Size Does Matter: Importance of Large Bubbles and Small-Scale Hot Spots for Methane Transport

Transcription

1 Size Does Matter: Importance of Large Bubbles and Small-Scale Hot Spots for Methane Transport T. DelSontro,*, D.F. McGinnis,, B. Wehrli, and I. Ostrovsky Eawag, Swiss Federal Institute of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland & Institute for Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland GEOMAR, Helmholtz Centre for Ocean Research Kiel, Marine Geosystems, Kiel, Germany Institute F.-A. Forel, Earth and Environmental Sciences, Faculty of Sciences, University of Geneva, 1227 Geneva, Switzerland Israel Oceanographic & Limnological Research, Yigal Allon Kinneret Limnological Laboratory, Migdal 14950, Israel *Corresponding author: Now at Département des sciences biologiques, Université du Québec à Montréal, 8888, succ. Centre-ville, Montréal, Québec H3C 3P8 Canada Supplemental Material Contents A. Methods 1. Echosounder calibration for bubble size.s2 2. Hydroacoustic assessment of bubble flux S3 3. BNS comparison with echo-counting...s5 B. Results and Discussion 1. Echosounder bubble calibration.. S5 2. BNS comparison with echo-counting...s6 C. Tables Table S1..S7 D. Figures Figure S1 S7 Figure S2...S8 Figure S3 S9 Figure S4 S10 Figure S5 S10 Figure S6 S11 Figure S7 S11 Figure S8 S12 Figure S9 S13 E. References.S14 S1

2 A. Methods 1. Echosounder calibration for bubble size. Bubble system for echosounder calibration. The bubble system frame was made from steel beams and held a CH4 tank (Pangas, >99.99%) along one of the edges with a regulator (Tescom, Germany) that was custom-made to be submersible (WSM-Armaturen, Germany). Air-tight tubing extended from the regulator to a hose fitting (Swagelok). An intermediate regulator allowed for more precise gas flow adjustment, which was then connected to air-tight tubing that contained a three-way stopcock that held the bubble-producing needles. Bubbles were released in front of an underwater video camera (SuperSeaCam D6000, DeepSea Power & Light, California, USA) that recorded at 30 frames per second. The camera and an underwater light (DeepSea Power & Light) were controlled (power, zoom, and focus) from the surface via connection by an underwater cable to the SuperSeaCam rackmount controller (S/N 104, Deepsea Power & Light, California, USA). The video was recorded using Dazzle Video Creator Platinum (DVC107, Pinnacle Systems, Avid Technology), which was connected via an S-video cable to the controller. There were two different set-ups for creating bubbles, similar to that of Ostrovsky et al.1 Small bubbles were produced using disposable medical needles (e.g., Terumo, Microlance) with various orifice diameters (from 0.4 to 1.2 mm) and two larger 2R2 stainless steel medical needles (see Table S1). Gas flow rate was adjusted at the surface to produce approximately one bubble per second before lowering the bubble system to the bottom. The large bubble setup also used a needle to produce bubbles, but the bubbles were then collected within an upside down cut syringe. The syringe was held in place by two aluminum pieces that were connected to a cylindrical steel rod and enabled the syringe to be rotated ca. 180 around the rod. When the gas had filled to one of the 1 ml marks on the syringe as observed via the live video streaming, we rotated the syringe upwards using an attached fishing line. The volume of the large bubble was known from the volume-calibrated syringe used to create it. The volumes of the bubbles released from needles were estimated from video images of the bubble just before the point of release using ImageJ (National Institutes of Health, USA), with the known width of the needle providing the pixel scaling. To find the volume of the bubble, a line was drawn down the center of the bubble and the length from each pixel in the middle to the outer edge was measured on both sides. The volume of each pixel segment was calculated using the formula of a cylinder, V = πr2h, where r (radius) was the distance of half a pixel and h (height) was the distance from the middle line to the outer edge of the bubble. The volumes of each segment were summed to find the volume of the bubble. For each bubble size, 15 bubbles were analyzed and the standard deviations of the calculated volumes were used to estimate the errors. Hydroacoustic processing and corrections. The echosounder (operating with a ping rate of 5 Hz) recorded the backscatter echo signal of bubbles released from our bubble system at 10 to 12 m depth. The size of an echosounder target is described by its backscattering crosssection, σbs (m2), or the log representation of σbs called target strength (TS = 10 log10 (σbs), [db re 1 m2]).2 Useful TS values could not be recorded for bubbles at the point of release due to echo interference from the bubble system frame; therefore, the TS values were measured approximately 0.5 m above the bottom and were thus slightly smaller due to dissolution than S2

3 the originally released bubble. To correct for this a discrete bubble model3 was used to backcalculate the change in volume of the ~100 % CH4 bubbles during rise to the acousticallymeasured depth. Local temperature and dissolved CH4 and oxygen concentrations were used in the model (background N2 was assumed in atmospheric equilibrium at local temperature). A single bubble may be observed several times depending on the echosounder beam angle and the duration of the bubble within the beam. Therefore, since bubbles were measured between 0.5 and 1.5 m above the release point, a single bubble was measured 15 to 30 times. An accurate TS distribution of numerous bubbles of the same size is needed for the bubble calibration; therefore only echoes holding to the following criteria were accepted for the TS distribution of each bubble size: (1) pulse lengths within 0.8 and 1.20 relative to the transmission pulse, (2) maximum angle standard deviation of 0.30 degrees, and (3) maximum gain compensation of ± 3 db. In addition, only bubbles released and remaining in the center of the beam (± 1 degree in both the along and athwart echosounder orientation) were used for analysis. 2. Hydroacoustic assessment of bubble flux. The bubble/non-bubble separation (BNS) method separates backscattered echosounder signals of rising bubbles from fish and particulate background readings (Figures S2 and S3). Bubble flux estimates from the sediment were based on bubble concentrations in the 3-m water layer above the bottom (red lines, Figure S2a,b). These data were the most robust as the targets are furthest away from the echosounder allowing the beam to be wider and capture a more representative volume of water.4 Furthermore, measurements within this distance from the bottom give more accurate assessment of target frequency distributions and therefore minimize biases related to possible dissolution of small bubbles or other large changes in bubble volume that occur during bubble ascent. Our bubble size calibration becomes more relevant as it was also conducted on bubbles closest to the bottom. Our echosounder does not allow hydroacoustic sampling of targets within ~1 m from the transducer due to near-field disturbance,5 and data collected in range < 2 m are difficult to use with the BNS method as the fish and bubble targets cannot be properly tracked and are not easily distinguishable. The BNS method will only work within water layers or a segment of a transect where the bubbleto-non-bubble density ratio can be assumed to be homogenous.6 Therefore, each transect was divided into several segments of similar distances (40 70 m) via visual inspection of the echograms for changing bubble/non-bubble densities. The segment distance must also be large enough to provide a sufficient number of detections for accurate approximation of TS distributions of the various targets. The BNS method was initially suggested by Ostrovsky.6 It is an echo-integration technique in which the acoustic backscattering of a volume of water, sv [m-3] (Figure S2a), and the TS distribution of all distinguishable single targets (or single echo detections, SEDs, Figure S2b) are used to determine overall target density, followed by an additional step to determine which proportion of those targets are bubbles. When dealing with hydroacoustic data of fish or bubbles, the number of truly identifiable single targets (SEDs) is less than the actual total number of targets in the water column (Figure S2c). This occurs when targets are in close proximity to each other, as in bubble plumes1 or fish schools,5 where individual/single targets are harder to distinguish. The BNS method first calculates an approximate TS distribution of all targets in the sample volume (Fa*) by relating the TS distributions for identifiable bubbles S3

4 (Fb) and identifiable non-bubbles (Fnb) with a factor that describes the approximate proportion of bubbles (Ab) and non-bubbles (1-Ab) in the sample volume (Figure S3): Fa* = Fb Ab + Fnb (1-Ab) (1) where Ab is a proportion between 0 and 1 and all TS distributions (F) are actual number of detected targets per TS bin. As Ab will change when the ratio of bubbles-to-non-bubbles changes, echosounder data were subdivided into segments based on varying proportions between bubble and non-bubble targets (two example segments shown in Figure S2). Prior to analysis, some non-bubble targets were erased from the echogram using Sonar 5 Pro tools, but only very large fish, fish schools, or other unidentifiable strong backscattering targets that would drastically affect the bubble density assessment. Next, bubbles and nonbubbles were distinguished based on rise velocity. Bubbles appear as diagonal tracks in echograms with a rise velocity between 15 and 50 cm s-1, while non-bubble tracks remain horizontal with almost no vertical velocity component (or <15 cm s-1). Fb and Fnb were then found by computing the bubble and non-bubble frequency distributions. Fa* and Fnb are only approximations of the TS distribution of various types of targets because they are based on the number of identifiable targets (bubbles and non-bubbles, respectively) for each TS bin, which is less than the total number of detected targets (i.e., Fa* > Fb+ Fnb). The latter is related to the fact that rise velocities cannot be accurately computed for short tracks. Ultimately, we aim to find the Fa* that most closely matches the true Fa, which relies on finding the appropriate Ab. The most appropriate Ab is found by testing all possibilities (between 0 and 1) in Eqn. 1 and choosing the one that provides the best correlation between Fa* and the actual Fa. For more details regarding this analysis see Ostrovsky.6 The following procedures are performed per TS bin per segment. We applied a 2-dB binning width for our histogram representation of TS from 30 to 70 db; this range covers all identifiable targets (bubbles and non-bubbles) in Lake Wohlen1. Once Ab is found, it is multiplied by the real Fa to find a new bin-specific Fb that represents the proportion of all bubbles and not just identifiable bubbles. The new Fb per bin is converted to a frequency, fb, by dividing each Fb per bin by the sum of all Fb. Bubble density per bin, Nb [# m-3], is found by multiplying bin-specific fb by the total target density in the insonified water volume, Na [# m-3]. Na was assessed with an echo-integration method, which permits the quantification of densely packed targets such as fish schools5 or bubble plumes.4 Sonar 5 Pro was used for processing the acoustic data collected in the field. This software provides a cross-filter detector7 for determination of single echoes, which further served as a basis for computing the representative TS distribution. The echo-integration method we used to determine Na scaled the total acoustic energy, sv [m-1] (also called the volume backscattering coefficient), of the observed volume of water by the mean σbs [m2]5. Finally, the gas volume represented by each bin, Vb [ml m-3], is found as the product of the mean bubble volume of that bin using the bubble calibration equation (bubble volume as a function of TS, see results) and Nb. The bubble flux (in ml m-2 s-1) is calculated as the product of Vb and the average bubble rise velocity (in m s-1) obtained using the hydroacoustic measurements. The sediment CH4 ebullition flux is calculated per bin by multiplying the bubble flux by the actual CH4 content (in %) measured in gas caught with near-bottom gas S4

5 traps8 and expressed as g CH4 m-2 d-1. Total CH4 flux from the sediment into the water column is found by summing all binned flux estimates. 3. BNS comparison with echo-counting The BNS method was compared with the echo-counting method (i.e., counting of individual targets of known volume) to assess its applicability in nature, and whether it is the most appropriate means to estimate bubble flux in the presence of non-bubble targets. Echointegration is widely used when targets of choice are densely aggregated; whereas the echocounting technique that involves counting all individual targets tends to bias the estimates under such conditions.5 Although echo-counting is not the best method for bubble density analysis (see also Ostrovsky4), we chose to employ it on the longest transect with the widest range of bubble densities in order to explore the relative performance of the BNS echointegration method. Thus, the bubble density for the echo-counting method was computed by counting the identifiable single echoes of bubbles, as opposed to the BNS echo-integration method that first found the bubble proportion of all identified targets and then applied that proportion to the density of all targets. The bubble densities were then used to compute bubble flux and the echo-counting and BNS results were compared. B. Results and Discussion 1. Echosounder bubble calibration We calibrated seven different bubble volumes ranging from to 1 ml (~1 to 12 mm equivalent diameter) with our split-beam echosounder (Table S1). Between 37 and 87 individual bubbles were measured for each bubble volume less than 1 ml and 15 for the 1 ml bubble (due to difficulties in creating 1 ml bubbles). Only bubble echoes from within the center of the transducer beam and within 50 cm of the bubble release point were used, which resulted in 100 to >800 individual TS measurements for each bubble size. The TS distributions for each bubble size across multiple replicates were narrow for the smallest bubbles and became wider for bubbles over 0.01 ml (equivalent diameter, 2.6 mm; Figure S8a), which is likely a result of larger bubbles varying more in shape and wobbling during ascent.1,9 We found, similar to Ostrovsky et al.,1 that σbs and bubble volume, vb, were strongly correlated logarithmically (R2 ~0.92, p<0.001; Figure S8b): log10(σbs) = log10(vb) (4) Therefore, vb can accurately be estimated based on σbs using the transformed equation: vb = σbs (5) The similarity of our calibration results and that in Ostrovsky et al.1 highlights the universality of such a bubble calibration approach, especially because the experimental setups were different. For example, our in situ calibration of a split-beam echosounder was performed in cooler (~15ºC) temperatures with pure CH4 bubbles as opposed to warm (>20ºC) laboratory conditions with only air bubbles and a dual-beam Biosonics echosounder as in Ostrovsky et al.1 Nevertheless, our TS bubble calibration corresponded closely to that from Ostrovsky et al.1 (see Figure S8b). Such a good agreement surpassed our initial expectations as a large fish S5

6 comparison study using several types of echosounders did not show full agreement in natural conditions.10 Our bubble calibration included the range of the most commonly observed CH4 bubble sizes in nature. Leifer and Culling11 found bubbles from natural marine seeps (California coast) produced bubbles ranging from 1.5 to 12 mm in equivalent diameter, while McGinnis et al.3 observed bubble diameters ranging from 1.3 to 11.3 (mean, 4.1 mm) in the Black Sea. Ostrovsky et al.1 hydroacoustically-determined that 90% of bubbles emitted from gassy sediments in Lake Kinneret were between 2.6 and 9 mm in diameter with 50% in the range of 4 to 6.4 mm. The 4 mm diameter range for bubbles is well-represented in our calibration, however producing larger bubbles and accurately measuring their size was a challenging task under field conditions. The relationship between bubble volumes and rise velocity of our measurements (Table S1) also agrees well with previous detailed experiments with air bubbles.1,12 Ultimately, our echosounder bubble calibration was confidently used to explore bubble size distributions in Lake Wohlen and to estimate the CH4 flux. 2. BNS comparison with echo-counting Bubble densities and resulting bubble fluxes computed using the echo-counting method were typically less than those obtained using the BNS method. Figure S9 portrays segmentspecific ebullition fluxes computed by both methods for a transect with high variability of bubble emissions. The data points positioned below the black 1:1 line show that echocounting consistently underestimated fluxes compared to echo-integration. This suggests that bubbles are frequently emitted from the bottom as clusters or in plumes, i.e., quite close together, and thus are not separated enough to be detected as single targets. The example transect shown in Figure S9 containing the largest variability in fluxes illustrates the shortcoming of the echo-counting method for quantification of gas emission in presence of fish or other backscattering targets. Ostrovsky4 has shown that echo-counting and echointegration provided similar densities in the epilimnion of Lake Kinneret where bubbles were well-separated and could be identified as single targets. Echo-counting underestimated bubble density in the hypolimnion because more multiple echoes were present closer to the bottom. This supports the notion that echo-integration via the BNS method is the better bubble density analysis method when calculating flux from bubble plumes. S6

7 C. Tables Table S1. Echosounder bubble calibration results Bubble Volume Analysis Bubble TS Analysis Bubbles Bubble Needle Standard Bubbles Standard Rise (echoes) Volume equivalent TS size deviation analyzed deviation speed analyzed diameter [mm] [#] [ 10-3 ml] ml 30 (469) 15 (276) 15 (299) 15 (475) 16 (491) 16 (861) 40 (146) [ 10-3 ml] [mm] [#] [db] [db] [m s-1] D. Figures Figure S1. Location of the run-of-river reservoir Lake Wohlen in Switzerland and Central Europe. White box in full lake photo delineates the field area shown in map below. Depth contours in meters are shown with darker shades representing shallower depths. S7

8 Figure S2. Example segments from 23 July 2008 showing sediment ebullition fluxes: a high flux (7100 mg CH4 m-2 d-1) and a low flux (230 mg CH4 m-2 d-1). (a) The full energy amplitude from all echoes in the segment. (b) The single echo detections (SED) used to scale sv, volumetric backscattering, during echointegration or the only echoes used during echo-counting. Bottom axis is ping number. Colorbar is of TS in db. Very right axis (z) is depth in m. (c) TS distributions for identifiable bubbles, non-bubbles, and all SEDs. (d) Real Fa (frequency distribution of all SEDs) in red and how it compares with the modeled Fa (or Fa*), which is based off of equation 1 (see extended methods and Figure S3 for more details). (e) Frequency of the number of bubbles per TS bin and the contribution of each TS bin to the total volume from all bubbles. Higher TS bubbles contribute the most volume despite existing much less in number. S8

9 Figure S3. Schematic of Bubble/non-bubble separation (BNS) method, based on the method described in Ostrovsky (2009). Details for steps 1-7 are in white boxes and ultimate results from each step are in blue boxes. Images from Figure S2 and methods in text will aid in understanding the method. S9

10 Figure S4. Weighted mean (a) and Sauter mean (b) diameter distributions of the bubble populations in each Lake Wohlen segment (gray bars). Contribution of each diameter to total flux volume (black line) highlights the importance of large bubbles for methane transport. Figure S5. Weighted mean diameter (WMD, gray circles) of the segment bubble population correlates better with maximum depth of the segment than the Sauter mean diameter (SMD, black dots). S10

11 Figure S6. Three surveys from June 2008 illustrating the weighted mean diameter (WMD) of the bubble population in each segment (represented by the size of the dot) and the sediment ebullition flux measured in each segment (represented by the color of the dot). Contours below dots represent depth with darker shades being shallower (see Figure S1 in SI for true depths). Figure S7. Surface area-to-volume ratio (in mm-1) of bubbles from 2 to 30 mm in diameter. S11

12 Figure S8. Echosounder bubble calibration results. (a) Target strength (TS) distribution for 7 bubble sizes. n is number of echoes analyzed. (b) Calibration results for Ostrovsky et al. (2008) (open circles) and our study (closed circles with error bars) displayed as bubble volume (ml) versus echosounder signature called the backscattering cross-section (σbs, m2). TS is related to σbs in the following manner: TS = 10*log(σbs). S12

13 Figure S9. (a) Echo-counting consistently underestimates bubble flux compared to the bubble/non-bubble separation (BNS) method because only identifiable bubbles are used in an echo-counting procedure, which is usually less than the true bubble population. S13

14 E. References (1) Ostrovsky, I.; McGinnis, D. F.; Lapidus, L.; Eckert, W. Quantifying gas ebullition with echosounder: the role of methane transport by bubbles in a medium-sized lake. Limnol. Oceanogr. Methods 2008, 6, (2) Maclennan, D. A consistent approach to definitions and symbols in fisheries acoustics. ICES J. Mar. Sci. 2002, 59, (3) McGinnis, D. F.; Greinert, J.; Artemov, Y.; Beaubien, S. E.; Wüest, A. Fate of rising methane bubbles in stratified waters: How much methane reaches the atmosphere? J. Geophys. Res. 2006, 111, C (4) Ostrovsky, I. Methane bubbles in Lake Kinneret: Quantification and temporal and spatial heterogeneity. Limnol. Oceanogr. 2003, 48, (5) Simmonds, J.; Maclennan, D. Fisheries Acoustics: Theory and Practice, 2nd ed.; Blackwell Science Ltd: Oxford, 2005; p (6) Ostrovsky, I. Hydroacoustic assessment of fish abundance in the presence of gas bubbles. Limnol. Oceanogr. Methods 2009, 7, (7) Balk, H.; Lindem, T. Improved fish detection in data from split-beam sonar. Aquat. Living Resour. 2000, 13, (8) Delsontro, T.; McGinnis, D. F.; Sobek, S.; Ostrovsky, I.; Wehrli, B. Extreme methane emissions from a Swiss hydropower reservoir: contribution from bubbling sediments. Environ. Sci. Technol. 2010, 44, (9) Clift, R.; Grace, J. R.; Weber, M. E. Bubbles, Drops, and Particles.; Academic Press: New York, 1978; p (10) Mason, D. M.; Schaner, T. Great Lakes Acoustics Workshop IV: Inter-Calibration of Scientific Echosounders in the Great Lakes; 2001; pp (11) Leifer, I.; Culling, D. Formation of seep bubble plumes in the Coal Oil Point seep field. Geo-Marine Lett. 2010, 30, (12) Haberman, W. L.; Morton, R. K. An experimental study of bubbles moving liquids. Proc. Am. Soc. Civ. Eng. 1954, 80, S14