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A wind tunnel and field evaluation of the efficacy
of various dust suppressants
A Thesis Submitted to the Committee on Graduate Studies in Partial Fulfillment of the
Requirements for the Degree of Master of Science in the Faculty of Arts and Science
TRENT UNIVERSITY
Peterborough, Ontario, Canada
© Copyright by Colette Alexia Preston 2017
Environmental & Life Sciences M.Sc Program
January 2018
ii
Abstract
A wind tunnel and field evaluation of the efficacy
of various dust suppressants
Colette Alexia Preston
A series of experiments was designed to assess the relative efficacy of various dust
suppressants to suppress PM10 emissions from nepheline syenite tailings. The experiments
were conducted in the Trent University Environmental Wind Tunnel, Peterborough,
Ontario, and on the tailings ponds at the Unimin Ltd Nephton mine near Havelock, Ontario.
Treated surfaces were subjected to particle-free airflow, abrasion with blown sand
particles, particle-free airflow after physical disturbance, and were measured independently
using a pin penetrometer. In the particle-free wind tunnel tests, three of the surfaces
performed well, and PM10 emissions scaled inversely with crust strength. Light
bombardment of each surface by saltating sand grains resulted in PM10 emission rates two
orders of magnitude higher. All treated surfaces emitted significantly more PM10 after
physical disturbance in both the laboratory and field research. The results suggest that the
site conditions, inclusive of the potential for dust advection and resuspension, must be
taken into account when considering the use of a commercial dust suppressant.
Keywords: dust suppression, mine tailings, wind tunnel experiment, field testing
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Acknowledgements
I would like to thank my supervisor, Dr. Cheryl McKenna Neuman, for her guidance and
encouragement throughout this research project. She was unfailingly positive and
understanding, and yet also challenged me to keep expanding my knowledge and outlook.
Cheryl was always available to consult about experimental design and data, and even ran
the PI-SWERL for a day in very hot conditions out on the tailings. I’m grateful for her
support, and for the opportunity to benefit from her extensive knowledge and experience.
My thanks to the management of the Unimin Ltd mine for their technical and
financial support of this project – in particular, Robert Marshall, Mikhail Clarkson, and
Cale Reeder. They were always willing to accommodate my many requests, and without
them this project would not have been possible. I would also like to recognize the
supervisory support from Wayne Boulton through RWDI. I’m grateful to Professor Chris
Hugenholtz (Department of Geography, University of Calgary) for the loan of the PI-
SWERL, which was of key importance to the field component of this project. Also, I would
like to thank the following for generously supplying the commercial dust suppressants
used in the project: Paul Goulet at Enssolutions, Randy Hudson at Landloc Environmental,
and Cheryl Detloff and Matt Mefford at Midwest Industrial Supply, Inc. In addition, I
would like to acknowledge the financial support of the Natural Sciences and Engineering
Research Council of Canada and the Ontario Graduate Scholarship Program.
I would also like to thank my colleagues at Trent University and in the Trent
University Wind Tunnel Research Group: Phaedra Cowden, Patrick O’Brien, Damilare
Ogungbemide, and Tamar Richards-Thomas. They were of invaluable assistance in terms
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of supporting the wind tunnel and field research, as well as during countless hours of
discussion and brain storming about the project.
Finally, I would like to thank my family and friends for their encouragement,
support, and belief in me during this project.
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Table of Contents
Abstract .............................................................................................................................. ii
Acknowledgements .......................................................................................................... iii
Table of Contents ...............................................................................................................v
List of Figures ................................................................................................................. viii
List of Tables ......................................................................................................................x
List of Equations ................................................................................................................x
List of Symbols and Abbreviations ............................................................................... xi
1 Introduction .................................................................................................................1
1.1 Overview ..............................................................................................................1
1.2 The Field Research Site ........................................................................................2
1.3 Fugitive Dust Emissions .......................................................................................4
1.4 Fugitive Dust Health Impacts and Environmental Standards ...............................5
1.5 Measuring Dust Emissions ...................................................................................7
1.6 Dust Suppressant Crusts .....................................................................................10
1.7 Crust Properties ..................................................................................................12
1.8 Dust Suppressant Studies ...................................................................................16
1.9 Dust Suppressant Selection ................................................................................18
1.10 Objectives ...........................................................................................................22
1.11 Study Structure ...................................................................................................24
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2 Methodology ..............................................................................................................25
2.1 Laboratory Methods ...........................................................................................25
2.1.1 Laboratory Test Tray Preparation ..................................................25
2.1.2 Laboratory Crust Preparation and Measurement ...........................27
2.1.3 The Trent University Environmental Wind Tunnel .......................29
2.1.4 Wind Tunnel Instrumentation and Configuration ..........................29
2.1.5 Wind Velocity Profiles ..................................................................33
2.1.6 Wind Tunnel Experimental Procedure ...........................................35
2.2 Field Methods .....................................................................................................38
2.2.1 Field Site Preparation .....................................................................38
2.2.2 Field PM10 Emission Measurements ..............................................42
2.2.3 Field Crust Measurements .............................................................44
2.2.4 Field Physical Disturbance Tests ...................................................44
2.2.5 Statistics .........................................................................................45
3 Laboratory Results and Discussion .........................................................................46
3.1 Laboratory Crust Penetrometer Tests .................................................................46
3.2 Laboratory Gravimetric Moisture Content .........................................................48
3.3 Wind Tunnel Clean Air Runs .............................................................................48
3.4 Wind Tunnel Saltation Runs ..............................................................................52
3.5 Wind Tunnel Disturbance Runs .........................................................................55
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3.6 Laboratory Crust Variability ..............................................................................58
3.7 Laboratory Laser Scans ......................................................................................60
3.8 Laboratory Discussion ........................................................................................69
4 Field Results and Discussion ....................................................................................77
4.1 Field PM10 Emission Measurements ..................................................................77
4.2 Field Crust Penetrometer Tests ..........................................................................82
4.3 Field Physical Disturbance Tests ........................................................................86
4.4 Field Site One Year Assessment ........................................................................92
4.5 Field Discussion .................................................................................................93
5 Conclusions, Study Limitations, Recommendations ............................................103
5.1 Conclusions ......................................................................................................103
5.2 Study Limitations .............................................................................................104
5.3 Recommendations ............................................................................................106
6 References ................................................................................................................108
7 Appendix ..................................................................................................................115
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List of Figures
Figure 1.1: Field research site .............................................................................................3
Figure 2.1: Particle size cumulative frequency of the laboratory tailings.........................26
Figure 2.2: Trent University Wind Tunnel configuration .................................................30
Figure 2.3: Wind velocity profiles ....................................................................................34
Figure 2.4: The board used to physically disturb the test trays .........................................37
Figure 2.5: Field site research plots ............................................................................ 39-40
Figure 2.6: Miniature PI-SWERL and the physical disturbance of the research plots .....41
Figure 3.1: Laboratory penetrometer results .....................................................................47
Figure 3.2: Clean air PM10 emission curves................................................................ 50-52
Figure 3.3: Saltation PM10 emission curves ................................................................ 53-54
Figure 3.4: Post-disturbance clean air PM10 emission curves ..................................... 56-57
Figure 3.5: LN clean air PM10 emission curves ................................................................59
Figure 3.6: SS saltation PM10 emission curves .................................................................59
Figure 3.7: EA post-disturbance clean air PM10 emission curves .....................................60
Figure 3.8: Range in surface elevation from laser scans ...................................................62
Figure 3.9: Laser scans of LN tray 1 and EN tray 1 .........................................................64
Figure 3.10: Laser scans of EA tray 1and SS tray 1 ........................................................65
Figure 3.11: Three of the test tray surfaces after the saltation run ....................................66
Figure 3.12: Four of the test tray surfaces after physical disturbance ........................ 67-68
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Figure 4.1: PI-SWERL concentration curve for EN test 1, 1 week after application .......80
Figure 4.2: PI-SWERL concentration curves for the LN tests,
1 week after application .....................................................................................................80
Figure 4.3: Average PI-SWERL concentration curves for all six research plots,
3 weeks after application....................................................................................................81
Figure 4.4: Field average PM10 emission fluxes and GMC ..............................................81
Figure 4.5: Field normalized PM10 emission fluxes ........................................................82
Figure 4.6: Field penetrometer results ........................................................................ 84-86
Figure 4.7: Field average PM10 emission fluxes from the disturbed sections ...................88
Figure 4.8: Complete and incomplete PI-SWERL ramp steps after disturbance ........ 88-89
Figure 4.9: Field normalized PM10 emission fluxes, disturbed sections ...........................89
Figure 4.10: Field research plot surfaces 4 weeks after physical disturbance ..................90
Figure 4.11: Field research plots after 13 months .............................................................91
x
List of Tables
Table 2.1: Dust suppressant application rates ...................................................................26
Table 2.2: Field measurement dates ..................................................................................42
Table 3.1: Laboratory PM10 emission fluxes ....................................................................49
Table 3.2: Range in surface elevation of the laboratory test trays ....................................61
Table 3.3: Comparison of the laboratory crusts by crust strength and PM10 flux .............73
Table 4.1: Comparison of the field crusts by crust strength and PM10 flux ......................94
List of Equations
Equation 2.1: Maximum penetration force, MPF .............................................................28
Equation 2.2: Modulus of Elasticity, MoE .......................................................................28
Equation 2.3: Gravimetric moisture content, GMC .........................................................28
Equation 2.4: Sediment feed rate, q ..................................................................................31
Equation 2.5: PM10 emission flux, F (laboratory) ............................................................32
Equation 2.6: Prandtl-von Kàrmàn equation ....................................................................33
Equation 2.7: PI-SWERL instantaneous emission rate, E ................................................42
Equation 2.8: Average PM10 emission flux for each PI-SWERL ramp, Fi ......................43
Equation 2.9: Average PM10 emission flux, F̅ (PI-SWERL) ............................................43
Equation 2.10: Normalized PM10 emission flux, F′ .........................................................43
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List of Symbols and Abbreviations
C – Control plot
CV – Coefficient of Variation (%)
DT – DustTrak™
E – Instantaneous emission rate in a PI-SWERL test (µg s-1)
EA – EcoAnchor (dust suppressant)
EN – Entac (dust suppressant)
F – PM10 emission flux (µg m-2 s-1)
Fi – Average PM10 emission flux for a PI-SWERL ramp (µg m-2 s-1)
F̅ – Average PM10 emission flux for a wind tunnel or PI-SWERL test (µg m-2 s-1)
F̿ – Average PM10 emission flux for all replicates of a dust suppressant (µg m-2 s-1)
F′ – PM10 emission flux normalized against the control
GMC – Gravimetric moisture content (%)
LN – Dust Fyghter LN100 (dust suppressant)
MoE – Modulus of Elasticity (N m-1)
MPF – Maximum penetration force (N)
PI-SWERL – Portable In-Situ Wind Erosion Laboratory
PM – Particulate matter
q – Sediment feed rate of the sand particles in the saltation runs (kg m-1 s-1)
RH – Relative humidity (%)
RPM – Revolutions per minute
SS – Soil Sement® (dust suppressant)
TEOM – Tapered Element Oscillating Microbalance
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TEWT – Trent University Environmental Wind Tunnel
VIVID 9i - Konica Minolta VIVID 9i laser scanner
W – Irrigated plot
σ – Standard deviation
1
Chapter 1 Introduction
1.1 Overview
Fugitive dust emissions are a major concern in the mining industry and are strictly
regulated. Fugitive dust may be generated as a direct result of mining activities, or may be
released from storage and service areas. Many methods are used to control dust emissions
including watering, applying a chemical dust suppressant, covering sensitive areas,
erecting windbreaks, and establishing protective vegetation. This study tested the efficacy
of four commercial dust suppressants and water in preventing fugitive dust emissions from
nepheline syenite mine tailings. Limited academic research has been conducted using
commercial dust suppressants, and industry testing tends to focus primarily on
environmental testing conducted by the product manufacturer, which does not usually
extend to comparative studies of the efficacy of dust suppressants in preventing fugitive
dust emissions.
This study included both laboratory and field research, using nepheline syenite
tailings from the field site in the laboratory research. The laboratory testing was conducted
in the Trent University Environmental Wind Tunnel, which allowed for a high degree of
control over environmental conditions and dust emission measurements, so that more
precise comparisons of the dust suppressants could be achieved. The field research,
conducted on the Unimin Ltd Nephton site tailings ponds, allowed for the dust suppressants
to be subjected to a wide range of naturally occurring weather conditions over the span of
a year. The combination of field and laboratory research allowed for a more comprehensive
comparison of the protective capabilities of the dust suppressants.
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1.2 The Field Research Site
The field research took place on nepheline syenite tailings ponds at the Unimin Ltd
Nephton mine near Havelock, Ontario (Figure 1.1). Nepheline syenite is a naturally-
occurring feldspathic, hydrocrystalline, plutonic igneous rock that forms an approximately
2 km long escarpment running north-south between the Nephton and Blue Mountain sites
of the Unimin mining operation (McLemore, 2006; Figure 1.1c). The nepheline syenite at
this site is unique due to the large size of the deposit and the fact that it is composed of over
50% albite, which gives the deposit its white colour. Waste from the milling process is
pumped as a slurry onto a series of tailings ponds. Some tailings ponds are constantly
submerged, some are mostly very damp or water-logged, and other ponds, including the
field site pond, are mostly dry.
In 2013, Unimin installed an extensive irrigation system on the Nephton site tailings
ponds to suppress dust emissions (Figure 1.1d). The irrigation system also afforded the
opportunity to establish vegetation cover on the drier areas of the ponds. As a result, there
has been extensive vegetation cover on tailings pond #4 since 2014. Committed to
facilitating research at their Nephton site, Unimin supported an undergraduate honours
thesis (Preston, 2015a) and commissioned a technical report (Preston, 2015b), which
assessed the extent of the vegetation cover on tailings pond #4. In addition, Unimin
supported research by fellow Trent University Wind Tunnel Research Group member
Damilare Ogungbemide as part of his PhD research project (2017) through the MITACS-
Accelerate Internship Program. In 2016, Unimin provided technical and financial support
3
Figure 1.1. The field research site. (a) Unimin Ltd Mine in southern Ontario (Google Earth, 2017); (b) Unimin Ltd Nephton tailings
pond #4 (Google Earth, 2017); (c) nepheline syenite escarpment; (d) tailings pond #4 with the irrigation system running, June 2014.
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for this project, which included clearing a section of tailings pond #4 to facilitate the field
research on the efficacy of commercial dust suppressants and water in preventing fugitive
dust emissions from the tailings pond.
1.3 Fugitive Dust Emissions
Fugitive dust refers to dust consisting of fine particles that are 10 µm or less in diameter,
or PM10. It is dust that may be of organic, synthetic, or geologic origin that is generated
from open sources rather than a confined flow stream such as a chimney, vent, or stack.
PM10 emissions are of concern since the particles are capable of travelling considerable
distances (McKenna Neuman, 2010), and are small enough to be inhaled, which can cause
health problems (WHO, 2005).
The manner in which dust moves when it is entrained is determined by its particle
size, with smaller particles such as PM10 usually moving in suspension in the airflow. A
particle will be entrained by the wind when the wind’s drag force exceeds the forces of
resistance that are exerted on the particle (Nickling & McKenna Neuman, 2009). These
forces are related to the particle’s mineralogy, density, size, shape, and packing, as well as
the possible presence of bonding agents such as organic matter, soluble salts, water films,
and synthetic dust suppressants. These forces are stronger for smaller particles such as
PM10, relative to their weight, so that very high wind speeds may be required to entrain the
particles unless larger saltating particles impart energy to the smaller particles causing them
to be loosened and ejected from the surface (McKenna Neuman, 2010).
Saltation refers to sand particles, of a diameter of 70 to 500 µm, that move in a
series of jumps along the surface. Despite their higher mass due to their larger size, sand
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particles are often more easily entrained by the wind than dust particles since they have
much lower cohesive forces and a rougher aerodynamic profile. Once sand particles are
entrained, they may in turn initiate the entrainment of smaller particles, including PM10,
when they impact the surface. The energy of these impactions is influenced by the particle
size, the wind speed, and the nature of the surface. For instance, in a study of collisions of
saltating particles over silt surfaces, the post-collision velocity of the sand particles was
44% greater over a surface that was saturated with water and then oven-dried into a brick,
as opposed to over a loose surface. As a result, the brick-like surface did not change
significantly, whereas the loose bed of silt absorbed sufficient energy to cause the
formation of ovoid craters and the loss of 14% of its material (Gordon & McKenna
Neuman, 2009). At the field site, the tailings consist of particles ranging from
approximately 1 µm to 1.3 mm, which means that there are sand-sized particles available
for saltation as well as small dust-sized particles which may be ejected into the airstream
by saltating particles (Preston, 2015a; Ogungbemide, 2017).
1.4 Fugitive Dust Health Impacts and Environmental Standards
Dust, or particulate matter (PM), is regulated at regional, national, and international levels
due to its possible health impacts. Airborne PM has the potential to cause a wide range of
negative health impacts, although predominant impacts are to the cardiovascular and
respiratory systems (WHO, 2005). Studies on PM have found that there is generally a
positive relationship between PM and mortality, particularly with regard to what is
commonly referred to as the “coarse” range of PM between 2.5 and 10 µm (Health Canada,
2016). “No effect” thresholds have not been established, since the susceptibility of people
6
to PM10 can vary widely with age and health, and health impacts may occur at PM10 levels
not greatly above common background levels (WHO, 2005). For instance, recent studies
conducted in Toronto and Vancouver found strong evidence of a link between short-term
exposure to coarse PM and hospitalizations in children due to asthma and respiratory
infection (Health Canada, 2016).
PM emissions in Canada come mainly from open sources such as roads and
agricultural fields, with only about 2% produced by industry. Of that 2%, 45% is produced
by rock quarrying and mining operations (Health Canada, 2016). The fact that mining
emissions only account for about 1% of overall PM emissions may in part be due to strict
government regulations. In Canada, the provinces have primary jurisdiction over mining,
and in Ontario PM10 is regulated as total suspended particulate matter (TSP) in the
Environmental Protection Act of Ontario, R. S. O. 1990 - Ontario Regulation 419/05 Local
Air Quality (1990). Schedule 2 states a half hour concentration limit of 100 µg m-3, and
Schedule 3 states a 24-hour average concentration limit of 120 µg m-3. Mining companies
are required to identify and estimate potential sources of PM emissions using an approved
model. On the national level, the Canada Council of Ministers of the Environment set new
Canada-wide standards in 2000 for fine particulate matter, PM2.5, establishing a daily limit
of 30 µg m-3 based on a 24 hour measuring time averaged over three years. It should be
noted that this limit is based on both primary and secondary particulate and, in practice,
very little of the PM2.5 measured is related to primary emission sources. On the
international level, the World Health Organization (2005) bases its PM limits on PM2.5
studies and considers that PM10 consists of approximately 50% PM2.5. The 24-hour
7
standards are 25 µg m-3 for PM2.5 and 50 µg m-3 for PM10, and the annual average standards
are 10_µg_m-3 for PM2.5 and 20 µg m-3 for PM10.
1.5 Measuring Dust Emissions
Methods for measuring PM10 have seen significant developments over the past two
decades. Early instruments able to measure PM10 in suspension, such as the Portable Filter
Sampler, a modification of a stacked filter unit (Cahill et al., 1996) and the Millipores® air
filter (Rajot et al., 2003), were filter-based and did not measure in real time, offering a more
coarse temporal resolution. The development of optical-based instruments allowed for
rapid, real-time sampling of PM10 emissions. One of the most widely used instruments in
dust research is the TSI DustTrak™, which incorporates light scattering laser photometry
and direct-reading, real-time measurement of both mass and size fraction. The DustTrak II
Model 8530 (DT) draws in the sample stream through an external filter that removes
particles of a diameter larger than 10 µm (TSI Incorporated, 2017). The air stream is then
pumped through a sample chamber whereupon the particles in the sample stream scatter
light, emitted by a laser diode, which is measured by a photodetector (Chung et al., 2001).
Studies comparing DTs to other methods of measuring PM10 such as the filter-based
Federal Method Reference Sampler (Chung et al., 2001), the Tapered Element Oscillating
Microbalance (TEOM; Kingham et al., 2006), and the Grimm Series 1.108 Aerosol
Spectrometer (Cheng, 2008) found that DTs tend to overestimate airborne particle
concentrations. However, DTs are factory calibrated using ISO 12103-1, A1 test dust (TSI
Incorporated, 2017) and may need to be calibrated taking into account the specific
characteristics of a given aerosol, such as its index of refraction, its particle size
8
distribution, and its light absorption, to ensure more accurate dust emission measurements
(Kim et al., 2004). For example, in a study conducted on PM10 emissions in both under-
ground and above-ground rail systems, correction factors for PM2.5 concentrations,
determined through calibration of the DTs, varied from 0.5 to 1.86 (Kam et al., 2011). In a
study measuring PM10 from diesel exhaust, no correction factor needed to be applied, and
DT measurements were found to be more accurate than aethalometer, photoacoustic
instrument, and smoke meter measurements, and on par with TEOM measurements
(Moosmüller et al., 2001). The researchers also noted that DTs have excellent signal-to-
noise ratio, good time resolution, are simple to use, and have good portability. It should be
noted that all of these DT comparative studies were conducted with the older, discontinued
model 8520 DT.
In terms of using DTs to measure PM10 in a wind tunnel setting, it has been
suggested that they may actually underestimate dust emissions (Houser & Nickling, 2001a;
2001b). This is because the DT intake flow rate of 3.0 L min-1 is lower than the wind speed
in the wind tunnel, meaning that the instrument is measuring subisokinetically. Therefore,
the faster the wind speed, the more likely that DTs will underestimate dust emission rates
in the wind tunnel. In fact, Houser and Nickling suggest that there may be as much as a
60% reduction in the measurement of PM10 emissions at the higher wind speeds.
In the field, DTs are used in a vertical array to create a concentration profile which
allows for the calculation of the vertical flux of dust particles, assuming that deposition due
to the force of gravity and advected dust are negligible (Gillette, 1978). In studies by
Houser & Nickling (2001a; 2001b) and Macpherson et al. (2008), a series of DTs placed
in a vertical array in a portable wind tunnel allowed for the calculation of the vertical dust
9
flux emitted from a surface for a given area. The Houser & Nickling studies were the first
to use DTs to measure PM10 emissions in a portable wind tunnel, and they also used a
vertically integrating, passive sediment trap to measure the rate of sand abrasion to which
the surface was subjected. Roney & White (2006) further developed PM10 emissions
measurement in a laboratory wind tunnel by applying a control volume approach, which
determines the PM10 emission rate from a surface using the difference between the PM10
mass flux into and out of a defined volume in the tunnel. This approach, which requires a
minimum of two DTs, has been used in recent wind tunnel studies at Trent University
(McKenna Neuman et al., 2009; Sanderson et al., 2014). In fact, in the Sanderson et al.
study, a comparison of the vertical approach to determining the dust flux with the control
volume method was found to have good agreement, except for very low friction velocities.
This would suggest that either method is acceptable when determining PM10 emissions
from surfaces being tested in the Trent University Environmental Wind Tunnel (TEWT).
In terms of field research, portable wind tunnels are large and cumbersome to use,
often needing several people to set up the tunnel as well as to move it to a new location,
which makes it challenging to obtain good spatial resolution or sample a wide range of soil
or terrain types. In response to these challenges, Etymezian et al. (2007) introduced a new
instrument called the Portable In-Situ Wind Erosion Laboratory (PI-SWERL) to measure
potential PM10 emissions in the field. It is a drum-like instrument which creates wind drag
on the surface by spinning an annular blade. The resulting dust emissions are measured by
a DustTrak II Model 8530. The PI-SWERL has a diameter of 57 cm and has been found to
measure dust emissions as reliably as the portable wind tunnel used in the Houser &
Nickling studies (Sweeney et al., 2008). A smaller, more portable, miniature PI-SWERL
10
was also developed, and it has been used effectively in several studies (Goosens & Buck,
2009; Kavouras et al., 2009; Sankey et al., 2011). In the miniature PI-SWERL, the annular
ring has an outer diameter of 25 cm and an inner diameter of 16 cm. A clean air flow of
100 L min-1 provides ventilation in the chamber. Because the miniature PI-SWERL has
also been determined to give results similar to a field wind tunnel but is much more
convenient, it has, for the most part, replaced research conducted with field wind tunnels.
Recent studies have further developed the efficacy of the PI-SWERL by correcting the
friction velocity calculation to account for vegetation (Sweeney et al., 2011) and surface
roughness (Etymezian et al., 2014).
1.6 Dust Suppressant Crusts
One of the most common approaches to preventing fugitive dust emissions in the mining
industry is the use of topically applied dust suppressants. The aim in applying dust
suppressants on a tailings pond is to establish a crust over the surface to protect the
underlying tailings from wind erosion. All five of the dust suppressants considered in this
study are capable of forming crusts on the surface. The four commercial dust suppressants
considered herein prevent wind erosion by binding the surface particles to form a crust.
Water, one of the most widely used dust suppressants in the mining industry, can also
promote the development of a physical and/or biological crust when applied to a surface.
Indeed, in locations where it is readily available, it may be one of the least expensive forms
of dust suppression. However, there are often regulations concerning how much water may
be used for this purpose, particularly if run-off is of concern. The level of concern is
11
affected by the nature of the tailings as well as the topography and sensitivity of the
surrounding watershed.
While there exists limited academic research on commercial dust suppressants,
there is a body of research, concerning both physical and biological crusts, which functions
as a useful analogue for both dust suppressant product crusts and the use of water as a dust
suppressant. Biological crusts are often quite varied and may consist of many different
species of cyanobacteria, algae, lichens, and/or mosses (Belnap, 2003). Physical soil crusts
are formed primarily through the action of water or the physical compression of the surface
(Belnap, 2001). If an area is treated with water, a physical crust may form with the washing
of the smaller particles into the spaces between the larger particles, creating a more tightly
packed surface. Therefore, there is also the possibility that an untreated area could form a
physical crust over time if there is sufficient rainfall. This was certainly the case in a dust
suppressant study, conducted north of Las Vegas, Nevada, in which dust emissions from
the untreated control surfaces declined over time because of the formation of a natural
physical crust due to precipitation events (Kavouras et al., 2009).
Even without the protection of a physical crust, the irrigation of mine tailings may
provide sufficient protection from wind erosion. Laboratory studies have shown that, for
sand-sized particles, a gravimetric moisture content (GMC) ≥ 0.2% may be sufficient to
suppress entrainment, and that as moisture content increases, higher wind speeds are
necessary to initiate fugitive dust emissions in soils including smaller particle sizes
(McKenna Neuman & Nickling, 1989; Fécan et al., 1999; McKenna Neuman, 2003).
However, for irrigation to be completely effective in preventing dust emissions in a field
setting, the surface must be carefully monitored since particles in the surface layers can dry
12
out quickly in sunny and/or windy weather. The establishment of a physical crust is
therefore more effective than relying on maintaining sufficient moisture levels to prevent
dust entrainment (McKenna Neuman, 2010). For example, McKenna Neuman & Langston
(2006) found that sand particle transport may begin once the surface GMC drops below
5%. They also point out that in conditions where the first few grains at the surface dry,
mass transport on a beach may be triggered despite there being sufficient levels of GMC
across the overall surface to prevent particle entrainment. Owing to the wide range of
particle sizes present in the nepheline syenite tailings, saltation of sand particles which have
become dry at the surface may result in dislodging smaller PM10 particles from the surface
into the airstream (Ogungbemide, 2017).
1.7 Crust Properties
In terms of assessing the efficacy of the crusts created by the dust suppressants under
consideration in this study, three main factors will be considered: strength, homogeneity,
and perseverance.
Crust strength is the most important characteristic when considering a crust’s resistance
to wind erosion. The strength of a crust is influenced by many factors including its structure
and thickness, and whether it exhibits brittle or ductile strength. Crust strength may be
measured directly using a pin penetrometer, which determines the overall strength of the
crust based on the maximum force required to penetrate the crust (Rice et al., 1996; Rice
et al., 1997; Rice et al., 1999; McKenna Neuman & Maxwell, 2002; Langston & McKenna
Neuman, 2005). Penetrometer data may also be used to determine the Modulus of
Elasticity, which is a measure of the elastic resilience of a crust.
13
Several of the strongest crusts created for use in wind tunnel studies consisted of
fine-textured soils that had been flooded with water and then oven-dried (Rice et al., 1996;
Gordon & McKenna Neuman, 2009). However, while these crusts exhibited higher levels
of resistance to wind erosion compared to dry, non-crusted surfaces and crusts which were
formed by spritzing soil with water, they were still found to be vulnerable to abrasion
during saltation tests. This suggests that even if a crust is very thick, under continuous
abrasion it may eventually erode to the point where the loose particles underneath become
exposed to the force of the wind and may be ejected by saltating sand particles. Previous
research on biological soil crusts also suggests that even crusts that are found to be strong
in penetrometer tests, and under wind drag, often become vulnerable when bombarded with
saltating sand particles (McKenna Neuman et al., 1996; McKenna Neuman & Maxwell,
1999; 2002). This group of studies, conducted in the TEWT, determined that moss crusts
were more resistant to wind erosion than fungal, cyanobacterial, and algal crusts. However,
both of the species of moss crusts studied were found to erode when abraded by saltating
sand particles. Interestingly, a comparison of salt crusts with these two moss crusts found
that the salt crusts broke down more quickly than the moss crusts despite having a greater
strength during penetrometer testing (Langston & McKenna Neuman, 2005). It was
suggested that this is due to the fact that salt crusts exhibit a more brittle behaviour, whereas
moss crusts exhibit more ductile behaviour and are better able to withstand bombardments
by sand particles.
Also of importance, in the context of the strength and protective capabilities of a
dust suppressant crust, is its resistance to disturbance. Some of the strongest crusts tested
in the laboratory, such as oven-dried blocks of pulverised tailings, required repeated
14
impacts with a hammer to break up (McKenna Neuman et al., 2009), and salt crusts tested
during pilot studies for this project proved virtually impervious to impacts with a heavy
metal bar. On mine tailings, even on a surface intended to be left undisturbed for a number
of years, maintenance and mining activities may require some vehicular or foot traffic
across the tailings surface. Therefore, it is important that the dust suppressant crusts be
subjected to disturbance and then tested for resistance to wind erosion in both the wind
tunnel and the field.
Crust homogeneity refers to the consistency of the crust cover that is created. This may
be affected by both the application of the product as well as the product consistency. Also
of importance is the roughness of the surface, since a more uneven surface may not end up
being covered as consistently by the dust suppressant, and areas with a higher surface
elevation may be more vulnerable to wind drag.
Previous studies have found that crusts that were determined to be strong enough
to resist wind erosion did erode, and at an uneven rate, under saltation abrasion. For
instance, salt crusts tested in the TEWT broke down under saltation abrasion due to the
formation of localised erosion pits, which suggested that there was spatial variability in the
crust strength (Langston & McKenna Neuman, 2005). Likewise, in moss crusts, sand
abrasion caused the development of abrasion pits due to localized ruptures within the moss
filaments which continued throughout the saltation tests, also suggesting considerable
spatial heterogeneity in the crusts (McKenna Neuman & Maxwell, 2002).
Crust perseverance refers to the ability of a crust to endure through a variety of conditions
over time. In the context of preventing dust emissions from mine tailings, the ability of a
15
dust suppressant crust to persevere is obviously important. Depending on site conditions,
a crust may experience a wide variety of weather conditions such as high summer
temperatures with very dry periods and high UV values, rain and sporadic thunderstorms,
strong winds, and freeze-thaw cycles over the winter months.
It is possible that precipitation could affect crusts formed by a dust suppressant,
potentially altering and/or weakening them over time. Raindrop erosion has been proven
to be able to break the bonds between soil particles, although it is generally ineffective in
transporting the detached particles (Kinnell, 2005). If there is any unevenness in the
surface, subsequent surface flow of rain water could initiate transport of the detached
particles. Also, the presence of a surface crust will likely decrease the infiltration rate of
rain water, which has been found to increase the level of water erosion (Walker et al.,
2007). Particles detached by rain erosion and transported by surface runoff are likely to
settle based on their particle size, with the smallest particles being carried the farthest. On
many tailings ponds this results in a pooling of the finest particles in low-lying areas of the
tailings (W. Boulton, personal communication, August 31, 2017). Certainly under diverse
weather conditions, crust response and perseverance can be difficult to predict. For
example, in the previously mentioned study conducted in Nevada, the surface crusts created
by the application of a tall oil pitch dust suppressant, derived from pine pitch, were found
to deteriorate six months after application (Kavouras et al., 2009). Frequent spalling of the
crusts and the resulting exposure of loose soil particles resulted in increased PM10
emissions. In contrast, the untreated control plots emitted increasingly lower levels of PM10
over time because of the formation of a natural protective crust due to precipitation.
16
1.8 Dust Suppressant Studies
The Kavouras et al. (2009) study is one of a very small number of academic studies testing
commercial dust suppressants. It concerns only one product, albeit applied at three different
concentrations, and was primarily aimed at highlighting the potential of the relatively new
PI-SWERL instrument in assessing PM10 emissions from treated surfaces. It did not include
any tests of disturbed surfaces, or the resistance of the surfaces to abrasion by saltating
sand particles. An older wind tunnel study considered three biologically-based dust
suppressants, not currently available, which were formulated from potato starch, sugar
beets, and fermented potato waste (Ligotke et al., 1993). While this study did consider the
effects of disturbance and saltation, the effects of wind erosion were determined by loss of
mass, and dust emission was not measured.
Most other studies assessing dust suppressants concern unpaved road dust
emissions. Watson et al. (1996) summarizes several earlier studies and technical reports on
a wide variety of dust suppressants available to control road dust. Many of the studies
concerning dust suppressant use on unpaved roads have been conducted in hot, dry climates
and/or climates that are different than conditions at the field site at the Nephton mine. For
example, a Colorado study assessed three dust suppressants on unpaved roads, capturing
PM10 on filter paper in a specially designed “dustometer” mounted on the bumper of a truck
(Sanders et al., 1997). The researchers estimated that net aggregate loss was reduced after
treating the road surface with the dust suppressants: by 61% with a lignosulfonate, by 60%
with magnesium chloride, and by 42% with calcium chloride. However, dust emissions
from all of the treated test sections were similar to the untreated control section by the end
17
of the four and a half month measurement period. Another study considered the efficacy
of four dust suppressants tested on unpaved roads in California (Gillies et al., 1999). The
efficiency of each dust suppressant was defined as the percent reduction in dust emissions
compared to an untreated control section. The researchers found that the acrylic polymer
and the non-hazardous crude-oil-containing material performed with the highest efficiency,
exceeding 80% and 95%, respectively. However, the oil-based product was only tested for
8 months, rather than over the full 12 month study period. The petroleum emulsion with
polymer was only 49% efficient after 12 months, and the biocatalyst stabilizer was only
marginally efficient, 33%, a week after application, and was less efficient than the untreated
control section on subsequent measurement dates.
Studies have also been conducted on paved roads, although they tend to focus on
salt-based dust suppressants. Indeed, in a review paper, Amato et al. (2010) included ten
studies, seven of which concerned salt-based treatments. In one example, Norman &
Johansson (2006) found that calcium magnesium acetate reduced dust emissions from
paved roads in Sweden, although a reduction in the use of studded tires was also effective
in reducing PM10 emissions. A Norwegian study found that the application of magnesium
chloride reduced PM10 emissions by 56%, although its effectiveness gradually decreased
until there was no discernible reduction in dust emissions after ten days (Aldrin et al.,
2008).
While road studies may provide useful information on measurement methods and
experimental design, they are not comparable in terms of the application of dust
suppressants on mine tailings. One major difference is that the road studies involve
continuous disturbance by traffic on the road. In contrast, mines try to limit disturbance of
18
tailings storage areas since they are not as highly compacted or as stable as roads, and are
therefore much more vulnerable to disturbance. Also, salt-based treatments are not usually
applied on tailings since precipitation may cause them to leach through the tailings into the
surrounding ecosystem.
1.9 Dust Suppressant Selection
There are a wide variety of commercial dust suppressants currently available. Dust
suppressants which are applied topically in a liquid form may be placed into four broad
categories: salts, organic petroleum and oil-based products, pulp process co-products, and
acrylic polymers. In this project four products were tested: two pulp process co-products
and two acrylic polymers.
Salts
The two most common salts used for dust suppression are calcium chloride and magnesium
chloride. Since salts are highly water soluble, they have the capacity to move easily through
soil (Piechota et al., 2004). As a result, salts are primarily used on roads where the hard-
packed surface minimizes infiltration by dissolved salt ions. On the Unimin Nephton
tailings ponds, water applied by the irrigation system and rain water move through the
tailings to the low-lying clarification pond at the southern tip of the tailings. This water is
then pumped back onto the tailings through the irrigation system. Therefore, chloride ions
dissolved in rain and irrigation water are likely to move through the tailings and enter the
re-circulating water system. Also, because salts are water soluble, salt-based dust
suppressants are usually applied more frequently than other water-resistant products,
19
however the study design for this project is based on a one-time application. It is for these
reasons that a salt-based dust suppressant was not selected for inclusion in this study.
Organic petroleum and oil-based products
Many of these products have been found to contain high levels of heavy metals as well as
other toxic and carcinogenic compounds, and some have been banned for use in the USA
(Piechota et al., 2004). In addition, oils tend to evaporate quickly and are prone to UV
breakdown (Piechota et al., 2002). Therefore, it was decided that it was not advisable to
apply any products from this group on the field site tailings ponds.
Pulp process co-products
The paper pulping process results in two groups of co-products: sugar-based products, and
gum-based products. The most common of the first group of products are the
lignosulfonates, also called ligninsulfonates or simply lignins. The most common of the
second group of products are the tall oil pitches (Stantec Consulting Ltd., 2005)
Lignosulfonates are co-products of the sulphite paper pulping process. Lignin is a
natural polymer that performs as a glue to hold together the cellulose fibers to provide
strength and stiffness in a woody plant (Midwest Industrial Supply, 2017). Lignosulfonates
have a chemistry that provides three synergistic mechanisms to suppress dust: (1) The
adhesion properties of lignosulfonates bind soil particles together in a cohesive matrix. (2)
Lignosulfonates contain sugars which absorb moisture from the air and the surface,
reducing dust emissions. (3) Lignosulfonates, and particularly those formulated from
ammonium ligninsulfonate such as the Dust Fyghter LN100 tested in this project, function
as dispersants which coat soil particles, interfering with their tendency to aggregate and
20
allowing for better compaction and vertical water flow through the substrate (Midwest
Industrial Supply, 2017). As it dries, water evaporates from Dust Fyghter LN100 producing
a highly viscous material which may be rejuvenated when it becomes wet during
precipitation events and which is also freeze-thaw stable. It is commonly used on roads,
but may also be applied on tailings storage areas (Midwest Industrial Supply, 2017).
The name tall oil pitch is derived from the Swedish for pine, and is also called pine
oil pitch. It is a co-product of the sulphate pulp process which uses high temperatures and
high alkalinity to convert fatty acid esters and rosin into soaps. These soaps are then heated
and acidified to produce crude pine rosin, which is fractionated into four groups, one of
which is the tall oil pitch (Stantec Consulting Inc., 2005). Tall oil pitch is a dark brown,
sticky substance that is extremely viscous and is insoluble. The pitch is heated and
emulsified to create a product that penetrates, coats, and adheres to granular base materials
(EnsSolutions, 2017). The tall oil pitch tested in this project, Entac, has a very fine particle
size distribution which is engineered to be chemically and physically consistent and stable.
The performance of the product depends on the precise control of the size of the pitch
particles, and on achieving a balance between the positive and negative charges of the
emulsifiers. It works by completely coating the particles at the surface and then curing, a
process in which the product hardens and binds the particles together to form a waterproof
and insoluble seal (EnsSolutions, 2017).
Acrylic polymers
Acrylic polymers are engineered using nanotechnology to create molecules capable of
forming long chains that bind to the soil surface creating a protective cover over the surface
21
(EP&A Envirotac, Inc., 2017). The molecules form very long, straight chains which then
cross link to create other chains and, ultimately, a mesh-like grid over the soil surface
(Midwest Industrial Supply, 2017). Two acrylic polymers were chosen to be tested in this
project.
Soil Sement® is a widely used product that is capable of forming chains of up to a
million molecules in length. This is considerably longer than most naturally occurring
lignin polymers which usually range from 100 to 10000 molecules. Soil Sement® forms a
strong protective grid over the surface which is water-resistant and weather-resistant, and
is reputed to be as strong as steel and as resilient as rubber (Midwest Industrial Supply,
2017).
EcoAnchor forms a three-dimensional network structure in the upper 1 – 2 cm of
the soil, increasing the interconnection between the soil particles and creating a
homogeneous surface material. Treated areas exhibit an increase in tensile and compressive
strength and remain hard when wet and during freeze-thaw conditions. This product is
reputed, through repeated applications, to be able to form a surface that is strong enough
to resist heavy machinery traffic (Landloc Environmental, 2017).
The four dust suppressants tested in this study may be roughly divided into two
crust types: (1) the two viscous pulp process co-products, Dust Fyghter LN100 and Entac,
which protect the surface primarily through gluing the surface particles together; (2) the
two acrylic polymers, EcoAnchor and Soil Sement®, which protect the surface by forming
a strong grid comprised of long chains of molecules. The acrylic polymers are expected to
form a crust that is strong and elastic in nature, whereas the pulp co-products are expected
22
to form weaker crusts that are likely to be more ductile in nature. This dichotomy of
characteristics makes it challenging to predict dust suppressant crust performance since the
literature suggests that while strong crusts, such as physical crusts formed by fine-textured
soils and/or salts, are expected to offer superior protection to wind erosion, they can be out-
performed by more ductile biological crusts.
1.10 Objectives
The main objective of this study was to compare the protective capabilities of the four
commercial dust suppressants in preventing fugitive dust emissions. Laboratory tests
allowed for the dust suppressants to be tested under precisely controlled and consistent
conditions, including temperature, relative humidity, wind speed, abrasion by sand
particles, physical disturbance, and penetrometer tests to determine crust strength and
elasticity. In the field study, the goal was not only to compare the dust suppressants to each
other, but also to consider their efficacy in comparison with an irrigated water treatment
plot and an untreated control plot. This was important since the laboratory tests could not
subject the dust suppressants to uncontrolled, “real world” site conditions, such as a wide
range in temperatures, freeze-thaw cycles over the winter, precipitation, high winds and
storm events, physical disturbance by wildlife, vegetation growth, potential abrasion from
on-site sand-sized particles, and potential deposition and resuspension of on-site dust
sources. The field study also included an experiment involving a more realistic physical
disturbance of the test plots. As such, the field results are more applicable to industrial
applications where the performance of a given dust suppressant is under review. The
following hypotheses are noted as follows:
23
WT laboratory tests:
H1: When nepheline syenite tailings are treated with a dust suppressant, left to dry, and
then tested with a pin penetrometer, the crusts formed by the acrylic polymers, EcoAnchor
and Soil Sement®, are expected to have a higher maximum penetration force and Modulus
of Elasticity than the crusts formed by the pulp process co-products, Dust Fyghter LN100
and Entac.
H2: When nepheline syenite tailings are treated with a dust suppressant and subjected to
abrasion by saltating sand particles, the PM10 emission rate is expected to escalate through
time.
H3: When nepheline syenite tailings are treated with a dust suppressant and subjected to
either particle-free wind drag or abrasion by saltating sand particles, the PM10 emission
rate is predicted to scale inversely with the strength of the crust formed.
H4: When nepheline syenite tailings are treated with a dust suppressant and left to dry
undisturbed, the PM10 emission rate is expected to be very low, as compared to that
following physical disturbance.
Nephton field tests:
H5: PM10 emission rates measured on the test plots are predicted to scale inversely with
the strength of the crust formed.
H6: Weathering of the protective crusts will result in increases over time in PM10 emission
rates from all of the treated plots.
24
H7: PM10 emission rates measured from disturbed sections of the test plots are expected to
be substantially higher than those measured from undisturbed sections of the plots.
1.11 Study Structure
This thesis consists of a laboratory and a field study, each with several components:
(i) Laboratory Study
The laboratory study was conducted in the Trent University Environmental Wind Tunnel
laboratory using nepheline syenite tailings obtained from the field site. Dust suppressant
crusts were tested with a penetrometer to determine crust strength and Modulus of
Elasticity. Wind tunnel experiments tested the ability of the four commercial dust
suppressants to prevent PM10 emissions under clean air conditions, as well as during
abrasion by saltating sand particles. The dust suppressant surfaces were also tested for their
efficacy in preventing dust emissions after physical disturbance.
(ii) Field Study
The field study took place on nepheline syenite tailings pond #4 at the Unimin Ltd Nephton
Mine in Southern Ontario. Six test plots included four treated with each of the commercial
dust suppressants, one treated with water, and one control plot that was untreated and not
irrigated. PM10 emissions were measured over a six month period and the plots were also
qualitatively assessed after one year. Crust samples were tested for strength and elasticity
with a penetrometer, as well as for gravimetric moisture content. One section of each test
plot was physically disturbed and tested for PM10 emissions.
25
Chapter 2 Methodology
2.1 Laboratory Methods
The laboratory study was conducted in the Trent University Environmental Wind Tunnel
laboratory using nepheline syenite tailings obtained from the field site.
2.1.1 Laboratory Test Tray Preparation
Nepheline syenite tailings obtained from the Nephton tailings pond #4 field site were used
in the laboratory experiments. The tailings were first sieved by hand to remove any visible
organic matter and then oven-dried at 105°C. The tailings were then sieved by hand again
to remove any remaining visible organic matter and any small aggregate clumps that
formed during the drying process. The tailings had a median particle diameter of 35 µm
and a range of diameter between 1 µm and 344 µm, with 19% of the particles being in the
PM10 range (Figure 2.1).
The tailings were placed in aluminum trays specifically fabricated for this study.
The trays are 35 cm wide with a length of 100 cm and a depth of 2.5 cm. Each dimension
was carefully chosen to suit the experiment and wind tunnel dimensions: (1) A tray width
of 35 cm is the widest that can be placed in the tunnel while still avoiding the wall effects
of the tunnel on the boundary layer flow. (2) A tray length of 100 cm allows for sufficient
fetch such that the sand particles released from the particle feed during the abrasion runs
will impact the tray several times. Also, experiments conducted in the TEWT found that
26
Figure 2.1. The particle size cumulative frequency distribution of the tailings used in the
laboratory experiments (Horiba LA-950V2 laser particle size analyzer).
Table 2.1. Dust suppressant application rates as recommended by the respective manufacturer or
supplier.
______________________________________________________________________________
Dust Suppressant Abbreviation Application rate
(Product : water ratio / area)
______________________________________________________________________________
A) Pulp process co-product:
Dust Fyghter LN100 LN 1 L product : 3 L water / 4.9 m2
(Midwest Industrial Supply)
Entac EN 1 L product : 4.4 L water / 3.6 m2
(Enssolutions Ltd.)
B) Acrylic polymer:
EcoAnchor EA 1 L product : 8 L water / 0.9 m2
(Landloc Environmental)
Soil Sement® SS 1 L product : 9 L water / 9.8 m2
(Midwest Industrial Supply)
______________________________________________________________________________
The application rates have been converted to metric units for consistency. All products were applied
according to the manufacturer’s specifications listed in this table.
0
20
40
60
80
100
1 10 100
Cu
mu
lati
ve
Fre
qu
ency
(%
)
Grain Diameter (µm)
27
the effects of fetch on beds longer than 50 cm have no influence on the PM10 emission rate
(Sanderson et al., 2014). (3) A tray depth of 2.5 cm can easily be accommodated within the
tunnel floor, while still having sufficient depth for the tailings in the tray to be coated with
the dust suppressants, without the dust suppressants leaching through the tailings to the
bottom of the tray.
For each dust suppressant, three trays were filled with tailings and then carefully
levelled using a large plastic ruler. Each tray was sprayed with the dust suppressant
following the specifications of the supplier (Table 2.1), with EN being applied in two coats,
20 minutes apart, and the other three suppressants in a single coat. The dust suppressants
were applied using a 1L plastic bottle with a small, hand-operated lever spray nozzle. The
direction of spray angle was regularly changed so that the tailings were as evenly and as
thoroughly coated as possible. Each tray was then placed in the Trent University
Environmental Wind Tunnel laboratory, at 20°C and 20% relative humidity, and allowed
to cure for a minimum of seven days before the wind tunnel tests were conducted.
2.1.2 Laboratory Crust Preparation and Measurement
A small sample crust was prepared in a Petri dish for each dust suppressant, so that the
treated surface could be measured for strength and elasticity with a pin penetrometer. The
Petri dish filled with tailings was placed beside one test tray for each dust suppressant and
was sprayed at the same time and in the same manner as the tray. The plastic Petri dishes
measured 9 cm in diameter with a depth of 2 cm. After drying in the wind tunnel for seven
days under the same conditions as the test trays, each Petri dish crust was placed on a weigh
scale and punched with a pin penetrometer 24 times in a random pattern spread evenly
28
across the surface. The penetrometer pin had a flat tip with a diameter of 0.25 mm and was
lowered at a rate of 50.8 µm s-1. The load on the crust was recorded each second until the
crust was fully penetrated by the pin. These measurements allowed for determination of
the maximum penetration force (MPF, N) required to break through the crust from the
equation:
MPF = m * g (2.1)
where m is the maximum applied load (kg) and g is gravitational acceleration (m s-2). The
degree or modulus of elasticity (MoE, N m-1) exhibited by the crust during the penetration
was also calculated from the equation:
MoE = ∆m
∆d* g (2.2)
where ∆m and ∆d are the changes in the maximum applied load (kg) and the penetration
depth of the pin (m) over the most linear part of the penetration curve. After the
penetrometer tests were completed, each crust was weighed, oven-dried at 105°C for 24
hours, and then weighed again to determine the gravimetric moisture content (GMC) from
the equation:
GMC (%) = Weight of moist sample - Weight of dry sample
Weight of dry sample * 100 (2.3)
29
2.1.3 The Trent University Environmental Wind Tunnel
The laboratory PM10 emission tests were conducted in the Trent University Environmental
Wind Tunnel (TEWT). The TEWT is a suction-type boundary layer wind tunnel with a
cross section 70 cm (wide) by 77 cm (high), and a 13.8 m long working section. The tunnel
intake consists of a honeycomb straw filter that straightens the airflow and minimizes
turbulence. An array of 2 cm diameter wooden dowels located at the opening of the tunnel
promotes the development of boundary-layer flow. Detailed descriptions of the TEWT may
be found in Nickling & McKenna Neuman (1997) and McKenna Neuman (2003).
The TEWT laboratory has an environmental system which controls the humidity
and temperature. All of the laboratory tests were conducted with the environmental controls
in the tunnel set to 20°C and 20% relative humidity (RH). Because the wind tunnel runs
were conducted in December and January, it was necessary to use the baseboard heaters to
supplement the environmental system. This resulted in a fairly large range of temperature
around the requested temperature, such that temperature varied by approximately ±2°C.
RH was generally more consistent, with a variability of approximately ±0.5%.
2.1.4 Wind Tunnel Instrumentation and Configuration
The instrumentation and configuration of the TEWT used in the laboratory research is
shown in Figure 2.2. The TEWT is equipped with a sediment feed located near the entrance
of the tunnel above the array of wooden dowels (Figure 2.2). In this study, quartz sand of
a median diameter of 334 µm (Horiba laser particle size analyzer) was released in order to
30
TEWT Configuration
1. Tunnel Entrance 4. Upwind pitot tube 7. Downwind DT intake tubes
2. Sediment Feed 5. Test tray 8. Downwind pitot tube
3. Upwind DT intake 6. VIVID 9i laser scanner 9. Sediment trap 0 1 2
tubes (background) (above test tray) m
Figure 2.2. Schematic and photograph of the TEWT configuration and instrumentation used in the laboratory research
(TEWT Research Group, 2013).
31
initiate saltation to abrade the test surfaces. The rate of sediment feed was measured using
a vertically integrating, wedge-shaped passive sediment trap located near the tunnel outlet,
3.92 m downstream from the leading edge of the test tray (Figure 2.2). The sand trap has a
2 cm wide opening with a height of 23.1 cm and a downstream width of 13.1 cm. The
sediment feed rate, q (kg m-1 s-1), was determined from the equation:
q = mw * ∆t
(2.4)
where m is the total sediment mass (kg), w is the width of the sand trap opening (m), and
∆t is the duration of sampling time in seconds. A detailed evaluation of the sediment trap
may be found in Nickling & McKenna Neuman (1997).
A Konica Minolta VIVID 9i laser scanner was placed in an opening in the roof of
the tunnel directly over the test trays (Figure 2.2). The VIVID 9i emits a horizontal light-
sheet through a cylindrical lens and determines the distance to an object by triangulating
the light reflected by the object. It scans an area of 0.05 m2, which represents 14% of the
total surface area of each test tray. The software Polygon Editing Tool 2.40 runs the VIVID
9i, expressing points located in three-dimensional space as three-dimensional Cartesian
coordinates. The initial scan data were saved as CDK files which could be used to create
three-dimensional images of the surfaces with the Polygon Editing Tool 2.40 software. The
scan data were also exported as ASCII files so that they could be imported into EXCEL to
calculate the range in the surface elevation after each wind tunnel test.
Four TSI DustTrak™ II Aerosol Monitors 8530 (DTs) were used to measure the
PM10 concentration in the airstream. PM10 inlet filters were attached and the DTs were set
to record concentrations in mg m-3 every second. The DTs were attached with
32
approximately 20 cm of Tygon® tubing to copper tubes with an outer diameter of 8 mm
and a length of 1 m. Two DTs were located 2.13 m upwind of the leading edge of the test
surface to determine the background dust concentration (Figure 2.2). The wind tunnel was
thoroughly cleaned before each run and no run was started until a background PM10 level
of less than 0.02 mg m-3 was achieved. Typically, the majority of runs were conducted with
an initial background level of less than 0.01 mg m-3. Two more DTs were located 1.15 m
downwind from the leading edge of the test tray to measure dust emissions from the test
tray surface (Figure 2.2). The intake tubes both upwind and downwind were placed 5 cm
and 10 cm above the tunnel floor. These heights were chosen based on previous research
conducted in the TEWT (Sanderson et al., 2014; Ogungbemide, 2017), as well as a series
of pilot tests conducted prior to the commencement of the tunnel runs. The main concerns
in setting the DT intake heights were to ensure that the lower DT intake was capturing a
high level of PM10 without being too close to the tunnel floor such that the airflow was
perturbed by flow acceleration beneath the tube, and to ensure that the upper DT intake
was still in the dust plume released by the test tray and was not simply recording
background dust levels. With regards to the DT limitations discussed in section 1.5, the DT
concentrations can be considered in a comparative manner since all of the test trays were
subjected to the same conditions and wind speeds, and contained only one dust source. The
background PM10 measurements from the two upwind DTs were averaged using a running
average over ten seconds, and these background dust levels were subtracted from the
downwind PM10 readings. The resulting values were used to determine the PM10 emission
rate, F (µg m-2 s-1), for each test surface from the equation (Gillette, 1978):
F = -0.41* u**(c2- c1 )
ln (z2 / z1) (2.5)
33
where u* is the friction velocity (m s-1) and c1 and c2 are the PM10 concentrations (µg m-3)
at heights z1 (0.05 m) and z2 (0.10 m). The average emission rate for an entire wind tunnel
test, F̅, was calculated from the emission fluxes determined for each second of the test
duration. The values for F̅ from the three replicate test trays were also averaged to
determine the overall emission rate, F̿, for a given dust suppressant.
2.1.5 Wind Velocity Profiles
Vertical profiles of the horizontal wind speed were measured to determine the friction
velocity (u*, m s-1) and aerodynamic roughness (zo, m) based on the Prandtl equation:
kuz
u* = ln (
z
zo) (2.6)
where k is the von Kármán constant. Wind velocity, uz, at height z was sampled with micro-
pitot tubes with an outer diameter of 3 mm and an inner diameter of 1 mm, beginning at
z_=_0.005m above the surface and traversing vertically through the boundary layer to a
height of z = 0.4 m in the freestream. Velocity profiles were measured simultaneously at
two locations in the tunnel, with upwind and downwind pitot tubes located 5.5 m and
9.1_m, respectively, from the entrance of the tunnel (Figure 2.2). The results from five
clean air profiles using both pitot tubes were averaged to determine the friction velocity,
u* = 0.35 m s-1, and aerodynamic roughness, zo = 2.83 * 10-5 m, for a requested freestream
velocity of 7 m s-1 (Figure 2.3). Five more profiles measured in the presence of a saltation
cloud were averaged to give u* = 0.33 m s-1 and zo = 3.21 * 10-5 m for a requested freestream
velocity of 7_m_s-1 (Figure 2.3). As expected, the friction velocity was lower in the
presence of the saltation cloud since the sand particles moving in saltation extract
34
momentum from the near-surface wind, which results in a reduction of the near-bed wind
speed (Nickling & McKenna Neuman, 2009). In a natural system, the transfer of
momentum between saltating particles and the near-surface wind creates a negative
feedback; that is, as the wind picks up saltating grains, the windspeed is reduced, until the
system reaches a ‘steady state saltation’ (Anderson & Haff, 1991). In the wind tunnel, the
continuous introduction of sand particles from the sand feed located at the entrance of the
tunnel creates a consistent level of saltation and, therefore, a consistent velocity profile.
Figure 2.3. Wind velocity profiles obtained at u∞ = 7 m s-1 under clean air conditions and
saltation conditions (q = 0.0077 kg m-1 s-1) , corresponding to friction velocities (u*) of 0.35
m s-1 and 0.33 m s-1, respectively, and aerodynamic roughness values (z0) of 2.83 * 10-5 m
and 3.21 * 10-5 m, respectively.
y = 3E-05e1.1761x
R² = 0.9984
y = 3E-05e1.2278x
R² = 0.999
0.001
0.01
0.1
1
3 4 5 6 7 8
z (m
)
uz (m s-1)
uz clean air uz saltation
35
2.1.6 Wind Tunnel Experimental Procedure
The test trays were placed so that the leading edge of the tray was located 6.68 m from the
tunnel entrance (Figure 2.2). Each test tray was inserted into the tunnel floor so that the
surface of the tray was flush with the surrounding bed surface. The edges of the test trays
were taped to the surface so that there were no gaps between the tray and the tunnel floor.
Each test surface was then subjected to wind tunnel experiments as outlined below:
i) Initial Laser Scan: A digital contour map of the surface before testing.
ii) Clean Air Run: A ramped velocity run with eight requested freestream velocities in
which PM10 concentrations were measured to determine the emission rate in response to
gradually increasing wind speeds. Beginning with a speed of 5 m s-1, the wind speed was
increased by 1 m s-1 every 90 seconds to a maximum wind speed of 12 m s-1. These
requested wind speeds corresponded to a low initial friction velocity of 0.26 m s-1, which
was gradually increased to 0.63 m s-1, a range which encompasses the range of mean
friction velocities measured at the Nephton field site in 2014 and 2015 (D. Ogungbemide,
personal communication, October 20, 2016).
iii) Saltation Run: An hour long run in which the surfaces were abraded with sand of a
median diameter of 334 µm. The lengthy test time was chosen because the test surfaces
were expected to be strong enough to withstand this level of abrasion, based on results from
previous studies conducted in the TEWT (McKenna Neuman & Maxwell, 1999; 2002;
Langston &McKenna Neuman, 2005; McKenna Neuman et al., 2009). The wind tunnel
was set for a ramped run with 12 randomly chosen wind speeds lasting for five minutes
each. The pattern of requested freestream wind velocities was: 6, 8, 7, 10, 7, 9, 6, 10, 8, 9,
36
7, 8 m s-1. The variable wind speeds were chosen to approximate fluctuations in wind speed
that may be experienced in a natural setting. After every second wind speed setting, i.e.
every ten minutes, an additional minute of run time was added so that the DT intake tubes
could be cleared of sand particles using an air compressor. The PM10 measurements during
these cleaning intervals were not included in the test data.
iv) Post-Saltation Run Laser Scan: A digital contour map of the surface after abrasion by
sand particles.
v) Physical Disturbance: The surface was physically disturbed by pushing an array of nail
heads through the crust. A 50 cm by 30 cm board, approximately half the size of the tray
area, held 30 nails, with a head diameter of 1 cm and a stem diameter of 0.33 cm, placed
in pairs at regular intervals and protruding 3 cm from the board so that the surface of the
board would not make contact with the surface of the tray (Figure 2.4). The board was
pushed as far as possible into each half of the tray taking care to avoid removing any
sections of the crusts that cracked due to the disturbance. This approach ensured that each
test tray was disturbed as consistently as possible, although the amount of force used to
fully penetrate each crust was not consistent. The nails were placed to ensure variation in
the orientation of the pairs of nail heads.
vi) Post-Disturbance Laser Scan: A digital contour map of the surface after physical
disturbance.
vii) Post-Disturbance Clean Air Run: A ramped velocity run with the same specifications
as the initial clean air run.
37
viii) Final Laser Scan: A digital contour map of the surface at the completion of the wind
tunnel tests.
Figure 2.4. The 50 cm by 30 cm board used to disturb the test tray crusts. The board held
30 nails with a head diameter of 1 cm and a stem diameter of 0.33 cm, each protruding 3
cm from the surface. The length of the bar represents 4 cm.
38
2.2 Field Methods
2.2.1 Field Site Preparation
The field study took place on the nepheline syenite tailings ponds at the Unimin Ltd
Nephton Mine in Southern Ontario. On June 1, 2016 a small section of tailings pond #4
near the berm road at the western edge of the pond was scraped clear of vegetation by
Unimin personnel (Figure 2.5). Tailings pond #4 has an irrigation system in place, and the
test plot using water as a dust suppressant was placed within range of two of the berm
sprinklers, which were activated by the mine on dry days as part of the standard operating
procedures at the facility. The other five test plots, including the untreated control plot and
the four commercial dust suppressant plots, were located perpendicular to the berm road
out of range of the sprinklers (Figure 2.5). The area was levelled with rakes and the plots
were measured and outlined with bamboo stakes and synthetic twine. The irrigated test plot
measured 3_m by 15 m, while the other five test plots measured 2.5_m by 16 m. The slight
differences in plot size were necessary due to the size and shape of the area cleared by the
mine personnel for this project. The commercial dust suppressants were then applied with
a “Workhorse” 25 gallon economy sprayer powered by a 12 volt pump according to the
manufacturers specifications (Table 2.1, Figure 2.5d). Entac was applied in three coats
approximately 20 minutes apart, while the other three products were applied in single
applications. One plot was left untreated to serve as a control plot.
39
(a) N
40
Figure 2.5. The field research site at the Unimin Ltd Nephton mine. (a) Tailings pond #4, the research plots, and a wind rose from July 2015 (Google Earth, 2017; Ogungbemide, 2017); (b) location of research plots on the Nephton tailings ponds; (c) tailings pond #4 with established vegetation before being cleared; (d) application of Entac, June 1, 2016; (e) the cleared area of tailings pond #4 showing five of the research plots one week after dust suppressant application, from left to right: EN, C, LN, EA, SS.
(b) (c)
(d) (e)
41
Figure 2.6. (a) The miniature Portable In-Situ Wind Erosion Laboratory (PI-SWERL; Dust-Quant LLC, 2011); (b) under the drum of a Miniature PI-SWERL (Dust-Quant LLC, 2011); (c) the physical disturbance of one end of the research plots by two passes of a Caterpillar 257B3 skid steer loader; (d) the tracks caused by the physical disturbance of the research plots.
(a) (b)
(c) (d)
42
2.2.2 Field PM10 Emission Measurements
PM10 emission rates from the test plots were measured with a miniature Portable In-Situ
Wind Erosion Laboratory (PI-SWERL; Figure 2.6a&b). Each PI-SWERL test was
preceded by the operation of the clean air blower for 90 seconds to ensure that there was
no dust in the chamber. Each ramped test run consisted of five target RPMs: 400, 1400,
2400, 3400, and 4400, lasting for 90 seconds each. Four replicate measurements were made
on each plot on each of seven site visits (Table 2.2).
Table 2.2. Field Measurement Dates.
________________________________________________________________________
Measurement Time Elapsed Measurement Date
________________________________________________________________________
M1 1 week June 6, 2016
M2 3 weeks June 21, 2016
M3 7 weeks July 18, 2016
M4 11 weeks August 15, 2016
M5 15 weeks September 12, 2016
M6 21 weeks October 24, 2016
M7 24 weeks November 14, 2016
________________________________________________________________________
The PI-SWERL output, a sample of which may be found in Table A of the
appendix, includes the PM10 concentration (mg m-3) measured by the DT each second, the
PM10 mass for each target revolution per minute (RPM; µg), the instantaneous emission
rate E (µg s-1), and the total mass emitted during the test (µg). The instantaneous emission
rate is calculated from:
E = C * Qf (2.7)
43
where C is the PM10 concentration (µg m-3) sampled each second, and Qf is the clean air
flow rate (m3 s-1). From this the average emission flux, Fi, is given for each ramp as:
Fi = ∑ ( C * Qf *
end,ibegin,i ∆t )
(tend,i- tbegin,i) * Aeff (2.8)
where ∆t is the sample rate, Aeff (0.035 m2) is the effective area under the PI-SWERL ring
(Etyemezian, et al., 2014) and (tend,i- tbegin,i) is the duration of the ramp in seconds. The
average PM10 emission flux, F̅, over the full length of each of the four replicate PI-SWERL
tests was calculated by:
F̅ = ∑ E / Aeff
n (2.9)
where n is the number of concentrations sampled. In order to determine the efficacy of the
dust suppressants, the PM10 emission fluxes from each dust suppressant surface were
normalized by the PM10 emission flux of the control, F′surfacetreatment:
F'surfacetreatment = F̿surface treatment
F̿control (2.10)
Where F̿ is the average PM10 emission flux for all of the replicate emission measurements
for a given dust suppressant. Note, for a given treated surface, an F′ value between 0 and 1
indicates that the surface is less emissive than the control, a value of 1 indicates that the
surface is equally as emissive as the control, and a value higher than 1 indicates that the
surface is more emissive than the control.
44
2.2.3 Field Crust Measurements
The following measurements were also undertaken at each site visit:
i) Crust strength and elasticity: Three crust samples were obtained by pushing small plastic
cups with the bottoms cut out into each test surface. The bottom of each cup was then
carefully re-attached and the crust samples were returned to the laboratory. The crusts were
then tested with the same pin penetrometer described in the laboratory methods. 24 punches
were distributed in a random pattern among the three crusts collected from each research
plot, taking care to avoid any cracks or uneven areas, and ensuring that penetration was
perpendicular to the surface.
ii) Gravimetric Moisture Content (GMC): Five samples were collected from each test
surface and were oven-dried at 105°C for 24 hours to determine GMC (Equation 2.3). Note,
no samples were collected on the first measurement date, M1.
2.2.4 Field Physical Disturbance Tests
On August 2nd, 2016, two months after the dust suppressants were applied, a Caterpillar
257B3 skid steer track loader was driven twice over the distal ends of the research plots,
once forward, and once in reverse (Figure 2.6c&d). Emissions from the disturbed area were
then measured immediately with the PI-SWERL, as well as four and ten weeks after
disturbance, on August 29th and October 11th. Due to very high emission rates on the day
of the disturbance, it was necessary to reduce the target RPMs for the PI-SWERL tests, and
all but one test still had to be terminated before completion, due to unacceptably high DT
readings. The decision to terminate PI-SWERL tests early was made because, as with the
45
wind tunnel experiments, there was concern that the DT would become clogged if a large
amount of dust was drawn through the instrument. Therefore, even though the DT is
capable of measuring PM10 concentrations of up to 300 mg m-3, the PI-SWERL was shut
down when the DT concentrations exceeded 110 mg m-3, and no such data were included
in the analysis when DT values exceeded this PM10 concentration. The subsequent
measurements, conducted four and ten weeks after the disturbance of the plots, followed
the target RPMs of the undisturbed PI-SWERL tests, although about 33% of the tests
conducted four weeks after disturbance also had to be terminated before their conclusion
due to high PM10 concentrations. The PI-SWERL test data from the runs on the disturbed
sections of the test plots were treated in the same manner as described in section 2.2.2.
2.2.5 Statistics
All data were tested for normality using the Shapiro-Wilk test. Relations involving non-
normal data were tested for significance using the Kruskal-Wallis and Wilcoxon Signed-
Rank tests in R. All normally distributed data were tested for significance using ANOVA
and Tukey’s honest significant difference (HSD) tests in Excel. All tests were conducted
with a confidence interval of 0.95.
46
Chapter 3 Laboratory Results and Discussion
3.1 Laboratory Crust Penetrometer Tests
Note, all of the results, both laboratory and field, are colour coded: Dust Fyghter LN100
(LN) is represented by dark green; Entac (EN) is represented by light green; EcoAnchor
(EA) is represented by dark orange; Soil-Sement® (SS) is represented by light orange. For
the wind tunnel tests, the emissions curves represent the ‘”intermediate” replicate run for
each test surface, i.e. the run that had the second highest overall emission rate.
As expected, the pin penetrometer results demonstrated a marked contrast between
the two acrylic polymers, EA and SS, and the two pulp-based products, LN and EN. The
strong and brittle acrylic polymers exhibit stress-stress curves which have a steep and fairly
linear ascending limb followed by abrupt brittle failure and a dramatic fall-off in applied
load (Figure 3.1a). On the other hand, the stress-strain relationship for the weaker and more
ductile pulp process co-product crusts have a much more gradual ascending limb – note
the much lower secondary axis range – followed by a gradually descending limb and
exhibit no brittle failure at the maximum penetration force point (Figure 3.1a).
As compared to the pulp process co-products, the acrylic polymer crusts,
EcoAnchor and Soil-Sement®, had significantly higher values for the maximum force
(MPF) required to penetrate the crusts (Figure 3.1b). EA formed a stronger crust than SS,
although the difference was not statistically significant. In terms of the two pulp co-
products, there was no significant difference in the strength of the two crusts. The two
47
Du
ctil
e
Bri
ttle
(a)
(b)
Figure 3.1. Laboratory penetrometer results: (a) Sample stress-strain curves of a strong
and brittle crust (SS replicate 18) and a weak and ductile crust (LN replicate 13); (b) The
mean maximum penetration force (MPF) and Modulus of Elasticity (MoE) for each dust
suppressant crust prepared in the laboratory.
0
10
20
30
40
50
60
70
80
90
100
0
100
200
300
400
500
600
700
0 1 2 3 4
Ap
plied
Lo
ad (g
) -W
eak an
d D
uctile C
rust
Appli
ed L
oad
(g)
-S
tro
ng a
nd
Bri
ttle
Cru
st)
Distance (mm)
SS R18 LN R13
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0
1
2
3
4
5
6
7
8
9
10
11
LN EN EA SS
Mo
du
lus
of
Ela
stic
ity (
N m
-1)
Max
imu
m P
enet
rati
on F
orc
e (N
)
Dust Suppressant
MPF
MoE
48
acrylic polymers were also significantly more elastic than the pulp process co-product
crusts, and the SS crust was slightly more brittle than the EA crust. For the pulp process
co-products, the LN crust was more slightly more elastic than the EN crust (Figure 3.1b).
All compared values for MoE were statistically different (Kruskal-Wallis, p = 1.8 * 10-15)
while those for MPF were statistically different (Kruskal-Wallis, p = 0.017) except for the
following two pairings: LN – EN and EA – SS.
3.2 Laboratory Gravimetric Moisture Content
With the TEWT set to a fairly low RH of 20%, each Petri dish crust formed by the
application of each of the four dust suppressants was quite dry after 7 days. The GMC
measurement of each crust reflected only slight differences between the crusts: LN –
0.23%, EN – 0.27%, EA – 0.15%, SS – 0.15%, with the EN exhibiting a slightly higher
GMC than the other three dust suppressants, particularly compared to the two polymers.
On the test trays, the EN crust did feel slightly soft to the touch and was easily dented by
the fingers at the termination of the tunnel tests. However, all four surface crusts had very
low GMC levels below 0.3%.
3.3 Wind Tunnel Clean Air Runs
During the clean air runs, the EN, EA, and SS emitted very low levels of PM10, all
with peak emission fluxes (Equation 2.5) of less than 10 µg m-2 s-1 (Figure 3.2). In addition,
these three dust suppressants exhibited similarities in the magnitude and pattern of their
PM10 emissions. In general, the clean air runs did not produce ‘typical’ emission curves in
49
Table 3.1. Average emission flux values, F̅ (µg m-2 s-1), for the initial clean air runs, the saltation
runs, and the post-disturbance clean air runs in the wind tunnel tests. Data also include the mean
emission flux, F̿, the standard deviation (σ), and the Coefficient of Variation (σ
F̿ *100, CV, %) for
each dust suppressant.
______________________________________________________________________________
Surface Clean Air Runa Saltation Runb Post-Disturbance
Treatment Clean Air Runc
______________________________________________________________________________
Dust Fyghter LN100
Tray 1 100.98 ------ 107.93
Tray 2 36.24 ------ 86.95
Tray 3 127.81 ------ 191.07
F̿ 88.34 ------ 128.65
σ 47.07 55.07
CV (%) 53 43
Entac
Tray 1 0.38 9.21 26.09
Tray 2 0.09 18.10 67.74
Tray 3 0.31 8.17 16.31
F̿ 0.26 11.83 36.71
σ 0.15 5.46 27.31
CV (%) 57 46 74
EcoAnchor
Tray 1 0.02 2.09 11.81
Tray 2 0.00 11.24 3.39
Tray 3 0.44 15.94 17.96
F̿ 0.15 9.76 11.06
σ 0.25 7.04 7.31
CV (%) 160 72 66
Soil Sement®
Tray 1 0.04 22.80 156.82
Tray 2 0.08 59.54 19.83
Tray 3 0.41 14.66 19.12
F̿ 0.18 32.33 65.25
σ 0.20 23.91 79.30
CV (%) 115 74 122
______________________________________________________________________________
Each set of three replicates was tested for normality using the Shapiro-Wilk test: (a, c) not normally
distributed, no significant difference (Kruskal-Wallis, p = 0.082 and 0.055, respectively); (b)
normally distributed, no significant difference (ANOVA, p = 0.0.093).
50
which increases in wind speed cause a spike in PM10 emission followed by a decay
throughout the step until the next ramp in wind speed. Instead, dust emissions were very
low in the initial stages, less than 1 µg m-2 s-1, and only began to spike at the highest two
wind speeds. The LN test trays were much more emissive, exhibiting peak PM10 emissions
several orders of magnitude higher than the other three dust suppressants (Figure 3.2). The
average PM10 emission rate of LN during the clean air runs also was at least two orders of
magnitude higher than the other three dust suppressants. Despite small differences, the
average emission rate of the other three dust suppressants, EA, EN, and SS, was low and
of a similar magnitude (Table 3.1).
(a)
0
0.1
0.2
0.3
0.4
0.5
0.6
0
400
800
1200
1600
2000
0 200 400 600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
LN tray 1
u*
51
(b)
(c)
0
0.1
0.2
0.3
0.4
0.5
0.6
0
1
2
3
4
5
6
7
8
0 200 400 600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
EN tray 3
u*
0
0.1
0.2
0.3
0.4
0.5
0.6
0
1
2
3
4
5
6
7
8
0 200 400 600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
EA tray 1
u*
52
(d)
Figure 3.2. Time series of the emission fluxes measured in the clean air wind tunnel runs
for the intermediate test run of each dust suppressant. (a) LN tray 1; (b) EN tray 3; (c) EA
tray 1; (d) SS tray 2.
3.4 Wind Tunnel Saltation Runs
Abrading the surfaces with saltating sand caused the EN, EA, and SS test trays to emit
substantially more PM10 compared to the clean air runs (Table 3.1). In addition, all of the
test trays emitted PM10 consistently throughout the hour-long test (Figure 3.3). In the
intermediate run of each of these three dust suppressants, the test surfaces emitted PM10 at
levels two orders of magnitude higher than during the clean air runs. However, when
considering the mean PM10 emission flux for the three replicates, F̿, for each dust
suppressant, SS emitted three orders of magnitude more PM10 than the clean air runs (Table
3.1). This was due to the fact that the second replicate SS tray emitted between 2.5 and 4
times more PM10 than the other two SS trays. The reasons for this are unclear, since the
0
0.1
0.2
0.3
0.4
0.5
0.6
0
1
2
3
4
5
6
7
8
0 200 400 600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
SS tray 2
u*
53
tray was subjected to the same experimental conditions as all of the test trays, and was
similar in appearance to the other two SS trays.
The LN test trays were not able to withstand any sand abrasion without emitting
extreme amounts of dust, and are therefore not included in the results presented in this
section. During the LN test runs, upon initiation of the sand feed in the tunnel, the DTs
downstream of the test trays immediately reached PM10 concentrations in excess of the
predetermined cut-off level of 20 mg m-3. This level was established based on previous
research conducted in the TEWT in which allowing higher levels of PM10 concentrations
was found to block the DT intake tubes, causing them to malfunction (C. McKenna
Neuman, personal communication, December 20, 2016). As a result, all three attempts to
subject the LN trays to sand abrasion had to be terminated within seconds of initiating the
sand feed in the wind tunnel.
(a)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0
100
200
300
400
500
600
0 600 1200 1800 2400 3000 3600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
EN tray 1
u*
54
(b)
(c)
Figure 3.3. Time series of the emission fluxes measured in the saltation runs for the
intermediate test run of three of the dust suppressants. (a) EN tray 1 – mean sand transport
rate 0.00804 kg m-1 s-1; (b) EA tray 2 – mean sand transport rate 0.00728 kg m-1 s-1; (c) EN
tray 1 – mean sand transport rate 0.00886 kg m-1 s-1.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0
100
200
300
400
500
600
0 600 1200 1800 2400 3000 3600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
EA tray 2
u*
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0
100
200
300
400
500
600
0 600 1200 1800 2400 3000 3600
u* (
m s
-1)
F (
µg m
-2s-1
)
Time (s)
SS tray 1
u*
55
3.5 Wind Tunnel Disturbance Runs
The PM10 emission curves for the disturbance runs generally exhibited more ‘typical’
emission curves than the initial clean air runs (Figure 3.4). That is, each increase in wind
speed generated increasingly higher peaks in dust emissions, which then gradually decayed
through the 90 second step as the supply of dust on the surface was exhausted, until the
next ramp in wind velocity. Similar to the runs with sand abrasion, the clean air runs
conducted after the surfaces were physically disturbed resulted in significantly higher
levels of PM10 emission fluxes compared to the initial clean air runs for all of the test
surfaces (Table 3.1). LN was the most emissive, exhibiting an average PM10 emission rate
an order of magnitude higher than the other three dust suppressants. In comparison to the
initial clean air runs before the surfaces were disturbed, the LN emission rate after
disturbance was, again, an order of magnitude higher. The other three dust suppressants
exhibited PM10 emission rates two orders of magnitude higher than during the initial clean
air runs. However, EN, EA, and SS still had lower emission rates after disturbance than
LN since they exhibited very low PM10 emission rates, less than 0.3 µg m-2 s-1, in the initial
clean air runs (Table 3.1). These results suggest that physical disturbance of the test
surfaces exposed particles, initially protected by the surface crust formed by the application
of the dust suppressants, to wind drag.
56
(a)
(b)
0
0.1
0.2
0.3
0.4
0.5
0.6
0
200
400
600
800
1000
1200
1400
0 200 400 600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
LN tray 1
u*
0
0.1
0.2
0.3
0.4
0.5
0.6
0
50
100
150
200
250
300
350
400
0 200 400 600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
EN tray 1
u*
57
(c)
(d)
Figure 3.4. Time series of the emission fluxes measured in the clean air wind tunnel runs
after physical disturbance of the test tray crusts for the intermediate test run of each of the
four dust suppressants. (a) LN tray1; (b) EN tray 1; (c) EA tray 1; (d) SS tray 2.
0
0.1
0.2
0.3
0.4
0.5
0.6
0
20
40
60
80
100
120
140
160
0 200 400 600
u*
(m
s-1
)
F (
µg m
-2s-1
)
Time (s)
EA tray 1
u*
0
0.1
0.2
0.3
0.4
0.5
0.6
0
50
100
150
200
250
300
350
400
450
500
0 200 400 600
u* (
m s
-1)
F (
µg m
-2s-1
)
Time (s)
SS tray 2
u*
58
3.6 Laboratory Crust Variability
Each test tray was subjected to a series of three wind tunnel runs as well as physical
disturbance. As already discussed, the magnitude of PM10 emission flux varied greatly
depending on the dust suppressant applied and the nature of the test (Table 3.1). In addition,
while every effort was made to ensure that each test tray received consistent treatment,
there was also considerable heterogeneity in the level of dust emissions within the replicate
test trays for each dust suppressant. For example, in the initial clean air runs for the LN test
trays, the three trays exhibited the same general emission pattern (Figure 3.5). However,
the actual spikes in PM10 emissions were quite variable and somewhat unpredictable. The
highest peaks in PM10 for each of the trays were 1692, 1781, and 2391 µg m-2 s-1,
respectively, with those for tray 1 and 3 occurring at a requested freestream wind speed of
11 m s-1, and that for tray 2 occurring earlier at 9 m s-1. The average PM10 emission flux,
F̅, for each of the three trays were 100.98, 36.24, and 127.81 µg m-2 s-1, respectively, which
represents a range of 91.57 µg m-2 s-1 and a Coefficient of Variation (CV) of 53%.
Variability can also be seen in the patterns of dust emissions during the saltation
runs for the SS trays (Figure 3.6). Trays 1 and 3 tended to exhibit more moderate dust
peaks, mostly remaining below 400 µg m-2 s-1, whereas tray 2 had several PM10 emission
spikes between 400 and 800 µg m-2 s-1. The average PM10 emission flux, F̅, for the three
trays, 22.80, 59.54, and 14.66 µm m-2 s-1, respectively, demonstrated a smaller range in the
dust emission fluxes, 44.78 µg m-2 s-1, than the LN clean air runs, but a greater degree of
variability with a CV of 74%.
59
Figure 3.5. Time series of the emission fluxes for the clean air wind tunnel runs for all
three LN trays.
Figure 3.6. Time series of the emission fluxes for the wind tunnel saltation runs for all
three SS trays; mean sand transport rate was 0.0086 kg m-1 s-1.
0
0.1
0.2
0.3
0.4
0.5
0.6
0
400
800
1200
1600
2000
2400
0 100 200 300 400 500 600 700
u*
(m s
-1)
F (
µg
m-2
s-1)
Time (s)
Tray 1
Tray 2
Tray 3
u*
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0
100
200
300
400
500
600
700
800
0 600 1200 1800 2400 3000 3600
u*
(m s
-1)
F (
µg
m-2
s-1)
Time (s)
Tray 1
Tray 2
Tray 3
u*
60
Figure 3.7. Time series of the emission fluxes for the clean air wind tunnel runs after
physical disturbance for all three EA trays.
Likewise, for the clean air runs after physical disturbance of the EN trays, once
again the general emissions patterns were quite similar (Figure 3.7). However, the levels
of PM10 emissions varied substantially, with tray 3 peaking at a much higher level,
344_µg m-2 s-1, at the highest requested wind speed compared to the other two test trays:
150 µg m-2 s-1 for tray 1 and 118 µg m-2 s-1 for tray 2. The PM10 emission fluxes for the
three trays were 26.09, 67.74, and 16.31 µg m-2 s-1, respectively, which encompasses a
range of 51.43 µg m-2 s-1 and a CV of 74%. This level of variability among the test trays
was fairly common across the four dust suppressants in all three of the wind tunnel tests.
3.7 Laboratory Laser Scans
There were two reasons for scanning the test surfaces with the VIVID 9i laser scanner. The
first was to determine how consistently the test trays were prepared, since it was important
0
0.1
0.2
0.3
0.4
0.5
0.6
0
50
100
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300
350
400
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(m s
-1)
F (
µg
m-2
s-1)
Time (s)
Tray 1
Tray 2
Tray 3
u*
61
that the tailings were as level as possible in each tray, and there was some unavoidable
‘splash’ effect during the initial passes with the spray bottle when applying the dust
suppressants. Using the vertical distance values from the laser scanner data, the range in
the surface level was consistent among all the trays and among the dust suppressants, with
values ranging between 4.0 and 6.0 mm (Table 3.2).
Table 3.2. Comparison of the ranges in surface elevation of the test trays prior to wind tunnel
testing. Data are normally distributed, while no significant differences exist between the
topographies of the prepared test surfaces (ANOVA, p = 0.91)
______________________________________________________________________________
Dust Suppressant Tray Deviation from level (mm)
______________________________________________________________________________
LN 1 6.0
2 4.0
3 5.6
Average 5.2
σ 1.1
______________________________________________________________________________
EN 1 5.8
2 4.1
3 4.3
Average 4.7
σ 0.9
______________________________________________________________________________
EA 1 5.8
2 4.4
3 5.1
Average 5.1
σ 0.7
______________________________________________________________________________
SS 1 5.7
2 4.1
3 4.8
Average 4.8
σ 0.8
______________________________________________________________________________
62
Figure 3.8. The average range in surface elevation, based on three replicate test trays for
each dust suppressant, indicating the degree of surface roughness, from VIVID 9i laser
scans obtained: (a) initially before any wind tunnel testing; (b) after sand saltation; (c) after
physical disturbance; and (d) after the final wind tunnel run. Statistical tests determined
that: (a,b,c) data are normally distributed, however, they are not significantly different
(ANOVA, p = 0.91, 0.37, 0.099, respectively); (d) data are normally distributed and the
pairings LN – EA, LN – SS, and EN – SS are significantly different (ANOVA, p = 0.0016).
The second purpose in scanning the test surfaces was to determine how much the
topography of each tray surface changed as a result of particle entrainment, sand abrasion,
and physical disturbance. The laser scans obtained after the saltation runs indicate very
similar surface topographies as the initial scans (Figures 3.8, 3.9, 3.10). This suggests that
while all surfaces were determined from the DT data to have lost PM10 mass, it was likely
not sufficient, given the very small size of the particles, to cause a substantial change in the
overall surface roughness. Also, if there were any slight irregularities or erosion marks
created on the surface as a result of saltation bombardment, these may have in turn been
0
5
10
15
20
25
30
35
LN EN EA SS
Var
iati
on
in
Su
rfac
e E
levat
ion
(m
m)
Surface Treatment
Initial (a) Saltation (b) Disturbance (c) Final (d)
63
‘filled in’ by the sand particles. Indeed, there were grains of sand visible on all of the test
surfaces after the saltation runs (Figure 3.11).
The physical disturbance of the crusts resulted in a much higher level of surface
variation in all four treatments (Figures 3.8, 3.9, 3.10). The LN, and to some extent EN,
surface crusts had less pronounced increases in the amount of surface roughness. This was
likely because these surface crusts exhibited more ductile behaviour during disturbance
(see also Figure 3.1), allowing the nail heads to pass through with minimal disturbance to
the surrounding crust areas that did not come into direct contact with the impingement
(Figure 3.12a-d). Note that the tailings appear light grey in colour in all of the laser scans,
and the sites of puncture made by the nails appear as grey “circles” where the tailings have
been exposed beneath the weaker and more ductile LN and EN surface crusts (Figure 3.9).
In contrast, the EA and SS surface crusts, being more brittle, tended to fracture into
polygonal plates when penetrated by the nails, manifesting as a greater level of disturbance
and variation in the surface topography (Figures 3.8, 3.10, 3.12e-h). Again, the tailings are
clearly visible at the edges of the crust fragments. Despite the fact that the disturbance of
the crusts resulted in higher PM10 emissions during the post-disturbance wind tunnel runs
compared to the initial clean air runs, the PM10 mass emitted was not sufficient to cause
substantial changes in the variability of the surface topography in the laser scans obtained
after the final wind tunnel runs compared to those immediately after disturbance (Figure
3.8).
64
Figure 3.9. Minolta VIVID 9i laser scans of the tests trays: (a) LN tray 1 initial scan; (b)
after saltation; (c) after disturbance; (d) final scan; (e) EN tray 1 initial scan; (f) after
saltation; (g) after disturbance; (h) final scan. Scale is approximately 1:4.
(b)
(d)
(e) (f)
(g) (h)
(a)
(c)
65
Figure 3.10. Minolta VIVID 9i laser scans of the tests trays: (a) EA tray 1 initial scan; (b)
after saltation; (c) after disturbance; (d) final scan; (e) SS tray 1 initial scan; (f) after
saltation; (g) after disturbance; (h) final scan. Scale is approximately 1:4.
(a) (b)
(c) (d)
(e) (f)
(g) (h)
66
Figure 3.11. Photographs of three of the test trays immediately after the saltation runs showing lighter, beige sand particles sitting on
the dust suppressant crusts. (a) EN tray 3 showing the pattern of sand on the entire tray; (b) EN tray 3 close up; (c) EA tray 3 close up;
(d) SS tray 2 close up. The circles indicate examples of sand particles perched on the surface of the crust.
(a) (b)
(c) (d)
67
(a) (b)
(c) (d)
68
Figure 3.12. Photographs of four of the test crusts immediately after physical disturbance. (a) LN tray 3; (b) LN tray 3 close up; (c)
EN tray 3; (d) EN tray 3 close up; (e) EA tray 1; (f) EA tray 1 close up; (g) SS tray 2; (h) SS tray 2 close up.
(f)
(g) (h)
(e)
69
3.8 Laboratory Discussion
HI: When nepheline syenite tailings are treated with a dust suppressant, left to dry, and
then tested with a pin penetrometer, the crusts formed by the acrylic polymers, EcoAnchor
and Soil Sement®, are expected to have a higher maximum penetration force and Modulus
of Elasticity than the crusts formed by the pulp process co-products, Dust Fyghter LN100
and Entac.
As expected, EcoAnchor and Soil-Sement® formed crusts that were significantly
stronger and significantly more elastic than the two pulp co-product dust suppressants
(Figure 3.1). In comparing the two acrylic polymers, EA formed a stronger crust than SS.
On the other hand, the SS crust was more elastic than the EA crust. The pulp co-products,
Dust Fyghter LN100 and Entac, were significantly less elastic and more ductile than the
polymers (Figure 3.1). This would suggest that while they rupture more easily, they may
be able to withstand greater localized strain without experiencing extensive breakdown of
the overall crust surface. Therefore, the crusts tended to divide into two extremes: very
strong and relatively brittle polymer crusts, and weak but ductile pulp co-product crusts.
H2: When nepheline syenite tailings are treated with a dust suppressant and subjected to
abrasion by saltating sand particles, the PM10 emission rate is expected to escalate through
time.
As expected, the results demonstrate that all of the dust suppressants, and particularly LN,
are vulnerable to sand abrasion (Table 3.1, Figure 3.3). In fact, the LN test trays were
unable to withstand any abrasion by saltating sand particles without saturating the DTs
with PM10, so that saltation runs could not be conducted on the LN trays. The other three
70
surfaces remained intact during the saltation runs, exhibiting no substantial changes in
surface roughness or visual evidence of the development of erosion pits (Figures 3.8, 3.11).
However, contrary to expectations, they all emitted PM10 continuously and consistently
throughout the hour-long test, regardless of crust type or strength (Figure 3.3). This result
is in good agreement with the findings of Houser & Nickling (2001b) where crusted playa
soils emitted PM10 continuously throughout ten minute saltation tests in a field wind tunnel.
The authors suggest that each impact from a saltating sand particle was able to cause the
release of dust particles, and that the erosion of the surface was not dependent on repeated
impacts of sand particles which might be expected to gradually break down the crust. This
result contradicted laboratory wind tunnel experiments in which both biological and
physical crusts were found to gradually break down over time under sand abrasion (Rice
et al., 1996; McKenna Neuman & Maxwell, 1999; 2002; McKenna Neuman et al., 2005;
O’Brien & McKenna Neuman, 2012). However, it should be noted that these laboratory
studies measured wind erosion by mass loss and/or crust deterioration and did not measure
PM10 emission. Also, the biological crusts were cultured and the salt crusts were formed
on medium sand which did not contain PM10, whereas the tailings used in the test trays in
this study had a particle size distribution comprising 19% PM10. A study conducted in the
TEWT on a mine tailings slurry, consisting of approximately 30% PM10 by volume, also
found that the test surfaces emitted PM10 throughout sand abrasion runs lasting 110 minutes
(McKenna Neuman et al., 2009). The tailings tested in this study were wetted to a GMC of
65%, oven-dried, and then pulverized before the commencement of wind tunnel testing.
Dust emissions remained well above background levels for the duration of the saltation
runs, and the researchers concluded that abrasion by sand of the crusted tailings produced
71
a renewable source of PM10. The results from the current study are in agreement with the
McKenna Neuman et al. (2009) and Houser & Nickling (2001b) studies, and suggest that
crusted surfaces may be more likely to release PM10 immediately and continuously when
abraded by sand particles. This also suggests that much of the literature pertaining to crust
resilience to wind drag and sand abrasion may not be as relevant when considering PM10
emission rates rather than crust erosion through mass loss and, ultimately, crust failure.
The intensity of abrasion established in the wind tunnel saltation runs was likely
higher and/or more sustained than might be expected to occur on crusts in nature. However,
the fact that all of the surfaces instantaneously exhibited PM10 emissions from the onset of
sand abrasion would suggest that the surfaces may also be vulnerable to lower or more
sporadic sand transport. For instance, at mine sites where there are haul and berm roads,
sand-sized particles are inevitably present and could act as abraders. Also, if only small
portions of tailings storage areas are treated with a dust suppressant, adjacent areas may
provide a source of sand-sized particles that could saltate in a wind event. For example, the
nepheline syenite tailings used in this study included particles larger than 70 µm in
diameter (Figure 2.1), and in a recent field study also conducted on Nephton tailings pond
#4, saltating sand particles were captured in Fryrear sand traps (Ogungbemide, 2017). In
areas that were well-vegetated, the amount of sand captured in the traps was low. However,
in areas of primarily unprotected tailings, the sand traps captured ten times the amount of
sand-sized particles than in the well-vegetated areas. In fact, a sand trap located in an area
of unprotected tailings captured an average of 2 kg m-2 wk-1 over a six week period in July
and August 2014. This mass of sand particles available to abrade areas treated with a dust
72
suppressant could result in higher levels of dust emission than expected from a treated
surface during wind events.
H3: When nepheline syenite tailings are treated with a dust suppressant and subjected to
either particle-free wind drag or abrasion by saltating sand particles, the PM10 emission
rate is predicted to scale inversely with the strength of the crust formed.
In order to determine the relationship between the PM10 emission flux and crust
strength, the average values for maximum penetration force, Modulus of Elasticity, and
PM10 emission flux in the initial clean air and saltation wind tunnels tests are compared for
each dust suppressant in Table 3.3. Note that a ranking of #1 indicates the highest value for
each parameter rather than the most favourable performance: for MPF the #1 ranking
represents the highest MPF value, indicating the strongest crust; for MoE the #1 ranking
represents the highest MoE value, indicating the most elastic crust; for PM10 emissions the
#1 ranking represents the highest PM10 emission flux, indicating the most emissive crust.
The dotted line separates the two extremes in MPF and MoE values: EA and SS – strong
and brittle; LN and EN – weak and ductile.
For the initial particle-free runs, crust strength does scale inversely with PM10
emissions for the stronger acrylic polymer crusts EA and SS. However, despite the fact that
the pulp process co-product crusts LN and EN had very similar low MPF values, the most
emissive surface, LN, emitted three orders of magnitude more dust than EN (Table 3.1).
This was unexpected, since previous wind tunnel experiments suggest that both physical
and biological crusts are resilient to wind drag at wind speeds as high as 19 m s-1 (McKenna
Neuman et al., 1996; McKenna Neuman & Maxwell, 1999; 2002). However, during
73
physical disturbance and penetrometer testing, and upon termination of the wind tunnel
tests when the trays were emptied, no LN crust was visible on the surface of the tailings,
although the surface did appear darker than untreated tailings. This suggests that LN did
not form as strong a protective crust over the surface as the other three dust suppressants.
Table 3.3. A comparison of the dust suppressants according to the mean values for maximum
penetration force, Modulus of Elasticity, and PM10 emission rate, F̿.
______________________________________________________________________________
Crust characteristics PM10 emission rate, F̿
MPF MoE Clean Air Saltation
LN 3 3 1 1
EN 4 4 2 3
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
EA 1 2 4 4
SS 2 1 3 2
______________________________________________________________________________
The scaling of crust properties to PM10 emission fluxes during runs conducted with
saltating sand particles is more challenging. It would appear that the superior strength of
the EA resulted in the best resistance to saltation with the lowest PM10 emission rate.
However, the second strongest crust, SS, exhibited the second highest dust emission rate,
with the much weaker EN crust proving more effective at preventing dust emissions during
the saltation runs. This may be a consequence of its ranking as the least elastic, and
therefore most ductile, of the crusts formed by the dust suppressants. This result is in
agreement with a TEWT study that found that salt crusts that were determined to be much
stronger than biological crusts in penetrometer tests broke down more quickly under
saltation than the biological crusts (Langston & McKenna Neuman, 2005). It was suggested
74
that the more ductile biological crusts were better able to absorb the energy of the saltating
sand particles than the stronger and more brittle salt crusts.
Another consideration in the scaling of crust strength properties and dust emissions
is the length and intensity of the saltation run. For example, the salt crusts which
experienced substantial erosion under saltation conditions in the Langston & McKenna
Neuman study (2005) had an MPF of 0.52 N, which is considerably lower than the EA and
SS crusts in this study, but higher than the LN and EN crusts (Figure 3.1). In terms of MoE,
the salt crusts tested roughly midway between the two extremes found in this study, being
less elastic than the EA and SS crusts but considerably more elastic, and therefore less
ductile, than the LN and EN crusts (Figure 3.1). Despite the fact that the EA and SS crusts
were considerably stronger, and the LN and EN crusts were more ductile, than the salt
crusts, they might also eventually breakdown if the runs with saltation were temporally
extended. However, the length of time required is difficult to predict and may be
impractical in a laboratory setting
Overall, attempting to predict the potential for dust emission, when a surface is
abraded with sand, based on penetrometer measurements is problematic since both MPF
and MoE likely affect the emissivity of a crust. For instance, in the current study, the
strongest crust, EA, and the most ductile crust, EN, had PM10 emission rates of a similar
magnitude during the saltation runs (Table 3.1). In addition, several studies have found that
relating penetrometer strength tests to expected performance under wind drag and sand
abrasion can be challenging and unpredictable due to the fact that most penetrometer data
are inherently variable and demonstrate a high degree of spatial heterogeneity (Rice et al.,
1997; McKenna Neuman & Maxwell, 2002; Langston & McKenna Neuman, 2005).
75
Certainly based on the penetrometer results alone, it would be difficult to predict how a
crust would perform when subjected to sand abrasion, which also makes it challenging to
predict efficacy when considering the two types of dust suppressant crusts.
H4: When nepheline syenite tailings are treated with a dust suppressant and left to dry
undisturbed, the PM10 emission rate is expected to be very low, as compared to that
following physical disturbance.
The results for the initial clean air runs indicate that EA, EN, and SS afforded
excellent protection against entrainment through fluid drag in clean air, since the
underlying tailings were protected by the crusts created by the dust suppressants (Table
3.1, Figure 3.2). This result is supported by many wind tunnel and field studies which found
that both physical and biological crusts were able to withstand particle-free winds without
eroding. For instance, studies conducted in the TEWT suggest that many different types of
biological crusts are stable under winds as high as 19 m s-1 (McKenna Neuman et al., 1996;
McKenna Neuman & Maxwell, 1999; 2002). In a field wind tunnel study, after an initial
PM10 peak upon initiation of the airflow, dust emissions remained low over playa soils
bearing a physical crust (Houser & Nickling, 2001b).
However, the LN trays had an average PM10 emission rate that was about 400 times
higher than the other three sets of test trays (Table 3.1). Again, this may reflect the fact
that, as previously mentioned, the application of LN did not appear to create a visible crust
on the surface of the tailings, although it did offer some protection against dust emission at
the lower wind speeds. However, even at moderate wind speeds, some of the peaks in the
PM10 emission curves were two orders of magnitude higher than the those at the highest
76
wind speeds for the trays treated with the other three dust suppressants (Figure 3.2).
Therefore, the Dust Fyghter LN100 afforded much lower resistance to particle-free wind
drag than the Entac, EcoAnchor, and Soil Sement®.
As expected, all of the surfaces exhibited higher dust emissions after physical
disturbance regardless of crust characteristics (Table 3.1). Indeed, many laboratory and
field studies involving both physical crusts (Cahill et al., 1996; Houser & Nickling, 2001a;
McKenna Neuman et al., 2009) and biological crusts (Williams et al., 1995; Belnap &
Gillette, 1997; Leys & Eldridge, 1998) found that crusted surfaces were significantly more
erosive under wind drag after they were disturbed. In a mining application, this would
suggest that the physical disturbance of a surface treated with a commercial dust
suppressant would increase the likelihood of PM10 emission from the surface. The
magnitude of dust emission would likely scale directly with the degree of disturbance,
depending on the size of the disturbed area, the depth of disturbance, and the frequency of
disturbance, which would affect the supply of dust particles created by exposing the
underlying tailings.
77
Chapter 4 Field Results and Discussion
In addition to the laboratory studies conducted in the TEWT, the performance of the same
four dust suppressants was evaluated under the conditions of a more ‘typical’ field
application, relative to an irrigated water plot (W) and an untreated control plot (C). Unlike
the laboratory, weathering processes in the field, such as extremes of temperature,
precipitation, and freeze-thaw cycles, as well as potential spring flooding, wildlife
disturbance, formation of physical and/or biological crusts, and establishment of vascular
vegetation, are expected to affect the efficacy of the dust suppressants. The field
experiment was conducted on nepheline syenite tailings at the Unimin Ltd Nephton site in
southern Ontario. The results and discussion are presented below.
4.1 Field PM10 Emission Measurements
The DT data from the PI-SWERL tests produced emission curves that were very similar in
shape to ‘typical’ emission curves obtained in wind tunnel tests. That is, spikes in PM10
concentrations occurred at each new RPM and then gradually decayed over the step
duration. Each spike was generally larger than the previous spike as the RPM, or shear
stress on the surface, was increased for each step of the PI-SWERL run (Figure 4.1).
When comparing the test data for the different dust suppressants and measurement
dates, what is noticeable is the high level of variability in the data. This variability is evident
within replicate measurements carried out on the same dust suppressant plot, as well as
between dust suppressant plots on a single measurement date. For example, the four PI-
SWERL PM10 concentration curves for the LN plot conducted one week after application,
78
M1, all follow the same general pattern, but demonstrate marked differences in the
magnitude of the emissions, particularly at the highest target u* of 0.75 m s-1 (Figure 4.2).
The first test was the most emissive, peaking at 330 µg m-3 with an average PM10 emission
flux of 1.38 µg m-2 s-1. On the other hand, test 4 exhibited much lower dust emissions with
the highest peak of only 11 µg m-3 and an average PM10 flux of 0.14 µg m-2 s-1, and the
four test runs had a high CV of 100%
The average PM10 concentration curves for all of the test plots three weeks after
application, M2, demonstrate a similar degree of variability between the research plots
(Figure 4.3). Note, the curve for each dust suppressant was obtained by averaging the
corresponding PM10 concentrations measured each second during the four replicate PI-
SWERL tests. For these tests, the control and Entac plots had similar high peaks in PM10
concentrations of 1600 µg m-3 and 1700 µg m-3, respectively, although the control did decay
more quickly. At the other extreme, LN emitted much lower levels of PM10 throughout the
tests, only ‘peaking’ slightly at 100 µg m-3, also at the highest requested u* of 0.75 m s-1.
In terms of the average emission fluxes, LN had a PM10 flux of 0.67 µg m-2 s-1, and EN had
a flux of 8.54 µg m-2 s-1, with the other four surfaces exhibiting PM10 fluxes falling between
these two values (Figure 4.4). These comparisons suggest that there was a considerable
amount of variability between the surface crusts.
The mean PM10 emission flux for each dust suppressant normalized by the control
plot emission flux, F′, also illustrates a large amount of variability in the PM10 emissions
data (Figure 4.5). In fact, over the span of the field season, all of the dust suppressants
exhibited emission fluxes significantly higher and/or lower than the control, and the values
for F′ were often quite erratic. For example, after one week, M1, all of the plots, except the
79
irrigated plot, were more emissive than the control, with SS being 7 times more emissive
than the control. However, after three weeks, M2, EN had the highest emission rate, 2.5
times more emissive than the control, whereas LN was only half as emissive as the control.
In examining the data over the seven measurement dates, all of the surfaces were more
emissive than the control more frequently than they were less emissive than the control:
LN four times, EN six times, EA five times, SS five time, W four times.
As computed for the wind tunnel data, the mean PM10 emission flux, F̿, from all
four replicate tests within each dust suppressant provides a measure of the relative
performance of each dust suppressant under identical environmental and test conditions
(Figure 4.4). These data also reflect the variability between the dust suppressants
temporally across the measurement period. The overall average emission rate across the
field season indicates that the surfaces rank from least to most emissive: W, C, EA, LN,
SS, EN. However, the emission rates were all of a similar magnitude, ranging from 8.97
µg m-2 s-1 for the irrigated test surface to 12.15 µg m-2 s-1 for the EN test surface. Also, over
the duration of the field season, there was no pattern of emission fluxes increasing as the
crusts weathered.
One factor that seems to have strongly affected the PM10 emission rates was the
GMC of the surface. The GMC was noticeably lower (below 5%) at M3, seven weeks after
application, which corresponds to the highest PM10 emission fluxes for all of the test
surfaces except for the water plot (Figure 4.5). In addition, two PI-SWERL measurements,
C test 2 and LN test 4, had to be stopped before completion due to high DT concentrations
(Appendix, Table B). Subsequent to this, the test surfaces remained relatively wet (15% <
GMC < 20%) at levels well known to arrest sand drifting or saltation.
80
Figure 4.1. A typical PM10 concentration curve from a PI-SWERL test for the first replicate
measurement on the EN plot, 1 week after application (M1).
Figure 4.2. PI-SWERL PM10 concentration curves for all four replicate tests on the LN
plot, 1 week after application (M1).
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
50
100
150
200
250
300
350
0 90 180 270 360 450
u*
(m
s-1
)
PM
10
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(µ
g m
-3)
Time (s)
PM10
u*
0
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PM
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n (
µg m
-3)
Time (s)
Test 1
Test 2
Test 3
Test 4
u*
81
Figure 4.3. Average PI-SWERL PM10 concentration curves for the six test plots, 3 weeks
after application (M2).
Figure 4.4. Comparison of the average PM10 emission fluxes measured during PI-SWERL
tests for each measurement date with average gravimetric moisture content levels.
Individual PI-SWERL test values, average test plot values, σ, CV, and the results from
statistical significance tests are found in Table B in the appendix. GMC data are not
normally distributed, and all pairings are significant except M5 – M7 and M6 – M7
(Kruskal-Wallis, p = 0.048).
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 90 180 270 360 450
u* (
m s
-1)
PM
10
con
cen
trat
ion
(µ
g m
-3)
Time (s)
C LN EN EA
SS W u*
0
5
10
15
20
25
0.1
1
10
100
1 week
(M1)
3 weeks
(M2)
7 weeks
(M3)
11 weeks
(M4)
15 weeks
(M5)
21 weeks
(M6)
24 weeks
(M7)
Season
Mean
Gra
vim
etri
c M
ois
ture
Conte
nt
(%)
F (
µg m
-2s-1
)
Measurement Date
C LN EN EA SS W GMC
82
Figure 4.5. Average emission fluxes for the five test surfaces normalized by the control
surface emission flux. The horizontal axis crosses at an F′ value of 1, which indicates a
treated surface that is equally emissive as the control.
4.2 Field Crust Penetrometer Tests
At the first measurement date, M1, the penetrometer measurements were similar to the
laboratory penetrometer results: the two acrylic polymers, EA and SS, were stronger and
more brittle, whereas the two pulp process co-products, LN and EN, were weaker but more
ductile (Figure 4.6a). Note, the data sets are not normally distributed, but are significantly
different except for C – EN and LN – EN (Kruskal-Wallis, p = 0.0025). However, the field
EA and SS crusts were weaker than the laboratory EA and SS crusts, despite the fact that
the products were applied at the same rate and the crusts had been allowed to cure for about
a week. In the field, at M1, EA and SS had mean maximum penetration force values of 1.1
and 1.6 N, respectively, whereas the laboratory EA and SS crusts had mean MPF values of
8.4 and 6.8 N, respectively.
0
5
10
15
20
25
1 week
(M1)
3 weeks
(M2)
7 weeks
(M3)
11 weeks
(M4)
15 weeks
(M5)
21 weeks
(M6)
24 weeks
(M7)
Fʼ
Measurement Date
LN EN EA SS W
83
In addition, the MPF results from the field plots do not correlate with the emissions
data, since the stronger EA and SS crusts had a higher emission rate than the control, with
SS exhibiting the highest emission rate. Also, this differentiation in crust strength did not
continue through the season. In fact, throughout the season, the penetrometer results also
exhibited a large degree of variability in the strength properties of the field crusts. Again,
this variability was evident between the different field crusts as well as within individual
dust suppressant crusts both for a single measurement date, and across the measurement
period. For instance, the penetration curves for the EA crusts on M5 demonstrate a wide
range in maximum penetration force values, 489.3 N for the highest MPF (run #12), and
4.8 N for the lowest MPF (run #9) (Figure 4.6c). Likewise, a comparison of the range in
MPF values for SS indicates a high degree of variability within the measurements for one
dust suppressant over the measurement season (Figure 4.6d). All of the crusts had lower
average strength considering all of the field season measurements, except for the EN crust
which was, on average, almost twice as strong as its initial MPF measurement at M1. The
average crust strength values for the field season indicate that the crusts may be considered
strongest to weakest: Soil Sement®, EcoAnchor, Entac, irrigated plot, Control, Dust
Fyghter LN100 (Figure 4.6a). The data series are normally distributed, but are not
significantly different (ANOVA, p = 0.25).
The Modulus of Elasticity results for M1 are in agreement with the MoE results for
the WT crusts in that the two acrylic polymer crusts were more elastic than the two pulp
co-product crusts (Figure 4.6b). In fact the four commercial dust suppressants ranked from
most to least brittle in the same order: SS, EA, LN, EN. With the inclusion of the control
and irrigated plots the surfaces rank from most to least brittle: SS, W, EA, C, LN, EN,
84
which is somewhat surprising since the irrigated and control plots had only a week for the
tailings to begin to pack down and possibly begin to form a physical crust. The data are not
normally distributed, although all of the data sets are significantly different from each other
(Kruskal-Wallis, p = 0.0027).
The average MoE results for the season indicate that all of the crusts became more
elastic as the season progressed, particularly EN (Figure 4.6b). In fact, contrary to the
laboratory results, the Entac proved to be the most brittle crust on average in the field, and
may have cured into a more brittle crust over time. The data are normally distributed but
are not significantly different (ANOVA, p = 0.54).
(a)
0
0.5
1
1.5
2
2.5
3
C LN EN EA SS W
Max
imu
m P
enet
rati
on F
orc
e (N
)
Dust Suppressant
M1
Season average
85
(b)
(c)
0
200
400
600
800
1000
1200
1400
1600
1800
C LN EN EA SS W
Modu
lus
of
Ela
stic
ity (
N m
-1)
Dust Suppressant
M1
Season average
0
100
200
300
400
500
600
0 0.5 1 1.5 2 2.5 3
Ap
pli
ed L
oad
(g)
Distance (mm)
R5 R22 R9 R12 R24
86
(d)
Figure 4.6. Penetrometer data from the field test plots. (a) Mean maximum penetration
force (N), 1 week after application, M1, and the field season average; (b) mean Modulus
of Elasticity (N m-1), M1, and the field season average; (c) representative EA penetrometer
curves at M5, including the runs with the lowest MPF (run 9), the median MPF (run 24),
and the highest MPF (run 12); (d) comparison of the minimum and maximum MPF values
for the SS crusts.
4.3 Field Physical Disturbance Tests
Once disturbed by two passes of a skid steer track loader, all of the field plots emitted
substantially more PM10 than from the main, undisturbed sections of the plots (Figures 4.4,
4.7). As a result, for the PI-SWERL tests on the day of the disturbance, D1, the target RPMs
had to be lowered by more than 50%, yet all but one of the tests still had to be terminated
before completion due to unacceptably high DT concentrations (Figure 4.8a). The
normalized emissions rates for D1 indicate that all of the commercial dust suppressant
surfaces were more emissive than the control plot, whereas the irrigated plot was less
emissive (Figure 4.9).
0
1
2
3
4
5
6
M1 M2 M3 M4 M5 M6 M7
Max
imu
m P
enet
rati
on
Fo
rce
(N)
Measurement Date
Min Max
87
The tests conducted 4 weeks after physical disturbance, D2, were run at the same
ramped RPM values as the main, undisturbed areas of the research plots, although 33% of
the runs still had to be terminated before completion due to high PM10 concentrations
(Figure 4.8b). Three of the surfaces, those treated with EN, EA, and SS, had PM10 emission
rates 2 to 4.5 times lower than the control. The LN surface emitted dust at approximately
twice the rate of the control, and the irrigated plot was by far the most emissive, emitting
5 times more dust than the control (Figure 4.9) The disturbed surfaces had visual signs that
they had likely been affected by compaction due to rain drop impact (Figure 4.10). In
particular, the three plots with no visible crusting (LN, W, and C) exhibited a marked
decrease in the size of the ridges created by the loader’s tracks. On the other hand, the more
visibly crusted, EN, EA, and SS retained a higher degree of roughness with visual evidence
of crust fragmentation.
By 10 weeks after the plots were disturbed, D3, the dust flux of all of the surfaces
was quite low, except for LN, which emitted much higher levels of PM10 (Figure 4.7). The
reasons for this are uncertain, although it should be noted that, similar to the laboratory test
trays, LN did not form a visible crust on the surface in the same manner as EN, EA, and
SS. Therefore, there were no visible LN crust segments overlying the tailings after
disturbance (Figure 4.10). On D3, LN emitted dust at an order of magnitude higher rate
than the control, although all the treated surfaces were more emissive than the control
(Figure 4.9).
88
Figure 4.7. Comparison of the average PM10 emission fluxes, F, for each test surface from
the disturbed sections of the research plots. Note that the initial tests conducted on the day
of disturbance were conducted at PI-SWERL RPMs reduced by more than 50%. Individual
test mean emission fluxes, test plot mean fluxes, σ, CV, and the results from statistical
significance tests are found in Table B in the appendix.
(a)
1
10
100
1000
Disturbance (D1) 4 weeks (D2) 10 weeks (D3)
F (
µg m
-2s-1
)
Measurement Date
C LN EN EA SS W
C1
C2
C3
C4
LN
1
LN
2
LN
3
LN
4
EN
1
EN
2
EN
3
EN
4
EA
1
EA
2
EA
3
EA
4
SS1
SS2
SS3
SS4
W1
W2
W3
W4
TRPM2000
TRPM1600
TRPM1200
TRPM800
TRPM 400
Surface Treatment and Run Number
89
(b)
Figure 4.8. Complete and incomplete PI-SWERL target revolutions per minute (TRPM)
ramp steps for the four replicate measurements for each research plot. (a) On the day of
disturbance (D1); (b) 4 weeks after disturbance (D2).
Figure 4.9. Average PM10 emission fluxes for the disturbed test surfaces, normalized by
the control surface emission flux, F′. The horizontal axis crosses at an F′ value of 1, which
indicates a treated surface that is equally emissive as the control.
C1
C2
C3
C4
LN
1
LN
2
LN
3
LN
4
EN
1
EN
2
EN
3
EN
4
EA
1
EA
2
EA
3
EA
4
SS1
SS2
SS3
SS4
W1
W2
W3
W4
TRPM4400
TRPM3400
TRPM2400
TRPM1400
TRPM400
Surface Treatment and Run Number
0
5
10
15
20
25
Disturbance (D1) 4 weeks (D2) 10 weeks (D3)
Fʼ
Measurement Date
LN EN EA SS W
90
Figure 4.10. The test surfaces 4 weeks, D2, after physical disturbance. The EN, EA, and SS plots retained a visibly higher degree of
roughness after physical disturbance. The loader tracks are less pronounced on the C, LN, and W plots, and the rings created by the PI-
SWERL are visible on the surfaces of the LN and W plots.
C
EA
LN
SS
EN
W
91
Figure 4.11. The field research plots after 13 months. (a) The Dust Fyghter LN100 plot with moss cover and sparse but consistent
aster growth; (b) spalled crust on the Soil Sement® plot; (c) close-up of surface cracks and asters growing on the EcoAnchor plot; (d)
moss cover on the near, undisturbed, end and lighter-coloured physical crusting on the far, physically disturbed, end of the water plot.
(a) (b)
(c) (d)
92
4.4 Field Site One Year Assessment
After a very wet period in spring and early summer, the research area was still too damp in
June, 2017 to measure the plots with the PI-SWERL. Therefore, it was not possible to
conduct a final measurement of emissions from the research plots one year after
application. However, a qualitative assessment was conducted on July 7, 2017, about 13
months after the application date. In general, the plots were remarkably consistent in their
appearance. The undisturbed ends were relatively dark in appearance due to the extensive
establishment of the moss Gemmabryum caespiticium (Hedw.) J.R. Spence, which has
established extensive colonies across tailings pond #4 (Figure 4.11a). There was little
evidence of residual dust suppressant crusts, with just small patches of spalled crust on the
EA and SS, and to a lesser degree the EN, plots (Figure 4.11b). Large, deep, and fairly
extensive tension cracks were evident, as well as sparse but consistently spaced vascular
vegetation, throughout the plots (Figure 4.11c). The disturbed ends of the plots were lighter
in colour with limited moss establishment (Figure 4.11d). They seemed to exhibit some
crusting, although the crusting was very similar among all six plots, suggesting the
development of a physical crust due to high levels of precipitation on the tailings.
93
4.5 Field Discussion
H5: PM10 emission rates measured on the test plots are predicted to scale inversely with
the strength of the crust formed.
Crust strength properties can be an important factor in how effectively a dust
suppressant crust prevents fugitive dust emissions. However, the penetrometer results were
highly variable over the field season, which makes it challenging to rank the strength of
the dust suppressants. Therefore, in order to more easily compare the dust suppressants,
the season average values for PM10 mass released, maximum penetration force, and
Modulus of Elasticity are compared in Table 4.1. As with the ranking of the laboratory
results, the ranking of #1 indicates the highest value rather than the most favourable
performance: for MPF the #1 ranking represents the highest MPF value, indicating the
strongest crust; for MoE the #1 ranking represents the highest MoE value, indicating the
most elastic crust; for PM10 mass the #1 ranking represents the highest PM10 emission flux,
indicating the most emissive crust.
Unlike the laboratory results, the field crust strength measurements do not scale
inversely with the PM10 emission rates, since the two strongest crusts, the acrylic polymers,
SS and EA, were the second and fourth most emissive, respectively, and the four plots
treated with the commercial dust suppressants were more emissive than both the irrigated
and control plots. Also, the least emissive W and C were the fourth and fifth weakest crusts,
with only LN have a lower MPF. On the other hand, the MoE results do scale directly with
the emissions results, with one slight misalignment in LN, which was the least brittle crust,
94
but only the third most emissive. If only the other five plots are considered, PM10 emissions
and MoE scale directly.
Table 4.1. A comparison of the dust suppressants according to the season mean values for
maximum penetration force, Modulus of Elasticity, and PM10 emission rate, F̿.
______________________________________________________________________________
Crust Strength Properties PM10 emission rate, F̿
MPF MoE
______________________________________________________________________________
LN 6 6 3
EN 3 1 1
EA 2 3 4
SS 1 2 2
W 4 5 6
C 5 4 5
______________________________________________________________________________
According to the average PM10 emission fluxes of the research plots, the results
suggest the following ranking, from most effective to least effective, in preventing dust
emission: Water, Control, EcoAnchor, Dust Fyghter LN100, Soil Sement®, Entac. This
result is particularly interesting since it means that none of the commercial dust
suppressants afforded better protection from fugitive dust emissions than the control or
irrigated plots. In considering only the commercial products, in the wind tunnel clean air
experiments EA was also the least emissive, however LN was found to be the most
emissive. Also, the values for the overall emission rate, F̿, for EN, EA, and SS were two
orders of magnitude higher in the field than in the tunnel. This also corresponds to the MPF
measurements which were much lower in the field than for the wind tunnel crusts. LN was
95
the only surface that exhibited a lower emission rate in the field, 11.40 µg m-2 s-1, than in
the wind tunnel, 88.34 µg m-2 s-1, since the LN wind tunnel surfaces were significantly
more emissive than the other three dust suppressants. Of note is that, in the field, LN
emitted the highest emission rate for a single measurement date on M3, which was also the
measurement date with the lowest measured GMC. This suggests that LN may be more
vulnerable to dry conditions than the other three commercial dust suppressants since the
wind tunnel experiments were conducted at a relatively low RH of 20%. Also, based on
qualitative observation and similar to the wind tunnel test trays, the application of LN on
the field research plot did not create a visible crust on the surface, whereas the other three
commercial products created a thin yet clearly visible physical crust over the tailings.
Therefore, in the field, the LN plot resembled the control and water plots, and tended to
align with them in terms of being more effective at preventing dust emission than two of
the other three commercial dust suppressants, EN and SS. This also suggests that crust
type, and more particularly crust strength, may not be important in a field setting in
predicting PM10 emission rates.
Beginning with the first dust emission measurements conducted a week after the
dust suppressants were applied, the control plot was often less emissive than the treated
plots. In fact, on M1 and again on M6, all of the plots treated with the commercial dust
suppressants were more emissive than the control plot (Figure 4.4). These results may have
been influenced by the development of a physical crust on the control plot. As previously
mentioned, an earlier study that compared three different application rates of a tall oil pitch
emulsion discovered that dust emission rates from the untreated control plot decreased over
the season due to the physical compaction of the surface associated with precipitation
96
events (Kavouras et al., 2009). Previous research conducted on tailings pond #4 found that
the tailings are capable of physically compacting, creating a stable surface for the initial
development of an algal followed by a moss crust if a gravimetric moisture content of at
least 10%, and ideally 15%, is maintained (Preston, 2015a; 2015b). In fact, the average
GMC of the research plots on the final four measurement dates was above 15%, which
suggests that there may have been sufficient moisture on the research plots to support the
development of a biological crust. In parallel research conducted on the research plots
established for this study, algae were also found to be present in the surface crusts of all of
the plots throughout the field season (Gilbert-Parkes, 2017), and a previous study
determined that algae are present in the irrigation water that is sourced from the
clarification pond to the south of tailings pond #4 (Preston, 2015a). Therefore, it is likely
that the moss-dominated biological crust, observed on the tailings in July, 2017, was the
result of the natural succession of algal crust development throughout the field season. The
gradual compaction of the surface and potential for the development of a biological crust
may have also affected the irrigated plot as well as the LN plot, since it did not have a
visible crust formed by the application of LN on the surface.
It is important to note that the irrigated plot tended to be either considerably more
or less emissive than the control on different measurement dates (Figure 4.5). The fact that
the irrigated plot was less emissive than the control on M1, M3, and M7 may have been
due to recent irrigation of the plot, which was not measured in this study. In contrast, W
was much more emissive than the control plot on M4 and D2, which may also be due to
the amount of irrigation the plot received. In research that measured the effectiveness of
irrigation on tailings pond #4, Ogungbemide (2017) discovered that low levels of irrigation
97
on the tailings could actually result in higher dust emissions as compared to not irrigating
the tailings. This is because, at low levels, the surface particles may be physically disturbed
by the drops of water from the sprinklers, such that the combination of low irrigation and
high winds could result in direct entrainment of PM10 particles dislodged by the water
droplets. This concept is supported by research on raindrop impact, which found that dust
emissions can actually increase in the early stages of rainfall due to the splashing action of
the raindrops before the surface is sufficiently wet to lock down the dust particles (Van
Duk & Stroosnijder, 1996). It is also possible that if insufficient water is applied to wet the
tailings, high wind speeds could initiate the saltation of sand particles, which could then
dislodge dust particles into the airstream. This implies that if water is used as a dust
suppressant, it is important to monitor the level of irrigation closely to ensure that the
surface is consistently wet enough to prevent fugitive dust emissions caused by the abrasion
of the surface by sand-sized particles. While laboratory research found that sand particles
resist entrainment by even high winds when the GMC is above 1-2% (McKenna Neuman
& Nickling; 1989; Cornelis et al., 2004), field research has observed sand transport at GMC
levels as high as 5% due to localized drying, which resulted in the entrainment of sand
particles through wind drag (Wiggs et al., 2004a; 2004b; McKenna Neuman & Langston,
2006). After a comprehensive investigation of wind erosion and site conditions on the
Nephton tailings ponds, Ogungbemide (2017) recommended a depth of 10 mm of water be
applied on hot summer days with RH less than 60%.
98
H6: Weathering of the protective crusts will result in increases over time in PM10 emission
rates from all of the treated plots.
Previous studies concerning dust suppressants have found that treated surfaces
exhibit temporal increases in PM10 emission rates due to the weathering of the dust
suppressant crusts. For instance, a study testing three concentrations of a tall oil pitch found
that all of the crusts gradually eroded over time, experiencing temporal increses in crust
spalling and PM10 emission rates (Kavouras et al., 2009). Likewise, a study of dust
suppressant efficacy on unpaved roads found that the treated surfaces deteriorated to the
point that they exhibited similar rates of dust emissions as the untreated control surface by
four and a half months after application (Sanders et al., 1997). However, the PM10 emission
fluxes of the test plots in the current study were quite inconsistent over the measurement
season, with only one measurement date, M3, exhibiting substantially higher dust
emissions in all of the plots, which coincided with considerably drier conditions on the
tailings (Figure 4.4). There was definitely no temporal increase in dust emissions over the
season, with the dates exhibiting the lowest levels of PM10 emissions occurring near the
end of the field season at 15 and 21 weeks. In fact, the emission rates generally scaled more
closely with GMC rather than demonstrating a temporal increase in emissions. The higher
GMC values would likely also align with higher relative humidity on the tailings pond. RH
has been shown to be a key driver in dust emissions from the tailings, and in both wind
tunnel and field experiments, dust emission was completely suppressed at RH > 60%
(Ogungbemide, 2017).
Overall, the field emissions results were quite variable. Certainly a high level of
variability has been found to be common in many PI-SWERL studies reflecting the
99
challenges of accurately measuring dust emission in nature (Sweeney et al., 2008;
Kavouras et al., 2009; Bacon et al., 2011; King et al., 2011). One factor which must be
considered in terms of the variability and unpredictability of the field results is the
possibility of dust deposition onto the research plots from adjacent areas as supported by
the following evidence:
(1) While most of tailings pond #4 had well established vegetation cover, there were small
areas of tailings immediately beside the plots that were exposed during the clearing of the
field site.
(2) In terms of the prevailing wind direction, tailings pond #4 is downwind of one of the
berm roads, as well as the main haul road from the mine entrance to the mill (Figure 2.5a).
(3) There was a large flock of Canada geese and some White-tailed deer on the tailings
ponds, which were often seen trampling on the research plots. While no measurements
were conducted on any visibly disturbed areas, wildlife activity may have caused some
release of dust onto the plots.
(4) There is sufficient fetch across the tailings ponds to allow for high levels of sand-sized
particles moving in saltation, and saltation has been observed at the field site
(Ogungbemide, 2017). The rate and effectiveness of saltation transport generally increases
as fetch increases (Gillette et al., 1996), and may reach 80% capacity at 100 m and full
capacity at 300 m (Zobeck et al., 2003).
To some extent, dust from these external sources likely settled on the surfaces of the
research plots, and it may have been, at least in part, the re-suspension of this dust that was
measured in the PM10 emission tests.
100
After one year, the research plots were visually very similar. Other than small
patches of spalled crust on the EA and SS plots, and to a lesser extent on the EN plot, no
extensive crusting was visible on any of the surfaces. All of the research plots had
approximately 50% moss cover as well as fairly sparse but consistent vegetation growth
throughout. This pattern in the establishment of vegetation – beginning with an algal crust,
developing into a moss-dominated biological crust, and culminating with the encroachment
and establishment of native vascular species – has been observed in previous research on
tailings pond #4 (Preston, 2015a; 2015b). In fact, the research plots all demonstrated
remarkably similar patterns and extent of vegetation cover a year after treatment. This
suggests that all of the treatments are comparable with respect to encouraging or restricting
the growth of vegetation on the tailings. Therefore, it would appear that site conditions
were the dominant factor influencing dust emission rates, with no temporal trend in PM10
emission rates observed in any of the treated research plots, regardless of the type of crust
formed by each dust suppressant.
H7: PM10 emission rates measured from disturbed sections of the test plots are expected to
be substantially higher than those measured from undisturbed sections of the plots.
As expected, all of the test plots proved vulnerable to physical disturbance (Figure
4.7). In fact, the PI-SWERL test parameters had to be reduced by over 50% on the day of
disturbance, and all but one test had to be terminated before completion due to excessively
high dust concentrations (Figure 4.8a). The results were also similar to the undisturbed
emissions tests in that none of the commercial dust suppressants performed measurably
better than the control surface on the first measurement date, although only the EA was
substantially more emissive, by a factor of 2.5, than the control (Figure 4.9). However, all
101
four of the commercial dust suppressants had higher emission rates on the day of
disturbance than either the irrigated plot or the control plots (Figures 4.7, 4.9). This is
possibly due to the fact that the visibly more brittle crusts on the EN, EA, and SS plots,
which exhibited the highest PM10 emission fluxes, tended to fracture to the extent that large
areas of the under-lying tailings were exposed.
However, it is possible that the crust fragments created by the physical disturbance
of the plots did afford some protection during subsequent measurements. Indeed, when
measured 4 weeks after disturbance, D2, the three visibly crusted plots, EN, EA, and SS,
were all less emissive than the control, whereas the LN and W plots were more emissive
than the control (Figure 4.9). Certainly the LN, W, and C plots only displayed track marks
upon disturbance, and did not appear to have a sufficient level of crusting to break into
segments. 10 weeks after disturbance, all of the surfaces were more emissive than the
Control (Figure 4.9). However, the emission fluxes for EN, EA, and W were at least one
magnitude lower than those after 4 weeks except for LN, which had a emission flux which
was lower by approximately 50%. On the other hand, SS had an emission flux that was two
times higher than at the previous measurement date. The temporal reduction in dust
emissions in most of the plots could be due to several factors: wet weather conditions could
have contributed to the packing of the exposed tailings, large pieces of crust that remained
intact may have afforded protection over some of the tailings, and, in the more exposed
sections, most of the dust particles exposed to the wind by disturbance may have already
been emitted, creating a supply limitation.
The results from the current study are consistent with those from other field studies.
For instance, in a comparison of four commercial dust suppressants conducted on unpaved
102
roads in California, the surfaces that were found to have the most brittle crusts tended to
break up and emit higher levels of PM10 (Gillies et al., 1999). The authors concluded that
repeated disturbances caused the breakdown of the crusts to the extent that the resulting
small crust particles were contributing to the dust emissions. Research concerning
biological crusts has shown them to be vulnerable to disturbance such that they experienced
significant wind erosion after physical disturbance (Belnap & Gillette, 1998; Houser &
Nickling, 2001a; Eldridge & Leys, 2003). In the current study, it was clear that all of the
research plots were immediately vulnerable to disturbance regardless of the dust
suppressant applied. This implies that no tailings should be treated with a commercial dust
suppressant if a site is expected to experience disturbance events, particularly if the
disturbance is to occur on a regular basis. Irrigation, where water is available, ideally before
physical disturbance, may be an effective form of dust suppression if moisture levels and
site conditions are carefully monitored.
103
Chapter 5 Conclusions, Study Limitations, and
Recommendations
5.1 Conclusions
1) In the laboratory, the acrylic polymer dust suppressants, EcoAnchor and Soil Sement®,
formed significantly stronger and more elastic crusts than the pulp process co-product dust
suppressants, Dust Fyghter LN100 and Entac.
2) In the laboratory study, three of the four commercial dust suppressants, Entac,
EcoAnchor, and Soil Sement®, protected the tailings from particle-free wind drag. Dust
Fyghter LN100 was less effective, with PM10 emission fluxes two orders of magnitude
higher than the other three dust suppressants. The efficacy of the dust suppressants scaled
with the penetrometer strength results and ranked from most effective to least effective as:
EcoAnchor, Soil Sement®, Entac, Dust Fyghter LN 100.
3) In the laboratory study, Dust Fyghter LN100 proved unable to withstand any abrasion
by sand particles. The other test surfaces remained visibly unchanged after saltation;
however, all three emitted 200-300 times more dust than during the particle-free tests, and
ranked from least emissive to most emissive as: EcoAnchor, Entac, Soil Sement®. In an
industrial application, it would be important to assess the specific site conditions to
determine the availability of sand-sized particles that may act as abraders.
4) In the laboratory study, all of the surfaces emitted considerably more dust compared to
the particle-free runs after being physically disturbed.
104
5) In the field, all of the research plots were also very vulnerable to physical disturbance,
and most emission tests conducted on the disturbed areas had to be terminated before
completion, despite the fact that the PI-SWERL test RPMs were reduced by more than
50%.
6) In the field, the ranking of the treatment efficacy scaled with the MoE but not with the
MPF measurements and was markedly different from the ranking of the laboratory
surfaces. The research plot surfaces ranked from most effective to least effective as:
Irrigated plot, Control, EcoAnchor, Dust Fyghter LN100, Soil Sement®, Entac. Therefore,
all four dust suppressants were less effective in preventing dust emissions than the
untreated control plot and the irrigated plot, which may have developed a physical and/or
biological crust.
7) In the field, there was no temporal pattern in the dust emission rates and the results
exhibited a high degree of variability, which suggests that they may have been influenced
more by site conditions, especially moisture content and the deposition and resuspension
of advected dust, than dust suppressant efficacy.
5.2 Study Limitations
1) Dust emissions are difficult to measure accurately because of the complex mechanisms
involved in the entrainment of dust particles and the small size of the particles, which move
in much less predictable patterns than larger sand particles. Therefore, as is common in
studies that aim to characterize dust emissions, the emissions data contain a lot of scatter.
Also, despite the high level of control of experimental conditions that is possible in the
wind tunnel, the majority of the wind tunnel emissions data were not normally distributed
105
and comparisons between the dust suppressants were often not statistically significant due
to the small sample size. Constraints on wind tunnel availability would make it difficult to
run a larger number of replicate trays, and there would likely still be a considerable degree
of variability in the data since dust emission and dispersion are highly stochastic processes
driven by turbulence.
2) The dust suppressants were applied on very level tailings in the wind tunnel study. This
does not reflect a natural surface which would likely be quite uneven and not completely
level. Applying the dust suppressants on an uneven surface could result in a less consistent
coverage of the tailings since there might be some tendency for the liquid to pool in low
lying areas before curing. During wind tunnel tests, higher lying areas would be more
exposed and would likely be more vulnerable to the shear stress exerted by the airflow as
well as to saltating sand particles.
3) This project only tested the dust suppressants on mine tailings from a nepheline syenite
operation. The application of these products on tailings with different physical and/or
chemical characteristics, as well as different site conditions, are expected to yield different
results.
4) The field plots were only tested on discrete measurement dates, rather than continuously
in order to capture crust response to changing weather and site conditions. In order to do
so would have required a much larger test area and a much more comprehensive array of
instruments to measure dust emissions and wind speed and direction.
5) There was no way to differentiate between dust released from the surface crusts on the
field plots and dust that was re-suspended after settling on the plots from adjacent areas.
106
Therefore, field dust emissions rates were likely strongly influenced by site characteristics,
particularly the re-suspension of settled dust.
6) The inclusion of seeded field plots to determine whether the dust suppressants support
or inhibit the growth of vegetation may have been insightful. However, the decision was
made to focus solely on dust emission rates for two reasons: (1) the area cleared by the
mine was not large enough to establish separate plots for a paired vegetation experiment,
and the entire area of the plots was required to perform the emissions and disturbance tests;
and (2) previous observations on the Unimin tailings ponds demonstrated a consistent
pattern in which vegetation encroachment begins from the edges of cleared areas and then
moves towards the center of the plots (Preston, 2015a; 2015b). In the current experimental
design, the plots treated with the four commercial dust suppressants and the control plot
were immediately adjacent to each other, meaning that any natural encroachment by native
vegetation would be greater in the two outer plots, thus creating a research bias.
5.3 Recommendations
1) Three of the commercial dust suppressants were effective at preventing dust emissions
under controlled laboratory conditions and in particle free airflow. However, in both the
laboratory and field, all of the dust suppressants were immediately and acutely vulnerable
to physical disturbance. Therefore, none of the commercial dust suppressants would be
recommended for use at a site if the surface is expected to experience physical disturbance.
2) When considering an industrial application of a dust suppressant, site conditions must
be carefully assessed. The particle size distribution of the tailings is particularly important
since the percentage of PM10 can be a critical factor in dust emissions (Bacon et al., 2011)
107
and may also affect the strength of the crusting (Cahill et al., 1996; Leys & Eldridge, 1998;
Rice & McEwan, 2001). The presence of sand-sized particles is also important, since they
may function as abraders, causing PM10 emission during wind events.
3) On the Nephton tailings ponds, no commercial dust suppressants are recommended due
to the nature of the tailings and the site conditions. Recent research has found that the
irrigation of the tailings ponds, if properly monitored, is sufficient to prevent fugitive dust
emission and may promote the formation of a physical crust. Following patterns seen in
2014 and 2015, the presence of algae on the tailings and in the irrigation water can promote
the development of an algal crust, which may develop into a moss crust, which in turn may
support vascular vegetation, in a manner similar to natural succession, if sufficient moisture
levels are present.
4) A large body of work exists concerning the resistance of both physical and biological
crusts to wind erosion. However, most of the studies are primarily concerned with the
movement of sand particles and erosion due to loss of mass or visible deterioration of the
surface crust. Further research is recommended to better understand the efficacy of physical
and biological crusts in preventing fugitive dust emissions.
108
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115
Appendix
Table A. A sample PI-SWERL PM10 emission output from the first replicate run on the control
plot, M1. Note, the PI-SWERL output also includes optical gate sensor data which were not used
in this study and are not included in this Table.
Datetime TestID RPM TRPM RPM_Norm Flow(LPM)
DT_PM10
(mg/m3)
InstantFlux
(ug/s)
StepMass
(ug)
TotalMass
(ug)
TestDur
(sec)
StepDur
(sec)
2016-06-06 12:32 3.55E+09 0.135269 400 0.000338 98.090106 0.002 0.00327 0.00327 1.104991 91 1
2016-06-06 12:32 3.55E+09 5.974799 400 0.014937 98.853374 0.002 0.003295 0.006565 1.108286 92 1
2016-06-06 12:32 3.55E+09 55.79783 400 0.139495 100.480154 0.002 0.003349 0.009914 1.111635 93 2
2016-06-06 12:32 3.55E+09 146.741 400 0.366853 101.317757 0.002 0.003377 0.013291 1.115013 94 3
2016-06-06 12:32 3.55E+09 273.9352 400 0.684838 100.203516 0.002 0.00334 0.016631 1.118353 95 4
2016-06-06 12:32 3.55E+09 362.2906 400 0.905726 101.346286 0.002 0.003378 0.02001 1.121731 96 5
2016-06-06 12:32 3.55E+09 405.7415 400 1.014354 99.738879 0.002 0.003325 0.023334 1.125056 97 6
2016-06-06 12:32 3.55E+09 398.7362 400 0.99684 100.579709 0.002 0.003353 0.026687 1.128408 98 7
2016-06-06 12:32 3.55E+09 395.6874 400 0.989219 98.73908 0.002 0.003291 0.029978 1.1317 99 8
2016-06-06 12:32 3.55E+09 393.69 400 0.984225 99.111554 0.002 0.003304 0.033282 1.135003 100 9
2016-06-06 12:32 3.55E+09 394.6529 400 0.986632 99.2212 0.002 0.003307 0.036589 1.138311 101 10
2016-06-06 12:32 3.55E+09 393.1654 400 0.982913 99.889918 0.003 0.004994 0.041584 1.143305 102 11
2016-06-06 12:32 3.55E+09 391.6351 400 0.979088 100.580848 0.002 0.003353 0.044937 1.146658 103 12
2016-06-06 12:32 3.55E+09 394.5246 400 0.986311 100.795397 0.002 0.00336 0.048296 1.150018 104 13
2016-06-06 12:32 3.55E+09 390.7411 400 0.976853 99.491239 0.002 0.003316 0.051613 1.153334 105 14
2016-06-06 12:32 3.55E+09 390.8515 400 0.977129 99.887761 0.002 0.00333 0.054942 1.156664 106 15
2016-06-06 12:32 3.55E+09 387.8032 400 0.969508 100.291028 0.002 0.003343 0.058285 1.160007 107 16
2016-06-06 12:32 3.55E+09 389.2328 400 0.973082 101.582001 0.002 0.003386 0.061671 1.163393 108 17
2016-06-06 12:32 3.55E+09 389.3874 400 0.973468 99.996574 0.002 0.003333 0.065005 1.166726 109 18
2016-06-06 12:32 3.55E+09 394.3533 400 0.985883 98.497255 0.002 0.003283 0.068288 1.170009 110 19
2016-06-06 12:32 3.55E+09 392.6525 400 0.981631 99.186495 0.002 0.003306 0.071594 1.173315 111 20
2016-06-06 12:32 3.55E+09 392.4833 400 0.981208 100.291453 0.002 0.003343 0.074937 1.176658 112 21
2016-06-06 12:32 3.55E+09 389.837 400 0.974592 99.571868 0.002 0.003319 0.078256 1.179977 113 22
2016-06-06 12:32 3.55E+09 386.063 400 0.965157 102.052637 0.002 0.003402 0.081658 1.183379 114 23
2016-06-06 12:32 3.55E+09 392.9041 400 0.98226 100.161846 0.002 0.003339 0.084997 1.186718 115 24
2016-06-06 12:32 3.55E+09 397.2919 400 0.99323 100.528466 0.002 0.003351 0.088348 1.190069 116 25
2016-06-06 12:32 3.55E+09 399.8086 400 0.999521 98.81463 0.002 0.003294 0.091642 1.193363 117 26
2016-06-06 12:32 3.55E+09 395.8355 400 0.989589 101.345432 0.002 0.003378 0.09502 1.196741 118 27
2016-06-06 12:32 3.55E+09 391.4581 400 0.978645 97.81985 0.002 0.003261 0.09828 1.200002 119 28
2016-06-06 12:32 3.55E+09 391.8364 400 0.979591 100.001065 0.003 0.005 0.10328 1.205002 120 29
2016-06-06 12:32 3.55E+09 388.367 400 0.970917 101.504864 0.002 0.003383 0.106664 1.208385 121 30
2016-06-06 12:32 3.55E+09 389.1954 400 0.972989 98.94868 0.003 0.004947 0.111611 1.213333 122 31
2016-06-06 12:32 3.55E+09 392.1128 400 0.980282 100.232798 0.003 0.005012 0.116623 1.218344 123 32
2016-06-06 12:33 3.55E+09 394.3054 400 0.985764 99.492291 0.002 0.003316 0.119939 1.221661 124 33
2016-06-06 12:33 3.55E+09 390.8151 400 0.977038 100.376127 0.002 0.003346 0.123285 1.225006 125 34
2016-06-06 12:33 3.55E+09 392.4894 400 0.981223 100.681924 0.002 0.003356 0.126641 1.228363 126 35
2016-06-06 12:33 3.55E+09 388.0877 400 0.970219 100.389686 0.002 0.003346 0.129988 1.231709 127 36
2016-06-06 12:33 3.55E+09 391.7747 400 0.979437 99.096065 0.002 0.003303 0.133291 1.235012 128 37
2016-06-06 12:33 3.55E+09 391.9606 400 0.979901 99.009839 0.002 0.0033 0.136591 1.238312 129 38
2016-06-06 12:33 3.55E+09 394.1615 400 0.985404 99.428854 0.002 0.003314 0.139905 1.241627 130 39
2016-06-06 12:33 3.55E+09 395.6714 400 0.989179 99.895607 0.002 0.00333 0.143235 1.244957 131 40
116
Table B. Field emissions data, F (µg m-2 s-1) with average emission values for each PI-SWERL
test, F̅, average values for each test plot, F̿, σ, CV, and statistical test results.
______________________________________________________________________________
Surface F̅, tests 1/2/3/4 F̿ σ CV(%)
M1
C 0.95 / 0.24 / 0.14 / 0.33 0.42 0.37 88
LN 1.38 / 0.50 / 0.24 / 0.14 0.57 0.57 100
EN 2.28 / 0.79 / 0.87 / 0.45 1.10 0.81 74
EA 1.97 / 0.94 / 0.49 / 0.23 0.91 0.76 84
SS 1.54 / 0.67 / 7.76 / 1.02 2.75 3.36 122
W 0.28 / 0.48 / 0.18 / 0.19 0.28 0.14 49
Data not normally distributed, not significant (Kruskal-Wallis, p = 0.091)
______________________________________________________________________________________
M2
C 8.29 / 0.84 / 0.70 / 0.43 2.56 3.81 149
LN 0.38 / 0.28 / 1.3 / 0.65 0.67 0.49 74
EN 18.7 / 8.26 / 4.22 / 3.04 8.54 7.10 83
EA 6.62 / 11.26 / 7.14 / 2.54 6.89 3.57 52
SS 2.98 / 3.00 / 1.54 / 6.22 3.43 1.98 58
W 4.25 / 1.12 / 3.88 / 7.16 4.10 2.47 60
Data not normally distributed except C, significant differences between C – EN, C – EA, LN – EN,
LN – EA, LN – SS, LN – W, EN – SS, EN – W, EA – SS, EA – W (Kruskal-Wallis, p = 0.035)
______________________________________________________________________________________
M3
C 32.98 / 84.12* / 76.31 / 55.95 62.34 22.89 37
LN 87.70 / 60.42 / 50.69 / 97.94** 74.19 22.27 30
EN 43.86 / 63.86 / 39.57 / 21.62 42.23 17.35 41
EA 56.97 / 47.39 / 59.46 / 66.59 57.60 7.94 14
SS 83.67 / 43.64 / 83.33 / 52.50 65.78 20.77 32
W 14.31 / 40.93 / 26.11 / 23.07 26.11 11.08 42
Data normally distributed, significant difference between LN and W (ANOVA, p = 0.016)
Tests terminated due to high DT concentrations: * after 370 seconds, ** after 290 seconds
______________________________________________________________________________________
M4
C 2.62 / 0.87 / 1.55 / 0.38 1.35 0.97 72
LN 2.09 / 1.16 / 4.65 / 2.62 2.63 1.48 56
EN 2.77 / 4.21 / 50.28 / 5.82 15.77 23.04 146
EA 0.51 / 0.39 / 0.73 / 14.31 3.99 6.88 172
SS 0.29 / 0.42 / 0.29 / 0.40 0.35 0.07 20
W 106.04 / 4.78 / 2.42 / 2.03 28.82 51.49 179
Data not normally distributed, significant differences between C – EN, C – W, LN – EN, LN – W,
EN – SS, EN – W, EA – W, SS – W (Kruskal-Wallis, p = 0.016)
______________________________________________________________________________________
117
_____________________________________________________________________________________
Surface F̅, tests 1/2/3/4 F̿ σ CV(%)
M5
C 1.05 / 0.45 / 0.30 / 0.49 0.57 0.33 57
LN 0.32 / 0.42 / 0.25 / 0.22 0.30 0.09 30
EN 0.46 / 3.14 / 0.85 / 3.80 2.06 1.65 80
EA 0.32 / 0.14 / 0.12 / 0.13 0.18 0.10 55
SS 0.15 / 0.14 / 0.12 / 0.13 0.14 0.01 10
W 2.99 / 0.19 / 0.15 / 0.14 0.87 1.41 164
Data not normally distributed, significant differences between C – EN, LN – EN, LN – W, EA – W,
SS – W (Kruskal-Wallis, p = 0.0082)
______________________________________________________________________________________
M6
C 0.17 / 0.18 / 0.10 / 0.09 0.13 0.05 34
LN 0.10 / 0.12 / 0.26 / 0.46 0.23 0.17 71
EN 6.84 / 0.57 / 0.37 / 0.41 2.05 3.19 156
EA 0.10 / 0.97 / 0.24 / 0.17 0.59 0.45 76
SS 0.73 / 0.28 / 0.26 / 0.322 0.40 0.23 57
W 3.43 / 0.49 / 0.24 / 0.26 1.11 1.55 140
Data not normally distributed, significant differences between C – EN, C – W, LN – EN, EN – EA,
EN – SS, EN – W (Kruskal-Wallis, p = 0.048)
______________________________________________________________________________________
M7
C 3.39 / 1.97 / 1.12 / 1.12 1.90 1.07 56
LN 1.06 / 1.17 / 1.10 / 1.40 1.18 0.15 13
EN 15.51 / 11.36 / 8.03 / 18.30 13.30 4.52 34
EA 7.70 / 5.32 / 3.88 / 1.71 4.65 2.52 54
SS 4.63 / 7.43 / 5.40 / 10.58 7.01 2.66 38
W 2.26 / 1.38 / 1.42 / 1.08 1.53 0.51 33
Data normally distributed, significant differences between C – EN, LN – EN, LN – SS, EN – EA, EN – SS,
EN – W, SS – W (ANOVA, p = 7.5 * 10-6)
______________________________________________________________________________________
D1
C 164.53 / 228.12 / 60.24 / 45.33 124.56 87.06 70
LN 43.71 / 316.57 / 68.90 / 122.45 137.91 123.55 90
EN 143.76 / 153.84 / 152.19 / 191.79 160.40 21.39 13
EA 350.69 / 160.22 / 463.26 / 232.66 301.71 133.27 44
SS 174.63 / 63.79 / 52.40 / 255.63 136.61 96.62 71
W 31.12 / 70.06 / 54.33 / 42.23 49.43 16.70 34
Data normally distributed, significant differences between C – EA, C – W, LN – EA, LN – W, EN – EA,
EN – W, EA – SS, EA – W, SS – W (ANOVA, p = 0.029)
______________________________________________________________________________________
118
______________________________________________________________________________________
Surface F̅, tests 1/2/3/4 F̿ σ CV(%)
D2
C 32.76 / 27.13 / 123.41 / 360.67 135.99 156.15 114
LN 84.65 / 189.43 / 546.33 / 95.11 228.88 216.82 95
EN 42.27 / 13.28 / 9.51 / 17.90 20.74 14.76 71
EA 77.63 / 71.48 / 16.37 / 23.27 47.19 31.83 67
SS 25.12 / 27.90 / 12.85 / 9.64 18.88 8.98 48
W 793.91 / 607.67 / 562.35 / 839.66 698.40 134.18 19
Data normally distributed, significant differences between C – LN, C – EN, C – EA, C – SS, C – W,
LN – EN, LN – EA, LN – SS, LN – W, EN – W, EA – W, SS – W (ANOVA, p = 1.88 * 10-6)
______________________________________________________________________________________
D3
C 8.07 / 6.43 / 3.32 / 3.85 5.42 2.23 41
LN 24.08 / 41.15 / 346.53 / 29.31 110.27 157.67 142
EN 14.46 / 3.55 / 6.95 / 3.48 7.11 5.16 73
EA 16.02 / 14.19 / 14.51 / 16.07 15.20 0.99 7
SS 25.44 / 26.51 / 13.77 / 96.20 40.48 37.59 93
W 5.81 / 9.54 / 8.39 / 11.36 8.77 2.33 27
Data not normally distributed, significant differences between C – LN, C – SS, LN – EN, LN – EA,
LN – SS, LN – W, EN – SS, EA – SS, SS – W (Kruskal-Wallis, p = 0.0025)
______________________________________________________________________________________