Developing Bayesian networks in managing the risk of Legionella colonisation of groundwater aeration systems

Yunana, Danladi; Maclaine, Stuart; Tng, Keng Han; Zappia, Luke; Bradley, Ian; Roser, David; Leslie, Greg; MacIntyre, C. Raina; Le‐Clech, Pierre

doi:10.1016/j.watres.2021.116854

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…However, BN is designed to conduct more realistic effect assessments by using case-based reasoning to search for analogous contamination cases based on experimental ecosystem type, exposure pattern, and mode of action of the evaluated substance (Van Den Brink et al 2006;Larras et al 2022). Incorporating BN into risk estimation and decision outputs offers a complementary and transparent alternative approach to quantitatively analyzing uncertainties (Yunana et al 2021). Bayesian influence diagrams enable inclusive decision modeling by incorporating multiple lines of evidence, including process-related information from existing data and expert judgment (Carriger and Newman 2012).…”

Section: Interpretation Of Bayesian Network (Bn) Predictions For Pcb ...mentioning

confidence: 99%

Investigating PCB degradation by Indigenous Fungal Strains Isolated from the Transformer Oil-Contaminated Site: Degradation Kinetics, Bayesian Network, Artificial Neural Networks, QSAR with DFT, Molecular Docking, and Molecular Dynamics Simulation

Singh,

Mukherjee

2024

Preprint

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The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian Network (BN), Quantitative Structure-Activity Relationship-Density Functional Theory (QSAR-DFT), Artificial Neural Network (ANN), Molecular docking (MD) and Molecular dynamics stimulation (MS) of PCBs biodegradation i.e. PCB-10, PCB-28, PCB-52, PCB-138, PCB-153, and PCB-180 in the soil system using fungi isolated from the transformer oil-contaminated sites. Results revealed that the efficacy of PCBs biodegradation best fits the first-order kinetics (R2 ≥ 0.93). The consortium treatment (29.44–74.49%) exhibited more efficient degradation of PCBs than those of Aspergillus tamarii (27.09–71.25%), Corynespora cassiicola sp. MN69 (23.76–57.37%), and Corynespora cassiicola sp. MN70 (23.09–54.98%). 3'-methoxy-2, 4, 4'-trichloro-biphenyl as an intermediate derivative was detected in the fungal consortium treatment. The BN analysis predicted that the biodegradation efficiency of PCBs ranged from 11.6–72.9%. The ANN approach showed the importance of quantum chemical descriptors (QCD) i.e., LUMO > Molecular weight > Ionization Potential > No. of Chlorine > HOMU in PCBs removal. Furthermore, the QSAR-DFT model between the QCD and rate constant (Log K) exhibited a high fit and good robustness of R2 = 99.12% in predicting ability. The MD and MS analyses showed the lowest binding energy through Normal Mode Analysis (NMA), implying stability in the interactions of the docked complexes. These findings provide crucial insights for devising strategies focused on natural attenuation, holding substantial potential for mitigating PCBs contamination within the environment.

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Section: Interpretation Of Bayesian Network (Bn) Predictions For Pcb ...mentioning

confidence: 99%

Investigating PCB degradation by Indigenous Fungal Strains Isolated from the Transformer Oil-Contaminated Site: Degradation Kinetics, Bayesian Network, Artificial Neural Networks, QSAR with DFT, Molecular Docking, and Molecular Dynamics Simulation

Singh,

Mukherjee

2024

Preprint

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“…The risk frequency interval division standards established by the International Tunnel Association (ITA) are presented in Table 1 [47]. The natural probability P N of the occurrence of a risk event obtained by Bayesian network inference is related to the log probability P as: The diagnostic reasoning function of a Bayesian network is employed to analyze the primary factors and combinations of factors that contribute to accidents.…”

Section: Causal Reasoningmentioning

confidence: 99%

“…Describing risk factors pertaining to accidents in the transportation of hazardous materials is a significant challenge, prompting the introduction of language evaluation levels to effectively characterize variables [47,50]. Table 2 shows seven natural language variables expressed as risk levels, as well as their corresponding triangular fuzzy numbers and probability range.…”

Section: The Evaluation Level Establishmentmentioning

confidence: 99%

Risk Assessment of Sudden Water Pollution Accidents Associated with Dangerous Goods Transportation on the Cross-Tributary Bridges of Baiyangdian Lake

Yang,

Yan,

Tian

et al. 2023

Water

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The issue of sudden water pollution resulting from accidents is a challenging environmental problem to address. The frequency of transport accidents involving hazardous materials over tributary bridges is steadily rising due to rapid industrialization and urbanization processes. This trend poses a significant threat to both the water’s ecological environment and human well-being. To effectively mitigate the risks associated with water pollution caused by accidents during the transportation of dangerous goods, this research focused on Baiyangdian Lake, the largest freshwater lake in North China. Thid study employed the expert judgment fuzzy language method and Bayesian network model as analytical tools to assess and analyze the potential risks associated with sudden water pollution accidents caused by the transportation of hazardous materials on bridges spanning tributaries. Through an examination of the various risk factors involved, the research identified four primary indicators and ten secondary indicators. Additionally, an oil leakage accident scenario was simulated, and recommendations for risk prevention and control measures were provided. The findings of the study indicated that: (1) The likelihood of risk associated with driver factors, vehicle emergency factors, fuel tank emergency factors, road factors, and lighting factors is elevated. (2) The probability of a dangerous goods transportation accident occurring on the Baiyangdian cross-tributary bridge is substantial, thereby presenting a potential hazard to both the water environment and human health. (3) Vehicle emergency factors, vehicle wear factors, and weather factors exert a significant influence on the incidence of accidents. (4) The highest likelihood of accidents is associated with a combination of factors, including driver fatigue, vehicle and fuel tank deterioration, and adverse weather conditions. (5) In instances where the vehicle and fuel tank are well-maintained, the probability of accidents is greatest on the cross tributary bridge, particularly when the driver is fatigued, weather conditions are unfavorable, and there is a lack of street lighting during nighttime. Implementing emergency prevention and control measures proved to be an effective approach in mitigating the risk of sudden water pollution accidents. This study offers valuable insights into risk mitigation and management strategies for emergent water pollution incidents, and the framework presented herein can be readily applied to other rivers worldwide confronting comparable risk challenges.

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“…Various studies have associated several physio-chemical factors with the survival of Legionella and the formation of biofilms in pipeline surfaces with the colonization of Legionella in an attempt to create mathematical models that predict the risk of the bacterium' s growth and human exposure, models which would help develop control strategies to prevent legionellosis (Yunana et al, 2021). Despite the fact that most "traditional" entero-pathogens, enter artificial water systems in the rare case of sewage system malfunction or greater water age (Falkinham et al, 2015;De Giglio et al, 2021), Legionella are considered opportunistic environmental pathogens, a distinct category of pathogens, similarly to Mycobacterium spp., and Pseudomonas aeruginosa.…”

Section: Introductionmentioning

confidence: 99%

Multi-criterion analysis of the effect of physico-chemical microbiological agents on Legionella detection in hotel water distribution systems in Crete

Chochlakis,

Sandalakis,

Ntoukakis

et al. 2023

Front. Cell. Infect. Microbiol.

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IntroductionWater distribution systems in hotels have been related to outbreaks caused by Legionella spp. Certain measures, including disinfection by chlorination, maintaining increased temperatures are usually undertaken to prevent Legionella outbreaks. However, these preventive strategies are not always effective, since there are several factors (e.g., synergistic interactions with other microbes, physico-chemical factors, biofilm formation, availability of nutrients) that promote survival and proliferation of the pathogen in water pipes., Accordingly, there is a need of a holistic approach in development of preventive models for Legionella outbreaks associated with water distribution systems.MethodsWater samples were collected from hotel water systems and were tested for the presence of Legionella, E. coli, total coliforms, total mesophilic count and Pseudomonas. In each sample, temperature and chlorine were also tested. Other epidemiological factors were additionally recorded including number of rooms, stars, proximity of sampling point to the boiler, etc. Data were processed by generalized linear analysis, and modeling based on logistic regression analysis to identify independent predictive factors associated with the presence of Legionella in hotel water systems.ResultsAccording to the generalized linear model, temperature affected (p<0.05) the presence of Legionella regardless of the species or the water supply (hot or cold). Additionally, opportunistic (P. aeruginosa) or non-opportunistic (E. coli, coliforms) pathogens were significantly associated (p<0.05) with the presence of all Legionella species. Temperature also exhibited a positive effect to all pathogens tested except for Pseudomonas according to the linear model. Multivariate analysis showed that Pseudomonas, total coliforms, HPC and temperature had a statistically significant effect on the presence of Legionella. Based on a binomial model, cold water had a positive effect on Legionella. Type of sampling and proximity of the sample to the boiler seemed to pose different effect on Legionella depending on the cfu/L. The number of hotel stars and rooms did not appear to have any effect in all tested models.DiscussionCollectively, these results indicate the need for development of individualized water safety plans tailored by the presence of other microbiological agents, and unique physico-chemical factors, which could facilitate the survival of Legionella.in hotel water systems.

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Developing Bayesian networks in managing the risk of Legionella colonisation of groundwater aeration systems

Cited by 9 publications

References 39 publications

Investigating PCB degradation by Indigenous Fungal Strains Isolated from the Transformer Oil-Contaminated Site: Degradation Kinetics, Bayesian Network, Artificial Neural Networks, QSAR with DFT, Molecular Docking, and Molecular Dynamics Simulation

Investigating PCB degradation by Indigenous Fungal Strains Isolated from the Transformer Oil-Contaminated Site: Degradation Kinetics, Bayesian Network, Artificial Neural Networks, QSAR with DFT, Molecular Docking, and Molecular Dynamics Simulation

Risk Assessment of Sudden Water Pollution Accidents Associated with Dangerous Goods Transportation on the Cross-Tributary Bridges of Baiyangdian Lake

Multi-criterion analysis of the effect of physico-chemical microbiological agents on Legionella detection in hotel water distribution systems in Crete

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