Joël Champeau
email: joel.champeau@ensta-bretagne.fr

Vincent Leildé
email: vincent.leilde@openflexo.org

Papa Issa Diallo
email: diallopa@ensta-bretagne.fr

Model Federation in toolchains

Keywords: tool integration, model federation, roles, OSLC

In this paper we introduce the toolchain topic as a federation of models based on an abstraction of differents tool definitions. We consider the toolchain in the context of embedded systems, in particular the co-design which implies a co-engineering approach with many tools. Our main goal is to define a tool integration model to carry out an abstraction of several data formats and for a domain model as a reference vocabulary. This model gathers the concepts for managing the development process artifacts and the roles attributed to these artifacts over the process. We have experimented this approach during the europeen ARTEMIS iFEST project over the OSLC layer (Open Services for Lifecycle Collaboration).

Introduction and challenges of the integration of tools

Embedded systems are becoming increasingly complex in the last recent years and the higher level of expected requirements, both in terms of robustness, scalability and design costs, also increases. This change requires designers to adapt their practices to use specialized software tools for the different phases of the system development cycle, from requirements elicitation to product deployment. These tools are by nature independents and allow to develop different kinds of artifacts during the system development, such as models in several formats, documentations and source code in several languages. The main objective of this situation is the separation of concerns and the use of dedicated tool for each concern. During the development process, it is necessary to enable the collaboration and the exchange of process artifacts. To do so tools supporting the process artifacts should be connected to each other to constitute a toolchain. The embedded systems and specially the co-design domain is highly representative of this situation due to an association of the software and hardware design. In this context, the heterogeneity of languages and models is an evidence and requires specific attention to build toolchains to cover the whole process development.

Currently, the toolchains exist in two forms, the point-to-point chains, and integration frameworks. The point-to-point one is an ad-hoc connection between two tools, thereby efficient but these toolchains are not scalable if we want to integrate a new tool interacting with the other tools. The second form of integration is more durable, it integrates a set of tools based on common rules, such as a common format and data type definitions. In this context the tools can be attached or detached more easily on this toolchain. However defining such kind of framework remains complex and mainly based on a trade off on the common definition. A generic definition allows the integration of many tools, in contrario the specialization, maximizing efficiency while reducing the number of connectable tools. In the modeling context, the modelBus [START_REF] Hein | Model-Driven tool integration with ModelBus[END_REF] is one of the most significant approaches based on transformations and models to create a toolchain.

In the case of modeling approaches, the common definition can be achieved from a common metamodel. Then models conform to this metamodel can be generated or created from model transformations. The scope of this common meta-model is the major issue with this kind of approaches. The challenge is to have a relevant common definition that must be abstract enough to cover a large scope but also contain details enough to take into account specialized concepts. Most of the time, this relevant trade-off is difficult to achieve if we want to limit the size of the metamodel. Another challenge is the management of the evolution of this common metamodel that constrains to update the transformations and the resulting models over time.

So abstract the tool inputs/outputs by a model and implement the tool connections by transformations, this is not sufficient to obtain a powerful toolchain framework. Mainly this kind of approaches promotes solutions where each tool element is created, or instantiated, for each model element and afterwards transformed, and so recreated, for another model. Most often after applying a transformation, no semantics link is maintained between the tool elements. In this case the semantics link is the information that a specific transformation was performed on tool elements. So this semantics information, that the new tool element was created from another tool element, is lost. By extension, several transformations are applied during the process development, the links between all the tool elements are lost and the evolution of the primary tool element is disconnected from the other tool elements. At the opposite, the last tool element is agnostic of its past history in the process development which prevents any impact analysis, for example.

The current modeling approaches to abstract over the tool format are too fragile and too close of the input/output format and don't provide any support to manage the semantics evolution of a tool element during all the process development.

To improve the tool integration, we promote a flexible approach, independent and non intrusive of the tool format (or model or metamodel) as well as independent of the tool integration platform. This approach ensures semantics consistency between tool elements all along the process development. Also this kind of approach can support semantics evolution of the tool element, impact analysis and support of collaborative works. Our approach is similar to a virtualization of the tool integration platform. We produce an abstraction based on the artifact concept, to represent the tool elements, and semantic roles are associated to this artifact, to symbolize all the types of the artifact evolution.

The article is organized as follows: the section 2 presents the state of the art of the tool integration frameworks. Secondly the section 3 presents our approach and the core concepts. The section 4 is related our experiments included in the iFEST project, and based on OSLC, dedicated to a tool integration framework for the embedded systems. Finally we conclude on our approach and experiments before some further works.

Context and state of the art

Tool integration is an active research area since the begining of 90's [START_REF] Wicks | A new research agenda for tool integration[END_REF] and several characterizations were attempted. In the paper [START_REF] Brown | Past and future models of CASE integration[END_REF], a clear separation is achieved on 2 axes, one Conceptual which try to define the integration, itself, and the other Mechanical which focuses on the technological space of the integration space. For [START_REF] Wasserman | Tool integration in software engineering environments[END_REF], the tool integration is based on five dimensions control, data, presentation, process and platform. [START_REF] Brown | Learning from ipse's mistakes[END_REF] characterizes the integration levels of interoperability among the syntactic level which represents the degree of agreement between the tools on a data structure, and semantics that addresses the meaning of the data exchanged. Many frameworks have been established and have never converged, often a toolchain is a coexistence of several frameworks and several individual tools.

As presented in the introduction two integration patterns exist, point to point, which is an ad-hoc connection between two tools, and common frameworks can be based on standardized architectures [START_REF] Group | The common object request broker: Architecture and specifcation[END_REF], formats [START_REF] Flatscher | Metamodeling in eia/cdif|meta-metamodel and metamodels[END_REF], or infrastructure to facilitate the inter-tools [14] collaborations. Some aspects are most important for us like the communication layer between tools, the sharing of data on both levels syntactic and semantic, but also the adaptation of the tool chain and its products to the context and specially to the engineering domain targeted.

Through the years, communication between tools was established on several technologies and more recently the service approach and specially web services are applied with standardized communication protocols (WSOA), favoring a weak coupling between the tools. More recently, an open initiative was launched and called OSLC for Open Services for Lifecycle Collaboration. This community provides a framework based on a REST architecture [START_REF] Fielding | Architectural Styles and the Design of Network-based Software Architectures[END_REF] and a common format. This emerging standard has also been chosen by many research projects and including IFEST [START_REF]iFEST -industrial Framework for Embedded Systems Tools[END_REF] in where we were involved.

The tool integration is increasingly based on the models, taking advantage of a meta level which provides the defintiion of the models. The models, conform to the metamodel, become the data exchanged between tools [START_REF] Kapsammer | Model-based tool integration-state of the art and future perspectives 1[END_REF]. There are different use of these metamodels in the integration of tools, Fujaba [START_REF] Henkler | Legacy component integration by the fujaba real-time tool suite[END_REF] approach provided a generic integration solution based on different patterns, VMTS [START_REF] Gergely Mezei | Integrating model transformation systems and asynchronous cluster tools[END_REF] targets only design tools based models, MOFLON [START_REF] Amelunxen | Metamodel-based tool integration with moflon[END_REF] GeneralStore [START_REF] Reichmann | Generalstore -a casetool integration platform enabling model level coupling of heterogeneous designs for embedded electronic systems[END_REF] and Semantic integration [START_REF] Karsai | Component generation technology for semantic tool integration[END_REF] is based on a generic integration but do not take into account the context of integration. Wotif [START_REF] Karsai | The model-integrated computing toolsuite: Metaprogrammable tools for embedded control system design[END_REF] Jeti [START_REF] Margaria | jETI: A Tool for Remote Tool Integration Tools and Algorithms for the Construction and Analysis of Systems[END_REF], and ModelBus [START_REF] Sriplakich | Collaborative software engineering on large-scale models: requirements and experience in modelbus[END_REF], are also based on web services. Besides using metamodels, some approaches manipulate ontologies as more favorable to the reasoning. In this case, the ontology is considered as a domain model and defines the model of this area. ModelCVS [START_REF] Kramler | Towards a semantic infrastructure supporting model-based tool integration[END_REF] uses ontologies to define the semantics of the data and seeks to establish a link between the tool metamodels.

Such tools can be combined in different contexts, the semantics of the produced data changes over the context of the tool usage and objectives of the toolchain. The role concept provides a dynamic adaptation by allowing to objects to have different needs depending on the roles that are attached [START_REF] Reenskaug | Working with objects: the Ooram software engineering method[END_REF] [START_REF] Bäumer | Role Object[END_REF]. [START_REF] Steimann | On the representation of roles in object-oriented and conceptual modelling[END_REF] provides an overview of these different defintion and properties, while [START_REF] Seifert | Anticipating Unanticipated Tool Interoperability using Role Models[END_REF] provides an example of metamodeling approach based on roles for tool integration.

Semantics integration in toolchains

The context of our toolchain is applied on embedded system domain and specially focused on the co-design. This domain involves a co-engineering development process for the software and the hardware part. In this domain, the tools are mainly specific to dedicated tasks like code parallelization, mapping on hardware, dedicated modeling, etc. This heterogeneity is also valid for the input/output formats with several programming and modeling languages. Regarding this situation, many times one domain concept, such as Parallel Entity, is realized in different languages and formats like a behavior function in Haskell, a C structure with a set of functions or a UML class with a MARTE stereotype.

In this context to improve our toolchain, we focus our approach on defining an engineering domain model, the co-design domain, to have a reference and a usage context for the tools. But also mainly, we define a mechanism to manage at the same level, the metatypes of the tool elements and our engineering domain types. Our goal is to create a federated model for our toolchain, based on our tool integration model to aggregate information at several level of abstraction.

First, we present our domain model in the goal to manage it in the tool integration model, detailed in the second part of this section.

A domain model for the semantics integration

Most of the time, tool integration consists of exchanging not directly interpretable data between tools, thus transformations are required. As such in order to keep the semantics consistency in the process, the semantics of tools as well as the mappings between the tools must be defined. We take as an example two tools used in the context of design and implementation.

A UML modeler produces UML models representing various concerns of the system under study. In our case study described latter, some of these models have to be processed by the Bluebee compiler/mapper for heterogeneous hardware architectures. This compiler requires two kinds of inputs either algorithmic or architectural ones, in two different formats respectively C and XML. In the UML model a StructuredComponent, a specialized metatype, can be interpreted as a functional bloc as well as a hardware component, so transformation rules towards Bluebee require specific information to generate either C or XML. And if we extend the development process towards the verification and validation phases, the semantics of the StructuredComponent would change again as a test case definition, for example. So a context definition is required to ensure the semantics consistency in the tool integration framework.

In this context, the most classical way is to base the tool integration on a common metamodel which provides the shared semantics according to the addressed domain. The tool models can be independent of the common metamodel but model transformations are required to produce models to and from this metamodel. In the one hand this common metamodel gathers all the concepts of each integrated tool, but in this case the metamodel must evolve when new tools are integrated and thus might be hard to manage. In the other hand if this metamodel is fixed to a set of concepts, and thus properties can be lost if new tools with new concepts are inserted into the toolchain.

To bring both flexibility and efficiency, another approach is necessary for not defining a common metamodel from which models are conformed to. But rather an independent domain model is created to qualify the tool models. This domain model captures a semantics reference among all the domain tools and applies it on every new inserted tool. In any case, this domain model is never instantiated since it is strictly used as metadata to qualify the model elements exchanged between tools.

In conformity with ISO/CEI 15940 we define an engineering domain as a set of tools and process activities. In the domain of embedded systems engineering, there is unfortunately no consensus on a standard to represent the domain with the purpose of tool integration. Therefore we propose a new model called D&I (Design and Implementation) adapted to tool integration and aligned with current standards like the MARTE UML profile. Our model consists of three viewpoints namely application, architecture and mapping. Application viewpoint defines functional aspects of the system under study, while the Architecture viewpoint describes the topology of the physical platform in which the application will be allocated thanks to the rules given by the Mapping viewpoint.

The main objective of this model is to define a contextual reference regarding the tool usage in the engineering domain, design and implementation. The content of this domain model can be improved certainly but our purpose is to explicit the management of this model for an integration scenario between tools rather to define yet another new domain model. The experiment performed in our integration scenario shows that the mechanism used to manage the mapping between the tool model and the engineering domain is not specialized to a domain and also independent of the content of the engineering domain model.

A tool integration model to explicit semantics consistency

In order to define a general mechanism to manage this domain model and the tool models, we based our work on a modeling approach to create an abstraction to ensure semantics consistency in the framework. The purpose of this model is to support a general mechanism to manage the evolution of the tool elements through the development process from a semantics point of view. Our artifact and role model is the support of the semantics management.

Artifact and role model

Each tool manipulates models composed of elements conform to the tool metamodel, for instance an UML tool manipulates UML classes. In this example the metamodel is the tool metamodel. For a C compiler, we must create a dedicated metamodel which is not necessarily the C language metamodel. This metamodel is more an abstraction of the structure of a C program dedicated to hardware synthesis or hardware mapping. Because, this kind of compiler takes into account C programs with some coding restrictions and annotations.

Our main goal is to avoid point-to-point model transformations between tools or between a tool and the engineering domain model. So we have created an abstraction only dedicated to the integration framework to be independent of the tool models and to provide a support for the semantics evolution in the tools integration space. Our approach is similar to a virtualization of this tools integration space with an abstraction based on an artifact concept. The artifact concept is an abstract representation of tool models or tool modeling elements. This artifact owns only semantic properties favoring the tool integration like tool intern properties or lifecycle properties.

While the artifact is the representation of modeling elements inside the tool chain, we associate the role concept to the artifact as a property of the artifact. The role concept was used to modeling knowledge [START_REF] Guarino | Concepts, attributes and arbitrary relations[END_REF] and also to provide flexibility in object oriented programming [START_REF] Steimann | On the representation of roles in object-oriented and conceptual modelling[END_REF]. The role concept provides three kinds of properties to another concept, or an object, that lacks semantic rigidity regarding its primary type, secondly a role depends on relationships between objects and finally an object may play different roles simultaneously. We reuse the role concept according to the properties, extracted from the Steimann's paper [START_REF] Steimann | On the representation of roles in object-oriented and conceptual modelling[END_REF]:

-Of course an artifact is created to represent a dedicated tool element but in the tool integration space and during the process development, the semantics of this artifact is evolving in flexible way to take into account several transformations. The flexible way means that a role can be added or removed dynamically during the process development. -A role is modeling the relationships between an artifact and a tool element without applying a particular transformation. The role models a tool specific property for a given artifact. -At a specific step in the process development, an artifact can play several roles simultaneously according to several tools. Each role models the result of applying a transformation on the original tool element. A role can be also the support of the engineering domain type independently of any tools.

This modeling approach based on artifact and roles is orthogonal to the tool elements and completely dedicated to the tool integration space. Figure 1 illustrates concepts and relations of the integration model. As described previously an artifact plays a set of roles, which are related to the engineering domain (like D&I:Architecture increase the decoupling between the tool integration space and the tool elements, a artifact accesses to a modeling element through the roleproxy allowing different representations of model

Artifact and

Adding roles to artifacts may be assimilated to annotation mechanisms or in the UML context as stereotypes. provide a generic mechanism to manage space independently of the tool formats. the dynamic adding and removing of the roles during the process development. Indeed the roles being dynamic, an artifact fits always to the current tooling and all along the process development. dynamically in the tool integration space without any impact on the previous role definitions and the previous life of the tool integration space.

Whether roles are linked to a tool/language, they make explicit the semantic interpretation of tool elements, in the context of the engineering domain. This might be useful to improve the semantics relationships between several tool elemen example, characterizing the model of computation combined with a UML component helps to improve the underlying transformations to another tool. Once these roles are shared between different tools, it becomes possible to c between tool concepts. For example, a UML StructuredComponent can be referenced by an artifact with the role ApplicationComponent of the D&I domain and so interpreted as a set of functions by the Bluebee compiler, but also an StructuredComponent can be referenced by an artifact with the role ArchitectureComponent of the D&I domain and so interpreted as a computing ressource (like a processor) in the scope of the Bluebee compiler.

:Architecture role) or relying to a tool (Rhapsody UML:Class increase the decoupling between the tool integration space and the tool elements, a artifact accesses to a modeling element through the roleproxy allowing different representations of model elements in a same tool for the same artifact.

Entities and relations of the integration model and role properties

Adding roles to artifacts may be assimilated to annotation mechanisms or in the UML context as stereotypes. In contrast with stereotypes, the purpose of the roles provide a generic mechanism to manage the types of the artifact in the tool integration space independently of the tool formats. Also, this approach is more flexible due to the dynamic adding and removing of the roles during the process development. Indeed the roles being dynamic, an artifact fits always to the current tooling and all the process development. New roles, related to new tooling, can be also added dynamically in the tool integration space without any impact on the previous role definitions and the previous life of the tool integration space.

Whether roles are linked to an engineering domain like D&I and related to a tool/language, they make explicit the semantic interpretation of tool elements, in the context of the engineering domain. This might be useful to improve the semantics relationships between several tool elements and the engineering domain. For example, characterizing the model of computation combined with a UML component helps to improve the underlying transformations to another tool. Once these roles are shared between different tools, it becomes possible to create execution semantic links between tool concepts. For example, a UML StructuredComponent can be referenced by an artifact with the role ApplicationComponent of the D&I domain and so interpreted as a set of functions by the Bluebee compiler, but also an StructuredComponent can be referenced by an artifact with the role ArchitectureComponent of the D&I domain and so interpreted as a computing ressource (like a processor) in the scope of the Bluebee compiler. UML:Class role). To increase the decoupling between the tool integration space and the tool elements, an artifact accesses to a modeling element through the roleproxy allowing different Adding roles to artifacts may be assimilated to annotation mechanisms or in the UML of the roles is to types of the artifact in the tool integration lso, this approach is more flexible due to the dynamic adding and removing of the roles during the process development. Indeed the roles being dynamic, an artifact fits always to the current tooling and all can be also added dynamically in the tool integration space without any impact on the previous role n engineering domain like D&I and related to a tool/language, they make explicit the semantic interpretation of tool elements, in the context of the engineering domain. This might be useful to improve the semantics ts and the engineering domain. For example, characterizing the model of computation combined with a UML component helps to improve the underlying transformations to another tool. Once these roles are semantic links between tool concepts. For example, a UML StructuredComponent can be referenced by an artifact with the role ApplicationComponent of the D&I domain and so interpreted as a set of functions by the Bluebee compiler, but also another UML StructuredComponent can be referenced by an artifact with the role ArchitectureComponent of the D&I domain and so interpreted as a computing

Experiments

We study the feasability of our approach with a tool integration scenario made in the context of the Europeen Artemis iFEST project which aims to define a tool integration framework for embedded system tools. More precisely tools involved are part of Design and Implementation process phase. In our scenario models representing the system are defined using a UML modeler (Rhapsody) in the respect of MoPCoM methodology. MoPCoM defines three levels of abstraction addressing particular concerns, including the functional specification of the system, the representation of the platform and the allocation of the functional elements onto the platform are described. At one point of the process the UML/MoPCoM model representing the system is transformed to Bluebee comprehensible code in order to generate the system for the target architecture. Bluebee requires annotated C code as input in order to describe the mapping onto hardware. An XML file describes features of the target architecture. The transformations are done with MDWorkbench, a model to model transformation tool [20]. This scenario is interesting because one must take care of exchanged model's semantics to keep their consistency over transformations. Indeed MoPCoM allows to describe functional as well as hardware elements thanks to a same UML element (Structured Component), although Bluebee mades a distinction with hardware elements which are required to be in XML code and functional's ones required to be in C. Besides iFEST extends OSLC, Open Services for Lifecycle Collaboration [START_REF] Oslc | Services for Lifecycle Collaboration[END_REF], with specifications for embedded systems. OSLC is a community providing specifications for tool integration based on REST web services and RDF data formalism. In addition iFEST proposes a set of tool adapters facilitating tool integration by making the bridge between tools and OSLC. An adapter is a client/server providing services to OSLC connected tools. It exposes OSLC ressources wrapping or referencing real model/tool elements. In our approach with artifact and roles, each tool(MoPCoM, Bluebee, MDWorkbench...) is linked to OSLC via an adapter. The adapter maps artifacts(in a form of OSLC ressource) to a tool model element(for instance a UML class). Artifacts and Roles are OSLC ressources managed by the Model Federation Adapter(MFA), adapter for the Model Federation Tool. The MFA provides basic services to other OSLC adapter in order to manage Artifacts and Roles(CRUD, mappings) as well as more higher level services such as finding artifacts playing a set of roles. The various services are sequenced by the orchestrator tool. In the whole tool chain given by the figure 2, Bluebee tool is connected to OSLC via the Automation Adapter Server providing a set of common services to automation tools(this same automation adapter server is used for Forsyde tool as well as DiVine tool).

Figure 2: The experimental toolchain

The lifecycle of the artifact is the following 1. When a model element is produced by a tool, an artifact is also created in the model federation tool, and plays a rôle associated to the tool. Besides the adapter of the tool handle a rôle proxy which make a bridge between artifact and model element.

2. If there is a mapping defined between the tool rôle and an engineering domain rôle, then engi Thus the artifact plays two roles, one relative to the tool and one relative to the engineering domain.

3. The artifact is used by an another tool(a transformation tool for instance), to produce a new model. In this case two scenarios are possible, either no artifacts are produced for this new model, and thus this artifact just plays a new rôle, or an artifact is produced and so it might plays two roles, one from the tool and one from the engineergin domain. This scenario can be extended to several engineering domains, in this case artifact plays roles from different engineering domains.

The sequence diagram illustrates how integration occurs in our scenario

The experimental toolchain

The lifecycle of the artifact is the following : When a model element is produced by a tool, an artifact is also created in the ation tool, and plays a rôle associated to the tool. Besides the adapter of the tool handle a rôle proxy which make a bridge between artifact and model element.

If there is a mapping defined between the tool rôle and an engineering domain rôle, then engineering domain rôle is attached dynamically to the artifact. Thus the artifact plays two roles, one relative to the tool and one relative to the engineering domain.

The artifact is used by an another tool(a transformation tool for instance), to new model. In this case two scenarios are possible, either no artifacts are produced for this new model, and thus this artifact just plays a new rôle, or an artifact is produced and so it might plays two roles, one from the tool and one from the n domain. This scenario can be extended to several engineering domains, in this case artifact plays roles from different engineering domains.

The sequence diagram illustrates how integration occurs in our scenario :

Step 1: Tools declare their mappings to the MFA by referencing their own set of roles, and their semantics(mapping(RoleX, RoleY) regarding an engineering (RTES, short for Real Time Embedded Systems)). For instance MoPCoM concept of FunctionalBehavior and Bluebee concept of CFunction, re both likened to an RTES concept of StructuredComponent in the RTES domain model

Step 2: Orchestrator gets a FunctionalBehavior model from MoPCoM Adaptor (funcBehavior(updateTargets)). Consequently an Artifact is created (MopcomFuncBehavior) for this model (createArtifact()). Its semantics is given by Semantics Roles dynamically attached to the artifact according to the predefined mapping

Step 3: A transformation service is called through a MDWorkbench service (transfo(mopcomMM, bluebeeMM, MopcomFuncBehavior Artifact)). This service requires three parameters: the source metamodel (mopcomMM), the When a model element is produced by a tool, an artifact is also created in the ation tool, and plays a rôle associated to the tool. Besides the adapter of the tool handle a rôle proxy which make a bridge between artifact and model element.

If there is a mapping defined between the tool rôle and an engineering neering domain rôle is attached dynamically to the artifact. Thus the artifact plays two roles, one relative to the tool and one relative to the The artifact is used by an another tool(a transformation tool for instance), to new model. In this case two scenarios are possible, either no artifacts are produced for this new model, and thus this artifact just plays a new rôle, or an artifact is produced and so it might plays two roles, one from the tool and one from the n domain. This scenario can be extended to several engineering domains, to the MFA by referencing their own set regarding an engineering ). For instance MoPCoM concept of FunctionalBehavior and Bluebee concept of CFunction, re both likened to an RTES concept of StructuredComponent in the RTES

Step 2: Orchestrator gets a FunctionalBehavior model from MoPCoM Adaptor (funcBehavior(updateTargets)). Consequently an Artifact is created model (createArtifact()). Its semantics is given by Semantics Roles dynamically attached to the artifact according to the

Step 3: A transformation service is called through a MDWorkbench service cBehavior Artifact)). This service requires three parameters: the source metamodel (mopcomMM), the target metamodel (bluebeeMM) as well as the Artifact representing the source model. Bluebee C code is generated (transformMopcom2bb()) and represented by a new Artifact (bluebeeCFunction Artifact). Roles (Bluebee CFunction and RTES StrucComponent) are dynamically attached to this Artifact.

• Step 4: Bluebee tool generates the system with a predefined target architecture and this Artifact (executeBluebee(bluebeeCFunction Artifact)).

Conclusion

In the field of embedded systems there is still no satisfactory solutions for tools integration. To facilitate this integration, the IFEST project proposed an innovative framework based in particular on OSLC. Mainly oriented lifecycle, OSLC is not dedicated to the concepts of embedded systems. Our approach provides primarily a conceptual model independent of technologies/standards. This model provides the capacity to build high level services on top on all the federated models like impact analysis or semantic consistency checkings.

Our model allows both to represent the elements of the tools in the tool integration space (artifact) and associates their semantics via the roles. A tool chain is prototyped and semantics of handled data depends on the co-design engineering domain. This model can be extended to any engineering domain for defining and managing the context of the use of the toolchain.

Figure 1 :

 1 Figure 1: Entities and relations of the integration model

Figure 3 :

 3 Figure 3: sequence diagram of the experiment

Acknowledgments

The authors would like to thanks the support of the european ARTEMIS iFEST project and all the partners that were contributed to this work directly and indirectly.


Khalil Hanna
email: khalil.hanna@ensc-rennes.fr

Muhammad Usman

Vincent Chatain

Sorption and transport of aqueous Fe II in a goethite-coated sand column under anoxic conditions

Keywords: goethite, Fe II, sorption, transport, column, modeling

come

Sorption and transport of aqueous Fe(II) in a goethite-coated sand column under anoxic conditions.

Introduction

In natural environments, the iron oxides exhibit considerable crystallographic heterogeneity, ranging from poorly crystalline phases such as 2-line ferrihydrite to well crystalline ones like goethite and hematite with different characteristics [START_REF] Cornell | The Iron Oxides: Structure, Properties, Reactions, Occurrence and Uses[END_REF][START_REF] Schwertmann | Iron Oxides in the Laboratory: Preparation and Characterization[END_REF]. Goethite (α-FeOOH) is by far the most common iron oxide in soils and sediments due to its high thermodynamic stability [START_REF] Cornell | The Iron Oxides: Structure, Properties, Reactions, Occurrence and Uses[END_REF][START_REF] Schwertmann | Iron Oxides in the Laboratory: Preparation and Characterization[END_REF]. The morphology, crystallography and specific surface area of goethite can vary widely [START_REF] Cornell | The Iron Oxides: Structure, Properties, Reactions, Occurrence and Uses[END_REF][START_REF] Schwertmann | Iron Oxides in the Laboratory: Preparation and Characterization[END_REF].

Dissolved Fe II is formed as a result of many biotic and abiotic processes in natural systems [START_REF] Stumm | The cycling of iron in natural environments: Considerations based on laboratory studies of heterogeneous redox processes[END_REF]. The interactions of Fe II with environmental surfaces such as iron oxides have attracted a great deal of attention not only because of the complicated chemical processes such as sorption and mineralogical transformation, but also because the reaction has significant environmental implications [START_REF] Coughlin | Nonreversible adsorption of divalent metal ions (MnII, CoII, NiII, CuII, and PbII) onto goethite: effects of acidification, FeII addition, and picolinic acid addition[END_REF][START_REF] Liger | Surface catalysis of uranium(VI) reduction by iron(II)[END_REF][START_REF] Hofstetter | Complete reduction of TNT and other (poly) nitroaromatic compounds under iron-reducing subsurface conditions[END_REF][START_REF] Elsner | Reactivity of Fe(II)-Bearing Minerals toward Reductive Transformation of Organic Contaminants[END_REF][START_REF] Williams | Spectroscopic Evidence for Fe(II)-Fe(III) Electron Transfer at the Iron Oxide-Water Interface[END_REF][START_REF] Pedersen | Fast transformation of iron oxyhydroxides by the catalytic action of aqueous Fe(II)[END_REF][START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF][START_REF] Larese-Casanova | Fe (II) sorption on hematite: New insights based on spectroscopic measurements[END_REF][START_REF] Tobler | Assessing Iron-Mediated Oxidation of Toluene and Reduction of Nitroaromatic Contaminants in Anoxic Environments Using Compound-Specific Isotope Analysis[END_REF][START_REF] Amstaetter | Redox transformation of arsenic by Fe (II)-activated goethite (α-FeOOH)[END_REF][START_REF] Handler | Atom exchange between aqueous Fe (II) and goethite: An Fe isotope tracer study[END_REF]Usman et al., 2012a;Usman et al., 2012b). The studies of the interaction between Fe II and mineral species focus on the catalytic effects of Fe II for contaminants reduction and also on mineralogical transformation of Fe III -oxyhydroxides. Aqueous Fe II complexes can reduce a number of contaminants but sorbed Fe II or structural Fe II are often more powerful reductants than dissolved Fe II [START_REF] Hofstetter | Complete reduction of TNT and other (poly) nitroaromatic compounds under iron-reducing subsurface conditions[END_REF][START_REF] Elsner | Reactivity of Fe(II)-Bearing Minerals toward Reductive Transformation of Organic Contaminants[END_REF][START_REF] Tobler | Assessing Iron-Mediated Oxidation of Toluene and Reduction of Nitroaromatic Contaminants in Anoxic Environments Using Compound-Specific Isotope Analysis[END_REF][START_REF] Amstaetter | Redox transformation of arsenic by Fe (II)-activated goethite (α-FeOOH)[END_REF]. The interaction of aqueous Fe II with iron oxides can produce a variety of reactions including sorption, electron transfer between Fe II and Fe III -oxide, conduction, dissolution, atom exchange and/or transformation to secondary minerals [START_REF] Williams | Spectroscopic Evidence for Fe(II)-Fe(III) Electron Transfer at the Iron Oxide-Water Interface[END_REF][START_REF] Pedersen | Fast transformation of iron oxyhydroxides by the catalytic action of aqueous Fe(II)[END_REF][START_REF] Larese-Casanova | Fe (II) sorption on hematite: New insights based on spectroscopic measurements[END_REF][START_REF] Handler | Atom exchange between aqueous Fe (II) and goethite: An Fe isotope tracer study[END_REF].

Interfacial electron transfer reactions between sorbed Fe II and well crystallized iron oxides (e.g. goethite, hematite) has been experimentally evidenced using Mössbauer spectroscopy [START_REF] Williams | Spectroscopic Evidence for Fe(II)-Fe(III) Electron Transfer at the Iron Oxide-Water Interface[END_REF]. Moreover, at low Fe II dose, sorption of Fe II on these iron oxides have been quantified and described through both macroscopic and surface complexation modeling approaches [START_REF] Coughlin | Nonreversible adsorption of divalent metal ions (MnII, CoII, NiII, CuII, and PbII) onto goethite: effects of acidification, FeII addition, and picolinic acid addition[END_REF][START_REF] Liger | Surface catalysis of uranium(VI) reduction by iron(II)[END_REF][START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF][START_REF] Hiemstra | Adsorption and surface oxidation of Fe(II) on metal (hydr)oxides[END_REF]. Both sorption extent and rate have also been determined although sorbed Fe II may transfer an electron to the solid matrix [START_REF] Liger | Surface catalysis of uranium(VI) reduction by iron(II)[END_REF][START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF][START_REF] Hiemstra | Adsorption and surface oxidation of Fe(II) on metal (hydr)oxides[END_REF].

The effect of crystal structure and morphology on Fe II sorption is not yet fully evaluated that will be done in this study by involving three kinds of same iron oxide (i.e. goethite). Large variability of goethite exists in both natural and engineering systems but no study has been reported comparing sorption of Fe II onto different goethites. While the interactions of Fe II with Fe-oxides are widely studied in laboratory batch reactors [START_REF] Coughlin | Nonreversible adsorption of divalent metal ions (MnII, CoII, NiII, CuII, and PbII) onto goethite: effects of acidification, FeII addition, and picolinic acid addition[END_REF][START_REF] Williams | Spectroscopic Evidence for Fe(II)-Fe(III) Electron Transfer at the Iron Oxide-Water Interface[END_REF][START_REF] Pedersen | Fast transformation of iron oxyhydroxides by the catalytic action of aqueous Fe(II)[END_REF][START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF][START_REF] Hiemstra | Adsorption and surface oxidation of Fe(II) on metal (hydr)oxides[END_REF][START_REF] Larese-Casanova | Fe (II) sorption on hematite: New insights based on spectroscopic measurements[END_REF][START_REF] Handler | Atom exchange between aqueous Fe (II) and goethite: An Fe isotope tracer study[END_REF][START_REF] Usman | Transformation of various kinds of goethite into magnetite: Effect of chemical and surface properties[END_REF], reports on transport of Fe II in goethite-packed column under anoxic flow through conditions are still missing in literature. In contrast to batch studies, continuous flow experiments allow studying the impact of non-equilibrium sorption on transport and impact of hydrodynamic parameters such as dispersion on the breakthrough of solute. Column tests can also accommodate more accurate field estimation by providing an appropriate soil/aqueous phase ratio. In addition, the byproducts of redox reaction are flushed out in continuous flow experiments, which can modify the kinetics and extent of reaction.

In the present study, the sorption extent of Fe II onto three kinds of goethite (G1, G2 and G3) having different crystal habits, morphologies and surface properties was compared. Sorption of Fe II was evaluated vs. time and pH in batch experiments. In order to evaluate the implication of non-equilibrium/kinetics processes in the breakthrough behavior, flow through experiments were conducted at different flow rates and column residence times. Predictions of breakthrough curves are developed from surface complexation modeling parameters derived from batch sorption data. The influence of goethite specific reactivity on Fe II sorption under batch vs. advective-dispersive flow and the mobility of Fe II in the column system are discussed.

Materials and methods

Chemicals

Ferrous chloride tetrahydrate (FeCl 2 . 4H 2 O) and sodium phosphate (Na 3 PO 4 .12H 2 O) were purchased from Sigma Aldrich. Fontainebleau sand (Prolabo) was used.

Goethite samples

This study involves three different types of goethite which were provided by Dr. F. Gaboriaud (LCPME). These goethites are referred as G1, G2 and G3. These goethites were synthesized and characterized in the context of previous studies by our research group [START_REF] Gaboriaud | Effects of different crystal faces on the surface charge of colloidal goethite (α-FeOOH) particles: an experimental and modeling study[END_REF][START_REF] Prélot | Morphology and surface heterogeneities in synthetic goethites[END_REF]. Briefly, Transmission Electron Microscopy (TEM) and Atomic Force Microscopy (AFM) images showed typical acicular shapes for all goethites samples. The AFM images demonstrated that (101) and/or (001) faces are always dominant on crystallized goethites. The crystal faces (101) and the (001) were identified on the single crystals of G2 and G3, while the main crystallographic faces of G1 particles were found as (001), ( 101) and ( 121) or (021) [START_REF] Gaboriaud | Effects of different crystal faces on the surface charge of colloidal goethite (α-FeOOH) particles: an experimental and modeling study[END_REF][START_REF] Prélot | Morphology and surface heterogeneities in synthetic goethites[END_REF]. Their main characteristics including the estimated maximum density of singly coordinated sites per surface unit, and PZC values are summarized in Table 1. The BET surface areas were redetermined in this work and was found almost similar to that determined in previous works ( [START_REF] Gaboriaud | Effects of different crystal faces on the surface charge of colloidal goethite (α-FeOOH) particles: an experimental and modeling study[END_REF][START_REF] Prélot | Morphology and surface heterogeneities in synthetic goethites[END_REF] and Table 1).

Goethite coated sand (GCS) was prepared by using the method reported elsewhere [START_REF] Scheidegger | Coating of silica sand with goethite: preparation and analytical identification[END_REF]. Fontainebleau sand (France), with a grain size range of 100-150 µm was used. The mineralogy of the sand was characterized by X-ray diffraction and was found to be exclusively quartz. The quartz sand was cleaned with 1 M HCl for 48 h and then rinsed with pure water. The quartz sand was also cleaned with H 2 O 2 to remove organic matter and then rinsed with pure water (Hanna, 2007a).

Iron oxides coating was obtained by shaking a suspension containing the iron oxide and the silica sand. The purified quartz sand was then added to the goethite suspension containing 10 mM NaCl brought to pH 5 with HCl and the mixture was agitated again for 24 h. All synthetic solids were washed to remove electrolytes and stored in an anaerobic N 2 chamber at ambient temperature. X-ray diffraction (XRD) and Raman spectroscopy confirmed that the coating procedure did not alter goethite particles.

Each goethite was deposited on the quartz sand surface to reach an equal amount of goethite (0.5 m 2 of goethite per gram of sand). BET surface area measurements and the goethite content of each coated sand as determined by acid digestion analysis, confirmed the desired value. Attachment strength of iron oxide to silica sand was evaluated by shaking suspension of coated sand at pH 3 for 24 h. Amounts of iron in supernatants (after removal of coated sand) before and after acid digestion were then measured. This test showed that the iron oxide was strongly attached to the silica surface and the percentage of detachment was low enough that its effect could be ignored over experimental conditions (Hanna, 2007a;[START_REF] Tanis | Experimental and modeling studies of sorption of tetracycline onto iron oxides-coated quartz[END_REF].

Batch experiments

The determination of Fe II sorption extent is difficult because sorbent solid (ferric oxyhydroxide) may be transformed into other compounds (e.g. magnetite), via Fe II -to-Fe III electron transfer processes. Recent studies have shown that possible transformation of goethite upon Fe II action is slow and requires special conditions such as high Fe II dosage and alkaline pH (Usman et al., 2012a;Usman et al., 2012b;[START_REF] Usman | Transformation of various kinds of goethite into magnetite: Effect of chemical and surface properties[END_REF]. Therefore sorption of Fe II onto goethite can be normally done at a reasonable time interval and at low Fe II concentration, as described by [START_REF] Liu | Kinetic Analysis of the Bacterial Reduction of Goethite[END_REF]. Batch sorption experiments were conducted in a 500 mL polyethylene bottles at 20 °C in a glove box, an anoxic chamber (N 2 : H 2 = 98:2) and in the absence of light. The goethite reactive phase concentration was fixed at 50 m 2 /L, equivalent to a solid-to-liquid ratio of 100 g of GCS per L (0.5 m 2 /g of GCS)). This high solid-to-liquid ratio (100g GCS/L) was chosen in order to be comparable with the column tests.

The pH of the suspension was maintained by titrating with 0.01M HCl or 0.01M NaOH solutions as required. The sorption of Fe II on three goethites was carried out vs. time (0-80 min) at pH 6 ± 0.1. Moreover, sorption of Fe II on three goethites was also evaluated vs. pH at a fixed Fe II concentration (0.5 mM). An equilibration period of about 60 min was allowed between each increment, after which a 2 mL sample was taken from the suspension. Before analysis, the suspensions were filtered through 0.22 µm polyvinylidene fluoride (PVDF) syringe filters (Millipore) that were shown not to sorb or oxidize ferrous ion. The filtrates were immediately acidified by using 5 M HCl. The residual Fe II concentration was measured by 1-phenanthroline method at 510 nm on a UV-Vis spectrophotometer. To insure if aqueous Fe II was sorbed and not oxidized by trace amounts of oxygen or other chemical phenomena, mass balance on solid-and aqueous-phase Fe II was conducted for each reactor. Blank tests conducted with uncoated sand showed that the sand did not sorb Fe II under our experimental conditions. The initial concentration of Fe II was determined in parallel reactors without solid.

To determine if interparticle diffusion is occurring for sorption of Fe II on goethites, ultrasonic mixing was used in order to disperse loosely formed aggregates in the solution. The sorption rate constants obtained with ultrasonication and magnetic stirring were, then, compared.

Additional batch experiments were conducted in glove box to test the impact of surfacesorbed phosphate on the sorption and uptake of Fe II by goethite surface. Phosphate was chosen as model compound because it is representative of naturally occurring inorganic ligands and it forms strong inner sphere complexes with the iron oxide surface. Phosphate was pre-sorbed to the goethite, and Fe II (0.1 mM) was added subsequently to the goethite/phosphate suspension containing 10 m 2 /L of goethite and 0.1 mM of phosphate.

These conditions were chosen to avoid precipitation of vivianite and achieve the theoretical maximum for surface adsorption of ~ 2.5 µmol/m 2 [START_REF] Torrent | Phosphate adsorption and desorption by goethites differing in crystal morphology[END_REF][START_REF] Strauss | Effects of crystallinity of goethite: II. Rates of sorption and desorption of phosphate[END_REF].

Sorption breakthrough column experiments

Column studies were conducted in duplicate to evaluate the sorption behavior under flowthrough conditions. GCS was dry packed into glass chromatographic columns (20 cm long, 1.6 cm internal diameter; XK 16, GE Healthcare). The porous bed had a length of 6 cm and a dry mass of about 20 g (equivalent to 10 m 2 of goethite). After packing to a uniform bulk density (1.65 ±0.01 g/cm 3 ), the column was wetted upward with a background electrolyte solution (NaCl, 10 -2 mol/L) at a constant flow rate. Once the column was water saturated, a non-reactive tracer experiment was performed in order to identify the flow characteristics through the column. The column was fed upwards at the same constant flow rate with the tracer solution composed of potassium bromide at 10 -2 mol/L in a pulse mode: injection of 1 mL of tracer solution followed by background solution. Bromide concentrations were measured by ion chromatography.

Aqueous transport can be characterized from the analysis of tracer experiments. The pulse injection of bromide induces a bell shaped elution curve characterized by a slight asymmetrical shape with little tailing (data not shown). Solutes were transported through water-driven convection and dispersion, thereby ensuring contact with all particles contacted by interstitial waters. The data analysis was carried out with the method of moments and the advective-dispersive model. The classical convection dispersion equation (CDE) generally describes accurately the 1D transport of a non-reactive solute under steady-state water flow in a saturated column:

x C v x C D t C         2 2 (1)
where c denotes the water solute concentration (M/L 3 ), t is time (T), x the spatial coordinate (L), D the dispersion coefficient (L 2 /T), v the flow velocity (L/T), θ the porosity (L 3 /L 3 ), and q the darcian velocity (L/T). The concentration of non-reactive solute in the outflow was analyzed using CDE developed on MATHCAD software to obtain the values of the hydrodynamic parameters  and D. The fit of bromide elution curve provided estimations of  and D that characterize of flow homogeneity [START_REF] Sardin | Modeling the nonequilibrium transport of linearly interacting solutes in porous media: A review[END_REF]. Modeling with the MIM code (mobile immobile water) that considers a mobile zone where flow is allowed, an immobile zone with no flow, and exchange of solute between the two zones by diffusion, did not improve the fit. Also, the estimate for immobile water was negligibly low. These results thus indicate that the classical advection-dispersion model (ADE) (Eq. 1) is sufficient to describe solute transport in the GCS-packed column.

The dispersivity α (L) was calculated neglecting molecular diffusion, according to:

v D   (2)
The Darcy velocity (q), porosity θ, v (pore water velocity), D (dispersion coefficient) are reported in Table 2. The dispersivity α was around 105 µm, close to the grain size particle (100-150 m). The Péclet number (Pe = vL/D) in the column was higher than 500, indicating the predominance of an advective regime in all columns (Table 2).

Column experiments were performed to predict the impact of goethite type on the mobility of Fe II in GCS column. The column was injected with Fe II solution (0.5 mM, dissolved form) at pH 6 in a continuous mode at the same constant flow rate, in a glove box. For this purpose, the column was carefully evacuated and flushed with N 2 to remove as much oxygen as possible. The column was then slowly saturated by upward flow of the degassed background solution. The porosity and pore volume was determined gravimetrically, and the injected solution was flushed continuously with a N 2 gas.

During water saturation and tracer experiments, total Fe in outflow was almost negligible, and therefore possibility of dissolution and dispersion of goethite particles is excluded from the column throughout the course of the experiment.

The flow-through experiments were duplicated for each goethite column. In order to study the impact of non-equilibrium sorption in the G1-containing column, two lower flow rates (0.1 and 0.5 mL/min) were used to ensure greater column residence times. Dissolved Fe II and total Fe concentrations in the collected fractions were measured by 1-phenanthroline method (UV visible spectrophotometry).

Surface complexation modeling

The double layer model (DLM) [START_REF] Dzombak | Surface Complexation Modeling: Hydrous Ferric Oxide[END_REF]) investigated by [START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF] was used here to describe the Fe II sorption vs. pH. This DLM is implemented in PHREEQC2 code [START_REF] Parkhurst | User's guide to PHREEQC (Version 2) a computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations[END_REF]Hanna, 2007b). The protonation constants of the surface hydroxyl groups and surface parameters previously determined in [START_REF] Gaboriaud | Effects of different crystal faces on the surface charge of colloidal goethite (α-FeOOH) particles: an experimental and modeling study[END_REF][START_REF] Prélot | Morphology and surface heterogeneities in synthetic goethites[END_REF] were used to predict Fe II sorption onto goethite. All calculations were carried out using the constants listed in Table 1.

PHREEQC-2 was also used to calculate species transport in the GCS-packed column [START_REF] Rusch | Sorption and Transport of Salicylate in a Porous Heterogeneous Medium of Silica Quartz and Goethite[END_REF]. This program enables calculation of chemical equilibria, including different types of interaction of dissolved species with solid phases, in combination with one dimensional conservative advective dispersive mass transport. The specific transport procedure and solute concentration calculations in PHREEQC2 are explained elsewhere [START_REF] Parkhurst | User's guide to PHREEQC (Version 2) a computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations[END_REF]Hanna et al., 2010;[START_REF] Rusch | Sorption and Transport of Salicylate in a Porous Heterogeneous Medium of Silica Quartz and Goethite[END_REF].

Results and discussion

Sorption of Fe II in batch experiments

Kinetic results

The kinetics data obtained under batch conditions revealed that Fe II uptake reached a steady state at 30 min for G2 and G3, and at around 60 min for G1 (Fig. 1a). Different kinetic models, namely a pseudo-first-order, a pseudo-second-order and an intraparticle diffusion model were applied to the data. One way to assess the goodness of fit of experimental kinetic data to these equations is to check the regression coefficients obtained during the regression analysis. The pseudo first-order expression provided the best fit, whereas the pseudo-second-order and the intraparticle diffusion models did not fit well the data. Sorption data were, therefore, treated according to the first-order kinetics by plotting ln(Q e /Q e -Q t ) as a function of time, t, and applying linear regression analysis to obtain the rate constant according to the following equation:

(3)

where Q e and Q t (µmol/m 2 ) are the amount of sorption at equilibrium and at time t (min), respectively and k is the rate constant of the first order sorption process. The order of the

kt Q Q Q Ln t e e  
kinetic rate constants (min -1 ) can be classified as G2 (0.094) > G3 (0.070) > G1 (0.052). G1 exhibited the slowest sorption reaction rate, which could be due to the heterogeneity of the surface site bonding energy or because of other chemical reactions occurring on the surface [START_REF] Liger | Surface catalysis of uranium(VI) reduction by iron(II)[END_REF][START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF][START_REF] Hiemstra | Adsorption and surface oxidation of Fe(II) on metal (hydr)oxides[END_REF]. As largely stated in literature, the interactions of aqueous Fe II with iron oxides can produce a variety of reactions including sorption, electron transfer, dissolution and atom exchange, and so it is difficult to argue that all these processes might be described by a first-order equation [START_REF] Williams | Spectroscopic Evidence for Fe(II)-Fe(III) Electron Transfer at the Iron Oxide-Water Interface[END_REF][START_REF] Pedersen | Fast transformation of iron oxyhydroxides by the catalytic action of aqueous Fe(II)[END_REF][START_REF] Larese-Casanova | Fe (II) sorption on hematite: New insights based on spectroscopic measurements[END_REF][START_REF] Handler | Atom exchange between aqueous Fe (II) and goethite: An Fe isotope tracer study[END_REF].

The sorption results with both ultrasonication and magnetic stirring are almost the same, suggesting that interparticle diffusion is not the rate-limiting step. In all cases, mass balance on solid-and aqueous-phase Fe II was determined, and showed that Fe II was removed only by sorption while no oxidation by trace amounts of oxygen or other phenomena occur.

Sorption vs. pH

The effect of varying solution pH on the sorption of Fe II is illustrated in Figure 1b. The shape of the sorption envelopes is consistent with cationic species interacting with the oxide surfaces [START_REF] Liger | Surface catalysis of uranium(VI) reduction by iron(II)[END_REF][START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF][START_REF] Hiemstra | Adsorption and surface oxidation of Fe(II) on metal (hydr)oxides[END_REF] references cited within). The observed sorption behavior can be attributed to a combination of pH-dependent speciation of ferrous ion and surface charge characteristics of the mineral oxide. Based upon surface charge, sorption is negligible at low pH values and then increases with an increase in pH. The effect of varying pH on Fe II sorption is similar for all goethites, although G1 exhibits a higher sorption extent than G2 or G3. These observations are consistent with literature [START_REF] Liger | Surface catalysis of uranium(VI) reduction by iron(II)[END_REF][START_REF] Hiemstra | Adsorption and surface oxidation of Fe(II) on metal (hydr)oxides[END_REF] which reported that the sorption of Fe II onto iron oxyhydroxide phases increases strongly at a pH higher than 7 regardless of the tested iron oxide.

In order to describe the sorption data vs. pH, surface complexation modeling (SCM) incorporated in PHREEQC2 was used. According to previous works [START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF][START_REF] Hiemstra | Adsorption and surface oxidation of Fe(II) on metal (hydr)oxides[END_REF], Fe II sorption in the goethite/water interface can be best described by assuming two surface complexes with and without electron transfer. The equations predicting Fe II sorption at low Fe II concentration on goethite are shown in Table 3.

These equations are based on the work of [START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF] who proposed two monodenate mononuclear reactions implying one type of site with a positively charged surface species and an uncharged surface species. The relative contribution of each equation varies as a function of pH where the sorption of Fe(OH) + is more pH dependent than that of Fe 2+ [START_REF] Dixit | Sorption of Fe(II) and As(III) on goethite in single-and dualsorbate systems[END_REF].

Surface complexation constants of the equations 1 and 2 were obtained by fitting the experimental data vs. pH (Table 3, Fig. 1b). The experimental data for all three goethites were successfully described by using the same surface complexation constant values, only the site density value was changed (Table 2). These values of site density are, however, different from those previously determined by considering maximum site density per crystal face present in each goethite (Table 1 and references [START_REF] Gaboriaud | Effects of different crystal faces on the surface charge of colloidal goethite (α-FeOOH) particles: an experimental and modeling study[END_REF][START_REF] Prélot | Morphology and surface heterogeneities in synthetic goethites[END_REF]).

Effect of phosphate adsorption

The impact of sorbed phosphate on the sorption and uptake of Fe II by goethite surface was tested by presorbing phosphate to the goethite and then adding aqueous Fe II to the suspension.

The three goethites were used at the same exposed surface area per volume unit (i.e. 10 m 2 /L) and the total phosphate loading was 10 µmol/m 2 . In these conditions, no significant Fe II sorption was observed for all goethites, suggesting that phosphate strongly affects the Fe II sorption on the goethite surface.

Phosphate binds strongly to Fe III -oxides through monodentate and/or multidentatemononuclear surface complexes [START_REF] Torrent | Phosphate adsorption and desorption by goethites differing in crystal morphology[END_REF][START_REF] Luengo | Kinetics of phosphate adsorption on goethite: Comparing batch adsorption and ATR-IR measurements[END_REF], decreasing significantly the availability of surface sites. These results are consistent with the findings of previous works where phosphate was found to interfere with the Fe II induced recrystallisation of ferric oxyhydroxides [START_REF] Benali | Effect of orthophosphate on the oxidation products of Fe(II)-Fe(III) hydroxycarbonate: the transformation of green rust to ferrihydrite[END_REF][START_REF] Borch | Phosphate Imposed Limitations on Biological Reduction and Alteration of Ferrihydrite[END_REF][START_REF] Usman | Transformation of various kinds of goethite into magnetite: Effect of chemical and surface properties[END_REF]. However, recent study [START_REF] Latta | Fe Electron Transfer and Atom Exchange in Goethite: Influence of Al-Substitution and Anion Sorption[END_REF] showed that both Fe II -Fe III electron transfer and Fe atom exchange are unaffected by phosphate sorption on goethite by investigating 57 Fe Mössbauer spectroscopy and isotope tracer method. These contradictory results may result from the difference in the experimental conditions used (e.g. exposed surface area per volume unit, ratio P/Fe, etc.) and also in the nature of the underlying goethite. Indeed, phosphate sorption has been tested on eight samples of goethite ranging in surface area from 18 to 132 m 2 /g and concluded that the duration and extent of the sorption reaction depended on the crystallinity of the goethite [START_REF] Strauss | Effects of crystallinity of goethite: II. Rates of sorption and desorption of phosphate[END_REF]. [START_REF] Torrent | Phosphate adsorption and desorption by goethites differing in crystal morphology[END_REF] have observed that the amount of phosphate adsorbed per unit surface area at pH of 6 was similar for all tested goethites (2.51, µmol/m 2 ), because of the existence of only (110) faces in their goethite samples. They, however stated that the crystal morphology affected desorption extents, since samples consisting of multidomainic laths retain more phosphate than those having monodomainic crystals [START_REF] Torrent | Phosphate adsorption and desorption by goethites differing in crystal morphology[END_REF]. Therefore, the impact of strongly bonded ligand on the Fe II sorption also points out the importance of morphology, crystal structure and particle size of the underlying goethite surface.

Sorption and transport of Fe II in column

Sorption of Fe II in columns

Column experiments were performed under conditions to predict the impact of goethite type on the mobility of Fe II in GCS column. The breakthrough curves (BTC) of both pH and aqueous Fe II are shown for each goethite in Figure 2, while those of aqueous Fe II only are regrouped for the three goethites in Figure 3a. The breakthrough point of Fe from G3 and G2 lies respectively at about 4 and 5 V/V p , while complete breakthrough occurs at ~7 injected pore volumes (PV).

The total sorbed amount of Fe II at complete breakthrough is 1.1 and 1.3 mol/m 2 for G3 and G2 respectively, which is in agreement with that expected from the batch experiments.

The breakthrough point for G1 column starts at ~6 PV and is completed at ~10 PV. Fe II surface loading (1.6 mol/m 2 ) is, however, less than derived from batch sorption experiments (Fig. 1). The pH coincides with the Fe II breakthrough slope and reaches a constant value when solute sorption achieves steady state and breakthrough completion (Fig. 2).

Additional breakthrough experiments were conducted by injecting aqueous Fe II solution in columns containing dried GCS-sorbed phosphate. Prior to column experiments, preliminary batch tests were conducted to prepare the GCS by mixing 20 g of GCS (equivalent to 10 m 2 of goethite) with 0.1 mM of phosphate solution (total ligand loading 10 µmol/m 2 ). The breakthrough curves of Fe II superposed with that of bromide tracer (Fig. 3a), and thus no Fe II sorption occurred in the column whatever the goethite used. These results confirm those observed in batch, i.e. phosphate hinders the adsorption sites and therefore inhibits the Fe(II) sorption.

Transport modeling was carried out with PHREEQC2, using hydrodynamic parameters defined by a Br -tracer breakthrough experiment and the surface complexation parameters of this study (Table 2). Fe II sorption in dynamic conditions (columns) can be satisfactory predicted by coupling aqueous transport (convection and dispersion) and the surface complexation model (batch experiments) for the G2 and G3 columns (solid lines in Fig. 3a).

The predicted breakthrough, however, overestimated Fe sorption in the G1 column system and showed a larger retardation (Fig. 3a). Indeed, experimental breakthrough was at about 6 PV, while calculations predicted around 8 PV.

The inability of the model simulation of a laboratory column experiment to describe outflow concentrations could possibly be related to the lack of local geochemical equilibrium in the column [START_REF] Altfelder | Nonequilibrium sorption of dimethylphthalate -Compatibility of batch and column techniques[END_REF][START_REF] Hanna | Sorption of two naphthoic acids to goethite surface under flow through conditions[END_REF]. The comparison of sorption kinetic rate and the residence time in the column at 1 ml/min (about 5 min) as well as the short tail observed in the BTC suggests that kinetic limitations of sorption might take place in the column. While BTC of G1 exhibits less dispersion on the initial limb, the tailing observed on desorption limb may be an indication of a kinetics effect and/or dispersion effect.

Firstly, when solute BTCs normalized by its retardation factor was compared to that of the Br - tracer, BTC was more tilted than that of the tracer (not shown). This observation is in favor of nonequilibrium sorption in the column. In contrast, if there is no influence of sorption kinetics, the steepness of the solute BTC is determined only by dispersion and must coincide with the tracer BTC. Another way to test the lack of local equilibrium is by estimating the Damkohler numbers (Da), representing the ratio of hydrodynamic residence time to characteristic time for sorption of a compound, as described elsewhere [START_REF] Bi | Simulation of nonlinear sorption of N-heterocyclic organic contaminates in soil columns[END_REF][START_REF] Hanna | Sorption of two naphthoic acids to goethite surface under flow through conditions[END_REF]Hanna et al., 2010;[START_REF] Clervil | Sorption of nalidixic acid onto sediments under batch and dynamic flow conditions[END_REF]. Da= τ(R-1)L/v, where τ is the mass transfer coefficient estimated from batch kinetic experiment (0.052 -0.094 min -1 ), R is retardation factor estimated from moment analysis of the breakthrough curves, L is the column length (cm) and v is the average pore water velocity (cm min -1 ). Da values varied between 1.5 (G1), 2 (G2) and 1.6 (G3) inferior to 20. Consequently, lower values of Da are not in favor of local equilibrium in a 1-D column [START_REF] Maraqa | Retardation coefficients of nonionic organic compounds determined by batch and column techniques[END_REF][START_REF] Bi | Simulation of nonlinear sorption of N-heterocyclic organic contaminates in soil columns[END_REF].

Sorption of Fe II in the G1 column at different pore water velocities

The best experimental method for testing the impact of kinetics in a packed column is to conduct the breakthrough experiment at different flow rates or residence times. For this aim, flow-through experiments were conducted at three different flow rates (0.1, 0.5 and 1 ml min - 1 ) at pH 6 for the G1 packed column (Fig. 3b). Interestingly, breakthrough curves were found to be dependent on flow rates, indicating that the sorption kinetics impact Fe II transport over the time scale of column experiment. The three sets of flow-through experiments gave rise to different retardation factors at complete breakthrough. The breakthrough curves at 1 and 0.5 ml/min were sigmoidal in shape and showed no extended tailing; however the BTC at the lowest flow rate (0.1 ml/min) exhibits an irregular shape (Fig. 3b). This behavior observed at a high residence time (about 50 min) might come from Fe dissolution/re-precipitation or Fe IIinduced transformation of goethite. Note that the dissolved ferrous ion can be flushed out under flow-through conditions, which could modify the extent of reaction in the column and explain the irregular shape of the BTC (Fig. 3b). However, XRD or Raman spectroscopy conducted on the goethite coated sand before and after column test did not show any significant difference (data not shown). As the Fe III loading in GCS (less than 1 wt%) is very low as well as the Fe II binding amount, a detection of mineralogical transformation of goethite or an identification of secondary minerals is quite difficult. Note that this degree of coating was chosen to correspond to the Fe oxide coatings range (0.074 to 44.2 mg Fe by gram of sand) found in natural settings [START_REF] Wang | Ferrihydrite, lepidocrocite, and goethite in coatings from east Texas vertic soils[END_REF], and also help to do column experiments in a reasonable period of time.

Based upon these results, transport of Fe II in goethite packed column was found dependent on time scale especially in the G1 system. Despite the agreement observed between batch and column data in term of sorbed amount (at low flow rate), we cannot suppose that the equilibrium is established in the column.

In our previous reports on the transport of organic ligands in GCS columns, compatibility of batch and column methods was found strongly dependent on ligand structure, sorbent nature and sorption mechanism [START_REF] Hanna | Sorption of two naphthoic acids to goethite surface under flow through conditions[END_REF]Hanna et al., 2010;[START_REF] Rusch | Sorption and Transport of Salicylate in a Porous Heterogeneous Medium of Silica Quartz and Goethite[END_REF]. In present work, we can imagine that the disparity between batch and column data may also result from the modification of sorption process and/or surface properties under flow through conditions. However, it is difficult to test these hypotheses here due to the complexity of interactions between Fe II and iron oxide (sorption with or without electron transfer, possible transformation to secondary minerals, etc.). Moreover, the goethite content in columns is too low to allow a relevant characterization and monitoring of iron reactive phases.

In order to test if the observed disparity resulted from the specific interactions of Fe II with Fe III oxide, BTCs were determined as for previous experiments but by using fluoride as a reactive tracer. Fluoride was used as a model compound because the interactions of fluoride with iron oxides have been largely described in literature [START_REF] Sigg | The interaction of anions and weak acids with the hydrous goethite (α-FeOOH) surface[END_REF]. The breakthrough results showed that, like the Fe II , the disparity between batch and column data was only observed for G1. This observation indicates that this behavior is directly related to the specific phase reactivity of the used goethite (i.e. G1). In our batch experiments, G1 has the highest sorption extent and lowest sorption rate constant.

Under batch conditions, kinetic data of Fe II sorption suggested some heterogeneity of chemical reactions occurring on the G1 surface. The high sorption capability of G1 might have resulted from the surface roughness and edge surface faces enhancing surface site densities. [START_REF] Manceau | Influence of Anionic Layer Structure of Fe-Oxyhydroxides on the Structure of Cd Surface Complexes[END_REF] suggested that the sorption of divalent cations may probably be dominated by the crystal faces that terminate the chains (021/001 like faces) and the presence of the face 021 or 121 favored the formation of bidentate or tridentate complex.

Therefore, the presence of the edges faces, as in the case of G1 [START_REF] Gaboriaud | Effects of different crystal faces on the surface charge of colloidal goethite (α-FeOOH) particles: an experimental and modeling study[END_REF][START_REF] Prélot | Morphology and surface heterogeneities in synthetic goethites[END_REF], might favor the formation of inner-sphere complex, making the Fe II sorption stronger.

In addition, longer residence time in the breakthrough experiments allowed for greater sorption and dispersion with an unusual breakthrough shape. [START_REF] Appelo | Variable dispersivity in a column experiment containing MnO2 and FeOOH-coated sand[END_REF] suggested that chemical reactions inside column can induce changes in the pore structure and then influence the flow properties of media, since they observed 10-fold increase in dispersion due to the reduction of the sorbent solid by Fe II in the column system.

All these observations suggest that the strong Fe II sorption on G1 makes it kinetically limited under flow through conditions. Therefore, the crystal structure and surface site density of goethite are needed to describe and predict the transport of Fe II in column.

Implications for contaminant attenuation and transport

Sorption of Fe II onto iron oxide minerals plays an important role both in natural and engineered systems. Sorption of Fe II to the iron oxides has possible consequences on the fate of contaminants in the environment. On the one hand, presence of Fe II in a goethite suspension can induce increases in sorption of metal cations [START_REF] Coughlin | Nonreversible adsorption of divalent metal ions (MnII, CoII, NiII, CuII, and PbII) onto goethite: effects of acidification, FeII addition, and picolinic acid addition[END_REF]. On the other hand, Fe II associated with goethite or other Fe III -hydroxide surfaces was shown to be a very powerful reductant of several environmental contaminants [START_REF] Elsner | Reactivity of Fe(II)-Bearing Minerals toward Reductive Transformation of Organic Contaminants[END_REF]. The rate and extent of contaminant reduction depends on the composition of the mineral, amount of Fe II sorbed, and possibly the speciation of sorbed Fe II [START_REF] Hofstetter | Complete reduction of TNT and other (poly) nitroaromatic compounds under iron-reducing subsurface conditions[END_REF][START_REF] Elsner | Reactivity of Fe(II)-Bearing Minerals toward Reductive Transformation of Organic Contaminants[END_REF][START_REF] Tobler | Assessing Iron-Mediated Oxidation of Toluene and Reduction of Nitroaromatic Contaminants in Anoxic Environments Using Compound-Specific Isotope Analysis[END_REF]Usman et al., 2012a). We notably demonstrated that the sorption of Fe II on goethite is strongly affected by the specific surface properties of the underlying phase.

In addition, the mobility of Fe II under anoxic flow conditions was dependent on the goethite phase. For two goethites G2 and G3, sorption breakthrough predictions using sorption parameters derived from batch experiments accurately predicted mobility of Fe II . Those for G1, however, predicted less sorption reactions than in the batch sorption experiments.

Additional breakthrough experiments and test calculations showed that these differences were related to kinetics behavior. Therefore, the effect of sorption kinetics was especially observed in the G1 column. The specific properties of G1 (i.e. surface roughness, edge surface faces, high site density) leading to a high sorption extent and a low sorption rate, may affect the transport and mobility of aqueous Fe II in the column system.

The dependence of transport of Fe II on the type of goethite phase under advective-flow conditions raise important consequences in prediction mobility models. This aspect should be taken into account in transport and attenuation studies of environmental contaminants.

Tables

  Figures captions

Figure 2 :

 2 Figure 2: Experimental breakthrough curves of compound (symbols) and pH (solid line) for

Figure 3 :

 3 Figure 3: (a) Experimental breakthrough curves of Br -and Fe II . Flow rate = 1mL/min.

  Fig.1(a)

Table 1 . Main characteristics of tested goethites (G1, G2 and G3) (Gaboriaud and Ehrhardt, 2003; Prélot et al., 2003).

 1

	G1	G2	G3

Table 2 . Characteristics of column experiments with three goethites (G1, G2 and G3)

 2

		G1	G2	G3
	Porous bed length (cm)	6 ± 0.2	6 ± 0.2	6 ± 0.2
	Section (cm 2 )	2.01	2.01	2.01
	Column volume (cm 3 )	12.06	12.06	12.06
	GCS amount (g)	20 ±1	20 ±1	20 ±1
	Bulk density (g.cm -3 )	1.65 ±0.1	1.65 ±0.1	1.65 ±0.1
	Experimental Vp (ml)	4.5±0.2	4.5±0.2	4.5±0.2
	Porosity (%)	0.37	0.37	0.37
	Flow rate (mL/min)	1.00	1.00	1.00
	Darcy velocity (cm/min)	0.50	0.50	0.50
	Pore-water velocity (cm/min) 1.35	1.35	1.35
	D cm 2 /min	0.015	0.014	0.014
	α µm	111	104	104
	Pe	540	580	580

Acknowledgment. We gratefully acknowledge Dr. F. Gaboriaud for providing the three goethites samples and G. Ollivier for XRD analyses.