Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling

Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: A comparison with conventional methods

Groundwater provides much of the world's potable water. Nevertheless, groundwater quality monitoring programmes often rely on a sporadic, slow, and narrowly focused combination of periodic manual sampling and laboratory analyses, such that some water quality deficiencies go undetected, or are detected too late to prevent adverse consequences. In an effort to address this shortcoming, we conducted enhanced monitoring of untreated groundwater quality over 12 months (February 2019-February 2020) in four shallow wells supplying potable water in Finland. We supplemented periodic manual sampling and laboratory analyses with (i) real-time online monitoring of physicochemical and hydrological parameters, (ii) analysis of stable water isotopes from groundwater and nearby surface waters, and (iii) microbial community analysis of groundwater via amplicon sequencing of the 16S rRNA gene and 16S rRNA. We also developed an early warning system (EWS) for detecting water quality anomalies by automating real-time online monitoring data collection, transfer, and analysis - using electrical conductivity (EC) and turbidity as indirect water quality indicators. Real-time online monitoring measurements were largely in fair agreement with periodic manual measurements, demonstrating their usefulness for monitoring water quality; and the findings of conventional monitoring, stable water isotopes, and microbial community analysis revealed indications of surface water intrusion and faecal contamination at some of the studied sites. With further advances in technology and affordability expected into the future, the supplementary methods used here could be more widely implemented to enhance groundwater quality monitoring - by contributing new insights and/or corroborating the findings of conventional analyses.

Risk assessment of Cryptosporidium intake in drinking water treatment plant by a combination of predictive models and event-tree and fault-tree techniques

Risk-informed decision making permits a more effective water safety management. In this framework, this article introduces the rationale and proposes a new approach to carry out a quantitative risk assessment along the water chain, from river source to tap water, by integrating predictive modelling combined with event-tree and fault-tree techniques. The model developed by this approach could not only account for normal but also for abnormal process conditions in the water treatment plant, as well as assess the real impact of the applied safety controls, such as turbidity control. A sensitivity study was conducted to determine the effect of considering a typical drinking water treatment plant (DWTP), i.e. coagulation, sedimentation and filtration with two turbidity controls (on intake and after filtration) on the risk of infection due to exposure to Cryptosporidium in tap water. The results showed that, with the current effectiveness of turbidity reduction in the DWTP, the first control did not minimise the annual risk of Cryptosporidium infection (3.6E-04) and only limiting turbidity after filtration to below 0.01NTU provided a clear reduction in risk (7.7E-05) at the cost of rejecting 60 % of the water after the control. The lowest risk was found when turbidity reduction was set at 4 logs (8.48E-06), although this means that the effectiveness of turbidity reduction should be greatly improved. It was therefore concluded that supplementing the current treatment with alternative barriers such as UV or ozone disinfection and/or implementing direct control of Cryptosporidium concentration should be considered.

A review of the application of machine learning in water quality evaluation

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.

Seawater desalination based drinking water: Microbial characterization during distribution with and without residual chlorine

Monitoring the changes that occur to water during distribution is vital to ensure water safety. In this study, the biological stability of reverse osmosis (RO) produced drinking water, characterized by low cell concentration and low assimilable organic carbon, in combination with chlorine disinfection was investigated. Water quality at several locations throughout the existing distribution network was monitored to investigate whether microbial water quality changes can be identified. Results revealed that the water leaving the plant had an average bacterial cell concentration of 10³ cells/mL. A 0.5-1.5 log increase in bacterial cell concentration was observed at locations in the network. The residual disinfectant was largely dissipated in the network from 0.5 mg/L at the treatment plant to less than 0.1 mg/L in the network locations. The simulative study involving miniature distribution networks, mimicking the dynamics of a distribution network, fed with the RO produced chlorinated and non-chlorinated drinking water revealed that distributing RO produced water without residual disinfection, especially at high water temperatures (25-30 °C), poses a higher chance for water quality change. Within six months of operation of the miniature network fed with unchlorinated RO produced water, the adenosine triphosphate (ATP) and total cell concentration (TCC) in the pipe biofilm were 4 × 10² pg ATP/cm² and 1 × 10⁷ cells/ cm². The low bacterial cell concentration and organic carbon concentration in the RO-produced water did not prevent biofilm development inside the network with and without residual chlorine. The bacterial community analysis using 16S ribosomal RNA (rRNA) gene sequencing revealed that mesophilic bacteria with higher temperature tolerance and bacteria associated with oligotrophic, nutrient-poor conditions dominated the biofilm, with no indication of the existence of opportunistic pathogenic species. However, chlorination selected against most bacterial groups and the bacterial community that remained was mainly the bacteria capable of surviving disinfection regimes. Biofilms that developed in the presence of chlorine contained species classified as opportunistic pathogens. These biofilms have an impact on shaping the water quality received at the consumer tap. The presence of these bacteria on its own is not a health risk indicator; viability assessment and qPCRs targeting genes specific to the opportunistic pathogens as well as quantitative microbiological risk assessment (QMRA) should be included to assess the risk. The results from this study highlight the importance of implementing multiple barriers to ensure water safety. Changes in water quality detected even when high-quality disinfected RO-produced water is distributed highlight microbiological challenges that chlorinated systems endure, especially at high water temperatures.

Surface Water Intrusion, Land Use Impacts, and Bacterial Community Composition in Shallow Groundwater Wells Supplying Potable Water in Sparsely Populated Areas of a Boreal Region

Rural communities often rely on groundwater for potable water supply. In this study, untreated groundwater samples from 28 shallow groundwater wells in Finland (<10 m deep and mostly supplying untreated groundwater to <200 users in rural areas) were assessed for physicochemical water quality, stable water isotopes, microbial water quality indicators, host-specific microbial source tracking (MST) markers, and bacterial community composition, activity, and diversity (using amplicon sequencing of the 16S rRNA gene and 16S rRNA). Indications of surface water intrusion were identified in five wells, and these indications were found to be negatively correlated, overall, with bacterial alpha diversity (based on amplicon sequencing of the 16S rRNA gene). High levels of turbidity, heterotrophs, and iron compromised water quality in two wells, with values up to 2.98 nephelometric turbidity units (NTU), 16,000 CFU/ml, and 2,300 μg/liter, respectively. Coliform bacteria and general fecal indicator Bacteroidales bacteria (GenBac3) were detected in 14 and 10 wells, respectively (albeit mostly at low levels), and correlations were identified between microbial, physicochemical, and environmental parameters, which may indicate impacts from nearby land use (e.g., agriculture, surface water, road salt used for deicing). Our results show that although water quality was generally adequate in most of the studied wells, the continued safe use of these wells should not be taken for granted.

IMPORTANCE Standard physicochemical water quality analyses and microbial indicator analyses leave much of the (largely uncultured) complexity of groundwater microbial communities unexplored. This study combined these standard methods with additional analyses of stable water isotopes, bacterial community data, and environmental data about the surrounding areas to investigate the associations between physicochemical and microbial properties of 28 shallow groundwater wells in Finland. We detected impaired groundwater quality in some wells, identified potential land use impacts, and revealed indications of surface water intrusion which were negatively correlated with bacterial alpha diversity. The potential influence of surface water intrusion on groundwater wells and their bacterial communities is of particular interest and warrants further investigation because surface water intrusion has previously been linked to groundwater contamination, which is the primary cause of waterborne outbreaks in the Nordic region and one of the major causes in the United States and Canada.