CN117312784A - Pollution source tracing method based on receptor model and big data combination - Google Patents
Pollution source tracing method based on receptor model and big data combination Download PDFInfo
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Abstract
The invention relates to an environment pollution tracing system based on the combination of a receptor model and big data, which comprises the following steps: step one: the method comprises the steps of rapidly acquiring pollution monitoring information, pollution emission information, environment information, geographic information and traffic flow information by using Internet big data; step two: locally storing and screening the acquired information, and carrying out data labeling; step three, carrying out label classification on the label data, and carrying out cross reference on the classified data to generate a pollution source database; step four: carrying out pollution area division by combining a pollution source database to generate a pollution characteristic block; step five: sampling the recipient atmosphere using an unmanned aerial vehicle; step six: performing source analytic calculation through a receptor model; step seven: generating a pollution source diffusion path by using a pollution diffusion model; step eight: and (3) sampling, positioning and tracing the pollution source of the pollution diffusion path by using the unmanned aerial vehicle, and generating a tracing report by combining an emission database and a source analysis result.
Description
Technical Field
The invention relates to the technical field of environmental protection, in particular to an environmental pollution tracing system based on receptor model and big data combination.
Background
The pollution tracing has very important guiding function on pollution management, can help pollution managers to better find pollution problems, and makes more accurate treatment countermeasures. The existing technology for tracing pollution sources through the receptor model consumes a great deal of manpower and material resources because of the establishment of pollution source data in the using process.
However, the method can only judge that the pollutant comes from a certain type of pollution source, cannot realize the geospatial source judgment, gradually accurately trace the source from a certain type of pollution source to a specific pollution point by using big data and a mode of combining an unmanned aerial vehicle sampling and diffusion model, realize efficient, quick and accurate pollution tracing, and cannot quickly locate the distribution of various types of pollution sources in urban areas and the contribution rates of the various types of pollution sources to environmental pollution.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the pollution source tracing method based on the combination of the receptor model and the big data is provided, the existing data resources and big data computing capacity are fully utilized, the tracing range is continuously narrowed by combining the receptor model, the diffusion model and the unmanned aerial vehicle in-situ positioning, the distribution of various pollution sources in urban areas and the contribution rate of the pollution sources to the environmental pollution are rapidly positioned, accurate and reliable pollution source data are provided for atmosphere control managers, and the basis and the direction are provided for the establishment of an atmosphere control scheme and the implementation of an emergency scheme.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a pollution source tracing method based on the combination of a receptor model and big data comprises the following steps:
the method comprises the steps of firstly, rapidly acquiring monitoring points of each base station, micro sites, on-line emission monitoring points of enterprises, meteorological monitoring points, construction site monitoring points and catering emission points by adopting an internet big data technology, and acquiring air monitoring data, meteorological data and dye emission data. Geographic information data, traffic flow data.
And step two, carrying out local storage, screening and management on the acquired data, carrying out uninterrupted updating uploading, and carrying out label processing on the related data.
And thirdly, classifying the data stored in the second step, establishing a pollution emission source database, and updating the dynamics in real time.
And step four, carrying out pollution block division by combining the pollution source database with the geographic space position of the area of the space geographic information, the traffic flow data and the pollution monitoring data.
And fifthly, setting an unmanned aerial vehicle matrix in the polluted atmosphere, and sampling the atmosphere of the unmanned aerial vehicle.
And step six, adopting a receptor model to carry out source analysis on the sample collected in the step five to determine the pollution source.
And step seven, determining a pollution source transmission path of the receptor area by using a pollution diffusion model and combining the monitoring data and the meteorological data.
And step eight, sampling a pollution transmission path by using an unmanned aerial vehicle matrix, comparing the pollution transmission path with a pollution source analysis result and a pollution source database, and generating a tracing report to finish tracing.
Preferably, in the first step, the data acquired by the monitoring station and the micro station are basically six parameters, and the meteorological station data mainly comprise temperature, humidity, wind speed, wind direction and ground air pressure; the enterprise online monitoring data and the restaurant emission data mainly comprise emission pollutant types, emission flow rates and emission periods; the worksite monitoring data mainly contains PM10 and PM2.5 and relative humidity.
Preferably, in the second step, the data processed by the tag is mainly pollution monitoring sites, enterprise emission monitoring data, site monitoring data, restaurant emission data, meteorological data and traffic flow data, and the data is mainly used for classifying the data.
Preferably, in the third step, each of the tag data is classified according to the collection time, the geographic information, the environmental information, the pollution factor monitoring information, the collection process information and the traffic flow information, and the pollution source emission database is generated by crossing.
The pollutant emission database data comprises specific geographic positions of pollution sources, emission flow rates and main substance types of emission pollutants, and the pollutant emission data is statistically analyzed according to a pollutant emission time sequence.
Preferably, in the fourth step, the affinity of each monitored data is calculated by using a W model of an environmental pollution tracing system F based on the combination of a receptor model and big data, and then atmospheric pollution area division is performed by using a K-Me ns clustering method of an environmental pollution tracing system based on the combination of the receptor model and the big data.
K-Me is an environment pollution traceability system ns clustering method main algorithm flow based on receptor model and big data combination:
c category initial centers are randomly selected or c initial centers are regulated according to actual scenes; the distances from any sample point to the centers of c categories are respectively calculated in the ith iteration, so that the classification result of the sample in the current iteration can be obtained: the method comprises the steps of obtaining the average value in the class, and the like;
and (3) for all c class centers, if the class centers are updated according to the modes of the second step and the third step, the clustering is finished, otherwise, the first two steps are repeated until the clustering center value converges.
Preferably, in the fifth step, the unmanned aerial vehicle carries an SDS021 dust sensor and an N module of an environmental pollution tracing system based on combination of a receptor model and big data as a monitoring module, and a GT-U7GPS module and a small BMP180 air pressure sensor are used as an accurate positioning module; and forming an unmanned aerial vehicle matrix through different heights, and carrying out multiple sample collection on the receptor atmosphere in the same area according to the height division.
Preferably, in the sixth step, the analysis of the contaminants is performed by NKCMB1.0 receptor model software, and the concentration and contribution rate of each contaminant are calculated to perform technical diagnosis.
The CMB receptor model calculation method is as follows:
wherein: c is the total mass concentration of the receptor atmospheric particulates in μg/square meter; s is S j The mass concentration contributed to the j-class source in μg/square meter; j is the number of source classes, j=1, 2, …, J.
If the concentration of chemical component i of the acceptor particulate matter is C i Then the calculation method is:
wherein: c (C) i Is a measure of the concentration of chemical component i in the atmospheric particulate of the receptor in μg/square meter; f (F) ij A concentration measurement value of the chemical component i in the particulate matter of the j-th type source, wherein the unit is g/g; s is S j Calculating a value for the concentration of the j-th class of sources; i is the number of chemical components. Only when i ∈ j, the above-mentioned party has a solution, and the contribution ratio of source class j is:
η i ＝S i /C×100％
preferably, in the seventh step, a backward trajectory model of HYSPLIT-4 is adopted to analyze the backward trajectory of the pollutant, calculate the air mass trajectory, simulate the air mass diffusion sedimentation process, treat the delivery conditions of different pollutant emission sources, and simulate various physical processes in combination with meteorological condition calculation. And (3) performing cluster analysis by using Meteolnfo, analyzing the source of the airflow track and the distance of source transmission, and clustering backward tracks with different heights to obtain an analysis result. And generating a model for the generated pollutant transmission track period by adopting a big data deep learning mode, and accumulating the pollutant transmission paths under different seasons under different meteorological conditions to generate a characteristic model.
Preferably, in the eighth step, the unmanned aerial vehicle is controlled to perform pollution transmission path tracing, and a mountain climbing algorithm is mainly used for performing pollution tracing. The tracing report mainly takes the form of pollution thermodynamic diagram and pollution diffusion dynamic diagram as tracing explanation.
Mountain climbing algorithm main principle: randomly selecting a certain space position on a pollution path as a mountain climbing starting point in the mountain climbing simulating process; each time, comparing the adjacent points in the detection range with the current point, taking the better of the two as the next step of mountain climbing, and ensuring that each step moves towards the better direction; the above steps are repeated until there are no more points around the point than they are.
The beneficial effects of the invention are as follows: the pollution source database is built quickly by fully utilizing the big data technology, and the accurate tracing purpose is achieved by combining a receptor model, a HYSPLIT-4 backward track model and unmanned aerial vehicle sampling tracing, taking the polluted receptor atmosphere as a tracing departure point and continuously reducing the tracing range from the surface to the line to the point. And an environmental pollution supervision system is built, an auxiliary supervision layer is used for rapidly managing and controlling pollution enterprises, pollution restaurant and pollution sites in time after environmental pollution occurs, and the pollution influence is effectively prevented and controlled to be continuously enlarged. The comprehensive capacity of the environmental protection department on sudden environmental pollution events is improved, scientific basis is made for environmental pollution control by the environmental protection department, and the environmental control direction is provided.
(III) beneficial effects
The invention provides a pollution source tracing method based on receptor model and big data combination. The beneficial effects are as follows:
(1) The pollution source tracing method based on the combination of the receptor model and the big data makes full use of big data technology to quickly establish a pollution source database, combines the receptor model, the HYSPLIT-4 backward track model and the unmanned aerial vehicle sampling tracing, and uses the pollution receptor atmosphere as a tracing starting point to continuously reduce the tracing range from the surface to the line to the point so as to carry out intelligent research and judgment on the pollution source.
(2) The pollution source tracing method based on the combination of the receptor model and the big data provides different pollution source tracing information for an environment manager, wherein the different pollution source tracing information comprises a non-point source pollution condition, an emission source condition, a pollution source and a pollution path track, a specific pollution period pollutes pollution points of enterprises and the like, and the provided pollution information is comprehensive and three-dimensional.
(3) According to the pollution source tracing method based on the combination of the receptor model and the big data, the environment pollution supervision system is built, and the auxiliary supervision layer is used for rapidly managing and controlling pollution enterprises, pollution restaurant and pollution sites in time after the environment pollution occurs, so that the pollution influence is effectively prevented and controlled to be continuously enlarged.
(4) According to the pollution source tracing method based on the combination of the receptor model and the big data, the environment manager is helped to quickly determine the characteristic pollutants and the characteristic pollution areas by the source analysis and the characteristic division blocks of the receptor model in emergency management, effective decision support data and definite control directions are provided for the environment manager, and after pollution occurs, a pollution responsibility main body or area is definitely polluted, so that the comprehensive treatment capacity of the environment pollution treatment is improved.
Drawings
Fig. 1 is a pollution tracing flow chart of a pollution source tracing method based on the combination of a receptor model and big data.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent by the following detailed description of the present invention with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
The invention provides a technical scheme that: a pollution source tracing method based on the combination of a receptor model and big data comprises the following steps:
the method comprises the steps of firstly, rapidly acquiring monitoring points of each base station, micro sites, on-line emission monitoring points of enterprises, meteorological monitoring points, construction site monitoring points and catering emission points by adopting an internet big data technology, and acquiring air monitoring data, meteorological data, pollution emission data, geographic information data, urban functional area distribution and traffic trip data.
The acquired base station and micro site monitoring data main data indexes comprise, but are not limited to SO2, NO2, O3, PM2.5, PM10, VOCs and CO. The construction site monitoring points are mainly monitoring data of PM2.5 and PM10 in a monitoring range.
The on-line emission monitoring data and the restaurant emission data of enterprises mainly comprise specific pollutant types such as sulfur dioxide, carbon monoxide, nitrogen dioxide, nitrogen oxides, hydrocarbon, volatile Organic Compounds (VOCs), particulate matters (fly ash, dust fall, oil smoke and the like), ammonia, oxygen-containing, nitrogen, chlorine, sulfur organic compounds and the like, and comprise specific emission pollutant flow and emission time period.
The acquired meteorological site data mainly comprise temperature, humidity, wind speed, wind direction and ground air pressure.
And step two, carrying out local storage, screening and management on the acquired data, carrying out uninterrupted updating uploading, and carrying out label processing on the related data.
The acquired data are marked by geographic information position, acquisition time, acquisition equipment, acquisition data type and four acquisition dimensions, effective data are screened, invalid data are removed, and data are updated along with the time dimensions.
And thirdly, classifying each piece of marked data according to time data, geographic information, environment information, pollution factor monitoring information and acquisition process information, and carrying out cross generation on the pollution factor monitoring information, the geographic information, the acquisition process information, the time information and the traffic flow information to generate a pollution source emission database.
The pollutant emission database data should contain specific geographical locations of the pollutant sources, emission flows, and types of main substances of the emitted pollutants, and statistically analyze the pollutant emission data according to pollutant emission time sequences.
And step four, carrying out pollution block division by combining the pollution source database with the geographic space position of the area of the space geographic information, the traffic flow data and the pollution monitoring data. The divided pollution areas are mainly divided into geographical positions and pollution characteristics as main division standards. The method comprises the steps of calculating the affinity of each monitoring data by using a W model of an environment pollution tracing system F based on combination of a receptor model and big data, and dividing an atmosphere pollution area by using a K-Me ns clustering method of an environment pollution tracing system based on combination of the receptor model and the big data.
K-Me is an environment pollution traceability system ns clustering method main algorithm flow based on receptor model and big data combination: c category initial centers are randomly selected or c initial centers are regulated according to actual scenes; the distances from any sample point to the centers of c categories are respectively calculated in the ith iteration, so that the classification result of the sample in the current iteration can be obtained: the method comprises the steps of obtaining the average value in the class, and the like; and (3) for all c class centers, if the class centers are updated according to the modes of the second step and the third step, the clustering is finished, otherwise, the first two steps are repeated until the clustering center value converges.
And fifthly, setting an unmanned aerial vehicle matrix in the receptor polluted atmosphere, and sampling the unmanned aerial vehicle atmosphere.
Unmanned aerial vehicle carries on SDS021 dust sensor and SINGO's environmental pollution traceability system N module based on receptor model and big data allies oneself with uses GT-U7GPS module and small-size BMP180 air pressure sensor as accurate positioning module as monitoring module.
And controlling the matrix setting of the unmanned aerial vehicle on the space position by combining the information center with big data.
The top of the unmanned aerial vehicle is provided with an air suction device and a plurality of air quality sensors in sequence along the machine head of the unmanned aerial vehicle to the machine tail direction of the unmanned aerial vehicle. And sequentially sampling according to different time intervals, and marking a sampling process by using a plurality of sampled samples.
And step six, adopting a receptor model to carry out source analysis on the sample collected in the step five to determine the pollution source.
The concentration and contribution of each contaminant was calculated by analysis of the contaminants using CMB analysis by NKCMB1.0 receptor model software.
CMB receptor assay calculation method:
wherein: c is the total mass concentration of the receptor atmospheric particulates in μg/square meter; s is S j The mass concentration contributed to the j-class source in μg/square meter; j is the number of source classes, j＝1，2，…，J。
If the concentration of chemical component i of the acceptor particulate matter is C i Then the calculation method is:
wherein: c (C) i Is a measure of the concentration of chemical component i in the atmospheric particulate of the receptor in μg/square meter; f (F) ij A concentration measurement value of the chemical component i in the particulate matter of the j-th type source, wherein the unit is g/g; s is S j Calculating a value for the concentration of the j-th class of sources; i is the number of chemical components. Only when i ∈ j, the above-mentioned party has a solution, and the contribution ratio of source class j is:
η i ＝S i /C×100％
the calculated value result is calculated by using T statistics, percentage mass ratio (PM), residual square sum (chi or x) 2 ) Regression coefficient (R) 2 ) The diagnosis technology performs result diagnosis and judges whether the result is acceptable.
When the calculation result Tst is an environment pollution tracing system based on combination of a receptor model and big data, the t is more than or equal to 2.0, the PM is 80-120%, and the x is x 2 ≤2，R 2 And when the ratio of the calculated value to the measured value is 0.5-2.0, the result can be accepted.
The calculation index and the method are as follows:
delta in the table Sj Is a pollution source S j J is the total number of pollution sources
And step seven, determining a pollution source transmission path of the receptor area by using a pollution diffusion model and combining the monitoring data and the meteorological data.
And analyzing a backward track of the pollutant by adopting a HYSPLIT-4 backward track model, calculating an air mass track, simulating an air mass diffusion sedimentation process, treating the conveying condition of different pollutant emission sources, and simulating various physical processes by combining meteorological condition calculation.
And (3) performing cluster analysis by using Meteolnfo, analyzing the source of the airflow track and the distance of source transmission, and clustering backward tracks with different heights to obtain an analysis result.
And generating a model for the generated pollutant transmission track period by adopting a big data deep learning mode, and accumulating the pollutant transmission paths under different seasons under different meteorological conditions to generate a characteristic model.
The clustering analysis method for Meteolnfo is as follows:
wherein D is ij i, the distance from the stay point of the J-th hour in the track to the corresponding point of the average track; t is the time length of the track; x is the number of tracks in the cluster.
The total spatial dissimilarity (TSV) is the sum of all clusters SPV, an environmental pollution traceability system R based on the combination of the receptor model and big data, and the relationship between TSV and n determines the number of final clusters and the spatial distribution of the average trajectories of each type.
And step eight, sampling and tracing the pollution transmission path by using an unmanned aerial vehicle matrix, combining the sampling and tracing result with a pollution source analysis result and a pollution source database, and generating a tracing report to finish tracing.
And the information center controls the unmanned aerial vehicle to carry out pollution tracing by using a mountain climbing algorithm.
The tracing report mainly takes the form of pollution thermodynamic diagram and pollution diffusion dynamic diagram as tracing explanation.
Mountain climbing algorithm main principle: randomly selecting a certain space position on a pollution path as a mountain climbing starting point in the mountain climbing simulating process; each time, comparing the adjacent points in the detection range with the current point, taking the better of the two as the next step of mountain climbing, and ensuring that each step moves towards the better direction; the above steps are repeated until there are no more points around the point than they are.
The pollution tracing report is generated by combining a pollution database and a meteorological data with a receptor model and a diffusion model to generate a pollution diffusion time sequence dynamic simulation diagram, so that the pollution process is more intuitively embodied; pollution thermodynamic diagrams are formed by pollution data discharged by pollution dividing areas, so that pollution conditions of all areas are defined, and source tracing analysis is assisted.
The invention utilizes the characteristic of rapid collection and analysis data of big data technology, combines a receptor model backward track diffusion model and unmanned aerial vehicle on-site sampling to trace pollution.
The whole tracing process is divided into the track analysis and the fixed point sampling from the region, the progressive tracing process is carried out from three different layers, the environment management guidance is more comprehensively carried out for the environment manager from a plurality of dimensions, and the environment management and control direction is clear.
The above preferred embodiments of the invention are provided only to help illustrate the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. A pollution source tracing method based on the combination of a receptor model and big data is characterized in that: comprises the following steps:
step one, data acquisition: the internet big data technology is adopted to rapidly acquire the monitoring points, the micro sites, the online emission monitoring points of enterprises, the meteorological monitoring points, the construction site monitoring points and the catering emission points, and the air monitoring data, the meteorological data, the dye emission data, the geographic information data and the traffic flow information data;
step two, storing local data: carrying out local management, storage and screening on the acquired data, and carrying out dynamic data updating in real time;
step three, pollution source database establishment: classifying the data stored in the second step, establishing a pollution emission source database, and updating the dynamics in real time;
step four, polluted area division: combining a pollution source database, and dividing pollution blocks at the geographic space positions of the areas;
step five, collecting the atmospheric pollution of the receptor: arranging an unmanned aerial vehicle matrix in a receptor area, and sampling the receptor atmosphere;
step six, analyzing a pollution source: carrying out source analysis on the sample collected in the fifth step by adopting a receptor model to determine a pollution source;
step seven, obtaining a pollution transmission path: determining a pollution source transmission path of the receptor area by using a pollution diffusion model and combining the monitoring data and the meteorological data;
step eight, unmanned aerial vehicle matrix field sampling: and sampling the pollution transmission path by using an unmanned aerial vehicle matrix, comparing the pollution transmission path with a pollution source analysis result and a pollution source database, and generating a tracing report to finish tracing.
2. The pollution source tracing method based on the combination of the receptor model and the big data according to claim 1 is characterized in that: in the first step, the information acquisition center rapidly acquires data in an internet big data mode, and the data acquisition comprises the following steps: base station monitoring data, micro-site data, meteorological site data, enterprise online monitoring data, construction site monitoring information and restaurant emission monitoring data.
3. The pollution source tracing method based on the combination of the acceptor model and the big data according to claim 1, wherein in the second step: the information storage mode comprises the steps that after the image-text data monitored by the weather monitor and the pollutant monitor are stored by the storage equipment, the image-text data are imported into the information marking equipment in real time to mark different types of data, wherein the tag data comprise: monitoring factors, concentration, wind speed, temperature, humidity, air pressure, acquisition time, warehouse entry time, equipment number, geographic information and traffic flow.
4. The pollution source tracing method based on the combination of the acceptor model and the big data according to claim 1, wherein in the third step, each piece of marked data comprises time data, geographic information, environment information, pollution factor monitoring information and acquisition process information, so as to generate a pollution source emission database.
5. The pollution source tracing method based on the combination of the acceptor model and the big data according to claim 1, wherein the atmospheric pollution characteristic area division in the step 4 comprises the following steps:
firstly, calculating the affinity of each monitoring data by using a W model of an environmental pollution traceability system F based on the combination of a receptor model and big data;
and carrying out atmospheric pollution area division by using a K-Me environmental pollution traceability system ns clustering method based on the combination of a receptor model and big data.
6. The pollution source tracing method based on the combination of the receptor model and the big data according to claim 1, wherein in the step 5, the unmanned aerial vehicle comprises a dust collection module, a precise positioning module, an air suction device and a plurality of air quality sensors.
7. The method for tracing a pollution source based on the combination of a receptor model and big data according to claim 1, wherein the analyzing the collected sample source in the step six comprises the following steps:
using a CMB receptor model, analyzing pollutants through NKCMB1.0 receptor model software, and calculating the concentration and contribution rate of each pollutant;
and diagnosing the result by adopting a diagnosis technology, and judging whether the result is acceptable.
8. The pollution source tracing method based on the combination of the receptor model and the big data according to claim 1 is characterized in that; in the seventh step, the generation of the contamination diffusion path includes the steps of:
setting up a local diffusion model, setting ground data and high-altitude environment data of an atmospheric pollution diffusion model, and finishing parameter setting;
constructing meteorological scenes according to different historical meteorological data, determining a local meteorological parameter range, and classifying cross simulation scenes of data in different seasons, morning, evening and morning time periods according to temperature, wind speed, humidity, low cloud quantity and high cloud quantity;
and combining the meteorological scene classification and the diffusion model to simulate diffusion paths in different time and space, so as to form a complete pollution diffusion model.
9. The pollution source tracing method based on the combination of the receptor model and the big data according to claim 1 is characterized in that; in the eighth step, the unmanned aerial vehicle tracing and the generation of the tracing report include the following steps:
the method comprises the steps that an information control center sends a pollution transmission path to an unmanned aerial vehicle, and the unmanned aerial vehicle is controlled to perform sampling tracing and upload sampling data to the information center;
and determining pollution points through comparison of the pollution source database and the source analysis result by the information center, and generating a tracing report.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117689399A (en) * | 2024-01-31 | 2024-03-12 | 四川省生态环境科学研究院 | Surface water pollution source tracing method and system for monitoring abnormal increase of index |
| CN118897059A (en) * | 2024-10-09 | 2024-11-05 | 暨南大学 | A method and system for tracing air pollution sources based on numerical simulation target area pointing |
| CN119293463A (en) * | 2024-12-12 | 2025-01-10 | 湖南工商大学 | Intelligent prediction method, system and medium for PM2.5 and ozone combined pollution |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117689399A (en) * | 2024-01-31 | 2024-03-12 | 四川省生态环境科学研究院 | Surface water pollution source tracing method and system for monitoring abnormal increase of index |
| CN117689399B (en) * | 2024-01-31 | 2024-04-12 | 四川省生态环境科学研究院 | Surface water pollution source tracing method and system for monitoring abnormal increase of index |
| CN118897059A (en) * | 2024-10-09 | 2024-11-05 | 暨南大学 | A method and system for tracing air pollution sources based on numerical simulation target area pointing |
| CN119293463A (en) * | 2024-12-12 | 2025-01-10 | 湖南工商大学 | Intelligent prediction method, system and medium for PM2.5 and ozone combined pollution |
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