BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-15-3085-2018Use of argon to measure gas exchange in turbulent mountain streamsGas exchange measured with argonHall Jr.Robert O.bob.hall@flbs.umt.eduhttps://orcid.org/0000-0002-0763-5346MadingerHilary L.Department of Zoology and Physiology, University of Wyoming, Laramie, WY 82071, USAProgram in Ecology, University of Wyoming, Laramie, WY 82071, USACurrent address: Flathead Lake Biological Station, University of Montana, Polson, MT 59860, USARobert O. Hall Jr. (bob.hall@flbs.umt.edu)18May201815103085309227February20186March201823April201826April2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://bg.copernicus.org/articles/15/3085/2018/bg-15-3085-2018.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/15/3085/2018/bg-15-3085-2018.pdf
Gas exchange is a parameter needed in stream metabolism and trace gas
emissions models. One way to estimate gas exchange is via measuring the
decline of added tracer gases such as sulfur hexafluoride (SF6).
Estimates of oxygen (O2) gas exchange derived from SF6 additions
require scaling via Schmidt number (Sc) ratio, but this scaling is
uncertain under conditions of high gas exchange via bubbles because scaling
depends on gas solubility as well as Sc. Because argon (Ar) and O2
have nearly identical Schmidt numbers and solubility, Ar may be a useful
tracer gas for estimating stream O2 exchange. Here we compared rates of
gas exchange measured via Ar and SF6 for turbulent mountain streams in
Wyoming, USA. We measured Ar as the ratio of Ar:N2 using a membrane inlet
mass spectrometer (MIMS). Normalizing to N2 confers higher precision than simply
measuring [Ar] alone. We consistently enriched streams with Ar from 1 to
18 % of ambient Ar concentration and could estimate gas exchange rate using
an exponential decline model. The mean ratio of gas exchange of Ar relative
to SF6 was 1.8 (credible interval 1.1 to 2.5) compared to the
theoretical estimate 1.35, showing that using SF6 would have
underestimated exchange of Ar. Steep streams (slopes 11–12 %) had high rates
of gas exchange velocity normalized to Sc=600 (k600, 57–210 md-1),
and slope strongly predicted variation in k600 among all streams. We
suggest that Ar is a useful tracer because it is easily measured, requires no
scaling assumptions to estimate rates of O2 exchange, and is not an
intense greenhouse gas as is SF6. We caution that scaling from rates of
either Ar or SF6 gas exchange to CO2 is uncertain due to solubility
effects in conditions of bubble-mediated gas transfer.
Introduction
Air–water gas flux is a key process in aquatic ecosystems because it defines
the flow of material between water and the atmosphere. Knowing this flux is
needed for questions ranging from global CO2 balance
to short-term O2 budgets to estimate ecosystem
metabolism . Gas flux is the product of air–water gas
exchange velocity (k, md-1) and the relative saturation in water,
i.e., F=k(αCair-Cwater), where Cair and Cwater are
the concentrations of gas in the air and water and α is the unitless
Ostwald solubility coefficient. The gas exchange velocity, k (md-1),
is a central variable for estimating gas flux, and it is much harder to
measure than the air–water concentration gradient in gases. k can vary
greatly through time and space and thus requires many empirical measurements
or robust predictive models to accurately estimate gas exchange.
There are several ways to measure gas exchange in aquatic ecosystems. In
places with high rates of primary production and low gas exchange, it is
possible to measure gas exchange rates via diel curves of oxygen with time
. Direct measures with domes are practical in
low-exchange habitats . Tracer gas addition
is another effective means of measuring gas exchange across all types of
aquatic habitats .
Tracer additions are particularly useful because they represent direct
measures at spatial scales similar to that of turnover length of gases. Given
enough estimates of k, it is then possible to build the theory of gas exchange
across time and space , e.g.,
among small high-energy streams. A trade-off with gas exchange measured by
tracer gases is that it is necessary to scale exchange rates measured for the
tracer gas (e.g., SF6, propane, 3He) with that of gases of ecological
interest (e.g., O2, CO2, CH4). This scaling is not always
straightforward because high rates of bubble-mediated gas exchange cause
scaling to depend on differences in solubility of gases as well as their
diffusivity . Thus, an ideal
tracer gas would not require scaling if its solubility and diffusivity were
similar to the gas of ecological interest. Here we demonstrate the use of
argon (Ar) as a tracer gas; Ar has similar solubility and diffusivity to
O2, a gas of major biological interest in the context of estimating
metabolism in aquatic ecosystems
.
Bubble-mediated gas exchange rate (Kb) normalized to that of
argon. Temperature was at 20 ∘C. Contours are equivalent to Kb,Ar/Kb,2, where Kb,2 varies as a function of solubility and Schmidt
number. At low solubilities (α, Ostwald solubility coefficient),
scaling among gases depends only on variation of Schmidt number (SF6). As
solubility increases, scaling depends on both Schmidt number and α
(CO2). O2 is similar to Ar. Propane has similar properties to SF6.
Analysis is based on Eq. (13) in .
In the absence of extensive bubbles, one can scale gas exchange rates between
gases based on the ratio of their Schmidt numbers (Sc); Sc is the
dimensionless ratio of kinematic viscosity of water (ν) and the diffusion
coefficient of the gas (D), i.e., Sc=νD. Given Sc for two
gases, scaling gas exchange rates is given by
k1k2=Sc1Sc2-n, where n is a coefficient ranging from 0.67 for smooth
water to 0.5 for wavy water. When bubbles are present, scaling between gases
depends upon solubility of the gases in addition to their diffusivity
. This bubble effect kb is additive to that of an
unbroken surface (ko) such that kw=ko+kb. One model for the bubble-mediated component of
gas exchange, kb, is given by Eq. (13) in :
kb=Qbα×1+χ1/f-fχ=Sc0.514α,
where Qb is the bubble flux and f=1.2. We can compare the ratios of the
bubble-mediated component of gas exchange kb,1/kb,2 for two gases with
varying solubility α1 and α2 as
kb,1kb,2=α2α1×1+χ11/f1+χ21/f-f.
This model shows that the effect of varying solubility on scaling kb among
gases depends on the solubility (Fig. ). For
low-solubility gases such as Ar and SF6, this model predicts only a
Schmidt number effect. For more soluble gases, such as CO2, the scaling
factor is higher than what would be predicted because of the higher
solubility of CO2 (Fig. ). Here, we test the gas
exchange scaling of two sparingly soluble gases, Ar and SF6, in high-energy
mountain streams with presumably high rates of bubble-mediated gas exchange.
Argon is promising for measuring gas exchange because it has low background
concentrations in water, it is inert, it is cheaply available from welding
supply stores, it has similar solubility and diffusivity to O2
(Fig. ), and it is easily detected using membrane inlet
mass spectrometry. We compared Ar to SF6, another commonly used tracer gas
that supersedes Ar in detectability but has a higher Schmidt number and lower
solubility in addition to being an intense greenhouse gas. Our objectives
were as follows.
Develop a method to measure gas exchange in streams using Ar tracer additions.
Test scaling of Ar to SF6 in turbulent streams with high rates of bubble-mediated gas transfer.
Site data for streams sampled including average stream width, reach
length, solute travel time (t), water velocity v, discharge (Q),
average stream depth (z¯), stream slope, and average stream
temperature during plateau. Gas exchange parameters kAr600 and a are
reported with 95 % credible intervals. We also include the per-time rate of
gas exchange, K600 (d-1).
SiteDateWidthReach lengthTravel timeVelocityQz¯SlopeTemp.K600kAr600a(m)(m)(min)(mmin-1)(m3s-1)(m)(mm-1)(∘C)d-1md-1Spring 113 Aug 20152.330025120.0840.180.00717.4315.8 (4.7,7.1)2.0 (1.5,2.6)Spring 230 Jun 20161.68605615.40.0700.170.00712.6285.6 (4.2,7.2)0.92 (0.67,1.2)Blair 12 Jul 20150.8420449.50.0200.160.01517.56911 (9.3,14)1.8 (1.4,2.2)Blair 215 Jul 20150.8420449.50.0200.160.01518.27712 (10,15)2.0 (1.6,2.4)Pole12 May 20170.9300983.10.0210.450.059.47947 (38,58)1.5 (1.2,1.9)Gold Run11 Oct 20163.31403540.0970.440.1134.49564 (54,75)2.3 (1.9,2.8)NoName 126 Jun 20151.3233376.30.0220.300.126.723097 (80,120)0.93 (0.73,1.2)NoName 214 Jul 20160.7110215.20.0220.200.126.3740208 (170,270)3.4 (2.7,4.5)MethodsSites
We sampled five streams across a gradient of predicted gas exchanges to
compare performance of Ar and SF6 as tracers. Streams were headwaters in
southeast Wyoming ranging from three mountain streams in Snowy and Laramie
ranges (NoName Creek,
Pole Creek, and Gold Run); one urban spring stream
(Spring Creek); and a low-slope, meadow stream in the Vedauwoo area of the
Laramie Range (Blair Creek) (Table ). The three mountain
streams were steep channels with step-pool morphology and presumed high rates
of gas exchange.
Gases and NaCl injection
We added Ar and SF6 gases to each stream and modeled their downstream
evasion to estimate their relative exchange rates. Prior to injection, we
collected pre-plateau samples at each of six sampling locations and an
upstream location. We collected dissolved Ar:N2 samples using a
3.8 cm
diameter PVC pipe with an attached outlet tube (3.2 mm
ID × 20 cm
vinyl tube) at the downstream end. As water flowed through the pipe, we
capped the downstream end with a stopper. Lifting from the stream, water
flowed through outlet tube to > triple overflow a 12 mL Exetainer vial.
These vials were capped immediately without bubbles. We did not use preservative because we
analyzed samples within a week and we found no change in concentration of
these nearly inert gases in this time period using laboratory tests. We
measured specific conductivity using a handheld conductivity sensor or
conductivity and temperature using a Onset HOBO conductivity logger and
converted the values to specific conductivity at each sampling location. We
also recorded the stream temperature using a reference
Thermapen (ThermoWorks, American Fork, UT) and barometric pressure in millimeters of mercury (mmHg) using a
handheld barometer (Extech, Nashua, NH, USA) to calculate saturated dissolved gas
concentrations. We assumed SF6 concentration was 0 before the addition.
Following pre-injection sampling, we simultaneously injected Ar, SF6,
and a NaCl solution. We bubbled Ar using a micro bubble ceramic diffuser
(Point Four Systems Inc., Coquitlam, BC, Canada) from a compressed Ar tank at a
constant bubbling rate ∼ 0.2 m3h-1. SF6 was bubbled at
∼ 100 mLmin-1 through a needle valve attached to a variable area
flow meter and to a 30 cm aquarium air stone. Concurrently we injected a
NaCl
solution at a constant rate using a peristaltic pump. Salt solution flow
rates were enough to increase stream conductivity by 20 to 50 µScm-1.
Once the downstream station reached plateau conductivity, we
sampled each station for specific conductivity, stream temperature,
barometric pressure, and triplicate dissolved gas concentration as for the
pre-injection sampling. Additionally, we sampled SF6 by sucking 45 mL of
stream water into a 60 mL plastic syringe and adding 15 mL of air. The
syringe was shut using a stopcock and shaken for 5 min. The 15 mL of
headspace was injected into an evacuated 12 mL Exetainer. We collected
three SF6 samples at each station. We collected all samples in an upstream to
downstream sequence and we stored these samples at cooler-than-stream
temperature to prevent outgassing.
We measured stream physical variables. We estimated stream discharge, Q,
based on dilution of the NaCl tracer. Nominal transport time (t) was
estimated as the time to reach one half of the plateau concentration of
conductivity. Stream velocity (v) was reach length, measured by meter tape,
divided by t. We measured the stream mean wetted width at more than eight locations at
constant intervals through the sampling reach.
Ar and N2 analysis
We measured dissolved Ar:N2 in water samples using a membrane inlet mass
spectrometer (MIMS) (Bay Instruments Inc., Easton, MD, USA)
. We used a two-point calibration by setting water
bath temperatures at ±2 ∘C of
the sample collection temperature. Round-bottom flasks in each water bath were equilibrated with the atmosphere by
stirring at ∼ 200 rpm. We bracketed groups of 5–10 samples with
calibration samples from each water bath. We recorded the currents at m/z 28 and 40,
and their ratio from the mass spectrometer
.
We converted the ratio currents m/z 40 :m/z 28 to Ar:N2 ratios. We
normalized all Ar measures to N2 because the MIMS is more precise with gas
ratios than absolute concentrations. We calculated the Ar:N2 in each of
the two standard flasks assuming that they were in equilibrium with the
atmosphere at a known temperature and barometric pressure. We estimated
saturation concentrations in each flask based on .
Unknown Ar:N2 in each sample was calibrated using a linear relationship
derived from the Ar:N2 in the two standard flasks. Despite adding Ar to the
streams, the amount of Ar was not high relative to ambient Ar. Based on the
small enrichment of Ar, we assumed that N2 concentration changed little
during the injection due to bubble exchange with Ar. In addition we assumed
no biologically driven N2 fluxes. Denitrification would cause a uniform
and small increase to the N2 concentration compared to saturation
throughout the reach.
SF6 analysis
We measured SF6 at the Utah State University Aquatic Biogeochemistry Lab
using a gas chromatograph (GC) (SRI Instruments, Torrance, CA, USA) with an
electron capture detector. We injected 5–20 µL of samples into the GC for
analysis. From each measurement, we estimated the relative SF6
concentration as area of the peak divided by injection volume. We assumed no
SF6 present in streams naturally and therefore use a pre-plateau value
of 0. Blanks showed no SF6.
Data analysis and inference
We estimated gas exchange rates assuming a first-order decay with distance.
Let A represent the excess Ar:N2 and S excess SF6 (measured as peak
area × injection volume) in stream water corrected for groundwater
inputs. C is specific electrical conductivity (µScm-1). First, at
each site, x, we estimated a groundwater-corrected Ax and Sx as
Ax=[Ar:N2]x,plateau-[Ar:N2]x,ambientCx,plateau-Cx,ambientSx=[SF6]x,plateauCx,plateau-Cx,ambient,
where “plateau” and “ambient” indicate samples collected during and before
the gas and salt additions. We estimated ambient Ar based upon temperature at
each site during the collection time of the plateau samples. Measured ambient
Ar:N2 accurately matched the calculated ambient but had higher within-site
variability due to measurement error; thus, we assumed that ambient Ar:N2
was that estimated based on saturation calculations (see Supplement).
We normalized Ax and Sx to that of their upstream-most concentrations,
i.e., at the first sampling station below the injection (A0, S0).
Anx=AxA0,Snx=SxS0
We fit exponential decay statistical models to the data
Anx∼N(An0×e-Kd×x,σA)Snx∼N(Sn0×e-Kda×x,σS),
where Kd is the per-length evasion rate of Ar and a is the ratio of
exchange rates between Ar and SF6. This model assumes that both Ar and
SF6 declined exponentially with distance downstream (x) and that
residual errors were normally distributed with a mean of 0 and standard
deviations σA for Ar and σS for SF6.
Parameters in this model are An0, Sn0, Kd, a, σA, and σS.
Exponential decline of normalized argon and SF6 at each
downstream sampling site for each stream shows that rates of decline for
SF6 (blue) are lower than that for Ar (red). Points are normalized tracer
gas concentrations, Anx and Snx, and lines are exponential model fits
(Eq. ).
We fit these models within a hierarchical Bayesian framework. We were most
interested in the value of a, i.e., the ratio of gas exchange for Ar and
SF6. For any stream, j, we estimated aj by using partial pooling
across additions such that its prior probability was
aj∼N(amean,σa),
where amean had a prior distribution of amean∼N(1.36,1). This distribution had a mean of 1.36, which is the
expected ratio of kAr:kSF6 based on Eq. (), and a
standard deviation of 1 allowing for considerable variation in amean
from 1.36. The among-stream variation aj (σa) had a half-normal
prior distribution of σa∼|N(0,2)|. Prior probability
for Kd was Kd∼N(0,0.1), where -0.1 would be a very high
rate of gas exchange. Prior probabilities for An0 and Sn0 were Kd∼N(1,0.05).
We fit this model by simulating the posterior parameter distributions
using the program Stan via the
rstan package in R .
Stan uses a Markov chain Monte Carlo (MCMC) method to simulate posterior distributions. For each parameter we ran
four MCMC chains with 500 steps for burn-in and 1000 for sampling. We visually checked
the chains for convergence and that of the scale reduction factor,
R^<1.1, for all parameters.
We converted the per-distance rate to gas exchange of Ar to per-unit time
(K, d-1) as K=Kd/v, where v is stream velocity (md-1).
Gas exchange velocity (k, md-1) was calculated as
k=Q×Kdw.
To facilitate comparison with other studies, we scaled our
temperature-specific estimates of k from each stream to k at a Schmidt number of 600
(k600) following Eq. () using equations to estimate Sc
from .
Results
We enriched all streams with Ar and estimated gas exchange rates with varying
precision. Enriched Ar:N2 at the first station downstream from the
addition site averaged 7 % higher than the ambient Ar:N2 (range 1.2 to
18 %). This low enrichment was large enough to easily measure a decline in
Ar:N2 to ambient (Fig. ), but low enough to minimally
affect absolute N2 concentration via degassing of N2 if we had, for example,
enriched Ar 10-fold. Gas exchange rates, Kd, ranged from 0.00067 to 0.050 m-1 and
the 95 % credible interval on these rates averaged 0.42 % (range
36–54 %) of the rate itself. Precision on our Ar:N2 measures was high. The
median standard deviation of replicate samples at each station was
3.31×10-5, corresponding to a coefficient of variation (cv) of
0.09 %. The cv for Ar concentration was 2.5 times higher at 0.23 %, showing that
normalizing Ar by N yielded more precise estimates. The coefficient of variation
for replicates of SF6 analyses was 5 %, much higher than that for Ar:N2.
Ratios of KAr:KSF6 measured in each injection varied greatly and were
higher than the expected ratio of 1.36. These ratios (aj) varied from 0.6
to 3.4 (Table ) and the mean of the pooled ratio (amean)
was 1.8 with a 95 % credible interval, 1.1–2.5. Variation among releases was
high, with σa=0.9. The credible interval in a averaged 49 % of
a, showing that estimates of SF6 evasion had slightly more uncertainty
than that for Kd. This finding is despite the fact that σS was
lower than σA, likely because some values of normalized A
(Anx) were negative. Negative values of Anx increase σS, but
do not necessarily increase uncertainty in the estimate of Kd because the
predicted Anx values are always > 0 in an exponential model.
Variability in a led to potential for error in estimating k600 between Ar
and SF6. K600 based on SF6 was lower than that for Ar for six of the
eight additions (Fig. ). Deviance from a 1 : 1 line exceeded that
of the statistical errors around Kd in the models because the posterior
distributions themselves deviated from the 1 : 1 line
(Fig. ).
Gas exchange was high at our steep streams. Gas exchange velocity (k600)
ranged from 5.4 to 208 md-1 and covaried tightly with variation in
stream slope (Fig. ). The k600 from our streams were
much higher than most literature values; the four sites with slopes ≥0.05
exceeded 99 % of the values in . The per-time rate
of gas exchange ranged from 28 to 740 d-1 (Table ).
k600 measured from SF6 was lower than predicted from k600
measured from argon in six of the eight injections. Each injection is represented
by a cloud of points that represents 6000 draws from the posterior
distributions of kj and aj, from which we calculated gas exchange
velocity (k) following Eq. () and converted to k600 using
Eq. (). Line is 1 : 1. Note log-scaled axes.
Discussion
Despite low enrichment of Ar:N2, we estimated Kd based upon exponential
declines of this tracer gas signal. On the surface, one might consider Ar to
be a poor tracer gas because it is the third most abundant gas in the
atmosphere at 1 % concentration, thus requiring a large increase in
concentration to detect a decline. However, because MIMS is highly precise when
measuring gas ratios , it is not necessary to elevate
concentrations greatly. This low enrichment has two advantages. One is the
practical aspect of not needing to haul a big tank of gas to a remote stream
(a 2.2 kg tank lasted us for several additions). The second is that the Ar
bubbling stripped little of the N2 from the stream. A potential concern
when conducting these experiments is that excess Ar bubbled to the stream will
strip N2 as Ar diffuses from bubbles and N2 diffuses in. If this N2
flux is large, one would need to model the concomitant invasion of N2 as
well as the evasion of Ar. How much N2 did the Ar strip? We averaged an
enrichment of 7 % of ambient Ar concentration with a high of 18 %. This high
value corresponds to in an increase in dissolved Ar from 0.476 to
0.561 mgL-1, which is an enrichment of 0.00214 mmolArL-1.
Assuming a mole for mole exchange with N2 gas, there would be a 0.00214 mmolL-1
decline in N2 from its saturation concentration of 0.455 mmolL-1.
This value represents a 0.47 % decline in dissolved N2, a
small amount relative to the 18 % increase in Ar.
Gas exchange velocity increased as a power relationship with stream
slope. Equation is ln(k600)=1.055×ln(slope)+6.90. Error bars are
the 95 % credible intervals.
However, added Ar must exceed a threshold to have a high enough signal-to-noise
ratio to detect a decline in Ar. We suggest at least a 3 % increase in
the Ar concentration. Given that measurement error with the MIMS is constant
across a range of concentrations, all things equal, higher values of Ar:N2
are better, until such an amount that it is necessary to model concomitant
N2 invasion. We did not test the conditions under which we could increase
the incorporation rate of Ar into streams, but it seems reasonable to assume
that higher Ar flow rate, larger air stones, and deeper pools in which to
inject Ar would all increase values of Ar:N2. We used a fine-bubble air
stone and suggest that this device greatly facilitated Ar exchange. One needs
to be aware of changing temperature between the ambient and during plateau
samples. Changing temperature 5 ∘C can cause a 1 % change in Ar:N2;
hence, one needs to estimate ambient Ar:N2 during plateau if temperature
is changing either by calculating ambient Ar:N2 at sampling temperature or
monitoring at an upstream station.
Our estimates of the ratio of KdA:KdS (a) were higher than the 1.36
expected based on Schmidt number scaling and the 1.33
based on Eq. (). This ratio a also varied greatly among
injections, such that we had high uncertainty on the actual value of a
(Table ). Thus, there are two problems. One is estimates of
Kd for either tracer gas contained substantial error, leading to high
variation in estimates of a. The other is that a was inexplicably higher
than predicted for both smooth surface and bubble-mediated transfer. Either
the theory for scaling in Eqs. () and
() did not work
in our case or we estimated either KdA or KdS with bias. From a
theoretical perspective, this question behind a>1.36 is compelling,
because if true it complicates models of bubble-mediated gas exchange
. From a practical perspective – where one simply
needs to estimate k600 for O2 exchange – this question is less germane
given that one could simply use Ar rather than SF6. If one uses tracer
estimates for SF6, and our estimate of a was in fact 1.8, then, all else
equal, gas exchange will be estimated at 1.36/1.8=0.75 times lower than the true
value, which we observed for six of the eight injections
(Fig. ). If using these gas exchange rates to estimate
metabolism, then estimates of ecosystem respiration will also be 0.75 times
too low. This bias in ecosystem respiration may be small relative to the
effects of groundwater, probe calibration, and process error
, but this bias adds to the complications
in estimating ecosystem respiration from diel O2 data
.
In steep, turbulent streams and rivers, bubbles likely cause most of the gas
exchange , complicating scaling among gases because one
must consider variation in solubility as well as variation in Sc. Theory
from suggests that at low solubilities variation in Sc
is all that is needed to scale among gases. Thus, scaling from SF6 to Ar or
O2 may be constant as kb approaches Kw. Although we did not assess
propane in this study, based on the similarity of propane Sc and α
with SF6, it is likely that there is not a strong solubility effect on its
rate of kb. For gases with much higher solubility, i.e., CO2, scaling
may deviate strongly when bubbles dominate gas exchange
(Fig. ) because bubbles do not reach equilibrium and
this scaling depends upon both Sc and solubility. Such streams have high
rates of gas exchange and error in estimating k for CO2 that may greatly
affect flux estimates in these streams. Thus, we caution using the findings
here for estimating CO2 flux in streams with high turbulence. In addition,
our subsequent work (Amber J. Ulseth, unpublished data, 2018) will show that it
is not possible to predict k600 in highly turbulent stream based on models
from low-energy streams and rivers . Streams with
steep slopes, such as our four steepest streams, have much higher gas exchange
than would be predicted from current empirical models
.
Conclusions
We recommend using Ar as a tracer gas in small streams. Argon is an inert and
easily obtained gas that one can precisely measure using MIMS. In addition,
Ar is not a greenhouse gas. While SF6 is inert and easily detectable, thus
making a potentially ideal tracer, SF6 has 23 500 times the greenhouse
forcing of CO2. It is somewhat ironic to
study carbon cycling using a tracer gas with that much greenhouse forcing. If
one is interested in O2 exchange, then Ar is an optimal tracer because it
has nearly the same solubility and diffusivity of O2, thus eliminating the
need to scale between gases. Given uncertainty with scaling due to bubbles
and the higher-than-predicted scaling ratio (a) found here, scaling from
SF6 to O2 is somewhat uncertain. SF6 does hold the advantage as a
gas tracer for large streams and rivers. We focused only on small streams
here and have not tested this method on larger streams and rivers. One would
need to add much more Ar, which is difficult, but possible with larger tanks
and air stones. SF6 is so detectable that it is used in very large rivers
. But it may be easier to measure gas exchange in large
rivers using diel cycling of O2 in lieu of a tracer .
In fact, with low gas exchange, diel O2 cycling may provide more accurate
estimates of k600 than tracer additions that extend for multiple kilometers
downstream and with a long time series of diel
O2, one can obtain even better estimates of k600. The Ar method we present here, however,
worked well in small, steep streams where high rates of gas exchange required
empirical measurements for accurate estimates of k600.
Code and data for all analyses are available in the
Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-15-3085-2018-supplement.
ROH Jr. and HLM designed the study, conducted fieldwork,
measured SF6, and analyzed data. HLM measured Ar:N2. ROH Jr. wrote the first draft of the paper and made the figures.
The authors declare that they have no conflict of interest.
Acknowledgements
Ina Goodman, Alison Appling, Pavel Garcia, Keli Goodman, Brady Kohler,
Brittany Nordberg, and Rachel Usher assisted with fieldwork. Michelle Baker
and Autumn Slade set us up with their GC and provided food. Financial support
came from National Science Foundation grants EPS-1208909 and EF-1442501.
Amber Ulseth, Tom Battin, Lauren Koenig, Daniel F. McGinnis, and an anonymous
reviewer read and commented on early drafts of this paper. We dedicate this
paper to the memory of Ina Goodman.
Edited by: Tom J. Battin
Reviewed by: D. F. McGinnis and one anonymous referee
ReferencesAlin, S., Maria de Fátima, F., Salimon, C., Richey, J. E., Holtgrieve,
G. W., Krusche, A. V., and Snidvongs, A.: Physical controls on carbon
dioxide transfer velocity and flux in low-gradient river systems and
implications for regional carbon budgets, J. Geophys. Res.-Biogeosci., 116, G01009, 10.1029/2010JG001398, 2011.Appling, A. P., Hall, Jr., R. O., Yackulic, C. B., and Arroita, M.: Overcoming
equifinality: Leveraging long time series for stream metabolism
estimation, J. Geophys. Res.-Biogeosci., 123, 624–645,
10.1002/2017JG004140, 2018.
Asher, W. and Wanninkhof, R.: Transient tracers and air-sea gas transfer,
J. Geophys. Res.-Oceans, 103, 15939–15958,
1998a.
Asher, W. E. and Wanninkhof, R.: The effect of bubble-mediated gas transfer on
purposeful dual-gaseous tracer experiments, J. Geophys. Res.-Oceans, 103, 10555–10560, 1998b.Bernhardt, E., Heffernan, J., Grimm, N., Stanley, E., Harvey, J., Arroita, M.,
Appling, A., Cohen, M., McDowell, W., Hall, R., Read, J., Roberts, B., Stets,
E., and Yackulic, C.: The metabolic regimes of flowing waters, Limnology and
Oceanography, 63, s99–s118, 10.1002/lno.10726, 2018.Borges, A., Delille, B., Schiettecatte, L., Gazeau, F., Abril, G., and
Frankignoulle, M.: Gas transfer velocities of CO2 in three
European estuaries (Randers Fjord, Scheldt, and Thames), Limnol.
Oceanogr., 49, 1630–1641, 2004.Cole, J. J. and Caraco, N. F.: Atmospheric exchange of carbon dioxide in a
low-wind oligotrophic lake measured by the addition of SF6,
Limnol. Oceanogr., 43, 647–656, 1998.
Demars, B. O., Thompson, J., and Manson, J. R.: Stream metabolism and the open
diel oxygen method: Principles, practice, and perspectives, Limnol.
Oceanogr.-Methods, 13, 356–374, 2015.
Goddijn Murphy, L., Woolf, D. K., Callaghan, A. H., Nightingale, P. D., and
Shutler, J. D.: A reconciliation of empirical and mechanistic models of the
air-sea gas transfer velocity, J. Geophys. Res.-Oceans, 121,
818–835, 2016.
Hall, R. O., Kennedy, T. A., and Rosi-Marshall, E.: Air-water oxygen exchange
in a large whitewater river, Limnol. Oceanogr.-Fluids Environ., 2, 1–11, 2012.
Hall, R. O., Tank, J. L., Baker, M. A., Rosi-Marshall, E. J., and Hotchkiss,
E. R.: Metabolism, gas exchange, and carbon spiraling in rivers, Ecosystems,
19, 73–86, 2016.
Hamme, R. and Emerson, S.: The solubility of neon, nitrogen and argon in
distilled water and seawater, Deep-Sea Res. Pt. I, 51, 1517–1528,
2004.
Ho, D. T., Schlosser, P., and Orton, P. M.: On factors controlling air–water
gas exchange in a large tidal river, Estuar. Coast., 34, 1103–1116,
2011.
Holtgrieve, G. W., Schindler, D. E., Branch, T. A., and A'mar, Z. T.:
Simultaneous quantification of aquatic ecosystem metabolism and reaeration
using a Bayesian statistical model of oxygen dynamics, Limnol.
Oceanogr., 55, 1047–1063, 2010.Holtgrieve, G. W., Schindler, D. E., and Jankowski, K.: Comment on Demars et
al. 2015, “Stream metabolism and the open diel oxygen
method: Principles, practice, and perspectives”,
Limnol. Oceanogr.-Methods, 14, 110–113, 2015.
Hornberger, G. M. and Kelly, M. G.: Atmospheric reaeration in a river using
productivity analysis, J. Env. Eng.-Div.-ASCE,
101, 729–739, 1975.
Jähne, B., Münnich, K. O., Bösinger, R., Dutzi, A., Huber, W., and
Libner, P.: On the parameters influencing air-water gas exchange, J. Geophys. Res.-Oceans, 92, 1937–1949, 1987.Kana, T. M., Darkangelo, C., Hunt, M. D., Oldham, J. B., Bennett, G. E., and
Cornwell, J. C.: Membrane inlet mass spectrometer for rapid high-precision
determination of N2, O2, and Ar in environmental water samples,
Anal. Chem., 66, 4166–4170, 1994.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D.,
Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic
and natural radiative forcing, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, 658–740, Cambridge University
Press, 2013.
Nicholson, D. P., Wilson, S. T., Doney, S. C., and Karl, D. M.: Quantifying
subtropical North Pacific gyre mixed layer primary productivity from
Seaglider observations of diel oxygen cycles, Geophys. Res. Lett.,
42, 4032–4039, 2015.
Odum, H. T.: Primary production in flowing waters, Limnol.
Oceanogr., 1, 102–117, 1956.
Raymond, P., Hartmann, J., Lauerwald, R., Sobek, S., McDonald, C. P., Hoover,
M., Butman, D., Striegl, R., Mayorga, E., Humborg, C., Kortelainen, P.,
Dürr, H., Meybeck, M., Cais, P., and Guth, P.: Global carbon dioxide
emissions from inland waters, Nature, 503, 355–359, 2013.
Raymond, P. A., Zappa, C. J., Butman, D., Bott, T. L., Potter, J., Mulholland,
P., Laursen, A. E., McDowell, W. H., and Newbold, D.: Scaling the gas
transfer velocity and hydraulic geometry in streams and small rivers,
Limnol. Oceanogr.-Fluids Environ., 2, 41–53, 2012.R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/, last access: 30 November 2016.Stan Development Team: Stan Modeling Language Users Guide and Reference Manual, Version 2.17.0, available at: http://mc-stan.org, last access: 11 May 2018.
Wanninkhof, R.: Relationship between wind speed and gas exchange, J.
Geophys. Res.-Oceans, 97, 7373–7382, 1992.
Wanninkhof, R., Mulholland, P. J., and Elwood, J.: Gas exchange rates for a
first-order stream determined with deliberate and natural tracers, Water
Resour. Res., 26, 1621–1630, 1990.
Woolf, D., Leifer, I., Nightingale, P., Rhee, T., Bowyer, P., Caulliez, G.,
De Leeuw, G., Larsen, S., Liddicoat, M., and Baker, J.: Modelling of
bubble-mediated gas transfer: Fundamental principles and a laboratory test,
J. Marine Syst., 66, 71–91, 2007.