An air quality assessment method

Abstract

This invention discloses an air quality assessment method, comprising: acquiring hyperspectral satellite data and meteorological data of the area to be assessed, and obtaining the trace gas plume structure from the hyperspectral satellite data and meteorological data; determining the trace gas linear density based on the trace gas plume structure; fitting the trace gas linear density and a constructed transport-diffusion model to obtain transport-diffusion model parameter information, and constructing the transport-diffusion model; and estimating the effective emissions and effective lifetime of trace gases based on the constructed transport-diffusion model. The implementation of this invention enables real-time and effective monitoring of atmospheric trace gas emissions based on hyperspectral satellite data acquisition, achieving large-scale global observation and assessment of the atmospheric environment, so that timely and relevant real-time data can be obtained to assess pollution emissions caused by hotspot areas and hot events.

Description

Technical Field

[0001] This invention relates to the field of intelligent environmental monitoring technology, and in particular to an air quality assessment method. Background Technology

[0002] Assessing urban air pollutant emissions is crucial for controlling urban pollution and improving air quality. Furthermore, some trace atmospheric gases can pose serious health threats, causing respiratory illnesses and diseases in infants and young children, thus severely endangering the health of urban residents.

[0003] To assess urban air pollutant emissions, current traditional methods rely primarily on interannual emission inventories. However, these inventories are often updated slowly, making it difficult to reflect sudden hotspot events or human activities that cause emission changes. In other words, there is currently no method for real-time monitoring and assessment of emission changes caused by sudden hotspot events or human activities, thus hindering effective real-time monitoring of air pollution.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an air quality assessment method to enable real-time and effective monitoring of emissions changes caused by sudden hotspot events or human activities, thereby solving the problems existing in the prior art.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] An air quality assessment method, comprising:

[0008] Acquire hyperspectral satellite data and meteorological data of the area to be evaluated, and obtain trace gas plume structures from the hyperspectral satellite data and meteorological data;

[0009] The trace gas linear density is determined based on the aforementioned trace gas plume structure.

[0010] Fit the trace gas linear density and the constructed transport-diffusion model to obtain the transport-diffusion model parameter information;

[0011] Air quality is assessed based on the parameters of the transport and diffusion model.

[0012] The hyperspectral satellite data includes concentration information of glyoxal, formaldehyde, sulfur dioxide, and nitrogen dioxide, and the concentration information is tropospheric column concentration.

[0013] The hyperspectral satellite data is further screened before application, and the screening process includes:

[0014] The quality assurance value should meet the reference value provided by the trace gas inversion algorithm, the regular grid size of the satellite data, i.e., the spatial resolution, should be better than the predetermined value, and the temporal resolution should meet the requirement of daily or monthly global coverage.

[0015] The meteorological data includes:

[0016] The wind field data for the area to be evaluated includes average grid wind field and wind speed data, and the wind field data includes zonal wind and meridional wind, and the average grid wind field and wind speed data correspond one-to-one with the latitude and longitude of the satellite data.

[0017] The process for obtaining the trace gas plume structure includes:

[0018] Determine the center latitude and longitude of the area to be evaluated, and rotate the area with the center latitude and longitude as the center and a set distance value as the radius to determine the rotated trace gas grid result as the trace gas plume structure;

[0019] The rotation is calculated using the following formula:

[0020] lat a =lat b cos(-θ)+lon b sin(-θ);

[0021] lon a =-lat b sin(-θ)+lon b cos(-θ);

[0022] Among them, lat a and lon a For the rotated latitude and longitude, lat b and lon b Here are the latitude and longitude before rotation, and θ is the angle between the wind direction at the center latitude and longitude grid and the due east direction.

[0023] The process of determining the linear density of trace gases based on the aforementioned trace gas plume structure includes:

[0024] Based on the rotated trace gas grid results, a portion of the rotated concentration distribution data is extracted, and the trace gas linear density is obtained along the wind direction at a predetermined distance.

[0025] The calculation formulas used to construct the transport-diffusion model M(x) include:

[0026]

[0027] Where E is the emission factor, B is the background concentration, and e is the cutoff exponent function; and the corresponding cutoff exponent function e is expressed as:

[0028]

[0029] Where X is the center latitude and longitude, x0 is the distance when the trace gas linear density drops to 1 / e, and x is the predetermined distance for obtaining the trace gas linear density.

[0030] In the transport-diffusion model M(x), e is also convolved with a Gaussian function G(x), which is expressed as:

[0031]

[0032] Where σ is the standard deviation of the Gaussian function.

[0033] The process of obtaining the transmission diffusion model parameter information includes:

[0034] The trace gas linear density L(x) and the transport-diffusion model M(x) are fitted using least squares to obtain the transport-diffusion model parameter information of the transport-diffusion model M(x); and the transport-diffusion model parameter information includes:

[0035] Emission factor E, background concentration B, distance x0 when the linear density of trace gases decreases to 1 / e, and standard deviation σ of the Gaussian function.

[0036] The process of assessing air quality based on the transport and diffusion model parameters includes estimating the effective emissions and effective lifespan of trace gases based on the transport and diffusion model parameters.

[0037] The process for estimating the effective emissions and effective lifespan of trace gases includes:

[0038] The formulas for calculating the effective emission limit P and effective lifetime τ of trace gases are as follows:

[0039] P = E / τ, τ = x0 / w;

[0040] Where E is the emission factor used as a parameter in the transport-diffusion model, x0 is the distance at which the linear density of trace gases decreases to 1 / e, and w is the wind speed at the center latitude and longitude.

[0041] Compared with existing technologies, the air quality assessment method provided by this invention can effectively monitor atmospheric trace gas emissions in real time based on data acquisition from hyperspectral satellites, and realize large-scale global observation and assessment of the atmospheric environment, so as to obtain timely data on pollution emissions caused by hot spots and hot events. Attached Figure Description

[0042] 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.

[0043] Figure 1 A schematic diagram illustrating the implementation process of the method in an embodiment of the present invention. Detailed Implementation

[0044] 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.

[0045] First, the following explanations are provided for the terms that may be used in this article:

[0046] 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".

[0047] The terms “including,” “comprising,” “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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] The following is a detailed description of an air quality assessment method 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 shall be performed according to conventional conditions in the art or conditions recommended by the manufacturer. Reagents or instruments used in the embodiments of this invention, unless otherwise specified by the manufacturer, are all commercially available conventional products.

[0054] The air quality assessment method provided by this invention can be implemented based on multivariate satellite data from hyperspectral satellite observations. For example, it can be based on satellite data containing information on atmospheric pollutants such as glyoxal, formaldehyde, sulfur dioxide, and nitrogen dioxide. Hyperspectral satellites typically enable daily, large-scale, global observations of the atmospheric environment to obtain multivariate satellite data. This multivariate satellite data is then used to estimate pollutant emissions in hotspot cities and regions. In other words, the corresponding multivariate satellite data can provide real-time data support for assessing pollution emissions in hotspot areas and events.

[0055] Specifically, the objective of this invention is to provide a remote sensing algorithm capable of air quality assessment. This algorithm can use multivariate satellite data retrieved by hyperspectral satellite instruments to calculate pollutant emissions from hotspot events or regions, thereby enabling real-time monitoring and assessment of pollution emissions in the atmospheric environment.

[0056] This invention provides an air quality assessment method, specifically a satellite remote sensing method for calculating pollutant emissions, and the implementation process of this method is described in reference to... Figure 1 As shown, the following steps may be included:

[0057] Step 11, Acquisition of hyperspectral satellite data;

[0058] The hyperspectral satellite data may include concentration information of glyoxal, formaldehyde, sulfur dioxide, and nitrogen dioxide, etc. In this embodiment of the invention, the atmospheric trace gas data (i.e., the concentration information of glyoxal, formaldehyde, sulfur dioxide, and nitrogen dioxide, etc. in the atmosphere) retrieved from the hyperspectral satellite data are mainly used to assess the pollutant emissions of hotspot events and hotspot areas.

[0059] Typically, glyoxal and formaldehyde are used to assess volatile organic compound emissions, sulfur dioxide is used to assess sulfide emissions, and nitrogen dioxide is used to assess nitrogen oxide emissions. Therefore, by obtaining the concentration information of pollutants such as glyoxal, formaldehyde, sulfur dioxide, and nitrogen dioxide, the emission of air pollutants in the atmospheric environment can be assessed, thereby enabling an effective assessment of air quality.

[0060] It should be noted that the atmospheric trace gas data used in the embodiments of the present invention are all tropospheric column concentrations, in order to reduce the influence of stratospheric background values ​​on the evaluation results.

[0061] Step 12: Screen the hyperspectral satellite data;

[0062] In this step, the Level 3 data (i.e., atmospheric trace gas data) retrieved from hyperspectral satellites needs to be screened before application. The screening criteria include:

[0063] The quality assurance value should meet the official reference value provided by the trace gas inversion algorithm, the regular grid size of the satellite data, i.e., the spatial resolution, should be better than 5km, and the temporal resolution should meet the requirement of daily or monthly global coverage;

[0064] The Quality Assurance Value (QA) is typically a number between 0 and 1. For example, a QA greater than 0.5 indicates reliable data, while a QA less than 0.5 indicates unreliable data. However, some data may have QA values ​​of 0, 1, or 2. A QA of 0 indicates "good" (reliable data), a QA of 1 indicates "bad" (unreliable data), and a QA of 2 indicates high-reflectivity pixels with a large error. The QA values ​​vary between different data products; therefore, this invention requires that the corresponding Quality Assurance Values ​​meet the official reference values ​​provided by the trace gas inversion algorithm.

[0065] The corresponding spatial resolution should be better than 5km, which refers to the spatial resolution of the smallest grid of satellite pixels. Typically, the smallest pixel can be 5km, 2km, or 1km. Therefore, when studying urban emissions, the corresponding spatial resolution should be better than 5km, i.e., <5km.

[0066] Step 13, assisting in the acquisition of meteorological data;

[0067] In this step, relevant meteorological data needs to be acquired for application; the main focus is on acquiring the plume structure of atmospheric trace gases in hotspot events or regions, which mainly includes wind field data.

[0068] For example, average grid wind field and wind speed data of near-surface area (e.g., air pressure range of 1000hpa to 900hpa) centered on hot events or regions with a fixed range (e.g., 200km) as the radius can be obtained; the wind field and wind speed data should correspond one-to-one with the latitude and longitude of the satellite grid, and the wind field data should include zonal winds and meridional winds.

[0069] Step 14, obtaining the trace gas plume structure;

[0070] In this step, it is necessary to acquire the trace gas plume structure from the hyperspectral satellite data and meteorological data. The specific process for acquiring the trace gas plume structure may include:

[0071] First, determine the central latitude and longitude of the hot topics or hot regions for research;

[0072] Specifically, when the research target is urban emissions, the center latitude and longitude are the latitude and longitude of the city center; when the research target is a biomass combustion event, the center latitude and longitude are the latitude and longitude of the maximum flare value; when the research target is a transient industrial activity, the center latitude and longitude are the latitude and longitude of the event.

[0073] Then, the latitude and longitude of the trace gas concentration distribution grid of the region grid with the determined center latitude and longitude as the center and a fixed range (e.g., 200 km) as the radius are rotated to obtain the rotated latitude and longitude grid, that is, the rotated trace gas grid result is used as the corresponding trace gas plume structure.

[0074] The rotation process is calculated using the following formula:

[0075] lat a =lat b cos(-θ)+lon b sin(-θ);

[0076] lon a =-lat b sin(-θ)+lon b cos(-θ);

[0077] Among them, lat a and lon a For the rotated latitude and longitude, lat b and lon b Here are the latitude and longitude before rotation, and θ is the angle between the wind direction at the center latitude and longitude grid and the due east direction.

[0078] Step 15: Obtain the trace gas linear density and construct the transport-diffusion model M(x);

[0079] This step involves extracting a portion of the rotated concentration distribution data from the rotated trace gas mesh and obtaining the trace gas linear density along the wind direction at a predetermined distance. The specific implementation process may include:

[0080] First, using the rotated trace gas grid result (i.e. trace gas plume structure) obtained in step 14 above, extract a portion of the rotated concentration distribution data; for example, data within a range of 50km upwind, 150km downwind, and 100km vertically from the center latitude and longitude.

[0081] Then, along the wind direction, the trace gas linear density L(x) is obtained, where x is the distance. For example, a linear density point is taken every 5km. This linear density point is the sum of the concentrations of all grids within a 100km range perpendicular to the wind direction. Based on the corresponding linear density point, the trace gas linear density can be determined.

[0082] Finally, the transmission-diffusion model parameter information is obtained by performing least-squares fitting on the linear density L(x) and the transmission-diffusion model M(x);

[0083] The transport-diffusion model M(x) is constructed using the following formula:

[0084]

[0085] Where E is the emission factor, B is the background concentration, and e is the cutoff exponent function; and the corresponding cutoff exponent function e is expressed as:

[0086]

[0087] Where X is the center latitude and longitude, and x0 is the distance when the trace gas linear density decreases to 1 / e.

[0088] Furthermore, in the formula for constructing the transport-diffusion model M(x), e also needs to be convolved with a Gaussian function G(x), and the corresponding G(x) can be expressed as:

[0089]

[0090] Where σ is the standard deviation of the Gaussian function.

[0091] Based on the above formula, the parameter information E, B, x0, σ, etc. of the transport-diffusion model M(x) can be obtained by performing least-squares fitting between the linear density L(x) and the transport-diffusion model M(x).

[0092] In the obtained parameter information:

[0093] The corresponding parameter information E is the emission factor in the corresponding transport and diffusion model parameter information;

[0094] The corresponding parameter information B is the background concentration, and the value of B is helpful for understanding the background value of pollutants in the study area;

[0095] The corresponding parameter information x0 is the distance required for the pollutant linear density to decrease to 1 / e, which can characterize the pollution diffusion capacity of the region;

[0096] Therefore, after obtaining the corresponding E, B, x0, and σ parameter information, the pollution information of the study area can be evaluated accordingly. That is, the air quality can be evaluated based on the parameter information of the transport and diffusion model. For example, the subsequent step 16 can be performed to estimate the effective emission and effective lifespan of trace gases.

[0097] Step 16: Estimate the effective emission and effective lifetime of trace gases based on the transport-diffusion model parameter information;

[0098] Specifically, the effective emission amount P and effective lifetime τ of trace gases in hotspot areas or events can be estimated using the model parameters (i.e., the transmission and diffusion model parameter information) obtained in step 15.

[0099] Furthermore, the estimation can be performed using the following formula:

[0100] τ = x0 / w, P = E / τ;

[0101] Where w is the wind speed at the center latitude and longitude, E is the emission factor in the corresponding transport and diffusion model parameters, and x0 is the distance when the trace gas linear density drops to 1 / e in the corresponding transport and diffusion model parameters.

[0102] After calculating the effective emission amount P and effective lifetime τ of trace gases in the corresponding hotspot area or event, real-time monitoring and assessment of air quality in the atmospheric environment corresponding to the hotspot area or event can be achieved.

[0103] In summary, the technical solution provided by this invention enables real-time and effective monitoring of atmospheric trace gas emissions based on data acquisition from hyperspectral satellites. This allows for large-scale global observation and assessment of the atmospheric environment, providing timely data on pollution emissions from assessment hotspots and events, and offering reliable technical support for real-time monitoring of the atmospheric environment.

[0104] 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. An air quality assessment method, characterized in that, include: Acquire hyperspectral satellite data and meteorological data of the area to be evaluated. Based on the hyperspectral satellite data and meteorological data, after determining the center latitude and longitude of the area to be evaluated, rotate the area with the center latitude and longitude as the center and a set distance value as the radius to obtain the rotated trace gas grid result as the trace gas plume structure. Based on the rotated trace gas grid results, a portion of the rotated concentration distribution data is extracted, and the trace gas linear density is obtained along the wind direction at a predetermined distance. Fit the trace gas linear density and the constructed transport-diffusion model to obtain the transport-diffusion model parameter information, including emission factor, background concentration, distance when the trace gas linear density drops to 1 / e, and standard deviation of the Gaussian function. The transport and diffusion model The construction calculation formula includes: ； in, As emission factors, Background concentration, To truncate the exponential function; and, the corresponding truncation exponential function Represented as: ； in, With the center latitude and longitude, The distance at which the linear density of the trace gas decreases to 1 / e, and x is the predetermined distance at which the linear density of the trace gas is obtained; In the transport-diffusion model middle, Also with Gaussian function Perform convolution, and the Gaussian function Represented as: ； in, The standard deviation of the Gaussian function; The effective emission and effective lifetime of trace gases are estimated based on the parameters of the transport and diffusion model.

2. The method according to claim 1, characterized in that, The hyperspectral satellite data includes concentration information of glyoxal, formaldehyde, sulfur dioxide, and nitrogen dioxide, and the concentration information is tropospheric column concentration.

3. The method according to claim 2, characterized in that, The hyperspectral satellite data is further screened before application, and the screening process includes: The quality assurance value should meet the reference value provided by the trace gas inversion algorithm, the regular grid size of the satellite data, i.e., the spatial resolution, should be better than the predetermined value, and the temporal resolution should meet the requirement of daily or monthly global coverage.

4. The method according to claim 1, characterized in that, The meteorological data includes: The wind field data for the area to be evaluated includes average grid wind field and wind speed data, and the wind field data includes zonal wind and meridional wind, and the average grid wind field and wind speed data correspond one-to-one with the latitude and longitude of the satellite data.

5. The method according to any one of claims 1 to 4, characterized in that, In the rotation to obtain the rotated trace gas mesh result, the rotation is calculated using the following formula: ； ； in, and These are the rotated latitude and longitude. and The latitude and longitude before rotation. It is the angle between the wind direction at the central latitude and longitude grid and the due east direction.

6. The method according to claim 5, characterized in that, The process of obtaining the transmission diffusion model parameter information includes: The linear density of the trace gas and the transport-diffusion model Perform least squares fitting to obtain the transport-diffusion model. The transmission and diffusion model parameter information.

7. The method according to claim 5, characterized in that, The process for estimating the effective emissions and effective lifespan of trace gases includes: Effective emissions of trace gases and effective lifespan The calculation formula is: ， ； in, The emission factor serves as a parameter for the transport and diffusion model. This is the distance at which the linear density of the trace gas, which serves as a parameter for the transport-diffusion model, decreases to 1 / e. The wind speed at the center latitude and longitude.

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