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Different Apparent Gas Exchange Coefficients for CO2 and CH4: Comparing a Brown-Water and a Clear-Water Lake in the Boreal Zone during the Whole Growing Season Miitta Rantakari,*,†,# Jouni Heiskanen,† Ivan Mammarella,† Tiina Tulonen,‡ Jessica Linnaluoma,§ Paula Kankaala,∥ and Anne Ojala§,⊥ †

Department of Physics, University of Helsinki, P.O. Box 48, 00014 University of Helsinki, Finland Department of Environmental Sciences, University of Helsinki, P.O. Box 65, 00014, Finland ‡ Lammi Biological Station, University of Helsinki, 16900 Lammi, Finland § Department of Environmental Sciences, University of Helsinki, Niemenkatu 73, 15140 Lahti, Finland ∥ Department of Biology, University of Eastern Finland, Joensuu Campus, Tulliportinkatu 1, 80130 Joensuu, Finland ⊥ Department of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 University of Helsinki, Finland #

ABSTRACT: The air−water exchange of carbon dioxide (CO2) and methane (CH4) is a central process during attempts to establish carbon budgets for lakes and landscapes containing lakes. Lake−atmosphere diffusive gas exchange is dependent on the concentration gradient between air and surface water and also on the gas transfer velocity, often described with the gas transfer coefficient k. We used the floating-chamber method in connection with surface water gas concentration measurements to estimate the gas transfer velocity of CO2 (kCO2) and CH4 (kCH4) weekly throughout the entire growing season in two contrasting boreal lakes, a humic oligotrophic lake and a clear-water productive lake, in order to investigate the earlier observed differences between kCO2 and kCH4. We found that the seasonally averaged gas transfer velocity of CH4 was the same for both lakes. When the lakes were sources of CO2, the gas transfer velocity of CO2 was also similar between the two study lakes. The gas transfer velocity of CH4 was constantly higher than that of CO2 in both lakes, a result also found in other studies but for reasons not yet fully understood. We found no differences between the lakes, demonstrating that the difference between kCO2 and kCH4 is not dependent on season or the characteristics of the lake.

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the gas transfer coefficient k.20 In theory, k varies as a function of the turbulent energy exchange between surface water and the atmosphere.21,22 Cole et al.23 demonstrated that k can be measured, using simple chambers, and since CH4 is not affected by algal production and is chemically inert, it is better than CO2 for determining k. However, since CH4 is much less soluble in water than CO2, it occurs in bubble form more often than CO2. The occurrence of macrobubbles in lakes is a well-known phenomenon,10,24,25 and in the surface water of oceans, the formation of microbubbles as a result of wave action has been shown to occur.26−28 These oceanic microbubbles have been assumed to contain a mixture of the atmospheric gases, and furthermore, it has been suggested that the bubble-mediated supersaturation will be greatest for the least soluble gases.29

INTRODUCTION Air−water exchange of CO2 is a central process during attempts to establish carbon budgets of lakes in landscapes up to the global level.1 The efflux of methane CH4 is a minor factor in terms of carbon budget, but due to its high global warming potential, CH4 is a very significant greenhouse gas released from lakes.2 Lakes are usually supersaturated with CO2 and CH4 and are therefore sources of these gases to the atmosphere.3−10 However, in productive lakes algal photosynthesis during the summer months decreases the efflux of CO2 or turns lakes from net sources to net sinks of CO2.6,11−13 Similarly, CH4 also has a biological origin in microbial processes, being produced by archaeal methanogenesis.14−16 CH4 is also biologically consumed by methanotrophic bacteria,17−19 but in contrast to CO2, phytoplankton uptake does not alter epilimnetic CH4 concentrations. Diffusive lake−atmosphere gas exchange is dependent not only on the concentration gradient between air and surface water but also on the gas transfer velocity, often described with © XXXX American Chemical Society

Received: March 20, 2014 Revised: September 11, 2015 Accepted: September 11, 2015

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DOI: 10.1021/acs.est.5b01261 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology More recent work has suggested that microbubbles may enhance kgas.30 Prairie and del Giorgio31 observed higher k600 values for CH4 than for CO2 and suggested that the presence of semistable CH4 microbubbles may cause higher evasion rates than strictly Fickian diffusive processes suggest. Campeau et al.32 observed higher CH4 emission in streams and small rivers in northeastern Canada than is possible by the diffusive transport of the gas. Furthermore, Beaulieu et al.33 observed differences in k600 values measured with CH4 and CO2 in large rivers, and although their finding did not completely support the microbubble theory, they strongly suggested the need for further measurements of k with both CH4 (kCH4) and CO2 (kCO2) to achieve better understanding of the phenomenon. McGinnis et al.34 found a strong dependence between kCH4 and wind speed, and furthermore, the difference between kCH4 and kCO2 increased with increasing wind speed, suggesting that higher turbulence created more microbubbles and enhanced the transfer of CH4 into the atmosphere. The physics of boundary-layer and gas exchange is complex, and there are great uncertainties in models predicting k. According to the latest studies, k is mediated by turbulence caused not only by wind speed but also by convection.35−37 Convection occurs when surface water loses heat to the atmosphere and resulting colder and denser surface water starts to sink, creating convective cells. This could lead to seasonal differences in k if autumnal cooling-driven convection enhances the gas transfer significantly. It has been estimated that compared to clear-water lakes, dark-water lakes have higher epilimnetic temperatures, leading to higher heat losses,38 which could contribute to higher k due to increased convection. Lopez Bellido et al.39 observed different k values for spring and autumn mixing periods. High-frequency direct measurements of gas concentrations and fluxes from which k can be derived are still rare, and thus widely used, unreliable models hinder development of accurate carbon budgets. In the present study, we calculated the gas transfer velocity from the weekly measurements of gas concentrations and fluxes throughout the growing season in two boreal lakes of differing water quality and water color. Lake Päaj̈ ärvi, with low phosphorus and high nitrogen concentration, is a dark-colored, humic lake with pH close to neutral, whereas Lake Ormajärvi nearby is a clear-water lake with a higher phosphorus concentration and a naturally high pH.40,41 We measured the gas transfer velocity of CH4 (kCH4) and CO2 (kCO2) to investigate the previously observed difference between kCO2 and kCH4, which has been suggested to result from the formation of semistable microbubbles by CH4. Theoretically, if the transfer of CH4 is enhanced by microbubbles, the formation of CH4-rich microbubbles should be at the highest when both CH4 supersaturation in water and the windspeed are high, and thus, simultaneously also the difference between kCO2 and kCH4 should be at its highest. Furthermore, we wanted to explore the role of convection in the gas exchange during different seasons in lakes with contrasting water color and thus differing thermal stratification.

Table 1. Characteristics of the Study Lakes humic lake (HL)

clearwater lake (CL)

Lake Päaj̈ ärvi

Lake Ormajärvi

Lake Morphometry lake area (LA) (km2) 13.4 maximum depth (m) 85 mean depth (m) 14.4 retention time (yr) 3.3 lake volume (106 m−3) 206 Catchment catchment area (CA) (km2) 199 CA/LA 15 forest (%) 59 peatland (%) 11 water (%) 6.7 agricultural area (%) 18 built-up area (%) 0.4 other (%) 4.9 Water Quality total nitrogen TN (μg L−1) 1351 total phosphorus (TP) (μg L−1) 9.9 DOC (mg L−1) 12.3 Chl a (μg L−1) 4.41 pH 7.2 water color (mg pt L−1) 100

6.53 30 10.7 2.9 67 116 18 55 6 5.6 26 3 4.4 756 16.9 7.6 8.1 7.5 20

terms of land use, the catchments are similar, the largest difference being in the proportion of peatlands and urban settlements; the first is higher in HL and the latter in CL (Table 1). There are clear differences in soil types, since the soil in the HL catchment consists mainly of unsorted deposits and bedrock outcrops, whereas in the CL catchment sorted glaciofluvial deposits predominate.42 CL is more productive, with higher concentrations of chlorophyll a and total phosphorus than HL.39 There is an extensive littoral vegetation zone surrounding CL with high diversity of aquatic plants. Furthermore, cyanobacterial blooms are regular in CL. The water color and dissolved organic carbon (DOC) concentration are higher in HL, and furthermore, dissolved organic matter (DOM) is suggested to be more recalcitrant in HL.40 Due to humic acids in HL and calcerous geological settings of CL, the alkalinity is higher in CL.40 Measurements. Both lakes were sampled weekly in 2004, HL at the beginning of the workweek and CL in the middle of the week. The sampling points were in the middle of the lakes, ensuring the longest possible fetches. Sampling was initiated in early May, and the last samples were taken in mid-November. The weekly samples were collected between 8:00 h and 11:00 h (solar time; Greenwich Mean Time +2). For the chamber measurements, we used three parallel chambers (height 12.5 cm and volume 4.9 L) made of acrylic plastic and equipped with a single sampling port and a digital thermometer. Both CO2 and CH4 were sampled simultaneously from the same chambers. Chamber placement allowed 2 cm of the walls to penetrate under the water surface. Four gas samples were drawn from each chamber every 10 min during the 30 min sampling period. The samples were drawn into 60 mL polypropylene syringes equipped with three-way stopcocks. Both CO2 and CH4 were analyzed from the same syringe in the laboratory with a gas chromatograph (GC) (see below for details of the apparatus) without any pretreatment. The floating-chamber (FC) technique used in this study is explained

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MATERIALS AND METHODS The Study Lakes. The study lakes are situated in southern Finland 5 km apart. The humic Lake Päaj̈ ärvi (hereafter referred to as humic lake, HL) is larger (13.4 km2) and deeper (maximum depth 87 m) than the clear-water Lake Ormajärvi (hereafter referred to as clearwater lake, CL), with a surface area of 6.53 km2 and maximum depth of 30 m (Table 1). In B

DOI: 10.1021/acs.est.5b01261 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Table 2. Average k600 Converted Gas Exchange Coefficients Measured with either CO2 (k600CO2) or with CH4 (k600CH4) (range in parentheses)

a

k (cm h−1)

k600CO2

k600CH4

HL, all data HL, WSa < 4 m s−1 HL, WS > 4 m s−1 CL, all data CL, WS < 4 m s−1 CL, WS > 4 m s−1 HL + CL HL + CL, WS < 4 m s−1 HL + CL, WS > 4 m s−1

7.8 (2.2−12.1) 7.3 (2.2−10.7) 8.6 (6.5−12.1) 7.4 (2.3−13.0) 3.9 (2.3−6.4) 10.9 (7.6−13.0) 7.7 (2.2−14.1) 6.1 (2.2−10.7) 9.4 (6.5−13.0)

14.1 (4.5−60.4) 12.2 (4.5−60.4) 16.4 (9.5−32.5) 12.6 (4.7−27.6) 9.5 (4.7−16.2) 16.5 (7.6−27.6) 13.3 (4.5−60.4) 10.1 (4.5−60.4) 16.5 (7.6−32.5)

k600CH4/k600CO2 1.8 1.8 1.9 1.8 2.2 1.5 1.8 1.9 1.7

(1.1−3.4) (1.1−2.8) (1.3−3.4) (1.1−3.3) (1.1−3.3) (1.1−2.1) (1.0−6.8) (1.1−3.3) (1.1−3.4)

WS = wind speed.

in detail by Kankaala et al.43 The average of the fluxes calculated from these three parallel chamber measurements was used as a flux estimate for each sampling occasion. In the parallel flux measurements, the variation between the chambers was relatively small, and the coefficient of variation (CV) of three parallel flux measurements varied between 0.0031 and 0.85, the median CV of all the parallel measurements being 0.14. The samples for the concentrations of CO2 and CH4 in the surface water (CCO2, CCH4) were taken with a Limnos water sampler (total volume 2.1 L) from the surface (0−30 cm) of both lakes. These samples were measured for gas concentrations (CCO2 and CCH4) with the headspace equilibrium technique as described by Ojala et al.40 Two replicates of water samples (volume 30 mL) were drawn into 60 mL polypropylene syringes, which were closed with three-way stopcocks after removing any gas bubbles. The syringes were kept in crushed ice until further processing in the laboratory within 1 h of arrival. For determination of the dissolved gases, the duplicated water samples were placed in a 20 °C water bath for 5 min before 50 mL of N2 gas was added to the headspace of the syringes. The syringes were then shaken vigorously. Replicate 20 mL subsamples from the well-mixed headspace were injected into preevacuated 12 mL Exetainer vials (Labco Ltd., Lampeter, Ceredigion, UK). The samples from the overpressurized vials were then delivered by a Gilson 222 XL autosampler (Gilson Inc., Middleton, WI) through a 1 mL Valco 10-port valve (VICI Valco Instrument Co. Inc., Houston, TX) into an Agilent 6890 N (Agilent Technologies, Santa Clara, CA) GC equipped with a flame-ionization detector (temperature 210 °C) and thermal conductivity detector (temperature 120 °C, oven 40 °C, PlotQ capillary column, flow rate 12 mL min−1, He as a carrier gas). The GC was calibrated with CO2 at concentrations of 103 and 999 μmol mol−1 and with CH4 at concentrations of 10 and 493 ppm (ppm) (Oy AGA Ab, Espoo, Finland). The concentration values in situ were calculated using the appropriate temperature relationships for gas solubility and Henry’s law.44 This technique is not suitable for net CO2 intake situations, because the reaction CO2 + H2O ↔ H2CO3 ↔ HCO3− is reasonably fast in the case of dehydration; i.e., bicarbonate is transformed to CO2 when samples are subsaturated with respect to CO2.45 In the supersaturation situations, chemical transformation of CO2 is not a problem, because hydration of CO2 is a very slow reaction.46 Thus, for further k calculations, we only used values obtained when the lakes were net sources of CO2. Wind-speed data were obtained from the nearest measuring station in Hämeenlinna operated by the Finnish Meteorological

Institute. The measuring station is situated about 40 km west of the study lakes. Wind-speed recordings were taken every 15 min and were averaged over the daily sampling period. During the study period, the average wind speed was very similar for the study sites, 4.0 m s−1 for HL and 3.9 m s−1 for CL. Calculation of the Gas Transfer Coefficient. We estimated the gas transfer velocity k using the FC technique in connection with surface water gas concentration (Cgas) measurements. The value for k can be calculated according to the method of Wanninkhof and Knox47 Fgas = kgas(Cgas − Ceq)

(1)

where Fgas is the air−water flux of the gas studied (here CO2 or CH4), kgas is the transfer velocity for the given gas, Cgas is the concentration of the gas in the surface water, and Ceq is the concentration of the gas in the surface water when in equilibrium with the atmosphere. The equilibrium concentrations (Ceq) were calculated according to Henry’s law with the constants corrected to the in situ temperature, using the initial concentration of the gas in the chamber as an approximation for the atmospheric concentration. Jähne et al.48 presented that k values for any gas can be converted into k values of any other gas by the ratio of the Schmidt numbers kgas1/kgas2 = (Scgas1/Scgas2)−n

(2)

where Scgas is the Schmidt number for a given gas at a given temperature and n = 2/3 for wind speed 3.7 m s−1.48 We used the ScCO2 given by Jähne et al.48 and the ScCH4 by Jähne et al.48 and Wania et al.49 The kCO2 and kCH4 values are often standardized to a Schmidt number (Sc) of 600, which corresponds to kCO2 at 20 °C. To compare the measurements regardless of the measuring season (i.e., in situ temperature), we calculated k600 values utilizing eq 3 as follows: k600 = kgas(600/Scgas)−n

(3)

These values standardized to a Schmidt number of 600, k600CO2 and k600CH4, were used for all the analysis. Statistical Analysis. The Pearson correlation coefficients were determined between the gas supersaturation ratio (Cgas/ Ceq) and the kgas. Furthermore, k600CO2 and k600CH4 were compared using the t-test (p < 0.05) both within lake and interlake. The interdependencies of k600CH4, k600CH4, and k600CH4/k600CO2 ratio with supersaturation rate and wind speed were modeled with stepwise multiple regression model. C

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Environmental Science & Technology All the statistical analyses were performed with SAS 9.3 for Windows software.50

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RESULTS HL acted as a source of CO2 throughout the growing season, apart from three sampling occasions when the lake was a small sink of CO2.40 Until late July, CL acted mainly as a sink of CO2, after which the lake turned from a sink to a clear source of CO2.40 Both lakes were small sources of CH4 throughout the growing season.40 The surface water pH varied between 7.2 and 7.6 in HL, whereas the corresponding pH variation in CL was 7.5−8.0. The ratio of supersaturation (Cgas/Ceq) with respect to CO2 varied between 1.3 and 3.7 (average 2.2) in HL and between 1.3 and 3.3 (average 2.1) in CL (subsaturated samples dismissed). Correspondingly, p(CO2) varied between 684 and 1486 μatm (942 μatm, 0.24) in CL and between 575 and 1674 μatm (927 μatm, 0.26) in HL, where the mean and CV are respectively in parentheses. The average CH4 fluxes in the study lakes were 0.096 mmol m−2 d−1 in HL and 0.160 mmol m−2 d−1 in CL. The lakes were always supersaturated with respect to CH4; the average supersaturation ratio (Cgas/Ceq) was 14 (4.3−37) in HL and 24 (3.1−70) in CL. The partial pressure p(CH4) varied between 5.1 and 119 μatm (37 μatm, 0.56) in CL and between 3.2 and 78 μatm (22 μatm, 0.76) in HL, where the mean and CV are respectively in parentheses. In both lakes, k600CH4 was always higher than k600CO2 (Table 2, Figures 1 and 2) (one measurement of k600CH4 was omitted, since the CCH4 value was very close to the detection limit), and there were no significant differences in average k600CH4/k600CO2

Figure 2. Relationship between k600CH4 (k measured with CH4) and k600CO2 (k measured with CO2) for CL and HL. The 1:1 line is displayed. Please note that one observation with kCH4 > 40 was removed, because the measured CH4 was at the detection limit.

values between the lakes (Table 2). In CL, the data from early summer when the lake was a sink of CO2 had to be left out from k600CO2 calculations because of the problems with the CCO2 measurements, and also in HL, three measurement occasions were dismissed for the same reason. Calculated k600CO2 was very similar in both lakes. Although the average k600CH4 appeared slightly higher in HL than in CL, the difference was not statistically significant (Table 2). In terms of inter- and intralake comparison, the values for k600CH4 and k600CO2 were significantly different (t-tests, p = 0.001 and 0.05, respectively). In both lakes, the difference between k600CH4 and k600CO2 was smallest in August and in late September/early October, when k600CH4 and k600CO2 were nearly the same. In HL, the difference was small also in the turn of June and July and in CL in early November (Figure 1). In HL there were two occasions when k600CH4 was markedly greater than k600CO2, in early June and in late July (Figure 1), the first occurring at the same time with very low surface water CH4 concentration; i.e., the concentration was very near the detection limit (Figure 3). We did not find a significant relationship between the supersaturation ratio of CH4 and k600CH4 (Pearson correlation coefficient, r = 0.019, p > 0.05), suggesting that high supersaturation did not contribute to a high k600CH4 value (Figure 3) nor did the multivariate regression analysis suggest any contribution of supersaturation of CH4 to k600CH4. However, we found a positive correlation between k600CH4 and k600CO2 in both lakes (r = 0.66, p < 0.001 in HL and r = 0.78, p < 0.001 in CL, respectively) and a positive correlation between wind speed and both k600CH4 and k600CO2 (Figure 4). The wind-speed-derived increment in k600 values was similar for CH4 and CO2 (i.e., slopes of the regression equations were similar) (Figure 4), and also the k600CH4/k600CO2 ratio remained rather constant, independent of the wind speed (Table 2).

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DISCUSSION Many authors have suggested that the FC technique is a costefficient and reliable method for estimating the k600 values for different gases,22,23 even though under conditions with very low turbulence it may overestimate the k600 values.22 The k600 values

Figure 1. Gas transfer coefficients (cm h−1) in (a) CL and (b) HL calculated from the chamber measurements of CO2 and CH4. D

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we measured in this study were higher (average k600CH4 = 13.3 ± 1.2 cm h−1, average k600CO2 = 7.7 ± 2.1 cm h−1) than those reported by Cole et al.,23 who used the same method but with incubation times of 24 h (average 1.75 ± 0.025 cm h−1) and only CH4 measurements. In our study, the lakes were considerably larger with longer fetches and thus, presumably, were exposed to higher wind shear, resulting in higher aquatic turbulence.46 However, our results were lower than the k600 measured by Vachon et al.22 [(15.8 ± 4.8)−(19.5 ± 7.0) cm h−1], who included also large lakes but whose measurements were performed only with CO2. Our results of k600CH4 were in good agreement with the estimations by McGinnis et al.,34 but the k600CO2 results in their study were lower than our estimations. Theoretically, k600CO2 should be the same or greater than k600CH4 because the diffusion speeds of these gases are nearly the same and k600CH4 is not affected by chemical enhancement.48 However, the k600CH4 values were on average 1.8 times higher (Table 2) or, in terms of gas transfer velocity, on average 4.8 cm h−1 higher than the k600CO2, and there were no interlake differences (average k600CH4/k600CO2 1.8 both in HL and CL). This is in agreement with the observations by Prairie and del Giorgio31 showing 2.3 times or 2.1 m d−1 higher gas transfer velocity of CH4. In HL, there were two distinctive peaks in k600CH4 in late May and in mid-June co-occurring either with very high CH4 flux or with very low CH4 concentration in water, i.e., the concentration being near the detection limit and thus unreliable. We observed higher k600 values for CH4 than for CO2, corresponding to the results of Prairie and del Giorgio31 and of McGinnis et al.,34 who suggested that the presence of semistable CH4 microbubbles was the reason behind the higher evasion rates of CH4 and thus higher k600. We found that k600CH4 and k600CO2 were correlated in the study lakes (with the highest values dismissed), similar to the results of Beaulieu et al.,33 who suggested that this correlation served as evidence against the microbubble theory, because one would not expect the microbubbles to be constantly present at the same proportion. Also the reasonably steady k600CH4/k600CO2 ratio (∼2) in our study suggests either a rather constant presence of microbubbles in the surface water or, more likely, some other explanation for the difference between k600CH4 and k600CO2. The results showed no evidence of macrobubbles being the contributors to higher k600CH4, because the increase of both CH4 and CO2 in the chambers was mainly linear through the 30 min measurements. For the few occasions of nonlinear increase, these measurements were dismissed as outliers. Prairie and del Giorgio31 proposed higher microbubble formation when water is clearly supersaturated with CH4,31 but we did not observe higher k600CH4 when lakes were highly supersaturated with respect to CH4 (Figure 3) nor did the multivariate regression analysis suggest any contribution of supersaturation of CH4 to k600CH4. McGinnis et al.34 found a strong dependence between k600CH4 and wind speed and, furthermore, an increasing difference between k600CH4 and k600CO2 with an increasing wind speed, suggesting that higher turbulence created more microbubbles and enhanced the transfer of CH4 into the atmosphere. However, unlike McGinnis et al.,34 we did not find a significant increase of k600CH4 with an increasing wind speed or a significant difference between the wind speed dependence of k600CH4 and k600CO2. Although k600CH4 was mainly clearly higher than k600CO2, we observed several occasions when k600CH4

Figure 3. Methane concentrations (CH4 gas) and the concentration in water in equilibrium with the atmosphere (CH4 equiv) and the k600CH4 in (a) CL and (b) HL.

Figure 4. Relationship between wind speed (m s−1) and k600CH4 (cm h−1) and k600CO2 (cm h−1) in (a) CL and (b) HL. E

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(5) Smith, L. C.; Lewis, M. W. Seasonality of methane emissions from five lakes and associated wetlands of the Colorado Rockies. Glob. Biogeochem. Cycles 1992, 6, 323−338. (6) Cole, J. J.; Caraco, N. F.; Kling, G. W.; Kratz, T. K. Carbon dioxide supersaturation in the surface waters of the lakes. Science 1994, 265, 1568−1570. (7) Hope, D.; Kratz, T. K.; Riera, J. L. Relationship between PCO2 and dissolved organic carbon in Northern Wisconsin lakes. J. Environ.Qual. 1996, 25, 1442−1445. (8) Del Giorgio, P. A.; Cole, J. J.; Cimbleris, A. Respiration rates in bacteria exceed phytoplankton production in unproductive lakes. Nature 1997, 385, 148−151. (9) Striegl, R. G.; Michmerhuizen, C. M. Hydrologic influence on methane and carbon dioxide dynamics at two north-central Minnesota lakes. Limnol. Oceanogr. 1998, 43, 1519−1529. (10) Bastviken, D.; Cole, J. J.; Pace, M.; Tranvik, L. Methane emissions from lakes: Dependence of lake characteristics, two regional assessments, and a global estimate. Glob. Biogeochem. Cycles 2004, 18, GB4009. (11) Hanson, P. C.; Pollard, A. I.; Bade, D. L.; Predick, K.; Carpenter, S. R.; Foley, J. A. A model of carbon evasion and sedimentation in temperate lakes. Global Change Biol. 2004, 10, 1285−1298. (12) Balmer, M. B.; Downing, J. A. Carbon dioxide concentrations in eutrophic lakes: undersaturation implies atmospheric uptake. Inland Waters 2011, 1, 125−132. (13) Huotari, J.; Ojala, A.; Peltomaa, E.; Nordbo, A.; Launiainen, S.; Pumpanen, J.; Rasilo, T.; Hari, P.; Vesala, T. Long term direct CO2 flux measurements over a boreal lake: Fiver years of eddy covariance data. Geophys. Res. Lett. 2011, 38, L18401. (14) Kelly, C. A.; Dise, N. B.; Martens, C. S. Temporal variations in the stable carbon isotopic composition of methane emitted from Minnesota peatlands. Glob. Biogeochem. Cycles 1992, 6, 263−269. (15) Schulz, S.; Conrad, R. Influence of temperature on pathways to methane production in permanently cold profundal sediment of Lake Constance. FEMS Microbiol. Ecol. 1996, 20, 1−14. (16) Avery, G. B.; Shannon, R. D.; White, J. R.; Martens, C. S.; Alperin, M. J. Effects of seasonal changes in the pathways of methanogenesis on the δ13C values of pore water methane in a Michigan peatland. Glob. Biogeochem. Cycles 1999, 13, 475−484. (17) Rudd, J. W. M.; Hamilton, R. D.; Campbell, N. E. Measurement of microbial oxidation of methane in lake water. Limnol. Oceanogr. 1974, 19, 519−524. (18) Harrits, S. M.; Hanson, R. S. Stratification of aerobic methaneoxidizing organisms in Lake Mendota, Madison, Wisconsin. Limnol. Oceanogr. 1980, 25, 412−421. (19) Rudd, J. W. M.; Taylor, C. D. Methane cycling in aquatic environments. Adv. Aquat. Microbiol. 1980, 2, 77−150. (20) Jähne, B.; Haußecker, H. Air-water gas exchange. Annu. Rev. Fluid Mech. 1998, 30, 443−468. (21) McKenna, S. P.; McGillis, W. R. The role of free-surface turbulence and surfactants in air-water gas transfer. Int. J. Heat Mass Transfer 2004, 47, 539−553. (22) Vachon, D.; Prairie, Y. T.; Cole, J. J. The relationship between near-surface turbulence and gas transfer velocity in freshwater systems and its implications for floating chamber measurements of gas exchange. Limnol. Oceanogr. 2010, 55, 1723−1732. (23) Cole, J. J.; Bade, D. L.; Bastviken, D.; Pace, M. L.; Van de Bogert, M. Multiple approaches to estimating air-water gas exchange in small lakes. Limnol. Oceanogr.: Methods 2010, 8, 285−293. (24) Mattson, M. D.; Likens, G. E. Air pressure and methane fluxes. Nature 1990, 347, 718−719. (25) Fendinger, N. J.; Adams, D. D.; Glotfelty, D. E. The role of gas ebullition in the transport of organic contaminants from sediments. Sci. Total Environ. 1992, 112, 189−201. (26) Johnson, B. D.; Cooke, R. C. Generation of stabilized microbubbles in seawater. Science 1981, 213, 209−211. (27) Medwin, H. In situ acoustic measurements of bubble populations in coastal ocean waters. J. Geophys. Res. 1970, 75, 599− 611.

and k600CO2 were the same or only slightly different, for example, in both lakes between late September and early October. However, we could not find a single explanation for this phenomenon; for example, the wind speed varied from low ∼2 m s−1 to high >5 m s−1 during those days. In autumn, when the lake water is cooling after summer, convection might be an important variable affecting the gas exchange.37,51 However, we observed no clear seasonal trend in k nor did we observe differences in k between the lakes with different water clarities. While these observations could indicate that heat flux was a minor component in gas transfer in our study, the implication is more probably that weekly sampling is not precise enough to capture complex processes behind the gas exchange, and thus, there is a need for direct and continuous measurements of these gases and heat fluxes, such as the eddy covariance technique.52,53 Furthermore, the boundary layer where the gas exchange occurs is very thin, and therefore, it is difficult to measure the gas concentrations in the boundary layer. Thus, the Cgas concentrations used for kgas calculations are typically measured deeper in the watercolumn, possibly causing bias to the k results. For out-scaling purposes, greenhouse gas fluxes often are calculated from the surface water concentrations according to eq 1. Therefore, it is very important that all factors used in the equation are correct to avoid distorted estimates. Our results do not lend strong support to the microbubble theory. The results also suggest that we need studies designed to resolve the chemistry of CH4 as well as CO2 in water in order to find an explanation for the difference between k600CO2 and k600CH4.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: miitta.rantakari@helsinki.fi; phone: +358-504486479. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Lammi Biological Station for the working facilities. This study was funded by the Academy of Finland projects BORWET (No. 201623), TRANSCARBO (No. 1116347), and PACE (No. 139291) and the Nordic Centre of Excellence for Studies of Ecosystem Carbon Exchange (NECC). The study was supported by EU projects InGOS and GHG-LAKE (project no. 612642), Nordic Centre of Excellence DEFROST, and National Centre of Excellence (272041), ICOS (271878), ICOS-FINLAND(281255), ICOS-ERIC (281250), CarLAC (281196).

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DOI: 10.1021/acs.est.5b01261 Environ. Sci. Technol. XXXX, XXX, XXX−XXX