Atmospheric pollution cause type determination method and computing equipment

Abstract

The embodiment of the invention provides an atmospheric pollution cause type determination method and computing equipment. The atmospheric pollution cause type determination method comprises the following steps: determining first target components related to a first pollution cause, and obtaining an overall correlation coefficient of each first target component; determining a second target component related to the first pollution cause and a proportion of the second target component; and under the condition that the total correlation coefficient is greater than a first preset correlation coefficient and the proportion of the second target component is greater than a first preset proportion, determining that the atmospheric pollution cause comprises the first pollution cause. By adopting the scheme of the embodiment of the invention, automatic identification of part of atmospheric pollution cause types can be realized.

Description

Technical Field

[0001] The present disclosure relates to the technical field of environmental monitoring, and in particular to a method for determining the type of causes of air pollution and a computing device. Background Art

[0002] In order to determine the causes of air pollution, relevant technologies have proposed using monitoring equipment to periodically monitor the components of atmospheric particulate matter and determine the various pollutant components in atmospheric particulate matter. Although various atmospheric pollutant components have been identified, it is still necessary to analyze and determine the atmospheric pollutant components to determine the causes of air pollution. However, currently, judging the causes of air pollution based on atmospheric pollutant component data still requires manual data analysis and cannot be automated. Summary of the invention

[0003] The embodiments of the present disclosure provide a method for determining the cause of air pollution and a computing device.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for determining the cause of air pollution, comprising:

[0005] When the mass concentration of fine particulate matter is greater than the threshold concentration and the duration exceeds the set duration, a single component mass concentration sequence of various components in the atmospheric particulate matter within the duration is obtained;

[0006] Determine the first target component related to the first pollution cause, and sort the single component mass concentration data sequences of each first target component in the same direction, and determine the re-ranking position of the mass concentration in each single component mass concentration sequence;

[0007] For any two target components in the first target components, a correlation evaluation is performed according to the reordering positions of the mass concentrations located at the same position in the corresponding single-component mass concentration sequence to obtain the correlation coefficients of the two target components; and the mean of each of the correlation coefficients is calculated to obtain the overall correlation coefficient;

[0008] Determine a second target component related to the first pollution cause, calculate a partial summed mass concentration mean according to the corresponding single component mass concentration sequence; calculate an overall mass concentration mean based on all single component mass concentration sequences; and calculate a proportion of the second target component based on the partial summed mass concentration mean and the overall mass concentration mean;

[0009] When the overall correlation coefficient is greater than the first preset correlation coefficient and the proportion of the second target component is greater than the first preset proportion, it is determined that the cause of air pollution includes the first cause of pollution.

[0010] Optionally, for any two target components in the first target components, the correlation coefficients of the two target components are obtained by performing a correlation evaluation according to the reordering positions of the mass concentrations at the same position in the corresponding single-component mass concentration sequence, including:

[0011] Calculating the square of the difference in the reordered positions of the mass concentrations at the same position in the single-component mass concentration sequence of the two target components;

[0012] The correlation coefficient of the two target components is calculated according to the square of the difference and the length of the mass concentration sequence.

[0013] Optionally, the first pollution source is biomass burning pollution;

[0014] The determining of the first target component associated with the first pollution cause includes: determining organic carbon, potassium ions, chloride ions, and organic matter associated with the biomass burning pollution;

[0015] The determining of the second target component related to the first pollution cause includes: determining organic matter related to the biomass burning pollution.

[0016] Optionally, the first pollution source is pollution caused by fireworks;

[0017] The determining of the first target component associated with the first pollution cause includes: determining sulfate, sodium, magnesium and chloride ions associated with the fireworks pollution;

[0018] The determining of the second target component related to the first pollution cause includes: determining the trace element component related to the fireworks pollution.

[0019] Optionally, the first pollution source is secondary organic pollution;

[0020] The determining of the first target component associated with the first pollution cause includes: determining secondary organic carbon associated with the secondary organic pollution;

[0021] The determining of the second target component related to the first pollution cause includes: determining the organic component related to the secondary organic pollution.

[0022] Optionally, it also includes: obtaining a fine particle mass concentration sequence of the fine particle mass within the duration and an inhalable particle mass concentration sequence of the inhalable particle mass within the duration, calculating a fine particle mass concentration mean based on the fine particle mass concentration sequence, and calculating a inhalable particle mass concentration mean based on the inhalable particle mass concentration sequence;

[0023] Calculating the proportion of fine particles based on the average mass concentration of fine particles and the average mass concentration of inhalable particles;

[0024] Determine the crustal material components related to dust / sand pollution in the various components, and calculate the mean value of the summed mass concentration of the crustal material according to the corresponding single-component mass concentration sequence;

[0025] Calculating the proportion of crustal materials based on the mean value of the summed mass concentration of the crustal materials and the mean value of the overall mass concentration;

[0026] When the proportion of fine particulate matter is less than the second preset proportion, and the proportion of crustal material is greater than the third preset proportion, it is determined that the cause of air pollution includes dust / sand pollution.

[0027] Optionally, the method further includes: acquiring a remote sensing image of the duration, and analyzing whether the remote sensing image is an image affected by dust and sandstorms;

[0028] The determining that the cause of air pollution includes dust / sand pollution includes: when the remote sensing image is a dust and sand impact image, determining that the cause of air pollution includes dust / sand pollution.

[0029] Optionally, determining the secondary inorganic salts in various components, and calculating the mean mass concentration of the secondary inorganic salts based on the corresponding single-component mass concentration sequence;

[0030] Calculate the ratio of the secondary inorganic salt concentration mean to the overall mass concentration mean to obtain the secondary inorganic salt ratio;

[0031] When the proportion of secondary inorganic salts is greater than the fourth preset proportion, it is determined that the cause of air pollution includes secondary inorganic salt pollution.

[0032] Optionally, the secondary inorganic salt includes secondary nitrate and secondary sulfate; the method further includes:

[0033] Comparing the mean values ​​of mass concentrations of the secondary nitrate and secondary sulfate;

[0034] When the average mass concentration of the secondary nitrates is greater than the average mass concentration of the secondary sulfates, it is determined that the secondary inorganic salt pollution includes secondary nitrate pollution; and when the average mass concentration of the secondary sulfates is greater than the average mass concentration of the secondary nitrates, it is determined that the secondary inorganic salt pollution includes secondary sulfate pollution.

[0035] In a second aspect, an embodiment of the present disclosure provides 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 aforementioned method for determining the cause of air pollution.

[0036] The embodiment scheme of the present disclosure determines whether the overall correlation coefficient of the first target component related to the first pollution cause is greater than the first preset correlation coefficient, and determines whether the proportion of the second target component related to the first pollution cause in the atmospheric particulate matter component is greater than the first preset proportion. If both of the above two conditions are met, it is determined that the first pollution cause is one of the causes of atmospheric pollution. The embodiment scheme of the present disclosure can realize the automatic identification of some types of atmospheric pollution causes. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] 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.

[0038] 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 work, including:

[0039] Figure 1 is a flow chart of a method for determining an air pollution source provided by an embodiment of the present disclosure;

[0040] Figure 2 is a flow chart of a method for calculating a correlation coefficient of two target components in some embodiments;

[0041] Figure 3 is a flow chart of a method for determining whether dust / sand pollution is a cause of air pollution in some embodiments of the present disclosure;

[0042] Figure 4 is a flow chart of a method for determining whether secondary inorganic salt pollution is a cause of air pollution in some embodiments of the present disclosure;

[0043] Figure 5 It is a schematic diagram of the structure of a computing device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] 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.

[0045] 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.

[0046] The embodiment of the present disclosure provides a method for automatically determining the type of cause of air pollution when air pollution is determined to occur. The method for determining the type of cause of air pollution provided by the embodiment of the present disclosure is executed by a computing device.

[0047] Figure 1 : is a flow chart of the method for determining air pollution sources provided by the embodiment of the present disclosure. Figure 1 As shown, the method for determining air pollution sources provided by the embodiment of the present disclosure includes S110-S170.

[0048] S110: When the mass concentration of fine particulate matter is greater than the threshold concentration and the duration exceeds the set duration, a single-component mass concentration sequence of various components in the atmospheric particulate matter within the duration is obtained.

[0049] In the disclosed embodiment, the computing device obtains the mass concentration data of fine particulate matter obtained by periodically monitoring the particle size of atmospheric particulate matter using a monitoring device such as a particle size spectrometer through networking. The aforementioned fine particulate matter value may refer to particulate matter with a diameter less than or equal to 2.5 μm, or may refer to particulate matter of other standards. In a specific implementation, the particle size spectrometer may monitor the number of particulate matter in various particle size segments and determine the mass concentration of fine particulate matter by converting the number to mass.

[0050] When the mass concentration of fine particulate matter is obtained, the computing device will determine whether the mass concentration of fine particulate matter obtained in each period is greater than the threshold. When it is determined that the mass concentration of fine particulate matter at a certain moment is greater than the threshold, the computing device starts the duration timing, and maintains the aforementioned timing when the mass concentration of fine particulate matter is subsequently obtained to be greater than the threshold, and then records the duration of the fine particulate matter mass concentration being greater than the threshold concentration. When it is determined that the duration of the fine particulate matter mass concentration being greater than the threshold concentration exceeds the set duration, the computing device determines that an air pollution event has occurred within the aforementioned duration.

[0051] In a specific implementation, the computing device may set different durations for different threshold concentrations, and then determine air pollution events according to different time-concentration standards. For example, in some embodiments, the computing device determines a pollution event in the following manner: (1) upon determining that the mass concentration of fine particulate matter exceeds 75 μg / m 3 If the duration of pollution reaches 8 hours, it is judged that a Level 3 pollution event has occurred; (2) When the fine particulate matter mass concentration exceeds 115 μg / m 3 If the duration of pollution reaches 4 hours, it is judged that a secondary pollution event has occurred; (3) When the fine particulate matter mass concentration is determined to be more than 150 μg / m 3 If the duration reaches 2 hours, it is judged that a first-level pollution event occurs. In addition, in the specific implementation, if the aforementioned multiple pollution event determination rules are triggered, it is classified as a high-level pollution event, and the corresponding duration and single-component mass concentration sequence are determined.

[0052] When it is determined that an air pollution event has occurred, the computing device obtains the single-component mass concentration sequence obtained by monitoring various components in the atmospheric particulate matter by the monitoring equipment during the aforementioned duration and sorting the various components in chronological order.

[0053] In a specific implementation, monitoring equipment such as an online particle chromatograph, a carbonaceous component analyzer, and an atmospheric heavy metal online detector can be used to measure the concentration of the particulate matter components, obtain the mass concentration of the corresponding components, and construct the corresponding single-component mass concentration sequence. The aforementioned components include ionic components, organic components, various types of trace element components, etc. in atmospheric particulate matter. For example, in a specific application, the monitored components in atmospheric particulate matter may include but are not limited to NO 3 - SO4 2 —、NH 4 +, Cl—, K+, Ca 2 +、Mg 2 +, OC, EC, K, Ca, V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Sn, Sb, Ba, Pb, Al, Si, Cl, Ti, Fe.

[0054] S120: Determine the first target component related to the first pollution cause, and sort the single component mass concentration data sequences of each first target component in the same direction to determine the re-ranking position of the mass concentration in each single component mass concentration sequence.

[0055] The first pollution cause is a pre-determined cause that may cause air pollution. The first pollution cause may be the result of a high proportion of specific components in the atmospheric particulate matter, such as biomass burning pollution, fireworks pollution, and secondary organic pollution, and the mass concentrations of certain components have a strong correlation.

[0056] Determining the first target component related to the first pollution cause is to determine the typical components produced by the first pollution cause. For example, when the first pollution cause is biomass burning pollution, the first target component includes organic carbon, potassium ions, chloride ions and burning organic matter; when the first pollution cause is fireworks pollution, the first target component includes sulfate, sodium ions, magnesium ions and chloride ions produced by fireworks; when the first pollution cause is secondary organic pollution, the first target component is secondary organic carbon.

[0057] After determining the first target component related to the first pollution cause, the single component mass concentration data sequence corresponding to the first target component is determined. Subsequently, the single component mass concentration data sequence of a first target component is sorted by size to determine the re-ranking of each mass concentration in the single component mass concentration data sequence.

[0058] Assuming that the single component mass concentration data sequence of a target component is {100, 140, 180, 123, 145}, the above sequence is reordered from small to large to {100, 123, 140, 145, 180}, and the reordering positions corresponding to each number are 100-1, 140-3, 180-5, 123-2, 145-4. Assuming that the single component mass concentration data sequence of another target component is {38, 49, 86, 47, 40}, the above sequence is reordered from small to large to {38, 40, 47, 49, 86}, and the reordering positions corresponding to each number are 38-1, 49-4, 86-5, 47-3, 40-2.

[0059] It should be noted here that when sorting the single-component mass concentration data of various first target components, they should be sorted in the same way (either from small to large or from large to small), and the sorting order cannot be reversed (the sorting order will directly affect the subsequent correlation analysis).

[0060] S130: For any two target components in the first target components, a correlation evaluation is performed according to the reordering positions of the mass concentrations at the same position in the corresponding single-component mass concentration sequence to obtain a correlation coefficient of the two target components.

[0061] As analyzed above, the first target component is a typical component produced by the first pollution cause. In actual situations, when the causes of atmospheric pollution include the first pollution cause, the component mass concentrations of various first target components will change in the same direction as the intensity of the first pollution increases or decreases, and the component mass concentrations of various first target components have a correlation. Accordingly, if it is determined that there is a strong correlation between the single component mass concentration data series of various first target components, it can be inferred that the first pollution cause is indeed the atmospheric pollution cause.

[0062] To achieve the aforementioned calculation, the computing device selects any two target components from the first target components, performs correlation evaluation based on the reordered positions of the mass concentrations at the same position in the single-component mass concentration sequence, and obtains the correlation coefficients of the two components.

[0063] Figure 2 4 is a flow chart of a method for calculating the correlation coefficient of two target components in some embodiments. Figure 2 As shown, in some embodiments, calculating the correlation coefficient of two target components includes the following S121-S132.

[0064] S131: Calculate the square of the difference in the reordered positions of the mass concentrations at the same position in the single-component mass concentration sequences of the two target components.

[0065] In order to obtain the aforementioned square difference, it is necessary to first calculate the difference in the reordering positions of the mass concentrations at the same position in the single component mass concentration sequence of the two target components. Taking the two target components in the example, the differences in the reordering positions of the corresponding positions are 0 (1-1 = 0), -1 (3-4 = -1), 0 (5-5 = 0), -1 (2-3 = -1) and 2 (4-2 = 2). The corresponding square differences are 0, 1, 0, 1 and 4 respectively.

[0066] S132: Calculate the correlation coefficient of the two target components based on the square of the difference and the length of the mass concentration sequence.

[0067] After determining the square of the difference, the computing device uses the square of the difference and the length of the mass concentration sequence (5 in the above example) to calculate the correlation coefficient of the two target components. The correlation coefficient r is calculated, where s i is the square of the difference at the ith position, and N is the length of the mass concentration sequence. According to the previous example, the correlation coefficient can be obtained to be 0.4.

[0068] S140: Calculate the mean of each correlation coefficient to obtain the overall correlation coefficient.

[0069] After obtaining the correlation coefficients of any two target components, that is, after obtaining the correlation coefficients of all target components, the mean of the aforementioned correlation coefficients can be calculated to obtain the overall correlation coefficient. According to the above analysis, each correlation coefficient should be greater than 0 and less than 1, and the corresponding overall correlation coefficient is also greater than 0 and less than 1.

[0070] According to the analysis above, the overall correlation coefficient reflects the coefficient that determines the overall correlation between various first target components in atmospheric particulate matter. If the first pollution cause is the atmospheric pollution cause, the corresponding overall correlation coefficient will be larger.

[0071] S150: Determine a second target component related to the first pollution cause, calculate a partial summed mass concentration mean according to the corresponding single-component mass concentration sequence; and calculate an overall mass concentration mean based on all single-component mass concentration sequences.

[0072] In the embodiment of the present disclosure, the second target component related to the first pollution cause is the main component produced by the first pollution cause. For example, when the first pollution cause is biomass burning pollution, the second target component includes organic matter formed by burning; when the first pollution cause is fireworks pollution, the second target component includes trace element components produced by fireworks; when the first pollution cause is secondary organic pollution, the second target component is an organic component.

[0073] After determining the second target component, the mean of the single component mass concentration sequence of each second target component can be calculated, and the aforementioned mean values ​​are added together to obtain the partial sum mass concentration mean. That is, the partial sum mass concentration mean is the mean of the mass concentration sum of the aforementioned various second target components in each period within the duration.

[0074] The overall mass concentration mean is calculated based on all the single-component mass concentration sequences, which can be obtained by first calculating the mean of each single-component mass concentration sequence, and then adding the aforementioned means to obtain the overall mass concentration mean. In other words, the overall mass concentration mean is the mean of the sum of the mass concentrations of all components in each period within the duration.

[0075] S160: Calculate the proportion of the second target component based on the partial summed mass concentration mean and the overall mass concentration mean.

[0076] After obtaining the aforementioned partial summed mass concentration mean and overall mass concentration mean, the second target component proportion can be obtained by dividing the partial summed mass concentration mean as the numerator and the overall mass concentration mean as the denominator.

[0077] As analyzed above, the second target component is the main pollution component produced by the first pollution cause. The higher the proportion of the second target component in the atmospheric particulate matter component, the higher the possibility that the first pollution cause is the cause of atmospheric pollution.

[0078] S170: When the overall correlation coefficient is greater than the first preset correlation coefficient and the proportion of the second target component is greater than the first preset proportion, it is determined that the cause of air pollution includes the first pollution cause.

[0079] After obtaining the overall correlation coefficient and the proportion of the second target component, the computing device determines whether the overall correlation coefficient is greater than the first preset correlation coefficient, and determines whether the proportion of the second target component is greater than the first preset proportion. As analyzed above, the larger the overall correlation coefficient and the larger the proportion of the second target component, the higher the probability that the corresponding first pollution cause is the cause of air pollution. Based on this, in the embodiment of the present disclosure, when it is determined that the overall correlation coefficient is greater than the first preset correlation coefficient and the proportion of the second target component is greater than the first preset proportion, it is determined that the first pollution cause is the cause of air pollution.

[0080] In some embodiments, when the first pollution cause is biomass burning pollution, when it is determined that the overall correlation coefficient is greater than the set correlation coefficient and the proportion of organic matter components is greater than 0.4, it is determined that biomass burning pollution is the cause of air pollution. Considering that biomass burning is mostly burning crop straw or forest fires, it is also possible to identify whether there is a fire point in the corresponding area through remote sensing monitoring images, and then determine whether there is indeed a biomass burning event.

[0081] In some embodiments, when the first cause of pollution is fireworks pollution, when it is determined that the overall correlation coefficient is greater than the set coefficient and the proportion of trace element components is greater than 0.1, it is determined that fireworks pollution is the cause of air pollution.

[0082] In some further embodiments, when the first pollution cause is secondary organic pollution, when it is determined that the overall correlation coefficient is greater than the set coefficient, and the proportion of organic components is greater than 0.4, it is determined that the secondary organic pollution is the cause of air pollution.

[0083] The method for determining the cause of air pollution provided by the embodiment of the present disclosure determines whether the overall correlation coefficient of the first target component related to the first pollution cause is greater than the first preset correlation coefficient, and determines whether the proportion of the second target component related to the first pollution cause in the atmospheric particulate matter component is greater than the first preset proportion. If both of the above two conditions are met, it is determined that the first pollution cause is one of the causes of air pollution. By adopting the embodiment of the present disclosure, the automatic identification of the causes of some types of air pollution can be achieved.

[0084] In actual situations, the causes of air pollution may include the aforementioned biomass burning pollution, fireworks pollution and secondary organic pollution, as well as dust / sand pollution, secondary inorganic salt pollution caused by vehicles and factory and mining enterprises. The aforementioned two causes of pollution can also be identified using corresponding methods.

[0085] Figure 3 is a flow chart of a method for determining whether dust / sand pollution is the cause of air pollution in some embodiments of the present disclosure. Figure 3 As shown, the method for determining whether dust-sand is the cause of air pollution includes S310-S360. It should be noted that S310-S360 are executed after an air pollution event is identified (that is, after the aforementioned S110 is executed).

[0086] S310: Obtaining a fine particulate matter mass concentration sequence within a duration and an inhalable particulate matter mass concentration sequence within a duration.

[0087] In the disclosed embodiment, the inhalable particulate matter may be, for example, particulate matter of 10.0 μm or particulate matter of other standards. However, it should be noted that the inhalable particulate matter includes the aforementioned fine particulate matter. As previously analyzed, the computing device can determine the particle mass concentration sequence and the inhalable particle mass concentration sequence through the periodic monitoring data of the device such as the particle size spectrometer.

[0088] S320: Calculate the mean mass concentration of fine particles based on the mass concentration sequence of fine particles, and calculate the mean mass concentration of inhalable particles based on the mass concentration sequence of inhalable particles.

[0089] The calculation of the mean fine particle mass concentration based on the fine particle mass concentration sequence is to average the mass concentrations in the above sequence. Similarly, the calculation of the mean inhalable particle mass concentration based on the inhalable particle mass concentration sequence is also to average the mass concentrations.

[0090] S330: Calculate the proportion of fine particles based on the average mass concentration of fine particles and the average mass concentration of inhalable particles.

[0091] After obtaining the aforementioned mean mass concentration of fine particles and mean mass concentration of inhalable particles, the proportion of fine particles can be obtained by dividing the mean mass concentration of fine particles as the numerator and the mean mass concentration of inhalable particles as the denominator.

[0092] S340: Determine the crustal material components related to dust / sand pollution among various components, and calculate the mean of the summed mass concentration of the crustal materials based on the corresponding single-component mass concentration sequence.

[0093] Dust / sand pollution is caused by strong winds blowing up dust from the ground. Correspondingly, the pollutants caused by this pollution are mostly Si, Ca 2 +, Al and other material components. Correspondingly, the crustal material components in the atmospheric particle components can be regarded as components related to dust / sand pollution. After determining the above components, the means of the mass concentration sequences of each single component corresponding to the above crustal material components can be calculated, and then the above means can be added to obtain the mean of the summed mass concentration of the crustal material. In other words, the overall mass concentration mean is the mean of the mass concentrations of all crustal material components in each period within the duration.

[0094] S350: Calculate the proportion of crustal materials based on the mean of the summed mass concentration of crustal materials and the mean of the overall mass concentration.

[0095] After obtaining the aforementioned mean value of the summed mass concentration of crustal materials, the proportion of crustal materials can be obtained by dividing the mean value of the summed mass concentration of crustal materials as the numerator and the mean value of the overall mass concentration as the denominator.

[0096] S360: When the proportion of fine particulate matter is less than the second preset proportion and the proportion of crustal material is greater than the third preset proportion, it is determined that the cause of air pollution includes dust / sand pollution.

[0097] If dust / sand pollution occurs, the diameter of atmospheric particles will be generally larger. In this case, the proportion of fine particles between the mass concentration of fine particles and the mass concentration of inhalable particles is relatively small. At the same time, dust Aachen pollution will cause the crustal material component in atmospheric pollution to account for a large proportion. Based on this, in the embodiment of the present disclosure, when it is determined that the proportion of fine particles is less than the second preset proportion, and the proportion of crustal materials is greater than the third preset proportion, it is determined that the cause of atmospheric pollution includes dust / sand pollution.

[0098] In some embodiments, the computing device may also obtain remote sensing images such as those obtained by satellite telemetry or aircraft telemetry. Because the spectral absorption characteristics of sand and dust are significantly different from those of conventional pollutants, remote sensing images obtained when there is sand and dust pollution are significantly different from remote sensing images obtained when there is no sand and dust pollution. Therefore, it is possible to determine whether a remote sensing image is an image affected by sand and dust through image analysis. In the case where it is determined that the remote sensing image is an image affected by sand and dust / raising dust, it can be further determined that the cause of atmospheric pollution includes raising dust / sand and dust pollution.

[0099] Figure 4 is a flow chart of a method for determining whether secondary inorganic salt pollution is the cause of air pollution in some embodiments of the present disclosure. Figure 4As shown, the method for determining whether dust-sand is the cause of air pollution includes S410-S430. It should be noted that S410-S430 are executed after an air pollution event is identified (that is, after the aforementioned S110 is executed).

[0100] S410: Determine the secondary inorganic salts in various components, and calculate the average mass concentration of the secondary inorganic salts based on the corresponding single-component mass concentration sequence.

[0101] In a specific implementation, the secondary inorganic salts may be components such as sulfates and nitrates obtained by reoxidation of pollutants emitted by vehicles and factories and mines. After determining the secondary inorganic salts, the mean of the single component mass concentration sequence of each secondary inorganic salt can be calculated, and the aforementioned mean values ​​can be added together to obtain the mean mass concentration of the secondary inorganic salts. That is, the mean mass concentration of the secondary inorganic salts is the mean of the mass concentrations of the aforementioned various secondary inorganic salts in each period within the duration.

[0102] S420: Calculate the ratio of the average secondary inorganic salt concentration to the average overall mass concentration to obtain the secondary inorganic salt ratio.

[0103] After obtaining the aforementioned mean value of secondary inorganic salt concentration in crustal materials, the proportion of secondary inorganic salt can be obtained by dividing the mean value of secondary inorganic salt concentration as the numerator and the mean value of overall mass concentration as the denominator.

[0104] S430: When the proportion of secondary inorganic salts is greater than a fourth preset proportion, determining that the cause of air pollution includes secondary inorganic salt pollution.

[0105] If secondary inorganic salt pollution is the cause of air pollution, it will cause a large proportion of secondary inorganic salts. Accordingly, in the disclosed embodiment, when it is determined that the proportion of secondary inorganic salts is greater than the fourth preset proportion, it is determined that the cause of air pollution includes the aforementioned secondary inorganic salt pollution. In a specific implementation, the aforementioned fourth preset proportion can be set to 0.5.

[0106] In a specific implementation, secondary inorganic salt pollution can be divided into secondary nitrate pollution and secondary sulfate pollution due to different pollutants discharged. Secondary nitrate pollution may be caused by vehicle exhaust emissions, and secondary sulfate pollution may be caused by coal burning and other reasons. In order to more accurately determine the type of pollution source, when executing the above S430, the following S440-S450 may also be executed.

[0107] S440: Compare the mean mass concentrations of secondary nitrate and secondary sulfate.

[0108] S450: When the average mass concentration of secondary nitrates is greater than the average mass concentration of secondary sulfates, it is determined that the secondary inorganic salt pollution includes secondary nitrate pollution; and, when the average mass concentration of secondary sulfates is greater than the average mass concentration of secondary nitrates, it is determined that the secondary inorganic salt pollution includes secondary sulfate pollution.

[0109] In a specific implementation, after determining the specific cause of air pollution by using the method of the above embodiment, the above pollution cause can also be output and displayed to prompt the relevant manager of the type of pollution source. It should be noted here that the cause of air pollution may include at least two of the above causes at the same time.

[0110] As analyzed above, in order to determine various types of pollution, it is necessary to obtain particulate matter mass concentration data and single component mass concentration data of various components. In practical applications, there may be problems with abnormal monitoring equipment and accidental jump data for the aforementioned data. In order to avoid the impact of the aforementioned abnormal data on the determination of pollution sources, after obtaining the aforementioned monitoring data, the monitoring data can also be integrated, stored and processed, and abnormal elimination can be performed.

[0111] 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 method of the embodiment 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.

[0112] like Figure 5 As 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.

[0113] Typically, the following devices may be connected to the I / O interface 505: input devices 506 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. 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.

[0114] 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.

[0115] 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.

[0116] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus 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, apparatus, or device.

[0117] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer readable program codes. 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, device, 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.

[0118] In some embodiments, the client and the server 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.

[0119] The computer-readable medium may be included in the computing device, or may exist independently without being incorporated into the computing device.

[0120] 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 computer, partially on the tester computer, as a separate software package, partially on the tester computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the tester computer through 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).

[0121] 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.

[0122] 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, in some cases, constitute a limitation on the unit itself. The functions described above herein 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 may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0123] 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 these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the type of atmospheric pollution, characterized in that: include: When the mass concentration of fine particulate matter is greater than the threshold concentration and the duration exceeds the set duration, a single component mass concentration sequence of various components in the atmospheric particulate matter within the duration is obtained; Determine the first target component related to the first pollution cause, and sort the single component mass concentration data sequences of each first target component in the same direction, and determine the re-ranking position of the mass concentration in each single component mass concentration sequence; For any two target components in the first target components, a correlation evaluation is performed according to the reordering positions of the mass concentrations located at the same position in the corresponding single-component mass concentration sequence to obtain the correlation coefficients of the two target components; and the mean of each of the correlation coefficients is calculated to obtain the overall correlation coefficient; Determine a second target component related to the first pollution cause, calculate a partial summed mass concentration mean according to the corresponding single component mass concentration sequence; calculate an overall mass concentration mean based on all single component mass concentration sequences; and calculate a proportion of the second target component based on the partial summed mass concentration mean and the overall mass concentration mean; When the overall correlation coefficient is greater than the first preset correlation coefficient and the proportion of the second target component is greater than the first preset proportion, it is determined that the cause of air pollution includes the first cause of pollution.

2. The method according to claim 1, characterized in that For any two target components in the first target components, the correlation coefficients of the two target components are obtained by performing a correlation evaluation according to the reordering positions of the mass concentrations at the same position in the corresponding single-component mass concentration sequence, including: Calculating the square of the difference in the reordered positions of the mass concentrations at the same position in the single-component mass concentration sequence of the two target components; The correlation coefficient of the two target components is calculated according to the square of the difference and the length of the mass concentration sequence.

3. The method according to claim 1 or 2, characterized in that: The first pollution source is biomass burning pollution; The determining of the first target component associated with the first pollution cause includes: determining organic carbon, potassium ions, chloride ions, and organic matter associated with the biomass burning pollution; The determining of the second target component related to the first pollution cause includes: determining organic matter related to the biomass burning pollution.

4. The method according to claim 1 or 2, characterized in that: The first pollution source is pollution caused by fireworks; The determining of the first target component associated with the first pollution cause includes: determining sulfate, sodium, magnesium and chloride ions associated with the fireworks pollution; The determining of the second target component related to the first pollution cause includes: determining the trace element component related to the fireworks pollution.

5. The method according to claim 1 or 2, characterized in that: The first pollution cause is secondary organic pollution; The determining of the first target component associated with the first pollution cause includes: determining secondary organic carbon associated with the secondary organic pollution; The determining of the second target component related to the first pollution cause includes: determining the organic component related to the secondary organic pollution.

6. The method according to claim 1 or 2, characterized in that: Also includes: Obtaining a fine particle mass concentration sequence of the fine particle mass within the duration and an inhalable particle mass concentration sequence of the inhalable particle mass within the duration, calculating a fine particle mass concentration mean based on the fine particle mass concentration sequence, and calculating a inhalable particle mass concentration mean based on the inhalable particle mass concentration sequence; Calculating the proportion of fine particles based on the average mass concentration of fine particles and the average mass concentration of inhalable particles; Determine the crustal material components related to dust / sand pollution in the various components, and calculate the mean value of the summed mass concentration of the crustal material according to the corresponding single-component mass concentration sequence; Calculating the proportion of crustal materials based on the mean value of the summed mass concentration of the crustal materials and the mean value of the overall mass concentration; When the proportion of fine particulate matter is less than the second preset proportion, and the proportion of crustal material is greater than the third preset proportion, it is determined that the cause of air pollution includes dust / sand pollution.

7. The method according to claim 6, characterized in that Also includes: Acquire the remote sensing image of the duration, and analyze whether the remote sensing image is an image affected by dust and sandstorms; The determining that the cause of air pollution includes dust / sand pollution includes: when the remote sensing image is a dust and sand impact image, determining that the cause of air pollution includes dust / sand pollution.

8. The method according to claim 1 or 2, characterized in that: Also includes: Determine the secondary inorganic salts in various components, and calculate the mean mass concentration of the secondary inorganic salts based on the corresponding single component mass concentration sequence; Calculate the ratio of the secondary inorganic salt concentration mean to the overall mass concentration mean to obtain the secondary inorganic salt ratio; When the proportion of secondary inorganic salts is greater than the fourth preset proportion, it is determined that the cause of air pollution includes secondary inorganic salt pollution.

9. The method according to claim 8, characterized in that The secondary inorganic salt includes secondary nitrate and secondary sulfate; the method further includes: Comparing the mean values ​​of mass concentrations of the secondary nitrate and secondary sulfate; When the average mass concentration of the secondary nitrates is greater than the average mass concentration of the secondary sulfates, it is determined that the secondary inorganic salt pollution includes secondary nitrate pollution; and when the average mass concentration of the secondary sulfates is greater than the average mass concentration of the secondary nitrates, it is determined that the secondary inorganic salt pollution includes secondary sulfate pollution.

10. A computing device, characterized in that It comprises 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 method for determining the cause of air pollution as claimed in any one of claims 1 to 9.

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