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a Dep. Forest Sciences, Univ. of British Columbia, Vancouver, BC, V6T 1Z4 Canada
b Canadian Forest Service, 506 West Burnside Road, Victoria, BC, V8Z 1M5 Canada
* Corresponding author (cpres{at}interchange.ubc.ca)
Received for publication August 1, 2000.
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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Abbreviations: CPMAS, cross-polarization and magic-angle spinning • DD, dipolar dephased • LCI, lignocellulose index • LOI, loss on ignition • NMR, nuclear magnetic resonance • OM, organic matter
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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The technique of solid-state carbon-13 nuclear magnetic resonance (13C NMR) spectroscopy offers an alternative approach to characterize directly the overall composition of organic materials and the changes that occur during decomposition (Piotrowski et al., 1984; Cogle et al., 1989; Baldock et al., 1997; Preston, 1996, 2001; Preston et al., 1997; Chefetz et al., 2000). Nuclear magnetic resonance spectroscopy may also be useful for predicting rates of decomposition and N mineralization based on initial C chemistry (Preston et al., 2000; Prescott and Preston, 1994).
The objective of this study was to relate rates of N mineralization of biosolids and other organic materials to their initial chemistry, in order to determine the best predictors of N mineralization. Biosolids from four sewage treatment plants (STPs), wheat straw, paper fines, and Douglas-fir needle litter were characterized in terms of C and N concentration, and C chemistry measured by proximate analysis and NMR spectroscopy. The initial chemistry was compared with rates of decomposition and net N mineralization during a greenhouse incubation to develop a model to predict rates based on substrate chemistry. Concurrent measurements of decomposition and N mineralization at two field sites were used to develop correction factors to facilitate use of the model at different field sites in British Columbia.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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Two high C to N ratio materials were selected as a contrast to the low C to N ratio biosolids. Paper fines (waste fiber from unbleached thermo-mechanical pulping) were provided by the Scott Paper mill in Burnaby, BC, and wheat straw was purchased from a commercial supplier. Douglas-fir needle litter was collected from a 40-yr-old Douglas-fir stand in the University of British Columbia Research Forest near Maple Ridge, BC.
Chemical Analysis Procedures
The initial moisture content of each material was determined by oven-drying for 24 h at 70°C. For all other analyses, samples were oven-dried, ground, and sieved (to 16 mesh) using a Tecator centrifuge mill (1093 Cyclotech sample mill; Foss Tecator, Höganäs, Sweden). Organic matter content was determined by loss on ignition (LOI; Nelson and Sommers, 1996). Carbon content was determined with a Leco (St. Joseph, MI) carbon analyzer. Total N was determined with a Lachat (Milwaukee, WI) auto-analyzer (Kopp and McKee, 1983) following a modified micro-Kjeldahl acid digestion (Bremner, 1996). Inorganic N (NH4–N and NO3–N) was determined using the auto-analyzer, following extraction with 2 M KCl (Mulvaney, 1996). Organic N was calculated as the difference between total and inorganic N. Proximate analysis was carried out using the forage fiber method described by Ryan et al. (1990) to estimate the concentrations of the extractable (hemicelluloses plus other labile C compounds), acid-soluble, and acid-insoluble fractions. Although the acid-soluble and acid-insoluble fractions are largely composed of cellulose and lignin, respectively, they contain many compounds and their composition probably varies among different types of organic matter (Preston et al., 1997; Ryan et al., 1990). Therefore, we refer to the fractions based on proximate analyses as extractable, acid-soluble, or acid-insoluble. Finally, the lignocellulose index (LCI = lignin/[lignin + acid-soluble carbohydrates]) of each material was calculated according to Melillo et al. (1989) from the values for the acid-insoluble fraction (lignin) and the acid-soluble fraction.
Nuclear Magnetic Resonance Spectroscopy
Carbon-13 NMR spectra with cross-polarization and magic-angle spinning (CPMAS NMR) of dry, finely ground samples were obtained on a Bruker (Karlsruhe, Germany) MSL 300 spectrometer at 75.47 MHz (Lorenz et al., 2000). Dipolar dephased (DD) spectra were generated by inserting a delay period of 45 to 50 µs without 1H decoupling between the cross-polarization and acquisition portions of the CPMAS sequence. The DD spectra were acquired using the total suppression of spinning sidebands (TOSS) pulse sequence since they have a serious effect on the aromatic and carboxyl regions at 300 MHz (Preston, 2001). In DD spectra, intensity is rapidly lost from C with attached hydrogens in rigid structures. Intensity is retained for nonprotonated C, and to a lesser extent for carbons having some mobility in the solid state. These include methoxyl and methyl C and long CH2 chains.
Spectra were divided into chemical shift regions as follows: 0 to 48 ppm, alkyl C; 48 to 60 ppm, methoxyl C; 60 to 93 ppm, O-alkyl C; 93 to 112 ppm, di-O-alkyl and some aromatic C; 112 to 140 ppm, aromatic C–C and C–H; 140 to 160 ppm, phenolic C; and 160 to 190 ppm, carboxyl C (including amides and esters). Areas of the chemical shift regions were determined from the integral curves and were expressed as a fraction of total area (relative intensity, which in this study is also designated index, e.g., alkyl index). The 48 to 60 ppm region designated methoxyl also includes intensity arising from C–N in amino acids and protein; the latter is rapidly lost in DD spectra, in contrast with the retention of methoxyl C intensity (Pichler et al., 2000). As several factors influence the intensity distributions in CPMAS NMR spectra (Preston, 1996; 2001), the relative intensities were only used for comparative purposes.
Incubations
The seven organic materials were incubated in three parallel trials for 391 d beginning in July 1997. In the greenhouse trial, the materials were incubated in microcosms consisting of 250-mL clear plastic tubs with nylon mesh (0.25-mm openings) on the bottom to allow drainage. The tubs were covered with a thin polyethylene film to limit moisture loss while allowing aeration and placed in plastic seedling trays lined with coarse sand. The initial weight of biosolids in each microcosm (10–20 g) approximated typical operational application rates of 15 to 30 dry tonnes per hectare. Samples of the other materials were of approximately equivalent volume to the biosolids. All microcosms were inoculated prior to incubation with 10 mL of a water extract of forest floor from a mixed conifer stand, to provide a decomposer microflora typical of a forest site. The microcosms were irrigated approximately weekly to maintain adequate moisture (neither dry or saturated) as determined by visual inspection; temperatures in the greenhouse ranged from 15 to 41°C during the incubation. After 0, 14, 33, 62, 92, 145, 209, 271, 323, and 391 d, six samples of each material were collected, dried, and weighed. Samples from the harvests at 0, 14, 145, and 371 d were analyzed for LOI, total C, and total and inorganic N, as described earlier.
Concurrent field incubations were conducted at two sites of contrasting climate. The dry interior forest site was a Douglas-fir stand about 20 km northwest of Kamloops, BC. Mean annual precipitation is about 500 mm, and mean daily temperature ranges from -4.8°C in January to 20.8°C in July. The wet coastal forest site was a Douglas-fir stand about 7 km south of Whistler, BC. Mean annual precipitation is 1291 mm and mean daily temperature ranges from -3.5°C in January to 15.6°C in July. Samples of known dry weight of each material were placed into 12- x 14-cm bags of nylon mesh with 0.25-mm openings and pinned to the forest floor to simulate surface application of biosolids. Samples were allowed to equilibrate under ambient moisture conditions. After 0, 33, 92, 271, 323, and 391 d, eight replicates of each material were collected, dried, and weighed. Six samples from the initial and final harvests were analyzed for LOI, total C, and total and inorganic N, as described earlier.
Model Development
Models were constructed based on data from the greenhouse trial, to relate rates of decomposition and net N mineralization to the suite of substrate chemistry parameters measured. Simple, two-term linear models (including day and each independent variable) were used to assess the most effective variables, which were then used in more comprehensive multiple linear models.
Multiple linear models were constructed using the SAS general linear model procedure (proc glm) (SAS Institute, 1982). The assumption of normality was tested using the SAS procedure proc univariate to test for skewness of the data. The assumption of homoscedasticity was tested by plotting model residuals against the predicted value of the dependent variable, using the SAS procedure proc plot. Lack of fit was also assessed using the plot of residuals against the predicted values of the dependent variable. Each of the preferred multiple linear models was selected on the basis of a maximum or near-maximum correlation coefficient (r2) with as few independent variables as possible.
The greenhouse models were assessed for their ability to describe the variation in the data from the two field sites, using the coefficient of determination (I2). This was defined as:
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RESULTS AND DISCUSSION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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The NMR spectrum of the paper fines (Fig. 1b) is similar to that reported by Jackson and Line (1997), and indicates a composition of almost pure cellulose. It has lower alkyl, aromatic, phenolic, and carboxyl intensity than the wheat straw (Table 2), and the phenolic region is typical of hardwood lignin, with the mixture of syringyl and guaiacyl lignin giving the peak at 153 ppm with a shoulder at 147 ppm. The DD spectrum enhances the phenolic and methoxyl signals of lignin, and is consistent with the very low alkyl and carboxyl content.
The NMR spectra of the paper fines are consistent with the proximate analyses, which show low extractives, high acid-solubles (mainly cellulose), and the lowest acid-insolubles, resulting in the lowest LCI (in this case, the acid-insolubles can genuinely be equated to Klason lignin as there is no tannin and only a trace of resins or waxes). It had the lowest N content, and essentially no protein, consistent with the very weak carboxyl signal.
The spectrum of the Douglas-fir needle litter (Fig. 1c) can also be understood in light of previous work (Prescott and Preston, 1994; Preston et al., 1997, 2000). The O-alkyl and di-O-alkyl regions are similar to those of wheat straw, indicating a high proportion of cellulose and hemicellulose. However, peaks in the other regions indicate considerable proportions of three biopolymers, cutin, condensed tannins, and lignin. The alkyl signal is split with peaks at 30 and 33 ppm. This is typical of long-chain CH2 arising from a mixture of surface waxes and cutin. The peak at 30 ppm, from the more mobile fraction, is retained in the DD spectrum, while that of the more rigid component at 33 ppm has been lost. Cutin and waxes also contribute to the carboxyl region. The methoxyl signal of lignin at 56 ppm is present in both the normal and DD spectrum, but the phenolic region indicates a mixture of lignin and condensed tannins (Almendros et al., 2000; Lorenz et al., 2000; Preston, 1996; Preston et al., 1997, 1998). The partially resolved peaks at 145 and 154 ppm are typically found with a mixture of condensed tannins and guaiacyl lignin. The broad signal at 105 ppm in the DD spectrum is also a strong indicator of condensed tannins.
The Douglas-fir needle litter had the highest content of acid-insolubles and the highest LCI of all materials. Whereas for wheat straw and paper fines, these originate largely from lignin, for Douglas-fir litter, the sources include lignin, cutin, and tannin (Preston et al., 1997). The lignin and tannin content together resulted in the highest phenolic index of all materials. It had a high organic matter content, but also the highest C content, resulting in the highest C to organic matter (OM) ratio of all materials. Its alkyl index was similar to the biosolids, but arising from plant waxes and cutins rather than fatty acids and proteins. It had a similar carboxyl index to the biosolids which, considering its high lignin and low N content, was probably due more to cutins and waxes (Preston et al., 1997) than the protein amides and fatty acids dominant in biosolids.
Nuclear Magnetic Resonance Spectra of Biosolids
There was also a large variation in the NMR spectra of the four biosolids, and few studies available for comparison, apart from a detailed study of the digestion and composting process of the Philadelphia Water Department (Pfeffer et al., 1984; Piotrowski et al., 1984). In that study, both the raw input (<2.5-cm fraction) and the sludge after anaerobic digestion and dewatering were high in alkyl and O-alkyl C, and low in aromatic and carboxyl C. The major organic components of the sludge were lipids, proteins, and carbohydrates, with a small proportion of lignin.
Some related information is also available from studies of manure. Carbon-13 CPMAS NMR and chemical analysis of whole cattle slurry (Beyer et al., 1997) indicated a composition similar to that of the raw sewage and sewage sludge, while the fibrous fraction of manure was high in cellulose (Cyr et al., 1988; Inbar et al., 1989). By contrast, a colloidal fraction separated from hog manure was enriched in a variety of lipids, including sterols (Dinel et al., 1998). Nuclear magnetic resonance studies of microbial biomass showed that fungi were higher in O-alkyl C, while bacteria had high proportions of alkyl and carboxyl C; both were very low in aromatic and phenolic C (Baldock et al., 1990). These organisms also secrete nonstructural polysaccharides with broad NMR signals similar to hemicelluloses (Golchin et al., 1996).
Of the four biosolids, the Lionsgate, Annacis Island, and Chilliwack samples are all anaerobic and form a progression with increasing secondary characteristics, while the Whistler biosolids differ in being aerobic and autothermophilic, as well as high in iron, which broadens the spectrum. The increasing secondary component is accompanied by an increase in total and organic N, decrease in C to N ratio, increase in extractables, and decrease in acid-solubles and acid-insolubles. The LCI increases while the ratios of lignin to N decrease.
The Lionsgate sludge (thermophilic, anaerobic, primary; Fig. 1d) has the highest O-alkyl C, with the sharp features typical of cellulose. The alkyl intensity retained in the DD spectrum probably comes from both plant cutin and waxes, and from proteins and lipids of microbial origin. The DD spectrum also shows phenolic and methoxyl signals of lignin (similar to those of pulp sludge) and a large carboxyl signal from proteins and lipids. The Lionsgate sludge has the lowest total N and highest C to N ratio of the four biosolids, consistent with the NMR spectrum, which shows the highest O-alkyl C and strongest cellulose signals. It also has the lowest extractables and highest acid-solubles, consistent with structural polysaccharides such as cellulose.
From Lionsgate to Annacis Island (Fig. 1e) to Chilliwack (Fig. 1f) biosolids, the NMR spectra show increasing alkyl, methoxyl, and carboxyl C, decreasing O-alkyl C, and increasing ratios of alkyl to O-alkyl C. The DD spectra also show a progression with decreasing intensity from phenolic, aromatic, methoxyl, and long-chain CH2. While the intensity at 56 ppm in the Lionsgate sample is largely from methoxyl C, the loss of intensity in the DD spectra of the other two samples means that this region comes increasingly from protein C–N rather than methoxyl C (Pichler et al., 2000; Preston, 2001). The decrease in lignin and increase in protein is also reflected in the lower intensity of phenolic and nonprotonated aromatic C in the DD spectra.
It is impossible to separate the contributions of protein versus lipid C to the alkyl region, and while there is some reduction in the proportion of long CH2 chains, overall both lipid and protein C increase in the biosolids progression. The changes in the NMR spectra reflect a reduction in less-soluble structural biopolymers (lignin, cellulose, and long-chain CH2) and an increase in protein and lipids from microbial biomass. These trends are consistent with the chemical analyses showing increasing N and extractives, and decreasing acid-solubles and acid-insolubles. However, indices of organic matter quality or readiness to decompose give conflicting results, as C to N and lignin to N ratios decrease, but values of LCI increase.
The Whistler biosolids (Fig. 1g) were derived from a different process (autothermophilic, aerobic, secondary in that digestive microflora was retained), and interpretation of the spectrum is complicated by the presence of iron. This causes broadening of the peaks and decreases the reliability of the intensity distribution. The almost complete loss of methoxyl, aromatic, and phenolic intensity in the DD spectrum is consistent with high protein content and absence of lignin. The Whistler sample has a high phenolic index, but the very low phenolic intensity in the DD spectrum indicates that this may be largely from unresolved signal spread from other regions. Both lipids and protein can contribute to the alkyl and carboxyl peaks in the DD spectrum. While high iron content reduced the quantitative reliability of the Whistler spectra compared with those of the other samples, the NMR results are still reasonably consistent with the chemical data. Compared with the other samples, Whistler was high in extractables (partly salts), low in acid-solubles and acid-insolubles, and intermediate in organic N. All of these data indicate considerable breakdown of large structural plant biopolymers and accumulation of microbial biomass and protein. It also had the highest content of inorganic N (0.81% compared with 0.20 to 0.25% for the others), presumably a result of the aerobic process.
For the biosolids, there was a strong positive relationship between organic N concentration and NMR indices of protein (methoxyl and carboxyl indices), suggesting that most of the organic N in biosolids occurred in a protein pool. This is consistent with Hattori and Mukai (1986), who estimated that 40 to 50% of the organic N in a range of six different sewage sludges was in amino acids. The levels of organic N and the protein and alkyl indices appeared to be linked to the sewage treatment process generating each type of biosolids. For example, Chilliwack biosolids, which had the highest organic N content and highest protein indices, had a short detention time and were the only mesophilic biosolids. Both detention time and digestion temperature negatively influence the level of labile organic matter such as proteins in biosolids (Parkin and Owen, 1986). Mesophilic temperatures are also less efficient than thermophilic temperatures at digesting the recalcitrant fatty acids in sewage (Parkin and Owen, 1986), which are strongly associated with alkyl index in biosolids. Conversely, Lionsgate biosolids, which had the lowest organic N content, the lowest protein indices, and the lowest alkyl index, were digested at thermophilic temperatures, and were detained in digestion tanks for the longest period of all biosolids. The extent of secondary treatment also appeared to be associated with greater proportions of microbial biomass (presumably derived from waste-activated sludge).
Decomposition and Nitrogen Mineralization
Decomposition, expressed as organic matter loss (Fig. 2), was fastest in paper fines, followed by wheat straw, followed by the four biosolids and the Douglas-fir litter. The rapid decomposition of paper fines and wheat straw is consistent with their high cellulose and low lignin contents, hence low LCI. Cumulative organic matter loss during the 391-d field incubations averaged 83 and 71% of that measured over the same period in the greenhouse at the Whistler and Kamloops site, respectively (Table 3). Net N mineralization was expressed as g of organic N lost per kg of organic matter applied to facilitate direct comparison of N release from the organic materials, irrespective of their initial N or ash contents. In the greenhouse, patterns of net N mineralization were quite distinct among the organic materials (Fig. 3, Table 3). Paper fines and needle litter both immobilized N throughout the incubation while wheat straw mineralized a small amount of N. All of the biosolids immobilized N initially and then released N for the rest of the year. Chilliwack biosolids immobilized the least N initially and released the most N during the 391 d. The relative ranking of N release rates from the seven materials in the field were fairly similar to those in the greenhouse (Table 3).
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The next best multiple linear models employed LCI (r2 = 0.93), alkyl index (r2 = 0.88), and carboxyl index (r2 = 0.88).
The effectiveness of the C to OM ratio as an index of decomposition was interesting. It was a measure of the content of elements other than C (i.e., oxygen and hydrogen) in the organic material. In the wheat straw and paper fines, it reflected the size of the polysaccharide pool (cellulose and hemicellulose), which has a relatively high proportion of O and H. The polysaccharides should have less effect on decomposition of the biosolids, which probably had a large labile protein pool (Lerch et al., 1992), and are relatively low in polysaccharides, as evidenced by the generally weaker O-alkyl signals. Nevertheless, the C to OM ratio is remarkably constant among the biosolids and correlated well with their rate of decomposition relative to the other materials.
The decomposition model exhibited low I2 values and large negative biases indicating that the model overestimated the rate of decomposition at both field sites (Table 4). The corrected I2 values were much improved over the initial I2 values, indicating that the bias in the greenhouse was fairly constant, irrespective of the individual materials, and that the influence of substrate chemistry as determined in the greenhouse incubation also held true under field conditions. The best net N mineralization model used time, the initial organic N concentration of the material, the phenolic index of the material, and the organic matter concentration of the material:
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The model exhibited fairly low I2 values, indicating a substantial difference between the predicted and observed mineralization rates at both field sites (Table 4). The biases were not large at either site, suggesting the low I2 values were due to error other than an overall model bias. The corrected I2 values were not much improved over the initial I2 values, indicating that the net N mineralization model (unlike the decomposition model) cannot be readily adjusted to field conditions using a simple correction factor. A more complex approach to predicting nitrogen mineralization from organic matter and residues such as suggested by Honeycutt (1999) is recommended.
Net N mineralization from the four biosolids was most highly correlated with the alkyl index (Table 5). The phenolic to Norganic ratio, the alkyl to O-alkyl ratio, LCI, and the initial organic N concentration were also good predictors. The C to N (total N) ratio and the lignin to N ratios were fairly poor predictors of net N mineralization. The effectiveness of the alkyl index and the alkyl to O-alkyl ratio as single predictive variables of net N mineralization from biosolids are probably due to the reflection of proteins in the alkyl region of the CPMAS NMR spectra. A large part of the organic N in biosolids has been shown to be proteinaceous in origin (Hattori and Mukai, 1986) and proteins form a particularly labile fraction of the organic pool (Lerch et al., 1992). Hattori and Mukai (1986) found a correlation between mineralization of organic N from biosolids–soil mixtures and crude protein in the biosolids. Hattori (1988) found a correlation between C and N mineralization in biosolids–soil mixtures and the level of proteinase activity in the soil. Lerch et al. (1992) found a correlation between N mineralization and the concentrations of low molecular weight amines (assumed to be proteins) in biosolids–soil mixtures. In the three anaerobically digested biosolids in our study, progressively higher content of secondary (waste-activated) sludge corresponded with progressively higher organic N content, higher protein content, and higher rate of N mineralization over the incubation period. It appears likely therefore that N mineralization from biosolids is mainly a consequence of catabolism of the labile pool of protein, rather than decomposition of the material as a whole. This would explain the poor relationship between rates of decomposition and net N mineralization from these materials.
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ACKNOWLEDGMENTS |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONREFERENCES |
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