Air pollutant and greenhouse gas collaborative emission reduction prediction model construction method

Abstract

The invention discloses an atmospheric pollutant and greenhouse gas collaborative emission reduction prediction model construction method, and particularly relates to the technical field of emission reduction model construction. The method comprises the following steps: acquiring activity data and pollutant emission data of a plurality of emission sources, respectively calculating emission reduction amounts of various pollutants, and carrying out weighting processing on the emission reduction amounts and pollutant effect factors to obtain weighted collaborative emission reduction benefit values; according to the method, a cooperative benefit evaluation index system based on the benefit value is constructed, a multi-objective optimization model is established based on the system, the emission reduction effect and the implementation cost are comprehensively considered, the optimal emission reduction path is finally identified, and by means of the method, emission reduction prediction results and recommended implementation schemes under different strategy scenes can be output, a scientific decision basis is provided, and the method is suitable for popularization and application. And the scientificity and priority rationality of emission reduction measures are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of emission reduction model construction, and in particular to a method for constructing a prediction model for the coordinated reduction of emissions of atmospheric pollutants and greenhouse gases. Background Art

[0002] The construction of prediction model for coordinated emission reduction of atmospheric pollutants and greenhouse gases refers to the comprehensive consideration of atmospheric pollutants (such as PM 2.5 、NO x The model analyzes the sources, changing trends, and mutual influences of emissions of greenhouse gases (such as CO2 and CH4) and other greenhouse gases (such as SO2 and CO2), and predicts the synergistic emission reduction effects of different technical measures. The goal of the model is to assess how to effectively reduce environmental pollution and address climate change simultaneously when implementing certain emission reduction strategies, thereby providing a scientific basis for decision makers to achieve the dual goals of environmental protection and low-carbon development.

[0003] The existing technology has the following shortcomings:

[0004] In the evaluation of collaborative emission reduction benefits, PM 2.5 、NO x The unified measurement of different pollutants such as PM2.5 and SO2 in terms of emission reduction tons ignores their essential differences in health hazards, life cycle and spatial impact, which can easily lead to distorted evaluation results. For example, in the evaluation of a city’s emission reduction project, due to the 2.5 The emission reduction is less than that of CO2, and the synergy benefit score is too low and it is not adopted, but in fact PM 2.5 It has a significant impact on local public health, but it is the object that should be controlled first, which ultimately leads to the wrong exclusion of key local emission reduction strategies. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for constructing a prediction model for the coordinated reduction of atmospheric pollutants and greenhouse gases to address the shortcomings of the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing a prediction model for the coordinated reduction of atmospheric pollutants and greenhouse gases, comprising:

[0007] Obtain basic activity data and pollutant emission data for multiple emission sources, including atmospheric pollutants and greenhouse gases;

[0008] Calculate emission reductions for each type of pollutant based on the pollutant type;

[0009] Introducing pollutant health hazard weights, life cycle impact factors, and spatial exposure impact parameters to construct a pollutant effect factor library;

[0010] Weighting the emission reduction amount and the corresponding pollutant effect factor to obtain a weighted synergistic emission reduction benefit value;

[0011] Constructing a synergistic benefit evaluation index system based on the weighted synergistic emission reduction benefit value;

[0012] Based on the evaluation of synergistic benefits, a multi-objective optimization model is established to comprehensively consider the emission reduction effect and implementation cost, and identify the optimal synergistic emission reduction path;

[0013] Output emission reduction forecast results under different strategic scenarios and recommend priority implementation plans.

[0014] Preferably, the acquisition of basic activity data and pollutant emission data of multiple emission sources includes: multiple emission sources including fixed sources, mobile sources and surface sources / scattered sources; collecting activity data of various emission sources, including fuel consumption, vehicle traffic, livestock population and agricultural production data; using the activity data × emission factor method to calculate the emission of each pollutant.

[0015] Preferably, the emission reductions of various pollutants are calculated based on the types of pollutants, including: clarifying the emission changes before and after the implementation of each emission reduction measure, establishing an emission reduction comparison analysis model, calculating the emissions before and after the emission reduction measures are taken and the emissions after the emission reduction measures are implemented; using standard emission factors related to the pollutant types, and combining them with activity data, calculating the pollutant emission reductions of each measure, and obtaining pollutant emission reduction data under different source types.

[0016] Preferably, the method for obtaining the health hazard weight is:

[0017] The health impact indicators are quantified into a unified evaluation scale, including: mortality risk increment corresponding to unit concentration; DALY value corresponding to unit pollutant; exposure-response function slope; the normalized indicators are used as the health hazard weight of the pollutant. The specific calculation expression of the health hazard weight is: Where H i is the health hazard weight of the i-th pollutant; D i To express the quantitative value of the impact of unit concentration or unit emission of pollutants on human health, D min The smallest health impact value among all pollutants; D max It has the highest health impact value among all pollutants.

[0018] Preferably, the method for obtaining the life cycle impact factor is as follows: each pollutant is assigned a normalized life cycle impact factor, and the life cycle impact factor L i The calculation expression is: Where: L i is the life cycle impact factor of the i-th pollutant; GWP iGWP is the relative ratio of the contribution of unit mass of pollutants to global warming over a time scale to CO2. min 、GWP max are the minimum and maximum GWP values of the sample pollutants.

[0019] Preferably, the spatial exposure impact parameter is obtained by running the CMAQ model to simulate pollutant concentrations; inputting emission, meteorological, and topographic data; setting a simulation time period and outputting gridded pollutant ground concentration data; and calculating the population-weighted exposure concentration for each grid as the spatial exposure impact parameter: Where: E i is the spatial exposure impact parameter of the i-th grid unit; C i is the ground concentration of the pollutant in the grid; Pop i is the population corresponding to the grid; n is the total number of grids in the area.

[0020] Preferably, the health hazard weight, life cycle impact factor and spatial exposure impact parameter are uniformly converted into values within the range of 0–1 to construct the composite effect factor EI i , the expression is: EI i =w h ·H i +w l ·L i +w e ·E i Where w h 、w l 、w e are the weights of the three dimensions.

[0021] Preferably, the emission reduction and pollutant effect factors are collected: emission reduction ΔE i For each pollutant, the emission reduction amount under each emission reduction measure is obtained by calculating the activity data and emission factors; ΔE i is the emission reduction of the i-th pollutant; the emission reduction of each pollutant is weighted by the effect factor to calculate the weighted synergistic emission reduction benefit value: Where: S is the weighted synergistic emission reduction benefit value, which represents the overall emission reduction benefit under a certain emission reduction strategy; m is the total number of pollutant types;

[0022] The obtained weighted synergistic emission reduction benefit value is compared with the preset threshold. If the weighted synergistic emission reduction benefit value is greater than or equal to the preset threshold, it means that the strategy can effectively reduce the environmental burden and health hazards of pollutants and should be implemented as a priority. If the weighted synergistic emission reduction benefit value is less than the preset threshold, it is necessary to optimize emission reduction measures or change the strategy priority.

[0023] Preferably, a synergistic benefit evaluation index system is constructed based on the weighted synergistic emission reduction benefit value. The index system covers: environmental benefits, health benefits, climate benefits and economic benefits, and a comprehensive score is given to different benefit dimensions through weighting factors.

[0024] Preferably, a multi-objective optimization model is established to comprehensively consider the emission reduction effect and implementation cost, and identify the optimal coordinated emission reduction path, including:

[0025] Establish a multi-objective optimization model and identify the optimal emission reduction path through model calculation;

[0026] The model includes two objective functions: maximizing emission reduction benefits and minimizing implementation costs;

[0027] Set relevant constraints;

[0028] Use multi-objective optimization algorithms to search and optimize solutions;

[0029] Based on the results of the multi-objective optimization model, output emission reduction prediction results under different strategy scenarios;

[0030] Based on different scenarios, priority implementation plans are recommended.

[0031] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0032] 1. The present invention constructs a pollutant effect factor library by introducing health hazard weights, life cycle impact factors and spatial exposure impact parameters, ensuring that the multi-dimensional impacts of different pollutants on health, environment and climate can be considered in the collaborative emission reduction benefit assessment. By weighting the emission reduction amount and the pollutant effect factor, a weighted collaborative emission reduction benefit value is obtained, and a collaborative benefit evaluation index system is constructed based on this value. In addition, through a multi-objective optimization model, the emission reduction effect and implementation cost are comprehensively considered, and the optimal emission reduction path can be identified, providing decision makers with scientific emission reduction plans, and outputting emission reduction forecast results and recommended implementation plans under different strategic scenarios, thereby improving the accuracy and operability of emission reduction decisions.

[0033] 2. This invention overcomes the problem of existing methods neglecting differences in pollutant hazards through meticulous pollutant classification, precise calculation of emission reductions, comprehensive effect factor introduction, and multi-dimensional synergistic benefit assessment. Ultimately, through a multi-objective optimization model based on weighted synergistic emission reduction benefit values, it provides a practical and applicable emission reduction path selection method, effectively achieving rational resource allocation and optimizing emission reduction targets, promoting the implementation of environmental protection and sustainable development, and achieving significant environmental, health, and socioeconomic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0035] Figure 1 This is a mind map of the method of the present invention. DETAILED DESCRIPTION

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0037] For examples, see Figure 1 As shown, the method for constructing a prediction model for coordinated reduction of atmospheric pollutants and greenhouse gases described in this embodiment includes:

[0038] Obtain basic activity data and pollutant emission data for multiple emission sources, including atmospheric pollutants and greenhouse gases;

[0039] Calculate emission reductions for each type of pollutant based on the pollutant type;

[0040] Introducing pollutant health hazard weights, life cycle impact factors, and spatial exposure impact parameters to construct a pollutant effect factor library;

[0041] Weighting the emission reduction amount and the corresponding pollutant effect factor to obtain a weighted synergistic emission reduction benefit value;

[0042] Constructing a synergistic benefit evaluation index system based on the weighted synergistic emission reduction benefit value;

[0043] Based on the evaluation of synergistic benefits, a multi-objective optimization model is established to comprehensively consider the emission reduction effect and implementation cost, and identify the optimal synergistic emission reduction path;

[0044] Output emission reduction forecast results under different strategic scenarios and recommend priority implementation plans.

[0045] In order to accurately capture the sources of pollutants and greenhouse gases, emission sources need to be carefully classified, mainly including the following three categories: stationary sources: industrial boilers, thermal power plants, steel, cement, chemical companies, etc.; building heating / cooling systems; solid waste incineration facilities.

[0046] Mobile sources: Road traffic: private cars, buses, freight trucks, etc.; non-road traffic: construction machinery, agricultural machinery, ships, aircraft, etc.; area sources: coal and wood used for heating and cooking; agricultural non-point sources (such as ammonia and methane emissions); construction dust, road dust, etc.

[0047] Industrial and energy activity data: Annual consumption of various fuels (coal, oil, natural gas, biomass, etc.); industrial output (e.g., tons of steel, tons of cement, chemical production); various process routes and rates of wastewater treatment and emission control equipment. Data sources: enterprise reporting system, pollution source census; monthly reports from power and heat operators.

[0048] Traffic and mobile activity data: Vehicle ownership and traffic volume by vehicle type; average annual mileage, vehicle fuel types; engine emission standards; and electric vehicle penetration rate. Data sources: Ministry of Transport, Public Security Vehicle Management System; urban traffic surveys, road traffic monitoring; and statistics from oil product sales units.

[0049] Agricultural and domestic activity data: livestock population (cattle, pigs, sheep, etc.) and feeding methods; fertilizer use and crop planting area; number and quantity of households using coal and firewood; residential heating types and coverage rates. Data sources: Ministry of Agriculture and Rural Affairs Statistical Yearbook; Urban and Rural Residential Energy Survey; third-party survey questionnaires or remote sensing analysis.

[0050] Types of pollutants covered: Air pollutants: PM 2.5 , SO2, NO x , CO, VOCs, NH3, etc. Greenhouse gases: CO2, CH4, N2O, and in some scenarios, high greenhouse effect gases such as SF6 and HFCs are also considered. Emissions calculation method: using the general "activity data × emission factor" method: E i,j =A j ×EF i,j ;E i,j A is the emission of the i-th pollutant from the j-th source; j is the activity level of the source (e.g. fuel consumption, vehicle kilometres); EF i,j The corresponding pollutant emission factor for this source type. Emission factor sources include standard data sources: IPCC Guidelines (International Greenhouse Gas Protocol); national / local pollutant emission factor manuals; measured correction data: actual enterprise monitoring data, CEMS system; scientific research projects, third-party survey sampling results; and adaptation and correction of time, space, and process characteristic sub-factors.

[0051] Spatial accuracy: divided by city, county, street or grid (such as 1×1km); use GIS system for data mapping and distribution estimation.

[0052] Time accuracy: Based on the annual basis, monthly or daily adjustments are made based on seasonal activities (heating season, busy farming season); mobile sources can be corrected using hourly traffic pattern data.

[0053] Fill in missing data (interpolation, simulation); handle extreme values or abnormal data (IQR method, empirical rules); conduct Monte Carlo simulation analysis on the variability of emission factors and activity data and provide confidence intervals.

[0054] The output is in the standard emission inventory format: a four-dimensional structure based on pollutants / industries / regions / time; supports the import of prediction models such as LEAP, MARKAL, and use in simulators; and can be seamlessly integrated with the collaborative emission reduction optimization module.

[0055] Types of pollutants: including but not limited to the following two categories: Air pollutants: Particulate matter (PM 2.5 、PM 10 ), nitrogen oxides (NO x ), sulfur dioxide (SO2), carbon monoxide (CO), volatile organic compounds (VOCs) and ammonia (NH3);

[0056] Greenhouse gases: carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O).

[0057] Typical emission reduction scenarios: such as coal substitution, motor vehicle restrictions, green building promotion, and farmland fertilizer control.

[0058] The calculation of emission reduction is based on comparative analysis, namely: ΔE i =E i,基准情景 -E i,措施情景 Where, ΔE i is the emission reduction of the i-th pollutant; E i,基准情景 Indicates the emissions when no emission reduction measures are taken (Baseline); E i,措施情景 Indicates the emissions after the implementation of emission reduction measures (WithMeasures).

[0059] Use the aforementioned activity data × emission factor method to calculate the current emission levels of each pollutant; using a certain time point as the base year, establish a complete baseline emission table.

[0060] Identify the impact of technological or behavioral changes on activity data; for example: replacing coal-fired boilers with electric boilers, natural gas substitution, emission standard upgrades, vehicle upgrades, etc.

[0061] After the measures are implemented, activity data are updated (e.g., coal use decreases, share of electric vehicles increases); emission factors are selected to match the new activity levels; and the total emissions of each pollutant are recalculated.

[0062] Calculate the difference based on the type of pollutant to obtain separate emission reduction data; it can be broken down by industry, region, time and other dimensions.

[0063] Dealing with heterogeneity between pollutants: Different pollutants have different units and hazards, and the calculation of emission reductions should unify the dimensions but not confuse the essence; emission reductions should be calculated for each pollutant separately and should not be directly summed up as the total emission reduction; if a synergistic benefit indicator is required later, a hazard weighting factor should be introduced (see the above content).

[0064] Certain measures (such as high-efficiency combustion and SCR denitrification) will affect emission factors and should be adjusted dynamically. For example, due to vehicle aging and the promotion of new energy vehicles, emissions decrease nonlinearly over time; this can be achieved through periodic simulation (annual or quarterly) and step-by-step calculations. Certain measures may lead to transfer effects (such as increased NH3 escape from desulfurization), necessitating a pollutant-linked assessment.

[0065] To scientifically optimize strategies for the coordinated reduction of atmospheric pollutants and greenhouse gases, model construction requires comprehensive consideration of the actual impacts of various pollutants on health, the environment, and spatial exposure, rather than solely relying on tonnage reductions as the evaluation basis. To this end, this method incorporates multidimensional indicators to construct a pollutant effect factor library to ensure fairness and accuracy in the assessment.

[0066] First, health hazard weights are used to measure the direct impact of pollutants on human health. Different pollutants have different effects on the respiratory system, cardiovascular system, and disease risk. For example, PM 2.5 Widely considered one of the most harmful air pollutants, long-term exposure is closely linked to asthma, stroke, and heart disease. Health weights are derived from authoritative epidemiological research, disease burden data (such as daily average lifetime deaths per year), and incremental mortality risk, assigning corresponding impact scores to different pollutants.

[0067] The method to obtain health hazard weights is to determine the type of pollutants to be assessed (such as PM 2.5 、NO x , SO2, CO2, CH4, etc.), covering two major categories: atmospheric pollutants and greenhouse gases. Data on the impact of various pollutants on human health are collected, with priority given to the following authoritative sources:

[0068] Epidemiological data released by the National Center for Disease Control; disease risk coefficients or mortality rate changes per unit concentration of exposure given in academic literature.

[0069] Quantify health impact indicators into a unified evaluation scale. Commonly used indicators include:

[0070] The mortality risk increment (RR) corresponding to unit concentration;

[0071] DALY value corresponding to unit pollutant (years of health loss caused by exposure per ton or per 10,000 people);

[0072] Exposure-response function slope (e.g., μg / m 3 →% health impact).

[0073] To facilitate comparison among multiple pollutants, all indicators are standardized to the interval [0,1]:

[0074] Use Min-Max normalization or Z-score normalization;

[0075] The normalized values of high-risk pollutants are close to 1, and those of low-risk pollutants are close to 0.

[0076] The normalized indicators are used as the health weights of pollutants and stored in the database as the basis for subsequent weighted calculation of synergistic effect evaluation. The specific calculation expression of health hazard weight is: Where H i is the health hazard weight of the i-th pollutant (after standardization, the value is between 0 and 1); D i To express the quantitative value of the impact of pollutants per unit concentration or unit emission on human health, common forms include: DALY caused by each ton of pollutants emitted; 3 Percentage of increased mortality; slope of the exposure-response relationship curve; D min The smallest health impact value among all pollutants; D max It has the highest health impact value among all pollutants.

[0077] The health hazard weight represents the relative strength of a pollutant's health impact among all pollutants. A higher value indicates a more severe health hazard and should be given a higher priority in coordinated emission reduction efforts.

[0078] For example, PM 2.5 The health impact value of CO2 (such as the number of DALYs caused by emissions per ton) is much higher than that of CO2 or CH4. Therefore, through the above calculation, its normalized weight is close to 1. Although CO2 has climate impacts, it has no obvious impact on direct human health, and its health hazard weight may be less than 0.1.

[0079] Secondly, the life cycle impact factor reflects the impact of pollutants on climate change or ecosystems during their life cycle. Among greenhouse gases, carbon dioxide has long-term residence characteristics. Although its global warming potential is relatively low, it has a significant climate impact due to its large emissions. Nitrous oxide and methane, although their emissions are relatively small, have a stronger greenhouse effect per unit mass and therefore need to be given high weight in the comprehensive evaluation. The life cycle impact factor is mainly derived from indicators such as the international LCA database and the global warming potential (GWP) published by the IPCC.

[0080] Select greenhouse gases to be assessed (such as CO2, CH4, N2O) and some atmospheric pollutants with life cycle environmental impacts (such as SO2, VOCs, NO x ) as the evaluation object.

[0081] Mainly based on the Life Cycle Assessment (LCA) database or authoritative reports, collect the full-cycle impact values of unit pollutant emissions on the environment, including:

[0082] Global Warming Potential (GWP) provided by the IPCC;

[0083] Neutralization period (residence time);

[0084] Potential impacts on ecosystem toxicity, acidification, eutrophication, etc.

[0085] The Global Warming Potential (GWP) is preferred as the main reference indicator because it covers a clear time scale, has mature data and is highly applicable, especially for greenhouse gases. Other pollutants (such as SO2, NO x ) can be supplemented by indicators such as acidification potential (AP) and photochemical ozone formation potential (POCP).

[0086] Normalize the obtained life cycle impact indicators to the range [0, 1] to ensure comparability across pollutants. This can be done using normalization techniques such as range normalization or Z-score.

[0087] Each pollutant is assigned a normalized life cycle impact factor for subsequent synergistic benefit weighted calculation and multi-objective optimization analysis.

[0088] Life cycle impact factor L i The calculation expression is as follows: Where: L i is the life cycle impact factor of the i-th pollutant; GWP iIt indicates the relative ratio of the contribution of a unit mass of pollutants to global warming over a certain time scale (usually 100 years) to CO2. For example: GWP of CO2 = 1 (baseline); GWP of CH4 ≈ 28–34; GWP of N2O ≈ 265–298; GWP min 、GWP max The minimum and maximum GWP values of the sample pollutants are used for normalization.

[0089] L i It represents the life cycle impact level of the i-th pollutant among all assessed pollutants. The higher the value, the greater the life cycle impact, and the model should give it a higher synergistic weight.

[0090] For example, when constructing a collaborative evaluation model for emission reduction in a certain region, although CH4 emissions are much lower than CO2, its life cycle impact factor (after normalization) can reach above 0.9, while that of CO2 is only 0.1-0.2. This makes methane emission reduction have higher environmental benefits in the selection of collaborative paths, and its priority is significantly improved.

[0091] Spatial exposure impact parameters take into account the retention capacity and exposure level differences of pollutants in densely populated areas after release. The population density, meteorological conditions, and topographic structure (such as urban street canyon effect) of different regions will affect the concentration distribution of pollutants in local space and the degree of human exposure. For example, NO x High concentration areas are formed in traffic-intensive areas, increasing the health threat to sensitive groups such as children and the elderly.

[0092] The method for obtaining spatial exposure impact parameters is as follows: pollutant emission inventory (emission source data) includes CO2, NO x 、PM 2.5 , SO2, etc.; derived from national or local emission inventories (such as MEIC), including source categories, spatial locations, temporal resolution, etc. Meteorological data are generated using the WRF model to ensure the temporal continuity and spatial accuracy of parameters such as wind speed, temperature, and boundary layer height; the resolution is within 3 to 9 km. Topography and land use data are used to simulate pollutant diffusion, deposition, and photochemical reaction processes; they are usually derived from USGS or MODIS. Population distribution grid data has an accuracy of 1 km × 1 km or higher; the data source can be WorldPop, LandScan, or national statistical grid data.

[0093] Run the CMAQ model to simulate pollutant concentrations;

[0094] Input emission, meteorological, and topographic data;

[0095] Set the simulation time period (recommended to be more than 30 days to cover the typical pollution period);

[0096] Output gridded pollutant ground concentration data (unit: μg / m 3 ), stored in hourly or daily average format.

[0097] For each grid, the population-weighted exposure concentration is calculated as the spatial exposure impact parameter: Where: E i is the spatial exposure impact parameter of the i-th grid unit; C i is the ground concentration of the pollutant in the grid (derived from CMAQ simulation); Pop i is the population corresponding to the grid; n is the total number of grids in the area; P i The values are normalized to the range of [0,1] to facilitate weight integration with other pollutant factors; high values indicate that the retention and exposure intensity of pollutants in the grid are high, which is suitable for key control.

[0098] For a specific city, a CMAQ simulation generated a NO2 concentration distribution map, which was then overlaid with a high-resolution population distribution grid to calculate weighted exposure values within each grid. The GWR model was then used to analyze the sensitivity of variables such as population density, wind speed, and traffic density to NO2 exposure. This generated an exposure factor layer, which was then used as a spatial exposure parameter in the collaborative emission reduction model.

[0099] Health hazard weights, life cycle impact factors, and spatial exposure impact parameters were uniformly converted to values within the range of 0–1; Min-Max normalization or entropy weight method was used for processing.

[0100] Constructing compound effect factor EI i , the expression is: EI i =w h ·H i +w l ·L i +w e ·E i ;w h 、w l 、w e are the weights of the three dimensions (which can be determined by AHP or Delphi method).

[0101] The health hazard weights, life cycle impact factors, spatial exposure impact parameters and composite effect factors corresponding to different pollutants are organized into corresponding databases.

[0102] Collect emission reductions and pollutant effect factors:

[0103] Emission reduction ΔE i : For each pollutant, the emission reduction under each emission reduction measure is calculated by activity data and emission factors; ΔE iis the emission reduction of the i-th pollutant (unit: ton); pollutant effect factor EI i ; represents the comprehensive assessment value of the environmental and health effects of each pollutant.

[0104] The emission reduction of each pollutant is weighted by the effect factor to calculate the weighted synergistic emission reduction benefit value: Where: S is the weighted synergistic emission reduction benefit value, which represents the overall emission reduction benefit under a certain emission reduction strategy (unit: dimensionless or harmonized unit, which can be set according to specific goals), and m is the total number of pollutant types.

[0105] The obtained weighted synergistic emission reduction benefit value is compared with the preset threshold. If the weighted synergistic emission reduction benefit value is greater than or equal to the preset threshold, it means that the strategy can effectively reduce the environmental burden and health hazards of pollutants and is suitable for priority implementation; if the weighted synergistic emission reduction benefit value is less than the preset threshold, it is necessary to optimize emission reduction measures or change the strategy priority.

[0106] The synergy benefit evaluation index system should include the following key dimensions:

[0107] Environmental benefits: measure the effect of emission reduction measures on improving air quality, reducing pollutant concentrations and the impact on the ecological environment. For example, reducing PM 2.5 , SO2 and other pollutants on the environment.

[0108] Health benefits: measures the effect of emission reduction measures on improving the health of the population. For example, reducing PM 2.5 Emissions can reduce pollution-related health risks such as respiratory diseases and cardiovascular diseases.

[0109] Climate benefits: This measures the contribution of mitigation measures to mitigating climate change and reducing greenhouse gas emissions. For example, it measures the impact of reducing greenhouse gas emissions such as CO2 and methane on the global warming potential.

[0110] Economic benefits: measure the impact of emission reduction measures on the social economy, considering the cost-benefit ratio of emission reduction, such as reduced medical expenses and increased productivity.

[0111] Based on the weighted synergistic emission reduction benefit value (S) calculated above, it is allocated to different benefit dimensions to form a single score for each dimension. This step ensures that the contribution of different emission reduction measures in each benefit dimension can be independently evaluated.

[0112] Environmental Benefit Score SE env : Among them, EI env,i It is the environmental effect factor of the i-th pollutant, which represents the comprehensive impact of the pollutant on the environment.

[0113] Health benefit score SE health : Among them, EI health,i is the health effect factor of the i-th pollutant, indicating the harm of the pollutant to human health.

[0114] Climate Benefit Score SE climate : Among them, EI climate,i is the climate effect factor of the i-th pollutant, which represents the impact of greenhouse gases on climate change.

[0115] Economic benefit score SE eco : Among them, EI eco,i is the economic effect factor of the i-th pollutant, which represents the economic returns of emission reduction measures, such as reduced public health costs and increased productivity.

[0116] After evaluating the individual scores of each dimension, the scores are weighted to obtain the overall synergy benefit value Stotal. The weighting coefficient should be determined based on the priority or importance of factors such as environment, health, and climate.

[0117] Stotal is the final evaluation value obtained by weighting the scores of all benefit dimensions, representing the comprehensive benefits of a particular emission reduction measure or strategy. The higher the value, the better the overall effectiveness of the strategy.

[0118] For example, suppose there are two emission reduction measures:

[0119] Measure A: Focus on reducing PM 2.5 and SO2 emissions, improving health and environmental benefits.

[0120] Measure B: Mainly reduce CO2 emissions, focusing on climate benefits and long-term economic benefits.

[0121] The above method is used to calculate the comprehensive synergistic benefit value of the two measures, and finally the Stotal value is compared to help decision makers choose the best emission reduction strategy.

[0122] The goal of the multi-objective optimization model is to optimize multiple decision variables, taking into account both emission reduction benefits and costs, and forming a comprehensive optimal emission reduction path. Specific goals include:

[0123] Maximize emission reduction benefits: While ensuring emission reduction targets, maximize the emission reduction effects of various pollutants, especially the comprehensive improvements to health, the environment, and the climate.

[0124] Minimize implementation costs: Consider the implementation costs of emission reduction measures (including initial investment, operating costs, technology promotion, etc.) to ensure that the emission reduction path is achieved within the budget.

[0125] Balancing environmental and economic goals: Find a balance between environmental and economic benefits, and avoid over-reliance on one goal.

[0126] Decision variables are the core of the optimization problem and determine the choice of emission reduction path. Common decision variables include:

[0127] x j : The input amount or implementation degree of the j-th emission reduction measure (for example, equipment investment amount, subsidy coverage rate).

[0128] h: The number of all possible emission reduction measures.

[0129] The multi-objective optimization model has multiple objective functions. The two main objectives are maximizing emission reduction benefits and minimizing costs. Its objective function can be expressed as:

[0130] Goal of maximizing emission reduction benefits:

[0131] Cost minimization goal: Where: C j is the unit implementation cost of the jth emission reduction measure (such as the cost per ton of emission reduction).

[0132] The optimization model must meet certain constraints, which ensure the feasibility and reality of the optimization results.

[0133] The total cost of implementing emission reduction measures cannot exceed the budget limit Btotal;

[0134] Emission reduction target constraints: Certain emission reduction targets need to be achieved within a set time period to ensure that environmental and health benefits are achieved.

[0135] Implementation measure constraints: The implementation amount or investment amount of each emission reduction measure cannot exceed its practical feasible range.

[0136] Time constraints: All measures must be implemented within the specified time to meet the time requirements of the emission reduction plan.

[0137] Since multiple objectives are involved, optimization models are generally solved using multi-objective optimization algorithms. Common algorithms include:

[0138] Genetic Algorithm (GA): It searches for the optimal solution set by simulating the natural selection process and is suitable for solving complex multi-objective optimization problems.

[0139] Particle Swarm Optimization (PSO): It finds the global optimal solution in the solution space by simulating the movement process of a particle swarm.

[0140] Non-dominated sorting genetic algorithm II (NSGA-II): widely used in multi-objective optimization problems, especially suitable for solving nonlinear, multi-objective emission reduction optimization problems.

[0141] Pareto optimization: Convert the objective function into a Pareto frontier and generate multiple optimal solution sets to demonstrate the compromise results under different objectives.

[0142] By using a multi-objective optimization algorithm (such as NSGA-II), multiple Pareto-optimal solutions are obtained. These solutions represent trade-offs between different emission reduction pathways. For example, some pathways may prioritize cost minimization, while others may prioritize emission reduction effectiveness. By using the Pareto frontier, decision makers can select the emission reduction pathway that best meets their needs.

[0143] In the multi-objective optimization model, we first set four different policy scenarios, which reflect different goals and priorities:

[0144] Scenario 1: Prioritizing reduction of air pollutants:

[0145] In this scenario, the goal is to reduce PM 2.5 、NO x The government will prioritize improvements to health and the environment by reducing emissions of air pollutants such as sulfur dioxide and sulfur dioxide. Emission reduction measures include traffic control, the application of industrial denitrification technologies, and improved heating methods.

[0146] Scenario 2: Prioritizing greenhouse gas emissions reduction:

[0147] This scenario aims to reduce greenhouse gas emissions, including CO2 and CH4, primarily to address climate change. Emission reduction measures focus on clean energy substitution (such as solar and wind power), electrification of transportation, and the promotion of coal-substituting technologies.

[0148] Scenario 3: Balancing air pollutant and greenhouse gas emissions reductions:

[0149] The goal in this scenario is to simultaneously reduce emissions of air pollutants and greenhouse gases, achieving dual benefits. Emission reduction measures include comprehensive energy optimization, clean transportation systems, and the promotion of green buildings.

[0150] Scenario 4: Cost First:

[0151] In this scenario, the selection of emission reduction strategies focuses on cost-effectiveness, giving priority to measures that can achieve greater emission reductions at lower costs, such as energy-saving renovations and the promotion of low-cost transportation technologies.

[0152] Based on the above scenarios, the effects of different emission reduction measures were obtained through multi-objective optimization models (such as NSGA-II). The following is a description of the emission reduction prediction results under different strategy scenarios:

[0153] Scenario 1: Prioritizing reduction of air pollutants:

[0154] In the scenario where air pollutant reduction is prioritized, the most effective measure is traffic control, which can significantly reduce PM 2.5 、NO x emissions, while improving urban air quality. Industrial denitrification technology follows closely behind, which can significantly reduce SO2 and NO x Although the emission reduction effect of residential heating reform is relatively low, it can still serve as an effective supplementary measure to reduce pollutant emissions in winter.

[0155] Scenario 2: Prioritizing greenhouse gas emissions reduction:

[0156] For the scenario of prioritizing greenhouse gas emission reduction, the most important emission reduction measure is clean energy substitution, which can significantly reduce CO2 emissions and mitigate climate change. In addition, electric transportation can also effectively reduce CO2 and NO x Emissions, especially in urban transportation systems, are reduced. While coal replacement technologies are costly, their long-term climate benefits are significant and can be considered a long-term strategy.

[0157] Scenario 3: Balancing air pollutant and greenhouse gas emissions reductions:

[0158] In the context of balancing air pollutant and greenhouse gas emissions reduction, integrated energy optimization is a very important measure that can simultaneously reduce PM 2.5 、NO x Clean transportation systems and the promotion of green buildings are also considered important emission reduction measures. The former reduces emissions from transportation, while the latter optimizes energy use and reduces carbon emissions from buildings.

[0159] Scenario 4: Cost First:

[0160] In the cost-first scenario, energy-saving retrofits are considered the most effective emission reduction measure. They are relatively low-cost to implement and can deliver significant emissions reduction benefits, particularly in industry and buildings. Implementing low-cost transportation technologies (such as hybrid and fuel-efficient vehicles) is also a cost-effective option, effectively reducing transportation emissions. Industrial efficiency improvements, while relatively costly, play a significant role in reducing CO2 and other greenhouse gas emissions.

[0161] Based on the emission reduction forecasts under different strategic scenarios, the following are the recommended priority implementation plans for each scenario:

[0162] Scenario 1: Prioritize air pollutant reduction. Recommended plan: In this scenario, priority is given to implementing traffic control measures to reduce motor vehicle emissions, especially NO in urban centers. x and PM 2.4 In addition, promote the application of industrial denitrification technology to reduce SO2 and NO from industrial sources x While residential heating reforms have lower emission reduction benefits, they can still be complementary, especially in areas where winter pollution is more severe.

[0163] Scenario 2: Prioritize greenhouse gas emissions reduction Recommended solution: For climate change issues, clean energy substitution should be prioritized, promoting the application of renewable energy such as solar and wind energy to reduce dependence on fossil energy. At the same time, promote the electrification of transportation to reduce CO2 and NO x Emissions, especially from urban transportation. Coal replacement technologies should be implemented as a long-term strategy. Although their initial costs are high, their positive impact on climate change in the long term cannot be ignored.

[0164] Scenario 3: Balancing Air Pollutant and Greenhouse Gas Emissions: Recommended Solution: In this scenario, comprehensive energy optimization is the optimal solution, capable of simultaneously reducing both air pollutant and greenhouse gas emissions. Combining clean transportation systems with the promotion of green buildings can simultaneously reduce environmental pollution while optimizing energy efficiency and lowering carbon emissions. This approach not only achieves dual benefits but also enhances urban sustainability.

[0165] Scenario 4: Cost-Prioritized Recommendations: In the cost-prioritized scenario, energy-saving retrofits are the most effective, low-cost emissions reduction measures, particularly in buildings and industry. Low-cost transportation technologies (such as hybrid vehicles) should also be prioritized, as they offer a high cost-benefit ratio. While industrial efficiency improvements may face higher initial costs, their long-term economic and environmental benefits make them a worthwhile measure.

[0166] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0167] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0168] It should be understood that the term "and / or" herein is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone, where A and B may be singular or plural. In addition, the character " / " herein generally indicates that the objects associated with each other are in an "or" relationship, but it may also indicate an "and / or" relationship, which can be understood by referring to the context. A person of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0169] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for constructing a prediction model for synergistic reduction of atmospheric pollutants and greenhouse gases, characterized by: include: Obtain basic activity data and pollutant emission data for multiple emission sources, including atmospheric pollutants and greenhouse gases; Calculate emission reductions for each type of pollutant based on the pollutant type; Introducing pollutant health hazard weights, life cycle impact factors, and spatial exposure impact parameters to construct a pollutant effect factor library; Weighting the emission reduction amount and the corresponding pollutant effect factor to obtain a weighted synergistic emission reduction benefit value; Constructing a synergistic benefit evaluation index system based on the weighted synergistic emission reduction benefit value; Based on the evaluation of synergistic benefits, a multi-objective optimization model is established to comprehensively consider the emission reduction effect and implementation cost, and identify the optimal synergistic emission reduction path; Output emission reduction forecast results under different strategic scenarios and recommend priority implementation plans.

2. The method for constructing a prediction model for synergistic reduction of atmospheric pollutants and greenhouse gases according to claim 1, characterized in that: The acquisition of basic activity data and pollutant emission data of multiple emission sources includes: multiple emission sources include fixed sources, mobile sources and surface sources / scattered sources; collecting activity data of various emission sources, including fuel consumption, vehicle traffic volume, livestock population and agricultural production data; using the activity data × emission factor method to calculate the emission of each pollutant.

3. The method for constructing a prediction model for synergistic reduction of atmospheric pollutants and greenhouse gases according to claim 1, characterized in that: Calculate the emission reductions of various pollutants based on the types of pollutants, including: clarifying the emission changes before and after the implementation of each emission reduction measure, establishing an emission reduction comparison analysis model, and calculating the emissions before and after the emission reduction measures are implemented; using standard emission factors related to the pollutant types and combining them with activity data to calculate the pollutant emission reductions of each measure, and obtain pollutant emission reduction data under different source types.

4. The method for constructing a prediction model for synergistic reduction of atmospheric pollutants and greenhouse gases according to claim 1, characterized in that: The method for obtaining health hazard weights is: The health impact indicators are quantified into a unified evaluation scale, including: mortality risk increment corresponding to unit concentration; DALY value corresponding to unit pollutant; exposure-response function slope; the normalized indicators are used as the health hazard weight of the pollutant. The specific calculation expression of the health hazard weight is: Where H i is the health hazard weight of the i-th pollutant; D i To express the quantitative value of the impact of unit concentration or unit emission of pollutants on human health, D min The smallest health impact value among all pollutants; D max It has the highest health impact value among all pollutants.

5. The method for constructing a prediction model for coordinated reduction of atmospheric pollutants and greenhouse gases according to claim 4, characterized in that: The method for obtaining the life cycle impact factor is as follows: each pollutant is assigned a normalized life cycle impact factor, and the life cycle impact factor L i The calculation expression is: Where: L i is the life cycle impact factor of the i-th pollutant; GWP i GWP is the relative ratio of the contribution of unit mass of pollutants to global warming over a time scale to CO2. min 、GWP max are the minimum and maximum GWP values of the sample pollutants.

6. The method for constructing a prediction model for coordinated emission reduction of atmospheric pollutants and greenhouse gases according to claim 5, characterized in that: The spatial exposure impact parameter is obtained by running the CMAQ model to simulate pollutant concentrations; inputting emission, meteorological, and topographic data; setting the simulation time period and outputting gridded ground-level pollutant concentration data; and calculating the population-weighted exposure concentration for each grid as the spatial exposure impact parameter: Where: E i is the spatial exposure impact parameter of the i-th grid unit; C i is the ground concentration of the pollutant in the grid; Pop i is the population corresponding to the grid; n is the total number of grids in the area.

7. The method for constructing a prediction model for coordinated reduction of atmospheric pollutants and greenhouse gases according to claim 6, characterized in that: The health hazard weight, life cycle impact factor and spatial exposure impact parameter are uniformly converted into values within the range of 0 to 1 to construct the composite effect factor EI i , the expression is: EI i =w h ·H i +w l ·L i +w e ·E i Where w h 、w l 、w e are the weights of the three dimensions.

8. The method for constructing a prediction model for coordinated reduction of atmospheric pollutants and greenhouse gases according to claim 7, characterized in that: Collect emission reduction and pollutant effect factors: emission reduction ΔE i For each pollutant, the emission reduction amount under each emission reduction measure is obtained by calculating the activity data and emission factors; ΔE i is the emission reduction of the i-th pollutant; the emission reduction of each pollutant is weighted by the effect factor to calculate the weighted synergistic emission reduction benefit value: Where: S is the weighted synergistic emission reduction benefit value, which represents the overall emission reduction benefit under a certain emission reduction strategy; m is the total number of pollutant types; The obtained weighted synergistic emission reduction benefit value is compared with the preset threshold. If the weighted synergistic emission reduction benefit value is greater than or equal to the preset threshold, it means that the strategy can effectively reduce the environmental burden and health hazards of pollutants and should be implemented as a priority. If the weighted synergistic emission reduction benefit value is less than the preset threshold, it is necessary to optimize emission reduction measures or change the strategy priority.

9. The method for constructing a prediction model for coordinated reduction of atmospheric pollutants and greenhouse gases according to claim 8, characterized in that: Based on the weighted synergistic emission reduction benefit value, a synergistic benefit evaluation index system is constructed. The index system covers: environmental benefits, health benefits, climate benefits and economic benefits, and a comprehensive score is given to different benefit dimensions through weighting factors.

10. The method for constructing a prediction model for coordinated emission reduction of atmospheric pollutants and greenhouse gases according to claim 9, characterized in that: Establish a multi-objective optimization model, comprehensively consider emission reduction effects and implementation costs, and identify the optimal coordinated emission reduction path, including: Establish a multi-objective optimization model and identify the optimal emission reduction path through model calculation; The model includes two objective functions: maximizing emission reduction benefits and minimizing implementation costs; Set relevant constraints; Use multi-objective optimization algorithms to search and optimize solutions; Based on the results of the multi-objective optimization model, output emission reduction prediction results under different strategy scenarios; Based on different scenarios, priority implementation plans are recommended.

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