our understanding of the drinking water microbial communities, which has been traditionally under-studied. In the medium-term, it will enable the predictive management of the drinking water systems, which will help prevent microbial contamination problems, rather than tackle them once they have occurred which can be risky, expensive, and laborious. Further, predictive models may also allow us to isolate and treat sources of microbial risk within the drinking water treatment plant, thus preventing its entry into the distribution system. Over the medium-long term, we anticipate that building a predictive microbial management capacity in the drinking water sector will enable us to beneficially manipulate the drinking water microbiome to transform the way we treat and deliver water.

饮用水中充满了微生物。事实上，每升饮用水中可以含有数百万到数亿个微生物细胞，它们都具有极其不同的进化历史和能力。例如，微生物可以（1）通过引起疾病影响人类健康，（2）腐蚀基础设施，以及（3）也会恶化水的美学质量。为了限制这些有害情况，饮用水公司投入大量劳动力、能源和资金通过使用消毒来限制微生物的存在。虽然消毒方法在减少水传播疾病的发病率方面是有效的，但它们并不是100%成功的，微生物群落仍然存在。因此，饮用水公司也通过实施严格的采样计划来进行微生物管理，目的是及早发现微生物污染事件。这些早期检测程序本质上是反应性的，只能在问题发生后才能检测到问题，并且仅限于告知试图减轻对消费者构成的迫在眉睫的风险的策略。此外，它们通常只关注病原微生物，而忽略所有其他微生物的影响（例如使供水管道恶化的腐蚀细菌）。通过将现有的早期事件检测和缓解方法转变为饮用水行业的事件预测和校正（EPC）框架，可以弥补微生物管理中的这些低效。EPC框架将使饮用水公司能够预测由一系列有害微生物带来的风险（疾病/腐蚀/气味/味道的原因），并允许启动衡量和积极的纠正行动战略，以消除这种风险之前，它表现出来。开发一个强大的EPC框架的关键是（1）确定饮用水系统中可以作为预测指标的关键位置，（2）量化这些位置的时间动态及其与整个饮用水系统的相关性，以及（3）开发由微生物群落数据提供信息的统计模型来预测污染事件。在这项研究中，我们将从事广泛的努力，在苏格兰的多个饮用水系统的细菌，原生动物和真菌群落的特征。随后将在一个有代表性的饮用水系统进行长期采样活动，以量化饮用水微生物组的时空动态。通过利用正在进行的核酸革命，我们将能够以前所未有的详细程度描述饮用水微生物群落。这一详细的定量分析将被用于建立一个统计模型，该模型描述了复杂微生物群落的组装，并预测了它们在饮用水系统中的命运。在短期内，该项目将大大提高我们对饮用水微生物群落的理解，这是传统上研究不足的。从中期来看，它将实现对饮用水系统的预测性管理，这将有助于防止微生物污染问题，而不是在微生物污染问题发生后立即解决，这可能是危险的，昂贵的和费力的。此外，预测模型还可以让我们隔离和处理饮用水处理厂内的微生物风险源，从而防止其进入分配系统。从中长期来看，我们预计在饮用水领域建立预测性微生物管理能力将使我们能够有利地操纵饮用水微生物组，从而改变我们处理和输送水的方式。

项目成果

Uncertainties Associated with Characterisation of Bulk Water Bacterial Communities in Drinking Water Systems

与饮用水系统中大量水细菌群落特征相关的不确定性

• 期刊: Water Quality and Technology Conference - 2014
• 作者: Bautista De Los Santos, Q. M.

Emerging investigators series: microbial communities in full-scale drinking water distribution systems - a meta-analysis

• DOI: 10.1039/c6ew00030d
• 发表时间: 2016-01-01
• 期刊: ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY
• 影响因子: 5
• 作者: Bautista-de los Santos, Quyen M.;Schroeder, Joanna L.;Pinto, Ameet J.
• 通讯作者: Pinto, Ameet J.

Metagenomic Insights into Bacteria that Dominate Drinking Water Bacterial Communities

对主导饮用水细菌群落的细菌的宏基因组学见解

• 期刊: Water Quality and Technology Conference - 2014
• 作者: Pinto, A.J

Probabilistic models to describe the dynamics of migrating microbial communities.

• DOI: 10.1371/journal.pone.0117221
• 发表时间: 2015
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Schroeder JL;Lunn M;Pinto AJ;Raskin L;Sloan WT
• 通讯作者: Sloan WT

Spatial-temporal survey and occupancy-abundance modeling to predict bacterial community dynamics in the drinking water microbiome.

• DOI: 10.1128/mbio.01135-14
• 发表时间: 2014-05-27
• 期刊: mBio
• 影响因子: 6.4
• 作者: Pinto AJ;Schroeder J;Lunn M;Sloan W;Raskin L
• 通讯作者: Raskin L