Continuous Measurement of Diffusive and Ebullitive Fluxes of

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Environmental Measurements Methods

Continuous measurement of diffusive and ebullitive fluxes of methane in aquatic ecosystems by an open dynamic chamber method Oscar Alejandro Gerardo-Nieto, Abner Vega-Peñaranda, Rodrigo Gonzalez-Valencia, Yameli Azeneth Alfano-Ojeda, and Frederic Thalasso Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b00425 • Publication Date (Web): 28 Mar 2019 Downloaded from http://pubs.acs.org on March 29, 2019

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Environmental Science & Technology

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Continuous measurement of diffusive and ebullitive

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fluxes of methane in aquatic ecosystems by an open

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dynamic chamber method

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Oscar Gerardo–Nieto,† Abner Vega–Peñaranda,‡ Rodrigo Gonzalez–Valencia,† Yameli Alfano–

5

Ojeda,† Frederic Thalasso*,†

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†

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Pedro Zacatenco, 07360 Mexico

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‡

Department of Biotechnology and Bioengineering, Cinvestav, Mexico City, Av. IPN 2508, San

University of Francisco de Paula Santander, Av. Gran Colombia 12E-96, San José de Cúcuta,

Colombia

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* Corresponding author: [email protected]

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KEYWORDS: Lakes, laser spectroscopy, spatiotemporal, carbon dioxide, long-term.

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Abstract

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An open dynamic chamber for the continuous monitoring of diffusive and ebullitive fluxes of

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methane (CH4) in aquatic ecosystems was designed and developed. This method is based on a

ACS Paragon Plus Environment

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standard floating chamber in which a well–defined carrier gas flows. The concentration of CH4 is

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measured continuously at the outlet of the chamber, and the flux is determined from a mass balance

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equation. The method was carefully tested in a laboratory and was subsequently applied to two

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lakes, in Mexico, with contrasting trophic states. We show here that the method allows for the

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continuous quantification of CH4 diffusive flux higher than 25 × 10-6 g m-2 h-1, the determination

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of ebullitive flux, and the individual characterization of bubbles larger than 1.50–1.72 mm in

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diameter. The method was also applied to determine carbon dioxide emissions (CO2). In that case,

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the method was less sensitive but allowed for the characterization of diffusive fluxes higher than

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10 mg CO2 m-2 h-1 and of bubbles larger than 5.3–8.4 mm in diameter. This high–throughput

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method can be adapted to any gas detector at low cost, making it a convenient tool to better

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constrain greenhouse gas emission from freshwater ecosystems.

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1. Introduction

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Methane (CH4) emission from freshwater ecosystems is a major component of carbon cycling,

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with a total annual emission estimated at 122 Tg CH4, 75% of which is emitted from lakes and

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reservoirs.1,2 Despite this global estimate, global CH4 emission from lakes and reservoirs is still

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considered uncertain,3 and its magnitude and variability need to be further constrained. The present

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uncertainty is mostly caused by the coexistence of several emission modes that can occur

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simultaneously or separately, each one of which being highly variable, both in space and in time.4,5

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The current consensus is that CH4 can be emitted to the atmosphere through three emission modes,

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which have been identified as follows: diffusive, ebullitive, and plant–mediated fluxes.6,7 Among

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these emission modes, diffusive and ebullitive fluxes are considered to be predominant.6,8


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Diffusive and/or ebullitive fluxes are conventionally determined by several methods, including:

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(i) floating chamber methods, in which flux is determined from the gas accumulation within the

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chamber; (ii) boundary layer methods, which are based on the determination of the concentration

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gradient at the water/atmosphere interface and in which fluxes are determined from Fick's first

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law;9 (iii) micrometeorological methods, such as the eddy covariance, which are based on the

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measurement of vertical turbulent fluxes derived from the analysis of high–frequency wind and

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atmospheric concentration data series;10 and (iv) mass balance methods, which are based on a

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global budget estimation at the ecosystem scale.11 A comparison of these methods and the flux

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magnitude determined by each of them has been discussed by Deemer et al.12 In addition to these

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methods, several others have been suggested for the determination of ebullitive flux, including the

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use of submerged inverted funnels,13–15 hydroacoustic detectors,16 optical detectors,17 survey of

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ebullition seeps,18 gas detectors mounted on robotic boats,19 and hyperspectral remote flux

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quantification.20

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Among these methods, the floating chamber method is extensively used because it is a simple

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and low–cost method that is based on the direct recovery of total gas flux, i.e., diffusive and

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ebullitive flux, in a floating container. It has been suggested as the best method to elucidate

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spatiotemporal variations in CH4 emission.21,22 According to Livingston and Hutchinson,23 two

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major chamber techniques are commonly used: (i) non–steady–state non–through–flow chambers,

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also called “closed static chambers,” which are based on the discrete sampling of the gas contained

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in a chamber for further analysis; and (ii) non–steady–state through–flow chambers, also called

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“closed dynamic chambers” (CDCs), which are based on the continuous measurement of the gas

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contained in the chamber. In both cases, flux determination is derived from the slope of the

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concentration increase in the floating chamber over time, which takes from a few minutes to


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several hours, depending on the flux magnitude and the detector sensitivity. These non–steady–

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state methods present several drawbacks, and, among them, it is important to mention the potential

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changes in the partial pressure of CH4 in the chamber headspace, which may alter the air/water

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equilibrium.24 In addition, these methods are relatively time demanding, resulting in a relatively

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reduced number of measurements.

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To avoid these obstacles, Edwards and Sollins25 suggested the use of a chamber in which a well–

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defined carrier gas flows continuously. The concentration of the gas of interest is measured

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continuously at the outlet of the chamber, and the flux is determined from a mass balance equation.

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According to the Livingston and Hutchinson23 classification, that design would correspond to a

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steady–state through–flow chamber, hereinafter called “open dynamic chamber” (ODC).

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Compared to non–steady–state chambers, the ODC concept presents several advantages, including

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a limited gas concentration build–up in the chamber. Moreover, an ODC coupled to a continuous

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detector allows for instantaneous flux measurements since the derivative of the gas concentration

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inside the chamber is a function of the flux. That characteristic further increases the potential

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interest in the use of the ODC in lakes since bubbles can be detected. Thus, the ODC may allow

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for an easy segregation between diffusive and ebullitive fluxes and potentially for bubble

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characterization.

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A literature review shows that ODCs have been already used for the determination of fluxes

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from soils, trees, and peatlands of a variety of gases, including but not limited to; carbon dioxide,

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nitrous and nitric oxides, ammonia, CH4 and water vapor (Table S1). Surprisingly, and to the best

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of our knowledge, the ODC has not been used so far for the determination of CH4 emissions from

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freshwater ecosystems. Since, on the contrary to other ecosystems where ODCs have been tested,

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emissions from aquatic ecosystems are predominantly distributed into diffusive and ebullitive


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fluxes, the objective of the present work was to design a floating dynamic chamber based on the

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Edwards and Sollins25 concept, and to assess the method for the determination of both emission

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modes of CH4 in aquatic ecosystems. In addition to the quantification of emissions, this work also

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focused on the characterization of bubbles. Although our main objective was the characterization

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of CH4 emissions, we also applied the ODC method for the characterization of carbon dioxide

92

(CO2) emissions.

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2. Materials and Methods

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2.1. Open dynamic chamber

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The original concept design of the ODC is shown in Figure 1. A continuous flow of CH4– and

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CO2–free nitrogen (Infra, Mexico) was used as the carrier gas. The carrier gas flow rate was

98

regulated at 7.72 L min-1 by a mass flow controller (GFC 17, Aalborg, Denmark). The carrier gas

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was continuously injected in a floating chamber (self–fabricated from a commercial polypropylene

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basin; volume, 8.9 L; area in contact with water, 0.11 m2), equipped with a battery–operated fan,

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to maintain the homogeneity of the chamber headspace. The ODC was also equipped with a

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styrofoam belt to guarantee floatability, while ensuring that the chamber was submerged for 1–2

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cm to avoid exchange with the atmosphere. This depth of immersion is standard, compared to the

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literature,26,27 and allows for flux measurements in the absence of short period waves with an

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amplitude superior to the penetration depth; i.e., which may cause a potential loss of hermeticity.

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The gas inside the chamber was extracted using the internal pump of an ultraportable greenhouse

107

gas analyzer (UGGA, model 915–0011, Los Gatos Research, Inc., California, USA) to determine

108

the CH4, CO2, and water vapor concentration, with a measurement frequency of 1 Hz. All the

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tubings used in this prototype were made of polyurethane and had 6 mm of external diameter


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(Festo, Mexico). The gas flow rate extracted by the UGGA was about 0.52 L min-1, which was

111

lower than the gas flow rate of the carrier nitrogen gas injected into the chamber. Therefore, the

112

excess of carrier gas was expelled through a purge, maintaining ODC pressure in equilibrium with

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the atmosphere. A gas injection port was added to the influent carrier gas line (Fig. 1, no. 5) to

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allow for the injection of a known volume of CH4/CO2 for calibration purposes. The ODC was

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floating, freely, close to a boat where the operator, the UGGA, two nitrogen cylinders (BT–80,

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Infra, Mexico) connected in parallel, and a car battery (50 A h capacity) were located. Under that

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configuration the autonomy of the experimental set–up was in the range of 8–10 h (Table S2).

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2.2. Mass balance and instantaneous flux measurements

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The flow chart of the ODC method as well as the mass balance equations are shown in the

120

Supporting Information (Section S3). We established that the flux can be determined from

121

Equation 1 (presented and demonstrated in Section S3):

(

(

𝑑 𝛩𝐷

𝑑𝐶𝐷 𝑑𝑡

+ 𝐶𝐷

)

𝛩𝐷

𝑑𝐶𝐷 𝑑𝑡

)

+ 𝐶𝐷 ― 𝐶0

𝑉𝑂𝐷𝐶

122

𝐹=

123

where F is the instantaneous flux (g m-2 s-1); CD is the concentration (g m-3) measured by the

124

UGGA; C0 is the potential concentration of the influent gas (g m-3; nil in our case); ΘODC and ΘD

125

are the residence time (s) in the ODC and the cavity of the UGGA, respectively (Eqs. S2 and S7);

126

VODC is the volume (m3) of the headspace of the ODC; and AODC is the area (m2) of the ODC in

127

contact with the aquatic ecosystem.

𝑑𝑡

+

𝛩𝑂𝐷𝐶

∙ 𝐴𝑂𝐷𝐶 ,

(1)

128

Since Equation 1 contains a double derivative, it is extremely sensitive to noise. The UGGA is

129

characterized by a high signal–to–noise ratio (SNR), i.e., the ratio of the mean to the standard

130

deviation, which was reported to be 1520 ± 415.28 Despite the high SNR, data smoothing was


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necessary to reduce the noise during the instantaneous flux determinations. We opted for a double

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pondered smoothing of F (𝐹′′𝑡), described by Equations 2 and 3, respectively:29

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𝐹′𝑡 = 0.1 𝐹𝑡 ― 2 +0.2 𝐹𝑡 ― 1 +0.4 𝐹𝑡 +0.2 𝐹𝑡 + 1 +0.1 𝐹𝑡 + 2,

(2)

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𝐹′′𝑡 = 0.1 ∙ 𝐹′𝑡 ― 2 +0.2 ∙ 𝐹′𝑡 ― 1 +0.4 ∙ 𝐹′𝑡 +0.2 ∙ 𝐹′𝑡 + 1 +0.1 ∙ 𝐹′𝑡 + 2,

(3)

135

2.3. Flux data interpretation

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As it will be shown in the results section (Fig. 4), each time a bubble was entering the ODC, an

137

abrupt increase in CD and its corresponding peak flux response was observed. From flux data (e.g.,

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Fig. 4C), total flux was determined by averaging the flux observed over the total measurement

139

time. For the determination of diffusive flux, the flux dataset was first filtered by excluding peak

140

responses which corresponded to ebullitive events. Since, after each peak response, an instability

141

period was observed, 30 s of data were also discarded after each peak response. Diffusive flux was

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then determined by averaging the filtered flux dataset. Ebullitive flux was then determined as the

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difference between total and diffusive fluxes. This strategy is detailed in the supporting

144

information (S4).

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2.4. Bubbles characterization

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First, from the abrupt increases in CD and their corresponding peak flux responses, we

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determined the specific bubble frequency (fB; m-2 h-1), which is the number of bubbles detected

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(nB) per unit of time and area (Eq. 8):

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𝑛𝐵

𝑓𝐵 = 𝑡 ∙ 𝐴𝑂𝐷𝐶 ,

(4)


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This quantity is of interest, in complement to ebullitive flux, not only because it allows to

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quantify the ebullitive intensity but also because it is a simple way to determine the average mass

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of CH4 contained in the bubbles; i.e., dividing the average ebullitive flux by fB.

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Although considered to be ancillary, the ODC method allows for a more detailed and discrete

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bubble characterization. Indeed, the abrupt CD increase, after each bubbling event, is proportional

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to the volume of CH4 contained in the bubble. This increase was used to characterize the mass of

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CH4 and the corresponding volume of CH4 contained in each bubble (MB,CH4 and VB,CH4,

157

respectively; see Figure S3 and Section S5 for more details), as follows;

158

∆𝐶𝑂𝐷𝐶 = 𝛽 ∙ ∆𝐶𝐷,

(5)

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𝑀𝐵,𝐶𝐻4 = 𝛽 ∙ ∆𝐶𝐷 ∙ 𝑉𝑂𝐷𝐶,

(6)

160

𝑉𝐵,𝐶𝐻4 =

161

where β is a proportionality constant between the concentration increase measured by the UGGA

162

and the increase of concentration in the chamber, and VM is the molar volume of CH4 (Eq. S11).

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The proportionality factor β was determined in the laboratory and in the field.

(

𝑀𝐵,𝐶𝐻4 16

) ∙ 𝑉 𝑀,

(7)

164

2.5. ODC calibration and laboratory testing

165

We first determined the SNR observed during flux measurements using the method described in

166

Section S4.2. We also determined the residence times of the ODC and the UGGA; i.e., ΘODC and

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ΘD, respectively. For the determination of ΘODC, we used the experimental set–up shown on Figure

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1, and a continuous flow of compressed atmospheric air (1.8 ppm v/v CH4) was injected through

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the ODC with a gas flow rate regulated at 7.72 L min-1 by a mass flow controller (GFC 17, Aalborg,

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Denmark). Once stable readings were observed, air was substituted by CH4–free nitrogen, at the


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same flow rate. A slow asymptotic decrease of CH4 concentration was then observed, which was

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fitted to Equation 8, with a least square error minimization performed in R30 using ΘODC as the

173

sole adjustment parameter, and where CD,atm is the steady state initial atmospheric concentration

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of CH4. The calibration of ΘD was done by causing a step increase of the CH4 concentration. First

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a continuous flow of CH4–free nitrogen was diverged directly to the UGGA through the 3–way

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valve (#9; Fig. 1). Once CH4 was no longer detected by the UGGA, the 3–way valve was returned

177

to normal position; i.e., shortcut closed, and simultaneously, the nitrogen flow was interrupted by

178

closing the flow control valve (#3; Fig. 1), forcing atmospheric air to enter the UGGA, using the

179

internal pump of the detector, through the purge (#8, Fig. 1). An asymptotic increase of CH4

180

concentration was then observed that was fitted to Equation 9, with the same adjustment method

181

than for the determination of ΘODC. Both ΘODC and ΘD were determined in quintuplicate.

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𝐶𝐷 = 𝐶𝐷,𝑎𝑡𝑚 ∙ exp

183

𝐶𝐷 = 𝐶𝐷,𝑎𝑡𝑚 ∙ 1 ― exp

[ ( )]

(8)

[

(9)

―𝑡 𝛩𝑂𝐷𝐶

( )] ―𝑡 𝛩𝐷

184

The proportionality constant β (Eq. 5) was determined after injecting volumes of 0.05–5 mL of

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CH4/CO2 (60/40 vol.%) at port no. 5 (Fig. 1), with syringes that were previously calibrated (volume

186

coefficient of variation ranging from 0.3 to 1%). According to Eq. 5, ΔCD was determined from

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the observed increase in concentration while ΔCODC was estimated from the CH4 concentration of

188

the gas injected, multiplied by the dilution factor Vg/VODC; where Vg is the volume of gas injected.

189

Laboratory testing of the ODC method was done using a 120–L plastic container. The laboratory

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testing included the calibration of ΘD and β, the measurement of diffusive and ebullitive fluxes

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with the ODC and their comparison to fluxes measured with a standard CDC (Fig. S4). Prior to

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flux measurements, the water was loaded up to several concentrations of dissolved CH4 by


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bubbling a standard CH4 and CO2 gas (60/40 vol.%) under strong agitation for an arbitrary time

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(Fig. S4). At the end of the arbitrary time, the flow of CH4 and CO2 was suspended, and mixing

195

was reduced to lessen perturbations at the air/water interface, while maintaining homogeneity. For

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each CH4 concentration, the ODC or the standard CDC was then placed at the surface of the water

197

and UGGA readings were sustained until significant results were obtained, typically 10 min. In

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order to compare fluxes measured with the ODC and the standard CDC, these were alternate, using

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the same batch of water loaded with CH4. Ebullitive fluxes were determined with the injection of

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known volumes of CH4/CO2 (60/40%vol.) or 100% CH4, underwater and below the chamber, with

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glass syringes (Fig. S4). First, several bubbles sizes were simulated, ranging from 0.05 to 5.0 mL

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of CH4/CO2, and from 1.0 to 5.0 mL of CH4, which corresponds to bubbles of 4.6–21.2 mm of

203

diameter. Then, to determine the capability of the ODC method to segregate multiple bubbles in

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sequence, three 2 mL bubbles (CH4/CO2; 60/40%vol.) were injected with different time intervals;

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i.e., 60, 30, 20, 10 and 5 s.

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2.6. Field testing

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For field testing, the ODC method was deployed, in October 2017, in two contrasting reservoirs:

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Lake “Guadalupe” (LG; 19.6274 latitude, -99.2686 longitude), a highly polluted reservoir with

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large diffusive and ebullitive fluxes, and Lake “Llano” (LL; 19.6552 latitude, -99.5086 longitude),

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which is a meso–oligotrophic reservoir, where the diffusive flux was predominant. These

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ecosystems have been previously described.31,32 After field calibration of ΘD and β, using the same

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protocol as the laboratory calibration, the ODC was positioned on the surface of the ecosystem.

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After 30 s, required for the system to stabilize, the CH4 and CO2 concentration were continuously

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acquired at a frequency of 1 Hz for approximately 30 minutes. The ODC method was deployed at

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18 locations of LG and 6 locations of LL, varying in distance to the shore and water column depth.


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In the regions of these lakes where no ebullitive flux had been observed, diffusive fluxes were

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measured with the ODC and compared to the measurements done with the standard CDC. To

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assess the ODC method over longer periods of time, a continuous measurement was performed, in

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November 2018, over 12 hours in LG, at a position where moderate ebullition was observed

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(19.6305 latitude, -99.2546 longitude). To compare day and night CH4 emissions, this

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measurement was performed from midnight to noon, and a supplementary measurement was done

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the same day from 3 to 6 pm.

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2.7. Statistical analysis

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Statistical analyses were carried out to compare the significant differences between datasets.

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Since large SNR datasets were obtained, normality was first assessed by the Shapiro–Wilk test,

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and we used the Chi–squared test followed by the two tailed Mann–Whitney U test to compare

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SNR medians of CDC and ODC, and SNR medians of lab and field experiments. Differences

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between large datasets of total, ebullitive and diffusive fluxes at night, morning, and afternoon

229

during the long–term measurement of CH4 emissions were tested by ANOVA mean comparison,

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after the Shapiro–Wilk normality test. We also tested significant differences between ΘD observed

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over 3 months of experimentation between β determined from different bubble volumes, and

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between VB,CH4 for bubbles in sequence at different time intervals. In these cases, the datasets were

233

not large enough to assess normality and thus, a Levene variance test was performed, followed by

234

median comparison with the Kruskal–Wallis test. The coefficient of determination (R2) was used

235

to assess the linearity of measured fluxes with CDC and ODC, and with measured volumes vs

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injected bubble volumes in the ODC. These R2 were confirmed by testing normality and

237

homoscedasticity of the residuals. The bias between CDC and ODC was visually assessed by the


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Bland–Altman plot, and calculated as the mean difference between CDC and ODC. All statistical

239

analyses were conducted using the R software30 with a significance level α = 0.05.

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3. Results and discussion

242

3.1. Laboratory testing

243

First, the residence time of the UGGA (ΘD) was established. The result was ΘD = 11.11 ± 0.79

244

s (mean ± standard deviation), which is about 85% higher than the ΘD that would be observed in

245

a perfectly mixed cavity (Eq. S2). In over 3 months of the experiments, ΘD did not change

246

significantly. Then, the ODC method was tested in the laboratory for diffusive flux (Fig. 2A). The

247

diffusive CH4 flux determined with the ODC matched the measurements done with the standard

248

CDC. The coefficient of determination (R2) was higher than 0.98, with a bias < 0.0001. When the

249

ODC and the CDC were compared, the mean difference between the two methods was 4.0% ±

250

1.8%. During the measurements, both methods gave a similar SNR, i.e., 3400 ± 950. This SNR

251

was higher than that previously observed in a similar but older UGGA model (i.e., 1520 ± 41528).

252

The observed correlation between the diffusive flux measurements by the standard CDC and the

253

ODC suggests that the latter produced similar results to those of the former while allowing for

254

continuous flux measurements and avoiding CH4 concentration buildup within the chamber

255

headspace.

256

The ODC was tested for the characterization of bubbles. First, prior to the bubble

257

characterization, in both laboratory and field testing, β (Eq. 5) was determined. The β parameter

258

for CH4 was remarkably stable, with a mean value of 2.03 ± 0.03, with no significant difference

259

between the different volumes injected. Figure 3 shows the correlation observed between the


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260

volume of CH4 injected, thus simulating bubbles of different sizes, and the CH4 volume determined

261

through the ODC method. The value of R2 was higher than 0.99, with a bias < 0.0001, which

262

confirms that the ODC method allows for bubble detection and characterization. Next, we injected

263

bubbles in sequences of three, with different time intervals. Figure S5 presents the CD observed

264

during this test, which shows that, in all cases, except for the interval time of 5 s, the bubbles were

265

discriminable. It is worthwhile to mention that during these sequences of bubbles injection, no

266

significant difference was observed in terms of β, compared to single bubbles measurements.

267

Combining all the tests of successive ebullitive events, we found the mean error on the equivalent

268

CH4 bubble volume determination to be 4.4% ± 7.6%, without significant difference among tests.

269

These results indicate that there is a minimum time, which we call the minimum discrimination

270

time (MDt), below which two ebullitive events cannot be discriminated, which was 10 s in our

271

case. However, as will be discussed in the next section, this MDt can be substantially reduced.

272

3.2. Field deployment

273

The ODC was deployed in two reservoirs. In both of them, a region with no apparent ebullition;

274

i.e., no bubbles observed over several minutes, was first selected and diffusive flux was measured

275

with the standard CDC and the ODC (Fig. 2B). The absence of ebullitive events during these

276

measurements was confirmed by no abrupt increase in CD. A comparison between the two methods

277

gave an R2 value higher than 0.99, with a bias < 0.0001, thus confirming the similitude between

278

the two methods, as previously observed in the laboratory. During field deployment, we observed

279

that the SNR was 1153 ± 379 (median = 1045), which is significantly lower (U = 104; p = 0.0009)

280

than previously observed in the laboratory; i.e., 3400 ± 950 (median = 3277), which is an indication

281

of background noise. An example of the measurement of combined diffusive and ebullitive CH4

282

fluxes in LG and data processing is shown in Figure 4. It should be recalled that, as described in


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283

the Materials and Methods section and detailed in the Supporting Information (Section S4), flux

284

data obtained after double smoothing (Fig. 4C) were only used to determine the total and the

285

diffusive CH4 flux, whereas bubble characteristics were determined by another set of equations

286

(Eqs. 5–7).

287

On average, we observed in LG a total flux ranging from 2.5 to 649.7 mg CH4 m-2 h-1, with a

288

mean of 147.8 mg m-2 h-1. From this total flux, 39.0% to 96.5% was caused by ebullition. As

289

previously reported,33 we observed that ebullition was more important in the littoral regions of the

290

lake (Fig. 5). In LL, the total flux ranged from 0.4 to 2.9 mg CH4 m-2 h-1, with a mean of 1.1 mg

291

m-2 h-1. In LG and LL, the diffusive fluxes averaged 20.4 ± 30.05 and 0.7 ± 0.3 mg CH4 m-2 h-1,

292

respectively. Regarding the lower limit of detection of diffusive fluxes, the use of CH4–free

293

nitrogen as well as the high sensitivity of the UGGA (reported by the manufacturer to be below 10

294

ppb) makes the ODC method very sensitive. Under the experimental conditions used, we

295

theoretically estimated the minimum detectable diffusive flux of CH4 to be about 25 × 10-6 g m-2

296

h-1, which is below the 98% average diffusive flux reported in 393 lakes by Bastviken et al.1 and

297

which is also within the lowest fluxes reported by Rasilo et al.34 in 224 boreal lakes.

298

During a total of 12 h of field deployment in the two lakes, we observed 233 ebullitive events,

299

mostly in LG (99% of all bubbles detected). From these observations, we determined fB. In LG,

300

we observed ebullitive events in all locations, and, consequently, we observed fB ranging from

301

25.0 to 919 m-2 h-1, with a mean of 346 m-2 h-1. On the contrary, in LL, we observed only two

302

ebullitive events. We defined a minimum peak height, higher than the background noise, to be

303

identified as an ebullitive event. We arbitrarily chose an instantaneous flux of 0.04 g CH4 m-2 h-1

304

as a cut-off index (shown in Fig. 4B), which was visually above the magnitude and the noise

305

observed during diffusive fluxes measurements. Under the experimental conditions, this cut–off


14

Page 15 of 34


306

flux corresponded to bubbles containing (VB,CH4) 1.6 × 10-3 mL of CH4. Assuming a conservative

307

bubble composition of 60–90% v/v CH4,4,35,36 this cut–off flux corresponds to bubbles of 1.50–

308

1.72 mm in diameter, which can be considered as the detection limit of our method, under its

309

present configuration, and which is below the range of bubble diameters previously reported in

310

lakes, i.e., 2.6–11.4 mm.16,17,37,38 However, according to the Baulch et al.39 review, bubbles

311

containing less than 60% v/v of CH4 have been often reported. Thus, assuming the lower bubble

312

diameter range; i.e., 2.6 mm, the ODM method presented here, would only detect bubbles

313

containing more than 17% v/v of CH4. It can therefore be considered that the ODM method may

314

detect ebullitive flux caused by most of the bubbles reaching the surface of lakes and reservoirs,

315

but probably not all of them. The undetected bubbles reaching the ODC would therefore be

316

interpreted as a component of diffusive flux.

317

The ODM method is mostly designed for the continuous characterization of diffusive and

318

ebullitive fluxes, while bubbles characterization is ancillary. However, bubble size and

319

composition are important parameters to describe transport processes and the fate of rising bubbles

320

in stratified waters.40,41 In addition to the limitation regarding the potential failure of the method

321

to detect small bubbles containing low percentage of CH4, described above, a potential weakness

322

of the method is the segregation of several bubbles reaching the ODC in a short period of time.

323

During the field testing, we observed that the raw flux data (Fig. 4B) presented, in some cases, a

324

double peak, as illustrated in the insets of Figure 4A and 4B. In this example, the time between the

325

two detected peaks was 5 s (shorter than the MDt of 10 s determined during the laboratory testing).

326

Thus, the interpretation of raw flux data can be considered as a useful tool for detecting bubble

327

events, in a way that is better than the observation of abrupt increases in CD (Fig. 4A, Section S5).

328

However, the UGGA used in the present work has a measurement frequency of 1 Hz, which makes


15


Page 16 of 34

329

short MDts (lower than 5 s) uncertain; i.e., the two peaks would be separated by only four or less

330

CD data. Thus, during our field deployment, we used the raw CD data to identify ebullitive events,

331

but discriminated the bubbles only when the separation time was longer than 5 s. The segregation

332

capability of the ODC method could be significantly improved by using a high–frequency detector

333

since some commercial detectors have a frequency ≥10 Hz, or by reducing the area of the ODC to

334

lessen the probability of simultaneous ebullitive events. However, even after reducing substantially

335

the MDt, it is unlikely that the ODC method would allow the individual characterization of bubbles

336

combined in a bubble stream; i.e., large group of bubbles emitted together. Nevertheless, the

337

method would not discard this specific ebullition type when determining total ebullitive flux.

338

3.3. ODC for CO2 fluxes

339

As previously mentioned, the main objective of our work was to assess the ODC for the

340

determination of diffusive and ebullitive fluxes of CH4 in lakes. Since our UGGA detected both

341

CH4 and CO2, we also characterized CO2 fluxes by the ODC method. We tested the ODC method

342

in the laboratory, and in the field for diffusive and ebullitive CO2 fluxes as well as for the

343

characterization of bubbles, as previously done for CH4. The results obtained are detailed in

344

Section S7.2. Briefly, the results obtained in laboratory were like those observed with CH4, but

345

during field deployment, we observed noisier signals from the UGGA as well as a lower

346

sensitivity. The relatively high level of noise as well as the lower sensitivity limited the ODC

347

method to CO2 diffusive fluxes above 10 mg CO2 m-2 h-1, which is higher than the lower range of

348

CO2 fluxes reported by Rasilo et al.34 in 224 boreal lakes. The latter suggests that the ODC method,

349

under its present configuration, would fail to determine CO2 fluxes in many cases.

350

3.4. Long term measurement of CH4 emissions


16

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351

The use of the ODC method for long term measurement of CH4 emissions was assessed through

352

a 12–h period in a single location in November 2018. Figure S7 shows the results obtained in terms

353

of total Flux while Table S3 shows the average total, diffusive, and ebullitive fluxes observed in

354

three contrasting periods of the day (night, morning, and afternoon). It should be noted that the

355

total emissions were significantly lower than the mean emissions observed during field testing, in

356

October 2017, at 18 locations of LG. The main reason is probably that that the long–term

357

measurement was done the day after a heavy rain episode, which was not the case in October 2017.

358

Indeed, Sanches et al.42 have shown that precipitation is a driving factor of CH4 emissions. A

359

higher number of ebullitive events were observed during the 20 first minutes of the experiment,

360

but this might be a result of the experimental setup installation; e.g., anchoring of the chamber.

361

Excluding this initial period, no clear trend was observed, with ebullitive event distributed over

362

the 12–h period and with no significant differences between night, morning and afternoon,

363

according to equally weighted running means of 15 minutes data subsets. However, Fig. S7 shows

364

relatively high variations in flux and bubble occurrences. These results are in accordance to Joyce

365

and Jewell8 and to Grinham et al.,19 who observed no clear diel effect on diffusive and ebullitive

366

fluxes but short term variations attributed to wind.

367

3.5. Strengths and weaknesses of the ODC method

368

We observed that the ODC method allows for the continuous characterization of diffusive and

369

ebullitive CH4 fluxes, over long periods of time, with a minimum detectable diffusive flux of 25 ×

370

10-6 g m-2 h-1. In terms of ebullitive fluxes, the method detects any bubble containing more than

371

1.6 × 10-3 mL of CH4. The application of the ODC method for CO2 flux characterization presented

372

more weaknesses than CH4 measurements, as fluxes below 10 mg CO2 m-2 h-1, or bubbles with a

373

diameter smaller than 8.4 mm (assuming they contained a very large percentage of CO2; i.e., 10%),


17


Page 18 of 34

374

could not be quantified. These detection limits are specific to the floating chamber design used in

375

the present work and it is worthwhile to mention that they can be easily modified. Among the most

376

important design/operational parameters that can be modified, the gas flow rate of the carrier gas

377

and the area of the ODC in contact with water will have a larger impact on the sensitivity of the

378

method and its capability to detect ebullitive and diffusive fluxes. A larger surface in contact with

379

water would improve the capability of the ODC to capture a larger number of bubbles in a given

380

time as well as to detect low diffusive fluxes. A lower carrier gas flow rate would increase the

381

sensitivity of the method and allow for the quantification of low fluxes. Thus, for example, a large

382

surface and a low flow rate would be better adapted for oligotrophic lakes. Fortunately, floating

383

chambers are simple and low cost, if excluding the detector (e.g., Table S2), thus making the

384

development of several chamber designs feasible. During field deployment, the operator could

385

select an adequate chamber design and the optimal operational conditions, according to the level

386

of emissions. In addition, it is worthwhile to mention that the design of the ODC can be easily

387

switched to standard CDC by closing the input of the carrier gas and returning the output of the

388

detector to the floating chamber, in case the ODC does not fulfill adequately the expected

389

measurements.

390

Regarding bubble characterization, which is important to describe transport processes and the fate

391

of rising bubbles in stratified waters, the method permitted the measurement of the frequency of

392

ebullitive events and the quantification of the CH4 content of bubbles, but failed, in most cases, to

393

quantify the CO2 content. As bubbles contain significant levels of other gases, including large

394

fractions of N2,39 the ODC method, under its present configuration, failed to fully characterize the

395

bubbles composition, and gas partial pressures, which are the factors controlling gas exchange

396

during bubble transport.40,41


18

Page 19 of 34


397

The ODC method requires the use of compressed gas; i.e., nitrogen in the present work, which

398

somewhat limits the autonomy of the method and increases the operational cost. In this regard,

399

compressed nitrogen or air can be used, without or with fixed concentration of CH4 and CO2, as

400

the presence of known trace amounts of both gases, considered in the mass balance Equation 1,

401

would not affect the measurements. Thus, a cylinder filled with an on–site air compressor may

402

advantageously be used. On the contrary, the use of ambient air, pumped through the chamber, is

403

not advisable as variations of CH4 and/or CO2 concentration may significantly affect the

404

measurements, mostly during diffusive flux determination. Another pertinent inconvenience of the

405

ODC method is that it requires a high sensitivity detector with relatively large power requirements

406

(Table S2), increasing the cost of the method while reducing its autonomy. However, it is

407

worthwhile to mention that the method is not detector–specific, and the current trend is the

408

development of higher sensitivity detectors at lower cost and power requirements.

409

To the best of our knowledge, this is the first time that an ODC method has been used for the

410

characterization of CH4 emissions from surface waters. Compared to other methods, listed in Table

411

S4, boundary layer methods allow for diffusive flux measurements with high resolution during

412

boat motion,43 but do not allow for the measurements of ebullitive flux and require the

413

measurement of wind speed. Unlike boundary layer methods, inverted funnel methods13–15 allow

414

for the direct quantification of ebullition at a given depth of the water column, but do not quantify

415

diffusive flux measurements and are not easily transportable i.e., requiring a set–up time, at each

416

location. Optical detectors,17 which are a novel development of inverted funnels, allow for a high–

417

definition analysis of bubble size, but do not quantify individual bubble composition nor diffusive

418

fluxes. Bubble detection and measurement by hydroacoustic detectors37 are mobile and of major

419

interest for bubble detection, but do not quantify fluxes or bubble composition. The survey of


19


Page 20 of 34

420

ebullition seeps18 quantifies globally ebullitive fluxes, but only during the winter period and in

421

lakes subject to freezing. In the case of gas detectors mounted on robotic boats,19 the detection of

422

ebullitive events can be done on a mobile platform; however, it does not quantify diffusive flux

423

nor does it segregate bubbles. When compared to other floating chamber methods, the main

424

advantage of the ODC method is that it reduces headspace concentration build–up within the

425

chamber, thus allowing for continuous measurements over longer periods of time. However, some

426

automated chambers with routine headspace ventilation reduce the headspace concentration build–

427

up and allow for discrete measurements over long periods of time.44–46 Another advantage of the

428

ODC compared to other chamber techniques is the high–throughput continuous measurements of

429

both ebullitive and diffusive flux. Thus, each method presents some specific advantages and we

430

see the ODC as a potential method that complements other methods and supports the current efforts

431

of the scientific community to better constrain greenhouse gas emission from freshwater

432

ecosystems.

433 434

Supporting Information.

435

Detailed discussion of mass balance equations, non–perfectly stirred behavior of the UGGA,

436

calibration, bubbles characterization, laboratory testing, data processing and results (PDF). The

437

following files are available free of charge via the Internet at http://pubs.acs.org

438 439

Corresponding Author

440

Frederic Thalasso, phone: +52 55.57.47.33.20; e–mail: [email protected]

441


20

Page 21 of 34


442

Author Contributions

443

The manuscript was written through contributions of all authors. All authors have given approval

444

to the final version of the manuscript.

445 446

Funding Sources

447

This work was supported by “Consejo Nacional de Ciencia y Tecnología” (Conacyt, project

448

255704). We also gratefully acknowledge Conacyt for the financial support to Oscar Gerardo–

449

Nieto (grant # 277238), to Rodrigo Gonzalez–Valencia (grant # 27101), and to Yameli Alfano–

450

Ojeda (grant # 485166).

451 452

Acknowledgment

453

The authors are thankful to Victoria T. Velázquez-Martínez, Juan Corona-Hernández, Francisco

454

Silva-Olmedo, David Flores-Rojas, Laurette Prince, and Andrés Rodriguez-Castellanos for their

455

technical assistance. The authors declare that they have no conflicts of interest.

456 457

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flux chamber for investigating gas flux at water-air interfaces. Environ. Sci. Technol. 2013, 47 (2),

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968–975.

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(45)

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floating chamber for continuous measurements of carbon dioxide gas flux on Lakes.

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Biogeosciences. 2018, 25 (February), 1–12.

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(46)

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Sommer, M.; Augustin, J. A Simple calculation algorithm to separate high-resolution CH4 flux

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measurements into ebullition- and diffusion-derived components. Atmos. Meas. Tech. 2017, 10

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(1), 109–118.

Duc, N. T.; Silverstein, S.; Lundmark, L.; Reyier, H.; Crill, P.; Bastviken, D. Automated

Martinsen, K. T.; Kragh, T.; Sand-Jensen, K. A simple and cost-efficient automated

Hoffmann, M.; Schulz-Hanke, M.; Garcia Alba, J.; Jurisch, N.; Hagemann, U.; Sachs, T.;

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Page 29 of 34


P

CH4–, CO 2–free Nitrogen

Floating Open Dynamic Chamber UGGA

602 603

Continuous CH 4/CO 2 flux measurement

Graphical abstract

604 605


29


2

5.45

P

4 5

3 9

1

8

1 0 1 1

Page 30 of 34

7

LGR

6

606 607 608

Figure 1. Concept design of the open dynamic chamber for the continuous CH4 and CO2 flux

609

measurements: 1. CH4-free nitrogen (carrier gas); 2. pressure control; 3. flow control valve; 4.

610

mass flow controller; 5. septum port for standard gas injection (calibration); 6. ODC; 7. battery-

611

operated fan; 8. purge; 9. 3–way valve to shortcut the floating chamber; 10. air filter; 11.

612

ultraportable greenhouse gas analyzer (UGGA).


30

Page 31 of 34


613 614 615 616

Figure 2. Correlation between diffusive CH4 fluxes measured with a standard CDC method and

617

ODC, during laboratory testing (A) and field deployment (B; □, LG; Δ, LL).

618


31


Page 32 of 34

619 620 621 622

Figure 3. Correlation between the volume of CH4 injected as artificial bubbles and the volume

623

quantified by the ODC.

624 625


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Page 33 of 34


6.0 4.0

449 Time (s)

0.6 0

120

240

F (g m-2 h-1)

F (g m-2 h-1)

0.8 0.0 0.4

0.6

360 0.4 0.2

480 600 Time (s)

720

840

B

960

0.0

0.2

449 Time (s)

0.3 0.0 F (g m-2 h-1)

3.0 1.0

2.0

0.2

A

5.0 CD (mg m-3)

CD (mg m-3)

8.0

0

120

240

360

480 600 Time (s)

720

840 C 960

0

120

240

360

480 600 Time (s)

720

840

0.1 0.0

626

960

627 628

Figure 4. Example of the continuous CH4 flux measurement by the ODC in LG: A. CD as

629

measured; B. unfiltered flux (Eq. 1); C. doubly smoothed flux (Eqs. 2 and 3). Horizontal line in

630

panel B shows the cut-off index. The insets in panels A and B show a close-up of a double-peak

631

detection. Please note the difference in scale between panel B and panel C, which was caused by

632

the smoothing of the data.

633


33


Page 34 of 34

Percentage of total flux (%)

100 80 EBULLITIVE FLUX

60 40 20

DIFFUSIVE FLUX

0 0

5

10

15

Depth (m)

634 635 636

Figure 5. Percentage of diffusive and ebullitive CH4 fluxes observed in LG as a function of the

637

water column depth.

638 639


34

Continuous Measurement of Diffusive and Ebullitive Fluxes of

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