Inhalation of aerosols produced during showering exposes people to chemical and microbial contaminants present in the water. To improve quantitative estimates of exposure and to inform the efficacy of potential interventions to reduce exposures, the number and size distributions of aerosols generated during showering events were monitored and a mass balance model of the generated aerosols was developed. The aerosol generation rates were calculated through calibrating the model with the measured aerosol data. Specifically, aerosol count concentrations and size distributions were measured with an aerodynamic particle sizer over the duration of mock showering events under various conditions, including different water temperatures and different showerhead types (conventional and rain showers). The empirical data were then used to fit a mass balance model to obtain aerosol generation rates and decay rates for each aerosol size class through least square fitting. An initial high peak concentration of aerosols was observed under hot water conditions relative to cold water conditions which resulted in a rapid increase in aerosol exposure during the first 1–2 min of showering. This suggests that people showering in hot water conditions will have a potentially increased exposure during the first 1–2 min. The model-fitted values suggest large inter-experiment variation in estimated aerosol generation and decay rates, even among triplicates of the same showering conditions. Current exposure assessment approaches assume constant aerosol concentrations during showers which might lead to miscalculated cumulative risk for microbial hazards because of their uneven distribution in building plumbing systems and biofilm detachment process during flushing. Thus, considering aerosol dynamics is beneficial during shower exposure assessments to inform risk management interventions. The data set and associated modeling results provided can support this, as they can be readily integrated into microbial risk assessments for waterborne pathogens such as Legionella spp., nontuberculous Mycobacteria (NTM) and Pseudomonas aeruginosa.

Quantitative polymerase chain reaction (qPCR) offers a rapid, automated, and potentially on-site method for quantifying L. pneumophila in building potable water systems, complementing and potentially replacing traditional culture-based techniques. However, its application in assessing human health risks is complicated by a tendency to overestimate risks due to the detection of genomic copies unassociated with viable, infectious bacteria. This study examines the relationship between L. pneumophila measurements via qPCR and culture-based methods, aiming to establish qPCR-to-culture concentration ratios needed to inform associated health risks. Eligible studies collected quantitative data on L. pneumophila concentrations using molecular and culture-based methods within paired water samples. We developed a Poisson lognormal ratio model and a random-effects meta-analysis model to analyze variations in qPCR-to-culture ratios within and across sites. Of the 17 studies in the systematic review, seven, including 23 site-specific data sets, were used for meta-analysis. Our findings indicate these ratios typically vary from 1:1 to 100:1, with ratios close to 1:1 predicted at all sites. Consequently, adopting a default 1:1 conversion factor appears necessary as a cautious approach to convert qPCR concentrations to culturable concentrations for use in health risk models, such as quantitative microbial risk assessment (QMRA). Where this approach may be too conservative, viability-qPCR could improve the accuracy of qPCR-based QMRA. Standardizing qPCR and culture-based methods and reporting site-specific environmental factors affecting L. pneumophila culturability would improve understanding of the relationship between the two methods. The ratio model introduced here advances beyond simple correlation analyses, facilitating investigations of temporal and spatial heterogeneities in the relationship. This analysis is a step forward in the integration of QMRA and molecular biology, and the framework demonstrated for L. pneumophila is applicable to other pathogens monitored in the environment.

Legionella are natural inhabitants of building plumbing biofilms, where interactions with other microorganisms influence their survival, proliferation, and death. Here, we investigated the associations of Legionella with bacterial and eukaryotic microbiomes in biofilm samples extracted from 85 shower hoses of a multiunit residential building. Legionella spp. relative abundance in the biofilms ranged between 0–7.8%, of which only 0–0.46% was L. pneumophila. Our data suggest that some microbiome members were associated with high (e.g. Chthonomonas, Vrihiamoeba) or low (e.g. Aquabacterium, Vannella) Legionella relative abundance. The correlations of the different Legionella variants (30 Zero-Radius OTUs detected) showed distinct patterns, suggesting separate ecological niches occupied by different Legionella species. This study provides insights into the ecology of Legionella with respect to: (i) the colonization of a high number of real shower hoses biofilm samples; (ii) the ecological meaning of associations between Legionella and co-occurring bacterial/eukaryotic organisms; (iii) critical points and future directions of microbial-interaction-based-ecological-investigations.

In the present study, triplicate rings of 360° pipe surfaces of an operational drinking water distribution pipe were swabbed. Each ring was equally divided into 16 parts for swabbing. The collected swabs were grouped into 3 sections and compared with the biofilm samples sampled by sonication of specimens from the same pipe. The results showed that the biofilm is unevenly distributed over the 16 parts and the 3 sections of the pipe surface. Both the active biomass and the number of observed OTUs increased as the measurements proceeded from the top to the bottom of the pipe. The bacterial community was dominated in all sections by Proteobacteria. At the genus level, Nitrospira spp., Terrimonas spp., and Hyphomicrobium spp. were dominant in all sections. Gaiella spp. and Vicinamibacter spp. dominated in S-I, Blastopirellula spp. and Pirellula spp. dominated in S-II, while Holophaga spp. and Phaeodactylibacter spp. dominated in S-III. When swabbing and pipe specimen sonication were compared, the results showed that the sampling strategy significantly influences the obtained biofilm bacterial community. A consistent multisectional swabbing strategy is proposed for future biofilm sampling; it involves collecting swabs from all sections and comparing the swabs from the same position/section across locations.

The bacterial growth potential is important to understand and manage bacterial regrowth-related water quality concerns. Bacterial growth potential depends on growth promoting/limiting compounds, therefore, nutrient availability is the key factor governing bacterial growth potential. Selecting proper tools for bacterial growth measurement is essential for routine implementation of the growth potential measurement. This study proposes a growth potential assay that is universal and can be used for different water types and soil extract without restrictions of pure culture or cultivability of the bacterial strain. The proposed assay measures the sample bacterial growth potential by using the indigenous community as inocula. Flow cytometry (FCM) and adenosine tri-phosphate (ATP) were used to evaluate the growth potential of six different microbial communities indigenous to the sample being analyzed, with increasing carbon concentrations. Bottled mineral water, non-chlorinated tap water, seawater, river water, wastewater effluent and a soil organic carbon extract were analyzed. Results showed that indigenous bacterial communities followed normal batch growth kinetics when grown on naturally present organic carbon. Indigenous bacterial growth could detect spiked organic carbon concentrations as low as 10 μg/L. The indigenous community in all samples responded proportionally to the increase in acetate-carbon and proportional growth could be measured with both FCM and ATP. Bacterial growth was proportional to the carbon concentration but not the same proportion factor for the different water samples tested. The effect of inoculating the same water with different indigenous microbial communities on the growth potential was also examined. The FCM results showed that the highest increase in total bacterial cell concentration was obtained with bacteria indigenous to the water sample. The growth potential assay using indigenous bacterial community revealed consistent results of bacterial growth in all the different samples tested and therefore providing a fast, more stable, and accurate approach for monitoring the biological stability of waters compared to the previously developed assays. The growth potential assay can be used to aid in detecting growth limitations by compounds other than organic carbon.

Drinking water utilities and researchers continue to rely on the century-old heterotrophic plate counts (HPC) method for routine assessment of general microbiological water quality. Bacterial cell counting with flow cytometry (FCM) is one of a number of alternative methods that challenge this status quo and provide an opportunity for improved water quality monitoring. After more than a decade of application in drinking water research, FCM methodology is optimised and established for routine application, supported by a considerable amount of data from multiple full-scale studies. Bacterial cell concentrations obtained by FCM enable quantification of the entire bacterial community instead of the minute fraction of cultivable bacteria detected with HPC (typically < 1% of all bacteria). FCM measurements are reproducible with relative standard deviations below 3% and can be available within 15 min of samples arriving in the laboratory. High throughput sample processing and complete automation are feasible and FCM analysis is arguably less expensive than HPC when measuring more than 15 water samples per day, depending on the laboratory and selected staining procedure(s). Moreover, many studies have shown FCM total (TCC) and intact (ICC) cell concentrations to be reliable and robust process variables, responsive to changes in the bacterial abundance and relevant for characterising and monitoring drinking water treatment and distribution systems. The purpose of this critical review is to initiate a constructive discussion on whether FCM could replace HPC in routine water quality monitoring. We argue that FCM provides a faster, more descriptive and more representative quantification of bacterial abundance in drinking water.

A systematic approach is presented for the assessment of (i) bacterial growth-controlling factors in drinking water and (ii) the impact of distribution conditions on the extent of bacterial growth in full-scale distribution systems. The approach combines (i) quantification of changes in autochthonous bacterial cell concentrations in full-scale distribution systems with (ii) laboratoryscale batch bacterial growth potential tests of drinking water samples under defined conditions. The growth potential tests were done by direct incubation of water samples, without modification of the original bacterial flora, and with flow cytometric quantification of bacterial growth. This method was shown to be reproducible (ca. 4% relative standard deviation) and sensitive (detection of bacterial growth down to 5 μg L^-1 of added assimilable organic carbon). The principle of step-wise assessment of bacterial growth-controlling factors was demonstrated on bottled water, shown to be primarily carbon limited at 133 (±18) × 10³ cells mL^-1 and secondarily limited by inorganic nutrients at 5,500 (±1,700) × 10³ cells mL^-1. Analysis of the effluent of a Dutch full-scale drinking water treatment plant showed (1) bacterial growth inhibition as a result of end-point chlorination, (2) organic carbon limitation at 192 (±72) × 10³ cells mL^-1 and (3) inorganic nutrient limitation at 375 (±31) × 10³ cells mL^-1. Significantly lower net bacterial growth was measured in the corresponding full-scale distribution system (176 (±25) × 10³ cells mL^-1) than in the laboratory-scale growth potential test of the same water (294 (±35) × 10³ cells mL^-1), highlighting the influence of distribution on bacterial growth. The systematic approach described herein provides quantitative information on the effect of drinking water properties and distribution system conditions on biological stability, which can assist water utilities in decision-making on treatment or distribution system improvements to better control bacterial growth during water distribution.

Large seasonal variations in microbial drinking water quality can occur in distribution networks, but are often not taken into account when evaluating results from short-term water sampling campaigns. Temporal dynamics in bacterial community characteristics were investigated during a two-year drinking water monitoring campaign in a full-scale distribution system operating without detectable disinfectant residual. A total of 368 water samples were collected on a biweekly basis at the water treatment plant (WTP) effluent and at one fixed location in the drinking water distribution network (NET). The samples were analysed for heterotrophic plate counts (HPC), Aeromonas plate counts, adenosine-tri-phosphate (ATP) concentrations, and flow cytometric (FCM) total and intact cell counts (TCC, ICC), water temperature, pH, conductivity, total organic carbon (TOC) and assimilable organic carbon (AOC). Multivariate analysis of the large dataset was performed to explore correlative trends between microbial and environmental parameters. The WTP effluent displayed considerable seasonal variations in TCC (from 90 × 103 cells mL-1 in winter time up to 455 × 103 cells mL-1 in summer time) and in bacterial ATP concentrations (<1–3.6 ng L-1), which were congruent with water temperature variations. These fluctuations were not detected with HPC and Aeromonas counts. The water in the network was predominantly influenced by the characteristics of the WTP effluent. The increase in ICC between the WTP effluent and the network sampling location was small (34 × 103 cells mL-1 on average) compared to seasonal fluctuations in ICC in the WTP effluent. Interestingly, the extent of bacterial growth in the NET was inversely correlated to AOC concentrations in the WTP effluent (Pearson’s correlation factor r = -0.35), and positively correlated with water temperature (r = 0.49). Collecting a large dataset at high frequency over a two year period enabled the characterization of previously undocumented seasonal dynamics in the distribution network. Moreover, high-resolution FCM data enabled prediction of bacterial cell concentrations at specific water temperatures and time of year. The study highlights the need to systematically assess temporal fluctuations in parallel to spatial dynamics for individual drinking water distribution systems.

Fluorescent staining coupled with flow cytometry (FCM) is often used for the monitoring, quantification and characterization of bacteria in engineered and environmental aquatic ecosystems including seawater, freshwater, drinking water, wastewater, and industrial bioreactors. However, infrequent grab sampling hampers accurate characterization and subsequent understanding of microbial dynamics in all of these ecosystems. A logic technological progression is high throughput and full automation of the sampling, staining, measurement, and data analysis steps. Here we assess the feasibility and applicability of automated FCM by means of actual data sets produced with prototype instrumentation. As proof-of-concept we demonstrate examples of microbial dynamics in (i) flowing tap water from a municipal drinking water supply network and (ii) river water from a small creek subject to two rainfall events. In both cases, automated measurements were done at 15-min intervals during 12-14 consecutive days, yielding more than 1000 individual data points for each ecosystem. The extensive data sets derived from the automated measurements allowed for the establishment of baseline data for each ecosystem, as well as for the recognition of daily variations and specific events that would most likely be missed (or miss-characterized) by infrequent sampling. In addition, the online FCM data from the river water was combined and correlated with online measurements of abiotic parameters, showing considerable potential for a better understanding of cause-and-effect relationships in aquatic ecosystems. Although several challenges remain, the successful operation of an automated online FCM system and the basic interpretation of the resulting data sets represent a breakthrough toward the eventual establishment of fully automated online microbiological monitoring technologies.