technological advances to improve the quantification of
TRANSCRIPT
vEGU21
Technological advances to improve the quantification
of volcanic emissions
Christopher Fuchs1, Jonas Kuhn1,2, Nicole Bobrowski1,2 and Ulrich Platt1,2
1Institut of Environmental Physics, Heidelberg, Germany2Max Planck Institute for Chemistry, Mainz, Germany
GI6.1: Remote sensing for environmental monitoring, EGU21-1308, 29 April 2021
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021 1
Imaging trace gases in volcanic emissions
Why monitoring volcanic trace gas emission?
• Changes in emission fluxes (e.g. CO2, SO2) can indicate changes in the volcanic activity• Variations in emission ratios (e.g. SO2/BrO) can identify changes in the volcanoes' magmatic system• Insights into trace gas concentration distributions to better understand atmospheric chemistry (e.g. halogens)
➔ Facilitating volcanic monitoring & activity assessment and increase understanding in atmospheric chemistry
Prerequisites for the imaging technique?
➔ Resolve plume processes at their intrinsic time and spatial scales
1. Differentiation between numerous trace gases → use characteristic differential absorption structures
2. Sufficient selectivity and sensitivity→ spectral resolution in the “sub nm” scale
3. Quantification on concentration distributions→ column densities (CDs, number density of trace gas along light path)
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021 2
Applied imaging techniques
Imaging DOAS
2D detector: one dimension for spatial information, other dimension for spectral information → scanning required for a 2D column density image
High spectral informationdetection of various trace gas species simultaneously
Too slow due to scanninglimited light throughput + complex scanning mechanism
acquisition time in the order of minutes
SO2 UV-cameras
2D detector solely used for spatial information → spectral selection by wavelength selective elements (e.g. spectral filter using 2 spectral channels exploiting broad band absorption)
High spatio-temporal resolutionplume propagation direction from images; flux monitoring
Low spectral selectivity and high cross interferencesonly for high SO2 CDs due to broadband interference withwith clouds, aerosols and ozone
Louban et al., 2009
SO2 CD
BrO CD
volcanicplume
volcanicplume
SO2 CD image@ 1 fps & 1 MP
channel A channel B
≈ 25 nm gap
broadbandinterference
Lübcke et al., 2013
By selecting appropriate FPI quantities (n, d, R, α) the FPIs’ transmission spectrum can be matched to periodic absorption features of the investigated trace gas (here: SO2)
Transmission at max.SO2 absorption(on-band, setting A)
Transmission at min. SO2 absorption(off-band, setting B)
➔ High specificity to the trace gas by using narrowband structures and reduction in broad band interferences due to small spectral shift of ≈ 1 nm (20 nm for SO2 UV-cameras)
Application of an Fabry-Perot interferometer (FPI) as wavelength selective element
Radiation traversing an FPI undergoes multiple reflections causing either constructive or destructive interference resultingin a comb shaped transmission spectrum with periodic maxima.
Free spectral range: Finesse:
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021 3
Imaging Fabry-Pérot interferometer correlation spectroscopy (IFPICS)
FPI sketch
∆λ ≈λ2
2 ∙ n ⋅ d ⋅ cos(α) F =∆λ
δλ≈
π R
1 − R
∆λδλ
≈ 1nm shift
tuning = shifting(i.e. alter α)
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021 4
The IFPICS instrument
Light entering from the left:1. Parallelization of incoming light by aperture and lens1 2. Traversing the FPI; FPI mounted on a motor allows for tilting
(tilting = change incident angle α = tuning the spectrum)3. Selection of spectral range of interest by a band bass filter
(BPF) with 10 nm FWHM and 308 nm central wavelength4. Projection onto 2D UV detector by lens 2
Dimensions [mm]: 200 x 350 x 130
Weight:≈ 5 kg
Power consumption:
< 30 W
Instrument is robust, compact, light-weight and can be operated several hours using a battery pack
➔ Ideal field instrument to access remote locations and to operate under harsh environmental conditions
Fuchs et al., 2021, Atmos. Meas. Techhttps://doi.org/10.5194/amt-14-295-2021
i ∈
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021 5
The IFPICS detection principle
dI λ = I0 λ ∙ e−𝛔 𝛌 ∙𝑺 dλ= I0 λ ∙ e−𝛕 dλ
τi=−lnλ1,iλ2,i I0,i λ dλ
λ1,iλ2,i Ii λ dλ
= 𝜎𝑖 ⋅ S
𝐀𝐀 = 𝛕𝐀 − 𝛕𝐁 = 𝝈𝑨 − 𝝈𝑩 ⋅ 𝐒𝐒𝐎𝟐 = 𝐤𝐒𝐎𝟐 ∙ 𝐒𝐒𝐎𝟐⇔
𝐒𝐒𝐎𝟐 = ൗ𝑨𝑨𝒌𝑺𝑶𝟐
Lambert-Beer´s law: I0: incident radiationσ: abs. cross sectionS: column density𝜏: optical density
A: on-bandB: off-band
i ∈
Apparent absorbance ⇔ column density:
on-band, τA
off-band, τB
Line 240
Line 240
Line 240
AA = 𝛕𝐀 − 𝛕𝐁
calibration
SSO2= AA / kSO2
Fuchs et al., 2021
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021 6
The calibration of IFPICS data
For the calibration of the recorded data apparent absorbance AA into column densities CD a forward instrument model is used.
The model is based on high resolution solar atlas spectrum and literature absorption cross sections.
Thereby the model includes:• potentially interfering trace gas species• the divergence of the light beams inside the instrument • optical losses due to imperfect optics• UV detector characteristics and quantum efficiency• Inherent including saturation effects due to high gas concentrations
Model calculations:Divergence of the light beam results in a decrease of the FPIs effective Finesse (damping the peaks of max. FPI transmission)
Consistency check of the forward instrument model by a conventional calibration based on SO2 gas cells:Measurement of 3 SO2 gas cells using:1. IFPICS instrument for apparent absorbance AA values2. DOAS system to retrieve SO2 column densities (CD)
➔ Instrument model represents measured data within rangeof uncertainties.
➔ A polynomial function AA(CDSO2) is fitted to the model. The inversion of the polynomial is used to calibrate AA into CD by CDSO2(AA) = [ AA(CDSO2) ]-1
Fuchs et al., 2021
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021 7
Camera performance & measurement setup at Mt. Etna, 02 October 2020
Google Earth ©; Gorelick, N., et al.
FOV
• Location: 37°42‘ N, 15°01‘ W, ”Schiena del Asino“, ≈ 2000 m A.S.L
• Spatial resolution & pixel size: 400x400 pix with 3.8m x 3.8m size
• Temporal resolution: 3.28 s
• Detection limit: 2.61+1017 molec cm-2 s-1/2
• Camera viewing direction: 357°N, 18° Elevation
• Camera field of view (FOV): 18°
• Plume & wind direction: 45° W
• Windspeed: ≈ 10.8 m/s (UWYO; atmo. soundings)
• Plume distance: ≈ 4700 m
Etnacrater
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021
Results: 1.) Plume (wind) velocity retrieval from imaging data
Plume propagation velocity data can be retrieved directly from imaging data using optical-flow algorithms the cross-correlation approach.
Cross-correlation: 1. Integrate recorded signal (AA, or CD) along different columns
perpendicular to the observed plume propagation direction (i.e.: col. 1: row 250, col. 2: row 300, col. 3: row 350, col. 4: row 400).
2. Find max correlation by shifting signal over time (images) with respect to certain column (i.e. with respect to column 1)
3. Max correlation determines time required for plume to travel certain pixel → allows to retrieve velocity using pixel size
Results:
• Col 1-2: lag of 5 images = 16.4s, 50 pix = 190m, correlation 87%• Col 1-3: lag of 10 images = 32.8s, 100 pix = 380m, correlation 77%• Col 1-4: lag of 15 images = 49.2s, 150 pix = 570m, correlation 66%
➔ Plume propagation velocity: 11.58 m/s (wind data: ≈ 10.8 m/s) (http://weather.uwyo.edu/upperair/sounding.html)
Column:1 2 3 4
Google Earth ©; Gorelick, N., et al.
1
2
1
34
234
8
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021
Results: 2.) SO2 mass fluxes from imaging data
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The volcanic SO2 mass flux Φ𝑆𝑂2 can be determined directly from
calibrated imaging data by:
Integration along a plume transect of length L of the evaluated SO2 CD image series ℎ𝑝𝑖𝑥 ⋅ 𝐶𝐷𝑆𝑂2 𝑑𝐿. A subsequent multiplication with the
perpendicular wind velocity 𝑣⊥𝑤𝑖𝑛𝑑 then yielding the SO2 flux through
the plume transect.As the pixel of the detector are discrete (finite size) the integration is replaced by summation:
We retrieved SO2 mass fluxes of:
𝚽𝑺𝑶𝟐 = (726 ± 130) t/day
Preliminary results!
Φ𝑆𝑂2 = නℎ𝑝𝑖𝑥 ⋅ 𝐶𝐷𝑆𝑂2 𝑑𝐿 ⋅ 𝑣⊥𝑤𝑖𝑛𝑑 =
𝑖
ℎ𝑝𝑖𝑥𝑖 ⋅ 𝐶𝐷𝑆𝑂2,𝑖 ⋅ 𝑣⊥𝑤𝑖𝑛𝑑
L
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021
Conclusion & Outlook
10
Conclusion:• First successful application of the new technique IFPICS• The instrument is robust and small hence applicable even
under harsh environmental conditions• Good sensitivity and low cross-interferences• Calibration is based on a forward model making infield
calibration redundant
Outlook: • Revising instrument (already done) and perform new
measurements with improved 2D UV detector• Field measurements of weaker SO2 emitter• Extension of the IFPICS technique to further trace gases,
e.g., BrO, HCHO, NO2
➔Have a look at EGU contribution: EGU21-2811, A. Nies et al., IFPICS measurements of HCHOLink: https://meetingorganizer.copernicus.org/EGU21/EGU21-2811.html
Thank you for [email protected]
vEGU21 Christopher Fuchs, GI6.1, EGU21-1308, 29 April 2021
References:
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• Louban et al., 2009: Louban, I., Bobrowski, N., Rouwet, D. et al. Imaging DOAS for volcanological applications. Bull Volcanol 71, 753–765 (2009). https://doi.org/10.1007/s00445-008-0262-6
• Lübcke et al., 2013: Lübcke, P., Bobrowski, N., Illing, S., Kern, C., Alvarez Nieves, J. M., Vogel, L., Zielcke, J., Delgado Granados, H., and Platt, U.: On the absolute calibration of SO2 cameras, Atmos. Meas. Tech., 6, 677–696, https://doi.org/10.5194/amt-6-677-2013, 2013.
• Fuchs et al., 2021: Fuchs, C., Kuhn, J., Bobrowski, N., and Platt, U.: Quantitative imaging of volcanic SO2 plumes using Fabry–Pérot interferometer correlation spectroscopy, Atmos. Meas. Tech., 14, 295–307, https://doi.org/10.5194/amt-14-295-2021, 2021.
• UWYO: University of Wyoming, Department of Atmopsheric Science, atmospheric soundings; Station data: Trapani (LICT), Italy 2019-07-22 12:00, available at: http://weather.uwyo.edu/upperair/sounding.html, last access: April 2021.
• Google Earth ©; Gorelick, N., et al.: Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. Accessed: April 2021