Abstract
The shredding of waste of electrical and electronic equipment (WEEE) and other products, incorporated with nanomaterials, can lead to a substantial release of nanomaterials. Considering the uncertainty, complexity, and scarcity of experimental data on release, we present the development of a Bayesian belief network (BBN) model. This baseline model aims to give a first prediction of the release of nanomaterials (excluding nanofibers) during their mechanical shredding. With a focus on the description of the model development methodology, we characterize nanomaterial release in terms of number, size, mass, and composition of released particles. Through a sensitivity analysis of the model, we find the material-specific parameters like affinity of nanomaterials to the matrix of the composite and their state of dispersion inside the matrix to reduce the nanomaterial release up to 50%. The shredder-specific parameters like number of shafts in a shredder and input and output size of the material for shredding could minimize it up to 98%. The comparison with two experimental test cases shows promising outcome on the prediction capacity of the model. As additional experimental data on nanomaterial release becomes available, the model is able to further adapt and update risk forecasts. When adapting the model with additional expert beliefs, experts should be selected using criteria, e.g., substantial contribution to nanomaterial and/or particulate matter release-related scientific literature, the capacity and willingness to contribute to further development of the BBN model, and openness to accepting deviating opinions.
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References
Arvidsson R, Molander S, Sandén BA (2011) Particle flow analysis: exploring potential use phase emissions of TiO2 nanoparticles from sunscreen, paint and cement. J Ind Ecol 16:343–351
Beaudrie CEH, Kandlikar M (2011) Horses for courses: risk information and decision making in the regulation of nanomaterials. J Nanopart Res 13(4):1477–1488. https://doi.org/10.1007/s11051-011-0234-1
Bilal M, Liu H, Liu R, Cohen Y (2017) Bayesian network as a support tool for rapid query of the environmental multimedia distribution of nanomaterials. Nano 9:4162–4174
Bystrzejewska-Piotrowska G, Golimowski J, Urban PL (2009) Nanoparticles: their potential toxicity, waste and environmental management. Waste Manag 29:2587–2595
Caballero-Guzman A, Sun T, Nowack B (2015) Flows of engineered nanomaterials through the recycling process in Switzerland. Waste Manag 36:33–43
Chien YC, Ton S, Lee MH, Chia T, Shu HY, Wu YS (2003) Assessment of occupational health hazards in scrap-tire shredding facilities. Sci Total Environ 309(1-3):35–46. https://doi.org/10.1016/S0048-9697(03)00009-3
Deng J, Guo J, Zhou X, Zhou P, Fu X, Zhang W, Lin K (2014) Hazardous substances in indoor dust emitted from waste TV recycling facility. Environ Sci Pollut Res 21(12):7656–7667. https://doi.org/10.1007/s11356-014-2662-9
Ding Y, Kuhlbusch TAJ, van Tongeren M, Jiménez AS, Tuinman I, Chen R, Alvarez IL, Mikolajczyk U, Nickel C, Meyer J, Kaminski H, Wohlleben W, Stahlmecke B, Clavaguera S, Riediker M (2017a) Airborne engineered nanomaterials in the workplace- a review of release and worker exposure during nanomaterial production and handling processes. J Hazard Mater 322(Pt A):17–28. https://doi.org/10.1016/j.jhazmat.2016.04.075
Ding Y, Wohlleben W, Boland M, Vilsmeier K, Riediker M (2017b) Nano-object release during machining of polymer-based nanocomposites depends on process factors and the type of nanofiller. Ann Work Expo Health 61(9):1132–1144. https://doi.org/10.1093/annweh/wxx081
Froggett SJ, Clancy SF, Boverhof DR, Canady RA (2014) A review and perspective of existing research on the release of nanomaterials from solid nanocomposites. Part Fibre Toxicol 11(1):17–45. https://doi.org/10.1186/1743-8977-11-17
FutureNanoNeeds (2017) https://www.futurenanoneeds.eu/outputs/fnn-bbn-shredding-model/. Accessed 06 June 2017
Gohler D, Stintz M, Hillemann L, Vorbau M (2010) Characterization of nanoparticle release from surface coatings by the simulation of a sanding process. Ann Occup Hyg 54(6):615–624. https://doi.org/10.1093/annhyg/meq053
Gomez-Rivera F, Field JA, Brown D, Sierra-Alvarez R (2012) Fate of cerium dioxide nanoparticles in municipal waste water during activated sludge treatment. Bioresour Technol 108:300–304. https://doi.org/10.1016/j.biortech.2011.12.113
Goswami L, Kim KH, Deep A, Das P, Bhattacharya SS, Kumar S, Adelodun AA (2017) Engineered nano particles: nature, behavior, and effect on the environment. J Environ Manag 196:297–315. https://doi.org/10.1016/j.jenvman.2017.01.011
Gottschalk F, Lassen C, Kjoelholt J, Christensen F, Nowack B (2015) Modeling flows and concentrations of nine engineered nanomaterials in the Danish environment. Int J Environ Res Public Health 12:5581–5602
Hauck M, Ligthart T, Schaap M, Boukris E, Brouwer D (2017) Environmental benefits of reduced electricity use exceed impacts from lead use for perovskite based tandem solar cell. Renew Energy 111:906–913
Hsu LY, Chein HM (2007) Evaluation of nanoparticle emission for TiO2 nanopowder coating materials. J Nanopart Res 9:157–163
Hung H-L, Altschuld JW, Lee Y-F (2008) Methodological and conceptual issues confronting a cross-country Delphi study of educational program evaluation. Eval Program Plann 31(2):191–198. https://doi.org/10.1016/j.evalprogplan.2008.02.005
Kaegi R, Ulrich A, Sinnet B, Vonbank R, Wichser A, Zuleeg S, Simmler H, Brunner S, Vonmont H, Burkhardt M, Boller M (2008) Synthetic TiO2 nanoparticles emission from exterior facades into the aquatic environment. Environ Pollut 156:233–239
Kandlikar M, Ramachandran G, Maynard A, Murdock B, Toscano WA (2007) Health risk assessment for nanoparticles: a case for using expert judgment. J Nanopart Res 9(1):137–156. https://doi.org/10.1007/s11051-006-9154-x
Kearney AT (2017) White paper on technology and innovation for the future of production: accelerating value creation. World Economic Forum. Accessed 28 Sept 2017 http://www3.weforum.org/docs/WEF_White_Paper_Technology_Innovation_Future_of_Production_2017.pdf
Knol AB, Slottje P, van der Sluijs JP, Lebret E (2010) The use of expert elicitation in environmental health impact assessment: a seven step procedure. Environ Health 9:19. https://doi.org/10.1186/1476-069X-9-19
Kohler AR, Som C, Helland A, Gottschalk F (2008) Studying the potential release of carbon nanotubes throughout the application life cycle. J Clean Prod 16(8-9):927–937. https://doi.org/10.1016/j.jclepro.2007.04.007
Li J, Yang R, Yu J, Liu Y (2008) Natural photo-aging degradation of polypropylene nanocomposites. Polym Degrad Stab 93(1):84–89. https://doi.org/10.1016/j.polymdegradstab.2007.10.022
Ling MP, Lin WC, Liu CC, Huang YS, Chueh MJ, Shih TS (2012) Risk management strategy to increase the safety of workers in the nanomaterials industry. J Hazard Mater 229-230:83–93. https://doi.org/10.1016/j.jhazmat.2012.05.073
Lowry GV, Hotze EM, Bernhardt ES, Dionysiou DD, Pedersen JA, Wiesner MR, Xing B (2010) Environmental occurrences, behavior, fate, and ecological effects of nanomaterials: an introduction to the special series. J Environ Qual 39(6):1867–1874. https://doi.org/10.2134/jeq2010.0297
Lynch I (2014) Compendium of projects in the European nanosafety cluster. Accessed 29 Sept 2017 https://www.enanomapper.net/sites/default/files/pictures/docs/nsc-compendium14.pdf
Marcot BG (2012) Metrics for evaluating performance and uncertainty of Bayesian network models. Ecol Model 230:50–62. https://doi.org/10.1016/j.ecolmodel.2012.01.013
Marcoux MA, Matias M, Olivier F, Keck G (2013) Review and prospect of emerging contaminants in waste—key issues and challenges linked to their presence in waste treatment schemes: general aspects and focus on nanoparticles. Waste Manag 33(11):2147–2156. https://doi.org/10.1016/j.wasman.2013.06.022
Marvin HJP, Bouzembrak Y, Janssen EM, van der Zande M, Murphy F, Sheehan B, Mullins M, Bouwmeester H (2017) Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment. Nanotoxicology 11(1):123–133. https://doi.org/10.1080/17435390.2016.1278481
Mitrano DM, Motellier S, Clavaguera S, Nowack B (2015) Review of nanomaterial aging and transformations through the life cycle of nano-enhanced products. Environ Int 77:132–147. https://doi.org/10.1016/j.envint.2015.01.013
Money ES, Reckhow KH, Wiesner MR (2012) The use of Bayesian networks for nanoparticle risk forecasting: model formulation and baseline evaluation. Sci Total Environ 426:436–445. https://doi.org/10.1016/j.scitotenv.2012.03.064
Murphy F, Sheehan B, Mullins M, Bouwmeester H, Marvin HJP, Bouzembrak Y, Costa AL, Das R, Stone V, Tofail SAM (2016) A tractable method for measuring nanomaterial risk using Bayesian networks. Nanoscale Res Lett 11(1):503. https://doi.org/10.1186/s11671-016-1724-y
Oguchi M, Sakanakura H, Terazono A, Takigami H (2012) Fate of metals contained in waste electrical and electronic equipment in a municipal waste treatment process. Waste Manag 32(1):96–103. https://doi.org/10.1016/j.wasman.2011.09.012
Part F, Zecha G, Causon T, Sinner EK, Huber-Humer M (2015) Current limitations and challenges in nanowaste detection, characterisation and monitoring. Waste Manag 43:407–420. https://doi.org/10.1016/j.wasman.2015.05.035
Raynor PC, Cebula JI, Spangenberger JS, Olson BA, Dasch JM, D’Arcy JB (2012) Assessing potential nanoparticle release during nanocomposite shredding using direct-reading instruments. J Occup Environ Hyg 9(1):1–13. https://doi.org/10.1080/15459624.2012.633061
Reijnders L (2009) The release of TiO2 and SiO2 nanoparticles from nanocomposites. Polym Degrad Stab 94(5):873–876. https://doi.org/10.1016/j.polymdegradstab.2009.02.005
Renn O, Roco MC (2006) White paper on nanotechnology risk governance. International Risk Governance Council (IRGC).. Accessed on 02 Oct 2017. http://irgc.org/wp-content/uploads/2012/04/IRGC_white_paper_2_PDF_final_version-2.pdf
Riedmann RA, Gasic B, Vernez D (2015) Sensitivity analysis, dominant factors, and robustness of the ECETOC TRA v3, Stoffenmanager 4.5, and ART 1.5 occupational exposure models. Risk Anal 35(2):211–225. https://doi.org/10.1111/risa.12286
Roman HA, Hammitt JK, Walsh TL, Stieb DM (2012) Expert elicitation of the value per statistical life in an air pollution context. Risk Anal 32(12):2133–2151. https://doi.org/10.1111/j.1539-6924.2012.01826.x
Sachse S, Silva F, Zhu H, Irfan A, Leszczynsk A, Pielichowski K (2012) The effect of nanoclay on dust generation during drilling process of polyamide 6 nanocomposites. J Nanomater Article ID 189386
Schneider T, Brouwer DH, Koponen IK, Jensen KA, Fransman W, van Duuren-Stuurman B, van Tongeren M, Tielemans E (2011) Conceptual model for assessment of inhalation exposure to manufactured nanoparticles. J Expo Sci Environ Epidemiol 21(5):450–463. https://doi.org/10.1038/jes.2011.4
Shandilya N, Le Bihan O, Morgeneyer M (2014a) A review on the study of the generation of (nano) particles aerosols during the mechanical solicitation of materials. J Nanomater Article ID 289108
Shandilya N, Le Bihan O, Morgeneyer M (2014b) Effect of the normal load on the release of aerosol wear particles during abrasion. Tribol Lett 55(2):227–234. https://doi.org/10.1007/s11249-014-0351-y
Shandilya N, Le Bihan O, Morgeneyer M (2015a) First development to model aerosol emission from solid surfaces subjected to mechanical stresses: II. Experiment-theory comparison, simulation and sensibility analysis. J Aerosol Sci 89:1–17
Shandilya N, Le Bihan O, Bressot C, Morgeneyer M (2015b) Emission of titanium dioxide nanoparticles from building materials to the environment by wear and weather. Environ Sci Technol 49:2163−2170
Tielemans E, Schneider T, Goede H, Tischer M, Warren N, Kromhout H, Van TM, Van HJ, Cherrie JW (2008) Conceptual model for assessment of inhalation exposure: defining modifying factors. Ann Occup Hyg 52(7):577–586. https://doi.org/10.1093/annhyg/men059
Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modelling. Ecol Model 203(3-4):312–318. https://doi.org/10.1016/j.ecolmodel.2006.11.033
Wiesner MR, Bottero JY (2011) A risk forecasting process for nanostructured materials, and nanomanufacturing. C R Physique 12(7):659–668. https://doi.org/10.1016/j.crhy.2011.06.008
Wohlleben W, Brill S, Meier MW, Mertler M, Cox G, Hirth S, von Vacano B, Strauss V, Treumann S, Wiench K, Ma-Hock L, Landsiedel R (2011) On the lifecycle of nanocomposites: comparing released fragments and their in-vivo hazards from three release mechanisms and four nanocomposites. Small 7(16):2384–2395. https://doi.org/10.1002/smll.201002054
Xu J, Futakuchi M, Shimizu H, Alexander DB, Yanagihara K, Fukamachi K, Suzui M, Kanno J, Hirose A, Ogata A, Sakamoto Y, Nakae D, Omori T, Tsuda H (2012) Multi-walled carbon nanotubes translocate into the pleural cavity and induce visceral mesothelial proliferation in rats. Cancer Sci 103(12):2045–2050. https://doi.org/10.1111/cas.12005
Acknowledgements
The authors are grateful to Hilde Cnossen (TNO Zeist, Netherlands), Bas Henzing (TNO Utrecht, Netherlands), Richard Laucournet (CEA, France), Thomas Kuhlbusch (BAuA, Germany), Bob van der Vecht (TNO The Hague, Netherlands), Margherita Cioffi (Rina Consulting group, Italy), and Derk Brouwer (WITS University, South Africa) for their valuable contributions during the study.
Funding
This study was funded by European Union Seventh Framework Programme (FutureNanoNeeds project; grant number 604602).
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Shandilya, N., Ligthart, T., van Voorde, I. et al. A nanomaterial release model for waste shredding using a Bayesian belief network. J Nanopart Res 20, 33 (2018). https://doi.org/10.1007/s11051-018-4137-2
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DOI: https://doi.org/10.1007/s11051-018-4137-2