CN107391951B - Air pollution tracing method based on annular neighborhood gradient sorting - Google Patents
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Abstract
The invention discloses an air pollution tracing method based on annular neighborhood gradient sorting, which comprises the following steps of: 1) acquiring air pollution data of each monitoring sampling point in a preset area from a database; 2) cleaning the data, and then performing kriging interpolation to obtain air pollution concentration data in a regular grid form; 3) and calculating the concentration data by using a neighborhood gradient sorting traceability method to obtain a backtracking line and a backtracking face. The method is used for realizing rapid air pollution source tracing in a small scale range, backtracking is carried out by using a neighborhood gradient sorting method in an interval time period, a gradient is used as an evaluation distance, an annular neighborhood is used as a search area, the self-definition of an azimuth angle and a search radius can be ensured, the maximum gradient sorting and the interval gradient sorting can be used, backtracking lines and backtracking faces can be obtained, the area where pollution occurs can be effectively backtracked, the precision and the efficiency are good, and the method can be practically applied to the development of embedded equipment and an environment-friendly big data platform.
Description
Technical Field
The invention belongs to the technical field of air pollution source tracing, and particularly relates to an air pollution source tracing method based on annular neighborhood gradient sorting.
Background
With the economic development and industrialization process of China being accelerated, air pollution happens frequently, and fast and efficient pollution tracing for gas pollution is an urgent need. However, the current vacancy pollution tracing method is mainly based on a meteorological model in a large scale range and is used for solving the problem of air pollution source tracing and positioning in the large scale range, such as the province and the province of PM2.5 and the monitoring between countries. However, with the acceleration of the urbanization process, the air pollution tracing in a small scale range shows the real value, such as the quick tracing for urban sudden pollution, the quick positioning for straw burning in urban and rural junctions, and the like. However, a pollution tracing method for a small-scale range is relatively lacking, and the reasons are manifold, namely, the data are limited by the number and the distribution density of pollution monitoring sites at present, so that a scholars cannot obtain small-area air pollution data with high density and high precision, and research work is difficult to develop directly; secondly, the utility of the air model in a small scale range is lost, and the air flow is complex and difficult to accurately simulate due to standing of buildings in the urban range.
Aiming at the rapid detection of sudden atmospheric pollution events in small areas, the conventional method is that samples are collected on site manually and then are brought back to a laboratory for monitoring, and the method takes long time. With the improvement of equipment, researchers begin to use portable monitoring equipment, namely, Shouyang (Chinese zodiac, Wang New Juan, Haliang, and the like), the rapid determination of the VOCs in the atmosphere and the initial application thereof in the emergency tracing of environmental air pollution [ J ]. environmental monitoring management and technology, 2016 (02):65-68 ], the monitoring equipment of the portable VOCs is used, after receiving the report, the monitoring equipment is carried to the local by professionals to carry out real-time monitoring, and then the pollution source tracing is realized by contrasting with the characteristic directory of the pollution source. This manual method is still not time efficient if the contamination event is rapid or if the contaminant uses intermittent discharge.
The prediction and tracing of the air flow based on the neighborhood sampled data at multiple time intervals is a stable and reliable method, and the method has good scale adaptability according to the spatial resolution and the time resolution of the data. The Wangchen machine learning research and the application thereof in wind forecasting [ D ] Fudan university, 2012 ] are verified by using a large amount of experiments and data, and the effectiveness and accuracy can be better ensured. However, the neighborhood-based method has many disadvantages, such as that it is difficult to effectively utilize the existing model by using the air pollution data at irregular positions, and the conventional 8-neighborhood or 16-neighborhood taking the pixel center as a unit cannot customize the search range shape and distance of the neighborhood.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects in the prior art, the invention aims to provide an air pollution tracing method based on annular neighborhood gradient sorting, which is used for realizing rapid air pollution tracing in a small-scale range.
The technical scheme is as follows: in order to achieve the purpose of the invention, the invention adopts the technical scheme that:
an air pollution tracing method based on annular neighborhood gradient sorting comprises the following steps:
1) acquiring air pollution data of each monitoring sampling point in a preset area from a database;
2) cleaning the data, and then performing kriging interpolation to obtain air pollution concentration data in a regular grid form;
3) and calculating the concentration data by using a neighborhood gradient sorting traceability method to obtain a backtracking line and a backtracking face.
In the step 2), cleaning the data to pre-process and convert the acquired data; the data preprocessing comprises the steps of carrying out time calibration on data obtained by each sensor, and enabling the time scales of the air quality data obtained by each sensor to be consistent; the data conversion is to convert the acquired dat format into csv format and list data structure in python.
In the step 2), in the kriging interpolation process: setting the administrative range of a preset area as B, and setting the air pollution measured by an air pollution monitoring station as Z (x), wherein the x represents the spatial position of the monitoring station, and the x is a non-measurement point0Z (x) of the attribute value of (2)0) The estimate is a weighted sum of the air pollution concentration values of a plurality of known monitored sites, as follows:
in the formula, xiFor monitoring the longitude and latitude values of the stations, x0For interpolating regional warp and weft values, Z (x)i) Is the air pollution grid data value, lambda, obtained by interpolationi(i ═ 1, 2., n) is a distance weight, which is calculated from a variance function under the assumption of minimum variance and unbiased features; the following sets out the common kriging equations:
the variance is minimum according to the common kriging estimation and is expressed as:
wherein C represents a covariance function and E is a mathematical expectation value
The process of interpolation calculation by using kriging comprises the following steps:
(1) calculating two distances and a half variance between monitoring stations;
(2) finding the relation between the fitting distance of a fitting curve and the half variance, thereby calculating the corresponding half variance r according to any distanceij(ii) a For unknown point zoCalculate it to all known points ziOf (a) half variance rio(ii) a Solving an equation set to obtain an optimal coefficient lambada i; the most usedWeighting and summing the attribute values of the known points by the optimal coefficients to obtain the unknown points zoAn estimate of (d).
In step 3), the specific process of the neighborhood gradient sorting method is as follows:
(1) inputting a head of line element, and determining the time period t0 and a neighborhood gradient threshold;
(2) finding the neighborhood of the head of line element and calculating the gradient of the neighborhood element in the last time period t1 and the current time period t0 of the head of line element;
(3) sorting the neighborhood gradients;
(4) judging whether a gradient larger than a threshold exists, if so, turning to step 5; otherwise, turning to the step 6;
(5) outputting all points greater than a threshold;
(6) the single point with the largest rank is output.
Storing the backtracking line and the backtracking surface obtained in the step 3) into a GeoJson format, and outputting the backtracking line and the backtracking surface to a front-end system for visual display.
According to the air pollution tracing method based on annular neighborhood gradient sorting, air pollution data is SO2Concentration data.
Has the advantages that: compared with the prior art, the air pollution tracing method based on annular neighborhood gradient sorting is used for realizing rapid air pollution tracing in a small-scale range, the neighborhood gradient sorting method in an interval time period is used for tracing back, the gradient is used as an evaluation distance, the annular neighborhood is used as a search area, the customization of an azimuth angle and a search radius can be ensured, the maximum gradient sorting and the interval gradient sorting can be used for obtaining a trace back line and a trace back face, and the method can effectively trace back the pollution occurrence area by verifying the result of favorable measured data, has better precision and efficiency, and can be practically applied to the development of embedded equipment and an environment-friendly big data platform.
Drawings
FIG. 1 is a diagram of the spatial locations of a sample area and a data monitoring site; (b) is a partial enlarged view of (a);
FIG. 2 is a method flow diagram;
FIG. 3 is a flow chart of a circular neighborhood gradient ranking method;
FIG. 4 is a schematic diagram of an improved circular neighborhood;
fig. 5 is a result diagram, in which (a) is a single-point tracing mode and (b) is a multi-point tracing mode.
Detailed Description
The invention will now be described with reference to specific embodiments.
Example 1
The sample district of this embodiment is Yixing city of Jiangsu province. The Yixing city has a north latitude of 31 degrees 07 '-31 degrees 37', an east longitude of 119 degrees 31 '-120 degrees 03', is positioned at the junction of the three provinces of Jiangsu, Anhui and Zhejiang, and the east is connected with the water surface of the Taihu lake. The total area of Yixing city is approximately 1997 square kilometers. More than 1500 environment-friendly equipment production enterprises and more than 3000 matching enterprises exist in Yixing city, and an environment-friendly industry cluster has 10 industrial workers, so that the application of the method can effectively utilize enterprise resources and is fully supported by the government.
The example used is the air SO of Yixing city in 4 months of 20172Monitoring data, wherein the data come from embedded equipment based on ARM architecture and produced by Jiangsu Zongyi information technology corporation, and SO is selected2The sensor is from SPEC company, and the model is 3SP _ SO2_20P Package 110-. And totally laying 24 monitoring sites in the whole city, and performing Krigin interpolation on the data of the sites to obtain grid data, wherein the distance between grids is 0.05 degrees, namely the grids are approximately 500 meters by 500 meters, and a Geojson format is generated. The spatial position of the spatial distribution is shown in fig. 1.
The air pollution tracing method based on annular neighborhood gradient sorting of the invention has a flow chart shown in figure 2, and the general process is as follows: firstly, the SO of each monitoring sampling point is obtained from a database2And cleaning the data (mainly preprocessing and converting the acquired data, wherein the preprocessing of the data is mainly to perform time calibration on the data acquired by each sensor so as to enable the air quality data acquired by each sensorThe time scales of the data are consistent; the data conversion is to convert the acquired dat format into csv format and list data structure in python, which can be directly used by the method), and perform kriging interpolation to obtain SO in regular grid form2Concentration data. And calculating the concentration data by using a neighborhood gradient sorting traceability method, storing the concentration data in a GeoJson format, and finally outputting the concentration data to a front-end system for visual display.
1. Kriging interpolation
The method for interpolating air pollution data by using a spatial interpolation algorithm to obtain grid data is a conventional method [ study on spatial variation and distribution rule of nitrate nitrogen pollution of underground water in Shandong province planting region [ D ]. Zhongnan university, 2011 ]. Aiming at the defects of the inverse distance weighting method for the spatial point interpolation, such as the lack of theoretical basis for determining the weighting coefficient of the geographic space, the inaccurate description of the spatial correlation degree, and the like, a Kriging interpolation (Kriging) algorithm is produced. The algorithm is able to fit a data function to a number of point values within a specified range to determine an output value for each location. The kriging interpolation algorithm has various specific implementation methods, wherein a common kriging (Ordinary kriging) method is used more because the principle is simple and stable, and the robustness and universality are better, and the method selects the common kriging as the interpolation algorithm.
In the method, the administrative scope of Yixing city is set as B, and SO measured by an air pollution monitoring station is set2And Z (x), then { Z (x) epsilon B }, wherein x represents the spatial position of the monitored station, and the method is a two-dimensional vector representing longitude and latitude. According to the principle of the common kriging algorithm, non-measurement points x0Z (x) of the attribute value of (2)0) The estimated value is the SO of a plurality of known monitored sites2Weighted summation of concentration values, see the following equation:
in the formula, xiFor monitoring the longitude and latitude values of the stations, x0For interpolating regional warp and weft values, Z (x)i) Is a need forAnd (4) interpolating to obtain the data value of the air pollution grid. Lambda [ alpha ]i(i ═ 1, 2., n) is the distance weight, which is calculated from the variance function under the assumption of minimum variance and unbiased features. The following sets out the common kriging equations:
the variance is minimum according to the common kriging estimation and is expressed as:
in the formula, C represents a covariance function, and E is a mathematical expectation value.
The process of interpolation calculation using kriging in the method is represented as follows:
(1) calculating two distances and a half variance between monitoring stations;
(2) finding the relation between the fitting distance of a fitting curve and the half variance, thereby calculating the corresponding half variance r according to any distanceij(ii) a For unknown point zoCalculate it to all known points ziOf (a) half variance rio(ii) a Solving the equation set in the step 4 to obtain an optimal coefficient lambada i; weighting and summing the attribute values of the known points by using the optimal coefficients to obtain the unknown points zoAn estimate of (d).
2. Circular neighborhood gradient ordering
The basic process of the neighborhood gradient ranking method is:
(1) inputting the queue head element and determining the time interval t0And a neighborhood gradient threshold;
(2) find the neighborhood of the head of line element and calculate the last time period t1Neighborhood element and head of line element this time period t0A gradient of (a);
(3) sorting the neighborhood gradients;
(4) judging whether a gradient larger than a threshold exists, if so, turning to step 5; otherwise, turning to the step 6;
(5) outputting all points greater than a threshold;
(6) the single point with the largest rank is output.
The specific flow chart is shown in fig. 3 below.
In the conventional neighborhood-based method, it is conventional to perform window neighborhood calculation based on a central pixel, such as 8 neighborhood calculation of 3 pixels by 3 pixels and 24 neighborhood calculation of 5 pixels by 5 pixels, using a pixel as a unit. However, the use of the pixel-based window analysis method has the following disadvantages that (1) the neighborhood radius of the method is fixed, namely, the neighborhood of the central pixel is used as the adjacent pixel, and the search range shape and the distance of the neighborhood cannot be customized. This also leads to inaccuracy of the search distance because the diagonal pixels are not the same distance from the vertical pixels. (2) More precise orientations cannot be determined using neighborhood pixels, e.g., 8 neighborhoods have only 8 orientations (due north, northeast, due east, southeast, due south, southwest, due west, northwest), while 24 neighborhoods can determine 16 orientations. Positioning using any azimuth angle cannot be achieved. Aiming at the defects, an improved annular neighborhood method is adopted. The principle of the method is shown in fig. 4, and the specific steps are as follows:
(1) inputting a head of line element, and determining the time period t0 and a neighborhood gradient threshold;
(2) finding the neighborhood of the head of line element and calculating the gradient of the neighborhood element in the last time period t1 and the current time period t0 of the head of line element;
(3) sorting the neighborhood gradients;
(4) judging whether a gradient larger than a threshold exists, if so, turning to the step (5); otherwise, turning to the step (6);
(5) outputting all points greater than a threshold;
(6) the single point with the largest rank is output.
In fig. 4, (a) represents a 3 × 3 8-pixel neighborhood; (b) representing an annular neighborhood with the radius of 1 unit and 8 sampling points; (c) representing an annular neighborhood with the radius of 2 units and the sampling point of 16; (d) representing an annular neighborhood with the radius of 2 and the sampling point of 8; (e) representing a single-point tracing mode, and forming a tracing route; (f) the multi-point tracing mode is represented, and a tracing range area is formed.
By utilizing the annular neighborhood method, the neighborhood search radius can be customized, the search points and the search azimuth angle can be increased at will, and not only can a good experimental result be obtained in the method, but also better expandability is realized. With the increase of monitoring sites, the annular neighborhood still has good applicability as the grid data of polluted air becomes more and more accurate and dense.
And storing the generated source tracing process data in a GeoJSON format. The result is shown in fig. 5, wherein (a) is a single-point tracing mode, the maximum gradient value is adopted for tracing each time, the actual pollution thermodynamic diagram is referred, and the tracing effect is accurate; and (b) the graph is a multipoint tracing graph, wherein the multipoint tracing graph is 3 gradient maximum values, the last tracing result is a plurality of tracing lines which are converged into a tracing surface, and the effect is accurate by referring to an actual pollution thermodynamic diagram.
Therefore, the method can effectively trace back the area where the pollution occurs, has better precision and efficiency, and can be practically applied to development of embedded equipment and an environment-friendly big data platform.
Claims (4)
1. An air pollution tracing method based on annular neighborhood gradient sorting is characterized by comprising the following steps:
1) acquiring air pollution data of each monitoring sampling point in a preset area from a database;
2) cleaning the data, and then performing kriging interpolation to obtain air pollution concentration data in a regular grid form; cleaning the data, namely preprocessing and converting the acquired data; the data preprocessing comprises the steps of carrying out time calibration on data obtained by each sensor, and enabling the time scales of the air quality data obtained by each sensor to be consistent; the data conversion is to convert the acquired dat format into csv format and list data structure in python;
3) calculating the concentration data by using a neighborhood gradient sorting traceability method to obtain a backtracking line and a backtracking surface; the specific process of the neighborhood gradient ranking method is as follows:
(1) input queue head element, trueBook determination period t0And a neighborhood gradient threshold;
(2) find the neighborhood of the head of line element and calculate the last time period t1Neighborhood elements and this time period t0The gradient of the head of line elements;
(3) sorting the neighborhood gradients; wherein the neighborhood is an annular neighborhood with 8 or 16 sampling points;
(4) judging whether a gradient larger than a threshold exists, if so, turning to step 5; otherwise, turning to the step 6;
(5) outputting all points greater than a threshold;
(6) the single point with the largest rank is output.
2. The air pollution tracing method based on annular neighborhood gradient sorting according to claim 1, wherein in step 2), in a kriging interpolation process: setting the administrative range of a preset area as B, and setting the concentration value of air pollution measured by an air pollution monitoring station as Z (x), then { Z (x) is epsilon of B }, x represents the spatial position of the monitoring station, wherein x is a non-measuring point0Z (x) of the attribute value of (2)0) The estimate is a weighted sum of the air pollution concentration values of a plurality of known monitored sites, as follows:
in the formula, xiFor monitoring the longitude and latitude values of the stations, x0For interpolating regional warp and weft values, Z (x)i) Is the air pollution grid data value, lambda, obtained by interpolationi(i ═ 1, 2., n) is a distance weight, which is calculated from a variance function under the assumption of minimum variance and unbiased features; the following sets out the common kriging equations:
the variance is minimum according to the common kriging estimation and is expressed as:
in the formula, C represents a covariance function, E is a mathematical expected value, and u is a mean value;
the process of interpolation calculation by using kriging comprises the following steps:
(1) calculating the distance between two points and the half variance between the monitoring stations;
(2) finding the relation between the fitting distance of a fitting curve and the half variance, thereby calculating the corresponding half variance r according to any distanceij(ii) a For unknown point zoCalculate it to all known points ziOf (a) half variance rio(ii) a Solving the equation set to obtain the optimal coefficient lambdai(ii) a Weighting and summing the attribute values of the known points by using the optimal coefficients to obtain the unknown points zoAn estimate of (d).
3. The air pollution tracing method based on annular neighborhood gradient sorting according to claim 1, wherein the traceback line and the traceback surface obtained in step 3) are stored in a GeoJson format and output to a front-end system for visual display.
4. The annular neighborhood gradient ranking-based air pollution tracing method according to claim 1, wherein the air pollution data is SO2Concentration data.
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