Insights into natural organic matter and pesticide characterisation and distribution in the Rhone River

2017 
Thorough characterisation of natural organic matter (NOM) in natural surface waters remains vital for evaluating pollutant dynamics and interactions with NOM under realistic environmental conditions. Here, we present the characterisation of NOM and pesticide compositions for nine sampling sites over the length of the Rhone River, also evaluating the advantages and limitations of different analytical techniques to determine how they complement one another. Together with dissolved and particulate organic carbon analyses, the dissolved organic matter (DOM, <0.8 mu m) or NOM (unfiltered organic matter) was characterised with gel permeation chromatography, the polarity rapid-assessment method, excitation-emission matrix fluorescence, and pyrolysis-gas chromatography-mass spectrometry to evaluate both composition and distribution. An additional objective was the determination of the NOM degradation state (i.e. constantly produced autochthonous or weakly degraded allochthonous species), an important factor in assessing potential NOM-pollutant interactions. The NOM compositions (i.e. proteins, polyhydroxy aromatics, polysaccharides, amino sugars) and proportions were similar between sites, but variations were observed in the relative proportions of autochthonous and allochthonous material from north to south. Anionic proteins and polyhydroxy aromatics in a molecular weight range of similar to 1000-1200 Da comprised the majority of the DOM. As a pollutant case study, five pesticides (glyphosate, metalochlor, chlortoluron, isoproturon, propyzamide) and some of their metabolites (aminomethylphosphonic acid, metolachlor ethanesulfonic acid and metolachlor oxanilic acid) were measured. Several exhibited trends with the NOM, particulate organic carbon and suspended particulate matter distributions in the Rhone waters, suggesting a significant influence on pesticide fate and transport in the river.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    68
    References
    11
    Citations
    NaN
    KQI
    []