Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data

Assessing long-term ecological impacts of PCE contamination in groundwater using a flow cytometric fingerprint approach

This study aims to develop and validate a comprehensive method for assessing ecological disturbances in groundwater ecosystems caused by tetrachloroethylene (PCE) contamination, utilizing flow cytometry (FCM) fingerprint approach. We hypothesized that the ecological disturbance resulting from PCE contamination would exhibit 'press disturbance', persisting over extended periods, and inducing notable phenotypic differences in the microbial community compared to undisturbed groundwater. We collected 40 groundwater samples from industrial district with a history of over twenty years of PCE contamination, along with 56 control groundwater from the national surveillance groundwater system. FCM revealed significant alterations in the phenotypic diversity of microbial communities in PCE-contaminated groundwater, particularly during the dry season. The presence of specific dechlorinating bacteria (Dehalococcoides, Dehalogenimonas, and Geobacter) and their syntrophic partners was identified as an indicator of contamination. Phenotypic diversity measures provided clearer and more direct reflections of contamination impact compared to taxonomic diversity measures. This study establishes FCM fingerprinting as a simple, robust, and accurate method for evaluating ecological disturbances, with potential applications in early warning systems and continuous monitoring of groundwater contamination. The findings not only underscore the sensitivity of FCM in detecting phenotypic variations induced by environmental stressors but also highlight its utility in understanding the complex dynamics of microbial communities in contaminated groundwater ecosystems.

Investigating the suitability of online flow cytometry for monitoring full-scale drinking water ozone system disinfection effectiveness

While online monitoring of physicochemical parameters has widely been incorporated into drinking water treatment systems, online microbial monitoring has lagged behind, resulting in the use of surrogate parameters (disinfectant residual, applied dose, concentration × time, CT) to assess disinfection system performance. Online flow cytometry (online FCM) allows for automated quantification of total and intact microbial cells. This study sought to investigate the feasibility of online FCM for full-scale drinking water ozone disinfection system performance monitoring. A water treatment plant with high lime solids turbidity in the ozone contactor influent was selected to evaluate the online FCM in challenging conditions. Total and intact cell counts were monitored for 40 days and compared to surrogate parameters (ozone residual, ozone dose, and CT) and grab sample assay results for cellular adenosine triphosphate (cATP), heterotrophic plate counts (HPC), impedance flow cytometry, and 16S rRNA gene sequencing. Online FCM provided insight into the dynamics of the full-scale ozone system, including offering early warning of increased contactor effluent cell concentrations, which was not observed using surrogate measures. Positive correlations were observed between online FCM intact cell counts and cATP levels (Kendall's tau=0.40), HPC (Kendall's tau=0.20), and impedance flow cytometry results (Kendall's tau=0.30). Though a strong correlation between log intact cell removal and CT was not observed, 16S rRNA gene sequencing results showed that passage through the ozone contactor significantly changed the microbial community (p < 0.05). Potential causes of the low overall cell inactivation in the contactor and the significant changes in the microbial community after ozonation include regrowth in the later chambers of the contactor and varied ozone resistance of drinking water microorganisms. This study demonstrates the suitability of direct, online microbial analysis for monitoring full-scale disinfection systems.

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t - 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.

Predicting the performance of chain elongating microbiomes through flow cytometric fingerprinting

As part of the circular bio-economy paradigm shift, waste management and valorisation practices have moved away from sanitation and towards the production of added-value compounds. Recently, the development of mixed culture bioprocess for the conversion of waste(water) to platform chemicals, such as medium chain carboxylic acids, has attracted significant interest. Often, the microbiology of these novel bioprocesses is less diverse and more prone to disturbances, which can lead to process failure. This issue can be tackled by implementing an advanced monitoring strategy based on the microbiology of the process. In this study, flow cytometry was used to monitor the microbiology of lactic acid chain elongation for the production of caproic acid, and assess its performance both qualitatively and quantitatively. Two continuous stirred tank reactors for chain elongation were monitored flow cytometrically for over 336 days. Through community typing, four specific community types could be identified and correlated to both a specific functionality and genotypic diversity. Additionally, the machine-learning algorithms trained in this study demonstrated the ability to predict production rates of, amongst others, caproic acid with high accuracy in the present (R² > 0.87) and intermediate accuracy in the near future (R² > 0.63). The identification of specific community types and the development of predictive algorithms form the basis of advanced bioprocess monitoring based on flow cytometry, and have the potential to improve bioprocess control and optimization, leading to better product quality and yields.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Predicting heterotrophic plate count exceedance in tap water: A binary classification model supervised by culture-independent data

Culture-independent data can be utilized to identify heterotrophic plate count (HPC) exceedances in drinking water. Although HPC represents less than 1% of the bacterial community and exhibits time lags of several days, HPC data are widely used to assess the microbiological quality of drinking water and are incorporated into drinking water standards. The present study confirmed the nonlinear relationships between HPC, intact cell count (ICC), and adenosine triphosphate (ATP) in tap water samples (stagnant and flushed). By using a combination of ICC, ATP, and free chlorine data as inputs, we show that HPC exceedance can be classified using a 2-layer feed-forward artificial neural network (ANN). Despite the nonlinearity of HPC, the best binary classification model showed accuracies of 95%, sensitivity of 91%, and specificity of 96%. ICC and chlorine concentrations were the most important features for classifiers. The main limitations, such as sample size and class imbalance, were also discussed. The present model provides the ability to convert data from emerging measurement techniques into established and well-understood measures, overcoming culture dependence and offering near real-time data to help ensure the biostability and safety of drinking water.

Single-Cell Techniques in Environmental Microbiology

Environmental microbiology has been an essential part of environmental research because it provides effective solutions to most pollutants. Hence, there is an interest in investigating microorganism behavior, such as observation, identification, isolation of pollutant degraders, and interactions between microbial species. To comprehensively understand cell heterogeneity, diverse approaches at the single-cell level are demanded. Thus far, the traditional bulk biological tools such as petri dishes are technically challenging for single cells, which could mask the heterogeneity. Single-cell technologies can reveal complex and rare cell populations by detecting heterogeneity among individual cells, which offers advantages of higher resolution, higher throughput, more accurate analysis, etc. Here, we overviewed several single-cell techniques on observation, isolation, and identification from aspects of methods and applications. Microscopic observation, sequencing identification, flow cytometric identification and isolation, Raman spectroscopy-based identification and isolation, and their applications are mainly discussed. Further development on multi-technique integrations at the single-cell level may highly advance the research progress of environmental microbiology, thereby giving more indication in the environmental microbial ecology.

Plastibodies for multiplexed detection and sorting of microplastic particles in high-throughput

Sensitive high-throughput analytic methodologies are needed to quantify microplastic particles (MPs) and thereby enable routine monitoring of MPs to ultimately secure animal, human, and environmental health. Here we report a multiplexed analytical and flow cytometry-based high-throughput methodology to quantify MPs in aqueous suspensions. The developed analytic MPs-quantification platform provides a sensitive as well as high-throughput detection of MPs that relies on the material binding peptide Liquid Chromatography Peak I (LCI) conjugated to Alexa-fluorophores (LCI_F16C-AF488, LCI_F16C-AF594, and LCI_F16C-AF647). These fluorescent material-binding peptides (also termed plastibodies) were used to fluorescently label polystyrene MPs, whereas Alexa-fluorophores alone exhibited a negligible background fluorescence. Mixtures of polystyrene MPs that varied in size (500 nm to 5 μm) and varied in labeled populations were analyzed and sorted into distinct populations reaching sorting efficiencies >90 % for 1 × 10⁶ sorted events. Finally, a multiplexed quantification and sorting with up to three plastibodies was successfully achieved to validate that the combination of plastibodies and flow cytometry is a powerful and generally applicable methodology for multiplexed analysis, quantification, and sorting of microplastic particles.

Investigation of the effect of sustainable magnetic treatment on the microbiological communities in drinking water

The drinking water scarcity is posing a threat to mankind, hence better water quality management methods are required. Magnetic water treatment, which has been reported to improve aesthetic water quality and reduce scaling problems, can be an important addition to the traditional disinfectant dependent treatment. Despite the extensive market application opportunities, the effect of magnetic fields on (microbial) drinking water communities and subsequently the biostability is still largely unexplored, although the first patent was registered already 1945. Here flow cytometry was applied to assess the effect of weak magnetic fields (≤10 G) with strong gradients (≈800 G/m) on drinking water microbial communities. Drinking water was collected from the tap and placed inside the magnetic field (treated) and 5 m away from the magnet to avoid any background interferences (control) using both a static set-up and a shaking set-up. Samples were collected during a seven-day period for flow cytometry examination. Additionally, the effects of magnetic fields on the growth of Pseudomonas aeruginosa in autoclaved tap water were examined. Based on the fluorescent intensity of the stained nucleic acid content, the microbial cells were grouped into low nucleic acid content (LNA) and high nucleic acid content (HNA). Our results show that the LNA was dominant under nutrient limited condition while the HNA dominates when nutrient is more available. Such behavior of LNA and HNA matches well with the long discussed r/K selection model where r-strategists adapted to eutrophic conditions and K-strategists adapted to oligotrophic conditions. The applied magnetic fields selectively promote the growth of LNA under nutrient rich environment, which indicates a beneficial effect on biostability enhancement. Inhibition on an HNA representative Pseudomonas aeruginosa has also been observed. Based on our laboratory observations, we conclude that magnetic field treatment can be a sustainable method for microbial community management with great potential.

Bacterial dynamics in drinking water distribution systems and flow cytometry monitoring scheme optimization

Water biostability is desired within drinking water distribution systems (DWDSs) to limit microbiologically-related operational, aesthetic and, eventually, health-related issues. However, variations in microbiological quality can take place both spatially along DWDS pipelines and temporally at single locations due to biofilm detachment, water quality seasonality and other processes. In this study, long- and short-term trends of bacterial concentration and community structure were investigated in a secondary branch of an unchlorinated DWDS for several months using high-frequency flow cytometry (FCM) and traditional laboratory monitoring campaigns. Long-term trends of bacterial concentrations and community structures were likely caused by changes in the water physical-chemical quality (i.e. pH and conductivity). Short-term daily pattern, instead, resulted in significant variations between the bacterial concentrations and community structures at different hours, likely due to biofilm detachment and loose deposits resuspension related to changes in the local water flow. These patterns, however, showed broad variations and did not persist during the entire monitoring campaign presumably due to the stochasticity of local instantaneous demand and seasonal changes in water consumption. During periods without sensible long-term trends, the sampling hours explain a comparable or larger fraction of the bacterial community diversity compared to dates. The variations observed with FCM were poorly or not detected by traditional laboratory analyses, as the correlation between the two were rather weak, highlighting the limited information provided by traditional approaches. On the other hand, FCM data correlated with water pH and conductivity, underlining the relation between physical-chemical and microbiological water quality. Such results suggest that the advanced control of the physical-chemical water quality could minimize the microbiological water quality variations. Moreover, monitoring campaign planning should take into account the sampling time to reduce the noise caused by daily fluctuations and/or assess the overall quality variations. Finally, as monitoring costs are one of the barriers which prevent a more widespread use of FCM, a monitoring scheme optimization strategy was developed. Such strategy employs the data from an initial high-frequency sampling period to select the sampling hours which maximize the observed variations of bacterial concentration and community composition.

Automated Online Flow Cytometry Advances Microalgal Ecosystem Management as in situ, High-Temporal Resolution Monitoring Tool

Microalgae are emerging as a next-generation biotechnological production system in the pharmaceutical, biofuel, and food domain. The economization of microalgal biorefineries remains a main target, where culture contamination and prokaryotic upsurge are main bottlenecks to impair culture stability, reproducibility, and consequently productivity. Automated online flow cytometry (FCM) is gaining momentum as bioprocess optimization tool, as it allows for spatial and temporal landscaping, real-time investigations of rapid microbial processes, and the assessment of intrinsic cell features. So far, automated online FCM has not been applied to microalgal ecosystems but poses a powerful technology for improving the feasibility of microalgal feedstock production through in situ, real-time, high-temporal resolution monitoring. The study lays the foundations for an application of automated online FCM implying far-reaching applications to impel and facilitate the implementation of innovations targeting at microalgal bioprocesses optimization. It shows that emissions collected on the FL1/FL3 fluorescent channels, harnessing nucleic acid staining and chlorophyll autofluorescence, enable a simultaneous assessment (quantitative and diversity-related) of prokaryotes and industrially relevant phototrophic Chlorella vulgaris in mixed ecosystems of different complexity over a broad concentration range (2.2–1,002.4 cells ⋅μL^–1). Automated online FCM combined with data analysis relying on phenotypic fingerprinting poses a powerful tool for quantitative and diversity-related population dynamics monitoring. Quantitative data assessment showed that prokaryotic growth phases in engineered and natural ecosystems were characterized by different growth speeds and distinct peaks. Diversity-related population monitoring based on phenotypic fingerprinting indicated that prokaryotic upsurge in mixed cultures was governed by the dominance of single prokaryotic species. Automated online FCM is a powerful tool for microalgal bioprocess optimization owing to its adaptability to myriad phenotypic assays and its compatibility with various cultivation systems. This allows advancing bioprocesses associated with both microalgal biomass and compound production. Hence, automated online FCM poses a viable tool with applications across multiple domains within the biobased sector relying on single cell–based value chains.

PhenoGMM: Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure

Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA.

Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics.

IMPORTANCE Microorganisms are vital components in various ecosystems on Earth. In order to investigate the microbial diversity, researchers have largely relied on the analysis of 16S rRNA gene sequences from DNA. Flow cytometry has been proposed as an alternative technology to characterize microbial community diversity and dynamics. The technology enables a fast measurement of optical properties of individual cells. So-called fingerprinting techniques are needed in order to describe microbial community diversity and dynamics based on flow cytometry data. In this work, we propose a more advanced fingerprinting strategy based on Gaussian mixture models. We evaluated our workflow on data sets from both synthetic and natural ecosystems, illustrating its general applicability for the analysis of microbial flow cytometry data. PhenoGMM supports a rapid and quantitative analysis of microbial community structure using flow cytometry.

A palette of fluorophores that are differentially accumulated by wild-type and mutant strains of Escherichia coli: surrogate ligands for profiling bacterial membrane transporters

Our previous work demonstrated that two commonly used fluorescent dyes that were accumulated by wild-type Escherichia coli MG1655 were differentially transported in single-gene knockout strains, and also that they might be used as surrogates in flow cytometric transporter assays. We summarize the desirable properties of such stains, and here survey 143 candidate dyes. We eventually triage them (on the basis of signal, accumulation levels and cost) to a palette of 39 commercially available and affordable fluorophores that are accumulated significantly by wild-type cells of the 'Keio' strain BW25113, as measured flow cytometrically. Cheminformatic analyses indicate both their similarities and their (much more considerable) structural differences. We describe the effects of pH and of the efflux pump inhibitor chlorpromazine on the accumulation of the dyes. Even the 'wild-type' MG1655 and BW25113 strains can differ significantly in their ability to take up such dyes. We illustrate the highly differential uptake of our dyes into strains with particular lesions in, or overexpressed levels of, three particular transporters or transporter components (yhjV, yihN and tolC). The relatively small collection of dyes described offers a rapid, inexpensive, convenient and informative approach to the assessment of microbial physiology and phenotyping of membrane transporter function.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

Nanosecond pulsed electric field processing of microalgae based biorefineries governs growth promotion or selective inactivation based on underlying microbial ecosystems

Nanosecond pulsed electric field treatment (nsPEF) is a technology-driven, resource-efficient approach fostering microalgae biorefineries for transforming them into economically viable scenarios. A processing window of 100 ns, 7 Hz, and 10 kV cm^-1 significantly leveraged phototrophic Chlorella vulgaris and bacterial counts up to + 50.1 ± 12.2% and + 77.0 ± 37.4%, respectively (n = 4; p < 0.05) in non-axenic cultures. Applying the same processing window decreased C. vulgaris (-17.1 ± 13.8%) and prokaryotic (-82.7 ± 14.6%) counts owing to alterations in the prokaryotic community diversity. Principle coordinate analysis of prokaryotic phenotypic fingerprints indicated that phenotype or metabolism related diversity changes in the prokaryotic community affected the treatment outcome. The study fosters the upsurge of industrial-scale nsPEF realization and the economic viability of microalgae biorefineries through improved process understanding and thus control. It perpetuates nsPEF applicability for microalgae feedstock production and several other applications within single-cell biorefineries in the bio-based domain.

Rapid Detection Methods for Bacterial Pathogens in Ambient Waters at the Point of Sample Collection: A Brief Review

The world is currently facing a serious health burden of waterborne diseases, including diarrhea, gastrointestinal diseases, and systemic illnesses. The control of these infectious diseases ultimately depends on the access to safe drinking water, properly managed sanitation, and hygiene practices. Therefore, ultrasensitive, rapid, and specific monitoring platforms for bacterial pathogens in ambient waters at the point of sample collection are urgently needed. We conducted a literature review on state-of-the-art research of rapid in-field aquatic bacteria detection methods, including cell-based methods, nucleic acid amplification detection methods, and biosensors. The detection performance, the advantages, and the disadvantages of the technologies are critically discussed. We envision that promising monitoring approaches should be automated, real-time, and target-multiplexed, thus allowing comprehensive evaluation of exposure risks attributable to waterborne pathogens and even emerging microbial contaminants such as antibiotic resistance genes, which leads to better protection of public health.

Reuse of ultrafiltered effluents for crop irrigation: On-site flow cytometry unveiled microbial removal patterns across a full-scale tertiary treatment

Reuse of treated wastewater for crop irrigation has been widely adopted to mitigate the effects of water scarcity on agricultural yields and to help preserving the integrity of aquatic ecosystems. This paper presents the outcomes of one-year monitoring of a full-scale agro-industrial wastewater treatment plant designed for water reuse, with a multistage tertiary treatment based on sand filtration, membrane ultrafiltration, storage and on-demand UV disinfection. We aimed to test flow cytometry as a monitoring tool to provide on-site indications on tertiary treatment performances and on the quality of treated wastewater along the treatment scheme. Membrane ultrafiltration retained prokaryotic cells and E. coli (>3 log). During storage of treated effluents, a significant decay of E. coli was observed together with the growth of prokaryotic and eukaryotic cells, and the UV disinfection was effective only against fecal indicators. The microbial quality of the treated effluent was comparable to the control groundwater locally used for irrigation. On-site rapid assessments by flow cytometry allowed unveiling crucial aspects affecting the microbiological quality of ultrafiltration permeate and treated effluent immediately after sampling, including plant operating performances and microbial removal patterns across the treatment train.

Online microbial fingerprinting for quality management of drinking water: Full-scale event detection

Microbial regrowth during drinking water distribution can result in a variety of problems such as a deviating taste and odor, and may even pose a risk to public health. Frequent monitoring is essential to anticipate events of biological instability, and relevant microbial parameters for operational control of biostability of drinking water should be developed. Here, online flow cytometry and derived biological metrics were used to assess the biological stability of a full-scale drinking water tower during normal and disturbed flow regime. Pronounced operational events, such as switching from drinking water source, and seasonal changes, were detected in the total cell counts, and regrowth was observed despite the short hydraulic residence time of 6-8 h. Based on the flow cytometric fingerprints, the Bray-Curtis dissimilarity was calculated and was developed as unambiguous parameter to indicate or warn for changing microbial drinking water quality during operational events. In the studied water tower, drastic microbial water quality changes were reflected in the Bray-Curtis dissimilarity, which demonstrates its use as an indicator to follow-up and detect microbial quality changes in practice. Hence, the Bray-Curtis dissimilarity can be used in an online setup as a straightforward parameter during full-scale operation of drinking water distribution, and combined with the cell concentration, it serves as an early-warning system for biological instability.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Monitoring, sampling, and automated analysis

A review of the literature published in 2018 on topics related to monitoring, sampling, and automated analysis is presented. The review includes current developments in monitoring, sampling, and analysis of water, wastewater, and groundwater. This review includes the following sections: brief introduction; sample preparation and extraction techniques; real-time, high-frequency, and/or in situ monitoring (microbiological, inorganic, organic, metals, and others); passive monitoring; and the biosensors. In the end, the authors have discussed future of the topic. PRACTITIONER POINTS: Advances in monitoring, sampling and automated analysis of water and wastewater are summarized. Real-time, high-frequency, and in-situ monitoring and analysis of pollutants are summarized. Topics include sample preparation and extraction and passive monitoring, and biosensors for pollutants.

Online assessment of sand filter performance for bacterial removal in a full-scale drinking water treatment plant

Microbiological risks associated with drinking water can be minimized by providing enhanced integrity monitoring of bacterial removal by water treatment processes. This study aimed to evaluate the efficacy of real-time bacteriological counters for continuously assessing the performance of a full-scale sand filter to remove bacteria. Over the course of an 8-day evaluation, online counting of bacteria was successfully performed, providing continuous bacterial counts in the sand filter influent and effluent over approximate ranges from 17 × 10⁴ to 94 × 10⁴ and from 0.2 × 10⁴ to 1.3 × 10⁴ counts/mL, respectively. Periodic variations were observed with online bacterial counts in the sand filter influent because of the changes in the performance of flocculation and sedimentation processes. Overall, online removal rates of bacteria determined during the full-scale test were 95.2-99.3% (i.e., 1.3-2.2-log), indicating that online bacterial counting can continuously demonstrate over 1.3-log removal in the sand filter. Real-time bacteriological counting technology can be a useful tool for assessing variability and detecting bacterial breakthrough. It can be integrated with other online water quality measurements to evaluate underlying trends and the performance of sand filters for bacterial removal, which can enhance the safety of drinking water.

Biological As(III) oxidation in biofilters by using native groundwater microorganisms

Arsenic (As) contamination in drinking water represents a worldwide threat to human health. During last decades, the exploitation of microbial As-transformations has been proposed for bioremediation applications. Among biological methods for As-contaminated water treatment, microbial As(III)-oxidation is one of the most promising approaches since it can be coupled to commonly used adsorption removal technologies, without requiring the addition of chemicals and producing toxic by-products. Despite the As(III) oxidation capability has been described in several bacterial pure or enrichment cultures, very little is known about the real potentialities of this process when mixed microbial communities, naturally occurring in As contaminated waters, are used. This study highlighted the contribution of native groundwater bacteria to As(III)-oxidation in biofilters, under conditions suitable for a household-scale treatment system. This work elucidated the influence of a variety of experimental conditions (i.e., various filling materials, flow rates, As(III) inflow concentration, As(III):As(V) ratio, filter volumes) on the microbially-mediated As(III)-oxidation process in terms of oxidation efficiency and rate. The highest oxidation efficiencies (up to 90% in 3 h) were found on coarse sand biofilters treating total initial As concentration of 100 μg L^-1. The detailed microbial characterization of the As(III) oxidizing biofilms revealed the occurrence of several OTUs affiliated with families known to oxidize As(III) (e.g., Burkholderiaceae, Comamonadaceae, Rhodobacteraceae, Xanthomonadaceae). Furthermore, As-related functional genes increased in biofilter systems in line with the observed oxidative performances.