Optimal control method for atmospheric fine particulate matters and ozone

Abstract

The invention provides an atmospheric fine particulate matter and ozone optimization control method, and relates to the technical field of environmental control, and the method comprises the steps: collecting the mass concentration of PM2.5 and O3 of a target city, an emission reduction policy and precursor emission data of each pollution source; dividing the target city into a VOCs control area and a VOCs and NOx cooperative control area according to the ozone sensitivity; based on an EKMA curve, obtaining an optimal emission reduction proportion of a precursor of each control region; calculating the control potential of different pollution sources VOCs and NOx of the target city, and establishing an O3 concentration optimization control constraint equation set; calculating PM2.5 sensitivities of different areas of the target city to the receptor points, dividing the target city into a plurality of grade sensitive areas, and establishing a PM2.5 optimization control constraint equation set; and solving the equation set to obtain an O3 and PM2.5 optimization emission reduction scheme. According to the invention, a refined PM2.5 and O3 pollution emission reduction scheme is provided from the city scale.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental control, and in particular to a method for optimizing the control of atmospheric fine particulate matter and ozone. Background Art

[0002] In recent years, China has actively promoted the prevention and control of air pollution, and the atmospheric fine particulate matter (PM 2.5 ) The pollution situation has been significantly improved, but the PM 2.5 The concentration has not yet reached the standard. With the high concentration of ozone (O3) in the summer, the atmospheric environment situation remains severe. 2.5 Together with O3, it has become the main air pollutants affecting the air quality in Chinese cities and regions.

[0003] Because PM 2.5 and O3 have common precursors. Pollutants emitted by emission sources react chemically after entering the atmosphere and will affect PM2.5. 2.5 and O3 have a dual impact, and through scientific emission reduction of various emission sources, PM 2.5 And the O3 concentration decreased simultaneously.

[0004] Existing atmospheric pollution control programs typically develop ozone pollution prevention and control plans by using scenario simulations to reduce NOx and VOCs at different rates, generating EKMA curves and optimal reduction ratios for O3 precursors. These precursors are then reduced based on these optimal reduction ratios. However, due to the complexity of the O3 generation process, relevant scientific research and project applications remain limited to controlling the total amount of VOC precursors or simply proposing that pollution sources with higher ozone generation potential should be prioritized. These efforts fail to quantify the reduction results or ratios of ozone generation potential across multiple emission sources, nor to propose scientifically feasible optimized reduction plans for improving O3 concentrations.

[0005] Since VOCs and NOx are PM 2.5 A common precursor of O3, reducing PM 2.5 and O3 concentrations require coordinated control of VOCs and NOx. 2.5 Most of the research results on the coordinated reduction of PM2.5 and O3 emissions are based on the assumed emission reduction scenario to reduce the total amount of precursors in the target area, but do not consider the future policy changes in the target area, the four major structural adjustments, the cost of pollutant emission reduction and the emission reduction potential of each pollution source from the city scale. 2.5 and the influence of O3 concentration changes. At the same time, due to PM 2.5 Due to the complex relationship between PM2.5 and O3, when environmental managers implement emission reduction plans, the concentration of one pollutant may decrease while the concentration of another pollutant increases. How can we achieve PM2.5 based on the actual situation of the city? 2.5There is still a lack of scientific methods to support the proposal of feasible optimization control plans that can improve both industrial development and O3 concentration while taking into account both industrial development and governance costs.

[0006] PM 2.5 O3 pollution is complex, and a single pollution source control model is not universal. Currently, there is no scientific guidance for PM2.5 pollution control at the city level. 2.5 There is little research on technical solutions for continuous improvement of O3.

[0007] Currently, optimization control technology is widely used in a variety of fields, including industrial pollution control, energy structure optimization, and regional pollution prevention and control, and has achieved significant results in improving air quality. The core of optimization control technology lies in achieving the optimal research objective while satisfying constraints through mathematical modeling and algorithm optimization. The three basic elements of an optimization control model are: ① The objective function, which defines the optimization goal, such as minimizing pollution emissions, costs, or energy consumption; ② Decision variables, which define adjustable control parameters or solutions, such as emissions and technology costs; and ③ Constraints, which aim to avoid infeasible or unreasonable restrictions on the optimization solution, such as environmental standards, resource constraints, and technology penetration rates. Optimization control technology can find the optimal balance among multiple influencing factors and provide managers with decision-making solutions.

[0008] At present, optimization control technology has been widely used in the field of environmental planning and management. Therefore, how to design a scientific and feasible emission source optimization reduction method based on optimization control technology and propose a universal PM 2.5 and O3 optimization control scheme to achieve PM 2.5 The research goal is to improve both PM and O3 concentrations at the city level. 2.5 Providing theoretical support for the formulation of collaborative control plans with O3 has become an urgent issue to be resolved. Summary of the Invention

[0009] In view of the problems in the background technology, the present invention provides an optimization control method for atmospheric fine particulate matter and ozone, with PM 2.5 With the dual improvement of O3 and various emission sources in the target city as the core goal, the emission volume of various emission sources, policy reduction targets, ozone generation potential and other factors are taken as constraints, and the minimum total economic cost of pollutant treatment is taken as the objective function. 2.5The optimal control model of the response of the air quality numerical model to the O3 concentration is used to solve the optimal pollutant emission reduction plan. This method conducts multiple scenario simulations on the target city based on the air quality numerical model to construct the response relationship between pollutant emission reduction and the air quality of the target city. The EKMA curve is used as an analysis tool to determine the optimal emission reduction ratio of nitrogen oxides (NOx) and volatile organic compounds (VOCs) from the emission source; based on linear programming theory, the relationship between sulfur dioxide (SO2), NOx, volatile organic compounds (VOCs), particulate matter, ammonia (NH3) emission reduction and PM is established. 2.5 The response relationship between the concentrations.

[0010] To achieve the above objectives, the present invention provides a method for optimizing the control of atmospheric fine particulate matter and ozone, comprising:

[0011] Collect PM in target cities 2.5 and O3 mass concentrations, emission reduction policies, and precursor emission data for each pollution source;

[0012] Target cities are divided into VOCs control areas and VOCs and NOx coordinated control areas based on ozone sensitivity;

[0013] Based on the EKMA curve, the optimal emission reduction ratio of precursors in each VOCs control area and VOCs and NOx coordinated control area was obtained;

[0014] Calculate the control potential of VOCs and NOx from different pollution sources in the target city, and establish a set of constraint equations for optimizing the control of O3 concentration by combining the optimal emission reduction ratio of precursors, emission reduction costs, emission reduction amounts, O3 reduction ratio, and the minimum emission reduction ratio of VOCs and NOx corresponding to the VOCs control area and the VOCs and NOx coordinated control area;

[0015] Calculate PM2.5 of different areas of the target city to the receptor point 2.5 Sensitivity, and divide the target city into multiple levels of sensitive areas;

[0016] PM based on different pollution sources in the target city 2.5 Control potential, multiple levels of sensitive areas, emission reduction costs, emission reduction amount, pollutant reduction amount, PM 2.5 Target reduction values ​​for PM concentrations are established 2.5 Optimize the control constraint equations;

[0017] Based on the target city PM 2.5 The O3 mass concentration, emission reduction policy and precursor emission data of each pollution source are used to solve the O3 concentration optimization control constraint equations and PM 2.5Optimize the control constraint equations to obtain the O3 optimization reduction plan for each VOCs control area and VOCs and NOx coordinated control area in the target city, as well as the PM2.5 optimization plan for each sensitive area in the target city. 2.5 Optimize emission reduction plans.

[0018] As a further improvement of the present invention, the optimal emission reduction ratio of precursors in each VOCs control area and VOCs and NOx coordinated control area is obtained based on the EKMA curve; including:

[0019] Based on the actual feasibility and scenario analysis method, the emission reduction range of precursor VOCs and NOx was set from 0% to 60%, and the EKMA curves of each VOCs control area and VOCs and NOx coordinated control area in the target city were obtained;

[0020] Based on the reduction ratio of VOCs and NOx and the O3 concentration, the optimal emission reduction ratio of precursors in each VOCs control area and VOCs and NOx coordinated control area was obtained.

[0021] As a further improvement of the present invention, the O3 optimization emission reduction scheme based on each VOCs control area, VOCs and NOx coordinated control area, and PM in each level sensitive area 2.5 Optimize the emission reduction plan, carry out proportional emission reduction on the precursor emission data of each pollution source in the target city, and obtain a proportional emission reduction list.

[0022] As a further improvement of the present invention, the control potential of VOCs and NOx from different pollution sources in the target city is calculated using the following formula:

[0023]

[0024] Where,

[0025] A pij is the control potential of pollutant p from pollution source j in region i, %;

[0026] σ pj is the control efficiency of pollutant p of pollution source j under the optimal control measures, %;

[0027] ω pj is the control efficiency of pollutant p of pollution source j under the current control measures, %.

[0028] As a further improvement of the present invention, a set of O3 concentration optimization control constraint equations is established by combining the optimal emission reduction ratio of precursors in the VOCs control area and the VOCs and NOx coordinated control area, the emission reduction cost, the emission reduction amount, the O3 reduction ratio, and the minimum VOCs and NOx emission reduction ratios corresponding to the VOCs control area and the VOCs and NOx coordinated control area. The formula is:

[0029]

[0030] Where,

[0031] Z is the total cost of controlling O3, i is the emission reduction area, j is the pollution source, and p represents the pollutants, including VOCs and NOx;

[0032] M pj is the control cost of pollutant p representing pollution source j;

[0033] X pij represents the reduction amount of pollutant p from pollution source j in region i under the pollutant emission reduction ratio constraint;

[0034] Q pij is the emission of pollutant p from pollution source j in region i, A pij is the control potential of pollutant p from pollution source j in region i;

[0035] t represents different control areas, C t represents the optimal VOCs:NOx emission reduction ratio in control area t; X vij 、X nij are the VOCs and NOx reductions of pollution source j in region i; Q vi , Q ni are the total emissions of anthropogenic VOCs and NOx, respectively; a is a constant, indicating that the ozone emission reduction ratio is within a certain range;

[0036] N represents the O3 decrease ratio, O vj is the normalized coefficient of O3 generation potential of pollution source j, R vij is the total VOCs emission of pollution source j in region i; α and β are the minimum VOCs and NOx emission reduction ratios corresponding to the VOCs control area and the VOCs and NOx coordinated control area, respectively.

[0037] As a further improvement of the present invention, according to PM 2.5 Source analysis results and pollutant emission calculations for PM2.5 at receptor points in different areas of the target city 2.5 Sensitivity, the formula is:

[0038]

[0039] Where,

[0040] S ij is the pollutant sensitivity coefficient;

[0041] i is the emission reduction area, j is the pollution source, C ij is the average contribution concentration of pollutant j emitted from emission area i to all receptor points, E ij is the annual emission of pollutant j in emission area i;

[0042] The target city is divided into multiple levels of sensitive areas, including: core urban areas, high-sensitivity areas, high-load pollution areas, medium-sensitive areas and low-sensitivity areas.

[0043] As a further improvement of the present invention, based on PM2.5 of different pollution sources in the target city, 2.5 Control potential, multiple levels of sensitive areas, emission reduction costs, emission reduction amount, pollutant reduction amount, PM 2.5 Target reduction values ​​for PM concentrations are established 2.5 Optimize the control constraint equations; the formula is:

[0044]

[0045]

[0046] Where,

[0047] A pij is the control potential of pollutant p from pollution source j in region i, σ pj is the control efficiency of pollutant p of pollution source j under the optimal control measures, ω pj is the control efficiency of pollutant p of pollution source j under the current control measures;

[0048] Z is the total cost of controlling O3, and p represents pollutants, including SO2, NOx, PM 2.5 , VOCs and NH3, M pj is the control cost of pollutant p from pollution source j, X pij represents the reduction of pollutant p from pollution source j in region i;

[0049] W pij is the policy emission reduction amount of pollutant p from pollution source j in region i in the emission reduction policy;

[0050] Q pij is the emission of pollutant p from pollution source j in region i;

[0051] δ pi is the sensitivity coefficient of pollutant p in area i, C0 is PM 2.5 Target reduction in concentration.

[0052] As a further improvement of the present invention,

[0053] Solving the O3 concentration optimization control constraint equations and PM 2.5 Optimize the control constraint equations to obtain the precursors of SO2, NOx, PM in each control area and each level of sensitive area of ​​the target city 2.5 , VOCs, and NH3 emission reduction amounts and reduction ratios.

[0054] As a further improvement of the present invention, based on the target city PM 2.5 The O3 mass concentration, emission reduction policy and precursor emission data of each pollution source are used to solve the O3 concentration optimization control constraint equations and PM 2.5 Optimize the control constraint equations to obtain the O3 concentration and O3 precursor NOx and VOCs emission reduction plans for each VOCs control area and VOCs and NOx coordinated control area in the target city, as well as the PM 2.5 concentration and PM 2.5 Precursor PM 2.5 , SO2, NOx, VOCs, and NH3 emission reduction plans.

[0055] As a further improvement of the present invention, the O3 optimization emission reduction scheme of each VOCs control area and VOCs and NOx coordinated control area of ​​the target city, as well as the PM 2.5 Optimize the emission reduction plan to calculate the emission reduction list, and evaluate the optimization control effect by subtracting the numerical simulation results of the optimized emission reduction plan from the benchmark results

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] Based on the EKMA curve, the present invention obtains the optimal emission reduction ratio of precursors in different control areas of the target city, and then constrains the O3 concentration reduction plan, while taking into account the PM 2.5 The VOCs emission reduction results of the emission reduction scheme were obtained, and the minimum emission reduction ratios α and β of pollutants in different control areas were obtained. The O3 concentration reduction target was determined by combining α and β with the EKMA curve. Based on multiple constraints, the emission reduction schemes for the precursor NOx and VOCs emission reduction on O3 concentration in multiple divided control areas were finally obtained; for PM 2.5 , calculate the comprehensive sensitivity coefficient of pollutants according to the source analysis results and pollutant emissions, divide the sensitive areas according to the sensitivity results, pollutant emissions, industrial conditions of emission reduction areas, etc., and finally obtain the PM 2.5 PM 2.5 , SO2, NOx, VOCs, NH3 emission reduction scheme, the present invention achieves the city-scale PM 2.5 and O3 concentrations were both improved.

[0058] The present invention introduces the normalized coefficient of O3 generation potential into the optimization control equation, that is, by weightedly summing the VOCs emission reduction amount and the total VOCs emission amount of each emission source with the O3 generation potential coefficient of the corresponding emission source to obtain a ratio, thereby constraining the O3 concentration reduction target, comprehensively considering the differences in the ozone generation potential of VOCs from different emission sources in actual emission reduction, and realizing optimal control of O3 concentration. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a schematic diagram of the overall framework of the method for optimizing the control of atmospheric fine particulate matter and ozone disclosed in one embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of the EKMA curve of the VOCs control area and the optimal emission reduction ratio disclosed in one embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of the EKMA curve of the VOCs and NOx coordinated control area and the optimal emission reduction ratio disclosed in an embodiment of the present invention;

[0062] Figure 4 Schematic diagram of the optimized O3 emission reduction plan for each control area based on 2022 as the base year, disclosed in an embodiment of the present invention

[0063] Figure 5 The PM values ​​of various sensitive areas with 2022 as the base year are disclosed in an embodiment of the present invention. 2.5 Schematic diagram of optimized emission reduction plan;

[0064] Figure 6 A schematic diagram of the effectiveness evaluation of an optimized emission reduction scheme disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] 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 in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0066] The present invention will be described in further detail below with reference to the accompanying drawings:

[0067] like Figure 1 As shown, the present invention provides an optimization control method for atmospheric fine particulate matter and ozone, comprising the steps of:

[0068] S1. Collect PM in target cities 2.5 and O3 mass concentrations, emission reduction policies, and precursor emission data for each pollution source;

[0069] in,

[0070] like Figure 1 As shown, it includes collecting atmospheric pollutant emission data from anthropogenic and vegetation sources, obtaining PM 2.5 and O3 precursor emission data, PM 2.5and O3 precursors including PM 2.5 , SO2, NOx, VOCs, NH3;

[0071] The acquired data is input into the numerical simulation system for subsequent use.

[0072] S2. Divide the target cities into VOCs control areas and VOCs and NOx coordinated control areas based on ozone sensitivity;

[0073] in,

[0074] Ozone sensitivity is a core concept in the control of complex atmospheric pollution. It refers to the degree to which ozone (O3) concentration responds to changes in nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions in different regions or emission scenarios. It describes how changes in ozone concentration will cause changes in NOx or VOCs emissions.

[0075] S3. Based on the EKMA curve, the optimal emission reduction ratio of precursors in each VOCs control area and VOCs and NOx coordinated control area is obtained;

[0076] in,

[0077] According to the actual feasibility and scenario analysis method, the VOCs and NOx emission reduction range of precursors is set to 0-60%, and the PM 2.5 The EKMA curves of each VOCs control area and VOCs and NOx coordinated control area in the target city were obtained based on the mass concentration of O3, emission reduction policies and precursor emission data of each pollution source;

[0078] Based on the reduction ratio of VOCs and NOx and the O3 concentration, the optimal emission reduction ratio of precursors in each VOCs control area and VOCs and NOx coordinated control area was obtained.

[0079] S4. Calculate the control potential of VOCs and NOx from different pollution sources in the target city, and establish a set of constraint equations for optimizing the control of O3 concentration by combining the optimal emission reduction ratio of precursors, emission reduction costs, emission reduction amounts, O3 reduction ratios, and the minimum emission reduction ratios of VOCs and NOx corresponding to the VOCs control area and the VOCs and NOx coordinated control area as constraints;

[0080] in,

[0081] The control potential of VOCs and NOx from different pollution sources in the target city is calculated using the following formula:

[0082]

[0083] Where,

[0084] Apij is the control potential of pollutant p from pollution source j in region i, %;

[0085] σ pj is the control efficiency of pollutant p of pollution source j under the optimal control measures, %;

[0086] ω pj is the control efficiency of pollutant p of pollution source j under the current control measures, %.

[0087] Furthermore, the O3 concentration optimization control constraint equations are as follows:

[0088]

[0089] Where,

[0090] Z is the total cost of controlling O3 (10,000 yuan), i is the emission reduction area, j is the pollution source, p represents the pollutants, including VOCs and NOx, M pj is the control cost of pollutant p representing pollution source j (10,000 yuan / t);

[0091] X pij represents the reduction amount (t) of pollutant p from pollution source j in region i under the emission reduction ratio constraint;

[0092] Q pij is the emission of pollutant p from pollution source j in region i (t), A pij is the control potential of pollutant p from pollution source j in region i (%);

[0093] t represents different control areas, C t represents the optimal VOCs:NOx emission reduction ratio in control area t;

[0094] X vij and X nij are the VOCs and NOx reductions (t) of pollution source j in region i, Q vij and Q nij are the total anthropogenic VOCs and NOx emissions (t), respectively, and a is a constant, indicating that the ozone emission reduction ratio takes a value within a certain range;

[0095] N represents the O3 reduction ratio (%), O vj is the normalized coefficient of O3 generation potential of pollution source j, R vij is the total VOCs emission from pollution source j in region i (t);

[0096] α and β are the minimum emission reduction ratios of VOCs and NOx corresponding to the VOCs control area and the VOCs and NOx coordinated control area, respectively.

[0097] S5. Calculate PM2.5 levels in different areas of the target city for the receiving points (national monitoring stations) 2.5 Sensitivity, and divide the target city into multiple levels of sensitive areas;

[0098] in,

[0099] According to PM 2.5 Source analysis results and pollutant emission calculations for PM2.5 at receptor points in different areas of the target city 2.5 Sensitivity, the formula is:

[0100]

[0101] Where,

[0102] S ij is the pollutant sensitivity coefficient;

[0103] i is the emission reduction area, j is the pollution source, C ij is the average contribution concentration of pollutant j emitted from emission area i to all receptor points, E ij is the annual emission of pollutant j in emission area i;

[0104] The target city is divided into multiple levels of sensitive areas, including: core urban areas, high-sensitivity areas, high-load pollution areas, medium-sensitive areas and low-sensitivity areas.

[0105] S6. Compare PM2.5 of different pollution sources in the target city 2.5 Control potential, multiple levels of sensitive areas, emission reduction costs, emission reduction amount, pollutant reduction amount, PM 2.5 The target reduction value of concentration is used as a constraint to establish PM 2.5 Optimize the control constraint equations;

[0106] Among them, PM 2.5 Optimize the control constraint equations; the formula is:

[0107]

[0108]

[0109] Where,

[0110] A pij is the control potential of pollutant p from pollution source j in region i, σ pj is the control efficiency of pollutant p of pollution source j under the optimal control measures, ω pj is the control efficiency of pollutant p of pollution source j under the current control measures;

[0111] Z is the total cost of controlling O3, and p represents pollutants, including SO2, NOx, PM 2.5, VOCs and NH3, M pj is the control cost of pollutant p from pollution source j, X pij represents the reduction of pollutant p from pollution source j in region i;

[0112] W pij is the policy emission reduction amount of pollutant p from pollution source j in region i in the emission reduction policy;

[0113] Q pij is the emission of pollutant p from pollution source j in region i;

[0114] δ pi is the sensitivity coefficient of pollutant p in area i, C0 is PM 2.5 Target reduction in concentration.

[0115] S7. Based on PM in target cities 2.5 The O3 mass concentration, emission reduction policy and precursor emission data of each pollution source are used to solve the O3 concentration optimization control constraint equations and PM 2.5 Optimize the control constraint equations to obtain the O3 optimization reduction plan for each VOCs control area and VOCs and NOx coordinated control area in the target city, as well as the PM2.5 optimization plan for each sensitive area in the target city. 2.5 Optimize emission reduction plans.

[0116] in,

[0117] Based on the target city PM 2.5 The O3 mass concentration, emission reduction policy and precursor emission data of each pollution source are used to solve the O3 concentration optimization control constraint equations and PM 2.5 Optimize the control constraint equations to obtain the O3 concentration and O3 precursor NOx and VOCs emission reduction plans for each VOCs control area and VOCs and NOx coordinated control area in the target city, as well as the PM 2.5 concentration and PM 2.5 Precursor PM 2.5 , SO2, NOx, VOCs, and NH3 emission reduction plans.

[0118] Solving the O3 concentration optimization control constraint equations and PM 2.5 Optimize the control constraint equations to obtain the precursors of SO2, NOx, PM in each control area and each level of sensitive area of ​​the target city 2.5 , VOCs, and NH3 emission reduction amounts and reduction ratios.

[0119] Further,

[0120] Based on the O3 optimization reduction plan for each VOCs control area, VOCs and NOx coordinated control area, and PM in each level of sensitive area2.5 Optimize the emission reduction plan, carry out proportional emission reduction on the precursor emission data of each pollution source in the target city, and obtain a proportional emission reduction list.

[0121] Furthermore,

[0122] According to the O3 optimization reduction plan of each VOCs control area and VOCs and NOx coordinated control area in the target city, as well as the PM 2.5 The emission reduction plan is optimized to calculate the emission reduction list, and the numerical simulation results of the optimized emission reduction plan are subtracted from the benchmark results to evaluate the optimization control effect.

[0123] Example:

[0124] The year 2022 is taken as the base year and the future scenario year 2035 is taken as the target year. 2.5 The mass concentration is 39ug / m 3 , O3 mass concentration is 170ug / m 3 According to the existing policy plan, the PM of Cangzhou City in 2035 is set. 2.5 The annual average concentration should be 32ug / m 3 The annual evaluation index of the 90% percentile of the daily maximum 8-hour O3 concentration achieved a 5% concentration reduction, and the emission reduction areas were set as various districts and counties in Cangzhou City.

[0125] Based on the ozone sensitivity assessment results, the VOCs control area of ​​Cangzhou City (main urban area, Renqiu City, and the eastern part of Huanghua City) and the VOCs and NOx coordinated control area (Huanghua City coordinated control area and other districts and counties) were divided;

[0126] like Figure 2 、 3 As shown in the figure, based on the actual feasible situation and the scenario analysis method, the emission reduction range of precursor VOCs and NOx is set to 0-60%, and the EKMA curves and the optimal emission reduction ratio of precursors in different O3 control areas (VOCs control area, VOCs and NOx coordinated control area) are obtained;

[0127] Calculate the emission reduction potential of each pollutant from different pollution sources and establish an O3 optimization control model:

[0128]

[0129]

[0130] Where,

[0131] A pij is the control potential of pollutant p from pollution source j (%), σ pj is the control efficiency (%) of pollutant p of pollution source j under the optimal control measures, ωpj is the control efficiency (%) of pollutant p from pollution source j under the current control measures;

[0132] Z is the total cost of controlling O3 (10,000 yuan), i is the emission reduction area, j is the pollution source, p represents the pollutants, including VOCs and NOx, M pj is the control cost of pollutant p representing pollution source j (10,000 yuan / t);

[0133] X pij represents the reduction amount (t) of pollutant p from pollution source j in region i under the emission reduction ratio constraint;

[0134] Q pij is the emission of pollutant p from pollution source j in region i (t), A pij is the control potential of pollutant p from pollution source j in region i (%);

[0135] t represents different control areas, C t represents the optimal VOCs:NOx emission reduction ratio in control area t;

[0136] X vij and X nij are the VOCs and NOx reductions (t) of pollution source j in region i, Q vij and Q nij are the total anthropogenic VOCs and NOx emissions (t), respectively, and a is a constant, indicating that the ozone emission reduction ratio takes a value within a certain range;

[0137] N represents the O3 reduction ratio (%), O vj is the normalized coefficient of O3 generation potential of pollution source j, R vij is the total VOCs emission from pollution source j in region i (t);

[0138] α and β are the minimum emission reduction ratios of VOCs and NOx corresponding to the VOCs control area and the VOCs and NOx coordinated control area, respectively.

[0139] The PM values ​​of each district and county in Cangzhou City at the receiving point (national monitoring station) were calculated. 2.5 Sensitivity; Cangzhou City is classified according to PM 2.5 The sensitivity, emission load and industrial distribution are divided into core urban areas, high-sensitivity areas, high-load pollution areas, medium-sensitivity areas and low-sensitivity areas;

[0140] Establish PM 2.5 Optimization control model:

[0141]

[0142]

[0143] Where,

[0144] A pij is the control potential of pollutant p from pollution source j in region i, σ pj is the control efficiency of pollutant p of pollution source j under the optimal control measures, ω pj is the control efficiency of pollutant p of pollution source j under the current control measures;

[0145] Z is the total cost of controlling O3, and p represents pollutants, including SO2, NOx, PM 2.5 , VOCs and NH3, M pj is the control cost of pollutant p from pollution source j, X pij represents the reduction of pollutant p from pollution source j in region i;

[0146] W pij is the policy emission reduction amount of pollutant p from pollution source j in region i in the emission reduction policy;

[0147] Q pij is the emission of pollutant p from pollution source j in region i;

[0148] δ pi is the sensitivity coefficient of pollutant p in area i, C0 is PM 2.5 Target reduction in concentration.

[0149] Genetic algorithm is used to calculate the optimized emission reduction plan. According to the emission reduction plan, each pollutant in each area of ​​the collected baseline list is reduced to obtain the optimized emission reduction list, such as Figure 4 As shown in the figure on the right, it includes the emission reduction amount and emission reduction ratio of NOx and VOCs in each control area; Figure 5 As shown in the right figure, including SO2, NOx, PM 2.5 , VOCs and NH3 reduction ratios and emission reduction amounts, and based on the optimized emission reduction results, the baseline inventory is subjected to equal-proportional emission reductions in each region to obtain an equal-proportional emission reduction inventory, such as Figure 4 As shown in the left figure, it includes the NOx and VOCs emission reduction of each pollution source in each control area, such as Figure 5 As shown in the left figure, SO2, NOx, PM and other pollution sources in sensitive areas of different levels are included. 2.5 , VOCs and NH3 emission reduction;

[0150] Keeping other conditions unchanged, the simulation results of different inventories are subtracted from the simulation results of the baseline scenario to evaluate the options, such as Figure 6 shown.

[0151] Advantages of the present invention:

[0152] The present invention obtains the optimal emission reduction ratio of precursors in different control areas of the target city based on the EKMA curve, and then constrains the O3 concentration reduction plan, while taking PM into consideration. 2.5 The VOCs emission reduction results of the emission reduction scheme were obtained, and the minimum emission reduction ratios α and β of pollutants in different control areas were obtained. The O3 concentration reduction target was determined by combining α and β with the EKMA curve. Based on multiple constraints, the emission reduction schemes for the precursor NOx and VOCs emission reduction on O3 concentration in multiple divided control areas were finally obtained; for PM 2.5 , calculate the comprehensive sensitivity coefficient of pollutants according to the source analysis results and pollutant emissions, divide the sensitive areas according to the sensitivity results, pollutant emissions, industrial conditions of emission reduction areas, etc., and finally obtain the PM 2.5 PM 2.5 , SO2, NOx, VOCs, NH3 emission reduction scheme, the present invention achieves the city-scale PM 2.5 and O3 concentrations were both improved.

[0153] The present invention introduces the normalized coefficient of O3 generation potential into the optimization control equation, that is, by weightedly summing the VOCs emission reduction amount and the total VOCs emission amount of each emission source with the O3 generation potential coefficient of the corresponding emission source to obtain a ratio, thereby constraining the O3 concentration reduction target, comprehensively considering the differences in the ozone generation potential of VOCs from different emission sources in actual emission reduction, and realizing optimal control of O3 concentration.

[0154] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the control of atmospheric fine particulate matter and ozone, characterized in that: include: Collect PM in target cities 2.5 and O3 mass concentrations, emission reduction policies, and precursor emission data for each pollution source; Target cities are divided into VOCs control areas and VOCs and NOx coordinated control areas based on ozone sensitivity; Based on the EKMA curve, the optimal emission reduction ratio of precursors in each VOCs control area and VOCs and NOx coordinated control area was obtained; Calculate the control potential of VOCs and NOx from different pollution sources in the target city, and establish a set of constraint equations for optimizing the control of O3 concentration by combining the optimal emission reduction ratio of precursors, emission reduction costs, emission reduction amounts, O3 reduction ratio, and the minimum emission reduction ratio of VOCs and NOx corresponding to the VOCs control area and the VOCs and NOx coordinated control area; Calculate PM2.5 of receptor points in different areas of the target city 2.5 Sensitivity, and divide the target city into multiple levels of sensitive areas; PM based on different pollution sources in the target city 2.5 Control potential, multiple levels of sensitive areas, emission reduction costs, emission reduction amount, pollutant reduction amount, PM 2.5 Target reduction values ​​for PM concentrations are established 2.5 Optimize the control constraint equations; Based on the target city PM 2.5 The O3 mass concentration, emission reduction policy and precursor emission data of each pollution source are used to solve the O3 concentration optimization control constraint equations and PM 2.5 Optimize the control constraint equations to obtain the O3 optimization reduction plan for each VOCs control area and VOCs and NOx coordinated control area in the target city, as well as the PM2.5 optimization plan for each sensitive area in the target city. 2.5 Optimize emission reduction plans.

2. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 1, characterized in that: Based on the EKMA curve, the optimal emission reduction ratio of precursors in each VOCs control area and VOCs and NOx coordinated control area is obtained; including: Based on the actual feasibility and scenario analysis method, the emission reduction range of precursor VOCs and NOx was set from 0% to 60%, and the EKMA curves of each VOCs control area and VOCs and NOx coordinated control area in the target city were obtained; Based on the reduction ratio of VOCs and NOx and the O3 concentration, the optimal emission reduction ratio of precursors in each VOCs control area and VOCs and NOx coordinated control area was obtained.

3. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 1, characterized in that: Based on the O3 optimization reduction plan for each VOCs control area, VOCs and NOx coordinated control area, and PM in each level of sensitive area 2.5 Optimize the emission reduction plan, carry out proportional emission reduction on the precursor emission data of each pollution source in the target city, and obtain a proportional emission reduction list.

4. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 1, characterized in that: The control potential of VOCs and NOx from different pollution sources in the target city is calculated using the following formula: Where, A pij is the control potential of pollutant p from pollution source j in region i, %; σ pj is the control efficiency of pollutant p of pollution source j under the optimal control measures, %; ω pj is the control efficiency of pollutant p of pollution source j under the current control measures, %.

5. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 4, characterized in that: The optimal emission reduction ratio of precursors, emission reduction cost, emission reduction amount, O3 reduction ratio, and the minimum emission reduction ratio of VOCs and NOx corresponding to the VOCs control area and the VOCs and NOx coordinated control area are combined to establish the O3 concentration optimization control constraint equation group, which is as follows: 0≤X pij ≤Q pij A pij Where, Z is the total cost of controlling O3, i is the emission reduction area, j is the pollution source, and p represents the pollutants, including VOCs and NOx; M pj is the control cost of pollutant p representing pollution source j; X pij represents the reduction amount of pollutant p from pollution source j in region i under the pollutant emission reduction ratio constraint; Q pij is the emission of pollutant p from pollution source j in region i, A pij is the control potential of pollutant p from pollution source j in region i; t represents different control areas, C t represents the optimal VOCs:NOx emission reduction ratio in control area t; X vij 、X nij are the VOCs and NOx reductions of pollution source j in region i; Q vi , Q ni are the total emissions of anthropogenic VOCs and NOx, respectively; a is a constant, indicating that the ozone emission reduction ratio is within a certain range; N represents the O3 decrease ratio, O vj is the normalized coefficient of O3 generation potential of pollution source j, R vij is the total VOCs emission of pollution source j in region i; α and β are the minimum VOCs and NOx emission reduction ratios corresponding to the VOCs control area and the VOCs and NOx coordinated control area, respectively.

6. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 1, characterized in that: According to PM 2.5 Source analysis results and pollutant emission calculations for PM2.5 at receptor points in different areas of the target city 2.5 Sensitivity, the formula is: Where, S ij is the pollutant sensitivity coefficient; i is the emission reduction area, j is the pollution source, C ij is the average contribution concentration of pollutant j emitted from emission area i to all receptor points, E ij is the annual emission of pollutant j in emission area i; The target city is divided into multiple levels of sensitive areas, including: core urban areas, high-sensitivity areas, high-load pollution areas, medium-sensitive areas and low-sensitivity areas.

7. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 6, characterized in that: PM based on different pollution sources in the target city 2.5 Control potential, multiple levels of sensitive areas, emission reduction costs, emission reduction amount, pollutant reduction amount, PM 2.5 Target reduction values ​​for PM concentrations are established 2.5 Optimize the control constraint equations; the formula is: IN pij ≤X pij 0≤X pij ≤Q pij A pij Where, A pij is the control potential of pollutant p from pollution source j in region i, σ pj is the control efficiency of pollutant p of pollution source j under the optimal control measures, ω pj is the control efficiency of pollutant p of pollution source j under the current control measures; Z is the total cost of controlling O3, and p represents pollutants, including SO2, NOx, PM 2.5 , VOCs and NH3, M pj is the control cost of pollutant p from pollution source j, X pij represents the reduction of pollutant p from pollution source j in region i; W pij is the policy emission reduction amount of pollutant p from pollution source j in region i in the emission reduction policy; Q pij is the emission of pollutant p from pollution source j in region i; δ pi is the sensitivity coefficient of pollutant p in area i, C0 is PM 2.5 Target reduction in concentration.

8. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 1, characterized in that: Solving the O3 concentration optimization control constraint equations and PM 2.5 Optimize the control constraint equations to obtain the precursors of SO2, NOx, PM in each control area and each level of sensitive area of ​​the target city 2.5 , VOCs, and NH3 emission reduction amounts and reduction ratios.

9. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 1, characterized in that: Based on the target city PM 2.5 The O3 mass concentration, emission reduction policy and precursor emission data of each pollution source are used to solve the O3 concentration optimization control constraint equations and PM 2.5 Optimize the control constraint equations to obtain the O3 concentration and O3 precursor NOx and VOCs emission reduction plans for each VOCs control area and VOCs and NOx coordinated control area in the target city, as well as the PM 2.5 concentration and PM 2.5 Precursor PM 2.5 , SO2, NOx, VOCs, and NH3 emission reduction plans.

10. The method for optimizing and controlling atmospheric fine particulate matter and ozone according to claim 1, characterized in that: According to the O3 optimization reduction plan of each VOCs control area and VOCs and NOx coordinated control area in the target city, as well as the PM 2.5 The emission reduction plan is optimized to calculate the emission reduction list, and the numerical simulation results of the optimized emission reduction plan are subtracted from the benchmark results to evaluate the optimization control effect.

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