Probabilistic Risk Assessment Model Research Articles

Postal IEDs and risk assessment of work health and safety considerations for postal workers

Postal improvised explosive devices (IEDs) provide criminals and terrorists with a convenient mechanism for delivering an energetic payload to an intended victim with little operational risk. Postal IEDs formed 7% of IED attacks reported in the West between 1998-2015, are often dispatched in groups and can bring postal systems to a standstill. Nearly 30% of postal IED explosions occur in the postal worker environment and a third of the casualties caused by postal IEDs are postal workers. Postal IEDs are debatably a reasonably foreseeable cause of harm to postal workers and should be considered under the work health and safety (WHS) constructs of many Western nations. This paper considers this problem, using a probabilistic risk assessment model to inform a cost-benefit analysis considering potential risk reduction options for postal workers. It identifies that the control measures identified were not cost-effective where only the direct WHS costs pertaining to unintentional postal IED detonation within the mail delivery system were considered given the risk levels identified.

Which Parameters Are Important? Differential Importance Under Uncertainty.

In probabilistic risk assessment, attention is often focused on the expected value of a risk metric. The sensitivity of this expectation to changes in the parameters of the distribution characterizing uncertainty in the inputs becomes of interest. Approaches based on differentiation encounter limitations when (i) distributional parameters are expressed in different units or (ii) the analyst wishes to transfer sensitivity insights from individual parameters to parameter groups, when alternating between different levels of a probabilistic safety assessment model. Moreover, the analyst may also wish to examine the effect of assuming independence among inputs. This work proposes an approach based on the differential importance measure, which solves these issues. Estimation aspects are discussed in detail, in particular the problem of obtaining all sensitivity measures from a single Monte Carlo sample, thus avoiding potentially costly model runs. The approach is illustrated through an analytical example, highlighting how it can be used to assess the impact of removing the independence assumption. An application to the probabilistic risk assessment model of the Advanced Test Reactor large loss of coolant accident sequence concludes the work.

Probabilistic risk-based pollution prevention model for a foundry: a case study of casting.

Hazardous air pollutants from industrial activities have long been associated with serious health effects. Traditional health risk assessment uses point estimates of inhalation concentrations based on standard Gaussian diffusion models with steady-state emission rate assumptions. This traditional approach was criticized because it does not account for variability and thus leading to a potential overestimate of the health risk from the batch processes. To overcome this deficiency, a probabilistic risk assessment model is proposed. The foundry industry with processes typically associated with several hazardous air pollutants is identified and iron casting is chosen as a case study to compare risk estimates. Existing data, representing historical proprietary information of the case study, were used to deliver representative risk values and help identify potential replacements or interventions in the manufacturing process. A probability distribution function of emitted concentrations was simulated to model the batch process emissions from mold and core resin binders, a major source of pollution. The same method was applied to exposure factors to feed into the risk model resulting in a probabilistic risk evaluation. Several alternative resin binders in commercial use were examined to offer a risk-based substitute to the resin binder in use. The risk results provided an opportunity to consider newer and environmentally friendlier options. A comparison of the resultsfrom this approach and those from the point estimate analysis reveals a gross over estimation of risks. The point estimate risk values were about eight time larger than the mean value and about twice the 95th percentile values of the probabilistic risk approach. The wide range of variability among resin binders associated risk results, close to two orders of magnitude in some cases, presented opportunities to select from a variety of binders with lesser emissions and lower risk. Optimal selection will depend on several pollutants emitted from this process to help address cumulative impacts of multiple pollutants. Investigations are underway for a multi-pollutant strategy including trade-offs, and other quality controls vital to the decision-making.

Integrated Level 1–Level 2 decommissioning probabilistic risk assessment for boiling water reactors

Abstract This article describes an integrated Level 1–Level 2 probabilistic risk assessment (PRA) methodology to evaluate the radiological risk during postulated accident scenarios initiated during the decommissioning phase of a typical Mark I containment boiling water reactor. The fuel damage scenarios include those initiated while the reactor is permanently shut down, defueled, and the spent fuel is located into the spent fuel storage pool. This article focuses on the integrated Level 1–Level 2 PRA aspects of the analysis, from the beginning of the accident to the radiological release into the environment. The integrated Level 1–Level 2 decommissioning PRA uses event trees and fault trees that assess the accident progression until and after fuel damage. Detailed deterministic severe accident analyses are performed to support the fault tree/event tree development and to provide source term information for the various pieces of the Level 1–Level 2 model. Source terms information is collected from accidents occurring in both the reactor pressure vessel and the spent fuel pool, including simultaneous accidents. The Level 1–Level 2 PRA model evaluates the temporal and physical changes in plant conditions including consideration of major uncertainties. The goal of this article is to provide a methodology framework to perform a decommissioning Probabilistic Risk Assessment (PRA), and an application to a real case study is provided to show the use of the methodology. Results will be derived from the integrated Level 1–Level 2 decommissioning PSA event tree in terms of fuel damage frequency, large release frequency, and large early release frequency, including uncertainties.

The construction dust-induced occupational health risk using Monte-Carlo simulation

Abstract A probabilistic risk assessment model was developed to explore the health effects of construction dust on the practitioners in the construction industry based on the United States Environmental Protection Agency (USEPA) risk assessment model and the Monte-Carlo simulation method. The health risk was assessed for workers in five zones during the superstructure construction stage of residential projects in Beijing. Health impairment assessment models were applied to translate health risks into disability-adjusted life years (DALYs), which are more intuitive and easy to understand. Sensitivity analysis methods were used to analyse the various exposure parameters and influencing factors that affect the health risk assessment results. The results indicated that workers in the template zone were exposed to the largest health risk, which followed a lognormal distribution of 1.14 × 10−6±9.43 × 10−7, and the probability exceeding 10−6 was more than 40%. The average health risks for workers in the steel, concrete and floor zones were slightly below 10−6, and the workers in the office zone had the lowest health risk. The spearman rank correlation coefficient method and the full factorial designs method were used for a sensitivity analysis and validation. Sensitivity analysis showed that the sensitivities of the average exposure time (AT), exposure duration (ED), exposure frequency (EF) and concentration (C) were larger, while AT and body weight (BW) exhibited a negative sensitivity. Workers in the template and steel zones had the larger health damage, with an average of more than 0.1a.

Probabilistic landslide risk assessment: Case study of Bujumbura

A spatial probabilistic landslide risk assessment and mapping model has been applied in a data scare region. The probabilistic model is based on a physical model based on Mohr coulomb failure criterion. A Monte Carlo simulation technique is applied to field collected data. The results are integrated and a probability of landslide is obtained at each cell level. The results are compared to a prepared landslide inventory. The overall accuracy of the model is 79.69%.

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions.

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments.

Assessing the oseltamivir-induced resistance risk and implications for influenza infection control strategies.

BackgroundOseltamivir-resistant mutants with higher drug resistance rates and low trans-mission fitness costs have not accounted for influenza (sub)type viruses. Predicting the impacts of neuraminidase inhibitor therapy on infection rates and transmission of drug-resistant viral strains requires further investigation.ObjectivesThe purpose of this study was to assess the potential risk of oseltamivir-induced resistance for influenza A (H1N1) and A (H3N2) viruses.Materials and methodsAn immune-response-based virus dynamic model was used to best fit the oseltamivir-resistant A (H1N1) and A (H3N2) infection data. A probabilistic risk assessment model was developed by incorporating branching process-derived probability distribution of resistance to estimate oseltamivir-induced resistance risk.ResultsMutation rate and sensitive strain number were key determinants in assessing resistance risk. By increasing immune response, antiviral efficacy, and fitness cost, the spread of resistant strains for A (H1N1) and A (H3N2) were greatly decreased. Probability of resistance depends most strongly on the sensitive strain number described by a Poisson model. Risk of oseltamivir-induced resistance increased with increasing the mutation rate for A (H1N1) only. The ≥50% of resistance risk induced by A (H1N1) and A (H3N2) sensitive infected cells were 0.4 (95% CI: 0.28–0.43) and 0.95 (95% CI 0.93–0.99) at a mutation rate of 10−6, respectively. Antiviral drugs must be administrated within 1–1.5 days for A (H1N1) and 2–2.5 days for A (H3N2) virus infections to limit viral production.ConclusionProbabilistic risk assessment of antiviral drug-induced resistance is crucial in the decision-making process for preventing influenza virus infections.

Probabilistic quantitative microbial risk assessment model of farmer exposure to Cryptosporidium spp. in irrigation water within Kumasi Metropolis-Ghana

Cryptosporidium is a protozoan parasite which can be transmitted via food and water. Some studies have shown irrigation water to be routes of transmission for Cryptosporidium into the food chain, however, little information is known about Cryptosporidium levels in wastewater used for irrigation in the Kumasi Metropolis of Ghana. Kumasi and for that matter Ghana is not immune to the widespread practice of wastewater irrigation for farm produce in developing countries which has attracted attention of both, policy makers and academia. However, most previous studies of microbial risk assessment focus on the possible health effects and risk estimation for consumers of wastewater irrigated produce, whereas farmers who actually come into direct contact with the wastewater have received little attention. This study estimated the possible risk/diseases from farmer exposure to Cryptosporidium, a zoonotic pathogen causing gastroenteritis. The results indicate high positive levels of Cryptosporidium in the irrigation water, however, the levels of Cryptosporidium decreases during the rainfall seasons, risk assessment results show that, farmers face a higher risk of being infected by Cryptosporidium due to frequent exposure to wastewater. An adoption of a possible on-farm wastewater treatment option was found to reduce the risk of infection of the farmers. The results of this study highlight the need for a proactive policy to integrate a multi-barrier approach to reduce direct contact of farmers with wastewater for irrigation, to minimise risk of infection.

Probabilistic quantitative microbial risk assessment model of norovirus from wastewater irrigated vegetables in Ghana using genome copies and fecal indicator ratio conversion for estimating exposure dose

The need to replace the commonly applied fecal indicator conversions ratio (an assumption of 1:10−5 virus to fecal indicator organism) in Quantitative Microbial Risk Assessment (QMRA) with models based on quantitative data on the virus of interest has gained prominence due to the different physical and environmental factors that might influence the reliability of using indicator organisms in microbial risk assessment. The challenges facing analytical studies on virus enumeration (genome copies or particles) have contributed to the already existing lack of data in QMRA modelling. This study attempts to fit a QMRA model to genome copies of norovirus data. The model estimates the risk of norovirus infection from the intake of vegetables irrigated with wastewater from different sources. The results were compared to the results of a corresponding model using the fecal indicator conversion ratio to estimate the norovirus count. In all scenarios of using different water sources, the application of the fecal indicator conversion ratio underestimated the norovirus disease burden, measured by the Disability Adjusted Life Years (DALYs), when compared to results using the genome copies norovirus data. In some cases the difference was >2 orders of magnitude. All scenarios using genome copies met the 10−4 DALY per person per year for consumption of vegetables irrigated with wastewater, although these results are considered to be highly conservative risk estimates. The fecal indicator conversion ratio model of stream-water and drain-water sources of wastewater achieved the 10−6 DALY per person per year threshold, which tends to indicate an underestimation of health risk when compared to using genome copies for estimating the dose.

Modelling improvised explosive device attacks in the West – Assessing the hazard

Improvised Explosive Devices (IEDs) continue to be a terrorist weapon of choice. With increasing pressures on the economies of Western nations, spending on counter-terrorism is subject to greater scrutiny. Homeland security agencies are no longer exempt from government fiscal due diligence, needing to justify how their spending achieves best value-for-money. Probabilistic Risk Assessment (PRA) is a valuable tool that can assist in this endeavour. This paper introduces a PRA model that characterises IED attacks in Western nations, and can be used to assess the risk reduction associated with IED attack countermeasures. When using the model with the START open-source terrorism database we identified that current IED attack countermeasures provide a risk reduction of at approximately 22%, and that terrorists using IEDs in Western nations cannot generally be considered adaptive, with the operational effectiveness of terrorists being approximately 7%.

Benefit of Distributed Security Queuing for Reducing Risks Associated With Improvised Explosive Device Attacks in Airport Terminals

Abstract Brussels Airport ceased operations for 12 days after a coordinated improvised explosive device (IED) attack by suicide bombers in March 2016, demonstrating that critical transport hubs can be disrupted for significant durations by terrorists. Designers of critical infrastructure need to consider countermeasures to such attacks, reducing a target's attractiveness and improving opportunities for business continuity. This can be achieved by considering the cost–benefit of potential countermeasures during the design phase for infrastructure. This paper uses a probabilistic risk assessment (PRA) model for IED attack to assess the costs and benefits of using distributed security queuing at airport terminals. Our results demonstrate that the use of distributed security queuing will offer casualty reductions when used in preference to centralized security queuing. However, when considering the cost–benefit of introducing distributed security queuing, on the basis of a single small to medium IED attack, it is likely that implementing this countermeasure would not be deemed cost-effective from a purely financial perspective, particularly when the threat likelihood is very low.

A probabilistic asteroid impact risk model: assessment of sub-300 m impacts

A comprehensive asteroid threat assessment requires the quantification of both the impact likelihood and resulting consequence across the range of possible events. This paper presents a probabilistic asteroid impact risk (PAIR) assessment model developed for this purpose. The model incorporates published impact frequency rates with state-of-the-art consequence assessment tools, applied within a Monte Carlo framework that generates sets of impact scenarios from uncertain input parameter distributions. Explicit treatment of atmospheric entry is included to produce energy deposition rates that account for the effects of thermal ablation and object fragmentation. These energy deposition rates are used to model the resulting ground damage, and affected populations are computed for the sampled impact locations. The results for each scenario are aggregated into a distribution of potential outcomes that reflect the range of uncertain impact parameters, population densities, and strike probabilities. As an illustration of the utility of the PAIR model, the results are used to address the question of what minimum size asteroid constitutes a threat to the population. To answer this question, complete distributions of results are combined with a hypothetical risk tolerance posture to provide the minimum size, given sets of initial assumptions for objects up to 300m in diameter. Model outputs demonstrate how such questions can be answered and provide a means for interpreting the effect that input assumptions and uncertainty can have on final risk-based decisions. Model results can be used to prioritize investments to gain knowledge in critical areas or, conversely, to identify areas where additional data have little effect on the metrics of interest.

Life cycle toxicity assessment of earthworms exposed to cadmium-contaminated soils.

Cadmium (Cd) is of great concern in the soil environment and it can damage terrestrial organisms. The purpose of this study was to employ a toxicokinetic/toxicodynamic (TK/TD) approach to investigate the effects of toxicologically relevant Cd accumulation on the life cycle growth of earthworms (Lumbricus rubellus and Eisenia fetida) and to assess potential terrestrial ecosystem risk. We reanalyzed growth toxicity and whole body and pellet accumulation data linked with TK/TD and life cycle growth models to estimate key rate constants. The growth risk of earthworms exposed to Cd was also assessed. This study found that the estimated whole body killing rate constant (0.114 g d μg-1) was much lower than that of pellet (0.248 g d μg-1). The recovery rate constant for whole body (6.02 d-1) was much higher than that of pellet (2.91 d-1). We also employed a life cycle-based probabilistic risk assessment model to estimate the growth inhibition risk for earthworms in response to environmentally relevant concentrations of Cd in Taiwan. Results showed that earthworms had a 90% growth inhibition probability risk of body weight, which was lower than 872.33 mg based on assessment of toxicologically relevant Cd accumulation. This study suggests that toxicologically relevant Cd accumulation could accurately reflect the capacity of Cd toxicity to earthworms. The integrated life cycle toxicity of earthworms exposed to Cd in this study provides a robust and applicable tool for the management of ecological risk assessment of Cd-contaminated soil.

Deciding with Thresholds: Importance Measures and Value of Information

Risk-informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value-of-information-based importance measure that brings together the risk metric, risk importance measures, and the risk threshold in one expression. The new importance measure does not impose additional computational burden because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large loss of coolant accident sequence of the Advanced Test Reactor helps us in illustrating the risk analysis insights.

Health risk assessment for carcinogenic and non-carcinogenic heavy metal exposures from vegetables and fruits of Bangladesh

AbstractMost popular vegetables and fruits and their corresponding soil from the sub-urban industrial area of Bangladesh were collected and the concentration of carcinogenic (Pb, As, and Cd) and non-carcinogenic (Fe, Co, V, Cu, Cr, Zn, Mn, and Ni) heavy metals was determined. Health risk was evaluated by estimating daily heavy metal intake and computing cancer and non-cancer risks (ILCR and THQ) using probabilistic risk assessment model of US-EPA. Heavy metals in vegetables varied with vegetable species as well as metal types. Higher daily intake of As, Fe, Mn, and Pb was observed from the consumption of root and leafy vegetables. Moreover, the probability of an adult for developing cancer from the consumption of studied vegetables was greater than US-EPA threshold risk limit (>10−4) for As and Cd. In addition, cumulative cancer risk (∑ILCR) of all the studied vegetables and fruits exceeded the limit for fruit, root, leafy vegetables, and fruits (22, 15, 59, and 4%) with As, Cd, and Pb as 17, 81, and 2%, ...

Risk of Progressive Collapse of Buildings from Terrorist Attacks: Are the Benefits of Protection Worth the Cost?

AbstractThe paper describes a probabilistic risk assessment model to identify the risks associated with vehicle-borne improvised explosive device (VBIED) attacks to large federal government buildings in the United States. Probability of terrorist threat, hazard, damage, fatality, and economic and social loss for progressive collapse are modeled as stochastic variables. It was found that the existing annual fatality risk for building occupants is lower than acceptable risk criteria, and that progressive collapse is an exceedingly rare event in Western countries. A performance-based design using cost-benefit analysis of U.S. design provisions to mitigate against progressive collapse showed that these design measures only becomes cost-effective when the threat likelihood is a very high 1 in 1,000 per building per year. However, such provisions are more likely to be cost-effective in regions of high seismicity due to a lower cost premium.

Assessing the inhalation cancer risk of particulate matter bound polycyclic aromatic hydrocarbons (PAHs) for the elderly in a retirement community of a mega city in North China.

Assessment of the health risks resulting from exposure to ambient polycyclic aromatic hydrocarbons (PAHs) is limited by the lack of environmental exposure data among different subpopulations. To assess the exposure cancer risk of particulate carcinogenic polycyclic aromatic hydrocarbon pollution for the elderly, this study conducted a personal exposure measurement campaign for particulate PAHs in a community of Tianjin, a city in northern China. Personal exposure samples were collected from the elderly in non-heating (August-September, 2009) and heating periods (November-December, 2009), and 12 PAHs individuals were analyzed for risk estimation. Questionnaire and time-activity log were also recorded for each person. The probabilistic risk assessment model was integrated with Toxic Equivalent Factors (TEFs). Considering that the estimation of the applied dose for a given air pollutant is dependent on the inhalation rate, the inhalation rate from both EPA exposure factor book was applied to calculate the carcinogenic risk in this study. Monte Carlo simulation was used as a probabilistic risk assessment model, and risk simulation results indicated that the inhalation-ILCR values for both male and female subjects followed a lognormal distribution with a mean of 4.81 × 10-6 and 4.57 × 10-6, respectively. Furthermore, the 95% probability lung cancer risks were greater than the USEPA acceptable level of 10-6 for both men and women through the inhalation route, revealing that exposure to PAHs posed an unacceptable potential cancer risk for the elderly in this study. As a result, some measures should be taken to reduce PAHs pollution and the exposure level to decrease the cancer risk for the general population, especially for the elderly.

An integrated modeling approach for event sequence development in multi-unit probabilistic risk assessment

To obtain a complete site risk profile for multi-unit nuclear power plants (NPPs), the existing single-unit probabilistic risk assessment (PRA) model needs to be upgraded to address both intra-unit and inter-unit dependencies. The high temperature gas cooled reactor-pebble bed module (HTR-PM), with two reactor-steam generator modules and one shared steam turbine-generator set, is a ready-in-hand pilot for multi-unit PRA (MUPRA) studies. In this paper, a general framework for MUPRA is firstly developed based on HTR-PM PRA methodology. An Integrated Modeling (IM) approach is then proposed for multi-unit event sequence modeling. The approach is discussed in detail and illustrated with a loss of offsite power (LOOP) case study. Also, the limitations of the current analysis are put forward for open discussion.

Impaired Spatial Memory Performance in Adult Wistar Rats Exposed to Low (5-20 cGy) Doses of 1 GeV/n (56)Fe Particles.

Prolonged deep space missions to planets and asteroids will expose astronauts to galactic cosmic radiation, comprised of low-linear energy transfer (LET) ionizing radiations, high-energy protons and high-Z and energy (HZE) particles, such as (56)Fe nuclei. In prior studies with rodents exposed to HZE particle radiation at doses likely to be encountered during deep space missions (<20 cGy) investigators reported impaired hippocampal-dependent neurocognitive performance and further observed substantial variation among the irradiated animals in neurocognitive impairment, ranging from no observable effects to severe impairment. These findings point to the importance of incorporating quantitative measures of interindividual variations into next generation risk assessment models of radiation risks on neurocognition. In this study, 269 male proven breeder Wistar rats were exposed to 1 GeV/n (56)Fe at doses of 0, 5, 10, 15 and 20 cGy, and tested for spatial memory performance on the Barnes maze at three months after exposure. The radiation response data were compared using changes in mean cohort performance and by the proportion of poor responders using the performance benchmark of two standard deviations below the mean value among the sham-irradiated cohort. Acute exposures to mission-relevant doses of 1 GeV/n (56)Fe reduced the mean spatial memory performance at three months after exposure (P < 0.002) and increased the proportions of poor performers, 2- to 3-fold. However, a substantial fraction of animals in all exposure cohorts showed no detectable change in performance, compared to the distribution of sham-irradiated animals. Our findings suggest that individualized metrics of susceptibility or resistance to radiation-induce changes in neurocognitive performance will be advantageous to the development of probabilistic risk assessment models for HZE-induced neurocognitive impairment.