Atmospheric particulate pollution rapid tracing method based on actual measurement

Abstract

The invention discloses an actual measurement-based rapid source tracing method for atmospheric particulate pollution, and belongs to the technical field of rapid source tracing methods for atmospheric particulate pollution. A regional particulate emission information base is established, and the information base comprises particulate emission amounts and spatial distribution of industrial, traffic, construction site dust raising and non-road mobile machinery departments; the method comprises the following steps: determining a particulate matter pollution period according to monitoring data or a forecast result; simulating regional meteorological characteristics of the pollution period by using a meteorological mode; acquiring meteorological data; determining a receptor position; and determining a pollution emission source area according to the pollution source identification parameters, and screening targeted prevention and treatment objects of particulate matter pollution.

Description

Technical Field

[0001] The present invention relates to a method for rapid source tracing of atmospheric particulate pollution, in particular to a method for rapid source tracing of atmospheric particulate pollution based on actual measurement, and belongs to the technical field of methods for rapid source tracing of atmospheric particulate pollution. Background Art

[0002] In recent years, with the rapid development of social economy and the accelerated advancement of urbanization, the problem of atmospheric particulate matter pollution has become increasingly serious. Many regions have frequently suffered from large-scale severe particulate matter pollution incidents, posing a serious threat to people's health and arousing widespread concern from all walks of life.

[0003] In the field of atmospheric particulate matter pollution source analysis, existing technologies mainly include inventory methods, source model methods, receptor model methods, and monitoring methods. However, these traditional methods each have obvious limitations:

[0004] Monitoring method: This method is highly dependent on professional monitoring equipment, which has high purchase costs and requires a large amount of manpower and material resources to accumulate basic data. In addition, its results are poorly targeted and lack the ability to predict future pollution conditions, which greatly limits the widespread application and effectiveness of this method in actual business.

[0005] Inventory method and receptor model method: Although these two methods are relatively simple to operate, they have the problem of poor timeliness. The analysis results are not very targeted and cannot predict future pollution conditions. It is difficult to effectively improve the accuracy of pollution prevention and control. Their application in actual governance and prediction work is severely restricted.

[0006] Source model method: Although it has predictive capabilities, it is difficult to operate and requires a huge amount of calculations. It also has extremely high requirements for software and hardware configuration. In order to ensure the timeliness of the analysis results, high-performance computing resources are required, which greatly limits the scope of application of this method.

[0007] In view of the many defects of the above-mentioned prior art, the present invention aims to provide an innovative method for rapid tracing of atmospheric particulate pollution to overcome these problems. Summary of the Invention

[0008] The main purpose of the present invention is to provide a method for rapid tracing of atmospheric particulate pollution based on actual measurements.

[0009] The purpose of the present invention can be achieved by adopting the following technical solutions:

[0010] A method for rapid source tracing of atmospheric particulate matter pollution based on field measurements includes the following steps:

[0011] Step S1: Establishing a regional particulate matter emission information database, which contains particulate matter emissions and their spatial distribution from industry, transportation, construction site dust, and non-road mobile machinery sectors;

[0012] Step S2: Determine the particulate matter pollution period based on monitoring data or forecast results;

[0013] Step S3: using a meteorological model to simulate regional meteorological characteristics during the pollution period and obtain meteorological data;

[0014] Step S4: determining the receptor location;

[0015] Step S5: Using a particle diffusion model, calculate the air mass residence time and pollution source identification parameters of different grids in the area;

[0016] Step S6: determining the pollution emission source area based on the pollution source identification parameters;

[0017] Step S7: Screening the targeted prevention and control objects of particulate matter pollution.

[0018] Preferably, the specific process of establishing the regional particulate matter emission information database in step S1 includes collecting activity level data of each emission department, calculating the particulate matter emissions using the material balance method or the emission factor method, and allocating the emissions to a 1km×1km grid space through a geographic information system, and constructing a corresponding relationship between the emission information and the grid.

[0019] Preferably, the emissions from the industrial sector are calculated using the material balance method, using the formula:

[0020] E 化石 =A×A ar ×(1-r a )×f PM ×(1-η);

[0021] E 其他 =A×F E ×(1-η);

[0022] Where, E is the emission;

[0023] A is the activity level of emission sources;

[0024] A ar is the average coal ash content;

[0025] r a is the proportion of ash entering the bottom ash;

[0026] f PM The proportion of particulate matter in a certain size range to the total particulate matter emissions;

[0027] F E is the emission factor;

[0028] η is the removal rate of the control measure.

[0029] Preferably, the simulation is performed using the WRF meteorological model in step S3, specifically including: selecting the improved mesoscale meteorological model WRF, selecting NCEP reanalysis data or GFS forecast data as initial and boundary conditions according to the nature of the pollution event, setting terrain and underlying surface input data, and obtaining regional meteorological data during the pollution period through grid interpolation processing.

[0030] Preferably, in step S5, the PSCF model is used to calculate the pollution source identification parameter, and the formula is:

[0031] PSCF ij =t′ ij ×p′ ij ×W(t′ ij );

[0032] Among them, PSCF ij is the probability that the particle emission of any grid ij will affect the particle concentration of the receptor;

[0033] t′ ij is the normalized air mass residence time of any grid ij;

[0034] p′ ij is the normalized atmospheric particulate matter emission of any grid ij.

[0035] Preferably, when the air mass residence time of any grid is small, the weight function W(t′ ij ), which is used to reduce the uncertainty of the PSCF of the grid with too small a residence time, as follows:

[0036]

[0037] The air mass residence time t and atmospheric particulate matter emission p that are input into the identification model are normalized. The following method is used to further normalize the air mass residence time t and atmospheric particulate matter emission p:

[0038]

[0039] Among them, t ij is the original air mass residence time of any grid ij;

[0040] t max and t min is the maximum and minimum air mass residence time within the regional grid;

[0041] p ij is the original atmospheric particulate matter emission of any grid ij;

[0042] p max and p min are the maximum and minimum atmospheric particulate matter emissions within the regional grid.

[0043] Beneficial technical effects of the present invention:

[0044] The present invention provides a method for rapid source tracing of atmospheric particulate pollution based on actual measurements. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of a method for rapidly tracing the source of atmospheric particulate matter pollution based on actual measurements according to a preferred embodiment of the present invention;

[0046] Figure 2 This is a flow chart of a particulate matter emission information database according to a preferred embodiment of a method for rapid tracing the source of atmospheric particulate matter pollution based on actual measurements of the present invention;

[0047] Figure 3 This is a flowchart of PSCF calculation according to a preferred embodiment of a method for rapid source tracing of atmospheric particulate pollution based on actual measurements of the present invention;

[0048] Figure 4 Schematic diagram of pollution period and meteorological data download period according to a preferred embodiment of a method for rapid tracing of atmospheric particulate pollution based on actual measurements of the present invention;

[0049] Figure 5 The present invention is a flowchart of screening targeted control objects according to a preferred embodiment of a method for rapid source tracing of atmospheric particulate pollution based on actual measurements. DETAILED DESCRIPTION

[0050] In order to make the technical solution of the present invention more clear and specific to those skilled in the art, the present invention is further described in detail below with reference to embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0051] like Figures 1 to 5 As shown, the rapid source tracing method for atmospheric particulate pollution provided in this embodiment is mainly aimed at a certain particulate pollution event. On the basis of the source inventory method, it innovatively combines the meteorological model and the particle diffusion model, and develops a pollution source area identification model to quickly and specifically identify the main emission units of atmospheric particulate pollution.

[0052] The particulate matter pollution events may be PM10 or PM2.5 pollution events that may occur in the future as monitored by a regional air quality monitoring network or as predicted by a regional air quality forecast.

[0053] The source list is a gridded, high-resolution atmospheric particulate matter emission list for the jurisdiction, including the particulate matter emissions and their spatial distribution from industry, transportation, construction site dust, and non-road mobile machinery sectors. Different emission sectors are subdivided into different specific emission units, including enterprises, road sections, construction sites, and non-road mobile machinery.

[0054] A rapid source tracing method for atmospheric particulate matter pollution based on field measurements, with the following specific steps:

[0055] Step S1: Establish a regional particulate matter emission information database.

[0056] The regional particulate matter emission information involved in this invention mainly considers emission sectors including fossil fuel stationary combustion, process, traffic road dust, construction site dust and non-road mobile machinery in industrial enterprises. The specific operations for establishing a regional particulate matter emission information database are as follows:

[0057] First, we collected data on various activities related to atmospheric particulate matter emissions within the jurisdiction, covering multiple emission sectors, including stationary combustion of fossil fuels in industrial enterprises, industrial processes, road dust from traffic, dust from construction sites, and non-road mobile machinery. For each sector, we collected detailed activity data on the following:

[0058] Table 1 Activity level data types;

[0059]

[0060] Then, refer to the Technical Guidelines for Compilation of Air Pollutant Emission Inventories to calculate regional particulate matter emissions. Particulate matter emissions from fossil fuel stationary combustion sources are calculated primarily using the material balance method, while particulate matter emissions from other emission sectors are calculated using the emission factor method, as shown in the following formula:

[0061] Efossil = A × Aar × (1-ra) × fPM × (1-η);

[0062] Eother = A × FE × (1-η);

[0063] Where E is emissions (tons), A is the activity level of the emission source, Aar is the average coal ash content, ra is the proportion of ash that enters the bottom ash, fPM is the proportion of particulate matter in a certain size range (PM10 or PM2.5) in total particulate matter emissions, FE is the emission factor, and η is the removal efficiency of the control measure. Emission factors are obtained based on the Technical Guidelines for Compilation of Air Pollutant Emission Inventories, literature research, or local monitoring.

[0064] Furthermore, the aforementioned emissions were spatially allocated on a 1km x 1km grid using a geographic information system. Industrial sources, including stationary fossil fuel combustion and industrial processes, were treated as point sources and located to corresponding grids using latitude and longitude information. Road dust was treated as area source, with the emission allocation grid determined based on the spatial distribution of the road network. Spatial differences in emissions from different roads were combined with road traffic volume and speed parameters to reflect the spatial variability of emissions. Construction site dust and non-road mobile machinery were treated as point sources and located to corresponding grids based on their latitude and longitude information.

[0065] Finally, information correspondences between different grids and industrial enterprises, traffic sections, construction sites and non-road mobile machinery are established to construct a regional particulate matter emission information database.

[0066] Step S2: Determine the particulate matter pollution period.

[0067] Based on the actual monitoring data of atmospheric particulate matter of different particle sizes in the region or the forecast results of the regional air quality forecast model for the concentration of atmospheric particulate matter of different particle sizes for a certain period of time in the future, when the concentration of atmospheric particulate matter of a certain particle size in the region reaches a light pollution level or above at a certain moment and persists for more than 24 hours, it is judged that particulate matter pollution has occurred. When the particulate matter concentration stabilizes and drops back to good, the pollution ends. The above period is defined as the particulate matter pollution period.

[0068] Step S3: Obtain regional meteorological characteristic distribution during the pollution period

[0069] Use the WRF meteorological model to simulate the regional meteorological environment during the pollution event period and obtain regional meteorological data during the pollution period. The specific steps are as follows:

[0070] Use the improved meteorological model to simulate the regional meteorological environment during the pollution event to obtain accurate regional meteorological data. The specific operations are as follows:

[0071] Meteorological Model Selection and Data Preparation: Based on the nature of the pollution event (actual monitoring or forecast), an appropriate meteorological model and initial and boundary data are selected. If the current event is based on actual pollution monitoring from the air quality monitoring network, the Improved Mesoscale Weather Model (WRF) is used, with optimized NCEP meteorological reanalysis data used as initial and boundary data. If the current event is a forecast of a possible future pollution event, the WRF model's initial and boundary data are optimized GFS meteorological forecast data. Topography and underlying surface input data are derived from the USGS30s global topography and MODIS underlying surface classification data, respectively.

[0072] NCEP meteorological reanalysis data: The resolution is 1°×1°, and the temporal resolution is 6 hours (00, 06, 12, and 18 UTC). After acquiring the data, it is preprocessed, including data cleaning, quality control, and interpolation, to improve the accuracy and applicability of the data.

[0073] GFS weather forecast data: The resolution is 0.25°×0.25°, and the time resolution is 6 hours (00, 06, 12, and 18 UTC). Similarly, data preprocessing is performed before use to ensure data quality.

[0074] WRF model settings and simulation:

[0075] Determine the calculation grid: Based on the particulate matter pollution period, add 24 hours as the model warm-up time, download the meteorological driving data from the corresponding website, determine the WRF calculation grid according to the grid inventory grid settings, and set the start and end times of the WRF calculation.

[0076] Interpolation processing: Using the terrain and underlying land use input data (USGS and MODIS30s data), the downloaded meteorological driving data are interpolated vertically and horizontally to meet the requirements of the calculation grid.

[0077] Preparation of initial and boundary condition files: Based on the interpolated files, prepare the meteorological initial and boundary condition files for WRF calculation.

[0078] Parameterization scheme setting and simulation: Set the parameterization scheme for different WRF calculation processes, simulate the regional meteorological environment during the pollution period, and obtain accurate regional meteorological data.

[0079] Step S4: Determine the receptor location.

[0080] The receptor location is usually set to the location of the monitoring site, including the longitude, latitude and sampling port height.

[0081] Step S5: Obtain the air mass residence time of different grids in the pollution period within the area and calculate the pollution source identification;

[0082] Use the Flexpart particle diffusion model, set the diffusion parameters, and calculate the grid consistent with the above grid list to obtain the air mass residence time of different grids in the pollution period in the area and the pollution source identification calculation:

[0083] PSCF ij =t′ ij ×p′ ij ×W(t′ ij )

[0084] Where PSCFij is the probability that the particle emission of any grid ij will affect the particle concentration of the receptor; t′ ij is the normalized air mass residence time at any grid ij; p′ ij is the normalized atmospheric particulate matter emission of any grid ij. Since PSCF is a conditional probability, its uncertainty increases with the distance between the grid point and the receptor position, especially when the air mass residence time of any grid is small. The weight function W(t′ ij ), which is used to reduce the uncertainty of the PSCF of the grid with too small a residence time, as follows:

[0085]

[0086] The air mass residence time t and atmospheric particulate matter emission p, which are input into the identification model, are normalized. This is mainly because there is a difference in the magnitude of the two values. If they are directly input into the model for calculation, the sensitivity of the calculation results will be affected. The present invention uses the following method to normalize the air mass residence time t and atmospheric particulate matter emission p:

[0087]

[0088] Among them, tij is the original air mass residence time of any grid ij, tmax and tmin are the maximum and minimum air mass residence times in the regional grid; pij is the original atmospheric particulate matter emission of any grid ij, pmax and pmin are the maximum and minimum atmospheric particulate matter emission in the regional grid.

[0089] Step S6: Determine the pollution emission source area;

[0090] The grids where the PSCF in step 5 is not 0 are the pollution emission source areas of this pollution process.

[0091] Step S7: Screening the targeted prevention and control objects of particulate matter pollution.

[0092] The information on atmospheric particulate matter emission sources within the region was retrieved from the database. Total particulate matter emissions were calculated by sector (industry, transportation, construction site dust, and non-road mobile machinery). The different emission units within each sector were then ranked from largest to smallest based on emission volume. This approach allowed the identification of the key atmospheric particulate matter emission sectors involved in the pollution process, as well as a list of key particulate matter emission units within each sector, providing strong support for targeted particulate matter pollution prevention and control efforts.

[0093] The above is only a further embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can replace or change the technical solution and concept of the present invention within the scope disclosed by the present invention, which falls within the scope of protection of the present invention.

Claims

1. A method for rapid source tracing of atmospheric particulate matter pollution based on field measurements, characterized by: The steps include: Step S1: Establishing a regional particulate matter emission information database, which contains particulate matter emissions and their spatial distribution from industry, transportation, construction site dust, and non-road mobile machinery sectors; Step S2: Determine the particulate matter pollution period based on monitoring data or forecast results; Step S3: using a meteorological model to simulate regional meteorological characteristics during the pollution period and obtain meteorological data; Step S4: determining the receptor location; Step S5: Using a particle diffusion model, calculate the air mass residence time and pollution source identification parameters of different grids in the area; Step S6: determining the pollution emission source area based on the pollution source identification parameters; Step S7: Screening the targeted prevention and control objects of particulate matter pollution.

2. The method for rapid source tracing of atmospheric particulate matter pollution based on field measurements according to claim 1 is characterized by: The specific process of establishing the regional particulate matter emission information database in step S1 includes collecting activity level data of each emission department, calculating particulate matter emissions using the material balance method or the emission factor method, and allocating the emissions to a 1km×1km grid space through a geographic information system, and establishing a corresponding relationship between emission information and grids.

3. The method for rapid source tracing of atmospheric particulate matter pollution based on field measurements according to claim 2 is characterized by: The emissions from the industrial sector are calculated using the material balance method, using the following formula: E 化石 =A×A ar ×(1-r a )×f PM ×(1-n); E 其他 =A×F E ×(1-n); Where, E is the emission; A is the activity level of emission sources; A ar is the average coal ash content; r a is the proportion of ash entering the bottom ash; f PM The proportion of particulate matter in a certain size range to the total particulate matter emissions; F E is the emission factor; η is the removal rate of the control measure.

4. The method for rapid source tracing of atmospheric particulate matter pollution based on field measurements according to claim 1 is characterized by: The simulation is performed using the WRF meteorological model in step S3, specifically including: selecting the improved mesoscale meteorological model WRF, selecting NCEP reanalysis data or GFS forecast data as initial and boundary conditions according to the nature of the pollution event, setting terrain and underlying surface input data, and obtaining regional meteorological data during the pollution period through grid interpolation processing.

5. The method for rapid source tracing of atmospheric particulate matter pollution based on field measurements according to claim 1 is characterized by: In step S5, the PSCF model is used to calculate the pollution source identification parameter, and the formula is: PSCF ij =t′ ij ×p′ ij ×W(t′ ij ); Among them, PSCF ij is the probability that the particle emission of any grid ij will affect the particle concentration of the receptor; t′ ij is the normalized air mass residence time of any grid ij; p′ ij is the normalized atmospheric particulate matter emission of any grid ij.

6. The method for rapid source tracing of atmospheric particulate matter pollution based on field measurements according to claim 1 is characterized by: When the air mass residence time of any grid is small, the weight function W(t′ ij ), which is used to reduce the uncertainty of the PSCF of the grid with too small a residence time, as follows: The air mass residence time t and atmospheric particulate matter emission p that are input into the identification model are normalized. The following method is used to further normalize the air mass residence time t and atmospheric particulate matter emission p: Among them, t ij is the original air mass residence time of any grid ij; t max and t min is the maximum and minimum air mass residence time within the regional grid; p ij is the original atmospheric particulate matter emission of any grid ij; p max and p min are the maximum and minimum atmospheric particulate matter emissions within the regional grid.

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