An atmospheric pollution source positioning method and device based on social network analysis

Abstract

The application discloses a kind of atmospheric pollution source positioning method and device based on social network analysis.The method comprises: obtaining the pollutant data of each monitoring station in a certain period in region and pre-processing;The pollutant data after pre-processing is filtered, and air pollution scene is established;Social network analysis is carried out on air pollution scene data to obtain the correlation strength between each station;According to the correlation strength between each station and the distance between each station, the intermediate centrality value of each station is calculated;According to the intermediate centrality value of each station and geographic coordinates, spatial interpolation is carried out using interpolation method, and the pollution source is positioned according to the plane data obtained after spatial interpolation.The positioning method of atmospheric pollution source proposed in the application can not only find the corresponding pollution area and key station corresponding to pollution event in complex environment, but also facilitate targeted management, and can further accurately locate the positioning range of pollution source to the vicinity of specific monitoring point, so as to efficiently and accurately locate the atmospheric pollution source.

Description

A method and apparatus for locating air pollution sources based on social network analysis Technical Field

[0001] The embodiments of the present invention relate to the field of pollution source location technology, and in particular to a method and apparatus for locating atmospheric pollution sources based on social network analysis. Background Technology

[0002] Air pollutants such as smog, dust, and inhalable particulate matter constantly threaten the health of residents, even triggering various diseases. Therefore, monitoring and forecasting air pollutants are crucial, providing residents with early warnings of air pollution and helping relevant departments take preventative measures to minimize adverse health effects. This is especially true for fine particulate matter (PM2.5). 2.5 Fine particulate matter (FPM), also known as respirable particulate matter, has a greater impact on human health and the atmospheric environment because it can adsorb a large amount of toxic and harmful substances, and because it has a long residence time in the atmosphere and can be transported over long distances. FPM is closely related to many global environmental problems such as ozone layer depletion, acid rain formation, and global climate change. FPM not only affects environmental quality, atmospheric visibility, and climate change, but also harms human health; therefore, controlling FPM pollution is crucial for the comprehensive prevention and control of complex air pollution.

[0003] In recent years, the analysis and application of big data from air pollutant monitoring has received increasing attention from researchers. However, big data still holds many untapped potential values. For example, publicly available air monitoring data may reflect patterns in typical pollution events and complex correlations between data from different monitoring stations. While researchers attempt to sift through air quality datasets from monitoring stations to identify representative pollution events or classify pollution patterns, using a single station's monitoring values ​​as a representative indicator of regional air pollution remains problematic. Therefore, fully exploring air quality monitoring data, uncovering deeper spatiotemporal relationships, and especially identifying pollution sources and key nodes in regional air pollution based on data analysis, is crucial for targeted prevention and control measures to fundamentally address air pollution.

[0004] Traditional pollution source location algorithms are based on gas concentration decay models. These methods, grounded in gas diffusion theory, calculate the location of pollution sources by analyzing the relationship between pollutant concentration and diffusion distance during gas diffusion. However, these gas concentration decay model-based pollution source location methods are often implemented under ideal conditions, neglecting the influence of environmental factors such as wind direction, wind speed, pollutant release rate, atmospheric pressure, temperature, and surface conditions. Yet, environmental factors can significantly impact the results of gas concentration decay models. Social Network Analysis (SNA), a quantitative analysis method developed by sociologists using graph theory and other mathematical methods, is a relatively mature analytical approach in sociology. By studying network relationships, it helps analyze the relationships between individuals and the rules and patterns of network structure. To address the vulnerability of gas concentration decay models to environmental factors in pollution source location, some researchers have recently applied a series of SNA methods to air pollution research. However, these studies and schemes using community networks for air pollution research have largely remained at the level of identifying polluted areas, with relatively little research on locating specific pollution sources. Summary of the Invention

[0005] This invention provides a method and apparatus for locating air pollution sources based on social network analysis, so as to accurately find the key stations causing air pollution in the region and provide pollution source location information in the region.

[0006] In a first aspect, the present invention provides a method for locating air pollution sources based on social network analysis, comprising:

[0007] S1. Obtain pollutant data from each monitoring station in the region within a certain time period and preprocess it to obtain preprocessed pollutant data from each monitoring station.

[0008] S2. Screen the preprocessed pollutant data to establish an air pollution scenario;

[0009] S3. Perform social network analysis on the air pollution scene data to obtain the correlation strength between each site;

[0010] S4. Calculate the median centrality value of each station based on the correlation strength between each station and the distance between each station;

[0011] S5. Based on the median centrality value and geographic coordinates of each site, spatial interpolation is performed using the Kriging interpolation method. The pollution source is located based on the planar data obtained after spatial interpolation.

[0012] Optionally, S2 includes:

[0013] The pre-processed pollutant data from each monitoring station were converted into a two-dimensional matrix using standard scores.

[0014] The two-dimensional matrix is ​​converted into a two-dimensional Boolean matrix X based on the standard score being greater than a set threshold, in order to establish an air pollution scenario.

[0015] Optionally, the set threshold is 2.05.

[0016] Optionally, S3 includes:

[0017] Using matrix multiplication Y = X T X transforms a two-dimensional Boolean matrix X into a correlation strength matrix Y, where the matrix elements Y... ij This indicates the strength of the association between site i and site j.

[0018] Optionally, S4 includes:

[0019] The association strength matrix is ​​redefined based on the ratio of the association strength between each site to the corresponding distance between each site.

[0020] The median centrality value of each site is calculated based on the redefined association strength matrix.

[0021] Optionally, S5 includes:

[0022] Based on the median centrality value and the geographic coordinates of each station, spatial interpolation is performed using the Kriging interpolation method to obtain planar data of the median centrality value.

[0023] The geographical coordinates of the maximum value and several maxima in the planar data are determined as the location of the pollution source.

[0024] Secondly, embodiments of the present invention also provide an atmospheric pollution source location device based on social network analysis, comprising:

[0025] The data acquisition and preprocessing module is used to acquire pollutant data from each monitoring station in the region within a certain time period and preprocess the data to obtain preprocessed pollutant data from each monitoring station.

[0026] An air pollution scenario creation module is used to filter the preprocessed pollutant data and create an air pollution scenario.

[0027] The social network analysis module is used to perform social network analysis on the air pollution scene data to obtain the correlation strength between each site.

[0028] And a tool for calculating the median centrality value of each site based on the association strength between the sites and the distance between the sites;

[0029] The pollution source location module is used to perform spatial interpolation using the Kriging interpolation method based on the median centrality value and geographic coordinates of each site, and to locate the pollution source based on the planar data obtained after spatial interpolation.

[0030] The beneficial effects of this invention are:

[0031] This invention utilizes social network analysis to analyze the spatial correlations of pollution events occurring at various monitoring stations, identifies key stations within a region, and locates pollution sources. This provides real-time early warnings for air pollution and effective regional air quality management strategies, aiding in the prevention and control of particulate matter pollution events within the region. Furthermore, this invention uses data visualization tools to determine the relationships between nodes, providing a reference for future real-time air quality management decisions and cross-regional boundary transport pollution early warning. The air pollution source location method proposed in this invention can not only locate corresponding pollution areas in complex environments but also further refine the location of pollution sources to the vicinity of specific monitoring points, thereby achieving efficient and accurate air pollution source location. Attached Figure Description

[0032] Figure 1 is a flowchart of an air pollution source localization method based on social network analysis provided by an embodiment of the present invention;

[0033] Figure 2 is a schematic diagram of the spatial distribution of a monitoring station provided in an embodiment of the present invention;

[0034] Figure 3 shows the matrix X provided in the embodiment of the present invention. * A schematic diagram of a two-part network;

[0035] Figure 4 is a schematic diagram of the associated network of pollution events occurring at sites s1 to s4 according to an embodiment of the present invention;

[0036] Figure 5 is a social network topology diagram provided in an embodiment of the present invention;

[0037] Figure 6 is a bar chart of the intermediate centrality values ​​of each monitoring station provided in the embodiments of the present invention;

[0038] Figure 7 is a schematic diagram of the planar data of the intermediate centrality provided in an embodiment of the present invention. Detailed Implementation

[0039] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0040] Example

[0041] Figure 1 is a flowchart of an air pollution source localization method based on social network analysis provided by an embodiment of the present invention, which specifically includes the following steps:

[0042] S1. Obtain pollutant data from each monitoring station within the region over a certain period of time and preprocess it to obtain preprocessed pollutant data from each monitoring station.

[0043] The pollutant data mentioned above may include PM 2.5 and PM 10 Data on major air pollutants, especially particulate matter, are used in this embodiment to obtain PM2.5 data. 2.5 Let's take data as an example to illustrate.

[0044] For example, air pollutant and meteorological data within the jurisdiction are captured by 33 monitoring stations, the spatial distribution of which is shown in Figure 2. The triangles in Figure 2 represent the locations of each monitoring station. This embodiment uses PM2.5 as an example. 2.5 Taking the data as an example, data on other air pollutants can also be retrieved for analysis as needed. This includes retrieving PM2.5 data from these monitoring stations. 2.5 The data was collected at 15-minute intervals, using a total of six months' worth of data (4 × 24 × 180 = 17280). For missing values, this scheme employed the Inverse Gravity Weighted (IDW) method, using data from surrounding stations to calculate and fill in the missing values, thus fully considering the spatial relationships between the station data. Then, the data for each hour was averaged to form a series of hourly averages, resulting in a total of 4320 hours × 33 stations of data.

[0045] S2. Screen the preprocessed pollutant data to establish an air pollution scenario.

[0046] For example, the Z-score can be used to construct an air pollution scenario. The Z-score represents the difference between a number and the mean, divided by the standard deviation. In statistics, the standard score is the sign of the standard deviation of an observed or data point's value from the mean of the observed or measured values.

[0047] Air pollution-related events and scenarios are widely used as fundamental elements in developing air pollution regulations, air quality simulations, and air quality management. However, simulating the spread and diffusion of air pollutants requires significant time and computational resources to handle the complex physical and chemical reactions involved. Therefore, it is not suitable for identifying a real-time pollution event. When such simulation methods are applied to real-time air pollution analysis, they need to be simplified using appropriate air pollution events.

[0048] In this embodiment, the preprocessed pollutant data of each monitoring station is first converted into a two-dimensional matrix by calculating the standard score; then, the two-dimensional matrix is ​​converted into a two-dimensional Boolean matrix X based on the standard score being greater than a set threshold.

[0049] Preferably, the threshold value set above can be 2.05. Define PM. 2.5 Pollution events are those PM events that correspond to a relatively high Z-score (>2.05). 2.5 Values, meaning these values ​​reached the PM of the site. 2.5 The size of the 98th percentile of the overall data. The use of the standard Z-score not only allows for better comparison of PM data from different sites. 2.5 The values ​​are statistically comparable, and the data is standardized for easier processing. The formula for calculating the Z-score is as follows:

[0050]

[0051] In the above formula, Z ik It is station i at PM in hour k. 2.5 Hourly average concentration C ik Z-score, μ i and σ i They are the PMs of site i. 2.5 The average concentration and standard deviation. The raw data from 4320 hours × 33 stations in step S1, after being transformed by Z-score calculation and filtered based on Z-score > 2.05, was converted into a Boolean matrix. That is, any element with a Z-score > 2.05 is recorded as 1, otherwise as 0, representing whether a pollution event occurred. The matrix size remains 4320 × 33, with each row representing data from the same time period and columns representing different stations. This matrix is ​​denoted as X.

[0052] S3. Perform social network analysis on the air pollution scene data to obtain the correlation strength between each site.

[0053] A social network is a group of social entities, such as individuals, organizations, or nations, that share certain patterns of relationships. A social network contains a series of nodes, sometimes called "actors" or "vertices," which are interconnected through some kind of relationship (or connection, link, arc, edge). In social network analysis (SNA), network graphs are used to explain patterns of social connections, where nodes represent social entities and lines represent social interactions between nodes. The SNA method can help us identify hidden relationships between nodes, which is particularly helpful in revealing the relationship between site contamination and regional contamination.

[0054] To illustrate the network analysis method here, let's use a simple example. For ease of demonstration, assume that the matrix X above only has five time points t1 to t5 and four stations s1 to s4. In other words, whether pollution occurred at the four sites at each time point is shown in the table below. 1 represents that a pollution event occurred at the corresponding time point at the site, and vice versa:

[0055]

[0056] A bipartite network, also known as a bimodal network or bipartite graph, consists of nodes representing two distinct sets of units. In this example, a station and a time represent a bipartite network. The connections between them represent contamination events occurring at the corresponding time points. For matrix X in this example... * The bipartite network is depicted in Figure 3. Correspondingly, matrix X... * This is called the bipartite matrix that describes the bipartite network.

[0057] Since most social networks represent the relationships between a set of nodes, which usually requires a partial matrix (similar to an adjacency matrix) or a network graph, matrix multiplication Y = X is used. T X transforms the bipartite matrix X into a unipartite matrix Y, where Y represents the strength or weight of the connection between any pair of nodes. Here, the off-diagonal part of Y represents the number of time points at which contamination events occur simultaneously between different sites, i.e., the number of times two sites experience simultaneous contamination events. For the aforementioned bipartite matrix X... * It can be converted into a matrix Y after calculation. * :

[0058]

[0059] From Y * The network of pollution events occurring at sites s1 to s4 can be obtained from this, and its topology is shown in Figure 4. The connections between sites indicate the number of times pollution events occurred simultaneously at two sites, i.e., Y. * Off-diagonal elements.

[0060] The 4320×33 bipartite matrix X established in step 2 is used for matrix multiplication Y = X. T X transforms the bipartite matrix X into a single-part matrix Y of size 33×33, which gives the connection strength or connection weight between any pair of nodes. In this embodiment, the connection strength refers to the simultaneous occurrence of PM2.5 at any pair of air quality monitoring stations (nodes). 2.5 Number of pollution events. Element Y ij This means that PM occurs simultaneously at both site i and site j. 2.5The number of pollution events is, in a sense, the strength of the association between site i and site j.

[0061] S4. Calculate the median centrality value of each station based on the correlation strength between each station and the distance between each station.

[0062] In this embodiment, in order to determine PM 2.5 For critical nodes in the pollution problem, it is necessary to determine the centrality of each site in the network. This embodiment uses the concept of intermediate centrality. Intermediate centrality reflects the potential ability of a node to control the exchange of information and resources with other nodes.

[0063] The calculation process for intermediate centrality is as follows: Assume that nodes j and k must exchange information and resources through node i. Therefore, node i has a significant impact on the timing and content of the information or resources exchanged between nodes j and k. Since nodes j and k generally exchange information and resources through the shortest path (there may be multiple shortest paths), the more frequently node i appears on the shortest path between nodes j and k, the more control node i has over the information and resource exchange between nodes j and k. If node i is on more shortest paths between nodes in the network, then it can influence more information exchange and communication in the network, meaning its intermediate centrality is higher. Assume g... jk It is the number of shortest paths between points j and k, g jk (n i ) represents all points between point j and point k that pass through node i (node ​​i is represented by n). i The number of shortest paths (g) represents the number of paths to the shortest path. jk (n i ) divided by g jk This gives us the proportion of shortest paths connecting points j and k through point i. The sum of the number of shortest paths between all other pairs of points in the network through point i is the center centrality of node i, calculated as follows:

[0064]

[0065] For an SNA network with g nodes, the standardized intermediate centrality can be obtained, denoted as . The formula is as follows:

[0066]

[0067] In this embodiment, stations with high median centrality are considered key nodes and will be the focus of subsequent analysis. Considering the characteristics of the monitoring station network, pollution propagation weakens with increasing distance; therefore, the correlation strength between two distant nodes will be relatively reduced. Thus, in this embodiment, a matrix Y is re-established using a method similar to a gravity model to re-establish the correlation strength.

[0068]

[0069] Where d ij The distance between station i and station j is represented by matrix Y. The network topology of pollution events at monitoring stations within the region, as shown in Figure 5, is obtained from the distance-corrected matrix Y. The thickness of the lines connecting nodes represents the correlation strength between nodes. This embodiment uses a portion of matrix Y that considers the distance between stations to calculate the median centrality value of each station. Referring to Figure 6, the calculation results show that stations 17, 04, and 28 have high median centrality and are considered to be PM2.5 concentrations within the region. 2.5 Key points of pollution require focused monitoring and investigation.

[0070] S5. Based on the median centrality value and geographic coordinates of each site, spatial interpolation is performed using the Kriging interpolation method. The pollution source is located based on the planar data obtained after spatial interpolation.

[0071] The variogram is a unique tool in spatial statistics, describing both the spatial structural changes and stochastic variations of regionalized variables. Furthermore, establishing the variogram is crucial for subsequent spatial interpolation using the Kriging method. Here, we calculate the experimental variogram values ​​for each monitored quantity using the discrete variogram formula. We define the variogram γ(x,h) along the x-axis as half the square deviation of a monitored quantity z(x) at spatial location x and station x+h, i.e.:

[0072]

[0073] Here we only consider that the mutation is isotropic, therefore the spatial mutation will only depend on the distance h. Under the assumption of second-order stationarity, for any h, we have E[z(x)] = E[z(x+h)], therefore the variogram function can be written as:

[0074]

[0075] Thus, the formula for calculating the experimental variability function of discrete samples is:

[0076]

[0077] Here, h is the spatial distance between the sample points of the monitoring quantity, and N(h) represents the number of all sample point pairs when the distance between the sample point pairs of the monitoring quantity is h.

[0078] This embodiment employs Kriging interpolation for spatial interpolation, a geostatistical interpolation method. Based on regionalized variable theory and using a variogram model as the primary tool, this method performs interpolation based on the spatial autocorrelation analysis of sampling points and the spatial variation patterns of natural phenomena, thereby obtaining an unbiased optimal estimator. Therefore, for Kriging interpolation, let the estimated value at x0 be... Here z(x) i ) represents the sampled value, n represents the total number of samples, and λ represents the sampled value. i λ is the weight coefficient for each spatial sample point. i The calculation of weighting coefficients must meet the following conditions.

[0079] ①z * (x0) is an unbiased estimator of z(x0), i.e., E[z * [(x0)-z(x0)]=0,

[0080] ② Minimize the estimated variance, i.e., D[z] * [(x0)-z(x0)]→min.

[0081] therefore Right now The minimum variance estimate is expressed as follows:

[0082]

[0083] Finding conditional extrema using the Lagrange multiplier method:

[0084]

[0085] Where μ is a Lagrange multiplier, let F with respect to λ i The partial derivatives of μ and μ are zero. Combining this with the variogram, we can obtain:

[0086]

[0087] This is the ordinary Kriging equation system expressed using the variogram, where γ(x) i ,x j Let x be two spatial points. i With x j The experimental variogram values ​​between γ(x0,x) j Let x0 and x be two spatial points.j The experimental variability function values ​​between.

[0088] By combining the median centrality data of each site with their geographic coordinates and performing spatial interpolation using the Kriging interpolation method, we can obtain the planar data of the median centrality, which is the spatial inference result of the median centrality. Areas with high median centrality values ​​are those with a high degree of connection to the respective sites. Obtaining the geographic coordinates of the maximum value and several maxima of this median centrality planar data yields the PM (middle centrality) data. 2.5 The spatial location inference of the pollution source is shown in Figure 7. The figure reveals that the highest inferred intermediate centrality values ​​occur near stations 04 and 17; in this example, these locations are considered to be PM2.5. 2.5 Location of the pollution source.

[0089] This invention also provides an air pollution source location device based on social network analysis, the device comprising:

[0090] The data acquisition and preprocessing module is used to acquire pollutant data from each monitoring station in the region within a certain time period and preprocess the data to obtain preprocessed pollutant data from each monitoring station.

[0091] An air pollution scenario establishment module is used to filter the preprocessed pollutant data using standard scores to establish an air pollution scenario;

[0092] The social network analysis module is used to perform social network analysis on the air pollution scene data to obtain the correlation strength between each site.

[0093] And calculate the median centrality value of each station based on the correlation strength between each station and the distance between each station;

[0094] The pollution source location module is used to perform spatial interpolation using the Kriging interpolation method based on the median centrality value and geographic coordinates of each site, and to locate the pollution source based on the planar data obtained after spatial interpolation.

[0095] The air pollution scenario creation module is specifically used for:

[0096] The pre-processed pollutant data from each monitoring station were converted into a two-dimensional matrix using standard scores.

[0097] The two-dimensional matrix is ​​converted into a two-dimensional Boolean matrix X based on the standard score being greater than a set threshold, in order to establish an air pollution scenario.

[0098] The set threshold is 2.05.

[0099] Furthermore, the social network analysis module is specifically used for:

[0100] Using matrix multiplication Y = X T X transforms a two-dimensional Boolean matrix X into a correlation strength matrix Y, where the matrix elements Y... ij This indicates the strength of the association between site i and site j.

[0101] And specifically used for:

[0102] The association strength matrix is ​​redefined based on the ratio of the association strength between each site to the corresponding distance between each site.

[0103] The median centrality value of each site is calculated based on the redefined association strength matrix.

[0104] Furthermore, the pollution source location module is specifically used for:

[0105] Based on the median centrality value and the geographic coordinates of each station, spatial interpolation is performed using the Kriging interpolation method to obtain planar data of the median centrality value.

[0106] The geographical coordinates of the maximum value and several maxima in the planar data are determined as the location of the pollution source.

[0107] The atmospheric pollution source location device based on social network analysis provided in this embodiment of the invention can execute the atmospheric pollution source location method based on social network analysis provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0108] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for locating air pollution sources based on social network analysis, characterized in that, include: S1. Obtain pollutant data from each monitoring station in the region within a certain time period and preprocess it to obtain preprocessed pollutant data from each monitoring station. S2. Filter the preprocessed pollutant data to establish an air pollution scenario; S3. Perform social network analysis on the air pollution scenario data to obtain the correlation strength between each site; S4. Calculate the median centrality value of each station based on the correlation strength and distance between them; S5. Perform spatial interpolation using Kriging interpolation based on the median centrality value and geographic coordinates of each station, and locate the pollution source based on the planar data obtained after spatial interpolation; S2 includes: converting the preprocessed pollutant data of each monitoring station into a two-dimensional matrix using standard scores; converting the two-dimensional matrix into a two-dimensional Boolean matrix X based on a standard score greater than a set threshold to establish an air pollution scenario; S3 includes: using matrix multiplication Y=X T X, transform the two-dimensional Boolean matrix X into a correlation strength matrix Y, matrix elements Y ij This indicates the strength of the association between site i and site j.

2. The method according to claim 1, characterized in that, The set threshold is 2.

05.

3. The method according to claim 2, characterized in that, S4 includes: redetermining the association strength matrix based on the ratio of the association strength between each site to the corresponding distance between each site; and calculating the median centrality value of each site based on the redetermined association strength matrix.

4. The method according to claim 1, characterized in that, S5 includes: using Kriging interpolation to perform spatial interpolation based on the median centrality value and the geographic coordinates of each site to obtain planar data of the median centrality value; and determining the geographic coordinates of the maximum value and several maxima in the planar data as the location of the pollution source.

5. An air pollution source location device based on social network analysis, characterized in that, include: The data acquisition and preprocessing module is used to acquire pollutant data from each monitoring station in the region within a certain time period and preprocess the data to obtain preprocessed pollutant data from each monitoring station. An air pollution scenario establishment module is used to filter the preprocessed pollutant data and establish an air pollution scenario, including: converting the preprocessed pollutant data of each monitoring station into a two-dimensional matrix using standard scores; converting the two-dimensional matrix into a two-dimensional Boolean matrix X based on a standard score greater than a set threshold to establish the air pollution scenario; and a social network analysis module is used to perform social network analysis on the air pollution scenario data to obtain the correlation strength between each station, including: using matrix multiplication Y=X. T X, transform the two-dimensional Boolean matrix X into a correlation strength matrix Y, matrix elements Y ij The system represents the correlation strength between site i and site j; and is used to calculate the median centrality value of each site based on the correlation strength between each site and the distance between each site; the pollution source location module is used to perform spatial interpolation using the Kriging interpolation method based on the median centrality value and geographic coordinates of each site, and to locate the pollution source based on the planar data obtained after spatial interpolation.

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