A Method for Constructing a Predictive Model for Synergistic Emission Reduction of Air Pollutants and Greenhouse Gases

Abstract

This invention discloses a method for constructing a synergistic emission reduction prediction model for air pollutants and greenhouse gases, specifically relating to the field of emission reduction model construction technology. By acquiring activity data and pollutant emission data from multiple emission sources, the emission reduction amounts for various pollutants are calculated separately, and these are weighted with pollutant effect factors to obtain a weighted synergistic emission reduction benefit value. A synergistic benefit evaluation index system based on this benefit value is constructed, and a multi-objective optimization model is established based on this system. Taking into account both emission reduction effects and implementation costs, the optimal emission reduction path is ultimately identified. This method can output emission reduction prediction results and recommended implementation schemes under different strategy scenarios, providing a scientific basis for decision-making and ensuring the scientific nature and rational prioritization of emission reduction measures.

Description

Technical Field

[0001] This invention relates to the field of emission reduction model construction technology, specifically to a method for constructing a predictive model for the coordinated emission reduction of air pollutants and greenhouse gases. Background Technology

[0002] The construction of a predictive model for the synergistic reduction of air pollutants and greenhouse gases refers to establishing a mathematical model or calculation method to comprehensively consider air pollutants (such as PM2.5, NO...). x This model aims to analyze the sources, trends, and interactions of emissions of carbon dioxide (SO2, etc.) and greenhouse gases (such as CO2, CH4, etc.), and predict the synergistic emission reduction effects under different technological measures. The goal of this model is to assess how to effectively reduce environmental pollution and address climate change simultaneously when implementing certain emission reduction strategies, thereby providing policymakers with a scientific basis to achieve the dual objectives of environmental protection and low-carbon development.

[0003] The existing technology has the following shortcomings:

[0004] In the evaluation of the benefits of synergistic emission reduction, PM2.5 and NO are often included. x Measuring different pollutants such as CO2 and SO2 uniformly by reducing emissions in tons ignores their fundamental differences in health hazards, life cycles, and spatial impacts, easily leading to distorted assessment results. For example, in the assessment of emission reduction projects in a certain city, PM2.5 emission reductions were not adopted because the synergy benefit score was low due to the smaller reductions compared to CO2. However, PM2.5 actually has a significant impact on local public health and should be a priority for control, ultimately resulting in the incorrect exclusion of key local emission reduction strategies. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing a predictive model for the synergistic emission reduction of air pollutants and greenhouse gases, in order to address the shortcomings of the prior art.

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

[0007] Acquire basic activity data and pollutant emission data from multiple emission sources, including air pollutants and greenhouse gases;

[0008] Based on the type of pollutant, calculate the emission reduction for each type of pollutant separately;

[0009] A database of pollutant effect factors is constructed by introducing pollutant health hazard weights, life cycle impact factors, and spatial exposure impact parameters.

[0010] The emission reduction amount is weighted with the corresponding pollutant effect factor to obtain the weighted synergistic emission reduction benefit value;

[0011] Construct 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 to identify the optimal synergistic emission reduction path.

[0013] Output emission reduction prediction results and recommended priority implementation schemes under different strategy scenarios.

[0014] Preferably, the acquisition of basic activity data and pollutant emission data from multiple emission sources includes: multiple emission sources including stationary sources, mobile sources, and area / dispersed sources; collecting activity data from various emission sources, including fuel consumption, vehicle traffic, livestock numbers, and agricultural production data; and calculating the emission amount of each pollutant using the activity data × emission factor method.

[0015] Preferably, the emission reduction of each type of pollutant is calculated based on the type of pollutant, including: clarifying the emission changes before and after the implementation of each emission reduction measure, establishing an emission reduction comparison analysis model, calculating the emission amount when no emission reduction measures are taken and the emission amount after the implementation of emission reduction measures; using standard emission factors related to the type of pollutant and combining activity data, calculating the pollutant emission reduction of each measure, and obtaining pollutant emission reduction data under different source categories.

[0016] The preferred method for obtaining health hazard weights is as follows:

[0017] Health impact indicators are quantified into a unified evaluation scale, including: the risk increment of mortality per unit concentration; the DALY value per unit of pollutant; and the slope of the exposure-response function. The normalized indicators are used as the health hazard weights of pollutants, and the specific calculation expression for the health hazard weights is as follows: In the formula, The health hazard weight of the i-th pollutant; To express the quantitative value of the impact of pollutants per unit concentration or per unit emission on human health, The lowest health impact value among all pollutants; This represents 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. The calculation expression is: ;in: The life cycle impact factor of the i-th pollutant; This represents the ratio of a unit mass of pollutant's contribution to global warming to CO2 over a given time scale. These represent the minimum and maximum GWP values ​​among the pollutants in the sample.

[0019] Preferably, the method for obtaining spatial exposure impact parameters is as follows: run the CMAQ model to simulate pollutant concentrations; input emission, meteorological, and topographic data; set the simulation time period and output gridded ground-level pollutant concentration data; for each grid, calculate the population-weighted exposure concentration as the spatial exposure impact parameter. ;in: Let i be the spatial exposure influence parameter of the i-th grid cell; This represents the ground-level concentration of pollutants in that grid. denoted by , where is the population corresponding to each grid cell; and 'n' is the total number of grid cells within the region.

[0020] Preferably, the health hazard weights, life cycle impact factors, and spatial exposure impact parameters are uniformly converted into values ​​within the range of 0–1 to construct a composite effect factor. The expression is: In the formula, The weights are for the three dimensions.

[0021] Preferably, emission reductions and pollutant effect factors are collected: emission reductions For each pollutant, the emission reduction amount under each emission reduction measure is calculated by using activity data and emission factors. Let be the emission reduction amount for pollutant i; weight the emission reduction amount for each pollutant with the effect factor to calculate the weighted synergistic emission reduction benefit value: Where: S is the weighted synergistic emission reduction benefit value, representing the overall emission reduction benefit under a certain emission reduction strategy, and 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 first. If the weighted synergistic emission reduction benefit value is less than the preset threshold, it is necessary to optimize the emission reduction measures or change the priority of the strategy.

[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 the different benefit dimensions are comprehensively scored through weighted factors.

[0024] Preferably, a multi-objective optimization model is established to comprehensively consider emission reduction effects and implementation costs, and to identify the optimal synergistic 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] Multi-objective optimization algorithms are used to search for and optimize the solution;

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

[0030] Based on different scenarios, we recommend prioritizing certain implementation methods.

[0031] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0032] 1. This invention constructs a pollutant effect factor library by introducing health hazard weights, life cycle impact factors, and spatial exposure impact parameters, ensuring that the multidimensional impacts of different pollutants on health, environment, and climate can be considered in the assessment of synergistic emission reduction benefits. By weighting emission reduction amounts and pollutant effect factors, a weighted synergistic emission reduction benefit value is obtained, and a synergistic benefit evaluation index system is constructed based on this value. Furthermore, through a multi-objective optimization model, comprehensively considering emission reduction effects and implementation costs, the optimal emission reduction path can be identified, providing decision-makers with scientific emission reduction solutions. It also outputs emission reduction prediction results and recommended implementation schemes under different strategy scenarios, improving the accuracy and operability of emission reduction decisions.

[0033] 2. This invention avoids the problem of existing methods neglecting the differences in pollutant hazards by employing detailed pollutant classification, accurate emission reduction calculation, comprehensive introduction of effect factors, and multi-dimensional synergistic benefit assessment. Ultimately, through a multi-objective optimization model based on weighted synergistic emission reduction benefit values, it provides emission reduction path selection with practical application value, effectively achieving rational resource allocation and optimization of emission reduction targets, promoting environmental protection and sustainable development, and demonstrating significant environmental, health, and socio-economic benefits. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

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

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] For examples, please refer to Figure 1 As shown in this embodiment, a method for constructing a predictive model for the synergistic reduction of air pollutants and greenhouse gases includes:

[0038] Acquire basic activity data and pollutant emission data from multiple emission sources, including air pollutants and greenhouse gases;

[0039] Based on the type of pollutant, calculate the emission reduction for each type of pollutant separately;

[0040] A database of pollutant effect factors is constructed by introducing pollutant health hazard weights, life cycle impact factors, and spatial exposure impact parameters.

[0041] The emission reduction amount is weighted with the corresponding pollutant effect factor to obtain the weighted synergistic emission reduction benefit value;

[0042] Construct 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 to identify the optimal synergistic emission reduction path.

[0044] Output emission reduction prediction results and recommended priority implementation schemes under different strategy scenarios.

[0045] To accurately identify 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 enterprises, etc.; building heating / cooling systems; solid waste incineration facilities.

[0046] Mobile sources: Road transportation: private cars, buses, freight trucks, etc.; Non-road transportation: construction machinery, agricultural machinery, ships, aircraft, etc. Area sources: coal and firewood used for residential 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., steel tons, cement tons, chemical output, etc.); various process routes and emission reduction equipment coverage rates. Data sources: enterprise reporting systems, pollution source surveys; monthly reports from power and heat operation units.

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

[0049] Data on agricultural and domestic energy activities: number of agricultural livestock (cattle, pigs, sheep, etc.) and feeding methods; fertilizer usage and crop planting area; number of households using coal or firewood; type and coverage of residential heating. Data sources: Statistical Yearbook of the Ministry of Agriculture and Rural Affairs; Urban and rural residents' energy survey; third-party survey questionnaires or remote sensing analysis.

[0050] The types of pollutants include: air pollutants: PM2.5, SO2, NO. x Greenhouse gases include CO, VOCs, and NH3. Other greenhouse gases include CO2, CH4, and N2O; in some scenarios, high greenhouse gas emissions such as SF6 and HFCs are also considered. Emissions are calculated using the common "activity data × emission factor" method. ; The emission amount of the i-th pollutant from the j-th source; The activity level of the source (e.g., fuel consumption, vehicle mileage). This refers to the corresponding pollutant emission factors for this type of source. Sources of emission factors include standard data sources: IPCC Guidelines (International Greenhouse Gas Accounting Standards); national / local pollutant emission factor manuals; measured and corrected data: actual monitoring data from enterprises, CEMS systems; research projects, third-party survey sampling results; and adaptation and correction of time, space, and process characteristic sub-factors.

[0051] Spatial precision: Divided by city, county, street or grid (e.g. 1×1 km); data mapping and distribution estimation are performed using a GIS system.

[0052] Time accuracy: Based on the year, adjusted monthly or daily 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 or outlier data (IQR method, empirical rules); perform Monte Carlo simulation analysis on the variability of emission factors and activity data to provide confidence intervals.

[0054] The output is in standard emission inventory format: a four-dimensional structure by pollutant / industry / region / time; it supports importing prediction models such as LEAP, MARKAL, and simulators; and it can be seamlessly integrated with the collaborative emission reduction optimization module.

[0055] Types of pollutants: including but not limited to the following two main categories: Air pollutants: particulate matter (PM2.5, PM2.5) 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 include: coal replacement, vehicle restrictions, promotion of green buildings, and fertilizer control in farmland.

[0058] The calculation of emission reductions is based on a comparative analysis method, namely: In the formula, Let be the emission reduction amount for the i-th pollutant; This indicates the emissions level when no emission reduction measures were taken (Baseline). This indicates the emissions after implementing emission reduction measures (With Measures).

[0059] Using the aforementioned activity data × emission factor method, the current emission levels of each pollutant are calculated; a complete baseline emission table is established with a certain time point as the base year.

[0060] Clearly define 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 replacements, etc.

[0061] After the measures are implemented, update activity data (e.g., decrease in coal consumption, increase in the proportion of electric vehicles); select emission factors that match the new activity level; and recalculate the total emissions of each pollutant.

[0062] The difference is calculated separately for each type of pollutant to obtain individual emission reduction data; it can be decomposed by industry, region, time and other dimensions.

[0063] Addressing the heterogeneity among pollutants: Different pollutants have different units and hazards, and emission reduction calculations should use unified dimensions without confusing their essence; emission reductions should be calculated separately for each pollutant, and they should not be directly summed up as the total emission reduction; if it is necessary to synthesize synergistic benefit indicators in the future, a hazard weighting factor should be introduced (see the above content).

[0064] Certain measures (such as high-efficiency combustion and SCR denitrification) can affect emission factors, and these factors should be adjusted dynamically. For example, emissions from vehicle aging and the promotion of new energy vehicles decrease non-linearly over time; phased simulations (annual or quarterly) can be used for step-by-step calculations. Some measures may lead to transfer effects (such as increased NH3 escape due to desulfurization), requiring pollutant linkage assessments.

[0065] To scientifically optimize strategies for synergistic emission reduction of air pollutants and greenhouse gases, the actual impacts of various pollutants on health, the environment, and spatial exposure must be comprehensively considered when constructing models, rather than solely relying on emission reduction tonnage as the evaluation criterion. Therefore, this method introduces multi-dimensional indicators to construct a pollutant effect factor library to ensure the fairness and accuracy of the assessment.

[0066] First, health hazard weights are used to measure the direct impact of pollutants on human health. Different pollutants have varying effects on the respiratory and cardiovascular systems and on disease risk. For example, PM2.5 is widely considered one of the most harmful air pollutants, and long-term exposure is closely related to the occurrence of asthma, stroke, and heart disease. Health hazard weights are derived from authoritative epidemiological studies, disease burden data (such as DALY), and mortality risk increments, assigning corresponding impact scores to different pollutants.

[0067] The method for obtaining health hazard weights is as follows: determine the types of pollutants to be assessed (e.g., PM2.5, NO). x (e.g., SO2, CO2, CH4, etc.), covering two major categories: air pollutants and greenhouse gases. Data on the impact of various pollutants on human health will be collected, prioritizing the following authoritative sources:

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

[0069] Quantifying health impact indicators into a unified evaluation scale, commonly used indicators include:

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

[0071] DALY value (Days of health loss per ton or per 10,000 people exposed to a pollutant);

[0072] Exposure-response function slope (e.g., μg / m³ → % health effect).

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

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

[0075] The normalization value for high-risk pollutants is close to 1, while that for low-risk pollutants is close to 0.

[0076] The normalized indicators are used as the health weights of pollutants and stored in the database, serving as the basis for subsequent weighted calculations for synergistic effect evaluation. The specific calculation expression for the health hazard weights is as follows: In the formula, The health hazard weight of the i-th pollutant (after standardization, the value is between 0 and 1). Common forms of quantitative values ​​for the impact of pollutants on human health per unit concentration or per unit emission include: the daily average risk of death (DALY) per ton of pollutant emitted; the percentage increase in mortality per 10 μg / m³ of exposure; and the slope of the exposure-response curve. The lowest health impact value among all pollutants; This represents the highest health impact value among all pollutants.

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

[0078] For example, the health impact value of PM2.5 (such as the DALY number per ton of emissions) is much higher than that of CO2 or CH4. Therefore, according to the above calculation, its normalized weight is close to 1. Although CO2 has climate impacts, it has no obvious direct impact on human health, and its health hazard weight may be less than 0.1.

[0079] Secondly, life cycle impact factors reflect the impact of pollutants on climate change or ecosystems during their lifetime. For greenhouse gases, carbon dioxide has a long-term residence characteristic; although its global warming potential is relatively low, its large emissions result in significant climate impacts. While emissions of gases such as nitrous oxide and methane are relatively small, their stronger greenhouse effect per unit mass necessitates higher weighting in comprehensive evaluations. Life cycle impact factors are primarily derived from international LCA databases and indicators such as the Global Warming Potential (GWP) published by the IPCC.

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

[0081] Primarily based on Life Cycle Assessment (LCA) databases or authoritative reports, the full-cycle impact of pollutant emissions on the environment is collected, including:

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

[0083] Neutralization period (residence time);

[0084] Potential impacts on ecosystems, such as toxicity, acidification, and eutrophication.

[0085] Global warming potential (GWP) is prioritized as the primary reference indicator due to its well-defined timescale, mature data, and strong applicability, particularly for greenhouse gases. Other pollutants (such as SO2 and NO) are also considered. x The assessment can be supplemented by indicators such as acidification potential (AP) and photochemical ozone formation potential (POCP).

[0086] The acquired life cycle impact indicators are standardized to a range of [0,1] to ensure comparability between different pollutants. Standardization techniques such as range normalization or Z-score can be used.

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

[0088] Life cycle impact factors The calculation expression is as follows: ;in: The life cycle impact factor of the i-th pollutant; This represents the relative contribution of a unit mass of pollutant to global warming over a specific time scale (usually 100 years) to CO2. For example: CO2 GWP = 1 (baseline); CH4 GWP ≈ 28–34; N2O GWP ≈ 265–298; These are the minimum and maximum GWP values ​​among the pollutants in the sample, used for normalization.

[0089] This indicates the life cycle impact level of pollutant i among all pollutants assessed. The higher the value, the greater the life cycle impact, and the higher the co-weight should be given to it in the model.

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

[0091] Spatial exposure impact parameters consider the retention capacity of pollutants in densely populated areas and the differences in exposure levels after release. Population density, meteorological conditions, and topographic structures (such as urban valley effects) in different regions all influence the local spatial concentration distribution of pollutants and the degree of public exposure. For example, NO... xHigh concentrations of pollutants in densely populated areas increase the health threat to vulnerable populations such as children and the elderly.

[0092] The method for obtaining spatial exposure impact parameters is as follows: the pollutant emission inventory (emission source data) includes CO2, NO... x Pollutants include PM2.5 and SO2; data sourced from national or local emission inventories (such as MEIC), including source category, spatial location, and temporal resolution. Meteorological data is generated using WRF models to ensure temporal continuity and spatial accuracy of parameters such as wind speed, temperature, and boundary layer height; resolution is within the range of 3–9 km. Topographic and land use data are used to simulate pollutant diffusion, deposition, and photochemical reaction processes; data are typically sourced from USGS or MODIS. Population distribution grid data has a resolution of 1 km × 1 km or higher; data sources can include WorldPop, LandScan, or national statistical grid data.

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

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

[0095] Set a simulation period (30 days or more is recommended to cover typical pollution periods).

[0096] Output gridded ground-level pollutant concentration data (unit: μg / m³), stored in hourly or daily average format.

[0097] For each grid cell, calculate the population-weighted exposure concentration as a spatial exposure impact parameter: ;in: Let i be the spatial exposure influence parameter of the i-th grid cell; The ground-level concentration of pollutants in this grid (obtained from CMAQ simulation); The population corresponding to this grid; n is the total number of grids in the region; The values ​​are normalized to the range of [0,1] to facilitate weighting and integration with other pollutant factors; high values ​​indicate that the pollutant has a high retention and exposure intensity in the grid, making it suitable for key control.

[0098] Taking a city as an example, a NO2 concentration distribution map was obtained through CMAQ simulation, and then overlaid with a high-resolution population distribution grid to calculate the weighted exposure value within each grid. Subsequently, the GWR model was used to analyze the sensitivity of variables such as population density, wind speed, and traffic density to NO2 exposure, forming an exposure impact factor layer, which was then used as a spatial exposure impact parameter input into the collaborative emission reduction model.

[0099] The health hazard weights, life cycle impact factors, and spatial exposure impact parameters are uniformly converted to values ​​within the range of 0–1; Min-Max standardization or entropy weighting is used for processing.

[0100] Constructing composite effect factors The expression is: The weights are for the three dimensions (which can be determined using AHP or Delphi methods).

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

[0102] Collect emission reduction data and pollutant effect factors:

[0103] Emission reduction For each pollutant, the emission reduction amount under various emission reduction measures is calculated by using activity data and emission factors. Emission reduction for pollutant i (in tons); pollutant effect factor This represents the comprehensive assessment value of the environmental and health effects of each pollutant.

[0104] The weighted synergistic emission reduction benefit value is calculated by weighting the emission reduction amount of each pollutant with the effect factors: Where: S is the weighted synergistic emission reduction benefit value, representing the overall emission reduction benefit under a certain emission reduction strategy (unit: dimensionless or harmonic unit, which can be set according to specific targets), 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 the emission reduction measures or change the priority of the strategy.

[0106] The evaluation index system for synergistic benefits should include the following key dimensions:

[0107] Environmental benefits: This measures the effectiveness of emission reduction measures in improving air quality, reducing pollutant concentrations, and minimizing their impact on the ecological environment. For example, the environmental impact of reducing emissions of pollutants such as PM2.5 and SO2.

[0108] Health benefits: This measures the effectiveness of emission reduction measures in improving the health of the population. For example, reducing PM2.5 emissions can reduce pollution-related health risks such as respiratory diseases and cardiovascular diseases.

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

[0110] Economic benefits: This measures the impact of emission reduction measures on the socio-economic situation, taking into account the cost-benefit ratio of emission reduction, such as reducing medical expenses and increasing productivity.

[0111] Based on the weighted synergistic emission reduction benefit value (S) calculated above, it is allocated to different benefit dimensions to form individual scores 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 ;in, is the environmental effect factor of the i-th pollutant, representing the comprehensive impact of the pollutant on the environment.

[0113] Health benefit score ;in, It is the health effect factor of the i-th pollutant, representing the harm of the pollutant to human health.

[0114] Climate benefit score ;in, is the climate effect factor of the i-th pollutant, representing the impact of greenhouse gases on climate change.

[0115] Economic benefit score ;in, is the economic effect factor of the i-th pollutant, representing the economic returns of emission reduction measures, such as reducing public health costs and increasing productivity.

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

[0117] The total score, calculated by weighting the scores across all benefit dimensions, represents the overall effectiveness of a particular emissions reduction measure or strategy. A higher score indicates a better overall effect of the strategy.

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

[0119] Measure A: Focus on reducing PM2.5 and SO2 emissions to improve health and environmental benefits.

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

[0121] The combined synergistic benefit value of the two measures is calculated using the above method. Finally, by comparing the total value, decision-makers are helped to select the best emission reduction strategy.

[0122] The goal of a multi-objective optimization model is to optimize multiple decision variables, balancing emission reduction benefits and costs, to form a comprehensively optimal emission reduction path. Specific objectives include:

[0123] Maximize emission reduction benefits: While ensuring emission reduction targets are met, maximize the emission reduction effects of various pollutants, especially the comprehensive improvement of health, environment and 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: Finding a balance between environmental and economic benefits and avoiding over-reliance on any one goal.

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

[0127] The amount of investment or implementation of the j-th emission reduction measure (e.g., equipment investment, subsidy coverage).

[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 to maximize emission reduction benefits and minimize costs, and their objective functions can be expressed as follows:

[0130] The goal of maximizing emission reduction benefits: ;

[0131] Cost minimization objective: ;in: This represents the unit implementation cost of the j-th emission reduction measure (e.g., the cost per ton of emission reduction).

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

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

[0134] Emissions 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 constraints: The amount of each emission reduction measure implemented or invested in cannot exceed its actual 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] Because 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) algorithm: finds the global optimal solution in the solution space by simulating the motion of a swarm of particles.

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

[0141] Pareto optimization: Transforms the objective function into a Pareto front, generating multiple optimal solution sets to demonstrate the trade-off results under different objectives.

[0142] By using multi-objective optimization algorithms (such as NSGA-II), multiple Pareto optimal solutions are obtained, representing trade-offs among different emission reduction paths. For example, some paths may prioritize cost minimization, while others may focus on emission reduction effectiveness. Through the Pareto front, policymakers can select the emission reduction path that best meets their needs.

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

[0144] Scenario 1: Prioritize reducing air pollutant emissions:

[0145] In this scenario, the goal is to reduce PM2.5 and NO. x Emissions of air pollutants such as SO2 should be prioritized for improvements in health and the environment. Emission reduction measures include traffic control, the application of industrial denitrification technologies, and improvements in heating methods.

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

[0147] The goal of this scenario is to reduce emissions of greenhouse gases such as CO2 and CH4, primarily addressing climate change. Emission reduction measures focus on clean energy alternatives (such as solar and wind power), transportation electrification, and promoting the application of coal-to-energy technologies.

[0148] Scenario 3: Balancing air pollutant and greenhouse gas emission reduction:

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

[0150] Scenario 4: Cost-first approach:

[0151] In this scenario, the choice of emission reduction strategies focuses on cost-effectiveness, prioritizing measures that can achieve significant emission reductions at a lower cost. Examples include energy-saving retrofits and the promotion of low-cost transportation technologies.

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

[0153] Scenario 1: Prioritize reducing air pollutant emissions:

[0154] In scenarios where reducing air pollutant emissions is the priority, the most effective measure is traffic control, which can significantly reduce PM2.5 and NOx. x This reduces emissions while improving urban air quality. Industrial denitrification technology follows closely behind, capable of significantly reducing SO2 and NO emissions. x Scenario 2: Prioritize greenhouse gas emission reduction:

[0155] For scenarios prioritizing greenhouse gas emissions reduction, the most important measure is clean energy substitution, which can significantly reduce CO2 emissions and mitigate climate change. Furthermore, the electrification of transportation can also effectively reduce CO2 and NOx emissions. x Emissions, especially in urban transportation systems. While coal alternatives are more expensive, their long-term climate benefits are significant, making them a viable long-term strategy.

[0156] Scenario 3: Balancing air pollutant and greenhouse gas emission reduction:

[0157] In a scenario balancing air pollutant and greenhouse gas emission reduction, integrated energy optimization is a crucial measure that can simultaneously reduce PM2.5 and NOx emissions. x And CO2 emissions. Clean transportation systems and the promotion of green buildings are also considered important emission reduction measures. The former reduces emissions in transportation, while the latter optimizes energy use and reduces carbon emissions in buildings.

[0158] Scenario 4: Cost-first approach:

[0159] In cost-priority scenarios, energy-saving retrofits are considered the most effective emission reduction measure. Their implementation costs are relatively low, yet they deliver significant emission reduction benefits, especially in the industrial and building sectors. Implementing low-cost transportation technologies (such as hybrid vehicles and energy-efficient vehicles) is also a cost-effective option, effectively reducing traffic emissions. Industrial efficiency improvements, while relatively more expensive, play a crucial role in reducing CO2 and other greenhouse gas emissions.

[0160] Based on the emission reduction predictions under different strategy scenarios, the following are the recommended priority implementation schemes for each scenario:

[0161] Scenario 1: Recommended Solution for Prioritizing Air Pollutant Emission Reduction: In this scenario, prioritize traffic control measures to reduce vehicle emissions, especially NOx emissions in urban centers. x And PM2.5 pollution. Furthermore, promoting the application of industrial denitrification technologies to reduce SO2 and NO from industrial sources. x Emissions. Scenario 2: Recommended Greenhouse Gas Emission Reduction Plan: For climate change issues, clean energy substitution should be a priority measure, promoting the application of renewable energy sources such as solar and wind power to reduce dependence on fossil fuels. Simultaneously, promoting the electrification of transportation will reduce CO2 and NOx emissions. x Emissions, especially those from urban transportation, are a significant concern. Coal substitution technologies should be implemented as a long-term strategy, despite their high initial costs, because their positive impact on climate change in the long run is undeniable.

[0162] Scenario 3: Recommended Solution for Balancing Air Pollutant and Greenhouse Gas Emission Reduction: In this scenario, integrated energy optimization is the optimal solution, capable of simultaneously reducing air pollutant and greenhouse gas emissions. Combining clean transportation systems and the promotion of green buildings can optimize energy efficiency and reduce carbon emissions while reducing environmental pollution. This solution not only achieves dual benefits but also enhances urban sustainability.

[0163] Scenario 4: Cost-First Recommendation: In a cost-first scenario, energy-saving retrofits are the most effective low-cost emission reduction measure, especially in the building and industrial sectors. Low-cost transportation technologies (such as hybrid vehicles) should also be prioritized due to their high cost-effectiveness. Although industrial efficiency improvements may face higher initial costs, they remain worthwhile measures considering their long-term economic and environmental benefits.

[0164] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0165] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. 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. 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 a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0166] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Additionally, the character " / " in this document generally indicates an "or" relationship between the preceding and following related objects, but it may also indicate an "and / or" relationship; please refer to the context for specific understanding. Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0167] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for constructing a predictive model for the synergistic reduction of air pollutants and greenhouse gases, characterized in that: include: Acquire basic activity data and pollutant emission data from multiple emission sources, including air pollutants and greenhouse gases; Based on the type of pollutant, calculate the emission reduction for each type of pollutant separately; A database of pollutant effect factors is constructed by introducing pollutant health hazard weights, life cycle impact factors, and spatial exposure impact parameters. The method for obtaining the life cycle impact factor is as follows: each pollutant is assigned a normalized life cycle impact factor, L. i The calculation expression is: Where: L i GWP is the life cycle impact factor of the i-th pollutant. i GWP represents the ratio of a unit mass of pollutant's contribution to global warming to CO2 over a time scale. min GWP max These are the minimum and maximum GWP values ​​among the pollutants in the sample; The method for obtaining spatial exposure impact parameters is as follows: run the CMAQ model to simulate pollutant concentrations; input emission, meteorological, and topographic data; set the simulation time period and output gridded ground-level pollutant concentration data; for each grid, calculate the population-weighted exposure concentration as the spatial exposure impact parameter. Among them: E i C represents the spatial exposure influence parameter of the i-th grid cell; i Pop represents the ground-level concentration of pollutants in this grid. i n represents the population corresponding to each grid cell; n is the total number of grid cells in the region. The emission reduction amount is weighted with the corresponding pollutant effect factor to obtain the weighted synergistic emission reduction benefit value; Construct 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 to identify the optimal synergistic emission reduction path. Output emission reduction prediction results and recommended priority implementation schemes under different strategy scenarios.

2. The method for constructing a predictive model for synergistic emission reduction of air pollutants and greenhouse gases according to claim 1, characterized in that: The acquisition of basic activity data and pollutant emission data from multiple emission sources includes: multiple emission sources including stationary sources, mobile sources, area sources, and scattered sources; collecting activity data from various emission sources, including fuel consumption, vehicle traffic, livestock numbers, and agricultural production data; and calculating the emission amount of each pollutant using the activity data × emission factor method.

3. The method for constructing a predictive model for synergistic emission reduction of air pollutants and greenhouse gases according to claim 1, characterized in that: The calculation of emission reductions for various pollutants based on pollutant types includes: clarifying the changes in emissions before and after the implementation of each emission reduction measure, establishing a comparative analysis model for emission reductions, calculating the emissions before and after the implementation of emission reduction measures; using standard emission factors related to pollutant types and combining activity data to calculate the pollutant emission reductions for each measure, and obtaining pollutant emission reduction data for different source categories.

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

5. The method for constructing a predictive model for synergistic emission reduction of air pollutants and greenhouse gases according to claim 1, characterized in that: The health hazard weights, life cycle impact factors, and spatial exposure impact parameters were uniformly converted to 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 In the formula, w h w l w e The weights are for the three dimensions.

6. The method for constructing a predictive model for synergistic emission reduction of air pollutants and greenhouse gases according to claim 5, characterized in that: Collect emission reductions and pollutant effect factors: Emission reduction ΔE i For each pollutant, the emission reduction amount under various emission reduction measures is calculated using activity data and emission factors; ΔE i Let be the emission reduction amount for pollutant i; weight the emission reduction amount for each pollutant with the effect factor to calculate the weighted synergistic emission reduction benefit value: Where: S is the weighted synergistic emission reduction benefit value, representing the overall emission reduction benefit under a certain emission reduction strategy, and 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 first. If the weighted synergistic emission reduction benefit value is less than the preset threshold, it is necessary to optimize the emission reduction measures or change the priority of the strategy.

7. The method for constructing a predictive model for synergistic emission reduction of air pollutants and greenhouse gases according to claim 6, characterized in that: Based on the weighted synergistic emission reduction benefit value, a synergistic benefit evaluation index system is constructed, which covers environmental benefits, health benefits, climate benefits and economic benefits. The different benefit dimensions are comprehensively scored through weighted factors.

8. The method for constructing a predictive model for synergistic emission reduction of air pollutants and greenhouse gases according to claim 7, characterized in that: Establish a multi-objective optimization model that comprehensively considers emission reduction effects and implementation costs to 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; Multi-objective optimization algorithms are used to search for and optimize the solution; Based on the results of the multi-objective optimization model, the emission reduction prediction results under different strategy scenarios are output. Based on different scenarios, we recommend prioritizing certain implementation methods.

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