Stijn Meganck
email: smeganck@vub.ac.be

Philippe Leray
email: philippe.leray@univ-nantes.fr

Bernard Manderick

UnCaDo: Unsure Causal Discovery

Keywords: Réseau Bayésien Causal, Apprentissage de Structure Causal Bayesian Networks, Structure Learning

La plupart des algorithmes pour découvrir des relations de causalité à partir de données font l'hypothèse que ces données reflètent parfaitement les (in)dépendances entre les variables étudiées. Cette hypothèse permet de retrouver le squelette théorique et le représentant de la classe d'équivalence de Markov du modèle dont sont réellement issues les données. Ce représentant est un graphe généralement partiellement orienté et sans circuit dont les arcs représentent des relations de causalité directe entre un ensemble de parents et le noeud enfant.

Nous relachons cette première hypothèse en permettant d'obtenir initialement un graphe pouvant contenir des arêtes "incertaines". Ces arêtes seront ensuite validées (et orientées) ou supprimées lors de l'obtention de nouvelles données expérimentales. Nous présentons alors l'algorithme UnCaDo (UNsure CAusal DiscOvery) qui propose le plain d'expériences nécessaire pour obtenir suffisamment d'informations pour obtenir une structure complétement causale.

ABSTRACT. Most algorithms to learn causal relationships from data assume that the provided data perfectly mirrors the (in)dependencies in the system under study. This allows us to recover the correct dependence skeleton and the representative of the Markov equivalence class of Bayesian networks that models the data. This complete partially directed acyclic graph contains some directed links that represent a direct causal influence from parent to child. In this paper we relax the mentioned requirement by allowing unsure edges in the dependence skeleton. These unsure edges can then be validated and oriented or discarded by performing experiments. We present the UnCaDo (UNsure CAusal DiscOvery) algorithm which proposes a number of necessary experiments that need to be done to gain sufficient causal information to complete the graph.

Introduction

Learning causal relationships from data and modeling them is a challenging task. One well known technique for causal modeling is a causal Bayesian network (CBN), introduced by [START_REF] Pearl | Causality : Models, Reasoning and Inference[END_REF]. In a CBN each directed edge represents a direct causal influence from the parent to the child. For instance a directed edge C → E in a CBN indicates that there exists at least one manipulation of C that would alter the distribution of the values of E given that all other variables are kept at certain constant values.

Learning the structure of Bayesian networks can be done from observational data. First the complete partially directed acyclic graph (CPDAG) is learned from data, and then a possible complete instantiation in the space of equivalent graphs defined by this CPDAG is chosen [START_REF] Spirtes | Causation, Prediction and Search[END_REF]. It is impossible to follow the same strategy for CBNs, because there is only one true causal network that represents the underlying mechanisms, so only one of the graphs in the space of equivalent graphs is the correct one. Learning algorithms for which it is proven that they converge to the true CPDAG are PC proposed by [START_REF] Spirtes | Causation, Prediction and Search[END_REF], GES by (Chickering, 2003) and conservative PC by [START_REF] Ramsey | Adjacency-Faithfulness and Conservative Causal Inference[END_REF]. [START_REF] Shimizu | Discovery of non-gaussian linear causal models using ICA[END_REF] showed that when assumptions are made or prior knowledge is available about the underlying distribution of the data it is sometimes possible to recover the entire DAG structure from observational data.

To learn a CBN, experiments are needed because in most cases from observational data alone we can only learn up to Markov equivalence. Sometimes it is possible that the entire structure is discovered (e.g. there is only one member in the Markov equivalence class), however in general we can only learn a subset of the causal influences. Several algorithms exist to learn CBNs based on experiments. For example, [START_REF] Tong | Active Learning for Structure in Bayesian Networks[END_REF] and [START_REF] Cooper | Causal Discovery from a Mixture of Experimental and Observational Data[END_REF] developed score-based techniques to learn a CBN from a mixture of experimental and observational data. In (Meganck et al., 2006a) we proposed a decision theoretic based algorithm. (Eberhardt et al., 2005a) performed a theoretical study on the lower bound of the worst case for the number of experiments to perform to recover the causal structure. All these results were based on performing structural interventions as experiments, i.e. randomization of a variable. Recently some work has been done on different types of interventions by [START_REF] Eberhardt | Interventions and Causal Inference[END_REF] and [START_REF] Eaton | Exact Bayesian structure learning from uncertain interventions[END_REF], but we will not go into detail in this paper.

We propose a constraint-based strategy to learn a causal Bayesian network using structural experiments in the case that observational data is not sufficient to learn all causal information and even insufficient to learn the correct skeleton of the network (i.e. imperfect data). We adapt an existing independence test and use this test to build a model able to represent unsure connections in a network. We then show how these unsure connections can be replaced either by a cause-effect relation or removed from the graph completely during the experiments phase.

The remainder of this paper is as follows. In the next section we provide notations and definitions needed in the remainder of this paper. Then we present our own ap-proach and illustrate it on a toy example. We end with a conclusion and an overview of possible future work.

Preliminaries

In this section we introduce the basic elements needed in the rest of the paper.

In this work uppercase letters are used to represent variables or sets of variables, V = {X 1 , . . . , X n }, while corresponding lowercase letters are used to represent their instantiations, x 1 , x 2 and v is an instantiation of all X i . P (X i ) is used to denote the probability distribution over all possible values of variable X i , while P (X i = x i ) is used to denote the probability that variable X i is equal to x i . Usually, P (x i ) is used as an abbreviation of P (X i = x i ). If two variables X i and X j are marginally dependent or independent we denote this as (X i X j ) and (X i ⊥ ⊥X j ) respectively. If the same relations count conditioned on some set of variables Z we denote it as (X i X j |Z) and (X i ⊥ ⊥X j |Z) respectively.

Ch(X i ), Π i , N e(X i ), Desc(X i ) respectively denote the children, parents, neighbors and descendants of variable X i in a graph. Furthermore, π i represents the values of the parents of X i .

Definition 1 A Bayesian network (BN) is a tuple, V, G, P (X i |Π i ) , with :

-V = {X 1 , . . . , X n }, a set of observable random variables -a directed acyclic graph (DAG) G, where each node represents a variable from V -conditional probability distributions (CPD) P (X i |Π i ) of each variable X i from V conditionally on its parents in the graph G such that the product of all P (X i |Π i ) is a distribution.

As mentioned by [START_REF] Pearl | Probabilistic Reasoning in Intelligent Systems[END_REF], several BNs can model the same independencies and the same probability distribution, we call such networks observationally (or Markov) equivalent. A complete partially directed acyclic graph (CDPAG) is a representation of all observationally equivalent BNs.

Definition 2 A partially directed acyclic graph (PDAG) is a graph containg both directed and undirected edges.

Definition 3 A complete partially directed acyclic graph CPDAG is a graph consisting of directed and undirected edges. An edge A → B is directed if all BNs in the equivalence class have an edge A → B with the same directionality. An edge is undirected if some have A → B and some A ← B.

Definition 4 A Causal BN (CBN) is a Bayesian network in which the directed edges are viewed as representing autonomous causal relations among the corresponding parents-child tuple, while in a BN the directed edges only represent a probabilistic dependency, and not necessarily a causal one.

With an autonomous causal relation, we mean that each CPD P (X i |Π i ) represents a stochastic assignment process by which the values of X i are chosen only in response to the values of Π i . In other words, each variable X j ∈ Π i is a direct cause of X i and no other variable is a direct cause of X i . This is an approximation of how events are physically related with their effects in the domain that is being modeled.

As [START_REF] Murphy | Active Learning of Causal Bayes Net Structure[END_REF][START_REF] Tong | Active Learning for Structure in Bayesian Networks[END_REF] and (Eberhardt et al., 2005b) have done, we will make some general assumptions about the domain being modeled.

Causal Markov condition :

Assuming that B = V, G, P (X i |Π i ) is a causal Baye-
sian network and P is the probability distribution generated by B. As mentioned by [START_REF] Spirtes | Causation, Prediction and Search[END_REF], G and P satisfy the Causal Markov condition if and

only if for every W in V , W is independent of V \(Desc(W ) ∪ Π W )) given Π W .
Faithful distribution : We assume the observed samples come from a distribution which independence properties are exactly matched by those present in the causal structure of a CBN.

Causal sufficiency :

We assume that there are no unknown (latent) variables that influence the system under study.

UnCaDo

Some existing constraint-based structure learning methods can converge to the correct CPDAG, however often the amount of data available does not permit this convergence. In this section we discuss our structural experiment strategy when the available observational data does not provide enough information to learn the correct CPDAG. We propose a constraint-based technique and assume causal sufficiency and the causal Markov property and that the samples come from a faithful distribution.

General description

The strategy of our approach consists of three phases. First an unsure dag, which is an undirected dependence structure, is learned using the observational data, that can include some unsure relations between nodes. In order to model these unsure relations we introduce a new type of edge, namely an unsure edge. Secondly all these unsure relations are removed from the graph by performing experiments in the system on the corresponding variables. In the final phase, possible remaining undirected edges are oriented.

Unsure independence test

The learning techniques we use are based on conditional (in)dependence tests. These tests need a certain amount of data in order to be reliable. If, for instance, not enough data was available then we can not draw a conclusion on the (in)dependence of two variables and thus are unsure whether an edge should be removed or added to the current graph. However most implementations of independence tests provide a standard answer in case there is not enough data. We propose an adapted independence test which in case there is not enough data or not enough evidence (this can be user dependent) for (in)dependence returns unsure as a result.

There are several ways to adapt existing independence tests, for instance :

-Using χ 2 , the number of data points has to be more than 10 * (degree of freedom), otherwise the test can not be performed reliably as mentioned by [START_REF] Spirtes | Causation, Prediction and Search[END_REF]. In most implementations "no conditional independence" is returned as default, while in our case unsure would be returned.

-There can be an interval for the significance level used to return unsure. Traditionally tests are done using α = 0.05 significance level. We allow to set two parameters α 1 and α 2 with α 1 > α 2 . We return independence if the test is significant for significance level α 2 and unsure if it is significant for α 1 and not for α 2 . For example Unsure could be returned where the test is significant with α 1 = 0.05, but no longer insignificant for α 2 = 0.02.

Initial phase : unsure observational learning

We use the adapted independence test and modify the skeleton discovery phase of the PC algorithm in order to form an unsure graph.

The independence test used in PC corresponds to our adapted independence tests. The classic independence test returns either true or f alse, using our adaptation there is a third possible response unsure. When independence is found the edge between the two nodes is removed as usual, however when the relation between the two variables is unsure, we include a new type of edge o-? -o. In order to find sets to test for conditional independence, arrows of type o-?-o are regarded as normal undirected edges.

Nodes in an unsure graph can have three graphical relations : no edge : X i and X j are found to be independent conditional on some subset (possibly the empty set).

edge X i o-oX j : X i and X j are dependent conditional on all subset of variables and all conditional independence tests returned f alse. This corresponds to the traditional undirected edge X i -X j and hence means either

X i ← X j or X i → X j .
unsure edge X i o-?-oX j : we can not determine whether X i and X j are (in)dependent, i.e. there exists at least one subset of variables for which the in-4) exp(X j )

X i + (3) ⇒ X i ← X j 5) exp(X j ) X i + (3) ⇒ X i X j
In a general graph there can be more than one path between two nodes, and we need to take them into account in order to draw conclusions based on the results of the experiments.

Therefore we introduce the following definition :

Definition 5 A potentially directed path (p.d. path) in an unsure PDAG is a path made of edges of types o-? -o, → and -? -o, with all arrowheads in the same direction. A p.d. path from X i to X j is denoted as X i X j .

If in a general unsure PDAG there is an edge X i o-? -oX j , the results of performing an experiment at X i are :

6) exp(X i ) X j ⇒ X i X j
, but since we want to find direct effects we need to block all p.d. paths of length ≥ 2 by a blocking set Z.

-(exp(X i ) X j )|Z ⇒ X i → X j -(exp(X i ) X j )|Z ⇒ X i X j 7) exp(X i ) X j ⇒ X i o-?-X j
In the case that exp(X i ) X j we have to perform an experiment at X j too. The results of this experiment are : 8) exp(X j ) X i + (7) ⇒ X i X j , but since we want to find direct effects we need to block all p.d. paths of length ≥ 2 by a blocking set Z.

-(exp(X j ) X i )|Z + (7) ⇒ X i ← X j -(exp(X j ) X i )|Z + (7) ⇒ X i X j 9) exp(X j ) X i + (7) ⇒ X i X j
After these experiments all unsure edges X i o-?-oX j are transformed into either directed edges or are removed from the graph.

It has to be noted that, like in the simplest example, the experiments only provide us with more data and that this still might not be enough to give a reliable answer for our statistical test. In this case the result of an experiment would leave the unsure edge and more experiments are needed until the test can be performed reliably.

Completion phase

At this point there are only the original undirected edges o-o and directed edges → found by resolving unsure edges, we can hence use Steps 3 and 4 of PC to complete the current PDAG into a CPDAG. If not all edges are directed after this we need to perform another set of experiments. In order to complete this we use the MyCaDo algorithm we proposed in (Meganck et al., 2006a)

X 1 X 3 X 2 X 1 X 3 X 2 X 1 X 3 X 2 ? X 1 X 3 X 2 (a) (b) (c) (e) X 1 X 3 X 2 ? (d)

Complete Learning Algorithm

All the actions described above combine to form the Unsure Causal Discovery algorithm (UnCaDo). The complete algorithm is given in Algorithm 1. We define a couple of notions to simplify the notation. In an unsure graph N e(X i ) are all variables connected to X i either by a directed, undirected or unsure edge. Test of independence in an unsure graph are performed using the unsure independence test we introduced above, in Algorithm 1 this test is referred to as the test of independence.

Toy Example

We demonstrate the different steps of the UnCaDo algorithm on a simple example. If the unsure independence test returns "unsure" for a test between X i and X j conditioned on some set Z we note this as (X i ⊥?⊥X j |Z). Assume the correct CBN is given in Figure 1(a). The algorithm starts with a complete undirected graph shown in Figure 1(b). Assuming that the first ordered pair of variables that will be checked is (X 1 , X 3 ) and that we find the following (in)dependence information :

-(X 1 X 3 ) -(X 1 ⊥?⊥X 3 |X 2 )
This means that the edge X 1 o-oX 3 will be replaced by X 1 o-? -oX 3 , cfr. Figure 1(c). To check for (in)dependence between the other sets of variables (X 1 , X 2 ) and (X 2 , X 3 ) we regard the unsure edge X 1 o-? -oX 3 as being a normal undirected edge. This means that we need to check for both the marginal as the conditional independence of these pairs. If the edge would be considered absent this might lead to missing necessary independence tests. Assume that we find the following independence information for (X 1 , X 2 ) and (X 2 , X 3 ) : and thus we can replace the edge X 1 o-?-oX 3 by X 1 o-?-X 3 as shown in Figure 1(d). Now we need to perform an experiment on X 3 , taking into account the p.d. path X 3 o-oX 2 o-oX 1 . We find that :

-(exp(X 3 ) X 1 |X 2 )
and we can remove the edge X 1 o-?-X 3 , leaving us the graph shown in Figure 1(e). Now that all unsure edges are resolved we can use the orientation rules of the PCalgorithm, including the search for v-structures which in some cases will immediatly find the correct structure if enough data is available or we need to run the MyCaDo algorithm to complete the structure.

Conclusions and Future work

In this paper, we discussed learning the structure of a CBN. In general, without making assumption about the underlying distribution, the full causal structure can not be retrieved from observational data alone and hence experiments are needed.

We proposed an algorithm for situations when observational data is not sufficient to learn the correct skeleton of the network. Therefore we proposed an adapted (in)dependence test which can return unsure if the (in)dependence can not be detected reliably. We suggested to change the skeleton discovery phase of the PC algorithm in order to be able to include the adapted (in)dependence test. We proposed a new graph, an unsure graph, which can represent the results of the new discovery phase by means of unsure edges. We then showed how these unsure connections can be replaced either by a cause-effect relation or removed from the graph completely during an experimentation phase. The experiment strategy indicates which experiments need to be performed to transform the unsure DAG into a PDAG. Using a combination of the orientation rules of PC and, if necessary, some experiments, this PDAG can then be turned into the correct CBN.

We would like to extend these results to a setting with latent variables for which we will use our previous results proposed in (Meganck et al., 2006b).

For future research regarding learning with imperfect data we would like to study the case in which not all unsure edges can be removed. In this case the completion phase would regard them as being absent as not to make any false propagation mistakes. We would like to study how we can use this to adapt our strategy to combine the experimentation and completion phase on a one-by-one basis instead of removing all the unsure edges first (Steps 4 and 5 in Algorithm 1) and then using one big completion phase (Steps 6 and 7 in Algorithm 1) as is presented in this article.

Bibliographie

Chickering D., « Optimal Structure Identification with Greedy Search », Journal of Machine Learning Research, vol. 3, p. 507-554, November, 2003.

Figure 1 .

 1 Figure 1. Simple example demonstrating the different steps in the UnCaDo algorithm.

L'objet. Volume 8 -n˚2/2005

dependence test returned unsure and none that return independent. This means that either X i X j , X i → X j or X i ← X j .

Note that as the number of data points N → ∞ the unsure edges will disappear, since we will work with perfect data. In general however when there are unsure edges more data is needed to distinguish between independence and dependence, therefore we are in need of experiments.

Experimentation phase : resolving unsure edges

In this section we show how we can resolve unsure edges using experiments. We denote performing an experiment at variable X i by exp(X i ).

In general if a variable X i is experimented on and the distribution of another variable X j is affected by this experiment, we say that X j varies with exp(X i ), denoted by exp(X i ) X j . If there is no variation in the distribution of X j we note exp(X i ) X j .

If we find when comparing the observational with the experimental data by conditioning the statistical test on the value of another set of variables Z that exp(X i ) X j we denote this as (exp(X i ) X j )|Z, if conditioning on Z cuts the influence of the experiment we denote it as (exp(X i )

X j )|Z. Note that conditioning on Z is done when comparing the data, not during the experiment. A suitable blocking set Z is N e(X j )\X i , since this is sure to block all incoming paths into X i .

We introduce additional notation to indicate that two nodes X i and X j are either not connected or connected by an arc into X j , we denote this by X i -?-oX j , where "-" indicates that there can be no arrow into X i .

If we take a look at the simplest example, a graph existing of only two variables X i and X j for which our initial learning phase gives X i o-? -oX j . After performing an experiment on X i and studying the data we can conclude one of three things :

The first case happens if the added experiments still do not provide us with enough data to perform an independence test reliably. We can repeat the experiment until sufficient data is available or the test can be performed at our desired significance level. If no sufficient experiments can be performed the link remains X i o-?-oX j , this possibility is a part of future work. The second case is the ideal one, in which we immediately find an answer for our problem.

In the third case, the only conclusion we can make is that X i is not a cause of X j and hence there is no arrow > into X j . To solve this structure completely we still need to perform an experiment on X j . So in this case the results of performing an experiment at X j are :

Algorithm 1 Adaptive learning of CBN for imperfect observational data and experiments. Require: A set of samples from a probability distribution faithful to a CBN P (V ). Ensure: A CBN. 1) G is complete undirected graph over V .

2) Skeleton discovery of PC. If any of the independence tests in this step of PC returned unsure, record the tuple (X i , X j ) into P ossU nsEdg.

3) For each tuple (X i , X j ) in P ossU nsEdg, if the edge X i -X j is still present in G replace that edge by X i o-?-oX j . 4) For each unsure edge X i o-?-oX j , Perform experiment at X i , If exp(X i ) X j , Find a (possibly empty) set of variables Z blocking all p.d. paths between X i and X j . If (exp(X i ) X j )|Z then orient X i o-?-oX j as X i → X j , else remove the edge. else replace X i o-?-oX j by X i o-?-X j . 5) For each edge X i o-?-X j , Perform experiment at X j , If exp(X j ) X i , Find a (possibly empty) set of variables Z blocking all p.d. paths between X j and X i . If (exp(X j ) X i )|Z then orient X i o-?-X j as X i ← X j , else remove the edge. else remove the edge. 6) Apply v-structure discovery and edge inference of PC. 7) Transform PDAG into CBN using the MyCaDo algorithm (Meganck et al., 2006a).

-

So at the end of our non-experimental phase we end up with the structure given in Figure 1(c).

We now need to perform experiments in order to remove the unsure edge X 1 o-?-oX 3 . Assume we choose to perform an experiment on X 1 and gather all data D exp . There is a p.d. path X 1 o-oX 2 o-oX 3 so we have to compare all conditional distributions to see whether there was an influence of exp(X 1 ) at X 3 . Hence we find that :

-(exp(X 1 ) X 3 |X 2 )


Introduction

The constant increase in waste volume and the consideration of environmental issues to its management has led European countries to adopt various regulatory measures in order to minimize waste and to enforce good management. Directive 2006/12/EC specified that member states shall take appropriate measures to encourage recycling of organic substances, including composting and other biological transformation processes (Parlement européen and Conseil, 2006).

Among solutions to manage organic wastes, home composting represents an interesting way to divert them from landfill but also from a collective management. Nevertheless, home composting systems have not yet been largely demonstrated as economically, sanitary and environmentally advantageous. Indeed, the amount of waste really diverted from collection by this way as to be verified. Moreover, voluntary households will commit themselves to compost their waste and will continue in this way only if they can be sure that composting is safe for them and that they can obtain a valuable product. However unlike industrial facilities, households can't easily order quality analysis of their composts. As a consequence simpler ways to assess home-made compost's quality have to be proposed and validated.

Thus, the objective of this document is to review which criteria can assess compost quality, which are the existing standards and which thresholds they specify. Moreover this document summarizes available data on home-made compost's quality and gathers information about other indicators that could be used for domestic composts quality assessment.

Which physico-chemical criteria for assessing compost quality?

Composting is a way to divert organic waste but the main interest of this biological treatment is to recycle organic matter in a valuable product that can return to soil. In this objective, compost quality must concern three main aspects:

• organic amendment properties: addition of organic amendments has been proven useful to restore the quality of degraded soil that can limit agricultural productivity [START_REF] Evanylo | Soil and water environmental effects of fertilizer-, manure-, and compost-based fertility practices in an organic vegetable cropping system[END_REF].

A soil amendment is a material added to soil that will improve its physical properties, such as water retention, permeability, water infiltration, aeration or structure. The goal is to provide a better environment for roots and thus enhance the plant development.

• Fertilizing effect: fertilizers are applied on soil to promote plant nutrition thanks to the provision of macronutrients (nitrogen, phosphorus and potassium) and other micronutrients. The major goal of fertilizer is thus to improve soil fertility and production yield of crop cultivation.

• Innocuousness: the safe use of organic wastes on land depends on several factors including its potential impacts on general environment (soils, water resources, air…) and possible impacts on animal and human health (infections for compost handlers and users, odour issues…). Using organic residues on agricultural land can bring environmental impacts such as groundwater pollution or harmful gaseous emissions. These later issues will not be properly discussed in this paper. They can be related to parameters presented here but they are predominantly influenced by the dose used on land and the period of application.

Indicators for organic amendment properties

A soil amendment is defined as a material added to soil whose principal aim is to maintain or improve soil physical properties or activity.

The main considered characteristics for amendment properties are the following:

• Dry matter content: important characteristic for the practical storage and use of the amendment.

• Organic matter content: It defines the amount of organic matter added to the soil to reinforce its structure and its potential as growing medium.

• Carbon and nitrogen content: carbon and nitrogen are the most important constituents of the organic matter. Depending on the balance between the organic and mineral forms of these elements, carbon and nitrogen will lead either to the improvement of the soil biological properties, either to the soil nutritive properties.

• Van Soest biochemical fractionation [START_REF] Van Soest | Use of detergents in the analysis of fibrous feeds. IV -Determination of plant cell-wall constituents[END_REF] (soluble organic fraction, hemicellulosic-like fraction, cellulosic-like fraction and ligno-cellulosic-like fraction): Organic amendment properties are defined by the structural nature of the organic matter added to soil. Biochemical fractionation of the organic matter quantifies categories of soluble compounds that are extracted in the same conditions. The extracted fractions have a decreasing biodegradability and growing structural properties for soils. That is why these amount of these different fractions have been used to define stability indices in France (IBS -index of biological stability, or Tr -proportion of residual organic matter)(AFNOR, 2005)

• Humic substances content: humic substances are the more stable component of the organic matter and are essential to improve soil structure, fertility and health. [START_REF] Campitelli | Effects of composting technologies on the chemical and physicochemical properties of humic acids[END_REF]. Indeed, humic acids can contribute to the soil buffer capacity and soil cation exchange capacity. Moreover, humic acids affects heavy metal solubility and mobility in soil [START_REF] Diacono | Long-term effects of organic amendments on soil fertility. A review[END_REF] • Cation exchange capacity (CEC): CEC addresses soil quality characterization as growing medium. Indeed it determines the substrate ability to absorb cations as those known as nutrients for plants (Ca, Mg, K, Na). As a consequence, increase of soil CEC is interesting for longer soil fertility. Evolution of CEC is also a good indicator of organic matter humification process. Moreover, high CEC can also contribute to improve contaminants retention avoiding leaching to groundwater [START_REF] Novoa-Munoz | Changes in soil properties and in the growth of Lolium multiflorum in an acid soil amended with a solid waste from wineries[END_REF])

Indicators for fertilizing effect

The fertilization potential of an organic product is estimated by its total content of nutrients and by the availability of these nutrients for plants.

• Nitrogen content: Total nitrogen, nitrates (NO 3 -) and ammonium (NH 4 +

) contents are important characteristics as they are indicative of nitrogen availability for plants growth.

• Potassium (K) and Phosphorus (P): P and K are the two others main nutrients for plant fertilization. Soluble forms of these components can be easily assimilated by plants.

• Other mineral contents like manganese (Mg), calcium (Ca) and sodium (Na) can also be considered.

Indicators for Innocuousness assessment

Innocuousness of organic products includes prevention of impacts on potential users, prevention of environmental problems (fauna, air, water and soil) and lastly prevention of phyto-toxicity for plants.

• Inorganic impurities: content of the compost in terms of inert material such as plastics, particles of glass or stones

• Heavy metals: heavy metals (as Cadmium, Cobalt, Chromium, Copper, Mercury, Nickel, Lead, Zinc): these substances are not concerned by biodegradation and may induce metal accumulation in soil. These potentially toxic elements may then be absorbed by plants, animal or micro-organisms.

• Ammonium content is important as fertilizing property of an organic product. But when too much concentrated ammonium may also lead to phytotoxic effects for plants.

• Organic contaminants: Here are considered organic molecules of two types (Polycyclic aromatic hydrocarbons and biphenyl polychlorinated compounds). These are pollutants persistent in the environment [START_REF] Hargreaves | A review of the use of composted municipal solid waste in agriculture[END_REF].

• Organic matter stability: Biological stability is an important characteristic for organic amendments and fertilizers as it avoids management issues such as further fermentation leading to odorous emissions and limits further decomposition in soil. Stability can be assessed by biological parameters such as respirometric methods (oxygen consumption or carbon dioxide production) [START_REF] Ponsa | Comparison of aerobic and anaerobic stability indices through a MSW biological treatment process[END_REF], self heating potential [START_REF] Brinton | Standardized Test for Evaluation of Compost Self-Heating[END_REF], or by chemical parameters as C/N ratio [START_REF] Mathur | Determination of Compost Biomaturity .1. Literature-Review[END_REF].

• pH, salinity and conductivity: pH is important for innocuousness as it controls the behaviour of metals in soils [START_REF] Kapanen | Ecotoxicity tests for compost applications[END_REF]. pH is also an indicator of the presence of organic acids that can be phyto-toxic for plant growth. Salinity has to be controlled because excess salt concentration has negative effects on plants germination and soil biological activity [START_REF] Hargreaves | A review of the use of composted municipal solid waste in agriculture[END_REF]. Finally conductivity is a way to measure salt concentration.

• Phyto-toxicity: It concerns characteristics affecting plant germination or root growth. To measure immediate phyto-toxicity, tests of seed germination inhibition are used (measurement of germination percentage en root length).

• Pathogens: Potential impacts on human health of an organic product can be assessed through the quantification of coliform bacteria. Escherichia Coli is reported to be the most representative faecal coliforms [START_REF] Hassen | Microbial characterization during composting of municipal solid waste[END_REF], but faecal streptococci are commonly considered as the best indicators of faecal pollution. Among pathogens, Salmonella are the most studied for indicating pathogens behaviour insludge and composts [START_REF] Moletta | Le traitement des déchets[END_REF].

Helminth eggs are also currently quantified as they present high resistance to environmental factors.

Existing standards concerning compost's quality

General knowledge on home-made compost's quality

Because of the lack of guidelines and standards concerning home-composting quality and also because of the expenses that represents composts analysis, data about home-made composts are very scarce.

Few articles presented home-composting studies in the literature review. Indeed only four scientific papers really dealing with home-made compost quality have been found:

• [START_REF] Preston | Characterization of Canadian Backyard Composts: Chemical and Spectroscopic Analyses[END_REF]: a Canadian study with the objective of characterising backyard composts samples from three provinces with different composting techniques (wood bin, plastic bin, pile, with or without mixing, etc.). Among samples, physical aspect of the compost varied but the general tendency was a brown fine and friable product.

• [START_REF] Jasim | The Practibility of Home Composting for the management of biodegradable Domestic Solid Waste -Final Report[END_REF]: a study performed in Great Britain about the practibility of Home composting for the management of biodegradable sold waste. Quality of 64 home-made composts, all obtained in plastic bins, was investigated.

• [START_REF] Alexander | An assessment of the suitability of backyard producced compost as potting soil[END_REF]: this study aimed at assessing backyard compost quality as potting medium.

• [START_REF] Ryckeboer | The fate of plant pathogens and seeds durong backyard composting of vegetable, fruit and garden wastes[END_REF]: these scientists studied the fate of plant pathogens and seeds during backyard composting.

Moreover even if some associations and local collectivities have performed compost sampling and analysis to assess their home-composting programs, results are rarely publicized.

Most relevant data that have been found are shown in table 2.

How assessing home-made compost's quality without chemical indicators?

As shown previously, compost quality can be largely characterized considering physico-chemical analysis. Inconvenience of these parameters is that they require sample analysis in a specialized laboratory and that these analyses are expensive. Thus these physico-chemical criteria can not be easily used by home-compost makers or masters-composters to assess home-made compost's quality. As a consequence more sensory parameters are often used by users to assess their compost quality and how they can use it.

Guidebooks given to home-composters propose some parameters to assess the end of the composting process and the quality of the compost (Jobard, 2008). Most frequently cited parameters are:

• Temperature

The biological activity developed during the composting treatment also produces heat. Thermal balance between this heat production and heat losses through the ambient air induces temperature variation in the composting mass (Tremier et al., 2007). Composting guidebooks generally indicates that ideal temperature for composting ranges from 40°C and 70°C. Nevertheless, these temperatures are more representative of large scale composting systems. Furthermore, temperature is linked to the stage of the process and not directly to the quality of the compost. Thus temperature is an interesting indicator of the composting behaviour but can not be interpreted in terms of compost agronomic quality and innocuousness.

• Moisture

Moisture is also important to state on a potential and practical use of the amendment. Actually important moisture can bring particular issues such as odours and difficulties to store and distribute the amendment (Teglia et al., 2010). Moisture can be measured by drying a compost sample but it can also be assessed thanks to a simple manual test: the fist test.

• Odours

Along composting bad odours may occur. They are due to organic molecules such as fatty acids, aldehydes and ketone and also to nitrogenous and sulphurous components. These bad odours can be linked to the raw waste added in the compost. These odours decrease during composting and tend to disappear. Other bad odours may occur along composting particularly when aeration is badly managed. The final compost is generally characterized by a humus-like odour, due to geosmine, a molecule that is produced along the curing phase of composting (Tremier et al., 2007). Thus odour can be used both as an indicator of the composting behaviour and as an indicator of the compost maturity stage.

Measuring odour is not a simple problem because the way to sample emissions is not standardized (Bockreis and Steinberg, 2005). Moreover analysis of the samples can be complex and expensive. In order to simplify odour characterization, odour wheels have been proposed. Odour wheels are a simple method of classification of the odour character (i.e. quality, note, type, group or category) and intensity of each characteristic (Suffet and Rosenfeld, 2007). A specific odour wheel has been established for composting and used to assess odours on industrial composting plants (Rosenfeld et al., 2007) • Presence of living organisms Presence of living organisms such as worms in the home-made compost is generally the sign of good environmental conditions (pH, salinity, temperature, moisture, etc.) for biological activity and as a consequence it is interpreted as the proof of a good management of the composting process.

• Colour of the compost On a colour point of view, humic substances, synthesized during the curing period of the composting process, are aromatic molecules with numerous double bonds, which are responsible for black colour of stabilized composts. Colour is often visually estimated and compost should be dark brown or black (Epstein, 1997). It does not exist a colour wheel for compost maturity.

• Texture

In order to be easily usable, guidebooks for home composting specify that good compost should have a fine and crumbly texture.

• Homogeneity Homogeneous colour and texture characterize a well managed composting treatment. Indeed it is the sign of frequent turning and homogenous conditions for biological activity.

As already noted, there is a good agreement among home-composting experts about the interest of these sensory indicators to assess the quality of home-made compost. Nevertheless it does not exist any correlation between physico-chemical characteristics assessing innocuousness and agronomic properties of composts and these sensory parameters.

Conclusion

Because of their large scale application to soil, a large amount of work has been performed concerning the quality of industrial composts. Standards and guidelines have been proposed to verify their innocuousness for humans, animals, plants and soils and also to asses their agronomic interest.

As home-made composts were not intended to be sold and spread at large scale, no standard or guideline was proposed for them until nowadays. However large promotion of home-composting leads to more and more production of home-made compost. Thus assessment of the quality of homemade composts becomes of crucial interest: (1) to assure public health and environmental safety;

(2) to encourage home-composters and to give them guidelines for composting management and compost use.

Parameters used for industrial compost characterization can be used to state on the quality of homemade compost. Sampling campaigns could be organised by local or regional municipalities to assess environmental impacts of home composting but it could be very expensive. Moreover, this physicochemical characterization can not be easily performed by individual composters who want to judge of their way to manage composting and of the quality of their composts. In these latter objectives, sensory parameters are much easier to use.

If relations could be found between physico-chemical parameters and sensory parameters, then it would be possible for individual composters to state on the quality of their composts and to inform local institutions of this quality, reducing by the way the need of large sampling and analitycal campaigns.

  < 20 gKCleq/kg DM (b) < 10 gKCleq/kg DM -

  > 50 % of the reference (b) > 75 % of the reference (a) > 25 % of the reference (b) > 50 % of the reference (a) > 50 % of the reference* (b) > 70 % of the reference (b) > 70 % of the reference* (b) > 70 % of the reference

  Different European standards exist on organic amendment properties. All consider different types of composts but none takes into account home composting.Considering industrial soil amendments and growing media, an EC report[START_REF] Afnor | XP U44-162 Amendements organiques et supports de culture -Fractionnement biochimique et estimation de la stabilité biologique -Méthode de caractérisation de la matière organique par solubilisations successives[END_REF] proposes a list of products that can be labelled in this way and the specification they have to comply with. But quality standards still depends on national responsibility.Examples of parameters and thresholds considered into soil amendments and growing media quality standards or guidelines (non exhaustive list) are listed in table 1.

	Germany -Quality Label RAL-GZ 251 (For (a) fresh compost ; (b) mature compost ; (c) mulch compost ; (d) substrate compost)	Great-Britain PAS 100:2005 (BSI, 2010) (apply to compost from source separated biowaste)	Switzerland -Quality Guidelines for composts 2010 (Inspektoratkommision des Gründgutbranche, 2010) (values given for horticultural use: (a)on field, (b) covered)	France -NF U 44-051 (AFNOR, 2006) (Apply to soil improvers excluding those coming from wastewater treatment sludge)	Canada -CCME Compost Quality Guidelines (CCME, 2005) (compost that can be used in any type of application)
	Dry matter (DM)

  -

						DM
	Molybdenum (Mo)	-	-	-	-	< 5 mg/kg DM
	Selenium (Se)	-	-	-	< 12 mg/kg DM	< 2 mg/kg DM
	Lead (Pb)	< 150 mg/kg DM	< 200 mg/kg DM	< 120 mg/kg DM	< 180 mg/kg DM	< 150 mg/kg DM
	Chromium (Cr)	< 100 mg/kg DM	< 100 mg/kg DM	-	< 120 mg/kg DM	< 210 mg/kg DM
	Nickel (Ni)	< 50 mg/kg DM	< 50 mg/kg DM	< 30 mg/kg DM	< 60 mg/kg DM	< 62 mg/kg DM
					< 600 mg/kg
	Zinc (Zn)	< 400 mg/kg DM	< 400 mg/kg DM	< 400 mg/kg DM	DM < 1200 mg/kg	< 700 mg/kg DM
					OM
	Cadmium (cd)	< 1.5 mg/kg DM	< 1.5 mg/kg DM	< 1 mg/kg DM	< 3 mg/kg DM < 3 mg/kg DM
					< 300 mg/kg
	Copper (Cu)	< 100 mg/kg DM	< 200 mg/kg DM	< 100 mg/kg DM	DM < 600 mg/kg	< 400 mg/kg DM
					OM
	Mercury (Hg)	< 1 mg/kg DM	< 1 mg/kg DM	< 1 mg/kg DM	< 2 mg/kg DM	< 0.8 mg/kg DM
	Grain size
	Impurities	(Total glass, metal, plastic and any other non-stone fragments > 2 mm) <0.5 % DM	(Total glass, metal, plastic and any other non-stone fragments > 2 mm) <0.5 % DM of which 0.25 is plastic	Films + PSE>5mm < 0.1 % DM (Total glass, metal, plastic and any other non-stone fragments > 2 mm) < 0.5 % DM	Films + PSE>5mm < 0.3% of DM Other plastics >5 mm <0.8% of DM Glass + metals > 2mm <2 % of DM	No sharp foreign matter>3mm per 500 ml compost No more than 1 piece of foreign matter>25 mm in 500 ml
			<8 or 16 % of
	Stones ( > 5 mm)	< 5 % DM	DM (depending on the grade of	As low as possible	-	-
			the compost)
	Arsenic (As)	-	-	-	< 18 mg/kg DM	< 13 mg/kg DM
	Cobalt (Co)	-	-	-	-	< 34 mg/kg

Table 1 -

 1 Example of quality standards or guidelines for compostsAs it can be seen in table 1, all quality standards or guidelines considered parameters for innocuousness assessment. But the thresholds differ from one country to another.Parameters assessing amendment properties or fertilizing effects are not largely considered. Only guidelines that described different types of composts linked with different uses give detailed thresholds for nutrient contents, salinity, etc.

Abstract

Among solutions to manage organic wastes, home composting represents an interesting way to divert them from landfill but also from a collective management. Nevertheless, home composting systems have not yet been largely demonstrated as economically, sanitary and environmentally advantageous. Indeed, the amount of waste really diverted from collection by this way as to be verified. Moreover, voluntary households will commit themselves to compost their waste and will continue in this way only if they can be sure that composting is safe for them and that they can obtain a valuable product. However unlike industrial facilities, households can't easily order quality analysis of their composts. As a consequence simpler ways to assess home-made compost's quality have to be proposed and validated.

Thus, the objective of this document is to review which criteria can assess compost quality, which are the existing standards and which thresholds they specify. Moreover this document summarizes available data on home-made compost's quality and gathers information about other indicators that could be used for domestic composts quality assessment.


Introduction

Home-composting is a way to divert domestic organic waste from collection, but the main interest of this biological treatment is to recycle organic matter in a valuable product that can return to soil. In this objective, compost quality must concern three main aspects:

• organic amendment properties: addition of organic amendments has been proven useful to restore the quality of degraded soil that can limit agricultural productivity [START_REF] Evanylo | Soil and water environmental effects of fertilizer-, manure-, and compost-based fertility practices in an organic vegetable cropping system[END_REF]. A soil amendment is a material added to soil that will improve its physical properties, such as water retention, permeability, water infiltration, aeration or structure. The goal is to provide a better environment for roots and thus enhance the plant development.

• Fertilizing effect: fertilizers are applied on soil to promote plant nutrition thanks to the provision of macronutrients (nitrogen, phosphorus and potassium) and other micronutrients. The major goal of fertilizer is thus to improve soil fertility and production yield of crop cultivation.

• Innocuousness: the safe use of organic wastes on land depends on several factors including its potential impacts on general environment (soils, water resources, air…) and possible impacts on animal and human health (infections for compost handlers and users, odour issues…). Using organic residues on agricultural land can bring environmental impacts such as groundwater pollution or harmful gaseous emissions. These later issues will not be properly discussed in this paper. They can be related to parameters presented here but they are predominantly influenced by the dose used on land and the period of application.

Few data on the home made compost quality can be found. Moreover, there is a large demand of user for methods to assess the development and the quality of their composts.

Thus this work had three objectives :

• To sample a large sample of home made composts to obtain a data base of compost quality (physicochemical prameters) ; • To compare the quality of the sampled composts to standards and guidelines given for industrial composts ;

• To ask users on their own assessment of their composts through the use of organoloeptic parameters and to compare with the analysed quality.

At the end of the document, the main information to be transfered to waste and prevention community services and to users is summarised.

Materials and methods

Physico-chemical assessment

Sampling panel

Along this experiment we chose to sample composts on the basis of • Composts must be aged of more than 5 months (considered as the minimal time required to obtain a usable compost).

• The season : end of autumn and end of spring • The type of housing/composting composting In order to have a good statistical analysis of the results we chose to work on a total panel of 60 samples:

• 40 for individual housing: 15 end of aut • 20 for collective housing: 5 end of autumn; 15 and of spring The sampling panel for individual compo the quantitative impact of home-composting were asked. Only part of the study on compost quality. Then, a call for volunteers was emailed among the Rennes Metropole staff. Finally only 39 individual composts were sampled (only 14 at the end of spring).

The sampling panel for collective housing/composting was chosen with the Rennes Metropole waste prevention service and their provider for the maintenance of collective composting site.

Method of sampling

In order to sample the most mature compost, samples were taken composting bin (Figure 1). To do so, in case of a composting bin, a side was opened to sample. In case of a pile, the upper part of the compost was discarded in order to

chemical assessment

Along this experiment we chose to sample composts on the basis of three parameters:

Composts must be aged of more than 5 months (considered as the minimal time required to obtain a and end of spring The type of housing/composting : individual housing and composting versus collective housing or In order to have a good statistical analysis of the results we chose to work on a total panel of 60 samples: housing: 15 end of autumn; 25 end of spring housing: 5 end of autumn; 15 and of spring The sampling panel for individual composting was based on volunteering. Firstly the volunteers from the study of composting were asked. Only about half of them answered and agreed to be part of the study on compost quality. Then, a call for volunteers was emailed among the Rennes Metropole staff. Finally only 39 individual composts were sampled (only 14 at the end of spring). l for collective housing/composting was chosen with the Rennes Metropole waste prevention service and their provider for the maintenance of collective composting site.

In order to sample the most mature compost, samples were taken in the bottom third of the composting bin (Figure 1). To do so, in case of a composting bin, a side was opened to sample. In case of a pile, the upper part of the compost was discarded in order to sample the most mature zone ng zone in a composting bin or compost pile sampled (for a 300 l bin): 2.5kg for physico-chemical analysis an 5

Composts must be aged of more than 5 months (considered as the minimal time required to obtain a : individual housing and composting versus collective housing or In order to have a good statistical analysis of the results we chose to work on a total panel of 60 samples: sting was based on volunteering. Firstly the volunteers from the study of about half of them answered and agreed to be part of the study on compost quality. Then, a call for volunteers was emailed among the Rennes Metropole staff. l for collective housing/composting was chosen with the Rennes Metropole waste prevention e bottom third of the compost heap or composting bin (Figure 1). To do so, in case of a composting bin, a side was opened to sample. In case of a pile, one.

chemical analysis and 0.5kg for bacterial

Characterization parameters and methods

The samples were analysed regarding the following criteria:

Chemical and biochemical criteria

• Total Nitrogen (% of WM) • Potassium (K2O % of WM) • Phosphorus (P2O5 % of WM)

Innocuousness parameters

• Heavy metals: Cadmium, Chrome, Copper, Nickel, Zinc, Mercury, Lead, Selenium, Arsenic (mg/ kg DM) • Organic pollutants : Benzo(a)pyrene, Benzo(b)fluoranthène, Fluoranthène (mg/kg DM) Phytotoxicity parameter

• Germination Index (GI): germination of cress seeds and ray-grass on composts aqueous extracts. These parameters were chosen following the specifications of French standard for soil improvers. Some parameters were added to better know the chemical and agronomic quality of the sampled composts.

Organoleptic assessment

At the occasion of each sampling, a form with different choice for sensory parameters was filled with the compost makers. These volunteers had also the possibility to add commentaries on the quality of their compost but also on the assessment form: adequacy of the used terms, other ways to state on the quality of their compost.

Assessment criteria

First page of the form concerned general information: name and address, type of composting, Age of compost or date of the last total mixing, date of sampling, weather.

On the second page the assessment began by the observation of living forms in or around the compost near the sampling point. Then the colour and the level of homogeneity of the compost at the sampling point were asked.

On the third page, the questions concerned the temperature, the odour (proposed odours are based on the odour wheel methodology [START_REF] Rosenfeld | The use of an odour wheel classification for the evaluation of human health risk criteria for compost facilities[END_REF]) and the texture of the sample. Lastly on the fourth page came the fist test and the opened questions.

Survey form

Survey form is presented on annex 1.

Results

Typology of the samples

Sampling panels are characterized by the type of housing, the type of composting performed and the age of the compost sampled.

As it was sometimes difficult to know the exact age of beginning of the compost, three categories were proposed: less than one year (remind that all composts are older than 5 months); between 1 and 2 years; more than 2 years.

Type of composting was also divided in three categories: composting in wood bin, composting in plastic bin and pile composting.

Individual compost

Around 75% of the composts sampled in individual housing were younger than 2 years (Figure 2), equally distributed between less than one year composts and one to 2 years composts. 22 % of the composts sampled were more than 2 years old. It corresponds to volunteers that made compost to divert waste from the general collection but that don't use the obtained composts. Most of the volunteers (around 46 %) used plastic composting bin. Around 38 % preferred wood bin. Only 15 % practised pile composting. Such a low representation of pile composting may probably be explained by the fact that most of the volunteers lived in urban or suburban area, where pile composting is not as convenient as bin composting. (Note that the Ademe survey (2008) [START_REF] Ademe | Enquête nationale sur la gestion domestique des déchets organiques -Rapport[END_REF] showed that in rural area the proportion of pile composting reached 64 %.)

Collective compost

Home composting in collective housing was always performed in wood bin. It is a choice made by Rennes Metropole. Collective composts, that were sampled, were quite equally distributed between compost younger than one year (and generally around 6 months) and composts elder than one year (and generally less than 18 months) (Figure 4). Depending on the composting site, there was on or two composting bin: one for waste feeding and one without waste feeding for maturation. In this last case, the sampling was made in the maturation bin.

Physico-chemical and biochemical quality

Results on compost quality are presented below. For each parameter, the global mean is firstly presented. Then, respectively for composts in individual housing and composts in collective housing, the results are presented as follows: the global mean per type of housing, the mean for winter samples and the mean for spring samples.

General results

Table 1: Chemical characteristics of sampled composts.

Home-made composts are slightly basic. Dry matter content is above 30% with rather low organic matter content on the basis of wet mass (16%). However one must note that organic matter content on the dry basis is significantly higher for collective compost than for individual composts (Figure 5). This can be explained by the fact that collective composts are globally younger and thus less biodegraded. Moreover in individual compost, more mineral substrates as animal litter or used soils are put in the composting bin. Concerning other global chemical characteristics (Table 1), quality does not really differ depending on the type of composting (individual or collective) and on the season (winter and spring). Total carbon to nitrogen ratio is around 15, that is to say a good level for agronomic use.

Among individual composts, soluble carbon and nitrogen content significantly differ between spring and winter. Soluble carbon and nitrogen (respectively 3 and 12 % of total carbon and nitrogen content) are higher for spring compost. Substrates as grass cut may supply more biodegradable organic matter and nutrient when added in the composting bin in spring. As soluble nitrogen is the most available for plants, spring composts will bring more fertilizing elements to the soil (Figure 6).

Soluble contents also differ between individual and collective composts, whatever the season. The higher soluble contents in collective composts (especially concerning carbon content) confirm that they are more biodegradable than individual composts. As already noted, the collective home-made composts contained more organic matter than the individual homemade composts. Distribution between biochemical fractions differs between the two types of composts. The distribution of biochemical fractions is difficult to interpret without the additional knowledge of carbon mineralization kinetics of the two types of composts. Fertilizing elements No significant difference can be observed among total fertilizing contents of home-made composts whatever the season or the type of composting, even if we have already noted that soluble nitrogen is higher for collective composts than individual composts. Total nitrogen, potassium and phosphorus content are lower than 1%. Such concentration are too low to enter the specification of fertilizers regarding the French standard NFU 42001 [START_REF] Afnor | NF U42-001/A8 -Engrais -Dénominations et Spécifications[END_REF]. It does not mean that home-made composts have no fertilizing effect. It just means that they are firstly soil improvers and growing medium.

Biochemical characteristics

Undesirable elements

Table 4: Heavy metals and organic pollutants content in sampled home-made composts

There is no significant difference between heavy metals content of collective and individual composts (Table 4), underlying the fact that main treated substrates are the same for both types of composting. It must be noted that in some samples of individual composts, high content of cupper or zinc have been found. It probably corresponds to yard waste that were treated with cupper or zinc-based product and metals accumulated in the compost. In one sample of collective compost, a high concentration of arsenic was measured. This composting site used sawdust as carbon additive. Arsenic is a product sometimes used in the treatment of wood and that can accumulate in sawdust.

Organic pollutants content is low in home-made composts but statistically higher for individual composts than collective composts. The analysed molecules belong to polycyclic aromatic hydrocarbons (PAH). Such molecules are potent atmospheric pollutants which are emitted during fuel and woods burning. Higher concentration in individual composts might come from the ashes often added in individual composts and not in collective ones. No analysis of pesticides was performed on these composts, but such molecules could also be found in individual composts because of the presence of yard waste in composting bins. Most of home-made composts (91%) are free of bacterial or parasites contamination (Table 5). Presence of salmonella was found in one compost sample. But this result has not been confirmed by the analysis of a second sampling. Helminth eggs are known to be very difficult to eliminate and generally require high temperature to disappear. Considering that they were found only in 4 samples of home-made composts, it means that they were not initially present in the treated organic waste or that high temperature is not the only way to eliminate them as temperature don't generally reach very high level in home composting.

Sanitary quality

Phytotoxicity Table 6: Germination index of cress seeds and ray-grass on compost water extracts

The germination index (GI) is the ratio (expressed in percent) of seeds that germinate and grow on an aqueous extract of compost compared to seeds that germinate and grow on water. With GI under 70 %, the compost aqueous extract is considered as phytotoxic for plant growth. Above 100 %, the compost aqueous extract proves to have a direct positive effect on plant growth [START_REF] Teglia | Valorisation par compostage de résidus solides de digestion anaérobie des déchets organiques[END_REF].

As shown in table 6, GI is globally higher for cress seeds than on for ray-grass seeds. Moreover, whatever the considered seeds, the GI is higher for individual compost than for collective compost. GI under 70 % were measured for collective composts. Phytotoxicity is generally related to the presence of phytotoxic molecules such as ammonia and easily biodegradable organic molecules such as volatile fatty acids. It underlines the fact that collective composts are mainly young composts, less biodegraded.

Comparison to marketable compost standards

The above table ( For analysed chemical parameters, home-made composts are generally in line with the proposed specifications. For example, organic matter content is lower than the French specifications for soil improvers but in line with the threshold proposed by ECN.

Considering heavy metals, home-made composts fulfil strictest specifications, unless for cadmium and mercury, those are just in line with French standards.

Regarding organic pollutants, home-made composts are far under the strictest specifications for PAH.

Regarding pathogens, at a European level, mainly salmonella are analysed. Sampled composts were free of salmonella (except 1 sample that should be verified).

Thus, on conclusion, regarding the European and Canadian guidelines, home-made composts are safe and can be used as soil improvers. Global physico-chemical characteristics don't discriminate quality among composts on a standard point of view. Nevertheless, added analyses that were performed can discriminate the level of biodegradation between the studied composts: soluble chemical parameters and germination index.

Link between phytotoxicity and physico-chemical quality

The previous conclusion stated that home-made composts generally fulfil with soil improver standard specifications. However, we also showed that more precise parameters as soluble oxygen chemical demand, carbon and nitrogen contents differ between individual and collective composts. Moreover, germination index also differs between individual and collective composts. That last parameter is an indicator of the stage of biodegradation and of the agronomic quality of the composts. We searched a linked between phytotoxicity and soluble contents of the composts (Multiple regression) and we found that GI was statistically linked with soluble Kjeldhal nitrogen content (with a probability of 98 %) and soluble chemical oxygen demand (with a probability of 80 %) (Table 8). ANOVA of the regression (Table 9) shows that the model really represents the variation of GI among composts (with a probability > 99,9 %) even if it is not very well adjusted (R² only about 31 %%). Such a low adjustment of the predicted data to experimental data may indicate that some parameters are missing in the model. For example, salinity may influence germination and was not analysed here.

Nevertheless, one can determine that with soluble COD under 40 mgO2/gDM and soluble Kjeldhal nitrogen below 3 mgN/gDM, composts don't show phytotoxicity limitations (Figure 7 and8). Living organisms are easy parameters to observe. As shown on figure 9 and 10, a higher diversity of living organisms was observed in individual composts than in collective composts. Moreover, the number of observed living organisms was also higher in individual composts (Figure 11 and 12). However, the average number of observed living organisms was 3. Colour is a sensory parameter very easy to observe. All recognized colours were distributed between light brown and dark Black. For individual composts (Figure 13), colour is not a discriminant parameter as more than 75% of samples were identified as dark brown. Collective composts are more regularly distributed between brown and black (Figure 14). Homogeneity and texture are two parameters difficult to evaluate by users. As a consequence they were not reliable to discriminate composts (Figures 15 and16). All users found that the fit test was a usable and easy to realise test. Rather no sampled composts presented an excess of water, even in winter. Main composts presented favourable moisture. Only one sample of collective compost showed a lack of moisture. Thus, even if moisture is an important parameter of composting conditions, it cannot be considered as a discriminant parameter for our study of the compost quality. Even if users primarily feared not to recognise the proposed odours, the test was finally quite easy to perform. The degree of odour was generally low. A larger diversity of odours was recognised in individual composts (Figure 19 and 20) than in collective composts (Figure 21 and22). 70% of the compost samples smelled an earthy odour. Bad odours (sewers, faecal odour, fermented vegetable odours) were rarely recognised. In collective composts, organic odours as solvent were recognised. Such solvent odours can be related to aldehydes and ketones emissions. These molecules are emitted in the early stage of the composting process [START_REF] Harrison | Compost Facilities: Off-Site Air Emissions and Health -Summary of the literature[END_REF]. This observation can also be linked to the age of the compost and its level of biodegradation that was proved to be lower for collective composts.

Homogeneity and texture

Moisture: Fist test

Odours

There were too much compost odour types proposed. Humus, wood, hay, fermented vegetable, sewage and solvants are probably sufficient to express the different stage of biodegradation of the biowaste.

Free remarks of users

The surveyed users found the form easy to complete and not too long.

One question to users was their personal assessment of their composts. Most users found that their composts showed good quality and were pleasant to use.

The other question was their best indicators to assess the quality of their composts. Colour and moisture were often cited. But the most cited indicator was the absence of recognisable waste.

Relation between physico-chemical and organoleptic assessment

As already noted, the average of sampled composts fulfils the quality specification imposed to marketable soil improvers. But these specifications don't discriminate the maturity of these composts and their agronomic quality.

Thus as no real difference can be observed between samples on a standard point of view, relation have been searched between the stage of biodegradation/maturity (Age and GI) and sensory parameters (colour, living forms and odours).

The index of germination is logically linked with the age of compost. As shown by Figure 23, GI below 70 % are mainly found among young composts, under one year. Number of living forms in compost seems to be a good indicator of the evolution of the biodegradation and maturity. Indeed, when the number of living forms is low there is more chance to measure a GI below 70 % (figure 25). Concerning odours it appears that odours decrease with the age of compost (figure 26) and that bad odours are mainly found in compost younger than 2 years (figure 27). But no real link have been found between GI and odours.

It probably means that molecules responsible of bad odours have no direct phytotoxic effect on plants.

Conclusion

The presented study followed three main objectives:

• To sample a large sample of home made composts to obtain a data base of compost quality (physicochemical prameters) ; • To compare the quality of the sampled composts to standards and guidelines given for industrial composts ;

• To ask users on their own assessment of their composts through the use of organoloeptic parameters and to compare with the analysed quality.

The physic-chemical analysis of the 59 compost samples demonstrated that the studied home-made composts had the following characteristics:

• Dry matter content around 30 % of wet mass • Organic matter content around 15 % of wet mass and around 50 % of dry mass • C/N ratio about 14

• pH about 8

• Fertilizing elements (Total N, P2O5 and K2O) less than 1 % of wet mass • Soluble N equals 10 % of total N • Low heavy metals content even if some punctual contamination may be measured in Copper, Zinc (probably linked to treatment made in garden and present on yard trimmings put in the composting bin) • Parasites were rarely detected. No pathogens.

• GI test did not demonstrate strong immediate phytotoxic of composts on plant growth.

These results tend to classify home-made compost as soil improver and growing medium. They can not be defined as fertilizers.

Above results were compared with standards and guidelines for marketable composts. Mean characteristics of composts proved to be in good agreement with the specification of the ECN quality assurance for compost. Only cadmium content and mercury could be above the ECN specifications but are in line with French ones. One may also note that whatever the appearance of the sampled compost, no significant difference was measured for global parameters among samples. Standard is mainly an assurance of innocuousness of the product but not a way to evaluate the extent of biodegradation (maturity) nor the agronomic effect.

As a consequence, sensory analysis results have not been correlated with the physic-chemical characteristics of composts, but relations have been searched between sensory parameters and biodegradation extent and phytotoxic effect of composts. The conclusions are the following:

• Colour is a very easy parameter to observe. It shows when compost is pleasant to use. But Colour doesn't discriminate compost on a quality point of view. Colour is not linked with compost age or germination index. • Moisture is an important parameter characterising the composting conditions. Nevertheless, in our study it was not a discriminant parameter as moisture conditions were good for all sampled composts. • Presence of living forms is an encouraging parameter for the extent of biodegradation and the number of observed living forms seems to be linked with germination index.

• Odours can be easily recognised and are linked to the extent of the biodegradation: the more biodegraded the compost is, the lowest is the odour intensity (earthy odour).

These conclusions are synthesized in the compost quality assessment protocol that follows.

Figure 1 :

 1 Figure 1 : Sampling zone in a composting bin or compost pile

  Statistical tests have been performed to compare results on the basis of the sampling season and type of composting (individual or collective): comparison of means (Student tests). Moreover relations have been searched between parameters: Analysis of Variance.

Figure 2 :

 2 Figure 2: Age distribution of individual composts

Figure 3 :

 3 Figure 3: Distribution of the type of composting within the individual panel.

Figure 4 :

 4 Figure 4: Age distribution of individual composts

Figure 5 :

 5 Figure 5: Comparison of mean of organic matter for collective (type 0) and individual compost (type 2)

Figure 6 :

 6 Figure 6: Comparison of mean soluble nitrogen for winter individual composts (2) and spring individual compost (3)

Figure 7 :Figure 8 :

 78 Figure 7: GI versus soluble COD

Figure 9 :Figure 10 :

 910 Figure 9: Type of living organisms observed in individual composts

Figure 11 :Figure 13 :

 1113 Figure 11: Number of different living organisms observed in individual composts

Figure 15 :

 15 Figure 15: Homogeneity of individual composts

Figure 17 :Figure 18 :

 1718 Figure 17: Fist test for individual composts

Figure 19 :Figure 21 :Figure 22 :

 192122 Figure 19: Recognised odours in individual composts

Figure 23 :

 23 Figure 23: Linked between mean GI and compost age.Colour is not statistically related to age or level of GI as shown by figure24. It explains that colour was not found as discriminant parameter among composts.

Figure 24 :

 24 Figure 24: Linked between GI and colour of composts (1: light brown, 2:dark brown, 3: light black, 4: dark black)

Figure 25 :

 25 Figure 25: Linked between GI and number of living forms

Figure 26 :Figure 27 :

 2627 Figure 26: Link between Number of recognised odours and age (0: unknown, 1: less than one year, 2: between one and 2 years, 3: more than 2 years)

Table 2 : biochemical fractions of compost organic matter (Van Soest or fibers fractionation)

 2

General Mean Individual Mean Winter individual Mean Spring Individual Mean Collective Mean Winter collective Mean Spring Collective Mean

	Soluble fraction (%DM)	23,3	25,3	26,5	24,7	19,4	22,6	18,4
	Hemicellulosic fraction (%DM)	12,9	10,0	9,4	10,4	18,4	14,8	19,6
	Cellulosic (%DM)	5,7	3,6	3,6	3,6	9,7	7,1	10,5
	Lignin (%DM)	8,2	6,5	6,0	6,7	11,6	9,1	12,5

Table 3 : fertilizing content in home-made composts

 3

Table 5 : Parasites and bacterial content of sampled home-made composts (number of detected presence/total samples)

 5

Table 7 )

 7 summarizes specifications that are proposed in standards or guidelines for marketable composts in different European countries and in Canada.

	Germany -
	Quality Label RAL-GZ 251 (For (a) fresh compost ; (b) mature compost ; (c) mulch compost ; (d) substrate	Great-Britain PAS 100:2005 [6] (apply to compost from source separated biowaste)	Switzerland -Quality Guidelines for composts 2010 [7] (values given for horticultural use: (a)on field, (b) covered)	France -NF U 44-051 [8] (Apply to soil improvers excluding those coming from sludge) treatment wastewater	Canada -CCME Compost Quality Guidelines [9] (compost that can be application) type of used in any	ECN Quality assurance for compost [10]
	compost)

Table 7 : Proposed standards and guidelines for composts in different countries.

 7

Table 8 : Multiple regression IG = f(soluble COD and soluble NTK)

 8

	Parameter	Estimation	Error	T	Probability
			type
	CONSTANT	102,946	4,00724	25,6901	0,0000
	Soluble Kjeldhal Nitrogen (mg/gMS)	-6,3376	2,66838	-2,37508	0,0216
	COD soluble (mgO2/gMS)	-0,182015	0,140878	-1,292	0,2025

Table 9 : Analysis of variance IG = f(soluble COD and soluble NTK)

 9

	Source	Square sum	Dof	Mean square	F	Probability
			freed
			om
	Model	5968,47	2	2984,23	12,73 0,0000
	Residue	11251,7	48	234,409
	Total (Corr.)	17220,1	50
	R² = 34,6598 %
	R²-Adjusted = 31,9373 %

Synthesis: Compost quality assessment sheet

Message to disseminate about home-made compost?

Home-made composts are safe.

When home-composting classically treats biowaste (food and kitchen waste) and green waste (grass clippings, yard trimmings), composts show:

• Low heavy metals contents • Low organic pollutants contents • No pathogens Home-made composts can be used on soil without fear. They fulfil the specifications of most existing standards for marketable composts Cautions:

• If heavy metals or toxic organic compounds are used as treatment in the garden, yard waste will contain these pollutants that will accumulate in the composting bin.

• Ashes may contain PAH that will accumulate in composting bin: add only little quantities of ashes.

• Beware of animal litters added in composts that could bring faecal parasites and pathogens. If added, take care of favouring biodegradation and temperature increase in the composting bin.

Frequent asked question by users about compost quality and composting

Is the presence of living forms in my compost normal?

YES

Living forms in the compost proved that compost is not toxic and give good condition for biological life.

Is the moisture of my compost convenient?

TEST IT! Use fist test to assess if the compost moisture is convenient. The handle is formed, does not disintegrate >> Your compost has a favorable moisture for good degradation The handle breaks down your compost is too dry >> add Water

What colour must have my compost?

Compost generally have dark brown colour. Colour is not directly linked with agronomic quality of compost.

What is the meaning of odours in my compost?

Two types of odours might be smelled:

• Known as pleasant: earthy and Humus, Hay • Generally known as unpleasant: fermented vegetables, ammonia, faecal odours, solvent odours Odours are linked with biodegradation extent. Fermented vegetables and ammonia odours can corresponds to fresh waste and beginning of biodegradation. Solvent odours correspond to uncompleted biodegradation of organic matter: biodegradation is still in progress. Faecal or sewage like odours are linked to bad conditions of biodegradation: lack of aeration, excess of water Mature compost is characterized by an earthy/humus odour. Intensity of odour decreases with the age of the compost.

Is my compost ready to use?

Compost is dark brown, moisture is convenient, odour is low and pleasant (earthy/humus), no more recognizable waste can be detected: compost is ready

How to use my compost?

Home-made composts are soil improvers:

• They can be used to improve soil physical properties • They can be used as growing medium Home-made compost will bring nutrients to soil but are not formal fertilizers as their content in N, P and K is less than 1% of wet mass (for each element) Caution:

Young compost may contain phytotoxic molecules that severally decrease germination index. Prefer compost elder than 1 year for use as plant growing medium: phytotoxic effect is inversely related to age.

How to control compost quality?

Sampling panel constitution

The constitution of the panel largely depends on the aim of the control.

What is the question?:

• Mean general quality? • Influence of composting practice? • Influence of season?

• etc.

In the first case, a large volunteer call can be used.

In the other cases, a previous survey on composting habits may help to target on geographic area, specific housing, etc. when searching volunteers.

At least 20 composts per studied conditions (type of housing, composting practice…) have to be analysed to calculate reliable statistics.

To be part of the sample panel composts must be elder than 5 months.

Information to collect

When sampling compost, information has to be collected from the users:

• Type of composting (bin, pile) • How long have they been composting? • What are they composting? • How are they composting (turning, adding water…)?

• Were they trained? • Do they use their compost

How to sample

In order to sample the most mature compost, samples have to be taken in the bottom third of the compost heap or composting bin (Figure 1). To do so, in case of a composting bin, open a side to sample. In case of a pile, the upper part of the compost has to be discarded in order to sample the most mature zone.

Parameters to analyse

Parameters to be analysed must include the parameters of national soil improver standard and if it doe the parameters proposed for compost European quality assurance:

• Organic matter content