A quantitative method for pollutant emissions from open burning of straw based on multi-source data fusion

Abstract

This invention relates to the field of environmental pollutant detection technology, specifically a quantitative method for pollutant emissions from open burning of straw based on multi-source data fusion. The method includes the following steps: based on air pollutant monitoring data, PM2.5, CO, and NOx concentrations are acquired using a sensor network; the thermal radiation intensity of the fire point is analyzed; meteorological data is collected; and the temporal changes of the fire point and meteorological parameters are combined to obtain a pollution source dataset. By integrating environmental monitoring sensors, satellite remote sensing, and ground meteorological data, the comprehensiveness and accuracy of pollution source monitoring are improved. The combination of fire point intensity and pollutant concentration temporal data optimizes the assessment of fire points and pollution hotspots. The comprehensive analysis of wind direction and meteorological conditions improves the accuracy of pollutant diffusion models. Dynamically adjusting pollutant release rates and using geographic information systems to correct diffusion models enhances the scientific rigor of pollution prediction and emission intensity assessment, providing more precise decision support for pollution prevention and control.

Description

Technical Field

[0001] This invention relates to the field of environmental pollutant detection technology, and in particular to a quantitative method for pollutant emissions from open burning of straw based on multi-source data fusion. Background Technology

[0002] The field of environmental pollutant detection technology encompasses the monitoring, analysis, and assessment of pollutants in the environment, particularly in the atmosphere, water bodies, and soil. The core of this field involves accurately and promptly detecting hazardous substances in the environment and analyzing their concentration, type, and source using various instruments and methods. These technologies are of great significance in environmental protection, public health, and safety, and are widely used in environmental monitoring, pollution source identification, environmental quality assessment, and other fields. Technical means include the use of gas sensors, chemical analysis instruments, remote sensing technology, and geographic information systems, aiming to achieve effective identification and quantitative assessment of environmental pollution.

[0003] The method for quantitatively calculating pollutant emissions from open straw burning based on multi-source data fusion refers to the quantitative calculation of pollutant emissions generated during open straw burning by combining multiple data sources. The technical aspects addressed in this patent include the comprehensive analysis of satellite remote sensing data, ground monitoring data, and meteorological data, combined with numerical models to quantitatively assess pollutant emissions. Specifically, it uses remote sensing images to monitor burning sites, analyzes the impact of atmospheric conditions on pollutant diffusion using meteorological data, and simultaneously acquires real-time pollutant concentration data from ground monitoring stations. By fusing these data from different sources, the accurate calculation of pollutant emissions during open straw burning can be achieved.

[0004] Current technologies for monitoring pollutant concentrations are limited by single data sources, resulting in incomplete spatial distribution information and biased identification of pollution hotspots. Fire point emission intensity assessments lack correlation analysis of pollutant concentration changes, leading to significant errors in pollutant release calculations. Analysis of pollutant diffusion trends fails to adequately consider factors such as wind direction, wind speed, humidity, and atmospheric stability, resulting in insufficient accuracy in predicting pollutant transport paths. Concentration change monitoring relies primarily on real-time data, lacking in-depth analysis of time-series concentration information, making it difficult to accurately identify areas of abnormal pollution concentration fluctuations. Pollutant emission assessments lack corrections for factors such as topography and vegetation cover, ignoring diffusion resistance and vegetation adsorption capacity, leading to biased predictions of pollutant diffusion ranges. The identification of abnormal pollution events lacks linkage analysis between emission intensity and concentration changes, causing delays in screening abnormally rising areas and affecting the efficiency of responding to sudden pollution events. These shortcomings affect the accuracy of pollutant diffusion simulations and reduce the effectiveness of pollution source tracing and pollution control measures. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a quantitative method for pollutant emissions from open burning of straw based on multi-source data fusion.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a quantitative method for pollutant emissions from open burning of straw based on multi-source data fusion, comprising the following steps:

[0007] S1: Based on air pollutant monitoring data, the sensor network is called to obtain PM2.5, CO, and NOx concentrations, analyze the thermal radiation intensity of fire points, collect meteorological data, and combine the temporal changes of fire points and meteorological parameters to obtain a pollution source dataset;

[0008] S2: Based on the pollution source dataset, filter areas with abnormal concentration fluctuations, analyze the coordinates and intensity of fire points, calculate the frequency of fire point activity, extract the time distribution information of pollutant concentration changes, and combine with time series data to obtain the distribution of pollution hotspot areas;

[0009] S3: Based on the distribution of the pollution hotspots, analyze the pollutant diffusion trend, calculate the pollutant transport rate and deposition rate, assess the concentration decrease gradient, calculate the diffusion radius in combination with the pollutant release rate, analyze the transport ratio of the prevailing wind direction by calling wind speed and wind direction data, and analyze the cross-regional transport impact in combination with meteorological conditions to obtain the pollutant diffusion range.

[0010] S4: Based on the pollutant diffusion range, analyze the current pollutant concentration change range, screen abnormally rising areas, calculate the pollutant release amount in combination with the heat radiation intensity of the fire point, and evaluate the pollutant retention time in combination with meteorological data, adjust the emission rate, and obtain the pollutant emission intensity.

[0011] As a further aspect of the present invention, the pollution source dataset includes PM2.5 concentration, CO concentration, NOx concentration, fire point thermal radiation intensity, air temperature, humidity, wind speed, and meteorological time series data. The distribution of pollution hotspot areas includes areas of abnormal concentration fluctuations, fire point intensity, fire point activity frequency, and pollutant concentration temporal distribution. The pollutant diffusion range includes pollutant diffusion trend, pollutant transport rate, pollutant deposition rate, pollutant concentration decrease gradient, pollutant diffusion radius, and pollutant transport ratio. The pollutant emission intensity includes the magnitude of pollutant concentration changes, areas of abnormal pollutant increase, pollutant release amount, pollutant retention time, and pollutant emission rate.

[0012] As a further aspect of the present invention, the specific steps for obtaining a pollution source dataset by using a sensor network to acquire PM2.5, CO, and NOx concentrations based on air pollutant monitoring data, analyzing the thermal radiation intensity of fire points, collecting meteorological data, and combining the temporal changes of fire points and meteorological parameters are as follows:

[0013] S101: Acquire PM2.5, CO, and NOx concentration data from the environmental monitoring sensor network, call up real-time measurement values ​​from monitoring points, filter out abnormal data and remove values ​​exceeding limits, sort pollutant concentration data according to data timestamps, calculate the average pollutant concentration within the time series, analyze the changing trend, and generate pollutant concentration time series data.

[0014] S102: Based on the pollutant concentration time series data, analyze the thermal radiation intensity data of the fire point area of ​​the satellite remote sensing equipment, match the monitoring time period of the fire point area, calculate the correlation between the thermal radiation intensity of the fire point and the pollutant concentration, and generate the fire point pollution impact coefficient.

[0015] S103: Based on the fire point pollution impact coefficient, collect meteorological parameters from ground meteorological monitoring stations, calculate the impact factor of wind speed changes on pollutant diffusion, and generate a pollution source dataset by combining the fire point pollution impact coefficient.

[0016] As a further aspect of the present invention, based on the pollution source dataset, the specific steps for screening areas with abnormal concentration fluctuations, analyzing fire point coordinates and intensity, calculating fire point activity frequency, extracting the temporal distribution information of pollutant concentration changes, and combining this with time-series data to obtain the distribution of pollution hotspot areas are as follows:

[0017] S201: Based on the pollution source dataset, call the pollutant concentration data to screen for abnormal concentration fluctuation areas, calculate the change range of pollutant concentration within the differentiated time interval, remove the fluctuation areas, extract the spatial coordinates of the concentration change areas, screen the areas where the pollutant concentration change rate exceeds the set threshold, and generate abnormal pollution fluctuation areas.

[0018] S202: Based on the pollution abnormal fluctuation area, analyze the fire point coordinate data, match the fire point location and fire point intensity data, calculate the fire point activity frequency and set a high-frequency threshold, filter high-frequency fire point areas, and generate high-frequency fire point areas.

[0019] S203: Call the high-frequency fire point area, extract the time distribution information of pollutant concentration changes, match the concentration time series data, calculate the pollutant concentration change trend, and generate the distribution of pollution hotspot areas.

[0020] As a further aspect of the present invention, based on the distribution of the pollution hotspot areas, the specific steps for analyzing pollutant diffusion trends, calculating pollutant transport rates and deposition rates, assessing concentration decrease gradients, calculating diffusion radius by combining pollutant release rates, analyzing the prevailing wind transport ratio using wind speed and direction data, and analyzing the impact of cross-regional transport in conjunction with meteorological conditions to obtain the pollutant diffusion range are as follows:

[0021] S301: Based on the distribution of the pollution hotspot areas, call the wind speed and wind direction data, analyze the diffusion trend of pollutants along the main wind direction, analyze the transmission rate of pollutants under different wind speed conditions, screen the pollutant transmission rate interval within the range of wind speed variation, analyze the diffusion trajectory of pollutants under different wind direction conditions, calculate the pollutant diffusion ratio along the main wind direction, and generate the pollutant diffusion trend along the wind direction.

[0022] S302: Based on the pollutant wind direction diffusion trend, call humidity and atmospheric stability data, calculate pollutant deposition rate, screen areas with deposition rate greater than the threshold, analyze the pollutant concentration decrease relationship, and generate pollutant deposition gradient;

[0023] S303: Based on the pollutant deposition gradient, the pollutant release rate at the fire point is called, and combined with the pollutant transport path, the impact of meteorological conditions on pollution diffusion is analyzed, the cross-regional transport index of pollutants is calculated, and the pollutant diffusion range is generated.

[0024] As a further aspect of the present invention, the formula for calculating the pollutant sedimentation rate is as follows:

[0025] ;

[0026] in, Indicates the pollutant settling rate. Indicates the pollutant concentration at the starting point of the path. Indicates the pollutant concentration at the end of the path. Indicates path length. This indicates the current relative humidity in the area where the path is located. This represents the average humidity level within the area. Indicates the atmospheric stability level of the area where the path is located. This represents the average level of stability within the region.

[0027] As a further aspect of the present invention, based on the pollutant diffusion range, the current pollutant concentration change range is analyzed, abnormally rising areas are screened, the pollutant release amount is calculated in conjunction with the fire point thermal radiation intensity, and the pollutant retention time is assessed in conjunction with meteorological data. The emission rate is then adjusted to obtain the specific steps for pollutant emission intensity.

[0028] S401: Based on the pollutant diffusion range, call the current pollutant concentration monitoring data, analyze the concentration change range of the monitoring points, screen the areas where the pollutant concentration increase exceeds the set threshold in a short period of time, remove the low-amplitude change areas, extract the spatial coordinates of the abnormal rise area, calculate the concentration change rate, and generate the abnormal rise area of ​​pollutants.

[0029] S402: Based on the abnormal rise area of ​​pollutants, call the thermal radiation intensity data of the fire point, calculate the pollutant release amount, match the pollutant release intensity with the fire point distribution, screen the areas where the pollutant release amount exceeds the set standard, and generate the pollutant release amount distribution.

[0030] S403: Based on the pollutant release distribution, combined with meteorological data, assess the pollutant concentration changes, analyze the pollutant retention time, adjust the emission rate, calculate the influence coefficient of concentration changes during pollutant transport, and generate the pollutant emission intensity.

[0031] As a further aspect of the present invention, the formula for calculating the total amount of pollutants released is specifically as follows:

[0032] ;

[0033] in, Indicates the total amount of pollutants released. Indicates the intensity of thermal radiation at the point of ignition. This indicates the actual burning area corresponding to the ignition point. This indicates the atmospheric temperature during the current observation period. This indicates the average temperature of the area over the past 24 hours. This indicates the actual vegetation height within the fire zone. This indicates the average vegetation height in the area.

[0034] As a further aspect of the present invention, the method further includes, S5: based on the pollutant emission intensity, calling terrain data to calculate diffusion resistance, assessing the impact of vegetation cover on pollutant adsorption capacity, analyzing the impact of ground elevation and wind field changes on flow path, and adjusting the diffusion time scale in conjunction with meteorological data to obtain a quantitative pollutant emission scheme.

[0035] The pollutant emission quantification scheme includes pollutant diffusion resistance, vegetation adsorption capacity, pollutant flow path, and pollutant diffusion time scale.

[0036] As a further embodiment of the present invention, S501: Based on the pollutant emission intensity, the topographic data of the fire point area is called to analyze the influence of terrain undulation on the pollutant diffusion path, calculate the diffusion resistance of the differentiated elevation area, screen the diffusion-obstructed area, calculate the diffusion rate attenuation ratio, and generate the pollutant diffusion resistance distribution.

[0037] S502: Based on the pollutant diffusion resistance distribution, call the vegetation cover information, analyze the adsorption capacity of vegetation for pollutants, screen areas where the adsorption rate exceeds the set threshold, analyze the influence of vegetation on pollutant flow, and generate the pollutant vegetation adsorption coefficient.

[0038] S503: Based on the pollutant adsorption coefficient of vegetation, combined with ground elevation data, analyze the pollutant flow path along the terrain, match meteorological data to adjust the diffusion time scale, calculate the transport adjustment amount of pollutants under different terrain conditions, and generate a quantitative pollutant emission scheme.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0040] This invention improves the comprehensiveness and accuracy of pollution source monitoring by integrating environmental monitoring sensors, satellite remote sensing, and ground meteorological data. The combination of fire intensity and pollutant concentration time series data optimizes the assessment of fire points and pollution hotspots. The comprehensive analysis of wind direction and meteorological conditions improves the accuracy of pollutant diffusion models. Dynamically adjusting pollutant release and using geographic information systems to correct diffusion models enhances the scientific nature of pollution prediction and emission intensity assessment, providing more accurate pollution prevention and control decision support. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0043] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0044] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0045] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0046] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0047] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0048] Please see Figure 1 A quantitative method for pollutant emissions from open burning of straw based on multi-source data fusion includes the following steps:

[0049] S1: Obtain air pollutant concentration data, call the environmental monitoring sensor network to monitor PM2.5, CO, and NOx concentrations, analyze the thermal radiation intensity of the fire point area through satellite remote sensing equipment (where fire point refers to the fire point of open burning of straw), collect temperature, humidity, and wind speed data collected by ground meteorological monitoring stations, analyze the time-series changes of meteorological parameters, and combine fire point and meteorological data to obtain pollution source dataset;

[0050] S2: Based on the pollution source dataset, pollutant concentration data is used to screen areas with abnormal concentration fluctuations. Fire intensity is analyzed by fire point coordinates, and the frequency of fire point activity is calculated based on remote sensing monitoring data. The time distribution information of pollutant concentration changes is extracted, and the distribution of pollution hotspot areas is obtained by combining the concentration time series data.

[0051] S3: Based on the distribution of pollution hotspots, analyze the diffusion trend of pollutants along the main wind direction by calling wind speed and wind direction data, calculate the pollutant transport rate, assess the pollutant deposition rate by humidity and atmospheric stability, analyze the pollutant concentration decrease gradient, analyze the diffusion radius by calling the pollutant release rate at the fire point, analyze the impact of differentiated meteorological conditions on pollution diffusion by combining pollutant transport paths and transport trends, calculate the cross-regional transport ratio of pollutants, and obtain the pollutant diffusion range.

[0052] S4: Based on the pollutant diffusion range, analyze the concentration change range by calling the current pollutant concentration monitoring data, screen out abnormally rising areas, calculate the pollutant release amount by the heat radiation intensity of the fire point, assess the pollutant concentration change by combining meteorological data, analyze the pollutant retention time, adjust the emission rate, and obtain the pollutant emission intensity.

[0053] S5: Based on pollutant emission intensity, topographic data of the fire point area is used to calculate pollutant diffusion resistance, vegetation adsorption capacity is assessed through vegetation cover information, pollutant flow path is analyzed based on ground elevation, and diffusion time scale is adjusted in combination with meteorological data to obtain a quantitative pollutant emission scheme.

[0054] The pollution source dataset includes PM2.5 concentration, CO concentration, NOx concentration, fire point thermal radiation intensity, air temperature, humidity, wind speed, and meteorological time series data. The distribution of pollution hotspot areas includes areas of abnormal concentration fluctuations, fire point intensity, fire point activity frequency, and pollutant concentration temporal distribution. The pollutant diffusion range includes pollutant diffusion trends, pollutant transport rates, pollutant deposition rates, pollutant concentration decrease gradients, pollutant diffusion radius, and pollutant transport ratios. The pollutant emission intensity includes the magnitude of pollutant concentration changes, areas of abnormal pollutant increases, pollutant release amounts, pollutant retention time, and pollutant emission rates. The pollutant emission quantification scheme includes pollutant diffusion resistance, vegetation adsorption capacity, pollutant flow paths, and pollutant diffusion timescales.

[0055] The specific steps of S1 are as follows:

[0056] S101: Acquire PM2.5, CO, and NOx concentration data from the environmental monitoring sensor network, call up real-time measurement values ​​from monitoring points, filter out abnormal data and remove values ​​exceeding limits, sort pollutant concentration data according to data timestamps, calculate the average pollutant concentration within the time series, analyze the changing trend, and generate pollutant concentration time series data.

[0057] First, it's necessary to clarify the deployment location of each type of sensor and the type of pollutant being monitored. For example, Type A sensors deployed in urban industrial areas collect PM2.5 concentrations, while Type B sensors deployed near major traffic arteries primarily collect CO and NOx data. Each type of sensor automatically uploads monitoring data at fixed time intervals. Each data entry includes a timestamp, latitude and longitude, pollutant type, and its corresponding concentration value. When accessing real-time measurement values ​​from monitoring points, the system retrieves the corresponding real-time data record based on the unique ID of each monitoring point and summarizes it in chronological order by timestamp, establishing time series for each pollutant type. For example, from 10:00 to 12:00 on March 12, 2025, three monitoring points in a certain location uploaded a total of 36 data entries. Data is collected every 5 minutes to form a continuous concentration record. During the screening of abnormal data, the average value and fluctuation range of the monitoring point over the past week are calculated first. The current concentration value of a pollutant is compared with the historical fluctuation level of its location. If the concentration value at a certain moment differs significantly from the daily average value of the past 7 days, exceeding a fixed fluctuation range (e.g., exceeding three times the daily average), it is considered abnormal. For example, if the average PM2.5 value at a monitoring point over the past week was 60 μg / m³, with a fluctuation range of approximately ±10 μg / m³, values ​​exceeding 60 ± 30 μg / m³ are considered abnormal. Simultaneously, the system sets a safe concentration threshold for pollutants, and data exceeding this upper limit is directly discarded. For example, if the threshold for PM2.5 is set to 1... The threshold values ​​were set at 50 μg / m³, CO at 10 mg / m³, and NOx at 0.5 ppm. Values ​​exceeding these limits were directly marked as invalid and removed from the dataset. During the sorting process, the retained valid data records were arranged in ascending order by timestamp to ensure continuity of concentration records at different times from the same monitoring point, facilitating subsequent time-series analysis. When calculating the mean pollutant concentration within a time series, the system selected the analysis period, such as hourly, 6-hourly, or daily. The mean was calculated by summing all valid concentration values ​​within that time interval and dividing by the number of records. For example, if 12 PM2.5 data values ​​were recorded between 00:00 and 01:00 on March 12, 2025, with values ​​[60, 62, 61, 59, ...], ... The values ​​are 63, 60, 61, 64, 65, 62, 60, 61 μg / m³, and the average is the sum of these values ​​divided by 12, resulting in an average of approximately 61.5 μg / m³. When analyzing the trend of pollutant concentration changes, the system compares the average values ​​over multiple adjacent analysis periods. A positive difference indicates an increase in pollutant concentration, while a negative difference indicates a decrease. If the difference between two average values ​​is less than 5 μg / m³, it is considered as no significant change. The system sets a judgment range; for example, PM2.5 concentration is divided into low concentration range (0-35 μg / m³), medium concentration range (36-75 μg / m³), and high concentration range (above 76 μg / m³), as in a certain location's PM2.5 concentration range.5. The average concentration was 68 μg / m³ at 03:00 and 82 μg / m³ at 06:00, indicating a shift from medium to high concentration, signifying an increase in pollution levels. Finally, the average values ​​and trends from all time periods were integrated into a time series to obtain continuous data on pollutant concentration changes.

[0058] S102: Based on the time series data of pollutant concentration, analyze the thermal radiation intensity data of the fire point area of ​​the satellite remote sensing equipment, match the monitoring time period of the fire point area, calculate the correlation between the thermal radiation intensity of the fire point and the pollutant concentration, and generate the fire point pollution impact coefficient.

[0059] The system extracts time-period data corresponding to fire point information recorded by satellite remote sensing equipment. This information includes specific latitude and longitude coordinates, fire point detection time, and thermal radiation intensity (TRI). TRI measures the energy release of the fire point. The system extends the fire point's timestamp by 15 minutes and extracts data from monitoring points within the pollutant time series that are closest to the fire point's latitude and longitude within that time period. For example, if a fire point was recorded on March 12, 2025, at 10:45 AM with a TRI of 65 MW, the system extracts data from multiple monitoring points within 5 kilometers of the fire point between 10:30 AM and 11:00 AM. If PM2.5 concentration is found to be 75 μg / m³, CO 1.5 mg / m³, and NOx 0.2 ppm, the system establishes a correlation between these data and the fire point. After matching multiple fire point data, the system batch integrates these records and compares the pollutant concentration changes corresponding to different fire points with the TRI. The system compares the concentration values ​​between the matched records for each fire point. To determine whether a high-intensity fire point causes a significant change in pollutant concentration within the same time window, such as when the fire point's thermal radiation intensity is 80 MW, and the nearby PM2.5 concentration rises from 60 μg / m³ to 90 μg / m³ within 30 minutes, a significant correlation is considered. Based on this, a fire point pollution impact coefficient is generated. The system normalizes pollutant concentrations according to different weights, which are set based on the proportion of the main components of pollutants in the region in the past. For example, if PM2.5 accounts for 6% of all pollutants in a certain area... If PM2.5 accounts for 5%, CO accounts for 25%, and NOx accounts for 10%, then the pollution impact coefficient is the sum of the normalized concentration values ​​of these three factors multiplied by their respective weights. For example, if the normalized PM2.5 concentration is 0.9, CO is 0.4, and NOx is 0.3, then the pollution impact coefficient is 0.65×0.9+0.25×0.4+0.10×0.3=0.765. The system uses this value as an indicator of the intensity of the pollution impact of the fire point at a specific time period. The higher the value, the greater the degree of pollution aggravation that the fire point may cause at that time.

[0060] S103: Based on the fire point pollution impact coefficient, collect meteorological parameters from ground meteorological monitoring stations, calculate the impact factor of wind speed changes on pollutant diffusion, and generate a pollution source dataset by combining the fire point pollution impact coefficient.

[0061] The system will further collect real-time meteorological data from ground meteorological monitoring stations covering the fire area, especially four parameters: wind speed, wind direction, temperature, and humidity. Meteorological monitoring data is generally uploaded to the central system in 10-minute sampling units. Through spatial matching with the fire point coordinates, meteorological station data points within 10 kilometers of the fire point and within 30 minutes before and after the fire point are selected as valid matches. This further identifies meteorological stations upwind of the fire point. When calculating the impact of wind speed changes on pollutant diffusion, the system first identifies the change in pollutant concentration at monitoring points downwind of the fire point and records the average wind speed value for the corresponding time period. The ratio between the two is used as the pollutant diffusion impact factor. For example, if a fire point is located in a southeast-east wind with a wind speed of 4.0 m / s, the PM2.5 concentration at its downwind monitoring point was 68 μg / m³ before the fire point appeared, and rose to [a higher value] within 30 minutes after the fire point appeared. The concentration was 84 μg / m³, with a change of 16 μg / m³. Dividing this value by the wind speed yielded a diffusion impact factor of 4.0, indicating a close relationship between pollution diffusion and wind speed; the higher the wind speed, the wider the pollution range. Combining this with the fire point pollution impact coefficient (e.g., 0.76) and the wind speed impact factor of 4.0, the comprehensive pollution source intensity of the fire point is calculated as 0.76 × 4.0 = 3.04. This indicates that the fire point has a significant pollution propagation capacity under the given time period and meteorological conditions. Finally, this information is integrated with the fire point's basic attributes to generate a pollution source dataset, including fields such as fire point number, latitude and longitude, recording time, thermal radiation intensity, concentrations of three types of pollutants and their time series mean, pollution impact coefficient, wind speed, wind direction, diffusion impact factor, and comprehensive pollution source intensity. This forms a standardized pollution source dataset that can be used for pollution analysis and scheduling.

[0062] The specific steps of S2 are as follows:

[0063] S201: Based on the pollution source dataset, call the pollutant concentration data to screen for abnormal concentration fluctuation areas, calculate the change range of pollutant concentration within the differentiated time interval, remove the fluctuating areas, extract the spatial coordinates of the concentration change areas, screen the areas where the pollutant concentration change rate exceeds the set threshold, and generate abnormal pollution fluctuation areas.

[0064] First, information such as pollutant type, spatial location, monitoring time, and concentration values ​​are extracted from the pollution source dataset. A time-series index is then established, and the data is organized into a continuous time series according to monitoring point ID and time order. For example, monitoring point ID001 recorded PM2.5 concentration data from 00:00 to 12:00 on March 12, 2025, with data in groups of 5 minutes, resulting in 144 data points. The system calculates the concentration difference between any two adjacent time points to determine whether the change exceeds a preset concentration fluctuation judgment standard. This judgment standard is determined by statistically analyzing the top 10% of samples with the most frequent changes in historical data from the same period. To analyze the situation, a threshold for concentration change is set. For example, if the PM2.5 fluctuation threshold is set to 10 μg / m³, then when the concentration change at two consecutive points within a certain time period exceeds this value, it is marked as a fluctuation segment. If the PM2.5 concentration at a monitoring point increases sharply from 65 μg / m³ to 78 μg / m³ between 01:30 and 01:35, a change of 13 μg / m³, exceeding the threshold of 10 μg / m³, then this interval is marked as an abnormal fluctuation area. When calculating the change in pollutant concentration within a differentiated time interval, the system will add one cycle before and after the abnormal segment for comparison, and compare the maximum concentration within three consecutive cycles with... The minimum value is subtracted to obtain the maximum fluctuation amplitude, which is then compared with multiple monitoring points. By comparing whether the fluctuation amplitude is higher than the 95th percentile of the concentration fluctuation within 7 days at each point, it is determined whether it belongs to an abnormal change region. During the process of eliminating fluctuation regions, the system removes all time periods marked as fluctuation regions from the master data sequence, retaining only segments with relatively stable continuous changes or concentrated abrupt changes. When extracting the spatial coordinates of concentration abrupt change regions, the system records the latitude and longitude of the monitoring point corresponding to the start time of the abrupt change segment. If multiple adjacent monitoring points experience abrupt changes in the same time period, the minimum envelope surface constructed from these points is used as the output spatial coordinates of the abrupt change region. When screening areas where the rate of change of pollutant concentration exceeds a set threshold, the system first calculates the rate of change of concentration at each monitoring point per unit time, i.e., the concentration value that changes every 5 minutes, and then compares it with the rate threshold. For example, if the rate threshold is set to 2 μg / m³ per minute, i.e. 10 μg / m³ every 5 minutes, and the concentration rise rate reaches 12 μg / m³ every 5 minutes in a certain period of time, it is determined to be an area with abnormal rate. The corresponding monitoring point ID and geographical coordinates are extracted and marked as abnormal rate points. Finally, the system merges all points that meet the conditions with the time period to form a pollution abnormal fluctuation area with both temporal and spatial characteristics.

[0065] S202: Based on the abnormal pollution fluctuation area, analyze the fire point coordinate data, match the fire point location and fire point intensity data, calculate the fire point activity frequency and set a high-frequency threshold, filter high-frequency fire point areas, and generate high-frequency fire point areas.

[0066] First, fire point monitoring data for the corresponding time period is extracted from the satellite remote sensing database. Extracted fields include fire point latitude and longitude, recording time, and thermal radiation intensity value. A fire point activity record set is constructed, and each fire point location is grouped and statistically analyzed by time. During the matching of fire point location and intensity data, the spatial envelope coordinates of each abnormal fluctuation zone are spatially overlapped with the fire point latitude and longitude. The overlap is determined by whether the fire point center falls within the spatial boundary of the abnormal fluctuation zone. If so, a mapping relationship between the fire point and the fluctuation zone is established. When calculating the fire point activity frequency, clustering is performed with fire point latitude and longitude accurate to 0.01 degrees (approximately 1 kilometer range). The number of fire points appearing in each cluster unit within a month is recorded, and the fire point activity level is determined. If a cluster unit has a certain number of fire points appearing within 30 days, the cluster unit is considered active. If the frequency reaches 20 times, the average daily occurrence frequency is 0.66 times / day. A high-frequency threshold is set based on the statistical results. This threshold is set with reference to the 85th percentile of the average daily frequency of fire points in the historical distribution. For example, if the 85th percentile of the historical monthly data is 0.6 times / day, then the high-frequency fire point judgment threshold is set to 0.6 times / day. When screening high-frequency fire point areas, the average daily frequency of all clustered areas is calculated, and those exceeding the threshold are classified as high-frequency fire point areas. At the same time, the average and maximum fire point intensity of the area are recorded for later use. For example, if the latitude and longitude range of a certain area is (104.32°E-104.34°E, 30.66°N-30.68°N), the cumulative number of fire point occurrences in the month is 25, and the average thermal radiation intensity is 68MW, then this area is marked as a high-frequency fire point area.

[0067] S203: Call the high-frequency fire point area, extract the time distribution information of pollutant concentration changes, match the concentration time series data, calculate the pollutant concentration change trend, and generate the distribution of pollution hotspot areas;

[0068] Based on the spatial range of each high-frequency fire zone, pollutant monitoring points within the corresponding area are located, and pollutant concentration records of these monitoring points within one hour before and after the fire activity period are extracted. For example, if a thermal radiation intensity of 70MW is recorded at the center point of a high-frequency fire zone between 10:00 and 10:10 on March 10, 2025, the system extracts PM2.5, CO, and NOx concentration data from monitoring points within a 5km radius of the fire zone between 09:00 and 11:00. A pollutant concentration time series is constructed for each monitoring point in minutes. When matching the concentration time series data, the time series data are aligned according to the time of fire occurrence. The concentration change curves of multiple monitoring points before and after the fire are synchronously integrated, and the concentration mean is smoothed over time. For example, the concentration curves of the 30 minutes before and after the fire are compared. After calculating and comparing the minute-by-minute average PM2.5, if the average concentration rises by more than 15 μg / m³ in the last 30 minutes, this period is marked as a pollution increase segment. In calculating the trend of pollutant concentration changes, the rate of concentration rise and fall is statistically analyzed every 10 minutes. The rate is classified and archived. For example, 0-0.5 μg / m³ / min is a slight change, 0.5-1.5 is a moderate change, and greater than 1.5 is a drastic change. The trend level of each monitoring point under the influence of fire points is statistically analyzed and spatially aggregated. Finally, the system classifies areas with drastic changes in pollutant concentration, continuous rise, and high spatial concentration as pollution hotspot areas, marking their spatial coordinate range, trend direction, and peak time period, generating pollution hotspot area distribution data, and forming key areas related to the correlation between pollution evolution and fire point activity.

[0069] The specific steps for S3 are as follows:

[0070] S301: Based on the distribution of pollution hotspots, wind speed and direction data are used to analyze the diffusion trend of pollutants along the main wind direction, analyze the transmission rate of pollutants under different wind speed conditions, screen the pollutant transmission rate range within the range of wind speed variation, analyze the diffusion trajectory of pollutants under different wind direction conditions, calculate the pollutant diffusion ratio along the main wind direction, and generate the pollutant diffusion trend along the wind direction.

[0071] First, the central coordinates of each pollution hotspot area and its corresponding time range are determined. Based on this, wind speed and direction data that coincide with the time period of the hotspot area and are spatially overlapping or within 10 kilometers are extracted from surface meteorological monitoring data. Each wind speed record includes fields such as timestamp, wind speed value (in m / s), wind direction angle (in degrees), and station latitude and longitude. For example, if a hotspot area is located near (104.33°E, 30.67°N) between 10:00 and 12:00 on March 10, 2025, the system matches wind speed and direction data records from three nearby meteorological stations during this time period. When analyzing the diffusion trend of pollutants along the prevailing wind direction, the direction of all wind direction records is statistically analyzed, and the wind direction is divided into 16 categories based on angle. The system classifies wind directions, each occupying a 22.5° angular interval. The wind direction category with the highest statistical frequency is the prevailing wind direction. For example, if the prevailing wind direction is southeast (135°) during the aforementioned time period, the system uses this prevailing wind direction as a reference axis to analyze the changing trend of pollutant concentrations at multiple monitoring points in the hotspot area downstream of the prevailing wind direction. It compares the concentration values ​​at monitoring points at different distances to determine whether pollutants show a decreasing trend with increasing distance. If the PM2.5 concentrations at sampling points every 2 kilometers along the prevailing wind direction are 95 μg / m³, 83 μg / m³, 74 μg / m³, and 65 μg / m³ respectively, then an effective diffusion trend is considered to exist. When analyzing the transport rate of pollutants under differentiated wind speed conditions, the wind speed interval within the monitoring time period is divided into [0-1 m / s]. Four wind speed levels were established: [1-3 m / s], [3-5 m / s], and [above 5 m / s]. The concentration change time of pollutants at downstream points along the prevailing wind direction was recorded for each wind speed level. The time it took for pollutants to travel from the origin to the downstream point was also measured. For example, at a wind speed of 3-5 m / s, if the delay in PM2.5 traveling from the hotspot center to a point 5 km southeast and reaching its peak was 40 minutes, the transmission rate was 7.5 km / h. After recording the transmission rates corresponding to each wind speed level, interval filtering was performed. When filtering the pollutant transmission rate intervals within the wind speed variation range, all transmission rates were distributed and sorted to identify the transmission rates located in the highest frequency interval. For example, at a medium wind speed [3-5 m / s], the transmission rate was concentrated at 6... In the -8km / h range, this range is selected as the effective velocity range. When analyzing the diffusion trajectory of pollutants under differentiated wind direction conditions, the pollutant concentration at downstream points within different wind direction angles is sampled and compared to construct concentration trajectory sequences corresponding to different wind directions. The concentration change trajectory in the main wind direction is compared with the difference in other directions. When calculating the pollutant diffusion ratio in the main wind direction, the maximum diffusion distance and average concentration amplitude downstream of the main wind direction are divided by the corresponding value downstream of the secondary main wind direction. For example, if the average concentration decrease within 5 kilometers downstream of the main wind direction is 30μg / m³, and the secondary main wind direction is only 15μg / m³, then the diffusion ratio is 2.0. Finally, by combining the information on wind speed, wind direction, concentration change, diffusion velocity, and ratio in the hotspot area, the pollutant wind direction diffusion trend is generated.

[0072] S302: Based on the pollutant wind direction diffusion trend, humidity and atmospheric stability data are used to calculate the pollutant deposition rate, screen areas with deposition rates greater than the threshold, analyze the pollutant concentration decrease relationship, and generate a pollutant deposition gradient.

[0073] The specific formula for calculating the pollutant settling rate is as follows:

[0074] ;

[0075] in, Indicates the pollutant settling rate. Indicates the pollutant concentration at the starting point of the path. Indicates the pollutant concentration at the end of the path. Indicates the path length. This indicates the current relative humidity in the area where the path is located. This represents the average humidity level within the area. Indicates the atmospheric stability level of the area where the path is located. The average level value representing the stability within the region;

[0076] This formula is used to calculate the sedimentation rate of pollutants;

[0077] To derive this formula, assume the actual monitoring data is as follows: starting concentration =150μg / m3, endpoint concentration =100μg / m3, path length =5km, current relative humidity =75%, regional average relative humidity =70%, current atmospheric stability level =3 (Level C), Regional average atmospheric stability =2 (Level B).

[0078] Substitute these values ​​into the formula to calculate:

[0079] ;

[0080] ;

[0081] ;

[0082] This result This indicates an average pollutant concentration reduction rate of 35.71 μg / m³ per kilometer along a given path. This value reflects the impact of atmospheric relative humidity and stability on pollutant dispersion under specific environmental conditions, as well as the magnitude of pollutant concentration reduction over a given distance. This calculation shows that environmental conditions and path length have a significant impact on pollutant deposition, thus providing effective data support for guiding environmental action plans, particularly in developing air quality improvement measures.

[0083] S303: Based on the pollutant deposition gradient, the pollutant release rate at the fire point is called, and combined with the pollutant transport path, the impact of meteorological conditions on pollution diffusion is analyzed, the cross-regional transport index of pollutants is calculated, and the pollutant diffusion range is generated.

[0084] Based on the fire point numbers and location coordinates recorded in the pollution source data, the total mass of pollutants released per unit time is extracted, with the release rate in μg / m²·s. This data is then spatially matched with the deposition gradient region. If the release rate of a fire point is 12 μg / m²·s, and the starting point of the corresponding deposition path is within 1 km of that fire point, a correlation is established. Combined with the pollutant transport path, the system constructs a main transport path from the fire point to the endpoint of the deposition region, recording the path length, average wind speed, transport duration, and concentration change. When analyzing the impact of meteorological conditions on pollution diffusion, the system compares the concentration change sequences under different combinations of humidity and stability, identifying that under conditions of high humidity (>85%), low wind speed (<1 m / s), and high stability (E and F levels), pollutant diffusion is slow and concentration peaks are sustained. The system tracks phenomena such as time-related growth and records the corresponding concentration delays and accumulation values ​​in the data. When calculating the cross-regional transport index of pollutants, the distance from the fire point to the boundary of another downstream area is used as the spatial span, and the time taken for transmission is used as the time span. The peak concentration attenuation ratio of pollutants along the path is multiplied by the transmission rate to obtain an index value reflecting the diffusion capacity. For example, if the initial concentration is still 80% at a distance of 5 kilometers and the transmission time is 1 hour, the transport index is at a high intensity level. When generating the pollutant diffusion range, the system delineates the boundaries of all path areas with transport indices higher than the set benchmark value (such as concentration attenuation ratio > 0.75 and distance > 4 km), forming a specific pollutant diffusion range layer, and records the corresponding fire point number, path length, start and end time, and spatial range.

[0085] The specific steps of S4 are as follows:

[0086] S401: Based on the pollutant diffusion range, call the current pollutant concentration monitoring data, analyze the concentration change range of the monitoring points, screen areas where the pollutant concentration increase exceeds the set threshold in a short period of time, remove areas with low amplitude changes, extract the spatial coordinates of the abnormal rise area, calculate the concentration change rate, and generate the abnormal rise area of ​​pollutants.

[0087] First, the system extracts the latest hourly pollutant concentration records from all monitoring points within the diffusion range from the real-time monitoring database. These records are sampled every 5 minutes, and each record includes the monitoring time, monitoring point coordinates, pollutant type, and corresponding concentration value. For example, the PM2.5 concentration data for a certain point from 10:00 to 10:30 is [72, 74, 76, 85, 96, 108] μg / m³. During the analysis of the concentration change range at the monitoring point, the system performs a difference calculation on every two adjacent data points to calculate the concentration increase every 5 minutes and records the maximum change value. In this example, the maximum increase is 108 − 96 = 12 μg / m³. When filtering areas where the pollutant concentration increase exceeds a set threshold within a short period, the system sets the threshold based on the 90th percentile of the PM2.5 concentration increase every 5 minutes over the past 30 days. For example, this value is 10 μg / m³. The data point with an increase of 12 μg / m³ is identified as an abnormal rise point. During the process of eliminating low-amplitude change areas, the system removes all monitoring points whose increase per unit time is below the threshold from the current record and does not participate in subsequent analysis. When extracting the spatial coordinates of the abnormal rise area, the latitude and longitude values ​​of the remaining points are extracted, and the minimum outer boundary range is calculated based on their spatial distribution. If three or more abnormal rise points appear in a concentrated area within 5 kilometers, they are marked as a continuous abnormal rise area. During the calculation of the concentration change rate, the system records the concentration change value per unit time of all monitoring points in the area and then performs an average calculation. If the average concentration increase value within 5 minutes is 11.5 μg / m³, the change rate is 2.3 μg / m³ per minute. Finally, all spatial coordinates, time ranges, and concentration change rate records that meet the conditions are integrated to generate the pollutant abnormal rise area.

[0088] S402: Based on the abnormal rise area of ​​pollutants, call the thermal radiation intensity data of the fire point, calculate the pollutant release amount, match the pollutant release intensity with the fire point distribution, screen the areas where the pollutant release amount exceeds the set standard, and generate the pollutant release amount distribution.

[0089] The specific formula for calculating the total amount of pollutants released is as follows:

[0090] ;

[0091] in, Indicates the total amount of pollutants released. Indicates the intensity of thermal radiation at the point of ignition. This indicates the actual burning area corresponding to the ignition point. This indicates the atmospheric temperature during the current observation period. This indicates the average temperature of the area over the past 24 hours. This indicates the actual vegetation height within the fire zone. This indicates the average vegetation height in the area;

[0092] Total amount of pollutants released This represents the intensity of pollutant emissions per unit time due to the thermal radiation from the fire point, calculated after adjustments based on fire point thermal radiation data, actual combustion area, regional temperature conditions, and vegetation height. Fire point thermal radiation intensity. The intensity was obtained through thermal intensity inversion in the remote sensing thermal infrared band, using VIIRS medium-resolution 375-meter fire point data, corresponding to an intensity of 64.8 MW. Actual combustion area. Based on the estimation of the fire point pixel area multiplied by the vegetation combustible load index, the pixel area is 0.14 km² (140,000 m²), corresponding to a vegetation coverage of 85%. The actual combustible distribution area is 140,000 × 0.85 = 119,000 m². Temperature during the observation period... The temperature, recorded at the automatic weather station corresponding to the fire location, was 31.2℃, which is the average temperature of the area over the past 24 hours. The temperature was 28.6℃, calculated as the average of the station's daily weather records. Vegetation height... The average vegetation height in the fire area, extracted via lidar or optical remote sensing, is 8.2m. The value is 6.5m, based on the average inversion value of similar vegetation within a 5-kilometer radius of the fire point.

[0093] Substitute the above values ​​into the formula to calculate:

[0094] ;

[0095] Calculate the denominator for the first part:

[0096] ;

[0097] ;

[0098] Calculate the first part of the molecules:

[0099] ;

[0100] The results for the first part are as follows:

[0101] ;

[0102] Calculate the adsorption correction factor for the second part:

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] Final result:

[0108] ;

[0109] The results indicate that, under the current conditions of thermal radiation intensity, temperature difference, and vegetation height, the total amount of pollutants released per unit time at this fire point is approximately 4,987,291.63 micrograms. This value is directly used to screen high-release areas. After comparing it with the set pollutant release threshold, it is determined whether the area constitutes a pollution anomaly, thereby supporting the generation of pollutant release distribution data.

[0110] S403: Based on the distribution of pollutant release, combined with meteorological data, assess the changes in pollutant concentration, analyze the pollutant retention time, adjust the emission rate, calculate the influence coefficient of concentration changes during pollutant transport, and generate pollutant emission intensity.

[0111] Meteorological records for the corresponding area, including wind speed, humidity, and stability information, are extracted. The concentration trends of pollutants in all directions during the release period are analyzed, and the concentration variation amplitude within each 10-minute interval is statistically analyzed. During the pollutant retention time, the system records the time span from when the pollutant concentration reaches its peak and begins to decline until the concentration recovers to 50% of its previous peak level. If the PM2.5 concentration reaches 110 μg / m³ at 10:20 and then drops below 55 μg / m³ at 11:00, the retention time is 40 minutes. Adjustments are then made to the emission levels. When calculating the emission rate, the system compares the degree of pollutant diffusion under different wind speeds and stability conditions with the actual concentration changes. If the actual holding time is much longer than the theoretical holding time under low stability and high wind speed conditions, the emission rate per unit time is adjusted upwards. For example, if the original estimated release rate is 10 μg / m²·s, it is adjusted to 12 μg / m²·s after considering the correction factor. When calculating the influence coefficient of concentration change during pollutant transport, the system compares the actual measured maximum concentration change with the theoretical release amount and path transport conditions to generate a coefficient value. If the release amount is 9 × 10⁻⁶, the system will adjust the coefficient value accordingly. 8 Given a pollutant concentration of μg, an average wind speed of 2.5 m / s, and a measured concentration increase of 50 μg / m³, the system calculates the impact coefficient of the pollutant under the current meteorological conditions to be 0.75. Finally, all relevant parameters are integrated, and the corrected emission rate, pollutant retention time, and impact coefficient for each region are recorded to generate the pollutant emission intensity.

[0112] The specific steps of S5 are as follows:

[0113] S501: Based on pollutant emission intensity, call the topographic data of the fire point area, analyze the impact of topographic relief on the pollutant diffusion path, calculate the diffusion resistance of the differential elevation area, screen the diffusion-obstructed area, calculate the diffusion rate attenuation ratio, and generate the pollutant diffusion resistance distribution.

[0114] First, the location of the fire point and its pollutant emission intensity are extracted. A digital elevation model is then established based on the topographic elevation data within a 5-kilometer radius around each fire point. Each elevation point records its specific latitude, longitude, and altitude. The system samples at 100-meter intervals to form a grid of elevation information. During the analysis of the impact of terrain undulations on the pollutant diffusion path, the elevation difference between any two adjacent grid points is calculated, and their slope direction is determined. The cumulative elevation difference along the path of pollutant diffusion from the fire point center along the prevailing wind direction is used as the basis for evaluating the topographic resistance in the diffusion direction. If there are multiple consecutive upward segments with elevation differences exceeding 50 meters on the main path, it is determined to be a diffusion obstruction path. In calculating the diffusion resistance of differentiated elevation areas, terrain areas with elevation differences greater than a certain threshold are designated as high-resistance areas. This threshold is referenced to the upper quartile of the regional average elevation difference. For example, if the regional elevation difference data shows a difference of 7... The 5% quantile is 40 meters. Areas with elevation differences greater than 40 meters are marked as high-resistance areas. When screening for areas with impeded diffusion, the cumulative elevation rise along the main diffusion path of pollutants is compared. If this value exceeds 150 meters or the average slope is greater than 15%, the entire path is considered an impeded diffusion path segment. In calculating the diffusion rate attenuation ratio, the system compares the time required for pollutants to reach downstream monitoring points and the degree of concentration change under the same wind speed conditions between flat path segments and resistance path segments. For example, a 5-kilometer flat path segment takes 40 minutes to transmit and has a PM2.5 concentration of 85 μg / m³ at the endpoint, while a 5-kilometer high-resistance path segment takes 70 minutes to transmit and has a concentration of 52 μg / m³ at the endpoint. The rate attenuation ratio is 70 divided by 40, which is 1.75. Finally, the resistance type, elevation difference distribution, attenuation rate, and path coordinates of different path segments are integrated to generate a pollutant diffusion resistance distribution.

[0115] S502: Based on the distribution of pollutant diffusion resistance, vegetation cover information is called to analyze the adsorption capacity of vegetation for pollutants, areas with adsorption rates exceeding a set threshold are screened, the influence of vegetation on pollutant flow is analyzed, and a pollutant vegetation adsorption coefficient is generated.

[0116] Based on the diffusion path, vegetation cover data is extracted from the area covered by the path. The NDVI value of each plot's vegetation data ranges from 0 to 1. A higher NDVI value indicates denser vegetation. In analyzing the adsorption capacity of vegetation for pollutants, the system categorizes NDVI values ​​into four levels: 0.0–0.2 (sparse), 0.2–0.5 (medium), 0.5–0.8 (high density), and above 0.8 (extremely high density). The corresponding pollutant adsorption capacities are set to 0.3 times, 0.6 times, 1.0 times, and 1.3 times the baseline adsorption rate, respectively. The baseline adsorption rate is set based on historical observation data for the region. For example, if the baseline adsorption rate for PM2.5 is set to 12%, then the adsorption rate for medium vegetation cover areas is 12% × 0.6 = 7.2%. During the screening process for areas with adsorption rates exceeding the set threshold, the system uses a 10... The percentage (%) is used to determine the adsorption rate of PM2.5. The system identifies all areas with an adsorption rate greater than 10% and records their spatial coordinates and area. When analyzing the impact of vegetation on pollutant flow, the system statistically analyzes the length of areas traversing high adsorption rates along the diffusion path and introduces additional adsorption adjustment factors based on vegetation type (e.g., shrubs, trees, herbs). For example, shrubs are adjusted to 1.0, trees to 1.2, and herbs to 0.8. Finally, the system combines parameters such as path length, NDVI level, and vegetation type to calculate the comprehensive adsorption capacity along the path. If a path is 2 kilometers long with an average NDVI value of 0.65, corresponding to an adsorption rate of 12%, and the vegetation type is trees, then the final adsorption capacity is 12% × 1.2 = 14.4%. The system records the adsorption rate of each path segment and combines it with spatial information to generate a pollutant vegetation adsorption coefficient.

[0117] S503: Based on the pollutant adsorption coefficient of vegetation and combined with ground elevation data, analyze the pollutant flow path along the terrain, match meteorological data to adjust the diffusion time scale, calculate the transport adjustment of pollutants under different terrain conditions, and generate a quantitative pollutant emission scheme.

[0118] According to the main diffusion direction of pollutants, the system extracts the elevation and adsorption coefficient values ​​corresponding to every 100 meters along the path. During the analysis of pollutant flow paths along the terrain, the system starts from the fire point and compares elevation values ​​sequentially downhill. If the elevation of adjacent points decreases, the pollutants preferentially flow along this path. If multiple paths exist, the path with the lowest adsorption coefficient is selected as the priority path, forming the main diffusion flow path. In the process of adjusting the diffusion time scale by matching meteorological data, the system calculates the required transmission time for the path by combining the transmission length and real-time wind speed data, and introduces a wind direction deviation correction coefficient into the transmission time. For example, with a wind speed of 3 m / s and a path length of 6 km, the theoretical transmission time is 2000 seconds. If the current wind direction deviates from the main path direction by more than 45 degrees, the transmission time is increased by 20%, resulting in an adjusted time of 2400 seconds. In calculating the transport adjustment of pollutants under differentiated terrain conditions, the system compares the concentration decay and time extension caused by three factors in the actual diffusion path: elevation increase, vegetation adsorption, and wind direction deviation. If the original emission intensity is 9 × 10⁻⁶... 8 Given that the pollutant's adsorption attenuation is 20% along the path, wind direction delay is 10%, and elevation causes a 15% decrease in transport rate, the actual pollutant transport volume is calculated using a comprehensive adjustment factor of 0.55, resulting in an effective emission of 4.95 × 10⁻⁶ μg. 8 μg, the system records the adjustment results of all diffusion paths, and integrates them with spatial range, path trajectory, meteorological conditions, transport duration and attenuation magnitude to generate a quantitative scheme for pollutant emissions.

[0119] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A quantitative method for pollutant emissions from open burning of straw based on multi-source data fusion, characterized in that, Includes the following steps: S1: Based on air pollutant monitoring data, the sensor network is called to obtain PM2.5, CO, and NOx concentrations, analyze the thermal radiation intensity of fire points, collect meteorological data, and combine the temporal changes of fire points and meteorological parameters to obtain a pollution source dataset; S2: Based on the pollution source dataset, filter areas with abnormal concentration fluctuations, analyze the coordinates and intensity of fire points, calculate the frequency of fire point activity, extract the time distribution information of pollutant concentration changes, and combine with time series data to obtain the distribution of pollution hotspot areas; S3: Based on the distribution of the pollution hotspots, analyze the pollutant diffusion trend, calculate the pollutant transport rate and deposition rate, assess the concentration decrease gradient, calculate the diffusion radius in combination with the pollutant release rate, analyze the transport ratio of the prevailing wind direction by calling wind speed and wind direction data, and analyze the cross-regional transport impact in combination with meteorological conditions to obtain the pollutant diffusion range. S4: Based on the pollutant diffusion range, analyze the current pollutant concentration change range, screen abnormal rise areas, calculate the pollutant release amount in combination with the heat radiation intensity of the fire point, and evaluate the pollutant retention time in combination with meteorological data, adjust the emission rate, and obtain the pollutant emission intensity. Based on the distribution of the pollution hotspots, the specific steps for analyzing pollutant diffusion trends, calculating pollutant transport rates and deposition rates, assessing concentration decrease gradients, calculating diffusion radius by combining pollutant release rates, analyzing the proportion of transport by the prevailing wind direction using wind speed and direction data, and analyzing the impact of cross-regional transport in conjunction with meteorological conditions to obtain the pollutant diffusion range are as follows. S301: Based on the distribution of the pollution hotspot areas, call the wind speed and wind direction data, analyze the diffusion trend of pollutants along the main wind direction, analyze the transmission rate of pollutants under different wind speed conditions, screen the pollutant transmission rate interval within the range of wind speed variation, analyze the diffusion trajectory of pollutants under different wind direction conditions, calculate the pollutant diffusion ratio along the main wind direction, and generate the pollutant diffusion trend along the wind direction. S302: Based on the pollutant wind direction diffusion trend, call humidity and atmospheric stability data, calculate pollutant deposition rate, screen areas with deposition rate greater than the threshold, analyze the pollutant concentration decrease relationship, and generate pollutant deposition gradient; S303: Based on the pollutant deposition gradient, call the pollutant release rate at the fire point, combine the pollutant transport path, analyze the impact of meteorological conditions on pollution diffusion, calculate the pollutant cross-regional transport index, and generate the pollutant diffusion range. The method also Including, S5: Based on the pollutant emission intensity, call terrain data to calculate diffusion resistance, assess the impact of vegetation cover on pollutant adsorption capacity, analyze the impact of ground elevation and wind field changes on flow path, and adjust the diffusion time scale in combination with meteorological data to obtain a quantitative pollutant emission scheme. The pollutant emission quantification scheme includes pollutant diffusion resistance, vegetation adsorption capacity, pollutant flow path, and pollutant diffusion time scale.

2. The method for quantifying pollutant emissions from open burning of straw based on multi-source data fusion according to claim 1, characterized in that, The pollution source dataset includes PM2.5 concentration, CO concentration, NOx concentration, fire point thermal radiation intensity, air temperature, humidity, wind speed, and meteorological time series data. The pollution hotspot area distribution includes areas of abnormal concentration fluctuations, fire point intensity, fire point activity frequency, and pollutant concentration temporal distribution. The pollutant diffusion range includes pollutant diffusion trend, pollutant transport rate, pollutant deposition rate, pollutant concentration decrease gradient, pollutant diffusion radius, and pollutant transport ratio. The pollutant emission intensity includes the pollutant concentration change range, areas of abnormal pollutant increase, pollutant release amount, pollutant retention time, and pollutant emission rate.

3. The method for quantifying pollutant emissions from open burning of straw based on multi-source data fusion according to claim 1, characterized in that, Based on air pollutant monitoring data, the specific steps for obtaining the pollution source dataset are as follows: utilizing sensor networks to acquire PM2.5, CO, and NOx concentrations; analyzing the thermal radiation intensity of fire points; collecting meteorological data; and combining the temporal changes of fire points and meteorological parameters. S101: Acquire PM2.5, CO, and NOx concentration data from the environmental monitoring sensor network, call up real-time measurement values ​​from monitoring points, filter out abnormal data and remove values ​​exceeding limits, sort pollutant concentration data according to data timestamps, calculate the average pollutant concentration within the time series, analyze the changing trend, and generate pollutant concentration time series data. S102: Based on the pollutant concentration time series data, analyze the thermal radiation intensity data of the fire point area of ​​the satellite remote sensing equipment, match the monitoring time period of the fire point area, calculate the correlation between the thermal radiation intensity of the fire point and the pollutant concentration, and generate the fire point pollution impact coefficient. S103: Based on the fire point pollution impact coefficient, collect meteorological parameters from ground meteorological monitoring stations, calculate the impact factor of wind speed changes on pollutant diffusion, and generate a pollution source dataset by combining the fire point pollution impact coefficient.

4. The method for quantifying pollutant emissions from open burning of straw based on multi-source data fusion according to claim 1, characterized in that, Based on the pollution source dataset, the specific steps for screening areas with abnormal concentration fluctuations, analyzing fire point coordinates and intensity, calculating fire point activity frequency, extracting the temporal distribution information of pollutant concentration changes, and combining this with time-series data to obtain the distribution of pollution hotspot areas are as follows: S201: Based on the pollution source dataset, call the pollutant concentration data to screen for abnormal concentration fluctuation areas, calculate the change range of pollutant concentration within the differentiated time interval, remove the fluctuation areas, extract the spatial coordinates of the concentration change areas, screen the areas where the pollutant concentration change rate exceeds the set threshold, and generate abnormal pollution fluctuation areas. S202: Based on the pollution abnormal fluctuation area, analyze the fire point coordinate data, match the fire point location and fire point intensity data, calculate the fire point activity frequency and set a high-frequency threshold, filter high-frequency fire point areas, and generate high-frequency fire point areas. S203: Call the high-frequency fire point area, extract the time distribution information of pollutant concentration changes, match the concentration time series data, calculate the pollutant concentration change trend, and generate the distribution of pollution hotspot areas.

5. The method for quantifying pollutant emissions from open burning of straw based on multi-source data fusion according to claim 1, characterized in that, The specific formula for calculating the pollutant sedimentation rate is as follows: ； in, Indicates the pollutant settling rate. Indicates the pollutant concentration at the starting point of the path. Indicates the pollutant concentration at the end of the path. Indicates path length. This indicates the current relative humidity in the area where the path is located. This represents the average humidity level within the area. Indicates the atmospheric stability level of the area where the path is located. This represents the average level of stability within the region.

6. The method for quantifying pollutant emissions from open burning of straw based on multi-source data fusion according to claim 1, characterized in that, Based on the pollutant diffusion range, the specific steps for analyzing the current pollutant concentration variation, screening abnormally rising areas, calculating pollutant release based on the heat radiation intensity at the fire point, assessing pollutant retention time based on meteorological data, and adjusting the emission rate to obtain the pollutant emission intensity are as follows: S401: Based on the pollutant diffusion range, call the current pollutant concentration monitoring data, analyze the concentration change range of the monitoring points, screen the areas where the pollutant concentration increase exceeds the set threshold in a short period of time, remove the low-amplitude change areas, extract the spatial coordinates of the abnormal rise area, calculate the concentration change rate, and generate the abnormal rise area of ​​pollutants. S402: Based on the abnormal rise area of ​​pollutants, call the thermal radiation intensity data of the fire point, calculate the pollutant release amount, match the pollutant release intensity with the fire point distribution, screen the areas where the pollutant release amount exceeds the set standard, and generate the pollutant release amount distribution. S403: Based on the pollutant release distribution, combined with meteorological data, assess the pollutant concentration changes, analyze the pollutant retention time, adjust the emission rate, calculate the influence coefficient of concentration changes during pollutant transport, and generate the pollutant emission intensity.

7. The method for quantifying pollutant emissions from open burning of straw based on multi-source data fusion according to claim 6, characterized in that, The specific formula for calculating the total amount of pollutants released is as follows: ； in, Indicates the total amount of pollutants released. Indicates the intensity of thermal radiation at the point of ignition. This indicates the actual burning area corresponding to the ignition point. This indicates the atmospheric temperature during the current observation period. This indicates the average temperature of the area over the past 24 hours. This indicates the actual vegetation height within the fire zone. This indicates the average vegetation height in the area.

8. The method for quantifying pollutant emissions from open burning of straw based on multi-source data fusion according to claim 1, characterized in that, Based on the pollutant emission intensity, the diffusion resistance is calculated using topographic data, the impact of vegetation cover on pollutant adsorption capacity is assessed, the influence of changes in ground elevation and wind field on flow paths is analyzed, and the diffusion timescale is adjusted in conjunction with meteorological data. The specific steps for obtaining the quantitative pollutant emission scheme are as follows: S501: Based on the pollutant emission intensity, call the topographic data of the fire point area, analyze the impact of topographic relief on the pollutant diffusion path, calculate the diffusion resistance of the differentiated elevation area, screen the diffusion-obstructed area, calculate the diffusion rate attenuation ratio, and generate the pollutant diffusion resistance distribution. S502: Based on the pollutant diffusion resistance distribution, call the vegetation cover information, analyze the adsorption capacity of vegetation for pollutants, screen areas where the adsorption rate exceeds the set threshold, analyze the influence of vegetation on pollutant flow, and generate the pollutant vegetation adsorption coefficient. S503: Based on the pollutant adsorption coefficient of vegetation, combined with ground elevation data, analyze the pollutant flow path along the terrain, match meteorological data to adjust the diffusion time scale, calculate the transport adjustment amount of pollutants under different terrain conditions, and generate a quantitative pollutant emission scheme.

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