Development of Exposure Scenarios for Manufactured Nanomaterials
Work Package 3
Occupational Exposure Scenarios
(Including Deliverables D3.1 and D3.2)
Derk Brouwer1, Rianda Gerritsen-Ebben1, Birgit van Duuren-Stuurman1, Iris Puijk1, Gaelle Uzu2, Luana Golanski2, Celina Vaquero3, Vasilis Gkanis; Panos4 Neofytou4,
Martie van Tongeren5
December 2010
1 Netherlands Organisation for Applied Scientific Research (TNO), Utrecht Area, Netherlands 2 French Alternative Energies and Atomic Energy Commission (CEA), Grenoble, France 3 Fundaciòn CDT – (LEIA), Miñano (Álava), Spain 4 National Center for Scientific Research 'Demokritos', Athens, Greece 5 Institute for Occupational Medicine (IOM), Edinburgh, United Kingdom
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Project website: www.nanex-project.eu
Funded under the seventh framework programme: NMP-2009-1.3-2: Exposure scenarios
to nanoparticles
Grant agreement no.: 247794
Funding scheme: Coordination and Support Actions (supporting action)
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Executive Summary
In order to build occupational exposure scenarios (ESs) for three types of manufactured
nanomaterials, i.e. single-walled carbon nanotubes (SWCNT) and multi-walled carbon
nanotubes (MWCNT), nano-silver (nano-Ag) and nano-titanium dioxide (nano-TiO2), the
following activities were performed.
• Identification and review of the open literature with respect to use for ES building requirements;
• Compilation of data generated by two major measurement campaigns, i.e. the NANOSH project (FP6) and the NanoINNOV project (CEA); and
• A performance check of existing exposure estimating models by comparison of modelled ES exposure estimates with actual data from the data campaigns mentioned above.
Based on 33 literature references, 22 ESs were entered into the NANEX Exposure
Scenario Database. A total of 14 ESs for CNT were developed, generating 35 contributing
exposure scenarios describing some facet of occupational exposure. Most of them were
related to ESs in the production/synthesis of carbon based nanomaterials or handling such
materials (weighing, removing, sonication, etc.) and two ESs addressed tasks related to
the machining of composites containing CNTs. A total of 5 ESs for nano-TiO2 were
developed, generating 12 contributing exposure scenarios. Of these, only two contained
sufficient information to fill in the ES; for the remaining ESs, only some elements of the
exposure scenario template could be completed. Three of these papers related to the
production of nano-TiO2 and two related to the production of materials containing nano-
TiO2.
Only two occupational ESs could be developed for nano-Ag, one describing its
manufacture in a wet chemistry process while the second was related to handling nano-Ag
in a fume hood.
In total, 35 ESs with 48 contributing scenarios were derived from the data sets of two large
measurement campaigns (NANOSH project (FP6) and the NanoINNOV project (CEA).
Most ESs were for CNTs (n=14), which were predominantly related to research-scale
activities. Most nano-TiO2 scenarios (n=8) were on commercial-scale manufacturing and
formulation. Only 2 ESs could be build for nano-Ag, whereas 11 ESs were build other
substances, including other metal oxides.
Based on the process of developing these ESs, several main conclusions could be drawn.
Most studies either reported in the literature or as part of the measurement campaigns had
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an explorative character and were focused on concentration/ emission analysis. Therefore,
the reports from these studies did not include most of the information necessary to build
ESs, e.g. amount used, frequency of activities. Basic characterization of the products used
was often not available and operational conditions were often not described. Most
concentration/emission-related measurement results were task-based and subsequently it
is difficult to assign a process category (PROC), as the same tasks can cover multiple
PROCs. The most important observation was the lack of harmonization either of the
measurement strategy or of the analysis and reporting of measurement data.
At this stage it was not possible to build exposure scenarios combining different
information sources (references). This was mainly due to the heterogeneity in the level and
quality of the description of the context (differences related to material characteristics,
processes, quantities handled, control systems, etc.) and in the exposure evaluation (the
absence of standards addressing different measurement strategies, equipments and data
treatment).
Information required to address the environmental release from the occupational exposure
scenarios is lacking, both from the open literature sources and from the measurement
campaigns. Information such as total quantities produced/year and some issues related to
air emissions (presence of vent hoods) was only rarely reported. No data were reported
related to water treatment.
ECETOC TRA and Stoffenmanager have been evaluated here with respect to their
applicability for estimating exposure to nanoparticles. Both models are based on a source-
receptor approach, distinguishing emission, transport, immission and personal exposure. It
was concluded that both models should, in principle, be able to predict exposure to
nanoparticles. However, the different categories within each model variable are not
particularly suitable for activities of nanomaterials and lack the required level of resolution,
which means that, in practice, many situations may fall into the same category, resulting in
the same or similar exposure estimates. Refinement of these categories in view of typical
activities for nanomaterial handling, amount of handling categories etc, is needed.
No correlation was observed between the model estimates (for mass concentration) and
the measured (particle-number) concentrations. In addition, no differences in the estimates
were observed between Stoffenmanager (activity-based) and ECETOC TRA (extrapolated
for full day exposure). Most probably the lack of correlation is largely dominated by the
variability of the (relatively few) data. The variability of the measured particle number
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concentrations was much larger than the variability of the Stoffenmanager and ECETOC
TRA exposure (mass concentration) predictions. This again suggests that there is a need
to refine the models to increase the resolution in the exposure estimates. As has been
demonstrated, due to lack of data or contextual information in the data sets, the entire
range within the model parameters categories could not be used, resulting in a loss of
power of discernment between exposure scenarios.
Both first tier models only provide mass concentration as proxy for exposure, where typical
devices used for nanoparticle exposure assessment use particle concentration as an
exposure metric. Since (nano) devices usually have size-windows up to 1000 nm, the
contribution of particles below 1000 nm to mass concentration will be low and might only
affect variations in the lower mass concentration ranges of the current model estimates.
This also indicates the need for recalibration of the models for nanomaterials exposure.
In summary it can be concluded that, in their current form:
• Both ECETOC TRA and Stoffenmanager are not suitable for providing estimates for nanoparticles. Neither of the models is tuned to and calibrated for nanomaterial exposure situations, and hence the actual model estimate will be inaccurate and possibly overestimate the (mass) concentration levels.
• Both models provide exposure estimates in mass concentrations, which may not be appropriate for expressing exposure to nanomaterials. Both will need to be modified to include more refined categories within several of the model variables. However, the main modification required is to address the exposure metrics.
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Table of Contents
Executive Summary ............................................................................................................. v Table of Contents ............................................................................................................... ix 1. Introduction ...................................................................................................................... 1
1.1 Objectives ................................................................................................................. 2 2. Methods ........................................................................................................................... 3
2.1 Introduction ............................................................................................................... 3 2.2 Review of literature .................................................................................................... 3 2.3 Data from NanoINNOV and NANOSH projects ......................................................... 3 2.4 Review of occupational exposure models and performance check ........................... 5 2.5 Building occupational exposure scenarios ................................................................ 5
3. Results ............................................................................................................................. 7 3.1. Use of information/data from open literature ............................................................. 7 3.2. Use of information/data from measurement campaigns ............................................ 8 3.3. Review of occupational exposure models and performance check ........................... 9
4. Discussion and Conclusions .......................................................................................... 11 4.1. Discussion and conclusions for ES from literature .................................................. 11 4.2. Discussion and conclusions for ES from the dataset generated by measurement
campaigns ............................................................................................................... 12 4.3. Discussion and conclusions from model review and performance check ................ 15
5. References .................................................................................................................... 19 6. Annexes ......................................................................................................................... 21
6.1. Overview of exposure scenarios in the NANEX Exposure Scenario Database ....... 22 6.2. Exposure models and exploration of their applicability for nanomaterials ............... 26
6.3.1 ECETOC TRA model ................................................................................... 26 Evaluation of model for suitability of nanomaterials under REACH ................................ 27
6.3.2 STOFFENMANAGER 4.0 ............................................................................. 28 6.3.3 Discussion and conclusion ........................................................................... 29
6.3. Performance check ECETOC TRA and STOFFENMANAGER with actual data ..... 31 6.4.1 Introduction ................................................................................................... 31 6.4.2 Assessments with ECETOC TRAv2 ............................................................. 31 6.4.2 Assessments with Stoffenmanager 4.0 ........................................................ 36 6.4.3 Performance check results ........................................................................... 37 6.4.4 Analysis of the performance check ............................................................... 47 6.4.5 Discussion and conclusion ........................................................................... 56 6.4.6 References ................................................................................................... 57
6.4. PDF extracts from the NANEX Exposure Scenario Database ................................. 58
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1. Introduction
NANEX aimed to develop a catalogue of generic and specific exposure scenarios (ESs)
for Manufactured NanoMaterials (MNMs), taking account of the entire lifecycle of these
materials. In WP3 (Occupational exposure scenarios) measurement and contextual
information was collected and reviewed to describe and characterize occupational
exposure and available tools and models to predict occupation exposure to MNMs
reviewed.
Within WP2 (Development of generic exposure scenario descr iptions) a database for
the collection of exposure scenarios according to REACH Guidance was developed
(NANEX Exposure Scenario Database). Annex 1 provides an overview of the 57 exposure
scenarios entered in this exposure scenario (ES) database, which were developed based
on information/data from open literature or existing databases.
The deliverables from WP3 were:
D3.1: Characterization of relevant occupational exposure scenarios. D3.2: Needs/ knowledge gaps to comply with REACH.
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1.1 Objectives
Information for describing and characterizing exposure were collected, collated and
reviewed for occupational exposure scenarios identified in WP2 relevant for the three
types of nanomaterials (High Aspect Ratio Nanomaterials (HARNs, mass produced
nanomaterials and speciality nanomaterials) using the format developed in WP2.
The Description of Work outlined the following objectives:
1. Collection, collation and summarizing of information on operational conditions and risk management measures for the scenarios identified from literature and amongst partners;
2. Collection, collation and review existing exposure data and (exposure) metrics; 3. Identification and review of available tools and models to predict occupation exposure
with respect to manufactured nanomaterials and identified scenarios; and 4. Characterization of relevant occupation exposure scenarios, if possible in terms of
operational conditions, risk management measures, estimated levels of exposure, numbers of workers involved etc, using the format developed in WP2.
The generic exposure scenarios identified in WP3 relevant for workers are:
• Carbon based nanomaterials, including SWCNT, MWCNT, carbon nanofibres and fullerenes, and their application in composite materials;
• Nano-silver (nano-Ag) and its use in textiles; and • Nano-TiO2 and its use in cosmetic products.
The development of these occupational ESs has been the core of the work of WP3.
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2. Methods
2.1 Introduction
In order to meet the objectives of the project, the following activities were performed:
• Identification of open literature related to occupational exposure; • Literature review; • Occupational exposure model review and performance check; • Compilation of data from the projects NanoINNOV and Nanosh; and • Building occupational exposure scenarios for the three nanomaterials.
2.2 Review of literature
Information on current and past FP6/7 and other projects, as well as results from a
literature search was made available by WP2 for review. The literature was reviewed
source by source and, for those papers that included relevant information for building
occupational exposure scenarios, an entry in the NANEX Exposure References Database
(developed by WP7 and WP2) was made. In addition the database has a section to import
occupational exposure scenarios (NANEX Exposure Scenario Database) which includes
the fields required for a REACHES as these fields were taken from the latest REACH
guidance on the web-site of the European Chemicals Agency (see also WP2 reporting). A
total of 40 references were added to the NANEX Exposure References Database of which
22 were eventually used to build occupational exposure scenarios.
2.3 Data from NanoINNOV and NANOSH projects
For the building of ESs, data was used from measurement campaigns from the
NanoINNOV/NanoSafe project and the NANOSH project.
The aim of both monitoring programmes was to assess the exposure of workers to
engineered nanoparticles. In this context, background nanoparticles that may be present in
the air from other sources (e.g. welding, grinding, and transportation) and from
outdoors/ambient aerosols can interfere with this assessment. The differentiation of
engineered nanoparticles from the background is one of the most important and difficult
challenges for the sampling strategy. Since there are no direct reading instruments
available that differentiate between engineered nanoparticles and ambient aerosols,
attention was paid to the issue of background levels and other aerosol-emitting sources.
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Determination of the air stream/ventilation patterns is a useful tool for the identification of
secondary sources. Other nano-sized aerosol emitting sources in the workplace were
identified and background concentrations were measured prior to and after the activity with
engineered nanoparticles.
As there is no firm agreement on the most appropriate exposure metric, it was decided to
employ a range of instruments for measuring mass, number and surface area
concentrations during the NANOSH project. In addition, samplers were used to collect
airborne nanoparticles for characterization purposes. Furthermore, contextual information
(e.g. description of process/situation, PPE), was collected by the completion of
questionnaires.
Direct measurements of number concentrations were obtained using Condensation
Particle Counter (CPCs) and Portacount. Number concentrations and number-based
size distributions were obtained using a Scanning Mobility Particle Sizer (SMPS) and
Electrical Low Pressure Impactor (ELPI). The SMPS characterizes particles by their
electrical mobility diameter, defined as the diameter of a spherical, singly-charged particle
having the same velocity as the particle in question in an electrostatic field. The 12-stage
ELPI monitors provides real-time aerodynamic particle size distributions ranging from
approximately 20 nm to 10000 nm. Direct measurements of surface area concentrations
of particles were obtained using Diffusion Charger (DC)-type instruments.
More specifically for the NANOSH measurement campaign, samples of airborne
nanoparticles were characterized, mainly by TEM analysis (parameters such as; shape,
size, state of agglomeration, composition). In the NANOSH study, a nanometer aerosol
sampler (NAS) was used to collect nanoparticles on Holey TEM grids (3 mm Holey Carbon
film 400 mesh Ni). Personal air samples (PAS) are taken in preference to
static/background samples (such as measurements performed using the NAS) given that,
for PAS measurements, the sample material is collected in the breathing zone of the
worker, which gives much more accurate information about the inhalation exposure. For
this purpose, a sampling device was developed consisting of an open-faced filter holder
including a 25 mm gold-coated polycarbonate filter on which a TEM grid was placed.
Data from the NanoINNOV and NANSOH projects resulted in a total of 35 entries in the
NANEX Exposure Scenario Database.
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2.4 Review of occupational exposure models and perf ormance check
Two of the main occupational exposure models, ECETOC TRA and Stoffenmanager,
which are both mentioned in the REACH Guidance R.14 from ECHA (2008), are often
used for estimating exposure to chemicals within a REACH chemical safety assessment
for occupational exposures.
The different modules of ECETOC-TRA and Stoffenmanager were reviewed for their
applicability for nanomaterials. This review is presented in Annex 6.3. Due to the
availability of data sets from measurement campaigns and literature references, no
exposure scenarios were built using these models. Instead a performance check of the
models in comparison with the collected data set was conducted and is presented in
Annex 6.4. Summaries of the reviews are provided in Chapter 3.
2.5 Building occupational exposure scenarios
This element of the study was the main integrating activity of NANEX WP3 and constitutes
the main chapters of this report. In total, 57 exposure scenarios were built from the
literature (22) and from data obtained from two large measurement campaigns
(NanoINNOV and NANSOH, 35). Chapter 3 addresses the processes of building ES using
data from these sources.
Overall findings, conclusions and recommendations from this work are given in Chapter 4.
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3. Results
3.1. Use of information/data from open literature
From the 57 occupational exposure scenarios in the NANEX Exposure Scenario
Database, 22 have been built from the literature, most of them related to carbon-based
nanomaterials (14), followed by nano-TiO2 (5) and nano-Ag (1). Annex 5.2 gives an
overview of the 22 literature-based exposure scenarios entered in the database and, in
Table 1, a summary of all the ES developed in WP3 is presented.
Carbon based nanomaterials
A total of 14 ESs for carbon-based nanomaterials were developed, generating 35
contributing exposure scenarios, describing some facet of occupational exposure. Most of
them were ESs from the production/synthesis of carbon-based nanomaterials or from
handling materials (weighting, removing, sonication, etc.); two scenarios addressed tasks
related to the machining of composites containing CNT (Bello, 2009; Methner, 2010).
Nano-TiO2
A total of 5 ESs for Nano-TiO2 were developed, generating 12 contributing exposure
scenarios. However, only two of the ESs describe full Operational Conditions and Risk
Management Measures relevant for occupational exposure. Three of the other ESs were
related to the production of nano-TiO2 and two references related to the production of
materials containing nano-TiO2.
Nano-Ag
Only two occupational ESs were developed for nanosilver (J. Park, 2009; S.J. Tsai, 2008),
the first one describing ES in a wet chemistry process while the second one related to
handling nanosilver in a fume hood.
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3.2. Use of information/data from measurement camp aigns
From the 57 occupational exposure scenarios in the NANEX Exposure Scenario
Database, 35 have been built from data campaigns, most of them related to carbon-based
nanomaterials (14), followed by Others (11) (e.g ZnO (2)), nano-TiO2 (8) and nano-Ag (2).
In total, 44 contributing exposure scenarios were identified. Both surveys were considered
as one reference and included in the NANEX Exposure References Database.
Annex 5.2 gives an overview of the 35 exposure scenarios entered in the NANEX
Exposure Scenario Database and, in Table 1, a summary of all the ES developed in WP3
is presented.
Table 1 Overview of the 57 occupational ESs developed in WP3 Sources ES IDs Substances Public Literature (research/ explorative studies)
22 TiO2 (5); CNT(14); Nano-Ag (2); others (1)
Data sets campaigns NanoINNOV & NANOSH
35 CNT(14); Nano-Ag (2); TiO2 (8). Others (11) e.g (ZnO) (2)
TOTAL 57
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3.3. Review of occupational exposure models and pe rformance check
The ECETOC TRA model estimates are based on the following input parameters:
• Vapour pressure or dustiness • Selection of a generic use scenario (25 PROCs) • Selection of limited exposure determinants (duration, LEV, RPE) • Selection of mixture or pure substance • Exposed skin surface (default for each PROC)
The inhalation exposure estimates are presented in ppm and mg/m³ and dermal values in
µgcm-2day-1 and, based on body weight, in mgkg-1day-1 and total exposure in mgkg-1day-1.
For Stoffenmanager, the following parameters and input data are needed:
• Vapour pressure or dustiness; • Percentage of substance in the product; • Handling category (7 classes for liquid, and 6 for solid); • Local controls; • Distance of worker to source; • Presence of other sources of same substance further than 1 metre from the worker; • Room volume (4 classes); • General ventilation (3 classes); • Immission control measures; • Personal Protective Equipment (5 classes); • Possibility of background exposure;
For both the ECETOC TRA and the Stoffenmanager it can be concluded that many of the
parameters used in the model will be similar for MNMs in comparison with conventional
compounds. However, the actual exposure values may not necessarily be accurate. This
is due to the possible specific characteristics of MNMs. Chemical composition, surface
structure, solubility, shape and aggregation behaviour might be completely different
compared with conventional compounds. Since the model is neither built nor calibrated for
MNMs caution is required when using either model to estimate the exposure to MNMs.
To check the performance of the models, the model predictions were compared with the
results from the particle number concentration measurements obtained during the two
surveys. Comparisons were carried out using the absolute difference in activity and
background concentrations and using the ratio of activity and background concentrations.
The results of the comparison of the model outcomes with actual workplace particle
number concentration data obtained by Condensation Particle Counters (CPC) and
Scanning Mobility Particle Sizers (SMPS) are presented in Table 2.
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The results show that there was no correlation between the model estimates and the
measurements. In order to find out possible explanations for the lack of any correlation a
more in-depth analysis was performed, based on the NANOSH dataset. (For more details
about this analysis see Annex 6.4). The main observations were that the category
variables within the model parameters were not evenly distributed over the various
scenarios, so that the potential resolution between the scenario exposure estimates was
not fully exploited.
Table 2 Pearson correlation coefficients for the ECETOC TRA model and Stoffenmanager for the Nanosh and NanoINNOV/NanoSafe datasets Nanosh dataset Pearson correlations
CPC results (N=13) SMPS results (N=23) Absolute difference activity minus non-activity
Ratio task vs. background
Absolute difference activity minus non-activity
Ratio task vs. background
Stoffenmanager Inhalable dust (mg/m³) -0.15 -0.17 <-0.001 0.05
ECETOC exposure level (mg/m³) 0.063 -0.02 <-0.09 -0.15
NanoINNOV/NanoSafe dataset
CPC results Absolute difference activity minus non-activity Ratio task vs. background
Stoffenmanager Inhalable dust (mg/m³) (N=15)
0.79$ 0.866$
ECETOC exposure level (mg/m³) (N=5) 0.35 0.35
$ The high correlation is biased by one single data point; in case that data point is omitted, the correlation coefficient is close to 0.3
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4. Discussion and Conclusions
4.1. Discussion and conclusions for ES from litera ture
In total, 22 ESs were developed from open literature and charged in the NANEX Exposure
Scenario Database (developed in WP2). Table 3 gives an overview of the main issues that
researchers have found when fulfilling the different fields in the database.
Table 3 Overview of the evaluation of the ES building process
Field in the database Evaluation of Exposure Scenarios building
Exposure Scenarios general details Title Ambiguous; standard phrases may be helpful
PROCs Difficult to assign PROCs from tasks/ processes Harmonization task description needed
Exposure Scenarios details
Product characteristics Most often incomplete (usually only name nanomaterial)
Amounts used Most often not reported
Frequency duration of use Frequency not reported; duration of activity reported
Human factors Not reported Other operational conditions Not reported Technical condition s source level Source enclosure reported
Technical conditions to control dispersion Most often reported
Organisational measures Not reported PPEs Frequently reported
Exposure Estimation
References Data sets (between) Lack of standardization Measurement strategy Measurement equipment Data analysis and report
Measurement strategy Data analysis and report
From the process of developing this ES the following main conclusions can be underlined:
• There are only a few references in the open literature that include data of workers’ exposure to nanomaterials which could be used to develop exposure scenarios. An example of this is nano-TiO2. Despite the numerous applications of TiO2 nanoparticles, it was not possible to find information on occupational exposure to TiO2 nanoparticles (no information on the size of the TiO2 particles or the concentration).
• Papers in the peer-reviewed literature often do not include most of the information needed to build comprehensive ESs.
• The majority of the ESs described are based on tasks/processes performed in research labs with few publications addressing exposure at manufacturing facilities.
• The peer-reviewed and other literature describing worker exposure do not generally include any information that could be used to estimate environmental release. On a few occasions the total quantities produced/year and information related to air
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emissions (presence of vent hoods) is reported. No data relating to water treatment were reported.
• At this stage it has not been possible to build exposure scenarios combining different information sources (references), even if different sources appear to address similar scenarios. The main reason for this was the heterogeneity in reporting of the context (differences related to material characteristics, processes, quantities handled, control systems, etc.) and in the exposure evaluation (the absence of standard approaches to measurement strategies, equipment and data treatment).
• Information on concentration and particle size is often lacking and therefore prevented the researchers from adding more incomplete ES in the NANEX Exposure Scenario Database.
4.2. Discussion and conclusions for ES from the da taset generated by
measurement campaigns
In total, 35 ESs were developed from measurement campaigns and entered into the
NANEX Exposure Scenario Database (developed in WP2). Table 4 gives an overview of
the main issues that researches reported when trying to complete the different fields.
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Table 4 Overview of the evaluation of the ES building process
Field in the database Evaluation of Exposure scenar ios building
Exposure Scenarios general details
Title Need of homogeneity and precision for further search in the database
List of all use descriptors related to the life cycle stage and the relevant uses under it; include market sector (by PC)
Mainly R&D workplaces/labs in dataset, few industrial cases.
PROCs Data from NANOSH, NanoINNOV and NanoSafe projects are workplace orientated so the scenario was the most often assigned with only one PROC.
Exposure Scenarios details
Product characteristics Few data are usually reported (normally only the name of nanomaterial; sometimes size/length).
Amounts used Not detailed but generally broadly reported. For some of the exposure scenarios derived from the NANOSH project detailed information for amount used is available.
Frequency duration of use Always reported. Periods of handling of nanomaterials are generally very short (<15 min), Operation of maintenance after handling the longest (~ 2h)
Human factors Generally not reported but they were assumed. (Not reported within the NANOSH project.)
Other operational conditions
Generally processes involving the handling of articles are dry whereas maintenance activities can be wet-based (washing) or dry (sanding). Information about type of workplace available (e.g. laboratory, size of company etc.) for exposure scenarios from the NANOSH project.
Tech. conditions source level It is reported when closed reactors an embedded process or fume cupboards are used, but technical measures to prevent release at source level are not usually applied.
Tech. conditions to control dispersion
Reported when tasks are performed in fume hood/vent hood, general extraction or natural ventilation.
Org. measures Not reported
PPEs Always reported. The most used PPEs are PPF3 mask, nitrile gloves, latex gloves and blouse for handling of MNMs. Maintenance operations can also be in clean room with air extraction mask.
Exposure Estimation
Measurement Techniques
Data from nanoINNOV, NanoSafe and NANOSH programs were obtained using: o Elpi (inertial impactor) o CPC (condensation particles, counter) o SMPS (Scanning Mobility Particle Sizer)
Calibration is always done before each measurement and levels of background are always measured before the activity.
Sample points o Two locations: background and near the source
Most often, during the tasks of handling, no increase of background was noticed. However, the most significant increase occurred during maintenance PROCs.
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From the process of developing this ES the following main conclusions can be drawn:
• The majority of the ESs described is based on tasks/processes performed in research labs.
• Because datasets from NanoINNOV and Nanosafe are workplace-based and related to worker exposure only limited information is reported related to environmental exposure/release. Conditions and measures related to on site/municipal sewage treatment plant were never reported. Finally, conditions and measures at level of article production process to prevent release during service life were not reported because R&D workplaces do handle NMM but no articles.
Exposure scenarios regarding handling of nano-TiO2, CNTs and Zinc oxide were obtained
from the NANSOH project database, with 46 measured situations. From the process of
developing these ESs the following main conclusions can be drawn:
• Data from the dataset campaigns are task-based and subsequently it is difficult to assign PROCs (tasks can be part of different PROCs)
• For ‘contributing exposure scenario 1’ (controlling environmental exposure for <name substance>) no information was collected during the field work. Consequently, these fields are empty.
• It would have been easier to complete a scenario if standard phrases were used for the different types of information that should be included in the exposure scenarios, since, in the REACH guidance, it is not always clear what information should be included, and vague terms are used. Also it is difficult to give the correct use descriptors.
• Currently, it is difficult to include measurement data into the NANEX Exposure Scenario Database since there is neither an agreed or harmonized appropriate method nor approach. Several options are available to report:
• Individual results for each instrument (for each measurement separately). But this will result in a long list of measurement data for one exposure scenario.
• Background levels and levels during the activity. It is difficult to report ranges during background measurements and during task measurements, as the two results are related (in other words: from the report the user should know which background belongs to which activity).
• Ratios between activity and background levels. Drawback is that the level of the background might affect the ratio, however this has not been observed in the NANOSH dataset. Alternatively, levels during activity and ratio should be reported.
• Currently, there are no exposure limits available for nanoparticles. Within REACH the exposure scenarios should demonstrate safe use scenarios. However, for exposure nanoparticles we are not able to do so.
• Not all the entry fields of the exposure scenarios could be completed since not all the information was collected during the NANOSH report.
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4.3. Discussion and conclusions from model review and performance
check
The following main conclusions can be drawn, based on the model review and the
performance check:
ECETOC TRA and Stoffenmanager have been evaluated here with respect to their
applicability for exposure to nanoparticles. Basically, both models use the same concepts
of the process of exposure, i.e. a source- receptor approach distinguishing emission,
transport, immission and personal exposure. Recently, Schneider et al.(2010, accepted)
proposed a conceptual model for exposure to nanoparticles based on the same
mechanism. The source strength or emission potential is determined by the activity or
process and the fugacity of the substance. In cases where the emission source is not
contained, the emission potential is further modified during transport e.g. by ventilation and
room volume, and will result in exposure. Both ECETOC TRA and Stoffenmanager follow
the same basic principles, however, both models are less detailed than the proposed
conceptual model or more elaborated models for ‘conventional’ inhalation exposure, e.g.
the Advanced Reach Tool (ART; Fransman et al., 2009). Schneider et al. (2010)
distinguished some mechanisms that will cause changes in size distributions, e.g.
coagulation; however, the effect on the mass concentration should be minimal. Therefore,
it is concluded that both models should, in principle, be able to predict exposure to
nanoparticles. However, the different categories within each model variable are not
particularly suitable for activities of nanomaterials, resulting in a lack of resolution with
many situations falling into the same category. Refinement of these categories in view of
typical activities for nanomaterial handling, the number of handling categories etc, is
needed. Calibration for nanomaterials/nanoparticle emission and exposure concentration
is another issue that should be addressed. Currently, the Stoffenmanager is being
adjusted for nanomaterials exposure (nano module Stoffenmanager) where, in contrast to
version 4.0, the model output will be only a category of exposure (exposure band) rather
than a quantitative estimate.
Both first tier models only provide mass concentration as a proxy for exposure. Currently,
in the field of risk assessment, there is no consensus on the most relevant exposure
metric. However, for insoluble particles, particle number concentration and (active) surface
area concentration are also candidates. In addition, the size frame issue is important in
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view of exposure assessment. Nanoparticles are usually defined as particles with (mobility,
aerodynamic, optical) sizes below 100 nm, however, agglomerates or aggregates of
primary nanoparticles may also be relevant to health if deagglomeration occurs in the
body. For typical ambient and workplace air situations, the numbers of particles with sizes
above about 300 nm will be relatively low compared to the nano-size particles. Therefore,
typical devices for particle concentration have size-windows up to 300 nm. However, in
cases where the size distribution is unknown, it is not possible to calculate the mass
concentration from the particle concentration without numerous assumptions. Based on
the physical relationship between size and mass the contribution of 100 nm or 300 nm
particles to mass compared to those of 1 or 3 µm is very low. So, if nanoparticle exposure
is focused on particle size ranges up to 300 nm, the resulting mass concentration would
always be in the lower ranges of the current model estimates. This indicates a further need
for recalibration of the models for nano-exposure.
From the results of the model performance check, it can be observed that there is no
correlation between the model outputs and the actual concentration data. In addition, no
difference can be observed between Stoffenmanager (activity-based) and ECETOC TRA
(extrapolated for full day exposure).
The particle number concentration data used to compare with the model scenario outputs
was obtained from devices that measured in the size ranges 10 -1000 nm (CPC), or 6- 700
nm (SMPS). As stated before, particles with these size ranges will contribute less to mass
compared to those particles with larger size ranges. It can therefore be hypothesized that
the resolution of the models for low mass concentration might be insufficient to show any
correlation.
In addition, the lack of any correlation is largely dominated by the large variability in the
(relatively few) data. The variability of the particle number concentration within the model
exposure scenarios is large compared to the between scenario model (mass
concentration) outputs, which would be expected to have a substantial effect on any
correlation. As has been demonstrated, due to lack of data or contextual information in the
data sets, the entire range of the model parameters categories could not be used, resulting
in a loss of power of discernment between exposure scenarios.
As been discussed for the NANOSH data set, the main observations were that the
category variables within the model parameters were not evenly distributed over the
17
various scenarios, so that the potential resolution between the scenario exposure
estimates was not fully exploited. Therefore it can be concluded that the data sets used to
check the performance of the exposure models for nanoparticle exposure scenarios were
not optimal for testing.
In summary, it can be concluded that, in their current form, both ECETOC TRA and
Stoffenmanager should not be used to estimate exposure to nanoparticles. Since the
models are not tuned to or calibrated for nanomaterial exposure situations, the actual
model estimate will be inaccurate and might overestimate the mass concentration levels.
18
19
5. References
Bello D, Hart AJ, Ahn K, Hallock M, Yamamoto N, Garcia EJ, Ellenbecker MJ, Wardle BL. Particle exposure levels during CVD growth and subsequent handling of vertically-aligned carbon nanotube films. Carbon 2008; 46(6): 974-977.
Bello D, Wardle BL, Yamamoto N, deVilloria RG, Garcia EJ, Hart AJ, Ahn K, Ellenbecker MJ, Hallock M. Exposure to nanoscale particles and fibers during machining of hybrid advanced composites containing carbon nanotubes. Journal of Nanoparticle Research, 2009; 11(1): 231-249.
Berges M. Workplace exposure characterization at TiO2 nanoparticle production. 3rd International Symposium on Nanotechnology, 2007.
Bullock WH, Laird LT. A Pilot Study of the Particle Size Distribution of Dust in the Paper and Wood Products Industry. American Industrial Hygiene Association Journal, 1994; 836-840.
Demou A, Peter P, Hellweg S. Exposure to Manufactured Nanostructured Particles in an Industrial Pilot Plant. Ann Occup Hyg, 2008; 52(8): 695–706.
Ellis ED, Watkins J, Tankersley W, Phillips J, Girardi D. Mortality Among Titanium Dioxide Workers at Three DuPont Plants. Journal of Occupational and Environmental Medicine, 2010: 303-309.
Fransman W, Cherrie J, van Tongeren M, Schneider T, Tischler M, Schinkel J, et al. Development of a Mechanistic Model for the Advanced REACH Tool (ART), Beta release. TNO report V8667, (2009) Zeist, The Netherlands, available from www.advancedreachtool.com
Fujitani Y, Kobayashi T, Arashidani K, Kunugita N, Suemura K. Measurement of the physical properties of aerosols in a fullerene factory for inhalation exposure assessment. Journal of Occupational and Environmental Hygiene, 2008; 5(6): 380-389
Fujitani Y, Kobayashi T. Measurement of aerosols in engineered nanomaterials factories for risk assessment. Nano, 2008; 3(4): 245-249.
Garabrant DH, Fine LJ, Oliver C, Bernstein L, Peters JM. Abnormalities of pulmonary function and pleural disease among titanium metal. Scand J Work Environ Health, 1987: 47-51
Han JH, Lee EJ, Lee JH, So KP, Lee YH, Bae GN, Lee SB, Ji JH, Cho MH, Yu IJ. Monitoring multiwalled carbon nanotube exposure in carbon nanotube research facility. Inhalation Toxicology, 2008; 20(8): 741-749.
Johnson DR, Methner MM, Kennedy AJ, Steevens JA. Potential for Occupational Exposure to Engineered Carbon-Based Nanomaterials in Environmental Laboratory Studies. Environmental Health Perspectives, 2010; 118(1): 49-54.
Korhonen K, Liukkonen T, Ahrens W, Astrakianakis G, Boffetta P, Burdorf A, Heederik D, Kauppinen T, Kogevinas M, Osvoll P, Rix BA, Saalo A, Sunyer J, Szadkowska-Stanczyk I, Teschke K, Westberg H, Widerkiewicz K. Occupational exposure to chemical agents in the paper industry. Int Arch Occup Environ Health, 2004: 451-460.
Lee JH, Lee SB, Bae GN, Jeon KS, Yoon JU, Ji JH, Sung JH, Lee BG, Lee JH, Yang JS, Kim HY, Kang CS, Yu IJ. Exposure assessment of carbon nanotube manufacturing workplaces. Inhalation Toxicology, 2010; 22(5): 369–381.
Maynard AD, Baron PA, Foley M, Shvedova AA, Kisin ER, Castranova V. Exposure to carbon nanotube material: aerosol release during the handling of unrefined single-walled carbon nanotube material. Journal of Toxicology and Environmental Health (Part A), 2004; 67(1): 87-107.
Mazzuckelli LF, Methner MM, Birch ME, Evans DE, Ku BK, Crouch K, Hoover MD.
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Identification and characterization of potential sources of worker exposure to carbon nanofibers during polymer composite laboratory operations. Journal of Occupational and Environmental Hygiene, 2007; 4(12): D125-30.
Methner M, Hodson L, Dames A, Geraci C. 2010, "Nanoparticle Emission Assessment Technique (NEAT) for the identification and measurement of potential inhalation exposure to engineered nanomaterials. Part B: Results from 12 field studies. Journal of Occupational and Environmental Hygiene, 2010; 7(3): 163-176.
Park J, Kyu Kwak B, Bae E, Lee J, Kim Y, Choi K, Yi J. Characterization of exposure to silver nanoparticles in a manufacturing facility. J Nanopart Res, 2009; 11:1705–1712.
Conceptual model for assessment of inhalation exposure to manufactured nanoparticles Schneider T, Brouwer DH, Koponen IK, Jensen KA, Fransman W, van Duuren-Stuurman
B, van Tongeren M, Tielemans E. 2011. J. Expos. Sci. Environ. Epidemiol. Nature America. (accepted for publication).
Tsai SJ, Hofmann M, Hallock M, Ada E, Kong J, Ellenbecker M. Characterization and evaluation of nanoparticle release during the synthesis of single-walled and multiwalled carbon nanotubes by chemical vapor deposition. Environmental Science and Technology, 2009; 43(15): 6017-6023.
Tsai SJ, Ada E, Isaacs JA, Ellenbecker MJ. Airborne nanoparticle exposures associated with the manual handling of nanoalumina and nanosilver in fume hoods. J Nanopart Res, 2008: DOI 10.1007/s11051-008-9459-z
Yeganeh B, Kull CM, Hull MS, Marr LC. Characterization of airborne particles during production of carbonaceous nanomaterials. Environmental Science and Technology, 2008; 42(12): 4600-4606.
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6. Annexes
6.1: Overview of exposure scenarios build in the NANEX Exposure Scenario Database 6.2: Exposure models and exploration of their applicability for nanomaterials 6.3: Performance checks of ECETOC TRA and STOFFENMANAGER with actual data 6.4: PDF extracts from the NANEX Exposure Scenario Database
22
6.1. Overview of exposure scenarios in the NANEX E xposure Scenario
Database
In table 1 an overview of the exposure scenarios presented in the NANEX Exposure Scenario Database is presented.
Table 1: Overview of exposure scenarios in the NANEX Exposure Scenario Database
Exposure type Title exposure scenario Substance name
Literature/ Database Reference
Use of substance by workers (Including Productions)
Production of MWCNT CNT Literature Mether et al, 2010
Use of substance by workers (Including Productions)
Production of Carbon Nanofibres
CNT Literature Mether et al, 2010
Use of substance by workers (Including Productions)
Handling of Carbon Nanofibres
CNT Literature Mether et al, 2010; Mazzuckelli et al, 2007
Use of substance by workers (Including Productions)
Handling of fullerenes CNT Literature Johnson et al, 2010
Use of substance by workers (Including Productions)
Handling of MWCNT CNT Literature Mether et al, 2010; Johnson et al, 2010
Use of substance by workers (Including Productions)
Handling of CNT CNT Literature Maynard et al, 2010
Use of substance by workers (Including Productions)
Manufacturing and handling of carbon nanotubes and handling (MWCNT)
CNT Literature Lee et al, 2010
Use of substance by workers (Including Productions)
Manufacturing of MWCNT
CNT Literature Han et al, 2008
Use of substance by workers (Including Productions)
Production of carbonaceous nanomaterials (fullerenes and other carbonaceous nanomaterials)
CNT Literature Yeganeh et al, 2008
Use of substance by workers (Including Productions)
Production of carbon nanomaterial
CNT Literature Fujitani et al, 2008b
Use of substance by workers (Including Productions)
Synthesis of fullerene CNT Literature Fujitani et al, 2008a
Use of substance by workers (Including Productions)
Synthesis and handling of CNT by CVD
CNT Literature Bello et al, 2008
Use of substance by workers (Including Productions)
Production and handling of metal-based nanoparticles in a gas-phase production process
Other Literature Demou et al, 2008
Use of substance by workers (Including Productions)
Synthesis of SWCNT and MWCNT by CVD
CNT Literature Tsai, S.J., 2009
23
Exposure type Title exposure scenario Substance name
Literature/ Database Reference
Use of substance by workers (Including Productions)
Machining of hybrid advanced composites containing CNT.
CNT Literature Bello et al, 2009
Use of substance by workers (Including Productions)
Manipulation of Nano-Ag into three different type of fume hoods (Conventional hood; By-pass hood; Constant velocity hood)
Nano-Ag Literature Tsai et al, 2008
Use of substance by workers (Including Productions)
Production of Nano-Ag during wet-chemistry process
Nano-Ag Literature Park, J. 2009
Use of substance by workers (Including Productions)
Autocombustion of lanthane, strontium, cobalt, iron
Other Database NanoINNOV
Use of substance by workers (Including Productions)
Cleaning of growth furnace producing nanothread Si
Other Database NanoINNOV
Use of substance by workers (Including Productions)
Maintenance of SiO2 PECVD equipment (Plasma-enhanced chemical vapour deposition)
Other Database NanoINNOV
Use of substance by workers (Including Productions)
preparation of inks with nanoZnO
Other Database NanoINNOV
Use of substance by workers (Including Productions)
Agitation of a solution of carbon black with N-methyl-pyrolidone
Other Database NanoINNOV
Use of substance by workers (Including Productions)
Opening of an epitaxy frame of an apparatus with a molecular beam with a Ti target
Other Database NanoINNOV
Use of substance by workers (Including Productions)
Opening of deposition equipment containing adsorption bed for chemical vapour deposition, used with diverse metal oxides.
Other Database NanoINNOV
Use of substance by workers (Including Productions)
Packaging of carbon black
Other Database NanoINNOV
Handling of articles by workers
Cutting of substrates coated by carbon black particles
Other Database NanoINNOV
Use of substance by workers (Including Productions)
Grinding of Nano-TiO2 TiO2 Database NanoINNOV
Use of substance by workers (Including Productions)
Maintenance of device polluted with NP with glassbead cabinets
Nano Ag Database NanoINNOV & NanoSafe
Handling of articles by workers
maintenance of physical vapour deposition (PVD)
Nano Ag Database NanoINNOV
Use of substance by workers (Including Productions)
Production of TiO2 by laser pyrolysis
TiO2 Database NanoINNOV
Use of substance by workers (Including Productions)
weighing of CNT CNT Database NanoINNOV
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Exposure type Title exposure scenario Substance name
Literature/ Database Reference
Use of substance by workers (Including Productions)
CNT in solution CNT Database NanoINNOV
Use of substance by workers (Including Productions)
Pouring of CNT CNT Database NanoINNOV
Use of substance by workers (Including Productions)
Handling small quantities of CNT
CNT Database NanoINNOV
Handling of articles by workers
Preparation of CNT pellets from CNT powder
CNT Database NanoINNOV
Use of substance by workers (Including Productions)
Production of MWCNT using gas-phase reactor
CNT Database NANOSH
Use of substance by workers (Including Productions)
production of paint TiO2 Database NANOSH
Use of substance by workers (Including Productions)
Production of pavement stones TiO2 Database NANOSH
Use of substance by workers (Including Productions)
Production of MWCNT at laboratory scale
CNT Database NANOSH
Use of substance by workers (Including Productions)
Production of TiO2 TiO2 Database NANOSH
Use of substance by workers (Including Productions)
Production of MWCNT using a tube furnace
CNT Database NANOSH
Use of substance by workers (Including Productions)
Production of TiO2 by laser ablation
TiO2 Database NANOSH
Use of substance by workers (Including Productions)
Laboratory activities on CNTs
CNT Database NANOSH
Use of substance by workers (Including Productions)
Production of filaments of CNTs
CNT Database NANOSH
Use of substance by workers (Including Productions)
Dry mounting of CNTs on to EM grids CNT Database NANOSH
Use of substance by workers (Including Productions)
Working at a research reactor to produce CNT
CNT Database NANOSH
Use of substance by workers (Including Productions)
Working at an extruder to produce polymer containing CNT
CNT Database NANOSH
Use of substance by workers (Including Productions)
CNT production using Chemical Vapour Deposition (CVD)
CNT Database NANOSH
Use of substance by workers (Including Productions)
Production of printing inks
TiO2 Database NANOSH
Use of substance by workers (Including Productions)
Production of cosmetics in a laboratory
TiO2 Database NANOSH
25
Exposure type Title exposure scenario Substance name
Literature/ Database Reference
Use of substance by workers (Including Productions)
Test 2. Transfer of substances or preparations (charging/discharging) from/to vessels/large containers at non-dedicated facilities (activities including sieving, bin filling of powder and bagging of powder)
Other Database NANOSH
Use of substance by workers (Including Productions)
Test 1. Production in laboratory of cosmetics containing zinc oxide powder
Other Database NANOSH
Use of substance by workers (Including Productions)
Occupational exposure scenario during the production of TiO2 at Umicore SA, Belgium
TiO2 Literature Berges, 2007
Use of substance by workers (Including Productions)
Exposure scenario in paper production
TiO2 Literature Bullock et al, 1994; Korhonen et al, 2004
Use of substance by workers (Including Productions)
Exposure to TiO2 in a lab and at a manufacturer
TiO2 Literature Methner et al, 2010
Use of substance by workers (Including Productions)
Exposure to TiO2 at three DuPont plants
TiO2 Literature Ellis et al, 2010
Use of substance by workers (Including Productions)
Exposure scenario in the production of titanium metal
TiO2 Literature Garabrant et al., 1987
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6.2. Exposure models and exploration of their appl icability for
nanomaterials
In this annex, the identification and review of available tools and models to predict occupational exposure with respect to nanomaterials and identified scenarios is addressed. The review is limited to the exposure models ECETOC TRA as a first tier tool and STOFFENMANAGER as a higher tier tool, both of which are mentioned in the REACH Guidance R.14 from ECHA (2008) dealing with the requirements and chemical safety assessment for the occupational exposure estimation.
6.3.1 ECETOC TRA model
Introduction ECETOC TRA (ECETOC, 2009) is a first tier tool developed by ECETOC (European Centre for Ecotoxicology and Toxicology Of Chemicals) to assess health and environmental risks from the supply and use of chemicals. The ECETOC TRA uses established exposure-prediction models, introduced in a more precise, structured and simplified approach as a web-based tool. The calculated basis for the approach is a modified version of the EASE (Estimation and Assessment of Substance Exposure) exposure model version 2.0, developed by the UK Health and Safety Executive (HSE) (Cherrie et al., 2003). The model is based on a relation between PROCs (product categories described in the REACH guidance) and basic exposure levels. The basic exposure levels can be modified by a limited number of operational conditions and risk management measures.
Inhalation and dermal model The EASE version 2.0 inhalation model, which forms the basis for the ECETOC TRA tool,is based on an exposure database from the HSE which contains about 100,000 exposure measurements. These measurements were grouped together in exposure ranges, based on processes with similar potential for exposure. The potential for exposure is based on the tendency of the substance to become airborne; the means of controlling exposure or of preventing the substance from entering the workroom atmosphere; and the way in which the substance is used. For liquids, the tendency to become airborne is indicated by the vapour pressure and thus the volatility of the substance and, for solids, the dustiness is considered an important parameter. There was little or no data available for dermal exposure and therefore a simple EASE dermal model was developed using the same parameters as for the inhalation model and adding the parameter ‘level of contact’ to give an indication of frequency and duration of direct exposure with the substance.
Estimating exposure using the model In the ECETOC TRA model the following input parameters are necessary to run the model and to make an occupational exposure estimate:
• Vapour pressure or dustiness
For vapour pressure the categories low (Vp >=0.00001- <0.5), medium (Vp 0.5 to 10) and high (> 10) are possible.
For dustiness the categories low (relative dustiness potential is 1 or 10-100 times dustier), medium (100 – 1,000 times dustier) and high (more than 1,000 times dustier) are also
27
possible. For these categories examples of typical materials are given.
• Selection of a generic use scenario
A choice can be made out of 25 process categories (PROCs).
• Selection of limited exposure determinants
For duration (modifying factor for > 4 hours (1), 1-4 hours (0.6), 15 min. – 1 hour (0.2) and <15 min. (0.1); use of local exhaust ventilation (yes or no); and use of RPE (no, 90 or 95% reduction).
• Selection of mixture or pure substance
No mixture (exposure modifying factor is 1); concentration >25 % w/w (modifying factor 1); concentration 5-25% w/w (modifying factor 0.6); concentration 1-5% w/w (modifying factor 0.2); and concentration is <1% w/w (modifying factor 0.1).
• Exposed skin surface
For each PROC a default for the exposed skin surface is given in order to reverse the predicted dermal exposure in µgcm-2day-1 to mgkg-1day-1.
The inhalation exposure estimates are presented in ppm and mg/m³ and dermal values in ugcm-2day-1 and, related to body weight, in mgkg-1day-1 and total exposure in mgkg-1day-1.
Validity and reliability of model The ECETOC TRA model is based on a modified version of the EASE 2.0 model. This EASE model has been validated with a number of studies in which the predictions of the model are compared with actual measurements and estimates of exposure. In addition an assessment of the degree of variation between different users of the model was undertaken, to determine whether it was capable of being used in a consistent fashion. In the ECETOC TRA model the EASE inhalation predictions have been modified for the use of ventilation and the dermal EASE predictions have been reviewed in light of available measured data (such as the RiskOfDerm and related projects) in order to ensure reasonable exposure estimates. Since the ECETOC TRA tool tends to underestimate dermal exposure in situations with LEV, the RiskOfDerm model should preferably be used for these cases (for an evaluation of the RiskofDerm model see annex 1 of the WP4 consumer report). Next to this the ECETOC TRA approach has also been validated and results showed that the approach offers the basis for a suitable cautionary scheme for the assessment of worker exposure.
The ECETOC scheme appears unsuitable for the assessment of workplace risks for:
• Mists (liquid aerosols); • Fumes arising from the use of a material within a process; • Working situations not described within the suite of generic scenarios, e.g. confined
spaces, abnormal exposure situations (e.g. spills).
Evaluation of model for suitability of nanomaterials under REACH A lot of the parameters that are used in the model will be similar for nanomaterials to those for conventional compounds, although the actual exposure values may not necessarily be equal. This is due to the possible specific characteristics of nanomaterials. Chemical
28
composition, surface structure, solubility, shape and aggregation behaviour might be completely different compared with conventional compounds. For example the same particle number for nanoparticles will result in completely different (i.e. lower) mass concentration compared to the same particle concentration for larger particles. This means that the model output as mass concentration exposure levels will probably overestimate the exposure to nanomaterials. However, since this model is a first tier model, this can be considered as a worst case estimate.
Since the model is neither built nor validated for nanomaterials, caution is necessary in using this model to estimate the exposure to nanomaterials.
Conclusion In principle ECETOC TRA will be suitable for estimate exposure to nanomaterials. However, since it is not calibrated for nanomaterials, the model estimate will most probably overestimate the exposure levels.
6.3.2 STOFFENMANAGER 4.0
Introduction The Stoffenmanager (Dutch for “substance manager”) tool originally is a web-based risk prioritizing tool for small and medium-sized enterprises. The first version of the Stoffenmanager was developed in 2003 by TNO, Arbo Unie and BECO on behalf of the Dutch Ministry of Social Affairs and Employment and the latest version is version 4.0. The exposure model developed in the latest version is a quantitative model for estimating inhalation exposure to vapours, aerosols of low volatility liquids and dusts. The Stoffenmanager would currently be regarded as a tool between a first Tier and higher Tier models. The model is based on exposure levels of tasks.
Inhalation model The model is based on the source-receptor approach (Cherrie et al., 1999, Marquart et al., 2008) but adapted in several ways. This approach indicates that exposure depends on the emission, transmission and imission factors. Determinants of exposure include task, local controls, and general ventilation and product characteristics and each are scored on a logarithmic scale. The exposure algorithm was quantified for dust and vapour scenarios for occupational exposure using 700 exposure measurements. The inhalation exposure is expressed in exposure concentration in mgm-³, based on the 90th percentiles of measured exposures.
The model uses process information, physiological characteristics and mass balance to assess exposure situations. The following parameters are needed as input data in the model:
• Vapour pressure or dustiness • Percentage of substance in the product • Handling category (7 classes for liquid, and 6 for solid) • Local controls • Distance of worker to source • Presence of other sources of same substance further than 1 metre from the worker • Room volume (4 classes) • General ventilation (3 classes) • Imission control measures
29
• Personal Protective Equipment (5 classes) • Possibility of background exposure
Estimating exposure using the model
To make an exposure assessment, values for the parameters above are entered into the model, and the resultant exposure estimates can be compared with REACH Derived No Effect Levels (DNELs) to derive safe uses.
Validity and reliability of model The Stoffenmanager is calibrated and validated with approximately 700 measurement data originating from STEAMBASE (Stoffenmanager Exposure and Modelling database). As well as the measured concentrations (mg/m³), this database contains all the contextual information necessary to make a Stoffenmanager exposure estimation.
Evaluation of model for suitability of nanomaterials under REACH As with ECETOC TRA, many of the parameters used for nanomaterials in the model will be similar to those for conventional compounds, although the actual exposure values may not necessarily be equal. This is again due to the possible specific characteristics of nanomaterials; meaning that the model output as mass concentration exposure levels will again probably overestimate the exposure to nanomaterials but can be considered as a worst case estimate.
Since the model is not built nor calibrated for nanomaterials caution is necessary in using this model to estimate the exposure to nanomaterials.
Conclusion: In principle, Stoffenmanager will be suitable for estimate exposure to nanomaterials. However, since it is not calibrated for nanomaterials, the model estimate will most probably overestimate the exposure levels.
Stoffenmanager Nano 1.0 is currently being developed. This model will contain emission, transmission and imission factors specifically for nanomaterials, greatly increasing the power of discernment for the identified scenarios. However this model has yet to be calibrated and validated with measurement data. In addition, the challenge to translate the assessment into REACH Use Descriptors remains.
6.3.3 Discussion and conclusion
The first-tier models for estimating occupational exposure under REACH, i.e. ECETOC TRA and Stoffenmanager have been evaluated here with respect to their applicability for exposure to nanoparticles. Both models use essentially the same concepts of the process of exposure, i.e. a source-receptor approach distinguishing emission, transport, imission and personal exposure. Recently, Schneider et al. (2010, accepted) proposed a conceptual model for exposure to nanoparticles, based on the same mechanism. The source strength or emission potential is determined by the activity or process and the fugacity of the substance. In cases where the emission source is not contained, the emission potential is further modified during transport e.g. by ventilation and room volume, and will result in exposure. Both ECETOC TRA and Stoffenmanager follow the same basics. However, both models are less detailed than the proposed conceptual model or more elaborated models for ‘conventional’ inhalation exposure, e.g. the Advanced Reach
30
Tool (ART; Fransman et al., 2009).
Schneider et al. (2010) distinguished some mechanisms that will affect shift in size distributions, e.g. coagulation; however, the effect on the mass concentration should be minimal. Therefore, it is concluded that both models should, in principle, be able to predict exposure to nanoparticles. However, the different categories within each model variable are not particularly well-fitted for activities involving nanomaterials. As a result, many situations may, in practice, fall into the same category. Refinement of these categories in view of typical activities for nanomaterial handling, the number of handling categories etc, is needed. Calibration for nanomaterials/nanoparticle emission and exposure concentration is another issue that should be addressed. Currently, the Stoffenmanager is in the process of adjustment for nanomaterial exposure (nano module Stoffenmanager), where in contrast to version 4.0, the model output will only be a category of exposure (exposure band) rather than a quantitative estimate.
An issue that has only implicitly been addressed during the evaluation of the models is the exposure metric. Both first-tier models only provide mass concentration as a proxy for exposure. Currently, in the field of risk assessment, there is no consensus on the most relevant exposure metric, however, for insoluble particles, particle number concentration and (active) surface area concentration are candidates. In addition, the size-frame issue is important in view of exposure assessment. Nanoparticles are usually defined as particles with (mobility, aerodynamic, optical) sizes below 100 nm. However, agglomerates or aggregates of primary nanoparticles might also be relevant for health if deagglomeration occurs in the body. For typical ambient and workplace air situations the numbers of particles with sizes above about 300 nm will be relatively low compared to the nano-sized particles. Therefore, typical devices for particle concentration have size-windows up to 300 nm. However, in cases where the size distribution is unknown, it is not possible to calculate mass concentration from particle concentration without numerous assumptions. Based on the physical relationship between size and mass the contribution of 100 nm or 300 nm particles to mass, compared to particles of 1 or 3 µm, is very low. So, if nanoparticle exposure is to be focused on particle size ranges up to 300 nm, the resulting mass concentration will always be in the lower ranges of the current model estimates. This also indicates the need for recalibration of the models for exposure to nanomaterials.
31
6.3. Performance check ECETOC TRA and STOFFENMANAG ER with
actual data
6.4.1 Introduction
To build exposure scenarios it was decided to estimate exposure for the scenarios obtained from the datasets of the NANOSH project (TNO and the NanoINNOV project (CEA). ECETOC TRA and Stoffenmanager were selected, as these models are recommended within the REACH guidance.
The applicability of these models for assessing exposure to nanoparticles is described in previous sections. The NANOSH dataset provided 46 scenarios to be assessed with the two models, with a further 15 scenarios obtained from nanoINNOV. A performance check of the models for nanomaterial exposure estimates was initiated by comparing the measurement values of the situations from the dataset with the results of the modelled exposure.
The first sections discuss how the exposure assessment has been performed with two different models and the required assumptions regarding the input. These are followed by the results, after which the consequences of the model assumptions for the results are discussed.
6.4.2 Assessments with ECETOC TRAv2
As ECETOC TRA is based on Process Category (PROC), a relationship needs to be established between the activity during the measurements from the Nanosh project and the process category. This will result in a basic exposure level which can be modified by a limited number of operational conditions and risk management measures. An overview of the different PROCs according to REACH is presented in Table 1.
32
Table 1 Overview PROCs Appendix R.12 -3: Descriptor -list for process categories [PROC]
Process category
Examples and explanations
PROC1 Use in closed process, no likelihood of exposure
Use of the substances in high integrity contained system where little potential exists for exposures, e.g. any sampling via closed loop systems.
PROC2
Use in closed, continuous process with occasional controlled exposure
Continuous process but where the design philosophy is not specifically aimed at minimizing emissions. Not high integrity and occasional exposure will arise e.g. through maintenance, sampling and equipment breakages
PROC3 Use in closed batch process (synthesis or formulation)
Batch manufacture of a chemical or formulation where the predominant handling is in a contained manner, e.g. through enclosed transfers, but where some opportunity for contact with chemicals occurs, e.g. through sampling.
PROC4
Use in batch and other process (synthesis) where opportunity for exposure arises
Use in batch manufacture of a chemical where significant opportunity for exposure arises, e.g. during charging, sampling or discharge of material, and when the nature of the design is likely to result in exposure.
PROC5
Mixing or blending in batch processes for formulation of preparations and articles (multistage and/or significant contact)
Manufacture or formulation of chemical products or articles using technologies related to mixing and blending of solid or liquid materials, and where the process is in stages and provides the opportunity for significant contact at any stage.
PROC6 Calendering operations Processing of product matrix. Calendering at elevated temperature and a large exposed surface area.
PROC7 Industrial spraying
Air dispersive techniques. Spraying for surface coating, adhesives, polishes/cleaners, air care products, sandblasting; Substances can be inhaled as aerosols. The energy of the aerosol particles may require advanced exposure controls; in case of coating, overspray may lead to waste water and waste.
PROC8a
Transfer of substance or preparation (charging/discharging) from/to vessels/large containers at non-dedicated facilities
Sampling, loading, filling, transfer, dumping, bagging in non- dedicated facilities. Exposure related to dust, vapour, aerosols or spillage, and cleaning of equipment to be expected.
PROC8b
Transfer of substance or preparation (charging/discharging) from/to vessels/large containers at dedicated facilities.
Sampling, loading, filling, transfer, dumping, bagging in dedicated facilities. Exposure related to dust, vapour, aerosols or spillage, and cleaning of equipment to be expected.
PROC9
Transfer of substance or preparation into small containers (dedicated filling line, including weighing)
Filling lines specifically designed to capture both vapour and aerosol emissions and minimise spillage
PROC10 Roller application or brushing
Low energy spreading of e.g. coatings. Including cleaning of surfaces. Substance can be inhaled as vapours, skin contact can occur through droplets, splashes, working with wipes and handling of treated surfaces.
PROC11 Non-industrial spraying
Air dispersive techniques, spraying for surface coating, adhesives, polishes/cleaners, air care products, sandblasting. Substances can be inhaled as aerosols. The energy of the aero-sol particles may require advanced exposure controls.
PROC12 Use of blow agents for foam production
33
Appendix R.12 -3: Descriptor -list for process categories [PROC]
Process category
Examples and explanations
PROC13 Treatment of articles by dipping and pouring
Immersion operations. The treatment of articles by dipping, pouring, immersing, soaking, washing out or washing in substances; including cold formation or resin type matrix. Includes handling of treated objects (e.g. after dying, plating,). Substance is applied to a surface by low energy techniques such as dipping the article into a bath or pouring a preparation onto a surface.
PROC14
Production of preparations or articles by tabletting, compression, extrusion, pelletisation
Processing of preparations and/or substances (liquid and solid) into preparations or articles. Substances in the chemical matrix may be exposed to elevated mechanical and/or thermal energy conditions. Exposure is predominantly related to volatiles and/or generated fumes, dust may be formed as well.
PROC15 Use as laboratory reagent. Use of substances at small scale laboratory (< 1 l or 1 kg pre-sent at workplace). Larger laboratories and R+D installations should be treated as industrial processes.
PROC16 Using material as fuel sources, limited exposure to unburned product to be expected.
Covers the use of material as fuel sources (including additives) where limited exposure to the product in its unburned form is expected. Does not cover exposure as a consequence of spill-age or combustion.
PROC17 Lubrication at high energy conditions and in partly open process.
Lubrication at high energy conditions (temperature, friction) between moving parts and substance; significant part of process is open to workers. The metal working fluid may form aerosols or fumes due to rapidly moving metal parts.
PROC18 Greasing in high energy conditions.
Use as lubricant where significant energy or temperature is applied between the substance and the moving parts.
PROC19 Hand-mixing with intimate contact and only PPE available.
Addresses occupations where intimate and intentional contact with substances occurs without any specific exposure controls other than PPE.
PROC20
Heat and pressure transfer fluids in dispersive, professional use but closed systems.
Motor and engine oils, brake fluids. Also in these applications, the lubricant may be exposed to high energy conditions and chemical reactions may take place during use. Exhausted fluids need to be disposed of as waste. Repair and maintenance may lead to skin contact.
PROC21 Low energy manipulation of substances bound in materials and/or articles.
Manual cutting, cold rolling or assembly/disassembly of material/article (including metals in massive form), possibly resulting in the release of fibres, metal fumes or dust;
PROC22
Potentially closed processing operations with minerals/metals at elevated temperatures in an industrial setting.
Activities at smelters, furnaces, refineries, coke ovens. Exposure related to dust and fumes to be expected. Emission from direct cooling may be relevant.
PROC23
Open processing and transfer operations with minerals/metals at elevated temperature.
Sand and die-casting, tapping and casting melted solids, drossing of melted solids, hot dip galvanising, raking of melted solids in paving. Exposure related to dust and fumes to be expected.
PROC24 High (mechanical) energy work-up of sub-stances bound in materials and/or articles
Substantial thermal or kinetic energy applied to substance (including metals in massive form) by hot rolling/forming, grinding, mechanical cutting, drilling or sanding. Exposure is pre-dominantly expected to be to dust. Dust or aerosol emission as result of direct cooling may be expected.
PROC25 Other hot work operations with metals
Welding, soldering, gouging, brazing, flame cutting Exposure is predominantly expected to fumes and gases.
34
Appendix R.12 -3: Descriptor -list for process categories [PROC]
Process category
Examples and explanations
PROC26 Handling of solid inorganic substances at ambient temperature
Transfer and handling of ores, concentrates, raw metal oxides and scrap; packaging, un-packaging, mixing/blending and weighing of metal powders or other minerals.23
PROC27a Production of metal powders (hot processes)
Production of metal powders by hot metallurgical processes (atomisation, dry dispersion).24
PROC27b Production of metal powders (wet processes)
Production of metal powders by wet metallurgical processes (electrolysis, wet dispersion).25
23 no corresponding TRA entry 24 no corresponding TRA entry 25 no corresponding TRA entry
The input parameters for the models are; • Vapour pressure or dustiness, industrial or professional setting • Selection of a generic use scenario/ process category (PROC) • Selection of limited exposure determinants: Percentage of the nanomaterials in the
product, duration of exposure , indoor/outdoor setting, use of local exhaust ventilation (LEV) and use of personal protection equipment.
Parameters to obtain the basic exposure level: 1) Vapour pressure and dustiness
These data are captured in the tool on an input data screen. They were used to categorize the material according to its fugacity (tendency of a substance to become airborne from a heterogeneous system) as defined in an availability banding for an initial assessment.
Since actual data are lacking, all nanomaterials were considered to be solids and assigned with the highest category of dustiness. Only nanomaterials in liquid suspension or solid articles were regarded as being in the lowest dustiness category.
2) Industrial or Professional setting.
Only scenarios in an industrial setting were considered.
3) Selection of a generic use scenario/ process category (PROC)
The generic use scenario/ process categories (PROC) for the measured situations were assigned as follows;
• When the measurement was performed at a completely closed system the situation was described by PROC1.
• When the measurement was performed at a closed system, but with some opportunity for exposure, the situation was described by PROC3.
• When the measurement was performed at a dedicated facility for transfer of chemicals (e.g. bagging at a dedicated facility, handling contaminated bags at a dedicated facility) the situation was described by PROC8b/9.
• When the measurement was performed at a non-dedicated facility for transfer of chemicals or included potential higher exposure due to other related activities (e.g transfer including sieving) the situation was described by PROC8a.
35
• When the measurement was performed during activities with small quantities (at least <1 l or <1 kg) the situation was described by PROC15.
• When the measurement was performed during mixing activities the situation was described by PROC5.
• When the measurement involved pelletisation or extrusion the situation was described by PROC14.
• When the measurement was performed at a site where exposure to nanoparticles was expected due to manipulation of articles or substances containing nanoparticles, the situation was described by PROC21.
When more than one of the PROC seemed applicable both were assessed, and the reasonable worst case (expert judgement) was considered valid.
Further modifications of the basic exposure level: a) Percentage of the nanomaterial in product.
Neither data set contained exact information on the concentration of nanomaterials in products. From the descriptions however it could be deduced that, in general, pure nanomaterials (concentration =100%), were handled during the activities
b) Duration of exposure.
The measurements within the datasets represent the total exposure during the specific activity that day. The assigned generic use scenario/process categories of processes and tasks contain periods of both activity and inactivity. Consequently, it was neither possible nor desirable to correct for the exposure duration of one activity and therefore the exposure measurement was considered be comparable to the full shift exposure estimate of the assigned PROC.
c) Indoor/outdoor setting
ECETOC TRA provides the option of an exposure reduction of 30% when the assessed situation is outside. Both datasets only contain scenarios in indoor settings.
This facility did not therefore offer any further modification and did not increase the power of discernment by the exposure estimates between the different measurements.
d) Risk management measures.
Within ECETOC TRA, two risk management measures could be applied: Local Exhaust ventilation (LEV); and respiratory protection.
The use of LEV is well documented within the datasets. Within ECETOC TRA, specific efficiencies are applicable for LEV for each basic exposure level. These efficiencies were applied to the exposure estimation when LEV was used during the measurements. Respiration protection is not applicable since the data in the datasets was derived from static measurements at fixed positions and not from personal measurements. However, for the calculations using the nanoINNOV dataset, respiratory protection was included.
In summary, due mainly to the lack of contextual information, the variables within ECETOC TRA potentially differentiating between the various measurement situations were limited
36
to: two categories of dustiness, i.e. high for all solids and low for liquids; the selection of a generic use scenario/ process category (PROC); and the possible application of LEV (PROC1 excepted).
6.4.2 Assessments with Stoffenmanager 4.0
This model uses process information, physicochemical characteristics, and mass balance to assess exposure situations. In contrast to ECETOC TRA, Stoffenmanager is, as with the measurement data in the dataset, activity based. This means that allocation of the handling category of Stoffenmanager to the activity in the dataset was more straightforward.
The input parameters of Stoffenmanager and considerations during the exposure estimation of the measured situations are:
1. Vapour pressure or dustiness
Since actual data were lacking, all nanomaterials were considered to be solids and assigned the highest category of dustiness. Only those nanomaterials in liquid suspension or solid articles were regarded as being in the lowest dustiness category.
2. Percentage of the substance in the product
Neither data set contains exact information on the concentration of nanomaterials in products. From the descriptions given however, it could be deduced that, in general, pure nanomaterials (concentration =100%), were handled during the activities.
3. Handling category
Solid substances can be assigned to one of six classes. These were assigned by comparing the description of the activity and quantity with the handling categories in Stoffenmanager.
4. Local controls and general ventilation (3 classes)
The use of local exhaust ventilation, general ventilation separation and segregation are well documented in the datasets and the categories were easily assigned in the Stoffenmanager tool.
5. Distance of the worker to the source (within 1 metre/further than one metre)
This type of information is not given in the datasets. It was assumed that all measurements were performed in close proximity to the source and that the near field exposure contributed to the exposure level.
6. Presence of other sources of the same substance further than one metre from the
worker (yes/no)
This information is given for most scenarios and, for the remaining ones, it was
37
assumed not to be the case.
7. Room volume (4 classes)
This information is well documented in the datasets and the actual room volume was easily assigned to the corresponding room volume class.
8. Imission control measures (worker in separate control room with clean air supply,
worker in cabin without specific ventilation system, no cabin).
Separation was not considered to be applicable for all measurement situations in the datasets.
9. Personal protective equipment used (5 classes)
Respiratory protection was not applicable since the data in the datasets was derived from static measurements at fixed positions and not from personal measurements.
10. Possibility of background exposure
For all measurement situations it was assumed that the working area was not cleaned on a daily basis and that equipment was not regularly maintained.
In contrast to ECETOC TRA, exposure duration and frequency are not required for Stoffenmanager as the event- or activity-based exposure will be comparable to the exposure measurement results.
In summary, due mainly to the lack of contextual information, the remaining variables within Stoffenmanager were limited to only four variables; i.e. two categories of dustiness (high for all solids and low for liquids); handling category; local controls/general ventilation; and room volume.
6.4.3 Performance check results
For both datasets, the particle number concentration data were compared with the estimates calculated for the same scenarios using the Stoffenmanager and ECETOC TRA models. A simple correlation was calculated, either for the absolute difference in concentrations or for the ratio of concentrations with and without activity (background).
6.4.3.1 Comparison of model outputs with data from the NANOSH data set
Table 2 shows the scenarios included in the NANEX Exposure Scenario Database which were used for comparison with exposure model outputs. Table 3 shows the type of data available, i.e. particle number concentration data obtained by Condensation Particle Counters (CPC) (usually in a size-range from 10 to 1000 nm) and data obtained by a Scanning Mobility Particle Sizer (SMPS) (usually in the size range from 6 to about 700 nm).
Table 2 Overview of exposure scenarios based on the NANOSH dataset in the NANEX Exposure Scenario Database for which a comparison with exposure models was made
38
Exposure type Title exposure scenario Substance name Contributing exposure scenario
Use of substance by workers (Including Productions)
Production of paint TiO2
14.1: Dumping large amount of powder into vessel 14.2: Dumping medium amount of powder into vessel
Use of substance by workers (Including Productions)
Production of MWCNT at laboratory scale CNT 12.1: Operating the oven
Use of substance by workers (Including Productions)
Production of TiO2 TiO2 11.2: Bag/bin filling
Use of substance by workers (Including Productions)
Production of MWCNT using a tube furnace
CNT 10.1: Production of MWCNT on a silicon substrate
Use of substance by workers (Including Productions)
Production of TiO2 by laser ablation
TiO2 9.1: Laser ablation (PROC 15, 26)
Use of substance by workers (Including Productions)
Laboratory activities on CNTs
CNT
8.1: Transfer of liquid containing CNTs (PROC 15) 8.2: Weighing of powder (PROC 15) 8.3: Manipulation of nanomaterial powder (PROC 15)
Use of substance by workers (Including Productions)
Production of filaments of CNTs
CNT 7.1: Production of filaments of CNTs
Use of substance by workers (Including Productions)
Dry mounting of CNTs on to EM grids CNT
6.1: Dry mounting of CNTs on to EM grids (PROC 15)
Use of substance by workers (Including Productions)
CNT production using Chemical Vapour Deposition (CVD)
CNT 3.1: Sampling from reactor (PROC 1) 3.2: Bagging from reactor (PROC 9)
Use of substance by workers (Including Productions)
Production of printing inks TiO2 2.1: Emptying bags in filling station (PROC 9 or 26)
Use of substance by workers (Including Productions)
Production of cosmetics in a laboratory
TiO2 1.1: Weighing powder, PROC 15 or 26
39
Table 3 Summary of available types of data for the various Contributing Exposure Scenarios (CES) used for the model performance check
CES 1.1 2.1 3.1 3.2 6.1 7.1 8.1 8.2 8.3 9.1 10.1 11.2 12.1 14.1 14.2
CPC* - + + + - - - - - - - - + + +
SMPS** + - - - + + + + + + + + + + +
* Additionally six handlings (bagging, box filling, pouring, sieving) involving ZnO2 were used (no contributing
exposure scenarios were available for ZnO2).
** Additionally 10 handlings (bagging, box filling, pouring, sieving, weighing, transferring) involving ZnO2 (no
contributing exposure scenarios were available for ZnO2).
The results of the comparisons between the model outcomes and actual workplace particle number concentration data obtained by CPC and SMPS are presented in Table 4 and plotted in figures 1-8.
Table 4 Pearson correlation coefficients for the ECETOC TRA model and Stoffenmanager
Pearson correlation coefficients CPC results N=13 SMPS results N=23 Absolute difference activity minus non-activity
Ratio of task vs. background
Absolute difference activity minus non-activity
Ratio of task vs. background
Stoffenmanager Inhalable dust (mg/m³) -0.15 -0.17 -0.0072 0.052
ECETOC exposure level (mg/m³) 0.063 -0.022 -0.097 -0.15
.
40
Figure 1 Stoffenmanager exposure estimate (mg/m³) versus ratio (GM concentration task versus GM concentration background) measured by CPC.
R2 = 0,0297
0
0,5
1
1,5
2
2,5
0,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0 16,0 18,0 20,0
Stoffenmanager exposure level (mg/m3)
ratio
task
ver
sus
back
grou
nd
GM
AM
Linear (GM)
Figure 2 Stoffenmanager exposure estimate (mg/m³) versus absolute difference (GM concentration task minus GM concentration background) measured by CPC.
R2 = 0,0217
-20000
-15000
-10000
-5000
0
5000
10000
15000
20000
25000
0,0 5,0 10,0 15,0 20,0
Stoffenmanager exposure level (mg/m3)
Abs
olut
e di
ffere
nce
activ
ity m
inus
non
-ac
tivity
GM
AM
Linear (GM)
41
Figure 3 ECETOC TRA exposure estimate (mg/m³) versus ratio (GM concentration task versus GM concentration background) measured by CPC.
R2 = 0,0005
0
0,5
1
1,5
2
2,5
0,0 5,0 10,0 15,0 20,0 25,0
ECETOC TRA level (mg/m3)
ratio
task
ver
sus
back
grou
nd
GM
AM
Linear (GM)
Figure 4 ECETOC TRA exposure estimate (mg/m³) versus absolute difference (GM concentration task minus GM concentration background) measured by CPC.
R2 = 0,004
-20000
-15000
-10000
-5000
0
5000
10000
15000
20000
25000
0,0 5,0 10,0 15,0 20,0 25,0
ECETOC TRA level (mg/m3)
Abs
olut
e di
ffere
nce
activ
ity m
inus
non
-ac
tivity
GM
AM
Linear (GM)
42
Figure 5 Stoffenmanager exposure estimate (mg/m³) versus ratio (GM concentration
task versus GM concentration background) measured by SMPS.
R2 = 0,0028
0
0,5
1
1,5
2
2,5
3
3,5
0,0 2,0 4,0 6,0 8,0 10,0
Stoffenmanager exposure level (mg/m3)
ratio
task
ver
sus
back
grou
nd
GM
AM
Linear (GM)
Figure 6 Stoffenmanager exposure estimate (mg/m³) versus absolute difference (GM concentration task minus GM concentration background) measured by SMPS.
R2 = 5E-05
-15000
-10000
-5000
0
5000
10000
15000
20000
25000
30000
0,0 2,0 4,0 6,0 8,0 10,0
Stoffenmanager exposure level (mg/m3)
Abs
olut
e di
ffere
nce
activ
ity m
inus
non
-ac
tivity
GM
AM
Linear (GM)
43
Figure 7 ECETOC TRA exposure estimate (mg/m³) versus ratio (GM concentration
task versus GM concentration background) measured by SMPS.
R2 = 0,0222
0
0,5
1
1,5
2
2,5
3
3,5
0,0 10,0 20,0 30,0 40,0 50,0 60,0
ECETOC TRA exposure level (mg/m3)
ratio
task
ver
sus
back
grou
nd
GM
AM
Linear (GM)
Figure 8 ECETOC TRA exposure estimate (mg/m³) versus absolute difference (GM concentration task minus GM concentration background) measured by SMPS.
R2 = 0,0093
-15000
-10000
-5000
0
5000
10000
15000
20000
25000
30000
0,0 10,0 20,0 30,0 40,0 50,0 60,0
ECETOC TRA exposure level (mg/m3)
Abs
olut
e di
ffere
nce
activ
ity m
inus
non
-ac
tivity
GM
AM
Linear (GM)
44
6.4.3.2 Comparison of model outputs with data NanoI NNOV data set
Fourteen Stoffenmanager scenarios could be calculated from the nanoINNOV project, with model outcomes ranging from 0.03 to 8.12 mg/m³. Only five scenarios for ECETOC TRA calculations could be extracted, with model outcomes ranging from 0.05 to 5 mg/m³.
Table 5 Overview exposure scenarios calculated with model outcomes
Exposure Type Exposure Scenario Title Substance name
Stoffen -manager
ECETOC TRA
Use of substance by workers (Including Productions)
Preparation of inks with nanoZnO ZnO + -
Use of substance by workers (Including Productions)
Agitation of a solution of carbon black with N-methyl-pyrolidone
Carbon black + -
Use of substance by workers (Including Productions)
Opening of deposition equipment containing adsorption bed for chemical vapour deposition, used with diverse metal oxides.
TiO2 + -
Use of substance by workers (Including Productions)
Packaging of carbon black carbon black + -
Handling of articles by workers
Cutting of substrates coated by carbon black particles carbon black + -
Use of substance by workers (Including Productions)
Grinding of NanoTiO2 TiO2 + -
Use of substance by workers (Including Productions)
Maintenance of device polluted with NP with glassbead cabinets
Nano Ag + -
Handling of articles by workers
Maintenance of physical vapour deposition (PVD)
Nano Ag + -
Use of substance by workers (Including Productions)
Production of TiO2 by laser pyrolysis
TiO2 + -
Use of substance by workers (Including Productions)
weighing of CNT CNT + PROC 3
Use of substance by workers (Including Productions)
CNT in solution CNT + PROC 5
Use of substance by workers (Including Productions)
Pouring of CNT CNT + PROC 3
Use of substance by workers (Including Productions)
Handling small quantities of CNT CNT + PROC 8a
Handling of articles by workers
Preparation of CNT pellets from CNT powder
CNT + PROC 3
The results from the comparison of the model outcomes with actual workplace particle number concentration data (obtained by CPC) are presented in Table 6 and plotted in figures 9 -12.
Table 6 Pearson correlation coefficients for the ECETOC TRA model and Stoffenmanager
CPC results Absolute difference activity minus non-activity
Ratio task vs. background
Stoffenmanager Inhalable dust (mg/m³) N=15 0.787 0.866 ECETOC exposure level ( mg/m³ ) N=5 0.346 0.346
.
45
Figure 9 Stoffenmanager exposure estimate (mg/m³) versus absolute difference (GM concentration task minus GM concentration background) measured by SMPS.
y = 0,9184x + 0,4534
R2 = 0,7516
0
2
4
6
8
10
12
0 2 4 6 8 10
Stoffenmanager
Inhalable dust (mg/m3)
CP
C r
esu
lts
Ra
tio
ta
sk v
s b
ack
gro
un
d
Figure 10 Absolute difference activity minus non-activity for Stoffenmanager
y = 6834,6x - 3180,4
R2 = 0,6205
-10000
0
10000
20000
30000
40000
50000
60000
70000
80000
0 2 4 6 8 10
Stoffenmanager
Inhalable dust (mg/m3)
CP
C r
esu
lts
Ab
solu
te d
iffe
ren
ce a
ctiv
ity
min
us
no
n-
act
ivit
y
46
Figure 11 Ratio task versus background for ECETOC
y = -0,0194x + 1,0831
R2 = 0,1171
0
1
2
3
0 1 2 3 4 5 6
ECETOC exposure level (mg/m3)
CP
C r
esu
lts
Rati
o t
ask
vs
back
gro
un
d
Figure 12 ECETOC TRA exposure estimate (mg/m³) versus absolute difference (GM concentration task minus GM concentration background) measured by SMPS.
y = -1548,1x + 6647,3
R2 = 0,1171
0
5000
10000
15000
20000
25000
30000
0 1 2 3 4 5 6
ECETOC exposure level (mg/m3)
CP
C r
esu
lts
Ab
solu
te d
iffe
ren
ce a
ctiv
ity
min
us
no
n-a
ctiv
ity
47
6.4.4 Analysis of the performance check
The results of the performance check showed that the correlations between the model estimates and the dataset from the NanoINNOV/ NanoSafe and NANOSH were poor. In order to find possible explanations for these poor correlations a more in-depth analysis was performed analyzing the data and the parameters underlying the exposure models. This analysis was limited to the NANOSH dataset only since it was expected that possible explanations would also be valid for the NanoINNOV/ NanoSafe data.
ECETOC TRA Theoretically, 30 different combinations were possible for the ECETOC TRA assessments, based on the available scenarios. However, some of these combinations resulted in the same exposure estimate. As a result, only 13 exposure values were obtained, as shown in figure 13, ranging from 0.01 - 50 mg/m³.
Figure 13 Overview possible estimated exposure values
Only eight different PROC could be assigned to the measurement data and the data used was not evenly distributed over these various PROCs (see figure 14). Moreover, low dustiness was applicable for only eight of the measured situations and these were again not evenly distributed over the different PROCs (see figure 15).
Assignment of low dustiness resulted both from solid articles containing nanoparticles and from solid particles dispersed in a liquid, because ECETOC TRA does not have a specific activity class for the latter.
48
Figure 14 Distribution of PROCs assigned to the measurement data
PROC1
PROC14
PROC5
PROC8a
PROC8b
PROC9
PROC15
PROC21
Figure 15 Distribution of dustiness category over the PROCs
dustiness distribution
0
2
4
6
8
10
12
14
PROC15 PROC14 PROC5 PROC21 PROC1 PROC8a PROC8b PROC9
num
ber of
mes
uram
ents
low dustiness
high dustiness
Local Exhaust ventilation (LEV) however was evenly distributed over the 46 assessed situations with 43% of the total measurements having LEV. As can be seen in figure 16, many PROCs contained both LEV and no LEV.
49
Figure 16 Distribution of LEV over the 46 measurements
LEV distribution
0
2
4
6
8
10
12
14
PROC1 PROC14 PROC15 PROC21 proc5 PROC8a PROC8b PROC9
num
ber
of m
easu
rmen
ts
With LEV
Without LEV
As shown previously, CPC and SMPS results were not available for all scenarios. The results from only 13 CPC and 23 SMPS measurements could be used for the performance check.
As can be seen in figures 17 and 18, the CPC results were slightly better distributed over the PROCS than the SMPS results. However, the measurements with the CPC only contained the high dustiness category while, for the SMPS results, the low dustiness category was present (see figure 19). The distribution of the LEV category was not distributed evenly over the PROCs for SMPS or CPC results (figures 20 and 21).
The performance check using the 23 SMPS resulted in six different outcomes in ECETOC TRA, ranging from 0.01 - 25 mg/m³. The performance check using the 13 CPC results in ECETOC TRA resulted in eight different outcomes ranging from 0.01 - 50 mg/m³. So, even though the CPC had a slightly better distribution, the number of data points was very limited. The number of measurement points for relative ranking was thus somewhat better with the SMPS where the distribution over the tool variables was much more limited, hence there were only eight different outcomes for 23 assessments.
Figure 17 Distribution of SMPS results over the PROCs
PROC1
PROC14
PROC5
PROC8a
PROC8b
PROC9
PROC15
PROC21
50
Figure 18 Distribution of CPC results over the PROCs
PROC1
PROC14
PROC5
PROC8a
PROC8b
PROC9
PROC15
PROC21
Figure 19 Distribution of dustiness category in SMPS
Dustiness distribution SMPS
0
2
4
6
8
10
12
PROC1 PROC14 PROC8a PROC9 PROC15
num
ber o
f mea
rem
ents
low dustiness
high dustiness
Figure 20 Distribution of LEV category in SMPS
LEV distribution SMPS
0
2
4
6
8
10
12
14
16
PROC1 PROC14 PROC8a PROC9 PROC15
num
ber
of m
easu
rem
ents
with LEV
Without LEV
51
Figure 21 Distribution LEV category CPC
LEV distribution CPC
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
PROC1 PROC14 PROC9 PROC15 PROC8a
num
ber
of m
easu
rem
ents
with LEBV
without LEV
STOFFENMANAGER Theoretically, 31 different exposure scenarios were assessed with Stoffenmanager, based on the 46 sets of measurement data and the information on operation conditions and risk management measures. However, some of the possible combinations resulted in the same exposure estimate. Eventually 15 different model outcomes were obtained, ranging from 0.17 - 42.6 mg/m³. Unfortunately, no useful data were available for the high end model outcomes, so the actual range for comparison was 0.17 - 9.3 mg/m³.
The distribution over the six handling categories of the solids with high dustiness was rather more even, as can be seen in figure 22. The liquids were only distributed over two handling categories.
Figure 22 Distribution of solids with high dustiness over the handling categories
34%
3%
7%
23%
30%
3%Handling of prodcuts with a relatively highspeed/force which may lead to some dispersion ofdust
Handling of products, where due to high pressure,speed or force large quantities of dust aregenerated and dispersed.
handeling products in closed containers
Handling of products in small amounts or insituations where only low quantities of productsare likely to be released.
Handling of products in very small amounts or insituations where release is highly unlikely.
Handling of products with low speed or with littleforce in medium quantities.
General ventilation was widely documented in the different activities. As a result the distribution was biased towards the more common activities meaning that the data were
52
less good for those activities which occurred less frequently. One effect of this was to limit the total variance in outcome, resulting in relatively poor variance data. The distribution is shown in figure 23.
Figure 23 Distribution of general ventilation over the activities
Overview general ventilation
0
2
4
6
8
10
12
14
16
1 2 3 4 5 6 7 8 9
number of this actvity class
total number of this category
general ventilation
Risk management measures (RMM) were widely distributed across the activities. However, this meant that there was only very limited data from activities without RMM, thus limiting the total variation in outcome (see figure 24).
Figure 24 Distribution on RMM over the activities
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Performance Check Ideally, for the performance check of Stoffenmanager using actual data, the data available should be sufficiently dispersed over the various combinations. However CPC and SMPS results are not available for all scenarios. In addition, where measurement results are available, the background and activity concentration is not reported in all cases. Consequently only 13 measurement results could be used for the CPC performance check and 23 for the SMPS performance check.
As can be seen in figures 25 and 26, the CPS results are very poorly distributed over the different activity classes; only 3 classes are presented and almost all of the measurements are located in one activity class. The SMPS results are slightly better; they are distributed over five different classes and are more equally distributed over the various classes. The measurements with the CPC also only contain the high dustiness category, while the SMPS measurements also describe some low dustiness scenarios. The performance check of the 23 SMPS results yielded six different outcomes using Stoffenmanager, ranging from 0.17 - 9.32 mg/m³. The performance check of the 13 CPC results using ECETOC TRA again yielded six different outcomes, ranging from 0.18 - 8.73 mg/m³. Although the SMPS has a better distribution the number of data points or model estimates for comparison is still very limited.
Figure 25 Distribution of CPC over the solid activity classes
72%
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0%Handling of prodcuts with a relatively highspeed/force which may lead to some dispersion ofdust
Handling of products with low speed or with littleforce in medium quantities.
handeling products in closed containers
Handling of products in small amounts or insituations where only low quantities of productsare likely to be released.
Handling of products in very small amounts or insituations where release is highly unlikely.
Handling of products, where due to high pressure,speed or force large quantities of dust aregenerated and dispersed.
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Figure 26 Distribution of SMPS over the activity classes
32%
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5%Handling of prodcuts with a relatively highspeed/force which may lead to some dispersion ofdust
Handling of products, where due to high pressure,speed or force large quantities of dust aregenerated and dispersed.
handeling products in closed containers
Handling of products in small amounts or insituations where only low quantities of productsare likely to be released.
Handling of products in very small amounts or insituations where release is highly unlikely.
Handling of products with low speed or with littleforce in medium quantities.
As expected, the distribution of general ventilation over the scenarios measured by the two devices is not homogeneous. The distributions are shown in figures 27 and 28.
Figure 27 Distribution of general ventilation over activity classes for SMPS
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Figure 28 Distribution of general ventilation over activity classes for CPC
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As for General Ventilation, the distribution of the type RMMs over the scenarios measured by the two devices is not homogeneous (figures 29 and 30)
Figure 29 Distribution of RMMs over activity classes for SMPS
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Figure 30 Distribution of RMMs over activity classes for CPC
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6.4.5 Discussion and conclusion
From the results given above, it can be seen that there is hardly any correlation between the model outputs and the actual concentration data. For both models, comparisons with either the NANOSH or the nanoINNOV data sets showed no substantial correlations. The relatively high correlation coefficients observed for the nanoINNOV data set is highly biased by only one data point and therefore it should be considered not to be meaningful. In addition, no difference can be observed between Stoffenmanager (activity-based) and ECETOC TRA (extrapolated for full day exposure).
The lack of any significant correlation is, most probably, largely attributable to the variability of the (relatively few) data. The variability of the particle number concentration within the Stoffenmanager and ECETOC TRA exposure scenarios is large compared to the between scenario model outputs. This would be expected to have a substantial effect on any correlation. As was shown earlier, in sections on the assessments using both models, due to lack of data or contextual information in the data sets not all variability within the model parameters could be used. This resulted in a loss of power of discernment between exposure scenarios. In addition, as been discussed for the NANOSH data set, the distribution of the variables within the model parameters was not even over the various scenarios so that, once more, the potential variability between the scenario exposure estimates was not fully exploited. Therefore it can be concluded that the data sets used to check the performance of the exposure models for nanoparticle exposure scenarios were not optimal for testing.
The particle number concentration data used to compare with the model scenario outputs was obtained from devices that measured in the size ranges 10 -1000 nm (CPC), or 6- 700 nm (SMPS). As stated before, particles within these size ranges will contribute less to mass compared to particles within larger size ranges, and it can be hypothesized that the resolution for low mass concentration of the models might be insufficient to show any correlation.
In summary it can be concluded that, theoretically, with respect to nanoparticle exposures, both ECETOC TRA and Stoffenmanager would be able to give an indication of exposure levels. Since the models are not attuned to, or calibrated for, nanomaterial exposure situations, the actual model estimates will be inaccurate and possibly overestimate the concentration levels.
Comparisons of the model estimates with actual data on the average particle concentrations measured during the nanomaterial handling activities revealed no significant correlation. This is most probably due to the large variability of the particle number concentration within the Stoffenmanager and ECETOC TRA exposure scenarios compared to the small between scenario model outputs. The latter is related to a combination of the typical scenarios that could be derived form the data sets; information that was lacking, and the inherent power of contrast of the models for nanomaterial exposure situations.
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6.4.6 References
ECETOC, Addendum to ECETOC Targeted Risk Assessment Report No. 93, December 2009, Technical Report No. 107.
Cherrie, J.W., Tickner, J., Friar, J., Creely, K.S., Soutar, A.J., Hughson, G, Warren N.D., 2003. Evaluation and further development of the EASE model 2.0. Health and Safety Executive, 2003.
ECHA, Guidance on information requirements and chemical safety assessment; Chapter R.14: Occupational Exposure Estimation, May 2008.
REACH Guidance on information requirements and chemical safety assessment. Chapter R.12: Use descriptor system (DRAFT Version 2.0 07/11/2009)
Marquart H, Heussen H, Le Feber M, Noy D, Tielemans E, Schinkel J, West J, Van der Schaar D. (2008) ‘Stoffenmanager’, a web-based control banding tool using an exposure process model. Ann. Occup. Hyg.; 52 (6), 429, doi:10.1093/annhyg/men032
Schinkel J, Fransman W, Heussen H, Kromhout H, Marquart H, and Tielemans E. (2010) Cross-validation and refinement of the Stoffenmanager as a first tier exposure assessment tool for REACH. Occup. Environ. Med. 2010 (67), 125
Tielemans E, Noy D, Schinkel J, Heussen H, van der Schaaf D, West J, Fransman W. (2008) Stoffenmanager exposure model: development of a quantitative algorithm. Ann. Occup. Hyg., 52(6) 443, doi:10.1093/annhyg/men033
Tielemans E, Schneider T, Goede H, Tischer M, Warren N, Kromhout H, van Tongeren M, van Hemmen J and Cherrie JW. (2008) Conceptual model for assessment of inhalation, exposure: defining modifying factors. Ann. Occup. Hyg., 52(7), 577, doi:10.1093/annhyg/men059
ECETOC (2009). Worker exposure tool (version 2.0) from the targeted risk assessment of ECETOC. (http://www.ecetoc.org/tra; access date: August 28, 2009)
ECETOC (2010). Integrated tool exposure tool (version 2.0) from the targeted risk assessment of ECETOC. (http://www.ecetoc.org/tra; access date: April 30, 2010)
ECETOC, Technical Report No. 86. Derivation of assessment factors for human health risk assessment. February 2003. ISSN-0773-6347-86.
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6.4. PDF extracts from the NANEX Exposure Scenari o Database
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