Benjamin Mathon
email: benjamin.mathon@gipsa-lab.inpg.fr

Patrick Bas
email: patrick.bas@gipsa-lab.inpg.fr

Francois Cayre
email: francois.cayre@gipsa-lab.inpg.fr

Practical Performance Analysis of Secure Modulations for WOA Spread-Spectrum based Image Watermarking

Keywords: H.4 [Information Systems Applications]: Miscellaneous; D.2.8 [Software Engineering]: Metrics-performance measures Experimentation, Performance, Security Security, Spread-Spectrum, Still Images

) and demonstrate the relevance of these modulations in a real-world application.

INTRODUCTION

This paper deals with the Watermark Only Attack (WOA) where an attacker has only access to watermarked contents and tries to estimate the secret key (see [START_REF] Cayre | Watermarking security: Theory and practice[END_REF] for details). In particular, he does not know about the embedded messages and/or the original works. A security level [START_REF] Cayre | Watermarking security: Theory and practice[END_REF] is the number of contents required to produce an estimation of the secret that is better by an order of magnitude. A security level MM&Sec'07, September 20-21, 2007, Dallas, Texas, USA.

essentially deals with unsecure data-hiding methods. A security class [START_REF] Cayre | Kerckhoffs based embedding security classes[END_REF] deals with the very nature of the secret that can be learned (or not) from watermarked contents. From [START_REF] Cayre | Kerckhoffs based embedding security classes[END_REF], one can devise four embedding security classes in the WOA framework:

• stego-security: the embedding method fulfills Cachin's requirement for steganography (which is obviously to be straightforwardly fitted into a WOA framework).

In particular, one cannot make any difference between host and watermarked contents;

• subspace-security: assuming the secret key lives in some private space, one cannot exhibit this space by observing watermarked contents -but steganalysis is possible;

• key-security: an attacker is able to disclose the private secret subspace but cannot make decision about the very secret that lives in it -optimal watermark removal is possible but security attacks are not;

• insecurity: an attacker is able to estimate the very secret that was used to embed the message into the host contents.

Recent works have proposed the use of two new modulations for spread-spectrum-based watermarking. These modulations were shown to belong to a better security class than unsecurity [1,[START_REF] Bas | Natural watermarking: a secure spread spectrum technique for woa[END_REF]. Most specifically, Natural Watermarking (NW) can be either stego-secure or subspace-secure. Circular Watermarking (CW) is key-secure. SS and ISS, on the other hand, were shown to be unsecure.

The very goal of this paper is to experiment a practical implemenation of such secure modulations in the WOA framework. Results were collected with the help of a 2000-image database. This paper is organized as follows: Sec. 2 recalls the basics of spread-spectrum based watermarking and the modulations that are to be used throughout this paper. Sec. 3 shows attacks that the new modulations prevent. Sec. 4 presents an implementation of the practical algorithm we used for benchmarking purposes against security attack and robustness against JPEG compression. Finally, Sec. 5 draws some conclusions about the present results.

SPREAD-SPECTRUM WATERMARKING

Let m ∈ [0, 1] Nc be a binary message to be embedded into a host content x ∈ R Nv to produce a watermarked version y of x. The watermark embedding operation can always be seen as follows:

y = x + w, (1)
where w is the watermark signal. One achieves the construction of w with the help of a modulation s : [0, 1] → R:

w = Nc X i=1 uis(m(i)), (2)
where the components of the ui ∈ R Nv are N (0, 1) realvalued carriers s.t.:

∀i = j < ui|uj >= 0, (3)
where < .|. > denotes the usual scalar product. The carriers are the output of a PRNG seeded with K the private secret key. From an attacker point of view, there's no difference between estimating the carriers and getting K because the dimension of the carriers is set and public (see for example watermarking schemes based on replication of a fixed-length pattern [START_REF] Deguillaume | Method for the estimation and recovering from general affine transforms[END_REF]). Security attacks traditionally target estimation of the ui. In a spread-spectrum framework, the private subspace is obviously Span(ui) and the secret is the set of the ui. Distorsion is assessed by means of the WCR (Watermark-to-Content Ratio) in dB:

W CR = 10 log 10 " σ 2 w σ 2 x « , (4)
where σ 2 w (resp. σ 2 x ) is the variance of w (resp. x).

Message decoding aims at producing an estimate m of the original message by using the normalized correlation z:

z u i ,y ′ = 1 Nv X j ui(j)y ′ (j), (5)
where y ′ is a (possibly) attacked version of y. Estimated message is produced as follows:

m(i) = sign(z u i ,y ′ ) (6)

Unsecure modulations

Classical modulations, although unsecure, include Spread-Spectrum (SS) and Improved Spread Spectrum (ISS). SS modulation [START_REF] Cox | Secure spread spectrum watermarking for multimedia[END_REF] is the analogon of the BPSK modulation for communications:

sSS(m(i)) = γ(-1) m(i) . (7)
ISS [START_REF] Malvar | Improved spread spectrum: a new modulation technique for robust watermarking[END_REF] uses side-information to improve both robustness and error probability:

sISS(m(i)) = α(-1) m(i) -λ < x|ui > ui 2 , (8)
where α and λ are computed to achieve host-interference rejection and error probability minimization. Please refer to [START_REF] Malvar | Improved spread spectrum: a new modulation technique for robust watermarking[END_REF] to see how they are computed.

SS and ISS are unsecure, as they were already shown to allow for carriers estimation given enough watermarked images [START_REF] Cayre | Watermarking attack: Security of wss techniques[END_REF].

Secure modulations

Recently, Natural Watermarking (NW) and Circular Watermarking (CW) modulations were introduced to provide better security at the cost of some robustness. Contrary to ISS, NW uses side-information to enhance security:

sNW (m(i)) = " η(-1) m(i) < x|ui > | < x|ui > | -1 « < x|ui > ui 2 . (9)
When η = 1, NW belongs to the so-called stego-secure class and is suitable for steganography applications. When η > 1, NW is only subspace-secure (at best).

Decreasing security requirements, one can devise another modulation called CW, based on ISS:

sCW (m(i)) = α(-1) m(i) d(i) -λ Nvzx,u i ui , (10)
where α and λ are computed the same way than with ISS and d is generated at each embedding as follows from g ∼ N (0, 1):

d(i) = |g(i)| g . ( 11
)
This parameter is used to randomly spread the correlations of the mixed signals on the whole decoding regions. CW belongs to the so-called key-secure security class. Next section recalls what are embedding security classes.

SECURITY ATTACKS

The very problem of assessing data-hiding security involves the knowledge of several (possibly) watermarked contents.

Let No denotes this number of observations a pirate has access to. Stating the No watermarking operations columnwise, one has the following matrix relation:

Y = X + W = X + US, (12)
where S are the modulations of the embedded messages, U is the matrix of the carriers and X is the matrix of the host contents. Given Y the matrix of the watermarked contents, the problem of disclosing U and S is known as blind source separation (BSS). One particular family of BSS method is called independent component analysis (ICA). It performs well when the sources (i.e. the modulations) are independently drawn. Obviously, ICA is relevant in the case of WOA attack where the messages can be supposed to be independent.

The goal of NW and CW is to break this independence so that ICA returns random sources and, most of all, random mixing matrix (i.e. random carriers). The reader may refer to [START_REF] Cayre | Watermarking security: Theory and practice[END_REF] and [START_REF] Hyvarinen | Fast and robust fixed-point algorithm for independent component analysis[END_REF] for further details. Methods to solve BSS when the sources are dependent is the subject of ongoing works.

TESTS ON STILL IMAGES 4.1 Implementation

We do not pay any attention to the problem of synchronization. Rather, we focus on robustness and security. Consider we want to watermark images of size M × N pixels. Using 9/7 Daubechies wavelete lifting scheme, we are able to construct a signal xt ∈ R N t from the finest detail subbands.

The signal xt is known not to have Gaussian distribution. Since we need the Gaussian assumption for the host content when using NW, we use the central limit theorem to construct the host (asymptotically Gaussian) signal x ∈ R Nv :

x(i) = 2 √ 3Nt N t X j xt(j)ai(j), (13)
where the ai are pseudo-random uniformly-distributed vectors and the ratio 2 √ 3 is used to normalize w.r.t. the variance of a uniformly-distributed variable. We produce w, the watermark signal. It depends on x for ISS, NW and CW modulations. Proper retro-projection is performed to produce wt the version of w in the wavelet domain:

wt(i) = 2 √ 3Nt Nv X j w(j)aj(i). ( 14
)
Also note that we set the ai only quasi-orthogonal to one another for computing complexity reasons. Distorsion results will show the influence of this quasi-orthogonality compared to strict orthogonality. Finally, we obtain yt, the marked signal, by summation of xt and wt.

In

Measures and specifications

Distortion

Distortion was specified not to be less than a certain PSNR. We linked the PSNR and the WCR with the formulae obtained from Appendix. A. We were able to specify distorsion both for constant embedding strength and variable embedding strength [START_REF] Piva | DCT-based watermark recovering without resorting to the uncorrupted original image[END_REF]. Variable strength embedding is perfomed as follows:

∀ k, yt(k) = xt(k) + d|xt(k)|wt(k) (15)
We report on Tab. 1 the distorsion results we had for our database of 2000 images with constant embedding strength, we also report the mean and the standard deviation of the original/marked PSNR for all images. As shown in Sec. 4. We depict on Fig. 2 to Fig. 5 the histograms of the obtained PSNR for the four modulations and constant embedding strength. It is worth noting that CW, NW and ISS are all supposed to achieve the target PSNR on average. Further, since our projection over the ai is only quasi-orthogonal, we pay this imprecision by 0.15dB on average, plus some additional variation as shown by the results for SS which should have yield perfect target PSNR. We believe this is not a big issue in practice.

Robustness

Robustness was assessed using JPEG compression. It was not the goal of this paper to treat optimal removal attack. Robustness results against JPEG compression are reported on Fig. 6 Cost in robustness due to security requirement vary substantially according to the desired target JPEG quality factor. However, we believe secure modulations (although not yet optimized) are already usable in practice. Optimal robustness attack deals with cancelling estimated carriers projections in the estimated subspace and is subject to ongoing work for the CW modulation.

Security

If projection (Eq. 13) and retro-projection (Eq. 14) are considered known by the attacker, the BSS problem stated in Eq. 12 is to be solved in the Nv-D space. One has to keep the following general BSS limitations in mind:

• BSS can only recover the carriers up to the sign;

• BSS cannot recover the order of the carriers (WOA framework -one would need the knowledge of some messages to do so).

Therefore, since we try to estimate basis vectors of the private subspace, we can use the following measure of precision for estimation of the carriers [START_REF] Bas | Practical security analysis of dirty paper trellis watermarking[END_REF]:

S = 1 Nc X i `max 1 j |z(uj, ûi)| -max 2 j |z(uj, ûi)| ´, (16)
where ûi are the estimated carriers and max 1 j |z(uj, ûi)| (resp. max 2 j |z(uj, ûi)|) is the first (resp. second) maximal absolute value of the normalized correlation z(uj, ûi). The score S will be close to one if we performed accurate estimation of the carriers. It will vegetate at low values without convergence in the case of inaccurate estimation of the carriers. Security results are collected on Fig. 7.

To further illustrate our views, we plot on Fig. 8 to Fig. 12 the 2D-distribution of the y projected over the two carriers when Nc = 2. For ISS and CW modulations, we precise the NCR (Noise-to-Content Ratio) in dB we used [START_REF] Malvar | Improved spread spectrum: a new modulation technique for robust watermarking[END_REF]:

N CR = 10 log 10 " σ 2 n σ 2 x « . (17)
This parameter sets the strength of AWGN attack by signal n that the marked signal should resist.

One can see on Fig. 8 and Fig. 9 that both SS and ISS modulations produce clusters in the private space. This is why it gets possible to estimate the carriers (up to the sign and the order).

On Fig. 10, no cluster appear. Actually, the watermarked distribution has the same shape than the host one. This allows for superior security in the WOA framework. The used estimator is presented in Eq. 16. SS and ISS are confirmed not to be secure. CW and NW show good security also in practice. was obtained when specifying target PSNR on average. We think it is quite insightful to compare with Fig. 11 where target PSNR was reached exactly. In this case, NW should better be called constant-PSNR-NW. This enables to think of NW as a special case of CW, which it is actually. Note that this is consistent with the circularity definition given in [START_REF] Cayre | Kerckhoffs based embedding security classes[END_REF].

On Fig. 12, we illustrate key-security with CW modulation. Even if the attacker can disclose the private subspace, he cannot make any decision in it about where are the secret carriers. CW delimits the fine line between robustness and security.

Security and psycho-visual masking

To illustrate the versatile capabilities of our scheme, we depict on Fig. 13 the distribution of the projection of the y over the ui in the case variable strength embedding (Eq. 15) is used with CW modulation. As one can see, adding psycho-visual masking capabilities to our scheme does not impair security (the circularity property still remains).

Further, we depict on Fig. 14 the two versions of the same image watermarked with CW modulation, with and without variable strength embedding.

CONCLUSION

This work demonstrates that while high security requirement in the WOA framework may induce robustness loss, CW and NW secure modulations are good candidates for practical use in real-world applications. This somewhat mitigates the conclusions of [START_REF] Cayre | Kerckhoffs based embedding security classes[END_REF] where CW and NW where shown to be significantly outperformed by SS and ISS from the robustness point of view. This paper used quite little payloads for proof-of-concept purposes. However, we are in the process of using these modulations in a more involved image watermarking scheme with pattern replication and more decent payload sizes. Future works therefore include assessment of security against the pattern size / the number of pattern replications and a more detailed study of the robustness / security tradeoff in a scheme designed to handle geometrical distorsions.

[1] P. Bas and F. Cayre. Achieving subspace or key security for woa using natural or circular watermarking. In Proc. ACM Multimedia Security PSNR is 30dB. Modulation: CW.

Image Processing (ICIP'97), Santa Barbara, California, Oct. 1997.

APPENDIX A. DISTORSION SPECIFICATIONS

The goal of this section is to rely the WCR to the PSNR in order to achieve the desired PSNR, possibly on average (depending on the modulation). We give results both for constant embedding strenght and for variable embedding strength [START_REF] Piva | DCT-based watermark recovering without resorting to the uncorrupted original image[END_REF].

A.1 Constant embedding strength

The first point is that, thanks to the nice normalization of projection (Eq. 13), distorsion keeps constant in the wavelet domain and in the projected space: With renormalization against space dimensions, one gets:

wt 2 = w 2 = d 2 (18)
σ 2 w t = d 2 Nt (19)
M SE = Nt M × N σ 2 w t , (22)
therefore, PSNR equals:

P SN R = 10 log 10 255 2 N t M ×N σ 2 w t ! . ( 23
)
From previous equation, one gets:

σ 2 w = 255 2 M × N Nv 10 -P SN R 10 , (24)
which gives, once plugged into Eq. 4:

W CR = 10 log 10

" 255 2 σ 2 x × M × N Nv « -P SN R (25)
A.2 Variable embedding strength

From [START_REF] Piva | DCT-based watermark recovering without resorting to the uncorrupted original image[END_REF], distorsion varies with the absolute value of the current wavelet coefficient to be watermarked. Eq. 21 therefore becomes:

σ 2 w = 1 E[x 2 ] Nt Nv σ 2 w t . (26)
The same lines as above lead to the final equation for variable strength embedding:

W CR = 10 log 10 " 255 2 σ 2 x × M × N Nv × 1 E[x 2 ] « -P SN R (27)

Figure 1 :

 1 Figure 1: Flowchart of our implementation for stills. Essential work of this paper deals with the modulation box.

Figure 2 :

 2 Figure 2: PSNR histogram for SS modulation.

Figure 3 :

 3 Figure 3: PSNR histogram for ISS modulation.

Figure 4 :

 4 Figure 4: PSNR histogram for NW modulation.

Figure 5 :

 5 Figure 5: PSNR histogram for CW modulation.

Figure 6 :

 6 Figure 6: JPEG compression robustness results for the four modulations.

  Fig.10

Figure 7 :

 7 Figure 7: Security results for the four modulations.The used estimator is presented in Eq. 16. SS and ISS are confirmed not to be secure. CW and NW show good security also in practice.

Figure 8 :

 8 Figure 8: Distribution of the projection of the y over the two carriers for SS.

Figure 9 :Figure 10 :

 910 Figure 9: Distribution of the projection of the y over the two carriers for ISS.

Figure 11 :Figure 12 :

 1112 Figure 11: Distribution of the projection of the y over the two carriers for NW. The PSNR was obtained exactly.

Figure 13 :

 13 Figure13: Distribution of the projection of the y over the two carriers for CW and variable strength embedding. Psycho-visual masking implies a higher variance in the target PSNR, which translates into a ring with larger width.

Figure 14 :

 14 Figure 14: Comparison between constant (up) and variable (down) strength embedding.PSNR is 30dB. Modulation: CW.

Figure 15 :

 15 Figure 15: Zoom of Fig. 14 on the sky above the trees.

  in the spatial domain is obviously:

  3, adding psycho-visual masking does not impair security.

	Modulation E[P SN R](dB) σP SN R(dB)
	SS	44.75	1.18e-1
	ISS	44.76	2.17e-1
	NW	45.19	1.89e0
	CW	44.76	2.16e-1
	Table 1: Distorsion caused by payload insertion.
	Target PSNR: 45dB.

Acknowledgments

This was supported, in part, by IST-2002-507932 ECRYPT European grant, and ANR-06-SETI-009 Nebbiano, RIAM Estivale and ARA TSAR French grants.


Jodelle Degois

Frederic Clerc

Xavier Simon

Cyril Bontemps

Pierre Leblond

Philippe Duquenne

Keywords: Waste sorting plant, Bioaerosol, Occupational exposure, Biodiversity, Nextgeneration sequencing, 16S rDNA Illumina sequencing, 18S rDNA 454 pyrosequencing

Waste sorting activities are source of occupational bioaerosol exposures that are associated with several health disorders. New analytical tools, based on next generation sequencing (NGS) technologies, provide powerful methods to assess the microbial composition of bioaerosols. The objectives of the study were (1) to assess the feasibility and the repeatability of NGS based biodiversity measurements and (2) to study the microbial biodiversity using NGS in bioaerosols emitted in a waste sorting plant (WSP). Three stationary parallel samples were collected in a sorting cabin using closed-face cassettes equipped with polycarbonate membranes. Bacterial and fungal diversity was assessed by sequencing 16S and 18S rDNA genes using either Illumina sequencing or 454 pyrosequencing methods. At sampling point, airborne bacteria were dominated by Proteobacteria, Firmicutes and Actinobacteria with prevailing genera assigned to unclassified Enterobacteriaceae, Staphylococcus, Acinetobacter, Leuconostoc, Pseudomonas and Lactobacillus. Airborne fungi were dominated by Ascomycota with prevailing genera assigned to Penicillium, Aspergillus, Rhizopus, Wallemia and Hemicarpenteles. The NGS biodiversity measurements revealed a higher biodiversity bioaerosols that previously reported for WSP in studies carried out using culture methods followed by identification of microorganisms. These results provide the first survey about taxonomic biodiversity in bioaerosols from WSPs using high-throughput sequencing.

First metagenomic survey of the microbial diversity in bioaerosols emitted in waste sorting plants Introduction

Household waste sorting activities are source of occupational exposure to bioaerosols including bacteria, fungi, endotoxins, (1,3)-β-D-glucans as well as inhalable dust [START_REF] Breum | Dustiness and bio-aerosol exposure in sorting recyclable paper[END_REF][START_REF] Gladding | Organic dust exposure and work-related effects among recycling workers[END_REF][START_REF] Hebisch | Workers' exposure to hazardous substances and biological agents in recycling enterprises[END_REF][START_REF] Schlosser | Extension of the sorting instructions for household plastic packaging and changes in exposure to bioaerosols at materials recovery facilities[END_REF]. Studies carried out in waste sorting plants (WSP) reported health disorders such as non-allergic symptoms (chronic bronchitis and mucous membrane irritations) as well as allergic symptoms (allergic asthma, allergic rhinitis and hypersensitivity pneumonitis) among the population of WSP workers exposed to bioaerosols [START_REF] Gladding | Organic dust exposure and work-related effects among recycling workers[END_REF][START_REF] Ivens | Gastrointestinal symptoms among waste recycling workers[END_REF][START_REF] Perez | Health effects associated with organic dust exposure during the handling of municipal solid waste[END_REF].

Bacteria, fungi and viruses found in bioaerosols at the workplaces, as well as the associated microbial compounds such as endotoxins, (1,3)-β-D-glucans, mycotoxins and allergens, were presented as the possible causative agents of occupational diseases [START_REF] Douwes | Bioaerosol health effects and exposure assessment: progress and prospects[END_REF][START_REF] Eduard | Bioaerosol exposure assessment in the workplace: the past, present and recent advances[END_REF]. However, the complex composition of bioaerosols still prevents identifying the specific microbial agents responsible for health effects as well as their precise role in symptoms. As a consequence, the dose-response relationships have not yet been established for most of non-infectious airborne microorganisms and no occupational exposure limit values are available. Thus, defining the precise composition of the bioaerosols to which employees are exposed to would provide a better understanding of biological risks at the workplaces.

So far, the microbial biodiversity data published in (WSP) were obtained by cultivation of microorganisms on nutrient media prior to identification. The corresponding studies found that the dominant fungi belonged to Penicillium sp., Aspergillus sp., Cladopsorium sp. [START_REF] Breum | Dustiness and bio-aerosol exposure in sorting recyclable paper[END_REF][START_REF] Lehtinen | Occupational hygiene in terms of volatile organic compounds (VOCs) and bioaerosols at two solid waste management plants in Finland[END_REF][START_REF] Tolvanen | Airborne bio-aerosols and noise in a dry waste treatment plant in Pietarsaari, Finland[END_REF][START_REF] Viegas | Assessment of fungal contamination in waste sorting and incineration-case study in Portugal[END_REF]. However, the culture method underestimates the total number of microorganisms in samples as dead cells and microorganisms at viable but non cultivable (VBNC) state due to the selecting effect of culture media and cultivation conditions [START_REF] Amann | Phylogenetic identification and in situ detection of individual microbial cells without cultivation[END_REF][START_REF] Heidelberg | Effect of aerosolization on culturability and viability of gram-negative bacteria[END_REF]. Thus, the cultured based biodiversity measurements previously performed in WSP surely have underestimated the real microbial taxon richness.

Advances in molecular biology and Next-generation sequencing (NGS) provided new powerful methods for biodiversity studies. These molecular methods were already used in bioaerosol studies carried out in different occupational environments such as composting facilities [START_REF] Partanen | Bacterial diversity at different stages of the composting process[END_REF], wastewater treatment plants [START_REF] Lin | Diversity and dynamics of microbial communities at each step of treatment plant for potable water generation[END_REF], and swine houses [START_REF] Kumari | Manure removal system influences the abundance and composition of airborne biotic contaminants in swine confinement buildings[END_REF]. NGS overcame the limitations of culture based methods and provided useful data (greater observed biodiversity, detection of non-preponderant taxa etc.)

for a better understanding of the microbial communities to which workers may be exposed.

However, the NGS have also some limitations due to the DNA extraction efficiency, PCR biases or the short size of the amplified DNA fragments which do not allow the identification at the species level. Nevertheless, the NGS could provide additional information as compared to culture-based methods [START_REF] Yoo | Molecular approaches for the detection and monitoring of microbial communities in bioaerosols: A review[END_REF]. To date, metagenomics methods were not applied to investigate the microbial biodiversity found in WSP. The aims of the present study were (1) to assess the feasibility and the repeatability of NGS based biodiversity measurements in WSP and (2) to apply this method in order to study the bioaerosol microbial biodiversity in bioaerosols emitted in a WSP.

Materials and methods

Description of the WSP

Bioaerosol biodiversity was assessed in July 2014 in a French WSP sorting journal newspapers, papers, cardboards, food packaging and other sorting wastes (4000 tons per month). The WSP employed about 50 workers mainly dedicated to manual sorting in three cabins.

Bioaerosol sampling

Three stationary parallel samples were collected in a cardboard sorting cabin (SC) and one outdoor reference (OR) sample was also collected at about 50 m upwind far from the WSP boundary to determine the background level. Bioaerosols were sampled with 37 mm closedface cassettes (CFC) equipped with a sterile polycarbonate filter (Nuclepore ® , 0.8 µm pore size) and a backing cellulose pad (Millipore ® , thick cellulose absorbent pad). The cassettes were connected to constant flow pumps (Bravo, Tecora, France) and sampling was performed at a flow rate of 10 L.min -1 . The flow rate was calibrated before and after sampling using a flow meter (Mass Flowmeter 4040, TSI Inc., USA). Sampling devices were placed at 1.7 m above ground level and the sampling was conducted for five hours during the work shift.

Temperature and relative humidity were monitored using a portable device (Thermohygrometer B6285C Pocket, Fischer, France) at the beginning and at the end of each sampling. Bioaerosol samples were transported to the laboratory during the day of sampling using a cold box and were then stored at 4°C until analysis. All samples were analyzed within 24 hours after being collected.

DNA extraction

A volume of 10 mL of a sterile solution (0.01 % Tween Chimera were detected and deleted using UNCHIME [START_REF] Edgar | UCHIME improves sensitivity and speed of chimera detection[END_REF] executed in Mothur.

Reads with 100 per cent of identity were clustered into a unique sequence. Then, sequences were clustered into Operational Taxonomic Units (OTU) at a threshold of 97 % sequence similarity. Bacterial taxonomy assignments were performed using the Mothur platform ("average" method) using SILVA database. The ten dominant eukaryotic OTUs were identified at the genus rank at 95% sequence similarity using BLASTn algorithm in Genbank (NCBI database; https://blast.ncbi.nlm.nih.gov).

The Shannon's index, H', was calculated with the following formula: H'=-∑ Pi x lnPi, where Pi= Ni/N, Ni is the OTU number in a genus and N is the total OTU number. The Simpson's index, D, was calculated with the following formula: D = ∑ (Ni x (Ni-1)/N x (N-1)). The Simpson's index measures the probability that two OTU chosen randomly will be associated with the same taxon (in that case the genus). The more the index value is near the value 0, the more the taxa are present in different proportions. On the contrary, the more the index value is near 1, the more the taxa are present in the same proportion. The values of Shannon's index are between 0 and S, the total taxa number and values of Simpson's index are between 0 and 1.

Repeatability of the biodiversity measurement process

The repeatability of the bioaerosol measurement process was assessed on the 16S rDNA OTUs found in the three parallel replicate samples collected in the SC. Simple descriptive statistics of the proportions of each phylum were calculated. The arithmetic mean (AM) and the arithmetic standard deviation (SD) allowed computing the coefficient of variation (SD / AM). The average coefficient of variation (CV) of all phyla was also computed and assumed as representing the degree of similarity between the three samples. The AM and the CV were also calculated for Shannon's and the Simpson's indexes from the replicates samples. The calculation of simple descriptive statistics of the proportions of each phylum revealed CV values below 10 % (data not shown). For example, the proportion of Proteobacteria was 34.57 % in SC1, 34.37 % in SC2 and 33.04 % in SC3 which corresponds to a CV(Proteobacteria) equals 2%. Furthermore, the average coefficient of variation of all phyla was 9%. Descriptive statistics calculated from the replicates samples regarding the proportions of phyla revealed CV values below 10 %. The calculated CV for the Shannon's and the Simpson's indexes were below 6 %. These statistics suggest that bacterial and Eukaryota biodiversity can be assumed as repeatable.

Results

The biodiversity was estimated in the sorting cabin (SC) (three replicates) and compared to that of the outdoor reference (OR).

Environmental and working conditions

The temperatures monitored in July 2014 in the SC and in the OR were 28°C and 29°C, respectively. The relative humidity of the air at the same points was 51% and 46%

respectively. During the sampling, the working activity in the WSP was continuous and regular and no particular malfunction such as stopping in the sorting line occurred. Two operators were working in the cardboard sorting cabin.

Assessment of biodiversity using deep sequencing

The outlines of high-throughput sequencing data obtained in the study are given in Table 1.

For bacterial 16S rDNA, Illumina sequencing provided about 78,600 reads in the OR sample and between 69,600 and 100,000 reads in the SC samples (Table 1). This corresponds to 581 OTU in the OR sample and between 842 and 1,233 OTU in the SC samples. More than 80% of the sequences were assigned to bacterial OTUs in all the samples. The Shannon's indexes were from 6.41 to 6.60 in the SC samples and 4.24 in the OR one (Table 1). In the three replicates, the mean Shannon's index was 6.48±0.10 (CV=1.61%). The Simpson's indexes were 0.1 in the OR and 0.02 in the SC samples, indicating that dominant bacterial genera were present in the two areas. For Eukaryota 18S rDNA, 454 pyrosequencing provided about 6,600 reads for in the OR sample and between 14,546 and 21,206 reads for the SC samples (Table 1). This corresponded to 15 OTUs in the OR sample and about 40 OTUs in the SC.

The Shannon's indexes were 1.83 in the OR and 1.25±0.07 (CV=5.60%) in the SC. The Simpson's indexes were 0.25 in the OR and about 0.45 in the SC.

Taxonomic diversity of airborne bacterial communities

The phylogenetic taxonomy analysis assigned more than 80% of the reads at the phylum rank, 70% at the family rank and 60 % at the genus rank (see supplementary data Table S1).

The four main phyla found in bioaerosol samples were Actinobacteria, Firmicutes, Proteobacteria and Bacteroidetes (Figure 1). They accounted for about 80% of the total reads for the SC and 83% for the OR. Unidentified 16S sequences represented 17% and 12% in the SC and in the OR, respectively. The other found phyla belonged to Chloroflexi, Cyanobacteria, Deinococcus-Thermus and Planctomycetes and account individually for less that 2.5 % of the total reads. Actinobacteria, Firmicutes and Bacteroidetes showed higher proportions in the SC than in the OR (Figure 1). Indeed, Actinobacteria and Firmicutes appeared to be more preponderant in bioaerosols that were emitted in the SC. Proteobacteria were the predominant phylum in all samples and in higher proportion in the OR sample.

At the family rank (see supplemental data Table S2), the specific richness was higher in the SC (78 families) than in the OR (59 families) samples. Most of these families belonged to the Proteobacteria phylum (26 in the SC and 23 in the OR) which accounted for 32% of the reads in the SC and 58% in the OR. In addition, 25% of the reads were assigned to Firmicutes families in the SC whereas they only account for 6.3% in the OR. The 20 main families found in the sorting cabin are represented in the Figure 2. The proportion of these families was between 9.34% (Entorobacteriaceae) and 0.83% (Aerococcaceae) in the SC and accounted for 57% of the bacterial reads. Others predominant families were Staphylococacceae (6.0%)

and Pseudomonadaceae (5.0%).

The analysis of sequences at the genus rank revealed 112 bacterial genera in the SC samples (Table S3). Brevibacterium (2.04 %). The analysis of sequences at the genus rank also highlighted differences between SC and OR. In the SC, 11 genera were observed with a 20-fold higher than in the OR (see Table S3). These 11 genera accounted for 25% (< 1% in the OR) of total bacterial reads and belonged to Staphylococcus, Enterococcus, Streptococcus, Lactobacillus, Lactococcus, Leuconostoc and Brevibacterium.

Biodiversity of airborne Eukaryota communities

The phylogenetic taxonomy analysis of 18S rDNA sequences revealed the occurrence of living organisms belonging to the Fungi and the Plantae kingdoms and assigned more than 99 % of the reads at the phylum rank in the 4 collected samples (Table S1).

In SC samples, Fungi represented 99.4 % of the reads and the remaining ones were assigned to Plantae (0.12 %) or remained unidentified (0.44 %). The three main fungal phyla found in

Discussion

Bacterial and fungal biodiversity in the WSP Very few data have been published regarding the biodiversity of airborne bacterial and fungal particles in industrial plants sorting household waste. Furthermore, the available biodiversity data were obtained by culture of microorganisms followed by macroscopic and/or microscopic identification as well as biochemical identification. Thus, we have found no equivalent results in previous published studies to which our findings could be compared to.

The first analysis of NGS data indicates a higher biodiversity in samples collected in the SC as compared with the outdoor one. Furthermore, biodiversity indexes showed that bacterial biodiversity was higher than Eukaryota biodiversity. Fungal genera were more present in equi-proportion than bacterial genera.

For airborne bacteria, in a plant located in Finland where household wastes were sorted, bioaerosols were dominated by unidentified Gram-positive and Gram-negative cultivated bacteria and by bacteria belonging to Pseudomonas, Bacillus and Micrococcus [START_REF] Rahkonen | Airborne contaminants at waste treatment plants[END_REF]. In a Danish paper sorting plant, Breum et al., found that airborne cultivated bacteria were dominated by Gram-positive bacteria, including Staphylococcus and Bacillus species, and Actinomycetes [START_REF] Breum | Dustiness and bio-aerosol exposure in sorting recyclable paper[END_REF]. Cultivated bacteria belonging to Staphylococcus, Bacillus, Streptococcus, or Micrococcus genera were also found in others studies carried during landfill (Breza-Boruta, 2012), glass sorting [START_REF] Vasconcelos Pinto | Airborne microorganisms associated with packaging glass sorting facilities[END_REF] and waste collection [START_REF] Nielsen | Occupational bioaerosol exposure during collection of household waste[END_REF][START_REF] Madsen | Waste workers' exposure to airborne fungal and bacterial species in the truck cab and during waste collection[END_REF]. In these studies, bacterial identification was usually achieved on a limited number of colonies and using Gram staining, metabolic tests, gas chromatography or mass spectrometry after cells culture. Data from previous biodiversity studies in the waste treatment sector provided similarities with our results regarding dominant bacterial taxa such as Staphylococcus, Pseudomonas, and actinomycetes. They also provided differences as Bacillus was not found in great portion in our study as compared previous published ones. Differences found in bacterial taxa might be explained by the methods used for assessing biodiversity as well as the geographic and climatic changes in bioaerosol biodiversity [START_REF] Smets | Airborne bacteria in the atmosphere: presence, purpose, and potential[END_REF]. Furthermore, due to the limits of the sequencing methods, the bacterial biodiversity could not be assessed at the species level in the study. Indeed, the DNA fragments sequenced by the Illumina technology were too short and the genetic target was too preserved to allow identification at the species level with a good accuracy. So, the NGS provided insufficient information for an accurate bacterial risk evaluation. To assess the bacterial risk, others methods such as the identification of cultured bacteria using MALDI-TOF could be used in addition with the sequencing.

For airborne fungi, findings from our study are in agreement with previous published studies for similar activities. Indeed, airborne cultivated fungi belonging to the Penicillium genus was found prevalent during sorting of household waste, while the Aspergillus, Rhizopus, Cladosporium, Geotrichum, and Chrysonilia genera were also common [START_REF] Breum | Dustiness and bio-aerosol exposure in sorting recyclable paper[END_REF][START_REF] Tolvanen | Airborne bio-aerosols and noise in a dry waste treatment plant in Pietarsaari, Finland[END_REF]Tolvanen and Hänninen, 2006;[START_REF] Malta-Vacas | Fungal and microbial volatile organic compounds exposure assessment in a waste sorting plant[END_REF][START_REF] Lehtinen | Occupational hygiene in terms of volatile organic compounds (VOCs) and bioaerosols at two solid waste management plants in Finland[END_REF][START_REF] Viegas | Assessment of fungal contamination in waste sorting and incineration-case study in Portugal[END_REF]. Aspergillus fumigatus was also detected in bioaerosol samples collected in WSP by the culture method [START_REF] Tolvanen | Airborne bio-aerosols and noise in a dry waste treatment plant in Pietarsaari, Finland[END_REF]Tolvanen and Hänninen, 2006) as well as by PCR [START_REF] Malta-Vacas | Fungal and microbial volatile organic compounds exposure assessment in a waste sorting plant[END_REF]. The genus Penicillium was also found to be dominant in other studies carried during out glass sorting [START_REF] Vasconcelos Pinto | Airborne microorganisms associated with packaging glass sorting facilities[END_REF] and during waste collection [START_REF] Madsen | Waste workers' exposure to airborne fungal and bacterial species in the truck cab and during waste collection[END_REF]. In the latter study, the authors found 11

Penicillium species as P. brevicompactum, P. camemberti or P. chrysogenum after identification of colony using MALDI-TOF MS. They also observed 6 Aspergillus species inclunding A. nidulans, A. niger and A. versicolor. This suggests that a substantial biodiversity can be found at the species level in bioaerosols emitted during waste collection.

Such a biodiversity is expected in waste soring plants, however, as for the bacterial biodiversity, the methods used in the present study did not allow identifying the fungal sequences at the species level.

The NGS biodiversity data from the present study revealed a far more important diversity than expected from the culture data as 112 different bacterial genera. In others studies, the authors observed from 20 bacterial genera and up to 38 bacterial species using culture-based methods [START_REF] Rahkonen | Airborne contaminants at waste treatment plants[END_REF][START_REF] Nielsen | Occupational bioaerosol exposure during collection of household waste[END_REF][START_REF] Breum | Dustiness and bio-aerosol exposure in sorting recyclable paper[END_REF][START_REF] Vasconcelos Pinto | Airborne microorganisms associated with packaging glass sorting facilities[END_REF][START_REF] Madsen | Waste workers' exposure to airborne fungal and bacterial species in the truck cab and during waste collection[END_REF]. The fungal diversity was more important than expected from culturing approach since 22 different genera were identified in our study versus only 2 to 8 in previous reports [START_REF] Breum | Dustiness and bio-aerosol exposure in sorting recyclable paper[END_REF][START_REF] Tolvanen | Airborne bio-aerosols and noise in a dry waste treatment plant in Pietarsaari, Finland[END_REF]Tolvanen and Hänninen, 2006;[START_REF] Malta-Vacas | Fungal and microbial volatile organic compounds exposure assessment in a waste sorting plant[END_REF][START_REF] Lehtinen | Occupational hygiene in terms of volatile organic compounds (VOCs) and bioaerosols at two solid waste management plants in Finland[END_REF][START_REF] Viegas | Assessment of fungal contamination in waste sorting and incineration-case study in Portugal[END_REF]. Thus, NGS provides a deeper accuracy of the types of microorganisms to which WSP workers might be exposed. To our knowledge, these results are the first report of bacterial and fungal biodiversity survey, using a metagenomics approach, published about bioaerosol emitted in waste sorting plant.

Cardboard sorting cabin vs. Outdoor

The first analysis of high-throughput sequencing data indicates a higher biodiversity in samples collected in the WSP than in the OR. The dominant bacterial genera were different in the OR and in the SC and the composition of bioaerosols were different in the two areas whatever the taxon rank considered. Differences in biodiversity were also observed between OR and SC samples; the outdoor Eukaryota community was specifically dominated by Cladosporium and Plantae. Such differences were already reported regarding the fungal community [START_REF] Malta-Vacas | Fungal and microbial volatile organic compounds exposure assessment in a waste sorting plant[END_REF][START_REF] Vasconcelos Pinto | Airborne microorganisms associated with packaging glass sorting facilities[END_REF]. Our findings showed that WSP workers are exposed to a specific bioaerosol at their workstation.

Significance of NGS biodiversity findings

Several bacterial genera found in the present study were associated with human or animal skin, mucous membrane or intestinal tract such as, Staphylococcus [START_REF] Schleifer | Volume three, the Firmicutes: Staphylococcus[END_REF],

Enterococcus [START_REF] Svec | Volume three, the Firmicutes: Enterococcus In De Vos[END_REF] and Streptococcus [START_REF] Whiley | Volume three, the Firmicutes: Streptococcus[END_REF].

Bacteria from dairy milk or meat were also found such as Lactobacillus [START_REF] Hammes | Volume three, the Firmicutes : Lactobacillus[END_REF], Lactococcus [START_REF] Teuber | Berguey's manual of systematic bacteriology[END_REF], Leuconostoc [START_REF] Holzapfel | Berguey's manual of systematic bacteriology[END_REF], and Brevibacterium [START_REF] Brennan | Biodiversity of the bacterial flora on the surface of a smear cheese[END_REF]. Bacterial biodiversity in the SC appears related to the occupational activities due to the origin of waste (domestic environment) as well as the presence of workers. This might explain the differences observed between the two sampling area.

The fungal genera found in our study include species that were associated with allergenic symptoms, opportunistic infection in immunosuppressed patients (Aspergillus fumigatus) or known as MVOC or mycotoxin producers [START_REF] Fischer | Exposure to airborne fungi, MVOC and mycotoxins in biowaste-handling facilities[END_REF]. The phylogenetic taxonomy analysis of 18S rDNA sequences allowed highlighting the occurrence of slow growing fungi belonging to the Stachybotrys and the Wallemia genera. These genera include harmful species that were almost never cited in previous works. Thus, culture based methods provide essential information, but they do not allow a global characterization of Eukaryota biodiversity. This confirms that NGS may provide a new view regarding the types of microorganisms to which WSP workers might be exposed to.

Limits and perspectives of the study

The present study was carried out with a limited number of samples and only three occupational bioaerosols and one reference were analyzed. This is not enough to draw definitive conclusion about the exposure of workers to microorganisms at the sorting plant.

Indeed, due to the complexity of the bioaerosols composition and to confirm the difference between the sorting cabin and the outdoor reference, additional samples need to be collected in order to confirm these first results of biodiversity in a waste sorting plant. Data analysis showed that the Simpson's and Shannon's indexes were very close among the three replicates.

Furthermore, the proportions of bacterial families found in the WSP bioaerosol appeared in the same range from a sample to another one. This suggests that the biodiversity measurement process is repeatable but additional statistical analyses are needed to comfort this. The study shows the usefulness of NGS based biodiversity measurements for occupational hygiene purposes in WSP but additional research is required to investigate more precisely the microbial composition of bioaerosols in this environment. Especially, studies are needed with more numerous bioaerosol samples collected in different WSP, at different working area in WSP and also at different moment of the year. The NGS is a qualitative method, to get a better understanding of the microbial exposure; the microbial concentration must be assessed using molecular biology methods such as qPCR or culture-based ones.

In the study, the 18S rDNA what used as target gene for the assessment of fungal biodiversity in bioaerosols. This approach reveals that Fungi were the most Eukaryota communities in the sorting cabin. Furthers studies, should focus on fungal biodiversity using the ITS (internal transcribed spacer) barcode which give a better taxonomic resolution for Fungi [START_REF] Schoch | Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi[END_REF].

NGS technologies have also some biases that were not addressed in our investigation. The metagenomics DNA must be of good quality to allow its sequencing. Biodiversity studied by NGS is also affected by the DNA extraction efficiency. Indeed, some microbial species can be preferably extracted depending on the DNA extraction method [START_REF] Haugland | Evaluation of different methods for the extraction of DNA from fungal conidia by quantitative competitive PCR analysis[END_REF]. In addition, errors were reported due to PCR biases in the sequences library preparation and to data analysis (sequences alignment). Illumina technologies provide short reads (150 pb for HiSeq and 250 pb for Miseq), these shorts reads are aligned with reference sequences to get a match probability. The incorrect annotation of reference sequences in the database can introduce mistakes in data analysis [START_REF] Nilsson | Taxonomic reliability of DNA sequences in public sequence databases: a fungal perspective[END_REF][START_REF] Yamamoto | Identification accuracy and diversity reproducibility associated with internal transcribed spacer-based fungal taxonomic library preparation[END_REF]. Because of these facts, the biodiversity, in our study, can only be assessing at the genus level (deeper assignment). But, an assessment of biodiversity at the species level is possible with an increase of the reads size using another sequencing technology as PacBio sequencing.

Furthermore, a sequencing of the entire ITS region, rather than the ITS2 sequencing, can provide an accurate method to assess the fungal biodiversity at the species level [START_REF] Yamamoto | Identification accuracy and diversity reproducibility associated with internal transcribed spacer-based fungal taxonomic library preparation[END_REF]. Error factors due to NGS methods are sometimes not suspected by the users or difficult to suppress. Previous studies showed that bioinformatics steps can reduce the impact of these factors. [START_REF] Bellemain | ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases[END_REF][START_REF] Ovaskainen | Identifying wood-inhabiting fungi with 454 sequencingwhat is the probability that BLAST gives the correct species?[END_REF][START_REF] Gihring | Massively parallel rRNA gene sequencing exacerbates the potential for biased community diversity comparisons due to variable library sizes[END_REF][START_REF] Van Dijk | Library preparation methods for nextgeneration sequencing: tone down the bias[END_REF][START_REF] O'rawe | Accounting for uncertainty in DNA sequencing data[END_REF]. Biodiversity studied by NGS is also affected by the DNA extraction efficiency and as well as the accuracy annotation of the sequences in the data basis [START_REF] Nilsson | Taxonomic reliability of DNA sequences in public sequence databases: a fungal perspective[END_REF][START_REF] Yamamoto | Identification accuracy and diversity reproducibility associated with internal transcribed spacer-based fungal taxonomic library preparation[END_REF]. Nevertheless, NGS is a useful approach to describe microbial communities in occupational area, in addition to cells culture methods.

To improve the biological risk evaluation, the colonies can be identified using MALDI-TOF MS for identification at the species level of the main culturable genera which has been demonstrated to provide complementary data as compared to Illumna sequencing [START_REF] Madsen | Microbial diversity in bioaerosol samples causing ODTS compared to reference bioaerosol samples as measured using Illumina sequencing and MALDI-TOF[END_REF]. The metagenomics approach makes it possible to assess the microbial diversity as never before by removing the limits of the microbial culturability biasing the previous biodiversity studies. On the other hand, if the sequencing showed a higher biodiversity at the genus level, it did not provide any information about the bacterial or fungal species present in the bioaerosols. In furthers studies, an identification of microbial colony could be performed in addition to the sequencing to get information about the microbial biodiversity at the species level. The use of metagenomics made it possible to describe for the first time the biodiversity of bioaerosol in WSP overcoming such culture bias and provided a finer description of bioaerosol composition that may be inhaled by exposed WSP workers.

Conclusion

In the present study, the bacterial and fungal abundance in bioaerosols from a WSP treating household waste were revealed for the first time using 16S rDNA Illumina sequencing and 18S rDNA 454 pyrosequencing methods, respectively. The results showed that the components of airborne microbial communities were assigned to 4 phyla and 112 genera for bacteria and to 3 phyla and 22 genera for fungi. In the WSP, airborne bacteria were dominated by Proteobacteria, Firmicutes and Actinobacteria with prevailing genera assigned to unclassified Enterobacteriaceae, Staphylococcus, Acinetobacter, Leuconostoc, Pseudomonas and Lactobacillus. Airborne fungi were dominated by Ascomycota with prevailing genera assigned to Penicillium, Aspergillus, Rhizopus, Wallemia and Hemicarpenteles.

The NGS biodiversity measurements performed in the present study revealed a higher biodiversity in WSP bioaerosols than the one previously reported in studies carried out using culture methods followed by identification of microorganisms. This suggests that NGS would provide a greater accuracy than culture based methods regarding the types of microorganisms to which WSP workers might be exposed to. Furthermore, different microbial communities were found in the sorting cabin as compare with the outdoor air, which support the hypothesis that biodiversity in bioaerosols emitted in the WSP maybe specific to waste sorting activities.

the biodiversity measurement process provided repeatable results. However, further studies based on a larger number of samples and other targeted genes (ITS, genes involved in microbial pathogenicity or in the expression of allergens etc.) are needed to confirm these primary results and to investigate the uncertainty of biodiversity measurements as well as the variation of airborne microbiomes in time and in space at occupational settings. A.

B.

Parameters

  bioaerosol samples were Ascomycota (91.1 %), an early diverging fungal lineage [Mucor and Rhizopus] (4.64 %) and Basidiomycota (3.70 %). Twenty two fungal genera were found. The ten dominant fungal OTUs represented 98.5 % of eukaryotic OTUs with a predominance of the Penicillium, Aspergillus and Rhizopus genera (Figure 3.a). Wallemia was the dominant Basidiomycota genus found in SC samples.In the OR sample, Fungi and Plantae represented 70.0 % and 29.9 % of the reads, respectively, and the remaining ones were unidentified (0.1 %). The two fungal phyla found in bioaerosol samples were Ascomycota (59.0 %) and Basidiomycota (11.0 %); fungi belonging to the early diverging fungal lineage were not found. The dominant outdoor fungal genera were Cladosporium (45.2 %), unidentified Ascomycota (8.3 %), Oligoporus (7.3 %), unidentified Basidiomycota (3.6 %) and, Penicillium (3.5 %). Biodiversity also consisted of others Eukaryota such as Viridiplantae (Figure3.b).

Figure 1 :

 1 Figure 1 : Bacterial biodiversity at the phylum Rank in bioaerosols from the WSP. Bioaerosol samples were collected in the Sorting Cabin (SC) and in the Outdoor Reference (OR). For practical reasons, only the mean of sequencing data from SC1, SC2 and SC3 are presented.

Figure 2 :

 2 Figure 2: Bacterial biodiversity at the family rank in bioaerosols from the WSP. Bioaerosol samples were collected in the Sorting Cabin (SC) and in the Outdoor Reference (OR). Data are given for the 20 dominant families found in SC samples. For practical reasons, only the mean of sequencing data from SC1, SC2 and SC3 are presented. *: corresponds to Bacteroidetes.

Figure 3 :

 3 Figure 3: Fungal biodiversity at the genus rank in bioaerosols from the WSP. Bioaerosol samples were collected A: in the Sorting Cabin and B: in the Outdoor Reference. For practical reasons, only the mean of sequencing data from SC1, SC2 and SC3 are presented.

  Dominant airborne bacteria in SC samples were unclassified Enterobacteriaceae

	(8.38 %), Staphylococcus (5.63 %), Acinetobacter (4.48 %), Leuconostoc (3.07 %),
	Pseudomonas (2.99 %), Lactobacillus (2.27 %), unclassified Planococcaceae (2.15 %) and

Table 1 :

 1 Overview of high-throughput sequencing data obtained from bioaerosol samples taken in the WSP. Bioaerosol samples were collected in the Sorting Cabin (SC) and in the Outdoor Reference (OR).

	SC1	SC2	SC3	OR

a : Illumina sequencing; b : 454 pyrosequencing.

ACKNOWLEDGEMENTS

The authors would to kindly acknowledge Catherine Coulais and Véronique Koehler for their valuable technical assistance.