Method for determining the type of atmospheric pollution causes and calculation equipment

Abstract

This disclosure provides a method and computing device for determining the cause type of air pollution. The method includes: determining a first target component related to a first pollution cause, and obtaining the overall correlation coefficient of various first target components; determining a second target component related to the first pollution cause and its proportion; and determining that the air pollution cause includes the first pollution cause if the overall 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. Using the scheme of this disclosure, the automated identification of some types of air pollution causes can be achieved.

Description

Technical Field

[0001] This disclosure relates to the field of environmental monitoring technology, specifically to a method and calculation device for determining the causes and types of air pollution. Background Technology

[0002] To determine the causes of air pollution, related technologies have proposed using monitoring equipment to periodically monitor the composition of atmospheric particulate matter and identify various pollutant components within it. Although various air pollutant components are identified, further analysis and judgment of these components are still needed to determine the causes of air pollution. However, currently, determining the causes of air pollution based on component data still requires manual data analysis and cannot be automated. Summary of the Invention

[0003] This disclosure provides a method and computing device for determining the causes of air pollution.

[0004] In a first aspect, embodiments of this disclosure provide a method for determining the causes of air pollution, including:

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

[0006] Identify 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 reordering position of the mass concentration in each single-component mass concentration sequence.

[0007] For any two target components in the first target component, the correlation is evaluated based on the reordering of the mass concentrations at the same position in the corresponding single-component mass concentration sequence to obtain the correlation coefficient between the two target components; and the mean of each correlation coefficient is calculated to obtain the overall correlation coefficient.

[0008] A second target component related to the first pollution cause is identified, and a partial summation mean mass concentration is calculated based on the corresponding single-component mass concentration sequence; the overall mean mass concentration is calculated based on all single-component mass concentration sequences; and the proportion of the second target component is calculated based on the partial summation mean mass concentration and the overall mean mass concentration.

[0009] If 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, the cause of air pollution is determined to include the first cause of pollution.

[0010] Optionally, the correlation evaluation for any two target components in the first target component, based on the reordering of mass concentrations at the same position in the corresponding single-component mass concentration sequence, yields a correlation coefficient between the two target components, including:

[0011] Calculate the squared difference of the reordering order of the mass concentrations at the same position in the single-component mass concentration sequences of the two target components.

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

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

[0014] The determination of the first target component related to the first pollution cause includes: determining organic carbon, potassium ions, chloride ions, and organic matter related to the biomass combustion pollution;

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

[0016] Optionally, the first cause of pollution is pollution from fireworks displays;

[0017] The determination of the first target component related to the first pollution cause includes: determining sulfate, sodium ions, magnesium ions, and chloride ions related to the pollution from the fireworks display;

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

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

[0020] The determination of the first target component related to the first pollution cause includes: determining secondary organic carbon related to the secondary organic pollution;

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

[0022] Optionally, it further includes: obtaining a fine particulate matter mass concentration sequence during the duration and an inhalable particulate matter mass concentration sequence during the duration, calculating a mean fine particulate matter mass concentration based on the fine particulate matter mass concentration sequence, and calculating a mean inhalable particulate matter mass concentration based on the inhalable particulate matter mass concentration sequence.

[0023] The proportion of fine particulate matter is calculated based on the average mass concentration of fine particulate matter and the average mass concentration of inhalable particulate matter;

[0024] Identify the crustal material components related to dust / dust pollution among the various components, and calculate the average sum of crustal material mass concentrations based on the corresponding single-component mass concentration sequences;

[0025] The proportion of crustal material is calculated based on the summation mean mass concentration of the crustal material and the total mean mass concentration.

[0026] If 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, the cause of air pollution is determined to include dust / sand pollution.

[0027] Optionally, it also includes: acquiring remote sensing images of the duration and analyzing whether the remote sensing images are images affected by sand and dust storms;

[0028] The determination that the cause of air pollution includes dust / dust pollution includes: when the remote sensing image is an image affected by dust and dust, determining that the cause of air pollution includes dust / dust pollution.

[0029] Optionally, secondary inorganic salts in various components are identified, and the mean mass concentration of secondary inorganic salts is calculated based on the corresponding single-component mass concentration sequence.

[0030] The ratio of the average concentration of the secondary inorganic salts to the average mass concentration of the total mass is calculated to obtain the proportion of secondary inorganic salts.

[0031] If 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] Compare the average mass concentrations of the secondary nitrate and secondary sulfate;

[0034] If the average mass concentration of the secondary nitrate is greater than the average mass concentration of the secondary sulfate, it is determined that the secondary inorganic salt contamination includes secondary nitrate contamination; and if the average mass concentration of the secondary sulfate is greater than the average mass concentration of the secondary nitrate, it is determined that the secondary inorganic salt contamination includes secondary sulfate contamination.

[0035] Secondly, embodiments of this disclosure provide 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 previously described method for determining the causes of air pollution.

[0036] The present invention discloses a scheme that determines whether the overall correlation coefficient of a first target component related to a first pollution cause is greater than a first preset correlation coefficient, and whether the proportion of a second target component related to the first pollution cause in the atmospheric particulate matter composition is greater than a first preset proportion. If both of the aforementioned conditions are met, then the first pollution cause is determined to be one of the causes of air pollution. Using the scheme of the present invention, the automated identification of some types of air pollution causes can be achieved. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0038] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort, wherein:

[0039] Figure 1 This is a flowchart of the method for determining air pollution sources provided in the embodiments of this disclosure;

[0040] Figure 2 This is a flowchart of a method for calculating the correlation coefficient between two target components in some embodiments;

[0041] Figure 3 This is a flowchart of a method for determining whether dust / sandstorm pollution is a cause of air pollution in some embodiments of this disclosure;

[0042] Figure 4 This is a flowchart of a method for determining whether secondary inorganic salt pollution is a cause of air pollution in some embodiments of this disclosure;

[0043] Figure 5 This is a schematic diagram of the structure of the computing device provided in the embodiments of this disclosure. Detailed Implementation

[0044] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0045] The term "comprising" and its variations as used herein are open-ended inclusion, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0046] This disclosure provides a method for automatically determining the type of air pollution cause when air pollution is assessed to be present. The method for determining the type of air pollution cause provided in this disclosure is executed by a computing device.

[0047] Figure 1 This is a flowchart of the method for determining air pollution sources provided in the embodiments of this disclosure. Figure 1 As shown, the method for determining air pollution sources provided in this embodiment includes steps S110-S170.

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

[0049] In this embodiment of the disclosure, the computing device acquires fine particulate matter mass concentration data obtained by periodically monitoring atmospheric particulate matter using a particle size spectrometer or other monitoring equipment via a network connection. The aforementioned fine particulate matter value can refer to particles with a diameter less than or equal to 2.5 μm, or other standard particulate matter. In specific implementations, the particle size spectrometer can determine the fine particulate matter mass concentration by monitoring the quantity of particles in various size ranges and converting the quantity to mass.

[0050] Upon obtaining the fine particulate matter (PM2.5) concentration, the computing device determines whether the PM2.5 concentration obtained in each cycle exceeds a threshold. If the PM2.5 concentration at a certain moment exceeds the threshold, the computing device starts a duration timer and maintains this timer for subsequent PM2.5 concentrations exceeding the threshold, thereby recording the duration of the PM2.5 concentration exceeding the threshold. If the duration of the PM2.5 concentration exceeding the threshold exceeds a set duration, the computing device determines that an air pollution event has occurred within the aforementioned duration.

[0051] In specific implementation, the computing device can 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 pollution events in the following manner: (1) when the concentration of fine particulate matter exceeds 75 μg / m³. 3 (1) If the duration of pollution reaches 8 hours, a Level III pollution event is determined to have occurred; (2) If the concentration of fine particulate matter exceeds 115 μg / m³, a Level III pollution event is determined to have occurred. 3 (2) If the duration of pollution reaches 4 hours, a level 2 pollution event is determined to have occurred; (3) If the concentration of fine particulate matter exceeds 150 μg / m³, a level 2 pollution event is determined to have occurred. 3 If the duration of pollution reaches 2 hours, it is considered a Level 1 pollution event. Furthermore, in practice, if the aforementioned multiple pollution event determination rules are triggered, it will be classified as a high-level pollution event, and the corresponding duration and single-component mass concentration sequence will be determined.

[0052] In the event of an air pollution incident, the computing device obtains the mass concentration sequence of each component by monitoring various components in the atmospheric particulate matter over the aforementioned duration and sorting them in chronological order.

[0053] In practice, monitoring equipment such as online particle chromatographs, carbonaceous component analyzers, and online atmospheric heavy metal detectors can be used to measure the concentration of particulate matter components, obtain the mass concentration of the corresponding components, and construct corresponding single-component mass concentration sequences. The aforementioned components include ionic components, organic components, and various types of trace element components 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: Identify 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 reordering position of the mass concentration in each single-component mass concentration sequence.

[0055] The primary cause of pollution is a predetermined cause that may lead to air pollution. Primary causes of pollution may include biomass combustion pollution, fireworks pollution, secondary organic pollution, etc., where the content of specific components in atmospheric particulate matter is relatively high, and there is a strong correlation between the mass concentrations of certain components.

[0056] Identifying the primary target component associated with the primary pollution cause involves determining the typical components produced by the primary pollution cause. For example, if the primary pollution cause is biomass combustion pollution, the primary target component includes organic carbon, potassium ions, chloride ions, and combustion organic matter; if the primary pollution cause is fireworks pollution, the primary target component includes sulfate ions, sodium ions, magnesium ions, and chloride ions produced by fireworks; and if the primary pollution cause is secondary organic matter pollution, the primary target component is secondary organic carbon.

[0057] After identifying the first target component associated with the first cause of pollution, the corresponding single-component mass concentration data sequence is also determined. Subsequently, by sorting the single-component mass concentration data sequence of the first target component by size, the reordering position of each mass concentration in the single-component mass concentration data sequence can be determined.

[0058] Assume the single-component mass concentration data sequence of a target component is {100, 140, 180, 123, 145}. Reordering this sequence in ascending order yields {100, 123, 140, 145, 180}, with the reordering positions of each number being 100-1, 140-3, 180-5, 123-2, 145-4. Assume the single-component mass concentration data sequence of another target component is {38, 49, 86, 47, 40}. Reordering this sequence in ascending order yields {38, 40, 47, 49, 86}, with the reordering positions of each number being 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 smallest to largest or from largest to smallest), 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 component, the correlation is evaluated based on the reordering of the mass concentrations at the same position in the corresponding single-component mass concentration sequence, and the correlation coefficient between the two target components is obtained.

[0061] As analyzed earlier, the first target component is a typical component produced by the first pollution cause. In reality, when the causes of air pollution include the first pollution cause, the 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 mass concentrations of various first target components are correlated. Accordingly, if a strong correlation is found between the data sequences of the mass concentrations of various first target components, it can be inferred that the first pollution cause is indeed an air pollution cause.

[0062] To perform the aforementioned calculations, the computing device selects any two target components from the first target component and evaluates their correlation based on the reordering of their mass concentrations at the same position in the single-component mass concentration sequence, thereby obtaining the correlation coefficient between the two components.

[0063] Figure 2 This is a flowchart illustrating methods for calculating the correlation coefficient between two target components in some embodiments. For example... Figure 2 As shown, in some embodiments, calculating the correlation coefficient between the two target components includes the following steps S121-S132.

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

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

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

[0067] After determining the aforementioned squared difference, the computing device uses the aforementioned squared difference and the length of the mass concentration sequence (5 in the aforementioned example) to calculate the correlation coefficient between the two target components. In this embodiment of the disclosure, the computing device uses... The correlation coefficient r is calculated, where s i Let N be the square of the difference at the i-th position, and N be the length of the mass concentration sequence. Following the example above, the correlation coefficient is 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. As analyzed above, each correlation coefficient should be greater than 0 and less than 1, and the corresponding overall correlation coefficient should also be greater than 0 and less than 1.

[0070] As analyzed above, the overall correlation coefficient reflects the coefficient that determines the overall correlation between various primary target components in atmospheric particulate matter. If the primary cause of pollution is air pollution, the corresponding overall correlation coefficient will be larger.

[0071] S150: Identify the second target component associated with the first pollution cause, calculate the partial summation mean mass concentration based on the corresponding single-component mass concentration sequence, and calculate the overall mean mass concentration based on all single-component mass concentration sequences.

[0072] In this embodiment of the disclosure, the second target component related to the first pollution cause is a major component generated by the first pollution cause. For example, if the first pollution cause is biomass combustion pollution, the second target component includes organic matter formed by combustion; if the first pollution cause is fireworks pollution, the second target component includes trace element components generated by fireworks; if the first pollution cause is secondary organic matter pollution, the second target component is an organic matter component.

[0073] After identifying the second target component, the mean of the individual component mass concentration sequence of each second target component can be calculated. These mean values ​​are then summed to obtain a partially summed mean mass concentration. In other words, the partially summed mean mass concentration is the average of the mass concentrations of the various second target components over each period within the duration.

[0074] The overall mean mass concentration can be calculated based on all individual component mass concentration sequences. This can be achieved by first calculating the mean of each individual component mass concentration sequence, and then summing these mean values ​​to obtain the overall mean mass concentration. In other words, the overall mean mass concentration is the average of the mass concentrations of all components over all periods within the duration.

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

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

[0077] As analyzed above, the second target component is the main pollutant component produced by the first pollution cause. The higher the proportion of the second target component in the atmospheric particulate matter composition, the higher the likelihood that the first pollution cause is the cause of air 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, the causes of air pollution are determined to include 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 a first preset correlation coefficient and 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 first pollution cause is air pollution. Based on this, in this embodiment of the disclosure, if 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, the first pollution cause is determined to be air pollution.

[0080] In some embodiments, where the primary pollution cause is biomass burning, if the overall correlation coefficient is greater than a set correlation coefficient and the proportion of organic matter is greater than 0.4, then biomass burning is determined to be the cause of air pollution. Considering that biomass burning often involves burning crop straw or forest fires, remote sensing images can also be used to identify whether there are fire points in the corresponding area, thereby determining whether a biomass burning event has indeed occurred.

[0081] In some embodiments, if the first cause of pollution is fireworks pollution, and the overall correlation coefficient is greater than a set coefficient and the proportion of trace element components is greater than 0.1, then fireworks pollution is determined to be the cause of air pollution.

[0082] In some other embodiments, when the primary pollution cause is secondary organic pollution, if the overall correlation coefficient is greater than a set coefficient and the proportion of organic components is greater than 0.4, then the secondary organic pollution is determined to be the cause of air pollution.

[0083] The method for determining the causes of air pollution provided in this disclosure determines whether the overall correlation coefficient of a first target component related to a first pollution cause is greater than a first preset correlation coefficient, and whether the proportion of a second target component related to the first pollution cause in the atmospheric particulate matter composition is greater than a first preset proportion. If both of the aforementioned conditions are met, then the first pollution cause is determined to be one of the causes of air pollution. Using the scheme of this disclosure, the automated identification of the causes of some types of air pollution can be achieved.

[0084] In reality, besides the aforementioned pollution from biomass burning, fireworks displays, and secondary organic pollutants, air pollution can also be caused by dust / sandstorms and secondary inorganic salt pollution from vehicle and industrial emissions. The aforementioned two types of pollution can also be identified using corresponding methods.

[0085] Figure 3 This is a flowchart illustrating a method for determining whether dust / sandstorm pollution is a cause of air pollution, as described in some embodiments of this disclosure. Figure 3 As shown, the methods for determining whether dust storms or sandstorms are a cause of air pollution include S310-S360. It should be noted that S310-S360 are executed after an air pollution event is identified (i.e., after executing the aforementioned S110).

[0086] S310: Obtain the fine particulate matter mass concentration sequence over a duration and the inhalable particulate matter mass concentration sequence over a duration.

[0087] In this embodiment of the disclosure, inhalable particulate matter can be particles such as 10.0 μm or other standard particulate matter. However, it should be noted that inhalable particulate matter includes the aforementioned fine particulate matter. As analyzed above, the computing device can determine the particulate matter mass concentration sequence and the inhalable particulate matter mass concentration sequence by periodically monitoring data from devices such as particle size analyzers.

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

[0089] Calculating the mean concentration of fine particulate matter (PM2.5) based on a PM2.5 concentration sequence involves averaging the concentrations of each individual particulate matter in the sequence. Similarly, calculating the mean concentration of inhalable particulate matter (IPM) based on a PM2.5 concentration sequence also involves averaging the concentrations of each individual particulate matter.

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

[0091] After obtaining the average mass concentrations of fine particulate matter and inhalable particulate matter, the proportion of fine particulate matter can be obtained by dividing the average mass concentration of fine particulate matter by the average mass concentration of inhalable particulate matter.

[0092] S340: Identify the crustal material components related to dust / dust pollution in various components, and calculate the mean of the sum of crustal material mass concentrations based on the corresponding single-component mass concentration sequences.

[0093] Dust / dust pollution is caused by strong winds blowing dust and sand up from the ground. Correspondingly, the pollutants produced by this type of pollution are mostly composed of elements such as Si and Ca. 2 Components such as α, β, and α are included. Correspondingly, crustal components within atmospheric particulate matter can be considered as components related to dust / dust pollution. After identifying these components, the mean of the mass concentration sequence of each individual component corresponding to each crustal component can be calculated. These mean values ​​are then summed to obtain the summed mean mass concentration of crustal materials. In other words, the overall mean mass concentration is the average of the mass concentrations of all crustal components over various periods within the duration.

[0094] S350: Calculate the proportion of crustal material based on the average summation mass concentration and the average total mass concentration of crustal material.

[0095] After obtaining the sum of the average mass concentrations of crustal materials, the proportion of crustal materials can be obtained by dividing the sum of the average mass concentrations of crustal materials as the numerator and the average mass concentration of the total mass 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, the causes of air pollution are determined to include dust / sand pollution.

[0097] If dust / sandstorm pollution occurs, the diameter of atmospheric particulate matter will generally be larger. In this situation, the proportion of fine particulate matter between the mass concentration of fine particulate matter and the mass concentration of inhalable particulate matter will be smaller. Simultaneously, dust / sandstorm pollution will result in a higher proportion of crustal material components in the air pollution. Therefore, in this embodiment of the disclosure, if the proportion of fine particulate matter is determined to be less than a second preset proportion, and the proportion of crustal material is determined to be greater than a third preset proportion, then the cause of air pollution is determined to include dust / sandstorm pollution.

[0098] In some embodiments, the computing device can also acquire remote sensing images such as those obtained through satellite telemetry or aircraft telemetry. Because the spectral absorption characteristics of dust storms are significantly different from those of conventional pollutants, remote sensing images acquired when dust storm pollution is present are significantly different from those acquired when dust storm pollution is absent. Therefore, image analysis can be used to determine whether a remote sensing image is affected by dust storms. If a remote sensing image is determined to be affected by dust / sand storms, it can be further determined that the cause of air pollution includes dust / sand storm pollution.

[0099] Figure 4 This is a flowchart illustrating a method for determining whether secondary inorganic salt pollution is a cause of air pollution, as described in some embodiments of this disclosure. Figure 4As shown, the methods for determining whether dust storms or sandstorms are a cause of air pollution include S410-S430. It should be noted that S410-S430 are implemented after an air pollution event is identified (i.e., after implementing the aforementioned S110).

[0100] S410: Identify the secondary inorganic salts in each component and calculate the mean mass concentration of the secondary inorganic salts based on the corresponding single-component mass concentration sequence.

[0101] In practice, secondary inorganic salts can be components such as sulfates and nitrates obtained from the re-oxidation of pollutants emitted from vehicle and industrial combustion. After identifying the secondary inorganic salts, the mean of the single-component mass concentration sequence of each secondary inorganic salt can be calculated, and these mean values ​​can be summed to obtain the mean mass concentration of the secondary inorganic salt. In other words, the mean mass concentration of the secondary inorganic salt is the average of the mass concentrations of the aforementioned various secondary inorganic salts over the duration of each period.

[0102] S420: Calculate the ratio of the average concentration of secondary inorganic salts to the average mass concentration of the total mass to obtain the proportion of secondary inorganic salts.

[0103] After obtaining the average concentration of secondary inorganic salts in the crustal material, the proportion of secondary inorganic salts can be obtained by dividing the sum of the average concentrations of secondary inorganic salts as the numerator and the average mass concentration of the total mass as the denominator.

[0104] S430: 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.

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

[0106] In practice, secondary inorganic salt pollution can be categorized into secondary nitrate pollution and secondary sulfate pollution based on the different pollutants emitted. Secondary nitrate pollution may be caused by vehicle exhaust emissions, while secondary sulfate pollution may be caused by factors such as coal combustion. To more accurately determine the type of pollution source, in addition to implementing the aforementioned S430, the following S440-S450 can also be implemented.

[0107] S440: Compare the average 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 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 secondary inorganic salt pollution includes secondary sulfate pollution.

[0109] In practice, after determining the specific causes of air pollution using the methods described in the preceding embodiments, the aforementioned pollution causes can be output and displayed to indicate the type of pollution source to relevant managers. It should be noted that the causes of air pollution may include at least two of the aforementioned reasons simultaneously.

[0110] As analyzed above, in order to determine various pollution types, it is necessary to obtain particulate matter mass concentration data and individual component mass concentration data for each component. In practical applications, issues may arise such as monitoring equipment malfunctions or occasional data jumps. To avoid the impact of such abnormal data on pollution source identification, after obtaining the aforementioned monitoring data, the data can be integrated, stored, and processed, and anomaly removal can be performed.

[0111] This disclosure also provides a computing device for implementing the aforementioned method. Figure 5 This is a schematic diagram of the structure of a computing device provided in an embodiment of this disclosure. See below for details. Figure 5 It shows a schematic diagram of the structure of a computing device 500 suitable for implementing the methods of the embodiments of the present disclosure. Figure 5 The computing device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0112] like Figure 5 As shown, the computing device 500 may include a processing unit (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 into a random access memory RAM 503. The RAM 503 also stores various programs and data required for the operation of the computing device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0113] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, cameras, microphones, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows computing device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 A computing device 500 with various devices is shown; however, 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 included alternatively.

[0114] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via 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, it performs the functions defined in the methods of embodiments of this disclosure.

[0115] It should be noted that the computer-readable medium described above in this disclosure may be a computer-readable storage medium, a computer-readable signal medium, or any combination thereof.

[0116] Computer-readable storage media can be, for example—but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can 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, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection 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 fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0118] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0119] The aforementioned computer-readable medium may be included in the aforementioned computing device; or it may exist independently and not assembled into the computing device.

[0120] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smarttalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the tester's computer, partially on the tester's computer, as a standalone software package, partially on the tester's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can 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 can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0122] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not necessarily limiting in certain circumstances. The functions described above can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), etc.

[0123] The above are merely specific embodiments of this disclosure, enabling those skilled in the art to understand or implement this disclosure. Various modifications to these embodiments will be readily 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 this disclosure. Therefore, this disclosure is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the causes and types of air pollution, characterized in that, include: When the concentration of fine particulate matter is greater than the threshold concentration and the duration exceeds the set duration, the single-component mass concentration sequence of various components in atmospheric particulate matter is obtained within the duration. Identify 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 reordering position of the mass concentration in each single-component mass concentration sequence. For any two target components in the first target component, the correlation is evaluated based on the reordering of the mass concentrations at the same position in the corresponding single-component mass concentration sequence to obtain the correlation coefficient between the two target components; and the mean of each correlation coefficient is calculated to obtain the overall correlation coefficient. A second target component related to the first pollution cause is identified, and a partial summation mean mass concentration is calculated based on the corresponding single-component mass concentration sequence; the overall mean mass concentration is calculated based on all single-component mass concentration sequences; and the proportion of the second target component is calculated based on the partial summation mean mass concentration and the overall mean mass concentration. If 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, the cause of air pollution is determined to include the first cause of pollution.

2. The method according to claim 1, characterized in that, For any two target components in the first target component, the correlation is evaluated based on the reordering of the mass concentrations at the same position in the corresponding single-component mass concentration sequence, resulting in a correlation coefficient between the two target components, including: Calculate the squared difference between the reordering positions of the mass concentrations at the same position in the single-component mass concentration sequences of the two target components. The correlation coefficient between the two target components is calculated based on 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 cause of pollution is biomass burning pollution; The determination of the first target component related to the first pollution cause includes: determining organic carbon, potassium ions, chloride ions, and organic matter related to the biomass combustion pollution; The determination of the second target component related to the first pollution cause includes: determining organic matter related to the biomass combustion pollution.

4. The method according to claim 1 or 2, characterized in that, The first cause of pollution is pollution from fireworks displays; The determination of the first target component related to the first pollution cause includes: determining sulfate, sodium ions, magnesium ions, and chloride ions related to the pollution from the fireworks display; The determination of the second target component related to the first pollution cause includes: determining the trace element components related to the pollution from fireworks displays.

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

6. The method according to claim 1 or 2, characterized in that, Also includes: Obtain the fine particulate matter mass concentration sequence and the inhalable particulate matter mass concentration sequence during the duration of the process; calculate the average fine particulate matter mass concentration based on the fine particulate matter mass concentration sequence; and calculate the average inhalable particulate matter mass concentration based on the inhalable particulate matter mass concentration sequence. The proportion of fine particulate matter is calculated based on the average mass concentration of fine particulate matter and the average mass concentration of inhalable particulate matter; Identify the crustal material components related to dust / dust pollution among the various components, and calculate the average sum of crustal material mass concentrations based on the corresponding single-component mass concentration sequences; The proportion of crustal material is calculated based on the summation mean mass concentration of the crustal material and the total mean mass concentration. If 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, the cause of air pollution is determined to include dust / sand pollution.

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

8. The method according to claim 1 or 2, characterized in that, Also includes: Secondary inorganic salts in various components were identified, and the mean mass concentration of secondary inorganic salts was calculated based on the corresponding single-component mass concentration sequences. The ratio of the average concentration of the secondary inorganic salts to the average mass concentration of the total mass is calculated to obtain the proportion of secondary inorganic salts. If 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 salts include secondary nitrates and secondary sulfates; the method further includes: Compare the average mass concentrations of the secondary nitrate and secondary sulfate; If the average mass concentration of the secondary nitrate is greater than the average mass concentration of the secondary sulfate, it is determined that the secondary inorganic salt contamination includes secondary nitrate contamination; and if the average mass concentration of the secondary sulfate is greater than the average mass concentration of the secondary nitrate, it is determined that the secondary inorganic salt contamination includes secondary sulfate contamination.

10. A computing device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program; when the computer program is loaded by the processor, it causes the processor to execute the method for determining the causes of air pollution as described in any one of claims 1-9.

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