RU2463556C1 - Method of verifying system of ground-based measurement of air condition of metropolises - Google Patents

Abstract

FIELD: physics.

SUBSTANCE: generalised, spectrozonal image in the red band of the visible range is obtained, having control industrial sites with known maximum allowable concentration values of the atmosphere. Image brightness pixels are calibrated based on the maximum allowable concentration values and relative pixel brightness of control sites. A programmed method is used to calculate local maxima on the calibrated image and the envelope of the Fourier spatial spectrum. Sampling points are identified with the detected maxima and the average distance r between sampling points and the volume of measurements V are determined.

EFFECT: statistical stability and reliability.

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Description

The invention relates to the field of ecology, in particular to remote methods for monitoring natural environments, and can find application in systems of sanitary and epidemiological control of industrial regions.

Industrial progress is inevitably associated with an increase in emissions of so-called "greenhouse" gases into the atmosphere, which are one of the causes of global climate change on the planet. Monitoring the state of air pollution is an integral part of the obligations of the states that have signed the Kyoto Protocol for environmental monitoring of natural environments.

In accordance with the regulation “On the creation of a unified state system of environmental monitoring” (EGSEM), the Ministry of Natural Resources of the Russian Federation, regional authorities are obliged to carry out planned monitoring and measurement of indicators of environmental monitoring objects. The metropolis contains hundreds of areal (industrial) and linear (transport) sources of air emissions. According to the all-Union regulatory document [OND-86 “Methodology for calculating atmospheric air concentrations of harmful substances contained in enterprise emissions", USSR, Gidrometeoizdat, Len, 1987, p. 32, paragraph 5.12], the calculation of the concentration of emissions of area targets is carried out in a grid, containing about 1500 nodes.

The measurement volume reaches several thousand samples at a cost of chemical analysis of one sample of ~ 10 thousand rubles. and analysis time of 8-12 hours. Thus, there is a methodological problem of the scientific justification of both the volume of measurements and the location of sampling over the area of the metropolis.

A known method for assessing the state of the atmosphere by calculating the total index q _Σ . Usually q _{Σ is} calculated for five components that determine the main contribution to air pollution, provided that q ₁ > q ₂ > q ₃ > q ₄ > q ₅ [see, for example, Yearbook of the State of Atmospheric Pollution in Cities in Russia, edited by E.Yu. Bezuglova, GGO named after A.I. Voeikova, St. Petersburg, 1994 ... 1996 - Analog]:

where m _i - [mg / m ³ ] - the average annual concentration of the i-th substance in the atmosphere, determined by local measurements on the ground;

CH _i - the maximum permissible sanitary norm for the concentration of the i-th substance in atmospheric air, according to GOST;

j is an indicator of the degree of isoefficiency of the harmful substance equal to 0.85; one; 1.3; 1.5 for substances of IV, III, II and I hazard classes.

The disadvantages of the known analogues are:

- statistical instability of the method of single local measurements on the ground at control points as such;

- the uncertainty of the choice of the sampling control points themselves and the significant influence on the result of measurements of the wind rose and random atmospheric turbulence at the sampling points;

- the inoperability of analytical chemistry methods by sampling, ranging from 6 to 12 hours per sample, the high cost of chemical analysis of the sample;

- the inability to simultaneously control the entire set of ingredients (up to 600) due to inoperability and high cost;

- the impossibility of documentary verification of subjectively compiled protocols based on the results of local measurements.

The closest analogue to the claimed technical solution is the "Method for assessing atmospheric pollution", patent RU No. 2117286, 1998.

The closest analogue method involves remotely obtaining a spectrozonal image of a region including control industrial sites in the form of digital values of spectral brightness I (x, y) of visible, G, R images, dimension | m × n | each element, with a linear resolution of each element larger than the fractal portion of the underlying surface, elementwise logical sorting of pixels in both matrices in accordance with the algorithm, if R≥G, then R, if R <G, then R = R _max - | k | -G , where k is the correlation coefficient of the chromatic coefficients r, g, the resulting matrix of the same size is obtained, the numerical characteristics of the resulting matrix are calculated - the mathematical expectation, variance, the envelope of the spatial spectrum, the autocorrelation function, the histogram of the distribution is calculated distributions of pixels in brightness, the obtained relative distribution law is linked to the absolute values of the index of the state of the atmosphere of the region according to its values and the area of the control sites.

The disadvantages of the closest analogue include:

- the inadequacy of the algorithm for logical sorting of pixels of the resulting matrix to the measured physical process and, as a result, the discrepancy between the obtained histograms and the physical process;

- the non-obviousness of the calibration of the relative law of the distribution of pixels in brightness by measurements of control sites.

The problem solved by the invention is to justify the minimum sampling sites and the volume of measurements by calculating the local maximums of atmospheric pollution and the geometric mean distance between the sampling points by the signal function of the spectral image of the metropolis.

, программный расчет (N) локальных максимумов на изображении и отождествление их с максимумами загрязнения атмосферы, определение среднегеометрического расстояния r (км) между точками забора проб, отсчитываемого от локальных максимумов и объема измерений системы V из соотношений:

, W=S/r²,The technical result is achieved by the fact that the verification method of the system of ground-based measurements of the atmosphere of megalopolises involves the remote acquisition of a generalized spectrozonal image of the region containing industrial control sites in the form of digital values of the spectral brightness of the image I (x, y) in the red band of the visible range (570 ... 670 nm) , calibration of brightness pixels in MPC values relative to the brightness of the pixels of the control sites, calculation of the spatial Fourier spectrum F (1 / m) of a calibrated matrix The signal and interval Δr (km) between discrete measurements on the ground:

, program calculation (N) of local maxima in the image and identifying them with the maximums of atmospheric pollution, determining the geometric mean distance r (km) between the points of sampling, counted from the local maxima and the measurement volume of the system V from the relations:

, W = S / r ² ,

where F _max - the maximum frequency of the spatial Fourier spectrum of a calibrated image matrix;

S is the image area of the metropolis, km ² ;

- среднее расстояние между локальными максимумами на изображении.

- the average distance between local maxima in the image.

The invention is illustrated by drawings, where

figure 1 is a fragment of the original spectrozonal image of the metropolis;

figure 2 is a histogram of the distribution of image pixels by brightness;

figure 3 - the distribution function of air pollution over the area of the metropolis, calibrated by the values of the MPC of the control sites;

figure 4 - envelope of the spatial Fourier spectrum of the function of the image signal;

figure 5 - finding local maxima in the window | 3 × 3 | element by finite difference methods;

6 is a functional diagram of a device that implements the method.

The technical essence of the invention is as follows.

, где F_max - максимальная частота спектра измеряемой функции [см., например, «Теоретические основы радиолокации», под ред. В.Е.Дулевича, учебник, М., Сов. Радио, 1964 г., стр.211]. Данные математические положения использованы при верификации системы наземных измерений. В качестве измеряемой функции в заявляемом способе используют космический снимок мегаполиса в виде коэффициента спектральной яркости функции I(х, y) системы «атмосфера-подстилающая поверхность».According to the analogs and the “Regulation on the establishment of the Unified State Examination of Ecological Energy and Energy Management”, ground-based measurements of the concentration of harmful emissions into the atmosphere are carried out at the source's output. The surface concentration of harmful substances in the atmosphere depends on many factors: the number of sources, the amount of emissions, wind rose, stratification of the temperature gradient, time of year, day, topography, regional infrastructure, azimuths of street directions and traffic flows. Therefore, the result of any local measurement on the ground is a random variable. Within megacities, above the hollows, “hanging” smogs or well-ventilated areas can be observed. From mathematics it is known that the position of the curve is completely determined by the points of maximum, minimum and inflection point [see N. S. Piskunov, “Differential and Integral Calculus for Higher Technical Schools,” Volume 1, 5th ed., Moscow: Nauka, 1964, pp. 168-172, §9. Convexity and concavity of the curve. Inflection points]. On the other hand, according to the Kotelnikov-Shannon sampling theorem, a continuous function defined on a finite interval [0 ... R] is uniquely determined by a set of discrete samples drawn through the interval

where F _max is the maximum frequency of the spectrum of the measured function [see, for example, “Theoretical Foundations of Radar”, ed. V.E.Dulevich, textbook, M., Owls. Radio, 1964, p. 211]. These mathematical provisions were used to verify the ground-based measurement system. As a measured function in the claimed method, a satellite image of the megalopolis is used in the form of a spectral brightness coefficient of the function I (x, y) of the “atmosphere-underlying surface” system.

The remotely recorded level of reflected solar radiation is the sum of reflections from the underlying surface and atmospheric haze. In a cloudy atmosphere, the spectral-energy and spatial characteristics of the underlying surface are “washed out”. There are methods for parametric separation of the underlying surface effects in the resulting brightness [see, for example, “The Final Report on Studying Atmosphere-Surface Parameters by Remote Methods”, Experiment MKS-M-MKF-6 at Salyut-7 station, 1983 ... 1985, M .: IKI, Academy of Sciences of the USSR, pp. 23-31]. One of the methods of parametric separation is image generalization, i.e. the use of low-resolution images in the red band (570 ... 670 nm) in the visible range.

The interaction of solar radiation with anthropogenic particles occurs at the molecular level. When photons of the light flux collide with gas molecules, energy quanta (hυ *) are transferred to the molecules, which transform into an excited state. For all types of possible interaction of the light flux with smog molecules over megacities, such as absorption, scattering, and fluorescence re-emission, the integral effect is to shift the spectrum of the visible range to its long-wavelength part (red region) [see, for example, R. Mezheris, Laser remote sensing, translation from English, Moscow: Mir, 1987, p. 124, Table 3.4 Raman wave numbers at a wavelength of 337.1 nm] The following are some extracts from this Table for some greenhouse smog molecules .

Type of substance molecule NO ₂ SO ₂ CO ₂ NH ₃ C ₂ H ₂ H ₂ s CO NO H ₂ O The wavelength of scattered radiation, nm 345.7 350.8 352.5 378.8 369 380.3 363.9 365.9 384.4 The absolute value of the displacement Δλ, nm 8.6 13.7 15.4 42.7 43,2 32 16.8 18.8 47.3

As a result of the Raman scattering of sunlight, energy is redistributed between the spectral components of the visible range, and the recorded spectral image of anthropogenically contaminated sites acquires a predominantly reddish or dark cherry hue. In addition, in cases of powerful anthropogenic haze of organic and photochemical origin, there is an additional intense absorption (red and near IR ranges), accompanied by “washing out” the pixels of the image of the underlying surface [see, for example, “Spectral brightness coefficients of airborne haze”, L.I. .Chapursky. “Reflective properties of natural objects in the range 400 ... 2500 nm”, part 1, Min. Defense of the USSR, 1986, pp. 103-107, 134-137, tab. P. 10].

Visual selectable signs of contaminated atmospheric portions during spectrozonal imaging from space are the yellow-orange shift of the spectrum of scattered re-radiation and the dark cherry hue of the regions of intense absorption.

To quantify the air pollution of a megalopolis, the image pixels are sorted by brightness and their distribution histogram is built. Since the absolute value of the brightness of pixels depends on the shooting conditions (the height of the Sun, viewing angle, time of day), they are brought to a single scale by normalizing with respect to the maximum brightness. This operation allows you to calibrate the relative brightness values in the MPC values (maximum permissible concentrations) against the relative brightness values of the control sites. A histogram of the distribution of pixels by brightness of the processed image fragment is illustrated in Fig.2.

The distribution function of the MPC values over the area of the metropolis, calibrated by the MPC values of the control sites, is illustrated in Fig. 3. Calibrated image matrix of the metropolis | m × n | the elements are subjected to software processing, for which they determine the rate of fluctuation of the desired signal function by spatial coordinates by calculating its spatial spectrum.

By definition, the two-dimensional spatial spectrum of the matrix signal G (F _x , F _y ), taking into account the linear dimensions (m, n) of the analyzed area, is calculated by the formula:

. Интервал дискретизации функции сигнала по теореме Котельникова-Шеннона составляет

. Следующей задачей анализа является нахождение локальных максимумов загрязнений атмосферы и отождествление их с точками забора проб наземной системы измерений. Поиск локальных максимумов двумерной функции сигнала цифровой матрицы реализуется следующим алгоритмом. Как известно, в точке экстремума производная функции равна нулю. Для нахождения производной, последовательно от начала массива, разбивают матрицу на окна размером [3×3] смежных элемента и рассчитывают конечные результаты по координатам x, y относительно центрального пиксела.The two-dimensional spatial spectrum is integrated over the ring segments and a one-dimensional envelope of the spatial spectrum of the signal is obtained as a function of the polar radius Φ (1 / R). This operation is based on fast Fourier transform (FFT) algorithms [see Marple S.A. Digital Spectral Analysis, trans. from English, Moscow: Mir, 1990, pp. 77-79]. FFT programs are included in the set of specialized software [see, for example, “Specialized Software MATH SAD.6.0 PLVS”, 2nd edition, stereotyped, M .: Inform. published Filin House, 1977, p. 411]. The calculated value of the envelope of the spatial spectrum of the analyzed image is illustrated in figure 4. The maximum spatial frequency of the function (figure 4) corresponds to

. The sampling interval of the signal function according to the Kotelnikov-Shannon theorem is

. The next task of the analysis is to find the local maximums of atmospheric pollution and identify them with the sampling points of the ground-based measurement system. The search for local maxima of the two-dimensional function of the digital matrix signal is implemented by the following algorithm. As is known, at the extremum point, the derivative of the function is zero. To find the derivative, sequentially from the beginning of the array, the matrix is divided into windows with the size [3 × 3] of adjacent elements and the final results are calculated by the x, y coordinates relative to the central pixel.

;

;

;

;

. Поскольку расстояние между точками забора проб рассчитывают двумя методами (Δr и b), то в качестве оптимального расстояния принимают среднегеометрическое

.The procedure for finding a local maximum in the window | 3 × 3 | element is illustrated in Fig.5. The text of the program for finding local maxima is given in the implementation example. After finding (N) points of local maxima, calculate the average distance between them. Considering that there are inflection points between local maxima (changes in the sign of the derivative), we can calculate the average area S / N per one maximum, whence the average distance between the maxima

. Since the distance between the sampling points is calculated by two methods (Δr and b), the geometric mean value is taken as the optimal distance

.

An example implementation of the method

The inventive method can be implemented on the basis of the device according to the scheme of Fig.7. Functional diagram of the device of Fig. 7 contains an orbital observation system 1, such as a Resurs spacecraft (SC) with a high-resolution digital camera 2 (type DCS760) installed on its board, for receiving a light flux signal reflected from the underlying surface 3 through a filter 4 in the red band of the visible range. Operational shooting of the planned objects and turning on the camera 2 is carried out by the onboard control complex (BCC) 5 according to the commands transmitted from the flight control center (MCC) 6 via the command control radio 7. The captured frames are recorded in the buffer memory 8 and in the visibility areas of the spacecraft from ground points , through an autonomous high-speed data transmission channel 9, they are dumped at information reception points (PPI) 10, where they are recorded on means 11. Information from the PPI is distilled over land communication channels 12 to an environmentally friendly th center 13, where they carry out the selection of captured frames on official grounds. Arranged arrays of images of megacities are placed in the database 14 of the Internet 15 (open access site for users "Resource"). Software image processing for the operations of the method is carried out on a personal computer 16 in a standard set of elements: a processor 17, a hard drive 18, a random access memory 19, a display 20, a printer 21, a keyboard 22. Programs for calculating local maxima and the Fourier spectrum are recorded on the hard drive 18. As An example was processed spectrozonal image of Mytishchi, Moscow region. The picture was ordered at the State Center "Nature", order No. 11 / 93-42. The amplitude quantization level of the signal according to the scale (0 ... 255). A fragment of the processed image (figure 1) corresponded to the matrix | m × n | = | 512 × 512 | elements.

The atmosphere is most polluted in the image above the territory of the Mytishchi Machine-Building Plant (MMZ), chosen as a reference industrial site. The average MPC level in the territory of the MMZ at the date of receipt of the image was 4.7 MPC, I _min = 92, I _max = 248. A histogram of the distribution of pixels by brightness of the analyzed image is illustrated in figure 2, where a) a histogram of the absolute values of brightness, b) a histogram of the distribution of MAC over the area of the metropolis. The minimum brightness in the image corresponds to the maximum MAC values (maximum signal attenuation). For a reference industrial site (MPC = 4.7), the relative value of the pixel brightness corresponds to: I _{industrial area} / I _max ≈92 / 248 = 0.37, whence the calibration coefficient inversely proportional for any point of the relative histogram brightness corresponds to:

For a calibrated (in the MPC values) image matrix, the spatial spectrum was calculated. The envelope of the spatial spectrum is illustrated in FIG. The maximum value of the frequency of the spatial spectrum F _max = 0.3 · 10 ^-3 1 / m The spatial sampling interval of the measurements of the desired function of air pollution is Δr = 1/2 F _max = 66 km.

Then, the software method calculates the number (N) of local maxima of the analyzed function.

The text of the program for determining the number of local maxima in the image matrix

{Counting the number of local maxima}

program Extrem070206;

uses crt;

const maxx = 500;

Kmem = 100;

vaf S: real;

ssrstring;

Buf1, Buf2, Buf3: array [1 ... maxx] of real;

f, ff, T: text;

z: byte;

Pixelx, Pixely, I, j, k, kolx, koly, kolM,

zmax, zmin, kolExtr: integer;

mashtab, Lx, Ly, dx, dy, dh, dpx, dpy, Ax, Ay,

z1, mm, sigm, sigm2, SonTree, Distreal;

function Ex (i: integer): boolean;

var x: real;

begin

x: = Buf2 (i);

Ex: = (x> = Buf1 [i-1]) and (x> = Buf1 [i]) and (x> = Buf1 [i + 1]) and

(x> = Buf2 [i + 1]) and (x> = Buf2 [i + 1])

and (x> = Buf3 [i-1]) and (x> = Buf3 [i]) and (x> = Buf1 [i + 1])

end;

begin (main)

clrscr;

readln (Pixelx); {The number of pixels by x in the picture}

readln (Pixely); {The number of pixels by y in the picture}

readln (Lx); {Image size by x in cm}

readln (Ly); {Image size by y in cm}

readln (mashtab); {Image scale of 1 cm 550 m}

kolM: = 0;

repeat

writeln ('BBeflMTe file name');

readln (ss);

assign (f, ss);

{SI-} reset (f); {SI +}

I: = IOresalt;

until j = 0;

assign (ff, 'Rez.txt');

rewrite (ff);

assign (T, 'Maxim.txt');

rewrite (T);

koly: = 0;

mm: = 0; zmax: = 0; zmin: = 255;

while not eof (f) do

begin

koly: = koly + 1;

kolx: = 0;

while not eoln (f) do

begin

read (ff.z); kolx: = kolx + 1; mm: = mm + z;

if z <zmin then zmin: = z;

if z> zmax then zmax: = z

end;

readln (f)

end; close (f);

mm: = mm / kolx / koly;

writeln ('Average z', mm: 12: 7);

writeln ('Maximum and minimum values', zmax: 4, zmin: 4);

writeln ('kolx =', kolx: 10);

writeln ('koly =', koly: 10);

writeln (ff, 'Average z', mm: 12: 7);

writeln (ff, 'Maximum and minimum values', zmax: 4, zmin: 4);

writeln (ff, 'kolx =', kolx: 10);

writeln (ff, 'koly =', koly: 10);

if (kolx> maxx) or (koly> maxx) then

begin

writeln ('flaHHbie don't fit in an array'); halt

end;

dx: = Lx / (Pixelx-1) * mashtab;

dy: = Ly / (Pixely-1) * mashtab;

Ax: = Lx / (Picselx-1) * (kolx-1) * mashtab;

Ay: = Ly / (Picsely-1) * (koly-1) * mashtab;

reset (f);

sigm2: = 0;

while not eof (f) do

begin

while not eoln (f) do

begin

read (f, z); sigm2: = sigm2 + sqr (z-mm) / (kolx * koly)

end;

readln (f);

end;

close (f);

sigm: = sqrt (sigm2);

writeln (7tycnepcnfl ', sigm: 12: 7);

writeln (ff, 'Dispersion', sigm: 12: 7);

dh: = 26.0 / (2 * sigm);

dh: = 1;

reset (f);

kolExtr: = 0;

for i: = 1 to kolx do

begin

read (f, z); zl: = z * dh; Bufl (i): = zl;

end;

readln (f);

for i: = 1 to kolx do

begin

read (f, z); zl: = z * dh; Bulf2 (i): = z1;

end;

readin (f);

for i: = 1 to kolx do

begin

read (f, z); zl: = z * dh; Bulf3 (i): = zl;

end;

readln (f);

j: = 2;

while not eof (f) do

begin

Buf1: = Buf2; Buf2: = Buf3;

for i: = 1 to kolx do begin

read (f, z); zl: = z * dh; Buf3 (i): = zl;

end;

readln (f);

for i: = 1 to kolx-1 do

begin

if Ex (i) then begin

kolExtr: = kolExtr + 1;

writeln (T, i * dx: 15: 2, **, j * dy: 15: 2);

end

end;

j: = j + 1

end;

Son: = Ax * Ay / kolExtr;

close (f);

close (T);

writeln ('Filename-', ss);

writeln ('dx =', dx: 10: 5, 'dy =', dy: 10: 5, 'dh =', dh: 10: 5);

writeln ('String -', koly: 5, 'Stobtsov -', kolx);

writeln ('Area Width', Ax: 15: 7);

writeln ('Area Length', Ay: 15: 7);

writeln ('Projection of the Square', Ax * Ay: 15: 3);

writeln ('Number of local maxima', kolExtr: 10);

writeln ('Area under one maximum', SonTree: 15: 7);

writeln (ff, 'File name -', ss);

writeln (ff, 'dx =', dx: 10: 5, 'dy =', dy: 10: 5, 'dh =', dh: 10: 5);

writeln (ff, 'Rows -', koly: 5, 'Columns -', kolx);

writeln (ff, 'Area Width', Ax: 15: 7);

writeln (ff, 'Area Length', Ay: 15: 7);

writeln (ff, 'Projection of the Square', Ah * Au: 15: 3);

writeln (ff, 'Number of local maxima', kolExtr: 10);

writeln (ff, 'Area under one maximum', Son: 15: 7);

close (ff);

end. (main)

Среднегеометрическое расстояние между точками забора проб:

. Объем измерений загрязнений атмосферы анализируемого фрагмента: V=S/r²=4/1,93≈2,1.With a sampling in amplitude of one MAC in the processed fragment, (N = 3) local maximums are allocated. The spectrozonal image scale corresponded to: 1: 55.000, the area of the processed fragment was S = 4 km. The objects of local maxima were distinguished: MMZ (MPC = 4.7), Khimvolokno (MPC = 3.5), Stroyplastmass (MPC = 2.5). The background (minimum) MPC level over the fragment area is 1.57, which corresponds to the data of ground-based measurements. In addition to local maxima, the program allows you to calculate the area under each local maximum to the inflection point. The average distance between local maxima:

Geometric mean distance between sampling points:

. The scope of measurements of atmospheric pollution of the analyzed fragment: V = S / r ² = 4 / 1.93≈2.1.

According to the measurement plan of Mytishchi SES, measurements are carried out at 8 points of sampling at the outlet of the main emission sources. Thus, the redundancy of information, with the established planned control of the ground-based measurement system, reaches (8: 2.1) ≈4 times.

The effectiveness of the claimed method is characterized by statistical stability, reliability, with minimal economic costs, compared with the regulation of measurements of the existing ground-based air pollution control system in Mytishchi.

Claims (1)

A method for verifying a system of ground-based measurements of the atmosphere state of megacities involves remotely obtaining a generalized spectrozonal image of a region containing industrial control sites in the form of digital values of the spectral brightness of the image I (x, y) in the red band of the visible range (570 ... 670 nm), calibration of brightness pixels in values MPC for the relative brightness of the pixels of the control sites, the calculation of the spatial Fourier spectrum F (l / m) of the calibrated signal matrix and the interval Δr (km) between discrete E measurements on site:

, program calculation (N) of local maxima in the image and their identification with the maximums of atmospheric pollution, determination of the geometric mean distance r (km) between the points of sampling, measured from local maxima,
and the measurement volume of the system V from the relations

V = S / r ² ,
where F _max - the maximum frequency of the spatial Fourier spectrum of a calibrated image matrix;
S is the image area of the metropolis, km ² ;

- the average distance between local maxima in the image.

Images