scholarly journals Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation

2021 ◽  
Vol 18 (21) ◽  
pp. 11106
Author(s):  
Ning Qin ◽  
Ayibota Tuerxunbieke ◽  
Qin Wang ◽  
Xing Chen ◽  
Rong Hou ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Goodness Of Fit ◽  
Carcinogenic Risk ◽  
Computational Technique ◽  
Average Risk ◽  
Key Factors ◽  
Correlated Parameters ◽  
Mcs Method

Monte Carlo simulation (MCS) is a computational technique widely used in exposure and risk assessment. However, the result of traditional health risk assessment based on the MCS method has always been questioned due to the uncertainty introduced in parameter estimation and the difficulty in result validation. Herein, data from a large-scale investigation of individual polycyclic aromatic hydrocarbon (PAH) exposure was used to explore the key factors for improving the MCS method. Research participants were selected using a statistical sampling method in a typical PAH polluted city. Atmospheric PAH concentrations from 25 sampling sites in the area were detected by GC-MS and exposure parameters of participants were collected by field measurement. The incremental lifetime cancer risk (ILCR) of participants was calculated based on the measured data and considered to be the actual carcinogenic risk of the population. Predicted risks were evaluated by traditional assessment method based on MCS and three improved models including concentration-adjusted, age-stratified, and correlated-parameter-adjusted Monte Carlo methods. The goodness of fit of the models was evaluated quantitatively by comparing with the actual risk. The results showed that the average risk derived by traditional and age-stratified Monte Carlo simulation was 2.6 times higher, and the standard deviation was 3.7 times higher than the actual values. In contrast, the predicted risks of concentration- and correlated-parameter-adjusted models were in good agreement with the actual ILCR. The results of the comparison suggested that accurate simulation of exposure concentration and adjustment of correlated parameters could greatly improve the MCS. The research also reveals that the social factors related to exposure and potential relationship between variables are important issues affecting risk assessment, which require full consideration in assessment and further study in future research.

scholarly journals Influencing factors and health risk assessment of microcystins in the Yongjiang river (China) by Monte Carlo simulation

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5955 ◽  
Author(s):  
Chan-Chan Xiao ◽  
Mao-Jian Chen ◽  
Fan-Biao Mei ◽  
Xiang Fang ◽  
Tian-Ren Huang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Drinking Water ◽  
Health Risk ◽  
Human Health ◽  
Health Risk Assessment ◽  
Carcinogenic Risk ◽  
Effective Control ◽  
Total Concentrations

The Yongjiang river is a large, shallow, hyper-trophic, freshwater river in Guangxi, China. To investigate the presence of microcystin-RR, microcystin-LR, and microcystin-YR (MC-RR, MC-LR, and MC-YR) in the Yongjiang river and describe their correlation with environmental factors, as well as, assess health risk using Monte Carlo simulation, 90 water samples were collected at three sample points from March to December 2017. Results showed that during the monitoring period, total concentrations of MC-RR (TMC-RR), MC-YR (TMC-YR), and MC-LR (TMC-LR) varied from 0.0224 to 0.3783 μg/L, 0.0329 to 0.1433 μg/L, and 0.0341 to 0.2663 μg/L, respectively. Total phosphorus (TP) content appeared to be related to TMC-LR and the total concentrations of microcystins (TMCs), while pH and total nitrogen (TN)/TP ratio appeared to be related to TMC-RR and TMC-YR, respectively. Using the professional health risk assessment software @Risk7.5, the risks of dietary intake of microcystins (MCs), including the carcinogenic risk and non-carcinogenic risk, were evaluated. It was found that the carcinogenic risk of MC-RR from drinking water was higher than MC-LR and MC-YR, and the presence of MCs would lead to high potential health risks, especially in children. The carcinogenic risk of MC-RR to children was >1 × 10−4, the maximum allowance level recommended by the US Environmental Protection Agency; as for adults, it was >5 × 10−5, the maximum allowance level recommended by the International Commission on Radiological Protection. The non-carcinogenic hazard index (HI) of MC-RR, MC-YR, and MC-LR increased successively, indicating that MC-LR was more hazardous to human health than MC-YR and MC-RR, but its HI was <1. This suggests that MCs pose less risk to health. However, it is necessary to strengthen the protection and monitoring of drinking water source for effective control of water pollution and safeguarding of human health.

scholarly journals Economic analysis and risk assessment of biomass gasification CHP systems of different sizes through Monte Carlo simulation

2021 ◽  
Vol 7 ◽  
pp. 1954-1961
Author(s):  
Andrea Colantoni ◽  
Mauro Villarini ◽  
Danilo Monarca ◽  
Maurizio Carlini ◽  
Enrico Maria Mosconi ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Economic Analysis ◽  
Biomass Gasification

scholarly journals Fault Risk Assessment of Underwater Vehicle Steering System Based on Virtual Prototyping and Monte Carlo Simulation

2016 ◽  
Vol 23 (3) ◽  
pp. 97-105
Author(s):  
Deyu He ◽  
Niaoqing Hu ◽  
Lei Hu ◽  
Ling Chen ◽  
YiPing Guo ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fault Injection ◽  
Fault Simulation ◽  
Virtual Prototyping ◽  
Underwater Vehicle ◽  
Steering System ◽  
Assessment Method ◽  
Risk Indicator

Abstract Assessing the risks of steering system faults in underwater vehicles is a human-machine-environment (HME) systematic safety field that studies faults in the steering system itself, the driver’s human reliability (HR) and various environmental conditions. This paper proposed a fault risk assessment method for an underwater vehicle steering system based on virtual prototyping and Monte Carlo simulation. A virtual steering system prototype was established and validated to rectify a lack of historic fault data. Fault injection and simulation were conducted to acquire fault simulation data. A Monte Carlo simulation was adopted that integrated randomness due to the human operator and environment. Randomness and uncertainty of the human, machine and environment were integrated in the method to obtain a probabilistic risk indicator. To verify the proposed method, a case of stuck rudder fault (SRF) risk assessment was studied. This method may provide a novel solution for fault risk assessment of a vehicle or other general HME system.

Monte Carlo Simulation Model for Predicting Salmonella Contamination of Chicken Liver as a Function of Serving Size for Use in Quantitative Microbial Risk Assessment

2021 ◽  
Author(s):  
Thomas Oscar
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Chicken Liver ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Serving Size ◽  
Monte Carlo Simulation Model

The first step in quantitative microbial risk assessment (QMRA) is to determine distribution of pathogen contamination among servings of the food at some point in the farm-to-table chain. In the present study, distribution of Salmonella contamination among servings of chicken liver for use in QMRA was determined at meal preparation. A combination of five methods: 1) whole sample enrichment; 2) quantitative polymerase chain reaction; 3) cultural isolation; 4) serotyping; and 5) Monte Carlo simulation were used to determine Salmonella prevalence (P), number (N), and serotype for different serving sizes. In addition, epidemiological data were used to convert serotype data to virulence (V) values for use in QMRA. A Monte Carlo simulation model based in Excel and simulated with @Risk predicted Salmonella P, N, serotype, and V as a function of serving size from one (58 g) to eight (464 g) chicken livers. Salmonella P of chicken livers was 72.5% (58/80) per 58 g. Four serotypes were isolated from chicken livers: 1) Infantis (P = 28%, V = 4.5); 2) Enteritidis (P = 15%, V = 5); 3) Typhimirium (P = 15%, V = 4.8); and 4) Kentucky (P = 15%, V = 0.8). Median Salmonella N was 1.76 log per 58 g (range: 0 to 4.67 log/58 g) and was not affected ( P &gt; 0.05) by serotype. The model predicted a non-linear increase ( P ≤ 0.05) of Salmonella P from 72.5% per 58 g to 100% per 464 g, minimum N from 0 log per 58 g to 1.28 log per 464 g, and median N from 1.76 log per 58 g to 3.22 log per 464 g. Regardless of serving size, predicted maximum N was 4.74 log, mean V was 3.9, and total N was 6.65 log per lot (10,000 chicken livers). The data acquired and model developed in this study fill an important data and modeling gap in QMRA for Salmonella and chicken liver.

A Weighted Monte Carlo Simulation Approach to Risk Assessment of Information Security Management System

2015 ◽  
Vol 11 (4) ◽  
pp. 63-78 ◽  
Author(s):  
Seyed Mojtaba Hosseini Bamakan ◽  
Mohammad Dehghanimohammadabadi
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Information Security ◽  
Risk Analysis ◽  
Management System ◽  
Security Management ◽  
International Standards ◽  
Information Security Management ◽  
Information Security Management System

In recent decades, information has become a critical asset to various organizations, hence identifying and preventing the loss of information are becoming competitive advantages for firms. Many international standards have been developed to help organizations to maintain their competitiveness by applying risk assessment and information security management system and keep risk level as low as possible. This study aims to propose a new quantitative risk analysis and assessment methodology which is based on AHP and Monte Carlo simulation. In this method, AHP is used to create favorable weights for Confidentiality, Integrity and Availability (CIA) as security characteristic of any information asset. To deal with the uncertain nature of vulnerabilities and threats, Monte Carlo simulation is utilized to handle the stochastic nature of risk assessment by taking into account multiple judges' opinions. The proposed methodology is suitable for organizations that require risk analysis to implement ISO/IEC 27001 standard.

scholarly journals Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 325
Author(s):  
Emad Mohamed ◽  
Parinaz Jafari ◽  
Simaan AbouRizk
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Wind Farm ◽  
Historical Data ◽  
Expert Knowledge ◽  
Bivariate Distribution ◽  
Expert Elicitation ◽  
Illustrative Case ◽  
Input Modeling

Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).

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