Adsorption and desorption behavior of selected pesticides as influenced by decomposition of maize mulch

Chemosphere 91 (2013) 1447–1455

Contents lists available at SciVerse ScienceDirect

Chemosphere journal homepage: www.elsevier.com/locate/chemosphere

Adsorption and desorption behavior of selected pesticides as influenced by decomposition of maize mulch Sohaib Aslam a, Patricia Garnier a, Cornelia Rumpel b, Serge E. Parent c, Pierre Benoit a,⇑ a

INRA, Institut National de la Recherche Agronomique, UMR 1091 EGC, 78850 Thiverval-Grignon, France CNRS, Biogéochimie et Écologie des Milieux Continentaux BIOEMCO UMR7618 (UPMC-CNRS-UPEC-ENS-IRD-AgroParisTech), 78850 Thiverval-Grignon, France c Department of Soils and Agrifood Engineering, Université Laval, Québec, Canada G1K 7P4 b

h i g h l i g h t s " The degree of mulch decomposition enhanced the adsorption of non-ionic pesticides. " Desorption of glyphosate increased with maize mulch decomposition. " Desorption of s-metolachlor and epoxiconazole decreased with mulch decomposition. " Sorption of non-ionic pesticides was predicted using compositional information of mulch.

a r t i c l e

i n f o

Article history: Received 5 October 2012 Received in revised form 3 December 2012 Accepted 9 December 2012 Available online 22 February 2013 Keywords: Maize mulch Adsorption–desorption Glyphosate S-metolachlor Epoxiconazole Compositional analysis

a b s t r a c t Assessing pesticide fate in conservation agricultural systems requires a detailed understanding of their interaction with decomposing surface crop residues (mulch). Adsorption and desorption behavior of glyphosate, s-metolachlor and epoxiconazole was investigated on maize mulch residues decomposed under laboratory and field conditions. Our conceptual approach included characterization of chemical composition and hydrophobicity of mulch residues in order to generate parameters to predict sorption behavior. Adsorption of s-metolachlor and epoxiconazole greatly increased with mulch decomposition, whereas glyphosate adsorption was less affected but its desorption was increased. Mulch characteristics including aromaticity, hydrophobicity and polarity indices were strongly correlated to Koc of the non-ionic pesticides. A predictive model based on compositional data (CoDa) analysis revealed that the sorption capacity of decomposing mulch can be predicted from descriptors such as aromatic and alkyl C corresponding respectively to lignin and NDF biochemical fractions. The decomposition degree of mulch residues should be taken into account while predicting the fate of pesticides. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Conservation agricultural (no-till) practices are increasingly replacing conventional practices in many regions of the world (Derpsch et al., 2010). One common feature of the broad spectrum of farming systems using conservation agriculture is the presence of surface mulch made of crop residues. The presence of mulch residues helps controlling soil erosion, conserving soil moisture by lowering water evaporation and increasing soil biodiversity (Boahen et al., 2007). Meanwhile, conservation agricultural practices can be considerably more dependent on pesticide use for weed and pest control compared to conventional agriculture practices (Alletto et al., 2010). The environmental fate of pesticides in these systems remains poorly understood (Locke and Bryson, 1997; ⇑ Corresponding author. Tel.: +33 (0)1 30 81 54 04; fax: +33 (0)1 30 81 55 63. E-mail address: [email protected] (P. Benoit). 0045-6535/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.chemosphere.2012.12.005

Alletto et al., 2010). Adsorption and desorption of pesticides by mulch are important processes, because they can further control other processes such as their degradation and/or dispersion into the environment through wash off, volatilization or leaching (Selim et al., 2003; Locke et al., 2005; Alletto et al., 2010). Crop residues on the soil surface may intercept pesticides (Selim et al., 2003), and may contribute to their retention depending on their chemical composition (Ahmad et al., 2001; Loganathan et al., 2009). The biochemical composition of plant residues may vary with their origin (Rovira and Vallejo, 2002) or decomposition stage (Baldock et al., 1997; Carvalho et al., 2009) that may influence the sorption of certain pesticide molecules (Dao, 1991; Benoit et al., 2008). In particular, the enrichment of aromatic and aliphatic compounds during decomposition leads to greater hydrophobicity (Alcântara et al., 2004; Carvalho et al., 2009) that may increase sorption of non-polar substances (Abelmann et al., 2005; Ahmad et al., 2006; Benoit et al., 2008). Decomposition thus changes the

1448

S. Aslam et al. / Chemosphere 91 (2013) 1447–1455

reactivity of crop residues by changing their chemical composition. Adsorption of many organic compounds such as metribuzin (Dao, 1991), pyrimethanil (Yu et al., 2010), naphthalene (Xing, 1997), atrazine (Loganathan et al., 2009), 2,4-D (Benoit et al., 1996) and polycyclic aromatic hydrocarbons (Sun and Zhou, 2008) has been reported to be affected by chemical composition of organic sorbents, notably aromaticity and polarity. The degree of decomposition may thus control pesticide retention by altering its sorption strength and reversibility, but to our best of knowledge, these interactions remain poorly understood. In a recent study, Rampoldi et al. (2011) compared glyphosate adsorption on maize and soybean mulchs originating from different field sites in Argentina. Crop residues were from different hybrids or cultivars but were collected in the same season and had therefore quite similar biochemical composition. In this study, we used maize straw residues decomposed under natural field conditions and compared their chemical composition and interaction with pesticides to maize residues decomposed under laboratory conditions. This allowed us to account for additional effects of outdoor climatic conditions such as photo-radiation and rainfall compared to laboratory conditions. We examined changes in chemical composition of these crop residues during decomposition and assessed modifications in their interaction with three types of pesticide molecules having contrasting physico-chemical characteristics, namely, epoxiconazole, s-metolachlor and glyphosate. The objectives of this study were (1) to investigate adsorption and desorption behavior of these pesticides on mulch residues at different stages of decomposition, (2) to identify parameters affecting these processes and (3) to include them into a predictive model.

the biochemical fractionation. An aliquot of 0.5 g was further ground and passed through a 200 lm sieve for elemental analysis, 13 C CPMAS NMR spectroscopy and contact angle measurement. 2.3. Elemental (C and N) analyses Organic carbon (OC) and nitrogen (N) content of mulch residues were quantified using a CHN auto-analyser (CHN NA 1500, Carlo Erba). 2.4. Biochemical fractionation We used a chemical fractionation procedure initially developed for fibrous feed characterization (Van Soest and Wine, 1967). The adapted procedure has been reported as the French standard XPU 44-162 procedure (AFNOR, 2008). Briefly, organic matter is divided into five C pools. About 1 g of mulch was successively extracted in glass crucibles with coarse porosity (40–100 lm) to yield (i) soluble substances (SOL, 30 min with boiling water and NDF, 60 min with hot neutral detergent); (ii) a hemicelluloses-like fraction (HEM, 60 min with hot acid detergent); and (iii) a cellulose-like fraction (CEL, 180 min in 72% sulfuric acid). The residue left after sulfuric acid attack is the residual fraction containing resistant components (lignin, cutin and tannins, LIC) (Rovira and Vallejo, 2002; AFNOR, 2008). Total organic matter content of the mulch was determined by loss on ignition at 480 °C. The distribution of each fraction was expressed as a percentage of total organic matter. We also calculated the ligno-cellulose index (LCI) (Melillo et al., 1989), using the ratio [LCI = LIC/(CEL + HEM + LIC)]. 13

2. Materials and methods

2.5.

2.1. Field sampling of maize mulch and soil

Solid-state 13C NMR spectra were obtained using a Bruker MSL 200 spectrometer (Bruker, Rheinstetten, Germany) operating at 50.33 MHz. Cross-polarization and magic angle spinning (CPMAS NMR) was applied using a spinning speed of 5 kHz. Samples of approximately 100–200 mg were placed in a zirconium oxide rotor of 7 mm in diameter. Spectra were acquired with 1 ms contact time, 10.2 ms acquisition time, and 2 s recycle time. Between 4000 and 9800 scans were acquired for each sample. Spectra were processed using 10 Hz line-broadening and baseline correction. Chemical shifts were reported relative to tetramethylsilane (TMS) at 0 ppm. Spectra were divided into chemical shift regions as follows: 0–50 ppm alkyl C; 50–60 ppm methoxyl C; 60–110 ppm O-alkyl C (including N-alkyl and di-O-alkyl C); 110–140 ppm aromatic (aryl) C; 140–160 ppm phenolic (O-aryl) C; 160–190 ppm carboxylic C. The peak assignments and their interpretation were made on the basis of previous NMR studies of plant material and natural organic matter (Baldock et al., 1997; Rovira and Vallejo, 2002; Carvalho et al., 2009). Integration of signals in the chemical shift regions were carried out by the integration routine of the spectrometer and expressed as percentages of total area. Owing to differences in chemical shifts of NMR data, polarity (Abelmann et al., 2005) of mulch residues was calculated by using the relative intensities of polar and non-polar groups as:

Maize residues (stems and leaves) were sampled after harvest on a plot managed by conservation tillage and mulching near Reims (Marne, France). Additionally, we collected maize residues left on the ground 150, 220, 250 and 300 d after harvest. These samples had decomposed under field conditions (variable temperature, rainfall and sunlight). Residues were washed with distilled water to remove adhering soil particles and then dried at 40 °C. They were cut into small pieces of 5–10 mm. The soil used in this study was a silt loamy Calcic Cambisol (FAO Classification) with 28% clay, 61% silt and 11% sand. It was sampled at 0–2 and 2–5 cm of depth. The field was located at INRA Experimental Unit in Grignon (Yvelines, France) and cultivated with notillage for more than 10 years. 2.2. Decomposition of maize mulch under laboratory conditions The maize residues were artificially decomposed. Residues were cut into 5–10 mm pieces and mulch was prepared at a leaf to stem ratio of 6:4 based on the weight ratio of stems and leaves in mulch samples collected in the field. PVC cylinders (5.6 cm internal diameter and 6 cm-high) were constructed with sieved soil sampled from two layers. The mulch residues (2 g-dry-equivalent) were placed on the soil surface and incubated in hermetic jars under controlled laboratory conditions (in the dark, at 28 ± 0.5 °C and at pF 2.5) for a maximum of 98 d. Because a faster decomposition was expected in laboratory conditions, we selected different sampling times for laboratory and field studies. At 0, 20, 49 and 98 d, mulch residues were recovered from six replicated incubation samples. Three replicates were taken for the adsorption–desorption study while the remaining residues from all repetitions were pooled, dried and stored for further treatment. Both types of mulch materials (field and laboratory) were first ground (1 mm size) for

C CP/MAS NMR spectroscopy

Polarity ðPÞ ¼

carbonyl þ O-alkyl þ O-aryl aryl þ alkyl

ð1Þ

Similarly, indices of aromaticity and hydrophobicity were calculated with same data according to Alcântara et al. (2004)

Aromaticity ðArÞ ¼

aromatic O-alkyl þ alkyl þ aromatic

Hydrophobicity ðHÞ ¼

aromatic þ alkyl O-alkyl þ carboxyl

ð2Þ

ð3Þ

1449

S. Aslam et al. / Chemosphere 91 (2013) 1447–1455

2.6. Contact angle measurement The sessile drop method, widely used due to its simplicity (Shang et al., 2008; Stalder et al., 2010), was employed to measure contact angle of ground mulch residues. Prior to contact angle measurements, three successive water washings of ground residue were made in order to remove soluble compounds that could interfere with accurate measurements. Briefly the mulch residues were thoroughly shaken with distilled water, centrifuged and the supernatant removed. After this treatment, residues were oven-dried at 45 °C until constant weight and compact pellets were prepared by pressing 125 mg of mulch residue in a Specac pellet press. Formation of pressed pellet were reported to improve results of the adopted procedure (Chibowski and Perea-Carpio, 2002; Bachmann et al., 2003). Contact angles were then measured at room temperature (23 °C) using a DSA 100 video contact angle measuring device (Krüss, Hamburg, Germany). The pellet was placed on a microscopic glass slide and a fine drop of distilled water (5 lL) was automatically deposited on surface of pellet with a very fine needle. The sessile drop-contact at sample surface was automatically recorded through a camera after the drop came in contact with the pellet surface. Image treatment through drop shape analysis software (DSA 1.9, Krüss) was carried out to measure the drop profile at the three-phase contact line of sample surface/water/air and finally the contact angle was calculated by tangent method. We made five contact angle measurements for each sample. 2.7. Pesticide molecules We studied two herbicides commonly used in no-tillage practices: glyphosate [N-(phosphonomethyl) glycine], a broad spectrum herbicide which is the most widely used in conservation agriculture, especially under maize cropping systems, and S-metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-[(1S)-2-methoxy-1-methylethyl] acetamide] which is used in cropping systems including maize, sunflower, sorghum, soybean. We also selected the fungicide epoxiconazole [(2RS,3SR)-1-[3-(2-chlorophenyl)-2,3-epoxi-2-(4-fluorophenyl) propyl]-1H-1,2,4-triazole], because of its lower water solubility and greater octanol-water partition coefficient (Table S1, supplementary material) compared to the other two molecules. All pesticide solutions were prepared with 14C-labeled molecules. [Methyl-14C] glyphosate was purchased from Sigma Chemicals (St. Louis, USA); S-metolachlor [Phenyl-U-ring 14C] and epoxiconazole [Epoxi-2-14C] were purchased from Syngenta (Greensboro, NC) and Izotop, Institute of isotopes Co., Ltd. (Budapest, Hungary) respectively. Non-labeled molecules were purchased from Sigma–Aldrich. Selected properties of all molecules are presented in Table S1 (Supplementary material). 2.8. Sorption experiments Adsorption was carried out with 60 mg of maize mulch and 3 mL of pesticide solution in glass centrifuge tubes by adapting standard batch equilibrium methods (OECD, 2000). Individual solutions of each pesticide were prepared in 0.01 M CaCl2. All solutions were prepared by using both 14C-labeled and unlabeled molecules to achieve the desired radioactivity. Adsorption isotherms with mulch decomposed for 0, 150 and 300 d were conducted with concentrations of 0.2, 0.4, 0.75, 1.5 and 3 mg L1 for each molecule whereas intermediate concentration of 0.75 mg L1 was selected to study adsorption on all other mulch samples. Centrifuge tubes with sorbents and pesticide solutions were rotated during 24 h with an end-over-head shaker and then centrifuged at 6000g (Sorvall Evolution RC, Kendro) for 15 min. Radioactivity in the supernatants was measured using 0.5 mL aliquots and 4 mL of scintillation cocktail (Ultima Gold XR Packard), and counting for

10 min in a Tri-Carb 2100 TR scintillation counter (Perkin Elmer Ins., Courtaboeuf, France). Control tests (without any sorbent) were also performed. Approximately, 1 ± 1%, 3 ± 1% and 9 ± 2% of initial radioactivity was adsorbed onto tubes for glyphosate, S-metolachlor and epoxiconazole, respectively. The amount of pesticide sorbed to mulch (Qads in mg kg1) was calculated as difference between initial and equilibrium concentrations Ce (mg L1). Adsorption isotherms were described using the Freundlich equation as follows:

Q ads ¼ K f C nf e

ð4Þ 1nf

1

nf

Freundlich adsorption parameters Kf (mg kg L ) and nf were obtained by non-linear regression. Adsorption coefficients for all samples were calculated as:

K d ¼ Q ads =C e

ð5Þ

Organic carbon normalized adsorption coefficients were determined by:

K foc ¼ K f =foc

ð6Þ

K oc ¼ K d =foc

ð7Þ

where foc is the organic carbon fraction (g/g) of mulch samples. Desorption experiments were conducted immediately after adsorption by replacing the supernatant with the same volume of 0.01 M CaCl2 solution. Suspensions were shaken for 24 h, centrifuged for 15 min and pesticide concentration was measured by liquid scintillation counting as described above. Four to six successive desorption steps were performed to reduce supernatant radioactivity. Desorbed amounts were determined at each step, and remaining sorbed quantities were (Qdes) were described by Freundlich isotherms as follows:

Q des ¼ K fd C ne fd

ð8Þ

Irreversibly sorbed amounts (Qirr) in % of the initially adsorbed amounts were also determined. 2.9. Data analysis A Pearson’s correlation coefficient matrix allowed examining relationships between elemental, biochemical, and NMR variables for mulch residues decomposed under laboratory and field conditions. Correlations were also calculated to examine the relationship between mulch characteristics and the adsorption and desorption parameters, i.e. Koc and Qirr respectively. Since the organic carbon contents of the different mulch residues were in a relatively narrow range of values (Table 1), we chose Koc to show the influence of organic matter composition. Because of the intrinsic co-linearity among concentration variables, we used compositional data analysis techniques (Parent et al., 2012) to get prediction of adsorption capacity from organic matter composition parameters without spurious correlations. First, concentration data were transformed into balances of concentrations using the isometric log-ratio technique (ilr) (Egozcue et al., 2003), which transforms a composition of D parts into D1 orthogonal balances of parts or groups of parts. Ilr balances are contrasts between two groups of CoDa parts and can be understood metaphorically as fulcrums in a CoDa dendrogram (Fig. 2). The way the composition is partitioned in the CoDa dendrogram does not influence the results of linear statistics. In the Eq. (9), balances are formulated between group +, the parts located on the left of the fulcrum, and group , to the right, where n+ and n are the number of parts in the respective group, c+ and c are the concentration values in the respective group and g is the geometric mean function.

1450

S. Aslam et al. / Chemosphere 91 (2013) 1447–1455

Table 1 Biochemical characteristics of different maize mulch residues used in our study. Maize

OC

N

C/N

SOL

NDF

HE

1

mg kg Laboratory 0d 20 d 49 d 98 d

422 ± 0.4 407 ± 0.4 400 ± 1.5 387 ± 5.0

Field 150 d 220 d 250 d 300 d

368 ± 1 380 ± 2 340 ± 1 371 ± 1

CEL

LIC

Contact angle (h)

% of OM

Indices LCIa

Pb

Arc

Hd

6±0 8 ± 0.1 11 ± 0.1 13 ± 0.1

70 ± 0.3 50 ± 0.3 38 ± 0.5 30 ± 0.5

11.7 12.5 9.4 8.5

4.2 5.9 9.6 12.7

32.7 32.0 28.8 25.9

46.8 41.6 40.2 38.7

4.8 8.1 12.0 14.2

91 ± 4 93 ± 2 88 ± 1 95 ± 2

0.057 0.099 0.149 0.180

7.4 6.2 5.0 4.5

0.04 0.10 0.11 0.14

0.15 0.21 0.26 0.30

5 ± 0.1 6 ± 0.1 7 ± 0.1 7 ± 0.1

73 ± 1.6 67 ± 1.2 48 ± 0.3 51 ± 0.5

3.7 4.5 5.4 5.2

6.7 7.1 8.9 7.7

34.5 32.3 29.0 25.9

47.3 48.6 45.0 46.4

7.7 7.3 11.7 14.8

82 ± 2 86 ± 2 80 ± 7 89 ± 3

0.086 0.083 0.136 0.170

10 ND ND 6.6

0.09 ND ND 0.12

0.14 ND ND 0.20

ND = not determined. OM = organic matter. a Ligno-cellulose index. b Polarity index. c Aromaticity index. d Hydrophobicity index.

ilr ¼

% of total NMR intensity

(a) 100 80

ð9Þ

CoDa analyses were conducted with R software and compositional data transformations were made with R ‘‘compositions’’ package (van den Boogaart et al., 2011).

0 day 20 day

60 40

3. Results

49 day 98 day

3.1. Characterization of mulch residues

20 0

(b) 100 % of total NMR intensity

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   nþ  n gðcþ Þ ln gðc Þ nþ þ n

80

150 day

60

300 day

40 20 0

Fig. 1. Distribution of carbon structural components derived from analysis: (a) for lab mulch and (b) for field mulch.

13

C NMR

Fig. 2. Partitioning CoDa dendrograms representing balances (a) between biochemical fractions and (b) between NMR components.

The C/N ratio of maize residues decreased during decomposition (Table 1). This decrease was larger following laboratory decomposition than following decomposition under field conditions. This was a surprising result considering the much shorter laboratory incubation (98 d) compared to the field exposure (300 d). The presumably more degradable fractions such as the soluble organic matter (SOL), hemicelluloses (HEMs) and cellulose (CEL) decreased and the presumably more recalcitrant fraction (LIC) increased during decomposition. The NDF fraction containing microbial wastes also increased. In contrast to mulch incubated under laboratory conditions, the mulch exposed under field conditions showed smaller proportions of SOL as well as NDF fractions due likely to the wash-off by rain. Dominant signals in 13C CPMAS NMR spectra (Fig. S1 – Supplementary material) of all mulch samples were observed in the O-alkyl region (45–110). This region is characterized by an intense signal at 72–74 ppm and weaker signals or shoulders at 56, 64, 84, and 89 ppm. The signals in this region are most likely assigned to cellulose and hemicelluloses. This region can be divided into three sub-regions; N-alkyl/methoxyl C (50–60), carbohydrate carbon (60–90) and di-O-alkyl C (90–110). Intensity in the alkyl region (0–50) arises mainly from waxes and cutins. In this region of the spectrum the main signal at 21 ppm represents methyl carbon of acetate of hemicelluloses. Two additional signals at 30 and 33 ppm appeared at later stages of decomposition and represent long chain CH2 (polymethylene type of carbon). Signals in the aryl region of the spectrum (110–160) may be assigned to lignins and condensed tannins. Within this region small signals at 116 and 130 ppm indicate presence of condensed aromatic structures, whereas phenols resonate at 147 and 153 ppm. These peaks of aromatic and phenolic carbon groups become wider and prominent in decomposed residues compared to much sampled at day 0 (Fig. S1 – Supplementary material). The main peak in the carboxylic region at 174 ppm may be attributed to carboxyl, amide and ester C. The relative contribution of major

1451

S. Aslam et al. / Chemosphere 91 (2013) 1447–1455 Table 2 Sorption coefficients (Kd and Koc) on mulch residues and proportions of un-desorbed (Qirr) amounts of glyphosate, S-metolachlor and epoxiconazole. Mulch

Glyphosate 1

Kd (L kg

)

1

Koc (L kg

)

S-metolachlor 1

Qirr (%)

Kd (L kg

)

Koc (L kg

1

)

Epoxiconazole 1

Qirr (%)

Kd (L kg

)

Koc (L kg1)

Qirr (%)

Laboratory 0d 20 d 49 d 98 d

10 ± 2 8±1 10 ± 1 9 ± 0.5

24 ± 5 19 ± 2 24 ± 3 23 ± 1

83 ± 10 38 ± 8 34 ± 5 26 ± 14

26 ± 1 40 ± 2 60 ± 3 70 ± 1

62 ± 2 97 ± 6 150 ± 7 182 ± 3

24 ± 2 27 ± 1 38 ± 5 33 ± 2

192 ± 8 389 ± 21 577 ± 32 583 ± 33

456 ± 19 955 ± 51 1441 ± 81 1504 ± 87

65 ± 3 73 ± 2 82 ± 3 80 ± 2

Field 150 d 220 d 250 d 300 d

13 ± 3 24 ± 2 24 ± 7 11 ± 0.4

34 ± 8 63 ± 7 71 ± 20 30 ± 1

63 ± 17 ND ND 65 ± 6

30 ± 1 41 ± 3 43 ± 2 44 ± 1

83 ± 3 107 ± 9 125 ± 6 118 ± 2

24 ± 3 ND ND 33 ± 3

319 ± 3 325 ± 21 331 ± 15 331 ± 24

866 ± 7 857 ± 56 973 ± 43 893 ± 65

73 ± 2 ND ND 78 ± 3

ND = not determined.

C-types as percentage of total spectral area is shown in Fig. 1. The proportion of O-alkyl carbon decreased and that of aromatic (aryl and O-aryl) carbon increased during mulch decomposition under both lab and field conditions. Alkyl C and carboxyl C contributions did not strongly increase or remained similar during laboratory decomposition whereas their proportions during field decomposition were slightly increased. The effect of decomposition may be illustrated by increasing trends of aromaticity and hydrophobicity and decreasing trend of polarity calculated from NMR data (Table 1). Contact angles, which give complementary information on hydrophobicity, were larger for mulch incubated under laboratory conditions (values >90°) than mulch exposed to field conditions, indicating contrasting surface properties for both residue types (Table 1). Under both conditions contact angles increased during the whole incubation period. 3.2. Adsorption of pesticides on mulch Adsorption isotherms on mulch residues (0, 150, and 300 d) were described by the Freundlich model with R2 P 0.998 for smetolachlor and epoxiconazole and R2 P 0.989 for glyphosate (Table S2 – Supplementary material). All s-metolachlor and epoxiconazole isotherms were almost linear (n P 0.9) whereas glyphosate isotherms for initial mulch and mulch sampled after 150 d of field exposure were non-linear (n < 0.9). Adsorption coefficients (Kf/oc) were significantly different (P < 0.01) for the three molecules. Glyphosate was least adsorbed while epoxiconazole was most strongly adsorbed followed by S-metolachlor. Linear isotherms allowed estimating adsorption coefficients (Kd and Koc) at single concentration of 0.75 mg L1 for all mulch residues (Table 2). We did not observe a significant effect of mulch decomposition degree on the adsorption behavior of glyphosate and almost similar adsorption coefficients were recorded for all mulch residues ranging from 24 to 23 and 30 L kg1 for maize residues decomposed under laboratory and field conditions respectively. In contrast, adsorption of S-metolachlor and epoxiconazole significantly (P < 0.01) increased with mulch decomposition. For both pesticides, this increase was larger following laboratory decomposition (threefold increase) than following decomposition under field conditions (twofold increase). Koc of S-metolachlor increased from 62 to 182 L kg1 after 98 d of laboratory incubation whereas Koc increased from 62 to 118 L kg1 after 300 d of field exposure. Similarly, Koc of epoxiconazole increased from 456 to 1504 L kg1 following laboratory decomposition and to 893 L kg1 following field decomposition (Table 2). 3.3. Desorption of pesticides from mulch Irreversible sorbed amounts (Qirr), expressed as percentage of initially adsorbed pesticides (Table 2) were determined in order

to evaluate the reversibility of adsorption. All three molecules exhibited different desorption potential. Desorption of glyphosate from fresh mulch residues (0 d) was small (83% of the initially adsorbed glyphosate remained un-desorbed), however we observed greater desorption for mulch with higher degree of decomposition. After 98 d of decomposition, almost 74% of glyphosate was desorbed. Mulch decomposition did not influence the adsorption of glyphosate, however, it influenced its desorption. For mulch exposed to field conditions, the extent of desorption was smaller compared to mulch incubated under laboratory conditions. Opposite behavior was observed for the two non-ionic molecules. Approximately, 24% of S-metolachlor and 65% of epoxiconazole remained un-desorbed for undecomposed much. Qirr values increased following mulch decomposition under both conditions (laboratory and field) indicating lower desorption potential for these molecules. Most of epoxiconazole (80% and 78% following laboratory and field conditions, respectively) remained adsorbed after four successive desorption steps while S-metolachlor sorption appeared to be more reversible. Nevertheless adsorption reversibility slightly decreased with increasing decomposition of maize residues, with Qirr values reaching 33% of initially adsorbed S-metolachlor (Table 2). 4. Discussion 4.1. Effect of decomposition on maize residue chemical composition The characterization methods used in this study provided complementary information and we investigated the relationships between elemental composition, biochemical fractions and NMR characteristics (Table S3 – Supplementary material). The O-alkyl C contribution in NMR spectra was significantly positively correlated to hemicelluloses (P < 0.05), cellulose (P < 0.05) while aromatic C was positively correlated (P < 0.05) to LIC. These correlations support the tentative assignments of chemical compounds to biochemical fractions and NMR chemical shift regions (see above). Contact angle was positively correlated with index of hydrophobicity and alkyl-C, although correlation was not significant (P > 0.05). Hydrophobicity index was positively correlated to NDF, aromatic carbon (P < 0.05) and negatively correlated to cellulose (P < 0.01) and O-alkyl carbon (P < 0.001). Hydrophobicity is reported to increase upon decomposition as a result of decreasing proportions of polysaccharides and increasing content of lignified material (Alcântara et al., 2004; Carvalho et al., 2009) which is also confirmed in our study by significant correlation (P < 0.05) of hydrophobicity with the ligno-cellulose index (LCI). The polarity of mulch during decomposition decreased as well as the O-alkyl C (P < 0.01) and cellulose contents (P < 0.05) supporting the hypothesis of defunctionalization of lignins and degradation of cellulose proposed by Abelmann et al. (2005).

1452

S. Aslam et al. / Chemosphere 91 (2013) 1447–1455

Table 3 Coefficients of Pearson Correlation matrix between mulch characteristics and adsorption coefficients (Koc) and irreversible sorbed (Qirr) amounts. Variables

C/N

SOL

NDF

HEM

CEL

LIC

LCI

h

Alkyl C

O-Alkyl C

Aromatic C

Carboxyl C

H

P

Koc Glyphosate s-metolachlor Epoxiconazole

0.23 0.91b 0.86b

0.65 0.06 0.02

0.04 0.97c 0.91b

0.11 0.79a 0.60

0.51 0.74a 0.82

0.05 0.82a 0.71a

0.11 0.87b 0.77a

0.74a 0.25 0.23

0.76 0.45 0.36

0.49 0.94b 0.87a

0.04 0.86a 0.84a

0.29 0.74 0.66

0.50 0.96b 0.89a

0.72 0.79 0.69

Qirr Glyphosate s-metolachlor Epoxiconazole

0.87a 0.84a 0.80

0.18 0.04 0.32

0.78 0.74 0.86a

0.44 0.80 0.72

0.94b 0.65 0.64

0.55 0.82a 0.89a

0.63 0.84a 0.90a

0.40 0.24 0.05

0.36 0.49 0.15

0.84a 0.85a 0.81a

0.80 0.67 0.89a

0.66 0.73 0.78

0.85a 0.83a 0.78

0.70 0.78 0.58

No. of observations (n = 6). Significance levels: a <0.05. b <0.01. c <0.001.

The greater compositional changes observed for laboratory incubated mulch may be attributed to constant and higher temperature and humidity levels (Best et al., 1990). In addition, field mulch was analyzed after its separation from attached soil particles, so it is likely that soil micro-organisms and their metabolites were retained on mulch surface and thus could have been analyzed by 13C NMR. These compounds would have mainly contributed to the carbohydrate region (Cogle et al., 1989). Alkyl C from microbial compounds could have accumulated on residues (Baldock et al., 1990) and its slight increase during field decomposition is attributable to its recalcitrance against microbial decay (Quideau et al., 2005). Alkyl C (Baldock et al., 1990; Carvalho et al., 2009) and NDF fractions (Francou et al., 2008; Zhang et al., 2012) both originate from decaying microbial biomass. The smaller proportions of alkyl C as well as SOL and NDF fractions measured in mulch exposed to field conditions can be attributed to the impact of rainfall wash-off as already suggested by Cogle et al. (1989) for wheat straw residues. In addition, solar irradiation could have increased the solubilization of aliphatic components of plant residues due to photo-degradation reactions (Feng et al., 2011). We suspect that this reduced the hydrophobicity of maize residues after field exposure and resulted in smaller contact angles as compared to residues incubated in the laboratory, which have higher proportions of both alkyl and NDF fractions. This also corroborates the hypothesis that mulch residues exposed to outdoor conditions will preferentially loose both soluble and microbial C due to the succession of humectation–dessication and wash-off by rainfall (Rovira and Vallejo, 2002). This indicates a relatively greater lability of plant residues under field conditions. 4.2. Interactions of pesticide sorption and mulch characteristics during decomposition Concerning the influence of biochemical composition on the sorption capacity of mulch residues, we observed major differences among three molecules. Glyphosate was weakly adsorbed on maize mulch and its adsorption was not affected by the degree of maize residue decomposition. Accinelli et al. (2005) also reported a weak adsorption of glyphosate on maize residues and explained it by the low affinity of cellulose and hemicelluloses for this ionic herbicide. Rampoldi et al. (2011) attributed low glyphosate adsorption on maize residues to its low molecular weight and high solubility. This finding is also consistent with the recent work of Lashermes et al. (2010) showing no clear effect of compost maturation on glyphosate adsorption. The Koc coefficient of glyphosate was not correlated to any variable of mulch composition (Table 3). In their study, Rampoldi et al. (2011) did not find significant correlation between glyphosate sorption and elemental or biochemical parameters, except a positive correlation with hemicellulose for

maize and a negative correlation with cellulose for soybean. A major difference with our study is that they used ground samples (mm size). The two non-ionic pesticides were sorbed in greater amounts due to intrinsic hydrophobicity and strong tendency to associate with organic matter (Simpson, 2006). Epoxiconazole was the most strongly adsorbed pesticide owing to its lower solubility and higher log Kow compared to the two other molecules (Table S1 – Supplementary material). Epoxiconazole adsorption to wetland and forest litter has been recently studied by Passeport et al. (2011). These authors reported strong adsorption of the molecule on plant material. In our study, the Koc of two non-ionic molecules were highly correlated with mulch characteristics and hydrophobicity index (Table 3). The decreased polarity of mulch residues with higher stage of decomposition resulted in stronger adsorption capacity for the non-polar/hydrophobic molecules S-metolachlor and epoxiconazole. Additionally, Koc values were positively correlated with NDF (P < 0.01); LIC, aromaticity and hydrophobicity (P < 0.05); contact angle and alkyl carbon (P > 0.05) and negatively with O-alkyl carbon, polarity index, cellulose and hemicelluloses fractions. Similar correlations have been reported previously for other non-ionic and hydrophobic molecules (Xing, 1997; Ahmad et al., 2001; Abelmann et al., 2005). Adsorption was stronger for mulch incubated in the laboratory probably due to its greater hydrophobicity compared to mulch exposed to field conditions, as previously discussed. We observed that for the last sampling point of the two experiments (98 d in the laboratory and 300 d in the field), LIC fractions and indices of aromaticity and hydrophobicity were similar (Table 1) but Koc of mulch incubated under laboratory conditions were much greater. Smaller contact angles and lower alkyl carbon contribution in mulch exposed to field conditions might have resulted in smaller adsorption capacities. Similarly, the NDF fraction, which was most strongly correlated (P < 0.001) to Koc, was found in smaller proportions in mulch after exposure to field conditions. Therefore, differences in chemical composition of mulch decomposed under the two different conditions explained their reactivity in the pesticide adsorption process. Desorption of pesticides is a key process describing their potential for release into the soil environment. Glyphosate was desorbed more easily from decomposed compared to fresh (0 d) residues. Therefore, Qirr of glyphosate (Table 3) was positively correlated to cellulose (P < 0.05) and O-alkyl C (P < 0.01). As polarity of decomposed residues strongly decreased, the association with the highly soluble and polar glyphosate might have been weakened and this facilitated its release from the more decomposed residues. Rampoldi et al. (2011) found no hysteresis of the glyphosate adsorption on maize and soybean crop residues with most of glyphosate recovery after the first desorption step. They hypothesized that glyphosate molecule could be physically and reversibly re-

1453

S. Aslam et al. / Chemosphere 91 (2013) 1447–1455

(a)

Field Lab

decreased with mulch decomposition and were inversely related to increasing adsorption capacity. Irreversibly sorbed amounts (Qirr) for these molecules show positive correlations (Table 3) with LIC, NDF, aromaticity, hydrophobicity and negative correlations with O-alkyl carbon, HEM, CEL and polarity of mulch. As discussed previously, decrease in polarity during decomposition could lead to stronger association with non-ionic molecules (Kile et al., 1999). Balances of parts (computed using the isometric log ratio technique) were used to create unbiased explanatory variables in a multiple linear regression model predicting pesticide sorption. For Van Soest and NMR approaches, balances were defined in the CoDa dendrogram respectively presented in Fig. 3a and 3b. The multiple regression models were defined as follows

K oc ¼ Intercept þ a: ½LIC; NDF j SOL; HEM; CEL þ b: ½SOL j HEM; CEL þ c: ½NDF j LIC

(b)

K oc ¼ Intercept þ a: ½alkyl; aromatic; carboxyl j O  alkyl þ b: ½alkyl j aromatic; carboxyl Field

þ c: ½aromatic j carboxyl

Lab

Fig. 3. (a) Evolution of Koc of S-metolachlor, epoxiconazole and glyphosate during decomposition of mulch residues in laboratory and field conditions. (b) Relationship between observed and predicted data for adsorption coefficients (Koc) of three pesticide molecules. Observed Koc values are represented for mulch decomposed under laboratory (N) and field conditions (d). Solid lines (—) show the values predicted by the model using biochemical fraction balances.

tained or occluded in the porous matrix formed by decomposed crop residues. S-metolachlor and epoxiconazole were desorbed in smaller amounts compared to glyphosate; epoxiconazole being the least desorbed. For the non-ionic molecules, desorbed amounts

Parameters of the models, as well as the associated p-values and R2, are shown in Table 4. Relationships between observed and predicted values are graphically shown in Fig. 3. Except for glyphosate’s Koc, whose R2 were relatively low, the model built on Van Soest fractions indicated that the balance [LIC, NDF | SOL, HEM, CEL] had more influence on adsorption than other balances. This was interpreted by the role of LIC and NDF fractions in the increase of the adsorption of S-metolachlor and epoxiconazole with mulch decomposition. The LIC contents increased due to a selective preservation compared to SOL and holocellulose fractions, therefore exposing more aromatic structures for the adsorption. Coefficients related to the [NDF | LIC] balances for the three pollutants were not significantly different from 0 at the 0.05 confidence level, even though the estimated coefficient was relatively high. NDF importance was previously discussed to explain differences between laboratory and field decomposed mulch. However we suspect that more aliphatic structures related to the increase in NDF content with microbial decay favored the sorption of both non-ionic pesticides. Owing to a small number of observations, we have cumulated lab and field data, which have variations in chemical composition and reactivity (as discussed above). A larger dataset should be considered in the prediction model to have a significance of the coefficients linked to this balance. The coefficient associated to the [SOL | CEL, HEM] balance was lower than other parameters

Table 4 Parameters of compositional analysis. S-metolachlor

Epoxiconazole

Glyphosate

Estimate

p-Value

Estimate

p-Value

Estimate

p-Value

Van Soest fractionation Intercept [LIC, NDF | SOL, HEM, CEL] [SOL | HEM, CEL] [NDF | LIC] Adjuted R2

2.63 0.324 0.169 0.228 0.942

3.2e06c 0.0004c 0.008b 0.090

3.58 0.332 0.164 0.315 0.725

2.9e05c 0.010b 0.119 0.266

1.1 0.057 0.340 0.266 0.243

0.034a 0.727 0.123 0.632

NMR spectroscopy Intercept [alkyl, aromatic, carboxyl | O-alkyl] [alkyl | aromatic, carboxyl] [aromatic | carboxyl] Adjuted R2

2.65 0.412 0.104 0.524 0.903

0.019a 0.071 0.445 0.199

3.10 0.280 0.158 0.909 0.924

0.013a 0.132 0.277 0.078

1.05 0.139 0.096 0.099 0.158

0.23 0.549 0.655 0.852

No. of observations (n = 6). Significance levels: a 0.05. b 0.01. c 0.001.

1454

S. Aslam et al. / Chemosphere 91 (2013) 1447–1455

for S-metolachlor, whereas it did not significantly diverge from 0 for epoxiconazole and glyphosate, suggesting less contribution of this balance in sorption. Similarly, a prediction model of pesticide Koc was developed by molecular composition information from 13C NMR spectroscopy (Table 4). In this model however, the results provided no significant evidence and were only indicative because all coefficients were not different significantly from zero (p-value > 0.05) except for intercept. Thus they should be interpreted with care. However the sign of coefficients was interpreted as potential indication trend. The model yielded more satisfactory prediction for the two non-ionic molecules with R2 > 0.90 compared to glyphosate (R2 = 0.158). The coefficient associated to the [alkyl, aromatic, carboxyl | O-alkyl] balance showed that relative high contents of resistant aromatic and alkyl functional groups relative to O-alkyl groups of mulch organic carbon tended to increase sorption capacity of non-ionic molecules thus supporting the same conclusion as drawn from Van Soest fractionation. The coefficient linked to the [alkyl | aromatic, carboxyl] balance was lower than that of [alkyl, aromatic, carboxyl | O-alkyl] balance in three cases and suggested a lesser influence of both carboxyl and aromatic compared to alkyl groups. Alkyl carbon was associated with organic matter hydrophobicity (Benoit et al., 2008); and its lower proportions in field mulch probably resulted in lower sorption reactivity of field mulch as discussed above. The coefficient associated with the [aromatic | carboxyl] balance suggested relatively more influence of aromatic carbon on sorption. More data are required to conclude about the significant influence of NMR balances on sorption. Previous studies using spectroscopic techniques have identified the descriptors of soil organic matter composition that can play significant role in organic pollutant sorption (Ahmad et al., 2006), and some recent works used PLS statistical approach to predict sorption capacity of soil organic matter (Ehlers et al., 2010). Our results suggest that composition data analysis can be a useful tool for predicting sorption capacity of non-ionic hydrophobic molecules on mulch residues with information obtained by biochemical fractionation and NMR spectroscopy.

5. Conclusions We characterized the chemical composition and physical properties of mulch residues exposed to microbial decomposition under two different conditions. Information of the mulch chemical composition was obtained from Van Soest fractionation, 13C CPMAS NMR spectroscopy and estimates of hydrophobicity from contact angle measurements. The approaches used provide complementary data for the understanding of ionic and non-ionic pesticide sorption and desorption properties on decomposing maize residues. This information was used to build a predictive model using the compositional data analysis (CoDa) approach of the sorption coefficient Koc. Our results showed that the changes in chemical composition of crop residues occurring during decomposition greatly influenced pesticide sorption properties, but results differed according to the type of pesticide. The adsorption capacity of the non-ionic molecules epoxiconazole and S-metolachlor was more strongly increased after decomposition compared to glyphosate and their adsorption coefficients were strongly correlated to LIC, NDF contents and aromaticity and hydrophobicity indices of mulch composition. Prediction models developed by CoDa analysis using separately the NMR and biochemical fractionation information supported these correlations. LIC and NDF fractions from Van Soest characterization in the first equation whereas aromatic and alkyl C in the second model seem to be the most important predictors of the adsorption capacities of S-metolachlor and epoxiconazole. In addition, mulch in natural field conditions exhibited

less reactivity, because of smaller proportions of alkyl and NDF contents probably due to rainfall and wash-off. This suggests that climatic outdoor conditions could be taken into account to correct the pesticide sorption capacities of decomposing surface mulch as predicted by the CoDa analysis model. Desorption of S-metolachlor and epoxiconazole from residues was inversely related to their adsorption. However, glyphosate was more easily desorbed from decomposed residues. As a result, changes of crop residues composition during decomposition will control differently the movement of non-ionic pesticides compared to ionic compounds such as glyphosate, largely used in conservation agriculture practices. Therefore the state of decomposition of mulch should be taken into account in models describing the behavior of pesticides in the conservation agriculture systems. Acknowledgements This research was supported by the ANR Pepites project. We thank to Pascal Thiebeau for his generous help in field mulch sampling, Valérie Bergheaud for her assistance in the experimental work with 14C labeled molecules, Muriel Jolly and Véronique Etievant for their help in biochemical fractionation. We thank Stephanie Baumberger and Valérie Méchin for their collaboration in the contact angle measurements, realized by Delphine Chuette during her laboratory training internship. The authors wish to express their appreciations to higher education commission (HEC) of Pakistan for financing doctoral fellowship of Sohaib Aslam. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.chemosphere. 2012.12.005. References Abelmann, K., Kleineidam, S., Knicker, H., Grathwohl, P., Kögel-Knabner, I., 2005. Sorption of HOC in soils with carbonaceous contamination: influence of organic-matter composition. J. Plant Nutr. Soil Sci. 168, 293–306. Accinelli, C., Koskinen, W.C., Seebinger, J.D., Vicari, A., Sadowsky, M.J., 2005. Effects of incorporated corn residues on glyphosate mineralization and sorption in soil. J. Agric. Food Chem. 53, 4110–4117. AFNOR, 2008. Norme française XPU 44–162. Amendements organiques et supports de culture – caractérisation de la matière organique par fractionnement biochimique et estimation de sa stabilité biologique. AFNOR, Paris, France. Ahmad, R., Kookana, R.S., Alston, A.M., Skjemstad, J.O., 2001. The nature of soil organic matter affects sorption of pesticides 1. Relationships with carbon chemistry as determined by 13C-CPMAS NMR spectroscopy. Environ. Sci. Technol. 35, 878–884. Ahmad, R., Nelson, P.N., Kookana, R.S., 2006. The molecular composition of soil organic matter as determined by 13C NMR and elemental analyses and correlation with pesticide sorption. Eur. J. Soil Sci. 57, 883–893. Alcântara, F.A.D., Buurman, P., Curi, N., Neto, A.E.F., Lagen, B.V., Meijer, E.L., 2004. Changes in soil organic matter composition after introduction of riparian vegetation on shores of hydroelectric reservoirs (Southeast of Brazil). Soil Biol. Biochem. 36, 1497–1508. Alletto, L., Coquet, Y., Benoit, P., Heddadj, D., Barriuso, E., 2010. Tillage management effects on pesticide fate in soils. A rev. Agron. Sustain. Dev. 30, 367–400. Bachmann, J., Woche, S.K., Goebel, M.O., Kirkham, M.B., Horton, R., 2003. Extended methodology for determining wetting properties of porous media. Water Resour. Res. 39. Baldock, J.A., Oades, J.M., Vassallo, A.M., Wilson, M.A., 1990. Significance of microbial activity in soils as demonstrated by solid-state 13C NMR. Environ. Sci. Technol. 24, 527–530. Baldock, J.A., Oades, J.M., Nelson, P.N., Skene, T.M., Golchin, A., Clarke, P., 1997. Assessing the extent of decomposition of natural organic materials using solidstate 13C-NMR spectroscopy. Aust. J. Soil Res. 35, 1061–1083. Benoit, P., Barriuso, E., Houot, S., Calvet, R., 1996. Influence of the nature of soil organic matter on the sorption-desorption of 4-chlorophenol, 2,4dichlorophenol and the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). Eur. J. Soil Sci. 47, 567–578. Benoit, P., Madrigal, I., Preston, C.M., Chenu, C., Barriuso, E., 2008. Sorption and desorption of non-ionic herbicides onto particulate organic matter from surface soils under different land uses. Eur. J. Soil Sci. 59, 178–189.

S. Aslam et al. / Chemosphere 91 (2013) 1447–1455 Best, E.P.H., Dassen, J.H.A., Boon, J.J., Wiegers, G., 1990. Studies on decomposition of Ceratophyllum demersum litter under laboratory and field conditions: losses of dry mass and nutrients, qualitative changes in organic compounds and consequences for ambient water and sediments. Hydrobiologia 194, 91–114. Boahen, P., Addo-Dartey, B., Delali-Dogbe, G., Asare-Boadi, E., Triomphe, B., Daamgard-Larsen, S., Ashburner, J., 2007. Conservation Agriculture as Practiced in Ghana, Conservation Agriculture in Africa Series. African Conservation Tillage Network, CIRAD and FAO, Nairobi, Kenya. 45 p. Carvalho, A.M., Bustamante, M.M.C., Alcantara, F.A., Resck, I.S., Lemos, S.S., 2009. Characterization by solid-state CPMAS 13C NMR spectroscopy of decomposing plant residues in conventional and no-tillage systems in Central Brazil. Soil Tillage Res. 102, 144–150. Chibowski, E., Perea-Carpio, R., 2002. Problems of contact angle and solid surface free energy determination. Adv. Colloid Interface Sci. 98, 245–264. Cogle, A.L., Saffigna, P.G., Barron, P.F., 1989. The use of 13C NMR for studies of wheat straw decomposition. Plant Soil 113, 125–128. Dao, T.H., 1991. Field decay of wheat straw and its effects on metribuzin and s-ethyl metribuzin sorption and elution from crop residues. J. Environ. Qual. 20, 203– 208. Derpsch, R., Friedrich, T., Kassam, A., Li, H., 2010. Current status of adoption of notill farming in the world and some of its main benefits. Int. J. Agric. Biol. Eng. 3, 1–25. Egozcue, J.J., Pawlowsky-Glahn, V., Mateu-Figueras, G., Barcelo-Vidal, C., 2003. Isometric logratio transformations for compositional data analysis. Math. Geol. 35, 279–300. Ehlers, G.A.C., Forrester, S.T., Scherr, K.E., Loibner, A.P., Janik, L.J., 2010. Influence of the nature of soil organic matter on the sorption behaviour of pentadecane as determined by PLS analysis of mid-infrared DRIFT and solid-state 13C NMR spectra. Environ. Pollut. 158, 285–291. Feng, X.J., Hills, K.M., Simpson, A.J., Whalen, J.K., Simpson, M.J., 2011. The role of biodegradation and photo-oxidation in the transformation of terrigenous organic matter. Org. Geochem. 42, 262–274. Francou, C., Lineres, M., Derenne, S., Le Villio-Poitrenaud, M., Houot, S., 2008. Influence of green waste, biowaste and paper-cardboard initial ratios on organic matter transformations during composting. Bioresour. Technol. 99, 8926–8934. Kile, D.E., Wershaw, R.L., Chiou, C.T., 1999. Correlation of soil and sediment organic matter polarity to aqueous sorption of nonionic compounds. Environ. Sci. Technol. 33, 2053–2056. Lashermes, G., Houot, S., Barriuso, E., 2010. Sorption and mineralization of organic pollutants during different stages of composting. Chemosphere 79, 455–462. Locke, M.A., Bryson, C.T., 1997. Herbicide-soil interactions in reduced tillage and plant residue management systems. Weed Sci. 45, 307–320. Locke, M.A., Zablotowicz, R.M., Bauer, P.J., Steinriede, R.W., Gaston, L.A., 2005. Conservation cotton production in the southern United States: herbicide dissipation in soil and cover crops. Weed Sci. 53, 717–727. Loganathan, V.A., Feng, Y.C., Sheng, G.D., Clement, T.P., 2009. Crop-residue-derived char influences sorption, desorption and bioavailability of atrazine in soils. Soil Sci. Soc. Am. J. 73, 967–974.

1455

Melillo, J.M., Aber, J.D., Linkins, A.E., Ricca, A., Fry, B., Nadelhoffer, K.J., 1989. Carbon and nitrogen dynamics along the decay continuum: Plant litter to soil organic matter. Plant Soil 115, 189–198. OECD, 2000. Adsorption/Desorption Using A Batch Equilibrium Method. Guideline 106, Paris, France. Parent, L.E., de Almeida, C.X., Hernandes, A., Egozcue, J.J., Gulser, C., Bolinder, M.A., Katterer, T., Andren, O., Parent, S.E., Anctil, F., Centurion, J.F., Natale, W., 2012. Compositional analysis for an unbiased measure of soil aggregation. Geoderma 179, 123–131. Passeport, E., Benoit, P., Bergheaud, V., Coquet, Y., Tournebize, J., 2011. Selected pesticides adsorption and desorption in substrates from artificial wetland and forest buffer. Environ. Toxicol. Chem. 30, 1669–1676. Quideau, S.A., Graham, R.C., Oh, S.W., Hendrix, P.F., Wasylishen, R.E., 2005. Leaf litter decomposition in a chaparral ecosystem Southern California. Soil Biol. Biochem. 37, 1988–1998. Rampoldi, E.A., Hang, S.S., Barriuso, E., 2011. The fate of glyphosate in crop residues. Soil Sci. Soc. Am. J. 75, 553–559. Rovira, P., Vallejo, V.R., 2002. Labile and recalcitrant pools of carbon and nitrogen in organic matter decomposing at different depths in soil: an acid hydrolysis approach. Geoderma 107, 109–141. Selim, H.M., Zhou, L., Zhu, H., 2003. Herbicide retention in soil as affected by sugarcane mulch residue. J. Environ. Qual. 32, 1445–1454. Shang, J., Flury, M., Harsh, J.B., Zollars, R.L., 2008. Comparison of different methods to measure contact angles of soil colloids. J. Colloid Interface Sci. 328, 299–307. Simpson, M.J., 2006. Nuclear magnetic resonance based investigations of contaminant interactions with soil organic matter. Soil Sci. Soc. Am. J. 70, 995–1004. Stalder, A.L.F., Melchior, T., Müller, M., Sage, D., Blu, T., Unser, M., 2010. Low-bond axisymmetric drop shape analysis for surface tension and contact angle measurements of sessile drops. Colloids Surf., A. 364, 72–81. Sun, H.W., Zhou, Z.L., 2008. Impacts of charcoal characteristics on sorption of polycyclic aromatic hydrocarbons. Chemosphere 71, 2113–2120. van den Boogaart, K.G., Tolosana-Delgado, R., Bren, M., 2011. Compositions: Compositional Data Analysis, R Package Version 1.10-2. . Van Soest, P.J., Wine, R.H., 1967. Use of detergents in the analysis of fibrous feeds. IV. Determination of plant cell wall constituents. J. Assoc. Off. Agric. Chem. 50, 50–55. Xing, B.S., 1997. The effect of the quality of soil organic matter on sorption of naphthalene. Chemosphere 35, 633–642. Yu, X.Y., Pan, L.G., Ying, G.G., Kookana, R.S., 2010. Enhanced and irreversible sorption of pesticide pyrimethanil by soil amended with biochars. J. Environ. Sci. 22, 615–620. Zhang, Y., Lashermes, G., Houot, S., Doublet, J., Steyer, J.P., Zhu, Y.G., Barriuso, E., Garnier, P., 2012. Modelling of organic matter dynamics during the composting process. Waste Manage. 32, 19–30.