domain: sdu.stu.gc sde phys

text:
Zélie Venel

Hervé Tabuteau
email: herve.tabuteau@univ-rennes1.fr

Alice Pradel

Pierre-Yves Pascal

Hind Bruno Grassl

El Hadri

Magalie Baudrimont

Julien Gigault
email: julien.gigault@takuvik.ulaval.ca

Environmental Fate Modeling of Nanoplastics in a Salinity Gradient Using a Lab-on-a-Chip: Where Does the Nanoscale Fraction of Plastic Debris Accumulate?

Keywords: Nanoplastics, aggregation, salinity, microfluidics, environmental

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Introduction

Global hydrodynamic models estimate that hundreds of thousands of tonnes of microplastics, i.e., with sizes ranging from 0.025 mm to a few mm, are likely floating in the oceans, mainly in subtropical gyres where they continuously accumulate, due to sea currents [START_REF] Eriksen | Plastic Pollution in the World's Oceans: More than 5 Trillion Plastic Pieces Weighing over 250,000 Tons Afloat at Sea[END_REF][START_REF] Andrady | Microplastics in the Marine Environment[END_REF][START_REF] Cózar | Plastic Debris in the Open Ocean[END_REF][START_REF] Jambeck | Plastic Waste Inputs from Land into the Ocean[END_REF] . It was recently demonstrated that microplastics can be also generated in terrestrial ecosystems before leaving land and reaching a gigantic gyre through rivers and other hydraulic systems [START_REF] Schmidt | Export of Plastic Debris by Rivers into the Sea[END_REF] . Despite this better understanding of the source of plastic in the oceans, we still only know where approximately 1% of the mass of plastics deposited on land has ended up. [START_REF] Sebille | A Global Inventory of Small Floating Plastic Debris[END_REF] . Clearly, microplastics (<5 mm) are lost somehow during their transportation from lands to the oceans.

It was recently demonstrated that during their transportation, plastic debris and microplastics are continuously weathered until they are tiny nanoscale pieces, i.e., nanoplastics [START_REF] Lambert | Characterisation of Nanoplastics during the Degradation of Polystyrene[END_REF][START_REF] Gigault | Marine Plastic Litter: The Unanalyzed Nano-Fraction[END_REF] . There is recent proof of nanoplastics appearing in consumer products and in environmental systems, such as oceans and soils [START_REF] Hernandez | Plastic Teabags Release Billions of Microparticles and Nanoparticles into Tea[END_REF][START_REF] Ter Halle | Nanoplastic in the North Atlantic Subtropical Gyre[END_REF][START_REF] Watteau | Microplastic Detection in Soil Amended With Municipal Solid Waste Composts as Revealed by Transmission Electronic Microscopy and Pyrolysis/GC/MS[END_REF] . However, due to the lack of appropriate analytical methods to identify and characterize trace concentations of nanoplastics in environmental systems, generally rich in natural and colloidal organic matter, there is very little data on the relative distribution of nanoplastics in the global environment. As with microplastics and other colloids, it is likely that nanoplastics are transported through river and estuarine systems as well [START_REF] Dai | The Significant Role of Colloids in the Transport and Transformation of Organic Carbon and Associated Trace Metals (Cd, Cu and Ni) in the Rhône Delta (France)[END_REF][START_REF] Pokrovsky | Iron Colloids/Organic Matter Associated Transport of Major and Trace Elements in Small Boreal Rivers and Their Estuaries (NW Russia)[END_REF] .

The main parameters that influence nanoplastic behavior in saline environments are ionic strength, organic matter, and pH [START_REF] Hotze | Nanoparticle Aggregation: Challenges to Understanding Transport and Reactivity in the Environment[END_REF][START_REF] Lowry | Environmental Occurrences, Behavior, Fate, and Ecological Effects of Nanomaterials: An Introduction to the Special Series[END_REF][START_REF] Petosa | Aggregation and Deposition of Engineered Nanomaterials in Aquatic Environments: Role of Physicochemical Interactions[END_REF][START_REF] Alimi | Microplastics and Nanoplastics in Aquatic Environments: Aggregation, Deposition, and Enhanced Contaminant Transport[END_REF] . In typical experiments, a given quantity of salt, such as NaCl or CaCl2, is added to a dilute dispersion of nanoplastics, and their aggregation is monitored as soon as the salt concentration becomes homogeneous [START_REF] Chen | The Impact of Nanoplastics on Marine Dissolved Organic Matter Assembly[END_REF][START_REF] Oriekhova | Heteroaggregation of Nanoplastic Particles in the Presence of Inorganic Colloids and Natural Organic Matter[END_REF] . Using these experimental approaches, the aggregation mechanisms of nanoplastics and the stability of their ionic strength can be characterized, principally through the critical coagulation concentration (CCC). However, these results can not be extrapolated to environmental systems that are experience continuous spatial and temporal variations of natural features (such as the ionic strength), so they cannot be considered at equilibrium [START_REF] Saleh | Aggregation Kinetics and Fractal Dimensions of Nanomaterials in Environmental Systems[END_REF][START_REF] Jungblut | Diffusion-and Reaction-Limited Cluster Aggregation Revisited †Electronic Supplementary Information (ESI) Available: Demonstrates That the Local Structure of the Aggregates Is Independent of the Volume Fraction of Particles Initially Present in the System[END_REF] . In particular, most land-sea continuum systems that nanoplastics are supposed to pass through are characterized by large salinity gradients and high flow rates. These two parameters are never investigated together to evaluate the fate of nanoplastics in the estuarine systems, since they cannot be jointly incorporated and controlled in classical experimental setups. Therefore, the major limitation of the present experimental approach used to determine the stability/aggregation of nanoplastics is the lack of environmental representativeness.

The present work is focused on the fate of model nanoplastics under flow conditions and with the presence of a salinity gradient (SG). These two conditions are generally encountered in mangrove swamps, which are some of the most fragile and essential ecosystems on Earth. Mangroves are characterized by a strong relationship with the ocean and land with both direct and indirect high anthropogenic pressures [START_REF] Giri | Status and Distribution of Mangrove Forests of the World Using Earth Observation Satellite Data[END_REF][START_REF] Abuodha | Human-Induced Stresses on Mangrove Swamps along the Kenyan Coast[END_REF] . These zones remain poorly investigated in terms of their aquatic physical and chemical properties. Based on physical and chemical parameter measurements in a mangrove swamp, the objective of this experiment is to study the nanoplastics aggregation dynamics under some of the specific initial conditions encountered in mangrove swamps when they pass through the salinity gradient. To do so, microfluidic experiments were designed to determine the behavior of nanoplastics under flow in the presence of a salinity gradient. These experiments allow us to (i) control the flow conditions of both the freshwater and the seawater, (ii) establish a spatial salt gradient in the channel, and monitor the diffusiophoretic drift of the nanoplastics due to this SG, and (iii) determine some of the features of the particle aggregation due to the shear flow, i.e., the orthokinetic aggregation. Indeed, it is demonstrated that the nanoplastics aggregation process results from their collision, and not from classical diffusion mechanisms. The new experimental results presented can be extrapolated to a mangrove swamp and open the door to new questions concerning these estuarine and coastal systems regarding the environmental behavior and impact of nanoplastics.

Materials and Methods

Chemical and sample preparation

Polystyrene nanoplastics (PS-nanoplastics) were synthesized to be representative (size and shape) of environmental nanoplastics as previously described [START_REF] El Hadri | Nanoplastic from Mechanically Degraded Primary and Secondary Microplastics for Environmental Assessments[END_REF] . PS-nanoplastics were formed using polystyrene pellets that are crushed by a ball-milling process. Such hydrophobic materials generally form carbon-based nanoplastics with a low surface charge. The z-average hydrodynamic diameter (dzH) of the PS-nanoplastics is centered at 380-400 nm, and they are highly polydispersed. Moreover, despite having a zeta potential of -32 mV in an aqueous system at pH 7 and low ionic strength, a relatively low -COOH functional concentration (0.5 mmol g -1 ) was measured [START_REF] El Hadri | Nanoplastic from Mechanically Degraded Primary and Secondary Microplastics for Environmental Assessments[END_REF] . No surfactants or other additives were present in the dispersing media.

Mangrove observation and analysis

Based on a previous study, nanoplastics were identified on Guadeloupe (a French Caribbean island) on beaches exposed to the North Atlantic gyre [START_REF] Davranche | Nanoplastics on the Coast Exposed to the North Atlantic Gyre: Evidence and Traceability[END_REF] . It has been identified that some of the debris comes directly from the garbage of the inhabitants of the island, as it comes from the largest open landfill on the island, the Gabarre landfill. This landfill is located between the city of Pointe-à-Pitres in the south and a large surrounding mangrove area, as illustrated on Fig. 1. There is a landfill located near the mangrove swamp's "rivière salée" or saline estuary, which leaches anthropogenic colloids and other landfill leachate into the estuary, which is the main channel of the mangrove swamp. In addition, the mangrove area is the contact area between the leached freshwater and the inundation of seawater. However, there is no precise information about salt distribution in the mangrove swamp (MSP), particularly inside the saline estuary that flows across the mangrove area. The salinity was measured (in October 30 th 2019) using an in situ conductimeter near one arm of the river that is close to the landfill (Fig. 2.b, blue point).

The salinity and temperature profile of the mangrove area water column were measured using in situ conductimeters (Ijinus, Quimperlé, France). Briefly, eight conductimeter sensors were placed along a 1.5 m z-axis arm and fixed to a boat. The total depth of the river arm was approximately 2 m. Measurements were performed over half of a tidal cycle, starting at the end of the afternoon and ending early the following morning. The mangrove swamp current was analyzed using a current meter (homemade setup). Due to their high affinity with colloids, the colloidal Cu(II) concentration was measured as a proxy of colloid presence in estuarine systems such as the mangrove swamp [START_REF] Shafer | Physical and Kinetic Speciation of Copper and Zinc in Three Geochemically Contrasting Marine Estuaries[END_REF][START_REF] Hoffmann | Strong Colloidal and Dissolved Organic Ligands Binding Copper and Zinc in Rivers[END_REF] . It was demonstrated that the size distribution of Cu(II) with colloids varied between marine systems and fresh water [START_REF] Roshan | Dissolved and Colloidal Copper in the Tropical South Pacific[END_REF] . A change in the relative size fraction of Cu(II) is therefore a releavant proxy of nanoplastics, especially since they are expected to be disseminated/mixed through the movement of natural and other anthropogenic colloids. Water samples were collected at different depths and Cu(II) was analyzed after preliminary sequential ultrafiltration using Amicon cell (Millipore, France) and a polyethersulfonated membrane (NADIR, Alting, France) with a molecular weight cut-off of 10kDa. Cu was analyzed by Inductively Coupled-Plasma Mass Spectrometry (7700x, Agilent Technologies, France).

Size and charge characterization

Hydrodynamic diameters (dH) were determined by in situ dynamic light scattering (DLS) using a Vasco-Flex model particle size analyzer (Cordouan Technologies). Each DLS measurement corresponded to an average of six measurements of 60 seconds each. Each sample was assigned a Z-average hydrodynamic diameter (dzH) using a cumulant algorithm. Additionally, the sparse Bayesian learning (SBL) algorithm was used to obtain the size distribution. All results presented have less than a 1% difference between all the points of the raw and fitted autocorrelation function. The size of large aggregates deposited on top of the microfluidic device (MD) was also evaluated using optical microscopy (Leica, France). The aggregate sizes were measured using ImageJ software.

Transport in the microfluidic devices

In the present work, to simulate the fate of nanoplastics that were initially transported by the fresh water that enters the mangrove swamp from inland, an experimental approach was based on a microfluidic set up. As previously described, a microfluidic device was developed to mimic a salinity gradient [START_REF] Gigault | Estuary-on-a-Chip: Unexpected Results for the Fate and Transport of Nanoparticles[END_REF] . The working principle is summarized in Figure 2. Two inlet and outlet arms of 5 mm long and 200 µm wide were connected to a 1.7 cm long, 800 µm wide channel. The height of the arms and channels was 75 µm. Aqueous dispersions of nanoplastics and NaCl solution were injected together in the two different inlets of the microfluidic device using syringe pumps (KDS 200 scientific) at the same flow rate Q (from 0.5 to 10 mL h -1 ). This range of average fluid velocity in the microfluidic device corresponds to the average fluid velocity within the water column that was measured at our sampling site and in the greater mangrove area [START_REF] Mclachlan | Impacts of Tidal-Channel Connectivity on Transport Asymmetry and Sediment Exchange with Mangrove Forests[END_REF] . Concerning the inlets, PS-nanoplastics dispersion (in deionized water) are injected into Inlet A, named Ain, while the NaCl solution is injected through Inlet B, named Bin.

The outlets are named Aout and Bout. Aout and Ain are arranged on the same side of the device, where PS-nanoplastics are principally eluted, and the same is true of Bout and Bin for the NaCl solution. According to the MD dimensions and the flow rates investigated, all experiments were performed at a low Reynold number (0.02<Re <0.4) and with a Péclet number higher than 10 4 , meaning that the nanoplastics did not diffuse around the fluid streamlines more than a few micrometers transverse to the flow direction over the whole length of the device. The liquids flowed through 20 cm tubing with an inner diameter of 360 µm before being collected in two vials connected to each outlet for one hour. Then, the flow was stopped, and the evolution of the dzH for the nanoplastics was monitored over 100 min using the in situ DLS probe. The device was first saturated with pure water and then filled with a 500 mM NaCl concentration from one inlet and PS-nanoplastics dispersion from the other. The final conductivity and NaCl concentration were measured at both outlets, Aout and Bout, for the different flow rates. The NaCl concentration ranged from 30 to 108 mM in Aout for decreasing Q (mL min -1 ), while it ranged from 390 to 470 mM in Bout for increasing Q (see SI, Fig. S3c). The evolution of the salt concentration in the vials indicates that salt diffusion occurred in the Aout.

Aggregation kinetics: theory and batch experiments

Nanoplastic aggregation kinetics were determined using the classical approach developed by Smoluchowski (Smoluchowski 1917). According to this theory, this is a two-step process: collision and attachment, which are determined by the collision rate constants and the attachment efficiency a, respectively. The evolution of the PS-nanoplastics concentration over time can be expressed as:

!" ! !# = $ % ∑ 𝛽 &' 𝑁 & 𝑁 ( &)'→( -𝛼𝑁 ( ∑ 𝛽 &( 𝑁 & & (Eq. 1)
where k is the mass of aggregate and N is the number of particles in the aggregate. bij is the collision frequency between two nanoplastics of different sizes with masses of i and j. For the colloidal population, bij is directly controlled by Brownian diffusion. In the case of homoaggregation, the equation is:

!" ! !# = -𝛼𝛽 (( ' " ! % ( % (Eq. 2)
The aggregation rate is determined by the slope in the linear part of the aggregation curve In the case of total aggregation, the attachment coefficient (𝛼 = 1) and the aggregation rate are solely dependent on the efficiency of transport. In batch mode experiments, NaCl is periodically injected into a closed and finite volume containing the nanoplastics. By measuring the z-average hydrodynamic diameter (dzH) over time, it is therefore possible to determine the aggregation kinetics. As illustrated elsewhere [START_REF] Ossadnik | Nonuniversal Diffusion-Limited Aggregation and Exact Fractal Dimensions[END_REF][START_REF] Meng | Aggregation Rate and Fractal Dimension of Fullerene Nanoparticles via Simultaneous Multiangle Static and Dynamic Light Scattering Measurement[END_REF] , steric and physical rearrangements lead to the formation of compact aggregates even for the diffusion-limited aggregation (DLA) mode.

Results & Discussion

Persistence of the salinity gradient in the mangrove swamp Figure 3 shows the temporal variation in the salinity and the temperature profile along the depth of the column. As expected, those variations are not the same in a rising tide and in a falling tide. For the rising tide, two depth zones were distinguished that had distinct salt concentrations (Fig. 3, Zone 2). In the first zone, in the first 0.5-0.75 m below the surface there is a salinity gradient, while deeper in the second zone, over 1 m, the salt concentration is rather homogeneous. These changes in salt concentration are directly linked to the incoming flows in this arm of the "rivière salée" (saline estuary) during the rising tide. While there is a continuous flux of fresh water from the east to the west, the seawater flows in the opposite direction. It invades the river arm below the surface at a depth of approximately 0.5-0.75 m below the surface, the depth at which the salt concentration is hence the highest. Further into the river, and farther from its connection to the saline estuary the slope of the SG decreases (Fig. S1).

During the inundation process, the salt contained in this finger of seawater diffuses into the two zones: from surface to -50cm depth and from -50cm to -150cm. Since the flow rate of freshwater in the first zone is faster than that in the second zone, the diffusion of salt is less important in this zone and leads to the formation of the salinity gradient that spreads throughout the first zone. In the second zone, salt diffusion occurs more quickly, the flow rate is slower, and a more or less homogeneous salt concentration develops rapidly. which changes the position of the maximum salt concentration. As a result, the SG is alternately stretched and compressed, a phenomenon for which no physical explanation has been reported in the literature yet. From all these results, in the river arm, there is always a salinity gradient above the first 0.5-1 m in depth, and thus, there is no global mixing phenomenon that would lead to a homogeneous salt concentration. This is the first time that such a salinity profile for a mangrove swamp has been obtained, opening new ways of thinking about the fate of colloidal suspensions that are present in the study area. In particular, it is interesting to study what will happen when nanoplastics produced in the landfill are transported through the soil [START_REF] Wahl | Nanoplastic Occurrence in a Soil Amended with Plastic Debris[END_REF] and reach the mangrove swamp. The colloidal Cu(II) concentrations were measured at 3.2 ng g -1 , 7.7 ng g -1 and 2.7 ng g -1 Cu at depths of 0 cm, 50 cm and 100 cm, respectively (see SI, Fig. S1). Clearly, a relative increase of the colloid concentration in the salinity gradient zone (30-50 cm) was observed, raising key questions about the fate and transport of nanoplastics from the land to the sea and through the mangrove area in between them.

Prior to microfluidic experiments, aggregation kinetics experiments were performed in batchmode conditions for the PS-nanoplastics, as detailed in the supplemental information (Fig. S2).

All these dispersion conditions and physical-chemical properties can explain the low critical coagulation concentration (CCC) value, which was around 30 mM. In the experiments, a salt concentration higher than the CCC was always used and only the concentration of the nanoplastics was varied. As expected, the mean aggregate size increased with the particle concentration (Fig. S2). The aggregate size varied linearly over time for particle concentrations from 1 to 11 mg L -1 . The dependence of the aggregation rate on the particle concentration is coherent with Equation (2) and is explained by the probability of collision between nanoplastics and aggregates. Under these experimental conditions, the aggregation is driven by particle diffusion, i.e., the DLA mode for which the attachment coefficient a is strongly dependent on the collision frequency bkk. At lower concentrations, the aggregation is rather limited, and the largest aggregates are doublets or triplets, while most of the nanoplastics remain isolated. Under these conditions, as expected, the aggregate size varied linearly over time.

Based on these batch experiments, different investigations have been tried using microfluidic devices, with the same nanoplastics concentration variation as summarized in Figure 4. During all the MD experiments, nanoplastics deposition was observed near the middle of the main channel along the flow direction (Fig. 4a), and the deposited nanoplastics included relatively large micrometric aggregates (Fig. 4.b). This deposition occurred inside a band whose width corresponded to the extension of the salinity gradient perpendicular to the flow direction. The position of this gradient depended on the flow rate and the Brownian diffusion of salt across the flow stream lines. The relevant length scale for this diffusion process for the salt was the saturation mixing length, ls =w². U/Ds, with w is the width and h is the height of the MD, U as the average velocity of the flow and Ds as the diffusion coefficient of the salt (DS= 1.52 10 -9 m 2 s -1 ). Here, the channel is much wider than it is tall, w>>h, so the salt diffuses first across the height of the channel as it enters the main channel and flows over the first micrometers.

Thereafter, it diffused across the width of the channel, perpendicular to the flow direction, until the salt water had reached the entire channel width. During this diffusion process, the salt moved by advection in the flow direction over a distance equal to ls from the entrance. Beyond this distance, the profile of the salt concentration in the MD cross-section did not continue to change further downstream. In our experimental conditions, the smallest value of ls was equal to 0.78 m, which is much greater than the length of the main channel of the MD for all the flow rates. Therefore, the salt had not moved across the whole width of the MD as it came out of the main channel. As a result, there was a constant broadening of the deposition band further downstream up to the exit zone of the main channel (Fig. 4). Inside this deposition band, the salt concentration was higher than the CCC of the nanoplastics, which thus enhanced their aggregation and successive deposition (Fig. 4a).

Nanoplastics deposition also occurred in both outlet arms. This process is more important in the outlet arms than in the main channel, since the salt concentration is consistently higher than the CCC and the particle concentration is higher. Since the Péclet number, Pe, with this Q range was always higher than 10 4 , nanoplastics scarcely diffused as they flowed through the MD.

Hence, particle aggregation in the whole MD was not due to particle diffusion, but rather to particle collisions that are transported to adjacent fluid streamlines with different velocities. If the collisions are caused by hydrodynamic motions (e.g convection or sedimentation) this is then referred to as orthokinetic aggregation. This process, called orthokinetic aggregation, is enhanced since the nanoplastics have no electrostatic barrier due to the high salt concentration.

However, the internal diameter of this tubing is equal to 360 µm, and the associated Pe number is comparable to that of the MD; therefore, there was also particle aggregation due to collisions and subsequent deposition in the tubing. The length of the tubing is approximately 20 cm, 10 times longer than the length of the main channel and the outlet arm of the MD; thus, most of the nanoplastics that exit the MD float, and are deposited at the top of the tubing. From the ratio between the length of the MD and that of the tubing, it was found that 90% of the particle aggregation takes place in the tubing. It is worth noting that nanoplastics deposition in the outlet, where only the saline solution is supposed to flow, is the result of the coupling of the salt migration and the diffusiophoretic drift of the nanoplastics [START_REF] Abécassis | Boosting Migration of Large Particles by Solute Contrasts[END_REF] . The diffusiophoresis is defined as the spontaneous motion of colloidal particles or molecules in a fluid induced by a concentration gradient of a different substance, as previously explained in controlled microfluidic experiments but not yet observed and characterized in the environment [START_REF] Ebel | Diffusiophoresis of Latex Particles in Electrolyte Gradients[END_REF] . This result suggests that there will be significant aggregation of nanoplastics in such places if the salt concentration is locally high enough to completely or partially screen the surface charge of the PS-nanoplastics.

Indeed, nanoplastics tended to accumulate near the region where the salt concentration is the highest. Due to the salt diffusion in the part of the channel where the nanoplastics flowed, the maximum salt concentration (MSC) was not located at the middle of the channel in the flow direction but inside the saline part of the channel (Fig. 4a). Therefore, the accumulated nanoplastics near the MSC will eventually come out of the MD through outlet Bout and not by Aout. Since the salt diffusion across the channel is higher at lower flow rates (Fig. S3), the location of the MSC is further inside the saline part of the channel, and thus, even more nanoplastics flow through the outlet Bout. The impact of the particle aggregation and deposition in the MD and in the tubing was measured in the vials through DLS measurements (Fig. 5). It is worth noting that there was no fluid agitation in the vial. Therefore, if particle aggregation takes place in the vials, it is only due to Brownian diffusion. In the Aout vial, since the ionic strength was always higher than the CCC, regardless of the flow rate, the particle concentration is assumed to be high enough to observe particle aggregation, as in the batch experiments. However, the aggregation process in the vial was much more limited than that in the batch experiments (Fig. S2b). There was almost no aggregation for 2< Q <10 mL/h and a slight increase in the aggregate size, up to 1.3Di, for the two lowest Q values (0.5 and 1 mL/h), with Di being the initial particle size (i.e. before injection to the microfluidic device), while in the batch experiments, the mean aggregate size was approximately 3Di at a similar concentration and constantly increased. The results for vial Bout are even more surprising, since aggregation occurred irrespective of the flow rate (Fig. 5), even if nanoplastics were not supposed to be present in this vial. Again, with a salt concentration well above the CCC (I > 470 mmol L -1 ) for all Q values, the mean aggregate size was smaller than in the batch experiments, except at the lowest flow rate. In this last case, the aggregate size ranged from 1.5 to 3Di after 120 min. Together, these measurements of the aggregate size in the vials suggest that the amount of nanoplastics that are lost in the MD and in the tubing is determined by a two-step process. First, there is a massive aggregation process that takes place in the bulk of the fluid, due to the collisions either between individual nanoplastics or between nanoplastics and aggregates, since the Pe number is high enough to consider that the aggregation is mostly orthokinetic, and the particle diffusion is negligible. In the second step, the aggregates that are close to the surface either migrate towards the wall due to the higher salt concentration near the surface [START_REF] Salmon | Transverse Transport of Solutes between Co-Flowing Pressure-Driven Streams for Microfluidic Studies of Diffusion/Reaction Processes[END_REF] , or are captured by physical interception by other immobile aggregates that lie on the surface. Based on a previous study on PSnanoplastics [START_REF] El Hadri | Nanoplastic from Mechanically Degraded Primary and Secondary Microplastics for Environmental Assessments[END_REF] , large micrometric aggregates of PS-nanoplastics mainly float and therefore are located near the top walls. Nanoplastics may also sink into the sediment at the bottom, since a substantial fraction of plastics are heavier than the seawater, as demonstrated in a previous study with fullerenes [START_REF] Gigault | Estuary-on-a-Chip: Unexpected Results for the Fate and Transport of Nanoparticles[END_REF] . Since the mean size of the deposited aggregates was quite large, this result may suppose that the flow does not fragment many of the aggregates, meaning that they have both great internal cohesion and strong adhesion on the surface of the PDMS, which the MD is made of [START_REF] Dersoir | Clogging of a Single Pore by Colloidal Particles[END_REF] , preventing them from being broken by shear forces. This deposition process mainly occurred inside the tubing, and it is so important that there were not enough nanoplastics in the vials to observe significant aggregation by diffusion. Even though aggregation can occur, the nanoplastics can form only very small aggregates (doublets, triplets) that are far from each other, and the DLS signal is still dominated by the individual nanoplastics. By comparison with the aggregate size evolution in the batch with a low particle concentration, it was estimated that the maximum amount of nanoplastics obtained at the lowest flow rate was approximately 1-2 and 10% in Aout and Bout, respectively, of the number of nanoplastics that were initially injected into the MD (Fig. S3). In order to extrapolate these results to mangrove swamps, two relevant time and length scales of particle transport in the microfluidic device were considered. First, the residence time inside the MD, and thereafter in the tubing, for the PS-nanoplastics that eventually reached the vials was between 4 and 80 seconds, depending on the flow rate. Everything happens on this short time scale where the aggregation induced by the SG is much faster than in the batch. Over longer times, this phenomenon will integrate all these aggregations throughout a mangrove swamp. As a result, only a small fraction of PS-nanoplastics, those which have not aggregated or slightly aggregated, will be released into the ocean, and the majority will remain flocculated (either floating or sunk to the sediment in various forms) in the mangroves (associated with natural colloids, sediment etc). Indeed, the results suggest that PS-nanoplastics will remain in the mangrove area for a much longer time, even though the average flow rate may be faster than that in the MD, since the mangrove swamp is much larger than the MD. This longer residence time will greatly increase the probability of the PS-nanoplastics to become involved in aggregation processes with either other PS-nanoplastics of the same type or, more likely, with the rest of the colloidal matter in suspension, in particular with organic matter. This longer residence time will also favor NP aggregation, since it will increase the diffusiophoretic drift of the PS-nanoplastics towards the high salt concentration zone in the water column, which will lead to a decrease in the surface charge of the nanoplastics.

Second, the growing aggregates cannot move more than 35-180 µm before becoming attached to the microfluidic device or the tubing walls. Therefore, aggregates cannot substantially grow in size before becoming immobile on a wall, i.e., they do not encounter more than one or two other nanoplastics/aggregates during their floating/sedimentation. In the mangrove area, the salt concentration is high at depths from tens of centimeters up to a few meters, which is at least three orders of magnitude deeper than the depth of the microfluidic experiment. Therefore, it is expected that in a mangrove swamp, the average mean size of the aggregate can become much greater than in the MD, since the aggregate can encounter many other aggregates as they either sediment or float. In addition, the collision rate increases as the mean aggregate size increases, since larger aggregates have a greater surface area and thus a higher chance of colliding with another aggregate. The results obtained demonstrate that the salinity gradient, and therefore mangrove swamps, act as a separator for nanoplastics according to their aggregation pathways. However, contrary to classical approaches, it was demonstrated that nanoplastic aggregation is driven by an orthokinetic process induced by the flow and the SG together. Indeed, all the aggregates formed in the SG will eventually either float to the surface, or settle down to the sediment in the MD or in the tubing, and only single nanoplastics and small aggregates are able to reach the vials. This rough analysis seems to suggest that PS-nanoplastics and especially nanoplastics that enter the mangrove swamp from land-based sources would be filtered, and that only a small fraction would reach the oceanic system as either nanoscale or microscale floating aggregates of nanoplastics. However, ongoing studies on the aggregation mechanisms of PS-nanoplastics in the mangrove swamp are performed considering the implication of turbulence and the natural organic matter in the MD. Indeed, such parameters could definitely play a role in the orthokinetic aggregation process along their transportation in the salinity gradient of the mangrove swamp. New sampling strategies need to be developed to detect (hetero-) aggregates of PSnanoplastics, taking into account the specific characteristics of these substances that lead to their different reactivities with contaminants and the other natural components. Finally, the results show the importance of protecting coastal mangrove ecosystems, since, apart from protecting the coast from possible storms and acting as habitat for terrestrial and aquatic species, they also act as natural filters and concentrator sinks for nanoplastics. Supporting Information. Description of the sampling points in the Mangrove (Figure S1); Size and stability evaluation of the nanoplastics model (Figure S2); Size evolution of nanoplastics model in the microfluidic device (Figure S3); NaCl distribution in the microfluidic device (Figure S4); Size evolution of spherical polystyrene nanoparticles in batch and microfluidic experiments (Figure S5).

Figure 1 :

 1 Figure 1: (a) Location of the sampling zone on the French Caribbean island of Guadeloupe. (b) Detail of the landfill in the mangrove swamp and the sampling location point (GPS coordinates of the blue point: 16°15'20.2"N 61°32'39.6"W)

Figure 2 :

 2 Figure 2: (a) Sketch of the experimental conditions in the microfluidic device (MD). After the nanoplastics flow through the MD, they flow through the tubing and are eventually collected in vials. (b) DLS allowed us to determine the evolution of the aggregate size over time. (c) View of the interface between the nanoplastic suspension (left) and a patent blue V dye suspension with the same diffusion coefficient as that of the saline suspension. The variation in the gray level across the channel allowed us to determine the salt concentration profile averaged over the height of the MD after a calibration experiment. This profile image is from an experiment with Q=2 ml h -1 and was taken in the middle of the straight part of the MD.

  dzH=f(t). The attachment efficiency a can be obtained by calculating the aggregation rate at different electrolyte concentrations. The ratio between the aggregation rate in favorable conditions (kfast, generally at high ionic strength, > critical coagulation concentration) and unfavorable conditions (kslow), normalized by the initial particulate concentration N0, allows the determination of a 31 and is expressed as:

Figure 3 :

 3 Figure 3: Temporal evolution of the salinity gradient along 1.5 m of water from the surface of the water. HT and LT correspond to high and low tide, respectively, while Zones 1 and 2 are the falling and rising phases of the tide.

Figure 4 :

 4 Figure 4: Microfluidic device (MD) experiment. (Top left) Image of the main straight part of the MD and the two outlet arms. Tubing connects the outlet to the vial. (A) Salt concentration profile along the width of the MD. The two yellow and green lines (color online) correspond to the lateral walls of the MD and to the salinity gradient in which the salt concentration is higher than the CCC, respectively. (B) Size distribution of the aggregates deposited on the top wall of the MD. (C) Concentration profile of the nanoplastics in the Bout arm and tubing. (D) Image of the colloidal deposit in the Aout arm and tubing. The blue arrows in 1 and 3 indicate the flow direction.

Figure 5 :

 5 Figure 5: Variation in the mean aggregate size, scaled by the initial size of the nanoplastics, with the flow rate for the two outlets.

Acknowledgments

We acknowledge funding and support from the French Agency for Research -ANR (ANR-17-CE34-0008). We acknowledge Sebastien Cordonnier for technical support in the Mangrove swamp.


Rashid Sbia
email: rashid.sbia@etu.univ-amu.fr

Helmi Hamdi

Remittances and Inflation: Evidence from O.P.E.C Countries

Keywords: Remittances, O.P.E.C, Inflation, CLSDV JEL:

The aim of this paper is to investigate the impact of remittances outflows on inflation for a panel of 14 OPEC countries during the period 1980-2012. Using bias-corrected least-squares dummy variable (CLSDV) estimator, empirical results reveal that remittance outflows have no effect on inflation rate while trade openness and current account deficits have a positive impact. Further, oil price appears to not have any effect on inflation in OPEC countries.

Introduction

For decades, explaining and fighting inflation has been a challenge for both scholars and central bankers. Inflation may hurt the economic and social stability by reducing the value of investments and increasing the costs of living.

There is vast literature explaining the sources and the determinants of inflation in order to detect the main factors behind its occurrence and to suggest adequate policy responses for fighting it. While some authors (Ubide 1997, [START_REF] Domac | The Main Determinants of Inflation in Albania[END_REF], Chhiber et al. 1998[START_REF] De Brouwer | Modeling Inflation in Australia[END_REF][START_REF] Leo | Money and Inflation in the Islamic Republic of Iran[END_REF][START_REF] Diouf | Modelling Inflation for Mali[END_REF]) have focused on the source of inflation using single country specific data, some others (Barnichon and Peiris 2007, Andersson et al 2009[START_REF] Crowley | Commodity Prices and Inflation in the Middle East, North Africa, and Central Asia[END_REF][START_REF] Kandil | Determinants of inflation in GCC[END_REF] have investigated the issue for a group of countries or regions. In both cases the evidence is mixed and it produced conflicting results.

The aim of this paper is to contribute to the development of this literature by exploring the possible economic consequences of remittances as a new variable on inflation rate. Generally speaking, remittances flows are private transfers of money between countries which could affect the level of prices in hosting and receiving countries as well. Literature examined the impact of remittances on the inflation rate is recent and very limited.

To the best of our knowledge, only few papers have investigated this topic. The most recent study was conducted by [START_REF] Narayan | Do Remittances Induce Inflation? Fresh Evidence from Developing Countries[END_REF] to inspect the determinants of inflation for 54 developing countries using a panel data set for a short period ranging from 1995 to 2004. The authors have used the Arellano and Bond panel dynamic estimator (GMM) and the Arellano and Bover and the Blundell and Bond system generalized method of moments estimator (SGMM). In addition to the usual economic variables that may affect inflation, the authors included remittance inflows and multiple institutional variables. The results revealed that remittances inflows increase inflation in developing countries (included in the sample). Furthermore, openness, debt, current account deficits, the agricultural sector, and the short-term U.S. interest rate appeared to have a significant positive effect on inflation. An interesting result showed that progress in democracy decreases inflation.

Another recent study was conducted by [START_REF] Termos | Remittance outflows and inflation: The case of the GCC countries[END_REF] to investigate the impact of remittance outflows on inflation in the Gulf Cooperation Council Countries (GCC). They used three panel estimations techniques including ordinary least squares (OLS), fixed effects (FE) and Anderson-Hsiao (AH) estimator. The empirical analysis exposed that the remittance outflows reduce the inflation pressures in the GCC sending countries. They concluded that remittance outflows in the GCC countries play a role of stabilizers.

The purpose of this study is to develop the literature on the inflation-remittances relationship for oil exporting countries with special focus on OPEC countries. OPEC is an interesting case study. In fact, with globalization, the movement of labor forces around the world increased considerably, especially to rich countries seeking a higher salaries and wages. In some oil rich countries such as in Kuwait, Qatar and United Arab Emirates, the stock of immigrants of local population reached a critical level: 68.8%, 86.5%, and 70% respectively. Basically, the rise of oil prices generates a rise in inflation as well as a rise in remittances outflows 1,2 . During, high periods of high oil price, remittance outflows could absorb the excess of money in OPEC countries and could play a role of stabilizer. While the share of remittance outflows increased during the past years, one could expect a strong relationship between inflation and remittances.

Do remittance outflows decrease/increase inflation rate?

To answer this question, we used a sample of fourteen OPEC member countries observed during the period 1980-2012. The estimation technique is based on bias-corrected least-squares dummy variable (CLSDV) estimator, a new technique developed by Bruno (2005a;2005b).

Results reveal that remittance outflows in OPEC countries have no effect on inflation rate while trade openness and current account deficits have a positive impact. We also found that oil prices do not have any inflationary belongings.

1 Generally, as we have seen in several OPEC countries during the past few years, an increase in oil price is followed by an increase in government spending in projects and improvement of infrastructure. In this case, the number of foreign workers increase which in turn also increase the volume of remittance outflows in oil rich countries. According to recent study of the World Bank (WDI 2012), the value of remittances outflows from O.P.E.C countries reached 67 billion dollars in 2010. 2 In the opposite scenario, a decrease in oil prices led automatically to a fall in remittance outflows because the demand for foreign workers falls.

The rest of the paper is organized as follows. In section 2, we present the econometric model and describe the estimation method. In section 3, we discuss empirical results and section 4 concludes.

Literature Review

Literature on the determinants of inflation has produced mixed and conflicting results either for single countries or for panel study.

For example, for country specific study, Ubide (1997) studied the determinants of inflation in Mozambique and showed that the marked tightening of monetary policy in 1996 was the ultimate reason for the control of inflation in 1996, and hence seems to correspond to a change in the fundamental trend of inflation that may have long lasting effects. Chhiber et al (1998) investigated the determinants of inflation in Zimbabwe from both structural and monetarist perspectives. They showed that inflation was influenced by nominal money growth, foreign prices, exchange rate, unit labor cost and real income. [START_REF] Domac | The Main Determinants of Inflation in Albania[END_REF] studied inflation in Albania using monthly data from January 1993 to September 1997. They decomposed inflation into four components: seasonal, cyclical, trend and random. By applying co-integration and error-correction techniques they found that in the long run, inflation is positively related to both money supply and the exchange rate, while it is negatively related to real income. To fight inflation and promote the competitiveness of exports, the authors suggest reducing both the budget deficit and, concomitantly, reducing credit to the government are crucial in fighting inflation.

De Brouwer and Ericsson (1998) have used quarterly data, covering 1976 Q3-1993 Q3 for Australia. By using different estimation techniques (cointegration, general to specific modeling, dynamic specification, model evaluation and testing, parameter constancy, forecasting and exogeneity), they found that continued low inflation through the 1990s appears to turn on sustained low growth rates in unit labor costs and import prices.

In the Brazilian context, Dureval (1998) studied the dynamic of chronic inflation in Brazil during the period 1968-1985 by the use of ECM. The long run, results reveal that domestic prices are determined by the exchange rate and world prices. Further, the author did not find evidence of money supply-led-inflation nor that money is a nominal anchor. However, he found that inflation increases when the rate of devaluation of the exchange rate increases and inflation decreases when output growth goes up. Sumaila and Laryea (2001)3 investigated the determinants of inflation in Tanzania. Their results show that inflation is influenced more by monetary factors and to a lesser extent by volatility in output or depreciation of the exchange rate in the short run and long run as well. The authors recommend policymakers to tighten monetary and fiscal policies. They also advised the government to increase food production to ease some of the supply constraints in the long run. [START_REF] Leo | Money and Inflation in the Islamic Republic of Iran[END_REF]; studied the determinants of inflation in Iran for the period 1988/89-2005/06.

By performing error correction model, he found that money has a prominent role in determining the equilibrium price level in the long run and short run. However, empirical results reveal no evidence of a structural change in the relationship between money and inflation. The author's results suggest that controlling money growth is the most important policy for fighting inflation in Iran. [START_REF] Diouf | Modelling Inflation for Mali[END_REF] investigated the determinants of inflation in Mali for the period 1979 Q1-2006 Q1. Using cointegration techniques and general-to-specific modeling, the empirical results show that inflation in Mali has both monetary and imported sources in both the short and the long term. To fight inflation, the author suggests the implementation of effective new policies to reduce food shortages and improve food security in Mali.

For the case of grouped countries, Barnichon and Peiris (2007) the euro area is primarily driven by different business cycle positions and to some extent by changes in product market regulations. While external factors such as differences in nominal effective exchange rates, energy intensity and the fiscal stance play a minor role. [START_REF] Crowley | Commodity Prices and Inflation in the Middle East, North Africa, and Central Asia[END_REF] inspected the relationship between factor input costs and inflation in the Middle East, North Africa, and Central Asia (MENACA) during the period 1996-2009 using different panel regression models. He found that inflation followed a perfect uniform pattern in all countries of the MENACA during the period under study. The same result was confirmed even when testing different cross sections of countries and when different estimators were included. The empirical analysis suggested that the pattern of inflation is explicated mainly by previous inflation, the strength of the US dollar, US inflation. Further, in some countries monetary and exchange rate policies and nonfuel commodity prices played an important role. This study came out with a number of controversial results that diverge from many works in related literature. For the author, the key one was that the changes in energy prices were statistically insignificant. This means that energy prices have no impact on inflation. This result was also confirmed using different models according to the variables used and subsets of countries. Ben Ali and Ben Mim (2011) evaluated the effect of both monetary and non-monetary determinants of inflation for a sample of 8 MENA countries over the period 1980-2009. The authors estimated multiples models (i.e. OLS, Fixed Effect, Random effect, GMM and SGMM) including different potential variables that may have an impact on inflation. Those potential variables were divided into 5 groups according to their kind namely structural, business-cycle-related, openness-related, external sector and monetary variables. The results revealed a clear persistence of inflation dynamics in these countries. Further, the world inflation and NEER have significant and positive effects on inflation. Moreover, a growth in money supply generates inflation. However, a strange result suggested that government spending has a negative impact on inflation. Recently, [START_REF] Kandil | Determinants of inflation in GCC[END_REF] explored the determinants of inflation in the GCC countries, using vector error-correction model including domestic and external variables that may affect inflation. The empirical analysis revealed that inflation in major trading partners seems to be affecting external variables the most. Further, oil returns increased inflation by allowing credit and spending expansions.

Data and Methodology

In our study, we used an unbalanced panel data for fourteen OPEC countries including Algeria, Angola, Ecuador, Gabon, Indonesia, Iran, Iraq, Kuwait, Libya, Nigeria, Qatar, Saudi Arabia, United Arab Emirates and Venezuela 5 . The dataset covers the period 1980 to 2012. Following previous studies on the determinants of inflation, we use the basic variables 6 such as: inflation rate, economic output in constant term (2005 US $), trade as a share of GDP, current account deficit as a share of GDP, total debt as a percentage of GDP, oil price, the U.S Treasury bill rate Anderson and Hsiao (AH, 1981) to explore the effect of remittances outflows on inflation, we used an alternative technique based on the bias-correction of the least square dummy variables estimator. Recently introduced by Kiviet, (1995), Judson and Owen (1999), [START_REF] Bun | On the diminishing returns of higher order terms in asymptotic expansions of bias[END_REF], it was extended by Bruno (2005) to unbalanced panels as considered in our work. This estimation method is chosen because, given the small time span over which this study expands, it produces unbiased and consistent estimates compared to other techniques applied in the estimation of dynamic panel datasets such as and Anderson and Hsiao (AH) (1992) and Arellano and Bond (AB) (1991) (Kiviet, 1995, Judson andOwen, 1998). Using Monte Carlo estimation, [START_REF] Bun | On the diminishing returns of higher order terms in asymptotic expansions of bias[END_REF] demonstrated that, in small samples, the LSDVC estimator outperforms consistent IV-GMM estimators such as the Anderson-Hsiao and Arellano-Bond estimators. 5 Gabon withdrew from OPEC in 1995 and Indonesia in 2008. 6 Narayan et al. (2011) explained thoroughly the effectiveness of these variables as a main determinant of inflation. 7 Arellano and Bond estimator reveals one weakness. Its properties hold only for N large and as a result, properties could be biased and inaccurate in panel data with a small number of cross-sectional units, which is our case in this study. The same situation occurs with other well-known estimators used to study dynamic panel model namely [START_REF] Anderson | Estimation of dynamic models with error components[END_REF] and [START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF] estimators.

Following [START_REF] Narayan | Do Remittances Induce Inflation? Fresh Evidence from Developing Countries[END_REF] and [START_REF] Termos | Remittance outflows and inflation: The case of the GCC countries[END_REF], the econometric model is specified as follows:

`, 0 , 1 , , , _ i t i t i t i t i i t inflation inflation X remit out v α β δ ε - = + + + + ; i = 1,.., N; t = 1,..,T (1)
Where inflationi,t is the inflation rate of country i at time t; remit_outi,t is remittances outflows in real term X is a vector of the explanatory variables; v is country-specific effects; and , i t ε is the error term. As the inflation rate is negative in some periods, the model was therefore estimated using log [l+( inf /100)] as a dependent variable as. All the variables except T-bill and oil price are expressed in logarithm form. Infi,t-1 is obtained by lagging , i t inf by one period.

Results

In the first stage of our econometrics analysis we use both pooled OLS and fixed effect techniques to estimate the equation ( 1). This approach is for comparative purposes only. Table 3 shows the results of both estimations. The empirical results reveal that the first lag of inflation rate positively affect the current inflation rate and it is statistically significant in both estimations. This is not the case for remittance outflows, which are not statistically significant in both models.

The remaining variables are not statistically significant under the OLS estimator. However, they are all significant under the fixed effect estimator except for oil price. Moreover, the signs are different. Gross Domestic Product and U.S Treasury bill have a negative impact on inflation while trade openness and current account deficit have a positive effect. It is well acknowledged in the literature that the inclusion of the lag of the independent variable makes the estimates of variables biased under OLS and FE estimators. In the second stage, we estimated our models using bias-corrected least-squares dummy variable (CLSDV) estimator. The different results are presented in Tables 3. We estimated multiple alternatives of the basic inflation model to check the robustness of the effect of remittance outflows on the inflation rate in 14 OPEC member countries.

In model 1, we investigate the impact of the economic output, the first lag of inflation and the real remittances outflows on the inflation rate. In model 2, we added to the model 1 variables' the trade openness as a share of gross domestic product. In model 3 to 5, in addition to the above variables, we include sequentially current account deficit as a share, oil price and U.S Treasury bill. When analyzing the results of model 1 to 5, we can distinguish two significant outcomes.

Firstly, the addition of different potential variables does not modify the sign and the statistical significance of the effect of remittance outflows on inflation. For example, through five models, we note that the effect of remittances outflows is negative and statistically insignificant.

Secondly, we observed that the magnitude and statistical significance of the remaining regressors are steady throughout the different models.

In all five different estimations, lag of inflation rate has a positive sign and shows a strong statistical significance. The coefficients range from 0.6938 to 0.8109 demonstrating that present current inflation is positively and strongly related to last year's inflation rate.

As for lag inflation rate, the gross domestic output is strongly statistically significant.

However, it has a negative effect on inflation. This result is consistent with the work done by [START_REF] Desai | Democracy, inequality and inflation[END_REF] which revealed a negative relationship between output growth and inflation for a panel of 100 countries. The same result found by Aisen and Viega (2006), who explored the same relationship for a panel of 75 developing countries, Recently, [START_REF] Narayan | Do Remittances Induce Inflation? Fresh Evidence from Developing Countries[END_REF] confirmed a negative effect of gross domestic product on inflation rate.

Surprisingly, remittance outflows variable has a negative impact but is statistically not significant on inflation. This result is found to be inconsistent with [START_REF] Termos | Remittance outflows and inflation: The case of the GCC countries[END_REF] who found that remittance outflows exert deflationary pressures in GCC economies. There are two possible explanations. The first one could be related to the estimator used by the authors which is unreliable in the case of small sample. The second explanation is that remittance outflows amount from GCC countries is bigger compared to OPEC countries as it has been shown in the introduction and their effect on inflation represents a special case.

Trade openness shows a positive and significant coefficient in all models where it was included. This result is consistent with cost-push hypothesis, which suggests that trade openness does not certainly decrease inflation; rather it increases it. In other words, there is a positive effect of trade openness on inflation [START_REF] Alfaro | Inflation, openness, and exchange-rate regimes: the quest for short-term commitment[END_REF][START_REF] Kim | Is free trade deflationary[END_REF][START_REF] Evans | Is openness inflationary? imperfect competition and monetary market power[END_REF].

However, empirical literature on the relationship between trade openness and inflation is

inconclusive. An alternative hypothesis, spillover hypothesis, put forward that trade openness is linked with falling prices and therefore protectionism is inflationary [START_REF] Musa | Tariffs and the distribution of income[END_REF][START_REF] Romer | Openness and inflation: theory and evidence[END_REF][START_REF] Lane | Inflation in open economies[END_REF][START_REF] Gruben | The openness-inflation puzzle revisited[END_REF][START_REF] Gruben | The openness-inflation puzzle revisited[END_REF].

The current account deficit as a share of gross domestic product exposes a positive effect on inflation rate and the coefficient is statistically significant. The current account deficit variable was included in 3 out of the 5 models. Our result indicates that deficit generates inflationary pressure. In comparison with the outcomes from empirical studies, Narayan et al.

(2011), for instance, found that current account deficit increases the inflation. [START_REF] Termos | Remittance outflows and inflation: The case of the GCC countries[END_REF] found that current account deficit has a positive effect on inflation but statistically not significant.

One of the most important result in this study is that the price of oil does not have any significant effect on inflation, while the U.S. Treasury bill rate has a negative and significant effect on the inflation rate.

Conclusion

In this empirical research, we contributed to the literature that to distinguishes the factors affecting inflation by modeling the influence of remittances outflows on the inflation for a panel of 14 OPEC countries over the period 1980 to 2010.

To avoid finite sample bias and inaccuracy when using some estimator including [START_REF] Anderson | Estimation of dynamic models with error components[END_REF], Arellano and Bover (1995) and the [START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF], we used bias-corrected least-squares dummy variable (CLSDV) estimator, recently introduced by Kiviet, (1995), Judson and Owen (1999), [START_REF] Bun | On the diminishing returns of higher order terms in asymptotic expansions of bias[END_REF] and extended by Bruno (2005), to unbalanced panels. It was necessary to check the robustness of our results suggesting that remittance outflows have negative but statistically insignificant effect on inflation in OPEC countries. We could confirm our results by including different potential factors that usually affect the inflation according to the literature.

We estimated five different models and we found the remittances effect on inflation kept the same characteristics across all models. Moreover, we found that real gross domestic product exerts a deflationary pressure in the case of our sample. At the opposite, the current account deficit, which increases the inflationary pressure. The positive effect of openness on inflation confirms the side effect of a big share of imports in the trade balance. A surprising result confirms that oil price does not have any effect on inflation in OPEC countries.

  examined the sources of inflation in nineteen Sub-Saharan Africa countries during the period 1960 to 2003. Using heterogeneous panel cointegration estimation techniques (GMM), they found that output gap and the real money gap both contain robust and considerable information regarding the evolution of inflation. Further, the authors showed that the real money gap plays a larger role in inflation processes than the output gap. In the European context, Andersson 4 et al. (2009) used dynamic panel estimations for the period 1999-2006. The results show that inflation differentials vis-à-vis

(

  3 months) and real remittances outflow. Data on remittances is obtained from the World Bank development indicator database (WDI 2014) except for United Arab Emirates which was extracted from the Arab Monetary Fund's online database. The other data was collected from different sources: the World Development Indicators (WDI), the International Financial Statistics (IFS) and Arab Monetary Fund online database. Unlike Narayan et al. (2011) 7 who used Arellano and Bond (AB, 1991) and Blundell and Bond (BB, 1998) estimators and Termos et al. (2013) who used

Table 3 . OLS and Fixed Effect Estimations Results

 3

	OLS	FE

*** significance at 1%.

Table 2 . LSDVC Estimations Results

 2

		Model 1	Model 2	Model 3	Model 4	Model 5
	Inflation.L1	0.8109***	0.7019***	0.6938***	0.6938***	0.6938***
		(0.0624)	(0.0611)	(0.0622)	(0.0623)	(0.0623)
	GDP	-0.4327***	-0.3612**	-0.4651**	-0.4651**	-0.4651**
		(0.1497)	(0.1806)	(0.2028)	(0.2028)	(0.2028)
	Remittances	-0.0010	-0.0091	-0.0089	-0.0091	-0.0091
	Outflows	(0.0228)	(0.0266)	(0.0268)	(0.0268)	(0.0268)
	Trade (% GDP)		0.1188*	0.1425*	0.14258*	0.1425*
			(0.0801)	(0.0805)	(0.0804)	(0.0804)
	Current Account			0.1917**	0.1917**	0.1917**
	deficit (% GDP)			(0.1016)	(0.1016)	(0.1016)
	Oil price				0.0025	-0.0004
					(0.0022)	(0.0021)
	Tbill					-0.0234**
						(0.0107)
	Notes: bootstrapped standard errors are in parentheses (50 iterations; see Bun and Kiviet (2003) and Bruno (2005b)).
	* significance at 10%.
	** significance at 5%.
	*** significance at 1%.

Ussif Rashid Sumaila and Samuel A. Laryea (2001) Determinants of inflation in Tanzania. Bergen: Chr. Michelsen Institute(CMI Working Paper WP 2001:12)

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