CN112687350A - Source analysis method of air fine particulate matter, electronic device, and storage medium - Google Patents
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Abstract
The application discloses source analysis method, electronic equipment and storage medium of fine particulate matters in air, and the method comprises the following steps: grouping the collected air fine particle concentration data according to the data collection time period; calculating the average value of the concentration data of the fine particulate matters in each group to obtain the air pollution concentration; calculating the average component concentration value of each component; calculating various branch characteristic parameters; and acquiring meteorological data corresponding to the acquisition time period of each group of data, and qualitatively analyzing the air fine particle source according to the air pollution concentration, various branch characteristic parameters and the meteorological data. The method analyzes atmospheric particulate pollution based on fine particulate components, deeply analyzes full-component and quality reconstruction component characteristics, performs quantitative source analysis, synthesizes gas pollutant data to assist in judging component analysis results, analyzes the causes and sources of fine particulate pollution more comprehensively through multi-angle comprehensive analysis, and has more reasonable and accurate analysis results.
Description
Technical Field
The application relates to the technical field of air quality monitoring, in particular to a source analysis method of fine air particles, electronic equipment and a storage medium.
Background
In recent years, fine Particulate Matter (PM) in air2.5) Increasingly, the pollution problem is prominent, and the Particulate Matter (PM) is2.5) It refers to particles with aerodynamic equivalent diameter less than or equal to 2.5 microns in ambient air. It can be suspended in air for a long time, and the higher the content concentration in the air, the more serious the air pollution is. Investigating fine particulate matter composition and chemical changes for PM2.5The pollution problem is very important, but at present, it is for PM2.5The analysis of pollution is mostly only for a single or few angles (e.g. PM)2.5Concentration analysis, PM2.5Component concentration and proportion analysis, PM only by model2.5Source analysis), and the reliability of the result is poor due to the lack of multi-angle fusion analysis.
Aerosols are relatively stable systems of fine solid particles and liquid droplets suspended in the atmosphere, and the various particles dispersed therein are referred to as atmospheric particulates. Can be divided into total suspended particulate matter (particle size less than 100 μm, TSP for short) and inhalable particulate matter (particle size less than 10 μm, PM for short) according to particle size10) Fine particulate matter (particle size less than 2.5 μm, PM for short)2.5)3 kinds of the Chinese herbal medicines. In recent years, regional haze frequently occurs in China, air pollution is serious, particularly in Jingjin Ji and Fenwei plain areas, and PM in the environment is generated2.5The large increase in concentration is the main cause of haze formation. PM (particulate matter)2.5The chemical components of (A) are complex and mainly divided into a carbon-containing component, a water-soluble ion component and an inorganic element component 3. Wherein the carbon-containing component mainly comprises Organic Carbon (OC) and Elemental Carbon (EC), and the water-soluble ionic component mainly comprises Nitrate (NO)3 -) Sulfate (SO)4 2-) Ammonium salts of the compounds(NH4 +) Fluorine salt (F)-) Chlorine salt (Cl)-) Sodium, sodium (Na)+) Magnesium (Mg)2+) Potassium (K)+) Calcium (Ca)2+) And the inorganic element components mainly include aluminum (Al), silicon (Si), phosphorus (P), sulfur (S), scandium (Sc), titanium (Ti), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), cadmium (Cd), tin (Sn), barium (Ba), lead (Pb), and the like.
PM in ambient air2.5The components are various, the chemical reaction mechanism and the emission source are complex and changeable, the visibility can be reduced by atmospheric particles, the health of people and other organisms is influenced, and climate change and other environmental effects can be caused. The chemical composition is the key for determining various environmental effects of the atmospheric particulates, so the research on the chemical composition of the atmospheric particulates is particularly important.
And (3) establishing a corresponding observation and sampling scheme (online monitoring or offline sampling) based on the geographical position, climatic weather, socioeconomic development condition and pollutant emission level of a certain city. By observing PM2.5Concentration, PM2.5The pollution related data such as component concentration and the like are comprehensively analyzed, and PM is included2.5Pollution characteristics, PM2.5Full component characteristics and variation rules, PM2.5Reconstructed Components feature analysis, PM2.5Source analysis research, pollution process analysis and prevention and control suggestions.
Prior art solutions to PM2.5The analysis of pollution is mostly only for a single or few angles (e.g. PM)2.5Concentration analysis, PM2.5Component concentration and proportion analysis, PM only by model2.5Source analysis), and the reliability of the result is poor due to the lack of multi-angle fusion analysis.
Disclosure of Invention
The application aims to provide a source analysis method of fine air particles, electronic equipment and a storage medium. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to an aspect of an embodiment of the present application, there is provided a method for resolving a source of fine air particles, including:
grouping the collected air fine particle concentration data according to the data collection time period to obtain a plurality of groups of data;
calculating the average value of the air fine particle concentration data of each group of data to obtain the air pollution concentration corresponding to each group of data;
calculating the average component concentration value of each component in each group of data;
calculating various branch characteristic parameters according to the calculated component concentration average value of each component in each group of data;
and acquiring meteorological data corresponding to the data acquisition time period of each group of data, and qualitatively analyzing the air fine particulate matter source of each group of data according to the air pollution concentration of each group of data, the multiple branch characteristic parameters and the meteorological data to obtain a qualitative source analysis result of the air fine particulate matter.
Further, the plurality of branch characteristic parameters include: the ratio of organic carbon to elemental carbon, the sulfur oxidation rate, the nitrogen oxidation rate, the enrichment factor of the crustal source, and the concentration of the oxidant.
Further, the oxidant concentration is calculated by the formula Ox ═ C(O3)/M(O3)*22.4+C(NO2)/M(NO2)*22.4，
Wherein Ox represents the oxidant concentration; c represents the mass concentration of the gaseous pollutants; m represents the relative molecular mass of the gaseous contaminant; 22.4 is the gas molar volume under standard conditions.
Further, the enrichment factor of the crustal source is calculated by the formula
EF＝(Ci/Cn)Data of/(Ci/Cn)Background of the soil
Wherein, CiDenotes the mass concentration of the inorganic element, CnRepresents the concentration of a reference element; (C)i/Cn)Data ofRepresents the ratio of inorganic elements to reference elements in the data; (C)i/Cn)Background of the soilRepresenting the ratio of inorganic elements in the crust to the reference element.
Further, the sulfur oxidation rate is calculated by the formula SOR ═ SO4 2-]/([SO4 2-]+[SO2]) In which [ SO ] is4 2-]Represents SO4 2-Concentration of [ SO ]2]Represents SO2The concentration of (c).
Further, the formula for calculating the nitrogen oxidation rate is NOR ═ NO3 -]/([NO3 -]+[NO2]) In which [ NO ]3 -]Represents NO3 -Concentration of [ NO ]2]Represents NO2The concentration of (c).
Further, the plurality of branch characteristic parameters further include a concentration of secondary organic carbon; the concentration of the secondary organic carbon is calculated by the formula
[SOC]＝[OC]-[EC]×([OC]/[EC])min(ii) a Wherein OC represents organic carbon, EC represents elemental carbon, [ OC ]]Represents the organic carbon concentration, [ EC ]]Represents the carbon concentration of the element, ([ OC ]]/[EC])minRepresents [ OC ]]And [ EC ]]Minimum value of the ratio.
Further, the plurality of branch characteristic parameters further include a degree of anion-cation equilibrium, and a calculation formula of the degree of anion-cation equilibrium is L ═ CE ]/[ AE ], wherein,
CE represents a cation, AE represents an anion, [ CE ]]Represents the concentration of cation, [ AE ]]Represents the concentration of anions, [ Na ]+]Represents Na+Concentration of [ NH ], [ NH ]4 +]Represents NH4 +Concentration of [ K ]+]Represents K+Concentration of [ Mg ]2+]Represents Mg2+Concentration of [ Ca ]2+]Represents Ca2+Concentration of [ SO ]4 2-]Represents SO4 2-Concentration of [ NO ]3 -]Represents NO3 -Concentration of [ F ]-]Represents F-Concentration of [ Cl ]-]Represents Cl-The concentration of (c).
Further, the method further comprises:
and quantitatively analyzing the fine particulate matter source of each group of data according to the air pollution concentration of each group of data and the average value of the component concentration of each component to obtain a quantitative source analysis result of the air fine particulate matter.
Further, the quantitative source analysis is realized by adopting a positive matrix factorization model.
According to another aspect of the embodiments of the present application, there is provided an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for resolving the source of fine air particles.
According to another aspect of embodiments of the present application, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method for resolving a source of fine air particles as described above.
The technical scheme provided by one aspect of the embodiment of the application can have the following beneficial effects:
the source analysis method of the fine particulate matters in the air provided by the embodiment of the application is based on fine particulate matter component analysis atmospheric particulate matter pollution, deep analysis is carried out on the characteristics of full components and quality reconstruction components, source analysis is carried out, the analysis result of the components is judged in an auxiliary mode through comprehensive gaseous pollutant data, multi-angle comprehensive analysis is carried out, the cause and the source of the fine particulate matter pollution are analyzed more comprehensively, and the analysis result is more reasonable and accurate.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the application, or may be learned by the practice of the embodiments. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 illustrates a flow chart of a method for source resolution of fine air particulate matter according to one embodiment of the present application;
FIG. 2 shows a flow chart of a method for source resolution of fine air particulate matter according to another embodiment of the present application;
fig. 3 shows a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For PM2.5And analysis of the contamination characteristics of its components, mainly with PM2.5Pollution signature analysis, PM2.5Compositional analysis, PM2.5And the correlation analysis between the components of the composition. As shown in fig. 1, one embodiment of the present application provides a method for resolving a source of fine air particles, comprising the steps of:
and S10, grouping the collected air fine particle concentration data according to the data collection time period to obtain a plurality of groups of data.
The data collection time period may be a date, month, or year, etc. For example, the air fine particle concentration data collected at the same station may be grouped according to the collection date, and the data collected at the same day may be grouped into one group; or, grouping is performed according to collected months, data collected in the same month are divided into a group, and the like. For example, the air pollution data collected by the air pollution data collection site a within one year are grouped by collection date, and the data collected on the same day are grouped into one group and 365 groups in total. Assuming a total of 10 stations, the air fine particle concentration data collected at each station is grouped.
And S20, calculating the average value of the air fine particle concentration data of each group of data, and obtaining the average pollution concentration corresponding to each group of data as the air pollution concentration of the data acquisition time period.
For example, the daily average pollution concentration data of the same day may be obtained by averaging all data on the same day, or the monthly average pollution concentration data, the annual average pollution concentration data, or the like may be obtained. For example, if a total of 24 data are collected by the site a in a certain day, the average value of valid data of the 24 data is taken as the air pollution concentration of the day.
In some embodiments, analysis angles of time series variation, year/season/month/day variation and spatial feature variation (limited to multiple points), same-ratio/ring-ratio feature variation may be employed.
The time-series change can be, for example, a time-varying trend graph of the concentration of the fine particles in the period of national day festival 10.1-10.7, and the concentration fluctuation of the pollutants in the period, the time when the concentration peak or the pollution occurs, and the like.
The annual/seasonal/monthly/daily variation can be, for example, respectively counting the average value of the concentration of the fine particulate matters of a certain site/city in each year/season/month/day in a certain period of time, and performing comparative analysis; the daily change is to average the data of the same hour in a period of time, finally obtain 24 average data of one hour in 0-23 hours, make a time change trend graph, and determine the concentration change between days, such as to see whether there is a phenomenon of sudden concentration increase during the peak of the morning and evening trip.
The spatial characteristic change (limited to multiple points) can be, for example, the spatial change of fine particles in day 10 and 1 in Jingjin Ji area, and the Taihang mountain is found to be seriously polluted along the line and presumably possibly adversely affected by terrain diffusion.
The equivalence/ring ratio can be determined, for example, by averaging the concentration over time and performing an equivalence/ring ratio analysis.
And S30, calculating the average component concentration value of each component in each group of data.
The components included in each set of the air fine particulate matter concentration data include a carbon-containing component, a water-soluble ion component, an inorganic element component, and a reconstituted component, and the average value of the component concentration of each of these components is calculated.
And analyzing the change and the proportion of the concentration of each component of the fine particles.
And (3) dividing the monitored fine particulate component species into three major components, namely a carbon-containing component, a water-soluble ion component and an inorganic element component, according to different chemical characteristics.
Each class of components comprises several species, among which:
the carbon-containing component mainly comprises Organic Carbon (OC) and Elemental Carbon (EC);
the water soluble ionic component comprises mainly Nitrate (NO)3 -) Sulfate (SO)4 2-) Ammonium salt (NH)4 +) Fluorine salt (F)-) Chlorine salt (Cl)-) Sodium, sodium (Na)+) Magnesium (Mg)2+) Potassium (K)+) Calcium (Ca)2+)；
The inorganic element components mainly comprise aluminum (Al), silicon (Si), phosphorus (P), sulfur (S), scandium (Sc), titanium (Ti), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), cadmium (Cd), tin (Sn), barium (Ba) and lead (Pb).
The concentration of each species in each component is averaged to obtain the overall concentration of each species, and each component can be subjected to internal proportion calculation.
The reconstruction components of the fine particles comprise organic matters, nitrates, sulfates, ammonium salts, elemental carbon, chlorine salts, crustal substances and inorganic elements, and the pollution concentration of each reconstruction component is obtained.
Analysis angles of time series changes, daily changes and spatial feature changes (limited to multiple points), and component feature changes of different pollution levels can be adopted.
The reconstruction component is organic matter + nitrate + sulfate + ammonium salt + element carbon + chloride + crustacean substance + inorganic element;
organic OM ═ OC × 1.6; nitrate is NO3 -(ii) a Sulfate Salt (SO)4 2-(ii) a Ammonium salt ═ NH4 +(ii) a The element carbon is EC; chloride salt ═ Cl-(ii) a Crustacean material-Al × 2.2+ Si × 2.49+ Ca2+X 1.63+ Fe x 2.42+ Ti x 1.94; inorganic element K++Mg2++F-+Ba+Cd+Sn+V+Cr+Mn+Co+Ni+Cu+Zn+As+Se+Pb+Sc+P+Na+(ii) a Other fine particulate masses-OM-nitrate-sulfate-ammonium-elemental carbon-chloride-crustacean-inorganic elements.
The method for reconstructing the chemical composition of the fine particulate matter is to convert all main components of the fine particulate matter into mass concentration, then to sum up and to calculate the proportion of all the components. The reconstituted fraction comprises 8 water-soluble ionic components (including SO)4 2-、NO3 -、Cl-、NH4 +、K+、Ca2+、Na+、Mg2+) Organic OM (converted based on OC), EC, vanadium, manganese and ironGallium, arsenic, selenium, cadmium, platinum, gold, lead, copper, zinc, silver, bromine and other elements.
In some embodiments, analysis angles of time series variation, fractional feature variation, and spatial feature variation (limited to multiple points) may be employed.
And S40, calculating various branch characteristic parameters according to the calculated component concentration average value of each component in each group of data.
In certain embodiments, the plurality of branch characterization parameters include a ratio of organic carbon to elemental carbon, a sulfur oxidation rate, a nitrogen oxidation rate, a degree of cation-anion balance, a concentration factor ratio of crustal source, and an oxidant concentration.
In certain embodiments, the plurality of branch characterization parameters further includes a concentration of secondary organic carbon.
A carbon-containing component: the ratio of OC to EC can be used to determine the source of the fine particulates (e.g., OC/EC is between 1.0 and 4.2, indicating motor vehicle exhaust emissions); secondary Organic Carbon (SOC) can be used to determine secondary generation characteristics of fine particulate matter, and secondary generation of organic matter and its proportion relationship in OC can be preliminarily determined by calculating SOC.
[SOC]＝[OC]-[EC]×([OC]/[EC])min；
[OC]Represents the organic carbon concentration, [ EC ]]Represents the carbon concentration of the element, [ SOC ]]Represents the secondary organic carbon concentration, ([ OC ]]/[EC])minRepresents [ OC ]]And [ EC ]]Minimum value of the ratio.
Water-soluble ionic component: which contain species and ratios of individual species that are indicative of the source of the contamination (e.g., NO)3 -Mainly from motor vehicle exhaust emissions, NO3 -/SO4 2-A ratio of (d) is greater than 1, indicating that the mobile source contribution is large), the source of the fine particulate matter can be preliminarily determined;
sulfur Oxidation Rate (SOR) and Nitrogen Oxidation Rate (NOR) can be used to analyze the secondary conversion characteristics of the fine particulate matter, with higher values indicating greater secondary conversion. The Sulfur Oxidation Rate (SOR) and Nitrogen Oxidation Rate (NOR) are calculated as follows:
SOR＝[SO4 2-]/([SO4 2-]+[SO2])；
NOR＝[NO3 -]/([NO3 -]+[NO2]) (ii) a Wherein [ SO4 2-]Represents SO4 2-Concentration of [ SO ]2]Represents SO2Concentration of [ NO ]3 -]Represents NO3 -Concentration of [ NO ]2]Represents NO2The concentration of (c).
The quality control of the quality of the component data is realized by calculating the balance degree of the anions and the cations according to the ion concentration contained in the water-soluble ion component (for example, the higher the correlation between the anions and the cations, the closer the slope is to 1, the higher the ion data quality is).
CE represents a cation, AE represents an anion, [ CE ]]Represents the concentration of cation, [ AE ]]Represents the concentration of anions, [ Na ]+]Represents Na+Concentration of [ NH ], [ NH ]4 +]Represents NH4 +Concentration of [ K ]+]Represents K+Concentration of [ Mg ]2+]Represents Mg2+Concentration of [ Ca ]2+]Represents Ca2+Concentration of [ SO ]4 2-]Represents SO4 2-Concentration of [ NO ]3 -]Represents NO3 -Concentration of [ F ]-]Represents F-Concentration of [ Cl ]-]Represents Cl-The concentration of (c).
The calculation formula of the anion-cation balance degree is L ═ CE ]/[ AE ], and L represents the anion-cation balance degree. The closer the value of L is to 1, the higher the correlation between cations and anions.
Inorganic element components: the species and ratios of individual species are indicative of the source of the contamination (e.g., Al, Si, Ca, Fe, Mg are indicative of dust sources, Cu/Zn is indicative of mobile sources), and the source of the fine particles can be determined preliminarily; the source characteristics of each element can also be identified by calculating the Enrichment Factor (EF) of the crustal source in the inorganic element components, and if the EF value is more than 10, the element is considered to be enriched, which indicates that the element not only originates from the contribution of crustal substances, but also has large artificial contribution.
Because the same source element can maintain better chemical quantitative relation when being transmitted through the atmosphere, the source can be identified by the concentration ratio of the element in the particulate matter to the reference element.
And identifying the source characteristics of each inorganic element by calculating the enrichment factor of the crustal source in the inorganic element components. EF is calculated as follows:
EF＝(Ci/Cn)data of/(Ci/Cn)Background of the soil
In the formula, CiDenotes the mass concentration of the inorganic element, CnRepresents the concentration of a reference element; (C)i/Cn)Data ofRepresents the ratio of inorganic elements to reference elements in the data; (C)i/Cn)Background of the soilRepresenting the ratio of inorganic elements in the crust to the reference element.
Detecting the concentration of the nitrogen oxide and the ozone, and calculating the concentration of the oxidant according to the concentration of the nitrogen oxide and the ozone, wherein the calculation formula is
Ox＝C(O3)/M(O3)*22.4+C(NO2)/M(NO2)*22.4
Wherein Ox represents the oxidant concentration (in ppb); c is the mass concentration of gaseous pollutants (in μ g/m)3) (ii) a M is the relative molecular mass of the gaseous contaminant; 22.4 is the gas molar volume (in L/mol) in the standard conditions. The oxidation (Ox) value can be used as an index for evaluating the air oxidation ability.
The oxidant concentration (Ox) value is used to determine the extent to which the fine particulate matter is affected by oxidation during secondary conversion.
In certain embodiments, the correlation between the gaseous contaminant and the secondary component can also be studied (e.g., a higher correlation between the gaseous contaminant and the secondary component indicates the secondary component and the secondary componentThe more pronounced the conversion process is when the primary precursor is present, e.g. Sulfate (SO)4 2-) Control of the SO precursor should also be considered at high concentrations2Discharging of (3); if a humidity range is highly correlated with a component therein, it indicates that the production of the component is more affected by the humidity range; if the correlation between the two species is extremely high, the two species are likely to point to the same pollution source), assisting in analyzing the source of the fine particles; according to the inventory data, the emission positions of local industrial enterprises and other pollution sources and the like can be known, and the results can be compared and analyzed with the component concentration data.
PM2.5Middle Nitrate (NO)3-, Sulfate (SO)4 2-) Ammonium salt (NH)4 +) With gaseous SO2、NO2、NOxAnd NH3And the primary precursors have complex conversion relationship. NH can be known from the correlation numbers (table) of the particulate matter, the gaseous pollutant and the secondary component4 +、SO4 2-、NO3 -And PM2.5There is a higher correlation of concentration.
S50, acquiring meteorological data corresponding to the data acquisition time period of each group of data, and qualitatively analyzing the air fine particulate matter source of each group of data according to the air pollution concentration of each group of data, the multiple branch characteristic parameters and the meteorological data to obtain a qualitative source analysis result of the air fine particulate matter.
Qualitative source analysis generally adopts a characteristic species and species ratio method, namely components of fine particulate matters emitted by different pollution sources are different in composition, and source characteristics can be judged through specific tracer species or specific ratio differences of the species in the components.
In some embodiments, qualitative source analysis may also enable source-tracking analysis of the contaminated air mass by backward trajectory and trajectory clustering methods, estimating the most likely transmission path from the source region to the recipient region at a particular time. The tool adopts TrajStat software, and the data source adopts the global assimilation system data, fine particle concentration data or component concentration data provided by the NCEP.
The potential source area of the pollutant can be analyzed and the pollutant and the source can be determined by a track gas mass statistical analysis method (PSCF, CWT) (tool: TrajStat software, data source: global assimilation system data, fine particle concentration data or component concentration data provided by the NCEP).
And according to the obtained source analysis result, obtaining the pollution characteristics of the fine particulate matters and the components thereof, determining a pollution source according to the local social and economic development condition, the industrial structure, the source analysis result and the like, and taking the pollution source as a reference to provide a corresponding control suggestion to control and prevent the fine particulate matter pollution.
And S60, quantitatively analyzing the fine particle sources of each group of data according to the air pollution concentration of each group of data and the average value of the component concentration of each component to obtain a quantitative source analysis result of the fine particle sources of the air.
Quantitative source analysis generally uses a receptor source analysis model PMF (Positive Matrix Factorization) method. The receptor model PMF is to combine the identification components of the respective emission sources and the calculation results to infer the emission source type and its contribution to the receptor when the receptor composition spectrum is known and the source spectrum is unknown.
The PMF model is used for deducing the type of the emission source and the contribution of the emission source to the receptor by combining the identification component and the operation result of each emission source under the condition that the receptor composition spectrum is known and the source spectrum is unknown. The PMF model can be used for analyzing to obtain a factor spectrum and a non-negative source contribution rate, and the analysis result is more consistent with the actual situation. Like other factor analysis methods, the PMF model cannot directly determine the number of factors, needs to consider the actual situation of the study area, and tries to run software many times to determine reasonable number of factors according to the analysis result, error, Q value, relative change of Q value when changing the number of factors, and the like. The PMF model requires: the selection of input chemical components should contain typical markers, and the importance of the markers is judged according to the data proportion, the signal-to-noise ratio and the like of the concentration lower than the detection limit; uncertainty in receptor data; the amount of data; factor identification can be determined based on the identity of the marker component and the time distribution characteristics (seasonal distribution). The contents of the PMF model and Q value can be found in "atmospheric particulate source analysis: principles, techniques and applications are described in Juntan, Von Yin Mill; scientific publishers, 2012.
As shown in fig. 3, another embodiment of the present application further provides an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the source resolution method for fine air particles according to any of the above embodiments. For example, the electronic device 20 in an embodiment may include: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the computer program to perform the method for resolving the source of fine air particles provided in any of the foregoing embodiments of the present application.
The Memory 201 may include a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and may include a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201, and completes the steps of the method for analyzing the source of the fine air particles in combination with the hardware thereof.
Another embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, where the program is executed by a processor to implement the method for resolving a source of fine air particles as described in any of the above embodiments.
The source analysis method of the fine air particulate matters comprises the steps of analyzing atmospheric particulate matters based on fine particulate matters, deeply analyzing full-component and quality-reconstructed component characteristics, carrying out quantitative source analysis or qualitative source analysis, comprehensively analyzing causes and sources of the fine particulate matters through comprehensive gaseous pollutant data to judge component analysis results in an auxiliary mode, comprehensively analyzing the causes and the sources of the fine particulate matters through multiple methods and cross-verifying results, and enabling the results to be more reasonable and accurate.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The above-mentioned embodiments only express the embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (12)
1. A method for resolving the source of fine air particles, comprising:
grouping the collected air fine particle concentration data according to the data collection time period to obtain a plurality of groups of data;
calculating the average value of the air fine particle concentration data of each group of data to obtain the air pollution concentration corresponding to each group of data;
calculating the average component concentration value of each component in each group of data;
calculating various branch characteristic parameters according to the calculated component concentration average value of each component in each group of data;
and acquiring meteorological data corresponding to the data acquisition time period of each group of data, and qualitatively analyzing the air fine particulate matter source of each group of data according to the air pollution concentration of each group of data, the multiple branch characteristic parameters and the meteorological data to obtain a qualitative source analysis result of the air fine particulate matter.
2. The method of claim 1, wherein the plurality of branch characterization parameters comprises: the ratio of organic carbon to elemental carbon, the sulfur oxidation rate, the nitrogen oxidation rate, the enrichment factor of the crustal source, and the concentration of the oxidant.
3. The method of claim 2, wherein the oxidant concentration is calculated by the formula Ox ═ C(O3)/M(O3)*22.4+C(NO2)/M(NO2)*22.4，
Wherein Ox represents the oxidant concentration; c represents the mass concentration of the gaseous pollutants; m represents the relative molecular mass of the gaseous contaminant; 22.4 is the gas molar volume under standard conditions.
4. The method of claim 2, wherein the enrichment factor of the crustal source is calculated by the formula
EF＝(Ci/Cn)Data of/(Ci/Cn)Background of the soil
Wherein, CiDenotes the mass concentration of the inorganic element, CnRepresents the concentration of a reference element; (C)i/Cn)Data ofRepresents the ratio of inorganic elements to reference elements in the data; (C)i/Cn)Background of the soilRepresenting the ratio of inorganic elements in the crust to the reference element.
5. The method according to claim 2, wherein the sulfur oxidation rate is calculated by the formula SOR ═ SO4 2-]/([SO4 2-]+[SO2]) In which [ SO ] is4 2-]Represents SO4 2-Concentration of [ SO ]2]Represents SO2The concentration of (c).
6. The method of claim 2, wherein the formula for calculating the oxynitride ratio is NOR ═ NO3 -]/([NO3 -]+[NO2]) In which [ NO ]3 -]Represents NO3 -Concentration of [ NO ]2]Represents NO2The concentration of (c).
7. The method of claim 2, wherein the plurality of branch characterization parameters further comprises a concentration of secondary organic carbon; the concentration of the secondary organic carbon is calculated by the formula
[SOC]＝[OC]-[EC]×([OC]/[EC])min(ii) a Wherein OC represents organic carbon, EC represents elemental carbon, [ OC ]]Represents the organic carbon concentration, [ EC ]]Represents the carbon concentration of the element, ([ OC ]]/[EC])minRepresents [ OC ]]And [ EC ]]Minimum value of the ratio.
8. The method according to claim 2, wherein the plurality of branch characteristic parameters further includes a degree of anion-cation equilibrium, the degree of anion-cation equilibrium being calculated by the formula L ═ CE/[ AE ], wherein,
CE represents a cation, AE represents an anion, [ CE ]]Represents the concentration of cation, [ AE ]]Represents the concentration of anions, [ Na ]+]Represents Na+Concentration of [ NH ], [ NH ]4 +]Represents NH4 +Concentration of [ K ]+]Represents K+Concentration of [ Mg ]2+]Represents Mg2+Concentration of [ Ca ]2+]Represents Ca2+Concentration of [ SO ]4 2-]Represents SO4 2-Concentration of [ NO ]3 -]Represents NO3 -Concentration of [ F ]-]Represents F-Concentration of [ Cl ]-]Represents Cl-The concentration of (c).
9. The method of claim 1, further comprising:
and quantitatively analyzing the fine particulate matter source of each group of data according to the air pollution concentration of each group of data and the average value of the component concentration of each component to obtain a quantitative source analysis result of the air fine particulate matter.
10. The method of claim 9, wherein the quantitative source resolution is implemented using a positive matrix factorization model.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method for source resolution of fine air particles according to any one of claims 1 to 10.
12. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor to implement the method for source resolution of fine air particles according to any one of claims 1 to 10.
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