CN116381152A - Method, system, electronic equipment and storage medium for determining pollution source type - Google Patents
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- WEVYAHXRMPXWCK-UHFFFAOYSA-N Acetonitrile Chemical compound CC#N WEVYAHXRMPXWCK-UHFFFAOYSA-N 0.000 description 3
- YMWUJEATGCHHMB-UHFFFAOYSA-N Dichloromethane Chemical compound ClCCl YMWUJEATGCHHMB-UHFFFAOYSA-N 0.000 description 3
- BZLVMXJERCGZMT-UHFFFAOYSA-N Methyl tert-butyl ether Chemical compound COC(C)(C)C BZLVMXJERCGZMT-UHFFFAOYSA-N 0.000 description 3
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Abstract
The invention discloses a method, a system, electronic equipment and a storage medium for determining a pollution source type, and relates to the technical field of atmospheric aerosol source analysis, wherein the method comprises the following steps: acquiring sample data of various pollutant concentrations; the contaminant concentration sample data includes: sampling time, type name and concentration of the contaminant; constructing a concentration data matrix and an uncertainty data matrix based on all contaminant concentration sample data; determining a factor matrix group of the preset group based on the preset group number, the preset factor number, the concentration data matrix, the uncertain data matrix and the positive matrix factorization acceptor software; the factor matrix group includes: a factor contribution matrix and a factor distribution matrix; determining a target factor distribution matrix based on the target function; the target distribution function is a function of the factor contribution matrix and the factor distribution matrix; and comparing the factor distribution matrix with the source component spectrum to determine the type of the pollution source corresponding to each pollutant. The invention improves the accuracy of identifying the pollution source type.
Description
Technical Field
The present invention relates to the field of atmospheric aerosol source analysis technologies, and in particular, to a method, a system, an electronic device, and a storage medium for determining a pollution source type.
Background
As the source of volatile organic compounds (Volatile Organic Compounds, VOCs) in the atmosphere is distinguished and identified, quantitative or qualitative research is carried out, the technology for analyzing the source contribution rate and the identification method of pollution source types are carried out, and the identification method for analyzing the pollution source types by the VOCs source provides basis for the control and treatment policies of urban VOCs responsibility division, industry emission reduction and the like. The recognition method of the pollution source type by the VOCs source analysis technology mainly comprises a diffusion model method and a receptor model method.
The positive matrix factorization (Positive Matrix Factorization, PMF) acceptor model is one of the most widely used identification methods for analyzing pollution source types of pollutant sources at present, and is a multi-element factor analysis tool, factor quantity and factor contribution are identified through a multi-linear multi-iteration algorithm by decomposing a sample content data matrix of an input model into two matrices of factor contribution (G) and factor component spectrum (F). The PMF receptor model has strong resolving power, can carry out mathematical statistics optimization, and does not need to grasp the pollution source component spectrum in advance; however, the PMF receptor model still has great uncertainty, namely, the determination of the number of factors and how to identify the sources corresponding to the factors after the PMF calculates the factor component spectrum. The method is completely dependent on experience and subjective judgment of a PMF operator in factor identification; in determining the optimal factor number, the operator also calculates the scheme combination of a plurality of factor numbers repeatedly, and determines that a plurality of factors should be selected finally by experience and subjective judgment. The experience of operators comes from the literature of the reading predecessors and general knowledge of the chemical components contained in the pollution sources, so that source analysis results obtained by different operators are different from person to person, and the standard and the accepted correct results are not unified.
The pollution source factor can be accurately obtained, and the identification of the pollution source type information is realized, so that the effectiveness of emission reduction and treatment decision of a user of a final source analysis result is affected.
Disclosure of Invention
The invention aims to provide a method, a system, electronic equipment and a storage medium for determining a pollution source type, which improve the accuracy of identifying the pollution source type.
In order to achieve the above object, the present invention provides the following solutions:
a method of determining a type of contamination source, the method comprising:
acquiring sample data of various pollutant concentrations; the contaminant concentration sample data includes: sampling time, type name and concentration of the contaminant;
constructing a concentration data matrix and an uncertainty data matrix based on all of the contaminant concentration sample data;
determining a factor matrix set of a preset set of sets based on a preset set number, a preset factor number, the concentration data matrix, the uncertainty data matrix and positive matrix factorization acceptor software; the factor matrix set includes: a factor contribution matrix and a factor distribution matrix;
determining a target factor distribution matrix based on the target function; the target distribution function is a function of the factor contribution matrix and the factor distribution matrix;
comparing the factor distribution matrix with a source component spectrum to determine the type of a pollution source corresponding to each pollutant; the types include: transportation, biomass combustion, fixed fossil fuel combustion, industrial processing, and solvent utilization.
Optionally, the construction process of the concentration data matrix specifically includes:
and constructing the concentration data matrix by taking the sampling time as a row identifier, the category name as a column identifier and the concentration of pollutants as elements.
Optionally, the construction process of the uncertainty data matrix specifically includes:
judging whether the concentration of each pollutant is greater than the minimum detection limit of an element measuring instrument; the element detecting instrument is a device for detecting the concentration of the contaminant;
if yes, calculating uncertainty of the corresponding pollutants according to the preset uncertainty percentage, the concentration of the pollutants and the minimum detection limit of the element measuring instrument;
if not, calculating uncertainty of the corresponding pollutants according to the minimum detection limit of the element measuring instrument;
and constructing the concentration data matrix by taking the sampling time as a row identifier, the category name as a column identifier and the uncertainty of the pollutant as an element.
Optionally, calculating the uncertainty of the corresponding contaminant according to a preset uncertainty percentage, the concentration of the contaminant and the minimum detection limit of the element measuring instrument is achieved by a first uncertainty formula, wherein the first uncertainty formula is as follows:
where Unc is the uncertainty of the contaminant; EF is a preset uncertainty percentage; c is the concentration of the contaminant; MDL is the minimum detection limit of an elemental measurement instrument.
Optionally, the expression of the objective function is:
wherein Q is an objective function value; i is the serial number of the sampling time; n is the total sampling time; j is the serial number of the pollutant; m is the total number of pollutants; x is x ij The concentration of the jth contaminant at the ith sampling time in the concentration data matrix; k is the sequence number of the factor; p is the preset factor number; u (u) ij Uncertainty for a jth contaminant at an ith sample time in the uncertainty matrix; g ik Contributing the kth factor in the factor contribution matrix to the concentration of all contaminants in the ith sample time; f (f) ki And (3) the concentration of the jth pollutant in the kth factor in the factor distribution matrix.
Optionally, comparing the factor distribution matrix with a source component spectrum to determine the type of the pollution source corresponding to each pollutant, which specifically includes:
calculating a geometric difference value based on the concentration of the pollutants corresponding to all the factors in the factor distribution matrix and the concentration of the pollutants corresponding to all the pollution sources in the source component spectrum;
determining the pollution source with the smallest geometric difference of the first d as the type of the corresponding pollution source of the pollutant; d is a preset number.
Optionally, the calculation formula of the geometric difference is:
wherein j is the serial number of the pollutant; m is the total number of pollutants; c a,j The mass fraction of the jth pollutant corresponding to the a factor in the factor distribution matrix in the m pollutants is the duty ratio of the concentration of any pollutant to the total concentration of all pollutants; e, e j The mass fraction of the jth, i.e., contaminant, in the m contaminants corresponding to the b-th contaminant source in the source composition spectrum.
A system for determining a type of a source of contamination, the system comprising:
the concentration sample data acquisition module is used for acquiring various pollutant concentration sample data; the contaminant concentration sample data includes: sampling time, type name and concentration of the contaminant;
a matrix construction module for constructing a concentration data matrix and an uncertainty data matrix based on all the contaminant concentration sample data;
the matrix decomposition module is used for determining a factor matrix group of a preset group based on a preset group number, a preset factor number, the concentration data matrix, the uncertain data matrix and positive matrix decomposition acceptor software; the factor matrix set includes: a factor contribution matrix and a factor distribution matrix;
the target factor distribution matrix determining module is used for determining a target factor distribution matrix based on the target function; the target distribution function is a function of the factor contribution matrix and the factor distribution matrix;
the pollution source determining module is used for comparing the factor distribution matrix with a source component spectrum and determining the type of the pollution source corresponding to each pollutant; the types include: transportation, biomass combustion, fixed fossil fuel combustion, industrial processing, and solvent utilization.
An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of determining a type of contamination source as described above.
A storage medium having stored thereon a computer program which, when executed by a processor, implements a method of determining a type of contamination source as described above.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method, a system, electronic equipment and a storage medium for determining a pollution source type, which are used for directly connecting a PMF factor with a source component spectrum, so that the source of pollutants can be determined more objectively, the analysis result of a PMF model is more similar to the real situation, the reliability of the analysis result of the PMF source is greatly improved, the automation of the pollution source type is realized, and the accuracy of identifying the pollution source type is improved.
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 flow chart of a method for determining a type of a pollution source according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram of a system for determining a type of a pollution source 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.
The invention aims to provide a method, a system, electronic equipment and a storage medium for determining a pollution source type, which aim to realize automation of the pollution source type and improve accuracy of identifying the pollution source type.
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
Fig. 1 is a flow chart of a method for determining a type of a pollution source according to embodiment 1 of the present invention. As shown in fig. 1, the method for determining the type of the pollution source in this embodiment includes:
step 101: acquiring sample data of various pollutant concentrations; the contaminant concentration sample data includes: sampling time, type name and concentration of contaminants.
As a specific example, a concentration data time series of two independent period hours resolution was obtained based on the guangzhou city Tianhe district and university of south air super monitoring station, 7 months (2016, 7, 4, to 2016, 7, 31), and 9 to 10 months (2016, 9, 28, to 2016, 11, 1). A total of 57 effective species (i.e., contaminants) were obtained, including 25 alkanes, 8 alkenes, 1 alkyne, 16 aromatics, 4 atmospheric fluorine-containing volatile organics OVOCs, and 3 other species (methyl tert-butyl ether MTBE, dichloromethane, and acetonitrile).
Step 102: a concentration data matrix and an uncertainty data matrix are constructed based on all contaminant concentration sample data.
Step 103: determining a factor matrix group of the preset group based on the preset group number, the preset factor number, the concentration data matrix, the uncertain data matrix and the positive matrix factorization acceptor software; the factor matrix group includes: factor contribution matrix and factor distribution matrix.
As a specific example, when the number of preset factors is 6, which is equivalent to that the PMF software operates according to the precondition of 6 factors (i.e. p=6), the PMF will obtain a result of 6 factors by calculation, each factor has its species concentration feature, and then the user will determine what sources the factors are, and rename the factors:
wherein i is the number of samples (i.e. the serial number of the sampling time), and one sample corresponds to the concentration of all pollutants in one sampling time; j is the component number (number of contaminants); x is x ij The concentration of the jth contaminant at the ith sample time in the jth sample data matrix, which is the volume fraction of component j in the ith sample; g ik A relative contribution of factor k to the ith sample (i.e., the contribution of the kth factor in the factor contribution matrix to the concentration of all contaminants in the ith sample time); f (f) ki For the volume fraction of component j in factor k (i.e., the concentration of the jth contaminant in the kth factor in the factor distribution matrix); e, e ij Is the random error of component j in the ith sample.
Step 104: determining a target factor distribution matrix based on the target function; the target distribution function is a function of the factor contribution matrix and the factor distribution matrix.
Step 105: comparing the factor distribution matrix with a source component spectrum, and determining the type of each pollutant corresponding to the pollution source; the types include: transportation, biomass combustion, fixed fossil fuel combustion, industrial processing, and solvent utilization.
Specifically, the source component spectrum in the emission list is determined from published literature by referring to the existing literature data. The source composition spectrum includes: a. types of pollution sources, including: transportation, biomass combustion, fixed fossil fuel combustion, industrial processing, and solvent utilization. b. The species class and mass fraction (i.e., the mass concentration of contaminants as a percentage of the total concentration of contaminants in the contaminated source) of VOCs (i.e., contaminants) per source.
More specifically, transportation, biomass combustion, fixed fossil fuel combustion, industrial processing, and solvent utilization are five primary categories. Further divided, in a hierarchical source class system, each primary source class consists of a number of secondary-quaternary classes, as shown in Table 1.
TABLE 1 pollution source class table
As an alternative embodiment, the construction process of the concentration data matrix specifically includes:
and (3) taking the sampling time as a row mark, taking a category name as a column mark, and taking the concentration of the pollutant as an element to construct a concentration data matrix.
As an alternative embodiment, the construction process of the uncertain data matrix specifically includes:
judging whether the concentration of each pollutant is greater than the minimum detection limit of the element measuring instrument; an element detecting instrument is a device for detecting the concentration of a contaminant.
If yes, calculating the uncertainty of the corresponding pollutant according to the preset uncertainty percentage, the concentration of the pollutant and the minimum detection limit of the element measuring instrument.
If not, calculating uncertainty of the corresponding pollutants according to the minimum detection limit of the element measuring instrument.
And (3) taking the sampling time as a row identifier, the category name as a column identifier, and taking the uncertainty of the pollutant as an element to construct a concentration data matrix.
Wherein MDL refers to the minimum detection limit (Minimum Detection Limit), which refers to the lowest concentration of a substance that can be detected in a sample. One instrument can measure many species, up to hundreds, and the MDL for each species is different, so MDL refers to the lowest concentration of a substance that the instrument can detect in a sample.
As an alternative embodiment, according to a preset uncertainty percentage, the concentration of the contaminant and the minimum detection limit of the element measuring instrument, calculating the uncertainty of the corresponding contaminant is achieved by a first uncertainty formula, where the first uncertainty formula is:
where Unc is the uncertainty of the contaminant; EF is a preset uncertainty percentage; c is the concentration of the contaminant; MDL is the minimum detection limit of an elemental measurement instrument.
Specifically, the uncertainty percentage is set by the user according to experience, and is usually set to be 5% -20%.
As an alternative embodiment, the calculation of the uncertainty of the corresponding contaminant is performed by a second uncertainty formula based on the minimum detection limit of the elemental measurement instrument, the second uncertainty formula being:
where Unc is the uncertainty of the contaminant; MDL is the minimum detection limit of an elemental measurement instrument.
As an alternative embodiment, the expression of the objective function is:
wherein Q is an objective function value; i is the serial number of the sampling time; n is the total sampling time; j is the serial number of the pollutant; m is the total number of pollutants; x is x ij The j of the ith sample time in the concentration data matrixConcentration of individual contaminants; k is the sequence number of the factor; p is a preset factor number; u (u) ij Uncertainty for the jth contaminant at the ith sample time in the uncertainty matrix; g ik Contribution of the kth factor in the factor contribution matrix to the concentration of all contaminants in the ith sample time; f (f) ki Is the concentration of the jth contaminant in the kth factor in the factor distribution matrix.
As an optional implementation, step 104 specifically includes:
and determining a factor distribution matrix corresponding to the minimum objective function value as an objective factor distribution matrix in a factor matrix group of a preset group array.
As an alternative embodiment, step 105 specifically includes:
the geometric difference is calculated based on the concentration of the contaminant corresponding to all of the factors in the factor distribution matrix and the concentration of the contaminant corresponding to all of the sources of contamination in the source composition spectrum.
Determining the pollution source with the smallest geometric difference of the first d as the type of the corresponding pollution source of the pollutant; d is a preset number.
The calculation formula of the geometric difference is as follows:
wherein GD b The geometrical difference value corresponding to the b-th pollution source in the source component spectrum is j, and the j is the serial number of the pollutant; m is the total number of pollutants; c a,j The mass fraction of the jth VOCs (i.e. pollutants) corresponding to the a-th factor in the factor distribution matrix in the m pollutants is the mass fraction of the jth VOCs (i.e. pollutants) obtained by PMF in the m pollutants, wherein the mass fraction is the ratio of the concentration of a certain pollutant to the total concentration of all pollutants; e, e j The mass fraction of the m contaminants in the jth VOCs species (i.e., contaminants) corresponding to the jth source of contamination in the source composition spectrum.
The first 15 pollution sources with the smallest geometric difference (specific data can be adjusted according to actual conditions) can well explain what factors are in the PMF, for example, the factors calculated by the PMF are the 15 pollution sources with the smallest geometric difference, 10 pollution sources are transportation sources and 5 pollution sources are petrochemical sources, and the factors are named as transportation sources and mainly mixed with partial petrochemical emission sources, so that the types of pollution sources corresponding to pollutants are determined.
Example 2
Fig. 2 is a schematic diagram of a system for determining a type of a pollution source according to embodiment 2 of the present invention. As shown in fig. 2, the system for determining a type of a pollution source in this embodiment includes:
a concentration sample data acquisition module 201 for acquiring a plurality of contaminant concentration sample data; the contaminant concentration sample data includes: sampling time, type name and concentration of contaminants.
A matrix construction module 202 for constructing a concentration data matrix and an uncertainty data matrix based on all contaminant concentration sample data.
The matrix decomposition module 203 is configured to determine a factor matrix set of the preset set based on the preset set number, the preset factor number, the concentration data matrix, the uncertain data matrix, and the positive matrix decomposition acceptor software; the factor matrix group includes: factor contribution matrix and factor distribution matrix.
A target factor distribution matrix determining module 204, configured to determine a target factor distribution matrix based on the objective function; the target distribution function is a function of the factor contribution matrix and the factor distribution matrix.
The pollution source determining module 205 is configured to compare the factor distribution matrix with the source component spectrum, and determine a type of a pollution source corresponding to each pollutant; the types include: transportation, biomass combustion, fixed fossil fuel combustion, industrial processing, and solvent utilization.
Example 3
An electronic device, comprising:
one or more processors.
A storage device having one or more programs stored thereon.
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of determining a type of contamination source as in embodiment 1.
Example 4
A storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method of determining a type of contamination source as in embodiment 1.
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 (10)
1. A method of determining a type of contamination source, the method comprising:
acquiring sample data of various pollutant concentrations; the contaminant concentration sample data includes: sampling time, type name and concentration of the contaminant;
constructing a concentration data matrix and an uncertainty data matrix based on all of the contaminant concentration sample data;
determining a factor matrix set of a preset set of sets based on a preset set number, a preset factor number, the concentration data matrix, the uncertainty data matrix and positive matrix factorization acceptor software; the factor matrix set includes: a factor contribution matrix and a factor distribution matrix;
determining a target factor distribution matrix based on the target function; the target distribution function is a function of the factor contribution matrix and the factor distribution matrix;
comparing the factor distribution matrix with a source component spectrum to determine the type of a pollution source corresponding to each pollutant; the types include: transportation, biomass combustion, fixed fossil fuel combustion, industrial processing, and solvent utilization.
2. The method for determining a type of a pollution source according to claim 1, wherein the constructing process of the concentration data matrix specifically comprises:
and constructing the concentration data matrix by taking the sampling time as a row identifier, the category name as a column identifier and the concentration of pollutants as elements.
3. The method for determining a type of a pollution source according to claim 1, wherein the construction process of the uncertainty data matrix specifically comprises:
judging whether the concentration of each pollutant is greater than the minimum detection limit of an element measuring instrument; the element detecting instrument is a device for detecting the concentration of the contaminant;
if yes, calculating uncertainty of the corresponding pollutants according to the preset uncertainty percentage, the concentration of the pollutants and the minimum detection limit of the element measuring instrument;
if not, calculating uncertainty of the corresponding pollutants according to the minimum detection limit of the element measuring instrument;
and constructing the concentration data matrix by taking the sampling time as a row identifier, the category name as a column identifier and the uncertainty of the pollutant as an element.
4. A method of determining a type of a source of contamination according to claim 3, wherein calculating the uncertainty of the corresponding contaminant is accomplished by a first uncertainty formula based on a predetermined uncertainty percentage, a concentration of the contaminant, and a minimum detection limit of an elemental measurement instrument, the first uncertainty formula being:
where Unc is the uncertainty of the contaminant; EF is a preset uncertainty percentage; c is the concentration of the contaminant; MDL is the minimum detection limit of an elemental measurement instrument.
5. The method of claim 1, wherein the expression of the objective function is:
wherein Q is an objective function value; i is the serial number of the sampling time; n is the total sampling time; j is the serial number of the pollutant; m is the total number of pollutants; x is x ij The concentration of the jth contaminant at the ith sampling time in the concentration data matrix; k is the sequence number of the factor; p is the preset factor number; u (u) ij Uncertainty for a jth contaminant at an ith sample time in the uncertainty matrix; g ik Contributing the kth factor in the factor contribution matrix to the concentration of all contaminants in the ith sample time; f (f) ki And (3) the concentration of the jth pollutant in the kth factor in the factor distribution matrix.
6. The method for determining the type of a pollution source according to claim 1, wherein comparing the factor distribution matrix with a source component spectrum to determine the type of a pollution source corresponding to each pollutant, specifically comprises:
calculating a geometric difference value based on the concentration of the pollutants corresponding to all the factors in the factor distribution matrix and the concentration of the pollutants corresponding to all the pollution sources in the source component spectrum;
determining the pollution source with the smallest geometric difference of the first d as the type of the corresponding pollution source of the pollutant; d is a preset number.
7. The method of claim 6, wherein the geometric difference is calculated by the formula:
wherein j is the serial number of the pollutant; m is the total number of pollutants; c a,j The mass fraction of the jth pollutant corresponding to the a factor in the factor distribution matrix in the m pollutants is the duty ratio of the concentration of any pollutant to the total concentration of all pollutants; e, e j The mass fraction of the jth, i.e., contaminant, in the m contaminants corresponding to the b-th contaminant source in the source composition spectrum.
8. A system for determining a type of a source of contamination, the system comprising:
the concentration sample data acquisition module is used for acquiring various pollutant concentration sample data; the contaminant concentration sample data includes: sampling time, type name and concentration of the contaminant;
a matrix construction module for constructing a concentration data matrix and an uncertainty data matrix based on all the contaminant concentration sample data;
the matrix decomposition module is used for determining a factor matrix group of a preset group based on a preset group number, a preset factor number, the concentration data matrix, the uncertain data matrix and positive matrix decomposition acceptor software; the factor matrix set includes: a factor contribution matrix and a factor distribution matrix;
the target factor distribution matrix determining module is used for determining a target factor distribution matrix based on the target function; the target distribution function is a function of the factor contribution matrix and the factor distribution matrix;
the pollution source determining module is used for comparing the factor distribution matrix with a source component spectrum and determining the type of the pollution source corresponding to each pollutant; the types include: transportation, biomass combustion, fixed fossil fuel combustion, industrial processing, and solvent utilization.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of determining a pollution source type as claimed in any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method of determining a type of pollution source as claimed in any one of claims 1 to 7.
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