Selena Sironi

Laura Capelli

Billy Pottier
email: billy.pottier@mines-ales.fr

Veronica Artigue

Jérôme Tixier

Sylvain Olivier

Mathilde Chaignaud

Jean-Louis Fanlo

Odour Nuisance: A New Methodology to Evaluate and Anticipate the Risk

Odour annoyance is the second most common source of complaints after noise in France. This leads to a direct impact on the quality of life of residents and an indirect impact on economic activities. This is why regulations require each emitting activity to take into account the annoyances it could generate (Grenelle II of the environment). It is clear that the forecasting of these impacts represents a strategic issue. In this context, this article presents a new methodology to evaluate and predict the risk of odour nuisance on a territory but also the potential economic or social damage associated. The objective is to provide a planning tool to decision-makers, based on maps of the odour impact and the associated risk of nuisance of an existing or future site. This work follows those conducted on the same theme by Popa (2013). The approach is based on the concept of risk to assess both the potential annoyance of a site and the vulnerability of populations or activities. The annoyance potential is calculated on the basis of the intensity and acceptability of the odour, two messages returned by our olfactory sense. These two parameters are intrinsically linked to the odour concentration. They are therefore calculated in each cell of the studied territory (200x200m) according to the odour concentrations obtained using a Gaussian dispersion software. The risk of odour nuisance is evaluated by combining the potential of annoyance thus calculated and the human vulnerability to odour, defined by the number of inhabitants in the cell. The risk of economic damage is obtained in a similar way, by combining the annoyance potential with the vulnerability of the activities present in each cell. The levels of annoyance potential and associated risks are defined using criticality matrices (Merad, 2004). The results have to be compared with the reports of a panel of residents around the site studied to assess the quality of the model.

Introduction

Since the middle of the 18th century, tolerance to odour problems has decreased significantly. The industrialization of societies has multiplied environmental degradation and increased the apprehension of a health risk [START_REF] Pierrette | L'environnement olfactif : de la perception à la gêne[END_REF]. A certain awareness of environmental issues is developing worldwide, making populations increasingly attentive to the quality of their environment, associating sometimes odours to dangerous atmospheric pollutants [START_REF] Conti | Measurements techniques and models to assess odor annoyance: A review[END_REF]. Even if there is no notion of toxicity behind these unpleasant odours, they have an impact on the image of the emitting activity, on the quality of life of the residents and, to a larger extent, on the functioning of the territory. Odour generated by wastewater treatment plants, composting plants, intensive livestock farming, chemical industry, etc. can lead the local residents to nuisance and complaint action. In this context, several methods have been developed to evaluate odour nuisance. Most of them are a posteriori assessment methods, used to characterize an established situation. The following article focuses on the presentation of a new model able to anticipate odour nuisance. This model is tested around an industrial plant and provides results that must be validated on the field.

Method development 2.1 The odour nuisance considered as a risk

The basic concept in risk assessment is that the overall risk depends on the events probability (hazard) as well as the probable consequences (stakes) if this event occurs (Tixier et al., 2006)(Eq(1)).

To quantify this risk, this equation is transposed as follows (Eq(2)).

This equation translates the hazard and the stakes into a typology which can be quantified and qualified. The risk is translated into an index that can be mapped [START_REF] Tena-Chollet | Development of a spatial risk assessment tool for the transportation of hydrocarbons: Methodology and implementation in a geographical information system[END_REF]. Applied to the odour domain, the risk of odour nuisance becomes the combination of the odour annoyance potential, considered as the probability of exposure with a given intensity (impact), and the stakes as the effect on receptors (local population, economic and social activities) (Eq(3)).

Anticipation of odour nuisance caused by an industry requires an process description, from emission to its effect on local residents. The model is part of the Source-Pathway-Receptor (S-P-R) concept that is now part of the guidelines for environmental risk assessment and management [START_REF] Bull | IAQM Guidance on the assessment of odour for planningversion 1.1[END_REF]. The S-P-R concept describes the potential relationship between the characteristics of the odour source (S), the pathway (P) which represents exposure on the territory and the receptors (R) that could be impacted. This approach considers both the annoyance and the impacted populations/activities. It characterizes both the source (S) and the receptors (R) of the S-P-R concept. To model the impact of the odour on the territory (P), a Gaussian atmospheric dispersion software (Aria Impact) simulates the transport of odorant molecules in the atmosphere and thus the odour concentration on the ground in a defined spatial and temporal domain [START_REF] Pottier | Odour nuisance: Decision support tools to anticipate and represent levels of odour nuisance on a territory[END_REF].

Definition of annoyance potential

Annoyance potential is defined as the capacity of an odour to constitute an annoyance. It describes the impact of the odour according to its intensity, acceptability and frequency in time and space. Its evaluation is based on two of the main information returned by our olfactory sense: the acceptability and the intensity of the odour, which are closely related to the odour concentration. The equation used to calculate a value of intensity and acceptability in each cell is inspired by Weber Fechner's law (Eq(4)). In psychophysics, this law describes the relationship between a mental sensation and the physical magnitude of a stimulus.

Where I is the intensity of the sensation, S the magnitude of the stimulus, k a constant. Applied to odours, the slope coefficient of the line (k) is determined by olfactometry for each gaseous effluent according to the responses of a sniffing team. This method developed by Olentica measures the intensity and acceptability on an open scale [START_REF] Chaignaud | A New method to Evaluate Odour Annoyance Potential[END_REF]. The stimulus magnitude (S) corresponds to the concentration in odour units. This equation is used to calculate an intensity value in each cell (Eq(5)).

Where I is the odour intensity value in the cell, k is the constant measured by Olentica, is the concentration of odour units in the cell and 0.5 is an interception constant (0.5 by definition, since 50% of the population can perceive the odour when the odour concentration is equal to 1 OUE/m

3 ).

The equation used to calculate odour acceptability (Acc) differs slightly from the intensity equation. The interception constant in this case is equal to 0 (at 1 OUE/m 3 the odour quality is not perceptible, so the odour acceptability is equal to 0 (neutral odour)).The slope coefficient (k') is determined in a similar way, based on the answers of a jury. This equation is used to calculate an acceptability value in each cell (Eq(6)).

Using the intensity and acceptability values of the odour calculated in each cell, the annoyance potential is evaluated according to this equation, which gives equal importance to both parameters (Eq(7)).

The discretization of the variables is based on the range of the two parameters (0 to 20) and on the jury's verbalization scale (very weak/ slightly unpleasant odour, weak/ unpleasant odour, etc.). A level of annoyance potential (1 to 10) is calculated for each cell in our study area (200 × 200 m) (Figure 1).

Figure 1: Discretization of odour intensity/acceptability and construction of annoyance potential

Defining vulnerability

Vulnerability is considered as "the propensity of a human, material, environmental or economic stake to be damaged". [START_REF] Renard | An urban vulnerability assessment method applied to the conurbation of Lyon[END_REF]. In the context of a territory subject to an odour annoyance, only human, economic and social stakes are considered. The objective of this method is to enlighten the decision makers on the odour impact of an existing or future industry under the prism of three types of stakes. Instead of providing a global index of the risk of odour nuisance, several thematic maps are proposed to the actors involved: risk of odour nuisance at home, risk of economic prejudice and risk of social prejudice.

Defining vulnerability at home

Applied to odours, human vulnerability can be defined as the sensitivity of an individual to an odorous compound. This sensitivity depends on a set of complex processes (neurosensorial, cognitive, mnesic, social, cultural, etc.) [START_REF] Popa | Approche du potentiel de nuisance et analyse de la vulnérabilité du territoire (Predictive method for the assessment of odor annoyance on a territory submitted to an industrial activity[END_REF]. The vulnerability of human stakes is reduced to a geo-referenced census of the population on the territory. It corresponds to the vulnerability of people when they are at home. A densely populated cell increases the probability that populations complain against the industrialist and the public authorities. The population living in the territory is estimated from the LCSQA data (Laboratoire de Contrôle et de Surveillance de la Qualité de l'Air). This annualized data is based on a precise method using the MAJIC files (cadastral information) to estimate the population at the building scale. [START_REF] Létinois | Méthodologie de répartition spatiale de la population (Methodology for spatial distribution of the population)[END_REF]. This population at home is then aggregated into the 200m INSEE squares, which constitute our analysis grid.

The different levels of vulnerability at home were obtained by dividing the statistical distribution into equal amplitudes. The discretization method is applied from the maximum value observed (2000 inhabitants in a 200×200 m square in a dense urban environment). These matrices, inspired by the assessment of major risks are used to transform a quantitative result into a qualitative result (Figure 2). Where a1, a2, etc. is a weighting factor according to a geometric progression (1, 10, 100, 1000), Nb_Rep1,Nb_Rep2, etc. is the number of responses collected according to each category (a little, moderately, very, extremely bothered) and Nbna the number of people who answered don't know.

In general, it appears that unpleasant odours are not accepted very much, respondents are very sensitive to the presence of an odour, and more particularly at home. In detail, the answers are more nuanced and reveal situations where the presence of an odour is unanimously judged as intolerable (i.e. a negative odour perceived in a restaurant), and others more easily tolerated (i.e. an errand at the tobacconist's). For example, a tennis club or a bakery may have a similar sensibility to the annoyance, but the economic impact on the business will be different. This sensibility variable is associated with an economic variable: the value-added rate of the activity. It is assumed that a high value-added rate indicates an activity more vulnerable to a decline in attendance. A high value-added rate means that the production cycle creates wealth to cover the other expenses of the business related to its economic model: salaries and expenses. According to the large and simplified nomenclature of the INSEE, a multiplicative factor (1 to 3) is attributed to each activity according to the value-added rate of the sector of activity (Table 1). The economic vulnerability is evaluated for each activity according to this equation (Eq(9)).

At the cell scale, the economic vulnerability is obtained by adding up all the activities which are present.

Defining social vulnerability

Social vulnerability represents the impact of the odour annoyance on the functioning of public infrastructure and on those who attend it (school, hospital, public space, etc.).

In the same way as economic vulnerability, a multiplicative factor is associated with the sensibility variable of the public infrastructure, in this case related to the average daily attendance. The vulnerability of a public facility is thus considered to depend on its sensitivity to an odour annoyance (impact on the quality of service and/or its functioning) but also on the number of people affected. Each type of public facility is assigned to one of three main attendance classes (Table 2). 9)).

At the cell scale, the social vulnerability is obtained by adding up all the infrastructure which are present.

Results

Result of the nuisance index at home

The model is being tested in the surrounding of an industrial site, in France. Originally located outside of the city, the site is currently in the heart of a very dense urban area (10,000 inhabitants/km²). The area is therefore particularly vulnerable to the presence of an odour annoyance (Figure 3(a)). Figure 3 (d) illustrates the levels of annoyance potential evaluated over a period of one year. This time scale gives a global representation of the odour impact on the territory and will be completed later by the modelling of particularly penalizing episodes (calm wind, summer, technical problems, etc.). In each cell, the level of annoyance potential is associated with the number of inhabitants to provide the cartography of the risk of odour nuisance at home (Figure 3(e)). The highest levels of nuisance are mainly located around the site, but also in particularly populated city centers and collective housing areas.

Result of the economic damage index

The risk of economic damage is assessed in each cell by combining the annoyance potential (Figure 3(d)) and the economic vulnerability of activities face to an odour annoyance (Figure 3(b)). The aim of this analysis is not to measure or quantify the economic damage but to highlight the cells that are particularly vulnerable. A cell with a high level of risk of economic damage indicates the presence of activities which are particularly vulnerable to the presence of an odour annoyance (hotels, restaurants, etc.), a very high level indicates a clustering of these activities, while a low risk indicates economically less vulnerable activities (Figure 3(f)).

Result of the social prejudice index

The risk of social damage is similarly assessed by combining the annoyance potential (Figure 3(d)) with the vulnerability of public infrastructure face to an odour annoyance (Figure 3(c)). The cartography of the risk of social damage (Figure 3(g)) reveals the most critical infrastructures that are impacted by an odour annoyance. These include schools and health care facilities where the presence of an odour annoyance is considered unacceptable. In contrast, a low value indicates the presence of public administration (town hall, police station, etc.) where the presence of odour is comparatively more acceptable.

Figure 3: Aggregation of annoyance potential with vulnerability and cartography of odour nuisance levels

These maps should be used to support decision-making. They will provide information to local stakeholders and industrialists to anticipate the impact of the settlement of a new odorous industry.

Conclusion

The model integrates the different stages of the process leading to odour nuisance, in accordance with the S-P-R concept. This concept, based on the risk analysis method, provides a spatial and temporal representation of the odour nuisance on a territory. The initial work initiated by Popa led to a reproducible method requiring less data (64 to 4 variables to describe vulnerability). In an attempt to simplify, sensitive variables such as income or property title are no longer taken into account in the vulnerability assessment. This simplification places everyone on an equal footing when faced with an odour annoyance and simplifies the adaptation of the model to other parts of the world.

Only two sets of geographical data are required (OSM, LCSQA). These data are accessible and regularly updated. The OSM data have the advantage of being available on a global scale, whereas the LCSQA data is limited to the French territory. For a global application, population grids exist, but the analysis scale is sometimes a limit (1 km grid) or the data is not free of rights.

An innovation was introduced in the assessment of the annoyance potential, product of the acceptability and intensity of the odour. These two parameters are measured on an open scale for each type of gaseous effluent (Olentica methodology) and then evaluated according to the concentration of odour units in any point of the territory.

To provide a decision support tool for the stakeholders involved, this model must be validated in Maisons-Alfort by confronting the results with the responses of a panel of local residents around the site. It can then be tested in other areas with different populations, types of odours, topography, etc.

There is a paradigm shift from a posteriori measurement of odour nuisance to a preventive approach used as a decision-making tool. This predictive model is intended for stakeholders and industrialists to proactively manage odour problems. This does not mean that other methods should be abandoned. The British Institute for Air Quality (IAQM) therefore recommends using empirical observation tools, where available and applicable, and combining them with a model. Using these different assessment tools in combination can minimise individual limitations and improve the reliability of conclusions.

Figure 2 :

 2 Figure 2: Discretization of vulnerability at home and construction of odour nuisance indexDefining economic vulnerabilityEconomic vulnerability represents the fragility of economic activities which suffer from an odour annoyance (attendance decrease, change in behaviour, etc.). For each activity (economic and social) of the Open Street Map nomenclature (224 activities), a sensitivity value is assigned, indicating the level of tolerance of a bad smell in that location. These values were collected through a questionnaire open to all (365 responses have been collected), providing information on the capacity to bear a nasty odour in various situations which allow us to define a sensibility value and rank all the activities according to their sensibility to odour annoyance. (Eq(8)).

Table 1 :

 1 Allocation of a multiplicative factor according to the nature of the activity and its value-added rate

	Value added rate	Less than 25%	25-40%	40% and more
	Economic factor	1	2	3
	Examples of OSM objects Superstore/supermarket/	Bookstore/butcher	Restaurant/bar/hotel/golf/
		car dealer/furniture store	shop/clothing store, game	amusement park
			store, etc./auto repair

Table 2 :

 2 Allocation of a multiplicative factor according to the public facility and its average daily attendance

	Attendance	Less than 100	Between 100 and 500	500 and more
	Attendance factor	1	2	3
	Examples of OSM objects City Hall/Court/Police	Social center/sports	University/hospital/clinic
			center/jail/ school/college
	Social vulnerability is assessed for each public infrastructure according to this equation (Eq(


M Joly

M Marlet

C Durieu

C Bene

J Launay

P Temple-Boyer

STUDY OF CHEMICAL FIELD EFFECT TRANSISTORS FOR THE DETECTION OF AMMONIUM AND NITRATE IONS IN LIQUID AND SOIL PHASES

Keywords: ion-sensitive field effect transistor, potentiometric sensor, ion-sensitive layers, ammonium NH4 + ion, nitrate NO3 -ion, soil analysis

the development of ChemFET-based sensors for the soil analysis of nitrogen-based ions is described in this work. Focusing on the fluoropolysiloxane (FPSX) polymer-based matrix, nonactin and tetradodecylammonium nitrate (TDDAN) were shown to have the best properties for the detection of ammonium NH4 + and nitrate NO3 -ions respectively. Thus, FPSX-based pNH4-ISFET and pNO3-ISFET microsensors exhibited good detection properties (sensitivity around 56 mV/pX in concentration ranges adapted to soil analysis) and acceptable selectivity to soil main interferent ions (K + , Na + , Ca 2+ , Cl -, H2PO4 2-, SO4 2-,…). Following, the two different ISFET sensors were applied to the in-situ soil analysis. Thus, using standard relative moistures, pH analysis in clay-silt matrixes was demonstrated on a six-month period (compatible with agriculture applications during the fertilization period). Then, experimental studies were successfully extended to the monitoring of nitrogen mineralization through pNH4 measurement, as well as to the analysis of soil nitration processes.

Finally, long-term analyses were performed, showing contradictory result according to the chosen ionophore: a "zero" temporal drift on a six-month period for nonactin and PFSX-based pNH4-ISFET, and a huge temporal drift for TDDAN and PFSX-based pNO3-ones. This work paves the way for future long-term ionic analyses in soil matrix.

Introduction

The nitrogen element, symbol N, is essential for the growth and development of plants. It represents between 1% and 5% of their dry matter [START_REF] Hawkesford | Chapter 6: Functions of macronutrients[END_REF] and plays an important role for the synthesis of their amino-acids, proteins, enzymes and chlorophyll-related molecules [START_REF] Andrews | Do plant needs nitrate ? The mechanisms by which nitrogen forms affect plants[END_REF]. However, despite the abundance of diazote N2 in the atmosphere (volume ratio: 78%), the sources of nitrogen for plants are mainly limited to the "N element" main mineral forms: the ammonium NH4 + and nitrate NO3 - ions, present in soils at different concentration ranges, i.e. 20 -200 µM and 1 -5 mM respectively [START_REF] Owen | Competition for amino acids between roots and rhizosphere microorganisms and the role of amino acids in plant N acquisition[END_REF]. Because of their greater mobility, nitrate ions are more easily absorbed by roots than ammonium ions, which are partly retained in the soil matrix. Nevertheless, both ionic species, as well as their derivative chemicals, are largely involved into the nitrogen cycle that governs the soil-plant system [START_REF] Di | Nitrate leaching in temperate agroecosystems: sources, factors and mitigating strategies[END_REF]. As a matter of fact, the development of nitrogen-based fertilization has become essential for improving production yields as well as agricultural product quality, for example in the frame of wheat culture [5,[START_REF] Liang | Cultivar and N fertilizer rate affect yield and N use efficiency in irrigated durum wheat[END_REF].

From another point of view, the excessive use of nitrogen-based fertilizers in modern farming leads today to a worldwide disruption of the nitrogen cycle: atmospheric and hydrological flows of nitrogen no longer compensate and are finally responsible for the degradation of ecosystems through soil leaching, eutrophication of fresh and marine waters, pollution of groundwaters and drinking waters, emission of ammoniac and nitrogen-oxide based gas,… [START_REF] Billen | The nitrogen cascade from agricultural soils to the sea: modelling nitrogen transfers at regional watershed and global scales[END_REF][START_REF] Billen | Anthropogenic nitrogen autography and heterotrophy of the world's watersheds: past, present and future trends[END_REF][9][START_REF] Masclaux-Daubresse | Nitrogen uptake, assimilation and remobilization in plants: challenges for sustainable and productive agriculture[END_REF] In order to guard against the consequences of agriculture on environment and health, regulations and standards were introduced at a worldwide scale and limit concentrations were defined for ammonium NH4 + and nitrate NO3 - ions in soils. This policy implies the development of analytical methods in order to measure ionic concentrations in soil samples. In this context, the first bottleneck was related to sampling techniques [START_REF] Conklin | Chapter 7: Soil and soil solution sampling, sample, transport and storage[END_REF]. Firstly, they should allow measurements representative of the studied agricultural parcel, while preventing any interferences from soil microorganisms. Secondly, samples must be kept unmodified during transport from field to lab. Thirdly, to be finally analysed, soil samples have to be diluted into aqueous solutions without changing their chemical composition [START_REF] Maynard | Chapter 6: Nitrate and exchangeable ammonium nitrogen[END_REF].

Starting from water-based liquid samples, different analysis techniques were developed in order to measure ammonium NH4 + ions as well as nitrate NO3 -ions concentrations: UV spectrophotometry [START_REF] Norman | Determination of nitrate in soil extracts by dualwavelength ultraviolet spectrophotometry[END_REF], ion chromatography in liquid phase [START_REF] Michalski | Determination of nitrogen species (nitrate, nitrite and ammonium ions) in environmental samples by ion chromatography[END_REF], chemical and electrochemical techniques [START_REF] Jimenez-Jorquera | ISFET-based microsensors for environmental monitoring[END_REF][START_REF] Parvez Mahmud | Recent progress in sensing nitrate, nitrite, phosphate and ammonium in aquatic environments[END_REF]. These last techniques are of particular interest since they allow the development of integrated chemical/electrochemical sensors based on ion-sensitive electrodes (ISE) and/or ionsensitive field effect transistors (ISFET). Thus, potentiometric, amperometric or impedimetric detection methods were thoroughly developed for the detection of nitrogen-based ions [START_REF] Parvez Mahmud | Recent progress in sensing nitrate, nitrite, phosphate and ammonium in aquatic environments[END_REF]. Since the ammonium NH4 + ion has no real electroactivity in water-based solutions, potentiometry was mainly proposed for its detection, focusing mainly on nonactin as ionophore [START_REF] Cuartero | Why ammonium detection is particularly challenging but insightful with ionophore-based potentiometric sensors -an overview of the progress in the last 20 years[END_REF] and leading to the realization of solid-state microsystems in the frame of environmental analysis [START_REF] Chudy | Multi-ion analysis on versatile sensor head[END_REF][START_REF] Artigas | Application of ion field-effect-transistor-based sensors to soil analysis[END_REF][START_REF] Temple-Boyer | Study of front-side connected chemical field effect transistor for water analysis[END_REF][START_REF] Humenuyk | Development of pNH4-ISFET microsensors for water analysis[END_REF][START_REF] Gutiérrez | Electronic tongue for the determination of alkaline ions using a screen-printed potentiometric sensor array[END_REF][START_REF] Pankratova | Potentiometric sensing array for monitoring aquatic systems[END_REF][START_REF] Choosang | Simultaneous detection of ammonium and nitrate in environmental samples using on ion-selective electrode and comparison with portable colorimetric assays[END_REF][START_REF] Garland | Flexible laser-induced graphene for nitrogen sensing in soil[END_REF][START_REF] Schwarz | Solid-Contact Ion-Selective Electrodes Based on Graphite Paste for Potentiometric Nitrate and Ammonium Determinations[END_REF][START_REF] Gallardo-Gonzalez | A fully integrated passive microfluidic Lab-on-a-Chip for real-time electrochemical detection of ammonium: sewage applications[END_REF]. Concerning the nitrate NO3 -ion, competing development paths were considered since its detection is possible using amperometric detection techniques, based on the reduction phenomena on different electroactive surface [START_REF] Badea | New electrochemical sensors for the detection of nitrites and nitrates[END_REF][START_REF] Zhang | Improvement of amperometric sensor used for determination of nitrate with polypyrrole nanowires modified electrode[END_REF][START_REF] Matsushima | The influence of boron content on electroanalytical detection of nitrate using BDD electrodes[END_REF][START_REF] Bui | A dual detection of nitrate and mercury in water using disposable electrochemical sensors[END_REF][START_REF] Chen | Square wave voltammetry measurements of low concentrations of nitrate using Au/AgNPs electrode in chloride solutions[END_REF][START_REF] Lebon | In-situ metalorganic deposition of silver nanoparticles on gold substrate and square wave voltammetry: a highly efficient combination for nanomolar detection of nitrate ions in sea water[END_REF][START_REF] Zhao | Palladium-gold modified ultramicro interdigital array electrode chip for nitrate detection in neutral water[END_REF][START_REF] Wang | Simultaneous detection of ammonia and nitrate using a modified electrode with two regions[END_REF], as well as potentiometric ones, based on the use of varied ionophores/ion-sensitive-membranes [18-20, 23-27, 36-38]. Applied to soil analysis, these works led to two different approaches: the "on the go" detection associated with the embedding of electrochemical sensors on agricultural vehicles [START_REF] Birrell | Real-time multi ISFET/FIA soil analysis system with automatic sample extraction[END_REF][START_REF] Adamchuk | Direct measurement of soil chemical properties on-the-go using ion-selective electrodes[END_REF][START_REF] Sibley | Field-scale validation of an automated soil nitrate extraction and measurement system[END_REF][START_REF] Sinfield | Evaluation of sensing technologies for the on-the-go detection of macro-nutrients in cultivated soils[END_REF], and the "in situ" detection associated with the burying of electrochemical (micro)sensors in fields [START_REF] Artigas | Development of a screen-printed thick-film nitrate sensor based on a graphite-epoxy composite for agricultural applications[END_REF][START_REF] Futagawa | A miniature integrated multimodal sensor for measuring pH, EC and temperature for precision agriculture[END_REF][START_REF] Tully | Ion-selective electrode offers accurate, inexpensive method for analyzing soil solution nitrate in remote regions[END_REF][START_REF] Caron | Practical application of electrochemical nitrate sensor under laboratory and forest nursery conditions[END_REF][START_REF] Jiang | In-situ, real-time monitoring of nutrient uptake on plant chip integrated with nutrient sensor[END_REF].

This paper deals with the development of pNH4-ISFET and pNO3-ISFET microsensors for aqueous solution analysis and soil analysis. It proposes the adaptation of a generic pH-ChemFET (pH-sensitive chemical field effect transistor) platform for ion detection thanks to fluoropolysiloxanebased ion-sensitive layers. While dealing with wheat culture application, it focuses on the detection performances optimization in the frame of the "in situ" soil detection approach.

Experimental

Microdevice fabrication

Silicon technologies were used in order to mass integrate microelectrodes as well as pHsensitive chemical field effect transistors on the same chip (figure 1), according to microfabrication processes previously studied [START_REF] Sant | On-line monitoring of urea using enzymatic field effect transistors[END_REF][START_REF] Christophe | Elaboration of integrated microelectrodes for the detection of antioxidant species[END_REF]. Thus, the proposed technological process can be described briefly as following (figure 2):

-step n°1: field oxidation (800 nm) -step n°2: field oxide opening (photomask 1) and P-well implantation (boron) -step n°3: field oxide opening (photomask II) and P++ guard ring implantation (boron) -step n°4: field oxide opening (photomask III) and N++ Source and Drain implantation (arsenic) -step n°5: gate opening (photomask IV) and deposition of a SiO2(50nm)/Si3N4(50 nm) insulative layer -step n°6: contact opening (photomask V) -step n°7: Ti(20nm)/Pt(150nm) metallization for contact/microelectrode fabrication (photomask VI) -step n°8: epoxy dry film wafer-level passivation (photomask VII) Thus, P-well, N-channel, SiO2/Si3N4-gate, pH-sensitive chemical field effect transistors (pH-ChemFET) were fabricated on 4-inch, (100)-oriented, N-type (500 .cm) silicon wafers. Furthermore, thanks to the penultimate technological step, associated to a titanium/platinum metallization lift-off (see below), a specific lithographic mask was used in order to fabricate simultaneously pH-ChemFET contact pads as well as platinum-based microelectrodes Pt-µE (for future measurement of soil conductivity). At least, a wafer-level passivation was performed using the photosensitive DF-1050 epoxy dry film (purchased from EMS company) [START_REF] Temiz | A comparative study on fabrication techniques for on-chip microelectrodes[END_REF], defining the microelectrode active surfaces and leaving the pH-ChemFET sensitive zone uncovered for the future integration of ion-sensitive layers.

The multi-sensor devices were manufactured on 6.7 × 5.3 mm 2 chips. These chips were stuck on specifically-coated printed circuit board using an epoxy insulating glue. After wire bonding, packaging was finally performed at the system level using a silicone glop-top in order to adapt the final sensor to the detection in liquid phase (figure 3).

Integration of ion-sensitive membrane for the fabrication of FET-based sensors

Ion detection was investigated using fluoropolysiloxane polymer (730 FS FPSX purchased from Dow Corning) [START_REF] Dumschat | Filled fluorosiloxane as matrix for ion-selective membranes[END_REF][START_REF] Hogg | Novel membrane material for ion-selective field effect transistors with extended lifetime and improved selectivity[END_REF]. This polymer was chosen according to previous results [START_REF] Cazalé | Study of field effect transistors for the sodium ion detection using fluoropolysiloxane-based sensitive layers[END_REF][START_REF] Cazalé | Physiological stress monitoring using sodium ion potentiometric microsensors for sweat analysis[END_REF] since, requiring no additional surface treatment it allows good adhesion properties on silicon-based films, and therefore important lifetimes for the associated ion-sensitive layers. Moreover, since fluorosilicone-based coatings, were shown to be good anti-biofouling candidates in the frame of marine application [START_REF] Hu | Silicone-based fouling release coatings for marine antifouling[END_REF], the FPSX polymer could ensure higher lifetime in soils for ion-sensitive layers.

The initial polymeric solution was made of 200 mg of FPSX centrifuged in 1.5 mL of tetrahydrofuran (to eliminate the binder that causes non-optimal functioning). Except for the NI-V (see hereafter) purchased form Santa Cruz Biotechnology, all chemical reagents were purchased from Sigma-Aldrich. For the NH4 + -sensitive layer, 3.5 mg of nonactin (used as ionophore) and 1.5 mg of "potassium tetrakis(4-chlorophenyl) borate)" KTpClBP ionic additive were diluted into 95 mg of the FPSX-based solution to improve ion exchanging properties, selectivity and lifetime [START_REF] Parvez Mahmud | Recent progress in sensing nitrate, nitrite, phosphate and ammonium in aquatic environments[END_REF][START_REF] Cuartero | Why ammonium detection is particularly challenging but insightful with ionophore-based potentiometric sensors -an overview of the progress in the last 20 years[END_REF][START_REF] Dumschat | Filled fluorosiloxane as matrix for ion-selective membranes[END_REF][START_REF] Hogg | Novel membrane material for ion-selective field effect transistors with extended lifetime and improved selectivity[END_REF].

In the same way, for the NO3 --sensitive one, three ionophores were studied, e.g. tridodecylmethylammonium nitrate (TDMAN), nitrate ionophore V (NI-V) and tetradodecylammonium nitrate (TDDAN), while using two different ionic additives: potassium tetrakis[3,5-bis(trifluoromethyl)phenyl] borate (KTFBP) and tridodecylmethylammonium chloride (TDMAC). Preparing the initial FPSX-based polymeric solution as previously, the different NO3 -sensitive layers were developed according to the following dilutions: matrix #1: 3 mg of TDMAN and 2.5 mg of KTFBP diluted into 94.5 mg of FPSX solution, matrix #2: 1.8 mg of NI-V and 1.1 mg of TDMAC diluted into 97.1 mg of FPSX solution, matrix #3: 4 mg of TDDAN and 2.4 mg of KTFBP diluted into 93.6 mg of FPSX solution.

In all cases, ion-sensitive solutions were mixed in an ultrasonic bath (duration: 30 minutes) to insure homogeneity, and was finally deposited by dip coating using a Hamilton micro-syringe (volume: 5 mm 3 ) installed on a micrometric positioner. Finally, the reticulation reaction was performed at ambient temperature thanks to atmospheric moisture. Mimicking previous results while using low solution volumes (0.1 mm 3 ) [START_REF] Cazalé | Study of field effect transistors for the sodium ion detection using fluoropolysiloxane-based sensitive layers[END_REF], the dip coating process was automatized in order to ensure repeatability and reproducibility. Thus, ion-sensitive, FPSX-based, quasi-semi-ellipsoidal membranes were deposited precisely (deposition accuracy: 50 µm) with dimensions (diameter: 1100 ± 100 µm, height: 7 ± 1 µm) compatible with the pH-ChemFET sensitive zone (figure 4).

According to this ultimate technological step (step n°9: dip-coating deposition of ion-sensitive FPSX-based layers), the SiO2/Si3N4 pH-sensitive ChemFET chips were adapted to ion detection, leading to the realisation of SiO2/Si3N4/FPSX NH4 + -sensitive and NO3 --sensitive field effect transistors respectively, called pNH4-ISFET and pNO3-ISFET hereafter (figure 2).

Electrochemical characterization in liquid phase and soil measurements

The different field-effect devices, i.e. pH-ChemFET, pNH4-ISFET and pNO3-ISFET, were characterized using a specific "source-drain follower" measurement interface, called ChemFETmeter hereafter. Thus, during measurement experiments, the Gate-Source voltage VGS of the FETbased microsensors was monitored continuously while in saturation mode thanks to constant Drain-Source voltage VDS and Drain-Source current IDS (typically VDS = 2 V and IDS = 0.1 mA). A commercial calomel reference electrode (XR110 model from Radiometer Analytical) was used to bias the analysed sample solution to the mass (VG = 0). For soil analysis, a specific burying procedure was used for ensuring electrical contacts while preventing any undesired breakage.

Concerning pH-ChemFET devices, titration experiments using hydrochloric acid (HCl: 10 -2 M) and tetra-methyl-ammonium hydroxide (TMAH: 10 -1 M) were performed with a background electrolyte (CH3COOLi 0.1 M) solution to fully study their pH detection properties. Their sensitivities to alkaline ions was studied by successive adding of KCl or NaCl solutions in a buffer solution (pH = 4.66 ± 0.1 pH).

For pNH4-ISFET and pNO3-ISFET devices, detection properties in liquid phase were also studied in deionized water (resistivity > 18 M.cm) while increasing regularly the [NH4 + ] and [NO3 - ] concentrations (maximal range: 10 -8 M -10 -2 M) thanks to successive additions of ammonium nitrate NH4NO3. Solutions with different specific salt concentrations (KCl, NaCl, CaCl 2 , MgSO4 and CH3COOLi) were also studied and potentiometric selectivity coefficients were found out according to the fixed interference method (FIM) [START_REF] Umezawa | Selectivity coefficients for ion-selective electrodes: recommended methods for reporting KAB pot values[END_REF].

Then, soil analysis was performed by burying the different ChemFET-based sensors into real samples corresponding to two different clay-silt matrices (provided by the Agronutrition company and analysed thanks to the pH-H2O ISO10390 norm): an acidic one (pH = 4.7 ± 0.1) and an alkaline one (pH = 8.3 ± 0.1). In all cases, a commercial calomel reference electrode (XR110 model purchased from Radiometer Analytical) was used to applied the Gate voltage to the soil sample. After calibrating the pH-ChemFET sensors in three buffer solutions (4, 7 and 10), pH measurements were first performed to check their ability for soil measurement, while considering relative moisture (RM) ranging from 40 to 100% (figure 5).

Finally, ion analysis was also studied in real samples using the acidic clay-silt matrix, characteristic of wheat cultures in the south-west of France. For the NH4 + ion, the nitrogen mineralization due to soil micro-organisms was activated thermally to alter biologically the soil samples. Otherwise, the NH4 + ion, the NO3 --ion detection was performed while modifying soil samples to obtain nitrate concentrations ranging from 0.4 to 10 mM. In both cases, SMS19.21.05F lysimeters (purchased from Rhizon) were used to extract liquid samples from the studied earthen pots. These liquid samples were finally characterized by ionic chromatography using Dionex ICS-5000+ (anions) and DX-120 (cations) equipments. All chemical products were purchased from Sigma-Aldrich and all experiments were done at ambient temperature (~ 21°C).

Results and discussion

pH-ChemFET characterization

The pH detection properties of the SiO2/Si3N4 pH-ChemFET devices were validated by HCl/TMAH titration experiments (results not shown). In a standard way, the developed technology is characterized by a quasi-Nernstian response (sensitivity: 55 ± 1 mV/pH) with no hysteresis on the [2 -12] pH range, low temporal drift (< 1 mV/day) and long lifetime (more than 6 months). This is associated to the ChemFET process quality as well as to the ability of pH-ChemFET sensors to be used for long-term applications. Nevertheless, it should be mentioned that the studied pH-ChemFET batch was characterized by a detection sensitivity around 51 ± 1 mV/pH. Since this lower sensitivity should be related to a non-stoichiometric silicon nitride film, it will have no influence on the pNH4and pNO3-ISFET detection properties but the value will be considered accordingly hereafter, highlighting the ability of the ChemFET technology to cope with real and in-situ soil analysis.

Concerning their sensitivities to alkaline ions, similar results were obtained for both sodium Na + and potassium K + ions. On one hand, for the lowest concentrations (lower than 10 -3 M), there was no influence on the pH-ChemFET response. On the other hand, for the highest concentrations (higher than 10 -3 M), linear analytical responses were found, evidencing sensitivities around 7.5 ± 0.5 mV/decade [START_REF] Hajji | pH, pK and pNa detection properties of SiO2/Si3N4 ISFET chemical sensors[END_REF] (results not shown). Nevertheless, in the frame of soil analysis applied to agriculture, since alkaline ion concentrations are generally lower than 10 -3 M, their interference will be finally negligible on pH measurement.

pNH4-ISFET and pNO3-ISFET characterization

In the following, the nitrogen-based ion detection was studied while focusing on concentration ranges compatible with soil analysis: [10 -5 -10 -3 M] for pNH4 and [10 -4 -10 -2 M] for pNO3 [START_REF] Owen | Competition for amino acids between roots and rhizosphere microorganisms and the role of amino acids in plant N acquisition[END_REF].

Concerning the detection of the ammonium NH4 + ion, only nonactin was studied as ionophore while using a FPSX-based polymer matrix. As expected for cations, the pNH4-ISFET output voltage decreased with the [NH4 + ] concentration increase (figure 6), enabling the definition of the sensor analytical response (figure 7). It appears that the FPSX-based pNH4-ISFET is characterized by a quasi-Nernstian variation in the [10 -2.5 -10 -5 M] concentration range (sensitivity estimated for nine different sensors: 56 ± 1 mV/pNH4). It should be mentioned that the measurement discontinuities / transitions evidenced on figure 6 are related to the experimental transfer from one liquid sample to another. These rough data were not treated mathematically, allowing to characterize the sensor transient response. As a result, the pNH4-ISFET response time was found around two minutes and, after stabilization, its measurement accuracy was estimated to ± 2.5 mV, i.e. to ± 0.05 pNH4. Such response time value will have no influence for real and in-situ soil analysis since concentration variations in soil occurs on more important time scales (typically few hours or few days).

Then, measurement interferences were estimated for the main soil cations: potassium K + , sodium Na + , lithium Li + , calcium Ca 2+ and magnesium Mg 2+ (figure 8). According to the various interfering ions, the different pNH4-ISFET analytical responses were largely shifted. This phenomenon should be related to interfering ion trapping into the nonactin-based FPSX matrix [START_REF] Cuartero | Why ammonium detection is particularly challenging but insightful with ionophore-based potentiometric sensors -an overview of the progress in the last 20 years[END_REF].

For the Na + and K + ions, interferences were also responsible for a sensitivity decrease (towards 47 and 42.5 mV/pNH4 respectively). In order to go further, the different potentiometric selectivity coefficients were estimated using the fixed interference method (FIM) (table 1). Except for the K + ion, excellent selective properties were obtained while compared to results from literature [START_REF] Ghauri | Poly(vinyl chloride) type ammonium ion-selective electrodes based on nonactin: solvent mediator effects[END_REF][START_REF] Wroblewski | NH4 + -sensitive chemically modified field effect transistors based on siloxane membranes for flow-cell applications[END_REF][START_REF] Karakus | A new potentiometric ammonium electrode for biosensor construction[END_REF][START_REF] Novell | Paper-based ion-selective potentiometric sensors[END_REF], mainly due to the choice of the 730 FS fluoropolysiloxane polymer. In the case of the potassium ion, the Log K(NH4 + , K + ) value was estimated to -1.2. As previously shown [START_REF] Cuartero | Why ammonium detection is particularly challenging but insightful with ionophore-based potentiometric sensors -an overview of the progress in the last 20 years[END_REF], such result should be related directly to the nonactin ionophore. Whereas Na + ions are rarely present in the frame of agriculture, only K + ion interferences will have to be considered in field applications but it should not be a major bottleneck for the monitoring of soil nitrogen cycle using FPSX-based pNH4-ChemFET sensors.

Concerning the detection of the nitrate NO3 -ion, the FPSX-based polymer matrix was studied for the integration of three different ionophores: tridodecylmethylammonium nitrate (TDMAN), nitrate ionophore V (NI-V) and tetradodecylammonium nitrate (TDDAN). In all cases and as expected for anions, the [NO3 -] concentration increase is responsible for the pNO3-ISFET output voltage increase (figure 9, associated to the TDDAN matrix). Thus, sensor analytical responses were defined according to the studied matrices (figure 10, associated to the TDDAN matrix). In all cases, the FPSX-based pNO3-ISFET were associated to quasi-Nernstian curves while evidencing the following characteristics (estimated for three different sensors): matrix #1 (TDMAN): sensitivity: 53 ± 1 mV/pNO3 on in the [10 -1.5 -10 -4.5 M] concentration range, matrix #2 (NI-V) sensitivity: 56 ± 1 mV/pNO3 on in the [10 -1.5 -10 -4.5 M] concentration range, matrix #3 (TDDAN): sensitivity: 56 ± 1 mV/pNO3 on in the [10 -1.5 -10 -5.5 M] concentration range.

As previously, the rough data analysis allowed to characterize the ISFET device transient response (figure 9, associated to the TDDAN matrix). Consequently, the pNO3-ISFET measurement accuracy was estimated to ± 1 mV, i.e. to ± 0.02 pNO3, and its response time to around two minutes (still negligible if compared to concentration variations time scales in soil).

Interferences of the main soil anions (chloride Cl -, acetate CH3COO -, dihydrogen phosphate H2PO4 -, sulphate SO4 2-, and phosphate PO4 3-) were also estimated. Whereas the different nitratesensitive matrices were characterized by similar detection sensitivities and ranges (see below), the interference measurement studies showed degraded analytical responses for the TDMAN-based ionsensitive matrix as well as for the NI-V-based one (results not shown). Therefore, in the following, focus will be done on the TDDAN-based matrix (figure 11). As previously, large potentiometric shifts were evidenced for the pNO3-ISFET analytical responses, as a result of interfering ion trapping phenomena associated to the TDDAN ionophore. However, according to measurement accuracy, quasi-Nernstian pNO3 sensitivities were still evidenced whatever the interfering ions.

Then, the fixed interference method was also used to determine the potentiometric selectivity coefficients associated to the different studied anions. Table 2 compares finally the so-obtained values to results extracted from literature [START_REF] Wroblewski | Nitrate-selective chemically modified field effect transistors for flow-cell applications[END_REF][START_REF] Masadome | Microfluidic polymer chip with an embedded ion-selective electrode detector for nitrate-ion assay in environmental samples[END_REF][START_REF] Zhang | Comparative investigation on soil nitrate-nitrogen and available potassium measurement capability by using solid-state and PVC ISE[END_REF], emphasizing the use of the 730 FS fluoropolysiloxane polymer. According to table 2, it should be also noted that, although TDDAN is an ion exchanger, selectivity towards phosphate PO4 3-ions is surprisingly worse than for the sulphate SO4 2-ion, in disagreement with the Hofmeister series [START_REF] Zhang | Interactions between macromolecules and ions: the Hofmeister series[END_REF]. Moreover, according to literature [START_REF] Wroblewski | Nitrate-selective chemically modified field effect transistors for flow-cell applications[END_REF][START_REF] Masadome | Microfluidic polymer chip with an embedded ion-selective electrode detector for nitrate-ion assay in environmental samples[END_REF][START_REF] Zhang | Comparative investigation on soil nitrate-nitrogen and available potassium measurement capability by using solid-state and PVC ISE[END_REF], the replacement of the poly-vinyl-chloride (PVC) polymer by the fluoropolysiloxane (FPSX) one as well as the use of the KTFBP ionic additive did not generate an improvement in selectivity (in fact, this additive is primordial for the stability of the ISFET sensor in solution, result not shown). As a matter of fact, for both polymers, results obtained with the TDDAN ionophore remain excellent. In the frame of agricultural applications, chloride Cl -appears to be the main interfering ion but its potentiometric selectivity coefficient Log K(NO3 -, Cl -) around -2.5 should be sufficient for nitrate ion analysis in soil.

Thus, according to sensitivity and selectivity results, the TDDAN-based FPSX matrix appears to be the best nitrate-sensitive layer, and should be fully compatible with the NO3 -ion detection in soils using ChemFET sensors.

Application of the ChemFET technology to soil analysis

The first soil analysis concerned the standard SiO2/Si3N4-pH-ChemFET technology while considering two different clay-silt soil matrices: acidic (pH ≈ 4.7) and alkaline (pH ≈ 8.3).

Considering both soil samples, pH-ChemFET devices were buried into earthen pots for relative moistures (RM) ranging from 40% to 100%, in agreement with soil-plant system and agriculture considerations [START_REF] Di | Nitrate leaching in temperate agroecosystems: sources, factors and mitigating strategies[END_REF]. During pH measurements, the ChemFET-meter experimental curves showed often initial transient variations (result not shown). Such phenomenon should be related to the electrical contact establishment between the pH-ChemFET sensitive structure and the soil matrix.

Nevertheless, a steady-state regime was evidenced after thirty minutes in soil, enabling the determination of a constant output voltage whatever the relative moisture values (table 3). This demonstrate that such soil RM levels allow the creation of a liquid interface compatible with the Electrolyte-Insulator-Semiconductor detection structure. So, pH-ChemFET sensor results were quite stable and in good agreement with initial pH analyses. Whatever the RM values, measurement errors were lower than 0.5 pH and the acidic and alkaline soils were respectively associated to pH values around 4.9 ± 0.3 and 8.1 ± 0.3.

Then, experimental studies dedicated to pH-ChemFET drift and lifetime in the acidic clay-silt soil (pH = 4.7) were performed on a six-month period, while keeping constant the soil relative moisture constant around 75%, representative of a wheat culture applications [5] (figure 12). Linear output voltage variations were evidenced, with a potential shift around + 100 mV on a 165-day duration (temporal drift: ~ 0.6 mV/day or ~ 0.012 pH/day). Such phenomenon could be related to an effective pH variation of the clay-silt matrix on the six-month duration, but also to a drift of the pH-ChemFET device. In order to go further, HCl/TMAH titration experiments were performed on the pH-ISFET sensors to study their analytical responses after six months of use in earth (result not shown). Linear curves were still obtained, evidencing similar sensitivity (~ 51 mV/pH) as well a positive threshold voltage shift around + 35 mV. So, considering the previous global potential shift (~ 100 mV), around 65 mV should be associated to an effective pH increase of the clay-silt soil matrix, and the pH-ChemFET drift was finally estimated to 0.2 mV/day (or 0.004 pH/day). Last but not least, the pH-ChemFET sensor lifetime in soil was found to be higher than six months, in agreement with wheat culture specifications.

As a matter of fact, these preliminary experiments highlight the potential of the ChemFET technology for in-situ soil analysis on a six-month growth period, even in rain conditions or dry weather periods. Compared to the agriculture-related current methods, that require a time-consuming analysis of a soil extract, such technology offers the possibility of an almost immediate result on site, with sufficient accuracy in the frame of precision farming.

In-situ soil analysis using pNH4-ISFET and pNO3-ISFET sensors

In-situ experiments were systematically performed while using the acidic clay-silt soil (pH = 4.7 ± 0.1, RM ≈ 75%, see below), characteristic of wheat culture in the south-west of France.

The first study was dedicated to the measurement of the ammonium ion concentration. In order to do so, one earth pot was incubated at 38°C in an oven in order to activate the nitrogen mineralization due to soil micro-organisms whereas the second one was maintained at 4°C in a fridge in order to prevent it. After one month, a pNH4-ISFET was used to characterized the different pots according to the following routine: calibration into NH4NO3 solutions (10 -4 M and 10 -3 M), analysis of the NH4 + -rich and NH4 + -poor soil samples after burying (two times), and final calibration into NH4NO3 solutions (10 -4 M and 10 -3 M), and its output voltage was monitored accordingly (figure 13). Thanks to the initial calibration steps (output voltage shift around 50 mV), the pNH4-ISFET operation was validated. Between each burying steps, there is clearly a drift in the sensor signal.

Again, this should be related to transient regime due the improvement of the contact impedances between the sensor and the soil matrix. Nevertheless, the two-time analysis of the NH4 + -rich and NH4 + -poor earth pots showed another voltage shift estimated around 40 mV. The final calibration step showed lastly that the pNH4-ISFET is still operational and that the previous values are correlated to effective variations of the ammonium ion concentration [NH4 + ]. In all, the monitoring of the nitrogen mineralization due to soil micro-organisms was demonstrated and the ISFET behaviour seemed unaffected by any biofouling phenomena or associated ones.

Compared to the pNH4-ISFET calibration procedure in 10 -4 M and 10 -3 M solutions (sensitivity: ~ 50 mV/pNH4), the ammonium ion concentrations of the NH4 + -poor and NH4 + -rich samples were respectively estimated to 150 ± 50 M and 800 ± 100 M (table 4). Nevertheless, such results have to be analysed in a critical way by considering the interference of the potassium ion concentration (see table 2). According to our previous results (see §3.2, Log K(NH4 + , K + ) ≈ -1.2), the final [NH4 + ] concentrations of both soil samples could be respectively estimated to approximately 150 M and 700 M, in quite good agreement with measurement performed by ionic chromatography analysis on lysimeters liquid samples (table 4).

All in all, even if drift phenomena involve important measurement errors, these experiments allowed to discriminate fully two levels of soil mineral nitrogen, and to analyse, on a monthly basis, the temperature-stimulated activity of micro-organisms in clay-silt soils.

Then, the nitrate ion detection was also studied. First, using the standard method based on soil sample dilution into pure water, the NO3 -ion and the Cl -ion concentrations were respectively estimated to 0.4 mM and 2.5 mM for the acidic clays-silt soil. According to this result and considering that nitrate ions are chemically available in the soil matrix, four earth pots were prepared with different concentrations [NO3 -] and were characterized by ionic chromatography thanks to soilrelated liquid samples. Finally, a pNO3-ISFET was used to characterized the different pots according to the following routine: calibration into NH4NO3 solutions (10 -3 M and 10 -2 M), analysis of the four soil samples after burying (four times), and final calibration into NH4NO3 solutions (10 -3 M and 10 -2 M).

Results show a quite good agreement between theoretical [NO3 -] concentrations and measured ones (table 5). They show that ISFET sensors tends to overestimate the nitrate content, certainly due to the Cl -chloride ion interferences. Nevertheless, a quite good and monotonous agreement was evidenced, demonstrating the ability of ISFET sensors to measure fully nitrate NO3 -ion concentration in soil.

Finally, the ISFET sensor lifetime in soil has to be studied. In this case, in order to prevent any deterioration of the FPSX-based ion-sensitive membranes, ISFET sensors were buried into earthen pots containing the acidic clay-silt soil saturated with water (pH = 4.7 ± 0.1, RM ≈ 100%). Then, during six months, they were periodically and cautiously removed, cleaned into pure water, calibrated into NH4NO3-based aqueous solutions to estimate their detection properties, and finally buried again into their earthen pots. Since linear analytical responses were systematically obtained, results were finally analysed according to their detection sensitivity as well as their output voltage for a 10 -4 M NH4NO3 solution (pNH4 = pNO3 = 4). Thus, it appeared that, for both pNH4-ISFET and pNO3-ISFET devices, detection responses remained quasi-Nernstian and their sensitivity decreased from 56 to 50 mV/decade during the six-month burying period. Contradictory results were rather obtained for the temporal drift analysis thanks to the reference value obtained at pNH4 = pNO3 = 4 (results not shown). On one hand, for the pNH4-ISFET, no real potential shift was evidenced on a six-month burying period (output voltage: 1,18 ± 0.02 V). Since the sensor was not in use during the burying period, this "zero" temporal drift value cannot be compared to the previous one determined for pH-ISFET in clay-silt soil (cf. §3.3). Nevertheless, this result demonstrates that the lifetime of fluorosiloxane-based ion-sensitive layers is also higher than six months, and that the FPSX-based pNH4-ISFET sensors could be used for soil monitoring on a six-month growth period. On another hand, for the pNO3-ISFET, a huge potential shift was found, leading to major dysfunctions. According to the previous results (see below), this huge drift should be related to the physico-chemical integration of the TDDAN ionophore into the FPSX membrane.

Besides, a similar phenomenon was also obtained in liquid phase (results not shown). As a matter of fact, this demonstrates that the improvement of NO3 --sensitive layers still remains a bottleneck for the development of pNO3 potentiometric ChemFET-based sensors.

Conclusion

Silicon-based technologies were used to develop ChemFET-based sensors for the nitrogen cycle monitoring in soil. Thus, focusing on nonactin and tetradodecylammonium nitrate (TDDAN) ionophores respectively, pH-ChemFET devices were adapted to the detection of ammonium NH4 + and nitrate NO3 -ions thanks to fluoropolysiloxane-based, ion-sensitive membranes deposited by dipcoating. The pNH4-ISFET and pNO3-ISFET were characterized by quasi-Nernstian detection properties (sensitivity: ~ 56 mV/decade) in a [10 -2 -10 -5 M] concentration range adapted to soil analysis. Their selectivity properties were also studied, evidencing the potassium K + and chloride Cl - ions as the main interfering species respectively (potentiometric selectivity coefficients: Log K(NH4 + , K + ) ≈ -1.2 and Log K(NO3 -, Cl -) ≈ -2.5).

Focusing on the "in situ" detection approach, soil analysis was finally performed during burying periods of six-month, as required for nitrogen-based fertilization procedure in the frame of agriculture applications. Whatever their acid/basic and moisture characteristics, pH-ChemFET were found to be fully compatible with clay-silt soil analysis, evidencing stable analytical responses for six months (temporal drift: ~ 0.004 pH/day). Similar studies were also done for the nitrogen-based ion-sensitive sensors. So, pNH4-ISFET were used for analysing, on a monthly basis, the nitrogen mineralization due to temperature-stimulated activity of micro-organisms in soil. In addition, pNO3-ISFET were successfully used to measure nitrate ion concentration [NO3 -] in soil. Overall, the ability of FPSXbased ISFET sensors to operate in-situ in clay-silt soil during a six-month period was demonstrated.

Nevertheless, some bottlenecks were also highlighted. The first one is minor since it is related to the potassium ion interferences on the intrinsic detection properties of the nonactin ionophore and therefore on the pNH4-ISFET analytical responses. Since there is no real alternative in terms of ionophore, this phenomenon should be only considered for future applications of pNH4-ChemFET sensors in soils. The second one is related to physico-chemical properties of the TDDAN ionophore into the FPSX polymeric membrane. Even if pNO3-ISFET were successfully developed in the frame of soil analysis, major dysfunctions were finally evidenced after few days, preventing their efficient use for long durations. Consequently, research works have to be continued. They have to concern the improvement of FPSX-based nitrate-sensitive layers. Nevertheless, in the frame of the "in situ" approach for modern farming, efforts must also be focused on the development of a functional (micro)system analysis, fully compatible with the soil analysis. Thus, it should be possible to monitor the nitrogen cycle in soils or to study the influence of nitrogen-based fertilizers in fields.

Tables and figures caption

Table 1:

Figure 1 :

 1 Figure 1: mass fabrication of SiO2/Si3N4 pH-ChemFET chips using silicon-based technologies

Figure 3 :Figure 4 :Figure 6 :Figure 7 :Figure 8 :Figure 10 :Figure 11 :Figure 12 :Figure 13 :

 3467810111213 Figure 3: integration of pH-ChemFET silicon chips on printed circuit board

Figure 1 :Figure 2 :

 12 Figure 1: mass fabrication of SiO2/Si3N4 pH-ChemFET chips using silicon-based technologies

Figure 3 :

 3 Figure 3: integration of pH-ChemFET silicon chips on printed circuit board

Figure 4 :

 4 Figure 4: dip-coating deposition of the fluoropolysiloxane-based ion-sensitive layer

Figure 5 :Figure 6 :Figure 8 : 4 Figure 9 :Figure 10 : 3 Figure 12 :Figure 13 :

 568491031213 Figure 5: analysis of clay-silt soil matrices using ChemFET-based sensors

Table 3 :

 3 pH-ISFET measurement in acidic (pH ≈ 4.7) and alkaline (pH ≈ 8.3) clay-silt soil samples

	-3 M)

Table 4 :

 4

	inhibit or activate the nitrogen mineralization due to soil micro-organisms
	(ionic chromatography was performed on liquid samples extracted by lysimetry)

comparison of in-situ pNH4-ISFET measurements and ionic chromatography analysis of different clay-silt soil pots (pH = 4.7 ± 0.1, RM ≈ 75%) incubated in order to -] concentrations, comparison of the pNO3-ISFET analysis to ionic chromatography performed on soil-related liquid samples

Acknowledgements

This project was financially supported by the French "Fonds Unique Interministériel" and the French "Midi-Pyrénées Région" (FUI project, AAP 16, n°F1401015M). The technological realisations and associated research works were partly supported by the French RENATECH network.


Oriane Thiery
email: oriane.thiery@lip6.fr

Garance Martin
email: garance.martin@lip6.fr

Isabelle Bloch
email: isabelle.bloch@lip6.fr

Martin Dres
email: martin.dres@aphp.fr

Umar Saleem
email: umar.saleem@bioserenity.com

Andrea Pinna
email: andrea.pinna@lip6.fr

Semi-Automatic A-Line Detection and Confidence Scoring in Lung Ultrasound

Keywords: Lung Ultrasound, A-lines, automatic detection, confidence estimation

Weaning a patient from mechanical ventilation is a critical task in Intensive Care Units, but it can be made safer by using Lung Ultrasound scoring. This scoring is currently done visually by specialists based on Lung Ultrasound artifacts among which are A-lines. Automating this scoring may help standardizing results and saving time for clinicians. In this paper, we propose a method to automatically detect A-lines by using both the intensity profile of the LUS image and morphological operations. A score is then assigned to significant lines and represents the possibility of them being A-lines. The proposed method shows promising results in differentiating Alines from other elements with an Area Under the Curve of 0.95; furthermore, using a threshold at 0.5 to detect A-lines leads to very good performances with an accuracy of 95% and a F0.5-score of 0.84.

I. INTRODUCTION

Knowing when to wean a patient from mechanical ventilation is a critical task in Intensive Care Units (ICU): waiting too long makes the patient less likely to manage natural breathing, and weaning him too quickly is associated with an increased risk of postextubation distress. One way to predict whether the patient is ready for the weaning is to use Lung Ultrasound (LUS) to estimate aeration changes during the spontaneous breathing trial [START_REF] Soummer | Ultrasound assessment of lung aeration loss during a successful weaning trial predicts postextubation distress[END_REF]. The estimation of aeration changes in the lung is based on the scoring of LUS images according to the following criteria: 1) normal aeration: presence of lung sliding with A-lines (thin, horizontal or slightly curved artifacts), or less than two isolated B-lines (vertical artifacts originating from the pleural line); 2) moderate loss of lung aeration: multiple, well-defined B-lines; 3) severe loss of lung aeration: multiple coalescent B-lines; and 4) lung consolidation: presence of a tissue pattern characterized by dynamic air bronchograms [START_REF] Soummer | Ultrasound assessment of lung aeration loss during a successful weaning trial predicts postextubation distress[END_REF]. However, this scoring, being done visually, requires both formation and experience for the clinician, and may vary depending on the operator. Therefore, image processing methods that identify the significant lines on LUS images could be a great help for both novice practitioners and for specialists to save time.

These last years, there has been a promising increase in the research for automating the evaluation of LUS images because (among other reasons) of the outbreak of COVID-19, which can be detected by LUS [START_REF] Mcdermott | Sonographic diagnosis of COVID-19: A review of image processing for lung ultrasound[END_REF], [START_REF] Zhao | A review of deep learning applications in lung ultrasound imaging of COVID-19 patients[END_REF]. However, while some research proposes automatic scoring of the LUS images [START_REF] Roy | Deep learning for classification and localization of Covid-19 markers in Point-of-Care lung ultrasound[END_REF]- [START_REF] Baloescu | Automated lung ultrasound B-line assessment using a deep learning algorithm[END_REF], none of these methods is based on the scoring system used to predict the risk of postextubation distress. Some other approaches are directed towards the identification of special patterns on LUS images, but most of this research is focused on the detection and evaluation of B-lines, these being one of the most significant elements to estimate the state of a lung in LUS images [START_REF] Baloescu | Automated lung ultrasound B-line assessment using a deep learning algorithm[END_REF]- [START_REF] Wang | Quantifying lung ultrasound comets with a convolutional neural network: Initial clinical results[END_REF]. Only few approaches try to segment the A-lines, mostly by using the Radon transform of the LUS image [START_REF] Karakus | Detection of line artefacts in lung ultrasound images of Covid-19 patients via non-convex regularization[END_REF]- [START_REF] Anantrasirichai | Line detection as an inverse problem: Application to lung ultrasound imaging[END_REF], or a morphological top-hat operation followed by an adaptive filter [START_REF] Susanti | Image processing framework for pleural line (A-line) detection in video lung ultrasonography[END_REF]. These methods have not been evaluated quantitatively on their performances in A-lines detection, either because it was not the main objective of the study, or because it was a more qualitative evaluation.

In this context, we propose a method to identify the A-lines in LUS images in a flexible way: instead of a binary estimation of the presence of an A-line, the method presented in this paper gives a level of confidence related to the possibility of a significant line detected in the LUS image being an A-line. This allows clinicians to know how much confidence they can have in the given result and whether or not they should doublecheck it.

This article is organized as follows: Section II describes the proposed method, Section III presents the used dataset, and finally, Section IV discusses the results of the testing phase.

II. METHOD

This section describes the proposed method for A-lines detection and confidence estimation. Before anything else, pre-processing is necessary in order to remove any annotation added by the imaging ultrasound machine on the LUS frame, to rescale the intensity values between 0 and 255, and to crop the frame to keep only the smallest rectangular image containing all the LUS information. The result of this operation is presented in Figure 1a. In our dataset, A-lines appear as arcs of a circle of 3-7 pixels thickness. Our method for their detection is thus based on the detection of bright arcs of a circle, which are considered A-line candidates, and the evaluation of three of their characteristics: their position, the contrast with their neighborhood and the continuity of the brightness in the detected line.

In order to study these characteristics, five steps are considered: pleural line detection, study of the vertical intensity profile of the LUS image. morphological operations to enhance potential lines in the image, scoring of each candidate line, and final decision among the candidates lines using a threshold. These steps are described hereafter.

1) Pleural line detection: as the following steps rely on the pleural line position to estimate if the detected line position makes it likely to be an A-line, the pleural line is identified by the clinician (cf Figure 1b). Doing it manually prevents errors on the detection of this element which is crucial to identify the A-lines, and in extreme cases, where the pleural line cannot be detected in the image, it leads to the immediate conclusion that the LUS image contains no A-lines (because they are reflections of the pleural line that cannot occur in the absence of pleural line).

From this point on, everything is done automatically.

2) LUS vertical intensity profile: in order to determine A-line candidates, the intensity profile of the LUS image is studied so as to identify any peak of brightness (relative to its surroundings). To this end, the pleural line being identified, the area under it is scanned to get the profile of the mean intensity of each arc of a circle having for center the peak of the LUS image cone (cf Figure 1c). The resulting profile is smoothed with a Gaussian filter (σ = 5) to remove the high-frequency variations due to the noisy nature of the LUS images, while preserving the lines (cf Figure 1c). Every maximum detected on the final curve is a candidate line for being an A-line.

3) Morphological operations: In order to evaluate if the candidate lines have not been detected thanks to a localized peak of brightness (and that they do present a long and thin bright intensity profile), some morphological operations are applied to the LUS frame to highlight long and thin elements. First, the contrast of the LUS frame is greatly increased by setting to 0 every pixel whose value is under the 98th percentile of intensity of the image, and rescaling the rest between 0 and 255 (cf Figure 1d). Then, an Alternate Sequential Filter (ASF) is applied to this image (cf Fig. 1e). This step consists in alternating morphological openings and closings with a structuring element of growing size. Here, the structuring element is chosen as an horizontal line (because most of the A-lines, independently of their orientation, contain at least an horizontal part) of length increasing from 2 to 30 pixels. To identify more precisely the long bright regions characteristic of A-lines, an opening with a structuring element of size 5 × 40 is then applied on the result of the ASF (cf Figure 1f). 4) Scoring: To estimate the possibility that the candidate lines are A-lines, both the intensity profile of the frame and the result of the morphological operations are used. A score between 0 and 1 is given to each line for each of the three parameters taken into account, and these scores are then aggregated. The three considered parameters are:

• position of the candidate line: it corresponds to the radius of the arc of a circle considered as a candidate line. As this position p should be a multiple of the distance between skin and the pleural line (denoted by d sp here, and typically between 40 and 130 pixels in our dataset), the score s 1 given to this parameter is determined by Algorithm 1 (with m being an imprecision margin considered on each side of the position of the pleural line, equal to 3 pixels).

Algorithm 1 Scoring of the position of a candidate line

Input: p, m, d sp Output: s 1 1: s 1 ← 0 2: for k in [2,3,4] do 3: if p ≥ k(d sp -m) & p ≤ k(d sp + m) 4: s 1 ← 1 5: else if p > k(d sp + m) & p < (k + 0.5)d sp then 6: s 1 ← p-dsp(k+0.5) -0.5dsp+km 7: else if p < k(d sp -m) & p > (k -0.5)d sp then 8: s 1 ← p-dsp(k-0.5) 0.5dsp-km 9:
end if 10: end for The score is 1 when the position of the A-line is a multiple of the distance between the skin and the pleural line, and it decreases as the line goes away from these positions until it reaches 0;

• contrast between the line and its neighborhood: it is assessed through the derivative of the intensity profile, both before (score s 2,1 ) and after (score s 2,2 ) the position of the candidate line. This derivative is calculated on the smoothed profile of intensity of the LUS image, and its value at five pixels before the position of the candidate line and at five pixels after are studied. In order to ensure that the line is bright enough compared to its surroundings, the higher in absolute value those two derivatives are, the higher the score associated to this parameter is. The function giving the score to one of the derivative d depending on its absolute value has been determined experimentally and is detailed in Algorithm 2;

• result of the ASF and opening operations: they are used to ensure that the line is continuous and has not been detected thanks to a single peak of brightness. Let ASF pres and O pres be Boolean values that indicate whether something has been detected after the ASF and the opening operations, respectively (note that O pres ≤ ASF pres ). Furthermore, we define L as the length in pixels of the detected element (in the opening if something has been detected there or in the ASF otherwise) and H its height. Considering these notations, the score s 3 given to this parameter is determined by Algorithm 3.

s3 ← max(s3([L/H] -2), 0) 17: end if
This algorithms means that if nothing has been detected by the ASF at the position of the line candidate (every pixel of the line in the image after the ASF operation is equal to 0) then the score is 0.4: this is because experience has shown that some Alines are not horizontal enough to be highlighted by the ASF, and thus the absence of detection after the ASF operation does not mean that there is no A-line at the considered position. If something has been detected at the position of the line after the ASF operation but disappeared after the opening, then the score is a little higher; and if something has been detected after the ASF and is still perceptible after the opening, then the score increases once again. After this first score is given to the considered line, the shape of the detected element is taken into account: if it is long, like an A-line should be, then the score is increased, and it is decreased if it is too thick (because it means that a bright extensive region is located here, which does not match the profile of an A-line).

In order to conclude on the number and position of the Alines in the frame, the scores are then aggregated by a weighted average operation that gives the confidence level c (Equation 1) that the line is an A-line. This operation has been chosen because some A-lines do not respect one of their theoretical characteristics (e.g. their position is slightly off from where it should be). In this case, this operation allows for compensation if the other parameters are strong enough to make it clear that the detected line is an A-line in spite of that. The scores are aggregated as follows:

c = (0.5s 1 + s 2,1 + s 2,2 + s 3 )/3.5 (1)
This gives a lot of influence to whether the line is contrasted enough from its neighborhood; and a little less influence to the position in order to limit false detections which only have a truly accurate position. Furthermore, if the line is distant from the skin more than five times the distance between the pleural line and the skin, c is set to 0 as there is almost never an A-line at this position.

5) Validation: The final step to identify A-lines is to consider three regions (because there is almost never more than three A-lines in a LUS image). Each region is centered around the depth of a multiple of the distance between the skin and the pleural line. This is to ensure that there is at most one A-line detected in each region: thus, for each region, only the candidate line with the best score is kept. This line is considered to be an A-line if the confidence level which has been attributed to it is higher than a threshold τ (cf Figure 1g obtained for τ = 0.5). This validation step, for τ = 0.5, enables the eliminate 3.66 false detections per image in average, while only discarding 0.19 true detections per image.

In the example in Figure 1, the confidence level of the two detected lines are both 0.89, and the confidence level of the line detected in the third region is 0.25, which means that this candidate line is not an A-line, as it can been seen in Figure 1a.

III. DATASET

The dataset used to test the proposed method includes LUS images from seven patients admitted to the ICU of the hospital La Pitié-Salpêtrière in Paris, which have undergone a successful spontaneous breathing trial and as such have been extubated afterwards. The clinical information of these patients (mean values) is detailed in Table I. For each patient, LUS has been conducted just before the extubation as well as 2 hours after the extubation, 24 hours and 48 hours after the extubation if the patient was still at the ICU, and at the time of his discharge if he was discharged before 48 hours.

Lung Ultrasound was performed with a Philips Sparq, 2to 4-MHz convex probe (cardiac probe) set to 40 frames/s. Each LUS sequence contains 81 frames and lasts for about 2 seconds. Each intercostal space of upper and lower parts of the anterior, lateral, and posterior regions of the left and right chest wall was carefully examined, for a total of 12 LUS sequences by patient and time of acquisition. This amount to a total of 244 sequences and 19763 LUS frames.

IV. RESULTS AND DISCUSSION

The proposed method has been applied on one image of every sequence of the aforementioned dataset, and has been judged on the score given to every candidate line. Thus, a true positive here is an A-line whose score is higher than τ and a false positive is a candidate line which is not an A-line but has a score higher than τ as well. With these definitions, the ROC (Receiver Operating Characteristic) curve representing the sensitivity of the method against its specificity shows an area under the curve of 0.95 (cf Figure 2), demonstrating that the proposed scoring method has a great capacity to distinguish between A-lines and other elements. Because A-lines are patterns indicating a healthy lung, it is more important not to detect them wrongly than to detect all of them. Thus, to evaluate the choice of a threshold of 0.5 for the A-lines detection we use the F0.5-score, which corresponds to the harmonic mean between the precision and the recall by considering the precision to be two times as important as the recall. The performance of the method with τ = 0.5 for this measure is 0.84, and its accuracy is 95%, proving this choice of threshold to be relevant for the given situation.

The proposed method is, to our knowledge, the only one that has been quantitatively evaluated on A-lines detection. It has been developed with the intention of being used for an automatic classification estimating the aeration of the lung, and to this end, is based on the scoring of the level of confidence that can be given to the fact that the detected line is an A-line. Indeed, for future works such as developing a classifier for the whole LUS scoring, having such an information may allow the classifier to estimate more accurately the score to give to the LUS image. Finally, the proposed method obtained great results on the task of A-lines scoring and detection, that we hope to confirm on an independent cohort of future patients.

Fig. 1 :

 1 Fig. 1: Steps of the proposed method: (a) Pre-processed image; (b) Pleural line (in blue) identification; (c) Original and smoothed intensity profile of the image; (d) Image after contrast adjustment; (e) Image after the ASF operation; (f) Image after the opening operation; (g) Result of the detection with the detected A-lines displayed in green.

Fig. 2 :

 2 Fig. 2: ROC curve and its Area Under the Curve (AUC).

Algorithm 2

 2 Scoring of the contrast of a candidate line

	Input: d
	Output: s2
	1: if |d| < 0.15 then
	2:	s2 ← 0
	3: else if |d| < 0.5 then
	4:	s2 ← 0.57 × |d| -0.085
	5: else if |d| < 1.25 then
	6:	s2 ← 1.067 × |d| -0.33
	7: else
	8:	s2 ← 1
	9: end if
	Algorithm 3 Scoring of the result of the morphological
	operations for a given candidate line
	Input: Opres, ASFpres, L, H
	Output: s3
	1: if Opres then
	2:	s3 ← 0.6
	3: else if ASFpres then
	4:	s3 ← 0.5
	5: else
	6:	s3 ← 0.4
	7: end if
	8: if L > 100 then
	9:	s3 ← s3 + 0.4
	10: else if L > 80 then
	11:	s3 ← s3 + 0.3
	12: else if L > 60 then
	13:	s3 ← s3 + 0.2
	14: end if
	15: if L/H < 3 then
	16:

TABLE I :

 I Patient data.

	No. of patients	Sex	Age	BMI (kg/m²)	MV a (days)
	7	M(4)-F(3)	52	24	7
	a MV: Mechanical Ventilation

ACKNOWLEDGMENT

The authors are thankful to Dr Vincent Joussellin and Dr Vincent Bonny who acquired the data at the hospital La Pitié-Salpêtrière (AP-HP) under the protocol NCT04180410 promoted by Bioserenity.