Quantitative microbial risk assessment for ingestion of antibiotic resistance genes from private wells contaminated by human and livestock fecal sources.

We used quantitative microbial risk assessment to estimate ingestion risk for intI1, erm(B), sul1, tet(A), tet(W), and tet(X) in private wells contaminated by human and/or livestock feces. Genes were quantified with five human-specific and six bovine-specific microbial source-tracking (MST) markers in 138 well-water samples from a rural Wisconsin county. Daily ingestion risk (probability of swallowing ≥1 gene) was based on daily water consumption and a Poisson exposure model. Calculations were stratified by MST source and soil depth over the aquifer where wells were drilled. Relative ingestion risk was estimated using wells with no MST detections and >6.1 m soil depth as a referent category. Daily ingestion risk varied from 0 to 8.8 × 10-1 by gene and fecal source (i.e., human or bovine). The estimated number of residents ingesting target genes from private wells varied from 910 (tet(A)) to 1,500 (intI1 and tet(X)) per day out of 12,000 total. Relative risk of tet(A) ingestion was significantly higher in wells with MST markers detected, including wells with ≤6.1 m soil depth contaminated by bovine markers (2.2 [90% CI: 1.1-4.7]), wells with >6.1 m soil depth contaminated by bovine markers (1.8 [1.002-3.9]), and wells with ≤6.1 m soil depth contaminated by bovine and human markers simultaneously (3.1 [1.7-6.5]). Antibiotic resistance genes (ARGs) were not necessarily present in viable microorganisms, and ingestion is not directly associated with infection. However, results illustrate relative contributions of human and livestock fecal sources to ARG exposure and highlight rural groundwater as a significant point of exposure.IMPORTANCEAntibiotic resistance is a global public health challenge with well-known environmental dimensions, but quantitative analyses of the roles played by various natural environments in transmission of antibiotic resistance are lacking, particularly for drinking water. This study assesses risk of ingestion for several antibiotic resistance genes (ARGs) and the class 1 integron gene (intI1) in drinking water from private wells in a rural area of northeast Wisconsin, United States. Results allow comparison of drinking water as an exposure route for antibiotic resistance relative to other routes like food and recreational water. They also enable a comparison of the importance of human versus livestock fecal sources in the study area. Our study demonstrates the previously unrecognized importance of untreated rural drinking water as an exposure route for antibiotic resistance and identifies bovine fecal material as an important exposure factor in the study setting.

Occurrence, transmission and risks assessment of pathogens in aquatic environments accessible to humans.

Pathogens are ubiquitously detected in various natural and engineered water systems, posing potential threats to public health. However, it remains unclear which human-accessible waters are hotspots for pathogens, how pathogens transmit to these waters, and what level of health risk associated with pathogens in these environments. This review collaboratively focuses and summarizes the contamination levels of pathogens on the 5 water systems accessible to humans (natural water, drinking water, recreational water, wastewater, and reclaimed water). Then, we showcase the pathways, influencing factors and simulation models of pathogens transmission and survival. Further, we compare the health risk levels of various pathogens through Quantitative Microbial Risk Assessment (QMRA), and assess the limitations of water-associated QMRA application. Pathogen levels in wastewater are consistently higher than in other water systems, with no significant variation for Cryptosporidium spp. among five water systems. Hydraulic conditions primarily govern the transmission of pathogens into human-accessible waters, while environmental factors such as temperature impact pathogens survival. The median and mean values of computed public health risk levels posed by pathogens consistently surpass safety thresholds, particularly in the context of recreational waters. Despite the highest pathogens levels found in wastewater, the calculated health risk is significantly lower than in other water systems. Except pathogens concentration, variables like the exposure mode, extent, and frequency are also crucial factors influencing the public health risk in water systems. This review shares valuable insights to the more accurate assessment and comprehensive management of public health risk in human-accessible water environments.

Evolution and health risk of indicator microorganisms in landscape water replenished by reclaimed water.

As an important means to solve water shortage, reclaimed water has been widely used for landscape water supply. However, with the emergence of large-scale epidemic diseases such as SARS, avian influenza and COVID-19 in recent years, people are increasingly concerned about the public health safety of reclaimed water discharged into landscape water, especially the pathogenic microorganisms in it. In this study, the water quality and microorganisms of the Old Summer Palace, a landscape water body with reclaimed water as the only replenishment water source, were tracked through long-term dynamic monitoring. And the health risks of indicator microorganisms were analyzed using Quantitative Microbial Risk Assessment (QMRA). It was found that the concentration of indicator microorganisms Enterococcus (ENT), Escherichia coli (EC) and Fecal coliform (FC) generally showed an upward trend along the direction of water flow and increased by more than 0.6 log at the end of the flow. The concentrations of indicator microorganisms were higher in summer and autumn than those in spring. And there was a positive correlation between the concentration of indicator microorganisms and COD. Further research suggested that increased concentration of indicator microorganisms also led to increased health risks, which were more than 30% higher in other areas of the park than the water inlet area and required special attention. In addition, (water) surface operation exposure pathway had much higher health risks than other pathways and people in related occupations were advised to take precautions to reduce the risks.

Combined Infection Control Interventions Protect Essential Food Workers from Occupational Exposures to SARS-CoV-2 in the Agricultural Environment.

Essential food workers experience elevated risks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection due to prolonged occupational exposures in food production and processing areas, shared transportation (car or bus), and employer-provided shared housing. Our goal was to quantify the daily cumulative risk of SARS-CoV-2 infection for healthy susceptible produce workers and to evaluate the relative reduction in risk attributable to food industry interventions and vaccination. We simulated daily SARS-CoV-2 exposures of indoor and outdoor produce workers through six linked quantitative microbial risk assessment (QMRA) model scenarios. For each scenario, the infectious viral dose emitted by a symptomatic worker was calculated across aerosol, droplet, and fomite-mediated transmission pathways. Standard industry interventions (2-m physical distancing, handwashing, surface disinfection, universal masking, ventilation) were simulated to assess relative risk reductions from baseline risk (no interventions, 1-m distance). Implementation of industry interventions reduced an indoor worker's relative infection risk by 98.0% (0.020; 95% uncertainty interval [UI], 0.005 to 0.104) from baseline risk (1.00; 95% UI, 0.995 to 1.00) and an outdoor worker's relative infection risk by 94.5% (0.027; 95% UI, 0.013 to 0.055) from baseline risk (0.487; 95% UI, 0.257 to 0.825). Integrating these interventions with two-dose mRNA vaccinations (86 to 99% efficacy), representing a worker's protective immunity to infection, reduced the relative infection risk from baseline for indoor workers by 99.9% (0.001; 95% UI, 0.0002 to 0.005) and outdoor workers by 99.6% (0.002; 95% UI, 0.0003 to 0.005). Consistent implementation of combined industry interventions, paired with vaccination, effectively mitigates the elevated risks from occupationally acquired SARS-CoV-2 infection faced by produce workers. IMPORTANCE This is the first study to estimate the daily risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across a variety of indoor and outdoor environmental settings relevant to food workers (e.g., shared transportation [car or bus], enclosed produce processing facility and accompanying breakroom, outdoor produce harvesting field, shared housing facility) through a linked quantitative microbial risk assessment framework. Our model has demonstrated that the elevated daily SARS-CoV-2 infection risk experienced by indoor and outdoor produce workers can be reduced below 1% when vaccinations (optimal vaccine efficacy, 86 to 99%) are implemented with recommended infection control strategies (e.g., handwashing, surface disinfection, universal masking, physical distancing, and increased ventilation). Our novel findings provide scenario-specific infection risk estimates that can be utilized by food industry managers to target high-risk scenarios with effective infection mitigation strategies, which was informed through more realistic and context-driven modeling estimates of the infection risk faced by essential food workers daily. Bundled interventions, particularly if they include vaccination, yield significant reductions (>99%) in daily SARS-CoV-2 infection risk for essential food workers in enclosed and open-air environments.

Social Distance Approximation on Public Transport Using Stereo Depth Camera and Passenger Pose Estimation.

In order to effectively balance enforced guidance/regulation during a pandemic and limit infection transmission, with the necessity for public transportation services to remain safe and operational, it is imperative to understand and monitor environmental conditions and typical behavioural patterns within such spaces. Social distancing ability on public transport as well as the use of advanced computer vision techniques to accurately measure this are explored in this paper. A low-cost depth-sensing system is deployed on a public bus as a means to approximate social distancing measures and study passenger habits in relation to social distancing. The results indicate that social distancing on this form of public transport is unlikely for an individual beyond a 28% occupancy threshold, with an 89% chance of being within 1-2 m from at least one other passenger and a 57% chance of being within less than one metre from another passenger at any one point in time. Passenger preference for seating is also analysed, which clearly demonstrates that for typical passengers, ease of access and comfort, as well as seats having a view, are preferred over maximising social-distancing measures. With a highly detailed and comprehensive set of acquired data and accurate measurement capability, the employed equipment and processing methodology also prove to be a robust approach for the application.

The probability of bacterial spores surviving a thermal process: The 12D myth and other issues with its quantitative assessment.

The concepts of "D-value," "thermal death time" and "commercial sterility," innovative and useful at their inception, are based on untenable assumptions, notably that the log-linear isothermal inactivation model has universal applicability, that extrapolation over several orders of magnitude below the detection level is permissible, and that total microbial inactivation is theoretically impossible. Almost all commonly observed inactivation patterns, the log-linear is just a special case, can be described by both deterministic and fully stochastic models, examples of which are given. Unlike the deterministic, the stochastic models predict either complete elimination of the targeted cells or spores in realistic finite time, or residual survival. In most cases, the published survival data do not contain enough information to establish which actually happens. The microbial safety of thermally processed foods can be compromised not only by under-processing but also by a variety of mishaps whose occurrence probabilities are unrelated to the inactivation kinetics. Moreover, the available sampling plans to detect microbial contamination in sterilized containers through incubation alone are insensitive to levels of potential safety concerns. In principle, many of these issues could be resolved by developing new dramatically improved detection methods and/or verifiable methods to predict very low levels of microbial survival.

Statewide Quantitative Microbial Risk Assessment for Waterborne Viruses, Bacteria, and Protozoa in Public Water Supply Wells in Minnesota.

Infection risk from waterborne pathogens can be estimated via quantitative microbial risk assessment (QMRA) and forms an important consideration in the management of public groundwater systems. However, few groundwater QMRAs use site-specific hazard identification and exposure assessment, so prevailing risks in these systems remain poorly defined. We estimated the infection risk for 9 waterborne pathogens based on a 2-year pathogen occurrence study in which 964 water samples were collected from 145 public wells throughout Minnesota, USA. Annual risk across all nine pathogens combined was 3.3 × 10-1 (95% CI: 2.3 × 10-1 to 4.2 × 10-1), 3.9 × 10-2 (2.3 × 10-2 to 5.4 × 10-2), and 1.2 × 10-1 (2.6 × 10-2 to 2.7 × 10-1) infections person-1 year-1 for noncommunity, nondisinfecting community, and disinfecting community wells, respectively. Risk estimates exceeded the U.S. benchmark of 10-4 infections person-1 year-1 in 59% of well-years, indicating that the risk was widespread. While the annual risk for all pathogens combined was relatively high, the average daily doses for individual pathogens were low, indicating that significant risk results from sporadic pathogen exposure. Cryptosporidium dominated annual risk, so improved identification of wells susceptible to Cryptosporidium contamination may be important for risk mitigation.

Developing an Integrated "Regression-QMRA method" to Predict Public Health Risks of Low Impact Developments (LIDs) for Improved Planning.

Worldwide Low Impact Developments (LIDs) are used for sustainable stormwater management; however, both the stormwater and LIDs carry microbial pathogens. The widespread development of LIDs is likely to increase human exposure to pathogens and risk of infection, leading to unexpected disease outbreaks in urban communities. The risk of infection from exposure to LIDs has been assessed via Quantitative Microbial Risk Assessment (QMRA) during the operation of these infrastructures; no effort is made to evaluate these risks during the planning phase of LID treatment train in urban communities. We developed a new integrated "Regression-QMRA method" by examining the relationship between pathogens' concentration and environmental variables. Applying of this methodology to a planned LID train shows that the predicted disease burden of diarrhea from Campylobacter is highest (i.e. 16.902 DALYs/1000 persons/yr) during landscape irrigation and playing on the LID train, followed by Giardia, Cryptosporidium, and Norovirus. These results illustrate that the risk of microbial infection can be predicted during the planning phase of LID treatment train. These predictions are of great value to municipalities and decision-makers to make informed decisions and ensure risk-based planning of stormwater systems before their development.

Assessment of the risk of Legionella pneumophila in water distribution systems in hospitals of Tehran city.

When a sensitive host inhales aerosols containing these bacteria, Legionella infection occurs. Therefore, monitoring and assessing Legionella in the environment and water distribution systems of such places are critical due to the prone population in hospitals. However, the health risks of Legionella bacteria in the environment are not adequately evaluated. In this study, for hospitalized patients, we performed a quantitative health risk assessment of Legionella in selected hospitals in Tehran city using two scenarios of shower and toilet faucet exposure. This study identified Legionella in 38 cases (38%) out of 100 samples collected from toilet faucets and showers in 8 hospitals. The information gathered was used for quantitative microbial risk assessment (QMRA). The microbial load transmitted by inhalation was calculated using the concentration of Legionella in water. Other exposure parameters (inhalation rate and exposure time) were obtained using information from other studies and the median length of hospital stay (3.6 days). The exponential model was used to estimate the risk of infection (γ = 0.06) due to Legionella pneumophila (L. pneumophila) inhalation for each exposure event. For the mean concentration obtained for Legionella (103 CFU/L), the risk of infection for toilet faucets and showers was in the range of 0.23-2.3 and 3.5-21.9, respectively, per 10,000 hospitalized patients. The results were compared with the tolerable risk level of infection determined by the US EPA and WHO. The risk values exceeded the WHO values for waterborne pathogens in hospitals in both exposure scenarios. As a result, our QMRA results based on monitoring data showed that despite using treated water (from distribution networks in the urban areas) by hospitals, 38% of the samples were contaminated with Legionella, and faucets and showers can be sources of Legionella transmission. Hence, to protect the health of hospitalized patients, the risk of Legionella infection should be considered.

Legionnaires' disease in dental offices: Quantifying aerosol risks to dental workers and patients.

Legionella pneumophila is an opportunistic bacterial respiratory pathogen that is one of the leading causes of drinking water outbreaks in the United States. Dental offices pose a potential risk for inhalation or aspiration of L. pneumophila due to the high surface area to volume ratio of dental unit water lines-a feature that is conducive to biofilm growth. This is coupled with the use of high-pressure water devices (e.g., ultrasonic scalers) that produce fine aerosols within the breathing zone. Prior research confirms that L. pneumophila occurs in dental unit water lines, but the associated human health risks have not been assessed. We aimed to: (1) synthesize the evidence for transmission and management of Legionnaires' disease in dental offices; (2) create a quantitative modeling framework for predicting associated L. pneumophila infection risk; and (3) highlight influential parameters and research gaps requiring further study. We reviewed outbreaks, management guidance, and exposure studies and used these data to parameterize a quantitative microbial risk assessment (QMRA) model for L. pneumophila in dental applications. Probabilities of infection for dental hygienists and patients were assessed on a per-exposure and annual basis. We also assessed the impact of varying ventilation rates and the use of personal protective equipment (PPE). Following an instrument purge (i.e., flush) and with a ventilation rate of 1.2 air changes per hour, the median per-exposure probability of infection for dental hygienists and patients exceeded a 1-in-10,000 infection risk benchmark. Per-exposure risks for workers during a purge and annual risks for workers wearing N95 masks did not exceed the benchmark. Increasing air change rates in the treatment room from 1.2 to 10 would achieve an ∼85% risk reduction, while utilization of N95 respirators would reduce risks by ∼95%. The concentration of L. pneumophila in dental unit water lines was a dominant parameter in the model and driver of risk. Future risk assessment efforts and refinement of microbiological control protocols would benefit from expanded occurrence datasets for L. pneumophila in dental applications.

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

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.

Impact of sludge bulking on receiving environment using quantitative microbial risk assessment (QMRA)-based management for full-scale wastewater treatment plants.

During sludge bulking in wastewater treatment plants (WWTPs), high amounts of potentially pathogenic bacteria would release into the environment, causing various human-health risks. This is the first study attempting to assess the microbial infections associated with the reuse of WWTP effluents under various bulking conditions. Three common waterborne pathogens, viz., E. coli O157:H7, Salmonella, and Mycobacterium, were quantified from full-scale WWTPs using DNA extraction and qPCR at different sludge volume indices (SVIs). The detected pathogens were incorporated into a quantitative microbial risk assessment (QMRA) model to determine the applicability of WWTP discharge for recreational (bathing) activities and agricultural practices. The QMRA exposures were children, women, and men during swimming, and farmers and vegetable consumers during irrigation. Bacterial abundance in the treated wastewater increased in response to SVIs, and the QMRA values at all bulking events exceeded the tolerable risk of one case of infection per 10,000 people per year. Hence, various disinfection scenarios (chlorination, ultraviolet, and ozonation) were hypothetically tested to control the risks associated with pathogenic bacteria, allowing for safe disposal and reuse of the treated effluent. The ultraviolet application provided the highest ability to inactivate the pathogenic bacteria, except for the case of children exposed to Salmonella infection during swimming. The reduction of Mycobacterium infection risks with either chlorination or ozonation showed inefficient results. This study would be helpful for the management of human health risks associated with effluent wastewater containing pathogens, i.e., particularly concerning the case of sludge bulking.

Application of a QMRA Framework to Inform Selection of Drinking Water Interventions in the Developing Context.

The aim of this study was to develop a modified quantitative microbial risk assessment (QMRA) framework that could be applied as a decision support tool to choose between alternative drinking water interventions in the developing context. The impact of different household water treatment (HWT) interventions on the overall incidence of diarrheal disease and disability adjusted life years (DALYs) was estimated, without relying on source water pathogen concentration as the starting point for the analysis. A framework was developed and a software tool constructed and then implemented for an illustrative case study for Nepal based on published scientific data. Coagulation combined with free chlorine disinfection provided the greatest estimated health gains in the short term; however, when long-term compliance was incorporated into the calculations, the preferred intervention was porous ceramic filtration. The model demonstrates how the QMRA framework can be used to integrate evidence from different studies to inform management decisions, and in particular to prioritize the next best intervention with respect to estimated reduction in diarrheal incidence. This study only considered HWT interventions; it is recognized that a systematic consideration of sanitation, recreation, and drinking water pathways is important for effective management of waterborne transmission of pathogens, and the approach could be expanded to consider the broader water-related context.

Risk of norovirus gastroenteritis from consumption of vegetables irrigated with highly treated municipal wastewater--evaluation of methods to estimate sewage quality.

Quantitative microbial risk assessment was used to assess the risk of norovirus gastroenteritis associated with consumption of raw vegetables irrigated with highly treated municipal wastewater, using Melbourne, Australia as an example. In the absence of local norovirus concentrations, three methods were developed: (1) published concentrations of norovirus in raw sewage, (2) an epidemiological method using Melbourne prevalence of norovirus, and (3) an adjustment of method 1 to account for prevalence of norovirus. The methods produced highly variable results with estimates of norovirus concentrations in raw sewage ranging from 10(4) per milliliter to 10(7) per milliliter and treated effluent from 1 × 10(-3) per milliliter to 3 per milliliter (95th percentiles). Annual disease burden was very low using method 1, from 4 to 5 log10 disability adjusted life years (DALYs) below the 10(-6) threshold (0.005-0.1 illnesses per year). Results of method 2 were higher, with some scenarios exceeding the threshold by up to 2 log10 DALYs (up to 95,000 illnesses per year). Method 3, thought to be most representative of Melbourne conditions, predicted annual disease burdens >2 log10 DALYs lower than the threshold (∼ 4 additional cases per year). Sensitivity analyses demonstrated that input parameters used to estimate norovirus concentration accounted for much of the model output variability. This model, while constrained by a lack of knowledge of sewage concentrations, used the best available information and sound logic. Results suggest that current wastewater reuse behaviors in Melbourne are unlikely to cause norovirus risks in excess of the annual DALY health target.

Evaluating the potential for a Helicobacter pylori drinking water guideline.

Helicobacter pylori is a microaerophilic, gram-negative bacterium that is linked to adverse health effects including ulcers and gastrointestinal cancers. The goal of this analysis is to develop the necessary inputs for a quantitative microbial risk assessment (QMRA) needed to develop a potential guideline for drinking water at the point of ingestion (e.g., a maximum contaminant level, or MCL) that would be protective of human health to an acceptable level of risk while considering sources of uncertainty. Using infection and gastric cancer as two discrete endpoints, and calculating dose-response relationships from experimental data on humans and monkeys, we perform both a forward and reverse risk assessment to determine the risk from current reported surface water concentrations of H. pylori and an acceptable concentration of H. pylori at the point of ingestion. This approach represents a synthesis of available information on human exposure to H. pylori via drinking water. A lifetime risk of cancer model suggests that a MCL be set at <1 organism/L given a 5-log removal treatment because we cannot exclude the possibility that current levels of H. pylori in environmental source waters pose a potential public health risk. Research gaps include pathogen occurrence in source and finished water, treatment removal rates, and determination of H. pylori risks from other water sources such as groundwater and recreational water.

Greywater recycling for diverse collection scales and appliances: Enteric pathogen log-removal targets and treatment trains.

In light of increasingly diverse greywater reuse applications, this study proposes risk-based log-removal targets (LRTs) to aid the selection of treatment trains for greywater recycling at different collection scales, including appliance-scale reuse of individual greywater streams. An epidemiology-based model was used to simulate the concentrations of prevalent and treatment-resistant reference pathogens (protozoa: Giardia and Cryptosporidium spp., bacteria: Salmonella and Campylobacter spp., viruses: rotavirus, norovirus, adenovirus, and Coxsackievirus B5) in the greywater streams for collection scales of 5-, 100-, and a 1000-people. Using quantitative microbial risk assessment (QMRA), we calculated LRTs to meet a health benchmark of 10-4 infections per person per year over 10'000 Monte Carlo iterations. LRTs were highest for norovirus at the 5-people scale and for adenovirus at the 100- and 1000-people scales. Example treatment trains were designed to meet the 95 % quantiles of LRTs. Treatment trains consisted of an aerated membrane bioreactor, chlorination, and, if required, UV disinfection. In most cases, rotavirus, norovirus, adenovirus and Cryptosporidium spp. determined the overall treatment train requirements. Norovirus was most often critical to dimension the chlorination (concentration × time values) and adenovirus determined the required UV dose. Smaller collection scales did not generally allow for simpler treatment trains due to the high LRTs associated with viruses, with the exception of recirculating washing machines and handwashing stations. Similarly, treating greywater sources individually resulted in lower LRTs, but the lower required LRTs nevertheless did not generally allow for simpler treatment trains. For instance, LRTs for a recirculating washing machine were around 3-log units lower compared to LRTs for indoor reuse of combined greywater (1000-people scale), but both scenarios necessitated treatment with a membrane bioreactor, chlorination and UV disinfection. However, simpler treatment trains may be feasible for small-scale and application-scale reuse if: (i) less conservative health benchmarks are used for household-based systems, considering the reduced relative importance of treated greywater in pathogen transmission in households, and (ii) higher log-removal values (LRVs) can be validated for unit processes, enabling simpler treatment trains for a larger number of appliance-scale reuse systems.

Evaluation of virus removal in membrane bioreactor (MBR) and conventional activated sludge (CAS) processes based on long-term monitoring at two wastewater treatment plants.

The membrane bioreactor (MBR) process always offers better wastewater treatment than conventional activated sludge (CAS) treatment. However, the difference in their efficacy of virus reduction remains unknown. To investigate this, we monitored virus concentrations before and after MBR and CAS processes over 2 years. Concentrations of norovirus genotypes I and II (NoV GI and GII), aichivirus (AiV), F-specific RNA phage genotypes I, II, and III (GI-, GII-, and GIII-FRNAPHs), and pepper mild mottle virus (PMMoV) were measured by a quantitative polymerase chain reaction (qPCR) method at two municipal wastewater treatment plants (WWTPs A and B) in Japan. Virus concentration datasets containing left-censored data were estimated by using both maximum likelihood estimation (MLE) and robust regression on order statistics (rROS) approaches. PMMoV was the most prevalent at both WWTPs, with median concentrations of 7.5 to 8.8 log10 copies/L before treatment. Log10 removal values (LRVs) of all viruses based on means and standard deviations of concentrations before and after treatment were consistently higher following MBR than following CAS. We used NoV GII as a model pathogen in a quantitative microbial risk assessment of the treated water, and we estimated the additional reductions required following MBR and CAS processes to meet the guideline of 10-6 DALYs pppy for safe wastewater reuse.

Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed.

The purpose of this study was to evaluate the performance of HF183 Bacteroides for estimating pathogen exposures during recreational water activities. We compared the use of Bacteroides-based exposure assessment to exposure assessment that relied on pathogen measurements. We considered two types of recreational water sites: those impacted by combined sewer overflows (CSOs) and those not impacted by CSOs. Samples from CSO-impacted and non-CSO-impacted urban creeks were analysed by quantitative polymerase chain reaction (qPCR) for HF183 Bacteroides and eight human gastrointestinal pathogens. Exposure assessment was conducted two ways for each type of site (CSO-impacted vs. non-CSO impacted): 1) by estimating pathogen concentrations from HF183 Bacteroides concentrations using published ratios of HF183 to pathogens in sewage and 2) by estimating pathogen concentrations from qPCR measurements. QMRA (quantitative microbial risk assessment) was then conducted for swimming, wading, and fishing exposures. Overall, mean risk estimates varied from 0.27 to 53 illnesses per 1,000 recreators depending on exposure assessment, site, activity, and norovirus dose-response model. HF183-based exposure assessment identified CSO-impacted sites as higher risk, and the recommended HF183 risk-based threshold of 525 genomic copies per 100 mL was generally protective of public health at the CSO-impacted sites but was not as protective at the non-CSO-impacted sites. In the context of our urban watershed, HF183-based exposure assessment over- and under-estimated risk relative to exposure assessment based on pathogen measurements, and the etiology of predicted pathogen-specific illnesses differed significantly. Across all sites, the HF183 model overestimated risk for norovirus, adenovirus, and Campylobacter jejuni, and it underestimated risk for E. coli and Cryptosporidium. To our knowledge, this study is the first to directly compare health risk estimates using HF183 and empirical pathogen measurements from the same waterways. Our work highlights the importance of site-specific hazard identification and exposure assessment to decide whether HF183 is applicable for monitoring risk.

Integrating quantitative microbiological risk assessment and disability-adjusted life years to evaluate the effects of urbanization on health risks for river recreationists.

Urban rivers provide an excellent opportunity for water recreation. This study probabilistically assessed health risks associated with water recreation in urban rivers in the Bitan Scenic Area, Taiwan, by employing quantitative microbial risk assessment and disability-adjusted life years (DALYs). Moreover, the effects of urbanization on the health risks of river recreation induced by waterborne pathogenic Escherichia coli (E. coli) were investigated. First, data on river E. coli levels were collected in both the Bitan Scenic Area and the upstream river section, and model parameters were obtained through a questionnaire administered to river recreationists. Monte Carlo simulation was then employed to address parameter uncertainty. Finally, DALYs were calculated to quantify the cumulative effects in terms of potential life lost and years lived with disability. The results indicated that the 90 % confidence intervals for the disease burden (DB) were 0.2-74.1 × 10-6, 0.01-94.0 × 10-6, and 0.3-128.9 × 10-6 DALY per person per year (pppy) for canoeing, swimming, and fishing, respectively, in the Bitan Scenic Area. Furthermore, urbanization near the Bitan Scenic Area approximately doubled the DB risks to river recreationists in upstream rural areas. At the 95th percentile, the DB risks exceeded the tolerances recommended by the World Health Organization (1 × 10-6) or U.S. Environmental Protection Agency (1 × 10-4). The findings suggest that the simultaneous implementation of effluent sewer systems and best management practices can reduce health risks to river recreationists by at least half, reducing the DALY levels below 1 × 10-4 or even 1 × 10-5 pppy.

Harnessing the theoretical foundations of the exponential and beta-Poisson dose-response models to quantify parameter uncertainty using Markov Chain Monte Carlo.

Dose-response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose-response model parameters are estimated using limited epidemiological data is rarely quantified. Second-order risk characterization approaches incorporating uncertainty in dose-response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta-Poisson dose-response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta-Poisson dose-response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta-Poisson dose-response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta-Poisson model are proposed, and simple algorithms to evaluate actual beta-Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta-Poisson dose-response model parameters is attributable to the absence of low-dose data. This region includes beta-Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility.