Method and system for comprehensively evaluating risk potential of chemical substance

Abstract

To provide a method that comprehensively evaluates risk potential of a chemical substance by combining comprehensive non-target qualitative and quantitative analysis with various hazard evaluation support databases.SOLUTION: A method for comprehensively evaluating risk potential for a chemical substance comprises: a chromatographic separation step of separating a measurement sample into components; a qualitative analysis step of qualitatively analyzing the separated components; a quantitative analysis step of oxidizing and reducing the separated components to make methane, and then quantifying the methane; and a risk evaluation step, for a target substance identified in the qualitative analysis step and the quantitative analysis step, extracting a chemical substance with a similar structure by referring to a database, extracting a chemical substance with physical properties similar to those of the target substance, predicting a toxicity value or arithmetic mean of the toxicity values of the extracted chemical substance as toxicity potential of the target substance, and estimating risk potential.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a comprehensive risk potential assessment method and assessment system for chemical substances. [Background technology]

[0002] In recent years, non-target analysis has become popular as a method for comprehensively analyzing chemical substances. In contrast to targeted analysis, in which the target to be measured is determined in advance and the analysis is carried out according to an official method, in non-target analysis, the target to be measured is not determined in advance, and all peaks obtained by analyzing a sample are analyzed.

[0003] One example is the comprehensive two-dimensional gas chromatograph mass spectrometer (GC×GC-MS). A two-dimensional gas chromatograph (GC×GC) uses two different types of columns connected in series via a GC×GC modulator to analyze samples. The sample eluted from the first column is accumulated for a certain period of time by the modulator, then focused and injected into the second column for high-speed analysis. Repeated accumulation and injection are performed, and the resulting chromatogram is expanded in two dimensions to obtain a two-dimensional chromatogram with boiling point and polarity on the respective axes. One issue with GC×GC is the need for standard substances for quantitative analysis. Preparing standard substances for the hundreds of substances detected by non-targeted analysis is time- and economically challenging, and the number of commercially available standard substances is limited. This means that the number of substances that can be quantified is limited by the number of standard substances available.

[0004] There is also the Automatic Identification and Quantitation Database System (AIQS-GC). Because the performance of conventional GC-MS analysis equipment changes daily, qualitative and quantitative analysis is possible by using standard substances to confirm the retention time and mass spectrum of chemical substances for each measurement and by creating a calibration curve. In contrast, the Automatic Identification and Quantitation Database System (AIQS) maintains constant GC-MS instrument performance, allowing for the use of mass spectra, retention indices, and calibration curves measured under the same conditions. This enables screening analysis (qualitative and semi-quantitative analysis) without the use of standard substances. The Fully Automatic Identification and Quantitation Database System (AIQS-DB) contains the retention times, mass information, and calibration curves of over 1,000 chemical substances. One issue with AIQS-GC-MS is that semi-quantitative analysis is limited to those registered in the database, meaning that not all substances can be quantified. Furthermore, while the column generally used in AIQS-GC-MS is the DB-5ms, the column used in volatile organic compound (VOC) analysis is the 624 series, and there are issues with the VOC version of AIQS, such as the lack of an established method for checking tuning results and whether the equipment is being maintained in the specified condition.A large amount of information on SVOCs is registered in the AIQS-DB, and the database for VOCs is currently lacking.

[0005] Even if quantitative analysis were possible, there is the problem that the number of chemical substances is so large that hazard assessments cannot keep up.

[0006] One type of hazard assessment method is in silico testing (data analysis using a computer). Because it does not require actual measurement testing, it can reduce the time and cost required for safety assessment, and as an alternative to animal testing, it also helps comply with the three principles of humane animal testing. However, there is an issue of low predictive accuracy for substance groups about which there is little knowledge.

[0007] QSAR and Read-across are used in in silico testing. QSAR refers to the correlation between the structural characteristics of a chemical substance and its biological activity (toxicity, etc.). It is a method for predicting the physical properties, environmental fate, and toxicity of substances for which no data is available, using a model formula derived from parameters such as physical properties. A system for quantitatively calculating toxicity, etc. based on structural information is called a QSAR model. Read-across is a method for predicting the properties of a target substance from information on multiple substances with identical properties, such as toxicity information, when there is structural similarity. Examples of QSAR models include the QSAR Toolbox and KATE (Biological Toxicity Prediction System). There is also the Hazard Evaluation Support System Integrated platform (HESS). This is a system that groups chemicals with similar chemical structure characteristics and supports the evaluation of repeated-dose toxicity of untested chemicals using Read-across.

[0008] There are quantitative systems that convert organic matter separated by gas chromatography (GC) into methane using an oxidation-reduction catalyst, then detect it with a flame ionization detector (FID), and convert it to methane. However, these known systems aim to quantify organic matter by target analysis, achieving a uniform response and high sensitivity by converting organic matter into methane.

[0009] Patent Document 1 discloses a gas chromatograph (GC) that can easily measure the concentration of a target component contained in a sample gas, even if the target component is unknown. Even if the sample contains an unknown organic compound, the organic compound corresponding to the retention time (the time it takes for the organic compound to pass through the column) can be determined (qualified) by referring to a retention time table. The retention time table is a table in which the expected retention times of multiple known organic compounds are measured in advance, and each measured retention time is associated with the corresponding organic compound. As a method for identifying the number of carbon atoms (C) contained in one molecule of an organic compound, the number of carbon atoms contained in one molecule of the organic compound is obtained by qualitatively characterizing the compound based on its retention time. The retention time is calculated based on the detection timing of each organic compound detected by a flame ionization detector (FID). In another embodiment, the organic compound that has passed through a column is measured with a mass spectrometer (MS) to obtain an MS spectrum, which is then compared with a database to identify the number of carbon atoms contained in one molecule of the organic compound. The MS spectrum is a spectrum obtained as a result of mass analysis, with the horizontal axis representing mass and the vertical axis representing detection intensity. Furthermore, the accurate mass of the molecular ion peak, which is the largest mass number in the MS spectrum, can be obtained to identify the number of carbon atoms contained in one molecule of the organic compound. [Prior art documents] [Patent documents]

[0010] [Patent Document 1] Patent No. 6549990 Summary of the Invention [Problem to be solved by the invention]

[0011] Advances in non-targeted analysis technology have revealed that a wide variety of chemicals exist in our daily lives. However, while existing non-targeted analysis methods are capable of qualitative analysis of a large number of chemicals, quantitative analysis requires the creation of calibration curves using standard substances. Furthermore, even if non-targeted analysis can detect a large number of chemicals, there is a lack of information on the toxicity of many of these chemicals, resulting in insufficient hazard assessment of the detected chemicals. Existing in silico methods require expert judgment to select endpoints and similar substances, making it difficult to predict the toxicity of hundreds of chemicals.

[0012] Therefore, this disclosure provides a method for comprehensive risk potential assessment of chemical substances by combining comprehensive non-target qualitative and quantitative analysis with various hazard assessment support databases. [Means for solving the problem]

[0013] The method for comprehensively assessing the risk potential of chemical substances disclosed herein includes: Optionally, a sample collection step (using a collection device) to collect a measurement sample; Optionally, a pretreatment step of heating the measurement sample (using a thermal desorption device (TD)) to desorb volatile components; a chromatographic separation step in which the measurement sample (or the volatile components separated from it) is separated into components (using a gas chromatograph (GC) or a separation column); A splitting step in which the separated components are split and sent to two systems (a qualitative analysis step and an oxidation-reduction methanation quantification step); a qualitative analysis step of qualitatively analyzing the components sent to the first system (using a mass spectrometer (MS)); a quantitative analysis step in which the components sent to the second system are oxidized and reduced to methane (using a detector) and quantified; For the target substance identified (qualitatively and quantitatively) in the qualitative analysis step and quantitative analysis step, chemical substances with similar structures are extracted by referring to the database, and chemical substances with predetermined physical property values ​​(e.g., octanol / water partition coefficient (LogKow)) close to those of the target substance are extracted. The toxicity value (TOX) or arithmetic mean of the toxicity values ​​(TOXp) (e.g., arithmetic mean of no observed effect level (NOELp)) of the extracted chemical substances is predicted as the toxicity potential of the target substance, and the risk potential (MOE p ) risk assessment step, may also include:

[0014] The comprehensive risk potential assessment system (1) for chemical substances disclosed herein is: optionally, a sample collection device for collecting a measurement sample; Optionally, a thermal desorption device (TD) for heating the measurement sample to desorb volatile components; A gas chromatograph (GC) separates the volatile components sent from the thermal desorption device (TD) into individual components. a dividing section (31) that divides the separated components into two systems and sends them therethrough; A mass spectrometer (MS) that qualitatively analyzes the components sent to the first system, A post-column reactor (2) that oxidizes and reduces the components sent to the second system to produce methane; A detector (FID) to quantify methane, For the qualitatively and quantitatively determined target substance, chemical substances with similar structures are extracted by referring to the database, and chemical substances with predetermined physical property values ​​close to the target substance are extracted. The toxicity value (TOX) or arithmetic mean of the toxicity values ​​(TOXp) of the extracted chemical substances is predicted as the toxicity potential of the target substance, and the risk potential (MOE) is calculated. p ) and a risk assessment unit (5) that estimates an output section (6) that outputs the results of the arithmetic mean (TOXp) and risk potential (MOEp) of the toxicity values ​​obtained in the risk assessment section (5); The device may also include:

[0015] The information processing device used in the comprehensive risk potential assessment system for chemical substances disclosed herein comprises: For target substances that have been qualified by a mass spectrometer (MS) and quantified by a detector that quantifies methane, chemical substances with similar structures are extracted by referring to a database, and chemical substances with predetermined physical property values ​​close to the target substance are extracted. The toxicity value (TOX) or arithmetic mean of the toxicity values ​​(TOXp) of the extracted chemical substances is predicted as the toxicity potential of the target substance, and the risk potential (MOE) is calculated. p ) risk assessment department, an output section that outputs the toxicity value (TOX) or the arithmetic mean of the toxicity values ​​(TOXp) and the risk potential (MOEp) obtained in the risk assessment section; The device may also include:

[0016] The program for comprehensive risk potential assessment of chemical substances disclosed herein comprises: A computer or one or more processors For target substances that have been qualified by a mass spectrometer (MS) and quantified by a detector that quantifies methane, chemical substances with similar structures are extracted by referring to a database, and chemical substances with predetermined physical property values ​​close to the target substance are extracted. The toxicity value (TOX) or arithmetic mean of the toxicity values ​​(TOXp) of the extracted chemical substances is predicted as the toxicity potential of the target substance, and the risk potential (MOE) is calculated. p ) risk assessment step, The program may also be a program that realizes an output step of outputting the toxicity value (TOX) or arithmetic mean of toxicity values ​​(TOXp), or risk potential (MOEp) obtained in the risk assessment step.

[0017] Another program for comprehensive risk potential assessment of chemicals is A computer or one or more processors a pretreatment step of controlling the thermal desorption device to heat the measurement sample and desorb volatile components; a chromatographic separation step of controlling a gas chromatograph to separate the volatile components separated from the measurement sample into individual components; a qualitative analysis step of controlling a mass spectrometer (MS) to qualitatively analyze the components sent to the first system; a quantitative analysis step of controlling the post-column reactor to oxidize and reduce the components sent to the second system to methane, and controlling a detector (FID) to quantify the methane; For the target substance identified (qualitatively and quantitatively) in the qualitative analysis step and the quantitative analysis step, chemical substances with similar structures are extracted by referring to the database, and chemical substances with predetermined property values ​​close to the target substance are extracted. The toxicity value (TOX) or arithmetic mean of the toxicity values ​​(TOXp) of the extracted chemical substances is predicted as the toxicity potential of the target substance, and the risk potential (MOE) is calculated. p ) risk assessment step, The program may also be a program that realizes an output step of outputting the toxicity value (TOX) or arithmetic mean of toxicity values ​​(TOXp), or risk potential (MOEp) obtained in the risk assessment step.

[0018] The database may be, for example, a database of the Hazard Assessment Support System Integrated Platform (HESS). The database may be stored in advance in a storage device, or may be stored in a temporary memory for real-time access.

[0019] (Action and effect) (1) By qualitatively confirming the presence of chemicals using a mass spectrometer (MS) and quantifying the presence of chemicals using a detector such as an FID, non-target (comprehensive) analysis can be performed to understand multiple and complex exposures to chemicals. (2) Toxicity can be predicted by combining the quantitative value calculated as exposure (intake) with harmfulness (toxicity). (3) It is possible to provide comprehensive risk indicators for unknown and unregulated chemical substances. [Brief explanation of the drawings]

[0020] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a risk potential assessment system. [Figure 2] FIG. 10 is a diagram illustrating an example of a processing flow of a risk assessment unit. [Figure 3] FIG. 1 is a diagram showing a calibration curve of methane-equivalent concentration of BTX. [Figure 4] FIG. 10 is a diagram showing a part of the qualitative and quantitative results of a riding mode A1 of a vehicle A. [Figure 5] FIG. 10 is a diagram showing a part of the qualitative and quantitative results of parking mode A2 of vehicle A. [Figure 6] FIG. 10 is a diagram showing a part of the qualitative and quantitative results of parking mode B of vehicle B. [Figure 7] FIG. 10 is a diagram showing a part of the qualitative and quantitative results of parking mode C of vehicle C. DETAILED DESCRIPTION OF THE INVENTION

[0021] 1 shows a risk potential assessment system according to embodiment 1. Note that the inventive technique of the present disclosure is not limited to the following embodiment, but includes various modifications.

[0022] (Sample collection) The collection device in Figure 1 is a device for collecting chemical substances in the atmosphere. For example, it consists of a collection tube filled with an adsorbent, a suction device for sucking in the air and sending it to the collection tube, etc. In this embodiment, an automatic gas sampling device (GSP-400FT) is used, and the collection material is a heat-resistant resin with a 2,6-diphenyl-p-phenylene oxide structure.

[0023] (Pretreatment) The pretreatment method varies depending on the form of the measurement sample and the characteristics of the compound to be measured. Examples of pretreatment devices include a headspace sampler, a purge and trap concentration introduction device, a solid-phase microextraction device (SPME), a thermal desorption device (TD), a pyrolyzer, and a simultaneous thermogravimetric / differential thermal analysis device (TG / DTA). In this embodiment, a thermal desorption device (TD) is used. A collection tube is set in the TD and heated, capturing volatile components from the collection tube for primary adsorption. The adsorbed volatile components are then sent to the injection tube 11 of the gas chromatograph.

[0024] (gas chromatograph) The gas chromatograph 1 comprises an injection tube 11 and a separation column 13 inside a heating device 12. The volatile components are further heated by the heating device 111 in the injection tube 11 and transported by the carrier gas. The carrier gas is regulated by a pressure gauge and a pressure / flow regulator V_p, and is sent while maintaining a constant pressure and flow rate. The volatile components heated to a predetermined temperature are separated into individual components as they pass through the separation column 13. In other words, the different components take different amounts of time to pass through the separation column 13 (retention time), making it possible to qualitatively and quantitatively identify each component. The separated components are detected by a detector. Two types of detectors are used: a mass spectrometer (MS) for qualitative analysis and a flame ionization detector (FID) for quantitative analysis. Details will be described later. The separation column 13 is, for example, a packed column or a capillary column. In this embodiment, a capillary column is used. A dividing section 31 is provided after the separation column 13. The dividing section 31 is a device that divides and sends each component separated by the separation column 13 to a first system for qualitative analysis and a second system for quantitative analysis.

[0025] (qualitative analysis) Examples of qualitative analysis methods include methods using a quadrupole mass spectrometer (QMS), a triple quadrupole mass spectrometer (TQMS), and a time-of-flight mass spectrometer (TOFMS). For non-target analysis, a time-of-flight mass spectrometer (TOFMS) is particularly preferred. In this embodiment, a time-of-flight mass spectrometer (TOFMS) is used as the mass spectrometer (MS). Quantitative analysis may be performed using the following procedure. Mass spectra obtained by electron ionization (EI) and soft ionization (SI) can be used to compare with any organic compound database and quantify the compound. Unknown components not included in the database may be estimated using the following methods, for example: 1) Estimate the compositional formula from the mass number of the molecular ionization peak. 2) Narrow down the compositional formula candidates from the isotope pattern of the molecular ion. 3) Estimate the compositional formula from the mass number of the fragment ions obtained by EI.

[0026] (Post-column reactor PR) In the post-column reactor 2, for example, each component separated in the separation column 13 is oxidized to carbon dioxide by catalytic combustion using an oxidation catalyst, and then the carbon dioxide is reduced to methane by catalytic reduction using a reduction catalyst and a reducing agent. The produced methane is sent to a detector for quantitative analysis. The oxidation reaction section 21, which is filled with an oxidation catalyst and causes an oxidation reaction, and the reduction reaction section 26, which is filled with a reduction catalyst and causes a reduction reaction, are each temperature-controlled by a heating device. The heating temperature is set to promote each catalytic reaction and also helps prevent condensation of moisture generated by the reaction.

[0027] Before being sent to the oxidation reaction section 21, clean air is supplied at a fixed amount while being controlled by a flow rate regulator 22 and mixed with each component. The mixed components are sent to the oxidation reaction section 21 and oxidized to carbon dioxide.

[0028] The oxidation catalyst is a catalyst for promoting the oxidation reaction of each component. Examples of the oxidation catalyst include palladium and platinum. In this embodiment, the palladium is a palladium fiber supported on a catalyst carrier. CaHbOcNdCleBrf→aCO2(1.1)

[0029] Before the carbon dioxide produced in the oxidation reaction section 21 is sent to the reduction reaction section 26, hydrogen (H2) is supplied at a constant rate while being controlled by the flow rate regulator 24. The carbon dioxide and hydrogen are sent to the reduction reaction section 26, where a reduction reaction produces methane and water. The switch 33 switches between a first route connecting the oxidation reaction unit 21, the reduction reaction unit 26, and the detector (FID), and a second route connecting the oxidation reaction unit 21 and the detector (FID). The second route allows the base value in the detector to be set.

[0030] The reduction catalyst is a catalyst for promoting the reduction reaction of reducing carbon dioxide to methane. Examples of the reduction catalyst include nickel-based catalysts, ruthenium-based catalysts, and rhodium-based catalysts. In this embodiment, the reducing agent is hydrogen, and a methanizer is used as the nickel-based catalyst device. CO2 + 4H2 → CH4 + 2H2O (1.2)

[0031] (Quantitative analysis) The detector detects, for example, methane and quantifies the methane concentration. The detected components can be quantified using a calibration curve converted into methane. Examples of detectors include a flame ionization detector (FID), a thermal conductivity detector (TCD), and a mass spectrometer (MS). In this embodiment, a flame ionization detector (FID) is used. The flame ionization detector (FID) converts the concentration of methane produced in the reduction reaction section 26 into an electrical signal and detects it.

[0032] (Information processing device) The information processing device 5 has memories 51, 52, 54, a chemical substance identification unit 53, a risk assessment unit 55, and a use information evaluation unit 56. The information processing device 5 receives components identified by the qualitative analyzer (MS) (including retention time, peak area, and identification information of the qualified chemical substance) and stores them in the memory 51. Furthermore, the information processing device 5 receives and stores in the memory 51 the components (including retention time, peak area, and methane concentration) quantified by the flame ionization detector (FID). The chemical substance identification unit 53 links the chemical substance name of each component with the methane concentration using the retention time as a key, and calculates the concentration of the chemical substance name based on a calibration curve converted into methane concentration. The chemical substance identification unit 53 identifies the chemical substance names and concentrations of all volatile components contained in the measurement sample. Information on the identified components (chemical substance names, concentrations, etc.) is stored in memory 52. ​​Memory 54 stores the HESS database and various databases of usage information such as the Ministry of Health, Labor and Welfare Workplace Safety Site, the Ministry of the Environment Initial Environmental Risk Assessment of Chemical Substances, the NITE Comprehensive Chemical Substance Information System, the J-CHECK Chemical Substances Control Law Database, and the Chemical book.

[0033] The information processing device 5 may be, for example, a cloud server, an on-premise server, a general-purpose computer, a dedicated computer, a mobile terminal, a smartphone, or a tablet. The memories 51, 52, and 54 may be temporary memories, or may be memory configurations such as HDD, SSD, or cloud storage. The risk assessment unit 55 and the usage information assessment unit 56 may be configured as a program (risk assessment program) that realizes their functions, or may be configured as hardware such as a memory, an electronic circuit, or a processor, or may be configured to be realized by having both of these. The program may be downloaded from a cloud server, and the information processing device 5 may send a processing command to the program on the cloud server, and the information processing device 5 may receive the processing result.

[0034] The output unit 6 may be composed of, for example, a monitor for displaying data, printing data, communicating data, storing data, a printer, a communication device for sending data to an external device, a storage device, and the like. The information processing device 5 may include an output unit 5.

[0035] (Prediction of Toxicity Potential (TOXp)) The risk assessment unit 55 extracts the common name, CAS No., toxicity value, and endpoint of a chemical substance from the HESS database. If multiple toxicity test results are registered, the highest toxicity (lowest toxicity value) among all endpoints is extracted from the viewpoint of a safer evaluation.

[0036] Examples of toxicity values ​​include the no observed effect level (NOEL), the lowest observed adverse effect level (LOAEL), the benchmark dose (BMD) on the safe side (BMDL: Benchmark Dose Lower Limit), the tolerable daily intake (TDI), and the reference dose (RfD: Reference Dose).

[0037] The risk assessment unit 55 performs the following process, the process flow of which is shown in Figure 2. (S1) The risk assessment unit 55 extracts chemical substances with similar structures for each target substance identified by the chemical substance identification unit 53 while referring to the database (HESS). Extraction of chemical substances with similar structures is performed based on a similarity evaluation using, for example, the Tanimoto coefficient, Dice coefficient, etc. from a fingerprint (morgan fingerprint (radius=2)). In this embodiment, the Tanimoto coefficient is used. The risk assessment unit 55 converts SMILES from the CAS No. in the database (HESS), extracts the IUPAC name, and calculates the LogKow and molecular weight. The risk assessment unit 55 calculates the Tanimoto coefficient.

[0038] (S2) The risk assessment unit 55 extracts chemical substances whose physical property values ​​are similar to those of the target substance. For example, the following extraction methods can be used. In this embodiment, the octanol / water partition coefficient (Log Kow) is used as the physical property value. In another embodiment, other physical property values ​​may be used as the physical property value. Examples of physical property values ​​include molecular weight, the number of specific elements or functional groups, structures, vapor pressure, oxidation-reduction potential, and surface tension. (i) From the chemical substances with the top n Tanimoto or Dice coefficients, extract chemical substances whose LogKow difference with the target substance is m or less. (ii) Extract up to p chemical substances in order of LogKow closest to the target substance. (iii) The chemical substance with the lowest no-effect level among the top n chemical substances in terms of Tanimoto or Dice coefficients may be extracted. For example, n is selected to be 5 or more, 10 or more, 20 or more, 50 or more, etc. For example, m may be selected as 0.2, 0.4, 0.5, 0.8, 1.0, 1.2, 1.5, etc. For example, p is selected to be 2, 3, 4, 5, etc.

[0039] (S3) The risk assessment unit 55 predicts the arithmetic mean (TOXp) of the toxicity values ​​of the extracted chemical substances as the toxicity potential of the target substance. The arithmetic mean (TOXp) of the toxicity values ​​of the chemical substances extracted above is calculated. In this embodiment, extraction is performed using the following five methods, and the arithmetic mean (TOXp) of the toxicity values ​​is calculated. The calculated arithmetic mean (TOXp) of the toxicity values ​​is stored in the memory 52.

[0040] (Method 1) From the top 20 chemicals with Tanimoto coefficients, extract chemicals whose LogKow difference with the target substance is 0.5 or less, and calculate the arithmetic mean (TOXp) of their toxicity values. (Method 2) From the top 20 chemicals with the highest Tanimoto coefficients, extract chemicals whose LogKow difference with the target substance is 1.0 or less, and calculate the arithmetic mean of their toxicity values ​​(TOXp). (Method 3) From the top 20 chemicals with the highest Tanimoto coefficients, select three chemicals with LogKow closest to the target substance, and calculate the arithmetic mean of their toxicity values ​​(TOXp). (Method 4) From the top 10 chemicals with the highest Tanimoto coefficients, select three chemicals in order of LogKow closest to the target substance, and calculate the arithmetic mean (TOXp) of their toxicity values. (Method 5) From the top 20 chemicals with the Tanimoto coefficient, the chemical with the lowest toxicity value (TOX) is extracted and that toxicity value (TOX) is used. Table 4 below shows the results using methods 1 to 5.

[0041] (S4) The risk potential (MOEp) is estimated. The risk potential (MOEp) may be estimated using the following formula. The estimated risk potential (MOEp) is linked to the chemical substance and stored in memory 52. ​​"****p" means the arithmetic mean. MOEp=TOXp / EHE (2.0) MOEp=NOELp / EHE (2.1) MOEp = (LOAELp / UF) / EHE (2.2) UF: Uncertainty factor MOEp=BMDLp / EHE (2.3) MOEp=TDIp / EHE (2.4) MOEp=RfDp / EHE (2.5)

[0042] (Exposure scenario) An exposure scenario will be constructed, and the estimated human exposure (EHE) will be estimated using qualitative and quantitative data obtained by non-target analysis. The exposure scenario will be based on the Ministry of the Environment's Initial Environmental Risk Assessment of Chemical Substances and the Ministry of Economy, Trade and Industry's Consumer Product Guidance for human health risks, and will use a respiratory volume of 15 m 3 day -1 ,The human weight is set to 50 kg. The exposure time is 9.0 h / day, based on the "Standards for Improving Working Hours of Automobile Drivers." -1 Set. EHE=(Ca t ×Q×t) / BW (2.0.1) Ca t is the average air concentration [mg m -3 ]: Calculated from quantitative values ​​of chemical substances Q is the respiration rate per hour [m 3 h -1 ]:15m 3 day -1 t is the exposure time per day [h·day -1 ]:9.0h·day -1 BW is body weight [kg]: 50kg

[0043] The use information evaluation unit 56 extracts use information of the identified chemical substance by referring to a database of various use information. The extracted use information is stored in the memory 52 in association with the chemical substance.

[0044] (Example) Using the configuration of the above-described first embodiment (TD-GC-MS / PR-FID device), non-target analysis of chemical substances contained in the air inside a vehicle was carried out. Sample: Vehicles A, B, and C immediately after production Sample collection: An automatic gas sampling device (GSP-400FT) was used, and the sampling position was set at the driver's nose, with a flow rate of 0.2 L / min and a sampling time of 5.0 min. (1) Vehicle A: In a large environmental chassis dynamometer laboratory (compliant with ISO11219-19), the vehicle was ventilated for 30 minutes, the doors and windows were fully closed, the interior temperature was raised to 35±2°C, and after waiting for 210 minutes, the vehicle was collected in a thermal desorption collection tube (Tenax-TA) (n=6) (parked mode A1). The air conditioner was then turned on and the vehicle was left to stand for 135 minutes, after which the vehicle was collected in a thermal desorption collection tube (Tenax-TA) (n=3) (riding mode A2). (2) Vehicles B and C: The vehicles were ventilated outdoors for 30 minutes, the doors and windows were fully closed, and the interior temperature was raised to 30°C. After that, the gases were collected in a thermal desorption collection tube (Tenax-TA) (n=3) (parking modes B and C).

[0045] Table 1 shows the analytical conditions for the TD-GC-MS / PR-FID instrument.

[0046] [Table 1]

[0047] Table 2 shows the analytical conditions for the TD-GC-MS device.

[0048] [Table 2]

[0049] Regarding peaks obtained by MS and FID, even if their retention times overlap, they can be separated by m / z. However, because FID cannot separate peaks, it was confirmed that the peaks of multiple chemicals identified by MS overlap with each other. Furthermore, because the measurement sensitivity of FID is lower than that of MS, it was also confirmed that the FID peaks for chemicals identified by MS are below the lower limit of quantification. Therefore, from the perspective of comprehensive screening, chemicals with overlapping FID peaks were quantified based on the overlapping FID peak area, with a view to making a conservative assessment. Therefore, it was decided to overestimate the concentration of each chemical identified by MS. Furthermore, for substances whose FID peaks were below the lower limit of quantification, the peak area was set at 100, which is the lower limit of quantification for FID. By making such a conservative assessment (which increases the risk potential), comprehensive screening becomes possible.

[0050] The number of chemical substances detected that could be qualitatively and quantitatively analyzed is shown in Table 3. The number of detected chemical substances is the number of chemical substances detected in all three measurements, which were performed three times. [Table 3]

[0051] The results of non-target analysis and exposure assessment of chemical substances in the air inside a vehicle using a TD-GC-MS / PR-FID system are shown below. Figure 4 shows the results for vehicle A in riding mode A1, and Figure 5 shows the results for vehicle A in parking mode A2. In riding mode A1, 205 chemical substances were detected, and in parking mode A2, 197 chemical substances were detected, for a total of 238 chemical substances. 161 substances were detected in both riding mode A1 and parking mode A2, 36 substances in riding mode A1 only, and 33 substances in parking mode A2 only. The indoor concentration guideline value components detected in both modes were the same in riding mode A1 and parking mode A2: ethylbenzene, 1,3-xylene, 1,4-xylene, styrene, and toluene, but most of the detected substances were not included in the indoor concentration guideline values ​​or the Organic Solvent Poisoning Prevention Regulations. It was confirmed that a wide variety of chemical substances are present in the air inside a vehicle.

[0052] The results of non-target analysis and exposure assessment of chemical substances in the air inside a vehicle using a TD-GC-MS / PR-FID system are shown below. Figure 6 shows the results for parking mode B for vehicle B, and Figure 7 shows the results for parking mode C for vehicle C. The indoor concentration guideline value components detected in vehicle B were ethylbenzene, 1,3-xylene, 1,4-xylene, styrene, toluene, formaldehyde, bis(2-ethylhexyl)benzene-1,2-dicarboxylate, and tetradecane. In vehicle C, the following were detected: ethylbenzene, 1,3-xylene, formaldehyde, 1,4-dichlorobenzene, acetaldehyde, styrene, dibutyl benzene-1,2-dicarboxylate, bis(2-ethylhexyl)benzene-1,2-dicarboxylate, tetradecane, and toluene. As with vehicle A, many chemicals not included in the indoor concentration guideline values ​​or the Organic Solvent Poisoning Prevention Regulations were detected in vehicles B and C as well. Of the chemicals detected in vehicles B and C, 95 types of chemicals were common, and it was confirmed that approximately 100 types of chemicals were detected as chemicals specific to each mode. By using the TD-GC-MS / PR-FID system, we were able to obtain estimated concentrations of unregulated chemical substances and chemical substances for which it is difficult to prepare standard substances.

[0053] A total of 795 substances were detected in the four measurement samples from vehicles A, B, and C. Of these, use information was available for 423 chemical substances. There were 29 types of substances with overlapping FID peaks in vehicle A's parking mode A2, 22 types in vehicle A's riding mode A1, 57 types in vehicle B's parking mode B, and 96 types in vehicle C's parking mode C. By using the TD-GC-MS / PR-FID system, it was possible to obtain estimated concentrations for all chemical substances. In addition, by estimating the risk potential value, information was obtained that would help determine whether the peak overlap needed to be improved or whether improvements should be made from the peak overlap.

[0054] Figure 3 shows the results of creating a calibration curve using a BTX standard substance for vehicle A's riding mode A1. Creating a methane-equivalent calibration curve using benzene, toluene, and xylene (BTX) makes it possible to measure the estimated concentration of detected chemicals. A comparison was made between analysis using a conventional TD-GC-MS device and analysis using the TD-GC-MS / PR-FID device of embodiment 1. When compared with the conventional method for four types of chemical substances (toluene, styrene, p-xylene, and o-xylene), good agreement was observed.

[0055] (Comparison of predicted toxicity potential with actual test data) In this experiment, the arithmetic mean of the no observed effect levels (NOELp) is used as the toxicity value. Table 4 shows a comparison of the arithmetic mean no-observed-effect level (NOELp) results (Vehicle A, riding mode A1) obtained by the above five methods with the test data for the compounds registered in HESS (actual HESS values). HESS actual values ​​contain multiple toxicity values. Compared with the actual HESS values, the number of chemicals for which the predicted values ​​of methods 1 to 5 fell within the range of one-third to three times was 27 for method 1, 26 for method 2, 23 for method 3, 25 for method 4, and 5 for method 5, with method 1 having the most.

[0056] [Table 4]

[0057] Underestimating toxicity values ​​may result in health effects, so overestimating toxicity values ​​is considered acceptable from the perspective of toxicity prediction in screening. Therefore, we investigated the number of substances for which the NOELp was smaller than the HESS measured value, and found that Method 1 was the best predictive method for 9 substances, Method 2 was 11 substances, Method 3 was 13 substances, Method 4 was 11 substances, and Method 5 was 35 substances. In other words, Method 5 was the best predictive method from a screening perspective, and only Method 5 was capable of making conservative predictions for all chemical substances. However, because overly conservative predictions may result in too many substances being caught in screening, it may be possible to select any of Methods 1 to 5 for evaluation.

[0058] (Estimation of risk potential MOEp) The predicted NOELp and MOEp were calculated. MOEp was lowest for 2-methylpropyl acetate in vehicle A in riding mode A1 and parking mode A2. Hexane had the lowest value in vehicles B and C in parking modes B and C. The results of examining the toxicity information for the top 10 substances with the smallest MOEp for each sample are shown in Tables 5, 6, 7, and 8. Detailed repeated dose toxicity data was available for six chemicals in vehicle A's riding mode A1, five in vehicle A's parking mode A2, three in vehicle B, and five in vehicle C. By using this method, it has become possible to quantitatively consider the toxicity potential in the form of MOEp even for chemicals for which insufficient toxicity data is available. The detected chemicals were analyzed not only qualitatively but also quantitatively, allowing us to estimate the estimated human exposure. Furthermore, by predicting NOELp in accordance with the HESS toxicity prediction system, we obtained information that will be useful in selecting chemicals that should be prioritized for detailed risk assessment.

[0059] [Table 5]

[0060] [Table 6]

[0061] [Table 7]

[0062] [Table 8] [Industrial Applicability]

[0063] Quantitative concentration information can be obtained for chemical substances that could not be quantified due to the unavailability of standard substances. From the vast number of chemical substances obtained through non-target analysis, it will be possible to efficiently and systematically select those substances that should be prioritized for more detailed risk assessment. This will dramatically improve the efficiency of chemical safety management.

[0064] It is possible to assess the human health risks of chemicals contained in indoor air, house dust, and indoor products, evaluate the safety of food (including unintentional contamination), evaluate the human and ecological risks of chemicals in the air, water, and soil environments, investigate chemical contamination during accidents and disasters, evaluate pollution sources using chemical fingerprinting methods, evaluate the safety of recycled materials and products, investigate chemicals in biological samples for disease diagnosis, and investigate information on chemicals that will contribute to product development.

Claims

1. A method for comprehensively assessing the risk potential of chemical substances, comprising: a chromatographic separation step of separating the measurement sample into components; a qualitative analysis step of qualitatively analyzing the separated components; oxidizing and reducing the separated components to methane and quantifying them; a risk assessment step of extracting chemical substances with similar structures to the target substance identified in the qualitative analysis step and the oxidation-reduction methanation quantification step while referring to a database, extracting the chemical substances with physical property values ​​similar to those of the target substance, predicting the toxicity value (TOX) or arithmetic mean of the toxicity values ​​(TOXp) of the extracted chemical substances as the toxicity potential of the target substance, and estimating the risk potential (MOEp); A risk potential assessment method, including:

2. A comprehensive risk potential assessment system for chemical substances, comprising: a gas chromatograph (GC) for separating volatile components into individual components; A division section that divides the separated components into two systems and sends them in. a mass spectrometer (MS) for qualitatively analyzing the components sent to the first system; A post-column reactor that oxidizes and reduces the components sent to the second system to produce methane; a detector for quantifying methane; a risk assessment unit that extracts chemical substances with similar structures from the qualitatively and quantitatively determined target substance while referring to a database, extracts chemical substances with physical property values ​​similar to those of the target substance, predicts the toxicity value (TOX) or arithmetic mean of the toxicity values ​​(TOXp) of the extracted chemical substances as the toxicity potential of the target substance, and estimates the risk potential (MOEp); A risk potential assessment system comprising:

3. 1. A program for comprehensive risk potential assessment of chemical substances, comprising: A computer or one or more processors, This program implements a risk assessment step in which, for a target substance that has been qualified using a mass spectrometer (MS) and quantified using a detector that quantifies methane, chemical substances with similar structures are extracted by referring to a database, chemical substances with physical properties close to those of the target substance are extracted, the toxicity value (TOX) or arithmetic mean of the toxicity values ​​(TOXp) of the extracted chemical substances are predicted as the toxicity potential of the target substance, and the risk potential (MOEp) is estimated.

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