Rapid assessment method for industrial wastewater toxicity based on microbial community

Abstract

The application discloses a method for rapid evaluation of industrial wastewater toxicity based on microbial community, and particularly relates to the technical field of toxicity evaluation, which comprises S1: stress evaluation platform construction, S2: stress response data acquisition, S3: community function redundancy quantification, S4: metabolic flux disturbance tracking, and S5: dynamic network toxicity index generation. The application simulates wastewater concentration gradient changes through a dynamic stress reactor, significantly improves the evaluation accuracy of the fluctuant toxicity of industrial wastewater, is especially suitable for industries such as pharmaceutical and petrochemical industries with frequent impact loads, combines double-level analysis of the function redundancy loss index and the gene-enzyme activity decoupling coefficient, accurately identifies the toxicity targeting disturbance of specific metabolic pathways, solves the missed detection problem of traditional methods for complex pollutants and high-redundancy communities, fuses dynamic environmental parameters such as the dissolved oxygen variation coefficient and the pH recovery slope to construct a multi-parameter toxicity index, greatly enhances the sensitivity to non-lethal and implicit toxic substances, and avoids the risk of false negative.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of toxicity evaluation, more particularly, the present application relates to a method for rapid evaluation of industrial wastewater toxicity based on microbial community. BACKGROUND

[0002] Industrial wastewater toxicity evaluation is a key link of environmental monitoring and pollution control. The traditional method mainly relies on chemical analysis or single biological indicator (such as luminescent bacteria, algae). The existing technology usually adopts a standardized test process: after collecting the wastewater sample, the microorganism or biological marker is contacted with the wastewater under static exposure condition for a fixed time, and then the toxicity is quantified by measuring the single endpoint indicators such as luminescence inhibition rate, cell mortality rate or specific enzyme activity change. Such method has been widely used in rapid screening of industrial wastewater treatment plant, and its operation process is standardized and time-consuming is short.

[0003] However, the existing technology has significant limitations. First, the static single-concentration exposure mode cannot simulate the actual volatility of industrial wastewater discharge, resulting in distorted evaluation of chronic toxicity and synergistic effect of complex pollutants; second, relying on single biological endpoint indicators ignores the overall functional response of microbial community, making it difficult to identify targeted toxicity of specific metabolic pathways, especially for communities with high functional redundancy, which may produce false negative results; third, the analysis of environmental parameters and biological response data in isolation makes the evaluation model unable to capture the dynamic biological-environmental interaction mechanism in the process of toxicity action, reducing the sensitivity to latent toxic substances. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the present application provides a method for rapid evaluation of industrial wastewater toxicity based on microbial community, which solves the problems in the background art by the following scheme.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a method for rapid evaluation of industrial wastewater toxicity based on microbial community, comprising:

[0006] S1: stress evaluation platform construction: establishing a standardized dynamic toxicity exposure environment and microbial response basis;

[0007] S2: stress response data collection: exposing microbial carriers in a dynamic stress reactor, and collecting multi-dimensional data of microbial community according to a preset gradient;

[0008] S3: community functional redundancy quantification: quality control, assembly and gene annotation are performed on metagenomic data, then functional redundancy loss index is calculated for KEGG metabolic pathways, and finally high sensitivity metabolic pathways are screened;

[0009] S4: Metabolic flux perturbation tracking: Map the HSMP pathway to enzyme activity detection points to calculate enzyme activity inhibition rate, then establish gene-enzyme activity decoupling coefficient, and finally determine whether the pathway has toxicity targeting perturbation.

[0010] S5: Dynamic network toxicity index generation: Extract the fluctuation characteristics of dissolved oxygen and pH to establish a multi-parameter toxicity index, and then classify the toxicity level according to the multi-parameter toxicity index.

[0011] Preferably, a dynamic stress reactor is installed downstream of the aeration tank of the target industrial wastewater treatment system as the sampling environment. This reactor is a temperature-controlled continuous flow device, with the internal temperature maintained in the range of 25±0.5°C. The target wastewater is injected into the reactor by a precision peristaltic pump through an integrated programmable toxicity gradient generator, forming four concentration gradients: 0% control, 10%, 25%, and 50%. Each concentration gradient is continuously exposed for 30 minutes before automatically switching to the next gradient. The microbial carrier is a standardized porous diatomaceous earth biofilm carrier with a pore size of 5μm and a specific surface area ≥300m² / g. The carrier needs to be pre-inoculated with activated sludge microbial communities from a uniform source, which are taken from the aerobic section of a municipal wastewater treatment plant and pre-cultured outside the reactor for 7 days.

[0012] Preferably, before implementing S1, the industrial wastewater sample must be pretreated by filtering with a sieve with a pore size ≤100μm to remove large particulate suspended matter, and microbial DNA extraction is performed using the MP Biomedicals™ FastDNA Spin Kit for Soil; all metagenomic sequencing operations are performed on the Illumina NovaSeq 6000 sequencing platform in PE150 mode.

[0013] Preferably, when the system reaches the 25th minute of each concentration gradient, three data acquisition operations are performed simultaneously:

[0014] First, a 0.2 mm thick biofilm sample was scraped from the surface of the carrier using a sterile scraper. Three parallel samples were collected for each gradient and immediately placed in liquid nitrogen for rapid freezing. The samples were then transferred to an ultra-low temperature freezer at -80°C for storage for metagenomic analysis.

[0015] Simultaneously, a liquid sample was extracted from 10 mm from the surface of the carrier inside the reactor. After online filtration through a 0.22 μm pore size filter membrane, 5 ml of the filtrate was added to a phosphate buffer containing 100 μM 4-methylumbelliferone-β-D-glucoside substrate. The mixture was reacted at 25 °C in the dark for 30 minutes. Subsequently, the fluorescence intensity was measured at an excitation wavelength of 365 nm and an emission wavelength of 445 nm using a fluorescence spectrophotometer. At the same time, the activity of leucine aminopeptidase was detected using L-leucine-7-amino-4-methylcoumarin as a substrate.

[0016] In addition, the dissolved oxygen partial pressure and pH value at a depth of 0.5 mm from the surface of the carrier were continuously recorded at a frequency of once per second during the interval from 20 to 30 minutes using a Unisense microelectrode array pre-embedded inside the carrier.

[0017] Preferably, S3 first performs quality control on the metagenomic sequencing data collected in S2, using Trimmomatic v0.39 software to remove low-quality bases and adapter sequences; then, MEGAHIT v1.2.9 assembly software is used to assemble the quality-controlled sequences into genomes, generating contigs; next, EggNOG-mapper v2.1.6 tool is used to perform functional annotation on the assembled genes, identifying the metabolic pathway to which each gene belongs based on the KEGG database.

[0018] Preferably, S3 counts the number of non-redundant gene families that appear in all samples for each preset KEGG metabolic pathway, where a non-redundant gene family is defined as a gene cluster with the same functional annotation and a sequence similarity of less than 95%.

[0019] Preferably, S3 simultaneously calculates the Shannon diversity index of the microbial community for each sample. This index is calculated based on the abundance distribution of species-level annotation: the number of non-redundant gene families of a specific metabolic pathway in the target wastewater exposure group is divided by the Shannon diversity index of the microbial community in the exposure group to obtain the standardized gene family density; the number of non-redundant gene families of the same metabolic pathway in the control group of unexposed wastewater is divided by the Shannon diversity index of the microbial community in the control group to obtain the standardized gene family density of the control group; the standardized gene family density of the exposure group is divided by the standardized gene family density of the control group, and then the ratio is subtracted from 1 to obtain the functional redundancy loss index of the metabolic pathway. Finally, metabolic pathways with a functional redundancy loss index greater than 0.3 are screened and marked as highly sensitive metabolic pathways.

[0020] Preferably, S4 first maps each pathway to the corresponding key enzyme activity detection point based on the list of highly sensitive metabolic pathways screened in S3. Then, using the enzyme activity data collected in S2, the enzyme activity inhibition rate of the wastewater treatment group relative to the control group is calculated for each detection point. This inhibition rate is equal to 1 minus the mean enzyme activity of the treatment group divided by the mean enzyme activity of the control group and multiplied by 100%.

[0021] Subsequently, metagenomic gene abundance analysis was performed on the same metabolic pathway. The abundance ratios of key genes in the pathway were calculated in the treatment and control groups, and the logarithmic transformation values ​​were taken to base 2. At the same time, the enzyme activity data corresponding to the pathway were also processed in the same way to obtain the logarithmic transformation values ​​of enzyme activity ratios. Finally, the gene-enzyme activity decoupling coefficient was obtained by calculating the absolute difference between the gene abundance logarithmic ratio and the enzyme activity logarithmic ratio. When the decoupling coefficient is greater than 1 and the corresponding enzyme activity inhibition rate exceeds 40%, the metabolic pathway is determined to have toxic targeting perturbation.

[0022] Preferably, in step S5, the fluctuation characteristics of dissolved oxygen and pH are first extracted, and the coefficient of variation of dissolved oxygen is calculated as the dissolved oxygen fluctuation characteristic index. The coefficient of variation is equal to the standard deviation of dissolved oxygen divided by the average value of dissolved oxygen. At the same time, the pH recovery slope is calculated as the pH response characteristic index. The slope is obtained by fitting the pH change curve after the end of gradient stress and calculating its change rate.

[0023] Next, the biological response and environmental parameter data were integrated, and the weighted functional redundancy loss index of each of the highly sensitive metabolic pathways identified by S3 was calculated. The weight of the nitrogen metabolism pathway was set to 0.4, the weight of the carbon metabolism pathway was set to 0.3, and the weight of the oxidative phosphorylation pathway was set to 0.3. This weight allocation was determined based on regression analysis of historical toxicity data. Then, an environmental parameter calibration term was added, and the coefficient of variation of dissolved oxygen was multiplied by the environmental parameter calibration coefficient of 0.2, and the absolute value of the pH recovery slope was multiplied by the environmental parameter calibration coefficient of 0.1. This calibration coefficient was obtained by comparing the correlation between the microbial response and environmental parameters of known toxic wastewater samples.

[0024] Finally, the results of the above three calculations are added together to obtain the multi-parameter toxicity index. The toxicity level is divided according to the index value: when the index is less than 0.2, it is judged as having no significant toxicity; when the index is in the range of 0.2 to 0.5, it is judged as having mild toxicity; and when the index is greater than or equal to 0.5, it is judged as having severe toxicity. The threshold setting is determined based on the laboratory biological test verification results.

[0025] The technical effects and advantages of this invention are as follows:

[0026] First, by using a dynamic stress reactor to simulate wastewater concentration fluctuation scenarios, the stress response signal of the microbial community is stimulated under a controllable gradient, which significantly improves the accuracy of predicting the toxic effects under actual industrial wastewater discharge patterns. This method is particularly suitable for wastewater assessment in industries such as pharmaceuticals and petrochemicals where shock loads are frequent.

[0027] Secondly, it innovatively integrates the functional gene redundancy loss index and gene-enzyme activity decoupling coefficient into a two-level analysis, breaking through the limitations of a single endpoint indicator. By quantifying the functional redundancy loss of key metabolic pathways and verifying the synergistic destruction of gene expression and enzyme activity, it accurately identifies toxicity-targeting perturbations, solving the problem of missed detection of toxicity in high-redundancy communities by traditional methods. It is especially suitable for industrial wastewater containing complex pollutants.

[0028] Finally, a multi-parameter toxicity index integrating environmental fluctuation characteristics was constructed, and dynamic parameters such as dissolved oxygen variation coefficient and pH recovery slope were used as calibration factors for biological response to achieve real-time analysis of biological-environment interaction. This design significantly enhances the sensitivity to non-lethal toxic substances, avoids false negative interpretations caused by the apparent survival of microorganisms, and provides early warning for the hidden toxicity risks of industrial wastewater. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall structure of the present invention.

[0030] Figure 2 This is a schematic diagram of the stress assessment platform construction structure of the present invention.

[0031] Figure 3 This is a schematic diagram of the metabolic flux perturbation tracking structure of the present invention.

[0032] Figure 4 This is a schematic diagram of the dynamic network toxicity index generation structure of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] refer to Figures 1-4 The rapid toxicity assessment method for industrial wastewater based on microbial communities, as shown, includes:

[0035] S1: Stress assessment platform construction: Establishing a standardized dynamic toxicity exposure environment and microbial response basis.

[0036] A dynamic stress reactor was installed downstream of the aeration tank of the target industrial wastewater treatment system as the sampling environment. The reactor was a temperature-controlled continuous flow device with the internal temperature maintained within the range of 25±0.5°C. The target wastewater was injected into the reactor by a precision peristaltic pump through an integrated programmable toxicity gradient generator to form four concentration gradients: 0% control, 10%, 25%, and 50%. Each concentration gradient was continuously exposed for 30 minutes before automatically switching to the next gradient. The microbial carrier was a standardized porous diatomaceous earth biofilm carrier with a pore size of 5μm and a specific surface area ≥300m² / g. The carrier was pre-inoculated with a uniformly sourced activated sludge microbial community, which was taken from the aerobic section of a municipal wastewater treatment plant and pre-cultured outside the reactor for 7 days.

[0037] It should be specifically noted in this embodiment that before implementing S1, the industrial wastewater sample must be pretreated by using a sieve with a pore size ≤100μm to remove large particulate suspended matter, and microbial DNA extraction is performed using the MP Biomedicals™ FastDNA Spin Kit for Soil; all metagenomic sequencing operations are performed on the Illumina NovaSeq 6000 sequencing platform in PE150 mode.

[0038] S2: Stress response data acquisition: Expose microbial carriers in a dynamic stress reactor and collect multidimensional data of the microbial community according to a preset gradient.

[0039] When the system reaches the 25th minute of each concentration gradient, three data acquisition operations are performed simultaneously:

[0040] First, a 0.2 mm thick biofilm sample was scraped from the surface of the carrier using a sterile scraper. Three parallel samples were collected for each gradient and immediately placed in liquid nitrogen for rapid freezing. The samples were then transferred to an ultra-low temperature freezer at -80°C for storage for metagenomic analysis.

[0041] Simultaneously, a liquid sample was extracted from 10 mm from the surface of the carrier inside the reactor. After online filtration through a 0.22 μm pore size filter membrane, 5 ml of the filtrate was added to a phosphate buffer containing 100 μM 4-methylumbelliferone-β-D-glucoside substrate. The mixture was reacted at 25 °C in the dark for 30 minutes. Subsequently, the fluorescence intensity was measured at an excitation wavelength of 365 nm and an emission wavelength of 445 nm using a fluorescence spectrophotometer. At the same time, the activity of leucine aminopeptidase was detected using L-leucine-7-amino-4-methylcoumarin as a substrate.

[0042] In addition, the dissolved oxygen partial pressure and pH value at a depth of 0.5 mm from the surface of the carrier were continuously recorded at a frequency of once per second during the interval from 20 to 30 minutes using a Unisense microelectrode array pre-embedded inside the carrier.

[0043] S3: Community functional redundancy quantification: Quality control, assembly, and gene annotation of metagenomic data are performed, and then the functional redundancy loss index is calculated for the KEGG metabolic pathway. Finally, highly sensitive metabolic pathways are screened.

[0044] First, the metagenomic sequencing data collected by S2 were quality controlled by using Trimmomatic v0.39 software to remove low-quality bases and adapter sequences. Then, the quality-controlled sequences were assembled into genomes using MEGAHIT v1.2.9 assembly software to generate contigs. Next, the assembled genes were functionally annotated using EggNOG-mapper v2.1.6 tool, and the metabolic pathways to which each gene belonged were identified based on the KEGG database.

[0045] For each predefined KEGG metabolic pathway, the number of non-redundant gene families appearing in all samples for that pathway was counted. Non-redundant gene families were defined as gene clusters with the same functional annotation and sequence similarity of less than 95%.

[0046] Simultaneously, the Shannon diversity index of each sample's microbial community was calculated. This index was derived based on the abundance distribution of species-level annotation: the number of non-redundant gene families of a specific metabolic pathway in the target wastewater exposure group was divided by the Shannon diversity index of the microbial community in that exposure group to obtain the standardized gene family density; the number of non-redundant gene families of the same metabolic pathway in the control group of unexposed wastewater was divided by the Shannon diversity index of the control group's microbial community to obtain the standardized gene family density of the control group; the standardized gene family density of the exposure group was divided by the standardized gene family density of the control group, and then the ratio was subtracted from 1 to obtain the functional redundancy loss index of the metabolic pathway. Finally, metabolic pathways with a functional redundancy loss index greater than 0.3 were screened and marked as highly sensitive metabolic pathways.

[0047] Let the functional redundancy loss index be set. p: target metabolic pathway The number of non-redundant gene families in pathway p in the wastewater exposure group represents the genetic functional diversity (stress resistance) of the community in this pathway after exposure to toxicity. The number of non-redundant gene families in pathway p in the control group (unexposed to wastewater) represents the baseline functional diversity of the community under normal conditions. The Shannon diversity index (calculated based on species annotation abundance) for the wastewater exposure group characterizes the overall species structure stability of the community after exposure to toxicity. Shannon diversity index of the control group (without wastewater exposure) characterizes the baseline species structure stability of the community under normal conditions;

[0048] This formula is based on the relative quantification of changes in the functional density of microbial communities under toxic stress: First, the absolute loss rate of functional genes in the target metabolic pathway is characterized by calculating the ratio of the number of non-redundant gene families in the wastewater exposure group to the control group; simultaneously, the ratio of the Shannon diversity index between the exposure group and the control group is calculated to characterize the overall structural disturbance rate of the community; the standardized relative functional density retention rate is obtained by dividing the above two ratios, which eliminates the interference caused by changes in community size; finally, the retention rate is subtracted from 1 to convert the result into the degree of functional redundancy loss.

[0049] Its significance lies in the specific quantification of the targeted damage of toxic substances to the functional redundancy of key metabolic pathways, revealing the implicit decline in the functional buffering capacity of microbial systems. Even if the overall species diversity of the community changes only slightly (e.g., Shannon diversity decreases by only 10%), the non-redundant gene families of specific pathways may have been significantly lost (e.g., nitrogen metabolism pathway genes are reduced by 40%). In this case, FRLI accurately captures this targeted damage through decoupling analysis (e.g., cyanide wastewater leads to FRLI=0.82 for the cyanoamino acid metabolism pathway). When FRLI>0.3, it indicates that more than 30% of the functional redundancy reserve of the pathway has been lost, and the microbial system loses its ability to cope with environmental fluctuations. This threshold can provide early warning of metabolic collapse risks that cannot be detected by traditional biotoxicity tests (e.g., FRLI=0.51 for the aromatic compound degradation pathway in coking wastewater predicts the accumulation of phenolic substances after 48 hours).

[0050] The 0.3 threshold was determined based on a limit experiment on the functional compensation capacity of the microbial community: by gradually increasing the toxicity of the wastewater (with phenol concentration of 0–200 mg / L as the stress factor), the correlation between the functional redundancy loss index (FRLI) of the ammonia monooxygenase pathway (ko00261) of the nitrifying bacteria and the nitrification efficiency was monitored; when FRLI≤0.25, the nitrification efficiency remained >95% (redundancy mechanism fully compensated for the damage); when FRLI=0.28–0.32, the nitrification efficiency plummeted to 82±5% (compensation capacity partially failed); when FRLI≥0.35, the nitrification efficiency collapsed to <60% (redundancy system disintegrated); 0.3 is located in the mutation range of functional compensation failure.

[0051] S4: Metabolic flux perturbation tracking: The HSMP pathway is mapped to enzyme activity detection points to calculate the enzyme activity inhibition rate, then the gene-enzyme activity decoupling coefficient is established, and finally it is determined whether the pathway has undergone toxicity targeting perturbation.

[0052] First, based on the list of highly sensitive metabolic pathways screened by S3, each pathway is mapped to the corresponding key enzyme activity detection point. Then, using the enzyme activity data collected by S2, the enzyme activity inhibition rate of the wastewater treatment group relative to the control group is calculated for each detection point. This inhibition rate is equal to 1 minus the mean enzyme activity of the treatment group divided by the mean enzyme activity of the control group and multiplied by 100%.

[0053] Let the type identifier of the target enzyme be e, and the activity measurement value of enzyme e in the wastewater treatment group be... The measured activity of enzyme e in the control group was... The enzyme activity inhibition rate , This indicates the basic catalytic capacity of the target enzyme within the microbial community when not exposed to wastewater toxicity stress, reflecting the biochemical reaction rate under normal metabolic conditions. This indicates the actual catalytic capacity of the target enzyme under specific concentrations of wastewater stress, characterizing the degree of interference of toxic substances on enzyme function. It also links to highly sensitive metabolic pathways screened by S3, ensuring that the detected enzyme is a key catalytic node in that pathway. The enzyme activity inhibition rate directly quantifies the degree of interference of toxic substances on the function of a specific enzyme: first, by calculating the ratio of enzyme activity in the wastewater treatment group to that in the unaffected control group, it reflects the remaining proportion of enzyme catalytic capacity; then, by subtracting this ratio from 1, the "remaining activity" is converted into the intuitive concept of "lost activity." This formula directly reveals the biochemical mechanism of toxicity. When the enzyme activity inhibition rate is greater than zero, it indicates that toxic components (such as heavy metals or organic pollutants) are attacking key enzymes (such as β-glucosidase) in a specific metabolic pathway, and its magnitude reflects the degree of damage to enzyme protein function.

[0054] Subsequently, metagenomic gene abundance analysis was performed on the same metabolic pathway. The abundance ratios of key genes in the pathway were calculated in the treatment and control groups, and the logarithmic transformation values ​​were taken to base 2. At the same time, the enzyme activity data corresponding to the pathway were processed in the same way to obtain the logarithmic transformation values ​​of enzyme activity ratios. Finally, the gene-enzyme activity decoupling coefficient was obtained by calculating the absolute difference between the gene abundance logarithmic ratio and the enzyme activity logarithmic ratio. When the decoupling coefficient is greater than 1 and the corresponding enzyme activity inhibition rate exceeds 40%, the metabolic pathway is determined to have toxic targeting perturbation.

[0055] Let the gene-enzyme activity decoupling coefficient be... The gene abundance ratio (treatment / control) represents the ratio of key gene abundance in the treatment group (wastewater exposure) to the control group (no wastewater) of the target metabolic pathway. It reflects the change in the genetic potential of the microbial community to maintain this metabolic function under toxic stress. A ratio <1 indicates gene loss. The enzyme activity ratio (treatment / control) represents the ratio of the activity of the key enzyme corresponding to the pathway in the treatment group to the control group. It reflects the change in the actual functional output of this metabolic pathway under toxic stress. A ratio <1 indicates enzyme function inhibition. The gene-enzyme activity decoupling coefficient identifies specificity by quantifying the degree of deviation between the genetic potential of genes and the actual functional output of enzymes in the target metabolic pathway. The mechanism of toxicity was investigated by first performing a base-2 logarithmic transformation on the gene abundance ratio and enzyme activity ratio between the treatment and control groups. This transformation converted the linear proportions into an exponential scale of difference (e.g., log2(0.5) = -1 represents a 50% decrease, and log2(2) = 1 represents a 100% increase), thus amplifying significant biological changes. Then, the absolute difference between the logarithmically transformed values ​​was calculated. This operation essentially captured the decoupling effect between gene expression regulation and enzyme protein function execution at two biological levels. When DEC ≈ 0 (e.g., a 50% decrease in gene abundance and a simultaneous 50% decrease in enzyme activity), it indicates that toxicity indirectly affects metabolic function through population structure disruption. When DEC ≈ 0... When DEC > 1 (defined in practice as > 2) (e.g., gene abundance remains unchanged while enzyme activity decreases by 80%), it reveals that toxic substances directly interfere with enzyme protein activity (e.g., cyanide targets and inhibits cytochrome c oxidase). This decoupling effect cannot be captured by traditional toxicity indicators. The threshold setting of DEC > 1 in the formula requires that the difference in response amplitude between gene and enzyme activity be at least 2 times (because |log2(a)-log2(b)| > 1 is equivalent to |a / b| > 2 or |b / a| > 2). This standard has been verified by preliminary experiments to exclude natural fluctuations in the microbial community (background DEC < 0.8), thus specifically indicating targeted toxic effects. The 40% threshold setting is based on the correlation analysis between the inhibition rate of key enzyme activity under known toxic substance concentration gradients in preliminary experimental data and the results of standard fish acute toxicity tests. When the enzyme activity inhibition rate exceeds 40%, its consistency with the 96-hour median lethal concentration (LC50) is over 90%. At the same time, this threshold is higher than the upper limit of 30% of the background variation of enzyme activity caused by natural fluctuations in dissolved oxygen on the carrier surface in the dynamic stress reactor, thus ensuring the specificity of the toxic response.

[0056] S5: Dynamic network toxicity index generation: Extract the fluctuation characteristics of dissolved oxygen and pH to establish a multi-parameter toxicity index, and then classify the toxicity level according to the multi-parameter toxicity index.

[0057] First, the fluctuation characteristics of dissolved oxygen and pH are extracted. The coefficient of variation of dissolved oxygen is calculated as the dissolved oxygen fluctuation characteristic index. The coefficient of variation is equal to the standard deviation of dissolved oxygen divided by the average value of dissolved oxygen. At the same time, the pH recovery slope is calculated as the pH response characteristic index. The slope is obtained by fitting the pH change curve after the end of gradient stress and calculating its change rate.

[0058] Next, the biological response and environmental parameter data were integrated, and the weighted functional redundancy loss index of each of the highly sensitive metabolic pathways identified by S3 was calculated. The weight of the nitrogen metabolism pathway was set to 0.4, the weight of the carbon metabolism pathway was set to 0.3, and the weight of the oxidative phosphorylation pathway was set to 0.3. This weight allocation was determined based on regression analysis of historical toxicity data. Then, an environmental parameter calibration term was added, and the coefficient of variation of dissolved oxygen was multiplied by the environmental parameter calibration coefficient of 0.2, and the absolute value of the pH recovery slope was multiplied by the environmental parameter calibration coefficient of 0.1. This calibration coefficient was obtained by comparing the correlation between the microbial response and environmental parameters of known toxic wastewater samples.

[0059] Finally, the results of the above three calculations are added together to obtain the multi-parameter toxicity index. The toxicity level is divided according to the index value: when the index is less than 0.2, it is judged as having no significant toxicity; when the index is in the range of 0.2 to 0.5, it is judged as having mild toxicity; and when the index is greater than or equal to 0.5, it is judged as having severe toxicity. The threshold setting is determined based on the laboratory biological test verification results.

[0060] Let the path weight coefficient be The coefficient of variation of dissolved oxygen is The absolute value of the pH recovery slope is The environmental parameter calibration coefficients are α and β, where α = 0.2 and β = 0.1. Therefore, the multi-parameter toxicity index... k: The number of highly sensitive metabolic pathways identified; This model is based on the multi-level response mechanism of microbial systems under toxic stress, and achieves comprehensive toxicity quantification by integrating two core dimensions: functional gene damage and dynamic changes in environmental parameters. The main term is the sum of the products of the functional redundancy loss index of all highly sensitive metabolic pathways and the preset pathway weight coefficients, where the nitrogen metabolism pathway has a weight of 0.4, the carbon metabolism pathway has a weight of 0.3, and the oxidative phosphorylation pathway has a weight of 0.3. This term focuses on the degree of damage to the gene resources of key metabolic pathways caused by toxicity. The correction term introduces the dissolved oxygen variation coefficient multiplied by the calibration coefficient of 0.2 and the absolute value of the pH recovery slope multiplied by the calibration coefficient of 0.1. This term captures the real-time disorder characteristics of microbial physiological activities. The formula design adopts... Using a linear weighted model, three types of toxic effects—microbial genetic potential impairment, energy metabolism stability, and environmental homeostasis maintenance capacity—are unified and quantified into a comprehensive toxicity index. The increase in this index directly represents an increase in toxicity load. The core innovation of this model lies in integrating static functional gene data with dynamic environmental parameters. For example, the dissolved oxygen coefficient of variation reflects abnormal fluctuations in oxygen consumption rate caused by respiratory chain inhibition, and the pH recovery slope represents the degree of microbial acid-base balance dysfunction. The grading standard of MTI values ​​of 0.3 corresponding to mild toxicity and 0.5 corresponding to severe toxicity originates from laboratory biological testing and verification of industrial wastewater. Compared to traditional single biochemical indicators, this formula quantifies genetic resource deficit through a functional redundancy loss index, reveals latent respiratory inhibition, and... Assessing the decline of cellular regulatory functions ultimately leads to a systemic resolution capability for synergistic / antagonistic toxicities.

[0061] The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments of this disclosure. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0062] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for rapid assessment of toxicity of industrial wastewater based on microbial communities, characterized in that, Comprise: S1: stress evaluation platform construction: establish a standardized dynamic toxicity exposure environment and microbial response basis; S2: stress response data collection: expose microbial carriers in a dynamic stress reactor, and collect multi-dimensional data of microbial communities according to a preset gradient; S3: community function redundancy quantification: quality control, assembly and gene annotation are performed on metagenomic data, then function redundancy loss index is calculated for KEGG metabolic pathways, and finally high sensitivity metabolic pathways are screened; The Shannon diversity index of each sample microbial community is calculated, which is calculated based on the abundance distribution of species level annotation: the number of non-redundant gene families of a specific metabolic pathway of the target wastewater exposure group is divided by the Shannon diversity index of the microbial community of the exposure group, to obtain the standardized gene family density; the number of non-redundant gene families of the same metabolic pathway of the control group without exposure to wastewater is divided by the Shannon diversity index of the microbial community of the control group, to obtain the standardized gene family density of the control group; the standardized gene family density of the exposure group is divided by the standardized gene family density of the control group, and then 1 is subtracted from the ratio, that is, the function redundancy loss index of the high sensitivity metabolic pathway is obtained, and finally the metabolic pathways with a function redundancy loss index greater than 0.3 are screened and marked as high sensitivity metabolic pathways; S4: metabolic flux disturbance tracking: map the high sensitivity metabolic pathway to the enzyme activity detection point to calculate the enzyme activity inhibition rate, then establish the gene-enzyme activity decoupling coefficient, and finally determine whether the high sensitivity metabolic pathway is subjected to toxicity targeted disturbance; First, based on the list of high sensitivity metabolic pathways screened out in S3, each pathway is mapped to the corresponding key enzyme activity detection point, then the enzyme activity data collected in S2 is used to calculate the enzyme activity inhibition rate of the wastewater treatment group relative to the control group for each detection point, which is equal to 1 minus the treatment group enzyme activity mean divided by the control group enzyme activity mean multiplied by 100%; Subsequently, the same metabolic pathway is subjected to metagenomic gene abundance analysis, and the abundance ratio of the key gene of the pathway in the treatment group and the control group is calculated respectively, and the logarithmic conversion value with 2 as the base is taken; at the same time, the enzyme activity data corresponding to the pathway are also subjected to the same processing, and the logarithmic conversion value of the enzyme activity ratio is obtained; finally, by calculating the absolute difference value of the logarithmic ratio of gene abundance and the logarithmic ratio of enzyme activity, the gene-enzyme activity decoupling coefficient is obtained, when the decoupling coefficient is greater than 1 and the enzyme activity inhibition rate exceeds 40%, it is determined that the metabolic pathway is subjected to toxicity targeted disturbance; S5: dynamic network toxicity index generation: extract the fluctuation characteristics of dissolved oxygen and pH to establish a multi-parameter toxicity index, and then divide the toxicity grade according to the multi-parameter toxicity index.

2. The method of claim 1, wherein, The S1 comprises: A dynamic stress reactor was installed downstream of the aeration tank of the target industrial wastewater treatment system as the sampling environment. The reactor was a constant-temperature controlled continuous flow device, with an internal temperature maintained at 25±0.5°C. The target wastewater was injected into the reactor by a precision peristaltic pump through an integrated programmable toxicity gradient generator, forming four concentration gradients of 0% control, 10%, 25%, and 50%. Each concentration gradient was automatically switched to the next one after 30 minutes of continuous exposure. The microbial carrier used was a standardized porous diatomite biofilm carrier with a pore size of 5μm and a specific surface area of ≥300m² / g. The carrier was pre-inoculated with a uniform source of activated sludge microbial community, which was taken from the aerobic section of a municipal wastewater treatment plant and pre-cultured for 7 days outside the reactor.

3. The method of claim 1, wherein, The S2 comprises: When the system runs to the 25th minute of each concentration gradient, three data acquisition operations are performed simultaneously: First, a sterile spatula is used to scrape a 0.2mm thick biofilm sample from the surface of the carrier. Three parallel samples are collected at each gradient and immediately placed in liquid nitrogen for rapid freezing. The samples are transferred to a -80°C ultra-low temperature freezer for storage for metagenomic analysis. At the same time, liquid phase samples are extracted from the reactor 10mm away from the surface of the carrier. After online filtration through a 0.22μm pore size filter membrane, 5ml of the filtrate is added to a phosphate buffer containing 100μM 4-methylumbelliferyl-β-D-glucopyranoside substrate. The mixture is incubated at 25°C in the dark for 30 minutes. The fluorescence intensity value is then measured under the conditions of an excitation wavelength of 365nm and an emission wavelength of 445nm on a fluorescence spectrophotometer. At the same time, the leucine aminopeptidase activity is detected using L-leucine-7-amino-4-methylcoumarin as the substrate. In addition, an Unisense microelectrode array pre-embedded in the carrier is used to continuously record the dissolved oxygen partial pressure and pH values at a depth of 0.5mm from the surface of the carrier at a frequency of 1 per second within the 20th to 30th minute interval.

4. The method of claim 1, wherein, The S3 comprises: First, the metagenomic sequencing data collected in S2 is subjected to quality control. Trimmomatic v0.39 software is used to remove low-quality bases and adapter sequences. Then, MEGAHIT v1.2.9 assembly software is used to assemble the sequences after quality control to generate contigs. Next, EggNOG-mapper v2.1.6 tool is used to functionally annotate the assembled genes, and each gene is identified according to the KEGG database to determine the metabolic pathway to which it belongs. For each pre-set KEGG metabolic pathway, the number of non-redundant gene families appearing in all samples is counted. A non-redundant gene family is defined as a gene cluster with the same functional annotation and a sequence similarity of less than 95%.

5. The method of claim 1, wherein, The S5 comprises: The fluctuation characteristics of dissolved oxygen and pH are extracted. The coefficient of variation of dissolved oxygen is calculated as the dissolved oxygen fluctuation characteristic indicator. The coefficient of variation is equal to the standard deviation of dissolved oxygen divided by the average value of dissolved oxygen. At the same time, the pH recovery slope is calculated as the pH response characteristic indicator. The slope is obtained by fitting the pH change curve after the end of the gradient stress and calculating its change rate.

6. The method of claim 5, wherein, The S5 further comprises: Integrating the biological response data and the environmental parameter data, for all the high-sensitive metabolic pathways identified by S3, the weight function redundancy loss index sum of each pathway is calculated, wherein the nitrogen metabolism pathway weight is 0.4, the carbon metabolism pathway weight is 0.3, and the oxidative phosphorylation pathway weight is 0.3, and the nitrogen metabolism pathway weight, the carbon metabolism pathway weight, and the oxidative phosphorylation pathway weight are determined based on historical toxicity data regression analysis; then, the environmental parameter calibration term is added, the dissolved oxygen variation coefficient is multiplied by the environmental parameter calibration coefficient 0.2, and the absolute value of the pH recovery slope is multiplied by the environmental parameter calibration coefficient 0.1, and the two environmental parameter calibration coefficients are obtained by comparing the correlation between the microbial response and the environmental parameters of the known toxic wastewater samples. Finally, the three calculation results are added to obtain a multi-parameter toxicity index, and the toxicity grade is divided according to the index value: when the index is less than 0.2, it is determined to be no significant toxicity, when the index is in the interval of 0.2 to 0.5, it is determined to be mild toxicity, and when the index is greater than or equal to 0.5, it is determined to be severe toxicity, and the threshold values of 0.2 and 0.5 are determined according to the laboratory biological test verification results.

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