SO2 emission traceability method and computing equipment

Abstract

The embodiment of the invention provides an SO2 emission tracing method and computing equipment. The SO2 emission traceability method comprises the following steps: acquiring SO2 concentration monitoring data of a plurality of monitoring points in a target time period and meteorological data in a management and control area; based on the SO2 concentration monitoring data and the meteorological data, determining a diffusion path corresponding to each piece of SO2 concentration monitoring data and SO2 concentration prediction data of a traceability point location on the diffusion path; sorting the SO2 concentration prediction data corresponding to each traceability point location according to time identifiers, and determining an SO2 concentration prediction sequence; on the basis of the SO2 concentration prediction sequence of each traceability point location and the proximity relation of each traceability point location, determining and predicting the SO2 standard exceeding pollution discharge point location. Based on the scheme provided by the embodiment of the invention, the predicted SO2 standard-exceeding pollution discharge point with relatively high credibility can be more accurately screened. And subsequently, targeted troubleshooting can be carried out aiming at the predicted SO2 standard-exceeding pollution discharge point positions, and standard-exceeding pollution discharge enterprises in the SO2 standard-exceeding pollution discharge point positions are determined.

Description

Technical Field

[0001] The present disclosure relates to the technical field of atmospheric environment monitoring, and in particular to a SO2 emission tracing method and computing device. Background Art

[0002] Energy companies, petrochemical companies and other companies will produce gaseous pollutants such as SO2 during the production process. In order to ensure air quality and avoid secondary disasters caused by excessive emissions of the above-mentioned pollutants, relevant laws, regulations and administrative rules have strict regulations on the concentration and scale of pollutant emissions from pollutant-emitting companies, requiring companies to build supporting pollution treatment equipment when installing production equipment and use pollution treatment equipment during the production process to avoid excessive emissions of gaseous pollutants. However, due to the drive to actively reduce production costs, aging of corporate equipment, improper management and maintenance, etc., the above-mentioned companies may still have problems with excessive emissions of pollutants in their production.

[0003] In order to strengthen the monitoring and management of corporate pollutant emissions and constrain corporate behavior, relevant technologies have considered tracing the source of pollutants based on atmospheric SO2 concentration monitoring data monitored by air quality monitoring stations. Specifically, when high-value SO2 concentration monitoring data is detected at air quality monitoring stations, relevant technologies are mostly used to determine the tracing direction in combination with local meteorological conditions, and then inspectors will check the tracing points in the corresponding direction. However, the aforementioned method only checks the vicinity of the target air quality station, and does not take into account the problem of high values ​​in monitoring equipment caused by the diffusion of pollutants in a large area. The directionality and target of the tracing points are not strong, resulting in a huge workload for manual tracing but a low timeliness in discovering pollution sources. Summary of the invention

[0004] In order to solve the problem that the existing pollution point tracing has weak directionality and targeting, the embodiments of the present disclosure provide a new SO2 emission tracing method and computing device.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for tracing the source of SO2 emissions, comprising: Obtain SO2 concentration monitoring data of multiple monitoring points within a target period and meteorological data within a control area, wherein both the SO2 concentration monitoring data and the meteorological data have time stamps, and the control area includes traceability points; Based on the SO2 concentration monitoring data and the meteorological data, reverse diffusion simulation is performed according to the pollutant diffusion mechanism to determine the diffusion path corresponding to each SO2 concentration monitoring data, as well as the SO2 concentration prediction data of the traceability point on the diffusion path; each SO2 concentration prediction data also has a time mark; The SO2 concentration prediction data corresponding to each traceability point are sorted according to the time stamp to determine the SO2 concentration prediction sequence; Based on the SO2 concentration prediction sequence of each tracing point and the proximity relationship of each tracing point, the predicted SO2 discharge points exceeding the standard are determined.

[0006] Optionally, the method further includes obtaining terrain feature data within the controlled area; The reverse diffusion simulation is performed based on the SO2 concentration monitoring data and the meteorological data according to the pollutant diffusion mechanism to determine the diffusion path corresponding to each SO2 concentration monitoring data, including: Based on the SO2 concentration monitoring data, the meteorological data and the terrain feature data, a reverse diffusion simulation is performed according to the pollutant diffusion mechanism to determine the diffusion path corresponding to each SO2 concentration monitoring data.

[0007] Optionally, the method further includes: obtaining a SO2 emission concentration monitoring sequence of the pollutant-discharging enterprise at each traceability point; After determining and predicting the SO2 discharge point exceeding the standard, the method further includes: Based on the SO2 emission concentration monitoring sequence of each pollutant-discharging enterprise and the SO2 concentration prediction sequence, the SO2 emission-exceeding enterprises are predicted to be determined.

[0008] Optionally, the step of determining and predicting SO2 excessive emission enterprises based on the SO2 emission concentration monitoring sequence of each emission enterprise and the SO2 concentration prediction sequence includes: Process the SO2 emission concentration monitoring sequence of each pollutant-discharging enterprise to determine the corresponding monitoring change trend; and process the SO2 concentration prediction sequence corresponding to the predicted SO2 exceeding emission point to determine the corresponding prediction change trend; A target monitoring change trend that is at least partially identical to the predicted change trend is selected from the monitoring change trends, and the pollutant-discharging enterprise corresponding to the target monitoring change trend is used as the predicted SO2 excessive pollutant-discharging enterprise.

[0009] Optionally, the step of determining and predicting SO2 excessive emission enterprises based on the SO2 emission concentration monitoring sequence of each emission enterprise and the SO2 concentration prediction sequence includes: Based on the SO2 emission concentration monitoring series of each pollutant-discharging enterprise, a weighted sum of multiple weights is performed to obtain multiple combined concentration prediction series; Processing each of the combined concentration prediction sequences to determine the corresponding combined change trend; and processing the SO2 concentration prediction sequence corresponding to the predicted SO2 exceeding standard discharge point to determine the corresponding prediction change trend; Selecting a target combined change trend that is at least partially the same as the predicted change trend from the combined change trends, and determining a corresponding pollutant-discharging enterprise with a higher weight based on the target combined change trend; The polluting enterprises with the highest weights are taken as the predicted SO2 exceeding the emission standard enterprises.

[0010] Optionally, before obtaining the SO2 concentration monitoring data of multiple monitoring points within the target period and the meteorological data within the control area, the method further includes: Determine whether the SO2 concentration monitoring data at the first monitoring point at the first time triggers a high value alarm; Determine to trigger a high value alarm, and determine the target time period based on the first time point and a preset duration; the first monitoring point is at least one monitoring point among the multiple monitoring points.

[0011] Optionally, the determining whether the SO2 concentration monitoring data at the first monitoring point at the first time point triggers a high value alarm includes: In response to the SO2 concentration monitoring data at the first monitoring point at the first time point satisfying at least one of the following conditions, it is determined that a high value alarm is triggered: The SO2 concentration monitoring data at the first monitoring point at the first time point exceeds the set comparison threshold; The ratio of the SO2 concentration monitoring data at the first monitoring point at the first time point to the SO2 concentration monitoring data at the previous time point is greater than a preset ratio, and the preset ratio is at least greater than 1; The ranking of the SO2 concentration monitoring data of the first monitoring point at the first time point is higher than the ranking of the SO2 concentration monitoring data at the previous time point by a set change ranking; The difference between the SO2 concentration monitoring data of the first monitoring point at the first time point and the average value of the SO2 concentration monitoring data of the adjacent points is greater than the set outlier threshold.

[0012] Optionally, after checking the predicted SO2 excessive discharge point to determine the excessive discharge, the method further includes: The SO2 concentration monitoring data when the first monitoring point triggers a high value alarm is used as high value alarm triggering data.

[0013] Optionally, before obtaining the SO2 concentration monitoring data of multiple monitoring points within the target period and the meteorological data within the control area, the method further includes: The historical SO2 concentration monitoring data of multiple target monitoring points are analyzed to determine the target time period.

[0014] In a second aspect, the present disclosure implements a computing device, including a processor and a memory, wherein the memory is used to store a computer program; when the computer program is loaded by the processor, the processor executes the SO2 emission tracing method as described above.

[0015] The solution provided by the embodiment of the present disclosure utilizes the SO2 concentration monitoring data of multiple monitoring points and the meteorological data of the control area to perform reverse diffusion simulation, and after determining the SO2 concentration prediction data of each traceability point at different times, a SO2 concentration prediction sequence of each traceability point is constructed. Based on the basic premise that pollutants are either diffused or discharged within the traceability point, through the SO2 concentration prediction sequence of each traceability point and the proximity relationship of the traceability point, according to the predetermined logical judgment rules, the predicted SO2 excessive discharge points can be determined. Based on the solution provided by the embodiment of the present disclosure, the predicted SO2 excessive discharge points with higher credibility can be screened more accurately. Subsequently, targeted investigations can be carried out on the predicted SO2 excessive discharge points to identify the excessively discharged enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor, including Figure 1 is a flow chart of the SO2 emission source tracing method provided by the embodiment of the present disclosure; Figure 2 is a flow chart for determining enterprises that discharge pollutants exceeding the standards in some embodiments of the present disclosure; Figure 3 is a flow chart of determining enterprises that discharge pollutants exceeding the standards in other embodiments of the present disclosure; Figure 4 is a schematic diagram of a SO2 emission source tracing device provided in an embodiment of the present disclosure; Figure 5 It is a schematic diagram of the structure of a computing device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0019] The term "including" and its variations used in this document are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. In this document, relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0020] In order to solve the problem that the traceability points determined by the existing solutions are not very directional and targeted, resulting in a huge workload for traceability but a low timeliness in discovering the pollution source, the present disclosure provides a new SO2 emission traceability method. The traceability method provided by the present disclosure is executed by a computing device.

[0021] Figure 1 This is a flow chart of the SO2 emission source tracing method provided by the embodiment of the present disclosure. Figure 1 As shown, the SO2 emission tracing method provided in the embodiment of the present disclosure includes S110-S140.

[0022] S110: Obtain SO2 concentration monitoring data of multiple monitoring points within the target period and meteorological data within the control area.

[0023] In order to trace the pollution source, reverse simulation analysis needs to be performed based on the data of the monitoring points in the subsequent operation steps. Reverse simulation analysis requires a traceability basis, which means that it is necessary to obtain the corresponding data within a period of time as a basis, so it is also necessary to obtain traceability data within a period of time, that is, traceability data of the target period is required.

[0024] According to the positioning theory, a point can only be determined by the intersection of at least two lines. Correspondingly, at least two paths need to be determined in the tracing process, and the point including the pollution source can be determined by cross-comparison of the paths. Based on this goal, the embodiment of the present disclosure obtains SO2 concentration monitoring data of multiple monitoring points to determine multiple tracing paths (that is, to obtain the diffusion path mentioned later).

[0025] The SO2 concentration monitoring data is time-stamped data, which means that the acquisition time of the SO2 concentration monitoring data can be determined. The aforementioned time stamp will serve as the basis for subsequent reverse diffusion simulation analysis.

[0026] The control area is a pre-determined area where pollutant dischargers need to be monitored to see if they are exceeding the discharge standards. In practical applications, the control area generally corresponds to the administrative management area of ​​an environmental regulatory body.

[0027] The control area includes traceability points. The traceability points are the points where the pollutant-discharging enterprises are located. In real scenarios, the traceability points can be industrial zones, high-tech development zones, etc. in a region. Here, the traceability points are treated as points, but they still represent a regional range. For example, when the control area is a prefecture-level city, the traceability point can represent a range with a radius of 5 km.

[0028] Acquiring meteorological data in the control area during the target time period is to obtain meteorological data in the control area at each time point during the target time period according to a predetermined time period. In the specific implementation, in order to ensure the accuracy of the subsequent reverse diffusion simulation, the meteorological data in the control area should be as detailed as possible. For example, if the control area is a prefecture-level city, in order to ensure the accuracy of the simulation, the meteorological data should at least be county-level meteorological data or township-level meteorological data.

[0029] Since wind speed and wind direction directly affect the diffusion of pollutants, meteorological data at least include wind speed and wind direction data. The aforementioned wind speed and wind direction include not only the wind speed and wind direction parallel to the ground surface, but also the wind speed and wind direction of the rising or falling airflow. In addition, in some cases, pollutants may settle or transform due to the effect of water vapor in the air, so in some cases, meteorological data may also include air humidity data, water content data, etc.

[0030] S120: Based on the SO2 concentration monitoring data and meteorological data, reverse diffusion simulation is performed according to the pollutant diffusion mechanism to determine the diffusion path corresponding to each SO2 concentration monitoring data, as well as the SO2 concentration prediction data of the traceability point on the diffusion path.

[0031] In the disclosed embodiment, each SO2 concentration prediction data also has a time stamp.

[0032] According to the actual situation, after air pollutants are discharged by polluting enterprises, they diffuse according to a specific diffusion mechanism under the action of external forces such as wind. Through a large amount of historical experimental data and simulation experiments, the diffusion mechanism of pollutants is determined. For example, for some pollutants, the Lagrangian particle diffusion model can be used to characterize the pollutant diffusion mechanism. When the diffusion mechanism and diffusion power (that is, the size and direction of the wind represented by meteorological data) are determined, the pollutant concentration at the downstream location can be determined by the pollutant concentration in the upstream direction.

[0033] Accordingly, on the premise of determining the SO2 concentration monitoring data of the downstream location point (that is, the target monitoring point) and the meteorological data in the control area, a reverse simulation is performed based on the pollutant diffusion mechanism, that is, a reverse diffusion simulation is performed, and a reverse diffusion path can be obtained. In the case where the traceability point is located on a certain diffusion path, the SO2 concentration forecast data at this traceability point at a certain point in time can also be determined (here it is only called SO2 concentration forecast data from the perspective of prediction, and does not mean that it is data at a future time point).

[0034] According to the above method, for the SO2 concentration monitoring data obtained at the same monitoring point at different time points in the target time period, multiple diffusion paths can be obtained (from the spatial dimension, the multiple diffusion paths here may be the same or different). Similarly, for the SO2 concentration monitoring data obtained at different monitoring points at the same time point at the target time point, multiple diffusion paths can be obtained (from the time dimension, the multiple diffusion paths here are the same, but their spatial dimensions are different).

[0035] According to the aforementioned method, for a traceability point, SO2 concentration prediction data on multiple diffusion paths can be obtained, that is, there will be multiple SO2 concentration prediction data for a traceability point. It should be noted here that the aforementioned multiple SO2 concentration prediction data for a traceability point should be prediction data for different time points, that is, the multiple pollution source concentration prediction data for a traceability point have corresponding (different) time stamps. Of course, in some cases, due to problems such as inaccurate calculations and model simulations, some traceability points may have more than one SO2 concentration prediction data at one time point (that is, one time stamp). At this time, data processing can be used to exclude data with poor confidence, or several data can be weighted to obtain unique data for the corresponding traceability point at a time point.

[0036] S130: Sort the SO2 concentration prediction data corresponding to each traceability point according to the time stamp to determine the SO2 concentration prediction sequence.

[0037] According to the above method, SO2 concentration prediction data with different time stamps can be obtained for each traceability point. According to the order of time stamps, the corresponding SO2 concentration prediction data are sorted to obtain the SO2 concentration prediction sequence of each traceability point. The SO2 concentration prediction sequence represents the change of pollutant concentration at the traceability point within the target time period.

[0038] S140: Based on the SO2 concentration prediction sequence of each tracing point and the proximity relationship of each tracing point, determine the predicted SO2 discharge point exceeding the standard.

[0039] Here, we first assume that other areas outside the control area do not produce pollutants (of course, this assumption may not be true, this is an ideal situation), then the reason why the monitoring point detects the concentration of pollutants is that the pollutant-discharging enterprises in the control area discharge pollutants. Pollutant-discharging enterprises are located in the area corresponding to the traceability point, and the pollutants discharged by the corresponding pollutant-discharging enterprises will inevitably be reflected in the SO2 concentration prediction sequence. If a pollutant-discharging enterprise in a certain traceability point exceeds the discharge of pollutants in a short period of time, it will inevitably be reflected in the corresponding SO2 concentration prediction sequence.

[0040] Correspondingly, by comparing the SO2 concentration prediction sequences of each nearby tracing point, it can be found that the excessive pollutant concentration at some tracing points at certain times is not caused by the diffusion of pollutants from surrounding tracing points. It is most likely caused by excessive emissions from the polluting enterprises within this tracing point. Accordingly, this tracing point can be regarded as an excessive pollution discharge point.

[0041] In a specific implementation, the computing device can screen the SO2 concentration prediction sequence of each traceability point through a pre-set data threshold to determine the high-value point segment sequence. After obtaining the high-value point segment sequence, the segment sequence of the corresponding time period of the adjacent traceability point is searched based on the time period corresponding to the high-value point segment sequence. By comparing and logically reasoning the SO2 concentration prediction data in the aforementioned segment sequences, it can be determined that the high-value points are not caused by the diffusion of pollutants, and the aforementioned high-value points can also be used as predicted SO2 discharge points that exceed the standard.

[0042] In specific implementations, the computing device can compare the excessive pollution discharge data of nearby tracing points based on predetermined comparison logic rules (this comparison logic rule is determined based on the diffusion mechanism of pollutants under meteorological factors) and meteorological data to determine the predicted SO2 excessive pollution discharge points.

[0043] The SO2 emission source tracing method provided by the embodiment of the present disclosure uses the SO2 concentration monitoring data of multiple monitoring points and the meteorological data of the control area to perform reverse diffusion simulation, and after determining the SO2 concentration prediction data of each tracing point at different times, constructs the SO2 concentration prediction sequence of each tracing point. Based on the basic premise that pollutants are either diffused or discharged within the tracing point, through the SO2 concentration prediction sequence of each tracing point and the proximity relationship of the tracing point, according to the predetermined logical judgment rules, it is also possible to determine and predict the SO2 excessive discharge point.

[0044] Based on the SO2 emission source tracing method provided by the embodiment of the present disclosure, it is possible to more accurately screen out the predicted SO2 emission points with higher credibility. Subsequently, targeted investigations can be conducted on the predicted SO2 emission points with excessive emission to identify the enterprises that have exceeded the emission standards.

[0045] The above scheme is to simulate reverse diffusion according to the pollutant diffusion mechanism based on SO2 concentration monitoring data and meteorological data, assuming that the control area is an ideal plane area or an approximate plane area, that is, assuming that the control area is a plain area or a plateau area. However, in actual situations, the control area may not be the aforementioned ideal plane area or approximate plane area. The topographical features of the control area will affect the diffusion of pollutants. In addition, it may also affect the wind speed and wind direction in the local area (that is, it will affect the meteorological parameters). For this reason, in some embodiments, while executing the above S110, the computing device will also obtain the terrain feature data in the control area. Correspondingly, in the case of obtaining the terrain feature data in the control area, the above S120 is specifically: based on the SO2 concentration monitoring data, meteorological data and terrain feature data, reverse diffusion simulation is performed according to the pollutant diffusion mechanism to determine the diffusion path corresponding to each SO2 concentration monitoring data.

[0046] In actual applications, determining the predicted SO2 excessive emission points is not the goal, but screening the enterprises predicted to have excessive SO2 emission points and predicting the SO2 excessive emission enterprises is the goal. To this end, after executing the aforementioned S140, the enterprises within the predicted SO2 excessive emission points must be screened to determine the predicted SO2 excessive emission enterprises.

[0047] In some embodiments of the present disclosure, a real-time online monitoring system is provided in the pollutant discharge enterprise to monitor the concentration of pollutant discharge, and the real-time online monitoring system can monitor the concentration of pollutants. Accordingly, after executing the aforementioned S140, the computing device can also execute the following S150.

[0048] S150: Based on the SO2 emission concentration monitoring sequence and SO2 concentration prediction sequence of each pollutant-discharging enterprise, determine the SO2 emission enterprises predicted to exceed the standard.

[0049] In the specific implementation, after determining the SO2 emission concentration monitoring sequence and SO2 concentration prediction sequence of each pollutant-discharging enterprise, it is possible to determine which pollutant-discharging enterprises have a higher correlation between their emission concentration monitoring sequence and SO2 concentration prediction sequence by mutual comparison, and use the pollutant-discharging enterprises with higher correlation as predicted SO2-exceeding pollutant-discharging enterprises.

[0050] Fig. 2 is a flowchart of determining enterprises that discharge pollutants exceeding the standard in some embodiments of the present disclosure. As shown in Fig. 2, in some embodiments of the present disclosure, the computing device may determine and predict enterprises that discharge pollutants exceeding the standard using the following S151-S154.

[0051] S151: Process the SO2 emission concentration monitoring sequence of each pollutant-discharging enterprise and determine the corresponding monitoring change trend.

[0052] In a specific implementation, the computing device can perform derivative operations on adjacent data in the SO2 emission concentration monitoring sequence of each pollutant discharger to determine the first-order derivative and / or second-order derivative, and then sort the first-order derivatives or sort the second-order derivatives according to the time mark to determine the corresponding monitoring change trend. The first-order monitoring change trend constructed by the first-order derivative represents the change rate of the pollutant discharge concentration of the pollutant discharger, and the second-order derivative represents the degree of change of the pollutant discharge change rate of the pollutant discharger.

[0053] S152: Process the predicted SO2 concentration sequence corresponding to the predicted SO2 exceeding discharge point, and determine the corresponding predicted change trend.

[0054] The computing device may process the SO2 concentration prediction sequence using the aforementioned method for processing the SO2 exhaust concentration monitoring sequence to determine the corresponding predicted change trend.

[0055] In actual implementation, in order to simplify subsequent comparisons, the monitoring change trends determined in the previous text by first-order derivatives, second-order derivatives and other methods can be simply normalized to obtain normalized monitoring change trends and predicted change trends, and the normalized monitoring change trends and predicted change trends can be subsequently compared.

[0056] S153: Compare the monitored change trend and the predicted change trend to determine whether there is a target monitored change trend that is at least partially identical to the predicted change trend; if so, execute S154.

[0057] S154: The pollutant-discharging enterprises corresponding to the target monitoring change trend are used as the predicted SO2 pollutant-discharging enterprises exceeding the standard.

[0058] When comparing the monitored change trend and the predicted trend, a change trend sequence can be intercepted from the predicted change trend and compared with the corresponding time period data in the monitored change trend, or compared with the monitored change trend by sliding window processing to determine whether there is a segment in the monitored change trend that is the same or similar to the aforementioned change trend sequence. If it is determined that there is the aforementioned segment, the corresponding monitored change trend is determined as the target monitored change trend, and the pollutant discharger corresponding to the target monitored change trend is used as the predicted SO2 excessive discharger.

[0059] By using the aforementioned S151-S154 to calculate the monitoring change trend and the predicted change trend, the influence of pollutant emissions of non-changing data on the data is eliminated, and it is easier to identify and predict SO2 pollutant discharge enterprises that exceed the standard. In specific implementation, the aforementioned method can more accurately identify pollutant discharge enterprises that exceed the standard when there is only one pollutant discharge enterprise at the pollutant discharge point that exceeds the standard, or when the pollutant discharge change trends of multiple pollutant discharge enterprises are consistent.

[0060] Figure 3Flowcharts for determining enterprises that discharge pollutants exceeding the standards in other embodiments of the present disclosure. Figure 3 As shown, in some embodiments of the present disclosure, the computing device may use the following S155-S159 to determine and predict the SO2 pollution-exceeding enterprises.

[0061] S155: Perform weighted summation of multiple weights based on the SO2 emission concentration monitoring sequence of each pollutant-discharging enterprise to obtain multiple combined concentration prediction sequences.

[0062] In actual applications, there may be multiple excessive pollutant discharge enterprises in the excessive pollutant discharge points, and the pollutant discharge changes of multiple excessive pollutant discharge enterprises are random and have no correlation with each other. Therefore, based on the SO2 emission concentration monitoring sequence of each pollutant discharge enterprise, the SO2 excessive pollutant discharge enterprise cannot be predicted according to the previous method. In addition, the unit time emission of each pollutant discharge enterprise may not be the same, which will also affect the determination and prediction of the SO2 excessive pollutant discharge enterprise in the previous article.

[0063] To solve this problem, the disclosed embodiment considers that the SO2 emission concentration monitoring sequence of each pollutant-discharging enterprise can be processed by weighted summation of multiple weights to obtain multiple combined concentration prediction sequences. The weight coefficients used in the aforementioned weighted summation can be determined based on empirical data such as the production characteristics of each enterprise and the emission volume of each enterprise, or can be determined by mining historical data through machine learning methods. By setting reasonable weight coefficients, or setting a large number of weight coefficient combinations, some or a certain combined concentration sequence obtained may characterize the changes in emission concentration of excessive emission points caused by the emission of pollutants from multiple enterprises.

[0064] S156: Process each combined concentration prediction sequence to determine the corresponding combined change trend; and process the SO2 concentration prediction sequence corresponding to the predicted SO2 exceeding standard discharge point to determine the corresponding prediction change trend.

[0065] The execution process of S156 is the same as S151-S152 in the previous text, which will not be repeated here. For details, please refer to the previous description.

[0066] S157: Select a target monitoring change trend that is at least partially identical to the predicted change trend from the combined change trend, and determine the corresponding ranked enterprise with the highest weight based on the target monitoring change trend.

[0067] S158: The pollutant-emitting enterprises with the highest weights are used as the predicted SO2 pollutant-emitting enterprises that exceed the standard.

[0068] When comparing the combined monitoring change trend and the predicted trend, a change trend sequence can be intercepted from the predicted change trend and compared with the corresponding time period data in the combined change trend, or it can be compared with the combined change trend by sliding window processing to determine whether there are fragments in the combined change trend that are the same or similar to the aforementioned change trend sequence. If it is determined that there are the aforementioned fragments, the corresponding combined change trend is determined as the target combined change trend. Because the change trend of the pollutant-discharging enterprise with the highest weight in the target combined change trend has a greater impact on the combined change trend, it is more likely to be an enterprise that discharges pollutants in excess of the standard, so it is used as the predicted SO2 pollutant-discharging enterprise.

[0069] By adopting the aforementioned S151-S154 or S155-S158, even if the SO2 emission concentration monitoring sequence of the pollutant-emitting enterprise is inaccurate or falsified, it is possible to determine and predict the SO2 pollutant-emitting enterprise exceeding the standard through data reasoning.

[0070] In the above scheme, there is no restriction on the target time period. In practical applications, if the above method is used, a large amount of computing resources may be wasted, and the predicted SO2 excessive discharge points and pollutant discharge enterprises obtained have no practical application significance. Based on this, in some embodiments, the following S160-S170 may also be executed before executing S110.

[0071] S160: Determine whether the SO2 concentration monitoring data at the first monitoring point at the first time point triggers a high value alarm.

[0072] In a specific implementation, the computing device can be connected to the monitoring devices at each monitoring point in communication, and receive the SO2 concentration monitoring data sent by each monitoring point in real time. After receiving the SO2 concentration monitoring data, the computing device will judge with the high-value alarm rule of the corresponding monitoring device to determine whether the SO2 concentration monitoring data triggers a high-value alarm. In the embodiment of the present disclosure, if a high-value alarm is triggered based on the SO2 concentration monitoring data of a certain monitoring point, the corresponding monitoring point is the first monitoring point.

[0073] In a specific implementation, the computing device may determine whether the pollutant concentration at the first monitoring point triggers a high value alarm in the following manner.

[0074] (1) The SO2 concentration monitoring data at the first monitoring point at the first time exceeds the set comparison threshold; in specific implementation, the comparison threshold can be reasonably set according to national standards, industry standards or local standards of the control area. For example, if the SO2 concentration in a certain place reaches 150 micrograms per cubic meter, it is determined that a high value alarm is triggered.

[0075] (2) The ratio of the SO2 concentration monitoring data at the first monitoring point at the first time point to the SO2 concentration monitoring data at the previous time point is greater than a preset ratio, and the preset ratio is at least greater than 1. In actual situations, the SO2 concentration monitoring data in a certain area will remain within a certain numerical range, and there will be no sudden increase. If a sudden increase occurs, it is most likely caused by excessive emissions. Based on this, the embodiment of the present disclosure can set an abnormal sudden increase multiple greater than 1. If the ratio of the SO2 concentration monitoring data collected at the first time point to the average value of the SO2 concentration monitoring data in the previous period is greater than the abnormal sudden increase multiple, a high value alarm is triggered. In some applications, the abnormal sudden increase multiple is set at 1.5.

[0076] (3) The ranking of the SO2 concentration monitoring data of the first monitoring point at the first time point is higher than the set change ranking compared with the ranking of the SO2 concentration monitoring data at the previous time point. The ranking mentioned here refers to the ranking of the SO2 concentration monitoring data of the monitoring points in the monitoring area. Under normal circumstances, the SO2 concentration monitoring data of each monitoring point is basically stable, and the corresponding ranking will not change. If the ranking of the SO2 concentration monitoring data of the first monitoring point suddenly rises by several places (that is, the ranking deteriorates), the SO2 concentration monitoring data of the first monitoring point is likely to be abnormal. Accordingly, a high value alarm can be triggered. For example, in some embodiments, if the pollution discharge ranking of the SO2 concentration monitoring data of the first monitoring point deteriorates by five places, a high value alarm is triggered.

[0077] (4) The difference between the SO2 concentration monitoring data of the first monitoring point at the first time point and the average value of the SO2 concentration monitoring data of the adjacent points is greater than the set outlier threshold.

[0078] Under certain environmental conditions, the SO2 concentration monitoring data of each monitoring point is within a regional range, and the data distribution conforms to the normal distribution or specific distribution. If the SO2 concentration monitoring data of the first monitoring station is significantly higher than the data of other monitoring stations, specifically, the SO2 concentration monitoring data of the first monitoring station is significantly higher than the average value of the SO2 concentration monitoring data of all the occupied areas, that is, it is significantly higher than the set outlier threshold, it can be judged that the SO2 concentration monitoring data of the first monitoring station is abnormal, and a high value warning can be triggered.

[0079] S170: Determine whether to trigger a high-value alarm, and determine a target time period based on the first time point and a preset duration.

[0080] After determining that a high value alarm is triggered, the first time point and the predetermined preset duration can be used to determine the target period. Specifically, the first time point can be used as the end time point or middle time point of the target period, and the target period is determined based on the preset duration.

[0081] In some other embodiments, the computing device does not determine the target time period according to the aforementioned method, but determines the target time period by analyzing historical data and performing high-value analysis on the historical SO2 concentration monitoring data of multiple target monitoring points. For example, some polluting enterprises have obvious time characteristics for exceeding the pollution standards, such as exceeding the pollution standards during the midnight period. By analyzing the historical SO2 concentration monitoring data of multiple target monitoring points, the time pattern of the high-value data is found to determine the target time period. For example, if it is found that some monitoring points have high-value pollutant data at night for a long time from 20:00 to 23:00, the target time period can be determined as 20:00-23:00.

[0082] As mentioned above, it is necessary to obtain the SO2 concentration monitoring data of the monitoring points or the pollution concentration monitoring data of the pollutant-discharging enterprises. In practical applications, after receiving the aforementioned data, the computing device will clean and pre-process the data to eliminate invalid data caused by the monitoring equipment itself, operation and maintenance operations, or environmental factors to ensure the availability of the data. In the specific implementation, it is necessary to consider excluding zero values, negative values, excluding out-of-range data, excluding continuous constant data (which is likely to be fake data), and excluding abnormal surge data (specifically, the second-order difference method can be used to eliminate data with obviously excessive second-order change rates).

[0083] In the process of using the method to determine the excessive discharge points and predict SO2 excessive discharge enterprises, a large amount of data will be obtained, including abnormal high-value data and normal data. Data can also be sorted based on the aforementioned data to form a typical high-value database for subsequent data analysis, and the high-value screening rules can be optimized based on the data analysis characteristics of the typical high-value database. In addition, the aforementioned data can also be used to construct high-value identification and traceability reports to provide suggestions for administrative departments to carry out pollution control.

[0084] In addition to providing the aforementioned SO2 emission tracing method, the embodiment of the present disclosure also provides a SO2 emission tracing device. Figure 4 Schematic diagram of the SO2 emission source tracing device provided in the embodiment of the present disclosure. Figure 4 As shown, the SO2 emission source tracing device 400 includes a data acquisition unit 401, a numerical analysis unit 402, a sequence construction unit 403 and a screening unit 404.

[0085] The data acquisition unit 401 is used to obtain SO2 concentration monitoring data of multiple monitoring points within the target period and meteorological data in the control area. The SO2 concentration monitoring data and meteorological data both have time stamps, and the control area includes traceability points.

[0086] The numerical analysis unit 402 is used to perform reverse diffusion simulation according to the pollutant diffusion mechanism based on the SO2 concentration monitoring data and meteorological data, determine the diffusion path corresponding to each SO2 concentration monitoring data, and the SO2 concentration prediction data of the traceability point on the diffusion path; each SO2 concentration prediction data also has a time mark.

[0087] The sequence construction unit 403 is used to sort the SO2 concentration prediction data corresponding to each traceability point according to the time mark to determine the SO2 concentration prediction sequence.

[0088] The screening unit 404 is used to determine the predicted SO2 exceeding the standard discharge point based on the SO2 concentration prediction sequence of each tracing point and the proximity relationship of each tracing point.

[0089] In some examples, the data acquisition unit 401 also acquires terrain feature data within the control area. Accordingly, the numerical analysis unit 402 performs reverse diffusion simulation according to the pollutant diffusion mechanism based on the SO2 concentration monitoring data, meteorological data and terrain feature data to determine the diffusion path corresponding to each SO2 concentration monitoring data.

[0090] In some embodiments, the data acquisition unit 401 also acquires the SO2 emission concentration monitoring sequence of the pollutant dischargers in each traceability point. Accordingly, the screening unit 404 determines the predicted SO2 excessive pollutant dischargers based on the SO2 emission concentration monitoring sequence and SO2 concentration prediction sequence of each pollutant discharger.

[0091] In some embodiments, the screening unit 404 processes the SO2 emission concentration monitoring sequence of each pollutant discharging enterprise to determine the corresponding monitoring change trend; and processes the SO2 concentration prediction sequence corresponding to the predicted SO2 exceeding emission point to determine the corresponding prediction change trend; selects a target monitoring change trend that is at least partially identical to the prediction change trend from the monitoring change trend, and uses the pollutant discharging enterprise corresponding to the target monitoring change trend as the predicted SO2 exceeding emission enterprise.

[0092] In some embodiments, the screening unit 404 performs weighted summation of multiple weights based on the SO2 emission concentration monitoring sequences of each pollutant discharge enterprise to obtain multiple combined concentration prediction sequences; processes each combined concentration prediction sequence to determine the corresponding combined change trend; and processes the SO2 concentration prediction sequence corresponding to the predicted SO2 exceeding emission point to determine the corresponding predicted change trend; selects a target combined change trend that is at least partially identical to the predicted change trend from the combined change trend, and determines the corresponding pollutant discharge enterprise with a higher weight based on the target combined change trend; and uses the pollutant discharge enterprise with a higher weight as the predicted SO2 exceeding emission enterprise.

[0093] In some embodiments, before obtaining the SO2 concentration monitoring data of multiple monitoring points within the target time period and the meteorological data within the control area, the data acquisition unit 401 determines whether the SO2 concentration monitoring data of the first monitoring point at the first time point triggers a high value alarm; determines whether the high value alarm is triggered, and determines the target time period based on the first time point and the preset time length; the first monitoring point is at least one monitoring point among the multiple monitoring points.

[0094] In some embodiments, the data acquisition unit 401 determines that a high value alarm is triggered in response to the SO2 concentration monitoring data of the first monitoring point at the first time point satisfying at least one of the following conditions: the SO2 concentration monitoring data of the first monitoring point at the first time point exceeds a set comparison threshold; the ratio of the SO2 concentration monitoring data of the first monitoring point at the first time point to the SO2 concentration monitoring data at the previous time point is greater than a preset ratio, and the preset ratio is at least greater than 1; the ranking rank of the SO2 concentration monitoring data of the first monitoring point at the first time point and the ranking rank of the SO2 concentration monitoring data at the previous time point are higher than a set change rank; the difference between the SO2 concentration monitoring data of the first monitoring point at the first time point and the average value of the SO2 concentration monitoring data of the adjacent points is greater than a set outlier threshold.

[0095] In some embodiments, after checking the predicted SO2 excessive discharge point to determine that it exceeds the discharge standard, the method further includes: using the SO2 concentration monitoring data when the first monitoring point triggers a high value alarm as high value alarm triggering data.

[0096] In some embodiments, before acquiring the SO2 concentration monitoring data of multiple monitoring points within the target period and the meteorological data within the control area, the data acquisition unit 401 analyzes the historical SO2 concentration monitoring data of multiple target monitoring points to determine the target period.

[0097] The embodiment of the present disclosure also provides a computing device for implementing the aforementioned method. Figure 5 Schematic diagram of the structure of the computing device provided by the embodiment of the present disclosure. Figure 5 , which shows a schematic diagram of the structure of a computing device 500 suitable for implementing the embodiments of the present disclosure. Figure 5 The computing device shown is only an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present disclosure.

[0098] like Figure 5As shown, the computing device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 502 or a program loaded from a storage device 508 to a random access memory RAM 503. Various programs and data required for the operation of the computing device 500 are also stored in the RAM 503. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output I / O interface 505 is also connected to the bus 504.

[0099] Typically, the following devices may be connected to the I / O interface 505: input devices 505 including, for example, a touch screen, a touch pad, a camera, a microphone, etc.; output devices 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 508 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 509. The communication devices 509 may allow the computing device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The computing device 500 is shown with various devices, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have instead.

[0100] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0101] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable storage medium, a computer-readable signal medium, or any combination of the above two.

[0102] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0103] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than a computer readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0104] In some embodiments, the client and computing devices may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0105] The computer-readable medium may be included in the computing device, or may exist independently without being incorporated into the computing device.

[0106] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the tester's computer, partially on the tester's computer, as a separate software package, partially on the tester's computer and partially on a remote computer, or entirely on a remote computer or computing device. In cases involving a remote computer, the remote computer may be connected to the tester's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system based on hardware that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0108] The units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. The name of the unit does not constitute a limitation on the unit itself in some cases. The functions described above in this article may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: field programmable gate array (FPGA), application specific integrated circuit (ASIC), application specific standard product (ASSP), system on chip (SOC), complex programmable logic device (CPLD), etc.

[0109] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for tracing the source of SO2 emissions, characterized in that: include: Obtain SO2 concentration monitoring data of multiple monitoring points within a target period and meteorological data within a control area, wherein both the SO2 concentration monitoring data and the meteorological data have time stamps, and the control area includes traceability points; Based on the SO2 concentration monitoring data and the meteorological data, reverse diffusion simulation is performed according to the pollutant diffusion mechanism to determine the diffusion path corresponding to each SO2 concentration monitoring data, as well as the SO2 concentration prediction data of the traceability point on the diffusion path; each SO2 concentration prediction data also has a time mark; The SO2 concentration prediction data corresponding to each traceability point are sorted according to the time stamp to determine the SO2 concentration prediction sequence; Based on the SO2 concentration prediction sequence of each tracing point and the proximity relationship of each tracing point, the predicted SO2 discharge points exceeding the standard are determined.

2. The method according to claim 1, characterized in that The method also includes obtaining terrain feature data within the controlled area; The reverse diffusion simulation is performed based on the SO2 concentration monitoring data and the meteorological data according to the pollutant diffusion mechanism to determine the diffusion path corresponding to each SO2 concentration monitoring data, including: Based on the SO2 concentration monitoring data, the meteorological data and the terrain feature data, a reverse diffusion simulation is performed according to the pollutant diffusion mechanism to determine the diffusion path corresponding to each SO2 concentration monitoring data.

3. The method according to claim 1 or 2, characterized in that: The method further includes: obtaining a monitoring sequence of SO2 emission concentration of pollutant discharge enterprises in each traceability point; After determining and predicting the SO2 discharge point exceeding the standard, the method further includes: Based on the SO2 emission concentration monitoring sequence of each pollutant-discharging enterprise and the SO2 concentration prediction sequence, the SO2 emission-exceeding enterprises are predicted to be determined.

4. The method according to claim 3, characterized in that The determining and predicting of SO2 excessive emission enterprises based on the SO2 emission concentration monitoring sequence of each emission enterprise and the SO2 concentration prediction sequence includes: Process the SO2 emission concentration monitoring sequence of each pollutant-discharging enterprise to determine the corresponding monitoring change trend; and process the SO2 concentration prediction sequence corresponding to the predicted SO2 exceeding emission point to determine the corresponding prediction change trend; A target monitoring change trend that is at least partially identical to the predicted change trend is selected from the monitoring change trends, and the pollutant-discharging enterprise corresponding to the target monitoring change trend is used as the predicted SO2 excessive pollutant-discharging enterprise.

5. The method according to claim 3, characterized in that: The determining and predicting of SO2 excessive emission enterprises based on the SO2 emission concentration monitoring sequence of each emission enterprise and the SO2 concentration prediction sequence includes: Based on the SO2 emission concentration monitoring series of each pollutant-discharging enterprise, a weighted sum of multiple weights is performed to obtain multiple combined concentration prediction series; Processing each of the combined concentration prediction sequences to determine the corresponding combined change trend; and processing the SO2 concentration prediction sequence corresponding to the predicted SO2 exceeding standard discharge point to determine the corresponding prediction change trend; Selecting a target combined change trend that is at least partially the same as the predicted change trend from the combined change trends, and determining a corresponding pollutant-discharging enterprise with a higher weight based on the target combined change trend; The polluting enterprises with the highest weights are taken as the predicted SO2 exceeding the emission standard enterprises.

6. The method according to claim 1 or 2, characterized in that: Before obtaining the SO2 concentration monitoring data of multiple monitoring points within the target period and the meteorological data within the control area, the method further includes: Determine whether the SO2 concentration monitoring data at the first monitoring point at the first time triggers a high value alarm; Determine to trigger a high value alarm, and determine the target time period based on the first time point and a preset duration; the first monitoring point is at least one monitoring point among the multiple monitoring points.

7. The method according to claim 6, characterized in that The determining that the SO2 concentration monitoring data at the first monitoring point at the first time point triggers a high value alarm includes: In response to the SO2 concentration monitoring data at the first monitoring point at the first time point satisfying at least one of the following conditions, it is determined that a high value alarm is triggered: The SO2 concentration monitoring data at the first monitoring point at the first time point exceeds the set comparison threshold; The ratio of the SO2 concentration monitoring data at the first monitoring point at the first time point to the SO2 concentration monitoring data at the previous time point is greater than a preset ratio, and the preset ratio is at least greater than 1; The ranking of the SO2 concentration monitoring data of the first monitoring point at the first time point is higher than the ranking of the SO2 concentration monitoring data at the previous time point by a set change ranking; The difference between the SO2 concentration monitoring data of the first monitoring point at the first time point and the average value of the SO2 concentration monitoring data of the adjacent points is greater than the set outlier threshold.

8. The method according to claim 7, characterized in that After checking the predicted SO2 excessive discharge point to determine that the excessive discharge is carried out, the method further includes: The SO2 concentration monitoring data when the first monitoring point triggers a high value alarm is used as high value alarm triggering data.

9. The method according to claim 1 or 2, characterized in that: Before obtaining the SO2 concentration monitoring data of multiple monitoring points within the target period and the meteorological data within the control area, the method further includes: The historical SO2 concentration monitoring data of multiple target monitoring points are analyzed to determine the target time period.

10. A computing device, characterized in that: It includes a processor and a memory, wherein the memory is used to store a computer program; when the computer program is loaded by the processor, the processor executes the SO2 emission tracing method as described in any one of claims 1 to 9.

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