Damien David
email: damien.david@insa-lyon.fr

Kevyn Johannes

Frederic Kuznik

Quantification of the natural convection perturbations on differential scanning calorimetry measurements of PCMs

Keywords: Phase Change Materials, Differential Scanning Calorimetry, Natural Convection, Thermogram, Lattice Boltzmann model

The paper investigates the effect of natural convection during differential scanning calorimetry measurements of phase change materials. We performed series of numerical simulations to characterize and quantify the perturbation on the measured thermogram. This perturbation becomes significant at the end of the fusion process. It manifests itself as an increase of the heat flux amplitude which accelerates the fusion process. Three correlations are built from the simulation results. The two first correlations quantify the amplitude of the heat flux deviation. The third correlation locates the time at which the perturbation occurs. The correlations are valid for large DSC capsules (around 1ml), and high Prandtl materials (paraffins, salt hydrates, fatty acids). They enable to define the experimental conditions for which natural convection becomes negligible.

Introduction

Latent heat storage system use Phase Change Materials (PCMs) to store heat. The PCM characteristic which has the strongest impact on the dynamics of the storage system is the PCM enthalpy curve (Darkwa and O'Callagan [START_REF] Darkwa | Simulation of phase change drywalls in a passive solar building[END_REF], Izquierdo-Barrientos et al. [START_REF] Izquierdo-Barrientos | A numerical study of external building walls containing phase change materials (pcm)[END_REF], Mathieu-Potvin and Gosselin [START_REF] Mathieu-Potvin | Thermal shielding of multilayer walls with phase change materials under different transient boundary conditions[END_REF], Arkar and Medved [START_REF] Arkar | Influence of accuracy of thermal property data of phase change material on the result of a numerical model of packed bed latent heat storage with spheres[END_REF], Kuyznik et al. [START_REF] Kuznik | Impact of the enthalpy function on the simulation of a building with phase change material wall[END_REF]). This curve describes the evolution of the material specific enthalpy h as a function of the temperature T .

The most popular way to obtain an enthalpy curve is to perform a Differential Scanning Calorimetry (DSC) measurement. A sample of the material is subjected to a temperature ramp, and the time evolution of the heat flux around the sample is measured. The relation between the temperature and the heat flux is called thermogram.

It is usually recommended to use small capsules (around 10 -3 ml) and low temperature scan speeds when performing DSC measurements. The idea behind those recommendations is to approach the homogeneous thermal state in the PCM sample (homogeneous temperature, and homogeneous liquid or solid state), and to limit the effects of natural convection and conduction in the liquid phase of the sample.

However, using a small capsule and a low temperature scan speed requires a rigorous measurement procedure to reduce measurement errors. Extremely low heat flux amplitudes are expected. Those amplitudes approach the limit of sensitivity of the DCS device. The calibration of the device should be flawless to avoid bias in the heat flux measurements. Since the mass of the PCM sample is very small, it should be measured with an appropriate weighting scale in a non perturbed atmosphere. The contact between the PCM sample and the furnace should not be spoiled by impurities. Otherwise, the sample might not be homogeneously heated, and there might be temperature gradients between the sensor locations and the sample surface. PCMs are not necessarily pure materials, they can be mixtures of pure substances. For a PCM mixture, the composition of the PCM in a small sample might not be representative of the composition of the whole PCM. Several samples are needed to statistically approach the behaviour of an average PCM composition.

The rigour which is required for a DSC measurement with small capsules an low temperature scan speed makes the measurement procedure tedious. We are forced to notice that this rigour is not always met. Lazaro et al. [START_REF] Lazaro | Intercomparative tests on phase change materials characterisation with differential scanning calorimeter[END_REF] and Castellon et al. [START_REF] Castellón | Determination of the enthalpy of pcm as a function of temperature using a heat-flux dsc-a study of different measurement procedures and their accuracy[END_REF] conducted benchmark measurements all over Europe with n-Octadecane. They highlighted considerable discrepancies on thermograms due to non-rigorous experimental protocols.

The reproducibility of the calorimetry measurements is improved by using calorimeters with large capsules. A large capsule is a capsule whose volume is approximatively equal to 1ml. The measurement principle is the same: the sample is subjected to a ramp of external temperature, and the heat flux which passes through the PCM sample is measured. Those calorimeters are commonly used to measure the heat capacity of standard solid materials. They can provide very precise heat flux measurements, with a great repeatability.

The utilization of large capsule calorimeters to get PCM thermograms rises two major issues. The first issue is due to the diffusion of the heat within the sample. Let's imagine the phase change front is near the core of a large PCM sample. It releases some latent energy. The latent energy takes some time to reach the sample surface. When it reaches the surface, it is measured by the device. So the heat flux which is measured at the surface does not reflects what happens in the sample right at the measurement time. It reflects what happened in the sample in the past. The diffusion process within the PCM sample must be taken into account for a reasonable interpretation of the thermogram.

Actually, heat diffusion is not an issue which is specific to large capsule measurements. Dumas et al. [START_REF] Pierre Dumas | Model for the DSC thermograms of the melting of ideal binary solutions[END_REF] pointed out that every thermogram was distorted by heat diffusion effects. That's why PCM thermograms depend so much on the temperature scan speed, whatever the device used. Franquet et al. [START_REF] Franquet | Inverse method for the identification of the enthalpy of phase change materials from calorimetry experiments[END_REF] and Gibout et al. [START_REF] Gibout | On the use of a reduced model for the simulation of melting of solutions in dsc experiments[END_REF] decided to develop a numerical tool to take into account the heat diffusion when interpreting thermograms. Their tool consists in an inverse method based on a heat diffusion model. It performs successive guesses of the PCM enthalpy curve, until the corresponding simulated thermogram converges toward the measured thermogram. With this tool, heat diffusion perturbations can be extracted from the thermograms.

The second issue is the natural convection flow in the liquid region. It is well known that natural convection accelerates the heat transfer between the phase front and the heat transfer fluid in PCM storage units. The same phenomenon might occur in large DSC samples. If the volume of the liquid region is too large, and if the thermal gradients are too high, the viscosity might not be sufficient to restrain the natural convection flow. The subsequent perturbation on the thermogram is the central issue tackled in the present paper.

Through this study, we wanted to answer three questions:

• how does the natural convection flow disturb the thermogram?

• how can the perturbation of the flow be quantified?

• how can it be related to the DSC measurement conditions, such as capsule size, temperature scan speed, etc?

We present in this paper the results of series of numerical simulations performed to answer the three previous questions. The first section of the document is devoted to the presentation of the numerical model of the PCM. This model was designed to reproduce as close as possible all the heat transfer phenomena in the PCM sample. Then, we focus on the shape of the natural convection perturbation, and on the way to parametrize this perturbation.

The two next sections concern the design of a tool to quantify the natural convection pertubation. We propose three correlations: two correlations concern the perturbation amplitude, and one correlation concern the time location of this perturbation. Those correlations are expressed as functions of the PCM material properties and the DSC measurement conditions.

Finally, the last section of the paper illustrates the proper way to use the three correlations through an example.

Numerical model of the PCM sample

Geometry of the model and boundary conditions

The geometry of the simulated system is an ideal representation of a DSC sample (Fig. 1, right). The sample volume is a perfect cylinder; there is no volume compression or expansion during the phase change. Given the symmetry of the system, we use an axis-symmetric model: only a half-section of the sample is simulated. The thermal boundary conditions are shown in Fig. 2. The symmetry axis is adiabatic. DSC devices are designed to impose a ramp of temperature T ∞ (t) on the sample:

Simulation domain

T ∞ = T 0 + b ∞ × t (1)
An ideal DSC device would apply the temperature T ∞ directly on the sample surface. However, the temperature sensor from which the temperature T ∞ is regulated is never located exactly at the surface of the PCM sample. We model the heat transfer between the furnace location where T ∞ is measured and the sample surface nodes through a heat transfer coefficient h S . h S is homogeneous all over the sample surface. The boundary temperatures T S and heat flux densities q S are related to each other with mixed boundary conditions:

q S = h S (T ∞ -T S ) (2)
with Solid Liquid The simulated material is a pure PCM. Its enthalpy curve is shown in Fig. 4. The latent heat of the material is L f , its fusion temperature is T f . The specific heat c p is the same in the liquid and in the solid regions. The relation between the material specific enthalpy h, its temperature T and its liquid fraction l f is: The output of the simulation is the evolution of the heat flux Q S (t) which crosses the sample boundary. Since no convection exists during solidification, only fusion cycles are computed (b ∞ > 0).

h =      c p T solid phase (T < T f ) c p T f + l f L f phase change (T = T f ) c p T + L f liquid phase (T > T f ) (3)

Fluid flow model

The fluid flow model is activated only in the liquid region, where l f > 0.5. It is an axis-symmetric Lattice Boltzmann model, which is based on the model developed by Guo [11]. This is a D2Q9 model: two dimensions / nine particle velocities. The mesh is a square mesh. The width of each lattice is ∆X. On each mesh node, the nine particle velocities join the node with itself and with the eight neighbouring nodes during one time step:

- → c 0 = (0; 0) - → c 1-4 = (±1; 0)∆X/∆t, (0; ±1)∆X/∆t - → c 5-8 = (±1; ±1)∆X/∆t (4)
One distribution function f i is associated to each particle velocity. The distribution functions are related to the macroscopic density ρ, velocities -→ u and forces -→ F through their zero-th and first order moments:

i f i = rρ i - → c i f i = rρ - → u - ∆t 2 r - → F (5)
The distribution functions follow a discrete evolution given by equation 6. Each time step is divided into two sub steps: the streaming step (left hand side), the collision step (right hand side).

f i ( - → x + - → c i ∆t, t + ∆t) -f i ( - → x , t) = 1 τ (f i -f eq i ) + ∆t 1 - 1 2τ R i (6)
The equilibrium distribution function is:

f eq i = rρω i 1 + - → c i . - → u c 2 S + ( - → c i . - → u ) 2 2c 4 S - - → u . - → u c 2 S ( 7
)
Where c 2 S = ∆X 2 /(3∆t 2 ) is the speed of sound. The relaxation time τ depends on the fluid kinematic viscosity: ν = ∆tc 2 S (τ -0.5). The source terms R i are related to the macroscopic forces:

R i = rω i - → c i -- → u c 2 s + - → c i . - → u c 4 s - → c i . - → F (8)
The force -→ F is the sum of the real force applied to the fluid -→ F 0 , and a corrective term -→ F which is needed by the Lattice Boltzmann scheme to recover the Navier Stokes equations in cylindrical coordinates. Here, -→ F 0 is the natural convection force. It is approximated by the Boussinesq formulation:

- → F 0 = ρgβ(T -T ref ) - → e z (9)
The corrective term is:

- → F = ρ c 2 S r -ρν 2u r r 2 - → e r (10)
The no-slip boundary conditions on the walls and the solid-liquid interface are modelled with a bounceback scheme (Gallivan et al. [START_REF] Gallivan | An evaluation of the bounce-back boundary condition for lattice boltzmann simulations[END_REF]). The free surface is simulated with a mirror boundary condition technique (Kuo and Chen [START_REF] Kuo | Numerical implementation of thermal boundary conditions in the lattice boltzmann method[END_REF]). The symmetry condition on the axis is simulated with a specific scheme developed by David et al. [START_REF] David | Numerical analysis of truncation error, consistency, and axis boundary condition for axis-symmetric flow simulations via the radius weighted lattice Boltzmann model[END_REF].

Energy conservation model

The energy conservation model is based on the enthalpy method. For the sake of simplicity, the fluid model and the energy model share the same time an space meshes. The time scheme of the energy model is Euler explicit. The diffusion terms are discretized using the control volume technique from Patankar [START_REF] Patankar | Numerical heat transfer and fluid flow[END_REF]. The convection terms are treated with a second order, centred finite difference scheme.

The simulation domain is split up into square control volumes. Let qr-, qr+ , qz-and qz+ be the estimations of the heat flux densities on the four sides of a control volume. Let ∂ r T and ∂ z T and be the estimations of the thermal gradients on the radial and vertical directions. The subscript + refers to the quantities at the time t + ∆t, the absence of subscript corresponds to the quantities at the time t. We get the average specific enthalpy in the control volume h + from the following equation:

ρh + = ρh + ∆t -ρc p (u r ∂ r T + u z ∂ z T ) + qr-+ qr+ + qz-+ qz+ ∆X + qr+ + qr- 2r∆X (11)
The average temperature T + and the liquid fraction l + f are obtained from h + by using the enthalpy curve of the material (Eq.3 and Fig. 4) .

Stability, precision of the model

The three following criterion were fulfilled to ensure the stability of the energy conservation model (Pletcher et al. [START_REF] Tannehill | Computational Fluid Mechanics and Heat Transfer[END_REF]):

• Diffusion criterion: α∆t/∆X 2 < 0.25

• CFL criterion: U 0 ∆t/∆X < 0.5 • Mixed criterion: U 2 0 ∆t/α = 1
The reference velocity is U 0 = √ qβ∆T L. The consistency between the Lattice Boltzmann model and the Navier Stokes equations is ensured by setting M a = U 0 /c s < 0.1, where M a is the Mach number.

An optimal number of grid points was determined by a mesh dependency study for each capsule geometry. The studies were carried out for the most sensitive set of parameter: low Rayleigh and high Stefan numbers. The mesh was refined until we reached a stabilization of ∆N u max within 2% (The definition of ∆N u max is given below).

Description of the convection perturbation

In this section, we analyse one simulation result to describe the mechanism of natural convection in the PCM. The results present the fusion of Capric Acid (Table 2), in a 11mm diameter and 11mm height furnace, with a scanning speed b ∞ = 11K/h. An analysis of the velocity streamlines is provided in the first part of the present section. This analysis reveals different stages in the formation of vertices in the liquid region. Their effect on the thermogram are described in the second part of this section, and some parameters which quantify the convection perturbation on the thermogram are defined in the last part.

Flow patterns

The Figure 5 shows four snapshots of the velocity streamlines during the melting process. At the beginning, the liquid area is too thin to enable a strong convective current. At this stage the natural convection should not disturb the heat flux evolution.

Then, as the fusion front moves on, it leaves enough space for two convection vertices: one at the top and one at the bottom corner of the furnace. The vertices are driven by the temperature difference between the fusion front (cold surface) and the furnace wall (hot surface). The more they grow, the stronger should be the convection perturbation.

After a moment, the two vertices merge into one single toroidal vortex. At this stage, the convection perturbation should be maximum. It accelerates the heat transfer between the solid PCM and the furnace wall. The last picture show that the fluid movement persists after the end of the phase change.

Specific thermogram representation

This paper presents the thermograms in a dimensionless form. The definition of dimensionless quantities involves a reference heat flux Q ref . The reference heat flux is the heat flux that we can expect when the PCM is fully solid or fully liquid. After some time, a stationary regime occurs. During the stationary regime, the temperature growth rate is homogeneous within all the PCM volume, it is equal to b ∞ . The expected heat flux is:

Q ref = V ρc p b ∞ = πR 2 Hρc p b ∞ ( 12
)
The thermograms are expressed in terms of Nusselt number N u evolutions over the Fourier number F o. The Nusselt number is the dimensionless form of the measured heat flux Q S : it is the ratio between Q S and the reference heat flux Q ref . The Fourier number is the dimensionless form of the time t. It depends on the diffusivity α of the PCM, and a reference length which will be defined later in the paper:

• Nusselt number: N u = Q S Q ref • Fourier number: F o = αt L 2
Let's now have a look on Fig. 6. For the moment, we focus on the simulation results without convection effect (dashed blue lines). The top figure contains the thermogram N u CD (F o). The middle figure contains the evolution of the global liquid fraction l f (F o). The global liquid fraction is the fraction of the PCM volume which is occupied by the liquid region.

The fusion process causes an increase of the Nusselt number N u, which occurs between two stationary regimes: the solid state stationary regime (F o ≤ 0), and the liquid state stationary regime (F o ≥ 23). The Nusselt number is equal to 1 during both stationary regimes (Q S = Q ref ). The line N u = 1 is the base line of the calorimetry measurement.

The time scale was shifted so that F o = 0 when T ∞ = T f . We notice that the rise of the heat flux does not start exactly when F o = 0, but shortly after. This delay is due to the thermal resistance between the temperature T ∞ and the PCM surface temperature.

The middle curve shows that l f = 1 from F o ≈ 17.3. From this limit, the PCM is fully liquid (grey area), the fusion process is over. However, we still observe N u > 1 after F o ≈ 17.3. This is due to the heat diffusion process inside the PCM sample.

The shape of the convection perturbation

The thermogram N u CV (F o) was obtained with the entire model (energy + fluid flow) (top of Fig. 6). It represents the Nusselt evolution which is distorted by the natural convection flow. The convection perturbation is the difference between the curves N u CV (F o) and N u CD (F o).

Consequently, a new quantity called the absolute convection perturbation: ∆N u = N u CV -N u CD is defined. The evolution of ∆N u is shown in the bottom curve of Fig. 6. As expected, the convection perturbation is null at the beginning of the fusion (F o < 10). At this stage, the natural convection flow is too weak to significantly increase the heat transfer rate of in the liquid region.

From F o = 10 the perturbation gradually increases until it reaches a maximum value. We can't see on the perturbation evolution any accident that could be interpreted by the fusion of the two vertices.

Since the natural convection increases the heat transfer between the solid region and the furnace wall, the solidification process ends up earlier with the natural convection perturbation (F o ≈ 17). The decrease of ∆N u starts shortly after. It continues until ∆N u reaches negative values. Negative values of ∆N u are due to the fact that the fusion process is longer without convection effect: after some time, we get N u CD > N u CV The second quantity is (∆N u/N u) max . It also reflects the amplitude of the convection perturbation, but in terms of instantaneous relative heat flux value. It is the maximum value of ∆N u/N u CD during the whole fusion process.

The third quantity F o max reflects the stage of fusion process at which the maximum perturbation occurs. This parameter is important because the later the convection perturbation occurs, the lower is its influence on the inverse model which is used to retrieve the PCM enthalpy curve from the thermogram. Let F o 0 and F o end be the values of the Fourier number at the beginning and the end of the phase change effect (Fig. 6). F o 0 occurs when N u CD has risen 5% of its way to the maximal value N u max :

N u CD (F o 0 ) = 1 + 0.05 × (N u max -1) (13)
F o end occurs when N u CD has returned to the previously given value. The maximum value of ∆N u is reached when F o = F o max . The maximum perturbation location F o max is defined by equation 14. It is close to 1 when the convection effect occurs at the end of the fusion process, and 0 when convection occurs at the beginning.

F o max = F o max -F o 0 F o end -F o 0 (14)

Presentation of the parametric study

Methodology

The rest of the paper is devoted to the construction of correlations for ∆N u max , (∆N u/N u) max , and F o max as functions of the DSC experimental conditions. Those correlations might be used by DSC users to quantify the convection perturbation they can expect on their thermogram, and adjust the PCM quantity to limit undesirable effects.

The first step of the methodology was to define a set of parameters which fully describe the conditions of a DSC measurement. Five parameters are sufficient. They are are expressed in terms of dimensionless numbers. They are defined in the first part of this section.

An interval of study was defined for each representative parameter. The union of those intervals constitutes the range of validity of the future correlations. The intervals of study were established from an inventory of available phase change material thermal properties and available characteristics DSC devices. The inventories are described in the second part of the present section.

We used the full map of experiment method to define the list of simulations to perform in order to get a sufficient amount of data to build the correlations. The full map of experiment consists in performing one simulation for each combination of extreme values of the representative parameters. For each simulation, each representative parameter is set either to its maximum value, or to its minimum value. A total of 2 5 = 32 simulations were performed.

A sensitivity analysis was performed on the simulation results, to determine which representative parameter was the the most influential on the values of ∆N u max , (∆N u/N u) max , and F o max . The results of the sensitivity analysis drove the determination of the correlation shapes. Finally, the correlation parameters were fitted to the simulations results. The simulation results and the different stages of the correlation design are presented in the next section.

Definition of the representative parameters

The dimensionless parameters depend on a characteristic length L and a characteristic temperature difference ∆T . The characteristic length is the ratio between the sample volume and its outer area:

L = V A = πR 2 H 2πRH + 2πR 2 = RH 2(R + H) (15)
The characteristic temperature difference ∆T depends on the reference heat flux density Q r ef /A and on

L ∆T = Q ref A L k = V ρc p b ∞ A L k = L 2 b ∞ α (16)
The governing equations of the system are the Navier-Stokes equations with the Boussinesq approximation, and the energy conservation equation. By turning those equations into their dimensionless formulation, five dimensionless parameters appear:

• Prandtl number: P r = ν α • Rayleigh number: Ra = gβ∆T L 3 αν • Stefan number: St = cp∆T L f • Shape factor: Γ = H R • Biot number: Bi = h S L k
The five dimensionless parameters are sufficient to describe entirely the conditions under which the scanning calorimetry simulation is performed.

Intervals of study 4.3.1. Inventory of DSC devices characteristics

In this paper, we focus on calorimetry devices with a large furnace volume. We identified three appropriate products: the µDSC7evo calorimeter from SETARAM, the C80 calorimeter from SETARAM, and the MMC274 Nexus calorimeter from NETZSCH. The characteristics of those devices are gathered in table 1. The values of the heat transfer coefficient h S were estimated by Gibout [START_REF] Gibout | Discussion with S. Gibout[END_REF] with the inverse conduction model.

Product R H b ∞ h S V mm mm K/h W/

Inventory of PCM thermal characteristics

We selected 14 phase change materials to get a representative set of PCM thermophysical data. Those data were found in Hasan et al. [START_REF] Hasan | Phase change materials for thermal regulation of building integrated photovoltaics[END_REF], Su et al. [START_REF] Su | Review of solid liquid phase change materials and their encapsulation technologies[END_REF] and Humphries and Griggs [START_REF] Humphries | A design handbook for phase change thermal control and energy storage devices[END_REF]. They are gathered in Table 2.

Type Material Name ρ cp k α ν β L f kg/m 3 J/kgK W/

Intervals of study

The minimum and the maximum values of the dimensionless numbers were computed from tables 1 and 2. They are listed in Table 3. They form the interval of study of each parameter.

It is not correct to directly plan our simulations based on the five dimensionless parameters: St, Ra, Γ, P r and Bi. In that case, half of the simulations would represent unrealistic conditions. This is because both parameters Ra and St are proportional to b ∞ . The space occupied by the values of Ra and St is represented in Fig. 7. We never get a maximum value of Ra when St is minimal. Similarly, we never get a minimum value of Ra when St is maximum.

To avoid non-physical simulation conditions, we replace Ra by a new parameter called RS. RS is the ratio between Ra and St (Eq.17

RS

= Ra St = gβ∆T L 3 αν cp∆T L f = gβL 3 c p L f αν (17)
The interval of study of RS is given in Tab.3. The simulation settings will be based on the intervals of study of the following five non dimension parameters: St, RS, Γ, P r and Bi.

Results of the parametric study

Results of the simulation campaign

The simulated values of (∆N u/N u) max , ∆N u max , and F o max are represented graphically in Fig. 9. The combination of parameters of each simulation is indicated under the lower axis (+ for maximum value,for minimum value).

The relative perturbation amplitude (∆N u/N u) max ranges from 0.02% (Simulation 7) to 100% (simulation 26). The perturbation amplitude ranges from 0.02 (simulation 6), to 60 (simulation 20). The relative Fourier values range between 47% and 91%: the convection perturbation occurs mostly during the second half of the fusion process. It is clear from Fig. 9 that the parameters which have the strongest influence on the convection perturbation are the Stefan number St and the parameter RS. A calorimetry measurement with a low value of St is either the calorimetry measurement of a PCM which has a high latent heat L f , or a calorimetry measurement with a very low scan speed b ∞ . For a given value of St, the parameter RS reflects the sensitivity of the fluid to thermal expansion.

Simulations 1 to 8 are the simulations with low St and low RS. They are the only simulations with a maximum relative perturbation below 10%. For those simulations, the maximum perturbation occurs at the very end of the fusion process, corresponding to the physics of the heat transfer with natural convection.

The remaining simulations show stronger perturbations. The thermograms of simulations 1,9 17 and 25 are drawn in Fig. 10. Those four simulations correspond to four different combinations of [St; RS]. The other parameter values are fixed. The perturbation is negligible in simulation 1, because of the low values of RS and St.

When St (or b ∞ ) increases (simulation 1 to 9), the baseline heat flux gets stronger compared to the heat flux due to the phase change. Thus, the Nusselt reaches smaller values during the phase change. In these conditions, the thermogram becomes more sensitive to the convection perturbation, which is responsible of a distinct second peak. The resulting thermogram is very similar to the thermogram of a binary mixture. It could lead to a misinterpretation of the curve.

Increasing RS obviously increases the absolute perturbation amplitude ∆N u max . But this increase is much more pronounced when St is low (simulation 1 to 17) than when St is higher. It is clear from Figs.9 and 10 that any modification of St and RS which would lead to an increase of the convection perturbation, would also lead to a decrease of F o max . The stronger is the perturbation, the sooner it occurs. The shape factor Γ also influences the convection perturbation amplitude. A high value of Γ results in a lower perturbation value, this is especially true when St and RS are low. Since the top surface of the sample is free, the convection movement is not slowed down at this location. A high value of Γ reduces the proportion of free surface at the top. It increases the contact with the walls, which damps the convection flow.

The Prandtl number and the Biot number have only a little effect on the convection perturbation amplitudes.

The shape of the correlations

Initially, we intended to build correlations by using the formalism given in equation 18. Q is the quantity to be correlated: either ∆N u max , (∆N u/N u) max or F o max . The coefficients C 0 , C RA , C P r , C St and C Bi were obtained with a least square method.

Q = C 0 Ra C Ra P r C P r St C St Γ CΓ Bi C Bi (18)
We obtained a poor agreement between the simulation results and the correlation results. This is because the formalism of equation 18 does take into account the interactions between the system parameters. However, some interactions were highlighted in the previous section: the effect of Γ on the perturbation amplitude is stronger when St and RS (and Ra) are small, the effect of RS is much lower when St is low than when it is high. Moreover, the parameters P r and Bi have a negligible influence on the simulation results. For those reasons, we decided to perform a sensitivity analysis to determine a more adequate formalism for the correlations.

The equation 18 is turned into a linear equation by applying the natural logarithm on each side:

ln(Q) = ln(C 0 ) + C Ra ln(Ra) + C P r ln(P r) + C St ln(St) + C Γ ln(Γ) + C Bi ln(Bi) (19)
Then, the logarithm values are normalized to get values between -1 and 1: The interactions are the products of all the possible combinations of ln(Ra), ln(P r), ln(St) , ln(Γ), and ln(Bi), multiplied by a corresponding coefficients. The 32 coefficients values are obtained by solving a linear system using the 32 simulation results. The resulting coefficient values are displayed in Fig. 11.

The values of the sensitivity coefficient for ∆N u max and (∆N u/N u) max are displayed in the top curve and the middle curve. As expected the coefficients for P r and Bi are very low. We highlight two significant interactions: the interaction between Ra and St, and the interaction between Ra and Γ. Despite the small value of C Γ , the term in Γ will remain in the final form of the correlation to get a better resolution when Ra and St are small. We obtain the following formalism for ∆N u max and (∆N u/N u) max :

ln(Q) = ln(C 0 ) + C Ra ln(Ra) + C St ln(St) + C Γ ln(Γ) + C RaSt ln(Ra)ln(St) + C RaΓ ln(Ra)ln(Γ) (22)
The bottom curve concerns F o max . Here, we only keep the terms in Ra and St to get a reasonable approximation of the maximum perturbation location:

ln(Q) = ln(C 0 ) + C Ra ln(Ra) + C St ln(St) (23) 0 0.5 1

Resulting correlations

We built the correlations 24 and 25 and 26 by using the formalisms given in equation 22 and 23, and by computing the coefficients with a least square method.

∆N u max = 0.41 • St -0.38 • Γ -1. 13 • Ra [-0.23-0.12 ln(St)+0.12 ln(Γ)]

(24

) ∆N u N u max = 1.03 • St 0.077 • Γ -1.37 • Ra [-0.3-0.11 ln(St)+0.15 ln(Γ)] (25)
F o max = 0.67 • St -0.021 • Ra -0.018 (26)
The correlation values are confronted to the simulation results in Fig 9 . There is a good agreement between the correlation and the simulation values for the extremal values of the system parameters.

How to use the correlations

In this section, we illustrate how to use the correlation through an example. The PCM which is tested is a salt hydrate. Its latent heat is known from a previous measurement. It is equal to 180kJ/kg. The objective is to determine its enthalpy curve by using a MMC274 Nexus calorimeter from NETZSCH. The constraint is less than 10% of perturbation due to natural convection on the thermogram.

The correlations enable us to calculate the amplitudes and the location of the convection perturbation as a function of the temperature scan speed b ∞ . This is done in Fig. 12. If some PCM thermal properties are missing, we can select the thermal properties of similar materials in table 2 to compute the non dimension parameters. Here we selected the properties of the Calcium Choride +12 H 2 0.

Since we want to limit the amplitude of the natural convection perturbation to 10%, we need to set (∆N u/N u) max < 0.1. This limit corresponds to a temperature scan speed which is equal to 18K/h (Fig. 12 For that speed, we can expect the maximum perturbation to occur at 75% of the fusion process, with a value equal to 1.6 times the base line heat flux. This configuration was simulated. The resulting thermogram is given in Fig. 13. Here, the base line heat flux is 55.6mW . Since the correlation gave ∆N u max = 1.6 (Fig. 12, middle curve), the maximum natural convection perturbation value should be 1.6 × 55.6 = 88.96mW . This perturbation should occur at 75% of the fusion process (Fig. 12, bottom curve).

The expected perturbation is drawn on the thermogram in Fig. 13. The correlation slightly overestimates the perturbation amplitude. This could have been expected, given the concavity of the curves (∆N u/N u) max (b ∞ ) and ∆N u max (b ∞ ). This overestimation is not an obstacle when we want to limit the amplitude of the perturbation.

Conclusions

In this paper, the effect of natural convection on DSC thermograms was quantified through series of simulations. The intervals of study were built around the case of DSC with large capsules (≈ 1ml), for the characterization of organic PCMs and Salt Hydrates (PCMs with high Prandtl values).

The results of the simulation campaign are aggregated in three correlations. They predict the maximum amplitude, the maximum relative amplitude and the time location of the maximum convection perturbation. The authors are aware of the limits of the model on which the correlation was built (non-moving solid phase in the capsule, homogeneous value of h S around the PCM, axis-symmetric system, etc). They are also aware of the tendency of the correlations to overestimate the perturbation amplitudes. Nevertheless, those correlations remain the first tool from which we can evaluate whether or not the natural convection would spoil the results of calorimetry measurement. The correlations will be refined in a future work, by focusing on cases where (∆N u/N u) max < 0.2. From the simulation results, it is clear that small capsules are preferable to get low convection effect. However, small capsules require special care for the sample preparation, which may be a source of measurement errors. If one prefer bigger sample size, he should select narrow capsules and then adjust the temperature scan speed to reach acceptable convection effect.

Figure 1 :

 1 Figure 1: From the DSC calorimeter (left) to the sample simulation domain (right). (Initial picture from the commercial brochure of the calorimeter µDSC7evo from Setaram)

Figure 2 :

 2 Figure 2: Boundary conditions for the energy conservation model

Figure 3 :

 3 Figure 3: Boundary conditions for the fluid flow model

Figure 4 :

 4 Figure 4: Enthalpy curve of the simulated PCM

Figure 5 :

 5 Figure 5: Evolution of the velocity streamlines in the liquid region during the melting process. The symmetry axis of the furnace is located on the left side of the pictures. The fluid is moves couter-clockwise. The solid region is the black area

Figure 6 : 3 . 4 .

 634 Figure 6: Typical result from one simulation. Conduction and conduction + convection thermograms in the top figure, evolutions of the global liquid fraction in the middle figure, convection perturbation in the bottom figure

  ). It does not depend on b ∞ . The space occupied by RS and St is represented in Fig.8. It has a rectangle shape with four physical corners.

Figure 7 :

 7 Figure 7: Surface occupied by the values of Ra and St. The space occupied by the extremum values of St and Ra is represented with a grey rectangle. It contains two non physical extremal points located on the top left and on the bottom right corners.

Figure 8 :

 8 Figure 8: Space occupied by the values of RS = Ra/St and St. The resulting rectangle of study is drawn in grey. It does not have non physical corners

Figure 10 :

 10 Figure 10: Nusselt evolutions for simulations 0, 8, 16 and 24

ln(Ra) = 2 ×

 2 ln(Ra) -ln(Ra min ) ln(Ra max ) -ln(Ra min ) -1 (20) An the interactions are added to equation 20: ln(Q) = ln(C 0 ) + C Ra ln(Ra) + C P r ln(P r) + C St ln(St) + C Γ ln(Γ) + C Bi ln(Bi) + interactions (21)

Figure 11 :

 11 Figure 11: Sensibility of the non dimension parameters on the perturbation amplitudes and location. Effect of the coupling parameters

  ,

Figure 12 :

 12 Figure 12: Example of how to use the correlation. Prediction of the natural convection perturbation amplitudes and location as a function of the temperature scan speed. The temperature scan speed b∞ is expressed in K/h

Figure 13 :

 13 Figure 13: Example of how to use the correlation: Location of the convection perturbation on the thermogram

Table 1 :

 1 Characteristics of scanning calorimetry devices with large furnace volumes

	m 2	ml

Table 2 :

 2 Thermal and physical properties of phase change materials

	mK m 2 /s	m 2 /s	1/K	kJ/K

Table 3 :

 3 Intervals of study of the non dimension numbers

	Setaram µDSC evo7	Netzsch MMC274
	Setaram C80
	100000
	10000
	1000
	100
	Ra
	10
	1
	0.1
	0.01
	1e-06 1e-05 0.0001 0.001	0.01	0.1	1
	St

Acknowledgments

This study was funded by the ANR project Stock-E MICMCP.


Frédéric Cosnier
email: frederic.cosnier@inrs.fr

Benoît Cossec

Manuella Burgart

Hervé Nunge

Céline Brochard

Marie-Josèphe Décret

Aurélie Rémy

Biomarkers of toluene exposure in rats: mercapturic acids vs. traditional indicators (urinary hippuric acid and o-cresol and blood toluene)

Keywords: toluene, mercapturic acids, ortho-cresol, hippuric acid, inhalation, biomonitoring

1. Toluene (TOL) is a neurotoxic, ototoxic and reprotoxic solvent which is metabolized via the glutathione pathway, producing benzylmercapturic, o-, m-and p-toluylmercapturic acids (MAs).

These metabolites could be useful as biomarkers of TOL exposure.

2. The aims of this work were 1) to provide data on MAs excretion in rat urine following TOL exposure by inhalation, 2) to compare them to data from traditional TOL biomarkers i.e., TOL in blood (Tol-B), and urinary hippuric acid (HA) and o-cresol (oCre), and 3) to establish the relationships between these different indicators and the airborne TOL concentration (Tol-A).

3. Sprague-Dawley rats were exposed to a range of TOL concentrations. Blood and urine were collected and analyzed to determine biomarker levels.

4. Levels of the four MAs correlate strongly with Tol-A (comparable to the correlation with Tol-B).

5. MAs are thus clearly superior to oCre and HA as potential markers of exposure to TOL.

Introduction

In industry, toluene (TOL) is the most commonly used monocyclic aromatic hydrocarbon. It is mainly used as a solvent and is a component in paints, varnishes, printing inks, adhesives, waxes, etc. It is also used as feedstock for the manufacture of multiple products (ATSDR, 2000).

A number of occupational studies have examined the effects of TOL exposure via inhalation. The most sensitive effects observed in humans following inhalation exposure are neurological, including altered colour vision, dizziness, fatigue, headache, and decreased performance in neurobehavioral tests (USEPA, 2005). TOL is also considered ototoxic and can exacerbate the effects of noise in humans [START_REF] Hoet | Ototoxicity of toluene and styrene: State of current knowledge[END_REF]. TOL has been classed by the European Union as a reprotoxic product, it may cause miscarriages during early pregnancy at exposure levels below 100 ppm (ATSDR, 2000, Hannigan and[START_REF] Hannigan | Reproductive Toxicology and Teratology of Abused Toluene[END_REF]. These two effects combined with the regular observation of visual impairments led the American Conference of Governmental Industrial Hygienists (ACGIH) to recommend 20 ppm as the time-weighted average threshold limit value (TWA-TLV). In France, the permissible upper limit value for TOL exposure over 8 h is 50 ppm (100 ppm for 15 min).

Biological monitoring of occupational exposure to this solvent is based on TOL metabolism (Figure 1). Various biomarkers have been used or proposed to assess this exposure. The major metabolic pathway (83-94% of urinary metabolites) produces hippuric acid (HA), which has been the marker of choice for years. Many studies have compared HA excretion to o-cresol (oCre) excretion. oCre is excreted in urine as a sulfate or glucuronide as a result of ring hydroxylation of TOL [START_REF] Apostoli | Biomonitoring of occupational toluene exposure[END_REF][START_REF] Dossing | Urinary hippuric acid and orthocresol excretion in man during experimental exposure to toluene[END_REF][START_REF] Hasegawa | Hippuric acid and o-cresol in the urine of workers exposed to toluene[END_REF][START_REF] Rosa | Hippuric acid and ortho-cresol as biological indicators of occupational exposure to toluene[END_REF][START_REF] Angerer | Occupational chronic exposure to organic solvents. XVI. Ambient and biological monitoring of workers exposed to toluene[END_REF][START_REF] Truchon | o-cresol: a good indicator of exposure to low levels of toluene[END_REF]. These two markers correlate with the airborne TOL concentration (Tol-A).

However, in humans, HA is only valid for relatively intense TOL exposure. When TOL-A is low (e.g., at less than 10 ppm) this marker only correlates poorly with exposure levels. At high exposure levels, oCre performs similarly to HA as a marker. In contrast, oCre appears to perform better than HA at lower TOL concentrations (below 50 ppm) [START_REF] Truchon | o-cresol: a good indicator of exposure to low levels of toluene[END_REF]. However, due to background levels, oCre cannot be used as an indicator of very low concentrations, close to 2 ppm [START_REF] Inoue | Limited validity of o-cresol and benzylmercapturic acid in urine as biomarkers of occupational exposure to toluene at low levels[END_REF].

As efforts are being made to reduce TOL exposure levels, more specific markers are needed for biological monitoring. At the moment, to assess low TOL exposure levels (e.g., 10 ppm or less), unmetabolized TOL in blood (Tol-B) [START_REF] Mizunuma | Toluene in blood as a marker of choice for low-level exposure to toluene[END_REF] or in urine (Tol-U) seem to be better indicators than urinary HA or oCre [START_REF] Kawai | Toluene itself as the best urinary marker of toluene exposure[END_REF][START_REF] Fustinoni | Comparison between urinary o-cresol and toluene as biomarkers of toluene exposure[END_REF], Ukai et al., 2007[START_REF] Ducos | Biological monitoring of exposure to solvents using the chemical itself in urine: application to toluene[END_REF], Ikeda et al., 2008, Inoue et al., 2008, Kawai et al., 2008). Because of the invasive nature of blood sampling, Tol-U is preferred [START_REF] Fustinoni | Comparison between blood and urinary toluene as biomarkers of exposure to toluene[END_REF]. However, care must be taken to avoid contaminations or possible losses due to evaporation [START_REF] Ikeda | Solvents in urine as exposure markers[END_REF][START_REF] Ducos | Biological monitoring of exposure to solvents using the chemical itself in urine: application to toluene[END_REF].

Unmetabolized TOL can also be measured in saliva [START_REF] Ferrari | Saliva as an analytical tool to measure occupational exposure to toluene[END_REF] or in exhaled air [START_REF] Apostoli | Biomonitoring of occupational toluene exposure[END_REF][START_REF] Pierce | Biological monitoring of controlled toluene exposure[END_REF][START_REF] Pierce | Toluene metabolites as biological indicators of exposure[END_REF][START_REF] Ghittori | A field method for sampling toluene in end-exhaled air, as a biomarker of occupational exposure: Correlation with other exposure indices[END_REF], but fewer studies have been published.

Other urinary TOL metabolites are known and can be measured. Benzyl alcohol is one such compound [START_REF] Kawai | Benzyl alcohol as a marker of occupational exposure to toluene[END_REF] which was proposed as an exposure marker, but it was shown to be of limited value in estimating TOL exposure levels [START_REF] Ukai | Comparative evaluation of biomarkers of occupational exposure to toluene[END_REF]. Finally, a whole family of metabolites which may be useful biomarkers of exposure (BE) has not been extensively investigated: the mercapturic acids (MAs). Recent studies confirm that MAs are useful as BE for electrophilic chemicals (Maestri et al., 1997, Haufroid and[START_REF] Haufroid | Mercapturic acids revisited as biomarkers of exposure to reactive chemicals in occupational toxicology: a minireview[END_REF]. TOL produces two MAs, S-benzylmercapturic acid (BMA) [START_REF] Takahashi | Determination of S-benzyl-Nacetyl-L-cysteine by gas-chromatography mass-spectrometry as a new marker of toluene exposure[END_REF][START_REF] Maestri | Determination of specific mercapturic acids as an index of exposure to environmental benzene, toluene, and styrene[END_REF][START_REF] Inoue | Urinary benzylmercapturic acid as a marker of occupational exposure to toluene[END_REF] and S-p-toluylmercapturic acid (pTMA) [START_REF] Angerer | S-p-toluylmercapturic acid in the urine of workers exposed to toluene: a new biomarker for toluene exposure[END_REF] through side-chain oxidation and ring oxidation, respectively. Thus, BMA is thought to be formed via conjugation of benzyl alcohol with glutathione (catalyzed by glutathione-S-transferases), followed by the concerted catalytic action of γ-glutamyltranspeptidase, releasing glutamic acid, amino peptidase M, releasing glycine, and N-acetyltransferase, which adds an acetyl group. pTMA is thought to be produced from a 3,4-TOL epoxide intermediate by a similar series of reactions. No study has compared pTMA to known BE.

BMA has been compared with other markers, and seems to be a better indicator than HA or oCre [START_REF] Inoue | Urinary benzylmercapturic acid as a marker of occupational exposure to toluene[END_REF][START_REF] Inoue | Benzylmercapturic acid is superior to hippuric acid and o-cresol as a urinary marker of occupational exposure to toluene[END_REF]. However, at low concentrations (< 15 ppm) it does not correlate as well as Tol-U with Tol-A [START_REF] Ikeda | Changes in correlation coefficients of exposure markers as a function of intensity of occupational exposure to toluene[END_REF], Inoue et al., 2008[START_REF] Lovreglio | Validity of new biomarkers of internal dose for use in the biological monitoring of occupational and environmental exposure to low concentrations of benzene and toluene[END_REF].

These poorer correlations may partly be explained by the analytical techniques employed which, until very recently, were probably not sensitive enough to quantify the BMA concentrations induced by environmental exposure. In corroboration of this, Aylward et al. [START_REF] Aylward | Biomonitoring Equivalents (BE) dossier for toluene (CAS No. 108-88-3)[END_REF] estimate that insufficient data on quantitative relationships between these metabolites and TOL exposure, or blood levels, are currently available.

Recently, we developed a new sensitive gas chromatography -tandem mass spectrometry (GC-MS/MS) method which allows simultaneous determination of the two known TOL-related MAs, BMA and pTMA, and two other ring oxidation TOL mercapturic acids, o-and m-toluylmercapturic acids (oTMA and mTMA) in urine. These other metabolites were detected in rat urine for the first time using this technique [START_REF] Cosnier | Mercapturic acids derived from toluene in rat urine samples: identification and measurement by gas chromatography-tandem mass spectrometry[END_REF].

The purpose of the present study was to provide data on the excretion of MAs in rat urine following exposure to TOL by inhalation. TOL metabolism is similar in humans and rats, making it possible to use animal experiments with single, stable and controlled exposures. These conditions clearly cannot be found in real occupational situations. To determine whether MAs are good BE candidates, it will be examined how MAs correlate with Tol-A and how they compare with Tol-B and the TOL biomarkers currently used: HA and oCre.

Materials and methods

Animals

Male Sprague-Dawley rats were purchased from Charles Rivers Laboratories (Saint Germain sur l'Arbresle, France) and weighed between 200 and 220 g on arrival at the animal facility. Before exposure, all animals were acclimatized for at least 2 weeks with a 12:12 h day/night cycle in polycarbonate cages containing irradiated corn cob LabCob12 bedding (DIETEX, Saint Gratien, France) and covered with spun-bonded polyester cage filter. A04 irradiated food (SAFE, Augy, France) and tap water were available ad libitum. The temperature in the animal quarters was 22 ± 2 °C and the relative humidity was 55 ± 10%. Animal experiments were performed according to ethical regulations of animal experimentation (European Directive 2010/63/EU) in a French Ministry of Agriculture-accredited animal facility (Accreditation N° C 54-547-10).

TOL exposure

Groups of eight rats (one concentration per group) were exposed to TOL vapours using a whole-body exposure system. Animals were exposed to 2, 5, 20, 50, 200 and 500 ppm for 6 hours/day (from 09:00 am to 03:00 pm), 5 days/week for one week in 200 L glass/stainless steel inhalation chambers. During the 6-hour exposure period, the rats did not have access to food or water. In the inhalation chambers, the animals were separated from each other by small wire mesh fences. The inhalation chambers are designed to sustain a dynamic and adjustable airflow (5-6 m 3 .h -1 ), they were maintained at a negative pressure of no more than 5 mm H2O to prevent any leakage of the test compound. Air entering the chamber was filtered and conditioned at a temperature of 22 ± 1 °C and a humidity of 55 ± 10%. TOL vapours were generated using a thermoregulated glass streamer. The TOL, delivered by a pump, was instantaneously vaporized upon contact with the heated surface and carried forward with the help of additional airflow through the streamer, into the main air inlet pipe of the exposure chambers.

Unexposed rats (controls) were maintained in similar chambers, but exposed to clean air. Exposure levels were measured three times during the 6h-exposure period (at 10:00 am, 12:00 pm and 2:00 pm) and by collecting samples of the chamber's atmosphere using glass tubes packed with activated charcoal 18-35 mesh (VWR International S.A.S, Fontenay-sous-Bois, France). TOL was desorbed from the activated charcoal with carbon disulfide. Styrene was added as an internal standard and samples were analyzed on a Varian CP-3800 gas chromatograph equipped with a flame ionization detector. TOL samples were assayed on a 30 m x 0.53 mm (1.5 µm film thickness) CP-Sil 5 column (Varian), using helium as the carrier gas at a constant flow of 10 mL/min. The column temperature program was: 70 °C for 1 min then increased to 200 °C at a rate of 10 °C/min. The temperature was held at 200 °C for 1 min. The injector (Flash 1061) and detector were maintained at 240 °C and 250 °C, respectively. These analyses allowed daily calibrations to be performed.

During exposure, a second GC (Perkin Elmer Clarus 500) equipped with a flame ionization detector and an automatic gas sampling valve was also used to perform online concentration measurements every minute to follow the stability of vapour generation throughout exposure.

Blood sample collection

The blood TOL concentration was measured after the first and fifth days of exposure. The rats' tail tip was first warmed in water at 39 °C before sectioning with a razor blade to collect blood (~ 0.8 mL) in 2-mL heparinised screw top vials, with silicone/PTFE screw caps. Collection was performed under anaesthesia with isoflurane gas. For this, a vaporizer, Isotec 4 (Ohmeda), and a generator, O 2 Millennium 5 Concentrator (Respironics), were used. Anaesthesia was induced with 4% isoflurane and then maintained at 2% during sampling. The collection procedure was completed within 5 min.

Blood samples were frozen at -20 °C until analysis. Under these conditions, the blood TOL concentration remains unchanged for at least 4 months. For analysis, blood samples were extracted with 0.5 mL CS 2 containing p-xylene as an internal standard. After 10 min shaking, the samples were centrifuged at 1250 rpm for 10 min at 4 °C. The lower CS 2 layer contained the TOL, 0.3 mL of this layer was transferred to a vial with a glass insert. The sample (1 µL) (2 µL for animals exposed to 2 ppm) was analyzed using the chromatographic conditions described for atmospheric monitoring. In these conditions, the recoveries for TOL and p-xylene were equal to 98 ± 4% and the TOL limit of detection (LOD) was 0.007 μg/g of blood (~ 0.004 µg/g for the 2 µL injection).

Urine collection

Following the first and fifth day of exposure, and after blood collection, animals were placed in individual metabolic-type stainless steel cages with free access to food and water from 3:00 pm to 9:00 am the following day. During this 18h-period, their urines were collected and refrigerated by a cooling system attached to the collection tubes. Urine samples were frozen immediately after collection and stored at -20 °C. They were thawed just before analysis.

To account for variability in urine volumes, metabolite concentrations were expressed relative to creatinine concentration. Creatinine concentrations in urine were determined by the Jaffé method using a creatinine test kit (Randox Laboratories, France).

Hippuric acid (HA) analysis HA concentrations were determined as previously described [START_REF] Campo | Toluene toxicokinetics and metabolism parameters in the rat and guinea pig[END_REF]. Briefly, urine and standard samples were first cleaned using a solid-phase extraction (SPE) cartridge. Then, each sample was centrifuged (1750 × g) for 10 min at 10 °C. A 60 μL aliquot of cleared, centrifuged sample (urine or standard) was applied to an activated strong anion exchange column (BondElut cartridge; Varian).

Then, an 800 μL aliquot of the internal standard (IS), 3-methyl hippuric acid (MHA; Aldrich 98%; 1 g/L) was added. After rinsing the column with water, HA and MHA were eluted using 3 mL of methanol/acetic acid (MeOH/AcOH: 9/1; v/v) mixture. The eluate was dried at 70 °C for 40 min and reconstituted in an aliquot of HPLC buffer (4 mL; water/methanol/AcOH: 60/40/1; v/v/v).

Chromatographic separation was performed at a flow rate of 1 mL/min in a Lichrospher C8 column (length 250, i.d. 4.6 mm, 5 μm). The Shimadzu SPD 10 AVp UV detector was set at 254 nm.

Recoveries were 98 ± 2% for HA and MHA, reproducibility was ≤ 2% and the LOD was 4 mg/L in water.

Cresols analysis

The concentrations of oCre and its two isomers, the m-cresol (mCre) and the p-cresol (pCre), in rat urine were measured according to [START_REF] Truchon | Gas chromatographic determination of urinary ocresol for the monitoring of toluene exposure[END_REF] with slight modifications. Briefly, 50 µL of sulphuric acid (95%) was added to a 0.5 mL aliquot of urine to hydrolyze cresol conjugates. Then, 50 µL of a 4 mg/L aqueous solution of 2-ethylphenol (oEtPhOH) (Aldrich, 99%) was added as internal standard. After shaking, the mixture was heated at 95 °C for 2 hours, and allowed to cool to room temperature. Excess acidity was neutralized with 1 mL of Na 2 CO 3 (100 g/L), and cresols and standards were extracted with 500 µL of diisopropyl ether. The mixture was shaken for 15 s. After separation of the two layers by centrifugation at 1250 rpm at 4 °C for 10 min, approximately 300 µL of the upper organic layer was collected and analyzed. Under these conditions, recoveries were 80 ± 7% for oCre, 88 ± 8% for oEtPhOH, 79 ± 10% for mCre and 74 ± 7% for pCre.

Cresols were separated by gas chromatography (GC Shimadzu 2010+) on a Supelcowax-10 column (length 60 m, i.d. 0.25 mm, film thickness 0.25 µm) with helium as carrier gas at a constant velocity of 40 cm/s. The injector (splitless mode for 0.5 min) temperature was set at 250 °C, and that of the detector at 270 °C.

The oven temperature program was as follows: 60 °C (0.5 min) then increased to 180 °C at a rate of 30 °C/min, further increased to 225 °C at 10 °C/min (held 3 min), and, finally increased to 250 °C at 10 °C/min (held 4.5 min). The total run time was 19 min. Under these conditions, the retention times were: 10.39 min for oCre, 10.98 for oEtPhOH, 11.19 for pCre, and 11.29 for mCre. The oCre chromatographic peak was well resolved and could be quantified; the peaks for pCre and mCre were not completely separated, but nevertheless allowed quantification.

Calibration ranges were prepared for each cresol: 0.1 to 15 mg/L for oCre, 0.01 to 1.5 mg/L for mCre, and 0.3 to 45 mg/L for pCre. Because pCre is naturally present in blank urine, these standard samples were prepared in water by adding aliquots of a 100 mg/L stock solution of the appropriate cresol dissolved in methanol. For these assays, LODs were 30 µg/L for oCre, 50 µg/L for mCre, and 300 µg/L for pCre.

Mercapturic acid analysis

The procedure for analyzing MAs has been exhaustively described [START_REF] Cosnier | Mercapturic acids derived from toluene in rat urine samples: identification and measurement by gas chromatography-tandem mass spectrometry[END_REF]. Briefly, frozen urine samples were allowed to equilibrate to room temperature. Samples were shaken, and 200-µL aliquots were then transferred into 5-mL glass tubes; 60 µL of 1 M HCl and 50 µL of 20 mg/L IS (IS = 3,5-dimethylbenzylmercapturic acid = 3,5-dMBMA) were added, and the total volume was made up to 510 µL with distilled water. The mixtures were then extracted three times with 1 mL chloroform (CHCl 3 ). The three CHCl 3 layers were collected in 5-mL hermetically closed PTFE-lined screw cap glass tubes and evaporated to dryness at 60 °C. After adding 60 µL of 1M HCl-isopropanol and 300 µL of TOL to the residue, MAs were esterified in a domestic microwave oven (FT331, Whirlpool) at 750 W for 4 min. After heating, remaining acidity was neutralized with 60 µL of Na 2 CO 3 . The mixture was then vortexed and centrifuged (2500 rpm for 15 min) before collecting the organic layer. Samples were then analyzed by GC-MS/MS. Recovery yields for the complete procedure are close to 100 % for all compounds for urine samples spiked at 0.1 and 1.0 mg/L. A 10 % to 15 % decrease in yield was observed at higher concentrations (10 mg/L).

Gas chromatography (GC) was performed on a 450-GC system (Bruker, Wissembourg, France) equipped with a CP-8400 autosampler (Bruker). Compounds were separated on an Rxi-5Sil MS capillary column with integra-guard (30 m length × 0.25 mm I.D. × 0.25 µm film thickness), using helium as the carrier gas at a constant flow of 1.3 mL/min. The sample (1 µL) was injected in splitless mode (injector type 1177) at an injection temperature of 250 °C, the split delay and split ratio were set at 0.5 min and 1/50, respectively. The oven temperature was held at 75 °C for 0.5 min, then increased to 195 °C at a rate of 20 °C/min and held at 195°C for 10 min, it was then increased to 270 °C at 10 °C/min and finally held at 270 °C for 1 min (total time: 25 min). The transfer line was maintained at 270 °C throughout. GC-MS/MS analyses were acquired using a 320 triple quadrupole mass spectrometer (Bruker, Wissembourg, France) in electronic impact (EI) mode. Ion source temperature was set at 200 °C and ionization energy at 70 eV. Selected reaction monitoring (SRM) data were acquired using argon 99.9999 % (Air Liquide) as collision gas at a pressure of 2.0 mtorr in the collision cell. The source was flushed with helium gas for the first 14 min of injection. Two SRM ion transitions per compound were chosen based on selectivity and abundance. To maximize signal-to-noise ratio in matrix extracts, one of these transitions was used as quantifier and the second as qualifier. Dwell time was fixed at 0.1 s for all transitions.

With this method, LOD was 2.8 for oTMA, 3.6 for mTMA, 4.6 for pTMA, and 5.5 µg/L for BMA.

Statistical analysis

All statistical analyses were performed using Stata 12.1 (Stata College station, Texas, USA). Each condition and indicator was analyzed using paired Student's t-test followed by Bonferroni correction to determine overall significant differences at the 95% confidence interval between days 1 and 5 of collection.

For HA, the endogenous toluene indicator, an ANOVA test was followed by a Bonferroni test to compare the mean HA concentrations in urine for controls and exposed rats. In occupational health studies, and where experimental data are not normally distributed, a ln-transformation is generally used to normalize data and homogenize variances. This was used here. However, due to the low number of values (n = 8), to facilitate interpretation and comparison with other investigations, only the means and standard errors (rather than the geometric mean and standard deviation) are reported for all indicators (Table 1). In addition to calculating simple summary statistics, toluene exposure indicators were analyzed using linear mixed effect models on the ln-transformed scale. A fixed effect (interesting in itself) was used to model the effect of atmospheric toluene (on a log scale). A random effect (interesting in the underlying population) was used to model systematic differences between rats, these appear with repeated measurements for the same animal.

The models can be represented as follows: ln(Y ij ) = α+β ln(X ij ) + ξ j +ε ij where Y ij is the i th measurement of an exposure indicator for the j th rat, α is the intercept of the model (overall mean of the model on a log scale), and β is the fixed effect coefficient (slope) for the ln-transformed atmospheric toluene concentration. The random effect ξ j , and residual error ε ij (withinindividual error) were assumed to be normally distributed with a mean of zero and standard deviation of ψ and θ, respectively.

For HA, the endogenous toluene indicator, we used the ln-transformed atmospheric toluene concentration plus one as a fixed effect. This allowed inclusion of the HA values for animals not exposed to toluene.

The models were validated by analyzing the residuals from the models.

Results

The summary statistics (means and standard deviations) for all inhalation exposures are presented for the eight indicators (Tol-B, HA, oCre, mCre, BMA, oTMA, mTMA and pTMA) following the first and fifth days of exposure (Table 1).

Day-effect

Statistical analysis of the atmospheric concentrations revealed no difference between day 1 and day 5.

The day-effect was also tested using paired t-test and Bonferroni correction for each indicator, and each concentration. No overall statistical difference between day 1 and 5 was found for any indicator.

Hippuric acid analysis

As HA is an endogenous product, all the urine samples analyzed, including controls, contained a significant amount. Except for TOL20, mean urinary concentrations of control animals were similar.

All experiments combined, the mean level for controls is equal to 0.52 (± 0.15). A statistically significant difference between exposed and controls was only observed for rats exposed to 50 ppm toluene (on day 5 only) or higher. Below this concentration, the amount of HA present in urine cannot be clearly attributed to toluene exposure.

Correction for creatinine is controversial since it does not always improve the correlation between the external doses and urinary exposure marker levels [START_REF] Inoue | Limited validity of o-cresol and benzylmercapturic acid in urine as biomarkers of occupational exposure to toluene at low levels[END_REF][START_REF] Kawai | Validation of Urine Density Correction in Cases of Hippuric Acid and Unmetabolized Toluene in Urine of Workers Exposed to Toluene[END_REF].

Nevertheless, its influence on the HA concentration was investigated. In this case, this correction does not reduce the significance level. This is essentially due to the homogeneity of creatinine levels: mean concentration = 0.42 ± 0.09 (n=144). For this reason, creatinine-corrected values are not reported in this paper.

The limit HA values recommend in humans after exposure to 50 ppm TOL-A by the French and Swiss authorities are 2.5 g/g creatinine (end of shift) and 2 g/g creatinine (end of shift after several shifts), respectively. These values are slightly higher than the results reported here: around 0.73 g/L (1.74 g/g creatinine).

Cresols analysis

No results for pCre are reported due to the high baseline level in controls (resulting from the degradation of phenylalanine and phenol compounds in vegetables) and the heterogeneity of responses in treated animals, as previously observed [START_REF] Inoue | Increased o-and p-cresol/hippuric acid ratios in the urine of four strains of rat exposed to toluene at thousands-ppm levels[END_REF]. For oCre and mCre, the concentrations in controls were systematically lower than the LOD for the analytical method. With mCre, exposure to at least 20 ppm is necessary to exceed the LOD (50 µg/L). This complicates its use as an indicator of toluene exposure. Considering all the measurements obtained for Tol-A > 20 ppm, the average ratio between oCre and mCre is 9.7 ± 2.0.

The oCre concentration excreted in urine after exposure to 20 ppm and 50 ppm Tol-A (US and EU TLV) was approximately 0.4 and 1.2 mg/L, respectively. These values are similar to those recommended for humans by the ACGIH (Biological Exposure Indice: 0.3 mg/g creatinine, end of shift after several shift) or by the German regulations (Biologischer Arbeitsstoff-Toleranz-Wert: 600 µg/L, end of shift).

Mercapturic acids analysis

The amounts of the different MA isomers are within similar ranges to the cresols (a few mg/L). As shown in figure 2A, the distribution of MAs produced by ring-or side-chain-oxidation depends on exposure level. At low concentrations, ring oxidation MAs (3 compounds) predominate (≈ 60% of the total amount excreted). In contrast, at high exposure levels, BMA is preponderant (55%). pTMA is the major TMA isomer (Figure 2B). Its relative abundance tends to increase with Tol-A. On average, the ratio of oTMA, mTMA and pTMA was approximately 2/1/6.

Regression of biomarker levels with airborne toluene

The relationship between the different indicators (in blood and urine) and atmospheric toluene are reported using a logarithm scale (Figure 3). These relationships were independently modelled using linear mixed-effect models. The fitted model, with prediction limits, is also presented in Figure 3. The regression equations calculated for each model, and some related estimates (needed to compare the different models) are presented in Table 2. R² is the percentage of variance explained by a fixed effect. ρ(rat) is the intra-class correlation coefficient (ICC, its value varies between 0 and 1). ρ(rat) can be considered a reliability test as it is the part of variance explained by random effects, after inclusion of the fixed effect in the model.

The linear regression analysis revealed a highly significant relationship between Tol-B and Tol-A, R² = 0.98 and ICC = 0.72. The corresponding fitted equation between the natural logarithm for both parameters is: ln Tol-B = -5.37 (± 0.09) + 1.29 (± 0.02) × ln Tol-A. This equation is similar to that previously obtained from inhalation exposure of Long-Evans rats to 50, 100, 500 and 1000 ppm toluene for 3 hours [START_REF] Benignus | Toluene levels in blood and brain of rats as a function of toluene level in inspired air[END_REF] (ln Tol-B = -6.53+1.43 × ln Tol-A; R² = 0.98).

The four MAs (BMA, o, m and pTMA) also significantly correlate with Tol-A (R²=0.96). This correlation is better than that obtained with oCre (R² = 0.92). In contrast, correlation was significantly poorer for HA (R² = 0.52) due to the absence of correlation between HA and Tol-A when Tol-A was < 50 ppm.

When Tol-A ≤ 20 ppm (Table 3), Tol-B is still very well correlated with Tol-A (R² = 0.96). R² obtained for MAs range from 0.80 (oTMA) to 0.87 (pTMA) while that found for oCre drops to 0.50.

Discussion

The goal of this study performed in rat exposed to toluene vapours (2-500 ppm) by inhalation was, primarily, to provide data on the urinary concentrations of four MAs derived from side-chain (BMA) and ring oxidation of toluene (o, m and pTMA). These data were compared to those obtained for the commonly used biomarkers, HA, and cresols (especially oCre).

The MAs concentrations detected in rat urine are of similar magnitude to cresol concentrations (a few mg/L) and about 1,000-fold lower than HA levels (measured in g/L). Rats and humans differ by the relative proportion of MAs in the total metabolites excreted. Indeed, occupational studies investigating BMA and pTMA in human urine revealed concentrations in the tens to hundreds of µg/L range [START_REF] Maestri | Determination of specific mercapturic acids as an index of exposure to environmental benzene, toluene, and styrene[END_REF][START_REF] Angerer | S-p-toluylmercapturic acid in the urine of workers exposed to toluene: a new biomarker for toluene exposure[END_REF][START_REF] Inoue | Urinary benzylmercapturic acid as a marker of occupational exposure to toluene[END_REF][START_REF] Inoue | Benzylmercapturic acid is superior to hippuric acid and o-cresol as a urinary marker of occupational exposure to toluene[END_REF], Ukai et al., 2007, Ikeda et al., 2008, Inoue et al., 2008[START_REF] Kawai | Validation of Urine Density Correction in Cases of Hippuric Acid and Unmetabolized Toluene in Urine of Workers Exposed to Toluene[END_REF][START_REF] Lovreglio | Validity of new biomarkers of internal dose for use in the biological monitoring of occupational and environmental exposure to low concentrations of benzene and toluene[END_REF]. The BMA to pTMA ratio also appears to differ between humans and rats, it nears 9.5 (median BMA and pTMA equal to 190 and 20 µg/L, respectively) for humans [START_REF] Angerer | S-p-toluylmercapturic acid in the urine of workers exposed to toluene: a new biomarker for toluene exposure[END_REF], compared to between 1.5 and 2 for rats (this study). This suggests that oxidation of the aromatic ring occurs more frequently in rats than in humans, a hypothesis supported by the lower HA / oCre ratio observed in rats. In rat, the ratio BMA / TMA depends also on the concentration. TMAs predominate at low concentrations whereas BMA is preponderant at high exposure levels. This inversion is probably due to the first oxidation step during TOL metabolism; indeed, the formation of arene oxides is either saturated faster or displays slower kinetics than benzyl alcohol formation.

The period and duration of sample collection are essential criteria in this type of study. In human studies, urine samples are generally spot samples taken at the end of a shift. In the protocol used here, all the urine excreted over the 18-h post exposure period was collected. With this method, elimination kinetics for individual animals have little influence on results. In humans, 65% of the HA produced is excreted within 4 hours, 80% after 20 hours, and elimination is complete within 24 hours [START_REF] Lof | Toxicokinetics of toluene and urinary excretion of hippuric acid after human exposure to 2 H 8 -toluene[END_REF]. oCre has a biphasic elimination half-life: 3-4 hours and 22 hours (its elimination kinetics vary considerably between individuals). For MAs, the elimination kinetics have yet to be determined. [START_REF] Angerer | S-p-toluylmercapturic acid in the urine of workers exposed to toluene: a new biomarker for toluene exposure[END_REF] assume that the half-life of pTMA is similar to that of S-phenylmercapturic acid (MA produced from benzene), at about 10 h in humans. In corroboration of this, data from a pilot animal study (own unpublished results) involving Sprague-Dawley ratsexposed for 6 hours to toluene (5-2000 ppm) by inhalation through the nose only -showed that, for all concentrations, 65 ± 15% of BMA and 67 ± 19% of pTMA excreted over the 24 hours following the start of exposure was excreted during the following 18h-post exposure period. BMA and pTMA amounts were revealed by HPLC/fluorescence, based on a method adapted from Maestri [START_REF] Maestri | Determination of specific mercapturic acids as an index of exposure to environmental benzene, toluene, and styrene[END_REF][START_REF] Campo | Toluene toxicokinetics and metabolism parameters in the rat and guinea pig[END_REF]. Contrary to HA and oCre, MAs are thus mainly produced after the exposure. It could be a good thing for the biological monitoring if observations in rat are transposable to human. The best collection period need nevertheless to be determined. Indeed, in this study the target was not to identify the peak of excretion.

The ratios for the different TMA isomers (2/1/6: o/m/p) are similar to those found for the corresponding ring oxidation mercapturic acids of ethylbenzene in rat (1/2/6) [START_REF] Cossec | Glutathione pathway in ethylbenzene metabolism: Novel biomarkers of exposure in the rat[END_REF] and for styrene in mice (2/1/6) [START_REF] Linhart | New urinary metabolites formed from ring-oxidized metabolic intermediates of styrene[END_REF]. Thus, the rates for glutathione's reaction with the 2,3-oxide and 3,4-oxide appear to be the same for all monosubstituted aromatic compounds in rodents. Very recently, Linhart et al. [START_REF] Linhart | Vinylphenylmercapturic acids in human urine as biomarkers of styrene ring oxidation[END_REF] showed that vinylphenylmercapturic acids (VPMA) were present in the urine of hand-lamination workers exposed to styrene. In this case, p-VPMA was found to predominate to an extent suggesting a more pronounced regioselectivity for VPMA formation in humans. However, traces of o-and m-VPMA isomers were also detected in urine samples taken at the end of the work-shift for the most exposed operators. We therefore anticipate that oTMA and mTMA will be detectable in urine from TOL-exposed humans, but in smaller amounts than pTMA.

No day-effect was observed for any marker or ratio throughout this study, indicating that no induction phenomenon is present, even at 500 ppm. This observation is in agreement with the literature (ATSDR, 2000); in rat, TOL induces hepatic CYP enzymes involved in its own metabolism starting from 1000 ppm. All the data collected after day 1 and day 5 of exposure were thus used to construct regression curves comparing Tol-A to the different indicators.

Tol-U and Tol-B are considered by many studies as the indicators of choice for TOL exposure [START_REF] Fustinoni | Comparison between blood and urinary toluene as biomarkers of exposure to toluene[END_REF], Ikeda et al., 2008). The following order of preference is often cited: Tol-U ~ groups [START_REF] Angerer | S-p-toluylmercapturic acid in the urine of workers exposed to toluene: a new biomarker for toluene exposure[END_REF]. This study demonstrates that rats excrete more BMA and TMAs than humans, however, neither was detected in rat control urine. These biomarkers are very sensitive; they would detect low toluene exposure. Using our GC-MS/MS analysis method, at just 2 ppm Tol-A, excretion was around 25 and 10 times the LODs for BMA and mTMA, respectively. Therefore, MAs offer clear advantages over traditional urinary biomarkers of TOL exposure. Further studies on TMAs are necessary to increase our knowledge in occupational fields beyond what was revealed by the single study of pTMA [START_REF] Angerer | S-p-toluylmercapturic acid in the urine of workers exposed to toluene: a new biomarker for toluene exposure[END_REF].

In an occupational environment, workers are exposed to toluene at various levels and for different durations. They are also generally co-exposed to other chemicals such as acetone, ethylacetate, ethylbenzene, methylethylketone, hexane, isopropyl alcohol, xylenes, etc. [START_REF] Truchon | o-cresol: a good indicator of exposure to low levels of toluene[END_REF], Ukai et al., 2007, Inoue et al., 2008). These other compounds may interact with toluene to modify its biotransformation. As a result, the blood TOL burden and excretion of TOL metabolites may be affected. Life-style factors including alcohol consumption and smoking may also affect TOL metabolism. Some of these interactions have already been identified indirectly in field studies, but their real impact on each indicator of TOL exposure has yet to be clarified. This study has paved the way for a broader study devoted to the interactions observed during controlled mixed exposures and their influence on the different biomarkers of toluene exposure, including the MAs.

Declaration of interest

  This work was funded by French National Institute for Research and Safety (INRS) for the prevention of occupational accidents or diseases. The authors report no conflicts of interest.

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Schematic representation of TOL metabolism in rat and human

  029 a three measurements per day b n = 8 except for TOL20 on day 5 (= 7) c limit of quantification (LOQ) = 3 × LOD = 150 µg/L

Table 1 . Summary statistics -Mean concentrations (± SD)

 1

		Day	TOL500	TOL200	TOL50	TOL20	TOL5	TOL2
	Tol-A a	1	534.0 ± 24.3	205.0 ± 6.0	51.6 ± 0.4	21.8 ± 0.9	5.39 ± 0.41	2.03 ± 0.11
	(ppm)	5	506.3 ± 16.3	197.0 ± 1.7	49.1 ± 1.5	19.5 ± 0.8	5.11 ± 0.09	2.06 ± 0.02
	Tol-B b	1	18.12 ± 2.18	5.51 ± 1.15	0.52 ± 0.06 0.18 ± 0.04 0.033 ± 0.003 0.017 ± 0.003
	(µg/g)	5	18.16 ± 3.36	5.05 ± 1.04	0.55 ± 0.08 0.15 ± 0.03 0.040 ± 0.005 0.018 ± 0.003
	HA b (g/L)	0 1 5	0.58 ± 0.13 1.95 ± 0.18 2.11 ± 0.19	0.58 ± 0.07 1.75 ± 0.22 1.68 ± 0.32	0.57 ± 0.15 0.36 ± 0.17 0.68 ± 0.14 0.51 ± 0.10 0.77 ± 0.17 0.50 ± 0.10	0.50 ± 0.17 0.42 ± 0.09 0.44 ± 0.13	0.54 ± 0.07 0.41 ± 0.17 0.42 ± 0.25
	oCre b	1	8.15 ± 2.40	5.43 ± 1.00	1.23 ± 0.25 0.51 ± 0.20	0.23 ± 0.05	0.18 ± 0.04
	(mg/L)	5	8.43 ± 0.90	5.17 ± 1.52	1.15 ± 0.34 0.34 ± 0.12	0.22 ± 0.04	0.22 ± 0.06
	mCre b	1	0.74 ± 0.13	0.57 ± 0.13	0.14 ± 0.04	< LOQ c	< LOD	< LOD
	(mg/L)	5	0.84 ± 0.09	0.52 ± 0.15	0.15 ± 0.06	< LOQ c	< LOD	< LOD
	BMA b	1	15.21 ± 4.48 13.41 ± 4.60 2.04 ± 0.36 1.14 ± 0.50	0.20 ± 0.03	0.12 ± 0.03
	(mg/L)	5	18.53 ± 5.14 12.57 ± 3.70 2.44 ± 0.53 1.06 ± 0.39	0.19 ± 0.06	0.15 ± 0.06
	oTMA b	1	3.04 ± 0.85	2.20 ± 0.47	0.47 ± 0.09 0.28 ± 0.16 0.069 ± 0.009 0.044 ± 0.006
	(mg/L)	5	3.44 ± 0.33	2.11 ± 0.49	0.50 ± 0.10 0.26 ± 0.14 0.065 ± 0.018 0.052 ± 0.011
	mTMA b	1	1.42 ± 0.43	1.15 ± 0.24	0.32 ± 0.07 0.15 ± 0.06 0.053 ± 0.003 0.036 ± 0.003
	(mg/L)	5	1.55 ± 0.21	1.04 ± 0.28	0.33 ± 0.06 0.15 ± 0.07 0.050± 0.007 0.038 ± 0.006
	pTMA b	1	7.77 ± 2.25	6.93 ± 1.52	1.69 ± 0.33 0.95 ± 0.49 0.175 ± 0.031 0.091 ± 0.017
	(mg/L)	5	7.77 ± 8.39	6.52 ± 1.52	1.73 ± 0.32 0.85 ± 0.38 0.164 ± 0.052 0.110 ± 0.

Table 2 . Linear mixed-effects models for ln-transformed toluene exposure indicators.

 2

	Indicator	Tol-B	HA	oCre	BMA	oTMA	mTMA	pTMA
	n	96	144	96	95	95	95	95
	Fixed part
	Intercept α	-5.37	-0.87	-2.61	-2.91	-3.83	-3.97	-2.95
	(SE)	(0.09)	(0.06)	(0.11)	(0.10)	(0.08)	(0.08)	(0.09)
	Slope β	1.29	0.19	0.74	0.95	0.81	0.71	0.85
	(SE)	(0.02)	(0.02)	(0.03)	(0.02)	(0.02)	(0.02)	(0.02)
	Random part
	Between-individual SD ψ	0.27	0.25	0.34	0.31	0.26	0.25	0.30
	Within-individual SD θ	0.17	0.4	0.25	0.22	0.17	0.14	0.17
	Derived estimates
	ρ(rat)	0.72	0.28	0.65	0.67	0.70	0.76	0.76
	R²	0.98	0.52	0.92	0.96	0.96	0.96	0.96

Acknowledgement

The authors gratefully acknowledge Philippe Chaumont, Lionel Dussoul, Jean-Claude Gondrexon and Sylvie Michaud for their technical assistance.

Tol-B > oCre ~ BMA > HA. In the present study, the sequence was found to be: Tol-B (R² = 0.98) > BMA ~ oTMA ~ mTMA ~ pTMA (R² = 0.96) > oCre (R² = 0.92) > HA (R² = 0.52) (Table 2). Tol-U was not measured here since the urine collection process did not include all the necessary precautions to avoid TOL loss by evaporation and/or oxidation. Correlation coefficient obtained with Tol-B shows that it closely correlates with TOL-A, even at low concentrations (R² = 0.96 when Tol-A ≤ 20 ppm).

Unlike for the urine samples, this excellent correlation cannot be attributed to how blood samples were collected (spot collection, as would be used in an occupational situation). This strong correlation is probably related to the exposure conditions, particularly the stability of the Tol-A concentration over the 6-h exposure period.

When considering the full range of Tol-A (2 -500 ppm), R² values for the surrogate biomarkers tested are also very high except for HA. This low R² for HA was expected because it is well known that the presence of these compounds in the urine is not a definitive proof of toluene exposure since it is also produced by metabolism from the normal diet (from benzoates or benzoic acid in fruits, vegetables, and food preservatives) [START_REF] Lof | Toxicokinetics of toluene and urinary excretion of hippuric acid after human exposure to 2 H 8 -toluene[END_REF][START_REF] Kawai | Toluene itself as the best urinary marker of toluene exposure[END_REF][START_REF] Pierce | Biological monitoring of controlled toluene exposure[END_REF]. MAs correlations with Tol-A were almost as good as those of Tol-B. At low concentrations (when Tol-A ≤ 20 ppm), oCre appear to be limited (R² = 0.50). Previous reports have already made this observation for oCre and BMA [START_REF] Ikeda | Changes in correlation coefficients of exposure markers as a function of intensity of occupational exposure to toluene[END_REF], Inoue et al., 2008). In this study, correlation coefficients obtained with MAs decrease at low TOL concentrations (mean R² equal 0.84 instead of 0.96) but they remain high. This is probably because in this study endogenous levels are too low to be detected. The background levels of MAs in non-exposed populations must be determined as they are key to the potential usefulness of MAs as biomarkers of TOL exposure. Indeed, a biomarker with a low background may be particularly useful for smokers for whom oCre levels may be an unreliable indicator of exposure [START_REF] Dossing | Urinary hippuric acid and orthocresol excretion in man during experimental exposure to toluene[END_REF][START_REF] Truchon | Gas chromatographic determination of urinary ocresol for the monitoring of toluene exposure[END_REF][START_REF] Pierce | Biological monitoring of controlled toluene exposure[END_REF][START_REF] Fustinoni | Comparison between urinary o-cresol and toluene as biomarkers of toluene exposure[END_REF], Ukai et al., 2007[START_REF] Ducos | Biological monitoring of exposure to solvents using the chemical itself in urine: application to toluene[END_REF], Inoue et al., 2008). Currently, a doubt remains; only two studies have reported a physiological background level of BMA. Values for this metabolites were in the range 20-40 µg/L for a control group [START_REF] Angerer | S-p-toluylmercapturic acid in the urine of workers exposed to toluene: a new biomarker for toluene exposure[END_REF], or 6.60 ± 6.43 µg/g creatinine [START_REF] Lovreglio | Validity of new biomarkers of internal dose for use in the biological monitoring of occupational and environmental exposure to low concentrations of benzene and toluene[END_REF]. pTMA has never been detected in urine samples from non-exposed control