A method for evaluating the relationship between fine particulate matter concentration and carbon emissions in the atmospheric environment

Abstract

The present invention discloses a method for evaluating the relationship between the concentration of fine particulate matter in the atmospheric environment and carbon emissions. First, the data observed by atmospheric pollutants are preprocessed; PM 2.5 Reconstruct chemical components; according to PM 2.5 Chemical component reconstruction results analysis PM 2.5 Chemical composition variation characteristics; compile an emission inventory of important local pollutant sources; use numerical simulation to distinguish PM from external transmission and local pollution sources 2.5 ; Get PM emissions per unit time from local pollution sources 2.5 Energy input data of each industry; Based on the energy input data obtained, the energy input data of each industry is multiplied by the energy CO2 emission coefficient of each industry to obtain the CO2 emissions of each industry; Establish the PM in the atmospheric environment 2.5 The relationship between PM and CO2 emissions. This method can effectively evaluate the PM in local atmospheric monitoring environments. 2.5 The relationship between concentration and CO2 emissions can reduce emission reduction costs and achieve coordinated emission reduction.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric environment monitoring, and in particular to a method for evaluating the relationship between fine particulate matter concentration and carbon emissions in an atmospheric environment. Background Art

[0002] my country's high proportion of fossil energy consumption and large volume are one of the main causes of air pollution and the main source of greenhouse gas emissions. 2.5 The relationship between concentration and CO2 emissions. In the context of pollution reduction and carbon reduction, coordinated emission reduction, a method is needed to establish the relationship between PM2.5 and CO2 emissions in the atmospheric environment. 2.5 The relationship between CO2 emissions.

[0003] In the prior art, there is a source-process-end collaborative emission reduction potential assessment system and method based on the pollution production and discharge process. The assessment system includes an emission reduction potential module, a carbon emission control module and a cost-effectiveness control module. The source-process-end collaborative emission reduction potential assessment is carried out based on the assessment system, and the emission reduction potential, economic benefits and carbon emissions of the entire source-process-end of the evaluated region and the evaluated industry are calculated respectively. However, the existing technology solution can only assess its emission reduction potential, and cannot well establish the relationship between atmospheric pollutants and CO2 emissions. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the relationship between fine particulate matter concentration and carbon emissions in the atmospheric environment, which can effectively evaluate the PM2.5 concentration in the local atmospheric monitoring environment. 2.5 The relationship between concentration and CO2 emissions can be used to calculate the PM2.5 content in the local atmospheric environment. 2.5 The reduction in CO2 emissions when the concentration is reduced by a certain amount, and the formulation of reasonable environmental control strategies.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A method for evaluating the relationship between fine particulate matter concentration and carbon emissions in an atmospheric environment, the method comprising:

[0007] Step 1: First, preprocess the data of atmospheric pollutant observations;

[0008] Step 2: Use offline filter membrane sampling to analyze the pre-processed data and analyze the PM 2.5 Reconstruct chemical components;

[0009] Step 3: PM according to step 2 2.5 Chemical component reconstruction results analysis PM 2.5 Chemical composition change characteristics, using mathematical statistics methods to analyze PM 2.5The changing trend of component content; specifically including analysis of water-soluble ions, carbon components, inorganic element pollution characteristics and correlation analysis;

[0010] Step 4: Identify local pollution sources, establish a classification system, use emission factors to calculate local pollutant emissions, and compile an emission inventory of important local pollutant sources;

[0011] Step 5: Use numerical simulation to distinguish PM from external transmission and local pollution sources 2.5 ;

[0012] Step 6: Use positive definite matrix factor analysis to factorize the PM 2.5 , OCEC conducts source analysis to obtain the contribution rates of different industries;

[0013] Step 7: Obtain PM emissions per unit time from local pollution sources 2.5 Energy input data of the industry; wherein the energy input data includes oil input and coal input;

[0014] Step 8: Based on the energy input data obtained in step 7, multiply the energy input data of each industry by the energy CO2 emission coefficient of each industry to obtain the CO2 emissions of each industry;

[0015] Step 9: Use statistical data and numerical simulation to verify the PM 2.5 The relationship between CO2 concentration and total CO2 emissions, CO2 concentration and PM 2.5 、NO X , SO2, and CO concentrations, and then establish the relationship between CO2 emissions and PM in the atmospheric environment. 2.5 、NO X , SO2, and CO concentrations.

[0016] It can be seen from the technical solution provided by the present invention that the above method can effectively evaluate the PM 2.5 The relationship between concentration and CO2 emissions can be used to calculate the PM2.5 content in the local atmospheric environment. 2.5 The reduction in CO2 emissions when the concentration is reduced by a certain amount, and the formulation of reasonable environmental control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flow chart of a method for evaluating the relationship between fine particulate matter concentration and carbon emissions in an atmospheric environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments, and do not constitute a limitation of the present invention. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] like Figure 1 FIG2 is a flow chart of a method for evaluating the relationship between fine particulate matter concentration and carbon emissions in an atmospheric environment according to an embodiment of the present invention. The method includes:

[0021] Step 1: First, preprocess the data of atmospheric pollutant observations;

[0022] In this step, the data preprocessing process includes: outlier processing and missing value filling for historical meteorological data, historical data on atmospheric pollutant concentrations, and historical data on emissions;

[0023] Among them, the 3σ rule was used to remove outliers, and the multiple interpolation (MICE) method was used to fill missing values.

[0024] Step 2: Use offline filter membrane sampling to analyze the pre-processed data and analyze the PM 2.5 Reconstruction of chemical components;

[0025] In this step, first determine the PM 2.5 The main chemical components of each PM 2.5 Reconstruction methods of components;

[0026] According to the existing PM 2.5 The conversion coefficient k1 between local primary organic carbon and secondary organic matter is calculated based on the emission inventory. The conversion coefficient k2 between secondary organic carbon and secondary organic matter is calculated based on the local VOCs emission inventory combined with the dual-product model inventory method.

[0027] According to each PM 2.5 Component reconstruction method and conversion coefficient calculation for each PM 2.5 The concentration of components and PM 2.5 Reconstruct the concentration and determine the reconstruction result.

[0028] Step 3: PM according to step 2 2.5 Chemical component reconstruction results analysis PM 2.5Chemical composition change characteristics, using mathematical statistics methods to analyze PM 2.5 Changing trend of component content;

[0029] Specifically, it includes analysis of pollution characteristics and correlation analysis of water-soluble ions (anions and cations), carbon components (OC, EC), and inorganic elements (heavy metals).

[0030] Step 4: Identify local pollution sources, establish a classification system, use emission factors to calculate local pollutant emissions, and compile an emission inventory of important local pollutant sources;

[0031] In this step, the main local pollutant emission sources are first divided into stationary combustion sources, process sources, mobile sources, solvent use sources, agricultural and animal husbandry sources, biomass combustion sources, storage and transportation sources, waste treatment sources and other emission sources;

[0032] Based on the characteristics of different emission sources, activity level data of various emission sources are collected. Specifically:

[0033] 1) Stationary combustion sources: These include thermal power plants, industrial combustion, and residential combustion. These sources are categorized into two levels based on local combustion type, using a top-down estimation approach. Activity data are derived from annual statistical data on pollutant emission registrations. This includes basic information such as the name and address of major local enterprises, their central latitude and longitude coordinates, combustion consumption, fuel sulfur content, installed capacity, power generation, and removal rates of desulfurization, denitrification, and dust control measures. Local coal, kerosene, fuel oil, liquefied petroleum gas, and natural gas usage are collected based on local statistical yearbooks and the China Energy Statistical Yearbook.

[0034] 2) Industrial Process Sources: Different industries emit varying types and intensities of atmospheric pollutants due to differences in raw material types and process technologies. The required activity data type is the product output of each process technology, primarily derived from two sources: first, the product output information of relevant enterprises from the annual statistical data on pollutant emissions registration; second, the product output information provided in the latest local statistical annual inspections and statistical bulletins.

[0035] 3) Mobile sources: The pollutant emission estimation formula for road mobile sources is as follows:

[0036] E i =∑P j ×M j ×EF i，j ×10 -3 Where i is the type of pollutant; j is the vehicle classification; P j is the number of motor vehicles of model j, vehicles; E i is the total annual pollutant emissions of motor vehicle i, t; M jis the average annual mileage of vehicle type j, km; EF i，j is the vehicle emission factor, g / (km·vehicle).

[0037] The data on motor vehicle ownership and the fuel ratio of each vehicle model can be referred to the data provided by the Vehicle Administration.

[0038] 4) Source of solvent use: The use of industrial solvents is mainly estimated based on the consumption of raw and auxiliary materials and the output of products. There are three main ways to obtain activity level data: one is the annual statistical data of pollutant discharge declaration and registration, the second is the information from the statistical yearbook and the statistical information website, and the third is the data survey of relevant industry reports; the use of architectural coatings is estimated based on the consumption of architectural coatings; the use of household solvents is estimated based on the population size, and the population data comes from the city statistical yearbook.

[0039] 5) Agricultural and pastoral sources: The pollutant emission process of agricultural and pastoral sources is closely related to the production activities of agriculture and animal husbandry. The main estimation is NH3 emissions from livestock and poultry farming and agricultural fertilization. The data can be referred to the local "Rural Statistical Yearbook".

[0040] 6) Biomass combustion sources: Estimated from a top-down basis based on biomass combustion volume, with data referenced from the China Energy Statistical Yearbook.

[0041] 7) Storage and transportation sources: Refer to the "Information Sheet for Finished Oil Wholesale Enterprises" and "Information Sheet for Finished Oil Retail Enterprises" provided by the local Economic and Information Bureau, and the data provided in the local energy balance sheet in the China Energy Statistical Yearbook.

[0042] 8) Other emission sources: Determine other local emission sources based on local industry types and human activities.

[0043] Then, the local emission factor is obtained by using the actual measurement method, literature research, and model estimation method. The emission factor estimation formula is:

[0044]

[0045] Where i and j are the types of pollutants (nitrogen, sulfur, heavy metals, CO, benzene, etc.) and emission source categories (waste treatment sources, catering pollution sources, etc.), respectively; E i is the total amount of pollutant emissions of type i, kg; A j is the activity level data of the jth emission source, kg; EF j is the emission factor, g / kg; η is the removal efficiency of the control measures;

[0046] Use emission factors to calculate pollutant emissions and compile an emission inventory of important local pollutant sources. For example, the atmospheric pollutant emissions are shown in the following table:

[0047]

[0048] Emissions of air pollutants (tons)

[0049] In specific implementation, the diffusion model can also be used to verify the weight factors of each pollutant in the local important pollutant source emission inventory and PM 2.5 Whether the weight factors of each pollutant in source apportionment are consistent.

[0050] Step 5: Use numerical simulation to distinguish PM from external transmission and local pollution sources 2.5 ;

[0051] In this step, first, localized information data is collected, including the compilation of regional files, original classification files, time distribution spectrum files, time distribution spectrum reference files, spatial distribution spectrum files, spatial distribution spectrum reference files, chemical species distribution spectrum reference files, and other auxiliary data files, and a SMOKE emission inventory processing model suitable for the local area is established. The established SMOKE emission inventory processing model is combined with the meteorological field output by the WRF model to convert the local important pollutant source emission inventory into the input format required by the air quality model;

[0052] Then, a Model-3 / CMAQ air quality model was built, and the results of the air quality model simulation were compared with the data monitored by external monitoring stations during typical periods. The air quality simulation was carried out by combining the SMOKE emission inventory processing model, WRF model and CMAQ air quality model, and the temporal and spatial distribution and industrial characteristics of major local pollutants were analyzed to distinguish PM2.5 from PM2.5 from PM2.5 from PM2.5 from PM2.5 2.5 .

[0053] For example, the SMOKE emission inventory processing model uses a high-performance sparse matrix algorithm to process the emission inventory into data with the spatiotemporal resolution required by the CMAQ air quality model;

[0054] The governing equations of the WRF model are as follows:

[0055] The model uses terrain-following coordinates in the vertical direction, which are defined as:

[0056] η=(p h -p hs ) / μ

[0057] where μ = p hs -p ht , ph represents the model layer pressure, p ht is the pressure at the top of the model layer, p hs Indicates the ground pressure; η ranges from 0 to 1, η = 1 is the upper boundary; η = 0 is the lower boundary;

[0058] μ(x, y) represents the mass of the entire atmosphere per unit area at any point, which varies with horizontal position;

[0059] Atmospheric variables in flux form are:

[0060]

[0061] Where v = (U, V, W) represents the horizontal and vertical velocities; η is the vertical velocity; θ is the potential temperature;

[0062] Using the variables defined above, we introduce three non-conserved variables Φ = gz (potential height), p (pressure), (inverse of density), the flux of the atmospheric control equations is derived from the Euler equations:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] The static equilibrium relationship is:

[0070]

[0071] The gas state equation is:

[0072] p=p0(R d θ / p0α) γ

[0073] Where γ = c p / c v =1.4, which is the ratio of the specific heat of dry air at constant pressure to the specific heat of dry air at constant volume; R d is the dry adiabatic lapse rate; p0 is the reference pressure, generally 10 5 Pascal; F u ,F,F w , F θ Represents the forcing terms due to the physical processes of the model, turbulence processes, spherical projection, and Earth rotation;

[0074] The specific gradient transport equation of the CMAQ air quality model is as follows:

[0075]

[0076] In the formula is the Jacobian determinant, used for coordinate transformation; is the pollutant concentration; is the horizontal divergence operator; are horizontal and vertical winds respectively; F qi and is the disturbance flux in the horizontal and vertical directions; is the mass mixing ratio change rate; is the rate of change of chemical reaction; is the source-sink term;

[0077] The meanings of each term in the equation are as follows: (a) is the rate of change of pollutant concentration; (b) is the horizontal advection process;

[0078] Item (c) is the vertical convection process; (d) is the error term, which ensures the conservation of the mixing ratio; (e) is the horizontal diffusion; (f) is the vertical diffusion; (g) is the non-diagnostic diffusion term, and the diffusion process belongs to sub-network-scale mixing; (h) is the chemical reaction or transformation process; (i) is the emission process; (j) is the aerosol process; (k) is the cloud mixing and liquid-phase chemical reaction process;

[0079]

[0080] where R aeroi is the growth and removal of ions; Q aeroi is the external source and sink term; is the inverse sedimentation velocity;

[0081]

[0082] The right formula is the ratio of unit volume emission rate to unit volume;

[0083] Cloud mixing liquid phase chemical reactions:

[0084]

[0085] The three items represent cloud, subgrid scale, and non-subgrid scale, respectively.

[0086] Step 6: Use the positive matrix factor analysis (PMF) to calculate the PM 2.5 , OCEC conducts source analysis to obtain the contribution rates of different industries;

[0087] In this step, the positive definite matrix factor analysis method PMF uses the least squares method to deal with the analysis factor problem. PMF constrains the factors to be non-negative and non-orthogonal, so that each factor has practical significance, that is:

[0088] X=GF+E

[0089]

[0090]

[0091] Where i = 1, 2…n; j = 1, 2…m; k = 1, 2…p, m is the number of chemical components; n is the number of samples; p is the number of major pollution sources; X is the concentration, which is an n×m matrix; G is the pollution source contribution, which is an n×p matrix; F is the source contour, which is a p×m matrix; E is the n×m residual matrix, which is the difference between the actual data and the analyzed results; sij is the standard deviation; Q(E) is the sum of the squares of the ratios of the residuals to the standard deviations of the observed data;

[0092] Among them, concentration X is the main input matrix, and the uncertainty of each data needs to be calculated. The uncertain data is also used as the main input data. The uncertainty formula is as follows:

[0093]

[0094] Where Unc represents uncertainty; MDL represents detection limit;

[0095] The pollution source contribution G and source contour line F matrices are the main output matrices. Since the PMF model imposes non-negative limits on each factor, all items in the G and F matrices are non-negative values.

[0096] Step 7: Obtain PM emissions per unit time from local pollution sources 2.5 Energy input data of the industry; energy input data includes oil input and coal input;

[0097] In this step, if energy input data is collected for a full year, the data is added to the last three months of the previous year. To account for the time delay required for energy to reach various sectors, the total energy input for the year is subtracted from the last three months. This includes electricity. Data on fossil energy consumption by industry can be found in the China Energy Statistical Yearbook.

[0098] Step 8: Based on the energy input data obtained in step 7, multiply the energy input data of each industry by the energy CO2 emission coefficient of each industry to obtain the CO2 emissions of each industry;

[0099] In this step, based on the China Energy Statistical Yearbook, we can obtain the consumption of various energy types by various industries, including coal, crude oil, natural gas, kerosene, gasoline, tar and electricity. At the same time, based on the carbon emission factors of various energy sources released by the IPCC (Intergovernmental Panel on Climate Change of the United Nations), combined with other parameters, we can calculate the CO2 emissions of various industries.

[0100] The energy CO2 emission coefficient of each industry is estimated as follows:

[0101] CO2 emission coefficient (kgCO2 / kg) = China's average lower calorific value of energy (kJ / kg) * IPCC carbon emission factor (kgC / GJ) * 10 6 *Carbon conversion rate*Carbon conversion coefficient;

[0102] Referring to the method recommended in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, the specific calculation formula for CO2 emissions generated by energy consumption is as follows:

[0103] CO2 emissions = (energy consumption * energy CO2 emission coefficient - energy carbon sequestration) * carbon oxidation coefficient * 3.67

[0104] Energy carbon sequestration refers to the amount of carbon that is fixed in the product as a raw material during the production process; 3.67 means that 1 ton of carbon can produce approximately 3.67 tons of CO2 after complete combustion; the carbon oxidation coefficient is always 1;

[0105] Summarize the calculated CO2 emissions of each industry to obtain the total CO2 emissions of these industries, and calculate the proportion of CO2 emissions of each industry in the total CO2 emissions to obtain the contribution rate of CO2 emissions of different industries;

[0106] Comparing the CO2 contribution rate and PM contribution rate of the same industry 2.5 Comparative analysis of contribution rates was conducted to verify the contribution rates of different industries to CO2 emissions and PM 2.5 Whether the weight relationship of contribution rate is consistent.

[0107] Step 9: Use statistical data and numerical simulation to verify the PM 2.5 The relationship between CO2 concentration and total CO2 emissions, CO2 concentration and PM 2.5 、NO X , SO2, and CO concentrations, and then establish the relationship between CO2 emissions and PM in the atmospheric environment. 2.5 、NO X , SO2, and CO concentrations.

[0108] It should be noted that the contents not described in detail in the embodiments of the present invention belong to the prior art known to those skilled in the art.

[0109] In summary, the method described in the embodiment of the present invention can effectively evaluate the PM 2.5 The relationship between concentration and CO2 emissions can be used to calculate the PM2.5 content in the local atmospheric environment. 2.5The reduction in CO2 emissions when the concentration is reduced by a certain amount, and the formulation of reasonable environmental control strategies to reduce emission reduction costs and achieve the goal of coordinated emission reduction.

[0110] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or any form of implication that the information constitutes prior art already known to those skilled in the art.

Claims

1. A method for evaluating the relationship between fine particulate matter concentration and carbon emissions in the atmospheric environment, characterized in that: The method comprises: Step 1: First, preprocess the data of atmospheric pollutant observations; Step 2: Use offline filter membrane sampling to analyze the pre-processed data and analyze the PM 2.5 Reconstruction of chemical components; Step 3: PM according to step 2 2.5 Chemical component reconstruction results analysis PM 2.5 Chemical composition change characteristics, using mathematical statistics methods to analyze PM 2.5 The changing trend of component content; specifically including analysis of water-soluble ions, carbon components, inorganic element pollution characteristics and correlation analysis; Step 4: Identify local pollution sources, establish a classification system, calculate local pollutant emissions using emission factors, and compile a local pollutant source emission inventory; Step 5: Use numerical simulation to distinguish PM from external transmission and local pollution sources 2.5 ; Step 6: Use positive definite matrix factor analysis to factorize the PM 2.5 , OCEC conducts source analysis to obtain the contribution rates of different industries; In step 6, the positive definite matrix factor analysis method PMF uses the least squares method to deal with the analysis factor problem. PMF constrains the factors to be non-negative and non-orthogonal, that is: Where i = 1, 2…n; j = 1, 2…m; k = 1, 2…p, m is the number of chemical components; n is the number of samples; p is the number of pollution sources; X is the concentration, which is an n×m matrix; G is the pollution source contribution, which is an n×p matrix; F is the source contour, which is a p×m matrix; E is the n×m residual matrix, which is the difference between the actual data and the analyzed result; Eij is the residual in the i-th row and j-th column; Xij is the element in the concentration matrix; Gik is the contribution of the k-th pollution source to the i-th sample in the pollution source contribution matrix; Fkj is the composition spectrum of the j-th pollutant in the k-th pollution source in the source contour matrix; sij is the standard deviation; Q(E) is the sum of the squares of the ratios of the residuals to the standard deviations of the observed data; Where concentration X is the input matrix, and the uncertainty of each data needs to be calculated. The uncertain data is used as input data. The uncertainty formula is as follows: Where Unc represents uncertainty; MDL represents detection limit; The pollution source contribution G and source contour line F matrices are output matrices. Since the PMF model imposes non-negative limits on each factor, all items in the G and F matrices are non-negative values; Step 7: Obtain PM emissions per unit time from local pollution sources 2.5 Energy input data of the industry; wherein the energy input data includes oil input and coal input; Step 8: Based on the energy input data obtained in step 7, multiply the energy input data of each industry by the energy CO2 emission coefficient of each industry to obtain the CO2 emissions of each industry; In step 8, the energy CO2 emission coefficients of each industry are estimated as follows: CO2 emission coefficient = China's average low calorific value of energy * IPCC carbon emission factor * 10 6 *Carbon conversion rate*Carbon conversion coefficient; The specific calculation formula for CO2 emissions generated by energy consumption is as follows: CO2 emissions = (energy consumption * energy CO2 emission coefficient - energy carbon sequestration) * carbon oxidation coefficient * 3.67 Energy carbon sequestration refers to the amount of carbon that is fixed in the product as a raw material during the production process; 3.67 means that 1 ton of carbon will produce approximately 3.67 tons of CO2 after complete combustion; the carbon oxidation coefficient is always 1; Summarize the calculated CO2 emissions of each industry to obtain the total CO2 emissions of these industries, and calculate the proportion of CO2 emissions of each industry in the total CO2 emissions to obtain the contribution rate of CO2 emissions of different industries; Comparing the CO2 contribution rate and PM contribution rate of the same industry 2.5 Comparative analysis of contribution rates was conducted to verify the contribution rates of different industries to CO2 emissions and PM 2.5 Whether the weight relationship of contribution rate is consistent; Step 9: Use statistical data and numerical simulation to verify the PM 2.5 The relationship between CO2 concentration and total CO2 emissions, CO2 concentration and PM 2.5 、NO X , SO2, and CO concentrations, and then establish the relationship between CO2 emissions and PM in the atmospheric environment. 2.5 、NO X , SO2, and CO concentrations.

2. The method for evaluating the relationship between fine particulate matter concentration and carbon emissions in the atmospheric environment according to claim 1, characterized in that: In step 1, the data preprocessing process includes: outlier processing and missing value filling for historical meteorological data, historical atmospheric pollutant concentration data, and historical emission data; Among them, the 3σ rule is used to remove outliers, and multiple interpolation is used to fill missing values.

3. The method for evaluating the relationship between fine particulate matter concentration and carbon emissions in the atmospheric environment according to claim 1, characterized in that: The process of step 2 is specifically as follows: First determine the PM 2.5 Chemical composition of each PM 2.5 Reconstruction methods of components; According to the existing PM 2.5 The conversion coefficient k1 between local primary organic carbon and secondary organic matter is calculated based on the emission inventory. The conversion coefficient k2 between secondary organic carbon and secondary organic matter is calculated based on the local VOCs emission inventory combined with the dual-product model inventory method. According to each PM 2.5 Component reconstruction method and conversion coefficient calculation for each PM 2.5 The concentration of components and PM 2.5 Reconstruct the concentration and determine the reconstruction result.

4. The method for evaluating the relationship between fine particulate matter concentration and carbon emissions in the atmospheric environment according to claim 1, characterized in that: The process of step 4 is specifically as follows: First, local pollutant emission sources are divided and activity data of each type of emission source is collected based on their characteristics; The local emission factor is obtained by using the actual measurement method, literature research, and model estimation method. The emission factor estimation formula is: Where i and j are the pollutant type and emission source category respectively; is the total amount of pollutant emissions of type i, kg; is the activity level data of the jth emission source, kg; is the emission factor, g / kg; η is the removal efficiency of the control measures; Use emission factors to calculate pollutant emissions and compile a local pollutant source emission inventory.

5. The method for evaluating the relationship between fine particulate matter concentration and carbon emissions in the atmospheric environment according to claim 1, characterized in that: The process of step 5 is specifically as follows: First, local information data is collected and a SMOKE emission inventory processing model suitable for the local area is established. The established SMOKE emission inventory processing model is combined with the meteorological field output by the WRF model to convert the local pollutant source emission inventory into the input format required by the air quality model; Then, a Model-3 / CMAQ air quality model was built, and the results of the air quality model simulation were compared with the data monitored by external monitoring stations during typical periods. The air quality simulation was carried out by combining the SMOKE emission inventory processing model, WRF model and CMAQ air quality model, and the temporal and spatial distribution and industrial characteristics of major local pollutants were analyzed to distinguish PM2.5 from PM2.5 from PM2.5 from PM2.5 from PM2.5 2.5 .

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