Waste Management 34 (2014) 2572–2580

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Combining chemical sequential extractions with 3D fluorescence spectroscopy to characterize sludge organic matter Mathieu Muller a,c,1, Julie Jimenez c,b,⇑, Maxime Antonini c, Yves Dudal a,1, Eric Latrille c, Fabien Vedrenne b, Jean-Philippe Steyer c, Dominique Patureau c a b c

INRA, UMR 1222, Ecologie microbienne et biogéochimie du sol, 2 Place Pierre Viala, Bâtiment 12, Montpellier cedex 2 F-34060, France Veolia Environnement R&D, Centre de Recherche sur l’Eau, Maisons-Laffitte F-78603, France INRA, UR050, Laboratoire de Biotechnologie de l’Environnement, Avenue des Etangs, Narbonne F-11100, France

a r t i c l e

i n f o

Article history: Received 23 April 2014 Accepted 30 July 2014 Available online 12 September 2014 Keywords: Sewage sludge Anaerobic digestion Bioaccessibility Biodegradability Organic matter fractionation Excitation–emission-matrix fluorescence spectroscopy

a b s t r a c t The design and management of anaerobic digestion of sewage sludge (SS) require a relevant characterisation of the sludge organic matter (OM). Methods currently used are time-consuming and often insufficiently informative. A new method combining chemical sequential extractions (CSE) with 3D fluorescence spectroscopy was developed to provide a relevant SS characterisation to assess both OM bioaccessibility and complexity which govern SS biodegradability. CSE fractionates the sludge OM into 5 compartments of decreasing accessibility. First applied on three SS samples with different OM stability, fractionation profiles obtained were in accordance with the latter. 3D fluorescence spectroscopy revealed that the bioaccessible compartments were mainly constituted of simple and easily biodegradable OM while the unaccessible ones were largely made of complex and refractory OM. Then, primary, secondary and anaerobically digested sludge with different biodegradabilities were tested. Complexity revealed by 3D fluorescence spectroscopy was linked with biodegradability and chemical accessibility was correlated with sludge bioaccessibility. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction In the European Union (EU), production of sewage sludge (SS) from urban origin raised from 5.5 million tons of dry matter per year in 1992 to 10.9 million tons of dry matter per year in 2005 and would reach 13 million tons of dry matter in 2020 (Kelissidis and Stasinakis, 2012). In the last two decades, EU policy has encouraged the recycling of this solid organic residue through anaerobic digestion or/and composting processes to produce both bioenergy and organic fertiliser for agriculture (Directive 86/278/ EEC; Directive 1999/31/CE; Directive 2008/98/CE). However, the limiting step of SS anaerobic digestion is the hydrolysis of particulate compounds (Vavilin et al., 2008). Consequently, SS anaerobic digestion efficiency is governed by notions as the bioaccessibility and biodegradability (i.e. chemical composition) of the sludge organic material (Aquino et al., 2008). The definitions of these ⇑ Corresponding author at: INRA, UR050, Laboratoire de Biotechnologie de l’Environnement, Avenue des Etangs, Narbonne F-11100, France. Tel.: +33 (0)4 68 42 51 70; fax: +33 (0)4 68 42 51 60. E-mail address: [email protected] (J. Jimenez). 1 Present address: Envolure SAS, CAP DELTA, 1682 rue de la Valsière, CS 67393, Montpellier cedex 4 F-34184, France. http://dx.doi.org/10.1016/j.wasman.2014.07.028 0956-053X/Ó 2014 Elsevier Ltd. All rights reserved.

two concepts are crucial. Depending on the field considered, definitions and concepts are indeed not used with the same meaning concerning bioaccessibility. According to Aquino et al. (2008), bioaccessibility is defined as the possible access to the molecule depending on several factors such as the contact time between the substrate and the microorganism, the hydrolytic activity efficiency or eventually the pre-treatment applied to the sludge. Nevertheless, there is also a notion of physical accessibility such as in the case of the cellulose protection by lignin or vegetals walls acting as a barrier and needing chemical or physical break-up to be accessible and then absorbed by microorganisms. Consequently, biodegradability is the ability of a microorganism to absorb a molecule but this biodegradation is limited by molecules bioaccessibility and complexity and/or toxicity. Therefore, the chemical characterisation of sludge organic matter (OM) is a crucial field of investigation which can supply knowledge and innovative tools to improve and to optimize the overall recycling of sludge. Classic characterisation of SS involves extracting and quantifying compounds according to biochemical families: proteins, lipids, carbohydrates, fibres, etc. (Reveillé et al., 2003; Parnaudeau et al., 2004). Such characterisation is tedious (numerous analytical

M. Muller et al. / Waste Management 34 (2014) 2572–2580

methods have to be used) and non-informative enough regarding the bioaccessibility of the OM. The biochemical methane potential (BMP) assay, related to the international standard NF EN ISO 11734, is commonly used to determine the anaerobic biodegradability of sludge (Aquino et al., 2008; Angelidaki et al., 2009). However, measuring BMP is also tedious and time consuming (usually, from 30 to 40 days). In addition, we do not know precisely which components of the sludge make the biodegradable and the nonbiodegradable fractions of the OM. In a recent effort to avoid BMP tests, Mottet et al. (2010) have proposed a biodegradability prediction model based on the biochemical composition of the sludge. Despite the fact that the method is faster than BMP to predict anaerobic biodegradability (about one week), prediction errors were high, ranging from 11% to 38%. According to the authors, the absence or the use of non-relevant data on bioaccessibility explained this lack of accuracy. Consequently, both bioaccessibility and complexity of the sludge OM should be simultaneously determined to accurately predict sludge biodegradability. Chemical sequential extractions (CSE) that simulate the bioaccessibility of the OM according to its chemical accessibility (Etcheber et al., 1985) could constitute an efficient alternative for sludge OM characterisation. In this procedure, the strength of the extractants increase progressively to recover the OM of decreasing accessibility, but independently of its biochemical origin. CSE were previously used to determine the OM-associated heavy metals distribution in solid waste (Prudent et al., 1996) but not for the OM fractionation itself. To date, studies about the OM extractions from solid organic residues have only focused on specific fractions like water-soluble OM for composts (Charest et al., 2004), extracellular polymeric substances – EPS (McSwain et al., 2005) and humic-like substances – HLS (Reveille et al., 2003) for sewage sludge. The HLS and EPS are considered as OM fractions since they have complex, heterogeneous and non-well defined composition (Etcheber et al., 1985; Nielsen et al., 2004). The accessibility of OM is also considered by the Van Soest fractionation aiming at characterizing the fibres (Van Soest, 1963). Indeed, the procedure consists in neutral to acid extractions in order to solubilize the carbohydrates. This methodology, created first for green wastes, works very well on compost or plants (Gunaseelan, 2009). The authors succeed in building correlations between these fractions and the BMP. However, as shown by Mottet et al. (2010), this method is not appropriate for sludge with correlations errors around 35%. Besides, the soluble fraction (extracted after hot water and hot neutral detergent addition) is too large (i.e. 55 to 70 g g TS1). Consequently, the accessible fraction is not accurately characterized with this method. Moreover, sewage sludge is composed of carbohydrates but the fraction of proteins is higher in secondary and digested sludge (Jimenez et al., 2013). A partial fractionation of the EPS compartment of sewage sludge has been previously reported (EsparzaSoto and Westerhoff, 2001). These authors have isolated two types of EPS of decreasing accessibility by increasing the strength of the extractant. Nevertheless, only a fraction of the total sludge OM was concerned, which is not sufficient for a precise evaluation of its bioaccessibility. An advanced fractionation of the whole sludge OM could indeed supply relevant information about its bioaccessibility. On the other hand, knowing the OM bioaccessibility is not sufficient to predict its biodegradability, and complexity of the OM needs to be assessed in each fraction. Spectroscopic analysis is a relevant method to characterize theses fractions and was successfully used to characterise organic residues (Lesteur et al., 2011; Richard et al., 2009). They are generally faster than classic biochemical analysis (few minutes versus several days). Among all available spectroscopic methods – i.e. UV–VIS, nuclear magnetic resonance, electronic paramagnetic resonance, infrared and fluorescence spectroscopy – fluorescence spectroscopy is the most

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appropriate method. Three dimensional liquid-phase fluorescence (3D-LPF) spectroscopy – i.e. excitation–emission-matrix (EEM) fluorescence spectroscopy – has been long used to describe dissolved OM (DOM) in marine and freshwaters, as well as in raw and treated wastewater (Henderson et al., 2009). It enables the visualization of proteins (Mayer et al., 1999), humic substances (Sierra et al., 2005) and melanoidins (Dwyer and Lant, 2008). The fluorescence of these compounds were also detected by 3D-LPF spectroscopy from DOM of sewage sludge (Wang et al., 2010) as well as in particulate OM (POM) extracted from sewage sludge and compost (Esparza-Soto and Westerhoff, 2001; Richard et al., 2009). In a simple spectrum acquisition, the 3D-LPF spectroscopy enables to detect all the fluorophores of a sample which can then be quantified using the fluorescence regional integration (FRI) approach (Chen et al., 2003). With the objective of proposing a new methodology for sludge characterization, we designed a sludge-specific CSE procedure to extract the OM compartments according to their bioaccessibility. We combined this innovative approach with 3D fluorescence spectroscopy for revealing the molecular complexity of the OM compartments. The resulting OM fractionation and fluorescence profiles were compared and discussed according to the type of sludge analysed. Then, the potential of the method to predict sludge biodegradability was tested. 2. Materials and methods 2.1. Chemicals Sodium hydrogen carbonate (>99%) and sodium hydroxide (>99%) were supplied by Sigma–Aldrich (St Quentin Fallavier, France). Sodium chloride (>99%) was purchased from Carlo Erba (Val de Reuil, France). Hydrochloric acid (0.1 M) was provided by Prolabo (VWR, Fontenay-sous-Bois, France). Deionised water was obtained through a Milli-Q system (Millipore, Molsheim, France). 2.2. Samples The CSE procedure was developed first and applied on three different sludge, a highly loaded secondary sludge with a sludge retention time (SRT) of 9 h (sludge A), a lowly loaded secondary sludge with a SRT of 20 days (sludge B) and a primary sludge (sludge C). These sludge samples differ from by the stabilisation of their OM: sludge C is supposed to contain rather partially hydrolysed and non-stabilised OM; sludge A, hydrolysed but non-fully stabilised OM and sludge B, highly stabilised and condensed OM. Samples were frozen after collection and stored at –20 °C before the fractionation analysis. Three other fresh sludge samples were used in order to assess the ability of the protocol to predict biodegradability of a primary sludge (SI), a secondary sludge (SII) and an anaerobically digested secondary sludge (SD). Their biodegradability was assessed with BMP tests according to Angelidaki et al. (2009). SD sludge corresponds to the digested sludge SII obtained after the BMP test. General characteristics of the sludge samples are presented in Table 1. 2.3. Classical analysis Dry solids (DS), volatile solids (VS) and mineral solids (MS) were determined by weight measurements at different temperatures. The DS consisted in the residual mass of sludge after a 24 h-stage at 105 °C. The MS consisted in the residual mass after a 2 h-stage at 550 °C. The VS was calculated as the difference between DS and MS. Chemical oxygen demand (COD) of sludge and extracts was measured according to the international standard ISO

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Table 1 Origin and characteristics of the sewage sludge samples. SS, VS, MS and COD values are expressed in g L1. Sample

A

B

C

SI

SII

SD

Effluent type Sludge type Plant capacity Organic load SRT DS (n = 2) VS (n = 2) MS (n = 2) COD (n = 4) BD (%COD) (n = 3)

Urban Secondary 250 000 p-eq High 9h 34.4 (0.2) 25.0 (0.1) 9.4 (0.0) 48.5 (6.7) –

Urban Secondary 120 000 p-eq Low 20 days 41.9 (0.3) 31.3 (0.3) 10.6 (0.0) 53.5 (3.9) –

Urban Primary 33 000 p-eq n.r. – 39.1 (0.7) 30.3 (0.6) 8.9 (0.2) 57.2 (2.3) –

Urban Primary 1 450 000 p-eq n.r. n.r. 66.45 (0.0) 51.9 (0.0) 14.47 (0.0) 49.1 (0.6) 61% (3%)

Urban Secondary 600 000 p-eq Low 11 days 72.5 (0.3) 52.5 (0.3) 20.0 (0.3) 79.7 (5.0) 47% (5%)

Urban Digested n.r. BMP test 60 days 3.7 (0.1) 0%

p-eq means ‘‘population-equivalent’’; n.r. means ‘‘not relevant’’; SRT means ‘‘sludge retention time’’; standard deviation are expressed in brackets; n means ‘‘number of replicates’’; ‘‘–’’ means not measured or not available.

15705:2002. The validity range of the assay is from 22 to 1 500 mg L1 of oxygen. A sample of 2 mL is required. Samples containing high OM concentrations can be previously diluted in milliQ water. For the negative control, 2 mL of milliQ water was added in the test tube. Tubes were then submitted to incubation at 150 °C for 2 h and the resulting oxygen consumption was determined by photometry. 2.4. Sequential extractions procedure The first step of the procedure was set to separate the DOM and the POM. For this purpose, a volume of 200 mL of raw sludge was centrifuged at 18 600g for 30 min at 4 °C. The supernatant, containing DOM, was recovered and filtered at 0.45 lm through cellulose acetate membrane (Minisart, Sartorius, Aubagne, France). The pellet containing POM was used in the following steps. The second step is designed to extract the soluble EPS (S-EPS) from the POM according to the procedure published by EsparzaSoto and Westerhoff (2001) with some modifications. For this purpose, 5 g of fresh pellet were shaked (15 min, 200 rpm, 30 °C) with 40 mL (mass ratio 1:8) of a solution of sodium chloride (10 mM) and sodium hydrogen carbonate (4 mM; pH = 8). The suspension was then centrifuged and the supernatant, containing the S-EPS, filtered similarly to the DOM. The residual pellet was used in the following step. The following step aims at recovering the readily extractable EPS (RE-EPS) from the POM. The method was also based on the work of Esparza-Soto and Westerhoff (2001). The residual pellet from the previous step was shaked (15 min, 200 rpm, 30 °C) with 40 mL (mass ratio 1:8) of a solution of sodium chloride (10 mM) and sodium hydroxide (10 mM; pH = 11). The suspension was then centrifuged and the supernatant, containing the RE-EPS, filtered as described previously. The residual pellet was used in the following step. The last step is carried out to extract the HLS (Humic-Like Substances) from the POM. A standardised method (Swift, 1996), developed for soils but also used for sediments (Giovanela et al., 2004), was applied with some modifications. The residual pellet from the previous step was first pre-treated by mixing with 40 mL (mass ratio 1:8) of hydrochloric acid (0.1 M) for 1 h at 200 rpm and 30 °C in order to eliminate the bound carbonates, sulfates and hydroxides. Hydrochloric acid was then eliminated after centrifugation (18 600g, 30 min, 4 °C). The resulting pellet was suspended in milli-Q water and neutralised using sodium hydroxide (1 M). After centrifugation (18 600g, 30 min, 4 °C), the milli-Q water was discarded and the HLS was extracted from the pre-treated pellet by shaking (4 h, 200 rpm, 30 °C) with 40 mL of sodium hydroxide (0.1 M; pH = 12) under nitrogen saturated-atmosphere (quality 4.5, Linde Gas, St. Priest, France) in order to avoid the OM oxidation. The HLS extract and the residual POM were

separated by centrifugation as described above. The residual pellet, constituting the non-extractable (NE) OM, was freeze-dried and crushed to 2 mm. The CSE procedure was carried out in triplicates on each sludge samples A, B and C. Before switching to the following one, all extraction steps were repeated until steady and negligible COD values were extracted from sludge. On the second part of the study, the same CSE procedure was applied on the sludge SI, SII and SD but the number of repetitions of each extraction was reduced in order to reach a compromise between OM extracted yield and experiment time. 2.5. 3D fluorescence spectroscopy The filtered extracts were analysed by 3D-LPF spectroscopy (LS55, Perkin Elmer, Courtaboeuf, France) in a 1 cm path length quartz cuvette. Samples were excited using a xenon lamp emitting a pulsed radiation (20 kW for 8 ls) from 200 to 600 nm with 10 nm steps. Fluorescence emission was recovered at a 90° angle from the excitation beam. Excitation and emission monochromator slit widths were set at 10 nm. Emission monochromator scan speed was 1200 nm s1 so that fluorescence emission values were recorded each 0.5 nm from 200 to 600 nm. Perkin Elmer FL Winlab 4.00.02 software was used to monitor the spectrofluorimeter and to record the signal. Fluorescence auto-quenching was avoided by checking the response linearity between fluorescence intensity and samples COD. Moreover, a protein standard solution (5 mg L1) was systematically analysed to correct potential dayto-day internal variation of the spectrofluorimeter. The fluorescence analysis of the non-extractable OM was carried out in the same conditions but using a specifically designed front face accessory for solid samples (Perkin Elmer, Courtaboeuf, France). 2.6. 3D spectra processing The resulting fluorescence EEM were processed using a homemade algorithm developed under the Matlab software (http:// www.mathworks.fr/products/matlab/). In few seconds, this algorithm interpolates the data measured to estimate intermediate fluorescence values. The matrix size is consequently increased from 40  792 (31 680 data points) to 792  792 (627 264 data points). This interpolation enables to shorten the sample analysis time without losing information. Finally, the algorithm converts the fluorescence EEM to grey values through a bitmap picture. This picture is processed using the Image J software (http://rsbweb. nih.gov/ij/) to quantify the fluorescence. Based on Chen et al. (2003) and He et al. (2011, 2013), spectra were decomposed on seven zones (zone I–VII) corresponding to each molecule families-like fluorescence (Fig. 2).

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From the analysis of model molecules and several types of natural matrices such as lignocellulose-like samples or thermal treated sludge (Muller et al., 2011 and personal data), the initial zone V of Chen et al. (2003) was split into 3 zones, now called V, VI and VII. The zone V is often due to an inner filter phenomena (proteins fluorescence is absorbed by aromatic molecules which reemit fluorescence inside this zone). The zone VI can represent several molecules depending on the sample studied:  Glycated protein-like molecules such as melanoidines in thermally treated DOM of sludge.  Lignocellulose-like compounds.  Humic acid-like compounds. The zone VII represents essentially humic-like and lipofuscinlike materials. Proteins-like molecules are named I–III (instead of I, II and IV in Chen et al. repartition). The zone IV represents the fulvic acid-like molecules fluorescence. Finally, by studying numerous spectra, the fluorescence zone limits based on Chen et al. (2003) were slightly modified in order to take into account the whole peak of a zone. Using the ‘‘Sync Measure 3D’’ additional plugin, available on the Image J website, up to 30 different spectra can be processed similarly and simultaneously. The proportion of fluorescence of a zone ‘‘i’’ Pf(i) was calculated from the fluorescence zone volume (Eqs. (1) and (2)).

V f ðiÞðU:A:=mg COD L1 Þ ¼

V image J ðiÞ 1  CODsample PSðiÞ 7 i¼1

ð1Þ

SðiÞ

V f ðiÞ Pf ðiÞð%Þ ¼ P7  100 i¼1 V f ðiÞ

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3. Results and discussion 3.1. OM fractionation profile: molecule bioaccessibility revealed For the three sludge samples analysed (A, B and C), CSE procedure showed that POM was constituted of fractions of different COD size. Indeed, when the same extraction step was repeated, the COD values of the successive extracts tended to decrease exponentially to reach a steady and negligible level (Fig. 1a–c). At this point, the extractant used was not efficient enough and only a more powerful extractant was able to release more COD from sludge. This means that another fraction is targeted from an extractant to another. Moreover, the quantities of COD extracted during the different extraction steps (Fig. 1a–c) as well as the total extracted COD (Fig. 1d) differed according to the sludge type. The OM fractionation profiles obtained were sludge specific (Table 2). For the sludge A, an important quantity of the OM (20% of COD) was contained in the DOM fraction and was supposed to be bioaccessible. Conversely, less than 5% of the COD was found in the HLS and about 53% was non-extractable (NE). For sludge B, the DOM and the S-EPS counted for less than 8% of the COD, most of the extractable OM was found in the RE-EPS and HLS fractions suggesting low bioaccessibility. Moreover, the NE part counted for 60% of COD. Sludge C appears to be intermediate with a DOM of 10% and a HLS of 4% of COD suggesting a higher availability of the extractable OM than sludge B but a NE OM higher than sludge A with about 61%. These results mean that the OM bioaccessibility differed according to the considered sludge with respect to its stabilisation degree. Consequently, the use of CSE procedure to characterise sludge appears to be highly relevant.

ð2Þ 3.2. OM fluorescence profiles: molecules complexity revealed 1

where Vimage J(i) (U.A./mg COD L ) is the raw volume obtained in Image J, CODsample (mg L1) is the COD concentration of the sample analysed, S(i) (nm2) is the area of a zone i, Pf(i)(%) is the fluorescence proportion of a zone i. FRI and Parallel Factor Analysis (PARAFAC) methods are the most commonly used techniques to analyze the emitted fluorescence signal (Sanchez et al., 2013). Although the PARAFAC method is very powerful for signals interpretation, we chose the FRI method because it allows a direct identification of biochemical families and a quantitative approach is possible to compare several samples. Besides, from the FRI method, we built a ‘‘complexity index’’ (Eq. (3)) based on Wang et al. (2010) and He et al. (2011, 2013). This index is defined by the ratio of the sum of the fluorescence volumes of the most complex fluorescence zones (IV–VII) over the sum of the fluorescence volumes of the protein-like zones (I–III).

P7 V f ðiÞ Complexity Index ¼ %CODfraction  Pi¼4 3 i¼1 V f ðiÞ

ð3Þ

Concerning this complexity ratio, a recent study performed by He et al. (2013) showed that upon 6 humification ratios, the use of FRI which enable to calculate a ratio between the sum of the fluorescence of ‘‘complex’’ zones volumes and the sum of the protein-like zones volumes (humification parameter) was the most consistent to predict humification during landfilling and compost. This ratio is quite similar to the complexity index proposed in this study where the sum of complex fluorescence zones and the sum of protein-like zones are used. Besides, the complexity index from this study takes into account the organic matter content of the sample through the COD concentration. This point provides more significancy to this index.

Focusing on sludge A, the complexity of OM contained in each fraction was investigated by 3D fluorescence spectroscopy. The fluorescence fingerprint of DOM (Fig. 2a) showed two peaks at about 350 nm emission wavelength when excited at 220 and 280 nm. These peaks are well known to be characteristic of protein-like compounds (Muller et al., 2011). This fluorescence fingerprint was reported as fresh organic matter which is rather easily biodegradable (Reynolds and Ahmad, 1997). It was also possible to measure the fluorescence fingerprint of the NE for this sludge. Indeed, Muller et al. (2011) showed that the 3D fluorescence applied to dark coloured sludge does not work properly. By removing layers of the sludge during CSE, the non-extracted part of some sludge, like sludge A, was less dark coloured and could be then measured by solid phase fluorescence analysis. Fig. 2b shows only one peak at 500 nm emission wavelengths when excited at 420 nm. Moreover, we can distinguish a shoulder below this main peak at excitation wavelength around 400 nm. We previously identified the main peak as lipofuscin-like products while fluorescence induced at 400 nm was rather recognised as a lignocellulosic marker (Olmstead and Gray, 1997; Muller et al., 2011). This fluorescence fingerprint could match humic-like material as well. Anyhow, ligno-cellulosic and humic-like materials are well known to be highly stabilised and not easily biodegradable compounds. Lipofuscins are made of highly condensed and degraded proteins resulting from the reaction with aldehydes produced next to the lipid oxidation. Lipofuscins are markers of OM maturation in foods chemistry and known as insoluble and not easily degradable molecules (Seehafer and Pearce, 2006). To sum up, DOM could be seen as an easily bioaccessible compartment mainly made of simple and easily biodegradable organic materials. Conversely, the non-extractable POM could be seen as a non bioaccessible

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Fig. 1. Quantities of COD (in mg) extracted from POM by the successive extractions for sludge A (a), B (b) and C (c); cumulative extracted COD (in mg) along the POM fractionation procedure for sludge A, B and C (d).

Table 2 COD fractionation profile of the sewage sludge samples (% of total COD). Sample

A (n = 3)

B (n = 3)

C (n = 3)

SI (n = 2)

SII (n = 2)

SD (n = 2)

DOM S-EPS RE-EPS HSL TOTAL

20.1 (0.9) 11.1 (0.5) 11.2 (0.1) 4.3 (1.1) 46.7 (2.6)

5.4 (0.1) 1.9 (0.1) 20.7 (0.8) 12.0 (1.6) 40.0 (2.6)

9.7 (0.2) 11.0 (0.2) 13.5 (0.4) 4.0 (1.1) 38.2 (1.9)

2.1 (0.7) 1.5 (0.3) 10.3 (1.3) 22.9 (4.5) 36.8 (4.8)

8.3 (0.9) 5.4 (0.1) 17.3 (1.0) 29.5 (2.3) 60.5 (2.3)

6.8 (0.9) 3.4 (0.1) 6.1 (0.1) 28.9 (0.4) 45.2 (0.7)

Standard deviation are expressed in brackets; n means ‘‘number of replicates’’.

compartment mainly made of complex and not easily biodegradable organic materials. Concerning the intermediate fractions, which represent the extractable POM, 3D fluorescence analysis were carried out in the first and final stages of each extraction type: S-EPS, RE-EPS and HLS. At the early stages of the S-EPS extraction (Fig. 3a), the fluorescence fingerprint of the extracts mainly revealed proteinlike fluorescence, as in the DOM fraction, but a second peak can be also observed at 420 nm emission wavelength when excited at 340 nm. This second peak is characteristic of the glycated proteins and the melanoidins compounds also known as Maillard’s compounds in food chemistry or advanced glycation end products (AGE) in the biomedical science. These molecules result from the chemical condensation of sugars on proteins followed by Amadori’s rearrangements which leads to highly polymerised and aromatic structures (Dwyer and Lant, 2008). These structures are not easily biodegradable. In order to compare results quantitatively, Fig. A.1 from the Appendix A presents the fluorescence percentage calculated at initial and final stage. At the final stages of the S-EPS extraction (Fig. 3b), the fluorescence of the extracts

showed the same peaks than at the early stages but the proteinlike fluorescence (regions I–III) tended to decrease from 80% to 70% while the fluorescence due to complex structures (regions IV–VII) increased from 20% to 30% (Fig. A.1). The first RE-EPS extracts (Fig. 3c) showed a pattern similar to the first S-EPS ones with a percentage of simple and complex materials of, respectively, 80% and 20%. This result suggests that OM of equivalent complexity, in this case essentially simple materials, could be found in sludge at different levels of bioaccessibility. So, the intrinsic biodegradability of simple materials could be reduced due to their unaccessibility. Conversely to the most bioaccessible OM compartments, the last RE-EPS extracts (Fig. 3d) did not show protein-like fluorescence but only glycated protein-like fluorescence as well as another peak at 300 nm emission wavelength when excited at 240 nm which was not identified but was located in the protein zone I. Anyhow, the fluorescence profile of the last RE-EPS extracts revealed more complex OM than the previous ones counting for about 40% of the total fluorescence (Fig. A.1). The fluorescence EEM of the HLS compartment (Fig. 3e and f) revealed that protein-like compounds remain in the lowest accessible OM. These

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Fig. 2. Excitation–emission-matrix fluorescence of the DOM (a) and the non-extractable residue (b) for sludge A. Fluorescence intensity in arbitrary units is plotted as isolines. Arrows indicate the main fluorescence peaks. Legend of FRI: I, II and III are protein-like materials, IV is fulvic-like materials, V is an inner filter area, VI is glycated protein-like materials and VII is humic-like and lipofuscin-like materials.

compounds counted for respectively 60 and 50% of the total fluorescence, in the early and final stages of the HLS extractions (Fig. A.1). A large part of the HLS fluorescence fingerprint was due to complex structures (from 40% to 50%). The non-identified fluorescence peak (240/300 nm) that we found in the spectra of the latest RE-EPS extracts was also observed in the latest extracts of the HLS fraction (Fig. 3f). Taking sludge A as an example, we showed that 3D fluorescence spectroscopy is a relevant method to estimate the OM complexity from the different fractions previously extracted. Indeed, we showed that simple OM (protein-like fluorescence) was found in the most bioaccessible fractions (DOM and EPS) while the least bioaccessible ones (HLS and the non-extractable residue) contained significant amounts of complex and refractory OM (glycated protein-like, ligno-cellulosic-like and lipofuscin-like fluorescence). These results were consistent with those previously reported in the literature dealing with the evolution of fluorescence fingerprints in the water line of wastewater treatment: usually, fluorescence due to simple organic materials predominated in the raw sewage and drastically decreased in the effluent while the fluorescence due to complex organic materials persisted in the effluent or increased compared to the raw sewage (Hur et al., 2011). However, in the present work, protein-like fluorescence was systematically found in the fractions excepted in the non-extractable residue. Similarly, glycated protein-like fluorescence was systematically found in all the fractions excepted in the DOM and the non-extractable residue. These results suggested that the intrinsic biodegradability of simple organic molecules could be reduced when located in unaccessible organic fractions and, conversely, bioaccessible organic fractions could be not easily biodegradable when constituted of complex and refractory molecules. This observation confirms that not only the complexity or accessibility but both are needed to accurately assess the biodegradability of sludge OM. 3.3. Potential of CSE-3D fluorescence spectroscopy to predict SS biodegradability In order to get an easier and quicker protocol, the number of extractions was reduced to 4 repetitions for each extraction step. Indeed, 80% of total fraction COD is recovered using 4 extractions.

An inconvenient of this modification would be that 20% of the remained COD from a fraction could pollute the next one. As shown by Fig. 3, there are some differences in fluorescence spectra between the first and the last extract. However, the 3D fluorescence is now made on the mixture composed of the 4 extractions generated by fraction and these 4 first extracts represent the main part of OM contained in the fraction. Consequently, we assumed that the interference generated by the remained COD is negligible. Besides, the main objective of the organic matter extraction was to extract sufficient organic matter from each sludge compartment to be representative of a fraction. By reducing the number of repetitions, the experimental time was reduced to 5 days. Primary, secondary and anaerobically digested sludge, respectively named SI, SII and SD, with different biodegradability values were used (cf. Table 1). In the current work, the COD extraction profiles from these 3 different sludge samples showed that there was a depletion of each fraction after 4 extractions (Fig. 4a). Whatever the sludge nature, the modified protocol did not affect the quality of the results. Indeed, most of the time, the lower detection limit of the COD analysis during the fourth extraction was reached and the chemical accessibility was respected as shown by the asymptotic curves of COD mass extractions (Fig. 4a). In terms of percentage of COD (Table 2), low COD concentrations in fractions of DOM (2–8% of COD) and S-EPS (1.5–5.4% of COD) were obtained in the three sludge samples. RE-EPS fractions concentrations were higher (6–17% of COD) as well as HLS (23–29% of COD). At the difference of the sludge A, B and C which have been frozen before the fractionation, DOM, S-EPS and RE-EPS obtained for fresh sludge were lower while HLS obtained for fresh sludge was higher. That means that accessibility of sludge has been impacted by freezing the sludge and led to another fractions repartition favorable to the accessibility with similar total COD yield (i.e. 38% to 47 for A, B and C versus 37% to 60% for SI, SII and SD). This explains the high differences of COD repartition obtained between the A, B, C sludge and SI, SII, SD sludge. Comparisons between fresh samples have been done. SII seemed to contain more accessible fractions than SD with higher values of DOM, S-EPS and RE-EPS fractions. Concerning HLS and the NE fractions, HLS was similar for SII and SD while the NE fraction which was less accessible was higher for SD. This result

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Fig. 3. Excitation–emission-matrix fluorescence at the early (on the left) and final stages (on the right) of each extraction step for the sludge A POM: from top to bottom, SEPS, RE-EPS and HSL. Fluorescence intensity in arbitrary units is plotted as iso-lines. Arrows indicate the main fluorescence peaks.

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Fig. 4. (a) Extraction profile with the CSE modified protocol for SI, SII and SD (in cumulated COD (mg/L) versus initial COD of sludge), (b) complexity index calculated from 3D fluorescence spectra of extracted fractions from the sludge SI, SII and SD.

is in accordance with the sludge stabilization. Indeed, COD mass balance was done between SII and SD, this latter being the sludge at the end of SII’s BMP test. Table 3 shows the quantity of COD biodegraded during the BMP test relative to each fraction and relative to the total COD degraded. It can be seen that 76% of the total COD biodegraded was contained in the extracted fraction and 24% was contained in the NE fraction. This result shows that even if the extraction yield was 60% of total COD extracted for SII, the modified CSE protocol extract the main part of biodegradable material. Concerning the biodegradation aspect, the most accessible fractions (DOM, S-EPS and RE-EPS) were biodegraded with high percentage of COD (from 60% to 83% of COD fraction) while the least accessible fractions HLS and NE were less biodegraded with respectively 52% and 32% of COD biodegraded. This result shows that the chemical accessibility defined by the CSE procedure was linked with bioaccessibility and biodegradability: the least accessible was the fraction; the least was the COD removal rate relative to the fraction. However, in order to predict the biodegradability of sludge, as mentioned in the Section 3.2, the knowledge of accessibility is not sufficient. Indeed, the most biodegradable sludge SI (i.e. biodegradability of 61% of COD) had low values for the accessible fractions DOM, S-EPS and RE-EPS (i.e. 13.8% versus 31% for SII and 16% for SD) and high values for the less accessible fractions HLS and NE (i.e. 86% versus 70% for SII and 84% for SD). Information about the OM biodegradability for each fraction was missing. In order to go further, 3D fluorescence spectra obtained for the fractions extracted from these sludge can reveal the OM complexity which can be correlated with biodegradability. These sludge samples had different biodegradability values (assessed with BMP tests) corresponding to their nature: primary (SI), secondary (SII) and anaerobically digested sludge (SD), respectively 61%, 47% and 0%. The complexity index defined previously was calculated for each spectra of each fraction as shown in the Fig. 4b. This index increased with the decreasing accessibility simulated by the fractionation. Another trend observed was that the least biodegradable was the total sludge, the most complex was the OM extracted in each fraction. The different biodegradabilities of the three sludge seemed to be correlated with the increasing complexity of the spectra of each extracted fraction. Moreover, RE-EPS and HLS fractions seemed to be the most discriminant as far as biodegradability is concerned. As shown in Fig. 6, SI contained Table 3 COD fractionation mass balance before and after anaerobic digestion of SII. Sample/fractions (mg COD)

DOM

S-EPS

RE-EPS

HLS

NE

SII (mg COD) SD (mg COD) Biodegradation %CODfraction Biodegradation %CODTotal

2188 871 60% 10%

1423 440 69% 7%

4558 790 83% 28%

7772 3717 52% 30%

10 229 6964 32% 24%

low complexity index values in RE-EPS and HLS while SII contained a complexity index higher in RE-EPS than SI and SD. In the same way, complexity index in HLS is higher in SII than in SI but lower than SD. This means that combining complexity information with fractionation could provide information about biodegradability of a sample. However, at this stage, no specific correlations between complexity index, fractionation and biodegradability could be found because of the limited number of samples analysed in this study. Nevertheless, these results are very promising because they demonstrate the high potential of combining sequential extractions and 3D fluorescence spectroscopy to predict both bioaccessibility and complexity of sludge OM.

4. Conclusions The SS biodegradability results from both the complexity and the bioaccessibility of its organic components. Up to now, in the field of organic solid waste treatment (and sewage sludge in particular), biochemical characterization was widely used to assess complexity of OM but bioaccessibility was not well addressed, anaerobic biodegradability being mainly measured by long and tedious BMP assays. In this study, we proposed a new procedure to quickly and precisely characterize sludge organic matter. Indeed, in less than one week, this procedure allows us to assess and quantify the sludge organic matter bioaccessibility, using CSE, together with its complexity, using fluorescence spectroscopy. This technique is very promising as far as biodegradability prediction concerned. Nevertheless, the next step should be to prove that this methodology is able to quantitatively predict the biodegradability. Another perspective would be to demonstrate that the chemical accessibility, which simulated the sludge bioaccessibility, is really able to predict the readily and slowly bioaccessible fractions from sludge. Knowing these characteristics can provide human operators with very valuable information for plants modelling and industrial plants optimization.

Acknowledgements The authors are grateful to Emmanuel Trouvé, Stéphane Déléris from Veolia Environment as well as Hélène Carrère, Alexis Mottet and Maialen Barret from INRA for their helpful advices, support and discussions about this work.

Appendix A See Fig. A.1.

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Fig. A.1. Relative fluorescence (in %) of the seven spectral regions at the initial and final stages of the S-EPS, RE-EPS and HSL extractions for the sludge A POM. The horizontal line splits the simple fluorescent OM from the complex one.

References Angelidaki, I., Alves, M., Bolzonella, D., Borzacconi, L., Campos, J.L., Guwy, A.J., Kalyuzhnyi, S., Jenicek, P., van Lier, J.B., 2009. Defining the biomethane potential (BMP) of solid organic wastes and energy crops: a proposed protocol for batch assays. Water Sci. Technol. 59 (5), 927–934. Aquino, S.F., Chernicharo, C.A.L., Soares, H., Takemoto, S.Y., Vazoller, R.F., 2008. Methodologies for determining the bioavailability and biodegradability of sludges. Environ. Technol. 29 (8), 855–862. Charest, M.H., Antoun, H., Beauchamp, C.J., 2004. Dynamics of water-soluble carbon substances and microbial populations during the composting of de-inking paper sludge. Bioresour. Technol. 91 (1), 53–67. Chen, W., Westerhoff, P., Leenheer, J.A., Booksh, K., 2003. Fluorescence excitation– emission matrix regional integration to quantify spectra for dissolved organic matter. Environ. Sci. Technol. 37 (24), 5701–5710. Directive 1999/31/CE. last access in 2013. Directive 2008/98/CE. last access in 2013. Directive 86/278/EEC. last access in 2013. Dwyer, J., Lant, P., 2008. Biodegradability of DOC and DON for UV/H2O2 pre-treated melanoidin based wastewater. Biochem. Eng. J. 42 (1), 47–54. Esparza-Soto, M., Westerhoff, P.K., 2001. Fluorescence spectroscopy and molecular weight distribution of extracellular polymers from full-scale activated sludge biomass. Water Sci. Technol. 43 (6), 87–95. Etcheber, H., Héral, M., Relexans, J., 1985. Protocoles d’extraction chimique de la matière organique particulaire: application au domaine estuarien. Océanis 11 (5), 409–428. Giovanela, M., Parlanti, E., Soriano-Sierra, E.J., Soldi, M.S., Sierra, M.M.D., 2004. Elemental compositions, FT-IR spectra and thermal behavior of sedimentary fulvic and humic acids from aquatic and terrestrial environments. Geochem. J. 38 (3), 255–264. Gunaseelan, V.N., 2009. Predicting ultimate methane yields of Jatropha curcus and Morus indica from their chemical composition. Bioresour. Technol. 100 (13), 3426–3429. He, X.S., Xi, B.D., Wei, Z.M., Jiang, Y.H., Yang, Y., An, D., Cao, J.L., Liu, H.L., 2011. Fluorescence excitation–emission matrix spectroscopy with regional integration analysis for characterizing composition and transformation of dissolved organic matter in landfill leachates. J. Hazard. Mater. 190 (1–3), 293–299. He, X.S., Xi, B.D., Li, X., Pan, H.W., An, D., Bai, S.G., Li, D., Cui, D.Y., 2013. Fluorescence excitation–emission matrix spectra coupled with parallel factor and regional integration analysis to characterize organic matter humification. Chemosphere 93, 2208–2215.

Henderson, R.K., Baker, A., Murphy, K.R., Hamblya, A., Stuetz, R.M., Khan, S.J., 2009. Fluorescence as a potential monitoring tool for recycled water systems: a review. Water Res. 43 (4), 863–881. Hur, J., Lee, T.H., Lee, B.M., 2011. Estimating the removal efficiency of refractory dissolved organic matter in wastewater treatment plants using a fluorescence technique. Environ. Technol. 32 (16), 1843–1850. Jimenez, J., Vedrenne, F., Denis, C., Mottet, A., Déléris, S., Steyer, J-P., Cacho Rivero, J.A., 2013. A statistical comparison of protein and carbohydrate characterisation methodology applied on sewage sludge samples. Water Res. 45 (5), 1752–1762. Kelissidis, A., Stasinakis, A.S., 2012. Comparative study of the methods used for treatment and final disposal of sewage sludge in European Countries. Waste Manage. 32, 1186–1195. Lesteur, M., Latrille, E., Maurel, V.B., Roger, J.M., Gonzalez, C., Junqua, G., Steyer, J.P., 2011. First step towards a fast analytical method for the determination of biochemical methane potential of solid wastes by near infrared spectroscopy. Bioresour. Technol. 102 (3), 2280–2288. Mayer, L.M., Schick, L.L., Loder, T.C., 1999. Dissolved protein fluorescence in two Maine estuaries. Mar. Chem. 64 (3), 171–179. McSwain, B.S., Irvine, R.L., Hausner, M., Wilderer, P.A., 2005. Composition and distribution of extracellular polymeric substances in aerobic flocs and granular sludge. Appl. Environ. Microbiol. 71 (2), 1051–1057. Mottet, A., Francois, E., Latrille, E., Steyer, J.P., Deleris, S., Vedrenne, F., Carrere, H., 2010. Estimating anaerobic biodegradability indicators for waste activated sludge. Chem. Eng. J. 160 (2), 488–496. Muller, M., Milori, M.B.P.D., Deleris, S., Steyer, J.P., Dudal, Y., 2011. Solid-phase fluorescence spectroscopy to characterize organic wastes. Waste Manage. 31 (9–10), 1916–1923. Nielsen, P.H., Thomsen, T.R., Nielsen, J.L., 2004. Bacterial composition of activated sludge – importance for floc and sludge properties. Water Sci. Technol. 49 (10), 51–58. Olmstead, J.A., Gray, D.G., 1997. Fluorescence spectroscopy of cellulose, lignin and mechanical pulps: a review. J. Pulp Pap. Sci. 23 (12), J571–J581. Parnaudeau, V., Nicolardot, B., Pages, J., 2004. Relevance of organic matter fractions as predictors of wastewater sludge mineralization in soil. J. Environ. Qual. 33 (5), 1885–1894. Prudent, P., Domeizel, M., Massiani, C., 1996. Chemical sequential extraction as decision-making tool: application to municipal solid waste and its individual constituents. Sci. Total Environ. 178 (1–3), 55–61. Reveille, W., Mansuy, L., Jarde, E., Garnier-Sillam, T., 2003. Characterisation of sewage sludge-derived organic matter: lipids and humic acids. Org. Geochem. 34 (4), 615–627. Reynolds, D.M., Ahmad, S.R., 1997. Rapid and direct determination of wastewater BOD values using a fluorescence technique. Water Res. 31 (8), 2012–2018. Richard, C., Guyot, G., Trubetskaya, O., Trubetskoj, O., Grigatti, M., Cavani, L., 2009. Fluorescence analysis of humic-like substances extracted from composts: influence of composting time and fractionation. Environ. Chem. Lett. 7 (1), 61–65. Sanchez, N., Skeniotis, A.T., Muller, C.M., 2013. Assessment of dissolved organic matter fluorescence PARAFAC components before and after coagulation filtration in a full scale water treatment plant. Water Res. 47 (4), 1679–1690. Seehafer, S.S., Pearce, D.A., 2006. You say lipofuscin, we say ceroid: defining autofluorescent storage material. Neurobiol. Aging 27 (4), 576–588. Sierra, M.M.D., Giovanela, M., Parlanti, E., Soriano-Sierra, E.J., 2005. Fluorescence fingerprint of fulvic and humic acids from varied origins as viewed by singlescan and excitation/emission matrix techniques. Chemosphere 58 (6), 715–733. Swift, R.S., 1996. Organic matter characterization. In: Sparks, D.L. et al. (Eds.), Methods of Soil Analysis: Part 3. Chemical Methods. SSSA Book Series 5. Soil Science Society of America, Madison, WI, pp. 1018–1020. Van Soest, P.J., 1963. Use of detergents in the analysis of fibrous feeds. II. A rapid method for the determination of fiber and lignin. J. Assoc. Off. Anal. Chem. 46, 829–835. Vavilin, V.A., Fernandez, B., Palatsi, J., Flotats, X., 2008. Hydrolysis kinetics in anaerobic degradation of particulate organic material: an overview. Waste Manage. 28 (6), 939–951. Wang, Z., Tang, S., Zhu, Y., Wu, Z., Zhou, Q., Yang, D., 2010. Fluorescent dissolved organic matter variations in a submerged membrane bioreactor under different sludge retention times. J. Membr. Sci. 355 (1-2), 151–157.