CN108229092B - Atmospheric pollution simulation prediction algorithm for increasing liquid phase chemistry and wet sedimentation process - Google Patents
Atmospheric pollution simulation prediction algorithm for increasing liquid phase chemistry and wet sedimentation process Download PDFInfo
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- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 38
- 238000001556 precipitation Methods 0.000 claims description 15
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- MGWGWNFMUOTEHG-UHFFFAOYSA-N 4-(3,5-dimethylphenyl)-1,3-thiazol-2-amine Chemical compound CC1=CC(C)=CC(C=2N=C(N)SC=2)=C1 MGWGWNFMUOTEHG-UHFFFAOYSA-N 0.000 claims description 9
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Abstract
The application discloses an atmospheric pollution simulation prediction algorithm for increasing liquid phase chemical and wet sedimentation processes, which is based on a chemical mode of CALGRID, and continuously introduces pollutant concentration change items caused by the liquid phase chemical process and the wet sedimentation process under the premise of considering the influence of atmospheric chemical reaction, atmospheric transportation and diffusion, sedimentation, ground surface source and overhead emission source, thereby increasing the CALGRID of the cloud and rain chemical process to better simulate the environmental pollution including SO 2 、NO 2 、PM 10 、PM 2.5 The conventional primary pollutants such as secondary pollutants, sulfate, nitrate, ammonium salt, black carbon, organic carbon and the like, and the wet settlement of S, N is well simulated. The improved CALGRID mode has high resolution and high timeliness, can conveniently and rapidly give out the response relation between the source and the receptor, and has reliable results.
Description
Technical Field
The application belongs to the technical field of atmospheric environmental pollutant detection, and particularly relates to an improved prediction model for mesoscale atmospheric photochemical pollution.
Background
The air quality mode is a method for realizing simulation and forecast of concentration distribution conditions and variation trends of air pollutants on different spatial scales by using research methods and computer technologies of subjects such as weather, environment, physics, chemistry and the like on the basis of understanding a series of physical and chemical processes such as transmission, diffusion, conversion, removal and the like after the pollutants are discharged into an atmosphere environment, has important practical application values in the aspects of air quality forecast, atmosphere pollution control, environment planning and management, urban construction, public health and the like, and has wide development prospect.
The CALGRID is developed by ARB (Air Resources Board) in California of the United states, is a Euler mesoscale atmospheric photochemical mode, has a good effect on simulating secondary pollutants such as ozone, is mainly suitable for simulating photochemical reactions under clear sky conditions, and comprises the processes of atmospheric transportation and diffusion, gas-phase chemical reactions, a manually discharged point-surface line source, dry sedimentation and the like.
With the acceleration of the process of urban treatment and the rapid development of social economy, the atmospheric combined pollution characterized by the mutual coupling and superposition of acid rain, photochemical pollution and dust haze pollutants becomes an increasingly prominent atmospheric environment problem in China. In recent years, research on formation mechanisms and comprehensive prevention and control of regional atmosphere combined pollution of heavy-point urban groups such as Jinjin Ji, yangtze river delta, zhujiang delta and the like is a hotspot problem of environmental protection research in China. Thus, air quality models are required to be able to simulate different types of pollution processes (gas phase, liquid phase, heterogeneous) for a variety of atmospheric pollutants (trace gases, aerosols, etc.) at different scales (regional, urban, etc.). In order to better utilize the calgarid model to recognize and understand the gas phase chemical process, aerosol micro-physical chemical process and liquid phase chemical process of trace gases, and provide theoretical support for regulating and controlling the atmosphere combined pollution, calgarid is required to be capable of simulating and solving the ozone pollution problem related to the gas phase chemical reaction, reasonably describing the physical chemical process of the aerosol, the liquid phase chemical process in cloud rainwater and the wet sedimentation process related to precipitation.
The calbrid mode lacks a description of liquid phase chemistry in cloud stormwater, and precipitation-related wet settling processes. Liquid phase chemistry and wet clean-up in the atmosphere are important components of atmospheric chemistry, both in connection with clouds and precipitation. Cloud and precipitation play an important role in the cyclic process of conveying, converting and removing trace gases and suspended particles in the atmosphere, and are mainly expressed in the following steps: (1) The strong ascending and sinking airflow exists in the accumulated cloud, so that pollutants are effectively mixed in the cloud; (2) The absorption of gaseous pollutants by cloud and rain drops and the removal of aerosol particles produce wet deposition; (3) The contaminants, after being absorbed by the cloud and rain drops, will undergo a series of dissolution, ionization and liquid phase oxidation reactions therein. Calgarid is required to accurately simulate the concentration of pollutant gases and particulate matter components in the atmosphere, including the cloud and precipitation chemistry described above.
Disclosure of Invention
Aiming at the problems existing in the prior art, the application provides an atmospheric pollution simulation prediction algorithm for increasing liquid phase chemistry and wet sedimentation processes.
In order to solve the technical problems, the application adopts the following technical scheme: an atmospheric pollution simulation prediction algorithm for increasing liquid phase chemistry and wet sedimentation processes, comprising the steps of:
step 1: based on the chemical mode of CALGRID, the influences of atmospheric chemical reaction, atmospheric transportation and diffusion, sedimentation, ground surface source and overhead emission source are considered, and pollutant concentration change terms caused by a liquid phase chemical process and a wet sedimentation process are introduced to obtain a chemical species concentration change equation as shown in a formula (1),
in the right polynomial of formula (1), the first termIs a second order turbulence diffusion term, second term->The third item (P-L) is a diffusion item GAS The fourth CHEM is a gas phase chemical change aq A variation term of the concentration of the pollutant caused by the liquid phase chemical process, a fifth term E ANT For artificial pollution source emission item, sixth item->For the change of the concentration of species caused by dry sedimentation, seventh item +.>Is the amount of wet sedimentation caused by wet sedimentation; wherein C is the average concentration of chemical species, V is the average amount of three-dimensional wind vector, K is the turbulence diffusion coefficient, wherein, the second-order turbulence diffusion term +.>The method is obtained through closed conversion of a turbulent diffusion coefficient K theory;
step 2: considering the liquid phase chemical process in the cloud of three substances of nitric oxide, nitrogen dioxide and sulfur dioxide, solving the substance concentration of the liquid phase in the cloud of the substances at the moment of n+1 by the formula (2):
C n+1,i ＝(1-Δt×N z ×K a )×C n (2)
C n the concentration of the substance in the liquid phase in the cloud at the time of N is the integral time step, and delta t is N z K is the total cloud a Is the liquid phase chemical conversion rate;
step 3: for the variation of the concentration of species caused by wet sedimentation, the species considered include nitric oxide, nitrogen dioxide, sulfur dioxide, nitric acid, sulfuric acid, PM 10 The wet removal rate K is obtained by first obtaining the sulfate, nitrate, ammonium salt, OC, EC, SOA through empirical formula (3) w ：
In the formula (3), P r For precipitation rate, a and b are empirical constants;
the concentration of the species at time n+1 after the wet clean process is then obtained by formula (4):
C n+1,i ＝(1-Δt×K w )×C n (4)
wherein C is n+1 C represents the concentration of the substance in the liquid phase in the cloud at the current time n The concentration of the liquid phase in the cloud at the previous time, Δt represents the integration time step, K w Is the wet clean rate;
in precipitation, the wet removal amount of each contaminant is obtained by the formula (5):
in the formula (5), the amino acid sequence of the compound,wet precipitation for time step at time n+1,/->Wet sedimentation for time step at time nAmount, K w For wet clearance rate, C n The contaminant concentration at the previous time is indicated, and Δz indicates the pattern level of precipitation.
Further, the empirical constants a and b in the solution equation (3) of the wet removal rate in step 3 are as follows:
for SO 2 When the device is in summer, the values of a and b are respectively 0.14 and 0.12; when in spring or autumn, the values of a and b are respectively 0.036 and 0.53; when in winter, the values of a and b are respectively 0.009 and 0.70;
for SO 4 2- When the device is in summer, the values of a and b are respectively 0.39 and 0.06; when in spring or autumn, the values of a and b are respectively 0.091 and 0.27; when in winter, the values of a and b are respectively 0.021 and 0.70.
Further, for NO and NO 2 The wet removal rate of (2) is SO 2 One quarter of the wet clean rate of (a).
Further, for HNO 3 The wet removal rate is taken as SO 2 One half of the wet clean rate of (a).
Further, for NO 3 - 、NH 4 + Is equal to SO and has the value of wet clearance rate 4 2- The wet removal rates of (2) are equal.
Further, for PM 10 The wet removal rate was determined, and the empirical constants a and b in equation (3) were 1.26 and 0.79, respectively.
Further, the values for the wet clearance rates of OC, EC and SOA are taken as PM 10 One half the value of the wet clean rate.
Description of the drawings
FIG. 1 is a schematic representation of a simulated region and topography of an embodiment of the present application;
FIG. 2 is a graph of the ground concentration of ozone in the region of the Yangtze river delta 2006, obtained by applying the simulation prediction algorithm of the present application;
FIG. 3 (a) is a graph showing the ground concentration profile of PM25 particulate matter in the Pingjiang delta 2006 using the simulation prediction algorithm of the present application;
FIG. 3 (b) is a graph showing the ground concentration profile of sulfate particles in the region of the Yangtze river delta 2006, obtained by applying the simulation prediction algorithm of the present application;
FIG. 3 (c) is a graph showing the ground concentration profile of nitrate granules in the region of Zhujiang delta in 2006 obtained by applying the simulation prediction algorithm of the present application;
FIG. 3 (d) is a graph showing the ground concentration distribution of ammonium salt particles in the region of Zhujiang delta in 2006 obtained by applying the simulation prediction algorithm of the present application;
FIG. 4 (a) is a graph of N settlement in the Pingjiang delta 2006 obtained by applying the simulation prediction algorithm of the present application;
fig. 4 (b) is a plot of the S settlement amount in the delta region of the pearl river in 2006 obtained by applying the simulation prediction algorithm of the present application.
Detailed Description
The application will be further elucidated with reference to the drawings and in the following by means of specific embodiments. It is to be understood that these examples are for the purpose of illustrating the application only and are not to be construed as limiting the scope of the application, since modifications to the application, which are various equivalent to those skilled in the art, will fall within the scope of the application as defined in the appended claims after reading the application.
An atmospheric pollution simulation prediction algorithm for adding liquid phase chemistry and wet sedimentation processes comprises the following steps:
step 1: based on the chemical mode of CALGRID, the influences of atmospheric chemical reaction, atmospheric transportation and diffusion, sedimentation, ground surface source and overhead emission source are considered, and pollutant concentration change terms caused by a liquid phase chemical process and a wet sedimentation process are introduced to obtain a chemical species concentration change equation as shown in a formula (1),
in the right polynomial of formula (1), the first termIs a second order turbulence diffusion term, second term->The third item (P-L) is a diffusion item GAS The fourth CHEM is a gas phase chemical change aq A variation term of the concentration of the pollutant caused by the liquid phase chemical process, a fifth term E ANT For artificial pollution source emission item, sixth item->For the change of the concentration of species caused by dry sedimentation, seventh item +.>Is the change in species concentration caused by wet sedimentation; wherein C is the average concentration of chemical species, V is the average amount of three-dimensional wind vector, K is the turbulence diffusion coefficient, wherein, the second-order turbulence diffusion term +.>Obtained by the closed conversion of the turbulent diffusion coefficient K theory. The following items of variation of concentration of pollutants CHEM mainly caused by liquid phase chemical process aq And species concentration variation term caused by wet sedimentation +.>The solution description is carried out:
CHEM aq is simplified for Nitric Oxide (NO), nitrogen dioxide (NO 2 ) Sulfur dioxide (SO) 2 ) Consider the liquid phase chemistry within its cloud: if cloud exists, the cloud is converted according to a certain proportion. Wherein the liquid phase chemical conversion rate of sulfur dioxide is 10% per hour, the liquid phase chemical conversion rate of nitrogen oxides (nitrogen monoxide and nitrogen dioxide) is 10% per hour in daytime, and 2% per hour in night. Thus, nitrogen monoxide (NO) and nitrogen dioxide (NO 2 ) Sulfur dioxide (SO) 2 ) The concentration change of (c) can be expressed as:
C n+1 ＝(1-Δt×N z ×K a )×C n (2)
(2) Wherein C is n+1 Indicating the current time of the pollutant (NO, NO 2 And SO 2 ) Concentration, C n Represents the concentration of the contaminant at the previous time, Δt represents the integration time step, N z Representing total cloud content, K a Representing the liquid phase chemical conversion.
For wet cleaning, consider nitric oxide, nitrogen dioxide, sulfur dioxide, nitric acid (HNO) 3 ) Sulfuric acid (H) 2 SO 4 )、PM 10 Sulfate (SO) 4 2- ) Nitrate (NO) 3 - ) Ammonium salt (NH) 4 + ) The wet removal process of OC, EC, SOA, et al, employing a simplified treatment method, introducing a wet removal rate K w . The method is expressed by an empirical formula:
K w ＝a×Pr b (3)
(3) Wherein P is r Is precipitation rate (mm/hr) and is output by the meteorological mode. a. b is an empirical constant for SO 2 And SO 4 2- The values of a and b are shown in Table 1.NO and NO 2 Wet removal rate of (2) is taken as SO 2 One quarter of the wet clean rate of HNO 3 Taking SO 2 One half of NO 3 - 、NH 4 + Taking and mixing SO 4 2- Equal. PM (particulate matter) 10 The values of a and b of (2) are 1.26 and 0.79. Wet removal rates for OC, EC and SOA were taken as PM 10 One half of (a) of (b).
TABLE 1SO 2 And SO 4 2- The values of a and b in the wet removal rate calculation formula of (2)
Then, nitric oxide, nitrogen dioxide, sulfur dioxide, nitric acid, sulfuric acid and PM in the atmosphere 10 The concentration of sulfate, nitrate, ammonium salt, OC, EC, SOA, etc. after the wet sedimentation process can be calculated from the following formula,
C n+1 ＝(1-Δt×K w )×C n (4)
(4) Wherein C is n+1 Indicating the concentration of the contaminant at the current time, C n On the representationThe contaminant concentration at time, Δt, represents the integration time step, K w Is the wet clean rate. While in precipitation, the wet removal of each contaminant is:
(5) In the method, in the process of the application,respectively represent the wet sedimentation quantity, K of the current time step and the next time step w For wet clearance rate, C n The contaminant concentration at the previous time is indicated, and Δz indicates the pattern level of precipitation. The following are specific examples of the application of the algorithm of the present application:
the CALGRID adopts the simplified algorithm of liquid phase chemistry and wet sedimentation, simulates main pollutants and S, N sedimentation in the Zhujiang delta area in 2006, and tests the stability of long-time running of the model and the validity of the result. The simulation period selected was 2006 year round. The selected simulation areas (21.8N-24N 112.16E-114.82E) comprise 10 areas of Guangzhou, shenzhen, buddha mountain, zhugai, dongguan, zhongshan, huizhou, qing Yuan, zhaoqing, jiangmen and the like and two special administrative areas of hong Kong and Australian China. The meteorological field is provided by a mesoscale meteorological pattern WRF, four layers of nesting are provided, and the nesting area of the innermost layer is a simulation area of an air quality pattern. Wherein D4 is the innermost nesting region, see table a. The source emission inventory used was that of the east Asia 2006 source emission materials of Zhang Qiang and D.G. Streets et al, with a spatial resolution of 0.5.
Table a
FIG. 2 is a simulated O 3 The annual average concentration of (C) is 45. Mu.g/m in most regions 3 About, the concentration of the large value area exceeds 70 mug/m 3 The region with greater concentration is in the Guangzhou bergan zone. Comparing air quality of Zhujiang delta in 2006As seen from the quantitative observation report, the concentration simulated by the regional source-receptor response model is similar to the observed result (the concentration value in most regions is 40-50 mug/m) 3 About, the large value is 70 mug/m 3 Left and right).
FIG. 3 shows the simulated ground concentration profile of particulate matter, showing that the regions of greater concentration are distributed in Guangzhou, fingered berg, and PM in the air quality observations of Zhujiang delta in 2006 10 Is similar in distribution. Table 2 gives the average of the months for each station of the monitoring station and pattern simulation, GZ1 for foot lake park (guangzhou), GZ2 for ten thousand hectares of sand (guangzhou), GZ3 for Tianhu (guangzhou), ZH for tangjia (bead sea), FS1 for the changshan school (bergamot), FS2 for Hui Jingcheng (bergamot), ZQ for city (culprit), HZ for down-hole (huizhou), HK for the gulf (hong kong in china). It can be seen that the error between simulation and observation is small.
Table 2-1 cantonese zhujiang delta area air monitoring network part site PM 10 Monitoring data
TABLE 2-2CALGRID Pattern lookup partial lattice point PM 10 Concentration of
FIG. 4 is a simulated spatial distribution of S, N sedimentation, which is seen to correspond to SO 2 ，NH 3 And the spatial distribution of sulfate, nitrate, ammonium salts. The results of calbrid simulation better reflect the settling of S and N compounds.
Overall, calgarid with added cloud chemistry not only mimics well the conventional primary pollutants (SO 2 、NO 2 、PM 10 、PM 2.5 Etc.), secondary pollutants (O) 3 Etc.), sulfate (SO 4 2- ) Nitrate (NO) 3 - ) Ammonium salt (NH) 4 + ) Black Carbon (BC), organic Carbon (OC), etc., also well simulated the wet settlement of S, N. The improved CALGRID mode has high resolution and high timeliness, can conveniently and rapidly give out the response relation between the source and the receptor, and has reliable results.
Claims (7)
1. An atmospheric pollution simulation prediction method for increasing liquid phase chemistry and wet sedimentation process is characterized by comprising the following steps:
step 1: based on the chemical mode of CALGRID, the influences of atmospheric chemical reaction, atmospheric transportation and diffusion, sedimentation, ground surface source and overhead emission source are considered, and pollutant concentration change terms caused by a liquid phase chemical process and a wet sedimentation process are introduced to obtain a chemical species concentration change equation as shown in a formula (1),
in the right polynomial of formula (1), the first termIs a second order turbulence diffusion term, second term->The third item (P-L) is a diffusion item GAS The fourth CHEM is a gas phase chemical change aq A variation term of the concentration of the pollutant caused by the liquid phase chemical process, a fifth term E ANT For artificial pollution source emission item, sixth item->The seventh item for the variation of species concentration due to dry sedimentationIs the amount of change in contaminant concentration caused by wet sedimentation; wherein C is the average concentration of chemical species, V is the average amount of three-dimensional wind vector, K is the turbulence diffusion coefficient, wherein, the second-order turbulence diffusion term +.>The method is obtained through closed conversion of a turbulent diffusion coefficient K theory;
step 2: consider the liquid phase chemical process in the cloud of three substances of nitric oxide, nitrogen dioxide and sulfur dioxide, and the pollutant concentration C of the liquid phase in the cloud at the moment of n+1 n+1,cloud Solving by formula (2):
C n+1,cloud ＝(1-Δt×N z ×K a )×C n,cloud (2)
C n,cloud the concentration of the pollutant in the liquid phase in the cloud at the time N is delta t, which is the integral time step, N z K is the total cloud a Is the liquid phase chemical conversion rate;
step 3: for the variation of the concentration of species caused by wet sedimentation, the species considered include nitric oxide, nitrogen dioxide, sulfur dioxide, nitric acid, sulfuric acid, PM 10 The wet removal rate K is obtained by first obtaining the sulfate, nitrate, ammonium salt, OC, EC, SOA through empirical formula (3) w ：
In the formula (3), P r For precipitation rate, a and b are empirical constants;
then, the contaminant concentration C after the wet clean-up process at time n+1 is obtained by the formula (4) n+1,wet ：
C n+1,wet ＝(1-Δt×K w )×C n,wet (4)
Wherein C is n+1,wet Indicating the concentration of the contaminant at the current time, C n,wet Represents the contaminant concentration at the previous time, Δt represents the integration time step, K w Is the wet clean rate;
in precipitation, the amount of change in the concentration of each contaminant due to wet sedimentation is obtained by the formula (5):
in the formula (5), the amino acid sequence of the compound,for the change in the concentration of the respective contaminant caused by wet sedimentation at time n+1,/for the time of the wet sedimentation>For each pollutant concentration change amount caused by wet sedimentation at time n, K w For wet removal rate, ΔZ represents the pattern level with precipitation.
2. The method for simulated prediction of atmospheric pollution to an enhanced liquid phase chemical and wet deposition process of claim 1, wherein: the empirical constants a and b in the solution equation (3) of the wet removal rate in step 3 are as follows:
for SO 2 When the device is in summer, the values of a and b are respectively 0.14 and 0.12; when in spring or autumn, the values of a and b are respectively 0.036 and 0.53; when in winter, the values of a and b are respectively 0.009 and 0.70;
for SO 4 2- When the device is in summer, the values of a and b are respectively 0.39 and 0.06; when in spring or autumn, the values of a and b are respectively 0.091 and 0.27; when in winter, the values of a and b are respectively 0.021 and 0.70.
3. The method for simulated prediction of atmospheric pollution to an enhanced liquid phase chemical and wet deposition process of claim 2, wherein: for NO and NO 2 The wet removal rate of (2) is SO 2 One quarter of the wet clean rate of (a).
4. The method of claim 2, wherein the method comprises increasing atmospheric pollution of liquid phase chemical and wet sedimentation processesThe simulation prediction method is characterized in that: for HNO 3 The wet removal rate is taken as SO 2 One half of the wet clean rate of (a).
5. The method for simulated prediction of atmospheric pollution to an enhanced liquid phase chemical and wet deposition process of claim 2, wherein: for NO 3 - 、NH 4 + Is equal to SO and has the value of wet clearance rate 4 2- The wet removal rates of (2) are equal.
6. The method for simulated prediction of atmospheric pollution to an enhanced liquid phase chemical and wet deposition process of claim 2, wherein: for PM 10 The wet removal rate was determined, and the empirical constants a and b in equation (3) were 1.26 and 0.79, respectively.
7. The method for simulated prediction of atmospheric pollution to an enhanced liquid phase chemical and wet deposition process of claim 6, wherein: the values for the wet clean rates of OC, EC and SOA were taken as PM 10 One half the value of the wet clean rate.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| KR101382507B1 (en) * | 2012-10-19 | 2014-04-10 | 사단법인대기환경모델링센터 | Air quality forecast and management system |
| CN105512485A (en) * | 2015-12-14 | 2016-04-20 | 中国科学院大气物理研究所 | Novel method for estimating environment capacity of fine particles and precursors of fine particles |
| CN106290163A (en) * | 2016-07-21 | 2017-01-04 | 中国科学院化学研究所 | Dinitrogen pentoxide and concentration of nitric acid on-line monitoring system and monitoring method in a kind of air |
| CN107064029A (en) * | 2017-02-14 | 2017-08-18 | 中国科学院化学研究所 | A kind of nitrous acid and concentration of nitric acid on-line measurement system and measuring method |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102701252A (en) * | 2012-05-23 | 2012-10-03 | 贵州红星发展股份有限公司 | Barium carbonate, preparation method thereof and carbonizer |
| KR101382507B1 (en) * | 2012-10-19 | 2014-04-10 | 사단법인대기환경모델링센터 | Air quality forecast and management system |
| CN105512485A (en) * | 2015-12-14 | 2016-04-20 | 中国科学院大气物理研究所 | Novel method for estimating environment capacity of fine particles and precursors of fine particles |
| CN106290163A (en) * | 2016-07-21 | 2017-01-04 | 中国科学院化学研究所 | Dinitrogen pentoxide and concentration of nitric acid on-line monitoring system and monitoring method in a kind of air |
| CN107064029A (en) * | 2017-02-14 | 2017-08-18 | 中国科学院化学研究所 | A kind of nitrous acid and concentration of nitric acid on-line measurement system and measuring method |
Non-Patent Citations (1)
| Title |
|---|
| 区域空气质量模拟中查表法的应用研究;谢旻;环境科学;第1409-1417页 * |
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