Dust haze discrimination standard construction method and device based on PM2.5 concentration weighting and medium

Abstract

The invention relates to the field of environmental monitoring, and discloses a PM2.5 concentration weighting-based dust-haze discrimination standard construction method, equipment and a medium. Compared with the existing three common dust-haze discrimination standards for setting a single threshold value based on visibility and relative humidity, the PM2.5 concentration weighting-based dust-haze discrimination standard construction method has the advantages that the PM2.5 concentration weighting-based dust-haze discrimination standard construction method is established by establishing the relationship between the PM2.5 mass concentration and the visibility and the relationship between the PM2.5 mass concentration and the relative humidity; and analyzing the occurrence frequency of fine particle aerosol pollution, providing a new standard of assigning different threshold values to different relative humidity intervals, and accurately identifying the haze day. According to the method, the characteristics of dust-haze fine particle aerosol pollution can be better represented, and fine particle aerosol pollution and non-fine particle aerosol pollution can be more accurately distinguished.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring, and in particular to a method, equipment and medium for constructing a haze discrimination standard based on PM2.5 concentration weighting. Background Art

[0002] Haze is a phenomenon characterized by large numbers of extremely fine dry dust particles uniformly suspended in the air, causing widespread air turbidity with horizontal visibility less than 10 km. This phenomenon gives distant bright objects a yellowish or reddish tint and dark objects a bluish tint. Niu et al. (Niu H, Hu W, Zhang D, et al. Variations of fine particle physiochemical properties during a heavy haze episode in the winter of Beijing. Science of the Total Environment, 2016, 571: 103-109.) analyzed the chemical composition and physical properties of fine particles in the air during and after the haze episode, revealing that haze is essentially fine particle aerosol pollution. Therefore, it is classified as atmospheric aerosol. Aerosols have a high hygroscopic growth characteristic. Haze in urban areas is often associated with high humidity, where strong air-to-particle transformation and hygroscopic growth of fine particle aerosols occur. Fog formation is inseparable from high relative humidity. The relative humidity and visibility conditions during the occurrence of haze and fog overlap to a certain extent. Misclassification of fog and haze affects the accuracy of haze day estimates, thereby affecting the spatiotemporal analysis characteristics of haze days. Accurately identifying haze days facilitates better analysis of the spatiotemporal variations of long-term historical haze events and is of great significance for haze pollution prevention and control.

[0003] The existing commonly used haze identification standards often use a single threshold value for ground meteorological parameters (relative humidity and visibility) to identify haze, but this method is bound to have misjudgments, and the extent to which these identification standards can characterize the characteristics of fine particle aerosol pollution remains to be verified. Based on the fine particulate matter characteristic of haze, our judgment of haze needs to include PM 2.5 Mass concentration is an important reference factor. The Ambient Air Quality Standard (GB 3095-2012) takes the 24-hour average PM 2.5 Mass concentration exceeds 75 μg / m 3 Defined as a fine particle pollution day.

[0004] PM 2.5 Monitoring of mass concentration of PM 2.5A large amount of historical data is missing. For a long time, meteorological data has been used instead of air environment monitoring data. The judgment of historical haze is mainly based on the relative humidity and visibility data to set different thresholds to divide haze days. To a certain extent, it reflects the low visibility and relative low humidity characteristics of fine particle aerosol pollution. 2.5 There is still a lack of mass concentration data to accurately identify haze days. Summary of the Invention

[0005] The purpose of the present invention is to propose a method, device and medium for constructing a haze discrimination standard based on PM2.5 concentration weighting, so as to solve the technical problem that the existing means of haze prediction based on PM2.5 concentration are still relatively insufficient.

[0006] Specifically, the present invention provides a method for constructing a haze discrimination standard based on PM2.5 concentration weighting, the method comprising the following steps: S1. PM2.5 at air environment monitoring stations 2.5 The quality of the data and ground meteorological observation data was screened to obtain the screened PM 2.5 data and surface meteorological observation data; S2, the screened PM 2.5 The data and ground meteorological observation data are spatially matched to obtain matched data samples; S3. Randomly sample the data sample by random sampling, and divide the sampled data into training samples and prediction samples according to a preset ratio; S4. Classify fine particle aerosol pollution days based on training samples and preset standards. Among the aerosol pollution days, analyze the frequency of fine particle pollution days corresponding to each visibility and relative humidity range; S5. Based on the frequency of fine particle pollution days corresponding to each visibility and relative humidity range, a new standard for differentiated visibility thresholds in different relative humidity ranges was constructed. The constructed new haze identification standard and three existing commonly used standards were used to identify haze days in the predicted sample data. The receiver operating characteristic (ROC) curve was used to evaluate the ability of the four standards to characterize the characteristics of fine particle aerosol pollution.

[0007] A storage medium stores instructions and data for implementing a method for constructing a haze discrimination standard based on PM2.5 concentration weighting.

[0008] A device for constructing a haze discrimination standard based on PM2.5 concentration weighting includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a method for constructing a haze discrimination standard based on PM2.5 concentration weighting.

[0009] The beneficial effects provided by the present invention are: based on the fine particle characteristics of haze, the present invention utilizes land surface PM 2.5 The relationship between mass concentration observation data, visibility and relative humidity is analyzed. The frequency of fine particle pollution days corresponding to each visibility and relative humidity range is analyzed, and the frequency is calculated according to the PM values ​​of different sizes. 2.5 By assigning certain weights to obtain weighted frequencies, a new haze identification standard is proposed, assigning differentiated visibility thresholds to different relative humidity ranges. When the weighted frequency of fine particle pollution exceeds the set frequency threshold, the observation group is considered to be haze. This method can better characterize the characteristics of haze fine particle aerosol pollution, improve the accuracy of using visibility and relative humidity to distinguish fine particle pollution from non-fine particle pollution, accurately identify haze days, and obtain long-term, large-scale historical haze spatiotemporal distribution data. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a simple flow chart of the method of the present invention; Figure 2 Based on China's regional environmental monitoring stations and meteorological stations from 2014 to 2020, the relationship between relative humidity, visibility and the frequency of fine particle aerosol pollution is shown, as well as a comparison of the fine particle characteristics of the three existing haze discrimination standards and the constructed new standard evaluated by the ROC curve; Figure 3 This is a comparison chart of the new haze identification standard and the three existing common standards for analyzing the trend changes of the annual average haze days in the region; Figure 4 It is a schematic diagram of the working of the hardware device of an embodiment of the present invention. DETAILED DESCRIPTION

[0011] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0012] Before formally explaining the present invention, the scheme of the present invention is first generally explained for easy understanding.

[0013] Please refer to Figure 1 The present invention provides a method for constructing a haze discrimination standard based on PM2.5 concentration weighting, comprising: S1. PM2.5 at air environment monitoring stations 2.5 The quality of the data and ground meteorological observation data was screened to obtain the screened PM 2.5 data and surface meteorological observation data; S2, the screened PM 2.5 The data and ground meteorological observation data are spatially matched to obtain matched data samples; S3. Randomly sample the data sample by random sampling, and divide the sampled data into training samples and prediction samples according to a preset ratio; S4. Classify fine particle aerosol pollution days based on training samples and preset standards. Among the aerosol pollution days, analyze the frequency of fine particle pollution days corresponding to each visibility and relative humidity range; S5. Based on the frequency of fine particle pollution days corresponding to each visibility and relative humidity range, a new standard for differentiated visibility thresholds in different relative humidity ranges was constructed. The constructed new haze identification standard and three existing commonly used standards were used to identify haze days in the predicted sample data. The receiver operating characteristic (ROC) curve was used to evaluate the ability of the four standards to characterize the characteristics of fine particle aerosol pollution.

[0014] The embodiment of the present invention is based on the land surface PM in China from 2014 to 2020. 2.5 By analyzing the relationship between mass concentration observation data, visibility, and relative humidity, a new haze identification standard with differentiated thresholds is proposed, which includes the following steps: S1. PM2.5 at air environment monitoring stations 2.5 The quality of the data and ground meteorological observation data was screened to obtain the screened PM 2.5 data and surface meteorological observation data; Regarding the PM used in this embodiment 2.5 To ensure data validity, stations with less than 300 days of data per year were excluded. Furthermore, to minimize the impact of meteorological factors, low visibility observations due to specific weather conditions such as precipitation, floating sediments, blowing sand, and sandstorms were excluded. These weather conditions usually have high relative humidity and are not representative of air pollution conditions.

[0015] S2, the screened PM 2.5 The data and ground meteorological observation data are spatially matched to obtain matched data samples; First, the distances between all environmental monitoring stations and meteorological observation sites were calculated. Then, the data of meteorological observation sites with distances less than 20 km from environmental monitoring stations were filtered out to spatially match the data of air environment monitoring stations and meteorological observation sites. The matched sites were evenly distributed across China.

[0016]

[0017] in d is the distance between the environmental monitoring station and the meteorological station, R is the Earth's equatorial radius, R is 6371.39km, It is the latitude and longitude of two points, both measured in radians.

[0018] S3. Randomly sample the data sample by random sampling, and divide the sampled data into training samples and prediction samples according to a preset ratio; Of all the samples obtained after spatial matching, 75% of the data were randomly selected as training samples for the subsequent construction of a new haze discrimination standard; the remaining 25% were used as prediction samples to evaluate the three existing common haze discrimination standards and the constructed new standard.

[0019] S4. Classify fine particle aerosol pollution days based on training samples and preset standards. Among the aerosol pollution days, analyze the frequency of fine particle pollution days corresponding to each visibility and relative humidity range; In step S4, fine particle aerosol pollution days are divided according to the training samples and the preset standards, specifically: according to the daily average PM2.5 mass concentration in the training samples, the fine particle aerosol pollution days are divided into 75 μg / m 3 Fine particle aerosol pollution days are divided according to the standard.

[0020] Step S4 is specifically as follows: The relative humidity a % is the step length, for visibility b km is the step length, a 、 b As the preset value, the original frequency of fine particle aerosol pollution corresponding to each relative humidity and visibility range is counted, and the original frequency is assigned a certain weight value to obtain the weighted frequency Fr :

[0021] in, is the number of days with fine particle aerosol pollution; is the number of samples corresponding to different visibility and relative humidity intervals, and K is the weight function.

[0022] In this paper, the relative humidity is set at a step size of 5% and the visibility is set at a step size of 1 km. The original frequency of fine particle aerosol pollution corresponding to each relative humidity and visibility interval is counted, and a certain weight value is assigned to the original frequency to obtain the weighted frequency value. Fr .

[0023] At 75 μg / m 3 As a benchmark, according to PM 2.5 The numerical value assigns a certain weight to the frequency of fine particle pollution in the corresponding relative humidity and visibility range. 2.5 The larger the value is, the greater the weight value is assigned, and the greater the probability of being judged as fine particle aerosol. The calculation formula of the weight function K is as follows:

[0024] Among them, is the average value of the PM 2.5 data corresponding to each interval.

[0025] S5. Based on the frequencies of the occurrence of fine particle pollution days corresponding to each visibility and relative humidity interval, a new standard for differential visibility thresholds in different relative humidity intervals is constructed. The prediction sample data is discriminated for haze days by using the newly constructed haze discrimination standard and three existing common standards respectively. The ability of the four standards to characterize the fine particle aerosol pollution characteristics is evaluated through the ROC curve.

[0026] In the present invention, a certain threshold is set for Fr, and different intervals of the new haze discrimination standard correspond to different thresholds. The area under the curve AUC value of this new standard is calculated by the ROC curve method. When the AUC value is greater than the three existing common standards, this new standard is adopted; otherwise, it is discarded. Finally, the interval of the haze discrimination standard corresponding to the largest AUC value is selected.

[0027]

[0028] AUC reflects the classification ability of the classifier, and this index is widely used for the comparison of ROC curves. The value range of AUC is 0 - 1. When 0.5 < AUC < 1, the closer AUC is to 1, the better the performance of the classifier; an AUC value of 0.5 is equivalent to random guessing, and the model performance is poor.

[0029] The effect of the present invention is as Figure 2 , shown in Figure 3.

[0030] Figure 2 Based on the environmental monitoring stations and meteorological stations in the Chinese region from 2014 to 2020, it is a relationship diagram of relative humidity, visibility and the occurrence frequency of fine particle aerosol pollution, as well as a comparison diagram evaluating the ability of three existing haze discrimination standards and the newly constructed standard for fine particle characteristics through the ROC curve. Figure 2 The legends in 2.5 are as follows: (a) Relationship diagram of relative humidity, visibility and the occurrence frequency of fine particle aerosol pollution (daily average PM 3 mass concentration greater than 75 μg / m

[0031] Figure 2 The phenomenon of differential distribution of the occurrence frequencies of fine particle aerosol pollution days corresponding to different relative humidities and visibilities is found. As the relative humidity decreases, the visibility corresponding to the occurrence frequency of fine particle aerosol pollution continuously increases. From Figure 2It can be seen that the AUC value of the new standard is 0.8015, which is a significant improvement compared to the previous standard. This shows that the new haze discrimination standard constructed by the present invention achieves the best classification performance. Compared with the three existing common haze discrimination standards, the new standard can better characterize the fine particle pollution characteristics of haze.

[0032] Figure 3 This is a comparison chart of the new haze identification standard and the three existing common standards for analyzing the trend changes in the annual average haze days in the region. Figure 3 The examples are as follows: (a) Beijing-Tianjin-Hebei; (b) Yangtze River Delta; (c) Pearl River Delta; (d) Sichuan Basin.

[0033] Taking the Beijing-Tianjin-Hebei region, the Yangtze River Delta, the Pearl River Delta, and the Sichuan Basin as examples, the three existing haze identification standards (standards one, two, and three) and the constructed new haze identification standard were used to identify haze days in the four regions from 1980 to 2020. Figure 3 It can be found that the interannual trends in haze days under the four criteria are generally similar, with the new standard showing good consistency with Standard 1. Standards 2 and 3 have a higher incidence of haze days compared to the new standard. Generally speaking, as relative humidity increases, the visibility corresponding to the frequency of high-fine particle pollution decreases. However, the three existing haze criteria set the visibility threshold at 10 km for all relative humidity ranges, inevitably resulting in a large number of non-fine particle pollution in high humidity ranges being misclassified as haze. The new standard better characterizes the fine particle aerosol characteristics of haze, distinguishing fine particle aerosol pollution from non-fine particle pollution with greater accuracy.

[0034] See Figure 4 , Figure 4 4 is a schematic diagram of the working of the hardware device of an embodiment of the present invention, wherein the hardware device specifically includes: a device 401 for constructing a haze discrimination standard based on PM2.5 concentration weighting, a processor 402 and a storage medium 403.

[0035] A device 401 for constructing a haze discrimination standard based on PM2.5 concentration weighting: The device 401 for constructing a haze discrimination standard based on PM2.5 concentration weighting implements the method for constructing a haze discrimination standard based on PM2.5 concentration weighting.

[0036] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the method for constructing a haze discrimination standard based on PM2.5 concentration weighting.

[0037] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the method for constructing a haze discrimination standard based on PM2.5 concentration weighting.

[0038] In general, the beneficial effects of the present invention are as follows: based on the fine particle characteristics of haze, the present invention utilizes land surface PM 2.5 The relationship between mass concentration observation data, visibility and relative humidity is analyzed. The frequency of fine particle pollution days corresponding to each visibility and relative humidity range is analyzed, and the frequency is calculated according to the PM values ​​of different sizes. 2.5 By assigning certain weights to obtain weighted frequencies, a new haze identification standard is proposed, assigning differentiated visibility thresholds to different relative humidity ranges. When the weighted frequency of fine particle pollution exceeds the set frequency threshold, the observation group is considered to be haze. This method can better characterize the characteristics of haze fine particle aerosol pollution, improve the accuracy of using visibility and relative humidity to distinguish fine particle pollution from non-fine particle pollution, accurately identify haze days, and obtain long-term, large-scale historical haze spatiotemporal distribution data.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for constructing a haze discrimination standard based on PM2.5 concentration weighting, characterized by: The following steps are involved: S1. PM2.5 at air environment monitoring stations 2.5 The quality of the data and ground meteorological observation data was screened to obtain the screened PM 2.5 data and surface meteorological observation data; S2, the screened PM 2.5 The data and ground meteorological observation data are spatially matched to obtain matched data samples; S3. Randomly sample the data sample by random sampling, and divide the sampled data into training samples and prediction samples according to a preset ratio; S4. Classify fine particle aerosol pollution days based on training samples and preset standards. Among the aerosol pollution days, analyze the frequency of fine particle pollution days corresponding to each visibility and relative humidity range; S5. Based on the frequency of fine particle pollution days corresponding to each visibility and relative humidity range, a new standard for differentiated visibility thresholds in different relative humidity ranges was constructed. The constructed new haze identification standard and three existing commonly used standards were used to identify haze days in the predicted sample data. The receiver operating characteristic (ROC) curve was used to evaluate the ability of the four standards to characterize the characteristics of fine particle aerosol pollution.

2. The method for constructing a haze discrimination standard based on PM2.5 concentration weighting according to claim 1, characterized in that: The specific formula for matching in step S2 is as follows: Where d is the distance between the environmental monitoring station and the meteorological station, R is the equatorial radius of the earth, Lat1Lat2 and Lon1Lon2 are the latitude and longitude of the environmental monitoring station and meteorological station.

3. The method for constructing a haze discrimination standard based on PM2.5 concentration weighting according to claim 2, characterized in that: In step S4, fine particle aerosol pollution days are divided according to the training samples and the preset standards, specifically: according to the daily average PM2.5 mass concentration in the training samples, the fine particle aerosol pollution days are divided into 75μg / m 3 Fine particle aerosol pollution days are divided according to the standard.

4. The method for constructing a haze discrimination standard based on PM2.5 concentration weighting according to claim 3, characterized in that: Step S4 is specifically as follows: For relative humidity, the step length is a%, and for visibility, the step length is b km. a and b are preset values. The original frequency of fine particle aerosol pollution corresponding to each relative humidity and visibility range is counted. The original frequency is assigned a certain weight value to obtain the weighted frequency Fr: in, is the number of days with fine particle aerosol pollution; N visrh is the number of samples corresponding to different visibility and relative humidity intervals, and K is the weight function.

5. The method for constructing a haze discrimination standard based on PM2.5 concentration weighting according to claim 4, characterized in that: The calculation of the weight function K is as follows: in, is the PM corresponding to each interval 2.5 The mean of the data.

6. The method for constructing a haze discrimination standard based on PM2.5 concentration weighting according to claim 4, characterized in that: Step S5 is as follows: A certain threshold is set for the frequency Fr. Different thresholds correspond to different new standard intervals for haze discrimination. The area under the curve (AUC) value of the new standard is calculated using the ROC curve method. If the AUC value is greater than the three existing commonly used standards, the new standard is adopted; otherwise, it is discarded. Finally, the new standard interval for haze discrimination corresponding to the largest AUC value is selected.

7. A storage medium, characterized in that: The storage medium stores instructions and data for implementing a method for constructing a haze discrimination standard based on PM2.5 concentration weighting as described in any one of claims 1 to 6.

8. A device for constructing a haze discrimination standard based on PM2.5 concentration weighting, characterized by: include: A processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a method for constructing a haze discrimination standard based on PM2.5 concentration weighting as described in any one of claims 1 to 6.

Images