A method and system for evaluating the risk priority of disinfection by-products in drinking water based on multi-dimensional data
Patent Information
- Application Number
- CN202610065972.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-19
- Publication Date
- 2026-10-27
AI Technical Summary
[0011]为此，本发明实施例提供了一种基于多维度数据的饮用水中消毒副产物风险优先级评价方法及系统，用于解决现有技术中评价维度单一、未充分考虑生物体内过程与环境稳定性导致优先级排序不准确的问题
（1）现有主流方法（如TIC-Tox）通常仅综合考虑浓度和细胞毒性。本发明创造性地构建了CBT(S)评价体系，首次系统性地将消毒副产物在人体内的生物过程（吸收、转运、代谢、排泄）以及在环境中的稳定性（生物降解与水解）作为核心评价维度，与浓度、多重毒性共同进行量化集成。这克服了现有技术忽略毒物动力学过程和环境持久性的缺陷，使得风险优先级排序能够更真实地反映化合物从暴露到产生有害效应的完整路径，从而得出更为科学、准确的评价结果。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of environmental health risk assessment and computational toxicology, and in particular to a method and system for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data. Background Technology
[0002] Drinking water disinfection is a key technology for ensuring public health safety, and it greatly reduced the occurrence and spread of waterborne infectious diseases such as cholera, typhoid fever, and dysentery in the 20th century. However, disinfectants (such as chlorine, chloramine, and ozone) can react with natural organic matter, bromide / iodide ions, nitrogenous substances, and other precursors in water to generate a series of disinfection by-products (DBPs) with potential health risks. Since the first detection of trihalomethanes (THMs) in drinking water in 1974, more than 700 DBPs have been identified in various disinfected water bodies. Epidemiological and toxicological studies have shown that long-term exposure to DBPs may increase the risk of bladder and colon cancer and is associated with adverse health outcomes such as osteoarthritis, hypertension, miscarriage in pregnant women, and birth defects in newborns. Therefore, how to scientifically assess and effectively manage the health risks of DBPs in drinking water has become an important issue of ongoing concern in the fields of environmental science and public health.
[0003] Among the numerous DBPs, there are significant differences in their concentration, toxicity, fate in the human body (absorption, distribution, metabolism, and excretion, i.e., ADME processes), and environmental stability (such as biodegradability and hydrolysis). Given limited regulatory resources and research efforts, establishing a scientific prioritization method to identify the highest-risk DBPs for focused monitoring and research is particularly necessary.
[0004] Currently, several methods have been proposed for prioritizing DBPs. The most typical is the TIC-Tox (Total Intake Cytotoxicity) method established by Plewa et al., which integrates the concentration (C) of DBPs in drinking water with their in vitro cytotoxicity data (Tox), assessing the relative risk of each DBP by calculating the product of its concentration and toxicity contribution. This method, due to its clear concept and experimental validation, has been applied to assess the main toxic factors of DBPs in drinking water in specific areas. However, this method mainly relies on concentration and cytotoxicity data, failing to systematically consider the complex biotransport and biotransformation processes (ADME) of DBPs after entering the human body. These processes directly affect the dose and form of DBPs reaching the target organs, thus influencing actual toxicity. Furthermore, the chemical stability and biodegradability of DBPs in water distribution networks and the environment determine their persistence and exposure probability, which current mainstream methods also fail to adequately consider.
[0005] Besides the TIC-Tox method, other pollutant prioritization methods or patents (e.g., ranking methods for new pollutants) have attempted to introduce more parameters, such as genotoxicity, carcinogenicity classification (IARC), and detection frequency, but still have significant limitations: on the one hand, there is a severe lack of relevant toxicological data (such as carcinogenicity and mutagenicity classification) for many emerging DBPs, which limits the wide applicability of these methods; on the other hand, existing methods generally lack a systematic integration of the ADME process and quantitative consideration of the environmental stability of compounds.
[0006] In summary, existing technical solutions for determining the regulatory priority of DBPs in drinking water have the following main shortcomings: 1. Insufficient consideration of the complete kinetics of human exposure: Most of them do not systematically incorporate the absorption, transport, metabolism and excretion (ADME) parameters of DBPs in the human body, resulting in insufficient estimation of the actual effective dose and toxicokinetics in vivo.
[0007] 2. The toxicological endpoints are relatively singular or limited: they often focus on one or a few types of toxicity (such as cytotoxicity) and fail to integrate a more comprehensive spectrum of potential toxic effects (such as neurotoxicity, hepatotoxicity, nephrotoxicity, carcinogenicity, etc.).
[0008] 3. Environmental stability factors were ignored or simplified: Insufficient attention was paid to the degradation and transformation persistence of DBPs in water treatment and distribution processes, which may have underestimated the cumulative risk of certain transformation products (such as THMs and HAAs) under long hydraulic retention time conditions.
[0009] 4. High data dependence and limited applicability: Many methods rely heavily on incomplete or difficult-to-obtain specific experimental toxicology data, making it difficult to effectively evaluate emerging, data-scarce DBPs.
[0010] Therefore, there is an urgent need in this field for a more comprehensive and scientific prioritization method. This method should be able to integrate multi-dimensional information such as the exposure concentration (C) of DBPs, their fate behavior in organisms (B, covering absorption, transport, metabolism, and excretion), multiple toxic effects (T), and environmental stability (S). It should also be able to use existing prediction tools to fill the gaps in experimental data, thereby enabling a more accurate and universal risk prioritization of DBPs in drinking water, especially emerging DBPs with missing data. Summary of the Invention
[0011] To address this issue, this invention provides a method and system for risk prioritization of disinfection byproducts in drinking water based on multi-dimensional data. This system solves the problem of inaccurate prioritization caused by the single evaluation dimension and insufficient consideration of in vivo processes and environmental stability in existing technologies.
[0012] To address the aforementioned technical problems, embodiments of the present invention provide a method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data. This method includes the following steps: S1: Obtain the exposure concentration data of each disinfection byproduct in drinking water from the target disinfection byproduct list; obtain the biological process characteristic data, toxicity characteristic data and environmental stability characteristic data of each disinfection byproduct; wherein, the biological process characteristic data includes parameters related to human absorption, transport, metabolism and excretion; S2: Based on the exposure concentration data, the biological process characteristic data, the toxicity characteristic data, and the environmental stability characteristic data, the concentration score, the comprehensive biological process score, the toxicity score, and the stability score are calculated respectively. S3: Combine the concentration score, the comprehensive biological process score, and the toxicity score to obtain a first comprehensive risk score; and / or combine the concentration score, the comprehensive biological process score, the toxicity score, and the stability score to obtain a second comprehensive risk score; S4: Determine the risk priority of various disinfection byproducts in the target disinfection byproduct list based on the first comprehensive risk score and / or the second comprehensive risk score.
[0013] Preferably, in step S1, the exposure concentration data is the average molar concentration obtained by statistical analysis of historical monitoring literature data; in step S2, the concentration score is calculated by assigning a value based on the percentile range of the average molar concentration in the target disinfection byproduct list.
[0014] Preferably, in step S1, the biological process characteristic data and the toxicity characteristic data are obtained by calculating a toxicological prediction model; the environmental stability characteristic data are obtained by an environmental fate prediction model.
[0015] Preferably, the computational toxicology prediction model includes the ADMET prediction model, which is used to predict parameters related to human absorption, transport, metabolism, excretion, and multiple toxic endpoints; the environmental fate prediction model is used to predict biodegradation potential and hydrolysis rate.
[0016] Preferably, in step S2, calculating the comprehensive score of the biological process includes: S21: Calculate the absorber score, transporter score, metabolite score, and excretion score based on the parameters related to absorption, transport, metabolism, and excretion, respectively. S22: Assign percentile intervals to the sum of the absorber score, transporter score, metabolite score and excretion score to obtain the absorber score, transporter score, metabolite score and excretion score. S23: Calculate the average of the absorption score, transport score, metabolism score and excretion score to serve as the comprehensive score of the biological process.
[0017] Preferably, in step S2, calculating the toxicity score includes: scoring and summing the predicted results of multiple toxicity endpoints, and then assigning a value to the percentile range of the summation value in the target disinfection byproduct list.
[0018] Preferably, step S2 further includes a stability score correction step: for the trihalomethanes and haloacetic acid disinfection byproducts in the target disinfection byproduct list, a correction coefficient is calculated based on the highest value of their stability scores, and the correction coefficient is added to the stability scores of the trihalomethanes and haloacetic acid disinfection byproducts respectively to obtain a corrected stability score, which is used to calculate the second comprehensive risk score in step S3.
[0019] Preferably, in step S3, the formula for calculating the first comprehensive risk score is: ,in, The first comprehensive risk score, This indicates a multiplication operation, where Score-C is the concentration score, Score-B is the comprehensive biological process score, and Score-T is the toxicity score; and / or, the formula for calculating the second comprehensive risk score is: ,in, The second comprehensive risk score, This is the corrected stability score.
[0020] Preferably, in step S4, the risk priority of various disinfection byproducts in the target disinfection byproduct list is determined based on the first comprehensive risk score and / or the second comprehensive risk score, specifically including: The Score-CBT and / or Score-CBTS are standardized to obtain the normalized score: ; in, For the Score-CBT or Score-CBTS to be standardized, and These represent the maximum and minimum values of Score-CBT or Score-CBTS among all target disinfection byproducts; Multiple risk level ranges are set, and disinfection byproducts that fall into different ranges with a Normalized Score are classified as different risk priorities.
[0021] This invention also provides a risk prioritization system for disinfection byproducts in drinking water based on multi-dimensional data. This system is used to implement the aforementioned risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data, specifically including: The data acquisition module is used to acquire the exposure concentration data of each disinfection byproduct in the target disinfection byproduct list in drinking water; and to acquire the biological process characteristic data, toxicity characteristic data and environmental stability characteristic data of each disinfection byproduct; wherein, the biological process characteristic data includes parameters related to human absorption, transport, metabolism and excretion; The basic scoring calculation module is used to calculate the concentration score, biological process comprehensive score, toxicity score and stability score based on the exposure concentration data, the biological process characteristic data, the toxicity characteristic data and the environmental stability characteristic data, respectively. The comprehensive scoring calculation module is used to combine the concentration score, the comprehensive biological process score, and the toxicity score to obtain a first comprehensive risk score; and / or, to combine the concentration score, the comprehensive biological process score, the toxicity score, and the stability score to obtain a second comprehensive risk score. The risk level determination module is used to determine the risk priority of various disinfection byproducts in the target disinfection byproduct list based on the first comprehensive risk score and / or the second comprehensive risk score.
[0022] As can be seen from the above technical solutions, this invention application has the following beneficial effects: (1) Existing mainstream methods (such as TIC-Tox) typically only consider concentration and cytotoxicity. This invention creatively constructs a CBT(S) evaluation system, which for the first time systematically integrates the biological processes (absorption, transport, metabolism, and excretion) of disinfection byproducts in the human body and their stability in the environment (biodegradation and hydrolysis) as core evaluation dimensions, and quantitatively integrates them with concentration and multiple toxicities. This overcomes the shortcomings of existing technologies that ignore toxicokinetics and environmental persistence, enabling risk prioritization to more realistically reflect the complete pathway of compounds from exposure to the production of harmful effects, thereby obtaining more scientific and accurate evaluation results.
[0023] (2) In response to the severe lack of experimental data (especially toxicity and ADMET data) on a large number of emerging disinfection byproducts, this invention explicitly proposes and applies mature prediction tools such as ADMETlab, EPI Suite, and QSAR Toolbox to obtain the required multi-dimensional parameters. This technical feature makes the method not entirely dependent on limited and costly experimental data, enabling effective evaluation of compounds with missing data, greatly expanding the applicability of the method, and enabling it to conduct prospective risk screening of newly emerging disinfection byproducts, providing a powerful tool for regulation and research.
[0024] (3) This invention pays special attention to trihalomethanes and haloacetic acids, two types of disinfection byproducts that may be continuously generated or accumulated in water distribution networks. By using a stability scoring correction step, the risk of these byproducts is specifically adjusted upwards, avoiding an underestimation of their hazards under long hydraulic retention times. Simultaneously, the method designs two comprehensive scoring formulas: Score-CBT (for conventional areas) and Score-CBTS (for areas with high hydraulic retention times), enabling differentiated evaluation of different water supply system characteristics. This provides an operable technical strategy for precise risk management in different regions and under different conditions. Attached Figure Description
[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Referring to the drawings will make the features and advantages of the present invention clearer. The drawings are illustrative and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart of a risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data, provided by the present invention. Figure 2 This is a block diagram of a risk priority assessment system for disinfection byproducts in drinking water based on multi-dimensional data, provided by the present invention. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0027] Example 1:
[0028] To address the problems of inaccurate prioritization caused by the single evaluation dimension and insufficient consideration of in vivo processes and environmental stability in existing technologies, such as... Figure 1 As shown, this invention proposes a risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data. The method includes the following steps: S1: Obtain the exposure concentration data of each disinfection byproduct in drinking water from the target disinfection byproduct list; obtain the biological process characteristic data, toxicity characteristic data and environmental stability characteristic data of each disinfection byproduct; wherein, the biological process characteristic data includes parameters related to human absorption, transport, metabolism and excretion; S2: Based on the exposure concentration data, the biological process characteristic data, the toxicity characteristic data, and the environmental stability characteristic data, the concentration score, the comprehensive biological process score, the toxicity score, and the stability score are calculated respectively. S3: Combine the concentration score, the comprehensive biological process score, and the toxicity score to obtain a first comprehensive risk score; and / or combine the concentration score, the comprehensive biological process score, the toxicity score, and the stability score to obtain a second comprehensive risk score; S4: Determine the risk priority of various disinfection byproducts in the target disinfection byproduct list based on the first comprehensive risk score and / or the second comprehensive risk score.
[0029] As can be seen from the above technical solution, this invention proposes a risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data. This method first obtains exposure concentration data of disinfection byproducts through literature retrieval, and then uses the ADMET prediction model and environmental fate model to obtain their biological processes, toxicity, and stability characteristics, thereby integrating four key dimensions: exposure, in vivo processes, hazards, and environmental fate, overcoming the shortcomings of existing methods with only one dimension. Subsequently, the data for each dimension are normalized and converted based on percentile scores to calculate the concentration, comprehensive biological processes, toxicity, and stability scores. This step quantifies the contribution of different parameters and uses prediction tools to compensate for missing experimental data, enhancing the method's universality. Next, the concentration, comprehensive biological processes, and toxicity scores are combined using a multiplication formula to obtain a first comprehensive risk score (Score-CBT), or further incorporated with a modified stability score to obtain a second comprehensive risk score (Score-CBTS). This design enables targeted evaluation for regions with different hydraulic retention times and avoids underestimation of the risk of key byproducts through specific corrections. Finally, the comprehensive score is used for standardization and risk level classification, resulting in a scientific, quantitative, and actionable risk priority ranking, providing a direct basis for precise supervision and research resource allocation. The entire solution, through the systematic integration of multi-dimensional data and intelligent prediction tools, achieves a more comprehensive, accurate, and applicable risk assessment of disinfection byproducts, especially emerging pollutants.
[0030] Further, in step S1, multi-dimensional feature data is obtained.
[0031] For each DBP in the target disinfection byproduct list, obtain its exposure concentration data, biological process characteristic data, toxicity characteristic data, and environmental stability characteristic data in drinking water.
[0032] The specific implementation method is as follows: 1. Obtaining Exposure Concentration Data: To comprehensively reflect the pollution status of DBPs in drinking water in recent years, a systematic search can be conducted on relevant literature published in the past five years (e.g., 2021-2025). Search strategies can use keywords such as "disinfection by product OR DBP," "occurrence OR concentration," and "water." Through screening, only quantitative research data from actual treated water, piped water, and tap water are included, excluding data from laboratory simulated water samples, hot water storage tanks, household water tanks, cooking water, and bottled water, which may affect the calculation of the average concentration in conventional drinking water. For each retrieved DBP, its concentration in each study is recorded, and its average molar concentration is calculated as the exposure concentration data for that DBP. Simultaneously, basic information such as the CAS number, SMILES structure, relative molecular mass, structural type, and halogen substitution type of the DBPs are obtained from professional databases (such as CompTox Chemicals Dashboard, Reaxys, and PubChem).
[0033] 2. Obtaining biological process and toxicity characteristic data: Computational toxicology prediction models are used to obtain data on biological processes (absorption, transport, metabolism, and excretion, i.e., ADME) and multiple toxicity endpoints. In a preferred embodiment, the highly accurate free online deep learning model ADMETlab 3.0 is used for prediction.
[0034] Absorption-related parameters may include permeability to human colon adenocarcinoma cells (Caco-2), human intestinal absorption (HIA), and oral bioavailability (F50%).
[0035] Transport-related parameters may include plasma protein binding rate (PPB) and plasma free fraction (Fu).
[0036] Metabolic parameters may include liver microsomal stability (HLM), etc.
[0037] Excretion-related parameters: may include plasma clearance rate ( ) and in vivo half-life ( )wait.
[0038] Toxicity-related parameters: covering a wide range of toxic effects, such as neurotoxicity, ototoxicity, hematologic toxicity, nephrotoxicity, genotoxicity, immunotoxicity, cytotoxicity, cardiotoxicity, hepatotoxicity, mutagenicity, acute toxicity, carcinogenicity, ocular toxicity, respiratory toxicity, endocrine disruption, oxidative stress toxicity, and DNA damage stress toxicity.
[0039] 3. Obtaining Environmental Stability Characteristic Data: The biodegradation and hydrolysis potential of DBPs is predicted using an environmental fate prediction model. In one embodiment, the time required for biodegradation is predicted using the BIOWIN4 module of the conventional machine learning tool EPI Suite 4.11, and the hydrolysis rate is predicted using QSAR Toolbox 4.7.
[0040] Further, in step S2, the base score is calculated.
[0041] Based on the multi-dimensional data obtained in step S1, the concentration score (Score-C), the comprehensive biological process score (Score-B), the toxicity score (Score-T), and the stability score (Score-S) are calculated respectively.
[0042] The specific implementation method is as follows: 1. Calculate the concentration score (Score-C): Calculate the average molar concentration of all DBPs in the target list over the past five years and sort these concentrations from low to high. Divide the sorted concentration data into 10 equal intervals based on their percentiles (e.g., 0-10%, 10-20%, ..., 90-100%), with each interval corresponding to a score from 1 to 10. This score is the Score-C.
[0043] 2. Calculate the overall score for biological processes (Score-B): a. Calculate sub-scores: The results of each absorption, transport, metabolism, and excretion-related parameter predicted by ADMETlab 3.0 are converted into a single score of 0-1 based on their contribution to the risk of chronic toxicity. For example, 0 represents poor absorption by the human body, poor binding to plasma proteins, easy metabolism, and easy clearance; 1 represents the opposite characteristics.
[0044] b. Summation and Percentile Assignment: Summate all individual scores for each dimension of absorption, transport, metabolism, and excretion to obtain Total-A (total absorption score), Total-D (total transport score), Total-M (total metabolism score), and Total-E (total excretion score). Then, based on the percentile intervals (divided into 10 intervals) of Total-A, Total-D, Total-M, and Total-E among all DBPs in the target list, assign absorption score (Score-A), transport score (Score-D), metabolism score (Score-M), and excretion score (Score-E) from 1 to 10.
[0045] c. Calculate the overall score: Calculate the arithmetic mean of Score-A, Score-D, Score-M, and Score-E as the overall biological process score (Score-B) reflecting the overall situation of DBPs in biotransport and biotransformation in the human body.
[0046] 3. Calculate the toxicity score (Score-T): Convert each toxicity endpoint result predicted by ADMETlab 3.0 into a single score of 0-1 (0 represents low toxicity, 1 represents high toxicity). Sum the single scores of all toxicity endpoints to obtain the Total-T (total toxicity score). Assign a toxicity score (Score-T) of 1-10 based on the percentile range of Total-T among all DBPs in the target list (divided into 10 intervals).
[0047] 4. Calculate the stability score (Score-S) and its correction: a. Calculate the baseline stability score: Convert the biodegradation and hydrolysis grades predicted by EPI Suite 4.11 and QSAR Toolbox 4.7 into individual scores of 0-1 (0 represents easy degradation / hydrolysis, 1 represents difficult degradation / hydrolysis). Sum all stability-related individual scores to obtain Total-S (total stability score). Assign a stability score (Score-S) of 1-10 based on the percentile range of Total-S among all DBPs in the target list (divided into 10 intervals).
[0048] b. Stability score correction for specific DBPs: Given that trihalomethanes (THMs) and haloacetic acids (HAAs) are both degradation products of many DBPs after long hydraulic retention time (HRT) and major products of residual chlorine conversion in water distribution networks, their stability may be underestimated. The Score-S of these two types of DBPs needs to be corrected. Specifically, subtract the highest Score-S of any individual DBP among all THMs and HAAs from 10 to obtain a correction factor. Then, add this correction factor to the original Score-S of each THM and HAA to obtain the corrected stability score (Corrected Score-S). For example, if the highest Score-S is 7, the correction factor is 3, and the Corrected Score-S for each THM and HAA = Score-S + 3 (if it exceeds 10, take 10).
[0049] Further, in step S3, a comprehensive risk score is calculated.
[0050] The basic scores obtained in step S2 are combined and calculated to obtain the comprehensive risk score.
[0051] Specifically, the following two formulas can be used: 1. First Comprehensive Risk Score (Score-CBT): Used for priority evaluation under normal conditions.
[0052] ; in, This indicates a series multiplication operation.
[0053] 2. Second Comprehensive Risk Score (Score-CBTS): It is particularly suitable for scenarios with high hydraulic retention times (such as rural and underdeveloped areas) where environmental durability needs to be considered.
[0054] .
[0055] Further, in step S4, risk priorities are determined.
[0056] Based on the comprehensive risk score obtained in step S3, the DBPs in the target list are classified into risk levels.
[0057] 1. Standardization: To facilitate comparison and interpretation, Score-CBT and / or Score-CBTS are standardized and mapped to the range of 0-100.
[0058] ; in, For a given DBP, the Score-CBT or Score-CBTS value. and These are the maximum and minimum values of Score-CBT or Score-CBTS for all DBPs in the target list, respectively.
[0059] 2. Risk Level Classification: Set risk level ranges and classify DBPs into different risk priorities based on the Normalized Score.
[0060] Very high risk: Normalized Score between 75 and 100.
[0061] High risk: Normalized Score between 50 and less than 75.
[0062] Medium risk: Normalized Score between 25 and less than 50.
[0063] Low risk: Normalized Score is between 0 and less than 25.
[0064] Through the above steps, the risk priority ranking of various DBPs in the target disinfection byproduct list can be obtained. For example, in a specific application example (as shown in Table 1), according to the Normalized Score-CBT, 13 DBPs in drinking water in conventional areas were identified as very high risk, 46 DBPs as high risk, 80 DBPs as medium risk, and 121 DBPs as low risk. According to the Normalized Score-CBTS, 6 DBPs in drinking water in high HRT areas were identified as very high risk, 33 DBPs as high risk, 67 DBPs as medium risk, and 154 DBPs as low risk. The very high-risk DBPs identified in drinking water in conventional areas include 3 HCPDs, 3 HKs, 2 HBQs, 2 HPs, 1 HAnil, 1 HNQ, and 1 Oxyhalide. Unfortunately, among these very high-risk DBPs, only Chlorate has been controlled to date. In drinking water from high-HRT areas, seven dichlorochemical pollutants (DBPs) identified as very high-risk in conventional drinking water have been downgraded to high-risk. The remaining very high-risk DBPs include three HCPDs, one HK, one HBQ, and one HANil. Notably, the DBPs identified as very high-risk in both conventional and high-HRT drinking water are characterized by high chlorination. Focusing on the reaction kinetics and influencing factors of highly chlorinated DBPs derived from various precursors will significantly contribute to reducing very high-risk DBPs across the entire population. These results provide a scientific basis for identifying research priorities and developing targeted regulatory strategies.
[0065] Table 1. Priority of DBPs in Drinking Water Example 2:
[0066] like Figure 2As shown, this invention provides a risk prioritization system for disinfection byproducts in drinking water based on multi-dimensional data. This system is used to implement the risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data in Embodiment 1 above, specifically including: The data acquisition module 100 is used to acquire the exposure concentration data of each disinfection byproduct in the target disinfection byproduct list in drinking water; acquire the biological process characteristic data, toxicity characteristic data and environmental stability characteristic data of each disinfection byproduct; wherein, the biological process characteristic data includes parameters related to human absorption, transport, metabolism and excretion; The basic score calculation module 200 is used to calculate the concentration score, the comprehensive biological process score, the toxicity score, and the stability score based on the exposure concentration data, the biological process characteristic data, the toxicity characteristic data, and the environmental stability characteristic data, respectively. The comprehensive scoring calculation module 300 is used to combine the concentration score, the comprehensive biological process score, and the toxicity score to obtain a first comprehensive risk score; and / or, to combine the concentration score, the comprehensive biological process score, the toxicity score, and the stability score to obtain a second comprehensive risk score. The risk level determination module 400 is used to determine the risk priority of various disinfection byproducts in the target disinfection byproduct list based on the first comprehensive risk score and / or the second comprehensive risk score.
[0067] Further, the data acquisition module 100 is used to perform step S1. Specifically, it is configured to: acquire exposure concentration data of each DBP in the target DBPs list in drinking water from literature databases and historical monitoring data; call or integrate computational toxicology prediction models (such as ADMETlab 3.0) and environmental fate prediction models (such as EPI Suite, QSAR Toolbox) to acquire biological process characteristic data, toxicity characteristic data and environmental stability characteristic data of each DBP.
[0068] Furthermore, the basic score calculation module 200 is used to execute step S2. Specifically, it is configured as follows: A concentration score (Score-C) is calculated based on the exposure concentration data.
[0069] Based on biological process characteristic data, a comprehensive biological process score (Score-B) is calculated by calculating sub-scores, summation, percentile assignment, and averaging.
[0070] Based on the toxicity characteristic data, a toxicity score (Score-T) is calculated by summing the scores and assigning percentile values.
[0071] Based on environmental stability characteristic data, a (corrected) stability score (Score-S or Corrected Score-S) is calculated by summing scores, assigning percentile values, and correcting for THMs and HAAs.
[0072] Further, the comprehensive score calculation module 300 is used to execute step S3. Specifically, it is configured to: call the scores obtained by the basic score calculation module 200, and calculate them according to the predefined formula (the multiplication formula of Score-CBT and / or Score-CBTS) to obtain the first comprehensive risk score and / or the second comprehensive risk score.
[0073] Further, the risk level determination module 400 is used to execute step S4. Specifically, it is configured to: standardize the comprehensive risk score output by the comprehensive score calculation module 300, and then map the standardized score to the corresponding risk level according to the preset risk level range (e.g., 75-100 is very high risk), thereby outputting the final target DBPs risk priority list.
[0074] The system can be implemented in the form of software, hardware, or a combination of both, and can be deployed on servers, computers, or cloud platforms to provide users with automated DBPs risk prioritization services.
[0075] This embodiment provides a risk prioritization system for disinfection byproducts in drinking water based on multi-dimensional data. This system is used to implement the aforementioned risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data. Therefore, the specific implementation of this system can be found in the embodiments of the aforementioned risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data. For example, the data acquisition module 100, the basic score calculation module 200, the comprehensive score calculation module 300, and the risk level determination module 400 are respectively used to implement steps S1, S2, S3, and S4 in the aforementioned risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data. Therefore, the specific implementation can be referred to the descriptions of the corresponding embodiments. To avoid redundancy, further details are omitted here.
[0076] In summary, this invention provides a more comprehensive, scientific, and scalable method and system for risk prioritization assessment of disinfection byproducts in drinking water by constructing a CBT(S) evaluation system that integrates concentration (C), biological processes (B), toxicity (T), and stability (S), and by making full use of existing computational toxicology and environmental fate prediction tools, effectively overcoming the limitations of existing technologies.
[0077] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0078] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0080] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.
Claims
1. A method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data, characterized in that, Includes the following steps: S1: Obtain the exposure concentration data of each disinfection byproduct in drinking water from the target disinfection byproduct list; obtain the biological process characteristic data, toxicity characteristic data and environmental stability characteristic data of each disinfection byproduct; wherein, the biological process characteristic data includes parameters related to human absorption, transport, metabolism and excretion; S2: Based on the exposure concentration data, the biological process characteristic data, the toxicity characteristic data, and the environmental stability characteristic data, the concentration score, the comprehensive biological process score, the toxicity score, and the stability score are calculated respectively. S3: Combine the concentration score, the comprehensive biological process score, and the toxicity score to calculate a first comprehensive risk score; and / or combine the concentration score, the comprehensive biological process score, the toxicity score, and the stability score to calculate a second comprehensive risk score; S4: Determine the risk priority of various disinfection byproducts in the target disinfection byproduct list based on the first comprehensive risk score and / or the second comprehensive risk score.
2. The method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data according to claim 1, characterized in that, In step S1, the exposure concentration data is the average molar concentration obtained based on historical monitoring literature data. In step S2, the concentration score is calculated by assigning a value based on the percentile range of the average molar concentration in the target disinfection byproduct list.
3. The method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data according to claim 1, characterized in that, In step S1, the biological process characteristic data and the toxicity characteristic data are obtained by calculating a toxicological prediction model; the environmental stability characteristic data are obtained by an environmental fate prediction model.
4. The method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data according to claim 3, characterized in that, The computational toxicology prediction model includes the ADMET prediction model, which is used to predict parameters related to human absorption, transport, metabolism, excretion, and multiple toxic endpoints; the environmental fate prediction model is used to predict biodegradation potential and hydrolysis rate.
5. The method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data according to claim 1, characterized in that, In step S2, calculating the comprehensive score of the biological process includes: S21: Calculate the absorber score, transporter score, metabolite score, and excretion score based on the parameters related to absorption, transport, metabolism, and excretion, respectively. S22: Assign percentile intervals to the sum of the absorber score, transporter score, metabolite score and excretion score to obtain the absorber score, transporter score, metabolite score and excretion score. S23: Calculate the average of the absorption score, transport score, metabolism score and excretion score to serve as the comprehensive score of the biological process.
6. The method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data according to claim 1, characterized in that, In step S2, calculating the toxicity score includes: scoring and summing the predicted results of multiple toxicity endpoints, and then assigning a value to the percentile range of the summation value in the target disinfection byproduct list.
7. The method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data according to claim 1, characterized in that, Step S2 further includes a stability score correction step: for the trihalomethanes and haloacetic acid disinfection byproducts in the target disinfection byproduct list, a correction coefficient is calculated based on the highest value of their stability scores, and the correction coefficient is added to the stability scores of the trihalomethanes and haloacetic acid disinfection byproducts respectively to obtain the corrected stability scores, which are used to calculate the second comprehensive risk score in step S3.
8. The method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data according to claim 1, characterized in that, In step S3, the formula for calculating the first comprehensive risk score is: ,in, The first comprehensive risk score, This indicates a multiplication operation, where Score-C is the concentration score, Score-B is the comprehensive biological process score, and Score-T is the toxicity score; and / or, the formula for calculating the second comprehensive risk score is: ,in, The second comprehensive risk score, This is the corrected stability score.
9. The method for risk prioritization assessment of disinfection byproducts in drinking water based on multi-dimensional data according to claim 8, characterized in that, In step S4, based on the first comprehensive risk score and / or the second comprehensive risk score, the risk priority of each disinfection byproduct in the target disinfection byproduct list is determined, specifically including: The Score-CBT and / or Score-CBTS are standardized to obtain the normalized score: ； in, For the Score-CBT or Score-CBTS to be standardized, and These represent the maximum and minimum values of Score-CBT or Score-CBTS among all target disinfection byproducts; Multiple risk level ranges are set, and disinfection byproducts that fall into different ranges with a Normalized Score are classified as different risk priorities.
10. A risk prioritization system for disinfection byproducts in drinking water based on multi-dimensional data, characterized in that, The system is used to implement the risk prioritization method for disinfection byproducts in drinking water based on multi-dimensional data as described in any one of claims 1 to 9, specifically including: The data acquisition module is used to acquire the exposure concentration data of each disinfection byproduct in the target disinfection byproduct list in drinking water; and to acquire the biological process characteristic data, toxicity characteristic data and environmental stability characteristic data of each disinfection byproduct; wherein, the biological process characteristic data includes parameters related to human absorption, transport, metabolism and excretion; The basic scoring calculation module is used to calculate the concentration score, biological process comprehensive score, toxicity score and stability score based on the exposure concentration data, the biological process characteristic data, the toxicity characteristic data and the environmental stability characteristic data, respectively. The comprehensive scoring calculation module is used to combine the concentration score, the comprehensive biological process score, and the toxicity score to obtain a first comprehensive risk score; and / or, to combine the concentration score, the comprehensive biological process score, the toxicity score, and the stability score to obtain a second comprehensive risk score. The risk level determination module is used to determine the risk priority of various disinfection byproducts in the target disinfection byproduct list based on the first comprehensive risk score and / or the second comprehensive risk score.