This document discusses validating the predicted environmental concentrations from the SimpleBox4nano model using the NanoFASE Water-Soil-Organism spatiotemporal multimedia fate model. SimpleBox4nano is a screening level model that uses steady state conditions, while NanoFASE-WSO is a higher tier model that is time explicit and spatially distributed. The authors compare concentration outputs between the two models for titanium dioxide nanoparticles in the Thames catchment area. They find that SimpleBox4nano predictions are generally valid for soil and sediment compartments but may underestimate concentrations in water. Further scenario specification based on NanoFASE-WSO is recommended, particularly regarding resuspension rates.

1. SimpleBox4nano
Towards validating nanomaterial
predicted environmental concen-
trations from SimpleBox4nano
using the NanoFASE Water-Soil-
Organism (WSO) spatiotemporal
multimedia fate model
Joris TK Quik, Sam Harrison, Jaap Slootweg,
Joris Meesters, Veronique Adam, Jeroen
Kuenen, Stephen Lofts and Willie Peijnenburg

2. Environmental Exposure assessment
Emission Fate Exposure
Tools available: the European Union System for the Evaluation of
Substances (EUSES), implemented also in ECETOC TRA and Chesar.
SimpleBox basis for EUSES Multimedia fate module
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v.d. Meent, et al. 2014
Hollander et al., 2016
rivm.nl/SimpleBox

3. Adaptation of SimpleBox 4.0 to SimpleBox4Nano
● Transport between compartments
(air, soil, water and sediment)
based on rate constants instead
of thermodynamic equilibrium
(partitioning)
● Inclusion of speciation in “free”
and heteroagglomerated
nanoparticles
● Account for transformation to
other forms, e.g. due to
dissolution
water
sediment
Meesters et al., 2013
Meesters et al., 2014
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4. Adaptation of SimpleBox 4.0 to SimpleBox4Nano
● Transport between compartments
(air, soil, water and sediment)
based on rate constants instead
of thermodynamic equilibrium
(partitioning)
● Inclusion of speciation in “free”
and heteroagglomerated
nanoparticles
● Account for transformation to
other forms, e.g. due to
dissolution
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Nanoparticle (1-100 nm)
Colloidal
heteroagglomerate (<450 nm)
Coarse
heteroagglomerate (>450 nm)
Transformed form (i.e. dissolved metal)

5. Uncertainty & Variability compared
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Meesters et al., 2016
Meesters et al., 2018

6. ● PEC sensitive to transformation
rates above approx. 10-12
s-1
● PEC sensitive to attachment
efficiency for heteroaggregation
– Varied sensitivity between
compartments.
Critical ranges of parameters need
quantification/measurement.
PEC insensitive
Sensitivity
PEC sensitiveCritical range
2.5-97.5th
percentile
Meesters et al., 2019
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7. Towards validating nanomaterial
predicted environmental concentrations from
SimpleBox4nano using the NanoFASE-WSO
spatiotemporal multimedia fate model
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8. Difference in characteristics of the two models
SimpleBox4nano NanoFASE WSO Water-Soil-Organism
Screening level (second tier) Greater realism (higher tier)
Steady state conditions Time explicit
Multimedia:
air, water, sediment, soil
Multimedia: water, sediment, soil
Air input from LOTOS-EUROS
Regional to continental scale Gridded: 5x5 km
Background, regional
concentrations
Local concentrations
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9. Why compare these two models?
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0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 5 10 15 20 25 30 35 40 45
Ce(μgl-1)
Distance (km)
αhet=0
αhet=0.1
αhet=0.5
αhet=1
measurements

10. What can we learn from the comparison?
● The relation between SimpleBox4nano (SB4N) PEC[steady state] and the
spatially distributed PEC [local] from the NanoFASE-WSO model.
– Understand the relation to hotspots
– Quantify the validity in relation to the percentage of locations
protected by the steady state regional PEC: 90%.
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11. Scenario
● Thames catchment (size of SB4N compartments adjusted)
● TiO2 nPs with average size from the NF-WSO PSD
● Emission redistributed to 3 soil sub-compartments and 1 air and
fresh water compartment.
› Based on dynamic MFA model
● Parameters based on NF-WSO:
– Suspended Particulate Matter (SPM) concentration, size and
density adjusted based on NF-SWO
– Attachment efficiencies and dissolution rate constants
● Other parameters left at defaults
● Concentrations from NF-WSO = after 1 year
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12. ● PEC[steady state]
Same order of
magnitude as 90th
percentile of
NF-WSO PEC
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Fresh Water

13. ● PEC[steady state] ~valid
Same order of
magnitude as 90th
percentile of
NF-WSO PEC
● PEC[1 year]
An order of magnitude
lower than 90th
percentile
of
NF-WSO PEC
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Fresh Water

14. ● PEC[steady state] and PEC[1year]
valid
Orders of magnitude
larger than 90th
percentile of
NF-WSO PEC
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Sediment

15. ● PEC[steady state] valid
Orders of magnitude
larger than 90th
percentile of
NF-WSO PEC
● PEC[1 year] valid
Larger than 90th
percentile
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Soil

16. Conclusions
● SB4N PECs (steady state or 1 year)
– Valid for soil and sediment
compartments
– Sinks for ENMs
● Water compartment
– PEC[steady state] is ok
– PEC[1 year] seems low
● Recommend to further specify the
scenario based on NF-WSO Thames
implementation - e.g. resuspension rate.
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17. Outlook
● SimpleBox4nano is robust modelling tool
for predicting exposure concentrations of
nanomaterials
– Screening-level exposure assessment
– Research tool; needs incorporation in
end-user tools such as EUSES for
broader usability.
Further inquiries:
joris.quik@rivm.nl
rivm.nl/SimpleBox
SimpleBox4nano
Download here

18. Acknowledgements
Willie Peijnenburg, Eric Bleeker, Martine Bakker, Eleni Tsitsiou, Dik
van de Meent
CEH/NERC – TNO – WUR – SLU – UoGEN– EMPA - LIETAT
This work is supported by funding from the European Union’s Horizon
2020 research and innovation programme under grant agreement No
686239 “caLIBRAte” and No 646002 “NanoFASE”
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19. Literature
• Hollander, A., M. Schoorl, and D. van de Meent. 2016. 'SimpleBox 4.0: Improving the model while keeping it
simple', Chemosphere, 148: 99-107.
• Klein, J. J. M. d.; Quik, J. T. K.; Bäuerlein, P. S.; Koelmans, A. A., Towards validation of the NanoDUFLOW
nanoparticle fate model for the river Dommel, The Netherlands. Environ. Sci.: Nano 2016, 3, (2), 434-441.
• Meesters, J.A.J., K. Veltman, A.J. Hendriks, and D. van de Meent. 2013. 'Environmental exposure assessment of
engineered nanoparticles: why REACH needs adjustment', Integr Environ Assess Manag, 9: e15-26.
• Meesters, J.A.J., A.A. Koelmans, J.T.K. Quik, A.J. Hendriks, and D. van de Meent. 2014. 'Multimedia Modeling of
Engineered Nanoparticles with SimpleBox4nano: Model Definition and Evaluation', Environ Sci Technol, 48: 5726-
36.
• Meesters, J.A.J., J.T.K. Quik, A.A. Koelmans, A.J. Hendriks, and D. van de Meent. 2016. 'Multimedia environmental
fate and speciation of engineered nanoparticles: a probabilistic modeling approach', Environ Sci Nano, 3: 715-27.
• Jacobs, R., J.A.J. Meesters, C.J. ter Braak, D. van de Meent, and H. van der Voet. 2016. 'Combining exposure and
effect modeling into an integrated probabilistic environmental risk assessment for nanoparticles', Environ Toxicol
Chem, 35: 2958-67.
• Meent, van de, Dik, J.T.K. Quik, T. Traas, 2014. Identification and preliminary analysis of update needs for EUSES,
ECHA/2014/253
• Meesters, J.A.J. 2017. 'Environmental Exposure Modeling of Nanoparticles', PhD thesis, Radboud University
Nijmegen.
• Meesters, J.A.J. 2019. "A Model Sensitivity Analysis to Determine the Most Important Physicochemical Properties
Driving Environmental Fate and Exposure of Engineered Nanoparticles." ES nano 10.1039/C9EN00117D
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