SUPPLEMENTAL MATERIAL
S1. SOURCES OF CHEMICAL INTERFERENCE
In addition to spectroscopic interferences from other molecules, the production of IO
from the laser-induced photolysis of iodine-containing species should be considered. The
Ti:Sapphire laser is operated at high repetition rates (5 kHz) with low pulse energies to reduce
the probability that the laser beam will produce IO. Given the experimental flow rates (~1.5
meters/second), a sample passes through the detection volume in 2 × 10-2 seconds. However, the
rate (k = 1.2 × 10-12 cm3 molecule-1 s-1 1) at which the reaction of I + O3 proceeds is too slow for
significant production of IO. Assuming ambient O3 concentrations of 100 ppbv, the calculated
lifetime for an I atom is 12.8 s within the detection axis, 4 orders of magnitude longer than the
residence time of the sample within the detection region.
While the residence time in the detection region is insufficient for the production of IO
from I atoms, the photolysis of precursor species that can produce an IO fragment directly would
contribute to the observed fluorescence signal. HOI, a reservoir of atmospheric iodine in the
absence of sunlight, photolyzes to OH and I with a near unity yield.2 While the yields of the
photolysis products of OIO are uncertain,3-6 the absorption cross section of OIO is less than 1 ×
10-18 cm2 molecule-1.7 The lifetime of OIO against photolysis (~1 s) is long relative to the
residence time of a sample in the excitation/detection region (2 × 10-2 s).
IO and I can react with NO2 forming IONO2 and INO2 respectively.1 In semi-polluted
environments with elevated NO2 mixing ratios (> 1ppbv), IONO2 may be the dominant iodinecontaining species.8 While the absorption cross sections for these molecules are not reported
explicitly in the literature for 445 nm, there is no evidence to suggest that there are strong
absorption features at this wavelength.1 Joseph et al.5 reported experimental and theoretical
evidence to support a minimal (< .02 ) photolytic yield of IO from IONO2. Photolysis yields of
INO2 are not well-characterized, but this species is not expected to be the major reservoir of I
atoms in polluted environments.8 In the absence of known absorption cross sections, quantum
yields, or measurements of atmospheric concentration, interference due to these species cannot
be quantified. Assuming a unity yield for the production of IO from photolysis, and atmospheric
concentrations of 1 ppb of IONO2 and INO2, the absorption cross section at 445 nm would have
to be on the order of 1 x 10-18 cm2 molecule-1 for these species to create a detectable interference
given the instrumental sensitivity. Whalley et al.9 predicted insignificant interference for a
similar instrument with a single-pass LIF detection axis and higher average laser power at 445
nm.
S2. UNCERTAINTY ANALYSIS USING THE BOOTSTRAP
The first step in the bootstrap analysis was to analyze calibration data with the same
procedures used to analyze observational data. The peak fitting process, described previously,
yields Gaussian models for calibrated amounts of IO. Since an approximately constant
concentration of IO was maintained for several minutes, at least 20-30 scans across the IO line
were recorded for each IO mixing ratio. The residuals of these fits were binned based on
wavelength, providing a frequency dependent population of residuals for a given concentration
of IO. Some autocorrelation was apparent in the residuals, so a first-order autoregressive model
(AR1) was used to determine the autocorrelation coefficient for each scan. Autocorrelation
coefficients for a given scan were typically on the order of 0.35. The autocorrelation factor was
then used to infer a “white noise” contribution from the correlated noise.
The frequency dependent white noise data were then randomly resampled to generate
“simulated peaks” from the combination of the Gaussian model and binned residuals. Figure S1a
shows how the simulated IO peaks are generated. First, the wavelength dependent white noise
was randomly resampled. The autocorrelation was reproduced by randomly resampling the
autocorrelation coefficients observed in the data and applying correlation to the wavelength
dependent white noise. This correlated noise was then added to the Gaussian model of the IO
lineshape (representing a calibrated amount of IO) to create a simulated IO peak with noise
characteristics analogous to those observed in the experimental dataset. These simulated peaks
were then fit to a Gaussian using the nonlinear fitting procedure. The generation of simulated IO
peaks followed by subsequent fitting was repeated 1000 times for each calibrated concentration
of IO.
Confidence intervals associated with the fitting parameters were calculated based on the
1000 fits at each IO concentration. The 95% confidence intervals from this exercise, as presented
in Figure S1b, show that the absolute uncertainty in the fit does not scale strongly with the
concentration of IO. This indicates that in absolute terms, the spread in the fit does not vary
significantly with the IO signal strength. The precision derived from the peak fitting is on
average ± 0.44 pptv (95% confidence interval). The accuracy of the fit is high as demonstrated
by linear regression with a high coefficient of determination (>0.99) and slope of approximately
1. None of the points deviates from the expected value by more than 10%. The peak fitting
procedure, is thus, considered to be an appropriate method of identifying the strength of IO
signal in a robust and reliable way.
Power Normalized Signal
(counts × second-1 mV-1)
(a)
simulated white noise
simulated AR1 noise
modeled IO peak
model + simulated
AR1 noise
Gaussian fit
1000
800
600
400
200
0
-200
2.865
2.87
2.875
2.88
2.885
2.89
Strain Gauge Amplifier Voltage (V)
IO Mixing Ratio from Fit (pptv)
(b)
bootstrapped data
linear fit
45
40
35
30
25
20
15
10
5
0
Fit Parameters:
y = (1.10 ± 0.05)x - 0.9 ± 3.4
R2 = 0.99
0
5
10
15
20
25
30
35
40
Calibrated IO Mixing Ratio (pptv)
FIG. S1. Uncertainty from peak fitting determined from a bootstrap analysis. (a) White noise is
generated by randomly sampling the wavelength dependent residuals from calibration data
observed at a constant concentration of IO. Using a randomly sampled autocorrelation factor (lag
= 1), correlated noise representative of that observed in an experiment can be reproduced from
the white noise. The correlated noise is then added to the Gaussian model to create a simulated
peak. The nonlinear least squares Gaussian fitting procedure is applied and the resulting fit
represents a single step in the bootstrap analysis. (b) Uncertainty analysis of the peak fitting
procedure conducted by bootstrapping. The mixing ratios of IO derived from fitting are plotted
as a function of the known concentrations established during a calibration experiment. The error
bars represent the 95% confidence intervals associated with a single fit.
S3. DIRECT SAMPLING FROM MACROALGAE
During the deployment, the strength of individual macroalgae species as a source of IO
was examined qualitatively. The macroalgae tests were carried out at the end of the deployment
(September 3-5). The predominant species observed in the intertidal range were Fucus
vesiculosus, commonly known as bladder wrack, and Ascophyllum nodosum, commonly known
as rockweed. These species were abundant and exposed during the majority of low tides. Several
other species were tested that had washed ashore and were not growing directly at the
measurement site, including: Chondrus crispus; Codium fragile; Palmaria palmata; Saccharina
latissima; and Laminaria digitata.
Samples of similar mass of each species were collected during low tide. The inlet of the
instrument was connected via a 1” inner diameter Teflon tube to a covered bucket holding the
seaweed sample. Air was first sampled from the bucket to determine if the seaweed emitted
iodine passively. The sample was then exposed to ozonized air generated by flowing oxygen
through a 750 sccm frit along the axis of a Hg pen ray lamp and directly into the bucket
containing the algae sample. The exposure to ozone serves as an approximation of extreme
oxidative stress for the macroalgae. This testing reflects the qualitative potential of a species to
emit iodine rather than a quantitative metric of emissions in a natural setting.
Table S1 summarizes the species tested, including the dry weight content of iodide as
measured by Martinelango et al.10 The time dependence and magnitude of observed emissions of
IO from the species are presented in Figure S2. Of the 7 seaweed samples examined, only 3
emitted significant quantities of IO. The strongest emission came from a mixed sample of
Laminaria digitata and Saccharina latissima. These species were grouped together because there
was a very limited amount of Laminaria digitata. Fucus vesiculosus also produced a relatively
large IO signal. Finally, Palmaria palmata yielded IO both passively and while under oxidative
stress, though the IO signal was much smaller in magnitude.
While the mixture of Laminaria digitata and Saccharina latissima showed the strongest
emissions, it should be noted that neither of these species were observed growing locally. The
test of these the species was conducted on September 3rd. On September 5th, Saccharina
latissima was retested alone and very little IO signal was observed. It is possible that the
Laminaria digitata was responsible for the emissions as suggested by other studies.10, 11
Alternatively, the aged samples may no longer have been capable of emitting iodine since
Hurricane Irene uprooted the microalgae samples 7 days prior to the measurement.
IO Signal by Mass (pptv / kg)
60
Laminaria digitata
and Saccharina latissima
Chondrus Crispus
Codium fragile
Fucus vesiculosus
Ascophyllum nodosum
Palmaria palmata
Saccharina latissima
50
40
30
20
10
0
0
50
100
150
200
Time (min)
FIG. S2. Emission of IO from ozonized samples of local seaweed collected at Shoals Marine
Laboratory. Ascophyllum nodosum and Fucus vesiculosus were growing locally, while other
samples were harvested after they had washed ashore.
Table 1. Summary of tests of emission of IO from macroalgae
Species
Common Name
Dry Weight
Max IO
of Iodide
per weight
(mg/kg)*
(pptv / kg)
Laminaria digitata
+Saccharina
Horsetail kelp ,
latissima
Sugar kelp
3134
56.37
Fucus vesiculosus
Bladder wrack
108
29.97
Saccharina latissima
Sugar kelp
574
3.5592
Palmaria palmata
Dulse
39
3.6353
Codium fragile
Green sea fingers
~
2.3147
Chondrus crispus
Irish moss
22
1.133
Ascophyllum
nodosum
Knotted wrack
275
0.15
* from Martinelango et al.
Relative
Integrated
IO signal
1.00
0.46
0.04
0.04
0.01
0.00
0.00
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