Wide-range major air pollution identification method
By using DBSCAN algorithm and air pollution data to identify the spatial scope and duration of large-scale major air pollution events, the problem of identifying difficult points in the existing technology is solved, and the accurate identification and classification of major air pollution events is achieved.
Patent Information
- Application Number
- CN202411829676.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-12
- Publication Date
- 2025-04-11
AI Technical Summary
The prior art lacks methods to effectively identify large-scale major air pollution events, especially in identifying the spatial scope and duration of events.
The DBSCAN algorithm is used to combine air pollution concentration and population data to identify the spatial range and duration of pollution events, and to divide the stability and mobility of pollution events according to the movement of the pollution center of gravity.
It has achieved effective identification of large-scale major air pollution events, can accurately determine the spatial scope and duration of events, and distinguish different types of pollution events.
Smart Images
Figure CN119807812A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of ecological environment protection, and specifically relates to a method for identifying large-scale serious air pollution. Background Art
[0002] Large-scale major air pollution events are a hot topic of environmental protection and public health. However, there is currently a lack of identification methods for large-scale major air pollution events. Although my country has issued corresponding heavy pollution weather emergency plans at the provincial and municipal levels, which stipulate warning levels corresponding to different pollution conditions, these regulations are only applied to urban scales and forecast warnings. Major air pollution events usually have a wide spatial coverage, and some pollution events may last for more than a week. In this case, how to identify and select these pollution events is an important content of the present invention, which has the following technical difficulties:
[0003] (1) How to quantitatively identify the spatial scope of major pollution incidents. During the identification process, the spatial scope and the population exposed to pollution should be considered comprehensively.
[0004] (2) How to quantitatively identify the duration of a major pollution incident and, during the identification process, how to classify single pollution incidents with adjacent dates as one pollution incident.
[0005] (3) For major pollution incidents that last for two days or more, how to classify pollution incidents into two categories: stable and mobile, based on the movement of the pollution center of gravity.
[0006] Currently, there is a lack of methods to identify large-scale major air pollution events, and the existing emergency plans for heavy pollution weather are only applied to the urban scale and forecasting and warning, and do not give the spatial scope and time period of specific pollution events. Summary of the invention
[0007] The purpose of the present invention is to provide a method for identifying large-scale serious air pollution, aiming to effectively identify the scope and time period of serious pollution events.
[0008] In order to achieve the above-mentioned purpose of the invention, the technical scheme adopted by the present invention is as follows: a method for identifying large-scale serious air pollution, comprising the following steps: step 1, data collection and preprocessing; step 2, preliminary identification of the spatial scope of pollution events based on the DBSCAN algorithm; step 3, identification of pollution event boundaries; step 4, identification of the duration of serious pollution events; step 5, classification of serious pollution events.
[0009] Further, as a preferred technical solution of the present invention, the step 1 specifically includes the following steps: the data includes a first category of data and a second category of data, the first category of data is air pollution concentration grid data, with a spatial resolution of 1 km × 1 km and a daily time resolution; the second category of data is population grid data, with a spatial resolution of 1 km × 1 km and a yearly time resolution; the air pollution concentration grid data and the population grid data are integrated into 11 km × 11 km.
[0010] Further, as an optimal technical solution of the present invention, the step 2 specifically includes the following steps: based on the 11 km × 11 km air pollution concentration grid data, according to the pollution concentration threshold Thre1, the grid points with pollution concentration higher than the threshold are screened; the screened grid points include spatially densely distributed grid points and spatially dispersed grid points; the spatially densely distributed grid points belong to the coverage range of heavy pollution events; the spatially dispersed grid points do not belong to the coverage range of heavy pollution events; the DBSCAN algorithm is used to classify the spatially densely distributed grid points to obtain different clusters, and different clusters correspond to different pollution events on the same day; the pollution event takes into account the number of affected population factors, and sets a population threshold Thre2 of 10 million, and combines the 11 km × 11 km population grid data, for the grid points of the same cluster family, calculates its total population, and if the total population is less than 10 million, deletes the cluster.
[0011] Further, as a preferred technical solution of the present invention, the step 3 specifically includes the following steps: for each grid point belonging to each cluster, the boundaries surrounding these grid points are calculated, and all the grid points within the boundaries are defined as the grid points contained in the spatial range of the pollution event. The total area corresponding to these grid points is calculated. If the area is less than the spatial range threshold Thre3, that is, 100,000 square kilometers, the pollution event is deleted.
[0012] Further, as a preferred technical solution of the present invention, step 4 specifically includes the following steps: identifying the spatial scope of major pollution events on each day, for the scenario where major pollution events occur for n consecutive days and n ≥ 2, calculating the area of these pollution events on each day in the previous period, and the spatial areas of the pollution events on two consecutive days are Area 1 and Area 2 respectively, and the smaller value of the two is Area min , if the two spatial ranges overlap and the overlapping area is greater than 0.4*Area min , then these two adjacent pollution events are classified as the same pollution event, and this judgment is applied to each of the n days in turn; according to the start time and end time of the same pollution event, the corresponding duration days are obtained.
[0013] Further, as a preferred technical solution of the present invention, step 5 specifically includes the following steps: based on the spatial scope and duration of the pollution event, according to the duration, it is classified into a mild large-scale pollution event, i.e., a duration of 1 day, a moderate large-scale pollution event, i.e., a duration of 2 days, and a severe large-scale pollution event, i.e., a duration of 3 days or more.
[0014] Further, as a preferred technical solution of the present invention, for pollution events that last for 2 days or more, they are divided according to the moving range of the pollution gravity center, where the calculation formula of the pollution gravity center is as follows:
[0015]
[0016] In the above formula, X center and Y center are the horizontal and vertical coordinate values of the pollution center of gravity, k is the total number of grid points contained in the spatial range of the pollution event, APC i is the air pollution concentration value corresponding to the i-th grid point, X i and Y i are the horizontal and vertical coordinate values corresponding to the grid point respectively;
[0017] Calculate the pollution centers of the same pollution event on different days and the maximum spatial distance between these pollution centers. If the distance exceeds 300 kilometers, it is classified as a mobile large-scale pollution event, otherwise it is classified as a stable large-scale pollution event.
[0018] The method for identifying large-scale serious air pollution described in the present invention adopts the above technical solution and has the following technical effects compared with the prior art:
[0019] The present invention can effectively identify the scope and time period of major pollution incidents. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 is a flow chart of a method according to an embodiment of the present invention;
[0021] Figure 2 Different clusters of PM2.5 concentration distribution based on DBSCAN algorithm;
[0022] Figure 3 Schematic diagram of the boundaries corresponding to different clusters;
[0023] Figure 4 PM for 4 days 2.5 Schematic diagram of a large-scale major pollution incident;
[0024] Figure 5 PM for 2 days 2.5 Schematic diagram of a large-scale major pollution incident. DETAILED DESCRIPTION
[0025] The present invention is further explained below in detail with reference to the accompanying drawings so that those skilled in the art can more deeply understand the present invention and be able to implement it. However, the following reference examples are only used to explain the present invention and are not intended to limit the present invention.
[0026] Reference Figure 1 ,A large-scale major air pollution identification method, including the following steps: Step 1, data collection and preprocessing; In the specific implementation, the period from January 11, 2019 to January 14, 2019 in China is selected as an example, focusing on PM 2.5 Pollution. PM 2.5 The pollution concentration grid data comes from the CHAP public dataset (https: / / zenodo.org / records / 6398971), and the population grid data comes from the WorldPop dataset (https: / / www.worldpop.org / ). The spatial resolution of these two grid data is 1 km × 1 km. These data are combined into 11 km × 11 km to facilitate subsequent processing.
[0027] Step 2: Preliminarily identify the spatial scope of pollution events based on the DBSCAN algorithm; for each day in this embodiment, based on the PM2.5 pollution concentration grid data of 11 km × 11 km, according to the pollution concentration threshold Thre1 (150 μg / m 3 ), and select grid points with pollution concentration higher than the threshold. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to classify these selected grid points (using the dbscan function module in Matlab) to obtain different clusters, which correspond to different pollution events on the same day. Taking January 12, 2019 as an example, we get Figure 2 The different clusters shown in Figure 1 are further selected based on the population threshold (10 million). Figure 2 The population of the yellow cluster does not reach the threshold, so the cluster is deleted.
[0028] Step 3, identify the boundary of the pollution event; for each grid point belonging to the cluster, calculate the boundary surrounding these grid points, and all the grid points within the boundary are defined as the grid points contained in the spatial range of the pollution event. Calculate the total area corresponding to these grid points. If the area is less than the spatial range threshold Thre3 (100,000 square kilometers), delete the pollution event. Figure 2 The boundaries of the clusters corresponding to blue and green are as follows Figure 3 As shown in (a) and (b), Figure 3 After filling the blank grid points, the different clusters in the dataset all meet the condition that the spatial range is greater than 100,000 square kilometers.
[0029] Step 4, identify the duration of major pollution incidents; Based on the above steps, identify the spatial scope of major pollution incidents on each day from January 11, 2019 to January 14, 2019, and calculate the overlapping area of the spatial scope of pollution incidents on two adjacent days. If the overlapping area is greater than 40% of the spatial scope of the two adjacent days, the pollution incidents on these two days are classified as the same pollution incident. The corresponding duration can be obtained based on the start and end time of the same pollution incident. From January 11, 2019 to January 14, 2019, two pollution incidents were identified, lasting 4 days and 2 days respectively. The pollution incident lasting 4 days is as follows: Figure 4 As shown, a pollution event lasting 2 days is Figure 5 shown.
[0030] Step 5: Classification of major pollution incidents. The above steps obtain the spatial scope and duration of pollution incidents. According to the duration, they are classified. Figure 4 and Figure 5 They are severe large-scale pollution incidents and moderate large-scale pollution incidents respectively.
[0031] For pollution events that last for 2 days or more, they are divided according to the moving range of the pollution gravity center. The calculation formula of the pollution gravity center is as follows:
[0032]
[0033] In the above formula, X center and Y center are the horizontal and vertical coordinate values of the pollution center of gravity, k is the total number of grid points contained in the spatial range of the pollution event, APC i is the air pollution concentration value corresponding to the i-th grid point, X i and Y i are the horizontal and vertical coordinate values corresponding to the grid point respectively;
[0034] Calculate the pollution centers of the same pollution event on different days and the maximum spatial distance between these pollution centers. If the distance exceeds 300 kilometers, it is classified as a mobile large-scale pollution event, otherwise it is classified as a stable large-scale pollution event.
[0035] It can be further divided according to the movement range of the pollution center. Figure 4 and Figure 5 The triangles in the figure are the pollution centers. The maximum spatial distances of these pollution centers on different days are calculated and found to be less than 300 kilometers. Therefore, Figure 4 and Figure 5All of them are large-scale stable contamination events.
[0036] The specific implementation scheme described above further describes in detail the purpose, technical scheme 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 the 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 identifying large-scale serious air pollution, characterized in that: The following steps are involved: Step 1: Data collection and preprocessing; Step 2: Preliminary identification of the spatial scope of pollution events based on the DBSCAN algorithm; Step 3: Identify the pollution event boundary; Step 4: Identify the duration of major pollution incidents; Step 5: Classify major pollution incidents.
2. A method for identifying large-scale serious air pollution according to claim 1, characterized in that: The step 1 specifically includes the following steps: the data includes first-category data and second-category data, the first-category data is air pollution concentration grid data, with a spatial resolution of 1 km × 1 km and a temporal resolution of daily; the second-category data is population quantity grid data, with a spatial resolution of 1 km × 1 km and a temporal resolution of annual; the air pollution concentration grid data and the population quantity grid data are integrated into 11 km × 11 km.
3. A method for identifying large-scale serious air pollution according to claim 2, characterized in that: The step 2 specifically includes the following steps: based on the 11 km × 11 km air pollution concentration grid data, according to the pollution concentration threshold Thre1, the grid points with pollution concentration higher than the threshold are screened; the screened grid points include spatially densely distributed grid points and spatially dispersed grid points; the spatially densely distributed grid points belong to the coverage range of heavy pollution events; the spatially dispersed grid points do not belong to the coverage range of heavy pollution events; the DBSCAN algorithm is used to classify the spatially densely distributed grid points to obtain different clusters, and different clusters correspond to different pollution events on the same day; the pollution event takes into account the number of affected population factors, and sets a population threshold Thre2 of 10 million, and combines the 11 km × 11 km population grid data, and calculates the total population of the grid points in the same cluster family. If the total population is less than 10 million, the cluster is deleted.
4. A method for identifying large-scale serious air pollution according to claim 3, characterized in that: The step 3 specifically includes the following steps: for each grid point belonging to each cluster, the boundaries surrounding these grid points are calculated, and all the grid points within the boundaries are defined as the grid points contained in the spatial range of the pollution event. The total area corresponding to these grid points is calculated. If the area is less than the spatial range threshold Thre3, that is, 100,000 square kilometers, the pollution event is deleted.
5. A method for identifying large-scale serious air pollution according to claim 4, characterized in that: The step 4 specifically includes the following steps: identifying the spatial scope of major pollution events on each day, and for the scenario where major pollution events occur for n consecutive days and n ≥ 2, calculating the area of these pollution events on each day in the previous period, and the spatial areas of the pollution events on two consecutive days are Area 1 and Area 2 respectively, and the smaller value of the two is Area min , if the two spatial ranges overlap and the overlapping area is greater than 0.4*Area min , then these two adjacent pollution events are classified as the same pollution event, and this judgment is applied to each of the n days in turn; according to the start time and end time of the same pollution event, the corresponding duration days are obtained.
6. A method for identifying large-scale serious air pollution according to claim 5, characterized in that: The step 5 specifically includes the following steps: based on the spatial scope and duration of the pollution event, the event is classified into a mild large-scale pollution event lasting 1 day, a moderate large-scale pollution event lasting 2 days, and a severe large-scale pollution event lasting 3 days or more according to the duration.
7. A method for identifying large-scale serious air pollution according to claim 6, characterized in that: For pollution events that last for 2 days or more, they are divided according to the moving range of the pollution gravity center. The calculation formula of the pollution gravity center is as follows: In the above formula, X center and Y center are the horizontal and vertical coordinate values of the pollution center of gravity, k is the total number of grid points contained in the spatial range of the pollution event, APC i is the air pollution concentration value corresponding to the i-th grid point, X i and Y i are the horizontal and vertical coordinate values corresponding to the grid point respectively; Calculate the pollution centers of the same pollution event on different days and the maximum spatial distance between these pollution centers. If the distance exceeds 300 kilometers, it is classified as a mobile large-scale pollution event, otherwise it is classified as a stable large-scale pollution event.