Polycyclic aromatic hydrocarbon emission and environmental health assessment method based on carbon emission scene

Abstract

The invention relates to the technical field of pollutant emission evaluation, in particular to a polycyclic aromatic hydrocarbon emission and environmental health evaluation method based on a carbon emission scene. According to the method provided by the invention, the energy activity data under different scenes are constructed through the multi-climate comprehensive evaluation model GCAM-China, the polycyclic aromatic hydrocarbon emission lists under various carbon emission scenes are constructed based on the energy activity data under different scenes and the polycyclic aromatic hydrocarbon emission factors, and the polycyclic aromatic hydrocarbon emission lists under various carbon emission scenes are constructed based on the polycyclic aromatic hydrocarbon emission lists under various carbon emission scenes. And outputting a pollutant concentration space-time distribution diagram under multiple scenes, and based on the pollutant concentration space-time distribution diagram under multiple scenes, evaluating collaborative emission reduction benefits of climate policy implementation on pollution control and health improvement. The method can realize quantitative evaluation of carbon dioxide and polycyclic aromatic hydrocarbon cooperative emission reduction benefits, environmental medium concentration simulation and crowd health risk priority identification under different climate policy paths, and breaks through the limitation of a traditional method in the aspect of systematic evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pollutant emission evaluation, in particular to a polycyclic aromatic hydrocarbon emission and environmental health evaluation method based on carbon emission scenarios. BACKGROUND

[0002] Polycyclic aromatic hydrocarbons (PAHs) are a typical class of persistent organic pollutants (POPs) with molecular structures composed of two or more benzene rings, which are widely present in processes such as energy combustion, industrial production, and transportation emissions. Numerous studies have shown that PAHs have significant mutagenicity, carcinogenicity, and teratogenicity, and even at low exposure levels, they can have adverse effects on human immune function, host resistance, and humoral immune system, significantly increasing the incidence of respiratory and cardiovascular diseases and mortality risk. The United States Environmental Protection Agency (USEPA) has listed 16 typical PAHs as priority controlled pollutants, and their environmental and health impacts have received international attention.

[0003] China is a large PAHs emitter and one of the countries with the highest PAHs emissions in the world. With the acceleration of industrialization and urbanization, China's PAHs emissions have continued to rise. Research data shows that China's annual PAHs emissions reached 25,300 tons in 2005, and rose to 77,800 tons by 2017. This growth trend indicates that traditional end-of-pipe control measures have been ineffective in curbing PAHs emissions and multi-path exposure, and there is an urgent need to systematically promote pollution prevention and control from the aspects of source control, process optimization, and comprehensive evaluation.

[0004] In the face of the dual challenges of climate change and air quality improvement, it is urgent to build a systematic and scientific pollution control framework. Through quantitative evaluation of PAHs emission trends, multi-medium simulation of migration and transformation processes, and comprehensive assessment of their impact on environmental quality and public health, scientific basis and technical support can be provided for achieving coordinated reduction of multiple pollutants and control of health risks. SUMMARY

[0005] The present application aims to provide a PAHs emission and environmental health evaluation method based on carbon emission scenarios, which can realize quantitative evaluation of carbon dioxide and PAHs co-emission reduction benefits under different climate policy paths, environmental medium concentration simulation, and identification of population health risk priorities, breaking through the limitations of traditional methods in systematic evaluation.

[0006] The technical solution of the present application is to provide a PAHs emission and environmental health evaluation method based on carbon emission scenarios, comprising the following steps:

[0007] S1: Based on the climate comprehensive evaluation model GCAM-China, and for the emission sectors not covered in the climate comprehensive evaluation model GCAM-China, collect relevant historical data of the uncovered emission sectors, and combine the climate comprehensive evaluation model GCAM-China output to build a machine learning prediction model, and build energy activity data under different scenarios;

[0008] S2: Based on different emission sectors, fuel types and technical paths, build polycyclic aromatic hydrocarbon emission factors, calibrate and optimize through technical decomposition and regression models, and combine the energy activity data under different scenarios in step S1, use a top-down method to build polycyclic aromatic hydrocarbon emission inventories under multiple carbon emission scenarios;

[0009] S3: Based on the emission inventory built in step S2, build a regional scale multi-medium environmental fate model, divide the region into several spatial network units, use a high spatial resolution of 0.5km x 0.5km, select benz[a]pyrene as a representative species, input its physicochemical parameters, dynamically simulate the multi-medium fate of polycyclic aromatic hydrocarbons at the regional scale, and output the pollutant concentration spatiotemporal distribution map under multiple scenarios;

[0010] S4: Based on the pollutant concentration spatiotemporal distribution map under multiple scenarios in step S3, combined with toxicology parameters, build a multi-medium-multi-path health risk assessment model, and based on the lifetime incremental carcinogenic risk method to assess population exposure level, build a high-resolution grid polycyclic aromatic hydrocarbon health risk distribution map, identify key exposure areas, to evaluate the synergistic emission reduction benefits of climate policy implementation on pollution control and health improvement.

[0011] Preferably, in step S1, the plurality of carbon emission scenarios includes a baseline scenario, a carbon peak scenario, a carbon peak-carbon neutralization scenario, and a physical examination carbon peak-carbon neutralization scenario; the plurality of carbon emission scenarios are all set in a shared social and economic path SSP2, and can dynamically simulate the energy system structure, energy consumption level and carbon dioxide emission under each scenario. The shared social and economic path SSP2 is a medium-speed social and economic development path, assuming that the population and income show a moderate growth trend, the climate comprehensive evaluation model GCAM-China uses version v6, takes 2015 as the base year, runs to 2100 with a 5-year time step, and solves the market equilibrium state through supply and demand balance at each time step, and the model input includes population, GDP, energy price, consumption preference and technology adoption, etc. Social and economic driving factors. The baseline scenario is a no-policy intervention scenario; the carbon peak scenario is to implement carbon peak in 2030; the carbon peak-carbon neutralization scenario is to implement carbon peak in 2030 and achieve carbon neutralization in 2060; the carbon peak-carbon neutralization scenario is to implement carbon peak in 2025 and achieve carbon neutralization in 2060. In step S1, the machine learning includes vector regression, random forest, gradient boosting decision tree and extreme gradient boosting tree algorithms; the prediction model improves the performance through hyperparameter optimization and cross-validation to determine the coefficient of determination R 2 and mean square error MSE as the core evaluation index, and selects the optimal model for prediction to improve the prediction accuracy.

[0012] Preferably, in step S2, five typical production process emission sources are additionally added to the polycyclic aromatic hydrocarbon emission inventory, including iron ore sintering, open hearth steelmaking, converter steelmaking, electric arc furnace steelmaking and hot rolling process, to supplement typical polycyclic aromatic hydrocarbon emission sources in high carbon dioxide emission processes; the emission sources in the polycyclic aromatic hydrocarbon emission inventory include coal, oil, natural gas, biomass, solid waste and industrial processes, and the emission sources are divided into five departments of energy production, industry, transportation, business / residents and agriculture; the polycyclic aromatic hydrocarbons include the following 16 priority pollutants: naphthalene, acenylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, dibenzo[a,h]anthracene, indeno[1,2,3-cd]pyrene and benzo[g,h,i]perylene.

[0013] Preferably, in step S2, the regression model uses the following general calculation formula:

[0014] GDP + ;

[0015] wherein, (mg / t) is the emission factor of the i th polycyclic aromatic hydrocarbon emitted by the transportation source in year y, GDP GDP per capita (in purchasing power parity, in $1000 USD) in year y, is the slope for the ith polycyclic aromatic hydrocarbon species, representing the influence (sensitivity) of the change in GDP on the logarithm of the emission factor. is the intercept term, representing the logarithm emission factor value without any influence of GDP (theoretical initial state).

[0016] Preferably, in step S2, the construction of the emission inventory is based on energy activity level data and corresponding emission factors, using the following general emission calculation formula:

[0017] E =

[0018] where E is the total emission of polycyclic aromatic hydrocarbons (PAHs) in milligrams (mg); AC i,j is the consumption activity level of fuel i in department j, in tons (t); EF i,j is the corresponding emission factor, in mg / t.

[0019] Preferably, in step S3, the media in the multi-media environmental fate model include upper atmosphere, near-surface atmosphere, natural soil, agricultural soil, urban soil, freshwater, seawater, sediment, natural vegetation, and agricultural vegetation.

[0020] The physicochemical property parameters of polycyclic aromatic hydrocarbons input into the multi-media model include molecular weight, vapor pressure, water solubility, octanol / water partition coefficient, acid dissociation constant, and multi-media half-life.

[0021] The model simulates the interaction between grid cells by introducing a non-directional advection connection method.

[0022] Preferably, in step S4, the health risk assessment model combines the concentrations of pollutants in different media, comprehensively considers multiple population exposure pathways such as inhalation, ingestion, and skin contact, quantitatively assesses the health risk of the population, and uses the assessment results to draw a high-resolution grid-based health risk distribution map and identify high-risk exposure areas.

[0023] The beneficial effects of the present application are:

[0024] 1. The method provided by the present application can realize the quantitative evaluation of the synergistic emission reduction benefits of carbon dioxide and polycyclic aromatic hydrocarbons under different climate policy paths, the simulation of environmental medium concentrations, and the identification of population health risk priorities, breaking through the limitations of traditional methods in systematic evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0025] The above and additional aspects of the present application will become apparent and easy to understand from the following description of the embodiments, combined with the accompanying drawings.

[0026] Figure 1 is a schematic flow chart of a polycyclic aromatic hydrocarbon emission-migration-risk integrated assessment method according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application will be further described in detail below in conjunction with the drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0028] Embodiment 1

[0029] Referring to Figure 1 , the present embodiment provides a polycyclic aromatic hydrocarbon emission and environmental health assessment method based on carbon emission scenarios. The method is based on the GCAM-China climate change comprehensive assessment model, constructs multiple carbon emission scenarios, realizes high-precision construction of polycyclic aromatic hydrocarbon emission inventory under the double carbon background, and realizes multi-scenario comparison and evaluation. On this basis, a multi-medium environmental fate model at the regional scale is further constructed to simulate the migration and transformation behavior of polycyclic aromatic hydrocarbon species in air, water, soil and other environmental media. Subsequently, based on the polycyclic aromatic hydrocarbon concentration results in the multi-medium, a multi-path health risk assessment model is established to quantify the pollution control and health benefit synergistic emission reduction potential under different carbon emission paths. Including the following steps:

[0030] S1: Carbon emission scenario construction and future energy activity level data prediction

[0031] This step uses the GCAM-China model to construct four typical carbon emission scenarios under the assumption of shared social and economic path SSP2, i.e., setting a medium population growth, medium economic development and limited sustainability transformation social development background. Specifically, they include:

[0032] 1. Baseline scenario (Baseline): Assuming that the current policy intensity remains unchanged, the future energy system and emission path continues to follow the historical trend, and no new climate reduction policy is introduced.

[0033] 2. Carbon peak scenario (Peak): Set to achieve the peak of carbon dioxide emissions by 2030, but do not consider long-term carbon neutrality constraints, simulate the emission path of medium-term policy force.

[0034] 3. Carbon peak-carbon neutral scenario (Peak+Neutrality): Set to achieve carbon peak in 2030 and carbon neutral in 2060, introduce gradually strengthened energy structure adjustment and emission reduction measures in the scenario.

[0035] 4. Early Peak+Neutrality: The peak carbon emission time is further advanced to 2025, and the short-term emission reduction efforts are strengthened to explore the system response under more aggressive climate policy paths, while the carbon neutrality target remains unchanged.

[0036] Through the above multi-scenario setting, the GCAM-China model can simulate the energy system evolution process of 31 provincial administrative regions from 2020 to 2060, covering the whole process of energy supply-conversion-terminal consumption, and output the consumption of various energy types (coal, oil, natural gas, electricity, biomass, hydrogen, etc.), technology penetration path and corresponding carbon dioxide emission level, providing sufficient data support for subsequent pollutant emission inventory construction and coordinated control path analysis.

[0037] This embodiment aims at the key polycyclic aromatic hydrocarbon emission departments not covered in the GCAM-China model, such as coking production, waste disposal and biomass fuel utilization, etc. A scenario supplement module is constructed to improve the integrity and accuracy of the emission inventory construction. This module collects and organizes historical activity level data from authoritative data sources such as the National Bureau of Statistics, China Environmental Statistics Yearbook, Industry Yearbook and local statistical bulletins, combined with machine learning algorithms such as random forest and gradient boosting decision tree, to establish a regression prediction model. This model can use relevant variables in GCAM-China (such as energy consumption, industrial output value, population size, etc.) to dynamically predict the energy activity level of the above-mentioned departments under different carbon emission scenarios, thus effectively supplementing the missing department emissions in the model, and improving the integrity and accuracy of the emission inventory construction.

[0038] S2: Polycyclic aromatic hydrocarbon emission factors and emission inventory construction

[0039] In this step, a polycyclic aromatic hydrocarbon emission factor database is systematically integrated and constructed for different industry sectors (such as industry, transportation, residents and agriculture, etc.), different fuel types (such as coal, oil, natural gas and biomass) and different production and energy utilization technology paths. On this basis, the technology splitting method is used to refine and decompose various emission processes, and the regression model is used to calibrate and adapt the emission factors to ensure their dynamic availability under different carbon reduction paths, ensuring their applicability to future simulation needs under different carbon reduction paths.

[0040] The regression model uses the following general calculation formula:

[0041] GDP + ,

[0042] (mg / t) is the emission factor of the i-th polycyclic aromatic hydrocarbon emitted by the traffic source in year y, GDP is the per capita GDP in year y (in purchasing power parity, unit: $1000 USD), is the slope of the i-th polycyclic aromatic hydrocarbon species, representing the impact of GDP changes on the logarithm of the emission factor (sensitivity degree) intercept term, represents the logarithmic emission factor value without any GDP impact (in the theoretical initial state).

[0043] The emission inventory adopts a top-down construction method, taking the energy activity level data in step S1 under different carbon emission scenarios as the core driving factor, combined with the constructed polycyclic aromatic hydrocarbon emission factor, to systematically account for the annual emissions of 16 kinds of polycyclic aromatic hydrocarbons in each emission department. This method can realize the conversion of macro energy activity data into total polycyclic aromatic hydrocarbon emissions, taking into account the adaptability and overall calculation efficiency of the model. On this basis, this method further refines and expands five key emission processes in the steel industry, including iron ore sintering, open hearth steelmaking, converter steelmaking, electric arc furnace steelmaking, and hot rolling process, filling the gap in the coverage of polycyclic aromatic hydrocarbon emissions in high-carbon processes in traditional emission inventories.

[0044] Specifically, first, referring to the national statistical classification and the IPCC industry division standard, the energy consumption activities are divided into multiple refined departments (such as power generation, industrial boilers, transportation, residential burning, agricultural use, etc.), and further sub-classified according to energy varieties (coal, oil, natural gas, electricity, biomass, etc.) and combustion technology paths. For each "department-fuel-technology" combination, select the corresponding polycyclic aromatic hydrocarbon emission factor, and according to the following general calculation formula, calculate the annual polycyclic aromatic hydrocarbon emissions under different emission scenarios:

[0045] E =

[0046] Where E is the total emission of polycyclic aromatic hydrocarbons, unit: milligrams (mg); AC i,j is the consumption activity level of fuel i in department j, unit: t; EF i,j is the corresponding emission factor, unit: mg / t.

[0047] The PAHs list covers 16 priority-controlled PAH species, including naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benz[a]pyrene, dibenzo[a,h]anthracene, indeno[1,2,3-cd]pyrene, and benzo[g,h,i]perylene. The above PAHs exhibit different migration, persistence and toxicity characteristics in the environment, covering structural types from low-ring to high-ring compounds, with good representativeness and environmental indication significance.

[0048] The final multi-scenario emission inventory has the following characteristics: the inventory covers 16 preferred PAHs in multiple sectors at the provincial scale in China; supports the quantitative analysis of the synergistic benefits of carbon dioxide and PAH pollution reduction under different climate policy paths; and provides high spatial and temporal resolution emission input data for subsequent environmental fate modeling and health risk assessment.

[0049] S3: Multi-medium environmental fate simulation

[0050] Based on the multi-scenario PAH emission inventory constructed in step S2, a regional-scale multi-medium environmental fate model is developed and deployed. To achieve high-precision pollution migration and diffusion simulation, the target research area is divided into several spatial grid units, with a high spatial resolution grid division strategy of 0.5 km x 0.5 km, taking into account simulation accuracy and computational efficiency. Taking the representative strong carcinogen benz[a]pyrene as an indicative PAH species, its molecular weight, vapor pressure, water solubility, organic carbon distribution coefficient, half-life, and other physicochemical parameters are input. By coupling diffusion and migration models, deposition and volatilization models, soil adsorption / leaching models, and water dilution-deposition models, the dynamic migration and fate behavior of typical PAHs in various environmental media are simulated.

[0051] Table 1: Physicochemical parameters of BaP input into the model

[0052]

[0053] The fate model can achieve quantitative output of pollutant concentrations in time (annual scale) and space (grid scale) under different carbon emission scenarios, generate pollutant concentration spatiotemporal distribution maps covering the entire country / region, support identification of key pollution accumulation areas, pollution high-value migration paths, and their time lag and intensity changes in response to climate policy, and provide high-resolution pollution exposure background values for the next step of health risk assessment.

[0054] Table 2: Default environmental parameters in the multi-medium model

[0055]

[0056]

[0057] The degradation rate in different media with temperature change is calculated as follows:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064] wherein, represents a temperature correction coefficient of the reaction rate in air; represents a temperature correction coefficient of the reaction rate in water, sediment and soil; is the proportion of the chemical substance in the gas phase in air; is the proportion of the chemical substance in the water phase, including surface fresh water and seawater; is the activation energy of the OH radical reaction (6000 J / mol in the present application); is the multiple of the reaction rate per 10℃ increase (2 in the present application).

[0065] S4: Health risk assessment

[0066] Based on the multi-medium polycyclic aromatic hydrocarbon concentration simulation results obtained in step S3, a multi-medium-multi-exposure pathway population health risk assessment model is constructed. The model considers exposure media such as air (inhalation pathway / contact), water (ingestion / contact), soil (contact / inhalation), integrates the toxicological parameters (toxic equivalent factor TEF) of the pollutants, exposure dose factors (such as inhalation rate, body weight, contact time, etc.) and population characteristic parameters (such as life cycle exposure time, age structure, etc.), and uses the lifetime incremental carcinogenic risk calculation method to quantitatively estimate the comprehensive health hazards of polycyclic aromatic hydrocarbons in different media.

[0067] ​​​​​​Based on the polycyclic aromatic hydrocarbon concentration data in different media, the exposure dose of polycyclic aromatic hydrocarbons in different media is calculated, converted into BaPeq toxic equivalent concentration, and then the total ILCR value is obtained. The risk value result is output in the form of a spatial grid, realizing the generation of a grid health risk distribution map at the national or regional scale. This method can effectively identify high exposure risk areas and key population exposure areas, and support health risk trend analysis under multiple scenarios. Through comparative analysis of different carbon emission scenarios, the synergistic emission reduction benefits of air pollution control and population health improvement brought by the implementation of climate policy can be quantitatively evaluated, providing scientific support for multi-target synergistic governance strategies.

[0068] The general formula for calculating ILCR is:

[0069]

[0070] Among them,

[0071] : the concentration of pollutants in medium i (air: , water: , soil: );

[0072] : the intake rate of the corresponding medium (such as respiratory rate , drinking water , soil intake ); : exposure frequency (days / year);

[0073] : exposure period (years);

[0074] : carcinogenic slope factor, unit ;

[0075] : body weight ( );

[0076] : average life expectancy (days).

[0077] For the skin exposure path, the skin penetration coefficient , exposure surface area, contact time and soil adhesion factor and other parameters are further considered in the ILCR calculation. In addition, the present application sets different exposure parameters for different age groups (children, adults, and the elderly) to reflect the differences in physiological characteristics and exposure sensitivity of different populations. When the ILCR value is greater than 1×10 -6When an area is deemed to have an unacceptable carcinogenic risk, requiring focused intervention and risk control, spatial processing is employed to generate a high-resolution gridded health risk distribution map. This map is then coupled with population distribution data to calculate the proportion and spatial distribution of potentially high-risk individuals, providing scientific support for policy development.

[0078] Although the invention has been disclosed in detail with reference to the accompanying drawings, it should be understood that these descriptions are merely exemplary and not intended to limit the application of the invention. The scope of protection of the invention is defined by the appended claims and may include various modifications, alterations, and equivalents made to the invention without departing from the scope and spirit of the invention.

Claims

1. A method for polycyclic aromatic hydrocarbon emissions and environmental health assessment based on carbon emission scenarios, characterized in that, The method comprises the following steps: S1: based on the climate comprehensive assessment model GCAM-China, and for the emission sectors not covered in the climate comprehensive assessment model GCAM-China, collecting relevant historical data of the uncovered emission sectors, and combining the climate comprehensive assessment model GCAM-China output to build a machine learning prediction model, and building energy activity data under different scenarios; S2: based on different emission sectors, fuel types and technical paths, build polycyclic aromatic hydrocarbon emission factors, calibrate and optimize through technical decomposition and regression models, and combine the energy activity data under different scenarios in step S1, and use a top-down method to build polycyclic aromatic hydrocarbon emission inventories under multiple carbon emission scenarios; S3: based on the emission inventory built in step S2, build a regional scale multi-medium environmental fate model, divide the region into several spatial network units, use a high spatial resolution of 0.5 km x 0.5 km, select benz[a]pyrene as a representative species, input its physicochemical parameters, and dynamically simulate the multi-medium fate of polycyclic aromatic hydrocarbons at the regional scale, and output the pollutant concentration spatiotemporal distribution map under multiple scenarios; S4: based on the pollutant concentration spatiotemporal distribution map under multiple scenarios in step S3, combine the toxicology parameters to build a multi-medium-multiple-path health risk assessment model, and based on the lifetime incremental carcinogenic risk method to assess the exposure level of the population, build a high-resolution grid polycyclic aromatic hydrocarbon health risk distribution map, and identify key exposure areas to evaluate the synergistic emission reduction benefits of climate policy implementation on pollution control and health improvement.

2. The method for polycyclic aromatic hydrocarbon emission and environmental health assessment based on carbon emission scenarios according to claim 1, characterized in that, In step S1, the multiple carbon emission scenarios include a baseline scenario, a carbon peak scenario, a carbon peak-carbon neutral scenario, and a physical carbon peak-carbon neutral scenario; The multiple carbon emission scenarios are all set under the shared social and economic path SSP2, and can dynamically simulate the energy system structure, energy consumption level and carbon dioxide emissions under each scenario.

3. The method for PAHs emission and environmental health assessment based on carbon emission scenarios according to claim 2, characterized in that, The shared social and economic path SSP2 is a medium-speed social and economic development path, assuming that the population and income show a moderate growth trend, the climate comprehensive assessment model GCAM-China uses version v6, takes 2015 as the base year, runs to 2100 with a 5-year time step, and solves the market equilibrium state through supply and demand balance in each time step, and the model input includes population, GDP, energy price, consumption preference and technology adoption, etc. Social and economic driving factors.

4. The method for PAHs emission and environmental health assessment based on carbon emission scenarios according to claim 2, characterized in that, The baseline scenario is a no-policy intervention scenario; The carbon peak scenario is to implement carbon peak in 2030; The carbon peak-carbon neutral scenario is to implement carbon peak in 2030 and achieve carbon neutral in 2060; The advanced carbon peak-carbon neutral scenario is to implement carbon peak in 2025 and achieve carbon neutral in 2060.

5. The method for PAHs emission and environmental health assessment based on carbon emission scenarios according to claim 1, characterized in that, In step S1, the machine learning includes vector regression, random forest, gradient boosting decision tree and extreme gradient boosting tree algorithm; The prediction model is improved in performance by hyperparameter optimization and cross-validation to determine the coefficient R 2 And the mean square error MSE is the core evaluation index, and the optimal model is selected for prediction to improve the prediction accuracy.

6. The method for PAHs emission and environmental health assessment based on carbon emission scenarios according to claim 1, characterized in that, In step S2, five typical production process emission sources, including iron ore sintering, open hearth steelmaking, converter steelmaking, electric arc furnace steelmaking and hot rolling process, are added to the PAHs emission inventory to supplement typical PAHs emission sources in high carbon dioxide emission processes; The emission sources in the PAHs emission inventory include coal, oil, natural gas, biomass, solid waste and industrial processes, and the emission sources are divided into five sectors, namely energy production, industry, transportation, business / residents and agriculture; The PAHs include the following 16 priority pollutants: naphthalene, acenylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, chrysene, benzo[b]fluoranthene, benzo[k]fluoranthene, benz[a]pyrene, dibenzo[a,h]anthracene, indeno[1,2,3-cd]pyrene and benzo[g,h,i]perylene.

7. The method for PAHs emission and environmental health assessment based on carbon emission scenarios according to claim 1, characterized in that, In step S2, the regression model uses the following general calculation formula: GDP + ; where, is the emission factor of the i-th PAH species in the traffic source in year y, GDP is the per capita GDP in year y (in purchasing power parity, unit: $1000 USD), is the slope of the i-th PAH species, representing the influence of GDP change on the log of emission factor (sensitivity) intercept term represents the log emission factor value without any GDP influence (in the theoretical initial state).

8. The method for PAHs emission and environmental health assessment based on carbon emission scenarios according to claim 1, characterized in that, In step S2, the construction of the emission inventory is based on energy activity level data and corresponding emission factors, and the following general emission calculation formula is used: E = ； wherein E is the total emission of polycyclic aromatic hydrocarbons in milligrams (mg); AC i,j is the consumption activity level of fuel i in sector j in tons (t); EF i,j is the corresponding emission factor in mg / t.

9. The method for PAHs emission and environmental health assessment based on carbon emission scenarios according to claim 1, characterized in that, In step S3, the media in the multi-medium environmental fate model include upper atmosphere, near-surface atmosphere, natural soil, agricultural soil, urban soil, freshwater, seawater, sediment, natural vegetation and agricultural vegetation; The physicochemical property parameters of PAHs input into the multi-medium model include molecular weight, vapor pressure, water solubility, octanol / water partition coefficient, acid dissociation constant and multi-medium half-life; The multi-medium environmental fate model simulates the interaction between grid cells by introducing a non-directional advection connection method.

10. The method for polycyclic aromatic hydrocarbon emission and environmental health assessment based on carbon emission scenarios according to claim 1, characterized in that, In step S4, the health risk assessment model combines the concentrations of pollutants in different media, considers multiple population exposure pathways such as inhalation, ingestion and skin contact, quantitatively assesses population health risks, and the assessment results are used to draw high-resolution grid-based health risk distribution maps and identify high-risk exposure areas.

Images