The use of Bayesian networks for nanoparticle risk forecasting: Model formulation and baseline evaluation

Money, Eric S.; Reckhow, Kenneth H.; Wiesner, Mark R.

doi:10.1016/j.scitotenv.2012.03.064

Cited by 59 publications

(52 citation statements)

References 31 publications

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“…There are a number of quantitative models, based on insights from colloidal chemistry, which account for the agglomeration/aggregation, sedimentation, re-suspension and dissolution dynamics of MNs (Ardvisson et al, 2011, Meesters et al, 2014, Money et al, 2012, Dale et al, 2013. The majority of these approaches cannot be characterised as user-friendly tools.…”

Section: Dynamic Modelsmentioning

confidence: 99%

“…In order to achieve similar results, in addition to these phenomena, the model by Quik et al (2014) takes into consideration also processes such as advection, volatilisation, chemical degradation, dissolution, and deposition, which makes it less strictly nano-specific and therefore applicable also to chemicals. Money et al (2012) adopted a different quantitative approach applying Bayesian networks in combination with expert elicitation as a tool for nanomaterial risk forecasting to develop a baseline probabilistic model that incorporates nano-specific characteristics and environmental parameters, along with elements of exposure potential, hazards, and risks from MNs. The Bayesian nature of FINE (Forecasting the Impacts of Nanomaterials in the Environment) allows for updating as new data become available.…”

Section: Dynamic Modelsmentioning

confidence: 99%

“…The CEINT/TINE tool was tested with nanoscale Ag data from freshwater wetland mesocosms. Similarly, the tool by Money et al (2012) was applied to estimate nano Ag concentrations in aquatic environments (Money et al, 2012).…”

Section: Dynamic Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Frameworks and tools for risk assessment of manufactured nanomaterials

Hristozov

Gottardo

Semenzin

et al. 2016

Environment International

106

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Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screeninglevel assessments rather than to support regulatory RA and risk management. Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs.

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Section: Dynamic Modelsmentioning

confidence: 99%

Section: Dynamic Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Frameworks and tools for risk assessment of manufactured nanomaterials

Hristozov

Gottardo

Semenzin

et al. 2016

Environment International

106

View full text Add to dashboard Cite

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“…Based on the given distributions, the distributions of the dependent values are then inferred using Monte--Carlo (MC) simulation. Money et al (2012) proposed a Bayesian network of several stages for forecasting environmental concentrations of na--noparticles.…”

Section: Introductionmentioning

confidence: 99%

A dynamic probabilistic material flow modeling method

Bornhöft

Sun

Hilty

et al. 2016

Environmental Modelling & Software

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Material flow modeling constitutes an important approach to predicting and understanding the flows of materials through the anthroposphere into the environment. The new "Dynamic Probabilistic Material Flow Analysis (DPMFA)" method, combining dynamic material flow modeling with probabilistic modeling, is presented in this paper. Material transfers that lead to particular environmental stocks are represented as systems of mass-balanced flows. The time-dynamic behavior of the system is calculated by adding up the flows over several consecutive periods, considering changes in the inflow to the system and intermediate delays in local stocks. Incomplete parameter knowledge is represented and propagated using Bayesian modeling. The method is implemented as a simulation framework in Python to support experts from different domains in the development of their application models. After the introduction of the method and its implementation, a case study is presented in which the framework is applied to predict the environmental concentrations of carbon nanotubes in Switzerland.

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“…Recently, this risk quantification method was extended (Coll et al, 2015) to include more toxicity data and extra uncertainty on the assessment factors used in the probabilistic SSD method. Money et al (2012) developed the Forecasting the Impacts of Nanomaterials in the Environment model for nanoparticle risk forecasting. This model is a Bayesian network model.…”

Section: Probabilistic Risk Assessment Of Enps In Litera-turementioning

confidence: 99%

Statistical modelling of variability and uncertainty in risk assessment of nanoparticles

Jacobs

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Engineered nanoparticles (ENPs) are used everywhere and have large technological and economic potential. Like all novel materials, however, ENPs have no history of safe use.Insight into risks of nanotechnology and the use of nanoparticles is an essential condition for the societal acceptance and safe use of nanotechnology.Risk assessment of ENPs has been hampered by lack of knowledge about ENPs, their environmental fate, toxicity, testing considerations, characterisation of nanoparticles and human and environmental exposures and routes. This lack of knowledge results in uncertainty in the risk assessment. Moreover, due to the novelty of nanotechnology, risk assessors are often confronted with small samples of data on which to perform a risk assessment. Dealing with this uncertainty and the small sample sizes are main challenges when it comes to risk assessment of ENPs. The objectives of this thesis are (i) to perform a transparent risk assessment of nanoparticles in the face of large uncertainty in such a way that it can guide future research to reduce the uncertainty and (ii) to evaluate empirical and parametric methods to estimate the risk probability in the case of small sample sizes.To address the first objective, I adapted an existing Integrated Probabilistic Risk Assessment (IPRA) method for use in nanoparticle risk assessment. In IPRA, statistical distributions and bootstrap methods are used to quantify uncertainty and variability in the risk assessment in a two-dimensional Monte Carlo algorithm. This method was applied in a human health (nanosilica in food) and an environmental (nanoT iO 2 in water) risk context. I showed that IPRA leads to a more transparent risk assessment and can direct further environmental and toxicological research to the areas in which it is most needed.For the second objective, I addressed the problem of small sample size of the critical effect concentration (CEC) in the estimation of R = P (E xpC > C EC ), where E xpC is the exposure concentration. First I assumed normality and investigated various parametric and non-parametric estimators. I found that, compared to the non-parametric estimators, the parametric estimators enable us to better estimate and bound the risk when sample sizes and/or small risks are small. Moreover, the Bayesian estimator out-ABSTRACT performed the maximum likelihood estimators in terms of coverage and interval lengths.Second, I relaxed the normality assumption for the tails of the exposure and effect distributions. I developed a mixture model to estimate the risk, R = P (E xpC > C EC ), with the assumption of a normal distribution for the bulk data and generalised Pareto distributions for the tails. A sensitivity analysis showed significant influence of the tail heaviness on the risk probability, R, especially for low risks.In conclusion, to really be able to focus the research into the risks of ENPs to the most needed areas, probabilistic methods as used and developed in this thesis need to be implemented on a larger scale. With these methods,...

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The use of Bayesian networks for nanoparticle risk forecasting: Model formulation and baseline evaluation

Cited by 59 publications

References 31 publications

Frameworks and tools for risk assessment of manufactured nanomaterials

Frameworks and tools for risk assessment of manufactured nanomaterials

A dynamic probabilistic material flow modeling method

Statistical modelling of variability and uncertainty in risk assessment of nanoparticles

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