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Quantification of fugitive emissions from an oil sands tailings pond by eddy covariance
Fuel 237 (2019) 457–464
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Full Length Article
Quantification of fugitive emissions from an oil sands tailings pond by eddy covariance ⁎
T
⁎
Lucas Zhanga, Sunny Choa,b, , Zaher Hashishoa, , Casandra Brownc a
University of Alberta, Department of Civil and Environmental Engineering, Edmonton, AB T6G 1H9, Canada Government of Alberta, Alberta Environment and Parks, Edmonton, AB T5J 1G4, Canada c University of Alberta, Department of Earth and Atmospheric Sciences, Edmonton, AB T6G 2W2, Canada b
A R T I C LE I N FO
A B S T R A C T
Keywords: Greenhouse gases Alberta oil sands region Eddy covariance Emission flux Fugitive emissions
Oil sands tailings ponds are sources of greenhouse gasses (GHG) and air pollutants. The flux chamber technique, typically used to measure emissions from tailings ponds, samples a small area for a short duration, which may not account for the spatial and temporal variability of emissions from oil sands tailings ponds. The eddy covariance (EC) technique, with large spatial coverage and better temporal resolution, is a promising method to improve the accuracy of emission flux quantification. A field campaign was conducted to measure emissions from an oil sands tailings pond in Alberta using an EC system. Average CH4 and CO2 emission fluxes were 4.56 × 10−2 g/(m2-d) and 3.59 g/(m2-d), respectively. Diurnal and daily variations of CH4 and CO2 emission fluxes were strong with relative standard deviations of 97–158%. Nighttime (18:30 to 8:00, inclusive) CH4 average emission flux (6.55 × 10−2 g/(m2-d)) was 2.8 times daytime (8:30 to 18:00, inclusive) CH4 flux (2.32 × 10−2 g/(m2-d)) while nighttime CO2 average emission flux (2.97 g/(m2-d)) was 0.7 times daytime CO2 emission flux (4.29 g/(m2-d)). Pearson correlation test results suggest that short-term (i.e., days to weeks) variations of CH4 and CO2 emission fluxes measured in this study were not strongly (but can be weakly) correlated with meteorological variables or the 90% cumulative flux contour distance. The CH4 and CO2 emission fluxes determined in this study were of the same order of magnitude as those from a previous study that used the EC technique at the same tailings pond. CO2 fluxes in this study were similar while CH4 fluxes in this study were more than an order of magnitude lower than fluxes based on flux chamber measurements conducted by a 3rd party at the same location and in the same month and year as part of routine regulatory monitoring requirements. Continuous, real-time, and long-term monitoring of tailings ponds emissions is necessary to reduce uncertainty and improve representativeness and accuracy of emission flux quantification.
1. Introduction Alberta’s oil sands deposits is the third largest proven reserve, after Saudi Arabia and Venezuela [18,20,27]. The oil sands consist of a mixture of quartz sand, silt, clay, bitumen, organics, trapped gasses, and pore water and trace metals and minerals [36,46]. Extracting bitumen from the oil sands can be done in-situ for deep reserves or through surface mining followed by bitumen-sand separation processes for shallow oil sands reserves [10]. Various additives (such as caustic soda), diluents (naphtha or paraffin), and large quantities of water are used to separate bitumen from the mined oil sands and increase bitumen recovery rates [38]. The waste stream, i.e., the oil sands tailings, is stored on-site in constructed tailings ponds and consists of a mixture of process and connate water, sand, silt, clays, residual bitumen, diluent, and inorganic ⁎
and organic by-products of the extraction process. Alberta has set up thresholds to ensure that fluid tailings volumes are contained and new and legacy tailings ponds will be reclaimed in a timely and progressive manner [1,21]. The understanding of many tailings treatment technologies at a sufficiently large scale has not been fully achieved and technological innovation to meet environmental challenges faced is encouraged [21]. Pollutants emitted from tailings ponds include volatile organic compounds (VOCs), reduced sulphur compounds (RSCs) and greenhouse gasses (GHG) including methane (CH4) and carbon dioxide (CO2) [6,45,51,44,46]. CH4 and CO2 are produced by microbes native to oil sands tailings through biodegradation of unrecovered bitumen and solvent (e.g., naphtha, a mixture of aliphatic and aromatic hydrocarbons) [26,6,40,44]. The amount of CH4 and CO2 emitted from tailings ponds can be substantial [51,44]. GHG and air pollutants emissions
Corresponding authors at: University of Alberta, Department of Civil and Environmental Engineering, Edmonton, AB T6G 1H9, Canada (S. Cho). E-mail addresses: [email protected] (S. Cho), [email protected] (Z. Hashisho).
https://doi.org/10.1016/j.fuel.2018.09.104 Received 12 June 2018; Received in revised form 20 September 2018; Accepted 22 September 2018 0016-2361/ Crown Copyright © 2018 Published by Elsevier Ltd. All rights reserved.
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(a)
from tailings ponds are difficult to characterize because of the daily and seasonal variation of emissions as well as the complexity of the processes generating these emissions [46]. There are different techniques and methods for measuring fugitive emissions and developing emission factors from oil sands tailings ponds. Flux chamber (FC) has emerged as one of the most widely used techniques for the measurement of GHG and VOC emissions in the Alberta oil sands region [46,2]. Most estimations of tailings emissions are based on FC emission surveys conducted at various tailings ponds locations in the spring, summer, and fall. The FC technique can be used to measure a variety of emissions (including VOCs, RSCs, CO2, and CH4) from surface impoundments. FC gathers information from a small sample area (typically 0.13 m2) over a short duration (typically 0.5–1 h) which may not account for the variability in emissions over time or over a non-uniform source area such as a tailings pond [24,46]. Multiple measurements are taken over one or a few days to obtain more spatially representative emissions estimates for various zones of a pond; however, the temporal coverage is very limited due to cost and labour constraints. In addition, the FC disturbs the surface of the pond and decouples the surface from the natural atmosphere, potentially impacting the processes controlling the flux being measured [13]. Even for a homogeneous source, FC results are estimated to be 50–124% of the true emission rate [30]. For these reasons, it is of interest to explore other methods for measuring emissions from the oil sands tailings ponds that would sample a larger area at higher temporal resolution without interfering with the emitting surface. Micrometeorological flux measurement techniques, such as the eddy covariance (EC) technique, could be used for this purpose; however, these techniques have not been tested at Alberta’s oil sands tailings ponds, which can be large (e.g., a few km2 in area), have a dusty environment, and are surrounded by industrial activities. The EC technique is one of the most direct and defensible methods to measure fluxes. The general principle of the EC technique is to measure the number of molecules moving upward and downward over time, as well as the traveling speeds of these molecules [8]. The EC technique has several advantages over other techniques including its ability to directly and continuously measure the vertical flux at the point of measurement [13,8]. Additional information about flux measurement techniques, including the EC technique, can be found in Hashisho et al. [24], Zhang [52], and Small et al. [46]. In this study, a pilot field campaign was conducted to measure concentrations and emission fluxes of GHG at one of the tailings ponds in Northeastern Alberta during the summer of 2014 using the EC technique. The objectives of this study are to quantify emission fluxes of CH4 and CO2, understand the variation in GHG emissions from the pond, especially at nighttime when little to no emission flux data are available, and compare emission flux results determined by the EC technique in this study to EC results from a prior field campaign and to reported FC results.
LI-7700 CH4 analyzer LI-7500A CO2/H2O analyzer
CSAT3 sonic anemometer
(b)
Fig. 1. Picture of eddy covariance instrumentation mounted on top of the flux tower (a) and a schematic of the flux tower location, with the bearing of sonic anemometer (yellow arrow line) and discarded wind direction (334.3° to 154.3°) indicated by blue arc arrow (b). Note: the grey oval shape represents the tailings pond area, however, it is mainly meant to indicate the relative location of the EC tower to the pond instead of to closely resemble the actual perimeter or shape of the pond. Percentages indicate the frequency of time the wind was of a particular direction. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
gas analyzer also measures the ambient temperature and pressure of the gas in the sampling path. (3) A CO2/H2O open-path gas analyzer (LI-7500A, LI-COR Inc.) measures CO2 and H2O in the atmosphere simultaneously at a high frequency up to 40 Hz. It is a high performance, non-dispersive, open-path analyzer with typical root mean square noise of 0.11 ppmv for CO2 and 0.0047 pptv for H2O.
2. Methodology A datalogger (CR3000, Campbell Scientific, Inc.) was used to record the raw data (at 10 Hz) generated by the instruments described above. Fig. 1 shows the instrumentation setup and location including the EC instrumentation mounted on top of the flux tower (Fig. 1(a)) and a schematic of the flux tower location, with the bearing (orientation) of sonic anemometer and discarded wind direction indicated (Fig. 1(b)). The instruments were mounted at the top of the flux tower with the centre of the sensor paths located 11 m above the tailings pond surface and the sonic anemometer positioned in between the gas analyzers. The EC system was also equipped with a modem to allow remote access to the datalogger to check on the recorded data and signals quality and assess the need for user intervention. The system was powered by four 12-volt batteries and two solar panels. The strength of the signals from both gas analyzers was recorded as
2.1. Instrumentation The main components of the EC system used in this study are briefly introduced as follows: (1) A 3-D sonic anemometer (CSAT3; Campbell Scientific Inc.) measures the horizontal (u and v) and vertical (w) wind velocity components, as well as the temperature at a frequency up to 20 Hz. The sonic anemometer determines the wind speed (WS) by measuring the speed of the ultrasonic signal it emits and receives. (2) A CH4 open-path gas analyzer (LI-7700, LI-COR Inc.) is a high-speed (data output frequency up to 40 Hz), high precision (Root Mean Square (RMS) noise of 5 ppbv at 10 Hz and typical ambient levels) open-path methane analyzer. Along with CH4 concentration, the 458
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a dimensionless value, the Residual Signal Strength Indicator (RSSI), which was used as an indicator for QA/QC and the need for mirror cleaning. As the surrounding environment could be dusty, two sets of automated dust blow-off systems [23] were built and used to blow the dust off the top and bottom optical surfaces of the gas analyzers using nitrogen (N2) from compressed gas tanks. RSSI thresholds (20% for CH4 analyzer and 50% for CO2 analyzer) were used to trigger the dust blowoffs with a maximum of five attempts (puffs of gas) per trigger and a 2hour break before the system can be triggered again. These thresholds were chosen based on experience and the local environment, aiming to maintain RSSI above thresholds for as long as possible without disturbing the wind flow too frequently and draining the gas tanks too soon. Signal strengths were used as “flags” to filter/select the raw data. For CH4, data collected with RSSI below 20% were discarded, while CO2 data with RSSI below 55% were removed (based on manufacturer instruction and practical experience) [35,34]. RSSIs need to be above certain thresholds to ensure best instrument performance. Based on the bearing of the sonic anemometer (244.3°), the data recorded with wind direction between 334.3° and 154.3° were discarded. The measured meteorological parameters (i.e., wind direction, wind speed, temperature) during the field study are summarized in Table S1. The raw data were processed using the software EddyPro (Version 5, LI-COR Inc). Major data processing functions performed by EddyPro include coordinate rotation, frequency response correction, WebbPearman-Leuning (WPL) density correction, and sonic anemometer virtual temperature correction [35,34]. The averaging interval for time series data and fluxes is 30 min. To indicate how well the measured flux is representative of the source strength, the 90% cumulative flux contour distance (x_90%) (i.e., the along-wind distance that represents the area providing 90% cumulative contribution to total fluxes) [32,31] was calculated using EddyPro and is shown in Fig. S1.
Fig. 2. σw/u* plotted against |z/L|: comparison between measured and modelled results. σw is the standard deviation of vertical wind speed; u* is friction velocity; z is measurement height (11 m) and L is Obukhov length. Model 1 (solid line): σw/u∗ = 1.0 × (1 − 4.5z/L)1/3, Model 2 (dashed line): σw/ u∗ = 1.25 × (1 − 3z/L)1/3.
the final data after all filtering processes unless otherwise stated. Fig. 2 indicates that the relationship between the normalized standard deviation of vertical velocity (σw/u*) and stability |z/L| (unstable conditions only) is consistent with an existing model [50] for an undisturbed surface layer, although the observed data points (this study) are mostly lower than the curve of the model described by Kaimal and Finnigan [29]. In general, the sonic anemometer was operating as expected and the EC technique was suitable for the site, according to Fig. 2.
2.2. Description of study area 3. Results and discussion
GHG concentrations and fluxes were measured at one of the tailings ponds in the Alberta oil sands region from June 5 to 18, 2014. The study area is located in the Athabasca oil sands area, which is approximately 72 km north of the regional municipality of Wood Buffalo. The tailings pond had an area of 3.3 km2 in 2014 and received froth treatment tailings and middlings stream tailings [2]. Residual bitumen and solvent, in addition to coarse sand, silt, and processed water were the major components of the tailings pond. Nearby (within 7 km) emission sources include, but are not limited to, open-pit mining faces, in-situ, and other oil sands tailings ponds. The temperature measured during the field campaign varied from 5.7 to 26.0 °C, with median and average temperatures of 16.8 °C and 16.9 °C, respectively. The wind speed recorded ranged from 0.3 m/s to 8.4 m/s, with median and average values of 3.2 m/s and 3.6 m/s, respectively. The prevailing wind direction was southerly and southsoutheasterly. Daily averages of temperature, wind speed and direction are presented in Fig. S2.
3.1. Pollution roses and diurnal variations o GHG concentrations The data quality of concentration measurements made by the gas sensors is not affected by wind direction or the possible flow disturbance caused by the instruments or tower supporting the instruments. Therefore, it is meaningful to investigate the relationships between concentrations and all wind directions. Under all wind directions, CH4 concentrations (average 2.1 ppmv; relative standard deviation 6%) ranged from 2.0 to 2.9 ppmv while CO2 concentrations (average 399.8 ppmv; relative standard deviation 2%) varied between 382.2 and 433.5 ppmv. High CH4 (> 2.2 ppmv) and CO2 (> 410 ppmv) concentrations were recorded under 40°, suggesting the presence of CH4 and CO2 emission source(s) northeast (i.e., upwind) of the studied tailings pond (Fig. 3). Possible emission sources northeast of the studied tailings pond include, but are not limited to, three mining face sites (relative locations indicated as M1, M2 and M3 in Fig. 3). Data from the whole dataset after all filterings were averaged to generate 48 composite 30-min values representing the average gas concentrations at each half-hour of a day. Composite diurnal CH4 concentration ranged from 2.00 ppmv to 2.23 ppmv (Fig. 4a) while composite diurnal CO2 concentration ranged from 387.9 ppmv to 412.1 ppmv (Fig. 4b). CH4 and CO2 concentrations were higher (> 2.1 ppmv and > 395 ppmv, respectively) at night and in the early morning (21:00 to 10:00), likely due to stable thermal stratification (indicated by low or decreasing air temperature; Fig. 4a and b) and low turbulent mixing (indicated by u* < 0.2 m/s; Fig. 4c and d) during this time period [3].
2.3. Data QA/QC Table S2 provides the percentages of data points remaining after each data filtering process. 16% of the total data points were removed due to unacceptable RSSI caused by precipitation events and/or dust etc. In addition, about half of the total data points were discarded due to unfavorable (according to the bearing of the sonic anemometer) wind direction (334.3–154.3°). Furthermore, an additional 7% of the total data points were removed because errors (e.g., flux footprint estimation beyond reasonable range) were reported by the software EddyPro for these data points. These errors were likely due to calm wind conditions (minimal vertical mixing). All data presented hereafter are 459
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30%
N
(a)
M2
30%
CH4 (ppmv)
20% 10% W
M3
2.6 to 2.8
E
2.4 to 2.6
N
M2
CO2 (ppmv)
20%
2.8 to 3
M1
(b)
430 to 440
M1
10%
M3 E
W
410 to 420
2.2 to 2.4
400 to 410
2 to 2.2
390 to 400
1.8 to 2
380 to 390
ppmv S
420 to 430
ppmv
mean = 2.1 2.0912 calm = 0 %
S
Frequency of counts by wind direction (%)
8 mean = 399.76 calm = 0 %
Frequency of counts by wind direction (%)
Fig. 3. Concentration (ppm) of CH4 (a) and CO2 (b) against wind direction (0 to 360°, wind direction filtering not applied). Note: Centers of the concentration roses indicate the location of the flux tower, which was placed northeast of the tailings pond (Fig. 1b). Relative locations of three mining face sites, located within approximately 7 km of the tower, are marked as M1, M2, and M3. Percentage values labeled on the circular grid lines indicate frequency of counts by wind direction (%).
For the whole study period, average CH4 and CO2 fluxes (30 min averages) were 4.56 × 10−2 g/(m2-d) and 3.59 g/(m2-d), respectively. Composite diurnal variations of CH4 fluxes correlated negatively
(b)
(c)
(d)
u* (m/s)
(a)
u* (m/s)
(correlation coefficient r = −0.6, Pearson test value p = 3.3 × 10−5), while composite diurnal CO2 fluxes correlated slightly positively (r = 0.3, p = 0.019), with diurnal variations of temperature (Fig. 5a and b). Both CH4 and CO2 diurnal fluxes showed large variability with relative standard deviations (RSD) of 123% and 97%, respectively. No
3.2. Temporal variations of GHG fluxes
Fig. 4. Composite diurnal variations of CH4 and CO2 concentrations against temperature (a and b) and friction velocity (u*) (c and d) with error (standard deviation) bars for each composite data points. 460
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Fig. 5. Composite diurnal variation of CH4 flux (a) and CO2 flux (b) against temperature (T) in June 2014.
uncertainties in and variabilities of the EC emission flux results than during the daytime. Furthermore, changes in daytime and nighttime industrial activities, if any, at the tailings pond may be another factor that influences nighttime/daytime ratios of emission fluxes. Prior to this study using the EC technique, little to no nighttime emission flux data are available as FC measurements on tailings ponds are usually taken during daytime due to safety concerns. The significant differences between daytime and nighttime CH4 and CO2 emission fluxes in this study further demonstrate the importance of having continuous emission flux measurements using techniques such as EC. Positive emission fluxes from tailings ponds are expected as they are considered to be a source of GHGs instead of a sink. The CH4 and CO2 emission fluxes (30-min averages) were negative for approximately 25% of the measurement period. These values are unlikely to be due to poor instrument performance, as the velocity statistics provided by the sonic anemometer fit the expected pattern for an undisturbed surface layer (Fig. 2) and the gas analyzers were calibrated prior to the field campaign. Further, the negative values were not found to be associated with undesirable (unreasonably large or beyond the pond perimeter) x_90% estimates. The negative emission flux values may or may not have occurred under conditions wherein the assumptions inherent to the EC technique (e.g., horizontal homogeneity and steady state conditions) were not valid. Therefore, these negative values were not discarded. Daily average CH4 and CO2 fluxes (Fig. 6) varied greatly with RSD of 158% and 131%, respectively. However, the number of data points (30-min averages, after data filtering applied) used in calculating the daily average fluxes ranged from 1 to 34 and only 2 of the 13 days had at least 24 (50% data completeness) data points, indicating that the
data about diurnal variability of emissions from oil sands tailings ponds is available for direct comparison. However, the negative correlation between diurnal CH4 and temperature variations in this study differs from the results at a biosolids lagoon [53] and a swine lagoon [42] where positive correlations were found between CH4 emission flux and air temperature. In addition, nighttime (18:00 to 8:30) CH4 average emission flux (6.55 × 10−2 g/(m2-d); RSD = 105%) was 2.8 times daytime CH4 flux (2.32 × 10−2 g/(m2-d); RSD = 97%) while nighttime CO2 average emission flux (2.97 g/(m2-d); RSD = 90%) was 0.7 times daytime CO2 flux (4.29 g/(m2-d); RSD = 100%). The nighttime/daytime ratios of emission fluxes could be impacted by different mechanisms of CH4 and CO2 productions. CH4 might be produced mainly by methanogenesis of hydrocarbons under anaerobic conditions (mainly in mature fine tailings at the bottom of the pond and thus less affected by ambient weather conditions) while aerobic biodegradation of residual bitumen and oil films on the surface of the pond (more likely affected by ambient weather conditions) might be key to CO2 production [43,40,44,9,46]. In addition, meteorological conditions could impact nighttime/daytime ratios of emission fluxes as well. Generally, the EC technique has been found to be more accurate when the meteorological conditions are steady as well as when the vegetation and terrain are flat and homogeneous [4]. More specifically, the accuracy of the EC technique is determined to be lower at nighttime than daytime [37,47,5,4]. At nighttime, atmospheric conditions such as low wind, stable thermal stratification (temperature inversion), intermittent (and thus sometimes insufficient) turbulent mixing [3], and periodic break-up of the nocturnal atmospheric boundary layer [28] could have a strong impact on emission fluxes produced by the EC method [19,4], indicating larger
Fig. 6. Daily variations of CH4 (a) and CO2 (b) fluxes and count of data points. 461
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daily averages may not be representative of the day on most days (Fig. 6). Hence, daily trends or correlations between emission fluxes and meteorological or other variables were not evaluated. Instead, Pearson correlation tests using 30-min averaging data were performed to estimate the strength of association between variables (meteorological including temperature, wind speed, and wind direction as well as x_90%) and fluxes of CH4 and CO2. CH4 flux had a weak negative correlation with temperature, weak positive correlations with wind direction and x_90%, and no significant correlation with wind speed. CO2 flux had no significant correlation with temperature, wind speed, wind direction, nor x_90% (Table S3). The correlation test results suggest that short-term (i.e., days to weeks) variability of emission fluxes measured in this study were not strongly driven by any of the individual meteorological variables or x_90% discussed above. However, the combined effects of the meteorological variables and x_90%, although hard to quantify, might have a strong impact on short-term emission flux variability. Other factors such as tailings discharge method and flow rate, unrecovered diluent, and residual bitumen, which could change the characteristics (e.g., total carbon, soluble carbon, and volatile solid contents) of the tailings pond water, may impact long-term CH4 and CO2 emissions from the pond significantly [46]. x_90% ranged from 120 m to 9904 m, with an average of 1238 m and a median of 784 m. Fig. 7a indicates that the x_90% was typically higher at 240 ± 10° (∼1500 m) than that at 180 ± 10° (∼800 m). The majority of the x_90% values were less than 1500 m, which was not sufficient to cover the entire tailings pond under southerly wind. However, under southwesterly wind, the x_90% could reach or go beyond the pond edge. x_90% for these two wind direction ranges are marked approximately proportional to the pond on Fig. 7b. Fluxes of CH4 and CO2 for 180° ± 10° were approximately 1.8 × 10−2 g/(m2-d) and 2.9 g/ (m2-d), respectively. For 240° ± 10°, fluxes of CH4 and CO2 were about 0.18 g/(m2-d) (10 times that for 180° ± 10°) and 5.8 g/(m2-d) (two times that for 180° ± 10°), respectively. The differences in emission fluxes under these two wind direction ranges show strong heterogeneity of the tailings pond and the importance of having representative wind directions and x_90%. Note that wind direction or x_90% do not impact how pollutants are emitted from the source. However, wind direction and x_90% can impact what part of the source is measured and how representative (due to source heterogeneity) the measured emission flux is of the whole source. Higher x_90% (i.e., more pond area being measured) do not necessarily mean higher emission fluxes being measured, depending on the average source strength of the area represented
50%
(a)
Table 1 Emission fluxes obtained using eddy covariance at the same tailings pond in June 2014 and July 2012. Fluxes are reported as an average ± standard deviation. Emission Flux
Eddy Covariance (This study1)
Eddy Covariance (2012 study2*)
CH4 (10−2 g/(m2-d)) CO2 (g/(m2-d))
4.56 ± 8.58 3.59 ± 7.11
0.27 ± 8.11 7.09 ± 9.93
Note: 1sampling duration: 13-day and 2sampling duration: 6-day. *Source from Brown et al. [7].
by the x_90%. Similarly, lower x_90%, e.g., a x_90% that covers a small area nearby the flux tower where there are tailings being discharged into the pond, could mean higher emission fluxes being measured, also depending on the average source strength of the area sensed by the flux tower. Hence having a taller flux tower or multiple flux towers on opposing sides of the pond and a longer field campaign is necessary to obtain emission fluxes more representative of the whole tailings pond and to have a better understanding of long-term trends of the emissions.
3.3. Quasi-comparisons of emission flux measurements Since no co-located measurements were completed using different emission quantification techniques at the same time, emission flux results obtained over different time frames at the same tailings pond were used for quasi-comparisons. Table 1 presents the CH4 and CO2 emission fluxes reported in this study (June 2014) and from an earlier study (July 2012) [7] at the same tailings pond using the same EC setup, with the exception that no dust blow-off systems were used in the 2012 study. Measured CH4 flux was higher in June 2014 compared to July 2012, with a difference of approximately 180% between the two studies, while CO2 flux was lower, with a difference of approximately 73% between the two studies. For the June 2014 study, the average temperature was 4 °C lower than that of the July 2012 campaign; wind speed was about the same with July 2012; prevailing wind direction was about 10° higher than July 2012, and x_90% was at the same level (Table S1). The difference in the emission fluxes between the two campaigns could be attributed to changes in the tailings pond (e.g., tailings composition, microbial activity), and differences in conditions (e.g., meteorological conditions) affecting emissions during the measurement periods.
N
(b) x_90% (m)
40% 30%
3000 to 9904 20%
2500 to 3000 10%
2000 to 2500
W
E
1500 to 2000 1000 to 1500 500 to 1000 0 to 500 m
S
mean = 1237.7 calm = 0 %
Frequency of counts by wind direction (%) Fig. 7. 90% cumulative flux contour distance (x_90%) plotted against wind direction (a) and x_90% marked as distances (yellow and blue solid lines) in meters on a schematic drawing of the tailings pond (b). Note: The green arc in (b) indicates the wind directions used for deriving emission fluxes. The grey oval shape represents the tailings pond area and serves to indicate the location of the EC tower relative to the pond, rather than to closely resemble the actual perimeter of the pond. Percentage values labeled on the circular grid lines indicate frequency of counts by wind direction (%). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 462
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(i.e., 6, 20 and 60 m) on an intensively managed grassland that was a largely homogeneous land cover [39]. The three-level EC measurement system provided CH4 fluxes with different spatial averaging due to the different footprint sizes, which increased with measurement height. Therefore, various EC system configurations, such as height and location with respect to source and wind direction, are required for testing proper spatial representativeness of measured flux at the oil sands tailings ponds.
Table 2 Emission fluxes obtained using eddy covariance and flux chamber at the same tailings pond in June 2014. Fluxes are reported as average ± standard deviation. Species
Method
−2
2
CH4 (10 g/(m -d)) CO2 (g/(m2-d))
Eddy covariance (this study)
Flux chamber*
All data1
Daytime
Daytime
4.6 ( ± 8.6) 3.6 ( ± 7.1)
2.3 ( ± 4.9) 4.3 ( ± 8.1)
76 3.5
4. Summary and conclusion The EC technique was used to quantify fugitive emissions of CH4 and CO2 from an oil sands tailings pond in Alberta in June 2014. The relationship between σw/u* and |z/L| shows that the EC flux tower site had an undisturbed surface layer conducive to good quality emission flux measurements. Under all wind directions, 30-min average CH4 concentrations (average 2.1 ppmv; relative standard deviation 6%) ranged from 2.0 to 2.9 ppmv while CO2 concentrations (average 399.8 ppmv; relative standard deviation 2%) varied between 382.2 and 433.5 ppmv. Diurnal variations (composite 30-min averages; after wind direction and other filtering) showed lower CH4 (< 2.1 ppmv) and CO2 (< 395 ppmv) concentrations during the daytime when temperatures were high and air flow was usually unstable. At nighttime, when temperatures were lower and mixing was often reduced due to a temperature inversion, CH4 and CO2 concentrations were higher (> 2.1 ppmv and > 395 ppmv, respectively). Daily variation of CH4 and CO2 concentrations were small with relative standard deviations less than 6%. Average CH4 and CO2 fluxes were 4.56 × 10−2 g/(m2-d) and 3.59 g/(m2-d), respectively. The differences in the daytime/nighttime ratios of diurnal (composite 30-min averages) CH4 and CO2 fluxes (2.8 and 0.7 times, respectively) and their different correlations (positive for CH4 and weakly negative for CO2) with diurnal air temperature variations may be due to different mechanisms (e.g., anaerobic methanogenesis vs. aerobic biodegradation) of CH4 and CO2 productions, differences in nighttime and daytime meteorological conditions (e.g., wind speed, thermal stratification, turbulent mixing, and depth of atmospheric boundary layer) as well as changes in nighttime and daytime industrial activities (if any). Both diurnal and daily CH4 and CO2 emission fluxes showed strong variability (relative standard deviation 97–158%). Only weak or no correlations were found between CH4 or CO2 fluxes and other variables (air temperature, wind speed, wind direction, and x_90%), suggesting that short-term (i.e., days to weeks) variability of emission fluxes measured in this study were not strongly driven by any of the meteorological variables or x_90% discussed above. Emission fluxes measured under different wind directions and x_90% could be different by one order of magnitude, showing the importance of having representative x_90%. The CH4 and CO2 emission fluxes determined in this study were of the same order of magnitude as those from a previous study that used the EC technique at the same tailings pond. CO2 fluxes in this study were similar while CH4 fluxes in this study were one order of magnitude higher than FC measurement results. To allow rigorous technical assessment, different emission quantification techniques should be deployed simultaneously and for extended test durations. The EC technique can be autonomously operated and can provide continuous, high (temporal) resolution monitoring as well as better temporal and spatial coverage than the FC technique. Additional EC flux towers at different sides and heights of the tailings pond could be used to minimize data loss due to unfavorable wind directions and increase spatial and temporal coverage of emissions measurement in future studies.
Note: * source from Alberta Environment and Parks (2017) (field work conducted by a 3rd party as part of the routine regulatory monitoring requirements, not as part of this study). 1includes day- and night-time data. Eddy covariance (EC) fluxes are reported as the average ± standard deviation. The flux chamber (FC) data is an average flux value from three different location measurements, outfall far-field, outfall mid-field and outfall near-field for total 19 sampling datasets. Both EC and FC measurements were conducted at the same pond location, but different days in June 2014.
Table 2 presents emission fluxes measured using EC in this study and reported FC results (i.e., field work conducted by a 3rd party in summer 2014 as part of the routine regulatory monitoring requirements; [2]). The FC data were collected over two days during daytime while the EC data were collected over a period of 13 days for day- and night-time in summer 2014 at the same sampling location. While the EC and FC measurements were not carried out simultaneously, a pseudocomparison was conducted. The average CO2 flux obtained from EC and FC were comparable while for CH4, the FC results were 17 and 33 times greater than the EC fluxes for all data and daytime only data, respectively (Table 2). The large difference in methane fluxes between the two techniques is possibly due to the occurrence of bubbling and the inherent limitation (e.g., small temporal and spatial coverages) of a FC when sampling heterogeneous sources. CH4 emission from a tailings pond originates from biogenic activities of methanogenic microorganisms present in the tailings, particularly in the mature fine tailings (MFT, a thick suspension of tailings containing solid material such as sands and clays). Within the MFT, methane bubbles move towards the water surface, at which point they burst and contents are emitted to the atmosphere [16]. If the chamber is overwhelmed by the eruption of bubbles, a surge in the emission flux will be detected while the equilibrium vapor-phase concentration in the chamber can be disrupted, suppressing the true emission rate. Several researchers have compared EC and FC techniques to quantify GHG fluxes at various study areas such as a forest [48,11,17], a small wind shielded lake [41], landfills [12,33,22] and agricultural fields [39] worldwide. The results showed non-conclusive trends in underestimating or overestimating of GHG emission fluxes between the two techniques. FC measurements have often been used for studying spatial variation [14,15]. However, spatial variation studies are often limited to survey-type measurements with one chamber (i.e., a snapshot of the emission flux at that sampling location) while EC measurements are also able to detect differences between different wind sectors [39]. An oil sands tailings pond is a largely heterogeneous source due to the variable chemical composition of the tailings and the non-uniform distribution of these tailings [46]. As such, emission factors obtained with an FC may not accurately account for the spatial and temporal variation of a heterogeneous tailings pond. These shortcomings can be overcome with the EC technique. The EC technique can be autonomously operated and can provide continuous, high (temporal) resolution monitoring as well as better temporal and spatial coverage than the FC technique. At the same time, there was clear differences between nighttime and daytime flux measurements of the EC technique [39,25,49]. The spatial representativeness of EC CH4 measurements was also examined by comparing parallel CH4 fluxes from three different heights
Acknowledgement This research was supported by the Government of Alberta’s EcoTrust funding grant number 11GREA06. The authors would like to 463
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thank the industry operator for providing site access and logistical support during the field campaign.
Environment, Edmonton, Alberta; 2012. [25] Haslwanter A, Hammerle A, Wohlfahrt G. Open- vs. closed-path eddy covariance measurements of the net ecosystem carbon dioxide and water vapour exchange: a long-term perspective. Agric For Meteorol 2009;149(2):291–302. https://doi.org/ 10.1016/j.agrformet.2008.08.011. [26] Holowenko FM, MacKinnon MD, Fedorak PM. Methanogens and sulfate-reducing bacteria in oil sands fine tailings waste. Can J Microbiol 2000;46(10):927–37. [27] Honarvar A, Rozhon J, Millington D, Walden T, Murillo CA, Walden Z. Economic impacts of new oil sands projects in Alberta (2010-2035), CERI; 2011. [28] Kabat P. Vegetation, water, humans and the climate: a new perspective on an internactive system. Springer Science & Business Media; 2004. [29] Kaimal JC, Finnigan JJ. Atmospheric boundary layer flows: their structure and measurement. New York: Oxford University Press; 1994. [30] Klenbusch M. Measurement of gaseous emission rates from land surfaces using an emission-isolation flux chamber. User’s Guide, U.S. Environmental Protection Agency, Washington, D.C., EPA/600/8-86/008; 2002. [31] Kljun N, Calanca P, Rotach M, Schmid H. A simple parameterisation for flux footprint predictions. Bound-Layer Meteorol 2004;112(3):503–23. [32] Kormann R, Meixner FX. An analytical footprint model for non-neutral stratification. Bound-Layer Meteorol 2001;99(2):207–24. [33] Liao W, Chou FS. Measurement of methane emission from a landfill with flux chamber. In: Proceedings of the air & waste management association’s 91st annual meeting & exhibition, June 14–18 San Diego, California; 1998. [34] LI-COR Inc., 2013. EddyPro instruction manual. [35] LI-COR Inc. RSSI thresholds for best performance of LI-7700 and LI-7500A instruments (Personal Communication). L. Zhang; 2013. [36] Lo CC, Brownlee BG, Bunce NJ. Mass spectrometric and toxicological assays of Athabasca oil sands naphthenic acids. Water Res 2006;40(4):655–64. [37] Moore C. Frequency response corrections for eddy correlation systems. Bound-Layer Meteorol 1986;37(1–2):17–35. [38] Oil Sands Developers Group. Responsible Oil Sands Development: the Process. Oil Sands Developers Group (http://www.oilsandsdevelopers.ca/index.php/oil-sandstechnologies/mining/the-process/); 2009. [39] Peltola O, Hensen A, Belelli Marchesini L, Helfter C, Bosveld FC, van den Bulk WCM, Haapanala S, van Huissteden J, Laurila T, Lindroth A, Nemitz E, Röckmann T, Vermeulenb AT, Mammarella I. Studying the spatial variability of methane flux with five eddy covariance towers of varying height. Agric For Meteorol 2015;214–215(15):456–72. [40] Penner TJ, Foght JM. Mature fine tailings from oil sands processing harbour diverse methanogenic communities. Can J Microbiol 2010;56(6):459–70. [41] Schubert CJ, Diem T, Eugster W. Methane emissions from a small wind shielded lake determined by eddy covariance, flux chambers, anchored funnels, and boundary model calculations: a comparison. Environ Sci Technol 2012;46(8):4515–22. [42] Sharpe R, Harper L. Methane emissions from an anaerobic swine lagoon. Atmos Environ 1999;33(22):3627–33. [43] Siddique T, Fedorak PM, MacKinnon MD, Foght JM. Metabolism of BTEX and naphtha compounds to methane in oil sands tailings. Environ Sci Technol 2007;41(7):2350–6. [44] Siddique T, Penner T, Semple K, Foght JM. Anaerobic biodegradation of longerchain n-alkanes coupled to methane production in oil sands tailings. Environ Sci Technol 2011;45(13):5892–9. [45] Simpson IJ, Blake NJ, Barletta B, Diskin GS, Fuelberg HE, Gorham K, et al. Characterization of trace gases measured over Alberta oil sands mining operations: 76 speciated C2–C10 volatile organic compounds (VOCs), CO2, CH4, CO, NO, NO2, NOy, O3 and SO2. Atmos Chem Phys 2010;10(23):11931–54. [46] Small CC, Cho S, Hashisho Z, Ulrich AC. Emissions from oil sands tailings ponds: review of tailings pond parameters and emission estimates. J Petrol Sci Eng 2015;127:490–501. [47] Soegaard H, Nordstroem C, Friborg T, Hansen BU, Christensen TR, Bay C. Trace gas exchange in a high-arctic valley: 3. Integrating and scaling CO2 fluxes from canopy to landscape using flux data, footprint modeling, and remote sensing. Global Biogeochem Cycles 2000;14(3):725–44. [48] Wang H. Effects of tree species mixture on soil organic carbon stocks and greenhouse gas fluxes in subtropical plantations in China. For Ecol Manage 2013;300:4–13. [49] Wang M, Guan DX, Han SJ, Wu JL. Comparison of eddy covariance and chamberbased methods for measuring CO2 flux in a temperate mixed forest. Tree Physiol 2010;30(1):149–63. https://doi.org/10.1093/treephys/tpp098. [50] Wilson J. Monin-Obukhov functions for standard deviations of velocity. BoundLayer Meteorol 2008;129(3):353–69. [51] Yeh S, Jordaan SM, Brandt AR, Turetsky MR, Spatari S, Keith DW. Land use greenhouse gas emissions from conventional oil production and oil sands. Environ Sci Technol 2010;44(22):8766–72. [52] Zhang L. Quantification of fugitive emissions from a biosolids lagoon. Master’s Thesis. University of Alberta; 2014. [53] Zhang L, Shariaty P, Hashisho Z, Brown C, Wilson JD, Cho S. Quantification of fugitive emissions from a biosolids lagoon. A&WMA’s 107th annual conference & exhibition, Long Beach, California, U.S.A; 2014b.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.fuel.2018.09.104. References [1] Alberta Energy Regulator. Directive 085: fluid tailings management for oil sands mining projects. Edmonton, Alberta, Canada; 2017. [2] Alberta Environment and Parks (AEP). Air Pollutant and GHG Emissions from Mine Faces and Tailings Ponds. Edmonton, Alberta, Canada, in press. Prepared by Clearstone Engineering Ltd. and Stantec; 2017. [3] Aubinet M. Eddy covariance CO2 flux measurements in nocturnal conditions: an analysis of the problem. Ecol Appl 2008;18(6):1368–78. [4] Baldocchi DD. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob Change Biol 2003;9(4):479–92. [5] Berger BW, Davis KJ, Yi C, Bakwin PS, Zhao CL. Long-term carbon dioxide fluxes from a very tall tower in a northern forest: flux measurement methodology. J Atmos Oceanic Technol 2001;18(4):529–42. [6] Bordenave S, Kostenko V, Dutkoski M, Grigoryan A, Martinuzzi RJ, Voordouw G. Relation between the activity of anaerobic microbial populations in oil sands tailings ponds and the sedimentation of tailings. Chemosphere 2010;81(5):663–8. [7] Brown C, Small C, Wilson J, Hashisho Z. Quantitative characterization of greenhouse gases and volatile organic compounds emissions from an oil sands tailings pond in Western Canada. University of Alberta; 2012. [8] Burba G. Eddy covariance method for scientific, industrial, agricultural, and regulatory applications. Lincoln, Nebraska: LI-COR, Inc.; 2013. [9] Burkus, Z., Wheler, J., Pletcher, S., 2014. GHG emissions from oil sands tailings ponds: overview and modelling based on fermentable substrates. Part I: Review of the tailings ponds facts and practices. Alberta Environment and Sustainable Resource Development, November 2014. [10] Chow DL, Nasr TN, Chow RS, Sawatzky RP. Recovery techniques for Canada’s heavy oil and bitumen resources. J Can Pet Technol 2008;47(5):12–7. [11] Clement RJ. Relating chamber measurements to eddy correlation measurements of methane flux. J Geophys Res: Atmos 1995;100(D10):21047–56. https://doi.org/10. 1029/95JD02196. [12] Cooper CD, Reinhart DR, Rash D. Seligman, Keely D. “Landfill gas emission: final report,” prepared for the Florida Center for Solid and Hazardous Waste Management; 1992. [13] Denmead OT. Approaches to measuring fluxes of methane and nitrous oxide between landscapes and the atmosphere. Plant Soil 2008;309(1–2):5–24. [14] Eklund B. Practical guidance for flux chamber measurements of fugitive volatile organic emission rates. J Air Waste Manage Assoc 1992;42(12):1583–91. https:// doi.org/10.1080/10473289.1992.10467102. [15] Eun S. Hydrogen sulfide flux measurements and dispersion modeling form construction and demolition debris landfills. Master’s Thesis, University of central Florida Orlando, FL; 2004. [16] Fuentes E, Coe H, Green D, Leeuw Gd, McFiggans G. Laboratory-generated primary marine aerosol via bubble-bursting and atomization. Atmos Meas Tech 2010;3(1):141–62. [17] Gaumont-Guay D, Black TA, McCaughey H, Barr AG, Krishnan P, Jassal RS, et al. Soil CO2 efflux in contrasting boreal deciduous and coniferous stands and its contribution to the ecosystem carbon balance. Glob Change Biol 2009;15(5):1302–19. [18] Giesy JP, Anderson JC, Wiseman SB. Alberta oil sands development. Proc Natl Acad Sci 2010;107(3):951–2. [19] Goulden ML, Munger JW, FAN SM, Daube BC, Wofsy SC. Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy. Glob Change Biol 1996;2(3):169–82. [20] Government of Alberta, About the resource (http://www.oilsands.alberta.ca/resource.html); 2011. [21] Government of Alberta. Lower Athabasca region tailings management framework for the mineable Athabasca oil sands. http://esrd.alberta.ca/focus/cumulative-effects/cumulative-effects-management/management-frameworks/documents/ LARP-TailingsMgtAthabascaOilsands-Mar2015.pdf (Retrieved as of April 29, 2015); 2015. [22] Gowing A, Farquhar GJ. Laboratory assessment of a flux chamber to determine landfill gas emissions. In: Proceedings of the air & waste management association’s 90th annual meeting & exhibition, June 8–13, Toronto, Ontario, Canada; 1997. [23] Ham J, Williams C, Shonkwiler K. Automated dust blow-off system for the LI-7700 methane analyzer. Colorado State University; 2012. [24] Hashisho Z, Small CC, Morshed G. Review of technologies for the characterization and monitoring of VOCs, reduced sulphur compounds and CH4, oil sands research and information network, University of Alberta, School of Energy and the
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Full Length Article
Quantification of fugitive emissions from an oil sands tailings pond by eddy covariance ⁎
T
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Lucas Zhanga, Sunny Choa,b, , Zaher Hashishoa, , Casandra Brownc a
University of Alberta, Department of Civil and Environmental Engineering, Edmonton, AB T6G 1H9, Canada Government of Alberta, Alberta Environment and Parks, Edmonton, AB T5J 1G4, Canada c University of Alberta, Department of Earth and Atmospheric Sciences, Edmonton, AB T6G 2W2, Canada b
A R T I C LE I N FO
A B S T R A C T
Keywords: Greenhouse gases Alberta oil sands region Eddy covariance Emission flux Fugitive emissions
Oil sands tailings ponds are sources of greenhouse gasses (GHG) and air pollutants. The flux chamber technique, typically used to measure emissions from tailings ponds, samples a small area for a short duration, which may not account for the spatial and temporal variability of emissions from oil sands tailings ponds. The eddy covariance (EC) technique, with large spatial coverage and better temporal resolution, is a promising method to improve the accuracy of emission flux quantification. A field campaign was conducted to measure emissions from an oil sands tailings pond in Alberta using an EC system. Average CH4 and CO2 emission fluxes were 4.56 × 10−2 g/(m2-d) and 3.59 g/(m2-d), respectively. Diurnal and daily variations of CH4 and CO2 emission fluxes were strong with relative standard deviations of 97–158%. Nighttime (18:30 to 8:00, inclusive) CH4 average emission flux (6.55 × 10−2 g/(m2-d)) was 2.8 times daytime (8:30 to 18:00, inclusive) CH4 flux (2.32 × 10−2 g/(m2-d)) while nighttime CO2 average emission flux (2.97 g/(m2-d)) was 0.7 times daytime CO2 emission flux (4.29 g/(m2-d)). Pearson correlation test results suggest that short-term (i.e., days to weeks) variations of CH4 and CO2 emission fluxes measured in this study were not strongly (but can be weakly) correlated with meteorological variables or the 90% cumulative flux contour distance. The CH4 and CO2 emission fluxes determined in this study were of the same order of magnitude as those from a previous study that used the EC technique at the same tailings pond. CO2 fluxes in this study were similar while CH4 fluxes in this study were more than an order of magnitude lower than fluxes based on flux chamber measurements conducted by a 3rd party at the same location and in the same month and year as part of routine regulatory monitoring requirements. Continuous, real-time, and long-term monitoring of tailings ponds emissions is necessary to reduce uncertainty and improve representativeness and accuracy of emission flux quantification.
1. Introduction Alberta’s oil sands deposits is the third largest proven reserve, after Saudi Arabia and Venezuela [18,20,27]. The oil sands consist of a mixture of quartz sand, silt, clay, bitumen, organics, trapped gasses, and pore water and trace metals and minerals [36,46]. Extracting bitumen from the oil sands can be done in-situ for deep reserves or through surface mining followed by bitumen-sand separation processes for shallow oil sands reserves [10]. Various additives (such as caustic soda), diluents (naphtha or paraffin), and large quantities of water are used to separate bitumen from the mined oil sands and increase bitumen recovery rates [38]. The waste stream, i.e., the oil sands tailings, is stored on-site in constructed tailings ponds and consists of a mixture of process and connate water, sand, silt, clays, residual bitumen, diluent, and inorganic ⁎
and organic by-products of the extraction process. Alberta has set up thresholds to ensure that fluid tailings volumes are contained and new and legacy tailings ponds will be reclaimed in a timely and progressive manner [1,21]. The understanding of many tailings treatment technologies at a sufficiently large scale has not been fully achieved and technological innovation to meet environmental challenges faced is encouraged [21]. Pollutants emitted from tailings ponds include volatile organic compounds (VOCs), reduced sulphur compounds (RSCs) and greenhouse gasses (GHG) including methane (CH4) and carbon dioxide (CO2) [6,45,51,44,46]. CH4 and CO2 are produced by microbes native to oil sands tailings through biodegradation of unrecovered bitumen and solvent (e.g., naphtha, a mixture of aliphatic and aromatic hydrocarbons) [26,6,40,44]. The amount of CH4 and CO2 emitted from tailings ponds can be substantial [51,44]. GHG and air pollutants emissions
Corresponding authors at: University of Alberta, Department of Civil and Environmental Engineering, Edmonton, AB T6G 1H9, Canada (S. Cho). E-mail addresses: [email protected] (S. Cho), [email protected] (Z. Hashisho).
https://doi.org/10.1016/j.fuel.2018.09.104 Received 12 June 2018; Received in revised form 20 September 2018; Accepted 22 September 2018 0016-2361/ Crown Copyright © 2018 Published by Elsevier Ltd. All rights reserved.
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(a)
from tailings ponds are difficult to characterize because of the daily and seasonal variation of emissions as well as the complexity of the processes generating these emissions [46]. There are different techniques and methods for measuring fugitive emissions and developing emission factors from oil sands tailings ponds. Flux chamber (FC) has emerged as one of the most widely used techniques for the measurement of GHG and VOC emissions in the Alberta oil sands region [46,2]. Most estimations of tailings emissions are based on FC emission surveys conducted at various tailings ponds locations in the spring, summer, and fall. The FC technique can be used to measure a variety of emissions (including VOCs, RSCs, CO2, and CH4) from surface impoundments. FC gathers information from a small sample area (typically 0.13 m2) over a short duration (typically 0.5–1 h) which may not account for the variability in emissions over time or over a non-uniform source area such as a tailings pond [24,46]. Multiple measurements are taken over one or a few days to obtain more spatially representative emissions estimates for various zones of a pond; however, the temporal coverage is very limited due to cost and labour constraints. In addition, the FC disturbs the surface of the pond and decouples the surface from the natural atmosphere, potentially impacting the processes controlling the flux being measured [13]. Even for a homogeneous source, FC results are estimated to be 50–124% of the true emission rate [30]. For these reasons, it is of interest to explore other methods for measuring emissions from the oil sands tailings ponds that would sample a larger area at higher temporal resolution without interfering with the emitting surface. Micrometeorological flux measurement techniques, such as the eddy covariance (EC) technique, could be used for this purpose; however, these techniques have not been tested at Alberta’s oil sands tailings ponds, which can be large (e.g., a few km2 in area), have a dusty environment, and are surrounded by industrial activities. The EC technique is one of the most direct and defensible methods to measure fluxes. The general principle of the EC technique is to measure the number of molecules moving upward and downward over time, as well as the traveling speeds of these molecules [8]. The EC technique has several advantages over other techniques including its ability to directly and continuously measure the vertical flux at the point of measurement [13,8]. Additional information about flux measurement techniques, including the EC technique, can be found in Hashisho et al. [24], Zhang [52], and Small et al. [46]. In this study, a pilot field campaign was conducted to measure concentrations and emission fluxes of GHG at one of the tailings ponds in Northeastern Alberta during the summer of 2014 using the EC technique. The objectives of this study are to quantify emission fluxes of CH4 and CO2, understand the variation in GHG emissions from the pond, especially at nighttime when little to no emission flux data are available, and compare emission flux results determined by the EC technique in this study to EC results from a prior field campaign and to reported FC results.
LI-7700 CH4 analyzer LI-7500A CO2/H2O analyzer
CSAT3 sonic anemometer
(b)
Fig. 1. Picture of eddy covariance instrumentation mounted on top of the flux tower (a) and a schematic of the flux tower location, with the bearing of sonic anemometer (yellow arrow line) and discarded wind direction (334.3° to 154.3°) indicated by blue arc arrow (b). Note: the grey oval shape represents the tailings pond area, however, it is mainly meant to indicate the relative location of the EC tower to the pond instead of to closely resemble the actual perimeter or shape of the pond. Percentages indicate the frequency of time the wind was of a particular direction. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
gas analyzer also measures the ambient temperature and pressure of the gas in the sampling path. (3) A CO2/H2O open-path gas analyzer (LI-7500A, LI-COR Inc.) measures CO2 and H2O in the atmosphere simultaneously at a high frequency up to 40 Hz. It is a high performance, non-dispersive, open-path analyzer with typical root mean square noise of 0.11 ppmv for CO2 and 0.0047 pptv for H2O.
2. Methodology A datalogger (CR3000, Campbell Scientific, Inc.) was used to record the raw data (at 10 Hz) generated by the instruments described above. Fig. 1 shows the instrumentation setup and location including the EC instrumentation mounted on top of the flux tower (Fig. 1(a)) and a schematic of the flux tower location, with the bearing (orientation) of sonic anemometer and discarded wind direction indicated (Fig. 1(b)). The instruments were mounted at the top of the flux tower with the centre of the sensor paths located 11 m above the tailings pond surface and the sonic anemometer positioned in between the gas analyzers. The EC system was also equipped with a modem to allow remote access to the datalogger to check on the recorded data and signals quality and assess the need for user intervention. The system was powered by four 12-volt batteries and two solar panels. The strength of the signals from both gas analyzers was recorded as
2.1. Instrumentation The main components of the EC system used in this study are briefly introduced as follows: (1) A 3-D sonic anemometer (CSAT3; Campbell Scientific Inc.) measures the horizontal (u and v) and vertical (w) wind velocity components, as well as the temperature at a frequency up to 20 Hz. The sonic anemometer determines the wind speed (WS) by measuring the speed of the ultrasonic signal it emits and receives. (2) A CH4 open-path gas analyzer (LI-7700, LI-COR Inc.) is a high-speed (data output frequency up to 40 Hz), high precision (Root Mean Square (RMS) noise of 5 ppbv at 10 Hz and typical ambient levels) open-path methane analyzer. Along with CH4 concentration, the 458
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a dimensionless value, the Residual Signal Strength Indicator (RSSI), which was used as an indicator for QA/QC and the need for mirror cleaning. As the surrounding environment could be dusty, two sets of automated dust blow-off systems [23] were built and used to blow the dust off the top and bottom optical surfaces of the gas analyzers using nitrogen (N2) from compressed gas tanks. RSSI thresholds (20% for CH4 analyzer and 50% for CO2 analyzer) were used to trigger the dust blowoffs with a maximum of five attempts (puffs of gas) per trigger and a 2hour break before the system can be triggered again. These thresholds were chosen based on experience and the local environment, aiming to maintain RSSI above thresholds for as long as possible without disturbing the wind flow too frequently and draining the gas tanks too soon. Signal strengths were used as “flags” to filter/select the raw data. For CH4, data collected with RSSI below 20% were discarded, while CO2 data with RSSI below 55% were removed (based on manufacturer instruction and practical experience) [35,34]. RSSIs need to be above certain thresholds to ensure best instrument performance. Based on the bearing of the sonic anemometer (244.3°), the data recorded with wind direction between 334.3° and 154.3° were discarded. The measured meteorological parameters (i.e., wind direction, wind speed, temperature) during the field study are summarized in Table S1. The raw data were processed using the software EddyPro (Version 5, LI-COR Inc). Major data processing functions performed by EddyPro include coordinate rotation, frequency response correction, WebbPearman-Leuning (WPL) density correction, and sonic anemometer virtual temperature correction [35,34]. The averaging interval for time series data and fluxes is 30 min. To indicate how well the measured flux is representative of the source strength, the 90% cumulative flux contour distance (x_90%) (i.e., the along-wind distance that represents the area providing 90% cumulative contribution to total fluxes) [32,31] was calculated using EddyPro and is shown in Fig. S1.
Fig. 2. σw/u* plotted against |z/L|: comparison between measured and modelled results. σw is the standard deviation of vertical wind speed; u* is friction velocity; z is measurement height (11 m) and L is Obukhov length. Model 1 (solid line): σw/u∗ = 1.0 × (1 − 4.5z/L)1/3, Model 2 (dashed line): σw/ u∗ = 1.25 × (1 − 3z/L)1/3.
the final data after all filtering processes unless otherwise stated. Fig. 2 indicates that the relationship between the normalized standard deviation of vertical velocity (σw/u*) and stability |z/L| (unstable conditions only) is consistent with an existing model [50] for an undisturbed surface layer, although the observed data points (this study) are mostly lower than the curve of the model described by Kaimal and Finnigan [29]. In general, the sonic anemometer was operating as expected and the EC technique was suitable for the site, according to Fig. 2.
2.2. Description of study area 3. Results and discussion
GHG concentrations and fluxes were measured at one of the tailings ponds in the Alberta oil sands region from June 5 to 18, 2014. The study area is located in the Athabasca oil sands area, which is approximately 72 km north of the regional municipality of Wood Buffalo. The tailings pond had an area of 3.3 km2 in 2014 and received froth treatment tailings and middlings stream tailings [2]. Residual bitumen and solvent, in addition to coarse sand, silt, and processed water were the major components of the tailings pond. Nearby (within 7 km) emission sources include, but are not limited to, open-pit mining faces, in-situ, and other oil sands tailings ponds. The temperature measured during the field campaign varied from 5.7 to 26.0 °C, with median and average temperatures of 16.8 °C and 16.9 °C, respectively. The wind speed recorded ranged from 0.3 m/s to 8.4 m/s, with median and average values of 3.2 m/s and 3.6 m/s, respectively. The prevailing wind direction was southerly and southsoutheasterly. Daily averages of temperature, wind speed and direction are presented in Fig. S2.
3.1. Pollution roses and diurnal variations o GHG concentrations The data quality of concentration measurements made by the gas sensors is not affected by wind direction or the possible flow disturbance caused by the instruments or tower supporting the instruments. Therefore, it is meaningful to investigate the relationships between concentrations and all wind directions. Under all wind directions, CH4 concentrations (average 2.1 ppmv; relative standard deviation 6%) ranged from 2.0 to 2.9 ppmv while CO2 concentrations (average 399.8 ppmv; relative standard deviation 2%) varied between 382.2 and 433.5 ppmv. High CH4 (> 2.2 ppmv) and CO2 (> 410 ppmv) concentrations were recorded under 40°, suggesting the presence of CH4 and CO2 emission source(s) northeast (i.e., upwind) of the studied tailings pond (Fig. 3). Possible emission sources northeast of the studied tailings pond include, but are not limited to, three mining face sites (relative locations indicated as M1, M2 and M3 in Fig. 3). Data from the whole dataset after all filterings were averaged to generate 48 composite 30-min values representing the average gas concentrations at each half-hour of a day. Composite diurnal CH4 concentration ranged from 2.00 ppmv to 2.23 ppmv (Fig. 4a) while composite diurnal CO2 concentration ranged from 387.9 ppmv to 412.1 ppmv (Fig. 4b). CH4 and CO2 concentrations were higher (> 2.1 ppmv and > 395 ppmv, respectively) at night and in the early morning (21:00 to 10:00), likely due to stable thermal stratification (indicated by low or decreasing air temperature; Fig. 4a and b) and low turbulent mixing (indicated by u* < 0.2 m/s; Fig. 4c and d) during this time period [3].
2.3. Data QA/QC Table S2 provides the percentages of data points remaining after each data filtering process. 16% of the total data points were removed due to unacceptable RSSI caused by precipitation events and/or dust etc. In addition, about half of the total data points were discarded due to unfavorable (according to the bearing of the sonic anemometer) wind direction (334.3–154.3°). Furthermore, an additional 7% of the total data points were removed because errors (e.g., flux footprint estimation beyond reasonable range) were reported by the software EddyPro for these data points. These errors were likely due to calm wind conditions (minimal vertical mixing). All data presented hereafter are 459
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30%
N
(a)
M2
30%
CH4 (ppmv)
20% 10% W
M3
2.6 to 2.8
E
2.4 to 2.6
N
M2
CO2 (ppmv)
20%
2.8 to 3
M1
(b)
430 to 440
M1
10%
M3 E
W
410 to 420
2.2 to 2.4
400 to 410
2 to 2.2
390 to 400
1.8 to 2
380 to 390
ppmv S
420 to 430
ppmv
mean = 2.1 2.0912 calm = 0 %
S
Frequency of counts by wind direction (%)
8 mean = 399.76 calm = 0 %
Frequency of counts by wind direction (%)
Fig. 3. Concentration (ppm) of CH4 (a) and CO2 (b) against wind direction (0 to 360°, wind direction filtering not applied). Note: Centers of the concentration roses indicate the location of the flux tower, which was placed northeast of the tailings pond (Fig. 1b). Relative locations of three mining face sites, located within approximately 7 km of the tower, are marked as M1, M2, and M3. Percentage values labeled on the circular grid lines indicate frequency of counts by wind direction (%).
For the whole study period, average CH4 and CO2 fluxes (30 min averages) were 4.56 × 10−2 g/(m2-d) and 3.59 g/(m2-d), respectively. Composite diurnal variations of CH4 fluxes correlated negatively
(b)
(c)
(d)
u* (m/s)
(a)
u* (m/s)
(correlation coefficient r = −0.6, Pearson test value p = 3.3 × 10−5), while composite diurnal CO2 fluxes correlated slightly positively (r = 0.3, p = 0.019), with diurnal variations of temperature (Fig. 5a and b). Both CH4 and CO2 diurnal fluxes showed large variability with relative standard deviations (RSD) of 123% and 97%, respectively. No
3.2. Temporal variations of GHG fluxes
Fig. 4. Composite diurnal variations of CH4 and CO2 concentrations against temperature (a and b) and friction velocity (u*) (c and d) with error (standard deviation) bars for each composite data points. 460
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Fig. 5. Composite diurnal variation of CH4 flux (a) and CO2 flux (b) against temperature (T) in June 2014.
uncertainties in and variabilities of the EC emission flux results than during the daytime. Furthermore, changes in daytime and nighttime industrial activities, if any, at the tailings pond may be another factor that influences nighttime/daytime ratios of emission fluxes. Prior to this study using the EC technique, little to no nighttime emission flux data are available as FC measurements on tailings ponds are usually taken during daytime due to safety concerns. The significant differences between daytime and nighttime CH4 and CO2 emission fluxes in this study further demonstrate the importance of having continuous emission flux measurements using techniques such as EC. Positive emission fluxes from tailings ponds are expected as they are considered to be a source of GHGs instead of a sink. The CH4 and CO2 emission fluxes (30-min averages) were negative for approximately 25% of the measurement period. These values are unlikely to be due to poor instrument performance, as the velocity statistics provided by the sonic anemometer fit the expected pattern for an undisturbed surface layer (Fig. 2) and the gas analyzers were calibrated prior to the field campaign. Further, the negative values were not found to be associated with undesirable (unreasonably large or beyond the pond perimeter) x_90% estimates. The negative emission flux values may or may not have occurred under conditions wherein the assumptions inherent to the EC technique (e.g., horizontal homogeneity and steady state conditions) were not valid. Therefore, these negative values were not discarded. Daily average CH4 and CO2 fluxes (Fig. 6) varied greatly with RSD of 158% and 131%, respectively. However, the number of data points (30-min averages, after data filtering applied) used in calculating the daily average fluxes ranged from 1 to 34 and only 2 of the 13 days had at least 24 (50% data completeness) data points, indicating that the
data about diurnal variability of emissions from oil sands tailings ponds is available for direct comparison. However, the negative correlation between diurnal CH4 and temperature variations in this study differs from the results at a biosolids lagoon [53] and a swine lagoon [42] where positive correlations were found between CH4 emission flux and air temperature. In addition, nighttime (18:00 to 8:30) CH4 average emission flux (6.55 × 10−2 g/(m2-d); RSD = 105%) was 2.8 times daytime CH4 flux (2.32 × 10−2 g/(m2-d); RSD = 97%) while nighttime CO2 average emission flux (2.97 g/(m2-d); RSD = 90%) was 0.7 times daytime CO2 flux (4.29 g/(m2-d); RSD = 100%). The nighttime/daytime ratios of emission fluxes could be impacted by different mechanisms of CH4 and CO2 productions. CH4 might be produced mainly by methanogenesis of hydrocarbons under anaerobic conditions (mainly in mature fine tailings at the bottom of the pond and thus less affected by ambient weather conditions) while aerobic biodegradation of residual bitumen and oil films on the surface of the pond (more likely affected by ambient weather conditions) might be key to CO2 production [43,40,44,9,46]. In addition, meteorological conditions could impact nighttime/daytime ratios of emission fluxes as well. Generally, the EC technique has been found to be more accurate when the meteorological conditions are steady as well as when the vegetation and terrain are flat and homogeneous [4]. More specifically, the accuracy of the EC technique is determined to be lower at nighttime than daytime [37,47,5,4]. At nighttime, atmospheric conditions such as low wind, stable thermal stratification (temperature inversion), intermittent (and thus sometimes insufficient) turbulent mixing [3], and periodic break-up of the nocturnal atmospheric boundary layer [28] could have a strong impact on emission fluxes produced by the EC method [19,4], indicating larger
Fig. 6. Daily variations of CH4 (a) and CO2 (b) fluxes and count of data points. 461
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daily averages may not be representative of the day on most days (Fig. 6). Hence, daily trends or correlations between emission fluxes and meteorological or other variables were not evaluated. Instead, Pearson correlation tests using 30-min averaging data were performed to estimate the strength of association between variables (meteorological including temperature, wind speed, and wind direction as well as x_90%) and fluxes of CH4 and CO2. CH4 flux had a weak negative correlation with temperature, weak positive correlations with wind direction and x_90%, and no significant correlation with wind speed. CO2 flux had no significant correlation with temperature, wind speed, wind direction, nor x_90% (Table S3). The correlation test results suggest that short-term (i.e., days to weeks) variability of emission fluxes measured in this study were not strongly driven by any of the individual meteorological variables or x_90% discussed above. However, the combined effects of the meteorological variables and x_90%, although hard to quantify, might have a strong impact on short-term emission flux variability. Other factors such as tailings discharge method and flow rate, unrecovered diluent, and residual bitumen, which could change the characteristics (e.g., total carbon, soluble carbon, and volatile solid contents) of the tailings pond water, may impact long-term CH4 and CO2 emissions from the pond significantly [46]. x_90% ranged from 120 m to 9904 m, with an average of 1238 m and a median of 784 m. Fig. 7a indicates that the x_90% was typically higher at 240 ± 10° (∼1500 m) than that at 180 ± 10° (∼800 m). The majority of the x_90% values were less than 1500 m, which was not sufficient to cover the entire tailings pond under southerly wind. However, under southwesterly wind, the x_90% could reach or go beyond the pond edge. x_90% for these two wind direction ranges are marked approximately proportional to the pond on Fig. 7b. Fluxes of CH4 and CO2 for 180° ± 10° were approximately 1.8 × 10−2 g/(m2-d) and 2.9 g/ (m2-d), respectively. For 240° ± 10°, fluxes of CH4 and CO2 were about 0.18 g/(m2-d) (10 times that for 180° ± 10°) and 5.8 g/(m2-d) (two times that for 180° ± 10°), respectively. The differences in emission fluxes under these two wind direction ranges show strong heterogeneity of the tailings pond and the importance of having representative wind directions and x_90%. Note that wind direction or x_90% do not impact how pollutants are emitted from the source. However, wind direction and x_90% can impact what part of the source is measured and how representative (due to source heterogeneity) the measured emission flux is of the whole source. Higher x_90% (i.e., more pond area being measured) do not necessarily mean higher emission fluxes being measured, depending on the average source strength of the area represented
50%
(a)
Table 1 Emission fluxes obtained using eddy covariance at the same tailings pond in June 2014 and July 2012. Fluxes are reported as an average ± standard deviation. Emission Flux
Eddy Covariance (This study1)
Eddy Covariance (2012 study2*)
CH4 (10−2 g/(m2-d)) CO2 (g/(m2-d))
4.56 ± 8.58 3.59 ± 7.11
0.27 ± 8.11 7.09 ± 9.93
Note: 1sampling duration: 13-day and 2sampling duration: 6-day. *Source from Brown et al. [7].
by the x_90%. Similarly, lower x_90%, e.g., a x_90% that covers a small area nearby the flux tower where there are tailings being discharged into the pond, could mean higher emission fluxes being measured, also depending on the average source strength of the area sensed by the flux tower. Hence having a taller flux tower or multiple flux towers on opposing sides of the pond and a longer field campaign is necessary to obtain emission fluxes more representative of the whole tailings pond and to have a better understanding of long-term trends of the emissions.
3.3. Quasi-comparisons of emission flux measurements Since no co-located measurements were completed using different emission quantification techniques at the same time, emission flux results obtained over different time frames at the same tailings pond were used for quasi-comparisons. Table 1 presents the CH4 and CO2 emission fluxes reported in this study (June 2014) and from an earlier study (July 2012) [7] at the same tailings pond using the same EC setup, with the exception that no dust blow-off systems were used in the 2012 study. Measured CH4 flux was higher in June 2014 compared to July 2012, with a difference of approximately 180% between the two studies, while CO2 flux was lower, with a difference of approximately 73% between the two studies. For the June 2014 study, the average temperature was 4 °C lower than that of the July 2012 campaign; wind speed was about the same with July 2012; prevailing wind direction was about 10° higher than July 2012, and x_90% was at the same level (Table S1). The difference in the emission fluxes between the two campaigns could be attributed to changes in the tailings pond (e.g., tailings composition, microbial activity), and differences in conditions (e.g., meteorological conditions) affecting emissions during the measurement periods.
N
(b) x_90% (m)
40% 30%
3000 to 9904 20%
2500 to 3000 10%
2000 to 2500
W
E
1500 to 2000 1000 to 1500 500 to 1000 0 to 500 m
S
mean = 1237.7 calm = 0 %
Frequency of counts by wind direction (%) Fig. 7. 90% cumulative flux contour distance (x_90%) plotted against wind direction (a) and x_90% marked as distances (yellow and blue solid lines) in meters on a schematic drawing of the tailings pond (b). Note: The green arc in (b) indicates the wind directions used for deriving emission fluxes. The grey oval shape represents the tailings pond area and serves to indicate the location of the EC tower relative to the pond, rather than to closely resemble the actual perimeter of the pond. Percentage values labeled on the circular grid lines indicate frequency of counts by wind direction (%). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 462
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(i.e., 6, 20 and 60 m) on an intensively managed grassland that was a largely homogeneous land cover [39]. The three-level EC measurement system provided CH4 fluxes with different spatial averaging due to the different footprint sizes, which increased with measurement height. Therefore, various EC system configurations, such as height and location with respect to source and wind direction, are required for testing proper spatial representativeness of measured flux at the oil sands tailings ponds.
Table 2 Emission fluxes obtained using eddy covariance and flux chamber at the same tailings pond in June 2014. Fluxes are reported as average ± standard deviation. Species
Method
−2
2
CH4 (10 g/(m -d)) CO2 (g/(m2-d))
Eddy covariance (this study)
Flux chamber*
All data1
Daytime
Daytime
4.6 ( ± 8.6) 3.6 ( ± 7.1)
2.3 ( ± 4.9) 4.3 ( ± 8.1)
76 3.5
4. Summary and conclusion The EC technique was used to quantify fugitive emissions of CH4 and CO2 from an oil sands tailings pond in Alberta in June 2014. The relationship between σw/u* and |z/L| shows that the EC flux tower site had an undisturbed surface layer conducive to good quality emission flux measurements. Under all wind directions, 30-min average CH4 concentrations (average 2.1 ppmv; relative standard deviation 6%) ranged from 2.0 to 2.9 ppmv while CO2 concentrations (average 399.8 ppmv; relative standard deviation 2%) varied between 382.2 and 433.5 ppmv. Diurnal variations (composite 30-min averages; after wind direction and other filtering) showed lower CH4 (< 2.1 ppmv) and CO2 (< 395 ppmv) concentrations during the daytime when temperatures were high and air flow was usually unstable. At nighttime, when temperatures were lower and mixing was often reduced due to a temperature inversion, CH4 and CO2 concentrations were higher (> 2.1 ppmv and > 395 ppmv, respectively). Daily variation of CH4 and CO2 concentrations were small with relative standard deviations less than 6%. Average CH4 and CO2 fluxes were 4.56 × 10−2 g/(m2-d) and 3.59 g/(m2-d), respectively. The differences in the daytime/nighttime ratios of diurnal (composite 30-min averages) CH4 and CO2 fluxes (2.8 and 0.7 times, respectively) and their different correlations (positive for CH4 and weakly negative for CO2) with diurnal air temperature variations may be due to different mechanisms (e.g., anaerobic methanogenesis vs. aerobic biodegradation) of CH4 and CO2 productions, differences in nighttime and daytime meteorological conditions (e.g., wind speed, thermal stratification, turbulent mixing, and depth of atmospheric boundary layer) as well as changes in nighttime and daytime industrial activities (if any). Both diurnal and daily CH4 and CO2 emission fluxes showed strong variability (relative standard deviation 97–158%). Only weak or no correlations were found between CH4 or CO2 fluxes and other variables (air temperature, wind speed, wind direction, and x_90%), suggesting that short-term (i.e., days to weeks) variability of emission fluxes measured in this study were not strongly driven by any of the meteorological variables or x_90% discussed above. Emission fluxes measured under different wind directions and x_90% could be different by one order of magnitude, showing the importance of having representative x_90%. The CH4 and CO2 emission fluxes determined in this study were of the same order of magnitude as those from a previous study that used the EC technique at the same tailings pond. CO2 fluxes in this study were similar while CH4 fluxes in this study were one order of magnitude higher than FC measurement results. To allow rigorous technical assessment, different emission quantification techniques should be deployed simultaneously and for extended test durations. The EC technique can be autonomously operated and can provide continuous, high (temporal) resolution monitoring as well as better temporal and spatial coverage than the FC technique. Additional EC flux towers at different sides and heights of the tailings pond could be used to minimize data loss due to unfavorable wind directions and increase spatial and temporal coverage of emissions measurement in future studies.
Note: * source from Alberta Environment and Parks (2017) (field work conducted by a 3rd party as part of the routine regulatory monitoring requirements, not as part of this study). 1includes day- and night-time data. Eddy covariance (EC) fluxes are reported as the average ± standard deviation. The flux chamber (FC) data is an average flux value from three different location measurements, outfall far-field, outfall mid-field and outfall near-field for total 19 sampling datasets. Both EC and FC measurements were conducted at the same pond location, but different days in June 2014.
Table 2 presents emission fluxes measured using EC in this study and reported FC results (i.e., field work conducted by a 3rd party in summer 2014 as part of the routine regulatory monitoring requirements; [2]). The FC data were collected over two days during daytime while the EC data were collected over a period of 13 days for day- and night-time in summer 2014 at the same sampling location. While the EC and FC measurements were not carried out simultaneously, a pseudocomparison was conducted. The average CO2 flux obtained from EC and FC were comparable while for CH4, the FC results were 17 and 33 times greater than the EC fluxes for all data and daytime only data, respectively (Table 2). The large difference in methane fluxes between the two techniques is possibly due to the occurrence of bubbling and the inherent limitation (e.g., small temporal and spatial coverages) of a FC when sampling heterogeneous sources. CH4 emission from a tailings pond originates from biogenic activities of methanogenic microorganisms present in the tailings, particularly in the mature fine tailings (MFT, a thick suspension of tailings containing solid material such as sands and clays). Within the MFT, methane bubbles move towards the water surface, at which point they burst and contents are emitted to the atmosphere [16]. If the chamber is overwhelmed by the eruption of bubbles, a surge in the emission flux will be detected while the equilibrium vapor-phase concentration in the chamber can be disrupted, suppressing the true emission rate. Several researchers have compared EC and FC techniques to quantify GHG fluxes at various study areas such as a forest [48,11,17], a small wind shielded lake [41], landfills [12,33,22] and agricultural fields [39] worldwide. The results showed non-conclusive trends in underestimating or overestimating of GHG emission fluxes between the two techniques. FC measurements have often been used for studying spatial variation [14,15]. However, spatial variation studies are often limited to survey-type measurements with one chamber (i.e., a snapshot of the emission flux at that sampling location) while EC measurements are also able to detect differences between different wind sectors [39]. An oil sands tailings pond is a largely heterogeneous source due to the variable chemical composition of the tailings and the non-uniform distribution of these tailings [46]. As such, emission factors obtained with an FC may not accurately account for the spatial and temporal variation of a heterogeneous tailings pond. These shortcomings can be overcome with the EC technique. The EC technique can be autonomously operated and can provide continuous, high (temporal) resolution monitoring as well as better temporal and spatial coverage than the FC technique. At the same time, there was clear differences between nighttime and daytime flux measurements of the EC technique [39,25,49]. The spatial representativeness of EC CH4 measurements was also examined by comparing parallel CH4 fluxes from three different heights
Acknowledgement This research was supported by the Government of Alberta’s EcoTrust funding grant number 11GREA06. The authors would like to 463
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thank the industry operator for providing site access and logistical support during the field campaign.
Environment, Edmonton, Alberta; 2012. [25] Haslwanter A, Hammerle A, Wohlfahrt G. Open- vs. closed-path eddy covariance measurements of the net ecosystem carbon dioxide and water vapour exchange: a long-term perspective. Agric For Meteorol 2009;149(2):291–302. https://doi.org/ 10.1016/j.agrformet.2008.08.011. [26] Holowenko FM, MacKinnon MD, Fedorak PM. Methanogens and sulfate-reducing bacteria in oil sands fine tailings waste. Can J Microbiol 2000;46(10):927–37. [27] Honarvar A, Rozhon J, Millington D, Walden T, Murillo CA, Walden Z. Economic impacts of new oil sands projects in Alberta (2010-2035), CERI; 2011. [28] Kabat P. Vegetation, water, humans and the climate: a new perspective on an internactive system. Springer Science & Business Media; 2004. [29] Kaimal JC, Finnigan JJ. Atmospheric boundary layer flows: their structure and measurement. New York: Oxford University Press; 1994. [30] Klenbusch M. Measurement of gaseous emission rates from land surfaces using an emission-isolation flux chamber. User’s Guide, U.S. Environmental Protection Agency, Washington, D.C., EPA/600/8-86/008; 2002. [31] Kljun N, Calanca P, Rotach M, Schmid H. A simple parameterisation for flux footprint predictions. Bound-Layer Meteorol 2004;112(3):503–23. [32] Kormann R, Meixner FX. An analytical footprint model for non-neutral stratification. Bound-Layer Meteorol 2001;99(2):207–24. [33] Liao W, Chou FS. Measurement of methane emission from a landfill with flux chamber. In: Proceedings of the air & waste management association’s 91st annual meeting & exhibition, June 14–18 San Diego, California; 1998. [34] LI-COR Inc., 2013. EddyPro instruction manual. [35] LI-COR Inc. RSSI thresholds for best performance of LI-7700 and LI-7500A instruments (Personal Communication). L. Zhang; 2013. [36] Lo CC, Brownlee BG, Bunce NJ. Mass spectrometric and toxicological assays of Athabasca oil sands naphthenic acids. Water Res 2006;40(4):655–64. [37] Moore C. Frequency response corrections for eddy correlation systems. Bound-Layer Meteorol 1986;37(1–2):17–35. [38] Oil Sands Developers Group. Responsible Oil Sands Development: the Process. Oil Sands Developers Group (http://www.oilsandsdevelopers.ca/index.php/oil-sandstechnologies/mining/the-process/); 2009. [39] Peltola O, Hensen A, Belelli Marchesini L, Helfter C, Bosveld FC, van den Bulk WCM, Haapanala S, van Huissteden J, Laurila T, Lindroth A, Nemitz E, Röckmann T, Vermeulenb AT, Mammarella I. Studying the spatial variability of methane flux with five eddy covariance towers of varying height. Agric For Meteorol 2015;214–215(15):456–72. [40] Penner TJ, Foght JM. Mature fine tailings from oil sands processing harbour diverse methanogenic communities. Can J Microbiol 2010;56(6):459–70. [41] Schubert CJ, Diem T, Eugster W. Methane emissions from a small wind shielded lake determined by eddy covariance, flux chambers, anchored funnels, and boundary model calculations: a comparison. Environ Sci Technol 2012;46(8):4515–22. [42] Sharpe R, Harper L. Methane emissions from an anaerobic swine lagoon. Atmos Environ 1999;33(22):3627–33. [43] Siddique T, Fedorak PM, MacKinnon MD, Foght JM. Metabolism of BTEX and naphtha compounds to methane in oil sands tailings. Environ Sci Technol 2007;41(7):2350–6. [44] Siddique T, Penner T, Semple K, Foght JM. Anaerobic biodegradation of longerchain n-alkanes coupled to methane production in oil sands tailings. Environ Sci Technol 2011;45(13):5892–9. [45] Simpson IJ, Blake NJ, Barletta B, Diskin GS, Fuelberg HE, Gorham K, et al. Characterization of trace gases measured over Alberta oil sands mining operations: 76 speciated C2–C10 volatile organic compounds (VOCs), CO2, CH4, CO, NO, NO2, NOy, O3 and SO2. Atmos Chem Phys 2010;10(23):11931–54. [46] Small CC, Cho S, Hashisho Z, Ulrich AC. Emissions from oil sands tailings ponds: review of tailings pond parameters and emission estimates. J Petrol Sci Eng 2015;127:490–501. [47] Soegaard H, Nordstroem C, Friborg T, Hansen BU, Christensen TR, Bay C. Trace gas exchange in a high-arctic valley: 3. Integrating and scaling CO2 fluxes from canopy to landscape using flux data, footprint modeling, and remote sensing. Global Biogeochem Cycles 2000;14(3):725–44. [48] Wang H. Effects of tree species mixture on soil organic carbon stocks and greenhouse gas fluxes in subtropical plantations in China. For Ecol Manage 2013;300:4–13. [49] Wang M, Guan DX, Han SJ, Wu JL. Comparison of eddy covariance and chamberbased methods for measuring CO2 flux in a temperate mixed forest. Tree Physiol 2010;30(1):149–63. https://doi.org/10.1093/treephys/tpp098. [50] Wilson J. Monin-Obukhov functions for standard deviations of velocity. BoundLayer Meteorol 2008;129(3):353–69. [51] Yeh S, Jordaan SM, Brandt AR, Turetsky MR, Spatari S, Keith DW. Land use greenhouse gas emissions from conventional oil production and oil sands. Environ Sci Technol 2010;44(22):8766–72. [52] Zhang L. Quantification of fugitive emissions from a biosolids lagoon. Master’s Thesis. University of Alberta; 2014. [53] Zhang L, Shariaty P, Hashisho Z, Brown C, Wilson JD, Cho S. Quantification of fugitive emissions from a biosolids lagoon. A&WMA’s 107th annual conference & exhibition, Long Beach, California, U.S.A; 2014b.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.fuel.2018.09.104. References [1] Alberta Energy Regulator. Directive 085: fluid tailings management for oil sands mining projects. Edmonton, Alberta, Canada; 2017. [2] Alberta Environment and Parks (AEP). Air Pollutant and GHG Emissions from Mine Faces and Tailings Ponds. Edmonton, Alberta, Canada, in press. Prepared by Clearstone Engineering Ltd. and Stantec; 2017. [3] Aubinet M. Eddy covariance CO2 flux measurements in nocturnal conditions: an analysis of the problem. Ecol Appl 2008;18(6):1368–78. [4] Baldocchi DD. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob Change Biol 2003;9(4):479–92. [5] Berger BW, Davis KJ, Yi C, Bakwin PS, Zhao CL. Long-term carbon dioxide fluxes from a very tall tower in a northern forest: flux measurement methodology. J Atmos Oceanic Technol 2001;18(4):529–42. [6] Bordenave S, Kostenko V, Dutkoski M, Grigoryan A, Martinuzzi RJ, Voordouw G. Relation between the activity of anaerobic microbial populations in oil sands tailings ponds and the sedimentation of tailings. Chemosphere 2010;81(5):663–8. [7] Brown C, Small C, Wilson J, Hashisho Z. Quantitative characterization of greenhouse gases and volatile organic compounds emissions from an oil sands tailings pond in Western Canada. University of Alberta; 2012. [8] Burba G. Eddy covariance method for scientific, industrial, agricultural, and regulatory applications. Lincoln, Nebraska: LI-COR, Inc.; 2013. [9] Burkus, Z., Wheler, J., Pletcher, S., 2014. GHG emissions from oil sands tailings ponds: overview and modelling based on fermentable substrates. Part I: Review of the tailings ponds facts and practices. Alberta Environment and Sustainable Resource Development, November 2014. [10] Chow DL, Nasr TN, Chow RS, Sawatzky RP. Recovery techniques for Canada’s heavy oil and bitumen resources. J Can Pet Technol 2008;47(5):12–7. [11] Clement RJ. Relating chamber measurements to eddy correlation measurements of methane flux. J Geophys Res: Atmos 1995;100(D10):21047–56. https://doi.org/10. 1029/95JD02196. [12] Cooper CD, Reinhart DR, Rash D. Seligman, Keely D. “Landfill gas emission: final report,” prepared for the Florida Center for Solid and Hazardous Waste Management; 1992. [13] Denmead OT. Approaches to measuring fluxes of methane and nitrous oxide between landscapes and the atmosphere. Plant Soil 2008;309(1–2):5–24. [14] Eklund B. Practical guidance for flux chamber measurements of fugitive volatile organic emission rates. J Air Waste Manage Assoc 1992;42(12):1583–91. https:// doi.org/10.1080/10473289.1992.10467102. [15] Eun S. Hydrogen sulfide flux measurements and dispersion modeling form construction and demolition debris landfills. Master’s Thesis, University of central Florida Orlando, FL; 2004. [16] Fuentes E, Coe H, Green D, Leeuw Gd, McFiggans G. Laboratory-generated primary marine aerosol via bubble-bursting and atomization. Atmos Meas Tech 2010;3(1):141–62. [17] Gaumont-Guay D, Black TA, McCaughey H, Barr AG, Krishnan P, Jassal RS, et al. Soil CO2 efflux in contrasting boreal deciduous and coniferous stands and its contribution to the ecosystem carbon balance. Glob Change Biol 2009;15(5):1302–19. [18] Giesy JP, Anderson JC, Wiseman SB. Alberta oil sands development. Proc Natl Acad Sci 2010;107(3):951–2. [19] Goulden ML, Munger JW, FAN SM, Daube BC, Wofsy SC. Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy. Glob Change Biol 1996;2(3):169–82. [20] Government of Alberta, About the resource (http://www.oilsands.alberta.ca/resource.html); 2011. [21] Government of Alberta. Lower Athabasca region tailings management framework for the mineable Athabasca oil sands. http://esrd.alberta.ca/focus/cumulative-effects/cumulative-effects-management/management-frameworks/documents/ LARP-TailingsMgtAthabascaOilsands-Mar2015.pdf (Retrieved as of April 29, 2015); 2015. [22] Gowing A, Farquhar GJ. Laboratory assessment of a flux chamber to determine landfill gas emissions. In: Proceedings of the air & waste management association’s 90th annual meeting & exhibition, June 8–13, Toronto, Ontario, Canada; 1997. [23] Ham J, Williams C, Shonkwiler K. Automated dust blow-off system for the LI-7700 methane analyzer. Colorado State University; 2012. [24] Hashisho Z, Small CC, Morshed G. Review of technologies for the characterization and monitoring of VOCs, reduced sulphur compounds and CH4, oil sands research and information network, University of Alberta, School of Energy and the
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