A method, device, equipment and storage medium for processing atmospheric hygroscopicity parameters

Abstract

The present invention discloses a method, apparatus, device, and storage medium for processing atmospheric hygroscopicity parameters. The method comprises: calculating the time difference between a total particle concentration data sequence and a cloud condensation nucleus concentration data sequence, and correcting the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence; determining whether the temperature of a cloud condensation nucleus counter remains stable within each preset time period, and then eliminating the cloud condensation nucleus concentration data sequence within the temperature-unstable time period; and fitting the corresponding atmospheric aerosol hygroscopicity parameter activation particle size based on the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after multi-charge correction. The present invention can automatically correct the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and eliminate the cloud condensation nucleus concentration data sequence within the temperature-unstable time period.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric measurement technology, and in particular to a method, device, equipment and storage medium for processing atmospheric hygroscopicity parameters. Background Art

[0002] Cloud condensation nuclei play a key role in cloud formation and precipitation, indirectly influencing Earth's radiation balance. A cloud condensation nucleus counter (CCNc) can measure CCN concentrations at varying degrees of supersaturation in the atmosphere. A scanning mobility particle sizer (SMPS = DMA + CPC) can measure the concentrations of particles in different atmospheric size ranges. Moore et al. proposed a combined CCNc and SMPS measurement method (SMCA). Using data from both methods, they can quickly and efficiently determine the CCN size spectrum and particle activation size.

[0003] The prior art uses a data processing method based on VBS Excel to process the data obtained by the two instruments to obtain the cloud condensation nucleus particle size spectrum and the activated particle size. The specific steps are: (1) reading the required data from the original files of the two instruments and extracting the data into a data file of a specific format; (2) copying the extracted data into the data processing file as required; (3) manually fitting the data at each time point, first judging based on the obtained temperature gradient, and judging by the naked eye whether the temperature gradient is stable; (4) manually aligning the tail ends of the particle concentrations obtained by CCN and SMPS, and judging by the naked eye; (5) performing charge correction; (6) modifying the fitting parameters, fitting according to the formula, and obtaining the activated particle size (D50) at that time.

[0004] However, the existing technology has the following defects: (1) manual data processing requires a lot of time and energy; (2) due to the different lengths of the pipes connected to the two instruments during measurement, there is a time deviation in the data measured by the two instruments; when judging whether the time series of particulate matter concentrations obtained by the two instruments overlap, the existing technology requires visual judgment, which is not scientific and accurate; (3) because the temperature gradient of the cloud condensation nucleus counter (CCNc) will change when the supersaturation is changed, the existing technology needs to rely on personal experience when judging whether the temperature gradient is stable, which is not scientific and accurate. Summary of the Invention

[0005] The present invention provides a method, device, equipment and storage medium for processing atmospheric hygroscopicity parameters to solve the technical problems of inaccurate judgment of time series deviation and temperature gradient stability when manually processing particulate matter concentration data and cloud condensation nucleus concentration data.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for processing atmospheric hygroscopicity parameters, comprising:

[0007] Obtaining total particulate matter concentration data generated by a particle counter and cloud condensation nucleus concentration data generated by a cloud condensation nucleus counter;

[0008] sorting the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence, and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0009] calculating a time difference between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series, and correcting a time series deviation between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series according to the time difference;

[0010] Determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period, and then remove the cloud condensation nucleus concentration data series within the time period where the temperature is unstable based on the calibrated cloud condensation nucleus concentration data series;

[0011] Multi-charge correction is performed on the corrected total particle concentration data series and the cloud condensation nucleus concentration data series after data removal. Then, the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is fitted based on the multi-charge corrected total particle concentration data series and the cloud condensation nucleus concentration data series.

[0012] As a preferred solution, before sorting the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence, the method further includes:

[0013] obtaining a preset supersaturation of a cloud condensation nucleus counter, and determining a time series range and an activated particle size range according to the supersaturation;

[0014] The total particle concentration data and the cloud condensation nucleus concentration data are screened according to the time series range and the activated particle size range, and the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range are eliminated.

[0015] As a preferred solution, the calculating the time difference between the total particulate matter concentration data sequence and the cloud condensation nucleus concentration data sequence includes:

[0016] According to a preset measurement cycle, extracting a plurality of first data sequences of a first time length from the total particulate matter concentration data sequence, and extracting a plurality of second data sequences of a second time length from the cloud condensation nucleus concentration data sequence;

[0017] Within the same measurement period, calculating a first mean square error between the first data sequence and the second data sequence, and extracting a third data sequence from the cloud condensation nucleus concentration data sequence at one time point forward and a fourth data sequence from one time point backward, respectively, and calculating a second mean square error between the third data sequence and the fourth data sequence;

[0018] The time difference between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series is calculated based on the first mean square error and the second mean square error.

[0019] As a preferred solution, the first mean square error is calculated by the following formula:

[0020]

[0021] Among them, σ t is the first mean square error corresponding to time point t, N CN,t,i is the first data sequence, N CCN,t,i is the second data sequence, and n is the measurement period.

[0022] As a preferred solution, the step of determining whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period includes:

[0023] Obtain the temperature gradient time series corresponding to the cloud condensation nucleus counter;

[0024] According to the preset sliding window length, the sliding average value corresponding to each time point in the temperature gradient time series is calculated, and then the sliding average value is used to determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period.

[0025] As a preferred solution, the corresponding atmospheric aerosol hygroscopic parameter activation particle size is obtained by fitting according to the following formula:

[0026]

[0027] Among them, N CCN is the cloud condensation nucleus concentration data series after multi-charge correction, N CN is the total particle concentration data series after multi-charge correction, D P is the particle size value measured by scanning mobility particle size spectrometer, D 50 To fit the parameters to be obtained, B and C are fitting parameters.

[0028] Based on the above embodiment, another embodiment of the present invention provides an atmospheric hygroscopicity parameter processing device, comprising: a data acquisition module, a data sorting module, a time correction module, a data elimination module, and a data fitting module;

[0029] The data acquisition module is used to acquire total particulate matter concentration data generated by the particle counter and cloud condensation nucleus concentration data generated by the cloud condensation nucleus counter;

[0030] The data sorting module is configured to sort the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence, and sort the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0031] The time correction module is configured to calculate a time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correct a time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference;

[0032] The data elimination module is used to determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period, and then eliminate the cloud condensation nucleus concentration data sequence within the time period where the temperature is unstable based on the corrected cloud condensation nucleus concentration data sequence;

[0033] The data fitting module is used to perform multi-charge correction on the corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after data removal, and then fit the corresponding atmospheric aerosol hygroscopicity parameter activation particle size based on the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after multi-charge correction.

[0034] As a preferred solution, the atmospheric hygroscopicity parameter processing device further includes: a data preprocessing module;

[0035] The data preprocessing module is used to obtain the supersaturation of a preset cloud condensation nucleus counter, determine a time series range and an activated particle size range based on the supersaturation; based on the time series range and the activated particle size range, screen the total particle concentration data and the cloud condensation nucleus concentration data, and eliminate the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range.

[0036] Based on the above embodiments, another embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for processing atmospheric hygroscopicity parameters described in the above embodiments of the invention.

[0037] Based on the above embodiment, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the atmospheric hygroscopicity parameter processing method described in the above embodiment of the invention.

[0038] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0039] The present invention provides a method for processing atmospheric hygroscopicity parameters. Through the present invention, atmospheric hygroscopicity parameters can be automatically processed. Based on the total particle concentration data generated by the particle counter and the cloud condensation nucleus concentration data generated by the cloud condensation nucleus counter, the time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence is calculated, and the time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence is automatically corrected based on the time difference, thereby avoiding the problem that the existing technology requires manual correction and needs to be judged based on subjective experience. In addition, the present invention will also automatically determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period, and then, based on the corrected cloud condensation nucleus concentration data sequence, the cloud condensation nucleus concentration data sequence within the time period with unstable temperature is eliminated, thereby avoiding the problem that the existing technology requires subjective experience judgment and has no objective scientific standards. Finally, multi-charge correction is performed on the corrected total particle concentration data series and the cloud condensation nucleus concentration data series after data removal. Then, the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is fitted based on the multi-charge corrected total particle concentration data series and the cloud condensation nucleus concentration data series. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 1 is a flow chart of a method for processing atmospheric hygroscopicity parameters provided by one embodiment of the present invention;

[0041] Figure 2 It is a diagram of the interface of the data processing software of the present invention;

[0042] Figure 3 It is a data fitting flow chart of the present invention;

[0043] Figure 4 It is a structural schematic diagram of a device for processing atmospheric hygroscopicity parameters provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0046] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0047] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0049] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0050] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0051] Example 1

[0052] Please refer to Figure 1 , which is a flow chart of a method for processing atmospheric hygroscopicity parameters provided by one embodiment of the present invention, comprising the following specific steps:

[0053] S1. Obtaining total particulate matter concentration data generated by a particle counter and cloud condensation nucleus concentration data generated by a cloud condensation nucleus counter;

[0054] Preferably, before sorting the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence, and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence, it also includes: obtaining a preset supersaturation of a cloud condensation nucleus counter, and determining a time series range and an activated particle size range according to the supersaturation; screening the total particulate matter concentration data and the cloud condensation nucleus concentration data according to the time series range and the activated particle size range, and eliminating the total particulate matter concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range.

[0055] Specifically, the following describes in detail the method for processing atmospheric hygroscopic parameters of the present invention, taking the online observation experiment of hygroscopic parameters conducted during the 2022 South China Sea scientific expedition as an example:

[0056] Step 1: Particles enter the differential mobility analyzer (DMA) from the air intake for size screening. They then enter the particle counter (CPC) and cloud condensation nucleus counter (CCNc), respectively, to calculate the total particle number concentration and cloud condensation nucleus number concentration. Finally, two data files are generated. In one embodiment, depending on the DMA's set time, a scan cycle typically lasts 5-8 minutes. Therefore, two segments of total particle concentration and cloud condensation nucleus concentration data are obtained every 5-8 minutes to calculate the atmospheric aerosol hygroscopicity parameters.

[0057] Step 2: Please refer to Figure 2, which shows the interface of the data processing software of the present invention. After entering the folder locations of the data files generated by the two instruments and setting the measurement time and other related parameters according to the prompts, run the software and the software will begin processing the data files of the two instruments. The specific parameters that need to be set are as follows:

[0058] (1) The path where the cloud condensation nucleus data file is stored and the last two digits of the current measurement year;

[0059] (2) The path where the scanning mobility spectrometer data is located, the number of time lines in the data file, the number of dN / d logDp data starting lines, the number of particle size lines, the total time of one scan, and the number of the starting lines of the raw data;

[0060] (3) Select the column used for CCNc instrument measurement. If CCNc has only one column, column A is selected by default. Then enter the path of the CCN_uptake_A / B.csv file generated in step 1 and the path where the images of the fitting process are saved.

[0061] (4) Input the parameters required for fitting: the first step is to estimate the time difference between the particles coming out of DMA and entering CCNc and CPC; the second step is to fill in the supersaturation set on the CCNc instrument during the measurement process; the third step is to select the fitting time series range according to different supersaturations; the fourth step is to select the fitting activation particle size range (the upper and lower limits of the activation particle size) according to different supersaturations, and then screen the total particle concentration data and the cloud condensation nucleus concentration data according to the time series range and the activation particle size range, and eliminate the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activation particle size range; the fifth step is to select the range for matching in the tail matching algorithm; the sixth step is to select the temperature gradient judgment algorithm, and method I is selected by default, which is the main judgment algorithm of the present invention;

[0062] (5) Fill in the initial guess value of the activated particle size for different supersaturation fittings, and the range must be within the set upper and lower limits.

[0063] S2. Sort the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence, and sort the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0064] Step 3, please refer to Figure 3 , which is the data fitting flow chart of the present invention. First, the tail matching algorithm is used to automatically correct the measurement time error between the two instruments caused by factors such as pipeline and air flow velocity differences. The specific steps are as follows:

[0065] (1) extracting data from the data files of the two instruments, sorting the total particulate matter concentration data according to the time sequence in the data files to obtain a corresponding total particulate matter concentration data sequence, and sorting the cloud condensation nucleus concentration data according to the time sequence to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0066] S3. Calculating a time difference between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series, and correcting a time series deviation between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series according to the time difference;

[0067] Preferably, the calculation of the time difference between the total particulate matter concentration data sequence and the cloud condensation nucleus concentration data sequence includes: according to a preset measurement period, extracting several first data sequences of a first time length from the total particulate matter concentration data sequence, and extracting several second data sequences of a second time length from the cloud condensation nucleus concentration data sequence; within the same measurement period, calculating the first mean square error of the first data sequence and the second data sequence, and respectively extracting the third data sequence of the cloud condensation nucleus concentration data sequence at a previous time point and the fourth data sequence at a backward time point, and calculating the second mean square error of the third data sequence and the fourth data sequence; and calculating the time difference between the total particulate matter concentration data sequence and the cloud condensation nucleus concentration data sequence based on the first mean square error and the second mean square error.

[0068] Preferably, the first mean square error is calculated by the following formula:

[0069]

[0070] Among them, σ t is the first mean square error corresponding to time point t, N CN,t,i is the first data sequence, N CCN,t,i is the second data sequence, and n is the measurement period.

[0071] (2) The time difference between the two instruments is corrected by the tail matching algorithm to ensure that the two time series of the total particulate matter concentration data series and the cloud condensation nucleus concentration data series are measured at the same time.

[0072] The tail matching algorithm is as follows:

[0073] First, according to the start time of a SMPS scan and the total time of a SMPS scan (e.g., 120 seconds, s), a time series N of total particle data is selected. CN,t (i.e. the first data series) and a time series N of cloud condensation nucleus concentration data CCN,t (ie the second data sequence), assuming N CN,tThe time length is 120s (i.e. the first time length), N CCN,t The time length is 180 (ie the second time length).

[0074] Since particles will eventually activate into cloud condensation nuclei after growing to a certain particle size (about >80-180nm) at different supersaturation levels, the same measurement period N CN,t and N CCN,t The number concentrations at the tail of the time series should be equal or very close. Therefore, according to the matching range n selected above, CN,t and N CCN,t The mean square error is calculated for the values ​​within this range to obtain the first mean square error Then put N CCN,t The sequence moves forward one time point to get N CCN,t-1 (ie the third data sequence), or move back one time point to get N CCN,t+1 (i.e. the fourth data sequence), and then calculate σ t-1 or σ t+1 (Second mean square error); Repeat the above steps 60 times to obtain 60 σ. Finally, select the σ with the smallest value k The time point k corresponding to the time point k is the time difference between the two time series scanned at time t. Then, the time series deviation between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series is corrected based on the time difference.

[0075] S4. Determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period, and then, based on the calibrated cloud condensation nucleus concentration data sequence, remove the cloud condensation nucleus concentration data sequence within the time period where the temperature is unstable;

[0076] Preferably, the determination of whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period includes: obtaining a temperature gradient time series corresponding to the cloud condensation nucleus counter; calculating a sliding average value corresponding to each time point in the temperature gradient time series according to a preset sliding window length, and then determining whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period based on the sliding average value.

[0077] Step 4: Use the temperature gradient judgment algorithm to determine whether the temperature in the cloud condensation nucleus counter remains stable during the time measurement data. If it remains stable, enter the final step of calculating the hygroscopicity parameters. If the temperature is unstable, discard the cloud condensation nucleus concentration data for this time period.

[0078] The specific implementation method of the temperature gradient judgment algorithm is as follows:

[0079] The scan started at time t obtains a temperature gradient time series ΔTt Set the sliding window to 5 and calculate the sliding average of each time point n in the time series Then, all the obtained τ are screened. If τ n -τ n-1 The absolute value of is greater than 10(abs(τ n -τ n-1 )>10), the temperature of the data measured at time t is unstable and cannot be fitted in the next step. It should be removed from the cloud condensation nucleus concentration data sequence. Otherwise, it can pass the screening and enter the next step of fitting.

[0080] S5. Perform multi-charge correction on the corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after data removal, and then fit the corresponding atmospheric aerosol hygroscopicity parameter activation particle size based on the multi-charge corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence.

[0081] Preferably, the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is obtained by fitting according to the following formula:

[0082]

[0083] Among them, N CCN is the cloud condensation nucleus concentration data series after multi-charge correction, N CN is the total particle concentration data series after multi-charge correction, D P is the particle size value measured by scanning mobility particle size spectrometer, D 50 To fit the parameters to be obtained, B and C are fitting parameters.

[0084] Step 5: Perform multi-charge correction on the corrected total particulate matter concentration data series and the cloud condensation nucleus concentration data series after data removal. Finally, the atmospheric aerosol hygroscopicity parameter activation particle size D is obtained by fitting. 50 , the specific fitting formula is as follows: N CCN and N CCN is the value of cloud condensation nucleus concentration and total particulate matter obtained by the instrument, D P is the particle size value measured by the scanning mobility particle size spectrometer, D 50 is the parameter to be obtained by fitting, and B and C are the fitting parameters.

[0085] It can be seen that the present invention provides a method for processing atmospheric hygroscopicity parameters. In view of the problem that the existing technology requires multiple steps and manual processing when calculating atmospheric hygroscopic parameters, which is extremely time-consuming, the present invention integrates all steps and automates them in a unified manner; in view of the problem that the time error generated when measuring two instruments in the existing technology requires manual and subjective judgment calibration, the present invention automatically corrects it through the tail matching algorithm; in view of the problem that the existing technology requires subjective judgment on whether the temperature gradient is stable, the present invention establishes scientific standards through a temperature gradient judgment algorithm and performs automatic judgment.

[0086] In general, the present invention can achieve the following beneficial effects:

[0087] (1) The entire atmospheric aerosol hygroscopicity parameter calculation process is integrated to quickly obtain accurate atmospheric aerosol hygroscopicity parameters during online observation;

[0088] (2) The tail matching algorithm can automatically and accurately correct the measurement time errors between the two instruments caused by factors such as differences in pipelines and airflow velocities, making the final atmospheric hygroscopicity parameters more accurate. The existing technology requires manual correction and judgment based on subjective experience. The present invention basically overcomes the above shortcomings and makes the processing process more scientific and effective.

[0089] (3) Through the temperature gradient algorithm, the period of unstable temperature gradient can be automatically and accurately screened out; the existing technology requires subjective experience to judge, and there is no objective scientific standard. The present invention basically solves this problem, establishes a clear judgment standard, and makes the data processing process more scientific and accurate.

[0090] Example 2

[0091] Please refer to Figure 4 , is a schematic structural diagram of a device for processing atmospheric hygroscopicity parameters provided by one embodiment of the present invention, the device comprising: a data acquisition module, a data sorting module, a time correction module, a data elimination module, and a data fitting module;

[0092] The data acquisition module is used to acquire total particulate matter concentration data generated by the particle counter and cloud condensation nucleus concentration data generated by the cloud condensation nucleus counter;

[0093] The data sorting module is configured to sort the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence, and sort the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence;

[0094] The time correction module is configured to calculate a time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correct a time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference;

[0095] The data elimination module is used to determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period, and then eliminate the cloud condensation nucleus concentration data sequence within the time period where the temperature is unstable based on the corrected cloud condensation nucleus concentration data sequence;

[0096] The data fitting module is used to perform multi-charge correction on the corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after data removal, and then fit the corresponding atmospheric aerosol hygroscopicity parameter activation particle size based on the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after multi-charge correction.

[0097] Preferably, the processing device for atmospheric hygroscopicity parameters also includes: a data preprocessing module; the data preprocessing module is used to obtain the supersaturation of a preset cloud condensation nucleus counter, and determine a time series range and an activated particle size range according to the supersaturation; according to the time series range and the activated particle size range, the total particulate matter concentration data and the cloud condensation nucleus concentration data are screened, and the total particulate matter concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range are eliminated.

[0098] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0099] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0100] Example 3

[0101] Accordingly, an embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for processing atmospheric hygroscopicity parameters described in the above-mentioned embodiment of the invention.

[0102] The electronic device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0103] The processor may be a central processing unit (CPU), other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, connecting various parts of the entire device using various interfaces and lines.

[0104] Example 4

[0105] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for processing atmospheric hygroscopicity parameters described in the above-mentioned embodiment of the invention.

[0106] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0107] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0108] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for processing atmospheric hygroscopicity parameters, characterized in that: include: Obtaining total particulate matter concentration data generated by a particle counter and cloud condensation nucleus concentration data generated by a cloud condensation nucleus counter; sorting the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence, and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence; calculating a time difference between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series, and correcting a time series deviation between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series according to the time difference; Determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period, and then remove the cloud condensation nucleus concentration data series within the time period where the temperature is unstable based on the calibrated cloud condensation nucleus concentration data series; Multi-charge correction is performed on the corrected total particle concentration data series and the cloud condensation nucleus concentration data series after data removal. Then, the corresponding atmospheric aerosol hygroscopicity parameter activation particle size is fitted based on the multi-charge corrected total particle concentration data series and the cloud condensation nucleus concentration data series.

2. The method for processing atmospheric hygroscopicity parameters according to claim 1, wherein: Before sorting the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence and sorting the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence, the method further includes: obtaining a preset supersaturation of a cloud condensation nucleus counter, and determining a time series range and an activated particle size range according to the supersaturation; The total particle concentration data and the cloud condensation nucleus concentration data are screened according to the time series range and the activated particle size range, and the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range are eliminated.

3. The method for processing atmospheric hygroscopicity parameters according to claim 1, wherein: Calculating the time difference between the total particulate matter concentration data sequence and the cloud condensation nucleus concentration data sequence includes: According to a preset measurement cycle, extracting a plurality of first data sequences of a first time length from the total particulate matter concentration data sequence, and extracting a plurality of second data sequences of a second time length from the cloud condensation nucleus concentration data sequence; Within the same measurement period, calculating a first mean square error between the first data sequence and the second data sequence, and extracting a third data sequence from the cloud condensation nucleus concentration data sequence at one time point forward and a fourth data sequence from one time point backward, respectively, and calculating a second mean square error between the third data sequence and the fourth data sequence; The time difference between the total particulate matter concentration data series and the cloud condensation nucleus concentration data series is calculated based on the first mean square error and the second mean square error.

4. The method for processing atmospheric hygroscopicity parameters according to claim 3, wherein: The first mean square error is calculated by the following formula: Among them, σ t is the first mean square error corresponding to time point t, N CN,t,i is the first data sequence, N CCN,t,i is the second data sequence, and n is the measurement period.

5. The method for processing atmospheric hygroscopicity parameters according to claim 1, wherein: The determining whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period includes: Obtain the temperature gradient time series corresponding to the cloud condensation nucleus counter; According to the preset sliding window length, the sliding average value corresponding to each time point in the temperature gradient time series is calculated, and then the sliding average value is used to determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period.

6. The method for processing atmospheric hygroscopicity parameters according to claim 1, wherein: According to the following formula, the corresponding atmospheric aerosol hygroscopic parameter activation particle size is fitted: Among them, N CCN is the cloud condensation nucleus concentration data series after multi-charge correction, N CN is the total particle concentration data series after multi-charge correction, D P is the particle size value measured by scanning mobility particle size spectrometer, D 50 To fit the parameters to be obtained, B and C are fitting parameters.

7. A device for processing atmospheric hygroscopicity parameters, characterized in that: include: Data acquisition module, data sorting module, time correction module, data rejection module and data fitting module; The data acquisition module is used to acquire total particulate matter concentration data generated by the particle counter and cloud condensation nucleus concentration data generated by the cloud condensation nucleus counter; The data sorting module is configured to sort the total particulate matter concentration data in chronological order to obtain a corresponding total particulate matter concentration data sequence, and sort the cloud condensation nucleus concentration data in chronological order to obtain a corresponding cloud condensation nucleus concentration data sequence; The time correction module is configured to calculate a time difference between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence, and correct a time series deviation between the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence according to the time difference; The data elimination module is used to determine whether the temperature of the cloud condensation nucleus counter remains stable within each preset time period, and then eliminate the cloud condensation nucleus concentration data sequence within the time period where the temperature is unstable based on the corrected cloud condensation nucleus concentration data sequence; The data fitting module is used to perform multi-charge correction on the corrected total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after data removal, and then fit the corresponding atmospheric aerosol hygroscopicity parameter activation particle size based on the total particle concentration data sequence and the cloud condensation nucleus concentration data sequence after multi-charge correction.

8. The atmospheric hygroscopicity parameter processing device according to claim 7, characterized in that: Also includes: Data preprocessing module; The data preprocessing module is used to obtain the supersaturation of a preset cloud condensation nucleus counter, determine a time series range and an activated particle size range based on the supersaturation; based on the time series range and the activated particle size range, screen the total particle concentration data and the cloud condensation nucleus concentration data, and eliminate the total particle concentration data and the cloud condensation nucleus concentration data that are not within the time series range and the activated particle size range.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for processing atmospheric hygroscopicity parameters according to any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is run, the device where the storage medium is located is controlled to execute the method for processing atmospheric hygroscopicity parameters according to any one of claims 1 to 6.

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