Method and system for real-time determination of the risk index for growth of legionella bacteria in indoor water systems of a building

Abstract

The present invention relates to a method for determining the risk index for growth of Legionella in water systems of a building, said risk index being expressed as a numerical value, said method comprising the following steps: I) measurement of at least two values corresponding to variables selected from the group comprising physical, physicochemical and biological characteristics of said water system, each of said values corresponding to a risk value; and II) calculation of the overall risk index from the risk values by means of weighted composition using automated means; characterized in that: the method comprises measuring bioactivity in the water system by means of at least one biosensor installed in said water system, and determining by automated means the difference between an expected bioactivity value and the one actually measured; and in that, in the calculation of the risk index in step II, said difference is used as one of said measured risk values from step I to determine the risk index.

Description

[0001] METHOD AND SYSTEM FOR DETERMINING THE RISK INDEX OF LEGIONELLA BACTERIA GROWTH IN INDOOR WATER INSTALLATIONS OF A BUILDING IN REAL TIME

[0002] DESCRIPTION

[0003] The present invention belongs to the hygiene and health sector. Specifically, it is applicable to facilities that are likely to become sources of human exposure to Legionella bacteria in any building, with a greater potential in buildings in the hospital, hotel, nursing home, educational centers, sports centers, and penitentiary sectors.

[0004] In particular, the present invention relates to a method for automatically determining a risk index for the growth of Legionella bacteria in a building's water networks or installations. To this end, the method takes into account the correlation and weighting of physical, chemical, and biological parameters of the water with parameters related to the hydraulic design of the installations and the building's usage and structural characteristics. Furthermore, the invention considers and adapts the risk assessment in real time and assesses the corrective action to be taken, if necessary.

[0005] Legionella is the common name for the genus Legionella, which groups Gram-negative, rod-shaped bacteria. These bacteria can cause an infection in humans called legionellosis. This can present as a mild febrile illness, with no specific pulmonary focus (Pontiac fever), or as severe as atypical pneumonia called Legionnaires' disease. The main sources of transmission for these bacteria are the water systems of large buildings, hotels and hospitals, humidifiers, misting machines, spas and hot springs, and air conditioning systems.

[0006] The risk is such that, in 2020, Directive (EU) 2020 / 2184 of the European Parliament and of the Council of 16 December 2020 on the quality of water intended for human consumption was published, requiring the implementation of a Water Health Plan (WHP) in existing facilities in tertiary sector buildings designated as priority. The regulations on this matter incorporate, as one of the most innovative aspects, a new risk management methodology based on identifying and controlling the risks in these types of facilities.This regulation has been implemented in the 27 Member States of the European Union, as in Spain, through Royal Decree RD3 / 2023. In some countries, this has resulted in the establishment of semi-quantitative methods for risk assessment. The severity of the hazard and the probability of the hazardous event occurring if appropriate corrective or preventive measures are not taken must be assessed. The risk of the existence and growth of Legionella is considered among the most serious. In some cases, the method of carrying out this assessment has resulted in a risk assessment matrix. This is shown in the following table, included in the Spanish Royal Decree RD3 / 2023:

[0007] Table 1. Legionella outbreak risk matrix according to RD3 / 2023

[0008] Thus, a need is emerging to assess and determine the risk of Legionella appearing or growing in a building, in order to prevent a Legionellosis outbreak.

[0009] Currently, determining the risk of Legionella growth in a facility considered susceptible to the bacteria is based on analytical and measurement methods using a fluid sample from the facility. However, numerous scientific articles and studies demonstrate that the risk of Legionella growth is associated with parameters beyond water quality, such as the hydraulic design characteristics of a facility, fluid temperatures, material types, lengths of facilities, use, building type and geometry, among others.

[0010] Podemos encontrar ejemplos en la literatura sobre la influencia de la temperatura en la presencia de Legionella en estudios como los de Buse et al., 2012 (Buse, H. Y., Schoen, M. E., & Ashbolt, N. J. (2012). Legionellae in engineered systems and use of quantitative microbial risk assessment to predict exposure. Water Research, 46, 921- 933), Marchesi et al., 2011 (Marchesi, I., Marchegiano, P., Bargellini, A., Cencetti, S., Frezza, G., Miselli, M., & Borella, P. (2011). Effectiveness of different methods to control Legionella in the water supply: ten-year experience in an Italian university hospital. Journal of Hospital Infection, 77, 47-51), Mouchtouri et al., 2007 (Mouchtouri, V., Velonakis, E., & Hadjichristodoulou, C. (2007). Thermal disinfection of hotels, hospitals, and athletic venues hot water distribution systems contaminated by Legionella species. American Journal of Infection Control, 35, 623-627) o Ndiongue et al., 2005 (Ndiongue, S., Huck, P. M., & Slawson, R. M. (2005).Effects of temperature and biodegradable organic matter on control of biofilms by free chlorine in a model drinking water distribution system. Water Research, 39, 953-964).

[0011] Regarding the influence of other parameters such as pipe length and stagnation, there is broad consensus on their impact on the precision, accuracy, and effectiveness of methods for estimating the risk of Legionella growth. However, few studies have empirically evaluated these factors in practice. Some studies have provided information on the parameters considered to be the best predictors of Legionella contamination risk, such as those by Eboigbodin et al., 2008 (Eboigbodin, KE, Seth, A., & Biggs, CA (2008). A review of biofilms in domestic plumbing. Journal of the American Water Works Association, 100, 131-138) or Volker et al., 20016 (Volker, S., Schreiber, C. Kistemann, T. (2016) Modelling characteristics to predict Legionella contamination risk - Surveillance of drinking water plumbing systems and identification of risk areas. International Journal of Hygiene and Environmental Health, 219, 101-109).Studies such as that of De Giglio et al., 2019 (De Giglio, O. (2019) Legionella and legionellosis in touristic-recreational facilities: Influence of climate factors and geostatistical analysis in Southern Italy (2001-2017) Environmental Research, 178, 108721), have even taken into account parameters such as climatic influence.

[0012] That is, the state of the art clearly identifies the different parameters that impact the development of Legionella. However, despite having correctly identified the main parameters, the risk indices calculated from these do not predict the existence and growth of Legionella with the desirable accuracy, since they do not take into account the changes that occur in the facilities over time or base their predictions on microbiological controls that do not provide a real risk value (De Giglio, O. (2019)).To date, there is no standardized system or method that simultaneously and periodically measures and evaluates all the determining factors for determining the risk of Legionella growth in building facilities that use or may use water as a transmission vector. This allows for the early prediction, control, and prevention of nosocomial and / or environmental infections, as well as the corrective measures to be employed. Therefore, there is a need in the sector to improve the determination of predictive indices for the risk of Legionella growth, based on which the necessary preventive measures can be taken. The present invention addresses this need.

[0013] The present invention relates to methods for determining by automatic means a risk index for the growth of Legionella in a water installation of a building, said risk index being expressed as a numerical value, comprising the following steps:

[0014] I) measurement and storage in an electronic memory of at least two values ​​corresponding to variables selected from the group comprising the physical, physical-chemical and biological characteristics of the water installation; each of these values ​​corresponding to a risk value; and

[0015] II) calculating the general risk index by automatic means from the values ​​by weighted composition; characterized in that: the method comprises measuring bioactivity in the water facility by means of at least one biosensor placed in said water facility, determining by automatic means the difference between an expected bioactivity value and the one actually measured and in that, in calculating the risk index in step II, said difference is used as one of the said measured and stored values ​​from step I to determine the risk index.

[0016] Preferably, the method comprises measuring and storing at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9 or at least 10 different values ​​in step I. Preferably, the number of variables measured and stored in step I is as large as possible.

[0017] In a preferred embodiment, said automatic means are processors programmed to perform different operations such as those selected from the group comprising accessing the values ​​recorded in the memory, calculating the difference between expected and measured bioactivity values ​​and calculating the general risk index by weighted composition.

[0018] The values ​​corresponding to physical parameters refer to the characteristics and hydraulic design of the installation and the geometric parameters of the building. Therefore, in a preferred embodiment, the method includes, among the measured values ​​from step 1, characteristic values ​​of the installation selected from a group comprising: the age of the installation, the installation material, the existence and type of recirculation system, the distribution network, and the use of water storage tanks.

[0019] After studying the data obtained from more than 900 buildings in the tertiary sector of various typologies, over a period of 8 years, technical publications and scientific documents, where the characteristics and hydraulic design of the facilities and geometric parameters of the building have been compiled, the inventors have come to the conclusion that a method is needed for determining a Legionella growth risk index such as that defined by the present invention, capable of taking into account the physical, physicochemical and / or biological characteristics of the facility and which includes the forecast of the growth of the facility's own biofilm in the calculation of the risk index.By understanding the bioactivity data in the water facility, the biofilm growth cycle is understood. The method allows for the application of morphological filters to assess the growth risk, for example, through a biofilm growth base curve, to identify growth trends over a duration attributable to a risk of Legionella growth.

[0020] In a preferred embodiment, the method comprises automated means for analyzing and defining bioactivity within the water system based on measurements from at least one biosensor taken over a period of at least one month. This would allow the method to have a sufficient number of measurements to perform a correct analysis of bioactivity without losing information. More preferably, the number of measurements analyzed over a one-month period is at least 2,800. Even more preferably, the number of measurements analyzed over a one-month period is between 2,800 and 3,000.

[0021] Preferably, the present invention comprises automated means for assessing the overall biofilm growth trend and / or the medium-term growth trend and / or the short-term growth trend, by analyzing successive bioactivity measurements obtained using the biosensors. Preferably, said automated means are programming environments and specialized software for the creation, implementation, and analysis of mathematical models. More preferably, said automated means utilize a programming and numerical computing platform for data analysis, algorithm development, creation of mathematical models, simulation, and visualization of the aforementioned. Preferably, the overall growth trend is measured from the point of origin. More preferably, the point of origin is the first day of monitoring or when any event considered to be a new point of origin occurs.Preferably, a point of origin can be after performing a corrective shock treatment on the facility. Preferably, the medium-term growth trend is that obtained from measurements taken over a period of 60 days. Also preferably, the short-term growth trend is that obtained from measurements taken over a period of 30 days.

[0022] In a preferred embodiment, the method comprises measuring temperature using at least one temperature sensor placed in the facility and using it to determine the bioactivity value by automated means. Preferably, the at least one temperature sensor is installed in the same location as the at least one biosensor.

[0023] More preferably, the method comprises measuring the redox potential (ORP) using at least one ORP sensor placed in the facility and using it to determine the bioactivity value using said automatic means. Preferably, said sensor is installed at the point where the facility's final disinfection treatment is performed.

[0024] In another preferred embodiment, the method comprises measuring both the redox potential (ORP) and the temperature using at least one sensor each installed in the water facility, and using them to determine the bioactivity value by automatic means. Preferably, the at least one ORP sensor is installed at the point where the final disinfection treatment of the facility is performed. Preferably, the at least one temperature sensor is installed in the same location as the at least one biosensor.

[0025] There are physical and chemical factors related to water quality that can affect the determination of the Legionella growth risk index. Therefore, in a preferred embodiment, the method uses measurements in step 1, which comprise measuring at least two risk values ​​using at least one sensor installed in the water system, from the values ​​selected from the group comprising: temperature, total chlorine content, free chlorine content, pH, and redox potential (ORP).

[0026] Each measured value is weighted automatically based on a proprietary database. Preferably, said database includes data obtained from water facility studies and state-of-the-art knowledge. In a preferred embodiment, said database includes data specific to the facility where the risk is being assessed. More preferably, said database includes historical data from the facility, taking into account the historical behavior, in terms of Legionella, positive cases or outbreaks of Legionella in the facility, their recurrence over time, and their improvement or failure depending on the corrective treatment performed.

[0027] Preferably, the method comprises automatic means for updating its database with the values ​​resulting from measurements of the water facility for which the Legionella growth risk index is being determined. Even more preferably, said database is automatically updated with each measurement taken, creating a history.

[0028] In a preferred embodiment, the method comprises automatic means for adjusting the weighting of the risk value given to a value measured in step 1 based on its lower and / or upper threshold value. Currently, defined threshold values ​​exist for some of the aforementioned risk values; however, each installation has its own thresholds. Preferably, the method comprises automatic means for determining the threshold values ​​for the measured risk values. Thus, in embodiments where the method adjusts the threshold for each value, the threshold is better adapted to the installation. Preferably, at least in a first approximation, the lower threshold value for each measured risk value is its mean value minus three times the standard deviation, and / or the upper threshold value for each measured risk value is its mean value plus three times the standard deviation.

[0029] More preferably, the method takes into account the existing correlation of values ​​or factors considered to be risk factors, and therefore, a preferred embodiment of the method comprises automatic means for adjusting the dependence between different measured risk values. Preferably, the automatic means model the incidence between the measured risk values ​​and reject dependent values ​​based on the percentages of dependence obtained.

[0030] In a preferred embodiment, the calculation of the risk index is carried out by automatic means that apply generalized linear mathematical models (GLM) based on multivariate strategies and the risk index that the growth and exposure of bacteria in water facilities represents for humans using the quantitative microbial risk assessment methodology (QMRA).

[0031] This QMRA methodology has been included in the World Health Organization (WHO) guidelines since 2016 as Quantitative Microbial Risk Assessment: Application to Water Safety Management. It is based on a four-step risk assessment procedure:

[0032] (1) Identification of the potential danger to humans

[0033] (2) Exposure assessment

[0034] (3) Dose-response analysis: Determining the probability of infections through mathematical models.

[0035] (4) Risk characterization. In a preferred embodiment, the present invention comprises automated means, such as software, that allows a user to view, manipulate, and enter values. In another preferred embodiment, the method comprises automated means that inform the user of the Legionella growth risk index. In a preferred embodiment, the automated means informs the user whether the growth risk index value is so high as to require corrective measures, after comparing said value with the risk values ​​assigned according to the reference risk matrix. Preferably, said corrective measures comprise cleaning the water system. Preferably, the method comprises automated means for indicating the effectiveness of a corrective measure carried out. In a preferred embodiment, the method comprises the use of automated means that allow access to said information to a user.More preferably, said automatic means are the set of programs included in a computer. Even more preferably, the present invention comprises automatic means for adjusting the risk values ​​assigned to each measured value based on a risk matrix considered. Preferably, said risk matrix adjusts the weighting of the risk values ​​based on a specific regulation.

[0036] Preferably, the method according to any of the aforementioned embodiments relates to a method for determining the risk index for Legionella growth in a building's cold water system or network. In another preferred embodiment, the method relates to a method for determining the risk index for Legionella growth in a building's domestic hot water (DHW) system or network. In another preferred embodiment, the method relates to a method for determining the risk index for Legionella growth in both water systems or networks.

[0037] The present invention also provides a system for estimating the risk index of Legionella growth in the interior facilities of a building that makes use of the method described in any of the previously mentioned embodiments.

[0038] The present invention is novel and advantageous, since it considers the difference between the expected bioactivity value in the water installation and the measured value as an additional risk value to be considered, along with other measurements, in order to determine a Legionella growth risk index with greater precision than the methods included in the state of the art. Furthermore, the method makes it possible to adjust the expected value based on the measurements carried out and also to weight each variable based on the knowledge of the state of the art in contrast to a history of own data, taking into account the parameters recognized as fundamental and the behavior and history of the installations where it will be applied, its repetition over time and its improvement or not depending on the corrective treatment carried out.Furthermore, preferred embodiments of said method allow adjusting the bioactivity risk values ​​based on the measurement of other variables that may influence bioactivity, such as temperature and / or redox potential (ORP).

[0039] The method described takes into account the effect of risk values ​​and how their weight directly impacts the existing risk index, as well as its evolution over time, having been underestimated until now in the risk assessment methodology, so we find ourselves before a unique invention that innovates in the estimation of the risk of outbreaks of nosocomial and environmental legionellosis, which includes the weighting of factors and their temporal evolution.

[0040] As a fundamental point, the described method determines the Legionella growth risk index of the facility in question, beyond the information provided through prediction techniques and physical analysis techniques, with the aim of being able to respond in advance of a possible health alarm.

[0041] It is understood that for a person skilled in the art any of the different embodiments explained above that make up the present invention are combinable with each other.

[0042] In the context of the present invention, the term "variable", "parameter" or "factor" refers to a magnitude that can be measured or quantified in the context of a system, process or installation, and is essential for the analysis and control of said systems. The term "risk value" refers to the value assigned to a "variable", "parameter" or "factor" considered as a risk variable for the growth of Legionella, which will be used subsequently in the calculation of the risk index by means of weighted composition.

[0043] In the field of physical-chemical risk values, the term "physicochemical variable" in the present invention refers to those magnitudes that involve physical or chemical properties, such as temperature, concentration of chemical substances, conductivity, among others, which are relevant to the operation of the water installation.

[0044] In the field of means for performing physicochemical or biological growth measurements, methods for determining measurement variables in water are understood to refer to specific procedures for quantifying parameters in water. Such methods may refer to the use of "physicochemical sensors" and "biosensors," which entail devices capable of measuring and detecting physical, chemical, or biological quantities in the system, respectively. Such methods may also refer to measurements derived from sample collection and analysis or cultivation of these in laboratories. In the context of the present invention, the term "sensors" refers to devices capable of measuring physical or chemical quantities in the system, providing quantitative information on relevant variables. These sensors are essential for monitoring and controlling the system, allowing for real-time data collection for informed decision-making.It should be understood that the sensors referred to in this invention have automatic means for telematically sending the data of the measurements they perform, which are recorded in an electronic memory and processed by another automatic means, capable of using each measured value and calculating the risk index as described in this invention.

[0045] On the other hand, "bioactivity sensors" or "biosensors" are a specific category of sensors capable of measuring and detecting quantities associated with biological processes. These devices are designed to detect and measure biological quantities, such as the presence of microorganisms or specific biomolecules such as ATP or the content of polysaccharides and proteins, bioactivity, for example through the electrochemical signal of biofilm, or the amount of biomass. In the context of the present invention, biosensors are crucial tools for monitoring biological activity or bioactivity relevant in water systems. A biofilm sensor or sensor for monitoring biofilm growth is considered a type of biosensor or bioactivity sensor. An exemplary biofilm monitoring sensor that measures bioactivity (i.e., biosensor) is the one described by Pavanello, G., Faimali, M., Pittore, M., Mollica, A., Mollica, A., & Mollica, A. (2011 ).Exploiting a new electrochemical sensor for biofilm monitoring and water treatment optimization. Water Research, 45(4). Specifically, this example of a biosensor measures bioactivity indirectly through potentiostatic and intensiostatic modes and records bioactivity in BES (biofilm electrochemical signal), expressed as a current or potential density (mV vs Zn). Specifically, this type of biosensor is used in the present invention to measure bioactivity.

[0046] The term "biofilm" in this document refers to a layer formed by microorganisms adhering to the surfaces of a building's internal water systems, and is relevant in the context of the present invention, which seeks to understand and control its growth in order to calculate the risk value associated with said growth.

[0047] "Morphological filters" in this invention refer to data or image processing methods that alter their shape or structure, being used for specific analyses of biofilm growth.

[0048] In the context of the present invention, the term "automatic means" refers to devices, systems, or processes that operate in an automated manner, that is, without direct human intervention. These means may include electronic components, software, sensors, actuators, or other technologies that enable the automatic execution of specific functions within the patented system.

[0049] In situations involving Legionella in building water systems, the "corrective treatments" mentioned herein are defined as actions or procedures applied to address and correct situations considered to be at risk, specifically related to the degree of presence or potential presence of Legionella. An example of a corrective measure would include cleaning and / or disinfection of the water system. "Weighting" in the present invention refers to the process of assigning relative values ​​to different variables or factors according to their importance in the system, which is essential for informed decision-making.

[0050] Weighting uses a correlation network specifically designed to adapt the value to a large number of variables, increasing the accuracy and precision of the results as it is used. It can range from weighted compositions to complex equations conditioned by the input values.

[0051] Weighted composites are mathematical functions, such as a weighted average, where a value—in this case, the risk index—is obtained by multiplying each of the data points (the risk value assigned to the measured values) by its weight and then adding them together, thus obtaining a weighted sum. The sum of the weights is then divided by the weights, resulting in the weighted average, or risk index.

[0052] Complex equations are the relationships established between different parameters that, directly or indirectly, allow us to obtain, in some cases, quantifiable indicators, similar to digital twins, to obtain numerical assessments based on different dimensions of the installation. The parameterization functions of the different variables depend on the dimension addressed by different subsections—or calculation levels. The same variable can be found at different calculation levels, both for the same equation and for different correlations. Among the different operators, we find multiplications, conditionals, and converters of qualitative signals into quantitative ones.

[0053] In the context of the present invention, the term “threshold value” refers to a minimum or maximum value of a magnitude from which a certain effect occurs, specifically, the minimum or maximum value of a variable from which its value changes in the weighting to determine the risk index of the system.

[0054] In the context of the present invention, the term "mathematical model" refers to an abstract and structured representation of a system or process using mathematical equations and relationships. These models seek to describe and predict the behavior of the system, facilitating analysis and decision-making. Within the scope of the present invention, mathematical models are essential tools for understanding and optimizing the performance of the facilities and processes involved.

[0055] The acronym "QMRA" stands for "Quantitative Microbial Risk Assessment." Within the scope of this invention, QMRA is a systematic approach that uses mathematical models to quantify the risks associated with the presence of microorganisms, such as Legionella, in water systems. This approach allows for the precise assessment and management of microbiological risks to humans within the scope of the patent.

[0056] The acronym "GLM" stands for "Generalized Linear Model." In the patent, GLM is a type of mathematical model used to analyze relationships between variables, even when the data distribution does not follow a normal distribution. This approach is valuable for understanding and modeling the complex phenomena present in the invention, considering different types of parameters and nonlinear relationships.

[0057] The "geometric variables" in the present invention are magnitudes related to the dimensions and shapes of the pipes and the water installation.

[0058] When referring to "installation design" in this invention, it refers to the process of planning and configuring a system or installation, considering various variables for its optimal operation.

[0059] The "hydraulic design of a facility" in the present invention refers to the specific planning of hydraulic aspects within the facility, such as those aspects that allow the flow or stagnation of water and its control.

[0060] For a better understanding, some figures related to a possible embodiment of the method carried out in accordance with the present invention are attached for explanatory and non-limiting purposes.

[0061] Figure 1 shows the analysis over time of bioactivity, the measurement of temperature and redox potential (ORP) values ​​and the determination of thresholds for the latter two during the period of 21000 (2.1 x 10 4 ) measurements of an embodiment of the present invention in a particular building and facility.

[0062] Figure 2 shows an example of the same embodiment as Figure 2 over the period of 23000 (2.3 x 10 4 ) measurements.

[0063] Figure 3 shows an example of the same realization of Figures 2 and 3 during the period of 25000 (2.5 x 10 4 ) measurements.

[0064] Figure 4 shows the study of the dependence between detailed variables and statistical results for consideration or rejection.

[0065] A non-limiting example of the method of the present invention comprises automatic means such as a computer where a user can select and / or adjust the ranges of the measured values ​​so that the Legionella growth risk index is obtained within the framework of said regulations.

[0066] Thus, in the same non-limiting example, a user can enter values ​​based on the characteristics of the building's water system where the method is being applied, such as physical parameters related to the characteristics and hydraulic design of the facility and geometric parameters of the building. Furthermore, in this embodiment, the method includes the measurement of other bioactivity and physical-chemical variables through the use of sensors placed in the building's water system or network, which collect measured values ​​that are sent electronically to the same computer.This computer features automated means for recording measured values, and automated means, such as a programming and numerical computing platform for data analysis, algorithm development, mathematical model creation, simulation, and visualization of the above, to analyze the facility's biofilm growth based on bioactivity measurements. It also calculates the difference between the expected growth value and the measured value, and uses this as a risk value. This value is used to determine the Legionella risk index along with the other measured values. In this way, the risk index is a value calculated and adjusted for each measurement, which will alert the user of the need to take corrective measures if the risk index approaches high values, according to the risk matrix entered.If a risk level considered high is exceeded, the system alerts the user to take corrective measures, such as cleaning and / or disinfecting the water system.

[0067] Figures 1, 2 and 3 are examples showing the periodic measurement of bioactivity risk values, temperature and redox potential (ORP) for this same implementation of the method. Specifically, the three figures show the evolution over time of the risk values ​​taken from 21000 (2.1 x 10 4 ) data in Figure 1, of 23000 (2.3 x 10 4 ) data in Figure 2, and 25000 (2.5 x 10 4) data in Figure 3. In these figures it is possible to observe how the threshold values ​​(dashed lines) of the temperature and redox potential values ​​vary as the number of data increases. In addition, the three figures show an embodiment of the method that comprises automatic means to display a first graph (Bio) corresponding to the data from the bioactivity sensor to monitor the growth of the biofilm in the system, and where three different curves are observed, resulting from the application of morphological filters by the automatic means to identify growth trends based on the measured bioactivity data at a global, medium-term and short-term level.

[0068] Figure 4 shows how the same example of the invention comprises automatic means for representing an analysis of the dependence between measured values. This embodiment of the method comprises automatic means for modeling the incidence between values ​​and rejecting dependent parameters based on the percentage of dependence, in order to specify the degree of incidence between risk values ​​and obtain a more precise risk index. In a first approximation, when the percentage of dependence is greater than 75%, the resulting data are rejected as valid data for calculating the risk index and are not considered. Specifically, the figure highlights that 79.4% (result -0.79445) of the bioactivity measurement is given by the temperature variable during said period, which means that said bioactivity data should not be considered as risk since they are the result of the dependence on another variable.

[0069] The inventors have tested the method described by the present invention in vapour-laden buildings and demonstrated that it works. When the bioactivity value determined by the bioactivity sensor was higher or lower than expected, said index value was adjusted. When the index value exceeded an acceptable index value according to the risk framework (e.g., a national regulation) to which it was adjusted, the relevant preventive and / or corrective measures were taken, such as cleaning and / or disinfecting the facility, allowing the growth of the biofilm to be controlled and preventing the occurrence of legionellosis outbreaks.

[0070] Using all this data, the method example obtains a series of risk values ​​from which a weighted composition can be used to calculate the Legionella growth rate in the water system. The method thus comprises measuring these values, adjusting these risk values ​​based on the threshold value of each variable, and compensating for these values ​​when they are dependent. Furthermore, to calculate the risk value due to bioactivity in the water system, the method comprises analyzing bioactivity measurements obtained by a biosensor using automated means to determine the expected biofilm growth and comparing it with the measured data.

[0071] Although the invention has been described and represented based on representative examples, it should be understood that said exemplary embodiment is in no way limiting for the present invention, so that any of the variations that are included directly or by way of equivalence in the content of the appended claims, should be considered included within the scope of the present invention.

Claims

CLAIMS 1. Method for determining the risk index of Legionella growth in the water installations of a building, said risk index being expressed as a numerical value, which comprises the following steps: I) measurement and storage in an electronic memory of at least two values ​​corresponding to variables selected from the group comprising physical, physicochemical and biological characteristics of said water facility, each of said values ​​corresponding to a risk value; and II) calculation of the general risk index by automatic means from the values ​​by weighted composition; characterized in that: the method comprises measuring bioactivity in the water installation by means of at least one biosensor installed in said water installation, determining by automatic means the difference between an expected bioactivity value and the one actually measured by the biosensor, and in that, in the calculation of the risk index in step II, said difference is used as one of the said measured and stored values ​​from step I to determine the risk index.

2. Method according to claim 1, wherein the method comprises measuring the temperature by means of at least one temperature sensor installed in the installation and its use in determining the value of bioactivity by automatic means.

3. Method according to claim 2, wherein said sensor is located in the same location as the at least one biosensor.

4. Method according to any of claims 1 to 3, wherein the method comprises measuring the redox potential (ORP) by means of at least one ORP sensor installed in the water installation and its use in determining the value of the bioactivity by automatic means.

5. Method according to claim 4, wherein said sensor is located at the point where the last disinfection treatment of the facility is carried out.

6. Method according to any of the preceding claims, wherein the measurement of step I comprises measuring at least two variables by means of at least one sensor installed in the water installation, of the variables selected from the group comprising: temperature, total chlorine quantity, free chlorine quantity, pH, and redox potential (ORP).

7. Method according to any of the preceding claims, wherein the method comprises automatic means for determining the threshold values ​​of the values ​​measured in step I.

8. Method according to claim 7, wherein the lower threshold value of each value is the mean value of the value minus three times its standard deviation and the upper threshold value of each value is the mean value of the value plus three times its standard deviation.

9. Method according to claim 7 or 8, wherein the method comprises automatic means for adjusting the weighting of the measured values ​​according to their threshold values.

10. Method according to any of the preceding claims, wherein the method comprises automatic means for adjusting the dependency between the different measured values.

11. Method according to any of the preceding claims, wherein the measurement of step I comprises measuring at least two risk values ​​characteristic of the installation selected from a group comprising: the age of the installation, the material of the installation, the existence and typology of recirculation of the system, the distribution network and the use of water storage tanks.

12. Method according to any of the preceding claims, wherein the method 13. A method according to claim 12, wherein the method comprises automatic means for updating its database with the values ​​resulting from measurements of the water installation for which the Legionella growth risk index is being determined.

14. A method according to any of the preceding claims, wherein the method refers to a method for determining the Legionella growth risk index in a domestic hot water (DHW) installation of a building.

15. A system for determining the Legionella growth risk index in the interior installations of a building, characterized by comprising a processor programmed to use the method described according to any of the preceding claims.

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