CN114418434B - Method for selecting pollutant treatment measures - Google Patents

Method for selecting pollutant treatment measures Download PDF

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CN114418434B
CN114418434B CN202210098699.4A CN202210098699A CN114418434B CN 114418434 B CN114418434 B CN 114418434B CN 202210098699 A CN202210098699 A CN 202210098699A CN 114418434 B CN114418434 B CN 114418434B
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解洪兴
何新
门高闪
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Environmental Technology Center Of Keling El Beijing
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Abstract

Aiming at the defect of selecting a mode for pollution treatment measures in the background technology, the invention provides a method which is efficient, quick, convenient and scientificA method for selecting air pollution treatment measures. The method is a dynamic selection method for optimizing air pollution control measures for cities according to the characteristics of the air pollution of the cities. The method is based on urban PM 2.5 、O 3 、SO 2 、NO 2 And selecting air pollution treatment measures according to the pollutant concentration standard exceeding rate and the corresponding emission source constitution (obtained according to an emission list), and preferably selecting measures suitable for the air quality status situation of each city according to the score of each measure in the measure library.

Description

Method for selecting pollutant treatment measures
Technical Field
The invention relates to a method for selecting an environmental pollution treatment measure, and belongs to the field of environmental treatment.
Background
In 2016, a new atmosphere law establishes an urban air quality period standard-reaching mechanism, cities become main bodies of air quality improvement, however, over 64% of cities in China still do not achieve standard air quality at present, and the situation of air pollution prevention and control is still severe. With the continuous deepening of air pollution prevention and control in China, a plurality of treatment works enter deep water areas, the difficulty of policy making and execution is continuously upgraded, the fine management requirement on the urban air quality is higher and higher, and the requirement of the customization measures taking cities as units is more and more urgent.
At present, the situation of unclear bottom is an important bottleneck for restricting the work of preventing and controlling the atmospheric pollution in China. Decision makers in many cities have insufficient information grasp of main pollutant emission total amount, space-time distribution, industry contribution, emission reduction potential and the like, when relevant measures for atmosphere pollution prevention and control are selected, local actual conditions are separated, effective scientific support is lacked, the measures cannot be matched with local actual air pollution conditions, and accurate haze control is difficult to achieve.
At present, some control Quality prediction and evaluation systems are used for providing suggestions for relevant decision-making personnel, such as a CMAQ (Community Multi-scale Air Quality prediction) which is a third-generation Air Quality prediction and evaluation system proposed by the United states Environmental Protection Agency (EPA) after a Lagrange trajectory model and an Euler grid model, and the model is based on source Emission models such as a mesoscale meteorological model and an SMOKE (sparse Matrix Operator Emission) under the guidance of an atmosphere theory, considers the influences of horizontal transmission, vertical transmission, a diffusion process, source Emission, a chemical reaction, a removal process and the like on pollutant concentration in an atmospheric pollution process, and comprehensively processes complex Air pollution conditions. The model can simulate advection transmission, turbulent diffusion, gas phase chemical reaction, aerosol dynamics, a discharge process, a sedimentation process, a cloud process and a liquid phase process, and can be used for evaluating the sedimentation pollution level of fine particles, troposphere ozone, aerosol and acid in the atmosphere. However, the system is mainly used for air quality prediction, has certain reference to measure selection, and has no systematic evaluation mode of the journey.
The CMAx (Comprehensive Air Quality Model with Extensions) Model is also an Euler type chemical transmission Model, a gas-liquid-solid multiphase chemical mechanism is considered under the guidance of the concept of 'one atmosphere', a gas image field simulation result is utilized, a source emission list is processed through an SMOKE source emission Model, and finally a CAMx Model simulates the pollutant concentration, and the CAMx Model is different from the models3-CMAQ Model in that the CAMx Model has a two-way nested grid structure, can calculate multiple grids simultaneously, and is more precise in simulation in a time range and a space range. CAMx also includes various analytical tools such as Ozone Source Analysis Technology (OSAT), particulate source tracking technology (PAST), grid plume module (PiG), and the like. The CAMx tool is also mainly used for pollution prediction and is used for assisting some pollution source analysis tools, and pollution treatment measures cannot be effectively selected.
A WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) model is also a commonly used technology in the field of atmospheric environment governance, wherein a meteorological model and a chemical transmission module of the model use the same lattice point, time step, transmission scheme and physical scheme to avoid errors caused by difference values and the like, and meanwhile, the two are synchronously calculated to complete coupling on time and spatial resolution and realize real on-line transmission, thereby completing coupling and feedback on multiple processes such as solar radiation, atmospheric power, aerosol Chemistry and the like. WRF-Chem can predict the air quality, and pollution treatment measures cannot be efficiently and scientifically selected.
The above technologies for treatment measures are mainly used for predicting air quality, and suitable measures or measures combinations cannot be selected for decision makers according to pollution characteristics. The use of these techniques requires the use of complex mathematical, physical and chemical models, the implementation of specialized personnel, the use of specialized large computers for the prediction process, and high time and cost.
Related papers used for selecting pollution measures are few, the most related one is mode selection and strategy measures for Shenzhen comprehensive traffic jam management, which is from the annual meeting corpus of 2016 China urban traffic planning in the year, and the paper mainly introduces a mode selection and strategy measure method in the aspect of traffic management.
In the related patent, no method patent for pollution abatement measure selection is retrieved.
Therefore, according to the search, the selection of the current atmospheric pollution treatment measures is mainly carried out by means of manual experience, and no scientific and efficient technical means is provided for evaluating and selecting the related pollution treatment measures.
Disclosure of Invention
Related terms
Emission list number i: i =1,2,3,4 \8230;, representing category numbers in a discharge list, such as road movement sources, non-road movement sources, dust sources, industrial sources, etc.
Discharge list number (i) example table
Value of i 1 2 3 4
Class of pollution source Road moving source Non-road mobile source Dust raising source Industrial source
Action list number j: j = A, B, C, D \8230; \8230andrepresentative action numbers, such as upgrading an engine locomotive: diesel electric hybrid vehicle, airplane ground support apparatus: pollutant number k that electric power substituted fuel, bank base electric power, coal washing measure can be administered: k =1,2,3,4 \8230;, the k value represents contaminants such as NO that can be remediated by this measure X 、PM 2.5、 SO 2 、O 3
Pollutant number (k) example table capable of treating
k value 1 2 3 4
Measures taken NO X PM 2.5 SO 2 O 3
Feature number α of measure: α =1,2,3 \8230, characteristics representative of measures such as implementation cycle characteristics, capital requirement characteristics.
Measure class weight W i Coefficient: w i The weighting coefficient represents a class of emission measures, the weighting coefficient is related to pollutant overproof conditions and source composition, the higher the urban pollutant overproof rate is, the larger the proportion of emission source categories (the categories of the measure library are consistent with the categories of the emission sources in an emission list) corresponding to the measure library in the pollutant source composition is, the greater the contribution is made to the weighting coefficient; such as W 1 Weight for pollution control measures of motor vehicles, W 2 The weight is taken as a measure for treating coal pollution.
Pollutant treating effect coefficient of measure
Figure BDA0003492030800000031
Showing the treatment effect of the measure j on the pollutant k.
Characteristic parameter of measure
Figure BDA0003492030800000032
Characteristic parameter of measure
Figure BDA0003492030800000033
Representing some necessary characteristics reflected by the implementation of the measures, such as implementation period and the like, and capital requirement conditions; alpha is the characteristic parameter number of the measure, e.g.
Figure BDA0003492030800000034
The periodic characteristics may be implemented for the action j,
Figure BDA0003492030800000035
may be the capital requirement characteristic of action j.
Coefficient of contribution of emission source to pollutant
Figure BDA0003492030800000036
Representing the contribution rate of class i emissions sources to contaminant k.
F j : score of measure score.
Corresponding pollutant discharge amount parameter in discharge source
Figure BDA0003492030800000037
Indicating the amount of pollutant k emitted from the class i emission source. Pollutant emission quantity parameter E k : the amount of pollutant k discharged.
Over-standard rate epsilon k :ε k Is the ratio of the monitoring value of the annual average concentration of the pollutants to the limit value of the annual average concentration of the pollutants in the national standard.
Limit of pollutant concentration S k :S k Represents the air pollutant average concentration limit value regulated in the national air quality standard. Pollutant concentration monitoring value T k :S k A monitored value representing the concentration of the air contaminant.
According to epsilon k Judging the overproof condition of each pollutant by the value, and judging different overproof coefficients e corresponding to different overproof conditions k
At present, decision makers in many cities have insufficient information grasp on main pollutant emission total amount, space-time distribution, industry contribution, emission reduction potential and the like, and when relevant measures for preventing and controlling atmospheric pollution are selected, local actual conditions are separated, effective scientific support is lacked, and the measures cannot be matched with local actual air pollution conditions.
Measures for reducing atmospheric pollution are basically improved at present, but in order to achieve accurate and effective haze treatment in a city or an area, the treatment measures need to be reasonably, efficiently and accurately selected under limited resources, and meanwhile, the selected measures need to meet certain time resource and cost limits. The current measure selection mode is mainly carried out by means of manual experience, scientific basis is not provided, decision errors are easily caused, the resource use efficiency is low, and the problem cannot be effectively solved in a targeted mode. Although some auxiliary means such as an atmospheric pollution prediction model are established, the models are basically used for predicting air quality, a decision maker cannot be directly helped to select air pollution treatment measures, the auxiliary effect is small, the models basically need a super computer to operate, high-level professional operation is needed, the time consumption is long, the cost is high, and the operation difficulty is high. In summary, at present, there is no scientific, systematic, convenient and efficient technical means for evaluating and selecting relevant pollution treatment measures.
Aiming at the defects of the selection mode of pollution treatment measures in the background technology, the invention provides a high-efficiency, quick, convenient and scientific selection method of air pollution treatment measures. The method is a dynamic selection method for optimizing the air pollution control measures for the city according to the urban air pollution characteristics. The method is based on urban PM 2.5 、O 3 、SO 2 、NO 2 And selecting air pollution treatment measures according to the pollutant concentration standard exceeding rate and the corresponding emission source constitution (obtained according to an emission list), and preferably selecting measures suitable for the air quality status situation of each city according to the score of each measure in the measure library.
The air pollution treatment measure selection method provided by the invention can help a decision maker to scientifically judge the applicability and effectiveness of each treatment measure according to local air pollution characteristics, and further solve the local air pollution problem in a targeted manner. The air pollution treatment measure selection method provided by the invention avoids blindness and inefficiency caused by selection depending on experience on one hand, and improves scientificity and pertinence; on one hand, compared with auxiliary means such as various atmospheric pollution prediction models and the like, the method is simple, efficient and low in cost, does not need advanced professional operation, can enable a decision maker to relatively quickly select air pollution treatment measures suitable for local actual conditions, and improves the measure selection efficiency.
The invention relates to a method for preventing and controlling urban preferred air pollution according to urban air pollution characteristicsThe dynamic selection method of (3). The basic basis for the measure selection is urban PM 2.5 、SO 2 、NO 2 And its corresponding emission source (from the emission inventory). The method can be used for grading each measure in the measure library and preferably selecting the measure which is suitable for the current air quality situation of each city according to the score.
The specific selection process comprises the steps of establishing a measure library, analyzing the pollutant monitoring data standard exceeding condition, analyzing the pollutant source composition, calculating measure weight, calculating measure score and selecting measures.
A local air pollution treatment measure library is required to be established, and the measures are classified according to the application range of the measures according to the classification method of the local pollutant emission list emission source to form different types of measure libraries. Each measure in the measure library has a removal applicability coefficient for each pollutant; the pollutant treatment effect coefficient of the measure is used for describing whether a certain measure is suitable for removing a certain pollutant or not, and the specific value is determined according to the pollutant removal efficiency of the measure.
The pollutant monitoring data standard exceeding condition needs to be analyzed, the standard exceeding rate of pollutant concentration can be calculated according to the corresponding environmental air quality standard, and different standard exceeding coefficients are determined according to the standard exceeding condition.
It is necessary to analyze the pollutant source constitution and analyze each pollutant (such as PM) according to the emission list of each pollutant in the city 2.5 、SO 2 、NO 2 ) The emission source of (2). The pollutant emissions list refers to the collection of the amount of atmospheric pollutants emitted into the atmosphere by various emission sources over a span of time and a spatial region. And calculating the contribution rate of each emission source to each pollutant according to the emission list.
And according to the overproof condition and the source of each pollutant, configuring each measure type in the measure library to be distributed with different weights. And calculating the scores of the measures according to the category weights of the measures, the pollutant exceeding conditions and the pollutant removal effect coefficients of the measures, and selecting the optimal measures according to a certain selection method according to the scores. The simplest method of selection may be a method of scoring the ranking.
The method can also be used for treating water pollution and greenhouse gas pollution, and the corresponding pollutants to be treated are water pollution pollutants or greenhouse gas pollutants; the treatment measures are corresponding water pollution and greenhouse gas treatment measures.
Establishing a measure library
The measure library is a list library of all effective measures (such as DPF addition of motor vehicles, ultralow emission modification of coal-fired power plants, clean heating and the like) capable of treating environmental pollution. The measures in the measure library are classified according to the classification method of the pollutant emission list emission sources in the city and the geographic position area and according to the application range of the measures, the types of the measure library are consistent with the types of the pollution sources in the emission list, and different types of measure libraries are formed, such as measures of industrial sources, road mobile sources, non-road mobile sources, dust raising sources, VOC related sources, thermal power plants, natural sources and the like. Each measure in the measure library has a pollutant treatment effect coefficient of the measure aiming at each pollutant, and the pollutant treatment effect coefficient of the measure is used for describing whether a certain measure is suitable for removing a certain pollutant or not. The pollutant treatment effect coefficient of the measure can be directly obtained from a measure list (such as the U.S. EPA measure list); or the pollutant treatment effect coefficient of the measure can be correspondingly converted from the treatment effect of the measure, and the value range is (0, 1).
Treatment effect correspondence table of measure
Actual removal efficiency of the measure to the contaminant Pollutant treating effect coefficient of measure
0%-25% 0
26%-75% 0.5
76%-100% 1
Pollutant treating effect coefficient table example table of measure
Figure BDA0003492030800000051
E.g. contaminant 1 may be NO X The pollutant 2 may be PM 2.5 Contaminant 3 may be SO2; the pollutant number can be edited and selected according to the city condition.
Analyzing the exceeding standard condition of urban air quality monitoring data
Analyzing the overproof rate of air pollutants: and obtaining the exceeding rate of each pollutant according to the air pollutant monitoring condition and an air quality standard, wherein the air quality standard can be national standard GB3095-2012, a local air quality standard, an international air quality standard and the like.
Limit of pollutant concentration S k :S k Represents the air pollutant annual average concentration limit, PM, specified in the air quality standard 2.5 、SO 2 、NO 2 Are concentration limits, respectively.
Pollutant concentration monitoring value T k :T k Monitoring value, T, representing the concentration of air pollutants PM2.5 、T SO2 、T NO2 、T O3 Are respectively PM 2.5 、SO 2 、NO 2 、O 3 Concentration monitor value (PM) 2.5 、SO 2 、NO 2 The annual average concentration monitoring value can be adopted; if O is selected 3 ,O 3 May be the 90 th percentile of daily maximum 8 hour moving average) calculates PM based on ambient air quality standards 2.5 、O 3 、SO 2 、NO 2 The concentration overproof rate of (2) is calculated as follows.
Figure BDA0003492030800000052
ε k The ratio of the annual average concentration monitoring value of the pollutants to the annual average concentration limit value of the pollutants in the national standard is shown in the following table, and each pollutant concentration limit value can be obtained from the national standard and can also be obtained from other air quality standards.
Chinese air pollutant concentration national standard restriction table
Contaminants Statistical index National standard
PM 2.5 Average concentration per year 35μg/m 3
SO 2 Average concentration per year 60μg/m 3
NO 2 Annual average concentration 40μg/m 3
O 3 90 th percentile of daily maximum 8 hour sliding averages 160μg/m 3
When epsilon is obtained by calculation k Then, the overproof conditions of each pollutant can be judged, and the overproof coefficient e obtained corresponding to different overproof conditions k As shown in the following table, the corresponding manner of the superscalar coefficient can be adjusted according to different local conditions.
Over-standard proportion epsilon k Over-standard condition judgment Coefficient of excess e k
0-1 Not exceeding standard 0
1-1.2 Slight over standard 1
1.2-1.5 Moderate degree exceeds standard 2
1.5-2 Severe over-standard 5
2 or more Severe standard exceeding 10
When PM 2.5 、SO 2 、NO 2 When the exceeding standard condition is in the following conditions, the monitoring value can be corrected:
1)PM 2.5 slight excess of SO 2 And NO 2 At least one of which does not exceed the standard and approaches the emission limit;
2)PM 2.5 not exceeding standard and approaching emission limit, SO 2 And NO 2 At least one of them is slightly out of standard;
3)PM 2.5 、SO 2 and NO 2 The same over-standard condition (respective ε) k Similar in value);
the correction method comprises the following steps:
according to PM 2.5 Obtaining PM as a result of the source analysis 2.5 The mass percentage of sulfate in the composition
Figure BDA0003492030800000061
Mass percent of the nitrate
Figure BDA0003492030800000062
Applications of
Figure BDA0003492030800000063
And
Figure BDA0003492030800000064
for PM 2.5 、SO 2 、NO 2 The concentration monitor value of (1) is corrected to obtain a correction value (T ') of the concentration' k ) The correction method comprises the following steps:
Figure BDA0003492030800000065
Figure BDA0003492030800000066
Figure BDA0003492030800000071
according to
Figure BDA0003492030800000072
Recalculating
Figure BDA0003492030800000073
Re-judging PM 2.5 、SO 2 、NO 2 Over-standard condition.
The analysis source is configured to analyze each Pollutant (PM) according to the emission list of each pollutant in the city 2.5 、O 3 、SO 2 、NO 2 ) The emission source of (2). The pollutant emissions list refers to the collection of the amount of atmospheric pollutants emitted into the atmosphere by various emission sources over a span of time and a spatial region. And calculating the contribution rate of each emission source to each pollutant according to the emission list, wherein the specific calculation method comprises the following steps:
Figure BDA0003492030800000074
Figure BDA0003492030800000075
coefficient of contribution of emission source to pollutant
Figure BDA0003492030800000076
Representing the contribution rate of class i emissions sources to contaminant k.
Figure BDA0003492030800000077
Corresponding pollutant discharge amount parameter in discharge source
Figure BDA0003492030800000078
Indicating the amount of pollutant k emitted from the class i emission source.
E k : pollutant emission quantity parameter E k Indicating the amount of pollutant k discharged.
Action category weight calculation
Different weights are distributed to each measure in the measure library according to the standard exceeding condition and the source constitution (contribution rate) of each pollutant, and the weight calculation method comprises the following steps:
the measure is primarily screened, and epsilon can be selected k Prevention measures corresponding to pollutants larger than 1 (i.e. exceeding pollutants);
and distributing the weights of various measure libraries related to the overproof pollutants according to the overproof rate of the overproof pollutants and the proportion of each pollution source in an emission list of the overproof pollutants, and finally adding the weights of the measure libraries related to each measure to obtain the weight of each measure.
Figure BDA0003492030800000079
W i : weight of class i action library.
i: and the measure library category related to the overproof pollutant, namely the pollution source category.
k: the pollutant name is exceeded.
Figure BDA00034920308000000710
The contribution rate of class i emission sources to contaminant k.
Some measures can be applied to control of various pollution sources at the same time, so that the same control measure can be presented in a measure library of various categories at the same time, and the calculation of the measure weight needs to take the weights of the measure in different measure categories into account and calculate the measure weight in the scoring.
Measure score calculation combining basic data and measure library
The evaluation of the measure is performed according to a scoring formula, which is as follows.
Figure BDA0003492030800000081
Figure BDA0003492030800000082
F j : measure scoring
For example:
Figure BDA0003492030800000083
the above formula represents the fractional case of action A in the class 1 action library.
The measure selection mode calculates the score F of each measure according to the formula j Then according to F j The applicable measures are selected. The specific selection methods include a score direct ranking method and a threshold value screening method.
Direct ranking method
The measures are scored according to their scores F j Size arranged, F j The larger the measure is, the more the measure is ranked, and finally the measure with the top 10% of the rank is selected.
For example: there were a total of 10 measures, the scores of which were F j And ranking cases are given in the following table:
measures taken A B C D E F G H I J
F F A =10 F B =9 F B =8 F D =7 F E =6 F F =5 F G =4 F H =3 F I =2 F J =1
Ranking 1 2 3 4 5 6 7 8 9 10
According to the table above, the measure of the top 10% is the measure of the rank 1 (10 × 10% = 1), i.e., measure a, and finally measure a is selected.
Threshold value screening method: choose the largest score F j 0.5 x F j Setting as threshold, and selecting score F j Measures greater than a threshold.
For example: a total of 10 measures, the scores of which are E j See table below:
Figure BDA0003492030800000084
Figure BDA0003492030800000091
according to the table above, threshold =0.5 × 10=5, score F j If the measures A, B, C, D and E are more than 5, the measures A, B, C, D and E are selected finally.
Threshold value screening method
After the measures are primarily screened by a direct ranking method, and the number of the measures obtained by primary screening is more than 1, the measures are secondarily screened by comprehensively considering the actual capital budget and time requirements.
When the time requirement is considered preferentially, namely a certain treatment effect is required to be achieved within a certain time range: according to the implementation period selection measure of each measure, a measure having an implementation period less than or equal to the required time range is selected. When the fund budget is preferentially considered, namely a certain amount of fund cost is needed to achieve a certain treatment effect: the measure is selected based on a capital cost for each measure, and the measure is selected such that the capital cost is less than or equal to a budget value for capital. When time requirements and capital budgets are considered in combination: grading F of measure selected by applying basic selection method according to implementation period and capital cost of measure j The correction is carried out by the following method:
Figure BDA0003492030800000092
F′ j : revised action scores
Figure BDA0003492030800000093
Period of execution of the measures
Figure BDA0003492030800000094
Capital cost of the measure
After correction, E 'is rejected' j The measure with the smallest value and the rest measures are finally optimized measures.
Drawings
FIG. 1: and the flow diagram of the basic scheme for selecting the measures is shown.
FIG. 2 is a schematic diagram: introducing a measure selection scheme schematic diagram with improved applicability conditions.
FIG. 3: introduction of PM 2.5 And the flow diagram of the measure selection scheme of the source analysis correction improvement.
FIG. 4: combining applicable conditions with PM 2.5 And the flow diagram of the measure selection scheme for source analysis modification is shown.
FIG. 5: first city PM 2.5 And source analysis result source constitution.
FIG. 6: first city SO 2 And source analysis result source constitution.
FIG. 7: first city NO 2 And source analysis result source constitution.
Detailed Description
Example one
The method for selecting the measures is applied to screen out the optimal pollution treatment measures for the city A. The first city air quality monitoring results are shown in the following table.
Monitoring results of concentration of each air pollutant in first city
Figure BDA0003492030800000111
First city PM 2.5 、SO 2 、NO 2 、O 3 The concentration superstandard ratio of (d) is calculated as follows:
Figure BDA0003492030800000112
Figure BDA0003492030800000113
Figure BDA0003492030800000114
the analytic results of each air pollutant source in city A are shown in the following table.
First city pollutant emission source composition table
Figure BDA0003492030800000115
The available pollution source control measures in city A are as follows.
Available pollution source prevention and control measure table for city A
Figure BDA0003492030800000116
Figure BDA0003492030800000121
As the SO2 standard exceeding rate is less than 1, the SO2 in the market reaches the standard and the important treatment is not needed to be carried out by taking related measures, PM is selected from the measure library 2.5 、NO 2 And (4) corresponding prevention and treatment measures.
The measure category weights are calculated as follows:
Figure BDA0003492030800000122
Figure BDA0003492030800000123
Figure BDA0003492030800000124
Figure BDA0003492030800000125
Figure BDA0003492030800000126
Figure BDA0003492030800000131
Figure BDA0003492030800000132
Figure BDA0003492030800000133
the treatment measures in the embodiment are from the American EPA list, and the pollutant removal applicability coefficient of each measure is obtained according to the treatment effect of the measure
Figure BDA0003492030800000134
The following were used:
Figure BDA0003492030800000135
such as, for example,
Figure BDA0003492030800000136
representative of action 1 on the contaminant NO x Has a removal suitability coefficient of 1, i.e. measure 1 is very suitable for removing contaminant NO x
By the formula:
Figure BDA0003492030800000137
the scoring for each measure j can be calculated as follows:
non-road mobile source Road moving source Industrial source Thermal power plant Other + natural sources Dust raising source Total points of measures
Action
1 0.6 0.6
Action 2 0.1 0.1
Action 3 0.6 0.6
Action 4 0 0
Action 5 0.1333 0.1333
Action 6 0 0
Action 7 0 0
Measure 8 0.46 0.46
Measure 9 4.3 4.3
Measure 10 0 0
Measure 11 1.2665 1.2665
Example of calculation:
Figure BDA0003492030800000141
since the main removal of measures 4, 6 and 7 is SO 2 In this example SO 2 The standard does not exceed, so the final scores of the three measures are 0; or due to the treatment of SO 2 Measure (A) SO 2 In the case of the scheme, the related SO is not exceeded 2 Measures are not taken into account. Since the four-term pollutant removal rate of the measure 10 is less than 25%, the removal adaptability coefficients of the measure 10 to the four-term pollutants are all 0, and therefore the final score of the measure 10 is 0.
Example two
The method for selecting the measures is applied to screen out the optimal pollution treatment measures for the city A. The first city air quality monitoring results are shown in the following table.
Results of monitoring concentration of each air pollutant in city A
Figure BDA0003492030800000142
First city PM 2.5 、SO 2 、NO 2 、O 3 The concentration superstandard ratio is calculated as follows:
Figure BDA0003492030800000151
Figure BDA0003492030800000152
Figure BDA0003492030800000153
the analytical results of the air pollutant sources in city A are shown in the following table.
First city pollutant emission source composition table
Figure BDA0003492030800000154
In the selection of the available pollution source control measures in the city A, the same measure has the treatment effect on a plurality of pollution sources, as shown in the following table.
Available pollution source prevention and control measure table for city A
Measure numbering Name of measure Non-road mobile source Road moving source Industrial source Thermal power plant
1 Using restructured gasoline (RFG) standards
2 Aircraft ground support apparatus: electric power alternative fuel
3 Diesel fuel reforming
4 Coal washing
5 Low NOx burner
6 A cement kiln: absorber of spray dryer
7 ESP electrostatic precipitator
The treatment measures in the embodiment are from the American EPA list, and the pollutant removal applicability coefficient of each measure is obtained according to the treatment effect of the measure
Figure BDA0003492030800000155
The following:
Figure BDA0003492030800000156
Figure BDA0003492030800000161
the weights occupied by the measure classes are calculated as follows:
Figure BDA0003492030800000162
Figure BDA0003492030800000163
Figure BDA0003492030800000164
Figure BDA0003492030800000165
Figure BDA0003492030800000166
by the formula
Figure BDA0003492030800000167
Each measure j can be calculated, and the scoring in the field of source resolution i is as follows:
Figure BDA0003492030800000171
measure score result table
Non-road mobile source Road moving source Industrial source Thermal power plant Total points of measures
Action
1 0 0 0 0 0
Action 2 0.1 0 0 0 0.1
Action 3 0.05 0.43 0 0 0.48
Action 4 0 0 0 0 0
Action 5 0 0 0.1333 0.23 0.3633
Action 6 0 0 0 0 0
Action 7 0 0 1.333 2.3 3.633
Since the main removal of measures 4, 6 is SO 2 In this example SO 2 The standard does not exceed, so the final score of the three measures is 0; or due to the treatment of SO 2 Measure (A) SO 2 In the case of the present embodiment, there is no over-standard, related SO 2 Measures are not taken into account. Since the four pollutant removal rates of the measure 1 are less than 25%, the removal adaptability coefficients of the measure 1 to the four pollutants are all 0, and therefore the final score of the measure 1 is 0. Final action 5 has the highest overall score, using a score ranking approach, then action 5 is ultimately preferred.

Claims (9)

1. A method for selecting pollutant treatment measures for cities according to urban air pollution characteristics is characterized by comprising the following steps:
(1A) Establishing a pollution treatment measure library: the pollution treatment measure library is classified according to a classification method of the pollutant emission list emission source to obtain different types of pollution treatment measure libraries; the classification method classifies the emission sources according to the pollutant emission list;
(1B) Calculating the superstandard coefficient: the standard exceeding coefficient is calculated by an environmental quality standard limit value and an environmental monitoring value;
(1C) And (3) correcting the standard exceeding coefficient: judging whether the overproof coefficient of the pollutant needs to be corrected according to the overproof conditions of different air pollutants; if the correction is needed, correcting by combining with the analysis result of the air pollution source and recalculating the overproof coefficient; the correction method comprises the steps of removing PM according to the analysis result of the air pollution source 2.5 、SO 2 、NO 2 Interference of sulfate and nitrate in the monitored values; the corrected pollutant concentration is a gaseous pollutant concentration containing PM generated by secondary reaction 2.5 The concentration of gaseous contaminants consumed;
(1D) Analyzing the pollutant source composition: according to the urban pollutant emission source list, analyzing and calculating the contribution rate of each emission source to each pollutant;
(1E) Calculating the treatment measure category weight: calculating measure category weight according to the exceeding rate of the pollutants and the contribution rate of the pollutants;
(1F) Calculating a measure score: calculating the score of each measure according to the measure category weight, the pollutant exceeding condition and the pollutant removal effect coefficient of the measure;
(1G) Selecting measures: and screening the measure scores to obtain the optimal pollution treatment measure.
2. The method of claim 1, wherein after screening the measure, if the number of screened measures is greater than 1, the measure is screened a second time using the fund limit parameter and the time limit parameter.
3. The method of claim 2, wherein said superscalar coefficients are calculated by a method comprising the steps of:
(3A) Calculating the overproof proportion epsilon of each air pollutant k : the calculation method is
Figure FDA0003822540340000011
Said S k Is the contaminant concentration limit, said T k Is a pollutant concentration monitoring value;
(3B) Determining the superstandard coefficient: the overproof coefficient is obtained by correspondingly judging the overproof proportion, and the overproof proportion epsilon k Is between 0 and 1, the overproof condition is judged as 'not overproof', and the overproof coefficient e k Is 0; the superscalar proportion ε k Is between 1 and 1.2, the overproof condition is judged as 'slight overproof', and the overproof coefficient e k Is 1; the superscalar proportion ε k Is between 1.2 and 1.5, the standard exceeding condition is judged as 'moderate standard exceeding', and the standard exceeding coefficient e k Is 2; the superscalar proportion ε k Is between 1.5 and 2, the standard exceeding condition is judged as 'serious standard exceeding', and the standard exceeding coefficient e k Is 5; the superscalar proportion ε k If the standard exceeding condition is more than 2, judging the standard exceeding condition as 'serious standard exceeding', and judging the standard exceeding coefficient e k Is 10.
4. The method of claim 3, wherein said superscalar coefficient correction method comprises the steps of:
(4A) Judging whether the monitoring value needs to be corrected: when epsilon PM2.5 Is 1-1.2,. Epsilon PMSO2 Is 0-1 or epsilon PMNO2 When the value is 0-1, the monitoring value needs to be corrected; when epsilon PM2.5 Is 0-1, epsilon PMSO2 Is 1-1.2 or epsilon PMNO2 When the value is 1-1.2, the monitoring value needs to be corrected; when epsilon PM2.5 Is 0-1 and ε PMSO2 Is 0-1 and ε PMNO2 When the value is 0-1, the monitoring value needs to be corrected; when epsilon PM2.5 1.2-1.5 and epsilon PMNO2 1.2-1.5 and epsilon PMNO2 When the value is 1.2-1.5, the monitoring value needs to be corrected; when epsilon PM2.5 Is 1.5-2 and epsilon PMNO2 Is 1.5-2 and epsilon PMNO2 When the value is 1.5-2, the monitoring value needs to be corrected; when epsilon PM2.5 Greater than 2 and epsilon PMNO2 Greater than 2 and epsilon PMNO2 When the value is larger than 2, the monitoring value needs to be corrected;
(4B) And (3) correcting the monitored value: the correction is calculated by
Figure FDA0003822540340000021
Figure FDA0003822540340000022
Wherein,
Figure FDA0003822540340000023
is the corrected PM 2.5 The value of the concentration is determined,
Figure FDA0003822540340000024
is the monitored PM 2.5 The value of the concentration is,
Figure FDA0003822540340000025
is the mass percentage of sulfate in the source analysis result,
Figure FDA0003822540340000026
is the mass percentage of the nitrate in the source analysis result,
Figure FDA0003822540340000027
is the corrected concentration value of the sulfur dioxide,
Figure FDA0003822540340000028
is the monitored concentration value of the sulfur dioxide,
Figure FDA0003822540340000029
is the corrected concentration value of the nitrogen dioxide,
Figure FDA00038225403400000210
is the monitored concentration value of nitrogen dioxide;
(4C) And recalculating the standard exceeding proportion by using the corrected monitoring value and redetermining the corrected standard exceeding coefficient.
5. The method of claim 4, wherein the actual removal efficiency of the pollutant abatement measure is 0% to 25%, and the pollutant abatement effect coefficient is 0; the actual removal efficiency of the pollutant treatment measures is 26-75%, and the pollutant treatment effect coefficient is 0.5; the actual removal efficiency of the pollutant treatment measures is 76% -100%, and the pollutant treatment effect coefficient is 1.
6. The method of claim 1, wherein the contribution rate is calculated in a manner such that
Figure FDA00038225403400000211
The above-mentioned
Figure FDA00038225403400000212
Is the contribution rate of the class i emission source to the pollutant k; the described
Figure FDA00038225403400000213
Is the emission of pollutant k from class i emission sources; said E k Is the amount of pollutant k discharged.
7. The method of claim 6, wherein the measure category weight is calculated in a manner such that
Figure FDA00038225403400000214
Figure FDA00038225403400000215
The W is i Is the weight of the class i measure; the calculation mode of calculating the measure score is
Figure FDA00038225403400000216
Figure FDA00038225403400000217
The described
Figure FDA00038225403400000218
Is the treatment effect coefficient of the measure j on the pollutant k.
8. The method of claim 7, wherein the score is modified according to the characteristic parameters of the measure by
Figure FDA00038225403400000219
Figure FDA00038225403400000220
Is a parameter of the implementation period of the measure,
Figure FDA00038225403400000221
is a capital cost parameter for the action.
9. A computer-readable storage medium having a computer program recorded thereon, wherein the program is capable of executing the method according to any one of claims 1 to 8.
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