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Envisioning nano release dynamics in a changing world: using dynamic probabilistic modelling to assess future environmental emissions of engineered nanoparticles Tianyin Sun, Denise M. Mitrano, Nikolaus A Bornhöft, Martin Scheringer, Konrad Hungerbuehler, and Bernd Nowack Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b05702 • Publication Date (Web): 03 Feb 2017 Downloaded from http://pubs.acs.org on February 6, 2017

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Environmental Science & Technology

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Envisioning nano release dynamics in a changing world: using dynam-

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ic probabilistic modelling to assess future environmental emissions of

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engineered nanoparticles

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Tian Yin Sun1,2, Denise M. Mitrano1, Nikolaus A. Bornhöft1,3, Martin Scheringer2,4, Konrad

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Hungerbühler2 and Bernd Nowack1*

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1)

Empa

–

Swiss

Federal

Laboratories

for

Materials Science

and

Technology,

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Technology and Society Laboratory, Lerchenfeldstrasse 5, CH-9014 St. Gallen, Switzerland

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2) Institute for Chemical and Bioengineering, ETH Zürich, CH-8093 Zürich, Switzerland

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3) Department of Informatics, University of Zurich, Binzmühlestrasse 14, 8050 Zurich, Switzer-

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land

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4) RECETOX, Masaryk University, 625 00 Brno, Czech Republic

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* Corresponding author

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[email protected]

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Tel: +41 (0)58 765 76 92

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Fax: +41 (0)58 765 78 62

19 20 21

ACS Paragon Plus Environment


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TOC Art

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Abstract

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The need for an environmental risk assessment for engineered nanomaterials (ENM) necessi-

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tates the knowledge about their environmental emissions. Material flow models (MFA) have

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been used to provide predicted environmental emissions but most current nano-MFA models

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consider neither the rapid development of ENM production nor the fact that a large proportion

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of ENM are entering an in-use stock and are released from products over time (i.e. have a lag

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phase). Here we use dynamic probabilistic material flow modelling to predict scenarios of the

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future flows of four ENM (nano-TiO2, nano-ZnO, nano-Ag and CNT) to environmental com-

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partments and to quantify their amounts in (temporary) sinks such as the in-use stock and (“fi-

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nal”) environmental sinks such as soil and sediment. In these scenarios, we estimate likely fu-

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ture amounts if the use and distribution of ENM in products continues along current trends (i.e.

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a business-as-usual approach) and predict the effect of hypothetical trends in the market devel-

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opment of nanomaterials, such as the emergence of a new widely used product or the ban on

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certain substances, on the flows of nanomaterials to the environment in years to come. We show

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that depending on the scenario and the product type affected, significant changes of the flows

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occur over time, driven by the growth of stocks and delayed release dynamics.

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Introduction

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The fast-paced growth of the nanotechnology industry brings novel materials into commercially

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available products on a frequent basis and these engineered nanomaterials (ENM) will be re-

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leased during the products’ life-cycle. Modelling approaches play an indispensable role in un-

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derstanding and predicting nanomaterial concentrations in the environment that cannot be re-

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placed by analytical measurements. Current modelling efforts have approximated the mass

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flows to and the concentrations of ENM in environmental and technical compartments, allowing

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researchers from many disciplines to perform more realistic studies concerning environmental

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health and safety.1-10 The preferred approach follows a life-cycle principle tracking ENM mass-

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flows (material flow analysis, MFA) from the production of ENM through to their final settling

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place in environmental or waste streams. Validation of these models proves difficult as analyti-

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cal measurements in target systems remain largely elusive11. However, all these models were

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static and did not consider time-dependent processes with respect to the use of nanoproducts and

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release of ENM, which will become an increasingly important factor in the future.

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Static MFA models for ENM represent an oversimplification of the system in two respects; 1)

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only input from production, manufacturing and consumption that occurs in one year is account-

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ed for and then distributed over the system immediately and 2) the assumption that all ENM



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produced and applied in products are released to waste streams and the environment in the same

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year in which they enter the system. These pitfalls will become increasingly apparent as the

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number of products that contain nanomaterials begin to increase on the market and the accumu-

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lation of significant stocks of ENM in the anthroposphere, which currently are not considered,

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continues. It is particularly important to account for realistic releases into technical and envi-

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ronmental compartments over a product life-time of many years. The release from these stocks

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is not necessarily an immediate or linear process but rather one that is dependent on how ENM

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are used in products and is related to specific releases dictated by their use and disposal path-

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ways12, 13.

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While first attempts have been made in considering accumulation in environmental sinks, such

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as soil, and sediment7, 14, these models cannot predict accurate and realistic emissions into these

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compartments because the dynamics of production and release from products have not been tak-

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en into account. For a limited system only involving waste incineration plants, Walser and

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Gottschalk attempted to adapt older models for dynamic material transfer between compart-

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ments.15

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A paradigm shift in the MFA modelling process for ENM is needed to ensure that new models

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are developed which have a dynamic nature that can encompass the inclusion of ENM stocks.

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This is an indispensable element of MFA for modelling substance metabolism in the anthropo-

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sphere16. Conceptually, this modelling approach is different from previous models used to ap-

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proximate ENM emissions/flows into various environmental compartments in its levels of so-

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phistication, accuracy and therefore applicability. This step towards a more holistic modelling

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scheme comes from the convergence of two existing modelling methods: probabilistic materials

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flow analysis (P-MFA) and dynamic material-flow analysis (D-MFA). With P-MFA, uncertainty

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in the data used for a given model parameter is addressed as a probability distribution, therefore

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representing the comprehensive picture of the current understanding. D-MFA17-19 is able to as-

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sess the past, present and future flows of a material relying on knowledge of how the target sys-

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tem behaves and does not use the simplified assumption of immediate releases into the system

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with the additional inclusion of stocks and timed releases. Uncertainty in the system is not usu-

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ally emphasised in D-MFA20. The dynamic probabilistic MFA (DP-MFA) method recently de-

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veloped by Bornhöft et al. fills this gap 21. This method provides information about the behav-

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iour of the system as a function of time while representing all uncertain system parameters as

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probability distributions. Built on this general DP-MFA framework, Sun et al.22 developed a

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customised dynamic probabilistic material flow model predicting the mass-flows of four ENM –

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nano-TiO2, nano-ZnO, nano-Ag, and CNT – to technical and environmental compartments and

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the resulting concentrations in these compartments over time. One of the main assumptions of

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Sun et al. is that the share of ENM applied in different products remains constant over time.


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However, this is a simplification, especially in a world with a rapid advancement of technology

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and the appearance and establishment of new products alongside increased regulatory measures

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that seek to limit facets of this technology.

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The aims of this work were to predict scenarios of future flows of four ENM (nano-TiO2, nano-

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ZnO, nano-Ag and CNT) to environmental compartments. In these scenarios, we estimate future

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amounts if the use of ENM in products continues along current trends and predict the effect of

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hypothetical trends in the market development of nanomaterials.

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Methods

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General principle

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The general principle of the DP-MFA21 model can be summarised by the following three fea-

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tures: 1) the use of a life-cycle concept, 2) the application of a probabilistic approach and 3) dy-

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namic considerations. Following a life-cycle concept, the model tracks the mass-flows of four

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ENM (nano-Ag, nano-TiO2, nano-ZnO, and CNT) from ENM production to incorporation into

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commercial products and finally from the products to technical and environmental compart-

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ments during/after their use and disposal12. We estimated the release of ENM from products as

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described in Sun et al22. Probabilistic methods were employed for all the parameters used in the

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modelling processes to address the inherent uncertainty in the raw data used23. The dynamic

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considerations in this work are comprised of three aspects: the input dynamics, the dynamics of

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use scenarios and the release dynamics. The input dynamics describe the annual production of

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ENM as inflows into the system within a given period. The dynamics of the allocation to nano-

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products address the change of the share of ENM in different products over time. The release

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dynamics describe the time-dependent ENM release kinetics from a specific product category

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over its entire lifecycle.

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DP-MFA is separated into two modules21: the release module and the distribution module, as

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depicted in Figure 1. The release module addresses the input dynamics, the use scenarios and

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the (product) release dynamics. It describes the annual ENM production entering the anthropo-

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sphere system in the European Union (EU) over a period, the changes of the ENM-shares in

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product categories, and the flows from product categories by immediate release or into in-use

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stocks and finally the release from in-use stocks. “In-use stocks” in our model represent those

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ENM that are contained in products or applications during a use-phase that is longer than one

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year. The total annual release of ENM is then transferred to the compartments of the distribution

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module. The distribution module is built upon our previous static model3, which describes the

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ENM transfers within and between technical and environmental compartments.



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Use: Use phase release; EoL: End of Life phase release.

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Figure 1: Schematic visualization of the release module (input dynamics, mass distribution and

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the time dependent ENM release dynamics) and the distribution module (movement of ENM

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to/among technical and environmental compartments after product release). Hourglass symbols

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represent a phase in the release cycle where product-dependent timed releases occur defined by

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the life cycle of the given product and the expected yearly releases associated with use and dis-

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posal over the products lifetime. Stop watches indicate places where releases of ENM are

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stocked and “stored” for later release in future yearly cycles. In the distribution module, tech-

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nical systems/compartments include wastewater treatment, waste incineration, landfill and re-

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cycling; environment indicates compartments of air, soil, surface water and sediment. Releases

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from production/manufacturing are considered but not depicted in this scheme.

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Input dynamics

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The estimation of production of ENM over time was determined by multiplying the base year

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production (2012) with retrospective and prospective scaling factors as described in the Sup-

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porting Information. The production of the four ENM in 2012 is based on updated probability

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distributions3, 22. The scaling factors for each individual year were based on ENM market pro-

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jections, nanotechnology patent analysis and direct information on ENM production when

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available22. We used the assumption that the development of ENM production is proportional to

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nanotechnology development.

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Product release dynamics


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In our definition, release refers to ENM that leaves the production, manufacturing and consump-

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tion (use) phase and is subsequently transferred to technical or environmental compartments.

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ENM that reside in the in-use stock are not yet considered released. The scheme in Figure 1 de-

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picts how ENM are released in a time-dependent manner from products. Modelling the release

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of ENM from products to environmental and/or technical compartments proceeds in three steps:

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1) separation of ENM allocated in one product category into the “Use release” and/or “End of

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Life (EoL) release”; 2) scheduling of Use and EoL releases, i.e. allocating timed release from

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each product category in the use phase or product disposal; and 3) distribution of ENM into

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technical and/or environmental compartment(s) after leaving the Use release and/or EoL release

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at the specified time. The magnitude of the mass of ENM moving from one stage in a compart-

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ment to the next takes into account the amount of ENM in the originating compartment but also

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hinges on the transfer coefficients (TC), where TC values are summarized in our previous stud-

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ies3, 22. An example of the release parameters is given in Table S2 (Supplementary Information).

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SI Table S3 provides the new TC values for CNT applied in tyres.

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The time line of ENM release from products and into the environment or technical compart-

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ments were established alongside the complete product life times to establish a timed-release

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schedule. The release kinetics of ENM are specific to which products the ENM are used in,

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which ENM is applied and how the ENM is bound in a product. Preferably, this information is

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based on experimental data but it was estimated by expert judgement when no experimental re-

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sults were available. The EoL release is dictated by the lifetime of the product. Product lifetimes

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are often independent of ENM application, therefore the period of time a product is used before

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it is disposed of is either well known or can be easily estimated. A detailed description of the pa-

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rameters used for the product use schedule and the EoL release schedule is given in the Support-

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ing Information.

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The model includes some ENM transformation reactions during release or in technical com-

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partments3, but no further fate in environmental sinks, such as soils, is considered. Additionally,

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some transformation processes of ENM may make them inherently loose their “nano” proper-

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ties, such as when ENM dissolve, become irreversibly complexed to natural particles, or be

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transformed during some stages of the product life-cycle.24 Therefore, the values given for ENM

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in environmental sinks represent upper limits for total ENM concentrations.

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Definition of use scenarios

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We defined six scenarios to explore possible ENM developments. The total ENM production is

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assigned to different nano-enabled product categories (e.g. cosmetics, textiles, paints, etc.) in

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shares based on the information provided by our previous work.3 This allocation of ENM to ACS Paragon Plus Environment


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product categories is assumed to remain constant over the timeline considered in this study

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(1990 to 2020) for the base scenario. For hypothetical scenarios, such as a ban or increase of

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ENM in a specific application, a gradual change of the share of ENM allocated to a specific

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product is considered and the timeline is extended to better exemplify the ramifications of the

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situation. The settings and assumptions of the chosen scenarios are summarised in Table 1.

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Business as usual (Base scenario - BASE)

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The business as usual approach is based on the production and mass allocation to products of

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ENM from 1990 to 2020 and assumes that after 2020 the release remains at the same values as

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in 2020. No realistic market projections after this date are available and thus a conservative ap-

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proach is adopted and the base scenario is defined as the production value and product distribu-

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tion with no changes. This scenario serves as a baseline to the product-specific scenarios listed

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below and is a snapshot of how the situation would develop if current ENM development and

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usage trends persist.

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Cease nano production and application (ENM Ban - BAN)

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To better understand the model dynamics, specifically to uncover the importance of in-use

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stocks and timed releases of ENM into technical and environmental compartments, we supposed

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a hypothetical ban on the use of nano-enhanced products in the year 2020. Where previous

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models considered ENM to be immediately released to various system compartments the year

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after production, the in-use stocks central to the DP-MFA model show more complex release

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dynamics in the years leading up to and after halting ENM production and incorporation into

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new products. With production completely halted, we observe the evolution of ENM mass ac-

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cumulation in various compartments over time until the in-use stocks of ENM have been com-

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pletely depleted. This thought experiment provides an analysis of how long ENM would remain

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in the consumer realm and how releases to various system compartments would evolve although

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there are no new inputs to the system aside from the remaining in-use stock.

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Selective increase or ban of individual nano-enhanced products

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A more refined approach in understanding more probable ENM dynamics opposed to a simple

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overarching ban was considered as a next step. The flexibility of the model allows us to amend

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selected product categories in terms of total production input, release rate, etc. Here we explore

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the possibilities of innovative products coming to market, which would drastically increase the


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production and implementation of a given ENM in a certain product category or, conversely, a

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legislative ban on ENM in a certain product for e.g. health and safety concerns. In each case, we

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can observe the dynamic changes one product category can make due to its application volume

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on the long-term release potential of a specific ENM. This results in a chain reaction affecting

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the in-use stock and the final releases into environmental and technical compartments in the

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coming years. In both cases, the hypothetical augmentation of production values was compared

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to the business as usual (BASE) values. The scenarios put forward in this work include: a ban of

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nano-TiO2 in cosmetics 25, a ban of nano-Ag in textiles 26, increased use of nano-TiO2 in con-

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crete in building and road constructions 28, 29

27

and finally the increased production of CNT for use

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in tyres

. Many of these scenarios have been previously advocated by NGOs, scientists or

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companies and thus the results presented in this study can help to determine the impact of these

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calls to ENM management. The detailed transfer factors used for the scenarios are given in the

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Supporting Information.

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In the cases of banned items, a new policy to phase out products is not instantaneous from one

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year to another and therefore the total share of ENM in a certain product category of interest

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was linearly decreased from the material distribution in the year 2015 until the ban year (2020),

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thus gradually reaching the target proportion (0%) over a span of five years. Two hypothetical

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scenarios are shown representing divergent ENM release patterns and product market shares.

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One product (nano-TiO2 in cosmetics) makes up a dominant fraction (60%) of total use of nano-

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TiO2 to date and has total, immediate release in one year; thus exemplifying a situation where

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no in-use stock will be carried over in future years once the ban has taken place. Conversely, the

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examination of a ban of nano-Ag in textiles constitutes a decidedly smaller market share of Ag

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products (25%) but the in-use stock has longer viability; thus having smaller yet longer-term

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impact on total nano-Ag output into various environmental and technical sectors over time.

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As was the case with banned materials, implementation and adoption of new technology slowly

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gains popularity and thus a linear increase of ENM in target categories was used. The justifica-

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tion for increasing nano-TiO2 in concrete from 0% to 10% arose from recent estimates that this

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use might be one of the main high-volume applications for photocatalytic nano-TiO2 27. This ap-

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plication serves to illustrate the increased use of a material that has a long life span and there-

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fore slow releases of large in-use stocks. Currently, CNT usage in polymer nano-composites

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drives the flow of this material with more than 99% of the annual flows of CNT coming from

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the in-use stock and only very little immediate release. Given that CNT can improve tyre dura-

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bility, this technology may come to the market in the future 28, 29 and so we hypothesized an in-

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creased market share from 0% to 10% of tyres produced which contain CNT. This application

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serves as an example where the innovation drives a large increase in ENM (CNT) production

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and causes immediate and steady release over the use phase of the products’ lifetime.



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Table 1: Summary of the hypothetical scenarios indicating selective bans or increases in production of various ENM containing products from 2015 to 2030 (or

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2120 for the BAN scenario); previous studies refer to Sun et al. 20143 and Sun et al. 201622. 2020 was chosen as the year to end the model prediction because this

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is the year where most of the available trend information ends (e.g. ENM production projections). Scenarios

Abbreviations

Target ENM

Business as usual

BASE

All ENM

Complete ENM ban

BAN

All ENM

Ban of nano-TiO2 in cosmetics

BAN-Cos

nano-TiO2

Ban of nano-Ag in textiles

BAN-Tex

nano-Ag

INC-Concr

nano-TiO2

INC-Tyr

CNT

Increase of nano-TiO2 in concrete Increase of CNT in tyres

Production development and time scope 1990-2020: Real production based on Sun et al. (2016) 2021-2030: Using for each year the value from 2020 1990-2020: Real production based on Sun et al. (2016) 2021-2120: No production 2015-2020: On the basis of real production as modelled in Sun et al. (2016) deducting the smaller amount used in cosmetics 2021-2030: Using for each year the value from 2020 2015-2020: On the basis of real production as modelled in Sun et al. (2016) deducting the smaller amount used in textiles 2021-2030: Using for each year the value from 2020 2015-2020: On the basis of real production as modelled in Sun et al. (2016) adding the increase of application in concrete 2021-2030: Using for each year the value from 2020 2015-2020: On the basis of real production as modelled in Sun et al. (2016) adding the increase of application in tyres 2021-2030: Using for each year the value from 2020


Application shares of target ENM Based on Sun et al. 2014 Based on Sun et al. 2014 Based on Sun et al. 2014, the share applied in cosmetics declines from 60% to 0% from 2015 to 2020, mass allocation to other products remains unchanged Based on Sun et al. 2014, the share of application in textiles declines from 25% to 0% from 2015 to 2020, mass allocation to other products remains unchanged, Based on Sun et al. 2014, the share of application in concrete increase from 0% to 10% from 2015 to 2020 mass allocation to other products remains unchanged Based on Sun et al. 2014, the share of application in tyres increases from 0% to 10% from 2015 to 2020, mass allocation to other products remains unchanged

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Results and discussion

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ENM production over time

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Figure 2a depicts the modelled production development of nano-TiO2 in the EU between 1990 and

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2020. Results for nano-TiO2 are demonstrated here as an example because they have the largest pro-

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duction volume amongst the four ENM considered in this study; the results for other ENM can be

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found in SI Figures S1. The full probability spectrum of the production development is used as the

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main input for the dynamic flow modelling. The grey dots represent single model runs with single val-

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ues randomly selected out of the underlying probability distributions. The denser the grey dots appear,

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the more likely the modelled value is. The mean value is shown by the red line. The uncertainty is

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quantified by the width of the gap between the 15% and 85% quantiles (dashed blue lines). The large

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spread of the values is a consequence of combining a suite of raw data on development of nanotech-

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nology that diverge quite significantly from each other. The probabilistic modelling is able to synthe-

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size these data. This approach is needed because no historical data on the actual development of the

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production of ENM are available (with the exception of CNT)30. We have based our model on the as-

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sumption that the development of nanotechnology also reflects the trend of the production of ENM,

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where more research, patents and scientific papers correlate to real increases in production of nano-

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enhanced products.

276 277

Evolution of ENM in stocks and sinks from realistic estimation and hypothetical extension of produc-

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tion (BASE scenario)

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Figure 2b provides a full picture of the likelihood of the distribution of the nano-TiO2 development in

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in-use stocks of products in the EU from 1990 to 2030, with modelled values from 1990 to 2020 based

281

on real production values and after 2020 the hypothetical BASE scenario is envisioned. This is visual-

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ized by single simulations (grey lines) of 100,000 iterations with the mean value (red line) and 15%

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and 85% quantiles (dashed blue lines). Notably, while the total net amount of ENM in the in-use stock

284

is currently increasing because of assumed increased input from new nanomaterials in the BASE sce-

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nario, other scenarios may not always exhibit sustained growth in all sectors of the in-use stock over

286

time. Figure 2c highlights the mean values of nano-TiO2 accumulated after production and the amount

287

accumulated in the in-use stock, landfills, sludge treated soil and sediment, from which we can deter-

288

mine the dominant fate of nano-TiO2 in a holistic way. In the BASE scenario, all the stocks exhibit an

289

exponential-like increase over time. This is caused by both the primary input increasing yearly (i.e. in-

290

creased stock production) and ENM accumulation in each compartment from year to year; especially

291

in those stocks which are currently considered as final sinks (landfills, soil and sediment).

292



293 294

Figure 2: a. Estimated annual production development of nano-TiO2 in the EU from 1990 to 2020 with

295

hypothetical extension to 2030 assuming the production after 2020 remains the same. Short grey lines

296

(dots) indicate single modelled values. The red curve is the average trend of all simulated values.

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Dashed blue lines indicate the 15% and 85% quantile range of the probability density distribution of

298

the production. b. The evolution of nano-TiO2 amount in the in-use stock. Each grey line is a develop-

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ment trend of a single iteration out of 100 000 simulation runs. The mean (red line) and 15% and

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85 % quantiles are shown (blue dashed lines). The vertical width of the grey area is indicative of the

301

degree of uncertainty. c. Mean values of the evolution of nano-TiO2 in stocks and sinks as well as the

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total accumulative production in the EU from 1990 to 2020 with hypothetical extension to 2030 as-

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suming the production and release after 2020 remain the same. The vertical dashed green lines at year

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2020 indicate the distinction between the results based on modelled production and release according

305

to Sun et al. (2016) and the hypothetical extension after 2020 using the values from 2020 for each ad-

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ditional year. “Soil” here indicates the sewage sludge treated soil. Information for nano-ZnO, nano-

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Ag and CNT are given in SI Figure S1.

308 309

Total cessation of ENM use (BAN Scenario): Understanding memory effects

310

Yearly ENM production inputs to the consumption, release and distribution system are allocated by re-

311

leases into environmental and technical compartments which are split into two fractions: the portion

312

which is immediately released and that which stays in the in-use stock for a pre-determined amount of

313

time depending on the product life-cycle. The differentiation between these two fractions is most evi-

314

dent if we hypothesise a ban of new ENM production in order to visualise the continued release of

315

ENM material from the in-use stock when there is no new production input.

316

In some cases, such as with nano-TiO2, immediate releases dominate the total flow of material, with a

317

lesser fraction being retained in products and released over many years (Figure 3a). This is based on

318

the fact that a significant fraction of products containing nano-TiO2 (e.g. cosmetics, sunscreens at 60%

319

of the total current material distribution) are slated for immediate release whereas a smaller fraction

320

(e.g. paints, consisting of 9% of the total material distribution) have a greater proportion of material al-


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located to the in-use stock for releases at later times. Because there is a high amount of initial release

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in consumer products that have fast releases, when a ban is introduced the amount of product in the

323

market will fall immediately; i.e. the in-use stock from these sources will be fully depleted soon after

324

the ban is enforced. Other uses of nano-TiO2, such as the inclusion in paints and coatings, have a long-

325

er lifespan due to their association with building materials and so a smaller released mass will continue

326

even after the ban is in place as depicted by the small trough in Figure 3a. However, since this use

327

makes up a smaller mass of nano-TiO2, only about 12% of total nano-TiO2 released or produced would

328

be available for release in the longer-term (i.e. approximately around 80 years after its production) in-

329

use stock.

330

In other cases, as with CNT (Figure 3b), the release from the long-term in-use stocks dominates all

331

immediate releases. As the main source of using CNT is currently in polymer composites and only 1%

332

of this material is released in the first year of use, the bulk of the material flow will be dominated by

333

the scheduled in-use stock release. The lifetime of plastic items is distributed around the 8 years and

334

releases of CNT into environmental and technical compartments are scheduled according to the dis-

335

posal of these items. Therefore, even after the ban has come into place, release of CNT from products

336

produced before the ban will continue. Similar to the case of nano-TiO2, due to the fact that a very

337

small fraction (around 3%) of CNT is applied in paints, there is also a much delayed emergence of re-

338

lease after the ban takes place.

339

This hypothetical ban exemplifies how the improved DP-MFA and its possibility to model memory ef-

340

fects can more accurately assess the timeline of ENM releases into environmental and technical sys-

341

tems, because releases are more closely coupled with the life-cycle of individual products and realistic

342

release rates from year to year. Older, static versions of the model did not account for timed releases

343

and so this model memory effect (i.e. the accumulation of ENM in stocks and timed releases) was not

344

considered. As more products reach the market which contain ENM, the in-use stock will become in-

345

creasingly important as essentially an additional, delayed source of particles to environmental and

346

technical systems. In previous models, the production values in one year were completely distributed

347

to the environment in a single year, an assumption that our dynamic modelling has clearly shown to

348

not be representative for the ENM investigated.

349 350



351 352

Figure 3: Stacked graph of total mass of nano-TiO2 (a.) and CNT (b.) released either immediately

353

(blue sectors) or from in-use stocks (orange sectors) before and after the hypothetical ban of all pro-

354

duction and application in 2020 (BAN scenario). Mass evolution of accumulated productions (orange

355

lines) for nano-TiO2 (c.) and CNT (d.) and the accumulated mass in sink compartments (landfill, soil,

356

sediment and export) until in-use stocks (red trace) are depleted in the BAN scenario.

357 358

Importance of timed release for the accumulation of ENM in sinks

359

The model memory of scheduled releases has further-reaching effects than a simple delayed release to

360

the distribution portion of the model. This also influences the timing of ENM mass that reaches each

361

system compartment and final sink. Following the hypothetical ban of all ENM production in 2020,

362

we can note a few trends differentiating the various ENM and how they are allocated to environmental

363

and technical compartments, even with no further inputs to the system (Figures 3c and 3d.).

364

For example, nano-TiO2 exhibits an initial drop of the in-use stock with a longer tail until it is depleted

365

and this is directly due to the importance of the distribution of the ENM in each of the product sectors

366

(Figure 3c). In this case, the products which dominate the distribution of ENM in products are those

367

where there is a high amount of ENM that are released on a short time scale to one environmental sec-

368

tor (e.g. nanoTiO2 release from cosmetics/sunscreens release to the wastewater treatment system and

369

ultimately to the landfill). This will cause an initial depletion of the in-use stock shortly after the ban in


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370

2020 (Figure 3c), and subsequent distribution of most of the ENM mass amongst the affected sinks.

371

Therefore the accumulation of nano-TiO2 in sediment and soil will not significantly increase a few

372

years after the ban because the in-use stock and flow of ENM contributing to these compartments has

373

been depleted. However, other uses of nano-TiO2, such as the inclusion in paints and coatings associ-

374

ated with building materials, have a longer lifespan (80 years) and so additional releases of ENM to

375

the landfill are anticipated even after the ban is in place. In brief, environmental and technical com-

376

partments remain at a near steady-state condition with no further input into the system almost immedi-

377

ately after the ban is in effect for nearly 80 years until the release of nano-TiO2 from construction

378

waste is scheduled for release, after which the in-use stocks of nano-TiO2 are finally diminished from

379

the pre-ban timeline.

380

Conversely, CNT are currently dominantly used in consumer products such as plastics that have a very

381

low initial release (1%) and thus a longer lagged release from the product into environmental and

382

technical systems. While the in-use stock of CNT is slowly consumed post ban, a continuous addition

383

of CNT to sink compartments will occur several years after the ban due to the delayed releases of CNT

384

from plastics (see from 2020 to 2040 in Figure 3d). It is only when these stocked products are finally

385

disposed of at the end of the life cycle into the landfill (averaging 10 years post-production) that the

386

mass of ENM emitted reaches a steady state; a situation that is directly dictated by the length of prod-

387

uct use and release rates of ENM.

388 389

Influence of variable ENM product incorporation, use and selective ban scenarios on ENM mass flows

390

When hypothetical developments in the use of ENM in products based on selective policy bans or de-

391

velopment and implementation of new technology are considered, the mass of ENM reaching surface

392

water and soil in some instances could change substantially in the near future. Factors influencing the

393

change in the magnitude of release compared to the BASE scenario include 1) share of products af-

394

fected by the change in ENM usage, 2) ENM timed release scheme over the product lifetime (i.e. short

395

vs. long in-use phase), 3) destination of ENM after product release (i.e. environmental or technical

396

compartment) and 4) the transfer parameters over the entire process, both in the release and distribu-

397

tion modules. The development of ENM emissions between 2015 and 2030 in the various scenarios

398

envisioned can be compared to the BASE scenario to better grasp to what extent changes in ENM us-

399

age may affect the mass of ENM reaching the environment (Figure 4).

400



401 402

Figure 4. Developments of ENM emissions to soil and surface water compartments between 2015 and

403

2030 based on the different scenarios as described in Table 1 compared to the base scenario (BASE).

404

For surface water (panels a. and d., shown in blue and green colours), which is a flow-through com-

405

partment, the annual emissions are shown; for soil (both natural, urban and sewage sludge treated

406

soils, shown in red and grey colours), which is a sink-compartment, panels b. and e. show the annual

407

emissions and panels c. and f. the accumulated mass. The average evolution of emissions and cumula-

408

tive mass in each scenario is indicated by darker lines with yearly markers (dots). Corresponding col-

409

oured bands express the range between the 15% and 85% quantiles, which delineates the uncertainty

410

in the results. The small year-to-year fluctuations of the annual emission are derived from random var-

411

iations of the stochastic simulations.

412 413

Given that cosmetics constitute up to a 60% share of all nano-TiO2 uses3 and the majority of this frac-

414

tion is released immediately to surface water after the product use phase, once the mass of this fraction

415

of the nano-TiO2 product distribution decreases there will be a significant reduction of total nano-TiO2

416

emitted to the surface water (Figure 4a). In the BASE scenario, the evolution of emissions into surface

417

water has a positive correlation with annual nano-TiO2 input, as noted by the steady increase in nano-

418

TiO2 to surface water between 2015 and 2020, but remains constant thereafter because the input mass

419

was fixed to the 2020 value in future projections. The discrepancy between nano-TiO2 emissions of the

420

BASE and BAN-Cos scenarios demonstrate the effect of this cosmetics ban, where the annual emis-


Page 16 of 22

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421

sions of nano-TiO2 to surface water would be projected to decrease by nearly three quarters. From

422

2020 onwards, the difference between the BASE and BAN-COS remains steady because the total in-

423

put into both modelled systems remains constant due to steady inputs from other product sectors using

424

nano-TiO2, for example cleaning agents and consumer appliances.

425

Conversely, little difference in both the annual emission to and the accumulation of nano-TiO2 in soil

426

between the BASE scenario and INC-Concr were observed between 2015 and 2030 (Figure 4b and

427

4c). This result stems from two sources. Firstly, the transfer data for nano-TiO2 applied in concrete

428

were the same as those used for paints, where only 1% is released during use and 99% is defined as

429

EoL release. Because emissions to natural and urban soils account for only 25% of the total use re-

430

lease, a mere 0.25% additional yearly increase derives from the increased use of nano-TiO2 in con-

431

crete. Secondly, because the bulk of the release is slated to occur at the EoL phase, averaging 80 years

432

post-production, we would only expect the in-use stocks of our hypothetical system to deviate from

433

the BASE scenario on a longer time frame. However, even then low emissions to soil are expected

434

given that after building demolition, as 30% of concrete goes to landfill and 70% is recycled. There-

435

fore the addition of nano-TiO2 to concrete appears to not expose soils to a large increase of ENM when

436

compared to all other uses of nano-TiO2 and the accompanying releases.

437

There is approximately a 20% reduction in nano-Ag emissions to surface water when the BASE and

438

BAN-Tex scenarios are compared (Figure 4d). The mass of Ag released from the textile during the use

439

phase, which has shown to be variable under different use conditions31, 32, has significant ramifications

440

both on the initial release to water but also on the mass of Ag remaining on the textile that will further

441

be distributed during the EoL phase. Secondly, releases to the surface water from nano-textiles first

442

must pass through the wastewater treatment plant, and so any variations in this transfer factor would

443

directly affect the calculated amount of nano-Ag emitted to surface water.

444

A significant difference between the BASE scenario and a hypothetical change to ENM use is found in

445

the case of increased CNT application in tyres (INC-Tyr) and the subsequent annual emission to and

446

accumulation of CNT in soil (Figure 4e and 4f). Approximately 9% of the total mass of the tyre is ex-

447

pected to be lost during the use phase (Table S2), which corresponds to the 9% out of the 10% (Table

448

1) of CNT applied in tyres being released during the use phase. Out of this fraction, approximately

449

half is estimated to be destined for the soil environmental compartment. This application accounts for

450

up to 0.5% of the total share of CNT consumption in all product categories. By the year 2030, the an-

451

nual CNT emission to soil rises to 40 tonnes in contrast to approximately 20 tonnes for the BASE sce-

452

nario; for the accumulated CNT mass in soil, the value of the INC-Tyr scenario is projected to rise to

453

approximately 500 tonnes by 2030.



454

Calculating the ratio of the accumulated mass in the soil to the annual emission to this compartment,

455

we see that in the early stages of increased CNT use (e.g. 2015), the annual flow accounts for approx-

456

imately one fifth of the accumulated mass, by 2020 this value drops to less than one tenth.

457 458

Implications of the model results

459

For all the scenarios developed in this work and the flows quantified for extended periods of time, we

460

did not include any transformation reactions of the ENM after they end up in environmental compart-

461

ments. We have only taken into account some specific and well-studied transformations of ENM dur-

462

ing wastewater treatment (sulfidation of nano-Ag and ZnO) and incineration processes (combustion of

463

CNT), of which the transformed fraction is represented by the virtual compartment “elimination”. The

464

emissions to landfill, soil, surface water, sediment that we provide therefore constitute a likely upper

465

bound for material flows depending on the ENM investigated. The mass flows given in Figure 4 may

466

therefore be lower for a reactive material, such as nano-Ag, which can dissolve or undergo chemical

467

transformation reactions. These mass flows are also one of the primary input data for environmental

468

fate models that describe mechanistically the fate of ENM in natural systems33-36. In these models the

469

further dissolution/transformation over time of the deposited masses can be predicted.

470

The ability to predict the dynamic flows of ENM to several environmental and technical compartments

471

based on prospective changes in materials development is an important development for the risk as-

472

sessment of ENM. The risk, comprised of the combination of hazard and exposure, of using nano-

473

enhanced products can change over time depending on the flow of ENM to the environment. The

474

emissions data generated from our previous (static) modelling efforts3, 7 have long stood as bench-

475

marks for the likely exposure values in the risk equation and therefore were used as realistic ENM

476

concentrations when evaluating the fate of ENM in the environment33, 37, 38 or to assess environmental

477

risks of ENM39, 40. A more refined, time-resolved analysis of this emissions data assists in more realis-

478

tic risk assessments as well as provides improved input data for environmental fate models, such as to

479

follow the heteroagglomeration in natural waters38. By better assessing the relative impact of the use

480

of given nano-enabled products over time both researchers and regulators may be able to better assess

481

the pros and cons of implementing new products and the potential for far reaching affects that curbing

482

ENM usage may have on various environmental sectors.

483 484

Acknowledgements

485

Tian Yin Sun was supported by project 406440_131241 of the Swiss National Science Foundation

486

within the National Research Program 64. Nikolaus A. Bornhöft was supported by the European

487

Commission within the Seventh Framework Programme (FP7; MARINA project - Grant Agreement ACS Paragon Plus Environment

Page 18 of 22

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488

n° 263215). Martin Scheringer acknowledges financial support by the Czech Ministry of Education,

489

Youth, and Sports (LM2015051) and Masaryk University (CETOCOEN PLUS project).

490 491

Supporting Information

492

Description of input dynamics; raw data for production volume; data used for parameterization; trans-

493

fer coefficients; Figure with dynamic evolution of production and amounts in stocks for nano-ZnO,

494

nano-Ag and CNT; product use release schedule, EOL release schedule.

495



496

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