Analysis of Avoided Water Utility Costs from Wildfire Risk Mitigation

Forested watersheds are severely threatened by wildfire in western Canada. The eastern slopes of the Rocky Mountains in southwestern Alberta produce the majority of surface water supplies supporting Alberta’s population, and recent increases in magnitude and severity of wildfires along with provincial water demand result in a pressing need to evaluate wildfire risk to downstream drinking water supply and treatment. Work from this project will better enable the coordination of land management and utility operations to ensure appropriate protection and treatment of drinking water in Alberta and potentially other wildfire-prone areas such as British Columbia

Analyse des coûts évités dans les services d’eau par l’atténuation des risques de feux de forêt

Les bassins versants forestiers sont gravement menacés par les feux de forêt dans l’ouest du Canada. La majeure partie de l’eau de surface utilisée par la population de l’Alberta provient du versant est des montagnes Rocheuses, dans le sud-ouest de la province. L’augmentation récente de l’ampleur et de la gravité des incendies de forêt, conjuguée à la demande en eau, se traduisent par un besoin pressant d’évaluer les risques que présentent ces incendies pour l’eau potable traitée et distribuée en aval. Les travaux réalisés dans le cadre de ce projet permettront de mieux coordonner la gestion des terres et les activités des services publics afin d’assurer la protection et le traitement appropriés de l’eau potable en Alberta, et éventuellement dans d’autres zones sujettes aux incendies de forêt, comme la Colombie- Britannique

Automated enumeration and size distribution analysis of Microcystis aeruginosa via fluorescence imaging

Due to climate change, toxic cyanobacteria and algae blooms and the associated exposure risk to humans has become a global issue. As a result, routine monitoring to evaluate cell concentrations is increasingly required to ensure safe water supplies. Current methods for cyanobacteria and algae cells enumeration are time consuming and cost-intensive due to the need for manual labor, which prevents their widespread adoption for routine water monitoring.. Automated enumeration with computer-assisted image analysis has strong potential to become a viable solution for continuous routine monitoring; however, the design of such automated systems is challenging due to: a) poor contrast between the target cells and the background, b) presence of confounding cells and abiotic particles and b) image quality variability depending on factors such as the underlying microscopy system in use and the sample condition. In this study, we introduce a novel integrated imaging-based method for automated enumeration and size distribution of Microcystis aeruginosa, a species of freshwater cyanobacteria that can originate harmful blooms. The target cells were excited using a 546nm light source and the resulting fluorescent imaging signal was acquired. A probabilistic unsupervised classification approach was taken to detect Microcystis cells from the surrounding background based on the fluorescent signal. A Gaussian mixture model was learned from the fluorescent imaging signal. The detected Microcystis cells were then enumerated and statistics regarding their size distribution automatically computed. When compared to the manual enumeration data using an hemacytometer, the developed method achieved higher accuracy using much less time and resources, without cell staining. These preliminary results demonstrate the potential of the proposed method as a powerful and robust tool for water quality monitoring and safe water quality control when used alongside gold standard methods

Computerized Enumeration and Bio-volume Estimation of the Cyanobacteria Anabaena flos-aquae

One of the most noticeable consequences of global climate change is the increased occurrence of algae and cyanobacteria blooms in surface waters. Some of these organisms may release hazardous toxins which represent a threat for human and animal health worldwide. Accordingly, the identification of threshold levels of toxic cyanobacteria cells has become common practice to ensure successful water management. The majority of current methods for cyanobacteria enumeration and bio-volume assessment are very time-consuming and costly. Furthermore, when dealing with multicellular organisms (i.e., filaments, colonies, agglomerates etc.), none of the existing enumeration methods can achieve good accuracy and all tend to underestimate cell concentrations and bio-volume. In this study, we introduce an integrated method for automated enumeration and bio-volume estimation of Anabaena flos-aquae, a common filamentous species of cyanobacteria often present in water blooms. Since Anabaena filaments are often long and tangled, a sample of its culture was first sonicated to isolate individual cells, and then imaged while being excited by a 546nm light source to considerably improve contrast. A probabilistic unsupervised classification was introduced to detect the target cells, and the size distribution of the cells was used for model calibration. Using this learned cell model, subsequent samples with natural Anabaena filaments were automatically enumerated and the bio-volume estimated. Compared to traditional manual enumeration using a hemacytometer, the developed method achieved equivalent accuracy in much less time, with less resources, and provided additional bio-volume information. These preliminary results demonstrate the potential of the developed method as a robust tool for water quality monitoring

Non-linear, non-monotonic effect of nano-scale roughness on particle deposition in absence of an energy barrier: Experiments and modeling

© APS, Jin, C., Glawdel, T., Ren, C. L., & Emelko, M. B. (2015). Non-linear, non-monotonic effect of nano-scale roughness on particle deposition in absence of an energy barrier: Experiments and modeling. Scientific Reports, 5, 17747. https://doi.org/10.1038/srep17747Deposition 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.We thank the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Water Network of financial support

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

This is the peer reviewed version of the following article: Schmidt, P. J., Emelko, M. B., & Thompson, M. E. (2020). Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit. Risk Analysis, 40(2), 352–369, which has been published in final form at https://doi.org/10.1111/risa.13386. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.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.Natural Sciences and Engineering Research Council of Canada (NSERC), RGPIN-2016-04655 || Alberta Innovates, Grant 3360-E086

Confirming the need for virus disinfection in municipal subsurface drinking water supplies

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.watres.2019.03.057. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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 and 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 water supplies were used herein to evaluate treatment needs for subsurface drinking 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.Natural Sciences and Engineering Research Council of Canada (NSERC), RGPIN-2016-04655 || U.S. EPA STAR, Grant R831630

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.Fil: Ramesh, Ashwin K.. Virginia-Maryland College of Veterinary Medicine; Estados UnidosFil: Parreño, Gladys Viviana. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Virología E Innovaciones Tecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Pque. Centenario. Instituto de Virología E Innovaciones Tecnológicas; ArgentinaFil: Schmidt, Philip J.. University of Waterloo; CanadáFil: Lei, Shaohua. Virginia-Maryland College of Veterinary Medicine; Estados UnidosFil: Zhong, Weiming. Cincinnati Children’s Hospital Medical Center; Estados UnidosFil: Jiang, Xi. Cincinnati Children’s Hospital Medical Center; Estados UnidosFil: Emelko, Monica B.. University of Waterloo; CanadáFil: Yuan, Lijuan. Virginia-Maryland College of Veterinary Medicine; Estados Unido