Given the rise in products developed with the use of nanotechnology, it is unclear if this technology will lead to new or unidentified risks to human health.^(1,2) In particular, little is known about how to safely handle nanoparticles, or what, if any, immediate risks may arise from inhaling nanoparticles in an occupational environment.⁽³⁾ Furthermore, there is some uncertainty as to the best exposure metric, as well as a lack of proper equipment to measure them at their source of production.⁽²⁾ To address these limitations, more strategic and risk-related research is necessary to ensure that people working in nanotechnology industries are protected from adverse health effects caused by aerosolized nanopowders.

Excessive airborne dust levels in occupational settings are undesirable because: they can cause adverse health effects, can be costly to control or costly in terms of lost product, and can contaminate machinery and products.⁽⁴⁾ Thus, it is advantageous to have detailed information about the propensity of nanoparticles to become airborne if emitted as a fugitive dust – that is, their “dustiness” – to fully characterize the risks involved with their production. Dustiness is generally considered to be the propensity of a powder to produce airborne dust as a result of handling the powder, which includes storing, filling, conveying and mixing.^{(2, 5–8)} Therefore, a potential hazard of nanotechnology is related to the dustiness of the nanoparticle bulk powder when handled during the production of nanomaterials.⁽⁵⁾

As provided in previous papers addressing this subject,^(8–10) the identification of a powder’s dustiness may aid in the predication of occupational dust exposure and is therefore a meaningful health-risk attribute when assessing their overall risk to workers. For example, Shulte et al. advocate for the use of dustiness as a risk parameter when utilizing a control banding approach to manage exposures to engineered nanoparticles.⁽¹⁰⁾ Likewise, Schneider et al. suggest that dustiness tests can be translated to estimates of workplace exposures as part of a comprehensive conceptual model of nanoparticle exposures used as the basis of a nanoparticle risk assessment strategy.⁽¹¹⁾ However, a direct link between powder dustiness and worker exposure has been difficult to verify. As summarized by Liden,⁽⁸⁾ studies conducted to relate dustiness to worker exposure^(6,9,12,13) have shown a positive correlation between the two, but other factors such as control technologies and powder handling may often contribute more to overall exposure than the dustiness of the powder. Therefore Liden suggests that dustiness should be viewed as a risk factor contributing to the “source strength” of potential aerosol generation rather than as a surrogate for occupational exposure.

A number of factors will affect the dustiness of a powder. Pujara and Kildig discuss these properties and review the available literature on this topic. ⁽¹⁴⁾ They identify powder mass, bulk density, and moisture content, as well as particle shape, size and cohesion as the most important factors determining the dustiness of a powder, with size being the most influential. Boundy et al. add factors such as powder surface chemistry and prior handling, such as whether the powder was previously compressed or dried, as also affecting powder dustiness.⁽¹⁵⁾

Standard methods for determining the dustiness of powders have been developed in Europe, but not in the United States. For example, the German standard, DIN 55992-2⁽¹⁶⁾, describes the single-drop method to determine the dustiness index for pigments and fillers for quality control purposes. Whereas, the European standard, EN 15051⁽¹⁷⁾, utilizes a rotating drum or a continuous drop device to create a dust cloud. These methods are well described by Hamelmann and Schmidt^(5,7) and are briefly reviewed here.

The single-drop dustiness device is comprised of a vertical tube that generates dust by placing a sample on a shutter atop the tube then opening the shutter allowing the sample to drop through the tube into a chamber at the bottom. The resulting cloud of dust is measured over time with an aerosol photometer viewing the particle cloud in the chamber. Following DIN 55992-2, dustiness is indicated by computing the ratio of the integrated concentration from 16 – 30 s to the integrated concentration from 1 – 30 s. No explanation for the significance of the 16-second start value in the numerator integral is provided. However, Bach and Schmidt used a commercial version of this apparatus, the Palas Dustview (Palas GmbH, Karlsruhe, Germany), and chose the concentrations at 0.5 s and 30 s as representative of the inhalable and respirable fraction of dust, respectively.⁽¹⁸⁾ The device, therefore, provides a dustiness “characteristic” of the test powder since the concentration value obtained is not related to the mass of powder applied to the device.

The rotating drum apparatus, as the name suggests, supplies mechanical energy to a bulk sample with the use of a rotating drum. The Heubach dust meter, originally developed to determine the dustiness of pigments, is an example of a rotating drum dustiness tester that has been evaluated for use as a dustiness tester for powders of occupational health significance.^(18–20) With this device, a steady stream of air is supplied through the drum and exiting particles are collected on a filter or measured with a direct-reading instrument. Unlike the single-drop device, the rotating drum provides a dustiness “mass fraction” by calculating the ratio of the mass collected on the filter after 1 min to the mass of powder supplied to the device.⁽¹⁸⁾ If the aerosol sampling device has a size-selective inlet, then the dustiness mass fraction can be segregated between respirable and inhalable dust. This technique is employed by the rotating drum device described in EN 15051. However, use of that device requires between 300–600 g of bulk powder. Schneider and Jensen augmented the method so that only 8 g was needed by employing an Aerodynamic Particle Sizer (APS, TSI Inc., Shoreview, MN).⁽²¹⁾

EN 15051 also describes a continuous drop dustiness tester.⁽¹⁷⁾ Dust is continuously augured into a 1.1 m by 150 mm tube which meets an upward flow of air pulled at a rate 53 L min⁻¹. Sample inlets to measure the inhalable and respirable size fractions of dust generated are located 0.8 m up from the bottom of the tube. Only one study could be found that used this method⁽²²⁾ where a weak correlation was found between the dustiness values obtained with this method and that of the rotating drum.

This study was motivated by the need for a nanopowder dustiness measurement technique that utilized milligram quantities of test powder and could size-segregate particles in the dust cloud. The first criterion is related to the large expense of some nanopowders such as purified single-walled carbon nanotubes (~$250.00 US/gm). Of the available standard methods, only the single-drop device could achieve that goal. However, following DIN 55992-2 does not allow for either a verifiable aerosol size-segregation process or the calculation of a dustiness mass fraction useful for risk assessment efforts. Therefore, the objective of this study was to develop a single-drop tester that could meet those criteria.

Concentration pulses created by representative powders are given in Figure 2. The curves in Figure 2 are the average of counts made for each of the 3 trials performed for each powder for the size bin with a median diameter of 2.2 μm. The peak in the pulse occurred within 3 to 4 seconds after the valve was opened for all powder types, and, as shown in Figure 2, the pulse decayed to near 0 counts after 20 seconds. Photomicrographs of particles collected at the base of the vertical tube from representative powders are provided in Figure 3. In general, there was a wide range in particle sizes for all powder types but the majority of particles were > 1 μm. Likewise, at a magnification needed to see the full range of agglomerates present in the falling powder it was not possible to distinguish the individual primary nanoparticles. Those primary particles can be seen in Figure 4 where transmission electron micrographs of agglomerates from some of the smallest particles found from two different TiO₂ powders are shown.

The reproducibility of this method was evaluated by determining the coefficient of variation (CV) for respirable concentrations for each powder type. The average CV for all powders was 19.2%. The reagent TiO₂ and Cloisite 20A had the highest CV (>33%), and low CV’s were obtained when dropping the 10-nm Al₂O₃ and 50-nm Al₂O₃ (<9%).

Table II provides the count (CMAD) and mass median (MMAD) aerodynamic diameters, and the corresponding geometric standard deviation (GSD), averaged for the three trials performed with each dust type. The average size distributions of 3 trials at the peak concentration for four representative powders are shown are shown in Figure 5. The CMAD ranged from 1.46 – 2.40 μm. MMAD values were computed from the CMAD and GSD⁽²⁷⁾ and ranged from 3.48 – 31.11 μm, corresponding to ARD and 10-nm Al₂O₃, respectively. The variation in the CMAD over the time period of a pulse for representative powders is given in Figure 6. The time course shown in Figure 6 for each powder ended when counts shown in Figure 2 fell below 5 cm⁻³ at which point a size distribution could not be accurately determined. In general the CMAD remained relatively stable over the sample time. The aerosols produced by the falling powders did not create an exceedingly wide-range in sizes as evidenced by the relatively low GSD values which ranged from 1.63 – 2.30. As shown in Figure 5, the entire distribution of particle sizes produced in the LMDT occurred within the limits of the APS sizing range for most powder types.

D values for each powder type are reported in Table III and ranged from 3.9 for Cu to 121.4 for SiO₂. Table III also contains a column to indicate a dustiness classification for each dust type. The classification was based on that provided in EN 15051 for the rotating drum. An analysis of the four category names (“very low” to “high”) relative to the dustiness mass fraction limits for each revealed the application of an exponential increase of the form, limit = k exp(ln(5)x), where x is a value between 1 and 4 corresponding to the four categories and k has a value of 2. In a similar way, a k value of 0.8 was determined to adequately separate the D values obtained here into 4 classifications with limits of <4, 4 – 20, 20 – 100, and > 100 corresponding to categories of “very low”, “low”, “moderate”, and “high”, respectively.

The ANOVA analysis for respirable dustiness revealed a significant difference between powder types (p<0.001). Results from the Tukey’s procedure showed SiO₂ produced significantly higher concentrations than all other dusts after which there were overlapping associations between the remaining dust types (Table III). 5-nm TiO₂ and 21-nm TiO₂ were next highest but less than half of the value for SiO₂. Powders producing low respirable concentrations included Al₂O₃ whiskers and Cu.

The comparison of TiO₂ powders also demonstrated a decline in D with an increase in primary particle size. We were unable to find a manufacturer’s report on the reagent TiO₂ but the TEM photos of that dust (Figure 4b) demonstrate primary particles ranging in size between 50–250 nm. Likewise the 10-nm Al₂O₃ is dustier than the 50-nm Al₂O₃.

An analysis of the five factors that may best predict respirable revealed that mass density (ρ_m), log-transformed values of bulk density (ρ_b), and the dichotomous variable to indicate whether the powder had nanosized particles (N) produced significant coefficients with r² = 0.80 The resulting regression equation for predicting respirable dustiness is: Eq. 6D=18.97-17.723lnρb-6.818ρm+30.950N

A single-drop, low-mass powder dustiness tester that utilizes 15 mg of powder per test was capable of distinguishing the respirable dustiness of various powder types. The mass of powder applied to each test was sufficient to provide measureable counts in each of the APS size channels without exceeding its count/channel limit (1,000/cm³) (although a commercially-available diluter is available for the APS). The use of an APS was a departure from the specifications for a single-drop dustiness tester described in DIN 55992-2. However, using a particle counter that sized by aerodynamic diameter permitted calculations to determine the mass of powder collected along with the size segregation needed to estimate a respirable dustiness mass fraction. By contrast, DIN 55992-2 calls for the use of an aerosol photometer which can indicate mass concentration but cannot size segregate. Another difference between this method and the standard is that the APS requires active sampling whereas the standard dictates the use of a passive photometer. Furthermore, unlike other dustiness testing methods, a measure of the inhalable dustiness cannot be obtained with the LMDT method. Given the emphasis on its use for expensive nanopowders, which are primarily inorganic compounds that primarily effect the pulmonary region, a measure of respirable dustiness is the most relevant of the two for nanopowders.

Using the method described here the powder settling down the tube enters a cross-flow volume where particles that can be captured in the sample flow stream to the APS are measured whereas all others fall through to the collecting jar. Therefore, the results obtained in this study are dependent on the APS sample flow rate, the aspiration efficiency of the entry nozzle to the APS, the cross-fitting geometry that will influence particle mixing and air velocity profiles, and the counting efficiency of the APS. Other researchers that used an APS as part of the sample collection of a dustiness apparatus^(21–29) also point out that the counting efficiency of an APS decreases for particles below 1.0 μm based on studies such as the one by Volcken and Peters.⁽³⁰⁾ However, the APS counting efficiency is higher for the dry particles produced during a dustiness test than for droplets. No correction to the counts obtained from the APS were made to compensate for particle count efficiency.

A Reynolds number in the 6.35-mm I.D. air inlet tube of 1113 for the sample flow rate of the APS indicates laminar flow and thus suggests that the inlet flow from the HEPA filter does not produce a turbulent jet to disturb and mix falling particles as they pass through the sampling volume. Particle capture into the exhaust tube to the APS is therefore a function of the terminal settling velocity of the particles relative to the horizontal velocity created in the sampling volume as well as the turning radius provided to deflect particles into the exit tube.

The aerosol size distribution data given in Table II indicates that the distribution of particles produced by this dustiness testing method produced many micrometer-sized particles from nanopowders due to the agglomeration of the primary particles. As particle size decreases, attractive forces such as van der Waals, electrostatic and covalent, increasingly favor the formation of agglomerates.⁽²⁸⁾ This suggests the creation of a biomodal distribution consisting of small agglomerates at the low end, and large unseparated clusters at the high end of the distribution. A bimodal distribution of this sort has been reported by researchers who used both an APS and a scanning mobility particle sizer to measure the size distribution between 10–10,000 nm exiting a rotating drum tester.^(21,29) However, the standard index of dustiness reported in other studies is on a mass-per-mass basis. Therefore, there may be an interest in determining the counts of particles in the nanometer range but their mass will be negligible compared to micrometer-sized particles. Thus an APS, which measures the heavier micrometer-sized particles, is sufficient for determining the dustiness mass fraction.

An APS would not be an adequate measurement device if the dustiness tester reduced a nanopowder to an aerosol with an entire distribution of diameters below 1 μm; in which case the APS would not adequately detect the many resulting particles that could contribute to a respirable mass. For this to occur, the tester would have to impart enough energy to reduce the bulk powder to nano-sized agglomerates. However, Liden suggests that dustiness tester should not impart enough energy to “divide the primary particles.”⁽⁸⁾ For a settling dust, the most important energy addition would be that from turbulent air flowing around the powder as it fell. As explained by sources on this subject,^(28,31) an analysis of turbulence is complicated by the large differences in particle sizes and that the falling powder will achieve, each with different settling velocities, a bulk movement with maximum velocity at the center and less in the outer region. However, after applying ten different nanopowders to the device, all produced distributions with median diameters in the micrometer range. Therefore, it may be concluded that the settling bolus of powder in the tester tube does not produce enough energy to break up the powder so that the majority of the particles are less than the lower limit of detection of the APS.

The use of an APS to determine particle mass as developed here was based on the assumption that the particles measured are spheres, and those spheres have a density equivalent to the mass density of the compound comprising the powder. The assumption that the particles are spheres is likely most valid for the nanopowders which tend to form large spherical aggregates as shown in Figure 3, whereas the ARD dust, for example, is composed largely of individual irregularly shaped particles. However, the powders comprised of larger, individual particles will have a density close to the mass density of their parent compound whereas the nano-agglomerates will necessarily have void spaces that reduce their density. Although not tested, it may be assumed that this method will then overestimate the dustiness of nanopowders.

Interestingly, density also affects dustiness in a counter-intuitive way. Given counts in bins separated by aerodynamic diameter, to determine the mass of those counts density is used to adjust the size of the particles to their true diameter and then their volume and, ultimately, mass is calculated. High density particles result in smaller physical diameters to have the same settling velocity, which significantly reduces a particle’s volume and mass by the cube of diameter. Therefore, for the same number of counts based on aerodynamic diameter made by the APS, higher density particles will result in lower mass and therefore lower dustiness than particles with low density.

In general, our results demonstrated that powders consisting of granular nanoparticles are more dusty than powders with fibrous nanoparticles or powders containing granular particles that are greater than 100 nm. Smaller primary particles are expected to develop smaller agglomerates which will settle more slowly and therefore are more easily captured in the sample stream of the APS. When using a rotating drum, Plinke et al. found a strong positive correlation between particle diameter and dustiness.⁽²⁰⁾ Fibrous nanoparticles, however, are known to develop tightly bound “bird nests” which result, for example, in the difficulty researchers have experienced when trying to generate a carbon nanotube aerosol from the bulk powder.^(32,33) Conversely, the amorphous SiO₂ nanopowder tested visually exhibited a morphology that was light and “fluffy” that readily de-aggregated as it fell through the tube contributing to its exceptionally large dustiness mass fraction.

The majority of other studies conducted to obtain a powder dustiness mass fraction (D) used a rotating drum device.^{(18,21,22,29)} For example, Tsai et al. obtained a respirable D of 15 mg/kg for the same 21-nm TiO₂ used in this study, which compares to a 45.3 mg/kg using the LMDT.⁽²⁹⁾ Likewise, the EN 15051 dustiness method provides a respirable D of 140 mg/kg for bentonite when using the rotating drum and 170 mg/kg when using the continuous drop method, which can be compared to the Nanofil^® 5 respirable D value of 29 mg/kg.⁽¹⁷⁾ Despite differences in the D value obtained, a “moderate” risk category was applied to this powder type via both EN 15051 methods and this LMDT method. Future work will involve a comparison of results obtained with the LMDT with those given in Annex D of EN 15051 for a suite of seven powder types. Bach and Schmidt describe results from a comparison of two alternative dustiness testers that followed the guidelines provided in Annex D.⁽¹⁸⁾ They found it difficult to derive the same classifications as those given in EN 15051 for all powder types when using the alternative methods but noted that there was a higher equivalency for the respirable particle fraction than for the inhalable fraction. Pensis et al. also compared the rotating drum with the continuous drop tester and found that the respirable mass fractions were comparable, but the continuous-drop tester overestimated the inhalable mass fraction 3 to 17 times higher compared to the rotating drum depending on powder type.⁽²²⁾

A low mass dustiness tester (LMDT) designed to report the respirable dustiness from milligram quantities of sample powder was tested with a variety of powder. The LMDT incorporated an APS that permitted a calculation of the respirable dustiness mass fraction from counts in particle size channels delineated by aerodynamic diameter. When applied to a variety of powder types, those consisting of primary particles that were granular and with diameters in the nanoparticle range demonstrated the highest dustiness. The respirable dustiness of SiO₂, a light fluffy powder, was significantly higher than all others. However, many nanopowders were not significantly more dusty than powders containing larger primary particles. A multivariable regression analysis was developed that can be used to estimate the respirable dustiness mass fraction of these powders given the powder’s bulk and mass densities and whether it contains nano-sized, granular primary particles. Furthermore, a classification scheme similar to that provided in a standard method for dustiness was developed. The regression model and classification scheme may be useful for efforts to incorporate dustiness as a factor in risk assessment models for the powder and nanopowder industries.