A high-spatial-resolution real-time monitoring data acquisition method for ambient air quality

Abstract

The application provides a high-spatial-resolution real-time environmental air quality monitoring data acquisition method, and belongs to the technical field of environmental air quality monitoring. The method establishes a nationwide weather field weather typing database and an air quality historical simulation database to establish the relationship between simulated meteorological characteristics and weather typing data and air quality data of each region, and then based on the above relationship, real-time air quality data of the place is calculated through real-time acquisition of current position coordinates and monitoring data of nearby national control stations. The application solves the problem that there are no environmental air quality monitoring stations in suburban areas, rural areas, mountainous areas, tourist attractions, urban communities and other places, and there is no accurate monitoring data, and has the advantages that real-time and accurate air quality data of any region in the country can be quickly obtained.

Description

Technical Field

[0001] This invention relates to the field of ambient air quality monitoring technology, specifically to a method for acquiring real-time ambient air quality monitoring data with high spatial resolution. Background Technology

[0002] Considering cost factors, existing ambient air quality monitoring stations are mainly concentrated in urban built-up areas. Data from national monitoring stations is transmitted in real-time to the National Air Quality Monitoring Center and released to the public through a network platform. Because these stations are primarily located in urban built-up areas, there are virtually no monitoring stations in the vast suburbs and rural areas. Residents in these areas cannot access surrounding air quality data and can only refer to air quality monitoring data from their local prefecture-level city. Furthermore, due to the extremely limited number of monitoring stations located in urban residential areas, many residents also lack access to accurate monitoring data.

[0003] Currently, many scholars primarily utilize spatial interpolation methods such as Kriging and inverse distance weighting to obtain air quality data for areas without national monitoring stations. These methods often use only the distance to the station as a parameter, obtaining concentration data for the interpolation point through a function related to spatial location. However, the spatial distribution of air pollutants is highly complex, influenced by physicochemical factors such as emissions, diffusion, transport, and deposition, and changes over time rather than solely depending on distance. Therefore, using these interpolation methods often yields erroneous interpolation results. Summary of the Invention

[0004] To address the lack of ambient air quality monitoring stations and accurate monitoring data in urban suburbs, rural areas, mountainous regions, tourist attractions, and urban residential communities, this invention proposes a high-resolution method for acquiring real-time ambient air quality monitoring data. Using this method, real-time and accurate air quality data for any region nationwide can be obtained, with spatial resolution reaching the micro-level (within 100 meters) and temporal resolution reaching 1 hour.

[0005] To solve the above problems, the technical solution of the present invention is as follows:

[0006] A method for acquiring real-time ambient air quality monitoring data with high spatial resolution includes the following steps:

[0007] S1. Establish a national meteorological field weather classification database;

[0008] S2. Establish a historical air quality simulation database:

[0009] Simulations were conducted on the national air quality over the past 3 to 5 years to obtain daily and hourly simulated fields of national air quality. A historical air quality simulation field database was then established by combining the weather classification database.

[0010] S3. Obtain the current location coordinates and monitoring data of nearby national monitoring stations;

[0011] S4. Obtain the meteorological field data at the current moment and perform weather classification on the meteorological field data;

[0012] S5. Obtain the ratio of pollutant concentrations between the current location and nearby national monitoring stations:

[0013] In the historical air quality simulation database, locate the air quality simulation field that has the same weather classification as the current location coordinates and the time period before and after the current time as the current weather type obtained in step S4. If multiple air quality simulation fields are located, average the multiple air quality simulation fields to obtain the average air quality simulation field. Based on the average air quality simulation field, obtain the proportional relationship between the current location coordinates, the current time air pollutant concentration and the monitoring data of the national control station.

[0014] S6. Obtain real-time air quality data for the location based on the proportional relationship.

[0015] As another aspect of the present invention, the spatial resolution of the daily and hourly simulation field is 3km×3km.

[0016] As another aspect of the present invention, the historical air quality simulation field database stores hourly concentration simulation data of SO2, NO2, PM2.5, PM10, O3 and CO in various regions of China in recent years, as well as weather classifications corresponding to the daily and hourly simulation fields.

[0017] As another aspect of the present invention, the monitoring data are real-time hourly concentrations of SO2, NO2, PM2.5, PM10, O3 and CO in the air.

[0018] As another aspect of the present invention, the number of adjacent national-level monitoring stations is 1 to 5.

[0019] As another aspect of the present invention, step S1 includes the following: acquiring national meteorological field data in recent years and performing weather classification, obtaining weather classification data of various regions in various seasons and meteorological characteristics corresponding to the weather classification data, and then establishing a weather classification database based on the weather classification data of various regions in various seasons and the meteorological characteristics corresponding to the weather classification data.

[0020] As another aspect of the present invention, step S4 includes the following: taking the latest real-time weather forecast data as the weather field data at the current moment, and clustering the weather field data to obtain the weather type of the province where the current location coordinates are located at the most recent moment.

[0021] As another aspect of the present invention, the period before and after is 5 to 15 days.

[0022] As another aspect of the present invention, step S6 includes the following: based on the proportional relationship obtained in step S5, and based on the monitoring data of the national control stations released in real time by the monitoring station, the concentration data of the current location coordinates at the current time is calculated.

[0023] The beneficial effects of this invention are:

[0024] Based on the transmission and diffusion patterns of atmospheric pollutants, this invention integrates air quality simulation and meteorological classification results to obtain a spatial distribution field of pollutant concentrations with high spatial resolution. On this basis, it uses real-time monitoring data from national monitoring stations to calculate the concentration data at the current location coordinates at the current moment, thereby obtaining more scientific and realistic monitoring results and providing the public with the most reliable monitoring data. Attached Figure Description

[0025] Figure 1 This is a flowchart of a method for acquiring real-time monitoring data of ambient air quality with high spatial resolution, as described in Example 1. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0027] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0028] It should be understood that although the terms first, second, third, etc., may be used to describe... in the embodiments of the present invention, these... should not be limited to these terms. These terms are only used to distinguish... For example, first... may also be referred to as second... without departing from the scope of the embodiments of the present invention, and similarly, second... may also be referred to as first...

[0029] National monitoring stations are short for national automatic monitoring stations for ambient air quality.

[0030] Example 1

[0031] This embodiment describes a method for acquiring real-time ambient air quality monitoring data with high spatial resolution. High spatial resolution refers to the spatial resolution of 3km*3km and the temporal resolution of 1 hour achieved by this method. Figure 1 As shown, it includes the following steps:

[0032] S1. Establish a national meteorological field weather classification database:

[0033] We acquire national meteorological field data from recent years and perform weather classification to obtain weather classification data for various regions in different seasons and the corresponding meteorological characteristics. Then, we establish a weather classification database based on the weather classification data for various regions in different seasons and the corresponding meteorological characteristics.

[0034] Optionally, in this embodiment, the national meteorological field data for recent years is obtained through the website of the National Meteorological Science Data Center (www.nmic.cn).

[0035] Understandably, the weather classification database includes weather classification data for various regions of China in various seasons, as well as weather classification data.

[0036] Optionally, in this embodiment, each region refers to one of the seven major geographical regions: Northeast China, North China, Central China, East China, South China, Northwest China, and Southwest China. Weather classification data includes circulation patterns, typhoon periphery + isotropic field, typhoon periphery + high-pressure base, isotropic field + subtropical high, high-pressure base + southward shift of subtropical high, high-pressure base + eastward shift of subtropical high, modified high-pressure ridge + subtropical high edge, subtropical high, southward movement of weak high pressure, southward movement of cold high pressure, and southward movement of strong cold high pressure.

[0037] Optionally, in this embodiment, "recent years" refers to the last three years.

[0038] Optionally, this embodiment uses the clustering algorithm built into the weather classification software COST733 to perform weather classification on the national meteorological field data. As a commonly used weather classification software in this field, COST733 guarantees the accuracy of the classification results, thereby ensuring the feasibility and robustness of the method in this embodiment.

[0039] S2. Establish a historical air quality simulation database:

[0040] We simulated the national air quality in recent years to obtain daily and hourly simulated fields of national air quality, and established a historical air quality simulated field database by combining it with a weather classification database.

[0041] Optionally, in this embodiment, the spatial resolution of the daily and hourly simulation field is 3km×3km, and the data included in the daily and hourly simulation field is the concentration of pollutants in the air, which is the real-time monitoring value of the hourly concentration of SO2, NO2, PM2.5, PM10, O3 and CO in the air.

[0042] Optionally, in this embodiment, the national air quality over the past three years is simulated using the WRF / CAMx air quality model based on the MEIC national atmospheric emission source inventory provided by Tsinghua University.

[0043] Understandably, the historical air quality simulation field database stores hourly concentration simulation data of SO2, NO2, PM2.5, PM10, O3 and CO for various regions in China in recent years, along with the weather types corresponding to the daily and hourly simulation fields.

[0044] S3. Obtain the current location coordinates and monitoring data of nearby national monitoring stations.

[0045] Optionally, in this embodiment, the current location coordinates are obtained through mobile phone GPS positioning, and real-time monitoring data from nearby national monitoring stations are obtained through the China National Environmental Monitoring Centre's national urban air quality real-time release platform.

[0046] Optionally, this embodiment obtains the current location coordinates and monitoring data of nearby national monitoring stations through the data interface of the China National Environmental Monitoring Centre's National Urban Air Quality Real-time Release Platform (https: / / air.cnemc.cn:18007 / ).

[0047] Understandably, the nearest national monitoring station is selected as the one with the closest coordinates to the current location.

[0048] S4. Obtain the current meteorological field data and perform weather classification on the meteorological field data:

[0049] The latest real-time weather forecast data is used as the meteorological field data for the current moment. The meteorological field data is clustered to obtain the weather type of the province where the current location coordinates are located at the most recent moment.

[0050] Optionally, this embodiment obtains the most recent real-time weather forecast data through NATIONAL WEATHER SERVICE (https: / / nomads.ncep.noaa.gov / ).

[0051] The purpose of using real-time meteorological forecast data here is that the spatial distribution of air pollutant concentrations is only similar under the same weather classification. If the weather classifications are different, using simulated data as a benchmark to spatially interpolate the measured data will introduce significant uncertainty.

[0052] Optionally, in this embodiment, step S4 is the same as step S1, which is to quickly cluster the meteorological field data using the clustering algorithm built into the weather classification software COST733 to obtain the weather classification of the province where the current location coordinates are located at the most recent moment.

[0053] S5. Obtain the ratio of pollutant concentrations at the location to those at nearby national monitoring stations.

[0054] In the historical air quality simulation database, locate the air quality simulation field that has the same weather classification as the current location coordinates and the time period before and after the current time as the current weather type obtained in step S4. If multiple air quality simulation fields are located, average the multiple air quality simulation fields to obtain the air quality simulation average field. Based on the air quality simulation average field, obtain the proportional relationship between the current location coordinates, the current time air pollutant concentration and the monitoring data of the national control station.

[0055] Optionally, the time period before and after this embodiment is 5 days.

[0056] S6. Obtain real-time air quality data for the location based on the proportional relationship:

[0057] Based on the proportional relationship obtained in step S5, and using the monitoring data of national control stations released in real time by the monitoring center, the concentration data at the current location coordinates at the current moment is calculated.

[0058] For example, the specific operation process of steps S5 and S6 in this embodiment is as follows:

[0059] Based on the simulated concentration field of similar weather patterns, the ratio of the current air pollutant concentration in the local area (coordinate X1) to the current air pollutant concentration monitored by the nearest national monitoring station (coordinate Y1) is obtained, where the ratio is r = ρ(X1) / ρ(Y1). The local concentration (coordinate X1) is then obtained using the measured concentration ρ(Y1) at the national monitoring station (coordinate Y1), i.e., local concentration = r * ρ(Y1). If multiple national monitoring stations are involved, the above results are averaged.

[0060] Example 2

[0061] This embodiment is a method for acquiring real-time ambient air quality monitoring data with high spatial resolution. The difference from Embodiment 1 is that: the recent years are the past 4 years, the time period before and after is 10 days, and the nearest national monitoring stations are the two national monitoring stations closest to the current location coordinates.

[0062] Optionally, in this embodiment, the national air quality over the past four years is simulated using the WRF / CMAQ air quality model based on the MEIC national atmospheric emission source inventory provided by Tsinghua University.

[0063] Example 3

[0064] This embodiment is a method for acquiring real-time ambient air quality monitoring data with high spatial resolution. The difference from Embodiment 1 is that: the recent years are the past 5 years, the time period before and after is 15 days, and the nearest national monitoring stations are the 3 national monitoring stations closest to the current location coordinates.

[0065] Example 4

[0066] This embodiment is a method for acquiring real-time monitoring data of ambient air quality with high spatial resolution. The difference from Embodiment 1 is that the nearest national monitoring stations are selected as the four national monitoring stations closest to the current location coordinates.

[0067] Example 5

[0068] This embodiment is a method for acquiring real-time monitoring data of ambient air quality with high spatial resolution. The difference from Embodiment 1 is that the nearest national monitoring stations are selected as the 5 national monitoring stations closest to the current location coordinates.

Claims

1. A method for acquiring real-time monitoring data of ambient air quality with high spatial resolution, characterized in that, Includes the following steps: S1. Establish a national meteorological field weather classification database; S2. Establish a historical air quality simulation database: Simulations were conducted on the national air quality over the past 3 to 5 years to obtain daily and hourly simulated fields of national air quality. A historical air quality simulation field database was then established by combining the weather classification database. S3. Obtain the current location coordinates and monitoring data of nearby national monitoring stations; S4. Obtain the meteorological field data at the current moment and perform weather classification on the meteorological field data; S5. Obtain the ratio of pollutant concentrations between the current location and nearby national monitoring stations: In the historical air quality simulation database, locate the air quality simulation field that has the same weather classification as the current location coordinates and the time period before and after the current time as the current weather type obtained in step S4. If multiple air quality simulation fields are located, average the multiple air quality simulation fields to obtain the average air quality simulation field. Based on the average air quality simulation field, obtain the proportional relationship between the current location coordinates, the current time air pollutant concentration and the monitoring data of the national control station. S6. Obtain real-time air quality data for the location based on the proportional relationship.

2. The method for acquiring real-time ambient air quality monitoring data as described in claim 1, characterized in that, The spatial resolution of the daily and hourly simulation field is 3km × 3km.

3. The method for acquiring real-time ambient air quality monitoring data as described in claim 1, characterized in that, The historical air quality simulation field database stores hourly concentration simulation data of SO2, NO2, PM2.5, PM10, O3 and CO in various regions of China in recent years, along with weather types corresponding to the daily and hourly simulation fields.

4. The method for acquiring real-time ambient air quality monitoring data as described in claim 1, characterized in that, The monitoring data are real-time hourly concentrations of SO2, NO2, PM2.5, PM10, O3, and CO in the air.

5. The method for acquiring real-time ambient air quality monitoring data as described in claim 1, characterized in that, The number of nearby national-level monitoring stations is 1 to 5.

6. The method for acquiring real-time ambient air quality monitoring data as described in claim 1, characterized in that, Step S1 includes the following: We acquire national meteorological field data from recent years and perform weather classification to obtain weather classification data for various regions in different seasons and the corresponding meteorological characteristics. Then, we establish a weather classification database based on the weather classification data for various regions in different seasons and the corresponding meteorological characteristics.

7. The method for acquiring real-time ambient air quality monitoring data as described in claim 1, characterized in that, Step S4 includes the following: The latest real-time weather forecast data is used as the meteorological field data for the current moment. The meteorological field data is clustered to obtain the weather type of the province where the current location coordinates are located at the most recent moment.

8. The method for acquiring real-time ambient air quality monitoring data as described in claim 1, characterized in that, The time period before and after is 5 to 15 days.

9. The method for acquiring real-time ambient air quality monitoring data as described in claim 1, characterized in that, Step S6 includes the following: Based on the proportional relationship obtained in step S5, and using the monitoring data of national control stations released in real time by the monitoring center, the concentration data at the current location coordinates at the current moment is calculated.

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