Analysis of fluvial dissolved organic carbon using high resolution UV-visible spectroscopy and Raman spectroscopy

Coleman, Martin (2017) Analysis of fluvial dissolved organic carbon using high resolution UV-visible spectroscopy and Raman spectroscopy. PhD thesis, University of Glasgow.

Abstract

This dissertation focusses on some advancements in methodology for measuring and analysing dissolved organic carbon (DOC): analysing data from a high resolution sensor generating DOC concentrations, [DOC] and secondly the use of Raman spectroscopy to analyse the composition of DOC. Recent advances in sensor technology have enabled the collection of DOC data with greater frequency over extended time periods than was previously possible through manually collecting water samples. In this research a time series of 30 minute [DOC] data for 2.5 years from Drumtee water, a peaty catchment in Scotland, was generated and analysed using a Spectro::lyserTM from S::CanTM, with a customised algorithm for calculating [DOC]. The time series revealed details of events and strong seasonal variation in the [DOC], with a range of 8.0 mg/l to 55.7 mg/l. During the same time period measurements made using manual sampling of river water were very similar, ranging from 10.2 mg/l to 81.1 mg/l (with the second largest value at 64.1 mg/l).

Similar DOC export budgets were calculated from Spectro::lyserTM measurements and from the laboratory-analysed samples for both the hydrological year 2012/13 (HY 2012/13) and hydrological year 2013/14 (HY 2013/14). For the HY 2012/13 year the DOC budgets using the field collected data and the laboratory collected data were 16.6 gCm2.yr-1 and 19.8 gCm2.yr-1 respectively. For the HY 2013/14 year the DOC budgets using the field collected data and the laboratory collected data were 18.1 gCm2.yr-1 and 19.5 gCm2.yr-1 respectively. The similarity between the budgets calculated using the high-resolution [DOC] sensor and the budget calculated using laboratory measured [DOC] samples indicated that seasonal variation had a greater influence on export budgets than short term events had. GAMs were used to model the high resolution [DOC] data, and the model generated an R2 value of 0.75 and a p-value of < 2.2 x 10-16. It was also identified statistically that there were regular [DOC] dilutions during events and that these dilutions tended to coincide with the time period when discharge was increasing most rapidly.

To identify relationships and periodicities in the high resolution [DOC] time series that would otherwise be challenging to identify three forms of wavelet analysis were used. These were continuous wavelet transforms (CWTs), maximal overlap discrete wavelet transforms (MODWTs) and wavelet coherence transforms (WTCs). Using the WTCs, it was determined that there were short term correlations between the [DOC] and pH between 25 June 2013 and 17 July 2013, between [DOC] and SC during 7 August 2013 and 7 October 2013 and between [DOC] and water temperature during 19 June 2013 and 30 June 2013. Although the although the relationship between [DOC] and temperature is strong over a full year it was over these shorter time periods the weakest of the three relationships established. Identifying this coherence was not possible using bivariate analysis and the long periods of no coherence obscured these responses when analysing the data on scatter plots. Although wavelet analysis has been used in other applications this is one of the first instances in which this technique has been applied to [DOC] time series.

Raman spectroscopy, conducted using a 785 nm laser, was explored as an analytical tool that could enable a better understanding of DOC composition, as an alternative to the use of fluorescence spectroscopy. Tests were conducted using both Stokes and anti-Stokes Raman spectroscopy measurements with the best results obtained using anti-Stokes Raman spectroscopy. Solid phase measurements were made of glucose, fructose, sucrose, glycine, tyrosine, tryptophan and phenylalanine, but only the glucose produced a measurable spectrum of these substances. Measurements (powders and solutions) were made of humic and fulvic acids and these produced spectra that were measurably different from the background signals. The limit of detection was measured to be approximately 500 mg/l for both the humic acid and fulvic acid. It was identified that comparing the sections of the measured spectra between wavenumbers -1100 cm-1 to -1400 cm-1 to -1800 cm-1 to -2000 cm-1 could be used to differentiate between humic and fulvic acids.

In summary, this research has focussed on the use of use high resolution sensor technology to generate and then analyse a long time series in a fluvial system with a particularly high [DOC], and made advances in being able to model the [DOC] using a GAM model, despite the complex relationship measured between discharge and [DOC]. Additionally, wavelet analysis has been applied to a [DOC] data set to identify trends in the [DOC] time series that would otherwise be hard to identify. Wavelet analysis has been applied to other geophysical time series such as those found in atmospheric research, but this appears to be the first time it has been applied to [DOC]. Additionally, the use of the anti-Stokes region of the Raman spectra has allowed identification of humic and fulvic acids, and established a limit of detection. Furthermore, an absorbance ratio was identified that can be used to determine whether a solution of humic substances is dominated primarily by humic acid or fulvic acid. This research appears to be the first study to explore this.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Keywords: Carbon, dissolved organic carbon, DOC, [DOC], POC, [POC], Raman, wavelets, time series, high resolution, spectrolyser, spectro::lyser, in-situ.
• Subjects: H Social Sciences > HA Statistics; Q Science > QC Physics; Q Science > QD Chemistry
• Colleges/Schools: College of Science and Engineering > School of Geographical and Earth Sciences > Earth Sciences; College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Supervisor's Name: Waldron, Prof. Susan and Scott, Prof. Marian
• Date of Award: 2017
• Depositing User: Martin Coleman
• Unique ID: glathesis:2017-8539
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 01 Nov 2017 09:27
• Last Modified: 23 Nov 2017 08:37
• URI: https://theses.gla.ac.uk/id/eprint/8539