CN114077798B - Method for estimating grid concentration of atmospheric pollutants in small-scale area - Google Patents
Method for estimating grid concentration of atmospheric pollutants in small-scale area Download PDFInfo
- Publication number
- CN114077798B CN114077798B CN202210057543.1A CN202210057543A CN114077798B CN 114077798 B CN114077798 B CN 114077798B CN 202210057543 A CN202210057543 A CN 202210057543A CN 114077798 B CN114077798 B CN 114077798B
- Authority
- CN
- China
- Prior art keywords
- grid
- small
- concentration
- scale
- monitoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Abstract
The invention discloses a method for estimating the concentration of a small-scale plant pollutant, belongs to the technical field of plant pollution concentration estimation methods, and solves the technical problem that the accuracy of estimating the concentration of the plant pollutant by using the method in the prior art is low. Presetting a plurality of monitoring point locations in a small-scale region, and arranging monitoring equipment at the monitoring point locations; acquiring concentration data of atmospheric pollutants, meteorological data and longitude and latitude data of the monitoring equipment in real time through the monitoring equipment; obtaining the center of the small-scale region and determining a small-scale diffusion model; fusing a first grid corresponding to the average wind vector with a second grid corresponding to the real-time wind vector to obtain a first initial grid; acquiring a second initial grid according to the error of the background concentration and the first initial grid; a real-time concentration grid for the small scale region is determined. The assimilation method is not limited by the fineness of monitoring point positions and grids, and has strong applicability and portability.
Description
Technical Field
The invention belongs to the technical field of factory pollution concentration estimation methods, and particularly relates to a method for estimating grid concentration of atmospheric pollutants in a small-scale area.
Background
Aiming at enterprises in the important atmospheric pollutant emission industry, the concentration distribution situation of atmospheric pollutants in a factory area is often difficult to obtain. The existing three-dimensional area quality model, such as WRF-CMAQ and other modes, is difficult to carry out high-precision gridding simulation on factory scale pollutants on the resolution.
The AERMOD model is used for simulating the grid concentration of the pollutants in the plant area, real-time organized real-time emission data and unorganized real-time emission data of the plant area are generally difficult to obtain, under the condition of using historical pollution source data, the grid concentration lacks assimilation of an actual observation value and is often different from the actual concentration space-time distribution.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a method for estimating the grid concentration of atmospheric pollutants in a small-scale area, and solves the technical problem that the accuracy of estimating the concentration of pollutants in a factory area is low by the method in the prior art. The technical scheme of the scheme has a plurality of technical beneficial effects, which are described as follows:
there is provided a method of estimating the grid concentration of atmospheric pollutants in a small-scale area, the method comprising:
presetting a plurality of monitoring point locations in a small-scale region, and arranging monitoring equipment at the monitoring point locations;
acquiring concentration data of atmospheric pollutants, meteorological data and longitude and latitude data of the monitoring equipment in real time through the monitoring equipment;
acquiring the center of the small-scale area, and drawing an equally-divided grid by using GIS software according to the center of the small-scale area;
the small-scale diffusion model is determined by simulating the diffusion of the atmospheric pollutants according to organized pollution source data and unorganized pollution source data of the atmospheric pollutants in a small-scale area;
fusing a first grid corresponding to the average wind vector with a second grid corresponding to the real-time wind vector to obtain a first initial grid, wherein the fusing comprises:
Ciconcentration of the first initial grid, C, which is the fused grid iijThe concentration of a second grid of a grid i under the wind field type corresponding to the monitoring point j, d is the distance between the grid i and the monitoring point j, R is the influence radius of the monitoring point j, CiaThe concentration of the first grid of grid i under the average wind field;
acquiring a second initial grid according to the error of the background concentration and the first initial grid;
obtaining a real-time concentration grid of the small-scale region according to the second grid and the second initial grid obtained by the monitoring device in real time and assimilating the concentration of the second grid to the second initial grid, including:
wherein: x is the number ofbIs the concentration of the second grid; y is0The concentration of the point location is monitored; h (x) is the concentration of a second grid where the monitoring point is located, and B is the grid concentration error covariance; r is the error covariance of the monitored concentration; j (x) is a cost function in the three-dimensional variation method, and when the cost function obtains the minimum value, x is a real-time concentration grid of a small-scale area.
In a preferred or alternative embodiment, the monitoring sites include fixed monitoring sites and movable monitoring sites.
In a preferred or alternative embodiment, the small-scale area comprises an overhead point source, the first monitoring device is arranged on the overhead point source by adopting a single-arc ground axial center concentration reverse method, and the first monitoring device is positioned on a sampling arc line which is downwind all the year around the overhead point source.
In a preferred or optional embodiment, the small-scale region further comprises a low-surface source and an unstructured emission source, and second monitoring devices are respectively arranged on the low-surface source and the unstructured emission source by adopting a multi-arc ground axial concentration back-deducing method, and are located downwind of the low-surface source and the unstructured emission source.
In a preferred or optional embodiment, the small-scale area further comprises a plurality of sparse point sources, and a movable third monitoring device is arranged, wherein the third monitoring device is used for acquiring a plurality of sparse point sources of atmospheric pollutant grid concentration data and meteorological data;
the third monitoring device is arranged close to the position of the sparse point source, or the third monitoring device is arranged in the downwind direction of the sparse point source.
In a preferred or alternative embodiment, the number of rows and columns of the bisection grid covers the small-scale region; the bisecting grid is 30m x 30 m.
In a preferred or alternative embodiment, the small-scale diffusion model is determined by simulating atmospheric pollutant diffusion according to organized pollution source data and unorganized pollution source data of atmospheric pollutants in a small-scale region, and comprises:
and simulating according to the meteorological data in the small-scale area.
In a preferred or alternative embodiment, the meteorological data includes at least wind vector, temperature and humidity; the fusing the first grid corresponding to the average wind vector and the second grid corresponding to the real-time wind vector to obtain the first initial grid comprises:
obtaining the average wind vector through at least part of the wind vectors of the monitoring equipment;
the small-scale diffusion model obtains the first grid according to the average wind vector; and the small-scale diffusion model obtains the second grid according to the real-time wind vector.
In a preferred or alternative embodiment, the error in the background concentration comprises:
and calculating the difference value between the first background concentration simulated by the small-scale diffusion model and the second background concentration under the average wind vector.
In a preferred or alternative embodiment, said obtaining a second initial grid based on the error in the background concentration and the first initial grid comprises:
and adding the difference value to the first initial grid, wherein the obtained minimum value is the second initial grid.
In a preferred or optional embodiment, the determining a real-time concentration grid of a small-scale region according to the second grid and the second initial grid acquired by the monitoring device in real time includes:
acquiring the concentration of the second grid monitored by at least part of monitoring equipment in real time;
obtaining a real-time concentration grid of the small-scale region by assimilating the concentration of the second grid to the second initial grid.
In a preferred or optional embodiment, the obtaining the center of the small-scale region, and drawing an equally-divided grid by using GIS software according to the center of the small-scale region includes:
acquiring the latitude of the north end and the south end of the small-scale area, and determining an average latitude value a;
acquiring longitudes of the west end and the east end of the small-scale area, and determining an average longitude value b;
taking the average latitude value a and the average longitude value b as the latitude and longitude of the central point position of the small-scale area;
the average latitude value a and the average longitude value b are used as central grid points of the ArcGIS grids and respectively extend the grid numbers of the same number to the north, south, west and east directions; the bisection grid is a high-resolution bisection grid, and the high resolution ranges from 1:45 to 1: 450.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
the method comprises the steps of arranging a plurality of monitoring devices and at least a movable monitoring device in a pollution plant area according to preset distribution points, wherein the monitoring devices are used for acquiring real-time atmospheric pollutant concentration data and meteorological field data and acquiring longitude and latitude data of the devices. The method comprises the steps of considering the influence of factory unorganized emission on pollutant concentration, wherein the unorganized emission is carried out, for example, pollutant concentration calculation directly emitted through an overhead chimney is not carried out, theoretical model construction is carried out through wind vector average values, a second initial grid is determined by using average wind in a proper amount and is corrected for one time, the second initial grid is corrected again according to the second grid obtained by monitoring equipment in real time, and finally pollutant concentration data of a small-scale area are determined.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. 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.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in practical implementation, and the type, quantity and proportion of the components in practical implementation can be changed freely, and the layout of the components can be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that aspects may be practiced without these specific details. In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
The estimation software or method of the prior art can only estimate the pollution in a large area, and cannot calculate the estimation of the pollution in a small scale range, for example, in the range of 1-20 km. The invention aims to provide a method for estimating pollutants in a small scale range.
A method of estimating the grid concentration of atmospheric pollutants in a small-scale area as shown in fig. 1, the method comprising:
s101, presetting a plurality of monitoring point locations in a small-scale area, and arranging monitoring equipment at the monitoring point locations, specifically:
the preset point location arrangement detection device may preset the monitoring device according to longitude and latitude, or preset the arrangement according to a selected grid, for example, the distance between a chimney and the monitoring device. The small-scale regional monitoring point locations comprise fixed monitoring point locations and movable monitoring point locations, different arrangement schemes need to be made for different meteorological environments, and under the condition that monitoring is accurate, the cost of equipment investment is reduced as far as possible, and the method comprises the following steps:
if the small-scale area comprises an elevated point source, the first monitoring equipment is arranged on the elevated point source by adopting a single-arc ground axis concentration back-pushing method, the first monitoring equipment is positioned on a sampling arc line of the perennial downwind direction of the elevated point source, the influence of a peripheral high building on the wind direction is considered, the wind is nonlinear wind, the measurement is carried out along the downwind direction of the arc, namely, the downwind side of the elevated point source, the actual pollutant value is measured more accurately, and/or,
and if the small-scale region comprises a low surface source and an unorganized emission source, respectively arranging second monitoring equipment on the low surface source and the unorganized emission source by adopting a multi-arc ground axis concentration back-stepping method, wherein the second monitoring equipment is positioned in the downwind direction of the low surface source and the unorganized emission source. Considering the effect of the surrounding tall and medium buildings on the wind direction, and including the effect of the disorganized emission on the organized emission, the monitoring device is arranged downwind, closer to the actual value, and/or,
the small-scale area comprises a plurality of sparse point sources, and is provided with a movable third monitoring device, and the third monitoring device is used for acquiring the atmospheric pollutant grid concentration data and meteorological data of the plurality of sparse point sources. The system carries out periodic monitoring, the sparse point source is disorganized and irregular in emission, pollution data are collected through mobile monitoring, the accuracy is higher, and during fixed-point measurement, the data are often smaller, so that the influence of the disorganized emission in a plant area can be less considered through the mobile monitoring.
Optionally, a third monitoring device is disposed proximate to the sparse point source; in addition, the third monitoring device may also be arranged downwind of the sparse point source.
S102, acquiring concentration data of atmospheric pollutants, meteorological data and longitude and latitude data of monitoring equipment in real time through the monitoring equipment, and preparing basic data for detection and analysis, wherein the monitoring equipment can be equipment capable of interacting with data of a main server in the prior art and is provided with a GPS (global positioning system);
s103, acquiring the center of the small-scale area, and drawing the equal-division grids by the center of the small-scale area through GIS software, specifically:
the number of rows and columns of the grid is equally divided to cover a small-scale area, the grid adopts a high-resolution form, and preferably, the grid is set to be 30m multiplied by 30 m. For example, the small-scale factory floor range is 5km × 5km, the set grid is 30m × 30m, and the bisection grid is a high-resolution bisection grid, the high-resolution range is 1:45 to 1:450, preferably 1: 150;
s104, simulating the diffusion of the atmospheric pollutants by the small-scale diffusion model according to organized pollution source data and unorganized pollution source data of the atmospheric pollutants in the small-scale area, wherein the preferable conditions are as follows:
establishing an AERMOD diffusion model, acquiring historical pollution data of a small-scale area or meteorological data of the small-scale area in a preset time period, wherein the historical pollution data comprise organized pollution source data, unorganized pollution source data and the like, and simulating the diffusion model of the atmospheric pollutants in the whole area through the historical pollution data in an equally-divided grid. Inputting different meteorological fields under different solar terms to the diffusion model to output corresponding grid concentrations, wherein the meteorological fields comprise wind vectors, namely, the meteorological fields correspond to different pollution source data in different months or seasons, the corresponding theoretical grid concentrations can be generated through the input wind vectors, and the historical pollution data and the meteorological data at least comprise the wind vectors, the temperature and the humidity;
further, simulation is carried out according to meteorological data in a small-scale area;
s105, fusing the first grid corresponding to the average wind vector with the second grid corresponding to the real-time wind vector to obtain a first initial grid, specifically:
the meteorological data at least comprises wind vector, temperature and humidity;
obtaining an average wind vector through at least part of wind vectors of monitoring equipment, when sparse points are more, performing weighted average on all detected measurement values to determine the average wind vector, and when the detection equipment is densely arranged or only the concentration of part of areas is calculated, adopting wind vector data of part of monitoring equipment;
inputting an average wind vector by a small-scale diffusion model to obtain a first grid;
inputting a real-time wind vector by the small-scale diffusion model to obtain a second grid;
and fusing to determine a first initial grid, specifically:
Ciconcentration of the first initial grid, C, which is the fused grid iijThe concentration of a second grid of a grid i under the wind field type corresponding to the monitoring point j, d is the distance between the grid i and the monitoring point j, R is the influence radius of the monitoring point j, CiaThe concentration of the first grid of grid i under the average wind field.
It should be noted that R is the influence radius of the monitoring point j, and the purpose of the influence radius is: different wind fields may exist in different point locations, the influence radius of the point location is set according to the environment around the monitored point location, for example, the setting range of the influence radius is 50-100m, the grid concentration distribution in the influence radius is greatly influenced by the local meteorological diffusion around the monitored point location, and the grid concentration distribution outside the influence radius is greatly influenced by the concentration distribution under the average wind field, so that fusion is needed, and the accuracy of overall estimation is improved.
S106, determining a second initial grid according to the error of the background concentration and the first initial grid, specifically:
the error in background concentration is: calculating the difference between a first background concentration simulated by the small-scale diffusion model and a second background concentration under an average wind vector, wherein the first background concentration is considered by taking the minimum value, for example, 20 monitoring points are distributed in the whole area, the average wind is calculated as the north wind, the monitoring point at the most north side is taken as the monitoring data of the background point, and the minimum value is taken as the consideration;
and adding the difference to the first initial grid, and taking the obtained minimum value as a second initial grid.
S107, determining a real-time concentration grid of the small-scale area according to a second grid and a second initial grid which are acquired by the monitoring equipment in real time, specifically:
obtaining the concentration of the second grid monitored by at least part of the monitoring devices in real time, preferably all the monitoring devices in real time
As an optional scheme, the concentration of the second grid monitored by part of the monitoring devices in real time can be screened, and the important monitoring is performed for special points, so that the calculation amount can be reduced, and the calculation efficiency can be improved.
Determining the real-time concentration grid of the small-scale area by assimilating the concentration of the second grid to the second initial grid, namely further correcting the real-time concentration grid on the basis of the minimum value;
further, the assimilation method is as follows:
wherein: x is the number ofbIs the concentration of the second grid; y is0The concentration of the point location is monitored; h (x) is the concentration of a second grid where the monitoring point is located, and B is the grid concentration error covariance; r is the error covariance of the monitored concentration; j (x) is a cost function in the three-dimensional variation method, and when the cost function obtains the minimum value, x is a real-time concentration grid of a small-scale area. And checking by using the actual value to improve the estimation accuracy, namely, checking or correcting the model.
As a specific implementation manner provided in the present application, acquiring the center of the small-scale region, and drawing an equally-divided grid by using GIS software according to the center of the small-scale region includes:
acquiring the latitude of the north end and the south end of the small-scale area, and determining an average latitude value a;
acquiring longitudes of the west end and the east end of the small-scale area, and determining an average longitude value b;
taking the average latitude value a and the average longitude value b as the latitude and longitude of the central point position of the small-scale area or the central point coordinate;
the average latitude value a and the average longitude value b are used as central grid points of the ArcGIS grid, the grid numbers with the same number are respectively extended towards the north, south, west and east directions, the longitude and latitude of the upper vertex, the lower vertex, the left vertex and the right vertex of the grid are determined, and the equal grid determined by the method is calculated according to the average longitude and latitude, so that the accuracy of the model is improved, and the influence of errors is reduced.
The whole technical effect is as follows:
1. the method of the scheme is based on an algorithm for carrying out pollutant concentration assimilation and inversion on sparse/movable monitoring point monitoring data, the influence of unorganized pollutant emission is reduced, the detection cost is reduced, and the accuracy of real data obtained through monitoring is improved;
2. the method integrates pollutant point source emission data and unorganized source emission data, can quickly realize a pollutant gridding real-time inversion technology, and can well simulate the gridding distribution of pollutants under the condition that the unorganized pollutant real-time emission data is difficult to obtain.
3. Meanwhile, the observation data assimilation technology based on the pollutant concentration and the simulation field concentration of the monitoring weather is used for assimilating the simulation data according to the real-time weather conditions of each monitoring point besides assimilating the pollutant concentration of the simulation grid, and the accuracy of observation assimilation is improved.
4. The method is not limited by the fineness of monitoring point positions and grids, and has strong applicability and portability.
The method for estimating the carbon emission of the factory building in the small-scale range is provided, the small-scale or small-scale region and other description modes can be a certain region in a city or regions such as a cell, a factory, a storehouse and the like in the city, and the problem that the carbon emission in the small-scale range cannot be estimated in the prior art is solved.
The products provided by the present invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the core concepts of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the invention without departing from the inventive concept, and those improvements and modifications also fall within the scope of the claims of the invention.
Claims (12)
1. A method for estimating grid concentration of atmospheric pollutants in a small-scale area, the method comprising:
presetting a plurality of monitoring point locations in a small-scale region, and arranging monitoring equipment at the monitoring point locations;
acquiring concentration data of atmospheric pollutants, meteorological data and longitude and latitude data of the monitoring equipment in real time through the monitoring equipment;
acquiring the center of the small-scale area, and drawing an equally-divided grid by using GIS software according to the center of the small-scale area;
the small-scale diffusion model is determined by simulating the diffusion of the atmospheric pollutants according to organized pollution source data and unorganized pollution source data of the atmospheric pollutants in a small-scale area;
fusing a first grid corresponding to the average wind vector with a second grid corresponding to the real-time wind vector to obtain a first initial grid, wherein the fusing comprises:
Ciconcentration of the first initial grid, C, which is the fused grid iijThe concentration of a second grid of a grid i under the wind field type corresponding to the monitoring point j, d is the distance between the grid i and the monitoring point j, R is the influence radius of the monitoring point j, CiaThe concentration of the first grid of grid i under the average wind field;
acquiring a second initial grid according to the error of the background concentration and the first initial grid;
obtaining a real-time concentration grid of the small-scale region according to the second grid and the second initial grid obtained by the monitoring device in real time and assimilating the concentration of the second grid to the second initial grid, including:
wherein: x is the number ofbIs the concentration of the second grid; y is0The concentration of the point location is monitored; h (x) is the concentration of a second grid where the monitoring point is located, and B is the grid concentration error covariance; r is the error covariance of the monitored concentration; j (x) is a cost function in the three-dimensional variation method, and when the cost function obtains the minimum value, x is a real-time concentration grid of a small-scale area.
2. The method of claim 1, wherein the monitoring sites comprise fixed monitoring sites and movable monitoring sites.
3. The method of claim 1, wherein the small scale area comprises an elevated point source, wherein the first monitoring device is deployed at the elevated point source using a single-arc ground-based axial-concentration back-projection method, and wherein the first monitoring device is located on a sampling arc of the elevated point source that is downwind throughout the year.
4. The method of claim 3, wherein the small scale region further comprises a low-profile source and an unstructured emission source, and wherein a second monitoring device is deployed at the low-profile source and the unstructured emission source respectively by multi-arc ground-based axial concentration back-stepping, and wherein the second monitoring device is located downwind of the low-profile source and the unstructured emission source.
5. The method of claim 4, wherein the small-scale region further comprises a plurality of sparse point sources, and a third monitoring device is provided that is movable and is configured to acquire a plurality of sparse point sources of atmospheric pollutant grid concentration data and meteorological data;
the third monitoring device is arranged close to the position of the sparse point source, or the third monitoring device is arranged in the downwind direction of the sparse point source.
6. The method of claim 1, wherein the number of rows and columns of the aliquot grid cover the small-scale region; the bisecting grid is 30m x 30 m.
7. The method of claim 1, wherein the small-scale diffusion model is determined from modeling atmospheric pollutant diffusion from organized and unorganized pollution source data for atmospheric pollutants within a small-scale region, comprising:
and simulating according to the meteorological data in the small-scale area.
8. The method of claim 7, wherein the meteorological data includes at least a wind vector, a temperature, and a humidity; the fusing the first grid corresponding to the average wind vector and the second grid corresponding to the real-time wind vector to obtain the first initial grid comprises:
obtaining the average wind vector through at least part of the wind vectors of the monitoring equipment;
the small-scale diffusion model obtains the first grid according to the average wind vector; and the small-scale diffusion model obtains the second grid according to the real-time wind vector.
9. The method of claim 8, wherein the error in the background concentration comprises:
and calculating the difference value between the first background concentration simulated by the small-scale diffusion model and the second background concentration under the average wind vector.
10. The method of claim 9, wherein obtaining a second initial grid based on the error in background concentration and the first initial grid comprises:
and adding the difference value to the first initial grid, wherein the obtained minimum value is the second initial grid.
11. The method of claim 1, wherein determining a real-time concentration grid for a small-scale region from the second grid and the second initial grid acquired by the monitoring device in real-time comprises:
acquiring the concentration of the second grid monitored by at least part of monitoring equipment in real time;
obtaining a real-time concentration grid of the small-scale region by assimilating the concentration of the second grid to the second initial grid.
12. The method of claim 1, wherein the obtaining the center of the small-scale region and drawing an aliquot grid according to the center of the small-scale region by using GIS software comprises:
acquiring the latitude of the north end and the south end of the small-scale area, and determining an average latitude value a;
acquiring longitudes of the west end and the east end of the small-scale area, and determining an average longitude value b;
taking the average latitude value a and the average longitude value b as the latitude and longitude of the central point position of the small-scale area;
the average latitude value a and the average longitude value b are used as central grid points of the ArcGIS grids, the grids with the same number extend towards the east, south, west and north directions respectively, and the longitude and latitude of vertexes in all directions are determined; the bisection grid is a high-resolution bisection grid, and the high resolution ranges from 1:45 to 1: 450.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210057543.1A CN114077798B (en) | 2022-01-19 | 2022-01-19 | Method for estimating grid concentration of atmospheric pollutants in small-scale area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210057543.1A CN114077798B (en) | 2022-01-19 | 2022-01-19 | Method for estimating grid concentration of atmospheric pollutants in small-scale area |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114077798A CN114077798A (en) | 2022-02-22 |
| CN114077798B true CN114077798B (en) | 2022-04-22 |
Family
ID=80284576
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210057543.1A Active CN114077798B (en) | 2022-01-19 | 2022-01-19 | Method for estimating grid concentration of atmospheric pollutants in small-scale area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114077798B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116912459B (en) * | 2023-09-13 | 2023-12-19 | 中国海洋大学 | Variable-grid multi-scale mixed data assimilation method |
| CN117686669B (en) * | 2024-02-02 | 2024-04-23 | 北京市生态环境监测中心 | Method, device, equipment and medium for monitoring meshing of unstructured emission sources |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113740220A (en) * | 2021-09-07 | 2021-12-03 | 中国人民解放军国防科技大学 | Multi-scale three-dimensional variational assimilation method based on high-resolution aerosol data |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| IT201700064056A1 (en) * | 2017-06-09 | 2018-12-09 | Sense Square S R L S | MAPPING OF THE ATMOSPHERIC POLLUTION SOURCES AND TRACKING OF POLLUTANTS BY USING HIGH-RESOLUTION TEMPERATURE AIR QUALITY MONITORING NETWORKS |
| CN107436343A (en) * | 2017-07-31 | 2017-12-05 | 南京南瑞集团公司 | It is a kind of to simulate the method for calculating sensitizing range pollutant concentration |
| CN111126710B (en) * | 2019-12-27 | 2021-07-16 | 成都星时代宇航科技有限公司 | Atmospheric pollutant prediction method |
| CN113191096B (en) * | 2021-04-13 | 2022-04-29 | 中南建筑设计院股份有限公司 | WRF and XLow coupling-based multi-fineness fusion pollutant diffusion analysis method |
| CN113436045A (en) * | 2021-06-15 | 2021-09-24 | 深圳中兴网信科技有限公司 | Atmospheric pollution multi-scale fusion traceability method and device and readable storage medium |
-
2022
- 2022-01-19 CN CN202210057543.1A patent/CN114077798B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113740220A (en) * | 2021-09-07 | 2021-12-03 | 中国人民解放军国防科技大学 | Multi-scale three-dimensional variational assimilation method based on high-resolution aerosol data |
Non-Patent Citations (1)
| Title |
|---|
| 大气污染资料同化与应用综述;朱江 等;《大气科学》;20180515;第42卷(第3期);第607-620页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114077798A (en) | 2022-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114077798B (en) | Method for estimating grid concentration of atmospheric pollutants in small-scale area | |
| CN104865353B (en) | Industrial park atmosphere pollution collecting method based on unmanned plane | |
| Carmichael et al. | The MICS-Asia study: model intercomparison of long-range transport and sulfur deposition in East Asia | |
| CN105095589B (en) | A kind of mountain area power grid wind area is distributed drawing drawing method | |
| CN114371260A (en) | Gridding monitoring, diffusion early warning and tracing method for non-organized VOCs of industrial enterprise | |
| CN113297528A (en) | NO based on multi-source big data2High-resolution space-time distribution calculation method | |
| CN106597416A (en) | Ground-GPS-assisted method for correcting error of difference of elevation of LiDAR data | |
| CN113624921B (en) | Multi-mode comprehensive pollution tracing method | |
| US10775258B2 (en) | Heuristic based analytics for gas leak source identification | |
| CN114757807A (en) | Multi-mode fused online accounting method for actual emission of atmospheric pollutants | |
| Boon et al. | Analysis of the potential of near-ground measurements of CO 2 and CH 4 in London, UK, for the monitoring of city-scale emissions using an atmospheric transport model | |
| CN110658307A (en) | Method for evaluating influence of pollution source on environmental air quality | |
| KR101607878B1 (en) | System and method for providing air quality information by integrating virtual sensor and monitoring sensor | |
| RU2503042C1 (en) | Method for spatial quantitative evaluation of ambient air contamination level | |
| CN112100922A (en) | Wind resource prediction method based on WRF and CNN convolutional neural network | |
| CN110503348B (en) | Individual air pollution exposure simulation measurement method based on position matching | |
| CN111366195A (en) | Multi-scale observation method for surface hydrothermal flux | |
| CN106156756A (en) | The Method of fast estimating of construction land efficiency spatial distribution | |
| CN116089406B (en) | Barrier visualization processing system for ocean mapping | |
| CN109948175B (en) | Satellite remote sensing albedo missing value inversion method based on meteorological data | |
| CN106203891A (en) | Territorial development density three-dimensional comprehensive evaluation methodology based on Methods on Multi-Sensors RS Image | |
| CN115493657A (en) | Atmospheric pollution tracing method and device based on unmanned aerial vehicle | |
| CN112632799B (en) | Method and device for evaluating design wind speed of power transmission line | |
| de Haan et al. | Modification of an operational dispersion model for urban applications | |
| Malakan et al. | Comparison of AERMOD performance using observed and prognostic meteorological data. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |