Drinking water QMRA and decision-making: Sensitivity of risk to common independence assumptions about model inputs.

When assessing risk posed by waterborne pathogens in drinking water, it is common to use Monte Carlo simulations in Quantitative Microbial Risk Assessment (QMRA). This method accounts for the variables that affect risk and their different values in a given system. A common underlying assumption in such analyses is that all random variables are independent (i.e., one is not associated in any way with another). Although the independence assumption simplifies the analysis, it is not always correct. For example, treatment efficiency can depend on microbial concentrations if changes in microbial concentrations either affect treatment themselves or are associated with water quality changes that affect treatment (e.g., during/after climate shocks like extreme precipitation events or wildfires). Notably, the effects of erroneous assumptions of independence in QMRA have not been widely discussed. Due to the implications of drinking water safety decisions on public health protection, it is critical that risk models accurately reflect the context being studied to meaningfully support decision-making. This work illustrates how dependence between pathogen concentration and either treatment efficiency or water consumption can impact risk estimates using hypothetical scenarios of relevance to drinking water QMRA. It is shown that the mean and variance of risk estimates can change substantially with different degrees of correlation. Data from a water supply system in Calgary, Canada are also used to illustrate the effect of dependence on risk. Recognizing the difficulty of obtaining data to empirically assess dependence, a framework to guide evaluation of the effect of dependence is presented to enhance support for decision making. This work emphasizes the importance of acknowledging and discussing assumptions implicit to models.

Research gaps and priorities for quantitative microbial risk assessment (QMRA).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Framework to Quantify Uncertainty in Microplastic Concentrations in Wastewaters and Sludges Incorporating Analytical Recovery Information into Data Analysis.

Wastewater treatment plants (WWTPs) serve a pivotal role in transferring microplastics (MPs) from wastewater to sludge streams, thereby exerting a significant influence on their release into the environment and establishing wastewater and biosolids as vectors for MP transport and delivery. Hence, an accurate understanding of the fate and transport of MPs in WWTPs is vital. Enumeration is commonly used to estimate concentrations of MPs in performance evaluations of treatment processes, and risk assessment also typically involves MP enumeration. However, achieving high accuracy in concentration estimates is challenging due to inherent uncertainty in the analytical workflow to collect and process samples and count MPs. Here, sources of random error in MP enumeration in wastewater and other matrices were investigated using a modeling approach that addresses the sources of error associated with each step of the analysis. In particular, losses are reflected in data analysis rather than merely being measured as a validation step for MP extraction methods. A model for addressing uncertainty in the enumeration of microorganisms in water was adapted to include key assumptions relevant to the enumeration of MPs in wastewater. Critically, analytical recovery, the capacity to successfully enumerate particles considering losses and counting error, may be variable among MPs due to differences in size, shape, and type (differential analytical recovery) in addition to random variability between samples (nonconstant analytical recovery). Accordingly, differential analytical recovery among the categories of MPs was added to the existing model. This model was illustratively applied to estimate MP concentrations from simulated data and quantify uncertainty in the resulting estimates. Increasing the number of replicates, counting categories of MPs separately, and accounting for both differential and nonconstant analytical recovery improved the accuracy of MP enumeration. This work contributes to developing guidelines for analytical procedures quantifying MPs in diverse types of samples and provides a framework for enhanced interpretation of enumeration data, thereby facilitating the collection of more accurate and reliable MP data in environmental studies.

Hydrology Predominates Over Harvest History and Landscape Variation to Control Water Quality and Disinfection Byproduct Formation Potentials in Forested Pacific Coast Watersheds.

Despite the global importance of forested watersheds as sources of drinking water, few studies have examined the effects of forestry on drinking water treatability. Relatively little is known about how the interaction between landscape variation and flow impacts source water quality and what this interaction means for drinking water treatability. To address this knowledge gap, we examined variability in sediments, dissolved organic matter, and disinfection byproduct formation potentials (DBP-FPs) across a range of flow conditions in four small watersheds with contrasting forest harvest histories and soil characteristics on Vancouver Island. Storm event-driven change in streamflow was the primary driver of water quality and DBP-FPs at our sites, with greater changes during stormflow (e.g., a 3-fold increase in dissolved organic carbon concentrations) than those across contrasting watersheds. Flow-driven changes in water quality and DBP-FPs were not significantly different across watersheds with different harvest histories; muted responses may be attributed to widespread second growth forests (i.e., recent harvesting effects may be confounded by historical harvest), forestry practices (e.g., slash burning), or soils with low organic carbon storage. This study suggests that variation in hydrology predominates over harvest history and soil characteristics to drive water quality and DBP-FPs on the east coast of Vancouver Island.

Sporadic diurnal fluctuations of cyanobacterial populations in oligotrophic temperate systems can prevent accurate characterization of change and risk in aquatic systems.

Cyanobacteria increasingly threaten recreational water use and drinking water resources globally. They require dynamic monitoring to account for variability in their distribution arising from diel cycles associated with oscillatory vertical migration. While this has been discussed in marine and eutrophic freshwater contexts, reports of diurnal vertical migration of cyanobacteria in oligotrophic freshwater lakes are scant. Typical monitoring protocols do not reflect these dynamics and frequently focus only on surface water sampling approaches, and either ignore sampling time or recommend large midday timeframes (e.g., 10AM-3PM), thereby preventing accurate characterization of cyanobacterial community dynamics. To evaluate the impact of diurnal migrations and water column stratification on cyanobacterial abundance and composition, communities were characterized in a shallow well-mixed lake interconnected to a thermally stratified lake in the Turkey Lakes Watershed (Ontario, Canada) using amplicon sequencing of the 16S rRNA gene across a multi-time point sampling series in 2018 and 2022. This work showed that cyanobacteria are present in oligotrophic lakes and their community structure varies (i) diurnally, (ii) across the depth of the water column, (iii) interannually within the same lake and (iv) between different lakes that are closely interconnected within the same watershed. It underscored the need for integrating multi-timepoint, multi-depth discrete sampling guidance into lake and reservoir monitoring programs to describe cyanobacteria community dynamics and signal change to inform risk management associated with the potential for cyanotoxin production. Ignoring variability in cyanobacterial community dynamics (such as that reported herein) and reducing sample numbers can lead to a false sense of security and missed opportunities to identify and mitigate changes in trophic status and associated risks such as toxin or taste and odor production, especially in sensitive, oligotrophic systems.

Biological Filtration is Resilient to Wildfire Ash-Associated Organic Carbon Threats to Drinking Water Treatment.

Elevated/altered levels of dissolved organic matter (DOM) in water can be challenging to treat after wildfire. Biologically mediated treatment removes some DOM; here, its ability to remove elevated/altered postfire dissolved organic carbon (DOC) resulting from wildfire ash was investigated for the first time. Treatment of wildfire ash-amended (low, moderate, high) source waters by bench-scale biofilters was evaluated in duplicate. Turbidity and DOC were typically well-removed (effluent turbidity ≤0.3 NTU; average DOC removal ∼20%) in all biofilters during periods of stable source water quality. Daily DOC removal across all biofilters (ash-amended and controls) was generally consistent, suggesting that (i) the biofilter DOC biodegradation capacity was not deleteriously impacted by the ash and (ii) the biofilters buffered the ash-associated increases in water extractable organic matter. DOM fractionation indicates this was because the biodegradable low molecular weight neutral fractions of DOM, which increased with ash addition, were reduced by biofiltration while humic substances were largely recalcitrant. Thus, biological filtration was resilient to wildfire ash-associated DOM threats to drinking water treatment, but operational resilience may be compromised if the balance between readily removed and recalcitrant fractions of DOM change, as was observed during brief periods herein.

Realizing the value in "non-standard" parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology.

The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard" data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.

Ensuring That Fundamentals of Quantitative Microbiology Are Reflected in Microbial Diversity Analyses Based on Next-Generation Sequencing.

Diversity analysis of amplicon sequencing data has mainly been limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, amplicon sequence read counts obtained from a sample are random data linked to source properties (e.g., proportional composition) by a probabilistic process. Thus, diversity analysis has focused on diversity exhibited in (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, and most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Following description of types of random error, mechanisms such as clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and experimental). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, diversity analysis at the level of samples with normalized library sizes is discussed.

Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities.

Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. This process is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences, yet it remains prevalent in practice and the suitability of rarefying, relative to many other normalization approaches, for diversity analysis has been argued. Here, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the amplified source microbial community. Specifically, it evaluates which data might have been obtained if a particular sample's library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes.

PURPOSE: This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. METHODS: Web of Science and Google Scholar were used to review published papers spanning the period 2013-2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018-2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. SCOPE: Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. CONCLUSIONS: The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

Evaluation of the 50% Infectious Dose of Human Norovirus Cin-2 in Gnotobiotic Pigs: A Comparison of Classical and Contemporary Methods for Endpoint Estimation.

Human noroviruses (HuNoVs) are the leading causative agents of epidemic and sporadic acute gastroenteritis that affect people of all ages worldwide. However, very few dose-response studies have been carried out to determine the median infectious dose of HuNoVs. In this study, we evaluated the median infectious dose (ID50) and diarrhea dose (DD50) of the GII.4/2003 variant of HuNoV (Cin-2) in the gnotobiotic pig model of HuNoV infection and disease. Using various mathematical approaches (Reed-Muench, Dragstedt-Behrens, Spearman-Karber, exponential, approximate beta-Poisson dose-response models, and area under the curve methods), we estimated the ID50 and DD50 to be between 2400-3400 RNA copies, and 21,000-38,000 RNA copies, respectively. Contemporary dose-response models offer greater flexibility and accuracy in estimating ID50. In contrast to classical methods of endpoint estimation, dose-response modelling allows seamless analyses of data that may include inconsistent dilution factors between doses or numbers of subjects per dose group, or small numbers of subjects. Although this investigation is consistent with state-of-the-art ID50 determinations and offers an advancement in clinical data analysis, it is important to underscore that such analyses remain confounded by pathogen aggregation. Regardless, challenging virus strain ID50 determination is crucial for identifying the true infectiousness of HuNoVs and for the accurate evaluation of protective efficacies in pre-clinical studies of therapeutics, vaccines and other prophylactics using this reliable animal model.

Evaluation of groundwater bacterial community composition to inform waterborne pathogen vulnerability assessments.

Microbial water quality evaluations are essential for determining the vulnerability of subsurface drinking water sources to fecal pathogen intrusion. Rather than directly monitor waterborne pathogens using culture- or enumeration-based techniques, the potential of assessing bacterial community using 16S rRNA gene amplicon sequencing to support these evaluations was investigated. A framework for analyzing 16S rRNA gene amplicon sequencing results featuring negative-binomial generalized linear models is demonstrated, and applied to bacterial taxa sequences in purge water samples collected from a shallow, highly aerobic, unconfined aquifer. Bacterial taxa relevant as indicators of fecal source and surface connectivity were examined using this approach. Observed sequences of Escherichia, a genus suggestive of fecal source, were consistently detected but not confirmed by culture-based methods. On the other hand, episodic appearance of anaerobic taxa sequences in this highly aerobic environment, namely Clostridia and Bacteroides, warrants further investigation as potential indicators of fecal contamination. Betaproteobacteria sequences varied significantly on a seasonal basis, and therefore may be linked to understanding surface-water groundwater interactions at this site. However, sequences that are often encountered in surface water bodies (Cyanobacteria and Flavobacteriia) were notably absent or present at very low levels, suggesting that microbial transport from surface-derived sources may be rather limited. This work demonstrates the utility of 16S rRNA gene amplicon sequencing for contextualizing and complementing conventional microbial techniques, allowing for hypotheses about source and transport processes to be tested and refined.

Severe western Canadian wildfire affects water quality even at large basin scales.

Wildfires can have severe and lasting impacts on the water quality of aquatic ecosystems. However, our understanding of these impacts is founded primarily from studies of small watersheds with well-connected runoff regimes. Despite the predominance of large, low-relief rivers across the fire-prone Boreal forest, it is unclear to what extent and duration wildfire-related material (e.g., ash) can be observed within these systems that typically buffer upstream disturbance signals. Following the devastating 2016 Fort McMurray wildfire in western Canada, we initiated a multi-faceted water quality monitoring program that suggested brief (hours to days) wildfire signatures could be detected in several large river systems, particularly following rainfall events greater than 10 mm. Continuous monitoring of flow and water quality showed distinct, precipitation-associated signatures of ash transport in rivers draining expansive (800-100,000 km2) and partially-burned (<1-22 percent burned) watersheds, which were not evident in nearby unburned regions. Yields of suspended sediment, nutrients (nitrogen, phosphorus) and metals (lead, others) from impacted rivers were 1.2-10 times greater than from those draining unburned regions. Post-fire suspended sediment concentrations in impacted rivers were often larger than pre-fire 95% prediction intervals based on several years of water sampling. These multiple lines of evidence indicate that low-relief landscapes can mobilize wildfire-related material to rivers similarly, though less-intensively and over shorter durations, than headwater regions. We propose that uneven mixing of heavily-impacted tributaries with high-order rivers may partially explain detection of wildfire signals in these large systems that may impact downstream water users.

Describing water treatment process performance: Why average log-reduction can be a misleading statistic.

The degree to which a technology used for drinking water treatment physically removes or inactivates pathogenic microorganisms is commonly expressed as a log-reduction (or log-removal) and is of central importance to the provision of microbiologically safe drinking water. Many evaluations of water treatment process performance generate or compile multiple values of microorganism log-reduction, and it is common to report the average of these log-reduction values as a summary statistic. This work provides a cautionary note against misinterpretation and misuse of averaged log-reduction values by mathematically proving that the average of a set of log-reduction values characteristically overstates the average performance of which the set of log-reduction values is believed to be representative. This has two important consequences for drinking water and food safety as well as other applications of log-reduction: 1) a technology with higher average log-reduction does not necessarily have higher average performance, and 2) risk analyses using averaged log-reduction values as point estimates of treatment efficiency will underestimate average risk-sometimes by well over an order of magnitude. When analyzing a set of log-reduction values, a summary statistic called the effective log-reduction (which averages reduction or passage rates and expresses this as a log-reduction) provides a better representation of average performance of a treatment technology.

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit.

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose-response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.

Optimisation and improvement of in-line filtration performance in water treatment for a typical low turbidity source water.

Forest catchments can produce high quality source water with a low turbidity. However, the combination of low turbidity, low organic carbon, and low temperature water conditions presents operating challenges in conventional water treatment processes. In this study, in-line filtration was tested using pilot-scale filter columns, and was found to be an appropriate option to treat a typical low turbidity water originating from the Rocky Mountains near Calgary, Alberta, Canada. When alum and cationic polymer were dosed simultaneously, in-line filtration produced high quality effluent with a turbidity and a particle count value of less than 0.1 NTU and 50 counts/mL, respectively. However, the alum and polymer doses and their ratios played important roles in the filtration efficiency. In general, short filter ripening times (i.e. <15 min) required an alum dose of at least 3 mg/L and an alum to polymer dose ratio of less than 180:1. A longer filter stable period was associated with lower alum and polymer doses, as long as their doses were at least 2 and 0.024 mg/L, respectively, and their dose ratio was maintained in the range of 30:1 to 130:1. The optimal alum and polymer doses were observed to be 3 and 0.072 mg/L, respectively. Filter performance was enhanced when higher alum and polymer doses were used for ripening, and lower doses were applied during the stable filtration period. In addition, in-line filtration resulted in the reduction of microspheres by 3.6 logs under the tested water conditions. Hence, a similar removal efficiency is anticipated for Cryptosporidium.

Applying biotechnology for drinking water biofiltration: advancing science and practice.

Drinking water biofiltration processes have evolved over time, moving from unintentional to deliberate, with careful filter media selection, nutrient and trace metal supplementation, oxidant amendment, and bioaugmentation of key microorganisms, to achieve improvements in water quality. Biofiltration is on the precipice of a revolution that aims to customize the microbial community for targeted functional outcomes. These outcomes might be to enhance or introduce target functional activity for contaminant removal, to avoid hydraulic challenges, or to shape beneficially the downstream microbial community. Moving from the foundational molecular techniques that are commonly applied to biofiltration processes, such as amplicon sequencing and quantitative, real-time polymerase chain reaction, the biofiltration revolution will be facilitated by modern biotechnological tools, including metagenomics, metatranscriptomics, and metaproteomics. The application of such tools will provide a rich knowledge base of microbial community structure/function data under various water quality and operational conditions, where this information will be utilized to select biofilter conditions that promote the enrichment and maintenance of microorganisms with the desired functions.

Learning Something From Nothing: The Critical Importance of Rethinking Microbial Non-detects.

Accurate estimation of microbial concentrations is necessary to inform many important environmental science and public health decisions and regulations. Critically, widespread misconceptions about laboratory-reported microbial non-detects have led to their erroneous description and handling as "censored" values. This ultimately compromises their interpretation and undermines efforts to describe and model microbial concentrations accurately. Herein, these misconceptions are dispelled by (1) discussing the critical differences between discrete microbial observations and continuous data acquired using analytical chemistry methodologies and (2) demonstrating the bias introduced by statistical approaches tailored for chemistry data and misapplied to discrete microbial data. Notably, these approaches especially preclude the accurate representation of low concentrations and those estimated using microbial methods with low or variable analytical recovery, which can be expected to result in non-detects. Techniques that account for the probabilistic relationship between observed data and underlying microbial concentrations have been widely demonstrated, and their necessity for handling non-detects (in a way which is consistent with the handling of positive observations) is underscored herein. Habitual reporting of raw microbial observations and sample sizes is proposed to facilitate accurate estimation and analysis of microbial concentrations.

Quantification of cyanobacterial cells via a novel imaging-driven technique with an integrated fluorescence signature.

A novel imaging-driven technique with an integrated fluorescence signature to enable automated enumeration of two species of cyanobacteria and an alga of somewhat similar morphology to one of the cyanobacteria is presented to demonstrate proof-of-concept that high accuracy, imaging-based, rapid water quality analysis can be with conventional equipment available in typical water quality laboratories-this is not currently available. The results presented herein demonstrate that the developed method identifies and enumerates cyanobacterial cells at a level equivalent to or better than that achieved using standard manual microscopic enumeration techniques, but in less time, and requiring significantly fewer resources. When compared with indirect measurement methods, the proposed method provides better accuracy at both low and high cell concentrations. It extends the detection range for cell enumeration while maintaining accuracy and increasing enumeration speed. The developed method not only accurately estimates cell concentrations, but it also reliably distinguishes between cells of Anabaena flos-aquae, Microcystis aeruginosa, and Ankistrodesmus in mixed cultures by taking advantage of additional contrast between the target cell and complex background gained under fluorescent light. Thus, the proposed image-driven approach offers promise as a robust and cost-effective tool for identifying and enumerating microscopic cells based on their unique morphological features.