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.

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).

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.

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.

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.].

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.

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.

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.

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.

Describing variability of MSW composition data with the log-logistic distribution.

Variations in solid waste composition data are necessary as inputs to solid waste planning, yet uncertainty exists regarding which probability distributions might be generally valuable to describe the variability. Twenty-two detailed analyses of solid waste from British Columbia, Canada, were fitted to distributions using the BestFit software. Alternative distributions were ranked based on three goodness-of-fit parameters and twelve waste fractions. The log-logistic distribution was found to be the most able to fit over the wide range of composition types considered. The results were demonstrated to be insensitive to the number of waste components or to the choice of a two- or three-parameter distribution. Although other distributions were able to better match the waste composition for individual waste types, the log-logistic distribution was demonstrated to fit, overall, a wide variety of waste composition types.