Thomas A Darde

Pierre Gaudriault

Rémi Béranger

Clement Lancien

Annaelle Caillarec-Joly

Olivier Sallou

Nathalie Bonvallot

Cécile Chevrier

Séverine Mazaud-Guittot

Bernard Jégou

Rémi Beranger

Clément Lancien

Annaëlle Caillarec-Joly

Olivier Collin

Em- Manuelle Becker

Antoine D Rolland

Frédéric Chalmel
email: frederic.chalmel@inserm.fr

TOXsIgN: a cross-species repository for toxicogenomic signatures

come

Introduction

Humans today are exposed to numerous man-made environmental contaminants. By July 2017, over 130 million chemicals were listed in the Chemical Abstract Service (CAS), 120,000 of which are marketed in the European Union according to the European Chemicals Agency (ECHA) (https://echa.europa.eu/fr/information-on-chemicals). The potential toxicity of the overwhelming majority of these compounds remains almost uninvestigated [START_REF] Tweedale | The inadequacies of pre-market chemical risk assessment's toxicity studies-the implications[END_REF]. Growing concerns about their potential adverse effects on human health and the environment led the European Commission to promulgate the regulation on Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) in 2007 (European Commission). This regulation has prompted innovative scientific programs to: i) screen for novel potential toxicants to which humans are exposed, especially, novel possibly carcinogenic, mutagenic, or reprotoxic (CPR) substances; ii) investigate the molecular mechanisms underlying their actions; and, finally, iii) develop predictive methods for assessing chemical hazards, ultimately intended to reduce the number of experimental tests on model organisms [START_REF] Russell | The principles of humane experimental technique[END_REF]. Several landmark projects, such as Open TG-GATEs [START_REF] Igarashi | Open TG-GATEs: a large-scale toxicogenomics database[END_REF], DrugMatrix [START_REF] Ganter | Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database[END_REF], CMap [START_REF] Lamb | The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules[END_REF], and a myriad of other high throughput studies, have been undertaken on the hypothesis that toxi-R e v i s e d m a n u s c r i t p t . cogenomic data, i.e., gene expression profiles in response to chemical exposure, will improve the prediction of their toxicity and the understanding of their mechanisms of action [START_REF] Prathipati | Systems Biology Approaches to a Rational Drug R e v i s e d m a n u s c r i t p t Discovery Paradigm[END_REF]. This concept supplements the traditional ligand-and structure-based predictive approaches to assessing the safety of compounds by postulating that transcript profiling can effectively discriminate classes of compounds with similar adverse effects [START_REF] Steiner | Discriminating different classes of toxicants by transcript profiling[END_REF][START_REF] Kavlock | ToxCast TM : Developing predictive signatures for chemical toxicity[END_REF]. Recently, the lack of reproducibility of biomedical research (Must try harder, 2012), including in the field of toxicology [START_REF] Miller | Improving Reproducibility in Toxicology[END_REF][START_REF] George | Raising the bar for reproducible science at the U.S. Environmental Protection Agency Office of Research and Development[END_REF][START_REF] Poland | The elephant in the room: reproducibility in toxicology[END_REF], has sparked apprehensions. Funding agencies, such as the National Institutes of Health (NIH), and other institutions share this concern and are participating in discussions of ways to enhance reproducibility in the environmental sciences [START_REF] Collins | Policy: NIH plans to enhance reproducibility[END_REF]. Among the practical points to be considered when funding, planning and reporting toxicology studies, a crucial one is improving data transparency, including negative findings or contradictory results [START_REF] Poland | The elephant in the room: reproducibility in toxicology[END_REF]. One effort in this context comes from the European Commission, which through the European Research Infrastructure Consortium (ERIC) is attempting to establish a model service for systems biology data management. Its objectives are to make biological data FAIR: Findable, Accessible, Interoperable, and Reusable [START_REF] Wilkinson | The FAIR Guiding Principles for scientific data management and stewardship[END_REF]. These goals are especially important in the field of toxicology. Resources such as CTD [START_REF] Davis | The Comparative Toxicogenomics Database's 10th year anniversary: update 2015[END_REF] (http://ctdbase.org), diXa [START_REF] Hendrickx | diXa: a data infrastructure for chemical safety assessment[END_REF] (http://www.dixa-fp7.eu), ToxDB [START_REF] Hardt | ToxDB: pathway-level interpretation of drug-treatment data[END_REF] (http://toxdb.molgen.mpg.de), CEBS [START_REF] Lea | CEBS: a comprehensive annotated database of toxicological data[END_REF]) (https://www.niehs.nih.gov/research/resources/databases/cebs/index.cfm) , NIH LINCS [START_REF] Duan | L1000CDS2: LINCS L1000 characteristic direction signatures search engine[END_REF] and Drug2Gene [START_REF] Roider | Drug2Gene: an exhaustive resource to explore effectively the drugtarget relation network[END_REF] (http://www.drug2gene.com) have paved the way for improved storage, exchange, and analysis of toxicological data [START_REF] Miller | Data sharing in toxicology: beyond show and tell[END_REF]. The Toxicity Forecaster (ToxCast) developed by the US Environmental Protection Agency is another good example of an innovative tool designed to generate and share standardized data and predictive models for thousands of toxicants, including endocrine disruptors [START_REF] Richard | ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology[END_REF]. Toxicogenomics is a scientific field that covers the acquisition, interpretation and storage of information about gene expression and associated protein activity to study the adverse effects of environmental and pharmaceutical chemicals on human health and the environment. Such studies use "-omics" technologies (for example, transcriptomics, proteomics, epigenomics, and related approaches) to discriminate molecular signatures, also called toxicogenomic signatures, that strongly correlate with genetic toxicity [START_REF] Beedanagari | Genotoxicity biomarkers[END_REF]. It is now well established that investigators are supposed to submit their raw data to public databases such as the Gene Expression Omnibus [START_REF] Barrett | NCBI GEO: archive for functional genomics data sets--update[END_REF] and Ar-rayExpress [START_REF] Kolesnikov | ArrayExpress update--simplifying data submissions[END_REF]. To the best of our knowledge, none of the existing toxicogenomics resources, including CTD and NIH LINCS, allows scientists to submit toxicogenomic signatures, i.e., the sets of genes showing altered status (in terms of gene expression, protein activity or epigenetic status) in individuals or their descendants after exposure to single or combined environmental factors. Although these signatures constitute the heart of studies in toxicogenomics, they usually only appear as supplementary tables and are accordingly complicated to reuse directly or compare with other data. To meet the demands of scientists for easy access to such information (i.e., without the time-consuming downloading and re-processing of raw data), we developed TOXsIgN (for TOXicogenomic sIgNatures), a userfriendly resource that supports online submission, storage, and retrieval of toxicogenomic signatures. This repository is not intended either to archive raw data, as GEO and ArrayExpress [START_REF] Kolesnikov | ArrayExpress update--simplifying data submissions[END_REF][START_REF] Barrett | NCBI GEO: archive for functional genomics data sets--update[END_REF] do, or to replace existing toxicological databases [START_REF] Davis | The Comparative Toxicogenomics Database's 10th year anniversary: update 2015[END_REF][START_REF] Lea | CEBS: a comprehensive annotated database of toxicological data[END_REF][START_REF] Hendrickx | diXa: a data infrastructure for chemical safety assessment[END_REF][START_REF] Duan | L1000CDS2: LINCS L1000 characteristic direction signatures search engine[END_REF], but rather to complement these resources by acting as a distribution hub. One of the unique features of TOXsIgN is its ability to archive heterogeneous data and thus allows users to upload lists of overexpressed/underexpressed genes from different kinds of omics experiments (e.g., transcriptomic, proteomic, or epigenomic) and make them usable for cross-species and cross-technology comparisons. TOXsIgN is also intended to serve as a warehouse for toxicogenomics and predictive toxicology tools simultaneously based on and able to analyze the overall set of signatures deposited by the community. The TOXsIgN repository is freely accessible at https://toxsign.genouest.org.

Methods

Data storage, management, and retrieval

The database underlying TOXsIgN is based on MongoDB (https://www.mongodb.com), a free, open-source cross-platform document-oriented database program. This NoSQL database technology provides relevant features for TOXsIgN, such as flexible storage of massive and rapidly changing types of data, data replication, and JavaScript compatibility.

The TOXsIgN search engine is based on the implementation of an ElasticSearch server (https://www.elastic.co). Briefly, ElasticSearch is a NoSQL database manager with a powerful search engine primarily used to index textual data, which allows TOXsIgN to index all four layers of a TOXsIgN project information (the project, the studies, the assays, and the signatures) and simultaneously enables investigators to query the database according to several data categories, such as chemicals, genes, doses, species, cell lines, and tissues.

Web interface

The web interface of TOXsIgN was built with two open web frameworks, Pyramid (https://trypyramid.com/) and AngularJS (https://angularjs.org/). Pyramid embeds many features, such as a REST API, a JSON renderer, an SQLAlchemy Object-relational mapper (ORM), a Deform library to generate forms, and compatibility with SMTP servers. AngularJS, on the other hand, is an open JavaScript framework that extends traditional HTML vocabulary; it allows implementation of readable and quickly developable web environments. To handle website traffic and provide data security, scalability, and deployment, all components of TOXsIgN (i.e., website server, Mon-goDB database, and Elasticsearch server) are hosted on separate individual virtual machines, with Docker (https://www.docker.com) as a container system. Briefly, Docker is an open-source virtualization system allowing easy deployment of container images, which in turn are lightweight, stand-alone, executable packages embedding everything needed to run a piece of software, such as code, runtime, system tools, libraries, and settings.

Workspace

The workspace supports the database and includes a job submission system to execute local scripts written in Python, R, or Tcl, and standalone programs. The corresponding web interface is based on the Pyramid framework as well as Python scripts and allows users to run modules and access their results. Execution of a module creates a new job from a R e v i s e d m a n u s c r i t p t

Python wrapper. The status of each job is available in the table accessible through the "running jobs" web page, which lists queued, running, and complete jobs coded respectively in gray, orange, and green. Uploaded toxicogenomic signatures correspond to tab-delimited text files composed of Entrez Gene identifiers (IDs) [START_REF] Maglott | Entrez Gene: gene-centered information at NCBI[END_REF]. Before available modules are run from the workspace, these IDs are supplemented with related information, such as gene symbols, gene descriptions, taxonomy IDs, and HomoloGene IDs (NCBI Resource Coordinators, 2016) using "gene_info" and "homologene.data" files from the NCBI website (https://www.ncbi.nlm.nih.gov).

Results

TOXsIgN overview and current content

TOXsIgN information is organized in a four-layer architecture (Project > Study > Assay > Signature). Each layer is associated with a unique trackable identifier that can be reported in submitted manuscripts, like the raw data identifiers from GEO or ArrayExpress [START_REF] Barrett | NCBI GEO: archive for functional genomics data sets--update[END_REF][START_REF] Kolesnikov | ArrayExpress update--simplifying data submissions[END_REF] (Fig. 1). Scientists should thus submit projects (each of which receives an identifier with the "TSP" prefix), which are subsequently subdivided into studies (or subprojects, "TSE" prefix) addressing specific questions, and describing experimental assays ("TST" prefix) from which specific outcomes are extracted in the form of toxicogenomic signatures ("TSS" prefix), i.e., the set of genes positively or negatively affected in the corresponding assays. These assays can be performed directly in exposed individuals or in their descendants after exposure to a single substance or a combination of several. This definition underlines several unique features of TOXsIgN, specifically, its compatibility with: i) transgenerational studies; ii) chemical mixture studies -by default each assay is a mixture of at least one environmental factor; iii) a variety of environmental factors, including chemicals (e.g., pesticides, plasticizers, drugs, and endocrine disruptors), and eventually physical (e.g., radiations and temperature) and biological (e.g., pathogens and parasites) factors; and, iv) gene sets resulting from several kinds of (transcript-/prote-/epigen-)omics experiments. Importantly, TOXsIgN also allows scientists to submit and describe outcomes besides toxicogenomic signatures, such as physiological (e.g., association with a specific phenotype) and molecular (e.g., change in specific hormone concentrations) signatures for both interventional (participants undergo some kind of treatment so its impact can be evaluated) and/or observational studies (individuals for which different outcomes are measured) [START_REF] Thiese | Observational and interventional study design types; an overview[END_REF].

TOXsIgN thus collects a large number of heterogeneous signatures and simultaneously help to break down barriers between different fields in environmental sciences that remain boxed off from each other. Currently, the TOXsIgN repository includes 754 projects for 911 transcriptomic studies of more than 450 compounds performed in humans, rats, mice, or drosophila (Supplementary Table 1 and Supplementary Information). Together these experimental assays correspond to 8491 toxicogenomic signatures, extracted from 32,688 microarray experiments that used 10 different technologies, including two major toxicogenomics resources, DrugMatrix and the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (Open TG-GATES) [START_REF] Ganter | Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database[END_REF][START_REF] Igarashi | Open TG-GATEs: a large-scale toxicogenomics database[END_REF]. DrugMatrix, set up by the National Institute of Environmental Sciences (NIES, USA) aimed at studying transcriptional responses in rats after exposure to 376 compounds in five different tissues (at multiple doses and multiple exposure times). On the other hand, Open TG-GATES is a collaborative project between the National Institute of Biomedical Innovation (NIB), the NIES and about 15 pharmaceutical companies that sought to study 150 chemicals and their transcriptional responses (at multiple doses and for multiple exposure times) in two rat tissues (liver and kidney). Our repository also hosts 326 physiological and molecular signatures from four interventional studies and four observational studies (Supplementary Table 1). In the near future, toxicogenomic signatures from other research programs will be included in TOXsIgN, such as CMAP, CEBS, diXa, and NIH LINCS [START_REF] Lamb | The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules[END_REF][START_REF] Lea | CEBS: a comprehensive annotated database of toxicological data[END_REF][START_REF] Hendrickx | diXa: a data infrastructure for chemical safety assessment[END_REF][START_REF] Duan | L1000CDS2: LINCS L1000 characteristic direction signatures search engine[END_REF].

Signature submission and access

In this quick and easy submission procedure, investigators will record all required information in a dedicated Excel template that embeds one tab for each layer (Project, Study, Assay, and Signature). This document integrates a dozen landmark controlled vocabularies allowing scientists, using ontologies as recommended [START_REF] Hardy | Toxicology ontology perspectives[END_REF][START_REF] Smith | The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration[END_REF], to describe their toxicogenomic studies and their outcomes with precision (Supplementary Table 2). Once uploaded, the TOXsIgN webserver performs an initial evaluation of the Excel template to identify: i) "critical errors" about essential information that may not have been properly completed (such as the project title) and could therefore prevent the project upload; ii) "warnings" for important but not essential missing information (such as a PubMed identifier); and, iii) "information" for any other data not appropriately completed (such as additional information). If the system detects no "critical error", it next invites the user to upload the associated toxicogenomic signatures. Each signature comprises three one-column text files specifying: i) all interrogated genes; genes ii) positively (overexpressed for transcriptomic assays), and iii) negatively affected (underexpressed) in the corresponding assays.

Because reliable and consistent identifier conversion is a complex problem, toxicogenomic signatures should be converted to Entrez Gene IDs from up-to-date resources [START_REF] Huang | DAVID gene ID conversion tool[END_REF][START_REF] Mudunuri | bioDBnet: the biological database network[END_REF].

For studies using Affymetrix GeneChip technologies, it is highly recommended that users normalize their raw data (CEL files) with the R e v i s e d m a n u s c r i t p t .

Brainarray custom Chip Description Files (CDF) so that intensity values are not summarized for each probe set but directly for each Entrez Gene ID [START_REF] Dai | Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data[END_REF] (see Supplementary Information). By default, each submitted project and its related signatures are tagged with a "private" status, meaning that only authorized users (the owner but also coauthors) can access the uploaded data. At this stage, information can still be modified simply by uploading an updated version of the Excel template. A button is available on the web interface for each project to request the TOXsIgN administrators to switch it from private to public status. If "warnings" are still detected, this demand is rejected. The administrators will then help the investigators make the necessary modifications to activate the public status. Full instructions and examples of the submission procedure are provided in the website's tutorial section. The latest release of the Excel template document for uploading signatures in the repository and all ontologies (OBO files) used in TOX-sIgN are available in a dedicated web page accessible through the "Download" tab on the main interface. query", and "results table" are displayed. The "search parameters" box allows users to specify the details of their request from among projects, studies, assays, or signatures. For each request, the ElasticSearch query used to retrieve information is displayed on the top of the "results table". The "results table" is a three-tab panel displaying result sorted according to projects, studies, or signatures and providing a link to the corresponding page.

A powerful search engine is implemented to access all public information within TOXsIgN. Users can thus interrogate the database according to many distinct fields, such as environmental factors, organisms, tissues, and technologies. They can also easily make more advanced queries by using ontologies to describe toxicogenomic signatures. For instance, an investigator can retrieve 33 toxicogenomic signatures in which the expression of Cyp3a18 (Entrez Gene ID 252931, a gene encoding a member of the cytochrome P450 superfamily highly expressed in the liver [START_REF] Nagata | Isolation and characterization of a new rat P450 (CYP3A18) cDNA encoding P450(6)beta-2 catalyzing testosterone 6 beta-and 16 alpha-hydroxylations[END_REF]) is negatively affected in the liver after exposure to hydroxyl steroid compounds (Fig. 2). All scripts and data used are freely accessible in the TOXsIgN "Download" section.

Predicting adverse effects from toxicogenomic signatures

Predictive toxicology approaches based on toxicogenomic data seek to evaluate the toxicity of different compounds by using altered gene expression as an endpoint. In 2004, Steiner and colleagues established the proof-of-concept that similar toxicogenomic signatures imply similar adverse effects, using support vector machines (SVMs) to classify hepatotoxic and non-hepatotoxic chemicals based on transcriptomic data [START_REF] Steiner | Discriminating different classes of toxicants by transcript profiling[END_REF]. Although the parameters may have been overfitted due to the small number of compounds, this approach correctly predicted hepatotoxic effects for 90% of known hepatotoxicants.

To illustrate the usefulness of archiving massive toxicogenomic signatures in a public repository we sought to demonstrate, but on a larger scale, the hypothesis formulated by Steiner and colleagues: that chemicals with similar toxicogenomic signatures share similar toxicity profiles (Supplementary Information). We used a subset of the current TOXsIgN repository that includes 3022 toxicogenomic signatures from transcriptomic assays from five rat tissues after exposure to 410 toxicants to evaluate the toxicological distance (concordance, i.e., the number of shared adverse effects between two toxicants) as well as the linear correlation between their toxicogenomic signatures (Pearson's correlation and Euclidean distance). We first showed a significant association between the toxicogenomic and toxicological distances (r 2 = 0.051, P < 2.2 10 -16 ) (Fig. 3, panels A-B). This result is in line with the hypothesis that a strong correlation between gene expression profiles implies similar toxicity characteristics. Interestingly, we also observed a linear correlation (r 2 = 0.056, P < 2.2 10 -16 ; panels C-D) when we discretized the expression data thanks to an expression matrix in which fold-change values information were simplified to only three distinct statuses: 1, genes overexpressed after exposure; -1, underexpressed; and, 0, no differential expression (see Supplementary Information). This finding supports the idea that toxicogenomic signatures can be archived as simplified text files, which substantially facilitates their submission, without excessively penalizing their predictive potential.

3.4

The signature enrichment analysis module for comparing toxicogenomic signatures

The core feature of the TOXsIgN workspace consists in the bioinformatics and biostatistics modules that allow retrieval, analysis, and comparison of the toxicogenomic signatures uploaded to the repository. The cross-species and cross-technology compatibility of this workspace relies on the conversion of toxicogenomic signatures into HomoloGene IDs before analysis by the different tools. The web page for each toxicogenomic signature includes a "Save in workspace" button that allows investigators to transfer it into the workspace from which all of the available modules can be accessed for further analysis.

In addition to the search engine and conversion tools, three other modules are currently available via the interface:

(1) Signature comparison. This module embeds an interactive Venn diagram viewer, called jvenn, for easy comparison of up to six selected toxicogenomic signatures [START_REF] Bardou | jvenn: an interactive Venn diagram viewer[END_REF].

(2) Functional enrichment analysis. This tool allows users to explore the mechanisms of toxicity of a given environmental factor by identifying the biological processes, molecular functions, cellular components [START_REF] Ashburner | Gene ontology: tool for the unification of biology. The Gene Ontology Consortium[END_REF], and phenotypes (Human Phenotype Ontology web resource) [START_REF] Köhler | The Human Phenotype Ontology in 2017[END_REF] associated with its toxicogenomic signature. Briefly, it determines the significance of the resulting overlaps, according to the hypergeometric distribution.

(3) Signature enrichment analysis. This module seeks to identify toxicogenomic signatures in the repository closely related to the user's selected signature. Like the Functional enrichment analysis module, it uses the hypergeometric distribution to determine the significance of overlaps. It also includes a distance matrix calculation that uses either the Euclidean distance or Pearson's correlation to discriminate among the closely-related toxicogenomic signatures. To illustrate the relevance of these modules, we uploaded into the workspace a toxicogenomic signature comprising 381 overexpressed and 494 underexpressed genes in rat livers after exposure to diethylstilbestrol (i.e., DES, a well-known estrogenic endocrine-disrupting chemical) [START_REF] Korach | The role of the estrogen receptor in diethylstilbestrol toxicity[END_REF][START_REF] Li | Diethylstilbestrol (DES)-Stimulated Hormonal Toxicity is Mediated by ERα Alteration of Target Gene Methylation Patterns and Epigenetic Modifiers (DNMT3A, MBD2, and HDAC2) in the Mouse Seminal Vesicle[END_REF] for 5 days, at a dose of 2.8 mg/kg (TOXsIgN signature identifier: TSS230). We then ran the Signature enrichment analysis module with its default parameters. It took about one minute to complete the job: consistently, three of the top-10 toxicogenomic signatures included experiments performed in rat livers after exposure to DES, but at different doses (three experimental conditions) (Fig. 4). The seven other signatures corresponded to other well-known estrogenic compounds sharing mechanisms of action similar to that of DES (such as ethinylestradiol, estriol, mestranol, and ßestradiol) [START_REF] Muechler | Properties of the Estrogen Receptor in the Human Oviduct and Its Interaction with Ethinylestradiol and Mestranol in Vitro*[END_REF][START_REF] Simpson | Celebrating 75 years of oestradiol[END_REF]. This finding again confirms the hypothesis set forth by Steiner and colleagues [START_REF] Steiner | Discriminating different classes of toxicants by transcript profiling[END_REF].

Conclusion and perspectives

Our goal when designing TOXsIgN was to develop a new cross-species repository to allow scientists to submit the toxicogenomic signatures that they have published. Having been evaluated by experts during the peerreview process, these should be very high quality data. As pointed out in the Nature editorial "Must try harder" (Must try harder, 2012), it is essential to publish the results of well-conducted experiments, whether they are positive, negative, or uninterpretable (absent). We believe TOXsIgN constitutes a new alternative for toxicological data storage, accessibility, and especially reusability and should thus contribute to the transparency of toxicological experiments. The success of this repository obviously depends on scientists' willingness to produce data that fit the FAIR criteria. Inversely, the success of the FAIR consortium also depends on the availability of repositories such as TOXsIgN. We think that making raw data available to the community may be considered an insufficient response, in view of the community's demand for direct and easy access to analyzed and interpreted data. We propose that the same effort should be made to encourage scientists to upload toxicogenomic and toxicological signatures in TOX-sIgN before submission of manuscripts for publication. A win-win situation could thus be reached for all parts, since submitted studies would benefit from enhanced visibility, while the community would take R e v i s e d m a n u s c r i t p t . advantage of a continually updated user-friendly resource allowing TOXsIgN repository to expand. On our side, we are currently integrating additional toxicogenomic signatures from other major toxicogenomics projects, such as CMAP, CEBS, diXa, and NIH LINCS [START_REF] Lamb | The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules[END_REF][START_REF] Lea | CEBS: a comprehensive annotated database of toxicological data[END_REF][START_REF] Hendrickx | diXa: a data infrastructure for chemical safety assessment[END_REF][START_REF] Duan | L1000CDS2: LINCS L1000 characteristic direction signatures search engine[END_REF]. Likewise, we plan to make TOXsIgN compatible with other kinds of environmental agents, such as physical and biological factors. Altogether, we expect that this new resource can contribute significantly to risk assessment. In addition to serving as a public repository, TOXsIgN is also intended to be a warehouse for toxicogenomic and predictive toxicology tools. Its modular design facilitates the implementation of additional bioinformatics modules relying on the deposited toxicogenomic signatures that will help investigators analyze and predict adverse effects of environmental factors relevant to their specific interests.

The TOXsIgN database will remain under constant development to offer more tools and enhance user experience. We are currently developing prediction and prioritization systems for chemical toxicity but also a module to extract toxicogenomic signatures automatically from raw data, in our own workflow. Another potential aspect is the addition of social features in TOXsIgN to enable several investigators to work on the same data. Finally, we plan to incorporate the ISA framework (http://isatools.org/), which provides rich descriptions of experimental metadata, to manage the TOXsIgN database and improve the FAIRness of available data. Together these efforts will improve TOXsIgN's utility, making it a front-line resource relevant to a large audience, including toxicologists, biologists, epidemiologists, and environmental scientists in general.

Fig. 1 .

 1 Fig. 1. Organization of the TOXsIgN database. The TOXsIgN database is organized in a four-layer architecture (Project > Study > Assay > Signature) associated with a unique identifier. The project layer covers one or several studies addressing specific questions. Each study in turn is associated with at least one assay that assesses the exposure of a given study model (e.g., cell culture, living animal, human population) to at least one chemical (e.g., pesticide, plasticizer, drug, or endocrine disruptor), physical (e.g., type of radiation or temperature) or biological (e.g., pathogen or parasite) agent at a given dose and for a given time of exposure. This organization makes TOXsIgN compatible with mixtures and transgenerational studies. At the time of writing, the database contains 8491 toxicogenomic signatures described by 12 different ontologies.

Fig. 2 .

 2 Fig. 2. The TOXsIgN advance search engine. Results for toxicogenomic signatures in which the expression of Cyp3a18 (Gene id: 252931) is downregulated in the rat liver after exposure to hydroxyl steroid compounds. From top to bottom, "search parameters", "full

Fig. 3 .

 3 Fig. 3. Correlation between toxicogenomic and toxicological distances. The linear correlation between toxicogenomic distances (Pearson's correlation and Euclidean distance calculated between two toxicogenomic signatures) and toxicological distances (concordance, i.e., the number of adverse effects shared between two toxicants) was evaluated. Panels A and B show the association between toxicogenomic and toxicological distances, assessed by Pearson's correlation for the toxicogenomic distance on the logfold-change matrix. Panels C and D show the degree of association with the use of a

Fig. 4 .

 4 Fig. 4. Signature enrichment analysis for diethylstilbestrol. The toxicogenomic signature of diethylstilbestrol (DES) (2.8 mg/kg, 5 days, rat liver) was compared to all the toxicogenomic signatures indexed in TOXsIgN with the Signature enrichment analysis tool. N is the total number of HomoleGene IDs measured, R the total number of Homo-leGene IDs meeting the criterion, n the total number of HomoleGene IDs in the selected signature, r the number of HomoleGene IDs meeting the criterion in this signature, and the Ratio corresponds to r/[union(n, R)]. The P-value and the adjusted P-value (Benjamini-Hochberg correction method) are obtained with the hypergeometric probability distribution. Euclidean distance and Pearson's correlation are also calculated to estimate the distance between each pair of toxicogenomic signatures.

Acknowledgements

We thank the GenOuest bioinformatics facility for hosting the software as well as all members of the Research Institute for Environmental and Occupational Health (IRSET) for stimulating discussions. We also thank the Institut national de la santé et de la recherche médicale (Inserm), the Centre national de la recherche scientifique (CNRS), the Université de Rennes 1, and the French School of Public Health (EHESP) for supporting this work.

Funding

TOXsIgN is supported, built, and maintained by the Research Institute for Environmental and Occupational Health (IRSET), the French School of Public Health (EHESP), and the GenOuest BioInformatics core facility. This work was supported by the French agency for food and safety [ANSES n°EST-13-081 to F.C.]; the Fondation pour la recherche médicale [FRM n°DBI20131228558 to F.C.], and the European Union [FEDER to F.C ]. Funding for open access charge: Institut national de la santé et de la recherche médicale (Inserm) and l'Université de Rennes 1.

Conflict of Interest: none declared.


C Jovic

D Wagner

P Hervé

Gérard Gary

L Lazarotto

P Herve

L Lazzarotto

Mechanical behaviour and temperature measurement during dynamic deformation on split Hopkinson bar of 304L stainless steel and 5754 aluminium alloy

Mechanical behaviour and temperature measurement during dynamic deformation on split Hopkinson bar of 304L stainless steel and 5754 aluminium alloy

INTRODUCTION

A new type of machine for materials working has appeared for a few years. These machines are able to combine important speeds, a rate and a lifetime, associated to potential positive effects on the forged products properties. The platform "forge and simulation" of CETIM initiated -in collaboration with various laboratories -a study on cold working at high speed of massive products, as they wished to study the possibilities of application of this press to industrial circle. The high strain rates can generate two contradictory physical phenomena. On the one hand, the process is called adiabatic when -during the deformation of a material -the generate heat is not evacuated to the rest of the system. It creates a heating which can soften the material and decrease the effort necessary for forming it. On the other hand during a high speed loading, an increase of yield stress of the material can be observed at high strain rate, as well as a hardening of the material and an increase of the effort necessary for forming it. At last, another phenomenon can sometimes be observed. It is known as adiabatic shearing. When it occurs, it generates the rupture of the pieces [START_REF] Ranc | Etude des champs de température et de déformation dans les matériaux métalliques solicités à grandes vitesse de déformation[END_REF][START_REF] Pina | Mesure de températue de bandes de cisaillement adiabatique dans les alliages de titane[END_REF].

Data quoted in literature tallies with the fact that the behaviour in dynamics varies from tests into quasi-statics -for the majority of the materials -by a rise in the yield stress and in the flow stress [START_REF] Lee | Effect of prestrain and strain rate on dynamic deformation characteristics of 304L stainless steel; part 2 -Microstructural study[END_REF][START_REF] Schiotz | Modeling of dislocation generation and interaction during high-speed deformation of metals: Radiation Effect and Defects in Solids[END_REF]. The mechanical behaviour of the selected materials (304L stainless steel and 5754 aluminium alloy) were determined at variable speed rates on Split Hopkinson Bar. Temperatures were determined on the surface of sample when tests carried out on Split Hopkinson bar by optical pyrometry were executed. Taylor-Quinney coefficient corresponding to the plastic fraction of work converted into heat was also determined.

APPARATUS PRESENTATION

Split Hopkinson bar

The mechanical behaviour of studied materials (5754 aluminium alloy and 304L stainless steel) was carried out on Split Hopkinson Bar [START_REF] Gary | Comportement des matériaux à grande vitesse de déformation[END_REF]. We used Split Hopkinson Bar in compression (figure 1). The studied sample can be deformed at high strain rates on a state of uniaxial stresses. The strains, stresses and strain rates can be calculated by using the properties of elastic waves propagation in the bars. This technic consists in intercalating the sample to study between two identical bars. The material used in the design of the bars must have a higher yield stress than the material studied. A strike bar is propelled by a compressed-air gun. It comes to impact the incident bar at a variable speed which can be controlled by the user. The induced shock will produce a longitudinal elastic compression wave which is propagated in the entering bar. Part of this wave is transmitted to the sample first and then to the transmitter bar. The other part of the wave is thrown back to the bars interface. Two strain gages permit to measure the deformations generated by the incident, transmitted and reflected waves. The behaviour of the sample can be calculated by gauges signals (Figure 1 and figure 2).

Thermal tools

The temperatures measurement used in this study should not be intrusive, so that it won't disturb the determination of mechanical behaviour of samples and it will possess a high acquisition frequency. Optical pyrometry measurement is the most adapted in this study. A pyrometer with thermal radiation allows collecting part of the emission of a body. It is a thermo sensitive element which absorbs part of the power emitted by the source and which transforms it into electric signal [START_REF] Cabannes | Pyrométrie optique[END_REF]. The apparatus for temperature measurement on figure 1 is made up with:

• An Cincinnati InSb multidetector (16 sensitive elements distributed in line of 43 m by 43 m).

• Two parabolic mirrors used as focalisation system.

• Acquisition System Nicolet acquires at 1 MHz.

• A PC allowing the data acquisition.

The multidetector sweeps the parallelepipedic sample perpendicularly at bars axis. Contrary to the lenses, the parabolic mirrors do not induce any chromatic or geometrical aberration.

The measurement of the temperature requires that the emissivity of the material should be determined in the field wavelengths of the detector. The coefficient of emissivity represents the part of the radiation emitted by the material. Emissivities were measured under vacuum, in laboratories [START_REF] Hervé | Influence de l'état de surface sur le rayonnement thermique des matériaux solides[END_REF]. The total error made on the temperature is around ±6%, this error is mainly due to the determination of the emissivity.

RESULTS

Mechanical behaviour measurement

Tests carried out on Split Hopkinson Bar showed that 304L stainless steel and 5754 aluminium alloy are sensitive to strain-rate of loading. For both materials, the increasing strain-rate of loading generates an increasing of yield and flow stress (Figure 2 and Figure 3). However, the behaviour of both materials are different when strain-rate of loading increases. In the case of 304L stainless steel, yield and flow stress undergoes an important variation between a test realized in quasi-statics and another carried out in dynamic (Figure 2). On the contrary, the stress necessary to deform 5754 aluminium alloy, increases gradually with the strain-rate of loading (Figure 3). At last, we could notice that repetitivity of the test is excellent for both materials at the same strain-rates of loading.

Measurement of temperatures and computation of the Taylor-Quinney coefficient

Checking of the homogeneity of the temperatures and the deformations on Split Hopkinson Bar

Results of the tests on figures 4 and 5 clearly show the homogeneity of the temperatures on the surface of the sample. No localization of the deformation appears.

The rise in temperature between aluminium alloy and steel are not very different for the same deformation. For a true strain of 0.5, the increase in temperature is 110 • C for 304L stainless steel and 90 • C for 5754 aluminium alloy. The greatest part of the deformation energy is transformed into heat.

Effect on strain rate on temperatures

The temperature increases in a constant way throughout the deformation for 304L stainless steel (Figure 6). On the contrary, the temperature curves for 5754 aluminium alloy are not linearly (Figure 6 and Figure 7). For both materials, the rise in temperature is independent from strain rate. Whatever the strain-rate of loading, temperature increases linearly for 304L stainless steel and not for 5754 aluminium alloy. TEM studies could be necessary. Nevertheless, literature reported that this phenomenon is due to a reorganization of dislocations, which leads to a reduction in their density [START_REF] Lee | Impact properties and microstructure evolution of 304L stainless steel[END_REF][START_REF] Lee | Effect of prestrain and strain rate on dynamic deformation characteristics of 304L stainless steel; part 1 -mechanical behaviour[END_REF][START_REF] Yuritzinn | Impact resistance of 304L type stainless steel plates: Identification of Lemaitre and Chaboche damage model's parameter at different strain rates[END_REF][START_REF] Roos | High speed dislocation in high strain-rate deformations[END_REF].

Calculation of the Taylor-Quinney coefficient

The equation of heat in the case of the thermoplastic problem is written:

m * pl *t = C v *T *t (1)
Energy resulting from mechanical work Thermal energy [START_REF] Pina | Mesure de températue de bandes de cisaillement adiabatique dans les alliages de titane[END_REF] where : : Taylor-Quinney coefficient, m : mean stress, pl : plastic strain, : density, C v : specific heat coefficient corresponds with the plastic fraction of work converted into heat [START_REF] Rosakis | On the partition of plastic work into heat and stored energy in metal; Part I and Part II: Report of California Institute of Technology[END_REF]. It is generally noted in literature as constant and typically ranged between 0.8 and 1. Values of the Taylor-Quinney coefficient quoted in literature tally with the calculation of this coefficient carried out in our test (Fig. 8).

However, the waves measured with the incident and transmitted bars fluctuate. The measuring accuracy at the beginning of loading is thus worse and does not allow determining precisely the flow stress of the material. It explains, values superiors to 1 for the Taylor-Quinney coefficient could be obtained for weak deformations. Results achieved at the beginning of loading cannot be interpreted.

CONCLUSIONS

The yield stress of 304L stainless steel significantly increases with the strain rate of loading. Strain hardening. Strain hardening increases gradually with strain rate for 5754 aluminium alloy. Both effects suggest that fast industrial dieing processes will not be easier than quasi-static ones. The increase of the yield stress with the strain rate can be related with the evolutions of the microstructures during the deformation process, in particular with the generation of dislocations, the increase of their number, with the strain their accumulation during compression, in the evolution of their morphological characteristics [START_REF] Lee | Impact properties and microstructure evolution of 304L stainless steel[END_REF][START_REF] Lee | Effect of prestrain and strain rate on dynamic deformation characteristics of 304L stainless steel; part 1 -mechanical behaviour[END_REF][START_REF] Yuritzinn | Impact resistance of 304L type stainless steel plates: Identification of Lemaitre and Chaboche damage model's parameter at different strain rates[END_REF][START_REF] Roos | High speed dislocation in high strain-rate deformations[END_REF]. Temperatures generated during dynamics compression test have been measured for thin samples at high strain-rates. Those measurements allowed for the checking of the temperature homogeneity along the sample during the test. They show that assuming a homogeneous state of strains and stress in the sample tested with a SHPB is valid in the present case. For strains greater than 20%, temperature measurements permit the calculation of Taylor-Quinney showing its decrease with the strain rate increases.

Figure 1 .

 1 Figure 1. System of temperatures measurement for split Hopkinson bar.

Figure 2 .

 2 Figure 2. Stress versus strain for 304L stainless steel at various strain-rates.

Figure 3 .

 3 Figure 3. Stress versus strain for 5754 aluminium alloy at various strain rates.

Figure 4 .

 4 Figure 4. Rise in the temperature for 304L stainless steel sample deformed at 1740 s-1 for various detectors sweeping the whole of the bar.

Figure 5 .

 5 Figure 5. Rise in the temperature for 5754 aluminium alloy sample deformed at 1620 s-1 for various detectors sweeping the whole of the bar.

Figure 6 .

 6 Figure 6. Rise in temperature for 304L stainless steel at various strain rates.

Figure 7 .

 7 Figure 7. Rise in temperature for 5754 aluminium alloy at various strain rates.

Figure 8 .

 8 Figure 8. Evolution of Taylor-Quinney coefficient function of plastic strain for 304L stainless steel.


Sophie Tomaz

Jean-Luc Jaffrezo

Olivier Favez

Emilie Perraudin

Eric Villenave

Alexandre Albinet
email: alexandre.albinet@gmail.com-alexandre.albinet@ineris.fr

Sources and atmospheric chemistry of oxy-and nitro-PAHs in the ambient air of Grenoble (France)

Keywords: PAH, OPAH, NPAH, Aerosol, SOA, reactivity

Total individual concentrations (in both gaseous and particulate phases) of 80 polycyclic aromatic compounds (PACs) including 32 nitro-PAHs, 27 oxy-PAHs (polycyclic aromatic hydrocarbons) and 21 parent PAHs have been investigated over a year in the ambient air of Grenoble (France) together with an extended aerosol chemical characterization. The results indicated that their concentrations were strongly affected by primary emissions in cold period, especially from residential heating (i.e. biomass burning). Besides, secondary processes occurred in summer but also in cold period under specific conditions such as during long thermal inversion layer periods and severe PM pollution events. Different secondary processes were involved during both PM pollution events observed in March-April and in December 2013. During the first one, long range transport of air masses, nitrate chemistry and secondary nitro-PAH formation seemed linked. During the second one, the accumulation of primary pollutants over several consecutive days enhanced secondary chemical processes notably highlighted by the dramatic increase of oxy-PAH concentrations. The study of the time trends of ratios of individual nitro-or oxy-PAHs to parent PAHs, in combination with key primary or secondary aerosol species and literature data, allowed the identification of potential molecular markers of PAH oxidation.

Finally, 6H-dibenzo[b,d]pyran-6-one, biphenyl-2,2'-dicarboxaldehyde and 3-nitrophenanthrene have been selected to be the best candidates as markers of PAH oxidation processes in ambient air.

Introduction

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental substances, mainly emitted by anthropogenic incomplete combustion processes [START_REF] Keyte | Chemical reactivity and long-range transport potential of polycyclic aromatic hydrocarbons-a review[END_REF][START_REF] Ravindra | Atmospheric polycyclic aromatic hydrocarbons: Source attribution, emission factors and regulation[END_REF][START_REF] Shen | Global atmospheric emissions of polycyclic aromatic hydrocarbons from 1960 to 2008 and future predictions[END_REF]. PAHs are of major health concern, because of their carcinogenic, mutagenic and teratogenic properties [START_REF] Iarc | Some non-heterocyclic Polycyclic Aromatic Hydrocarbons and some related exposures[END_REF][START_REF] Kim | A review of airborne polycyclic aromatic hydrocarbons (PAHs) and their human health effects[END_REF].

In the atmosphere, PAH oxidation through homogeneous and heterogeneous reactions lead to the formation of oxy-and nitro-PAHs [START_REF] Atkinson | Mechanisms of the gas-phase reactions of aromatic hydrocarbons and PAHs with OH and NO 3 Radicals[END_REF][START_REF] Keyte | Chemical reactivity and long-range transport potential of polycyclic aromatic hydrocarbons-a review[END_REF]. These latter species are also emitted concomitantly with PAHs during incomplete combustion processes [START_REF] Chen | Increase in polycyclic aromatic hydrocarbon (PAH) emissions due to briquetting: A challenge to the coal briquetting policy[END_REF][START_REF] Karavalakis | The impact of soy-based biodiesel on PAH, nitro-PAH and oxy-PAH emissions from a passenger car operated over regulated and nonregulated driving cycles[END_REF][START_REF] Nalin | Fast oxidation processes from emission to ambient air introduction of aerosol emitted by residential log wood stoves[END_REF]. Oxy-and nitro-PAHs are potentially more mutagenic than PAHs [START_REF] Durant | Human cell mutagenicity of oxygenated, nitrated and unsubstituted polycyclic aromatic hydrocarbons associated with urban aerosols[END_REF]Jariyasopit et al., 2014a[START_REF] Jariyasopit | Heterogeneous reactions of particulate matter-bound PAHs and Nitro-PAHs with NO 3 /N 2 O 5 , OH radicals, and O 3 under simulated long-range atmospheric transport conditions: reactivity and mutagenicity[END_REF][START_REF] A N U S C R I P T A C C E P T E D Accepted Manuscript 26 Pedersen | Human cell mutagens in respirable airborne particles from the northeastern United States. 2. Quantification of mutagens and other organic compounds[END_REF][START_REF] Rosenkranz | The genotoxicity, metabolism and carcinogenicity of nitrated polycyclic aromatic hydrocarbons[END_REF] and some of these substances are also suspected to be carcinogenic (IARC 2012(IARC , 2013)). The identification of the origins of oxy-and nitro-PAHs is challenging, due to the coexistence of their primary and/or secondary sources [START_REF] Keyte | Chemical reactivity and long-range transport potential of polycyclic aromatic hydrocarbons-a review[END_REF]. Several compounds have been identified as typical PAH oxidation byproducts and may be used as indicators of such chemical processes. For instance, (E)-2formylcinnamaldehyde and 6H-dibenzo [b,d]pyran-6-one are typical by-products of naphthalene and phenanthrene oxidation processes, respectively [START_REF] Lee | Quantification and seasonal pattern of atmospheric reaction products of gas phase PAHs in PM 2.5[END_REF][START_REF] Sasaki | Products of the gas phase OH and NO 3 radical initiated reactions of naphthalene[END_REF].

Usually, molecular ratios between PAH derivatives and parent PAHs, or between well-known secondary and primary compounds are calculated in order to highlight the influence of primary or secondary oxy-and nitro-PAHs sources or to study the potential origin of these compounds. For instance, the concentration ratio of 2-nitrofluoranthene (2-NFlt, a secondary compound, [START_REF] Arey | The formation of nitro-PAH from the gas-phase reactions of fluoranthene and pyrene with the OH radical in the presence of NOx[END_REF][START_REF] Atkinson | Kinetics and nitro-products of the gas-phase OH and NO 3 radical-initiated reactions of naphthalene-d8, fluoranthene-d10, and pyrene[END_REF] to 1-nitropyrene (1-NP, a primary compound from diesel exhaust, [START_REF] Keyte | On-road traffic emissions of polycyclic aromatic hydrocarbons and their oxy-and nitro-derivative compounds measured in road tunnel environments[END_REF] has been extensively used to assess the primary vs secondary sources of nitro-PAHs in ambient air (e.g. [START_REF] Albinet | Polycyclic aromatic hydrocarbons (PAHs), nitrated PAHs and oxygenated PAHs in ambient air of the Marseilles area (South of France): Concentrations and sources[END_REF]Albinet et al., , 2008a;;[START_REF] Bamford | Nitro-polycyclic aromatic hydrocarbon concentrations and sources in urban and suburban atmospheres of the Mid-Atlantic region[END_REF][START_REF] Bandowe | PM 2.5bound oxygenated PAHs, nitro-PAHs and parent-PAHs from the atmosphere of a Chinese megacity: Seasonal variation, sources and cancer risk assessment[END_REF][START_REF] Ciccioli | Formation and transport of 2-nitrofluoranthene and 2-nitropyrene of photochemical origin in the troposphere[END_REF][START_REF] Huang | Phase distribution, sources and risk assessment of PAHs, Nitro-PAHs and Oxy-PAHs in a rural site of Pearl River Delta region, China[END_REF][START_REF] Marino | Nitro-PAH in ambient particulate matter in the atmosphere of Athens[END_REF]Ringuet et al., 2012a, b;[START_REF] Wang | Nitrated polycyclic aromatic hydrocarbon pollution during the Shanghai World Expo 2010[END_REF]. Considering the same degradation rates for both compounds [START_REF] Fan | Photostability of nitropolycyclic aromatic hydrocarbons on combustion soot particles in sunlight[END_REF], a ratio of [2-NFlt]/[1-NP] higher than 5 indicates the predominance of secondary formation

M A N U S C R I P T A C C E P T E D

ACCEPTED MANUSCRIPT 4 of nitro-PAHs while, a ratio lower than 5 highlights the strong influence of primary nitro-PAH emission sources. Quinones-to-parent PAH concentration ratios have also been investigated in order to evaluate the photochemical formation of oxy-PAHs during long range transport of air masses [START_REF] Alam | Using atmospheric measurements of PAH and quinone compounds at roadside and urban background sites to assess sources and reactivity[END_REF][START_REF] Alam | Investigating PAH relative reactivity using congener profiles, quinone measurements and back trajectories[END_REF][START_REF] Keyte | On-road traffic emissions of polycyclic aromatic hydrocarbons and their oxy-and nitro-derivative compounds measured in road tunnel environments[END_REF].

The overall objective of this work was to identify specific oxy-and nitro-PAHs, based on ambient air field analysis combined with literature knowledge, that could further be used as molecular markers of PAH oxidation and of secondary organic aerosol (SOA) formation in typical urban environment. The individual annual concentration trends of these compounds, specifically for substances known to be primary emitted or, conversely, by-products of secondary processes, together with characteristic polycyclic aromatic compound (PAC: PAHs, nitro-and oxy-PAHs) diagnostic ratios has also been investigated to evaluate the primary vs secondary sources of oxyand nitro-PAHs. This work is an additional analysis of the PAC data already reported in a previous paper [START_REF] Tomaz | Source apportionment of PM 10 in a north-western Europe regional urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions[END_REF].

Experimental

Sampling site

The measurement site was located at the urban background sampling station of "Les

Frênes", (45° 09' 41" N, 5° 44' 07" E, 220 m above sea level) in Grenoble (France) (Fig. A1), considered as the most densely populated urban area of the French Alps. The city is surrounded by three mountainous areas. Earlier studies showed that residential heating, mainly biomass combustion, accounts for the main source of PM 2.5 during winter [START_REF] Favez | Intercomparison of source apportionment models for the estimation of wood burning aerosols during wintertime in an Alpine city (Grenoble, France)[END_REF](Favez et al., , 2012)). In addition to traffic and residential emissions, a cement and two power plants are also local PM emitters. The frequent formation of thermal inversion layers in the Grenoble valley may lead to the stagnation of pollutants enhancing the PM pollution events at ground level.

Sample collection

Samples have been collected every third day for one year from 01/02/2013 to 01/03/2014, using two high volume samplers implemented in parallel (DA-80, Digitel) (sampling duration 24 h, 30 m 3 h -1 , PM 10 sampling head). ) were analyzed by ion chromatography according to [START_REF] Jaffrezo | Seasonal variations of the water soluble organic carbon mass fraction of aerosol in the two valleys of the French Alps[END_REF]. Levoglucosan was quantified using HPLC-PAD [START_REF] Tomaz | Source apportionment of PM 10 in a north-western Europe regional urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions[END_REF] and EC /OC were measured using a Sunset lab analyzer using the EUSAAR-2 thermal protocol [START_REF] Cavalli | Toward a standardized thermal-optical protocol for measuring atmospheric organic and elemental carbon: the EUSAAR protocol[END_REF].

PAHs, oxy-PAHs and nitro-PAHs were quantified using UPLC/UV-Fluorescence and GC/NICI-MS, respectively, after PLE or QuEChERS-like extractions. Details of the analytical procedures used have been previously reported [START_REF] Albinet | Simultaneous analysis of oxygenated and nitrated polycyclic aromatic hydrocarbons on standard reference material 1649a (urban dust) and on a natural ambient air samples by gas chromatography-mass spectrometry with negative ion chemical ionization[END_REF][START_REF] Albinet | A simple QuEChERS-like extraction approach for molecular chemical characterization of organic aerosols: application to nitrated and oxygenated PAH derivatives (NPAH and OPAH) quantified by GC-NICIMS[END_REF][START_REF] Tomaz | Source apportionment of PM 10 in a north-western Europe regional urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions[END_REF] and are available in the Supplementary Information A (Tables A1, A2, A3 and A4).

Quality assurance and quality control

Accuracy of the analytical methods used in this work have been validated by the participation to national and international inter-comparison exercises for the analysis of PAH,

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT
6 EC/OC and levoglucosan. Results obtained were all in good agreement with reference values [START_REF] Panteliadis | ECOC comparison exercise with identical thermal protocols after temperature offset correction -instrument diagnostics by in-depth evaluation of operational parameters[END_REF][START_REF] Favez | Emission of PAHs, Nitro-PAHs and Oxy-PAHs, from residential honeycomb coal briquette combustion[END_REF]Verlhac and Albinet, 2015).

PAH extraction efficiencies were evaluated following the EN 15549 and CEN/TS 16645 standard methods (CEN, 2008[START_REF] Cen | Ambient air -Method for the measurement of benz[a]anthracene, benzo[bfluoranthene, benzo[jfluoranthene, benzo[END_REF]. Oxy-and nitro-PAH extraction efficiencies were quantified using a certified solid material (urban dust, NIST SRM 1649b, Tables A5 to A7) and results were in good agreement with NIST values and with those previously reported in the literature (Albinet et al., 2014 and references therein). Additionally, an evaluation of the calibration drift and blank contaminations was performed for data validation purposes. All collected data was corrected using field blanks. Analytical method details are reported in the Supplementary Information A [START_REF] Tomaz | Source apportionment of PM 10 in a north-western Europe regional urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions[END_REF].

Finally, the concentration values of OC, EC, anions, cations, oxalate, levoglucosan, 21

PAHs, 27 oxy-PAHs and 32 nitro-PAHs were validated and discussed in this work.

Results and discussion

Overview of PM chemical composition and pollution events

The year 2013 was affected by two severe national PM pollution events (PM 10 concentrations > 50 µg m -3 for at least 3 consecutive days) occurring during the cold season (defined as the period from January 1 st to April 10 th and from the October 1 st to December 31 st 2013). The first PM pollution event could be divided into two parts (Fig. 1):

-February-March (02/25/2013-03/05/2013) with 63 µg m -3 of PM 10 concentration levels on average and with a large contribution of OM to the total PM composition and the presence of a thermal inversion layer.

-March-April (03/24/2013-04/08/2013) with PM 10 concentration levels around 50 µg m -3 and with a large contribution of secondary inorganic species (ammonium nitrate and sulfate) to the total PM composition and the absence of thermal inversion layer.

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 7
Similarly to the first PM pollution event, a second event was observed in December, with PM 10 concentrations about 56 µg m -3 on average and with a large contribution of organic matter (OM) to the total PM composition and the occurrence of thermal inversion layers over a long period (10 consecutive days).

Annual trends of individual compounds

The annual concentration trends of individual compounds were similar for all PAHs, oxy-and nitro-PAHs, with higher concentrations observed in winter and autumn than in spring and summer (Figs. 2, A6 to A26). Additional emission sources, namely residential heating, and the occurrence of thermal inversion layers in the Grenoble valley leading to the accumulation of pollutants (Fig. 1) explain the high concentrations observed in winter and autumn. Lower concentrations in summer and spring are due to lower PAC emissions and their degradation by photochemical processes, as already highlighted in a previous paper [START_REF] Tomaz | Source apportionment of PM 10 in a north-western Europe regional urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions[END_REF].

Overall, significant correlations (Pearson coefficient R>0.6, n=123, p<0.05, Supplementary Information B, Table B1) between known primary compounds such as EC, NO, levoglucosan, PAHs and a large number of oxy-(18 compounds over 27 oxy-PAHs) and nitro-PAHs (12 compounds over 32 nitro-PAHs) were observed (total concentrations for PACs: gaseous + particulate phases). However, these results should be discussed cautiously to avoid spurious deductions from the statistical analysis. As an example, the concentrations of levoglucosan, a specific marker of biomass burning [START_REF] Simoneit | Levoglucosan, a tracer for cellulose in biomass burning and atmospheric particles[END_REF], significantly correlated with those of 1nitropyrene (R=0.89, p<0.05, n=123, Table B1), which is a well-known marker of diesel emissions [START_REF] Keyte | On-road traffic emissions of polycyclic aromatic hydrocarbons and their oxy-and nitro-derivative compounds measured in road tunnel environments[END_REF]. In addition, these correlations could also indicate that these compounds are sorbed together or have similar fates. The local atmospheric dynamic of the valley with the frequent formation of inversion layers is one of the key parameter leading to such correlations (Fig. 1).

Only three substances, namely phthaldialdehyde, biphenyl-2,2'-dicarboxaldehyde, and 1,2naphthoquinone, showed a distinct annual pattern with higher concentrations during spring and summer than in winter and autumn (Figs. 2 and A12, A13, A14). These specific patterns seem to be related to their formation by secondary photochemical processes:

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 8
-Phthaldialdehyde is known as a second generation product of naphthalene photooxidation (i.e. from the oxidation of (E)-2-formylcinnamaldehyde) [START_REF] Bunce | Reaction of naphthalene and its derivatives with hydroxyl radicals in the gas phase[END_REF][START_REF] Chan | Secondary organic aerosol formation from photooxidation of naphthalene and alkylnaphthalenes: implications for oxidation of intermediate volatility organic compounds (IVOCs)[END_REF][START_REF] Kautzman | Chemical composition of gas-and aerosol-phase products from the photooxidation of naphthalene[END_REF][START_REF] Lee | Unique products from the reaction of naphthalene with the hydroxyl radical[END_REF]Wang et al., 2007a).

To the best of our knowledge, phthaldialdehyde has been observed in the emissions of primary combustion sources (wood burning) only at very low concentration levels [START_REF] Nalin | Fast oxidation processes from emission to ambient air introduction of aerosol emitted by residential log wood stoves[END_REF]. The non-correlation, on yearly basis (R=0.2, n=123, p>0.05), between (E)-2formylcinnamaldehyde and phthaldialdehyde shows that the latter is also a secondary byproduct of other parent compounds and not only PAHs. For instance, phthaldialdehyde has been identified as an oxidation product from tolualdehyde photolysis [START_REF] Clifford | The atmospheric photolysis of o-tolualdehyde[END_REF].

-1,2-Naphthoquinone has been identified in diesel and gasoline exhausts [START_REF] Cho | Determination of four quinones in diesel exhaust particles, SRM 1649a, and atmospheric PM 2.5[END_REF][START_REF] Jakober | Quinone emissions from gasoline and diesel motor vehicles[END_REF][START_REF] Oda | Mobile sources of atmospheric polycyclic aromatic hydrocarbons in a roadway tunnel[END_REF] but has also been reported as an OHinitiated oxidation product of naphthalene [START_REF] Lee | Unique products from the reaction of naphthalene with the hydroxyl radical[END_REF].

-Biphenyl-2,2'-dicarboxaldehyde has been identified in primary emissions from biomass burning at very low concentration levels [START_REF] Nalin | Fast oxidation processes from emission to ambient air introduction of aerosol emitted by residential log wood stoves[END_REF]. It has been reported as a secondary compound from the gas phase reaction of phenanthrene with OH radical [START_REF] Lee | Formation of oxidized products from the reaction of gaseous phenanthrene with the OH radical in a reaction chamber[END_REF], NO 3 radical (Wang et al., 2007b), and O 3 [START_REF] Kwok | Reactions of gas-phase phenanthrene under simulated atmospheric conditions[END_REF]Wang et al., 2007b;[START_REF] Zhang | Homogeneous and heterogeneous reactions of phenanthrene with ozone[END_REF]. This compound has also been identified as a by-product of the heterogeneous reaction of phenanthrene with ozone [START_REF] Perraudin | Identification and quantification of ozonation products of anthracene and phenanthrene adsorbed on silica particles[END_REF][START_REF] Zhang | Homogeneous and heterogeneous reactions of phenanthrene with ozone[END_REF].

Formation of nitro-PAHs: study of the [(2+3)-NFlt]/[1-NP] ratio

The primary emissions vs the gas-phase formation of nitro-PAHs in ambient air was assessed using the (2+3)-nitrofluoranthene-to-1-nitropyrene ([(2+3)-NFlt]/[1-NP]) concentration ratio (total concentrations) (Fig. 3).

The (from January to April 2013) while previous studies reported high ratio values in summer and low ones in winter [START_REF] Albinet | Polycyclic aromatic hydrocarbons (PAHs), nitrated PAHs and oxygenated PAHs in ambient air of the Marseilles area (South of France): Concentrations and sources[END_REF](Albinet et al., , 2008a, b;, b;[START_REF] Bamford | Nitro-polycyclic aromatic hydrocarbon concentrations and sources in urban and suburban atmospheres of the Mid-Atlantic region[END_REF][START_REF] Dimashki | Measurements of nitro-PAH in the atmospheres of two cities[END_REF]Ringuet et al., 2012a, b). Only a few studies reported similar results with ratios larger than 5 in winter; that were explained by specific conditions such as pollutant accumulation over several days (Albinet et al. 2008a;[START_REF] Lin | Sources, transformation, and health implications of PAHs and their nitrated, hydroxylated, and oxygenated derivatives in PM 2.5 in Beijing[END_REF].

Here, the two highest ratios were observed during the March-April period together with high (Fig. 3). The organic matter (OM) formed the main fraction of PM 10 mass in this last period and originated mainly from biomass burning (residential combustion), as indicated by the high concentrations of levoglucosan. Under these conditions, nitrate was probably emitted by biomass burning as supported by its correlation with levoglucosan (R=0.85, p>0.05, n=4). This result shows the existence of a link between the secondary origin of nitrate and the sources of nitro-PAHs in relation with photochemical conditions. High NO 3 -concentrations could be explained by the oxidation of NO 2 , that may be also able to react with PAHs to form nitro-PAHs after initiation by OH and/or NO 3 radicals.

In summer, several [(2+3)-NFlt]/[1-NP] ratios were higher than 5 for periods characterized by high SO 4 2contributions to PM 10 , indicating the impact of atmospheric secondary processes with an enhancement of the photochemical activity, possibly leading to the secondary formation of nitro-PAHs.

Cold season: influence of primary emissions and evidence of secondary formation processes under specific conditions

As discussed in the paragraph 3.3., the February-March period of the first PM pollution event seemed mainly influenced by primary emissions. In fact, wind speeds were low during this period, Zefir tool [START_REF] Petit | An Igor-based tool for comprehensive evaluation of the geographical origins of atmospheric pollution: wind and trajectory analyses[END_REF], sources of PM 10 seemed mainly local (Fig. 5). The low dispersion together with the formation of thermal inversion layers (Fig. 1) enhanced the stagnation of the pollution in the valley and the increase of primary pollutant concentrations. High concentrations of many oxy-(14 oxy-PAHs) and nitro-PAHs (6 nitro-PAHs) were observed during this period. Some substances showed their highest concentrations of the year at that time, such as 1naphthaldehyde, 1,4-naphthoquinone, 9-nitroanthracene, 2-nitroanthracene, 5nitroacenaphthene, and 7-nitrobenz[a]anthracene (Figs. 4, A12, A21, A22, A23, A24). All of them may originate from both primary and secondary sources [START_REF] Arey | Nitroarene products from the gasphase reactions of volatile polycyclic aromatic hydrocarbons with the OH radical and N 2 O 5[END_REF][START_REF] Atkinson | Atmospheric chemistry of gas-phase polycyclic aromatic hydrocarbons: formation of atmospheric mutagens[END_REF][START_REF] Chen | Formation of nitroanthracene and anthraquinone from the heterogeneous reaction between NO 2 and anthracene adsorbed on NaCl particles[END_REF][START_REF] Chen | Increase in polycyclic aromatic hydrocarbon (PAH) emissions due to briquetting: A challenge to the coal briquetting policy[END_REF]Huang et al., 2013;[START_REF] Karavalakis | The impact of soy-based biodiesel on PAH, nitro-PAH and oxy-PAH emissions from a passenger car operated over regulated and nonregulated driving cycles[END_REF][START_REF] Liu | Mitigation of PAH and Nitro-PAH emissions from nonroad diesel engines[END_REF][START_REF] Sauret-Szczepanski | Smog chamber study of acenaphthene: gas/particle partition measurements of the products formed by reaction with the OH radical[END_REF].The potential occurrence of secondary formation of nitro-PAHs is indeed highlighted by the high [(2+3)-NFlt]/[1-NP] ratio observes, with a value of 11, when PM 10 concentrations were the highest (on February 28) (Fig. 3). It is therefore likely that both primary emissions and secondary processes were enhanced during this period.

In comparison with the first part of the PM pollution event, during the March-April period, lower concentrations of primary compounds such as, NO, NO 2 , levoglucosan and 1-nitropyrene were observed, together with a lower contribution of OM to the PM concentrations. Conversely, concentrations of secondary species such as ammonium, nitrate and sulfate, (E)-2formylcinnamaldehyde [START_REF] Aschmann | Study of the atmospheric chemistry of 2formylcinnamaldehyde[END_REF][START_REF] Kautzman | Chemical composition of gas-and aerosol-phase products from the photooxidation of naphthalene[END_REF] and, as shown before,

[(2+3)-NFlt]/[1-NP] ratios (from 7 to 13), were significantly higher (Figs. 3 and4). This period was thus characterized by the transport of aged air masses, coming from the N-NE, to the sampling site of Les-Frênes (Figs. A27 andA28) and the absence of thermal inversion layers in the sampling area (Fig. 1). The secondary formation of nitro-PAHs, together with the nitrate chemistry, and, to a lesser extent, of oxy-PAHs, may have occurred during the transport and aging of these air masses.

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT

11

The December pollution event was characterized by a dramatic increase of the concentrations of all pollutants except ozone (Figs. 1, 2, 3, 4 and A4 to A19). Concentrations of primary species such as EC (7.80 µg m -3 ), some PAHs (11 PAHs, from 0.4 ng m -3 for 2methylfluoranthene to 48.6 ng m -3 for 2-methlynaphthalene), levoglucosan (2963 ng m -3 ), 1nitropyrene (46 pg m -3 ) and NO (about 110 µg m -3 ) showed their highest yearly concentrations, highlighting the impact of local combustion sources like residential heating (notably wood combustion) and traffic. Additionally, low wind speed (< 10 km h -1 ) and the occurrence of strong and durable thermal inversion layers (from 18 to 24 h per day during the overall PM event)

promoted the accumulation of pollutants in the Grenoble valley (Figs. 1, 5 andA3).

The highest concentrations of several individual nitro-and most of the oxy-PAHs were observed during this event (Figs. 4,A12 to A26). Total oxy-PAH concentrations (Σ 27 oxyPAHs) was even higher than total PAH concentrations (Σ 21 PAHs) i.e. about 183 and 136 ng m -3 , respectively [START_REF] Tomaz | Source apportionment of PM 10 in a north-western Europe regional urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions[END_REF]. Concentrations of many PAH derivatives were more than 10 times higher (ranging from 11 to 43) than the annual mean concentration levels, including phthalic As an example of evidences of secondary oxy-PAH processes during this period, (E)-2formylcinnamaldehyde concentrations were the highest during this PM pollution event (Fig. 6).

This compound originates from naphthalene oxidation [START_REF] Nishino | 2-Formylcinnamaldehyde formation yield from the OH radical-initiated reaction of Naphthalene: effect of NO 2 concentration[END_REF][START_REF] Sasaki | Products of the gas phase OH and NO 3 radical initiated reactions of naphthalene[END_REF]Wang et al., 2007a). No significant correlation with any primary compound was observed on the annual scale (e.g. with levoglucosan and 1-nitropyrene; R < 0.4, excluding the December event, n=120, p < 0.05) confirming the secondary origin of this compound. Interestingly, concentrations of phthaldialdehyde increased when its precursor concentration decreased and, were maximum on the 12/16/2013. Here again, the secondary processing during the PM pollution event seemed obvious and preponderant. Besides, phthalic anhydride, an oxidation product of phthaldialdehyde [START_REF] Wang | Kinetics and products of photolysis and reaction with OH radicals of a series of aromatic carbonyl compounds[END_REF], was measured in large concentration (42 ng m -3 ) with a maximum value reached at the same time as (E)-2-formylcinnamaldehyde. Discrepancy of temporal concentration trend was probably due to the dual origin of phthalic anhydride with also its direct emission by combustion processes and especially diesel exhaust (Fig. 6) [START_REF] Bayona | Characterization of polar polycyclic aromatic compounds in a heavy-duty diesel exhaust particulate by capillary column gas chromatography and high-resolution mass spectrometry[END_REF][START_REF] Sidhu | Endocrine disrupting chemical emissions from combustion sources: diesel particulate emissions and domestic waste open burn emissions[END_REF].

Warm season: Influence of photochemistry and identification of potential markers of PAH oxidation processes

Using both gaseous and particulate phase concentrations, the ratios of oxy-or nitro-PAHs to their parent PAH have been considered in order to investigate the PAH relative reactivity and the sources of oxy-and nitro-PAHs. Note that acenaphthenequinone and acenaphthenone may arise from the oxidation of both, acenaphthene and acenaphthylene but the latter one was not quantified in our study so, the ratios were calculated using acenaphthene only [START_REF] Zhou | Kinetics and products of the gas-phase reactions of acenaphthylene with hydroxyl radicals, nitrate radicals and ozone[END_REF]. The highest concentration ratios of the selected secondary candidate compounds were observed in July and August (Fig. 7, period framed). At the same time, [(2+3)-NFlt]/[1-NP] ratio was higher than 5 (with an average of 9) suggesting favorable photochemical conditions to induce a secondary formation of nitro-PAHs. Concentrations of oxalate, a marker of secondary processes for the organic matter [START_REF] Carlton | Atmospheric oxalic acid and SOA production from glyoxal: Results of aqueous photooxidation experiments[END_REF][START_REF] Legrand | Origin of C2-C5 dicarboxylic acids in the European atmosphere inferred from yearround aerosol study conducted at a west-east transect[END_REF][START_REF] Warneck | In-cloud chemistry opens pathway to the formation of oxalic acid in the marine atmosphere[END_REF], were also high (and the highest of the year, from 350 to 401 ng m -3 , Fig. 7) confirming the influence of the photochemical activity during this period. The whole period was characterized by long range transport, with air masses coming from the North or the South of the sampling location (e.g. Figs.

A29 and A30). Significant correlations between total concentrations of 6H-dibenzo [b,d]pyran-6one with 9,10-anthraquinone (R=0.93), 3-nitrophenanthrene (R=0.96) were obtained (from July to August, n=21, p<0.05). Only biphenyl-2,2'-dicarboxaldehyde was not significantly correlated with any identified secondary candidate compounds. Different chemical processes, unknown at the moment, may be involved for this last compound.

All of these compounds have been previously reported in the literature as by-products of gas phase and/or heterogeneous reactions of PAHs (Lee andLane, 2009, 2010;[START_REF] Perraudin | Identification and quantification of ozonation products of anthracene and phenanthrene adsorbed on silica particles[END_REF][START_REF] Reisen | Reactions of hydroxy radicals and ozone with acenaphthene and acenaphthylene[END_REF][START_REF] Zhang | Nitration of particle-associated PAHs and their derivatives (nitro-, oxy-, and hydroxy-PAHs) with NO 3 radicals[END_REF][START_REF] Zhang | Homogeneous and heterogeneous reactions of anthracene with selected atmospheric oxidants[END_REF]. However, except biphenyl-2,2'-dicarboxaldehyde, they also have all been identified in primary emissions from biomass burning or vehicle exhaust [START_REF] Alves | Polycyclic aromatic hydrocarbons (PAHs) and their derivatives (oxygenated-PAHs, nitrated-PAHs and azaarenes) in size-fractionated particles emitted in an urban road tunnel[END_REF][START_REF] Bayona | Characterization of polar polycyclic aromatic compounds in a heavy-duty diesel exhaust particulate by capillary column gas chromatography and high-resolution mass spectrometry[END_REF]Fine et al., 2001;Fine et al., 2002;Fitzpatrick et al., 2007;[START_REF] Nalin | Fast oxidation processes from emission to ambient air introduction of aerosol emitted by residential log wood stoves[END_REF]Zielinska et al., 2004).

The question to be answered is to identify which compounds could be here considered as a marker of oxidative processes involving PAHs to better understand the sources of these toxic observed at very low concentration levels in the atmosphere and quite difficult to quantify. 9,10-Anthraquinone does not seem to be a good marker candidate of PAH oxidation processes as it has been reported as largely emitted by vehicular emissions [START_REF] Alves | Polycyclic aromatic hydrocarbons (PAHs) and their derivatives (oxygenated-PAHs, nitrated-PAHs and azaarenes) in size-fractionated particles emitted in an urban road tunnel[END_REF]. [START_REF] Lee | Quantification and seasonal pattern of atmospheric reaction products of gas phase PAHs in PM 2.5[END_REF] suggested the use of 6H-dibenzo[b,d]pyran-6-one as a SOA marker for phenanthrene photooxidation. Here, in July-August 2013, low concentrations of this compound were observed during the days significantly impacted by primary emission sources (with high EC concentrations, Fig. 7). This indicates that it may mainly arise from secondary reactions and not from primary emission sources. 3-Nitrophenanthrene has also been reported as a by-product of SOA formation from phenanthrene oxidation (Di Filippo et al. 2010;[START_REF] Lin | Sources, transformation, and health implications of PAHs and their nitrated, hydroxylated, and oxygenated derivatives in PM 2.5 in Beijing[END_REF].

Conclusion

Individual concentrations of about 60 oxy-and nitro-PAHs were measured over a year in Grenoble-Les Frênes, jointly with specific primary or secondary aerosol species. In cold period, oxy-and nitro-PAH concentrations were controlled by emissions from residential heating (e.g. The approach to identify specific PAH derivatives typical of PAH oxidation and SOA formation based on ambient air field analysis combined with literature knowledge proposed here is limited by the time resolution of the PAC measurements as well by the current knowledge available in the literature. Further work will be needed to document the behavior of these compounds in the atmosphere using both, laboratory experiments and field measurements with a higher time resolution to understand the physicochemical processes involved including gas to particle phase conversion.

  annual mean value of the [(2+3)-NFlt]/[1-NP] concentration ratio of 6.3 ± 3.9 indicates a slight predominance of secondary formation of nitro-PAHs in the gas phase. The highest [(2+3)-NFlt]/[1-NP] values (>10) were surprisingly observed during the winter period and in early spring

  PM 10 concentration levels. During this period, the [(2+3)-NFlt]/[1-NP] ratio was significantly correlated with NO 3 -(Pearson coefficient: R=0.75, p<0.05, n=6), suggesting a link between the secondary formation of nitro-PAHs and the nitrate chemistry. Conversely, at the end of the February-March PM pollution event, a large NO 3 -contribution was observed but no significant correlation between nitrate and the [(2+3)-NFlt]/[1-NP] ratio was noticed (R=-0.3, p>0.05, n=4)

  value of 5 km h -1 , inducing a low atmospheric dispersion. As shown by the results of the non-parametric wind regression analysis(NWR, Henry et al., 2009) performed using

  anthraquinone, 2-methylanthraquinone, 9-phenanthrenecarboxaldehyde, 2-nitro-9fluorenone, benzo[a]fluorenone, benzo[b]fluorenone, benzanthrone, 1-pyrenecarboxaldehyde, aceanthrenequinone, benz[a]anthracene-7,12-dione, and 9-methyl-10-nitroanthracene. Low wind speeds and any potential long range transport could not alone explain the dramatic increase of their concentrations (Fig.5). Parent PAHs were accumulated for several consecutive days due to the low wind speeds and the long-time inversion layers formed (Figs.1 and A3). In the presence of atmospheric oxidants -such as OH radical, for which concentration levels are usually high in urban environments even in winter(Heard et al., 2004) -the accumulation of PAHs allowed enough time for their oxidation, the formation of by-products such as oxy-PAHs through gaseous and/or heterogeneous processes and their accumulation. Interestingly, secondary processes involved during the December PM pollution event seemed different than those involved during the February-March. Actually, only oxy-PAHs and no nitro-PAHs were effectively formed, as supported by the low [(2+3)-NFlt]/[1-NP] ratio value of 3.9.

  Overall, only concentration ratios to their parent PAHs of6H-dibenzo[b,d]pyran-6-one, 3nitrophenanthrene, biphenyl-2,2'-dicarboxaldehyde, 9,10-anthraquinone, 2-methyl-4nitronaphthalene and 6-nitrobenzo[a]pyrene showed a trend with significant higher values exclusively in the warm season (defined as the period from the 04/11/2013 to the 09/30/2013) A33, A34 and A35). This suggests the dominant secondary origin of these specific species. By comparison, known primary compounds such as 1-nitropyrene and benz[a]anthracene-7,12-dione did not exhibit the same trend. Note that, peak values of the concentration ratios in November were observed for all compounds and could be explained by the very low concentration levels of both parent PAHs and PAH derivatives (inducing larger uncertainties).

  marker compounds should mainly be specific of secondary processes, relatively stable in the atmosphere and easily quantified. 2-Methyl-4-nitronaphthalene and 6nitrobenzo[a]pyrene are rejected according to these criteria, as they are usually (like in this study)

Finally

  , only6H-dibenzo[b,d]pyran-6-one, biphenyl-2,2'-dicarboxaldehyde and 3nitrophenanthrene remain interesting candidates as markers of secondary reactions. 6H-Dibenzo[b,d]pyran-6-one has also the advantage to be present in large concentrations in the atmosphere by comparison to nitro-PAHs (about 2 orders of magnitude more abundant) allowing an easier quantification.

  biomass burning) together with secondary processes occurring under specific conditions such as pollutant accumulation or long range transport of air masses. The study of the time trends of the ratios of oxy-or nitro-PAHs on parent PAH concentrations highlighted higher ratios of a series of compounds in summer, concurrently with high oxalate concentrations. By combination with literature data, 6H-dibenzo[b,d]pyran-6-one, biphenyl-2,2'-dicarboxaldehyde and 3nitrophenanthrene have been identified as probably good marker candidates of PAH oxidation

Fig. 1 .

 1 Figure Captions

Fig. 2 .Fig. 3 .

 23 Fig. 2. Annual trends (2013) of total concentrations (gaseous and particulate phases) of phthaldialdehyde, 1,2-naphthoquinone (only in gas phase), biphenyl-2,2'-dicarboxaldehyde (only in gas phase) and indeno[1,2,3-cd]pyrene at the urban background station of Grenoble-Les Frênes.

Fig. 4 .

 4 Fig. 4. Annual trends (2013) of (E)-2-formylcinnamaldehyde (2-FCin), 6H-dibenzo[b,d]pyran-6one, 9-nitroanthracene, 1-naphthaldehyde and 1,8-naphthalic anhydride total concentrations together with NO at the urban background station of Grenoble-Les Frênes. Yellow tinted background areas show the periods of severe PM pollution events.

Fig. 5 .

 5 Fig. 5. Wind rose (km h -1 ) (left) and NWR for PM 10 (µg m -3 ) (right) from the 02/25/2013 to the 03/06/2013 (A) and from the 12/09/2013 to the 12/19/2013 (B).

Fig. 6 .

 6 Fig. 6. Temporal evolution of (E)-2-formylcinnamaldehyde, phthaldialdehyde and phthalic anhydride concentrations (in both gaseous and particulate phases) during the severe PM

Acknowledgments

The authors wish to thank the French Ministry of Environment (MEEM) and the French Ministry of Research for their financial support. This work was done as part of the LCSQA activities (French reference laboratory for air quality monitoring). Authors thank Air Rhône-Alpes for PAC sampling, air quality and meteorological data, Nadine Guillaumet and Noémie Nuttens for PAH analysis and Nathalie Bocquet and Robin Aujay for sample preparation.