A multivariate approach to the study of the composting process by means of analytical electrofocusing

A multivariate approach to the study of the composting process by means of analytical electrofocusing

Waste Management 27 (2007) 1072–1082 www.elsevier.com/locate/wasman A multivariate approach to the study of the composting process by means of analyt...

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Waste Management 27 (2007) 1072–1082 www.elsevier.com/locate/wasman

A multivariate approach to the study of the composting process by means of analytical electrofocusing Marco Grigatti *, Luciano Cavani, Claudio Ciavatta Department of Agroenvironmental Sciences and Technologies, Alma Mater Studiorum, University of Bologna, Viale G. Fanin, 40, 40127 Bologna, Italy Accepted 31 May 2006 Available online 24 July 2006

Abstract Three blends formed by: agro-industrial waste, wastewater sewage sludge, and their mixture, blended with tree pruning as bulking agent, were composted over a 3-month period. During the composting process the blends were monitored for the main physical and chemical characteristics. Electrofocusing (EF) was carried out on the extracted organic matter. The EF profiles were analyzed by principal component analysis (PCA) in order to assess the suitability of EF to evaluate the stabilisation level during the composting process. Throughout the process, the blends showed a general shifting of focused bands, from low to high pH, even though the compost origin affected the EF profiles. If the EF profile is analyzed by dividing it into pH regions, the interpretation of the results can be affected by the origin of compost. A good clustering of compost samples depending on the process time was obtained by analyzing the whole profile by PCA. Analysis of EF results with PCA represents a useful analytical technique to study the evolution and the stabilisation of composted organic matter.  2006 Elsevier Ltd. All rights reserved.

1. Introduction The correct use of compost requires knowledge of its maturity and the level of organic matter (OM) stabilisation reached at the end of the composting process. Maturity is normally related to phytotoxicity (Iannotti et al., 1993) while stability is linked to microbial activity, although these two characteristics are usually strictly linked because the phytotoxic compounds are mainly produced by microorganisms during the initial process stages (Bernal et al., 1998; Tiquia and Tam, 1998; Tiquia, 2005). Many studies have been carried out on composting in an attempt to properly understand its evolution path (Ciavatta et al., 1993; Govi et al., 1993; Chefetz et al., 1996; Veeken et al., 2000, 2001). However, at the moment, a specific technique is still missing to identify origin, stabilisation level and quality of the organic matter in such a complex matrix as compost. Many indexes have been proposed over the *

Corresponding author. Tel.: +39 0 51 209 62 14/17; fax: +39 0 51 209 62 03. E-mail address: [email protected] (M. Grigatti). 0956-053X/$ - see front matter  2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2006.05.011

years. Ciavatta et al. (1993) proposed the degree of humification (DH) which takes in account the humic like carbon on the total extractable carbon in an alkaline environment. Adani et al. (1997) proposed the OM evolution index (OMEI), which considers the amount of humic-like substances (HLS) vs. composting time and the ligno-humic fraction. Recently, many authors have studied the relationship between microorganism activity and composting time by means of the oxygen uptake rate during organic matter conversion (Iannotti et al., 1994; Adani et al., 2001; Barrena et al., 2005). Tremier et al. (2005) used the respirometric technique to highlight the important role played by substrate biodegradability in compost evolution over time. Isoelectrofocusing (IEF) has been used to study OM extracted from soil, sewage sludge, organic fertilisers and compost (De Nobili et al., 1986, 1990; Garcia et al., 1992; Ciavatta et al., 1993; Govi et al., 1993; Requena et al., 1996, 1997; Alianiello and Fiorelli, 1998; Alianiello, 2003). IEF is an electrophoresis carried out in a pH gradient. This technique allows us to separate the amphoteric molecules as a function of their isoelectric point (pI). Nevertheless, in the case of humic or HLS, the electromigration

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is principally controlled by the acidic functional groups and the separated bands are a function of the pKa rather than the pI (Duxbury, 1989). Therefore, with humic or humic-like substances, De Nobili (1988) and Duxbury (1989) suggest to use the term electrofocusing (EF) because the pH of the separate bands is only the ‘‘apparent’’ pI of the separated substances. Several authors have characterised by means of EF the HLS isolated from compost from different sources and increasing stabilisation time (De Nobili et al., 1986; Garcia et al., 1992; Govi et al., 1993; Alianiello and Fiorelli, 1998). The main differences observed in EF profiles of compost HLS were the appearance of some new bands focused at pH > 4.4 increasing with stabilisation time. A good relationship between composting time and the higher pH region of EF profiles of compost HLS from different sources was found (Ciavatta et al., 1993; Canali et al., 1998). This has been interpreted as an increase in the molecular size of compost HLS due to poorly understood processes occurring during the ‘‘stabilisation’’ phase of composting (De Nobili et al., 1986; Ciavatta et al., 1993); indeed, the increase of molecular size of compost HLS reduces the charge-to-mass ratio and consequently increases the ‘‘apparent’’ pI (or the pKa). These results show the potential of EF to study the composting process and to evaluate the stability of compost. Nevertheless, the interpretation of EF profiles is often subjective due to the different raw materials used and the great complexity of HLS isolated from compost. Furthermore, some results are not in agreement with the hypothesis that composting produces compounds with high molecular size. For example, Trubetskaya et al. (2001) showed an increase in content of the low molecular size fraction in compost HLS when coupling size exclusion chromatography (SEC) and polyacrylamide gel electrophoresis (PAGE). Alianiello and Baroccio (2004), analyzing molecular size fractions of humic acids from soil and peat obtained by ultrafiltration, observed that the dependence of EF profile on molecular size is not a general rule. With the objective to evaluate the capacity of EF to provide real and objective information on the stabilisation level of compost, we have studied the evolution of the composting process of three composts obtained by mixing tree pruning, agro-industrial waste and wastewater. These blends were formed to achieve contrasting characteristics while still studying biosolids commonly used for compost production in Italy (APAT-ONR, 2005). During the composting process the HLS were extracted from each compost and characterised by means of EF. Finally, the EF profiles were analyzed using multivariate analysis to achieve an objective interpretation of the EF profiles. 2. Materials and methods 2.1. Composting blends Compost piles were prepared by mixing different quantities of tree pruning (LC), agro-industrial waste mix

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(AI), composed by a blend (v/v) of 50% of starch (AI1) and 50% of poultry meat (AI2) processing waste, and wastewater sewage sludge (WW), as reported in Table 1. The main physical–chemical characteristics of the raw materials are reported in Table 2. In an external yard, each of the three blends were mixed by means of a specialised compost mixing machine (DOPPSTAD TDU 320, Germany) and three large piles (about 100 m3) were formed, one for each blend. The piles were then divided into three smaller piles (about 30 m3 each) to have three replicates and carefully arranged in the yard in a random block design. The piles were mixed weekly to ensure good aeration condition.

Table 1 Raw materials, compositions of composting blends and abbreviations Materials

Abbreviation

Raw materials Tree pruning as bulking ligno-cellulosic agent Agro-industrial waste mixa (v/v) Starch processing waste (50%) Poultry meat processing waste (50%) Wastewater sewage sludgeb

LC AI AI1 AI2 WW

Composting blends (v/v) 80% LC + 20% AI 80% LC + 20% WW 80% LC + 10% AI + 10% WW

CPAI CPWW CPAI+WW

a

Agro-industrial waste mix: blend of 50% (v/v) of starch and poultry meat processing wastes. b Wastewater sewage sludge was formed by mixing sludge from two different municipal plants in order to reproduce the average composting plant feeding.

Table 2 Physical–chemical characteristics of raw materials Parameters

Moisture (%) pH EC (mS cm 1) Ash (%) TOC (%) TKN (%) C/N HLS (%) Total Cd (mg kg Total Cr (mg kg Total Cu (mg kg Total Ni (mg kg Total Pb (mg kg Total Zn (mg kg

Tree pruning (LC)

1

) ) 1 ) 1 ) 1 ) 1 ) 1

45.0 6.84 0.93 59.0 1.12 25.2 22.5 8.10 <0.1 21 38 63 54 87

Agro-industrial waste mix (AI) Starch processing waste (AI1)

Poultry meat processing waste (AI2)

70.0 7.28 1.10 64.3 22.6 3.33 6.8 10.2 1.7 27 45 48 23 123

48.5 10.22 1.90 22.0 68.1 2.14 31.8 8.26 2.0 28 58 56 25 200

Wastewater sewage sludge (WW)

75.5 7.94 0.90 61.8 3.97 27.7 6.7 6.6 1.0 309 424 189 182 1190

All parameters excluding moisture, pH and EC, are expressed on a dry matter basis.

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2.2. Sampling and preparation of samples

ple with 20 ml of 70% HNO3 plus 5 ml of 37% HClO4 (MAF, 1986; MiPAF, 2001).

Compost samples at different stages of stabilisation (0, 7, 15, 30, 60, 90 days) were collected over a 3-month period. Mixed samples (2 kg) were formed by mixing five sub-samples taken from each pile (I.P.L.A., 1998), dried in an air-forced oven at 40 C until a constant weight was achieved and then sieved to 8 mm. The fraction <8 mm (B) was crushed using a Tecator Cyclotec, 1093 (PBI, Sweden), until all of the material passed through a 0.25 mm sieve to produce a wholemeal sample (I.P.L.A., 1998). 2.3. Determination of temperature and O2 saturation level Temperature and O2 saturation level were determined at a 30 cm depth (three replicates in each pile) at the time of sampling, by coupled platinum and polarographic sensors probe (Bio.Ge.Co. model D09709, Italy). 2.4. Determination of pH and electrical conductivity A suspension comprised of 5 g of sample and 50 ml of deionised water (dH2O) was stirred for 30 min at 25 C. After filtration, the pH was measured using a Micro TT 2022 (Crison, Spain) pH-meter and the electrical conductivity (EC) using a SAT type (Halosis, Italy) conducimeter (I.P.L.A., 1998). 2.5. Determination of total nitrogen and heavy metals Total nitrogen (TKN) was determined by the Kjeldahl method (Bremner, 1996). The total heavy metal content was determined by atomic absorption spectrophotometry (GBC model 903, Australia) after digestion of 1 g of sam-

2.6. Total, extracted and humic-like organic carbon Total (TOC), extracted (TEC) and humic-like (HLS) organic carbon were measured using dichromate oxidation methods (MiPAF, 2001). TEC was isolated as follows: 2 g of sample were extracted with 100 ml of 0.1 M NaOH + 0.1 M Na4P2O7 solution, at 65 C using a Dubnoff shaker bath at 110 rpm for 24 h under N2 atmosphere (Ciavatta et al., 1990a). The suspension was centrifuged at 5000g for 30 min, filtered through a 0.8 lm filter (mixed cellulose esters, Millipore, USA), and then 25 ml of the solution were acidified with 6 M HCl until pH < 2. After 1 h the suspension was centrifuged at 5000g for 15 min and the precipitate, called humic-like acid (HLA), was obtained. The soluble fraction was separated into fulvic-like acid (FLA) and non-humified organic carbon using polyvinylpyrrolidone insoluble resin (Ciavatta et al., 1990a). The sum of HLA + FLA gave the HLS. 2.7. Electrofocusing An aliquot of TEC (20–25 ml) was neutralised (pH  7.0) with 1.0 M HCl, dialyzed in a 1000 Da cut-off dialysis tube (Cellu Sep H1, Membrane Filtration Products Inc., USA) against dH2O at 4 C, and then freeze dried. The lyophilised was re-dissolved in dH2O at 5 mg ml 1 prior to the analysis. The EF experiment was carried out in a 5%T (total monomer concentration = (g acrylamide + g bis-acrylamide)/total volume · 100) and 3.33%C (cross-linking monomer concentration = g bis-acrylamide/(g acrylamide + g bis-acrylamide) · 100) polyacrylamide slab gel (Bio-Rad,

Table 3 Main physical–chemical characteristics of compost analyzed by main factor: ‘‘composting blend’’, ‘‘time’’ and their interaction ‘‘composting blend · time’’ Temperature (C)

O2 (%)

pH

EC (dS m 1)

TOC (%)

TKN (%)

TEC (%)

HLS (%)

Composting blend CPAI 43.5 CPWW 50.5 CPAI+WW 48.0 LSD(0.05) 0.68

49.5 49.7 49.7 n.s.

86.2 86.5 86.7 n.s.

7.53 7.25 7.57 0.24

1.04 0.80 1.00 0.11

24.5 25.4 25.4 0.55

1.31 1.51 1.51 0.06

11.2 10.5 10.9 0.32

8.1 7.8 8.0 n.s.

Time (days) 0 7 15 30 60 90 LSD(0.05)

25.7 73.0 66.0 55.3 41.7 36.0 1.81

89.6 80.1 82.3 86.1 89.6 91.0 0.62

7.65 7.70 7.35 7.35 7.33 7.32 n.s.

1.32 0.91 0.79 0.87 0.88 0.88 0.09

30.3 27.5 26.0 25.7 20.5 20.6 0.77

1.66 1.52 1.43 1.43 1.31 1.31 0.08

13.3 10.9 10.5 9.6 10.7 10.3 0.45

10.0 8.0 7.5 7.4 7.6 7.4 0.88

n.s. 49.6

1.08 86.5

n.s. 7.5

0.27 0.9

1.34 25.1

n.s. 1.4

0.78 10.9

n.s. 8.0

Factors

Moisture (%)

57.6 52.6 48.1 46.8 39.8 39.1 0.96

Composting blend · time 1.67 LSD(0.05) Mean 47.3

All chemical parameters excluding moisture, pH and EC, are expressed on a dry matter basis. LSD, least significant difference at a = 0.05; n.s., not significant.

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taking into account each single peak. For the graphical representation, the two principal components that recovered the highest variance were considered. The analyses were carried out using Statistica for Windows 5.0 (StatSoft. Inc., USA). 3. Results 3.1. Physical–chemical characteristics The raw materials chosen (LC, AI, and WW) fall among the main biosolids to be spread on land or to be composted (EC, 2001; APAT-ONR, 2005); their main physical–chemical characteristics are listed in Table 2. All of the parame35

CPAI CPWW CPAI+WW

TOC (%)

30

25

20

0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

15 14 13

TEC (%)

USA), containing a 2% mixture of carrier ampholytes. The carrier ampholytes used were composed of a mixture of 70% Ampholine (Amersham Biosciences, USA) with a pH gradient from 4.0 to 6.0 and 30% Ampholine with a pH gradient from 3.5 to 10.0 (Ciavatta et al., 1993). The slab (260 · 110 · 0.5 mm) was pre-run for 3 h in an electrophoretic cell (Multiphore II, LKB, Sweden), cooled to 2 C and powered by a 2197 Power Supply (LKB, Sweden). The distance between the 2 electrodes was about 90 mm. The pre-set values were as follows: voltage 1200 V; current 0.6 mA cm 1 and power 0.9 W cm 1. After loading the aliquot of samples (50 lL of dH2O solution containing 5 mg ml 1 of freeze-dried sample), the run was conducted for 2 h. The pH gradient of the gel slab was immediately verified after the run using a specific surface electrode (Ingold, Switzerland). The slab was stained for 2 h, under gentle, continuous stirring, in a solution containing 15% glacial acetic acid (100%, Merck, Germany), 15% ethyl alcohol (95%, Carlo Erba, Italy), 0.25 g l 1 Coomassie Brilliant Blue R-250 (Merck, Germany) and 10 g l 1 copper (II) sulphate (99.5%, Carlo Erba, Italy) (first bath). The Coomassie Brilliant Blue is a typical dye for proteins and does not stain the humic substances (Ciavatta et al., 1997). In order to stain the humic substances, the copper (II) sulphate was added in the staining solution (Cavani et al., 2003). The slab was then treated overnight, under gentle, continuous stirring, with a solution containing: 15% acetic acid, 15% ethyl alcohol, 0.025 g l 1 Coomassie Brilliant Blue R-250 and 10 g l 1 copper (II) sulphate (second bath) and finally with a solution prepared with 10% acetic acid and 10% ethyl alcohol (third bath) to completely de-stain the area without bands. After destaining, the focused bands were scanned at 633 nm with an Ultroscan XL Enhanced Laser Densitometer (LKB, Sweden). The bands of the EF densitograms were integrated and evaluated using the 2.1 version of the Gelscan Software (Pharmacia, Sweden). The electrofocusing profile analyses have been run on the relative area of regions or peaks (area of the region or peak/total area of profile · 100).

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12 11 10 9

2.8. Statistical analysis 1.8 1.7 1.6

TKN (%)

All physical–chemical data collected during composting were processed by two-way ANOVA. The main factors affecting composting were composting blends and time. The Fisher LSD (least significant differences) test was used to compare means (a = 0.05). Regarding EF, a principal component analyses (PCA) was run on selected standardised variables as columns. Compost samples of the different blends and composting times were selected from the bulk sample of different piles of the same treatment and used to form the rows (cases). Principal components (PC) were selected on the basis of Eigenvalue > 1 (Zbytniewski and Buszewski, 2005). The compost EF profiles were analyzed firstly by division into three pH zones: region A, pH 3.5–3.8; region B, pH 3.8–4.4; region C, pH > 4.4 (Cavani et al., 2003). They were then analyzed as a whole profile

1.5 1.4 1.3 1.2 1.1

Days of composting

Fig. 1. Total organic carbon (TOC), total extracted organic carbon (TEC) and total nitrogen (TKN) during the composting of the different blends.

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ters fall in the normal range for these waste types (EC, 2001). The main physical–chemical characteristics of the composting blends (CPAI, CPWW, CPAI+WW) analyzed by the main factors of ‘‘composting blend’’ and ‘‘time’’ (days of composting) and their interaction ‘‘composting blend · time’’ are shown in Table 3. The kind of composting blend greatly influenced the physical–chemical characteristics because it affects six of the nine parameters chosen: moisture, pH, EC, TOC, TEC and TKN. The time of composting, the second main factor, had a strong influence on the physical–chemical parameters since all of those were influenced, with the only exception of the pH. The interaction between composting blend · time was significant for six of the nine parameters with the exception of temperature, pH, TKN and HLS. On the whole, the physical–chemical analysis showed that moisture, EC, TOC and TEC were significant for the main factors and their interaction. TKN was influenced

Region A

by both the composting blend and time of composting, whereas O2 saturation level was influenced by time of composting and the interaction composting blend · time. The temperature and the HLS were only influenced by the time of composting, whereas the pH was influenced only by composting blend. The high content of HLS at the beginning of the composting process (Table 3) is probably an overestimate as a result of the analytical interferences due to the presence of non-humic substances (proteins and lipids) extracted and then precipitated in acidic media (Ciavatta et al., 1990b; Govi et al., 1991). Similar issues with HLS analysis of compost have been reported by Grigatti et al. (2004). Although the total HLS results may be biased, the trends seen appear to be real and accurate. The trends of TOC, TEC and TKN are reported in Fig. 1. The TOC content progressively decreased, 32% on average over the 3-month period (Fig. 1), due to the process of mineralization (Ciavatta et al., 1993; Grigatti

Region B

Region C

Absorbance at 633 nm

Tree pruning (LC)

Starch processing waste (AI1)

Poultry meat processing waste (AI2)

Waste water sewage sludge (WW)

3.5

3.8

4.4

6.0

pH

Fig. 2. Electrofocusing profiles of total extracted organic matter of raw materials: tree pruning as bulking ligno-cellulosic compound (LC), starch processing waste (AI1), poultry meat processing waste (AI2) and wastewater sewage sludge (WW).

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et al., 2004). CPAI had the highest mineralization (42.5%), occurring mostly during the first week, followed by CPAI+WW and CPWW, with 30.8% and 21.7%, respectively. CPAI had the highest TEC concentration at the beginning of the composting process (14.2%), as well as the highest overall reduction ( 31.9%), while CPAI+WW and CPWW showed smaller depletion with 21.8% and 10.2%, respectively. The TKN concentration was similar for CPAI+WW and CPWW (1.75%) and lowest for CPAI (1.47%) at the beginning of the process (Fig. 1), with a progressive depletion during composting. The highest TKN losses ( 26.6%) were found in CPAI while CPAI+WW and CPWW showed a similar reduction (about 16.6%) with respect to the initial TKN value. The TKN trend in CPAI was different from the other blends because any depletion was found during the first week, but after that the organic N mineralization occurred faster than in the other blends (Fig. 1).

this area was higher in CPAI+WW at the beginning of composting and this blend showed the highest loss ( 42.9%), followed by CPWW ( 39.0%) and CPAI ( 30.8%). The relative area of region B showed a 18.6% increase, with significant differences occurring among the blends (Fig. 3): CPAI+WW (+36.5%), CPWW (+10.9%) and CPAI (+8.3%). The variation of the relative area of region C from 0 to 90 days of composting was from 6.1% to 19.6% in CPAI, while over the same timeframe CPAI+WW increased from 2.3% to 21.0% and CPWW from 19.9% to 39.0%. 3.3. Principal component analysis (PCA) 3.3.1. PCA of the EF profile divided by pH region The generally adopted approach to testing compost stabilisation by EF (De Nobili et al., 1986; Ciavatta et al., 1993; Govi et al., 1993; Canali et al., 1998), in which the region with pH > 4.4 (region C) is considered the most important because of the appearance of new bands focused there, a PCA on regions A, B and C of grouped samples was run. The analysis recovered 98.0% of variance, 56.9% on PC1 in which region A and C revealed 0.78 and 0.99 loading, respectively (Table 4). PC2 was dominated by region B and it recovered 41.1% of variance. In the analysis of the three variables, there was an overlapping among blends and time. The effect of the raw materials is clear from this analysis, since the CPWW samples were all clustered in the lower right region of the scores plot. From 15 to 90 days, CPAI and CPAI+WW were almost

3.2. Electrofocusing 3.2.1. Raw materials The raw materials showed different EF profiles (Fig. 2). The LC profile was characterised by several bands in regions A and B, with the presence of two small bands in region C. WW showed a similar pattern even if no bands in the higher pH region were detectable. No bands were found in starch processing waste (AI1), whereas poultry meat processing (AI2) showed its main profile area in the higher pH region. These different patterns reflect the differences in the OM structure. 3.2.2. Composting blends The composting process was followed by EF of the samples taken at 0, 15, 30, 60 and 90 days. In Fig. 4 are shown the EF profiles at the beginning (0 days) and at the end (90 days) of the composting period. During the process, the relative area of EF profile regions of the different compost samples showed significant variations (Fig. 3). The relative area of region A had an average decrease of 37.6% from 0 to 90 days;

EF regions relative area (%)

80

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Table 4 Factor loadings of electrofocusing (EF) regions in composting blends EF regions

Factor 1

A B C

2 0.78 0.38 0.99

Variance (%)

0.62 0.93 0.14

56.9

80

80

60

60

60

40

40

40

20

20

20

41.1

Region A Region B Region C

CPAI 0

CPWW 0

0

20

40

60

80

Days of composting

100

CPAI+WW 0

0

20

40

60

Days of composting

80

100

0

20

40

60

Days of composting

Fig. 3. Trends in the relative area of the electrofocusing pH regions during the composting of the different blends.

80

100

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indistinguishable. The marked effect of AI on the interpretation of the EF profiles is evident, due to the high importance given to region B.

Region A

3.3.2. PCA of the whole EF profile The relative area of the 11 numbered peaks in the EF profiles (Fig. 4) were analyzed by PCA. The cumulative

Region B

4

Region C

6

1 2

3

8

5 7

9

8 6 5

2

1

7

CPAI 0 days

4

9

3 10 11 6 4

CPAI 90 days 5

3

1

7

2

8

Absorbance at 633 nm

9 8 6 5 2

1

7 9

4

CPWW 0 days

3 10 11

4

CPWW 90 days

3

6

2

1

5 7

8

9

8 1

CPAI+WW 0 days 6 5 2

9

7 3

4 10 11

CPAI+WW 90 days 3.5

3.8

4.4

6.0

pH

Fig. 4. Comparison of the initial (0 days) and final (90 days) electrofocusing profiles of the different blends.

M. Grigatti et al. / Waste Management 27 (2007) 1072–1082 Table 5 Factor loadingsa of whole profile as single peak of composting blends Peak

Factor 1

1 2 3 4 5 6 7 8 9 10 11 Variance (%)

2

3 0.69

0.90 0.83 0.70 0.95 0.87 0.75 0.92 0.89 0.76 0.82 40.9

26.3

15.5

a

Loadings <0.50 were removed in order to analyze highly significant relationships.

variance was 82.7% (40.9% PC1, 26.3% PC2, 15.5% PC3) (Table 5). On PC1, high loading was shown by peaks 2, 3, 4 (region A), 7, 8 (region B) and 9 (region C) with the highest (0.92) attributed to peak 8. On PC2, peaks 6, 10 and 11 were found. In both PCs, the peaks in lower pH region were negatively correlated to the higher ones. These results imply that the increase in peaks in higher pH regions was related to the reduction of peaks in lower pH regions (peak 6). For PC1, the increase in regions B (peaks 7, 8) and C (peak 9) was related to reduction of peaks in region A. Peaks 1 and 5 appeared to be unrelated to the evolution of other peaks. A good separation of samples as a function of composting time was obtained (Fig. 5). Note that the samples for early times tend to the lower left of Fig. 5b, while samples from late times tend to the upper right. The separation among initial and final samples was significant, while some overlapping was visible in the intermediate evolution compost samples. 4. Discussion During the composting process, the chemical and physical parameters were dependent on time as well as on the origin of raw materials used in the blends. TOC losses during composting, which were higher in the CPAI, significantly decreased with WW addition (Fig. 1). The TEC losses during composting were similar in all blends in the first 30 days, in which readily degradable organic carbon was mineralised. After that CPAI showed a flattening trend, probably related to the disappearance of readily degradable organic carbon. At the same time, the other blends showed an increase in the TEC, probably due to the presence of more stable organic carbon forms, hydrolysable over a longer time. CPAI showed an initial nitrogen immobilization, probably due to the presence of high labile organic carbon and after that it showed the most signifi-

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cant nitrogen losses, most likely due to the respiration of proteins. The results here obtained suggest that the EF behavior of the OM extracted from compost, when analyzed by pH regions, showed a certain distinctive character depending on the initial raw materials and the composting time. Previously in the study of the composting process, the bands focused in the highest pH region have been taken as an indication of compost stabilisation (De Nobili et al., 1986; Govi et al., 1993; Requena et al., 1996; Canali et al., 1998). In the present work the relative area of the region C (pH > 4.4) showed an increasing trend, but it did not always display a linear increase during the composting process (Fig. 3). For instance, the increase of the area of region C was not always significant since the 15-day and 90-day-old samples CPAI had the same relative area. Furthermore, CPWW showed a higher area of region C at the beginning of the process than the final stage of the other blends, suggesting the dependence of this parameter on the raw materials. The results of the PCA of the whole EF profile allowed a good separation of samples based on composting time (Fig. 5). On the contrary, the PCA analysis based on three pH regions of the EF profile showed clustering, which was driven more by blend than by composting time (Fig. 5). Comparing the results of the two PCA, we demonstrate the limitations in evaluations of the EF profile based only on the analysis of the highest pH region. Compost origin and age strongly affect EF profiles. All blends showed in the first stage of composting process the greatest decrease in the relative area of region A (pH < 3.8). Moreover, WW-based blends (CPAI+WW and CPWW) had a following decrease of this area not relevant in CPAI. This behavior appears related to the TEC trend, since more apparent acidic compounds probably with lower molecular size (on average) are focused in this pH region (Alianiello and Baroccio, 2004). The area of region B in all blends had smaller and positive variations. The PCA showed that part of this region (peaks 7 and 8) had an increase over time corresponding to the decrease of the area of region A. Another part of region B (peak 6) had a decrease over time corresponding to an increase in pH region C (peaks 10 and 11). Therefore, region B seems to be a transitional zone in which molecules can move in both directions. Region C seems to be formed, in all blends, by the evolution of molecules initially in region A and even from region B. The increase of the area of region C appears to be related to the stability level of compost. An increase of region C occurred in the first composting period, probably due to the formation of molecules produced by the intense microorganism activity during the thermophilic phase (Chang et al., 2006). The relationship of this phenomenon to the disappearance of readily degradable organic carbon seems to confirm this observation. A comparison of Figs. 2 and 4 shows that the EF results presented for composting mixtures are dependent on the

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a

2

b

3

AI+WW 60 AI+WW 90 2 AI 60

1

AI 90

AI 90 AI 15

0

AI+WW 90 1

PC2

PC2

AI+WW AI+WW AI15 3030

AI 0

WW 60 WW 90 AI+WW 0

AI+WW 60

0

WW 60 WW 90

AI 15 WW 0

WW 30

AI 0

-1 WW 15

-1

AI+WW 30 AI 30 WW 30 AI 60 AI+WW WW 15 15

WW 0

AI+WW 0 -2

-2 -2

-1

0

1

2

-3

-2

-1

0

PC1

c

1

2

3

PC1

d

1.5

1.0 Peak 11 Peak 10

Region B

0.5

PC2 (Var. 26.3%)

PC2 (Var. 41.1)

1.0

0.5

0.0

Peak 8 Peak 9 Peak 5 0.0

Peak 2 Peak 4 Peak 3

Region C

Peak 7

-0.5

Peak 1

-0.5 Region A Peak 6 -1.0

-1.0 -1.0

-0.5

0.0

0.5

1.0

1.5

PC1 (Var. 56.9%)

-1.0

-0.5

0.0

0.5

1.0

1.5

PC1 (Var. 40.9%)

Fig. 5. PCA scores (a and b) and loading (c and d) of the electrofocusing pH regions (left) and of the whole electrofocusing profiles (right) in the different blends (AI = CPAI, WW = CPWW, AI + WW = CPAI+WW).

ligno-cellulosic material used. It is unclear how much of the overall humification seen in the EF results is due to the degradation of the tree prunings used, and more study is needed into the effect that the choice of ligno-cellulosic material might have on the changes in EF results. In any case, because the same ligno-cellulosic material was used for all experiments, the differences between experiments would have been caused by the additives rather than the ligno-cellulosic material. 5. Conclusions The analysis of the EF profiles of 0- to 3-month-old compost samples showed significant variations in all of the pH regions (A, B and C). The variations occurring only

in the highest pH region (region C) do not allow one to discriminate the stability level of compost because it is affected by the characteristics of the raw materials. On the other hand, the analysis of the whole EF profile provides much more information than considering only the higher pH region, since it reduces the interferences due to the effects of raw materials. In this way, EF coupled to PCA represents a useful analytical technique in order to study the evolution of the compost organic matter. A future goal is to implement the purification techniques of TEC by means of enzymatic hydrolysis (Grigatti et al., 2004) and/or by adding denaturing agents (Trubetskaya et al., 2001) in order to improve profile resolution. It is hoped that this will allow a better understanding of the composting process, as well as testing the importance of

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