Plume rise parameterizations calculate the rise of pollutant plumes due to
effluent buoyancy and exit momentum. Some form of these parameterizations is
used by most air quality models. In this paper, the performance of the
commonly used Briggs plume rise algorithm was extensively evaluated, through
a comparison of the algorithm's results when driven by meteorological
observations with direct observations of plume heights in the Athabasca oil
sands region. The observations were carried out as part of the Canada-Alberta
Joint Oil Sands Monitoring Plan in August and September of 2013. Wind and
temperature data used to drive the algorithm were measured in the region of
emissions from various platforms, including two meteorological towers, a
radio-acoustic profiler, and a research aircraft. Other meteorological
variables used to drive the algorithm include friction velocity,
boundary-layer height, and the Obukhov length. Stack emissions and flow
parameter information reported by continuous emissions monitoring systems
(CEMSs) were used to drive the plume rise algorithm. The calculated plume
heights were then compared to interpolated aircraft
In large scale air-quality models, grid cell sizes may be on the order of 1 km or larger, while vertical resolution may be tens to hundreds of metres (see Makar et al., 2015a, b). The large-scale impacts of transport by winds and turbulence are handled in these models by algorithms dealing with advection and turbulent diffusion of tracers. However, the redistribution of mass from elevated stacks with high-temperature and/or high-velocity emissions sources requires parameterization in order to deal with issues such as the buoyancy and momentum of the emitted mass. Briggs and others developed a system of parameterizations for plume rise beginning in the late 1960s (e.g. Briggs, 1969, 1975). The parameterizations followed dimensional analysis to estimate plume rise based on meteorological measurements, atmospheric conditions, and stack parameters. Different variations of the Briggs plume rise parameterization equations are used in three-dimensional air-quality models such as GEM-MACH (Makar et al., 2015a, b), CMAQ (Byun and Ching, 1999), and CAMx (Emery et al., 2010), as well as AEROPOL, SCREEN3, and CALGRID models (see Holmes and Morawska, 2006, for a summary of these models). The Briggs equations are also used in the Regional Acid Deposition Model (RADM, Byun and Binowski, 1991) and have been incorporated into emissions processing systems such as SMOKE (CMAS, 2018) and SMOKE-EU (Bieser et al., 2011a).
As summarized by Briggs (1969), early observation of plume rise incorporated a wide variety of methods. Plumes were visually traced on Plexiglas screens, photographed, compared in height to nearby towers, and measured with lidar. Other techniques included the release of Geiger counters attached to balloons, and the release of balloons from within the stack chimneys. Bringfelt (1968) summarizes other techniques, using either theodolite, cloud height searchlights, or fluorescent particles sampled by aircraft-mounted instruments. Scaled wind tunnel simulations were also used. These observations were used to constrain the plume rise parameterizations and to choose appropriate constants following dimensional analysis (see Bieser et al., 2011b, for a summary).
Once a set of equations for plume rise had been developed, further
observations were used to test their accuracy. A report of these comparisons
(VDI, 1985) summarizes five studies in which plume rise parameterizations
were compared to observations. These studies consistently show a tendency to
overestimate plume rise when using the Briggs parameterizations. Giebel (1979)
measured pit coal power plant plumes with lidar, which averaged 50 % lower
than the parameterization. Rittmann (1982) reanalyzed the Bringfelt (1968)
and Briggs (1969) measurements from “industrial-sized sources” and found
most plume heights were between 12 % and 50 % of the predicted rise. England
et al. (1976) measured plume rise at a gas turbine facility with airborne
measurements of
In the summer of 2013, as part of the Canada-Alberta Joint Oil Sands
Monitoring (JOSM) Plan, aircraft measurements and monitoring stations were
used to study dispersion and chemical processing of pollutants emitted from
sources in the Athabasca oil sands region of northern Alberta. The GEM-MACH
model (nested to 2.5 km resolution) was run from August through September,
coincident with the measurement campaign, as an aid in directing aircraft
flights and in subsequent post-campaign analysis of the observations. The
model makes use of the Briggs plume rise algorithms. The large stacks in the
region emit many key pollutants, such as
This paper evaluates the performance of the Briggs plume rise
parameterization, as it is formulated in Environment and Climate Change
Canada's GEM-MACH model, in a “stand-alone/off-line” sense, using
meteorological observations as well as stack parameter data to drive the
Briggs algorithms. For comparison, another model proposed by Briggs (1984)
for irregular stability profiles is also evaluated. We also make use of
aircraft observations of emitted
In our companion paper (Akingunola et al., 2018) we examine the potential impact of the observed heterogeneity in meteorological data on plume rise predictions, comparing high-resolution GEM-MACH plume locations to aircraft observations, as well as the effects of different sources of stack data on simulated plume rise performance.
The plume rise (
The algorithm makes use of derived quantities (the buoyancy flux,
The flight tracks (black lines in
The atmosphere is considered neutral if
The only difference between Eqs. (3), (4), and (5) and the plume rise parameterizations used in SMOKE (described in Bieser et al., 2011, and Houyoux, 1998) is the option of the minimum values in unstable and neutral conditions. In the SMOKE model, only the second parameterizations within the minima of Eqs. (3) and (5) are used. Both of the approaches used in GEM-MACH and SMOKE are investigated in the following analysis.
Plume rise is also modified for situations where the stack height is less
than the boundary-layer height (
While the above formulae are used in GEM-MACH and other models, we also examine a layer-based approach suggested by Briggs, described below, and the companion paper, Akingunola et al. (2018), examines the impact of this approach within the GEM-MACH model itself.
In addition to the parameterization discussed above, Briggs (1984) suggests a
layer-based approach to calculate plume rise for complex stability profiles.
In this approach, the plume buoyancy (
Equation (7) is intended for use with stable (
While the Briggs parameterization discussed in Sect. 2.1 is driven by surface (or near-surface) observations, the layered method (Eq. 7) is driven by observations up to the height of the plume. The observed plume centreline heights (Sect. 2.7) vary between approximately 100 and 1000 m above the surface. Hence, the layered method can be used with the elevated observations from an aircraft measurement platform and an acoustic profiler (Sect. 2.4).
As part of the Continuous Emission Monitoring System (see CEMS, 1998),
measurements of 19 stacks in the region of study with valid hourly
measurements of
A flaring stack at the CNRL facility was added to the list (CNRL2) because
daily reports indicated a large amount of
Although NRPI data are available for the CNRL flaring stack, the other CNRL
stack used here (a “sulfur recovery unit”) has both CEMS and NPRI data
available. This allows for a test of the variability in
All eight stacks are listed in Table 1 and the locations of these eight stacks are
shown in Fig. 1. For comparison, average effluent velocities (calculated from
flow rate and stack diameter as
CEMS stack parameters for all stacks within the flight area that
emit significant
The relatively high flow rates and diameters of some stacks may lead to plume
rise due to momentum alone, especially under stable conditions. Briggs also
developed similar equations for rise due to momentum (see Briggs, 1984).
These equations are typically used when
Wind speed (
Example horizontal flight path of a box flight
The AMS03 tower measures wind speed, wind direction, and temperature at heights of 20, 45, 100, and 167 m (all heights above ground level). The AMS05 tower measures wind speed and direction at heights of 20, 45, 75, and 90 m and temperature at heights of 2, 20, 45, and 75 m. Tower measurements are reported as 1 h averages. The RASS measures wind speed and temperature (among other variables) between a minimum height of 40 m and a maximum height that varies depending on wind conditions (Cuxart et al., 2012). During the aircraft flight period, the maximum RASS measurement height varied from 130 to 800 m, with an average of 336 m. The RASS measurements are 15 min averages.
As part of JOSM, aircraft-based measurements were made in the Athabasca oil sands region between 13 August and 7 September 2013. The project included 22 flights, which were flown in some combination of either box formations (circumnavigating a facility at variable heights in order to determine facility pollutant emissions), screen formations (flown perpendicular to the plume centreline axis to characterize the transformation of the plumes), spiral ascent and descent (to characterize boundary-layer structure), or horizontal area coverage (to verify satellite observations over a larger spatial extent). Figure 1 shows all these flight formations. Within the 22 flights, there were 16 box-flight formations and 21 screens used for this analysis. Aircraft flight times varied from approximately 2.5 h to over 5 h, typically in the mid-afternoon, for a total of 84 h. Wind speeds and temperatures were measured from the aircraft with a Rosemount 858 probe, sampled at 32 Hz and averaged to 1 Hz. For details of the aircraft measurements, see Li et al. (2017), Liggio et al. (2016), and Gordon et al. (2015). The aircraft flew at a minimum height of 150 m a.g.l. The maximum height of box formations varied from 500 to 1300 m a.g.l., while the maximum height of screen formations ranged from 350 to 2000 m a.g.l.
Correlation coefficient (
Tower, RASS, and aircraft measurements were compared over the 84 flight
hours. The RASS was not operational until 17 August (thus missing 3 flights); hence, RASS data are compared for a reduced period. For comparison to the
tower measurements, the 15 min RASS and 1 s aircraft measurements were
averaged to concurrent 1 h values. For comparison to the RASS, the 1 s
aircraft measurements were averaged to 15 min values. The resulting
correlation coefficients are listed in Table 2. The aircraft wind and
temperature measurements are also compared with the highest tower (AMS03)
and the RASS. For comparison to aircraft measurements, the RASS measurements
at a height of 90 m were compared to all concurrent aircraft measurements
below 200 m. In the case of AMS03, the measurement at a height of 167 m was
compared to all concurrent aircraft measurements below 200 m. The wind speed
comparisons are best between the two towers (
We note that the Athabasca oil sands region is centred on the Athabasca River valley, with over 500 m of vertical relief within 60 km of the facilities; the flow within the valley may be complex, with frequent observations of shear between plumes from stacks at different elevations under stable conditions. The low correlations between the stations and between the stations and the aircraft reflect this variation in local meteorological conditions. We examine this possibility through the use of a high-resolution GEM-MACH simulation in our companion paper (Akingunola et al., 2018).
Stability, boundary-layer height, and friction velocity were all determined
from the observations using wind speed and temperature profiles from
multiple height measurements. Anemometers and temperature sensors on the towers, mounted at variable heights between 2 and 167 m, are within the surface layer and are best suited for these estimations. The
RASS, which has a minimum measurement height of 40 m, may not capture the
surface layer effectively. As the aircraft did not fly below a height of
150 m, aircraft-based measurements cannot be used to estimate the stability,
boundary-layer height, and friction velocity. For our analysis, we calculate
The atmospheric stability is determined using the Bulk Richardson Number,
which is defined (Garratt, 1994) as
Boundary-layer height can be parameterized for stable and unstable conditions
following Mahrt (1981) as
The boundary-layer height derived from Eq. (10) can be compared to the
boundary-layer height estimated from in situ aircraft measurements of the
The friction velocity (
It is noted that parameterizing stability without a measurement of heat flux
and estimating boundary-layer height based on near-surface measurements may
lead to significant uncertainties in these values. This will also affect the
estimation of
To drive the layered method discussed in Sect. 2.2, profiles of temperature and wind speed were derived for each box and each screen using RASS and aircraft observations. RASS layers were 10 m thick to match the instruments resolution. The lowest RASS measurement is at a height of 40 m, well below the lowest stack height (76 m). Because the maximum observation height of the RASS varies (with an average of 336 m), it was necessary to extrapolate temperature and wind speed above the maximum measurement height in some cases. This was done by assuming a constant wind speed and a constant temperature gradient, based on measurements in the highest 100 m of observations.
For aircraft observations, the box and screen flights were designed to
approximate 100 m vertical spacing between each box circuit or screen pass.
Based on this resolution we use a layer thickness of 100 m for the layered
method driven by aircraft observations. Testing demonstrates that the
algorithm is not sensitive to the layer thickness. Flight measurements of
wind (
Our temperature profiles for the layered method thus have the following as key assumptions: (1) that the profiles at the RASS location and derived from the aircraft are representative of conditions at the stacks, and (2) that the extrapolations and vertical resolution used here provide a reasonable representation of the atmospheric temperature profile.
The aircraft measured numerous pollutants, of which
A semi-empirical approach was used to match each stack to the observed plume locations. The wind direction measured from the aircraft was averaged for the duration of each box or screen. Tower or RASS-based wind direction measurements were not used, as an initial comparison of wind directions and observed plume locations demonstrated that the aircraft measurements are a better representation of the wind direction associated with plume transport than surface measurements. This agreement is most likely due to the consistent proximity of the aircraft to the stack sources; the towers and RASS locations can often be much further away (Fig. 1).
The interpolated images for the box flight
The average wind directions were then used to predict the direction of plume
transport downwind of each stack. The intercept of each plume's predicted
path with the box or screen (
Plume rise (
The flight path observations are converted to two-dimensional (
Non-stationarity of the wind speed, wind direction, and plume buoyancy
during the measurements is a potential source of uncertainty as each flight
circuit (or pass) around the facility can take between 10 and 15 min.
This effect is discussed in Gordon et al. (2015) for this flight campaign.
Although this can have significant effect on the calculation of emissions,
the effect on the estimation of plume height should be less than the
vertical distance between passes (
Each calculated plume location (
For the example of the 15 August screen flight (Figs. 2b, 3c, d), the forward
trajectory and Briggs algorithm model intercept the flight screen
approximately 2 km further south, and 140 m higher, than the observed plume
centre, indicating the possibility of more complex wind flow than a simple
trajectory. In the example of the 29 August box flight (Figs. 2a, 3a, b),
there are two observed plumes along the northwest–southeast oriented wall of the box. The
forward trajectory model places the plume intercept between these two plumes,
closer to the vertically higher and more northern observed plume at the
horizontal location given by
The topography of the Athabasca oil sands region can be generally described as a north–south river valley approximately 1 to 5 km in width, within a larger and more gradually sloped north–south valley between 10 and 50 km in width, and up to 500 m of vertical relief (Fig. 1a). Local surface wind patterns can be heterogeneous, especially within the valley. The AMS03 and AMS05 towers are in the vicinity of the Suncor stacks and the Syncrude stacks (Table 1), while the RASS is nearly equidistant to the eight stacks used for this analysis (Fig. 1b).
As an approximate measure of the uncertainty associated with local meteorology, plume rise values from the eight stacks are compared using the Briggs parameterization (Eqs. 1–6) with all three meteorological measurement platforms (i.e. AMS03, AMS05, and RASS) as well using the layered method (Eq. 7) with both RASS and aircraft measurements. This comparison was done for all concurrent times during which the aircraft was flying in box or screen patterns. There were approximately 26 h during which the aircraft flew in a box pattern and 20 h during which the aircraft flew in a screen formation, for a total of more than 46 h. The resulting distributions of calculated plume heights for these 46 h of flight time for the eight stacks are compared in Fig. 4.
The distributions of plume rise heights are similar for the Briggs parameterization with the three fixed, near-surface measurement platforms. Approximately 90 % of the plume rise values calculated with the AMS tower and RASS measurements are below approximately 250 m, with half or more below 75 m. With the layered method, the plume heights calculated with the RASS measurements are similar to those calculated with aircraft measurements. As with the Briggs parameterization, approximately 90 % of the plume rise values are below 250 m; however, more than half of the plume rise heights calculated with the layered method are above 125 m.
The distribution of calculated plume rise (
The plume rise was calculated for each flight for each stack with the Briggs
parameterization for each input (towers, RASS) as well as with the layered
method (RASS, aircraft). These plume rises were then paired with the measured
plume locations following the method described in Sect. 2.7. For simplicity,
the parameterized plume rise is described as
Using the tower or RASS measurements with the standard Briggs parameterization suggests an average underestimation (based on the average ratio) between 18 % (RASS) and 45 % (AMS03). The layered method using the RASS and aircraft-based measurements predicts a plume rise that is, on average, nearly half (47 %–49 %) of the observed value. In all cases, more than half of the plume rise values are underestimated by more than a factor of 2, and between 22 % and 42 % of predicted plume rise values are within a factor of 2 of the observations.
Comparison of the predicted plume rise from the Briggs
parameterization used in GEM-MACH with the measured plume rise as determined
by various atmospheric measurements described in the text. Black circles
indicate the Briggs parameterization (Eqs. 1–6) and red crosses indicate the
layered method (Eq. 7). Lines demonstrate
Statistics comparing the predicted-to-measured plume rises using
both the Briggs parameterization (Eqs. 1–6) and the layered method (Eq. 7).
The intercept (
Table 4 lists the frequency of each stability class during box and screen
flight times according to each measurement platform as determined by the sign
and magnitude of the Obukhov length (
Based on previous studies summarized in VDI (1985), the authors suggested a reduction of the Briggs parameterization by 30 % in neutral conditions. Although the atmospheric stability is predominantly classified as neutral in our analysis, we are seeing an underestimation by the Briggs parameterization, in contrast to the previous studies.
Stability was determined using the RASS and aircraft temperature profile
measurements based on a comparison of the temperature profile to the
adiabatic lapse rate (
Frequency of each stability type during flight times determined by
each measurement platform. Stability is either determined by parameterization
of Obukhov length (
A comparison is also made using the Pasquill–Gifford (P-G; Turner and Schulze,
2007) stability class, based on cloud cover and the wind speed at 10 m
(
Hence all three methods produce a different prominent stability class: the Obukhov length calculation predicts mostly neutral conditions; the lapse rate predicts mostly stable conditions; and the Pasquill–Gifford stability classes predict an approximately equal occurrence of unstable and neutral conditions. Both the Obukhov length and Pasquill–Gifford class approaches show a substantial difference in the frequency of occurrence of unstable conditions between towers AMS03 and AMS05, underscoring the local variability that may exist in temperature profiles. In light of this disagreement, we test the change in results with different stability classification schemes in Sect. 4.4 in order to estimate the extent to which the average plume rise depends on the stability classification.
The above analysis suggests the potential for substantial variability between
measurement locations, which may be due to heterogeneity of the terrain and
surface conditions in the area. Here we perform a simple test of the
sensitivity of the Briggs algorithm to uncertainties in input variables due
to this variability between measurement platforms. Input variables are
modified based on differences between the AMS03 and AMS05 measurement
platforms. First, the average plume rise is calculated for the box and screen
flight times for the eight stacks used in the analysis using AMS03 measurements
as input. The input variables were then modified by the ratio of the average
absolute difference between stations to the mean value (i.e.
Percentage changes in average plume height (
Average percentage changes in the plume rise for each modification for each
measurement platform are listed in Table 5. The largest differences between
the two measurement locations are boundary-layer height (
The table identifies the variables with the largest impact on the parameterization results, and hence which variables require the greatest accuracy when obtained from a meteorological model forecast. These results also help explain the low correlation coefficients of the observation-driven plume rise height comparisons (Table 3), as uncertainty in the estimation of these derived quantities will lead to uncertainty in individual plume rise estimations.
If the stacks are physically close enough to the interception of the plume
with the box walls or screens, it may be the case that the plumes have not
travelled a sufficient distance to reach the maximum plume rise that is
parameterized by the Briggs algorithms. Briggs (1984) also developed
parameterizations of downwind distance to maximum plume rise. A plume in
stable conditions will reach its final rise (Briggs, 1984) at
Using the AMS03 input data as an example, none of the 87 matched plumes have
distance from stack to measurement location (
Given that the observed plume rise is generally much higher than the
calculated plume rise, it should also be the case that distance to maximum
plume rise is also underestimated. If it is assumed that the plume reaches
its maximum height at the measurement location and the predicted plume rise
(
To investigate the underestimation of plume rise by the parameterization, we recalculate the predicted plume rise with a number of modifications. For ease of comparison, we use only the AMS03 tower data to drive the algorithm. Table 6 lists the results of these modifications. The “base case” is the analysis as described in the preceding sections with no modifications. The base case statistics are reprinted in Table 6 (case 0) from the first line of Table 3 in order to facilitate comparison. The results are presented as scatter plots for each case in the Supplement. Each of the comparison studies presented as different cases in Table 6 are described in more detail in the sub-sections that follow.
Cases 1 through 8 in Table 6 provide statistics for the stack–plume matching
separated by each of the eight stacks as listed in Table 2. Half of the stacks
demonstrate very strong underestimation of plume rise, with ratios of
calculated-to-observed plume rise between 4 % and 13 %. In the cases
of the Suncor stacks (1 and 3), these are large diameter stacks
(
Statistics comparing the predicted to measured plume rises using the Briggs parameterization (Eqs. 1–6) with either select conditions only or modification to the analysis. Cases are described in further detail in the text. Variables are defined as in Table 3. The listings of NA indicate the the correlation coefficient is not applicable for a fit with two data points.
Only the calculated to observed plume matches that originate from Syncrude1
(case 5) demonstrate good agreement between the Briggs equations and the
observations (with an average ratio of 1.0 and more than half the calculated
plume rise values with a factor of 2 of the observed plume rise values. This
stack is the largest of the eight stacks (
Three types of tests were done to determine the effect of atmospheric stability classification on the calculated plume rise: separation by stability class (cases 9 and 10), testing of sensitivity to the limits of neutral classification (cases 11 and 12), and testing of other stability classification methods (cases 13 and 14). These tests are described in more detail below.
We first compare the calculated to observed plume rise values that occur during neutral conditions only (case 9) and stable conditions only (case 10), with stability based on Obukhov length. For the times when plumes were observed (and matched to stack sources), there were no unstable classifications using the AMS03 tower site data (based on Obukhov length). There are 50 stack–plume matches during neutral conditions and 33 stack–plume matches during stable conditions. There is no significant difference between the stack–plume comparisons for the plume rise under neutral conditions versus stable conditions. The ratio of average predicted plume rise to observed plume rise is similar in both cases (0.55 compared to 0.53), and the fraction of plume rise values less than one-half the observed values is near 55 % in both cases. Hence, the underestimation of plume rise does not seem to be dependent on predicted stability classification.
Secondly, the sensitivity of the results to the limits of neutral conditions
(
Finally, the results discussed in Section 4.1 suggest that there is poor
agreement between the various methods used to classify stability. As
discussed previously, the estimation of Obukhov length based on the bulk
Richardson number may be considered less accurate than an estimation based on
heat flux measurements. We recalculate the plume rise values using the
stability classification based on the comparison of the negative temperature
gradient,
A number of modifications were made to test the sensitivity of the results to various assumptions and equations used to calculate plume rise in the base case. These include the assumption of validity of the equations beyond a given downwind distance (case 15), the estimation of maximum plume height for plumes that may still be ascending at the measurement location (case 16), the effect of limits and minima used in the equations (cases 17, 18), and finally an alternate plume rise equation used for neutral conditions (case 19).
Firstly, as discussed above, the distance between the stack and the horizontal point of measurement of plume height is limited in this analysis to less than 50 km. Removal of this criteria (case 15) adds a further 38 stack–plume matches to the original 83 stack–plume matches in the base case. The observed plume rise values of these distant plumes are generally higher, and the predicted plume rise values are lower. The resulting average ratio of calculated to observed is 0.40 (compared to 0.54 for the base case, which only includes plumes that have travelled less than 50 km before measurement).
As discussed in Sect. 4.3, the calculated distance to maximum plume rise is
less than the distance between the stack and the measurement location for all
stack–plume matches. However, when the distance to maximum plume rise is
modified by a factor equal to the ratio of observed plume rise to calculated
plume rise, approximately 13 % of the plumes should reach maximum plume
height further from the stack than the measurement location. To test whether
this is causing an under-prediction of plume rise, we adjust the calculated
plume rise values for those plumes with
The
As discussed in Sect. 2.1, the minimum criteria of Eqs. (4) and (5), which are used in the GEM-MACH model, are not used in other plume rise models, such as SMOKE. To investigate the difference between these two approaches, the plume rise is recalculated (case 18) using only the second (rightmost) term within the minimum functions of Eqs. (4) and (5). The resulting statistics are listed in Table 3. The removal of the minimum function results in three cases of extremely (i.e. unrealistically) high plume rise (between 6 and 41 km), all of which occur in neutral conditions. Because of these extreme values, the ratio of average predicted to average observed plume rise is 4.1. However, the majority of predicted values (54 %) are less than half of the observed plume rise values (similar to the base case), suggesting that the high ratio of predicted to observed values is due to a few outliers. This implies that a lower limit on wind speed and friction velocity should be used to prevent unrealistically high plume rise values when using these equations without the minimum functions, making the GEM-MACH choice of minima appropriate.
In order to test other parameterizations of plume rise, the equation for
plume rise in neutral conditions (Eq. 3) is replaced by an alternative
equation (De Visscher, 2013), given as
The plume rise due to momentum of stack effluent is not included in the
parameterization used in GEM-MACH (see Sect. 2.1). To investigate whether
neglect of momentum rise may be a significant contribution to the
underestimation of plume rise we test two sets of equations to include this
effect. Plumes are typically classified as either momentum driven or buoyancy
driven, and the maximum of
For the first test (case 20), parameterizations for momentum-dominated
plumes developed by Briggs are given in De Visscher (2013) for stable and
neutral conditions respectively as
For the second test (case 21) we follow the approach used in the CALPUFF
model in which buoyancy and momentum are considered simultaneously (De
Visscher, 2013). For plume rise in neutral or stable conditions, the plume
rise can be calculated as
The high fraction of under-predicted plume rise (48 %) and under-predicted
plume rise (35 %) using the combined buoyancy–momentum formula of
Eq. (20) warrants extra investigation. Of the 83 plume-to-stack matches used in this
analysis, 40 are under-predicted (ratio
Our focus within this work was the use of the available measurement data as
a proxy for the meteorological conditions at the stack locations themselves.
However, significant differences could be seen in the data between the
different measurement platform locations (see Table 2). In subsequent work
in our companion paper (Akingunola et al., 2018, this issue), high-resolution
meteorological model forecast simulations for the region were carried out.
These suggested the presence of significant spatial heterogeneity in the
meteorological parameters used to drive both the Briggs parameterization and
the layered method. Predicted meteorological parameters at the
meteorological measurement platform locations were substantially different
from those at stack locations. When tested using the model-predicted
at-stack meteorological values, and NPRI stack emissions data, the Briggs
parameterization and the layered approach resulted in very different plume
rise behaviour. Predicted surface
These results demonstrate a significant underestimation of plume rise using
the Briggs plume rise parameterizations. The ratio of average modelled plume
rise to average measured plume rise
(
These results are in direct contrast to the many studies summarized in VDI (1985), which consistently suggest that plume rise is overestimated by the Briggs equations. The more recent study of Webster and Thomas (2002) might possibly imply an underestimation of plume rise, owing to an overestimation of surface concentration measurements using a plume rise model; however, there may be other reasons for this overestimation unrelated to plume rise. The authors of the VDI report suggest that the Briggs parameterization should be reduced by a factor of 30 % in neutral conditions in order to better match observations. In contrast to this suggestion, our results would be improved significantly by increasing the Briggs parameterization by a factor of 30 %.
Much of the underestimation in this study appears to be driven by two stacks
(Suncor 1, 3) that have relatively low effluent exit velocities. Based on a
2010 CEMA inventory, these stacks are among the list of significant
By far, the best results of the Briggs parametrization (as used in the
GEM-MACH model) are for the largest stack, Syncrude1. This stack emits
between 11 and 40 times more
For both the Briggs parameterization and layered method and for all the
measurement platforms used in this study, the correlation of parameterized
plume rise to measured plume rise is low (
The aircraft-based measurements used for this study provide only a “snapshot” of plume rise and atmospheric conditions as measurements are made on a timescale of a few hours in the morning or afternoon over the course of a few weeks in summer. However, this consistent underestimation of plume height for these observations suggests that further investigation is warranted. Given the advancements in atmospheric measurement technology in recent decades (e.g. automated lidar, RASS, image analysis), there is an opportunity to make long-term measurements of plume rise and atmospheric conditions in an effort to improve predictability. Although the Briggs algorithms have been in use for nearly 4 decades, are used in many air-quality models (e.g. GEM-MACH, AEROPOL, SCREEN3, CALGRID, RADM, SMOKE, and SMOKE-EU), and are widely referenced in air quality and dispersion texts (Beychok, 2005; Arya, 1998), the verification of these algorithms relies on decades-old measurement techniques. More in situ measurements of plume height are clearly needed to attempt to quantify the uncertainties in these parameterizations and to suggest improvements to the algorithm.
Further, the observations compared here demonstrated the presence of considerable horizontal heterogeneity in meteorological conditions across this region, with towers within a 10 km distance providing substantially different statistics of stability conditions during the study period. This suggested that meteorological observations in close proximity to the stacks are necessary to further improve the algorithms. We examine the potential impact of this heterogeneity in our companion paper (Akingunola et al., 2018) using a high-resolution meteorological model.
The aircraft and RASS observations used in this study are publicly available on the ECCC data portal (ECCC, 2018a). The hourly surface monitoring network data (AMS03 and AMS05) are from the public website of the Wood Buffalo Environmental Monitoring Association (WBEA, 2018). The CEMS data used for this paper are available from the ECCC weblink (ECCC, 2018b) in the CEMS_Case folder.
The supplement related to this article is available online at:
MG and PAM were responsible for the study design and methodology, comparison to observations, and the writing of the paper and modifications of the same. RMS, JZ, AA, WG, and SML provided feedback and suggestions to the paper revisions. AA provided information and simulations using the GEM-MACH model. JZ contributed emissions data used in the analysis. SML contributed aircraft observation data. AA and PAM contributed information on the companion paper.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Atmospheric emissions from oil sands development and their transport, transformation and deposition (ACP/AMT inter-journal SI)”. It is not associated with a conference.
The authors wish to thank the Wood Buffalo Environmental Association (WBEA) for the use of the Lower Camp Met Tower (AMS03) and Mannix Tower (AMS05) data. The Continuing Emission Monitoring System (CEMS) data were provided by Marilyn Albert, Ewa Przybylo-Komar, Katelyn Mackay, and Tara-Lynn Carmody of Data Management and Stewardship, Corporate Services Division, Alberta Environment and Parks. Funding for the aircraft measurement study was provided by Environment and Climate Change Canada and the Oil Sands Monitoring Program. Edited by: Jeffrey Brook Reviewed by: Alex De Visscher and Franco DiGiovanni