High-risk production process environmental health risk assessment method

Abstract

The invention is suitable for the field of environmental health assessment, and provides a high-risk production process environmental health risk assessment method, which comprises new pollutant production and emission analysis, and high-risk new pollutant identification and early warning. A new pollutant list is obtained through data analysis and high-resolution mass spectrum actual measurement, a five-dimensional risk screening system is constructed to identify risk pollutants, exposure is simulated through a III-level fugularity model and the like, actual measurement correction is combined, and an agent model is constructed to achieve risk early warning. The method is based on model prediction and field measurement, can effectively represent the environmental health risk of new pollutants under different working conditions of an enterprise, and promotes evaluation work. Taking a non-phosgene method for producing diphenylmethane diisocyanate as an example, the risk of related pollutants can be controlled after optimization, and the environment and crowd health are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of environmental health assessment, and in particular relates to an environmental health risk assessment method for a high-risk production process. Background Art

[0002] Environmental health risk assessment has become an important foundation for refined environmental management. The technical standard system covers areas such as environmental and health surveys and environmental pollutant exposure assessments.

[0003] Most of the existing relevant assessment technical methods are based on regionality, and research on new pollutants mostly focuses on regional / watershed screening and monitoring technical methods. There is a lack of application methods for environmental health risk assessment of new pollutants for specific production and pollution-discharging enterprises. It is difficult to effectively characterize the environmental health risks involving new pollutants under different production conditions of enterprises, which is not conducive to the large-scale implementation of environmental health risk assessment work for new pollutants. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for environmental health risk assessment of high-risk production processes, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] This invention is achieved by providing a technical method for environmental health risk assessment of new pollutants in manufacturing enterprises. This method, based on model prediction and field measurement data, constructs a complete set of technical methods that can effectively characterize the environmental health risks of new pollutants under different production conditions of the enterprise, helping to promote the large-scale implementation of environmental health risk assessment of new pollutants. Specifically, it includes the following contents: Analysis of new pollutant production and emission from production enterprises The primary method is to utilize data analysis and on-site measurements to obtain a list of new pollutants for the assessed enterprises based on the results of these analyses. Data analysis involves reviewing documents such as the enterprise's feasibility study report and environmental impact assessment report, comparing them with the latest version of the new pollutant list, and identifying potential new pollutants in the enterprise's raw materials, products, and intermediates and by-products involved in the production process. On-site measurements, in accordance with relevant sampling technical specifications, involve setting up sampling points at the production enterprise's outfalls to collect samples of the "three wastes" (wastewater, waste gas, and solid waste). Based on the data analysis results, high-resolution mass spectrometry is used to perform suspicious / non-targeted screening on these samples to further supplement the list of new pollutants for the assessed enterprises.

[0006] Identification of high-risk new pollutants in production enterprises Based on the production and emission analysis, a new pollutant risk screening system was constructed, covering five indicators: confidence level, detection level, toxic effects, ecological risk, and physical and chemical properties. Initial risk screening was conducted for the new pollutants identified. Based on the comprehensive risk score results, a preliminary screening list of new pollutants with potential risks was established. For the new pollutants on the preliminary screening list, dose-response relationships between exposure and adverse effects were established, and environmental and health toxicity parameters were derived.

[0007] Early warning of high-risk new pollutants for production enterprises On the basis of risk identification, we further carry out environmental and health exposure simulation of high-risk new pollutants in production enterprises. Based on the raw material usage data and emission coefficients in the environmental impact assessment report of the production enterprise, we use the model to predict the occurrence of high-risk new pollutants in the surrounding environmental media; based on the measured data, we calibrate the simulation results, optimize and adjust the production process, and build a refined exposure prediction model for high-risk new pollutants. Based on the prediction results, we will issue early warnings for high-risk new pollutants. The beneficial effects of the present invention are: The environmental health risk assessment method for high-risk production processes provided by the present invention has built a complete technical system based on model prediction and on-site measured data. Through data analysis and on-site measurements, a list of new pollutants for the enterprise is obtained, a risk screening system covering multiple indicators is constructed to identify high-risk new pollutants, and an agent model is constructed by combining exposure simulation and measured correction to achieve risk warning. This method can effectively characterize the environmental health risks of the enterprise under different production conditions and promote the implementation of new pollutant assessment work; it can optimize production process parameters to reduce the environmental and health risks of pollutants to a controllable level and protect the health of the environment and the people; it provides a scientific and systematic technical solution for the environmental health risk assessment of new pollutants in production enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 Schematic diagram of the Pareto optimal frontier interval. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0010] Example 1 Analysis of new pollutant production and emission by production enterprises: Data analysis: By reviewing the company's feasibility study reports, environmental impact assessment reports and other documents, and comparing them with the latest versions of new pollutant lists such as the list of key controlled new pollutants and the list of toxic and hazardous pollutants, we systematically sort out potential new pollutants in the company's raw materials, products, intermediates and by-products in the production process.

[0011] On-site testing: In accordance with relevant sampling technical specifications, sampling points were set up at the sewage outlets of production enterprises to collect samples of "three wastes" (wastewater, waste gas, and solid waste). Based on the results of the aforementioned data analysis, a high-resolution mass spectrometer (Orbitrap-HRMS) was used to conduct suspicious / non-targeted screening of the collected "three wastes". The specific steps are as follows: 1. After the "three wastes" samples undergo appropriate pretreatment (keeping all components of the sample intact), isotope internal standard indicators are added for detection.

[0012] 2. Use Orbitrap-HRMS to collect mass spectrometry data. The specific parameters are set as follows: Electrospray ionization (H-ESI) source: spray voltage 3400 V in negative ion mode and 3500 V in positive ion mode; Sheath gas flow rate: 50Arb; Auxiliary gas flow rate: 10Arb; Purge gas: 1Arb; Ion transfer tube temperature: 325°C; Atomizer temperature: 350℃; Expected LC peak width: 6s; Default charge state: 2; Internal standard mass calibration: EASY-ICTM.

[0013] 3. Collect mass spectrometry data using the following four-step process: a. In full scan mode, set the mass spectrometer resolution to 12000; scan range: 85-1210 m / z; lens voltage: 70%; and scan positive and negative ions simultaneously.

[0014] b. Peak height threshold: 5.0×10 4 .

[0015] c. Dynamic exclusion: The dynamic exclusion mode is customized; ions that appear within 3 seconds after a scan are excluded; the mass deviation tolerance is set to 5 ppm; isotope exclusion is performed.

[0016] d. Fragment ion scan (ddMS2): Isolation window 2 m / z; collision energy type: normalized; HCD collision energies: 20%, 40%, 60%, and 80%; resolution: 15,000; scan range mode: automatic; AGC target: standard; and eight fragment ion spectra collected.

[0017] 4. Use commercial software such as CompoundDiscoverer to automatically process screening data. The processing process includes: a. Peak extraction.

[0018] b. Peak alignment.

[0019] c. Compound detection, where the mass tolerance was set to 5 ppm, the minimum peak response was set to 100,000, the most abundant isotope was used, the signal-to-noise ratio threshold was set to 5, and the ion adduction mode was [M+H]+1.

[0020] d. Compound combination: mass tolerance is set to 5 ppm, retention time tolerance is set to 0.1 min, peak type score threshold is 5 and it must be detected in at least 5 samples.

[0021] e. Compound identification and annotation: Based on database queries such as mzCloud (identification search method: Cosine; similarity search method: ConfidenceForward; match factor threshold: 50%), mzVault (using the HighChemHighRes search algorithm; match factor threshold: 50%), ChemSpider (search mode: ByFormulaorMass; mass error tolerance: 5 ppm), and MassLists (mass error tolerance: 5 ppm).

[0022] f. Filling the gaps: The mass tolerance was set to 5 ppm and the fill value was 1.5 times the signal-to-noise ratio.

[0023] g. QC calibration: The minimum detection rate of specific compounds in QC samples is 50%, the maximum relative standard deviation allowed for the peak area of ​​specific compounds in QC samples is 30%, and the maximum relative standard deviation allowed for the peak area of ​​specific compounds in QC samples after calibration is 25%.

[0024] h. Subtract background blank.

[0025] 5. The confidence levels for new contaminants obtained through screening are defined as follows: If it can match the local standard database and simultaneously meet the matching requirements of parent ion (mass error <5ppm), parent ion characteristic isotope pattern (match degree >60%), product ion (match at least one fragment ion) and retention time (RT <1min), the confidence level is defined as L1; If the online mzCloud database and the offline mzVault database can be matched, and the matching requirements of parent ion (mass error <5 ppm), parent ion characteristic isotope pattern (matching factor >60%), and product ion (matching factor >50%) are met at the same time, the confidence level is defined as L2.

[0026] Example 2 Identification of high-risk new pollutants in manufacturing enterprises: Based on Example 1, a new pollutant risk screening system was constructed, covering five indicators: confidence level, detection level, toxic effects, ecological risk, and physical and chemical properties. Initial risk screening was performed on the new pollutants obtained in Example 1. A preliminary screening list of new pollutants with potential risks was established based on the comprehensive risk score results. For the new pollutants on the preliminary screening list, a dose-response relationship between exposure and adverse effects was constructed, and environmental and health toxicity parameters were derived. The specific steps are as follows: 1. Methods for assigning values ​​to various indicators: a. Confidence level assignment: Assign confidence levels based on the new pollutant confidence levels obtained through screening. L1 and L2 are assigned values ​​of 1.0 and 0.5, respectively, and corresponding weighting factors are assigned.

[0027] b. Detection level assignment: Assign a value based on the logarithmic value of the high-resolution mass spectrometer response and assign a weighting factor.

[0028] c. Toxicity effect scores: Toxicity effect scores were calculated based on a classification model based on the 12 toxicity endpoints constructed by the U.S. Environmental Protection Agency (USEPA) Tox21 program [AR (androgen receptor), AhR (aryl hydrocarbon receptor), AR-LBD (androgen receptor ligand-binding domain), ER (estrogen receptor), ER-LBD (estrogen receptor ligand-binding domain), aromatase, PPAR-gamma (peroxisome proliferator-activated receptor-gamma), ARE (antioxidant response element), ATAD5 (ATPase family AAA domain protein 5), HSE (heat shock element), MMP (matrix metalloproteinase), and P53 (tumor protein 53)]. Each endpoint was assigned a score of 1 and a corresponding weighting factor.

[0029] d. Ecological risk assignment: A random forest model is constructed based on measured data to link compound structure with logPNEC. This model is used to predict and assign ecological risks to new pollutants obtained through screening.

[0030] e. Assignment of physicochemical properties: Use EPI.suit software to obtain physicochemical property parameters such as bioconcentration factor and octanol-water partition coefficient for screening new pollutants, and build an RF model to make predictions and assign values.

[0031] 2. Risk ranking and hazard characterization process: a. Risk ranking: A comprehensive index method is used to normalize the scores of various indicators, comprehensively rank the environmental risks of screened new pollutants, and form a preliminary screening list of new pollutants with potential risks.

[0032] b. Hazard Identification: Hazard identification is conducted through a combination of literature search (e.g., ECOTOX, IRIS databases, etc.) and computational toxicology (e.g., quantitative structure-activity and interspecies relationship prediction models). If necessary, relevant data can be supplemented through in vivo / in vitro experiments (e.g., cytotoxicity experiments, zebrafish toxicity experiments, and rat toxicity experiments).

[0033] c. Characterization of environmental hazards: Use statistical extrapolation or evaluation coefficient method to characterize water environmental hazards, use phase equilibrium distribution method and evaluation coefficient method to characterize sediment environmental hazards, and use phase equilibrium distribution method or evaluation coefficient method to characterize soil environmental hazards.

[0034] d. Characterization of health hazards: Reference dose (RfD) or reference concentration (RfC) is used to characterize the health toxicity parameters of non-carcinogenic new pollutants, and carcinogenic slope factor (SF) or unit risk factor (IUR) is used to represent the toxicity parameters of carcinogenic new pollutants.

[0035] Example 3: Early warning of high-risk new pollutants for production enterprises: On the basis of Example 2, further simulation of environmental and health exposure of high-risk new pollutants in production enterprises is carried out. Based on the raw material usage data and emission coefficients in the production enterprise's environmental impact assessment report, a model is used to predict the occurrence of high-risk new pollutants in the surrounding environmental media; the simulation results are corrected based on the measured data, and the production process is optimized and adjusted to construct a refined exposure prediction model for high-risk new pollutants, and early warning of high-risk new pollutants is issued based on the prediction results. The specific steps are as follows: 1. Data collection: Evaluate the use of raw and auxiliary materials, process technology routes, by-product generation and other information involved in the company's environmental impact assessment report and other materials; Collect emission coefficients of new pollutants to be evaluated through materials such as the "Technical Guidelines for Environmental and Health Exposure Assessment of Chemical Substances (Trial)"; Collect the physical and chemical properties of new pollutants to be evaluated; Collect information such as geographical and meteorological parameters of the area where the production enterprise is located.

[0036] 2. Emission simulation: Based on the data collection results, calculate the emission rate of high-risk new pollutants in raw materials and auxiliary materials; BioTransformer and other software are used to simulate the environmental transformation process of process raw materials and auxiliary materials. Combined with environmental impact assessment data, potential by-products in the production process are further analyzed and the production and emission rates of by-products are calculated.

[0037] 3. Exposure simulation: Use environmental migration and transformation models such as the Level III fugacity model to simulate the multi-media fugacity distribution of high-risk emerging pollutants under the geographical and meteorological conditions of the production enterprise's area, and understand the occurrence of pollutants involved in the main and side reaction processes in typical environmental media such as water, soil, sediment, and atmosphere.

[0038] 4. On-site measurement: Based on the enterprise's pollutant production and emission pathways, select representative environmental media to set up monitoring points, sample and analyze the concentrations of high-risk new pollutants in different environmental media, and compare and analyze them with the aforementioned multi-media simulation data. When the simulation results are far lower than the measured results, the parameters of the exposure simulation model need to be adjusted.

[0039] 5. Construction of a proxy model based on enterprise production data: Collect various production data of the enterprise to be evaluated over a period of time, such as reaction temperature, time, material addition ratio, etc., and use machine learning technology to construct a mathematical expression for the optimization of the enterprise's production process. The multi-media exposure simulation process based on pollutant production and emission information is embedded in the production process expression, and then a proxy evaluation model based on multivariate linearity is constructed between the enterprise's production data and the multi-media distribution law of high-risk new pollutants. Based on this model, the exposure prediction of high-risk new pollutants under different production conditions of the enterprise can be realized.

[0040] 6. Population exposure assessment: Based on the multi-media exposure prediction results of high-risk emerging pollutants under different production conditions of the enterprise, reasonable worst-case exposure scenarios are constructed for enterprise employees and surrounding residents, and the total human exposure to high-risk emerging pollutants through inhalation, ingestion and skin contact is calculated.

[0041] 7. Risk warning: Compare and analyze the multi-media exposure prediction results and total population exposure prediction results based on the company's production data with the environmental and health hazard characterization data of the new pollutants to be evaluated. When the prediction result is higher than the hazard characterization data, it indicates that there are unreasonable environmental and health risks under the current production process conditions, and corresponding emergency measures (such as adjusting process operating parameters, reducing production load, etc.) need to be taken to reduce the risk; when the prediction result is lower than the hazard characterization data, it indicates that the environmental and health risks under the current production process conditions are controllable, and the production company should reasonably set the operating process parameter range according to the risk threshold to ensure that the environmental and health risks of high-risk new pollutants in the production process are controllable.

[0042] Take the non-phosgene method for producing diphenylmethane diisocyanate as an example: (1) The high-risk pollutants in the production of diphenylmethane diisocyanate by a non-phosgene method mainly include aniline, o-dichlorobenzene and formaldehyde. Based on the raw material usage data under a certain production condition, the LEVELIII model is used to predict that the concentrations of aniline in the atmosphere, water, soil and sediment around the production enterprise are 46 ng / m3, 2.8 ng / L, 3.1E-02 ng / g and 3.1E-02 ng / g, respectively; the concentrations of o-dichlorobenzene are 1.4E-06 ng / m3, 7.4E-01 ng / L, 1.4E-02 ng / g and 4.1E-02 ng / g, respectively; and the concentrations of formaldehyde are 7800 ng / m3, 2700 ng / L, 9.8 ng / g and 1.3 ng / g, respectively. By comparing with the predicted no-effect concentrations of aniline, o-dichlorobenzene and formaldehyde in the atmosphere, water, soil and sediment, it can be seen that formaldehyde has unreasonable environmental risks under the current working conditions. In addition, based on the toxicity parameters of aniline, o-dichlorobenzene, and formaldehyde, the calculated lifetime excess carcinogenic risk of formaldehyde at the current predicted environmental concentration is 9.59E-06, which poses an unreasonable health risk, as shown in Table 1.

[0043] Table 1 Environmental health risks of aniline, o-dichlorobenzene and formaldehyde under certain working conditions

[0044] (2) AspenPlus process simulation software was used to construct the process flow of the production enterprise. The mathematical expressions of production process conditions and production and emission data were optimized through machine learning technology. The multi-media exposure simulation process based on pollutant emission information was embedded in the production process simulation and integrated optimization was performed. The Pareto optimal frontier interval of the optimization process was as follows: Figure 1 The comparison of working parameters before and after optimization is shown in Table 2.

[0045] Table 2 Typical operating parameters before and after optimization

[0046] (3) The predicted environmental exposure concentrations and environmental health risks of aniline, o-dichlorobenzene, and formaldehyde under the optimized working conditions are shown in Table 3. Under the production process conditions, the environmental risks of aniline, o-dichlorobenzene, and formaldehyde are all less than 1, and the health risks are all less than 10-6. Both environmental and health risks are at a controllable level, which can effectively protect the environment and human health.

[0047] Table 3 Environmental health risks of aniline, o-dichlorobenzene and formaldehyde under optimized working conditions

[0048] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0049] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for environmental health risk assessment of high-risk production processes, characterized in that: The method comprises: Conduct production and emission analysis of new pollutants from production enterprises and obtain a list of new pollutants through data analysis and on-site measurements; Construct a risk screening system covering confidence level, detection level, toxic effects, ecological risks, and physical and chemical properties. Use a comprehensive index method to normalize scores and generate a preliminary screening list of high-risk new pollutants for hazard identification and characterization. Collect the company's raw materials, process and environmental data, build a proxy assessment model through emission simulation, exposure simulation and actual measurement correction, predict the multi-media exposure concentration and total population exposure under different production conditions through the proxy assessment model, and compare the hazard characterization data for risk warning.

2. The method according to claim 1, characterized in that The on-site measurement includes collecting three waste samples at the sewage outlet and performing non-targeted screening. The specific steps include sample pretreatment, mass spectrometry data acquisition, data automation processing and compound confidence level definition.

3. The method according to claim 2, characterized in that The mass spectrometry data were collected using Orbitrap-HRMS, and the electrospray ion source parameters were: spray voltage 3400 V in negative ion mode, 3500 V in positive ion mode, sheath gas flow rate 50 Arb, auxiliary gas flow rate 10 Arb, ion transfer tube temperature 325°C, and nebulizer temperature 350°C.

4. The method according to claim 2, characterized in that The data were automatically processed using CompoundDiscoverer software, and the processing flow included peak extraction, peak alignment, compound detection, combination, identification and annotation, gap filling, and QC correction.

5. The method according to claim 1, wherein In the risk screening system, the confidence levels L1 and L2 were assigned 1.0 and 0.5, respectively. The toxic effects were calculated based on the 12 toxicity endpoints of the USEPATox21 program. The ecological risks and physicochemical properties were predicted using a random forest model and EPI.suit software combined with an RF model, respectively.

6. The method according to claim 1, characterized in that The exposure simulation adopts a Level III fugacity model, combines the geographical and meteorological parameters of the production enterprise to simulate the multi-media fugacity distribution, and calibrates the model through field measured data.

7. The method according to claim 4, characterized in that The agent assessment model is constructed by embedding multi-media exposure simulation into production process flow expressions through machine learning technology, and is used to predict pollutant exposure under different production conditions.

8. The method according to claim 1, characterized in that The risk warning is carried out by comparing the exposure prediction results with the environmental and health hazard characterization data. When the exposure is higher than the threshold, the process parameters are adjusted and the load is reduced. When the exposure is lower than the threshold, a safe operating parameter range is set.

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