Quantitative 3D Shape Description of Dust Particles from Treated

Article pubs.acs.org/est

Quantitative 3D Shape Description of Dust Particles from Treated Seeds by Means of X‑ray Micro-CT Wouter Devarrewaere,*,† Dieter Foqué,‡ Udo Heimbach,§ Dennis Cantre,† Bart Nicolai,† David Nuyttens,‡ and Pieter Verboven† †

BIOSYST-MeBioS, KU Leuven, Willem de Croylaan 42, 3001 Leuven, Belgium Agricultural Engineering, Technology and Food Science Unit, Institute for Agricultural and Fisheries Research (ILVO), 9820 Merelbeke, Belgium § Institute for Plant Protection in Field Crops and Grassland, JKI, 38104 Braunschweig, Germany ‡

S Supporting Information *

ABSTRACT: Crop seeds are often treated with pesticides before planting. Pesticide-laden dust particles can be abraded from the seed coating during planting and expelled into the environment, damaging nontarget organisms. Drift of these dust particles depends on their size, shape and density. In this work, we used X-ray micro-CT to examine the size, shape (sphericity) and porosity of dust particles from treated seeds of various crops. The dust properties quantified in this work were very variable in different crops. This variability may be a result of seed morphology, seed batch, treatment composition, treatment technology, seed cleaning or an interaction of these factors. The intraparticle porosity of seed treatment dust particles varied from 0.02 to 0.51 according to the crop and generally increased with particle size. Calculated settling velocities demonstrated that accounting for particle shape and porosity is important in drift studies. For example, the settling velocity of dust particles with an equivalent diameter of 200 μm may vary between 0.1 and 1.2 m s−1, depending on their shape and density. Our analysis shows that in a wind velocity of 5 m s−1, such particles ejected at 1 m height may travel between 4 and 50 m from the source before settling. Although micro-CT is a valuable tool to characterize dust particles, the current image processing methodology limits the number of particles that can be analyzed. significantly.12−17 Researchers have approached the problem from multiple angles, including the toxicology of neonicotinoid insecticides to honey bees18−26 but they also considered the effect of seeder design on dust drift.11,27,28 Although this example applies to the effects of neonicotinoid insecticides, the environmental risks of seed treatments in general have come to the attention of researchers, consumers and governmental bodies. The extent of drift of dust particles from treated seeds depends on environmental conditions, the seeder design and operation settings, the dust emission rates and the particle properties. Few field drift experiments have been reported so far.29 Modeling the dust drift phenomenon can be a useful addition to experimental studies, as demonstrated before in the cases of spray drift30−32 and the emission of dust33−35 and aerosols.36 For a model to predict accurate particle drift patterns, however, the physical properties of dust particles need to be accurately known, because

1. INTRODUCTION The seeds of many crops are commonly treated with plant protection products prior to planting, to protect the crop from pests and plagues in the early stages of its development.1 This technique is widely established since many years because of its important advantages. The active ingredients are applied directly to the seed and they can provide a durable protection of the plant, thereby reducing the need for postemergence spraying, saving the farmer time and labor. Recently, however, a number of independent honey bee poisoning incidents2−7 pointed out one of the possible disadvantages of the seed treatment technique.8,9 During the seed treatment process, transportation, handling and planting of the treated seeds, friction and other mechanical stresses can cause small particles to break off the coating layer on the seed.10 These dust particles can have high concentrations of active substances of the applied plant protection products. If pneumatic seed drills are used in windy circumstances during sowing, this dust can be expelled far into the environment and cause environmental damage.11 The importance of dust drift from seed drills cannot be overlooked. For example, following the honey bee colony collapses of recent years, research on the causes of the declines of honey bee populations has picked up © 2015 American Chemical Society

Received: Revised: Accepted: Published: 7310

November 27, 2014 May 28, 2015 May 29, 2015 May 29, 2015 DOI: 10.1021/acs.est.5b02250 Environ. Sci. Technol. 2015, 49, 7310−7318

Article

Environmental Science & Technology

Table 1. Physical Properties of Five Size Fractions of Dust from Treated Maize Seeds, Obtained from Quantitative Analysis of Micro-CT Imagesa 500 μm

33

50

μ±σ

48a ± 14

111b ± 21

271c ± 46

367d ± 100

659e ± 246

range μ±σ range μ±σ

17−78 7.33 × 104a ± 5.76 × 104 2.43 × 103−2.50 × 105 1.35 × 104a ± 8.32 × 103

51−159 7.91 × 105a ± 4.37 × 105 6.81 × 104−2.09 × 106 7.99 × 104a ± 2.78 × 104

171−383 1.14 × 107a ± 5.81 × 106 2.63 × 106−2.94 × 107 4.48 × 105ab ± 1.14 × 105

114−593 3.14 × 107a ± 2.20 × 107 7.77 × 105−1.09 × 108 8.99 × 105b ± 2.86 × 105

362−1751 2.31 × 108b ± 4.26 × 108 2.48 × 107−2.81 × 109 4.12 × 106c ± 3.83 × 106

range μ±σ range μ±σ range μ±σ range μ±σ

9.73 × 102−3.81 × 104 36a ± 11 14−58 108a ± 51 27−398 0.63a ± 0.13 0.37−0.93 162a ± 38

9.48 × 103−1.75 × 105 69a ± 18 35−125 265b ± 66 69−500 0.51b ± 0.11 0.30−0.85 129b ± 28

2.77 × 105−7.55 × 105 177b ± 45 87−324 614c ± 117 422−930 0.53b ± 0.13 0.25−0.79 75c ± 21

1.50 × 105−1.42 × 106 260c ± 83 93−519 935d ± 255 470−1513 0.50b ± 0.18 0.17−0.78 72c ± 18

6.72 × 105−1.87 × 107 498d ± 259 169−1881 1833e ± 649 895−3596 0.42c ± 0.15 0.16−0.70 54d ± 13

range μ±σ

109−236 0.02a ± 0.03

62−246 0.11b ± 0.10

47−143 0.35cd ± 0.14

41−106 0.39c ± 0.14

3 0.31c + 0.14

range

0.00−0.12

0.01−0.50

0.04−0.66

0.13−0.71

0.09−0.64

Different letters indicate which results are significantly different.

they determine the particle motion in the air. In particular, a particle’s mass, determined by its volume and density, defines its gravitational force. A particle’s size and shape affect its drag coefficient. Various authors developed correlation formulas that predict the drag coefficient of spherical and nonspherical particles based on the particle Reynolds number and one or more shape factors.37−42 It is clearly understood that particle shape is a very important factor in predicting the drag coefficient. The most often used shape factor in correlation formula is sphericity. Sphericity is defined as the ratio of the surface area of the volume equivalent sphere of a particle and the surface area of the particle itself. It is difficult, however, to measure the sphericity of an irregularly shaped object using microscopy or other techniques.42,43 X-ray computed microtomography (micro-CT) is a nondestructive technique that produces stacks of cross-sectional images of the scanned object, thus providing 3D shape and size information on the object. Images are formed with the following procedure.44 X-rays are emitted from a microfocus X-ray source to an X-ray detector, traveling through the sample along the way. In the object, X-rays are absorbed depending on the local density of the material. X-rays passing through the object are captured by a high resolution 2D detector providing a projection image (or radiograph) of the object. The radiograph represents the cumulative attenuation of the X-rays along a line through the object. The imaging is repeated for a large number of angles while the object is rotating, producing multiple radiographs. Using mathematical algorithms, the actual attenuation coefficient of the material at each point along the lines can be reconstructed into a cross-sectional image. By stacking the cross-sectional images, one obtains a three-dimensional image of the scanned material. X-ray micro-CT has been successfully applied to analyze microstructural properties of many biological materials, including fruit,45 leaves,46 roots47 and seeds.48

The aim of this work was to apply X-ray computed microtomography (micro-CT) to determine the size, shape and porosity of dust particles from treated seeds and to investigate effects on particle settling velocity. The CT scans were processed with imaging software and dust particle size and shape descriptors (length, width, surface area, volume, equivalent spherical diameter, sphericity) and particle porosity were calculated and analyzed. Particle settling velocity was calculated using the size, shape and porosity information obtained in the micro-CT analysis. The dust samples studied in this work were taken from seed batches of various crops (maize, wheat, rapeseed, rye, barley and pea) treated with different products.

2. MATERIALS AND METHODS 2.1. Seed Treatment Dust Samples. Dust from treated maize (Zea mays subsp., mays L.) seeds was retrieved by JKI (Institute for Plant Protection in Field Crops and Grassland, Braunschweig, Germany) during post-treatment filtering in a seed treatment facility in Germany. This dust sample therefore originated from multiple seed batches. The active ingredient was methiocarb, but the other treatment components are unknown. The dust particles were separated by mechanical sieving into five size fractions: 500 μm. The size fractions from 160 to 250 μm and from 450 to 500 μm were not used. JKI also provided dust samples from six batches of treated seeds of different crops. Two dust samples were from maize seeds, one from wheat (Triticum aestivum L.), one from barley (Hordeum vulgare L.), one from rye (Secale cereale L.) and one from winter rapeseed (Brassica napus L.). Dust from three batches of treated seeds was produced by mechanical agitation and sieving at ILVO (Institute for Agricultural and Fisheries Research, Merelbeke, Belgium), as described by Foqué.49 One dust sample originated from maize seeds, one from wheat and one from pea (Pisum sativum L.). The 7311

DOI: 10.1021/acs.est.5b02250 Environ. Sci. Technol. 2015, 49, 7310−7318

34−329 3.08 × 106bc ± 4.06 × 106 2.15 × 104−1.86 × 107 1.69 × 105c ± 1.60 × 105 4.32 × 103−6.64 × 105 101b ± 53 31−238 378b ± 224 50−912 0.54cd ± 0.10 0.35−0.86 111d ± 46 61−238 0.33b ± 0.20 0.00−0.61

range μ±σ

7312

a

μ±σ range μ±σ range μ±σ range μ±σ range μ±σ range

range

μ±σ

69−489 7.54 × 106a ± 1.11 × 107 1.74 × 105−6.12 × 107 2.36 × 105b ± 2.09 × 105 2.61 × 104−9.85 × 105 141a ± 67 47−348 392b ± 179 122−774 0.65b ± 0.14 0.30−0.88 125cd ± 64 67−253 0.24c ± 0.18 0.00−0.51

Different letters indicate which results are significantly different.

intraparticle porosity

mean pixel intensity

sphericity

length (μm)

width (μm)

surface area (μm2)

volume (μm3)

range

201 ± 95

149 ± 72

μ±σ

equivalent spherical diameter (μm)

50

50

number of analyzed particles a

methiocarb

clothianidin, thiram

active ingredients

b

maize b

maize a

size fraction

59

67−452 7.04 × 106a ± 1.00 × 107 1.56 × 105−4.85 × 107 2.44 × 105b ± 2.01 × 105 2.33 × 104−8.01 × 105 129a ± 69 45−370 428ab ± 187 133−944 0.59bc ± 0.11 0.39−0.93 87e ± 23 38−151 0.24c ± 0.20 0.03−0.82

199 ± 90 a

methiocarb, thiram

maize c

200

48c ± 19 21−167 496a ± 199 52−1085 0.42e ± 0.11 0.34−0.91 138bc ± 27 83−253 0.11e ± 0.06 0.00−0.42

4.92 × 103−1.34 × 105

2.99 × 104d ± 1.85 × 104

2.09 × 104−3.43 × 106

34−187 1.57 × 105d ± 3.15 × 105

60 ± 19 d

difenoconazole, fludioxonil, tebuconazole

wheat a

100

20−93 9.74 × 104d ± 8.36 × 104 4.19 × 103−4.21 × 105 2.38 × 104d ± 1.70 × 104 1.45 × 103−7.90 × 104 47c ± 18 15−102 408b ± 251 28−1020 0.48d ± 0.18 0.33−0.96 88e ± 19 44−157 0.16d ± 0.10 0.00−0.54

52 ± 16 d

prothioconazole, triticonazole

wheat b

40

107−350 6.02 × 106ab ± 4.54 × 106 6.33 × 105−2.24 × 107 3.16 × 105a ± 1.32 × 105 8.58 × 104−5.50 × 105 143a ± 49 43−257 532a ± 143 285−846 0.48de ± 0.11 0.29−0.73 56f ± 13 33−95 0.51a ± 0.13 0.14−0.74

213 ± 53 a

unknown

barley

55−187 8.04 × 105 cd ± 8.28 × 105 8.84 × 104−3.42 × 106 1.64 × 105bc ± 1.48 × 105 2.59 × 104−6.43 × 105 94b ± 53 37−254 386ab ± 175 143−766 0.28f ± 0.08 0.14−0.46 125bcd ± 20 87−150 0.16cde ± 0.09 0.05−0.35

105 ± 34

20

100

23−89 1.01 × 105d ± 8.56 × 104 6.07 × 103−3.73 × 105 1.24 × 104d ± 7.18 × 103 2.15 × 103−3.30 × 104 45c ± 15 16−90 88c ± 29 34−180 0.79a ± 0.09 0.49−0.92 150b ± 41 98−252 0.13de ± 0.10 0.00−0.34

53 ± 16 d

clothianidin, thiram

fludioxonil, tebuconazole c

rapeseed

rye

Table 2. Physical Properties of Dust Particles from Treated Seeds of Different Crops, Obtained from Quantitative Analysis of Micro-CT Imagesa

100

37−348 9.84 × 105 cd ± 2.37 × 106 2.64 × 104−2.20 × 107 5.43 × 104d ± 7.62 × 104 5.23 × 103−6.01 × 105 80b ± 37 26−222 165c ± 101 55−704 0.77a ± 0.09 0.43−0.91 171a ± 43 42−231 0.08e ± 0.17 0.00−0.79

100 ± 48 c

thiamethoxam, cymoxanil

pea

Environmental Science & Technology Article


Article


Figure 1. X-ray micro-CT cross sections of dust samples from treated seeds of different crops: maize b (a), barley (b), rapeseed (c), wheat b (d), rye (e) and pea (f). Intraparticle porosity is indicated with arrows. Image pixel size: 1.92 μm. Gray value (0−255) is related to material density.

labeled, i.e., they were assigned a number to identify them individually after statistical analysis. After labeling and 3D surface generation of individual particles, size and shape parameters were calculated. Of the individual particles in each sample, the volume, the equivalent spherical diameter, the surface area, the length and the width (the maximum and the minimum Feret diameter of the particle, respectively), the sphericity (ratio of the surface area of the volume equivalent sphere of a particle and the surface area of the particle itself), the particle mean pixel intensity (expressed as a gray value from 0 to 255) and the intraparticle porosity were calculated. Porosity was estimated by masking the automatically binarized images with the label field of the individual particles. The difference between the originally segmented particle and the mask is a measure for the air volume within each particle. Hence, porosity was defined as the fraction of this air volume within the volume of the particle mask. The open and closed fractions of total porosity was determined manually in a limited number of particles by means of 3D filling tools of the binarized images. The Tukey HSD test was performed in JMP software (JMP Pro, Version 11.2.0. SAS Institute Inc., Cary, NC, 2013) for multiple comparison of the means of all properties of the samples to find means that differ significantly from each other. The results were expressed by means of a letter report, i.e., means that have a different letter differ significantly from each other. 2.4. Settling Velocity. The settling velocity of all analyzed seed treatment dust particles was calculated. A falling object reaches its settling (or terminal) velocity when the sum of the drag force and buoyancy equals the downward force of gravity.41 When buoyancy in air is neglected, settling velocity is given by

active ingredients in the treatments of each seed batch are given in Table 1 and Table 2. 2.2. X-ray Computed Microtomography. Three subsamples of each dust batch were transferred into standard polypropylene 10 μL micropipette tips and the pipette openings at top and bottom were sealed with parafilm. Each micropipette tip contained approximately 1 mg of dust. The micropipette tips were placed upright in the center of rotation of the sample holder and scanned by means of micro-CT. Cross section slices were generated from the shadow projections using the Feldkamp reconstruction algorithm50 implemented in Nrecon 1.6.5.8 software to obtain 3D images of, respectively, 8 and 125 mm3 of dust, depending on the used pixel size of 1.92 or 4.87 μm. The technical details of the image acquisition and processing procedure can be found in the Supporting Information. 2.3. Image Processing. Image processing was performed in the software Avizo Fire Edition 8.0.0 (VSG, Bordeaux, France). The micro-CT tomographs were first cropped to the region of interest, in this case the interior of the micropipette tip. No image filtering or noise removal algorithms were used. The micro-CT scans were binarized with an automatic threshold of the pixel intensity, automatically assigning every voxel to either solid matter or air. After image binarization, touching particles were segmented in order to characterize their size, shape and porosity on an individual level. The segmentation of individual particles could not be automated with any of the available automatic segmentation algorithms because these performed poorly. As a result, the number of particles that could be analyzed was limited. Individual particles were segmented by interpolation of manually determined particle contours in single cross sections across the image stack. In other words, particles were wrapped around their outer surfaces. Both open and closed air-filled pores that may exist within a dust particle were thus considered to be part of the particle and contributed to its volume. Bias in particle selection was minimized by selecting as many particles (on average 80 particles per sample) as possible. Segmented particles were

Vt =

2mg ρfluid ACd

(1)

where Vt (m s−1) is the settling velocity, m (kg) is the mass of the falling object, g (9.81 m s−2) is the gravitational acceleration, ρfluid (kg m−3) is the fluid density (air, 1.205 kg m−3 at 20 °C), A (m2) is the projected area of the object and Cd is the drag coefficient. 7313


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Figure 2. 3D renderings of dust particles from maize a (a), maize b (b), maize c (c), wheat a (d), wheat b (e), rapeseed (f), rye (g), barley (h) and pea (i). Images of 1.92 μm pixel size were obtained with micro-CT. Particles are rendered in arbitrary colors for contrast between individual particles.

Mass was obtained by multiplying particle volume, determined by micro-CT, with the envelope density of the particle. The apparent density (ρa) of abraded coating was taken to be 1500 kg m−3, according to gas pycnometry measurements.49 The apparent density includes the volume of closed pores because the closed pores are inaccessible to the probe gas. The micro-CT measurement provided particle volume measured by enveloping the particle as well as the volume of open and closed air pores in the particle. The values of open porosity εo were used to estimate envelope density ρe for each individual particle: ρe = ρa (1 − εo)

Cd =

30 + 67.289e(−5.03ϕ) Re

(3)

Re =

ud ν

(4)

3. RESULTS 3.1. Micro-CT Images. Figure 1 shows 2D cross sections (or tomographs) of micro-CT scans of dust samples from the different crops. The denser regions (particles) appear in lighter gray (higher X-ray attenuation) and the less dense regions (air) are darker (lower X-ray attenuation). These unprocessed tomographs already demonstrate that the dust particles are generally irregular in size, shape and X-ray attenuation. Air was observed not only between dust particles but also within single particles to some extent. Intraparticle porosity is indicated in Figure 1 with arrows. Barley and maize in particular had rather large and porous seed treatment dust particles. Dust from treated rape seeds and pea seeds, on the other hand, appeared to be relatively small and dense without significant air inclusions. The wheat dust particles looked like dots in the 2D cross sections, suggesting rod-like structures in 3D. The rye dust particles, on the other hand, appeared as lines in the 2D cross sections,

(2)

The projected area of the particle was approximated by the length multiplied by the width. The drag coefficient is also dependent on the shape, as demonstrated by various authors.39,41,42 The drag correlation by Chien,51 which employs sphericity as a shape factor, was used to account for particle nonsphericity. In this equation, Cd is the drag coefficient, Re is the particle Reynolds number and ϕ is sphericity. In the particle Reynolds number, u (m s−1) is the velocity of the particle relative to the air, d (m) is the equivalent spherical diameter and ν is the kinematic viscosity of air (1.511 × 10−5 m2 s−1 at 20 °C). 7314


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Figure 3. Sphericity as a function of equivalent spherical diameter of dust particles from (left) treated maize seeds, and (right) treated seeds of various crops.

Figure 4. Intraparticle porosity as a function of equivalent spherical diameter of dust particles from (left) treated maize seeds, and (right) treated seeds of various crops.

One measure to quantify an object’s shape is sphericity (Table 1, Table 2). The needle-shaped wheat dust particles of two independent samples had a low sphericity, with mean values of 0.42 and 0.48. The flake-shaped rye dust particles also had a low sphericity (0.28). The spheroid dust particles of rapeseed and pea had a high mean sphericity (0.79 and 0.77, respectively). In general, particle sphericity slightly decreased with particle size (Table 1, Figure 3). The sphericity of wheat dust particles decreased strongly with increasing particle size. This negative correlation was weaker in the dust samples of pea and rye seeds. The dust particles of maize, barley and rapeseed even showed a slight increase of sphericity with particle size. The composition of the dust particles determines their X-ray attenuation. Differences in pixel intensity thus could be an indication for differences in composition of the dust. Particles containing high-density components (rapeseed, Figure 1c; pea, Figure 1f) will produce higher intensity images. However, because little is known about the actual composition of the seed coatings, no attempts were made to quantify these differences from the images. The porosity of the dust particles varied also among crops. Pea, wheat and rapeseed produced dust that was quite dense, with porosities of 0.08, 0.11 and 0.13, respectively. Dust from treated barley seeds, on the other hand, was very porous (0.51). The variation of porosity with particle size is plotted in Figure 4. The figure demonstrates that for a single dust sample, the intraparticle porosity significantly increases with particle size. All five size fractions of the dust from maize seeds had a trend of increasing porosity with particle size, with the intermediate size fractions having the strongest correlations. All dust samples from different

suggesting sheet-like particles in 3D. Depending on the size distribution and the shape of the particles, the stacking was dense (rapeseed and pea) or sparse (barley). 3.2. 3D Renderings of Dust of Treated Seeds of Different Crops. The micro-CT tomographs were processed to render 3D images of individual dust particles. Figure 2 shows dust particles of seed samples of different crops. In general, maize dust did not have a predominant shape, with particle shapes mainly including spheroids and disks. In other crops, dust particle shape could be defined more clearly. Barley dust particles were mostly shaped as plates and disks. Dust of rapeseed and pea was generally small and spheroid. Wheat dust was needle or rod shaped, whereas rye dust particles, ultimately, appeared to be thin flakes or sheets. 3D images of the dust particles from treated maize seeds of the JKI sample were also made (Figure S1, Supporting Information). The smaller size fractions consisted of more spherical particles and the larger size fractions were mainly composed of flatter and more elongated disk-shaped dust particles. 3.3. Shape and Size Characteristics of Seed Treatment Dust. After image processing, size and shape parameters of individual particles were calculated and the results were analyzed statistically. The data are summarized in Table 1 and Table 2. The visual observations in the CT images were confirmed by the size and shape descriptors. Overall, dust particles from treated seeds had an average equivalent spherical diameter in the range of tens to hundreds of micrometers. There were significant differences between different crops. For example, the mean equivalent spherical diameter of wheat (sample b) was 52 μm, whereas it was 213 μm in the case of treated barley seeds. 7315


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The mean histograms of the five size fractions of maize dust are plotted in Figure S3 (Supporting Information). The larger size fractions have higher shares of low-intensity voxels that constitute their particles. In Figure S4 (Supporting Information), mean pixel intensity is plotted as a function of equivalent spherical diameter, but the voxels that represent air, determined by automatic threshold binarization, are excluded from the calculation. On the basis of these results, the composition of dust from treated seeds is discussed. We considered dust of three maize samples. One was treated with clothianidin and thiram, one with methiocarb and thiram, and one with methiocarb and possibly other active ingredients. Analyzing the means of the properties of these samples allows us to estimate the effect of a different seed treatment on the properties of the dust particles, though different batches of seeds might also result in some variability. A seed treatment consists not only of one or more active ingredients of plant protection products, but it can also contain binders, colorants, flow agents, growth regulators, biological additives and other substances.52 The exact composition is usually not disclosed by seed treatment companies. Of all the components of the seed treatments of the samples studied in this work, only the active ingredients were known. It must therefore be noted that other factors, such as the other treatment components, seed cleaning and treatment technology, may also explain some of the differences in particle properties between the different dust samples. The seed treatment process itself can influence the amount of dust and its size distribution. The cleaning of seeds before treatment removes dust that is already present in the seed batch, including larger plant particles (e.g., the leaf-like glumes in maize, also influenced by the cultivar). Cleaning is especially relevant for maize and cereals. Transportation of seeds within the facility during the treatment process will influence abrasion, and the intensity of dust cleaning at several points during the treatment line will affect dust levels. The added substances in addition to the seed treatment product (such as water, stickers or drying substances, depending on the crop) will also affect dust levels of treated seed batches. These factors were not studied in this work. The two maize samples treated with methiocarb (b and c) originated from independent seed batches and different seed treatment companies. Yet the dust they produced had similar properties (Table 2). Indeed, all their studied properties except for the mean pixel intensity differed insignificantly. The maize sample treated with clothianidin and thiram (a), on the other hand, produced dust particles that were significantly smaller and more porous (Table 2). This finding suggests that the seed treatment composition affects the properties of the abraded dust, even in the same crop. However, differences in dust particle properties were generally much greater between seeds of different crops than between seeds of the same crop with different treatments. One of the principal findings of this study was the vast range of sizes and shapes of dust particles from treated seeds of different crops (Figure 2, Table 2). Differences in seed morphology and in the seed treatment process in different crops may help explain this observation. The smoothness of the seed surface, the shape and the hardness of the seed may affect how well a seed treatment sticks to the seed, and in which way particles are abraded from the treatment layer. Furthermore, the properties of the seeds of a certain crop may dictate the composition of the seed treatment in terms of carriers, binders, stickers and other components. The differences in dust particle shape and size may therefore be a

crops also showed this trend of increasing porosity with particle size. 3.4. Settling Velocity. Figure 5 shows the calculated settling velocity of seed treatment dust particles of all samples, as affected

Figure 5. Calculated settling velocity of seed treatment dust particles as affected by size, shape and porosity. Micro-CT measurements of particle shape and porosity were used to adjust the drag coefficient and the density, respectively. Apparent density of the particles was assumed 1500 kg m−3.

by particle size, shape and porosity. Deviation from sphericity changes the projected area and therefor the drag coefficient of a particle. Quantifying intraparticle porosity allowed the apparent density to be corrected to the relevant envelope density. MicroCT analysis indicated that closed pores in seed treatment dust are negligible. Therefore, values of total porosity were used in eq 2 to calculate the particle envelope density. The case in which nonspherical particle shape and porosity are accounted for is compared to the case in which the particles are simplified as spheres and porosity is neglected. The discrepancy between both cases is large and it increases with particle size. With the spherical simplification the settling velocity is on average twice as high for particles of 100 μm and four times higher for particles of 400 μm. Furthermore, due to the irregular shapes and porosities of the particles, the settling velocities show a wide variability across the size spectrum.

4. DISCUSSION Figure 2 and Table 1 demonstrate that, although sphericity is a commonly used shape descriptor, sphericity alone cannot fully describe a particle’s shape. For example, maize (sample a) had a statistically similar sphericity as wheat (sample b) (0.54 and 0.48, respectively), but the figures show different particle shapes. Combining sphericity information with length and width provides a more complete description of particle shape. Nevertheless, most correlation formulas that predict a particle’s drag coefficient only use sphericity as a shape factor.37,41,42 Therefore, quantifying particle sphericity is mostly sufficient if the goal of the study is the implementation of particle shape in existing particle drag correlations for use in a dust drift model. Xray micro-CT is an appropriate tool for quantifying any shape or size descriptor of dust particles. There is a strong link between the porosity and the mean pixel intensity of dust particles (Figure S2, Supporting Information). This is a logical result of the method that was applied to segment particles. Particles were “wrapped” as a whole, including air-filled pores. Air has a low X-ray attenuation and as a result, it is represented by dark voxels that decrease the mean pixel intensity. 7316


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available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b02250.

result of seed morphology, seed batch, treatment composition, treatment technology, seed cleaning or an interaction of these factors. Altogether, the number of seed batches tested in this study was quite low and more data should confirm our findings. Dust particle shape and porosity strongly affect settling behavior. Both factors were quantified by means of micro-CT. Deviation from sphericity changes the projected area of a particle and thus its drag coefficient. Density of dust samples is typically measured by means of gas expansion pycnometry, which produces apparent density. However, in settling studies, the envelope density, which includes both closed and open pores, is more relevant. In the micro-CT analysis, particle volume was measured by enveloping the particle in an imaginary, closely conforming skin and this volume thus includes both closed and open pores. The envelope density can be estimated if the total porosity and open fraction of porosity are quantified. Figure 5 shows that the settling velocity of particles of 200 μm equivalent diameter may vary between 0.1 and 1.2 m s−1 depending on their shape and density. In a wind velocity of 5 m s−1, such particles ejected at 1 m height may thus travel between 4 and 50 m from the source before settling. This observation underlines the importance of both accounting for particle shape and porosity as well as the need for methods to quantify size as well as shape descriptors of particles. The use of 3D X-ray micro-CT for quantifying the shape, size and porosity of particles has advantages and limitations. The advantages are that the technique is nondestructive and accurate. The main limitation is that the image acquisition and image analysis are time-consuming. Object separation currently cannot yet be fully automated with the available segmentation algorithms. Manual separation (or at least a manual check-up of automatic results) remains necessary. This also implies that the number of particles per sample that can be analyzed is relatively small, reducing reproducibility and statistical power. Because of the advantages and limitations of micro-CT, the technique is suitable for certain applications but not for others. It is ideal in materials science to measure and classify accurately the shape and porosity of dust and soil particles, sediments, powders and other types of particles. The time-consuming image acquisition and processing are acceptable if the goal is a onetime accurate measurement of material properties for future use in models, for example. Micro-CT is less suitable, however, if a continuous and fast screening of material properties is required.

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AUTHOR INFORMATION

Corresponding Author

*W. Devarrewaere. Tel.: +3216377083. E-mail: wouter. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors greatly acknowledge the financial support of IWT (Agency for Innovation by Science and Technology) for this research (project IWT 100848).

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

More detailed information about the image acquisition and image processing of the X-ray computed microtomography technique used in this work, a discussion about the composition of dust from treated seeds, as well as the following figures: Figure S1, 3D renderings of dust particles from treated maize seeds in five size fractions: 500 μm (e). Images of 1.92 μm pixel size were obtained with micro-CT. Particles are rendered in arbitrary colors for contrast between individual particles; Figure S2, mean pixel intensity as a function of intraparticle porosity of dust particles from (left) treated maize seeds, and (right) treated seeds of various crops; Figure S3, mean histograms of five size fractions of dust from treated maize seeds; Figure S4, mean pixel intensity as a function of equivalent spherical diameter of dust particles from (left) treated maize seeds, and (right) treated seeds of various crops. The voxels representing the air pores are excluded from the particles. The Supporting Information is 7317


Article


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Quantitative 3D Shape Description of Dust Particles from Treated

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