Brenda Elizabeth Olivas-Padilla

Dimitris Papanagiotou
email: dimitris.papanagiotou@minesparis.psl.eu

Gavriela Senteri
email: gavriela.senteri@minesparis.psl.eu

Sotiris Manitsaris
email: sotiris.manitsaris@minesparis.psl.eu

Alina Glushkova
email: alina.glushkova@minesparis.psl.eu

Improving Human-Robot Collaboration in TV Assembly through Computational Ergonomics: Effective Task Delegation and Robot Adaptation

Keywords: Human-Robot Collaboration, Wearable sensors, Ergonomics, Gesture recognition, Pose estimation, Assembly

The high prevalence of work-related musculoskeletal disorders (WMSDs) could be addressed by optimizing Human-Robot Collaboration (HRC) frameworks. In this context, this paper proposes a methodology for ergonomically effective task delegation and HRC for manufacturing applications based on two hypotheses. The first hypothesis states that it is possible to rapidly quantify ergonomically professional tasks using motion data from a few wearable sensors and then delegate the highrisk tasks to a collaborative robot. The second hypothesis is that, compared to typical HRC frameworks involving physical interaction, the ergonomics and safety of an HRC scenario can be enhanced by combining gesture recognition and pose estimation. These remove unnecessary motions that could expose operators to ergonomic risks, decrease the amount of physical effort, and make the robot aware and responsive to the presence and gestures of the operators. The methodology is evaluated by optimizing the HRC scenario of a television manufacturing process, yet it is described how it can be reconfigured for other industrial scenarios. The effect of the temporal and spatial adaptation on the operator's range of motion was analyzed through three separate experiments. The effectiveness of HRC is measured through the standard key performance indicators (KPIs); however, to evaluate the collaboration and required physical demand, two KPIs are proposed in this paper. These are the rate of spatial adaptation and the rate of reduction in the operator's motion. The results demonstrated that the methodology enhanced the ergonomics and efficiency of the production process. First, the robot was delegated two tasks identified as the most ergonomically dangerous for human operators. Then the optimized HRC achieved an average rate of spatial adaptation of 29.37% and a decrease in operator movement of 28.8% across 14 subjects compared to HRC frameworks that do not include spatial and temporal adaptation.

I. INTRODUCTION

Industry 4.0 has resulted in a rise in research in the field of Human-Robot Collaboration (HRC). Robotic agents are therefore being integrated into the work routine, not to take the position of human operators but to assist them in accomplishing complicated and physically demanding tasks. However, it is critical to consider ergonomic factors to design HRC installations properly and prevent operators from developing work-related musculoskeletal disorders (WMSDs). Thus, current approaches for task delegation and collaboration between operators and robots must be improved by considering ergonomics to maximize operators' comfort and production efficiency in industrial co-production cells. With this in mind, a methodology for ergonomically effective task delegation and HRC is proposed in this paper. This methodology is founded on two hypotheses. The first hypothesis (H1) is that by utilizing a reduced amount of motion capture (MoCap) data from wearable inertial sensors, operators' movements can be accurately measured, allowing for a more thorough ergonomic analysis of their actions and facilitating task delegation when implementing HRC frameworks. The ability to record accurate measurements for ergonomic analysis is essential as it provides quantitative measures of operators' performance. The second hypothesis (H2) is that gesture recognition used for temporal adaptation and pose estimation for spatial adaptation enable a more natural and ergonomic HRC. The reason is that only motions that are convenient for the operators are performed, avoiding unnecessary movements that may expose human operators to ergonomic risks. Gesture recognition is one of the methods used to achieve contactless communication between a robot and a human operator. It can be defined as the process of transforming movements into a form that a machine can easily interpret. On the other hand, spatial adaptation is the process through which the robot adjusts its movements according to the anthropometric characteristics of each operator. Including human pose estimation enriches the robot's perception and enables its spatial adaptation in the HRC. Thus, the robot can assist operators in reducing their range of motion and physical effort necessary to fulfill their professional duties. Fig. 1 illustrates the 2-step methodology proposed in this paper for ergonomically optimizing an industrial HRC scenario. First, wearable inertial sensors are used to conduct an in-depth analysis and ergonomic evaluation of professional tasks for task delegation. The proposed HRC then employs an RGB camera in egocentric vision and a frontal RGB-D camera for real-time gesture recognition and pose estimation, respectively. The methodology is evaluated based on its application to an actual television (TV) manufacturing process. Following, Section II discusses state of the art in ergonomic analysis using MoCap data and HRC frameworks. Section III explains the methodology for ergonomically evaluating professional tasks and the results obtained. Next, Section IV details the HRC framework and the calculated KPIs. Finally, Section V presents the conclusion and future work.

II. STATE OF THE ART

The activities performed by operators in the industrial sector are becoming more challenging and complex in order to meet market demands within certain time limits, job specifications, and budget constraints. Operators must go beyond their natural physical limitations to undertake repetitive jobs for long periods to complete the required tasks. Being subjected to such constant physical strain leads to WMSDs. Ergonomists have developed a variety of methods for evaluating workrelated tasks. The methods based their analysis on theoretical knowledge of human physical limitations and abilities indicated by known standards (e.g., ISO 11226:2000 andEN 1005-4). Popular methods are the Rapid Upper Limb Assessment (RULA) [START_REF] Mcatamney | RULA: A survey method for the investigation of work-related upper limb disorders[END_REF], Rapid Entire Body Assessment (REBA) [START_REF] Hignett | Rapid Entire Body Assessment (REBA)[END_REF], and European Assembly Worksheet (EAWS) [START_REF] Schaub | The European Assembly Worksheet[END_REF]. To implement these methodologies, the ergonomist observes an operator executing the task under evaluation and annotates various body part postures on a worksheet to estimate an score. Yet, because these approaches rely on the ergonomist's perception and experience, scoring can be subjective and have inter-variability. Previous studies have used biomechanical simulations to compute ergonomic metrics (posture, physical effort, and energy spent) [START_REF] Kim | Anticipatory robot assistance for the prevention of human static joint overloading in human-robot collaboration[END_REF], [START_REF] Muller | Motion-based prediction of external forces and moments and back loading during manual material handling tasks[END_REF]. Nevertheless, adopting these analyses in industrial applications that demand rapid reconfigurability has been hindered due to several factors [START_REF] Muller | Motion-based prediction of external forces and moments and back loading during manual material handling tasks[END_REF], [START_REF] Menolotto | Motion Capture Technology in Industrial Applications: A Systematic Review[END_REF], [START_REF] Kanko | Concurrent assessment of gait kinematics using marker-based and markerless motion capture[END_REF]. These include requiring specialized and costly equipment, frequent calibration procedures, limited capture volumes, and substantial installation and operational expenses. Thus, there is still a need for methodologies that employ MoCap technologies that are practical to implement in real-world settings and can accurately estimate the ergonomic risk of any representative set of manipulation actions performed in industry.

Collaborative robots are becoming increasingly common as an automated solution for making workplaces more ergonomic, cost-effective, and flexible. As a result, early re-search focused on creating HRC frameworks that physically couple humans and robots. For example, some applications are for co-carrying [START_REF] Yu | Human-Robot Co-Carrying Using Visual and Force Sensing[END_REF], [START_REF] Sirintuna | Human-Robot Collaborative Carrying of Objects with Unknown Deformation Characteristics[END_REF] and co-manipulation [START_REF] Al-Yacoub | Robotics and Computer-Integrated Manufacturing Improving human robot collaboration through Force / Torque based learning for object manipulation[END_REF]. However, most existing real-time HRC frameworks are developed around the concept of human avoidance to assure safety, i.e., preventing unintentional accidents between humans and robots [START_REF] Sharkawy | Human-robot collisions detection for safe human-robot interaction using one multi-input-output neural network[END_REF], [START_REF] El-Shamouty | Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning[END_REF]. A study that directly addressed human ergonomic demands was done by Bestick et al. [START_REF] Bestick | Implicitly Assisting Humans to Choose Good Grasps in Robot to Human Handovers[END_REF], who investigated the most convenient robot configurations when giving over an object to a human operator through offline biomechanical simulations. A priori assumptions were made regarding the cost function based on the distance of human joints from the neutral position. Lorenzini et al. [START_REF] Lorenzini | A New Overloading Fatigue Model for Ergonomic Risk Assessment with Application to Human-Robot Collaboration[END_REF] and Kim et al. [START_REF] Kim | A Human-Robot Collaboration Framework for Improving Ergonomics During Dexterous Operation of Power Tools[END_REF] used overloading joint torque as a cost in the offline simulation to optimize the human body configuration in co-carrying and co-manipulation activities based on the human body stability, task, and workspace conditions. Now, the primary focus of ergonomics research lies in incorporating and applying evaluation methods effectively within work environments without requiring offline biomechanical analysis in laboratory settings. Therefore, it is crucial to explore the potential use of user-friendly MoCap technologies that are comfortable and do not disrupt daily work activities. Also, future research should concentrate on designing methods for distributing roles in HRC frameworks and others that facilitate online planning and adjustment of robot movements. This online planning and robot adaptation can be evaluated based on Key Performance Indicators (KPIs) that consider ergonomic risk factors. Previous research provided metrics that primarily assessed the productivity of collaborative robots [START_REF] Steinfeld | Common metrics for human-robot interaction[END_REF], [START_REF] Crandall | Validating humanrobot interaction schemes in multitasking environments[END_REF]. For instance, neglect tolerance (NT), concerned with how long a human can ignore a robot, has been used to measure the robot's effectiveness in interacting with its operators. Another standard indicator is the interaction effort (IE) which denotes the time necessary to engage with the robot. The combination of NT and IE produces the robot attention demand (RAD), a dimensionless metric that quantifies the level of focus that the robot requires from the operator. Finally, there is also the measurement of the efficiency of the robot (ER), which involves the time it takes for the robot to move in relation to the total time of the whole routine. Consequently, to measure the job quality of operators in HRC frameworks online, rather than just their productivity performance, new KPIs that consider human factors must be developed.

To summarize, practical and rapidly reconfigurable frameworks with real-time data processing capabilities are necessary to handle the industry's ergonomics challenges while ensuring productivity. This paper presents a methodology for creating an ergonomic and reconfigurable HRC framework, following up on the authors' prior work while developing natural HRC [START_REF]Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots[END_REF] and analyzing postural risk factors [START_REF] Olivas-Padilla | Stochastic-biomechanic modeling and recognition of human movement primitives, in industry, using wearables[END_REF].

III. AUTOMATIC ERGONOMIC EVALUATION

This section describes the task delegation approach in which real professional tasks are automatically evaluated using a minimal set of inertial sensors.

A. Motion capturing of the television assembly scenario

For the initial study and recording of tasks performed on a TV production line, the BioMed bundle motion capture system from Nansense Inc. (Baranger Studios, Los Angeles, CA, USA) was used. This system consisted of a full-body suit composed of 52 IMUs strategically placed throughout the body and hands. At a rate of 90 frames per second, the IMUs were used to calculate the Euler joint angles of body segments on the articulated spine chain, shoulders, limbs, and fingertips. Wearable sensors were chosen for the ergonomic evaluation because they provide more precise measurements of the operators' posture than optical MoCap systems such as RGB cameras.

Two healthy adults, one male and one female, wore the IMU-based suit during the MoCap recording session. The recording took place on a real production line over the course of an eight-hour shift, with the operators placing circuit cards into the frame of TVs. The entire assembly procedure can be broken down into four main tasks, each illustrated in Fig. 2. The first task is grabbing a circuit card from a container (T 1 ); the second is taking a wire from a second container (T 2 ); the third involves connecting the card and wire and placing them on the TV frame (T 3 ); the fourth task corresponds to drilling the cards on the TV frame (T 4 ). The whole assembly was recorded 108 times. Following the recordings, the MoCap data was pre-processed offline before analysis. Low pass filtering with a Butterworth filter with a cut-off frequency of 5hz was used to reduce noise, and the common zero velocity update algorithm was used to eliminate drift that occurred in some iterations by electromagnetic interference. When the algorithm detects a period of zero or near-zero velocity, it updates the orientation and position of the subject to reduce accumulated errors. The full details of the processing of the recordings are given in [START_REF] Olivas-Padilla | Motion Capture Benchmark of Real Industrial Tasks and Traditional Crafts for Human Movement Analysis[END_REF].

B. Modeling of motion primitives for ergonomic evaluation

An automatic posture evaluation system was built to identify potentially hazardous postures in professional tasks. The system is comprised of Hidden Markov Models (HMMs) that learned to recognize motion primitives. The motion primitives consist of small sequences of postures with varying ergonomic The protocol contained 28 different motion primitives, each recorded three times. The motions can be classified into three broad categories: those performed standing, those performed while seated in a chair, and those performed while kneeling. The small movements (≈ 4 seconds) progressed from comfortable to increasingly uncomfortable postures (raising arms, bending forward, rotating the torso). Each motion has assigned an EAWS risk score, ranging from 0.5 to 26.5. The higher the ergonomic risk score, the greater the risk. In prior research of these motion primitives, their spatiotemporal dynamics were analyzed using a Gestural Operational Model (GOM) [START_REF] Olivas-Padilla | Stochastic-biomechanic modeling and recognition of human movement primitives, in industry, using wearables[END_REF].

As a result, it was discovered that high recognition accuracy can be achieved with only five inertial sensors. These sensors are positioned on the lumbar spine, the left upper arm, the right shoulder, the right upper leg, and the left forearm. Consequently, the proposed system for evaluating professional tasks only requires measurements from these five sensors.

C. Automatic postural evaluation of professional tasks

To conduct the ergonomic evaluation, the recordings of the professional tasks are segmented into short windows of similar duration to the motion primitives and then provided to the [START_REF] Schaub | The European Assembly Worksheet[END_REF], and the statistics are used to decide which tasks expose operators to the higher risk.

D. Results and discussion of the ergonomic evaluation

Before segmenting professional tasks into motion primitives, the 28 HMMs were trained using a left-right topology with seven internal states that yielded the maximum recognition performance. Their performance was evaluated using the all-shots approach and achieved an overall F-score of 95.03%, precision 94.97%, and recall 95.30%. The assembly tasks were then evaluated with the automatic evaluation system, resulting in the EAWS scores illustrated in Table I. Table I contains the mean, standard deviation, and mode of the scores calculated for each task.

According to these results, the majority of iterations of tasks T 1 and T 2 can be classified as medium-risk motions, while iterations of tasks T 3 and T 4 are classified as lowrisk motions. For T 1 , the motion primitives detected most frequently resembled motions in which the elbows are raised above shoulder level while the torso is laterally bent. These results are expected as operators must rotate and laterally bend their torsos to retrieve a circuit card from a container. Due to the container's location, operators must also raise their arms above shoulder level, as illustrated in Fig. 2(a). The motion primitives detected for T 2 corresponded more to motions where there is both bending and rotation of the torso and stretching of the arms. These results match T 2 , where the operators bend to pick up a wire from a container and then connect it to the card. The motion primitives detected for T 3 and T 4 were primarily torso rotations and working with arms bent around 90 • . These motion primitives are visible in Fig 2(c) and 2(d). In these tasks, operators were only required to slightly rotate their torso due to the constant movement of the TV frame caused by the conveyor belt, but they did not need to stretch their arms or strongly bend forward to reach the TV frame. Since T 1 and T 2 involve assuming awkward postures such as rotating the torso while bending forward or raising the arms above shoulder level, they represent a major ergonomic risk than T 3 and T 4 . EAWS recommends that, if possible, tasks with moderate risk must be redesigned; otherwise, the risk must be controlled through other means. Therefore, it is proposed to delegate T 1 and T 2 to the robot and leave T 3 and T 4 to the operators. These results were compared to those calculated by the factory's ergonomist, who manually calculated the ergonomic risk of each task using the Rapid Entire Body Assessment (REBA) worksheet [START_REF] Hignett | Rapid Entire Body Assessment (REBA)[END_REF]. According to the results of this study, this postural evaluation categorized T 1 and T 2 as medium-risk tasks with a REBA score of 7, which suggests redesigning the task to improve ergonomics. On the other hand, T 3 and T 4 were given a score of 2, meaning movements of low risk.

Consequently, the proposed task delegation approach that automatically detects postural risk factors utilizing a reduced set of inertial sensors was validated for this workplace scenario. This analysis can be applied to other professional tasks for rapid reconfigurability, just as it was done with TV assembly tasks. First, the professional tasks to be evaluated need to be recorded with the designated inertial sensors and placed according to the standards of the International Society of Biomechanics (ISB). Next, calculate the Euler angles of each body part measured by each sensor. Then, segment the data of the tasks into four-second windows, ideally with a two-second overlap to cover the entire task. Finally, apply the automatic postural evaluation to the segmented tasks, which would indicate the motion primitives detected and the estimated EAWS score. Depending on the nature of the identified high-risk tasks, it is determined whether they should be delegated to a collaborative robot or the production cell should be redesigned. Additionally, the proposed approach for task delegation can serve for high-frequency monitoring of the operators, providing human ergonomists with quantitative measures of the workers' performance and postural risk detected during the shift.

IV. OPTIMIZATION OF WORK-SPACE SCENARIO

Based on the results of the automatic ergonomic evaluation, this section describes the integration of a collaborative robot into the manufacturing process. To improve the interaction of the collaborative robot and the operator and reduce the operator's range of motion, gesture recognition and pose estimation modules were included. Three experiments were conducted using the TV assembly scenario to evaluate the proposed HRC framework using standard KPIs and two new KPIs proposed in this paper.

A. Gesture recognition with 3DCNNs

According to the task delegation results, the assembling routine is first divided into sub-tasks performed by the robot or operators. 3D Convolutional Neural Networks (3DCNNs) are used for the gesture recognition module in real-time, which establishes a gestural communication between the operator and the robot. As proven in recent research [START_REF]Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots[END_REF], gesture recognition decreases production time in a real-life industrial scenario and ensures workers use ergonomic postures.

3DCNNs were chosen since they are an end-to-end method that can get as input RGB images without the prior extraction of features like the case is for other methods, such as the Hidden-Markov models. These networks were trained using a dataset consisting of command gestures adapted for TV assembly and recorded with an RGB camera in an egocentric view. The challenge with using egocentric data for recognition is that the head and hands move in parallel but do not always follow each other. Even if the hands are a significant part of this module, they can be prominent within the frame but can also be partly or even totally out of the main view. Nevertheless, the added value of an egocentric perspective is that the network will provide gesture recognition results independent of the users' anthropometric characteristics and position in the environment.

To train the proposed network, a dataset was first created from this particular HRC scenario, consisting of RGB images recorded with a GoPro Black, placed with a headband on the head of the human operator. The recording was made at an 848x480 resolution and 20 frames per second. Concerning the architecture of the 3DCNNs, it consisted of 6 convolution layers, with max-pooling and batch normalization in between. The Adam optimizer was used with a learning rate of 0.000001. To facilitate the real-time HRC, a sliding window of 5 frames was used, as it was the one that gave the best results among those of 10, 15, and 20 frames. For the creation of the TV-assembly dataset, a group of 14 operators, four females and ten males, were recorded performing six gestures and five postures. Thus, there are 11 classes in total, each corresponding to a unique command for the collaborative robot, presented in Fig. 3. The objects involved are a TV frame and two circuit cards: the power supply (PSU-gold card) and the mainboard (chassis-green card). These gestures were adapted for TV assembly, but they can be customized for other scenarios by modifying the actions performed by the robot.

B. HRC for television manufacturing

For the proposed HRC scenario, the UR3 robotic arm (Universal Robots, Odense, Denmark)is used. The external parts that are used for grasping and releasing the circuit cards are from ROBOTIQ (gripper: 2F-140 & force torque sensor: FT-300-S). The Robot Operating System (ROS) is used to control the robotic arm. Through a UDP communication protocol, the robotic arm receives real-time ID messages from the gesture recognition module. In the new routine, the operator performs the start gesture (G 1 ) to notify the robot that the assembly routine starts. Next, the robot approaches the card container, retrieves the initial green card, and hands it to the operator in the pre-defined handover position. By pressing the Force sensor, the operator releases the card and verifies its functionality. If the card is functional, the operator performs G 2 and places it on the TV frame (G 3 ), while the robotic agent moves toward the card box to retrieve the gold card. If the green card is not functional, the operator executes G 10 to notify the robot, which then brings a replacement green card. When the robotic arm delivers a functional card, the operator performs G 2 and then places and screws the card on the TV frame (G 3 and G 4 ). This procedure is repeated until both the green and gold cards are placed on the TV frame. Then, the human operator executes G 11 to signal the routine's completion. Safety regulations for collaborative robotics are followed according to standards (ISO 10218 and TS 15066). The gesture recognition module is also designed with duallevel control to ensure the operator's safety in the HRC scenario. More precisely, the robot receives the ID of the recognized gesture only after it has been correctly recognized for 20 consecutive time frames. This may raise concerns about any latency observed during the TV assembly process. However, it was observed that the time interval between capturing a frame and its correct recognition was between 0 and 0.08 ms, indicating no significant latency. The second level of control was performed within ROS, where the robot accepted only gestural IDs that corresponded to the assembly workflow as defined by the routine. As a result, if an unintended incident occurs, the operator retains full control as the robotic arm strictly adheres to the routine.

The introduction of the robotic arm and a gesture recognition module requires only minor torso rotations from the human operator. To facilitate natural collaboration and assist the operator in performing only ergonomically safe motions, a posture estimation module is added that enables the robot to spatially adapt to the operator. The methodology for pose estimation and spatial adaptation of the robot is described in detail in an earlier publication by the authors at [START_REF]Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots[END_REF]. The spatial adaptation refers to the fact that the robotic arm does not place the cards in a fixed position but rather adapts to the operator's anthropometric characteristics, thereby improving the operator's posture. An Intel-RealSense RGB-D camera is utilized to capture the operator's upper body for pose estimation and the robot's spatial adaptation. The X and Y axes of the camera are first aligned with the X and Z axes of the robot. Then, the operator's skeleton and the position and velocity of the operator's wrists are extracted. The spatial adaptation then consists of the robotic arm tracking the operator's hand to approach it with a card when requested by the operator and when the hand is motionless and in an accessible position for the robot.

C. Key performance indicators

To evaluate the HRC framework, NT, IE, RAD, and ER were calculated, which essentially assess the interaction between the robot and the operator as well as their productivity in the HRC. This paper introduces two new KPIs to evaluate the physical demand required of the operator in the HRC. The first refers to the percentage of robot spatial adaptation (SA) by adding pose estimation, and the second is for determining how much effort from the operator is reduced due to the gesture recognition module; this KPI is designated as the reduction in the operator's motion effort (RiOM ). The KPI of robot spatial adaptation represents the ratio of the distance covered by the robot without spatial adaptation to the distance covered when the robot adjusts to the operator-specified position. The following formula is used to determine this KPI, where SA is the spatial adaptation percentage:

SA(%) = ∥AHP -W P ∥ -∥P HP -W P ∥ ∥P HP -W P ∥ (1)
By measuring the position of the robot's ending point, it is obtained the waiting point W P , which is the robot's position when waiting for the instruction of the operator to provide a circuit card. The adapted handover position AHP is the position where the robot gives the circuit card according to the operator's hand, using pose estimation. Finally, the pre-defined handover position P HP is the position the robot leaves the circuit card without pose estimation, fixed for all operators. Centimeters are used to measure distances. The higher the rate of adaptation, the more effort the operator had to put in to reach the circuit card during the HRC scenario when there was no spatial adaptation of the robot. The second KPI quantifies the difference in operators' motion before and after introducing gesture recognition. This KPI is calculated as follows:

RiOM (%) = ∥M woGR∥ -∥M wGR∥ ∥M woGR∥ (2)
The position of the operator's wrist is measured to calculate the distance covered by the wrist while interacting with the robot. M woGR is the distance covered by the wrist when there is no gesture recognition and the operator need to press the robot's force-torque sensor to continue the routine. M wGR is the distance covered by the wrist when there is gesture recognition and the operators only perform a gesture with their hand to continue their routine. The higher the percentage of reduction in distance, the greater the convenience for the user in completing their routine with fewer physical movements. Consequently, RiOM indicates the amount of effort reduced by the operator as a result of gesture recognition.

D. Experimental results

In order to evaluate the HRC scenario in terms of collaboration and operator performance, 14 operators, who did not participate in the creation of the training dataset, were recorded performing the proposed routine in three separate experiments. The 14 subjects consisted of seven males and seven females, either right-or left-handed, ranging in height from 1.60 to 1.90 meters.

The 3DCNNs were trained with the command gesture dataset using the 80:20 approach for the gesture recognition module. An F-score of 97.20% was achieved in recognizing the 11 gestures after 40 epochs and 98.60% by using earlystopping. For real-time recognition, the module presented a delay of 0.8 seconds. The confusion matrix is illustrated in Fig. 4, where it can be observed that the main false results are among G 4 , which is the gesture where the human operator screws the green card on top of the TV frame, and G 7 , where the operator waits for the robot for the next card. The main reason is the confusion caused by part of the frames from G 4 where the operator had the hands down as in G 7 . When the algorithm was deployed in real-time, for the collaboration of the human operator with the robot, the challenge in the recognition results was noticed between gestures G 2 and G 10 , as well as G 8 and G 11 . The primary difficulty arises from the fact that the TV frame contains edges that can be mistaken for the fingers of the human operator. Additionally, it was observed that in real-time scenarios, lightning conditions affect the recognition results for the gestures mentioned above due to the reflection of the light on the metallic TV frame. Nevertheless, despite these difficulties, all 14 new subjects successfully completed the collaboration procedure, indicating that the accuracy of the gesture recognition module is sufficient even for users that were not part of the training dataset.

The case of failed recognition was handled in two ways. Firstly, the TV assembly routine consists of a specific se- quence of gestures, and every class can be followed only by one or two at most other classes. The collaborative robot ignores recognition errors that do not follow the TV assembly procedure. Second, suppose an incorrect gesture is identified but is part of the normal assembly sequence. In that case, the collaborative robot only considers recognition results of 100% for 30 consecutive frames, which takes one second to compute.

Three experiments were conducted to assess the adaptation of the robotic arm and the operator's motion during each. In the first experiment, gesture recognition and spatial adaptation were disabled. Hence, operators were required to interrupt their routine and inform the robotic arm of their current action by pressing its force-torque sensor (vertically in order to proceed to the next step or horizontally if the received card is not functional). For the second experiment, only the gesture recognition module was enabled, so the operators received the circuit cards from a P HP . Finally, in the third experiment, gesture recognition and spatial adaptation were enabled and continuously provided information about the operators' actions to the robotic arm.

The KPIs NT, RAD, IE, and ER were calculated for each experiment to evaluate the different HRC frameworks and are given in Table II. However, for the proposed KPIs, SA is primarily used to compare the first experiment (physical interaction) with the third experiment (gesture recognition and spatial adaptation with pose estimation), whereas RiOM is used to compare the first experiment with the second experiment (only gesture recognition). The measured SA and RiOM for each operator are shown in Table III. Note that the greater the percentage of SA, the more difficult it was for the operator to receive the cards without spatial adaptation enabled. This is because the P HP was not possible to be convenient for every operator. For RiOM , the larger the percentage, the better, as it indicates a greater reduction in operator motion when gesture recognition is utilized.

E. Discussion

The four standard KPIs are designed to assess the effectiveness of the performance of a robot in an HRC scenario. According to TableII, a larger value of NT indicates that less information is being conveyed to the robot about the operator's actions and intentions, which is evident in the experiment where only physical interaction takes place. However, this limitation can be addressed by incorporating gesture recognition and pose estimation modalities. RAD is also displayed and reflects the robot's ability to understand and adapt to the operator. The implementation of gesture recognition modality enhances the RAD because the operator is able to interact with the robot not only explicitly but also implicitly through the use of gestures. The efficiency of the robot is determined by calculating the percentage of time it spends in motion while executing a routine. In the first and second experiments, the handover position is pre-defined, and the duration of the motion of the robot remains constant throughout the experiment; this is denoted by an asterisk (*) in TableII. In this case, where the efficiency falls below 50 percent, it indicates that a single robotic arm can be utilized to simultaneously handle two different product lines.

These standard metrics do not take into account the potential benefits of different modalities of HRC at the operator's convenience. To address this gap, this paper proposes the use of two new movement-based KPIs (SA and RiOM ). As stated previously, collaborative robotics can improve operators' working conditions and specifically contribute to preventing WMSD. According to the results achieved using the proposed HRC with gesture recognition and spatial adaptation, Table III illustrated a consistent decrease in the physical effort required of the operator. The spatial adaptation had the most impact on subject 11, who required more physical effort than the other subjects to reach the P HP . However, for subject 9, the difference in effort between using the P HP or AHP was the smallest, with a SA of 13.40%. Regarding RiOM , the gesture recognition module helped reduce motion the greatest for subject 9 and reduced motion by at least 20% for all subjects. This reduction can be illustrated in Fig. 5, where in Fig. 5(a), the operator was required to rotate his torso in order to touch the robot's sensor, which is located outside the TV frame. In Fig. 5(b), the robot recognizes when the operator has completed his task and can proceed to the next action. The average rate of spatial adaptation and reduction in operator motion was 29.37% and 28.37%, respectively, among the 14 subjects.

These results proved hypothesis H2 as the collaborative robot can adapt to human factors and aids in improving ergonomics and efficiency, evidenced by the KPIs. Since the collaborative robot would be assigned to the most dangerous tasks, similar results could be expected for other assembly processes. The operator would only need to perform command gestures and safe tasks during the operation. The proposed KPIs revealed motion variations across experiments, demonstrating that they can be a relevant indicator to include in the evaluation for measuring how physically demanding and comfortable the installation is for the operators.

V. CONCLUSION AND FUTURE WORK

This paper presents a methodology for task delegation and an HRC framework that maximize ergonomics and production efficiency in a television co-production cell. For this work, professional tasks performed on a television production line were captured using a small set of wearable sensors. Then, motion primitives with known ergonomic risks were detected across the entire professional tasks to estimate the tasks' ergonomic scores and identify the most dangerous ones. An optimized HRC was then proposed, in which hazardous tasks were delegated to the collaborative robot. The HRC scenario was enhanced by applying gesture recognition for temporal adaptation and pose estimation for spatial adaptation that allowed the human operator to collaborate with the robot using gestures while avoiding unnecessary movements that could cause physical strain. Lastly, two novel KPIs are proposed for measuring the physical demand required of operators when collaborating with a robot. These can complement the evaluation of HRC frameworks by considering not only the production efficiency but also the ergonomics and job quality of the operator.

Future research will focus on developing a frame-by-frame risk prediction method. The system would be capable of predicting future postures and their ergonomic risk. This type of system may be advantageous for HRC for various reasons. For example, the robot could provide preventive feedback to operators or protect them from risks by actively assisting them in adopting safer postures before and during a task.

Fig. 1 :

 1 Fig. 1: Pipeline for ergonomically optimizing industrial co-production cells with HRC.

Fig. 2 :

 2 Fig. 2: Professional tasks for TV assembly.risk levels, according to EAWS evaluation. Based on the detected motion, an ergonomic score is computed. For generating the dataset with the motion primitives, MoCap recordings were made in a laboratory. The neutral environment sessions involved ten healthy individuals, three females and seven males. None of them suffered from any musculoskeletal injury. The protocol contained 28 different motion primitives, each recorded three times. The motions can be classified into three broad categories: those performed standing, those performed while seated in a chair, and those performed while kneeling. The small movements (≈ 4 seconds) progressed from comfortable to increasingly uncomfortable postures (raising arms, bending forward, rotating the torso). Each motion has assigned an EAWS risk score, ranging from 0.5 to 26.5. The higher the ergonomic risk score, the greater the risk. In prior research of these motion primitives, their spatiotemporal dynamics were analyzed using a Gestural Operational Model (GOM)[START_REF] Olivas-Padilla | Stochastic-biomechanic modeling and recognition of human movement primitives, in industry, using wearables[END_REF]. As a result, it was discovered that high recognition accuracy can be achieved with only five inertial sensors. These sensors are positioned on the lumbar spine, the left upper arm, the right shoulder, the right upper leg, and the left forearm. Consequently, the proposed system for evaluating professional tasks only requires measurements from these five sensors.

Fig. 3 :

 3 Fig. 3: Command gestures of the gesture recognition module.

Fig. 4 :

 4 Fig. 4: Confusion matrix of the multi-class gesture recognition.

Fig. 5 :

 5 Fig. 5: TV assembly with and without gesture recognition.

TABLE I :

 I Summary statistics of the EAWS scores.

	Dangerous -26.5	Tasks	EAWS scores Mean σ Mode
	EAWS	T 1	16.02	2.65	17.50
	scoring	T 2	15.02	3.43	16.00
		T 3	10.76	3.68	8.50
	Safe -0.5	T 4	11.50	3.19	12.50

automatic evaluation system. The resulting likelihoods indicate the detected motion primitive. All motion primitives identified throughout each activity are annotated and utilized to calculate each task's mode and average ergonomic score. The score is determined using EAWS's score calculations

TABLE II :

 II Standard KPIs measured for each experiment.

	Experiments	Neglect tolerance Interaction effort	RAD	Efficiency of the robot
	1. Physical interaction	97.47 (σ: 4.60)	13.53 (σ: 2.80)	0.12 (σ: 0.001) 25.90*
	2. Gesture recognition	60.14 (σ: 4.20)	21.90 (σ: 3.10)	0.27 (σ: 0.001) 34.90*
	3. Gesture recognition and pose estimation 76.73 (σ: 2.40)	28.33 (σ: 3.20)	0.26 (σ: 0.001) 30.85 (σ: 0.30)

TABLE III :

 III Proposed KPIs to measure the collaboration of the robot with each operator.

	KPI	1	2	3	4	5	6	Operator 7 8	9	10	11	12	13	14
	SA (%)	39.10 33.30 21.10 27.50 30.40 31.90 27.10 31.80 13.40 33.90 43.50 32.10 18.70 27.40
	RiOM (%)	31.40 33.10 24.40 27.10 32.10 27.30 24.50 26.80 37.40 20.60 45.90 20.80 21.30 24.50

ACKNOWLEDGMENT

The authors like to express their gratitude to Arc ¸elik A.S. for their support in this work.

The research leading to these results has received funding by the EU Horizon 2020 Research and Innovation Programme under grant agreement No. 820767, project CoLLaboratE.


PhD Larissa Richter

PhD David Amouroux
email: david.amouroux@univ-pau.fr

PhD Emmanuel Tessier

Anne Hélène

Anne Helene

PhD Helene Fostier

Xuewu Fu
email: fuxuewu@mail.gyig.ac.cn

Bridget Begquist
email: bergquist@es.utoronto.ca

Jason Demers
email: jdemers@umich.edu

Dr Demers

Jean Remy

Davee Guimaraes

Ari Feinberg

Anne Hélène Fostier
email: annehfostier@gmail.com

Impact of forest fire on the mercury stable isotope composition in

Keywords: Isotopic composition, Soil, Litter, Ashes, Combustion, Atmospheric emissions Conceptualization, Methodology, Investigation, Formal analysis, Writingoriginal draft Conceptualization, Project administration, Writing-original Conceptualization, Project administration, Mercury, Isotopic composition, Soil, Litter, Ashes, Combustion, Atmospheric emissions

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Mercury (Hg) is a highly toxic and ubiquist element in the environment [START_REF] Outridge | Updated Global and Oceanic Mercury Budgets for the United Nations Global Mercury Assessment[END_REF].

In the atmosphere, 90% to 99% is present as gaseous elemental mercury (GEM), with the remaining portion composed of operationally-defined gaseous oxidized mercury (GOM) and particulate-bound mercury (PBM) [START_REF] Gustin | Do We Understand What the Mercury Speciation Instruments Are Actually Measuring? Results of RAMIX[END_REF]. Foliage uptake has been shown to be an efficient process to remove atmospheric GEM [START_REF] Jiskra | A vegetation control on seasonal variations in global atmospheric mercury concentrations[END_REF], but GOM and PBM can also be sorbed by or on leaves.

Mercury is then transferred to soil by litterfall and throughfall, where it accumulates. Forests have, therefore, been recognized to play a key and global role in the biogeochemical cycle of this element [START_REF] Wang | Assessment of Global Mercury Deposition through Litterfall[END_REF][START_REF] Obrist | A review of global environmental mercury processes in response to human and natural perturbations: Changes of emissions, climate, and land use[END_REF][START_REF] Zhou | Vegetation uptake of mercury and impacts on global cycling[END_REF].

Due to their specificities such as high biomass productivity, leaf area index and leaf lifespan [START_REF] Witt | Influence of the Forest Canopy on Total and Methyl Mercury Deposition in the Boreal Forest[END_REF], tropical and subtropical forests are especially efficient in removing atmospheric Hg [START_REF] Wang | Assessment of Global Mercury Deposition through Litterfall[END_REF] and further storing it in soil [START_REF] Wang | Climate and Vegetation As Primary Drivers for Global Mercury Storage in Surface Soil[END_REF]. On the other hand, these forests have also been submitted to intense rates of deforestation often associated with forest burning [START_REF] Carmenta | Understanding Human-Fire Interactions in Tropical Forest Regions: a Case for Interdisciplinary Research across the Natural and Social Sciences[END_REF]. A number of studies have provided evidence that part of the Hg stored in vegetation and soil is reemitted to the atmosphere during a forest fire [START_REF] Friedli | Initial Estimates of Mercury Emissions to the Atmosphere from Global Biomass Burning[END_REF][START_REF] Chen | High-resolution inventory of mercury emissions from biomass burning in China for 2000-2010 and a projection for 2020[END_REF][START_REF] De Simone | Model Study of Global Mercury Deposition from Biomass Burning[END_REF][START_REF] Shi | High-resolution inventory of mercury emissions from biomass burning in tropical continents during 2001-2017[END_REF][START_REF] Gworek | Mercury in the terrestrial environment: a review[END_REF], and it has been estimated that 43.3% of the total Hg budget emitted by forest burning can be attributed to tropical forests [START_REF] Kumar | Mercury from wildfires: Global emission inventories and sensitivity to 2000-2050 global change[END_REF].

The Amazon rainforest accounts for over half of the planet's remaining tropical forest, and it was estimated that it contributes to approximately 20% to 30% of the total Hg land sink [START_REF] Fostier | Litter mercury deposition in the Amazonian rainforest[END_REF][START_REF] Feinberg | Evaluating atmospheric mercury (Hg) uptake by vegetation in a chemistry-transport model[END_REF]. But, this biome also figures as an important Hg emitter due to enormous annual deforestation (INPE, 2022). However, the Amazon remains a region with few studies focused on this topic and with great uncertainties related to the emissions, thus increasing the difficulty in establishing accurate mass balance and mathematical models to understand Hg fate during the burning process in this region.

In recent years, studying Hg stable isotope composition appears as a very performant tool to improve knowledge on different processes that make up the complex Hg biogeochemical cycle [START_REF] Blum | Mercury Isotopes in Earth and Environmental Sciences[END_REF][START_REF] Bishop | Recent advances in understanding and measurement of mercury in the environment: Terrestrial Hg cycling[END_REF][START_REF] Kwon | Mercury stable isotopes for monitoring the effectivness of the Minamata Convention on Mercury[END_REF]. The basis of the technique is that Hg isotopes undergo both mass dependent fractionation (MDF, reported as δ 202 Hg (‰)) and mass independent fractionation (odd-MIF, reported as Δ 199 Hg (‰) and even-MIF, reported as Δ 200 Hg (‰)) processes, which enables investigation of possible Hg sources, its transformations and even fate in the environment.

Many studies have been performed aiming to investigate mercury isotopic composition in different types of forests [START_REF] Demers | Mercury isotopes in a forested ecosystem: Implications for air-surface exchange dynamics and the global mercury cycle[END_REF][START_REF] Zhang | Atmospheric mercury inputs in montane soils increase with elevation: evidence from mercury isotope signatures[END_REF][START_REF] Jiskra | Mercury Deposition and Re-emission Pathways in Boreal Forest Soils Investigated with Hg Isotope Signatures[END_REF][START_REF] Enrico | Atmospheric Mercury Transfer to Peat Bogs Dominated by Gaseous Elemental Mercury Dry Deposition[END_REF][START_REF] Zheng | Mercury isotope compositions across North American forests[END_REF][START_REF] Wang | Using Mercury Isotopes To Understand Mercury Accumulation in the Montane Forest Floor of the Eastern Tibetan Plateau[END_REF][START_REF] Liu | Mercury isotopic compositions of mosses, conifer needles, and surface soils: Implications for mercury distribution and sources in Shergyla Mountain, Tibetan Plateau[END_REF], but only a few have focused on the Amazon rainforest region (Fig, 1) [START_REF] Araujo | Mercury speciation and Hg stable isotope ratios in sediments from Amazon floodplain lakes-Brazil[END_REF][START_REF] Guedron | Mercury Isotopic Fractionation during Pedogenesis in a Tropical Forest Soil Catena (French Guiana): Deciphering the Impact of Historical Gold Mining[END_REF][START_REF] Miserendino | Mercury Pollution in Amapa, Brazil: Mercury Amalgamation in Artisanal and Small-Scale Gold Mining or Land-Cover and Land-Use Changes?[END_REF][START_REF] Schudel | An investigation of mercury sources in the Puyango-Tumbes River: Using stable Hg isotopes to characterize transboundary Hg pollution[END_REF]. Furthermore, the potential of Hg stable isotopes to trace forest fire Hg emissions has never been investigated.

In coal combustion processes, [START_REF] Sun | Mercury Stable Isotope Signatures of World Coal Deposits and Historical Coal Combustion Emissions[END_REF] were the first to observe that GEM emitted from coal-fire plants were enriched in heavier Hg isotopes, suggesting that isotopic fractionation may occur during coal burning and fly ash removal processes. However, [START_REF] Li | Isotope signatures of atmospheric mercury emitted from residential coal combustion[END_REF] observed that total mercury emissions in flue gas from residential feed coal had similar isotopic composition than coal itself, indicating that scale, temperature or other factors may influence isotopic fractionation during burning, although many uncertainties still remain about this issue. As forest burning involves a nonquantitative combustion process, it is expected to cause mercury isotopic fractionation.

The aim of this work was therefore to assess forest burning potential to cause mercury isotopic fractionation (MDF and MIF) in different soil compartments (litter, soils and remaining ashes). The study was performed in the Amazon rainforest in small-and larger-scale prescribed burning experiments to assess some factors that can influence such isotopic fractionation.

Materials and methods

Fire experiments and sampling

Fire experiments were carried out in two places (Fig. 1): Alta Floresta (AF) (9°56'40S, 56°19′48″W) and Candeias do Jamari (CJ) (8°45'1"S, 63°27'20"W). Both localities are in the socalled arc of deforestation, a vast region with high rates of fire-based deforestation resulting from efforts to transform forests in crop land [START_REF] Carvalho | Biomass fire consumption and carbon release rates of rainforest-clearing experiments conducted in northern Mato Grosso, Brazil[END_REF]. The vegetation type from the experimental areas is old-growth terra firme forest [START_REF] Chave | Regional and seasonal patterns of litterfall in tropical South America[END_REF] and the soils are leached ferralitic soils, oxisols [START_REF] Lacerda | The effects of land use change on mercury distribution in soils of Alta Floresta, Southern Amazon[END_REF][START_REF] Almeida | Mercury loss from soils following conversion from forest to pasture in Rondonia, Western Amazon, Brazil[END_REF]. The climate of the region is humid tropical, type Aw in the Koppen classification [START_REF] Ferraz | Landscape dynamics of Amazonian deforestation between 1984 and 2002 in central Rondonia, Brazil: assessment and future scenarios[END_REF]. The annual rainfall rate is around 2200 mm year -1 , with the rainy season lasting from November to April [START_REF] Brown | Uncertainty in the Biomass of Amazonian Forests -an Example from Rondonia, Brazil[END_REF][START_REF] Aparecido | Koppen-Geiger and Camargo climate classifications for the Midwest of Brasil[END_REF].

At AF, six burning sub-area 2 × 2 m 2 plots of land were delimited inside the forest and a variable amount of fuel (litter and branches) was placed within each experimental sub-area. Fuel material was weighed before burning, and the remaining ash was weighed after burning to calculate biomass consumption completeness (BCC (% m m -1 ) according to equation S1 (Text S1) in Supplementary Information (SI); detailed data on fuel loading and ash mass are also available in SI, Table S1. In these experiments litter, soils and ashes were collected in each sub-area. Soil sampling was carried out to depths of 0-1 cm and 1-2 cm before and after burning. [START_REF] Araujo | Mercury speciation and Hg stable isotope ratios in sediments from Amazon floodplain lakes-Brazil[END_REF][START_REF] Guedron | Mercury Isotopic Fractionation during Pedogenesis in a Tropical Forest Soil Catena (French Guiana): Deciphering the Impact of Historical Gold Mining[END_REF][START_REF] Miserendino | Mercury Pollution in Amapa, Brazil: Mercury Amalgamation in Artisanal and Small-Scale Gold Mining or Land-Cover and Land-Use Changes?[END_REF][START_REF] Schudel | An investigation of mercury sources in the Puyango-Tumbes River: Using stable Hg isotopes to characterize transboundary Hg pollution[END_REF] At CJ, a prescribed fire experiment was conducted in a 150 × 150 m 2 plot (Fire experiment authorization 204/2014 Secretaria de Estado do Desenvolvimento Ambiental do Rondônia). Details on this experiment are given in the SI (Text S2). Briefly: the forest was cleared at the beginning of the dry season (June-August) and burned in August. BCC was estimated according to equation S1 (SI, Text S1) through the results obtained for nine different 2 × 2 m 2 sub-area plots in which litter, soil (0-1, 1-2, 2-5 cm) and ash were sampled. It is important to notice, that at both sites, after burning the 0-1-cm layer visually corresponded to the burned O-horizon, and ash mainly to the burned biomass. Details on fuel loading, ash mass and BCC on each sub-area are presented in SI, Table S2.

In both experiments, soil and ash sampling were performed according to the method described by [START_REF] Melendez-Perez | Soil and biomass mercury emissions during a prescribed fire in the Amazonian rain forest[END_REF]. The samples were dried at room temperature on laminar flow wood, milled in mortar with liquid N2 and stocked at room temperature in HDPE flasks.

Total Hg determination

Total mercury (HgTot) determination was carried out by thermo-desorption atomic absorption spectrometry Milestone,Italy) according to [START_REF] Melendez-Perez | Assessment of direct Mercury Analyzer® to quantify mercury in soils and leaf samples[END_REF].

Accuracy was assessed in terms of recovery (%) by analyzing Standard Reference Materials and was 113 ± 8% for NIST 2689 (Trace elements in coal), 113 ± 5% for NIST 1632d (coal fly ash), 93 ± 2% for IAEA 336 (lichen) and 99 ± 5% for IAEA 433 (marine sediment). Precision expressed as the relative standard deviation of three analytical repetitions was <10%. Detection limit (DL) and quantification limit (QL) were 1.7 ± 0.1 and 5.6 ± 0.5 ng g -1 , respectively, for litter analyses, 1.3 ± 0.1 ng g -1 and 4.5 ± 0.4 ng g -1 for ash, and 0.75 ± 0.06 and 2.5 ± 0.2 ng g -1 for soil.

Isotopic composition analysis

For isotopic composition analysis, two sample preparation methods were used. In the first one, approximately 1000 mg of ash, litter or soil sample were digested with 5 mL HNO3 and 0.5 mL H2O2 in a high-pressure Asher (HPA-S, Anton Paar) according to [START_REF] Barre | Multi-element isotopic signature (C, N, Pb, Hg) in epiphytic lichens to discriminate atmospheric contamination as a function of land-use characteristics (Pyrenees-Atlantiques, SW France)[END_REF][START_REF] Barre | Comparison of the Isotopic Composition of Hg and Pb in Two Atmospheric Bioaccumulators in a Pyrenean Beech Forest[END_REF]. For the second one, between 500-800 mg of sample were decomposed in 50 mL PP vial with 5 mL of HNO/HCl/H2O2

(3:1:1, v:v:v) at 85 o C for 48 h, [START_REF] Guedron | Mercury Isotopic Fractionation during Pedogenesis in a Tropical Forest Soil Catena (French Guiana): Deciphering the Impact of Historical Gold Mining[END_REF]. For soils, the first method was only used for more complex samples and those with lower Hg concentration.

Mercury isotope analyses were performed with a cold vapor generation system (CVG) with SnCl2 reduction coupled to a multicollector (MC)mass spectrometer with inductively coupled plasma (ICP-MS) (Nu Plasma, Nu Instrument) as described by [START_REF] Barre | Comparison of the Isotopic Composition of Hg and Pb in Two Atmospheric Bioaccumulators in a Pyrenean Beech Forest[END_REF]. MDF and MIF are expressed according to the equations recommended by [START_REF] Bergquist | Mass-dependent and mass-independent fractionation of Hg isotopes in aquatic systems[END_REF]

Where xxx corresponds to the studied isotope in the sample and in the CRM NIST 3133 solution, monitored following a standard bracketing method as a Hg isotopic composition reference in the same concentration as the sample (1.0, 0.5 and 0.25 µg L -1 ); and βXXX is the kinetic massdependence scale factor, characteristic of each isotope mass (for 199 Hg is 0.2520, 200 Hg 0.5024, 201 Hg 0.7520 and for 204 Hg 1.493). The instrumental mass-bias was corrected by using the internal standard of Tl (NIST 997, 205 Tl/ 203 Tl = 2.38714). NIST 8610 (former UM-Almadén) standard solution was monitored during each analytical session as a quality control of the isotopic composition analyses.

Multiple measurements of the certified reference materials (CRM) BCR-482 (lichen), IAEA 336

(lichen) and IAEA 405 (marine sediment) were also performed. The consistency between the average isotopic composition of each material obtained in the present study and those previously published in the literature (Table S3) attests to the good accuracy of the method. The precision method, expressed as 2 standard deviations of the N measurements, is also given in Table S3.

For litter, ash, and soil samples, recovery (R%) was estimated as the total Hg concentration measured by CVG/MC-ICP-MS*100/total Hg concentration measured by TD-AAS. The precision measurement (expressed as 2SD for CVG/MC-ICP-MS analyses) was calculated from three analytical replicates. When considering all the analyzed samples, average recovery was 96 ± 11%, 99 ± 7% and 90 ± 8% for litter, ash, and soils respectively, and for δ 202 Hg, δ 200 Hg, Δ 200 Hg and Δ 201 Hg, 2SD was <0.15‰.

Uncertainties of the isotopic composition measurements were defined as follows: when the measurement precision of CRM NIST 8610 for a particular isotope was greater than the obtained value for a particular type of sample, the values of 2SD of NIST 8610 were considered, as observed for litter and soil samples; when a particular type of sample presented greater values of 2SD than NIST 8610, the samples 2SD were considered, as observed for ash samples.

Complementary analyses

The organic matter content was obtained by gravimetric method after calcination for 2 h at 360 °C (North Central Regional Research, 2012). Elemental composition (C, H, N) was determined with an elemental analyzer (PerkinElmer, 2400 System, Waltham, MA, US).

Statistical data treatment

Different data treatments were performed using Origin 8.1 ® , Microsoft Excel ® and Xlstat ® software. All the results were assessed for normal distribution using the Shapiro-Wilk test (=5%).

When the results were not normally distributed, nonparametric statistical tools (Kruskal Wallis test combined with Conover-Iman with Bonferroni correction) were used to compare the data to each other.

Results

Total Hg concentration in litter, ash, and soil

Concentration of HgTot in all analyzed samples are given in Table 1 (detailed data in Table S4 and S5). For litter, HgTot at AF (54 ± 9 ng g -1 ) was significantly higher (p = 0.002) than at CJ (33 ± 8 ng g -1 ). In wood, HgTot were 8.8 ± 1.1 ng g -1 and < LQ (1.3 ng g -1 ) for the samples from AF and CJ, respectively. In ash, HgTot was in the same range (12 to 31 ng g -1 ) in AF and CJ samples. In both sites the concentrations in ash were lower than in litter (p = 0.01 for AF and p = 0.03 for CJ). In AF soil, HgTot significantly decreased after burning for both depths (0-1 cm: p = 0.02 and 1-2 cm: p = 0.03), while at CJ significant decreases were only observed for the surface layer ( 0-1 cm: p = 0.02). S6 andS7), while probabilities for each comparison between different datasets are given in Table S8.

Mass dependent fractionation

Regarding δ 202 Hg in AF (Fig. 2a, Table 2), no significant difference (p>0.05, Table S8) was observed between litter, soil and ash, before and after burning. In contrast, in CJ (Fig. 2b) significant differences (p<0.05) were observed between 1) litter and soil before burning, 2) litter and soil after burning, 3) ash and soil before burning, and 4) ash and soil after burning. Comparing the results from AF and CJ, no significant difference was observed between δ 202 Hg of litter and ash from both sites (p = 0.94 for litter and p = 0.47 for ash). For soils, however, significant differences were obtained between AF and CJ (p<0.05 before/after burning).

Odd and even mass independent fractionation

Variations of Hg δ 200 Hg and δ 199 Hg as a function of δ 202 Hg, respectively presented on Fig. 2 and Fig. S1, allow to assess if both odd and even Hg isotopes follow a strictly MDF process or exhibit significant deviation inherent to Hg isotopes MIF. The δ 200 Hg/δ 202 Hg ratio of all AF (Fig. 2a) and CJ (Fig. 2b) samples clearly follows a linear distribution with the same slope as expected for Hg isotopes MDF [START_REF] Blum | Mercury Isotopes in Earth and Environmental Sciences[END_REF], suggesting no even Hg isotopes MIF. This observation is clearly validated in Fig. S2 (Δ 200 Hg compared to δ 202 Hg) and Fig. S3 (Δ 200 Hg compared to Δ 204 Hg), that explicitely illustrate that Δ 200 Hg and Δ 204 Hg extent and variations are not significant between the localities and samples (i.e. considering analytical precision). The variation of δ 199 Hg as a function of δ 202 Hg (Fig. S1) roughly follow the classical MDF line. However, for CJ (Fig. S1b), litter presented significant deviations from the MDF line, suggesting that MIF-odd is statistically significant. Odd MIF extents for Hg isotopes are indeed different from zero and exhibit negative average values as shown in Table 2. Variation of odd-MIF for Hg isotopes is highlighted by Fig. S2 showing Δ 199 Hg compared with Δ 201 Hg plot, in which a clear difference appears between CJ litter and other samples from both localities (p<0.05, Table S8).

Figure 3 represents odd-MIF compared with MDF of Hg isotopes (Δ 199 Hg × δ 202 Hg). It shows that for AF (Fig. 3a) the obtained isotopic composition of the different types of samples mainly overlap and no discrimination of specific sample types can be verified. However, for CJ (Fig. 3b), litter samples are separated from the others due to their enrichment in Δ 199 Hg when compared with other samples (ash and soil) from the same location (ε(Δ 199 Hg ) ~0.15‰, p<0.05). Ashes compared to soils are in the same odd-MIF range (Δ 199 Hg), while they present an enrichment (ε) in δ 202 Hg of about +0.2‰.

Discussion

Mercury distribution and its isotopic composition as a function of sample type and burning experiment

Litter: Total Hg concentration in litter from AF and CJ significantly differed (Table 1), which could be related to the different operational approach used in each experiment. The clearing of the forest at the beginning of the dry season in CJ left the litter exposed to solar radiation (i.e. UV-visible light and heat) and rain washout for several months. These conditions could have respectively promoted direct atmospheric evasion and leaching of Hg from exposed litter, and therefore, reduced the total Hg content in litter at this site. In contrast, the AF experiment was carried out under forest cover, below the canopy, preventing litter from being frequently exposed to intense solar radiation or rain washout, and consequently reduced potential Hg loss from the litter. This hypothesis is confirmed by the HgTot (57 ± 3 ng g -1 ) of an additional litter sample collected inside the forest at CJ, which is much closer to the concentration obtained in AF and to the average concentration reported for litter in the Amazon forest [START_REF] Fostier | Litter mercury deposition in the Amazonian rainforest[END_REF]. Despite these conclusions, the differences between the isotopic composition in litter from AF and CJ cannot only be explained by potential photochemical induced reduction and volatilization of Hg. Although such a process could generate significant MDF and odd-MIF of the Hg isotopes, the litter sample collected under the forest canopy of CJ exhibits very similar isotopic composition to the litter samples from the burning experiment area (Table 2). This suggests that the differences in litter Hg concentration and isotopic composition between CJ and AF are mainly caused by non-photochemical solar heating and/or rain washout processes for which only MDF and negligible MIF of the Hg isotopes are expected [START_REF] Blum | Mercury Isotopes in Earth and Environmental Sciences[END_REF]). However we do not have data regarding isotopic fractions for such specific pathways, and differences measured for both concentration and isotopic composition could also be attributed to specific regional and ecological differences between both locations such as atmospheric total gaseous Hg, predominant plant species, tree and leaf age, among other factors [START_REF] Yuan | Stable Isotope Evidence Shows Re-emission of Elemental Mercury Vapor Occurring after Reductive Loss from Foliage[END_REF].

The greater differences between the isotopic composition for litter from different localities appear for MDF (Fig. 4). In temperate forest system, Demers et al. ( 2013) obtained a MDF extent for litter of δ 202 Hg = -1.32 ± 0.14‰, which is markedly higher than the present study. Meanwhile, more negative MDF values were reported in another tropical rainforest [START_REF] Guedron | Mercury Isotopic Fractionation during Pedogenesis in a Tropical Forest Soil Catena (French Guiana): Deciphering the Impact of Historical Gold Mining[END_REF]: δ 202 Hg = -2.01 ± 0.18‰) and a boreal forest [START_REF] Jiskra | Mercury Deposition and Re-emission Pathways in Boreal Forest Soils Investigated with Hg Isotope Signatures[END_REF]: δ 202 Hg = -2.35 ± 0.09‰). These large differences can be related to many factors such as atmospheric HgTot, tree species and tree and leaf age that result in different uptake of atmospheric Hg [START_REF] Zhou | Vegetation uptake of mercury and impacts on global cycling[END_REF]. The large δ 202 Hg extent (~1.0‰) suggests that the type of forest doesnot have a clear influence on Hg MDF isotopic composition in litter.

For odd-MIF in litter from tropical forest, [START_REF] Guedron | Mercury Isotopic Fractionation during Pedogenesis in a Tropical Forest Soil Catena (French Guiana): Deciphering the Impact of Historical Gold Mining[END_REF] obtained Δ 199 Hg= -0.20 ± 0.15‰, values that are less negative than those obtained in the present study. This can be explained because those samples were collected close to a former gold mining site. Indeed, metallic Hg commonly used in small-scale gold mining has an odd-MIF close to zero. In [START_REF] Demers | Mercury isotopes in a forested ecosystem: Implications for air-surface exchange dynamics and the global mercury cycle[END_REF], odd MIF average was higher (Δ 199 Hg= -0.18 ± 0.03‰, but most likely reflects the isotopic composition of atmospheric Hg in a region largely influenced by industrial anthropogenic inputs. In [START_REF] Jiskra | Mercury Deposition and Re-emission Pathways in Boreal Forest Soils Investigated with Hg Isotope Signatures[END_REF], odd-MIF average (Δ 199 Hg= -0.44 ± 0.03‰) is similar to our study.

Nonetheless, results of δ 202 Hg, Δ 199 Hg and Δ 200 Hg in litter indicate that the origin of Hg in these samples is mostly atmospheric, as demonstrated in previous investigations, and exhibits negative MDF, slightly negative odd-MIF and close to zero even-MIF [START_REF] Demers | Mercury isotopes in a forested ecosystem: Implications for air-surface exchange dynamics and the global mercury cycle[END_REF][START_REF] Jiskra | Mercury Deposition and Re-emission Pathways in Boreal Forest Soils Investigated with Hg Isotope Signatures[END_REF][START_REF] Araujo | Mercury speciation and Hg stable isotope ratios in sediments from Amazon floodplain lakes-Brazil[END_REF][START_REF] Guedron | Mercury Isotopic Fractionation during Pedogenesis in a Tropical Forest Soil Catena (French Guiana): Deciphering the Impact of Historical Gold Mining[END_REF][START_REF] Miserendino | Mercury Pollution in Amapa, Brazil: Mercury Amalgamation in Artisanal and Small-Scale Gold Mining or Land-Cover and Land-Use Changes?[END_REF][START_REF] Yuan | Stable Isotope Evidence Shows Re-emission of Elemental Mercury Vapor Occurring after Reductive Loss from Foliage[END_REF][START_REF] Barre | Comparison of the Isotopic Composition of Hg and Pb in Two Atmospheric Bioaccumulators in a Pyrenean Beech Forest[END_REF][START_REF] Wang | Mercury cycling and isotopic fractionation in global forests[END_REF][START_REF] Zhou | Vegetation uptake of mercury and impacts on global cycling[END_REF].

Soil:

The average concentrations of HgTot in soils of both study sites before burning (Table 1) are in the same range as those previously reported in the literature (e.g. [START_REF] Lacerda | The effects of land use change on mercury distribution in soils of Alta Floresta, Southern Amazon[END_REF] and [START_REF] Michelazzo | Mercury emissions from forest burning in southern Amazon[END_REF] for AF, and [START_REF] Almeida | Mercury loss from soils following conversion from forest to pasture in Rondonia, Western Amazon, Brazil[END_REF] for CJ). At both sites, HgTot before burning was homogeneously distributed in the different soil layers.

At AF, both the organic layer (0-1 cm) as the mineral layer (1-2 cm) had significant mercury losses after burning, with 16 ± 12% of the mercury budget lost in the first layer and 6 ± 5% in the second layer. Conversely, at CJ only the organic layer (0-1 cm) had significant losses (13 ± 8%) (Table S5), indicating that even though at CJ the burning experiment was much more severe than in AF (as discussed in section 4.3), mercury emissions from soils may be affected by several parameters.

Regarding the isotopic composition, large differences for MDF (δ 202 Hg) were observed between AF and CJ. Hg isotopic composition in soils may vary widely depending on the geogenic Hg contribution, origin of the atmospheric Hg deposition, type of soil and even how the nutrient cycling occurs at each location [START_REF] Blum | Mercury Isotopes in Earth and Environmental Sciences[END_REF][START_REF] Zheng | Mercury isotope compositions across North American forests[END_REF], as illustrated in Fig. 4 reporting data from different forested ecosystems. For North American temperate forests not directly impacted by anthropogenic sources, the MDF and MIF-odd values reported by [START_REF] Demers | Mercury isotopes in a forested ecosystem: Implications for air-surface exchange dynamics and the global mercury cycle[END_REF], (δ 202 Hg = -0.87 ± 0.12‰; Δ 199 Hg= -0.19 ± 0.02‰) are very different from those obtained in this study.

Conversely, the values reported by [START_REF] Araujo | Mercury speciation and Hg stable isotope ratios in sediments from Amazon floodplain lakes-Brazil[END_REF] (δ 202 Hg = -2.60 ± 0.34‰; Δ 199 Hg = -0.38 ± 0.22‰) and [START_REF] Miserendino | Mercury Pollution in Amapa, Brazil: Mercury Amalgamation in Artisanal and Small-Scale Gold Mining or Land-Cover and Land-Use Changes?[END_REF] (δ 202 Hg = -2.12 to -2.23 ± 0.14‰; Δ 199 Hg= -0.55‰) who also assessed nonimpacted forests in the Amazon, are closer to those obtained in our study. Among the forests, systematic negative values of MDF and MIF falling in a close range confirm the importance of all forests for the scavenging of atmospheric gaseous Hg and the litterfall transfer of this Hg from the litter to the soil.

The Hg isotopic composition does not exhibit significant differences in the soils sampled before and after burning, except for Δ 199 Hg from CJ (Table S8). These results may indicate that although fire leads to quantifiable Hg emissions from soil, this does not necessarily result in a significant isotopic fractionation of the residual fraction remaining in the soil.

Ash:

The presence of Hg in ash after the burning experiments may be attributed to the incomplete emission of Hg initially present in the litter, and to the uptake of Hg emitted from the surface soil during burning. Despite some differences observed in the HgTot and Hg isotopic composition of the litter and soil samples, ash from AF and CJ presented similar HgTot (Table 1) and similar isotopic composition (p>0.05 for HgTot and Hg isotopes, Table S8). This can be related to the similarities in the chemical composition of the ash from both locations. Ash chemical composition did not differ significantly (p>0.05) between both sites for organic matter (Table S4), HgTot and major element composition (C, H, N) (Table S9). This suggests that Hg content and Hg isotopic composition in ashes can be significantly constrained by the ash chemical characteristics, that can vary under different burning experimental conditions. Therefore, the influence of the final ash composition, and the Hg fractionation confirm that Hg uptake by ashes is closely related to re-adsorption or condensation mechanisms of Hg onto ash particles during and after the burning experiment. This hypothesis can explain the final Hg isotopic composition of the ash, but not the exact main source of this Hg in the cleared forest system.

According to [START_REF] Sun | Mercury Stable Isotope Fractionation During Coal Combustion in Coal-Fired Boilers: Reconciling Atmospheric Hg Isotope Observations with Hg Isotope Fractionation Theory[END_REF] who reviewed Hg isotopic fractionation associated with the coal combustion process, if MDF processes are expected during coal combustion, MIF are not. Reviewed literature data mainly reports enrichments in heavier isotopes in ash material, following MDF law, when compared to coal Hg isotopic composition. Using the same assumption for forest fires, the differences measured in MIF extent between litter and ash at CJ (Fig. 3b) suggests that the litter contribution to the Hg recovered in ash was low or insignificant. Indeed, for CJ, Δ 199 Hg determined in ash was significantly lower than in litter (Δ 199 Hg litter= -0.37 ± 0.05‰; Δ 199 Hg ash = -0.58 ± 0.07‰).

Conversely, Δ 199 Hg in ash was like that in soils before burning (Δ 199 Hg ash =-0.59 ± 0.03‰), suggesting that Hg in ash mainly originated from soils. In addition, δ 202 Hg in ash at CJ was significantly higher than in soils before burning (Fig. 2b). This suggests that an MDF process occurs during soil heating and burning. This can be explained by the emission of lighter Hg isotopes to the atmosphere and a simultaneous ash enrichment of heavier Hg with higher δ 202 Hg. This corroborates the fact that forest fire can follow similar basic processes than coal combustion in a power plant as reported by [START_REF] Sun | Mercury Stable Isotope Fractionation During Coal Combustion in Coal-Fired Boilers: Reconciling Atmospheric Hg Isotope Observations with Hg Isotope Fractionation Theory[END_REF].

The fractionation of Hg and the associated isotopic composition can likely be explained by the equilibrium between the two main Hg species emitted during a combustion process, such as GEM and particular bound Mmercury (PBM). When Hg is emitted during biomass combustion, both species are occurring, but their proportion can vary according to moisture and fuel nature, among other factors [START_REF] Obrist | A review of global environmental mercury processes in response to human and natural perturbations: Changes of emissions, climate, and land use[END_REF]. Furthermore, PBM is more easily deposited after burning than GEM and can easily be adsorbed on ash [START_REF] De Simone | Particulate-phase mercury emissions from biomass burning and impact on resulting deposition: a modelling assessment[END_REF][START_REF] Sun | Mercury Stable Isotope Fractionation During Coal Combustion in Coal-Fired Boilers: Reconciling Atmospheric Hg Isotope Observations with Hg Isotope Fractionation Theory[END_REF]. For coal combustion, [START_REF] Sun | Mercury Stable Isotope Signatures of World Coal Deposits and Historical Coal Combustion Emissions[END_REF] reported significant isotopic fractionation following MDF pathways. They determined higher δ 202 Hg in the PBM associated with ash material, which was then enriched in heavier isotopes compared to GEM Hg isotopic composition. This resulting ion is very similar to our observation for ash isotopic composition at the CJ location. However, such an increase of δ 202 Hg in ash was not observed during the AF experiment, which indicates that other parameters are involved in the Hg fractionation processes from soils and that significant MDF of Hg stable isotopes will not necessarily occur in all fires.

Factors influencing Hg isotopic composition during forest fire

Although the present dataset provided some information on Hg isotopic fractionation during forest fires, it also highlights how fractionation processes can vary according to the type of forest fire, especially for soil emissions.

One of the factors that differentiates forest fires is the severity, which is related to the intensity of burning and depends on factors that govern the fire behavior (e.g. propagation rate, duration and height of the flames), climatic conditions (temperature, relative humidity, wind and rain), topography, and quantity, size and moisture of live and dead vegetal fuel and its chemical and structural composition [START_REF] Keeley | The 2007 Southern California Wildfires: Lessons in Complexity[END_REF][START_REF] Mataix-Solera | Fire effects on soil aggregation: A review[END_REF][START_REF] Abraham | Effects of prescribed fire and post-fire rainfall on mercury mobilization and subsequent contamination assessment in a legacy mine site in Victoria, Australia[END_REF]. Although measuring burning severity is still a challenge due to the many variables involved, some parameters are frequently used to this end [START_REF] Engle | Mercury distribution in two Sierran forest and one desert sagebrush steppe ecosystems and the effects of fire[END_REF][START_REF] Keeley | The 2007 Southern California Wildfires: Lessons in Complexity[END_REF].

In the experiments of AF and CJ, the consumption of biomass and the variations of organic matter content in soil were used to assess the burning severity. Biomass consumption completeness at AF averaged 80 ± 10%, while at CJ it was 90 ± 11 (Table S1 andS2). For the soil organic matter content [OM, % w w -1 ], different behaviors were observed between both locations (Table S10). In AF, [OM] did not change significantly after burning ([OM]0-1 cm before = 18 ± 6%; [OM]0-1 cm after =16 ± 5%; p = 0.55), while in CJ this variation was substantial ([OM]0-1 cm before = 21 ± 5%; [0M]0-1 cm after =14 ± 4%; p = 0.005). This indicates that burning at CJ resulted in an important consumption of soil organic matter, likely due to longer combustion and higher temperatures [START_REF] Merino | Inferring changes in soil organic matter in post-wildfire soil burn severity levels in a temperate climate[END_REF]. Thus, we hypothesize that the burning conditions at CJ allowed higher temperatures to be reached during the experimental fire, enabling the consumption of the soil organic matter and the emission and isotopic fractionation of Hg between soils and ashes.

Likewise, different parameters influencing the severity of burning in previous studies were found to modify the isotopic composition of other elements, such as carbon, oxygen and nitrogen [START_REF] Kato | Stable isotopic compositions of carbon monoxide from biomass burning experiments[END_REF][START_REF] Schumacher | Oxygen isotopic signature of CO<sub>2</sub> from combustion processes[END_REF]. [START_REF] Kato | Stable isotopic compositions of carbon monoxide from biomass burning experiments[END_REF] observed that during biomass burning processes the volatile carbon species formed during the initial flaming phase were enriched in isotopically heavier carbon species ( 13 C enrichment) compared to the initial fuel material, which exhibited lighter carbon isotopic composition ( 13 C depletion) throughout the burning process and during the smoldering phase (i.e. flameless burning phase with significant gas emissions). Therefore, different burning settings that can result in a longer or smaller smoldering phase may influence differently the isotopic composition of the burning products, favoring either the production of isotopically lighter or heavier species. Based on the same assumptions for Hg emissions from soils during biomass burning, more pronounced isotopic fractionations (i.e. MDF) may be expected in longer burning episodes with more pronounced smoldering phases. These conditions in which greater amount of energy is given throughout a longer burning period were likely achieved during CJ experiment. [START_REF] Schumacher | Oxygen isotopic signature of CO<sub>2</sub> from combustion processes[END_REF] evaluated the oxygen isotopic composition ( 16 O vs 18 O) in the CO2 formed after the combustion of different types of fuels and under different conditions. They reported that the composition of the fuel load and the comburent gases strongly influence the isotopic composition of the emitted CO2. However, several processes may affect whether the resulting gas will have an isotopic composition either more relatable to the comburent gas (i.e. lighter isotopic composition or 16 O enrichment), or to the fuel load (plant tissues, with heavier isotopic composition for carbon and oxygen). For wet fuel samples, the fractionation was smaller, resulting in lighter isotopic composition (depletion of 13 C and 18 O isotopes) of the CO2 compared with dry fuels. These authors also observed that for higher temperatures reached during burning, the gases produced had heavier carbon and oxygen isotopic composition compared to smaller temperatures, indicating that when higher temperatures are reached the influence of the fuel isotopic composition may increase in the final product.

Expanding these previous conclusions for lighter elements to Hg emissions during biomass burning, several parameters may affect Hg fractionation and isotopic composition during forest burning episodes. Even though the temperature was not measured during the burning experiments, we suggest that the combustion temperature and fuel moisture may act as important factors for Hg emission and fractionation in the soil/litter/ash system. These hypotheses are based on the fact that no significant losses of soil OM were observed for AF experiment with dominant wet fuels, while much drier fuels were burnt during the CJ experiment. During burning, OM consumption can only be observed above 300 °C [START_REF] González-Pérez | The effect of fire on soil organic matter--a review[END_REF] and fuel moisture directly impacts the combustion completeness and therefore, the fire temperature itself [START_REF] Certini | Effects of fire on properties of forest soils: a review[END_REF]. This infers that these two factors are both significantly influencing differences in the fate and fractionation of Hg observed between AF and CJ experiments.

Considering the isotopic composition of the fuel material (i.e. litter and surface soils) and of the resulting ash and soil after burning, we suggest that Hg from the litter was almost totally emitted to the atmosphere while Hg from surface soil was partially and variably emitted as a function of several parameters, such as fire temperature and moisture content of fuel. We highlight here that the burning conditions play a fundamental role in the isotopic fractionation of the Hg species emitted, suggesting that further controlled experiments are required to better understand this complex dynamic.

Mercury isotopic fingerprint of forest fire emissions

Rayleigh's equation is commonly used to access the isotopic composition during a particular chemical reaction. It describes the partition between two isotopes as the initial reservoir decrease along with reaction advancement and time. This equation can only be used if the following principles are applied: 1) the material is continually removed from a mixed system that contains all the evolved species; 2) The isotopic fractionation can be described in any instant by a fractionation factor (α) and 3) α does not change throughout the process [START_REF] Kendall | 5.11 -Stable Isotope Applications in Hydrologic Studies[END_REF][START_REF] Hintelmann | Tracking Geochemical Transformations and Transport of Mercury through Isotope Fractionation[END_REF].

As pointed out in the previous discussion, several variables act simultaneously during forest burning, making it almost impossible to determine a fractionation factor for such complex processes and environments. A conservative approach may be applied to provide an initial estimate of the forest burning Hg isotopic signature emitted to the atmosphere. Considering the evolved environmental compartments as part of a conservative mass relation, the isotopic composition of reagents and products of the burning can be described by the mass balance given in Equation 3. Up to now, studies on Hg isotopic composition due to combustion processes were only performed for coal [START_REF] Sun | Mercury Stable Isotope Signatures of World Coal Deposits and Historical Coal Combustion Emissions[END_REF][START_REF] Tang | Stable isotope composition of mercury forms in flue gases from a typical coal-fired power plant, Inner Mongolia, northern China[END_REF][START_REF] Sun | Mercury Stable Isotope Fractionation During Coal Combustion in Coal-Fired Boilers: Reconciling Atmospheric Hg Isotope Observations with Hg Isotope Fractionation Theory[END_REF]. For global coal, [START_REF] Sun | Mercury Stable Isotope Signatures of World Coal Deposits and Historical Coal Combustion Emissions[END_REF] reported δ 202 Hg = -1.2 ± 0.5‰ (1SD). For coals from different locations these authors concluded that due to the variability of δ 202 Hg between different coals, their emissions could result in different δ 202 Hg, reflecting the high variability observed in various locations (1SD=0.5‰). Considering that the isotopic composition of plants and soils varies depending on the location, type of plant, climatic conditions, among other factors [START_REF] Demers | Mercury isotopes in a forested ecosystem: Implications for air-surface exchange dynamics and the global mercury cycle[END_REF][START_REF] Fu | Significant Seasonal Variations in Isotopic Composition of Atmospheric Total Gaseous Mercury at Forest Sites in China Caused by Vegetation and Mercury Sources[END_REF], significant uncertainties can also be expected for the isotopic composition of Hg emissions from forest fires. The relatively high uncertainty obtained for the estimated isotopic composition of the Hg emitted to the atmosphere in this study (Table 3) is therefore consistent and overlaps with the expected variability for Hg emissions for different types of forest fire. The present estimate can therefore be considered as an initial fingerprint of atmospheric Hg isotopic composition resulting from forest fire.

Conclusion

The results obtained indicate that a burnt forest is a complex environment, with Hg emissions being affected by several parameters. Mercury bound to litter is mostly emitted to the atmosphere, while soil Hg may be partially emitted, varying accordingly to the burning conditions. In this way, Hg isotopic fractionation was determined during forest fires but may occur at very different extent. Several factors may act altogether, influencing the Hg isotopic composition in the fuels and the burning process itself. Based on our findings, we conclude that Hg isotopic fractionation may occur during forest burning, mainly following MDF, while no significant MIF variations can be observed. To better understand Hg emissions and isotopic fractionation processes due to forest burning more studies should be performed in different areas and types of forests, but also the influence of fuel and burning conditions should be investigated in controlled conditions.

Figure 1 -

 1 Figure1-Map of the Brazilian legal Amazon territory, location of the study areas (Candeias do Jamari and Alta Floresta) and other works reported in the literature including Hg isotopes data for the Amazonian region[START_REF] Araujo | Mercury speciation and Hg stable isotope ratios in sediments from Amazon floodplain lakes-Brazil[END_REF][START_REF] Guedron | Mercury Isotopic Fractionation during Pedogenesis in a Tropical Forest Soil Catena (French Guiana): Deciphering the Impact of Historical Gold Mining[END_REF][START_REF] Miserendino | Mercury Pollution in Amapa, Brazil: Mercury Amalgamation in Artisanal and Small-Scale Gold Mining or Land-Cover and Land-Use Changes?[END_REF][START_REF] Schudel | An investigation of mercury sources in the Puyango-Tumbes River: Using stable Hg isotopes to characterize transboundary Hg pollution[END_REF]

Figure 2 .

 2 Figure 2. Graph of δ 200 Hg versus δ 202 Hg for litter, ash and soil samples from a) Alta Floresta (AF) and b) Candeias do Jamari (CJ). Soil samples are divided into different depths (0-1, 1-2 or 2-5 cm) and before or after (aft.) fires. * For the CRM NIST 8610, IC is represented in another range of the graph, and results are inserted at the top of the graph just to represent the magnitude of the uncertainty of the validated methods.

Figure 3 .

 3 Figure 3. Graphic of Δ 199 Hg compared with δ 202 Hg for litter, ash and soil samples from a) Alta Floresta (AF) b) Candeias do Jamari (CJ). Soil samples are divided into different depths (0-1, 1-2 or 2-5 cm) and before or after (aft.) fires.* For the CRM NIST 8610, IC is represented in another range of the graph, and results are inserted at the top of the graph just to represent the magnitude of the uncertainty of the validated methods.

Figure 4 .

 4 Figure 4. Graphic of Δ 199 Hg as a function of average δ 202 Hg for litter, ash and soil samples analyzed in this work compared to other forest samples reported in the literature. Sources: Araujo et al. (2018), Demers et al. (2013), Guedron et al. (2018), Jiskra et al. (2018) and Miserendino et al. (2018).

  mass of Hg emitted by soils (in µg of Hg per m 2 ); 𝐸𝐹 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 ̅̅̅̅̅̅̅̅̅̅̅̅̅ = Average mass of Hg emitted by biomass (in µg of Hg per m 2 ); 𝐻𝑔 𝑎𝑠ℎ ̅̅̅̅̅̅̅̅ = Average mass of Hg remaining in ash (in µg of Hg per m 2 );

Table 1 .

 1 Total mercury concentration (ng g -1 ) in litterfall, ash and soil samples collected before (BB) and after (AB) burning at Alta Floresta (AF) and Candeia do Jamari (CJ) experimental sites.

			Litter	Ash			Soil
					0-1 cm	1-2 cm	2-5 cm
					BB	AF	BB	AF	BB	AF
		N (a)	5	5	6	6	6	6	-	-
		Range	40 -65 13 -31 68 -93 57 -77 67 -82 60 -79	-	-
	AF	Mean ±	54 ± 9 23 ± 6 79 ± 9	66 ± 7	75 ± 6	69 ± 8	-	-
		SD (b)
		Median	55	23	81	67	76	68	-	-
		N (a)	9	9	9	9	9	9	9	9
		Range	25 -50 12 -31	135 -	113 -	136 -	126 -	138 -	131 -
					172	167	172	169	167	168
	CJ
		Mean ±	33 ± 8 23 ± 8	148 ±	134 ±	148 ±	146 ±	150 ±	151 ±
		SD (b)			13	21	12	16	12	13
		Median	33	24	144	125	146	141	149	151
	(a) N: number of samples; (b) SD: standard deviation
	3.2 Hg isotopic composition in soil, litter and ash
		Averages of the isotopic composition and main results per type of sample and locality are
	given in Table 2. Complete data of Hg isotopic composition from samples are given in
	Supplementary Information (Table

Table 2 .

 2 Main results of the Hg isotopic composition per type of sample and locality 204

						δ 202 Hg	δ 200 Hg	∆ 200 Hg	∆ 199 Hg
		Locality	Type of sample N (a)
						(‰)	(‰)	(‰)	(‰)
					Mean	-1.66	-0.93	-0.09	-0.54
			Litter	4
					SD	0.26	0.11	0.05	0.07
					Mean	-1.64	-0.87	-0.04	-0.56
			Ash	3
		Alta			SD	0.32	0.19	0.06	0.02
		Floresta	Soils before		Mean	-1.56	-0.82	-0.03	-0.52
				8
			burning		SD	0.17	0.09	0.02	0.07
			Soils after	7	Mean	-1.50	-0.81	-0.06	-0.53
			burning		SD	0.06	0.05	0.05	0.04
					Mean	-1.62	-0.89	-0.07	-0.36
			Litter	10
					SD	0.17	0.11	0.04	0.06
					Mean	-1.78	-0.93	-0.03	-0.58
		Candeias	Ash	9
					SD	0.10	0.05	0.02	0.06
		do Jamari
			Soils before		Mean	-2.05	-1.07	-0.04	-0.59
				12
			burning		SD	0.10	0.07	0.04	0.03
			Soils after	13	Mean	-2.08	-1.07	-0.03	-0.63
			burning		SD	0.12	0.08	0.05	0.03
	205	(a): N, number of analyzed samples

Acknowledgements

This work was funded by The São Paulo Research Foundation (FAPESP) (Projects 2008(Projects /04490-4, 2010(Projects /19040-4, 2014(Projects /00555-5 and 2016/14227-5) /14227-5) and also partially funded by the Regional Council of Nouvelle Aquitaine (France) and French National Research Agency (ANR). Larissa Richter received a fellowship from the Brazilian Coordination for the Improvement of Higher Education Personnel, CAPES, (Financing code 001). We also thank the Brazilian Environmental Secretary for authorizing the experiments that enabled this work. For the experimental work, we thank the owners of the Caiabi Farm, the co-workers from the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA) and the Brazilian National Institute of Spatial Research (INPE). This work is a contribution to the European MercOx project (EMPIR EURAMET, Grant number 16ENV01). Jim Hesson copyedited the manuscript (https://www.academicenglishsolutions.com/editing-service).

Author Contributions Statement

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

𝐸𝐹 𝐴𝑡𝑚 ̅̅̅̅̅̅̅̅ = Average mass of total Hg emitted to the atmosphere due to forest burning (sum of all emitted species (GEM, PBM, etc.), in µg of Hg per m 2 ); 𝐼𝐶 𝑋 ̅̅̅̅̅ = estimated average isotopic composition δ 202 Hg, Δ 199 Hg , Δ 200 Hg for each studied matrix (where X is the soil, biomass, ash or atmosphere) (in ‰).

The detail of the calculations performed to obtain these different masses is presented in the SI (Text S3). By considering Equation 3 as an acceptable first approximation and using the different values obtained for Hg average isotopic composition of each compartment (Table 2) together with the average EF or remaining mass (Table S11) in each compartment, we estimate the average isotopic composition of the atmospheric Hg emissions presented in Table 3.


Gaëtan Roisné-Hamelin

Roshni M Pasanna

Dalila Azzout-Marniche

Daniel Tomé

Anura V Kurpad

Sarita Devi
email: sarita@sjri.res.in

Urine metabolomic signature of lysine deficiency in stunted children

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The methods used to test the hypothesis -A parallel group intervention trial of a daily 3-months lysine supplementation (80 mg/kg/day in an orange flavored drink) was conducted in stunted (heightfor-age Z-score <-2SD, n=24) 6-11 years old South Indian children to evaluate urinary biomarkers for EAAD, in comparison with control non-stunted children (n=27) who received an orange flavored placebo drink during study period.

A description of the experimental design and statistical analysis -At baseline and monthly intervals, clinical examinations, height, weight, circumferences (cranial, forearm, upper-arm, waist), skin folds, muscle strength, food intake-recalls were measured, along with urine and blood sampling at baseline and end-line. The urine metabolome was analyzed by Q Exactive orbitrap-based mass spectrometer (Thermo Scientific) using Compound Discoverer (Thermo Scientific). Differences in anthropometry were analyzed by t-test and repeated measures models.

A brief description of the main results -Anthropometric measurements were significantly different (p<0.01) at baseline and end of 3-months. Preliminary urine metabolomic profiles showed a difference between groups in lysine-related metabolites at baseline and an alteration with supplementation. Metabolites of tryptophan degradation and utilization, and related to threonine, methionine, cysteine and branched chain amino acids biosynthesis pathways were also different at baseline between groups and with lysine supplementation.

A synthetic conclusion derived from the data presented -The urine metabolomic profiles with EAAD is different between stunted and non-stunted children at baseline and in response to lysine supplementation. A validation of urine metabolomic profiles using the blood metabolomic profiles could provide more insights towards designing targeted nutritional therapies.