Keywords: Suspended Particulate Organic matter SPOM, Fluorescence Spectroscopy, parallel factor analysis PARAFAC modeling, Solar Irradiation, mixing experiments

Modelling of impact of presence/absence of suspended particulate organic matter from river and sea and effluent wastewater on fluorescence signal in the coastal area of Gapeau River

Ibrahim El-Nahhal, Roland Redon, Michel Raynaud, Yasser El-Nahhal,

Stéphane Mounier

To cite this version:

Ibrahim El-Nahhal, Roland Redon, Michel Raynaud, Yasser El-Nahhal, Stéphane Mounier. Modelling of impact of presence/absence of suspended particulate organic matter from river and sea and effluent wastewater on fluorescence signal in the coastal area of Gapeau River. Environmental Science and Pollution Research, inPress, 10.1007/s11356-021-13265-2. hal-03171038

Introduction

Organic matter in natural waters can be operationally classified and divided into dissolved organic matter (DOM) or suspended particulate organic matter (SPOM) depending on the filtration and filter size [START_REF] Osburn | Fluorescence tracking of dissolved and particulate organic matter quality in a river-dominated estuary[END_REF][START_REF] Gagné | Organic matter distribution and reactivity in the waters of a large estuarine system[END_REF]. DOM pool consists of a wide range of organic molecules originating from decaying dead stuff (plants and animals). Such organic molecules could be humic substances (i.e. humic and fulvic acid)or non-humic substances such as proteins, and carbohydrates with varying molecular size and functional groups (Her et al.2003).

Whereas, SPOM pool may consist of living microorganisms (e.g. bacteria and viruses), organic/inorganic particles organic polymers among others (Chin et al.1998;[START_REF] Leppard | A classification scheme for marine organic colloids in the Adriatic Sea: colloid speciation by transmission electron microscopy[END_REF]. SPOM in water plays an important role in characterizing the fate of (DOM) in ecosystems. SPOM and DOM are an important constituent in aquatic environments and plays a significant role in the transport, stability and bioavailability of several organic/inorganic pollutants that results from anthropogenic activity (e.g. heavy metals, pesticides and polycyclic aromatic hydrocarbons) [START_REF] Akkanen | Essential characteristics of natural dissolved organic matter affecting the sorption of hydrophobic organic contaminants[END_REF][START_REF] Hirose | Metal-organic matter interaction: ecological roles of ligands in oceanic DOM[END_REF][START_REF] Baker | Relating dissolved organic matter fluorescence and functional properties[END_REF][START_REF] Ishii | Behavior of reoccurring PARAFAC components in fluorescent dissolved organic matter in natural and engineered systems: a critical review[END_REF]. However, DOM in aquatic ecosystems is considered to be the most important and significant fraction of organic matter due to its involvement in so many environmental processes [START_REF] Søndergaard | Dissolved Organic Matter (DOM) in Aquatic Ecosystems: A Study of European Catchments and Coastal Waters[END_REF] In addition, both of them have a role in global biogeochemical cycling of carbon and nutrients. It is critical for the better understanding of carbon cycle to differentiate sources of DOM in aquatic environment and the factors which play important roles in its sources and sinks like biodegradation and photodegradation (McCallister et al. 2006a,b;[START_REF] Dalzell | Photodegradation of estuarine dissolved organic matter: an assessment of DOM transformation[END_REF]). More research attention has been given to study the role in environmental photochemistry of DOM than that of SPOM [START_REF] Mopper | Marine Photochemistry of Organic Matter: Processes and Impacts. Processes and Impacts[END_REF]. Solar irradiation of (SPOM) may result in production of dissolved nutrients and/or DOM in considerable amounts which may enrich the aquatic system. For instance, previous studies [START_REF] Liu | Solar radiation-enhanced dissolution (photodissolution) of particulate organic matter in Texas estuaries[END_REF][START_REF] Mayer | Photodissolution of particulate organic matter from sediments[END_REF][START_REF] Riggsbee | Suspended sediments in river ecosystems: photochemical sources of dissolved organic carbon, dissolved organic nitrogen, and adsorptive removal of dissolved iron[END_REF][START_REF] Southwell | Effects of sunlight on the production of dissolved organic and inorganic nutrients from resuspended sediments[END_REF][START_REF] Estapa | Photooxidation of particulate organic matter, carbon/oxygen stoichiometry, and related photoreactions[END_REF][START_REF] Pisani | Photo-dissolution of flocculent, detrital material in aquatic environments: contributions to the dissolved organic matter pool[END_REF]) investigated the influence of sunlight on POM and found that it undergo similar photochemical reactions as DOM due to absorbance of UV-VIS light which are the same wavelengths that DOM can absorb. Moreover, He et al. (2016) evaluated the effect of SPOM in attenuating the fraction of dissolved organic carbon and revealed that SPOM can reduce the concentration of dissolved organic carbon in water systems through adsorption process. Moreover, influence of SPOM in fluorometry of DOM were investigated by several authors (e.g. [START_REF] Laane R W P M | Natural fluorescence in the North Sea and its major estuaries[END_REF][START_REF] Souza | Simulation of fluorescence variability in estuaries[END_REF][START_REF] Baker | Characterization of dissolved organic matter from source to sea using fluorescence and absorbance spectroscopy[END_REF][START_REF] Boyd | Changes in CDOM fluorescence from allochthonous and autochthonous sources during tidal mixing and bacterial degradation in two coastal estuaries[END_REF][START_REF] Callahan | Distribution of dissolved organic matter in the Pearl river estuary, China[END_REF][START_REF] Kowalczuk | Characterization of CDOM in an organicrich river and surrounding coastal ocean in South Atlantic Bight[END_REF]Kowalczuk et al. , 2005;;[START_REF] Murphy | Distinguishing between terrestrial and autochthonous organic matter sources in marine environments using fluorescence spectroscopy[END_REF]. Evolution of fluorophores of DOM (i.e. FDOM) is being followed using spectrofluorometry which is a qualitative and semi-quantitative technique. Spectrofluorometry technique of three-dimensional excitation-emission matrix (EEM) spectroscopy has several advantages for the detection of fluorophores of DOM in aquatic environment because it is fast and non-destructive with no need for sample pre-treatment and is highly sensitive for detection of even-low concentrations of samples which is the case in several aquatic environments. In addition to the fact that type and origin of samples (riverine , marine, wetlands) can be figured out and types and relative concentrations of fluorophores constituting DOM can also be known using three-dimensional excitation-emission matrix (EEM) spectroscopy.

Previous studies [START_REF] He | Dynamic exchanges between DOM and POM pools in coastal and inland aquatic ecosystems: A review[END_REF]) investigated the influence of SPOM in light scattering, adsorption of DOM, attenuation of dissolved organic carbon and other matters, however the impact of presence/absence of SPOM on fluorescence signal of naturally occurring mixtures is not fully understood. Accordingly, this study was designed to bridge the gap of knowledge in the field of SPOM effect on fluorescence signal of naturally occurring mixtures of river water and effluent wastewater and sea water. Therefore, the research question of this study is that will the presence/absence of particulate matter from one of three water types (river water, seawater, effluent wastewater) affect the modelling and

Material and methods

Study area and Sample Collection

Gapeau river, a small coastal river, is situated in Var department in Provence-Alpes-Côte d'Azur region located in Southeastern France and is the second largest river in Var department. It discharges its runoff in Mediterranean Sea at Hyeres City. It is submitted to various anthropogenic inputs especially thirteen wastewater treatment plants in its water catchment. In this study, the focus was on anthropogenic organic matter from La Crau wastewater treatment plant WWTP which served 50,086 inhabitants. This WWTP uses secondary and tertiary technologies for wastewater treatment such as activated sludge technology, sand filter, prolonged aeration and anaerobic sludge digestion.

Sampling was done using plastic bottles of one liter each to sample 1 liter of River water , wastewater treatment plant effluent and Sea water. Exact GPS locations of these sampling sites are the same according to recent procedures [START_REF] El-Nahhal | Characterization of the fate and changes of postirradiance fluorescence signal of filtered anthropogenic effluent dissolved organic matter from wastewater treatment plant in the coastal zone of Gapeau river[END_REF]. Eight sampling cruises were conducted for solar irradiation experiments, sampling dates corresponding to each irradiation experiment are shown in Table 2.

Collected Samples Filtration

Having three types of water (River water RW, Sea water SW and wastewater effluent WW) and making permutations of filtering of two types of water and leaving the last one non-filtered, we end up with experiments described in Table 2. Filtration process (Removal of SPOM) was conducted using a filtration kit and MilliPore filters (Type GNWP 0.20

Preparing water mixtures

Vials of quartz were used because quartz allows absorption of UV-VIS energy. Different mixing percentages taken from the collected 1L RW, lL SW and 1L WW (filtered or not filtered as explained in the previous section) were taken by pipette and inserted in quartz vial according to table 1 (or Fig. 1). These mixing percentages are speculated to represent actual mixing in nature. After having the required mixing percentages, all quartz vials were hand-shaken to have representative mixtures. Another fifteen control samples had the same mixing percentages and prepared in dark vials. Each intersection point and summits in the ternary diagram in Fig. 1 represents a corresponding quartz vial that contains the indicated percentages in the ternary diagram of mixtures. Exact volume of RW, SW and WW in each quartz vial is indicated in Table 1. Total volume of each quartz vial is 50 mL.

Measurements of DOC and POC and UV-Vis spectra

Shimadzu TOC-5000A Total Organic Carbon Analyzer (catalytic combustion) with module ASI-5000A has been used

to measure dissolved organic carbon (DOC) at high temperature (720 °C) and with module SSM-5000A to measure particulate organic carbon (POC). Acidification was performed to pH < 2 using 2 N HCl on samples of 1L RW, 1L

SW and 1L WW in each irradiation experiment and CO2 was removed by purging samples with oxygen. UV-visible absorption spectra (between 250-800 nm) were measured using a PerkinElmer Lambda 10500 UV/VIS spectrophotometer with a 1 cm quartz cuvette with the blank as MilliQ water for the series of dilutions (100%, 50%, 25%, 12,5%) of 1L RW, lL WW, 1LSW to check for inner filter effect according to [START_REF] Tucker | Primary and secondary inner filtering: effect of K2Cr2O7 on fluorescence emission intensities of quinine sulfate[END_REF].

Irradiation experiments

Four irradiation experiments were conducted at different time of year (Table 2). The first two experiment were conducted in autumn and in winter season, and the last one was conducted in spring and the exact dates are described in (Table 2). Fifteen mixtures in quartz vials (according to previous section ) were prepared and transferred on rooftop of our laboratory MIO at Campus La Garde, Universite de Toulon in plank at sufficient distances from each other to have the same irradiation conditions as shown in Fig. 2. The used apparatus for these experiments is shown in Fig. 2.

Another fifteen control samples were prepared and incubated in dark box in vicinity of irradiated samples.

Measurements of light intensity.

Météo-France (www.meteofrance.com) provided solar irradiance (light intensity) measured in milliVolts mV for each day of irradiation as mentioned in table 2 and the cumulative light intensity was calculated and graphed for each irradiation experiment.

Excitation Emission Matrix EEM fluorescence spectroscopy

Spectrofluorometric measurements were completed using a 1-cm quartz cuvette with a Hitachi F-4500 spectrofluorometer with PMT voltage of 700 V, at 25 ℃ and sampling using a pipette a 3 mL from each irradiated quartz vial. A correction for the change in volume was done by replacing the taken 3mL aliquots by another 3 mL of deionized water therefore steady state concentration was kept till the end of each irradiation experiment. Sodium azide (100 µL of 1M NaN3) was added for each sampled 3mL to inhibit biodegradation during EEM fluorescence measurements. Addition of sodium azide NaN3 has no effect on fluorescence intensity in EEMs as revealed by [START_REF] Patel-Sorrentino | Excitation-emission fluorescence matrix to study pH influence on organic matter fluorescence in the Amazon basin rivers[END_REF].

Excitation wavelengths (Ex) spectra were measured from 200 to 400 nm at 5 nm-increment, and emission wavelengths (Em) spectra from 220 to 420 nm at 5 nm-intervals with scan speed set at 2,400 nm.min-1. Slit width of 5 nm was set for both excitation and emission wavelengths. EEM datasets of solar irradiation experiments were processed using Matlab 2013a (Math Works Inc., USA). All recorded EEMs were blank-corrected through the subtraction of EEMs of Sealed ultrapure Perkin Elmer deionized water cell. Integrated Raman signal of Sealed ultrapure Perkin Elmer deionized water cell was used to normalize values of each excitation-emission matrix and it was calculated by integrating the area under the curve from 370 to 420 nm [START_REF] Lawaetz | Fluorescence intensity calibration using the Raman scatter peak of water[END_REF]. All fluorescence intensity were in Raman units (RU) . Only EEMs before irradiation of Samples No. 1,2 and 3 in table 1 are presented in the results and discussion section whereas the remaining EEMs are not shown since the total number of EEMS for all four irradiation experiments is 648 EEMs.

Parallel factor analysis (PARAFAC) of EEM data

PARAFAC is a powerful multiway technique used to decompose and fully make use of the EEM dataset. The principle of PARAFAC is that it decomposes any given EEM dataset into its underlying EEM spectra constituents [START_REF] Murphy | Fluorescence spectroscopy and multi-way techniques[END_REF] which are a set of trilinear terms and a residual array using an alternating least squares algorithm to minimize sum of squared residuals in a trilinear model. Resulting PARAFAC components represents fluorophores having similar fluorescing properties which constitute the EEM dataset. PARAFAC modeling was performed on the whole EEM datasets of all irradiation experiments (Table 2) using MATLAB software (MathWorks R2015b, USA) and NWAY toolbox and DOMFluor toolbox (http://www.models.life.ku.dk) [START_REF] Micó | EEMlab: A graphical user-friendly interface for fluorimetry experiments based on the drEEM toolbox[END_REF][START_REF] Stedmon | Characterizing dissolved organic matter fluorescence with parallel factor analysis: A tutorial[END_REF] for a total of 648 EEMs. Numerical filter was taken as 25 nm to eliminate Raman and Rayleigh scattering according to Zepp method [START_REF] Zepp | Dissolved organic fluorophores in southeastern US coastal waters: correction method for eliminating Rayleigh and Raman scattering peaks in excitation-emission matrices[END_REF]. Based on CONCORDIA score, split-half analysis and visual inspection of spectral shapes of each PARAFAC component, the accepted number of PARAFAC components was determined [START_REF] Bro | Multi-way Analysis in the Food Industry[END_REF]. Splithalf analysis were performed for validation of PARAFAC model results [START_REF] Stedmon | Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy[END_REF][START_REF] Murphy | Fluorescence spectroscopy and multi-way techniques[END_REF].

Scores of each PARAFAC component represent relative concentration of each PARAFAC component in EEM dataset.

Quantitative and qualitative variations of the composition of organic matter can be extracted from PARAFAC modelling of EEM datasets. Normalization of contributions of PARAFAC components was performed by dividing each contribution with its corresponding daily maximum contribution.

Multilinear regression between scores of PARAFAC components and fRW and fSW

Based on the explanation recently described (EL-Nahhal et al. 2020), the final multilinear regression equation as a function of fRW and fSW is:

C*i = A WW i,0 + A WW i,1.fSW + A WW i,2.fRW (Eq.1)
Where fRW and fSW are percentages in mixture in a given quartz vial of RW and SW as described in Fig. 1;

A WW i,0 , A WW i,1 and A WW i,2 represent multilinear regression coefficients related to mixing equation when fWW is expressed in terms of percentages (fRW and fSW). i is the number of a given PARAFAC component (e.g. C1, C2, C3) .

More details and explanations are thoroughly given elsewhere [START_REF] El-Nahhal | Characterization of the fate and changes of postirradiance fluorescence signal of filtered anthropogenic effluent dissolved organic matter from wastewater treatment plant in the coastal zone of Gapeau river[END_REF].

Kinetics of Multilinear regression parameters A WW i,0 , A WW i,1 and A WW i,2

A WW i,0 , A WW i,1 and A WW i,2 values change for each day of irradiation in a given experiment. Changes in their values were modelized to rate order kinetic equation in order to get a model for the evolution of fluorescence signal as a function of irradiation energy expressed in volts V; with their kinetic formulas expressed as

A WW i,0 (V), A WW i,1 (V) and A WW i,2(V) .
Accordingly, multilinear regression model in eq. 1 can be expressed kinetically as follow:

C*i (V) = A WW i,0 (V) + A WW i,1(V) . fSW + A WW i,2(V) . fRW (Eq.2)

Statistical Analysis

Multi-regression analysis was used to investigate the strength of linear relationships between concentration scores of fluorescent components obtained from PARAFAC analysis and water mixing composition. Regression and correlation analyses and relative standard deviation for multilinear regression parameters in control non-irradiated samples were performed using Microsoft Excel 2016. Significances of correlations in the statistics were evaluated.

Results and Discussions

Measured light intensity in mV for Exp.

First experiment Exp.1 (in autumn) .

First irradiation experiment (Exp. 1) was conducted in the autumn season starting irradiation from November 10 to November 20 2015 (Table 2). EEMs of Samples no 1, 2 and 3 in table 1 (before irradiation and after irradiation and the difference between before and after irradiation) are presented in Fig. 5 for Exp.1 in which SPOM from effluent WW is present whereas SPOM from RW and SW were absent. Concentrations of POC of WW was 3 ± 0.23 mg/l whereas DOC concentration of RW and SW were 2.1±0.18mg/l and 2.4±0.19mg/l. It can be seen in Fig. 5 that unfiltered and unmixed effluent WW (sample no. 3) contains the highest fluorescence intensities before irradiation and after irradiation compared to samples no. 1 and 2. All fluorescence peaks got photodegraded as shown by the diminution of fluorescence intensity of them as clearly can be seen in color bar values.

Second experiment Exp. 2 (in autumn)

Second irradiation experiment (Exp. 2) was also conducted in autumn season starting irradiation from December 20

until December 17 2015 (Table 2). EEMs of Samples no 1, 2 and 3 in table (before irradiation and after irradiation and the difference between before and after irradiation) are presented in Fig. 6 for Exp. 2 in which particulate organic matter from river water is present whereas seawater and effluent wastewater were filtered. The concentration of POC of RW was 0.5 ± 0.14 mg/l whereas DOC concentration of SW and WW were 2±0.3mg/l and 3.5±0.2mg/l. It can be seen in March 4 2016 (Table 2). EEMs of Samples no 1, 2 and 3 in table (before irradiation and after irradiation and the difference between before and after irradiation) are presented in Fig. 7 for Exp. 3 in which particulate organic matter from sea water is present whereas river water and effluent wastewater were filtered. The concentration of POC of SW was 0.6 ± 0.07 mg/l whereas DOC concentration of RW and WW were 2±0.18 mg/l and 3.7±0.14 mg/l. It can be seen in Fig. 7 that sample no 2 ( unmixed and unfiltered SW) has the lowest fluorescence intensity for most peaks (C, A, M, B, and T) before irradiation and sustained more or less the same fluorescence intensity after irradiation. There was no inner-filter effect in this sample as checked by UV-VIS absorption spectra of SW as explained above. intensities of protein-like peaks in all the EEMS shown in figures (Fig. 5, 6, 7 and Fig. 8) are in accordance with the results of PARAFAC model shown in Fig. 9 where there was no protein-like PARAFAC component was found.

Parallel factor analysis (PARAFAC) of EEM data

EEMs of fifteen samples (Table 1) for each irradiation day and for all the irradiation experiments (Exp.1, Exp.2, Exp.3 and Exp.4) have been decomposed using PARAFAC for a total of 648 EEMs. PARAFAC decomposition of 648 EEM dataset of all irradiation experiments (Table 2) gives three independent components which are validated by CORCONDIA 74.9 % and split-half method. Fluorescence landscape of PARAFAC components (C1, C2 and C3)

with corresponding excitation and the emission loadings are shown in Fig. 9. It can be noticed that the nature of PARAFAC components seem to be present with varying contributions in every sample in the whole EEMs dataset of all Irradiation experiments. Excitation emission loadings of each component (C1, C2 and C3) were compared with

Openfluor.org database and previously reported PARAFAC components in literature and their corresponding equivalents are shown in Table 3. C1 presented excitation maximum at 340 nm and an emission maximum at 430 nm.

Previous studies have associated this component to UV-A humic-like fluorophores and Peak C [START_REF] Coble | Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy[END_REF]) and peak "∝ [START_REF] Parlanti | Dissolved organic matter fluorescence spectroscopy as a tool to estimate biological activity in a coastal zone submitted to anthropogenic inputs[END_REF]. It represents terrestrial humic-like and it can be suggested that it is used as wastewater/nutrient enrichment tracer [START_REF] Murphy | Organic Matter Fluorescence in Municipal Water Recycling Schemes: Toward a Unified PARAFAC Model[END_REF]). C2 showed excitation maximum at 375 nm and an emission maximum at 465 nm. This component represents more humificated fluorophores and resembles C2 found in the study of [START_REF] Abaker | UV-Visible and Fluorescence Green Waste Composts Monitoring: Material Dependency[END_REF] and it is of fluvic-like fluorophores Peak A [START_REF] Coble | Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy[END_REF]. C3 excitation/emission peak was located at wavelengths of 295/405nm. This may be attributed to anthropogenic humic materials, agricultural and microbial component. This component C3 was defined in literature as marine humic-like peak M [START_REF] Coble | Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy[END_REF] and has resemblance to Q3 which is a microbially oxidized component (Cory and McKnight 2005). This suggestion is in accordance with previous reports [START_REF] Murphy | Distinguishing between terrestrial and autochthonous organic matter sources in marine environments using fluorescence spectroscopy[END_REF][START_REF] Stedmon | Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis[END_REF]. Furthermore, no protein-like component was detected by PARAFAC which is validated by split-half method. The above-described PARAFAC fluorescent components were used to reconstruct the original whole EEMs dataset and for multilinear regression between each component (i.e. C1, C2 and C3) and sample composition (fSW and fRW).

Photochemical variation in

𝐶𝐿 -𝐶𝐿 → 2𝐶𝑙 * 𝐸𝑞. (3)

Free radicals produced in Eq. 3 react with oxygen to produce chlorine oxides may be classified in monochlor-and dichloroxi-derivatives as previously reported [START_REF] Lopez | The photochemical reaction between chlorine and chlorine perchlorate at 366 nm[END_REF] (Eq. 4)

2𝐶𝑙 * +𝑂 2 → 𝐶𝑙𝑂 + 𝐶𝑙 2 𝑂 𝐸𝑞. ( 4)

All these compounds are thermally unstable and may decompose readily in atmosphere. Nevertheless, these reactions are contentious and may lead to formation of C12O4 which may undergo a reformation reaction to reproduce Cl-Cl undergo continues reaction as shown in Eq. 5-6.

2𝐶𝑙 2 𝑂 4 → 𝐶𝑙 2 𝑂 7 + 0.5 𝑂 2 + 𝐶𝑙 2 𝐸𝑞. ( 5)

𝐶𝑙 2 𝑂 4 → 2𝑂 2 + 𝐶𝑙 2 𝐸𝑞. ( 6
)
These proposed reactions are in accordance with [START_REF] Lopez | The photochemical reaction between chlorine and chlorine perchlorate at 366 nm[END_REF] who indicated formation of these photochemical reaction under exposure to light at wavelength of 366 nm. So far, free radicals produced in Eq. 3 react with dissolved organic matter (Glucose, fatty acid and or protein) producing organochlorine compound according to Eq. ( 7)

2𝐶𝑙 * +𝑅 -𝐶𝐻 3 → 𝑅 -𝐶𝐻 2 𝐶𝑙 + 𝐻𝐶𝑙 𝐸𝑞. ( 7)

These chlorinated hydrocarbons (R-CH2Cl) are persistent in ecosystem and are able to absorb solar energy and release it again into environment to go to its stable stage according to Eq. 8.

𝑅 -𝐶𝐻 2 𝐶𝐻 2 𝐶𝑙 + 𝐶𝑙 * + 𝑙𝑖𝑔ℎ𝑡 𝑒𝑛𝑒𝑟𝑔𝑦 → 𝑅 -𝐶𝐻𝐶𝐻 2 𝐶𝑙 + 𝐻𝐶𝑙 → 𝐸𝑞. ( 8)

The produced compound R-CHCH2Cl is fluorescent matter due to double bond formation. Where R can be an aliphatic or aromatic moiety (Margulies et al. 1994). Presence of particulate matter in the system can reduce the rate of abovementioned reactions (3-8) but cannot stop them. The proposed reactions agree with previous reports [START_REF] Morris | The chemistry of aqueous chlorine in relation to water chlorination[END_REF][START_REF] El-Nahhal | Reduction of photodegradation and volatilization of herbicides in organo-clay formulations[END_REF][START_REF] Nir | Optimization of adsorption of hydrophobic herbicides on montmorillonite preadsorbed by monovalent organic cations: Interaction between phenyl rings[END_REF][START_REF] Muellner | Haloacetonitriles vs. regulated haloacetic acids: are nitrogen-containing DBFs more toxic?[END_REF] A WW 3,0 ) are of two order of magnitude higher than the absolute value of both coefficients of fSW (A WW 1,1 , A WW 2,1 , A WW 3,1 ) and fRW (A WW 1,2 , A WW 2,2 , A WW 3,2 ) for all PARAFAC components. The intercept (A WW 1,0 , A WW 2,0 , A WW 3,0 ) contains implicitly the impact of fWW on resulting fluorescence contribution of (C1, C2 and C3) as explained thoroughly in the methodology section of this manuscript . This indicates that increasing fSW or fRW result in decreasing the resulting fluorescence contribution of (C1, C2 and C3). Additionally, it can be suggested that presence of particulate matter slows the intensity of photochemical degradation, this is quite obvious with the slight changes in samples that contains large fraction of suspended particulate matter compared with filtrated samples. Furthermore, suspended organic matter may adsorb dissolved organic matter on its surfaces and provide photoprotection from degradation. This phenomenon is in accordance with previous reports (EL-Nahhal et al. 2001;[START_REF] Nir | Optimization of adsorption of hydrophobic herbicides on montmorillonite preadsorbed by monovalent organic cations: Interaction between phenyl rings[END_REF]Margulies 1996) that revealed the ability of organoclay complexes to provide photoprotection to photodegradable pesticides. Moreover, filtration of samples may increase the contribution of fluorescence of mixing process is predominated by wastewater treatment plant for PARAFAC components and filtration has a measurable effect on multilinear regression parameters. When SPOM of only one water source is present, there is a decrease of values of the intercept parameter which suggest that there is an effect on fluorophores of filtered particles. When SPOM from RW is present (i.e. Exp.2), values of intercept are greater than that when SPOM from SW (i.e. Exp.3) is present suggesting that removal of fluorophores of river water plays a role on values of intercept. In general, there is an influence of presence/absence of SPOM on the initial contribution of multilinear regression parameters.

Rate order and kinetic constant determination for the photodegradation of Multilinear regression parameters

( A WW *,0 , A WW *,1 , A WW *,2 )

Kinetic constant and rate order of these parameters indicate how contribution of each PARAFAC component (Eq.2)

will evolve with irradiation time; hence EEM at any given point of irradiation time can be reconstructed. All irradiation experiment (Exp.1, Exp.2, Exp.3 and Exp.4) showed continuous decrease of fluorescence signal with irradiation time as shown in Fig. 10. In comparison to other studies [START_REF] Song | Effects of irradiation and pH on fluorescence properties and flocculation of extracellular polymeric substances from the cyanobacterium Chroococcus minutus[END_REF]Zhu et al. 2017a), there was no increase of fluorescence intensity found in this study. The decrease in values of ( A WW *,0 , A WW *,1 , A WW *,2 ) in all experiments could be fit by second order reaction kinetics (Table 5) in agreement with previous works [START_REF] Yang | Sunlight-induced changes in chromophores and fluorophores of wastewater-derived organic matter in receiving waters -The role of salinity[END_REF]. Moreover, values of kinetic constant (k) are presented in Fig. 11(a). [START_REF] Wu | Removal of fluorescence and ultraviolet absorbance of dissolved organic matter in reclaimed water by solar light[END_REF] found pseudo-first order reaction kinetics for the removal of fluorescence region volumes using fluorescence regional integration "FRI" which is in contrast with our study where fluorescence EEMs were modelled by PARAFAC and multilinear regression was conducted between fSW and fRW and (C1, C2 and C3). In addition, those authors used simulated solar light during 12h and under 2.80 mW/cm 2 (visible) and 70.00 mW/cm 2 . Second order reaction kinetics suggest that organic matter reacts with excited organic matter itself. Values of kinetic constant for ( A WW *,0 , A WW *,1 , A WW *,2 ) are shown in Fig. 11 Accordingly, fWW contribution is refractory and resilient under long irradiation. Zhu et al. (2017b) found similar results between mixing of terrestrial and autochthonous organic matter. [START_REF] Wu | Removal of fluorescence and ultraviolet absorbance of dissolved organic matter in reclaimed water by solar light[END_REF]

Conclusions

The rationale of this study emerges from the need to develop previous models for the prediction of fluorescence signal of anthropogenic DOM based on mixing composition of sample and to study the impact of presence/absence of suspended particulate matter of three water types (River water RW, Seawater SW, effluent wastewater WW) on the resulting fluorescence signal of their mixtures and to simulate their natural mixing. Exact volume in each quartz vial (indicated in its corresponding number) is the vertical sum in mL Exp.1 8,17 -0,08 -0,08 0,99 11,86 -0,11 -0,10 0,99 28,31 -0,24 -0,21 0,99

Sample

Exp.2 62,36 -0,61 -0,53 0,99 71,53 -0,63 -0,54 0,98 63,44 -0,47 -0,43 0,98

Exp.3 61,75 -0,62 -0,56 0,98 63,89 -0,61 -0,55 0,98 55,68 -0,49 -0,45 0,98

Exp.4 94,56 -0,94 -0,90 0,99 93,17 -0,89 -0,83 0,99 99,49 -0,93 -0,86 0,99

Table 5

Kinetic order of coefficients of multilinear regression for each CP/PARAFAC .

C1 C2 C3

A WW 1,0

A WW 1,1 (fSW)
Exp.1

  kinetics of degradation of fluorescence signal of different mixtures of these water types after solar irradiation . So far, the objectives of this study are to investigate the effect of presence/absence of SPOM on fluorescence signal of naturally occurring mixtures through mixing experiments and to examine the impact of solar photodegradation on mixtures of different types of water (i.e. river water RW, Sea water SW and wastewater effluent WW) by using the technique of three-dimensional fluorescence spectroscopy and parallel factor analysis PARAFAC by developing a multilinear regression model for tracking the effect of SPOM on fluorescence signal of the above mentioned mixtures to simulate naturally occurring mixing in coastal zone which could be used in further work to develop online or real-time remotesensing monitoring software.

Fig. 6

 6 Fig.6 that peak C representing humic-like fluorophores and peak M representing marine humic-like fluorophores (Coble 1996) has medium intensity in sample no. 1 (unmixed RW i.e. 100% RW) and higher intensity peak C in the sample no. 2 (unmixed SW i.e. 100% SW) and the highest intensity found in sample no.3 (unmixed WW). In addition, peaks B and T which represent protein-like fluorophores (tyrosine-like and tryptophan-like) are also higher in fluorescence intensity compared to samples no. 1 and 2 before irradiation. After irradiation, fluorescence intensity of peak B (tyrosine-like fluorophores) and peak T (Tryptophan-like fluorophores) degraded as shown with their lowest fluorescence intensities shown as the values in the color bar. Furthermore, the light intensity in the second experiment in autumn is less than the light intensity in experiment Exp.1. This suggest slower photochemical degradation. Third experiment Exp. 3 (in winter season) Third irradiation experiment (Exp. 3) was conducted in the winter season starting irradiation from February 15 until

  Photochemical variation in fluorescent PARAFAC components Variation of C1, C2 and C3 in irradiation experiment Exp.4 before conducting multilinear regression indicates how the parameters of multilinear regression varies with irradiation process. Changes of maximum fluorescence intensity Fmax of each PARAFAC components with irradiation time are shown in Fig.10. Fig.10 shows variations of Fmax for C1, C2 and C3 which decrease with irradiation process which is very clear for irradiated samples (shown in black).Whereas, for control samples (dark) (shown in dotted gray), Fmax doesn't have a clear trend which explains the results presented in Fig.11. Similar trend has been found for the remaining irradiation experiments (Exp.1, Exp.2, Exp.3 and Exp.4) (data not shown). These findings suggest that photodegradation impact humic and fluvic-like fluorophores in water column in river and sea water. Additionally, SPOM may adsorb soluble organic matter from water resulting in a photoprotection to the DOM. This process may result in reducing photochemical degradation in which the fluorescence signal is not dramatically affected. In this regard, we suggest the following protection mode from suspended particulate organic matter. As many wastewater treatment plants use chlorine as a disinfectant material after secondary treatment and seawater in France is partly chlorinated[START_REF] Péron | Etude physicochimique de la chloration de l'eau de mer artificielle[END_REF]. Part of the added chlorine may undergo the following reaction either in treatment plants or receiving water bodies. Under sunlight, chlorine in effluent wastewater samples undergo the following reactions according to equations 3 and 4.Photodegradation of Cl2 under sunlight produces free radicals of as shown in Eq. (3).

  photodegradation of PARAFAC component. Photosensitivity variations are present between fWW and fRW or fSW.

  observed little variations between humic-like and protein-like fluorophores in reclaimed water under strong conditions of irradiation. Kinetic constant (k) is highest when SPOM of WW and SW were present (Exp.1 and Exp.3) which imply that SPOM from these WW and SW makes the photodegradation faster for C1 and C2 and C3. Impact of SPOM from RW, SW and WW varies according to fluorescent PARAFAC component. The nature of these SPOM should be investigated more in order to understand their role in photodegradation. Advanced SPOM separation or extraction and fractionation techniques should be used (e.g. XAD resins). Evolution with irradiation of initial values of ( A WW *,0 , A WW *,1 , A WW *,2 ) before irradiation for each corresponding PARAFAC component can be tracked using the values of their corresponding kinetic constants hence reconstruction of fluorescence signal of C1, C2, and C3 can be conducted hence the whole EEM of sample. Therefore, a dynamic model has been developed for the photodegradation of SPOM from different water types. No clear behavior for control dark samples was found due to biological activity (Yang et al. 2014); except the mean contributions relative deviation standard (RSD) and this is consistent with results shown above in Fig.10. Relative Standard deviation (RSD) of parameters of multilinear regression are shown in Fig.11(b). RSD values in Exp.4 are the smallest compared to all other irradiation experiments. This finding could be due to synergic effect of the microbial activity coming simultaneously from all the water types (RW, SW and WW). RSD values for C3 coefficients of multilinear regression are higher for all irradiation experiments compared to those of C1 and C2. The greater RSD value of C3 for all coefficients can be interpreted as having chaotic variations. Another observation is that RSD values for C1 and C2 have an order of Exp. 1 > Exp. 2 > Exp. 3 > Exp. 4. SPOM from WW seems to be responsible for variation with a greater degree compared to SPOM from SW and RW. Compared to irradiation experiment (Exp.4), the synergistic effect of all SPOM makes RSD values to be the smallest possible. This observation could be attributed to the fact that SPOM from each water type and microorganisms are competing which therefore stabilize fluorescence signal in non-sterile dark control. These data indicate statistical differences between control group and those exposed to sunlight during different time of year.

Figures CaptionsFig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9

 123456789 Figures CaptionsFig.1Ternary diagram of the mixing percentages of three endmember mixing components (freshwater (RW), wastewater treatment plant (WW), seawater (SW)). Each red point represents a solution that contains the mentioned and calculated percentages of each water source (endmember) Fig.2The used apparatus for all the irradiation experiments which was on the rooftop of MIO laboratory/ University of Toulon-France. GPS location: (43° 08' 11.2" N 6° 01' 16.7" E) Fig. 3 Light intensity measured in millivolts mV for the four irradiation experiments Fig. 4 Linearity of UV-Vis absorbance spectra with dilution of the sampled 1L RW, 1L WW, 1L SW from top to down respectively showing no Inner Filter Effect Fig. 5 EEMs of Samples 1,2,3 in Irradiation experiment Exp.1 Fig. 6 EEMs of Samples 1,2,3 in irradiation experiment Exp.2 Fig. 7 EEMs of Samples 1,2,3 in Irradiation experiment Exp.3 Fig. 8 EEMs of Samples 1,2,3 in Irradiation experiment Exp.4 Fig. 9 Fluorescence landscape of PARAFAC components identified from the decomposition of all EEM datasets on

Fig. 2

 2 Fig. 2

Figure

  Figure

Excitation Emission Matrix of Example Samples 1,2 and 3 before irradiation and after irradiation

  1, Exp.2, Exp.3 and Exp.4 are presented in Fig.3. It can be noticed from Fig.3 that light intensity in Exp.1 which was conducted in autumn and the third one Exp.3 which was conducted in winter season have the lowest light intensity compared to Exp.2 (conducted in autumn) and Exp.4 (conducted in spring). The high light intensity in autumn Exp.2 in December 2015 compared to autumn Exp.1 in November 2015 is due to un-Collected water samples (RW, SW and WW) have different origin to match the naturally occurring mixtures. Filtration of samples was done to match naturally occurring precipitation in ecosystems. For instance, at rough sea water movement, SPOM are at most whereas, at soft sea, SPOM tend to precipitate resulting in as clean solution as filtrated samples. Moreover, the use of different irradiation periods is to understand the effects of different light intensities on

	degradation processes and fluorescence signal of DOM. Three-dimensional fluorescence spectra (EEMs) of the first
	three samples in table 1 are presented for every irradiation experiment in Figures (5, 6, 7 and 8) as example EEMs of

predictable weather in PACA (Provence Alpes Cote d'Azur) region in southeastern France. This explains the rapid photodegradation in Exp.2 and Exp.4.

UV-VIS Absorption spectra of 1L RW, 1L WW, 1L SW

UV-VIS absorption spectra of sampled 1-liter river water RW, 1-liter effluent wastewater WW and 1-liter seawater and the dilution series 100%, 50%, 25% and 12,5% for each water type are shown in Fig.

4

. It can be seen from Fig

4

that UV-VIS absorption spectrum of all water types RW, WW and SW decrease linearly with dilution series (100%, 50%, 25% and 12,5%) showing no primary or secondary inner filter effect in these waters (RW,WW & SW) as previously described

[START_REF] Tucker | Primary and secondary inner filtering: effect of K2Cr2O7 on fluorescence emission intensities of quinine sulfate[END_REF][START_REF] Ohno | Fluorescence inner-filtering correction for determining the humification index of dissolved organic matter[END_REF]

. In addition, sand filter; a tertiary wastewater treatment technology; is used in the WWTP of La Crau city in this study removes inner filter effect caused by SPOM and this is in accordance with

[START_REF] Sgroi | Inner filter effect, suspended solids and nitrite/nitrate interferences in fluorescence measurements of wastewater organic matter[END_REF]

who found no inner filter effect for effluent tertiary wastewater after sand filtration.

the effect of solar irradiation (the remaining EEMs for each sample in table 1 are not shown).

  to samples no 1 and 2. Moreover, peaks C and M degraded the most which is in agreement withZhu et al. (2017b) who found that CDOM from terrestrial origins was more susceptible to photochemical degradation compared to CDOM from biological sources.Last irradiation experiment (Exp. 4) was conducted in spring season starting irradiation from May 05 until May 27 2016 (Table2). EEMs of Samples no 1, 2 and 3 in table (before irradiation and after irradiation and the difference between before and after irradiation) are presented in Fig.8for Exp.4 in which particulate organic matter from RW, SW and WW are present at the same time in all the samples of Table1. Concentrations of POC of RW, SW and WW were 0.6±0.1mg/l , 0.7±0.2mg/l and 2.75±0.45mg/l . It can be seen in Fig.8that the highest fluorescence intensity is found in Sample No.3 (100% WW) which is characterized by high peak C and M intensities which are degraded after irradiation. This finding is in accord with previous report (Seong-Nam and Gary 2008). The too low fluorescence

	Fourth experiment Exp.4 (in spring season)

For filtered WW (sample no. 3), it is characterized by the highest fluorescence intensities (before irradiation and after irradiation) compared

  Four Mixing and IrradiationExperiments(Exp.1, Exp.2, Exp.3 and Exp.4) were conducted during different time of year and the impact of irradiation and presence/absence of suspended particulate matter from three water types (river water RW, seawater SW, effluent wastewater WW) were studied using three-dimensional fluorescence spectroscopy coupled with parallel factor analysis EEM-PARAFAC. Evolution of fluorescence signal of PARAFAC components was investigated kinetically through kinetic evolution of multilinear regression parameters with irradiation. Three components (C1,C2 and C3) might be extracted from the whole EEM dataset of all irradiation experiments. Protein-like component might not be found which is due to its very low fluorescence intensity in the whole dataset. Second order kinetics were found for all parameters (intercept , coefficient of fSW and coefficient of fRW) indicating bimodal reaction of organic matter with itself and excited organic matter. It can be concluded that SPOM of one water type has profound impact on the "coefficient of fRW") were the highest in irradiation experiment Exp. 1 and Exp. 3 and the lowest in Exp. 4 and Exp.2. Thus, this study is a further step on the development of online or real time models of evolution of fluorescence signal coming from anthropogenic sources. Further studies are warranted for the application of this model on other urban river systems.

	resulting kinetic constants of multilinear regression parameters. Kinetic constants of (A WW	*,0 "i.e. intercept" , A WW	*,1
	"coefficient of fSW" and A WW	*,2

.10 0.12 0.14 Wavelength Absorbance Linearity of Absorbance with dilution of River water showing no Inner Filter Eff ect

	50% River water 25% River water 0.00 0.02 0.04 0.06 0.08 0100% River water 12,5% River water
	200	250	300	350	400
	0.0 0.1 0.2 0.3 0.4
	200	250	300	350	400
	Fig. 3
	Fig. 4

Exp.1 (Date: 10/11/2015 to 26/11/2015) Exp.2 (Date: 02/12/2015 to 17/12/2015) Exp.3 (Date: 15/02/2016 to 01/03/2016) Exp.4 (Date: 11/05/2016 to 27/05/2016)

Wavelength Absorbance Linearity of Absorbance with dilution of Effluent W astewater showing no Inner Filter Effect

				100% Effluent Wastewater
				50% Effluent Wastewater
				25% Effluent Wastewater
				12,5% Effluent Wastewater
	0.0 0.5 1
	200	250	300	350	400

.0 1.5 Wavelength Absorbance Linearity of Absorbance with dilution of Sea water showing no Inner Filter Eff ect

	100% Seawater
	50% Seawater
	25% Seawater
	12,5% Seawater

Table 1

 1

Table 2

 2 Irradiation Experiments dates and types.

	Experiment	Filtration state of RW, SW	Irradiation	Irradiation End	Duration
		and WW	Start Date	Date
	Exp. 1	RW(F), SW(F), WW (NF)	10/11/2015	26/11/2015	16 days
	Exp. 2	RW(NF), SW(F), WW(F)	02/12/2015	17/12/2015	16 days
	Exp. 3	RW(F), SW(NF), WW(F)	15/02/2016	01/03/2016	16 days
	Exp. 4	RW(NF), SW(NF), WW(NF)	11/05/2016	27/05/2016	16 days
	F indicates filtered state and NF means Not Filtered state

Table

Table 3

 3 Correspondence of PARAFAC components in this study with components reported elsewhere in literature.

	The present study		Correspondence	Correspondence with openfluor.org
				with literature	database
	Component	Ex/Em	Characterization
		(nm)
	C1	340/430	Wastewater/nutrient	G3(1) , Peak C	RecycleG7 C3; RecycleStM C1;
			enrichment tracer;	(2), C4(3);		RecycleWTP C3; RecycleWRAMS C4;
			terrestrial humic-			RecycleRH C1; RecyclePC C3;
			like			Peleato_OzoneAOP_biofilter	C1;
						MIEX-DOC-GOLD C2; Vines_WWEff
						C1; Fuirosos_Drought C2
	C2	375/465	More humificated or	Peak A (2); C450	osPARAFAC_RioNegro	C4;
			ligneous	(4); C2(7)		Masanbay_Korea C2; Partners C2;
			compounds			MIEX-DOC-GOLD C4; Drink C2;
						osPARAFAC_Lillsjoen C4
	C3	295/405	Anthropogenic	Peak	M(2);	FloridaKeys C1; Shutova_F C1;
			humic materials,	C2(5);C5(6);	WAIS_Holocene_3	C3;
			agricultural;	C2(8)		NeusePOMDOM C2; Kauai C1;
			Microbial			Vines_BWR C1; Fuirosos_Drought C1;
			component			Vines_WWEff	C2;
						Shakil_Peel2015t2017_5comp	C1;
						RaskaDOM C1; Arctic Seawater C2;
						Borisover_wastewater treatment plants
						C2;
						BengalBasin_GW_Nadia_Acidification
						C1
	1.

[START_REF] Murphy | Organic Matter Fluorescence in Municipal Water Recycling Schemes: Toward a Unified PARAFAC Model[END_REF]

; 2.

[START_REF] Coble | Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy[END_REF]

; 3. Lapierre and del Giorgio (2014); 4.

[START_REF] Wünsch | The One-Sample PARAFAC Approach Reveals Molecular Size Distributions of Fluorescent Components in Dissolved Organic Matter[END_REF]

; 5.

[START_REF] Murphy | Distinguishing between terrestrial and autochthonous organic matter sources in marine environments using fluorescence spectroscopy[END_REF]

; 6.

[START_REF] Stedmon | Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis[END_REF]

;7.

[START_REF] Abaker | UV-Visible and Fluorescence Green Waste Composts Monitoring: Material Dependency[END_REF] 8. Cohen et al., 2014

Table 4

 4 Values of Multilinear regression parameters of PARAFAC components and fSW and fRW before irradiation which are initial conditions for the second order model

		Coefficients of C1	Coefficients of C2	Coefficients of C3
	Exp.	A WW 1,0	A WW 1
		interce	,1
		pt

Acknowledgements

This work was funded by Hermes Programfrom European commission through its Erasmus Mundus foundation; in addition to the funding from Campus France through its research grant Al Maqdisi PHC Project n° 40229SD. Moreover, we are thankful toward Christian Martino and Gael Durrieu for being available in each sampling campaign.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest

Author


Christophe Guervilly

Stéphane Burtey

Florence Sabatier

Raphaël Cauchois

Guillaume Lano

Evelyne Abdili

Florence Daviet

Laurent Arnaud

Philippe Brunet

Sami Hraiech

Stephane Burtey

Noémie Jourde-Chiche

Marie Koubi

Romaric Lacroix

Léa Pietri

Yaël Berda

Thomas Robert

Clara Degioanni

Mélanie Velier

Laurent Papazian

Gilles Kaplanski

Françoise Dignat- George
email: francoise.dignat-george@univ-amu.fr

Affiliations

Pr Françoise Dignat-George

Circulating Endothelial Cells as a Marker of Endothelial Injury in Severe COVID -19

Keywords: Circulating endothelial cells, COVID-19, endothelial injury

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

The COVID-19 pandemic, caused by infection with SARS-CoV-2, has become a global threat to public health, affecting more than four million individuals worldwide. COVID-19 patients are usually mildly symptomatic, but 20% of them will evolve towards viral pneumonia, and among them, 5% will experience an atypical form of acute respiratory distress syndrome (ARDS) [START_REF] Richardson | Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area[END_REF]. SARS-CoV-2 binds to angiotensin-converting enzyme 2 (ACE2), which is highly expressed in the lungs. ACE2 is also expressed by the endothelium [START_REF] Khan | EndoDB: a database of endothelial cell transcriptomics data[END_REF], one of the largest organs in the human body. Moreover, cardiovascular complications [START_REF] Liu | The Science Underlying COVID-19: Implications for the Cardiovascular System[END_REF] consisting of strokes, pulmonary embolisms, high blood pressure, vasculitis and Kawasaki-like disease [START_REF] Jones | COVID-19 and Kawasaki Disease: Novel Virus and Novel Case[END_REF] are emerging as severe COVID-19 symptoms, suggesting that endothelial injury could constitute a key pathogenic mechanism. Despite widespread interest in the pathophysiology of the disease, little is known about the involvement of the endothelium during the course of COVID-19.

The vascular endothelium is a dynamic organ that plays key roles in vascular homeostasis, such as maintaining vascular tone, permeability and inflammatory response, preserving the hemostatic balance. Any endothelial injury, including infections, impairs regulatory functions of the endothelium with subsequent vasoconstriction, ischemia, inflammation and activation of the coagulation cascade, ultimately leading to vessels denudation and exposure of the thrombogenic subendothelium [START_REF] Godo | Endothelial Functions[END_REF].

Circulating endothelial cells (CEC) are stressed cells detached from injured vessels [START_REF] Blann | Circulating endothelial cells. Biomarker of vascular disease[END_REF]. They are detectable at very low levels in healthy conditions. Increased CEC counts have been reported in various diseases of inflammatory, infectious or ischemic origin, where they evidence a profound vascular insult and are indicative of disease severity [START_REF] Blann | Circulating endothelial cells. Biomarker of vascular disease[END_REF]. The objective of the present study was to measure CEC in the blood of patients with COVID-19, in relation to systemic inflammation and disease severity.

Quantification of CEC, soluble endothelial cell adhesion molecules and inflammatory cytokines

CEC were counted according to a previously published standardized protocol [START_REF] Blann | Circulating endothelial cells. Biomarker of vascular disease[END_REF]. Briefly, CEC were isolated by immunomagnetic separation with beads (Dynabeads M-450, Thermo Fisher, Carlsbad, CA) coated with CD146 (clone S-endo1, Biocytex, Marseille, France) and enumerated using a fluorescence microscope (Eclipse TE2000-S, Nikon, Tokyo, Japan) after acridine orange labeling. CEC were identified according to the following consensus criteria: stained rosette cells, size over 15 µm, bearing more than five beads.

Statistical analysis

Statistical analysis was performed using SPSS version 15.0 software. Categorical variables were presented as frequencies, and continuous variables were presented as the median [25-75 percentiles]. Comparisons between groups were performed using Mann-Whitney test for quantitative variables and Fisher's exact tests for frequencies. Associations between continuous variables were analyzed using Spearman's correlation tests. Multiple linear regression was performed to determine the impact of cardiovascular risk factors and comorbidities on CEC levels.

Statistical significance was defined as p<0.05.

RESULTS

Patients' characteristics are reported in table 1. The non-ICU patients were older and less frequently obese than the patients in the ICU. The prevalence of other comorbidities was higher in the non-ICU patients. During their stay in the ICU, all patients needed invasive mechanical ventilation. At inclusion, ICU patients had more severe lymphopenia and higher levels of leukocytes, neutrophils fibrinogen, C-reactive protein and lactate dehydrogenase than non-ICU patients.

Measurement of CEC revealed that 55% of patients had CEC counts above 20 cells/mL, considered as the upper limit of normal values. CEC count was significantly higher in ICU patients than in non-ICU patients (49 [24-103] vs 18 [6-70] CEC/mL, p=0.03) (Fig. 1A). CEC counts were negatively correlated A c c e p t e d M a n u s c r i p t 7 with platelet and lymphocyte counts (r=-0.334, p=0.0008 and r=-0.336, p=0.0007, respectively) (Figure 1B), previously identified as COVID-19 severity markers [START_REF] Zhang | Predictive factors for disease progression in hospitalized patients with coronavirus disease 2019 in Wuhan, China[END_REF]. Interestingly, a positive correlation between the CEC counts and the length of hospital stay (r=0•261, p=0•02) was observed.

The plasma concentrations of the inflammatory cytokines IL-6 and IP-10, and of two inflammationinduced endothelial adhesive molecules, E-Selectin and sVCAM-1, were significantly higher in ICU patients than non-ICU patients (table 1). CEC counts were correlated with sVCAM-1 (r=0.374, p=0.0002) and IP-10 concentrations (r=0.487, p<0.0001) (Figure 1C) but not with sE-Selectin and IL-6 concentrations.

Finally, because we analyzed the relationship between CEC count, comorbidities and cardiovascular risk factors in patients with COVID-19. In univariate analysis, increased CEC levels were associated with CKD but not with high blood pressure, diabetes, age, obesity or CVD. Multivariate analysis integrating age, obesity, hypertension, CKD, cardiovascular disease -and ICU hospitalization, indicated that among comorbidities factors only CKD could be identified as independently associated with CEC counts (p= 0.011). Admission in ICU was also associated with high CEC levels independently of all comorbidities that differ between ICU and non ICU patients (p=0.046). Consequently, after the exclusion of patients with CKD, the difference between CEC levels in ICU patients compared to non-ICU patients was even more pronounced (p=0.002) (Figure 1A).

DISCUSSION

This is the first observation of increased CEC counts in the blood of patients with COVID-19. CEC counts were significantly higher in patients admitted to the ICU, positively correlated with the length of hospital stay and were inversely correlated with platelet and lymphocyte counts. Increased CEC levels were also associated with increased concentrations of the cytokine IP10 and sVCAM-1. These CEC have been previously reported to be increased in patients with cardiovascular risk factors [START_REF] Blann | Circulating endothelial cells. Biomarker of vascular disease[END_REF].

Although our cohort showed a high prevalence of various comorbidities, CKD was the only condition identified as a major determinant of elevated CEC counts in patients with COVID-19. Consistently, previous studies indicated that increased CEC counts behave as a marker of endothelial dysfunction with pejorative prognosis value in hemodialysis patients [START_REF] Koc | Circulating endothelial cells are associated with future vascular events in hemodialysis patients[END_REF]. Thus, more than reflecting preexisting vascular dysfunction associated to typical risk factors, a rise in CEC counts in COVID-19 patients could be indicative of the acute deleterious impact of the disease process itself. However, CKD, which is increasingly recognized as a risk factor for severe COVID-19 development, may favor endothelial vulnerability.

Endothelial involvement across different vascular beds was shown in recent postmortem

histopathological observations of COVID-19 patients with multisystem organ failure. Ultrastructural damage, including disruption of intercellular junction, cell swelling and a loss of contacts with the basal membrane, were associated with accumulation of inflammatory cells together with the presence of apoptotic bodies and viral inclusion structures in endothelial cells, recapitulating features of endotheliitis [START_REF] Varga | Endothelial cell infection and endotheliitis in COVID-19[END_REF][START_REF] Ackermann | Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19[END_REF]. This suggests that both viral infection of endothelial cells and inflammation-induced microangiopathy could contribute to cell detachment and elevated CEC counts. Indeed, other coronaviruses are cytopathic [START_REF] Yue | SARS-Coronavirus Open Reading Frame-3a drives multimodal necrotic cell death[END_REF] and potent inducers of the intracytoplasmic NLRP3 inflammasome, known to control pyroptosis and to play a role in endothelial detachment in Kawasaki Disease [START_REF] Jia | Endothelial cell pyroptosis plays an important role in Kawasaki disease via HMGB1/RAGE/cathespin B signaling pathway and NLRP3 inflammasome activation[END_REF], a disease recently associated with the COVID-19 spectrum [START_REF] Jones | COVID-19 and Kawasaki Disease: Novel Virus and Novel Case[END_REF]. Excessive activation of the NLRP3 inflammasome, triggering IL-6 and Interferon-γ production may also mediate a severe inflammatory endothelial cell response. Consistent with the "cytokine storm" involved in the severe forms of COVID-19 [START_REF] Tay | The trinity of COVID-19: immunity, inflammation and intervention[END_REF], we found that CEC counts were positively correlated with the A c c e p t e d M a n u s c r i p t 9 concentrations of IP-10, and sVCAM-1, known to be induced by Interferon-γ. This suggests that the cytokine storm plays an important role in the endothelial cell monolayer disruption during SARS-CoV-2 infection, as already reported in other viral diseases [START_REF] Bonney | Gamma interferon alters junctional integrity via rho kinase, resulting in blood-brain barrier leakage in experimental viral encephalitis[END_REF]. Loss of adhesive contact of infected endothelial cells, involving inflammatory cytokines or leukocytes-derived proteases has also been documented during HSV or CMV infection [START_REF] Blann | Circulating endothelial cells. Biomarker of vascular disease[END_REF][START_REF] Percivalle | Circulating endothelial giant cells permissive for human cytomegalovirus (HCMV) are detected in disseminated HCMV infections with organ involvement[END_REF]. Markers allowing to stratify the progression of COVID-19 are helpful to improve the management of patients. Interestingly, we found that the CEC levels in COVID-19 patients were inversely correlated with lymphocytes and platelets counts, recognized as prognostic factors for early identification of severe COVID-19 cases [START_REF] Koc | Circulating endothelial cells are associated with future vascular events in hemodialysis patients[END_REF]. CEC counts were also correlated with the length of hospital stay. Thus, CEC could be proposed as a noninvasive and specific biomarker to quantify endothelial injury associated with the exacerbated inflammatory host response that characterizes severe COVID-19.

There are some limitations to this study. The small sample size and the overall low mortality rate limited the evaluation of the prognostic value of CEC, notably for the prediction of cardiovascular complications. In addition, because serial blood samples could not be available, future studies are required to investigate the kinetics of CEC and its relationship with COVID 19 evolution and therapeutic interventions.

In conclusion, increased CEC counts provide a direct proof of endothelial damage during the course of COVID-19 and a clinically informative biomarker of disease severity. The data may also bring rationale for therapies that stabilize the endothelium in addition to limiting inflammation in vulnerable patients. Future longitudinal studies are required to define the usefulness of CEC as a non-invasive vascular biomarker for risk stratification and response to treatment. A c c e p t e d M a n u s c r i p t

  adult patients with COVID-19 were enrolled in a retrospective study approved by a local medical ethics committee of Aix-Marseille University. Written informed consent was obtained from the patients or their relatives. COVID-19 was diagnosed when patients presented with viral pneumonia associated with objective findings on lung-CT scan or chest X-ray, and infection was confirmed by positive SARS-CoV-2 RT-PCR in nasopharyngeal swab, sputum or bronchoalveolar lavage. Patients were hospitalized either in the COVID-19 medicine or nephrology units (non-ICU) or in intensive care unit (ICU), depending on the severity of their oxygen requirements. Blood samples were collected by within 48 h of patient admission.

  ) levels of soluble Vascular Cell Adhesion Molecule (sVCAM-1) and E-Selectin (sE-Selectin) and serum levels of Interferon gamma-induced protein 10 (IP-10) and interleukin 6 (IL-6) were quantified by magnetic luminex assay (Bio-Techne, Minneapolis, MN)

A c c e p t e d M a n u s c r i p t 8 results

 8 provide in vivo evidence that a marked and widespread endothelial injury occurred in the course of proinflammatory vessel activation in the more severe forms of COVID-19.

  This work was supported by Aix-Marseille University, INSERM and AP-HMA c c e p t e d M a n u s c r i p t

Figure 1

 1 Figure 1

Table 1 : Baseline demographics, treatments, biological parameters and outcomes of the study patients.

 1

	14

a median (25-75 percentile); b Obesity was defined as an body mass index> 30kg/m²; c Chronic kidney disease was defined as an estimated glomerular filtration rate <60mL/min/1.73m² or requirement for hemodialysis for more than 3 months; d hemodialyzed patients were excluded; M. F, male/female; NA, not applicable; ns, non significant; ICU : intensive care unit; CEC : circulating endothelial cells; sVCAM-1: soluble vascular cell adhesion molecule 1;sE-Selectin: soluble E-Selectin; IP-10: Interferon gamma-induced protein 1; IL-6: interleukin 6.

Acknowledgements

We thank Patricia Berenger, Cécile Daou, and Léa Plantureux for their technical help during this study. We are also grateful to the many front-line medical and technical staff for their dedication in the face of this outbreak, despite the potential threat to their own lives and the lives of their family.

Potential conflicts of interest

F. DG and R.L disclose grants from Stago and a patent microvesicle fibrinolytic activity licensed to Stago. C.G reports personal fees from XENIOS FMC Inc. Similarly, S.B reports personal fees from Otsuka, Bayer and from Sanofi. All these disclosures are outside the submitted work.

Legends