Real-time evaluation of signal accuracy in wastewater surveillance of pathogens with high rates of mutation.

Wastewater surveillance of coronavirus disease 2019 (COVID-19) commonly applies reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater over time. In most applications worldwide, maximal sensitivity and specificity of RT-qPCR has been achieved, in part, by monitoring two or more genomic loci of SARS-CoV-2. In Ontario, Canada, the provincial Wastewater Surveillance Initiative reports the average copies of the CDC N1 and N2 loci normalized to the fecal biomarker pepper mild mottle virus. In November 2021, the emergence of the Omicron variant of concern, harboring a C28311T mutation within the CDC N1 probe region, challenged the accuracy of the consensus between the RT-qPCR measurements of the N1 and N2 loci of SARS-CoV-2. In this study, we developed and applied a novel real-time dual loci quality assurance and control framework based on the relative difference between the loci measurements to the City of Ottawa dataset to identify a loss of sensitivity of the N1 assay in the period from July 10, 2022 to January 31, 2023. Further analysis via sequencing and allele-specific RT-qPCR revealed a high proportion of mutations C28312T and A28330G during the study period, both in the City of Ottawa and across the province. It is hypothesized that nucleotide mutations in the probe region, especially A28330G, led to inefficient annealing, resulting in reduction in sensitivity and accuracy of the N1 assay. This study highlights the importance of implementing quality assurance and control criteria to continually evaluate, in near real-time, the accuracy of the signal produced in wastewater surveillance applications that rely on detection of pathogens whose genomes undergo high rates of mutation.

Understanding, controlling and optimising the cooling of waste thermal treatment beds including STARx Hottpads.

STARx (Self-sustaining Treatment for Active Remediation ex situ) is a thermal treatment strategy for contaminated soils and organic wastes. Key to this technology is that organics are embedded in porous matrix beds (e.g. sand). STARx induces a self-sustaining smouldering combustion front that traverses the bed, burning away the embedded contaminants/wastes. The time and cost effectiveness of this technology is largely dictated by the time required for cooling of the hot, clean, porous matrix bed that remains after treatment. This study is the first to explore the cooling of these beds. A suite of novel simulations investigated the influence of key parameters on bed-cooling time. The results reveal that cooling time decreased nearly linearly with decreases of volume-averaged bed temperature and bed bulk density. Increased injection air fluxes led to the non-linear decrease of cooling time. Also, cooling time was negatively impacted by bed temperature inhomogeneity, which influenced preferential air flow through cooler regions of the bed, bypassing hotter regions. From these results, using lower bulk density bed materials, increased air fluxes and enhancing wall insulation to improve bed temperature homogeneity were identified as system optimisations to reduce cooling times. While the aim of this research is to improve the STARx cooling process, the results are also highly applicable to many similar engineering systems that involve hot porous bed cooling.

Effects of flow velocity and bubble size distribution on oxygen mass transfer in bubble column reactors-A critical evaluation of the computational fluid dynamics-population balance model.

Computational fluid dynamics (CFD) is used to simulate a bubble column reactor operating in the bubbly (homogenous) regime. The Euler-Euler two-fluid model, integrated with the population balance model (PBM), is adopted to compute the flow and bubble size distribution (BSD). The CFD-PBM model is validated against published experimental data for BSD, global gas holdup, and oxygen mass transfer coefficient. The sensitivity of the model with respect to the specification of boundary conditions and the bubble coalescence/breakup models is assessed. The coalescence model of Prince and Blanch (1990) provides the best results, whereas the output is shown to be insensitive to the breakup model. The CFD-PBM study demonstrates the importance of considering the BSD in order to correctly model mass transfer. Results show that the constant bubble size assumption results in a large error in the oxygen mass transfer coefficient, while giving acceptable results for gas holdup. PRACTITIONER POINTS: Constant bubble size (CBS) and population balance model (PBM) are compared for a bubble column reactor. Both PBM and CBS can predict gas holdup; however, PBM can correctly predict gas-liquid mass transfer whereas CBS cannot. Best practices for selecting coalescence, breakup, and drag models are determined.

Uncertainty analysis of rising sewer models with respect to input parameters and model structure using Monte Carlo simulations and computational fluid dynamics.

Modelling conversion processes in sewers can help minimize odour and pipe corrosion issues, but model uncertainties and errors must be understood. In this study, the Wastewater Aerobic/Anaerobic Transformation in Sewers (WATS) model is implemented in two different frameworks; 1-D (CSTR-in-series) and computational fluid dynamics (CFD) to study the uncertainties due to model parameters and its mathematical form. The 1-D model is used to conduct uncertainty/sensitivity analysis using Monte Carlo simulations. Time-averaged outputs were represented using a general linearized model to quantify the importance of specific parameters. The sulfide formation rate per unit area of the biofilm is the most influential parameter. Parameters controlling anaerobic hydrolysis and fermentation are also significant. Uncertainty due to model structure is studied using CFD to explore the influences of non-homogeneous surface reactions and solids settling. These showed that the 1-D model provides a reasonable characterisation of the process for simple flows in pressure mains.

A Conjugate Fluid-Porous Approach for Simulating Airflow in Realistic Geometric Representations of the Human Respiratory System.

Simulation of flow in the human lung is of great practical interest as a means to study the detailed flow patterns within the airways for many physiological applications. While computational simulation techniques are quite mature, lung simulations are particularly complicated due to the vast separation of length scales between upper airways and alveoli. Many past studies have presented numerical results for truncated airway trees, however, there are significant difficulties in connecting such results with respiratory airway models. This article presents a new modeling paradigm for flow in the full lung, based on a conjugate fluid-porous formulation where the upper airway is considered as a fluid region with the remainder of the lung being considered as a coupled porous region. Results are presented for a realistic lung geometry obtained from computed tomography (CT) images, which show the method's potential as being more efficient and practical than attempting to directly simulate flow in the full lung.