Veronika Paluchova

Tomas Cajka

Thierry Durand

Claire Vigor

Chandra Dodia

Shampa Chatterjee

Aron B Fisher

Ondrej Kuda

The Role of Peroxiredoxin 6 in Biosynthesis of FAHFAs

Peroxiredoxin 6 (Prdx6) is a multifunctional enzyme, a unique member of the peroxiredoxin family, with an important role in antioxidant defense. Moreover, it has also been linked with the biosynthesis of anti-inflammatory and anti-diabetic lipids called fatty acid esters of hydroxy fatty acids (FAHFAs) and many diseases, including cancer, inflammation, and metabolic disorders. Here, we performed lipidomic profiling of subcutaneous adipose from mouse models with genetically modified Prdx6. Deletion of Prdx6 resulted in reduced levels of FAHFAs containing 13hydroxylinoleic acid (13-HLA). Mutation of Prdx6 C47S impaired the glutathione peroxidase activity and reduced FAHFA levels while D140A mutation, responsible for phospholipase A2 activity, showed only minor effects. Targeted analysis of oxidized phospholipids and triacylglycerols in adipocytes highlighted a correlation between FAHFA and hydroxy fatty acid production by Prdx6. FAHFA regioisomer abundance was negatively affected by the Prdx6 deletion, and this effect was more pronounced in longer and more unsaturated FAHFAs. The predicted protein model of Prdx6 suggested that the monomer-dimer transition mechanism might be involved in the preferred repair of longer peroxidized phospholipids, which were bound over two monomers. In conclusion, our work linked the peroxidase activity of Prdx6 with the levels of FAHFAs in adipose tissue.

ABBREVIATIONS

FAHFA -fatty acid ester of hydroxy fatty acid POHLA -palmitoleic acid ester of hydroxy linoleic acid HFA -hydroxy fatty acid (H)PA -(hydroxy) palmitic acid (H)PO -(hydroxy) palmitoleic acid (H)SA -(hydroxy) stearic acid (H)OA -(hydroxy) oleic acid (H)LA -(hydroxy) linoleic acid (H)AA -(hydroxy) arachidonic acid (H)DHA -(hydroxy) docosahexaenoic acid HODE -hydroxy octadecadienoic acid LPC -lysophosphatidylcholine PE -phosphatidylethanolamine PC -phosphatidylcholine TAG -triacylglycerol

INTRODUCTION

During conditions of oxidative stress, reactive oxygen species (ROS) readily attack phospholipids (PLs), which results in their peroxidation and consequently leads to disruption of the membrane

lipid bilayer arrangement [START_REF] Birben | Oxidative stress and antioxidant defense[END_REF]. Peroxiredoxin 6 (Prdx6), also called 1-Cys peroxiredoxin (UniProt ID O08709), exists as a dimer and it is the last identified mammalian enzyme of the peroxiredoxin family [START_REF] Kang | Characterization of a mammalian peroxiredoxin that contains one conserved cysteine[END_REF][START_REF] Zhou | Peroxiredoxin 6 homodimerization and heterodimerization with glutathione S-transferase pi are required for its peroxidase but not phospholipase A2 activity[END_REF]. It has been assumed to play an important role in several health disorders, including acute lung injury and inflammation [START_REF] Fisher | Inhibition of Peroxiredoxin 6 PLA2 Activity Decreases Oxidative Stress and the Severity of Acute Lung Injury in the Mouse Cecal Ligation and Puncture Model[END_REF][START_REF] Li | Antioxidant protein peroxiredoxin 6 suppresses the vascular inflammation, oxidative stress and endothelial dysfunction in angiotensin II-induced endotheliocyte[END_REF][START_REF] Wang | Peroxiredoxin 6 knockout aggravates cecal ligation and puncture-induced acute lung injury[END_REF], type 1 diabetes [START_REF] Novoselova | Role of Innate Immunity and Oxidative Stress in the Development of Type 1 Diabetes Mellitus. Peroxiredoxin 6 as a New Anti-Diabetic Agent[END_REF], male fertility [START_REF] Bumanlag | Peroxiredoxin 6 Peroxidase and Ca(2+)-Independent Phospholipase A2 Activities Are Essential to Support Male-Mouse Fertility[END_REF], cancer [START_REF] Xu | Differential Expression And Effects Of Peroxiredoxin-6 On Drug Resistance And Cancer Stem Cell-Like Properties In Non-Small Cell Lung Cancer[END_REF][START_REF] Xu | The phospholipase A2 activity of peroxiredoxin 6 promotes cancer cell death induced by tumor necrosis factor alpha in hepatocellular carcinoma[END_REF], and ferroptosis, which is highly related to cancer [START_REF] Lu | Identification of PRDX6 as a regulator of ferroptosis[END_REF] . The mechanism of the catalytic activity is based on a single active cysteine, in contrast to a 2-cys mechanism common for most other members of the peroxiredoxin family. The fact that Prdx6 uses glutathione (GSH) rather than thioredoxin as a reducing agent for peroxidase activity [START_REF] Kang | Characterization of a mammalian peroxiredoxin that contains one conserved cysteine[END_REF][START_REF] Fisher | Peroxiredoxin 6 in the repair of peroxidized cell membranes and cell signaling[END_REF], makes Prdx6 a unique member of the peroxiredoxin family. It also plays a crucial role in antioxidant defense as it can bind to peroxidized phospholipids (PLOOHs) and repair them through its enzymatic activities [START_REF] Fisher | Peroxiredoxin 6 in the repair of peroxidized cell membranes and cell signaling[END_REF][START_REF] Fisher | A novel lysophosphatidylcholine acyl transferase activity is expressed by peroxiredoxin 6[END_REF] (Figure 1A). Three enzymatic activities of Prdx6 have been described: a) glutathione peroxidase activity, b) phospholipase A 2 (PLA 2 ) activity, and c) lysophosphatidylcholine acyltransferase (LPCAT) activity.

Peroxidase activity

Prdx6 can reduce peroxidized phospholipids to the corresponding alcohols (PLOHs) through its peroxidase activity, which involves three main steps: peroxidation, resolution, and recycling. Peroxidation reduces hydroperoxides through the single conserved cysteine oxidation to a sulfenic form. The following resolution uses an external reducing agent, glutathione (GSH), namely the π isoform of the enzyme GSH S-transferase (πGST) as Prdx6 does not have a second resolving cysteine, resulting in glutathionylated Prdx6. Then it reacts with free GSH to return to a fully reduced state in a process called recycling [START_REF] Zhou | Peroxiredoxin 6 homodimerization and heterodimerization with glutathione S-transferase pi are required for its peroxidase but not phospholipase A2 activity[END_REF][START_REF] Hall | Structure-based insights into the catalytic power and conformational dexterity of peroxiredoxins[END_REF][START_REF] Ralat | Direct evidence for the formation of a complex between 1-cysteine peroxiredoxin and glutathione S-transferase pi with activity changes in both enzymes[END_REF]. The phospholipid hydroperoxidase GSH peroxidase (PHGPx) activity is dependent on catalytic cysteine (Cys) at position 47 (C47). Mutating cysteine to serine (C47S) abolishes the ability of Prdx6 to reduce hydroperoxides but does not affect PLA 2 activity [START_REF] Chen | 1-Cys peroxiredoxin, a bifunctional enzyme with glutathione peroxidase and phospholipase A2 activities[END_REF][START_REF] Fisher | Phospholipid hydroperoxides are substrates for non-selenium glutathione peroxidase[END_REF] (Figure 1B).

Phospholipase A2 activity

Prdx6 is the only member of the Prdx family that possesses phospholipase A 2 (PLA 2 ) activity, which also participates in the phospholipid turnover by hydrolyzing an oxidized fatty acid (FA) bound at the sn-2 position, thus generating lysoPLs [START_REF] Zhou | Peroxiredoxin 6 homodimerization and heterodimerization with glutathione S-transferase pi are required for its peroxidase but not phospholipase A2 activity[END_REF][START_REF] Fisher | Altered lung phospholipid metabolism in mice with targeted deletion of lysosomal-type phospholipase A2[END_REF]. It is a calcium-independent activity and requires a catalytic triad serine at position 32 (S32), histidine at position 26 (H26), and aspartate at position 140 (D140). Since H26 and S32 are necessary for binding Prdx6 to the phospholipid, a mutation of aspartate to alanine (D140A) was found to be the most suitable one for abolishing the PLA 2 activity [START_REF] Li | Critical role of peroxiredoxin 6 in the repair of peroxidized cell membranes following oxidative stress[END_REF][START_REF] Manevich | Structure and phospholipase function of peroxiredoxin 6: identification of the catalytic triad and its role in phospholipid substrate binding[END_REF] (Figure 1B).

Lysophosphatidylcholine acyltransferase activity

Lysophosphatidylcholine acyltransferase (LPCAT) activity is coupled to the PLA 2 activity and reacylates the sn-2 position of lysoPLs with a FA-CoA. This activity generates remodeled phosphatidylcholine (PC) and is important for dipalmitoylphosphatidylcholine lung surfactant [START_REF] Zhou | Peroxiredoxin 6 homodimerization and heterodimerization with glutathione S-transferase pi are required for its peroxidase but not phospholipase A2 activity[END_REF][START_REF] Fisher | A novel lysophosphatidylcholine acyl transferase activity is expressed by peroxiredoxin 6[END_REF].

Biosynthesis of FAHFA

A previous study linked Prdx6 with the biosynthesis of FAHFAs (fatty acid esters of hydroxy fatty acids) via QTL mapping, suggesting that PLOH, generated during the process of peroxidized phospholipid repair, could serve as a source of the hydroxy fatty acid (HFA) in FAHFA structure [START_REF] Kuda | Nrf2-Mediated Antioxidant Defense and Peroxiredoxin 6 Are Linked to Biosynthesis of Palmitic Acid Ester of 9-Hydroxystearic Acid[END_REF]. FAHFAs represent a diverse group of lipids. The position of the ester bond between the HFA and the (FA) defines the regioisomer and also the biological activities, mainly anti-diabetic and anti-inflammatory [START_REF] Yore | Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects[END_REF][START_REF] Aryal | Distinct biological activities of isomers from several families of branched fatty acid esters of hydroxy fatty acids (FAHFAs)[END_REF]. A review summarizing current knowledge about their metabolism, biological effects, and synthesis has been recently published [START_REF] Brejchova | Understanding FAHFAs: From structure to metabolic regulation[END_REF].

Here, we present a study exploring the effects of specific enzymatic activities of Prdx6 on FAHFA levels. We hypothesize that the glutathione peroxidase activity is important for the generation of HFA for FAHFA synthesis. For this purpose, we used mouse models with genetically altered Prdx6: one knock-out model (Prdx6 null), two knock-in models (C47S, D140A), and wild-type (WT) mice as a control group.

MATERIALS AND METHODS

Materials and Reagents

LC-MS-grade solvents and mobile phase modifiers were purchased from VWR International (Czech Republic). FAHFA standards were from Cayman Europe (Tallinn, Estonia), and all other chemicals, including selective Glutathione peroxidase 4 (GPX4) inhibitor (ML210), were ordered from Merck (Czech Republic) unless stated otherwise. GPX4 functions as a phospholipid hydroperoxidase to reduce phospholipid hydroperoxide (20:4-or 22:4-PE-OOH) to the corresponding phospholipid alcohol (PLOH) [START_REF] Kagan | Oxidized arachidonic and adrenic PEs navigate cells to ferroptosis[END_REF]. MJ33 is an inhibitor of the PLA 2 activity of Prdx6 [START_REF] Fisher | A competitive inhibitor of phospholipase A2 decreases surfactant phosphatidylcholine degradation by the rat lung[END_REF].

Animal Studies

Mouse models (Prdx6 null, Prdx6 D140A, and C47S knock-in mice) used in this study were bred in animal care facilities of the Institute for Environmental Medicine, Department of Physiology, Perelman School of Medicine of the University of Pennsylvania. The generation of these mouse models has been described previously [START_REF] Feinstein | Mouse Models of Genetically Altered Peroxiredoxin 6[END_REF]. Wild-type mice C57Bl/6 were obtained from the Jackson Laboratory (Bar Harbor, ME). We used male and female mice 10-13 weeks old in this study. Mice were maintained at 22 °C on a 12h light/dark cycle while feeding a standard chow diet and having unlimited access to water. Mice were fasted for 24 hours and then re-fed overnight with 15% sucrose in drinking water before scarification to stimulate de novo lipogenesis. All tissue samples (subcutaneous white adipose tissue (scWAT) and the liver)) were collected into cryovials, immediately frozen in liquid nitrogen, and stored at -80 °C freezer. The University of Pennsylvania Animal Care and Use Committee (IACUC) approved all procedures involving mice.

Cell Culture experiments

The 3T3-L1 cell line was differentiated according to the standard protocol [START_REF] Kuda | Docosahexaenoic Acid-Derived Fatty Acid Esters of Hydroxy Fatty Acids (FAHFAs) With Anti-inflammatory Properties[END_REF]. Differentiated adipocytes were incubated in a DMEM complete medium (25 mmol/L glucose, 10% FBS, 850 nmol/L insulin, penicillin/streptomycin) supplemented with a 40 µM mixture of polyunsaturated FAs (OA, LA, AA, DHA; 10 µM each, complexed to BSA at the ratio 3:1) for 96 hours to provide building blocks for more complexed FAHFAs. For the last 24 hours, inhibitors MJ33 and ML210 were added to the medium (10 µM) separately or combined to mimic the Prdx6 null mouse model.

Sample processing

Tissue samples were divided into aliquots in liquid nitrogen and three different extraction protocols and LC-MS analyzes were applied. Liquid nitrogen or ice was used during sample processing to prevent the degradation of oxidized PLs (PLOOHs, PLOHs). All samples were used for lipidomic profiling and targeted methods including FAHFA analysis. Only the liver samples were used to measure isoprostanes (IsoP) as oxidative stress biomarkers due to limited sample amounts.

FAHFA analysis

FAHFAs extraction was based on liquid-liquid extraction followed by solid-phase extraction performed according to the published methods [START_REF] Yore | Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects[END_REF][START_REF] Paluchova | Triacylglycerol-Rich Oils of Marine Origin are Optimal Nutrients for Induction of Polyunsaturated Docosahexaenoic Acid Ester of Hydroxy Linoleic Acid (13-DHAHLA) with Anti-Inflammatory Properties in Mice[END_REF]. The final dried and purified extract was resuspended in methanol and immediately analyzed using an UltiMate 3000 RSLC UHPLC system coupled to a QTRAP 5500/SelexION mass spectrometer (SCIEX, Darmstadt, Germany) with Triart C18 ExRS 250 × 2.0 mm; 3 µm column (YMC, Japan). FAHFAs were detected in negative ESI mode as before [START_REF] Kuda | Docosahexaenoic Acid-Derived Fatty Acid Esters of Hydroxy Fatty Acids (FAHFAs) With Anti-inflammatory Properties[END_REF].

Lipidomic profiling and targeted analysis

Nonpolar analytes were extracted using a mixture of cold water, methanol, and methyl tert-butyl ether (MTBE), as published before, with some modifications [START_REF] Brezinova | Exercise training induces insulin-sensitizing PAHSAs in adipose tissue of elderly women[END_REF][START_REF] Cajka | Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms[END_REF]. A Vanquish UHPLC System (Thermo Fisher Scientific, Bremen, Germany) coupled to a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) together with LIMeX (Lipids, Metabolites, and eXposome compounds) LC-MS workflow was used for the untargeted analysis of complex lipids and polar metabolites [START_REF] Lopes | Metabolomics atlas of oral 13C-glucose tolerance test in mice[END_REF]. Parallel Reaction Monitoring (PRM) mode and inclusion lists were used to identify and measure oxidized phospholipids.

Isoprostane analysis

Extraction of IsoPs was based on a published protocol [START_REF] Dupuy | Simultaneous quantitative profiling of 20 isoprostanoids from omega-3 and omega-6 polyunsaturated fatty acids by LC-MS/MS in various biological samples[END_REF] with certain modifications for the liver. Liver tissue (200 mg) was mixed with 50 µL of BHT (1% in ethanol) and 1 mL of a cold mixture of methanol and 5 mM EGTA in water (2:1) and spiked with 5 ng of each internal standard. The mixture was homogenized using a Fast Prep instrument (MP Biomedicals) for 30 s at 6.5 m/s. After transferring the homogenate into a new tube, the rest was re-extracted by adding cold methanol (830 µL) and phosphate buffer (800 µL). Finally, after the addition of cold chloroform (3 mL) to perform Folch extraction, the mixture was vortexed (30 s) and centrifuged (2,000×g, 5 min, 4 °C). The lower organic phase was carefully transferred in a Pyrex tube and dried under a flow of nitrogen (40 °C, 30 min). Then, extracted lipid fraction was hydrolyzed with 1 M KOH in methanol (0.95 mL) and incubated for 30 min at 40 °C while rotating. After cooling to room temperature, we added 2 mL of 40 mM formic acid and further extracted samples by SPE (OASIS MAX 200 mg, 3 mL). SPE columns were cleaned with methanol (2 mL) and conditioned with 2 mL of 20 mM of formic acid. After loading the samples, SPE columns were washed in several steps: 1) 2% NH 4 OH (2 mL), 2) 20 mM formic acid/methanol (70:30, v/v) (2 mL), 3) hexane (2 mL), and 4) hexane/ethyl acetate (70:30, v/v), (2 mL). The IsoPs were eluted with 2 mL of a mixture of hexane/ethanol/acetic acid (70:29.4:0.6, v/v/v), dried under a flow of nitrogen at 40 °C, resuspended in 100 µL of water/acetonitrile (87:17, v/v), centrifuged through a filter (10,000×g, 1 min, RT) and transferred in a plastic injection vial. An Eksigent microLC 200 Plus system coupled to a QTRAP 5500 (Sciex, Darmstadt, Germany) was used to analyze IsoPs [START_REF] Rund | Development of an LC-ESI(-)-MS/MS method for the simultaneous quantification of 35 isoprostanes and isofurans derived from the major n3-and n6-PUFAs[END_REF].

Prdx6 protein model

ColabFold pipeline v1.3 including MMseqs2 with AlphaFold2-multimer engine (databases UniRef30 2022_02 and PDB/PDB70 220313) was used to predict human Prdx6 dimer model (Uniprot # P30041, reduced form) [START_REF] Mirdita | ColabFold: making protein folding accessible to all[END_REF]. ChimeraX v1.4 (VinaDock v1.2) was used to dock the oxidized phosphatidylcholine (PC 16:0/13(S)-HpODE) ligand [START_REF] Pettersen | UCSF ChimeraX: Structure visualization for researchers, educators, and developers[END_REF]. 13(S)-HpODE (13-(S)hydroperoxyoctadecadienoic acid) is the most abundant peroxidized metabolite of LA. The PDB model is included in SI material (Figure Sx and ZIP).

Data Processing and Statistics

LC-MS data from the LIMeX workflow were processed using MS-DIAL ver. 4.70 [START_REF] Cajka | Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms[END_REF]. Metabolites were annotated using in-house retention time-m/z library and using MS/MS libraries available from public sources (MassBank, MoNA). Raw data were filtered using blank samples, serial dilution samples, and quality control (QC) pool samples with relative standard deviation (RSD) < 30%, and then normalized using the LOESS approach by means of QC pool samples for each matrix regularly injected between 10 actual samples. LC-MS/MS data from targeted analysis of FAHFAs and IsoPs were processed using MultiQuant software (SCIEX). Data were further processed via MetaboAnalyst [START_REF] Chong | MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis[END_REF] and GraphPad Prism to compare groups (Student t-test, p < 0.05 was considered significant). Lipid enrichment was calculated as before [START_REF] Brejchova | Triacylglycerols containing branched palmitic acid ester of hydroxystearic acid (PAHSA) are present in the breast milk and hydrolyzed by carboxyl ester lipase[END_REF].

RESULTS

Mild effect of Prdx6 deletion on scWAT lipidome

We explored the role of Prdx6 in scWAT lipid metabolism. We used a mouse model of Prdx6 deletion (global knockout), Prdx6 C47S, and D140A knock-in mice to target the particular enzymatic activities. Deletion of Prdx6 resulted in mild changes of the scWAT lipidome and we did not find any specific pattern in the downregulated lipids but enrichment in FA 18:2 (Figure 2A). Mice harboring the C47S mutation showed several downregulated and upregulated lipid species. Downregulated cluster was enriched in short chain TAG species containing saturated FAs with 8-14 carbons. The upregulated lipid cluster was characterized by ether phosphatidylenthanolamines (PE) containing mainly O-20:1 acyl chains and steroid sulfate conjugates (Figure 2B). The D140A mutation showed the weakest effect compared to wild type mice and only a few steroid sulfate conjugates were upregulated (Figure 2C). Based on the original hypothesis, we focused only on the lipids related to oxidation and FAHFA synthesis.

Oxidative stress markers were unaltered by deletion of Prdx6

Next, we checked the markers of oxidative stress. Levels of reduced glutathione in scWAT were similar between all genotypes and only the oxidized glutathione was increased in C47S mutation (Figure 2D). The marker of susceptibility to ferroptosis (PE O-16:1/20:4) was not altered in scWAT (Figure 2E) and the levels of isoprostanes did not suggest oxidative stress condition in the liver (Figure 2F). Therefore, we concluded that the missing Prdx6 enzymatic activities were compensated by other general antioxidant mechanisms.

Putative pathway of FAHFA biosynthesis

Lipids downregulated in Prdx6 null mice contained linoleic acid (FA 18:2). Therefore, we explored the levels of highly abundant FAHFAs containing 13-hydroxylinoleic acid (HLA or 18:2;O chain). Scheme in Figure 3A illustrated the putative metabolic pathway of FAHFA synthesis. Oxidized membrane phospholipids serve as a source of hydroxylated FAs which are then incorporated into TAGs. The ATGL biosynthetic transacylase activity combines two TAGs and creates a TAG estolide (TG-FAHFA). Free FAHFAs are liberated from TAG estolides by HSL or CEL [START_REF] Brejchova | Triacylglycerols containing branched palmitic acid ester of hydroxystearic acid (PAHSA) are present in the breast milk and hydrolyzed by carboxyl ester lipase[END_REF][START_REF] Patel | ATGL is a biosynthetic enzyme for fatty acid esters of hydroxy fatty acids[END_REF][START_REF] Brejchova | Distinct roles of adipose triglyceride lipase and hormone-sensitive lipase in the catabolism of triacylglycerol estolides[END_REF] (Figure 3A). Levels of 13-HLA-containing FAHFAs were significantly downregulated in Prdx6 null scWAT and this effect was also observed also in C47S mice. The lack of PLA 2 activity in D140A mice had the weakest effect on FAHFA levels. Interestingly, the longer and more unsaturated FAHFAs were affected more than the shorter and saturated species (Figure 3B). The untargeted lipidomic analysis also showed several TAG species containing HLA, which could be the intermediates in FAHFA synthesis (Figure 3C). Their profiles were similar to the FAHFA profiles. The untargeted lipidomic profiling cannot capture the peroxidized phospholipid species (putative intermediates in HFA synthesis). Therefore, we performed the targeted analysis using 3T3-L1 adipocytes exposed to ML210 (inhibitor of phospholipid peroxidase activity) and or MJ33 (inhibitor of PLA 2 activity of Prdx6). This situation mimicked the animal models (Figure 1). We selected the PC 16:0/18:2 as the tracer molecule. Peroxidized PC 16:0/18:2;2O and hydroxylated PC 16:0/18:2;O showed profiles that resembled the pattern observed in mouse scWAT (Figure 4A). Levels of 13-HLA were downregulated only by the combination of both inhibitors (Figure 4B), while the levels of HLAcontaining FAHFAs were similar to the animal profiles (Figure 4C and Figure 3). The animal and in vitro data suggest that Prdx6 participates in FAHFA biosynthesis and that peroxidase activity is the main enzymatic activity of Prdx6 involved in this process.

Prdx6 deletion affects FAHFA regioisomer abundance

To explore Prdx6 regioselectivity and substrate specificity affecting potential FAHFA precursors, we explored FAHFAs with different branching positions, chain lengths, and saturation levels of HFAs. The branching position and the type of HFA were dependent on Prdx6 presence (Figure 5). The further was the hydroxyl group from an α-carbon atom of HFA, the more significant was the effect of Prdx6 deletion on FAHFA levels (Figure 5A and Figure 5B). The palmitic acid and oleic acid chains were kept constant for each profile. We observed this effect also for saturated FAHFAs (Figure 5C), although Prdx6 deletion negatively affected levels of unsaturated FAHFA more than those of saturated FAHFAs (Figure 5 and Figure 2).

Prdx6 protein model

Prdx6′s activity is conformation driven process which involves monomer-dimer transition [START_REF] Chowhan | Structural basis of peroxidase catalytic cycle of human Prdx6[END_REF]. To visualize the process and the key amino acid residues, we used AlphaFold2-multimer algorithm implemented as ColabFold pipeline to predict Prdx6 dimer structure [START_REF] Mirdita | ColabFold: making protein folding accessible to all[END_REF]. Next, we used ChimeraX to dock an oxidized PC ligand (PC 16:0/13(S)-HpODE) and explore the putative active sites [START_REF] Pettersen | UCSF ChimeraX: Structure visualization for researchers, educators, and developers[END_REF] (Figure 6A). The prediction showed that two monomers created a deep valley at their interface to fit the oxidized 13(S)-HpODE chain close to the peroxidatic Cys47 residue (Figure 6A and6B). Simultaneously, the glycerol backbone and the ester bonds were positioned close to the Asp140 residue, important for the PLA 2 activity, on the flat surface of the protein [START_REF] Manevich | Structure and phospholipase function of peroxiredoxin 6: identification of the catalytic triad and its role in phospholipid substrate binding[END_REF]. While the peroxidase activity was localized within the monomer B, the PLA 2 activity residues were localized within the A chain (Figure 6C). This confirms the monomer-dimer transition mechanism and the proposed phospholipid binding over two monomers [START_REF] Kim | Crystal structures of human peroxiredoxin 6 in different oxidation states[END_REF]. It also partially explained the effect on regioisomer branching position (Figure 5) when the peroxidation at carbon atoms further from the α-carbon can be more easily processed by Prdx6 peroxidase activity.

DISCUSSION

Elucidation of FAHFA metabolism has been one of the main goals since their discovery in 2014 [START_REF] Yore | Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects[END_REF]. The synthesis of FAHFAs, FAHFA storage, degradation and metabolic regulations [START_REF] Patel | ATGL is a biosynthetic enzyme for fatty acid esters of hydroxy fatty acids[END_REF][START_REF] Brejchova | Distinct roles of adipose triglyceride lipase and hormone-sensitive lipase in the catabolism of triacylglycerol estolides[END_REF][START_REF] Paluchova | Lipokine 5-PAHSA Is Regulated by Adipose Triglyceride Lipase and Primes Adipocytes for De Novo Lipogenesis in Mice[END_REF][START_REF] Tan | Discovery of FAHFA-Containing Triacylglycerols and Their Metabolic Regulation[END_REF] have been described and summarized in a review [START_REF] Riecan | Branched and linear fatty acid esters of hydroxy fatty acids (FAHFA) relevant to human health[END_REF]. However, the mechanism of HFA synthesis has not been fully elucidated yet. Here, we report the role of the glutathione peroxidase activity of Prdx6 in the biosynthesis of FAHFA.

Lipidomic profiling of mouse models showed that whole body Prdx6 knockout had only a mild impact on the general lipidome of scWAT. We did not observe any massive increase of oxidized lipids, but the targeted methods highlighted that levels of specific oxidized lipids were in fact lower in Prdx6 null mice. We speculate that the antioxidant defense system for lipids, mainly GPX4, compensated the loss of Prdx6 in scWAT and that only Prdx6-specific role was dysregulated. This is supported by the unaltered oxidative stress and ferroptosis susceptibility markers.

The knock-in model C47S showed more dysregulated scWAT lipidome than the Prdx6 knockout and the D140A model was nearly identical to wild type mice. The physical presence of mutated Prdx6 protein might participate in the signaling cascades and the effect of the mutation could be more pronounced in the tissues that are primarily under oxidative stress, e.g. lungs.

The cluster of downregulated lipids in Prdx6 null mice highlighted altered metabolism of FA 18:2. We explored FAHFAs containing HLA, specifically the most abundant 13-HLA, and found significantly lower levels in Prdx6 null mice. C47S mouse model confirmed that the peroxidase activity is linked to FAHFA levels while the PLA 2 is dispensable. Next, we explored the putative metabolic intermediates in the synthesis of FA-HLAs, the hydroxy TAGs containing 13-HLA, and found the same concentration patterns. In general, the hydroxy-TAGs should be used by ATGL to generate TAG estolides [START_REF] Patel | ATGL is a biosynthetic enzyme for fatty acid esters of hydroxy fatty acids[END_REF]. However, this neutral lipid remodeling process creates a huge number of isomeric structures (TAG sn position, FAHFA regioisomers) and acyl combinations (FAHFA and two FAs) [START_REF] Brejchova | Distinct roles of adipose triglyceride lipase and hormone-sensitive lipase in the catabolism of triacylglycerol estolides[END_REF]. There is currently no analytical approach for this challenge and we detected only pooled signals for TAG estolide families that are approximately 100-times more concentrated than the free FAHFAs [START_REF] Tan | Discovery of FAHFA-Containing Triacylglycerols and Their Metabolic Regulation[END_REF]. Therefore, we could not track the fate of hydroxy-TAGs any further.

To explore the other branch of FAHFA synthesis, we focused on oxidized PCs, which are the preferred substrates of Prdx6 [START_REF] Akiba | Characterization of acidic Ca(2+)-independent phospholipase A2 of bovine lung[END_REF]. We used a cell culture model of adipocytes to precisely control the extraction procedure of oxidized lipids and a combination of inhibitors to mimics the mouse models. The levels of peroxidized PC 16:0/18:2;2O, hydroxylated PC 16:0/18:2;O, and 13-HLAcontaining FAHFA showed similar profiles to FAHFAs which we previously detected in mice. This suggests a mechanistic link between Prdx6 peroxidase activity and FAHFA levels. Although the expected effects of Prdx6 deletion or inhibition in mice and cells could be the increase of (per)oxidized lipids, we observed the opposite. We could speculate that the antioxidant system in adipose tissue compensated the loss of Prdx6 action and degraded the oxidized lipids [START_REF] Chen | iPLA2beta-mediated lipid detoxification controls p53-driven ferroptosis independent of GPX4[END_REF], while the alternative pathway storing oxidized acyls within lipid droplets was scaled down [START_REF] Jarc | A twist of FATe: Lipid droplets and inflammatory lipid mediators[END_REF]. The adipose tissue could store the oxidized lipids in lipid droplets in a form of oxidized TAGs or in lipid droplet surface phospholipid monolayer, and therefore provide the substrates for TAG estolide synthesis. Inhibition of this process could limit the availability of hydroxy-TAGs and further affect FAHFA levels.

Reduced levels of 13-HLA-containing FAHFAs in scWAT were dependent on the length and saturation of the FAHFA chain and a similar effect was observed for PA-an OA-containing FAHFAs, respectively. The protein model of Prdx6 dimer and Figure 5 heatmaps suggested that the peroxidase active residue Cys47 can repair oxidized carbon atoms from the position nine further to the tail of the PC acyl chain. The specific distribution of acyl composition in PCs and TAGs, adipose tissue depot and feeding status might affect the biosynthesis of FAHFAs within lipid remodeling cycle [START_REF] Kuda | Nrf2-Mediated Antioxidant Defense and Peroxiredoxin 6 Are Linked to Biosynthesis of Palmitic Acid Ester of 9-Hydroxystearic Acid[END_REF][START_REF] Paluchova | Triacylglycerol-Rich Oils of Marine Origin are Optimal Nutrients for Induction of Polyunsaturated Docosahexaenoic Acid Ester of Hydroxy Linoleic Acid (13-DHAHLA) with Anti-Inflammatory Properties in Mice[END_REF][START_REF] Patel | ATGL is a biosynthetic enzyme for fatty acid esters of hydroxy fatty acids[END_REF][START_REF] Brejchova | Distinct roles of adipose triglyceride lipase and hormone-sensitive lipase in the catabolism of triacylglycerol estolides[END_REF][START_REF] Paluchova | Lipokine 5-PAHSA Is Regulated by Adipose Triglyceride Lipase and Primes Adipocytes for De Novo Lipogenesis in Mice[END_REF].

In conclusion, our work linked the peroxidase activity of Prdx6 with the levels of FAHFAs in adipose tissue. The fate of oxidized acyl chains within lipid remodeling cycle, specificity of the enzymatic machinery, and the co-regulation with antioxidant defense system remains unknown for future research.

Duality of Interest

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was supported by a grant from the Ministry of Education, Youth and Sports of the Czech Republic (LTAUSA18104).

Figure 1 .

 1 Figure 1. Illustrative overview (A) Scheme of the function of Prdx6 and its enzymatic activities. Adapted from Manevich, 2009 [25]. (B) Overview of mouse models used in the study -wild-type mice and mice with genetically altered Prdx6 (Prdx6 null, D140A, and C47S).

Figure 2 :

 2 Figure 2: scWAT lipidome alterations. (A) Volcano plot comparing Prdx6 null mice and wild type mice (WT). Metabolites with fold change > 1.5 and FDR < 0.1 were highlighted in red. (B) Volcano plot for C47S and WT. (C) Volcano plot for D140A and WT. (D) Levels of reduced and oxidized glutathione in scWAT. (E) Levels of PE-O 16:1/20:4 in scWAT. (F) Levels of isoprostanes in the liver. Data are mean ± S.E., n = 8-9.

Figure 3 :

 3 Figure 3: FAHLA synthesis in scWAT. (A) Proposed scheme of FAHFA synthesis. ATGL, adipose triglyceride lipase; CEL, carboxyl ester lipase; DAG, diacylglycerol; FA, fatty acid; GPx, peroxidase

Figure 4 :

 4 Figure 4: Targeted analysis of 3t3-L1 cells. (A) Levels of peroxidized and oxidized (hydroxylated) PC 16:0/18:2. (B) Levels of 13-HLA (FA 18:2;O). (C) FAHFA levels. Data are mean ± S.E., n = 8-9, * significantly different at p < 0.05.

Figure 5 :

 5 Figure 5: FAHFA regioisomers in scWAT. (A) Heatmap of FAHFA regioisomers containing palmitic acid (PA) and different HFAs with 16 to 22 carbon atoms. (B) Heatmap of FAHFA regioisomers containing oleic acid (OA) and different HFAs with 16 to 22 carbon atoms. (C) Regioisomers of saturated FAHFA. * Statistically different at p < 0.05 by Student t-test comparing Prdx6 null vs. WT.

Figure 6 .

 6 Figure 6. Model of Prdx6 dimer. (A) Chain A in pink and chain B in cyan within the transparent protein surface. Ligand PC 16:0/18:2;2O is shown as balls & sticks model. (B) Detail of the active site within chain B. The distance between carbon number 13 in 13(S)-HpODE and thiol group of C47B is approx. 6.2 Å. (C) Annotated surface structure of the Prdx6 dimer.

9 Acknowledgment Conceptualization: A.B.F. and O.K.; Data curation: V.P.; Formal analysis: V.P., T.C. and O.K.; Funding acquisition: T.D., A.B.F. and O.K.; Investigation: V.P., C.V., B.Z., C.D., S.C., A.B.F. and O.K.; Methodology: V.P., T.D., C.V., B.Z., C.D. and S.C.; Project administration: V.P., A.B.F. and O.K.; Resources: C.D., S.C., A.B.F. and O.K.; Software: O.K.; Supervision: A.B.F. and O.K.; Validation: T.C. and O.K.; Visualization: V.P. and O.K.; Writing -original draft: V.P., A.B.F. and O.K.; Writing -review & editing: V.P., T.C., T.D., C.V., B.Z., C.D., S.C., A.B.F. and O.K.;

The authors would like to acknowledge the Metabolomics Core Facility at the Institute of Physiology of the Czech Academy of Sciences for lipidomics profiling.

Supplementary Information

PDB and details


Laurent Morel
email: laurent.morel@lecnam.net

Ouafae Moudni

Frédéric Durand

Joëlle Nicolas

Jean Michel Follin

Stéphane Durand

Eric Pottiaux

Joël Van Baelen

Paulo Sergio

De Oliveira

On the relation between GPS tropospheric gradients and the local topography

Keywords: GNSS, Zenith tropospheric delay, Zenith wet delay, tropospheric gradients

The estimation of tropospheric gradients in GNSS data processing is a well-known technique to improve positioning precision.

To study the correlation between the tropospheric gradients and the topography, we computed Zenith Wet Delay (ZWD) and tropospheric gradients using the GIPSY-OASIS II SOFTWARE from 2 years of GPS observations recorded at 52 worldwide permanent stations, focusing on regions with significant relief. We observe that gradient directions are stable over time and point towards the relief for most of the considered stations. Based on these results, we discuss the physical meaning of the tropospheric horizontal gradients and we investigate why gradients have this particular direction for stations located nearby high mountains. The GPS stations were selected and classified into four main categories: stations close to a mountain range or an isolated mountain (class 1 and 2), stations surrounded by isolated mountains in several directions or in all directions (class 3 and 4). The correlation between the gradient direction and their magnitude with respect to mountain slopes was analysed. A very clear correlation appears for stations of classes 1 and 2 whereas no correlation is obvious for stations of classes 3 and 4. For 89% of stations in classes 1 and 2, a relevant correlation appears, varying between 0.4 and 1. For 64% of stations in classes 1 and 2, a relevant correlation appears, varying between 0.6 and 1. Horizontal gradients estimation show very significant amplitude and a stable direction all along the year, this main direction is most of the time pointing towards the direction of mountains. This behaviour can be explained by a vertical shift of the tropospheric layer due to the presence of mountains, close to the station and up to the maximum distance of 60 km from the station. This orientation does not seem to depend on seasons because no annual or bimonthly means variations appear for all stations. Moreover diurnal variations do not appear on the spatial distribution of the gradients and results are similar for neighbouring stations, separated by few km, which show that local effects such as multipath propagation have influence.

Introduction

Taking into account the propagation delay of electromagnetic wave signals due to the troposphere is mandatory in space geodetic techniques such as Global Navigation Satellite Systems (GNSS). This tropospheric delay has to be mitigated by using a model such as those recommended in the IERS Conventions 2010 (Petit and Luzum, 2010). These models are usually expressed as a function of elevation (el) and azimuth (Az) of the satellite as in Eq. ( 1):

(1)

The total tropospheric delay observed at the zenith of a GNSS station, i.e. the Zenith Total Delay (ZTD), is divided into the Zenith Hydrostatic Delay (ZHD) and the Zenith Wet Delay (ZWD), which respectively represent about 90 % and 10 % of the total delay. The third part of tropospheric model accounts for the azimuthal anisotropy of tropospheric delay by introducing the two horizontal components of the gradient, i.e. its decomposition in the North-South (GN) and the East-West (GE) directions. The hydrostatic and wet delays and the horizontal gradients are mapped down to any elevation angle el using the corresponding hydrostatic, wet and gradient mapping functions (mfH , mfW and mfG respectively) to provide the Slant Total Delay (STD), i.e. the delay in the satellite direction. In these tropospheric models, the ZHD and the mapping functions are assumed to be known a priori (i.e. modelled) while the ZWD and the horizontal gradient components are estimated.

Several studies have shown that the estimation of tropospheric horizontal gradients benefits positioning using spatial geodetic techniques [START_REF] Bar-Sever | Estimating horizontal gradients of tropospheric path delay with a single GPS receiver[END_REF][START_REF] Chen | Effects of Atmospheric Azimuthal Asymetry on the Analysis of Space Geodetic Data[END_REF][START_REF] Iwabuchi | An impact of estimating tropospheric delay gradients on tropospheric delay estimations in the summer using the Japanese nationwide GPS array[END_REF][START_REF] Macmillan | Using meteorological data assimilation models in computing tropospheric delays at microwave frequencies[END_REF][START_REF] Tregoning | Accuracy of Absolute Precipitable Water Vapor Estimates from GPS Observations[END_REF][START_REF] Willis | Estimating horizontal tropospheric gradients in DORIS data processing, preliminary results[END_REF][START_REF] Zhou | The Impact of Estimating High-Resolution Tropospheric Gradients on Multi-GNSS Precise Positioning[END_REF].

In the recent past years, several authors have focused their studies on tropospheric gradients. By studying 102 worldwide permanent stations well distributed in latitude, [START_REF] Meindl | Tropospheric gradient estimation at CODE: Results from global solutions[END_REF] established that the mean annual value of the north gradient component GN is positive for all the stations in the southern hemisphere and negative for those in the northern hemisphere, corresponding to gradient vectors pointing towards the Earth's equator. They highlighted that this general gradient behaviour reflects the trend of increasing atmospheric water vapour content when moving towards the Earth's equator. They suggested that "it is "remarkable that such a global troposphere variation can be well detected by a GPS station with its restricted tropospheric field of view with a radius of about 200 km". Dousa et al (2016) noted that "GNSS-derived gradients demonstrated a nice ability for producing a homogeneous tropospheric gradient field revealing details in the actual state of the troposphere". Gradients were estimated with two GNSS software packages and two NWM global models and showed discrepancies in terms of the magnitude of gradient values while keeping consistency in directions. More interesting details could be observed in GNSS maps instead they are note visible or smoothed in NWM maps.

Recently, Zus et al. (2019a) demonstrated that ZWD interpolation can be improved by utilizing tropospheric gradients.

Moreover, Zus et al (2019b) showed that the assimilation of the tropospheric gradients in addition to the ZTDs improves the

          . . . .cos .sin H W G N E STD ZHD mf el ZWD mf el mf G Az G Az    
3 refractivity field in the vicinity of the considered station. Both studies provide clear evidence that tropospheric gradients contain information on the water vapor distribution in the troposphere. Kacmarik et al. (2019) analysed the behaviour of the tropospheric gradients estimation. They showed, firstly, that adding low-elevation GNSS data increase the robustness of the gradient estimation, secondly a weak impact of adding Glonass data on top of GPS observations, and finally a high sensitivity of real time gradients estimation to mapping functions.

Furthermore, [START_REF] Heki | GNSS climatology : origins of atmospheric delay gradients[END_REF] demonstrated that, at Japanese coastal stations, the sea breeze induces landward gradients in summer daytime and stationary southward gradients in winter due to their dominating dry component with a latitude dependence of surface pressure.

The current study is a follow-up to an earlier local study performed in Corsica [START_REF] Morel | Validity and behaviour of tropospheric gradients estimated by GPS in Corsica[END_REF]. In this previous paper, we studied the temporal and spatial distribution of horizontal tropospheric gradients estimated over one year (2011) at 13 permanent stations in Corsica Island (France). We demonstrated the correlation of the horizontal gradient amplitude with the temporal distribution of the water vapour all along the year 2011. Their amplitudes were very significant and their directions were stable all along the year for most of the stations. Their direction is most of the time pointing towards inland, in the opposite direction of the largest downslope and perpendicular to the coastline. These first results gave an indication of the physical behaviour of the horizontal gradients but the origin of such directions required further investigations.

In this paper, we study the temporal and spatial distribution of horizontal tropospheric gradients estimated over one year (2015 and 2016) at 52 permanent stations distributed all around the world. Our main motivation is to analyse the gradients estimated during the GNSS data processing in order to investigate their physical interpretation [START_REF] Bock | West African Monsoon observed with ground-based GPS receivers during African Monsoon Multidisciplinary Analysis (AMMA)[END_REF][START_REF] Brenot | Preliminary signs of the initiation of deep convection by GNSS[END_REF]Dousa et al., 2017). Our second goal is to investigate the possible systematic behaviour of the tropospheric gradients in mountainous areas. We study their correlation with the geographical context, particularly the environmental contrast between the continent and the sea and the very large topography variations that cause humidity content spatial variations, local sea breeze and land wind regimes [START_REF] Heki | GNSS climatology : origins of atmospheric delay gradients[END_REF]. Considering that humidity is concentrated in the troposphere near the surface (up to 3 km) and taking into account signals coming from satellites with a cut-off angle of 3 degrees, the horizontal gradient behaviour can be influenced by the humidity pattern distribution within a distance from the station up to 57 km.

The paper is organized as follows: we first describe our methodology to study a potential correlation between the gradient direction and the presence of mountains in the tropospheric field of view of the considered GPS station, up to 60 km. Then, the GPS data and data processing methods used to estimate the tropospheric parameters (ZWD and horizontal gradients) based on PPP (Precise Point Positioning) technique with the GIPSY-OASIS software [START_REF] Zumberge | Precise Point Positioning for the Efficient and Robust Analysis of GPS Data from Large Networks[END_REF] are presented.

Finally, the tropospheric gradients correlation with the relief is analysed and discussed.

Data and methods

GPS data

To analyse the correlation between the gradient direction and the relief around the GPS stations, we applied our methodology to 52 permanent stations distributed around the world. Several criteria have been considered to select them:

sites with strong contrast between plain and mountain, close enough to the mountains (up to 60 km), and different mountain location directions relative to the station. In order to analyse the relief effect on the gradients direction, only sites with significant topographic height contrasts close to the GPS stations have been considered, with a minimum height difference of over 500 m between the station elevation and the highest point of the surrounding mountains.

Then, the stations have been classified into four categories: class 1 for stations close to a mountain range, class 2 for stations close to an isolated mountain, class 3 for stations surrounded by isolated mountains in several directions and class 4 for stations surrounded by mountains in all the directions. Table 1 summarises the number of stations in each class and Fig. 1 presents the spatial distribution of these permanent stations.

Methodology

In a first step, the horizontal gradients are estimated each 5 min, and then averaged over 1 hour all along the year 2015. They are plotted as a point cloud corresponding to their annual spatial distribution, for the CRAL station shown in Fig. 2 as an example. Each point corresponds to the gradient north component as a function of the gradient east component. Then, the sum of the points in each 30° sector is computed and is affected to the corresponding sector. On an azimuthal plot centred on the considered GNSS station, the points affected to the different sectors are drawn at a distance from the centre proportional to the sum affected to the sector. Then, the points are connected by a red curve as shown on Fig. 3, to represent the spatial distribution of the gradients as a radar plot. A similar curve would have been obtained if we had considered the gradient intensity instead of the gradient counting because all the clouds are isotropic as in Fig. 2. We privileged counting for the purpose of simplicity and because it would have been more or less arbitrary to class the intensity of the points. The origin corresponds to the location of the GNSS permanent station. In a second step, to represent the relief around the GNSS station, the highest terrain elevation is determined for each azimuth sector, 30° by 30°. For that we use the Earth digital elevation model GTOPO15 from the SRTM v2 mission [START_REF] Farr | The Shuttle Radar Topography Mission[END_REF], with a resolution of 500 m after conversion. To identify the highest elevation point in all directions, three curves are drawn corresponding to three different distances from the station, 20, 40 and 60 km respectively, as illustrated in Fig. 4. In each sector of this figure, the distance from the centre is proportional to the height difference between the GNSS station and the height of the highest elevation point. Four circles are drawn to represent 1000 m, 2000 m, 3000 m and 4000 m height differences. Moreover, tests showed that a finer azimuth step doesn't lead to significant differences in the results.

GPS data processing

The data processing was done with the PPP technique using the GIPSY/OASIS 6.4 software [START_REF] Bertiger | Single receiver phase ambiguity resolution with GPS data[END_REF] for all the stations all along the years 2015 and 2016. The observation cut-off angle was set to 3 degrees in order to study the spatial distribution of the water vapour with the horizontal gradients [START_REF] Willis | Estimating horizontal tropospheric gradients in DORIS data processing, preliminary results[END_REF]. We used the JPL final products (Earth orientation parameters, orbits and clocks), no atmospheric pressure loading correction and the igs08 antenna calibration model [START_REF] Dow | The International GNSS Service in a changing landscape of Global Navigation Satellite Systems[END_REF][START_REF] Schmid | Generation of a consistent absolute phase center correction model for GPS receiver and satellite antennas[END_REF]. We followed the recommendations from the IERS Conventions 2010 (Petit and Luzum, 2010) for the tropospheric modelling: we used the a priori Vienna ZHD (VZHD) and the Vienna Mapping Function (VMF1) derived from Numerical Weather Model (NWM) data and provided by the Vienna University of Technology [START_REF] Boehm | Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data[END_REF]. The processing strategy options are summarised in Table 2 and very similar to the one used in our first study in Corsica [START_REF] Morel | Validity and behaviour of tropospheric gradients estimated by GPS in Corsica[END_REF]. The classic parameters for PPP were estimated: station position, receiver clocks, ZWDs, and horizontal tropospheric gradients. The ambiguity resolution is obtained behalf the additional products necessary for that (wide-lane phase bias files from JPL) [START_REF] Bertiger | Single receiver phase ambiguity resolution with GPS data[END_REF]. The tropospheric parameter is estimated using a random walk process at an interval of 5 min, i-e. the same as the measurement data rate since this software is using a Kalman filter solution. Results have been averaged over 1-hour-time bins. The 1-hour interval averaging was chosen because it is suitable to study the behaviour of gradients throughout days, months and years. Point by point variations allowed for ZWD (1 cm/√ℎ𝑜𝑢𝑟 ) and for gradients at zenith (0.03 cm/√ℎ𝑜𝑢𝑟 ), which corresponds for gradients to 1 cm/√ℎ𝑜𝑢𝑟 at 10°, have been chosen. In our first article [START_REF] Morel | Validity and behaviour of tropospheric gradients estimated by GPS in Corsica[END_REF], data was also processed with the double difference technique using the GAMIT software and the yearly average of the horizontal gradients estimated by the two software were in good agreement. In this article, we keep only the GIPSY software because it was necessary to be able to process stations all around the world and sometimes isolated. A double difference process would have needed several sub network processing which would have limited the number of stations to be studied whereas it was difficult to find a lot of them.

Table 2. Summary of the processing options used to compute the tropospheric parameters with the GIPSY-OASIS 6.4 software.

Results

Representative results

In this part, we illustrate the results with representative cases for each class defined in section 2.1. Fig. 6 shows representative results for 'class 1' stations: GHER, POL2, P572 and P467. These stations have significant mountains in different directions: towards the north for GHER, the south for POL2, the east for P572 and the west for P467. A visual correlation can be observed for all these stations whatever the direction of the mountains. Gradient curves in blue (filled with pink) have a very well identified orientation towards the highest elevation point, regardless of its distance from the station considered for the identification of the highest elevation point. The correlation coefficients between the gradient direction

Observations

GPS only Station Coordinates

Yes

Fixed

Atmospheric loading correction

and the relief at 20 km, 40 km and 60 km are 0.50, 0.63 and 0.67 respectively for GHER station (0.74, 0.73 and 0.68 for POL2; 0.81, 0.85 and 0.77 for P572; 0.58, 0.65 and 0.71 for P467). POL2 and P572 show stronger correlation because mountains are very well identified in a specific direction: south for POL2 and east for P572. The front of mountains appears larger and less oriented for GHER, and some little mountains exist also in the opposite direction of the main mountain for P467, which can explain lesser correlation for both stations. A marked increase of the correlation coefficients with respect to the distance of relief from the station is clear for GHER and P467 stations, while the correlation coefficients appear more or less constant for POL2 and P572. Again, it can be explained by the previous reasons even if the differences are not very significant. Since similar results have been retrieved for stations in class 2 whatever the isolated mountains direction, we do not show them here. In Fig. 7, no preferred direction can be noticed for the station MICH located in California near Sierra Nevada and belonging to class 3, which corresponds to a station relatively surrounded by isolated mountains. Gradient points are scattered in all directions and the gradient curve is not well identified. The correlation coefficients between the gradient direction and the relief at 20 km, 40 km and 60 km are 0.71, 0.21 and 0.04 respectively. We can do similar comment for the station BZRG located in Italia near Dolomites and belonging to class 4 which correspond to stations surrounded by mountains in all the directions (Fig. 8). Even if the gradient curve is a little better identified than for MICH station, it shows a symmetrical shape around the station. For this station, the correlation n coefficients between the gradient direction and the relief at 20 km, 40 km and 60 km are 0.10, 0.17 and 0.63 respectively. This station illustrates the typical results obtained for stations in class 4.

In these two previous classes 3 and 4, it is not surprising that no correlation appears since the surrounding mountains have no preferred direction with respect to the station. Horizontal gradient and relief curves (right). Red curve (filled with pink) represents the gradient and the other curves represent the relief at different distances from the station: blue for 20 km, orange for 40 km and green for 60 km. Y axis legend corresponds to the elevation in m. Other axis legend (at 135° azimuth) corresponds to the sum of gradient.

Complete and statistical results

We computed the radar charts with the horizontal gradient and the relief curves and the corresponding correlation coefficients, for all the 52 considered permanent stations, between gradient direction and the relief at 20 km, 40 km and 60 km. For all stations and for each class, Table 3 presents the number of stations and the mean correlation coefficient between the gradient's direction and the relief for the different distances from the considered stations to identify the highest elevation point (20, 40 and 60 km). A very clear correlation appears for stations of class 1 and 2 whereas no correlation is obvious for stations of classes 3 and 4. Figure 9 shows the correlation coefficients for all the stations of class 1 and 2 (36 stations) only considering the distance of 60 km from the stations to identify the highest elevation point. Very similar curves are observed for the other distances from the stations. For 89% of stations of class 1 and 2, a correlation between 0.4 and 1 was found, and 64% of stations presented a correlation between 0.6 and 1. For these 36 stations, the correlation coefficients range between 0.4 and 1 except for 4 stations (P041, P636, SEDR, CIT1) and range between 0.6 and 1 except for 5 additional stations (LINH, P037, P339, MAS1, RSVY). These complete results show clear correlation between gradient and relief due to the presence of mountains whatever the direction of the mountains.

Discussion

Our results clearly underline the influence of nearby mountains on the tropospheric gradient direction estimated from GPS data. In a preliminary study, realised before our previous article [START_REF] Morel | Validity and behaviour of tropospheric gradients estimated by GPS in Corsica[END_REF], gradients had been analysed for 10 stations in the Paris region (height variation < 200 m and far to the sea or mountains (> 200 km)). This preliminary study had shown no significant value for estimated gradient to allow us a statistical analysis. This influence of nearby mountains can probably be explained by a vertical shift of the tropospheric layer due to the presence of mountains, close to the station and up to the maximum distance of 60 km from the station as illustrated in Fig. 10. Then, the path delay through the troposphere is increased in the mountains direction and the lengthening depends on the mountains shape and altitude. If we consider the extreme case of a mountain range with a height difference of 3 km compared to the station elevation and a troposphere thickness of 3 km, this lengthening can reach twice as much compared to a station located in an area without relief (e.g. 114 km instead of 57 km for an elevation angle of 3 degrees). For an intermediate case (height difference of 2 km), as an isolated mountain, the extension is less important (e.g. 95 km instead of 57 km for an elevation angle of 3°) and stays significant even in the case shown in the Fig. 10 (76 km instead of 57 km for height difference of 1 km). Consequently, due to this path delay heterogeneity across the troposphere around the station, the tropospheric gradient should appear in the direction of the greatest path delay and thus of the highest relief. This particular gradient signature close to mountains can clearly be observed in Kacmarick et al (2019). For Andes mountains, Tibetan Plateau and Rocky mountains, the figure A1 in the appendix A of this paper shows this behaviour of tropospheric gradient. High mountains in the vicinity of the respective stations change the 'tilting' of the troposphere, such that the mean tropospheric gradients do not point towards the equator but instead the mean tropospheric gradients point towards the mountain crests.

In the following parts, three tests have been performed to investigate this hypothesis. 4 presents the comparison of the correlation coefficients obtained for these 5 stations during each considered year. Annual differences between these correlation coefficients are very weak and do not exceed 0.11, with a RMS of 0.05 for all the differences. This means that the correlation coefficients are very similar over these 2 years. Moreover, for all the considered stations, the radar charts match perfectly and no visual shift can be detected between the figures for the years 2015 and 2016. Since the gradient orientation is not dependent of the year, we can say that it corresponds to a systematic effect related to the environment around the station. Likewise, meteorological activities which are not similar every year seem to have no impact on the annual mean of gradient direction. Presence of mountains is the dominant effect. Considering that the differences of the correlation coefficients between these two years are not significant at all, no more stations were added to this specific study.

In order to go further in the tropospheric gradient behaviour analysis, we analysed monthly variations of their values for these 5 stations for the year 2016. Fig. 11 shows bimonthly means of the gradient sum counted in all the directions (30° by 30°). The relief around the stations is added (dashed line) considering the highest elevation point estimated at 60 km from the considered station. For all stations, no bimonthly means variations appear because all the bimonthly curves are correlated, less for KIT3. For this station, the sum of the gradients differs slightly according to the season, similar profiles are observed for all seasons.

Likewise, for these 5 stations, the May-June and July-August profiles systematically show higher values due to the higher amount of water vapour during summer. Nevertheless, the systematic orientation of the gradients is always correlated with the relief for these 5 stations regardless to the seasons, which confirms the predominant effect of the mountains.

Considering that the gradient orientation could be related to the diurnal variation of humidity [START_REF] Heki | GNSS climatology : origins of atmospheric delay gradients[END_REF], daily variations were studied for all stations. For each considered station in Fig. 12, we computed the averages of the gradient counting in each 30° sector separately during day (grey curve) and night (black curve) for all the year 2016. For easier interpretation, we added the topography (dashed line) around the stations considering the maximum altitude at 60 km from the station. For these 5 considered stations, the diurnal variations do not appear on the spatial distribution of the gradients because all the curves are correlated. Similar profiles are observed for all the stations and no gradient inversion is detected as in the study of [START_REF] Heki | GNSS climatology : origins of atmospheric delay gradients[END_REF] where gradients are clearly dominated by local weather effects. For AHUP and KOSM, situated on the Hawai island, the sum of the day and night gradients are different but always with a higher amplitude in the direction of the relief. Even if no diurnal variations (AHUP) or weak diurnal variations (KOSM) are observed, these increase of gradient during the day could be explained by the water vapour locking by relief. The developments of clouds would be encouraged by the tropical convection and the trade winds.

In summary, the gradient shows a clear and systematic orientation towards the highest elevation point. This occurs due to the presence of mountains around the station. This orientation does not seem to depend on seasonal or daily weather effects.

Meanwhile, other local effects such as multipath propagation could be responsible for this specific orientation. To analyse this potential effect, we analysed the tropospheric gradient behaviour of neighbouring stations (class 1 and 2). Fig. 13 shows radar charts for 3 pairs of stations (GHER and BESI located in Nepal near Himalaya, POL2 and CHUM located in Kazakhistan near the Alaa-Too mountains, KOSM and APNT located in Hawai,). BESI/GHER are separated by 18.7 km, POL2/CHUM by 35.7 km and KOSM/APNT by 16.3 km respectively. The gradient orientation is very similar for all these 3 pairs of stations, which means that if there is an effect of the multipath, it is not dominating. Even if this last test cannot be applied for all our 52 considered stations, these results confirm the influence of the mountains on the gradient orientation for these 6 particular stations and it does not deny these specific effects for the other stations.

Conclusions

By processing two complete years of GPS data (2015 and 2016) for 52 worldwide permanent stations, we studied the temporal behaviour and the spatial distribution of the horizontal tropospheric gradients. These gradients have been estimated

using the GIPSY-OASIS software and the PPP processing method. The GPS stations were selected and classified into four main categories: stations close to a mountain range (class 1), stations close to an isolated mountain (class 2), stations surrounded by isolated mountains in several directions (class 3) and stations surrounded by mountains in all directions (class 4). The correlation between the gradients' direction and magnitude with respect to mountain slopes was analysed. A very clear correlation appears for stations of classes 1 and 2 whereas no correlation is obvious for stations of classes 3 and 4. For 89% of stations in classes 1 and 2, a relevant correlation appears, varying between 0.4 and 1. In fact, 64% of the stations in classes 1 and 2 presented strong correlation between 0.6 and 1. Horizontal gradients estimation show very significant amplitude and a stable direction all along the year, for most of the stations considered. This main direction is most of the time pointing towards the direction of mountains. This orientation does not seem to depend on seasonal or daily weather cycle. Additionally, we investigated local effects such as multipath propagation for some specific stations, and no local influence was found.

Results achieved in our study can at least reinforce that tropospheric horizontal gradients do have a particular behaviour according to the relief around the station location. A specific study is necessary to compute accurately how the mountains change the tilting of the troposphere, with theoric and simulation approaches in a first part, and with an experimental campaign in a second part. Furthermore their addition as parameters during GNSS data processing can be explored to improve GNSS positioning mathematical model. Thus, in further researches, the improvement on positioning with and without the gradients estimation for stations surrounded by mountains must be assessed. ZTDs are operationally assimilated into numerical weather prediction models and if this is not the case for the moment for the horizontal gradients, they are used now for the reconstruction of STD and these results are very promising for GNSS meteorology and climatology.

Figure Captions

Fig. 1. Map of the 52 permanent GPS stations considered with a zoom on three particular areas. © Google Maps. The origin corresponds to the location of the GNSS permanent station.

Fig. 2 .

 2 Fig. 2. Spatial distribution of the horizontal gradients along the year 2015 for the station CRAL located in French Pyrenees.

Fig. 3 .

 3 Fig. 3. Spatial distribution of the horizontal gradients along the year 2015 for the station CRAL located in French Pyrenees. The cloud points are counted in each 30° azimutal sector and represented by their sum. All the points are connected by a red curve to represent the spatial distribution.

Fig. 4 .

 4 Fig. 4. Example of relief around the GHER station located in Himalaya (class 1). The spatial distribution of the relief is represented by three curves (blue, orange and green) corresponding to three distances from the station (20, 40 and 60 km) to identify the highest elevation point in all the directions. Distance from the centre is proportional to the height difference between the station elevation and the highest elevation point. The four circles drawn represent height differences of 2000 m, 4000 m and 6000 m respectively.Finally, gradient and relief curves are superimposed on the same plot (Fig.5) to perform a visual analysis of the correlation between the orientation of the horizontal gradients and the relief. When the gradient and relief curves have a similar pattern, it means that the gradients have a privileged direction and a strong correlation with the relief around the station. We then computed the correlation coefficients between the gradient direction values and the highest elevation in each sector by using the sample Pearson correlation coefficient. Three correlation coefficients are obtained for the relief at three considered distances: 20 km, 40 km, 60 km.

Fig. 6 .

 6 Fig. 6. Relative location of mountains from the station (top charts). Gradients and relief curves (bottom radar charts centred on station location) for class 1 stations: GHER (Himalaya), POL2 (Nepal), P572 and P467 (California near Sierra Nevada). Red curve (filled with pink) represents the gradient and the other curves represent the relief at different distances from the station: blue for 20 km, orange for 40 km and green for 60 km. Y axis legend corresponds to the elevation in m. (scale is different for GHER as it is situated in Himalaya, with height difference up to 6 km). Other axis legend (at 135° azimuth) corresponds to the sum of gradient.

Fig. 7 .

 7 Fig. 7. Location of mountains relative to the 'class 3' GNSS station MICH located in California near the Sierra Nevada (left). Horizontal gradient and relief curves (right). Red curve (filled with pink) represents the gradient and the other curves represent the relief at different distances from the station: blue for 20 km, orange for 40 km and green for 60 km. Y axis legend corresponds to the elevation in m. Other axis legend (at 135° azimuth) corresponds to the sum of gradient.

Fig. 9 .

 9 Fig. 9. Correlation coefficient for stations of class 1 and 2 between the gradient direction and the relief curves at 60 km from the station.

Fig. 10 .

 10 Fig. 10. Schematic path delay across troposphere of a GPS signal in the presence or not of mountains.

Fig. 11 .

 11 Fig. 11. Bimonthly variations of the mean tropospheric gradients sum all along the year 2016 for 5 stations. The dashed line represents the relief around the station estimated at 60 km from the station.

Fig. 12 .

 12 Fig. 12. Average of diurnal variations of gradients for 2016 for 5 stations. The dashed line represents the topography around the station at a distance of 60 km from the considered station.

Fig. 13 .

 13 Fig. 13. Radar charts of gradient curve (red curve filled with pink) and relief curves (dark blue, orange and green curves) for 3 couple of neighbouring stations (GHER and BESI (class 1) located in Nepal near Himalaya; POL2 (class 1) and CHUM (class 2) located in Kazakhistan near Alaa-Too mountains; KOSM and APNT (class 2) located Hawai).

Fig. 2 .

 2 Fig. 2. Spatial distribution of the horizontal gradients along the year 2015 for the station CRAL located in French Pyrenees.

Fig. 3 .

 3 Fig. 3. Spatial distribution of the horizontal gradients along the year 2015 for the station CRAL located in French Pyrenees. The cloud points are counted in each 30° azimutal sector and represented by their sum. All the points are connected by a red curve to represent the spatial distribution.

Fig. 4 .

 4 Fig. 4. Example of relief around the UNSA station located in Argentina near the Andes (class 1). The spatial distribution of the relief is represented by three curves corresponding to three distances from the station (20, 40 and 60 km) to identify the highest elevation point in all the directions. Distance from the centre is proportional to the height difference between the

Fig. 5 .

 5 Fig. 5. Gradient orientation curve (blue) and relief curves (red, green and purple) around the station ARAN located in France near the Alps considering three distances from the station (20, 40 and 60 km) for the relief representation.

Fig. 6 .

 6 Fig.6. Relative location of mountains from the station (top charts). Gradients and relief curves (bottom radar charts centred on station location) for class 1 stations: GHER (Himalaya), POL2 (Nepal), P572 and P467 (California near Sierra Nevada).Red curve (filled with pink) represents the gradient and the other curves represent the relief at different distances from the station: blue for 20 km, orange for 40 km and green for 60 km. Y axis legend corresponds to the elevation in m. (scale is different for GHER as it is situated in Himalaya, with height difference up to 6 km). Other axis legend (at 135° azimuth) corresponds to the sum of gradient.

Fig. 8 .

 8 Fig. 8. Location of mountains relative to the 'class 4' GNSS station BZRG located in Italia near the Dolomites (left). Horizontal gradient and relief curves (right). Red curve (filled with pink) represents the gradient and the other curves represent the relief at different distances from the station: blue for 20 km, orange for 40 km and green for 60 km. Y axis legend corresponds to the elevation in m. Other axis legend (at 135° azimuth) corresponds to the sum of gradient.

Fig. 9 .

 9 Fig. 9. Correlation coefficient for stations of class 1 and 2 between the gradient direction and the relief curves at 60 km from the station.

Fig. 10 .

 10 Fig. 10. Schematic path delay across troposphere of a GPS signal in the presence or not of mountains.

Fig. 11 .

 11 Fig. 11. Bimonthly variations of the mean tropospheric gradients sum all along the year 2016 for 5 stations. The dashed line represents the relief around the station estimated at 60 km from the station.

Fig. 12 .

 12 Fig. 12. Average of diurnal variations of gradients for 2016 for 5 stations. The dashed line represents the topography around the station at a distance of 60 km from the considered station.

Fig. 13 .

 13 Fig. 13. Radar charts of gradient curve (red curve filled with pink) and relief curves (dark blue, orange and green curves) for 3 couple of neighbouring stations (GHER and BESI (class 1) located in Nepal near Himalaya; POL2 (class 1) and CHUM (class 2) located in Kazakhistan near Alaa-Too mountains; KOSM and APNT (class 2) located in Hawai).

Table 1 .

 1 Number of GPS stations considered in each class.

	Classes	Class 1	Class 2	Class 3	Class 4
	Number of stations	24	12	10	6

5 Fig. 1. Map of the 52 permanent GPS stations considered with a zoom on three particular areas. © Google Maps. 6

Table 3 .

 3 Mean correlation coefficients between the tropospheric gradient orientation and the highest elevation point direction for each class of relief. The number of stations considered for each class is also indicated.

		number	Mean correlation coefficient 20 km 40 km 60 km
	All stations	52	0.36	0.41	0.41
		class 1 24	0.44	0.60	0.64
	Type of relief	class 2 12 class 3 10	0.62 0.00	0.64 -0.03	0.68 -0.14
		class 4 6	0.17	-0.05	-0.06

Table 4 .

 4 Correlation coefficients between the gradient direction and the relief for 5 stations for the years 2015 and 2016.

	Stations		Correlation in 2015			Correlation in 2016
		class	20 km	40 km	60 km	80 km	20 km	40 km	60 km	80 km
	AHUP	1	0.94	0.88	0.89	0.74	0.89	0.92	0.89	0.68

Acknowledgments

The authors acknowledge M. Florian Zus for scientific discussion. We are also grateful to all data and products providers without which this study wouldn't have been possible (JPL, IGS, UNAVCO).


Jerome Chesneau

Suzanne Crumeyrolle

Luc Dauchet

Benjamin Hanoune
email: benjamin.hanoune@univ-lille.fr

Methodology for the investigation of IAQ inside French dwellings: the PAMELA study

Keywords: Individual exposure, sensors, particles, citizen science, microenvironments and activities

The PAMELA (Particules Atmosphériques : Mesure de l'Exposition à Lille et aux Alentours) project aims to quantify the personal exposure to particles in the Lille area (Northern France), to differentiate the exposure according to the various environments, and to explore the drivers of exposure in terms of environment and activity. To that purpose we developed a compact particle sensor (0.3-10 µm, 6 size channels), also measuring additional parameters (T, RH, P, GPS positioning), that is worn during one week by participants recruited for the study. We present here the first results obtained, as well as general methodological considerations that emerged after a few weeks, concerning the design of the sensor units, the analysis of the data, and the issues related to the behavior of participants in such a study.

INTRODUCTION

The World Health Organization considers air pollution as the greatest environmental risk in the world, and estimates that seven million people die annually from exposure to air pollutants. The health impacts of acute and chronic exposure to particles have been the subject of many studies, with the most common effects on the cardiovascular and respiratory systems. Epidemiological studies have shown that particles even impact the health of healthy people [START_REF] Dauchet | Short-term exposure to air pollution: Associations with lung function and inflammatory markers in non-smoking, healthy adults[END_REF]. However, in these epidemiological studies, the individual exposure is not measured but only evaluated using the concentrations at the home address of the subjects of the cohort, calculated using chemistry-transport models and outdoors concentrations as input ("front door exposure"). While this approach benefits from its simplicity and availability of data from the national regulatory outdoor air monitoring networks, it does not consider the many environments in which we are exposed every day, or the diversity of activities and behaviors. In particular, this approach completely overlooks the indoor exposure of people (80 to 90 % of the time), starting with the home environment (50-60 % of the time). It is therefore necessary, to better assess the health impacts of air pollution, to improve the assessment of the exposure, and if possible by measuring it instead of inferring it indirectly. Time-resolved personal measurements will also, in the first place, provide a well-needed description of the pollution levels, and of the environmental parameters, in many indoors and outdoors microenvironments, as well as the concentration levels of pollutants as a function of activities performed by people or close to people.

The objectives of the PAMELA project (Particules Atmosphériques : Mesure de l'Exposition à Lille et aux Alentours -Atmospheric Particles : measuring individual exposure in and around Lille), are to develop wearable real-time particle sensors, to progressively enroll at least 500 persons over the next two years in the metropolitan area of Lille (Northern France), who will carry the sensors during one week, to measure their individual exposure, to discriminate between indoor and outdoor exposure, to relate their exposure to the microenvironments and activities, and ultimately to set the ground for future full-size epidemiological studies.

MATERIALS/METHODS

Wearable particle sensors (50 units so far) were developed in our laboratories and are presented in Fig. 1. They measure 8*5*5 cm, and include an optical particle sensing element (HK-A5 model; output: particle number concentration in 6 size channels from 0.3 to 10 µm, mass concentration PM1, PM2.5 and PM10), a temperature and relative humidity sensor (HDC 180), a pressure sensor (DPS 310), a GPS unit, a battery providing autonomy of about 10 hours, an SD storage card and a BLE communication system. Measurements are taken every second. Data are retrieved either by physically connecting the sensor unit to a computer after the measuring campaign or through a mobile phone app developed in our lab. This app allows quasi real-time transfer of the data to a server, and real-time visualization, but is not used in the present study, because its use by the wearers could influence their behaviour. The calibration (particle, T, RH) of the sensors is performed by installing them on the ATOLL atmospheric observation facility (http://www.loa.univ-lille1.fr/observations/plateformes.html) located at the University of Lille, which provides reference measurements for temperature, humidity and size-resolved number concentration up to 1 µm as well as PM1 mass concentration (ACSM and SMPS instruments). PM2.5 and PM10 reference measurements are taken from a reference monitoring station of the French air quality network, located 3 km away. Long-term measurements have shown that there are only rare moments with local pollution events that would perturb such a calibration.

Participants (40 so far) to the study are recruited among university members and staff, students, and local associations. For the moment, we do not consider the representativity of the participants with respect to age, sex, home address…, but these factors will be considered eventually as the number of volunteers increase. Each participant is given a sensor with instructions for use, and is requested to wear it or keep it close to himself or herself during one week, after which time they return it to the lab for data analysis and technical assessment of the sensor. The participants also complete questionnaires about themselves and their dwellings, and are asked to keep a space/time/activity log, under free format. The participants have no way of visualizing the measurements, so that they will not modify their behavior. After analysis of the data, a report is provided, containing a summary of their exposure, with comparison to French health guidelines and to the average results from all the previous participants of the study. Privacy issues are taken into account at all steps of data, questionnaires, logs and results handling.

Data analysis is performed using R language routines developed in the lab. They are used for calibration of the sensors, data preprocessing, and data analysis. Data preprocessing includes (i) removal of incomplete dataframes that can happen when turning off the sensor when data are being written or from data transfer errors, (ii) change of units for the particle number concentrations because of a specific format delivered by the sensor, (iii) addition of flags corresponding to the state of the battery (full but still on charge, see below) and to a null number of satellites detected by GPS, (iv) calculation of the absolute humidity. Data analysis aims to differentiate the environments and activities based on all measured parameters except the particles number and mass concentrations. For this we rely on segmentation methods, applied either to temperature, humidity (relative and absolute), number of visible satellites and speed as determined by the GPS, considering the mean values, slopes and fluctuations of each of these parameters, using the 'changepoint' R-package [START_REF] Killick | Optimal Detection of Changepoints With a Linear Computational Cost[END_REF]. Sensitivity of the segmentation methods still needs to be manually adjusted to optimize the number of significant environment/activity changes. Simultaneous use of all the parameters for segmentation should perform more efficient and robust segmentation (El Hafyani et al., 2020) but has not been implemented yet. The space/time/activity log is not used for the segmentation process, but only afterwards to explain and "put a name" on the different environments.

RESULTS

An example of data acquired by one participant is given in Figure 2. Pressure and absolute humidity were found, in this example as in others, to be the best indicators of change of microenvironment. The segmentation process identifies changes, most of them clearly visible on the pressure and humidity data, but not so on the PM10 mass concentration, with only three high concentration episodes sticking out from the background concentration.

The main episodes were identified by the participant in its activity log as subway ride in the morning, lunch break at the university cafeteria, and subway ride in the evening. While concentrations of PM10 in the subway in the mornings tend to be quite stable from day to day, concentrations in the evenings show unexplained much larger variations. From this segmentation, and by comparison with the activity log, we can derive the duration that the participant spent in each environment. For instance, for the data shown on 2, the participant spent 60% of his time at home, 28% in the office, 6 % in transportation (4% in the morning and 2% in the evening), 2% for lunch at the university cafeteria, and 4% outdoors.

Fig. 3 presents the statistical analysis of the PM concentrations during these events. The most polluted environments are the subway cars, which are well known confined environments often presenting high PM concentrations [START_REF] Moreno | Non-exhaust emissions -An urban air quality problem for public health; Impact and mitigation measures[END_REF]). PM10 concentrations measured at the home of the participant as well as outdoors are around 30 µg.m -3 , and lower in his workplace (university laboratory) and in the university cafeteria.

This figure also presents the average exposure levels calculated from our measurements (color lines), as well as the "front door" exposure levels. For PM10 and PM2.5 the measured exposure levels are 17.2 and 13.0 µg.m -3 respectively, lower than the 36.0 and 16.4 µg.m -3 front door exposure levels. For PM1, the exposure level is 9.2 µg.m -3 . This illustrates further the need to to improve epidemiological studies using individual measurements instead of extrapolated front door values. No other specific microenvironments have been identified so far, but activities (not shown here) such as smoking (outdoors), use of fireplace, and cooking activities are also clearly distinguished in the data from some participants.

DISCUSSION Sensor design issues

In spite of careful design planning and testing while developing the sensor unit prototypes, several technical issues appeared when starting to analyze the first results. The integration of the temperature and humidity sensor inside the sensor unit, close to the battery and electronic board, leads to biased temperature measurements that cannot be completely corrected even with careful calibration, because of the thermal inertia of the unit (~20 minutes). In addition, when the sensor is plugged on the sector, which we recommended when measuring indoors to extend the duration of the measurements, the charging cycle of the unit leads to power overshoots that also perturb the temperature and humidity sensor. This makes the temperature and relative humidity readings for the moment unreliable when the battery level is over 4.5 V, but does not prevent their use for the segmentation procedure, because we test for breakpoints in the time profiles and do not search specific values. Testing is under way with a modified position of the temperature sensor inside the unit and a modified charging board that should fix these problems.

No such effects have been noticed on the other sensors and outputs of the unit (P, GPS, particles), though the influence of temperature and humidity on the particle number and mass concentrations may not be correctly taken into account. However, even though the sensors have been calibrated against reference instruments, we expect that the uncertainty associated with the measurements may not be significantly changed with a better correction for environmental parameters.

User related issues

Many issues, some expected, some not, are connected with the incorrect use of the sensors by the participants of the study, and with the poor observance of the protocol. Some of the participants reported not being able to tell whether the sensor was on or off, in spite of the red and blue LED lights on the shield, and as a consequence they did not recharge the sensor. Some participants said that they simply forgot to recharge the sensor. In some cases, the participants only turned the sensor on when they suspected unsafe particle levels, instead of leaving the sensor active all the time. Some of the participants did not keep the sensor close to them, either they left it in one room when they were home, instead of carrying it with them, or they merely forgot to take it during their daily activities, which tended to happen more often after a three to four days, when the novelty of the experiment wore off. Some of them lent the sensor to a member of their family or a friend. While the questionnaires were in general correctly filled by the participants, this was not the case with the space/time/activity logs. In addition to being sometime illegible because of really bad handwriting, some logs contained irrelevant and unusable information, and were generally incomplete, even with participants educated in air pollution, in particular when the logs were filled either retrospectively, at the end of the day, leading to missing activities, but also when they were filled more often, because of rapid succession of environments and activities, such as leaving a building and entering another one, with a short smoking break outdoors in between, making it difficult to keep a written track of all these changes. As a result, the amount of data usable is far from 100%, and it is difficult for the moment to ascertain for each participant the correct observance of the protocol, and to always compare exposure, environments or activities. It will be therefore necessary to improve the initial information package and follow-up that we give them. However, the first results obtained during this work, can still be exploited, with meaningful analysis and results, and we are starting to question the necessity of asking all the participants to keep a log.

CONCLUSIONS

The sensors and analysis methodology developed within the PAMELA project framework are now operational. Some adjustments need to be made to the sensors, so to have a better and more significant measurement of the temperature. The protocols for analysis are only at their early stages of development, with improvement expected in the segmentation process, and in the automatic assignment of the microenvironment and/or activities based on environmental, GPS, and particles concentration and size, by way of unsupervised and supervised learning. Further work is underway to improve the information given to participants to improve the quality of the space/time/activity logs, in order to better document the physical measurements, and to improve their knowledge and concern about air pollution and its effects on health.

A database with information on environmental parameters and particle load in all the microenvironments, and in particular indoors, including workplace, dwellings, and public transportation systems, is progressively growing, which will provide eventually statistically meaningful information useful for all aspects of IAQ studies, and for epidemiological studies as well.

  Figure 1: a) inside view of the sensor unit b) sensor carried by a participant

Figure 2 :

 2 Figure 2: Time series acquired on Nov. 9, 2021 (volunteer #2, sensor unit APO_010). a) pressure and absolute humidity, b) PM concentration.

Figure 3 :

 3 Figure 3: Statistical analysis of PM in each environment identified in Fig 2.

ACKNOWLEDGEMENT

The work was supported by the French government through the Programme Investissement d'Avenir (I-SITE ULNE / ANR-16-IDEX-0004 ULNE), managed by the Agence Nationale de la Recherche as part of the project "Santé Environnement: du risque territorial au risque individual" (also funded by Lille European Metropole). This work is a contribution to the CaPPA project (Chemical and Physical Properties of the Atmosphere), funded by the French National Research Agency (ANR) through the PIA (Programme Investissement d'Avenir) under contract ANR-11-LABX-005-01, to the Institut de Recherches Pluridisciplinaires en Sciences de l'Environnement (IRePSE Fed 4129), and a contribution to the CPER research project CLIMIBIO, with financial support from the French Ministère de l'Enseignement Supérieur et de la Recherche, the Hauts de France Region and the European Funds for Regional Economic Development. One of the authors (JC) thankfully acknowledges the CaPPA project and the University of Lille for PhD support.