Letizia Abis
email: letizia.abis@inra.fr

Benjamin Loubet

Raluca Ciuraru

Florence Lafouge

Samuel Dequiedt

Sabine Houot

Pierre Alain Maron

Sophie Bourgeteau-Sadet

Keywords: VOC, soil, organic waste products, PTR-QiTOF-MS, VOCs fluxes

Volatile Organic Compounds (VOCs) are reactive compounds essential to atmospheric chemistry. They are mainly emitted by living organisms, and mostly by plants. Soil microbes also contribute to emissions of VOCs. However, these emissions have not yet been characterized in terms of quality and quantity. Furthermore, long-term organic matter amendments are known to affect the microbial content of soils, and hence the quantity and quality of VOC emissions. This study investigates which and how much of these VOCs are emitted from soil amended with organic waste products (OWPs). Four OWPs were investigated: municipal solid waste compost (MSW), green waste and sludge co-compost (GWS), bio-waste compost (BIOW) and farmyard manure (FYM). These OWPs have been amended every two years since 1998 until now at a rate of ~4 tC ha -1 . A soil receiving no organic inputs was used as a reference (CN). VOCs emissions were measured under laboratory conditions using a Proton Transfer Reaction-Quadrupole ion guide Time of Flight-Mass Spectrometry (PTR-QiToF-MS). A laboratory system was set up made of two Pyrex chambers, one for samples and the second empty, to be used as a blank. Our results showed that total VOC emissions were higher in BIOW than in MSW. Further findings outlined that the most emitted compounds were acetone, butanone, and acetaldehyde in all treatments, suggesting a common production mechanism for these compounds, meaning they were not affected by the OWP amendment. We isolated 21 VOCs that had statistically different emissions between the treatments and could, therefore, be considered as good markers of soil biological functioning. Our results suggest that organic matter and pH jointly influenced total VOC emissions. In conclusion, OWPs in soil affect the type of VOC emissions and the total flux also depends on the pH of the soil and the quantity of organic matter.

Background

Current investigation on climate science reveals that the air quality and global temperatures are positively or negatively affected by several biogeochemical processes. The biogeochemical processes involving greenhouse gases (CO 2, CH 4 and N 2 O) have been widely documented. However, another class of compounds, the volatile organic compounds (VOCs) family, has been widely investigated in the last decades. Despite the low VOCs concentration in the atmosphere (from part per billions -ppb to part per trillions -ppt) [START_REF] Kesselmeier | Biogenic volatile organic compounds (VOC): An overview on emission, physiology and ecology[END_REF], they are involved in several important reactions in the atmosphere such as: the reactions leading to the ozone (O 3 ) formation in the troposphere [START_REF] Seinfeld | Atmospheric Chemistry and Physics: From Air Pollution to Climate Change[END_REF], the generation of large quantities of organic aerosols [START_REF] Singh | High concentrations and photochemical fate of oxygenated hydrocarbons in the global troposphere[END_REF] or the production of the nitrates in the atmosphere [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF]. Those processes are directly responsible for atmospheric pollution. The consequences on the environment at a global scale, on human health and on ecosystems underline the importance, in Europe and more in general worldwide, of international agreements and investments in order to reduce and control the emissions of these pollutant gases in the atmosphere.

The purpose of this work is not to investigate how to reduce the impact of VOCs emissions in the atmosphere. Nevertheless, to reduce VOCs emissions, the study of the dynamics and the sources leading to the VOCs production is indispensable. Biogenic sources of VOCs are between 10 and 11 times higher than the VOCs emissions released from anthropogenic sources [START_REF] Guenther | Seasonal and Spatial Variations in Natural Volatile Organic Compound Emissions[END_REF]. Amongst the most important biogenic sources of VOCs we can find plants and forests, which, at the moment are the widest studied sources of VOCs. However, recently, [START_REF] Bachy | Are BVOC exchanges in agricultural ecosystems overestimated? Insights from fluxes measured in a maize field over a whole growing season[END_REF] highlighted the possibility of an overestimation of the VOCs derived from canopy compared to bare soils. Moreover, [START_REF] Potard | Organic amendment practices as possible drivers of biogenic Volatile Organic Compounds emitted by soils in agrosystems[END_REF] and Raza et al., (2017a) put forward the role played by the organic amendment on the VOCs emissions from soils. The use of organic amendment, and in particular of the organic waste products, has been widely encouraged in Europe (Europe commissions, 2010) during the last decades in order to recycle the exogenous organic matter, increase soil organic matter and partially substitute mineral fertilizers. The production of organic waste products in France is about 332 Mt per year, of which at least the 50 % return back to soil as organic amendment (ADEME, 2006). Furthermore, it has been reported that the emissions from organic waste products are potential precursors of the particulate matter formation in the atmosphere; this information underlines the need of a VOCs emissions characterization. In addition, several studies reported that VOCs emissions from soils are strongly related to the microbial activity [START_REF] Bäck | Variable emissions of microbial volatile organic compounds (MVOCs) from root-associated fungi isolated from Scots pine[END_REF][START_REF] Mayrhofer | Microbial community related to volatile organic compound (VOC) emission in household biowaste[END_REF][START_REF] Mcneal | Volatile Organic Metabolites as Indicators of Soil Microbial Activity and Community Composition Shifts[END_REF]. Despite, VOCs sources such as soil and microorganisms, are still poorly explored due to the very complexity of the ecosystem. For instance, to our knowledge, no information has been reported concerning the relationship between VOCs emissions, organic waste amendment and microbial diversity in soil. Thus, the aim of this work will be to add small pieces to the complex world of the VOCs emissions by soil and microorganisms.

Chapter I

General introduction I. General introduction I.1 What are Volatile Organic Compounds (VOCs)?

The article 2 of the European Council Directive 1999/13/EC of 11 March 1999 defines volatile organic compounds as:

"Volatile organic compound (VOC) shall mean any organic compound having at 293.15 K a vapour pressure of 0,01 kPa or more, or having a corresponding volatility under the particular conditions of use."

Volatile organic compounds are molecules containing atoms of carbon and hydrogen, often bonded with halogens, oxygen, sulfur, nitrogen, or phosphorous. The wide range of molecules they can form, with different functional groups, make the ensemble of these compounds difficult to be fully detected and identified. Indeed, a large number of VOCs have not been identified yet. The fact that they have different functional groups also implies different chemical and physical properties and thus differences in terms of toxicity [START_REF] Cicolella | Volatile Organic Compounds (VOC): definition, classification and properties[END_REF]. Several families of compounds are well-defined VOCs such as: ketones, aldehydes, alcohols, nitriles, hydrocarbons, diols etc... Sources of VOCs can be gathered in two important groups: anthropogenic sources and biogenic sources. Within the anthropogenic sources, the main ones are transportation, gas, bio-fuel, industrial and domestic solvents, house painting, glue and emissions from wastes (waste water and solid waste) [START_REF] Wei | Emission and speciation of non-methane volatile organic compounds from anthropogenic sources in China[END_REF]. Anthropogenic sources contribute up to 10% of the total emissions of VOCs in the atmosphere. Emissions from biogenic sources contribute to the rest [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF]. Biogenic VOCs (bVOCs) are released from plants, litter, forests, animals, soil, and microorganisms. The complexity of studying bVOCs is also related to the difficulty to discriminating sources in a very complex system such as the biosphere.

It is important to underline that even if methane can be defined as VOC, the dynamics and the properties of this compound are not similar to the other compounds of the family. For this reason, generally, methane is not taken into account in VOCs studies. Therefore, in this project, we will talk about non-methane VOCs (NMVOCs). For simplicity, we will use the term VOCs for NMVOCs in this manuscript.

I.2 The regulatory context I.2.1 VOCs affect human health and crop productions

Volatile organic compounds (VOCs) are an essential component of atmospheric chemistry that contributes to the production of pollutant harmful to the human health and the environment: ozone (O3) and secondary organic aerosols (SOA). It has been widely demonstrated that the increase in the atmosphere of O 3 [START_REF] Lippmann | Health effects of tropospheric ozone: review of recent research findings and their implications to ambient air quality standards[END_REF] and SOA [START_REF] Davidson | Airborne Particulate Matter and Human Health: A Review[END_REF] have significant consequences on human health and crop production. For instance, the tropospheric concentration of O 3 in 2050 has been estimated to increase by 23% (Ehhalt et al., 2001, IPCC) with a consequence of a 17% decrease on soybean crop production [START_REF] Morgan | Season-long elevation of ozone concentration to projected 2050 levels under fully open-air conditions substantially decreases the growth and production of soybean[END_REF]. With the global human population currently over 6 billion and expected to reach 8 billion by 2050 [START_REF] Lutz | The end of world population growth[END_REF], assessing the impact of changing atmospheric O 3 concentration on crops is crucial to maintaining food security. Moreover, in Europe, the consequences of the SOA concentration in the atmosphere on human health has been reported as 42000 premature deaths in 2000 (CAFE-AEAT/ED51014/Baseline Issue 5, 2005) while O 3 incease in the troposphere caused 21,000 premature deaths and 14,000 respiratory diseases in Europe (EU25) in 2000 [START_REF] Enarson | Respiratory diseases in the world: realities of today -opportunities for tomorrow[END_REF]. The worrying scenarios just described led to the need for law enforcement measures to reduce the impact of O 3 and SOA.

Since VOCs are precursors of the SOA and O 3 they represent a source of pollution as well.

I.2.2 The regulatory context

The regulatory context in France regarding VOCs emissions directly is not very restrictive. The most important regulations, resulting from the 1999 Gothenburg Protocol (ratified by France in 2007 and modified in 2012), concerned the limitation of VOCs emissions from industries and of the VOCs concentration in consumer products. However, fine particles (PM 2.5) are subject to very restrictive regulations. France must ratify the limitation imposed by the European "air quality" directive (2008/50/EC). To achieve these objectives, France has implemented the plan to reduce emissions of atmospheric pollutants (PREPA, established in 2003) supplemented by the Particulate Matter Plan (2010). The plan has the purpose of reducing particulate matter and is complemented at a local level by atmospheric protection plans and regional climate-air-energy schemes.

I.3 The role of VOCs in air pollution and climate change I.3.1 The atmosphere composition

The atmosphere is divided into four layers of varying thickness. The boundaries between these layers have been set according to temperature discontinuities and altitude (Figure I-1).

From the Earth's surface, the atmosphere is composed of the troposphere (0-10 km), stratosphere (12-50 km), mesosphere (50-80 km) and thermosphere (above 90 km altitude).

The O 3 layer is found within the stratosphere. O 3 layer absorbs high-energy ultraviolet (UV) (λ <290 nm) light from the Sun, converting the UV energy into heat. The troposphere is the densest layer and contains up to 75% of the mass of the atmosphere. The troposphere is composed of 78% N 2 , 21% O 2 , 1% Ar, a varying concentration of water vapour, CO 2 (between 350 and 450 ppm), and trace gases. The presence of O 3 in the troposphere is due to the net downward of O 3 by eddy diffusion from the stratosphere and to the interaction of VOCs, NO x , and sunlight [START_REF] Roelofs | Model study of the influence of cross-tropopause O3 transports on tropospheric O3 levels[END_REF]. Furthermore, within the troposphere, there is as a suspension of fine solid or liquid particles named particulate matter or aerosols.

To our knowledge, all reactions involving VOCs occurs within the troposphere.

I.3.2 Tropospheric air pollution

Air pollution is defined as a condition in which substances resulting from anthropogenic activities have tropospheric concentrations higher than a certain threshold, and that has measurable and undesirable effects on humans, animals, vegetation, or more generally on ecosystems. VOCs contribute to air pollution through several cycles where they interact with other elements within the troposphere. The purpose of this paragraph is to describe the different cycles producing harmful air pollutants that involve VOCs.

I.3.2.1 VOCs lifetime

VOCs compounds range from low to highly reactive molecules. Once in the atmosphere, their lifetime can be quite short. Table I-1 reports the lifetime of the most abundant VOCs in the atmosphere due to the reaction with OH, NO 3 radicals, O 3 and as a result of photolysis.

We see in Table I-1 that while terpenoids and acetaldehyde are very reactive with OH and NO 3 and have lifetimes smaller than a few hours, acetone and methanol are quite stable with regards to reaction with OH, NO 3, and O 3 . We should, however, stress that methanol and acetone are very soluble gases and can readily be adsorbed to wet surfaces including cloud droplets. Table I-1. Lifetime of the most abundant VOCs in the atmosphere reacting with OH radical, NO 3 radical O 3 and due to the photolysis. NA= not available, sd=standard deviation [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF][START_REF] Guenther | Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)[END_REF][START_REF] Sindelarova | Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years[END_REF].

I.3.2.2 The role of VOCs in ozone formation

The key reactive species in the troposphere is the hydroxyl radical (OH). Since it does not react with O 2 , its lifetime in the troposphere is long enough to allow the reaction with all organic molecules excluding chlorofluorocarbons and halons not containing H atoms. OH radical is the primary oxidizing species in the troposphere. The following equations describe the tropospheric mechanisms leading to OH formation.

Thanks to the photolysis of O 3 excited singlet oxygen, O ( 1 D), or ground states O are formed:

𝑎) 𝑂 3 + ℎυ → 𝑂 2 + 𝑂( 𝐷) 1 or (Equation 1)

𝑏) 𝑂 3 + ℎυ → 𝑂 2 + 𝑂
The ground state O combines with O 2 to form O 3 :

𝑂 2 + 𝑂 + 𝑀 → 𝑂 3 + 𝑀 (Equation 2)

The equation 1b and 2 lead to no O 3 production. Moreover, O ( 1 D) most often reacts with N 2

or O 2 removing the excited state of the singlet and becoming a ground state O.

𝑂( 𝐷)

1 + 𝑀 → 𝑂 + 𝑀 (Equation 3) So most of the time null cycles are produced (Eqns 1b and 2 or 1a, 3 and 2). However, occasionally, the excited singlet O( 1 D) collides with a water molecule producing 2 OH: 4) [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF]. Hence, the result of this second pathway is a net formation of O 3 in the atmosphere. and Ox in the presence of VOC [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF].

𝑂( 𝐷) 1 + 𝐻 2 𝑂 → 2𝑂𝐻 (Equation
The
Net photochemical formation of O 3 also depends on the VOC/NO x ratio. There is strong competition for the OH radical between VOCs and NO x . A rate of VOCs/NOx of 5.5/1 leads to an equal reaction rate of VOCs and NO x with OH. If the concentration of NO x in the atmosphere increases and the VOC/NO x ratio becomes lower than 5.5/1, the reaction between OH and NO x is predominant. The consequence is that OH radical reacts less in the VOCs oxidation cycle (since there is less availability of OH radical due to the higher rate of the OH-NO x reaction), reducing the O 3 production. However, if the VOC/NO x rate is higher than 5.5/1 the OH-VOCs reactions are predominant and the production of O 3 increases. In conclusion,

VOCs are not only involved in the tropospheric ozone production but they are able to shift the balance of the null cycle described in Figure 2a leading to a net O 3 formation [START_REF] Seinfeld | Atmospheric Chemistry and Physics: From Air Pollution to Climate Change[END_REF] .

I.3.2.3 VOCs as precursor of particulate matter (or secondary organic aerosol -SOA)

Within the troposphere, we can find aerosol particles. These aerosols play a crucial role in many atmospheric processes, both regarding climate and atmospheric reactivity. Aerosol particles are classified into two categories, according to their formations: (i) primary particles, which are emitted directly into the atmosphere in solid or liquid form and (ii) secondary particles, which are formed by reactions between precursors in the atmosphere. According to their size, particles are classified into three categories (Figure I-3): ultrafine particles, having an aerodynamic diameter less than 0.01 μm; fine particles, having an aerodynamic diameter between 0.01 and 1 μm; large particles, with an aerodynamic diameter greater than 2.5 μm.

Particle nucleation from gaseous precursors is the base mechanism of the formation of new atmospheric particles. The size of the particles formed is in the order of 10 -3 to 10 -2 μm. The lifetime of these particles is usually less than five hours. These particles will quickly increase in size to give birth to fine particles by coagulation or condensation processes (Figure I-3).

They are still too small to sediment and their lifetime in the atmosphere is about a few days. VOCs oxidation in the troposphere leads to the formation of lower volatile organic products. The addition of oxygen and nitrogen to organic compounds reduce their volatility and so do the addition of alcohol, aldehydes, ketone, and nitro groups; in these cases, the volatility can be reduced by several orders of magnitude. These semi-volatile organic compounds can efficiently initiate particle formation [START_REF] Lee | Gas-phase products and secondary aerosol yields from the photooxidation of 16 different terpenes[END_REF]. The reactions explained in paragraph 2.1 between VOC, OH and NOx can potentially all lead to the secondary organic aerosol formation. The ability of a VOC to form SOA depends on its concentration in the atmosphere, its chemical reactivity and on the volatility of its oxidation products [START_REF] Williams | Volatile Organic Compounds in the Atmosphere: An Overview[END_REF].

I.3.2.3 VOCs effects on air quality and climate change

It has been widely demonstrated that high ozone concentration in the troposphere, SOA formation and the methane lifetime augmentation due to the presence of VOCs (William et al. 2013) all contribute significantly to the earth radiating balance. Hence, VOCs emissions are directly and indirectly involved in climate change as well as air pollution and thus on human and ecosystem health. All the reactions involving VOCs emissions explained in paragraph I.3 have different consequences on air quality.

First, tropospheric O 3 is a greenhouse gas which anthropogenic origin accounts for 0.25 W m -2 (IPCC 5 th report). Furthermore, several consequences on human health have been reported. O 3 in the troposphere also leads to adverse health effects such as throat and eye irritation, headache, fatigue, and nausea [START_REF] Lippmann | Health effects of tropospheric ozone: review of recent research findings and their implications to ambient air quality standards[END_REF].

SOA are precursor of cloud condensation nuclei (CCN) 5 , responsible for scattering and absorbing solar and terrestrial radiation. The formation of clouds influences the radiation budget of the earth by absorbing direct solar radiations leading to a cooling effect (IPCC 5 th report). Aerosols also scatter solar radiation leading to an increased diffusive radiation which can increase CO 2 fixation by canopies [START_REF] Niyogi | Direct observations of the effects of aerosol loading on net ecosystem CO 2 exchanges over different landscapes[END_REF]. Consequences of PM2.5 (particulate matter with a diameter smaller than 2.5 µm) on human health have been reported. These small particles can irritate the respiratory system and promote respiratory diseases responsible for increased mortality [START_REF] Atkinson | Epidemiological time series studies of PM2.5 and daily mortality and hospital admissions: a systematic review and meta-analysis[END_REF].

At first, VOCs emissions were studied as compounds emitted from anthropological activities. During the last decades, literature underlined the importance of the biological 5 Cloud condensation nuclei (CCN) are small particles (0.2 μm) on which water vapor condenses and they are key players on clouds formation.

sources of VOCs, which contribute up to 90% of the total emission rate. Next paragraph will be focused on the description of the biogenic type of VOCs emissions in the atmosphere.

I.4 Biogenic Volatile Organic Compounds

The biosphere produces biogenic gases including volatile compounds other than carbon monoxide, carbon dioxide, and methane, called biogenic Volatile Organic Compounds (bVOCs). This classification allows differentiating the VOCs released from biogenic sources to the anthropogenic ones. BVOCs emissions represent the largest VOC source with an estimated annual emissions amount of 760 Tg (C) yr -1 . If we consider that global total carbon monoxide emissions (CO) is 90 Tg (C) yr -1 , we can notice that the impact of the VOCs emissions in the atmosphere is between 8 and 9 times higher than CO emissions [START_REF] Sindelarova | Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years[END_REF]. Nevertheless, bVOCs fluxes contribute up to 5-10% of the total net carbon exchange between the biosphere and the atmosphere [START_REF] Peñuelas | BVOCs: plant defense against climate warming?[END_REF]. Chemical compounds included in the BVOCs family are: isoprenoids such as isoprene (contributing up to 69% total BVOCs emissions) and monoterpenes (contributing up to 11% total BVOCs emissions), methanol (6%), acetone (3%), sesquiterpenes (2.5%) and other BVOC species each contributing less than 2% [START_REF] Sindelarova | Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years[END_REF].

I.4.1 Main sources and sinks

The predominant source of biogenic VOCs emissions is the terrestrial vegetation, which includes plant, forests, and grassland as well as anthropogenically induced vegetation such as crops. Forests contribute up to 55% of the total bVOCs emissions [START_REF] Karl | A new European plant-specific emission inventory of biogenic volatile organic compounds for use in atmospheric transport models[END_REF], while crops contribute up to 27% in Europe [START_REF] Karl | A new European plant-specific emission inventory of biogenic volatile organic compounds for use in atmospheric transport models[END_REF]. Another bVOCs source is the ocean. The total bVOCs emissions estimation from oceans is between 37 and 148 Tg y -1 [START_REF] Abbatt | Introduction to Atmospheric Chemistry[END_REF]. Oceans can be considered as sinks as well; they have the capacity to store acetone and methanol with an estimated deposition rate of 0.10 cm s -1 and 0.08 cm s -1 respectively [START_REF] Singh | Oxygenated volatile organic chemicals in the oceans: Inferences and implications based on atmospheric observations and air-sea exchange models[END_REF]. Soil and microorganisms are considered as sources and sinks as well, and since they are the object of the entire manuscript, a detailed description of the VOCs emissions from those sources will be given in chapter I.V.

I.4.2 Feedbacks effects of bVOC on climate

Lately, bVOCs gained attention because of their pivotal role in climate changes. In the past, bVOCs were precluded from having a significant role on climate change because of their short lifetime in the atmosphere. It is now clear that the increase of bVOCs due to the warming and global change have direct and indirect effects on the greenhouse gas budgets [START_REF] Peñuelas | BVOCs and global change[END_REF]. Indeed, stresses linked to climate changes (i.e., heat and drought) or other type of environmental stresses (i.e., insect infestations) affect the composition and quality of the VOCs emissions. For instance, [START_REF] Zhao | Environmental conditions regulate the impact of plants on cloud formation[END_REF], studied the type of VOCs emissions emitted from a boral forest under unstressed and stressed conditions.

VOCs emissions from unstressed condition were dominated by isoprene and monoterpenes. [START_REF] Zhao | Environmental conditions regulate the impact of plants on cloud formation[END_REF].

Figure I-6. Interactions between environmental conditions, biogenic volatile organic compounds emissions, secondary organic aerosol, cloud formation and climate as potential feedback loops. VOC constitutive (unstressed conditions) = higher emission rate of isoprene and monoterpenes, VOC induced (stressed conditions) = higher emission rate of sesquiterpenes, k=hygroscopicity [START_REF] Zhao | Environmental conditions regulate the impact of plants on cloud formation[END_REF].

Biogenic VOCs are also involved in the reactions explained in paragraph 3.2.2 leading to the ozone formation. Furthermore, bVOCs react with the ozone thus contributing to its consumption as well. Nevertheless, the net formation of O 3 depends mainly on the VOC/NOx ratio (see paragraph 3.2.2 for more details). Besides, bVOCs emissions are likely to indirectly increase other greenhouse gasses (i.e., methane) impoverishing OH radical concentration in the atmosphere. OH radicals are very reactive oxidants and act as the primary cleansing agent for the atmosphere [START_REF] Lelieveld | Atmospheric oxidation capacity sustained by a tropical forest[END_REF].

Several studies have been focused on bVOCs emissions by plants and forests. These sources of VOCs are considered the most important because of the higher VOCs flux emitted [START_REF] Kesselmeier | Biogenic volatile organic compounds (VOC): An overview on emission, physiology and ecology[END_REF]. Recently, other bVOCs sources gained attention because of their possible underestimated contribution on the total bVOCs emissions [START_REF] Bachy | Are BVOC exchanges in agricultural ecosystems overestimated? Insights from fluxes measured in a maize field over a whole growing season[END_REF].

Those sources are soil and microorganisms, and they will be detailed in the following paragraphs.

I.5 Soil as source and sink of VOCs

Soil emits VOCs between 1-2 orders of magnitude less than vegetation. BVOCs from soil are the result of multiple abiotic and biotic processes. Biotic processes leading to the bVOCs emissions in soils are the decomposition of litter and dead organic material, and the metabolism of microorganisms living in soil and on roots. Furthermore, VOCs mediate the interactions between organisms living in the soil. An overview of the interactions mediated by VOCs in soil is represented in Figure I-7. Since VOCs are often the result of complex interactions, understanding the processes leading to their emissions is challenging. For example, root exudate and litter increase the microbial activity in soil; this can lead to a shift in the consumption or production of VOCs in soil [START_REF] Rinnan | Off-season biogenic volatile organic compound emissions from heath mesocosms: responses to vegetation cutting[END_REF]. Moreover, it was shown that other abiotic factors affect the VOCs emissions from soil such as water content, nutrient availability and temperature (Asensio et al., 2007b). However, VOCs transfer in soils is also affected by adsorption mechanisms onto water, and the mineral and organic fraction of soil. The VOCs equilibrium between liquid and gaseous phase in soil follows the Henry's law: in thermodynamical equilibrium, the gas quantity dissolved in the liquid phase is proportional to the pressure of the VOCs on the liquid surface.

The concentration gradient depends on soil porosity, pore water content, organic carbon fraction, pH, soil particles distribution depending on the type of soil [START_REF] Uteau | Root and time dependent soil structure formation and its influence on gas transport in the subsoil[END_REF] and is finally affected by the chemical properties of the VOC (Williams et al., 1996). Another important factor is the soil organic matter, which, changing the soil porosity, affects the gas diffusivity [START_REF] Hamamoto | Organic Matter Fraction Dependent Model for Predicting the Gas Diffusion Coefficient in Variably Saturated Soils[END_REF]. Besides, high water content in pores boosts the emissions of non-soluble VOCs, while, in dry conditions, soluble VOCs will mainly be retained [START_REF] Provoost | Henry's Equilibrium Partitioning between Ground Water and Soil Air: Predictions versus Observations[END_REF]. The percolation threshold which represents the air content in soil at which diffusive gas transport decrease due to water pore blockage, is known to be higher in organic soils than in mineral6 ones [START_REF] Freijer | Calibration of Jointed Tube Model for the Gas Diffusion Coefficient in Soils[END_REF]. Soil properties and soil composition also affect the VOCs emissions and absorption in soil. [START_REF] Ruiz | Adsorption of Different VOC onto Soil Minerals from Gas Phase: Influence of Mineral, Type of VOC, and Air Humidity[END_REF] studied the absorbing VOCs potential of clay, sand, and limestone. This study demonstrated that for the 8 VOCs analyzed (n-hexane, n-heptane, n-octane, toluene, p-xylene, m-xylene, ethylbenzene, and methyl ethyl ketone) clay absorbed a higher rate of VOCs. Approximatively, clay absorbs an order of magnitude more than sand and two orders of magnitude more than limestone [START_REF] Ruiz | Adsorption of Different VOC onto Soil Minerals from Gas Phase: Influence of Mineral, Type of VOC, and Air Humidity[END_REF].

Furthermore, [START_REF] Serrano | Sorption study of 25 volatile organic compounds in several Mediterranean soils using headspace-gas chromatography-mass spectrometry[END_REF] studied the absorption of 25 VOCs compounds in alkaline and acid agricultural soils. They reported a higher absorption in alkaline soils than acid ones and a positive correlation between the content of organic carbon in soil and the absorption of VOCs for alkaline soil while for the acid ones VOCs absorption decreases with organic carbon content.

I.6 Microorganisms living in soil: structure and functions

The soil is a highly complex, heterogeneous, and nutrient-limited habitat consisting of a mixed organic-mineral matrix with liquid and gaseous pores, owning the highest microbial diversity on earth [START_REF] Jangid | Development of soil microbial communities during tallgrass prairie restoration[END_REF]. The range of microbial biomass in soil goes from 1 T ha -1 to 10 T ha -1 representing a considerable fraction of the biomass on Earth [START_REF] Fierer | Toward an ecological classification of soil bacteria[END_REF]. The diversity of terrestrial microbial communities is complex and variable at different levels of biological organization. It includes genetic variability within taxa (species), number (richness) and relative abundance of taxa (evenness) as well as functional groups within the community [START_REF] Torsvik | Microbial diversity and function in soil: from genes to ecosystems[END_REF]. Microorganisms colonizing soil are bacteria, archaea, nematodes, protozoa, and fungi. Besides, it has been estimated that only a very small fraction of the microorganisms colonizing soil is known. For bacteria and fungi we know 13000 and between 18000-35000 species respectively which correspond to 1-2 % of the total estimated species [START_REF] Barrios | Soil biota, ecosystem services and land productivity[END_REF].

Microorganisms are especially abundant in the narrow region of soil attached to plant roots, the rhizosphere. They are essential in soil ecosystem due to their essential role in all ecosystem functions and biogeochemical processes in soil [START_REF] Baumann | Soil microbial diversity affects soil organic matter decomposition in a silty grassland soil[END_REF][START_REF] Falkowski | The Microbial Engines That Drive Earth's Biogeochemical Cycles[END_REF]. The ecosystem services related to soil functioning driven by microorganisms are listed in [START_REF] Bodelier | Toward Understanding, Managing, and Protecting Microbial Ecosystems[END_REF].

As Table I-2 shows, different microorganisms in soil can accomplish the same task.

Species colonizing an ecosystem and contributing equally to an ecosystem function can be substituted by another. This characteristic is named functional redundancy. At first, functional redundancy gave less importance to the preservation of microbial diversity in soils since the soil functions remained constant even if erosion of microbial diversity due to environmental stresses were noticed [START_REF] Chapin | Biotic Control over the Functioning of Ecosystems[END_REF].

However, it has been reported that microbial species, surviving a perturbation, and which supposed to have the same ability to perform a function, might not have the same competitive ability or growth rate as the original community members [START_REF] Riah-Anglet | Soil microbial community structure and function relationships: A heat stress experiment[END_REF].

Furthermore, the new community shaped after a perturbation might not be able to perform the functions with the same efficiency, or finally, they might perturb directly or indirectly the activity of other population in the community [START_REF] Riah-Anglet | Soil microbial community structure and function relationships: A heat stress experiment[END_REF].

I.6.1 Factors affecting microbial diversity in soil

Generally, high-input agricultural practices decrease microbial biodiversity while the low-input practices enhance microbial diversity in soil [START_REF] Girvan | Soil Type Is the Primary Determinant of the Composition of the Total and Active Bacterial Communities in Arable Soils[END_REF][START_REF] Munyanziza | Agricultural intensification, soil biodiversity and agroecosystem function in the tropics: the role of mycorrhiza in crops and trees[END_REF]. [START_REF] Lupwayi | Bacterial diversity in water-stable aggregates of soils under conventional and zero tillage management[END_REF] has given an example recording higher microbial diversity on soils under conventional tillage than zero tillage. Furthermore, [START_REF] Wolińska | Microbial biodiversity in arable soils is affected by agricultural practices[END_REF], compared cultivate soils with non-cultivated soil reporting a decrease of the microbial diversity in soil up to 30% in the cultivated soil. Monoculture agricultural practice also reduced bacteria biodiversity in soil, while fungi biodiversity seems to not be affected [START_REF] Liu | Microbial Community Diversities and Taxa Abundances in Soils along a Seven-Year Gradient of Potato Monoculture Using High Throughput Pyrosequencing Approach[END_REF]. Soil microbial diversity decrease with increasing latitude and correlate positively with measures of atmospheric temperature and pH [START_REF] Staddon | Soil microbial diversity and community structure across a climatic gradient in western Canada[END_REF]. Soil microbial diversity may be lower in northern sites due to decreased productivity, nutrient limitation, and higher acidity. The diversity and abundance of soil bacteria and fungi are also reduced in the arid lands [START_REF] Maestre | Increasing aridity reduces soil microbial diversity and abundance in global drylands[END_REF].

I.6.2 Microorganisms and biogeochemical cycles in soil

The survival of plants and animal life depend on the microorganisms' activity because of their implication on the biogeochemical cycles of carbon, nitrogen, sulfur, and other elements.

In soil, microorganisms are involved on the humus formation, decomposition of plant litter and thus, energy production. An example of the roles played by microorganisms in soil is

given by the description of the nitrogen cycle (Figure I-8). The nitrogen cycle is mostly driven by microorganisms: nitrogen-fixing bacteria can transform atmospheric nitrogen (N 2 ) in ammonium by fixation and ammonification processes. Secondly, nitrifying bacteria transform ammonia in nitrites and nitrates, which are assimilated by the plant in order to build proteins, amino acids, RNA and DNA, and may lead to NO emissions. Finally, denitrifying bacteria use nitrate for the oxidation process releasing N 2 in the atmosphere, but also leading to N 2 O emissions.

Moreover, the mineralization of organic matter, a central process of the carbon cycle, is mostly carried out by heterotrophic soil microorganisms which reduce complex molecules into smaller molecules, easily assimilated by plants. Due to their metabolic plasticity, soil microorganisms are also involved in the degradation and immobilization of pollutants (i.e., pesticides) brought from the agricultural or industrial environment. Some microorganisms have also got a significant impact on plant health and growth by creating symbioses [START_REF] Barrios | Soil biota, ecosystem services and land productivity[END_REF] or by inducing diseases. Other microorganisms are involved in the formation and stability of soil aggregates through the production of binding agents or the establishment of physical bonds made by the hyphae of fungi [START_REF] Six | Soil organic matter, biota and aggregation in temperate and tropical soils -Effects of no-tillage[END_REF].

All the metabolic processes leading to the transformation of the elements in soil, such as the nitrogen and carbon cycles just described, can release VOCs [START_REF] Schmidt | Volatile affairs in microbial interactions[END_REF].

Moreover, in a complex system such as the soil, VOCs are also used to communicate or more in general for intraspecific and interspecific interactions [START_REF] Tyc | The Ecological Role of Volatile and Soluble Secondary Metabolites Produced by Soil Bacteria[END_REF]. In the following paragraphs, the description of possible metabolic processes leading to the production of VOCs is given.

I.6.3 Biosynthesis of VOCs from microorganisms

Microbial VOCs can be released from primary metabolism in order to produce energy or in the secondary metabolism implicated in the communication and defense mechanisms. In this paragraph, we will describe the primary metabolisms leading to VOCs production.

VOCs production from sugar degradation is mainly released from these three pathways:

Embden-Meyerhof, Entner-Doudoroff and heterolactic/homolactic pathway. During sugar degradation, several precursor compounds of the VOCs production are formed:

glyceraldehyde-3-phosphate, pyruvate, lactate, acetate, and CO 2 are among those. From the compounds listed above (except CO 2 ) Saccharomyces and a few more bacteria can synthesize ethanol [START_REF] Sniegowski | Saccharomyces cerevisiae and Saccharomyces paradoxus coexist in a natural woodland site in North America and display different levels of reproductive isolation from European conspecifics[END_REF]. Bacillus species can produce 2,3-Butanediol and acetoin [START_REF] Ryu | Bacterial volatiles promote growth in Arabidopsis[END_REF] while Clostridium species produce butanol and acetone from the fermentation process [START_REF] Smith | Common mesophilic anaerobes, including Clostridium botulinum and Clostridium tetani, in 21 soil specimens[END_REF]. Microorganisms in soil need to degrade the organic matter in order to produce energy. When the biodegradation of the soil organic matter is incomplete, several VOCs, as intermediate products of the degradation, are released [START_REF] Leff | Volatile organic compound (VOC) emissions from soil and litter samples[END_REF]. For example, pyruvate is an intermediate product of the hemicellulose and cellulose degradation and if the degradation is incomplete higher quantities of ethanol are released [START_REF] Lamsen | Recent progress in synthetic biology for microbial production of C3-C10 alcohols[END_REF]).

An essential factor affecting degradation processes is oxygen availability. When anaerobic conditions are present, the alcoholic fermentation becomes the most likely metabolic process.

Another important pathway generating microbial VOCs is the shikimate pathway which releases aromatic compounds [START_REF] Schulz | Bacterial volatiles: the smell of small organisms[END_REF]. The shikimate pathway (Shikimic acid pathway) is a seven-step metabolic route used by bacteria, archaea, fungi, algae, and plants for the biosynthesis of folates and aromatic amino acids (phenylalanine, tyrosine, and tryptophan). [START_REF] Gosset | Production of aromatic compounds in bacteria[END_REF] reported that the most emitted VOCs releases from the shikimate pathway are 2-phenylethanol, p-hydroxycinnamic acid, p-hydroxystyrene, and p-hydroxybenzoate.

Bacteria can synthesize sulphur-containing VOCs. An example is given by the lactic bacteria which generate a degradation product, the l-methionine, that leads to the formation of H 2 S, dimethyl sulphide (DMS), dimethyl disulphide (DMDS), and methanethiol [START_REF] Schulz | Bacterial volatiles: the smell of small organisms[END_REF].

Compounds such as alkenes, aliphatic alcohols, hydrocarbon compounds, and ketones are the result of fatty acid metabolism [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF]. During the fatty acid biosynthesis, several transformations occur such as the decarboxylation that leads to alkenes or methyl ketones or the reduction of carboxyl group which generates aldehydes and 1-alkanols [START_REF] Schulz | Bacterial volatiles: the smell of small organisms[END_REF].

Microorganisms are also responsible for the production of the VOCs giving the typical earthy odour: Geosmin (terpene) and 2-methyisoborneol. Those compounds are emitted by actinomycetes myxobacteria and cyanobacteria [START_REF] Citron | Terpenoids are Widespread in Actinomycetes: A Correlation of Secondary Metabolism and Genome Data[END_REF]. Finally, another class of compounds that is likely to be emitted from microorganisms is monoterpenes. More details , fatty acids (butyric acid), isoprene, lactic acid, lactones (gamma-butyrolactone), methylketones (acetone), monoterpenes (farnesol), nitrogen compounds (benzonitrile), sesquiterpenes (pinene) and sulphur compounds (dimethyl disulphide) [START_REF] Schmidt | Volatile affairs in microbial interactions[END_REF].

I.6.4 Interactions mediated by VOCs

Culture-based studies have revealed that even a single bacteria or fungi strain can produce a vast array of secondary metabolites which are not directly involved in the growth, development or reproduction of the microorganism [START_REF] Tyc | The Ecological Role of Volatile and Soluble Secondary Metabolites Produced by Soil Bacteria[END_REF]. Within those secondary metabolites, we can find VOCs. VOCs, thanks to their physiochemical properties (i.e., small molecules <300 Da, easy diffusion and volatility), are the perfect candidates to mediate the cooperation and the competition between soil microorganisms; even when they are not adjacent to each other. VOCs by microorganisms mediate several functions such as (Kai et al., 2009a;[START_REF] Ryan | Diffusible signals and interspecies communication in bacteria[END_REF]:

 Communication between inter and intra-specific organisms;

 Cell-to-cell signals;

 Growth promoting and inhibiting agents.

In the following paragraph, we will be focused on the VOCs emitted from bacteria and fungi which inhibit or promote growth.

I.6.4.1 VOCs inhibiting and promoting fungi and bacteria growth

For fungi, the exposure to bacterial VOCs can change fungal morphology, enzyme activity and gene expression (Kai et al., 2009a;[START_REF] Vespermann | Rhizobacterial Volatiles Affect the Growth of Fungi and Arabidopsis thaliana[END_REF][START_REF] Wheatley | The consequences of volatile organic compound mediated bacterial and fungal interactions[END_REF].

Some VOCs have antifungal properties even at low concentrations: trimethylamine, benzaldehyde, and N,N-demethyloctylamine (Chuankun et al., 2004a). Fungi emit several VOCs implied on antibiotic activity. For instance, the emissions profiles of the basidiomycetes Hypholoma Fasciculare and Resinicium Bicolor change when they are sharing the same space [START_REF] Huang | Changes in volatile production during the course of fungal mycelial interactions between Hypholoma fasciculare and Resinicium bicolor[END_REF]. The VOCs emitted while those two species are in contact inhibit mutually the growth of the fungi. Moreover, [START_REF] Chakraborty | Control of fusarium wilt of Solanum melongena by Trichoderma spp[END_REF], studied the Trichoderma spp. inhibiting activity mediates by antibiotic VOCs versus pathogenic fungi (i.e., Fusarium solani) on plants. The antibiotic VOCs released by Trichoderma spp. can reduce the pathogenic fungi colonization on plants up to 75% [START_REF] Chakraborty | Control of fusarium wilt of Solanum melongena by Trichoderma spp[END_REF].

VOCs which mediate bacteria-to-bacteria interactions can inhibit the growth of other bacteria species. For instance, Veillonella and Bacteroides fragilis inhibit the growth of enteropathogenic bacteria such as Escherichia coli, Salmonella, Shigella and Yersinia [START_REF] Wrigley | Inhibition of Clostridium perfringens sporulation by Bacteroides fragilis and short-chain fatty acids[END_REF]. A widespread VOCs produced among bacteria with a strong growth inhibitor power is Indole. Indole is generated through the degradation of tryptophan by tryptophanase enzyme and inhibits biofilm formation in E. coli, P. fluorescens, and P.

aeruginosa. Other studies as Kai et al., 2009a reported that the typical red colony coloration of the B. cepacia species diminishes on exposure to volatiles from S. odorifera and Serratia plymuthica, showing that volatiles of bacteria can influence the metabolism of other bacteria.

Another example is the release of volatile short chain fatty acids from Veilonella species and Bacteroides fragilis, which thus control the growth of the enteropathogens Salmonella typhimurium, Salmonella enteritidis, E. coli, and Pseudomonas aeroginosa [START_REF] Hinton | Antibacterial Activity of the Metabolic By-products of a Veillonella species and Bacteroides fragilis[END_REF]. Finally, positive interactions between bacteria and fungi have been reported.

I.7 Organic Waste Products (OWPs)

Organic waste products are defined as the exogenous organic matter applied in soils with the aim of improving fertilization and soil structure. OWPs include all the organic wastes deriving from agricultural (i.e. crops residues), urban and industrial activities ( [START_REF] Van-Camp | Established Under the Thematic Strategy for Soil Protection[END_REF]. In Europe every year about 1.6 billions tons of OWPs are produced: 61% comes from animal residues, 25% comes from crops residues, 7% comes from municipal wastes and finally 7 % comes from industrial wastes. In France, the majority of the annual production of OWPs is derived from manure (300 Mt, ADEME, 2006), and the 50% of this production is returned to the soil. Regarding municipal and industrial OWPs France produces about 32 Mt (ADEME, 2006). In this case, about 40% are recycled as OWP amendment to soil (ADEME, 2006). In this study four different organic waste products were taken into account:

 Municipal solid waste compost (MSW) made from residual municipal waste after the selective collection of dry and clean packaging;

 Biowaste compost (BIOW) made from selectively collected fermentable fraction of municipal wasted co-composted with green wastes;

 Green waste and sludge (GWS) a compost resulting from the co-composting of sewage sludge, green wastes, and wood chips;

 Farmyard manure (FYM) obtained from a dairy farm.

All those OWPs are the result of a composting process leading to a monitored transformation of the organic waste products driven by the microbial community. The final products are rich in stabilized organic products, humic compounds, and nutrients (ITAB, 2002).

I.7.1 Effects of organic waste products amendment on soil organic matter and microbial communities

The intensive agricultural practices widely used in agricultural land contribute to a decrease in soil organic matter content with negative consequences on soil fertility. To contrast, this trend the recycling of organic waste products (OWPs) have been widely

encouraged in Europe (European Commission, 2010) especially for cropped soils. In fact, OWPs improve soil fertility and contribute to carbon storage in soil by improving soil porosity, aggregation, bulk density and structure stability [START_REF] Abiven | The effects of organic inputs over time on soil aggregate stability -A literature analysis[END_REF]. Furthermore, OWPs represent a potential source of nutrients (N, P, K..) for crops that can partially substitute the use of mineral fertilizers [START_REF] Chalhoub | Increased nitrogen availability in soil after repeated compost applications: Use of the PASTIS model to separate short and long-term effects[END_REF]. In addition to positive effects on soil fertility, OWPs application improves soil biodiversity and biological activities [START_REF] García-Gil | Effects of Long-Term Sewage Sludge Amendment on the Composition, Structure and Proton Binding Activity of Soil Fulvic Acids[END_REF]. OWPs also impact on chemical fertility through the pH regulation, as well as increase CEC [START_REF] Diacono | Long-term effects of organic amendments on soil fertility[END_REF]. The effects of the OWP in soil have been investigated on long-term and short-term experiments. In the following paragraphs, an elucidation of the effects on microbial community and on the organic matter of both type of experiment will be given.

I.7.1.1 Long-term effects

Repeated fertilizations over several years are a commonly used practice under field conditions to maintain soil fertility and thus crop yields. It has been also observed that compared to punctual organic fertilization, long-term OWP applications had more persistent impacts on soil characteristics [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF], plant growth [START_REF] Clark | Environmental and plant community determinants of species loss following nitrogen enrichment[END_REF], and microbial diversity and activity [START_REF] Francioli | Mineral vs. Organic Amendments: Microbial Community Structure, Activity and Abundance of Agriculturally Relevant Microbes Are Driven by Long-Term Fertilization Strategies[END_REF][START_REF] Giacometti | Microplate-scale fluorometric soil enzyme assays as tools to assess soil quality in a long-term agricultural field experiment[END_REF]. Studies focusing on the impact on soil microbial community demonstrated an increase of soil microbial biomass with recurrent OWP amendment [START_REF] Sadet-Bourgeteau | Lasting effect of repeated application of organic waste products on microbial communities in arable soils[END_REF].

However, the response of other soil microbial community parameters such as diversity, composition, and structure seemed to depend on the time between the OWP application and soil sampling and the number of years of application. In fact, for applications shorter than 6 years no changes in soil microbial community structure and composition were observed [START_REF] Poulsen | Effects of fertilization with urban and agricultural organic wastes in a field trial -Prokaryotic diversity investigated by pyrosequencing[END_REF]. On the other hand, an increase of soil bacterial and fungal diversity and stimulation of some microbial groups such as Firmicutes, Proteobacteria, and Zygomycota were observed after more than 20 years of OWP application [START_REF] Francioli | Mineral vs. Organic Amendments: Microbial Community Structure, Activity and Abundance of Agriculturally Relevant Microbes Are Driven by Long-Term Fertilization Strategies[END_REF]. Others parameters, such as the quantity and the type of OWPs applied, seemed to modify the impact of the microbial communities in soil [START_REF] Diacono | Long-term effects of organic amendments on soil fertility[END_REF]Sadet-Bourgeteau et al., 2018).

Long term application of OWPs in soil have also negatives effects. OWPs may contain contaminants such as pathogens, organic contaminants or trace elements including metals [START_REF] Belon | An inventory of trace elements inputs to French agricultural soils[END_REF]. Those contaminants are accumulated in soil leading to a possible transfer to plant and water [START_REF] Cambier | Impact of long-term organic residue recycling in agriculture on soil solution composition and trace metal leaching in soils[END_REF]. Additional negative impacts are the decrease of the pH that leads to less O 2 availability in soil, or an excessive input of nutrients due to simultaneous N and P addition [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF]. These impacts on soil are strictly related to the quality of the organic amendment, the applied doses, the frequency of the application and the cropping system.

I.7.1.2 Short-term effects

Short-term effects of OWP amendments to soils were investigated by [START_REF] Leroy | The quality of exogenous organic matter: short-term effects on soil physical properties and soil organic matter fractions[END_REF] who studied aggregate stability, hydraulic conductivity, and on the OC in soil organic matter (SOM) fractions. Their study reported an increase of the aggregate stability and hydraulic conductivity after less than 1 year from the first OWP application. The OWP applications also improved the amount of OC in all the fractions of the SOM [START_REF] Leroy | The quality of exogenous organic matter: short-term effects on soil physical properties and soil organic matter fractions[END_REF]. Federici et al., (2017a) reported the same results as [START_REF] Leroy | The quality of exogenous organic matter: short-term effects on soil physical properties and soil organic matter fractions[END_REF] after only 120 days from the OWP application. These results showed that OWPs have pronounced effects on soil physical properties starting from the first year of application. Concerning the microbial community, after three days only, all OWPs induced rapid modifications of both fungal and bacterial communities (Federici et al., 2017a). However, Federici et al., (2017a) also reported that while the bacterial community restored to its initial state after 120 days, suggesting their high resilience capacity, the fungal community changes remained modified after this period.

I.7.2 Effects of the organic waste products amendment on the VOCs emissions from soil

Organic waste products affect soil biological and chemical parameters. Changes in soil characteristics also affect the VOCs emissions. It has been reported for instance that moisture content, pH and temperature leads to variation of VOCs flux (Asensio et al., 2007a;Raza et al., 2017a). Due to the complexity of the soil ecosystem, literature does not provide exhaustive information about the dynamics of the VOCs emitted by OWP, despite recent studies have started to fill this lack. For example, [START_REF] Seewald | Substrate-induced volatile organic compound emissions from compost-amended soils[END_REF] studied VOCs production by adding glucose to soils regularly amended by different composts and mineral fertilizers. They also reduced the O 2 availability in order to impair the degradation of the VOCs produced by microorganisms. Results reported by this study showed that organic waste composts did not alter VOC emissions compared to an untreated control, while sewage sludge composts and mineral fertilization showed the opposite effect. Moreover, [START_REF] Potard | Organic amendment practices as possible drivers of biogenic Volatile Organic Compounds emitted by soils in agrosystems[END_REF] showed that different soil organic fertilizers such as pig slurry and methanized pig slurry impacted on a different way soil VOCs emissions: pig slurry released double quantities of VOCs while methanized pig slurry emitted even less than the unamended samples. This is as expected since methanization tends to drastically reduce the carbon content of the slurry. [START_REF] Woodbury | Emission of Volatile Organic Compounds as Affected by Rate of Application of Cattle Manure[END_REF] reported an emission of volatile sulfurs compounds after the application of beef cattle manure. Contrasting type of VOCs emissions from soil have been reported from several studies. For instance, in Mediterranean soils the most emitted compound was methanol (Asensio et al., 2007a) while in soil amended with straw it was acetone [START_REF] Zhao | Volatile organic compound emissions from straw-amended agricultural soils and their relations to bacterial communities: A laboratory study[END_REF]. In fact, emissions of VOCs are strongly related to the substrate quality and it is important to note that even small variations in nutrient composition may change the type and the amount of individual VOCs produced [START_REF] Wheatley | The consequences of volatile organic compound mediated bacterial and fungal interactions[END_REF]. It is also important to bear in mind that a significant proportion of the VOCs are produced by microorganisms [START_REF] Isidorov | Volatile organic compounds from leaves litter[END_REF]. Hence, since the microbial communities, as explained in previous paragraphs, are widely altered by the OWP amendment [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF][START_REF] Sadet-Bourgeteau | Lasting effect of repeated application of organic waste products on microbial communities in arable soils[END_REF] VOCs are also altered.

Objectives

During these three years, my work has focused on the characterization of VOCs emissions from soil. In particular, the aim was to characterize, in terms of composition and quantities, the VOCs emitted from soils amended with different type of organic waste products and to determine the role of the microbial diversity in these emissions. We also Chapter II gives an overview of the methods used in this work, while chapters III, IV and V are the core of the work presenting results from three experiments designed to answer the three research questions listed above. Chapter VI is a general discussion that makes the link between these three questions and Chapter VII gives conclusions and perspectives to this work.

Chapter III was dedicated to question a. For this purpose, we measured the composition and quantity of VOCs emitted by soils regularly amended with OWPs. Soil samples were taken from the QualiAgro site where soils have received OWPs since 1998. We measured VOCs emissions from soil amended with 4 different OWPs and a control sample that never received organic amendment. Soils were sampled two years after the last application in order to avoid any short term emission effects. In this chapter, we discuss the differences in magnitude and composition of the VOCs emitted and we discuss the links with soil characteristics (pH, organic matter, I roc indicator, cation exchange capacity, carbon/nitrogen ratio, total nitrogen, and organic carbon). Chapter III was published in Science of the Total Environment journal.

The aim of chapter IV was to give an answer to question b. Soil samples were collected at the same site as the first experiment, one year after the last OWPs application. Soil samples were sterilized and inoculated with three different microbial diversity dilution levels (high, medium and low) to prepare microcosms that were incubated for 6 weeks. VOCs emissions were then measured and the microbial composition characterized. The links between the microbial community structure, as affected by diversity manipulation, and VOCs emissions composition and magnitude were explored.

Chapter V is dedicated to question c. In this experiment, we measured the dynamics of VOC emissions and microbial structure during two days following the fresh application of green waste and sludge (GWS). We further compared the response of a soil recurrently receiving OWPs to soil that had never received OWPs for ~20 years. Addition of GWS to these soils was performed 1h before the detection of VOCs emissions. The VOCs emissions from the microcosms were detected 10 times during 49h after the application of the GWS amendment. We characterized the microbial community structure before and at the end of the 49h. We then discussed the short term VOC emissions and the links with changes in the microbial community.

For all the performed experiments the detection of VOCs was made using the PTR-Qi-TOF-MS technique; while the evolution of the microbial diversity was characterized after the VOCs detection by a high throughput sequencing approach targeting 16S and 18S ribosomal genes. The experimental setups are presented in Chapter II in details. The soil has a loamy-clayey texture and is classified as a hortic glossic Luvisol (IUSS Working Group WRB, 2014), representative of the Parisian Basin. The characteristics of these soils are loam texture of a depth greater than 1.2 m, lack of clay (<15%) initial pH of 6.9

in the surface (0-30 cm), good drainage and it is based on loessic carbonate loam which appears at around 1.60 m. At the beginning of the experiment, in 1998, the site had an organic matter content lower than the critical threshold 8 (1.1 %) [START_REF] Meersmans | A high resolution map of French soil organic carbon[END_REF], which is not the case anymore, in fact, organic matter content is now around the 2%. The increase of organic matter content is due to the application of OWPs since 1998 at a rate of ~4 tC ha -1 every two years on the wheat stubbles in September, after harvesting. The crop rotation was wheat (Triticum aestivum L.) and maize (Zea mays L.). The wheat crop residues were exported, whereas the maize residues were returned to the soil. Finally, the site weather station reports an annual average precipitation is 583 mm with 11°C of annual average temperature. Mineral fertilization occurs twice per year for wheat (early March and early April) and once for maize (early May).

II.2 Site structure and sample collection

The QualiAgro site is a 6 ha experiment divided in 4 blocks of 10 plots (40 plots of 10x45 m each). The distance between plots in the same block was 6 m while the distance between plots in different blocks was 25 m, separated by a wide buffer strip (Erreur ! Source du renvoi introuvable.). The experiment was a randomized block design with 4 replicates comparing 4 organic waste products: BIOW (bio-waste compost derived from the cocomposting of green wastes and source-separated organic fractions of municipal solid wastes), GWS (compost derived from the co-composting of green wastes, 70%, with sewage sludge, 30%, collected from private and public gardens), FYM (farmyard manure) and MSW (municipal solid waste compost derived from the composting of residual solid wastes after removing dry and clean packaging); plus a control without organic input (CN). Table II-1 shows the composition of the different organic waste products regarding the dry matter content, the organic carbon, the total N, the organic carbon/total N ratio and the pH.

Furthermore, the OWPs application was crossed with 2 mineral N treatments. 20 field plots were additionally amended with mineral N in order to reach the N optimal quantity in soil. Application of N optimal was reached adding: 140 (±37) kg N ha -1 for wheat and 82 (±34) kg N ha -1 for maize.

Sampling was performed in the ploughed layer (0-30 cm). For each plot, 10 pooled subsamples were collected. The collection for the first experiment was made in September 2015 in the 20 plot receiving the N optimal amendment, 2 years after the application of OWPs.

8 Critical threshold of organic content 1.5% [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF].

For the second and the third experiment samples were collected in September 2016, 1 year after the application of OWPs and we only collected samples in the first block receiving the optimal N amendment.

II.3 Samples preparation

After the samples collection for the first experiment, samples of the same plot were mixed, homogenized and dried at room temperature during 2 weeks. The homogenization of the samples has been obtained by passing samples through a 2 mm sieve and removing aboveground plant materials, roots and stones.

For the second and the third experiment, samples were prepared as the first experiment except for the dry up temperature which was 40 °C for three days. Secondly, samples were sent to the sterilization process in order to proceed with the inoculation and thus the creation of the microcosms (see paragraph Microcosms preparation pg. 40).

II.4 Flux chambers

All the experiments described in this work were performed in a laboratory environment. In order to detect the VOCs emissions dynamics chambers were used. The two Pyrex chambers used in the first experiment soil samples had a surface of 106 cm 2 . Pirex chambers allow better detection of the VOCs emitted by soil because of their low interaction with VOCs molecules. Furthermore, the connection between the chambers and the PTR-QiTOF-MS was performed using PEEK tubes which are made of inert material. For the first experiment, 60 g of soil were homogeneously spread in the chamber surface. One chamber was used as a blank (no samples were inserted) in order to detect the VOCs emitted by the empty chamber.

For the second and the third experiment, 30 g of inoculated soil were transferred in a flask

II.5 Microcosms preparation

Humidity and water holding capacity (WHC) of the sterilized soils were measured for all samples. The samples collection was performed one month before the sterilization. Once soils were sterilized a period of 8 weeks passed before the inoculation. In fact, sterilized soils need a stabilization period in order to verify that the sterilization worked and no microbial contaminations were still colonizing the samples.

The day after the inoculation, on sterilized conditions, 30 g of soil for each sample was transferred in a flask; 5 mL of MilliQ water were added in order to reach the 50% of the 60 % of the WHC (Figure II-5).

Figure II-5. Sample humidification in sterilized conditions

The day of the inoculation samples were homogeneously hydrated. The preparation performed is explained in Figure II-6. Soil suspension was prepared by mixing 30 g of soil and 90 mL of sterilized water. Three levels of microbial dilution were created: one pure, the second one had a water ration of 1:10 3 and the third one with 1:10 5 water rates. After obtaining the different dilution levels, 5 mL of the different soil suspensions were added to the corresponding microcosms. The addition of the soil suspension allowed reaching the 60% of the WHC within the microcosms. Before incubation microcosms were hermetically sealed; the incubation was 6 weeks long at 20°C. Every week, on Monday, microcosms were aerated in order to avoid the accumulation of CO 2 . If a weight loss higher than 0.1g were observed by weighing the microcosms, the addition of pure water was carried out.

II.6

Biomolecular analysis

II.6.1 DNA extraction

The DNA extraction has been performed for all microcosms following the protocol developed by GenoSol platform (INRA, Dijon, France, www.dijon.inra.fr/plateforme_genosol) (Terrat et al., 2012) for application in large-scale soil survey (Terrat et al., n.d.). The protocol consists of mixing 1 g of each soil sample with 2 g of 100 mm diameter silica beads, 2.5 g of 1.

II.6.3 The high throughput sequencing and the bioinformatics analysis

The high throughput sequencing refers to a method used for determining the order of the nucleotides bases in a DNA molecule. In this study, we amplified 440-base 16S rRNA from and 350-base 18S rRNA each DNA sample in order to obtain the bacterial and the fungi diversity respectively. The primers used are detailed in Table II-3. The pool was sequenced with a MiSeq Illumina instrument (Illumina Inc, San Diego, CA) operating with V3 chemistry and producing 250 bp paired-reads. After the sequencing, we performed the Bioinformatic analysis using the GnS-PIPE developed by the Genosol platform (INRA, Dijon, France) (Terrat et al., 2012). At first, all the 16S and 18S raw reads were organized according to the multiplex identifier sequences. All raw sequences were checked and discarded if: (i) they contained any ambiguous base (Ns), (ii) if their length was less than 350 nucleotides for 16S reads or 300 nucleotides for 18S reads, (iii) if the exact primer sequences were not found (for the distal primer, the sequence can be shorter than the complete primer sequence, but without ambiguities). A PERL program was then applied for rigorous dereplication (i.e. clustering of strictly identical sequences). The dereplicated reads were then aligned using Infernal alignment [START_REF] Cole | The Ribosomal Database Project: improved alignments and new tools for rRNA analysis[END_REF], and clustered into operational taxonomic units (OTU) using a PERL program that groups rare reads to abundant ones, and does not count differences in homopolymer lengths. A filtering step was then carried out to check all single-singletons (reads detected only once and not clustered, which might be artifacts, such as PCR chimeras)

based on the quality of their taxonomic assignments. Finally, in order to compare the datasets efficiently and avoid biased community comparisons, the reads retained were homogenized by random selection. The details of the random selection are given in Chapter IV and V since the number of reads was different. The retained high-quality reads were used for taxonomyindependent analyses and determining the Shannon index.

Primer code Primers References

Bacteria

II.7 Techniques for the detection of VOCs

In order to detect VOCs emissions in a laboratory system, several techniques and instruments can be used. Several studies analysed the emissions in the field using detection chambers while other studies used soil samples in the field and analysing them in a laboratory. The most common instruments used for the detection of VOCs emissions are the gas chromatography mass spectrometer (GC-MS) and the Proton Transfer Reaction Mass Spectrometry (PTR-MS). The gas chromatography can be coupled with: a flame ionization detector (GC-FID), a mass spectrometer (GC-MS) and a flame photometric detector (GC-FPD). For all those VOCs detection systems a method for the extraction of VOCs is needed.

This means that they are off lines techniques requiring a pre-concentration of VOCs in absorption traps (eg. Solid phase micro-extraction -SPME). Only after the collection of these VOCs they can be subjected to the detection of a GC system. On the other hand, PTR-MS allows the online detection of the VOCs released from samples with a very high mass resolution, especially if coupled with a time of flight (TOF) detector.

In the present study, VOCs emissions were detected with a PTR-QiTOF-MS (Proton Transfer Reaction-Quadrupole Ion guide Time of Flight-Mass Spectrometry) technique.

II.7.1 PTR-QiTOF-MS

The advantages given by this technique are the online detection of the VOCs and the low limit of detection ( 10 In the first chamber a potential difference between a cathode and an anode is produced

(Figure II-8),
while in the second one the ions are created as follows:

𝐻 2 + + 𝐻 2 𝑂  𝐻 2 𝑂 + + 𝐻 2
Equation 5

or

𝐻 + + 𝐻 2 𝑂  𝐻 2 𝑂 + + 𝐻 Equation 6
or

𝑂 + + 𝐻 2 𝑂  𝐻 2 𝑂 + + 𝑂 Equation 7
Leading to the final equation:

𝐻 2 𝑂 + + 𝐻 2 𝑂 𝐻 2 𝑂 -𝐻 + + 𝑂𝐻 Equation 8
The final molecule colliding with the VOCs is the ion H 3 O + . Within the drift tube, the ionization of the VOCs is performed. The ionization happens due to the transfer of one proton of the H 3 O + to the emitted molecule (M) from the sample. The proton transfer was possible only for molecules having a proton affinity higher than the proton affinity of the water (691.7 kJ mol -1 ). The molecule resulting from the interaction is a protonated molecule (MH + ).

Furthermore, the ionization of the molecule can be possible thanks to the charge transfer reaction (𝐴++ 𝐵  𝐴 -𝐵 + 𝐶ℎ𝑎𝑟𝑔𝑒 𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟 𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 Equation 10).

𝐴𝐻 + + 𝐵  𝐴 -𝐵𝐻 + 𝑃𝑟𝑜𝑡𝑜𝑛 𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟 Equation 9

𝐴 + + 𝐵  𝐴 -𝐵 + 𝐶ℎ𝑎𝑟𝑔𝑒 𝑡𝑟𝑎𝑛𝑠𝑓𝑒𝑟 𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 Equation 10

𝐻 3 𝑂 + + 𝑅 𝐾 → 𝑅𝐻 + + 𝐻 2 𝑂 𝐶ℎ𝑒𝑚𝑖𝑐𝑎𝑙 𝑖𝑜𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛 𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 Equation 11
Once the molecule is protonated it passes through the ion-guide where only the range of molecule of the select m/z range pass to the time of flight. Finally, the molecule is detected with a Multiple Channel Plate (MCP), an electron multiplier. From the signal of the 𝐻 3 𝑂 + ions, the signal of the protoned mass (MH + ) we can calculate the concentration of the molecule [M] using the following equation [START_REF] Lindinger | On-line monitoring of volatile organic compounds at pptv levels by means of proton-transfer-reaction mass spectrometry (PTR-MS) medical applications, food control and environmental research[END_REF]:

[𝑀𝐻 + ] = [𝐻 3 𝑂 + ] 0 0(1 -𝑒 -𝑘[𝑀]𝑡 ) ≈ [𝐻 3 𝑂 + ] 0 [𝑀]𝑘𝑡 Equation 12
Where t is the reaction time of 𝐻 3 𝑂 + in the drift tube, and where k is the reaction rate coefficient between [M] an 𝐻 3 𝑂 + according to the tabulation provided by [START_REF] Cappellin | On Quantitative Determination of Volatile Organic Compound Concentrations Using Proton Transfer Reaction Time-of-Flight Mass Spectrometry[END_REF]. If k is not available a standard value can be used (2*10 -9 cm 3 s -1 ).

Despite all the advantages of the PTR-QiTOF-MS listed before, some disadvantages are reported. In fact 𝐻 3 𝑂 + can form a cluster leading to the formation of a molecule with a +19 m/z (𝑀 -𝐻 3 𝑂 + ) instead of +1 m/z (𝑀𝐻 + ). Furthermore, events like fragmentation can happen caused by the collision of the molecules within the drift tube. Those events lead to a complicated identification of the detected molecules. Finally, the PTR-QiTOF-MS is not able to separate isomers, for this reason, it is often coupled with a GC.

II.7.2 Peaks analyses and mass table

The detection of the peaks was performed using the software PTR viewer 3.1.019 (Ionicon, Analytik GmbH). The integration of the peaks has been done using the software and Once the selection of all peaks have been completed, the same mass table has been applied to all samples.

Once the mass table was ready calculations of the concentration of the compounds were performed, and the calculation of the average spectra was carried out. In order to do that, software using LabVIEW has been created (Figure II-10). The aim of this software is to cut the spectrum when the signal was stable. For all samples, we took a minimum of 60 spectra to calculate an average spectrum used on the further analysis.

III.1 Introduction

The family of volatile organic compounds (VOCs) is composed of a large range of molecules with different functional groups and different chemical and physical properties.

The common traits between VOCs are the high vapor pressure under ambient conditions and low boiling points which explain their presence in the atmosphere. Volatile organic compounds sources have been extensively studied because of their contribution to global warming and pollution [START_REF] Kesselmeier | Biogenic volatile organic compounds (VOC): An overview on emission, physiology and ecology[END_REF]. It has been demonstrated that VOCs are involved in many chemical reactions in the atmosphere among which the production cycle of tropospheric ozone (O 3 ) in combination with the NO x cycle [START_REF] Seinfeld | Atmospheric Chemistry and Physics: From Air Pollution to Climate Change[END_REF].

Another important chemical processes in the atmosphere that involves VOCs are the photolysis of formaldehyde and other carbonyls in presence of NO which are responsible of hydroxyl (OH) formation [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF]. Additionally, VOCs are precursors of secondary organic aerosol (SOA) [START_REF] Singh | High concentrations and photochemical fate of oxygenated hydrocarbons in the global troposphere[END_REF] and are involved in the production of nitrates in the troposphere [START_REF] Monson | Biospheric Trace Gas Fluxes and Their Control Over Tropospheric Chemistry[END_REF]. For these reasons VOCs alter the air quality and thus they have indirectly consequences on human and ecosystems health.

Several studies demonstrate that biogenic VOCs contribute to 90% of the total VOCs emissions [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF], while anthropogenic sources such as industries, solvents, transportations etc. contribute only for the 10% of the total VOCs emissions. Among the most important biogenic sources of VOCs are living plants, with a global emission of 1Pg C y -1 , mainly monoterpenes from broadleaves forests [START_REF] Harrison | Volatile isoprenoid emissions from plastid to planet[END_REF]. Furthermore, it has been shown that the soil and litter can release around 12 to 136 % of monoterpenes compared to canopy emissions in spring and fall [START_REF] Faiola | SOA Formation Potential of Emissions from Soil and Leaf Litter[END_REF].

Another source of biogenic VOCs is the interaction between soil and microorganisms. Soils

VOCs emissions are up to three times lower than the VOCs release from the canopy.

Nevertheless, it has been demonstrated that under certain temperatures and soil water contents, and for specific ecosystems, VOCs emissions from soil could reach the same magnitude as canopy emissions [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF]. Additionally, little is known about the emissions from agricultural fields, although they are recognised as large contributors of methanol and acetone to the atmosphere [START_REF] Bachy | Methanol exchange dynamics between a temperate cropland soil and the atmosphere[END_REF]. It however appears from recent measurements that soils may contribute to a large extent to the overall balance of VOC fluxes in crop rotations [START_REF] Bachy | Are BVOC exchanges in agricultural ecosystems overestimated? Insights from fluxes measured in a maize field over a whole growing season[END_REF].

From another perspective, intensive agriculture may lead to the decrease of soil organic matter contents [START_REF] Chan | Soil carbon fractions and relationship to soil quality under different tillage and stubble management[END_REF] and Europe is encouraging the use of organic waste products (OWPs) in crops (European Commision, 2010) to favour carbon storage in soils [START_REF] Peltre | RothC simulation of carbon accumulation in soil after repeated application of widely different organic amendments[END_REF]. The recycling of OWPs in soils also represents an alternative to waste management by landfilling or incineration. The application of OWPs also provides nutrients such as nitrogen (N) and phosphorous (P) which make possible the substitution of mineral fertilizer, improves soil biodiversity and has positive impacts on soil physical properties such as aggregate stability [START_REF] Diacono | Long-term effects of organic amendments on soil fertility[END_REF]. The negative impacts are not negligible though; OWPs release contaminants that can be transferred to plants, water or to the atmosphere [START_REF] Belon | An inventory of trace elements inputs to French agricultural soils[END_REF]. OWPs change the pH in soil. As [START_REF] Brinton | Volatile Organic Acids In Compost: Production and Odorant Aspects[END_REF] reported, the pH is one of the chemical properties that impact on VOCs emissions. Positive and negative impacts in soil vary with the applied organic waste product [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF]. For example, [START_REF] Seewald | Substrate-induced volatile organic compound emissions from compost-amended soils[END_REF] studied VOCs production by adding glucose to soils regularly amended by different composts and mineral fertilizers. They also reduced the O 2 availability in order to impair the degradation of the produced VOCs by microorganisms. Results reported by this study shown that organic waste compost did not alter the VOC emissions compared to the untreated control, while sewage sludge composts and mineral fertilization showed distinct effects. Moreover, [START_REF] Potard | Organic amendment practices as possible drivers of biogenic Volatile Organic Compounds emitted by soils in agrosystems[END_REF] showed that different soil organic fertilizers such as pig slurry and methanized pig slurry impact in a different way soil VOCs emissions: pig slurry releases double quantities of VOCs while methanized pig slurry emits even less than the unamended samples. Contrasting type of VOCs emissions from soil have been reported from several studies. For instance, in Mediterranean soils the most emitted compound was methanol (Asensio et al., 2007a) while in soil amended with straw it was acetone [START_REF] Zhao | Volatile organic compound emissions from straw-amended agricultural soils and their relations to bacterial communities: A laboratory study[END_REF]. In fact, emissions of VOCs are strongly related to the substrate quality and it is important to note that even small variations in nutrient composition may change the type and the amount of individual VOCs produced [START_REF] Wheatley | The consequences of volatile organic compound mediated bacterial and fungal interactions[END_REF].

Significant emissions of the VOCs released by soil are produced by microorganisms [START_REF] Isidorov | Volatile organic compounds from leaves litter[END_REF]. It has been reported that the composition and amount of VOCs emitted are linked to the microbial community living in the soil [START_REF] Seewald | Substrate-induced volatile organic compound emissions from compost-amended soils[END_REF].

Microorganisms produce VOCs as a result of the degradation of sugar, alcoholic fermentation, amino acid and fatty acid degradation, terpenoid biosynthesis and sulphur reduction. For instance, acetaldehyde, which is one of the compounds most emitted from soil, is formed during the alcoholic fermentation that produces ethanol [START_REF] Castaldelli | Decomposition dynamics of the bloom forming macroalga Ulva rigida C. Agardh determined using a C-14-carbon radio-tracer technique[END_REF].

Previous studies have reported that microorganisms degrade sugar following three major pathways: (1) Embden-Meyerof pathway, (2) Heterolactic/homolactic pathway and (3)

Entner-Douoroff pathway. In these processes, intermediate compounds are produced such as pyruvate, glyceraldehyde-3-phosphate, lactate and acetate which are precursors for the biosynthesis of several VOCs [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF]. VOCs are also released as molecules resulting from the secondary metabolisms of micro-organisms [START_REF] Werner | Belowground communication: impacts of volatile organic compounds (VOCs) from soil fungi on other soil-inhabiting organisms[END_REF].

Secondary metabolites might be produced under certain circumstances or development stages;

hence they might be used as indicator of the cellular state [START_REF] Patti | Innovation: Metabolomics: the apogee of the omics trilogy[END_REF]. Furthermore, the nature of the substrate can affect microorganisms' community and thus change VOCs emissions [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF]. Moreover, the application of OWPs in soil stimulate its diversity, the abundance and the activities of microorganisms in soil compared to soils that only received mineral fertilizers. This is a consequence of the addition of microorganisms previously present in the OWP [START_REF] Ros | Long-term effects of compost amendment of soil on functional and structural diversity and microbial activity[END_REF].

The aim of this study was to characterise VOCs emissions from soil amended with OWPs in order to quantify and qualify emissions from amended soil to the atmosphere. Furthermore, soils regularly amended with different organic waste products for a long period can have a differentiated volatile organic compound signature and different total VOCs fluxes. In order to reach this aim, four soils amended for 20 years with organic wastes products were considered: bio-waste compost (BIOW), green waste and sludge compost (GWS), municipal solid waste compost (MSW) and farmyard manure (FYM). All these soils were compared with a control sample that had never received any OWP. We measured VOCs fluxes under standardised laboratory conditions with a Proton Transfer Reaction -Quadrupole Ion guide Time of Flight -Mass Spectrometer (PTR-QiTOF-MS). The VOCs released were identified and quantified and the relationships between VOCs emissions and soil chemical characteristics (pH, organic matter, I roc , cation exchange capacity, carbon/nitrogen ratio, total nitrogen, organic carbon) were studied. (15.0% clay, 78.3% slit) and an initial pH of 6.9 in the surface horizon (0-30 cm) and good drainage. Moreover, the QualiAgro field experiment is in a cropland dominated region, which lead to a low organic carbon and low organic matter concentrations (initial content of 1.1%) [START_REF] Meersmans | A high resolution map of French soil organic carbon[END_REF]. On the other hand, observing QualiAgro site on a smaller scale, differences in the organic carbon content, total nitrogen or pH between samples are due to the different applications of OWPs.

III.2 Methods

III.2.1 Site description

The experiment was a randomized block design with 4 replicates comparing 4 organic waste products: BIOW (bio-waste compost derived from the co-composting of green wastes and source-separated organic fractions of municipal solid wastes), GWS (compost derived from the co-composting of green wastes with sewage sludge), FYM (farmyard manure) and MSW (municipal solid waste compost derived from the composting of residual solid wastes after removing dry and clean packaging); plus a control without organic input (CN). Moreover, the 20 field blocks studied were additionally amended with mineral N to reach optimal N application. The field was cropped with a winter wheatmaize rotation. The wheat crop residues were exported, whereas the maize residues were returned to the soil. Since 1998, the organic waste products (OWPs) have been applied at a rate of ~4 tC ha -1 every two years on the wheat stubbles in September, after harvesting. In each plot, 5 soil cores were randomly sampled at 0-30 cm depth using a core drill and stored in a cold chamber at 4°C prior to analysis. The sampling was performed in early September 2015, two years after the last amendment of OWPs. Each type of amended soil was sampled as described by [START_REF] Noirot-Cosson | Modelling the long-term effect of urban waste compost applications on carbon and nitrogen dynamics in temperate cropland[END_REF].

III.2.2 Experimental setup

III.2.2.1 Soil analysis

All soil analysis was realized once on the dried and 2mm-seived composite sample, further 250µm-ground when indicated. Soil pH was determined following the standard NF ISO 10390 protocol, where a glass electrode was used in a 1:5 (volume ratio) suspension of soil in MilliQ water. Total carbon in soil was measured by dry combustion of 0.05 g of 250 µm-ground soil and organic carbon calculated after correction of the carbonate content (ISO 10694). Total N was determined simultaneously, also by dry combustion (ISO 13878).

Finally, organic matter in soil was calculated by multiplying C organic x 1.72, while organic matter for composts was determined by loss on ignition of 1 g of ground composts at 550°C.

The soil cation exchange capacity was determined with cobaltihexamine following NF X 31-130 (AFNOR 1999). Briefly, 2.5 g of dried and 2mm-seived soil were shaken for 2 hours with 50 ml of 50 mmol/L of cobaltihexamine chloride. The soil cation exchange capacity was determined based on the difference of cobaltihexamine chloride before and after soil contact.

III.2.2.2 Soil sample preparation for PTR-QiTOF-MS analysis

Samples of the same plot were mixed and homogenized by passing through a 2 mm sieve to remove aboveground plant materials, roots and stones, then dried up at room temperature (~23°C) for 2 weeks.

III.2.2.3 Laboratory flux chambers

In order to measure VOC emissions, soil samples were first moisturised to the field capacity by adding ultrapure water (resistivity 18.2 MΩ). The amount of water necessary was determined on a sub-sample and used for all samples (15 mL of water for each sample). Soil samples were then inserted, one at a time, in a Pyrex chamber having a volume of 106 cm 3 .

The quantity of soil inserted in the chamber (𝑚 𝑑𝑟𝑦 𝑠𝑜𝑖𝑙 ) was 60 g (dry mass) for each measurement. Soil was homogeneously spread on the chamber surface (132 cm 2 ). An empty chamber was used as a reference for zero emissions. standard 0.5% Rd plus 0.1% FS, range: 0.2 L min -1 to 5 L min -1 air) was used to control the synthetic air flow rate. Air was sampled at the chamber outlet into a PTR-QiToF-MS with a 0.05 L min -1 flow rate with a 2 m long, 1 mm internal diameter PEEK tube, heated at 80 °C. A measurement cycle consisted in measuring the VOC mixing ratio at the outlet of the chamber containing the soil (𝑥 𝑉𝑂𝐶 𝑠𝑜𝑖𝑙 in ppb) for 180 s. Then air was sampled for 120 s on the empty chamber to determine 𝑥 𝑉𝑂𝐶 𝑒𝑚𝑝𝑡𝑦 (ppb). We sampled the empty chamber less than the chamber with soil because the signal was stable after only ~10 s. Only the last 60 s of each measurement were kept to calculate averaged mixing ratios in order to ensure a stable VOCs mixing ratio. The VOC emission (𝐸 𝑉𝑂𝐶 in nmol g -1 s -1 dry soil) was calculated as:

𝑬 𝑽𝑶𝑪 = 𝑸 𝒂𝒊𝒓 × (𝛘 𝑽𝑶𝑪 𝒔𝒐𝒊𝒍 -𝛘 𝑽𝑶𝑪 𝒆𝒎𝒑𝒕𝒚 ) 𝑽 𝒎𝒐𝒍 𝒂𝒊𝒓 × 𝒎 𝒅𝒓𝒚 𝒔𝒐𝒊𝒍 (1)
Where 𝑉 𝑚𝑜𝑙 𝑎𝑖𝑟 is the air molar volume at standard temperature and pressure (22.4 L mol -1 at 0°C and 1 atm). In total 15 samples were measured: 3 replicates for each of the 5 treatments. Each measurement was further repeated 3 times to compute the mean and standard deviation of the measurement. This led to 45 measurements in total. After each measurement, the chamber was cleaned with diluted sodium hydroxide in order to destroy any organic compound that could affect the next measurements, and then rinsed with ultrapure water (resistivity 18.2 MΩ). detector and a time-to-digital converter (Burle Industries Inc., Lancaster, PA, USA). In this study, ionisation was carried out with H 3 O + as proton donor. The transfer reaction was effective for VOCs with a proton affinity higher than 691.7 kJ mol -1 , which is the proton affinity of the water. In the drift tube, the pressure was tuned to 4 mbar, the temperature to

III.2.3.1 Peak detection of the mass spectra and mass calibration

We used the PTR viewer 3.1.0.29 software (Ionicon, Analytik GmbH) to detect mass peaks in each spectrum. The mass calibration was performed using the oxygen isotope of the ion source H 3 18 O + (21.022 m/z) and acetone, C 3 H 7 O + , (59.0449 m/z). These two were chosen as they were present in all samples. Mass calibration was made over the entire dataset allowing for a change of 0.1 m/z around the peak maximum between consecutive samples and using a moving average of 50 successive samples for better stability. Overall the instrument mass resolution, which is defined as the peak m / z divided by the peak width at mid-height was around 4000 (no unit). In order to create a peak table including the largest number of peaks, we used the automatic research feature of the software selecting as search mode "multiple peaks" with a threshold of 5 counts per second. Following this step, a manual check of each peak was performed in order to correct for any software miss-fits. Each peak was manually readjusted. The modes used to auto-select and manually adjust the peak areas that are used for mixing ratio calculation were the Gaussian or the multi-peak modes. The Gaussian peak mode was used for the analysis of isolated peaks; while for a better fit of multiple peaks we used the multi-peak mode. The range of the mass detection was limited between 31 and 400 m/z. Finally, masses deriving from the water cluster such as 37.03 m/z, 38.03 m/z, 39.03 m/z, and 55.03 m/z were not taken into account during the analysis of the dataset.

III.2.3.2 Isotopes and fragments identification

In order to make sure that a compound was not twice counted, we identified likely isotopes and fragments by performing a correlation analysis on the mass spectra. The Pearson's correlation coefficient was computed between each m/z based on all measured mixing ratios during the experiment (N=45). The m/z having a correlation coefficient larger than 0.99 were considered either fragments or isotopes depending on the m/z difference. In total 16 isotopes and 2 likely fragments were identified (Table S1).

III.2.3.3 PTR-QiTOF-MS mixing ratio calibration

The MCP provides the counts per seconds of detected ions after the TOF. In order to calculate the mixing ratio of gases in ppb that enter the PTR, a series of calculations were performed. The mixing ratio was calculated using the PTR-viewer software, which uses the following equation:

χ voc standard transmission =1.657e -11 U drift T act 2 [K] k p act 2 [mbar] R + H 3 O + TR H 3 O + TR R + (2)
Where χ 𝑖 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 is the mixing ratio of the compound in ppb, U drift is the voltage of the drift (V), T act is the drift-tube temperature in Kelvin, R + is the signal of the product ion in count per second (cps), k is the protonation reaction rate constant (2 × 10 -9 cm 3 s -1 ) assumed equal for all compounds, TR H 3 O + is the transmission factor for mass of primary ion, TR R + is the transmission factor for mass of product, p act is the pressure of the drift, ion H 3 O + is the primary ions signal in cps. The standard transmission curve from supplier was used to In order to account for the normal loss of sensitivity of the PTR-QiTOF-MS, due to the MCP degradation over time, the change of H 3 18 O + produced in the source, or other factors, we used a calibration procedure to account for the actual sensitivity of the analyser. We used benzene, toluene and ethylbenzene as reference compounds for this calibration. The mixing ratio of these compounds was modulated using a dilution system with a standard cylinder containing 102 ppb of benzene, 104 ppb of toluene, 130 ppb of ethylbenzene and 336 ppb of xylene (Btex, Messer®). We used the same synthetic air for the dilution system as the one described for the chamber set up. The sensitivity 𝑆 𝑛𝑜𝑟𝑚 was calculated as the slope of the regression between measured mixing ratio with the standard transmission provided by Ionicon and mixing ratio calculated with equation ( 2), and forcing a zero intercept. The calibrated mixing ratio of all compounds was then calculated as:

compute TR H 3 O + TR R + ⁄ (see
χ 𝑣𝑜𝑐 = [χ 𝑣𝑜𝑐 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 -χ 𝑣𝑜𝑐 (0) ] × 𝑆 𝑛𝑜𝑟𝑚 (3)
Where χ 𝑣𝑜𝑐 is the mixing ratio of the compound in ppb, χ 𝑣𝑜𝑐 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 is the mixing ratio calculated using the standard transmission as in equation ( 2), χ 𝑣𝑜𝑐 (0) is the mixing ratio of the compound measured for dry synthetic air.

The procedure was applied for the three compounds cited above for the calibration. We found that the sensitivity was 3.11±0.15. A single sensitivity value of 3 was applied for all masses, since the protonation reaction constant was assumed equal for all masses. In total we selected 754 peaks in the mass spectra used for these analyses.

III.2.3.4 VOC identification

We first identified the molecular formula with the Spectra Analyser within the PTR viewer 3.1.0.29 software (Ionicon, Analytik GmbH). The spectra analyser is a tool that allows the manual selection of the peaks chosen for the identification. The identification of the compounds corresponding to each mass peak was performed by searching the possible combinations of elements that led to the closest molecular weight. The following elements were selected for all peaks: C, N, O, and H. Other compounds were selected on purpose for masses higher than 70 m/z like S, Si, Cl, Ca and K, when C, N, O and H did not lead to a good agreement with the peak mass. The maximum number of atoms per molecule was set to 50, while the error on the molecular weight was fixed to ± 0.01 m/z. Subsequently, a list of possible compounds was produced. The compounds listed represent the best matches for the selected exact m/z. Finally, a match of the isotopes of each compound was evaluated. Once the molecular formula was determined, we tried to identify the potential compounds with the use of the literature reviews. We have to stress that this method does not provide an undoubtable identification of the compounds as several compounds could have the same molecular mass but not the same chemical structure. Moreover, even with this procedure, some compounds could not be identified at all.

III.2.4 Data analysis

Several statistics test were performed on the VOCs emissions calculated for each treatment in order to identify the VOCs that mark the differences between the OWP treatments. All statistical analysis were performed using R software (Version 1.0.153 -© 2009-2017 RStudio). First, a normality test (Shapiro-Wilk test, W> 0.9) has been applied to verify that the mean mixing ratios were normally distributed for each VOC. Secondly, the homogeneity of the variances was verified for each treatment using the Levene test. Once the normality and the homogeneity of de variances were validated, the ANOVA test followed by

III.3 Results

III.3.1 Soil characteristics of each treatment

The Figure 2 summarizes soils characteristics as a function of OWP. The carbon content is in the range 10-18 g kg -1 DM which spans from typical to high carbon content for a siltloam. Organic matter was mainly found in the clay fraction of the soil (60%) and in the smaller fraction of the particulate organic matter, mainly in the form of O-alkyls, Alkyls and Aryls [START_REF] Paetsch | Urban waste composts enhance OC and N stocks after long-term amendment but do not alter organic matter composition[END_REF].

The CN samples showed the lowest cation exchange capacity, organic matter content, total nitrogen content, pH and organic carbon while its C/N ratio was not different from BIOW and GWS treatments (Figure III-2). The treatment with the highest cation exchange capacity was BIOW while we found the highest C/N ratio in the FYM. Furthermore, the total N, organic carbon and organic matter were higher for the BIOW and the GWS treatments.

Concerning the pH, BIOW and MSW had the highest values, while CN and GWS had the lowest values.

These results show that the repeated application of OWP has significantly increased soil organic carbon content (OC in Figure 2) in all organic treatments compared to the CN, but not in the same amount. Although all OWP are applied based on the same input of organic C, their different characteristics can explain their varying efficiencies at increasing soil organic carbon [START_REF] Peltre | RothC simulation of carbon accumulation in soil after repeated application of widely different organic amendments[END_REF]. Indeed, the index of residual organic carbon (I roc ) that represents the portion of organic carbon potentially incorporated into soil organic carbon after the organic waste application [START_REF] Lashermes | Indicator of potential residual carbon in soils after exogenous organic matter application[END_REF] was similar to OC: I roc was the lowest for the MSW samples (I roc =49% Org C), and was the highest for BIOW and GWS (I roc =77% and 75 % Org C, respectively) [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF]. Total organic nitrogen (Ntot) was also greater in the organic treatments compared to CN was ordered similarly as soil organic carbon. The C/N ratio is in the same order of magnitude for all the samples. The soil CEC was increased in treatments receiving OWP, which is in line with previous finding, that CEC increased with organic matter in soils [START_REF] Kaiser | Cation Exchange Capacity and Composition of Soluble Soil Organic Matter Fractions[END_REF]. Finally, the BIOW and MSW treatments increased soil pH mainly because of their contents in carbonates. FYM only slightly increased pH compared to control while GWS had no effect on soil pH. [START_REF] Paetsch | Urban waste composts enhance OC and N stocks after long-term amendment but do not alter organic matter composition[END_REF] further

showed that the C/N ratio of the fine fraction of particulate organic matter rather represented the crops residues than organic waste inputs. They deduced lower degradability of organic matter in BIOW and GWS although they show the highest OM content.

III.3.2 VOCs Emissions

In order to verify whether the treatments affected the bulk production of VOCs, the fluxes of all compounds detected with the PTR-QiTOF-MS were summed up. They ranged between 36 ± 8 10 -4 nmol s -1 g -1 and 52 ± 12 10 -4 nmol s -1 g -1 (Figure III-3a and Supplementary material Table S3). The treatment with the highest total flux rate was the BIOW and the III-1 contribute between 70% and 91% of the total emission rate. For all treatments includes the control we found that acetone (contributing for the 30% of the total emissions), butanone (contributing for the 25% of the total emissions) and acetaldehyde (contributing for the 8.1% of the total emissions) were the most emitted compounds always as first, second and third. In Table III-1 we added a tentative identification of the compounds and the corresponding chemical class. BIOW generally showed larger emissions than other treatments for small weighed compounds while GWS showed clearly higher emissions than other treatments for compounds in the mass range 80-130 m/z. This study was focused on VOCs emissions from soils regularly amended with 4 different organic waste products compared to unamended soil. However, the soils were sampled two years after the last OWPs application. Thus the differences in VOCs emitted could be associated to long-lasting effects of the OWP applications and to changes in soil organic matter characteristics due to these repeated OWP applications. We first notice that in all treatments including the control, the 3 most emitted compounds were acetone, 2-butanone and acetaldehyde regardless of amendments.

This study showed acetone emissions of 1000 ± 240 µg m -2 h -1 . Smaller acetone emissions are usually reported from laboratory experiments such as those by Asensio et al. (2007a) for a Mediterranean soil who found 10 times smaller fluxes than what we measured here. On the contrary, [START_REF] Schade | Fluxes of oxygenated volatile organic compounds from a ponderosa pine plantation[END_REF] reported acetone emissions in the same range as our study using a field chamber and a dual GC-FID system on a ponderosa pine plantation; they indeed found peak emissions of 800 µg m -2 h -1 following rain but smaller emissions 260 µg m -2 h -1 on average. [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF], in their review, reported emissions ranging from 4 to 800 µg m -2 h -1 . Acetone is often observed in rural areas [START_REF] Lamanna | In situ measurements of C2-C10 volatile organic compounds above a Sierra Nevada ponderosa pine plantation[END_REF]. Acetone emissions seem to be mainly driven by biogenic sources such as litter decomposition [START_REF] Gray | Emissions of volatile organic compounds during the decomposition of plant litter[END_REF], microorganisms in soils [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF] but also functioning leaves [START_REF] Mozaffar | Biogenic volatile organic compound emissions from senescent maize leaves and a comparison with other leaf developmental stages[END_REF]. However, acetone was also found to be produced from abiotic degradation of plant material in the soil at a rate of 100 µg g -1 DM by [START_REF] Warneke | Acetone, methanol, and other partially oxidized volatile organic emissions from dead plant matter by abiological processes: Significance for atmospheric HOx chemistry[END_REF]. Emissions of acetone from soils depend on moisture, temperature and on the technique used to detect the fluxes which could explain the differences between the fluxes reported in the studies cited previously [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF].

Organic matter seem to enhance acetone emissions as reported by [START_REF] Zhao | Volatile organic compound emissions from straw-amended agricultural soils and their relations to bacterial communities: A laboratory study[END_REF] who found that acetone was the most emitted compound from soil amended with straw contributing to 50 ± 9% of the total VOCs emissions. For comparison, in our study acetone contributed to around 30% of the total mass of VOC emissions (Table III-1).

Butanone was the second most emitted compound from our samples contributing up to 25.1% of the total VOCs emissions. [START_REF] Zhao | Volatile organic compound emissions from straw-amended agricultural soils and their relations to bacterial communities: A laboratory study[END_REF] also reported butanone emissions with a contribution of 9 ± 2% to the total VOCs emissions. [START_REF] Mcneal | Volatile Organic Metabolites as Indicators of Soil Microbial Activity and Community Composition Shifts[END_REF] also detected butanone production by soil microcosm, while Larsen and Frisvad (1995a;1995b) showed that butanone was one of the VOCs emitted from fungi. Apart from these references butanone was not detected in other studies, at least not as one of the major compound emitted such as we found here.

The third most emitted compound from soil was acetaldehyde which was detected by Asensio et al. (2007a) as the second most emitted compound from Mediterranean soil samples. [START_REF] Schade | Fluxes of oxygenated volatile organic compounds from a ponderosa pine plantation[END_REF]detected a flux of acetaldehyde of 100 µg m -2 h -1 at 24°C quite similar to what we found ~280 µg m -2 h -1 . Similar to this study, though smaller, [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF] reported between 2 and 100 µg m -2 h -1 emissions of acetaldehyde for different type of soil, such as: forest soil, ponderosa pine plantation and shrubland soil. [START_REF] Gray | Biotic and abiotic controls on biogenic volatile organic compound fluxes from a subalpine forest floor[END_REF] reported much smaller emissions (0.2 µg m -2 h -1 ), but they demonstrated that acetaldehyde emissions were largely coming from the roots system which was partly removed from our measurements by sieving at 2 mm. [START_REF] Mancuso | Soil volatile analysis by proton transfer reaction-time of flight mass spectrometry (PTR-TOF-MS)[END_REF] also detected acetaldehyde as the most emitted compound. It is noticeable that acetaldehyde is an oxidation product of ethanol. We should also stress that measurements of acetaldehyde with PTR-MS (not TOF ones) may catch some CO 2 .H + and hence include some soil respiration. Indeed, although CO 2 has a proton-affinity lower than the one of water, we found that CO 2 was protonated in our TOF at m/z = 44.997 (Loubet, personal communication).

Methanol, isoprene and ethanol were also detected with a significant emission rate in this study (Table III-1). Methanol has been reported by [START_REF] Mancuso | Soil volatile analysis by proton transfer reaction-time of flight mass spectrometry (PTR-TOF-MS)[END_REF], [START_REF] Seewald | Substrate-induced volatile organic compound emissions from compost-amended soils[END_REF] and was the compound most emitted as reported by Asensio et al. (2007) and [START_REF] Ramirez | Microbial consumption and production of volatile organic compounds at the soil-litter interface[END_REF]. [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF] reported emissions in field conditions ranging from 4 to 530 µg m -2 h -1 , while [START_REF] Bachy | Methanol exchange dynamics between a temperate cropland soil and the atmosphere[END_REF] found a range from -50 to 200 µg m -2 h -1 , which agreed with [START_REF] Schade | OVOC emissions from agricultural soil in northern Germany during the 2003 European heat wave[END_REF] measuring the emissions from a bare soil. Our results (~ 66 µg m -2 h -1 ) are in good agreement with the cited studies. [START_REF] Ramirez | Microbial consumption and production of volatile organic compounds at the soil-litter interface[END_REF] showed that methanol contributed to 89% of the total VOCs emissions by a forest soil while in our study methanol emissions contributed around 3.1% of the total mass VOCs emissions only.

Methanol was also reported by [START_REF] Potard | Organic amendment practices as possible drivers of biogenic Volatile Organic Compounds emitted by soils in agrosystems[END_REF] as one of the most emitted compounds by soil. In fact it contributed between 20% to 40% of the total VOCs emissions in the control treatment and between 20% and 90% in soil amended with methanized pig slurry [START_REF] Potard | Organic amendment practices as possible drivers of biogenic Volatile Organic Compounds emitted by soils in agrosystems[END_REF]. Several studies showed different methanol sources from soil, i.e. abiotic production through SOM degradation [START_REF] Schade | OVOC emissions from agricultural soil in northern Germany during the 2003 European heat wave[END_REF], biotic production from the litter [START_REF] Ramirez | Microbial consumption and production of volatile organic compounds at the soil-litter interface[END_REF], Bachy et al., 2018). Methanol might be produced via the microbial breakdown of plant pectin [START_REF] Jayani | Microbial pectinolytic enzymes: A review[END_REF]. The small amount of plant residues in our samples may explain the lower contribution of methanol to VOCs emissions compared to [START_REF] Potard | Organic amendment practices as possible drivers of biogenic Volatile Organic Compounds emitted by soils in agrosystems[END_REF] and [START_REF] Ramirez | Microbial consumption and production of volatile organic compounds at the soil-litter interface[END_REF]. It is also important to stress that methanol, as a soluble compound is adsorbed and desorbed from water films [START_REF] Laffineur | Abiotic and biotic control of methanol exchanges in a temperate mixed forest[END_REF]. This mechanism may have boosted emissions due to water evaporation in the chamber exposed to dry air.

Butene contributed for the 5.3% of the total VOCs emissions. Emissions smaller than 2 µg m -2 h -1 were detected from forest floor [START_REF] Hellén | C2-C10 hydrocarbon emissions from a boreal wetland and forest floor[END_REF] and from agricultural soils amended with straw [START_REF] Wang | Effects of straw return on C2-C5 non-methane hydrocarbon (NMHC) emissions from agricultural soils[END_REF] with a contribution between 3-7 % of the total emissions. [START_REF] Wang | Effects of straw return on C2-C5 non-methane hydrocarbon (NMHC) emissions from agricultural soils[END_REF] also reported a positive correlation between butene and microbial biomass.

Ethanol (4.3% of the total emissions in our study, see Table III-1) is one of the compounds emitted during the sugar degradation as showed by [START_REF] Seewald | Substrate-induced volatile organic compound emissions from compost-amended soils[END_REF]. [START_REF] Schade | Fluxes of oxygenated volatile organic compounds from a ponderosa pine plantation[END_REF] reported emission of ethanol from bare soil between 300 and 500 µg m -2 h -1 while we found lower emissions around 100 µg m -2 h -1 ).

III.4.1.2 Other compounds detected

Several aromatic compounds have been observed as emitted from our soil samples.

Microorganisms and plants release aromatic compounds through the shikimate pathway or by the degradation of phenylalanine or tyrosine. Aromatic compounds are known to be emitted by plants under stress conditions [START_REF] Misztal | Atmospheric benzenoid emissions from plants rival those from fossil fuels[END_REF] and absorbed by humic substances in soil [START_REF] Lu | Sorption of Apolar Aromatic Compounds to Soil Humic Acid Particles Affected by Aluminum(III) Ion Cross-Linking[END_REF]. Toluene is one of the aromatic compounds most emitted by our samples (at a rate of 1.2%), but in a similar amount between treatments which was also reported by Asensio et al. (2007b). Toluene is rather a compound linked to oil, either natural (crude oil) or from derivatives, such as solvents, gasoline or other fuels as well as in the production process of coke from coal. The flux of this compound from soil is always very low but due to the several impacts that this compound might have on the health is important to quantify and characterize the emissions. It is interesting to notice that we also detected monoterpenes and p-cymene, which is an aromatic compound formed by the oxidation of monoterpenes [START_REF] Gratien | Surprising Formation of p-Cymene in the Oxidation of α-Pinene in Air by the Atmospheric Oxidants OH, O3, and NO3[END_REF]. We found around 0.018 nmol m -2 s -1 which is higher than the range of monoterpenes emissions detected by Asensio et al. (2007a) (between -0.004 nmol m -2 s -1 and 0.004 nmol m -2 s -1 ). [START_REF] Hayward | Monoterpene emissions from soil in a Sitka spruce forest[END_REF] reported that emissions of monoterpenes from soil of Sitka spruce forest were 0.026 nmol m -2 s -1 , and were strongly related to soil temperature (Raza et al., 2017b). It is also known that monoterpene emissions from forests exceed those from agriculture [START_REF] Kesselmeier | Biogenic volatile organic compounds (VOC): An overview on emission, physiology and ecology[END_REF]. Assuming soil emissions are linked to plant residues degradation in the soil this might explain the higher emissions from forest's soils compared to what we find here.

Other Bacillus, Pseudomonas and Actinomycetes [START_REF] Schöller | Volatile metabolites from some gram-negative bacteria[END_REF][START_REF] Wilkins | Volatile metabolites from actinomycetes[END_REF]. It represents also a precursor of atmospheric aerosols which hence has impacts on climate and health [START_REF] Ayers | The CLAW hypothesis: a review of the major developments[END_REF].

Propene is also in our list of characteristic compounds and was reported by Zhao et al.

(2016) from amended agricultural soil as one of the most emitted compounds with a significantly positive correlation between this compound and bacterial communities in soil. In [START_REF] Zhao | Volatile organic compound emissions from straw-amended agricultural soils and their relations to bacterial communities: A laboratory study[END_REF], propene contributed to 1.1% of the total emissions similar to what we found in this study (2.0 %).

Isoprene was reported as a compound emitted by agricultural soils amended with straw at only 0.1% of the total emissions by [START_REF] Zhao | Volatile organic compound emissions from straw-amended agricultural soils and their relations to bacterial communities: A laboratory study[END_REF]. Our results showed that around 6% of the total VOCs emissions were represented by isoprene emissions. [START_REF] Mancuso | Soil volatile analysis by proton transfer reaction-time of flight mass spectrometry (PTR-TOF-MS)[END_REF] measured isoprene emissions from agricultural and forest soil samples. Isoprene is a metabolite directly related to the presence of microorganisms in soil even if the role of this compound in microorganisms cell has not been clarified yet [START_REF] Hess | Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics[END_REF].

The VOCs emitted from the soil samples may be derivated from the microbial activity in soil. In fact, different VOCs derive from several metabolic pathways. Fermentative pathways such as ethanol fermentation, with acetaldehyde as an intermediate product, and butyric acid, acetone and methanol fermentation take part of the microorganisms' primary metabolism (Raza et al., 2017b). On the other hand, more complex molecules with a higher molecular weight may derive from secondary metabolism pathways, such as production of signaling compounds or antibiotics [START_REF] Mathivanan | The Effect of Fungal Secondary Metabolites on Bacterial and Fungal Pathogens[END_REF]. Some VOCs produced by these pathways are 2,4-diacetylphloroglucinol, 3-4-dihydroxycarotane and trichodermin.

It is important to bear in mind that in this study measurement of VOCs emissions is not bi-directional since the inlet was synthetic air which was free of VOCs. This may have led to consistent differences when compared to other in-situ measurements that captured the bidirectionality of some fluxes (e.i. [START_REF] Mozaffar | Biogenic volatile organic compound emissions from senescent maize leaves and a comparison with other leaf developmental stages[END_REF]. Moreover, we should also stress that evaluations of the percentage of total VOC emissions are affected by the number of peaks identified. Indeed, we could expect higher percentages by studies that use PTR-MS which detect a smaller number of peaks than those based on PTR-TOF that screens all peaks.

III.4.2 Effects of organic waste product applications on VOCs emission rates by soils

Total VOCs emissions were statistically higher in BIOW than in MSW (Figure III-3a).

Despite the fact that the amount of carbon imported in these two treatments was the same over 17 years, the I roc indicator which is the fraction of carbon stored in the soil to that applied was found to be lower in MSW (49% Org C) than in BIOW (77% Org C) [START_REF] Lashermes | Indicator of potential residual carbon in soils after exogenous organic matter application[END_REF].

As a consequence, the organic matter (OM) content was higher in BIOW compared to MSW (Figure III-2). This suggests that total VOCs production is linked to OM content in soil.

However, the OM content cannot be the only factor affecting total VOCs emissions.

Otherwise, GWS, which has the highest OM content, should have had also the largest total VOCs emissions and it was not the case.

BCA and ANOVA analysis showed that emitted compounds from BIOW and GWS were the most differentiated. BIOW and GWS were also differentiated from CN, MSW and FYM emissions, which were grouped. It is noticeable that the same classification was also found in the C/N ratio of the small particle organic matter (POM) fraction reported by [START_REF] Paetsch | Urban waste composts enhance OC and N stocks after long-term amendment but do not alter organic matter composition[END_REF] on the same site. Indeed, they found higher C/N ratios of these fractions in CN, MSW and FYM. Higher C/N ratios indicate higher organic matter degradability. This would mean that the VOC compounds that are mostly associated with CN, MSW and FYM treatments may be more representative of the degradation of the small fraction of POM than those measured in the BIOW and GWS treatments.

Overall, BIOW emitted more alcohols and alkenes, N compounds and terpenes than the other treatments, while GWS mainly emitted more aromatic compound (Figure S2). [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF] reported that bacterial VOCs emissions contain mostly alkenes, ketones, terpenes and pyrazines while fungi emit more aldehydes, benzenoids, nitriles and alkynes. We A notable difference in the measured soil characteristics between BIOW and GWS treatments is the soil pH. Since soil pH affects nutrient availability and the physiological state of microorganisms, we could also expect that pH may affect VOCs emission profiles. [START_REF] Insam | Volatile organic compounds (VOCs) in soils[END_REF] reported that VOCs are largely absorbed by the mineral and organic fraction of soil. This adsorption depends on soil pH, with higher adsorption in alkaline soils and lowers in acid soils. We tend to observe a complex dependency to pH which also interacts with organic matter. The total emission of VOC tends indeed to increase with the product of pH with organic matter (Figure S3).

Furthermore, it has been shown by [START_REF] Seewald | Substrate-induced volatile organic compound emissions from compost-amended soils[END_REF] that anaerobic conditions lead to increased amount and diversity of VOCs emitted as opposed to aerobic conditions that leads to CO 2 as the end product of microbial decomposition. The increase of soil organic matter in treatments receiving OWP is known to improve soil structure and aggregate stability [START_REF] Diacono | Long-term effects of organic amendments on soil fertility[END_REF]. [START_REF] Gupta | Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density[END_REF] have shown that OM content increases water soil retention which may promote anaerobic conditions in microsites and hence modify VOC emissions diversity and amounts. OM content may also modify the porosity and hence gas diffusivity which can affect the emission and storage processes of VOCs in soil system [START_REF] Peñuelas | Biogenic volatile emissions from the soil[END_REF]. One should however keep in mind that the soils used in our study were sieved before VOCs analysis, which would suppress any effects of OM on soil porosity above 2 mm. which is a persistent herbicide in soil [START_REF] Holtze | Transformation of the herbicide 2,6dichlorobenzonitrile to the persistent metabolite 2,6-dichlorobenzamide (BAM) by soil bacteria known to harbour nitrile hydratase or nitrilase[END_REF]. The last herbicide application was two years before the sampling period. The content of benzamide could be related also to the herbicide present in the green wastes used for the GWS compost. Similarly, CN can be differentiated from the other treatments with high emissions of 136.02 m/z and 221.15 m/z.

For the FYM and MSW no distinctive compound was found.

III.5 Conclusions

VOCs emissions by soils are involved in atmospheric chemistry and climate change. This study underlines the importance of the VOCs emissions characterization from soil in order to fill the lack of information in this field. Our results showed that VOCs emissions were affected by the type of OWP amended to soils. The BIOW compost emitted the highest total flux rate, while MSW compost the lowest. Our results further suggested that organic matter content and pH jointly influenced total VOC emissions, with larger emissions for higher joint pH and OM content.

The most emitted compounds were acetone, butanone and acetaldehyde whatever the OWP amended, suggesting a universal production mechanism for these compounds little affected by the OWP amendment. On the contrary, we found that 21 compounds had statistically different emissions between the treatments, and could be hence considered as good markers of soil biological functioning. Our results also suggest that the VOC emissions profiles are linked to the C/N ratio of the small fraction of particulate organic matter which is an indicator of OM degradability. It is however difficult to draw conclusions concerning the biological pathway (i.e. degradation of sugar, mineralization of the organic matter ;..) leading to VOCs emissions observed. Other analysis of microorganisms' activity and diversity are necessary to unveil these aspects.

Acknowledgement

We gratefully acknowledge ANAEE-France that provided the PTR-QiTOF-MS, ADEME for partially funding this study through the projects COV3ER (n°1562C0032) and DICOV

IV.1 Introduction

Volatile organic compounds (VOCs) have a large influence on atmospheric chemistry. In particular, VOCs play a role in atmospheric photochemistry by reducing hydroxyl (OH) concentration, increasing tropospheric ozone (O 3 ) and stimulating the secondary organic aerosol (SOA) in the atmosphere [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF]. VOCs are released from biogenic (i.e., plant, forests, soils) and anthropogenic (i.e., industries, solvents, transportation) sources.

Biogenic VOCs contribute up to 90% of the total VOCs emissions [START_REF] Guenther | A global model of natural volatile organic compound emissions[END_REF], with isoprene as the most emitted compound and broadleaves forest dominating the budget [START_REF] Guenther | Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)[END_REF]. Recent studies have been focused on biogenic VOCs released by aboveground biomass because of their contribution to atmospheric chemistry [START_REF] Karl | A new European plant-specific emission inventory of biogenic volatile organic compounds for use in atmospheric transport models[END_REF][START_REF] Lindfors | Biogenic volatile organic compound (VOC) emissions from forests in Finland[END_REF][START_REF] Zemankova | Emissions of biogenic VOC from forest ecosystems in central Europe: Estimation and comparison with anthropogenic emission inventory[END_REF]. Often, VOCs fluxes by soil, and in particular by microorganisms, are not directly considered. This is illustrated for example by the most used Guenther global emission model which does not include soil emissions, although it is based on Volatile organic compounds (VOCs) have a large influence on atmospheric chemistry. In particular, VOCs play a role in atmospheric photochemistry by reducing hydroxyl (OH) concentration, increasing tropospheric ozone (O 3 ) and stimulating the secondary organic aerosol (SOA) in the atmosphere [START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF]. VOCs are released from biogenic (i.e., plant, forests, soils) and anthropogenic (i.e., industries, solvents, transportation) sources. Biogenic VOCs contribute up to 90% of the total VOCs emissions [START_REF] Guenther | A global model of natural volatile organic compound emissions[END_REF], with isoprene as the most emitted compound and broadleaves forest dominating the budget [START_REF] Guenther | Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)[END_REF]. Recent studies have been focused on biogenic VOCs released by aboveground biomass because of their contribution to atmospheric chemistry [START_REF] Karl | A new European plant-specific emission inventory of biogenic volatile organic compounds for use in atmospheric transport models[END_REF][START_REF] Lindfors | Biogenic volatile organic compound (VOC) emissions from forests in Finland[END_REF][START_REF] Zemankova | Emissions of biogenic VOC from forest ecosystems in central Europe: Estimation and comparison with anthropogenic emission inventory[END_REF].

Often, VOCs fluxes by soil, and in particular by microorganisms, are not directly considered. This is illustrated for example by the most used Guenther global emission model which does not include soil emissions, although it is based on whole canopy flux measurements [START_REF] Guenther | A global model of natural volatile organic compound emissions[END_REF]. [START_REF] Bachy | Are BVOC exchanges in agricultural ecosystems overestimated? Insights from fluxes measured in a maize field over a whole growing season[END_REF] underlined that VOCs from vegetation could be overestimated compared to soil emissions; reporting comparable fluxes of methanol from bare soil and canopy. Moreover, VOCs emissions by soils are affected by agricultural practices. [START_REF] Woodbury | Emission of Volatile Organic Compounds as Affected by Rate of Application of Cattle Manure[END_REF] reported higher fluxes of volatile sulfur compounds under no-tillage condition than tilled one; also underlining the important role played by the organic amendment on the VOCs emissions. Furthermore, the role of the organic amendments on VOCs emissions by soil have been reported by several recent studies [START_REF] Abis | Profiles of volatile organic compound emissions from soils amended with organic waste products[END_REF][START_REF] Potard | Organic amendment practices as possible drivers of biogenic Volatile Organic Compounds emitted by soils in agrosystems[END_REF]Raza et al., 2017;[START_REF] Woodbury | Emission of Volatile Organic Compounds as Affected by Rate of Application of Cattle Manure[END_REF]. VOCs emissions may be boosted by organic amendment, due to soil organic matter (SOM) content and soil microbial biomass increase compared to soil receiving mineral amendments [START_REF] Girvan | Responses of Active Bacterial and Fungal Communities in Soils under Winter Wheat to Different Fertilizer and Pesticide Regimens[END_REF].

Among the different organic amendment, organic waste products (OWPs) resulting from human activities (i.e., sewage sludge, municipal solid waste composts, farmyard manure) are being increasingly used since they facilitate the recycling of nutrients and improve soil fertility. OWPs are also responsible for increasing soil microbial diversity in soil [START_REF] Sadet-Bourgeteau | Lasting effect of repeated application of organic waste products on microbial communities in arable soils[END_REF] due to the incorporation of exogenous microorganisms colonizing the organic amendment [START_REF] García-Gil | Effects of Long-Term Sewage Sludge Amendment on the Composition, Structure and Proton Binding Activity of Soil Fulvic Acids[END_REF]. Microbial diversity in soil ensures several soil ecosystems functions such as (i) carbon (C) balance between organic C sequestration and C mineralization [START_REF] Orgill | Sensitivity of soil organic carbon to grazing management in the semi-arid rangelands of south-eastern Australia[END_REF], (ii) organic matter break down [START_REF] Baumann | Soil microbial diversity affects soil organic matter decomposition in a silty grassland soil[END_REF], and more generally, (iii) nutrient recycling. Initially, no importance was given to preserve the microbial diversity since the functional redundancy ensured ecosystem functions. In other words, Species colonizing an ecosystem and contributing equally to an ecosystem function can be substituted by another [START_REF] Chapin | Biotic Control over the Functioning of Ecosystems[END_REF]. Recently, it has been demonstrated that independently of their redundancy, functions of soil organic matter decomposition are affected by a reduction in microbial diversity [START_REF] Baumann | Soil microbial diversity affects soil organic matter decomposition in a silty grassland soil[END_REF][START_REF] Philippot | Loss in microbial diversity affects nitrogen cycling in soil[END_REF].

Additionally, microorganisms are directly involved in the production of VOCs emitted by soil [START_REF] Stahl | Microbial production of volatile organic compounds in soil microcosms[END_REF] since they are involved in several processes as the communication between organisms [START_REF] Wenke | Belowground volatiles facilitate interactions between plant roots and soil organisms[END_REF], alterations of microbial nutrient cycles [START_REF] Bending | Inhibition of soil nitrifying bacteria communities and their activities by glucosinolate hydrolysis products[END_REF] and growth rate of microorganisms colonizing the ecosystem [START_REF] Stotzky | Volatile organic compounds and microorganisms[END_REF]. For instance, it has been reported that VOCs production by bacteria living in soil suppresses the growth of specific fungal phytopathogens [START_REF] Kai | Volatiles of bacterial antagonists inhibit mycelial growth of the plant pathogen Rhizoctonia solani[END_REF]. Other studies suggested that microbial VOCs may also be able to indirectly influence plant growth rate by inducing systemic tolerance to abiotic stresses (i.e., drought, heavy metals) in plants [START_REF] Farag | Dynamic Chemical Communication between Plants and Bacteria through Airborne Signals: Induced Resistance by Bacterial Volatiles[END_REF]. Microbial community is not only a source of VOCs but also a sink which means that microorganisms use VOCs as a nutrient source. For example, [START_REF] Paavolainen | Inhibition of nitrification in forest soil by monoterpenes[END_REF] reported an increase in respiration rate and microbial biomass by introducing a high concentration of VOCs in an in vitro soil incubation experiment. Another important information about microbial VOCs was reported by [START_REF] Isidorov | Volatile organic compounds from leaves litter[END_REF] and [START_REF] Misztal | Emission Factors of Microbial Volatile Organic Compounds from Environmental Bacteria and Fungi[END_REF] who demonstrated that the microbial VOCs emissions could also depend on the taxa. For instance, it has been shown that VOCs emissions from P. syringae were dominated by methanol while other microbial emissions were often dominated by compounds with higher carbon number [START_REF] Misztal | Emission Factors of Microbial Volatile Organic Compounds from Environmental Bacteria and Fungi[END_REF] Up to our knowledge, no studies reported VOCs emissions measurements in response to microbial diversity and organic waste amendment in the soil. Hence, the effect of microbial diversity dilution on VOCs emissions is still an unexplored land. Since microorganisms play a central role in soil VOCs emissions, we hypothesized that the manipulation of the microbial community in soil might lead to some variation in the quantity and quality of the VOCs emissions.

The manipulation of the microbial diversity was performed by inoculating 30 g of sterile soils with three dilution levels of a soil microbial suspension taken from the same soil.

After six weeks incubation at water holding capacity and 20°C in the dark, VOCs emissions

were measured with a dynamic chamber method using a highly sensitive proton transfer, quadrupole injection, time of flight mass spectrometer (PTR-Qi-TOF-MS, Ionicon). The VOCs concentration was measured every second during 3 min for each sample (n=45, replicates= 3). The mass spectra were analyzed using PTR-MS viewer-3 software in order to integrate each ion peak. The microbial biodiversity was measured after VOC emission measurements by a high throughput sequencing approach targeting 16S and 18S ribosomal genes. Four soils were selected that had 20 years of differentiated OWPs amendments together with a control soil that received no organic inputs during the same period were selected. Three replicates of each combination of soil and dilution were used for providing enough data for statistical analysis. All statistical analyses were performed with R (R Version 1.0.153 -© 2009-2017 R-Studio). In particular, ANOVA followed by a post hoc Tukey test were performed as well as principal component analysis (PCA, Package Ade4).

IV.2 Results

IV.2.1 Soil microbial biomass

After six weeks of incubation, microbial biomass was not significantly different between microbial dilution levels for each type of OWPs except for FYM (farmyard manure),

where the higher dilution level had the higher microbial biomass (Table IV-1). A normalization of the VOCs emissions by the biomass levels showed no significant difference in the variation of VOCs emissions with dilution compared to non-normalized data. This indicates that these small differences in microbial biomass did not affect conclusions on VOC emissions. Whatever the dilution levels considered, the microbial biomass was statistically higher in MSW (municipal solid waste compost derived from the composting of residual solid wastes after removing dry and clean packaging), FYM and GWS (compost derived from the co-composting of green wastes with sewage sludge) than CN (control without organic input)

and BIOW (bio-waste compost derived from the co-composting of green wastes and sourceseparated organic fractions of municipal solid wastes).

IV.2.2 Microbial diversity

The bacterial Shannon diversity index is a measure of bacterial diversity. It shows a significantly decreasing of bacterial diversity with increasing dilution (p.value < 0.05), confirming the efficiency of the diversity manipulation (Figure IV-1). The dilution of diversity was also efficient for the FYM samples, even though a higher biomass level was found for the highest dilution (D2, Table IV-1 As expected, the diversity manipulation tended to strengthen the presence of the most abundant bacteria species and reduce the presence of the less abundant ones. This was indeed observed with phyla Proteobacteria and Bacteroides which were the most abundant in the lowest dilution samples and showed an increased abundance in the highest dilution samples.

IV.2.3 VOCs emissions from manipulated soils

Total VOCs emissions increased with dilution rates and hence decreased with microbial diversity in soil (Figure IV-2). The higher dilution level reported a total VOCs flux between 0.5 and 3 times higher than the lower dilution levels. This was true for all the considered soils Significant results underlined by Table IV-S1 concerned the emissions of acetaldehyde, butanone, and acetoin that were emitted in double quantities in samples with lower dilution diversity levels compared to samples with higher dilution levels. By correlating the VOCs emissions with the bacterial and fungi phyla abundance, we found that in general VOC emission rates were negatively correlated with bacterial phyla except for Bacteroides and Proteobacteria which were positively correlated with almost all compounds (Figure IV-5). An opposite behavior was observed for the fungus phyla with positive correlations except for Cryptomicota. This result suggests that most bacteria were VOCs consumers while fungus were mostly VOCs emitters, although we cannot tell in which proportions. VOCs profiles of bacteria phyla showed higher consumption of VOCs than the fungal phyla.

IV.3 Discussion

IV.3.1 Microbial diversity

It was found that, after six weeks of incubation, the microbial biomass was similar in all dilutions levels except for FYM samples. As previously reported by [START_REF] Bevivino | Soil Bacterial Community Response to Differences in Agricultural Management along with Seasonal Changes in a Mediterranean Region[END_REF], pioneer species can colonize the microcosms faster than others, especially in reduced competition conditions, such as we have generated in D2. The larger biomass observed in the highest dilution level in FYM microcosms could hence result from a faster colonizing process promoted by reduced competition conditions and specific to that soil. Nevertheless, Tukey tests confirmed that the decrease of bacteria phyla diversity was effective in all D2 trials compared to D0. This decrease was explained by a lower abundance of phyla reported to be mainly composed of slow-growing organisms such as Acidobacteria, Actinobacteria, Planctomycetes, and Gemmatimonadetes [START_REF] Fierer | Toward an ecological classification of soil bacteria[END_REF], and an increased dominance of phyla described as fast-growing such as Proteobacteria and Bacteroidetes and Firmicutes [START_REF] Fierer | Toward an ecological classification of soil bacteria[END_REF].

IV.3.2 Interactions between bacteria and fungi diversity

The increase of biomass for some fungi phyla and the larger fungi diversity observed in the highest dilution level D2 might also be the consequence of interactions between fungi and bacteria. Indeed, [START_REF] Mille-Lindblom | Antagonism between bacteria and fungi: substrate competition and a possible tradeoff between fungal growth and tolerance towards bacteria[END_REF] reported that fungi, grown up in a substrate with a reduced competition with bacteria, had higher biomass than in substrates with higher competition with bacteria. Moreover, de [START_REF] De Boer | Microbial Community Composition Affects Soil Fungistasis[END_REF] and [START_REF] Garbeva | Fungistasis and general soil biostasis -A new synthesis[END_REF] reported that bacterial secondary metabolisms produced fungistatic VOCs limiting the colonization of the microcosms by fungi. More specifically, Chuankun et al., (2004) found that some VOCs were only found in strongly fungistatic soils and others VOCs had higher concentrations in fungistatic soils (Table S2). The bacteria emitting fungistatic volatiles span a wide phylogenetic spectrum (Chuankun et al., 2004). [START_REF] Campos | Volatiles produced by interacting microorganisms potentially useful for the control of plant pathogens[END_REF] further reported that several bacteria from the Firmicutes phylum inhibited the growth of common fungal species. These findings have been supported by [START_REF] Zou | Possible contributions of volatile-producing bacteria to soil fungistasis[END_REF] who observed that the benzaldehyde produced by Bacillus sp. (Firmicutes phylum) was one of the inhibitors of the fungi mycelia growth. Our results reported a higher relative abundance of Firmicutes in the lowest dilution level D0 compared to D2 (Figure 2). Higher emission rate of the mass tentatively identified as benzaldehyde in the higher dilution level (32.69 ± 6.16 nmol s -1 g -1 DW ˟ 10 4 ) than in the lower one (5.66 ± 8.2 nmol s -1 g -1 DW ˟ 10 4 ) were reported.. At the same time, the higher dilution level reported the lowest fungi-bacteria ratio, which is perfectly in line with [START_REF] Campos | Volatiles produced by interacting microorganisms potentially useful for the control of plant pathogens[END_REF] and [START_REF] Zou | Possible contributions of volatile-producing bacteria to soil fungistasis[END_REF] results. We hence hypothesize that the higher production of the benzaldehyde probably produced by Firmicutes phylum might be responsible for a lower quantity of fungi in the higher dilution. Another compound that was probably responsible for the lower F/B ratio in the higher dilution level was dimethyl disulfide which was emitted 10 times more in the higher dilution level than in the lower one. Kai et al., (2009) have reported that dimethyl disulfide was an inhibitor of the mycelium growth.

IV.3.3 Microbial diversity affects VOCs total emission rates

Although [START_REF] Abis | Profiles of volatile organic compound emissions from soils amended with organic waste products[END_REF] reported a significantly higher VOCs emission rate for BIOW treatment, in this study, we noticed that the effect of the substrate on VOC emissions was negligible compared to the effect of the bacteria diversity (Erreur ! Source du renvoi introuvable.Erreur ! Source du renvoi introuvable. and S3-S5). Furthermore, in the previous study of [START_REF] Abis | Profiles of volatile organic compound emissions from soils amended with organic waste products[END_REF], using the same soil samples without performing the microbial diversity manipulation, it was found that the most emitted compounds were acetone, butanone, and acetaldehyde. In this study, we found the masses m/z 121.097

(tentatively identified as propylbenzene, isopropylbenzene or 1,3,5-trimethylbenzene), m/z 93.066 (Toluene), m/z 107.081 (tentatively identified as xylenes, ethylbenzene or benzaldehyde) and m/z 135.113 (p-cymene). Acetone was not emitted, or at least not between the 50 most compounds emitted which meant that its contribution to the total VOCs emissions was lower than 0.01%. Acetaldehyde and butanone were emitted 100 and 10 times less respectively, compared to [START_REF] Abis | Profiles of volatile organic compound emissions from soils amended with organic waste products[END_REF]. Even for the most emitted compound, the influence of the microbial diversity in soil was more important than the OWPs amendment effect on VOCs emissions. It is important to note that differences on the VOCs emissions might also be due to a different samples treatment.

The three most emitted compounds reported in this study are aromatic compounds. It has been reported by [START_REF] Gosset | Production of aromatic compounds in bacteria[END_REF] and [START_REF] Schmidt | Volatile affairs in microbial interactions[END_REF] that the emissions of aromatic compounds are released from the shikimate pathway leading to the production of aromatic aminoacids. In particular, this pathway is used by microorganisms in order to produce amino acids like phenylalanine, tyrosine, and tryptophan used to build proteins.

It has been shown by several studies that microorganisms are a source and a sink of VOCs [START_REF] Müller | Volatile profiles of fungi -Chemotyping of species and ecological functions[END_REF][START_REF] Schulz-Bohm | A fragrant neighborhood: volatile mediated bacterial interactions in soil[END_REF]. Furthermore, [START_REF] Paavolainen | Inhibition of nitrification in forest soil by monoterpenes[END_REF] an in vitro soil incubation, reported that when VOCs were increased the respiration rate was higher, suggesting that VOCs were used as a nutrient source for some bacteria. In our study we found more than 250 VOC having a detectable flux. We found that VOC emissions rates increased with decreasing bacterial diversity. We further found that VOC emissions were positively correlated with only two bacterial phyla (Proteobacteria and Bacteriodetes), which also those were increasing with decreasing diversity. This suggests that the VOC increase may be due to increased emission from these two phyla together with decreased absorption by other bacteria. VOCs are released either by the process of the primary metabolisms or by secondary metabolisms sources [START_REF] Citron | Terpenoids are Widespread in Actinomycetes: A Correlation of Secondary Metabolism and Genome Data[END_REF]. Since Proteobacteria and Bacteriodetes are the most abundant phyla, the emissions of VOCs might be correlated to the secondary metabolism which is related to the colonization of the space within the microcosms.

IV.3.4 Bacterial and fungi VOCs emissions profiles

Bacteria and fungi are both capable of producing and consuming VOCs. The heatmap (Erreur ! Source du renvoi introuvable.) shows that some phyla are mostly positively correlated with VOC compounds emissions while others are mostly negatively correlated with VOC compounds emissions. Previous studies reported that the attribution of a unique VOCs emission pattern to a phylum was not possible [START_REF] Bäck | Variable emissions of microbial volatile organic compounds (MVOCs) from root-associated fungi isolated from Scots pine[END_REF]. For this reason, the aim of this study was not to find common intraspecific VOCs patterns. However, it is interesting to underline that the correlation between VOCs emissions and microorganisms have a lower correlation rate (Spearman coefficient <0.50) while the negative correlation was stronger (Spearman coefficient between -0.50 and -0.75). This meant that VOCs compounds that showed decreased emissions with increasing phyla abundance had a steeper decrease than those increasing. [START_REF] Mayrhofer | Microbial community related to volatile organic compound (VOC) emission in household biowaste[END_REF] found, as in the present study, positive and negative correlation between microorganisms and specific VOCs, and hypothesized two possible VOC-bacteria interaction, for both positive and negative correlation. A positive correlation with a VOC can occur due to the emissions from a microorganism or due to a stimulated growth of the microorganisms where the VOCs is present, suggesting that microorganisms are using VOCs as a carbon source. A negative correlation may either result from the gradual exhausting of a VOC used by microorganism as a carbon source or the presence of a specific VOC might inhibit the presence of a microorganism.

IV.3.5 VOCs mediating interaction between phyla

VOCs were shown to be essential intermediates in the interactions between bacteria, playing a role together with the competition for nutrients [START_REF] Schulz-Bohm | A fragrant neighborhood: volatile mediated bacterial interactions in soil[END_REF]. In particular, some VOC produced by bacteria having access to nutrients have shown to stimulate the activity, but suppress the growth of starved bacteria [START_REF] Schulz-Bohm | A fragrant neighborhood: volatile mediated bacterial interactions in soil[END_REF].

Compounds tentatively identified in this study as 1,3-butadiene are known to be used as biocontrol agents against other bacteria. In particular, Pseudomonas sp. which form part of the Proteobacteria phylum can inhibit the growth of Bacillus sp, which form part of the Firmicutes phylum [START_REF] Tahir | Bacillus volatiles adversely affect the physiology and ultra-structure of Ralstonia solanacearum and induce systemic resistance in tobacco against bacterial wilt[END_REF]. In this study, for the higher level of dilution, the higher flux rate of this compound was found.

Furthermore, in the same dilution level, the bacteria Proteobacteria phylum abundance increased while Firmicutes abundance decreased, giving indirect evidence of the effect of 1,3butadiene in the interaction between these two phyla. Another interesting compound detected was acetoin. Acetoin was detected in the three dilution levels, with a contribution in the total emissions increasing from 0.1 -0.8% in the lower dilution level to about 2% in the higher dilution level. The acetoin (3-hydroxy-2-butanone) is derived from the pyruvate fermentation under anaerobic conditions [START_REF] Ryu | Bacterial volatiles promote growth in Arabidopsis[END_REF], and was shown to increase the virulence factors of Proteobacteria in the colonization process [START_REF] Audrain | Role of bacterial volatile compounds in bacterial biology[END_REF]. [START_REF] Bunge | On-Line Monitoring of Microbial Volatile Metabolites by Proton Transfer Reaction-Mass Spectrometry[END_REF] also found a positive correlation between the bacteria cell numbers of Salmonella enterica (Proteobacteria phylum) with the signal of an unidentified signal from the mass 89, which can be identified as the ionized acetoin. Furthermore, acetoin is a promoter of plants growth inducing systemic resistance in plants (Chuankun et al., 2004;[START_REF] Farag | Dynamic Chemical Communication between Plants and Bacteria through Airborne Signals: Induced Resistance by Bacterial Volatiles[END_REF][START_REF] Ryu | Bacterial volatiles promote growth in Arabidopsis[END_REF]. We could hence hypothesize that acetoin could have been a vector promoting Proteobacteria, in reduced microbial diversity conditions, which might have a positive effect on plants growth.

Our results also showed a double emission rate of acetaldehyde and butanone emissions in the higher dilution level compared to the lower ones. [START_REF] Bunge | On-Line Monitoring of Microbial Volatile Metabolites by Proton Transfer Reaction-Mass Spectrometry[END_REF] reported a positive correlation between the number count of Shigella flexneri and Salmonella enterica (Proteobacteria phylum) cells and the emissions of acetaldehyde and butanone. Acetaldehyde and butanone emissions might also be correlated to the Proteobacteria phylum VOCs emissions.

IV.4 Conclusions and perspectives

The major important conclusion of this work is that decreases of the microbial dilution levels in soil increased VOCs total emissions rate while decreased the VOCs diversity. It is important to keep in mind that the experiment was under controlled laboratory conditions and studies on the field are needed in order to confirm our results. Furthermore, several microbial processes involved in the production of VOCs within the microcosms have been hypothesized, underlining the possibility of both VOCs production pathways: primary and secondary metabolisms production. Finally, Proteobacteria and Bacteroidetes might be phyla correlated with the emissions on VOCs from soils while other phyla seemed to be sources and sinks of VOCs at the same time. represented by the lack of clay, a silt-loam texture (15.0% clay, 78.3% slit) and an initial pH of 6.9 in the surface horizon (0-30 cm) and good drainage. Moreover, the QualiAgro field experiment is in a cropland dominated region, which lead to a low organic carbon and low organic matter concentrations (initial content of 1.1%) [START_REF] Meersmans | A high resolution map of French soil organic carbon[END_REF].

IV.5 Methods

IV.5.1 Sampling and site description

The experiment was a randomized block design with 4 replicates comparing 4 organic waste products: BIOW (bio-waste compost derived from the co-composting of green wastes and source-separated organic fractions of municipal solid wastes), GWS (compost derived from the co-composting of green wastes with sewage sludge), FYM (farmyard manure) and MSW (municipal solid waste compost derived from the composting of residual solid wastes after removing dry and clean packaging); plus a control without organic input (CN). Samples were collected in 5 blocks of the site amended with mineral N in order to reach optimal N application. Since 1998, the organic waste products (OWPs) have been applied at a rate of ~4 tC ha -1 every two years on the wheat stubbles in September, after harvesting. In each plot, 5 soil cores were randomly sampled at 0-30 cm depth using a core drill and stored in a cold chamber at 4°C prior to analysis. The sampling was performed in early September 2016, one year after the last amendment of OWPs.

IV.5.2 Microcosms and Experimental setup

Soil samples were sieved at 4 mm and homogenized before gamma-ray sterilization ( 35kGy; Conservatome, Dagneux, France). The sterility of the irradiated soil was verified by spreading serial dilutions of the soil onto nutrient agar plates. After the sterilization process soils were inoculated with a diluted soil suspension prepared with the same soil before sterilization [START_REF] Wertz | Maintenance of soil functioning following erosion of microbial diversity[END_REF]. The soil suspension was created by mixing 30 g of soil with 90 mL of sterilized water. From this soil suspension that was used pure (10 0 ) two levels of dilution were prepared with a water ratio of 1:10 3 for the first one and 1:10 5 for the second one. The second step was the re-inoculation of the sterilized soil with the three different soil suspensions (pure or 10 0 , 10 -3 and 10 -5 ). In order to create the microcosms, 30 g of every sample were transferred in a flask, and we added 50% of the water necessary to reach the 60% of the water holding capacity (WHC). We completed the microcosms adding one of the three soil suspension until 60% of the WHC. Soil samples consisting of soils amended with 4 different OWPs and a control without organic input. Finally, three replicates of every combination of soil and microbial diversity level were created obtaining thus 45 microcosms (n=45, replicates=3). Microcosms were sealed hermetically and pre-incubated at 20°C in the dark. Once a week during six weeks the microcosms have been aerated keeping constant the soil moisture at 60% of the WHC.

One week before the detection of the emissions with the PTR-TOF-MS caoutchouc the flask plugs were substituted with Teflon plugs. This was necessary in order to reduce the influence of the emissions of VOCs released from the caoutchouc. In fact, Teflon is an inert material reducing the VOCs emissions that would have been released from the caoutchouc plug. After the six week incubation, the microcosms were connected to the PTR-QiTOF-MS in order to detect the VOCs emissions.

IV.5.3 PTR-TOF-MS detection system

Every flask plug was equipped with two PEEK tubes, one allowing the connection with the PTR-QiTOF-MS and the other one was connected with a bottle of dry synthetic air (Alphagaz 1 Air: 80% nitrogen, 20% oxygen, 99.9999%, Air Liquide®). The flasks had a volume of 88 cm 3 . The detection of the VOCs emissions from the microcosms was performed during 180 s for every sample. An empty flask was used as a reference for zero emissions. In order to characterize the emissions released from the Teflon plug, after the detection of the emissions of the flask containing the microcosms the plug was moved to an empty flask and the emissions of the plug and the empty flask were detected during 180 s. An air flow (𝑄 𝑎𝑖𝑟 ) of 0.3 L min -1 (equivalent volumetric flow at 0°C and 1 atm) of dry synthetic air was passed through a hydrocarbons and humidity filter (Filter for fuel gas, final purity=99.999%, Restek®) and a Hydrocarbon Trap (Supelco, Supelpure® HC) prior to injection in the flask.

A mass flowmeter (Bronkhorst® model F-201CV, accuracy: standard 0.5% Rd plus 0.1% FS, range: 0.2 L min -1 to 5 L min -1 air) was used to control the synthetic air flow rate. Air was sampled at the chamber outlet into a PTR-QiToF-MS with a 0.05 L min -1 flow rate with a 2 m long, 1 mm internal diameter PEEK tube, heated at 80 °C. A measurement cycle consisted in measuring the VOC mixing ratio at the outlet of the flask containing the microcosms (𝑥 𝑉𝑂𝐶 𝑚𝑖𝑐 in ppb) for 180 s. Then air was sampled for 180 s on the empty chamber with the Teflon plug sealing the microcosms just measured to determine 𝑥 𝑉𝑂𝐶 𝑒𝑚𝑝𝑡𝑦 (ppb). Only the last 60 s of each measurement were kept to calculate averaged mixing ratios in order to ensure a stable VOCs mixing ratio. The VOC emission (𝐸 𝑉𝑂𝐶 in nmol g -1 s -1 dry soil) was calculated as:

𝑬 𝑽𝑶𝑪 = 𝑸 𝒂𝒊𝒓 × (𝛘 𝑽𝑶𝑪 𝒎𝒊𝒄𝒓𝒐 -𝛘 𝑽𝑶𝑪 𝒆𝒎𝒑𝒕𝒚 ) 𝑽 𝒎𝒐𝒍 𝒂𝒊𝒓 × 𝒎 𝒅𝒓𝒚 𝒔𝒐𝒊𝒍 (1)
Where 𝑉 𝑚𝑜𝑙 𝑎𝑖𝑟 is the air molar volume at standard temperature and pressure (22.4 L mol -1 at 0°C and 1 atm). After each measurement, the flask was cleaned and the soil transferred in a -40 °C chamber before the DNA extraction.

IV.5.3.1 VOCs data analysis

The analysis of the peaks of VOCs detected, the mass calibration, and the creation of the mass table with all the compounds detected were performed using Spectra Analyser within the PTR viewer 3.1.0.29 software (Ionicon, Analytik GmbH) following the protocol published in [START_REF] Abis | Profiles of volatile organic compound emissions from soils amended with organic waste products[END_REF]. Likely isotopes and fragments were identified using a correlation coefficient of 0.99. Hence, the ions having a correlation coefficient higher than 0.99 were considered as isotope or fragment depending on the m/z difference. Furthermore, correlated masses that are closer than the resolution of the used PTR-QiTOF-MS were removed in order to not count twice the same peak.

IV.5.4 Microbial analysis

IV.5.4.1 DNA extraction

The DNA extraction has been performed for all microcosms following the protocol developed by GenoSol platform (INRA, Dijon, France, www.dijon.inra.fr/plateforme_genosol) (Terrat et al., 2012) for application in large-scale soil survey (Terrat et al., n.d.). The protocol consist of mixing in a 15 mL Falcon tube 1 g of each soil sample with 2 g of 100 mm diameter silica beads, 2.5 g of 1.4 mm diameter ceramic beads and 4 glass bead of 4 mm diameter and 5 mL of a solution containing 100 mMTris (pH 8.0), 100 mMEDTA (pH 8.0), 100 mM NaCl, and 2% (wt/vol) sodium dodecyl sulfate. Then, we proceeded homogenizing the samples in a FastPrep-24 (MP-Biomedicals, Santa Ana, CA, USA) during 90 s and incubated for 30 min at 70 °C before centrifugation at 7000 g for 5 min at 20 °C. The deproteination was performed by collecting 1 mL of the supernatant and incubating for 10 min on ice with 1/10 volume of 3M potassium acetate (pH 5.5) and centrifuged at 14.000 g during 5 min. The precipitation of the proteins was performed with one volume of ice-cold isopropanol. The last step of the extraction consisted of washing the nucleic acid with 70% ethanol. DNA concentrations of crude extracts were determined by electrophoresis in 1% agarose gel stained with ethidium bromide using a calf thymus DNA standard curve, and used as estimates of microbial biomass [START_REF] Dequiedt | Biogeographical patterns of soil molecular microbial biomass as influenced by soil characteristics and management[END_REF]. After quantification, nucleic acids were separated from the residual impurities, particularly humic substances, by centrifuging through two types of minicolumn. Aliquots (100 µl) of crude DNA extract were first loaded onto polyvinyl polypyrrolidone minicolumns (BIORAD, Marne-la-Coquette, France) and centrifuged at 1000 × g for 4 min at 10 °C. The eluates were then purified using the Geneclean turbo kit (Mp Biomedicals, Illkirch, France) (Ranjard et al., 2003). (Ranjard et al., 2003). Purified DNA concentrations were finally measured using the Quantifluor (Promega, Lyon, France) staining kit, according to the manufacturer's instructions.

IV.5.4.2 Quantitative PCR (qPCR)

Quantitative real-time PCR was performed on extracted DNA to quantify 16S and 18S rRNA gene sequences (Lienhard et al., 2012;Plassart et al., 2012), which led to the estimation of the fungal to bacterial ratio (F:B). Bacterial and fungal quantitative PCR assays were performed using a StepONE (Applied Biosystems, Courtaboeuf, France) with a SYBRGreen® detection system. Purified DNA extract was amplified in a total reaction volume of 20 µl, containing 500 ng of T4 gene 32 protein (MP Biomedicals, France) and 10 µl of Power SYBR ® Green PCR master mix (Applied Biosystems, France).

For bacterial quantification, the reaction mixtures contained 1 µM of each primer min at 95°C for activation; followed by 35 cycles of 15 s at 95°C, 30 s at 50°C and 60 s at 70°C. Amplified DNA from a pure culture of Fusarium oxysporum 47 was used as a fungal standard. These measures were used to estimate the bacterial and fungal densities in the samples.

IV.5.4.3 High throughput sequencing of 16S and 18S rRNA gene sequences

Bacterial diversity was obtained by amplifying a 440-base 16S rRNA from each DNA samples. The corresponding primers were: F479 (5'-CAG CMG CYG CNG TAA NAC-3') and R888 (5'-CCG YCA ATT CMT TTR AGT-3') (Tardy et al., 2014). The amplification of the DNA was performed during a 25 µL PCR (with 5 ng of DNA for each sample) under the following set up conditions: 94 °C for 2 min, 35 cycles of 30 s at 94 °C, 52 °C for 30 s and 72

°C for 1 min, followed by 7 min at 72 °C.

For fungal diversity, a 350-base 18S rRNA fragment was amplified from each DNA sample

(5 ng) with the corresponding primers: FF390 (5'-CGA TAA CGA ACG AGA CCT-3') and FR1 (5'-ANC CAT TCA ATC GGT ANT-3') [START_REF] Prévost-Bouré | Validation and Application of a PCR Primer Set to Quantify Fungal Communities in the Soil Environment by Real-Time Quantitative PCR[END_REF]. For each sample, 5 ng of DNA were used for a 25 µL PCR conducted under the following conditions: 94 °C for 3 min, 35 cycles of 30 s at 94 °C, 52 °C for 1 min and 72 °C for 1 min, followed by 5 min at 72 °C.

The purification of the PCR products was performed using the Agencourt® AMPure® XP kit (Beckman Coulter, Italy, Milano) and quantified with the Quantifluor (Promega, Lyon, France) staining kit according to the manufacturer's instructions. Purified PCR products (7.5 ng of DNA for bacteria) were amplified twice in order to integrate to the 5′ end of the primers a 10-bp multiplex identifiers allowing the specific identification of the samples and the prevention of PCR biases. For bacteria, the second PCR conditions were the same than previously described but with only seven cycles. For fungi, the second PCR conditions were optimized, with the number of cycles being reduced to seven and the denaturation step processed at 94 °C during 1 min. PCR products were purified with the MinElute gel extraction kit (Qiagen NV) and quantified with the Quantifluor (Promega, Lyon, France)

staining kit according to the manufacturer's instructions. Equal amounts of each sample were pooled and then cleaned with the SPRI (Solid Phase Reverse Immobilization Method) using the Agencourt® AMPure® XP kit (Beckman Coulter, Italy, Milano). The pool was finally sequenced with a MiSeq Illumina instrument (Illumina Inc, San Diego, CA) operating with V3 chemistry and producing 250 bp paired-reads.

IV.5.4.4 Bioinformatic analysis of 16S and 18S rRNA gene sequences

The bioinformatics analyses were performed using the GnS-PIPE developed by the Genosol platform (INRA, Dijon, France) (Terrat et al., 2012). At first, all the 16S and 18S raw reads were organized according to the multiplex identifier sequences. All raw sequences were checked and discarded if: (i) they contained any ambiguous base (Ns), (ii) if their length was less than 350 nucleotides for 16S reads or 300 nucleotides for 18S reads, (iii) if the exact primer sequences were not found (for the distal primer, the sequence can be shorter than the complete primer sequence, but without ambiguities). A PERL program was then applied for rigorous dereplication (i.e. clustering of strictly identical sequences). The dereplicated reads were then aligned using Infernal alignment [START_REF] Cole | The Ribosomal Database Project: improved alignments and new tools for rRNA analysis[END_REF], and clustered into operational taxonomic units (OTU) using a PERL program that groups rare reads to abundant ones, and does not count differences in homopolymer lengths. A filtering step was then carried out to check all single-singletons (reads detected only once and not clustered, which might be artifacts, such as PCR chimeras) based on the quality of their taxonomic assignments. Finally, in order to compare the datasets efficiently and avoid biased community comparisons, the reads retained were homogenized by random selection (23 700 and 11900 reads for 16S and 18S rRNA gene sequences, respectively). The retained high-quality reads were used for: (i) taxonomy-independent analyses, determining the Shannon index (ii) taxonomy-based analysis using similarity approaches against dedicated reference databases from SILVA [START_REF] Quast | The SILVA ribosomal RNA gene database project: improved data processing and web-based tools[END_REF]. The raw datasets are available in the EBI database system under project accession number PRJEB29286.

IV.5.5 Statistical analysis

Microbial biomass, microbial diversity Index (Shannon) and the relative abundance of bacterial, and fungal phyla in the microbial composition were processed by the ANOVA test.

With the ANOVA test, we also analyzed the effect of the organic waste product on the microbial community for the different dilution level The dataset before the statistical analysis was made of 754 variables (number of peaks detected) and 45 samples comprising the 3 replicate for each sample. In order to select the most representing variables of the dataset, several statistical tests have been performed using the R software (Version 1.0.153 -© 2009-2017 RStudio). At first, the normality test (Shapiro-Wilk test, W> 0.9) has been applied to verify that the mean mixing ratios were normally distributed for each VOC. Secondly, the homogeneity of the variances was verified for each treatment using the Levene test. Once the normality and the homogeneity of the variances were validated, a t-test was performed in order to see if the VOC flux was significantly higher than 0. Moreover, correlated masses that are closer than the resolution of the used PTR-QiTOF-MS were removed in order to not count twice the same peak. Finally, an ANOVA test was performed. The final selected dataset comprised of 45 samples and 239 variables meaning that only 32% of the dataset was kept for further analysis. The ANOVA tests were followed by the Tukey post hoc test. , where the sum is over all VOCs recorded in the mass table. The correlation matrix between VOCs and microorganisms has been created

selecting the VOC having a R 2 of the correlation microorganisms/VOC larger than 0.6, the final number of compounds displayed was 107.

Microbial biomass, microbial diversity Index (Shannon) and the relative abundance of bacterial, and fungal phyla in the microbial composition were processed by the ANOVA test.

With the ANOVA test, we also analyzed the effect of the organic waste product on the microbial community for the different dilution levels. All significant effects were assessed by Tukey's Honestly Significant Difference (HSD) post hoc test (P < 0.05).

V.1 Introduction

The recycling of the organic matter via amendment with organic waste products (OWPs) is an extensively used agronomic practice to increase soil fertility. Lately, this practice has been encouraged in Europe (European Commission, 2010) in order to contrast the decrease of soil organic matter content due to intensive agricultural practices [START_REF] Chan | Soil carbon fractions and relationship to soil quality under different tillage and stubble management[END_REF]. OWPs are potential sources of nutrients for crops, and they can partially substitute mineral fertilizers [START_REF] Chalhoub | Increased nitrogen availability in soil after repeated compost applications: Use of the PASTIS model to separate short and long-term effects[END_REF]. The use of OWPs affects several chemical and physical properties and thus the microbial structure and activity in soil [START_REF] Blagodatskaya | Priming effects in Chernozem induced by glucose and N in relation to microbial growth strategies[END_REF]Federici et al., 2017;[START_REF] Sadet-Bourgeteau | Lasting effect of repeated application of organic waste products on microbial communities in arable soils[END_REF]. The effects of the OWPs application on soil chemical and physical characteristics and the microbial community depend on the duration of the OWPs application to soil. Repeated OWPs applications over several years maintain soil fertility and thus crop yields; they also have more persistent impacts on soil characteristics, plant growth [START_REF] Clark | Environmental and plant community determinants of species loss following nitrogen enrichment[END_REF] and microbial diversity [START_REF] Francioli | Mineral vs. Organic Amendments: Microbial Community Structure, Activity and Abundance of Agriculturally Relevant Microbes Are Driven by Long-Term Fertilization Strategies[END_REF][START_REF] Zhong | The effects of mineral fertilizer and organic manure on soil microbial community and diversity[END_REF] than punctual OWPs application. Some studies reported that repeated organic fertilization on soil microbial communities led to an increased soil microbial biomass [START_REF] Francioli | Mineral vs. Organic Amendments: Microbial Community Structure, Activity and Abundance of Agriculturally Relevant Microbes Are Driven by Long-Term Fertilization Strategies[END_REF][START_REF] Poulsen | Effects of fertilization with urban and agricultural organic wastes in a field trial -Prokaryotic diversity investigated by pyrosequencing[END_REF].

However, microbial communities parameters such as diversity, composition (i.e. number and relative abundance of present species in the community) and seemed to depend on the time between the OWP application and soil sampling, and the duration of the application [START_REF] Francioli | Mineral vs. Organic Amendments: Microbial Community Structure, Activity and Abundance of Agriculturally Relevant Microbes Are Driven by Long-Term Fertilization Strategies[END_REF][START_REF] Nasini | Effect of solid olive-mill waste amendment on soil fertility and olive (Olea europaea L.) tree activity[END_REF][START_REF] Poulsen | Effects of fertilization with urban and agricultural organic wastes in a field trial -Prokaryotic diversity investigated by pyrosequencing[END_REF][START_REF] Proietti | Effects of amendment with oil mill waste and its derived-compost on soil chemical and microbiological characteristics and olive (Olea europaea L.) productivity[END_REF][START_REF] Regni | Long Term Amendment with Fresh and Composted Solid Olive Mill Waste on Olive Grove Affects Carbon Sequestration by Prunings, Fruits, and Soil[END_REF].

Until now, it has been demonstrated that the most significant changes in the structure of the microbial community due to OWPs amendments occur in the short-term period [START_REF] Pezzolla | Influence of exogenous organic matter on prokaryotic and eukaryotic microbiota in an agricultural soil. A multidisciplinary approach[END_REF]. Experiments on short-term periods following the OWPs application reported wide changes in the microbial community structure caused by a rapid turnover of microbial biomass due to the r-strategists bacteria [START_REF] Blagodatskaya | Priming effects in Chernozem induced by glucose and N in relation to microbial growth strategies[END_REF]. Federici et al., (2017) reported a rapid modification of both fungal and bacterial communities after three days from the OWPs spreading. Furthermore, in Federici et al., (2017) study, the bacterial community restored its initial state after 120 days demonstrating high resilience capacity, while the fungal population changes remained until the end of the trial (120 days). Other studies reported substantial changes in bacterial structure for a larger period after the application of OWPs. [START_REF] Calbrix | Impact of organic amendments on the dynamics of soil microbial biomass and bacterial communities in cultivated land[END_REF] revealed that after the OWPs (~5 Mg ha -1 ) application, the microbial community was strongly impacted on its functional and genetic structure during the first 3 months succeeding the amendment, followed by a period of resilience leading, after 6 months, to similar communities in both amended and control plots. Previous research thus suggests that a single OWP application has a transient impact on the abundance and diversity of soil microbial communities.

Changes on the microbial community are known to affect the volatile organic compounds (VOCs) emissions by soil. During the last decades, an increasing awareness concerning the VOCs emissions from a large number of microorganisms was observed. The production of VOCs from soil and microorganisms gained attention because of their contribution to biogenic VOCs emissions [START_REF] Bachy | Methanol exchange dynamics between a temperate cropland soil and the atmosphere[END_REF][START_REF] Bachy | Are BVOC exchanges in agricultural ecosystems overestimated? Insights from fluxes measured in a maize field over a whole growing season[END_REF][START_REF] Mancuso | Soil volatile analysis by proton transfer reaction-time of flight mass spectrometry (PTR-TOF-MS)[END_REF]. Abis et al., (2018-Chapter IV) showed that VOCs emissions from the microbial community are linked to the microbial diversity in soil. The dilution of the microbial diversity in soil led to an increase of the VOCs emissions from the soil up to 2-3 times more than soil with high diversity levels (Abis et al., to be submitted-Chapter IV). Furthermore, the effect of the OWPs applications on VOCs emissions by soil was less important compared to the influence of the microbial diversity dilution levels.

In this context, this study aims at characterizing the short-term effects of OWPs application to soils with varying microbial dilution levels on microbial structure and VOCs emissions. These parameters were measured after a 49 hours period following OWP application. We further compared the response of a microbial community used to receive OWPs applications (GWS soil) with a microbial community that never received organic waste input to an OWP application (CN soil). The measurements of VOCs emissions were performed, under controlled laboratory conditions using a dynamic chamber approach, from 18 microcosms with three different microbial dilution levels (high, medium and low). The VOCs emissions from the microcosms were detected with a PTR-QiTOF-MS, 1 hour before the application of the green waste and sludge (GWS) and during the following 49h. The microbial community change due to the application of the GWS amendment was measured by a high throughput sequencing approach targeting 16S ribosomal genes.

V.2 Methods

V.2.1 Sampling and site description

The collection of the samples was performed in the QualiAgro site, located at Feucherolles, which is a station of the SOERE-PRO-network (https://www6.inra.fr/qualiagro_eng/Nospartenaires/The-SOERE-PRO-network). Feucherolles is in northwestern France (35 km west of Paris; 48•52'N, 1•57'E, alt 150 m), and the soil of the same zone are classified as glossic Luvisol (IUSS Working Group WRB, 2014). Important characteristics of these soils are: (i) low quantity of clay, (ii) slit-loam texture (15.0% clay, 78.3% slit), (iii) pH alkaline in the surface horizon (0-30 cm), and (vi) good drainage. QualiAgro site was used from September 1998 to September 2017 in order to study the long-term effect of the amendment with OWPs.

The experiment consisted of amending the site every two years, in a randomized block design with 4 replicates with 4 different organic waste products: BIOW (bio-waste compost derived from the co-composting of green wastes and source-separated organic fractions of municipal solid wastes), GWS (compost derived from the co-composting of green wastes with sewage sludge), FYM (farmyard manure) and MSW (municipal solid waste compost derived from the composting of residual solid wastes after removing dry and clean packaging); plus a control without organic input (CN).

The collection of the samples was performed in 2 plots of the site (GWS and CN). For each plot, we collected randomly five soil cores (0-30 cm depth) in early September 2016 one year following the last amendment (~4 tC ha -1 of every OWP).

V.2.2 Microcosms preparation

Soil samples were sieved at 4 mm and homogenized before gamma-ray sterilization (35 kGy;

Conservatome, Dagneux, France). Soil samples consisting of soils amended with GWS and control without organic input (CN). The sterility of the irradiated soil was verified by spreading serial dilutions of the soil onto nutrient agar plates. After the sterilization process soils were inoculated with a diluted soil suspension prepared with the same soil before sterilization [START_REF] Wertz | Maintenance of soil functioning following erosion of microbial diversity[END_REF]. The soil suspension, obtained by a mix of 30 g of soil with 90 mL of sterilized water, was used pure (D0=1), diluted with water 10 3 times (D1= 1:10 3 water/soil suspension ratio) and finally, diluted 10 5 times (D2=1:10 5 water/soil suspension ratio). Microcosms were set up by placing 30 g of dry sterile soil in 150 ml plasma flask. The three levels of dilution of the soil suspension were used as inoculum to create a gradient of diversity as follow: low, for samples inoculated with D0 soil suspension, medium, for samples inoculated with D1 soil suspension, and high, for samples inoculated with D2 soil suspension.

The soil moisture was fixed at 60% of the water holding capacity (WHC). Microcosms were at first sealed hermetically with a caoutchouc plug and pre-incubated at 20°C in the dark.

Once a week during six weeks the microcosms have been aerated keeping constant the soil moisture at 60% of the WHC. One week before the measurement with the PTR-QiTOF-MS the caoutchouc plug was substitute with a Teflon plug in order to reduce the VOCs emissions released from the plug.

After six weeks incubation (20 °C), GWS were thoroughly mixed with soil samples CN+GWS and GWS+GWS, in its dry form (dose equivalent to 4 t C ha-1). The main characteristics of the GWS amendment are reported in CN and GWS samples were lyophilized and stored at -40 °C until use for molecular analyses based on soil DNA extraction. CN+GWS and GWS+GWS samples were then incubated for 49 hours in the dark at 20 °C and always 60 % water-holding capacity to ensure proper biomass activation. In total, there were 36 microcosms (2 treatments × 3 levels of microbial diversity × 2 dates of sacrifice × 3 replicates). After 49h incubation, CN+GWS and GWS+GWS samples were sacrificed for microbial molecular analyses. In

V.3 PTR-QiTOF-MS measurement set up

Two PEEK tubes were inserted in the plug of every flask, one connected to the PTR-QiTOF-MS and the other one connected to a bottle of dry synthetic air (Alphagaz 1 Air: 80% nitrogen, 20% oxygen, 99.9999%, Air Liquide®). VOCs detection lasted 180 s for every microcosm. The PTR-QiTOF-MS detection system and the equation used in order to calculate the VOCs fluxes were the same as in [START_REF] Abis | Profiles of volatile organic compound emissions from soils amended with organic waste products[END_REF], apart from the air flow which was set to 0.3 L min -1 was used. Between each VOCs measurement, the microcosms were incubated in a 20°C chamber. After the last VOCs measurement, the flasks were cleaned and the soil transferred in a -40 °C chamber waiting for the DNA extraction.

V.2.4 Microbial Analyses

V.2.4.1 DNA extraction

The DNA extraction has been performed for all microcosms following the protocol developed by GenoSol platform (INRA, Dijon, France, www.dijon.inra.fr/plateforme_genosol) (Terrat et al., 2012) for application in large-scale soil survey (Terrat et al., n.d.). The protocol consist on mixing in a 15 mL Falcon tube 1 g of each soil sample with 2 g of 100 mm diameter silica beads, 2.5 g of 1.4 mm diameter ceramic beads and 4 glass bead of 4 mm diameter and 5 mL of a solution containing 100 mMTris (pH 8.0), 100 mMEDTA (pH 8.0), 100 mM NaCl, and 2% (wt/vol) sodium dodecyl sulfate. Then, we proceeded to homogenize the samples in a FastPrep-24 (MP-Biomedicals, Santa Ana, CA, USA) during 90 s and incubated for 30 min at 70 °C before centrifugation at 7000 g for 5 min at 20 °C. The deproteination was obtained by collecting 1 mL of the supernatant and incubating for 10 min on ice with 1/10 volume of 3M potassium acetate (pH 5.5) and centrifuged at 14.000 g during 5 min. The precipitation of the proteins was performed with one volume of ice-cold isopropanol. The last step of the extraction consisted of washing the nucleic acid with 70% ethanol. DNA concentrations of crude extracts were determined by electrophoresis in 1% agarose gel stained with ethidium bromide using a calf thymus DNA standard curve, and used as estimates of microbial biomass [START_REF] Dequiedt | Biogeographical patterns of soil molecular microbial biomass as influenced by soil characteristics and management[END_REF]. After quantification, nucleic acids were separated from the residual impurities, particularly humic substances, by centrifuging through two types of minicolumn. Aliquots (100 µl) of crude DNA extract were first loaded onto polyvinyl polypyrrolidone minicolumns (BIORAD, Marne-la-Coquette, France) and centrifuged at 1000 × g for 4 min at 10 °C. The eluates were then purified using the Geneclean turbo kit (Mp Biomedicals, Illkirch, France) (Ranjard et al., 2003). DNA concentration in each sample was fluorometrically quantified with the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, CergyPontoise, France) following the manufacturer's instructions.

V.2.4.2 High throughput sequencing of 16S rRNA gene sequences

Bacterial diversity was obtained by amplifying a 440-base 16S rRNA from each DNA samples. The corresponding primers were: F479 (5'-CAG CMG CYG CNG TAA NAC-3') and R888 (5'-CCG YCA ATT CMT TTR AGT-3') (Tardy et al., 2014). The amplification of the DNA was performed during a 25 µL PCR (with 5 ng of DNA for each sample) under the following set up conditions: 94 °C for 2 min, 35 cycles of 30 s at 94 °C, 52 °C for 30 s and 72

°C for 1 min, followed by 7 min at 72 °C.

The purification of the PCR products was performed using the Agencourt® AMPure® XP kit (Beckman Coulter, Italy, Milano) and quantified with the Quantifluor (Promega, Lyon, France) staining kit according to the manufacturer's instructions. Purified PCR products (7.5 ng of DNA for bacteria) were amplified twice in order to integrate to the 5′ end of the primers a 10-bp multiplex identifiers allowing the specific identification of the samples and the prevention of PCR biases. The second PCR conditions were the same than previously described but with only seven cycles. PCR products were purified with the MinElute gel extraction kit (Qiagen NV) and quantified with the Quantifluor (Promega, Lyon, France) staining kit according to the manufacturer's instructions. Equal amounts of each sample were pooled and then cleaned with the SPRI (Solid Phase Reverse Immobilization Method) using the Agencourt® AMPure® XP kit (Beckman Coulter, Italy, Milano). The pool was finally sequenced with a MiSeq Illumina instrument (Illumina Inc, San Diego, CA) operating with V3 chemistry and producing 250 bp paired-reads.

V.2.4.3 Bioinformatic analysis of 16S rRNA gene sequences

The bioinformatic analyses were performed using the GnS-PIPE developed by the Genosol platform (INRA, Dijon, France) (Terrat et al., 2012). At first, all the 16S raw reads were organized according to the multiplex identifier sequences. All raw sequences were checked and discarded if: (i) they contained any ambiguous base (Ns), (ii) if their length was less than 350 nucleotides for 16S reads, (iii) if the exact primer sequences were not found (for the distal primer, the sequence can be shorter than the complete primer sequence, but without ambiguities). A PERL program was then applied for rigorous dereplication (i.e., clustering of strictly identical sequences). The dereplicated reads were then aligned using Infernal alignment [START_REF] Cole | The Ribosomal Database Project: improved alignments and new tools for rRNA analysis[END_REF], and clustered into operational taxonomic units (OTU) using a PERL program that groups rare reads to abundant ones, and does not count differences in homopolymer lengths. A filtering step was then carried out to check all single-singletons (reads detected only once and not clustered, which might be artifacts, such as PCR chimeras)

based on the quality of their taxonomic assignments. Finally, in order to compare the datasets efficiently and avoid biased community comparisons, the reads retained were homogenized by random selection (15 000 reads for 16S rRNA gene sequences). The retained high-quality reads were used for taxonomy-independent analyses and determining the Shannon index.

V.2.5 Statistical Analysis

V.2.5.1 VOCs statistical Analyses

The dataset before the statistical analysis was made of 754 variables (number of peaks detected) and 18 samples comprising the 3 replicate for each sample. In order to select the most representing variables of the dataset, several statistical tests have been performed using the R software (Version 1.0.153 -© 2009-2017 RStudio). At first, the normality test (Shapiro-Wilk test, W> 0.9) has been applied to verify that the mean mixing ratios were normally distributed for each VOC. Secondly, the homogeneity of the variances was verified for each treatment using the Levene test. Once the normality and the homogeneity of de variances were validated, a t-test was performed in order to see if the VOC flux was significantly higher than 0.

Moreover, correlated masses that are closer than the resolution of the used PTR-QiTOF-MS were removed in order to not count twice the same peak. Finally, the ANOVA test was performed. The final selected dataset was made of 18 samples and 239 variables meaning that only 32% of the dataset was kept for further analysis. Then we performed the ANOVA test in order to analyze the statistical differences among the different timing. The performed ANOVAs were followed by the Tukey post hoc test.

The Shannon index of diversity (Figure 3b

V.2.5.2 Microbial statistical analyses

Microbial biomass, microbial diversity Index (Shannon) and the relative abundance of bacterial, and fungal phyla in the microbial composition were processed by the ANOVA test.

With the ANOVA test, we also analyzed the effect of the organic waste product on the microbial community for the different dilution levels. All significant effects were assessed by Tukey's Honestly Significant Difference (HSD) post hoc test (P < 0.05).

V.3 Results

V.3.1 Microbial biomass and dilution diversity manipulation

V.3.2 Bacterial relative abundance

The colonization from the different phyla within the microcosms showed a shift on the microbial community structure when the GWS was incorporated in both soil type (CN and GWS) (

V.4 Discussion

V.4.1 Microbial diversity manipulation

The microbial biomass was constant among the three different dilution levels, indicating that after 44 days incubation, the microbial biomass was not affected by the dilution diversity manipulation performed. The Shannon index, calculated for CN, GWS and CN+GWS and GWS+GWS, indicated a decrease of the microbial diversity in the higher dilution level for all samples. Thus, the Shannon index confirmed successful microbial diversity dilution which was even increased following GWS amendment.

V.4.2 Effect of the GWS amendment on the bacterial community

Results showed that Bacteroidetes phylum takes advantage of the addition of the new GWS amendment whatever the history of the soil (CN and GWS soils). This result is in line with [START_REF] Simmons | Characterization of bacterial communities in solarized soil amended with lignocellulosic organic matter[END_REF] study which reported an increase of the Bacteroidetes phylum in soil amended with organic amendment compared to soil that did not receive any organic amendment. [START_REF] Lupwayi | Soil bacterial community responses to black medic cover crop and fertilizer N under no-till[END_REF] explained that the relative abundance of Bacteroidetes increased linearly with increasing N rate. Since GWS, is a source of N (see Table V-1), we can hypothesize that the increase of the Bacteroidetes relative abundance might be due to the increase of the N content in the microcosms. Proteobacteria was the most dominant phylum in all the microcosms and as suggested by [START_REF] Kuppusamy | Pyrosequencing analysis of bacterial diversity in soils contaminated long-term with PAHs and heavy metals: Implications to bioremediation[END_REF] this might be because functionally diverse groups of bacteria fall within this phylum. Nevertheless, [START_REF] Bastida | Soil restoration with organic amendments: linking cellular functionality and ecosystem processes[END_REF] reported that the abundance of Proteobacteria was higher in compost-treated samples than in sludge-treated samples. Since our samples have been amended with GWS amendment that contains 30% of sludge, the development of the Proteobacteria might have been negatively influenced by the type of substrate added. Furthermore, in samples not receiving the new GWS amendment (CN and GWS) Proteobacteria, increased their relative abundance among the dilution levels may confirm that Proteobacteria are a great colonizer only when the conditions are favorable [START_REF] Cleveland | Increases in soil respiration following labile carbon additions linked to rapid shifts in soil microbial community composition[END_REF][START_REF] Fierer | Toward an ecological classification of soil bacteria[END_REF]. [START_REF] Fierer | Toward an ecological classification of soil bacteria[END_REF] reported that Bacteroidetes and Proteobacteria phyla were positively related with the C amendment level leading to the conclusion that these phyla can be classified as copiotrophic.

Copiotrophics, are microorganisms requiring a high level of a nutrient, able to grow exponentially, colonize the habitat quickly with a relative short life due to the initial huge waste of energy involved on the exponential growth [START_REF] Kjelleberg | Oligotrophic and copiotrophic marine bacteria-observations related to attachment[END_REF][START_REF] Poindexter | Oligotrophy[END_REF].

V.4.3 Dynamics of the total VOCs emissions

The detection of the VOCs emissions from the microcosms was performed 10 times during 49h. Total VOCs emissions of the GWS+GWS samples constantly increasing during the incubation hours. For the CN+GWS no statistical differences on the total VOCs emissions were observed along the incubation hours. This is possibly due to the faster degradation of the organic input from a microbial community used to the GWS application (GWS+GWS) than a microbial community not used to receive this organic input (CN+GWS). This efficiency in the organic matter degradation for the GWS+GWS microbial community might increase the VOCs emissions from soil. Even though, for CN and GWS higher VOCs emissions from the higher microbial dilution level were reported. Those results are in line with the previous study of Abis et al., (2018-chapter VI). Results concerning the Shannon index showed a lower diversity of the VOCs emitted while the total VOCs emissions increased (Figure V-10).

Previous studies as [START_REF] Abis | Profiles of volatile organic compound emissions from soils amended with organic waste products[END_REF] and Abis et al., (to be submitted-chapter IV) reported the same trend, lower VOCs diversity for higher emissions.

V.4.4 Bacteria VOCs production dynamic of the most and less emitted

VOCs

In order to explain this phenome, we analyzed the emission dynamics of the 6 most emitted compounds, representing the 70% of the total VOCs emissions. Results showed an increase of the emissions with time. The opposite dynamic was observed for the compounds less emitted (representing less than 1% of the total VOCs emissions). In more details, the most emitted compound showed a constant emissions increase with time following GWS amendment, while the less emitted compounds showed an exponential decrease.

Compounds as 84.084 m/z and 98.100 m/z, which were less emitted, were detected from the GWS alone by Ciuraru et al., (personal communication). We can hence hypothesize that emission of these compounds, and by extension, all compounds showing an exponential decrease dynamics were due to emissions by GWS organic matter being processed rather than an induced emission from soil. We can further hypothesize that the soil microbial community might have processed (consume) those VOCs as a carbon source. Indeed, the study of [START_REF] Mayrhofer | Microbial community related to volatile organic compound (VOC) emission in household biowaste[END_REF] reported a negative correlation between the cells count of Dysgonomonas caprocytophagoides and the emissions of the ion mass m/z 84 underlining either a possible absorption by this bacterial species of the m/z 84 mass as a carbon source gradually exhausted (which is the likely cause in our case), or a suppression or inhibition of the emitting bacteria species.

Among the compounds which constantly increase following the GWS amendment, we found indole (m/z 119.101). Indole has been reported in several studies as a promoter of plant growth [START_REF] Bhattacharyya | Volatile Indole Produced by Rhizobacterium Proteus vulgaris JBLS202 Stimulates Growth of Arabidopsis thaliana Through Auxin, Cytokinin, and Brassinosteroid Pathways[END_REF][START_REF] Park | Plant metabolomics for plant chemical responses to belowground community change by climate change[END_REF][START_REF] Yu | Plant growth promoting rhizobacterium Proteus vulgaris JBLS202 stimulates the seedling growth of Chinese cabbage through indole emission[END_REF]. Hence plants may benefit indirectly from GWS amendment via indole emissions. Other studies have reported indole emissions by E. coli culture [START_REF] Bhattacharyya | Volatile Indole Produced by Rhizobacterium Proteus vulgaris JBLS202 Stimulates Growth of Arabidopsis thaliana Through Auxin, Cytokinin, and Brassinosteroid Pathways[END_REF][START_REF] Mayrhofer | Microbial community related to volatile organic compound (VOC) emission in household biowaste[END_REF]. In particular, [START_REF] Mayrhofer | Microbial community related to volatile organic compound (VOC) emission in household biowaste[END_REF] reported a positive correlation between the increase of the E. coli cells count and the emissions of Indole.

V.5 Conclusions

This work aimed to characterize in terms of quantity and composition the effect of the GWS application on soil VOCs emissions and microbial community in the short-term period (49 h).

We can conclude that GWS amendment boosted Bacteroidetes growth while penalized Proteobacteria colonization. Furthermore, the new addition of GWS in soil increased soil

VOCs emissions in microcosms used to received GWS amendment and thus, more efficient on the degradation of the new organic input but not for soils unused to organic matter amendment. This work highlighted the possibility that the VOCs emissions released by the GWS amendment might be used as a carbon source by microorganisms. Finally, GWS might boost Indole emissions, promoter of plant growth.

Chapter VI

General discussions

VI. General discussions

In a global context were the emissions of VOCs gain more and more relevance due to the roles played on climate change and air pollution, with this work, we wanted to contribute by the addition of small tiles to the complex mosaic of the biogenic VOCs emissions. The 1. The effect of the timing following the OWPs amendment on VOCs emissions.

2. The effect of the soil microbial diversity on the VOCs emissions (the native soil, used for the first experiment, will be included as a non-manipulated soil)

3. The combined effects of OWPs amendment and microbial dilution levels on VOCs emissions.

The experimental conditions were similar in the three experiments, although some differences may arise due to soil manipulations: sterilization and incubation. However, samples for experiment two and three (Chapter V) were collected at the same time and manipulated following the same protocol for the microbial dilution diversity. The only differences between samples treatment of experiments two and three were: (i) the application of new OWP in experiment three before the VOCs detection, and (ii) the number of different type of OWPs in soil, 4 for experiment two (GWS, FYM, BIOW and MSW), and 1 for experiment three (GWS).

In the following analysis, we first analyzed total VOCs emissions as a function of time following the OWP application. We then analyzed total VOCs emissions in response to microbial dilution levels comprising the first experiment where no microbial diversity manipulation was performed. In order to validate and compare the results, we performed ANOVA tests followed by a Tukey HSD test and a T-test.

VI.1 Effect of time following the OWPs application on total VOCs emissions

Results regarding the analysis of the timing following the OWPs application are showed in Figure VI-1. Time h_0 is the timing just before the new application of the OWP which is equivalent to the VOCs emissions from experiment two (Chapter IV). The samples T1y showed high variability in the total VOCs emissions (from -30 nmol s -1 g -1 10 4 to 180 nmol s -1 g -1 10 4 ). This variability might be due to the higher number of OWPs analyzed crossed with the different microbial dilution levels.

The Tukey test showed that there was a significant difference between samples h_0, Ty2, and h_49 (Figure VI-1) in terms of total VOCs emissions. Total VOCs emissions at h_0 and Ty2 were statistically lower compared to h_49 total VOCs emissions. Besides, the total VOCs emissions 49 hours following OWPs amendment were two times higher than the total VOCs emissions before the amendment. A possible explanation of this total VOCs emissions increase during the 49 hours following the OWPs application could be the increase of the microbial activity caused by the priming effect. The priming effect is the enhanced decomposition of the existing soil organic matter due to a new C input [START_REF] Chen | Soil C and N availability determine the priming effect: microbial N mining and stoichiometric decomposition theories[END_REF][START_REF] Fontaine | The priming effect of organic matter: a question of microbial competition?[END_REF]. The quality of organic substances and their availability to decomposers affect the decomposition of soil organic matter [START_REF] Six | Soil organic matter, biota and aggregation in temperate and tropical soils -Effects of no-tillage[END_REF]. In this case, availability is defined as the biochemical recalcitrance of organic compounds, that is, their susceptibility to microbial enzymatic degradation. The GWS has a high recalcitrance level, as shown by its high index of residual organic carbon (I roc ), that represents the portion of organic carbon potentially incorporated into soil organic carbon after the organic waste application [START_REF] Lashermes | Indicator of potential residual carbon in soils after exogenous organic matter application[END_REF] which was equal to 75% org C [START_REF] Obriot | Multi-criteria indices to evaluate the effects of repeated organic amendment applications on soil and crop quality[END_REF]. We can hypothesize that an easy degradation of the GWS amendment may enhance the priming effect. Since VOCs production in the soil is related to the primary [START_REF] Insam | Volatile organic compounds (VOCs) in soils[END_REF][START_REF] Stahl | Microbial production of volatile organic compounds in soil microcosms[END_REF] and secondary microbial metabolisms (Kai et al., 2009b), we can also hypothesize that high microbial activity enhanced by the priming effect might lead to higher VOCs total emissions rate. Furthermore, the priming effect is a short-term (maximum one month) boost of the soil organic matter degradation [START_REF] Chen | Soil C and N availability determine the priming effect: microbial N mining and stoichiometric decomposition theories[END_REF], which is not in contradiction with the observed return to lower VOCs emissions one year following the last OWPs application.

Since several studies reported that organic waste products are also abiotic sources of VOCs emissions [START_REF] Kumar | Volatile organic compound emissions from green waste composting: Characterization and ozone formation[END_REF][START_REF] Leff | Volatile organic compound (VOC) emissions from soil and litter samples[END_REF][START_REF] Mayrhofer | Microbial community related to volatile organic compound (VOC) emission in household biowaste[END_REF], a second complementary hypothesis could be that the increase of VOCs emissions was in part due to the VOCs emitted by the GWS itself.

Figure VI-1.Total VOCs emission rates against time following the OWPs amendment. h_0= measure performed before the application of the OWP. h_1= 1h after the application of OWPs, h_3= 3h after the application of OWPs, h_6= 6h after the application of OWPs, h_9= 9h after the application of OWPs, h_25= 25h after the application of OWPs, h_27= 27 h after the application of OWPs, h_30 = 30h after the application of OWPs, h_33= 33h after the application of OWPs, h_49h= 49h after the application of OWPs, T1y=1 year after the application of OWPs, T2y= 2 years after the application of OWPs. Bold line=median, boxes= interquartile, whiskers = minimum and maximum, white point = outliers. Letters indicate significant differences according to the Tukey test with p.value <0.05.

Extrapolating our results to a larger scale, we could thus conclude that the amendment with OWPs might increase the VOCs emissions in the short-term period following the OWPs amendment.

VI.2 VOCs emissions among the different microbial dilution levels

Pooling all soils together, results concerning the total VOCs emissions from the different microbial dilution levels as reported in both chapters IV and V showed an increase of the VOCs total emissions in the higher microbial dilution level. The addition of the results from non-manipulated soils showed that DX and D0 behaved similarly and both showed a significant difference in total VOCs emissions with medium and high microbial dilution levels (Figure VI-2 and Erreur ! Source du renvoi introuvable.). In terms of percentages, the differences were significant (33% and 110%). We can conclude that there is a noticeable increase in the total VOCs emissions rate due to the microbial dilution diversity. The differences between the total VOCs emissions of the non-manipulated soils and the manipulated soils were probably due to the loss of microbial phyla correlated with the absorption of VOCs. Indeed, in experiments 2 and 3 we found that the increase in total VOCs emissions was always accompanied by an increase of the Proteobacteria and Bacteroidetes relative abundance. Moreover, those phyla had a VOCs profile systematically positively correlated with VOCs compounds emissions. A positive correlation of a given VOC with a given phyla can occur through the emission from a specific VOC or its stimulated growth by this VOC. Other phyla than Proteobacteria and Bacteroidetes were found to be negatively correlated with VOCs emissions (chapter IV). A negative correlation may result from a given phyla using a particular VOC as carbon source (absorbing this VOC), or an inhibition of the given phyla by this particular VOC. Hence we could interpret the increase in total VOC emissions with increased dilution (Figure VI-2 and Erreur ! Source du renvoi introuvable.), as a result of the loss of some phyla that were previously absorbing those VOCs together with the increase of Proteobacteria and Bacteroidetes that are emitting those VOCs.

We are aware that this study has been performed under laboratory conditions, by artificially manipulating the microbial diversity. Even though, reduced microbial diversity conditions, similar to our laboratory conditions, have been reported by several studies as a consequence of intensive soil management. Generally, high-input agricultural practices decrease microbial biodiversity while the low-input practices enhance microbial diversity in soil [START_REF] Girvan | Soil Type Is the Primary Determinant of the Composition of the Total and Active Bacterial Communities in Arable Soils[END_REF][START_REF] Munyanziza | Agricultural intensification, soil biodiversity and agroecosystem function in the tropics: the role of mycorrhiza in crops and trees[END_REF]. [START_REF] Lupwayi | Bacterial diversity in water-stable aggregates of soils under conventional and zero tillage management[END_REF] reported, for instance, higher microbial diversity in soils under conventional tillage than under zero tillage. Furthermore, [START_REF] Wolińska | Microbial biodiversity in arable soils is affected by agricultural practices[END_REF], compared cultivated soils with non-cultivated soils, reporting a decrease of the microbial diversity in soil up to 30% in the cultivated soil. Monoculture also reduces bacteria biodiversity in soil, while fungi biodiversity seems to be not affected [START_REF] Liu | Microbial Community Diversities and Taxa Abundances in Soils along a Seven-Year Gradient of Potato Monoculture Using High Throughput Pyrosequencing Approach[END_REF]. Furthermore, some studies reported soil microbial diversity decreasing with increasing latitude [START_REF] Staddon | Soil microbial diversity and community structure across a climatic gradient in western Canada[END_REF]. Soil microbial diversity may be lower in northern sites due to decreased productivity caused by nutrient limitation and higher acidity. The diversity and abundance of soil bacteria and fungi are also reduced in arid land. Indeed, aridity promoted shifts in the composition of soil bacteria, with increases in the relative abundance of Chloroflexi and Proteobacteria and decreases in Acidobacteria and Verrucomicrobia phyla [START_REF] Maestre | Increasing aridity reduces soil microbial diversity and abundance in global drylands[END_REF]. We should further add that drylands are expected to expand in the global area by 11-23% by 2100 [START_REF] Huang | Accelerated dryland expansion under climate change[END_REF].

Contextualizing our results in the aforementioned scenarios (intensive agricultural practices, climatic changes leading to the dryland expansion, etc.), we hence hypothesized that total VOCs emissions from soil might increase. It is important to note that, apart from this study, and to our knowledge, no effects of the reduced microbial diversity dilution level on the VOCs emissions have been reported before. This hypothesis should hence require to be confirmed by field measurements.

VI.3 Coupled effect of the OWPs and microbial dilution on VOCs emissions

Analyzing the total VOCs emissions for the different type of OWPs without differentiating for the microbial dilution levels we could both find differences between OWPs total VOCs emissions rates (Figure VI-3). [START_REF] Abis | Profiles of volatile organic compound emissions from soils amended with organic waste products[END_REF] reported higher emissions from BIOW treatment which is not the case when the soil microbial manipulation occurs. This suggests that the effect of microbial dilution level was more important than the effect of the OWPs in soil on the VOCs total emissions. Once more, our results highlight the importance of the microbial diversity on the VOCs emissions. Combining microbial dilution levels with the type of OWPs amendments in soil (Figure emissions, we can thus conclude that the total VOCs emissions increase in the high microbial diversity dilution levels was due to the high presence of these two phyla. If we sum relative abundance of the Bacteroidetes and Proteobacteria phyla, we found that was always higher than 70% for all the microcosms except BIOW_D0, where they were less than 25%.

VII. Conclusions and future perspectives

This work aimed at the characterization of the VOCs emissions from soils amended with OWPs and to determine the impact of the different microbial dilution levels on VOCs emissions. Within the three experiments performed in order to achieve this aim, three different timings between the OWPs application and sampling were studied: two years for the first experiment (chapter III), one year for the second one (chapter IV) and 49 hours for the third one (chapter V).

The first experiment (chapter III) concerned the characterization of the VOCs emissions from soils amended with OWPs for almost 20 years. We found that the total VOCs emissions rate was affected by the type of OWPs amendment in the soil. In particular, we showed that the BIOW compost in soil had the largest VOCs emissions rate, while MSW compost had the lowest VOCs emission rate. Furthermore, in chapter III, we showed that the organic matter content and pH jointly influenced total VOCs emissions with higher emissions for higher joint pH and OM content.

Chapter III, further revealed that the most emitted compounds were acetone, butanone, and acetaldehyde whatever the OWPs amendment. We finally found that 21 compounds had statistically different emissions between the OWPs amendment.

In chapter IV, we showed by microbial dilution manipulation that VOCs emissions increased when soil microbial diversity decreased. We further showed that the most present bacteria benefited from dilution diversity: bacteria from Proteobacteria and Bacteroidetes phyla. A correlation analysis revealed that these bacteria we probable emitters of most VOCs compounds detected while other bacteria which presence decreased with dilution were probable absorbers of those compounds.

Additionally, in chapter IV we found that the VOC compounds most emitted by these manipulated soils were different from those from soils in Chapter IV. We also showed that microbial dilution affected both the quantity and the composition of emitted VOCs much stronger than the soil historical OWPs amendment.

With chapter V we aimed at characterizing GWS application effects on soil VOCs emissions and microbial community over the short-term following GWS application. This work led to the conclusion that the GWS amendment boosted Bacteroidetes growth while penalized Proteobacteria colonization. Furthermore, the addition of GWS in soil increased soil VOCs emissions in soils accustomed to GWS amendment but not in soils that did not receive organic matter for 20 years. This may be attributed to increased activity of microbes specialized in degrading that OWP. This work finally suggested that the VOCs emissions induced by the GWS amendment might be used as a carbon source by microorganisms.

In order to give a global overview of the results found during these three years' work, we merged all the results in a unique dataset (chapter VI). This overview allows ordering factors affecting VOCs emissions. The effect of the microbial diversity was indeed stronger than the effect of the long-term OWPs application. At the same time, we demonstrate that the microbial diversity also affected the microbial community structure with consequences in term of composition and quantity on VOCs emissions. In contrast, in the short-term period following the last OWPs application (49h), the effect of the new OWPs application on total

VOCs emissions was stronger than the microbial dilution level (chapter V).

With this work, we just began the investigation of the microbial diversity and the OWPs in soil effects on VOCs emissions. We demonstrated that the VOCs emissions were widely affected by the microbial diversity level and the microbial structure in soil. Since VOCs

Supplementary material

Study of the effect of organic waste products amendments (OWPs) and microbial diversity on volatile organic compounds (VOCs) emissions by soil

Abstract : VOCs emissions play a pivotal role on the atmospheric pollution. Biogenic sources of VOCs are between 10 and 11 times higher than VOC emissions from anthropogenic sources. Recently, the importance of the characterization of the VOC fluxes by soils and microorganisms has been highlighted. Instead, VOCs emissions from soil and microorganisms are possible precursors of the particulate matter and the O 3 formation. This work is focused on the characterization of the VOCs emissions by soils amended with OWPs over the long and short terms application. The influence of the microbial diversity in soil on VOCs emissions was also analysed. VOC emissions were detected using the PTR-QiTOF-MS technique and all the experiments were performed under controlled laboratory condition using dynamic chambers for the detection of the VOCs emissions from samples. The results showed that different OWPs released different quantity of VOCs emissions and also the chemical and physical properties of the soil were linked to the emissions. Analyses on the influence of microbial biodiversity on VOCs emissions have shown that while the microbial diversity was higher VOC emissions by soils were lower. Furthermore, the diversity of the VOCs decreases when the VOCs emissions by soil are higher. Finally, the study of the dynamics of VOC emissions from microcosms amended with fresh OWPs, showed that the VOC emission flux increased in the first 49 hours after the OWP amendment, due to a disturbance of the microbial community in the soil.

Keywords : VOCs, microbial VOCs, microbial dilution diversity, PTR-QiTOF-MS, organic waste products

Figure

  Figure I-1. Layers of the atmosphere (Source: https://courses.lumenlearning.com/geophysical/chapter/layers-of-the-atmosphere/).

  Figure I-2b. The degradation reactions of VOCs start with the oxidation of the VOCs by the OH radical, leading to the formation of RO 2 and HO 2 radicals as intermediate products (Figure I-2b). The intermediate products RO 2 and HO 2 convert NO in NO 2 following the same scheme as in Figure I-2a[START_REF] Atkinson | Atmospheric chemistry of VOCs and NOx[END_REF]. Hence, the result of this second pathway is

Figure I- 2

 2 Figure I-2. (a) Reactions involving NOx and Ox in the ozone formation. (b) Reactions between NOx

  They are eliminated mainly by washing or by incorporation in liquid droplets. Largest particles are mainly emitted by soil erosion, as well as by plants (pollens and spores). They are generally removed by sedimentation.

Figure I- 3 .

 3 Figure I-3. Production, growth, and removal of atmospheric aerosols. (Jacob, 2000)

  Isoprene and monoterpenes were found to affect the size of the SOA particles (Figure I-4). Under stressed condition, VOCs emissions were dominated by sesquiterpenes. Sesquiterpenes were found to influence the higroscopicity of the SOA particles. The size and the higroscopicity of the SOA particles are crucial factors on the cloud condensation nuclei (CCN) formation. This study concluded that the CCN formation by SOA due to changes in biogenic VOC emissions, will influence cloud formation, and ultimately impact climate (Figure I-5).

Figure I- 7 .

 7 Figure I-7. Illustration of the complexity of VOCs exchanges in soil. rVOCs = emissions from plants roots, mVOCs = emissions from microorganisms, fVOCs = fungi VOCs emissions. Red lines indicate negative effects (like inhibition of growth, toxicity), while positive effects are indicated by the green arrows (i.e. growth promotion). Blue lines represent the flux of VOCs emitted by soil (reproduced from Peñuelas et al., 2014).

Figure I- 8 .

 8 Figure I-8. Nitrogen cycle (source: EarthLabs:Climate and the Carbon Cycle)

  concerning the different pathways leading to the VOCs formation are represented in Figure I-9.

Figure I- 9 .

 9 Figure I-9. Main metabolic pathways for the production of microbial volatiles. VOCs are represented in colored dashed rectangles indicating different chemical classes. Representative examples are given per class: alcohols (ethanol), aldehydes (benzaldehyde), alkanes (undecane), alkenes (1-undecene),aromatic compounds (2-phenylethanol), esters (2-phenylethyl ester), fatty acids (butyric acid), isoprene, lactic acid, lactones (gamma-butyrolactone), methylketones (acetone), monoterpenes (farnesol), nitrogen compounds (benzonitrile), sesquiterpenes (pinene) and sulphur compounds (dimethyl disulphide)[START_REF] Schmidt | Volatile affairs in microbial interactions[END_REF].

Feucherolles

  is located in the northwest of France 35km from Paris (48•52'N, 1•57'E, alt 150 m), in the Yvelines department (Figure II-1).

Figure II- 1 .

 1 Figure II-1. Localization of the QualiAgro site.

Figure

  Figure II-2. Design of the QualiAgro field experiment.

(

  Figure II-3). The flask had a volume of 150 cm 3 and a surface of 15 cm 2 . The flask had a caoutchouc plug with two PEEK tubes, one connected with the PTR-QiTOF-MS and the other connected to the synthetic air (Figure II-3). In Figure II-4 the laboratory system used for the detection of VOCs is shown.

Figure II- 3 .

 3 Figure II-3. Picture of the flasks used during the second and the third experiment.

Figure II- 4 .

 4 Figure II-4. Laboratory system used for the detection of the VOCs from the microcosms.

Figure

  Figure II-6. Preparation of the soil suspensions.

  4 mm diameter ceramic beads and 4 glass bead of 4 mm diameter and 5 mL of a solution containing 100 mMTris (pH 8.0), 100 mMEDTA (pH 8.0), 100 mM NaCl, and 2% (wt/vol) sodium dodecyl sulfate on mixing in a 15 mL Falcon tube. Then, we proceed homogenizing the samples in a FastPrep-24 (MP-Biomedicals, Santa Ana, CA, USA) during 90 s and incubated for 30 min at 70 °C before centrifugation at 7000 g for 5 min at 20 °C. The deproteination was achieved by collecting 1 mL of the supernatant and incubating for 10 min on ice with 1/10 volume of 3M potassium acetate (pH 5.5) and centrifuged at 14.000 g during 5 min. The precipitation of the proteins was performed with one volume of ice-cold isopropanol. The last step of the extraction consisted of washing the nucleic acid with 70% ethanol. DNA concentrations of crude extracts were determined by electrophoresis in 1% agarose gel stained with ethidium bromide using a calf thymus DNA standard curve, and used as estimates of microbial biomass[START_REF] Dequiedt | Biogeographical patterns of soil molecular microbial biomass as influenced by soil characteristics and management[END_REF]. After quantification, nucleic acids were separated from the residual impurities, particularly humic substances, by centrifuging through two types of minicolumn. Aliquots (100 µl) of crude DNA extract were first loaded onto polyvinyl polypyrrolidone minicolumns (BIORAD, Marne-la-Coquette, France) and centrifuged at 1000 × g for 4 min at 10 °C. The eluates were then purified using the Geneclean turbo kit (Mp Biomedicals, Illkirch, France)(Ranjard et al., 2003). DNA concentration in each sample was fluorometrically quantified with the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, CergyPontoise, France) following the manufacturer's instructions.II.6.2 Quantitative PCR (qPCR)The polymerase chain reaction (PCR) is an in vitro molecular biology method for DNA or RNA amplification. The quantitative real-time PCR is a technique used to monitor the progress of a PCR and quantify the amount of PCR product in real-time (DNA or RNA).The real-time PCR is based on the detection of the fluorescent molecules that bind to the double-strained DNA. In this study, we used the SYBR © Green detection system, which consists in the most common fluorescent dye that binds by intercalating between the DNA bases. The fluorescence can be measured at the end of each amplification cycle determining the quantity of amplified DNA. In this work, we used the quantitative real-time PCR in order to quantify 16S (bacterial quantification) and 18S (fungi quantification) ribosomal DNA (rDNA) sequences. It was thus possible to estimate the fungal bacterial ratio (F:B). Bacterial and fungal quantitative PCR assays were performed using a StepONE (Applied Biosystems, Courtaboeuf, France) with a SYBRGreen® detection system. The primers used for the bacterial and fungi quantification are reported in

  CGA TAA CGA ACG AGA CCT-3' (Prévost-Bouré et al., 2011) FR1 5'-ANC CAT TCA ATC GGT ANT-3'

  scheme summarizing the PTR-QiTOF-MS technique is shown in Figure II-7.

Figure

  Figure II-7. Scheme of the PTR-QiTOF-MS technique.

Figure

  Figure II-8. First chamber of the ion source.

  selecting the peaks one by one in order to include the largest number of peaks. An example of VOCs spectrum is reported in Figure II-9. For more details concerning the peak, selection see paragraph Peak detection of the mass spectra and mass calibration pg. 59.

Figure II- 9 .

 9 Figure II-9. Example of a spectrum detected from soil sample.

Figure

  Figure II-10. Image of the LabVIEW software developed for the selection of the stable signals used to calculate the average spectrum.

  Samples were collected in the QualiAgro site, a field station taking part of the SOERE-PRO-network (https://www6.inra.fr/qualiagro_eng/Nos-partenaires/The-SOERE-PROnetwork). The QualiAgro agronomic set up which is described in this study started in September 1998 and lasted until September 2015. The QualiAgro site is located at Feucherolles in northwestern France (35 km west of Paris; 48•52'N, 1•57'E, alt 150 m), on a silt loam textured soil. The soil of QualiAgro site of Feucherolles is classified as a hortic glossic Luvisol (IUSS Working Group WRB, 2014), representative of the Parisian Basin. The main characteristics of these soils are represented by the lack of clay, a silt-loam texture

  Figure III-1 represents the design of the measurement scheme. An air flow (𝑄 𝑎𝑖𝑟 ) of 0.2 L min -1 (equivalent volumetric flow at 0°C and 1 atm) of dry synthetic air (Alphagaz 1 Air: 80% nitrogen, 20% oxygen, 99.9999%, Air Liquide®) was passed through a hydrocarbons and humidity filter (Filter for fuel gas, final purity=99.999%, Restek®) and a Hydrocarbon Trap (Supelco, Supelpure® HC) prior to injection in the chambers. A mass flowmeter (Bronkhorst® model F-201CV, accuracy:

Figure

  Figure III-1. Design of the experimental set-up. The chamber on the left is the empty chamber used as a zero, the one to the right is the chamber used to detect the emissions released from the samples.

  80°C, and the drift voltage to 1000 V, while the extraction voltage at the end of the tube (UDx) was 44 V. The E/N ratio (Electric field/density of natural particles) was 132 Td where 1 Td is 10 V m 2 . These parameters were controlled in order to maintain constant ionization conditions within the drift tube. The setup of the time of flight timing was: TOF extraction period 40000 ns, pulse width 2000 ns, trigger delay 100 ns. The number of channels was 240.000. This gave a mass spectrum measurement up to 510 m/z. The measurement period was set to 1 s, which means that each sample corresponded to 60 acquisitions of 25000 individual spectra. Raw PTR-QiToF-MS data were recorded by TofDaq software (Tofwerk AG, Switzerland).

Figure

  Figure III-2. Average soil characteristics analysis for the different treatments. DM= dry matter, CEC= cation exchange capacity, CN ratio= carbon/nitrogen ratio, Ntot= total Nitrogen, OC= organic carbon, M= organic matter. MSW: Municipal solid waste compost, GWS: Green waste and sludge compost, BIOW: Bio-waste compost, FYM: farmyard manure, CN: control without organic inputs. Point= median, boxes= interquartile, whiskers= minimum and maximum.

  treatment with the lowest total flux rate MSW. The other three treatments had nonsignificantly different total VOC emissions (ANOVA and a post hoc Tukey test with pvalue < 0.05). Moreover, the Shannon diversity indexes (Figure III-3b) were similar in all treatments, though slightly smaller in BIOW indicating that BIOW less compounds contribute to the overall VOC flux.

Figure

  Figure III-3. (a) Total VOCs emission rates per soil treatment. Bold line=median, boxes= interquartile, whiskers= minimum and maximum. Letters indicate significant differences according to the Tukey test with p.value>0.05. (b) Shannon diversity index of the VOC signature indicating the diversity of VOCs emitted. BIOW= bio-waste compost, MSW= municipal solid waste compost, CN= control without organic input, FYM= farmyard manure, GWS= green waste and sludge compost.

  VOCs for each treatmentIn order to determine if OWP amended soils affect the profile of VOCs emissions, we performed a Between Component Analysis (BCA). The first two components of the BCA explained 40.2% and 33.8% of the total dataset variability. In Figure III-4 we observe that GWS and BIOW treatments are isolated from the other OWP (FYM, MSW, and CN), which means that they have a VOC profile which is different from the other OWP. The VOCs that most explained the variance in the two first components of the BCA were selected as those having a coordinate larger than 0.9 on the first component and 0.5 on the second one (in absolute value). The m/z and a tentative identification of these VOCs are shown in Figure III-4. Furthermore, an ANOVA analysis was performed in order to determine the VOCs that significantly differentiated the treatments. A Tukey test was performed on those compounds to detect significance in means differences. The emission rates of the 21 compounds statistically different are plotted in Figure S1. Table III-1summarises the most emitted VOCs and those highlighted by the BCA and the ANOVA analysis. These 49 compounds listed in Table

Figure III- 4 .

 4 Figure III-4. Effect of OWP on the VOCs emissions. The m/z of the compounds that are most explaining the variance in the two first components are shown on the graph. The eigenvalues show the percentage of the variance explained by the 4 first components (the percentage is also shown on each ax).

  could hence argue that BIOW promotes bacterial-mediated VOCs emissions as shown by the dominance of alkenes and terpenes, and also by the larger total VOC emissions (Figure III-3a). Since BIOW has also the lowest VOC diversity index (Figure III-3b); this suggests that BIOW may promote more specific VOC emission processes than other treatments.

(

  n°1662C0020). The Qualiagro site conducted in partnership with Veolia forms part of the SOERE-PRO integrated as a service of the infrastructure AnaEE-France, overseen by the French National Research Agency (ANR-11-INBS-0001). The QUALIAGRO field experiment forms part of the SOERE-PRO (network of long-term experiments dedicated to the study of impacts of organic waste product recycling) certified by ALLENVI (Alliance Nationale de Recherche pour l'Environnement) and integrated as a service of the ''Investment d'Avenir'' infrastructure AnaEE-France, overseen by the French National Research Agency (ANR-11-INBS-0001)'.

  ). For fungi, the Shannon index revealed a larger number of fungi phyla in the higher dilution level, which was unexpected (Figure IV-S1). In our microcosms the number fungi:bacteria ratio was, however, smaller compared to the number of fungi to bacteria ratio which was lower than previously observed in similar studies which was always higher than 0.35[START_REF] Maron | High microbial diversity promotes soil ecosystem functioning[END_REF] (Figure IV-S2).

Figure

  Figure IV-1. Shannon index for bacteria in the soil for each dilution level. D0: microbial dilution equal to 1. D1: microbial dilution equal to 10 -3 . D2: microbial dilution equal to 10 -5 . Bold line=median, boxes= interquartile, whiskers = minimum and maximum. Point = Shannon Index of each sample. Letters indicate significant differences according to the Tukey test with p-value > 0.05.

  Acidobacteria showed the opposite behavior Figure IV-2a. Concerning fungi, this was not the case: Basidiomycota, Chytridiomycota, Glomeromycota, and Neoclimastigomycota were more abundant in the highest dilution samples than in the although they were not the most abundant fungi in D0 (Figure IV-2b). The contrary was observed for Ascomycota and Blastoclamidiomycota which were the most present in D0.

Figure

  Figure IV-2. Relative abundances of the phylum. (a) Relative abundances of the bacterial phyla. (b) Relative abundances of the fungal phyla. D0: microbial dilution equal to 1. D1: microbial dilution equal to 10 -3 . D2: microbial dilution equal to 10 -5 .

(

  Figure VI-S3, Tukey test on OWPs). This meant that the effect of microbial dilution in driving soil VOC emissions overpasses the effect of OWPs amendment. The VOCs diversity (Shannon index on VOCs emissions) was lower in the highest dilution, meaning that the larger total emission rates were combined with a lower number of VOCs emitted (Figure VI-S4).

Figure

  Figure IV-3. Total VOCs emissions rate per microbial dilution in soil. Bold line=median, boxes= interquartile, whiskers = minimum and maximum, empty points (white) = outliers, points (grey) = value of the total VOCs emissions for each sample. Letters indicate significant differences according to the Tukey test with p.value >0.05. D0: microbial dilution equal to 1. D1: microbial dilution equal to 10 -3 . D2: microbial dilution equal to 10 -5 .

  Figure IV-4. Effect of the different microbial dilutions on VOCs emissions by soil. The m/z of the 20 compounds that are explaining the variance in the two first components are shown on the graph. The percentage of the variance explained by the 2 first components is shown on each axis. The ellipses represent the similarity between samples. Samples in the same ellipsis are more similar than samples displayed in two different ellipses. D0: microbial dilution equal to 1. D1: microbial dilution equal to 10 -3 . D2: microbial dilution equal to 10 -5 .

Figure

  Figure Correlation coefficients between VOCs emission rates and phyla abundance. Spearman correlation between log (emission rate + c) and log (phyla abundance + 1). The constant c was tuned to get always positive values. VOCs are named as ion mass to charge ratios (m/z). VOCs for which at least one correlation coefficient was larger than 0.6 with phyla are displayed.

  Samples were collected in the QualiAgro site, a field station taking part of the SOERE-PRO-network (https://www6.inra.fr/qualiagro_eng/Nos-partenaires/The-SOERE-PROnetwork). The QualiAgro agronomic set up which is described in this study started in September 1998 and lasted until September 2015. The QualiAgro site is located at Feucherolles in northwestern France (35 km west of Paris; 48•52'N, 1•57'E, alt 150 m), on a silt loam textured soil. The soil is classified as a hortic glossic Luvisol (IUSS Working Group WRB, 2014), representative of the Parisian Basin. The main characteristics of these soils are

(

  341F: 5′ -CCT ACG GGA GGC AGC AG -3′ and 515R: 5′ -ATT ACC GCG GCT GCT GGC A -3′) (López-Gutiérrez et al. 2004) and 2 ng of template DNA. The PCR conditions consisted of an initial step of 15 min at 95°C then 35 cycles of 15 s at 95°C, 30 s at 60°C, 30 s at 72°C and 20 s at 80°C. The 16S rDNA gene from a pure culture of Pseudomonas aeruginosa PAO was used as a standard for the bacterial quantitative PCR assay. Soil fungi were quantified using 1.25 µM of each primer (FR1: 5′-AIC CAT TCA ATC GGT AIT-3′, and FF390: 5′-CGA TAA CGA ACG AGA CCT-3′) (Chemidlin Prévost-Bouré et al. 2011), and 2 ng of template DNA. The PCR conditions were: an initial step of 10

A

  principal component analysis (PCA, Package Ade4 Version 1.0.153 -© 2009-2017 R Studio) was performed to see the different VOCs compounds differentiating the three dilution levels. The Shannon index of diversity (Figure 3b) was calculated with the diversity function of the vegan package (version 2.4-3) in the R software (version 3.2.3). The diversity index was calculated as 𝐻 = ∑ 𝐸 𝑉𝑂𝐶 log (𝐸 𝑉𝑂𝐶 ) 𝑉𝑂𝐶

  ) was calculated with the diversity function of the vegan package (version 2.4-3) in the R software (version 3.2.3). The diversity index was calculated as 𝐻 = ∑ 𝐸 𝑉𝑂𝐶 log (𝐸 𝑉𝑂𝐶 ) 𝑉𝑂𝐶 , where the sum is over all VOCs recorded in the mass table.

  After six weeks incubation, microbial biomass was not significantly different between the different microbial dilution levels (Figure V-1), while the Shannon index showed a significant decrease along the different microbial dilution levels (p.value < 0.05). The Shannon index with the microbial biomass results confirmed the efficiency of the diversity manipulation (Figure V-2).

Figure V- 1 .

 1 Figure V-1. Microbial biomass for the different dilution levels. Bold line=median, boxes= interquartile, whiskers = minimum and maximum. Letters represent the significant differences according to the Tukey Test p-value > 0.05.

Figure V- 2 .

 2 Figure V-2. Shannon index of the microbial diversity as a function of the dilution level for samples receiving GWS amendment (CN+GWS and GWS+GWS) and samples without any supplementary OWP addition (CN and GWS). Bold line=median, boxes= interquartile, whiskers = minimum and maximum, white point= outliers. Letters represent the significant differences according to the Tukey Test p-value > 0.05.

Figure V- 3

 3 and Figure V-4). According to the Tukey test, this shift was statistically different for Bacteroidetes and Proteobacteria phyla. The relative abundance of the Bacteroidetes statistically increased while Proteobacteria statistically decreased among the dilution levels in CN+GWS and GWS+GWS samples compared to the relative abundance recorded in CN and GWS, respectively. Furthermore, a higher presence of Bacteroidetes was reported in CN+GWS and GWS+GWS than CN and GWS, respectively, for all the dilution levels. For the other phyla, no statistical shifts concerning the microbial relative abundance were reported after the addition of GWS amendment. The bacterial structure shift caused by the OWPs amendment was confirmed by the PCA (Figure V-S1 and Figure V-S2).

Figure V- 3 .

 3 Figure V-3. Relative phyla abundance from (a) CN= control without organic input and (b) CN+GWS=control without organic input and green waste and sludge amendment microcosms receiving a GWS amendment for the three dilution levels.

Figure V- 4 .

 4 Figure V-4. Relative phyla abundance of microcosms (a) GWS=green waste and sludge and (b) GWS+GWS= green waste and sludge receiving a new GWS amendment for the three dilution levels.

Figure V- 5 .

 5 Figure V-5. Total VOCs emission rates per soil treatment and dilution rate after the GWS amendment. D0= microbial diversity pure or 1, D1= microbial dilution diversity equal to 10 -3 , D2= microbial dilution diversity equal to 10 -5 . Black vertical bars= standard deviation, black points= outliers, Bold line =median, boxes= interquartile. Letters represent the statistical differences according to the Tukey test.

Figure V- 7 .

 7 Figure V-7. Shannon index calculated along the incubation hours after the fresh amendment with GWS. D0= microbial diversity equal to 1, D1= microbial dilution diversity equal to 10 - 3 , D2= microbial dilution diversity equal to 10 -5 .

Figure V- 8 .

 8 Figure V-8. Linear regression of the 6 most emitted compounds. m/z 121.101 (tentatively identified as propylbenzene, isopropylbenzene or 1,3,5trimethylbenzene), m/z 93.066 (Toluene), m/z 135.113 (p-cymene), m/z 79.049 (benzene), m/z 119.101 (Indane) and m/z 105.079 (Styrene)

  present work aimed at the characterization of the VOCs emissions from soils amended with OWPs and to define the impact of different microbial dilution levels on the VOCs emissions from soil. In order to achieve the purpose, we performed three experiments. The first one was focused on the influence of the different type of OWPs in soil on the VOCs emissions. The second experiment concerned the study of the relationship between the VOCs emissions and the microbial diversity level in soils amended with the different type of OWPs. Finally, with the third experiment, we aimed to analyze the VOCs emissions short-term dynamics following OWP amendment, from different microbial dilution levels in soil and for a microbial community used to receive OWP and a one that never received OWPs amendment. Within these three experiments, three different timings between the OWPs application and sampling occurred: two years for the first experiment, one year for the second one and 49 hours for the third one. The results concerning the chapters as separate works have been discussed in detail in the previous single chapters. For this reason, in the following paragraphs, we will focus on giving a global overview of the whole dataset from the three experiments. This global overview will concern:

Figure

  Figure VI-2. Total VOC emissions rate for the different microbial dilution levels. DX=nonmanipulated soil used in the first experiment, D0: microbial dilution equal to 1. D1: microbial dilution equal to 10 -3 . D2: microbial dilution equal to 10 -5 . Bold line=median, boxes= interquartile, whiskers = minimum and maximum, white point = outliers. Letters indicate significant differences according to the Tukey test with p.value <0.05.

Figure

  Figure VI-4. Total VOC emissions rates for the different OWPs amendment in the soil. MSW: Municipal solid waste, GWS: Green waste and sludge, BIOW: Bio-waste, FYM: farmyard manure, CN: control without organic inputs. Bold line=median, boxes= interquartile, whiskers = minimum and maximum, white point = outliers. Letters indicate significant differences according to the Tukey test with p.value<0.05

Figure

  Figure VI-5. Total VOC emissions rate for the different OWPs amendment in the soil. MSW: Municipal solid waste, GWS: Green waste and sludge, BIOW: Bio-waste, FYM: farmyard manure, CN: control without organic inputs. Bold line=median, boxes= interquartile, whiskers = minimum and maximum, white point = outliers. Letters indicate significant differences according to the Tukey test with p.value < 0.05

  emissions are also influenced by several abiotic factors, we can also propose to study the effect of the microbial dilution levels as a function of temperature, soil pH, soil moisture, soil porosity, and organic matter content. Furthermore, VOCs emissions by soil are strictly related to the microbial enzymatic activity (i.e., sugar degradation). It could be thus interesting to perform enzymatic soil analysis. The analysis of the enzymatic activities allows the evaluation of soil processes for the degradation of cellulose, hemicelluloses, lignin, chitin, etc. by microorganisms. Furthermore, enzymes, give information about cycles and mobilization of the principal nutrient such as N, P and S. The complementary information obtained with the study of the enzymatic activity in soil will help to draw a bigger picture of the microorganisms role on the nutrient cycles in soil and the link with the VOCs emissions.With the characterization of the emissions by the OWPs in soil we can raise awareness on the role played by the flux of VOCs emitted from soil on the atmospheric reactions involving VOCs. Hence, an interesting follow up of the present work will be the evaluation of the impact on atmospheric chemistry of the VOCs emitted from OWPs in soil. We can propose an analysis of the role played by the OWPs emissions on the SOA and O 3 formation. We can also imagine that a similar work could be carried out for the different levels of microbial diversity in soil in order to clarify the real impact of these VOCs emissions on atmospheric reactions.

Figure

  Figure III-S1. Emission rates (in nmol s -1 g -1 DM x 10 4 ) per treatment for VOCs identified with the ANOVA test. MSW: Municipal solid waste, GWS: Green waste and sludge, BIOW: Bio-waste, FYM: farmyard manure, CN: control without organic inputs. Each plot label shows m/z of the VOC. Letters indicate significant differences according to the Tukey test with p.value < 0.05. Only VOCs with significant differences are shown.

Figure

  Figure III-S2. Relative abundance of chemical classes found for each treatment.

Figure

  Figure III-S3. Total VOC emissions as a function of the product of the soil pH (or -log[H + ]) with the organic matter.

Figure

  Figure IV-S2. Fungi/Bacteria ratio. Bold line=median, boxes= interquartile, whiskers = minimum and maximum. Letters indicate significant differences according to the Tukey test with p.value >0.05.

Figure

  Figure IV-S3. Total VOCs emission rates per soil treatment. BIOW= bio-waste compost, MSW= municipal solid waste compost, CN= control without organic input, FYM= farmyard manure, GWS= green waste and sludge compost, D0= microbial diversity pure or 10 0 , D1= microbial dilution diversity equal to 10 -3 , D2= microbial dilution diversity equal to 10 -5 .

Figure

  Figure IV-S5. (a) Shannon index for bacteria in soil per OWPs. Bold line=median, boxes= interquartile, whiskers = minimum and maximum. Point= value the Shannon Index for each sample. Letters indicate significant differences according to the Tukey test with p.value >0.05. (b) Shannon index for VOCs emissions in soil per OWPs. D0= microbial diversity pure or 10 0 , D1= microbial dilution diversity equal to 10 -3 , D2= microbial dilution diversity equal to 10 -5 .

Figure

  Figure V-S1.Comparaison of the microbial structure before (CN samples) and after GWS amendment (CN+GWS). a) Microbial community structure of CN samples. b) Microbial structure for the CN+GWS. D0= microbial dilution diversity equal to 1, D1= microbial dilution diversity equal to 10 -3 , D2= microbial dilution diversity equal to 10 -5 .

  ......................................................TABLE VI-1. MEAN OF THE TOTAL VOCS EMISSIONS RATE FOR EACH MICROBIAL DILUTION LEVELS AND THE NON-MANIPULATED SOIL. DX=NON-MANIPULATED SOIL USED IN THE FIRST EXPERIMENT, D0: MICROBIAL DILUTION EQUAL TO 1. D1: MICROBIAL DILUTION EQUAL TO VOCS EMISSIONS MEAN STATISTICALLY DIFFERENT ACCORDING TO THE T-TEST FROM DX TOTAL VOCS EMISSIONS MEAN (P.VALUE<0.05). ......................................................................................

  Table I-2.

	Microbial group	Process	Ecosystem service	Ecosystem service category
			Decomposition, nutrient
	HETEROTROPHIC	Organic matter breakdown,	recycling, climate	Supporting and
	BACTERIA/ARCHAEA	mineralization	regulation, water	regulating
			purification
	PHOTOAUTOTROPHIC BACTERIA	Photosynthesis	Primary production, carbon sequestration	Supporting and regulating
	CHEMO(LITHO)AUTOTROP HIC	Specific elemental transformations (e.g., NH+4, S-2, Fe+2,CH4oxidati on)	Nutrient recycling, climate regulation, water purification	Supporting and regulating
	UNICELLULAR PHYTOPLANKTON	Photosynthesis	Primary production, carbon sequestration	Supporting and regulating
	ARCHAEA	Specific elemental transformation (e.g., metals, CH4 formation, NH4 + oxidation), often in extreme habitats.	Nutrient recycling, climate regulation, carbon sequestration	Supporting and regulating
	PROTOZOA	Mineralization of other microbes	Decomposition, nutrient recycling, soil formation	Supporting
			Decomposition, nutrient
	FUNGI	Organic matter breakdown and mineralization	recycling, soil formation, primary production (i.e.,	Supporting
			mycorrhizal fungi)
	VIRUSES	Lysis of hosts	Nutrient recycling	Supporting
	ALL	Production of metabolites (e.g., antibiotics, polymers), degradation transformation, and rearrangement of xenobiotics, genetic	Production of precursors pharmaceutical products to industrial and	Provisional
		Enormous diversity, versatility,	Educational purposes,
	ALL	environmental and biotechnological	getting students	Cultural
		applications	interested in science

Table I -

 I 2. Ecosystem services and major functions of the microorganisms living in soil

Table I-3, however, summarizes

  the main items of the three experimental setups underlining this work.

					NUMBER OF
	CHAPTERS TITLE	SITE	EXPERIMENTAL CONDITIONS	SOIL MANIPULATION	SAMPLES (N) AND REPLICATES
					(R)

MICROBIAL DIVERSITY ANALYSIS VOCs EMISSIONS ANALYSIS TYPES OF OWPs DELAY AFTER THE LAST OWPs APPLICATION MEASUREMENTS Chapter III - Profiles of volatile organic compound emissions from soils amended with organic waste products

					 Total VOCs	GWS	2 years	VOCs: PTR-QiTOF-MS
					emissions	MSW
		  	Dried; 2mm sieved; 60% WHC	N=15, R=3	 Most emitted compounds  ANOVA  Shannon	FYM BIOW Vs CN
					index
					 BCA
		Dynamic chambers
	QualiAgro	under laboratory
		conditions

Chapter IV - Volatile organic compounds emissions from soils are linked to the loss of microbial

					Microbial	 Total VOCs	GWS	1 year	VOCs: PTR-QiTOF-MS
					Biomass;	emissions	MSW
					Ratio F/B;	 Most emitted	FYM	MICROBIAL
					Shannon index;	compounds;	BIOW	COMMUNITY:
			N=45, R=3		Bacterial and	 ANOVA	Vs	quantitative PCR and
	diversity	 	Dried; 2mm sieved;		fungi relative abundance	 Shannon index;	CN	High throughput sequencing
			60% WHC;			 PCA;
	Chapter V -		Sterilized;
			re-inoculated with a
			soil suspension of
			the same non-
			sterilized soil;
			6 weeks incubation

Microbial VOCs dynamics after green waste and sludge amendment

		Microbial	 Total VOCs	GWS	49h	VOCs: PTR-QiTOF-MS
		Biomass;	emissions;	Vs
		Shannon index;	 Most and	CN		MICROBIAL
		Bacterial	less emitted			COMMUNITY:
		abundance.	compounds;			quantitative PCR and
	N=18, R=3		 Shannon index;			High throughput sequencing
			 VOCs
			emissions
			dynamic
			along time.

Table I -

 I 3. Summary of the performed experiments. GWS= green waste and sludge, MSW=municipal soil waste, FYM=farmyard and manure, BIOW=biowaste, CN=control without organic input, ratio F/B= ratio Fungi/Bacteria.

Chapter II Materials and methods II. Materials and methods II.1 Site description

	The experimental site was situated in the Plain of Versailles, at Feucherolles. The site
	name is QualiAgro and it takes part of the SOERE-PRO-network 7 . SOERE-PRO is a network
	of experimental field sites for long duration experiments. The aim of the SOERE-PRO
	network was to observe the effects of organic waste products (OWPs) amendment on
	different crops. QualiAgro site was subdivided on plots allowing the study of the evolution in
	a long-term scale of soil, plants, air, and OWPs of the amended ecosystem.

Table II

 II

-2. Primers used for the quantification of fungi and bacterial DNA

Table II

 II

-3. Primers used for sequencing fungi (18S rRNA) and bacteria (16S rRNA).

Table S2 in supplementary material).

	We used the following primary ions mass: 21.02 m/z (H 3 18 O + , multiplier factor 487.56), 38.03
	m/z (water cluster H 2	18 O•H 3	18 O + with two 18 O isotopes, multiplier factor 819.51) and mass
	55.03 m/z (H 2 O•H 3 O + •H 3 O + multiplier factor 1.0008).

  Tukey post hoc test were performed. Finally, the Between Class Analysis (BCA, Package

	Ade4 Version 1.0.153 -© 2009-2017 R Studio) was performed. The ANOVA and BCA were

specifically used to identify the VOCs that differentiate between each OWP treatment. The Between-Class analysis is a particular case of a Principal Component analysis, it calculates the axes maximizing the covariance between the different treatments analysed. The Shannon index of diversity (Figure

3b

) was calculated with the diversity function of the vegan package (version 2.4-3) in the R software (version 3.2.3). The diversity index is calculated as 𝐻 = ∑ 𝐸 𝑉𝑂𝐶 log (𝐸 𝑉𝑂𝐶 )

𝑉𝑂𝐶

, where the sum is over all VOCs recorded in the mass table.

m/z Most likely formula Tentative identification Class of chemical compounds Detected in BCA or ANOVA Emission rates ± sd ( nmol s -1 g -1 (DW) ) x 10 4

	77.05 C 3 H 8 O 2 111.08 C 7 H 10 O 42.00 C 2 OH	Hydroxymethyl		X X X	Average ± sd emission 12 ± 2.6 0.056 ± 0.011 0.061 ± 0.007 0.069 ± 0.014 0.083 ± 0.011 0.066 ± 0.022 12 ± 1.9 0.082 ± 0.011 0.13 ± 0.031 0.1 ± 0.02 0.098 ± 0.02 0.08 ± 0.02 0.064 0.001 ± 0.001 0.002 ± 0.002 NA NA NA 0.041 ±	Averaged percentage contribution 0.36% 0.35% 0.001%
	63.02 C 2 H 6 S 111.07	Dimethysulfide	Organosulfur	X X	rates 1 0.049 ± 0.018 0.024 ± 0.014 0.043 ± 0.009 0.039 ± 0.01 0.039 ± 0.014 9.9 ± 2.4 (µg m -2 h -1 ) 0.044 ± 0.008 0.048 ± 0.005 0.05 ± 0.011 0.071 ± 0.011 0.05 ± 0.012 9.5 ± 1.9	0.29% 0.27%
	111.04 C 6 H 6 O 2			X	CN 0.044 ± 0.025 0.032 ± 0.018 0.046 ± 0.017 0.032 ± 0.012 0.042 ± 0.026 8.7 ± 1.5 BIOW FYM GWS MSW	0.25%
	59.04 C 3 H 6 O 135.10 C 10 H 14 73.06 C 4 H 8 O 157.11 137.10 C₁₀H₁₆	Acetone p-cymene 2-Butanone, MEK Monoterpenes	Carbonyl/ketone Aromatic Carbonyl/ketone	X X X	9.4 ± 2.4 0.023 ± 0.007 0.032 ± 0.021 0.047 ± 0.016 0.027 ± 0.006 0.05 ± 0.04 8.0 ± 2.7 15.0 ± 4.9 11.0 ± 2.7 10.0 ± 2.7 8.8 ± 1.1 1000 ± 240 6.8 ± 1.9 11.0 ± 3.1 6.7 ± 1.9 6.4 ± 1.4 5.5 ± 0.78 870 ± 0.028 ± 0.003 0.038 ± 0.016 0.033 ± 0.01 0.079 ± 0.012 0.038 ± 0.002 7.9 ± 3.7 260 0.032 ± 0.02 0.025 ± 0.017 0.042 ± 0.021 0.027 ± 0.014 0.03 ± 0.019 6.2 ± 1.3	30.0% 0.23% 0.23% 25.1% 0.18%
	45.03 C 2 H 4 O 57.06 C 4 H 8 75.06 C 3 H 9 NO 47.04 C 2 H 6 O 121.10 C 9 H 12	Acetaldehyde Butene Ethanol Propylbenzene/cumene/mesytilene aromatic Carbonyl/aldehyde N containing compound Alkene Alcohol	X X	3.5 ± 0.85 1.7 ± 0.29 0.049 ± 0.009 0.06 ± 0.012 0.064 ± 0.012 0.064 ± 0.011 0.048 ± 0.014 4.9 ± 1.4 3.7 ± 0.97 3.9 ± 1.2 3.1 ± 0.32 2.3 ± 0.54 1.9 ± 0.6 2.3 ± 0.96 1.6 ± 0.23 0.76 ± 0.12 0.85 ± 0.11 0.76 ± 0.24 0.9 ± 0.44 0.69 ± 0.079 150 ± 16 280 ± 51 180 ± 29 5.9 ± 0.83 0.74 0.023 ± 0.011 0.02 ± 0.008 0.025 ± 0.007 0.021 ± 0.004 0.029 ± 0.016 4.9 ±	8.1% 5.3% 0.17% 4.3% 0.14%
	33.03 CH 4 O 92.05 C 7 H 8	Methanol	Alcohol	X	0.50 ± 0.14 0.02 ± 0.005 0.011 ± 0.005 0.012 ± 0.004 0.011 ± 0.005 0.011 ± 0.003 4.8 ± 1.4 0.43 ± 0.085 0.53 ± 0.12 0.64 ± 0.19 0.47 ± 0.14 110 ± 17	3.1% 0.852%
	115.10 C 7 H 14 O 127.10 C 8 H 14 O	Heptanal/heptanone Octenal/octenone	Carbonyl /ketone Carbonyl/ketone		1.2 ± 0.16 0.017 ± 0.004 0.022 ± 0.004 0.019 ± 0.005 0.055 ± 0.009 0.026 ± 0.005 4.2 ± 2.3 2.0 ± 0.66 1.2 ± 0.27 1.2 ± 0.55 1.1 ± 0.6 100 ± 27	3.0% 0.12%
	43.05 C 3 H 6 129.10 C 8 H 16 O 95.02 C 2 H 6 S 2 87.07 C 5 H 10 O 113.05 C 6 H 12 N	Propene Octanal/Octanone Dimethydisulfide Pentanal, MBO	Alkene Carbonyl /ketone Organosulfur Carbonyl N containing compounds /aldehyde Heterocyclic	X X	0.54 ± 0.14 0.92 ± 0.24 0.026 ± 0.005 0.034 ± 0.008 0.03 ± 0.007 0.046 ± 0.008 0.03 ± 0.01 0.56 ± 0.088 0.44 ± 0.09 0.55 ± 0.22 0.4 ± 0.042 1.3 ± 0.51 1.5 ± 0.5 1.3 ± 0.53 1.0 ± 0.54 0.45 ± 0.24 0.33 ± 0.12 0.39 ± 0.16 0.46 ± 0.12 0.31 ± 0.12 0.015 ± 0.003 0.016 ± 0.002 0.017 ± 0.004 0.021 ± 0.003 0.015 ± 0.005 0.018 ± 0.007 0.01 ± 0.006 0.018 ± 0.004 0.033 ± 0.008 0.019 ± 0.009 3.0 ± 1.3 71 ± 10 66 ± 13 4.1 ± 0.93 59 ± 10 3.1 ± 0.45	2.0% 0.12% 1.9% 1.7% 0.089% 0.088%
	41.03 C 3 H 4 69.06 C 5 H 8 80.03 C 5 H 5 N	Propyne Isoprene Pyridine	Alkyne Terpene compound) organo-compound (N containing	X	0.42 ± 0.11 0.21 ± 0.023 0.29 ± 0.091 0.38 ± 0.1	0.41 ± 0.13 0.41 ± 0.1	0.47 ± 0.23 0.26 ± 0.056	0.36 ± 0.065 ± 4.6 0.41 ± 0.31 42 ± 12	1.3% 1.2%
	93.06 C 7 H 8 94.03 C 6 H 5 O	Toluene	Aromatic	X	0.55 ± 0.08 0.009 ± 0.002 0.008 ± 0.004 0.01 ± 0.001 0.028 ± 0.005 0.012 ± 0.002 2.1 ± 1.3 0.65 ± 0.12 0.55 ± 0.13 0.62 ± 0.17 0.47 ± 0.13 ± 4.9	1.2% 0.060%
	81.07 C 6 H 8 65.05 C 2 H 2 F 2 221.15 71.08 C 5 H 10 63.00 74.06 113.01 C 6 H 12 N	Fragment monoterpenes/Hexenal terpene Pentene Alkene N containing compounds	X X X X	0.13 ± 0.011 0.21 ± 0.036 0.26 ± 0.04 0.3 ± 0.05 0.021 ± 0.002 0.011 ± 0.003 0.02 ± 0.005 0.013 ± 0.002 0.013 ± 0.005 0.15 ± 0.03 0.59 ± 0.12 0.25 ± 0.03 0.31 ± 0.049 0.32 ± 0.087 0.27 ± 0.086 ± 2.9 39 ± 28 2.1 ± 0.56 0.26 ± 0.045 0.39 ± 0.095 0.26 ± 0.06 0.25 ± 0.031 0.23 ± 0.046 34 ± 8 0.008 ± 0.001 0.009 ± 0.003 0.009 ± 0.002 0.013 ± 0.002 0.007 ± 0.001 1.7 ± 0.4 0.27 ± 0.088 0.43 ± 0.14 0.29 ± 0.067 0.29 ± 0.14 0.25 ± 0.15 33 ± 8 0.27 0.007 ± 0.002 0.004 ± 0.001 0.005 ± 0.001 0.004 ± 0.002 0.004 ± 0.001 1.0 ±	1.1% 0.059% 1.0% 0.050% 1.0% 0.94% 0.030%
	60.04 101.00			X	0.54 ± 0.29 0.006 ± 0.003 0.004 ± 0.001 0.008 ± 0.004 0.007 ± 0.002 0.006 ± 0.004 0.53 ± 0.16 0.41 ± 0.18 0.5 ± 0.26 0.41 ± 0.12	32 ± 4 0.24 1.0 ±	0.9% 0.029%
	43.01 C 2 H 2 O 91.06 C 4 H 10 S 136.02 93.03 C 6 H 4 O 110.06 C 6 H 7 NO Benzamide Ethenone Diethyl sulphide	Carbonyl /ketone Organosulfur containing Amide (N	X X X	0.14 ± 0.041 0.068 ± 0.04 0.13 ± 0.022 0.12 ± 0.046 0.12 ± 0.036 0.1 ± 0.021 0.1 ± 0.055 0.12 ± 0.016 0.39 ± 0.057 0.16 ± 0.018 27 ± 19 30 ± 7 0.011 ± 0.001 0.006 ± 0.004 0.011 ± 0.002 0.012 ± 0.002 0.008 ± 0.002 0.99 ± 0.25 0.24 ± 0.044 0.35 ± 0.087 0.27 ± 0.054 0.25 ± 0.045 0.24 ± 0.029 ± 4.7 0.39 0.003 ± 0.001 0.003 ± 0.0 0.004 ± 0.001 0.008 ± 0.001 0.005 ± 0.001 0.83 ±	0.85% 0.029% 0.77% 0.77% 0.024%
	63.04 C 2 H 6 O 2 157.15 76.03 C 2 H 5 NO	1,2 Ethanediol	Diol compound) compound N containing	X X	0.3 ± 0.15 0.14 ± 0.036	0.36 ± 0.14 0.2 ± 0.048	0.23 ± 0.069 0.23 ± 0.087 0.23 ± 0.039 ± 4.3 0.18 ± 0.02 0.18 ± 0.034 0.14 ± 0.049 ± 2.9 0.29 48 ±	0.55% 0.50% 0.014%

0.005 ± 0.0 0.001 ± 0.001 0.004 ± 0.003 0.007 ± 0.002 0.003 ± 0.002 0.

1 average and standard deviations over all treatments.

4 Discussion II.4.1 Identification and quantification of VOCs emitted III.4.1.1 Most emitted VOCs: acetone, butanone, acetaldehyde, methanol, butene, and ethanol

  TableIII-1. VOCs identified as most emitted, most contributing to the BCA and most explaining the variance in the ANOVA test. The compounds are sorted in descending order of average emission rates in mass for all treatments.

III.

  noticeable compounds detected are the organosulfur compounds such as dimethylsulfide, dimethethydisulfide and diethylsulfide. Other studies have reported emissions of these compounds from soils. For instance,[START_REF] Potard | Organic amendment practices as possible drivers of biogenic Volatile Organic Compounds emitted by soils in agrosystems[END_REF],[START_REF] Veres | Volatile Organic Compound emissions from soil: using Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-TOF-MS) for the real time observation of microbial processes[END_REF],[START_REF] Zhao | Volatile organic compound emissions from straw-amended agricultural soils and their relations to bacterial communities: A laboratory study[END_REF],[START_REF] Mayrhofer | Microbial community related to volatile organic compound (VOC) emission in household biowaste[END_REF], and(Raza et al., 2017b)) reported emissions of dimethylsulfide from soils. Dimethethydisulfide is a fungistatic produced by

Organic Waste Product Dilution level Biomass µg DNA g sol -1 ± sd Tukey test Control without organic input

		D0	13.52 ±2.41	a
		D1	12.48 ±2.09	a
		D2	12.69 ±2.30	a
		D0	11.24±1.07	a
	Biowaste	D1	15.83±1.33	a
		D2	17.38±2.77	a
		D0	16.25 ±4.35	a
	Farmyard manure	D1	21.22 ±2.20	ab
		D2	28.52 ±3.48	b
		D0	18.30 ±1.18	a
	Green waste and sludge	D1	20.26 ±4.78	a
		D2	21.99 ±3.77	a
		D0	22.31 ±2.12	a
	Municipal solid waste	D1	21.52 ±1.19	a
		D2	23.97 ±1.39	a
	Table IV-1. Microbial biomass in soil samples. D0: microbial dilution equal to 1. D1: microbial
	dilution equal to 10 -3 . D2: microbial dilution equal to 10 -5 .

Table V -1.

 V

	Treatment		GWS
	Dry Matter (DM)	%	67.2 ± 1.3
	Organic Carbon	g kg -1 DM	257 ± 2
	Total N	g kg -1 DM	22.5 ± 0.5
	P2O5 (Olsen)	g kg -1 DM	0.8 ± 0.1
	C:N		13.2 ± 0.5
	pH (water)		7.7 ± 0.1
	Molecular Biomass µg of DNA g -1 of soil 54.9 ± 19.1

Table V -

 V 1. Main characteristics of the organic waste product used. GWS= green waste and sludge.

DM= dry matter.

Microbial dilution levels Mean of the total VOCs emission rates nmol s -1 g -1 (DM)) x 10 4 % of the Total VOC emissions rates increase from the non-manipulated soil Tukey test

				Significance
				(p.value<0.5)
	DX	40.6		a
	D0	47.1	16%	a
	D1	54.2*	33%	a
	D2	85.4*	110%	b

Table VI -

 VI 1.Mean of the total VOCs emissions rate for each microbial dilution levels and the nonmanipulated soil. DX=non-manipulated soil used in the first experiment, D0: microbial dilution equal to 1. D1: microbial dilution equal to 10 -3 . D2: microbial dilution equal to 10 -5 . *Total VOCs emissions means statistically different according to the T-test from DX total VOCs emissions mean (p.value<0.05).

  VI-5), we can see that for CN and GWS soils, VOCs emissions increased with microbial dilution. For BIOW, MSW, and FYM the lower total VOCs emissions rate was recorded for the low microbial dilution level (D0). MSW soils showed the highest VOCs emissions (followed by BIOW and FYM samples) for the highest microbial dilution level. The MSW_D2 samples were found to have the higher presence of Proteobacteria and Bacteroidetes phyla (FigureVI-6). Those phyla were also found to be positively correlated with all VOCs compounds (chapter IV). We can, therefore, hypothesize that the observed emissions shift in MSW samples can be explained by the high relative abundance of Proteobacteria and Bacteroidetes phyla within the microcosm.The BIOW_D0 reported the lower total VOCs emissions rate. Comparing this result with the relative bacterial abundance, we notice that the most abundant phylum was Firmicutes,

representing 50% of the total bacterial abundance (Figure

VI-6

). Usually, for the other microcosms, Firmicutes relative abundance was between 20% and 7%. At the same time, in BIOW_D0 the relative abundance of Proteobacteria was around 20% while for the other samples, was always higher than 50%. Besides, Bacteroidetes were also less present in BIOW_D0 than in other samples: for the other OWPs and microbial dilution levels, we reported Bacteroidetes relative abundance between 20% and 40%, while in BIOW_D0 was around 4%. Once again, we can hypothesize that the lower presence of the Proteobacteria and Bacteroidetes are responsible for a lower VOCs total emission rate in BIOW_D0.

We can assume that the high microbial diversity dilution level in soil promotes the growth of Proteobacteria and Bacteroidetes phyla (Figure

VI-6

). Those pioneers bacteria phyla, in reduced competitive condition (i.e., high microbial diversity dilution level), colonized the environment quickly than other phyla. Since those phyla are positively correlated with VOCs

Annex A -Supplementary material Chapter III -Profiles of VOCs emissions from soil receiving differing organic waste products

	Letizia Abis 1,2 , Benjamin Loubet 2 , Raluca Ciuraru 2 , Florence Lafouge 2 , Jean-Christohe
	Gueudet 2 Samuel Dequiedt 3 , Pierre-Alain Maron 3 , Sabine Houot 2 , Sophie Bourgeteau-Sadet
	3
	1 INRA, UMR ECOSYS, INRA, AgroParisTech, F-78850 Thiverval Grignon, France,
	Sorbonne Université, UPMC
	2 INRA, UMR ECOSYS, INRA, AgroParisTech, F-78850 Thiverval Grignon, France
	3 INRA, UMR AgroEcologie, AgroSup Dijon, BP 87999, 21079 Dijon cedex, France
	Corresponding author: letizia.abis@inra.fr

Table III -

 III S1. Isotopes and fragments found in the mass spectra by correlation analysis.

	Table III-S2. Standard Ionicon transmission curve used for computing the mixing ratio in equations
		(2) and (3)
	m / z	TR	TR H 3 O + TR R + ⁄
	< 21	0.018	1.000
	33	0.40	0.045
	59	0.65	0.028
	79	0.75	0.024
	107	0.86	0.021
	146	0.96	0.019
	> 181	1.00	0.018
	m/z	correlated m/z	m/z
				difference
	Likely isotopes
	37.01	39.03		2.02
	44.99	45.99		1.00
	106.07	108.08		2.01
	107.08	108.08		1.00
	120.10	121.10	122.10	1.00
	127.10	128.11		1.01
	135.11	136.11		1.00
	137.12	138.13		1.01
	151.14	152.14		1.00
	165.16	166.16	167.16	1.00
	179.17	180.18		1.01
	297.08	299.05		1.97
	355.07	356.08		1.01
	371.10	372.10		1.00
	Likely fragments
	167.17	179.17		12.00
	167.17	181.18		14.01

Table III -

 III S3. Total VOC emission rate and mixing ratio (summed up over all identified VOCs) for each treatment. Mean and standard deviation for each treatment calculated from replicates. BIOW= bio-waste compost, MSW= municipal solid waste compost, CN= control without organic input, FYM= farmyard manure, GWS= green waste and sludge compost.

		units	CN	BIOW	FYM	GWS	MSW
		nmol s -1 g -1 DW × 10 4	42 ± 8	52 ± 12	43 ± 8	39 ± 4	36 ± 8
	Total emission rate	µg C m -2 s -1
	Total mixing ratio at chamber outlet	ppb	1700 ± 310	2100 ± 470	1700 ± 330	1600 ± 150	1500 ± 300

Table IV -

 IV S1.

Most emitted compounds for the different microbial dilution levels D0, D1 and D2

TABLE I -

 I 1. LIFETIME OF THE MOST ABUNDANT VOCS IN THE ATMOSPHERE REACTING WITH OH RADICAL, NO 3 RADICAL O 3 AND DUE TO THE PHOTOLYSIS. NA= NOT AVAILABLE, SD=STANDARD DEVIATION (ATKINSON, 2000; GUENTHER ET AL., 2006; SINDELAROVA ET AL., 2014). ...................................................................... TABLE I-2. ECOSYSTEM SERVICES AND MAJOR FUNCTIONS OF THE MICROORGANISMS LIVING IN SOIL (BODELIER, 2011). ........................................................................................................................................ TABLE I-3. SUMMARY OF THE PERFORMED EXPERIMENTS. GWS= GREEN WASTE AND SLUDGE, MSW=MUNICIPAL SOIL WASTE, FYM=FARMYARD AND MANURE, BIOW=BIOWASTE, CN=CONTROL WITHOUT ORGANIC INPUT, RATIO F/B= RATIO FUNGI/BACTERIA. ............................................................. TABLE II-1. MEAN CHARACTERISTICS OF THE OWPS (OBRIOT ET AL., 2016). ...................................................... TABLE III-1. VOCS IDENTIFIED AS MOST EMITTED, MOST CONTRIBUTING TO THE BCA AND MOST EXPLAINING THE VARIANCE IN THE ANOVA TEST. THE COMPOUNDS ARE SORTED IN DESCENDING ORDER OF AVERAGE EMISSION RATES IN MASS FOR ALL TREATMENTS. .....................................................................................

TABLE IV -

 IV 1. MICROBIAL BIOMASS IN SOIL SAMPLES. D0: MICROBIAL DILUTION EQUAL TO 1. D1: MICROBIAL DILUTION EQUAL TO 10

TABLE III -

 III S1. ISOTOPES AND FRAGMENTS FOUND IN THE MASS SPECTRA BY CORRELATION ANALYSIS. ..........TABLE III-S2. STANDARD IONICON TRANSMISSION CURVE USED FOR COMPUTING THE MIXING RATIO IN EQUATIONS (2) AND (3) ............................................................................................................................. TABLE III-S3. TOTAL VOC EMISSION RATE AND MIXING RATIO (SUMMED UP OVER ALL IDENTIFIED VOCS) FOR EACH TREATMENT. MEAN AND STANDARD DEVIATION FOR EACH TREATMENT CALCULATED FROM REPLICATES. BIOW= BIO-WASTE COMPOST, MSW= MUNICIPAL SOLID WASTE COMPOST, CN= CONTROL WITHOUT ORGANIC INPUT, FYM= FARMYARD MANURE, GWS= GREEN WASTE AND SLUDGE COMPOST. ...................................................................................................................................................................

TABLE IV -

 IV S1. MOST EMITTED COMPOUNDS FOR THE DIFFERENT MICROBIAL DILUTION LEVELS D0, D1 AND D2 ...................................................................................................................................................................

	Etude de l'

effet des épandages avec des produits résiduaires organiques (PROs) et de la diversité microbienne sur les émissions des composés organiques volatiles (COVs) par les sols.

  Les émissions de COVs jouent un rôle central sur la pollution atmosphérique. Les sources biogéniques des COVs sont entre 10 et 11 fois plus élevées que les émissions des COVs provenant par des sources anthropiques. Récemment, l'importance de la caractérisation des flux de COVs par les sols et les microorganismes a été soulignée. En effet, les émissions des COVs provenant du sol et des microorganismes sont des possibles précurseurs des particules atmosphériques et de la formation d'O 3 . En particulier, ce travail est centré sur la caractérisation des émissions de COVs par des sols amendés avec des PROs en détectant les émissions toute de suite après l'apport, 1 an et 2 ans après l'apport des PROs. De plus, l'influence de la diversité microbienne du sol sur les émissions de COV a également été analysée. Les émissions de COVs ont été détectées à l'aide de la technique PTR-QiTOF-MS et la totalité des expériences a été réalisées dans des conditions de laboratoire contrôlées en utilisant des chambres dynamiques pour la détection des COVs émis par les échantillons. Les résultats ont montré que les différentes PROs émettent des quantités COVs variables et que les propriétés chimiques et physiques du sol influençaient également les émissions. L'analyse de l'influence de la biodiversité microbienne sur les émissions de COVs a montré que si la diversité microbienne est plus élevée, les émissions de COVs par les sols sont plus faibles. En outre, la diversité des COVs diminue lorsque les émissions de COVs par le sol sont plus élevées. Enfin, l'étude de la dynamique des émissions de COVs par de microcosmes récemment amendés avec du PRO, a montré que le flux des émissions de COV augmentait dans les premières 49 heures après l'apport des PRO en raison d'une perturbation de la communauté microbienne dans le sol.

	Résumé :

Mots clés : COVs, COVs microbiens, dilution de diversité microbienne, PTR-QiTOF-MS, produits résiduaires organiques

For a 12-h daytime average OH radical concentration of

2.0 x 10 6 molecule cm -3 2 For a 12-h nighttime average NO 3 radical concentration of 5.0 x 10 8 molecule cm -3 3 For a 24-h average O 3 concentration of 7.0 x 10 11 molecule cm -3 4 For overhead sun

Soils with organic content < 3 % are defined mineral soils, soils with organic content >3% and <15%, are classified as mineral soils with organics; when the organic content exceeds 15% but < 30% they are called organic soils.[START_REF] Huang | Changes in volatile production during the course of fungal mycelial interactions between Hypholoma fasciculare and Resinicium bicolor[END_REF]

https://www6.inra.fr/qualiagro_eng/Nos-partenaires/The-SOERE-PRO-network

(To be continued in the next page)

Acknowledgements Acknowledgments

We gratefully acknowledge ANAEE-France that provided the PTR-QiTOF-MS, ADEME for partially funding this study through the projects COV3ER (n°1562C0032) and DICOV (n°1662C0020). The Qualiagro site conducted in partnership with Veolia, forms part of the SOERE-PRO integrated as a service of the infrastructure AnaEE-France, overseen by the French National Research Agency (ANR-11-INBS-0001). The QUALIAGRO field experiment forms part of the SOERE-PRO (network of long-term experiments dedicated to the study of impacts of organic waste product recycling) certified by ALLENVI (Alliance Nationale de Recherche pour l'Environnement) and integrated as a service of the ''Investment d'Avenir'' infrastructure AnaEE-France, overseen by the French National Research Agency (ANR-11-INBS-0001)'.

VOCs emissions using a highly sensitive PTR-QiTOF-MS. Soil microcosms were prepared with three microbial dilution levels on soils amended with organic waste products. VOCs emissions and microbial RNA were measured after six weeks incubation. We found that microbial diversity in soil reduced VOCs emissions and increased VOCs diversity.

Furthermore, we found that Proteobacteria and Bacteroidetes phyla were VOCs emitters while other bacteria were both absorbing and emitting VOCs.

Authors contributions

L.

Abstract

During the last decades, soil amendments with organic waste products (OWPs) have been widely encouraged in Europe in order to improve soil fertility. Short-term effect of the OWPs application causes wide changes on the microbial community structure and diversity. Those changes are known to affect the volatile organic compounds (VOCs) emissions by soil. This work aimed to characterize, in terms of quantity and composition, the effect of the green waste and sludge (GWS) application on soil VOCs emissions and microbial community over a short-term period (49 h after the last GWS application). Moreover, we used two soil types:

one soil used to receive GWS input (GWS soil) and one that never received organic input (CN soil). This two were manipulated to generate a microbial dilution diversity gradient (low, medium and high) as explained in Abis et al., (2018-chapter IV). Results showed that Bacteroidetes phyla took advantage of the GWS application in all samples while Proteobacteria were penalized by the GWS amendment. Microbial structure differences between microbial diversity dilutions levels remained even after GWS application. GWS amendment induced an increase of total VOCs emissions for GWS soil only, while the VOCs Shannon diversity decreased among the incubation hours for both soils. We observed an increase in emissions of the most emitted compounds with GWS application: m/z 121.101

V.2.2.1 Timing of the VOCs measurements

During the 49 hours incubation of the sample receiving GWS addition (CN+GWS and GWS+GWS), 10 VOCs measurements were carried out. The measurements have been performed 1h (T1), 3h (T2), 6h (T3), 9h (T4), 25h (T5), 27h (T6), 30h (T7), 33h (T8), 49h (T9) following the addition of GWS. Furthermore, we performed the VOCs detection just before the GWS addition (T0).

Chapter VII

Conclusions and future perspectives