Passive sampling, a practical method for wastewater-based surveillance of SARS-CoV-2.

In search of practical and affordable tools for wastewater-based surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), three independent field experiments were conducted using three passive sampler sorbents (electronegative membrane, cotton bud, and gauze) in Guelph, Ontario, Canada. Total daily cases during this study ranged from 2 to 17/100,000 people and 43/54 traditionally collected wastewater samples were positive for SARS-CoV-2 with mean detectable concentrations ranging from 8.4 to 1780 copies/ml. Viral levels on the passive samplers were assessed after 4, 8, 24, 48, 72, and 96 hrs of deployment in the wastewater and 43/54 membrane, 42/54 gauze, and 27/54 cotton bud samples were positive. A linear accumulation rate of SARS-CoV-2 on the membranes was observed up to 48 hours, suggesting the passive sampler could adequately reflect wastewater levels for up to two days of deployment. Due the variability in accumulation observed for the cotton buds and gauzes, and the pre-processing steps required for the gauzes, we recommend membrane filters as a simple cost-effective option for wastewater-based surveillance of SARS-CoV-2.

A novel risk assessment method for landfill slope failure: Case study application for Bhalswa Dumpsite, India.

Rapid population growth of major urban centres in many developing countries has created massive landfills with extraordinary heights and steep side-slopes, which are frequently surrounded by illegal low-income residential settlements developed too close to landfills. These extraordinary landfills are facing high risks of catastrophic failure with potentially large numbers of fatalities. This study presents a novel method for risk assessment of landfill slope failure, using probabilistic analysis of potential failure scenarios and associated fatalities. The conceptual framework of the method includes selecting appropriate statistical distributions for the municipal solid waste (MSW) material shear strength and rheological properties for potential failure scenario analysis. The MSW material properties for a given scenario is then used to analyse the probability of slope failure and the resulting run-out length to calculate the potential risk of fatalities. In comparison with existing methods, which are solely based on the probability of slope failure, this method provides a more accurate estimate of the risk of fatalities associated with a given landfill slope failure. The application of the new risk assessment method is demonstrated with a case study for a landfill located within a heavily populated area of New Delhi, India.

Phosphate interference during in situ treatment for arsenic in groundwater.

Contamination of groundwater by arsenic is a problem in many areas of the world, particularly in West Bengal (India) and Bangladesh, where reducing conditions in groundwater are the cause. In situ treatment is a novel approach wherein, by introduction of dissolved oxygen (DO), advantages over other treatment methods can be achieved through simplicity, not using chemicals, and not requiring disposal of arsenic-rich wastes. A lab-scale test of in situ treatment by air sparging, using a solution with approximately 5.3 mg L(-1) ferrous iron and 200 µg L(-1) arsenate, showed removal of arsenate in the range of 59%. A significant obstacle exists, however, due to the interference of phosphate since phosphate competes for adsorption sites on oxidized iron precipitates. A lab-scale test including 0.5 mg L(-1) phosphate showed negligible removal of arsenate. In situ treatment by air sparging demonstrates considerable promise for removal of arsenic from groundwater where iron is present in considerable quantities and phosphates are low.

Evaluation of four human-associated fecal biomarkers in wastewater in Southern Ontario.

Human fecal biomarkers (HFBs) have a longstanding history in the field of microbial source tracking (MST) serving as indicators of human fecal contamination in drinking and recreational water. Further, HFBs have aided in recent efforts to monitor human pathogen transmission within communities. The dilution of wastewater from various sources throughout the sewershed cannot be controlled and human fecal biomarkers (HFBs) can be used to normalize target human pathogen concentrations so that fluctuations in fecal matter in wastewater can be accounted for. In the current study, we monitored the prevalence of four HFBs - including two viruses, Pepper mild mottle virus (PMMoV), cross-assembly phage (crAssphage), as well as two human-associated Bacteroides markers, HF183 and BacHuman - in wastewater samples from ten Southern Ontario wastewater treatment plants and evaluated their temporal and spatial variation in context of environmental factors that may impact the ability of HFB to normalize pathogen concentrations in wastewater. Environmental variables including precipitation, wastewater flow rate, temperature, and concentrated mass were also analyzed for their potential correlation with HFB variation in wastewater. The four HFBs were detected at high concentrations across all 10 sampling locations. The median concentrations across all sampling sites were: PMMoV 3.6 Log gene copies (GC)/mL; crAssphage 5.0 Log GC/mL; HF183 6.8 Log GC/mL and BacHuman 6.9 Log GC/mL. All HFBs were found to be similarly stratified across all 10 sites, and the bacterial markers were consistently found at higher concentration compared to the viral HFBs at all sites. The coefficient of variation (CV) for each HFB was used to characterize the variability of each biomarker at each sewershed. BacHuman and crAssphage were found to have lower CV than PMMoV and HF183, indicating that BacHuman and crAssphage may perform better in reflecting the variations in abundance of human feces in wastewater or MST applications.

Passive Samplers, an Important Tool for Continuous Monitoring of the COVID-19 Pandemic.

The global pandemic caused by COVID-19 has resulted in major costs around the world, costs with dimensions in every aspect, from peoples' daily living to the global economy. As the pandemic progresses, the virus evolves, and more vaccines become available, and the 'battle against the virus' continues. As part of the battle, Wastewater-Based Epidemiology (WBE) technologies are being widely deployed in essential roles for SARS-CoV-2 detection and monitoring. While focusing on demonstrating the advantages of passive samplers as a tool in WBE, this review provides a holistic view of the current WBE applications in monitoring SARS-CoV-2 with the integration of the most up-to-date data. A novel scenario example based on a recent Nanjing (China) outbreak in July 2021 is used to illustrate the potential benefits of using passive samplers to monitor COVID-19 and to facilitate effective control of future major outbreaks. The presented contents and how the application of passive samplers indicates that this technology can be beneficial at different levels, varying from building to community to regional. Countries and regions that have the pandemic well under control or have low positive case occurrences have the potential to significantly benefit from deploying passive samplers as a measure to identify and suppress outbreaks.

Use of AI to assess COVID-19 variant impacts on hospitalization, ICU, and death.

The rapid spread of COVID-19 and its variants have devastated communities worldwide, and as the highly transmissible Omicron variant becomes the dominant strain of the virus in late 2021, the need to characterize and understand the difference between the new variant and its predecessors has been an increasing priority for public health authorities. Artificial Intelligence has played a significant role in the analysis of various facets of COVID-19 since the early stages of the pandemic. This study proposes the use of AI, specifically an XGBoost model, to quantify the impact of various medical risk factors (or "population features") on the possibility of a patient outcome resulting in hospitalization, ICU admission, or death. The results are compared between the Delta and Omicron COVID-19 variants. Results indicated that older age and an unvaccinated patient status most consistently correspond as the most significant population features contributing to all three scenarios (hospitalization, ICU, death). The top 15 features for each variant-outcome scenario were determined, which most frequently included diabetes, cardiovascular disease, chronic kidney disease, and complications of pneumonia as highly significant population features contributing to serious illness outcomes. The Delta/Hospitalization model returned the highest performance metric scores for the area under the receiver operating characteristic (AUROC), F1, and Recall, while Omicron/ICU and Omicron/Hospitalization had the highest accuracy and precision values, respectively. The recall was found to be above 0.60 in most cases (with only two exceptions), indicating that the total number of false positives was generally minimized (accounting for more of the people who would theoretically require medical care).

Virus removal efficiency of Cambodian ceramic pot water purifiers.

Virus removal efficiency is described for three types of silver-impregnated, ceramic water filters (CWFs) produced in Cambodia. The tests were completed using freshly scrubbed filters and de-ionized (DI) water as an evaluation of the removal efficiency of the virus in isolation with no other interacting water quality variables. Removal efficiencies between 0.21 and 0.45 log are evidenced, which is significantly lower than results obtained in testing of similar filters by other investigators utilizing surface or rain water and a less frequent cleaning regime. Other experiments generally found virus removal efficiencies greater than 1.0 log. This difference may be because of the association of viruses with suspended solids, and subsequent removal of these solids during filtration. Variability in virus removal efficiencies between pots of the same manufacturer, and observed flow rates outside the manufacturer's specifications, suggest tighter quality control and consistency may be needed during production.

Incorporation of the Multiple Barrier Approach in drinking water risk assessment tools.

A number of existing risk assessment tools make reference to, or incorporate, a Multiple Barrier Approach to drinking water safety. Three waterborne disease outbreaks that occurred in developed nations were used as case studies to test a selected set of risk assessment tools. The outbreaks were used to determine how well the risk assessment tools identify hazards and vulnerabilities associated with different barriers to drinking water contamination.

The role of orthophosphate and dissolved oxygen in the performance of arsenic-iron removal plants in Bangladesh.

Arsenic iron removal plants (AIRPs) are used in some locations in Bangladesh to remove arsenic from groundwater to provide access to safer drinking water. In this study, the influence of orthophosphate in influent water on the performance of 21 (of 105) AIRPs installed in the Manikganj District was evaluated. The degree of aeration was also estimated, and the role of dissolved oxygen in AIRP performance is discussed. AIRP installations were done by a local non-governmental organization (The Society for People's Action in Change and Equity) with financial assistance from the Australian High Commission, Dhaka under the Direct Aid Program of the Australian Government. The presence of orthophosphate in the influent did not influence arsenic removal efficiency in the tested AIRPs, likely due to the high iron concentrations at all sites. The high iron provides adequate surface area for both orthophosphate and arsenic to be removed. Orthophosphate co-precipitated with iron oxides much more quickly than arsenic, in one cleaning cycle study, and is expected to play a more significant role in interfering with arsenic removal at sites with much lower iron concentrations. The aeration trays studied are estimated to introduce at least 2.4-3.7 mg/L of dissolved oxygen. In normal operation, sufficient oxygen is introduced through the aeration tray to fully oxidize all influent iron. The AIRPs studied show promise for use in areas of Bangladesh with high natural iron, where users are concerned with arsenic, iron, or both, in their drinking water.

Corrigendum: Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada.

[This corrects the article DOI: 10.3389/frai.2021.684609.].

Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada.

The worldwide rapid spread of the severe acute respiratory syndrome coronavirus 2 has affected millions of individuals and caused unprecedented medical challenges by putting healthcare services under high pressure. Given the global increase in number of cases and mortalities due to the current COVID-19 pandemic, it is critical to identify predictive features that assist identification of individuals most at-risk of COVID-19 mortality and thus, enable planning for effective usage of medical resources. The impact of individual variables in an XGBoost artificial intelligence model, applied to a dataset containing 57,390 individual COVID-19 cases and 2,822 patient deaths in Ontario, is explored with the use of SHapley Additive exPlanations values. The most important variables were found to be: age, date of the positive test, sex, income, dementia plus many more that were considered. The utility of SHapley Additive exPlanations dependency graphs is used to provide greater interpretation of the black-box XGBoost mortality prediction model, allowing focus on the non-linear relationships to improve insights. A "Test-date Dependency" plot indicates mortality risk dropped substantially over time, as likely a result of the improved treatment being developed within the medical system. As well, the findings indicate that people of lower income and people from more ethnically diverse communities, face an increased mortality risk due to COVID-19 within Ontario. These findings will help guide clinical decision-making for patients with COVID-19.

Asymptomatic Cases, the Hidden Challenge in Predicting COVID-19 Caseload Increases.

The numbers of novel coronavirus cases continue to grow at an unprecedented rate across the world. Attempts to control the growth of the virus using masks and social-distancing, and, recently, double-masking as well, continue to be difficult to maintain, in part due to the extent of asymptomatic cases. Analyses of large datasets consisting of 219,075 individual cases in Ontario, indicated that asymptomatic and pre-symptomatic cases are substantial in number. Large numbers of cases in children aged 0-9 were asymptomatic or had only one symptom (35.0% and 31.4% of total cases, respectively) and resulted in fever as the most common symptom (30.6% of total cases). COVID-19 cases in children were more likely to be milder symptomatic with cough not seen as frequently as in adults aged over 40, and past research has shown children to be index cases in familial clusters. These findings highlight the importance of targeting asymptomatic and mild infections in the continuing effort to control the spread of COVID-19. The Pearson correlation coefficient between test positivity rates and asymptomatic rates of -0.729 indicates that estimates of the asymptomatic rates should be obtained when the test positivity rates are lowest as the best approach.

Corrigendum: Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada.

[This corrects the article DOI: 10.3389/fpubh.2021.675766.].

Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada.

The Severe Acute Respiratory Syndrome Coronavirus 2 pandemic has challenged medical systems to the brink of collapse around the globe. In this paper, logistic regression and three other artificial intelligence models (XGBoost, Artificial Neural Network and Random Forest) are described and used to predict mortality risk of individual patients. The database is based on census data for the designated area and co-morbidities obtained using data from the Ontario Health Data Platform. The dataset consisted of more than 280,000 COVID-19 cases in Ontario for a wide-range of age groups; 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, and 90+. Findings resulting from using logistic regression, XGBoost, Artificial Neural Network and Random Forest, all demonstrate excellent discrimination (area under the curve for all models exceeded 0.948 with the best performance being 0.956 for an XGBoost model). Based on SHapley Additive exPlanations values, the importance of 24 variables are identified, and the findings indicated the highest importance variables are, in order of importance, age, date of test, sex, and presence/absence of chronic dementia. The findings from this study allow the identification of out-patients who are likely to deteriorate into severe cases, allowing medical professionals to make decisions on timely treatments. Furthermore, the methodology and results may be extended to other public health regions.

A critical evaluation of two point-of-use water treatment technologies: can they provide water that meets WHO drinking water guidelines?

Point-of-use (POU) technologies have been proposed as solutions for meeting the Millennium Development Goal (MDG) for safe water. They reduce the risk of contamination between the water source and the home, by providing treatment at the household level. This study examined two POU technologies commonly used around the world: BioSand and ceramic filters. While the health benefits in terms of diarrhoeal disease reduction have been fairly well documented for both technologies, little research has focused on the ability of these technologies to treat other contaminants that pose health concerns, including the potential for formation of contaminants as a result of POU treatment. These technologies have not been rigorously tested to see if they meet World Health Organization (WHO) drinking water guidelines. A study was developed to evaluate POU BioSand and ceramic filters in terms of microbiological and chemical quality of the treated water. The following parameters were monitored on filters in rural Cambodia over a six-month period: iron, manganese, fluoride, nitrate, nitrite and Escherichia coli. The results revealed that these technologies are not capable of consistently meeting all of the WHO drinking water guidelines for these parameters.

Nitrification, denitrification and ammonification in point-of-use biosand filters in rural Cambodia.

In order to address the United Nations Millennium Development Goal (MDG) target #7 for water and sanitation, the World Health Organization (WHO) has identified point-of-use (POU) water treatment technologies as an option for providing safe water to households. The BioSand filter (BSF) is a commonly used POU system that has been implemented in Cambodia and over 20 countries worldwide. While the health benefits of using a BSF in terms of reduction of diarrheal disease have been fairly well documented, little research has focused on the ability of this technology to treat for other contaminants that could pose health concerns. To address these concerns, a study was developed to evaluate this technology in rural Cambodia in terms of microbiological and chemical quality of the treated water. The study revealed that simultaneous nitrification and denitrification is occurring inside the BioSand filters. Nitrite concentrations in treated water consistently exceeded WHO guidelines. Seventeen of 20 filters on average did not meet the 3.0 mg l(-1) NO2- guideline and the combined nitrate-nitrite guideline ratio of 1. Denitrification seemed to predominate when BSFs were fed surface water. In addition, nitrate-ammonification occurred in some filters fed surface water, causing increases in ammonia in treated water.

Risk assessment of hybrid rain harvesting system and other small drinking water supply systems by game theory and fuzzy logic modeling.

The complexity and uncertainties affecting drinking water supply systems and threatening hazards require a comprehensive and effective risk assessment to increase the reliability of drinking water safety, especially for small or household systems. This study presents a hierarchical structure risk assessment model based on fuzzy logic and game theory to assess the quantity, treatment technology, distribution systems, and storage at household level risks, for small systems in remote areas. Game theory combined with an analytical hierarchy process with entropy weight method is employed. The efficiency tradeoffs in risk from use of this model are examined in a case study which includes three types of small systems (i.e., rainwater harvesting, surface water combined with rainwater and groundwater) in Gansu Province, China. Fifteen risk factors are employed, with evaluation results showing that "Source water" is the most important factor. The hybrid (surface & rainwater) system in the driest year has "Medium" risk with the highest aggregate risk value as a result of source water availability and the distribution system is the most susceptible to failure. The groundwater system consistently has the lowest risk in the case study area. The utility of the model provides scientific support to decision-makers to plan for the most effective risk mitigation measures for water supply systems in remote areas. A cloud-based online-platform employing this methodology has been developed to facilitate the adoption of the methodology in remote areas with mobile or internet access.

Ecological network analysis of an urban water metabolic system based on input-output model: A case study of Guangdong, China.

When considering a region as a superorganism, there are various processes of metabolism that reflect the growth and maintenances of the system, as well as the interactions with the surroundings. Ecological Network Analysis combined with the input-output model is utilized to analyze the water consumption structure and the interaction control relationships among different sectors within the urban ecosystem. The integrated approach is applied to the case study of Guangdong Province, China. The Water Ecological Network model is developed by the monetary I-O table of Guangdong province in 2012. The network control analysis is employed to describe the pathway of indirect water. In addition, the competitive and mutual relationship among different sectors is concluded by the approach of network utility analysis. This study also newly compares "virtual water (which is the indirect water) imports of per unit Gross Domestic Product (GDP)" and "water consumption of per unit GDP" to reveal which of them contributes more to the urban metabolism. The results indicate that the top three controllers are machinery, equipment and other services, followed closely by food and tobacco processing. The model results also show that the negative effects among different sectors accounting for the majority in all relationships, which indicates that the system under investigation is not in a mutualism state. This is due to that the competition of water uses between the economic sectors weaken the virtual water circulation within the system. The results are valuable to provide scientific suggestions for improving water use efficiency and make reasonable virtual water policy.

Using Bayesian statistics to estimate the coefficients of a two- component second-order chlorine bulk decay model for a water distribution system.

Most chlorine decay models for the bulk phase in a water distribution system consider only chlorine concentration and time. Clark [1998. Chlorine demand and trihalomethane formation kinetics: a second-order model. J. Environ. Eng. 124(1), 16-24] first proposed a two-component second-order chlorine decay model based on the concept of competing reacting substances. A corrected mathematical formulation is developed and, because the recent findings suggested that not all natural organic matter (NOM) is involved in the chlorine decay process, an additional parameter is introduced. A parameter assignment method employing Bayesian statistical analysis incorporating Monte Carlo Markov chain (MCMC) with Gibbs sampling to make inferences, is employed in the estimation of model parameters. Three parameters are estimated for the model, namely the ratio of chlorine to TOC, the chlorine reaction rate, and a fraction factor of TOC which represents the true amount of TOC involved in chlorine decay process. Water samples taken from Goderich in the summer of 2005, are used for estimating the parameters.

A methodology for solid waste characterization based on diminishing marginal returns.

A methodology is developed for estimating the number of waste sorts for characterizing solid wastes into categories based on diminishing minimum incremental information. Convergence in the square of the coefficient of variation with successive waste sorts is used to indicate cost-efficient termination of sampling at substantially reduced numbers of sorts in comparison with existing methodologies. These findings indicate that the numbers of waste sorts beyond that determined using the proposed methodology do not add substantial marginal gains in information and/or reduction in the confidence interval of the estimate. The methodology is demonstrated using waste composition analyses from the Greater Vancouver Regional District where 22 waste sorts are examined. The proposed methodology is simple, and the number of waste sorts can be estimated with a hand-held calculator and utilized in the field.