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Publication - Dr Chris Arthur

    Untargeted characterisation of dissolved organic matter contributions to rivers from anthropogenic point sources using direct infusion- and high-performance liquid chromatography-Orbitrap mass spectrometry

    Citation

    Pemberton, J, Lloyd, CEM, Arthur, C, Johnes, PJ, Dickinson, M, Charlton, AJ & Evershed, RP, 2019, ‘Untargeted characterisation of dissolved organic matter contributions to rivers from anthropogenic point sources using direct infusion- and high-performance liquid chromatography-Orbitrap mass spectrometry’. Rapid Communications in Mass Spectrometry.

    Abstract

    Fresh surface water is a fundamental resource not only for drinking water and irrigation, but also for supporting terrestrial and aquatic ecosystems.2 DOM is ubiquitous to all aquatic systems and is an extremely complex mixture of organic compounds although its composition has remained intractable due to the lack of suitable analytical methods.3 DOM has been asserted to be a nutrient for autotrophs.2,3 The range of compounds comprising DOM includes compounds generated naturally and through anthropogenic activities, and can include potentially toxic micropollutants which attract much attention in water quality legislation as these have been shown to have adverse impacts upon organisms within the aquatic ecosystems.4–6 Despite individual anthropogenically derived compounds being at low concentration, the chronic exposure of stream biota to these compounds has been shown to have a wide range of acute ecotoxicological and chronic adverse effects on organisms.4–7 These include the disruption of reproduction,8,9 a reduction in biodiversity,10 and, dysmorphia in the maturation of organisms.11 Furthermore, different compounds which affect organisms in a similar way can work synergistically amplifying the impact.7,12 Regulations only cover a very minor proportion of the commonly identified micropollutants, and many others almost certainly remain to be discovered.
    Micropollutants have been identified in different discharges including sewage treatment works.13,14 Sewage treatment works have been found to be a major gateway for the release of pharmaceuticals,15 personal care products16 and plasticisers17 into the environment. The concentration and presence/absence of target compounds across different sewage treatment works has been found to vary between different sites and over time.13,15,16,18,19 With over 9000 sewage treatment works in the UK and numerous other point sources the identification of potentially ecotoxicological compounds remains a challenge. Without identifying these micropollutants; the determination of ecotoxicity, effective mitigation solutions and environmental monitoring cannot be carried out.
    The most common approach to the determination of organic compounds in both wastewater and the natural aquatic environment ecosystem is targeted analysis using MS approaches focusing on known or suspected compounds.4,20 Optimised extraction methods are used to isolate and concentrate the target analytes with subsequent interrogation involving gas chromatography (GC)21,22 or high-performance liquid chromatography (HPLC)13,23 linked MS. Targeted studies have largely focused on pharmaceuticals,15,24 personal care products16,25 and pesticides,21,26 with their concentrations or load in the riverine environment being used to assess the effectiveness of sewage treatment and local sources.13,15,27 The obvious limitation of targeted analysis is that it requires a predetermined list of known compounds. Targeted analysis will only determine the selected compounds and exclude other compounds originating from a point source or the environment. The use of electrospray ionisation (ESI) and HRMS has revolutionised the analysis of complex mixtures of water soluble compounds, such as DOM, allowing the exact mass of individual molecular ions to be determined.28,29 The ionisation of intact molecules and their mass analysis using instruments with high resolving power and high mass accuracy means that each ion in a spectrum potentially corresponds to a unique compound (taking account of other adducts and isotopes). Application of this approach has revealed the extraordinary complexity and heterogeneity of DOM in the natural environment, as evidenced by the DI-HRMS spectra containing many thousands of resolved ions.30,31
    One of the major challenges of utilising these HR mass spectra of DOM lies in the interrogation of the data. Attempts have been made to assign formulae to the observed ions in the spectra, using rule-based calculations.32–34 All studies include carbon, oxygen, nitrogen and hydrogen, however, the inclusion of heteroatoms, e.g. P, Cl and S vary between studies.30,35,36 Increasing the number of heteroatoms results in an exponential increase in the number of possible formulae for a single ion, resulting in a high level of uncertainty and false positives.33 Isotopes and adducts, i.e. [M+Na]+, [M+K]+, [M+Cl]- , will be present in all DI-HRMS spectra, but are rarely accounted for. Hence, despite the high mass resolution attainable using modern Fourier-transform ion cyclotron resonance (FTICR) or Orbitrap™ MS instruments, the exceptional complexity of the mass spectra obtained largely defies conventional approaches to handling these unusual data sets.
    An alternative approach is to move toward data visualisation rather than more conventional peak identification approaches. One such approach is the use of van Krevelen diagrams. Such diagrams use the ratios of carbon:hydrogen and carbon:oxygen of the formulae assigned to ions as a basis for the comparison of DOM in water extracts. 37–41 These elemental ratios of formulae are used to classify ions to a compound class.30,31,40,41 However, the interpretation of a van Krevelen diagram relies on the correct assignment of formulae, including appropriate numbers of heteroatoms. Incorrect assignments will lead to the inaccurate interpretations of differences in the composition of DOM extracts. Furthermore, a single ion in a DI-HRMS spectrum maybe the result of multiple isomers and therefore, the full complexity is not fully revealed. In addition, the correct classification using a van Krevelen diagram of a compound class for one isomer maybe incorrect for another isomer with the same formulae. Despite this van Krevelen diagrams have found utility in visualising differences in composition of DOM extracts from different aquatic systems, addressing a range of questions relating DOM source and variability between ecosystems, e.g. differences between water bodies in different geographical locations.38,42,43
    While van Krevelen diagrams have proved useful for visualising differences between DOMs extract chemistries, the approach is non-statistical and is rather restricted in truly exploiting the full complexity of the data, e.g. ion intensities and molecular species of unassigned formulae. An alternative, but still less widely applied approach, is multivariate statistics, in particular PCA of DI-HRMS spectra. The latter has been used to determine and visualise differences between the composition of DOM extracts from different SPE extraction methods44 and different water bodies within the same pristine catchment.42 PCA requires only the detected ions and their intensities in different DI-HRMS spectra to determine if extracts are different. However, this has not been applied to point sources in comparison to their receiving environment.
    Herein, we address the challenge of how to deal with the question of the complexity of riverine DOM analysis by HRMS. We have taken a comprehensive approach in order to retain a broad view of DOM composition and developed a method for data reduction based on a difference algorithm to highlight complex anthropogenic DOM contributions against a natural or semi-natural DOM background. To achieve this, we first record DI-HRMS spectra of DOM recovered by SPE, then use PCA as a rapid qualitative screening method to determine if differences exist between DOM extracts of point sources and the receiving aquatic environment. Following this the difference algorithm, employing univariate statistics (Kruskal-Wallis analysis), was applied to allow the anthropogenic point source components to be identified in DI-HRMS spectra. Heatmaps and hierarchical cluster analysis are then used as data visualisation tools, which allow compositional differences to be recognised. The anthropogenic components highlighted through the untargeted difference analysis formed the basis for structural identification of specific molecular species by HPLC/HRMS/MS.

    Full details in the University publications repository