Differential Optical Absorption Spectroscopy (DOAS)

Transcription

1 Differential Optical Absorption Spectroscopy (DOAS) An example of methodology using ground based emission measurements of Popocateptl volcano, Mexico Group 6 Mike Perry Paula Salas de los Rios Moises Blanco Espinar Advisor: Leif Vogel

2 Research Problem The determination of SO 2 from both ground and satellite platforms is an issue of great scientific and technical complexity For volcanos extruding large quantities of SO 2 developing a method for quantifying this flux would be vital to understanding and predicting their more destructive behaviours

3 Aims Provide an introduction to DOAS (Differential Optical Absorption Spectroscopy). To determine the emissions of the Popocatepetl volcano using DOAS from: - Ground based car traverses of the volcanic plume. - Satellite measurement of SO 2 column To compare measurements from these different sources to acertain the validity of satellite retrieved SO 2

4 WHERE??

5 Popocateptl -Mexico - Active volcano -Height: 5426 m Data sources Satellite Traverses

6 WHY??

7 Motivations SO 2 is one of the most abundant compounds among volcanic gases and in itself can have ecological impacts from the formation of acid rain. Emitted aerosol can act as a cooling agent within the atmosphere interfering with the radiative transfer within the atmosphere Monitoring SO 2 flux can used as an eruption precursor.

8 HOW??

9 Theoretical Base Absorption spectroscopy: molecules can enter excited states when irradiated by electromagnetic radiation. Rayleigh Scattering Cross-section Beer-Lambert Law of absorption: Types of scattering process in absorption spectroscopy: Rayleigh Scattering Mie Scattering Raman Scattering Mie Scattering The problem?? Contributions of the different scattering can be distinguished with exact information on aerosol content but there is another solution.

10 How we can solve this problem? Differential Optical Absorption Spectroscopy (DOAS) The trace gas absorption is divided into two parts: - Broadband scattering (σ b ): small dependence on the wavelength. - Narrowband (σ ): differential absorption cross-section and has a strong dependence on the wavelength. With this separation, the Beer-Lambert Law is: Finally, the Beer-Lambert Law can be written: Approximately Polynomial [Platt & Stutz 2008]

11 Mobile DOAS transverse method It is an excellent technique for measuring volcanic emission fluxes. Measurements are conducted from a mobile platform (car) traversing under the plume. Wind Flux= cross section*wind Cross Section

12 Ground Based results

13 Traverses -14 th April 2010

14 Traverses - 17th April 2010

15 Processing DOASIS Data Data from the VIS/UV Espectometrer DOASIS Goal: identify SO 2 in the ground measures For processing correctly the information some corrections are needed: - Offset: happens in every exposure - Dark current: depends on time exposure

16 Processing DOASIS Data Spectrum obtained from the plume of the volcano

17 Processing DOASIS Data Beer-Lambert Law I 0 = Sky reference I = Measure σ i = Cross section C i = Density of the absorber Fitting -O 3 - SO 2 -Ring spectrum

18 DOASIS Data 15 th April 2010 O 3 Convolution SO 2 Convolution Fit Result Ring Effect Fit Residual Polynomial

19 DOASIS Data 15 th April 2010 O 3 Convolution Fit Result Ring Effect SO 2 Convolution Fit Residual Polynomial

20 What about fluxes? SOFTWARE: Mobile DOAS

21 Wind data Wind speed and direction was retrieved from the NOAA database for the traverse times for all 4 days for which we had data.

22 Fluxes calculated Date Start hour End hour Flux (ton/day) 14 18:30:59 19:49: :43:52 19:04: :21:12 20:27: :29:26 21:31: :48:16 22:53: :24:37 17:09: :12:34 17:50: :00:12 18:39:

23 Satellite measurements

24 Satellite measurements OMI is an instrument mounted on the NASA AURA satellite AURA It is capable of retrievals in the UV/visible range and is used to monitor aerosols within the Earth s atmosphere. Example: OMI composite for SO2 over Sierra Negra 2005

25 Satellite Measurements OMI data for SO2 column density over the period covered by the traverses was obtained. Usable data was found on the 15th and the 17th of April, however the data was sparse with only a few pixels over in each scene. Popocatapetl The data was initially in Dobson units and had to be converted to the SO 2 column mass in the pixels. Image produced on NASA s Giovanni The data for a single day is very sparse and only a few pixels are usable for analysis.

26 Satellite Results Given the low quantity of pixels available, a basic estimation was devised using the flux of SO 2 through only one or a small grouping of pixels. The pixels SO 2 column in Dobson units was converted into a number of molecules and subsequently mass per meter. SO 2 FLUX The area of available pixels was then calculated and thereby the mass of SO2 estimated.

27 Satellite Results The mean wind speed and direction for the period within which the satellite data was taken and a probable velocity for the plume SO 2 was estimated. The velocity was used to estimate the time taken for the plume to traverse the width of the pixel, corrected for the angular direction of the wind. The total column tonnage of the pixel could then be divided by this time to produce an estimate for the SO 2 flux of the pixel.

28 Date Time Wind Direction Wind Speed (m/s) Flux path time Ton/Pixel Tons/Day 15/04/ : : WIND DIRECTION Popocateptl

29 Date Time Wind Direction Wind Speed (m/s) Flux path time Ton/Pixel Tons/Day 17/04/ : : WIND DIRECTION Popocateptl

30 Satellite Evaluation The flux values from the satellite appear to be 2 or 3 orders different to those of the ground traverses Given the roughness of the method utilised by the satellite retrieval it is very likely that the highest degree of error in any comparison will lie within the product produced from the satellite. The satellite data definitely shows a SO 2 flux however it is difficult to draw conclusions from this given the sporadic nature of the useful pixels and the crudity of the method. A longer time series of data over a volcanic region might allow better visualisation of the plume, however variations in wind speed and direction would introduce more uncertainty into flux estimations.

31 Conclusion Lack of data on two of the study days limits the extent of comparison The satellite fluxes are noticeably higher than the ground fluxes Date Satellite Flux (ton/day) Ground Flux (ton/day) 14 - NO DATA NO DATA The limitations of wind data and pixel availability are major contributors Given the crude nature of the satellite method the values are surprisingly reasonable Currently the poor quality of the satellite results do not allow good prospects for comparison but further work over a longer time scale could improve this.

32 References FP18/FP38 Atmospheric Trace Gases Institute of Environmental Physics. University of Heidelberg. Germany Dissertation submitted to the Combined Faculties for the Natural Sciences and for Mathematics of the Ruperto-Carola University of Heidelberg, Germany

33 THANKS FOR YOU ATTENTION!