A Bayesian Belief Network Model Assessing the Risk to Wastewater Workers of Contracting Ebola Virus Disease During an Outbreak

Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms

The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure-activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs.

A probabilistic assessment of microbial infection risks due to occupational exposure to wastewater in a conventional activated sludge wastewater treatment plant

Exposure to pathogens during wastewater treatment could result in significant health risks. In this paper, a probabilistic approach for assessing the risks of microbial infection for workers in an activated sludge wastewater treatment plant is presented. A number of exposure routes were modelled, including hand-to-mouth and droplet ingestion of untreated wastewater, droplet ingestion and inhalation of aerosols after secondary treatment, and ingestion of sludge during drying. Almost all workers exposed to untreated wastewater could be infected with the three selected potential pathogens of pathogenic E. coli, Norovirus and Cryptosporidium spp. Hand-to-mouth ingestion is the single most significant route of exposure at the head of works. There is also a risk of infections resulting from ingestion of droplets or inhalation of aerosols at the aeration tanks or contaminated hands at the clarifiers during secondary wastewater treatment. For sludge, the risks of infection with Norovirus was found to be the highest due to accidental ingestion (median risks of 2.2 × 10^-2(±3.3 × 10^-3)). Regardless of the point and route of exposure, Norovirus and Cryptosporidium spp. presented the highest risks. The study finds that occupational exposure to wastewater at wastewater treatment plants can result in significant viral and protozoan infections. This risk assessment framework can be used to establish and measure the success of risk reduction measures in wastewater treatment plants. These measures could include the use of personal protective equipment and adherence to strict personal hygiene.

A new approach to a legacy concern: Evaluating machine-learned Bayesian networks to predict childhood lead exposure risk from community water systems

Lead in drinking water continues to put children at risk of irreversible neurological impairment. Understanding drinking water system characteristics that influence blood lead levels is needed to prevent ongoing exposures. This study sought to assess the relationship between children's blood lead levels and drinking water system characteristics using machine-learned Bayesian networks. Blood lead records from 2003 to 2017 for 40,742 children in Wake County, North Carolina were matched with the characteristics of 178 community water systems and sociodemographic characteristics of each child's neighborhood. Bayesian networks were machine-learned to evaluate the drinking water variables associated with blood lead levels ≥2 μg/dL and ≥5 μg/dL. The model was used to predict geographic areas and water utilities with increased lead exposure risk. Drinking water characteristics were not significantly associated with children's blood lead levels ≥5 μg/dL but were important predictors of blood lead levels ≥2 μg/dL. Whether 10% of water samples exceeded 2 ppb of lead in the most recent year prior to the blood test was the most important water system predictor and increased the risk of blood lead levels ≥2 μg/dL by 42%. The model achieved an area under the receiver operating characteristic curve of 0.792 (±0.8%) during ten-fold cross validation, indicating good predictive performance. Water system characteristics may thus be used to predict areas that are at risk of higher blood lead levels. Current drinking water regulatory thresholds for lead may be insufficient to detect the levels in drinking water associated with children's blood lead levels.

Estimating acceptable exposure time for bioaerosols emission in a wastewater treatment plant by reverse quantitative microbial risk assessment based on various risk benchmarks

Populations exposed to bioaerosols over time in wastewater treatment plants (WWTPs) will be infected. Then, the reverse quantitative microbial risk assessment (QMRA) provides a quantitative framework for the estimation of acceptable exposure time to protect people from excessive exposure and then manage their health risk. In this study, the acceptable exposure time for staffs and visiting researchers exposed to S. aureus or E. coli bioaerosols emitted from aeration ponds in WWTPs was estimated and analyzed by Monte Carlo simulation-based reverse QMRA (using the 1E-4 pppy suggested by the US EPA or 1E-6 DALYs pppy suggested by the WHO as benchmarks). The 1E-3 and 1E-2 pppy were selected as a series of loose annual infection risk benchmarks to calculate a practical acceptable exposure time. The results showed that for the acceptable exposure time in each specific exposure scenario, the exposure of females was consistently 0.3-0.4 times longer than that of males; the exposure of staffs was 3.6-3.9 times shorter than that of visiting researchers; the exposures of populations in the rotating-disc aeration mode were consistently 6.3-6.6 and 2.8-3.1 times longer than those in the microporous aeration mode for S. aureus and E. coli bioaerosols, respectively. The acceptable exposure time with the use of personal protective equipment (PPE) was 33.4-35.0 times as long as that without PPE. The US EPA benchmark is stricter than the WHO benchmark with regard to the estimation of the acceptable exposure time of S. aureus or E. coli bioaerosols. The 1E-3 pppy is more appropriate and practical than the US EPA benchmark, but the 1E-2 pppy is notably too loose for health risk management. This research can assist managers of WWTPs to formulate a justified exposure time and develop applicable administrative and personal intervention strategies. The results can enrich the knowledge bases of reverse QMRA to elect a series of loose health-based target risk benchmarks for health risk management.

Bioaerosolization and pathogen transmission in wastewater treatment plants: Microbial composition, emission rate, factors affecting and control measures

Environmental consequences during wastewater management are vital and getting increased attention to interrupt any possible disease transmission pathways. Evidence of bioaerosolization of pathogen from wastewater to atmosphere during wastewater treatment have been highlighted previously. Understanding aerosol-based transmission in wastewater treatment plant (WWTP) is important because of the hazard it presents to the workers involved or to the population around and appears to be very significant during pandemic occurrences. This work aims to evaluate the possibility of pathogenic content of wastewater getting aerosolized during treatment by synthesizing the evidence on the potential aerosol generating treatment phases of WWTP, bioaerosol microbial composition, emission load and the factors affecting the bioaerosol formation. We also present some potential control strategies to take up in WWTP which may be useful to avoid such occurrences. Implementation of Aeration based strategies (use of diffused, submerged aeration, reduction in aeration rate), Improved ventilation based strategies (effective ventilation with adequate supply of clean air, minimizing air recirculation, supplementation with infection control measures such as filtration, irradiation), Improved protection based strategy (periodic monitoring of disinfection efficiency, pathogenic load of wastewater, improved operation policy) and other strategies (provision of buffer zone, wind shielding, water spraying on aerosol, screened surface of treatment units) could be very much relevant and significant in case of disease outbreak through aerosol formation in wastewater environment. Recent progress in sensor-based data collection, analysis, cloud-based storage, and early warning techniques in WWTP may help to reduce the risk of infectious transmission, especially during a pandemic situation.

The bioaerosols emitted from toilet and wastewater treatment plant: a literature review

The aerosols harboring microorganisms and viruses released from the wastewater system into the air have greatly threatened the health and safety of human beings. The wastewater systems, including toilet and wastewater treatment plant (WWTP), are the major locations of epidemic infections due to the extensive sources of aerosols, as well as multifarious germs and microorganisms. Viruses and microorganisms may transport from both toilet and hospital into municipal pipes and subsequently into WWTP, which accounts for the main source of bioaerosols dispersed in the air of the wastewater system. This review aims to elaborate the generation, transmission, and diffusion processes of bioaerosols at toilet and WWTP. Moreover, the main factors affecting bioaerosol transmission and the corresponding prevention strategies for the airborne and inhaled bioaerosols are also discussed. Collectively, this review highlights the importance of managing bioaerosol occurrence in the wastewater system, which has aroused increasing concern from the public.

Protecting wastewater workers from disease risks: Personal protective equipment guidelines

The 2013-2016 Ebola epidemic revived concerns about infection risks to wastewater workers. Prior research has shown that wastewater can contain a variety of known and emerging pathogens and that wastewater workers are at increased risk of infectious illnesses. However, guidelines on using personal protective equipment (PPE) to decrease these risks are lacking. We engaged 34 wastewater utility personnel and public health experts to conduct a job safety analysis identifying tasks in which workers could be exposed to pathogens and to develop a PPE selection matrix for preventing those exposures. We identified 43 relevant job tasks. Recommended PPE ranges from durable gloves (all tasks) to safety glasses (24 tasks), Tyvek suits or coveralls (4 tasks), and respiratory protection (N95 mask or face mask, depending on the activity, 10 tasks). The PPE selection matrix can serve as a guide for protecting the 120,000 wastewater workers in the United States from known and emerging pathogens. PRACTITIONER POINTS: Wastewater workers are at increased risk of infectious illnesses. Policies to protect wastewater workers from these illnesses are lacking. We developed guidelines for use of personal protective equipment by wastewater workers to prevent exposure to infectious agents.

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

Quantitative risk assessment of Listeria monocytogenes in bulk cooked meat from production to consumption in China: a Bayesian approach

BACKGROUND

To estimate the public health risk related to cooked meat in bulk products contaminated with Listeria monocytogenes, a generic Bayesian network (BN) risk-assessment model was developed to simulate influencing factors and processes of products from the industry level to the consumer level. To quantify the model, parameter values of prior distributions were acquired from the literature, websites, and expert opinions. Using the Markov chain Monte Carlo (MCMC) simulation approach, posterior probability distributions were calculated according to the incorporated evidence, which allowed us to predict various risks affected by processing variability from production to consumption.

RESULTS

The average risks of listeriosis from consuming cooked meat in bulk products are 8.40 × 10^-7 , 2.58 × 10^-8 , 8.24 × 10^-7 , and 1.05 × 10^-6 per meal for children, young people, elderly people, and pregnant women, respectively. The estimated mean number of listeriosis cases is 5 per 100 000 people per year in China.

CONCLUSION

Although only a conceptual BN model is given, it manifests the principles and characteristics of mathematical methods. The BN model can also provide significant benefits for quantitative risk assessment by incorporating all available data and by updating beliefs. © 2018 Society of Chemical Industry.

A fully integrated passive microfluidic Lab-on-a-Chip for real-time electrochemical detection of ammonium: Sewage applications

The present work reports on the development of a new generation of Lab-on-a-chip (LOC) to perform in-situ and real-time potentiometric measurements in flowing water. The device consisted of two differentiated parts: a poly (dimethylsiloxane) (PDMS) microfluidic structure obtained by soft lithography and a fully integrated chemical sensing platform including four working microelectrodes, two reference microelectrodes and one counter microelectrode for detecting ammonium in a continuous mode. The performance of the device was evaluated following its potentiometric response when analyzing ammonium containing samples. As a key parameter, its time of response was compared to that of a commercially available electrical conductivity sensor used as reference sensor during tests in laboratory using flowing tap water and technical scale using flowing wastewater. As a result, the LOC showed a slope of 55 mV/decade, a limit of detection of 4·10^-5 M and a time of full response between 10 and 12 s. It was demonstrated that the device can provide fast and reliable data at real time when immersed in a laminar flow of water. Moreover, the test of robustness showed that it was still functional after immersion in sewage for at least 15 min. Besides, the LOC reported here can be helpful for a wide variety of flowing-water applications such as aqua culture outlets control, in-situ and continuous analysis of rivers effluents and sea waters monitoring among others.