New pollutant identification method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of environmental analysis, and discloses a new pollutant identification method and device, computer equipment and a storage medium, and the method comprises the steps: determining a target execution strategy through determining the application sequence of each non-target analysis method and each computational toxicology method in a high-throughput fusion method; the method can be realized by reasonably selecting and switching execution strategies whether searching corresponding chemical substances from the toxic effect or evaluating the toxic effect of the chemical substances from the found chemical substances. Furthermore, a target execution strategy is utilized to perform new pollutant risk assessment and calculate a plurality of risk values, various characteristics of the new pollutants and performance in an actual environment are comprehensively considered, and the potential risk of the new pollutants can be quantitatively reflected from different angles. And finally, multiple target new pollutants are further determined in combination with multiple risk values, and objects which have great threats to the environment and human health and need to be managed and controlled preferentially are screened out from numerous possible new pollutants.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental analysis, and in particular to a new pollutant identification method, device, computer equipment and storage medium. Background Art

[0002] The large-scale production and use of synthetic chemicals has greatly improved the quality of human life, but it has also led to their widespread entry into the environment, seriously threatening ecological safety and human health. The production and use of toxic and hazardous chemicals are the main sources of new pollutants. In the past few decades, the growth trend of synthetic chemicals has been obvious, and the growth rate has continued to accelerate. On the one hand, the use of many synthetic chemicals means a huge number of new pollutants. On the other hand, toxicity experimental data for most new pollutants are lacking. Therefore, it is crucial to develop a high-throughput fusion technology process that can comprehensively identify new pollutants in complex environmental media and human matrices and complete risk assessment.

[0003] Analytical methods and evaluation means are the basis and core of the development of the above processes. With the development and progress of modern high-resolution mass spectrometry instruments, non-target analysis has become one of the key chemical analysis methods for comprehensive identification of new pollutants in complex matrices. It does not rely on real standards and can complete the simultaneous qualitative and quantitative analysis of thousands of new pollutants. How to achieve reliable risk assessment of new pollutants through this analysis in the absence of toxicity experimental data is a key technical problem. Computational toxicology can accurately predict the toxic effects of massive compounds at different scales quickly and with high throughput. How to obtain compound data sets is the primary technical problem faced in using computational toxicology for toxicity prediction. Summary of the invention

[0004] In view of this, the present invention provides a method, apparatus, computer device and storage medium for identifying new pollutants to solve the problems in the prior art that reliable risk assessment of new pollutants cannot be analyzed in the absence of toxicity experimental data, and that compound data sets cannot be obtained in computational toxicology methods.

[0005] In a first aspect, the present invention provides a method for identifying a new pollutant, the method comprising: Obtain a high-throughput fusion method, which is obtained by fusing at least one non-target analysis method and at least one computational toxicology method; determine the application order of each non-target analysis method and each computational toxicology method in the high-throughput fusion method; based on the application order, determine the target execution strategy of the high-throughput fusion method, the target execution strategy is one or more of the toxic effect to chemical substance strategy and the chemical substance to toxic effect strategy; use the target execution strategy to conduct risk assessment of new pollutants to obtain multiple risk values; based on the multiple risk values, determine multiple target new pollutants.

[0006] The new pollutant identification method provided by the present invention integrates non-target analysis methods and computational toxicology methods, breaks the limitations of a single method, fully integrates their respective resources and capabilities, and realizes the synergy of multiple methods. Furthermore, by determining the application order of each non-target analysis method and each computational toxicology method in the high-throughput fusion method and further determining the target execution strategy, the workflow can be optimized according to the specific situation, work efficiency can be improved, and unnecessary repeated operations and waste of resources can be avoided. At the same time, whether it is to find the corresponding chemical substances based on toxic effects or to evaluate the toxic effects of discovered chemical substances, it can be achieved by reasonably selecting and switching execution strategies, which enhances the adaptability to diversified practical problems. Furthermore, the risk assessment of new pollutants is carried out using the determined target execution strategy, which comprehensively considers the various characteristics of new pollutants and their performance in the actual environment. Multiple risk values ​​are obtained through scientific calculation and analysis, which can quantitatively reflect the potential risk size of new pollutants from different angles. Finally, by combining multiple risk values, we further identified multiple target new pollutants, and screened out those that posed a greater threat to the environment and human health and required priority control from among many possible new pollutants. This avoided indiscriminate control of all new pollutants, allowing resources to be concentrated on key control targets, improving the efficiency and effectiveness of the implementation of control measures, and specifically alleviating the adverse effects of new pollutants on the ecological environment and human health, providing a clear direction and focus for environmental monitoring, governance, and government supervision.

[0007] In an optional implementation, the target execution strategy is a toxic effect to chemical substance strategy; the target execution strategy is used to perform a risk assessment on new pollutants to obtain multiple risk values, including: Computational toxicology methods are used to screen and determine the range of new pollutants that meet the selected toxicity endpoints from a preset database; samples are collected based on the new pollutant range to obtain multiple initial new pollutant samples; the multiple initial new pollutant samples are processed and mass spectrometrically analyzed to obtain a target mass spectrometry analysis data set; based on the new pollutant range, data mining analysis and toxicity prediction are performed on the target mass spectrometry analysis data set to obtain new pollutant concentration levels and toxicity prediction results; based on the new pollutant concentration levels and toxicity prediction results, multiple risk values ​​are calculated.

[0008] The new pollutant identification method provided by the present invention, when the target execution strategy is the toxic effect to chemical substance strategy, uses computational toxicology methods to screen and determine the range of new pollutants that meet the selected toxicity endpoint from a preset database, which can quickly exclude a large number of substances that are not related to the toxicity characteristics, narrow the scope of investigation, and improve the efficiency of locating new pollutants in a large number of possible substances, avoiding directionless comprehensive screening, saving resources and time costs. Furthermore, sample collection is carried out in combination with the scope of new pollutants, which improves the effectiveness and representativeness of the samples. Furthermore, the collected initial new pollutant samples are processed and mass spectrometrically analyzed, and the obtained target mass spectrometry data set is subjected to data mining analysis and toxicity prediction, and then multiple risk values ​​are calculated, so that the risk assessment results are closer to the actual situation, avoiding the assessment bias caused by focusing only on the single factor of concentration or toxicity, and can more accurately measure the potential risks of new pollutants to the ecological environment and human health from multiple angles.

[0009] In an optional embodiment, a plurality of initial new pollutant samples are processed and mass spectrometrically analyzed respectively to obtain a target mass spectrometric analysis data set, including: Acquire multiple sample matrix characteristics of multiple initial new pollutant samples; based on the new pollutant range and multiple sample matrix characteristics, use a preset sample pretreatment method to process the multiple initial new pollutant samples to obtain multiple target new pollutant samples; perform mass spectrometry analysis on the multiple target new pollutant samples to obtain an initial mass spectrometry analysis data set; preprocess the initial mass spectrometry analysis data set to obtain a target mass spectrometry analysis data set.

[0010] The new pollutant identification method provided by the present invention uses a preset sample pretreatment method to process multiple initial new pollutant samples to obtain multiple target new pollutant samples, which can fully consider the chemical properties of the target new pollutants and the interfering components that may exist in the samples, thereby reducing the analysis errors caused by factors such as matrix effects. Furthermore, by performing mass spectrometry analysis on multiple target new pollutant samples, an initial mass spectrometry analysis data set that more realistically and clearly reflects the characteristics of the target new pollutants can be obtained. Finally, the initial mass spectrometry analysis data set is preprocessed to improve the quality and availability of the target mass spectrometry analysis data set.

[0011] In an optional embodiment, based on the scope of new pollutants, data mining analysis and toxicity prediction are performed on the target mass spectrometry data set to obtain new pollutant concentration levels and toxicity prediction results, including: Based on the scope of new pollutants, non-target analysis methods are used to perform data mining on the target mass spectrometry analysis data set to obtain multiple second new pollutants; structural annotations are performed on multiple second new pollutants to obtain multiple chemical structures; semi-quantitative analysis is performed on multiple chemical structures to obtain concentration levels of new pollutants; the target mass spectrometry analysis data set is processed using computational toxicology methods to obtain toxicity prediction results.

[0012] The new pollutant identification method provided by the present invention is based on the scope of new pollutants and uses non-target analysis methods to perform data mining on the target mass spectrometry analysis data set, which can lock the target more accurately and increase the probability of mining real new pollutants. Furthermore, structural annotation of multiple second new pollutants can provide a deeper understanding of key information such as the molecular composition, chemical bond type, and functional group distribution of new pollutants. Furthermore, by performing semi-quantitative analysis of the chemical structure, the actual content of new pollutants in a specific environment or human matrix can be quantified, avoiding the one-sidedness of assessing risks only from a qualitative perspective. Furthermore, by using computational toxicology methods to obtain toxicity prediction results, it is possible to reasonably predict the toxic effects of new pollutants in the absence of actual toxicity experimental data.

[0013] In an optional implementation, the target execution strategy is a chemical substance to toxic effect strategy; the target execution strategy is used to perform a risk assessment on new pollutants to obtain multiple risk values, including: Acquire multiple initial new pollutant samples; process and perform mass spectrometry analysis on the multiple initial new pollutant samples respectively to obtain a target mass spectrometry analysis data set; perform data mining analysis and toxicity prediction on the target mass spectrometry analysis data set to obtain new pollutant concentration levels and toxicity prediction results; calculate multiple risk values ​​based on the new pollutant concentration levels and toxicity prediction results.

[0014] The new pollutant identification method provided by the present invention, when the target execution strategy is the chemical substance to toxic effect strategy, processes and performs mass spectrometry analysis on multiple initial new pollutant samples obtained, and performs data mining analysis and toxicity prediction on the obtained target mass spectrometry analysis data set, and then calculates multiple risk values, so that the risk assessment results are closer to the actual situation, avoiding the assessment bias caused by focusing only on a single factor of concentration or toxicity, and can more accurately measure the potential risks of new pollutants to the ecological environment and human health from multiple angles.

[0015] In an optional embodiment, data mining analysis and toxicity prediction are performed on the target mass spectrometry data set to obtain new pollutant concentration level and toxicity prediction results, including: The target mass spectrometry data set was subjected to data mining analysis using non-target analysis methods to obtain the concentration levels of new pollutants. The target mass spectrometry data set was processed using computational toxicology methods to obtain toxicity prediction results.

[0016] The new pollutant identification method provided by the present invention uses a non-target analysis method to perform data mining analysis to obtain the concentration level of the new pollutant, and can more accurately determine the content of the new pollutant in the actual sample based on complex mass spectrometry data. Furthermore, the toxicity prediction results are obtained by processing the data set by computational toxicology methods. In the absence of a large amount of actual toxicity experimental data, the toxicity of new pollutants can be reasonably estimated based on existing data characteristics and related models. Therefore, by implementing the present invention, a clear division of labor avoids the functional overlap and duplication of non-target analysis methods and computational toxicology methods, so that each link can focus on the tasks it is good at, and then reasonably allocate resources, reduce unnecessary calculations, analysis steps and time costs, and ensure the reliability of the final concentration level and toxicity prediction results.

[0017] In an optional embodiment, multiple target new pollutants are determined based on multiple risk values, including: Based on the multiple risk values, multiple new pollutant risk contribution values ​​are determined; based on the multiple risk values ​​and the multiple new pollutant risk contribution values, multiple target new pollutants are determined.

[0018] The new pollutant identification method provided by the present invention determines multiple new pollutant risk contribution values ​​through multiple risk values, and can determine the contribution of each new pollutant to the overall risk, and thus can reflect the potential threat of each new pollutant to the ecological environment and human health. Further, by combining multiple risk values ​​and multiple new pollutant risk contribution values ​​to determine multiple target new pollutants, it is possible to accurately identify new pollutants that contribute more to the overall risk and pose more prominent potential threats to the ecological environment and human health, avoiding indiscriminate control of all new pollutants, allowing resources to be concentrated on key control objects, improving the efficiency and effectiveness of the implementation of control measures, and reducing the adverse effects of new pollutants on the ecological environment and human health in a targeted manner, providing a clear direction and focus for environmental monitoring, governance, and government supervision.

[0019] In a second aspect, the present invention provides a new pollutant identification device, the device comprising: An acquisition module is used to acquire a high-throughput fusion method, which is obtained by fusing at least one non-target analysis method and at least one computational toxicology method; a first determination module is used to determine the application order of each non-target analysis method and each computational toxicology method in the high-throughput fusion method; a second determination module is used to determine the target execution strategy of the high-throughput fusion method based on the application order, and the target execution strategy is one or more of the toxic effect to chemical substance strategy and the chemical substance to toxic effect strategy; an evaluation module is used to use the target execution strategy to perform risk assessment of new pollutants to obtain multiple risk values; a third determination module is used to determine multiple target new pollutants based on multiple risk values.

[0020] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the new pollutant identification method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the new pollutant identification method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a schematic flow chart of a new pollutant identification method according to an embodiment of the present invention; Figure 2 is a schematic flow chart of another new pollutant identification method according to an embodiment of the present invention; Figure 3 is a flow chart of another new pollutant identification method according to an embodiment of the present invention; Figure 4 is a detailed flow chart of the fusion of high-throughput non-target analysis and computational toxicology according to an embodiment of the present invention; Figure 5 is a structural block diagram of a new pollutant identification device according to an embodiment of the present invention; Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] In non-target analysis, how to obtain the toxic endpoints of new pollutants that lack toxicity experimental data is a technical difficulty in risk assessment using non-target analysis; while computational toxicology can predict various toxic endpoints of new pollutants, the difficulty in its application lies in obtaining the dataset of new pollutants to be predicted in actual samples. Therefore, an embodiment of the present invention provides a method for identifying new pollutants, which develops a high-throughput fusion method that integrates the two methods based on the technical characteristics of non-target analysis and the methods of computational toxicology to achieve the effect of comprehensive identification and risk assessment of new pollutant mixtures in complex real environment scenarios.

[0026] According to an embodiment of the present invention, an embodiment of a new pollutant identification method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] In this embodiment, a new pollutant identification method is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 1 is a flow chart of a new pollutant identification method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps: Step S101, obtaining a high-throughput fusion method.

[0028] Specifically, the high-throughput fusion method can be obtained by fusing at least one non-target analysis method and at least one computational toxicology method.

[0029] Among them, non-target analysis refers to a chemical analysis method that does not rely on authentic standards and can perform simultaneous qualitative and quantitative analysis of numerous substances in complex matrices. It can be completed in sequence through procedures such as sample collection, sample pretreatment, instrument analysis, data preprocessing, data mining, structural annotation and (semi-) quantitative analysis.

[0030] Computational toxicology refers to a high-throughput method that uses computer technology and mathematical models to predict the toxicity of chemical substances, which may include (quantitative) structure-activity relationship models, machine learning, molecular docking, molecular dynamics simulation, etc.

[0031] Step S102, determining the application order of each non-target analysis method and each computational toxicology method in the high-throughput fusion method.

[0032] Specifically, the order of application of each non-target analysis method and each computational toxicology method in the high-throughput fusion method can be determined by comprehensively considering multiple factors, such as research objectives, existing data basis, and the degree of prior understanding of samples and pollutants.

[0033] For example, if the research aims to quickly identify the presence of new pollutants that may have high-risk toxicity in actual samples, and some toxicity-related theoretical models and public database resources have been mastered, then it may be inclined to use computational toxicology methods for preliminary screening first, that is, to carry out computational toxicology-related operations first in the application order.

[0034] If there are certain monitoring clues for the chemical substances that may be contained in a certain type of environmental sample (such as soil and water samples in a specific area), but there is a lack of information on their toxicity, the application order can give priority to non-target analysis methods.

[0035] In this embodiment, the above application order is not specifically limited and can be determined according to actual conditions.

[0036] Step S103, determining a target execution strategy of the high-throughput fusion method based on the application order.

[0037] Among them, the target execution strategy is one or more of the toxic effect to chemical substance strategy and the chemical substance to toxic effect strategy.

[0038] Specifically, high-throughput fusion methods can be divided into “top-down” (i.e., toxic effect to chemical substance) strategies and “bottom-up” (i.e., chemical substance to toxic effect) strategies according to the order of application.

[0039] Furthermore, "top-down" means starting from computational toxicology prediction, then collecting samples and subsequently completing non-target analysis to achieve the purpose of risk assessment; "bottom-up" means starting from sample collection and then performing non-target analysis and computational toxicology prediction in sequence to achieve the purpose of risk assessment.

[0040] Furthermore, in this embodiment, "top-down" and "bottom-up" are two independent execution strategies, and there is no order of precedence or logical relationship. That is, the two execution strategies can be implemented separately or used in series, and there is no order of application when used in series.

[0041] Specifically, single use refers to only adopting the "top-down" or "bottom-up" execution strategy to achieve comprehensive identification and risk assessment of new pollutants in samples; tandem use refers to simultaneously adopting at least one sub-step of the "top-down" and one "bottom-up" fusion strategy to achieve comprehensive identification and risk assessment of new pollutants in samples; further, in tandem use, the "top-down" strategy can be used first, or the "bottom-up" strategy can be used first, and the choice can be free.

[0042] Step S104: perform risk assessment on new pollutants using the target execution strategy to obtain multiple risk values.

[0043] Specifically, according to the description of step S104, a determined target execution strategy can be used to conduct risk assessment of new pollutants, which comprehensively considers the various characteristics of new pollutants and their performance in actual environments. Multiple risk values ​​are obtained through scientific calculation and analysis, which can quantitatively reflect the potential risk size of new pollutants from different angles.

[0044] Step S105, determining multiple target new pollutants based on multiple risk values.

[0045] Specifically, by combining the multiple risk values ​​obtained, multiple target new pollutants can be further determined, making it possible to screen out those that pose a greater threat to the environment and human health and require priority control from among many possible new pollutants, avoiding indiscriminate control of all new pollutants, allowing resources to be concentrated on key control objects, improving the efficiency and effectiveness of the implementation of control measures, and specifically alleviating the adverse effects of new pollutants on the ecological environment and human health.

[0046] The new pollutant identification method provided in this embodiment integrates non-target analysis methods and computational toxicology methods, breaking the limitations of a single method, fully integrating their respective resources and capabilities, and realizing the synergy of multiple methods. Further, by determining the application order of each non-target analysis method and each computational toxicology method in the high-throughput fusion method and further determining the target execution strategy, the workflow can be optimized according to the specific situation, work efficiency can be improved, and unnecessary repetitive operations and waste of resources can be avoided. At the same time, whether it is to find the corresponding chemical substance from the toxic effect, or to evaluate the toxic effect of the discovered chemical substance, it can be achieved by reasonably selecting and switching the execution strategy, which enhances the adaptability to diversified practical problems. Further, the risk assessment of new pollutants is carried out using the determined target execution strategy, which comprehensively considers the various characteristics of new pollutants and their performance in the actual environment. Through scientific calculation and analysis, multiple risk values ​​are obtained, which can quantify the potential risk size of new pollutants from different angles. Finally, by combining multiple risk values, we further identified multiple target new pollutants, and screened out those that posed a greater threat to the environment and human health and required priority control from among many possible new pollutants. This avoided indiscriminate control of all new pollutants, allowing resources to be concentrated on key control targets, improving the efficiency and effectiveness of the implementation of control measures, and specifically alleviating the adverse effects of new pollutants on the ecological environment and human health, providing a clear direction and focus for environmental monitoring, governance, and government supervision.

[0047] In this embodiment, a new pollutant identification method is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 2 is a flow chart of a new pollutant identification method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps: Step S201, obtain a high-throughput fusion method. For details, see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0048] Step S202, determining the application order of each non-target analysis method and each computational toxicology method in the high-throughput fusion method. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0049] Step S203: Determine the target execution strategy of the high-throughput fusion method based on the application order. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0050] Step S204: perform risk assessment on new pollutants using the target execution strategy to obtain multiple risk values.

[0051] Specifically, when the target execution strategy is the toxic effect to chemical substance strategy, the above step S204 includes: Step S2041, using computational toxicology methods to screen and determine the range of new pollutants that meet the selected toxicity endpoint from a preset database.

[0052] The toxicity endpoints can be determined based on the research purpose and actual focus. For example, if the focus is on the impact of new pollutants on aquatic ecosystems, the toxicity endpoints may be set as acute toxicity to fish, interference with the reproductive capacity of aquatic organisms, etc.; if the focus is on hazards to human health, the toxicity endpoints may be carcinogenicity, teratogenicity, damage to specific organs (such as liver, kidney, etc.), etc.

[0053] Furthermore, the preset database can be a public chemical database (such as PubChem, ChemSpider, etc., which contains a large amount of basic information such as the structure and properties of chemical substances), or a database of specific types of chemical substances accumulated within the enterprise, a collection of chemical substances related to a specific industry field, etc. It can be determined based on the actual research scenario and available data resources.

[0054] Specifically, according to the description in step S101, multiple new pollutants that meet the selected toxicity endpoints can be screened out from a preset database using methods such as (quantitative) structure-activity relationship models (QSAR / QSPR), machine learning, molecular docking, and molecular dynamics simulation.

[0055] In an optional embodiment, taking the (quantitative) structure-activity relationship model (QSAR / QSPR) as an example, if there are a large number of known chemical structures and corresponding toxicity data as a basis, the QSAR / QSPR model can be used. First, analyze these data to find the quantitative relationship between chemical structure characteristics (such as molecular size, shape, type and distribution of functional groups, etc.) and selected toxicity endpoints, and establish a mathematical model. Then, substitute the structural parameters of many chemical substances in the preset database into the model, calculate their activity prediction values ​​related to the selected toxicity endpoint, and screen out those chemical substances predicted to have the required activity (which means that they may have corresponding toxicity).

[0056] For example, it is known that some compounds containing specific functional groups are toxic to the liver. After analyzing this structure-toxicity relationship through the QSAR model, other compounds in the preset database are checked to see whether there are similar functional groups and related structural features in their structures, so as to determine whether they may be toxic to the liver and then screen them out.

[0057] Furthermore, the corresponding new pollutant range may be determined based on the multiple new pollutants.

[0058] Step S2042: collect samples based on the new pollutant range to obtain multiple initial new pollutant samples.

[0059] Specifically, the types of media in which new pollutants may be mainly stored in the environment can be determined by studying the characteristics of the substances included in the scope of the identified new pollutants. For example, if the scope of new pollutants is some organic compounds with low water solubility, high volatility and easy adsorption on particulate matter, then the new pollutants are likely to exist in the atmospheric environment, and may be adsorbed on atmospheric particulate matter or exist in gaseous form; if the scope of new pollutants is defined as some substances that are difficult to degrade, hydrophilic and easily combined with soil particles, then soil will be one of its main storage media; and for those substances that can be enriched in organisms and have bioaccumulation, it is necessary to focus on collecting them from biological samples (such as tissues in organisms such as fish and birds).

[0060] Furthermore, based on the above analysis results, the types of samples that need to be collected can be reasonably determined. For example, when it is determined that new pollutants are mainly present in the water environment, the sample type can be selected as a water sample, which can be further subdivided into water samples from different sources, such as industrial wastewater (for specific industrial discharge areas, it may contain relevant new pollutants generated in the production process), sewage treatment plant inlet and outlet water (which can reflect the changes in pollutants during sewage treatment and possible residual new pollutants), river water (representing the pollution status of natural water bodies after being affected by the surrounding environment), surface water (covering a wider range of natural water surface conditions), seawater (for coastal areas that may be affected by marine-related activities and new pollutants), drinking water (related to human health, to detect whether there are new pollutants), groundwater (affected by factors such as soil infiltration, it may contain some long-term accumulated pollutants), etc.

[0061] Furthermore, after determining the sample type, you can further select appropriate areas / groups for sample collection. For example, for water sample collection, if it is industrial sewage, you can select locations such as the vicinity of the corresponding industrial discharge port, the front end of the sewage treatment facility, etc. for sample collection.

[0062] Furthermore, after determining the sampling area and group, the time, frequency, quantity and other elements of sample collection can be planned in detail to form a complete sample collection plan.

[0063] Furthermore, according to the set sample collection plan, appropriate sampling tools and methods can be selected to perform sample collection operations and obtain corresponding multiple initial new pollutant samples.

[0064] Step S2043, processing and mass spectrometry analysis are performed on multiple initial new pollutant samples respectively to obtain a target mass spectrometry analysis data set.

[0065] In some optional implementations, the above step S2043 includes: Step a1, obtaining multiple sample matrix characteristics of multiple initial new pollutant samples.

[0066] Step a2: Based on the new pollutant range and multiple sample matrix characteristics, a preset sample pretreatment method is used to process multiple initial new pollutant samples to obtain multiple target new pollutant samples.

[0067] Step a3: performing mass spectrometry analysis on multiple target new pollutant samples to obtain an initial mass spectrometry analysis data set.

[0068] Step a4, preprocessing the initial mass spectrometry analysis data set to obtain a target mass spectrometry analysis data set.

[0069] Among them, the sample matrix characteristics represent the sample type; the preset sample pretreatment method can be ultrasonic vibration, Soxhlet extraction, solid phase extraction, liquid-liquid extraction, solid phase microextraction, liquid phase microextraction, pressurized solvent extraction, QuECHERS and other methods.

[0070] Specifically, it is possible to determine whether the multiple initial new pollutant samples are water samples, solid samples, gas samples, biological samples, etc., based on the environmental medium classification described in step S2042. For example, it is determined that the multiple initial new pollutant samples collected are river water samples, solid samples of sewage treatment plant sediment, and fish biological samples.

[0071] Furthermore, the matrix feature description can be refined. For example, for water samples, its specific source and related characteristics should be further clarified. For example, for river water samples, it is necessary to understand the area through which the river flows (whether it passes through industrial pollution areas, agricultural planting areas, etc.), water flow, water temperature, pH value, etc.; for solid samples of sewage treatment plant sediment, it is necessary to know the main source of sewage treated by the sewage treatment plant (domestic sewage or industrial sewage, etc.) and the texture and water content of the sediment; for fish biological samples, it is necessary to record the type of fish, growth environment (freshwater or seawater environment, whether it is in polluted waters, etc.), size and age of the fish, etc.

[0072] Furthermore, appropriate sample pretreatment methods can be selected based on the chemical properties of the target new pollutants determined by the scope of new pollutants (such as solubility, volatility, polarity, etc.) and the matrix characteristics of the initial sample. For example, if the scope of new pollutants is some organic compounds with strong polarity, and the initial sample is a river water sample, considering that the water sample may contain a large amount of impurities and the concentration of the target is low, a liquid-liquid extraction method can be used, and an organic solvent (such as dichloromethane, etc.) that is immiscible with water and has good solubility in the target organic compound can be selected for extraction, and the target new pollutants can be transferred from the water sample to the organic phase to achieve the purpose of enrichment and preliminary separation of impurities.

[0073] Furthermore, the sample pretreatment method can be selected and the corresponding operation procedures and specifications can be strictly followed. For example, when using ultrasonic oscillation, it is necessary to control the ultrasonic power, time and other parameters to ensure that the target substance can be effectively desorbed without destroying its chemical structure; when performing liquid-liquid extraction operations, it is necessary to accurately control the amount of extractant, the number of extractions, and the intensity and time of oscillation to ensure the extraction efficiency; in the process of solid phase extraction and solid phase microextraction, it is necessary to pay attention to the key points of operation of the activation, loading, elution and other links of the adsorbent, so that the sample pretreatment process can be completed smoothly, and finally multiple treated target new pollutant samples can be obtained.

[0074] Furthermore, the precursor ions (or adduct ions) corresponding to the predefined new pollutants can be input into the inclusion list (online priority collection of precursor ion library) in the instrument analysis, so that when the instrument is performing analysis, the substances in the list will be given priority analysis under the conditions of equal substance signal strength.

[0075] At the same time, according to the type of target new pollutant samples (such as water samples, solid samples, biological samples, etc.) and the properties of the target objects to be analyzed, other parameters of the mass spectrometer are reasonably set, such as ion source type (electrospray ion source ESI, atmospheric pressure chemical ion source APCI, etc.), scanning range (set the appropriate scanning range according to the approximate molecular weight range of the target object), resolution (select a high-resolution value that meets the analysis requirements), scanning speed, etc., to ensure that the instrument is in the best working state and can accurately detect the mass spectrometry signals of the target new pollutants.

[0076] Furthermore, multiple target new pollutant samples obtained after pretreatment can be injected into the mass spectrometer for analysis in sequence. During the analysis process, different target new pollutants will be ionized in the ion source to form charged ions, which are then separated and detected in the mass analyzer according to their different mass-to-charge ratios (m / z), generating mass spectrometry signal information containing various ions. These signal information reflects the molecular weight, ion fragments and other characteristics of the target new pollutants. The mass spectrometer records these data and forms the corresponding initial mass spectrometry analysis data set.

[0077] Furthermore, the initial mass spectrometry data set may be preprocessed to obtain a target mass spectrometry data set, wherein the preprocessing may include operations such as filtering, deconvolution, peak alignment, null value filling, normalization, and feature annotation.

[0078] Step S2044, based on the scope of new pollutants, data mining analysis and toxicity prediction are performed on the target mass spectrometry analysis data set to obtain new pollutant concentration levels and toxicity prediction results.

[0079] In some optional implementations, the above step S2044 includes: Step b1, based on the scope of new pollutants, using a non-target analysis method to perform data mining on the target mass spectrometry analysis data set to obtain multiple second new pollutants.

[0080] Step b2, structurally annotating the multiple second new pollutants to obtain multiple chemical structures.

[0081] Step b3, semi-quantitative analysis of multiple chemical structures to obtain new pollutant concentration levels.

[0082] Step b4, processing the target mass spectrometry analysis data set using a computational toxicology method to obtain a toxicity prediction result.

[0083] Among them, data mining methods include but are not limited to suspected target screening, isotope labeling, mass defect filtering, homologue retrieval, case control, and characteristic fragment ion labeling; structural annotation methods can include spectral database matching, spectral prediction, and characteristic fragmentation path analysis.

[0084] Furthermore, spectral databases include but are not limited to mzClould, massBank EU, MoNA, NIST, Wiley, GNPS, etc.

[0085] Specifically, the range of new pollutants is used as the screening database, and the suspected target screening method in non-target analysis is mainly used. At the same time, various structure-driven data mining methods such as isotope labeling, mass defect filtering, homologue retrieval, case control, characteristic fragment ion labeling, etc. can be combined to carry out mining of the target mass spectrometry analysis data set.

[0086] For example, the suspected target screening method will search for signal peaks that match or have a high degree of similarity in the entire target mass spectrometry analysis data set based on the characteristic ions (such as molecular ion peaks, characteristic fragment ion peaks, etc.) of the target substances determined in the new pollutant range in the mass spectrum and their relative abundance information, and preliminarily judge the possible existence of new pollutants; the isotope labeling method uses the isotope characteristics of certain elements in the target new pollutants to assist in determining possible new pollutants by observing signals with corresponding isotope peak ratios or difference relationships in the mass spectrometry data; mass loss filtering can screen out substances corresponding to the mass spectrometry signals in the data set that meet the mass loss characteristics based on the theoretical mass loss value range of substances in the known new pollutant range, thereby narrowing the search range and improving mining efficiency.

[0087] Furthermore, structural annotation methods such as spectral database matching, spectral prediction and characteristic fragmentation path analysis can be used to determine the chemical structure of the second new pollutant. Specifically, the spectral database matching method can be used to compare the mass spectrum of the mined second new pollutant with the standard spectrum in the known spectral database (such as mzClould, massBank EU, MoNA, NIST, Wiley, GNPS, etc.), and find the chemical structure corresponding to the spectrum with high similarity, which can be used as a reference to preliminarily determine its possible structure.

[0088] For example, the mass spectrum of a second new pollutant is uploaded to the NIST spectrum database for search and matching. If a highly similar standard spectrum is found and the chemical structure corresponding to the spectrum is clearly marked, then it can be preliminarily determined that the second new pollutant has a similar chemical structure.

[0089] Furthermore, for the second new pollutant that cannot find a completely matching spectrum in the spectrum database, the spectrum prediction tool can be used to predict the possible chemical structure based on the fragment ion information in its mass spectrum and some known chemical laws (such as the breaking law of chemical bonds, the characteristic fragmentation mode of functional groups, etc.). At the same time, it is also possible to combine the characteristic fragmentation path analysis to conduct in-depth research on how the characteristic fragment ions generated by the substance during the mass spectrometry analysis are formed step by step from its parent ion, and further improve and confirm its chemical structure through reverse deduction.

[0090] Among them, spectrum prediction tools include but are not limited to CFM-ID, CSI:FingerID, MetFrag, MS-FINDER, Mass Frontier, Molecular Structure Correlator, MS-Fragmenter, MOLGEN-MS, etc.

[0091] Furthermore, based on the chemical structures that have been determined, appropriate semi-quantitative analysis methods can be selected to conduct semi-quantitative analysis on multiple chemical structures according to the properties of different chemical substances and the available analysis conditions, so as to obtain the corresponding concentration levels of new pollutants, thus avoiding the one-sidedness of assessing risks only from a qualitative perspective.

[0092] Among them, semi-quantitative analysis methods can be implemented based on structurally similar standards, chromatographic retention times close to standards, ionization efficiency prediction, machine learning, etc.

[0093] Finally, based on the target mass spectrometry analysis data set, using primary mass spectra, secondary mass spectra, compound structure and other information as input parameters, computational toxicology methods are used to perform toxicity predictions and obtain corresponding toxicity prediction results. This enables reasonable prediction of the toxic effects of new pollutants in the absence of actual toxicity experimental data.

[0094] Step S2045, calculating multiple risk values ​​based on the new pollutant concentration level and toxicity prediction results.

[0095] Specifically, by combining the concentration levels of new pollutants and toxicity prediction results, the risk value of each new pollutant can be calculated, avoiding the assessment bias caused by focusing only on single factors such as concentration or toxicity. This can more accurately measure the potential risks of new pollutants to the ecological environment and human health from multiple angles.

[0096] Step S205: Based on the multiple risk values, multiple target new pollutants are determined. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0097] The new pollutant identification method provided in this embodiment, when the target execution strategy is the toxic effect to chemical substance strategy, uses the computational toxicology method to screen and determine the range of new pollutants that meet the selected toxicity endpoint from the preset database, which can quickly exclude a large number of substances that are not related to the toxicity characteristics, narrow the scope of investigation, and improve the efficiency of locating new pollutants in a large number of possible substances, avoiding directionless comprehensive screening, saving resources and time costs. Further, using the preset sample pretreatment method to process multiple initial new pollutant samples to obtain multiple target new pollutant samples, it is possible to fully consider the chemical properties of the target new pollutants and the interfering components that may exist in the sample, and reduce the analysis errors caused by factors such as matrix effects. Further, mass spectrometry analysis is performed on multiple target new pollutant samples, and an initial mass spectrometry analysis data set that more realistically and clearly reflects the characteristics of the target new pollutants can be obtained. Finally, the initial mass spectrometry analysis data set is preprocessed to improve the quality and availability of the target mass spectrometry analysis data set. Further, based on the scope of new pollutants, the target mass spectrometry analysis data set is mined using a non-target analysis method, which can more accurately lock the target and increase the probability of mining real new pollutants. Furthermore, structural annotation of multiple second new pollutants can provide a deeper understanding of key information such as the molecular composition, chemical bond type, and functional group distribution of new pollutants. Furthermore, through semi-quantitative analysis of chemical structures, the actual content of new pollutants in specific environments or human matrices can be quantified, avoiding the one-sidedness of assessing risks only from a qualitative perspective. Furthermore, by using computational toxicology methods to obtain toxicity prediction results, it is possible to reasonably predict the toxic effects of new pollutants in the absence of actual toxicity experimental data.

[0098] In this embodiment, a new pollutant identification method is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 3 is a flow chart of a new pollutant identification method according to an embodiment of the present invention. Figure 3As shown, the process includes the following steps: Step S301, obtain a high-throughput fusion method. For details, see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0099] Step S302, determining the application order of each non-target analysis method and each computational toxicology method in the high-throughput fusion method. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0100] Step S303: Determine the target execution strategy of the high-throughput fusion method based on the application order. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0101] Step S304: perform risk assessment on new pollutants using the target execution strategy to obtain multiple risk values.

[0102] Specifically, when the target execution strategy is the chemical substance to toxic effect strategy, the above step S304 includes: Step S3041, obtaining multiple initial new pollutant samples.

[0103] Specifically, sample collection can be performed through a pre-set collection plan to obtain a corresponding plurality of initial new pollutant samples.

[0104] Step S3042, respectively process and perform mass spectrometry analysis on a plurality of initial new pollutant samples to obtain a target mass spectrometry analysis data set.

[0105] The specific process can be referred to the description of step S2043 above, which will not be repeated here.

[0106] Step S3043, performing data mining analysis and toxicity prediction on the target mass spectrometry analysis data set to obtain new pollutant concentration levels and toxicity prediction results.

[0107] In some optional implementations, the above step S3043 includes: Step c1, using a non-target analysis method to perform data mining analysis on the target mass spectrometry analysis data set to obtain the concentration level of new pollutants.

[0108] For the specific process, please refer to the description of steps b1 to b3 above, which will not be repeated here.

[0109] Step c2, based on the target mass spectrometry analysis data set, the toxicity prediction result is obtained by processing it using computational toxicology methods.

[0110] Specifically, key information related to the chemical structure of new pollutants can be extracted from the target mass spectrometry data set. Furthermore, the extracted key information can be organized and quantified to be converted into a data format that can be used for computational toxicology analysis.

[0111] Further, according to the description of step S101, the toxicity of new pollutants can be predicted from different angles and to different degrees using methods such as (quantitative) structure-activity relationship models, machine learning, molecular docking, and molecular dynamics simulation, and corresponding toxicity prediction results can be obtained.

[0112] Step S3044, calculating multiple risk values ​​based on the new pollutant concentration level and toxicity prediction results.

[0113] The specific process can be referred to the description of step S2045 above, which will not be repeated here.

[0114] Step S305, determining multiple target new pollutants based on multiple risk values.

[0115] Specifically, the above step S305 includes: Step S3051, determining multiple new pollutant risk contribution values ​​based on multiple risk values.

[0116] Specifically, by calculating (such as adding up) the obtained multiple risk values, a total risk value can be obtained. Further, the total risk value can represent the overall risk level of all new pollutants in a specific environment or to a specific receptor group.

[0117] Furthermore, for each new pollutant, the proportion of its risk value in the total risk value, that is, the risk contribution value of the new pollutant, can be calculated.

[0118] Step S3052: determining multiple target new pollutants based on multiple risk values ​​and multiple new pollutant risk contribution values.

[0119] Specifically, the risk value and corresponding risk contribution value of each new pollutant can be taken into consideration at the same time, and all new pollutants can be comprehensively ranked according to the risk contribution level and the size of the risk value.

[0120] For example, suppose there is another group of new pollutants A, B, C, and D, whose risk values ​​are 0.2, 0.35, 0.3, and 0.15 respectively, and the corresponding risk contribution values ​​are 20%, 35%, 30%, and 15% respectively (if the total risk is assumed to be 1). On the whole, they are first sorted according to the risk contribution value, and the ranking is B (35%) > C (30%) > A (20%) > D (15%); if the risk contribution values ​​are the same, they are further sorted for a second time with reference to the size of the risk values ​​to determine the order of the new pollutants and obtain a ranking result similar to BCAD to clarify the order of the impact of different new pollutants on the overall risk.

[0121] Furthermore, based on the sorting results, some of the new pollutants ranked at the top can be selected as target new pollutants. These target new pollutants are usually substances that contribute more to the total risk and have relatively higher risk values, that is, those substances that pose more prominent potential threats to the ecological environment and human health among many new pollutants. They will be the objects of priority control.

[0122] The new pollutant identification method provided in this embodiment, when the target execution strategy is the chemical substance to toxic effect strategy, processes and mass spectrometry analyzes the multiple initial new pollutant samples obtained and obtains the target mass spectrometry analysis data set, and then uses the non-target analysis method to perform data mining analysis to obtain the new pollutant concentration level, which can accurately determine the content of the new pollutant in the actual sample based on the complex mass spectrometry data. Further, relying on the computational toxicology method to process the data set to obtain the toxicity prediction result, it can also reasonably estimate the toxicity of the new pollutant based on the existing data characteristics and related models in the absence of a large amount of actual toxicity experimental data. Further, considering the concentration level of the new pollutant and the toxicity prediction results to calculate multiple risk values, the risk assessment results are closer to the actual situation, avoiding the assessment deviation caused by focusing only on the concentration or toxicity single factor, and can more accurately measure the potential risk size of the new pollutants to the ecological environment and human health from multiple angles. Further, by determining multiple new pollutant risk contribution values ​​through multiple risk values, it is possible to determine the contribution of each new pollutant to the overall risk, and then reflect the potential threat of each new pollutant to the ecological environment and human health. Furthermore, combining multiple risk values ​​and multiple new pollutant risk contribution values ​​to determine multiple target new pollutants can accurately identify new pollutants that contribute more to the overall risk and pose more prominent potential threats to the ecological environment and human health, avoid indiscriminate control of all new pollutants, and enable resources to be concentrated on key control objects, thereby improving the efficiency and effectiveness of the implementation of control measures, and specifically reducing the adverse effects of new pollutants on the ecological environment and human health, providing a clear direction and focus for environmental monitoring, governance, and government supervision.

[0123] In one example, specific implementation processes of the two execution strategies are provided separately and in series.

[0124] Embodiment 1: Using in-laboratory testing as an example, the focus is on achieving comprehensive identification and risk assessment of new contaminants in samples using an established “top-down” execution strategy, such as Figure 4 As shown, the specific steps include: Step 1: To identify endocrine disruptors in synthetic chemicals and their hazards to human health, a fusion approach of computational toxicology and non-target analysis was used.

[0125] Step 2: After establishing the fusion method, conduct comprehensive identification and health hazard assessment of endocrine disruptors in the human body based on a “top-down” fusion strategy.

[0126] Step 3: First, based on the public database of synthetic chemicals, a structural database containing all organic synthetic chemicals used in production is constructed (containing a number of compounds). The molecular docking model is used to predict compounds with endocrine disrupting effects, and b compounds with different structures are obtained, which are pre-defined as the endocrine disruptor database (containing b number of compounds).

[0127] Step 4: Collect serum samples from c individuals of different age groups and genders in location A, and use liquid-liquid extraction method to extract endocrine disruptors in the samples.

[0128] Step 5: Select ultra-high performance liquid chromatography and high-resolution mass spectrometry for instrumental analysis based on the characteristics of the serum matrix and the physicochemical properties of the compounds in the endocrine disruptor database. The chromatographic column is Waters ACQUITY C18 (1.7 μm, 2.1×100 mm). Mass spectrometry data are acquired based on data-dependent acquisition mode. In the mass spectrometry method, the adduct ion form of compound b in the endocrine disruptor database is added to the secondary spectrum priority acquisition precursor ion library to complete data acquisition.

[0129] Step 6: Use software to preprocess the mass spectrometry raw data. The parameters involved in the filtering, deconvolution, peak alignment, null value filling, normalization and feature annotation procedures were set to the default values ​​of the software without modification. After preprocessing, d mass spectrometry features were identified from c serum samples.

[0130] Step 7: Construct a suspected target screening database from the endocrine disruptor database (with the number of compounds being b), and use the suspected target screening method in non-target analysis to confirm that e0 mass spectral features out of d mass spectral features belong to the compounds in the endocrine disruptor database (e0 ≤ d). Further, perform structure annotation on each of the e0 mass spectral features one by one, and determine that e of these compounds are positive matches (e ≤ e0), that is, initially identify e new endocrine disruptor pollutants from the serum samples.

[0131] Meanwhile, use a machine learning algorithm to identify f0 mass spectral features belonging to endocrine disruptors from the mass spectra corresponding to the preprocessed d mass spectral features (f0 < d). Further, perform structure annotation on each of the f0 mass spectral features one by one, and be able to give the definite or possible chemical structures of f1 of these mass spectral features (f1 ≤ f0). Use the molecular docking model in Step 3 to predict that f of the f1 compounds belong to endocrine disruptors (f ≤ f1), that is, further identify f new endocrine disruptor pollutants from the serum samples.

[0132] By comparing the e and f endocrine disruptors identified by the two methods respectively, it is found that there are g endocrine disruptors that are repeated in the two datasets. Therefore, finally, (e + f - g) endocrine disruptors are identified from c serum samples.

[0133] Through research on the existing real standards in the market, determine the number of new pollutants among which there are real standards. Subsequently, purchase the real standards and perform target confirmation on the new pollutants. Among them, the new pollutants are confirmed , that is, it is clear that they exist in the samples. Subsequently, perform quantitative analysis on the confirmed new pollutants to give the accurate concentration levels. For the new pollutants other than those lacking real standards , based on the ionization efficiency prediction model, perform semi-quantitative analysis to give the predicted concentration levels.

[0134] Step 8: Using estrogen and androgen effects as toxicity targets, calculate the endocrine disruption effect risk of the identified new pollutants in serum samples based on the quantitative and semi-quantitative concentration levels and their binding energies with the target receptors.

[0135] Step 9: Since the number of identified endocrine disruptors exceeds 50, it is difficult to include all of them in daily monitoring and government supervision activities. Based on the ToxPi ranking, select the top 10 compounds in the ranking for optimization and establish them as priority-controlled new pollutants.

[0136] Example 2: Using in-laboratory testing as an example, the focus is on achieving comprehensive identification and risk assessment of new contaminants in samples using an established “bottom-up” execution strategy, such as Figure 4 As shown, the specific steps include: Step 1: In order to identify per / polyfluoroalkyl substances (PFAS) in the surface water of Area B and their ecological risks to local algae, daphnia and fish, a fusion approach of non-target analysis and computational toxicology was used.

[0137] Step 2: After establishing the fusion method, conduct comprehensive identification and ecological risk assessment of PFAS in the surface water of Area B based on the “bottom-up” fusion strategy.

[0138] Step 3: Take the surface water receiving points of point source pollution such as the fluorine chemical industrial park and the effluent from the domestic sewage treatment plant as the sampling starting point, and collect a total of a surface water samples along the direction of water flow.

[0139] After all samples were filtered through a 0.45 μm filter membrane, 500 mL of each sample was taken and solid phase extraction technology was used to complete sample pretreatment extraction, enrichment and re-dissolution.

[0140] According to the water matrix characteristics and the physicochemical properties of PFAS, an ultra-high performance liquid chromatography and high-resolution mass spectrometry system were selected for instrumental analysis. The chromatographic column was Waters ACQUITY C18 (1.7 μm, 2.1×100 mm). Mass spectrometry data were acquired simultaneously based on data-dependent and data-independent acquisition modes.

[0141] The mass spectrometry raw data were preprocessed using software. The parameter settings involved in the filtering, deconvolution, peak alignment, null value filling, normalization and feature annotation procedures were set to the default values ​​of the software without modification. After preprocessing, b1 mass spectrometry features were identified from the data-dependent data of a surface water sample, and b2 mass spectrometry features were identified from the data-independent data.

[0142] Step 4: Perform PFAS structure-driven data mining on the preprocessed mass spectrometry data, which includes four parts.

[0143] Firstly, a suspected target database containing c0 PFAS was constructed based on the OECD database and PubChem database. The suspected target screening method in non-target analysis was used to screen out c mass spectral features from b1 mass spectral features that may belong to PFAS (c < b1 and c ≤ c0).

[0144] Secondly, based on the CF2-normalized Kendrick mass loss (≤ 0.1 or ≥ 0.8), the mass loss filtering method in non-target analysis was used to screen out d mass spectral features from b1 mass spectral features that may belong to PFAS (d < b1).

[0145] Then, based on the characteristic fragment ions C2F5−, C2F5O−, C3F7−, C3F5−, SO3F−, C2F5−, C5F12N−, etc. and the neutral loss fragment HF, the characteristic fragment ion labeling method in non-target analysis was used to label e0 mass spectral features from b2 mass spectral features as possibly belonging to PFAS (e0 < b2). According to the retention time, e of the e0 mass spectral features were screened out from b1 mass spectral features in the data-dependent data, which had both precursor ions and secondary fragment ions (e ≤ e0 and e < b1), and it was considered that the screened e mass spectral features might belong to PFAS.

[0146] By comparing the mass spectral features screened by the above three non-target analysis methods, it was found that g0 kinds of mass spectral features were repeated in the three data sets.

[0147] Finally, based on the identified (c+d+e-g0) mass spectral features that may belong to PFAS and the unit masses of PFAS homologues 49.9968 (CF2), 99.9936 (CF2CF2), 64.0125 (CH2CF2) and 65.9917 (CF2O), the homologue retrieval method in non-target analysis was used to screen out f mass spectral features that may belong to PFAS (f < b1) from b1 mass spectral features.

[0148] By comparing the mass spectrometry features screened by the above four non-target analysis methods, it was found that g mass spectrometry features were repeated in the four data sets. Therefore, (c+d+e+fg) mass spectrometry features were finally identified from a surface water sample as possibly belonging to PFAS.

[0149] Step 5: Structural annotation of the mined (c+d+e+fg) mass spectral features, including three parts.

[0150] First, we used the spectral databases such as mzClould, massBank EU and MoNA to retrieve the mass spectra of the h1' mass spectral features among the (c+d+e+fg) mass spectral features. Based on the 70% spectral similarity score threshold, we preliminarily identified the h1 mass spectral feature as belonging to PFAS.

[0151] Secondly, for the mass spectrometric features that may belong to PFAS that have not been identified, the spectrum prediction tool CFM-ID was used to predict their mass spectra and compared with the collected mass spectra in the data-dependent data. Based on the principle of at least 2 secondary mass spectrometric fragment matches, h2 mass spectrometric features were further identified as PFAS.

[0152] Then, for the mass spectral features that may belong to PFAS that were not identified in the spectral database and spectral prediction tools, h3 mass spectral features were manually identified as belonging to PFAS based on the high-resolution mass spectral characteristic fragmentation paths of new PFAS pollutants.

[0153] Through the above method, a total of a surface water samples were identified types of PFAS.

[0154] By investigating the real standard products on the market, PFAS Then, buy the authentic standard and PFAS were identified as targets. PFAS confirmed , that is, it is clearly present in the sample.

[0155] Step 6: Confirm Targeted quantitative analysis of various PFASs is performed to provide accurate concentration levels.

[0156] In the absence of authentic standards For PFAS other than those listed above, semi-quantitative analysis is performed based on the principle of structural similarity (Tanimoto coefficient>0.7) with the help of authentic PFAS standards with similar structures to give predicted concentration levels.

[0157] The number of PFAS with accurate concentration levels and predicted concentration levels is h .

[0158] Through searching the NORMAN, ECOTOX and EnviroTox ecotoxicology databases and literature searches, it was found that the (semi-)quantified ha, hb and hc PFASs had experimental half-lethal concentrations or half-growth inhibition concentrations for algae, daphnia and fish, respectively.

[0159] For PFAS without toxicity test data for algae (h-ha species), daphnia (h-hb species) and fish (h-hc species), the quantitative structure-activity relationship model is used to predict their ecotoxicity data. For PFAS with toxicity prediction concentration higher than water solubility, the toxicity prediction value is replaced by solubility.

[0160] Step 7: Use the ratio of PFAS concentration levels to toxic endpoint thresholds to assess the ecological risk of each PFAS to aquatic species such as algae, daphnia, and fish.

[0161] Based on the concentration addition model, the cumulative risks of h types of PFAS to algae, daphnia and fish were obtained respectively.

[0162] Step 8: Calculate the contribution of each PFAS to the cumulative risk of PFAS for each species. Select the top three PFAS in each species as the priority new pollutants. It can be seen that the number of priority PFAS does not exceed 9.

[0163] Embodiment 3: The test examples are focused on realizing the comprehensive identification and risk assessment of new contaminants in samples by using the established "top-down" and "bottom-up" fusion strategies in tandem, such as Figure 4 As shown, the specific steps include: Step 1: In order to identify new contaminants with neurotoxic effects in human cerebrospinal fluid and their neurotoxicity risks, a fusion approach of computational toxicology followed by non-target analysis and then computational toxicology was used.

[0164] Step 2: After establishing the fusion method, conduct comprehensive identification of new pollutants with neurotoxic effects in human cerebrospinal fluid and their neurotoxic risk assessment based on the "top-down" and "bottom-up" fusion strategy.

[0165] Step 3: Same as step 3 in Example 1, construct a database of new neurotoxic pollutants using machine learning; then, same as steps 4, 5 and 6 in Example 1, identify a mass spectral feature from the cerebrospinal fluid sample.

[0166] Step 4: Same as steps 4 and 5 in Example 2, from the a mass spectral features, b0 mass spectral features that may belong to new pollutants with neurotoxic effects are mined. Further, the b new pollutants with neurotoxic effects are confirmed by authentic standards. At the same time, there are c mass spectral features that may belong to new pollutants with neurotoxic effects.

[0167] Step 5: Same as step 7 in Example 1, obtain the quantitative concentration and semi-quantitative concentration of (b+c) new pollutants.

[0168] The quantitative structure-activity relationship model was used to predict the 10% growth inhibition concentration of the new pollutants (b+c) on human neuroblastoma cells SH-SY5Y.

[0169] Step 6: Same as step 7 in Example 2, the neurotoxicity risk of the identified new pollutant is obtained based on the concentration level and effect concentration ratio of the (b+c) new pollutants.

[0170] Combining the results of the above embodiments, it can be shown that the new pollutant identification method provided in this embodiment is highly reliable, and non-target analysis and computational toxicology methods are complementary; it can be applied in a variety of complex matrices, and is less affected by matrix types and interference; the types of new pollutants and risk types screened cover a wide range, and both known and unknown new pollutants and their risks can be efficiently identified; it has high-throughput characteristics and can complete the identification and risk assessment of a large number of new pollutants in a short time. Therefore, the new pollutant identification method provided in this embodiment has universal applicability and broad application prospects.

[0171] In this embodiment, a new pollutant identification device is also provided, which is used to implement the above embodiments and preferred implementations, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0172] This embodiment provides a new pollutant identification device, such as Figure 5 As shown, the device comprises: The acquisition module 501 is used to acquire a high-throughput fusion method, where the high-throughput fusion method is obtained by fusing at least one non-target analysis method and at least one computational toxicology method.

[0173] The first determination module 502 is used to determine the application order of each non-target analysis method and each computational toxicology method in the high-throughput fusion method.

[0174] The second determination module 503 is used to determine the target execution strategy of the high-throughput fusion method based on the application order, and the target execution strategy is one or more of the toxic effect to chemical substance strategy and the chemical substance to toxic effect strategy.

[0175] The evaluation module 504 is used to perform risk evaluation of new pollutants using the target execution strategy to obtain multiple risk values.

[0176] The third determination module 505 is used to determine multiple target new pollutants based on multiple risk values.

[0177] The further functional description of each of the above modules is the same as that of the above corresponding embodiments and will not be repeated here.

[0178] The new pollutant identification device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0179] The embodiment of the present invention also provides a computer device having the above Figure 5 The new contaminant identification device shown.

[0180] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6 As shown, the computer device comprises: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other, and can be installed on a common mainboard or installed in other ways as required. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0181] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0182] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0183] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0184] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0185] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0186] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0187] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0188] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A new pollutant identification method, characterized in that: The method comprises: Obtaining a high-throughput fusion method, wherein the high-throughput fusion method is obtained by fusing at least one non-target analysis method and at least one computational toxicology method; determining an order of application of each of the non-target analysis methods and each of the computational toxicology methods in the high-throughput fusion method; Based on the application order, determining a target execution strategy of the high-throughput fusion method, wherein the target execution strategy is one or more of a toxic effect to chemical substance strategy and a chemical substance to toxic effect strategy; Using the target execution strategy to conduct risk assessment of new pollutants to obtain multiple risk values; Based on the multiple risk values, multiple target new pollutants are determined.

2. The method according to claim 1, characterized in that The target implementation strategy is a toxic effect to chemical substance strategy; The target execution strategy is used to conduct risk assessment of new pollutants to obtain multiple risk values, including: Using the computational toxicology method, screening and determining the range of new pollutants that meet the selected toxicity endpoint from a preset database; Collect samples based on the new pollutant range to obtain multiple initial new pollutant samples; respectively processing and performing mass spectrometry analysis on the plurality of initial new pollutant samples to obtain a target mass spectrometry analysis data set; Based on the scope of the new pollutants, data mining analysis and toxicity prediction are performed on the target mass spectrometry analysis data set to obtain new pollutant concentration levels and toxicity prediction results; The plurality of risk values ​​are calculated based on the new pollutant concentration level and the toxicity prediction result.

3. The method according to claim 2, characterized in that The multiple initial new pollutant samples are processed and mass spectrometrically analyzed respectively to obtain a target mass spectrometric analysis data set, including: acquiring a plurality of sample matrix characteristics of the plurality of initial new contaminant samples; Based on the new pollutant range and the multiple sample matrix characteristics, the multiple initial new pollutant samples are processed using a preset sample pre-treatment method to obtain multiple target new pollutant samples; Performing mass spectrometry analysis on the multiple target new pollutant samples to obtain an initial mass spectrometry analysis data set; The initial mass spectrometry data set is preprocessed to obtain the target mass spectrometry data set.

4. The method according to claim 2, characterized in that: Based on the scope of the new pollutants, data mining analysis and toxicity prediction are performed on the target mass spectrometry analysis data set to obtain new pollutant concentration levels and toxicity prediction results, including: Based on the new pollutant range, using the non-target analysis method to perform data mining on the target mass spectrometry analysis data set to obtain a plurality of second new pollutants; Performing structural annotation on the plurality of second new pollutants to obtain a plurality of chemical structures; Performing semi-quantitative analysis on the multiple chemical structures to obtain the concentration level of the new pollutant; The target mass spectrometry analysis data set is processed by the computational toxicology method to obtain the toxicity prediction result.

5. The method according to claim 1, characterized in that The target implementation strategy is a chemical substance to toxic effect strategy; The target execution strategy is used to conduct risk assessment of new pollutants to obtain multiple risk values, including: Obtaining multiple initial new contaminant samples; respectively processing and performing mass spectrometry analysis on the plurality of initial new pollutant samples to obtain a target mass spectrometry analysis data set; Performing data mining analysis and toxicity prediction on the target mass spectrometry data set to obtain new pollutant concentration levels and toxicity prediction results; The plurality of risk values ​​are calculated based on the new pollutant concentration level and the toxicity prediction result.

6. The method according to claim 5, characterized in that Data mining analysis and toxicity prediction are performed on the target mass spectrometry data set to obtain new pollutant concentration levels and toxicity prediction results, including: Using the non-target analysis method to perform data mining analysis on the target mass spectrometry analysis data set to obtain the concentration level of the new pollutant; Based on the target mass spectrometry analysis data set, the toxicity prediction result is obtained after being processed by the computational toxicology method.

7. The method according to claim 1, characterized in that Based on the multiple risk values, multiple target new pollutants are determined, including: determining a plurality of new pollutant risk contribution values ​​based on the plurality of risk values; The plurality of target new pollutants are determined based on the plurality of risk values ​​and the plurality of new pollutant risk contribution values.

8. A new pollutant identification device, characterized in that: The device comprises: An acquisition module, used for acquiring a high-throughput fusion method, wherein the high-throughput fusion method is obtained by fusing at least one non-target analysis method and at least one computational toxicology method; A first determination module is used to determine the application order of each of the non-target analysis methods and each of the computational toxicology methods in the high-throughput fusion method; A second determination module is used to determine a target execution strategy of the high-throughput fusion method based on the application order, wherein the target execution strategy is one or more of a toxic effect to chemical substance strategy and a chemical substance to toxic effect strategy; An assessment module, used to perform risk assessment of new pollutants using the target execution strategy to obtain multiple risk values; The third determination module is used to determine a plurality of target new pollutants based on the plurality of risk values.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the new pollutant identification method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the new pollutant identification method according to any one of claims 1 to 7.

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