Environmental monitoring system and monitoring method

Abstract

The present invention discloses an environmental monitoring system and a monitoring method, which relates to the field of environmental monitoring technology. The system accurately collects the abundance vector MAV of the microbial community within a fixed period, performs concentration and separation treatment, then adds a substrate reaction and monitors the enzyme catalytic reaction in the water sample in real time, generates an enzyme activity feature set EAF, combines the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, forms a multidimensional data set Eac, and establishes a comprehensive water quality assessment algorithm based on this, thereby obtaining a comprehensive water quality detection and assessment index WQI, and realizing a quantitative assessment of the health status of water quality. By comparing with the water quality assessment standard threshold Sthe, an early warning notification is automatically triggered, water quality abnormalities are discovered and responded to in a timely manner, solving the problem that the traditional method cannot capture water quality changes in a timely manner, especially in terms of rapid response to environmental pollution and ecological changes. It has significant advantages.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an environmental monitoring system and a monitoring method. Background Art

[0002] Environmental monitoring, as a key technology in the field of environmental protection, involves multiple aspects of detection and evaluation, including monitoring of pollutants in the natural environment of air, water and soil. With the rapid development of human society, water pollution has attracted widespread attention. Water pollution not only affects the safety of human drinking water, but also threatens the ecological environment and affects agricultural irrigation and industrial water use.

[0003] At present, traditional water quality monitoring methods mostly rely on chemical analysis, physical testing and standardized biological testing methods. Although these methods have certain application value in actual operation, they also have some significant limitations. For example, conventional chemical analysis methods, such as COD and BOD determination, can only detect certain specific pollutants in water, and it takes a long time to get results, and it is impossible to achieve real-time and continuous water quality monitoring; while biological detection methods often rely on the collection and processing of water samples, and have high professional requirements for operators, and there are certain operating errors.

[0004] In addition, traditional monitoring methods often ignore the changes in microbial communities in water bodies, especially the response characteristics of those microbial communities to changes in water quality. The microbial communities in water bodies are not static, they will change dynamically with changes in water quality, but traditional methods often do not include changes in microbial communities in the monitoring scope, so they cannot fully and accurately reflect the trend and real-time status of water quality changes. This deficiency makes the existing water quality monitoring methods unable to provide effective monitoring and early warning in complex and changeable water quality environments. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an environmental monitoring system and a monitoring method, which solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an environmental monitoring system and a monitoring method, comprising the following steps:

[0007] S1. Collect water samples from monitoring points at fixed periods using an automated sampler, extract microorganisms from water samples after sample pretreatment, analyze the composition of microbial communities using polymerase chain reaction technology, and form a microbial community abundance vector MAV;

[0008] S2, by concentrating and separating the microbial community abundance vector MAV, and then adding substrates for reaction, the enzyme catalysis reaction of the microbial community in the water sample is monitored in real time, and the reaction data is recorded to form the enzyme activity feature set EAF;

[0009] S3, integrating the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional data set Eac, and inputting it into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F;

[0010] S4, by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, a water quality assessment data set is formed, and it is substituted into the established comprehensive water quality assessment algorithm formula for evaluation to obtain the comprehensive water quality detection evaluation index WQI;

[0011] S5. By comparing the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI, the water quality assessment results of the water samples collected at the monitoring point are obtained, and an early warning notification is triggered according to the water quality assessment results.

[0012] Preferably, said S1 includes S11, S12 and S13;

[0013] S11. Collect water samples from monitoring points at fixed periods through an automated sampler, including collecting water samples at fixed periods and fixed amounts from urban water sources, drinking water, and industrial wastewater, and filter suspended matter and sediment through a filter membrane set in the automated sampler, and then use centrifugation technology to enrich microorganisms in the water samples to obtain pretreated and concentrated water samples;

[0014] S12. Analyze the composition of microbial communities by using polymerase chain reaction technology, specifically extract microorganisms from pretreated and concentrated water samples, including using a commercial DNA extraction kit for extraction, and then perform qPCR amplification, including using primers of the 16SrRNA gene to amplify microbial DNA, to achieve quantitative analysis of different microbial species in water samples, and according to the specificity of the primers, qPCR can quantitatively analyze the abundance of aerobic bacteria aer and anaerobic bacteria anaer;

[0015] Among them, the primers include fixed primers for directional amplification of aerobic bacteria aer, anaerobic bacteria anaer and total in the total microbial community.

[0016] Preferably, S13, by comparing the amplification result of aerobic bacteria aer with the standard curve, the abundance information of aerobic bacteria aer in the water sample is calculated and marked as aerobic bacteria abundance Naer;

[0017] The aerobic bacteria abundance Naer is obtained by the following calculation formula:

[0018]

[0019] In the formula, Ctaer represents the Ct value of aerobic bacteria amplification, specifically represents the cycle number when the aerobic bacteria-specific primers reach the threshold in the qPCR reaction, Cstedaer represents the intercept of the standard curve of aerobic bacteria, Maer represents the slope of the standard curvature of aerobic bacteria, and Vsample represents the volume of the water sample;

[0020] By comparing the amplification results of anaerobic bacteria anaer with the standard curve, the abundance information of anaerobic bacteria anaer in the water sample is calculated and marked as anaerobic bacteria abundance Nanaer;

[0021] The anaerobic bacteria abundance Nanaer is obtained by the following calculation formula:

[0022]

[0023] In the formula, Ctanaer represents the Ct value of anaerobic bacteria amplification, specifically the cycle number when the anaerobic bacteria-specific primers reach the threshold in the qPCR reaction, Cstdanaer represents the intercept of the standard curve of anaerobic bacteria, and Manaer represents the slope of the standard curvature of anaerobic bacteria;

[0024] By amplifying aerobic bacteria aer and anaerobic bacteria anaer in the water sample, the abundance information of the total microbial community in the water sample is calculated and marked as the total microbial abundance Ntotal;

[0025] The total microbial abundance Ntotal is obtained by the following calculation formula:

[0026]

[0027] In the formula, Cttotal represents the Ct value of total microbial amplification, specifically the number of cycles when the total microbial specific primers reach the threshold in the qPCR reaction, Cstdtotal represents the intercept of the standard curve of total microorganisms, and Mtotal represents the slope of the standard curvature of total microorganisms;

[0028] The microbial community abundance vector MAV = {Naer, Nanaer, Ntotal} is formed by integrating the aerobic bacteria abundance Naer, the anaerobic bacteria abundance Nanaer and the total microbial abundance Ntotal.

[0029] Preferably, S2 includes S21 and S22;

[0030] S21, by concentrating and separating the microbial community abundance vector MAV, the impurities in the water sample are removed for a second time, the microbial community is retained, and the concentration of the microbial community is enhanced to obtain the concentrated water sample volume Vcon;

[0031] The water sample volume Vcon is obtained by the following calculation formula:

[0032]

[0033] Where CF represents the concentration factor, which specifically represents the ratio between the initial volume of the water sample and the concentrated volume.

[0034] Preferably, S22, adding a fixed substrate to the concentrated water sample volume Vcon, so that the substrate reacts with the enzyme in the microbial community abundance vector MAV, synchronously monitoring the enzyme catalytic reaction of the microbial community, and recording relevant data during the reaction, including measuring the concentration change of the reaction to obtain the catalytic rate E, obtaining the substrate concentration C by a photometric analysis method, and obtaining the reaction time T by intercepting the time from the start to the end of the reaction, to form an enzyme activity feature set EAF = {E, C, T};

[0035] The catalytic rate E is given by The calculation formula is obtained, where △P represents the change in product concentration produced during the reaction, △T represents the change in reaction time, and the product specifically represents the product generated by the substrate during the reaction;

[0036] The substrate concentration C is determined by The calculation formula is obtained, where Pfinal represents the product concentration at the end of the reaction, and Pinitial represents the product concentration at the beginning of the reaction.

[0037] Preferably, said S3 includes S31 and S32;

[0038] S31, integrating the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional dataset Eac, which reflects the abundance information and enzyme activity information of each microorganism;

[0039] The multidimensional dataset Eac={Naer, Nanaer, Ntotal, E, C, T};

[0040] S32. Input the multidimensional data set Eac into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F.

[0041] Preferably, the aerobic bacteria change rate Raer is obtained by the following calculation formula:

[0042]

[0043] Wherein, Naer(cur) represents the abundance of aerobic bacteria in the current water sample, specifically the abundance of aerobic bacteria in the water sample volume Vcon, and Naer(bas) represents the abundance of aerobic bacteria under the preset reference water quality;

[0044] The anaerobic bacteria change rate Ranaer is obtained by the following calculation formula:

[0045]

[0046] Wherein, Ranaer(cur) represents the abundance of anaerobic bacteria in the current water sample, specifically the abundance of anaerobic bacteria in the water sample volume Vcon, and Ranaer(bas) represents the abundance of anaerobic bacteria under the preset baseline water quality;

[0047] The correction factor F is obtained by the following calculation formula:

[0048]

[0049] Wherein, F represents the correction factor, which is used to reflect the relationship between the overall enzyme activity level and abundance of the microbial community; β represents the power function of substrate concentration, which is used to adjust the effect of substrate concentration on enzyme activity, and the range is 0<β≤1; γ represents the power function of reaction time, which is used to adjust the effect of time on reaction, and the range is 0<γ≤1; Ntotal represents the total microbial abundance.

[0050] Preferably, said S4 includes S41 and S42;

[0051] S41, by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, a water quality assessment data set {Eac, Raer, Ranaer, F} is formed;

[0052] S42, substituting the water quality assessment data set into the established comprehensive water quality assessment algorithm formula for evaluation, and obtaining a comprehensive water quality detection assessment index WQI;

[0053] The comprehensive water quality assessment index WQI is obtained by the following calculation formula:

[0054]

[0055] Where exp represents exponential function and ln represents natural logarithm.

[0056] Preferably, the S5 includes S51;

[0057] S51, obtaining the water quality assessment result of the water sample collected at the monitoring point by comparing the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI, and triggering an early warning notification according to the water quality assessment result;

[0058] The water quality assessment results are obtained by the following comparison method:

[0059] When the comprehensive water quality detection and evaluation index WQI ≥ the water quality evaluation standard threshold Sthe, the water quality evaluation result of the water sample collected at the monitoring point is qualified, and no warning notification is triggered;

[0060] When the comprehensive water quality detection and evaluation index WQI is less than the water quality evaluation standard threshold Sthe, the water quality evaluation result of the water sample collected at the monitoring point is unqualified, triggering an early warning notification, including extracting the location of the monitoring point and the unique identification of the automatic sampler to send an early warning notification to the relevant environmental protection processing department for processing.

[0061] An environmental monitoring system and monitoring method, including a water sample collection module, a water sample processing module, an integrated extraction module, a water quality assessment module and a decision-making module;

[0062] The water sample collection module collects water samples from the monitoring points at a fixed period through an automated sampler, extracts microorganisms in the water samples after sample pretreatment, analyzes the composition of the microbial community through polymerase chain reaction technology, and forms a microbial community abundance vector MAV;

[0063] The water sample processing module concentrates and separates the microbial community abundance vector MAV, then adds substrate for reaction, monitors the enzyme catalytic reaction of the microbial community in the water sample in real time, and records the reaction data to form the enzyme activity feature set EAF;

[0064] The integration and extraction module integrates the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional data set Eac, and inputs it into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F;

[0065] The water quality assessment module forms a water quality assessment data set by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, and substitutes it into the established comprehensive water quality assessment algorithm formula for evaluation to obtain the comprehensive water quality detection evaluation index WQI;

[0066] The decision module compares the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI to obtain the water quality assessment results of the water samples collected at the monitoring point, and triggers an early warning notification based on the water quality assessment results.

[0067] The present invention provides an environmental monitoring system and a monitoring method, which have the following beneficial effects:

[0068] (1) By accurately collecting the microbial community abundance vector MAV within a fixed period, concentrating and separating it, adding the substrate reaction and monitoring the enzyme catalytic reaction in the water sample in real time, the enzyme activity feature set EAF is generated, and the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F are combined to form a multidimensional data set Eac. Based on this, a comprehensive water quality assessment algorithm is established to obtain the comprehensive water quality detection and assessment index WQI, realizing the quantitative assessment of the health status of water quality. By comparing with the water quality assessment standard threshold Sthe, an early warning notification is automatically triggered to detect water quality abnormalities and respond in time. Real-time, dynamic and comprehensive monitoring of water quality changes improves the efficiency and accuracy of water quality management and environmental monitoring, and solves the problem that traditional methods cannot capture water quality changes in a timely manner, especially in terms of rapid response to environmental pollution and ecological changes.

[0069] (2) By integrating the microbial community abundance vector MAV and the enzyme activity feature set EAF, a multidimensional data set Eac was formed and input into the microbial community and enzyme activity relationship analysis algorithm, and the aerobic bacteria change rate Raer, anaerobic bacteria change rate Ranaer and correction factor F were successfully obtained. These calculation results can not only reflect the changes of microorganisms under different water quality conditions, but also provide a quantitative basis for the relationship between microbial communities and water quality.

[0070] (3) By integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, a water quality assessment data set was generated and substituted into the comprehensive water quality assessment algorithm to finally calculate the comprehensive water quality detection and assessment index WQI. It was compared with the preset water quality assessment standard threshold Sthe to determine whether the water quality is qualified, and the early warning notification was automatically triggered based on the assessment results, which not only improved the response speed of water quality monitoring, but also ensured that the treatment measures could be put in place in a timely manner, thereby effectively ensuring water quality safety and reducing the lag and errors in traditional manual monitoring and response. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of an environmental monitoring system and a monitoring method according to the present invention;

[0072] Figure 2 The present invention is an environmental monitoring system and a schematic diagram of the monitoring system block diagram. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0074] Example 1

[0075] The present invention provides an environmental monitoring system and a monitoring method. Figure 1 , including the following steps:

[0076] S1. Collect water samples from monitoring points at fixed periods using an automated sampler, extract microorganisms from water samples after sample pretreatment, analyze the composition of microbial communities using polymerase chain reaction technology, and form a microbial community abundance vector MAV;

[0077] S2, by concentrating and separating the microbial community abundance vector MAV, and then adding substrates for reaction, the enzyme catalysis reaction of the microbial community in the water sample is monitored in real time, and the reaction data is recorded to form the enzyme activity feature set EAF;

[0078] S3, integrating the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional data set Eac, and inputting it into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F;

[0079] S4, by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, a water quality assessment data set is formed, and it is substituted into the established comprehensive water quality assessment algorithm formula for evaluation to obtain the comprehensive water quality detection evaluation index WQI;

[0080] S5. By comparing the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI, the water quality assessment results of the water samples collected at the monitoring point are obtained, and an early warning notification is triggered according to the water quality assessment results.

[0081] In this embodiment, through the combination of automated samplers and polymerase chain reaction technology, the microbial community in the water sample can be accurately collected and analyzed within a fixed period to form a microbial community abundance vector MAV, thereby providing real-time and accurate microbial basic data for subsequent water quality monitoring. Secondly, after concentration and separation, the substrate reaction is added and the enzyme catalytic reaction in the water sample is monitored in real time to generate an enzyme activity feature set EAF, which provides a detailed analysis of enzyme activity for water quality health. This process not only improves the sensitivity of water quality monitoring, but also can more carefully reflect the changes in microbial activity and ecological functions in the water body. Further, by integrating MAV and EAF data, combined with the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, a multidimensional data set Eac is formed, and a comprehensive water quality assessment algorithm is established based on this, thereby obtaining a comprehensive water quality detection and evaluation index WQI, and realizing a quantitative assessment of the health status of water quality. This system can not only accurately reflect the dynamic changes of the water body microbial community, but also automatically trigger early warning notifications by comparing with the water quality assessment standard threshold Sthe, and promptly detect water quality abnormalities and respond. Real-time, dynamic and comprehensive monitoring of water quality changes has improved the efficiency and accuracy of water quality management and environmental monitoring, and solved the problem that traditional methods cannot capture water quality changes in a timely manner, especially in terms of rapid response to environmental pollution and ecological changes.

[0082] Example 2

[0083] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11, S12 and S13;

[0084] S11. Collect water samples from monitoring points at fixed periods through an automated sampler, including collecting water samples at fixed periods and fixed amounts from urban water sources, drinking water, and industrial wastewater, and filter suspended matter and sediment through a filter membrane set in the automated sampler, and then use centrifugation technology to enrich microorganisms in the water samples to obtain pretreated and concentrated water samples;

[0085] S12. Analyze the composition of microbial communities by using polymerase chain reaction technology, specifically extract microorganisms from pretreated and concentrated water samples, including using a commercial DNA extraction kit for extraction, and then perform qPCR amplification, including using primers of the 16SrRNA gene to amplify microbial DNA, to achieve quantitative analysis of different microbial species in water samples, and according to the specificity of the primers, qPCR can quantitatively analyze the abundance of aerobic bacteria aer and anaerobic bacteria anaer;

[0086] Among them, the primers include fixed primers for directional amplification of aerobic bacteria aer, anaerobic bacteria anaer and total in the total microbial community.

[0087] S13, by comparing the amplification result of aerobic bacteria aer with the standard curve, the abundance information of aerobic bacteria aer in the water sample is calculated and marked as aerobic bacteria abundance Naer;

[0088] The aerobic bacteria abundance Naer is obtained by the following calculation formula:

[0089]

[0090] In the formula, Ctaer represents the Ct value of aerobic bacteria amplification, specifically represents the cycle number when the aerobic bacteria-specific primers reach the threshold in the qPCR reaction, Cstedaer represents the intercept of the standard curve of aerobic bacteria, Maer represents the standard curvature slope of aerobic bacteria, and Vsample represents the volume of the water sample, wherein the intercept of the standard curve of aerobic bacteria Cstedaer and the standard curvature slope of aerobic bacteria Maer are specifically generated by aerobic bacteria standard samples of known concentrations;

[0091] By comparing the amplification results of anaerobic bacteria anaer with the standard curve, the abundance information of anaerobic bacteria anaer in the water sample is calculated and marked as anaerobic bacteria abundance Nanaer;

[0092] The anaerobic bacteria abundance Nanaer is obtained by the following calculation formula:

[0093]

[0094] Wherein, Ctanaer represents the Ct value of anaerobic bacteria amplification, specifically represents the cycle number when the anaerobic bacteria-specific primer reaches the threshold value in the qPCR reaction, Cstdanaer represents the intercept of the standard curve of anaerobic bacteria, and Manaer represents the standard curvature slope of anaerobic bacteria, wherein the standard curve intercept Cstdanaer of anaerobic bacteria and the standard curvature slope Manaer of anaerobic bacteria are specifically generated by anaerobic bacteria standard samples of known concentrations;

[0095] By amplifying aerobic bacteria aer and anaerobic bacteria anaer in the water sample, the abundance information of the total microbial community in the water sample is calculated and marked as the total microbial abundance Ntotal;

[0096] The total microbial abundance Ntotal is obtained by the following calculation formula:

[0097]

[0098] In the formula, Cttotal represents the Ct value of total microbial amplification, specifically the number of cycles when the total microbial specific primers reach the threshold in the qPCR reaction, Cstdtotal represents the intercept of the standard curve of total microorganisms, and Mtotal represents the slope of the standard curvature of total microorganisms;

[0099] The microbial community abundance vector MAV = {Naer, Nanaer, Ntotal} is formed by integrating the aerobic bacteria abundance Naer, the anaerobic bacteria abundance Nanaer and the total microbial abundance Ntotal.

[0100] In this embodiment, by using polymerase chain reaction technology qPCR and special primers to quantitatively analyze the microbial community, not only can the abundance of aerobic bacteria Naer and anaerobic bacteria Nanaer in the water sample be accurately determined, but also the total microbial abundance Ntotal can be comprehensively evaluated. This innovative method that combines molecular biology technology with traditional water quality analysis methods enables the monitoring process to accurately classify and quantify different types of microorganisms, especially for effective identification of microbial groups that are more sensitive to water pollution or ecological changes. The calculated microbial community abundance vector MAV provides more comprehensive and reliable data support for subsequent water quality assessment and pollution source tracking, especially in various water quality environments such as water sources, drinking water and industrial wastewater, and can accurately reflect the changing trends of microbial communities.

[0101] Example 3

[0102] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0103] S21, by concentrating and separating the microbial community abundance vector MAV, the impurities in the water sample are removed for a second time, the microbial community is retained, and the concentration of the microbial community is enhanced to obtain the concentrated water sample volume Vcon;

[0104] The water sample volume Vcon is obtained by the following calculation formula:

[0105]

[0106] Where CF represents the concentration factor, which specifically refers to the ratio between the initial volume of the water sample and the concentrated volume. It is usually determined based on the actual degree of concentration during the experimental setting or sample processing.

[0107] S22, adding a fixed substrate to the concentrated water sample volume Vcon, so that the substrate reacts with the enzyme in the microbial community abundance vector MAV, synchronously monitoring the enzyme catalytic reaction of the microbial community, and recording relevant data during the reaction, including measuring the concentration change of the reaction to obtain the catalytic rate E, obtaining the substrate concentration C by a photometric analysis method, and obtaining the reaction time T by intercepting the time from the start to the end of the reaction, forming an enzyme activity feature set EAF = {E, C, T};

[0108] The catalytic rate E is given by The calculation formula is obtained, where △P represents the change in product concentration produced during the reaction, △T represents the change in reaction time, and the product specifically represents the product generated by the substrate during the reaction;

[0109] The substrate concentration C is determined by The calculation formula is obtained, where Pfinal represents the product concentration at the end of the reaction, and Pinitial represents the product concentration at the beginning of the reaction.

[0110] In this embodiment, the microbial community is retained by performing a secondary treatment of removing impurities from the water sample, and on this basis, the volume of the water sample is controlled by the concentration factor CF, so that the microbial concentration is enhanced. The innovation of this process is that the concentration factor can be flexibly set according to the experimental conditions, the accuracy and efficiency of sample processing can be optimized, and the microbial abundance in the water sample can be more accurately reflected. Further, by adding a fixed substrate to the concentrated water sample and monitoring the enzyme reaction, the catalytic rate E, substrate concentration C and reaction time T in the reaction are measured in real time by photometry and reaction time analysis methods, and an enzyme activity feature set EAF is formed, which not only provides the enzyme reaction characteristics of the microbial community for subsequent water quality assessment, but also can provide detailed data support for studying the changes in microbial enzyme activity in water quality, the degradation process of pollutants, etc. Compared with the traditional single physical and chemical analysis method, this method not only refines the correlation analysis between microbial activity and water quality, but also can better monitor and predict the response of the microbial community to water quality changes, greatly improving the sensitivity and real-time performance of water quality monitoring.

[0111] Example 4

[0112] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0113] S31, integrating the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional dataset Eac, which reflects the abundance information and enzyme activity information of each microorganism;

[0114] The multidimensional dataset Eac={Naer, Nanaer, Ntotal, E, C, T};

[0115] S32. Input the multidimensional data set Eac into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F.

[0116] The aerobic bacteria change rate Raer is obtained by the following calculation formula:

[0117]

[0118] Wherein, Naer(cur) represents the abundance of aerobic bacteria in the current water sample, specifically the abundance of aerobic bacteria in the water sample volume Vcon, and Naer(bas) represents the abundance of aerobic bacteria under the preset reference water quality;

[0119] The anaerobic bacteria change rate Ranaer is obtained by the following calculation formula:

[0120]

[0121] Wherein, Ranaer(cur) represents the abundance of anaerobic bacteria in the current water sample, specifically the abundance of anaerobic bacteria in the water sample volume Vcon, and Ranaer(bas) represents the abundance of anaerobic bacteria under the preset baseline water quality;

[0122] The correction factor F is obtained by the following calculation formula:

[0123]

[0124] Wherein, F represents the correction factor, which is specifically used to reflect the relationship between the overall enzyme activity level and abundance of the microbial community. The larger the correction factor, the stronger the activity of the microbial community. β represents the power function of substrate concentration, which is specifically used to adjust the effect of substrate concentration on enzyme activity, and the range is 0<β≤1. γ represents the power function of reaction time, which is specifically used to adjust the effect of time on reaction, and the range is 0<γ≤1. Ntotal represents the total microbial abundance.

[0125] In this embodiment, a multidimensional data set Eac is formed by integrating the microbial community abundance vector MAV and the enzyme activity feature set EAF. This integration process provides comprehensive microbial community structure and enzyme reaction characteristic data, which enables detailed analysis of the activity state of the microbial community in the water sample and the kinetic characteristics of the enzyme catalytic reaction. Further, the multidimensional data set Eac is input into the microbial community and enzyme activity relationship analysis algorithm, and the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F are successfully obtained. These calculation results can not only reflect the changes of microorganisms under different water quality conditions, but also provide a quantitative basis for the relationship between microbial communities and water quality. In particular, the correction factor F, which comprehensively considers the effects of substrate concentration and reaction time on enzyme activity, provides a more sophisticated regulation mechanism for water quality assessment, thereby enhancing the precise correspondence between microbial community activity and water quality status. The advantage of this integration and analysis method is that it breaks the limitation of relying only on a single water quality indicator in traditional monitoring methods, can comprehensively reflect the dynamic changes of microbial communities in water samples and the comprehensive effects of enzyme activity, and helps to achieve more efficient and sensitive water quality monitoring and management.

[0126] Example 5

[0127] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: the S4 includes S41 and S42;

[0128] S41, by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, a water quality assessment data set {Eac, Raer, Ranaer, F} is formed;

[0129] S42, substituting the water quality assessment data set into the established comprehensive water quality assessment algorithm formula for evaluation, and obtaining a comprehensive water quality detection assessment index WQI;

[0130] The comprehensive water quality assessment index WQI is obtained by the following calculation formula:

[0131]

[0132] Wherein, exp represents an exponential function, which is specifically used to smooth the relationship between parameters; ln represents the natural logarithm, which is specifically used to avoid data being too large or too small and to limit the impact of extreme values ​​of parameters on the results; ln(1+Raer) can smooth the change in the aerobic bacteria change rate Raer, so that extremely high or extremely low aerobic bacteria change rates will not have an excessive impact on the final results; ln(1+Ranaer) can smooth the change in the anaerobic bacteria change rate Ranaer, so that extremely high or extremely low anaerobic bacteria change rates will not have an excessive impact on the final results; ln(1+F) is used to balance the impact of enzyme activity on water quality assessment and avoid excessive impact when enzyme activity is abnormally high or low.

[0133] The S5 includes S51;

[0134] S51, obtaining the water quality assessment result of the water sample collected at the monitoring point by comparing the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI, and triggering an early warning notification according to the water quality assessment result;

[0135] The water quality assessment results are obtained by the following comparison method:

[0136] When the comprehensive water quality detection and evaluation index WQI ≥ the water quality evaluation standard threshold Sthe, the water quality evaluation result of the water sample collected at the monitoring point is qualified, and no warning notification is triggered;

[0137] When the comprehensive water quality detection and evaluation index WQI is less than the water quality evaluation standard threshold Sthe, the water quality evaluation result of the water sample collected at the monitoring point is unqualified, triggering an early warning notification, including extracting the location of the monitoring point and the unique identification of the automatic sampler to send an early warning notification to the relevant environmental protection processing department for processing.

[0138] In this embodiment, a water quality assessment data set is generated by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, and it is substituted into the comprehensive water quality assessment algorithm to finally calculate the comprehensive water quality detection and evaluation index WQI. It is compared with the preset water quality assessment standard threshold Sthe to determine whether the water quality is qualified, and automatically triggers an early warning notification based on the evaluation results. It can evaluate the water quality of water samples in real time and accurately, and issue an early warning in time when the water quality is unqualified, and notify the relevant environmental protection department to handle it. This automated and efficient monitoring and feedback mechanism not only improves the response speed of water quality monitoring, but also ensures that the treatment measures can be in place in a timely manner, thereby effectively ensuring water quality safety and reducing the lag and errors in traditional manual monitoring and response.

[0139] Example 6

[0140] An environmental monitoring system and monitoring method, please refer to Figure 2,Specifically: including water sample collection module, water sample processing module, integrated extraction module, water quality assessment module and decision-making module;

[0141] The water sample collection module collects water samples from the monitoring points at a fixed period through an automated sampler, extracts microorganisms in the water samples after sample pretreatment, analyzes the composition of the microbial community through polymerase chain reaction technology, and forms a microbial community abundance vector MAV;

[0142] The water sample processing module concentrates and separates the microbial community abundance vector MAV, then adds substrate for reaction, monitors the enzyme catalytic reaction of the microbial community in the water sample in real time, and records the reaction data to form the enzyme activity feature set EAF;

[0143] The integration and extraction module integrates the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional data set Eac, and inputs it into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F;

[0144] The water quality assessment module forms a water quality assessment data set by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, and substitutes it into the established comprehensive water quality assessment algorithm formula for evaluation to obtain the comprehensive water quality detection evaluation index WQI;

[0145] The decision module compares the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI to obtain the water quality assessment results of the water samples collected at the monitoring point, and triggers an early warning notification based on the water quality assessment results.

[0146] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An environmental monitoring method, characterized in that: The following steps are involved: S1. Collect water samples from monitoring points at fixed periods using an automated sampler, extract microorganisms from water samples after sample pretreatment, analyze the composition of microbial communities using polymerase chain reaction technology, and form a microbial community abundance vector MAV; S2, by concentrating and separating the microbial community abundance vector MAV, and then adding substrates for reaction, the enzyme catalysis reaction of the microbial community in the water sample is monitored in real time, and the reaction data is recorded to form the enzyme activity feature set EAF; S3, integrating the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional data set Eac, and inputting it into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F; S4, by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, a water quality assessment data set is formed, and it is substituted into the established comprehensive water quality assessment algorithm formula for evaluation to obtain the comprehensive water quality detection evaluation index WQI; S5. By comparing the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI, the water quality assessment results of the water samples collected at the monitoring point are obtained, and an early warning notification is triggered according to the water quality assessment results.

2. An environmental monitoring method according to claim 1, characterized in that: Said S1 includes S11, S12 and S13; S11. Collect water samples from monitoring points at fixed periods through an automated sampler, including collecting water samples at fixed periods and fixed amounts from urban water sources, drinking water, and industrial wastewater, and filter suspended matter and sediment through a filter membrane set in the automated sampler, and then use centrifugation technology to enrich microorganisms in the water samples to obtain pretreated and concentrated water samples; S12. Analyze the composition of microbial communities by using polymerase chain reaction technology, specifically extract microorganisms from pretreated and concentrated water samples, including using a commercial DNA extraction kit for extraction, and then perform qPCR amplification, including using primers of the 16SrRNA gene to amplify microbial DNA, to achieve quantitative analysis of different microbial species in water samples, and according to the specificity of the primers, qPCR can quantitatively analyze the abundance of aerobic bacteria aer and anaerobic bacteria anaer; Among them, the primers include fixed primers for directional amplification of aerobic bacteria aer, anaerobic bacteria anaer and total in the total microbial community.

3. An environmental monitoring method according to claim 2, characterized in that: S13, by comparing the amplification result of aerobic bacteria aer with the standard curve, the abundance information of aerobic bacteria aer in the water sample is calculated and marked as aerobic bacteria abundance Naer; The aerobic bacteria abundance Naer is obtained by the following calculation formula: In the formula, Ctaer represents the Ct value of aerobic bacteria amplification, specifically represents the cycle number when the aerobic bacteria-specific primers reach the threshold in the qPCR reaction, Cstedaer represents the intercept of the standard curve of aerobic bacteria, Maer represents the slope of the standard curvature of aerobic bacteria, and Vsample represents the volume of the water sample; By comparing the amplification results of anaerobic bacteria anaer with the standard curve, the abundance information of anaerobic bacteria anaer in the water sample is calculated and marked as anaerobic bacteria abundance Nanaer; The anaerobic bacteria abundance Nanaer is obtained by the following calculation formula: In the formula, Ctanaer represents the Ct value of anaerobic bacteria amplification, specifically the cycle number when the anaerobic bacteria-specific primers reach the threshold in the qPCR reaction, Cstdanaer represents the intercept of the standard curve of anaerobic bacteria, and Manaer represents the slope of the standard curvature of anaerobic bacteria; By amplifying aerobic bacteria aer and anaerobic bacteria anaer in the water sample, the abundance information of the total microbial community in the water sample is calculated and marked as the total microbial abundance Ntotal; The total microbial abundance Ntotal is obtained by the following calculation formula: In the formula, Cttotal represents the Ct value of total microbial amplification, specifically the number of cycles when the total microbial specific primers reach the threshold in the qPCR reaction, Cstdtotal represents the intercept of the standard curve of total microorganisms, and Mtotal represents the slope of the standard curvature of total microorganisms; The microbial community abundance vector MAV = {Naer, Nanaer, Ntotal} is formed by integrating the aerobic bacteria abundance Naer, the anaerobic bacteria abundance Nanaer and the total microbial abundance Ntotal.

4. An environmental monitoring method according to claim 3, characterized in that: The S2 includes S21 and S22; S21, by concentrating and separating the microbial community abundance vector MAV, the impurities in the water sample are removed for a second time, the microbial community is retained, and the concentration of the microbial community is enhanced to obtain the concentrated water sample volume Vcon; The water sample volume Vcon is obtained by the following calculation formula: Where CF represents the concentration factor, which specifically represents the ratio between the initial volume of the water sample and the concentrated volume.

5. An environmental monitoring method according to claim 4, characterized in that: S22, adding a fixed substrate to the concentrated water sample volume Vcon, so that the substrate reacts with the enzyme in the microbial community abundance vector MAV, synchronously monitoring the enzyme catalytic reaction of the microbial community, and recording relevant data during the reaction, including measuring the concentration change of the reaction to obtain the catalytic rate E, obtaining the substrate concentration C by a photometric analysis method, and obtaining the reaction time T by intercepting the time from the start to the end of the reaction, forming an enzyme activity feature set EAF = {E, C, T}; The catalytic rate E is given by The calculation formula is obtained, where △P represents the change in product concentration produced during the reaction, △T represents the change in reaction time, and the product specifically represents the product generated by the substrate during the reaction; The substrate concentration C is determined by The calculation formula is obtained, where Pfinal represents the product concentration at the end of the reaction, and Pinitial represents the product concentration at the beginning of the reaction.

6. The environmental monitoring method according to claim 1, characterized in that: The S3 includes S31 and S32; S31, integrating the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional dataset Eac, which reflects the abundance information and enzyme activity information of each microorganism; The multidimensional dataset Eac={Naer, Nanaer, Ntotal, E, C, T}; S32. Input the multidimensional data set Eac into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F.

7. An environmental monitoring method according to claim 5, characterized in that: The aerobic bacteria change rate Raer is obtained by the following calculation formula: Wherein, Naer(cur) represents the abundance of aerobic bacteria in the current water sample, specifically the abundance of aerobic bacteria in the water sample volume Vcon, and Naer(bas) represents the abundance of aerobic bacteria under the preset reference water quality; The anaerobic bacteria change rate Ranaer is obtained by the following calculation formula: Wherein, Ranaer(cur) represents the abundance of anaerobic bacteria in the current water sample, specifically the abundance of anaerobic bacteria in the water sample volume Vcon, and Ranaer(bas) represents the abundance of anaerobic bacteria under the preset baseline water quality; The correction factor F is obtained by the following calculation formula: Wherein, F represents the correction factor, which is used to reflect the relationship between the overall enzyme activity level and abundance of the microbial community; β represents the power function of substrate concentration, which is used to adjust the effect of substrate concentration on enzyme activity, and the range is 0<β≤1; γ represents the power function of reaction time, which is used to adjust the effect of time on reaction, and the range is 0<γ≤1; Ntotal represents the total microbial abundance.

8. An environmental monitoring method according to claim 1, characterized in that: The S4 includes S41 and S42; S41, by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, a water quality assessment data set {Eac, Raer, Ranaer, F} is formed; S42, substituting the water quality assessment data set into the established comprehensive water quality assessment algorithm formula for evaluation, and obtaining a comprehensive water quality detection assessment index WQI; The comprehensive water quality assessment index WQI is obtained by the following calculation formula: Where exp represents exponential function and ln represents natural logarithm.

9. An environmental monitoring method according to claim 1, characterized in that: The S5 includes S51; S51, obtaining the water quality assessment result of the water sample collected at the monitoring point by comparing the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI, and triggering an early warning notification according to the water quality assessment result; The water quality assessment results are obtained by the following comparison method: When the comprehensive water quality detection and evaluation index WQI ≥ the water quality evaluation standard threshold Sthe, the water quality evaluation result of the water sample collected at the monitoring point is qualified, and no warning notification is triggered; When the comprehensive water quality detection and evaluation index WQI is less than the water quality evaluation standard threshold Sthe, the water quality evaluation result of the water sample collected at the monitoring point is unqualified, triggering an early warning notification, including extracting the location of the monitoring point and the unique identification of the automatic sampler to send an early warning notification to the relevant environmental protection processing department for processing.

10. An environmental monitoring system, applied to an environmental monitoring method according to any one of claims 1 to 9, characterized in that: It includes water sample collection module, water sample processing module, integrated extraction module, water quality assessment module and decision-making module; The water sample collection module collects water samples from the monitoring points at a fixed period through an automated sampler, extracts microorganisms in the water samples after sample pretreatment, analyzes the composition of the microbial community through polymerase chain reaction technology, and forms a microbial community abundance vector MAV; The water sample processing module concentrates and separates the microbial community abundance vector MAV, then adds substrate for reaction, monitors the enzyme catalytic reaction of the microbial community in the water sample in real time, and records the reaction data to form the enzyme activity feature set EAF; The integration and extraction module integrates the microbial community abundance vector MAV and the enzyme activity feature set EAF to obtain the multidimensional data set Eac, and inputs it into the established microbial community and enzyme activity relationship analysis algorithm formula to obtain the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F; The water quality assessment module forms a water quality assessment data set by integrating the multidimensional data set Eac, the aerobic bacteria change rate Raer, the anaerobic bacteria change rate Ranaer and the correction factor F, and substitutes it into the established comprehensive water quality assessment algorithm formula for evaluation to obtain the comprehensive water quality detection evaluation index WQI; The decision module compares the preset water quality assessment standard threshold Sthe with the comprehensive water quality detection assessment index WQI to obtain the water quality assessment results of the water samples collected at the monitoring point, and triggers an early warning notification based on the water quality assessment results.

Images