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Dust dispersion from haul roads in
complex terrain: the case of a mineral
reclamation site located in Sardinia
(Italy)
a
a
a
Letizia Piras , Valentina Dentoni , Giorgio Massacci & Ian S.
Lowndes
b
a
Department of Civil and Environmental Engineering and
Architecture (DICAAR), University of Cagliari, Cagliari, Italy
b
Faculty of Engineering, Process and Environmental Research
Division, University of Nottingham, UK
Published online: 06 Mar 2014.
To cite this article: Letizia Piras, Valentina Dentoni, Giorgio Massacci & Ian S. Lowndes (2014) Dust
dispersion from haul roads in complex terrain: the case of a mineral reclamation site located in
Sardinia (Italy), International Journal of Mining, Reclamation and Environment, 28:5, 323-341, DOI:
10.1080/17480930.2014.884269
To link to this article: http://dx.doi.org/10.1080/17480930.2014.884269
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International Journal of Mining, Reclamation and Environment, 2014
Vol. 28, No. 5, 323–341, http://dx.doi.org/10.1080/17480930.2014.884269
Dust dispersion from haul roads in complex terrain: the case of a
mineral reclamation site located in Sardinia (Italy)
Downloaded by [Universita Degli Studi di Cagliari] at 03:15 12 November 2014
Letizia Pirasa, Valentina Dentonia*, Giorgio Massaccia and Ian S. Lowndesb
a
Department of Civil and Environmental Engineering and Architecture (DICAAR), University of
Cagliari, Cagliari, Italy; bFaculty of Engineering, Process and Environmental Research Division,
University of Nottingham, UK
(Received 20 December 2013; accepted 13 January 2014)
In recent years, there has been significant research effort to investigate the use of
plume dispersion models to assess the environmental impact of fugitive dust
emissions from surface mining operations. In particular, the results of these studies
have identified challenges to the use of traditional Gaussian plume dispersion
models to satisfactorily reproduce fugitive dust dispersion and deposition
experienced from low elevation release heights within complex topography. This
paper presents a discussion of the results of a preliminary series of modelling
studies that have employed the UK-Atmospheric Dispersion Modelling System
(ADMS) model to investigate the dust dispersion and deposition from a former
mining site currently undergoing remediation. The remediation site is located within
a valley in south-western Sardinia that may be considered an aerodynamically
complex terrain. A series of field measurement surveys were conducted along the
length of an unpaved surface haul truck roadway to measure the PM2.5 and PM10
dust fractions within the emitted plumes. To investigate the potential effects that the
surrounding topography may have on the atmospheric dispersion and deposition
experienced a series of UK-ADMS dispersion models were solved for a range of
meteorological stability conditions typical of the area under investigation. A
preliminary analysis of the results of these simulations concludes that there was a
strong influence of the surrounding terrain on the dispersion and deposition
phenomena predicted.
Keywords: mine reclamation; unpaved roads; fugitive dust; emission factors;
atmospheric dispersion
Introduction
Mining and mineral site remediation activities can represent a significant contribution
to particulate matter (PM) emissions from a range of sources which may include:
overburden removal, drilling, blasting, loading, transit along unpaved roads, materials
handling and wind erosion from exposed surfaces [1]. These potential emission sources
are classified as fugitive dust sources by the US Environmental Protection Agency,
since they emit dust in the form of a non-confined flow stream [2].
National and International regulations to specify air quality standards have
established statutory exposure limit levels for airborne dust concentrations, and in particular for the PM10 and PM2.5 size fractions, deemed potentially hazardous to human
*Corresponding author. Email: [email protected]
© 2014 Taylor & Francis
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324
L. Piras et al.
health. In particular, since 2005 Annex XI (Limit values for the protection of Human
Health) of the European Directive on ambient air quality [3] established a daily PM10
exposure limit of 50 μg/m3, and that a daily exposure of 40 μg/m3 should not be
exceeded more than 35 times in a calendar year. As regards PM2.5, Annex XIV
(National exposure reduction target, target value and limit value for PM2.5) of the
Directive establishes an exposure-level limit value of 25 μg/m3 per calendar year to be
achieved by 2015, followed by a 20 μg/m3 limit value to be met by 2020.
A series of recent research studies have highlighted that Gaussian plume models
may not satisfactorily predict the atmospheric dispersion and deposition of fugitive dust
emitted from sources within mining and mine remediation operations. It was concluded
that this may be due to a number of factors including: the typically low release heights
of the fugitive dust sources and the complexity of the surrounding topography.
Recently, Cole and Zapert [4] employed the ISC3 Gaussian plume model to investigate
the dispersion of dust from a number of fugitive sources located within three stone
quarries in the state of Georgia, USA. The results of these studies demonstrated that
the model overpredicted the actual measured PM10 ground-level deposition
experienced by a factor of 5. It was concluded that this overprediction was due to both
an overestimation of the actual fugitive dust emission by the US EPA AP42 dust
emission factor equations used to estimate the source emissions, and to the inherent
limitations of the Gaussian plume model to predict the particulate dispersion and
deposition within areas of complex terrain. Other research studies have confirmed the
need to develop more representative emission factors to be employed with Gaussian
plume dispersion models in order to more satisfactorily predict the environmental
impact of surface mining operations [5,6].
Several recent research studies were performed to investigate the performance of
the UK-Atmospheric Dispersion Modelling System (ADMS) model [7] against other
Gaussian plume dispersion models [8–11]. The UK-ADMS code uses a modified
Gaussian dispersion model to simulate pollutant dispersion within the atmospheric
boundary layer. The ADMS code was developed to model the dispersion of buoyant or
neutrally buoyant particles and gases [10,12].
A recent study [13] employed the ADMS code to develop a simulation strategy to
investigate the fugitive dust emissions from in-pit quarry extraction and processing
activities. The model was used to replicate the fugitive dust emissions, dispersion and
deposition observed at a working quarry during the blasting, loading and haulage of
the mineral. The study concluded that the observed dust dispersion is strongly affected
by the local meteorological conditions and by the in-pit and surrounding terrains. A
further investigation [14] was carried out at the same quarry site to verify the reliability
of the predicted ADMS model simulations. The modelled monthly dust deposition rates
arising from the routine blasting, loading and haul road operations were compared
against the dust deposition data collected by a network of British Standard Frisbee
deposition gauges. A comparative analysis of this data concluded that the ADMS
models overpredicted the observed dust deposition rates; the discrepancy was attributed
to the use of non-site-specific emission factors and to the inherent limits of the flow
field model to simulate the in-pit turbulent flow fields [15].
This paper presents the results of a series of parametric modelling studies conducted
to investigate the predicted atmospheric dispersion of fugitive dust emissions measured
along an unpaved haul road of a mineral remediation site in the south‐west of Sardinia.
The UK-ADMS models were solved for a range of typical meteorological conditions
(wind speed, wind direction and atmospheric stability class). The results of these
International Journal of Mining, Reclamation and Environment
325
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simulation results were analysed to identify the effects that the surrounding topography
may have on the predicted dust dispersion and deposition. The dust emissions simulated were validated against a series of PM10 and PM2.5 dust measurements collected
along the near upwind and downwind lengths of an unpaved haul road travelled by
mineral haulage trucks [16]. The resultant fugitive dust emission rates were used to
determine emission factors that were used to represent the fugitive dust sources within
the UK-ADMS models.
The case study
This paper presents the results of a series of modelling studies to investigate the
predicted dust dispersion and deposition experienced from the unpaved haul roads
within a former mineral property undergoing remediation in south-western Sardinia.
There are a significant number of former mining and mineral sites currently undergoing
remediation in this region, which was an important former mining area in Italy for lead,
zinc and silver extraction. These mining activities ceased towards the end of the 1980s.
Since this time, an intensive remediation plan has been promoted by both the local and
national authorities to reclaim many of these former mining and mineral sites. Figure 1
shows a photograph of one of the major remediation sites in the province of
Carbonia–Iglesias (Monteponi): the remaining surface structures of the former mineral
plant are visible behind the red mud tailings bank that has been covered with a
geotextile fabric.
The principal reclamation activities conducted at the former mineral property are
the limestone capping of the abandoned tailings dumps, which are part of a wider
hydraulic management project to prevent the potential emission of acid leachate from
the residual ore remaining within the tailings dumps.
The emplacement of the limestone cap involves the following stages: the crushed
limestone rock is loaded from stock piles into haulage trucks by wheeled loaders and
then transported along unpaved haul roads to the emplacement sites on top of the
former tailings dumps; the limestone material is then unloaded from haulage trucks and
spread over the surface of the tailings dump by a shovel excavator.
A schematic representation of these activities is shown in Figure 2. Table 1
summarises the operational capacities of the vehicles.
A preliminary estimation of the fugitive dust emission rates associated with these
activities concluded that the total emissions inventory was dominated by that generated
by the transit of the haulage trucks along the unpaved roads.
Figure 1. The former mining and mineral site of Monteponi (Carbonia–Iglesias) illustrating the
red mud tailings dump in the foreground that is undergoing remediation.
326
L. Piras et al.
material
loading
material
transport
material
unloading
material
spreading
empty dumper
travel
Figure 2.
process.
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Table 1.
Flow diagram of the various stages of operational activities involved in the reclamation
Main operational characteristics of the vehicles used during the reclamation activities.
Vehicle
No. of wheels
Weight [t]
Capacity [m3]
4
6
tracked
18.2
13.8
23.0
3.0
12.5
1.2
Wheeled loader
Haulage truck
Excavator
As regards material spreading, it was concluded that the release height of this
potential source was very close to the ground and therefore the major dust deposition
occurred at short distances from the emission point. Therefore, the contribution of this
activity to the overall emission was not included during the subsequent modelling
studies.
With reference to the loading and unloading operations, the emission factors were
estimated by employing Equation 1, proposed by US EPA for mineral drop operations
[17]:
U 1=3
E ¼ k 0:0016 2:21=4
M
(1)
2
where E = emission factor in terms of mass of emitted dust per mass of handled
material [kg/Mg]; U = mean wind speed [m/s]; M = material moisture content [%]; and
k = numerical coefficient depending on the particulate size.
As shown above, the emission generated depends on the near-surface wind speed;
therefore, different emission factors were obtained for a range of different meteorological conditions and release heights, typical to the mineral operations under consideration.
For the atmospheric conditions simulated, the wind speeds at the release heights of
loading and unloading (4 m and 1.5 m, respectively) were estimated [18]. The crushed
Table 2.
TSP emission factors and TSP emission rates for the material handling operations.
Dust source
Material loading
Material unloading
Meteorological
conditions
Class
Class
Class
Class
Class
Class
A
D
F
A
D
F
TSP emission factor
[kg/Mg]
0.17
1.25
0.24
0.16
1.03
0.12
×
×
×
×
×
×
10−2
10−2
10−2
10−2
10−2
10−2
TSP emission rate
[g/s]
7.36
54.25
10.24
6.74
44.81
5.08
×
×
×
×
×
×
10−2
10−2
10−2
10−2
10−2
10−2
International Journal of Mining, Reclamation and Environment
327
limestone was assumed to have a moisture content of 0.7% [17]. The total suspended
particulate (TSP) emission factors and the corresponding emission rates determined for
the material loading and unloading operations are reported in Table 2. A comparison of
the computed emission rates for the material loading and unloading, and the truck
haulage operations concluded that the contributions of the dust emissions due to
material loading and unloading were assumed negligible.
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Field measurements
Dust emission from unpaved roads
The transit of the loaded and unloaded vehicles along the unpaved haul roads crushes
the surface road material into finer particles due to the force applied by the haulage
truck wheels. These generated fine particles which are then subsequently lifted up by
the action of the rolling wheels, to be potentially transported by the background wind
flows across large distances. Following the initial suspension of the fine dust by the
wheels of the trucks, the turbulent wake generated around and left behind by the
passage of the vehicles can continue to act on the surface material of the road, and thus
potentially generating further particulate emissions [19].
Unpaved haul roads are classified as fugitive dust sources by the US Environmental
Protection Agency, as they emit fugitive dust in the form of an unconfined flow stream.
The sampling of the dust plume arising from a vehicle travelling on a road is complex
and traditional sampling techniques cannot be used as they are designed for application
to confined flow streams. There are currently two accepted sampling methods suitable
to estimate the emissions from fugitive dust sources: either the upwind–downwind
method or the exposure-profiling method [20].
With reference to the case study under examination, a series of field measurements were performed to measure the PM2.5 and PM10 dust emissions generated
by the transit of haulage trucks along the unpaved roads. The exposure-profiling
method [16] was used to estimate the dust emission factors. A detailed description
of the sampling procedures and the measurement instruments used is reported
below.
Materials and methods
The PM2.5 and PM10 dust fractions generated by the transit of the haulage trucks
along an unpaved haul road within the remediation site were sampled using Respicone multistage Virtual Impactors. The dust samplers have three sampling stages in
which different size fractions are collected on fibreglass filters. The samplers combine
traditional inertial classification with a photometric detection system which analyses
the variability of dust concentration over time for each collected dust fraction. The
two pictures in Figure 3 illustrate the classification mechanism and stages employed
by the collectors. A total airflow of 3.10 l/min (Q) is pulled through each sampler
delivering the total inhalable fraction (IF). This total airflow is subsequently divided
into three airflow streams which exit the instrument at each of the three collection
stages (where Q1 = 2.66 l/min, Q2 = 0.33 l/min, Q3 = 0.11 l/min). The following three
PM fractions are captured in each of the three sampling stages, listing from top to
bottom the finest fraction PM2.5 (cut-off diameter 2.5 μm), the PM10 fraction (cut-off
diameter 10 μm) deprived of the PM2.5 fraction and the coarser IF deprived of the
PM10 fraction. The mass deposited on each of the three fibreglass filters enables the
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328
L. Piras et al.
Figure 3. Schematic representations of the Respicone multistage Virtual Impactor dust sampler
illustrating the 3 size fraction sampling and collection stages.
determination of the mean dust concentration for each fraction. The photometric
measurement system analyses the variability of dust concentration collected over time
by each size collection stage. The time histories of the collected dust concentration
for each of the three size fractions were recorded by a digital data logger connected
to the samplers.
The dust measurements were carried out in accordance with the protocols defined
within the US EPA approved exposure-profiling method for unpaved roads [20]. Three
Respicone multistage Virtual Impactors were mounted on a dust sampling tower
located 5 m downwind of the haul road, to sample the dust concentration at three
different heights above the haul road surface (0.75, 1.5 and 3 m above the tower base).
A fourth Virtual Impactor was installed at a height of 1.5 m above the ground level on
a second tower located upwind of the same road to measure the upwind background
dust concentration. A weather station connected to a portable PC was also mounted on
the upwind tower to record the wind speed and direction, the temperature and the
relative humidity of the air.
The wind speeds at the corresponding dust sampling heights were determined from
the average wind speed recorded by the weather station and the application of a power
law equation used to estimate the wind profile over a defined height range [18].
A schematic representation of the location of the dust samplers is shown in
Figure 4.
The dust measurements were collected on a sunny day under convective stable
atmospheric conditions. The haulage trucks travelled along a dry unpaved road surface,
which were the environmental conditions experienced on a typical working day during
the spring and summer seasons. A number of road surface dirt samples were collected
for subsequent analysis to determine the moisture content and the silt percentage.
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International Journal of Mining, Reclamation and Environment
329
Figure 4. A schematic of the relative locations of the upwind and downwind dust samplers and
weather station to comply with the sampling protocol of the exposure-profiling method.
Table 3. Truck haulage transit data recorded during the field dust survey collection
programmes.
Haul truck (loaded)
Haul truck (empty)
No. of transits
Min speed [km/h]
Max speed [km/h]
38
18
7.3
9.0
21.8
21.2
Table 3 indicates the number of sampled truck transits and the average vehicle speeds
recorded during the sampling periods.
Elaboration of emission factors
The PM10 and PM2.5 emission factors were determined for each truck transit according
to Equation (2) [21].
(
)
M
N
X
X
E¼
Dt cos hj uij Cij Dzi
(2)
j¼1
i¼1
where E = PM emission in grams per vehicle kilometre travelled [g/VKT]; Δt = sampling period of dust sampler (Δt = 1 s); θj = angle between the mean wind direction and
the perpendicular to the road centreline; uij = average wind speed at time tj at i‐th
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L. Piras et al.
Table 4. The average dust emission factors determined from an analysis of the collected field
dust measurements [16].
Vehicle
No. of transits
PM2.5
E [g/VKT]
PM10
E [g/VKT]
34
13
158
100
1560
812
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Dump truck (loaded)
Dump truck (empty)
sampler position on the downwind tower [m/s]; Cij = PM concentration at time tj as
measured by the i‐th sampler [mg/m3]; Δzi = vertical interval represented by the i‐th
sampler [m]; M = number of Δt included in the event; and N = number of samplers on
the downwind tower (N = 3).
The emission factors for the haul road under investigation were determined for the
transit of a fully loaded and an empty truck. Table 4 reports the mean value of the dust
emission factors determined for a truck travelling at different speeds. According to the
US EPA AP42 dust emission model guidelines for truck haulage on unpaved roads
[22], the transits that took place when the angle between the wind direction and the
perpendicular to the road centre line exceeded 45° were excluded from the calculation
of the average emission factors.
The ratio of the PM2.5 and PM10 emission factors (EFPM2.5/EFPM10) determined
from the field measurements collected was computed to be approximately 0.1 for both
the loaded and unloaded truck conditions, which is within the accepted range for the
US EPA method [23].
Comparison of measured and estimated emission factors
The emission factors determined from an analysis of the field measurements were
compared to the emission factors estimated by the use of Equation (3), which
represents the latest version of the US EPA AP-42 haul road fugitive dust emissions
estimate [22]:
s a W b
E¼k
(3)
12
3
where E = size-specific emission factor in grams per vehicle kilometre travelled
[g/VKT]; s = road surface material silt content [%]; W = mean vehicle weight [tons];
and a, b = parameters dependant on the particulate size range.
The emission factors derived from the field measurements (Measured E) and the
emission factors estimated on the basis of the EPA Equation (3) (Estimated E) are
reported in Table 5. A comparative analysis of the two sets of values concluded that
the computational model overestimates the measured values by a factor of between
1.08 and 1.25.
Modelling methodology
Representation of mobile sources
A number of different modelling methodologies have been proposed to represent the
fugitive dust emission sources within atmospheric dispersion models. The simplest
method is to represent the source as a continuous line source, the total emission being
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International Journal of Mining, Reclamation and Environment
331
uniformly distributed along the length of the road centreline and determined by the
speed, tonnage and frequency of the haul trucks [11]. The emission factor may be
either estimated from measurements taken on-site or in accordance with the haul road
dust emission models defined by US EPA AP-42 manual [22].
Organisiak and Reed [24] carried out field studies at a limestone quarry and coal
preparation plant, to quantify the fugitive dust emission and dispersion experienced from
the transit of haulage trucks along unpaved roads. The results of these studies support
the representation of these mobile sources as an area or volume-distributed sources [25]
and suggest the definition of an adjusted road width given by the actual road width plus
6 m, to represent the horizontal extent of the wake arising from the transit along the road.
Reed and Westman [6] developed a modified emission model to improve the reliability of the use of the Gaussian model ISC3 (Industrial Source Complex model) to
replicate the observed total fugitive dust emission arising from an unpaved road. The
emission is represented as a set of equidistant spaced point sources distributed along
the length of the road.
The results of a recent modelling study carried out by Joseph [26], to investigate
the use of the UK-ADMS Gaussian plume dispersion model, concluded that the use of
a series of non-overlapping point emission sources appeared to best replicate the haul
road emissions and dispersion experienced at a surveyed quarry under variable
meteorological conditions and downwind terrain conditions.
Adopting the modelling strategy proposed by Joseph, the haul road under investigation was modelled as a series of non-overlapping point sources with a 5 m diameter,
which corresponds to the width of the trailing truck plumes observed during field
measurements. The total haul road distance travelled by the haulage trucks on a single
trip from the crushed limestone source pile to the tailings dump was approximately
1200 m. Consequently, a series of 240 equidistant emission point sources were
distributed along the linear length of the unpaved haulage road within the UK-ADMS
model solution domain. Each individual fugitive dust source release point was set 1.5
m above the road surface, which corresponds to the mid height of the actual observed
emission plumes during the vehicle transits.
Emission rates
In accordance with the US EPA fugitive dust emission guidance notes on dust sampling, a
ratio of the PM10 to TSP emission factors, (EPM10/ETSP) of 0.3, was used to estimate the
TSP emission factors associated with the transits of both the empty and fully loaded haulage trucks: ETSP (empty haulage truck) = 2707 [g/VKT ] and ETSP (fully loaded haulage
truck) = 5200 [g/VKT]. The consistency of such an assumption is confirmed by the results
of the field study carried out by Organisiak and Reed [24], who found that the majority of
Table 5.
Measured and estimated emission factors for the three PM fractions.
Load condition
Size fraction
Empty dumper
PM2.5
PM10
TSP
PM2.5
PM10
TSP
Loaded dumper
Measured E [g/VKT]
Estimated E [g/VKT]
100
812
na
158
1560
na
109
1092
3399
176
1759
5475
332
L. Piras et al.
Table 6. TSP emission factors and TSP emission rates determined from the survey of the
unpaved haul road.
Dust source
Working condition
Haul road
empty haulage truck
loaded haulage truck
TSP emission factor [g/VKT]
2707
5200
TSP emission rate [g/s]
5.46
10.49
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dust generated from unpaved and untreated haulage roads is non-respirable and at least
80% of the airborne dust has an aerodynamic diameter size larger than 10 μm.
The TSP emission rates determined for the unpaved road on the mineral
remediation site are reported in Table 6.
Topography
The former mine reclamation site is located within a valley at an elevation of between
50 and 425 m above the sea level. The area may be considered as an aerodynamically
complex terrain, where the slopes of the valley sides in close vicinity to the haul road
have a gradient of between 25 and 75%.
To investigate the effect that the surrounding topography may have on the predicted
dust plume dispersion, a number of UK-ADMS model simulations were performed. To
define the solution domain for the models requires the specification of the surface
terrain in terms of the spatial variation in elevation. A digital elevation model of the
actual topography (with a spatial resolution of 10 m) was obtained from the Regione
Sardegna. A preliminary series of grid size solution sensitivity exercises were
performed to ensure that the predicted dispersion and deposition solutions obtained
were grid size independent [27].
Figure 5 shows a representative contour relief map of the area and indicates the
positions of the surveyed haul road (bold black line), the limestone stockpile (location
A) and the tailings dump emplacement site (location B).
Meteorological data
The meteorological data for surveyed region over the previous 30 years were analysed
to determine the statistical frequency distributions of the predominant seasonal wind
speeds and directions. The analysis confirmed that most frequent wind direction for this
region was from the north-west direction (315° measured clockwise from the North)
which corresponds to the dominant regional wind called the maestrale. This
predominant wind system was prevalent during the dust sampling survey period and
was therefore used to simulate the predicted plume dispersion and deposition within the
UK-ADMS model valleys. To investigate the potential dust plume dispersion that may
be experienced transversely across the same valley, model simulations were
subsequently performed employing a cross-valley 45° wind direction.
A series of parametric modelling studies were performed to predict the dispersion
of the observed dust plumes for both the identified principal wind directions under
unstable, neutral and stable atmospheric stability conditions. Table 7 summarises the
meteorological input parameters used to conduct the UK-ADMS model simulations.
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International Journal of Mining, Reclamation and Environment
333
Figure 5. Contour relief map of the area indicating the positions of the surveyed haul road (bold
black line), the limestone stock piles (location A) and the tailings dump emplacement site (location B).
Simulation results
Preliminary modelling across flat terrain
Preliminary benchmark model simulation studies were performed across a flat terrain to
investigate the plume dispersion arising from the haul road under different meteorological conditions in the absence of the influence of any surrounding complex topography.
As was expected, under convective atmospheric conditions (stability class A), the
horizontal plume dispersion is constrained and the maximum value of dust
Table 7. Meteorological input data used for the ADMS simulation models that correspond to
the unstable (A), neutral (D) and stable (F) Pasquill atmospheric stability classes [11].
Wind direction
[°]
315
315
315
45
45
45
Wind speed [m/
s]
Surface heat
flux
[W/m2]
1
5
2
1
5
2
113
0
−6
113
0
−6
Boundary layer height
[m]
1300
800
100
1300
800
100
Stability
class
A
D
F
A
D
F
Figure 6. Predicted hourly averaged TSP particulate concentrations across a flat terrain under convective (Class A), neutral (Class D) and stable (Class F)
atmospheric stability conditions under the influence of a 315° wind direction.
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L. Piras et al.
International Journal of Mining, Reclamation and Environment
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concentration is observed very close to the source. Larger downwind plume transport
distances are predicted under neutral stability conditions, which are also accompanied
by a wider predicted areal dispersion and deposition. The maximum plume advection is
found for very stable conditions (stability class F) for a low boundary layer height
(100 m above the ground) which suppresses the vertical rise of the plume. Figure 6
shows the hourly averaged TSP concentrations predicted across a flat terrain subject to
a 315° wind direction under representative unstable, neutral and stable atmospheric
conditions.
Influence of topography
A further series of model simulations were performed to study the influence of the
surrounding complex topography on the predicted plume dispersion across the range of
atmospheric stability conditions described in Table 7. The UK-ADMS simulation model
calculates the plume dispersion over a given terrain by first determining the flow field
generated by the defined wind direction and speed by using a calculation sub-model
called FLOWSTAR. The FLOWSTAR model computes the near-surface flow fields
over terrains with gradients up to 1:3. Validation studies performed by Carruthers et al.
[7] confirmed that the FLOWSTAR model simulations are reliable over ranges of
between tens of metres to several kilometres, for gradients up to 1:3 in hill wakes and
up to 1:2 for upwind slopes and hill summits [10]. The simulation of the plume
dispersion predicted along the length of the valley, which corresponds to the most
frequently experienced scenario, has been analysed using the actual topography together
subject to the predominant north-westerly wind direction. As shown in Figure 7, the
simulation of the three different stability conditions produces significantly different
behaviour in the predicted plume dispersions.
The relative confinement of the plume in the near vicinity of the source under
the influence of the highly convective unstable Class A stability conditions does not
indicate a strong influence of the topography. However, the higher wind speeds used
to simulate neutral stability conditions (class D) result in a distinct channelling of
the plume path by the confining valley sides. However, the downwind travel length
and lateral spread achieved by the predicted plume were found to be dependent on
the resolution scale of the flow field. The FLOWSTAR sub-model permits the use
of different grid resolutions to model the flow field, whilst maintaining the
same terrain file. An analysis of the flow grid resolution studies concluded that for
higher grid resolutions, the plumes were predicted to penetrate further along the
valley.
It was concluded that under stable atmospheric condition (class F), the downwind
travel of the predicted plume trajectory is arrested by the influence of a local flow
reversal (recirculation) experienced at the base of the hill slope.
Figure 8 shows the predicted plume dispersion across the valley subject to a 45°
wind direction under unstable, neutral and stable atmospheric conditions. Under the
highly convective unstable (Class A) conditions, the path of the plume is observed to
be channelled by the valleys slopes, with the maximum concentrations of the plume
being confined near to the source of emission. Under neutral atmospheric conditions
(class D), the path of the predicted plume is observed to be strongly channelled by the
combined influences of the higher surface wind flows and the confinement of the local
topography. Under stable atmospheric conditions (Class F), the areal extent of the
predicted dust dispersion is confined by the slopes of the surrounding valley. The
Figure 7. Hourly averaged TSP dust concentrations simulated within the actual field terrain under convective (Class A), neutral (Class D) and stable (Class
F) atmospheric stability conditions under the influence of a 315° wind direction.
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Figure 8. The predicted hourly averaged TSP plume concentrations for the actual terrain under convective (Class A), neutral (Class D) and stable (Class F)
atmospheric conditions subject to a 45° wind direction.
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lateral confinement of the plume is much greater than that predicted for the highly
convective stability class A.
The inclusion of the actual topography creates a reversal of the local flow field
within a 30 m height above the ground at the base of the rising slope of the adjacent
main hill.
A comparative analysis of the predicted plume concentrations at different heights
(0 m, 25 m, 50 m, 75 m, 100 m) above the ground level concluded that under the same
meteorological conditions, the vertical rise of the simulated plumes over the actual
terrain was higher than those predicted.
Figure 9. Deposition rates simulated for the flat and the actual complex real terrain under stable
conditions and 45° wind direction.
Dry deposition
The predicted dry deposition rates were determined for all the simulation scenarios
described above. The results predicted for 45° and 315° wind directions under both the
unstable convective (class A) and neutral (Class D) atmospheric stability conditions
were as expected. However, under the simulated stable (Class F) atmospheric
conditions, the predicted deposition rates were higher than expected for both the flat
and real complex topographies. For cross-valley plume dispersion under stable
conditions (class F), the comparison between the simulations performed with flat and
real topography produced a surprisingly higher rate of deposition over the flat terrain
(Figure 9).
An examination of the calculation method used by UK-ADMS to model dry
deposition may explain the differences in these predicted deposition rates. The dry
deposition rate is defined as equal to the product of the particle deposition velocity and
the ground-level concentration, Equation (4) [11]. Consequently, the lower predicted
ground-level dust concentrations determined for cross-valley dispersion under stable
atmospheric conditions will necessarily lead to lower deposition rates.
F ¼ vd Cðx; y; 0Þ
2
(4)
where F = rate of deposition per unit area [μg/ s·m ]; vd=particle deposition velocity
[m/s]; and C (x, y, 0) = ground-level concentration at the point (x, y).
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Conclusions
In recent years, there has been significant research effort to investigate the use of plume
dispersion models to assess the environmental impact of fugitive dust emissions from
surface mining operations. In particular, the results of these studies have identified
challenges to the use of traditional Gaussian plume dispersion models to satisfactorily
reproduce fugitive dust dispersion and deposition experienced from low elevation
release heights within complex topography. Recent studies have highlighted the need to
more accurately define and assess the fugitive dust emission factors used with
predictive atmospheric dispersion models.
This paper discusses the results of an application of the UK-ADMS model to
predict the fugitive dust plume dispersion and deposition experienced at a former
mining site currently undergoing remediation. The UK-ADMS model employs a
sub-model FLOWSTAR to compute the near-surface flow field over complex terrains.
Previous research studies have confirmed the reliability of this sub-model to replicate
these flows over ranges of between tens of metres to several kilometres from the dust
emission source, and across surfaces possessing gradients of up to 1:3 for hill wakes
and of up to 1:2 for upwind slopes and hill summits.
This paper summarises the results of a PM10 and PM2.5 dust sampling survey that
was conducted along the length of an unpaved haul road at a former mineral property
that is being remediated. The results of this survey were used to compute the actual
dust emission factors for the truck haulage activities conducted along this road.
Unpaved road haulage truck dust emission factors were also computed using the
revised AP42 emission models proposed by the US EPA. A comparative analysis of
the values produced by both methods showed satisfactory agreement.
To investigate the effect that surrounding topography may have on the dispersion of
generated dust plumes, a series of UK-ADMS models were solved for a number of
different meteorological and atmospheric stability conditions. An analysis of the results
of these studies concludes that the surrounding complex topography can have a strong
influence on the dispersion and deposition of the dust plumes generated from the haul
road. However, the results of this preliminary study need to be further investigated and
validated against further dust dispersion and deposition data collected in the field.
Acknowledgements
Investigation carried out in the framework of projects conducted by IGAG-CNR (Environmental
Geology and Geoengineering Institute of the National Research Council), Cagliari, Italy, and by
CINIGeo (National Inter-university Consortium for Georesources Engineering, Rome, Italy).
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