CN115526043B - Atmospheric environment capacity calculation method considering air resource endowment - Google Patents
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Abstract
The invention provides an atmospheric environment capacity calculation method considering air resource endowment, which comprises the following steps: establishing a three-dimensional iterative computation model of regional composite atmospheric pollution environment capacity based on a meteorological model, an emission model and an air quality model; setting an iterative simulation grid and dividing a control area according to the principle that the attribution distance of the grid is the nearest control point; manufacturing a gridding discharge list by using a discharge model according to a control area; adjusting the discharge amount of the artificial pollution source in the control area according to the ratio of the air quality simulation concentration of the control point to the target concentration; simulating gridding air resource endowment based on a meteorological model; after the iteration reaches the target concentration, the air resource endowment is used for redistributing the emission of each control area grid by grid, the iteration is continued to reach the target concentration, and the emission of each control area is the atmospheric environment capacity considering the air resource endowment. The method optimizes the division method of the control area, comprehensively considers air resource endowment and social factors, and makes up the defects of the existing environment capacity calculation method.
Description
Technical Field
The invention relates to the field of atmospheric environment science, in particular to an atmospheric environment capacity calculation method considering air resource endowments.
Background
The atmospheric environment capacity refers to the maximum load of pollutants which can be contained in the environment under the condition that the concentration of a certain environmental pollutant does not exceed a specified maximum allowable value of the air quality of the environment, and is an important basis for supporting the total control of national atmospheric pollutants and the air quality management. The amount of the pollutants is determined by two factors of nature and society, wherein the natural factors refer to the processes of transportation, diffusion, dry and wet sedimentation, various chemical removal and conversion and the like of the pollutants in the atmosphere; social factors include pollution source layout, emission characteristics, selection of control points, determination of environmental target values and the like, and different values can lead to different capacity accounting results. The current accounting methods include an a-value method (modified a-value method), a linear programming method, and a model iteration method.
The a-value method (modified a-value method) assumes that the contaminants are uniformly mixed within the air-mix layer above the study area, and that the amount of contaminants that can be accommodated is proportional to the height of the mix layer, the area of the study area, the contaminant purification capacity, and the regional contaminant concentration control limits (i.e., regional ambient air quality targets). Wherein the mixed layer height and the pollutant purification capacity of the research area are represented by a total quantity control coefficient A. Simple and easy to understand, and convenient to operate. However, the calculated capacity is "static capacity", only reflects the accommodation amount of the primary pollutants due to the "resources" of the atmospheric environment, does not consider the transmission influence of the surrounding pollutants and the chemical reaction among the pollutants, and cannot reflect the accommodation amount of the precursor of the secondary pollutants (such as sulfate, nitrate, secondary organic carbon and the like in PM 2.5).
The linear programming method is based on a linear programming theory, takes the environmental quality standards of different functional areas as constraint conditions, takes the maximization of the atmospheric bearing capacity of the areas as a target function, and takes the maximum discharge amount of the atmospheric pollutants corresponding to the functional areas meeting the standard as the atmospheric environmental bearing capacity of the areas. Can reflect the response relation of the pollution source and the receptor, and realize the optimal distribution. The linear programming method uses a model to calculate the transmission coefficient, is restricted by a linear relation, and generally cannot solve the problem of secondary pollution which is obvious in a nonlinear process. Only for small spatial dimensions.
And the model iteration method determines the target annual atmospheric environment capacity of the evaluation area by carrying out iterative calculation through an air quality numerical model. The method aims at the condition that the air quality meets the environmental management requirement, and performs numerical model iterative calculation according to the pollution source change of a target year to obtain the concentration of a control point, wherein when the concentration of the control point is equal to the target concentration required by the environmental management, the total emission amount of all the pollution sources is the environmental bearing capacity of the pollutant. When the environmental capacity is estimated based on the third-generation air quality model iteration method, the influence of regional pollutant transmission and a complex chemical reaction mechanism are considered, and the influence of social factors such as pollution source emission and the like on the atmospheric environmental capacity can be fully considered. However, the traditional model iteration method adjusts the emission source by taking an administrative division as a minimum unit for adjustment, the distribution of the administrative division is extremely irregular, and even the situation of the flying land occurs, and the environmental air quality monitoring station as a control point cannot well represent the situation of the whole area. And the spatial distribution of the capacity result is determined by the spatial distribution of the gridding discharge list manufactured in the simulation, and the large capacity rule of the heavily polluted area is presented. If the weather conditions are good and there is no industry in the layout, the capacity may be underestimated.
In summary, the a-value method, the linear programming method and the model iteration method have respective advantages and limitations in the estimation of the atmospheric environment capacity.
Disclosure of Invention
The invention aims to solve the problem of iterative computation of atmospheric environmental capacity by using a traditional model in the prior art, provides an atmospheric environmental capacity computation method considering air resources and provides two innovations on the basis of the traditional model iterative method. Firstly, in the iterative process, the adjustment of the emission source does not take an administrative division as a minimum unit of adjustment, but divides a control area according to the distance between a control point and each simulation grid, and takes the control area as a minimum space unit of adjustment. And secondly, after the traditional simulation iteration is finished, the air resource which is calculated based on the WRF simulation result is used for redistributing the discharge in the control area, and the simulation iteration is carried out again. By using the optimization algorithm, underestimation of environmental capacity by a traditional model iteration method can be avoided, more reasonable capacity space distribution is obtained, and support can be provided for the layout of industrial enterprises.
The purpose of the invention is realized by the following technical scheme:
mainly provides an atmospheric environment capacity calculation method considering air resource endowment, which comprises the following steps:
s1, setting a simulation grid according to a required atmospheric environment capacity area, simulating a weather field by using a mesoscale weather forecast mode WRF, and verifying a simulation result by using weather monitoring data;
s2, setting the concentration of a target pollutant of a control point by taking an air quality monitoring station as the control point, and dividing a control area according to the distance between the simulation grid and the control point;
s3, manufacturing a gridded discharge list according to the control area of the S2;
s4, inputting the results of the S1 and the S3 into a three-dimensional air quality model to simulate chemical transmission, and verifying the simulation result by using the monitoring data of the air quality monitoring station;
s5, judging whether the concentration of the control point simulated pollutants reaches the target pollutant concentration or not according to the result of the S4, if not, adjusting the pollution source emission of the control area according to the ratio of the concentration of the control point simulated pollutants to the target concentration, and then performing iterative calculation by using the three-dimensional air quality model until the target pollutant concentration is reached;
s6, calculating the air resource endowments of each simulation grid by using the WRF simulation three-dimensional meteorological field results;
s7, redistributing the discharge of each control area obtained by iteration in the S5 according to the air resource endowment calculated in the S6 grid by grid;
s8, inputting the distribution result of the S7 into a three-dimensional air quality model for iteration until the control point simulated pollutant concentration reaches a target pollutant concentration;
and S9, summing the gridded emission of the S8 according to administrative areas to obtain the atmospheric environment capacity of each administrative area considering chemical transmission influence and air resource endowment at the same time.
In a preferred embodiment, said making a gridded discharge list according to S2 said control area comprises:
and inputting the local man-source emission or other public emission list products of the research area which is subjected to environmental system, pollution discharge permission and enterprise research data accounting into an emission list processing model SMOKE according to the control area to obtain a gridded emission list.
In a preferred embodiment, said creating a gridded emissions list further comprises:
natural source emissions were simulated using the megan model.
In a preferred embodiment, the setting of the simulation grid according to the requested area of the atmospheric environment capacity includes:
and setting a simulation grid in a Cartesian coordinate system according to the size of the atmospheric environment capacity area.
In a preferred embodiment, said dividing control areas according to the distance between said simulation grid and said control points comprises:
and projecting the longitude and latitude coordinates of the control points to a Lambert projection coordinate system used by a WRF simulation grid, and calculating the distance between the control points and the central point of each grid, wherein the calculation formula is as follows:
wherein, (i, j) is a grid, and p is a control point; l (i,j)p Distance to control point p for grid (i, j); x is a radical of a fluorine atom (i,j) And y (i,j) Respectively, the horizontal and vertical projection coordinates of the grid (i, j); x is a radical of a fluorine atom p And y p Respectively, the horizontal and vertical projection coordinates of the control point p.
In a preferred embodiment, the mesh (i, j) is assigned to the control point P closest to it, and the set of all meshes assigned to the control point P is called the pcontrol zone.
In a preferred embodiment, the adjusting the pollutant source emission of the control area according to the ratio of the control point simulated pollutant concentration to the target concentration comprises:
and changing the intensity of the artificial emission source corresponding to the control point in the control area into k times of the original intensity, and reproducing the emission source, wherein k is the ratio of the concentration of the simulated pollutants of the control point to the target concentration.
In a preferred embodiment, the three-dimensional air quality model is CMAQ.
In a preferred embodiment, the setting of the target pollutant concentration of the control point comprises:
and setting the target pollutant concentration according to the limit value of the environmental air quality standard or the actual management requirement of a research area.
In a preferred embodiment, the redistribution of the emissions of the control areas obtained by the iteration in S5 according to the air resource endowment calculated in S6 comprises:
and (3) calculating the emission intensity after air resource intrinsic endowment redistribution, wherein the calculation formula is as follows:
wherein, E (i,j) The emission intensity of the grid (i, j) after redistribution according to the air resource endowment; e p The total emission amount of a certain pollutant in the p control area is obtained after the primary iteration is finished; a. The (i,j) Is the index of the atmospheric self-cleaning capacity of the grid (i, j);
is the sum of the self-cleaning ability indexes of all grids in the p control area.
It should be further noted that the technical features corresponding to the above options can be combined with each other or replaced to form a new technical solution without conflict.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides two innovations on the basis of the traditional model iteration method. The method is characterized in that the adjustment of the emission source in the iteration process does not take an administrative division as a minimum unit of adjustment, but divides a control area according to the distance between a control point and each simulation grid, and takes the control area as a minimum space unit of adjustment, so that the control point can better represent the control area. And secondly, after the traditional simulation iteration is finished, the air resource endowment calculated based on the WRF simulation result is used for redistributing the discharge in the control area, and the simulation iteration is carried out again. By using the optimization algorithm, the underestimation of the traditional model iteration method on the environmental capacity can be reduced, more reasonable capacity space distribution is obtained, and the support can be provided for the layout of industrial enterprises.
Drawings
Fig. 1 is a flowchart of an atmospheric environment capacity calculation method considering air resources endowment according to the present invention;
FIG. 2 is a flow chart of the specific calculation method of the present invention based on FIG. 1;
fig. 3 is a schematic diagram of a simulation grid according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The method is mainly based on a meteorological model, an emission model and an air quality model to establish a regional composite type atmospheric pollution environment capacity three-dimensional iterative computation model; setting an iterative simulation grid and dividing a control area according to the principle that the attribution distance of the grid is the nearest control point; manufacturing a gridding discharge list by using a discharge model according to a control area; adjusting the discharge amount of the artificial pollution source in the control area according to the ratio of the air quality simulation concentration of the control point to the target concentration; simulating the endowment of gridding air resources based on a meteorological model; and after the iteration reaches the target concentration, the air resource endowment is used for redistributing the emission of each control area grid by grid, the iteration is continued until the target concentration is reached, and the emission of each control area is the atmospheric environment capacity considering the air resource endowment. The method optimizes the division method of the control area, comprehensively considers air resource endowment and social factors, and makes up the defects of the existing environment capacity calculation method.
In an exemplary embodiment, as shown in fig. 1 and 2, there is provided an atmospheric environmental capacity calculation method considering air resources endowment, the method comprising the steps of:
s1, setting a simulation grid according to a required atmospheric environment capacity area, simulating a weather field by using a mesoscale weather forecast mode WRF, and verifying a simulation result by using weather monitoring data;
s2, setting the concentration of the target pollutants of the control point by taking an air quality monitoring station as the control point, and dividing a control area according to the distance between the simulation grid and the control point;
s3, manufacturing a gridded discharge list according to the control area of the S2;
s4, inputting the results of the S1 and the S3 into a three-dimensional air quality model to simulate chemical transmission, and verifying the simulation result by using the monitoring data of the air quality monitoring station;
s5, judging whether the concentration of the control point simulated pollutants reaches the target pollutant concentration or not according to the result of the S4, if not, adjusting the pollution source emission of the control area according to the ratio of the concentration of the control point simulated pollutants to the target concentration, and then performing iterative calculation by using the three-dimensional air quality model until the target pollutant concentration is reached;
s6, calculating the air resource endowments of each simulation grid by using a WRF simulation three-dimensional meteorological field result;
s7, redistributing the discharge of each control area obtained by iteration in the S5 according to the air resource endowment calculated in the S6 grid by grid;
s8, inputting the distribution result of the S7 into a three-dimensional air quality model for iteration until the control point simulated pollutant concentration reaches a target pollutant concentration;
and S9, summing the gridding emission of the S8 according to administrative districts to obtain the atmospheric environment capacity of each administrative district considering chemical transmission influence and air resource endowment at the same time.
Specifically, the WRF (The Weather Research and Weather Model) mesoscale numerical Weather Forecasting mode includes modules of terrain data processing, ground and sounding data processing, numerical simulation, post-processing and The like, and is suitable for various Weather applications from tens of meters to thousands of kilometers.
In a possible embodiment, the setting a simulation grid according to the requested atmospheric environment capacity region includes:
and setting a simulation grid in a Cartesian coordinate system according to the size of the atmospheric environment capacity area.
Specifically, according to the size of a research area, a simulation grid is set in a Cartesian coordinate system, the origin of coordinates is at the lower left corner, i represents a grid abscissa, j represents a grid ordinate, as shown in FIG. 3, terrain, land use types and global re-analysis weather data are input into a mesoscale weather forecast mode WRF, a physical parameter scheme is set, and a grid weather field of 4 seasons (each season can be selected to represent a month for simulation, such as 1, 4, 7 and 10 months) in a certain year is simulated. And extracting the simulation value of the meteorological monitoring station of the research area and meteorological observation data for comparison and verification, and optimizing the simulation result by adjusting a physical parameter scheme, updating land utilization type data and the like.
Further, the setting of the target pollutant concentration of the control point comprises:
and setting the target pollutant concentration according to the limit value of the environmental air quality standard or the actual management requirement of a research area.
Such as second grade annual average concentration SO 2 Is 60 mu g/m 3 ，NO 2 Is 40 mu g/m 3 ，PM 10 Is 70 mu g/m 3 ，PM 2.5 Is 35 mu g/m 3 (ii) a Target pollutant concentrations can also be set according to actual management requirements of the research area, such as atmospheric environmental quality targets required by planning and higher-level departments.
In a possible embodiment, the dividing the control area according to the distance between the simulation grid and the control point includes:
and projecting the longitude and latitude coordinates of the control points to a Lambert projection coordinate system used by a WRF simulation grid, and calculating the distance between the control points and the central point of each grid, wherein the calculation formula is as follows:
wherein, (i, j) is a grid, and p is a control point; l (i,j)p Distance to control point p for grid (i, j); x is a radical of a fluorine atom (i,j) And y (i,j) Respectively, the horizontal and vertical projection coordinates of the grid (i, j); x is the number of p And y p Respectively, the horizontal and vertical projection coordinates of the control point p.
Furthermore, the grids (i, j) are assigned to the nearest control point P, the set of all grids assigned to the control point P is called a P control area, and the control areas to which all grids belong are determined according to the method.
Further, the making of the gridded discharge list according to the control area of S2 includes:
and inputting emission list processing model SMOKE into local man-made source emission or other public emission list products (such as MEIC emission list of Qinghua university) of a research area which is checked by using a loop system, emission permission and enterprise research data according to a control area to obtain a gridded emission list. Among them, SMOKE (spark Matrix Operator Kernel emulsions) is a pollution source emission list processing tool developed by north carolina university in the united states. And (3) adopting a high-performance sparse matrix calculation algorithm to distribute time, space and species of annual average emission of pollutants in an emission source list to manufacture a meshed hourly emission list file meeting the air quality mode requirement.
Further, the making a gridded emissions list further comprises:
natural source emissions were simulated using the megan model.
Specifically, the artificial emission list of the research area is calculated by adopting an emission factor method according to data such as research area environment, pollution discharge permission, enterprise research, yearbook and the like. The point source determines the control area according to the longitude and latitude and falls into the simulation grid; the surface sources use population, roads, land use types, POIs and the like as space allocation factors to allocate the total amount of the surface sources counted by administrative regions to each control region, and then are allocated to the simulation grids from the control regions. And then obtaining a gridding emission list capable of inputting CMAQ through time and species distribution.
The gridded artificial source emission list is manufactured by using an emission list processing model SMOKE, natural source emission is simulated by using a MEGAN model, and the emission list outside a research area is an MEIC emission list of Qinghua university.
After setting simulation grids and control areas, a three-dimensional air quality model CMAQ and the like simulate 5 pollutants (CO, NOx and SO) in 4 seasonal research areas in a year 2 、PM 2.5 、PM 10 ) The average concentration of (c). And extracting a simulation value and monitoring data of an air quality control point of a research area for comparison and verification, and optimizing a simulation result by adjusting a parameterization scheme, checking an emission source list and the like.
In a possible embodiment, the adjusting the pollutant source emission of the control area according to the ratio of the control point simulated pollutant concentration to the target concentration comprises:
and (3) calculating the ratio k of the target limit concentration of each control point, each season, each pollutant and the simulated concentration (for CO, the highest daily average concentration value of CO in each month is selected), and changing the artificial emission source intensity (which is regarded as a unified whole and does not distinguish industries) of the control area corresponding to the control point into the original k times based on the assumption that the pollutant concentration and the emission amount are in a linear relation, namely resetting the emission source. And (4) using the reproduced emission source, calculating the average concentration of each pollutant in each season by using the WRF-SMOKE-CMAQ simulation again, and judging whether the concentration of the simulated pollutant at the control point reaches the target concentration. And repeating the steps until each pollutant is close to the target concentration, so as to obtain the primary iteration atmospheric environment capacity under the target concentration of each pollutant control point in each season, wherein the sum of the primary iteration atmospheric environment capacities in each season is the annual primary iteration atmospheric environment capacity.
In consideration of the self-purification capacity of the atmosphere, the index of the self-purification capacity of the atmosphere needs to be calculated, and specifically, the maximum total amount of pollutants which can be removed by the atmospheric advection diffusion and precipitation in unit time and unit area is the index A of the self-purification capacity of the atmosphere. The index of the self-purification capacity of the atmosphere has no relation with the emission of the atmospheric pollution and the air quality, and only represents the ventilation diffusion and precipitation removal capacity of the atmospheric pollution caused by the self-movement of the atmosphere. The larger the index value of the atmospheric self-cleaning capability is, the stronger the cleaning capability of the atmosphere on pollutants is, and the strong atmospheric self-cleaning capability is; on the contrary, the self-cleaning capacity of the atmosphere is weak, and the index of the self-cleaning capacity of the atmosphere is used for representing the intrinsic endowment of the air resources in the research area.
Atmospheric boundary layer meteorological elements required by calculating the atmospheric self-purification capacity, such as ground wind speed, height of a mixed layer and the like, can be output through WRF forecast in a mesoscale numerical mode. Hourly, gridded (1-3 km) atmospheric self-cleaning capability index is calculated based on the WRF simulation results. The specific algorithm is as follows:
in the formula: v E Is the ventilation volume in m 2 (s), R is the precipitation intensity in mm/d; s is unit area, taken as 100km 2 。
When the mechanical mixing layer height is below 200m, the ventilation is calculated as follows:
V E ＝(U 200 +U 10 )×0.5×L b
when the thermal or mechanical mixing layer height is above 200m, the ventilation is calculated as follows:
V E ＝200×(U 200 +U 10 )×0.5+(L b -200)×U 200
wherein, V E Is the ventilation volume in m 2 /s；U 200 Is the wind speed at 200m height, in m/s; u shape 10 Is the wind speed at 10m height, in m/s; l is a radical of an alcohol b When the height of the thermal or mechanical mixing layer is in unit m and the atmospheric stability is in A, B, C and D grades, the height of the mixing layer is calculated by the following formula:
wherein L is b Is the height of the thermal mixing layer in m/s, a s Are the thermodynamic mixing layer coefficients, see table 1.
TABLE 1 AS and bs values in various areas of China
u 10 The average wind speed is 10m above the height, the unit m/s is 6m/s when the unit m/s is more than 6m/s; f is a rotation parameter in degrees (°), which is directly extracted from the WRF simulation results in this study, and when the atmospheric stability is E-grade and F-grade, the height of the mixed layer is calculated by the following formula:
l in the formula b The height of the mechanical mixing layer is in m/s; b s Are mechanical mixing layer coefficients, see table 1.
The calculation formula of the atmospheric stability is as follows:
in the formula: u is the friction speed; c p =1004j/kg/K, which is specific heat at constant pressure; ρ =1.225kg/m 3 Air density; k =0.4, is Karman constant; t is the absolute temperature; g =9.8m/s 2 Is the gravitational acceleration; h is the surface heat flux; b is Bow en Ratio. Wherein u is * T and H are directly extracted from the WRF simulation result.
The relationship between the atmospheric stability rating and L is shown in Table 2.
TABLE 2 stability rating vs. L
In a possible embodiment, the redistribution of the emissions of the control areas obtained by iteration in S5 according to the air resource endowment calculated in S6 comprises:
and (3) calculating the emission intensity after air resource intrinsic endowment redistribution, wherein the calculation formula is as follows:
wherein, E (i,j) The emission intensity of the grid (i, j) after redistribution according to the intrinsic endowments of the air resources; e p The total emission amount of a certain pollutant in the p control area is obtained after the initial iteration is finished; a. The (i,j) Is the index of the atmospheric self-cleaning capacity of the grid (i, j);
is the sum of the self-cleaning ability indexes of all grids in the p control area.
Further, inputting a redistribution emission result of the S7 into a three-dimensional air quality model for simulation, calculating a ratio k of target limit concentration of each pollutant and simulated concentration (for CO, selecting the highest daily average concentration value of CO in each month) of each control point, each season, and changing artificial emission source intensity (regarded as a unified whole and does not distinguish industries) of a control area corresponding to the control point into the original k times based on the assumption that the pollutant concentration and the emission amount are in a linear relation, namely resetting the emission source;
and (4) using the reset emission source, calculating the average concentration of each pollutant in each season by using the WRF-SMOKE-CMAQ simulation again, and judging whether the concentration of the pollutant simulated at the control point reaches the target concentration. And repeating the steps until each pollutant is close to the target concentration, so as to obtain the final atmospheric environment capacity under the target concentration of each pollutant control point in each season, wherein the sum of the atmospheric environment capacities in each season is the annual atmospheric environment capacity.
And finally, summing the gridding atmospheric environment capacity in the step S8 according to administrative districts, namely, simultaneously considering the chemical transmission influence and the air resource endowment atmospheric environment capacity in each administrative district.
In a possible embodiment, based on the same inventive concept as the aforementioned method, the present invention also provides an atmospheric environmental capacity calculation system considering air resources endowment, the system comprising:
the simulation grid setting module is used for setting a simulation grid according to the required atmospheric environment capacity area, simulating a meteorological field in a mesoscale weather forecast mode WRF, and verifying a simulation result by adopting meteorological monitoring data;
the control area dividing module is used for setting the target pollutant concentration of the control point by taking an air quality monitoring station as the control point and dividing a control area according to the distance between the simulation grid and the control point;
the emission list making module is used for making a gridded emission list according to the control area;
and the model iteration module is used for inputting the results of the simulation grid setting module and the control area division module into the three-dimensional air quality model to simulate chemical transmission and verifying the simulation result by using the monitoring data of the air quality monitoring station.
During iteration, judging whether the concentration of the control point simulated pollutants reaches the target concentration of pollutants, if not, adjusting the pollution source emission of the control area according to the ratio of the concentration of the control point simulated pollutants to the target concentration, and then performing iterative calculation by using the three-dimensional air quality model until the target concentration of pollutants is reached; calculating the air resource endowments of each simulation grid by using a WRF simulated three-dimensional meteorological field result; redistributing the discharge of each control area obtained by iteration according to the calculated air resource endowment from grid to grid; inputting the distribution result into a three-dimensional air quality model for iteration until the control point simulated pollutant concentration reaches the target pollutant concentration;
and the atmospheric environment capacity output module is used for summing the gridding emission according to administrative districts to obtain the atmospheric environment capacity of each administrative district considering the influence of chemical transmission and the endowment of air resources.
In a possible embodiment, based on the same inventive concept as the aforementioned method, the present invention further provides a storage medium having stored thereon computer instructions which, when executed, perform the relevant steps of the atmospheric environmental capacity calculation method considering the air resource endowment.
Based on such understanding, the technical solutions of the present embodiment or portions of the technical solutions that substantially contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In a possible embodiment, based on the same inventive concept as the aforementioned method, the present invention further provides a terminal, comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the computer instructions to perform the relevant steps of the atmospheric environmental capacity calculation method considering the intrinsic characteristics of the air resources.
The processor may be a single or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.
Embodiments of the subject matter and the functional operations described in this specification can be implemented in: tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on a tangible, non-transitory program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to suitable receiver apparatus for execution by the data processing apparatus.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, both general and/or special purpose microprocessors, or any other type of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory and/or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer does not necessarily have such a device. Moreover, a computer may be embedded in another device, e.g., a mobile telephone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. In other instances, features described in connection with one embodiment may be implemented as discrete components or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
The above detailed description is for the purpose of describing the invention in detail, and it should not be construed that the detailed description is limited to the description, and it will be apparent to those skilled in the art that various modifications and substitutions can be made without departing from the spirit of the invention.
Claims (7)
1. An atmospheric environmental capacity calculation method considering air resources endowment, comprising the steps of:
s1, setting a simulation grid according to a required atmospheric environment capacity area, simulating a meteorological field in a mesoscale weather forecast mode WRF, and verifying a simulation result by adopting meteorological monitoring data; the setting of the simulation grid according to the required atmospheric environment capacity region comprises the following steps:
setting a simulation grid in a Cartesian coordinate system according to the size of the atmospheric environment capacity area;
s2, setting the concentration of the target pollutants of the control point by taking an air quality monitoring station as the control point, and dividing a control area according to the distance between the simulation grid and the control point; the dividing of the control area according to the distance between the simulation grid and the control point includes:
and projecting the longitude and latitude coordinates of the control points to a Lambert projection coordinate system used by a WRF simulation grid, and calculating the distance between the control points and the central point of each grid, wherein the calculation formula is as follows:
wherein,（i，j）is a grid of the shape of a square,pis a control point;
is a grid(i,j)To the control pointpThe distance of (d); />
And &>
Are respectively a grid（i,j）The horizontal and vertical projection coordinates of; />
And &>
Are respectively control pointspThe horizontal and vertical projection coordinates of; handle grid（i,j）Attribution to the control point closest theretopAll belonging to the control pointpThe set of grids is calledPA control area;
s3, manufacturing a gridded discharge list according to the control area of the S2;
s4, inputting the results of the S1 and the S3 into a three-dimensional air quality model to simulate chemical transmission, and verifying the simulation result by using the monitoring data of the air quality monitoring station;
s5, judging whether the concentration of the control point simulated pollutants reaches the target pollutant concentration or not according to the result of the S4, if not, adjusting the pollution source emission of the control area according to the ratio of the concentration of the control point simulated pollutants to the target concentration, and then, using the three-dimensional air quality model to perform iterative calculation until the target pollutant concentration is reached;
s6, calculating the air resource endowments of each simulation grid by using a WRF simulation three-dimensional meteorological field result;
s7, redistributing the discharge of each control area obtained by iteration in the S5 according to the air resource endowment calculated in the S6 grid by grid;
s8, inputting the distribution result of the S7 into a three-dimensional air quality model for iteration until the control point simulation pollutant concentration reaches the target pollutant concentration;
and S9, summing the gridded emission of the S8 according to administrative areas to obtain the atmospheric environment capacity of each administrative area considering chemical transmission influence and air resource endowment at the same time.
2. The method of claim 1, wherein the step of making a gridded emission list according to the control region of S2 comprises:
and inputting local man-source emission or other public emission list products of a research area subjected to cyclic system, pollution discharge permission and enterprise research data accounting into an emission list processing model SMOKE according to a control area to obtain a gridded emission list.
3. The method of claim 2, wherein the step of generating the latticed emission list further comprises:
natural source emissions were simulated using the megan model.
4. The method of claim 1, wherein the adjusting the pollutant source emission of the control area according to the ratio of the control point simulated pollutant concentration to the target concentration comprises:
and changing the intensity of the artificial emission source corresponding to the control point in the control area into k times of the original intensity, and reproducing the emission source, wherein k is the ratio of the concentration of the simulated pollutants of the control point to the target concentration.
5. The method of claim 1, wherein the three-dimensional air quality model is CMAQ.
6. The method of claim 1, wherein the setting of the target pollutant concentration at the control point comprises:
and setting the target pollutant concentration according to the limit value of the environmental air quality standard or the actual management requirement of a research area.
7. The method of claim 1, wherein the redistributing the discharge of each control area obtained by iteration in S5 according to the air resource assumption calculated in S6 comprises:
and (3) calculating the emission intensity after air resource intrinsic endowment redistribution, wherein the calculation formula is as follows:
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