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.

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).

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.

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.

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.].

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.].

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.

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.

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.

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.

Estimating Tortuosity Coefficients Based on Hydraulic Conductivity.

While the tortuosity coefficient is commonly estimated using an expression based on total porosity, this relationship is demonstrated to not be applicable (and thus is often misapplied) over a broad range of soil textures. The fundamental basis for a correlation between the apparent diffusion tortuosity coefficient and hydraulic conductivity is demonstrated, although such a relationship is not typically considered. An empirical regression for estimating the tortuosity coefficient based on hydraulic conductivity for saturated, unconsolidated soil is derived based on results from 14 previously reported diffusion experiments performed with a broad range of soil textures. Analyses of these experimental results confirm that total porosity is a poor predictor for the tortuosity coefficient over a large range of soil textures. The apparent diffusion tortuosity coefficient is more reliably estimated based on hydraulic conductivity.

Human health risk assessment from arsenic exposures in Bangladesh.

High arsenic exposures, prevalent through dietary and non-dietary sources in Bangladesh, present a major health risk to the public. A quantitative human health risk assessment is described as a result of arsenic exposure through food and water intake, tea intake, accidental soil ingestion, and chewing of betel quid, while people meet their desirable dietary intake requirements throughout their lifetime. In evaluating the contribution of each intake pathway to average daily arsenic intake, the results show that food and water intake combined, makes up approximately 98% of the daily arsenic intake with the balance contributed to by intake pathways such as tea consumption, soil ingestion, and quid consumption. Under an exposure scenario where arsenic concentration in water is at the WHO guideline (0.01 mg/L), food intake is the major arsenic intake pathway ranging from 67% to 80% of the average daily arsenic intake. However, the contribution from food drops to a range of 29% to 45% for an exposure scenario where arsenic in water is at the Bangladesh standard (0.05 mg/L). The lifetime excess risk of cancer occurrence from chronic arsenic exposure, considering a population of 160 million people, based on an exposure scenario with 85 million people at the WHO guideline value and 75 million people at the Bangladesh standard, and assuming that 35 million people are associated with a heavy activity level, is estimated as 1.15 million cases.

A critical review of arsenic exposures for Bangladeshi adults.

Groundwater, the most important source of water for drinking, cooking, and irrigation in Bangladesh, is a significant contributor to the daily human intake of arsenic. Other arsenic intake pathways, established as relevant for Bangladeshi adults through this review, include consumption of contaminated edible plant parts and animal-origin food, inhalation of contaminated air, soil ingestion, betel quid chewing, and tobacco smoking. This review qualifies and quantifies these arsenic intake pathways through analysis of the range of arsenic levels observed in different food types, water, soil, and air in Bangladesh, and highlights the contributions of dietary intake variation and cooking method in influencing arsenic exposures. This study also highlights the potential of desirable dietary patterns and intakes in increasing arsenic exposure which is relevant to Bangladesh where nutritional deficiencies and lower-than-desirable dietary intakes continue to be a major concern.

A risk-based approach to sanitary sewer pipe asset management.

Wastewater collection systems are an important component of proper management of wastewater to prevent environmental and human health implications from mismanagement of anthropogenic waste. Due to aging and inadequate asset management practices, the wastewater collection assets of many cities around the globe are in a state of rapid decline and in need of urgent attention. Risk management is a tool which can help prioritize resources to better manage and rehabilitate wastewater collection systems. In this study, a risk matrix and a weighted sum multi-criteria decision-matrix are used to assess the consequence and risk of sewer pipe failure for a mid-sized city, using ArcGIS. The methodology shows that six percent of the uninspected sewer pipe assets of the case study have a high consequence of failure while four percent of the assets have a high risk of failure and hence provide priorities for inspection. A map incorporating risk of sewer pipe failure and consequence is developed to facilitate future planning, rehabilitation and maintenance programs. The consequence of failure assessment also includes a novel failure impact factor which captures the effect of structurally defective stormwater pipes on the failure assessment. The methodology recommended in this study can serve as a basis for future planning and decision making and has the potential to be universally applied by municipal sewer pipe asset managers globally to effectively manage the sanitary sewer pipe infrastructure within their jurisdiction.

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.

In situ treatment of arsenic-contaminated groundwater by air sparging.

Arsenic contamination of groundwater is a major problem in some areas of the world, particularly in West Bengal (India) and Bangladesh where it is caused by reducing conditions in the aquifer. In situ treatment, if it can be proven as operationally feasible, has the potential to capture some advantages over other treatment methods by being fairly simple, not using chemicals, and not necessitating disposal of arsenic-rich wastes. In this study, the potential for in situ treatment by injection of compressed air directly into the aquifer (i.e. air sparging) is assessed. An experimental apparatus was constructed to simulate conditions of arsenic-rich groundwater under anaerobic conditions, and in situ treatment by air sparging was employed. Arsenic (up to 200 µg/L) was removed to a maximum of 79% (at a local point in the apparatus) using a solution with dissolved iron and arsenic only. A static "jar" test revealed arsenic removal by co-precipitation with iron at a molar ratio of approximately 2 (iron/arsenic). This is encouraging since groundwater with relatively high amounts of dissolved iron (as compared to arsenic) therefore has a large theoretical treatment capacity for arsenic. Iron oxidation was significantly retarded at pH values below neutral. In terms of operation, analysis of experimental results shows that periodic air sparging may be feasible.

Estimating water content in an active landfill with the aid of GPR.

Landfill gas (LFG) receives a great deal of attention due to both negative and positive environmental impacts, global warming and a green energy source, respectively. However, predicting the quantity of LFG generated at a given landfill, whether active or closed is difficult due to the heterogeneities present in waste, and the lack of accurate in situ waste parameters like water content. Accordingly, ground penetrating radar (GPR) was evaluated as a tool for estimating in situ water content. Due to the large degree of subsurface heterogeneity and the electrically conductive clay cap covering landfills, both of which affect the transmission of the electromagnetic pulses, there is much scepticism concerning the use of GPR to quantify in situ water content within a municipal landfill. Two landfills were studied. The first landfill was used to develop the measurement protocols, while the second landfill provided a means of confirming these protocols. GPR measurements were initially completed using the surface GPR approach, but the lack of success led to the use of borehole (BH) GPR. Both zero offset profiling (ZOP) and multiple offset gathers (MOG) modes were tried, with the results indicating that BH GPR using the ZOP mode is the most simple and efficient method to measure in situ water content. The best results were obtained at a separation distance of 2m, where higher the water content, smaller the effective separation distance. However, an increase in water content did appear to increase the accuracy of the GPR measurements. For the effective separation distance of 2m at both landfills, the difference between GPR and lab measured water contents were reasonable at 33.9% for the drier landfill and 18.1% for the wetter landfill. Infiltration experiments also showed the potential to measure small increases in water content.

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.