A method for inversion of atmospheric environmental pollution concentration based on multi-source information fusion

Abstract

The present invention belongs to the field of atmospheric environmental health assessment technology, and specifically relates to an atmospheric environmental pollution concentration inversion method based on multi-source information fusion. The present invention comprises the following steps: Step 1, PM 2.5 Data collection and preprocessing; Step 2, PM based on numerical model 2.5 Resample the concentration data; Step 3, resample the PM 2.5 Interpolate the error of concentration data; Step 4, resample PM 2.5 Correct the concentration data; Step 5: Integrate the PM2.5 data based on satellite inversion 2.5 Concentration data. This invention integrates three different PM 2.5 The advantage of the concentration observation method is that it uses PM2.5 concentration observations based on observation sites to 2.5 Concentration data, PM based on satellite inversion 2.5 Concentration data and PM based on numerical models 2.5 The concentration data can then provide PM2.5 concentrations per hour and at a 1km×1km grid. 2.5 Concentration data will ultimately provide data support for atmospheric environmental management.

Description

Technical Field

[0001] The present invention belongs to the technical field of atmospheric environmental health assessment, and in particular relates to an atmospheric environmental pollution concentration inversion method based on multi-source information fusion. Background Art

[0002] Monitoring PM 2.5 Concentration is an important part of atmospheric environmental monitoring. There are usually three technical means to obtain ground PM 2.5 Concentration data, first, PM2.5 is collected through ground-based air pollution observation stations. 2.5 The advantage of this technology is that it can achieve continuous observation every hour, but its disadvantage is that the observation data can only represent a limited spatial area near the station and cannot achieve the goal of full spatial coverage. Second, PM2.5 can be inverted through satellite remote sensing. 2.5 concentration, this technology can provide PM 2.5 The concentration inversion value can reach a spatial resolution of 1km×1km, but this method usually only gives the PM 2.5 The daily average of the concentration has poor time resolution. Third, the PM 2.5 concentration, this method can give a wide range of PM concentrations hour by hour. 2.5 However, due to the limitation of emission inventory or simulation calculation cost, numerical models can usually only give relatively rough PM 2.5 The spatial resolution of the concentration value is usually greater than 10km×10km.

[0003] In obtaining ground PM 2.5 The existing technical means of collecting concentration data cannot meet the requirements of high spatial resolution and high temporal resolution at the same time and cannot provide hourly PM2.5 data with a spatial resolution of 1 km × 1 km. 2.5 Concentration value, how to integrate the advantages of existing methods is to build a refined PM 2.5 Technical issues of inversion methods. Summary of the Invention

[0004] In view of the problems in the prior art, the present invention provides an atmospheric environmental pollution concentration inversion method based on multi-source information fusion.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for inverting atmospheric environmental pollution concentration based on multi-source information fusion includes the following steps:

[0007] Step 1: PM 2.5 Data collection and preprocessing; collected PM 2.5 Data include PM based on observation sites 2.5 Concentration data, PM based on satellite inversion2.5 Concentration data and PM based on numerical models 2.5 Concentration data; PM 2.5 Data preprocessing, based on satellite inversion and numerical model-based PM 2.5 The concentration data is clipped to the target area, and then the PM based on the observation site is extracted. 2.5 Concentration data, PM based on satellite inversion 2.5 Concentration data and PM based on numerical models 2.5 Concentration data to a unified target time period;

[0008] Step 2: PM based on numerical model 2.5 Concentration data were resampled; PM based on numerical model 2.5 The concentration data is grid data, each grid size is 12km×12km, based on the PM2.5 concentration inverted by satellite. 2.5 The concentration data is grid data, each grid size is 1km×1km, based on the PM2.5 concentration inverted by satellite. 2.5 The grid points of the concentration data are used as the benchmark, and the PM 2.5 The grid data were resampled to 1 km × 1 km using a linear resampling method;

[0009] Step 3: Resample PM 2.5 The error of concentration data is interpolated; the resampled PM obtained in step 2 2.5 The concentration data is hourly grid data with a spatial resolution of 1km×1km. It is considered that the PM 2.5 The concentration data is the real value, and the PM 2.5 The concentration data is matched to the corresponding 1km×1km grid according to its latitude and longitude, and then the PM concentration of the data generated in step 2 at each observation station is calculated. 2.5 Concentration error, which is an hourly error and only includes PM at the observation site 2.5 The grid point corresponding to the concentration data is interpolated to all 1km×1km grid points in the target area using the natural point interpolation method.

[0010] Step 4: Resample PM 2.5 The concentration data is corrected; PM is obtained through step 3 2.5 Concentration error data is applied to the PM generated in step 2. 2.5 concentration, and obtain the corrected PM 2.5 Grid data, which is hourly concentration value with a spatial resolution of 1km×1km;

[0011] Step 5: Integrate PM based on satellite inversion 2.5Concentration data; using satellite-based PM 2.5 The concentration data constrains the data generated in step 4. The PM generated in step 4 2.5 The concentration is a fusion of site observation data and numerical model data, taking into account the PM2.5 concentration based on satellite inversion. 2.5 Concentration data provides more reliable PM 2.5 Concentration value, so use this data to correct the data generated in step 4, the specific method is as follows:

[0012] Calculate the ratio F for the i-th grid point in the target area and the j-th day in the target time period ij :

[0013]

[0014] In the above formula, PM ij，step PM based on satellite inversion 2.5 The daily average concentration value of the data at the i-th grid point and the j-th day, PM ij，step4 The data generated in step 4 is the daily average concentration value at the i-th grid point and the j-th day. Since the data generated in step 4 is hourly, PM ij，step4 The 24-hour average value needs to be calculated;

[0015] For the data generated in step 4, the PM of each grid point and each hour in the data is 2.5 The concentration values ​​are multiplied by the corresponding F ij , and finally obtain PM with high temporal and spatial resolution 2.5 Concentration data, which is hourly value with a spatial resolution of 1km×1km, and the PM based on this data 2.5 Daily mean concentration and PM2.5 based on satellite retrieval 2.5 The daily average concentration was consistent.

[0016] Further as a preferred technical solution of the present invention, the PM based on the observation station 2.5 The concentration data is hourly observation, and the latitude and longitude information of the ground observation station is also required; the PM2.5 concentration data based on satellite inversion is 2.5 The concentration data are daily average data with a spatial resolution of 1km×1km; the PM 2.5 The concentration data are hourly with a spatial resolution of 12 km × 12 km.

[0017] The beneficial effects of the present invention compared to the prior art are:

[0018] The present invention integrates three different PM 2.5 The advantage of the concentration observation method is that it uses PM2.5 concentration observations based on observation sites to 2.5 Concentration data, PM based on satellite inversion2.5 Concentration data and PM based on numerical models 2.5 The concentration data can then provide PM2.5 concentrations per hour and at a 1km×1km grid. 2.5 Concentration data will ultimately provide data support for atmospheric environmental management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of a flow chart of an embodiment of the present invention;

[0020] Figure 2 PM of the embodiment of the present invention 2.5 Schematic diagram of the spatial distribution of observation sites;

[0021] Figure 3 The PM of different times in the embodiment of the present invention 2.5 Spatial distribution of concentration. DETAILED DESCRIPTION

[0022] The present invention will be described in further detail below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, a method for inverting atmospheric pollution concentration based on multi-source information fusion includes the following steps:

[0024] Step 1: PM 2.5 Data collection and preprocessing; collected PM 2.5 Data include PM based on observation sites 2.5 Concentration data, PM based on satellite inversion 2.5 Concentration data and PM based on numerical models 2.5 Concentration data; PM 2.5 Data preprocessing, based on satellite inversion and numerical model-based PM 2.5 The concentration data is clipped to the target area, and then the PM based on the observation site is extracted. 2.5 Concentration data, PM based on satellite inversion 2.5 Concentration data and PM based on numerical models 2.5 Concentration data to a unified target time period;

[0025] PM based on observation sites 2.5 The concentration data is hourly observation, and the latitude and longitude information of the ground observation station is also required; the PM2.5 concentration data based on satellite inversion is 2.5 The concentration data are daily average data with a spatial resolution of 1km×1km; the PM 2.5 The concentration data are hourly with a spatial resolution of 12 km × 12 km.

[0026] Step 2: PM based on numerical model 2.5Concentration data were resampled; PM based on numerical model 2.5 The concentration data is grid data, each grid size is 12km×12km, based on the PM2.5 concentration inverted by satellite. 2.5 The concentration data is grid data, each grid size is 1km×1km, based on the PM2.5 concentration inverted by satellite. 2.5 The grid points of the concentration data are used as the benchmark, and the PM 2.5 The grid data were resampled to 1 km × 1 km using a linear resampling method;

[0027] Step 3: Resample PM 2.5 The error of concentration data is interpolated; the resampled PM obtained in step 2 2.5 The concentration data is hourly grid data with a spatial resolution of 1km×1km. It is considered that the PM 2.5 The concentration data is the real value, and the PM 2.5 The concentration data is matched to the corresponding 1km×1km grid according to its latitude and longitude, and then the PM concentration of the data generated in step 2 at each observation station is calculated. 2.5 Concentration error, which is an hourly error and only includes PM at the observation site 2.5 The grid point corresponding to the concentration data is interpolated to all 1km×1km grid points in the target area using the natural point interpolation method.

[0028] Step 4: Resample PM 2.5 The concentration data is corrected; PM is obtained through step 3 2.5 Concentration error data is applied to the PM generated in step 2. 2.5 concentration, and obtain the corrected PM 2.5 Grid data, which is hourly concentration value with a spatial resolution of 1km×1km;

[0029] Step 5: Integrate PM based on satellite inversion 2.5 Concentration data; using satellite-based PM 2.5 The concentration data constrains the data generated in step 4. The PM generated in step 4 2.5 The concentration is a fusion of site observation data and numerical model data, which can well reflect the hourly changes, but PM 2.5 There may be systematic errors in the concentration values, especially for areas far away from the observation site. 2.5 Concentration data provides more reliable PM 2.5 Concentration value, so use this data to correct the data generated in step 4, the specific method is as follows:

[0030] For the i-th grid point in the target area, the j-th day in the target time period, the ratio F is calculated ij :

[0031]

[0032] In the above formula, PM ij，sate is the PM 2.5 data based on satellite inversion, PM ij，step4 is the daily concentration value of the data generated in step 4 at the i-th grid point and the j-th day. Since the data generated in step 4 is hourly, PM ij，step4 needs to calculate the 24-hour average value;

[0033] For the data generated in step 4, multiply the PM 2.5 concentration value of each grid point and each hour in the data by the corresponding F ij , and finally obtain the high spatio-temporal resolution PM 2.5 concentration data, which is hourly value, spatial resolution is 1km x 1km, and the daily average concentration based on the data is consistent with the PM 2.5 daily average concentration based on satellite inversion. 2.5

[0034] In specific implementation, Nanjing city is selected as the target area, and the target time period is selected from July 1, 2021 to July 31, 2021.

[0035] Step 1, PM 2.5 concentration data collection, PM 2.5 concentration data is as follows: PM 2.5 concentration data from the China Environmental Monitoring Center (http: / / www.cnemc.cn), which includes 13 stations, and the spatial distribution of the stations is shown in Figure 2 . PM 2.5 concentration data based on satellite inversion comes from CHAP public data set (https: / / weijing-rs.github.io / product.html), which is PM 2.5 daily average concentration value, spatial resolution is 1km x 1km, and spatial coverage is the whole China area. The data needs to be cut to Nanjing city according to the latitude and longitude grid. PM 2.5 concentration data based on numerical mode comes from air quality mode CMAQ, which is hourly average value, spatial resolution is 12km x 12km, and spatial coverage is the whole eastern China area. Data preprocessing, PM 2.5 concentration data based on satellite inversion and PM 2.5 ​The concentration data are all grid point data. Based on the latitude and longitude values ​​of the grid points, the two types of data are clipped to the Nanjing area, and the PM concentration of the target time period is extracted. 2.5 Concentration value.

[0036] Step 2: PM based on numerical model 2.5 The concentration data is resampled. During the resampling process, the PM concentration based on satellite inversion is first read. 2.5 The concentration data is gridded in latitude and longitude, and then the PM values ​​of the numerical model are resampled using the linear resampling method. 2.5 The coarse grid data (12 km × 12 km) is converted to fine grid data (1 km × 1 km).

[0037] Step 3: Calculate the resampled PM 2.5 The PM concentration data error is interpolated to 2.5 The concentration data is used as the true value, and each station in Nanjing is matched to the corresponding 1km×1km grid. The resampled PM 2.5 The errors in the concentration data were then interpolated to each 1km×1km grid in the entire Nanjing city.

[0038] Step 4: Resample PM 2.5 The concentration data were corrected and the PM concentration on each 1km×1km grid in Nanjing was obtained based on the above step 3. 2.5 The concentration error is added to the resampled data obtained in step 2 to obtain the corrected data, which combines the ground observations and the numerical model PM 2.5 Concentration data can better characterize PM 2.5 Hourly changes in concentration.

[0039] Step 5: Integrate PM based on satellite inversion 2.5 Concentration data. Using the data corrected in step 4 and the PM2.5 concentration data based on satellite inversion 2.5 Concentration data, calculate the ratio Fij for each grid point and each day in Nanjing, multiply the data generated in step 4 by the corresponding Fij to obtain the final high temporal and spatial resolution PM 2.5 The concentration data has a spatial resolution of 1 km × 1 km and is hourly averaged. Figure 3 Shows the PM values ​​for Nanjing at six different times on July 6, 2021 2.5 Concentration distribution.

[0040] The specific implementation scheme described above further illustrates in detail the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above is only a specific implementation scheme of the present invention and is not intended to limit the scope of the present invention. Any equivalent changes and modifications made by any technician in this field without departing from the concept and principle of the present invention should fall within the scope of protection of the present invention.

Claims

1. A method for inverting atmospheric pollution concentration based on multi-source information fusion, characterized in that: The following steps are involved: Step 1: PM 2.5 Data collection and preprocessing; collected PM 2.5 Data include PM based on observation sites 2.5 Concentration data, PM based on satellite inversion 2.5 Concentration data and PM based on numerical models 2.5 Concentration data; PM 2.5 Data preprocessing, based on satellite inversion and numerical model-based PM 2.5 The concentration data is clipped to the target area, and then the PM based on the observation site is extracted. 2.5 Concentration data, PM based on satellite inversion 2.5 Concentration data and PM based on numerical models 2.5 Concentration data to a unified target time period; Step 2: PM based on numerical model 2.5 Concentration data were resampled; PM based on numerical model 2.5 The concentration data is grid data, each grid size is 12km×12km, based on the PM2.5 concentration inverted by satellite. 2.5 The concentration data is grid data, each grid size is 1km×1km, based on the PM2.5 concentration inverted by satellite. 2.5 The grid points of the concentration data are used as the benchmark, and the PM 2.5 The grid data were resampled to 1 km × 1 km using a linear resampling method; Step 3: Resample PM 2.5 The error of concentration data is interpolated; the resampled PM obtained in step 2 2.5 The concentration data is hourly grid data with a spatial resolution of 1km×1km. It is considered that the PM 2.5 The concentration data is the real value, and the PM 2.5 The concentration data is matched to the corresponding 1km×1km grid according to its latitude and longitude, and then the PM concentration of the data generated in step 2 at each observation station is calculated. 2.5 Concentration error, which is an hourly error and only includes PM at the observation site 2.5 The grid point corresponding to the concentration data is interpolated to all 1km×1km grid points in the target area using the natural point interpolation method. Step 4: Resample PM 2.5 The concentration data is corrected; PM is obtained through step 3 2.5 Concentration error data is applied to the PM generated in step 2. 2.5 concentration, and obtain the corrected PM 2.5 Grid data, which is hourly concentration value with a spatial resolution of 1km×1km; Step 5: Integrate PM based on satellite inversion 2.5 Concentration data; using satellite-based PM 2.5 The concentration data constrains the data generated in step 4. The PM generated in step 4 2.5 The concentration is a fusion of site observation data and numerical model data, taking into account the PM2.5 concentration based on satellite inversion. 2.5 Concentration data provides more reliable PM 2.5 Concentration value, so use this data to correct the data generated in step 4, the specific method is as follows: Calculate the ratio F for the i-th grid point in the target area and the j-th day in the target time period ij : In the above formula, PM ij，sate PM based on satellite inversion 2.5 The daily average concentration value of the data at the i-th grid point and the j-th day, PM ij，step4 The data generated in step 4 is the daily average concentration value at the i-th grid point and the j-th day. Since the data generated in step 4 is hourly, PM ij，step4 The 24-hour average value needs to be calculated; For the data generated in step 4, the PM of each grid point and each hour in the data is 2.5 The concentration values ​​are multiplied by the corresponding F ij , and finally obtain PM with high temporal and spatial resolution 2.5 Concentration data, which is hourly value with a spatial resolution of 1km×1km, and PM based on this data 2.5 Daily mean concentration and PM2.5 based on satellite retrieval 2.5 The daily average concentration was consistent.

2. The atmospheric pollution concentration inversion method based on multi-source information fusion according to claim 1 is characterized in that: The PM based on observation sites 2.5 The concentration data is hourly observation, and the latitude and longitude information of the ground observation station is also required; the PM2.5 concentration data based on satellite inversion is 2.5 The concentration data are daily average data with a spatial resolution of 1km×1km; the PM 2.5 The concentration data are hourly with a spatial resolution of 12 km × 12 km.

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