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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.

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., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Although human health impacts of microplastics are not well understood, concern regarding chemical contaminants retained on or within them is growing. Drinking water providers are increasingly asked about these risks, but strategies for evaluating them and the extent of treatment needed to manage them are currently lacking. Microplastics can potentially induce health effects if the concentration of contaminants adsorbed to them exceeds predetermined drinking water guidelines (e.g., Maximum Contaminant Levels). The risk posed by microplastics due to adsorbed contaminants is difficult to determine, but a worst-case scenario can be evaluated by using adsorption capacity. Here, a "Threshold Microplastics Concentration" (TMC) framework is developed to evaluate whether waterborne microplastic concentrations can potentially result in the intake of regulated contaminants on/in microplastics at levels of human health concern and identify treatment targets for managing associated health risk. Exceeding the TMC does not indicate an immediate health risk; it informs the need for detailed risk assessment or further treatment evaluation to ensure particle removal targets are achieved. Thus, the TMC concept and framework provide an updateable, science-based screening tool to determine if there is a need for detailed risk assessment or treatment modification due to waterborne microplastics in supplies used for potable water production., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Co-published by Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, and American Chemical Society.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Schmidt, Acosta, Chik, D’Aoust, Delatolla, Dhiyebi, Glier, Hubert, Kopetzky, Mangat, Pang, Peterson, Prystajecky, Qiu, Servos and Emelko.)

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., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Schmidt, Cameron, Müller and Emelko.)

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., (© 2021. The Author(s).)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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 km 2 ) 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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 (ID 50 ) and diarrhea dose (DD 50 ) 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 ID 50 and DD 50 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 ID 50 . 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 ID 50 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 ID 50 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.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (© 2019 Society for Risk Analysis.)

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 .

Enteric viruses pose the greatest acute human health risks associated with subsurface drinking water supplies, yet quantitative risk assessment tools have rarely been used to develop health-based targets for virus treatment in drinking water sourced from these supplies. Such efforts have previously been hampered by a lack of consensus concerning a suitable viral reference pathogen and dose-response model as well as difficulties in quantifying pathogenic viruses in water. A reverse quantitative microbial risk assessment (QMRA) framework and quantitative polymerase chain reaction data for norovirus genogroup I in subsurface drinking water supplies were used herein to evaluate treatment needs for such water supplies. Norovirus was not detected in over 90% of samples, which emphasizes the need to consider the spatially and/or temporally intermittent patterns of enteric pathogen contamination in subsurface water supplies. Collectively, this analysis reinforces existing recommendations that a minimum 4-log treatment goal is needed for enteric viruses in groundwater in absence of well-specific monitoring information. This result is sensitive to the virus dose-response model used as there is approximately a 3-log discrepancy among virus dose-response models in the existing literature. This emphasizes the need to address the uncertainties and lack of consensus related to various QMRA modelling approaches and the analytical limitations that preclude more accurate description of virus risks., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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.

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.

Although it is widely believed that media/collector roughness can enhance particle deposition on surfaces, this effect has not been consistently observed nor systematically described. Here, column tests were conducted to: 1) evaluate media roughness impacts on particle deposition in the presence of an energy barrier (i.e., at low ionic strength conditions), and 2) describe the concurrent impacts of collector surface roughness and suspension fluid ionic strength on particle deposition in packed beds. This work presents a first, systematic demonstration that media/collector surface roughness consistently influences particle deposition in a non-linear, non-monotonic manner, irrespective of the presence of an energy barrier. Notably, ionic strength-associated changes in DLVO interaction energy could not solely explain observed differences in particle deposition associated with collector surface roughness. Particle-to-roughness element and particle-to-smooth/bottom surface interactions contributed to a critical roughness size associated with a minimum DLVO interaction energy; however, that critical size is not necessarily the same as the critical size associated with minimal particle deposition rates. Surface roughness and ionic strength concurrently affected particle deposition in a manner that is not simply additive; rather, particle deposition rates were highly correlated with inverse Debye-Hückel length (i.e., ln [κ -1 ]) using second-order polynomial functions. Notably, the secondary energy minimum alone appears inadequate for explaining the observed particle deposition behavior. These relationships may provide insight for further development of physico-chemical filtration models for describing particle deposition on surfaces., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

It is widely believed that media surface roughness enhances particle deposition-numerous, but inconsistent, examples of this effect have been reported. Here, a new mathematical framework describing the effects of hydrodynamics and interaction forces on particle deposition on rough spherical collectors in absence of an energy barrier was developed and validated. In addition to quantifying DLVO force, the model includes improved descriptions of flow field profiles and hydrodynamic retardation functions. This work demonstrates that hydrodynamic effects can significantly alter particle deposition relative to expectations when only the DLVO force is considered. Moreover, the combined effects of hydrodynamics and interaction forces on particle deposition on rough, spherical media are not additive, but synergistic. Notably, the developed model's particle deposition predictions are in closer agreement with experimental observations than those from current models, demonstrating the importance of inclusion of roughness impacts in particle deposition description/simulation. Consideration of hydrodynamic contributions to particle deposition may help to explain discrepancies between model-based expectations and experimental outcomes and improve descriptions of particle deposition during physicochemical filtration in systems with nonsmooth collector surfaces.

Global increases in the occurrence of large, severe wildfires in forested watersheds threaten drinking water supplies and aquatic ecology. Wildfire effects on water quality, particularly nutrient levels and forms, can be significant. The longevity and downstream propagation of these effects as well as the geochemical mechanisms regulating them remain largely undocumented at larger river basin scales. Here, phosphorus (P) speciation and sorption behavior of suspended sediment were examined in two river basins impacted by a severe wildfire in southern Alberta, Canada. Fine-grained suspended sediments (<125 μm) were sampled continuously during ice-free conditions over a two-year period (2009-2010), 6 and 7 years after the wildfire. Suspended sediment samples were collected from upstream reference (unburned) river reaches, multiple tributaries within the burned areas, and from reaches downstream of the burned areas, in the Crowsnest and Castle River basins. Total particulate phosphorus (TPP) and particulate phosphorus forms (nonapatite inorganic P, apatite P, organic P), and the equilibrium phosphorus concentration (EPC0 ) of suspended sediment were assessed. Concentrations of TPP and the EPC0 were significantly higher downstream of wildfire-impacted areas compared to reference (unburned) upstream river reaches. Sediments from the burned tributary inputs contained higher levels of bioavailable particulate P (NAIP) - these effects were also observed downstream at larger river basin scales. The release of bioavailable P from postfire, P-enriched fine sediment is a key mechanism causing these effects in gravel-bed rivers at larger basin scales. Wildfire-associated increases in NAIP and the EPC0 persisted 6 and 7 years after wildfire. Accordingly, this work demonstrated that fine sediment in gravel-bed rivers is a significant, long-term source of in-stream bioavailable P that contributes to a legacy of wildfire impacts on downstream water quality, aquatic ecology, and drinking water treatability., (© 2015 John Wiley & Sons Ltd.)

Deposition of colloidal- and nano-scale particles on surfaces is critical to numerous natural and engineered environmental, health, and industrial applications ranging from drinking water treatment to semi-conductor manufacturing. Nano-scale surface roughness-induced hydrodynamic impacts on particle deposition were evaluated in the absence of an energy barrier to deposition in a parallel plate system. A non-linear, non-monotonic relationship between deposition surface roughness and particle deposition flux was observed and a critical roughness size associated with minimum deposition flux or "sag effect" was identified. This effect was more significant for nanoparticles (<1 μm) than for colloids and was numerically simulated using a Convective-Diffusion model and experimentally validated. Inclusion of flow field and hydrodynamic retardation effects explained particle deposition profiles better than when only the Derjaguin-Landau-Verwey-Overbeek (DLVO) force was considered. This work provides 1) a first comprehensive framework for describing the hydrodynamic impacts of nano-scale surface roughness on particle deposition by unifying hydrodynamic forces (using the most current approaches for describing flow field profiles and hydrodynamic retardation effects) with appropriately modified expressions for DLVO interaction energies, and gravity forces in one model and 2) a foundation for further describing the impacts of more complicated scales of deposition surface roughness on particle deposition.

A lens-free spectral light-field fusion microscopy (LSLFM) system is presented for enabling contrast- and resolution-enhanced imaging of biological specimens. LSLFM consists of a pulsed multispectral lens-free microscope for capturing interferometric light-field encodings at various wavelengths, and Bayesian-based fusion to reconstruct a fused object light-field from the encodings. By fusing unique object detail information captured at different wavelengths, LSLFM can achieve improved resolution, contrast, and signal-to-noise ratio (SNR) over a single-channel lens-free microscopy system. A five-channel LSLFM system was developed and quantitatively evaluated to validate the design. Experimental results demonstrated that the LSLFM system provided SNR improvements of 6-12 dB, as well as a six-fold improvement in the dispersion index (DI), over that achieved using a single-channel, resolution-enhancing lens-free deconvolution microscopy system or its multi-wavelength counterpart. Furthermore, the LSLFM system achieved an increase in numerical aperture (NA) of ∼16% over a single-channel resolution-enhancing lens-free deconvolution microscopy system at the highest resolution wavelength used in the study. Samples of Staurastrum paradoxum, a waterborne algae, and human corneal epithelial cells were imaged using the system to illustrate its potential for enhanced imaging of biological specimens.

Column tests were conducted to investigate media roughness impacts on particle deposition in absence of an energy barrier (i.e., high ionic strength). Media/collector surface roughness consistently influenced colloid deposition in a nonlinear, nonmonotonic manner such that a critical roughness size associated with minimum particle deposition could be identified; this was confirmed using a convection-diffusion model. The results demonstrate that media surface roughness size alone is inadequate for predicting media roughness impacts on particle deposition; rather, the relative size relationship between the particles and media/collectors must also be considered. A model that quantitatively considers media surface roughness was developed that described experimental outcomes well and consistently with classic colloid filtration theory (CFT) for smooth surfaces. Dimensionless-scaling factors froughness and fPCIF were introduced and used to develop a model describing particle deposition rate (kd) and colloid attachment efficiency (α). The model includes fitting parameters that reflect the impact of critical system characteristics such as ionic strength, loading rate, hydrophobicity. Excellent agreement was found not only between the modeled outcomes for colloid attachment efficiency (α) and experimental results from the column tests, but also with experimental outcomes reported elsewhere. The model developed herein provides a framework for describing media surface roughness impacts on colloid deposition.

In many parts of the world, forests provide high quality water for domestic, agricultural, industrial, and ecological needs, with water supplies in those regions inextricably linked to forest health. Wildfires have the potential to have devastating effects on aquatic ecosystems and community drinking water supply through impacts on water quantity and quality. In recent decades, a combination of fuel load accumulation, climate change, extensive droughts, and increased human presence in forests have resulted in increases in area burned and wildfire severity-a trend predicted to continue. Thus, the implications of wildfire for many downstream water uses are increasingly concerning, particularly the provision of safe drinking water, which may require additional treatment infrastructure and increased operations and maintenance costs in communities downstream of impacted landscapes. A better understanding of the effects of wildfire on water is needed to develop effective adaptation and mitigation strategies to protect globally critical water supplies originating in forested environments.

Concentrations of particular types of microorganisms are commonly measured in various waters, yet the accuracy and precision of reported microorganism concentration values are often questioned due to the imperfect analytical recovery of quantitative microbiological methods and the considerable variation among fully replicated measurements. The random error in analytical recovery estimates and unbiased concentration estimates may be attributable to several sources, and knowing the relative contribution from each source can facilitate strategic design of experiments to yield more precise data or provide an acceptable level of information with fewer data. Herein, variance decomposition using the law of total variance is applied to previously published probabilistic models to explore the relative contributions of various sources of random error and to develop tools to aid experimental design. This work focuses upon enumeration-based methods with imperfect analytical recovery (such as enumeration of Cryptosporidium oocysts), but the results also yield insights about plating methods and microbial methods in general. Using two hypothetical analytical recovery profiles, the variance decomposition method is used to explore 1) the design of an experiment to quantify variation in analytical recovery (including the size and precision of seeding suspensions and the number of samples), and 2) the design of an experiment to estimate a single microorganism concentration (including sample volume, effects of improving analytical recovery, and replication). In one illustrative example, a strategically designed analytical recovery experiment with 6 seeded samples would provide as much information as an alternative experiment with 15 seeded samples. Several examples of diminishing returns are illustrated to show that efforts to reduce error in analytical recovery and concentration estimates can have negligible effect if they are directed at trivial error sources., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

There is increasing global concern regarding the impacts of large scale land disturbance by wildfire on a wide range of water and related ecological services. This study explores the impact of the 2003 Lost Creek wildfire in the Crowsnest River basin, Alberta, Canada on regional scale sediment sources using a tracing approach. A composite geochemical fingerprinting procedure was used to apportion the sediment efflux among three key spatial sediment sources: 1) unburned (reference) 2) burned and 3) burned sub-basins that were subsequently salvage logged. Spatial sediment sources were characterized by collecting time-integrated suspended sediment samples using passive devices during the entire ice free periods in 2009 and 2010. The tracing procedure combines the Kruskal-Wallis H-test, principal component analysis and genetic-algorithm driven discriminant function analysis for source discrimination. Source apportionment was based on a numerical mass balance model deployed within a Monte Carlo framework incorporating both local optimization and global (genetic algorithm) optimization. The mean relative frequency-weighted average median inputs from the three spatial source units were estimated to be 17% (inter-quartile uncertainty range 0-32%) from the reference areas, 45% (inter-quartile uncertainty range 25-65%) from the burned areas and 38% (inter-quartile uncertainty range 14-59%) from the burned-salvage logged areas. High sediment inputs from burned and the burned-salvage logged areas, representing spatial source units 2 and 3, reflect the lasting effects of forest canopy and forest floor organic matter disturbance during the 2003 wildfire including increased runoff and sediment availability related to high terrestrial erosion, streamside mass wasting and river bank collapse. The results demonstrate the impact of wildfire and incremental pressures associated with salvage logging on catchment spatial sediment sources in higher elevation Montane regions where forest growth and vegetation recovery are relatively slow., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Quantitative microbial risk assessment (QMRA) is a tool to evaluate the potential implications of pathogens in a water supply or other media and is of increasing interest to regulators. In the case of potentially pathogenic protozoa (e.g. Cryptosporidium oocysts and Giardia cysts), it is well known that the methods used to enumerate (oo)cysts in samples of water and other media can have low and highly variable analytical recovery. In these applications, QMRA has evolved from ignoring analytical recovery to addressing it in point-estimates of risk, and then to addressing variation of analytical recovery in Monte Carlo risk assessments. Often, variation of analytical recovery is addressed in exposure assessment by dividing concentration values that were obtained without consideration of analytical recovery by random beta-distributed recovery values. A simple mathematical proof is provided to demonstrate that this conventional approach to address non-constant analytical recovery in drinking water QMRA will lead to overestimation of mean pathogen concentrations. The bias, which can exceed an order of magnitude, is greatest when low analytical recovery values are common. A simulated dataset is analyzed using a diverse set of approaches to obtain distributions representing temporal variation in the oocyst concentration, and mean annual risk is then computed from each concentration distribution using a simple risk model. This illustrative example demonstrates that the bias associated with mishandling non-constant analytical recovery and non-detect samples can cause drinking water systems to be erroneously classified as surpassing risk thresholds., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Knowledge of the variability in pathogen or indicator concentrations over time at a particular location (e.g. in drinking water sources) is essential in implementation of concentration-based regulations and in quantitative microbial risk assessment. Microbial enumeration methods, however, are known to yield highly variable counts (even among replicates) and some methods are prone to substantial losses (i.e. only a fraction of the target microorganisms in a sample are observed). Consequently, estimated microorganism concentrations may be biased and only a fraction of the variability that is observed in temporally distributed concentration estimates is due to variability in concentration itself. These issues have often been ignored in the past, and approaches to integrate knowledge about the measurement error associated with enumeration methods into decisions have not been standardized. Here, an existing model that describes variability in microorganism counts as a function of sample volume and the analytical recovery of the enumeration method is expanded to include temporal concentration variability and sample-specific recovery information. This model is used to demonstrate that microorganism counts and analytical recovery are not independent (as has often been assumed), even if the correlation is obscured by other sources of variability in the data. It is also used as an experimental design tool to evaluate strategies that may yield more accurate concentration estimates. Finally, the model is implemented in a Bayesian framework (with a Gibbs sampling algorithm) to quantify temporal concentration variability with appropriate consideration of measurement errors in the data and the analytical recovery of the enumeration method. We demonstrate by simulation that this statistical approach facilitates risk analyses that appropriately model variability in microorganism concentrations given the available data and that it enables decisions based on quantitative measures of uncertainty., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Forests form the critical source water areas for downstream drinking water supplies in many parts of the world, including the Rocky Mountain regions of North America. Large scale natural disturbances from wildfire and severe insect infestation are more likely because of warming climate and can significantly impact water quality downstream of forested headwaters regions. To investigate potential implications of changing climate and wildfire on drinking water treatment, the 2003 Lost Creek Wildfire in Alberta, Canada was studied. Four years of comprehensive hydrology and water quality data from seven watersheds were evaluated and synthesized to assess the implications of wildfire and post-fire intervention (salvage-logging) on downstream drinking water treatment. The 95th percentile turbidity and DOC remained low in streams draining unburned watersheds (5.1 NTU, 3.8 mg/L), even during periods of potential treatment challenge (e.g., stormflows, spring freshet); in contrast, they were elevated in streams draining burned (15.3 NTU, 4.6 mg/L) and salvage-logged (18.8 NTU, 9.9 mg/L) watersheds. Persistent increases in these parameters and observed increases in other contaminants such as nutrients, heavy metals, and chlorophyll-a in discharge from burned and salvage-logged watersheds present important economic and operational challenges for water treatment; most notably, a potential increased dependence on solids and DOC removal processes. Many traditional source water protection strategies would fail to adequately identify and evaluate many of the significant wildfire- and post-fire management-associated implications to drinking water "treatability"; accordingly, it is proposed that "source water supply and protection strategies" should be developed to consider a suppliers' ability to provide adequate quantities of potable water to meet demand by addressing all aspects of drinking water "supply" (i.e., quantity, timing of availability, and quality) and their relationship to "treatability" in response to land disturbance., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

The erosion characteristics and bed stability of wildfire-affected stream sediment were measured in an annular flume. Biofilms were grown in the flume on cohesive streambed sediments collected from a wildfire affected stream and a reference undisturbed stream in southern Alberta, Canada. Examined factors that influence sediment erosion, settling and bed stability included applied shear stress, geochemical and physical properties of the sediment, floc structural characteristics and consolidation period (2, 7, 14 days). Erosion characteristics and sediment properties were strongly influenced by wildfire, consolidation period and bed biostabilization. The fire-modified sediment was more resistant to erosion than the reference unburned sediment. Settling velocities were lower in the burned sediment due to higher organic content and porosity. The critical shear stresses for erosion were 1.6 and 1.8 times higher for the burn-associated sediment after 7 and 14 days of consolidation. The differences are related to the greater degree and spatial extent (depth) of biofilm attachment in the burned sediment. Erosion depths were 4-8 times higher in burned sediment as a result of wildfire-associated biostabilization., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Many of the methods routinely used to quantify microscopic discrete particles and microorganisms are based on enumeration, yet these methods are often known to yield highly variable results. This variability arises from sampling error and variations in analytical recovery (i.e., losses during sample processing and errors in counting), and leads to considerable uncertainty in particle concentration or log(10)-reduction estimates. Conventional statistical analysis techniques based on the t-distribution are often inappropriate, however, because the data must be corrected for mean analytical recovery and may not be normally distributed with equal variance. Furthermore, these statistical approaches do not include subjective knowledge about the stochastic processes involved in enumeration. Here we develop two probabilistic models to account for the random errors in enumeration data, with emphasis on sampling error assumptions, nonconstant analytical recovery, and discussion of counting errors. These models are implemented using Bayes' theorem to yield posterior distributions (by numerical integration or Gibbs sampling) that completely quantify the uncertainty in particle concentration or log(10)-reduction given the experimental data and parameters that describe variability in analytical recovery. The presented approach can easily be implemented to correctly and rigorously analyze single or replicate (bio)particle enumeration data.

Enumeration-based methods that are often used to quantify microorganisms and microscopic discrete particles in aqueous systems may include losses during sample processing or errors in counting. Analytical recovery (the capacity of the analyst to successfully count each microorganism or particle of interest in a sample using a specific enumeration method) is frequently assessed by enumerating samples that are seeded with known quantities of the microorganisms or particles. Probabilistic models were developed to account for the impacts of seeding and analytical error on recovery data, and probability intervals, obtained by Monte Carlo simulation, were used to evaluate recovery experiment design (i.e., seeding method, number of seeded particles, and number of samples). The method of moments, maximum likelihood estimation, and credible intervals were used to statistically analyze recovery experiment results. Low or uncertain numbers of seeded particles were found to result in variability in recovery data that was not due to analytical recovery, and should be avoided if possible. This additional variability was found to reduce the reproducibility of experimental results and necessitated the use of statistical analysis techniques, such as maximum likelihood estimation using probabilistic models that account for the impacts of sampling and analytical error in recovery data.

Bacteriophages are bacterial viruses with unique characteristics that make them excellent surrogates for mammalian pathogenic viruses in environmental studies. Simple and reliable methodologies for isolation, detection, characterization and enumeration of somatic and F-specific bacteriophage are available in the literature. Limited information or methods are available for producing high-titer purified phage suspensions for studying microbial transport and survival in natural and engineered environments. This deficiency arises because most research on the production of high-titer phage suspensions was completed over half a century ago and more recent advances on these methods have not been compiled in a single publication. We present a review of the available methods and new data on the propagation, concentration and purification of two bacteriophage host systems (somatic PRD1/Salmonella thyphimurium and F-specific PR772/Escherichia coli) that are commonly utilized in laboratory and field-scale assessments of subsurface microbial transport and survival. The focus of the present study is to recommend the approach(es) that will ensure maximum bacteriophage yields while optimizing suspension purification (i.e. avoiding modification of surface charge of the phage capsids and/or inadvertent introduction of dissolved organic matter to the study system)., (Copyright 2009 Elsevier Ltd. All rights reserved.)

Basic oxygen furnace (BOF) slag media were studied as a potential treatment material in on-site sanitation systems. Batch and column studies were conducted to evaluate attenuation of the bacteriophage PR772 and 0.190 microm diameter microspheres by BOF media, and to delineate the relative contributions of two principle processes of virus attenuation: inactivation and attachment. In the batch studies, conducted at 4 degrees C, substantial inactivation of PR772 did not occur in the pH 7.6 and 9.5 suspensions. At pH 11.4, bimodal inactivation of PR772 was observed, at an initial rate of 2.1 log C/C(0) day(-1) for the first two days, followed by a much slower rate of 0.124 log C/C(0) day(-1) over the following 10 days. Two column studies were conducted at 4 degrees C at a flow rate of 1 pore volume day(-1) using two slag sources (Stelco, Ontario; Tubarão, Brazil) combined with sand and pea gravel. In both column experiments, the effluent microsphere concentration approached input concentrations over time (reductions of 0.1-0.2 log C/C(0)), suggesting attachment processes for microspheres were negligible. Removal of PR772 virus was more pronounced both during the early stages of the experiments, but also after longer transport times (0.5-1.0 log C/C(0)). PR772 reduction appeared to be primarily as a result of virus inactivation in response to the elevated pH conditions generated by the BOF mixture (10.6-11.4). On-site sanitation systems using BOF media should be designed to maintain sufficient contact time between the BOF media and the wastewater to allow sufficient residence time of pathogens at elevated pH conditions., (Copyright 2009. Published by Elsevier Ltd.)

Polystyrene latex microspheres are widely used as surrogates for biocolloid transport in porous media; however, relatively few studies directly compare microsphere transport with that of the microorganism it is intended to represent, particularly at the field scale. Here, we compared the transport behaviour of a bacterium (Escherichia coli RS2g; 1.2 microm in diameter) and three different sized microspheres (1.1, 3.9, and 4.8 microm in diameter) within undisturbed agricultural field soil following infiltration under partially saturated conditions. The soil contained significant macroporosity. A tension infiltrometer was used to control the application of a transport solution containing Brilliant Blue FCF dye to two plots. A >2 log reduction in the concentration of all colloids was observed from the soil surface to 5 cm depth in both plots. The concentration of colloids in the soil was generally proportional to the intensity of soil dye staining; however, both the E. coli RS2g bacterium and the 1.1 microm microspheres appeared to be transported deeper than the other colloids and the visible dye along root holes at the bottom of the profile in both plots. The similarities in size and zeta potential of the 1.1 microm microspheres and the E. coli RS2g likely contributed to that outcome. Colloid concentrations in dyed soil by depth were similar between the two plots, despite differences in soil properties and infiltration patterns. The properties of the colloids and macropore density were the most important factors affecting colloid transport. These results suggest that microspheres with size and surface properties similar to the microbe of interest are useful surrogates to trace potential pathways of transport in the subsurface., (Copyright 2009 Elsevier Ltd. All rights reserved.)

Maintenance of appropriate chemical pretreatment is a critical component of ensuring proper filtration performance. Pilot-scale in-line filtration studies were performed to investigate the relative impacts of chitosan, alum, and FeCl(3) coagulation on the removal of Cryptosporidium parvum oocysts and oocyst-sized polystyrene microspheres by granular media filtration. Similar removals of oocysts and microspheres were achieved when optimal coagulant doses were utilized. Sub-optimal alum and FeCl(3) coagulation resulted in a deterioration filter effluent turbidity (0.2-0.3NTU) and total particle counts (30-100 total particles > or =2microm/mL) that were accompanied by reduced (by approximately 2-3-log) median oocyst and microsphere removals by filtration. At all doses investigated, chitosan coagulation resulted in excellent turbidity and particle reductions by filtration. Nonetheless, chitosan coagulation at doses of 0.1, 0.5, and 1.0mg/L did not result in appreciable improvements in C. parvum oocyst removal relative to complete coagulation failure (median oocyst removals were < approximately 1-log). As well, oocyst-sized polystyrene microspheres appear to be reasonable indicators of C. parvum oocyst removal by in-line filtration preceded by alum and FeCl(3) coagulation, but not chitosan coagulation.

Eight saturated column experiments were conducted to examine the effects of solution chemistry and grain size on the transport of colloids through crushed silica sand. Two sizes of colloids, 0.025-microm bacteriophage (MS-2) and 1.5-microm carboxylated microspheres, were used as surrogates for the transport of pathogenic viruses and bacteria, respectively. Increasing the Ca(2+) concentration from 1 to 4.8 mM (along with background monovalent ions) resulted in complete attenuation (>6-log decrease in C/C(0)) of MS-2, but caused only a 1-log reduction (C/C(0)=0.1) in the concentration of the microspheres. Decreasing grain size from medium sand (d(50)=0.70 mm) to fine sand (d(50)=0.34 mm) resulted in substantial decreases in effluent concentrations of both the MS-2 (5-log decrease) and microspheres (>2.5-log decrease). Comparison of observed colloid retention to that predicted by a recently published correlation equation for colloid filtration revealed that the model can considerably underpredict (by 4 orders of magnitude or more) colloid retention by angular sand over distances as short as 20 cm. This indicates that state-of-the-art colloid filtration models are still limited in applicability to natural systems.

Erosion experiments were conducted in an annular flume to determine the effect of bed age and shear stress on the particle morphology (fractal dimensions D, D(1), and D(k)) of eroded cohesive river sediment. Sediment beds were deposited under low shear and left to consolidate for one, two and seven days. Fractal data and photomicrographs show particle morphology changed with shear stress and bed age. During the one-day experiment, flocs were highly branched and particle geometry became more complex with increasing shear. Microflocs present in suspension at low shear, formed larger more loosely bound flocs at moderate shear due to flocculation. At higher shear, larger flocs were less prevalent due to particle breakup. As bed age increased, less sediment was eroded and particles appeared less porous and more angular in shape for a given shear stress. Changes in floc morphology and eroded sediment mass at various shear stresses may be related to bed age-associated biostabilization of bed deposits.

Fluorescent microspheres are increasingly used in environmental studies to evaluate threats of viral and bacterial pathogens in drinking water and to investigate colloid-facilitated contaminant transport. A commonly accepted technique for the enumeration of viruses, bacteria, and virus- and bacteria-sized particles by microscopy involves a field-of-view (field) approach to estimate concentration. Few studies have focused on those factors that are most important in ensuring precise and accurate measures of concentration. Microsphere counts in suspensions of artificial groundwater and deionized water were contrasted in this study to gain a greater understanding of the effect of ionic strength and the presence of precipitates in groundwater matrices that can bias microsphere enumerations. To investigate microsphere enumeration with minimal bias from other factors, a commonly used standard method was used to prepare slides and enumerate microspheres, with particular care to randomly select fields for counting. A factorial experiment evaluated two factors, (1) the density of microspheres in each field and (2) the number of counts in an enumeration. Two parameters, relative standard deviation and percent error, were used to assess methodological precision and accuracy. Visual observations of the slides indicated that some biases, such as undulation in the filter membrane or bubble entrained in the mounting medium, create biases in microsphere enumeration. Additional biases were introduced by the presence of precipitates that form in artificial groundwater saturated with calcite. Microsphere density was found to be critical for ensuring methodological precision, whereas the total number of microspheres counted was essential to ensuring methodological accuracy. The results suggested that to minimize variability using the field approach, the enumeration of at least 350 microspheres and 25-40 microspheresfield (-1) is necessary.

The limited efficacy of disinfectants, other than ultraviolet irradiation and ozonation, as a barrier against Cryptosporidium parvum in drinking water treatment has underscored the increased importance of oocyst removal by filtration. Currently, no reliable surrogates have been identified for C. parvum removal by filtration. As a result, evaluations of the Cryptosporidium removal by treatment operations have been performed using oocysts. It has typically been assumed that chemically inactivated oocysts are suitable surrogates for viable oocysts. Measurements of electrophoretic mobility, however, have shown that chemical inactivation changes the surface charge of Cryptosporidium oocysts. The present bench-scale research indicated that formalin-inactivated oocysts are reliable surrogates for viable oocysts during both stable filter operation and periods where filtration processes are challenged, such as coagulation failure. This finding is important because of the practical difficulties associated with using viable oocysts in filtration investigations. Poor coagulation conditions severely compromised removal of viable and inactivated oocysts by dual- and tri-media filters compared to stable operating conditions and filter ripening, emphasizing the importance of optimized chemical pre-treatment (coagulation) for the successful removal of oocysts during filtration. The treatment optimization experiments also indicated that tri-media filters offered only marginally higher oocyst removals than dual-media filters.