Accepted Manuscript Understanding what we are measuring: standards and quantification of natural organic matter Montserrat Filella PII:

S0043-1354(13)01014-2

DOI:

10.1016/j.watres.2013.12.015

Reference:

WR 10374

To appear in:

Water Research

Received Date: 2 September 2013 Revised Date:

3 December 2013

Accepted Date: 11 December 2013

Please cite this article as: Filella, M., Understanding what we are measuring: standards and quantification of natural organic matter, Water Research (2014), doi: 10.1016/j.watres.2013.12.015. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Understanding what we are measuring: standards and

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Montserrat Filellaa,b

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Switzerland

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quantification of natural organic matter

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Institute F.-A. Forel, University of Geneva, 10 route de Suisse, CH-1290 Versoix,

SCHEMA, Rue Principale 92,L-6990 Rameldange, Luxembourg

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Tlf: +41223790300

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e-mail: [email protected]

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Abstract

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Some of the problems involved in quantifying operationally-defined parameters in natural

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waters are generally overlooked. In particular, the implications of the fact that standards

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have an effect on the results obtained that differs from usual analytical determinations are

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often ignored. On the basis of a revision of published data and personal experience, the

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case of natural organic matter types is discussed here for four different categories:

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carbohydrates, thiols, TEP (transparent exopolymers), and humic substances in

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freshwaters. In all cases, the results obtained are noticeably dependent on the standard,

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though the type of dependency varies. Key aspects are discussed in detail and advice is

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given about the procedure to follow when reporting data. Conclusions are applicable to

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other aquatic systems and natural organic matter fractions.

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In operationally defined parameters, the effect of standards differs from usual analysis

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The effect of standards depends on the type of NOM (e.g., carbohydrates, thiols, TEP, humics)

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Highlights

Results should always be expressed in standard equivalent units

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When expressing results in C units, the calculation procedure should always be

described in detail

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Keywords: Quantification, Standards, Natural Organic Matter, Thiols, Carbohydrates,

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TEP, Humics, Freshwaters

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1. Introduction

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It is generally accepted that nothing is more important in science than good data. And an

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essential condition for having good data is the need to know what we are measuring. This

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might appear self-evident to most readers yet, in practice, it is often overlooked in

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environmental chemistry, where many parameters are operationally defined. A key

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aspect, the effect of the standards used for calibration purposes in measured parameters

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when quantifying different categories of natural organic matter (NOM) types, is

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discussed here.

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The term ‘natural organic matter’ (NOM) is normally used to designate all the

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organic matter in a reservoir or natural ecosystem other than synthesis compounds (i.e.,

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organic micropollutants). Since the natural processes of formation and degradation

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involved are extremely diverse, the NOM found in natural waters has a broad range of

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properties and is composed of an extremely complex mixture of compounds. For this

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reason, NOM studies have often dealt not with pure compounds but rather with groups of

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compounds fractionated from the waters, or simply observed in them, by means of

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various techniques. These groups of so-called ‘homologous compounds’ have similar

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operationally defined physico-chemical characteristics (Altmann and Buffle, 1987).

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Humic and fulvic acids, as well as carbohydrates, are examples in the NOM field, as are

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hydrous metal oxides or clays in the inorganic one. In practice, to complicate things

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further, different ‘homologous compounds’ are not necessarily independent (e.g.,

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carbohydrates are present in humic substances, so-called TEP –transparent exopolymers–

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contain carbohydrates and proteins, etc.). The different types of NOM have been

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discussed in detail in Filella (2009 and references therein). The reader is referred to this

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publication for a detailed description of the different ‘homologous’ compounds and

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abbreviations used to designate them, given that preferred usage varies widely among

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research ‘communities’ (i.e., oceanography, water treatment, ecotoxicology, etc.).

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However, a simplified table (Table 1) is included here to help readers less familiar with

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certain aspects of the field.

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An impressive number of studies have focussed on characterizing different NOM

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homologous groups by using a wide range of experimental –and increasingly

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sophisticated– methods (Abbt-Braun et al., 2004). Some techniques, largely

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chromatographic in nature, have been developed to detect and quantify specific, well-

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characterised chemical compounds, but they cover only a very limited number of

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substances, such as some carbohydrates (Borch and Kirchman, 1997; Gremm and

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Kaplan, 1997; Hung et al., 2005) or low molecular weight sulphur-containing compounds

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(Mopper and Delmas, 1984; Vairavamurthy and Mopper, 1990; Tang et al., 2000; Zhang

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et al., 2004) while most quantitative measurements performed in research and monitoring

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concern homologous compounds. In this paper the focus will be on standards: a key

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problem encountered when trying to quantify them. As explained in classical analytical

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chemistry textbooks (e.g., Woodget and Cooper, 1987), whilst a few analytical methods

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are ‘absolute’ and do not require calibration, most methods are ‘comparative’ and require

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calibration against known standards. The use of standards is based on the implicit

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assumption that they give the same analytical signal as the compound whose

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concentration we want to measure. Unfortunately, this premise is often not entirely true

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when the substances being dealt with are operationally defined categories rather than

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well-defined chemical compounds as is the case of NOM homologous compounds. Four

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categories of NOM will be discussed here (carbohydrates, thiols, TEP and humics) but,

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obviously, the issues tackled also apply to other NOM categories. These have been

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chosen because they clearly illustrate conceptually different types of effects of standards

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on the meaning of parameters measured.

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2. Some methodological clarifications

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This paper is the fruit of reading several hundreds of papers along with personal

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experience. However, its aim is neither to describe methods (and their possible

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shortcomings) in detail nor to collate published values (i.e., it is not a compilation or a

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classical review paper). The approach followed consisted in evaluating in detail a

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reasonably comprehensive set of existing studies where the above-mentioned categories

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had been quantified, taking care to avoid cherry-picking. The information relevant for the

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objective of this article was extracted from the original sources and is given in table form

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in the Sup Info file for carbohydrates, thiols and TEP. In the case of humics, a similar

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table is freely available in the Sup Info file linked to Filella (2010). The table

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corresponding to carbohydrates is an update to a previously published table in Chanudet

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and Filella (2006). All the information contained in the tables is complementary and not

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essential for understanding this article. For this reason, it is offered in Sup Info form.

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The discussion will focus on freshwaters (with examples from seawater being given

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only in the case of thiols due to the scarcity of applications in freshwater bodies).

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Nonetheless, the problem exists for all types of water and conclusions are applicable to

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all of them. Obviously, the discussion in this article does not apply to the quantification of welldefined,

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chromatography or other analytical techniques (e.g., voltammetry). The usual

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identification and calibration methods apply to them and no particular conceptual and

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signification problem arises. The format in all sections is the same: a very general description of the NOM

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category is followed by a specific discussion of the standard problem.

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3. Discussion of cases

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3.1. Carbohydrates: when not all sugar is glucose

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Carbohydrates are ubiquitous in natural waters and account for a variable but significant

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percentage of the DOC pool in aquatic environments (Buffle, 1988; Panagiotopoulos and

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Semperé, 2005 and references therein). They serve not only as structural cell components

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for living organisms but also as energy storage and transport media for autotrophic and

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heterotrophic organisms in both terrestrial and marine ecosystems. In waters,

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carbohydrates are mainly products of phytoplankton photosynthesis and are released by

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exudation, cell lysis, zooplankton grazing, and microbial degradation; excretion by

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zooplankton and bacteria is a further source (section 5.22 in Filella, 2007).

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So called ‘total’ carbohydrate concentrations in freshwaters are obtained

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colorimetrically. The two more widely used methods are the MBTH (3-methyl-2-

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benzothiazolinone) (Pakulski and Benner, 1992) and TPTZ (2,4,6-tripyridyl-s-triazine)

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(Myklestad et al., 1997) methods, which take their names from the analytical reagent

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used. Both have in common that they measure monosaccharides liberated after acid

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hydrolysis and use a monosaccharide (usually glucose) as the standard.

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The underlying hypotheses in these methods are that: (i) all monosaccharides that

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constitute natural carbohydrates give the same analytical signal with the colorimetric 6

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reagent as glucose does and (ii) all polysaccharides hydrolyse completely and without

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any loss of monosaccharides. In practice, both hypotheses are probably wrong to some

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extent: (i) Chanudet and Filella (2006) showed that monosaccharides other than glucose

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(i.e., fructose, galactose, galactosamine, galacturonic acid) and polysaccharides

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systematically gave a much lower MBTH response than expected; (ii) the same authors

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(Chanudet and Filella, 2007) simultaneously determined the humic fraction and the

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MBTH-carbohydrates in freshwater samples and found that a significant part of the

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organic carbon remains undetected in some seasons (i.e., productivity periods), probably

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because a significant proportion of the carbohydrates present are not being ‘seen’ by the

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MBTH method and that this proportion changes depending on productivity. Therefore,

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the response of the technique, even if generally accepted as giving ‘total carbohydrates’,

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in practice is dependent on their nature and, significantly, most carbohydrates give a

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response that is lower, in a non predictable way, than the hypothetical case where only

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glucose is present. The difficulty of breaking down all carbohydrate structures by acid

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hydrolysis was also pointed out by Panagiotopoulos and Semperé (2005) as a possible

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explanation of the lower concentrations found by chromatographic and colorimetric

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methods in the case of marine samples. In conclusion, even if carbohydrates are a well-

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defined biochemical category, quantifying them in surface waters gives results dependent

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on the composition of the sample. This is partly due to incomplete hydrolysis but also

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derives from the choice of the standard.

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A further question to consider is that published results are not always adequately

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reported and the dependence on the standard not clearly acknowledged. Although this

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might appear to be a minor issue, it is not. Looking at the 26 studies listed in Table S1

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(Supplementary Material file), we will notice that the standard used for calibration is

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often not mentioned and that, when results are expressed in carbon units, how they were

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calculated is often not explained. More importantly, results are sometimes expressed in

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mg per litre and in µmol L-1 without specifying of what (carbon? glucose? other?). Since,

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as it is well known, glucose contains six carbon atoms, such values have no meaning.

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3.2. Thiols: an ‘homologous’ compound category?

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Thiols are low molecular weight (l.m.w.) organic compounds containing a sulfhydryl

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functional group. They are ubiquitous in intracellular media. It was long assumed that

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concentrations of thiols would be quite low –or zero– in oxic waters as they are readily

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oxidized by molecular oxygen and/or easily metabolized by bacteria. The first discoveries

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of thiols in oxic surface seawaters were unexpected (Matrai and Vetter, 1988; Le Gall and

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van den Berg, 1993) and aroused great interest mainly because organic thiol compounds

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are important complexing ligands for soft Lewis acids. Their presence can therefore

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significantly modify the speciation of many trace elements in natural waters by

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competing with other potential binders such as humic substances.

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The high affinity of reduced sulphur for mercury makes it easier to analyse them by

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cathodic stripping voltammetry (CSV) using mercury electrodes. The CSV peaks of

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many sulphur-containing compounds (e.g., sulphide, polysulphides, glutathione, thiourea,

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thioacetamide, etc.) fall in a narrow potential interval, resulting in coalescence and the

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possibility of collective quantification as an ‘homologous’ compound category. This

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approach has been applied in a number of studies in seawater (Table S2 in Supplementary

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Material file). The analytical method consists on electrode deposition at a convenient

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potential (see Table S2 for values used) followed by a cathodic scan. The most widely

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used compound as standard is glutathione. Apart from the fact that the position of the

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ligand peak can be modified by complexation of trace metals naturally present in the

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water (e.g., the glutathione peak can be the GSH-Cu(I) or the GSH-Hg(II) (Le Gall and

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van den Berg, 1993, Leal and van den Berg, 1998)), different thiol compounds will give

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different intensity responses. Therefore, results should always be expressed as mass units

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equivalent of standard per volume unit. However, this has not always been the case and,

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as shown in Table S2, thiol units with no reference to the standard used have been

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published. In some cases, it has even been suggested that glutathione was the only

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compound being determined when using the coalesced peak (Le Gall and van den Berg,

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1993, 1998), ignoring the possible contribution of other thiols to the signal. The use of

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CSV as a method for determining thiols as a category of compounds has recently been

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criticised and alternative methods for the quantification of individual thiols and other

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sulphur-containing compounds (e.g., thioamides) by CSV proposed (Laglera and Tovar-

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Sánchez, 2012; Pernet-Coudrier et al., 2013; Superville et al., 2013). Those methods are

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outside the scope of this study.

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3.3. TEP: a completely operational category (or two?)

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Transparent exopolymer particles (TEP) are a class of organic particles defined as those

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being stained by an acidic solution of Alcian Blue (Alldredge et al., 1993). Ubiquitously

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present in surface waters, they consist predominantly of acidic polysaccharides although,

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as noted by Uta Passow, “the chemical composition of TEP varies, is complex and

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unknown” (Passow, 2012). They are gel-like sticky particles formed out of algal

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exudates, bacterial mucus and material from the gelatinous envelopes surrounding

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phytoplankton. They initially attracted the attention of oceanographers, in the main, but

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they have also been studied in freshwaters (Table S3 in Supplementary Material file).

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Their importance in relation to water treatment and membrane fouling has received

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increased attention recently (Berman and Holenberg, 2005; Kennedy et al., 2009;

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Villacorte et al., 2009a, 2009b; Berman, 2010; Van Nevel et al., 2012).

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The presence of transparent substances like TEP in seawater was already noticed in

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the 1970’s (Gordon, 1970) but studying them became widespread only after Alice

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Alldredge and co-workers developed a staining technique to visualize them. The method

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usually applied consists of a colorimetric determination following 0.4 µm filtration

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(Passow and Alldredge, 1995). Xanthan gum is used as reference material and results

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obtained should always be expressed as xanthan equivalent mass unit per unit volume.

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The method is not easy to apply and variability between replicated measurements is high.

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For instance, the Alcian Blue staining solution is known to show purity and solubility

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variations and measured absorption of samples usually varies in different batches of

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staining solution (Passow and Alldredge, 1995; Kennedy et al., 2009). However, as far as

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the object of this article is concerned (i.e., the effect of the standards on the meaning of

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the measured values), there are no major problems, provided that users understand TEP is

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a category of NOM whose definition entirely relies on the method and standard used to

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determine it, and that units are adequately reported, which is largely the case in published

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studies (Table S3). A thornier issue is the conversion of concentrations in xanthan

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equivalents to concentrations in carbon units. The conversion factor: [organic carbon, µg

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L-1] = 0.75x[TEP, µg xanthan equivalent L-1], proposed by Engel and Passow (2001) for

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TEP produced in the laboratory from dissolved precursors by laminar or turbulent shear,

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is used in seawater and has also been applied in freshwaters (de Vicente et al., 2009,

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2010) without any further justification. However, legitimate doubts can be cast on the

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validity of using a value obtained for a particular system (i.e., culture of seawater

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diatoms) in largely different ones. There is also an alternative method for measuring TEP: microscopic particle

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counting (Alldredge et al., 1993; Passow and Alldredge, 1994). Although this method is

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quite time consuming, it has been largely used in freshwaters. In this case, concentrations

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are expressed as number of particles per volume unit and no standard is needed. It can be

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considered an ‘absolute’ analytical method (see introduction). In this case, the conversion

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of measured TEP particle size to carbon concentration units requires a large number of

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assumptions and is usually based on the work of Mari (Mari, 1999) that assumes a fractal

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scaling of TEP size to mass. In applying this method to freshwaters, Chateauvert et al.

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(2012) found that carbon in TEP exceeded 100% of total particulate organic carbon

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(POC) in seven out of 29 cases studied. They concluded that “there is some variation in

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carbon-content that requires further investigation” but did not explore the subject further.

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Comparison of counting-based results with those obtained by colorimetry is not

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straightforward. The issue was discussed in detail by Engel and Passow (2001) for

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seawaters. In freshwaters, Berman and Viner-Mozzini (2001) did not find a good

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correlation between colorimetric results and the microscopic determination of TEP

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particle abundance in lake waters. This is not surprising. Although based on the same

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staining principle, both methods give results in measurement scales based on different

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principles. Thus, direct comparison of measured values is more doubtful, particularly

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when the conversion of the measured parameters to carbon units is already uncertain,

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irrespective of the method used for TEP determination.

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3.4. Humic substances: the most difficult case?

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A significant proportion of the NOM present in surface freshwaters consists of

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compounds which are stable to degradation and are produced mostly in the soil but also

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in the water body (Buffle, 1988; Perdue and Gjessing, 1990; Stevenson, 1994). These

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compounds are occasionally referred to as refractory organic matter (ROM) because of

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their resistance to degradation, but they are most often called humic substances. This term

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stems from soil science where, historically, two fractions of NOM, humic and fulvic

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acids, were extracted based of their different solubility in concentrated acid and base

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solutions. These names have persisted in waters even if, in this media, the acid–base

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treatment follows an isolation procedure based not on solubility but on hydrophobicity.

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Here ‘humics’ will be used to collectively design fulvic and humic acids.

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‘Humics’ are not a well-defined biochemical category, as carbohydrates or thiols, for

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instance, are generally considered to be. ‘Humics’ are operationally defined but, unlike

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TEP (another 100% operationally defined group), they are not defined by the method of

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determination. In fact, their notorious elusive and non-constant composition and

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structure, makes it difficult to find an intrinsic ‘humic’ property that can be used to

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determine them quantitatively. Often the property measured (e.g., UV absorption,

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fluorescence, etc.) only characterises a portion of the ‘humics’ which, moreover, is

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system-dependent. No solution to this problem (i.e., the dependence of property intensity

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on both type and concentration) has been found so far.

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‘Humics’ are an interesting case because, although they are by far the most widely

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used type of organic matter used in laboratory studies as a model of NOM (e.g., in trace

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element complexation, ecotoxicity, nanoparticle behaviour, etc.), attempts to quantify

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them in natural waters are scarce. Rather, popular surrogate parameters have been

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broadly used in the literature to follow their presence, such as SUVA (specific UV

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absorbance) in water treatment, CDOM (coloured dissolved organic matter) in

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oceanography (Hansell and Carlson, 2002), fluorescence in different water systems (Ishii

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and Boyer, 2012), etc (Filella, 2010).

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However, in spite of its difficulty, attempts to develop quantification methods exist,

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even if they have never been extensively applied in practice. Standards used by

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developers and users was one of the main issues examined in a recent compilation of

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humic quantification methods (see Table S2 in Filella (2010)). This revision showed that,

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in the few cases where the dependence of the measured analytical signals on the type of

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‘humic’ was studied (in most studies, only one standard was used) or when observations

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about the variability of the response with different ‘humic’ substances was mentioned, a

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noticeable variability of the analytical responses with the type of ‘humic’ substance was

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observed. Indeed the variability of the response of different humic substances was

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confirmed in the only published systematic study where the responses of 13 different

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types of IHSS (International Humic Substance Society) standard humic materials were

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analysed when implementing an electroanalytical method based on cathodic stripping

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following the adsorptive collection of Mo(VI)–humic or fulvic acids complexes

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(Chanudet et al., 2006; Quentel and Filella, 2008). These authors suggested some rules to

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be generally applied when quantifying ‘humics’ in freshwaters: (i) in the case of

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extensive studies of NOM in a given freshwater system involving many measurements,

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prior isolation of the ‘humics’ in the system by following the IHSS procedure and the use

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of the ‘humics’ thus obtained as the standard was recommended; (ii) alternatively, in

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cases where this procedure was deemed too cumbersome, or when only occasional

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measurements in different systems were planned, the use of IHSS Suwannee River humic

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or fulvic standards was suggested. An alternative approach –using only one reference substance, irrespective of the

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sample considered– merits some thought in the case of ‘humics’. In principle, using a

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common reference substance makes it possible to compare results obtained in different

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studies and might be worth considering. However, it requires that the range of analytical

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responses for the type of NOM of interest is not too large. Otherwise, it might make it

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difficult to relate, even semi-quantitatively, concentrations of the different NOM fractions

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among themselves and with DOC concentrations.

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4. Conclusions and recommendations

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All the cases analysed here have in common the dependence of the results obtained on the standard used. However, the type of dependence is different in thiols and

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carbohydrates, well-defined biochemical categories, than in TEP, a category defined

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exclusively by methodology.

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The dependency on the standard is intrinsically different from the common use of standards in analytical chemistry (e.g., analysis of trace elements, organic

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micropollutants, etc.). This ‘hidden’ dependence should not be overlooked or plainly

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ignored by the users in failing to express their results in units relative to the standard.

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Using carbon-based units, though tempting, should be avoided and, when used

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(always accompanied by values in standard units), the converting factor always needs

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to be clearly indicated and justified. The use of carbon-based units has the apparent

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advantage of allowing comparison with total or dissolved organic carbon

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concentrations but gives the false impression that the given amount of carbon was

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‘really’ present when, after reading the above discussion, the reader will concur that it

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is rarely so. •

To ensure that results obtained through a given technique are comparable among

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studies and systems, it is preferable that a common standard is always used. However,

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among the NOM types discussed here, the case of ‘humics’ might be considered an

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exception because of the strong dependence of the signal on the type of ‘humic’. For

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these substances, the issue remains open to discussion.

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Acknowledgements

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I would like to thank reviewers of this manuscript and of passed papers of mine who

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showed, through their questions and comments, that, too often, what looks obvious to us

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is not necessarily obvious to everybody.

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BOD

biochemical oxygen demand

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CDOM

coloured dissolved organic matter

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COD

chemical oxygen demand

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CSP

Coomassie-stained proteinaceous

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CSV

cathodic stripping voltammetry

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DAPI

4’,6-diamidino-2-phenylindole

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DOC

dissolved organic carbon

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DYP

4’,6-diamidino-2-phenylindole

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IHSS

International Humic Substance Society

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MBTH

3-methyl-2-benzothiazolinone

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NOM

natural organic matter

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POC

particulate organic matter

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ROM

refractory organic matter

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SUVA

specific UV absorbance

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TEP

transparent exopolymer particles

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TPTZ

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Nomenclature

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2,4,6-tripyridyl-s-triazine

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Abbt-Braun, G., Lankes, U., Frimmel, F.H., 2004. Structural characterization of aquatic

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Alldredge, A.L., Passow, U., Logan, B.E., 1993. The abundance and significance of a

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EP

351

Filtration+Separation 42, 30–32.

356

Borch, N.H., Kirchman, D.L., 1997. Concentration and composition of dissolved

357

combined neutral sugars (polysaccharides) in seawater determined by HPLC-PAD.

358

Marine Chemistry 57, 85–95.

359 360

Buffle, J., 1988. Complexation Reactions in Aquatic Systems. An Analytical Approach. Ellis Horwood, Chichester, UK.

17

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Chanudet, V., Filella, M., 2006. The application of the MBTH method for carbohydrate

362

determination in freshwaters revisited. International Journal of Environmental

363

Analytical Chemistry 86, 693–712.

364 365

RI PT

361

Chanudet, V., Filella, M., 2007. Submicron organic matter in a peri-alpine, ultraoligotrophic lake. Organic Geochemistry 38, 1146–1160.

Chanudet, V., Filella, M., Quentel, F., 2006. Application of a simple voltammetric

367

method to the determination of refractory organic substances in freshwaters.

368

Analytica Chimica Acta. 569, 244–249.

M AN U

SC

366

369

Chateauvert, C.A., Lesack, L.F.W., Bothwell, M.L., 2012. Abundance and patterns of

370

transparent exopolymer particles (TEP) in Arctic floodplain lakes of the Mackenzie

371

River

372

doi:10.1029/2012JG002132.

Journal

of

Geophysical

Research

117,

G04013,

TE D

Delta.

de Vicente, I., Ortega-Retuerta, E., Mazuecos, I.P., Pace, M.L., Cole, J.J., Reche, I., 2010.

374

Variation in transparent exopolymer particles in relation to biological and chemical

375

factors in two contrasting lake districts. Aquatic Sciences 72, 443–453.

EP

373

de Vicente, I., Ortega-Retuerta, E., Romera, O., Morales-Baquero, R., Reche, I. 2009.

377

Contribution of transparent exopolymer particles to carbon sinking flux in an

378

oligotrophic reservoir. Biogeochemistry 96, 13–23.

AC C

376

379

Engel, A., Passow, U., 2001. Carbon and nitrogen content of transparent exopolymer

380

particles (TEP) in relation to their Alcian Blue adsorption. Marine Ecology Progress

381

Series 219, 1–10.

18

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Filella, M., 2007. Colloidal properties of submicron particles in natural waters. In:

383

Environmental Colloids and Particles. Behaviour, Separation and Characterisation.

384

Ed. by Wilkinson, K.J., Lead, J.R. Wiley, New York, pp. 17–93.

387 388 389 390

21–35.

Filella, M., 2010. Quantifying 'humics' in freshwaters: purpose and methods. Chemistry

SC

386

Filella, M., 2009. Freshwaters: which NOM matters? Environmental Chemistry Letters 7,

and Ecology 26, 177–186.

Gordon, D.C., 1970. A microscopic study of organic particles in the North Atlantic

M AN U

385

RI PT

382

Ocean. Deep-Sea Research 17, 175–185.

Gremm, T.J., Kaplan, L.A., 1997. Dissolved carbohydrates in streamwater determined by

392

HPLC and pulsed amperometric detection. Limnology and Oceanography 42, 385–

393

393.

394 395

TE D

391

Hansell, D.A., Carlson, C.A. (Eds.), 2002. Biogeochemistry of Marine Dissolved Organic Matter. Academic Press.

Hung, C.-C., Warnken, K.W., Santschi, P.H., 2005. A seasonal survey of carbohydrates

397

and uronic acids in the Trinity River, Texas. Organic Geochemistry 36, 463–474.

398

Ishii, S.K.L., Boyer, T.H., 2012. Behavior of reoccurring PARAFAC components in

399

fluorescent dissolved organic matter in natural and engineered systems: A critical

400

review. Environmental Science & Technology 46, 2006−2017.

AC C

EP

396

401

Kennedy, M.D., Tobar, F.P.M., Amy, G., Schippers, J.C., 2009. Transparent exopolymer

402

particle (TEP) fouling of ultrafiltration membrane systems. Desalination and Water

403

Treatment 6, 169–176.

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406 407 408 409

voltammetry of thiol/thioamide mixes in seawater. Talanta 89, 496–504. Le Gall, A.-C., van den Berg, C.M.G., 1993. Cathodic stripping voltammetry of glutathione in natural waters. Analyst 118, 1411–1415.

RI PT

405

Laglera, L.M., Tovar-Sánchez, A., 2012. Direct recognition and quantification by

Le Gall, A.-C., van den Berg, C.M.G., 1998. Folic acid and glutathione in the water column of the North East Atlantic. Deep-Sea Research I 45, 1903–1918.

SC

404

Mari, X., 1999. Carbon content and C:N ratio of transparent exopolymeric particles

411

(TEP) produced by bubbling exudates of diatoms, Marine Ecology Progress Series

412

183, 59–71.

M AN U

410

Matrai, P.A., Vetter, R.D., 1988. Particulate thiols in coastal waters—The effect of light

414

and nutrients on their planktonic production. Limnology and Oceanography 33, 624–

415

631.

TE D

413

Mopper, K., Delmas, D., 1984. Trace determination of biological thiols by liquid

417

chromatography and precolumn fluorometric labelling with o-phthalaldehyde.

418

Analytical Chemistry 56, 2557–2560.

EP

416

Myklestad, S.M., Skanoy, E., Hestmann, S., 1997. A sensitive and rapid method for

420

analysis of dissolved mono- and polysaccharides in seawater. Marine Chemistry 56,

421

279–286.

AC C

419

422

Panagiotopoulos, C., Sempéré, R., 2005. Analytical methods for the determination of

423

sugars in marine samples: A historical perspective and future directions. Limnology

424

and Oceanography: Methods 3, 419–454.

20

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Pakulski, J.D., and Benner, R., 1992. An improved method for the hydrolysis and MBTH

426

analysis of dissolved and particulate carbohydrates in seawater. Marine Chemistry

427

40, 143–160.

428 429

RI PT

425

Passow, U. 2012. The abiotic formation of TEP under different ocean acidification scenarios. Marine Chemistry 128–129, 72–80.

Passow, U., Alldredge, A.L., 1995. A dye-binding assay for the spectrophotometric

431

measurement of transparent exopolymer particles (TEP). Limnology and

432

Oceanography 40, 1326–1335.

M AN U

SC

430

433

Passow, U., Alldredge, A.L., 1994. Distribution, size and bacterial colonization of

434

transparent exopolymer particles (TEP) in the ocean. Marine Ecology Progress

435

Series 113, 185–198.

437

Perdue, E.M., Gjessing, E.T. Eds., 1990. Organic Acids in Aquatic Ecosystems. Wiley-

TE D

436

Interscience, Chichester.

Pernet-Coudrier, B., Waeles, M., Filella, M., Quentel, F., Riso, R., 2013. Simple and

439

simultaneous determination of glutathione, thioacetamide and refractory organic

440

matter in natural waters by DP-CSV. Science of the Total Environment 463–464,

441

997–1005.

AC C

EP

438

442

Quentel, F., Filella, M., 2008. Quantification of refractory organic substances in

443

freshwaters: further insight into the response of the voltammetric method. Analytical

444

and Bioanalytical Chemistry 392, 1225–1230.

445

Stevenson, F.J., 1994. Humus Chemistry, 2nd Ed. Wiley, New York.

21

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Tang, D., Wen, L.-S., Santschi, P.H., 2000. Analysis of biogenic thiols in natural water

447

samples by high-performance liquid chromatographic separation and fluorescence

448

detection with ammonium 7-fluorobenzo-2-oxa-1,2-diazole-4-sulfonate (SDB-F).

449

Analytica Chimica Acta 408, 299–307.

451

Vairavamurthy, A., Mopper, K., 1990. Field method for determination of traces of thiols in natural waters. Analytica Chimica Acta 236, 363–370.

SC

450

RI PT

446

Van Nevel, S., Hennebel, T., De Beuf, K., Du Laing, G., Verstraete, W., Boon, N., 2012.

453

Transparent exopolymer particle removal in different drinking water production

454

centers. Water Research 46, 3603–3611.

M AN U

452

Villacorte, L.O., Kennedy, M.D., Amy, G.L., Schippers, J.C., 2009a. The fate of

456

transparent exopolymer particles (TEP) in integrated membrane systems: removal

457

through pretreatment processes and deposition on reverse osmosis membranes.

458

Water Research 43, 5039–5052.

TE D

455

Villacorte, L.O., Kennedy, M.D., Amy, G.L., Schippers, J.C., 2009b. Measuring

460

transparent exopolymer particles (TEP) as indicator of the (bio)fouling potential of

461

RO feed water. Desalination and Water Treatment 5, 207–212.

463

Woodget, B.W., Cooper, D., 1987. Samples and Standards. Analytical Chemistry by

AC C

462

EP

459

Open Learning. Wiley, Chichester.

464

Zhang, J., Wang, F., House, J.D., Page, B., 2004. Thiols in wetland interstitial waters and

465

their role in mercury and methylmercury speciation. Limnology and Oceanography

466

49, 2276–2286.

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Table 1. Operationally-defined NOM categoriesa

Classification based on:

Examples

- origin

aquogenic, algogenic, anthropogenic, exopolymeric, extracellular, intracellular, pedogenic, sediment, soil, peat, terrestrial

- classical biochemical categories

carbohydrates, lipids, proteins, thiols

- physically-based fractionation methods

colloidal, coarse, dissolved, fine, high molecular weight, low molecular weight, nano-, particulate, pico-, ultrafiltered

- chemically-based fractionation methods

fulvic acids, humic acids, hydrophilic, hydrophobic

- lability criteria

assimilable, BOD (biochemical oxygen demand), biodegradable, COD (chemical oxygen demand), labile, recalcitrant, utilizable

- response to colorimetric reagents

CSP (Coomassie-stained proteinaceous) particles, DYP or DAPI (4’,6-diamidino-2-phenylindole) yellow particles, TEP (transparent exopolymer particles)

- response to spectroscopic techniques

chromophoric, coloured, fluorescent

TE D

M AN U

SC

RI PT

467

468

a

469

many associated acronyms, are discussed in detail.

This table is based on Filella (2009) where the different categories, together with the

AC C

EP

470

23

ACCEPTED MANUSCRIPT

SUPPLEMENTARY MATERIAL

RI PT

Understanding what we are measuring: standards and quantification of natural organic matter

a

SC

Montserrat Filellaa,b

Institute F.-A. Forel, University of Geneva, 10 route de Suisse, CH-1290 Versoix,

EP

TE D

SCHEMA, Rue Principale 92,L-6990 Rameldange, Luxembourg

3 Tables

AC C

b

M AN U

Switzerland

1

ACCEPTED MANUSCRIPT

RI PT

Table S1. Studies where ’dissolved’ carbohydrate concentrations in freshwater have been determined using colorimetric methods. Method

Standard

Units results

C units conversion clearly explained?

Reference

Lakes

Anthrone

Not given

mg L-1

-

Ochiai and Hanya, 1980

Lake

Phenol sulphuric method

Glucose

mg C L-1

“reported as carbon unit of equivalent glucose”

Satoh et al., 1986

River

MBTH

(glucose + manitol)/2

g C L-1

“expressed in g C L-1 of glucose equivalent”

Senior and Chevolot, 1991

Bog pool

MBTH / Phenol sulphuric method

Glucose

mg C L-1

No

Satoh et al., 1992

Lake

MBTH

Glucose

M (glucose equivalents)

Reservoir

MBTH

Not given

Lake

MBTH

Not given

River

MBTH

River

L-tryptophansulphuric acid

TE D

M AN U

SC

System

mg L-1

-

Striquer-Soares and Chevolot, 1996

mg C L-1

Polysaccharide concentrations were converted to mg C L-1 through a standardization proceedure by using the weighted average slope of the MTBH absorbance vs. the TOC for the standard polysaccharide

Wilkinson et al., 1997

EP AC C

1.7-5.5 M glucose equivalents Hanisch et al., 1996 = 130-396 g C L-1

Not given

g L-1

-

Lara et al., 1998

Glucose

M glucose equivalents

-

Kattner et al., 1999

2

ACCEPTED MANUSCRIPT

Method

Standard

Units results

C units conversion clearly explained?

Lake

Phenol-sulphuric acid

Glucose

mg L-1

calculated following Gnaiger and Bitterlich (1984) by using a conversion factor of 0.444

Pedrosa et al., 1999

Reservoir

MBTH

Glucose

mg L-1

was calculated assuming that 1 mg of glucose is equivalent to 0.4 mg carbon

Jugnia et al., 2000

River

MBTH

Starch, glucose

mol glucose L-1

-

Murrell and Hollibaugh, 2000

River

TPTZ

Not given

M-C

No

Hung et al., 2001

Lake

MBTH

Glucose

M glucose and M C

No

Hayakawa, 2004

Lakes

MBTH

Not given

M L-1

-

Laybourn-Party et al., 2004

Lake

MBTH

Not given

ng C L-1

No

Grigorszky et al., 2005

River

TPTZ

Not given

M C

No

Hung et al., 2005

Lake and rivers

MBTH

Glucose

mg C L-1

The carbohydrate concentrations obtained from the glucose calibration curves were converted to organic carbon concentrations: (1 mg of glucose = 0.4 mg C) assuming that all monomers were hexoses

Chanudet and Filella, 2006

River

TPTZ

mol C L-1

No

Guéguen et al., 2006

AC C

EP

TE D

M AN U

SC

RI PT

System

Not given

Reference

3

ACCEPTED MANUSCRIPT

Method

Standard

Units results

C units conversion clearly explained?

Lake and rivers

MBTH

Glucose

mg C L-1

No

River

TPTZ

Glucose

mol C L-1

No

River

TPTZ

Not given

% OC

No

River

TPTZ

Not given

mol C L-1

“calculated assuming per mole “glucose equivalent” has 6 mol C”

He et al., 2010

Lakes

TPTZ

Not given

mg L-1

-

de Vicente et al., 2010

Stream

MBTH

Glucose

mg L-1

“expressed as glucose equivalents”

Ylla et al., 2010

Lakes

TPTZ

Not given

mg C L-1

No

Chateauvert et al., 2012

River

TPTZ

Glucose

M- C glucose equivalents

expressed as M-C by the equivalent of glucose (C6H12O6) concentration multiplied by 6

Wang et al., 2013

SC

M AN U

TE D

Reference

Chanudet and Filella, 2007 Cai et al., 2008 Guo et al., 2009

AC C

EP

RI PT

System

4

ACCEPTED MANUSCRIPT

except when otherwise indicated.

RI PT

Table S2. Studies where thiols, as a ‘homologous’ compound, have been determined voltammetry. All by CSV (cathodic stripping voltammetry)

System

Edepa / V

Epeaka / V

Standard

Units in Figures/Tables

marine porewatersb

-

-

-

No thiol detected

Luther et al., 1985

porewaters, Great Marsh, Delawarec

-

-

-

RSH (M)

Luther et al., 1986a

porewaters, Great Marsh, Delawarec

-

-

-

porewaters, Great Marsh, Delawareb

-

-

cysteine hydrochloride or glutathione

Mersey estuary

-0.225 (pH 5)

-0.43 (pH 5)

-0.6

SC

Luther and Church, 1988

glutathione

glutathione (nmol L-1)

Le Gall and Van den Berg, 1993

glutathione

glutathione (nM)

Le Gall and Van den Berg, 1998

-

nM

EP

-0.1, -0.2

M AN U Thiol (M)

TE D

Luther et al., 1986b

AC C

Northern Adriatic Sea

Reference

Organic thiol (mM)

-0.250 (pH 8.5) -0.65 (pH 8.5) North East Atlantic

Comments

“expressed as equivalent to sulfide concentrations”

Ciglenečki and Ćosović, 1996

5

ACCEPTED MANUSCRIPT

System

Edepa / V

Epeaka / V

Standard

Units in Figures/Tables

Western North sea and English Channel

-0.05

Figure

thiourea

Thiols (nM)

Galveston Bay

-0.25

-0.6

glutathione

[Total reduced sulfur] (nM)

Scheldt estuary

-0.1

-0.5 – -0.6

none

Scheldt estuary

-0.1

-0.5

glutathione

deep-sea hydrothermal vent systems

-0.010, -0.2

-0.5

RI PT

Comments

Al-Farawati and van den Berg, 2001

“TRS as glutathioneequivalent concentration”

Tang et al., 2001

Current (nA)

Upper thiol concentration limit estimated from Cu titrations

Laglera et al., 2003

Thiol peak (nA)

“The quantification of thiols is not accurate as the standard used (glutathione) is not identical to the thiol in the estuary; for this reason, the thiol concentrations are indicated only as peak high rather than concentration”

Laglera and van den Berg, 2006

no quantification

“we can see that the hydrothermal ligands behave similar, but not exactly as GSH”

Sander et al., 2007

TE D

M AN U

SC

“Thiol concentrations are expressed in nM equivalents of thiourea”

EP AC C

glutathione

Reference

6

ACCEPTED MANUSCRIPT

Epeaka / V

Standard

Units in Figures/Tables

Venice lagoon

-0.1

-0.55 – -0.60

glutathione

Thiol (nM)

Rogoznica lake

-0.20

-0.68

not mentioned

RSS / nM

Deûle river

-0.2

-0.5 – -1.6

-

I / nA

a

“Thiol (glutathione equivalents)”

Reference

Chapman et al., 2009 Plavšić et al., 2011 Superville et al., 2013

M AN U

vs. Ag/AgCl Differential pulse polarography c ”polarographic methods”

Comments

RI PT

Edepa / V

SC

System

AC C

EP

TE D

b

7

ACCEPTED MANUSCRIPT

Method reference

Standard

Units

Lake

Counting

Passow et al., 1994

-

mL-1

Lake

Counting

Logan et al., 1994 -

mL-1

Lake

Counting

Logan et al., 1994 -

mL-1

Lake

Countinga

-

Lake

Counting

Logan et al., 1994 -

Lake

Colorimetric

Pasow and Alldredge, 1995

Counting

Alldredge et al., 1993; Mari and Kiørboe, 1996

Lake

Counting

Logan et al., 1994 -

Lakes

Counting

Passow and Alldredge, 1994

Lakes

Counting

Reservoir

Colorimetric

Conversion C

Reference

No

Logan et al., 1994

No

Grossart et al., 1997

No

Grossart et al., 1998

SC

Method

M AN U

System

RI PT

Table S3. Studies where TEP has been quantified in freshwaters.

mL-1

EP

TE D

Xanthan

Worm and Sondegaard, 1998

mL-1

No

Simon et al., 2000

g Xeq L-1

No

Berman and Viner-Mozzini, 2001

particles mL-1

mL-1

Brachvogel et al., 2001

L-1

No

Carrias et al., 2002

Passow and Alldredge, 1994

-

L-1

No

Lemarchand et al., 2006

Pasow and Alldredge, 1995

Xanthan

g Xeq L-1

0.75 factor (Engel and Passow, 2001)

de Vicente et al., 2009

AC C

-

8

ACCEPTED MANUSCRIPT

Method reference

Standard

Units

Surface water

Colorimetric

Pasow and Alldredge, 1995

Xanthan

g Xeq L-1

River, lake, canal

Colorimetric

Pasow and Alldredge, 1995

Xanthan

mg Xeq L-1

Lake

Counting

Passow and Alldredge, 1994

-

L-1

Surface water, saline groundwater

Colorimetric

Pasow and Alldredge, 1995

Xanthan

Lakes

Colorimetric

Pasow and Alldredge, 1995

Xanthan

Alldredge et al., 1993

-

Surface water, groundawater

Colorimetric

Passow and Alldredge, 1995; Villacorte et al., 2009

Xanthan

Reference

No

Kennedy et al., 2009

No

Villacorte et al., 2009

No

Arnous et al., 2010

g Xeq L-1

No

Berman and Parparova, 2010

g Xeq L-1

0.75 factor

de Vicente et al., 2010

M AN U

TE D

Counting

Engel and Passow, 2001

g-C mL-1

Mari, 1999b

Chateauvert et al., 2012

g Xeq L-1

No

Van Nevel et al., 2012

AC C

EP

Lakes

Conversion C

RI PT

Method

SC

System

These authors filtered through 3 m filters and used a non-acidified Alcian Blue solution for staining; they called observed particles Alcian Blue-stained particles (ABSP). b TEPcarbon = 0.25 x 10-6 R2.55 where TEPcarbon is in g C and R is the equivalent spherical radius (m). a

9

ACCEPTED MANUSCRIPT

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RI PT

North Sea and English Channel. Environmental Science & Technology 35, 1902– 1911.

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SC

class of large, transparent organic particles in the ocean. Deep Sea Research I 40, 1131–1140.

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Arnous, M.-B., Courcol, N., Carrias, J.-F., 2010. The significance of transparent exopolymeric particles in the vertical distribution of bacteria and heterotrophic nanoflagellates in Lake Pavin. Aquatic Sciences 72, 245–253. Berman, T., Parparova, R., 2010. Visualization of transparent exopolymer particles (TEP)

TE D

in various source waters. Desalination and Water Treatment 21, 382–389 Berman, T., Viner-Mozzini, Y., 2001. Abundance and characteristics of polysaccharide

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EP

and proteinaceous particles in Lake Kinneret. Aquatic Microbial Ecology 24, 255–

AC C

Brachvogel, T., Schweitzer, B., Simon, M., 2001. Dynamics and bacterial colonization of microaggregates in a large mesotrophic lake. Aquatic Microbial Ecology 26, 23–35. Cai, Y., Guo, L., Douglas, T.A., 2008. Temporal variations in organic carbon species and fluxes from the Chena River, Alaska. Limnology and Oceanography 53, 1408–1419. Carrias, J.F., Serre, J.P., Sime-Ngando, T., Amblard, C., 2002. Distribution, size, and bacterial colonization of pico- and nano-detrital organic particles (DOP) in two lakes of different trophic status. Limnology and Oceanography 47, 1202–1209. 10

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RI PT

Analytical Chemistry 86, 693–712. Chanudet, V., Filella, M., 2007. Submicron organic matter in a peri-alpine, ultraoligotrophic lake. Organic Geochemistry 38, 1146–1160.

SC

Chapman, C.S., Capodaglio, G., Turetta, C., van den Berg, C.M.G., 2009. Benthic fluxes of copper, complexing ligands and thiol compounds in shallow lagoon waters.

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TE D

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EP

de Vicente, I., Ortega-Retuerta, E., Romera, O., Morales-Baquero, R., Reche, I., 2009. Contribution of transparent exopolymer particles to carbon sinking flux in an

AC C

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11

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EP

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AC C

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12

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Hayakawa, K., 2004. Seasonal variations and dynamics of dissolved carbohydrates in Lake Biwa. Organic Geochemistry 35, 169–179.

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SC

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