KR20240018749A - aerosol PM2.5 retrieval method and system from aerosol extinction coefficient Using wavelength dependent aerosol extinction coefficients power exponent - Google Patents

Abstract

The purpose of the present invention is to provide a method and system for extracting PM2.5 particles from an extinction coefficient by using an exponent value according to wavelength, which obtains a scattering coefficient by ultrafine dust at two or more wavelengths, and estimates the value of PM2.5 by using a graph or table of the relationship between the corrected scattering coefficient and PM2.5. A system for extracting PM2.5 particles from an extinction coefficient comprises the following: an extinction coefficient obtaining unit(110) obtaining an extinction coefficient for each wavelength by using two or more wavelengths of light; an AE calculation unit(120) obtaining an oblong index by using the extinction coefficient for each wavelength; and PM2.5; a function generating unit(130) obtaining a correlation function of PM2.5 particles and the extinction coefficient obtained through a set range of the obtained Angstrom index; and a particle output unit(140) producing the total mass of PM2.5 particle in a unit volume. According to the present invention, a wide area can be quickly and accurately measured by remotely measuring the extinction coefficient at two or more wavelengths.

Description

Method and system for extracting PM2.5 particles from the dissipation coefficient using a power exponent value depending on the wavelength {aerosol PM2.5 retrieval method and system from aerosol extinction coefficient Using wavelength dependent aerosol extinction coefficients power exponent}

The present invention relates to a technology for obtaining the total amount of fine dust using the optical dissipation coefficient of fine dust. In detail, the scattering coefficient due to ultrafine dust is obtained at more than two wavelengths in a simple method, and the corrected scattering coefficient and PM2.5 It relates to a method and system for extracting PM2.5 particles from the dissipation coefficient using a power exponent value according to the wavelength to estimate the value of PM2.5 using a relationship graph or table.

Currently, the Ministry of Environment measures the mass of fine dust by classifying it into those smaller than 10 micrometers in diameter and those smaller than 2.5 micrometers in diameter. At this time, in obtaining the total amount of fine dust below a certain size, it takes a certain amount of time to obtain sufficient measurement values, and since only a small portion of fine dust is inhaled and measured locally, it is difficult to measure quickly, and it is difficult to represent a wide area. .

Therefore, the average dissipation coefficient by particles existing in atmospheric space can be obtained instantaneously using photography or other lasers, and is preferred as a value representing the entire space because it provides the average value in the entire space rather than a single point.

However, because the Ministry of Environment's standard measured physical quantity is mass that exists in a unit volume, the method of obtaining mass from the dissipation coefficient of other units is still bound to include many errors. In other words, the dissipation coefficient is not the mass of fine dust existing in a unit volume, but an area, so the problem arises from this difference in units. The physical quantity refers to the total mass of particles per unit volume, and the device separates particles in the air by part and size. Since it is divided into a part that measures the mass of particles and a part that measures the mass of particles, it is difficult to understand the entire city or a large area due to the cumbersome measurement method, the inability to represent a large area, and the high price. there is.

In addition, ultrafine particles that exist in the range of a given size or less (2.5 micrometers in the case of PM2.5) have a lognormal distribution with a certain width and a maximum number of a specific size depending on weather conditions or causes of occurrence. It is distributed. In other words, changes in the size and distribution of ultrafine dust particles change due to various causes such as the cause of particle generation and weather conditions.

At this time, the efficiency of the degree of dissipation by particles varies depending on the size of the particle and the wavelength used. For this reason, even if particles of the same mass exist, the dissipation coefficient value changes when the particle size changes. Therefore, without technology to detect changes in particle size, it is theoretically impossible to obtain the total amount of particles solely through changes in dissipation efficiency by particles, and in reality, there will be many errors.

Korean Patent No. 10-2139815 (2020.07.24)

The present invention was created to solve the above problems. The purpose of the present invention is to obtain the scattering coefficient due to ultrafine dust at two or more wavelengths in a simple method in order to solve the problem arising from the unit difference and calculate the corrected scattering coefficient. It provides a method and system for extracting PM2.5 particles from the dissipation coefficient using a power exponent value according to the wavelength that estimates the value of PM2.5 using a relationship graph or table between PM2.5 and PM2.5.

For the above purpose, the system for extracting PM2.5 particles from the dissipation coefficient of the present invention uses light of two or more wavelengths to obtain the dissipation coefficient for each wavelength in measuring ultrafine dust PM2.5 particles in the atmosphere. Dissipation coefficient acquisition unit; AE calculation unit that calculates the Angstrong index using the dissipation coefficient for each wavelength; A function generator that calculates the correlation function of the dissipation coefficient obtained through a set range of the obtained Angstrong index and the PM2.5 particles; a particle calculation unit that calculates the total mass of PM2.5 particles in unit volume using the correlation function; It is characterized by consisting of.

At this time, the dissipation coefficient acquisition unit obtains the scattering coefficient from means for measuring scattering phenomena, including LiDAR, the RGB wavelength of the camera, the optical depth of fine dust obtained by a solar estimation device, and the nepalometer that obtains the dissipation or scattering coefficient at one point. You can get it.

In addition, the dissipation coefficient acquisition unit can utilize wavelengths in the NIR (Near Infrared) region from the visible light with two different wavelengths according to different wide wavelength regions like a camera.

Additionally, the function generator may use the set range of the Angstrong index by dividing all areas between minus values and 4 into the selected range.

In addition, the function generator generates the total amount of ultrafine dust (V _f ), the total amount of coarse particles (V _c ), the center size of ultrafine dust (r _f ), the center size of coarse particles (r _c ), and the ultrafine dust. In the correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the six parameters of the center width (σ _f ) and the center width (σ _c ) of coarse particles, the Angstrong index is calculated by calculating only those within the set range. The relationship function can be obtained.

In addition, the correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the above six parameters is calculated by reflecting the range of the six parameters according to atmospheric measurements over a set period, but fixing the refractive index at a constant value. can be obtained.

The method of extracting PM2.5 particles from the dissipation coefficient of the present invention includes a dissipation coefficient acquisition step of obtaining the dissipation coefficient for each wavelength using light of two or more wavelengths in measuring ultrafine dust PM2.5 particles in the air; AE calculation step of calculating the Angstrong index using the dissipation coefficient for each wavelength; A function generation step of calculating the correlation function of the dissipation coefficient obtained through a set range of the obtained Angstrong index and the PM2.5 particles; A particle calculation step of calculating the total mass of PM2.5 particles in unit volume using the correlation function; It is characterized by consisting of.

At this time, the dissipation coefficient acquisition step is to obtain the scattering coefficient from means of measuring the scattering phenomenon, including LiDAR, the RGB wavelength of the camera, the optical depth of fine dust obtained by a solar estimation device, and the nepalometer that obtains the dissipation or scattering coefficient at one point. can be obtained.

In addition, the dissipation coefficient acquisition step can utilize wavelengths in the NIR (Near Infrared) region from visible light with two different wavelengths in a wide wavelength range like a camera.

Additionally, in the function creation step, the set range of the Angstrong index can be used by dividing all areas between minus values and 4 into the selected range.

In addition, the function generation step includes the total amount of ultrafine dust (V _f ), the total amount of coarse particles (V _c ), the center size of ultrafine dust (r _f ), the center size of coarse particles (r _{c ), and the total amount of ultrafine dust (V c} ). From the correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in six parameters of the center width of dust (σ _f ) and the center width of coarse particles (σ _c ), only the Angstrong index is calculated within the set range. The correlation function can be obtained.

In addition, the correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the above six parameters is calculated by reflecting the range of the six parameters according to atmospheric measurements over a set period, but fixing the refractive index at a constant value. You can get it.

The present invention remotely measures the dissipation coefficient at two or more wavelengths, making it possible to measure a large area quickly and accurately. In addition, by predicting how the particle size has moved, a more accurate particle mass (PM2.5) can be obtained from the dissipation coefficient. At the same time, by monitoring the change in particle size, the particle mass can be obtained with a small error by only measuring the particle dissipation coefficient. You can.

Methods for measuring the dissipation coefficient in a large area can be applied to either passive measurement using a camera or active measurement using a laser (CW-laser or pulse laser). However, the existing technology uses a 1:1 relationship between the dissipation coefficient and PM2.5 considering only humidity, so errors are severe and it is difficult to apply to sensitive matters such as environmental health. However, the present invention Even though the total integral amount of particles (PM2.5) is the same, the dissipation coefficient due to particles is very sensitive to the size distribution of particles, so correction is made considering changes in particle size.

Figure 1 is a graph of the volume and number density distribution of airborne particles by size;
Figure 2 is a graph according to the definition of six particle volume distribution determination parameters;
Figure 3 is a graph showing the dissipation efficiency (Q) by particle size (2πr/λ) normalized by the wavelength of scattered light;
Figure 4 is a graph showing the definition of volume dissipation efficiency (Q _v ), volume distribution characteristics by particle size, and dissipation coefficient;
Figure 5 is a graph according to the method of obtaining PM2.5 and PM10 from the volume distribution density of particles;
Figure 6 is a table showing the range of six variables of particles existing in the atmosphere;
Figure 7 is a distribution chart of the dissipation coefficient and PM2.5 correlation obtained from changes in the total amount of arbitrary ultrafine dust and coarse particles (V _f , V _c );
Figure 8 is a distribution chart of the dissipation coefficient and PM2.5 correlation obtained from changes in the central width (σ _f , σ _c ) of arbitrary ultrafine dust and coarse particles;
Figure 9 is a distribution chart of the dissipation coefficient and PM2.5 correlation obtained from changes in the center size (r _f , r _c ) of arbitrary ultrafine dust and coarse particles;
Figure 10 is a graph showing the dissipation coefficient and PM2.5 correlation (0.12<r _f <0.18) obtained from changes in six fine dust parameters (V _f , V _c , r _f , r _c , σ _f , σ _c ). ,
Figure 11 is a graph showing the dissipation coefficient and PM2.5 correlation (0.12<r _f <0.52) obtained from changes in six fine dust parameters (V _f, V _c , r _f , r _c , σ _f , σ _c ). ,
Figure 12 is a graph showing the dissipation coefficient and PM2.5 correlation (0.12<r _f <0.18) obtained from changes in six fine dust parameters (V _f, V _c , r _f , r _c , σ _f , σ _c ). ,
Figure 13 shows the correlation (0.12<r _f <0.52) with the dissipation coefficient and PM2.5 obtained from changes in six fine dust parameters (V _f, V _c , r _f , r _c , σ _f , σ _c ). Graph, (if only AE within a certain range is calculated)
14 is a block diagram of a system for extracting PM2.5 particles according to the present invention;
Figure 15 is a flowchart showing a method for extracting PM2.5 particles according to the present invention.

Hereinafter, with reference to the attached drawings, the method and system for extracting PM2.5 particles from the dissipation coefficient using the exponent value according to the wavelength of the present invention will be described in detail.

Basically, the three variables of ultrafine dust: the total amount of ultrafine dust, the center size, and the size distribution width affect the dissipation coefficient in a non-linear manner, so ultimately, the relationship between the dissipation coefficient and PM2.5 is a 1:1 relationship. It won't exist. In other words, PM2.5 cannot be obtained from the dissipation coefficient by simply measuring the dissipation coefficient.

In order to overcome this problem, the present invention uses a method of extracting PM2.5 based on AE (Angstrom Exponent, hereinafter referred to as 'AE'), which represents the change in particle size, that is, from a dissipation coefficient with the same AE value. Apply.

That is, while the existing method of obtaining PM2.5 independently finds the correspondence between the dissipation coefficient obtained at each wavelength and PM2.5, in the present invention, each dissipation coefficient obtained at multiple wavelengths is used to independently obtain PM2.5. Rather, AE obtained from dissipation coefficients obtained at various wavelengths is obtained, and this value is used as a new independent variable to increase the correlation of the 1:1 relationship to obtain PM2.5 more accurately.

Figure 14 is a block diagram of a system for extracting PM2.5 particles according to the present invention, and Figure 15 is a flow chart showing a method for extracting PM2.5 particles according to the present invention. The present invention is a block diagram of a system for extracting PM2.5 particles according to the present invention. The configuration for measuring particles includes a dissipation coefficient acquisition unit 110, an AE calculation unit 120, a function generation unit 130, and a particle calculation unit 140, through which PM2. 5 Measure the mass of the particle.

The first step is a dissipation coefficient acquisition step (S 110) in which dissipation coefficients for each wavelength are obtained using light of two or more wavelengths, and is performed through the dissipation coefficient acquisition unit 110.

At this time, in the dissipation coefficient acquisition step (S 110), the dissipation coefficient acquisition unit 110 is a LiDAR, the RGB wavelength of the camera, the optical depth of fine dust obtained by a solar estimation device, and the dissipation or scattering coefficient obtained at one point in Nepal. Scattering coefficients can be obtained from means of measuring scattering phenomena, including a rometer, and, like a camera, wavelengths in the NIR (Near Infrared) region from visible light can be utilized with two different wavelengths according to a wide range of wavelengths.

The second step is an AE calculation step (S 120) in which the Angstrong index is calculated using the dissipation coefficient for each wavelength, and is performed through the AE calculation unit 120.

The third step is a function generation step (S 130) that obtains the correlation function between the dissipation coefficient obtained through a set range of the obtained Angstrong index and the PM2.5 particles, and is performed through the function generator 130.

At this time, in the function generation step (S 130), the function generator 130 uses the set range of the Angstrong index by dividing all areas between the minus value and 4 into the selected range, and calculates the total amount of ultrafine dust (V _f ) and the total amount of coarse particles (V _c ), the center size of ultrafine dust (r _f ), the center size of coarse particles (r _c ), the center width of ultrafine dust (σ _f ), and the center width of coarse particles (σ From the correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the six parameters in _c ), the correlation function can be obtained by calculating only those Angstrong exponents within the set range. In addition, the correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the above six parameters is calculated by reflecting the range of the six parameters according to atmospheric measurements over a set period, but fixing the refractive index at a constant value. can be obtained.

The fourth step is a particle calculation step (S 140) in which the total mass of PM2.5 particles in unit volume is calculated using the correlation function, which is performed through the particle calculation unit 140.

The dissipation coefficient is obtained through AOD (Aerosol Optical Depth) obtained by LiDAR, a camera, and a solar estimation device, and a nepalometer that obtains the dissipation or scattering coefficient at one point, or a device that uses various scattering phenomena. You can.

To do this, obtain the dissipation coefficient at two or more wavelengths, calculate the AE from this, and then obtain PM2.5/PM10 from the relationship between PM2.5 and dissipation coefficient obtained with the same AE value using various existing methods. The relationship between dissipation coefficient and PM2.5 is needed.

In the present invention, this problem is solved by obtaining the relationship between the dissipation coefficient and PM2.5 according to the AE value from about 106 or more dissipation coefficients and PM2.5/PM10 obtained over a period of one year in various atmospheric conditions.

Figure 1 is a graph of the volume and number density distribution of particles suspended in the air by size, and Figure 2 is a graph according to the definitions of six parameters for determining the volume distribution of particles.

The distribution of the number (volume/area) of fine dust by size is composed of ultrafine dust and coarse particles, as shown in Figure 1, and is expressed as the sum of two curves with normal distribution on the log value scale of the size. do. Since the area or volume of suspended particles is a direct variable that causes actual natural phenomena (visibility distance, chemical reaction, degree of hazard), volume or area distribution is mainly used.

As can be seen in Figures 1 and 2, this is a phenomenon that occurs because the two types of particles have different sources of particle generation. Ultrafine dust is mainly created by the particles becoming larger through a physical/chemical transformation process in the gas, and coarse particles are large particles. It is a primary product created by a physical phenomenon such as particle splitting, and has such coarse particle distribution from the beginning of its creation.

In other words, among coarse particles, particles that are too large are difficult to float, so the floating number density (bulk density) is low, and particles smaller than 1 micron mean that they are not produced in large quantities as primary products, and the volume/area of the particles is The smaller it is, the smaller the area (volume) is, so it has a smaller value again.

In addition, in the 1 micron size area, ultrafine dust also shows a small volume density ratio. This is because the particle growth mechanism is related to the probability of collision and the speed of the two particles at the time of collision, making it difficult for ultrafine dust to grow up to the 1 micron size area. It shows that it does. In a small size region, the smaller the number density of ultrafine particles, the higher the number density (junge power law distribution), but as they get smaller, the area and volume become smaller in proportion to the square or third power of the size, so the volume and area distribution are very small. converges to 0. Therefore, the volume (area) distribution according to the size of coarse particles and ultrafine dust has the distribution shown in the figure.

Figure 3 is a graph showing the dissipation efficiency (Q) by particle size (2πr/λ) normalized by the wavelength of scattered light.

The dissipation coefficient by a particle is determined by the Mie scattering theory, and its efficiency (Q) generally varies depending on the size of the particle and the wavelength of light, but is basically proportional to the area of the particle for a particle of a given size, and the efficiency (Q) shows a tendency to converge to '2' when the ratio of particle size and wavelength becomes sufficiently large, as shown in Figure 3.

The horizontal axis (x-axis) of FIG. 3 represents the size of the particle's perimeter (2πr) divided by the wavelength (λ) (x=2πr/λ). In the figure, convergence to 2 means that if one given particle exists and the wavelength used is sufficiently larger than the size of the particle, about twice the area is scattered. This means that the amount of scattering can be obtained by multiplying the scattering efficiency and the area. Therefore, if the volume distribution of particles is as shown in Figure 1, the total amount scattered can be obtained by multiplying the volume scattering efficiency (Q _V = 3Q/4r) and the volume of a given particle instead of the scattering efficiency and area. Therefore, the dissipation coefficient is obtained as [Equation 1].

[Equation 1]

Figure 4 is a graph showing the definition of volume dissipation efficiency (Q _v ), volume distribution characteristics and dissipation coefficient by particle size, and Figure 5 is a graph according to a method of obtaining PM2.5 and PM10 from the volume distribution density of particles.

[Equation 1] is illustrated graphically as shown in Figures 4 and 5. Figures 4 and 5 indicate that there is a volume distribution of particles, which is obtained by multiplying the volume distribution and the volumetric scattering efficiency. As can be intuitively seen in Figures 4 and 5, changes in the center size of coarse particles do not have a significant effect on the dissipation coefficient, but the particle size of ultrafine dust has a large impact on the dissipation coefficient even with a small change in size.

PM10 and PM2.5 represent the combined mass of all suspended particles with a diameter of 10 micrometers and 2.5 micrometers or less, and the definitions are again shown in Figure 5. In general, PM2.5 does not affect the PM2.5 value even if the size of ultrafine particles (fine particles) changes significantly.

This means that PM10 and the threshold values (d=10, d=2.5um) are not sensitive to changes in the six parameters of the two-mode particle distribution (Figures 1 and 2). In other words, it shows a very different aspect from the dissipation coefficient. In other words, the dissipation coefficient has the characteristic of being very sensitive to changes in the size of ultrafine dust. Therefore, as mentioned, if a change in the size of ultrafine dust is not detected, it can be seen that the same PM2.5 maintains the same PM2.5 value in all the various volume distributions of ultrafine dust, but shows different dissipation coefficients.

PM2.5 refers to the total mass of particles of a certain size or less existing in a unit volume, and the dissipation coefficient refers to the total area of particles existing in a unit volume. Therefore, the units are g/㎥ and ㎡/㎥, respectively. In other words, even if particles of the same mass exist in the atmosphere, if they exist in a small state, the area of the particle becomes larger. In order to correspond 1:1 between area and mass (volume), information about the size of the particle must be considered at the same time.

Figure 6 is a table showing the range of six variables of particles existing in the atmosphere.

The volume distribution according to the size of the particles is limited to very complex shapes due to the suspension characteristics of the atmosphere, the development process and growth mechanism of the particles, and the volume characteristics of each particle depending on the size, and shows that it is normally distributed on the logarithmic scale of the particle size. This has already been explained through 1 and 2. The volume distribution according to particle size can be completely simulated by three variables: a value corresponding to the total amount of particles, an amount corresponding to the width of the particle volume distribution, and the center size of the particles. will be. Since there are two modes (ultrafine dust and coarse particles), there are a total of six variables: total amount of ultrafine dust (V _f ), total amount of coarse particles (V _c ), and center size of ultrafine dust (r _f ). , it can be said that there exist the central size of coarse particles (r _c ), the central width of ultrafine dust (σ _f ), and the central width of coarse particles (σ _c ).

In extracting the mass of particles (PM2.5) from the dissipation coefficient, it is very important to theoretically investigate the correlation between the dissipation coefficient and the mass of the particles according to the six independent variables and find a solution by revealing the correlation. In other words, it is difficult to measure the mass of particles with different units using only the dissipation coefficient when the six particle variables are unknown, but the value can be extracted under limited conditions.

1) Among these six variables, the total amount of particles is undoubtedly the first of the two measurements (dissipation coefficient and It can be predicted that there is a 1:1 correspondence between PM2.5).

Figure 7 is a distribution chart of the dissipation coefficient and PM2.5 correlation obtained from changes in the total amount (V _f , V _c ) of arbitrary ultrafine dust and coarse particles. Therefore, the change in the total volume of particles is linear at the same time as the dissipation coefficient and PM2.5. It can be predicted that the two values will have a good correlation even if the total amount of particles changes.

2) On the other hand, among the two modes, it can be easily expected that the distribution width of ultrafine dust and coarse particles (if the other four variables are constant) will have no effect on PM2.5. This is because, according to the definition of PM2.5, it integrates the total amount of particles with a diameter of 2.5um or less, so even if the width changes, the integrated value does not change significantly. However, the distribution width of the two modes can affect the dissipation coefficient, as can be seen in Figures 4 and 5. This means that changes in the distribution width of particles in Figures 4 and 5 can affect the dissipation coefficient, which is calculated as the product of dissipation efficiency and distribution.

Figure 8 is a distribution chart of the dissipation coefficient and PM2.5 correlation obtained from changes in the central width (σ _f , σ _c ) of arbitrary ultrafine dust and coarse particles, and the quantitative results can be seen later.

3) The size of the centers of the two modes affects not only PM2.5 but also the dissipation coefficient. Therefore, information about this should be taken into consideration. However, if the impact on PM2.5 and the dissipation coefficient are the same when the center size changes, there is no need to consider this because the correlation between the two variables (dissipation coefficient and PM2.5) does not change even if the center size changes.

Figure 9 is a distribution chart of the dissipation coefficient and PM2.5 correlation obtained from changes in the center size (r _f , r _c ) of arbitrary ultrafine dust and coarse particles. In Figure 9, the center size variable is related to the dissipation coefficient and PM2.5, respectively. We are going to find out how it affects us.

Previously, we looked at how the six variables of particles affect the particle dissipation coefficient and PM2.5 value. At this time, the range of each of the six variables was determined limited to categories that change in daily life. For example, the central size of ultrafine dust is in the range of 0.12-0.18um, regardless of the particle's origin, and coarse particles are in the range of 1.9 to 3.2um.

For example, Figure 7 shows the relationship between the dissipation coefficient and PM2.5 when the total amount of ultrafine dust and coarse particles changes randomly. The wavelength used in the calculation was 550 nm, which is used to measure the visibility distance. This shows that if only the total amount of ultrafine dust and coarse particles changes and the other four parameters are constant, PM2.5 can be specified very accurately just by measuring the dissipation coefficient. At this time, the four variables used as fixed values were the central values of Figure 6. And the refractive indices of 1.45 and 0.001 were used as fixed values, respectively.

Figure 8 shows the relationship between the two measured values (dissipation coefficient and PM2.5) obtained when the distribution width of the two mode particles is randomly changed in the same manner. At this time, the other four fine dust parameters used constant values fixed in the same manner as in Figure 7.

As can be seen from the figure, the change in distribution width does not contribute at all to the change in dissipation coefficient and PM2.5. In other words, the distribution width of particles does not contribute to changes in dissipation coefficient and PM2.5. In other words, the dissipation coefficient shows a stable value within 10% in a narrow range from 380 to 470×10 ^-6 m ^-1 , and PM2.5 varies by around 10% at around 41-45μgm ^-3 .

Figure 9 shows the relationship between the two measured values (dissipation coefficient and PM2.5) obtained when the center size of the two mode particles is randomly changed. At this time, other fine dust parameters used constant values fixed in the same manner as in Figure 7.

Figure 9 shows that the dissipation coefficient varies within 40% ^from 230 ^{to 320} The difference is too large to use the correlation of values, and in particular, as can be seen in Figure 9, the dissipation coefficient has a high distribution density near a certain value (in this case, 320×10 ^-6 m ^-1 ), which is arbitrarily sized ( randomly) This means that there is a high probability that it will be measured as a specific value.

In this case, it means that very different PM2.5 values can be obtained from the same dissipation coefficient (Δα/α≪ 1), so the correlation between the dissipation coefficient and PM2.5 cannot be used to obtain PM2.5.

Figure 10 is a graph showing the dissipation coefficient and PM2.5 correlation (0.12<r _f <0.18) obtained from changes in six fine dust parameters (V _f , V _c , r _f , r _c , σ _f , σ _c ). , Figure 11 shows the dissipation coefficient and PM2.5 correlation (0.12<r _f <0.52) obtained from changes in six fine dust parameters (V _f, V _c , r _f , r _c , σ _f , σ _c ). It's a graph.

Figures 10 and 11 are calculations made by changing all six fine dust parameters to arbitrary values while fixing only the refractive index to a constant value as shown in Figure 7. The six variables used in Figures 10 and 11 changed their values over the entire area shown in Figure 6. However, in order to obtain the effect of the ratio of V _f /V _c under various atmospheric conditions, the two values (V _f and V _c ) were set as independent variables and the size was changed up to 5 times.

In addition, Figure 10 shows changes in the center size of ultrafine dust from 0.12 to 0.18㎛ over a wider area, and Figure 11 shows the center size of ultrafine dust from 0.12 to 0.52㎛ to enable expansion to a larger area. Changes occurred in a wide area.

As shown in the figure, the calculation results can correspond to various PM2.5 values from the dissipation coefficient obtained at a given wavelength. Solving this problem is what the present invention seeks to solve.

Those skilled in the art are well aware of Angstrom Exponent (AE). This value represents the degree to which the dissipation coefficient varies depending on the wavelength, and is a value defined when the dissipation coefficient according to the wavelength is expressed as Equation 2.

[Equation 2]

In general, AE is a value that depends on the size of the particle and can have negative or positive values. Negative numbers mainly occur when only large particles exist in the air, and when phenomena such as fog, clouds, or yellow dust occur, this value becomes negative. In other words, if this value is constant, it has a similar meaning to the meaning that the particle size is constant.

Based on this physical meaning of AE, Figures 12 and 13 show how the correlations in Figures 10 and 11 change depending on the AE value.

Figure 12 is a graph showing the dissipation coefficient and PM2.5 correlation (0.12<r _f <0.18) obtained from changes in six fine dust parameters (V _f, V _c , r _f , r _c , σ _f , σ _c ). , Figure 13 shows the correlation (0.12<r _f <0.52) with the dissipation coefficient and PM2.5 obtained from changes in six fine dust parameters (V _f, V _c , r _f , r _c , σ _f , σ _c ). This is the graph shown. (However, if only AE is calculated within a certain range)

In FIG. 12, the AE = 1 to 1.1 case and the AE = 2 to 2.1 case are shown separately among the entire data in FIG. 10. As can be seen from the figure, in each case, the slope is expressed differently as a value of 0.1635×10 ⁶ (PM2.5/α(550)) and 0.1552×10 ⁶ (PM2.5/α(550)), and unlike Figure 10, The correlation between the two physical quantities (dissipation coefficient and PM2.5) also shows a better correlation of 0.995 and 0.986, respectively. Therefore, different correlation functions must be applied depending on the AE. Figure 13 also shows the relationship between the dissipation coefficient and PM2.5 obtained by extracting only specific AE values in Figure 11, showing that the slope and correlation coefficient have increased significantly compared to Figure 11.

The AE values obtained in Figures 12 and 13 are quantities that can only be known by obtaining dissipation coefficients at more than two wavelengths. Therefore, to increase the correlation between the dissipation coefficient and PM2.5 using the AE value as shown in Figures 12 and 13, the dissipation coefficient must be obtained at more than two wavelengths. The way to obtain the dissipation coefficient at more than two wavelengths is to use the RGB wavelengths of a camera, or in the case of LiDAR, multi-wavelength LiDAR is commonly used. Other methods include using solar radiation with a dissipation coefficient of a point or the entire atmosphere. By measuring the dissipation coefficient at multiple wavelengths of two or more, AE can be obtained using [Equation 2], and PM2.5 can be obtained using another correlation function (FIG. 13).

The rights of the present invention are not limited to the embodiments described above but are defined by the claims, and those skilled in the art can make various changes and modifications within the scope of the claims. This is self-evident.

110: Dissipation coefficient acquisition unit 120: AE calculation unit
130: Function generation unit 140: Particle calculation unit

Claims (12)

In measuring ultrafine dust PM2.5 particles in the air
A dissipation coefficient acquisition unit 110 that obtains a dissipation coefficient for each wavelength using light of two or more wavelengths;
An AE calculation unit 120 that calculates the Angstrom index using the dissipation coefficient for each wavelength;
A function generator 130 that calculates a correlation function between a dissipation coefficient obtained through a set range of the obtained Angstrong index and a correlation function of PM2.5 particles;
a particle calculation unit 140 that calculates the total mass of PM2.5 particles in unit volume using the correlation function; A system for extracting PM2.5 particles from the dissipation coefficient, characterized in that it consists of
According to paragraph 1,
The dissipation coefficient acquisition unit 110,
The dissipation coefficient is characterized by obtaining the scattering coefficient from means of measuring scattering phenomena, including LiDAR, the RGB wavelength of a camera, the optical depth of fine dust obtained by a solar estimator, and a nepalometer that obtains the dissipation or scattering coefficient at one point. A system for extracting PM2.5 particles from
According to paragraph 1,
The dissipation coefficient acquisition unit 110,
A system that extracts PM2.5 particles from the dissipation coefficient, which utilizes wavelengths in the NIR (Near Infrared) region from visible light with two different wavelengths in a wide wavelength range like a camera.
According to paragraph 1,
The function generator 130,
A system for extracting PM2.5 particles from a dissipation coefficient, characterized in that the set range of the Angstrong index is divided into the selected range from a negative value to 4.
According to paragraph 1,
The function generator 130,
Total amount of ultrafine dust (V _f ), total amount of coarse particles (V _c ), center size of ultrafine dust (r _f ), center size of coarse particles (r _c ), and center width of ultrafine dust (σ _f ). The feature is that the correlation function is obtained by calculating only those Angstrong exponents within the set range from the correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the six parameters of the center width (σ _c ) of the coarse particles. A system that extracts PM2.5 particles from the dissipation coefficient.
According to clause 6,
The correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the above six parameters is obtained by calculating the range of the six parameters according to atmospheric measurements over a set period while fixing the refractive index at a constant value. A system that extracts PM2.5 particles from the characteristic dissipation coefficient.
In measuring ultrafine dust PM2.5 particles in the air
A dissipation coefficient acquisition step (S 110) of obtaining dissipation coefficients for each wavelength using light of two or more wavelengths;
AE calculation step (S 120) of calculating the Angstrong index using the dissipation coefficient for each wavelength;
A function generation step (S 130) of calculating the correlation function of the dissipation coefficient obtained through a set range of the obtained Angstrong index and the PM2.5 particles;
A particle calculation step (S 140) of calculating the total mass of PM2.5 particles in unit volume using the correlation function; A method of extracting PM2.5 particles from the dissipation coefficient, characterized in that it consists of.
In clause 7,
In the dissipation coefficient acquisition step (S 110),
The dissipation coefficient is characterized by obtaining the scattering coefficient from means of measuring scattering phenomena, including LiDAR, the RGB wavelength of a camera, the optical depth of fine dust obtained by a solar estimator, and a nepalometer that obtains the dissipation or scattering coefficient at one point. How to extract PM2.5 particles from.
In clause 7,
In the dissipation coefficient acquisition step (S 110),
A method of extracting PM2.5 particles from the dissipation coefficient, which is characterized by utilizing wavelengths in the NIR (Near Infrared) region from visible light with two different wavelengths in a wide wavelength range like a camera.
In clause 7,
In the function creation step (S 130),
A method for extracting PM2.5 particles from a dissipation coefficient, characterized in that the set range of the Angstrong index is divided into the selected range from a negative value to 4.
In clause 7,
In the function creation step (S 130),
Total amount of ultrafine dust (V _f ), total amount of coarse particles (V _c ), center size of ultrafine dust (r _f ), center size of coarse particles (r _c ), and center width of ultrafine dust (σ _f ). The feature is that the correlation function is obtained by calculating only those Angstrong exponents within the set range from the correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the six parameters of the center width (σ _c ) of the coarse particles. A method of extracting PM2.5 particles from the dissipation coefficient.
According to clause 11,
The correlation between the dissipation coefficient and PM2.5 particle mass obtained from changes in the above six parameters is obtained by calculating the range of the six parameters according to atmospheric measurements over a set period while fixing the refractive index at a constant value. Method for extracting PM2.5 particles from the characterized dissipation coefficient.