Evaluation method for tracing main emission source of atmospheric particulates and effectiveness of intervention measures

Abstract

The invention discloses an evaluation method for tracing main emission sources of atmospheric particulates and effectiveness of intervention measures. The evaluation method comprises the following steps: 1, constructing isotope ratio big data of different components or elements of the atmospheric particulates and the emission sources of the different components or elements; 2, analyzing isotope ratio characteristics of different emission sources of target components or elements in the atmospheric particulates; 3, performing time sequence trend analysis on the target component or element isotope ratio of the atmospheric particulates; and step 4, analyzing the change trend of the isotope ratio of the target component or element of the atmospheric particulates according to the isotope ratio fingerprint characteristics of the emission source, identifying the main emission source of a certain component or element in the atmospheric particulates in a specific time period, and evaluating the effectiveness of the intervention measure according to the continuous change rate of the isotope ratio characteristics. According to the invention, the analysis of a main emission source of a certain component or element in the atmospheric particulates is realized by carrying out continuous time sequence analysis on the isotope ratio characteristics of the atmospheric particulates.

Description

Technical Field

[0001] The present invention relates to the technical field of air pollution source tracing and intervention measure design, and specifically to a method for tracing the main emission sources of atmospheric particulate matter and evaluating the effectiveness of intervention measures, especially a method for tracing the changes in the main emission sources of atmospheric particulate matter and the effectiveness of policy intervention measures by using isotope ratios of atmospheric particulate matter. Background Art

[0002] Atmospheric particulate matter (PM) pollution is a type of environmental pollutant that is ubiquitous and widely concerned, and is also the most important environmental factor that endangers human health. In order to deal with atmospheric particulate matter pollution, my country has implemented a targeted and well-organized clean air action since 2013, and has implemented a series of intervention and control measures for atmospheric particulate matter pollution. At present, the methods for evaluating the effectiveness of these policies mainly rely on the analysis of changes in the concentration of particulate matter or its specific components. However, this method can only roughly judge the control effect on a certain type of component, and it is difficult to accurately evaluate the effectiveness of policy intervention. In addition, although numerical simulation methods based on PM concentration (such as WRF-chem) combined with the calculation of emission inventories can predict PM pollution conditions under different policy scenarios and evaluate the effectiveness of intervention measures, this method often ignores the nonlinear process of atmospheric particulate matter components from emission sources to the formation of atmospheric particulate matter during the simulation process, and often targets PM as a whole and cannot analyze specific components, resulting in greater uncertainty in the evaluation results. Therefore, this research field urgently needs a more accurate tracing technology that can not only effectively distinguish the control policies for different emission sources, but also overcome the nonlinear problems in the formation process of atmospheric particulate matter, so as to improve the accuracy and reliability of the effectiveness evaluation of pollution intervention measures. Summary of the invention

[0003] The present invention provides a method for tracing the main emission sources of atmospheric particulate matter and evaluating the effectiveness of intervention measures. It can make full use of a wider range of time scales and more types of isotope ratio data, judge the changes in the main pollution sources of atmospheric particulate matter in different periods through continuous analysis, and realize the tracing evaluation of the effectiveness of pollution intervention measures, and explore the possibility of isotope ratio data as an indicator of pollution policy.

[0004] In order to solve the above technical problems, the technical solution proposed by the present invention comprises the following steps:

[0005] A method for tracing the main emission sources of atmospheric particulate matter and evaluating the effectiveness of intervention measures, characterized by comprising the following steps:

[0006] The first step is to build big data on isotope ratios of different components or elements of atmospheric particulate matter and their emission sources;

[0007] In the second step, analyze the isotope ratio characteristics of the target components or elements in atmospheric particulate matter from different emission sources;

[0008] In the third step, conduct a temporal trend analysis on the isotope ratios of the target components or elements in atmospheric particulate matter;

[0009] In the fourth step, by comparing with the isotope ratio fingerprint characteristics of the emission sources, analyze the changing trends of the isotope ratios of the target components or elements in atmospheric particulate matter, identify the main emission sources of a certain component or element in atmospheric particulate matter within a specific time period, and evaluate the effectiveness of intervention measures based on the rate of change of the continuous variation of the isotope ratio characteristics.

[0010] Furthermore, in step 1, the components or elements of atmospheric particulate matter include: organic carbon (OC), inorganic carbon (EC), nitrate (NO3 - ), ammonium (NH4 + ), sulfate (SO4 2- ), silicon (Si), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), strontium (Sr), neodymium (Nd), hafnium (Hf), lead (Pb), and mercury (Hg).

[0011] Furthermore, in step 1, the isotope types corresponding to different components or elements include: δ 13 C and f M -14C (for organic carbon and inorganic carbon), δ 15 N (for nitrate and ammonium), δ 34 S (for sulfate), δ 30 Si (for silicon), δ 56 Fe (for iron), δ 60 Ni (for nickel), δ 65 Cu (for copper), δ 66 Zn (for zinc), δ 87 Sr (for strontium), δ 144 Nd (for neodymium), δ 177 Hf (for hafnium), 207 Pb / 206 Pb (for lead) and δ 202 Hg (for mercury).

[0012] Furthermore, in step 1, the emission sources of atmospheric particulate matter include: coal combustion, biomass burning, vehicle exhaust, industrial fuel oil, municipal solid waste, livestock farming, chemical fertilizers, microbial activities, ore smelting, soil dust, non-exhaust traffic emissions, and waste incineration.

[0013] Furthermore, in step 2, the different time scales include long-term time and consecutive short-term times within the long-term time.

[0014] Further, in Step 2, for consecutive short-term times, the moving window method is used to determine the time range, with a window length of 5 years and a moving step of 1 year.

[0015] In Step 2, the method of trend analysis is the least squares method, and the positive or negative of the linear fitting slope is used to represent the increasing or decreasing trend of the isotope ratio value.

[0016] Further, in Step 2, for the isotope ratio of long-term time, the exhaustive method is used to find the trend turning points of the isotope ratio data and segment them, and the least squares method is used to fit each segment and obtain the change trend.

[0017] Further, in Step 2, for the isotope ratio of short-term time, the least squares method is used to fit all the isotopes within the time, and the change trend is represented by the slope of the linear fit.

[0018] Further, in Step 4, the main basis for determining the main emission sources is the long-term change trend of the atmospheric particulate matter isotope ratio and the isotope values of the emission sources. If the slope of the fitting line is positive / negative within a certain long-term time, then the emission source with a larger / smaller isotope value of a certain type of isotope may be the main emission source during this time period.

[0019] Further, in Step 4, the basis for judging the effectiveness of the intervention measures is the long-term change trend of the atmospheric particulate matter isotope ratio, the continuous short-term change trend, and the isotope values of the emission sources. If the slope of the fitting line in a certain short-term time has the same positive or negative as the slope of the fitting line within the long-term time where it is located, and the absolute value of the slope of the fitting line of adjacent short-term times gradually decreases, it indicates that effective intervention measures have been used for the main emission source during this time period.

[0020] On the other hand, the present application also claims protection for an electronic device,

[0021] The electronic device includes: one or more processors;

[0022] a memory; and

[0023] one or more applications, where the one or more applications are stored in the memory and configured to be executed by the processor to implement the method described in any one of the foregoing items.

[0024] On yet another aspect, the present application also claims protection for a computer-readable storage medium,

[0025] on which a computer program is stored, and the computer program is loaded by a processor to execute the steps in the method described in any one of the foregoing items.

[0026] The beneficial technical effects achieved by the present application are:

[0027] 1. Make full use of the stable and accurate characteristics of isotope ratios to qualitatively identify the main emission sources of atmospheric particulate matter in different time periods.

[0028] 2. Make full use of the continuity analysis of the isotope ratio datasets of atmospheric particulate matter on different time scales to judge and track the effectiveness of atmospheric particulate matter pollution intervention measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0030] Figure 1 It is the changing trend of f M -EC for the long-term period from 2001 to 2021 in the embodiments of the present application;

[0031] Figure 2 It is the changing trend of f M -EC for all short-term periods from 2001 to 2021 in the embodiments of the present application;

[0032] Figure 3 It is the changing trend of δ 13 C-EC for the long-term period from 2001 to 2021 in the embodiments of the present application;

[0033] Figure 4 It is the changing trend of δ 13 C-EC for all short-term periods from 2001 to 2021 in the embodiments of the present application;

[0034] Figure 5 It is the summary of f M -EC values of different types of emission sources in the embodiments of the present application;

[0035] Figure 6 It is the summary of δ 13 C-EC values of different types of emission sources in the embodiments of the present application;

[0036] Figure 7 It is a schematic structural diagram of an electronic device corresponding to an evaluation method for tracing the main emission sources of atmospheric particulate matter and the effectiveness of intervention measures in the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0038] An evaluation method for tracing the main emission sources of atmospheric particulate matter and the effectiveness of intervention measures provided by an embodiment of the present application is a method based on the isotope ratio data of a certain component or element in atmospheric particulate matter and its emission sources. It analyzes the main emission sources of the corresponding component or element in atmospheric particulate matter over different time periods, and is a method for tracking the effectiveness of pollution intervention measures through the temporal variation trend of the isotope ratio of the corresponding component or element. The following is a detailed description.

[0039] The evaluation method for tracing the main emission sources of atmospheric particulate matter and the effectiveness of intervention measures of the present invention consists of four parts: collecting and integrating the isotope ratio data of a certain component or element in atmospheric particulate matter and its emission sources; conducting trend analysis on the isotope ratios of atmospheric particulate matter at different time scales; differentiating the isotope ratios of different emission sources of this component or element and comparing them with the variation trend of the isotope ratio of atmospheric particulate matter; identifying the main emission sources of a certain component or element in atmospheric particulate matter during a specific time period and tracking the effectiveness of intervention measures.

[0040] Among them, δ x E refers to the isotope ratio of the corresponding component or element of atmospheric particulate matter or its emission source, which is usually expressed as the permil deviation (‰) of the isotope ratio of the sample relative to the reference material, as shown in Equation (1).

[0041]

[0042] Among them, E represents the selected element, and x and y represent the mass numbers of the isotopes of element E.

[0043] In particular, the isotope ratio data of Pb is usually not expressed as the permil deviation of the isotope ratio of the sample relative to the reference material, but directly uses the ratio of different types of isotopes, such as 207 Pb / 206 Pb, 208 Pb / 207 Pb.

[0044] For 14 C, this type of special radioactive isotope, the contemporary carbon fraction (f M ) is used to represent it, as shown in Equation (2).

[0045]

[0046] Among them, 14 C / 12 C) sample and 14 C / 12 C) standard respectively represent the isotope ratios of the sample and the reference material (NIST oxalic acid II 4990C).

[0047] In the first step, construct a big data (data platform) of isotope ratios of different components or elements of atmospheric particulate matter and their emission sources.

[0048] The method for obtaining the isotope fingerprint data of atmospheric particulate matter is as follows: Retrieve the published relevant research articles through the database, and extract the isotope fingerprint data and other relevant information (such as particulate matter type, sampling time, sampling location, etc.) from them.

[0049] The method for obtaining the types of emission sources and the corresponding isotope fingerprint data is as follows: First, investigate literature materials, etc., to investigate the main emission source categories of components or elements in atmospheric particulate matter, and collect and collate the isotope ratio data corresponding to each type of emission source. Taking EC as an example, it can generally be divided into three categories: biomass burning (BB), coal combustion (CC), and vehicle exhaust (VE). Among them, biomass burning can be further divided into C3 plant burning and C4 plant burning. Subsequently, according to the classification, collate and summarize as much as possible the isotope ratio data corresponding to each type of emission source, and calculate the average value of the isotope ratios of each type of emission source.

[0050] In the second step, conduct a trend analysis of the isotope ratios of atmospheric particulate matter on different time scales.

[0051] First, convert the sampling date of atmospheric particulate matter into a decimal form, as shown in Equation (3):

[0052]

[0053] Among them, Y represents the sampling year, DoY represents the day of the year when the sampling date is located, and NoY represents the total number of days in the sampling year.

[0054] For the long-term isotope ratio data of atmospheric particulate matter, the "segmented" package in R is used to perform piecewise linear regression on all the data. To ensure the reliability of the regression, the P-values of each piecewise linear regression result are calculated simultaneously, and as many P < 0.05 as possible are guaranteed. Taking the f M -EC and δ 13 C-EC from 2001 to 2021 as an example: The isotope ratio of f M -EC is divided into two segments, the break point coordinate is 2013.882, and the slopes of the two-piece linear fits are 0.007 and -0.008 respectively. δ 13The isotope ratios of C-EC are divided into four segments, and the break point coordinates are 2008.530, 2011.466, and 2016.949. The slopes of the four linear fittings are 0.225, -0.808, 0.243, and -0.717 respectively. At the same time, for consecutive short-term periods in the long term, with a five-year time span and a one-year moving step, the f of atmospheric particulate matter from 2001 to 2021 was analyzed. M -EC and δ 13 The changing trends of the C-EC isotope ratio.

[0055] In the third step, the isotope ratios of different emission sources of specific components are distinguished. By querying and retrieving published papers, the isotope ratio data of multiple types of emission sources are sorted out. For f M -EC, the isotope ratios of different emission sources from small to large are coal combustion emissions (0.03±0.04), vehicle exhaust (0.24±0.27), and biomass combustion (1.13±0.09). For δ 13 C-EC, the isotope ratios of different emission sources from small to large are C3 biomass combustion (-27.47±2.69), vehicle exhaust (-25.26±1.29), coal combustion emissions (-24.28±1.06), and C4 biomass combustion (15.38±2.74).

[0056] In the fourth step, the isotope ratios of different emission sources are compared and analyzed with the changing trends of the atmospheric particulate matter isotope ratios. Identify the main emission sources of a certain component or element in atmospheric particulate matter during a specific time period, and track the effectiveness of intervention measures.

[0057] The f of EC in global PM M -EC increased significantly before 2014 and then decreased, indicating that the main emission source from 2001 to 2013 was biomass combustion, while after 2014, the main emission source became fossil fuel combustion (coal combustion emissions or vehicle exhaust). Subsequently, by combining the analysis of δ 13 C-EC, further determine whether the biomass combustion before 2014 was mainly C4 plant biomass combustion or C3 plant biomass combustion. Among them, the increase in δ 13 C from 2001 to 2008 and from 2012 to 2014 indicates that C4 plant biomass combustion was the main source, while the decrease from 2009 to 2011 points to C3 plant biomass combustion.

[0058] Subsequently, by combining the short-term time change trends within the long time scale, during the periods from 2005 to 2007 and from 2012 to 2014, the growth rate of f M -EC continued to slow down, indicating effective intervention measures against C4 plant emissions during these periods.

[0059] In another embodiment of the present application, refer to Figure 7 , the present application further provides an electronic device, specifically:

[0060] The electronic device may include components such as a processor with one or more processing cores, a memory with one or more computer-readable storage media, a power supply, and an input unit. Among them:

[0061] The processor is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory, and by calling data stored in the memory, it executes various functions of the electronic device and processes data, thereby monitoring the entire electronic device. Optionally, the processor may include one or more processing cores; the processor may be a central processing unit (CPU), or it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. Preferably, the processor may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor.

[0062] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store the operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the electronic device. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0063] The electronic device also includes a power supply for supplying power to each component. Preferably, the power supply can be logically connected to the processor through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. The power supply may also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc.

[0064] The electronic device may further include an input unit, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0065] Although not shown, the electronic device may further include a display unit and the like, which will not be elaborated here. Specifically, in this embodiment, the processor in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory according to the following instructions, and the processor will run the application programs stored in the memory to implement various functions.

[0066] Those of ordinary skill in the art can understand that all or part of the steps in the above-mentioned various methods can be completed by instructions, or by controlling relevant hardware through instructions. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0067] In some embodiments of the present application, the present application further provides a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disc, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in the order processing method provided by the embodiments of the present application.

[0068] The above has introduced the embodiments of the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. An evaluation method for tracing the main emission sources of atmospheric particulate matter and the effectiveness of intervention measures, characterized in that, It includes the following steps: In the first step, construct big data of isotope ratios of different components or elements of atmospheric particulate matter and their emission sources; In the second step, analyze the isotope ratio characteristics of different emission sources of the target component or element in atmospheric particulate matter; In the third step, conduct a time-series trend analysis on the isotope ratios of the target component or element of atmospheric particulate matter; In the fourth step, by comparing with the isotope ratio fingerprint characteristics of the emission sources, analyze the changing trend of the isotope ratios of the target component or element of atmospheric particulate matter, identify the main emission sources of a certain component or element in atmospheric particulate matter within a specific time period, and evaluate the effectiveness of intervention measures based on the rate of change of the continuous change of the isotope ratio characteristics.

2. The method according to claim 1, wherein: In Step 1, the components or elements of atmospheric particulate matter include: organic carbon (OC), elemental carbon (EC), nitrate (NO3 - ), ammonium (NH4 + ), sulfate (SO4 2- ), silicon (Si), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), strontium (Sr), neodymium (Nd), hafnium (Hf), lead (Pb), and mercury (Hg).

3. The method according to claim 1, characterized in that: In step 1, the isotope types corresponding to different components or elements include: δ 13 C and f M -14C (for organic and inorganic carbon), δ 15 N (for nitrate and ammonium), δ 34 S (for sulfate), δ 30 Si (for silicon), δ 56 Fe (for iron), δ 60 Ni (for nickel), δ 65 Cu (for copper), δ 66 Zn (for zinc), δ 87 Sr (for strontium), δ 144 Nd (for neodymium), δ 177 Hf (for hafnium), 207 Pb / 206 Pb (for lead) and δ 202 Hg (for mercury).

4. The method according to claim 1, wherein: In step 1, the emission sources of atmospheric particulate matter include: coal combustion, biomass combustion, vehicle exhaust, industrial fuel oil, urban waste, livestock farming, chemical fertilizer, microbial activity, ore smelting, soil dust, non-exhaust traffic emissions, and waste incineration.

5. The method according to claim 1, wherein: In step 2, the different time scales include long-term time and continuous short-term time within the long-term time.

6. The method according to claim 1, wherein: In step 2, for the continuous short-term time, use the moving window method to determine the time range, with the window length of 5 years and the moving step of 1 year.

7. The method according to claim 1, characterized in that: In step 2, the method of trend analysis is the least squares method, and the positive or negative of the linear fitting slope is used to represent the increasing or decreasing trend of the isotope ratio value.

8. The method according to claim 1, characterized in that: In step 2, for the isotope ratios of the long-term time, use the exhaustive method to find the trend turning points of the isotope ratio data and segment them, and use the least squares method to fit each segment and obtain the changing trend.

9. The method according to claim 1, wherein: In step 2, for the isotope ratios of the short-term time, conduct the least squares fitting for all the isotopes within the time, and use the slope of the linear fitting to represent the changing trend.

10. The method according to claim 1, wherein: In step 4, the main basis for determining the main emission source is the long-term changing trend of the isotope ratios of atmospheric particulate matter and the isotope values of the emission sources. If the slope of the fitting line is positive / negative within a certain long-term time, then the emission source with a larger / smaller isotope value of a certain type of isotope may be the main emission source during this time period.

11. The method according to claim 1, characterized in that: In step 4, the basis for judging the effectiveness of the intervention measure is the long-term changing trend, continuous short-term changing trend of the isotope ratios of atmospheric particulate matter and the isotope values of the emission sources. If the slope of the fitting line in a certain short-term time has the same positive or negative as the slope of the fitting line within the long-term time where it is located, and the absolute value of the slope of the fitting line of adjacent short-term times gradually decreases, it indicates that an effective intervention measure has been used for the main emission source during this time period.

12. An electronic device, characterized in that, The electronic device includes: one or more processors; a memory; and one or more applications, where the one or more applications are stored in the memory and configured to be executed by the processor to implement the method according to any one of claims 1 to 11.

13. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and the computer program is loaded by the processor to execute the steps in the method according to any one of claims 1 to 11.

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