Guidance for the prognostic risk assessment of nanomaterials in aquatic ecosystems

Koelmans, Albert A.; Diepens, Noël J.; Velzeboer, Ilona; Besseling, Ellen; Quik, J.T.K.; Meent, Dik van de

doi:10.1016/j.scitotenv.2015.02.032

Cited by 56 publications

(69 citation statements)

References 58 publications

(103 reference statements)

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“…Hence, the CRs for nanoT iO 2 that do not ensure safe concentrations (Figure 3.4) are still conservative estimates and should thus be interpreted with caution. Nonetheless, the results emphasize the relevance of the debate whether aquatic toxicity of ENPs should be tested in their freely dispersed and unaggregated state or in a more environmentally realistic state that include ENPs present as aggregates (Koelmans et al, 2015). The CRs prove that only in- From the above discussion, we see that the nanoparticle form in current toxicity tests is not compatible with the nanoparticle form to which species are exposed to in the environment.…”

Section: Exposure Of Free Hetero-aggregated and Attached Enpsmentioning

confidence: 81%

“…Although the literature provides some evidence of probabilistic methods for the risk assessment of ENPs, the need for methods, including probabilistic methods, to address the problem of uncertainty in risk assessment of ENPs is still very topical (Koelmans et al, 2015). From the previous literature search, 7 papers concerned development of probabilistic risk assessment methodology for ENPs.…”

Section: Probabilistic Risk Assessment Of Enps In Litera-turementioning

confidence: 99%

“…Moreover, aggregation might even increase the uptake and bioavailability of ENPs. Depending on the feeding mechanism of the organism at stake, (hetero)-aggregated ENPs may have grown to a size so that they no longer pass the filtering apparatus of filter feeders Koelmans et al, 2015). On the other hand, an aggregated state probably yields higher effect thresholds, because particle toxicity would be lowered by aggregation or encapsulation of the nanoscale particles Koelmans et al, 2015).…”

Section: Exposure Of Free Hetero-aggregated and Attached Enpsmentioning

confidence: 99%

“…Depending on the feeding mechanism of the organism at stake, (hetero)-aggregated ENPs may have grown to a size so that they no longer pass the filtering apparatus of filter feeders Koelmans et al, 2015). On the other hand, an aggregated state probably yields higher effect thresholds, because particle toxicity would be lowered by aggregation or encapsulation of the nanoscale particles Koelmans et al, 2015). The CECs that are applied in the CR are based on toxicity testing of free and homo-aggregated ENPs (Coll et al, 2015) and do not account for such possible reduction of the toxicity of the hetero-aggregated form.…”

Section: Exposure Of Free Hetero-aggregated and Attached Enpsmentioning

confidence: 99%

“…This incompatibility between toxicity and exposure data constitutes extra uncertainty, which, if not resolved, could possibly be modelled. SB4N is a screening level model that is designed for exposure assessment of background concentrations on a regional or continental scale (Koelmans et al, 2015;Liu et al, 2014).…”

Section: Exposure Of Free Hetero-aggregated and Attached Enpsmentioning

confidence: 99%

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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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Section: Exposure Of Free Hetero-aggregated and Attached Enpsmentioning

confidence: 81%

Section: Probabilistic Risk Assessment Of Enps In Litera-turementioning

confidence: 99%

Section: Exposure Of Free Hetero-aggregated and Attached Enpsmentioning

confidence: 99%

Section: Exposure Of Free Hetero-aggregated and Attached Enpsmentioning

confidence: 99%

Section: Exposure Of Free Hetero-aggregated and Attached Enpsmentioning

confidence: 99%

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Statistical modelling of variability and uncertainty in risk assessment of nanoparticles

Jacobs

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Ecosafety of Nanomaterials in the Aquatic Environment

Bebianno¹,

Rocha²,

Pinheiro³

et al. 2022

Toxicology of Nanoparticles and Nanomaterials in Human, Terrestrial and Aquatic Systems

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Combining exposure and effect modeling into an integrated probabilistic environmental risk assessment for nanoparticles

Jacobs

Meesters

Braak

et al. 2016

Environmental Toxicology and Chemistry

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There is a growing need for good environmental risk assessment of engineered nanoparticles (ENPs). Environmental risk assessment of ENPs has been hampered by lack of data and knowledge about ENPs, their environmental fate, and their toxicity. This leads to uncertainty in the risk assessment. To deal with uncertainty in the risk assessment effectively, probabilistic methods are advantageous. In the present study, the authors developed a method to model both the variability and the uncertainty in environmental risk assessment of ENPs. This method is based on the concentration ratio and the ratio of the exposure concentration to the critical effect concentration, both considered to be random. In this method, variability and uncertainty are modeled separately so as to allow the user to see which part of the total variation in the concentration ratio is attributable to uncertainty and which part is attributable to variability. The authors illustrate the use of the method with a simplified aquatic risk assessment of nano-titanium dioxide. The authors' method allows a more transparent risk assessment and can also direct further environmental and toxicological research to the areas in which it is most needed.

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Guidance for the prognostic risk assessment of nanomaterials in aquatic ecosystems

Cited by 56 publications

References 58 publications

Statistical modelling of variability and uncertainty in risk assessment of nanoparticles

Statistical modelling of variability and uncertainty in risk assessment of nanoparticles

Ecosafety of Nanomaterials in the Aquatic Environment

Combining exposure and effect modeling into an integrated probabilistic environmental risk assessment for nanoparticles

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