Probabilistic Risk Assessment Approach Research Articles

A probabilistic approach for risk assessment of moisture-related degradation of building envelopes

A probabilistic approach for risk assessment of moisture-related degradation of building envelopes

Probabilistic risk assessment of residential exposure to electric arc furnace steel slag using Bayesian model of relative bioavailability and PBPK modeling of manganese.

Electric arc furnace (EAF) slag is a coproduct of steel production used primarily for construction purposes. Some applications of EAF slag result in residential exposures by incidental ingestion and inhalation of airborne dust. To evaluate potential health risks, an EAF slag characterization program was conducted to measure concentrations of metals and leaching potential (including oral bioaccessibility) in 38 EAF slag samples. Arsenic, hexavalent chromium, iron, vanadium, and manganese (Mn) were identified as constituents of interest (COIs). Using a probabilistic risk assessment (PRA) approach, estimated distributions of dose for COIs were assessed, and increased cancer risks and noncancer hazard quotients (HQs) at the 50th and 95th percentiles were calculated. For the residents near slag-covered roads, cancer risk and noncancer HQs were <1E - 6 and 1, respectively. For residential driveway or landscape exposure, at the 95th percentile, cancer risks were 1E - 6 and 7E - 07 based on oral exposure to arsenic and hexavalent chromium, respectively. HQs ranged from 0.07 to 2 with the upper bound due to ingestion of Mn among children. To expand the analysis, a previously published physiologically based pharmacokinetic (PBPK) model was used to estimate Mn levels in the globus pallidus for both exposure scenarios and further evaluate the potential for Mn neurotoxicity. The PBPK model estimated slightly increased Mn in the globus pallidus at the 95th percentile of exposure, but concentrations did not exceed no-observed-adverse-effect levels for neurological effects. Overall, the assessment found that the application of EAF slag in residential areas is unlikely to pose a health hazard or increased cancer risk.

Comparative deterministic and probabilistic approaches for assessing the aquatic ecological risk of pesticides in a mixed land use basin: A case study in Uruguay

Environmental concentrations of 25 pesticides in superficial water were employed to conduct an ecological risk assessment (ERA) in a mixed land-use basin utilized as a drinking water source. A deterministic risk assessment (RQ) was utilized to evaluate the chronic risk to aquatic biota, while a probabilistic risk assessment (PRA) approach was applied to assess the acute and chronic risk in the most sensitive species and at the community level. A high risk was identified for insecticides (pyrethroids, organophosphates and organochlorines). RQs ranged from 4.0e−4 (2,4-D) to 105.3 (ethion) considering median concentrations and from 8.0e−4 (2,4-D) to 230 (p,p’-DDT) considering extreme concentrations. Temporal variation in ΣRQs showed the highest risk during spring and summer months, which is related to the crop calendar and land use in the Laguna del Cisne basin. For PRA, the probability of exceeding the hazardous concentration HC5 (5th percentile) was higher for the most sensitive species in chronic exposure, especially for cypermethrin (38.9 %), permethrin (25.6 %), and chlorpyrifos (16.6 %). In the case of acute exposures, the probability of surpassing HC5 was higher for the entire freshwater biota, with the highest values observed for bifenthrin (28.3 %), cypermethrin (25.5 %), permethrin (11.75 %), and ethion (11.1 %). The advantages and disadvantages of PRA for assessing pesticide ecological risk were compared with the conventional deterministic RQs approach, highlighting that PRA offers improvements over the deterministic risk assessment, especially for organophosphate pesticides. Additionally, PRA provides a more comprehensive evaluation of risk for both short and long-term exposure, has the potential to incorporate others available toxicity data (e.g., LD50, Daily Intake), and utilizes different hazardous concentrations, such as HC5, HC10, and HC50. Our findings emphasize the urgent need to establish a national regulatory framework to evaluate and mitigate pesticide risks in aquatic ecosystems, especially in drinking water source like Laguna del Cisne.

Mitigating casualty risks from primary fragmentation hazards

Primary fragmentation from detonation of high-explosive metal-cased munitions imposes significant risks to the safety of related personnel and the public. Barricades or other protective structures are commonly used to stop fragments and reduce casualty risks caused by detonated munitions when a sufficient safety distance cannot be guaranteed. This study aims to provide decision support for the positioning of barricades that can reasonably mitigate primary fragmentation hazards from the detonation of large calibre munitions using a probabilistic risk assessment approach. This approach enables a stochastic characterization of fragment ejections, stacking effects, fragment trajectories, human vulnerability and fragment hazard reduction by barricade. In a case study, the assessments of casualty risks and effectiveness of barricades were conducted for a single and a pallet of 155 mm projectiles. It was found that barricades with heights exceeding the height of munitions can significantly reduce the hazardous fragment densities and casualty risks beyond the barricade. The benefit of increasing the barricade height becomes marginal when it exceeds the height of munitions.

Polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs) in human breast milk from Colombia: A probabilistic risk assessment approach

Polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), and organochlorine pesticides (OCPs) are ubiquitous environmental contaminants. They were produced in relatively large volumes in the last century and are now subject to long-term monitoring and regulated under the United Nations Stockholm Convention (SC) on persistent organic pollutants (POPs). Though restricted, human exposure is still a concern and in some regions of the globe the information on the health risk is limited. Sixty breast milk samples from nursing mothers were collected between 2014 and 2015, residing in Bogota, Cartagena, and Medellin, which are industrialized cities in Colombia. This is the first comprehensive study to determine the concentrations in breast milk of PBDEs (n = 7), PCBs (n = 29), and OCPs (n = 28) in Colombia. The detection frequency of POPs, including BDE-47, CB-138, CB-153, CB-156, and CB-180, as well as several OCPs such as chloroneb, aldrins, HCHs, DDTs, and heptachlor, was found to be 100% in all samples tested. The mean concentrations of the analyzed legacy POPs were ∑3DDTs (423 ng/g lw) > chloroneb (50.1 ng/g lw) > ∑2permetrins (17.5 ng/g lw) > ∑2aldrins (16.7 ng/g lw) > 29 PCBs (15.04 ng/g lw) > ∑2chlordanes (CHLs) (11.2 ng/g lw) ≈ ∑3endosulfans (11.1 ng/g lw) > ∑2heptachlors (2.43 ng/g lw) > 7PBDEs (2.1 ng/g lw) > ∑4HCHs (0.58 ng/g lw). The results of this study suggest that the concentrations of DDTs were present in breast milk samples from Colombia at levels comparable to those found in previous studies conducted in other countries such as Brazil, Uruguay, Chile, and various Asian countries. The concentrations of PBDE and PCB congeners, as well as many pesticides, were found to be significantly correlated with each other. This suggests that these substances may have similar sources of exposure. The strength of the pair correlation among concentrations of POPs was assessed using Spearman's correlation coefficients, which varied from r = 0.62 for the association between BDE-47 and CB-153, to a high correlation of 0.99 for the correlation between γ-Chlordane and heptachlor. This suggests that these POPs may share similar sources, such as diet. An exposure assessment model obtained by Monte Carlo simulation showed that infants were exposed to low concentrations of POPs with exception of p,p’-DDE and Aldrin, in which 25th, 50th and 95th percentiles were greater than the threshold reference values of non-carcinogenic effects suggested by US-EPA regulations while the 90th percentile of pg TEQ/Kg-bw/day for dl-PCBs was above of the tolerable daily intake (TDI) proposed by the World Health Organization (WHO). Therefore, the health risk of infants exposed to OCPs and dl-PCBs should be exanimated continually through biomonitoring programs in the Colombian population.

Tsunami flooding analysis graded-approach framework for tsunami probabilistic risk assessment

ABSTRACT A tsunami probabilistic risk assessment (PRA) is an effective tool that reviews the safety margins for beyond-design-basis events and strengthens the application of the defense-in-depth philosophy. It is recognized that tsunami hazard levels, assumed margins, and assumed risks differ significantly between sites. This paper proposes a framework of risk-based graded approach for a tsunami PRA. Since the predominant sources of uncertainties in an overall tsunami PRA may stem from tsunami hazard analysis, the framework selects a methodology for incorporating the uncertainties of tsunami hazard analysis in subsequent analyses, by grade. The framework provides five grades depending on the level of detail in the assessment. This graded approach framework was experimentally applied to the tsunami PRA for a virtual nuclear power station.

Comparative study of deterministic and probabilistic assessments of microbial risk associated with combined sewer overflows upstream of drinking water intakes

Combined sewer overflows (CSOs) are a source of microbial contamination of drinking water intakes located downstream from their discharge. To safeguard the quality of the source water, it is essential to evaluate the risk levels associated with these municipal structures. This study compares two risk assessment approaches to test their applicability for assessing the risk of CSOs to drinking water intakes in a highly urbanized watershed. The first approach was based on a deterministic equation that combines the characteristics of an overflow structure allowing the risk to be rated as very low, low, medium, high, or very high. The second probabilistic risk assessment approach yielded findings that are probabilistically distributed across the five levels of risk. This approach was developed by constructing a novel Bayesian network to probabilistically link the different factors defining the exposure of water intakes to the hazards of CSOs. The comparison between the results of these two approaches highlighted the importance of simultaneously considering many scenarios for assessing the risk of contamination of source waters. It was possible to use the Bayesian network rather than the deterministic equation, which only supports one scenario at a time. It was also shown that the deterministic approach often overestimated risk levels for CSO outfalls close to the water intake. This occurred because the assessment process emphasized the distance factor between the discharge point and the water intake, while neglecting other crucial characteristics of the overflow, such as duration and frequency. In particular, the deterministic approach tended to underestimate risk for CSOs associated with low overflow frequencies as it did not support scenarios of overflow duration, unlike the probabilistic approach. The validation and sensitivity analysis of the Bayesian model revealed that the population residing in the CSO's drainage basin, along with the frequency and duration of the overflows, exerted the greatest influence on the resulting risk levels. These factors outweighed other variables utilized in the risk assessment, including vulnerability of the drinking water intake, the type of overflow recorder, pipe diameter, and variables defining the exposure of the water intake to the discharge. In the context of implementing action plans, the Bayesian network is estimated as a cost-effective technique as it prioritized overflow structures needing special attention in a highly urbanized watershed, where the same CSOs were deterministically rated as having the same risk level. The results also demonstrated the effectiveness of the Bayesian model in addressing data gaps faced by water managers and stakeholders. The Bayesian model proved capable of assessing risks with uncertainties for CSOs, even with limited input data available. These findings can assist managers in identifying problematic structures by considering various scenarios, unlike the deterministic approach, which left almost half (n = 42) of the study site's overflow structures unassessed due to data limitations.

A probabilistic assessment of urban flood risk and impacts of future climate change

Climate change has resulted in the increased frequency of extreme storms and associated flood risk uncertainties in urban areas, which brings more challenges to future urban flood prevention. Assessing future trends in probabilistic urban flood risk can improve uncertainty estimation of urban floods under climate change, and thus it is becoming increasingly urgent and important. In this study, a probabilistic risk assessment approach of urban flooding was developed to quantify the probabilistic flood risk in urban areas and assess the impacts of future climate change on flood risk. Results showed probabilistic urban flood risk under the historical condition ranged from 0 to 0.508 with a mean value of 0.104, indicating probabilistic urban flood risk was at the medium risk level. The areas where probabilistic flood risk reached the medium risk level under historical condition accounted for 34.3 % of the study area. The ranges of probabilistic flood risk in the study area were 0–0.588 and 0–0.601 with mean values of 0.126 and 0.131 under SSP2-4.5 and SSP5-8.5 climate change scenarios, respectively. The areas where probabilistic flood risk reached the high risk level respectively accounted for 17.1 % and 32.7 % of the study area for SSP2-4.5 and SSP5-8.5 scenarios. Compared with the historical period, probabilistic risk of urban flooding in the study area was projected to increase by 51.3 % and 67.4 % on average for SSP2-4.5 and SSP5-8.5 scenarios, respectively. The largest increase of probabilistic flood risk was approximately 200 % and 250 % for the SSP2-4.5 and SSP5-8.5 scenarios, respectively, which mainly occurred for forestlands and grasslands. These results can effectively improve the uncertainty estimation of risk trends for urban floods under future climate change, and thus better-informed decision making for urban flood prevention.

Application of probabilistic methods to address variability and uncertainty in estimating risks for non-cancer health effects

Human health risk assessment currently uses the reference dose or reference concentration (RfD, RfC) approach to describe the level of exposure to chemical hazards without appreciable risk for non-cancer health effects in people. However, this “bright line” approach assumes that there is minimal risk below the RfD/RfC with some undefined level of increased risk at exposures above the RfD/RfC and has limited utility for decision-making. Rather than this dichotomous approach, non-cancer risk assessment can benefit from incorporating probabilistic methods to estimate the amount of risk across a wide range of exposures and define a risk-specific dose. We identify and review existing approaches for conducting probabilistic non-cancer risk assessments. Using perchloroethylene (PCE), a priority chemical for the U.S. Environmental Protection Agency under the Toxic Substances Control Act, we calculate risk-specific doses for the effects on cognitive deficits using probabilistic risk assessment approaches. Our probabilistic risk assessment shows that chronic exposure to 0.004 ppm PCE is associated with approximately 1-in-1,000 risk for a 5% reduced performance on the Wechsler Memory Scale Visual Reproduction subtest with 95% confidence. This exposure level associated with a 1-in-1000 risk for non-cancer neurocognitive deficits is lower than the current RfC for PCE of 0.0059 ppm, which is based on standard point of departure and uncertainty factor approaches for the same neurotoxic effects in occupationally exposed adults. We found that the population-level risk of cognitive deficit (indicating central nervous system dysfunction) is estimated to be greater than the cancer risk level of 1-in-100,000 at a similar chronic exposure level. The extension of toxicological endpoints to more clinically relevant endpoints, along with consideration of magnitude and severity of effect, will help in the selection of acceptable risk targets for non-cancer effects. We find that probabilistic approaches can 1) provide greater context to existing RfDs and RfCs by describing the probability of effect across a range of exposure levels including the RfD/RfC in a diverse population for a given magnitude of effect and confidence level, 2) relate effects of chemical exposures to clinical disease risk so that the resulting risk assessments can better inform decision-makers and benefit-cost analysis, and 3) better reflect the underlying biology and uncertainties of population risks.

Ambient water quality criteria derived using probabilistic risk assessment.

National recommendations for numeric human health ambient water quality criteria (AWQC) for toxic substances are derived by the US Environmental Protection Agency (USEPA) using a deterministic approach that combines point estimates for exposure, toxicity, and acceptable risk. In accordance with the Clean Water Act, states, territories, and authorized tribes must either adopt these recommendations or modify and replace them with criteria using an alternative, scientifically defensible method. Recent reports have criticized the deterministic approach, stating that it suffers from compounded conservatism by selecting upper percentiles or maximum values for multiple inputs and that it cannot directly determine what portion of the population a given criterion protects. As an alternative, probabilistic risk assessment (PRA) has been promoted as a more transparent and robust method for deriving AWQC. Probabilistic risk assessment offers several advantages over the deterministic approach. For example, PRA uses entire data distributions rather than upper-percentile point estimates to specify exposures, thereby reducing compounded conservatism. Additionally, because it links acceptable risk targets with specific segments of the exposed population, PRA-based AWQC demonstrably protects multiple subsets of the population. To date, no study has quantitatively compared deterministic and PRA approaches and resulting AWQC using national inputs consistent with USEPA guidance. This study introduces a PRA method for deriving AWQC and presents case studies to compare probabilistically derived AWQC with USEPA's 2015 recommendations. The methods and results of this work will help federal and state regulators, water quality managers, and stakeholders better understand available approaches to deriving AWQC and provide context to assumption- and method-specific differences between criteria. Integr Environ Assess Manag 2023;19:501-512.© 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Data-Driven Probabilistic Voltage Risk Assessment of MiniWECC System With Uncertain PVs and Wind Generations Using Realistic Data

It is found from actual data that due to generation dispatch and uncertain renewable generations and loads with complicated correlations, inferring the probabilistic distributions for uncertain inputs is challenging. Many probabilistic power flow approaches have been developed in the literature but their validations using realistic systems and data are lacking. This paper proposes a data-driven probabilistic analysis approach for system risk assessment of the miniWECC system using actual data. The sparse Gaussian process (SGP) is advocated to quantify the impacts of uncertain inputs on voltage security. SGP does not need the probability distribution function of uncertain inputs, can handle correlations and is highly computationally efficient. Results on the miniWECC system using realistic data show that SGP outperforms existing approaches and is able to quantify the voltage violation risks.

A Probabilistic Risk Assessment Approach for Surface Settlement Caused by Metro Tunnel Construction Using Credal Network

A Probabilistic Risk Assessment Approach for Surface Settlement Caused by Metro Tunnel Construction Using Credal Network

Ecological and human health risk assessment of chromite ore processing residue (COPR) dumpsites in Northern India: A multi–pathways based probabilistic risk approach

In this study, ecological and human health risk assessment (HHRA) of the subsurface environment was performed due to the presence of six different contaminants, i.e., Cr(VI), U, F-, As, NO3-, and Mo, in the proximity of chromite ore processing residue (COPR) dumping sites of Northern India. The analysis covered a probabilistic risk assessment approach comprised of 10,000 Monte Carlo simulations (MCS) for multi-exposure scenarios following soil and water pathways. The results of ecological risk indexes indicated considerable (contamination factor (CF) = 5.6) to very high (maximum CF ~ 11.0) contamination of soil near the Rania contaminant site. The maximum and 95th percentile of CF obtained due to Cr(VI) were 4.547 and 11.129, respectively, during the monsoon season. Cancer risk (CR) value for dermal contact exposure via soil pathway for the Rania site was > 1–2 orders higher than the safe limit, posing a serious threat to human health. Further, oral ingestion and skin dermal exposure to groundwater in the proximity of Chhiwali dumping site were posing the highest risk with CR = 18.5 and 54.9 (>10−6), respectively to the adult population. Overall, the study recommends an urgent need to take intensive remediation action for these COPR dumpsites to ensure human health safety.

Probabilistic approach for risk assessment of offshore hydrate wellbore during test production

The problem of wellbore risk due to complicated phase variation in coupled seepage in the process of hydrate production is one of a major obstacle restricting its commercial production. In response to this problem, a hybrid methodology for evaluating the risk of offshore hydrate test production wellbore is proposed using self-developed Particle Filter (PF) model, which realizes risk update through measurement data based on physical model. In this study, the physical model is constructed by a multi-field coupling model and the corresponding algorithm. The monitoring data is generated by Monte Carlo simulation to illustrate the method application. Then the probability prediction curve of the shear failure of the sediment around the wellbore can be calculated based on the PF. Finally, a novel dynamic risk matrix is developed to evaluate the risk of the current hydrate wellbore. A case study using geological parameters of hydrate sediments in the Shenhu area of the South China Sea was presented to illustrate the application of the proposed framework. Results indicated that the remaining safe production time at 90 days is between 210 days and 280 days, with a pressure drop of 3.0 MPa and 0.75 as the threshold. Subsequently, a dynamic risk matrix diagram of the risk of the hydrate wellbore is proposed based on Event Sequence Diagram (ESD) and Bayesian Network (BN). The result shows that the risk of abandonment caused by landslides should be prevented in the early stage, and the workover risk caused by damage to the wellbore structure should be paid special attention to in the middle and late stages. The proposed methodology can provide applied technology for wellbore risk management in the process of hydrate test production.

Integration of Toxicogenomics and Physiologically Based Pharmacokinetic Modeling in Human Health Risk Assessment of Perfluorooctane Sulfonate.

Toxicogenomics and physiologically based pharmacokinetic (PBPK) models are useful approaches in chemical risk assessment, but the methodology to incorporate toxicogenomic data into a PBPK model to inform risk assessment remains to be developed. This study aimed to develop a probabilistic human health risk assessment approach by integrating toxicogenomic dose-response data and PBPK modeling using perfluorooctane sulfonate (PFOS) as a case study. Based on the available human in vitro and mouse in vivo toxicogenomic data, we identified the differentially expressed genes (DEGs) at each exposure paradigm/duration. Kyoto Encyclopedia of Genes and Genomes and disease ontology enrichment analyses were conducted on the DEGs to identify significantly enriched pathways and diseases. The dose-response data of DEGs were analyzed using the Bayesian benchmark dose (BMD) method. Using a previously published PBPK model, the gene BMDs were converted to human equivalent doses (HEDs), which were summarized to pathway and disease HEDs and then extrapolated to reference doses (RfDs) by considering an uncertainty factor of 30 for mouse in vivo data and 10 for human in vitro data. The results suggested that the median RfDs at different exposure paradigms were similar to the 2016 U.S. Environmental Protection Agency's recommended RfD, while the RfDs for the most sensitive pathways and diseases were closer to the recent European Food Safety Authority's guidance values. In conclusion, genomic dose-response data and PBPK modeling can be integrated to become a useful alternative approach in risk assessment of environmental chemicals. This approach considers multiple endpoints, provides toxicity mechanistic insights, and does not rely on apical toxicity endpoints.

Distribution of some potentially toxic elements in the soils of the Jharia Coalfield: A probabilistic approach for source identification and risk assessment

AbstractPotentially toxic elements (PTEs) in coal mining soil degrade soil quality and adversely affect people's health. The increasing mining activities in the Jharia Coalfield (JCF) may increase the concentration of PTEs in soils. Therefore, it is vital to have an in‐depth analysis of land degradation caused by them, but there is a lack of accurate and efficient methods to estimate land degradation and health risk analysis in JCF. Therefore, finite mixture distribution model (FMDM), positive matrix factorization (PMF), and Monte Carlo simulation were applied to Jharia's PTEs dataset. The FMDM was employed to distinguish the background concentration and the contamination due to anthropogenic activity. In addition, the contribution of the source was determined through PMF. The FMDM suggested two possible sources of PTEs, while the PMF defines the influence of PTEs more broadly and identifies three sources of PTEs in soil. The results showed that PTEs contribution by a natural source, coal mining, and atmospheric deposition accounted for 45.72%, 27.34%, and 26.94%, respectively. Based on health risk results, this region does not pose a noncarcinogenic risk, but it does pose a carcinogenic risk, especially to infants. JCF comprises a relatively low concentration of PTEs as compared with other possible values for India. Probabilistic health risk assessment studies are scarce for Indian soils, especially in mining areas. In combination, these approaches will provide deeper insights and help identify potential solutions for PTEs contamination in these areas.

Development of a Bayesian network for probabilistic risk assessment of pesticides.

Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072-1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Risk Analysis Frameworks Used in Biological Control and Introduction of a Novel Bayesian Network Tool.

Classical biological control, the introduction of natural enemies to new environments to control unwanted pests or weeds, is, despite numerous successful examples, associated with rising concerns about unwanted environmental impacts such as population decline of nontarget species. Recognition of these biosafety risks is globally increasing, and prerelease assessments of biological control agents (BCAs) have become more rigorous in many countries. We review the current approaches to risk assessment for BCAs as used in Australasia, Europe, and North America. Traditionally, these assessments focus on providing assurance about the specificity of a proposed BCA, generally via a list of suitable versus nonsuitable hosts determined through laboratory specificity tests (i.e., by determining the BCA's physiological host range). The outcome of interactions of proposed agents in the natural environment can differ from laboratory-based predictions. Potential nontarget host testing may be incomplete, additional ecological barriers under field conditions may limit encounters between BCA and nontargets or reduce attack levels, and BCAs could disperse to habitats beyond those used by the target species and adversely affect nontarget species. We advocate for the adoption of more comprehensive, ecologically-based, probabilistic risk assessment approaches to BCA introductions. An example is provided using a Bayesian network that can integrate information on probabilities and uncertainties of a BCA to spread and establish in new habitats, interact with nontarget species in these habitats, and eventually negatively impact the populations of these nontarget species. Our new model, Biocontrol Adverse Impact Probability Assessment, aims to be incorporated into a structured decision-making framework to support national regulatory authorities.

Life stage and endpoint sensitivity differences of fathead minnow (Pimephales promelas) to chemicals with various modes of action

Fish Embryo Acute Toxicity (FET) test was proposed as an alternative to the traditional test methods using larval or adult fish. However, whether fathead minnow (Pimephales promelas) embryo is appropriate for FET remains uncertain. In the present study, ecological threshold of toxicological concern (ecoTTC) values and uncertainty factors (UFs) for each Verhaar et al. category in P. promelas were identified by employing probabilistic ecological risk assessment (PERA) approach with chemical toxicity distributions (CTDs). The sensitivity among different life stages and toxicity among different mode of actions (MOAs) classes were comprehensively compared by CTD comparisons. The results showed that embryo exhibited the less or similar sensitivity compared to larva or adult for Verhaar et al. MOA classes (1-4) while adults were more sensitive, followed by embryo than larval for non-classified chemicals. Considering growth effect as endpoint to class 1, class 3, and non-classified chemicals on P. promelas embryo and larva was more sensitive than mortality. Non-classified chemicals especially inorganic compounds were most toxic to P. promelas embryo for the four concerned Verharr et al. MOA-specific chemical classes. This study also derived uncertainty factors (UFs) as 26.5 (9.8, 109) for embryo-to-larva, 6.26 (3.94, 11.0) for embryo-to-adult, 15.6 (10.1, 36.1) for mortality-to-growth, and 3.03 (1.86, 7.08) for mortality-to-reproduction, which can be applied for extrapolations of life stage-to-life stage and effect-to-effect to reduce the underestimating and overestimating risk by the use of default UF such as 10, 100 or 1000. Our findings are vital for feasibility of FET test of P. promelas for ecotoxicity testing and ecological risk assessment for chemicals with different MOAs.

Probabilistic health risk assessment and concentration of trace elements in meat, egg, and milk of Iran

ABSTRACT This research was performed to assess the concentration and health risk of eight trace elements (manganese (Mn), mercury (Hg), copper (Cu), lead (Pb), iron (Fe), zinc (Zn), cadmium (Cd), and arsenic (As)) in animal product samples (n = 120) in Markazi province of Iran by inductively coupled plasma optical emission spectrometry (ICP-OES). Some trace elements such as Mn, Hg, Cu, Pb, Fe, and Zn were detected in samples while Cd and As non-detected. Fe had the highest mean level of trace elements in meat samples (1551.28 ± 83.25 μg/kg). The levels of Pb and Hg in some samples were upper than the limit of the European Commission (EC) and the Institute of Standards and Industrial Research of Iran (ISIRI), International/National Standards for Heavy Metals, and U.S. Food and Drug Administration (FDA). Deterministic and probabilistic health risk assessment approaches to evaluate of the target hazard quotients (THQ) and carcinogenic risks (CR) index were applied. The Monte Carlo probability distributions revealed that the rank order of total target hazard quotient (TTHQ) was milk > meat > egg. The non-carcinogenic and carcinogenic index of toxic elements indicated that exposed population in Markazi province was safe (1> THQ, 10−4> CR), except Hg exposure for children in milk.