Method for correcting trends in atmospheric pollution

Abstract

The application discloses a correction method for atmospheric pollution change trend, comprising the following steps: obtaining atmospheric pollutant data and corresponding meteorological data of atmospheric environment within a predetermined time length; performing gradient encapsulation on the atmospheric pollutant data and the corresponding meteorological data, and obtaining the change of the atmospheric pollutant data in each gradient; performing all-gradient summation and average processing on the change of the atmospheric pollutant data caused by the meteorological data, and obtaining the change of the atmospheric pollutant data after correction based on the meteorological data, so as to realize the correction of the atmospheric pollution change trend. The application can effectively correct the atmospheric pollutant data change trend, so as to obtain an accurate atmospheric pollutant data change trend, and provide more accurate and reliable data support for the research on the atmospheric environment, and effectively solve the problems in the prior art.

Description

Technical Field

[0001] This invention relates to the field of intelligent environmental monitoring technology, and in particular to a method for correcting the trend of air pollution changes. Background Technology

[0002] To better protect the atmospheric environment, it is necessary to understand the long-term evolution trends of air pollutants. The changing trends of air pollutants are mainly influenced by emissions from human activities. Currently, predictions of the long-term evolution trends of air pollutants are typically based on human-induced emissions, and strict emission standards and air pollution control policies are formulated accordingly to reduce air pollutants and thus improve the atmospheric environment for people's lives.

[0003] However, in addition to being directly affected by human activities, some pollutants in the atmosphere are also affected by biomass emissions and meteorological factors. For example, secondary atmospheric pollutants such as formaldehyde and glyoxal, as well as meteorological factors such as temperature and air pressure, can cause changes in biomass emissions, thus affecting the long-term trend of atmospheric pollutant observation results, and consequently affecting environmentalists' judgment on the long-term evolution trend of atmospheric pollutants.

[0004] Currently, there is no solution that can accurately predict the long-term evolution trend of air pollutants, thus making it impossible to protect the atmospheric environment based on accurate long-term evolution trends of air pollutants.

[0005] In view of this, the present invention is hereby proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a method for correcting the trend of air pollution changes, so as to improve the accuracy of air pollution trend prediction and solve the problems existing in the prior art.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A method for correcting trends in air pollution, comprising:

[0009] Acquire atmospheric pollutant data and corresponding meteorological data for a predetermined time period;

[0010] The atmospheric pollutant data and the corresponding meteorological data are encapsulated in a gradient, and the changes in atmospheric pollutant data within each gradient are obtained.

[0011] For the changes in the air pollutant data caused by the meteorological data, all gradients are summed and then averaged to obtain the changes in the air pollutant data after correction based on the meteorological data, so as to correct the trend of air pollution changes.

[0012] The gradient encapsulation includes:

[0013] The air pollutant data and the corresponding meteorological data are rearranged and packaged according to a predetermined meteorological data gradient. Within each meteorological data gradient, the air pollutant data is further divided into multiple stages according to the time of observation, and the degree of change of air pollutant data between multiple stages is calculated to obtain the gradient packaged result. The gradient of the meteorological data refers to the result obtained after dividing the meteorological data according to a predetermined rule.

[0014] The calculation process for the degree of change in air pollutant data between the multiple stages includes:

[0015] Calculate the average value of air pollutant data within each stage, and determine the degree of change of air pollutant data between each stage based on the average value.

[0016] The step of summing and averaging all gradients includes:

[0017] For the observed pixels of the aforementioned air pollutant data, calculate their long-term variation after correction based on meteorological data. The formulas include:

[0018] ;

[0019] in, and This represents the number of all observations in the first and second stages within pixel p and the meteorological data gradient t. The average change in air pollutant data over two periods is calculated using the following formula:

[0020] ;

[0021] in, and It is represented as the average of the air pollutant data in the first stage and the average of the air pollutant data in the second stage.

[0022] The method also includes:

[0023] The interannual growth rate of air pollutants unaffected by seasons is calculated, and the trend of air pollution change is corrected based on the interannual growth rate of air pollutants unaffected by seasons.

[0024] The calculation methods for the interannual growth rate of air pollutants that are not affected by seasons include:

[0025] The seasonally varying interannual growth rate model is constructed as follows:

[0026] ;

[0027] in, This represents the average concentration of air pollutants over a time interval t, where t is the observation time. A, B, C n D n All are fitting parameters, where C n D n To fit the parameters of the quarterly variation, A is the average pollutant concentration in the first year, and B is the interannual growth rate of air pollutants.

[0028] The atmospheric pollutant data obtained from the observation results are input into the seasonal variation interannual growth rate model, and the least squares fitting process is performed on each parameter term in the seasonal variation interannual growth rate model to obtain the interannual growth rate of atmospheric pollutants that is not affected by the season.

[0029] Compared with existing technologies, the present invention provides a method for correcting air pollution change trends. This method utilizes multivariate meteorological data to correct long-term trends in air pollutants, thereby obtaining an implementation scheme capable of accurately predicting air pollution change trends. Specifically, it uses air pollutant data and matching meteorological data as basic data to analyze and calculate the change trends of air pollutant data, and then effectively corrects the change trends of air pollutant data to obtain accurate long-term change trends of air pollutants. The implementation of this invention provides more and more accurate and reliable data support for atmospheric environment research. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0031] Figure 1 A schematic diagram illustrating the implementation process of the method in an embodiment of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them, and do not constitute a limitation on the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0033] First, the following explanations are provided for the terms that may be used in this article:

[0034] The term "and / or" means that either or both can be achieved simultaneously. For example, X and / or Y means that it includes both "X" or "Y" as well as the three cases of "X and Y".

[0035] The terms "comprising," "including," "containing," "having," or other similar semantic descriptions should be interpreted as non-exclusive inclusion. For example, including a technical feature element (such as raw material, component, ingredient, carrier, dosage form, material, size, part, component, mechanism, device, step, process, method, reaction conditions, processing conditions, parameter, algorithm, signal, data, product or article of manufacture, etc.) should be interpreted as including not only the expressly listed technical feature element, but also other technical feature elements that are not expressly listed and are well-known in the art.

[0036] The term "composed of" excludes any technical features not expressly listed. When used in a claim, it closes the claim to exclude all technical features other than those expressly listed, except for associated conventional impurities. If the term appears only in a clause of a claim, it limits the claim to the elements expressly listed in that clause; elements recited in other clauses are not excluded from the overall claim.

[0037] The term "parts by mass" indicates the mass ratio between multiple components. For example, if component X is described as x parts by mass and component Y as y parts by mass, then the mass ratio of component X to component Y is x:y. One part by mass can represent any mass; for example, one part by mass can be expressed as 1 kg or 3.1415926 kg, etc. The sum of the parts by mass of all components is not necessarily 100 parts; it can be greater than 100 parts, less than 100 parts, or equal to 100 parts. Unless otherwise stated, parts, proportions, and percentages mentioned herein are all measured by mass.

[0038] Unless otherwise explicitly specified or limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this document according to the specific circumstances.

[0039] When concentration, temperature, pressure, size, or other parameters are expressed as numerical ranges, such ranges should be understood to specifically disclose all ranges formed by any pairing of upper limits, lower limits, or preferred values ​​within that range, regardless of whether the range is explicitly stated; for example, if the numerical range "2 to 8" is stated, then that range should be interpreted to include ranges such as "2 to 7", "2 to 6", "5 to 7", "3 to 4 and 6 to 7", "3 to 5 and 7", "2 and 5 to 7", etc. Unless otherwise stated, the numerical ranges described herein include both their endpoints and all integers and fractions within that range.

[0040] The terms “center,” “longitudinal,” “lateral,” “length,” “width,” “thickness,” “upper,” “lower,” “front,” “back,” “left,” “right,” “vertical,” “horizontal,” “top,” “bottom,” “inner,” “outer,” “clockwise,” and “counterclockwise” indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience and simplification of description and do not imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this document.

[0041] The following is a detailed description of a method for correcting air pollution trends provided by this invention. Contents not described in detail in the embodiments of this invention are prior art known to those skilled in the art. Where specific conditions are not specified in the embodiments of this invention, they should be performed according to conventional conditions in the art or conditions recommended by the manufacturer. Where the manufacturers of the reagents or instruments used in the embodiments of this invention are not specified, they are all conventional products that can be purchased commercially.

[0042] In the process of realizing this invention, it was discovered that correcting the long-term variation trends of air pollutants based on multivariate meteorological elements from satellite and ground observations plays an important indicative role in assessing the impact of emission reduction measures on anthropogenic air pollutants. Based on this, embodiments of this invention provide a method for correcting air pollution variation trends. This method mainly provides an algorithm for correcting long-term variation trends of air pollutants using multivariate meteorological data, thereby obtaining accurate air pollution variation trends.

[0043] Specifically, the present invention provides a method for correcting the long-term variation trend of air pollutants, referring to... Figure 1 As shown, the specific implementation process may include the following steps:

[0044] Step 11, Acquisition and utilization of air pollutant data;

[0045] Specifically, the corresponding air pollutant data mainly includes the concentration information of secondary pollutants, which may include the concentration information of glyoxal, formaldehyde and secondary organic aerosols;

[0046] In this embodiment of the invention, atmospheric pollutant concentration information observed based on different platforms can be used to analyze the long-term changing trends of atmospheric pollutants; wherein, the corresponding observation platform can be the tropospheric column concentration results observed by spacecraft, or the point or vertical profile results observed by ground, etc.

[0047] Moreover, since it is necessary to accurately correct the long-term trend of air pollutants, the corresponding air pollution data should have long-term continuity. That is, only data obtained through long-term observation can truly reflect the accurate long-term trend of air pollutants. For example, the corresponding air pollution data should have observation results of more than 2 years, and the time resolution should be at least daily, with the corresponding missing values ​​not exceeding 20%.

[0048] Step 12, Acquisition of meteorological data;

[0049] In this embodiment of the invention, the collected meteorological data will also be used in the correction process. The relevant meteorological data may include, but is not limited to, temperature, air pressure, wind, solar radiation intensity, and humidity.

[0050] During the implementation of this invention, it was discovered that meteorological data contains elements that can affect the emission of some biomass pollutants, such as increased temperature, increased sunshine, increased biological activity, and increased emissions of substances such as isoprene and monoterpenes. These factors can also lead to an increase in the secondary generation of formaldehyde, glyoxal, and secondary organic aerosols, thereby affecting the trend judgment of anthropogenic pollutants. Therefore, during the implementation of this invention, it is also necessary to obtain relevant meteorological data as one of the basic data for correction.

[0051] Specifically, for each observation result (i.e., air pollutant data), corresponding meteorological data should be paired. That is, in order to obtain the air pollutant data, it is also necessary to obtain the meteorological data that matches it. The matching and correspondence refers to the air pollutant data and its corresponding meteorological data under the same spatiotemporal conditions. For example, for the pollutant grid results observed by satellite, each observation grid should be paired with corresponding near-surface average meteorological data (e.g., meteorological data from 1000 hPa to 900 hPa). This meteorological data can be the gridded result of model simulation. For air pollution characteristic data observed by the ground, each observation time should be paired with corresponding meteorological elements (i.e., meteorological data). These can be measured meteorological elements near the observation station or simulated results as the corresponding meteorological data.

[0052] Step 13: Encapsulate and redistribute the observation results of air pollutant data and meteorological data;

[0053] In this step, the atmospheric pollutant data and the corresponding meteorological data need to be encapsulated in a gradient, and the changes in atmospheric pollutant data within each gradient need to be obtained; that is, the acquired atmospheric pollutant data is redistributed and encapsulated into meteorological data of different gradients.

[0054] This involves encapsulating the one-to-one observed atmospheric pollutant data and the corresponding meteorological data in a gradient manner. In actual processing, all observation results and corresponding meteorological data can be encapsulated in ascending order and with a fixed gradient to obtain the changes in atmospheric pollutant data within each gradient, such as changes in atmospheric pollutant concentration.

[0055] Specifically, the corresponding gradient encapsulation (including redistribution) process may include: rearranging and encapsulating the air pollutant data and the corresponding meteorological data according to a predetermined meteorological data gradient to achieve the corresponding encapsulation and redistribution; moreover, within each meteorological data gradient, the air pollutant data is divided into multiple stages according to the time of observation, and the degree of change of air pollutant data between multiple stages is calculated to obtain the result after gradient encapsulation; wherein, the gradient of the meteorological data refers to the result obtained after dividing the meteorological data according to a predetermined rule, and the result includes multiple meteorological data segments that constitute the meteorological data;

[0056] In the above processing, the calculation process of the degree of change of air pollutant data between the multiple stages may include: calculating the average value of air pollutant data in each stage, and calculating and determining the degree of change of air pollutant data between each stage based on the average value.

[0057] For example, taking satellite observation grid data as the observation result and temperature data as meteorological data, for each satellite pixel p, all long-term observation results of atmospheric pollutant concentrations (i.e., atmospheric pollutant data observation results) and corresponding temperature results (i.e., meteorological data) can be arranged and packaged according to temperature gradients from 285K to 320K, in increments of 0.25K. For each temperature gradient t, all long-term observation results can be divided into two or more stages. For example, taking two stages as an example, the observation results from 2020 to 2025 can be divided into stage 1: 2020-2022 and stage 2: 2023-2025. Then, for that temperature gradient t of pixel p, the average change of atmospheric pollutants in the two stages is... The degree of change in air pollutant data between stages can be calculated using the following formula:

[0058] ;

[0059] in, and It is represented as the average of the air pollutant data in the first stage and the average of the air pollutant data in the second stage, that is, for pixel p, it is the average pollutant concentration in the two stages of the temperature gradient t respectively.

[0060] Step 14: The changes in atmospheric pollutant data after correction based on meteorological elements (i.e., meteorological data), that is, the changes in the concentration of atmospheric pollutants after correction.

[0061] For all changes in the air pollutant data (such as air pollutant concentration) caused by meteorological elements (i.e., the meteorological data), all gradients are summed and then averaged to obtain the changes in air pollutant data corrected based on the meteorological elements, or the change results.

[0062] Specifically, taking satellite observation data as atmospheric pollutant data and temperature data as meteorological data as examples, for a pixel p in the satellite observation data, the long-term change after correction based on temperature data is... The calculation can be performed using the following formula:

[0063] ;

[0064] in, and This represents the number of all observations in the first and second stages within pixel p and temperature gradient t. To ensure data quality and computational validity, the number of meteorological element gradients should be at least 20, and the number of observations within each gradient range should be at least 30.

[0065] For ground observation results, the corresponding pixel p can correspond to the ground observation point, and the corresponding calculation formula is the same as the calculation formula mentioned above.

[0066] Moreover, in addition to temperature data, meteorological elements such as air pressure, humidity, and solar radiation intensity can also be corrected for long-term changes in the observed data using the above processing method during this step.

[0067] Step 15, Calculation of changes in the interannual growth rate of air pollutants;

[0068] For secondary air pollutants affected by meteorological factors, their seasonal variation is significant, and different pollutants will exhibit different variation characteristics. For example, formaldehyde concentration is high in summer and low in winter. However, anthropogenic formaldehyde emissions often do not exhibit such seasonal variation characteristics. To obtain the long-term variation trend of air pollutants that are not affected by seasonal variation, i.e., the interannual growth rate of air pollutants, the processing procedure adopted in this embodiment of the invention includes:

[0069] Construct a seasonally varying interannual growth rate model, namely:

[0070] ;

[0071] in, This represents the average concentration of air pollutants over a time period t (e.g., within a month), where t is the observation time. A, B, C n D n All are fitting parameters, where C n D n To fit the parameters of the quarterly variation, A is the average pollutant concentration in the first year, and B is the interannual growth rate of air pollutants.

[0072] Furthermore, by inputting the observation results into the above... In the model, and for By performing least-squares fitting on each parameter in the model, the final interannual growth rate of air pollutants, which is unaffected by seasonal variations, can be calculated.

[0073] In this way, the trend of air pollution can be corrected based on the interannual growth rate of air pollutants, which is not affected by seasonal changes, and thus an accurate trend of air pollution can be obtained.

[0074] In summary, the technical solution provided by this invention utilizes atmospheric pollutant data and matching meteorological data as basic data to analyze and calculate the changing trends of atmospheric pollutant data, thereby effectively correcting these trends to obtain accurate long-term trends. This invention provides more and more accurate and reliable data support for atmospheric environment research, effectively solving the problem of inaccurate prediction of atmospheric pollution trends in existing technologies.

[0075] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims. The information disclosed in the background section is intended only to enhance the understanding of the overall background technology of the present invention and should not be construed as an admission or implication in any way that such information constitutes prior art known to those skilled in the art.

Claims

1. A method for correcting trends in air pollution, characterized in that, include: Acquire atmospheric pollutant data and corresponding meteorological data for a predetermined time period; The atmospheric pollutant data and the corresponding meteorological data are encapsulated in a gradient, and the changes in atmospheric pollutant data within each gradient are obtained. Gradient encapsulation includes: rearranging and encapsulating the atmospheric pollutant data and the corresponding meteorological data according to a predetermined gradient of meteorological data; within each gradient of meteorological data, dividing the atmospheric pollutant data into multiple stages according to the time of observation, and calculating the degree of change of atmospheric pollutant data between multiple stages to obtain the result after gradient encapsulation; wherein, the gradient of meteorological data refers to the result obtained after dividing the meteorological data according to a predetermined rule. For the changes in the air pollutant data caused by the meteorological data, all gradients are summed and then averaged to obtain the changes in the air pollutant data after correction based on the meteorological data, so as to correct the trend of air pollution changes. The seasonally varying interannual growth rate model is constructed as follows: ； in, This represents the average concentration of air pollutants over a time interval t, where t is the observation time. A, B, C n D n All are fitting parameters, where C n D n To fit the parameters of the quarterly variation, A is the average pollutant concentration in the first year, and B is the interannual growth rate of air pollutants. The atmospheric pollutant data obtained from the observation results are input into the seasonal variation interannual growth rate model, and the least squares fitting process is performed on each parameter in the seasonal variation interannual growth rate model to obtain the interannual growth rate of atmospheric pollutants that is not affected by the season. The trend of air pollution change is corrected based on the interannual growth rate of air pollutants that is not affected by seasons.

2. The method according to claim 1, characterized in that, The calculation process for the degree of change in air pollutant data between the multiple stages includes: Calculate the average value of air pollutant data within each stage, and determine the degree of change of air pollutant data between each stage based on the average value.

3. The method according to claim 1 or 2, characterized in that, The step of summing and averaging all gradients includes: For the observed pixels of the aforementioned air pollutant data, calculate their long-term variation after correction based on meteorological data. The formulas include: ； in, and This represents the number of all observations in the first and second stages within pixel p and the meteorological data gradient t. The average change in air pollutant data over two periods is calculated using the following formula: ； in, and It is represented as the average of the air pollutant data in the first stage and the average of the air pollutant data in the second stage.

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