CN107944213B - PMF online source analysis method, PMF online source analysis system, terminal device and computer readable storage medium - Google Patents
PMF online source analysis method, PMF online source analysis system, terminal device and computer readable storage medium Download PDFInfo
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Abstract
The invention relates to a PMF online source analysis method, a PMF online source analysis system, terminal equipment and a computer readable storage medium, wherein the PMF online source analysis method comprises the steps of collecting a particulate matter source, and performing online analysis to obtain original data; importing original data, selecting components according to the component categories of the particles to be detected, starting a data quality control process to automatically screen and process the original data to obtain effective data; inputting effective data into a PMF model, selecting the category of the particulate matter to be detected and the category of the particulate matter to be detected, setting parameters of the PMF model for calculation, and obtaining a source analysis result; and automatically identifying the source type of the obtained source analysis result through the formulated source identification rule to obtain the type of the pollution source. The invention has the beneficial effects that: obtaining effective data by the original data according to a data quality control process; and calculating the contribution value and uncertainty of the pollution source by the PMF algorithm, and automatically identifying the type of the pollution source by combining the source component spectrum and the relationship between factors, the interactive relationship between the factors, the important component information of the factors and the like.
Description
Technical Field
The invention belongs to the field of analysis of particulate pollution sources, and particularly relates to a PMF online source analysis method, a PMF online source analysis system, a PMF online source analysis terminal device and a computer readable storage medium.
Background
Dust haze pollution is one of the main atmospheric environment problems in cities and areas in China at present, and has the characteristics of high occurrence frequency and large haze area. The particle source analysis is the basis and the premise for scientifically and effectively developing dust-haze pollution control, and provides an indispensable scientific basis for formulating urban atmospheric particle pollution control strategies.
The current common method mainly obtains a pollution source component spectrum, a receptor chemical component and other auxiliary data through manual membrane sampling and an off-line analysis technology, and utilizes a receptor model to analyze the source of the particulate matter. The off-line analysis method has the advantages of longer sampling time, lower time resolution (24 hours), more complicated chemical analysis and data processing processes and stronger model calculation speciality, and can not meet the requirement of fast source analysis of the cause of the heavy pollution process which occurs in shorter time; the online source analysis technology established by combining the PMF model with online monitoring data can realize the rapid analysis of the particulate matter source in the heavy pollution weather process. Although the technology can automatically obtain the particulate component data with high resolution through an online monitoring instrument, because the online data volume is large, manual operation is required from data quality control to model calculation, the workload is huge, and the rapid analysis of the particulate source in the heavy pollution process is not facilitated. Data processing, model calculation, source identification and the like in an online source analysis technology established by combining a positive definite matrix PMF model with online monitoring data all need manual operation, are time-consuming and labor-consuming, have strong technical performance and are difficult to widely popularize.
Disclosure of Invention
In order to solve the above problems, the present invention provides a PMF (probabilistic Matrix factorization) online source parsing system, which integrates blocks of quality control of online data, PMF model (probability Matrix decomposition model) calculation, result source identification, etc., so as to implement automatic operations of online source parsing data processing and source identification, which is convenient and fast, and greatly improves the efficiency of source parsing work.
The technical scheme of the invention is as follows: the PMF online source analysis method is characterized by comprising the following steps:
step a, acquiring monitoring original data on line;
b, importing monitoring original data, selecting components according to the component categories of the particles to be detected, starting a data quality control process to automatically screen and process the original data to obtain quality control effective data;
inputting effective data into a PMF model, selecting the category of the particulate matter to be detected and the category of the particulate matter to be detected, setting parameters of the PMF model for calculation, and obtaining a source analysis result;
and d, automatically identifying the source type of the obtained source analysis result through a formulated source identification rule to obtain the type of the pollution source.
Further, the step b specifically includes the following steps:
the method comprises the following steps: importing online acquisition to obtain original data and initializing the original data;
step two: selecting components of the initialized original data according to the component categories of the particles to be detected to form sorting data;
step three: judging whether a data quality control process is started or not according to the atmospheric chemical mechanism and the monitoring factor relevance of the sorting data, and executing a fourth step after the data quality control process is started if the sorting data is judged to be yes; if judging whether the data is valid, directly obtaining quality control valid data;
step four: judging whether a data validity statistical rule is started or not, if so, executing a fifth step after the data validity statistical rule is started; if the judgment result is yes, directly executing the step five;
step five: judging whether data relevance diagnosis is started or not, and executing a sixth step after the data relevance diagnosis is started if the judgment is yes; if the judgment result is that whether the operation is finished or not is judged, the step six is directly executed;
step six: judging whether a large and small value judgment rule is started or not, if so, executing a seventh step after the large and small value judgment rule is started; if yes, directly executing the step seven;
step seven: judging whether a continuous data processing rule is started or not, and if so, starting the continuous data processing rule to obtain quality-controlled data; if the judgment result is that whether the data is valid or not is judged, the quality control valid data is directly obtained.
Further, the types of the particulate matter components to be detected specifically include 6 types: PM2.5 data: PM 2.5; EC/OC: EC. OC; anion: SO (SO)4 2-、NO3 -、Cl-All inorganic anion data that can be measured by the instrument; cation: NH (NH)4 +、K+、Na+Waiting all inorganic cation data which can be detected by all instruments; the element components are as follows: fe. All heavy metal ion data which can be detected by Ca and K instruments; gas composition: HCL, HONO, SO2Gas composition data measurable by all instruments;
preferably, the parameters of the PMF model include detection limits, input data uncertainty related parameters, and identified factors.
Further, the statistical rule of data validity in step four includes:
a: if the large types of components of PM2.5 data, EC/OC, anions, cations and element components 5 have no data, default data is lost, and the whole row is deleted;
b: deleting the whole row when the effective rate of certain type of data of components of EC/OC, anion, cation, element component and gas component is lower than a set value;
c: counting the data loss rate, wherein the loss rate is higher than a set value, and performing no source analysis calculation;
d: substitution with the first 6 hour sliding average for missing data;
preferably, the data association diagnosis rule in the fifth step includes:
e, setting a statistical step length: performing statistical analysis on the data in the setting according to the self-defined setting step range;
f, executing the judgment rule of the proportion of three types of substances to PM 2.5: analysis of PM2.5Concentration, EC/OC, anions and cations, element components, and the concentration of the three species is added/PM when the concentration is more than 30-50%2.5If the concentration is less than 60-80%, the batch of data is reasonable, and the weight of EC/OC, anions, cations and element components is set; adding the concentrations of the three species/PM which are not more than 30-50%2.5Deleting the whole segment of results from the data with the concentration less than 60-80%;
g, executing OCEC/ion/element relative ratio judgment rules: analyzing the relative relation between EC/OC, anions and elements, and meeting the requirements that the concentration sum of EC/OC/three species is 20-30% < 40-50%, the concentration sum of anions and anions/three species is 45-55% < 65-75%, and the concentration sum of elements/three species is 1-2% < 4-6%, so that the batch of data is reasonable; deleting the whole stage result of data which do not meet the requirements of 20-30% < EC/OC/three species concentration sum < 40-50%, 45-55% < zwitterion/three species concentration sum < 65-75%, and 1-2% < element/three species concentration sum < 4-6%;
preferably, the rule for determining the larger and smaller values in the sixth step includes:
setting a statistical step length: performing statistical analysis on the data in the setting according to the self-defined setting step range;
respectively and independently setting N values for each category in the statistical step length, and removing the values larger than the average value +/-N standard deviation;
j, replacing the data with larger or smaller abnormality by using the sliding average value in the first 6 hours;
preferably, the continuous data processing rule in the seventh step includes:
k, replacing data below the detection limit with 1/2 detection limit;
continuously judging the data which is more than or equal to 3 times of the detection limit, and deleting the data if the deviation is within a certain range for continuous N hours;
m-substitution with the first 6 hours running average for continuous data.
Furthermore, the source identification in the step d integrates a factor-feature-based component identification method, a factor spectrum and source component spectrum correlation identification method and a factor and component time sequence correlation identification method for source class identification.
Further, the specific process of initializing the original data in the step one is as follows: automatically identifying the imported data of which the time column is null, and deleting the row; import data with component concentrations at null values are automatically identified and assigned a value of "0".
The PMF online source resolution system is characterized by comprising:
the data acquisition module is used for acquiring a particulate matter source and analyzing online to obtain original data;
the online data quality control module is used for importing original data, selecting components according to the component categories of the particles to be detected, starting a data quality control process to automatically screen and process the original data to obtain quality control effective data;
the PMF model calculation module is used for inputting effective data into the PMF model, selecting the category of the particulate matter to be detected and the category of the particulate matter to be detected, and setting parameters of the PMF model for calculation to obtain a source analysis result;
and the result source identification module is used for automatically identifying the source analysis result through the formulated source identification rule to obtain a pollution source type module.
Further, the quality control module of the online data comprises:
importing an original data unit, importing a unit for obtaining original data through online analysis and initializing the original data;
the unit for selecting the component types of the particles to be detected selects the components of the initialized original data according to the component types of the particles to be detected to form a unit for sorting data;
and the data quality control flow unit is used for automatically screening and processing the original data to obtain a unit of quality inspection effective data.
A PMF online source resolution terminal device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the PMF online source resolution method when executing the computer program.
A computer-readable storage medium storing a PMF online source resolution program, the computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the PMF online source resolution method.
The invention has the beneficial effects that: selecting the category of the particulate matter to be detected and the category of the particulate matter to be detected for source analysis; the received original data can be automatically audited and optimized according to the atmospheric chemical mechanism and the monitoring factor correlation data quality control process, and high-quality effective quality inspection data can be finally obtained; calculating contribution values and uncertainty of various pollution sources by using a PMF algorithm, and displaying the contribution values and the uncertainty in a chart mode, wherein the contribution values and the uncertainty comprise an hour source analysis result of particulate matters in a specified time period, a pollution source time sequence stack diagram, a pollution source component spectrogram and the like; and automatically identifying the type of the pollution source by combining the source component spectrum and the relationship between factors, the interactive relationship between the factors, the important component information of the factors and the like.
Drawings
Fig. 1 is a data quality control flowchart of a PMF online source analysis method according to embodiment 1 of the present invention.
Fig. 2 is a block diagram of a PMF online source resolution system according to embodiment 1 of the present invention.
Fig. 3 is a flow chart of source class identification according to embodiment 1 of the present invention.
Fig. 4 is a diagram of an original data import page according to embodiment 1 of the present invention.
FIG. 5 is a screenshot of a portion of raw data of embodiment 1 of the present invention.
FIG. 6 is a raw data import and display interface according to embodiment 1 of the present invention.
FIG. 7 is a dialogue diagram of mandatory components and optional components of the quality control rule menu item in embodiment 1 of the present invention.
Fig. 8 is a dialogue diagram of each type of efficiency analysis of the validity statistics rule according to embodiment 1 of the present invention.
Fig. 9 is a data loss rate statistics dialog of the validity statistics rules of embodiment 1 of the present invention.
Fig. 10 is a moving average replacement dialog box for the first 6 hours of the validity statistics rule of example 1 of the present invention.
FIG. 11 is a screenshot of input data and optional chemical components incorporated into a PMF model calculation according to example 1 of the present invention.
Fig. 12 is a dialogue diagram of input data uncertainty setting of PMF model calculation in embodiment 1 of the present invention.
FIG. 13 is a factor spectrum display interface calculated by the PMF model in example 1 of the present invention.
FIG. 14 is a source contribution result presentation interface of example 1 of the present invention.
FIG. 15 is a time series of percentage results of source parsing and a pie chart of source parsing according to example 1 of the present invention.
Fig. 16 is a diagram of results obtained after three source identification rules are respectively given by the rule one interface, the rule two interface and the rule three interface in embodiment 1 of the present invention.
Detailed Description
An embodiment of the present invention will be described with reference to the accompanying drawings.
The PMF online source analysis method comprises the following steps:
step a, acquiring monitoring original data on line;
b, importing monitoring original data, selecting components according to the component categories of the particles to be detected, starting a data quality control process to automatically screen and process the original data to obtain quality control effective data;
inputting effective data into a PMF model, selecting the category of the particulate matter to be detected and the category of the particulate matter to be detected, setting parameters of the PMF model for calculation, and obtaining a source analysis result;
and d, automatically identifying the source type of the obtained source analysis result through a formulated source identification rule to obtain the type of the pollution source.
Wherein, the step b specifically comprises the following steps:
the first step is as follows: importing online acquisition to obtain original data and initializing the original data;
step two: selecting components of the initialized original data according to the component categories of the particles to be detected to form sorting data;
step three: judging whether a data quality control process is started or not according to the atmospheric chemical mechanism and the monitoring factor relevance of the sorting data, and executing a fourth step after the data quality control process is started if the sorting data is judged to be yes; if judging whether the data is valid, directly obtaining quality control valid data;
step four: judging whether a data validity statistical rule is started or not, if so, executing a fifth step after the data validity statistical rule is started; if the judgment result is yes, directly executing the step five;
step five: judging whether data relevance diagnosis is started or not, and executing a sixth step after the data relevance diagnosis is started if the judgment is yes; if the judgment result is that whether the operation is finished or not is judged, the step six is directly executed;
step six: judging whether a large and small value judgment rule is started or not, if so, executing a seventh step after the large and small value judgment rule is started; if yes, directly executing the step seven;
step seven: judging whether a continuous data processing rule is started or not, and if so, starting the continuous data processing rule to obtain quality-controlled data; if the judgment result is that whether the data is valid or not is judged, the quality control valid data is directly obtained.
The types of the particulate matter components to be detected specifically include 6 types: PM2.5 data: PM 2.5; EC/OC: EC. OC; anion: SO (SO)4 2-、NO3 -、Cl-All inorganic anion data that can be measured by the instrument; cation: NH (NH)4 +、K+、Na+Waiting all inorganic cation data which can be detected by all instruments; the element components are as follows: fe. All heavy metal ion data which can be detected by Ca and K instruments; gas composition: HCL, HONO, SO2Gas composition data measurable by all instruments;
the parameters of the PMF model include detection limits, input data uncertainty related parameters, and identified factors.
Wherein, the statistical rule of data validity in the fourth step includes:
a: if the large types of components of PM2.5 data, EC/OC, anions, cations and element components 5 have no data, default data is lost, and the whole row is deleted;
b: deleting the whole row when the effective rate of certain type of data of components of EC/OC, anion, cation, element component and gas component is lower than a set value;
c: counting the data loss rate, wherein the loss rate is higher than a set value, and performing no source analysis calculation;
d: substitution with the first 6 hour sliding average for missing data;
the data association diagnosis rule in the step five comprises the following steps:
e, setting a statistical step length: performing statistical analysis on the data in the setting according to the self-defined setting step range;
f, executing the judgment rule of the proportion of three types of substances to PM 2.5: analysis of PM2.5Concentration, EC/OC, anions and cations, element components, and the concentration of the last three species is more than 30-50%Degree of addition/PM2.5If the concentration is less than 60-80%, the batch of data is reasonable, and the weight of EC/OC, anions, cations and element components is set; adding the concentrations of the three species/PM which are not more than 30-50%2.5Deleting the whole segment of results from the data with the concentration less than 60-80%;
g, executing OCEC/ion/element relative ratio judgment rules: analyzing the relative relation between EC/OC, anions and elements, and meeting the requirements that the concentration sum of EC/OC/three species is 20-30% < 40-50%, the concentration sum of anions and anions/three species is 45-55% < 65-75%, and the concentration sum of elements/three species is 1-2% < 4-6%, so that the batch of data is reasonable; deleting the whole stage result of data which do not meet the requirements of 20-30% < EC/OC/three species concentration sum < 40-50%, 45-55% < zwitterion/three species concentration sum < 65-75%, and 1-2% < element/three species concentration sum < 4-6%;
the judgment rule of the larger value and the smaller value in the sixth step comprises the following steps:
setting a statistical step length: performing statistical analysis on the data in the setting according to the self-defined setting step range;
respectively and independently setting N values for each category in the statistical step length, and removing the values larger than the average value +/-N standard deviation;
j, replacing the data with larger or smaller abnormality by using the sliding average value in the first 6 hours;
the continuous data processing rule in the seventh step comprises the following steps:
k, replacing data below the detection limit with 1/2 detection limit;
continuously judging the data which is more than or equal to 3 times of the detection limit, and deleting the data if the deviation is within a certain range for continuous N hours;
m-substitution with the first 6 hours running average for continuous data.
In the step d, the source identification integrates a factor characteristic component identification method, a factor spectrum and source component spectrum correlation identification method and a factor and component time sequence correlation identification method for source identification.
The specific process of initializing the original data in the first step is as follows: automatically identifying the time column of the imported data as a null value, and deleting the row; automatically identifying the component concentration in the imported data as a null value and assigning a value of '0'; the units are processed uniformly, four digits after the decimal point are reserved for the element components, and two digits after the decimal point are reserved for the other components.
The PMF online source analysis system is characterized by comprising the following modules:
the data acquisition module is used for acquiring a particulate matter source and analyzing online to obtain original data;
the online data quality control module is used for importing original data, selecting components according to the component types of the particles to be detected, starting a data quality control process to automatically screen and process the original data to obtain effective data;
the PMF model calculation module is used for inputting effective data into the PMF model, selecting the category of the particulate matter to be detected and the category of the particulate matter to be detected, and setting parameters of the PMF model for calculation to obtain a source analysis result;
and the result source identification module is used for automatically identifying the source analysis result through the formulated source identification rule to obtain a pollution source type module.
The quality control module of the online data comprises the following units:
importing an original data unit, importing a unit for obtaining original data through online analysis and initializing the original data;
the unit for selecting the component types of the particles to be detected selects the components of the initialized original data according to the component types of the particles to be detected to form a unit for sorting data;
and the data quality control flow unit is used for automatically screening and processing the original data to obtain a unit of effective data.
A PMF online source analysis terminal device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that the steps of the PMF online source analysis method are realized when the processor executes the computer program.
A computer-readable storage medium storing a PMF online source resolution program, the computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the PMF online source resolution method.
Example 1
Fig. 1 is a data quality control flowchart of a PMF online source analysis method according to embodiment 1 of the present invention.
The PMF online source analysis method is characterized by comprising the following steps:
a, collecting a particulate matter source, and carrying out online analysis to obtain original data; specifically, the method comprises the following steps: monitoring data of an AMMS (advanced metering system) atmosphere heavy metal analyzer, a WAGA (wide area atomic gas) atmosphere water-soluble ion component online analyzer and an atmosphere OCEC (oxygen demand instrument) online analyzer are utilized for online analysis to obtain original data;
b, importing original data, selecting components according to the component categories of the particles to be detected, starting a data quality control process to automatically screen and process the original data to obtain effective data; the received original data can be automatically audited and optimized according to atmospheric chemical mechanism, monitoring factor correlation and the like, and a high-quality data set is finally obtained;
fig. 2 is a block diagram of a PMF online source resolution system according to embodiment 1 of the present invention. The data quality control process comprises the following steps:
the method comprises the following steps: importing online analysis to obtain original data and initializing the original data;
in the first step, the specific process of obtaining the original data by introducing online analysis and initializing the original data is carried out:
import pour raw data:
fig. 4 is a diagram of an original data import page according to embodiment 1 of the present invention. Specifically, a file named as 'original data, excel' is found, and an import file is opened by clicking.
FIG. 5 is a screenshot of a portion of raw data of embodiment 1 of the present invention. "original data. excel" file
"raw data. excel" document description: the first action is component information, the case and the format of the component refer to a necessary component and an optional component in the quality control rule, wherein brackets must be input in English font, and the sequence of the components is adjustable; the first column is a time sequence, time can not be empty, otherwise, when software is imported, the row is automatically identified and deleted. If the imported component data has null values, the program automatically recognizes and assigns a value of "0".
FIG. 6 is a raw data import and display interface according to embodiment 1 of the present invention. Clicking the import can view the loaded original data information in the original data interface.
Initialization of original data: automatically identifying the time column of the imported data as a null value, and deleting the row; automatically identifying the component concentration in the imported data as a null value and assigning a value of '0'; the units are processed uniformly, four digits after the decimal point are reserved for the element components, and two digits after the decimal point are reserved for the other components.
Step two: selecting components according to the component categories of the particles to be detected; FIG. 7 is a dialogue diagram of mandatory components and optional components of the quality control rule menu item in embodiment 1 of the present invention. The component selection in the step two specifically comprises 6 types: PM2.5 data: PM 2.5; EC/OC: EC. OC; anion: SO (SO)4 2-、NO3 -All inorganic anion data detectable by Cl-instrument; cation: NH (NH)4 +、K+、Na+Waiting all inorganic cation data which can be detected by all instruments; the element components are as follows: fe. All heavy metal ion data which can be detected by Ca and K instruments; gas composition: HCL, HONO, SO2 all instruments can measure gas composition data. The method comprises the following specific operations: in the menu item of the quality control rule, the optional components provide the function of component selection, and the detection limit, the value a and the value b of the components can be set by user, wherein the values a and b are used for calculating the uncertainty of the components, and the related setting can be completed by clicking a save button.
Step three: judging whether a data quality control process is started or not, if so, executing the step four after the data quality control process is started; if the judgment result is yes, waiting for the data after quality control. The data quality control process mainly comprises a data validity statistical rule, a data correlation diagnosis rule, a partial magnitude value judgment rule and a continuous data processing rule;
step four: judging whether a data validity statistical rule is started or not, if so, executing a fifth step after the data validity statistical rule is started; if yes, executing the step five;
the statistical rule of data validity in the fourth step is as follows:
a: the major classes of components of PM2.5 data, EC/OC, anion, cation, elemental composition 5 have type without data, default data is missing, delete whole row:
b: deleting the whole row when the effective rate of certain type of data of components of EC/OC, anion, cation, element component and gas component is lower than a set value; fig. 8 is a dialogue diagram of each type of efficiency analysis of the validity statistical rules of embodiment 1 of the present invention. For example, 10 element components are selected, and when 5 of the elements are non-null valid data, the data effective rate is 50%, where if the set effective rate is lower than 50%, the row is deleted, otherwise, the row is retained; fig. 8 is a dialogue diagram of each type of efficiency analysis of the validity statistics rule according to embodiment 1 of the present invention.
C: counting the data loss rate, wherein the loss rate is higher than a set value, and performing no source analysis calculation; fig. 9 is a data loss rate statistics dialog of the validity statistics rule of embodiment 1 of the present invention.
D: the missing data were replaced with the first 6 hour sliding average. Figure 10 the first 6 hours moving average replacement dialog of the validity statistics rules of example 1 of the present invention.
Step five: judging whether data relevance diagnosis is started or not, and executing a sixth step after the data relevance diagnosis is started if the judgment is yes; if yes, executing the step six;
the data association diagnosis rule in the fifth step is as follows:
e, setting a statistical step length: setting a step length range in a user-defined manner, and performing statistical analysis on data in the setting; the step size was set to 1000 in example 1;
f, judging rules of proportion of three types of substances to PM 2.5: PM (particulate matter)2.5Concentration, EC/OC, anions and cations, element components, and the concentration of the last three species is added/PM when the concentration is more than 40 percent (adjustable)2.5If the concentration is less than 70% (adjustable), the batch of data is reasonable, and the weight of EC/OC, anion, cation and element components can be set; for the concentration addition/PM of three species which do not meet the condition that 40 percent (adjustable) < the last three species2.5Deleting the whole segment of results from the data with the concentration less than 70% (adjustable) requirement;
OCEC/ion/element relative ratio judgment rule: the relative relationship between EC/OC, anions and elements, namely the three major components are normalized to meet the requirements that the concentration sum of 25% (adjustable) < EC/OC/three species is less than 45% (adjustable), the concentration sum of 50% (adjustable) < anions/three species is less than 70% (adjustable), and the concentration sum of 1% (adjustable) < elements/three species is less than 5% (adjustable); deleting the whole stage result for the data which satisfies the requirements of 25% (adjustable) < EC/OC/three species concentration sum < 45% (adjustable), 50% (adjustable) < zwitterion/three species concentration sum < 70% (adjustable), 1% (adjustable) < element/three species concentration sum < 5% (adjustable);
step six: judging whether a large and small value judgment rule is started or not, if so, executing a seventh step after the large and small value judgment rule is started; if yes, executing the step seven;
in the sixth step, the rule for judging the larger value and the smaller value is as follows:
setting a statistical step length: setting a step length range in a user-defined manner, and performing statistical analysis on data in the setting; the step size was set to 50 in example 1;
i, eliminating values larger than the standard deviation of the average value +/-N within the statistical step length, and independently setting an N value for each category; the standard deviation n in example 1 is 3;
and J, replacing the abnormally large or small data by using the sliding average value in the first 6 hours.
Step seven: judging whether a continuous data processing rule is started or not, and if so, starting the continuous data processing rule to obtain quality-controlled data; if the judgment result is that whether the data is not the quality control data or not is judged, the quality control data is directly obtained.
The continuous data processing rule in the seventh step is as follows:
data below the detection limit were replaced with the detection limit of 1/2.
Continuously judging the data more than or equal to 3 times of detection limit for N hours, and deleting the data when the deviation is within a certain range; example 1 was 6 hours continuous with a deviation of 5%;
m-substitution with the first 6 hours running average for continuous data.
And c, inputting effective data into the PMF model, selecting the category of the particles to be detected and the category of the particles to be detected, and setting parameters of the PMF model for calculation to obtain a source analysis result.
FIG. 11 is a screenshot of input data and optional chemical components incorporated into a PMF model calculation according to example 1 of the present invention. Input data of the PMF model are constructed, wherein the input data comprise anions and cations, EC/OC, 5 major components of element components and PM2.5 data. PM measurement by using particle online monitoring instrument2.5Concentration data. The EC/OC carbon component, including the concentrations of OC and EC, was measured using a semi-continuous OC/EC instrument. Measurement of water-soluble anions and cations, including NH, using an on-line ion chromatograph4 +、Na+、Mg2 +、S04 2-、NO3 -、Cl-The concentration of (c). The concentration of elements including Ca, Mn, Fe, Cu, Zn, As, Se, Ba, Hg and Pb is monitored by using a heavy metal on-line analyzer. (the component classification of each input data may vary somewhat depending on the actual monitored data). Four monitoring instruments simultaneously collect samples for a plurality of consecutive days, and the time resolution of the monitored data is 1 hour. Selecting the calculated sample and chemical components; and parameters input into the PMF model are set according to the detection limit of an actual analytical instrument and the uncertainty of input data, and comprise two parameters, namely a parameter a related to the detection limit of the analytical instrument and a parameter b related to the uncertainty of the input data. FIG. 12 is a block diagram of an input data uncertainty setting dialog of the PMF model calculation module of the present invention; factor of model extractionThe number is set to 4; the number of factors identified is input. Generally default to 0.6; fig. 12 is a dialogue diagram of input data uncertainty setting of PMF model calculation in embodiment 1 of the present invention.
And d, automatically identifying the source type of the obtained source analysis result through a formulated source identification rule to obtain the type of the pollution source.
The result calculated by the model can automatically identify the source class through the formulated source identification rule; the result source identification module integrates factor characteristic component identification, factor spectrum and source component spectrum correlation identification and factor and component time sequence correlation identification for source identification. FIG. 3 is a flow chart of result source class identification of embodiment 1 of the present invention. Rule one is as follows: factor-based feature component identification
Transversely normalizing the original result of the factor I spectrum;
II the identification rule is as follows:
firstly, the factor with the highest Ca proportion distribution is raised dust; the highest factor of the sum of OC and EC is the motor vehicle; the factor with the ratio of OC to EC being added to the next highest is fire coal; fourthly SO4 2-The factor with the highest proportion distribution is secondary sulfate; fifthly, NO3 -The factor with the highest proportion distribution is secondary nitrate; sixthly, the definition which is not identified is other "
Rule two: identification based on factor spectrum and source composition spectrum correlation
Firstly, embedding an actually measured source component spectrum (selecting a local actually measured source spectrum) into software, wherein the actually measured source spectrum is shown in a table 1:
TABLE 1
| Dust raising | Coal burning | Construction of buildings | Second sulfuric acid | Secondary nitric acid | Motor vehicle | |
| Na | 0.0173 | 0.0164 | 0.0156 | 0.0000 | 0.0000 | 0.0030 |
| Mg | 0.0114 | 0.0050 | 0.0137 | 0.0000 | 0.0000 | 0.0022 |
| K | 0.0085 | 0.0059 | 0.0212 | 0.0000 | 0.0000 | 0.0023 |
| Ca | 0.0800 | 0.0300 | 0.3921 | 0.0000 | 0.0000 | 0.0060 |
| Ti | 0.0043 | 0.0092 | 0.0036 | 0.0000 | 0.0000 | 0.0010 |
| Cr | 0.0000 | 0.0001 | 0.0003 | 0.0000 | 0.0000 | 0.0001 |
| Mn | 0.0004 | 0.0002 | 0.0005 | 0.0000 | 0.0000 | 0.0002 |
| Fe | 0.0236 | 0.0284 | 0.0173 | 0.0000 | 0.0000 | 0.0118 |
| Ni | 0.0001 | 0.0001 | 0.0000 | 0.0000 | 0.0000 | 0.0001 |
| Cu | 0.0003 | 0.0001 | 0.0003 | 0.0000 | 0.0000 | 0.0008 |
| Zn | 0.0006 | 0.0002 | 0.0000 | 0.0000 | 0.0000 | 0.0022 |
| Pb | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0003 |
| SO4 2- | 0.0650 | 0.1359 | 0.0160 | 0.7270 | 0.0000 | 0.0387 |
| NO3 - | 0.0030 | 0.0012 | 0.0000 | 0.0000 | 0.7750 | 0.0077 |
| NH4 + | 0.0002 | 0.0028 | 0 | 0.273 | 0.225 | 0.0242 |
| OC | 0.0630 | 0.1181 | 0.0041 | 0.0000 | 0.0000 | 0.3170 |
| EC | 0.0130 | 0.0826 | 0.0045 | 0.0000 | 0.0000 | 0.3020 |
Extracting common components of the factor spectrum and the actually measured source spectrum, and performing correlation analysis;
analysis of correlation coefficient: a source spectrum with the best significant correlation to the factor and a correlation coefficient >0.6 (adjustable) is identified as the source class and the unrecognized definition is "other".
Rule three: identification based on factor and component time series correlation
The factor contribution time sequence corresponding to a certain factor has the best correlation with the Ca component concentration time sequence, the correlation coefficient is more than 0.6 (adjustable), and the factor is identified as a dust source;
the correlation between the factor contribution time sequence corresponding to a certain factor and the OC component concentration time sequence is the best, the correlation coefficient is more than 0.6 (adjustable), and the factor is identified as a motor vehicle;
③ factor contribution time series and SO corresponding to certain factor4 2-The time series correlation of the component concentration is best, and the correlation coefficient>0.6 (tunable), this factor being identified as secondary sulfuric acid;
factor contribution time series corresponding to certain factor and NO3 -The time series correlation of the component concentration is best, and the correlation coefficient>0.6 (tunable), this factor being identified as secondary nitric acid;
sixthly, the factor contribution time sequence corresponding to a certain factor and SO2The concentration time series correlation is best, and the correlation coefficient>0.6 (tunable), this factor is identified as the source of the coal;
and the unrecognized definition is 'other'.
The concentration and percentage of the source contribution is output. The corresponding results can be viewed on the interfaces of 'factor spectrum', 'source contribution', 'graphical presentation', etc., and fig. 14 is a source contribution result presentation interface of embodiment 1 of the present invention. The source contribution interface gives a time series of concentrations of the various types of factors, where the time corresponds to the time after quality control.
Fig. 15 is a time series of results of percentage source analysis and a pie chart of plot source analysis according to embodiment 1 of the present invention. The graph display gives a stacking graph of the percentage of various factors, the source contribution pie chart of each hour can be switched in a self-defined mode, and the overall percentage proportion condition of various factors in the statistical time period is given in the statistical list.
Fig. 16 is a diagram of results obtained after three source identification rules are respectively given by the rule one interface, the rule two interface and the rule three interface in embodiment 1 of the present invention.
A PMF online source analysis terminal device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that the steps of the PMF online source analysis method are realized when the processor executes the computer program.
A computer-readable storage medium storing a PMF online source resolution program, the computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the PMF online source resolution method.
Compared with the prior art, the method has the advantages that the types of the particles to be detected and the component types of the particles to be detected are selected for source analysis; the received original data can be automatically audited and optimized according to the atmospheric chemical mechanism and the monitoring factor correlation data quality control process, and high-quality effective quality inspection data can be finally obtained; calculating contribution values and uncertainty of various pollution sources by using a PMF algorithm, and displaying the contribution values and the uncertainty in a chart mode, wherein the contribution values and the uncertainty comprise an hour source analysis result of particulate matters in a specified time period, a pollution source time sequence stack diagram, a pollution source component spectrogram and the like; and automatically identifying the type of the pollution source by combining the source component spectrum and the relationship between factors, the interactive relationship between the factors, the important component information of the factors and the like.
The above description is intended to illustrate an embodiment of the present invention, but the present invention is only a preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (12)
- The PMF online source analysis method is characterized by comprising the following steps:step a, acquiring monitoring original data on line;b, importing monitoring original data, selecting components according to the component categories of the particles to be detected, starting a data quality control process to automatically screen and process the original data to obtain quality control effective data;the method specifically comprises the following steps:the method comprises the following steps: importing online acquisition to obtain original data and initializing the original data;step two: selecting components of the initialized original data according to the component categories of the particles to be detected to form sorting data;step three: judging whether a data quality control process is started or not according to the atmospheric chemical mechanism and the monitoring factor relevance of the sorting data, and executing a fourth step after the data quality control process is started if the sorting data is judged to be yes; if not, directly obtaining quality control effective data;step four: judging whether a data validity statistical rule is started or not, if so, executing a fifth step after the data validity statistical rule is started; if not, directly executing the step five;step five: judging whether data relevance diagnosis is started or not, and executing a sixth step after the data relevance diagnosis is started if the judgment is yes; if not, directly executing the step six;step six: judging whether a large and small value judgment rule is started or not, if so, executing a seventh step after the large and small value judgment rule is started; if not, directly executing the step seven;step seven: judging whether a continuous data processing rule is started or not, and if so, starting the continuous data processing rule to obtain quality-controlled data; if not, directly obtaining quality control effective data;inputting effective data into a PMF model, selecting the category of the particulate matter to be detected and the category of the particulate matter to be detected, setting parameters of the PMF model for calculation, and obtaining a source analysis result;and d, automatically identifying the source type of the obtained source analysis result through a formulated source identification rule to obtain the type of the pollution source.
- 2. The PMF online source analysis method according to claim 1, wherein the categories of the particulate matter to be detected specifically include 6 categories: PM2.5 data: PM 2.5; EC/OC: EC. OC; anion: SO measurable by instrument4 2-、NO3-、Cl-Inorganic anion data; cation: instrumental measurable NH4+、K+、Na+Inorganic cation data; the element components are as follows: fe, Ca and K heavy metal ion data which can be detected by an instrument; gas composition: HCL, HONO, SO detectable by instrument2Gas composition data.
- 3. The PMF online source analytic method of claim 2, wherein the parameters of the PMF model comprise detection limit, input data uncertainty related parameters and identified factors.
- 4. The PMF online source parsing method according to claim 2, wherein the statistical rule of data validity in step four includes:a: if the large types of components of PM2.5 data, EC/OC, anions, cations and element components 5 have no data, default data is lost, and the whole row is deleted;b: deleting the whole row when the effective rate of certain type of data of components of EC/OC, anion, cation, element component and gas component is lower than a set value;c: counting the data loss rate, wherein the loss rate is higher than a set value, and performing no source analysis calculation;d: the missing data were replaced with the first 6 hour sliding average.
- 5. The PMF online source resolution method according to claim 4, wherein the data association diagnosis rule in the fifth step comprises:e, setting a statistical step length: performing statistical analysis on the data in the setting according to the self-defined setting step range;f, executing the judgment rule of the proportion of three types of substances to PM 2.5: analyzing PM2.5 concentration, EC/OC, anions and cations and element components, meeting the requirement that the concentration of the three species is added when the concentration of PM2.5 is less than 60-80% after the concentration is more than 30-50%, and then setting the weight of EC/OC, anions, cations and element components; deleting the whole stage result of the data which does not meet the requirement that the concentration of the three species is added and the concentration of PM2.5 is less than 60-80% after the concentration is more than 30-50%;g, executing (EC/OC)/anion/element relative ratio judgment rules: analyzing the relative relation of EC/OC, anions and cations and elements, and meeting the requirements that the concentration sum of EC/OC is more than 20-30% < 40-50%, the concentration sum of anions and cations/three species is more than 45-55% < 65-75%, and the concentration sum of elements/three species is more than 1-2% < 4-6%, so that the batch of data is reasonable; deleting the whole stage result of the data which does not meet the requirements that the concentration sum of EC/OC is more than 20-30% < 40-50%, the concentration sum of anions and cations is more than 45-55% < 65-75%, and the concentration sum of elements is more than 1-2% < 4-6%.
- 6. The PMF online source analysis method according to claim 5, wherein said rule of judging the magnitude of bias value in step six includes:setting a statistical step length: performing statistical analysis on the data in the setting according to the self-defined setting step range;respectively and independently setting N values for each category in the statistical step length, and removing the values larger than the average value +/-N standard deviation;j, replacing the data with larger or smaller abnormality by using the sliding average value in the first 6 hours;the continuous data processing rule in the seventh step includes:k, replacing data below the detection limit with 1/2 detection limit;continuously judging the data which is more than or equal to 3 times of the detection limit, and deleting the data if the deviation is 5 percent in continuous N hours;m-substitution with the first 6 hours running average for continuous data.
- 7. The online PMF source resolution method according to any one of claims 1 to 6, wherein the source identification in step d integrates a factor-feature-based component identification method, a factor spectrum-source component correlation identification method and a factor-component time-series correlation identification method for source class identification.
- 8. The PMF online source resolution method according to any one of claims 1 to 6, characterized in that the specific process of initializing the original data in the first step is as follows: automatically identifying the imported data of which the time column is null, and deleting the row; import data with component concentrations at null values are automatically identified and assigned a value of "0".
- The PMF online source resolution system is characterized by comprising the following modules:the data acquisition module is used for acquiring a particulate matter source and analyzing online to obtain original data;the online data quality control module is used for importing original data, selecting components according to the component categories of the particles to be detected, starting a data quality control process to automatically screen and process the original data to obtain quality control effective data;the method specifically comprises the following steps:the method comprises the following steps: importing online acquisition to obtain original data and initializing the original data;step two: selecting components of the initialized original data according to the component categories of the particles to be detected to form sorting data;step three: judging whether a data quality control process is started or not according to the atmospheric chemical mechanism and the monitoring factor relevance of the sorting data, and executing a fourth step after the data quality control process is started if the sorting data is judged to be yes; if not, directly obtaining quality control effective data;step four: judging whether a data validity statistical rule is started or not, if so, executing a fifth step after the data validity statistical rule is started; if not, directly executing the step five;step five: judging whether data relevance diagnosis is started or not, and executing a sixth step after the data relevance diagnosis is started if the judgment is yes; if not, directly executing the step six;step six: judging whether a large and small value judgment rule is started or not, if so, executing a seventh step after the large and small value judgment rule is started; if not, directly executing the step seven;step seven: judging whether a continuous data processing rule is started or not, and if so, starting the continuous data processing rule to obtain quality-controlled data; if not, directly obtaining quality control effective data;the PMF model calculation module is used for inputting effective data into the PMF model, selecting the category of the particulate matter to be detected and the category of the particulate matter to be detected, and setting parameters of the PMF model for calculation to obtain a source analysis result;and the result source identification module is used for automatically identifying the source analysis result through the formulated source identification rule to obtain a pollution source type module.
- 10. The PMF online source resolution system of claim 9, wherein the quality control module of the online data comprises the following units:importing an original data unit, importing a unit for obtaining original data through online analysis and initializing the original data;the device comprises a unit for selecting the component types of the particles to be detected, a unit for selecting the components according to the component types of the particles to be detected;and the data quality control flow unit is used for automatically screening and processing the original data to obtain a unit of quality control effective data.
- 11. PMF online source resolution terminal device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor implements the steps of the PMF online source resolution method according to claims 1-8 when executing said computer program.
- 12. A computer-readable storage medium storing a PMF online source resolution program, the computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the PMF online source resolution method according to claims 1-8.
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