Appendix: Detailed description of data evaluation procedures
Spectra were analysed using the DOASIS software (Kraus, 2003). Initially, the spectra were corrected
using measured dark spectra and electronic offset and following the procedure applied in Bobrowski
(2005) and Hoeninger (2002). The spectral quality below 365nm was examined and only spectra with
maximum intensity between 19,000 and 59,000 counts were included. All registered sets of spectra
were tested for stability of wavelength calibration and instrumental function. Small shifts of about
0.1 nm caused probably by imperfect stabilisation of the spectrometer’s optical bench temperature
have been observed between spectra registered during most of the data sets. To avoid the resulting
false modulation in the logarithm of the ratio S()/So() (where (S() denotes plume spectrum which
is being fitted, whilst So() is the corresponding background spectrum), the reciprocal of the
logarithm of background spectra was treated as a cross-section and was fitted to the logarithm of
S() together with the cross-sections of measured gases. The cross-sections used for DOAS fitting of
the measured spectra are listed in Table 3. All cross-sections were convolved by the spectrometer’s
instrumental function using QDOAS software (Danckaert et al., 2012). The sulphur dioxide crosssection was first deconvolved to high resolution by the same software. Further, a Ring spectrum was
calculated from the solar spectrum (Chance and Kurucz, 2010), also using QDOAS software. A
polynomial was fitted in order to account for all slow-varying absorbers and instrumental effects.
The degree of polynomial ranged from 2 to 4 and was selected to minimise the residual and
homogenise it. The fit was performed in wavelength space, i.e. all spectra and cross-sections
included in the non-linear DOAS fit had wavelength calibration. The shift and the squeeze permitted
during the fit were limited to -0.5 nm–0.5 nm and 0.97–1.03, respectively.
In order to improve the signal-to-noise ratio in the spectra and thus reduce the error on the BrO and
OClO retrievals, spectra were averaged in groups of five before fitting.
A systematic residual was present in all of the datasets, possibly caused either by temperature
effects, by ignoring the slight wavelength dependence of the instrumental function, or by non-
compensated slow-varying aerosol effects. While the residual varied from day to day, within each
dataset it was consistent. The residual was calculated for each dataset, and included in the fits. This
practice is discussed in detail by Chance (1998) and Allan et al (2000). Chance notes that it results in
a factor of two decrease in the error on BrO SCAs. In our case we observed a slightly stronger
decrease, about 2.0–2.5 times. We checked that there was no statistical relationship between the
residual cross-sections of all absorbers included in the fit.
There are a number of sources of error in DOAS measurements of trace gas column amounts. These
include instrumental errors and stray light, photon noise, radiative transfer effects due to aerosol
and Rayleigh scattering, the influence of clouds, the Io-correction effect when retrieving weak
absorbers, thermal effects on the spectrometer performance and errors due to non-perfect
absorption cross-sections and their temperature dependences (e.g. Platt et al., 1997; Platt and Sturz,
2008).
Instrumental error were minimised as noted above by correction of dark and offset spectra
registered at the correct temperatures. The USB2000+ spectrometer was precisely calibrated and
thermostabilised (see above) and the best available absorption cross-sections at corresponding
temperatures were included in the fit. Photon noise was reduced by averaging measured spectra. In
order to estimate the errors of DOAS retrievals we have first to select proper fitting interval,
consider necessity of Io-corrections and account for retrieval errors and influence of radiative
transfer effects.
Sulphur dioxide reveals strong absorption between 300 and 330 nm. Two effects compromise the
retrieval depending on the fit window used. The first one is the presence of stray light components
𝐴𝑜 (𝜆) and 𝐴(𝜆) in signals 𝑆0 (𝜆) and 𝑆(𝜆). When the magnitudes of stray light in the instrument and
spectra are comparable, the optical density derived in the DOAS retrieval is underestimated (Platt
and Stutz, 2008):
𝑆(𝜆) + 𝐴(𝜆)
)
𝑆0 (𝜆) + 𝐴0 (𝜆)
𝑙𝑛 (
𝑆(𝜆)
)
𝑆0 (𝜆)
≈ 𝑙𝑛 (
× (1 −
𝐴(𝜆)
)
𝑆(𝜆)
if
𝐴(𝜆)
𝑆(𝜆)
≈
𝐴0 (𝜆)
𝑆0 (𝜆)
.
The impact of radiative transfer effects on DOAS retrievals was first reported by Millan (1980) who
also provided a simplified theoretical model and some quantitative estimation. Later, Weibring et al.
(2002) and Mori et al. (2006) reported experimental confirmation of the dependence of DOAS
retrievals on scattering. Three effects may occur: widening of the plume due to multiple scattering in
the plume and surrounding atmosphere; increase of the retrieved column amount due to multiple
scattering in plume; or decrease of the retrieved column amount due to dilution effects. The
radiative transfer effects on DOAS retrievals have been explained theoretically by means of three
Monte-Carlo simulation models (Weibring et al., 2002; Kern et. al., 2010; Kern et al., 2012). None of
these models accounts for all the above-mentioned effects. The proposed method for correction of
the scattering effects requires presence of corresponding software and a-priori information about
plume and atmosphere. Furthermore, processing is time consuming and labour intensive. An
additional disadvantage is the common assumption of a cloudless atmosphere. In reality volcanic
plumes are quite often surrounded by clouds. The Monte-Carlo simulation requires a more
complicated approach in order to account for clouds (e.g. see Schröder, 2004, and references
therein). As a result radiative transfer effects are ignored when dealing with large data sets
(Bobrowski and Giuffrida, 2012; Mori et al.,2013; Lübcke et al. 2013).
Comparing our plume and atmospheric parameters, registration conditions and estimated column
amounts when ignoring the scattering effects with the estimations of “effective” air mass factors
reported by Kern (2010) for number of “fit scenarios” we concluded that our DOAS retrieved column
amounts for SO2 are underestimated by 11 to 33%. Further Kern et al (2010) showed that for
relatively transparent plume and surrounding atmosphere (as in our case) the “effective” air mass is
practically wavelength independent.
In order to minimise the impact of stray light and scattering effects on SO2 DOAS retrieval , and thus
to reduce any resulting error, an optimisation script was applied to find the most effective range for
SO2 retrievals (Tsanev and C. Oppenheimer, 2006; Tsanev, access on web-page). The script operates
by fixing the end point of the fitting interval, and progressively selecting minima and maxima of the
SO2 absorption cross section as start points in succession. Assessment of the fit residual from the
range of intervals allows the identification of the ideal fitting range, in which the air mass factor
defined by radiative transfer is approximately constant and where there is no stray light
interference. This algorithm is very similar to ‘Retrieval interval mapping’ proposed by Vogel (2013).
Its aim is to select a fitting interval where residual is minimised but homogeneous and at the same
time evaluated SO2 column amount are approximately constant. In this way the influence of stray
light and scattering effects is minimised as much as possible. The SO2, O3, NO2 and Ring cross
sections were included in the fit scenario. This algorithm suggested that the optimum range for SO2
retrievals was from 310.78 nm to 323.67nm for our data. The changes of fit residual in dependence
on the lower limit of fitting range are presented on Figure 2. At longer wavelengths, the absorption
of SO2 was significantly lower and a wider fitting range did not improve the results significantly, but
resulted in larger errors.
Figure A1: Maximum, mean and minimum residual intensity (arbitrary units) for the fit range to
323.67nm, varying the initial wavelength of the interval. The fit stabilises at 310.78 nm.
BrO and OClO were retrieved in two spectral ranges: 320-360 nm (to coincide with that used by
Bobrowski, 2003, 2007) and 331-357 nm, which contains three peaks of OClO and five of BrO (Kern
et al., 2009). In both of these ranges, the cross sections for BrO, OClO and HCHO are distinct (Figure
A2). Cross sections for O3, O4, BrO, OClO, NO2 and SO2 were included in the fit.
Figure A2: Cross-sections used in the fits, plus HCHO. Fit windows were 320-360nm and
331-357nm.
Small amounts of formaldehyde have been detected near some volcanoes (Bobrowski, 2005; Vogel,
2011; Lübcke et al., 2013). In all these cases formaldehyde probably originates from atmospheric
photochemical tranformations of volatile organic compounds (VOCs) or biomass burning (Luecken et
al., 2010). Further it is known that NO2 may exist in volcanic plumes if enough hot lava is in contact
with atmospheric air (Martin et al., 2012). We tested thoroughly all background spectra by using a
convolved solar spectrum as a reference (Salerno et al., 2009). We checked spectra for the presence
of NO2, and this species was not found above the detection limit. This is consistent with the
relatively low temperatures of the lava dome. Since HCHO is not of volcanic origin and we found no
HCHO in the background spectra or measured spectra, and there is no reason to expect it to be
present, we have not included HCHO in the fit.
Vogel et al (2013) discuss the role of the I0 effect in retrievals of BrO from volcanic plumes. This is
caused by the presence of Fraunhofer lines in the background spectra used in DOAS retrievals and
their absence in the cross-sections recorded in a laboratory (Aliwell et al., 2002). Vogel et al. (2013)
note that unlike zenith-sky DOAS retrievals for stratospheric weak absorbers, BrO in volcanic plume
retrievals experience considerably less error as a result of not accounting for the I0-effect. There is
currently no theoretical means to estimate the error on BrO and OClO measurements from multiple
scattering effects (Bobrowski et al., 2003; Lubcke et al., 2013; Vogel et al., 2013).
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that the results were artefacts of the processing due to high concentrations of SO2 and the overlap
of the ClO and SO2 cross sections. We have therefore omitted these results from the analysis.
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