WO2021026792A1 - Method for selecting pollutant treatment measure - Google Patents

Abstract

Aimed at the deficiency of pollution treatment measure selection modes in the background art, provided is an efficient, quick, convenient and scientific method for selecting an air pollution treatment measure. The method is a method for dynamically selecting, according to air pollution characteristics of a city, a preferred air pollution prevention and treatment measure for the city. The method comprises: selecting an air pollution treatment measure according to the concentration exceeding rate of pollutants, such as PM_2.5, O₃, SO₂ and NO₂, and emission source constituents (obtained according to an emission inventory) corresponding thereto; and preferably selecting, by means of scoring each measure in a measure library and according to the scores, for the city, a measure suited to the current air quality situation of the city.

Description

A method of selecting pollutant treatment measures Technical field

The invention relates to a method for selecting environmental pollution control measures, and belongs to the field of environmental control.

Background technique

In 2016, the new atmospheric law established a mechanism for meeting air quality standards within a time limit, and cities have become the main body of air quality improvement. However, at present, more than 64% of cities in my country still have not achieved air quality standards, and the situation of air pollution prevention and control is still severe. With the continuous deepening of air pollution prevention and control in my country, many governance works have entered deep water areas, and the difficulty of policy formulation and implementation will continue to escalate. The requirements for refined management of urban air quality will become higher and higher, so the city is the unit The demand for customized measures is becoming more and more urgent.

At present, the status of "unclear numbers" is an important bottleneck restricting my country's air pollution prevention and control work. Decision makers in many cities do not have sufficient information on the total emissions of major pollutants, their temporal and spatial distribution, industry contributions, and emission reduction potentials. When choosing air pollution prevention and control measures, they are out of local conditions and lack effective scientific support. To match the actual local air pollution situation, it is difficult to achieve precise haze control.

At present, there are some control quality forecasting and evaluation systems that are used to provide suggestions to relevant decision-makers, such as CMAQ (Community Multi-scale Air Quality), CMAQ is the United States Environmental Protection Agency (EPA) Lagrangian trajectory model and Euler. The third-generation air quality forecasting and assessment system proposed after the grid model. This model is guided by the "one atmosphere" theory, based on the mesoscale meteorological model and SMOKE (Spare Matrix Operator Kerenl Emission) and other source emission models. The influence of horizontal transmission, vertical transmission, diffusion process, source emission, chemical reaction and removal process on the concentration of pollutants in the air pollution process is comprehensively treated. The model can simulate advection, turbulent diffusion, gas-phase chemical reaction, aerosol dynamics, emission process, sedimentation process, cloud process and liquid phase process, and can be used to evaluate the level of fine particulate matter, tropospheric ozone, aerosol and acid deposition pollution in the atmosphere. However, this system is mainly used for air quality forecasting and has certain reference to the selection of measures, but there is no systematic evaluation method for itineraries.

The CMAx (Comprehensive Air Quality Model with Extensions) model is also an Euler-type chemical transport model. Under the guidance of the "one atmosphere" concept, the gas-liquid-solid multiphase chemical mechanism is considered, and the meteorological field simulation results are used to pass the SMOKE source emission model The source emission inventory is processed, and finally the CAMx model simulates the pollutant concentration. The difference from the models3-CMAQ model is that the CAMx model has a two-way nested grid structure, which can be calculated in multiple grids at the same time, in the time range and space range. The internal simulation is more refined. CAMx also includes a variety of analysis tools such as ozone source analysis technology (OSAT), particulate matter source tracking technology (PAST), grid plume module (PiG), etc. The CAMx tool is also mainly used for pollution prediction, and to assist some pollution source analysis tools, and it cannot effectively select pollution control measures.

The WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) model is also a commonly used technology in the field of atmospheric environmental governance. The weather mode and chemical transmission module of this model use the same grid point, time step, transmission scheme and physical scheme to avoid The errors caused by the difference, etc., at the same time, the two are calculated synchronously, and they are coupled in time and space resolution to achieve true online transmission, thereby completing the coupling and feedback of multiple processes such as solar radiation, atmospheric dynamics and aerosol chemistry. WRF-Chem can predict air quality, but also cannot efficiently and scientifically select pollution control measures.

The above technologies used for control measures are mainly used to predict air quality, and cannot be targeted to select appropriate measures or combination of measures for decision makers based on pollution characteristics. The use of these technologies requires the use of complex mathematics, physics, and chemical models, and requires professional staff to implement them. A large-scale computer is required for a prediction process, which is time-consuming and costly.

There are also relatively few related papers for the selection of pollution measures. The most relevant one is [Model selection and strategic measures for comprehensive management of traffic congestion in Shenzhen], from the 2016 China Urban Transport Planning Annual Conference Proceedings. This paper mainly introduces It is a method of model selection and strategic measures in traffic governance. This method first conducts investigations, collects other governance methods, and finally formulates and decides traffic congestion control strategies and measures after human evaluation. There are no quantitative indicators for selection, and science Method of argumentation.

In terms of related patents, no method patents for the selection of pollution control measures have been retrieved.

Therefore, according to our search, the current selection of air pollution control measures mainly relies on manual experience, and there is no scientific and efficient technical means to evaluate and select relevant pollution control measures.

Summary of the invention

Related terms

Emission inventory number i: i=1,2,3,4……., representing the category number in the emission inventory, such as road mobile sources, non-road mobile sources, dust sources, industrial sources, etc.

Example table of emission inventory number (i)

i value	1	2	3	4
Pollution source category	Road movement source	Non-road mobile source	Dust source	Industrial source

List of measures number j: j = A, B, C, D...... represents the number of measures, such as upgrading engine locomotives: diesel-electric hybrid locomotives, aircraft ground support equipment: electric alternative fuels, shore-based electricity, coal washing

The number of pollutants that can be treated by the measure is k: k = 1, 2, 3, 4....... The value of k represents the pollutants that can be treated by the measure, such as NO _X , PM _2.5 , SO ₂ , and O ₃ .

Example table of treatable pollutant numbers (k)

k value	1	2	3	4
Measures	NO X	PM 2.5	SO 2	O 3

The feature number of the measure α: α=1, 2, 3...... Represents the feature of the measure, such as the implementation cycle feature and the capital demand feature.

Measures category weights W _i coefficients: W _i represents the weight of a class of emissions measure the weight coefficient, the weight coefficient exceedances and pollutant source configuration, the higher urban pollutants exceeding the rate, source category (measures library action library corresponding The classification is consistent with the emission source classification in the emission inventory) The greater the proportion of the pollutant source composition, the greater the contribution to this weighting coefficient; for example, W ₁ is the weight of vehicle pollution control measures, and W ₂ is the weight of coal pollution control measures.

The pollutant control effect coefficient of the measure

Indicates the treatment effect of measure j on pollutant k.

Characteristic parameters of measures

Measures characteristic parameters

Represents some of the necessary features reflected in the implementation of the measure, such as the implementation cycle, etc., and funding requirements; α is the feature parameter number of the measure, such as

It can be the characteristic of the implementation period of measure j,

It can be the characteristic of the fund requirement of measure j.

Contribution factor of emission source to pollutant

Indicates the contribution rate of type i emission sources to pollutant k.

F _j : The score of the measure score.

Corresponding pollutant emission parameters in emission sources

Indicates the emission amount of pollutant k in the i-type emission source.

Pollutant discharge quantity parameter E _k : the discharge quantity of pollutant k.

Exceeding rate ε _k : ε _k is the ratio of the annual average concentration of pollutants monitored to the national standard.

Pollutant concentration limit _Sk : _Sk represents the annual average concentration limit of air pollutants specified in the national air quality standard.

Pollutant concentration monitoring value T _k : _Sk represents the monitoring value of air pollutant concentration.

According to the value of ε _k, judge the excess of each pollutant, and different excesses correspond to different excess coefficients e _k .

At present, decision makers in many cities do not have sufficient information on the total emissions of major pollutants, temporal and spatial distribution, industry contributions, and emission reduction potentials. When selecting air pollution prevention and control measures, they are out of local conditions and lack effective scientific support. Match the actual local air pollution situation.

The measures to reduce air pollution have been basically completed. However, in order to achieve accurate and effective control of smog in a city or a region, it is necessary to select reasonable, efficient and accurate control measures with limited resources, and the selected measures need to meet certain requirements. Time resources and cost constraints. The current method of selection of measures mainly relies on manual experience. There is no scientific basis, and it is easy to cause mistakes in decision-making, inefficient use of resources, and unable to effectively solve problems in a targeted manner. Although there are some auxiliary methods for formulating measures, such as air pollution prediction models, these models are basically used to predict air quality and cannot directly help decision makers in the selection of air pollution control measures. They play a small auxiliary role. These models basically require supercomputers to run, require high-level professionals to operate, and are time-consuming, costly, and difficult to operate. In summary, there is currently no scientific, systematic, convenient and efficient technical means for evaluating and selecting relevant pollution control measures.

Aiming at the deficiencies in the selection of pollution control measures in the background technology, the present invention provides an efficient, fast, convenient and scientific method for selecting air pollution control measures. The method is a dynamic selection method that optimizes air pollution prevention and control measures for cities according to the characteristics of urban air pollution. This method selects air pollution control measures based on the excessive concentration rate of urban PM _2.5 , O ₃ , SO ₂ and NO ₂ pollutants and their corresponding emission source composition (derived from the emission inventory). The measures are scored, and the measures that adapt to the current air quality of each city are selected according to the size of the score.

The method for selecting air pollution control measures provided by the present invention can help decision-makers to scientifically judge the applicability and effectiveness of various control measures based on local air pollution characteristics, and thereby solve local air pollution problems in a targeted manner. The method for selecting air pollution control measures provided by the invention avoids the blindness and inefficiency caused by the selection based on experience, and improves the science and pertinence; on the other hand, it is compared with auxiliary methods such as various air pollution prediction models. , It is simple, efficient, low cost, and does not require senior professionals to operate. It enables decision-makers to relatively quickly select air pollution control measures suitable for local conditions and improve the efficiency of measure selection.

The present invention is a dynamic selection method for optimizing air pollution prevention and control measures for cities according to the characteristics of urban air pollution. The basic basis for the selection of measures is the rate of urban PM _2.5 , SO ₂ , NO ₂ concentration exceeding the standard and the corresponding emission source composition (based on the emission inventory). This method can be used to score each measure in the measure library, and according to the size of the score, the measures that are adapted to the current air quality of each city can be selected.

The specific selection process includes establishing a measure database, analyzing pollutant monitoring data exceeding the standard, analyzing the composition of pollutants, calculating the weight of the measure, calculating the score of the measure, and selecting the measure.

It is necessary to establish a local air pollution control measure library and to classify the measures according to the scope of application of the local pollutant emission inventory according to the classification method of the local pollutant emission inventory 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 a measure is used to describe whether a measure is suitable for removing a certain pollutant, and the specific value is based on the removal efficiency of the pollutant. determine.

It is necessary to analyze the excess of pollutant monitoring data, calculate the excess rate of pollutant concentration according to the corresponding ambient air quality standards, and determine different excess coefficients according to the excess.

It is necessary to analyze the source composition of pollutants, and analyze the source composition of pollutants (such as PM _2.5 , SO ₂ , NO ₂ ) according to the city's pollutant emission inventory. Pollutant emission inventory refers to the collection of the amount of air pollutants discharged into the atmosphere by various emission sources in a certain time span and space area. The contribution rate of each emission source to each pollutant is calculated according to the emission inventory.

According to the over-standard situation and source composition of each pollutant, different weights are assigned to each measure category in the measure library.

Calculate the score of each measure according to the weight of each measure category, the pollutant excess status, and the pollutant removal effect coefficient of the measure, and select the best measure according to the score according to a certain selection method. The easiest way to choose is to score and rank.

This method can also be used for water pollution and greenhouse gas pollution treatment, and the corresponding required treatment pollutants are water pollution pollutants or greenhouse gas pollutants; the treatment measures can be corresponding water pollution and greenhouse gas treatment measures.

Establish a measure library

The measure library is the inventory library of all effective measures that can control environmental pollution (such as installing DPF on motor vehicles, ultra-low emission transformation of coal-fired power plants, clean heating, etc.). The measures in the measure library are classified according to the classification method of the emission source of the pollutant emission inventory of the city and geographical location, and are classified according to the scope of application of the measure. The category of the measure library is consistent with the category of the pollution source in the emission inventory, forming different types of measure libraries. Such as industrial sources, road mobile sources, non-road mobile sources, dust sources, VOC-related sources, thermal power plants, natural sources and other categories of measures.

Each measure in the measure library has a pollutant control effect coefficient for each pollutant. The pollutant control effect coefficient of a measure is used to describe whether a certain measure is suitable for removing a certain pollutant. The pollutant control effect coefficient of a measure can be directly derived from the list of measures (such as the US EPA measure list); or the pollutant control effect coefficient of a measure can be correspondingly transformed from the treatment effect of the measure, and the value range is (0, 1) .

Correspondence table of treatment effect of measures

The actual removal efficiency of pollutants by the measures	The pollutant control effect coefficient of the measure
0%-25%	0
26%-75%	0.5
76%-100%	1

Example table of the pollutant control effect coefficient table of the measures

The contaminant may be a NO _X, 2 contaminant may be a PM _2.5, 3 may be of SO2 contaminant; contaminant selected according to the numbers can be edited in cities.

Analysis of urban air quality monitoring data exceeding standards

Analyze the excess rate of air pollutants: According to the monitoring of air pollutants and air quality standards, the excess rate of each pollutant is obtained. The air quality standards can be the national standard GB3095-2012, local air quality standards, international air quality standards, etc.

Pollutant concentration limit S _k : _Sk represents the annual average concentration limit of air pollutants specified in the air quality standards, and PM _2.5 , SO ₂ , and NO ₂ are the concentration limits respectively.

Pollutant concentration monitoring value T _k : T _k represents the monitoring value of air pollutant concentration, T _PM2.5 , T _SO2 , T _NO2 , and T _O3 are the concentration monitoring values of PM _2.5 , SO ₂ , NO ₂ , and O ₃ respectively ( PM _2.5 , SO ₂ , and NO ₂ can be the annual average concentration monitoring values; if O ₃ is selected, O ₃ can be the 90th percentile of the daily maximum 8-hour moving average) Calculate PM _2.5 and O according to ambient air quality standards _{3. The} over-standard rate of SO ₂ and NO ₂ concentration is calculated as follows.

ε _k is the ratio of the pollutant annual average concentration monitoring value to the annual average pollutant concentration limit in the national standard. The concentration limit of each pollutant can be derived from our national standard or other air quality standards. See the table below.

China National Air Pollutant Concentration Standard Limit Table

Pollutants	Statistical indicators	National standard
PM 2.5	Annual average concentration	35μg/m 3
SO 2	Annual average concentration	60μg/m 3
NO 2	Annual average concentration	40μg/m 3
O 3	The 90th percentile of the daily maximum 8-hour moving average	160μg/m 3

When ε _{k is} calculated, it can be judged that each pollutant exceeds the standard. The corresponding excess coefficient e _k corresponding to different excess conditions is as follows. The corresponding method of the excess coefficient can be adjusted according to different local conditions.

Excess ratio ε k	Excessive judgment	Exceeding standard e k
0-1	Not exceeding	0

1-1.2	Slightly exceeded	1
1.2-1.5	Moderately exceeded	2
1.5-2	Severely exceeded	5
2 or more	Severely exceeded	10

When PM _2.5 , SO ₂ , NO ₂ exceed the standards in the following situations, the monitoring values can also be corrected:

1) PM _2.5 slightly exceeds the standard, and at least one of SO ₂ and NO ₂ does not exceed the standard and is close to the emission limit;

2) PM _2.5 does not exceed the standard and is close to the emission limit, and at least one of SO ₂ and NO ₂ slightly exceeds the standard;

3) PM _2.5 , SO ₂ and NO ₂ exceed the standard conditions are the same (the respective ε _k values are similar);

The correction method is as follows:

The PM _2.5 source analysis result obtained percentage composition by mass of sulfate PM _2.5

And nitrate mass percentage

application

versus

The concentration monitoring values of PM _2.5 , SO ₂ , and NO ₂ are corrected to obtain the corrected value (T′ _k ) of the concentration. The correction method is as follows:

according to

recalculate

Re-judge the excess of PM _2.5 , SO ₂ , and NO ₂ .

Analysis of source composition

Analyze the emission source composition of each pollutant (PM _2.5 , O ₃ , SO ₂ , NO ₂ ) according to the emission inventory of various pollutants in the city. Pollutant emission inventory refers to the collection of the amount of air pollutants discharged into the atmosphere by various emission sources in a certain time span and space area. The contribution rate of each emission source to each pollutant is calculated according to the emission inventory. The specific calculation method is as follows:

Contribution factor of emission source to pollutant

Indicates the contribution rate of type i emission sources to pollutant k.

Corresponding pollutant emission parameters in emission sources

Indicates the emission amount of pollutant k in the i-type emission source.

E _k : Pollutant discharge quantity parameter E _k represents the discharge quantity of pollutant k.

Measure category weight calculation

According to the excess of each pollutant and the source composition (contribution rate), different weights are assigned to each measure in the measure library. The weight calculation method is as follows:

Preliminary screening of measures is carried out, and the control measures corresponding to pollutants with ε _k greater than 1 (ie, pollutants exceeding the standard) can be selected;

According to the excess pollutants exceeding standard rate and the proportion of each pollution source in the emission inventory, the weights of the various measure libraries involved in the excess pollutants are allocated, and the weights of the measures involved in each measure are finally added together to obtain the weight of each measure. The calculation method is as follows.

W _i : the weight of the i-type measure library.

i: The category of the measure library involved in the excess pollutant, that is, the category of pollution source.

k: The name of the excessive pollutant.

Contribution rate of type i emission sources to pollutant k.

There are some measures that can be applied to the prevention and control of multiple pollution sources at the same time, so the same prevention and control measures can appear in multiple categories of measure libraries at the same time. The calculation of the weight of such measures needs to take their weights in different categories into consideration. , Is calculated in the score.

Combine basic data and measure database to calculate measure score

The evaluation of measures is carried out according to the scoring formula, which is as follows.

F _j : measure score

E.g:

The above formula represents the scores of measure A in the category 1 measure library.

Choice of measures

Calculate the score F _{j of} each measure according to the above formula, and then select the applicable measure according to F _j . There are two specific selection methods, namely, the score direct ranking method and the threshold screening method.

Direct ranking method

Arrange the measures according to their score F _j . The larger the F _j , the higher the ranking of the measures. Finally, the top 10% measures are selected.

For example: There are a total of 10 measures. The scores F _j and rankings of these measures are shown in the following table:

Measures	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
Rank	1	2	3	4	5	6	7	8	9	10

According to the above table, the top 10% measures are the first measures (10*10%=1), that is, measure A, and measure A is finally selected.

Threshold screening method: select the largest score F _j , set 0.5*F _j as the threshold, and finally select the measure with the score F _j greater than the threshold.

For example: There are 10 measures in total, and the scores E _{j of} these measures are shown in the table below:

Measures	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

According to the above table, the threshold=0.5*10=5, and the measures with a score F _j greater than 5 are A, B, C, D, E, and then the measures A, B, C, D, and E are finally selected.

Threshold screening

After preliminary screening of the measures by the direct ranking method, and the number of measures obtained by the preliminary screening is greater than 1, the measures shall be screened again in consideration of the actual budget and time requirements.

When priority is given to time requirements, that is, when a certain treatment effect is required to be achieved within a certain time range: measures are selected according to the implementation period of each measure, and measures with an implementation period less than or equal to the required time range are selected.

When the capital budget is prioritized, that is, when a certain amount of capital costs are needed to achieve a certain treatment effect: select measures based on the capital cost of each measure, and select the measure with the capital cost less than or equal to the capital budget value.

When considering the time requirements and capital budget: According to the implementation period and capital cost of the measures, the score F _j of the measures selected by the basic selection method is revised, and the revision method is as follows:

Revised measure score

Implementation cycle of measures

The capital cost of the measure

After correction, removed _'j measures a minimum value E, the rest of the measures is the preferred final measures.

Description of the drawings

Figure 1: Schematic diagram of the basic plan for the selection of measures.

Figure 2: Schematic diagram of the process of introducing measures to improve applicability conditions.

Figure 3: Schematic diagram of the process of the selection of measures for the introduction of PM _2.5 source analysis correction improvement.

Figure 4: Schematic diagram of the flow of the measure selection plan combined with applicable conditions and PM _2.5 source analysis modification.

Figure 5: Source composition of PM _2.5 source analysis results in City A.

Figure 6: Source composition of SO ₂ source analysis results in City A.

Figure 7: Source composition of NO ₂ source analysis results in City A.

detailed description

Example one

Apply the above-mentioned measure selection method to screen out the best pollution control measures for City A. The air quality monitoring results of City A are as follows.

Monitoring results of air pollutant concentrations in City A

The over-standard rate of PM _2.5 , SO ₂ , NO ₂ , and O ₃ concentration in City A is calculated as follows:

The analysis results of air pollutants in city A are as follows.

Composition of Pollutant Emission Sources in City A

The available pollution source prevention and control measures in City A are as follows.

List of available pollution source prevention measures in City A

Since the SO2 over-standard rate is less than 1, it shows that the city's SO2 has reached the standard and there is no need to take relevant measures to focus on governance. Therefore, the prevention and control measures corresponding to PM _2.5 and NO ₂ are selected in the measure library.

The weight of each measure category is calculated as follows:

The treatment measures in this example are from the US EPA list, and the pollutant removal applicability coefficient of each measure is corresponding to the treatment effect of the measure.

as follows:

Such as,

Removing Applicability coefficients represent a measure of pollutants of NO _x is 1, i.e., a very suitable measure to remove contaminants NO _x.

By formula:

The score of each measure j can be calculated as follows:

To	Non-road mobile source	Road movement source	Industrial source	Thermal power plant	Other + natural source	Dust source	Measure total score
Measure 1	0.6	To	To	To	To	To	0.6
Measure 2	0.1	To	To	To	To	To	0.1
Measure 3	0.6	To	To	To	To	To	0.6
Measure 4	To	To	0	To	To	To	0
Measure 5	To	To	0.1333	To	To	To	0.1333
Measure 6	To	To	0	To	To	To	0
Measure 7	To	To	To	0	To	To	0
Measure 8	To	To	To	0.46	To	To	0.46
Measure 9	To	4.3	To	To	To	To	4.3
Measure 10	To	To	To	To	0	To	0
Measure 11	To	To	To	To	To	1.2665	1.2665

Calculation example:

Was due mainly to remove SO ₂ measures as 4,6,7, and SO ₂ in this example is not exceeded, so the above three measures, the final score is 0; or measures to control because it is of SO _2, SO ₂ in the present case If there is no excess, the relevant SO ₂ measures are not taken into consideration. Since the removal rate of the four pollutants of Measure 10 is less than 25%, the removal adaptability coefficient of Measure 10 to the four pollutants is 0, so the final score of Measure 10 is 0.

Example two

Apply the above-mentioned measure selection method to screen out the best pollution control measures for City A. The air quality monitoring results of City A are as follows.

Monitoring results of air pollutant concentrations in City A

The over-standard rate of PM _2.5 , SO ₂ , NO ₂ , and O ₃ concentration in City A is calculated as follows:

The analysis results of air pollutants in city A are as follows.

Composition of Pollutant Emission Sources in City A

The available pollution source prevention and control measures in City A are in the selection, and the same measure has the effect of treating multiple pollution sources, as shown in the following table.

List of available pollution source prevention measures in City A

Measure number	Measure name	Non-road mobile source	Road movement source	Industrial source	Thermal power plant
1	Use Recombinant Gasoline (RFG) standards	√	√	To	To
2	Aircraft ground support equipment: electric alternative fuel	√	To	To	To
3	Diesel modification	√	√	To	To
4	coal washing	To	To	To	√
5	Low NOx burner	To	To	√	√
6	Cement kiln: spray dryer absorber	To	To	√	To
7	ESP electrostatic precipitator	To	To	√	√

The treatment measures in this example are from the US EPA list, and the pollutant removal applicability coefficient of each measure is corresponding to the treatment effect of the measure.

as follows:

The weight of each measure category is calculated as follows:

By formula

Each measure j can be calculated, and the score in the field of source analysis i is as follows:

Measure score result table

To	Non-road mobile source	Road movement source	Industrial source	Thermal power plant	Measure total score
Measure 1	0	0	0	0	0
Measure 2	0.1	0	0	0	0.1
Measure 3	0.05	0.43	0	0	0.48
Measure 4	0	0	0	0	0
Measure 5	0	0	0.1333	0.23	0.3633
Measure 6	0	0	0	0	0
Measure 7	0	0	1.333	2.3	3.633

Was due mainly to remove SO ₂ to 4, 6 measures, and SO ₂ in this example is not exceeded, so the above three measures, the final score is 0; or because the measures is SO ₂ treatment, SO ₂ is not exceeded in the present case , Related SO ₂ measures are not taken into consideration. Since the removal rate of the four pollutants of Measure 1 is less than 25%, the removal adaptability coefficient of Measure 1 to the four pollutants is 0, so the final score of Measure 1 is 0. The final measure 5 has the highest total score. Using the score ranking method, then the final measure 5 is preferred.

Claims (11)

1. A method for selecting pollutant treatment measures, characterized in that it comprises the following steps:

   (A) Establish a library of pollution control measures: the said pollution control measure library is classified according to the classification method of the emission source of the pollutant emission inventory, and different types of pollution control measure libraries are obtained;

   (B) Calculating the over-standard coefficient: The over-standard coefficient is calculated from the limits of environmental quality standards and environmental monitoring values;

   3) Analyze the composition of pollutant sources: According to the urban pollutant emission source list, analyze and calculate the contribution rate of each emission source to each pollutant;

   4) Calculate the weight of the treatment measures category: calculate the weight of the measure library category according to the over-standard rate of each pollutant and the contribution rate of each pollutant;

   5) Calculate the score of measures: Calculate the scores of each measure according to the weight of each measure category, the pollutant excess status, and the pollutant removal effect coefficient of the measure;

   6) Filter and sort the scores of measures, and select the best measures.
2. The method according to claim 1, characterized in that the method for classification of measures in the measure library is based on the emission source classification method of the pollutant emission inventory in the geographical area, and the classification methods include industrial sources, road mobile sources, and off-road sources. Mobile sources, dust sources, VOC related sources, thermal power plants, natural sources.
3. The method according to claim 1, characterized in that the pollutant excess coefficient is determined based on the pollutant excess ratio correspondingly, and the corresponding determination method is as follows:

   Excess ratio ε _k Excessive judgment Exceeding standard e _k 0-1 Not exceeding 0 1-1.2 Slightly exceeded 1 1.2-1.5 Moderately exceeded 2 1.5-2 Severely exceeded 5 2 or more Severely exceeded 10
4. The method according to claim 1, wherein the pollutant excess coefficient can also be directly equal to the pollutant excess ratio.
5. The method according to claims 3 and 4, characterized in that the calculation method of the excess ratio of ε _k is as follows:

   

   The pollutant concentration limit S _k , the pollutant concentration monitoring value T _k , ε _k is the proportion of excess.
6. The method of claim 1, wherein the contribution rate of each pollutant is calculated as follows:

   

   

   Contribution factor of emission source to pollutant

   

   Indicates the contribution rate of type i emission sources to pollutant k;

   

   Corresponding pollutant emission parameters in emission sources

   

   Indicates the emission amount of pollutant k in the i-type emission source;

   E _k : Pollutant discharge quantity parameter E _k represents the discharge quantity of pollutant k.
7. The method of claim 1, wherein the method for calculating the weight of the measure category is as follows:

   

   W _i : the weight of the i-type measure library;

   i: The category of the measure library involved in the excess pollutant, that is, the category of pollution source;

   k: The name of the excessive pollutant;

   

   Contribution rate of type i emission sources to pollutant k.
8. The method of claim 1, characterized in that the measure is calculated as follows:

   

   The pollutant control effect coefficient of the measure

   

   Indicates the treatment effect of measure j on pollutant k;

   F _j : measure score.
9. The method according to claim 1, characterized in that the method of screening and ranking the scores of the measures is a direct score ranking method, each measure is ranked according to its score F _j from large to small, and a certain proportion of the top ranked measures are selected , The certain ratio may be 5%, 10%, 20%, 30%, 40%.
10. The method of claim 1, wherein the method of screening and ranking the measure scores is a threshold screening method, and the threshold screening method is that when the result of more than one measure is obtained by using the direct ranking method, After correcting the scores on the characteristic parameters of the measures, the ranking is performed again from largest to smallest, and a certain proportion of the measures in the top ranking is selected. The certain proportion may be 5%, 10%, 20%, 30%, 40%.
11. The method of claim 10, wherein the calculation method for correcting the score according to the characteristic parameter of the measure is:

    

    F′ _j : the score of the revised measure;

    

    The implementation cycle of measures;

    

    The capital cost of the measure.

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