METHOD FOR ESTIMATING THE CONTENT OF PESTICIDES OR PESTICIDE METABOLITES IN WATER TO BE DRINKING WATER

Abstract

METHOD FOR ESTIMATING THE CONTENT OF PESTICIDES OR PESTICIDE METABOLITES IN WATER TO BE DRINKING A method for estimating the content of pesticides or pesticide metabolites such as ESA and OXA metabolites of chloroacetamides, in water to be made potable is described. The method comprises obtaining a nitrate content followed by calculating the content of pesticides or pesticide metabolites from the obtained nitrate content. Figure for abstract: Fig. 2

Description

Title of the invention: METHOD FOR ESTIMATING THE CONTENT OF pesticides or pesticide metabolites IN WATER TO BE DRINKING WATER FIELD OF THE INVENTION

[0001] The present invention relates to the field of treatment of water intended for human consumption. In particular, the present invention relates to a method for estimating the content of pesticides or pesticide metabolites, such as sulfonic acids and / or oxalic acids derived from active substances of the chloroacetamide family, in water to be made potable.

[0002] BACKGROUND OF THE INVENTION

[0003] S-metolachlor, alachlor, metazachlor, acetochlor and flufenacet are herbicidal active substances of the chloroacetamide family. With the exception of alachlor, which has been banned from the European Union market for several years, they are used in the composition of many herbicides, alone or in combination with other active substances. These active substances are particularly used for weed control in large crops (corn, sunflower, soybean, beet, sorghum, millet, etc.) in pre-emergence or early post-emergence of weeds. Thus, the application period for these active substances typically extends from March to June.

[0004] These active substances degrade rapidly in soils by biological means to give metabolites, in particular in the form of sulfonic acid (ESA) or oxalic acid (OXA), which may prove to be more stable than the parent molecules from which they are derived. Thus, alachlor ESA, alachlor OXA, acetochlor ESA, acetochlor OXA, metazachlor ESA, metazachlor OXA, metolachlor ESA and metolachlor OXA, flufenacet ESA and flufenacet OXA, collectively referred to as ESA / OXA metabolites, are metabolites regularly found in surface water but also in groundwater. Their content in the water to be made potable may depend on numerous factors, such as the quantity and intensity of precipitation, the date of their occurrence after herbicide treatment, the nature and structure of the soil, the characteristics of each molecule (adsorbability, polarity, octanol / water partition coefficient, solubility, leaching potential, etc.).

[0005] The French Agency for Food, Environmental and Occupational Health Safety (ANSES) has defined a method for assessing the relevance of pesticide metabolites in water intended for human consumption. This assessment focuses on the relevance with regard to the potential health risk after ingestion of water for the consumer. Thus, a metabolite is said to be relevant "if there is reason to consider that it could generate (itself or its transformation products) an unacceptable health risk for the consumer" and quality limits are set accordingly. According to the latest opinions issued by ANSES, alachlor OXA, meto-lachlor ESA, metolachlor NOA 413173 and flufenacet ESA are classified as relevant metabolites and thus set their quality limit at 0.1 pg / L per individual substance in water intended for human consumption. This list of relevant metabolites is regularly updated by ANSES through the publication of new opinions.

[0006] These metabolites are very weakly adsorbed in soils and are therefore very mobile. They therefore have a high leaching potential. The presence of ESA / OXA metabolites, and pesticide metabolites in general, in water to be made potable has become a major problem in the production of drinking water.

[0007] These metabolites, due to their hydrophilic, highly soluble and polar nature, are generally less easily eliminated by conventional treatments used for the production of drinking water than the active substances from which they are derived. Treatments based on activated carbon in all its forms, powder (PAC), grain (GAC), and according to all existing implementations (injection of PAC in clarification, filtration on GAC, finishing treatments (or refining) with PAC or GAC reactors are techniques allowing them to be eliminated or at least to limit their content in Water Intended for Human Consumption (EDCH). A dosage or renewal rate of carbon adapted to the quantity of pollution quantified or renewals of the GAC of the optimized filters are necessary for effective treatment of these metabolites over time.In some installations, several water resources, containing different metabolite contents, are available and can allow water mixing in order to limit the metabolite content in the water to be made potable. The necessary dilution rate or mixing percentages is / are to be defined according to the metabolite contents of each of the resources.

[0008] Regular monitoring, at best monthly or bimonthly, of ESA / OXA metabolite levels in industrial installations has made it possible to establish seasonal variations in ESA / OXA metabolite levels in water to be made potable. However, variations in metabolite concentrations (increase, decrease, minimum content, maximum content) vary from one year to the next and are the consequence of multiple factors, such as agricultural practices, meteorology, hydrology, etc.

[0009] Qualitative knowledge of the seasonal evolution of ESA / OXA metabolites makes it possible to modify the operating conditions of unit treatments based on activated carbon. On the other hand, without precise knowledge of the ESA / OXA metabolite contents, it does not make it possible to guarantee the effectiveness and performance of the treatments. for compliance with health standards on treated water before distribution. Indeed, without quantification, it is impossible to know if the activated carbon is under or overdosed or to know the short, medium or long term effectiveness of the preventive renewal of granular activated carbon filters before reaching a leak of pesticides or metabolites not respecting health standards. Current analytical materials and methods do not allow quantification of these metabolites in real time. Only analyses, which can be costly and carried out post-treatment, make it possible to determine the abatement yields obtained and to optimize the quantity of activated carbon (PAC, CAG) to use and / or the renewals of CAG filters and / or the dilution factors to implement. However, the return of analytical reports can be very long (several weeks to a month) making it difficult to optimize operational monitoring.In order to respect the quality limits set by the standards in force and to optimize the costs of treating water to be made potable, it is important to control as closely as possible the dose of activated carbon (PAC, CAG) used and / or the renewals of CAG filters and / or the dilution factors to be implemented.

[0010] Thus, a need remains for the provision of a simple, rapid and inexpensive method for estimating the concentration of ESA / OXA metabolites in water to be made potable, this method ultimately making it possible to determine in real time the optimal operating conditions necessary to achieve a reduction rate of ESA / OXA metabolites so as to produce potable water meeting the quality limits set by the standards in force.

[0011] BRIEF DESCRIPTION OF THE INVENTION

[0012] According to a first aspect, the invention relates to a method for estimating the content of pesticides or pesticide metabolites in a water resource to be made potable, comprising the implementation by data processing means of a client of the following steps:

[0013] (a) obtaining a nitrate content of the water to be made potable from said resource;

[0014] (b) calculation of the estimated content of pesticides or pesticide metabolites by ap application of a prediction model to at least the nitrate content obtained.

[0015] According to advantageous and non-limiting characteristics:

[0016] The content of pesticides or pesticide metabolites is the content of ES A / OXA metabolites.

[0017] The ESA / OXA metabolite content includes the content of metolachlor ESA and / or flufenacet ESA.

[0018] The ESA / OXA metabolite content is the total content of different ESA / OXA metabolites of chloroacetamides.

[0019] Said prediction model takes as input a vector of descriptive parameters of said water resource to be made potable including at least the nitrate content obtained.

[0020] Said vector of descriptive parameters of said water resource to be made potable further comprises at least one parameter of rainfall, temperature and / or flow rate of the resource.

[0021] The method comprises a step (aO) of learning, by data processing means of a server, the parameters of said prediction model from a learning base of pairs of a nitrate content of the water to be made potable from a resource and a reference value of said pesticide or pesticide metabolite content of this resource.

[0022] Step (aO) comprises constructing the learning base in the following manner:

[0023] (i) taking several samples of water to be made potable;

[0024] (ii) measuring at least the content of nitrates and pesticides or pesticide metabolites in said samples.

[0025] Step (b) comprises selecting a prediction model from among several possible prediction models, depending on the water resource, the applied model being said selected model.

[0026] Step (a) comprises the online measurement of the nitrate content carried out by means of an analyzer.

[0027] The water to be made potable is raw water.

[0028] According to a second aspect, the invention relates to a use of the method for estimating the content of pesticides or pesticide metabolites in a water resource to be made potable as defined according to the first aspect for optimizing the treatment of pesticides or pesticide metabolites in the water to be made potable, preferably by means of activated carbon.

[0029] According to a third aspect, the invention relates to a method for treating pesticides or pesticide metabolites in a water resource to be made potable by means of at least one treatment technique comprising the following steps:

[0030] (A) implementation of the method for estimating the content of pesticides or metabolites of pesticides in said water resource to be made potable according to the first aspect;

[0031] (B) regulating said treatment technique as a function of the estimated content of pesticides or pesticide metabolites.

[0032] According to advantageous and non-limiting characteristics:

[0033] Said treatment technique is with activated carbon, and a dose of activated carbon (CAP or CAG) and / or a renewal of CAG filters is regulated in step (B).

[0034] Said treatment technique is dilution and a mixing rate is regulated in step (B). Brief description of the drawings

[0035] Other characteristics and advantages of the present invention will appear on reading the following description of a preferred embodiment. This description will be given with reference to the appended drawings in which:

[0036] - [Fig.l] [Fig.l] is a diagram of an architecture for implementing the methods according to the invention; - [Fig.2] [Fig.2] illustrates the steps of a preferred embodiment of the methods according to the invention.

[0037] DEFINITIONS

[0038] The expression "water to be made potable" as used in the present invention designates the water which supplies a device or a plant for producing potable water. This is the captured, drawn or collected water which is conveyed to a treatment plant, in order to receive the treatments making it suitable for human consumption. This method allows the estimation of the content of pesticides or pesticide metabolites in a water resource to be made potable. By "pesticide" is meant a substance used to control organisms considered harmful. It is a generic term that includes insecticides, fungicides, herbicides and parasiticides designed to have a biocidal action. The term pesticide essentially refers to "phytosanitary products" used in agriculture, forestry and horticulture, although it also refers to household products such as anti-lice shampoo, although the associated quantities are marginal in comparison. It can thus be approximated that the pesticides in question in this application are of agricultural origin, but we will not be limited to any type of pesticide.

[0039] Pesticide metabolites are understood to mean the degradation, transformation and reaction products formed in the environment from active substances included in the composition of phytosanitary products and biocides, and in particular ESA / OXA metabolites, which cause significant degradation in the quality of our resources.

[0040] The expression "ESA / OXA metabolite(s)" as used in the present description designates the ESA / OXA metabolite(s) of chloroacetamides, in other words the sulfonic acids (ESA) and / or the oxalic acids (OXA) derived from active substances of the chloroacetamide family, in particular the compounds alachlor ESA, alachlor OXA, acetochlor ESA, acetochlor OXA, metazachlor ESA, metazachlor OXA, metolachlor ESA and metolachlor OXA, flufenacet ESA and flufenacet OXA.

[0041] The expression "ESA / OXA metabolite content" means the content of a single chloroacetamide metabolite selected from alachlor ESA, alachlor OXA, acetochlor ESA, acetochlor OXA, metazachlor ESA, metazachlor OXA, metolachlor ESA, metolachlor OXA, flufenacet ESA, flufenacet OXA or denotes the total content of several chloroacetamide metabolites selected from alachlor ESA, alachlor OXA, acetochlor ESA, acetochlor OXA, metazachlor ESA, metazachlor OXA, metolachlor ESA, metolachlor OXA, flufenacet ESA and flufenacet OXA. A content may in itself be expressed for example as a mass concentration or a molar concentration. DETAILED DESCRIPTION OF THE INVENTION

[0042] Architecture

[0043] The invention relates to a method for estimating the content of pesticides or pesticide metabolites in a water resource to be made potable.

[0044] As will be seen, this method may comprise one or more machine learning components, and in particular at least one so-called prediction model, which may in particular conform to one of the following artificial intelligence models:

[0045] - Gradient Boosting Regressor

[0046] - Vector Machine Regressor support

[0047] - Ridge Regression

[0048] - Neural network.

[0049] Indeed, unexpectedly, the inventors have demonstrated that the content of ESA / OXA metabolites, and generally the content of pesticides or pesticide metabolites, in the water to be made potable can be correlated with the nitrate content in the water. Without wishing to be bound by any theory, it is suggested that this correlation can be explained by similar agricultural practices and by similar behavior of nitrates and pesticides or pesticide metabolites in the soil. Pesticides or pesticide metabolites including ESA / OXA metabolites could be co-transported with nitrates of agricultural origin to groundwater and surface water.

[0050] Since the nitrate content in the water to be made potable can be measured in real time, the correlation established by the inventors makes it possible to estimate in real time the content of pesticides or pesticide metabolites in the water to be made potable. This real-time estimation makes it possible to adapt the treatments necessary for effective reduction of problematic pesticides or pesticide metabolites such as ESA / OXA metabolites. Thus, this estimation makes it possible to adapt as precisely as possible the dose of activated carbon (PAC, CAG) to be used and / or the renewals of the CAG filters and / or the dilution factors to be implemented necessary to achieve a reduction which makes it possible to produce water meeting the quality limits set by the standards in force.

[0051] In some embodiments, the pesticide or pesticide metabolite content comprises the individual pesticide or metabolite content, e.g. example for chloroacetamides and their ESA / OXA metabolites, it can include the metolachlor content or the metolachlor ESA content or the metazachlor ESA content etc.

[0052] In some embodiments, the pesticide or pesticide metabolite content comprises the total content of several pesticides or several pesticide metabolites or several pesticides and pesticide metabolites, for example for chloroacetamides and their ESA / OXA metabolites, the total content may comprise the sum of the contents of one or more parent molecules (metolachlor, alachlor etc.) or the sum of the contents of one or more metabolites (alachlor ESA, alachlor OXA, acetochlor ESA, acetochlor OXA, metazachlor ESA, metazachlor OXA, metolachlor ESA, metolachlor OXA, etc.) or the sum of the contents of one or more parent molecules and one or more metabolites.

[0053] By "prediction" is meant the determination of a candidate value of the content of pesticides or pesticide metabolites from an input data or from a learning data in the case of a learning phase of said model.

[0054] The input or learning data are vectors of descriptive parameters of a water resource (i.e. variables) including in particular said nitrate content, but also potentially other parameters relating to aspects of said resource such as:

[0055] - Additional physicochemical parameters that can be analyzed or measured directly on site at the same time as nitrates such as UV absorbance 254 nm (total or filtered 0.45 pm), turbidity, etc.

[0056] - Rainfall;

[0057] - Temperature; and / or

[0058] - Flow rate (upstream from the nearest water level gauge, but also tributaries close ones).

[0059] In a particularly preferred manner, said values of the parameters are a function of time, i.e. in practice there is a time series of values, so as to constitute a history.

[0060] This consists of creating accumulations (shifted or not), of different time steps (for example one value per day). For rainfall, we can thus use the following series of values:

[0061] - Precipitation height of the previous day

[0062] - Precipitation height from the day before yesterday

[0063] -...

[0064] - Precipitation height of D-30

[0065] Thus, in this example, the rainfall is defined by an object of dimension 30.

[0066] Alternatively we can have the same thing but with accumulations:

[0067] - Cumulative rainfall over the last 2 days

[0068] - Cumulative over the last 3 days

[0069] - etc.

[0070] In addition or alternatively, variables can also be introduced to translate the number of dry days:

[0071] - Number of consecutive days without rain;

[0072] - Number of consecutive days with less than 1mm of rain

[0073] Constructions of the same order can be carried out on the other variables. For temperatures, we can introduce the notions of maximum, minimum and average temperatures. These data can, for example, be directly linked to the decision for a farmer to carry out spreading or not.

[0074] The present methods are implemented within an architecture such as represented by [Fig.l], using a server 1 and a client 2. The server 1 is the learning equipment (implementing the learning method) and the client 2 is a user equipment (implementing the estimation method), for example a terminal of a water treatment plant.

[0075] It is entirely possible that the two devices 1, 2 are merged, but preferably the server 1 is a remote device, and the client 2 an on-site device, in particular an office computer, a laptop, etc. The client device 2 is advantageously connected to at least one acquisition device 10 called an analyzer (and any other necessary sensor), so as to be able to directly acquire said input data, typically to process it directly, alternatively this input data will be loaded onto the client device 2.

[0076] In all cases, each equipment 1, 2 is typically a remote computer equipment connected to a local network or a wide area network such as the Internet network for the exchange of data. Each comprises data processing means 11, 21 of the processor type, and data storage means 12, 22 such as a computer memory, for example a flash memory or a hard disk. The client 2 typically comprises a user interface 23 such as a screen for interacting, for example to display the estimated content of pesticides or pesticide metabolites, noted [ESA / OXA] for simplicity (although it is recalled that we are not limited to these pesticide derivatives in particular) and / or the measured content of nitrates, noted [NO3 ].The user interface can thus enable continuous monitoring of the levels of pesticides or pesticide metabolites [ESA / OXA] and / or nitrates [NO3] in the water to be made potable and to consult daily, monthly and annual concentration histories, thus facilitating the operation of the treatment plant.

[0077] The server 1 advantageously stores a learning database, i.e. a set of pairs of at least one nitrate content [NO3] of the water to be made potable. of a resource (preferably in a vector of descriptive parameters of said resource) and a “reference” value of said corresponding pesticide or pesticide metabolite content, i.e. an expected value for this resource. This reference value of said pesticide or pesticide metabolite content may have been obtained by measurement, see below. It is understood that each pair of the learning base is associated with a resource and that preferably the base comprises a large number of pairs corresponding to various resources. It is also possible, for each resource, to have several learning pairs if “specific” models are to be trained, see below.

[0078] Acquisition

[0079] With reference to [Fig.2], the present method begins with a step (a) of obtaining a nitrate content [NO3 ] of the water to be made potable from said resource, as input data. As explained, the prediction model can take as input a vector of descriptive parameters of said water resource including a nitrate content [NO3 ], so that step (a) comprises obtaining the values of each of the parameters of said vector.

[0080] Even if, as explained, the present method can obtain it in any way, preferably step (a) comprises the measurement of said nitrate content [NO3] of the water to be made potable from said resource by the device 10, in particular from a sample.

[0081] The sampling of water samples to be made potable is typically carried out upstream of a water treatment plant, for example at a draw-off or collection point, or within the water treatment plant, for example before any treatment installations (for example at the level of the supply of water to be made potable to the treatment installations).

[0082] The water resources to be made potable are typically groundwater or surface water (rivers, canals, reservoirs) alone or in a mixture.

[0083] The different samples of water to be made potable are preferably taken at different times t, for example one or more samples may be taken over the course of a month or several months, for example one value per day as explained above, so as to allow characterization of the water to be made potable over the days. The number of samples to be taken will thus be chosen so as to obtain a good representation of the possible variations in the concentrations of nitrates [NO3 ] and pesticides or pesticide metabolites [ESA / OXA] over the course of a week, a month or several months.

[0084] The measurement of nitrate [NO3 ] concentrations (and pesticides or pesticide metabolites [ESA / OXA] if we are seeking to constitute the learning base) in the samples taken is carried out according to methods well known to those skilled in the art. profession. Thus, nitrate concentrations [NO3 ] can be determined using spectrometric, chromatographic or other methods, for example in accordance with standard NF ISO 15923-1. Thus, concentrations of pesticides or pesticide metabolites [ESA / OXA] can be determined using liquid or gas chromatography methods followed by detection by tandem mass spectrometry.

[0085] Nitrate content is typically measured using an analyzer, or in laboratory analysis. Such analyzers are simple to implement and inexpensive. They also have the advantage of transmitting instantaneous measurement values. Examples of analyzers that may be suitable include analyzers using colorimetric or spectrometric methods.

[0086] The analyzer may be a continuous analyzer allowing users to equip themselves with automated alert stations and to have real-time measurements.

[0087] Ideally, the analyzer is placed upstream of a water treatment plant, for example at a draw-off or collection point, or within the water treatment station, for example before any treatment installations (for example at the level of the supply of water to be made potable to the treatment installations).

[0088] The other parameters of said vector such as those linked to rainfall, temperature or flow rate can be measured by other devices 10, in particular conventional sensors (rain gauge, thermometer, flow meter), or obtained for example from the internet.

[0089] All parameter values can, as explained, be measured over time so as to obtain a series of values.

[0090] Implementation of the model

[0091] The present method is particularly distinguished in that it then comprises a step (b) of calculating the estimated content of pesticides or pesticide metabolites [ESA / OXA] by applying the prediction model to at least the nitrate content obtained.

[0092] More precisely, the prediction model takes as input said vector of descriptive parameters of said water resource to be made potable (which includes at least the nitrate content obtained), and generates as output said estimate of the content of pesticides or pesticide metabolites [ESA / OXA].

[0093] Note that there may be several prediction models, in particular of a different nature (as explained chosen from Gradient Boosting Regressor, Support Vector Machine Regressor, Ridge Regression, etc.) and / or taking a vector of different parameters (with more or fewer parameters).

[0094] The idea is that the different types of models can have different performances depending on the site of the resource, and particularly preferably there is a optimal model associated with each resource, called specific model.

[0095] Thus, step (b) can comprise the selection of a prediction model from among several possible prediction models, depending on the water resource (and in particular the prediction model specific to the resource).

[0096] Note that alternatively or in addition, step (b) may comprise the use of several possible prediction models and the merging of the results (i.e. a candidate value of pesticide or pesticide metabolite content is estimated for each model and they are combined, for example by taking their average). The two approaches may be combined, for example by selecting more than one model and combining the results of the selected models.

[0097] Learning

[0098] The method advantageously comprises a step (aO) of learning the parameters of said prediction model from said learning base. This step is typically implemented very early on, in particular by the remote server 1. It does not need to be implemented repeatedly.

[0099] As explained, the learning base can comprise a large number of pairs of a nitrate content [NO3] of the water to be made potable from a resource (or directly of vectors of descriptive parameters of this resource) and a reference value of said pesticide or pesticide metabolite content [ESA / OXA] of this resource.

[0100] Conventionally, learning leads to determining the parameters which minimize a cost function comparing for each pair the estimated content of pesticides or pesticide metabolites [ESA / OXA] by applying the prediction model to at least the nitrate content of said pair (and preferably the vector of parameters), and the reference content.

[0101] For each model, an exploration can be made on the hyperparameters, of the "grid search" type, in particular on the number of estimators, and the learning rate. The other parameters are chosen then left fixed.

[0102] Step (aO) may further comprise validation of the model on a portion of the base left for this purpose. The model is tested and validated by comparing its predictions (estimated pesticide or pesticide metabolite [ESA / OXA] contents by application of the prediction model) with reality (the associated reference contents). Said comparison may be made on the basis of a predefined indicator.

[0103] Among the various known indicators, we can notably use:

[0104] - The coefficient of determination: R2 measures the adequacy between a model (more pre precisely the data estimated from the model) and the observed data (or the realizations of the random variables) which made it possible to establish it. It is defined as the proportion of variance explained in the total variance. The value taken by the variable that we are trying to explain can be broken down into two parts: one explained by the model and the other residual, due for example to measurement errors. The dispersion of all the observations is therefore broken down into variance explained by the regression and residual, unexplained variance. The total variance is the sum of the two.

[0105] - The mean absolute error: MAE which is the absolute value of the differences between the actual values and predicted values and allows to evaluate the accuracy of the predictions. The lower the MAE (close to zero), the better the prediction.

[0106] Note that several models of different nature can be learned by repeating this process.

[0107] If we learn a model per resource (specific models), then we can limit ourselves for each resource to the pairs in the base corresponding to this resource.

[0108] Alternatively or in addition, learning can be carried out with one explanatory variable, mainly nitrates, then two, for example nitrates and flow, then three, nitrates, flow and rainfall, then n parameters.... Then, the different predictions obtained from different models can be compared with reality. Thanks to the validation indicators, the most relevant model will be retained.

[0109] The model can then be used for the real-time prediction of step (b), once the model has been validated, i.e. providing data to the model on a day-to-day basis to predict the evolution of a system.

[0110] Note that step (aO) can advantageously comprise the construction of the learning base, in particular in the following manner:

[0111] (i) taking several samples of water to be made potable (potentially in various resources and / or at several time points for the same resource);

[0112] (ii) measuring at least the concentration of nitrates [NO3 ] and pesticides or pesticide metabolites [ESA / OXA] in said samples (and measuring the values of the other parameters of the vector). The two associated form a pair of said base.

[0113] As explained, the measurement of the concentration of pesticides or pesticide metabolites [ESA / OXA] is complex, but here only needs to be done once. Indeed, as soon as the model is learned, it can be used in real time to replace this measurement of the content of pesticides or pesticide metabolites [ESA / OXA].

[0114] Automated processing

[0115] This method of estimating the content of pesticides or pesticide metabolites also offers the possibility of automating, on certain treatment installations, the treatment (elimination or at least limitation) of pesticides or pesticide metabolites, in particular based on activated carbon, for optimized management.

[0116] Thus, the method of estimating the content of pesticides or pesticide metabolites in a water resource to be made potable according to the present invention can be used to optimize the treatment of pesticides or pesticide metabolites in the water to be made potable.

[0117] In certain embodiments, the pesticides or pesticide metabolites and in particular the ESA / OXA metabolites are eliminated by treatment with activated carbon involving a dosage of CAP (Powdered Activated Carbon) in clarification and / or finishing or refining treatments within CAP or CAG reactors where the carbon is in suspension.

[0118] By treatment, we mean any technical solution making it possible to reduce the content of pesticides or pesticide metabolites [ESA / OXA] in the resource to be treated, this therefore includes the dilution or mixing of water to be made potable.

[0119] The present invention therefore also relates, according to this second aspect, to a method for treating pesticides or pesticide metabolites in a water resource to be made potable by means of at least one treatment technique comprising the following steps:

[0120] (A) implementation of the method for estimating the content of pesticides or metabolites of pesticides [ESA / OXA] in said water resource to be made potable according to the process according to the first aspect described above;

[0121] (B) regulating said treatment technique as a function of the estimated content of pesticides or pesticide metabolites [ESA / OXA].

[0122] As explained, said treatment technique can be with activated carbon. Then, the regulation can take several forms.

[0123] In certain embodiments, a dose of activated carbon injected into the water to be made potable can be regulated. The regulation of the dose of activated carbon injected into the water to be made potable as a function of the estimated content of pesticides or pesticide metabolites [ESA / OXA] can be carried out by means of one or more injectors used to inject the activated carbon into the water to be made potable. Thus, the client 2 can be connected to one or more injectors 3 and transmit data to them in real time.

[0124] The regulation of the dose of activated carbon may consist of a reduction in the concentration of activated carbon injected when the estimated content of pesticides or pesticide metabolites [ESA / OXA] is lower than a predetermined threshold value or outside a range of predetermined values, or even a stopping of the injection of activated carbon.

[0125] Alternatively, the regulation of the dose of activated carbon may consist of an increase in the dose of activated carbon injected when the estimated content of pesticides or pesticide metabolites [ESA / OXA] is greater than a predetermined threshold value or outside a range of predetermined values or may consist of a resumption of the injection of activated carbon. Thus the method for treating pesticides or pesticide metabolites in the water to be made potable allows a constant reduction percentage of pesticides or pesticide metabolites to be maintained.

[0126] The optimal doses of activated carbon to be injected as a function of the desired percentage of reduction of pesticides or pesticide metabolites can be determined from standard curves expressing the percentage of reduction of pesticides or pesticide metabolites as a function of the dose of activated carbon used.

[0127] In certain embodiments, the pesticides or pesticide metabolites and in particular the ESA / OXA metabolites are treated by filtration on CAG.

[0128] Then, the regulation of said activated carbon technique may involve the planning of the renewal or regeneration of the CAG of the filter(s) (always depending on the estimated content of pesticides or pesticide metabolites [ESA / OXA] in the raw water) in the absence of pretreatment, in particular based on activated carbon.

[0129] In the presence of elimination pretreatment(s), in particular based on activated carbon, the renewal of the CAG filter(s) will be adapted according to the estimated content of pesticides or pesticide metabolites [ESA / OXA] in raw water and the residual content deduced from the reductions in the pretreatment(s).

[0130] In certain embodiments (alternatively or in addition to activated carbon), the dilution or mixing of water to be made potable from different resources makes it possible to limit the levels of pesticides and / or pesticide metabolites and in particular ESA / OXA metabolites in the water to be made potable.

[0131] Optimal dilution or mixing rates can then be regulated (always based on the estimated content of pesticides or pesticide metabolites [ESA / OXA] in each of the resources).

[0132] Similar to what is done for activated carbon, the regulation of the dilution or mixing rates may consist of a reduction in the dilution or mixing rates when the estimated content of pesticides or pesticide metabolites [ESA / OXA] is below a predetermined threshold value or outside a range of predetermined values, or even a stopping of the dilution or mixing.

[0133] The regulation of the dilution or mixing rates may on the other hand consist of an increase in the dilution or mixing rates when the estimated content of pesticides or pesticide metabolites [ESA / OXA] is higher than a predetermined threshold value or outside a range of predetermined values or may consist of a resumption of the dilution or mixing.

Claims

Claims

1. Method for estimating the content of pesticides or pesticide metabolites in a water resource to be made potable, comprising the implementation by data processing means (21) of a client (2) of the following steps: (a) obtaining a nitrate content of the water to be made potable from said resource; (b) calculating the estimated content of pesticides or pesticide metabolites by applying a prediction model to at least the nitrate content obtained; the method comprising a prior step (aO) of learning, by data processing means (11) of a server (1), the parameters of said prediction model from a learning base of pairs of a nitrate content of the water to be made potable from a resource and a reference value of said content of pesticides or pesticide metabolites of this resource.

2. The method of claim 1 wherein the pesticide or pesticide metabolite content is the ESA / OXA metabolite content.

3. A method according to claim 2 wherein the ESA / OXA metabolite content comprises the content of metolachlor ESA and / or flufenacet ESA.

4. A method according to either of claims 2 and 3, wherein the ESA / OXA metabolite content is the total content of different ESA / OXA metabolites of chloroacetamides

5. Method according to one of claims 1 to 4, in which said prediction model takes as input a vector of descriptive parameters of said water resource to be made potable comprising at least the nitrate content obtained.

6. Method according to claim 5, in which said vector of descriptive parameters of said water resource to be made potable further comprises at least one parameter of rainfall, temperature and / or flow rates of the resource.

7. Method according to one of claims 1 to 6 in which step (aO) comprises the construction of the learning base in the following manner: (i) taking several samples of water to be made potable; (ii) measuring at least the content of nitrates and pesticides or me- pesticide tabolites in said samples.

8. Method according to one of claims 1 to 7, in which step (b) comprises selecting a prediction model from several possible prediction models, depending on the water resource, the model applied being said selected model.

9. Method according to one of the preceding claims in which step (a) comprises the online measurement of the nitrate content carried out by means of an analyzer.

10. Use of the method for estimating the content of pesticides or pesticide metabolites in a water resource to be made potable as defined according to one of the preceding claims for optimizing the treatment of pesticides or pesticide metabolites in the water to be made potable, preferably by means of activated carbon.

11. 11. Method for treating pesticides or pesticide metabolites in a water resource to be made potable by means of at least one treatment technique comprising the following steps: (A) implementing the method for estimating the content of pesticides or pesticide metabolites in said water resource to be made potable according to one of claims 1 to 9; (B) regulating said treatment technique as a function of the estimated content of pesticides or pesticide metabolites.

12. Method according to claim 11 in which said treatment technique is activated carbon, and an activated carbon (CAP or CAG) dose and / or a renewal of CAG filters is regulated in step (B).

13. A method according to one of claims 11 and 12 wherein said treatment technique is dilution and a mixing rate is regulated in step (B).

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