Urban and rural area near-surface ozone concentration evaluation method and device

Abstract

The invention discloses an urban and rural area near-surface ozone concentration evaluation method and device. The method comprises the following steps: interpolating a population data image and a near-surface ozone image to a research area grid to obtain a research area grid complete set; correcting the interpolated near-surface ozone image through a least square regression model by using air quality station observation data distributed in the research area to obtain a corrected near-surface ozone image; based on the interpolated population data image, performing urban and rural region division through a preset population density threshold and a connectivity rule to obtain a plurality of region complete sets; extracting ozone concentration values of corresponding regions from the corrected near-surface ozone image aiming at the plurality of region universal sets; and calculating a near-surface ozone concentration index of each region based on the ozone concentration value of each region in the plurality of region sets.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically, to a method and apparatus for assessing near-surface ozone concentration in urban and rural areas. Background Technology

[0002] ground-level ozone As a typical secondary pollutant, nitrogen oxides exhibit significant differences in their formation mechanisms between urban and rural areas. In urban areas, nitrogen oxides are primarily caused by vehicle exhaust, industrial emissions, and the urban heat island effect. and anthropogenic volatile organic compounds (VOCs) Ozone concentrations remain high, and the ozone formation rate increases significantly under high temperature and strong sunlight conditions, resulting in a surge in ozone levels in the afternoon. Although anthropogenic emissions are lower in rural areas, biological sources play a significant role. (For example, isoprene released by vegetation) may far exceed urban levels in summer. This, combined with high concentrations in urban areas, exacerbates the situation. Ozone titration (high concentration NO and) Ozone pollution, due to urban ozone depletion caused by ozone reactions and the regional transport of precursors, results in higher background ozone concentrations and more persistent exceedances in rural areas. Furthermore, the health risks of ozone pollution are imbalanced between urban and rural areas. Rural residents, due to insufficient medical resources, poor housing ventilation, and weak awareness of hygiene, have lower physiological tolerance thresholds to ozone exposure. Accurate assessment of near-ground ozone concentrations in both urban and rural areas has become a pressing technical challenge. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and apparatus for assessing near-ground ozone concentration in urban and rural areas.

[0004] According to one aspect of the present invention, a method for assessing near-surface ozone concentration in urban and rural areas is provided, comprising: Population data imagery and near-surface ozone imagery are interpolated to the study area grid to obtain the complete study area grid set, which includes the interpolated population data imagery and near-surface ozone imagery. Using air quality monitoring data distributed throughout the study area, the interpolated near-surface ozone image was corrected using a least squares regression model to obtain the corrected near-surface ozone image. Based on the interpolated population data image, urban and rural areas are divided by preset population density thresholds and connectivity rules, resulting in multiple regional sets, which include: constrained urban area subdomains, constrained urban core area subdomains, urban area sets, urban core area sets, and non-urban areas. For multiple regions, ozone concentration values ​​for the corresponding regions are extracted from the corrected near-ground ozone images. The ozone concentration near the ground of each region is calculated based on the ozone concentration values of each region in the plurality of region full sets, wherein the ozone concentration near the ground includes an average value of ozone concentration near the ground and / or a population-weighted average value of ozone concentration near the ground.

[0005] According to another aspect of the present application, there is provided an urban and rural area ozone concentration near the ground evaluation device, comprising: An interpolation module is configured to interpolate the population data image and the ozone image near the ground to a research area grid to obtain a research area grid full set, wherein the research area grid full set includes the interpolated population data image and the ozone image near the ground; A correction module is configured to correct the interpolated ozone image near the ground by a least square regression model using air quality site observation data distributed in the research area to obtain a corrected ozone image near the ground; A division module is configured to divide the urban and rural areas based on the interpolated population data image by a preset population density threshold and connectivity rules to obtain a plurality of region full sets, wherein the plurality of region full sets include a constrained urban area sub-domain, a constrained urban core area sub-domain, an urban area full set, an urban core area full set and a non-urban area; An extraction module is configured to extract ozone concentration values of corresponding regions from the corrected ozone image near the ground for the plurality of region full sets; A calculation module is configured to calculate ozone concentration near the ground of each region based on the ozone concentration values of each region in the plurality of region full sets, wherein the ozone concentration near the ground includes an average value of ozone concentration near the ground and / or a population-weighted average value of ozone concentration near the ground.

[0006] According to still another aspect of the present application, there is provided a computer readable storage medium, which stores a computer program for executing the method according to any one of the above aspects of the present application.

[0007] According to still another aspect of the present application, there is provided an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; and the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of the above aspects of the present application.

[0008] Therefore, the present application divides urban and rural ozone evaluation units by population density and infrastructure data, which not only can analyze the regional coupling mechanism of precursor transport and photochemical reaction, but also can identify the spatial distribution of vulnerable population, thereby providing support for establishing an environmental fairness-oriented prevention and control strategy, and having important significance for relieving health inequality caused by environmental pollution. BRIEF DESCRIPTION OF DRAWINGS

[0009] The exemplary embodiments of the present application can be more completely understood in reference to the following drawings: The above-mentioned and other features and examples of the present application are described in more detail below. Figure 1 is a flowchart of a method for evaluating near-surface ozone concentration in an urban and rural area according to an example embodiment of the present application; Figure 2 is a structural diagram of an apparatus for evaluating near-surface ozone concentration in an urban and rural area according to an example embodiment of the present application; Figure 3 is a structure of an electronic device according to an example embodiment of the present application. DETAILED DESCRIPTION

[0010] Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent to those skilled in the art that the described embodiments are merely exemplary of the application and should not be considered limiting. It should also be appreciated that specific embodiments of the application can be used in any number of contexts and therefore the application should not be limited to the embodiments presented herein.

[0011] It should be noted that the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments are not limitations on the scope of the application, unless otherwise specifically indicated.

[0012] Those skilled in the art will understand that the terms "first", "second", and so on in the embodiments of the present application are only used to distinguish different steps, devices or modules, and do not represent any specific technical meaning, nor do they represent a necessary logical order between them.

[0013] It should also be understood that in the embodiments of the present application, "a plurality of" can mean two or more, and "at least one" can mean one, two or more.

[0014] It should also be understood that for any component, data or structure mentioned in the embodiments of the present application, unless specifically limited or given a contrary indication in the context, it can be understood as one or more in general.

[0015] In addition, the term "and / or" in the present application is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B together, and the existence of B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0016] It should also be understood that the description of each embodiment of the present application emphasizes the differences between the embodiments, and the same or similar parts can be referred to each other, and for the sake of brevity, will not be repeated.

[0017] At the same time, it should be understood that, for the convenience of description, the size of each part shown in the drawings is not drawn in accordance with the actual proportional relationship.

[0018] The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application or its application and uses.

[0019] Techniques, methods, and apparatus known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered part of the specification.

[0020] It is to be noted that like reference numerals and letters refer to like items in the following figures, and thus once something is defined in one figure, it need not be discussed further in subsequent figures.

[0021] Embodiments of the application can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations that can be suitable for use with terminal devices, computer systems, servers, etc. include, but are not limited to: personal computers, servers, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputers, mainframe computers, and distributed cloud computing environments that include any of the above systems or the like.

[0022] Terminal devices, computer systems, servers, etc. can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and the like, that perform particular tasks or implement particular abstract data types. Computer systems / servers can be practiced in distributed cloud-computing environments with remote processing devices that are linked through a communications network. In a distributed cloud-computing environment, program modules can be located in local or remote computer system storage media including memory storage devices.

[0023] Exemplary method Figure 1 FIG. 1 is a flowchart of a method for evaluating near-surface ozone concentration in urban and rural areas according to an exemplary embodiment of the present application. The present embodiment can be applied to electronic devices such as terminal devices, computer systems, servers, etc. Figure 1 As shown in FIG. 1, the method 100 for evaluating near-surface ozone concentration in urban and rural areas includes the following steps: At step 101, the population data image and the near-surface ozone image are interpolated to a research area grid to obtain a research area grid full set, wherein the research area grid full set includes the population data image and the near-surface ozone image after interpolation. Step 102, using the air quality site observation data distributed in the study area, the interpolated near-surface ozone image is corrected by a least square regression model to obtain a corrected near-surface ozone image; Step 103, based on the interpolated population data image, the urban and rural area division is carried out through a preset population density threshold and connectivity rule to obtain a plurality of regional sets, wherein the plurality of regional sets include: a constrained urban area sub-domain, a constrained urban core area sub-domain, an urban area set, an urban core area set, and a non-urban area. Step 104, for the plurality of regional sets, the ozone concentration value of the corresponding region is extracted from the corrected near-surface ozone image; Step 105, based on the ozone concentration value of each region in the plurality of regional sets, the near-surface ozone concentration index of each region is calculated, wherein the near-surface ozone concentration index includes the near-surface ozone average value and / or the population weighted near-surface ozone average value.

[0024] Specifically, in view of the technical problems in the background art, the present application divides the urban and rural ozone evaluation unit through population density and infrastructure data, which not only can analyze the regional coupling mechanism of precursor transmission and photochemical reaction, but also can identify the spatial distribution of vulnerable population, providing support for establishing environmental fairness oriented prevention and control strategy, and has important significance for relieving the health inequality of environmental pollution. In order to extract and evaluate the near-surface ozone concentration of urban and rural areas, the present application proposes an urban and rural near-surface ozone concentration evaluation method based on population density data, and the specific implementation steps are as follows: Specific scheme: Required data: annual 1km resolution WorldPOP grid population data; hourly near-surface remote sensing inversion ozone data (such as CHAP, TAP, etc.); hourly air quality site near-surface ozone data.

[0025] 1. According to the single time WorldPOP grid population data of the study area, the single time CHAP data is sampled to the same resolution as the image P, and the CHAP near-surface ozone image is cut to the same study area range as the image P. ;P and Both grid images are M rows and N columns.

[0026] Research area grid set: ; WorldPOP grid population image: ; CHAP near-surface ozone image: .

[0027] 2. Using hourly air quality station site latitude and longitude information, match ground-level ozone image point by point Corresponding pixel value, by least squares regression fitting, to the ground-level ozone image with station observations as true value

[0028] (1) Read the image and the station observation dataset S

[0029] Where A is the full set of grid in the study area (step 1); T is the time series (hourly); K is all stations.

[0030] (2) Match S with , get samples:

[0031] (3) Least squares regression model The correction model is:

[0032] The parameters (a, b) are determined by the following least squares problem:

[0033] Its closed-form solution can be written as:

[0034] Where, and represent all k, t CHAP ground-level ozone and air quality station ground-level ozone average. Put a, b into the correction model, correct the image to O.

[0035] 3. Divide into three areas: urban core area, urban area and non-urban area. Urban area sub-domain (subset) is composed of continuous grid cells with at least 300 residents per square kilometer. For pixels that do not meet the requirements, according to the eight-connected region of the grid cell (including the four-connected up, down, left and right plus four diagonal directions, a total of 8 directions), if there are 5 pixels in the eight pixels that meet the above requirements, the pixel that does not meet the requirements will be changed to the pixel that meets the requirements. For pixels that meet the requirements, the population in a urban area sub-domain (subset) is at least 5000 people.

[0036] The urban core area subdomain (subset) is based on the urban area subdomain (subset) and consists of continuous grid units with at least 1,500 residents per square kilometer. The edges of the urban core area are smoothed. For non-urban core area pixels, according to the eight-connected regions of the grid unit (including four connected directions: up, down, left, right, and four diagonal directions), if five out of the eight pixels meet the above requirements, then the urban area subdomain (subset) pixels are changed to urban core area subdomain (subset) pixels. An urban core area subdomain (subset) has at least 50,000 people.

[0037] Urban area + non-urban area = research area Urban core area + non-urban core area = urban area, and urban area completely includes urban core area.

[0038] Convert to formula: (1) Extraction of candidate sets for urban areas and candidate sets for urban core areas Define an 8-connected neighborhood of any cell (i,j):

[0039] (a) Candidate set of urban areas If any pixel (i,j) has ≥5 pixels within a 3×3 window that satisfy P≥300, then pixel (i,j) is included in the candidate set of urban areas.

[0040] (b) For the candidate set of the urban core area, if any pixel (i,j) has ≥5 pixels within a 3×3 window that satisfy P≥1500, then pixel (i,j) is included in the candidate set of the urban core area.

[0041] (2) Extraction of urban regional subdomains and urban core regional subdomains: In the set or In this context, any two pixels that can reach each other through an internal "eight-connected" path belong to the same connected component and the same subdomain (subset).

[0042] (a) to :

[0043] Each It is a maximum mutually disjoint 8-connected subset. The city region has K subdomains. (b) on :

[0044] Each It is a maximum mutually disjoint 8-connected subset. The city core area has L subdomains. (3) Threshold constraints for urban area subdomains and urban core area subdomains: (a) Urban area subdomain threshold constraint: the population of an urban area subdomain (subset) must be at least 5,000 people.

[0045] Represents the total population of subdomain k; To constrain the subdomains of the city area, ≥5000, reserve city area subdomains; otherwise, set to The final complete set of urban areas (U); (b) Threshold constraint for urban core area subdomains: The population of an urban core area subdomain (subset) must be at least 50,000.

[0046] express Total population of the subdomain; To constrain the subdomains of the post-urban core area, ≥50000, retain the core urban area subdomain; otherwise, set to [unclear]. The final core region complete set (R); Non-urban areas

[0047] 4. Based on urban area subdomains Urban core area sub-domain Near-surface ozone is extracted from any of the following regions: urban area U, urban core area R, and non-urban area N, combined with CHAP-corrected image O. The average value for each region is calculated, and weighted statistics are performed using population data P.

[0048] For any region:

[0049] Extracting near-ground ozone from various regions :

[0050] Calculate the average near-surface ozone concentration in each region. :

[0051] Calculate the population-weighted average near-ground ozone concentration for each region. :

[0052] Application example: For the first [unclear] in the core urban area Subdomain : Average near-surface ozone:

[0053] Population-weighted average ozone concentration:

[0054] Similarly, other regions U, R, N, or subdomains can be targeted. Or perform the corresponding calculations.

[0055] Therefore, this invention divides urban and rural ozone assessment units by population density and infrastructure data, which can not only analyze the regional coupling mechanism of precursor transport and photochemical reactions, but also identify the spatial distribution of vulnerable populations, providing support for the establishment of environmental equity-oriented prevention and control strategies, and is of great significance for alleviating health inequalities caused by environmental pollution.

[0056] Exemplary apparatus Figure 2 This is a schematic diagram of the structure of a near-surface ozone concentration assessment device for urban and rural areas provided in an exemplary embodiment of the present invention. Figure 2 As shown, the device 200 includes: Interpolation module 210 is used to interpolate population data images and near-surface ozone images to the study area grid to obtain a complete set of study area grids, wherein the complete set of study area grids includes the interpolated population data images and near-surface ozone images. The correction module 220 is used to correct the interpolated near-ground ozone image by using the observation data of air quality stations distributed in the study area and the least squares regression model to obtain the corrected near-ground ozone image. The segmentation module 230 is used to segment urban and rural areas based on the interpolated population data image through preset population density thresholds and connectivity rules, and obtain multiple regional sets, which include: constrained urban area subdomains, constrained urban core area subdomains, urban area sets, urban core area sets, and non-urban areas. Extraction module 240 is used to extract ozone concentration values ​​for corresponding regions from corrected near-ground ozone images for multiple regions; The calculation module 250 is used to calculate the near-ground ozone concentration index of each region based on the ozone concentration values ​​of each region in the multi-regional ensemble. The near-ground ozone concentration index includes the average near-ground ozone value and / or the population-weighted average near-ground ozone value.

[0057] Exemplary electronic device Figure 3 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 3 As shown, the electronic device 30 includes one or more processors 31 and memory 32.

[0058] The processor 31 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0059] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 33 and an output device 34, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0060] In addition, the input device 33 may also include, for example, a keyboard, a mouse, etc.

[0061] The output device 34 can output various information to the outside. The output device 34 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0062] Of course, for the sake of simplicity, Figure 3 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0063] Exemplary computer program product and computer readable storage medium In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0064] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0065] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0066] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0067] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0068] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0069] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0070] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0071] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0072] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for assessing the concentration of near-surface ozone in urban and rural areas, characterized by, The method comprises the following steps: interpolating population data images and near-surface ozone images to a research area grid to obtain a research area grid set, wherein the research area grid set comprises the interpolated population data images and the near-surface ozone images; correcting the interpolated near-surface ozone images by using air quality site observation data distributed in the research area through a least square regression model to obtain corrected near-surface ozone images; dividing urban and rural areas based on the interpolated population data images through a preset population density threshold and connectivity rules to obtain a plurality of area sets, wherein the plurality of area sets comprise a constrained urban area subdomain, a constrained urban core area subdomain, an urban area set, an urban core area set and a non-urban area; extracting ozone concentration values of corresponding areas from the corrected near-surface ozone images for the plurality of area sets; calculating near-surface ozone concentration indexes of the areas in the plurality of area sets based on the ozone concentration values of the areas, wherein the near-surface ozone concentration indexes comprise a near-surface ozone average value and / or a population-weighted near-surface ozone average value.

2. The method of claim 1, wherein, The method for correcting the interpolated near-surface ozone images by using air quality site observation data distributed in the research area through a least square regression model to obtain corrected near-surface ozone images comprises the following steps: matching pixels corresponding to the interpolated near-surface ozone images and the air quality site observation data to construct a correction sample set; The least square regression algorithm is used to fit the parameters of the correction model according to the correction sample set, and the correction model parameters are obtained a and b The correction model is: O ij ( t ) = a × O ij '( t ) + b ; applying the correction model parameters to correct all pixels in the near-surface ozone images to obtain the corrected near-surface ozone images.

3. The method of claim 2, wherein, The expression of the least square regression algorithm is as follows: Its closed-form solution is given by: where, where, and denotes the average of the near-surface ozone at all k, t near-surface ozone and air quality sites; is the interpolated near-surface ozone image; S is the air quality site observation data; A is a research area grid set; T is time series; K is all sites; is the CHAP near-surface ozone pixel value of the i-th row and j-th column at time T; K is all sites, and k is the k-th site; is the latitude value of the k-th site; is the longitude value of the k-th site; is the observation value of the k-th site at time t, where obs is the site observation identifier.

4. The method of claim 1, wherein, The method for dividing urban and rural areas based on the interpolated population data images through a preset population density threshold and connectivity rules to obtain a plurality of area sets comprises the following steps: calculating eight-connected neighborhoods of each pixel of the interpolated population data images; extracting a city area candidate set and a city core area candidate set according to the eight-connected neighborhoods of each pixel and a preset population density threshold; extracting the plurality of area sets according to the city area candidate set and the city core area candidate set.

5. The method of claim 4, wherein, The eight-connected neighborhood The expression is: the set of urban area candidates C 0 is expressed as: the set of city core area candidates H 0 is expressed as: wherein, is a pixel in the population data image; represents the row and column population values within the window. u row v column population values.

6. The method of claim 4, wherein, The expression of the plurality of area sets is as follows: wherein, wherein, is a largest disjoint 8-connected subset of the urban area candidate set; is a largest disjoint 8-connected subset of the urban core candidate set; denotes the total population of the k subdomain; is the constrained urban area subdomain; final urban area universe U; denotes the total population of the sub domain; is the constrained urban core subdomain; R is the final core universe; N is the non-urban area.

7. A device for assessing near-ground ozone concentration in urban and rural areas, characterized in that, The method comprises the following steps: an interpolation module is configured to interpolate population data images and near-surface ozone images to a research area grid to obtain a research area grid set, wherein the research area grid set comprises the interpolated population data images and the near-surface ozone images; a correction module is configured to correct the interpolated near-surface ozone images by using air quality site observation data distributed in the research area through a least square regression model to obtain corrected near-surface ozone images; a division module is configured to divide urban and rural areas based on the interpolated population data images through a preset population density threshold and connectivity rules to obtain a plurality of area sets, wherein the plurality of area sets comprise a constrained urban area subdomain, a constrained urban core area subdomain, an urban area set, an urban core area set and a non-urban area; An extraction module is configured to extract ozone concentration values of corresponding regions from the corrected surface ozone image for a complete set of regions; A calculation module is configured to calculate a surface ozone concentration index of each region based on the ozone concentration values of each region in the complete set of regions, wherein the surface ozone concentration index comprises a surface ozone average value and / or a population-weighted surface ozone average value.

8. The apparatus of claim 7, wherein, The correction module comprises: A construction sub-module is configured to match the pixels corresponding to the interpolated surface ozone image and the air quality site observation data, and construct a correction sample set; The fitting submodule is configured to perform parameter fitting on the constructed correction model according to the correction sample set by using a least square regression algorithm to obtain correction model parameters a and b , wherein the correction model is: O ij ( t ) = a × O ij '( t ) + b ; A correction sub-module is configured to correct all pixels in the surface ozone image by using the correction model parameters to obtain a corrected surface ozone image.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is used to execute the method in any one of claims 1-6.

10. An electronic device, comprising: The electronic device comprises: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method in any one of claims 1-6.

Images