Vegetation planning method, device and equipment for improving ambient air quality and storage medium

Abstract

The invention provides a vegetation planning method, device and equipment for improving environmental air quality and a storage medium, and belongs to the field of ecological environment. The method comprises the following steps: firstly, acquiring pollutant monitoring data and remote sensing image data of a target area, and obtaining vegetation index data; correlation analysis is carried out on the pollutant monitoring data and the vegetation index data, and the internal relation between pollutants and vegetation indexes is determined; and the preset vegetation planting scheme is optimized based on the correlation and the air quality standard, so that the planning is more targeted and scientific. On the basis of comprehensively considering pollution factors and air quality requirements, vegetation planning is scientifically optimized, the effect of vegetation planning on improvement of regional air quality is effectively improved, it is ensured that planning is reasonable and meets ecological and pollution control requirements, the environmental quality of a target region is improved, and more efficient and accurate vegetation planning is achieved.

Description

Technical Field

[0001] This invention relates to the field of ecological environment, and in particular to a vegetation planning method, apparatus, equipment and storage medium for improving ambient air quality. Background Technology

[0002] Vegetation, as a core component of the near-surface atmospheric self-purification system, plays an irreplaceable role in air purification. However, it is also a major source of naturally occurring volatile organic compounds (VOCs), which can affect photochemical smog processes, leading to increased pollution. To fully leverage the positive role of vegetation in air purification and climate regulation while effectively controlling the potential risk of exacerbating photochemical smog pollution through VOC release, and to achieve a balance between ecological benefits and environmental quality, rational vegetation planning is necessary. This will maintain ecosystem health, reduce pollution risks, and build a livable environment where humans and nature coexist harmoniously.

[0003] In traditional techniques, vegetation planning is mainly based on indicators such as vegetation coverage area and urban greening rate.

[0004] However, this vegetation planning method focuses only on the quantity of greenery, which may exacerbate air pollution, offset the ecological benefits of vegetation in purifying the air, and even worsen air quality. Summary of the Invention

[0005] This invention provides a vegetation planning method, apparatus, equipment, and storage medium for improving ambient air quality, aiming to solve the problem of how to effectively reduce pollution risks while maintaining the health of the ecosystem during the vegetation planning process.

[0006] In a first aspect, embodiments of the present invention provide a vegetation planning method for improving ambient air quality, comprising:

[0007] Acquire pollutant monitoring data and remote sensing image data of the target area, and obtain vegetation index data of the target area based on the remote sensing image data;

[0008] Correlation analysis was performed on the pollutant monitoring data and the vegetation index data to obtain the correlation relationship between the pollutant monitoring data and the vegetation index.

[0009] Based on the correlation and the air quality standards of the target area, the preset vegetation planting plan of the target area is optimized and adjusted to obtain the vegetation optimization plan of the target area.

[0010] Based on the vegetation optimization scheme, vegetation planning is carried out in the target area.

[0011] In one possible implementation, the vegetation index data includes the vegetation index of the target area at multiple historical time points within a historical period, and the pollutant monitoring data includes pollutant monitoring data of the target area at multiple historical time points within the historical period.

[0012] The pollutant monitoring data includes first pollutant monitoring data and second pollutant monitoring data; the first pollutant is set according to the adsorption capacity of vegetation, and the second pollutant is set according to the release capacity of vegetation.

[0013] The correlation analysis between the pollutant monitoring data and the vegetation index data to obtain the correlation relationship between the pollutant monitoring data and the vegetation index includes:

[0014] Using vegetation index as the independent variable and the first pollutant monitoring data as the dependent variable, regression analysis is performed on the first pollutant monitoring data and vegetation index at multiple historical time points to obtain a first function; wherein, the first function characterizes the first correlation between vegetation index and the first pollutant monitoring data;

[0015] Using vegetation index as the independent variable and the second pollutant monitoring data as the dependent variable, regression analysis is performed on the second pollutant monitoring data and vegetation index at multiple historical time points to obtain a second function, wherein the second function characterizes the second correlation between vegetation index and the second pollutant monitoring data.

[0016] In one possible implementation, the air quality standard includes a first pollutant concentration standard and a second pollutant concentration standard;

[0017] Based on the correlation and the air quality standards of the target area, the preset vegetation planting plan for the target area is optimized and adjusted to obtain the vegetation optimization plan for the target area, including:

[0018] Based on the first function, the second function, and the preset vegetation planting scheme, determine the first predicted pollutant data and the second predicted pollutant data corresponding to the preset vegetation planting scheme;

[0019] Determine whether the first predicted pollutant data meets the first pollutant concentration standard, and determine whether the second predicted pollutant data meets the second pollutant concentration standard;

[0020] If the first predicted pollutant data does not meet the first pollutant concentration standard, the preset vegetation planting scheme is optimized and adjusted based on the difference between the first predicted pollutant data and the first pollutant concentration standard, the second function, and the second pollutant concentration standard to obtain the vegetation optimization scheme for the target area.

[0021] Alternatively, if the second predicted pollutant data does not meet the second pollutant concentration standard, the preset vegetation planting scheme is optimized and adjusted based on the difference between the second predicted pollutant data and the second pollutant concentration standard, the first function, and the first pollutant concentration standard to obtain the vegetation optimization scheme for the target area.

[0022] In one possible implementation, optimizing the preset vegetation planting scheme based on the difference between the first predicted pollutant data and the first pollutant concentration standard, the second function, and the second pollutant concentration standard to obtain a vegetation optimization scheme for the target area includes:

[0023] Based on the difference between the first predicted pollutant data and the first pollutant concentration standard, determine the first vegetation index adjustment value corresponding to the vegetation index;

[0024] Based on the first vegetation index adjustment value and the second function, determine whether the new second predicted pollutant data corresponding to the first vegetation index adjustment value meets the second pollutant concentration standard;

[0025] If the new second predicted pollutant data corresponding to the first vegetation index adjustment value meets the second pollutant concentration standard, the preset vegetation planting plan is optimized and adjusted according to the first vegetation index adjustment value; wherein, the first predicted pollutant data is negatively correlated with the vegetation index, and the second predicted pollutant data is positively correlated with the vegetation index; the vegetation index represents the vegetation coverage.

[0026] In one possible implementation, optimizing the preset vegetation planting scheme based on the first vegetation index adjustment value includes:

[0027] Based on the first vegetation index adjustment value, the adjusted vegetation coverage information is calculated;

[0028] Based on the adjusted vegetation coverage information, the plant planting area in the preset vegetation planting plan is adjusted to obtain the vegetation optimization plan for the target area.

[0029] In one possible implementation, the vegetation planning includes at least one of plant planting area and plant type planning;

[0030] After performing vegetation planning in the target area according to the vegetation optimization scheme, the method further includes:

[0031] Obtain preset information on plant volatile emissions;

[0032] Based on the plant volatile emission information, the plant planting types in the preset vegetation planting plan are adjusted.

[0033] In one possible implementation, the remote sensing image data includes remote sensing image data of multiple grids within a preset range of the target area;

[0034] The step of obtaining the vegetation index data of the target area based on the remote sensing image data includes:

[0035] The vegetation index for each grid is obtained based on the reflectance of the near-infrared band and the reflectance of the infrared band corresponding to the remote sensing image data of each grid.

[0036] The vegetation index data of the target area is obtained based on the vegetation index of each grid cell.

[0037] Secondly, embodiments of the present invention provide a vegetation planning device for improving ambient air quality, the device comprising:

[0038] The acquisition unit is used to acquire pollutant monitoring data and remote sensing image data of the target area, and to obtain vegetation index data of the target area based on the remote sensing image data.

[0039] The first processing unit is used to perform correlation analysis on the pollutant monitoring data and the vegetation index data to obtain the correlation relationship between the pollutant monitoring data and the vegetation index.

[0040] The second processing unit is used to optimize and adjust the preset vegetation planting plan of the target area based on the correlation and the air quality standard of the target area, so as to obtain the vegetation optimization plan of the target area.

[0041] An execution unit is used to perform vegetation planning in the target area according to the vegetation optimization scheme.

[0042] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0043] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0044] This invention provides a vegetation planning method, apparatus, equipment, and storage medium for improving ambient air quality. First, it acquires pollutant monitoring data and remote sensing image data of the target area to obtain vegetation index data, accurately grasping the basic environmental information of the target area. Then, it performs correlation analysis on the pollutant monitoring data and vegetation index data to clarify the intrinsic relationship between pollutants and vegetation indices. Finally, based on the correlation and air quality standards, it optimizes the preset vegetation planting plan, making the planning more targeted and scientific. In summary, this embodiment can scientifically optimize vegetation planning based on a comprehensive consideration of pollution factors and air quality requirements, effectively improving the role of vegetation planning in improving regional air quality, ensuring that the planning is reasonable and meets ecological and pollution control needs, improving the environmental quality of the target area, and achieving more efficient and precise vegetation planning. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating the implementation of a vegetation planning method for improving ambient air quality provided in an embodiment of the present invention.

[0047] Figure 2 This is a flowchart illustrating another vegetation planning method for improving ambient air quality provided in this embodiment of the invention.

[0048] Figure 3 This is a schematic diagram of the vegetation planning device for improving ambient air quality provided in an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0050] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail. Invention Overview

[0052] my country's air quality is facing challenges caused by fine particulate matter (PM2.5). 2.5The main air pollutants are volatile organic compounds (VOCs) and ozone (O3). S As a precursor to both, its emissions directly affect secondary PM2.5. 2.5 And the generation of O3. VOCs are classified into anthropogenic VOCs according to their source. S (Anthropogenic Volatile Organic Compounds, AVOC S ) and natural source VOCs S (Biogenic Volatile Organic Compounds, BVOC S From regional and global scales, BVOC S It has far exceeded AVOC S .

[0053] Vegetation, as an important component of the ecosystem, plays a vital role in soil and water conservation, climate regulation, carbon sequestration and oxygen release, and air purification. However, it is also a major contributor to BVOCs (bioactive compounds). S One of the factors contributing to changes in emissions is the presence of green vegetation. Studies have shown that green plants play a major role in the removal of near-surface atmospheric pollutants; however, with the increase in the variety of tree species and planting area, green spaces have become a double-edged sword. The increase in vegetation inevitably leads to a rise in BVOCs. S Increased emissions, although BVOCs in urban areas S Emissions are lower than AVOC S However, it has high chemical reactivity and can generate secondary PM through atmospheric photochemical reactions. 2.5 Both oxygen and oxygen (O3) have a certain impact on the atmospheric environment. However, existing vegetation planning methods are mostly based on parameters such as current vegetation area and greening rate.

[0054] Based on the idea of ​​effectively reducing pollution risks while maintaining ecosystem health, the embodiments of this invention first acquire pollutant monitoring data and remote sensing image data of the target area and obtain vegetation index data. Then, a correlation analysis is performed on the pollutant monitoring data and vegetation index data to clarify the intrinsic relationship between pollutants and vegetation indices. Subsequently, based on the correlation and air quality standards, the preset vegetation planting plan is optimized to make the planning more targeted and scientific, effectively improve the role of vegetation planning in improving regional air quality, ensure that the planning is reasonable and meets the needs of ecological and pollution control, improve the environmental quality of the target area, and achieve more efficient and precise vegetation planning.

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0056] Figure 1 The implementation flowchart of the vegetation planning method for improving ambient air quality provided in this embodiment of the invention is described in detail below:

[0057] Step 101: Obtain pollutant monitoring data and remote sensing image data of the target area, and obtain vegetation index data of the target area based on the remote sensing image data.

[0058] In this embodiment, pollutant monitoring data can refer to pollutant data monitored by air monitoring stations. Correspondingly, the target area refers to the area formed by taking the air monitoring station as the geometric center and making a buffer zone at a preset distance. The pollutant data monitored by the air monitoring station can characterize the pollutant situation in the target area.

[0059] The determination of the target area corresponding to the air monitoring station is based on the principle that the buffer areas of each station do not overlap, and is determined within a radius of 500 meters to 4 kilometers.

[0060] Among them, vegetation index data refers to quantitative indicators that characterize the state of vegetation, such as the Normalized Difference Vegetation Index (NDVI). The higher the value, the higher the vegetation coverage and the more lush the growth.

[0061] In particular, the time corresponding to the remote sensing image data is synchronized with or close to the time corresponding to the pollutant monitoring data to ensure data consistency.

[0062] For example, this embodiment can collect PM2.5 concentration data within a target area through designated air quality monitoring stations. 2.5 Monitoring data of pollutants such as O3, or historical monitoring records of pollutants obtained from databases. Among them, pollutant monitoring data can be pollutant concentration data, pollutant concentration change data, etc.

[0063] This embodiment can acquire multispectral images covering the target area through a satellite remote sensing platform. Then, using remote sensing image processing software, the acquired remote sensing images are preprocessed with radiometric correction, atmospheric correction, etc., and then the NDVI of each pixel is calculated based on the reflectance of the near-infrared and infrared bands to generate a vegetation index raster map of the target area, i.e., the vegetation index data of the target area.

[0064] Step 102: Conduct a correlation analysis on the pollutant monitoring data and vegetation index data to obtain the correlation between the pollutant monitoring data and the vegetation index.

[0065] In one example, this embodiment first spatially matches pollutant monitoring data with vegetation index raster maps according to the target area coordinates, extracts the NDVI values ​​of the corresponding locations in the target area to form a paired dataset, and removes or smooths outliers to ensure data quality.

[0066] For example, this embodiment uses statistical software to calculate the correlation coefficient between pollutant concentration and NDVI value, such as the Pearson correlation coefficient, and analyzes the degree of linear or nonlinear correlation between the two; it establishes a functional relationship between pollutant concentration and NDVI through regression analysis, such as linear equations, logarithmic equations, etc., and performs significance tests.

[0067] In one feasible implementation, this embodiment can also group pollutant concentrations and NDVI by time dimensions such as season and month, and calculate the correlation coefficient between pollutant concentrations and NDVI in different time periods.

[0068] Step 103: Based on the correlation and the air quality standards of the target area, optimize and adjust the preset vegetation planting plan of the target area to obtain the vegetation optimization plan of the target area.

[0069] For example, in this embodiment, an optimization target is set based on the air quality standard corresponding to the target area, such as reducing PM2.5 levels. 2.5 The concentration control target value of O3 pollutant is used as the optimization target. The preset vegetation planting plan of the target area is optimized and adjusted so that the pollutant monitoring data meets the air quality standards or reaches a certain concentration control target value, thus obtaining the vegetation optimization plan for the target area.

[0070] In one example, this embodiment combines a correlation function with air quality standards to establish a vegetation-pollutant response model: by changing the vegetation index in the model, it simulates the changes in pollutant concentration under different planting schemes, and selects the vegetation index that can meet the pollutant optimization target. Based on this, the preset vegetation planting scheme for the target area is optimized and adjusted to obtain the vegetation optimization scheme for the target area.

[0071] Step 104: Based on the vegetation optimization plan, carry out vegetation planning in the target area.

[0072] For example, this embodiment uses information such as vegetation index in the optimization scheme, combined with the topography and land use status of the target area, to draw a vegetation planting planning map, which clarifies the types of vegetation, the specific planting location, area and boundary of each type of vegetation.

[0073] In one feasible implementation, after vegetation is planted, remote sensing images and pollutant monitoring data are collected regularly, the difference between the actual vegetation index and the planned target is compared, the effect of vegetation planning on improving air quality is evaluated, and then the vegetation planning is dynamically adjusted based on the monitoring results.

[0074] In summary, this embodiment first acquires pollutant monitoring data and remote sensing image data of the target area and obtains vegetation index data to accurately grasp the basic environmental information of the target area. Then, it performs correlation analysis on the pollutant monitoring data and vegetation index data to clarify the intrinsic relationship between pollutants and vegetation indices. Finally, based on the correlation and air quality standards, it optimizes the pre-set vegetation planting plan, making the planning more targeted and scientific. In conclusion, this embodiment can scientifically optimize vegetation planning based on a comprehensive consideration of pollution factors and air quality requirements, effectively enhancing the role of vegetation planning in improving regional air quality, ensuring that the planning is reasonable and meets ecological and pollution control needs, improving the environmental quality of the target area, and achieving more efficient and precise vegetation planning.

[0075] To more accurately balance the adsorption and purification effect of vegetation on pollutants with the pollution risks brought by the released substances, this embodiment classifies the pollutant monitoring data, distinguishes between the first pollutant affected by vegetation adsorption and the second pollutant affected by vegetation release, and constructs a functional relationship between different pollutants and vegetation indices based on multi-dimensional data from historical time points. Based on this, the preset vegetation planting plan for the target area is optimized and adjusted.

[0076] Figure 2 The following is a detailed flowchart of another vegetation planning method for improving ambient air quality provided by an embodiment of the present invention:

[0077] Step 201: Obtain pollutant monitoring data and remote sensing image data of the target area. The pollutant monitoring data includes first pollutant monitoring data and second pollutant monitoring data. The first pollutant is set according to the adsorption effect of vegetation, and the second pollutant is set according to the release effect of vegetation.

[0078] In this embodiment, the first pollutant refers to pollutants that can be purified by vegetation through physiological or physical processes such as adsorption by leaves, absorption through stomata, and surface retention, including fine particulate matter (PM2.5). 2.5 ), inhalable particulate matter (PM) 10 ), sulfur dioxide (SO2), nitrogen oxides (NO) X One or more of the following, in this embodiment, the first pollutant is PM2.5. 2.5 Let's take an example to illustrate.

[0079] The second pollutant refers to pollutants affected by substances released by vegetation, such as BVOCs (e.g., isoprene, terpenes) released by vegetation through photosynthesis or metabolic processes, which may participate in atmospheric photochemical reactions to generate O3. This example uses O3 as the second pollutant for illustration.

[0080] In one example, vegetation index data includes vegetation indices of the target area at multiple historical time points within a historical period, and pollutant monitoring data includes pollutant monitoring data of the target area at multiple historical time points within a historical period. In this embodiment, the target area can be an air monitoring station.

[0081] For example, this embodiment obtains the location of air monitoring stations and PM2.5 concentrations. 2.5 Monitoring data for PM2.5 and O3. Statistics were obtained from air monitoring stations. 2.5 The daily and monthly average concentrations of O3, the daily maximum 8-hour average concentration of O3, and the 90th percentile of the monthly daily maximum 8-hour average concentration of O3 were obtained. Furthermore, remote sensing image data synchronized with the pollutant monitoring time were acquired.

[0082] Step 202: Obtain vegetation index data for the target area based on remote sensing image data.

[0083] Among them, the vegetation index refers to a quantitative indicator that characterizes the vegetation status obtained through remote sensing technology, such as NDVI. The higher the value, the higher the vegetation coverage and the more lush the growth, which can reflect the potential strength of the vegetation's ability to adsorb or release pollutants.

[0084] In one feasible implementation, the remote sensing image data includes remote sensing image data of multiple raster grids within a preset range of the target area; step 202 includes the following steps:

[0085] The vegetation index for each grid is obtained based on the reflectance of the near-infrared band and the reflectance of the infrared band corresponding to the remote sensing image data of each grid.

[0086] The vegetation index data of the target area is obtained based on the vegetation index of each grid.

[0087] For example, in this embodiment, the NDVI raster value is calculated based on the NDVI calculation principle.

[0088] NDVI = (N NIR -R) / (N NIR +R)

[0089] In the formula: NNIR is the reflectivity of the near-infrared band, and R is the reflectivity of the infrared band.

[0090] In one example, this embodiment acquires monthly raster images of an air monitoring station, extracts NDVI raster values ​​within a certain buffer zone centered on the location of the air monitoring station, and uses the average NDVI value within the extracted buffer zone to represent the NDVI value of the air monitoring station.

[0091] Step 203: Using vegetation index as the independent variable and monitoring data of the first pollutant as the dependent variable, perform regression analysis on the monitoring data of the first pollutant and vegetation index at multiple historical time points to obtain the first function; wherein, the first function represents the first correlation between vegetation index and monitoring data of the first pollutant.

[0092] For example, in this embodiment, NDVI is used as the independent variable and the concentration of the first pollutant is used as the dependent variable. The mathematical expression established through regression analysis, namely the first function, describes the quantitative relationship between vegetation coverage and the adsorption and purification effect of pollutants (for example, the higher the vegetation index, the lower the concentration of the first pollutant may be).

[0093] In one feasible implementation, if it is assumed that the adsorption effect of vegetation on the first pollutant is linear, a univariate linear regression can be used, for example: C1 = a1 × NDVI + b1, where C1 is the concentration of the first pollutant, and a1 and b1 are regression coefficients; if a nonlinear trend is found (such as the purification effect tending to saturate under high vegetation coverage), a quadratic polynomial regression can be used, for example: C1 = a1 × NDVI 2 +b1. Or, C1 = a1ln(NDVI) + b1.

[0094] Step 204: Using vegetation index as the independent variable and second pollutant monitoring data as the dependent variable, regression analysis is performed on second pollutant monitoring data and vegetation index at multiple historical time points to obtain a second function, wherein the second function characterizes the second correlation between vegetation index and second pollutant monitoring data.

[0095] For example, this embodiment uses vegetation index as the independent variable and second pollutant concentration as the dependent variable to establish a mathematical expression, which is used to characterize the association between vegetation physiological activities (such as the release of volatile organic compounds) and pollutant generation (e.g., an increase in vegetation index may lead to an increase in the concentration of certain second pollutants).

[0096] In one feasible implementation, considering the complex impact of vegetation-released substances on the second pollutant, such as the nonlinear photochemical reaction between BVOCs and O3, this embodiment can employ a nonlinear regression model, such as the exponential function C2 = a2·e b ·NDVI +c, where C2 is the concentration of the second pollutant, a2 and b are regression coefficients, and c is a constant term. Alternatively, piecewise regression analysis can be used to distinguish the different impacts of peak and off-peak vegetation growth seasons.

[0097] In one example, this embodiment measures the explanatory power of the function based on the coefficient of determination, with a coefficient of determination closer to 1 indicating a better fit. Alternatively, it tests the significance of the function based on the F-statistic.

[0098] In one example, this embodiment summarizes the monthly PM2.5 levels from air monitoring stations.2.5 O3 concentration and NDVI value were used to calculate the correlation coefficient and perform qualitative analysis of PM2.5. 2.5 The trend of O3 concentration with NDVI value; with NDVI as the independent variable, PM 2.5 Using O3 as the dependent variable, regression analysis was performed and a functional equation was established to quantitatively analyze PM. 2.5 O3 concentration changes with NDVI value.

[0099] In one example, PM 2.5 A negative correlation coefficient between PM and NDVI, and a positive correlation coefficient between O3 and NDVI, indicate that PM... 2.5 The concentration of O3 decreases as the NDVI value increases, while the opposite is true for O3, where the concentration of O3 increases as the NDVI value increases.

[0100] This embodiment quantifies the impact mechanism of vegetation indices on different pollutants, providing a scientific mathematical model to support the balance between "purification benefits" and "pollution risks" in subsequent vegetation planning.

[0101] Step 205: Based on the first function, the second function, and the preset vegetation planting scheme, determine the first predicted pollutant data and the second predicted pollutant data corresponding to the preset vegetation planting scheme.

[0102] The preset vegetation planting plan can be either the current target area's planting plan or a vegetation planting plan that has not yet been implemented.

[0103] Vegetation index information is determined based on the vegetation area and / or vegetation coverage in a pre-defined vegetation planting plan, or the vegetation index information in a pre-defined vegetation planting plan is obtained. The vegetation index information may include vegetation indices at multiple different time points, such as the monthly vegetation index for the target area.

[0104] Since the first function quantifies the relationship between the first pollutant and the vegetation index, substituting the vegetation index into the first function yields the first predicted pollutant data; since the second function quantifies the relationship between the second pollutant and the vegetation index, substituting the vegetation index into the second function yields the second predicted pollutant data.

[0105] Step 206: The air quality standards include the first pollutant concentration standard and the second pollutant concentration standard; determine whether the first predicted pollutant data meets the first pollutant concentration standard, and determine whether the second predicted pollutant data meets the second pollutant concentration standard.

[0106] For example, if the first predicted pollutant data does not meet the first pollutant concentration standard, then the difference between the first predicted pollutant data and the first pollutant concentration standard is determined; if the second predicted pollutant data does not meet the second pollutant concentration standard, then the difference between the second predicted pollutant data and the second pollutant concentration standard is determined.

[0107] In one example, the preset vegetation planting plan includes the monthly vegetation index of the target area. Then, based on the first and second functions, the first and second predicted pollutant concentrations for each month can be obtained. The vegetation index at which the predicted pollutant concentration does not meet the standard is then used as the vegetation index to be optimized.

[0108] Step 207: If the first predicted pollutant data does not meet the first pollutant concentration standard, then the preset vegetation planting plan is optimized and adjusted according to the difference between the first predicted pollutant data and the first pollutant concentration standard, the second function, and the second pollutant concentration standard to obtain the vegetation optimization plan for the target area; or, if the second predicted pollutant data does not meet the second pollutant concentration standard, then the preset vegetation planting plan is optimized and adjusted according to the difference between the second predicted pollutant data and the second pollutant concentration standard, the first function, and the first pollutant concentration standard to obtain the vegetation optimization plan for the target area.

[0109] In one feasible implementation, step 207 includes the following steps:

[0110] If the first predicted pollutant data does not meet the first pollutant concentration standard, an adjustment value for the first vegetation index is determined based on the difference between the first predicted pollutant data and the first pollutant concentration standard. Then, based on the first vegetation index adjustment value and a second function, it is determined whether the new second predicted pollutant data corresponding to the first vegetation index adjustment value meets the second pollutant concentration standard. If the new second predicted pollutant data corresponding to the first vegetation index adjustment value meets the second pollutant concentration standard, the preset vegetation planting plan is optimized and adjusted based on the first vegetation index adjustment value. The first predicted pollutant data is negatively correlated with the vegetation index; the vegetation index represents the vegetation cover.

[0111] If the second predicted pollutant data does not meet the second pollutant concentration standard, the second vegetation index adjustment value corresponding to the vegetation index is determined based on the difference between the second predicted pollutant data and the second pollutant concentration standard; based on the second vegetation index adjustment value and the first function, it is determined whether the new first predicted pollutant data corresponding to the second vegetation index adjustment value meets the first pollutant concentration standard; if the new first predicted pollutant data corresponding to the second vegetation index adjustment value meets the first pollutant concentration standard, the preset vegetation planting plan is optimized and adjusted based on the second vegetation index adjustment value; wherein, the second predicted pollutant data is positively correlated with the vegetation index; the vegetation index represents the vegetation cover.

[0112] The optimization and adjustment of the preset vegetation planting plan based on the first vegetation index adjustment value includes: calculating the adjusted vegetation coverage information based on the first vegetation index adjustment value; and adjusting the plant planting area in the preset vegetation planting plan based on the adjusted vegetation coverage information to obtain the vegetation optimization plan for the target area.

[0113] Based on the second vegetation index adjustment value, the preset vegetation planting plan is optimized and adjusted, including: calculating the adjusted vegetation coverage information based on the second vegetation index adjustment value; adjusting the plant planting area in the preset vegetation planting plan based on the adjusted vegetation coverage information to obtain the vegetation optimization plan for the target area.

[0114] If the concentration of the first predicted pollutant is higher than the standard for the first pollutant concentration, it indicates that the vegetation's adsorption capacity is insufficient, and vegetation coverage needs to be increased to improve the purification effect. If the concentration of the second predicted pollutant is higher than the standard for the second pollutant concentration, it indicates that the substances released by the vegetation may exacerbate the pollution, and vegetation coverage needs to be reduced or the plant type adjusted to reduce the release amount.

[0115] In one example, we assume the standard for the concentration of the first pollutant is below 75 micrograms per cubic meter (μg / m³). 3 The standard for the second pollutant concentration is below 160 μg / m³. 3 .

[0116] If the predicted concentration of the first pollutant, i.e. PM2.5 2.5 The predicted concentration is 90 μg / m³. 3 It does not meet the first pollutant standard of 75 μg / m³ 3The required NDVI value is then calculated by working backward from the first function. For example, if the first function is PM2.5 = -100NDVI + 100, then for the target NDVI to be met, PM2.5 = -100NDVI + 100, yielding a target NDVI of 0.25. Therefore, the adjusted value for the first vegetation index should be greater than or equal to 0.25, meaning the NDVI needs to be increased to reduce the concentration of the first pollutant. At this point, it is also necessary to consider whether the predicted concentration of the second pollutant corresponding to the adjusted NDVI still meets the standard, avoiding a single adjustment that could exacerbate the exceedance of the second pollutant, thus forming a "multi-objective balance optimization."

[0117] If the predicted concentration of the second pollutant, i.e. O3, is 180 μg / m³ 3 The second pollutant standard of 160 μg / m³ is not met. 3 The required NDVI value is then calculated by working backward from the second function. For example, the second function is O3 = 75NDVI + 130, and to achieve the target, 160 = 75NDVI + 130, yielding a target NDVI of 0.4. This means that if the adjusted value of the second vegetation index is less than or equal to 0.4, the NDVI needs to be reduced to decrease the contribution of vegetation-released substances to O3 formation. At this point, it is also necessary to consider whether the predicted concentration of the first pollutant corresponding to the adjusted NDVI still meets the standard, to avoid a single adjustment exacerbating the exceedance of the first pollutant.

[0118] After adjustment, the predicted concentrations are recalculated by substituting the values ​​into the first and second functions to verify whether the standards for both types of pollutants are met simultaneously. This process continues until both types of pollutants meet the standards, ultimately resulting in a vegetation optimization plan.

[0119] Another feasible implementation method is to adjust the NDVI if the concentrations of both the first and second pollutants fail to meet the standards, taking into account local needs. For example, if the local area is more concerned about PM2.5 concentrations... 2.5 If the concentration is high, the NDVI value can be increased; if the local area is more concerned about the O3 concentration, the NDVI value can be decreased. Highly adsorbent plants can be retained, and some high BVOCs plants can be replaced with low-emission varieties to reduce the release without significantly reducing the adsorption capacity.

[0120] Step 208: Based on the vegetation optimization plan, carry out vegetation planning in the target area.

[0121] In one example, vegetation planning includes at least one of planting area and plant type planning; after vegetation planning is carried out in the target area according to the vegetation optimization scheme, the method further includes:

[0122] Obtain preset information on plant volatile emissions;

[0123] Based on information on plant volatile emissions, the types of plants planted in the preset vegetation planting plan are adjusted.

[0124] For example, in this embodiment, based on the vegetation index adjustment value or vegetation coverage determined in the vegetation optimization scheme, a specific planting area is delineated on the target area map, and the corresponding planting area is calculated. For instance, if the optimization scheme requires increasing the vegetation index of a certain area from 0.3 to 0.4, the existing vegetation coverage can be analyzed using GIS to mark the areas that need to be newly planted, and the required increase in planting area can be calculated.

[0125] In addition, this embodiment can also select suitable plants based on the main pollutant types in the target area. If the primary pollutant (adsorbable pollutant) exceeds the standard significantly, plants with large leaf surface area and strong adsorption capacity, such as oleander and privet, are preferred. If the secondary pollutant (pollutant affected by vegetation release) poses a risk, plants with low volatile emissions, such as palm trees, are selected. At the same time, the ecological adaptability of the plants is considered to ensure that they can grow well in the target area.

[0126] In one feasible implementation, this embodiment can query volatile organic compound (VOC) emission data of different plants from authoritative plant ecology research databases; refer to research findings on VOC emissions from plants in specific areas in scientific literature; or cooperate with local forestry and environmental protection departments to obtain VOC emission information from plants based on on-site monitoring. The obtained VOC emission information is then compared with a pre-set vegetation planting plan. If it is found that the VOC emissions of certain plants may lead to a risk of exceeding the standard for secondary pollutants, they can be replaced with low-emission plants in a timely manner. For example, if the original plan was to plant a large number of pine trees (high BVOC emission plants), but the obtained information shows that the ozone concentration in the area is already close to exceeding the standard, some pine trees can be replaced with camphor or banyan trees with low VOC emissions, thus ensuring the greening effect while reducing the pollution risk caused by plant VOCs.

[0127] In summary, this embodiment achieves scientific adjustment of vegetation planting schemes through pollutant classification monitoring, construction of quantitative relationships between vegetation indices and pollutants, difference analysis between predicted data and standards, and synergistic optimization of multi-pollutant concentration targets. It not only balances the purification benefits of vegetation adsorbing pollutants with the pollution risks of released substances, but also ensures through dynamic verification that the planning scheme simultaneously meets the control standards for both types of pollutants. This transforms vegetation planning from a single experience-based judgment to data-driven precision optimization, effectively improving the targeting and feasibility of regional air quality improvement.

[0128] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0129] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0130] Figure 3 A schematic diagram of a vegetation planning device for improving ambient air quality provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0131] like Figure 3 As shown, the vegetation planning device for improving ambient air quality includes:

[0132] The acquisition unit 31 is used to acquire pollutant monitoring data and remote sensing image data of the target area, and to obtain vegetation index data of the target area based on the remote sensing image data.

[0133] The first processing unit 32 is used to perform correlation analysis on pollutant monitoring data and vegetation index data to obtain the correlation relationship between pollutant monitoring data and vegetation index.

[0134] The second processing unit 33 is used to optimize and adjust the preset vegetation planting plan of the target area based on the correlation and the air quality standard of the target area, so as to obtain the vegetation optimization plan of the target area.

[0135] The execution unit 34 is used to perform vegetation planning in the target area according to the vegetation optimization scheme.

[0136] In one possible implementation, the vegetation index data includes the vegetation index of the target area at multiple historical time points within a historical period, and the pollutant monitoring data includes pollutant monitoring data of the target area at multiple historical time points within a historical period; the pollutant monitoring data includes first pollutant monitoring data and second pollutant monitoring data; the first pollutant is set according to the adsorption capacity of vegetation, and the second pollutant is set according to the release capacity of vegetation;

[0137] The first processing unit 32 is specifically used for:

[0138] Using vegetation index as the independent variable and monitoring data of the first pollutant as the dependent variable, regression analysis was performed on monitoring data of the first pollutant and vegetation index at multiple historical time points to obtain the first function; whereby the first function represents the first correlation between vegetation index and monitoring data of the first pollutant.

[0139] Using vegetation index as the independent variable and monitoring data of the second pollutant as the dependent variable, regression analysis was performed on monitoring data of the second pollutant and vegetation index at multiple historical time points to obtain a second function, which represents the second correlation between vegetation index and monitoring data of the second pollutant.

[0140] In one possible implementation, the air quality standard includes a first pollutant concentration standard and a second pollutant concentration standard; the second processing unit 33 is specifically used for:

[0141] Based on the first function, the second function, and the preset vegetation planting scheme, determine the first and second predicted pollutant data corresponding to the preset vegetation planting scheme.

[0142] Determine whether the data for the first predicted pollutant meets the concentration standard for the first pollutant, and determine whether the data for the second predicted pollutant meets the concentration standard for the second pollutant;

[0143] If the first predicted pollutant data does not meet the first pollutant concentration standard, the preset vegetation planting plan is optimized and adjusted based on the difference between the first predicted pollutant data and the first pollutant concentration standard, the second function, and the second pollutant concentration standard to obtain the vegetation optimization plan for the target area.

[0144] Alternatively, if the second predicted pollutant data does not meet the second pollutant concentration standard, the preset vegetation planting plan is optimized and adjusted based on the difference between the second predicted pollutant data and the second pollutant concentration standard, the first function, and the first pollutant concentration standard to obtain the vegetation optimization plan for the target area.

[0145] In one possible implementation, the second processing unit 33 is specifically used to: determine a first vegetation index adjustment value corresponding to the vegetation index based on the difference between the first predicted pollutant data and the first pollutant concentration standard; determine whether the new second predicted pollutant data corresponding to the first vegetation index adjustment value meets the second pollutant concentration standard based on the first vegetation index adjustment value and a second function; if the new second predicted pollutant data corresponding to the first vegetation index adjustment value meets the second pollutant concentration standard, optimize and adjust the preset vegetation planting plan based on the first vegetation index adjustment value; wherein, the first predicted pollutant data is negatively correlated with the vegetation index; the vegetation index characterizes the vegetation cover.

[0146] The second processing unit 33 is further configured to: if the second predicted pollutant data does not meet the second pollutant concentration standard, determine the second vegetation index adjustment value corresponding to the vegetation index based on the difference between the second predicted pollutant data and the second pollutant concentration standard; determine whether the new first predicted pollutant data corresponding to the second vegetation index adjustment value meets the first pollutant concentration standard based on the second vegetation index adjustment value and the first function; if the new first predicted pollutant data corresponding to the second vegetation index adjustment value meets the first pollutant concentration standard, optimize and adjust the preset vegetation planting plan based on the second vegetation index adjustment value; wherein, the second predicted pollutant data is positively correlated with the vegetation index; the vegetation index characterizes the vegetation cover.

[0147] In one possible implementation, the second processing unit 33 is further configured to calculate the adjusted vegetation coverage information based on the first vegetation index adjustment value; and adjust the plant planting area in the preset vegetation planting scheme based on the adjusted vegetation coverage information to obtain the vegetation optimization scheme for the target area.

[0148] In one possible implementation, vegetation planning includes at least one of plant planting area and plant type planning; after execution unit 34, the device further includes: a third processing unit, used to acquire preset plant volatile emission information; and to adjust the plant planting type in the preset vegetation planting plan based on the plant volatile emission information.

[0149] In one possible implementation, the remote sensing image data includes remote sensing image data of multiple raster grids within a preset range of the target area; the acquisition unit 32 is specifically used for:

[0150] The vegetation index of each grid is obtained by using the reflectance of the near-infrared band and the reflectance of the infrared band corresponding to the remote sensing image data of each grid; the vegetation index data of the target area is obtained by using the vegetation index of each grid.

[0151] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0152] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 4 As shown, the electronic device 4 in this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, it implements the steps in the various method embodiments described above. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module / unit in the various device embodiments described above.

[0153] For example, computer program 42 may be divided into one or more modules / units, which are stored in memory 41 and executed by processor 40 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 42 in electronic device 4.

[0154] Electronic device 4 may include, but is not limited to, processor 40 and memory 41. Those skilled in the art will understand that... Figure 4This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 4 may also include input / output devices, network access devices, buses, etc.

[0155] The processor 40 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0156] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 41 can include both internal and external storage units of the electronic device 4. The memory 41 is used to store the computer program 42 and other programs and data required by the electronic device 4. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0157] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0158] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0159] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0160] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0161] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0162] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A vegetation planning method for improving ambient air quality, characterized in that, include: Acquire pollutant monitoring data and remote sensing image data of the target area, and obtain vegetation index data of the target area based on the remote sensing image data; Correlation analysis was performed on the pollutant monitoring data and the vegetation index data to obtain the correlation relationship between the pollutant monitoring data and the vegetation index. Based on the correlation and the air quality standards of the target area, the preset vegetation planting plan of the target area is optimized and adjusted to obtain the vegetation optimization plan of the target area. Based on the vegetation optimization scheme, vegetation planning is carried out in the target area.

2. The vegetation planning method for improving ambient air quality according to claim 1, characterized in that, The vegetation index data includes the vegetation index of the target area at multiple historical time points within the historical period, and the pollutant monitoring data includes the pollutant monitoring data of the target area at multiple historical time points within the historical period. The pollutant monitoring data includes first pollutant monitoring data and second pollutant monitoring data; the first pollutant is set according to the adsorption capacity of vegetation, and the second pollutant is set according to the release capacity of vegetation. The correlation analysis between the pollutant monitoring data and the vegetation index data to obtain the correlation relationship between the pollutant monitoring data and the vegetation index includes: Using vegetation index as the independent variable and the first pollutant monitoring data as the dependent variable, regression analysis is performed on the first pollutant monitoring data and vegetation index at multiple historical time points to obtain a first function; wherein, the first function characterizes the first correlation between vegetation index and the first pollutant monitoring data; Using vegetation index as the independent variable and the second pollutant monitoring data as the dependent variable, regression analysis is performed on the second pollutant monitoring data and vegetation index at multiple historical time points to obtain a second function, wherein the second function characterizes the second correlation between vegetation index and the second pollutant monitoring data.

3. The vegetation planning method for improving ambient air quality according to claim 2, characterized in that, The air quality standards include standards for the concentration of a first pollutant and standards for the concentration of a second pollutant. Based on the correlation and the air quality standards of the target area, the preset vegetation planting plan for the target area is optimized and adjusted to obtain the vegetation optimization plan for the target area, including: Based on the first function, the second function, and the preset vegetation planting scheme, determine the first predicted pollutant data and the second predicted pollutant data corresponding to the preset vegetation planting scheme; Determine whether the first predicted pollutant data meets the first pollutant concentration standard, and determine whether the second predicted pollutant data meets the second pollutant concentration standard; If the first predicted pollutant data does not meet the first pollutant concentration standard, the preset vegetation planting scheme is optimized and adjusted based on the difference between the first predicted pollutant data and the first pollutant concentration standard, the second function, and the second pollutant concentration standard to obtain the vegetation optimization scheme for the target area. Alternatively, if the second predicted pollutant data does not meet the second pollutant concentration standard, the preset vegetation planting scheme is optimized and adjusted based on the difference between the second predicted pollutant data and the second pollutant concentration standard, the first function, and the first pollutant concentration standard to obtain the vegetation optimization scheme for the target area.

4. The vegetation planning method for improving ambient air quality according to claim 3, characterized in that, The step of optimizing and adjusting the preset vegetation planting plan based on the difference between the first predicted pollutant data and the first pollutant concentration standard, the second function, and the second pollutant concentration standard to obtain a vegetation optimization plan for the target area includes: Based on the difference between the first predicted pollutant data and the first pollutant concentration standard, determine the first vegetation index adjustment value corresponding to the vegetation index; Based on the first vegetation index adjustment value and the second function, determine whether the new second predicted pollutant data corresponding to the first vegetation index adjustment value meets the second pollutant concentration standard; If the new second predicted pollutant data corresponding to the first vegetation index adjustment value meets the second pollutant concentration standard, the preset vegetation planting plan is optimized and adjusted according to the first vegetation index adjustment value; wherein, the first predicted pollutant data is negatively correlated with the vegetation index, and the second predicted pollutant data is positively correlated with the vegetation index; the vegetation index represents the vegetation coverage.

5. The vegetation planning method for improving ambient air quality according to claim 4, characterized in that, The step of optimizing and adjusting the preset vegetation planting plan based on the first vegetation index adjustment value includes: Based on the first vegetation index adjustment value, the adjusted vegetation coverage information is calculated; Based on the adjusted vegetation coverage information, the plant planting area in the preset vegetation planting plan is adjusted to obtain the vegetation optimization plan for the target area.

6. The vegetation planning method for improving ambient air quality according to any one of claims 1-5, characterized in that, The vegetation plan includes at least one of the following: plant planting area and plant type planning; After performing vegetation planning in the target area according to the vegetation optimization scheme, the method further includes: Obtain preset information on plant volatile emissions; Based on the plant volatile emission information, the plant planting types in the preset vegetation planting plan are adjusted.

7. The vegetation planning method for improving ambient air quality according to any one of claims 1-5, characterized in that, The remote sensing image data includes remote sensing image data of multiple grids within a preset range of the target area; The step of obtaining the vegetation index data of the target area based on the remote sensing image data includes: The vegetation index for each grid is obtained based on the reflectance of the near-infrared band and the reflectance of the infrared band corresponding to the remote sensing image data of each grid. The vegetation index data of the target area is obtained based on the vegetation index of each grid cell.

8. A vegetation planning device for improving ambient air quality, characterized in that, The device includes: The acquisition unit is used to acquire pollutant monitoring data and remote sensing image data of the target area, and to obtain vegetation index data of the target area based on the remote sensing image data. The first processing unit is used to perform correlation analysis on the pollutant monitoring data and the vegetation index data to obtain the correlation relationship between the pollutant monitoring data and the vegetation index. The second processing unit is used to optimize and adjust the preset vegetation planting plan of the target area based on the correlation and the air quality standard of the target area, so as to obtain the vegetation optimization plan of the target area. An execution unit is used to perform vegetation planning in the target area according to the vegetation optimization scheme.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the vegetation planning method for improving ambient air quality as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the vegetation planning method for improving ambient air quality as described in any one of claims 1 to 7.

Images