# Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Materials and Work Environment

^{3}(Figure 1a,b). Filling of big bags (1200 kg) was carried out through a cylindrical opening. Both filling lines were not enclosed but were equipped with a LEV, with a theoretical flow rate of 18,000 m

^{3}h

^{−1}(Q

_{LEV}), and a subsequent bag filter. Filling line L had not a seal system for attaching the bags to the feed funnel whereas a partially closed seal system was in place at filling line M to reduce release of airborne dust. In both cases, the feed funnel was placed inside the bags, with an open-air drop of 0. The maximum material drop height inside the bags was 1.3 m from the feed to the bottom of the bags in both filling lines. Filling lines L and M were not operated at the same time but diesel-powered forklifts were used to move the filled bags to the storage area and occasionally other activities were performed in the plant.

^{3}, contained filling line H where filling of small bags (20–25 kg) was carried out through a lateral cylindrical opening (Figure 1c). The maximum material drop height was 0.6 m. The filling line was not enclosed but during filling the bags were sealed to the feed funnel equipped with a LEV system (Q

_{LEV}) (flow rate of 2400 m

^{3}h

^{−1}) with a subsequent bag filter.

_{GV}). Indoor air velocities were measured, and used to calculate flows (m

^{3}min

^{−1}) and the total general ACH for each day (see Supplementary Table S1).

#### 2.2. Aerosol Measurements

^{−1}sample flow rate were used. Description of all instrumentation used can be found in Ribalta et al. [30], and the uncertainties of the portable instruments are reported in Viana et al. [31] and Fonseca et al. [32].

#### 2.3. Dustiness

^{−}

^{1}) as well as categorical ranking of the powders according to EN 15051 classification are presented in Table 1 and described in detail in the Supplementary Section S2 and Supplementary Table S2.

^{−}

^{1}[33]. Sampling heads for inhalable (designed by Institut für Gefahrstoff-Forschung—IGF) (W

_{I}) and respirable (FSP-2, BGIA) (W

_{R}) fractions are located slightly above the discharge position of the material. Samples for gravimetric measurements of inhalable and respirable fractions were collected on cellulose thimbles, single thickness, 10 × 50 mm, and PVC filters of 37 mm Ø and 5 µm of porosity. Total material drop height during the test was approximately 1.2 m.

^{−}

^{1}. The dust sampling system consists of two sections of selective foam per particle size (one metal coated PE foam of 20 ppi and one metal coated PE foam 80 ppi) followed by a glass fibre filter, to gravimetrically analyse inhalable (W

_{I}), thoracic (W

_{T}) (data not shown) and respirable (W

_{R}) fractions.

#### 2.4. One- and Two-Box Models

#### 2.4.1. Emission Source Characterization and Parametrization

^{−}

^{1}(Table 1), H is the handling energy factor for the process, dM(t)/dt (kg min

^{−}

^{1}) is the mass flow of the material (Table 1), LC

_{bag}is the local control reduction factor due to the presence of the bag and attachment to the feed funnel, and LC

_{LEV}is the reduction due to the LEV effect. As efficiency reductions due to the enclosure of the bags could not be experimentally determined, literature values were used. Enclosures have been reported to reduce emissions from 10% up to more than 90% [25,26,27]. Additionally, several search combinations were made in the Exposure Control Efficacy Library (ECEL v3.0) (data are shown in Supplementary Section S3) and results were used to select the different reduction efficacies applied in the modelling. For bagging and pouring processes “containment without ventilation” had a reported efficacy of 30–85% in the ECEL library (Supplementary Figures S1 and S2). On the other hand, for general processes “low and medium level containment” had reported efficacies mainly between 35–75% (with median approximately at 65%) and 30–100% (with median approximately at 95%), respectively (Supplementary Figures S3 and S4). Based on these values, the reduction in particle emissions due to the effect of the bag (LC

_{bag}) were chosen as 70, 80 and 90% reduction for filling line L, M and H, respectively (Table 1). These reduction percentages were introduced in Equation (1) as the LC

_{bag}parameter. Similarly, for the LEV effect, no experimentally determined reduction values could be obtained. Thus, several reduction efficiencies were tested by using the OAT method in order to determine the impact on the model output (see Section 2.5). For bagging and pouring processes, “fixed capturing hoods” have reported efficacies between 50–90% in the ECEL library (see Supplementary Figures S5 and S6). Therefore, reduction values tested in the modelling were 50, 70, 80 and 90%. There are several standardized DI methods available, which intend to resemble different processes and activities, and thus provide different DI values. For modelling, it is advised to use the method, which most closely resembles the process under study. However, good dustiness/exposure correlations have been found during pouring of powders [25] when using both the CD and the RD dustiness methods. Thus, effects on modelling performance when using the CD and the RD, was studied. The handling energy factor was assumed to be 1 for filling in lines L and M, where bags of 1200 kg where packed, and 0.5 for filling line H, where small bags of 25 kg were packed (Table 1).

#### 2.4.2. One-Box Model

^{−}

^{1}) is the emission source, Q (m

^{3}min

^{−}

^{1}) is the total air flow including air flow due to general ventilation (Q

_{GV}) and LEV (Q

_{LEV}), ACH (h

^{−}

^{1}) is the air changes per h, V (m

^{3}) is the box volume, C is the (inside the box and outgoing) concentration and C

_{0}is the initial and incoming concentration.

#### 2.4.3. Two-Box Model

- −
- Mass balance in the NF:

- −
- Mass balance in the FF:

^{−}

^{1}) is the emission source in the NF, C

_{NF}and C

_{FF}are NF and FF concentrations, V

_{NF}and V

_{FF}(m

^{3}) is the volume in NF and FF, β is the air flow between NF and FF (m

^{3}min

^{−}

^{1}), and β

_{i}is the air flow between NF and FF including (Q

_{LEV}) air flow due to LEV (m

^{3}min

^{−}

^{1}).

#### 2.5. Model Parametrization and Evaluation

_{LEV}) and the inter-zonal (NF-FF) flow rate, β.

_{i}) consists of both β and Q

_{LEV}, complicating the estimation of the existing β using air velocity measurements. Literature reported β values for several indoor environments range between 0.24–30 m

^{3}min

^{−}

^{1}[7,8,10,18,35], with average values around 5 m

^{3}min

^{−}

^{1}. Therefore, 5 m

^{3}min

^{−}

^{1}was considered most likely β value and 0.25, 0.5, 1, 2.5, and 10 m

^{3}min

^{−}

^{1}were tested. Flow values from FF to NF due to LEV effect (Q

_{LEV}) were fixed at 10 and 5 m

^{3}min

^{−}

^{1}for filling lines L, M and H, respectively.

^{2}and Spearman correlation coefficients were calculated (criteria; >0.6) as well as the percentage of measured values exceeding modelled values (criteria; <10%). Moreover, the descriptive statistical mean absolute error (MAE) was calculated.

## 3. Results and Discussion

_{LEV}and β). Monitored WA measurements were compared to one-box and two-box FF model results as monitoring instruments were placed at 2 to 2.5 m from the emission source, therefore outside of the applied model limits of the NF. Modelling performance was assessed by considering dustiness method used as input for emission source, mass fraction modelled (inhalable and respirable) and type of model (one- and two-box). In addition, the effect of background concentrations, LEV efficacy reduction and β value were studied. Modelled concentrations and ratios of modelled/measured concentrations are given in Table 2. In addition, linear regression and Spearman correlation coefficient, as well as the statistical descriptors difference, absolute difference and mean absolute error (MAE) are provided in Supplementary Section S5, Tables S3 and S4, and Figures S7 and S8, respectively.

#### 3.1. Dustiness Method and Modelled Exposure to Inhalable and Respirable Dust

^{−3}, respectively with 46.7% and 86.7% of measured concentrations estimated with ranges between 0.5 and 5 with the one- and two-box models (data not shown). These observations show the need to continue studying how different dustiness test methods can be applied for modelling of powder handling scenarios and the importance of correct parametrization of the H factor, which is key for obtaining accurate modelling results, not only for the different dustiness methods but also fractions.

#### 3.2. Model Performance Comparison between One- and Two-Box Models

^{2}and Spearman correlation coefficient were calculated (Supplementary Figures S7 and S8). The calculated R

^{2}between measured and modelled concentrations with the one- and two-box models was <0.18 for the respirable concentrations, whereas Spearman correlation ranged from −0.20 to 0.24. Conversely, for the inhalable mass fraction, R

^{2}of 0.50 and 0.44 were obtained for the one- and two-box models, respectively when using the CD DI, and R

^{2}< 0.28 was observed when RD DI was used. Spearman correlations ranged from −0.11 to 0.25. These results are far from the criteria proposed in Fransman et al. [37] of a Spearman correlation > 0.6. However, it is important to keep in mind that (1) a low number of data points was used (<20), (2) the data was clustered, and (3) the concentration range is limited and not widely spread (See Supplementary Figures S7 and S8). Thus, in this specific case, care should be taken when interpreting these results as, if only looking at the R

^{2}and Spearman correlations one could have the impression that modelled concentrations for the inhalable fraction are in better agreement with measured concentrations than for the respirable fraction, which is not true. This shows that when assessing exposure models performance, it is highly relevant to use data sets, which are relatively large and with widely spread data points across the possible range.

#### 3.3. Effect of Background Concentrations on Modelled Respirable Mass

#### 3.4. Effect of LEV Reduction on Modelled Respirable Concentrations

_{bag}real value is probably higher than the selected (90%) and thus the better fit of the modelled concentrations for Feldspar 2 for which unexpected events occurred during filling (e.g., broken bags during filling). The one- and two-box models are developed for constant and cyclic emissions, which does not exactly correspond with the type of scenario in filling of Feldspar 2 in line H. Thus, this shows the relevance to consider also likely accidents in the modelling approach.

#### 3.5. Effect of Inter-Zonal Flows (β) on Modelled Respirable Concentrations

^{3}min

^{−1}underestimated monitored concentrations in 82 and 88% of the cases when using RD and CD DI. Conversely, β values of 2.5–10 m

^{3}min

^{−1}accurately estimated measured concentrations on 48–55% and slightly overestimated 27–39% of the cases (Figure 6). For filling line H, as previously described for LEV variations, high intra-filling line variability was observed, with Feldspar 2 ratio of modelled/measured concentrations showing a similar behaviour to materials in lines L and M, and Kaolin 2 ratios highly overestimating independently of the β values used (Figure 6).

^{3}min

^{−1}were used, and accurately estimated exposures when using β values of 5 and 10 m

^{3}min

^{−1}. Conversely, monitored concentrations for Clay 2 and Kaolin 1 were accurately and precisely estimated when using β 1–5 m

^{3}min

^{−1}(Figure 6a), with ratios modelled/measured between 0.5–1.9 and 0.9–1.6 for Clay 2 and Kaolin 1, respectively. In filling line M, and for both materials, most accurate values were obtained when using β values from 1–5 m

^{3}min

^{−1}(Figure 6b). Conversely, in filling line H, Feldspar 2 monitored concentrations were predicted accurately inside the 0.5–2 benchmark for β values 2.5–10 m

^{3}min

^{−1}, whereas for Kaolin 2, ratios modelled/measured were always >5 for those β values (Figure 6c).

^{3}min

^{−1}and a lower intra-filling line response to β value changes was observed compared to CD modelling results (Figure 6d). This effect was also observed, although less strong, for filling line M (Figure 6e). Finally, for filling line H, again high variability was observed between materials, with Feldspar 2 showing more accurate results with β values of 2.5–10 m

^{3}min

^{−1}whereas for Kaolin 2 with β values 0.5–2.5 m

^{3}min

^{−1}(Figure 6f).

^{3}min

^{−1}) should not lead to large differences on modelling outcomes. Strong effects on modelling outcomes when using different values of β have been previously reported for different environments such as industrial scenarios, chamber experiments or medical sites [10,11,13,43]. Therefore, efforts on measuring inter-zonal airflows for modelling are paramount, even though their exact characterisation is complex.

## 4. Conclusions

^{3}), modelled concentrations were more accurate and precise when using the two-box model than the one-box model, with one-box generally overestimating worker area monitored concentrations. The one-box model estimated 53% of the cases within the 0.5–5 ratio whereas the percentage increased up to 87% for the two-box model. The one-box model may be useful for simple scenarios with good air mixing, but for complex scenarios including enclosures and LEV systems, the use of a two-box model may provide more accurate and precise results. In summary, both, the one- and two-box models, when using DI as input parameter for the source emission characterization, were seen to accurately and precisely estimate respirable mass concentrations for different scenarios in a quite robust way if adequate parametrization for the given scenarios were applied. However, further understanding on how to scale dustiness to process by means of the H factor is needed. Additionally, studies are needed to identify most appropriate values for the determinant model parameters to improve the general model performance. Finally, it was shown how the use of the one- and two-box models for unexpected events should be conducted with care, by clearly identifying and determining specific emission rates for the different conditions or the results could lead to impaired decision making.

## Supplementary Materials

^{3}min

^{−1}) and corresponding air changes per hour (ACH h

^{−1}), Table S2: ranking categories for continuous drop (CD) and rotating drum (RD) dustiness methods according to the EN 15051, Table S3: Difference between modelled and measured inhalable concentration (Diff.), absolute difference (Abs. diff.) and mean absolute error (MAE) for each model run. C1: Clay 1; C2: Clay 2; K1: Kaolin 1; F1: Feldspar 1; Q1: Quarts 1; F2: Feldspar 2; K2: Kaolin 2, Table S4: Difference between modelled and measured respirable concentration (Diff.), absolute difference (Abs. diff.) and mean absolute error (MAE) for each model run. C1: Clay 1; C2: Clay 2; K1: Kaolin 1; F1: Feldspar 1; Q1: Quarts 1; F2: Feldspar 2; K2: Kaolin 2, Figure S1: Search selection of risk management measures (RMM) percentage reductions for tasks (bagging/dumping/filling, packing/bottling, transfer powders, transfer during packing and pouring of powders) in ECEL. Source: screenshot from https://diamonds.tno.nl/#ecel, accessed on 4 June 2021, Figure S2: Overview of percentages of reduction due to different risk management measures (RMM) on process selection from Figure S1. Source: screenshot from https://diamonds.tno.nl/#ecel, accessed on 4 June 2021, Figure S3: Search selection for risk management measures (RMM) percentage reductions due to isolation/segregation, containment without ventilation, low-level containment, not specified segregation and medium level containment on general tasks in ECEL. Source: screenshot from https://diamonds.tno.nl/#ecel, accessed on 4 June 2021, Figure S4: Overview of percentages of reduction due to due to isolation/segregation, containment without ventilation, low-level containment, not specified segregation and medium level containment on general processes. RMM: risk management measures. Source: screenshot from https://diamonds.tno.nl/#ecel, accessed on 4 June 2021, Figure S5: Search selection of “fixed capturing hoods” risk management measure on bagging, dumping, filling, packing/bottling, transfer of powders, transfer during packing and pouring of powders in ECEL. Source: screenshot from https://diamonds.tno.nl/#ecel, accessed on 4 June 2021, Figure S6: Overview of percentages of reduction due to due to “fixed capturing hoods” on process selection from Figure S5. RMM: risk management measures. Source: screenshot from https://diamonds.tno.nl/#ecel, accessed on 4 June 2021, Figure S7: Linear regression, R2 and Spearman’s correlation coefficient (c.c.) for respirable modelled concentration and measured concentrations when using (a) one-box model and CD DI, (b) one-box model and RD DI, (c) two-box model and CD DI, and (d) two-box model and RD DI, Figure S8: Linear regression, R2 and Spearman’s correlation coefficient (c.c.) for inhalable modelled concentration and measured concentrations when using (a) one-box model and CD DI, (b) one-box model and RD DI, (c) two-box model and CD DI, and (d) two-box model and RD DI.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Modelling layout for Industrial Setting #1 containing filling lines L (

**a**) and M (

**b**), and for Industrial Setting #2 containing filling line H (

**c**).

**Figure 2.**Vertical boxplot showing all materials one- (room) and two-box (FF) ratio modelled/measured (

**a**) inhalable concentrations using CD DI as input, (

**b**) using RD DI as input for the one-box model, (

**c**) using CD DI as input for the two-box model, and (

**d**) using RD DI as input for the two-box model. Ratios 1, 2 and 5 are marked as reference (red solid line, red dashed line and black dashed lines, respectively). The solid coloured line within the box indicates the median value, and the limits of the box indicate min and max values. Coloured dots represent the individual replicate values. Modelling parameters: LEV and β fixed at 70% and 5 m

^{3}min

^{−1}, respectively.

**Figure 3.**Vertical boxplot for ratio modelled/measured respirable concentrations for all materials without including incoming concentrations and including outdoor concentrations when using (

**a**) CD as input for the one-box model, (

**b**) RD as input for the one-box model, (

**c**) CD as input for the two-box model and (

**d**) RD as input for the two-box model. Ratios 1, 2 and 5 are marked as reference (red solid line, red dashed line and black dashed line, respectively). Solid coloured line within the box indicates the median value, and the limits of the box indicate min and max values. Coloured dots represent the individual replicate values. Modelling parameters: LEV and β fixed at 70% and 5 m

^{3}min

^{−1}, respectively.

**Figure 4.**Vertical boxplot for ratio one-box modelled/measured respirable concentrations for all materials when using CD DI (

**a**–

**c**) and RD DI (

**d**–

**f**) as input parameter for source characterisation. Ratios 1, 2 and 5 are marked as reference (red solid line, red dashed line and black dashed lines, respectively). Solid coloured line within the box indicates the median value, the limits of the box indicate min and max values. Coloured dots represent the individual replicate values. Modelling parameters: β

_{i}fixed at 5 m

^{3}min

^{−1}.

**Figure 5.**Vertical boxplot for ratio two-box modelled/measured respirable concentrations for all materials when using CD DI (

**a**–

**c**) and RD DI (

**d**–

**f**) as input parameter for source characterisation. Ratios 1, 2 and 5 are marked as reference (red solid line, red dashed line and black dashed lines, respectively). Solid coloured line within the box indicates the median value, and the limits of the box indicate min and max values. Coloured dots represent the individual replicate values. Modelling parameters: β

_{i}fixed at 5 m

^{3}min

^{−1}.

**Figure 6.**Vertical boxplot for ratio modelled/measured respirable concentrations for all materials when using CD DI (

**a**–

**c**) and RD DI (

**d**–

**f**) using RD DI as input parameter for source characterisation. Ratios 1, 2 and 5 are marked as reference (red solid line, red dashed line and black dashed lines, respectively). Solid coloured line within the box indicates the median value. Modelling parameters: LEV and fixed at 70%.

**Table 1.**Variables used in Equation 1. Dustiness index (CD and RD) for material, calculated dM/dt, H factor and LC due to the effect of enclosure of the bag.

Filling Line | Material | Equation (1) Variables (Unit) | ||||||
---|---|---|---|---|---|---|---|---|

Continuous Drop DI (mg kg^{−1}) | Rotating Drum DI (mg kg^{−1}) | dM/dt (kg min ^{−1}) | H (−) | LC_{bag} (−) | ||||

W_{I} ± SD | W_{R} ± SD | W_{I} ± SD | W_{R} ± SD | |||||

L | Clay 1 | 1733 * | 6 * | 96 * | 13 * | 800 | 1 | 0.3 (70% reduction) |

Clay 2 | 5170 ** | 16 * | 192 * | 20 * | 600 | 1 | ||

Kaolin 1 | 18,886 *** | 44* | 353 * | 18 * | 850 | 1 | ||

M | Feldspar 1 | 10,246 ** | 59 * | 455 * | 73 ** | 530 | 1 | 0.2 (80% reduction) |

Quartz 1 | 8891 ** | 43 * | 480 * | 75 ** | 550 | 1 | ||

H | Feldspar 2 | 9651 ** | 77 ** | 505 * | 34 * | 100–250 | 0.5 | 0.1 (90% reduction) |

Kaolin 2 | 12,325 ** | 104 ** | 721 ** | 80 ** | 0.5 |

**Table 2.**Measured stationary concentrations at the worker area, and one- and two-box modelling results for inhalable and respirable mass fractions (mg m

^{−3}), and for CD and RD dustiness index. Ratio modelled/measured shown in brackets. Modelling parameters: LEV and β fixed at 70% and 5 m

^{3}min

^{−1}, respectively. Rn: number of replicates for each material. * Not considering unexpected events.

Filling Line L | Filling Line M | Filling Line H | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Mass Fraction | Model | DI Method | Clay 1 | Clay 2 | Kaolin 1 | Feldspar 1 | Quartz 1 | Feldspar 2 | Kaolin 2 | ||||||||

R1 | R2 | R3 | R1 | R2 | R1 | R2 | R1 | R2 | R1 | R2 | R1 | R2 | R1 | R2 | |||

Inhalable | One-box | CD | 92.1 (50) | 94.5 (56) | 93.4 (68) | 263.6 (132) | 221.2 (143) | 1117 (422) | 1266.5 (269) | 198.4 (58) | 285.1 (201) | 155.2 (91) | 181.8 (158) | 170.9 (40/174 *) | 168.7 (107/129 *) | 217.9 (263) | 215.3 (761) |

RD | 5.1 (2.8) | 5.2 (3.1) | 5.2 (3.8) | 9.8 (4.9) | 8.2 (5.3) | 20.9 (7.9) | 23.7 (5.0) | 8.8 (2.6) | 12.7 (9.0) | 8.3 (4.8) | 9.7 (8.4) | 8.9 (2.1/9.1 *) | 8.8 (5.6/6.8 *) | 12.8 (15) | 12.6 (45) | ||

Two-box | CD | 30.9 (17) | 31.5 (19) | 31.3 (23) | 86.1 (43) | 72.3 (47) | 362.9 (137) | 410.1 (87) | 65.9 (19) | 94.6 (67) | 52.7 (31) | 61.8 (54) | 84.8 (20/86.5 *) | 83.2 (53/64.0 *) | 98.3 (118) | 96.8 (342) | |

RD | 1.7 (0.9) | 1.8 (1.0) | 1.7 (1.3) | 3.2 (1.6) | 2.7 (1.7) | 6.8 (2.6) | 7.7 (1.6) | 2.9 (0.9) | 4.2 (3.0) | 2.8 (1.6) | 3.3 (2.9) | 4.4 (1.0/4.5 *) | 4.4 (2.8/3.4 *) | 5.8 (6.9) | 5.7 (20) | ||

Measured | - | 1.85 | 1.70 | 1.37 | 2.00 | 1.54 | 2.65 | 4.71 | 3.42 | 1.41 | 1.71 | 1.15 | 4.26/0.98 * | 1.57/1.30 * | 0.83 | 0.28 | |

Respirable | One-box | CD | 0.32 (2.2) | 0.33 (2.0) | 0.32 (2.0) | 0.82 (5.9) | 0.68 (5.0) | 2.6 (11) | 3.0 (4.8) | 1.1 (2.0) | 1.6 (14) | 0.76 (5.0) | 0.89 (4.3) | 1.4 (1.9/8.0 *) | 1.4 (4.7/4.7 *) | 1.8 (13) | 1.8 (34) |

RD | 0.69 (4.8) | 0.71 (4.3) | 0.70 (4.3) | 1.0 (7.3) | 0.86 (6.4) | 1.1 (4.4) | 1.2 (2.0) | 1.4 (2.4) | 2.0 (17) | 1.3 (8.5) | 1.5 (7.3) | 0.60 (0.9/3.5 *) | 0.59 (2.1/2.0 *) | 1.4 (10) | 1.4 (26) | ||

Two-box | CD | 0.11 (0.8) | 0.11 (0.7) | 0.11 (0.7) | 0.27 (1.9) | 0.22 (1.6) | 0.85 (3.5) | 0.96 (1.6) | 0.38 (0.7) | 0.55 (4.6) | 0.26 (1.7) | 0.30 (1.4) | 0.68 (1.0/4.0 *) | 0.66 (2.3/2.3 *) | 0.83 (6.1) | 0.82 (15) | |

RD | 0.23 (1.6) | 0.24 (1.4) | 0.24 (1.5) | 0.33 (2.4) | 0.28 (2.1) | 0.35 (1.4) | 0.39 (0.6) | 0.47 (0.8) | 0.67 (5.6) | 0.44 (2.9) | 0.52 (2.5) | 0.30 (0.4/1.8 *) | 0.29 (1.0/1.0 *) | 0.64 (4.7) | 0.63 (12) | ||

Measured | - | 0.14 | 0.17 | 0.16 | 0.14 | 0.14 | 0.24 | 0.61 | 0.58 | 0.12 | 0.15 | 0.21 | 0.70/0.17 * | 0.29/0.26 * | 0.14 | 0.05 |

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## Share and Cite

**MDPI and ACS Style**

Ribalta, C.; López-Lilao, A.; Fonseca, A.S.; Jensen, A.C.Ø.; Jensen, K.A.; Monfort, E.; Viana, M.
Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale. *Toxics* **2021**, *9*, 201.
https://doi.org/10.3390/toxics9090201

**AMA Style**

Ribalta C, López-Lilao A, Fonseca AS, Jensen ACØ, Jensen KA, Monfort E, Viana M.
Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale. *Toxics*. 2021; 9(9):201.
https://doi.org/10.3390/toxics9090201

**Chicago/Turabian Style**

Ribalta, Carla, Ana López-Lilao, Ana Sofia Fonseca, Alexander Christian Østerskov Jensen, Keld Alstrup Jensen, Eliseo Monfort, and Mar Viana.
2021. "Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale" *Toxics* 9, no. 9: 201.
https://doi.org/10.3390/toxics9090201