Simulation of Organic Matter and Pollutant Evolution during Composting: The COP-Compost Model.

Organic pollutants (OPs) are potentially present in composts and the assessment of their content and bioaccessibility in these composts is of paramount importance. In this work, we proposed a model to simulate the behavior of OPs and the dynamic of organic C during composting. This model, named COP-Compost, includes two modules. An existing organic C module is based on the biochemical composition of the initial waste mixture and simulates the organic matter transformation during composting. An additional OP module simulates OP mineralization and the evolution of its bioaccessibility. Coupling hypotheses were proposed to describe the interactions between organic C and OP modules. The organic C module, evaluated using experimental data obtained from 4-L composting pilots, was independently tested. The COP-Compost model was evaluated during composting experiments containing four OPs representative of the major pollutants detected in compost and targeted by current and future regulations. These OPs included a polycyclic aromatic hydrocarbon (fluoranthene), two surfactants (4--nonylphenol and a linear alkylbenzene sulfonate), and an herbicide (glyphosate). Residues of C-labeled OP with different bioaccessibility were characterized by sequential extraction and quantified as soluble, sorbed, and nonextractable fractions. The model was calibrated and coupling the organic C and OP modules improved the simulation of the OP behavior and bioaccessibility during composting.

COP-compost: a software to study the degradation of organic pollutants in composts.

Composting has been demonstrated to be effective in degrading organic pollutants (OP) whose behaviour depends on the composting conditions, the microbial populations activated and interactions with organic matters. The fate of OP during composting involves complex mechanisms and models can be helpful tools for educational and scientific purposes, as well as for industrialists who want to optimise the composting process for OP elimination. A COP-Compost model, which couples an organic carbon (OC) module and an organic pollutant (OP) module and which simulates the changes of organic matter, organic pollutants and the microbial activities during the composting process, has been proposed and calibrated for a first set of OP in a previous study. The objectives of the present work were (1) to introduce the COP-Compost model from its convenient interface to a potential panel of users, (2) to show the variety of OP that could be simulated, including the possibility of choosing between degradation through co-metabolism or specific metabolism and (3) to show the effect of the initial characteristics of organic matter quality and its microbial biomass on the simulated results of the OP dynamic. In the model, we assumed that the pollutants can be adsorbed on organic matter according to the biochemical quality of the OC and that the microorganisms can degrade the pollutants at the same time as they degrade OC (by co-metabolism). A composting experiment describing two different (14)C-labelled organic pollutants, simazine and pyrene, were chosen from the literature because the four OP fractions simulated in the model were measured during the study (the mineralised, soluble, sorbed and non-extractable fractions). Except for the mineralised fraction of simazine, a good agreement was achieved between the simulated and experimental results describing the evolution of the different organic fractions. For simazine, a specific biomass had to be added. To assess the relative importance of organic matter dynamics on the organic pollutants' behaviour, a sensitivity analysis was conducted. The sensitivity analysis demonstrated that the parameters associated with organic matter dynamics and its initial microbial biomass greatly influenced the evolution of all the OP fractions, although the initial biochemical quality of the OC did not have a significant impact on the OP evolution.

Composting in small laboratory pilots: performance and reproducibility.

Small-scale reactors (<10 l) have been employed in composting research, but few attempts have assessed the performance of composting considering the transformations of organic matter. Moreover, composting at small scales is often performed by imposing a fixed temperature, thus creating artificial conditions, and the reproducibility of composting has rarely been reported. The objectives of this study are to design an innovative small-scale composting device safeguarding self-heating to drive the composting process and to assess the performance and reproducibility of composting in small-scale pilots. The experimental setup included six 4-l reactors used for composting a mixture of sewage sludge and green wastes. The performance of the process was assessed by monitoring the temperature, O(2) consumption and CO(2) emissions, and characterising the biochemical evolution of organic matter. A good reproducibility was found for the six replicates with coefficients of variation for all parameters generally lower than 19%. An intense self-heating ensured the existence of a spontaneous thermophilic phase in all reactors. The average loss of total organic matter (TOM) was 46% of the initial content. Compared to the initial mixture, the hot water soluble fraction decreased by 62%, the hemicellulose-like fraction by 68%, the cellulose-like fraction by 50% and the lignin-like fractions by 12% in the final compost. The TOM losses, compost stabilisation and evolution of the biochemical fractions were similar to observed in large reactors or on-site experiments, excluding the lignin degradation, which was less important than in full-scale systems. The reproducibility of the process and the quality of the final compost make it possible to propose the use of this experimental device for research requiring a mass reduction of the initial composted waste mixtures.

Modelling of organic matter dynamics during the composting process.

Composting urban organic wastes enables the recycling of their organic fraction in agriculture. The objective of this new composting model was to gain a clearer understanding of the dynamics of organic fractions during composting and to predict the final quality of composts. Organic matter was split into different compartments according to its degradability. The nature and size of these compartments were studied using a biochemical fractionation method. The evolution of each compartment and the microbial biomass were simulated, as was the total organic carbon loss corresponding to organic carbon mineralisation into CO(2). Twelve composting experiments from different feedstocks were used to calibrate and validate our model. We obtained a unique set of estimated parameters. Good agreement was achieved between the simulated and experimental results that described the evolution of different organic fractions, with the exception of some compost because of a poor simulation of the cellulosic and soluble pools. The degradation rate of the cellulosic fraction appeared to be highly variable and dependent on the origin of the feedstocks. The initial soluble fraction could contain some degradable and recalcitrant elements that are not easily accessible experimentally.

Sorption and mineralization of organic pollutants during different stages of composting.

The organic pollutant (OP) content is a key factor when determining compost quality. The OPs present in feedstock materials may either be degraded during composting or stabilized in the compost by sorption interactions with organic matter (OM), which may reduce the availability of OP to microorganism degradation. It is particularly important to identify the key stages during composting that are involved in OP mineralization so as to be able to optimize the composting process and determine whether OP sorption on OM is a limiting factor to OP mineralization. Four (14)C-labeled OPs were used during the study: a polycyclic aromatic hydrocarbon (fluoranthene), two surfactants (4-n-nonylphenol - NP and sodium linear dodecylbenzene sulfonate - LAS) and a herbicide (glyphosate). The potential for compost microflora to degrade OP, and compost sorption properties, were characterized at different stages of composting. The highest levels of LAS and glyphosate mineralization were found during the thermophilic stage, at the beginning of maturation for NP and at the end of maturation for fluoranthene. A specific microflora was probably involved in the biodegradation of fluoranthene while NP, LAS and glyphosate mineralization were linked to total microbial activity. OP sorption on compost was linked to their hydrophobicity, decreasing in the order: fluoranthene>NP>LAS>glyphosate. Moreover, sorption decreased as compost maturity increased, except for glyphosate. The sorption coefficients were positively correlated to mineralization kinetics parameters for NP, LAS and glyphosate, suggesting a positive effect of sorption on increasing mineralization rates.

Typology of exogenous organic matters based on chemical and biochemical composition to predict potential nitrogen mineralization.

Our aim was to develop a typology predicting potential N availability of exogenous organic matters (EOMs) in soil based on their chemical characteristics. A database of 273 EOMs was constructed including analytical data of biochemical fractionation, organic C and N, and results of N mineralization during incubation of soil-EOM mixtures in controlled conditions. Multiple factor analysis and hierarchical classification were performed to gather EOMs with similar composition and N mineralization behavior. A typology was then defined using composition criteria to predict potential N mineralization. Six classes of EOM potential N mineralization in soil were defined, from high potential N mineralization to risk of inducing N immobilization in soil after application. These classes were defined on the basis of EOM organic N content and soluble, cellulose-, and lignin-like fractions. A decision tree based on these variables was constructed in order to easily attribute any EOM to 1 of the 6 classes.