Research

Drinking water infrastructure requires constant monitoring and modeling to ensure system reliability and public health. To this end, utilities, regulating agencies and civil organizations run manual sampling campaigns at different points of the system throughout the year. These campaigns are expensive and time-intensive, not allowing for real-time analysis of the system.

Novel sensors, wireless communications, and data platforms developed in the recent decades have accompanied the boom of Internet of Things (IoT). A movement of interconnected and smart services, buildings, and cities. Despite continued calls for increased monitoring of our drinking water systems, few utilities - if any - have adopted comprehensive distributed and real-time monitoring techniques now made possible with technological advances. Here, our overarching goal is to develop real-time physicochemical and microbiome characterization tools of natural and man-made water systems.

This project forms part of the larger research effort towards developing near real-time microbiome monitoring. The aim of this project was to address the need for an automatic water microbiome sampler that is inexpensive, suitable for the average household, and compatible with the InnovaPrep Large Volume Concentrator (LVC). Currently, automatic water samplers can cost thousands of dollars and are not practical in household applications due to their size.

The team has conducted interviews with stakeholders, visited relevant labs, conducted a literature search, benchmarked design concepts, and performed concept generation and evaluation. Through this process the team has developed an autosampler prototype with multiple sub-functions including sample collection, water management, malfunction detection, laboratory communication, and temperature control. Overall, this autosampler has great potential to be integrated into the larger project and contribute to rethinking the way that water systems are monitored.

A comprehensive understanding of the drinking water environment can enable the development of strategies for improved monitoring and protection of the end-point water quality. Here, two doctoral students and a postdoctoral fellow are collaborating with the Ann Arbor Water Treatment Plant to conduct a year-long sampling campaign of the drinking water treatment and distribution system.

This project investigates, characterizes and monitors the general water quality and microbial communities of the city’s drinking water. The research is focused especially on understanding how the drinking water microbiome and opportunistic pathogens are impacted by various water quality parameters and engineered system characteristics over different temporal and spatial scales. Collected data will also be used to help develop a model that will help the utility better monitor the distribution system for undesirable water-quality changes in the system.

Studying key microbial processes and dynamics across the urban water cycle is essential to manage the microbiomes of engineered water cycles rationally. A critical technology gap towards realizing this vision is the lack of accurate and sensitive methods to evaluate the composition and abundances of discrete microbial populations quickly. Our research aims to address this technology gap by developing methods to characterize microbiomes in urban water systems in near real-time using nanopore sequencing.

Here, nanopore sequencing is ideally suited to support rapid decision making when coupled with appropriate data analysis techniques. Our work has also included evaluating methods to for rapid DNA extraction from drinking water and new methods for analyzing the absolute abundances of microbes as data is produced during sequencing. Moving forward, we also aim to integrate the real-time microbiome data into models, such as quantitative microbial risk assessments, that will guide decision-making around urban water systems to improve infrastructure, human, and environmental health.

Viruses are critical for regulating microbial population dynamics in many environments; however, the interactions between bacteria and viruses in drinking water are largely unknown. To fill this gap, it is essential to understand the abundances and types of viruses present in drinking water.

Here, a combination of computational and wet lab experimental techniques will be used for this project. This research will focus on mycobacteria and their associated viruses due to their prevalence in drinking water and public health concern (nontuberculosis mycobacteria (NTM) infections are on the rise in many cities in North America). By understanding these interactions, mycobacteriophage-based method for controlling NTM abundance in drinking water systems may be developed.