CN117589948B - Cloud condensation nucleus source analysis method and system based on particle number spectrum - Google Patents
Cloud condensation nucleus source analysis method and system based on particle number spectrum Download PDFInfo
- Publication number
- CN117589948B CN117589948B CN202410077679.8A CN202410077679A CN117589948B CN 117589948 B CN117589948 B CN 117589948B CN 202410077679 A CN202410077679 A CN 202410077679A CN 117589948 B CN117589948 B CN 117589948B
- Authority
- CN
- China
- Prior art keywords
- data
- aerosol
- aerosol particle
- particle size
- growth factor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002245 particle Substances 0.000 title claims abstract description 313
- 238000001228 spectrum Methods 0.000 title claims abstract description 106
- 238000004458 analytical method Methods 0.000 title claims abstract description 51
- 238000009833 condensation Methods 0.000 title claims abstract description 21
- 230000005494 condensation Effects 0.000 title claims abstract description 21
- 239000000443 aerosol Substances 0.000 claims abstract description 199
- 239000003102 growth factor Substances 0.000 claims abstract description 100
- 238000010521 absorption reaction Methods 0.000 claims abstract description 48
- 239000000126 substance Substances 0.000 claims abstract description 45
- 238000000034 method Methods 0.000 claims abstract description 41
- 201000008827 tuberculosis Diseases 0.000 claims abstract description 26
- 239000011159 matrix material Substances 0.000 claims abstract description 21
- 238000005070 sampling Methods 0.000 claims abstract description 19
- 238000004364 calculation method Methods 0.000 claims abstract description 18
- 239000013618 particulate matter Substances 0.000 claims abstract description 3
- 239000007789 gas Substances 0.000 claims description 49
- 230000032683 aging Effects 0.000 claims description 31
- 230000006911 nucleation Effects 0.000 claims description 19
- 238000010899 nucleation Methods 0.000 claims description 19
- 230000004913 activation Effects 0.000 claims description 18
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 16
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 16
- 239000003738 black carbon Substances 0.000 claims description 15
- 230000000694 effects Effects 0.000 claims description 15
- 238000010438 heat treatment Methods 0.000 claims description 15
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 14
- 230000007613 environmental effect Effects 0.000 claims description 14
- 239000000203 mixture Substances 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 11
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 8
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 claims description 8
- MGWGWNFMUOTEHG-UHFFFAOYSA-N 4-(3,5-dimethylphenyl)-1,3-thiazol-2-amine Chemical compound CC1=CC(C)=CC(C=2N=C(N)SC=2)=C1 MGWGWNFMUOTEHG-UHFFFAOYSA-N 0.000 claims description 7
- QGZKDVFQNNGYKY-UHFFFAOYSA-O Ammonium Chemical compound [NH4+] QGZKDVFQNNGYKY-UHFFFAOYSA-O 0.000 claims description 7
- 229910002651 NO3 Inorganic materials 0.000 claims description 7
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 claims description 7
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 claims description 7
- QAOWNCQODCNURD-UHFFFAOYSA-L Sulfate Chemical compound [O-]S([O-])(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-L 0.000 claims description 7
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 7
- 238000010411 cooking Methods 0.000 claims description 7
- 230000005012 migration Effects 0.000 claims description 7
- 238000013508 migration Methods 0.000 claims description 7
- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide Inorganic materials O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 claims description 2
- 238000005259 measurement Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 38
- 235000010269 sulphur dioxide Nutrition 0.000 description 5
- 239000005427 atmospheric aerosol Substances 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 230000003595 spectral effect Effects 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 3
- 235000013361 beverage Nutrition 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 229920000557 Nafion® Polymers 0.000 description 1
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 1
- 230000003213 activating effect Effects 0.000 description 1
- 239000008277 atmospheric particulate matter Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000015271 coagulation Effects 0.000 description 1
- 238000005345 coagulation Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 235000002639 sodium chloride Nutrition 0.000 description 1
- 239000011780 sodium chloride Substances 0.000 description 1
- 239000004291 sulphur dioxide Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
- G01N33/0068—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display using a computer specifically programmed
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Medicinal Chemistry (AREA)
- Data Mining & Analysis (AREA)
- Combustion & Propulsion (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Food Science & Technology (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Algebra (AREA)
- Pure & Applied Mathematics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Dispersion Chemistry (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a cloud condensation nucleus source analysis method and a system based on a particulate matter number spectrum, and relates to the technical field of atmospheric measurement, wherein the method comprises the steps of acquiring aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to an atmospheric environment sample at a sampling moment in a set time period, carrying out source analysis by using an orthogonal matrix factorization (PMF) algorithm, and judging according to the chemical component data and the atmospheric gas concentration data to obtain the particle size spectrum distribution data and the moisture absorption growth factor data of aerosol particle groups of different sources; and calculating by using a kappa-K foster hler theory to obtain the concentration of the cloud tuberculosis of different sources. The invention realizes the source analysis of the cloud tuberculosis based on PMF analysis of aerosol particle size spectrum data and moisture absorption growth factor data, and improves the accuracy of the calculation of the cloud tuberculosis concentration.
Description
Technical Field
The invention relates to the technical field of atmospheric measurement, in particular to a cloud condensation nucleus source analysis method and system based on a particle number spectrum.
Background
According to the "two effect" and the "Albrecht effect", aerosols indirectly affect the optical properties and life cycle of the cloud by participating in the cloud process as a cloud condensation nucleus (Cloud Condensation Nuclei, CCN), thereby significantly affecting global and regional climate systems, and the sixth evaluation report of IPCC recently released in 2021 indicates that the indirect climate effect of aerosols as CCN is one of the most uncertain factors in climate change evaluation. To alleviate this uncertainty, the biophysical link between aerosols and clouds has received widespread attention as the most important part. Whether the environmental atmospheric aerosol can be used as CCN to participate in the cloud process depends on the physical and chemical properties and the meteorological factors, and parameterization research is carried out on the process of activating the aerosol into the CCN, so that the indirect climate effect of the aerosol can be quantified. Thus, accurate acquisition of CCN number concentration of aerosol activation formation is critical to model assessment of the climate effect of the aerosol.
CCN refers to aerosol particles that enable water vapor to condense and grow on them under certain environmental supersaturation. Most of the CCN number concentration predictions currently made are calculated based on particle spectral distribution and overall hygroscopicity of aerosols, and do not adequately account for the differences in the hygroscopic properties of aerosol particles from different sources. Aerosols from different sources have different hygroscopic properties due to the variety of particle sizes and chemical compositions. Aerosols such as traffic and restaurant emissions typically have low hygroscopic properties, and marine aerosols typically have high hygroscopicity due to the presence of sea salt particles. And the size and hygroscopicity of the particles discharged to the atmosphere are also changed under the influence of an aging process such as condensation collision. Therefore, in order to improve the accuracy of CCN number concentration prediction, it is important to fully consider the hygroscopic properties of aerosols of different sources, and it is important to understand and simulate aerosol-cloud interactions in the atmosphere.
Disclosure of Invention
The invention aims to provide a cloud condensation nucleus source analysis method and a cloud condensation nucleus source analysis system based on a particle number spectrum, which can improve the calculation accuracy of the concentration of the cloud condensation nucleus.
In order to achieve the above object, the present invention provides the following solutions:
the invention provides a cloud coagulation nodule source analysis method based on a particle number spectrum, which comprises the following steps:
acquiring aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to atmospheric environmental samples at a plurality of sampling moments within a set time period; the chemical composition data includes concentrations of black carbon, organics, sulfate, nitrate, ammonium, and chloride; the atmospheric gas concentration data includes concentrations of carbon monoxide, sulfur dioxide, ozone, nitrogen dioxide, and nitric oxide; the aerosol particle size spectrum data is obtained by observing an atmospheric environment sample by a scanning mobility particle size spectrometer; the moisture absorption growth factor data are obtained by observing an atmospheric environment sample through a humidifying migration differential analyzer; the chemical component data are obtained by observing atmospheric environment samples by a high-resolution time-of-flight aerosol mass spectrometer and an AE33 type black carbon meter; and the atmospheric gas concentration data are obtained by observing an atmospheric environment sample by a gas analyzer.
For an atmospheric environment sample at each sampling moment, carrying out source analysis on the source of each aerosol particle group by utilizing an orthogonal matrix factorization algorithm according to aerosol particle size spectrum data, hygroscopic growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample to obtain particle size spectrum distribution data and hygroscopic growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group; the sources include nucleation processes, traffic emissions, cooking activities, primary aging, residential heating, and regional secondary aging; each aerosol particle set comprises a plurality of aerosol particles;
and for each aerosol particle group, calculating to obtain the final cloud tuberculosis concentration corresponding to the aerosol particle group by using a kappa-K foster hler theory according to the particle size spectrum distribution data and the moisture absorption growth factor data of the aerosol particle group.
The invention also provides a cloud condensation nucleus source analysis system based on the particle number spectrum, which comprises the following steps:
the observation data acquisition module is used for acquiring aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environmental samples at a plurality of sampling moments within a set time period; the chemical composition data includes concentrations of black carbon, organics, sulfate, nitrate, ammonium, and chloride; the atmospheric gas concentration data includes concentrations of carbon monoxide, sulfur dioxide, ozone, nitrogen dioxide, and nitric oxide; the aerosol particle size spectrum data is obtained by observing an atmospheric environment sample by a scanning mobility particle size spectrometer; the moisture absorption growth factor data are obtained by observing an atmospheric environment sample through a humidifying migration differential analyzer; the chemical component data are obtained by observing atmospheric environment samples by a high-resolution time-of-flight aerosol mass spectrometer and an AE33 type black carbon meter; and the atmospheric gas concentration data are obtained by observing an atmospheric environment sample by a gas analyzer.
The source analysis module is used for carrying out source analysis on the source of each aerosol particle group by utilizing an orthogonal matrix factorization algorithm according to aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample at each sampling moment to obtain particle size spectrum distribution data and moisture absorption growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group; the sources include nucleation processes, traffic emissions, cooking activities, primary aging, residential heating, and regional secondary aging; each of the aerosol particle sets comprises a number of aerosol particles.
And the final cloud tuberculosis concentration calculation module is used for calculating the final cloud tuberculosis concentration corresponding to each aerosol particle group by utilizing a kappa-K hler theory according to the particle size spectrum distribution data and the moisture absorption growth factor data of the aerosol particle group.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a cloud condensation nucleus source analysis method and a cloud condensation nucleus source analysis system based on a particle number spectrum, which are characterized in that firstly, aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to an atmospheric environment sample at a sampling moment in a set time period are obtained, source analysis is carried out by using an orthogonal matrix factorization algorithm (PMF), and the particle size spectrum distribution data and the moisture absorption growth factor data of aerosol particle groups with different sources are obtained according to the chemical component data and the atmospheric gas concentration data; and calculating by using a kappa-K foster hler theory to obtain the concentration of the cloud tuberculosis of different sources. The invention realizes the source analysis of the cloud tuberculosis based on PMF analysis of aerosol particle size spectrum data and moisture absorption growth factor data, and improves the accuracy of the calculation of the cloud tuberculosis concentration.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for resolving cloud nuclei based on a particle number spectrum according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a particle number spectrum distribution of different source factors obtained by source analysis according to embodiment 1 of the present invention; FIG. 2 (a) is a schematic diagram showing the particle-number spectrum distribution of nucleation-derived factors; fig. 2 (b) is a schematic diagram of a particle spectrum distribution of a factor of a traffic emission source; fig. 2 (c) is a schematic diagram of particle spectrum distribution of factors of food and beverage emission sources; fig. 2 (d) is a schematic diagram of a particle spectrum distribution of primary aging source factors; fig. 2 (e) is a schematic diagram of particle number spectrum distribution of a factor of origin for house heating; FIG. 2 (f) is a schematic diagram of the particle spectrum distribution of the regional secondary aging source factor;
FIG. 3 is a graph showing daily variation of particle concentration of different source factors obtained by source analysis according to embodiment 1 of the present invention; FIG. 3 (a) is a graph showing the daily variation of the particle number concentration of the nucleation source factor; fig. 3 (b) is a schematic diagram of the daily variation of the concentration of particles of the factor of the source of traffic emission; fig. 3 (c) is a schematic diagram of daily variation of particle number concentration of food discharge source factor; fig. 3 (d) is a graph showing the daily change of the particle number concentration of the primary aging source factor; fig. 3 (e) is a schematic diagram showing the daily variation of the particle count concentration of the house heating source factor; FIG. 3 (f) is a graph showing the daily variation of the particle number concentration of the regional secondary aging source factor;
FIG. 4 is a schematic diagram of probability distribution of hygroscopic growth factors obtained by source analysis according to embodiment 1 of the present invention; FIG. 4 (a) is a graph showing probability distribution of the hygroscopic growth factor of the nucleation source factor; fig. 4 (b) is a schematic diagram of the probability distribution of the hygroscopic growth factor of the traffic emission source factor; fig. 4 (c) is a schematic diagram of probability distribution of hygroscopic growth factor of restaurant emission source factor; fig. 4 (d) is a schematic diagram of probability distribution of hygroscopic increase factor of primary aging source factor; fig. 4 (e) is a schematic diagram of the probability distribution of the moisture absorption growth factor of the residential heating source factor; fig. 4 (f) is a schematic diagram of probability distribution of hygroscopic growth factor of regional secondary aging source factor;
FIG. 5 is a graph showing the daily variation of the probability distribution of the hygroscopic growth factors obtained by source analysis according to the embodiment 1 of the present invention; FIG. 5 (a) is a graph showing the daily variation of the probability distribution of the hygroscopic growth factor of the nucleation source factor; fig. 5 (b) is a schematic diagram of the daily variation of the hygroscopic growth factor probability distribution of the traffic emission source factor; fig. 5 (c) is a schematic diagram of daily variation of the probability distribution of the hygroscopic growth factor of the restaurant emission source factor; fig. 5 (d) is a graph showing the daily variation of the probability distribution of the hygroscopic propagation factor of the primary aging source factor; fig. 5 (e) is a schematic diagram showing the daily variation of the probability distribution of the moisture absorption growth factor of the house heating source factor; fig. 5 (f) is a graph showing the daily variation of the probability distribution of the hygroscopic growth factor of the regional secondary aging source factor;
FIG. 6 is a graph showing the results of the estimated and actual observed CCN concentration of the present invention (supersaturation of 0.23%) provided in example 1 of the present invention; FIG. 6 (a) is a time-series plot of the estimated CCN number concentration and the actual CCN number concentration using the method provided by the present embodiment; FIG. 6 (b) provides a plot of CCN number concentration versus scatter for the method calculation and measured for the present example at all times; FIG. 6 (c) is a histogram of the CCN number concentration of each source calculated by the method provided in this example;
fig. 7 is a block diagram of a cloud computing nodule source analysis system based on a particle number spectrum according to embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
K-th theory describes the process of hydroscopic growth activation of aerosols to CCN in atmospheric environments, quantitatively correlating aerosol particle number concentration to CCN number concentration, and when the environmental supersaturation ratio is greater than the critical supersaturation ratio of particles, the particles are activated and grow rapidly to CCN. Whether or not atmospheric aerosol particles can be activated as CCN depends largely on their physicochemical properties, including chemical composition, particle size, and state of mixing.
The invention aims to provide a cloud tuberculosis source analysis method and system based on a particle number spectrum, which are used for carrying out source analysis by adopting an orthogonal matrix factorization algorithm to obtain particle size spectrum distribution data and moisture absorption growth factor data of aerosol particle groups with different sources, and calculating the cloud tuberculosis concentration with different sources by utilizing kappa-K (K-K) theory, so that the calculation precision of the cloud tuberculosis concentration is improved.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1:
as shown in fig. 1, the embodiment provides a cloud condensation nucleus source analysis method based on a particle number spectrum, which includes:
s1: acquiring aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to atmospheric environmental samples at a plurality of sampling moments within a set time period; the chemical composition data includes concentrations of black carbon, organics, sulfate, nitrate, ammonium, and chloride; the atmospheric gas concentration data includes concentrations of carbon monoxide, sulfur dioxide, ozone, nitrogen dioxide, and nitric oxide; the aerosol particle size spectrum data is obtained by observing an atmospheric environment sample by a scanning mobility particle size spectrometer; the moisture absorption growth factor data are obtained by observing an atmospheric environment sample through a humidifying migration differential analyzer; the chemical component data are obtained by observing atmospheric environment samples by a high-resolution time-of-flight aerosol mass spectrometer and an AE33 type black carbon meter; and the atmospheric gas concentration data are obtained by observing an atmospheric environment sample by a gas analyzer.
S2: for an atmospheric environment sample at each sampling moment, carrying out source analysis on the source of each aerosol particle group by utilizing an orthogonal matrix factorization algorithm according to aerosol particle size spectrum data, hygroscopic growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample to obtain particle size spectrum distribution data and hygroscopic growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group; the sources include nucleation processes, traffic emissions, cooking activities, primary aging, residential heating, and regional secondary aging; each of the aerosol particle sets comprises a number of aerosol particles.
S3: and for each aerosol particle group, calculating to obtain the final cloud tuberculosis concentration corresponding to the aerosol particle group by using a kappa-K foster hler theory according to the particle size spectrum distribution data and the moisture absorption growth factor data of the aerosol particle group.
In a specific example in the present embodiment, S1 specifically includes: and selecting observation data of a scanning mobility particle size spectrometer (Scanning Mobility Particle Sizer, SMPS), a humidifying migration differential analyzer (Hygroscopic Tandem Differential Mobility Analyzer, H-TDMA), a High-resolution time-of-flight aerosol mass spectrometer (Aerodyne High-resolution time-of flight Aerosol Mass Spectrometer, HR-AMS), an AE33 type black carbon meter, a gas analyzer and a cloud tuberculosis counter (Cloud Condensation Nuclei Counter, DMT-CCNC) in 2016 in the winter period to obtain the corresponding observation data of atmospheric environment samples at a plurality of sampling moments in the 2016 winter period.
Atmospheric particles above 1 μm are first removed by a cutting head and then passed through a Nafion drying tube to ensure that the Relative Humidity (RH) of the particles is below 30%. The particle spectral distribution was measured using an SMPS equipped with a differential mobility analyzer and a agglomerated particle counter. Particles in the size range of 10-550nm (2016 years) were measured at 5 minute intervals. The hygroscopic growth factor data for five selected diameters (40, 80, 110, 150, and 200 nm) were measured at a given RH (90%) using an H-TDMA system in 2016. In addition, chemical composition data were measured by HR-AMS,comprises organic matters (Org) and sulfate) Nitrate (+)>) Ammonium (/ ->) And chloride (Chl) concentration. The black carbon concentration was measured by an AE33 type black carbon meter. The atmospheric gas concentration, i.e. carbon monoxide (CO), sulphur dioxide (++>) Ozone ()>) Nitrogen dioxide ()>) And Nitric Oxide (NO). CCN number concentration was measured by DMT-CCNC.
Before the source analysis is performed on the source of each aerosol particle group by using an orthogonal matrix factorization algorithm according to aerosol particle size spectrum data, hygroscopic growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample, the method further comprises:
preprocessing aerosol particle size spectrum data, hygroscopic growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample; the pretreatment includes a correction treatment and an assimilation treatment.
S2 specifically comprises: the particle size spectrum and hygroscopic Growth factor concentration files are collated and uncertainty files thereof are calculated, and orthogonal matrix factorization (Positive Matrix Factorization, PMF) is performed to obtain particle size spectrum distribution (Particle Number Size Distribution, PNSD) and hygroscopic Growth factor (Gf) data for aerosol particles of different sources.
According to aerosol particle size spectrum data, hygroscopic growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample, carrying out source analysis on the source of each aerosol particle group by utilizing an orthogonal matrix factorization algorithm to obtain particle size spectrum distribution data and hygroscopic growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group, wherein the method specifically comprises the following steps:
calculating the uncertainty of aerosol particles of the atmospheric environment sample according to aerosol particle size spectrum data and hygroscopic growth factor data corresponding to the atmospheric environment sample;
and judging the source of each aerosol particle group according to the uncertainty of aerosol particles, the chemical component data and the atmospheric gas concentration data of the atmospheric environment sample to obtain the particle size spectrum distribution data and the moisture absorption growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group.
In step S2, aerosol particle size spectrum data and aerosol hygroscopic growth factor data at particle sizes of 40, 80, 110, 150 and 200nm are input, source analysis is performed on the atmospheric particulate matter, and aerosol source determination is performed on each factor in combination with physical explanation such as factor daily variation and correlation with chemical components.
Concentration file: the time resolution of both SMPS and H-TDMA datasets (i.e. particle size spectrum data and hygroscopic growth factor data) are unified to hours and all missing data values are removed from the file. The total CN number concentration was calculated from SMPS aerosol particle size spectrum data, put into the file and set as the total variable with twice uncertainty. Where the Gf bins of the H-TDMA may be combined appropriately.
Uncertainty file: the observed uncertainty (i.e., aerosol particulate uncertainty of the atmospheric environmental sample) is calculated as follows:
。
。
wherein, row i corresponds to different sampling times, and row j represents data boxes with different particle sizes and moisture absorption growth factors.Aerosol particulate matter uncertainty (observation uncertainty) for a jth atmospheric environmental sample (sample box) at an ith sampling instant;the measurement error of the jth atmospheric environment sample at the ith sampling moment; />Is constant, equal to about 0.1; />Aerosol particle size spectrum data and moisture absorption growth factor data of a jth atmospheric environment sample at an ith sampling moment; />Is a constant equal to 0.01; />Is the average value of aerosol particle size spectrum data and moisture absorption growth factor data of the jth atmospheric environment sample.
PMF analysis is performed on the aerosol particle size spectrum data and the moisture absorption growth factor data to obtain potential sources of aerosol particles, and particle size spectrum distribution data and moisture absorption characteristics of aerosol particles from different sources are obtained, and the results are shown in figures 2-5. FIG. 2 is a schematic diagram of a particle spectrum distribution of different source factors obtained by source resolution; FIG. 2 (a) is a schematic diagram showing the particle-number spectrum distribution of nucleation-derived factors; fig. 2 (b) is a schematic diagram of a particle spectrum distribution of a factor of a traffic emission source; fig. 2 (c) is a schematic diagram of particle spectrum distribution of factors of food and beverage emission sources; fig. 2 (d) is a schematic diagram of a particle spectrum distribution of primary aging source factors; fig. 2 (e) is a schematic diagram of particle number spectrum distribution of a factor of origin for house heating; fig. 2 (f) is a schematic diagram of the particle spectrum distribution of the regional secondary aging source factor.
FIG. 3 is a graph showing the daily variation of the concentration of particles obtained by source analysis for different source factors; FIG. 3 (a) is a graph showing the daily variation of the particle number concentration of the nucleation source factor; fig. 3 (b) is a schematic diagram of the daily variation of the concentration of particles of the factor of the source of traffic emission; fig. 3 (c) is a schematic diagram of daily variation of particle number concentration of food discharge source factor; fig. 3 (d) is a graph showing the daily change of the particle number concentration of the primary aging source factor; fig. 3 (e) is a schematic diagram showing the daily variation of the particle count concentration of the house heating source factor; fig. 3 (f) is a graph showing the daily change in the particle number concentration of the regional secondary aging source factor.
FIG. 4 is a graph showing probability distribution of hygroscopic growth factors (i.e., hygroscopic growth factor data) obtained from source resolution for different source factors; FIG. 4 (a) is a graph showing probability distribution of the hygroscopic growth factor of the nucleation source factor; fig. 4 (b) is a schematic diagram of the probability distribution of the hygroscopic growth factor of the traffic emission source factor; fig. 4 (c) is a schematic diagram of probability distribution of hygroscopic growth factor of restaurant emission source factor; fig. 4 (d) is a schematic diagram of probability distribution of hygroscopic increase factor of primary aging source factor; fig. 4 (e) is a schematic diagram of the probability distribution of the moisture absorption growth factor of the residential heating source factor; fig. 4 (f) is a schematic diagram of the probability distribution of the hygroscopic growth factor of the regional secondary aging source factor.
FIG. 5 is a graph showing the daily variation of the probability distribution of the hygroscopic growth factors obtained by source analysis; FIG. 5 (a) is a graph showing the daily variation of the probability distribution of the hygroscopic growth factor of the nucleation source factor; fig. 5 (b) is a schematic diagram of the daily variation of the hygroscopic growth factor probability distribution of the traffic emission source factor; fig. 5 (c) is a schematic diagram of daily variation of the probability distribution of the hygroscopic growth factor of the restaurant emission source factor; fig. 5 (d) is a graph showing the daily variation of the probability distribution of the hygroscopic propagation factor of the primary aging source factor; fig. 5 (e) is a schematic diagram showing the daily variation of the probability distribution of the moisture absorption growth factor of the house heating source factor; fig. 5 (f) is a graph showing the daily variation of the probability distribution of the hygroscopic growth factor of the regional secondary aging source factor.
PMF is a factoring technique used to separate contributions from different sources from multivariate observations to learn the characteristics of the different sources in the system. It is assumed that the observation data matrix X can be decomposed into the product of two matrices, one being the source contribution matrix G and the other being the source component feature matrix F. This decomposition can be expressed by the following equation:
。
where X is the observation data matrix, G is the source contribution matrix, and F is the source component feature matrix.
And determining the sources of the factors according to the particle sizes and hygroscopicity distribution characteristics (namely the particle size spectrum distribution data and the hygroscopic growth factor data of the aerosol particle group) of different factors obtained by PMF analysis and by combining the daily changes of the factors and physical explanations such as correlation with chemical components. And calculating the CCN number concentration according to the particle spectrum distribution of each factor and the moisture absorption growth factor data obtained by analysis. Note that, since the data obtained from the previous observation are the hygroscopic growth factor data at 40, 80, 110, 150, and 200nm, respectively, the hygroscopic growth factor data at the particle diameter closest to the dominant peak particle diameter of each factor was used in determining the hygroscopicity of each factor.
In this embodiment, a concentration file (the concentration file includes chemical component data and atmospheric gas concentration data of an atmospheric environmental sample) and an uncertainty file (i.e., an aerosol particulate uncertainty of the atmospheric environmental sample) are input into a PMF model to perform source analysis, and 2-8 factors are selected and run 30 times respectively. For selected species, variables were designated as "bad" when the signal to noise ratio (S/N) ratio was below 0.5 and excluded from the model. When S/N is between 0.5 and 1.0, they are designated as "weak". The optimum number of factors is selected in combination with the variation and error estimation of the Q value in the running result and the physical interpretability of the factor (particle size and hygroscopicity distribution, daily variation and correlation with chemical components, etc.).
In the source analytical analysis of Beijing CCN, source analytical analysis was performed by applying the PMF model to PNSD and Gf-PDF datasets measured in the Beijing city field of 2016 to determine the particle size spectral distribution of aerosol particles of different sources (FIG. 2, FIG. 3) and the moisture absorption growth factor data (FIGS. 4 and 5). Six sources were analyzed in this example, namely nucleation process, traffic emission, cooking activity, primary aging, residential heating and regional secondary aging, with average hygroscopic growth factor Gf values of 1.23, 1.15, 1.17, 1.44, 1.48 and 1.47, respectively.
And S3, calculating to obtain the final cloud condensation nucleus concentration corresponding to the aerosol particle group by utilizing a kappa-K business hler theory according to the particle size spectrum distribution data and the moisture absorption growth factor data of the aerosol particle group, wherein the method specifically comprises the following steps of:
calculating the hygroscopicity parameters of the aerosol particle group according to the hygroscopicity growth factor data of the aerosol particle group;
calculating critical activated particle size corresponding to the aerosol particle group according to the hygroscopicity parameter and the set saturation ratio of the aerosol particle group;
calculating the final cloud tuberculosis concentration corresponding to the aerosol particle group according to the critical activation particle diameter corresponding to the aerosol particle group and the particle diameter spectrum distribution data of the aerosol particle group, wherein the method specifically comprises the following steps:
and adding the integral of the particle sizes of all aerosol particles larger than the critical activation particle size in the aerosol particle group to obtain the final cloud tuberculosis concentration corresponding to the aerosol particle group.
According to the K-K foster hler theory, the hygroscopicity parameters of different factors) It can be derived from Gf that the calculation formula of the hygroscopicity parameter is as follows:
。
wherein,is the ith gasHygroscopic parameters of the sol particle group; />Moisture absorption growth factor data for the ith aerosol particle set (+.>Gf, which is a factor i); />Is relative humidity>Is the relative humidity in HTDMA (in this example +.>90%); />The surface tension of pure water is assumed; />Is the molecular weight of water; />Is a universal gas constant;is the temperature; />Is the density of water; />Is of dry diameter.
Calculating the concentration of CCN number by using kappa-K (K) and hler theory, wherein the theory establishes critical dry particle size of particles) And setting the supersaturation ratio (+)>) Is a parameterized relationship of (2)I.e. hygroscopicity parameter +.>Is of a certain dry particle size (& gt)>) The critical supersaturation ratio is just ĸ -K, the supersaturation ratio corresponding to the highest point of the hler curve, the dry particle diameter of the particle is the critical dry particle diameter (/ in) under the condition>=/>) At this time +.>And->The dry particle size is greater than->The particles of (a) may be activated. According to the set environment supersaturation ratio (set critical saturation ratio)>Obtaining the corresponding critical activation particle size (/ -A)>) The CCN number concentration is greater than +.A particle size in PNSD>Particle integral sum of (a).
Wherein, the calculation formula of the critical activation particle size is as follows:
。
wherein,is critical activated particle size; />Droplet surface tension as activation point (+)>= 0.072J m -2 )；Molecular weight of water (+)>=0.018015 kg mol -1 ）；/>As a general gas constant (r= 8.315J K) -1 mol -1 )；Temperature (t=298.15K); />Is the density of water (+)>=997.1 kg m -3 ）；/>The hygroscopicity parameter of the aerosol particle group, namely +.>The value of which depends on the physical properties and chemical composition of the particles; />To set the supersaturation ratio.
The calculation formula of the final cloud nucleation concentration corresponding to the aerosol particle group is as follows:
。
wherein,the final cloud tuberculosis concentration corresponding to the ith aerosol particle group; />An upper integral limit for the particle spectrum distribution; />The critical activation particle size corresponds to the integral lower limit of the particle spectrum distribution; />(logDp-i) is a function of aerosol number concentration particle size distribution; />The particle size of the aerosol particles corresponding to the ith aerosol particle size spectrum data distribution.
The following aerosol particle sets of different sources were obtained based on winter data analysis of 2016 BeijingAnd PNSD data are subjected to CCN prediction and closure experiment research, and the accuracy of the CCN number concentration prediction by the method is explored.
After the contributions of aerosols of different sources to the CCN (the final cloud tuberculosis concentration corresponding to each aerosol particle group) are obtained based on the above steps, the number concentration of CCN in the atmosphere predicted in this embodiment is the sum of the contributions of aerosols of different sources to the number concentration of CCN, and the accuracy of the method is verified through a CCN closure experiment, so that a CCN prediction model based on aerosol particle size spectrum data and moisture absorption growth factor data is established.
Based on kappa-K theory, hler theory, PMF resolved Gf was applied to obtain critical diameters for each factor. Application of calculation to obtain 2016And particle spectrum distribution of each source, the CCN number concentration was predicted at a typical supersaturation (s=0.23%) and the experimental result table was closed with the actual CCN number concentrationIt is clear that the method can accurately estimate +.>（R 2 =0.89), as shown in fig. 6, (a) in fig. 6 is a graph of the CCN number concentration of 2016 years calculated by the method provided by the present embodiment and the actual CCN number concentration closing experiment result, the solid line is the CCN number concentration calculated by the method provided by the present embodiment, and the dotted line is the observation result of the actual CCN number concentration. Fig. 6 (b) is a graph of CCN number concentration versus scatter plot calculated and measured by the method provided in this example, and fig. 6 (c) is a histogram of CCN number concentration of each source obtained by the method provided in this example.
Compared with the prior art, the beneficial effects of the embodiment are as follows:
1. PMF source analysis is carried out on the atmospheric aerosol particle size spectrum data and the hygroscopic growth factor data to obtain the hygroscopic parameters of aerosols with different sources.
2. Based on PMF analysis, the spectral distribution of each factor and the data of hygroscopic growth factors are combined with kappa-K hler theory to quantify the contribution of aerosols from different sources to CCN.
3. The method establishes a CCN prediction model based on aerosol particle size spectrum data and moisture absorption growth factor data, has universality, can provide method guarantee for prediction and source analysis of CCN in the atmosphere, and further provides scientific reference for quantifying indirect climate effect of aerosol.
Example 2:
in order to execute the method corresponding to the above embodiment 1 to achieve the corresponding functions and technical effects, as shown in fig. 7, a cloud condensation nucleus source analysis system based on a particle number spectrum is provided below, including:
the observation data acquisition module T1 is used for acquiring aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to atmospheric environmental samples at a plurality of sampling moments within a set time period; the chemical composition data includes concentrations of black carbon, organics, sulfate, nitrate, ammonium, and chloride; the atmospheric gas concentration data includes concentrations of carbon monoxide, sulfur dioxide, ozone, nitrogen dioxide, and nitric oxide; the aerosol particle size spectrum data is obtained by observing an atmospheric environment sample by a scanning mobility particle size spectrometer; the moisture absorption growth factor data are obtained by observing an atmospheric environment sample through a humidifying migration differential analyzer; the chemical component data are obtained by observing atmospheric environment samples by a high-resolution time-of-flight aerosol mass spectrometer and an AE33 type black carbon meter; and the atmospheric gas concentration data are obtained by observing an atmospheric environment sample by a gas analyzer.
The source analysis module T2 is used for carrying out source analysis on the source of each aerosol particle group by utilizing an orthogonal matrix factorization algorithm according to aerosol particle size spectrum data, hygroscopic growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample at each sampling moment to obtain particle size spectrum distribution data and hygroscopic growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group; the sources include nucleation processes, traffic emissions, cooking activities, primary aging, residential heating, and regional secondary aging; each of the aerosol particle sets comprises a number of aerosol particles.
And the final cloud condensation nucleus concentration calculation module T3 is used for calculating the final cloud condensation nucleus concentration corresponding to each aerosol particle group by utilizing a kappa-K hler theory according to the particle size spectrum distribution data and the moisture absorption growth factor data of the aerosol particle group.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (4)
1. The cloud condensation nucleus source analysis method based on the particle number spectrum is characterized by comprising the following steps of:
acquiring aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to atmospheric environmental samples at a plurality of sampling moments within a set time period; the chemical composition data includes concentrations of black carbon, organics, sulfate, nitrate, ammonium, and chloride; the atmospheric gas concentration data includes concentrations of carbon monoxide, sulfur dioxide, ozone, nitrogen dioxide, and nitric oxide; the aerosol particle size spectrum data is obtained by observing an atmospheric environment sample by a scanning mobility particle size spectrometer; the moisture absorption growth factor data are obtained by observing an atmospheric environment sample through a humidifying migration differential analyzer; the chemical component data are obtained by observing atmospheric environment samples by a high-resolution time-of-flight aerosol mass spectrometer and an AE33 type black carbon meter; the atmospheric gas concentration data are obtained by observing an atmospheric environment sample by a gas analyzer;
for an atmospheric environment sample at each sampling moment, carrying out source analysis on the source of each aerosol particle group by utilizing an orthogonal matrix factorization algorithm according to aerosol particle size spectrum data, hygroscopic growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample to obtain particle size spectrum distribution data and hygroscopic growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group; the sources include nucleation processes, traffic emissions, cooking activities, primary aging, residential heating, and regional secondary aging; each aerosol particle set comprises a plurality of aerosol particles;
for each aerosol particle group, utilizing according to the particle size spectrum distribution data and the moisture absorption growth factor data of the aerosol particle groupThe final cloud tuberculosis concentration corresponding to the aerosol particle group is calculated according to theory, and the method specifically comprises the following steps:
calculating the hygroscopicity parameters of the aerosol particle group according to the hygroscopicity growth factor data of the aerosol particle group; the calculation formula of the hygroscopicity parameter is as follows:
wherein, kappa Gfi A hygroscopicity parameter for the ith aerosol particle set; gf (Gf) i Moisture absorption growth factor data for the ith aerosol particle set; RH is relative humidity; sigma (sigma) s/a Is the surface tension of pure water; m is M w Is the molecular weight of water; r is a universal gas constant; t is the temperature; ρ w Is the density of water; d (D) d Is of dry diameter;
calculating critical activated particle size corresponding to the aerosol particle group according to the hygroscopicity parameter and the set saturation ratio of the aerosol particle group; the calculation formula of the critical activation particle size is as follows:
wherein D is cri Is critical activated particle size; m is M w The surface tension of the droplet, which is the activation point; m is M w Is the molecular weight of water; r is a universal gas constant; t is the temperature; ρ w Is the density of water; kappa is the hygroscopicity parameter of the aerosol particle group; s is S c Setting a supersaturation ratio for the purpose of setting;
calculating the final cloud tuberculosis concentration corresponding to the aerosol particle group according to the critical activation particle diameter corresponding to the aerosol particle group and the particle diameter spectrum distribution data of the aerosol particle group, wherein the method specifically comprises the following steps:
adding the integral of the particle sizes of all aerosol particles larger than the critical activation particle size in the aerosol particle group to obtain the final cloud tuberculosis concentration corresponding to the aerosol particle group; the calculation formula of the final cloud nucleation concentration corresponding to the aerosol particle group is as follows:
wherein, CCN pre-i The final cloud tuberculosis concentration corresponding to the ith aerosol particle group; d (D) end An integral upper limit corresponding to the particle size spectrum distribution data; d (D) cri-i The critical activation particle size corresponding to the ith aerosol particle group corresponds to the integral lower limit of the particle size spectrum distribution data; n (log D) p-i ) As a function of aerosol number concentration particle size distribution; d (D) p-i Is the particle size of the ith aerosol particle group.
2. The method for analyzing cloud nuclei sources based on a particle count spectrum of claim 1, wherein prior to source analysis of the sources of each aerosol particle set using an orthogonal matrix factorization algorithm based on aerosol particle size spectrum data, hygroscopic growth factor data, chemical composition data, and atmospheric gas concentration data corresponding to the atmospheric environmental sample, further comprising:
preprocessing aerosol particle size spectrum data, hygroscopic growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample; the pretreatment includes a correction treatment and an assimilation treatment.
3. The method for analyzing cloud nuclei sources based on particle number spectra according to claim 1, wherein the method for analyzing sources of each aerosol particle group by using an orthogonal matrix factorization algorithm according to aerosol particle size spectrum data, hygroscopic growth factor data, chemical composition data and atmospheric gas concentration data corresponding to the atmospheric environment sample is characterized by obtaining particle size spectrum distribution data and hygroscopic growth factor data of a plurality of aerosol particle groups and sources of each aerosol particle group, and specifically comprises the following steps:
calculating the uncertainty of aerosol particles of the atmospheric environment sample according to aerosol particle size spectrum data and hygroscopic growth factor data corresponding to the atmospheric environment sample;
and judging the source of each aerosol particle group according to the uncertainty of aerosol particles, the chemical component data and the atmospheric gas concentration data of the atmospheric environment sample to obtain the particle size spectrum distribution data and the moisture absorption growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group.
4. A cloud nucleation source analysis system based on a particulate matter number spectrum, comprising:
the observation data acquisition module is used for acquiring aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environmental samples at a plurality of sampling moments within a set time period; the chemical composition data includes concentrations of black carbon, organics, sulfate, nitrate, ammonium, and chloride; the atmospheric gas concentration data includes concentrations of carbon monoxide, sulfur dioxide, ozone, nitrogen dioxide, and nitric oxide; the aerosol particle size spectrum data is obtained by observing an atmospheric environment sample by a scanning mobility particle size spectrometer; the moisture absorption growth factor data are obtained by observing an atmospheric environment sample through a humidifying migration differential analyzer; the chemical component data are obtained by observing atmospheric environment samples by a high-resolution time-of-flight aerosol mass spectrometer and an AE33 type black carbon meter; the atmospheric gas concentration data are obtained by observing an atmospheric environment sample by a gas analyzer;
the source analysis module is used for carrying out source analysis on the source of each aerosol particle group by utilizing an orthogonal matrix factorization algorithm according to aerosol particle size spectrum data, moisture absorption growth factor data, chemical component data and atmospheric gas concentration data corresponding to the atmospheric environment sample at each sampling moment to obtain particle size spectrum distribution data and moisture absorption growth factor data of a plurality of aerosol particle groups and the source of each aerosol particle group; the sources include nucleation processes, traffic emissions, cooking activities, primary aging, residential heating, and regional secondary aging; each aerosol particle set comprises a plurality of aerosol particles;
the final cloud condensation nucleus concentration calculation module is used for utilizing, for each aerosol particle group, according to the particle size spectrum distribution data and the moisture absorption growth factor data of the aerosol particle groupThe final cloud tuberculosis concentration corresponding to the aerosol particle group is calculated according to theory, and the method specifically comprises the following steps:
calculating the hygroscopicity parameters of the aerosol particle group according to the hygroscopicity growth factor data of the aerosol particle group; the calculation formula of the hygroscopicity parameter is as follows:
wherein, kappa Gfi A hygroscopicity parameter for the ith aerosol particle set; gf (Gf) i Moisture absorption growth factor data for the ith aerosol particle set; RH is relative humidity; sigma (sigma) s/a Is the surface tension of pure water; m is M w Is the molecular weight of water; r is a universal gas constant; t is the temperature; ρ w Is the density of water; d (D) d Is of dry diameter;
calculating critical activated particle size corresponding to the aerosol particle group according to the hygroscopicity parameter and the set saturation ratio of the aerosol particle group; the calculation formula of the critical activation particle size is as follows:
wherein D is cri Is critical activated particle size; sigma (sigma) w The surface tension of the droplet, which is the activation point; m is M w Is the molecular weight of water; r is a universal gas constant; t is the temperature; ρ w Is the density of water; kappa is the hygroscopicity parameter of the aerosol particle group; s is S c To set the satietyAnd the sum ratio;
calculating the final cloud tuberculosis concentration corresponding to the aerosol particle group according to the critical activation particle diameter corresponding to the aerosol particle group and the particle diameter spectrum distribution data of the aerosol particle group, wherein the method specifically comprises the following steps:
adding the integral of the particle sizes of all aerosol particles larger than the critical activation particle size in the aerosol particle group to obtain the final cloud tuberculosis concentration corresponding to the aerosol particle group; the calculation formula of the final cloud nucleation concentration corresponding to the aerosol particle group is as follows:
wherein, CCN pre-i The final cloud tuberculosis concentration corresponding to the ith aerosol particle group; d (D) end An integral upper limit corresponding to the particle size spectrum distribution data; d (D) cri-i The critical activation particle size corresponding to the ith aerosol particle group corresponds to the integral lower limit of the particle size spectrum distribution data; n (log D) p-i ) As a function of aerosol number concentration particle size distribution; d (D) p-i Is the particle size of the ith aerosol particle group.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410077679.8A CN117589948B (en) | 2024-01-19 | 2024-01-19 | Cloud condensation nucleus source analysis method and system based on particle number spectrum |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410077679.8A CN117589948B (en) | 2024-01-19 | 2024-01-19 | Cloud condensation nucleus source analysis method and system based on particle number spectrum |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117589948A CN117589948A (en) | 2024-02-23 |
| CN117589948B true CN117589948B (en) | 2024-04-02 |
Family
ID=89912024
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410077679.8A Active CN117589948B (en) | 2024-01-19 | 2024-01-19 | Cloud condensation nucleus source analysis method and system based on particle number spectrum |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117589948B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6330060B1 (en) * | 1997-10-10 | 2001-12-11 | California Institute Of Technology | Cloud condensation nucleus spectrometer |
| WO2004027380A2 (en) * | 2002-09-18 | 2004-04-01 | The Regents Of The University Of California | Stream-wise thermal gradient cloud condensation nuclei chamber |
| WO2013173320A1 (en) * | 2012-05-17 | 2013-11-21 | Regents Of The University Of Minnesota | Drift tube ion mobility spectrometer for aerosol measurement |
| CN104076136A (en) * | 2014-06-30 | 2014-10-01 | 哈尔滨工业大学 | Method for analyzing aging mechanism of silicone rubber by utilizing variable activation energy |
| CN106018706A (en) * | 2016-07-15 | 2016-10-12 | 上海纳米技术及应用国家工程研究中心有限公司 | Stannic oxide-loaded porous nickel oxide gas sensor material as well as preparation and application thereof |
| EP3570004A1 (en) * | 2018-05-17 | 2019-11-20 | Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. | A supersaturation scanning ccn activity meter |
| CN110631967A (en) * | 2019-10-16 | 2019-12-31 | 复旦大学 | Raman spectrum-based atmospheric black carbon aerosol source analysis method |
-
2024
- 2024-01-19 CN CN202410077679.8A patent/CN117589948B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6330060B1 (en) * | 1997-10-10 | 2001-12-11 | California Institute Of Technology | Cloud condensation nucleus spectrometer |
| WO2004027380A2 (en) * | 2002-09-18 | 2004-04-01 | The Regents Of The University Of California | Stream-wise thermal gradient cloud condensation nuclei chamber |
| WO2013173320A1 (en) * | 2012-05-17 | 2013-11-21 | Regents Of The University Of Minnesota | Drift tube ion mobility spectrometer for aerosol measurement |
| CN104076136A (en) * | 2014-06-30 | 2014-10-01 | 哈尔滨工业大学 | Method for analyzing aging mechanism of silicone rubber by utilizing variable activation energy |
| CN104634950A (en) * | 2014-06-30 | 2015-05-20 | 哈尔滨工业大学 | Method for analyzing silicone rubber aging mechanism by utilizing variable activation energy |
| CN106018706A (en) * | 2016-07-15 | 2016-10-12 | 上海纳米技术及应用国家工程研究中心有限公司 | Stannic oxide-loaded porous nickel oxide gas sensor material as well as preparation and application thereof |
| EP3570004A1 (en) * | 2018-05-17 | 2019-11-20 | Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. | A supersaturation scanning ccn activity meter |
| CN110631967A (en) * | 2019-10-16 | 2019-12-31 | 复旦大学 | Raman spectrum-based atmospheric black carbon aerosol source analysis method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117589948A (en) | 2024-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Russell et al. | Aerosol production and growth in the marine boundary layer | |
| Nieminen et al. | Trends in atmospheric new-particle formation: 16 years of observations in a boreal-forest environment | |
| Quinn et al. | Dimethylsulfide/cloud condensation nuclei/climate system: Relevant size‐resolved measurements of the chemical and physical properties of atmospheric aerosol particles | |
| Lee et al. | Atmospheric new particle formation characteristics in the Arctic as measured at Mount Zeppelin, Svalbard, from 2016 to 2018 | |
| Kulmala et al. | On the existence of neutral atmospheric clusters | |
| Kannosto et al. | Mode resolved density of atmospheric aerosol particles | |
| Hsieh et al. | Origin of low-molecular-weight dicarboxylic acids and their concentration and size distribution variation in suburban aerosol | |
| Burrows et al. | OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions–implementation in a global climate model and impacts on clouds | |
| CN105784556A (en) | Soft measuring method of air particulate matter 2.5 (PM2.5) based on self-organizing fuzzy neural network | |
| Peng et al. | Source apportionment using receptor model based on aerosol mass spectra and 1 h resolution chemical dataset in Tianjin, China | |
| Zaidan et al. | Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach | |
| CN112733419A (en) | Method for improving inversion accuracy of sudden atmospheric pollution accident source | |
| CN116793911A (en) | Method for measuring dynamic evolution of atmospheric secondary organic aerosol | |
| CN117077460A (en) | Method for quantifying daily influence of meteorological factors on pollutant concentration | |
| Kompalli et al. | Aging of biomass burning emissions in the Indo-Gangetic Plain outflow: Implications for black carbon light-absorption enhancement | |
| CN117589948B (en) | Cloud condensation nucleus source analysis method and system based on particle number spectrum | |
| O'Dowd et al. | Mid-latitude North-Atlantic aerosol characteristics in clean and polluted air | |
| Petters et al. | Morphology and hygroscopicity of nanoplastics in sea spray | |
| Jamalani et al. | Monthly analysis of PM10 in ambient air of Klang Valley, Malaysia | |
| CN115730467A (en) | Quantitative evaluation method for influence of meteorological conditions on atmospheric particulate pollution | |
| CN116337693A (en) | Detection and estimation method for generating organic aerosol by atmosphere liquid phase | |
| Ren et al. | Identifying the hygroscopic properties of fine aerosol particles from diverse sources in urban atmosphere and the applicability in prediction of cloud nuclei | |
| Dal Maso et al. | Spring recovery of photosynthesis and atmospheric particle formation | |
| Sellegri et al. | Role of the volatile fraction of submicron marine aerosol on its hygroscopic properties | |
| Blanco-Alegre et al. | Evolution of size-segregated aerosol concentration in NW Spain: A two-step classification to identify new particle formation events |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |