GB2600072A - Method for selecting pollutant treatment measure - Google Patents
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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 PM2.5, 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
METHOD FOR SELECTING POLLUTANT TREATMENT
MEASURE
FIELD OF THE INVENTION
This invention relates to a method of selecting environmental pollution treatment measures, and this invention is in the environmental treatment field.
BACKGROUND OF THE INVENTION
In 2016, the new air law established a deadline mechanism for city air quality, and cities have become the main body for air quality improvement. However, more than 64% of cities in China have not achieved the air quality standard. The situation of air pollution prevention and control is still difficult. With China's deepening air pollution prevention and control, much governance has entered dilemmas. The difficulty of policymaking and implementation would continue to upgrade, and the requirements of city air quality fine management would increase. Therefore, the demand for customized measures based on city level has become more urgent.
Currently, the situation, which is called "unclear basic matter," is a critical bottleneck that restricts the prevention and control of air pollution in China. In many cities, policymakers do not have enough information on the total emission, spatiotemporal distribution, industrial contribution, emission reduction potential, and many other aspects. As a result, policymakers are always out of the local situation knowledge when selecting air pollution prevention arid control measures mid lack effective scientific support. Therefore, their measures cannot match the local air pollution situation, and it is difficult to achieve accurate smog control.
At present, there have been some quality control prediction and evaluation systems that could provide suggestions to relevant policymakers, such as CMAQ (Community Multi-scale Air Quality). CMAQ is the third generation air quality prediction and evaluation system proposed by the U.S. Environmental Protection Agency (EPA) based on the Lagrange Trajectory and Euler Grid models. The "one-atmosphere" theory guides this model. The model is also based on the mesoscale meteorological model, the smoke (Spare Matrix Operator Kernel Emission), and other emission source models. The effects of horizontal transmission, vertical transmission, diffusion process, source emission, chemical reaction, and removal process on pollutant concentration in air pollution are considered. The complex air pollution conditions would be comprehensively treated. This model could simulate advection transport, turbulent diffusion, gas-phase chemical reaction, aerosol dynamics, emission process, sedimentation process, cloud process, and liquid phase process, and this model can be used to evaluate the pollution levels of fine particles, tropospheric ozone, aerosol and acid deposition in the atmosphere. Although this system is mainly used for air quality prediction, which has some reference for selecting measures, but there is no systematic evaluation method yet.
CAMx (Comprehensive Air Quality Model with Extensions) model is also an Eulerian chemical transport model. This model considers the gas-liquid-solid multiphase chemical mechanism under the concept of "one atmosphere." Using the meteorological field simulation results, the source emission list is processed through the SMOKE source emission model. Then, the CAMx model simulates the pollutant concentration. Unlike the models3-CMAQ model, the CAMx model has a two-way nested grid structure, which can be calculated in multiple grids simultaneously. Therefore, the CA Mx model could refine the simulation in time and space. CA Mx also includes a variety of analysis tools, such as ozone source analysis technology (OSAT), particulate source tracking technology (PAST), grid plume module (PiG), and other tools. CAMX tools could also use for pollution prediction and assist some pollution source analysis tools. However, the CAMx model cannot select pollution control measures effectively.
WRF -Chem (Weather Research and Forecasting model coupled with Chemistry) model is also a standard technology in atmospheric environment treatment. The meteorological model and chemical transmission module of this model use the same grid, time step, transmission scheme, and physical scheme to avoid errors caused by errors. At the same time, they are calculated synchronously. This model realizes the online transmission by completing the time and space resolution coupling. Then, the coupling and feedback of multiple processes such as solar radiation, atmospheric dynamics, and aerosol chemistry would be completed. As a result, WRF -Chem can predict the air quality but cannot select pollution control measures efficiently and scientifically.
The technologies of treatment measures above are mainly used to predict air quality but cannot select appropriate measures or a combination of measures according to pollution characteristics for policymakers. These technologies require complex mathematical, physical, mid chemical models. Professional staff is required to implement these technologies. Also, a prediction process requires using a dedicated mainframe computer, which is time-consuming and costly.
There are only a few relevant papers on the selection of pollution measures. The title of the most relevant one is Mode Selection and Strategic Measures for Comprehensive Theatment of Marne Congestion in Shenzhen. This paper is from the 2016 China Annual Conference on city traffic planning proceedings. This paper mainly introduces model selection methods and strategic measures in traffic control. The method of this paper conducts research and collects other treatment methods lirst. After artificial evaluation, traffic congestion control strategies and measures arc finally formulated and decided. There arc no quantitative selection indicators and scientific demonstration methods in the method.
As for relevant patents, no method patents for selecting pollution control measures were retrieved. Therefore, according to our search, the selection of air pollution control measures mainly depends on manual experience currently. There arc no scientific and efficient technical ways to evaluate and select relevant pollution control measures.
INVENTION CONTENT
Relative Terms Emission inventory parameter i i=1,2,3 Parameter i represents the pollution source category number in the inventory, such as mobile source, off-road source, dust source, industrial source, etc. Emission list number (i) sample table Value of i 1 2 3 4 Pollution Source Category mobile source off-road source dust source industrial source Control measurement inventory parameter j: j=2k, B, C, D Parameter j represents the control measures, such as engine upgraded: diesel electric hybrid locomotive, aircraft ground support equipment: Electric alternative fuel, shore based power and coal washing.
Parameter k is the pollutants that could be treated k: k = 1,2,3,4 The value of k represents the pollutants that can be treated by a measure, such as NOx, PM2.3, SO2, and 03.
Example table of manageable pollutant number (k) Value of k 1 2 3 4 Measures NOx PM2.5 SO2 03 The characteristic number of die measures a:a= 1, 2, 3 Number a represents the characteristics of measures, such as implementation cycle characteristics and fund demand characteristics.
Measure Category Weight Coefficient (MCWC) Wi: 1/17i represents the weight coefficient of the category of measures. This coefficient is related to the exceeding standard situation and source composition of pollutants. The higher the exceeding standard rate of city pollutants, the greater the proportion of the corresponding emission source category of the measure Library (the classification of the measure library is consistent with the classification of emission sources in the emission list) in the source composition of pollutants and the contribution to this weight coefficient would be. For example, I/V1 is the weight of motor vehicle pollution control measures, and W2 is the weight of coal pollution control measures.
Control Effect Coefficient of Measures (CECM) . DJ' represents the control effect of measure j on pollutant k.
Characteristic Parameters of Measures (CPM) (//7: cpy represents some necessary characteristics reflected by the implementation of the measures, such as implementation cycle and fund demand. a is the number of the characteristic parameters of the measure. For example, co can be the implementation cycle characteristic of measure j, and (p can be the fund demand characteristics of measure j.
source to pollutant contribution coefficient Cj represents the contribution rate of category i emission source to pollutant k.
Fj: score of measures.
Corresponding pollutant emission parameter E< for emission source: E represents the emission of pollutant k from class i emission sources.
Pollutant discharge parameter Ek: Ek represents the discharge of pollutants k.
Exceeding standard ratio Ek: Ek is the ratio of the monitored value of the average annual concentration of pollutants to the limited value of the average annual concentration of pollutants in the national standard.
Pollutant concentration limited value Sk: Sk refers to the annual average concentration limited value of air pollutants specified in the national air quality standard.
Pollutant concentration monitoring value Tk:Tk represents the monitored value of air pollutant concentration.
The exceeding condition of each pollutant is determined by Ek value Different exceeding conditions correspond to different exceeding coefficient ek.
In many cities, policymakers do not have enough information on the total emission, spatiotemporal distribution, industrial contribution, emission reduction potential, and many other aspects. As a result, when selecting air pollution prevention and control measures, policym akers are always out of local situation knowledge and lack effective scientific support. Therefore, their measures cannot match the local air pollution situation.
At present, the measures to reduce air pollution have been improved basically. However, to achieve accurate and effective control of smog, a city or a region needs to select the control measures under limited resources reasonably, efficiently, and accurately. At die same time, the selected measures need to satisfy the need for the specific time, resources, and cost constraints. The selection of measures mainly depends on manual experience, which always contains little scientific basis. This situation could easily lead to decision-making mistakes, inefficient resource use, and ineffectively solving problems. Although there are some auxiliary' methods for the formulation of measures, such as air pollution prediction models, most of these models are used to predict air quality. Therefore, these models could offer little auxiliary help and cannot directly help policymakers select air pollution control measures.
Moreover, most of these models need supercomputer operation and require high-level professionals to operate. That means they are time-consuming, costly, and difficult to operate. To sum up, there are no scientific, systematic, convenient, and efficient technical methods for evaluating and selecting relevant pollution control measures.
Given the deficiency of the pollution control measures selection method in the technology background, this invention provides an efficient, fast, convenient, and scientific method for selecting air pollution control measures. This method is a dynamic selection method to optimize air pollution prevention and control measures for cities according to the characteristics of city air pollution. In this method, the air pollution control measures are selected according to the concentration exceeding the rate of city pollutants, such as PM3.3, 03, SO2, and NO2. and their corresponding emission source composition (obtained according to the emission list). Then, by scoring cach measure in the measure library, the suitable measures for the current air quality situation are optimized for each city according to the score.
The method for selecting air pollution control measures provided by this invention can help policymakers scientifically evaluate the applicability and effectiveness of various control measures according to local air pollution characteristics to solve local air pollution problems. On the one hand, this selection method of air pollution control measures avoids the blindness and low efficiency caused by relying on experience and improves the scientific and pertinence in the selection. On the other hand, compared with various air pollution prediction models and other auxiliary methods, this method is simple, efficient, low-cost, and does not need professionals to operate. This advantage can improve the efficiency of measure selection and enable policymakers to quickly select air pollution control measures suitable for actual local conditions.
This invention relates to a dynamic selection method for optimizing air pollution prevention measures for cities according to the characteristics of city air pollution. The basis for the measure selection is the concentration exceeding the city PIY1225, SO2, and NO2 and the corresponding emission source composition (obtained according to the emission list). This method can score the measures in the measure library and select suitable measures for the current air quality situation for each city according to the score.
The specific selection process includes establishing a measure library, analysis of excessive pollutant monitoring data, analysis of pollutant source composition, calculation of measure weight, calculation of Measure score, and selection of measures.
It is necessary to establish a local library of air pollution control measures. These measures should be classified according to the classification method of cmission sources and the scope or application of the measures themselves. After that, a library of different types of measures can be formed. Each measure in the measure library has a removal applicability coefficient for each pollutant. The pollutant treatment effect coefficient of the measure is used to describe whether a measure is suitable for removing a certain pollutant. The specific value is determined according to the pollutant removal efficiency of the measure.
It is necessary to analyze the excess situation of pollutant monitoring data. Then, the excess rate of pollutant concentration can bc calculated according to the corresponding air quality standards, and different excess coefficients can be determined according to the excess situation.
It is necessary to analyze the composition of the pollutant emission source and analyze the composition of pollutant (such as P1M2"5, 502, and NO2) emission source according to the emission list of pollutants in the city. A pollutant emission list refers to the collection of air pollutants discharged into the atmosphere by various emission sources in a certain period and space arca. The contribution rate of each emission source to each pollutant can be calculated according to the emission list.
Assign different weights to each measure category in the measure library according to the excess situation and the composition of each pollutant source.
Calcubte the score of each measure according to the weight of each measure category, excess situation, and pollutant removal effect coefficient of the measure, and select the best measure according to a certain selection method based on the score. The easiest choice can be the method of scoring and ranking.
This method can also be used in water pollution and greenhouse gas pollution. The corresponding pollutants to be treated are water pollutants or greenhouse gas pollutants. The treatment measures are the corresponding water pollution and greenhouse gas treatment measures.
Establish measure library The measure library is the list Library of all effective measures that can control environmental pollution (such as adding DPF to 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 emission sources in the pollutant emission list of cities and geographical locations and the scope of application of the measures themselves. The category of the measure library is consistent with that of the pollution sources in the emission list. Therefore, different categories of measure libraries, such as industrial sources, road sources, off-road sources, dust sources, VOC-related sources, thermal power plants, natural sources, and other categories can be formed.
Each measure in the measure library has the pollutant treatment effect coefficient that aims at die effect of each measure for each pollutant. The pollutant treatment effect coefficient of the measure is used to describe whether a measure is suitable for removing a pollutant. The pollutant control effect coefficient of measures can be directly derived from the list of measures (such as the list of EPA measures in the United States). Also, the pollutant treatment effect coefficient of the measure can be transformed from the treatment effect of the measure, and the value range is (0,1).
Corresponding table of treatment effect of measures Actual removal efficiency for pollutants ofmeasures Pollutant control effect coefficient of measures 0%-25% 0 26%-75% 0.5 76%-1 0% I Example table of pollutant control effect coefficient of measures Measures MCWC CECM 1 CECM 2 CECM 3 CECM 4 CECM 5 CPM 1 CPM 2 CPM 3 Measure A IN, D.11 Di Dj 14 D? rilA (pi rp,3, Measure B DA DA D:b3 DE; kJ (pL 4, 3 enn Measure C DA: E). D3 DA' De'. Vic- 2 3 gic CPC Measure A W2 DI Dj Di DI D.? (pA (pi (14 Measure D Di? DI, D.1, 14, li;1 4 4 3
PD
Measure E DA Di. DI. DA: W. 'Vnit Pi Vi
E
For example, pollutant 1 can be NOx, pollutant 2 can be PM2 5, and pollutant 3 can be SO2, The pollutant number can be edited and selected according to the city's situation. Analyze the Situation of City Air Quality Monitoring Data Exceeding Situation Analyze the exceeding rate of air pollutants: the exceeding rate of each pollutant is obtained according to the monitoring of air pollutants and air quality standards. The air quality standards can be national standard GB3095-2012, local air quality standards, international air quality standards, etc. Pollutant concentration limit Sk: Sk represents the annual average concentration limit of air pollutants specified in the air quality standard. Spm2.5, Ss02, SN 02 arc the concentration limits for PM2.5. SO2, and NO2.
Pollutant concentration monitoring value Tk Tk represents the monitored value of air pollutant concentration. Tpm2.5, T502., TN 02 T03 is the concentration monitoring value of PM2.5, NO2, and 03 respectively (PM2.5, SO2, and NO2 can be the average annual concentration monitoring value; if 03 is selected, 03 can be the 90th percentile of the daily maximum 8-hour moving average) The concentration exceeding rate of PNE.5, O. SO?, and NO2 can be calculated according to the ambient air quality standard. The calculation method is as follows. Tk
Ek = Sk Ek is the ratio of the monitoring value of the annual concentration of pollutants to the annual concentration limit of pollutants in the national standard. The concentration limit of each pollutant can be obtained from national standards or other air quality standards. The limitations are shown in the table below.
National standard lim able of air pollutant concentration in China pollutant statistical indicators National standard PM2.5 Annual average concentration 35t.ig/m3 SO? Annual average concentration 6thig/m3 NO2 Annual average concentration 4thig/m3 01 90th percentile of the daily maximum 8-hour moving average 16Oug/m3 After the calculation of Ek, the exceeding situation of each pollutant can be judged. The exceeding coefficient ek corresponding to different exceeding standard conditions can be obtained and arc shown in the table below The corresponding mode of exceeding coefficient can be adjusted according to different local conditions.
Exceeding ratio SI, Exceeding situation judgment exceeding coefficient ek 0-1 Not exceed 0 1-1.2 Slightly exceed 1 1.2-1.5 Moderately exceed 2 1.5-2 Seriously exceed 5 2± Severe exceed 10 When PM2.5, 502, and NO2 exceed the standard in the following situations, the monitoring values can also be corrected: 1) PM, 5 slightly exceeds the standard, and at least one of SO2 and NO2 does not exceed the standard and is close to the emission limit.
2) Pis{2 5 does not exceed the standard and is close to the emission limit, and at least one of SO2 and NO2 exceeds the standard slightly.
3) The exceeding situation of PM25, SO2, and NO2 is the same (respective e K values are similar).
The correction method is as follows The source apportionment of PM2.5 can be obtained according to the mass percentage of sulfate (Pso,) and nitrate (PNo2). Using P -so2 and PNO2 can modify the concentration monitoring values of PM25. SO2, and NO2, The corrected value of their concentration (T'k) can be obtained. The correction method is as follows: T1 pAn = Tpm X (100% '.2.5 -2 5 -P502 -PA502 T'502 = T502 + Tpm2 s X Ps02 T' NO2 = TNO2 TPM2s X PNO2 EPA12s7 E502, ENO2 can be recalculated, and PM2.5., SO2, and NO3 can be rejudged according to TrPM2 T'S02, T'NO2 * Analyze Source Composition The emission source composition of various pollutants (PM2.5, 03, SO2, and NO2) can be analyzed according to the emission list of various pollutants in the city. A pollutant emission list refers to the collection 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 list. The specific calculation method is as follows: Ck = -Ek CI,: contribution coefficient of emission source to pollutant Ck represents the contribution rate of class i emission source to pollutant k.
Eki: emission parameter of corresponding pollutants in emission source 4 represents the emission of pollutant k from class i emission sources.
Ek: pollutant em ission parameter Ek represents the emission of pollutant k.
Calculate Measure Category Weight According to the exceeding situation 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 could select prevention and control measures corresponding to pollutants with Ek greater than 1 (i.e. excessive pollutants).
According to the exceeding rate of exceeding pollutants and the weight of various measure libraries involved in exceeding standard pollutants based on the proportion of each pollution source in its emission list. The weight of each measure can be calculated by adding the weight of measure libraries involved in each measure together The specific calculation method is as follows.
E Ek x ck vv, -2, Ek weight of class i measure library.
I: the category of measure library involved in excessive pollutants, i.e. the category of pollution source.
k: name of excessive pollutants.
Ck: contribution rate of class i emission source to pollutant k.
Some measures can be applied to the prevention and control of multiple pollution sources simultaneously. Therefore, the same prevention mid control measure can appear in multiple categories of the measure Library simultaneously. The weight in different measure categories of such measures needs to be considered when in the score calculated.
Measure Scores Are Calculated in Combination with Basic Data and Measure Database The measures are evaluated according to the scoring formula, as follows.
=1Wiek131 Measure Measure category Pollutant k applicability of characteristic weight measure j parameter Measure wi 1 1 Fj: measure score. For example: DiO2 _L Al414VOX) FA =W1(ePM2.SDIM2.5 eS02 ' 'Ux The formula above represents the score of measure A in measure library class 1.
Measure Selection Method Calculate the score Fj of each measure according to the formula above. Then select the applicable measure according to Fj. There are two specific options: the score direct ranking method and the threshold screening method Direct ranking method The measures can be ranked according to their score Fj. The larger the Fj, the higher the measure ranking. Finally, the top 10% measures could be selected.
For example, there are 10 measures in total. The score Fj and ranking of these measures are shown in the table below: Measures A B C D E F CI H I F FA= I 0 F8=0 FB=8 FD=7 FE=6 FF=5 Fc,=4 FE=3 F1=2 Fj= I Ranking 1 2 3 4 a 6 7 8 9 10 According to the above table, the top 10% measures are the first measures (10 * 10% = 1), Which means measure A is selected finally.
Threshold screening method: select the maximiun Fj score, mid set 0.5 * F) as the threshold. Select the measure that the score Fj is greater than the threshold finally.
For example, there are 10 measures in total, mid the scores of these measures are shown in the
table below:
measures A B C D E E G H I J F FA=10 FE=9 FB=8 F11=7 FE=6 FF=5 Fer=4 FH=3 FT=2 FJ=1 According to the table above, threshold = 0.5 * 10 = 5. Measures wit Fj greater than 5 include a, B, C, D and E. Measures A, B. C. D. and E are finally selected.
Threshold screening method The secondary screening of measures shall be carried out carefully considering the actual capital budget and time requirements after the preliminary screening of measures through the direct ranking method. the number of measures obtained through the preliminary screening is greater than 1. When the time requirement is given priority, that is, when it is required to achieve a certain treatment effect within a certain time range: select measures according to the implementation cycle of each measure, and select measures whose implementation cycle is less than or equal the required time range.
When prioritizing the capital budget, that is, when a certain amount of capital cost is required to achieve a certain treatment effect: select measures according to the capital cost of each measure, and select measures whose capital cost is less than or equal to the capital budget value.
When comprehensively considering the time requirements and capital budget, the Fj score of the measures selected by applying the basic selection method needs to bc corrected according to the measures implementation cycle and capital cost. The correction method is as follows:
F - j
T X cO7
r * revised measure score 1 CpT: implementation cycle of measures At : capital cost of measures After die correction, eliminate measures with the minimum value of Ejf, and the remaining measures are finally optimized.
BRIEF DESCRIPTION OF DRAWINGS
Figure 1: flow chart of basic scheme for measure selection.
Figure 2: flow chart of measure selection scheme for ntroducing applicability condition improvement.
Figure 3: flow chart of measure selection scheme for introducing PM25 source analysts correction improvement.
Figure 4: flow chart of measure selection scheme combined with applicable conditions and PIV12.5 source analysis modification.
Figure 5: PIV12.5 source composition analysis results in city A. Figure 6: SO2 source composition analysis results in city A. Figure 7: NO2 source composition analysis results in city A.
EMBODIMENTS
Embodiment 1 The optimal pollution control measures for city A are selected using the above measures selection methods. The air quality monitoring results of city A are shown below Monitoring Results of Air Pollutant Concentration in City A Pollutants EIVI2.5 SO2 NO2 Concentration (1.ig/m3) 90 30 50 Exceeding rate 2.6 0.5 1.3 Thc concentration exceeding rate of PM2,5. SO2, NO2, and 03 in city A can be calculated as follows: EF,m2 = = 2.6 Es°2 = -= 0.5 ENO2 = -40 = 1.3 The analysis results of air pollutant sources in city A are shown in the table below Composition of City Pollutant Emission Sources in City A pollutants 00-road mobile Industrial Thermal Others Dust source source source power source plant PM2.5 2% 38% 1% 18% 3% 38% SO2 38% 4% 10% 43% 4% 1% NO2 11% 53% 2% 33% 1% 0% The prevention and control measures for available pollution sources in city A are shown in the table below.
Table of available pollution source prevention and control measures n city A Measure number Measures Off- Mobile source. Thermal others Dust source road Industrial power source source plant Upgraded engine locomotive: dieselelectric hybrid locomotive i 2 Aircraft ground support equipment: i power alternative fuel 3 Shore based power -cold ironing 4 Coal washing / Low N Ox burner i 6 Cement kiln: absorber for spray dryer 7 Wet washing technology v 8 Selective catalytic reduction v 9 Driving efficiency strategy v Driving efficiency strategy v 11 Driving efficiency strategy v Since the exceeding rate of SO2 is less than I. it indicates that SO2 in the city has reached the standard, and no relevant measures need to be taken to focus on the treatment. Therefore, the prevention and control measures corresponding to PM2.5 and NO2 are selected in the measure library. The weight of each measure category is calculated as follows: x Cf-tm2, + EN02 X CgO2 ERM25 EN02 2.6 x 0.02 + 1.3 x 0.11 2.6 + 1.3 = 0.05 E pm X ann EN2 X Cb S ^ r.2.5 O NO2 EPM2.5 EN02 2.6 x 0.38 + 1.3 x 0.53 2.6 + 1.3 = 0.43 EP/I42.5 x C pC PNO2 X C/V02 Epm Emn -2.5 ^^*-.2 2.6 x 0.01 + 1.3 x 0.02 2.6 + 1.3 = 0.1333 W4= X Cpam rd £ 2 5 + EN02 " PM2 5 Epm2 5 ± EN02 2.6 x 0.18 + 1.3 x 0.33 2.6 + 1.3
-
-
= 0.23 Ws - Epm2 X Ct:m2 2 ± ENO X Ck02 EEM2.2 E.NO2 2.6 X 0.01 ± 1.3 X 0.01 2.6 ± 1.3 = 0.01 r f Epivi25X C vpm2s± EN02 " 14/76 2.6 X 0.38 ± 1.3 X 0 2.6 + 1.3 = 0.2533 Measure Wi off-road W., mobile. W; W4 W5 others W6 dust source category source source industrial thermal weight source power plant 0.05 0.43 0.1333 0.23 0.01 0.2533 The treatment measures in this embodiment are obtained from the U.S. EPA list. The pollutant removal applicability coefficient DI' of each measure is obtained according to the treatment effect of the measures, which are as follows: Control Effect Coefficient of Measures D7 NOx PM SO2 Measure 1 1 1 0 Measure 2 1 0 0 Measure 3 1 1 0 Measure 4 0 0 0.5 Measure 5 0.5 0 0 Measure 6 0 0 1 Measure 7 0 0 1 Measure 8 1 0 0 Measure 9 0 1 0 Measure 10 0 0 0 Measure 11 0 0.5 0 EEM2.2± E.NO2 NOr For example, = 1 means that the applicability coefficient of measure 1 for pollutant NOx removal is 1. which means measure 1 is very suitable for pollutant NOx removal. Through the formula: =1Wieklit The score of each measure J can be calculated as follows: Off- Mobile source Industrial source Thermal Others mid Dust Total score for road power natural source measures source plant source Measure 1 0.6 0.6 Measure 2 0.1 0.1 Measure 3 0.6 0.6 Measure 4 0 0 Measure 5 0.1333 0.1333 Measure 6 0 0 Measure 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
Calculation example:
= WiekM, Nox\ = (epm2.5 PM2.5 ± eNoxD, ) = 0.05 * (10 x 1 + 2 x 1) =0.6 Since the primary removal pollutant of measures 4, 6, and 7 is SO2, and the SO2 in this embodiment does not exceed the standard, the final score of the above three measures is 0. Alternatively, relevant SO2 measures are not considered because 502 does not exceed the standard in this case. Since the removal rate of the four pollutants in measure 10 is less than 25%, the removal adaptability coefficient of measure 10 for the four pollutants is 0. That means the final score of measure 10 is 0.
Embodiment 2 The optimal pollution control measures are selected for city A using the measures selection method above. The air quality monitoring results of city A arc shown in the table below.
Monitoring results of air pollutant concentration in city A Pollutants PM2,3 SO2 NO2 Concentration 90 30 50 (tig/1/13) Exceeding rate 2.6 0.5 1.3 The concentration exceeding rate of PM 25. SO2, NO2 and 03 in a city a is calculated as follows: Epp,52 = = 2.6 Ecn= -= 0.5 -2 60 EN02 40 1.3 The analysis results of air pollutant sourccs in city A are shown below.
Table of c pollutant emission sources composition pollutants a. Non b. Road c. d. Thermal 3, others f. Dust road mobile Industrial power and natural source mobile source source plant source source PM2,5 2% 38% 1% 18% 3% 38% SO2 38% 4% 10% 43% 4% 1% NO2 11% 53% 2% 33% 1% 0% In the selection of available pollution source prevention and control measures in city A. the same measure could have treatment effect on multiple pollution sources, as shown in the table below.
Table of available pollution source prevention and control measures in city A Measure number Measures Off-road Mobile Industrial Thermal source source source power plant standard 2 Aircraft ground support vi equipment: power alternative fuel 3 Diesel transformation V V 4 Coal washing V Low NOx buner i V 6 Cement kiln: absorber for spray V dryer 7 ESP electrostatic precipitator V V The treatment measures in this embodiment arc obtained from the U.S. EPA list. The pollutant removal applicability coefficient Dik of each measure is obtained according to the treatment effect of the measures, which are as follows: Control Effect NO- PM SO2 Coefficient of Measures ac Measure 1 0 0 0 Measure 2 1 0 0 Measure 3 0.5 0 0 Measure 4 0 0 0.5 Measure 5 0.5 0 0 Measure 6 0 0 1 Measure 7 0 1 0 The weight of each measure category is calculated as follows: EPM2 x CjIm2 EN02 X Q02 W1 = Epro Emn --2 5 EN O2 2.6 x 0.02 + 1.3 x 0.11 2.6 + 1.3 = 0.05 Epm2 X avi 2.5 EN02 X CR/02 W2 = 2.6 x 0.38 + 1.3 x 0.53 2.6 + 1.3 EPA12 s EiV02.
= 0.43 w3- Epm2 X CM 25 EN02 X CCT;02 EEM2.s E.NO2 2.6 x 0.01 + 1.3 x 0.02 2.6 + 1.3 = 0.1333 EPM2PI42 + E NO, X CA5102 X cd VV4 2.6 x 0.18 + 1.3 x 0.33 2.6 + 1.3 = 0.23 Measure Vv't Off-road source W3 Mobile source W3 industrial source W4 thermal category weight power plant 0.05 0.43 0.1333 0.23 Through formula: Fj =>jWiekDt The score of each measure j in the field of source analysis i can be calculated as follows: F3 =IWiek,01 NO, x eNox X D3 ± W2 X eNox x DNOx3 = 0.05 x 2 x 0.5 + 0.43 x 2 x 0.5 = 0.48 Table of measures score results Off-road Mobile Industrial Thermal power plant Total score for source source source measures 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 EPM2 s EN02 Measure 6 0 0 0 0 0 Measure 7 0 0 1.333 2.3 3.633 Since the main removal pollutant of measures 4 and 6 is 502, and the SO2 in this example does not exceed, the final score of the above three measures is 0. Or because SO2 does not exceed in this case, and relevant SO2 measures are not considered. Since the removal rate of the four pollutants in measure 1 is less than 25%, the removal adaptability coefficient of measure 1 for the four pollutants is 0, so the final score of measure 1 is 0. Finally, the total score of measure 5 is the highest. If the score ranking method is used, the final preferred measure would be measure 5.
Claims (15)
- What is Claimed: 1. A method for selecting pollutant treatment measure for a city based on the urban air pollution characteristic, comprising the following steps: (1A) Establish a library of pollution treatment measures: the said pollution treatment measure library is classified according to the emission source classification method of the pollutant emission inventory, and different types of pollution treatment measure libraries are obtained; the classification method is based on the emission source of the pollutant emission inventory classification; (1B) Calculate exceeding coefficient: Calculate the exceeding coefficient from the environmental quality standard limit and environmental monitoring values; (1C) Correct exceeding coefficient: correct exceeding coefficient when needed according to the exceeding situation of air pollutants; the said exceeding coefficient is based on the source apportionment analysis, to exclude the interference of sulfates and sulphates in the monitored values of PM25. SO2, and NO2, and recalculate the exceeding coefficient to correct of the exceeding coefficient; (1D) Analyze the pollutant source apportionment: According to the city pollutant emission source inventory, analyze and calculate the contribution rate of each emission source to each pollutant; (1E) Calculate the weight of the treatment measures category: calculate the weight of the measures category according to the pollutant's over-standard rate and the pollutant's contribution rate; (1F) Calculate the scores of the measures: Calculate the scores of each measure according to the weight of the measure category, the pollutant exceeding the standard, and the pollutant removal effect coefficient of the measure; (1G) Select preferred measures: rank or filter the scores of measures to select the optimal pollution treatment measures.
- 2. A method for selecting pollutant treatment measures as defined in claim 1, wherein the categories include one or more of the following categories: industrial sources, road mobile sources, non-road mobile sources, dust sources, VOC-related sources, thermal power plants, Natural source.
- 3. A method for selecting pollutant treatment measures as defined in claim 2, wherein the calculation of the exceeding coefficient comprising the following steps: 7:k (3A) Calculate exceeding standard ratio sic, the calculation method is Ek = -, the Sk Sk is the limit of pollutant concentration, and the Tk is the monitored pollutant concentration value; (3B) Corresponding the exceeding coefficient: the exceeding coefficient is determined by the exceeding standard ratio; when the said exceeding ratio Ek is between 0-1, which exceeds the standard the situation is judged as "not exceeded", the said exceeding coefficient ek is 0; when the said exceeding ratio Ek is between 1-1.2, and the exceeding situation is judged as " Slightly exceed ", the said exceeding coefficient ek is 1; when the said exceeding ratio ek is 1.2-1.5 Among them, the over-standard condition is judged as " Moderately exceed ", the said exceeding coefficient ek is 2; when the said exceeding ratio ek is between 1.5-2, and the over-standard condition is judged as " Seriously exceed ", the said exceeding coefficient ek is 5; when the said exceeding ratio Ek is greater than 2, the said exceeding situation is judged as "Severe exceed", and said exceeding coefficient ek is 10.
- 4. A method for selecting pollutant treatment measures as defined in claim 3, wherein the Correction of exceeding coefficient comprising the following steps: (4A) Determine whether the monitored value needs to be corrected: when Epm2.5 is 1-1.2, EPM502 is 0-1 or F -PMNO2 is 0-1, the monitored value needs to be corrected; when Epm2.5 is 0-1, Epmso2 is 1-1.2 or E -PMNO2 is 1-1.2, the monitored value needs to be corrected; when Epm2.5 is 0-1 and EpAlso 2 is 0-1 and EpiviNO2 is 0-1, the monitored value needs to be corrected; when Epmas is 1.2-1.5, Epmgo2 is 1.2-1.5, and EpmNO2 is 1.2-1.5, the monitored value needs to be corrected; when Epm2.5 is 1.5-2 and Epms02 is 1.5 -2 and EpmNO2 is 1.5-2, the monitored value needs to be corrected; when Epm2.5 is greater than 2 and spms02 is greater than 2 and E PMNO2 is greater than 2, the monitored value needs to be corrected; (4B) Correct the monitored value: T'pm25 = Tpm25 X (100% -PSO, PN02) I T'502 = T502 Tpm25 X Ps02, Tf No2 = TNOz TPM2.5 X PNO2, Among them, rpm25 is the corrected PM2.5 concentration value, Tpm2.5 is the monitored PM2.s concentration value, P02 2 is the sulfate content in the source analysis result Mass percentage, P -NO2 is the mass percentage of nitrate in the source analysis result, T'502 is the modified sulfur dioxide concentration value, T502 is the monitored sulfur dioxide concentration value, TrNO2 is the corrected nitrogen dioxide concentration value, TN02 is the monitored nitrogen dioxide concentration value; (4C) Recalculate the exceeding standard ratio by applying the corrected monitored value and re-determine the corrected exceeding coefficient.
- 5. A method for selecting pollutant treatment measures as defined in claim 4, wherein the pollutant treatment measures are entitled with parameters of the measures specifications, and the characteristic parameters of the measures include one or more of the following parameters: pollutant treatment effect coefficients, time limit parameters, capital limit parameters.
- 6. A method for selecting pollutant treatment measures as defined in claim 4, wherein the removal rate of the pollutant treatment measures is 0%-25%, the pollutant treatment effect coefficient is 0; the actual removal efficiency of the pollutant treatment measures is 26 %-75%, the pollutant treatment effect coefficient is 0.5; the actual removal efficiency of the pollutant treatment measures is 76%400%, and the pollutant treatment effect coefficient is 1.
- 7. A method for selecting pollutant treatment measures as defined in claim 6, wherein the corrected pollutant concentration is a gaseous pollutant concentration, and the corrected gaseous pollutant concentration includes the consumption of PM2.5 due to the secondary reaction. Concentration of gaseous pollutants.
- 8. A method for selecting pollutant treatment measures as defined in claim 1, wherein the contribution rate calculation method is Ckl, = , and the Ck is the contribution Ek of type i emission source to pollutant k TheEk is the emission amount of pollutant k in the I type emission source; the Ek is the emission amount of pollutant k.
- 9. A method for selecting pollutant treatment measures as defined in claim 1, wherein EEkxck the method for calculating the weight of the measure category is MT, - and Eek xxc the VV, = Ekk is the i type The weight of the measure. 2, Ek
- 10. A method for selecting pollutant treatment measures as defined in claim 7, wherein the score of the measure is positively correlated with the measure category weight, the exceeding coefficient or the exceeding coefficient, and the pollutant removal effect coefficient of the measure.
- 11. A method for selecting pollutant treatment measures as defined in claim 10, wherein the calculation method for calculating the score of the measure is Fj = Elf WiekD; the DI' is the pollution effect of measure j The coefficient of treatment effect of item k.
- 12. A method for selecting pollutant treatment measures as defined in claim 1, wherein the method for selecting measures is a direct ranking method, and the direct ranking method is to arrange each measure according to its score F./ from large to small, and select the ranking in the design Measures within the range of a certain ratio, the set ratio is any one of 5%, 10%, 20%, 30%, and 40%.
- 13. A method for selecting pollutant treatment measures as defined in claim 12, wherein the implementation period of the selected measure is less than the time limit parameter; the budgeted cost of the selected measure is less than the funding limit parameter.
- 14. A method for selecting pollutant treatment measures as defined in claim 13, characterized in that the method of correcting the score according to the characteristic F./ parameters of the measure is P7 - 7. , and col' is the measure The cpjx<pj implementation time parameter of cpj. is the investment parameter of the measure.
- 15. A computer program, wherein the computer program can execute the method for selecting pollutant treatment measures as defined in the claims from 1 to 14.
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| WO2021026792A1 (en) | 2021-02-18 |
| CN114418442B (en) | 2022-09-27 |
| CN114418434A (en) | 2022-04-29 |
| CN111727447B (en) | 2022-03-01 |
| CN111727447A (en) | 2020-09-29 |
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