Method and related electronic devices for determining the flux of airborne particles

Abstract

A method for determining the flux of airborne particles, the method comprising: - s1) controlling a maneuverable lidar device (2) so that the lidar device (2) scans a control surface (S) by emitting several laser pulses directed along different radiation axes and acquiring corresponding backscattered light signals; - s2) processing the backscattered light signals to determine particle density values ​​and collinear wind velocity values ​​at different distances from the lidar device along each radiation axis; - s3) calculating the particle flux passing through the control surface (S) based on the particle density values ​​and collinear wind velocity values.

Description

[Technical Field]

[0001] The technical field involves measuring and monitoring atmospheric aerosol particulate matter emissions. [Background technology]

[0002] Monitoring airborne particulate matter emissions from operational areas such as agricultural or industrial areas is extremely useful for monitoring air quality and / or processes taking place in such areas.

[0003] Video monitoring using automated image analysis is sometimes used to monitor exhaust fumes from industrial chimneys. However, this technique is limited to localized suspended sources and generally does not provide a quantitative estimate of the mass of emitted particulate matter.

[0004] LiDAR scanning is also used to provide maps of particulate density over open-air operation areas, imaging the amount of particulate matter in the air above that area. While useful, such maps do not provide information about the amount of particulate matter emitted by that area, i.e., information about the emission itself, or at least only very indirect information. [Overview of the Initiative] [Means for solving the problem]

[0005] In connection therewith, a method for determining atmospheric flux as described in claim 1 is provided.

[0006] By basing this particulate flux determination on wind speed values ​​derived from lidar acquisition, accurate estimation of this flux becomes possible. Indeed, wind direction and speed generally change over time, and in some cases, even from location to location over the outdoor operating area; therefore, it is beneficial to use simultaneous and co-localized measurements of particulate density and wind speed (or its components) at different locations on the control surface. In particular, using localized wind speed measurements rather than single-point or average wind speed measurements performed with a cup anemometer is preferable in terms of accuracy.

[0007] Nevertheless, it should be noted that calculating the flux passing through the control surface from the data obtained during lidar scanning is not immediate. Indeed, the backscattered light signal acquired by lidar is a collinear wind velocity ω m This makes it possible to determine the value of the wind speed itself, but the wind speed itself

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[0008] The control surface and the outdoor operating area can, in particular, form a closed or partially closed surface together (in other words, the control surface can enclose the outdoor operating area). This closed or partially closed surface defines the volume of air extending over the outdoor operating area (the volume of air scanned by the lidar when the lidar scans the control surface).

[0009] When a control surface encloses and covers an outdoor area (the outdoor area, together with the control surface, forms a closed surface), it is necessary to know the value of the vertical component of the wind velocity for several parts of the control surface in order to calculate the particulate flux. And determining the vertical component of the wind velocity from lidar scans at many different points required specific developments, as described in the detailed explanation.

[0010] The method according to the present invention may include one or more additional features as defined in claims 2 to 16, which may be considered individually or in combination.

[0011] The present invention also relates to an electronic device connected to or integrated therein a LiDAR device, as defined by claim 17. The electronic device has, for example, the structure of a computer. Additional features of claims 2 to 14 presented with respect to the method can also be applied to this electronic device.

[0012] The present invention also relates to a system comprising a operable lidar device and a processing device, as defined by claim 18.

[0013] The present invention also relates to computer programs, as defined by claim 17. The additional features of claims 2 to 14 presented with respect to the method can also be applied to this computer program.

[0014] In this document, LiDAR devices (referring to "Light Detection And Ranging" devices) are referred to simply as "LiDAR devices" or "LiDAR" without distinction.

[0015] Next, the present invention will be described and illustrated in more detail by reference to the accompanying drawings, without introducing any limitations. [Brief explanation of the drawing]

[0016] [Figure 1] This is a top view of the tan area monitored using this method. [Figure 2] This diagram schematically represents this area in perspective, along with the control surface used to calculate the particulate flux emitted by this area. [Figure 3] This is a schematic diagram illustrating the system used to implement that method. [Figure 4] This diagram schematically represents the radiation axis from which the laser pulse is emitted. [Figure 5] This diagram schematically illustrates, in perspective view, the different azimuth scans performed to scan the air across the monitored area. [Figure 6] This diagram schematically represents such an azimuth angle scan as viewed from above. [Figure 7] This diagram schematically shows several azimuth angle scans viewed from the side. [Figure 8] This is a sequence diagram showing the series of steps in this method. [Figure 9] This diagram schematically represents a collection of discrete cells from above, where the particle density is measured thanks to a lidar device, and these cells intersect with a control surface. [Figure 10] This diagram schematically represents the close-range continuous laser radiation used to estimate the vertical component of wind speed. [Figure 11] This diagram schematically represents the close-range continuous laser radiation used to estimate the horizontal component of wind speed. [Figure 12] This is a schematic diagram showing, from above, an additional lateral surface used for checking the redundancy of the method's reliability. [Modes for carrying out the invention]

[0017] This method is for determining the emission of airborne particulate matter from outdoor areas Zo (Figure 2), such as industrial or mining areas.

[0018] As shown in Figure 1, Area Zo is the primary mineral yard 3, part of Industrial Facility 1, which in this case is a steelmaking plant. Various piles of raw materials for minerals and coal are stored in this primary mineral yard 3. The area has various roads to allow for the transport of materials when necessary, and various heavy machinery is also intended to carry out this collection and transport (reclaimers, etc.). Generally, the piles are about 10 to 20 meters high.

[0019] In other embodiments, the monitored area Zo may include other elements of a steelmaking plant, such as a secondary mineral storage area, a sintering plant, or a blast furnace facility. The area monitored thanks to this method may include just one or more of these elements. In yet another embodiment, the area may encompass an open-pit mine (mineral yard) or another type of outdoor operating area. More generally, the expression outdoor area specifies a plot of land (a plot of ground) on which, if any, equipment or structures are located.

[0020] To determine the amount of particulate matter released by the outdoor area Zo, follow these steps: - s1) The LiDAR device 2 is controlled to scan zone V located above area Zo (see Figure 2) by emitting several laser pulses directed along different radiation axes and acquiring the corresponding backscattered light signals; - s2) Process the backscattered light signal along each radiation axis at different distances d from the LiDAR device 2. i The values ​​of particulate matter density (PM) and collinear wind speed ω in the region. m Steps to determine the value, - Step s3) of determining the airborne particulate emissions from area Zo (see Fig. 2) by calculating the particulate flux φ passing through the control surface S surrounding area Zo, wherein the particulate flux φ is determined from the value of the particulate density PM determined from the backscattered light signal acquired by the lidar device 2 and the value of the collinear wind speed ω m is calculated based on and is the determining step is executed.

[0021] In the following description, the particulate flux φ is a mass flux corresponding to the total mass of particulate matter crossing the control surface S per unit time. Further, in other embodiments, the particulate flux can correspond to the number of particulate matter (e.g., particulate matter within a given size range) or the volume of particulate matter crossing the control surface per unit time.

[0022] The control surface S is the boundary (or in other words, the envelope surface) of the volume of air V extending from this area Zo above area Zo. The control surface S extends from the ground above area Zo and then back to the ground again (the control surface S completely covers area Zo anyway). The outdoor area Zo, together with the control surface S, forms a closed surface (completely enclosing the volume V).

[0023] In the example described herein, referring to the figure, the control surface S is - an upper surface S that is horizontal (in some cases, with a slight deviation, e.g., within 2 degrees of horizontal) T , - and a lateral surface S that surrounds area Zo and is vertical (in some cases, with a slight deviation, e.g., within 2 degrees of vertical) L and consists of.

[0024] The lateral surface S L extends all around the perimeter of area Zo and surrounds area Zo in the sense of surrounding it anyway, enclosing it.

[0025] The horizontal portion S T and the vertical side portion S LIt is beneficial to use such a control surface consisting of the above. Indeed, the horizontal surface has been found to be well suited, as will be discussed later, for deriving the vertical wind velocity component at different points on the control surface from lidar scanning. In addition, such outdoor operating areas have two typical sources of airborne particulate matter emissions: suspended sources (such as sintered cooler exhaust or ventilation from building roofs) and diffusion release sources (such as wind erosion, road emissions, or handling at low heights). In suspended sources, the emitted particulate matter has sufficient thrust to be lifted and transported to a distance far from their source, and the majority of such emissions end up on the upper surface S T It is expected to cross the area. On the other hand, at the diffusion source, most of the ejected material is on the lateral surface S L It is expected to traverse the area. Therefore, the horizontal plus vertical structure of the control surface S allows access to additional information regarding the nature of the emission.

[0026] As shown in the figure, area Zo has a rectangular boundary, and therefore the control surface S has a parallelepiped shape. Upper surface S T It is rectangular, and the lateral surface S L It has four lateral flat surfaces S L,1 S L,2 S L,3 , and S L,4This consists of (Figure 2). Furthermore, the control surface S can have different shapes. For example, if the outdoor area is circular, the top surface will also be circular, and the lateral surfaces will have the shape of the lateral surfaces of a cylinder. In the example above, the outdoor area, together with the control surface, forms a completely closed surface. Furthermore, in some cases, they can together form a mostly closed surface, meaning that it is closed except for one or more openings that account for less than 50%, less than 20%, or even less than 10% of the total area of ​​the control surface S. Such cases where the control surface is not completely enclosed can occur when there is an obstacle, such as a chimney, on a radial axis considered for some of the radial axes, which prevents measurements from being taken at some positions behind the obstacle. In such cases, the method can still be applied, but with slightly less accuracy than when the control surface completely encloses the outdoor area being monitored. In some cases, the control surface also does not extend all the way to the ground (it may stop above the ground), which also means that it does not completely enclose the control surface. In this case as well, the method can still be applied.

[0027] The complete scanning and computing process that enables the determination of the particle flux φ is achieved by system 4, which comprises a lidar device 2 and a processing device 10 (Figure 3). The processing device 10 performs at least the following steps (Figure 8): - Step s1 (a step in which Rider 2 is controlled and as a result scans the air above Area Zo), - Step 2': From Rider Device 2: • Backscattered light signals acquired during the scan, and / or • The values ​​of particulate matter density PM and collinear wind speed ω are derived from the above signal along different radial axes. m Appropriate data to determine the value of, and / or • Particulate matter density (PM) values ​​and collinear wind speed (ω) along different radial axes directly determined by the LiDAR device m Step to receive the value, - Step s3 It is configured, for example, to perform a certain action, and is programmed to do so.

[0028] Particulate matter density (PM) and collinear wind speed ω m Step s2, which involves processing the backscattered light signal to determine the value of , may be performed by the lidar device 2; or by the processing device 10; or by both the lidar device (for part of its processing) and the processing device (for the rest of its processing).

[0029] In the embodiment shown in Figure 3, the LiDAR device 2 is a separate, possibly remote, standalone device from the processing device 10. The LiDAR device 2 and the processing device are connected together, possibly through a network, using either a wired or wireless connection. Thus, they can exchange data and instructions. The processing device 10 may comprise at least a processor and memory. It can take the form of a standalone computer, electronic unit, or server. However, it may also be implemented in a distributed manner (effectively, anyway) using so-called "cloud" resources (computational and storage resources distributed among separate physical systems in a network).

[0030] In an alternative embodiment, the processing device may be integrated with (i.e., part of) the LiDAR device 2, rather than being separate from it.

[0031] Next, the different steps of this method will be explained in more detail.

[0032] Step 1 In step s1, the processing device 10 controls the lidar device 2 to scan the control surface S by scanning zone V (air) located above area Zo. To this end, the processing device 10 sends a scan command or scan parameters to the lidar device 2 (in step s1), and the lidar device 2 then performs the scan (in step s10, see Figure 8). The characteristics of this scan will now be described in more detail.

[0033] During this scan, LiDAR 2 each uses a different radial axis X. j,k Several laser pulses are emitted along the line (see, for example, Figures 2 and 6). For each emitted laser pulse, the lidar acquires a corresponding backscattered light signal, i.e., a light signal that is scattered in response to the emission of that laser pulse and returns to the lidar. This light signal is scattered back by air and particles located in the path of the laser pulse.

[0034] For each laser pulse, the pulse radiation axis X j,k The direction is as shown in Figure 4, azimuth angle α j and elevation angle β k It is specified by. In Figure 4 and other figures, three axes x, y, and z are represented. The radial axes x, y, and z are perpendicular to each other. The axis z is vertical (and upward). The axis x corresponds to the reference fixed horizontal direction (for example, axis x points north). Azimuth angle α j This refers to axis x and axis X j,k This is the angle between the projection onto the horizontal plane x,y and the actual plane.

[0035] The scan commanded in step s1 includes at least a first “azimuth scan” (in a sense, a “horizontal” scan). An “azimuth scan” means a scan performed by changing the azimuth angle while maintaining a fixed angle of attack. Such an “azimuth scan” is a scan in so-called Plan Position Indicator (PPI) mode (the lidar maintains a constant angle of elevation but changes its azimuth angle). Elevation angle β kThis includes the horizontal plane and the radial axis X. j,k It is the angle between [the specified value] and [the specified value]. In this document, all elevation angles considered are elevation angles (not height angles).

[0036] Therefore, in step s1, the lidar determines that some of the laser pulses have different but the same elevation angle α1, α2, ... α i , ..., α J These laser pulses are controlled to be emitted in a certain direction. These laser pulses form a first set of laser pulses, or in other words, a first scan (i.e., a first "azimuth scan"). Figure 5 shows the zone covered by this first scan (labeled "first scan" in Figure 5) when β1 = 0°. This first scan covers the corresponding set of radiation axes, i.e., X j,k=1 j=1...J is implemented to completely cover the area Zo being monitored. In other words, the radial axis X j,k=1 , j=1…J is distributed across a zone that completely covers area Zo (it looks like an angular sector - see Figure 5). Radial axis X j=1,k=1 and the radial axis X j=J,k=1 The zone encompassed between (the axis corresponding to the minimum azimuth angle and the axis corresponding to the maximum azimuth angle in this first scan) covers the entire area Zo (i.e., it overlaps at least the entire area Zo). The number of different azimuth angles in this first “azimuth scan,” denoted by J, is, for example, greater than 40, or even greater than 90. The angular step dα (a step that is not necessarily constant) between two adjacent azimuth angles in this scan is, for example, 0.1 to 3 degrees, or even greater than 0.1 to 1 degree.

[0037] The complete scan achieved by LiDAR 2, as shown here, differs from β1 and has different elevation angles from each other: β2, ...β k , ...β K This may also include one or more additional "azimuth scans" achieved for [the specified value].

[0038] In the examples in Figures 5 and 7, the number of "azimuth scans" K is equal to 5, and the different elevation angles are: β1=0°, β2=0.5°, β3=1°, β4=2°, and β5=3°. Figure 7 schematically shows the zones scanned between the first (β=β1), third (β=β3), and fifth (β=β5) "azimuth scans" (each having the shape of an angular sector).

[0039] K is, for example, 3 or greater. It can be from 3 to 10. The angle step dβ (not necessarily constant) between two consecutive elevation angles is, for example, 0.3 to 5 degrees, or further 0.5 to 3 degrees. The elevation angle used can be either positive (upward scan) or negative (downward scan).

[0040] In any case, the embodiments described herein use different elevation angles β1, ...β K Within this, at least one elevation angle is close to zero, meaning its absolute value is less than 10 degrees, or even less than 5 degrees, or less than 2 degrees. And at least one other elevation angle is close to that elevation angle, with an angular difference dβ of less than 10 degrees, or even less than 5 degrees, or less than 2 degrees. These characteristics are preferable for determining the value of the vertical component of wind speed from the lidar scan, as will be further detailed below. Having at least one elevation angle close to zero is also preferable for the lateral surface S L This facilitates the calculation of flux passing through different elevation angles β1, ...β. K The absolute values ​​of each are preferably less than 20 degrees, or even less than 10 degrees.

[0041] Here, the azimuth angles of the laser pulse emission are set: α1, ...α J This is the same for different "azimuth scans": the same set of azimuths is used for the azimuth scan performed with β=β1, the azimuth scan performed with β=β2, and so on.

[0042] Step s2 As shown in Figure 8, step 2, which includes processing the backscattered light signal, can be performed partially by the lidar device 2 in step s20 and partially by the processing device 10 in step s21.

[0043] Backscattered light signals are acquired using optical sensors in the LiDAR device, such as photodiodes or photomultiplier tubes. Each of these can represent the backscattered light power over time (depending on the round-trip travel time for the light, which is a kind of echo time). They can further take the form of carrier-to-noise ratios over time, or signal-to-noise ratios (SNRs) over time (noise is defined, for example, as the standard deviation of the optical signal within a limited time window).

[0044] In step s20, the lidar device, at different distances from lidar 2 (in fact, for different time intervals distributed over the entire duration of each backscattered light signal), has a radial backscatter coefficient β opt core (for example, meter) -1 · Stellarian -1 (represented by) and collinear wind speed ω m Determine the value of collinear wind speed ω. m The value of may be determined by intra-signal cross-correlation. Radial backscattering coefficient β opt The value of is determined here from the SNR of the backscattered light signal using the classical Klett LiDAR inversion method.

[0045] The above distance d i (Related to the rider's measurement range and accuracy) can be from 20m to 2km. Two consecutive distances d i The gap between them can be 5 to 200 meters, especially when using techniques to overlap pulses to increase LiDAR resolution. (Distance sample d) iThe total number of I units with i=1 is generally between 25 and 500.

[0046] Next, in step s2', the processing unit 10 processes the ω transmitted by the lidar m and β opt It receives the value.

[0047] In step s21, the processing unit β opt Each of the values ​​is converted to a particulate density PM value. For this purpose, a conversion coefficient, formula, or algorithm stored in the memory of the processing device is used. Here, particulate density PM is, for example, the mass density expressed as micrograms per cubic meter. Here, β opt The conversion from to PM is such that the PM value is a mass density representing the mass of all particulate matter having dimensions less than 10 microns in a given volume of air (such fine particles are commonly referred to as "PM10" in this art). This conversion is not exclusively limited to PM10 particles and can determine other fractions of particulate matter with diameters of environmental interest (e.g., particles with an aerodynamic diameter of less than 2.5 microns, i.e., PM2.5).

[0048] Particulate matter refers to atmospheric aerosol particles such as suspended sand, suspended minerals, dust, quarry dust, mineral processing dust, wind-eroded dust, combustion residues, and smoke components.

[0049] β opt Regarding the conversion from to PM, this method may include a pre-calibration step (performed before step s1), during which β opt The relationship between the PM is determined.

[0050] This calibration step is achieved, for example, by acquiring the backscattered light signal relative to a given fixed radiation axis several times in succession at different moments, and then acquiring the particle density value measured using a particle sensor (such as an air sampling pump or optical particle counter). Unlike lidar, the particle sensor achieves particle density measurement at a fixed position. This position is selected to be close to the fixed radiation axis of the lidar.

[0051] For each acquired backscattered light signal, the β at the distance corresponding to its fixed measurement position is calculated. opt The value of is determined. Next, β opt A time series containing several consecutive values ​​of β is correlated with a time series containing several consecutive values ​​of particulate matter density (PM) acquired by a particulate matter sensor, opt The relationship between β and PM is determined (for example, by linear regression). opt The time series and PM time series can be temporally aligned with respect to each other before determining the relationship between them. For this purpose, the possible temporal offset between the two signals is β relative to PM. opt It is determined by identifying (and then compensating for) the maximum value of the time correlation function of β. opt The fixed location where measurements and direct PM measurements are taken is preferably well above ground level (more than 10 meters or even more than 20 meters above ground level) and far from localized airborne sources (more than 100 meters away). For calibration purposes, this fixed location is preferably within or near the monitored area Zo.

[0052] It should be noted that alternatives to those presented above are possible regarding the distribution of the decision step s2 between the lidar device 2 and the processing device 10. For example, the lidar device can directly transfer the backscattered light signal (time-dependent optical power, or time-dependent SNR) to the processing unit, which can then perform all processing on these signals, such as ω m And the value of PM is determined. Alternatively, the lidar device determines ω mThe value and backscattered light signal can be transferred to a processing device, which then processes the backscattered light signal from β opt Next, PM is determined. Alternatively, LiDAR device 2 achieves complete processing of the backscattered light signal, and then ω m The PM value can also be sent to the processing unit 10.

[0053] Step s3 In the example described here, to calculate the particle flux φ passing through the control surface S, the zone V scanned by the lidar 2 is cell C i,j,k The element flux is discretized into a grid G ​​(Figure 6), and the element flux corresponds to the flux at the cell level.

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[0054] More specifically, step s3 is the following step: - Different radial axes X j,k The center is located at a different measurement distance d from the LiDAR device 2. i Cell C located at i,j,k A step of numerically constructing a grid G ​​composed of; a step in which the cells of the grid are adjacent to each other, - Cell C of grid G ​​intersecting the control surface S i,j,k Frontier cell C o,l Identification step (Figure 9), - Each Frontier Cell C o,l against element flux

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[0055] Numerically constructing grid G ​​involves each cell C i,j,k This means determining the position of the boundary (or the edge or vertex of that boundary) and / or the position of the center of the cell.

[0056] As can be seen in Figure 6, each cell C i,j,k This is the distance d from the rider's position O. i Located on the radial axis X j,k The center is at the point shown above. The cells are adjacent to each other. The boundary between two adjacent cells lies midway between the centers of those two cells. Figure 6 is simply a top view of one "azimuth scan"; therefore, only one layer of cells in grid G ​​(i.e., layer C 2D corresponding to elevation angle β1) i,j,k=1 This shows that, however, grid G ​​in this embodiment is a three-dimensional grid and includes several such layers superimposed on one another.

[0057] Regarding the vertical expansion of a cell, the cell's area expands in the same proportion as the height difference between two consecutive scans considered. At the bottom layer of the cell (here, cell C), i,j,k=1 In this layer, each cell extends downwards until it reaches the ground, and interpolation can be used in some cases to make accurate predictions of the concentration near ground level (see Figure 7).

[0058] Due to the radial nature of lidar scanning, grid G ​​has a radial structure rather than a Cartesian structure. Each cell C i,j,k The surfaces that define the radial axis X are, as shown here, considered separately. j,k It can be either parallel or perpendicular to it.

[0059] As described above, Frontier Cell C o,lis a cell of the grid that intersects the control surface S. Frontier cell C o,l is labeled with an integer l from 1 to L. Some of the frontier cells are cells that intersect the lateral surface SL (some of these "lateral" frontier cells are shown in FIG. 9), while other frontier cells are cells of the grid that intersect the upper surface.

[0060] Here, for the lateral surface S L intersecting frontier cell C o,l each elemental flux

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[0061] The upper surface S T and the frontier cell C that intersects o,l For each element flux

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[0062] Here, the wind speed

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[0063] The total particle flux φ is on the upper surface S T For both the frontier cell and the frontier cell of the lateral surface SL, element flux

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[0064] In some embodiments, the lateral surface S LThe particulate flux passing through is calculated taking into account only one layer of cells (even if grid G ​​contains layers of two or more cells), and taking into account that the element area A (in eqn1) is equal to h·a, where h is the lateral surface S L This is the height of the frontier cell C in the case of a horizontal cross-section (see Figure 7), where a is the height of the frontier cell C o,l Side surfaces S intersected by L This is the length (see Figure 9). In other words, in these embodiments, the calculation of the lateral flux is performed as if there were simply one cell (of height h) in the vertical direction.

[0065] As presented above, element flux

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[0066] Next, the collinear wind speed ωm From the measured values, wind speed

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[0067] In any case, at least one of the elevation angles (e.g., β1) is close to zero, for example, less than 10 degrees, or even less than 5 degrees, or less than 2 degrees. The difference between the two elevation angles (e.g., β1 and β2) is small, for example, less than 10 degrees, or even less than 5 degrees, or less than 2 degrees. Under such conditions, the value of the vertical component u ul is, with good approximation, u1≒(w m,2 -w m,1 ) / (β2-β1) (eqn3) It can be calculated as follows.

[0068] The corresponding situation is shown in Figure 10. The collinear wind speed ω m,1 and ω m,2 These are, respectively, cell C i,j,k=1 Position and cell C i,j,k This is a measurement at position 2. That is, both are at the same distance d. i It is located at the same azimuth angle α j This is in relation to ω m,1 This is in relation to the elevation angle β1, and ω m,2 This relates to the elevation angle β2 = β1 + dβ.

[0069] In general, ω m teeth

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[0070] Since dβ is small, wind speed

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[0071] therefore,

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[0072] However, in reality, ν o ·dβ and ν o ·sin(β1) is small compared to u, for example, u = 1 m / s and ν o For =20m / s (which is actually very high when considering only u=1m / s), using the above example values ​​of β1=0° and dβ=0.5°, ν o ·sin(β1)=0≪1m / s and νo ·dβ = 0.17 m / s ≪ 1 m / s is obtained.

[0073] Therefore, w m,2 ≒w m,1 +u·dβ, that is: u l ≒(w m,2 -w m,1 ) / (β2-β1) (eqn3)

[0074] It should be noted that in alternative embodiments, the value of u can be derived from the measured collinear wind speeds using a relationship between u and two or more values ​​of collinear wind speed (measured for two or more different elevation angles), which is different (and possibly more precise) from eqn3. The value of u may also be determined from two or more values ​​of collinear wind speed (measured for two or more different elevation angles) using statistical analysis methods. For example, ω m (β) The predicted theoretical variation is ω m By adjusting the value of u to fit the measured value of (β). Such a fitting method yields equations (3) to (5) in the following paper, which can be used instead of eqn3 above: "Validating precision estimates in horizontal wind measurements from a Doppler lidar", Rob K. Newsom et al., Atmos.Meas.Tech., 10, 1229-1240, 2017.

[0075] Partially using a similar method, wind speed

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[0076] Two such measurements ω' m,1 and ω' m,2 from

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[0077] Using a different relationship than eqn4 and eqn5, from two or more values ​​of collinear wind speed

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[0078] Step 4 In step s4, the processing unit 10 outputs the particulate flux φ determined in step s3. The particulate flux φ may be output by sending it to a human-computer interface such as a screen to allow an operator to monitor the flux. It may also be output by sending it to a database that records the value of the particulate flux φ over time.

[0079] verification To evaluate the reliability of the method presented above, the first test was performed using an alternative control surface S' that was identical to control surface S, except that it was slightly shifted laterally (Figure 12). The upper surfaces of both S and S' are identical. Lateral surface S' L is, S LIn comparison, it is shifted slightly by a distance that is generally the dimension of one or two cells in the grid. Then, based on the same lidar acquisition, the particulate flux φ is calculated using both S and S' as the control surface. In practice, no significant difference (less than 5% of the total value) was found between these two redundant calculations.

[0080] Using the LiDAR technique presented above, a further comparison was made between the theoretical estimate of the particle flux φ and the measured value of φ.

[0081] The first test was conducted on primary mineral storage area 3 in Figure 1. In this first test, the lidar height ho (see Figure 7) was 20 meters, and the lidar scan was one of those shown in Figures 5 and 7. Theoretical estimations were made based on the methods outlined in the "WRAP Fugitive Dust Handbook" (particularly Chapter 9 of that handbook) by Countess Environmental, 4001 Whitesail Circle, Westlake Village, CA 91361 (2006). The survey was conducted over a three-month period, during which rain and wind conditions varied considerably. Table 1 summarizes the corresponding results. Table 1 provides particulate flux values ​​corresponding to the average over one month for both theoretical estimations and lidar measurements. These values ​​are in arbitrary units (corresponding to mass per unit time).

[0082] [Table 1]

[0083] As can be seen in Table 1, the lidar-based results agree fairly well with theoretical estimates (within approximately 30%), which supports the reliability of the lidar-based measurement method. Nevertheless, compared to theoretical estimates, lidar-based measurements provide much more detailed information about the temporal variation of radiation. Moreover, it is considered more reliable because, since the flux is measured directly, all possible causes of flux variation are directly taken into account. While some causes of emission may be overlooked in theoretical estimates (e.g., the number of times the material in the pile is disturbed—the number of times it is consumed, the number of times it is refreshed, etc.—strongly affects the results—and therefore, if operations on the pile are overlooked in theoretical estimates, the results will be considerably impaired), lidar measurements are not affected by such errors.

[0084] The tests were also conducted in areas including industrial facilities. Tests were performed in areas including a sintering plant (Table 2) and an area including a blast furnace (Table 3). The quantities expressed are the same as in Table 1. For these two cases, the theoretical estimates take into account the actual process parameters of these facilities.

[0085] [Table 2]

[0086] [Table 3]

[0087] Again, a good agreement is found between lidar-based measurements and theoretical estimates.

[0088] The inventors further tested the total-mass variation method, in which the total mass of particles in volume V is calculated by summing the element masses for all cells in a grid within volume V, with each element mass being equal to the particle density value in the cell under consideration multiplied by the cell volume. The total mass at time t+dt is then compared to the total mass at time t, and the mass flux is estimated from there. However, this method proved to yield considerably inaccurate results (far less accurate than the flux method presented above). Possible explanations for this are the limited temporal resolution in a complete volume scan (which takes a considerable amount of time to perform) and the fact that the primary quantity determined is total mass instead of total flux.

[0089] With reference to the drawings, embodiments different from those described above are possible without departing from the scope of the Art. For example, instead of scanning in PPI mode, then changing the elevation angle, and scanning again in PPI mode, the entire scan may be achieved by scanning in so-called Range Height Indicator (RHI) mode (the lidar keeps the azimuth angle constant but changes the elevation angle, then changes the azimuth angle, and scans again in RHI mode, etc.). Furthermore, the particulate flux may be determined for a control surface that does not completely (or almost completely) enclose the outdoor area being monitored. In fact, the method can also be usefully applied to calculate the flux of airborne particulates passing through other types of control surfaces. For example, passing through a control surface that forms (or is located at) the boundary of a residential area or other densely populated area (the control surface does not necessarily completely enclose the area). The control surface also does not necessarily completely cover (or completely enclose) the area, and may extend over or around the outdoor area being monitored. Indeed, determining the airborne flux escaping through the lateral surfaces extending around such areas already provides valuable information about emissions originating from that area.

Claims

1. A method for determining the flux (φ) of airborne particles, wherein the method is: - s1) Lidar device (2) has different radial axes (X ｊ，ｋ Controlling a maneuverable LiDAR device (2) to scan a control surface (S) by emitting several laser pulses directed along the ) and acquiring the corresponding backscattered light signals, - s2) Process the backscattered light signal along each radiation axis at different distances (d) from the lidar device (2). ｉ The particulate matter density (PM) and collinear wind speed (ω) in ) ｍ ) Determining the value, - s3) The value of the particulate matter density (PM) and the collinear wind speed (ω ｍ Based on the value of ), calculate the particulate flux (φ) passing through the control surface (S). Methods that include...

2. The method according to claim 1, wherein the control surface (S) extends over, around, or both over and around an outdoor operating area (Zo), and the emission of airborne particulate matter from the outdoor operating area (Zo) is determined by calculating the flux (φ).

3. The method according to claim 1 or 2, wherein the control surface (S) and the outdoor operating area (Zo) together form a closed or partially closed surface that determines the volume (V) of air extending over the outdoor operating area (Zo).

4. The control surface (S) is the upper surface (S) which is horizontal. Ｔ ) and the lateral surface (S) that surrounds the area (Zo) and is perpendicular to it. Ｌ The method according to claim 2 or 3, comprising:

5. The rider device is controlled in step s1 such that at least some of the emitted laser pulses form a set of laser pulses emitted at respective azimuth angles (α １ , α ２ , α ｉ ) that are different from each other. The method according to any one of claims 1 to 4.

6. - The set of laser pulses is called the first set of laser pulses, and the laser pulses of the first set are at the same first elevation angle (β １ ) is emitted, - Some of the other laser pulses that are emitted are at the first elevation angle (β １ ) the same second elevation angle (β) which is different from ２ ) forms a second set of laser pulses emitted at different azimuth angles (α), and the laser pulses of the second set form a second set of laser pulses that emit at different azimuth angles (α) １ , α ２ , α ｉ The method according to claim 5, which is emitted by )

7. Step s3 is the first elevation angle (β １ ) and the second elevation angle (β ２ For each of the following, the wind speed obtained is at least the collinear wind speed (ω ｍ The first value of (ω ｍ，１ ) and the second value (ω ｍ，２ ) from wind speed [Math 1] The method according to claim 6, comprising calculating the value of the vertical component (u) of the

8. wind speed [Math 2] The value of the vertical component (u) is - Collinear wind speed (ω ｍ The second value of (ω ｍ，２ ) and the first value (ω ｍ，１ The difference between ) - Second elevation angle (β ２ ) and the first elevation angle (β １ The difference between ) The method according to claim 7, which is calculated by division.

9. Step s3 includes calculating the upper particulate flux, where the upper particulate flux is the upper surface (S) of the control surface (S) Ｔ The flux passing through the upper particulate flux is determined by the value of the particulate density (PM) and the wind speed determined according to the method of claim 7 or 8. [Math 3] The method according to claims 4 and 7 or the method according to claims 4 and 8, calculated from the value of the vertical component (u).

10. Step s3 is the azimuth angle (α １ , α ２ For two or more of these, the collinear wind speed (ω ｍ Wind speed [Math 4] horizontal component [Math 5] The method according to any one of claims 5 to 9, comprising calculating the amplitude (v) and / or direction of a signal.

11. Step s3 includes calculating the lateral particulate flux, where the lateral particulate flux is on the lateral surface (S) of the control surface (S). Ｌ The flux passing through the lateral particulate flux is determined by the value of the particulate density (PM) and the wind speed determined according to the method of claim 8. [Math 6] horizontal component [Number 7] The method according to claims 4 and 10, calculated from the amplitude (v) and / or direction values ​​of

12. Step s3 is the following step, namely, - Different radial axes (X ｊ，ｋ The center is at ) and at different distances (d) from the Rider device (2). ｉ Cell (C) located at ) ｉ，ｊ，ｋ A step of numerically constructing a grid (G) consisting of ) - Cells (C) of the grid (G) intersecting the control surface (S) ｉ，ｊ，ｋ ) is a frontier cell (C ｏ，ｌ ) step of identifying - Each Frontier Cell (C ｏ，ｌ ) against element flux [Number 8] A step of calculating the element flux, wherein the element flux is a particulate flux passing through a portion (A) of a control surface (S) intersected by a frontier cell under consideration. - Elemental particle flux [Number 9] A step to calculate the particulate flux (φ) passing through the control surface (S) by summing them together. The method according to any one of claims 1 to 11, including the method described in any one of claims 1 to 11.

13. Lateral surface (S Ｌ ) and the intersecting frontier cell (C ｏ，ｌ ) [Number 10] Each element flux shown is given by the following equation eqn1, i.e., [Math 11] It is calculated according to the following, where, - PM ｌ The frontier cell (C) to be considered ｏ，ｌ This is the value of the particulate density within ). - A is the frontier cell to be considered (C ｏ，ｌ ) intersecting lateral surfaces (S Ｌ This is the area of ​​the part of ) - v ｌ The frontier cell (C) to be considered ｏ，ｌ This is the amplitude of the horizontal component of the wind speed in ) - θ ｌ The frontier cell (C) to be considered ｏ，ｌ ) lateral surface (S Ｌ A vector perpendicular to ) [Math 12] And, wind speed [Number 13] horizontal component [Number 14] The method according to claims 4 and 12, wherein the angle is between [a certain value] and [a certain value].

14. Further including a pre-calibration step, the pre-calibration step is - A step of controlling the lidar device (2) to acquire a calibrated backscatter light signal several times in succession at different moments with respect to a given fixed radiation axis passing through a fixed measurement position, and together acquiring the value of the particle density at the position measured by a particle sensor, - Process each calibrated backscatter light signal to obtain the backscatter coefficient β at the measurement position. ｏｐｔ Steps to determine the value, - β at the aforementioned moment ｏｐｔ A step of aligning a set of values ​​with a set of particulate density values ​​measured by a particulate sensor in time, - β ｏｐｔ By correlating the set of values ​​for β with the set of values ​​for particulate density, ｏｐｔ A step to determine a numerical relationship relating to particulate matter density. The method according to any one of claims 1 to 13, including the method described in any one of claims 1 to 13.

15. The method according to any one of claims 1 to 14, wherein the outdoor operating area (Zo) includes or is part of the industrial facility (1).

16. The method according to any one of claims 1 to 15, wherein the outdoor operating area (Zo) includes a mineral storage area (3) or an open-cut mine.

17. The following steps, namely, - s1) Lidar device (2) has different radial axes (X ｊ，ｋ The steps include controlling a maneuverable LiDAR device (2) to scan a control surface (S) by emitting several laser pulses directed along the surface and acquiring corresponding backscattered light signals, - s2') From the Rider device, - Along each radiation axis, different distances (d) from the lidar device are determined by the lidar device from the backscattered light signal. ｉ The particulate matter density (PM) and collinear wind speed (ω) in ) ｍ The value of ) or - Different distances from the Rider device (2) (d ｉ In this case, along each radial axis, the particulate matter density (PM) and the collinear wind speed (ω ｍ Data that includes or is derived from a backscattered light signal, suitable for determining the value of ) Steps to receive - s3) The value of the particulate matter density (PM) and the collinear wind speed (ω ｍ A step of calculating the particulate flux (φ) passing through the control surface (S) based on the value of ). An electronic device (10) configured to perform the following.

18. A system (4) comprising a controllable lidar device (2) and a processing device (10), wherein the following steps are taken: - s10) A controllable Rider device (2) allows for different radial axes (X ｊ，ｋ The steps include scanning the control surface (S) by emitting several laser pulses directed along the line and acquiring the corresponding backscattered light signals, - s2) The processing device (10) and / or the controllable LiDAR device (2) process the backscattered light signal along each radiation axis at different distances (d) from the LiDAR device (2). ｉ The particulate matter density (PM) and collinear wind speed (ω) in ) ｍ ) Steps to determine the value, - s3) The value of the particulate matter density (PM) and the collinear wind speed (ω ｍ A step of calculating the particulate flux (φ) passing through the control surface (S) based on the value of ). A system (4) configured to perform the following.

19. A computer program containing instructions, wherein the execution of instructions on a computer device (10) connected to a controllable LiDAR device (2) involves the following steps on the computer device: - s1) Lidar device (2) has different radial axes (X ｊ，ｋ The steps include controlling a maneuverable LiDAR device (2) to scan a control surface (S) by emitting several laser pulses directed along the surface and acquiring corresponding backscattered light signals, - s2') From the Rider device, - Along each radiation axis, different distances (d) from the lidar device (2) are determined by the lidar device from the backscattered light signal. ｉ The particulate matter density (PM) and collinear wind speed (ω) in ) ｍ The value of ) or - Different distances from the Rider device (2) (d ｉ In this case, along each radial axis, the particulate matter density (PM) and the collinear wind speed (ω ｍ Data that includes or is derived from a backscattered light signal, suitable for determining the value of ) Steps to receive - s3) The value of the particulate matter density (PM) and the collinear wind speed (ω ｍ A step of calculating the particulate flux (φ) passing through the control surface (S) based on the value of ). A computer program that executes something.

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