Salma Fitouri
email: fitouri_trabelsi-salma@hotmail.comcarlos

Alberto Nunes Cosenza

Luis Gustavo
email: lgustavozelaya@gmail.com

Zelaya Cruz

Felix Mora

AN OPERATIONAL APPROACH FOR GROUND HANDLING MANAGEMENT AT AIRPORTS WITH IMPERFECT INFORMATION

Keywords: ground handling, multi fleet assignment, airport operations, fuzzy modelling

In this communication the ground handling fleet management problem at airports is considered with the aim of improving aircraft service at arrival and departure while the operational cost of the ground service fleets is taken into account. The complexity of the considered problem, as well as, operational considerations lead to propose an on-line decentralized management structure where the criticality of each aircraft demand for service is evaluated using a particular fuzzy formalism. Then after detailing the assumed collaborative scheme between ground handling fleet managers, airlines and airport authorities, a heuristic approach is proposed to solve this multi fleet assignment problem. A case study considering a large airport is discussed.

Introduction

The ground handling operations represent the airside activities at airports in charge of processing passengers, cargo, facilities and supplies at and around parked aircraft. Most of these operations are performed by different service providers, using vehicles which are specific to each type of operation. Ground handling is not a prominent activity within the air transportation system (RHLG, 2000), however this activity is an important enabler for efficient airport operation and its management is an important issue. Over the last decades, the complexity of ATS has increased to face the worldwide growth of air traffic. Today the operation of this system involves global actors (airports, airlines, air traffic control (ATC), air traffic management (ATM)) as well as local actors (ground handlers, local suppliers…) whose coordination, while pursuing different and sometimes contradictory objectives, is difficult to achieve. The main objectives of traffic management at airports are to improve operational efficiency by reducing aircraft delays, to optimize the use of airport resources to reduce operation costs and to improve the predictability of air transportation operations (flight arrivals and departures). So, to face the current situation with acceptable safety and efficiency standards a new concept has been developed: Airport Collaborative Decision Making (CDM) which tries to create a common ground for the different component of the ATS. This concept is based on an improved communication between the different actors of the airport. As an example, for Roissy-CDG airport, where CDM has been implemented since 2010, departure times are respected in more than 85% of the cases, against 80% before, the ground traffic is more fluid (taxiing times have been shortened by 2 to 4 minutes), a reduction of 14.5 tons of fuel in daily consumption and also a significant decrease in CO 2 emissions (The Boeing Company, 2000). So far the management of the different service fleets which perform the turnaround process of ground handling has not been considered specifically in the CDM approach, even if it has an important part in the fluidity of aircraft ground movements, however ([3] shows that 10% of all the flight delays are caused by the inefficient management of the different fleet of the ground handling operation. This paper after identifying possible objectives and essential constraints for ground handling at airports, proposes a decentralized structure to cope with this multi-fleet assignment problem. Considering that many parameters are imperfectly known, the fuzzy modelling formalism is introduced to take into account this important characteristic of this ground handling multi-fleet (GHMF) problem.

Scope of the Study

Aircraft turnaround defines the process of servicing an aircraft while it is on the ground between two successive flights it operates. The turnaround term implies a fast sequence between an arrival and a departure, however for many air transport operations, in particular for long haul flights, a large time interval may be programmed between them. During the turnaround, an aircraft must undergo a complex process composed of a set of elementary ground handling activities such as landing / boarding, unloading / loading of luggage, fuelling, catering, cleaning, water and sanitation processes. Fig. 1 describes the main ground handling operations taking place around a grounded aircraft as well as their precedence constraints. Arrival and departure ground handling tasks are distinguished. Ground handling operations are in general carried out by different service companies, using vehicle which are specific to each type of operation. To perform the turnaround process for each aircraft within the allocated time, these different companies have to coordinate between each other while respecting the constraints of scheduling tasks for each aircraft and the constraints related to the use of service vehicles. The duration of each ground handling operation is variable from one flight to another and depends in general of the type of aircraft, the volumes of passengers/luggage to be processed as well as of other external factors such as the current weather conditions at the airport. Then the large variability of elementary task durations should be taken into account when managing the different ground handling fleets. Each ground handling fleet type is supposed homogenous so that the same task can be performed with the same efficiency by any vehicle of each considered ground handling fleet.

The duration of an elementary task t on aircraft a(i) assigned to flight i can be estimated either by an airline ground station manager or the corresponding ground handling manager who has received information about the load of the flight from the airline. It is here supposed that this duration is given by a dual fuzzy number

i t i t i t d d ~
where i t d is the current central value of the duration of task t and i t is the uncertainty range. Part 3 provides a brief introduction to fuzzy dual numbers and associated basic operations. A set of fuzzy rules can be built to generate these fuzzy dual task durations where the backbone is the nominal processing times with scaling factors and the fuzzy rules generate the dual part of the elementary task durations. Each of the ground handling activities makes use of specialized equipment which must be turned available at the aircraft parking place at the right time to avoid delays. Some of the ground handling activities should be performed as soon as possible after the arrival of the aircraft at their parking stand and others must be performed only some time before departure from their parking stand. Depending of aircraft operation these two sub sets of activities can be performed in immediate sequence or are separated by an idle period of variable duration according to arrival and departure schedules of a given aircraft. Fig. 2 displays a standard situation for an aircraft undergoing a turnaround process where space is a rather scarce resource and some tasks cannot be performed simultaneously (mainly for safety reasons). It appears that the efficient operations of such complex process which repeats with each aircraft arrival or departure is very difficult to be achieved while it is a critical issue for airport operations performance. Then advanced management tools are necessary to cope in a satisfactory way with this problem. Few publications covering Fuzzy VRP or Fuzzy Scheduling are available in the literature. The Fuzzy VRP has been introduced as a VRP problem with time window constraints where the customer demand, the service and the travel times are given by fuzzy numbers. In (Jia, 2005), a simple description of a VRP problem with fuzzy travelling times is introduced and its solution is obtained through a genetic algorithm. In [START_REF] Tang | Ant Colony Algorithm Based on Information Entropy Theory to Fuzzy Vehicle Routing Problem[END_REF] where the duration of the time window of a VRP problem is considered as a fuzzy variable, the solution has been computed with an Ant algorithm whose monitoring is based on the evolution of the entropy of the solution. With regard to Fuzzy Scheduling, [START_REF] Dubois | Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge[END_REF] presents an overview of fuzzy approaches to scheduling and emphasizes the representation of preference profiles and the modelling of uncertainty distributions.

Fuzzy dual variables and basic calculus

The set of fuzzy dual numbers is the set ~of numbers [START_REF] Cosenza | Nombres et ensembles duaux flous et applications[END_REF] of the form

b a such as R b R a ,
where a is the primal part and b is the dual part of the fuzzy dual number. Here is the unity pure dual number. A crisp fuzzy dual number will be such as b is zero, it loses both its dual and its fuzzy attributes. The lower and upper bounds of (1) A graphical representation of a fuzzy dual number is given below where μ is a symmetrical membership function defined over R: The fuzzy dual (FD) addition of dual fuzzy numbers, written ~ is given by:

) ( ) ( ) ( ) ( 2 1 2 1 2 2 1 1 y y x x y x y x
(2) Its neutral element is

) 0 0 ( , written 0 ~.
Then the sum of a real number 1

x and a fuzzy dual number

2 2 y x is such as: ) ( ) ( ) 0 ( ) ( 2 1 1 2 2 1 2 2 1 y y x y x x y x x
(2.2) The pseudo norm of a dual fuzzy number is given by: (3) where is a shape parameter. Figure 4 displays standard fuzzy dual numbers with different shape parameters.

A Decentralized Approach of Ground Handling Multi-Fleet Management Problem

In this case, it is considered that airlines communicate with the ground handling fleet managers through their own ground station managers which are in charge of monitoring the ground handling activities at arrival or departure of each flight. For example, one of their objective with respect to flight arrivals is to minimize the waiting time for de-boarding passengers and luggage, another one is to make sure that passengers board the aircraft in due time before scheduled flight departure time. So, they will be in charge of requesting in due time the necessary ground handling resources for flight arrival or departure processing. In the case of a decentralized management of the different fleets of ground handling vehicles, the size of each fleet management problem is of course smaller. With respect to the definition of the corresponding decision problems, some objectives of the ground handling problem can be expressed as constraints at the individual fleet level. Once these constraints are set, a major objective for each ground handling fleet manager will consist in minimizing its ground handling variable costs related mainly to the fleet operations costs. This can be considered to be achieved by minimizing the travelled distance of the corresponding ground handling fleet, contributing also to airport environment protection (chemical emissions and noise). To be feasible, a decentralized approach, nominal or on-line, must be cooperative. Each ground handling fleet assignment (GHFA) problem must be executed according to a sequence compatible with the organization of the ground handling activities (Fig. 1). Then each GHFA problem should integrate time constraints generated from the solution of the uphill GHFA problems or from the updated expected flight arrival schedules. The ground handling services are delivered in a disturbed environment with many operational uncertainties. For example, the expected arrival times for flights are subject to frequent delays, the duration of ground handling tasks is sensitive to unexpected events such as additional travel time due to traffic congestion on airside service ways or machine breakdown. Airport air traffic control services update the predicted arrival times which are forwarded to airport services, including airlines and ground handling. This starts the process of updating the assignment and scheduling of tasks for each ground handling fleet. In the case in which repeated aircraft arrival schedule perturbations are expected, according for instance to meteorology conditions, the horizon of fleet management can be commonly limited to some hours ahead, while ground handling resources computed from the daily nominal GHMF problem must remain on the lookout. In theory, each ground handling fleet manager should solve the new instance of each GHFA problem by taking into account the scheduling constraints generated and provided by the uphill GHFA problems or from the updated aircraft arrival schedule and parking positions forwarded by the airline. The predicted completion time of his activities on each aircraft should be sent to the other ground handling managers and the corresponding airline. Then, when a fleet manager decides to generate a new plan he should communicate the result to the downhill ground handling operators so that they update their own plans. However, this scheme appears too complex to be adopted on an on-line situation, specially when we know that the duration of the elementary activities at grounded aircraft are subject to variations.

Here we consider that the estimated arrival or departure time of an aircraft will be a common time reference for all involved ground handling operators which will have to send the corresponding service vehicle to the considered aircraft. Then coordination between the different tasks will take place according to the corresponding scheduling constraints (see for example figure 1). The immediate uphill ground handling managers will be able then to compute the estimated time margins for each task by comparing their processing time plus the nominal duration of their task with the earliest processing time of the following tasks in the turnaround process. The delay of a service vehicle to arrive at the parking position or to execute its task there may have two types of consequences: some following task at this parking position may be delayed, some subsequent assignments of this vehicle to other arriving or departing flights may be reviewed to avoid delays.

In both cases, the ground handling fleet manager of the delayed vehicle should communicate an estimate of this delay to the other ground handlers and to the involved airline. Then they will be able to check the feasibility of the current planned assignment scheme of each ground handling fleet. In the case a current assignment scheme is no more feasible without additional delay, a reassignment of the corresponding ground handling fleet should be achieved and communicated to the other parties if involving confirmed delays.

5

A Fuzzy Heuristic for the On-line GHMF Assignment

The problem for each ground handling fleet is here to assign ground handling vehicles to arriving or departing aircraft so that each aircraft is serviced by a vehicle while, according to the current operational situation, no delay or a minimum delay is produced. For that, the airline ground station managers generate resources requests to the ground handling fleet managers. The produced schedules are based on the predicted arrival times as well as the scheduled departure times. These schedules take not only into consideration the possible variation of the ground handling tasks durations by using a fuzzy dual formalism [START_REF] Cosenza | Fuzzy sets and dual numbers: an integrated approach[END_REF] , but consider also the criticality of the flight. This criticality depends on the current predicted delay as well as the operational consequences on other flights. Then more critical flights may get their ground handling solution treated before earlier less critical scheduled flights. The following notations are adopted: Each task of the turnaround process Departure schedule when the arriving aircraft must be ready to start a new flight with a tight schedule. When considering connection constraints, let i C be the set of departing flights connected to arriving flight i. The fuzzy time margin between flight i and each flight j in C i is given by:

i ij i ul ij i db a i d j a ij C j d T d t t m , max ˆ (7)
Here ij T ~and ij ~ are respectively the connecting delay for passengers and luggage between flights i and j. The margin between arrival flight i and departure flight j which are serviced in immediate succession by the same aircraft is: Then, the fuzzy margin of the arriving aircraft i is given by:

a ij i C j a i m m i min ~) ( (10)
The amended arrival time for flight i is then given by:

a i a i a i m t t ~ (11)
To each departing flight i I D , can be assigned the difference

d i d i d i t t t ˆ
between the predicted departure time d i t ˆ and the scheduled departure time d i t . Here also, d i t ˆ and d i t can be either real numbers or fuzzy dual numbers. Symmetrically, each departing flight must cope with operational constraints related with successive flights by the same aircraft and flight connections for passengers and cargo. In the case in which the ground handling tasks are relative to a departing flight j, the amended predicted time to start grand handling activities at the corresponding parking position is now given by:

a ij j i and C j i d j d j m t t i min ~) ( 1 (12) with pb wa bd ca ll fu a i i d d d d d d m max ~) ( (13)
Then, to each flight i, either arriving or departing, is assigned a time parameter i such as:

) ( a i low i t B
for arriving flights (14.a)

) ( d j low i t B
for departing flights (14.b) Then the flights, either arriving or departing, present in the considered period of operation can be ranked according to an increasing i index. Let the integer r a (i) be the amended rank of flight i.

Ground Handling Fleets assignment to flights

Each ground handling fleet manager will process the different flights according to the produced order r a (i) but taking into account that the estimated arrival (departure) time is still

a i t ˆ( d i t ˆ),
where ground handling vehicles are assigned to the corresponding aircraft. In the case of an arriving flight, ground handling arrival tasks (unloading luggage, de-boarding, cleaning and sanitation) are coped with by assigning the corresponding vehicles in accordance to their previous assigned tasks with other aircraft, their current availability, and their current distance to the considered aircraft. Here the common reference time schedule for the ground handling arrival tasks is

A a i I i t , ˆ.
In the case of a departing flight, ground handling departure tasks (fuelling, catering, luggage loading, boarding, water and push back) are also coped with by assigning the corresponding vehicles in accordance to their previous assigned tasks with other aircraft, their current availability, and their current distance to the considered aircraft. Here the common reference time schedule for the ground handling departure tasks is

D d i I j t , ˆ.
In both cases it is considered that the whole set of different ground handling vehicles necessary at arrival or departure is assigned by considering the common reference time schedule. This assignment of vehicles to flights either arriving or departing is performed on a greedy base by considering the closest vehicle available to perform the required task. This will make that at the start of ground handling activities for an arrival or departure flight, all necessary resources will be nearby the parking place and that scheduling constraints between elementary ground handling tasks (see fig. 1) will be coped locally by the vehicle operators without need of communication between the different ground handling fleet managers. This is a rather simple greedy heuristic which provides for each fleet facing the current service demand a complete solution through a reduced computational effort. So there is no limitation in calling back this solution process, including the criticality ranking of flights, any time a significant perturbation occurs. In general, this process will be called back systematically any time a new aircraft enters set A I or set D I . Observe that the ranking of flights in

B A I I
is a global ranking which should be adopted by the ground handling fleet managers as a constraint with respect to the global performance of the airport with respect to flights. Given this ranking, it is up to each ground handling fleet manager to minimize its operations costs while meeting this overall common soft constraint. The sets A I and D I are updated according to current time t. It is considered that assignment of ground handling vehicles to arriving or departing flights is definitive at time i . Then at time i , A I or D I will be updated depending if i is an arriving or a departing flight:

Case Study

To validate the proposed cooperation scheme and the associated heuristics real traffic data from Palma de Mallorca (PDM) Airport has been considered. PDM Airport is, with respect to aircraft and passengers traffic, the third largest Spanish airport. The evaluation of the proposed decentralized approach has been performed using aircraft traffic data for a 24h period with ground handling activities taking place at the four parking areas related with the four terminals of PDM Airport. Except for aircraft staying at night at the airport, a large majority of ground handling operations are done in the context of fast turnaround operations. Different sizes for each of the ground handling fleets have been considered in various scenarios. Fig. 7 displays one of the considered compositions for ground handling fleets, while figure 8 displays the fuzzy dual durations adopted for a full A320 airplane. Perturbations have been also introduced for some arriving aircraft with updated predictions available with fifteen minutes ahead.

The proposed heuristic approach has been tested for the aircraft traffic at 1 st of August, 2007 (345 aircraft turnarounds on that day). The resulting earliest departure time for aircraft have been compared with the real time departure data, showing that with rather reduced ground handling fleets, available at each terminal, the proposed decentralized heuristic, does not generate additional delays. The application of the proposed heuristic approach has led to delays with respect to departure schedule involving only 36 aircraft, with a maximum delay of 16 minutes. Figure 9 displays the hourly distribution of delayed aircraft at departure resulting from the application of the proposed decentralized approach. Clearly, the occurrence of these delays corresponds to the busiest aircraft traffic periods at the airport.

Conclusion

In this communication the problem of managing in a decentralized way airport ground handling has been considered. Then, adopting a decentralized management structure, where airline station managers and ground handling fleet managers interact, an heuristic taking explicitly into account the uncertainty about elementary processing times has been developed. This heuristic is based on the cooperation between the different tactical decision makers, providing an efficient reactive ground handling multi fleet management structure. This cooperation scheme appears to be compatible with an overall collaborative decision making approach for the airside management at airports. A case study considering aircraft and ground handling traffics at PDM Airport during a typical summer day has been developed through simulation, showing the interest of the proposed approach.

Figure 1 :

 1 Figure 1: Ground handling activities at arrival and departure

Figure 2 :

 2 Figure 2: Ground handling arrangement [2] .Few publications covering Fuzzy VRP or Fuzzy Scheduling are available in the literature. The Fuzzy VRP has been introduced as a VRP problem with time window constraints where the customer demand, the service and the travel times are given by fuzzy numbers. In(Jia, 2005), a simple description of a VRP problem with fuzzy travelling times is introduced and its solution is obtained through a genetic algorithm. In[START_REF] Tang | Ant Colony Algorithm Based on Information Entropy Theory to Fuzzy Vehicle Routing Problem[END_REF] where the duration of the time window of a VRP problem is considered as a fuzzy variable, the solution has been computed with an Ant algorithm whose monitoring is based on the evolution of the entropy of the solution. With regard to Fuzzy Scheduling,[START_REF] Dubois | Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge[END_REF] presents an overview of fuzzy approaches to scheduling and emphasizes the representation of preference profiles and the modelling of uncertainty distributions.

Figure 3 :

 3 Figure 3: Example of dual representation of a fuzzy number

Figure 4 :

 4 Figure 4: Examples of shapes for fuzzy dual numbers The shape parameter is given in general by:b

Figure 5 :

 5 Figure 5: Information flows structure for GHMF operation

t

  an aircraft a(i) associated to a flight i, i I, (I=I A I D , I A is the set of arriving flights and I D is the set of departing flights) by a specific service provider Fuzzy-based ranking of flightsThe first step of the proposed heuristic consists in performing an initial ordering of the flights in accordance with their current predicted arrival time a i t ˆat their assigned parking amended by considering their criticality. To each arriving flight i I A , can be assigned the difference can be either real numbers or fuzzy dual numbers, where a i t ˆis provided by the ATC. Each arriving flight must cope with two types of operational constraints: Connection constraints when arriving passengers must reach without delay another departing flight.

  minimum fuzzy dual duration of ground handling around arrival of flight i and departure of flight j. Here ) (i provides the number of the next flight serviced by the aircraft operating flight i. Then, according to figure 1:

.

  Depending on the intensity of traffic, the delaysA and D can be extended (low traffic situation) or retracted (high traffic situation).

Figure 6 :

 6 Figure 6: 01/08/2007 PDM Airport Aircraft Hourly Traffic

Figure 7 :

 7 Figure 7: Example of composition of ground handling fleets

Figure 8 :

 8 Figure 8: Fuzzy dual duration of handling tasks for A320 aircraft The average delay among delayed aircraft has been of 7 minutes. Historical data from 01/08/2007 at Palma de Mallorca Airport indicate that about 200 aircraft departures where delayed for multiple reasons, including one of the main reasons, ground handling delays.Figure9displays the hourly distribution of delayed aircraft at departure resulting from the application of the proposed decentralized approach. Clearly, the occurrence of these delays corresponds to the busiest aircraft traffic periods at the airport.

Figure 9 :

 9 Figure 9: Hourly distribution of resulting delays

  During the summer period it is one of the busiest airports in Europe, with 22.7 million of passengers in 2011. The airport is the main base for the Spanish carrier Air Europa and also a focus airport for German carrier Air Berlin. It occupies an area of 6.3 km2 (2.4 sq mi). Due to rapid growth of aircraft traffic and passenger flows along the last decades, additional infrastructure has been added to the two original terminals A(1965) and B (1972). PDM Airport is composed now of two runways, four terminals and 180 parking stands (27 of them at aprons)[20]. It can handle up to 25 million passengers per year, with a capacity to dispatch 12,000 passengers per hour. Figure6displays the hourly traffic of arriving and departing aircraft on a typical summer day at this airport. It appears that aircraft traffic remains intense from early morning until the beginning of night hours.


Salma Fitouri-Trabelsi
email: fitouri_trabelsi_salma@hotmail.com

Alberto Carlos

Nunes Cosenza

Felix Mora-Camino
email: felix.mora@enac.fr

Ground Handling Management at Airports with Fuzzy Information

In this communication the ground handling fleet management problem at airports is considered with the aim of improving aircraft service at arrival and departure while the operational cost of the ground service fleets is taken into account. The complexity of the considered problem, as well as, operational considerations lead to propose an on-line decentralized management structure where the criticality of each aircraft demand for service is evaluated using a fuzzy formalism. Then after detailing the proposed collaborative scheme between ground handling fleet managers, airlines and airport authorities, a heuristic approach is proposed to solve each fleet assignment problem. A case study considering a large airport is discussed.



INTRODUCTION

The ground handling operations represent the airside activities at airports in charge of processing passengers, cargo, facilities and supplies at and around parked aircraft. Most of these operations are performed by different service providers, using vehicles which are specific to each type of operation.

Ground handling is not a prominent activity within the air transportation system (ATS), however this activity is an important enabler for efficient airport operation and its management is an important issue. Over the last decades, the complexity of ATS has increased to face the worldwide growth of air traffic. Today the operation of this system involves global actors (airports, airlines, air traffic control (ATC), air traffic management (ATM)) as well as local actors (ground handlers, local suppliers…) whose coordination, while pursuing different and sometimes contradictory objectives, is difficult to achieve. The main objectives of traffic management at airports are to improve operational efficiency by reducing aircraft delays, to optimize the use of airport resources to reduce operation costs and to improve the predictability of air transportation operations (flight arrivals and departures). So, to face the current situation with acceptable safety and efficiency standards a new concept has been developed: Airport Collaborative Decision Making (CDM) which tries to create a common ground for the different component of the ATS. This concept is based on an improved communication between the different actors of the airport. As an example, for Roissy-CDG airport, where CDM has been implemented since 2010, departure times are respected in more than 85% of the cases, against 80% before, the ground traffic is more fluid (taxiing times have been shortened by 2 to 4 minutes), a reduction of 14.5 tons of fuel in daily consumption and also a significant decrease in CO 2 emissions [START_REF] Boeing | B777-Airplane Characteristics for Airport Planning AC[END_REF].

So far the management of the different service fleets which perform the turnaround process of ground handling has not been considered specifically in the CDM approach, even if it has an important part in the fluidity of aircraft ground movements, however [START_REF] Jaw | A heuristic algorithm for the multi-vehicle advance request dial-a-ride problem with time windows[END_REF] shows that 10% of all the flight delays are caused by the inefficient management of the different fleet of the ground handling operation. This paper after identifying possible objectives and essential constraints for ground handling at airports, proposes a decentralized structure to cope with this multi-fleet assignment problem. Considering that many parameters are imperfectly known, the fuzzy modelling formalism is introduced to take into account this important characteristic of this ground handling multi-fleet (GHMF) problem.

WORK CONTEXT

Aircraft turnaround defines the process of servicing an aircraft while it is on the ground between two successive flights it operates. The turnaround term implies a fast sequence between an arrival and a departure, however for many air transport operations, in particular for long haul flights, a large time interval may be programmed between them. During the turnaround, an aircraft must undergo a complex process composed of a set of elementary ground handling activities such as landing / boarding, unloading / loading of luggage, fuelling, catering, cleaning, water and sanitation processes. Fig. 1 describes the main ground handling operations taking place around a grounded aircraft as well as their precedence constraints. Arrival and departure ground handling tasks are distinguished. Ground handling operations are carried out by various service companies, using vehicle which are specific to each type of operation. To perform the turnaround process for each aircraft within the allocated time, these different companies have to coordinate between each other while respecting the constraints of scheduling tasks for each aircraft and the constraints related to the use of service vehicles. The duration of each ground handling operation is variable from one flight to another and depends in general of the type of aircraft, the volumes of passengers/luggage to be processed as well as of other external factors such as the current weather conditions at the airport. Then the large variability of elementary task durations should be taken into account when managing the different ground handling fleets. Each ground handling fleet type is supposed homogenous so that the same task can be performed with the same efficiency by any vehicle of each considered ground handling fleet. The duration of an elementary task t on aircraft a(i) assigned to flight i can be estimated either by an airline ground station manager or the corresponding ground handling manager who has received information about the load of the flight from the airline. It is here supposed that this duration is given by a dual fuzzy number

i t i t i t d d      ~where i t d is the current central value
of the duration of task t and i t  is the uncertainty range. See appendix A for an introduction to fuzzy dual numbers. A set of fuzzy rules can be built to generate these fuzzy dual task durations where the backbone is the nominal processing times with scaling factors and the fuzzy rules generate the dual part of the elementary task durations.

Each of the ground handling activities makes use of specialized equipment which must be turned available at the aircraft parking place at the right time to avoid delays. Some of the ground handling activities should be performed as soon as possible after the arrival of the aircraft at their parking stand and others must be performed only some time before departure from their parking stand. Depending of aircraft operation these two sub sets of activities can be performed in immediate sequence or are separated by an idle period of variable duration according to arrival and departure schedules of a given aircraft. Fig. 2 displays a standard situation for an aircraft undergoing a turnaround process where space is a rather scare resource and some tasks cannot be performed simultaneously (mainly for safety reasons). It appears that the efficient operations of such complex process which repeats with each aircraft arrival or departure is very difficult to be achieved while it is a critical issue for airport operations performance. Then advanced management tools are necessary to cope in a satisfactory way with this problem. Fig. 2. Ground handling arrangement [START_REF] Boeing | B777-Airplane Characteristics for Airport Planning AC[END_REF] .

Few publications covering Fuzzy VRP or Fuzzy Scheduling are available in the literature. The Fuzzy VRP has been introduced as a VRP problem with time window constraints where the customer demand, the service and the travel times are given by fuzzy numbers. In [START_REF] Jia | Genetic Algorithm for Fuzzy Logistics Distribution Vehicle Routing Problem[END_REF], a simple description of a VRP problem with fuzzy traveling times is introduced and its solution is obtained through a genetic algorithm. In [START_REF] Tang | Ant Colony Algorithm Based on Information Entropy Theory to Fuzzy Vehicle Routing Problem[END_REF] where the duration of the time window of a VRP problem is considered as a fuzzy variable, the solution has been computed with an Ant algorithm whose monitoring is based on the evolution of the entropy of the solution. With regard to Fuzzy Scheduling, [START_REF] Dubois | Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge[END_REF] presents an overview of fuzzy approaches to scheduling and emphasizes the representation of preference profiles and the modelling of uncertainty distributions.

DECENTRALIZED MANAGEMENT OF GHMF

In this case, it is considered that airlines communicate with the ground handling fleet managers through their own ground station managers which are in charge of monitoring the ground handling activities at arrival or departure of each flight. For example, one of their objective with respect to flight arrivals is to minimize the waiting time for de-boarding passengers and luggage, another one is to make sure that passengers board the aircraft in due time before scheduled flight departure time. So, they will be in charge of requesting in due time the necessary ground handling resources for flight arrival or departure processing. In the case of a decentralized management of the different fleets of ground handling vehicles, the size of each fleet management problem is of course smaller. With respect to the definition of the corresponding decision problems, some objectives of the ground handling problem can be expressed as constraints at the individual fleet level. Once these constraints are set, a major objective for each ground handling fleet manager will consist in minimizing its ground handling variable costs related mainly to the fleet operations costs. This can be considered to be achieved by minimizing the travelled distance of the corresponding ground handling fleet, contributing also to airport environment protection (chemical emissions and noise).

To be feasible, a decentralized approach, nominal or on-line, must be cooperative. Each ground handling fleet assignment and scheduling (GHFAS) problem must be executed according to a sequence compatible with the organization of the ground handling activities (Fig. 1). Then each GHFAS problem should integrate time constraints generated from the solution of the uphill GHFAS problems or from the updated expected flight arrival schedules. The ground handling services are delivered in a disturbed environment with many operational uncertainties. For example, the expected arrival times for flights are subject to frequent delays, the duration of ground handling tasks is sensitive to unexpected events such as additional travel time due to traffic congestion on airside service ways or machine breakdown.

Airport air traffic control services update the predicted arrival times which are forwarded to airport services, including airlines and ground handling. This starts the process of updating the assignment and scheduling of tasks for each ground handling fleet. In the case in which repeated aircraft arrival schedule perturbations are expected, according for instance to meteorology conditions, the horizon of fleet management can be commonly limited to some hours ahead, while ground handling resources computed from the daily nominal GHMF problem must remain on the lookout. Each ground handling fleet manager may solve the new instance of each GHFAS problem by taking into account the scheduling constraints generated and provided by the uphill GHFAS problems or from the updated aircraft arrival schedule and parking positions forwarded by the airline. The predicted completion time of his activities on each aircraft are sent to the other ground handling managers and the corresponding airline. When a fleet manager decides to generate a new plan he communicates the result to the downhill ground handling operators so that they update their plans. The immediate uphill ground handling managers will be able then to compute the estimated time margins for each task by comparing their processing time plus the nominal duration of their task with the earliest processing time of the following tasks in turnaround process. Then, if some vehicle is delayed but remains within the computed time margin, no delay warning is sent to the following task providers. When delays of vehicles overcome time margins new scheduling constraints are generated and the following ground handling fleet managers solve an updated version of their GHFAS problem.

Fig. 3. Information flows structure

The solutions of the successive updated GHFAS problems are forwarded to the airlines which produce new estimates for the departure schedule of their aircraft. However, it appears that this approach generates a lot of communication between each fleet managers as well as a large amount of computation to update detailed assignment solutions. So in the next section a simplified approach to decentralized ground handling management is introduced with the corresponding heuristic to produce on-line solutions to the GHMF assignment problem.

A FUZZY HEURISTIC FOR ON-LINE GHMF ASSIGNMENT PROBLEM

The problem for each ground handling fleet is here to assign ground handling vehicles to arriving or departing aircraft so that each aircraft is serviced by a vehicle while, according to the current operational situation, no delay or a minimum delay is produced. For that, the airline ground station managers generate resources requests to the ground handling fleet managers. The produced schedules are based on the predicted arrival times as well as the scheduled departure times. These schedules take not only into consideration the possible variation of the ground handling tasks durations by using a fuzzy dual formalism [START_REF] Cosenza | Nombres et ensembles duaux flous et applications[END_REF][START_REF] Cosenza | Fuzzy sets and dual numbers: an integrated approach[END_REF] , but consider also the criticality of the flight. This criticality depends on the current predicted delay as well as the operational consequences on other flights. Then more critical flights may get their ground handling solution treated before earlier less critical scheduled flights. The following notations are adopted: Each task of the turnaround process

  T t ,..., 1 
is carried out on an aircraft a(i) associated to a flight i, iI, (I=I A I D , I A is the set of arriving flights and I D is the set of departing flights) by a specific service provider

  K k ,..., 1  .

Fuzzy-based ranking of flights

The first step of the proposed heuristic consists in performing an initial ordering of the flights in accordance with their current predicted arrival time -Connection constraints when arriving passengers must reach without delay another departing flight.

-Departure schedule when the arriving aircraft must be ready to start a new flight with a tight schedule.

When considering connection constraints, let i C be the set of departing flights connected to arriving flight i. The time margin between fight i and each flight j in C i is given by:

  i ij i ul ij i db a i d j a ij C j d T d t t m        , max ˆ (1)
Here ij T ~and ij  ~ are respectively the connecting delay for passengers and luggage between flights i and j. The margin between arrival flight i and departure flight j serviced in immediate succession by the same aircraft is:

ij a i d j a ij D t t m    with ) (i j   (2)
D max ~                       (3)
Then, the fuzzy margin of arriving aircraft i is given by:

a ij i C j a i m m i min ~) (     (4)
The amended arrival time for flight i is then given by:

a i a i a i m t t   (5)
To each departing flight i  I D , can be assigned the difference In the case in which the ground handling tasks are relative to a departing flight j, the amended predicted time to start grand handling activities at the corresponding parking position is now given by:

d i d i d i t t t    ˆ between
a ij j i and C j i d j d j m t t i min ~) ( 1       (6) with pb wa bd ca ll fu a i i d d d d d d m max ~) (                (7)
Then, to each flight i, either arriving or departing, is assigned a time parameter i  such as: where is the fuzzy dual pseudo norm defined in the appendix. Then the flights, either arriving or departing, present in the considered period of operation can be ranked according to an increasing i  index. Let the integer r a (i) be the amended rank of flight i.

Ground Handling Fleets assignment to flights

Then flights are processed in the produced order r a (i) where ground handling vehicles are assigned to the corresponding aircraft. In the case of an arriving flight, ground handling arrival tasks (unloading luggage, de-boarding, cleaning and sanitation) are coped with by assigning the corresponding vehicles in accordance to their previous assigned tasks with other aircraft, their current availability, and their current distance to the considered aircraft. Here the common reference time schedule for the ground handling arrival tasks is

A a i I i t  , ˆ.
In the case of a departing flight, ground handling departure tasks (fuelling, catering, luggage loading, boarding, water and push back) are also coped with by assigning the corresponding vehicles in accordance to their previous assigned tasks with other aircraft, their current availability, and their current distance to the considered aircraft. Here the common reference time schedule for the ground handling departure tasks is

D d i low I i t B  ), ( .
In both cases it is considered that the whole set of different ground handling vehicles necessary at arrival or departure is assigned by considering the common reference time schedule. This assignment of vehicles to flights either arriving or departing is performed on a greedy base by considering the closest vehicle available to perform the required task. This will make that at the start of ground handling activities for an arrival or departure flight, all necessary resources will be nearby the parking place and that scheduling constraints between elementary ground handling tasks (see fig. 1) will be coped with locally without need of communication between the different ground handling fleet managers. This is a rather simple greedy heuristic which provides for each fleet facing the current service demand a complete solution through a reduced computational effort. So there is no limitation in calling back this solution process any time a significant perturbation occurs. The evaluation of the proposed decentralized approach has been performed using aircraft traffic data for a 24h period with ground handling activities taking place at the four parking areas related with the four terminals of PDM Airport. Except for aircraft staying at night at the airport, a large majority of ground handling operations are done in the context of fast turnaround operations. Different sizes for each of the ground handling fleets have been considered in various scenarios. Fig. 5 displays one of the considered compositions for ground handling fleets. Perturbations have been also introduced for some arriving aircraft with updated predictions available with fifteen minutes ahead.

The proposed heuristic approach has been tested for the aircraft traffic at 1 st of August, 2007 (345 aircraft turnarounds on that day). The resulting earliest departure time for aircraft have been compared with the real time departure data, showing that with rather reduced ground handling fleets, available at each terminal, the proposed decentralized heuristic, does not generate additional delays. The application of the proposed heuristic approach has led to delays with respect to departure schedule involving only 36 aircraft, with a maximum delay of 16 minutes. where delayed for multiple reasons, including one of the main reasons, ground handling delays. Figure 6 displays the hourly distribution of delayed aircraft at departure resulting from the application of the proposed decentralized approach.

Clearly, the occurrence of these delays corresponds to the busiest aircraft traffic periods at the airport. In this communication the problem of managing in a decentralized way airport ground handling has been considered. Then, adopting a decentralized management structure, where airline station managers and ground handling fleet managers interact, an heuristic taking explicitly into account the uncertainty about elementary processing times has been developed. This heuristic is based on the cooperation between the different tactical decision makers, providing an efficient reactive ground handling multi fleet management structure. This cooperation scheme appears to be compatible with an overall collaborative decision making approach for the airside management at airports. A case study considering aircraft and ground handling traffics at PDM Airport during a typical summer day has been developed through simulation, showing the interest of the proposed approach. Observe that here the max and min operators produce new fuzzy dual numbers.

Fig. 1 .

 1 Fig. 1. Ground handling activities at arrival and departure

D

  where ij ~is the minimum fuzzy dual duration of ground handling around arrival of flight i and departure of flight j. Here ) (i  provides the number of the next flight serviced by the aircraft operating flight i. Then:

the predicted departure time d

  numbers or fuzzy dual numbers. Symmetrically, each departing flight must cope with operational constraints related with successive flights by the same aircraft and flight connections for passengers and cargo.

Fig. 4 .

 4 Fig.4. 01/08/2007 PDM Airport Aircraft Hourly Traffic

Fig. 5 .

 5 Fig.5. Example of composition of ground handling fleets The average delay among delayed aircraft has been of 7 minutes. Historical data from 01/08/2007 at Palma de Mallorca Airport indicate that about 200 aircraft departureswhere delayed for multiple reasons, including one of the main reasons, ground handling delays. Figure6displays the hourly distribution of delayed aircraft at departure resulting from the application of the proposed decentralized approach. Clearly, the occurrence of these delays corresponds to the busiest aircraft traffic periods at the airport.

Fig. 6 .

 6 Fig.6. Hourly distribution of resulting delays

  APPENDIX A Fuzzy dual variables and basic calculusThe set of dual numbers is the set  ~of numbers (Cheng, 1994the primal part and b is the dual part of the fuzzy dual number. Here  is the unity pure dual number. A crisp fuzzy dual number will be such as b is zero, it loses both its dual and its fuzzy attributes. The lower and upper bounds of b a fuzzy dual number is given below where μ is a symmetrical membership function defined over R:

Fig. A 1 .

 1 Fig. A 1. Example of dual representation of a fuzzy number The fuzzy dual (FD) addition of dual fuzzy numbers, written  ~ is given by:)( ) ( ) ( ) ( 2 1 2 1 2 2 1 1 y y x x y x y x          

  Figure A.2 displays standard fuzzy dual numbers with different shape parameters.

Fig. A 2 .

 2 Fig. A 2. Examples of fuzzy dual numbersThe shape parameter is given by:


Bashar Qasim

Mikael Motelica-Heino

Potentially toxic element fractionation in technosoils using two sequential extraction schemes

Keywords: Potentially toxic element fractionation, Sequential extraction, Mining and smelting activities, contaminated technosoils

This study reports the chemical fractionation of several potentially toxic elements (Zn, Pb, Cd, As, and Sb) in contaminated technosoils of two former smelting and mining areas using two sequential extraction schemes. The extraction schemes used in this st udy were the Tessier's scheme and a modified BCR scheme. The fractions were rearranged into four equivalent fractions defined as acid soluble, reducible, oxidizable, and residual to compare the results obtained from two sequential extraction schemes. Surfa ce soils were samples from a waste landfill contaminated with Zn, Pb, and Cd located at Mortagne -du-Nord (MDN; North France) and from a settling basin contaminated with PTE such as As, Pb, and Sb located at La Petite Faye (LPF; Limoges, France). The study of the Zn, Pb, Cd, As, and Sb partitioning in the acid soluble, reducible, oxidizable, and residual fractions of the technosoils revealed that Zn, Cd, and Pb were mainly associated with the acid soluble and reducible fractions for MDN site, while As, Sb, and Pb were associated with residual fraction for LPF site. Fractionation results indicate that the percentages of Zn, Pb, Cd, As, and Sb extracted in Fe-Mn oxide bound fraction of Tessier's scheme were always higher than those extracted by modified BCR scheme. This may be attributed to the stronger Tessier's scheme conditions used to extract this fraction. In contrast the percentages of Zn, Pb, Cd, As, and Sb extracted in the organic fraction of the modified BCR scheme were always higher than those of the Tessier's scheme. The order of mobility of PTE was as follows: Cd > Zn > Pb in MDN site and As > Sb > Pb in LPF site. PTE were distributed in all soil fractions, with the most relevant enrichments in extractable and residual fractions. A significant amount of Cd, Pb, and Zn were rather mobile, which suggests that these elements can be readily available to plants and soil organisms.

Introduction

Mining and smelting activities are considered as highly polluting sources of potentially toxic elements (PTEs) in the environment [START_REF] Shu | Acidification of lead/zinc mine tailings and its effect on heavy metal mobility[END_REF][START_REF] Schuwirth | Vertical distribution and speciation of trace metals in weathering flotation residues of a zinc/lead sulfide mine[END_REF]). In fact both mining and smelting processes produce large amounts of wastes resulting in soil contamination [START_REF] Davies | Heavy Metal Contamination from Base Metal Mining and Smelting: Implications for Man and His Environment[END_REF][START_REF] Adriano | Trace Elements in Terrestrial Environments[END_REF]. These processes introduce PTE into the environment through gaseous and particulate emissions but also liquid and solid wastes [START_REF] Dudka | Environmental impacts of metal ore mining and processing: a review[END_REF]. The exposure of the population to these PTE involves a risk to human health by consuming the plants grown on the contaminated sites [START_REF] Nabulo | Assessing risk to human health from tropical leafy vegetables grown on contaminated urban soils[END_REF].

Since the determination of the total concentration of PTE in soils does not give sufficient information about the mobility and toxicity of these elements [START_REF] Nowak | Sequential extraction of metal forms in the soil near a roadway in southern Poland[END_REF][START_REF] Szulczewski | Comparison of XANES analyses and extractions to determine chromium speciation in contaminated soils[END_REF][START_REF] Rauret | Extraction procedure for the determination of heavy metals in contaminated soil and sediment[END_REF][START_REF] Petit | Sequential extractions for determination of cadmium distribution in coal fly ash, soil and sediment samples[END_REF], additional information on the concentrations of particular physicochemical forms of the PTE is in fact required as their behavior in the environmental system depends on their chemical speciation [START_REF] Merian | Metals and their compounds in the environment: occurrence, analysis, and biological relevance[END_REF][START_REF] Rodriguez | Heavy metal distribution and chemical speciation in tailings and soils around a Pb-Zn mine in Spain[END_REF]. Speciation can be defined as the identification and quantification process of different species or binding forms in which an element occurs [START_REF] Ure | Single and sequential extraction schemes For Trace metal speciation in soil and sediment[END_REF][START_REF] Ure | Chemical Speciation in the Environment[END_REF]. PTE in soils are partitioned between the different phases present, i.e., organic matter, oxyhydroxides of iron, aluminum and manganese carbonates, sulfides, clays, etc., and are retained on these solid phases by different mechanisms, i.e., ion exchange, complexation, precipitation or co -precipitation [START_REF] Weber | Fate and behavior of herbicides in soil[END_REF][START_REF] Benedetti | Metal ion binding to humic substances: application of the non-ideal competitive adsorption model[END_REF], or are retained in the lattice of soil minerals. Since the behavior of PTE in the soil-water-plant system depends on their speciation, the determination of PTE partition in soils assessed by sequential extraction schemes can give useful information. Different extraction schemes have been widely used to mimic differing environmental conditions, with strong acids being used to determine the total concentration of PTE and weaker solutions to determine compounds that are relatively soluble and bioavailable [START_REF] Anju | Comparison of two sequential extraction procedures for heavy metal partitioning[END_REF].

The main goal of sequential extraction is to convert PTE bound in the different solid phases into soluble forms with a specific chemical extractants used at each step (Tokalioglu et al. 2000a, b). In these sequential extraction schemes, a series of progressively harsher reagents is used to dissolve increasingly refractory forms of these elements, from the more mobile to the more refractory [START_REF] Maclaren | Studies on soil copper. I. The fractionation of copper in soils[END_REF][START_REF] Shuman | Fractionation method for soil micro elements[END_REF].

However, sequential extraction schemes are greatly influenced by experimental factors such as the choice of chemical reagents, specific extraction time, and weight ratio of sample to volume of the extraction solution. Thus, there are many criticisms of these schemes as several operationally defined artifacts may occur including the lack of specificity of reagents, the possibility of re-adsorption of released PTE to the remaining fractions [START_REF] Belzille | Testing readsorption of trace elements during partial chemical extractions of bottom sediments[END_REF][START_REF] Hass | Sequential selective extraction procedures for the study of heavy metals in soils, sediments, and waste materials -a critical review[END_REF] and the potential alteration of other phases during one extraction step [START_REF] Li | Chemical partitioning of trace and major elements in soils contaminated by mining and smelting activities[END_REF].

The best-known sequential extraction scheme [START_REF] Tessier | Sequential extraction procedure for speciation of particulate trace elements[END_REF]) consists of five steps in which PTTE are considered to be distributed among different soil fractions:(1) exchangeable, (2) bound to carbonate, (3) bound to Fe-Mn oxides, (4) bound to organic matter, and (5) residual. Many sequential extraction schemes derived from the Tessier one have been applied to soils and sediments to fractionate PTE by using different chemical extractants to obtain useful information about the bioavailability and mobility of these elements [START_REF] Elliott | Content and fractionation of heavy-metals in water treatment sludges[END_REF][START_REF] Krishnamurti | Speciation of particulate-bound Cadmium of soils and its bioavailability[END_REF] in particular, one of the Community Bureau of Reference (BCR) (Ure et al.1993)

Materials and methods

Soil sampling

Two sites were selected for this study. The first one is metallophyte grassland located in Mortagne-du-Nord (MDN) in Northen France. Thus area has been occupied for over 60 years by a Zn smelter unit linked to a sulfuric acid production unit and a Pb smelting unit for a few years. The site has been back-filled with slags and smelting crucible to a thickness of about 3 meters. More than 30 metal-bearing (Zn, Pb and Cd) mineral species have been identified such as sulfides, sulfates, carbonates, oxides and silicates [START_REF] Thiry | Les phases minérales majeures et mineures d'une friche industrielle de métallurgie des métaux non ferreux: état d'altération, évolution géochimique et devenir des métaux polluants du site de Mortagne-du-Nord[END_REF]. The second site is the former mine tailing of La Petite Faye (LPF) located 60 km North-East of Limoges (France), where mining wastes have been stored in a settling basin from 46 years ago. The main PTE present in soils are As, Pb and Sb [START_REF] Néel | Factors affecting natural development of soil on 35-year-old sulphide-rich mine tailings[END_REF][START_REF] Nénert | Les districts aurifères du Haut-Limousin, Massif central français: approche minéralogique et géochimique[END_REF].

Three distinct locations (zone1, zone2 and zone3) from each site were selected according to the level of PTE concentrations and spatial distribution of the vegetation cover. One kg of surface soil (0-20 cm) was sampled at each location with a stainless steel spade. In the MDN site the vegetation cover consists of essentially Arabidopses hallari and Avena sativa L. whereas, in LPF site, Graminea, Equisetum telmateia, Betula pendula and Pteridium aquilinum were present with large horsetails of black alder and poplar [START_REF] Dutreuil | Grain size and mineralogical controls on vegetal colonisation of tailings in a former gold mine (La Petite Faye Massif central, France)[END_REF].

All soil samples collected were carefully transferred to clean polyethylene bags before transport to the laboratory. Collected soil samples were oven-dried at a constant temperature of 40°C, manually homogenized and then quartered. Two equivalent fractions were obtained from each quartered sample. One fraction was used for the determination of the soil physicochemical properties whereas the other fraction was used for chemical analysis of PTE.

Samples for chemical analysis were sieved first using a 2mm mesh sieve, to remove gross plant matter and subsequently crushed to pass through a 250μm sieve [START_REF] Brooks | Biological Methods of Prospecting for Minerals[END_REF].

Reagents and standards

All the reagents used to prepare the extracting solutions were products of analytical-grade quality (Merck pro-analysis, Darmstadt, Germany). All solutions and dilutions were prepared using doubly deionized water (18.2MΩcm -1 ) (Thermo Scientific Barnstead Easy pure II systems). Standard stock solutions of 1000mg.l -1 of different elements were prepared from metal wires or salts of purity higher than 99.998% (VWR international, BDH Prolabo ICP Standards, Belgium). Diluted standard working solutions were prepared from these on a daily basis. All laboratory glassware and plastic ware were rinsed three times with double deionized water after being soaked in a HNO 3 (10%, v/v) bath for 24h.

Analysis of the soil physico-chemical properties

Physico-chemical properties of the samples ( 2mm fraction) were characterized by routine soil testing laboratories according to standardized French (AFNOR 1999) or international (ISO 1999) procedures (Table 3). pH and EC of each samples were determined in distilled water extracts (1:2.5 w/v) (NF ISO 10390 (2005)). Total organic carbon concentrations were determined according to [START_REF] Iso | Soil Quality. Guidance on the ecotoxicological characterization of soils and soil materials Guidelines no[END_REF]. Cation exchange capacity (CEC) was determined by the 0.05N cobalthexamine method [START_REF] Aran | A spectrophotometric measurement of soil cation exchange capacity based on cobaltihexamine chloride absorbance[END_REF]. Organic matter was determined according to [START_REF] Storer | A simple high sample volume ashing procedure for determining soil organic matter[END_REF] procedure depends on loss of weight on ignition (LOI).

X-ray fluorescence spectrometer (XRF)

The total concentration of PTE in the contaminated soil samples was determined from X-ray fluorescence using a XMET 5100 X-ray spectrophotometer (Oxford Instruments Analytical GmbH, Germany). PTE in the soil were determined by XRF method by pressed soil pellets with an acquisition delay time of 3 min. for each sample.

Soil mineralogical characterization

Powder X-ray diffraction was used for mineralogical characterization in bulk studied soils using a diffractometer INEL XRM3000 montage transmission (geometry Debye-Scherrer), (CoKα,40 kV,30mA) between 5 to 90°2. The analyses of minerals were carried out on thin section of the bulk sample using INEL diffractometer equipped with an INEL CPS 120 detector (Debye-Scherrer geometry).

Sequential extractions

Sequential extraction schemes of [START_REF] Tessier | Sequential extraction procedure for speciation of particulate trace elements[END_REF] and of the European Union's Standards, Measurements and Testing Program (SM&T) [START_REF] Sahuquillo | Use of a certified reference material for extractable trace metals to assess sources of uncertainty in the BCR three stage sequential extraction procedures[END_REF] were used in this study and are summarized in (Table1 and Table 2) respectively. Each extraction step was performed in triplicate, starting with 1g of original soil material. The aqua regia step performed on the residue from step 4 for (Tessier's scheme) or step 3 for (BCR scheme) was based on the ISO 11466 method (ISO norm 11466 (1995).

Metal concentrations in all extracts from these sequential extraction schemes were determined by ICP-MS (Finnigan Element XR, Thermo Electron, Germany). Additionally blanks of the different extractants were analyzed in triplicate and further subtracted from the soil samples.

The accuracy of the sequential chemical extraction scheme was determined by comparing the sum of the concentrations obtained at the different steps of the extraction scheme with total metal content [START_REF] Cuong | Metal speciation in coastal marine sediments from Singapore using a modified BCR sequential extraction procedure[END_REF]. 3 Results and discussion

Physico-chemical properties of the studied soils

The main physico-chemical properties of the soil samples are summarized in (Table3). The pH of MDN soils was slightly acid and ranged from 6.14 to 6.92, while the pH of LPF soils was acidic to slightly acid and ranged from 3.65 to 5.75, this could be because of the acid forming process occurring during mine tailing weathering producing acid mine drainage leading to the increase in hydrogen ion activity. Organic matter ranged from 8.26 to13.84%

and from 5.27 to 6.83% for MDN and LPF respectively. Organic carbon of both MDN and LPF ranged from 3.35 to 6.45% and 0.3 to 1.67% respectively. Total PTE concentrations for MDN site (n=3) were as follows: Zn (3111-7721 mg kg -1 ), Pb (879 -3558 mgkg -1 ) and Cd (53 -72 mgkg -1 ), while, total PTE concentrations for LPF site (n=3) were as follows: As (2677 -83937 mgkg -1 ), Pb (362 -16100 mgkg -1 ) and Sb (100 -871 mgkg -1 ). The concentrations of Zn, As and Pb are greater than: (i) the possible toxicity values (10 -300, 5-20 and 30-300 mg/kg, respectively; [START_REF] Kabata-Pendias | Trace Elements in Soils and Plants[END_REF][START_REF] Kiekens | Zinc[END_REF][START_REF] Mcgarth | The Soil Geochemical Atlas of England and Wales[END_REF]) and (ii) the normal background levels given (e.g. 1 -50 mgkg -1 for Zn, 1-30 mgkg -1 for As and 9-50 mgkg -1 for Pb [START_REF] Wilson | The chemistry and behavior of antimony in the soil environment with comparisons to arsenic: A critical review[END_REF] for soils of diverse origins and lithologies throughout the world.

Soil mineralogical composition

As derived from the XRD analysis, there is no significant differences in mineralogical composition between the different soil samples are found for the LPF site. Surface soils in the studied former mining area are mainly constituted by silicate species such as quartz, muscovite, clinoclore, feldspar together with clay minerals such as illite, kaolinite and chlorite. The MDN surface soils are mainly constituted by quartz, muscovite, albite, dolomite, orthoclase and calisite.

Fractionation of the PTE

Results of the PTE fractionation in the studied soils for the different zones for the two sequential extraction schemes are shown in (Fig. 1and Fig. 2). The contents of these elements extracted in each steps of sequential extractions scheme are summarized in (Tables 4 and5).

In the Tessier' scheme the equivalent fraction used were T1 (Exchangeable fraction), T2

(Bound to carbonate), T3 (reducible (Fe-Mn oxide bound) fraction), T4 (oxidisable (organic/sulphide) fraction) and T5 (residual fraction). Whereas, in modified BCR scheme, the equivalent fraction used were B1 (acid soluble (carbonate bound) equivalent to (T1+T2)), B2 (reducible (Fe-Mn oxide bound) fraction equivalent to T3), B3 (oxidizable (organic/sulphide) fraction), equivalent to T4) and B4 (residual fraction, equivalent to T5).

MDN site

Zinc

Tessier's extraction scheme showed that Zn recovery was between 95.7% and 98.4% for all studied samples which can be regarded as acceptable in comparison with other studies (Hongyu Liu 2005;[START_REF] Rodriguez | Heavy metal distribution and chemical speciation in tailings and soils around a Pb-Zn mine in Spain[END_REF]). Zn was mainly associated with carbonate fraction and accounted for 38.2%, 31.1% and 30.3 % of total Zn for zones1, 2 and 3 respectively (Fig. 1a).

The concentration of Zn in reducible fraction ranged from 25.8 to 28.3% of total Zn for all three zones. On average, the concentration of Zn in exchangeable fraction accounted for 17-21.7% and in residual fraction 14.3-17.2% for all studied zones. Data in (Fig. 1a) indicate that general distribution of Zn into the various fractions for all studied zones followed the order: T2 T3 T1 T5 T4. This observation is in agreement with that of [START_REF] Pardo | Determination and speciation of heavy metals in sediments of Pisuerga river[END_REF].

BCR extraction scheme showed that Zinc recovery was ranged between 96.1% and 99.7% for all studied samples. Zn distribution pattern in each fraction followed the order: B1 B2 B4 B3 (Fig. 2a). About 53.4%, 42.2% and 48.8% of total Zn occurs in soluble fraction (B1) for zone1, 2 and zone 3 respectively. 21.2 -26.1% is distributed in reducible fraction, 18 -20 % is incorporated into silicate lattice, while fraction of Zn bound to oxidisable fraction represents less than11% of total Zn for all studied samples. The first extraction step (B1) is important for this metal. A possible explanation for this is the presence of calcareous minerals, which act as a strong adsorbent for Zn in mine tailings [START_REF] Anju | Comparison of two sequential extraction procedures for heavy metal partitioning[END_REF][START_REF] Nannoni | Fractionation and geochemical mobility of heavy elements in soils of a mining area in Northern Kosovo[END_REF].

Collectively, both extraction schemes gave similar results for Zn. In fact the relative Zn associations, as provided by both extraction schemes are similar for all four equivalent fractions (i.e. acid soluble > reducible > residual > organic/sulphide bound) for all studied zones. Zn fractionation is dominated by acid soluble fraction. However it was observed that the amount of Zn extracted in acid soluble fraction by BCR scheme was higher than Tessier's scheme , as more Zn was extracted in (B2) by BCR scheme than by Tessier's scheme (T3).

Lead

Lead recovery in Tessier's extraction scheme ranged between 92% and 110% for all studied samples. The relative distribution of Pb among different fractions in zone1 followed the order: T2≈T3 T4 T5 T1 (Fig. 1b). It can be seen that Pb was mainly associated with carbonate and reducible fractions accounting for 31.4% and 30.8% respectively of total Pb content. Pb in zone1 is shown to be strongly associated with carbonate fraction. This is coherent with thermodynamic prediction [START_REF] Brookins | Use of sequential extraction to evaluate the heavy metals in mining wastes[END_REF]) that cerrusite (PbCO 3 ) would be the dominant lead mineral at the Eh-pH conditions of mine tailing. Additionally CaCO 3 may act as a strong adsorbent for Pb and could complex Pb as (PbCO 3 ) [START_REF] Li | Chemical partitioning of trace and major elements in soils contaminated by mining and smelting activities[END_REF]. The next Pb fractions were oxidisable and residual which represents 18% and 15% respectively.

The fractionation pattern of Pb in both zone 2 and zone 3 was: T3 T2 T4 T5 T1 (Fig. 1b).

This was comparable for the two zones, demonstrating that Fe-Mn oxide bound fraction (42.9%) for zone 2 and (32.8%) for zone 3 is the predominant fraction. Fe-Mn oxides are in fact important scavengers of metals in soils particularly at a high pH range (pH > 7.0) [START_REF] Tipping | Effects of pH on the release of metals from naturally-occurring oxides of Mn and Fe[END_REF]). The remaining Pb content was distributed among carbonate, oxidisable and residual fractions represents 29%, 13% and 10 for zone 2 and 21%, 26% and 13% for zone 3 respectively.

BCR extraction scheme showed a percentage recovery for Pb ranging between 97% and 113% for all studied samples. Pb association in zone1 followed the order: B1 B2 B3 B4

(Fig. 2b), indicating that highest percentages of Pb were associated with acid soluble (32.6%), Fe-Mn oxide bound (28.9%), organic/sulphide (27.2%) and residual fraction (11%). In contrast Pb distribution among different fractions in zone2 and zone3 followed the order: B2 B3 B1 B4, indicating the relative importance of Fe-Mn oxide bound (33.7% and 30.6%)

and organic/sulphide fraction (31.1% and 30.1%) for both zone 2 and 3 respectively. This finding is in agreement with that of [START_REF] Ramos | Sequential fractionation of copper, lead, cadmium and zinc in soils from or near Donana National Park[END_REF][START_REF] Romaguera | Comparison of three sequential extraction procedures for trace element partitioning in three contaminated Mediterranean soils[END_REF]Zemberyova et al. 2006.

Fractionation results obtained for Pb from the two extraction schemes were also similar for all studied zones. The relative distribution of Pb in all four fractions was also similar. The most abundant fractions were acid soluble and Fe-Mn oxide bound. Again, a higher amount of Pb was extracted with reducible fraction by Tessier's extraction scheme compared to BCR scheme. In contrast for organic/sulphide bound fractions it was the opposite.

Cadmium

Cd recovery in Tessier's extraction scheme was ranged between 84% and 105% for all studied samples. The fractionation of Cd in each fraction followed the order: T1 T5 T3 T2 T4 (Fig. 1c) for zone1 and 2, while it followed the order: T1 T2 T3 T4 T5 for zone 3, matching the usual distribution of this element in contaminated soils [START_REF] Narwal | Association of cadmium, zinc, copper, and nickel with components in naturally heavy metal-rich soils studied by parallel and sequential extractions[END_REF][START_REF] Sanchez | Forms of cadmium, lead, and zinc in polluted mining soils and uptake by plants (Soria Province, Spain)[END_REF][START_REF] Kaasalainen | Use of sequential extraction to assess metal partitioning in soils[END_REF]. Soil Cd was mainly associated with exchangeable fraction representing about 46.1%, 46.1 % and 37.64 % of total Cd for zones 1, 2 and 3 respectively. This fraction includes Cd held by electrostatic adsorption and specifically adsorbed. This fraction may be also called easily soluble and/or exchangeable. This form of Cd is considered more mobile and phytoavailable [START_REF] Dudka | Effect of solid-phase speciation on metal mobility and phytoavailability in sludge amended soil[END_REF].

It can be seen that for the non-exchangeable fraction, the highest percentage of Cd 22.3% and 23.9% in zone1 and 2 was associated with residual fraction followed by Fe-Mn oxide bound, carbonate bound and organic/sulphide fractions whereas, in zone3, the fractionation pattern revealed that Cd was most abundant in carbonate bound fraction and at least in organic/sulphide bound fraction.

Cd recovery in BCR extraction scheme was ranged between 93% and 110% for all studied samples. Cd fractionation for both zones1 and 2 decreased in the order: B1 B4 B2 B3 (Fig. 2c). It can be further seen that for the majority of samples analyzed, Cd is distributed between acid soluble 53.5% and 45.8%, residual 16.8% and 31.6%, Fe-Mn oxide bound 15% and 17% and organic/sulphide 14% and 5.4% fraction for zones1 and 2 respectively. Cd distribution in zone3 was followed the order: B1 B2 B3 B4 (Fig. 2c), indicating the relative importance of the acid soluble fraction followed by the Fe-Mn oxide bound, organic/sulphide and residual fractions.

The relative Cd associations, as revealed by both extraction schemes, are similar in all four equivalent fractions for all studied samples indicating the suitability and comparability of both methods for Cd. The fractionation is dominated by acid soluble fraction. It was also observed that more Cd was extracted in reducible (Fe-Mn oxide bound) fraction by Tessier's extraction scheme than by BCR scheme in B2. Again, more Cd was extracted in organic fraction (B3) by BCR scheme than by Tessier's scheme. However, for both methods Cd is extracted maximally in the most mobile phases. -

-------------------------------------------------------------------------------------------------------------------------- Zone Exchangeable Carbonate Reducible Oxidizable Residual % Recovery (T1) (T2) (T3) (T4) (T5) ---------------------------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------------------------
a All values are in percentages.

Table 5: Potentially toxic element fractionation in the different solid phases as obtained from the modified BCR scheme b .

-

------------------------------------------------------------------------------------------------------------------------------------- Zone Acid soluble Reducible Oxidizable Residual % Recovery (B1) (B2) (B3) (B4) ---------------------------------------------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------------------------------------------- b
All values are in percentages.

LPF site

Arsenic

Tessier's extraction scheme exhibits As recovery ranged between 97% and 99% for all studied samples. Data obtained during the sequential extraction steps carried out on soil samples of LPF site indicate that As was distributed in the order: T5 T4 T3 T2 T1 (Fig. 1d) for all three zones. Results showed that in zone1 more than 90% of As was distributed in residual fraction, while, in zone2 and 3 it represents more than 80% and 50% respectively of total As content. The majority of As remained from residual fraction was distributed among other different fractions. The sum of As extracted in fractions T1, T2, T3

and fraction T4 was almost negligible for zone1 and 2 samples, whereas, in zone 3, Fe-Mn oxide and organic/sulphide fractions represent 21.3% and 14.5% respectively.

As recovery by BCR extraction scheme ranged between 93% and 99% for all studied samples. More than 90% of total As was bound to residual fraction in zone1, while both zone 2 and 3 represents 80% and 60% respectively of total As content. The other fractions showed a lower As content, especially for B1and B2 which showed less than 1% of total As content.

In contrast zone 3 presents 7.4 and 12.7 % for B1 and B2 respectively. As was concentrated in residual fractions (Fig. 2d), which in term of mobility may be considered immobile. The important quantities of As in residual fraction were in agreement with results of other studies investigating the distribution of this element in contaminated soils (Fernandez et al. 2004;Novoa-Munoz et al. 2007;[START_REF] Pueyo | Use of the modified BCR three-step sequential extraction procedure for the study of trace element dynamics in contaminated soils[END_REF]. The distribution of As into soil fractions B4 B3 B2 B1 did not change significantly as a function of contamination level;

nevertheless, the anthropogenic input mainly affected residual and reducible fractions.

It can be seen that fractionation results obtained from the two methods were similar (Table 2), the most abundant fraction being the residual fraction. Despite of the highest As contents leached from residual fraction, it can be seen that percentage of As extracted by Tessier's scheme for reducible fraction is little higher than that of BCR scheme, while As extracted in oxidisable fraction of BCR scheme was higher than that extracted in Tessier's T4 (oxidisable) fraction.

Antimony

In Tessier's extraction scheme, Sb recovery ranged between 88% and 91% for all studied samples. Sb was largely distributed in residual fraction accounting for 93.5%, 98% and 70.9% for zones1, 2 and zone 3 respectively. Results showed that highest percentage of Sb was associated with residual fraction which represents metal bound to crystalline phase, followed by oxidisable fraction which represents metal bound to organic/sulphide fraction with a percentage of (0.7 -15.6 %) for each three zones. Sb amount relate to other fractions was very low and represents less than 3% for all zones. In all three zones, the distribution of Sb into various fractions was as follows: T5 T4 T3 T2≈T1 (Fig. 1f).

BCR extraction results showed a suitable Sb recovery ranged between 90% and 96% for all studied samples. More than 90% of total Sb was found to be bound with refractory minerals in residual fraction in zone1and 2, while zone3, represented only 57% of total Sb content. Sb distribution followed the order: B4 B3 B2 B1 (Fig. 2f). This finding is in agreement with that of Alvarez-Ayuso et al. 2012;[START_REF] Nannoni | Fractionation and geochemical mobility of heavy elements in soils of a mining area in Northern Kosovo[END_REF], which they found that more than 90%

of Sb was partitioned in residual fraction in former mining sites. In non-residual fraction, the highest percentage of Sb (27.2%) in zone 3 was associated with organic /sulphide fraction (B3), while in zones1 and zone 2 it represents less than 2% and 1% respectively. Reducible fraction represents less than 10% in zone 3 and less than 2% in zones1 and zone2.

It can be seen that a uniform fractionation pattern (residual > organic > oxidisable > reducible > exchangeable) was observed for Sb by both extraction schemes. The most abundant fraction was residual bound; amount of Sb extracted in reducible fraction by Tessier's method was in general higher than that by BCR scheme. However, more Sb was extracted in B3 fraction than in T4 (oxidisable) fraction.

Lead

Pb recovery in Tessier's scheme was ranged between 87% and 98% for all studied samples. It was apparent from the preliminary Tessier's sequential extraction procedure that most of Pb was mainly associated with residual fraction accounting more than 90% of total Pb content in zone1, while it represents 82% and 62% for zones 2 and 3 respectively. Pb distribution in both oxidisable and reducible fractions was variable for all zones accounted 6.26 to 26.5% for oxidisable fraction and 0.39 to 9.89% for reducible fraction in all zones. The low amount of Pb was associated with T1 and T2 fraction representing less than 1% for all zones. Despite the variability in total concentrations, Pb in all zones showed a similar distribution into various fractions: T5 T4 T3 T2 T1 (Fig. 1e). The high percentage of Pb in residual fraction is in broad agreement with that observed by [START_REF] Paulo | Selective chemical extraction of heavy metals in tailings and soils contaminated by mining activity: Environmental implications[END_REF] in soil contaminated by mining activity. The input of Pb in contaminated soils was redistributed among various soil fractions, with highest enrichment in residual and reducible fractions.

BCR extraction scheme exhibits Pb recovery ranged between 91% and 98% for all studied samples. The relative distribution of Pb in different soil fractions followed the order: B4 B3 B2 B1 (Fig. 2e). This fractionation pattern revealed that Pb was most abundant in residual fraction showing a comparable percentage with that of Tessier's scheme. The amount of Pb distributed in oxidisable fraction was ranged between 6.9 to 28.7%, while, it represents 0.22 to 7.45 % in reducible fraction for all zones. T1 and T2 also showed lowest Pb percentages < 1%. Pb partitioning indicates that this element is rather stable, since it is mainly associated with residual fraction.

It can be seen that the distribution of this metal among different equivalent fractions is similar for the two methods. Also, the extracted amounts of Pb in the two schemes are slightly comparable. Pb content in oxidisable fraction extracted by BCR scheme was slightly higher than those of Tessier's scheme. Despite the analogous pattern in all fractions, fractionation by both extraction schemes substantiate that acid soluble bound fraction is the least abundant for all zones.

Potential mobility of PTE in the technosoils

The knowledge of the mobility and bioavailability of PTE depends not only upon the total concentration but also upon the chemical form of the PTE. In fact little information is provided by the total soil concentration. For instance of more soluble metal compounds are more mobile and toxic (Kwon et al. 2001). On the other hand, the first extraction steps in any sequential extraction procedure represent the most mobile and bioavailable fractions and may provide useful information about the PTE.

The order of mobility of PTE considering their abundance in different soil fractions followed the order: exchangeable bound to carbonate bound to Fe-Mn oxide bound to organic matter residual fraction [START_REF] Tessier | Sequential extraction procedure for speciation of particulate trace elements[END_REF]. Exchangeable elements are a measurement of those elements which are released most readily into environment under acidic conditions and may be considered the most dangerous fraction to environment [START_REF] Stone | Trace metal composition and speciation in street sediment: Sault Ste. Marie, Canada[END_REF].

Changes in ionic composition, influencing adsorption-desorption reactions or lowering of pH could cause remobilization of these PTE from this fraction [START_REF] Ure | Chemical Speciation in the Environment[END_REF][START_REF] Marin | Reproducibility testing of a sequential extraction scheme for the determination of trace metal speciation in a marine reference sediment by inductively coupled plasma-mass spectrometry[END_REF][START_REF] Sutherland | Comparison between non-residual Al, Co, Cu, Fe, Mn, Ni, Pb and Zn released by a three-step sequential extraction procedure and a dilute hydrochloric acid leach for soil and road deposited sediment[END_REF]. PTE bound to Fe-Mn oxides that would be released under reducing conditions represent the reducible fraction. These oxides are well known "sinks" in the surface environment for these elements (Baruah et al. 1996b) and are thermodynamically unstable under anoxic conditions (Tokalioglu 2000). PTE bound to organic matter / sulphides would be released under oxidizing conditions and thus represent the oxidisable fraction. PTE associated with oxidisable phases are assumed to remain in the soil for a longer period but may be mobilized by decomposition processes [START_REF] Kennedy | Use of single and sequential chemical extractants to assess radionuclide and heavy metals availability from soils for root up-take[END_REF]. Residual fraction represents the elements that have the strongest association with crystalline structures of the minerals and are therefore the most difficult to mobilize.

The determination of PTE mobility in soils is based on the relative amount of the metal in exchangeable fraction (T1) containing the mobile forms and in carbonate bound fraction (T2)

containing easily mobilisable phases. The mobility index of PTE was calculated as the mobility factor (MF) based on the following equation by [START_REF] Kabala | Fractionation and mobility of copper, lead and zinc in soil profiles in the vicinity of a copper smelter[END_REF][START_REF] Narwal | Association of cadmium, zinc, copper, and nickel with components in naturally heavy metal-rich soils studied by parallel and sequential extractions[END_REF][START_REF] Salbu | Characterization of radioactive particles in the environment[END_REF]):

MF= × 100

Consequently, the mobility factor value determines the relative mobility and biological availability of PTE in the soil. On this basis high mobility factor (MF) values have been reported or interpreted as evidence of relatively high reactivity, high lability and high biological availability of PTE in soil (Karczewska 1996;Ma and Rao 1997).

On the basis of the MF values, the average mobility of PTE in the studied soil samples is shown graphically in Fig. 3 [START_REF] Clevenger | Lead speciation of particles on air filters collected in the vicinity of a lead smelter[END_REF][START_REF] Ettler | Mineralogy and environmental stability of slags from the tsumeb smelter, Namibia[END_REF].

The average potential mobility factor of As was 4.86 %. In our site, As can be considered a rather immobile element as most of this metalloid is bound to the residual mineral fraction.

The chemistry of arsenic precipitation-dissolution and adsorption-desorption is complex and depends on the charge of the absorbing species and the soil surface characterization. The

Potential mobility

results for arsenic fractions should reflect the geochemical behavior of arsenic in the surface environment. In our study on sequential extraction of arsenic from contaminated technosoils, we found that only a very small proportion of arsenic was extracted in the early fractions compare to the total arsenic content for both extraction schemes. However, high concentrations of arsenic found in residual fractions indicate that arsenic present in these soils is difficulty released by chemical extractants from the first steps of the schemes. The important quantities of As in residual fraction were in agreement with the results of other studies investigating the distribution of this element in contaminated soils [START_REF] Filippi | Arsenic in contaminated soils and anthropogenic deposits at the Mokrsko, Roundy, and Kasperske Hory gold deposits, Bohemiam Massif (CZ)[END_REF][START_REF] Ghosh | Assessment of a sequential extraction procedure for fractionation of soil arsenic in contaminated soils[END_REF][START_REF] Matera | A methodological approach for the identification of arsenic bearing phases in polluted soils[END_REF].

Sb potential mobility factor in the LPF zones was 0. and non-crystalline oxide and hydroxide minerals is often assumed to be immobile (Filella et al., 2009).

Conclusion

This study aimed to evaluate the soil fractionation of of Zn, Pb, Cd, As and Sb in selected technosoils of two PTE contaminated sites.

The percentages of Zn, Pb, Cd, As and Sb extracted in Fe-Mn oxide bound fraction of Tessier's scheme were always higher than those extracted by modified BCR scheme. In contrast the percentages of Zn, Pb, Cd, As and Sb extracted in organic fraction of modified BCR scheme were always higher than those of Tessier's scheme.

Zn, Pb and Cd from MDN technosoils were distributed in all fractions, with greatest enrichments in acid soluble fraction in the case of Cd. The results showed that PTE from LPF zones have the highest abundance in residual fraction. This indicates that the PTE were rather immobile. The largest proportions for As, Pb and Sb were extracted in residual phase, in which PTE are strongly retained in soil minerals. This shows that the environmental risk of these PTE may be limited. However, the relatively high PTE content in LPF zones may represents some indirect environmental risk due to clay dispersion, and disaggregated soil particles bearing PTE may eventually reach and accumulate in ground waters and food chain.

The order of mobility in technosoils was: Cd>Zn>Pb in MDN zones and As>Sb>Pb in LPF zones. The mobile and potentially bioavailable pools of these heavy elements were considerably higher in contaminated soils with respect to uncontaminated ones. In MDN zones, Cd, Zn and Pb can be considered the most mobile elements and potentially available to the biosphere, representing therefore a more significant environmental risk than Pb, Sb and

As in LPF zones. The quantity of mobile and potentially bioavailable PTE in MDN soils could threaten the quality of crops, with the real risk that these elements enter the food chain.

The partitioning patterns of PTE in soils of this region will therefore be a sound basis for studies focusing on soil organisms (such as earthworms), cultivated and wild vegetables and the safety of food stuffs. Comparison of both sequential extraction schemes indicates that the results are only empirically defined and are not totally exclusive to the specified mineral phase.

  scheme.The aims of this work was thus to: (1) investigate the chemical fractionation of PTE, respectively Zn, Pb, and Cd, in contaminated surface technosoils from a former smelting site (Mortagne-du-Nord (MDN), France) and As, Pb, and Sb from a former mining site (La Petite Faye (LPF), Limoges, France), and (2) compare the efficiency and suitability of two sequential extraction schemes (Tessier and a modified BCR) for these soils.

Fig. 1 :

 1 Fig.1: Chemical distribution of Zn (a), Pb (b and e), Cd (c), As (d), Sb (f) in the different soil fractions in MDN and LPF (Tessier's scheme): T1 (exchangeable), T2 (carbonates), T3 (reducible), T4 (oxidizable), T5 (residual).

Fig. 2 :

 2 Fig.2: Chemical distribution of Zn (a), Pb (b and e), Cd (c), As (d), Sb (f) in the different soil fractions in MDN and LPF (modified BCR scheme): B1 (acid soluble), B2 (reducible), B3 (oxidizable), B4 (residual).

Fig. 3 :Fig. 4 :

 34 Fig.3: Potential mobility of the potentially toxic elements from the MDN site

  96 %. Similar to arsenic, Sb distribution into the soil fractions indicates that this element was rather immobile in the contaminated soils as being largely present in residual fraction. In a recent study on sequential extraction of Sb from contaminated soils, Tighe and Lockwood 2007; He 2007; Denys et al. 2008 found that a large amount (up to 90%) of Sb was associated with the residual fraction. In general, a number of similar general trends in As and Sb fractionation in soil are evident. These include the low mobility of both As and Sb and the high proportion of both metalloids associated with the residual fraction depending on metalloid source. Metalloid associated with the crystalline

Table 1 :

 1 Extraction conditions of the Tessier's scheme

	Fraction	Extractants and conditions
	Exchangeable	1 M MgCl 2 (pH = 7), 20 ml, 1 h.
	Bound to carbonate	1 M NaOAc (pH = 5), 20 ml, 1 h.
	Reducible	0.04 M NH 2 OH HCl, 20 ml, 95 °C, 6 h.
	Oxidizable	15 ml 30 % H 2 O 2 and 3 ml 0.02 M HNO 3 , 2 h, 85 °C, 3.2 M NH 4 OAc, 5 ml, 85 °C, 3 h.
	Residual	HF, HClO 4 , HNO 3

Table 2 :

 2 Extraction conditions of the modified BCR

scheme Fraction Extractants and conditions

	Exchangeable	0.11 M CH 3 COOH, 40 ml, 16 h
	Reducible	0.1 M NH 2 OH HCl (pH = 2), 40 ml, 16 h
	Oxidizable	8.8 M H 2 O 2 , 10 ml, 85 C, 1 h and8.8 M H 2 O 2 , 10 ml, heated to dryness, and 1 M NH 4 OAc, (pH = 2), 50 ml, 16 h
	Residual aqua regia	(ISO 11466 (ISO 1999))

Table 3 :

 3 Physicochemical characteristics of the soils and PTE total concentrations of the selected zones from Mortagne-du-Nord (MDN) and La Petite Faye (LPF)

			EC			CEC	Zn	Pb	Cd
	Zone	pH		OM% TOC%
		(μs cm -1 )			(cmol kg -1 )		(mg kg -1 )
	MDN1 6.92	112	8.26	3.35	7.2	7721	3558	72
	MDN2 6.35	112	10.28	4.39	8.5	3111	876	63
	MDN3 6.14	113	13.84	6.45	6.7	3124	879	53
							As	Pb	Sb
	LPF1 3.65	258	6.46	0.3	18.3	83937 16100 871
	LPF2 4.93	200	5.27	0.47	39.7	37458	8536	498
	LPF3 5.75	150	6.83	1.67	29.1	2677	362	100
	EC electrical conductivity, OM organic matter, TOC total organic carbon, CEC cation
	exchange capacity

Table 4 :

 4 Potentially toxic element fractionation in the different solid phases as obtained from the Tessier'sscheme a .

  and 4 to help compare the MF values of PTE in each studied sites.On average, the potential mobility of Cd was 52.89 % for MDN studied samples. Cd can be considered the most mobile element, as about half of its total content was in exchangeable fraction. The relationship between Cd concentrations in exchangeable fraction and carbonate content suggest that a substantial portion of Cd was contained in minerals such as calcite (in which Cd may replace Ca). As an overview of the study area, the fractionation data indicate a high mobility and bioavailability of Cd. The results are in accordance with those obtained by[START_REF] Kapusta | Direct and indirect effects of metal contamination on soil biota in a Zn-Pb post-mining and smelting area (S Poland)[END_REF][START_REF] Vaněk | Mobility of lead, zinc and cadmium in alluvial soils heavily polluted by smelting industry[END_REF], indicating that Cd is the most mobile of the three metals tested (Cd, Zn, Pb).The average potential mobility factor of Zn was 42.24%. Under reducing and acidic conditions, Zn will easily be mobilized to the environment. Therefore compared with other PTE they were mostly abundant in the residue fraction. Thus Zn was more available and potentially toxic in the environment. Part of Zn was likely related to carbonates such as Znrich calcite and/or smithsonite (ZnCO 3 ). Other acid-soluble minerals affecting the occurrence of Zn in acid soluble fraction could be sulphates (ZnSO 4 ) and oxide-sulphates [ZnO.ZnSO 4 ;

	Zn (OH) 2 . ZnSO4], whose presence was commonly detected in contaminated soils collected
	in mining and smelting areas (Li and Thornton 2001).
	Potential mobility of Pb in MDN and LPF zones were 33.44% and 0.66 % respectively.
	Change in the concentration was low because Pb in soil in this environment was relatively
	immobile. It could be noted that any change to anoxic soil conditions (e.g. caused by water-
	flooding) could mobilize significant amounts of adsorbed Pb through the destabilization of the

Fe-Mn oxyhydroxide lattices. Many researchers referred to that Pb in acid soluble fraction of the most contaminated soils was mainly in carbonate phases such as cerussite (PbCO 3 ) and

Acknowledgements

The authors wish to acknowledge Dr. Emmanuel Joussein (Université de Limoges) for help in soil sampling and XRF analysis, and Dr. Arnaud Gauthier (Université Lille 1) for help in soil sampling.