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Estimation of inhalation exposure on the base of airborne nanomaterial release data and propagation modelling Daniel Göhler, Ralf Gritzki, Markus Rösler, Clemens Felsmann, and Michael Stintz ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b01678 • Publication Date (Web): 08 Jun 2018 Downloaded from http://pubs.acs.org on June 8, 2018

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ACS Sustainable Chemistry & Engineering

Estimation of inhalation exposure on the base of airborne nanomaterial release data and propagation modelling Daniel Göhler†, Ralf Gritzki‡, Markus Rösler‡, Clemens Felsmann‡, and Michael Stintz*,† †

Research Group Mechanical Process Engineering, Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, D-01062 Dresden, Germany ‡ Chair of Building Energy Systems and Heat Supply, Institute of Power Engineering, Technische Universität Dresden, D01062 Dresden, Germany * corresponding author: [email protected] KEYWORDS: Nanomaterial risk assessment, particle release, propagation modelling, exposure estimation, CFD, EHS

ABSTRACT: The lack of data for inhalation exposure concerning the handling of nanostructured materials is limiting the current nanomaterial risk assessment. However, there are numerous studies dealing with the origin of exposure, i.e., the release. The link between release and exposure are transport and transformation phenomena. Thus, propagation modelling was used to simulate transport from source to breathing zone in order to estimate quantitative exposure levels from measured release data for selected scenarios. On the base of three ventilation and five release scenarios, exposure levels were calculated with and without human thermal plumes for both the nearfield and the farfield. Results show, that the often neglected human induced convective airflow can have a strong impact on the level of exposure, especially for natural ventilation or non-well designed technical ventilation systems as typical for non-industrial consumers or handicraft business.

especially in the area of occupational safety. The former ones can be applied solely for exposure estimation either in the nearfield17 or the farfield10,17, whereas the latter ones consider both nearfield and farfield exposure12. In a recently published article3 we demonstrated the complex relationships between release and exposure for selected scenarios by the combination of measured release data and propagation modelling. Based on similar scenarios, propagation modelling was used in the present work to consider beside nearfield also farfield exposure and to examine the impact of human thermal plumes. Furthermore, the results for exposure based on propagation modelling were compared to the ones received by two compartment models.

INTRODUCTION As essential part of the nanomaterial risk assessment, the estimation of exposure is also subjected to an intensified research in the last decade. In this context, an increased attention was paid especially on the release (or emission), i.e., the origin of exposure. Thereby, release describes the process-induced separation and transfer of discrete particulate or ionic objects from a material into the environment1. Ideally, release characterization can be performed under optimal analytical conditions in laboratory2 and needs only minimal contextual information for data interpretation3. In contrast, exposure assessment requires beside release data additional information for the description of the exposure scenario like data on geometric boundaries, interior, ventilation or the whereabouts of the subject to be exposed.4,5 Today, the number of published experimental release studies exceeds those for exposure, since exposure studies are complex to perform6-8, require high effort and provide solely limited exposure data.9 Thus, little is known about quantitative exposure levels.5 Only under worst case conditions (e.g. accidents or misuse) release data can become exposure ones.3 However, in reality if at all, only a fractional amount of released material will reach the breathing zone. Thus, the question arises which quantities of release become relevant for exposure. In this context, simplified (i.a. well mixed conditions without thermal impacts) one compartment models10 and two compartment models11-13 based on quantitative release data14-16 are state of the art for indoor exposure estimation,

MODELLING DETAILS Fundamentals of computation. In analogy to previous work3, fluid dynamic and thermodynamic phenomena were modelled by operating the computational fluid dynamic calculation module ParallelNS18 and the thermal building simulation module TRNSYS-TUD19 coupled within a parallel virtual machine20. For solving unsteady Reynolds-averaged NavierStokes equations, ParallelNS uses a Galerkin Least-Squares finite element approach. TRNSYS-TUD emanates on the program TRNSYS21 and considers thermal aspects like environmental climate or heat transfer properties of heating systems as well as construction materials. Simplifications, assumptions and limitations. To minimize computational effort for modelling aerosol propagation, some

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simplifications and assumptions on release and aerosol characteristics were preceded according to previous work3. At first, it was appointed that the simulated adiabatic particle supply (i.e., particle release at local room temperature) occurs interference free without directional momentum. This approach allows the modelling of a relative propagation based on passive scalars, whose results can be converted subsequently with measured release quantities. Accordingly, modeling of aerosol propagation was performed with an additional dimensionless transport equation, which is valid for air-equivalent gaseous systems:22

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Ventilation scenarios. Of course, there are several ventilation scenarios (VS) in reality. This work focuses in analogy to previous work3 on three VSs, i.e., natural ventilation by door slit infiltration (NVD), natural ventilation by pivot-hung window (NVW) and one technical ventilation system (TVS). According to Table 1, the VSs differ in their air exchange rate (nAER), their air inlet temperature (ϑin), their inlet (LVS,in) and outlet location (LVS,out) and the cross-sectional areas of air inlet (Ain) and air outlet (Aout). Note that the VSs were designed with regard to comfort aspects according to ISO 7730:200524. This includes beside thermal conditions also airflow conditions. Table 1. Parameter set of analyzed ventilation scenarios.

, ∘ ∘

Where c is the substance concentration, t the time, u the velocity vector, ν the kinematic viscosity, Sc the molecular Schmidt-Number (index turb specifies quantities for turbulence model), mt,V the volume-specific mass flow and ρ0 the reference density. Secondly, it was supposed that airborne submicron particles behave gas-like. Thus, some transport and transformation phenomena based on particle-wall-interactions (e.g. particle loss by deposition), particle-particle-interaction (e.g. concentration decrease and increase of size by agglomeration) and particle-fluid interactions (e.g. size dependent segregation by inertia or gravitational settling) were neglected. Note that the mentioned particle-particle, particle-fluid and particle-wall interactions have less relevance for the present release scenarios. Geometric and thermal conditions of the model room. Figure 1 shows the examined model room that was endued for propagation modelling with a grid of 800000 tetrahedron elements according to previous work3.

IDVS

LVS,in

LVS,out

nAER -1

ϑin

Ain

Aout

[-]

[-]

[-]

[h ]

[°C]

[m²]

[m²]

NVD

ANVD,1

ANVD,2

0.5

20

0.05

0.4

NVW

ANVW,1

ANVW,2

1.5

5

0.217

0.15

TVS

ATVS,1

ATVS,2

8.0

20

0.4

0.4

Release scenarios and release processes. Starting point for propagation modelling are five release scenarios (RSs, Table 2), which differ primarily in the source of release (i.e., source location LRS and source volume VRS), the duration of release (tRS = 10 s, 60 s) and specific release data (i.e., quantities and size distributions of released objects) of three release processes (i.e., wiping, sanding and spraying). Details on the given release data, which are based on measurement data for spray can application of a liquid coating25 as well as for weak26 and moderate27 mechanical treatment of cured coatings, are provided in previous work3. The RSs for wiping (WIP) and sanding (SAN) based on the treatment of a work piece area of 1 m × 0.5 m = 0.5 m² and area-specific release numbers (nA), while for spraying (SPR) release rates (nt) came into use. For both sanding and spraying, two single RSs were modeled for different release duration. For the sanding scenarios (SAN 1, SAN 2) the area-specific number of released particles (nA) was kept constant that goes along with different release rates (nt), while in the case of the spraying scenarios (SPR1, SPR2) equal release rates were used that lead to different total release quantities. Table 2. Parameter set of analyzed release scenarios: wiping (WIP), sanding (SAN1, SAN2) and spraying (SPR1, SPR2).

Figure 1. Model room; blue areas = air inlets, red areas = air outlets; particle sources (R1, R2), exposure sensors (S1, S2).

The envelop of the room (3.0 m × 5.0 m × 3.0 m = 90 m³) is equipped with a door, a triple-part window (1.5 m × 5.0 m = 7.5 m²) and inlets as well as outlets for ventilation. Only a workbench (1.0 m × 4.0 m × 0.95 m) and a person (1.8 m) were placed inside. A floor heating provides a mean room temperature of ϑroom = 20°C at an outdoor temperature of ϑoutdoor = 5°C. The wall with the window is the only exterior wall and fulfills like all the other ones the requirements of the German Energy Saving Ordinance (EnEV 2009)23. Since humans are thermal-active, the surface of the person was also divided in relevant thermal regions (ϑclothes = 26°C, ϑhead = 35°C, ϑhands = 30°C) according to ISO 7730:200524.

IDRS

LRS VRS tRS ARS nA

nt

x50,0

σg

dNF

dFF

[-]

[-]

[L]

[s] [m²] [#/m²]

[#/s]

[nm] [-]

[m]

[m]

WIP

R1

50

10 0.5

5.0E05

2.5E04

n.a.

n.a. 0.65 3.45

SAN 1 R1

50

10 0.5

1.0E11

5.0E09

240

1.4

0.65 3.45

SAN 2 R1

50

60 0.5

1.0E11

8.3E08

240

1.4

0.65 3.45

SPR 1 R2

8

10 n.d.

n.d.

7.5E09

120

2.0

0.53 3.54

SPR 2 R2

8

60 n.d.

n.d.

7.5E09

120

2.0

0.53 3.54

Exposure scenarios. The geometric as well as the thermal conditions of the environment, the VSs and the RSs form the propagation scenarios (PSs), which become exposure ones

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(ESs) by defining the whereabouts of the subject to be exposed. Thus, exposure data recording was realized by two breathing zone sensors (S1, S2) located at 1.6 m height; one right in front of the person and one near the corner to the left behind the person. The sensor positions were chosen to characterize nearfield (NF) and farfield (FF) exposure. Note that in this context, FF is defined as the region outside of a cube with an edge length of 2 m, centered on the head of the person.11,12 The actual source to sensor distances (dNF, dFF) are provided in Table 2.

shown in Figure 2 a), which is colored by the local air exchange index28 ILAE, makes the different VSs more comparable. Regions of poor air exchange quality are indicated by ILAE < 1 and regions of efficient air exchange are described by ILAE > 1, whereas in the case of ILAE = 1 the local air age equals the reciprocal of the air exchange rate (nAER). Accordingly, the direct comparison between the VSs shows that: i) NVD suffers on regions with efficient air exchange (ILAE > 1, on the floor behind the person), ii) regions of efficient air exchange for NVW are limited to the front of the window and the floor, and iii) TVS goes along with the highest volumetric amount of regions with effective air exchange. The stationary flow conditions in the sectional plane through the workplace are illustrated by the mean airflow velocity vm in Figure 2 b). No significant airflow through the working area can be observed for the natural VSs (NVD, NVW), whereas the TVS shows a high throughput. All VSs show in common the presence of convective flows around and above the person towards the ceiling, which are induced by thermal plumes around the person. The mean airflow velocities in this region are between 0.05 m/s and 0.25 m/s and are in good agreement with measurement data.29,30

RESULTS AND DISCUSSION Ventilation conditions and aerosol propagation. In order to illustrate all relevant phenomena within the model room, a total time frame of 120 min was chosen for simulation. At first, the flow conditions for the VSs were modeled. Since stationary flow conditions were achieved for < 30 min, the RSs were initiated at 30 min. A survey of the stationary flow conditions and the quality of air exchange for the VSs is provided in both Figure 2 a) and Figure 2 b). The quality of air exchange for the VSs was already discussed in previous work3 but will be explained here again in some extent to improve the intelligibility. The velocity vector field

Figure 2. Stationary flow conditions and quality of local air exchange (a, b) in the model room for the three ventilation scenarios (NVD, NVW, TVS) and state of aerosol propagation 50 s (c) resp. 135 s (d) after release initiation (release scenario SPR2).

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ACS Sustainable Chemistry & Engineering As already mentioned, the RSs were initiated for a duration of 10 s resp. 60 s at a simulated time of 30 min, at which steadystate airflow conditions prevailed in the model room. Henceforward, the actual aerosol propagation happens. The propagation behavior for each PS was than computed for further 90 min. Note that selected video sequences, which show the propagation behavior for the first 100 s after release initiation, are provided in the supplement. However, Figure 2 c) and Figure 2 d) show the aerosol propagation 50 s resp. 135 s after release initiation (on the example of release scenario SPR 2) for the three VSs. The state of aerosol propagation 50 s after release initiation (Figure 2 c) reveals that human thermal plumes have a significant impact on the aerosol propagation behavior and can thus lead to particle availability in the breathing zone. According to Figure 2 d), the spatial aerosol propagation correlates to the air exchange rate of the VSs, i.e., the lowest spatial propagation at 135 s after release initiation can be observed for NVD, while in the case of the TVS the aerosol cloud is already distributed over the whole model room. Note that a higher spatial propagation is accompanied with an increased dilution and thus with lower local concentrations. Exposure, inhalation and deposition. Figure 3 a) provides the temporal evaluation of the relative particle concentration (in strictly speaking the mass mixing ratio between mass of pseudo polluted air and mass of clean air) in the NF, while Figure 3 b) shows the ones for the FF. Considering at first the relative concentration over time for the NF in Figure 3 a), similar temporal behaviors can be observed for each ES. After release initiation, the relative concentration increases at first rapidly, followed by a phase of alleviated increase. The primary increase phase lasted with about 3 min in each case even longer than the duration of the release process. After reaching the maximum, the relative concentration decreases rapidly to an obvious primary local minimum. Afterwards, the relative concentration increases again to a secondary maximum that is attributed to the returning but expanded particle cloud. Note that this behavior recurs alternately with steady attenuation. For each RS configuration (i.e. release duration and release source) the concentration peak in the NF depends significantly on the VS and the decrease follows the corresponding air exchange rates of the VSs (see Table 1). Thus, highest peak concentrations in the NF result for NVD followed by NVW, while the lowest ones occur for the TVS. Furthermore, it can be observed that minor changes within the RSs (i.e., release source and release duration) lead already to considerable differences in the NF peaks; for the given examples each by two orders of magnitude. The relative concentrations over time for the FF given in Figure 3 b) differ significantly from the ones of the NF. At first, the FF shows lower concentration peaks at a higher time shift to release initiation and the temporal evaluation of the concentration is more attenuated. Secondly, the peak sort order based on the VSs reverses for each RS configuration, i.e. highest concentration peaks in the FF arose for the TVS, while the lowest one results for the NVD. Note that a lower exposure level in the FF as observed for the analyzed scenarios is not a general circumstance. For certain conditions, especially for VSs with airflows directed on the release source, exposure in the FF can become higher than for the NF. In analogy to previous work3, the temporal courses of particle concentration (Figure 3) were used to determine total exposure

quantities. At first, specific release quantities (Table 2) and size distribution characteristics (see Figure S2) were used to calculate number-weighted and mass-weighted exposure concentrations. Note that for the determination of the latter ones unity particle density was assumed. Afterwards, a gender and activity averaged breathing rate of 386 cm³/s was assumed to calculate cumulative amounts of inhaled particles over the total time frame of 90 min after release initiation. Since fractions of inhaled particles will also be exhaled, a gender and activity averaged model of the human respiratory tract (modified ICRP 66 model) was chosen (see Figure S1) for the determination of total particle deposition as well as local particle deposition in the head airways (HA), the tracheobronchial region (TB) and alveolar region (AL).31

a)

1.0E-01 R2, 60 s, NVD R2, 60 s, NVW R2, 60 s, TVS R2, 10 s, NVD R2, 10 s, NVW R2, 10 s, TVS R1, 60 s, NVD R1, 60 s, NVW R1, 60 s, TVS R1, 10 s, NVD R1, 10 s, NVW R1, 10 s, TVS

1.0E-02

relative concentration [-]

1.0E-03 1.0E-04 1.0E-05 1.0E-06 1.0E-07 1.0E-08 1.0E-09 30

35

40

45

50

55

60

elapsed time [min]

b)

1.0E-01 R2, 60 s, NVD R2, 60 s, NVW R2, 60 s, TVS R2, 10 s, NVD R2, 10 s, NVW R2, 10 s, TVS R1, 60 s, NVD R1, 60 s, NVW R1, 60 s, TVS R1, 10 s, NVD R1, 10 s, NVW R1, 10 s, TVS

1.0E-02 1.0E-03

relative concentration [-]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1.0E-04 1.0E-05 1.0E-06 1.0E-07 1.0E-08 1.0E-09 30

35

40

45

50

55

60

elapsed time [min]

Figure 3. Relative concentration over time for a) NF (sensor S1) and b) FF (sensor S2) based on the three ventilation scenarios (NVD, NVW, TVS), the two sources of release (R1, R2) and the two release durations (10 s, 60 s).

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Therefore, lognormal particle size distributions (see Figure S2) were approximated from measured ones for sanding and spraying. Figure S3 provides the thus determined numberweighted and mass-weighted fractions for regional deposition, which were the base for further calculations. Figure 4 shows the total quantities of inhaled and deposited particles in the NF for each ES. Note that these exposure values are easy to be converted into FF ones using the NF/FF ratios for the thermal active person shown in Figure 7. Graphical representations for number- and mass-weighted exposure levels in the FF are also provided in the supplement (Figure S4, Figure S5).

courses, the NF exposure levels decrease with increasing air exchange rate of the VSs, i.e., in each analysed RS lowest exposure levels were calculated for the TVS followed by NVW, while NVD shows the highest ones. In contrast to the NF exposure levels provided in Figure 4, the highest exposure levels in the FF (Figure S4 and Figure S5) were determined for NVW, while values for NVD and TVS were very similar to each other. Furthermore, no direct proportionality between exposure level and duration of release could be found, neither for equal release rate nor for equal release quantity. For instance, a six times higher release time leads in the case of equal release rates to an exposure increase (for both NF and FF) by a factor of 29 for sanding resp. 4.3 times for spraying, whereas for equal release quantities exposure increases by 4.8 times for sanding and decreases by a factor of 0.7 for spraying. Proportionalities for generalization. In order to assess the fractional amount of released material that becomes relevant for exposure, ratios of released and inhaled particles (designated in the following as R/I ratio) were determined for both the nearfield and farfield as shown in Figure 5.

2.6E+8 5.5E+7

9.5E+06

4.5E+07

3.7E+07 5.2E+07

7.8E+06 1.1E+07

1.0E+01

far field ratios

2.1E+04

5.1E+03 4.4E+03

4.9E+03 1.8E+03

near field ratios

7.6E+03

1.0E+02

1.7E+03 2.1E+03

1.0E+03

4.1E+02 4.9E+02

2.2E+04 1.9E+04 NVD NVW

1.0E+00

1.0E+04

8.0E+05 3.2E+06

1.0E+01

1.0E+05

6.0E+05

1.0E+02

1.0E+06

3.8E+06 1.5E+07

TVS

NVD

NVD NVW

SPR 2 R2, 60 s

2.1E+0 1.7E+0 4.7E-1 8.8E+0 7.4E+0 2.0E+0

b) 1.0E+03

SPR 1 R2, 10 s

1.0E+07

NVD

SAN 2 R1, 60 s

NVW TVS

NVD

NVW TVS

NVD

SAN 1 R1, 10 s

1.0E+08

NVW TVS

WIP R1, 10 s

NVW TVS

NVD

NVW TVS

1.0E-02

1.0E+09

2.9E+06

9.4E+3

7.1E+3 1.8E+3

n_inh n_dep n_dep_HA n_dep_TB n_dep_AL

ratio of released and inhaled particles [-]

1.0E-01

1.9E+3

1.0E-03 1.0E-04 1.0E-05

1.0E-07 1.0E-08

TVS

TVS

SPR 1 R2, 10 s

NVD NVW

TVS

SAN 2 R1, 60 s

NVD NVW

TVS

NVD NVW

NVD

NVW TVS

NVD NVW

SAN 1 R1, 10 s

SAN 2 R1, 60 s

TVS

NVD

TVS

SPR 1 R2, 10 s

SPR 2 R2, 60 s

As it can be observed in Figure 5, the R/I ratios varied from 4.1⋅102 up to 1.5⋅107 for the NF and resp. between 4.4⋅103 and 5.2⋅107 for the FF. For NF exposure the R/I ratios depend significantly on the VS and the increase follows the corresponding air exchange rates. In the case of FF exposure, R/I ratios balanced almost out among the VSs. In contrast to the current literature, also human thermal plume was considered in the present work for propagation modelling and thus for exposure estimation. Note that human thermal plume was simulated only for the person in the NF and not for the FF to reduce computational effort. But it is expected that this neglect has only a minor impact on calculated exposure levels, since aerosol propagation with increasing distance to source goes along with a considerable expansion of the cloud volume and thus with an more balanced spatial concentration homogeneity. However, in order to get an impression on the

1.0E-09

WIP R1, 10 s

SAN 1 R1, 10 s

Figure 5. Ratios of released and inhaled particles for nearfield (sensor S1) and farfield (sensor S2).

m_inh m_dep m_dep_HA m_dep_TB m_dep_AL

2.9E-5 2.3E-5 5.7E-6 1.4E-4 1.1E-4 2.7E-5

1.0E-06

WIP R1, 10 s

NVD NVW

1.0E-02

NVW TVS

2.1E-4 1.6E-4 4.0E-5 1.0E-3 7.7E-4 1.9E-4 5.5E-1 4.5E-1 1.2E-1 2.3E+0 1.9E+0 5.4E-1

NVD

1.0E+00

1.0E-01

NVW TVS

1.0E+00

1.5E+3 3.7E+2

1.0E+01

8.5E-2

1.0E+02

4.5E+07

1.0E+10

1.0E+03

3.7E+07 5.2E+07

1.0E+04

2.4E+8 2.0E+8

1.0E+05

8.3E+4

1.7E+4

1.0E+06

1.3E+4 3.3E+3

1.0E+07

6.3E+4 1.6E+4

1.0E+08

3.8E+06 1.5E+07

2.6E+8

2.1E+8 5.9E+7

5.7E+7

1.0E+09

4.6E+7 1.3E+7

1.0E+10

6.5E-2 1.6E-2

number of inhaled/deposited particles [#]

1.0E+11

1.1E+9 9.2E+8

a) 1.0E+12

mass of inhaled/deposited particles [µg]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


2.9E+06

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SPR 2 R2, 60 s

Figure 4. Number a) and mass b) of inhaled/deposited particles in the nearfield (sensor S1).

According to Figure 4 a), the total number of inhaled particles in the NF varies over all analysed NF exposure scenarios from quasi zero in the case of WIP up to 1.9⋅109 for SPR 2. In analogy to the findings concerning the temporal concentration

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ACS Sustainable Chemistry & Engineering impact of human thermal plume on exposure, all scenarios were also computed instead of a thermal active person with an adiabatic one. For the purpose of comparison, the number of inhaled particles received for the thermal active person was related to the ones for the adiabatic person as shown in Figure 6. Accordingly, the negligence of convective airflows induced by human thermal plume leads for all scenarios to considerable differences, which are more pronounced for NF than for FF exposure. In the case of the natural ventilation (i.e., NVD, NVW), the NF exposure level would be underestimated for 2.5 up to 5 times, whereas FF exposure goes along with an overestimation for 1.29 up to 1.82 times. The NF overestimation in the case of the TVS for both spraying scenarios (SPR 1, SPR 2) suggests, that in the case of stagnating aerosol clouds human thermal plumes can have also a positive cleaning effect. Furthermore, it can be observed that within each RS configuration the highest false estimation results for NVW. This is attributed to the higher local temperature gradients that accompany the cold air inlet flow.

SUMMARY AND CONCLUSIONS In order to estimate the inhalation exposure on the base of measured airborne particle release data for nanostructured materials, propagation modelling was used to simulate particle transport from source to breathing zone. Therefore, 30 exposure scenarios including nearfield and farfield considerations were specified for a 90 m³ model room equipped i.a. with a geometric and thermal active person. Accordingly, inhalation exposure levels were determined on the base of three ventilations scenarios (i.e., natural ventilation by door slit infiltration and pivot-hung window and a technical ventilation system) and five short-term (10 s, 60 s) release scenarios for three release processes (i.e., dry wiping as well as sanding of a coated work piece, spray can application of a liquid coating). 2.0E+01 15.7

15.2

9.0

8.8

9.8

9.7

12.5

12.8

15.2 9.7

8.0E+00

2.8 1.3 2.2

2.2

1.3

2.8 3.0

3.1

3.5 0.9

1.2

3.4 2.4 0.9

1.2

2.0E+00

2.5

4.0E+00

3.4

6.0E+00

0.9

1.39 1.12

WIP R1, 10 s

SAN 1 R1, 10 s

SAN 2 R1, 60 s

SPR 1 R2, 10 s

NVW TVS

TVS NVD

NVD NVW

NVW TVS

TVS NVD

NVD NVW

NVD

0.0E+00 NVW TVS

0.40

1.0E+01

0.27

0.39

0.27

0.20

1.2E+01

0.37

0.37 0.20

0.20

0.37 0.21

0.21

0.20

5.0E-01

1.10

1.0E+00

adiabatic person

1.4E+01

2.5

1.63 1.39

1.40

1.59

1.71

1.70 1.40 1.32

1.70 1.40 1.29

1.29

1.5E+00

1.82

far field impact

2.0E+00

1.82

near field impact

1.6E+01

thermal-active person

1.2

NF/FF ratio of inhaled particles [-]

1.8E+01

2.5E+00

SPR 2 R2, 60 s

WIP R1, 10 s

SAN 1 R1, 10 s

SAN 2 R1, 60 s

SPR 1 R2, 10 s

NVD

NVW TVS

NVD

NVW TVS

NVW TVS

TVS NVD

NVW

TVS NVD

NVD

0.0E+00 NVW

inh. ratio of thermal-active and adiabatic person [-]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Figure 7. NF/FF ratios of inhaled particles on the base of a thermal-active person and an adiabatic one.

SPR 2 R2, 60 s

Beside particle number and mass concentrations in the breathing zone, number- and mass-weighted quantities of inhaled and deposited particles in the human respiratory tract were determined. Across all analyzed exposure scenarios, exposure levels varied over 11 orders of magnitude. In contrast, the exposure levels within the individual release scenarios varied due to the different ventilation conditions (i.e., 0.5 h-1, 1.5 h-1 and 8 h-1) below one order of magnitude. To answer the question which quantities of release become relevant for exposure, ratios of released and inhaled particles (R/I ratios) were determined. For the chosen exposure scenarios, these ratios varied also over orders of magnitude; for the nearfield from 4.1⋅102 to 1.5⋅107 and for the farfield between 4.4⋅103 and 5.2⋅107. Since state of the art exposure modeling (i.e., two compartment modelling) neglects human thermal plume, propagation modelling was performed also for an adiabatic person. A direct comparison between the exposure levels of a thermal active person and an adiabatic one shows considerable differences. For the analyzed exposure scenarios, the negligence of human thermal plumes leads i) for the nearfield to an underestimation of exposure levels up to 5 times and ii) for the farfield to an overestimation up to 1.8 times. Furthermore it could be

Figure 6. False estimation of exposure levels due to negligence of convective air flows induced by human thermal plumes; inhalation ratio of thermal active person and adiabatic person.

Interestingly, the determined values on the overestimation for FF are similar to the ones between 1.3 and 1.6 identified by comparing two-box-model results and measurements data by Cherry & Schneider (1999)32. Figure 7 provides the nearfield to farfield ratios (designated as NF/FF ratios) of inhaled particles for all ES. Across all ES, the NF/FF ratios of inhaled particles varied from 2.75 up to 15.74, respectively between 0.9 and 3.1 when convective flows due to human thermal plumes are not considered. Note that the values of the latter case are in the same magnitude of order as the ones (1.0 - 5.8) recalculated by Koivisto et al. (2018)13 on the base of Cherry (1999)11 for a model room of 100 m³ (air exchange rates between 0.3 h-1 and 10 h-1) by operating a two compartment box model. Furthermore, the NF/FF ratios show a strong dependence on the prevailing VS. With increasing air exchange rate the NF/FF ratio decreases, i.e., NVD (0.5 h-1) goes along with NF/FF ratios > 12, NVW (1.5 h-1) with NF/FF ratios ≈ 10 and the TVS (8h-1) with NF/FF ratios < 4.

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shown that by neglecting human thermal activity, results for exposure on the base of the performed propagation modelling are very similar to those received by state of the art exposure modelling.11,32 Therefore it is recommended that, i.a. human thermal activity have to be considered in future work for exposure estimation, especially for natural ventilation or non-well designed technical ventilation systems as typical for nonindustrial consumers or handicraft business. For the purpose of sustainable nanotechnology the design of save nanomaterials or release-free processes should be evaluated at first in terms of particle release since release quantities can be determined independently from specific exposure scenarios. On the base of propagation modelling, release data can be interpreted in terms of exposure data. But this requires the specification of numerous parameters. Therefore, we recommend an agreement to standardized parameter sets for characteristic scenarios with relevant consumer or handicraft business associations, managed possibly by the Organization for Economic Co-operation and Development (OECD) or the International Standardization Organization (ISO). According to this strategy, exposure limits can also be transferred to release ones in the future.

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(3) Göhler, D.; Gritzki, R.; Stintz, M.; Rösler, M. & Felsmann, C. Propagation modelling based on airborne particle release data from nanostructured materials for exposure estimation and prediction. J. Phys.: Conf. Ser., 2017, 838 (1), DOI: 10.1088/17426596/838/1/012010. (4) Clark, K.; van Tongeren, M.; Christensen, F.M.; Brouwer, D.; Nowack, B.; Gottschalk, F.; Micheletti, C.; Schmid, K.; Gerritsen, R.; Aitken, R.; Vaquero, C.; Gkanis, V.; Housiadas, C.; de Ipina, J.M.L. & Riediker, M. Limitations and information needs for engineered nanomaterial-specific exposure estimation and scenarios: recommendations for improved reporting practices. J. Nanopart. Res., 2012, 14 (9), DOI: 10.1007/s11051-012-0970-x. (5) Ding, Y.; Kuhlbusch, T.A.; Tongeren, M.V.; Jiménez, A.S.; Tuinman, I.; Chen, R.; Alvarez, I.L.; Mikolajczyk, U.; Nickel, C.; Meyer, J.; Kaminski, H.; Wohllebeni, W.; Stahlmecke, B.; Clavaguera, S. & Riediker, M. Airborne engineered nanomaterials in the workplace - a review of release and worker exposure during nanomaterial production and handling processes. J. Hazard. Mater., 2016, 322, Part A, DOI: 10.1016/j.jhazmat.2016.04.075. (6) Fonseca, A.S.; Viana, M.; Pérez, N.; Alastuey, A.; Querol, X.; Kaminski, H.; Todea, A.M.; Monz, C. & Asbach, C. Intercomparison of a portable and two stationary mobility particle sizers for nanoscale aerosol measurements. Aerosol Sci. Technol., 2016, 50 (7), DOI: 10.1080/02786826.2016.1174329. (7) Asbach, C.; Neumann, V.; Monz, C.; Dahmann, D.; van Tongeren, M.; Alexander, C.; MacCalman, L. & Todea, A.M. On the effect of wearing personal nanoparticle monitors on the comparability of personal exposure measurements. Environ. Sci.: Nano, 2017, 4 (1), DOI: 10.1039/C6EN00362A. (8) Bau, S.; Payet, R.; Witschger, O. & Jankowska, E. Performance study of portable devices for the real-time measurement of airborne particle number concentration and size (distribution). J. Phys.: Conf. Ser., 2017, 838 (1), DOI: 10.1088/1742-6596/838/1/012001. (9) Brouwer, D.; van Duuren-Stuurman, B.; Berges, M.; Jankowska, E.; Bard, D. & Mark, D. From workplace air measurement results toward estimates of exposure? Development of a strategy to assess exposure to manufactured nano-objects. J. Nanopart. Res., 2009, 11, 8, 1867-1881. doi: 10.1007/s11051-009-9772-1. (10) Hewett, P. & Ganser, G.H. Models for nearly every occasion: Part I - One box models. J. Occup. Environ. Hyg., 2017, 14 (1), DOI: 10.1080/15459624.2016.1213392. (11) Cherrie, J.W. The Effect of Room Size and General Ventilation on the Relationship Between Near and Far-Field Concentrations. Appl. Occup. Environ. Hyg., 1999, 14 (8), DOI: 10.1080/104732299302530. (12) Ganser, G.H. & Hewett, P. Models for nearly every occasion: Part II - Two box models. J. Occup. Environ. Hyg., 2017, 14 (1), DOI: 10.1080/15459624.2016.1213393. (13) Koivisto, A.J.; Jensen, A.C.Ø. & Koponen, I.K. The general ventilation multipliers calculated by using a standard Near-Field/FarField model. J. Occup. Environ. Hyg., 2018, 15(5), DOI: 10.1080/15459624.2018.1440084. (14) Koivisto, A.J.; Jensen, A.C.O.; Kling, K.I.; Nørgaard, A.; Brinch, A.; Christensen, F. & Jensen, K.A. Quantitative material releases from products and articles containing manufactured nanomaterials: Towards a release library. NanoImpact, 2017, 5, DOI: 10.1016/j.impact.2017.02.001. (15) Koivisto, A.J.; Jensen, A.C.; Kling, K.I.; Kling, J.; Budtz, H.C.; Koponen, I.K.; Tuinman, I.; Hussein, T.; Jensen, K.A.; Nørgaard, A. & Levin, M. Particle emission rates during electrostatic spray deposition of TiO2 nanoparticle-based photoactive coating. J. Hazard. Mater., 2018, 341, DOI: 10.1016/j.jhazmat.2017.07.045. (16) Koivisto, A.J.; Kling, K.I.; Fonseca, A.S.; Bluhme, A.B.; Moreman, M.; Yu, M.; Costa, A.L.; Giovanni, B.; Ortelli, S.; Fransman, W.; Vogel, U. & Jensen, K.A. Dip coating of air purifier ceramic honeycombs with photocatalytic TiO2 nanoparticles: A case study for occupational exposure. Sci. Total Environ., 2018, 630, DOI: 10.1016/j.scitotenv.2018.02.316. (17) Göhler, D.; Nogowski, A.; Fiala, P. & Stintz, M. Nanoparticle release from nanocomposites due to mechanical treatment at two

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: XYZ. Selected video sequences (AVI) of aerosol propagation in tenfold playback speed; gender and activity averaged deposition curves for the human respiratory tract (mod. ICRP 66 model); measured and approximated size distributions; number- and mass-weighted deposition fractions for approximated size distribution of released particles for sanding and spraying; number/mass of inhaled particles for the farfield.

AUTHOR INFORMATION Corresponding Author * E-mail: [email protected]. Tel.: +49 351 46335176. Fax: +49 351 463-37058 (Germany)

Author Contributions The manuscript was written through contributions of all authors. Daniel Göhler and Ralf Gritzki performed study design, data acquisition, drafting of the manuscript and critical revision for important intellectual content of the manuscript. Propagation modelling was performed by Ralf Gritzki, while Daniel Göhler processed the relative modelling results in the context of release and exposure. Michael Stintz, Markus Rösler and Clemens Felsmann supervised the study, performed data interpretation and revised critically the manuscript according to their field of research.

ORCID Michael Stintz: Daniel Göhler:

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0000-0001-5239-058X 0000-0003-0769-9187

Notes The authors declare no competing financial interests.

ACKNOWLEDGMENT This work was supported financially by the German Paint Industry Association (VdL e.V., Frankfurt/Main, Germany, representing about 180 German companies). The authors wish to thank all involved members of the VdL e.V. for the suggestions and the discussions during the project.

ABBREVIATIONS AL, alveolar region of the human respiratory tract; ES, exposure scenario; FF, farfield; HA, head airways of the human respiratory tract; NF, nearfield; NVD, natural ventilation by door slit infiltration; NVW, natural ventilation by pivot-hung window; PS, propagation scenario; RS, release scenario; SAN; sanding, SPR, spraying; TB, tracheobronchial region of the human respiratory tract; TVS, technical ventilation system; VS, ventilation scenario; WIP, wiping

REFERENCES (1) Harper, S.; Wohlleben, W.; Doa, M.; Nowack, B.; Clancy, S.; Canady, R. & Maynard, A. Measuring Nanomaterial Release from Carbon Nanotube Composites: Review of the State of the Science. J. Phys.: Conf. Ser., 2015, 617 (1), DOI: 10.1088/17426596/617/1/012026. (2) Kuhlbusch, T.A.J.; Asbach, C.; Fissan, H.; Göhler, D. & Stintz, M. Nanoparticle exposure at nanotechnology workplaces: A review. Part. Fibre Toxicol., 2011, 8 (1), DOI: 10.1186/1743-8977-8-22.

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stages of the life-cycle. J. Phys.: Conf. Ser., 2013, 429 (1), DOI: 10.1088/1742-6596/429/1/012045. (18) Knopp, T.; Lube, G.; Gritzki, R. & Rösler, M. A near-wall strategy for buoyancy-affected turbulent flows using stabilized FEM with applications to indoor air flow simulation. Comput. Methods Appl. Mech. Engrg., 2005, 194 (36-38), DOI: 10.1016/j.cma.2004.10.003. (19) Perschk, A. Gebäude- und Anlagensimulation – „Ein Dresdner Modell“. Gesundheitsingenieur. Haustechnik – Bauphysik Umwelttechnik, 2010, 131 (4), 178–183. (20) Gritzki, R.; Richter, W. & Rösler, M. How to predict the air exchange efficiency for hybrid ventilation systems. Int. J. Vent., 2003, 1 (4), DOI: 10.1080/14733315.2003.11683642. (21) Klein, S.A.; Duffie, J.A. & Beckmann, W.A. TRNSYS - A Transient Simulation Program. ASHRAE Transactions, 1976, 82, 1, 623-633. (22) Gritzki, R. Bestimmung der Effektivität nutzerbedingter Fensterlüftung mit Hilfe numerischer Simulationsverfahren. Ph.D. Dissertation, Technische Universität Dresden, Germany, 2001. (23) EnEV 2009. German energy saving ordinance, 2009. (24) ISO 7730:2005. Ergonomics of the thermal environment – Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. (25) Göhler, D. & Stintz, M. Granulometric characterization of airborne particulate release during spray application of nanoparticledoped coatings. J. Nanopart. Res., 2014, 16 (8), DOI: 10.1007/s11051-014-2520-1.

(26) Vorbau, M.; Hillemann, L. & Stintz, M. Method for the characterization of the abrasion induced nanoparticle release into air from surface coatings. J. Aerosol Sci., 2009, 40 (3), DOI: 10.1016/j.jaerosci.2008.10.006. (27) Göhler, D.; Stintz, M. & Rommert, A. Im Lack und drum herum. Partikelfreisetzung beim Umgang mit nanostrukturierten Materialien. Farbe und Lack, 2016, 122 (3), 52-60. (28) Wildeboer, J. & Miller, D. Lüftungseffektivität als Qualitätskriterium für Quellluftsysteme – die Auswirkung des Luftaustauschwirkungsgrads auf die Höhe der Frischluftschicht. KI Luft- und Kältetechnik, 2006, 42 (10), 439-443. (29) Lewis, H.; Foster, A.; Mullan, B.; Cox, R. & Clark, R. Aerodynamics of the human microenvironment. The Lancet, 1969, 293 (7609), DOI: 10.1016/S0140-6736(69)92220-X. (30) Naseri, A.; Abouali, O. & Ahmadi, G. Effect of turbulent thermal plume on aspiration efficiency of micro-particles. Build. Environ., 2017, 118, DOI: 10.1016/j.buildenv.2017.03.018. (31) Hinds, W.C. Aerosol Technology - Properties, Behavior, and Measurement of airborne particles; John Wiley & Sons Inc., New York, 1999. (32) Cherrie, J.W. & Schneider, T. Validation of a New Method for Structured Subjective Assessment of Past Concentrations. Ann. Occup. Hyg., 1999, 43 (4), DOI: 10.1016/S0003-4878(99)00023-X.

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SYNOPSIS Exposure estimation based on modern calculation methods improves risk assessment and supports thus occupational safety and consumer protection.

TOC/ABSTRACT GRAPHIC (for Table of Content Use only)

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