CN117591907B - Pollution occurrence and propagation sensing method based on intensive air quality micro-station monitoring - Google Patents
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Abstract
The invention discloses a pollution occurrence and propagation sensing method based on intensive air quality micro-station monitoring, which relates to the field of environmental pollutant monitoring and comprises the following steps of: acquiring peak value data in air quality micro-station monitoring data; correcting the peak value data based on the fixed site data, and obtaining a local pollution area through clustering; comparing peak data of all air quality micro stations in each local pollution area, and calculating the most likely pollution center point by a maximum likelihood estimation method; and calculating the pollution propagation direction through the spatial relationship between the peak value data and the pollution center point and the pollutant time sequence data between the peak value data. The method reasonably and effectively utilizes the data of the atmosphere monitoring network, combines the space and time information, extracts useful information such as trend, peak value and the like by jointly mining the data of a plurality of air quality micro stations, further improves the robustness to random noise, and analyzes the pollution transmission direction with the maximum probability.
Description
Technical Field
The invention relates to the field of environmental pollutant monitoring, in particular to a pollution occurrence and propagation sensing method based on intensive air quality micro-station monitoring.
Background
The environment monitoring system takes the higher standard ensuring environment monitoring data as the root, the sound, accurate, full, fast and new as the main line, the sound, scientific, independent, authoritative and efficient ecological environment monitoring system as the main line, the environment quality monitoring is consolidated, the pollution source monitoring is enhanced, the ecological quality monitoring is expanded, the ecological environment monitoring is comprehensively pushed from the quantity scale type to the quality efficiency type, and the improvement of the modernization level of the ecological environment monitoring is the target of the current environment monitoring.
But the problems that the environmental monitoring data need to overcome at present include:
1. the construction cost of fixed stations such as national control stations, provincial control stations and the like is high, the quantity is small, and the global monitoring and the local pollution monitoring cannot be realized;
2. the self-built air quality micro-station has the advantages of low cost and more quantity, but has the problems of limited data quality control, indirect comparison among data, relatively sensitive local accidental pollution of individual stations and the like.
Therefore, if only the data of the fixed station is used as the monitoring basis, the problem that local monitoring cannot be realized is caused; if the data of the air quality micro-station is used as the monitoring basis, the problem of low reliability of the monitoring result can be caused.
Disclosure of Invention
Aiming at the defects in the prior art, the pollution occurrence and propagation sensing method based on the intensive air quality micro-station monitoring combines the monitoring data of the fixed station and the air quality micro-station to locate the local pollution and calculate the propagation direction of the pollution, thereby realizing high-reliability pollution monitoring.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
provided is a pollution occurrence and propagation sensing method based on intensive air quality micro-station monitoring, which comprises the following steps:
s1, acquiring air quality micro-station monitoring data and mining to obtain peak value data;
s2, gridding the monitoring area, correcting and filtering peak data in the grid based on fixed site data, and obtaining processed peak data; clustering the processed peak data to generate a local pollution area;
s3, comparing peak data of all air quality micro-stations in each local pollution area, and calculating the most likely pollution center point through a maximum likelihood estimation method;
and S4, calculating the pollution propagation direction through the spatial relationship between the peak value data and the pollution center point and the pollutant time sequence data among the peak value data.
Further, the specific method of step S1 comprises the following sub-steps:
s1-1, acquiring the serial numbers, the point positions and the original monitoring data of the air quality micro-stations;
s1-2, for each pollutant data in the original monitoring data of each air quality micro station, performing burr filtering on the pollutant data by a Savitzky-Golay filter to obtain smooth data of each pollutant;
s1-3, searching the peak value of the smooth data of each pollutant according to significance by using a signal processing find_peaks method to obtain a peak value set of each pollutant data of each air quality micro station;
s1-4, reserving a point position higher than the average value of the peak points in the peak value set of each pollutant data of each air quality micro station, and acquiring longitude and latitude information of the point position to obtain peak value data.
Further, the division window width of the Savitzky-Golay filter in the step S1-2 in the burr filtering is 9, and the order is 2.
Further, the distance parameter in the process of looking up the peak of the smoothed data for each contaminant by significance using the process find_peaks method in step S1-3 is set to 30.
Further, the specific method of step S2 comprises the following sub-steps:
s2-1, meshing a monitoring area, and if a fixed monitoring station exists within 2km of a mesh where the air quality micro station is located, according to the formula:
acquiring pollutant monitoring values in peak value data corrected by the air quality micro stationThe method comprises the steps of carrying out a first treatment on the surface of the Otherwise, directly reserving a pollutant monitoring value in peak data of the corresponding air quality micro station; wherein->A monitoring value for a fixed monitoring site; />Monitoring values for contaminants in peak data of air quality micro-stations located within 2km of a fixed monitoring site in the same grid; />Weights for fixed monitoring sites; />Weight for air quality micro-station +.>，/>The method comprises the steps of carrying out a first treatment on the surface of the The further apart an air quality micro-station is from a corresponding stationary monitoring station, the +.>The smaller the value of (2);
s2-2, carrying out air index calculation on pollutant monitoring values of the air quality micro station and the fixed monitoring station, and reserving station data with the air index exceeding 50 as a pollution point;
s2-3, clustering all pollution points according to longitude and latitude by a K-means method, and marking each class after clustering as a pollution area to obtain a local pollution area.
Further, the specific method of step S3 comprises the following sub-steps:
s3-1, setting peak weights according to the pollutant concentration for a single local pollution area, and simulating coordinate points around each peak according to the peak weights to obtain a peak coordinate point set; wherein the concentration of the contaminant is proportional to the peak weight, and the peak weight is proportional to the number of coordinate points;
s3-2, calculating a central coordinate point of the peak coordinate point set by a maximum likelihood estimation method based on the fact that the peak coordinate points obey two-dimensional Gaussian distribution, and taking the central coordinate point as the most likely pollution central point.
Further, the specific method for calculating the center coordinate point of the peak coordinate point set by the maximum likelihood estimation method in the step S3-2 is as follows:
according to the formula:
acquiring a center coordinate pointThe method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps ofnThe total number of peak coordinate points in the peak coordinate point set is the total number of peak coordinate points; />Is the firstjCoordinate values of the peak coordinate points; />Standard deviation of normal distribution; pi is the circumference ratio; />The base 10 logarithm is shown; />Is a likelihood function of the center coordinate point.
Further, the specific method of step S4 comprises the following sub-steps:
s4-1, marking the central point as sittingAcquiring a peak data coordinate nearest to the center point coordinateThe construction direction is->To->Vector of->；
S4-2, searching for a satisfaction vectorVector->The included angle of (2) is smaller than 30 DEG, and the distance is +.>Nearest peak data coordinate point->The method comprises the steps of carrying out a first treatment on the surface of the Wherein vector->Is +.>To->；
S4-3, obtaining peak value data coordinatesMonitoring sequence of the corresponding monitoring station +.>And peak data coordinates->Monitoring sequence of the corresponding monitoring station +.>The method comprises the steps of carrying out a first treatment on the surface of the The monitoring stations comprise an air quality micro station and a fixed monitoring station; />For corresponding monitoring stationmMonitoring data for each hour; />Is the hysteresis order; />For the corresponding monitoring site->Monitoring data for each hour;
s4-4, pair monitoring sequenceAnd->Performing a Grangel causal test, and determining the monitoring sequence when the test result is less than a set significance level>The change of (2) causes the monitoring sequence->Will->To->The direction is taken as the primary pollution propagation direction;
s4-5, dividing one peak value data coordinate which is selected to be nearest to the central point coordinate, and obtaining an unselected peak value data coordinate set;
s4-6, traversing all peak data coordinates in the unselected peak data coordinate set by adopting the same method as the steps S4-1 to S4-5 to obtain a plurality of preliminary pollution propagation directions;
s4-7, selecting the direction with the minimum corresponding Grangel causal test in all the preliminary pollution propagation directions as the final pollution propagation direction.
The beneficial effects of the invention are as follows: the method reasonably and effectively utilizes the data of the atmosphere monitoring network, combines the space and time information, can grasp pollution dynamics at a higher granularity, extracts useful information such as trend, peak value and the like by carrying out combined excavation on a plurality of air quality micro-station data, further improves the robustness to random noise, analyzes the pollution propagation direction with the maximum probability, can provide effective auxiliary information for law enforcement activities of related departments, avoids risks in advance, and inhibits further propagation of pollution.
Drawings
FIG. 1 is a schematic flow chart of the method;
FIG. 2 is a diagram of raw PM2.5 monitoring data for an air quality substation in an embodiment;
FIG. 3 is a data diagram of the data of FIG. 2 after spike filtering;
fig. 4 is a schematic diagram of the result of extracting peak points based on the data of fig. 3.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, the pollution occurrence and propagation sensing method based on intensive air quality micro-station monitoring comprises the following steps:
s1, acquiring air quality micro-station monitoring data and mining to obtain peak value data;
s2, gridding the monitoring area, correcting and filtering peak data in the grid based on fixed site data, and obtaining processed peak data; clustering the processed peak data to generate a local pollution area;
s3, comparing peak data of all air quality micro-stations in each local pollution area, and calculating the most likely pollution center point through a maximum likelihood estimation method;
and S4, calculating the pollution propagation direction through the spatial relationship between the peak value data and the pollution center point and the pollutant time sequence data among the peak value data.
The specific method of the step S1 comprises the following substeps:
s1-1, acquiring the serial numbers, the point positions and the original monitoring data of the air quality micro-stations;
s1-2, for each pollutant data in the original monitoring data of each air quality micro station, performing burr filtering on the pollutant data by a Savitzky-Golay filter to obtain smooth data of each pollutant;
s1-3, searching the peak value of the smooth data of each pollutant according to significance by using a signal processing find_peaks method to obtain a peak value set of each pollutant data of each air quality micro station;
s1-4, reserving a point position higher than the average value of the peak points in the peak value set of each pollutant data of each air quality micro station, and acquiring longitude and latitude information of the point position to obtain peak value data.
The specific method of the step S2 comprises the following substeps:
s2-1, meshing a monitoring area, and if a fixed monitoring station exists within 2km of a mesh where the air quality micro station is located, according to the formula:
acquiring pollutant monitoring values in peak value data corrected by the air quality micro stationThe method comprises the steps of carrying out a first treatment on the surface of the Otherwise, directly reserving a pollutant monitoring value in peak data of the corresponding air quality micro station; wherein->A monitoring value for a fixed monitoring site; />Monitoring values for contaminants in peak data of air quality micro-stations located within 2km of a fixed monitoring site in the same grid; />Weights for fixed monitoring sites; />Weight for air quality micro-station +.>，/>The method comprises the steps of carrying out a first treatment on the surface of the The further apart an air quality micro-station is from a corresponding stationary monitoring station, the +.>The smaller the value of (2);
s2-2, carrying out air index calculation on pollutant monitoring values of the air quality micro station and the fixed monitoring station, and reserving station data with the air index exceeding 50 as a pollution point;
s2-3, clustering all pollution points according to longitude and latitude by a K-means method, and marking each class after clustering as a pollution area to obtain a local pollution area.
The specific method of the step S3 comprises the following substeps:
s3-1, setting peak weights according to the pollutant concentration for a single local pollution area, and simulating coordinate points around each peak according to the peak weights to obtain a peak coordinate point set; wherein the concentration of the contaminant is proportional to the peak weight, and the peak weight is proportional to the number of coordinate points;
s3-2, calculating a central coordinate point of the peak coordinate point set by a maximum likelihood estimation method based on the fact that the peak coordinate points obey two-dimensional Gaussian distribution, and taking the central coordinate point as the most likely pollution central point.
The specific method for calculating the central coordinate point of the peak coordinate point set by the maximum likelihood estimation method in the step S3-2 is as follows:
according to the formula:
acquiring a center coordinate pointThe method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps ofnThe total number of peak coordinate points in the peak coordinate point set is the total number of peak coordinate points; />Is the firstjCoordinate values of the peak coordinate points; />Standard deviation of normal distribution; pi is the circumference ratio; />The base 10 logarithm is shown; />Is a likelihood function of the center coordinate point.
The specific method of step S4 comprises the following sub-steps:
s4-1, marking the central point as sittingAcquiring a peak data coordinate nearest to the center point coordinateThe construction direction is->To->Vector of->；
S4-2, searching for a satisfaction vectorVector->The included angle of (2) is smaller than 30 DEG, and the distance is +.>Nearest peak data coordinate point->The method comprises the steps of carrying out a first treatment on the surface of the Wherein vector->Is +.>To->；
S4-3, obtaining peak value data coordinatesMonitoring sequence of the corresponding monitoring station +.>And peak data coordinates->Monitoring sequence of the corresponding monitoring station +.>The method comprises the steps of carrying out a first treatment on the surface of the The monitoring stations comprise an air quality micro station and a fixed monitoring station; />For corresponding monitoring stationmMonitoring data for each hour; />Is the hysteresis order; />For the corresponding monitoring site->Monitoring data for each hour;
s4-4, pair monitoring sequenceAnd->Performing a Grangel causal test, and determining the monitoring sequence when the test result is less than a set significance level>The change of (2) causes the monitoring sequence->Will->To->The direction is taken as the primary pollution propagation direction;
s4-5, dividing one peak value data coordinate which is selected to be nearest to the central point coordinate, and obtaining an unselected peak value data coordinate set;
s4-6, traversing all peak data coordinates in the unselected peak data coordinate set by adopting the same method as the steps S4-1 to S4-5 to obtain a plurality of preliminary pollution propagation directions;
s4-7, selecting the direction with the minimum corresponding Grangel causal test in all the preliminary pollution propagation directions as the final pollution propagation direction.
In one embodiment of the present invention, each set of monitoring data of the air quality micro-station includes monitoring data of six pollutants (PM 2.5, PM10, sulfur dioxide, carbon monoxide, nitrogen dioxide, ozone), each of which is processed independently and identically, and taking PM2.5 as an example, fig. 2 is original PM2.5 monitoring data, and it can be seen that there are a plurality of sharp points, i.e. there are more burrs. As shown in fig. 3, the burr filtering processing is performed on the data by adopting a Savitzky-Golay filter, and then smooth data with the same trend as the original data is obtained. Parameters of the Savitzky-Golay filter are set as follows: the division window width is 9 and the order is 2.
Then using the signal processing find_peaks method, finding peaks by significance, setting finding parameters: the distance was 30. The abscissa of the peak point of the PM2.5 of the station is shown in fig. 4, and the dot in fig. 4 is the peak point found according to the significance. And filtering the peak points, only reserving the point positions higher than the average value of all the peak points, acquiring longitude and latitude information carried by the rest data, and further performing subsequent operation to obtain the final pollution propagation direction of PM 2.5.
In summary, the method reasonably and effectively utilizes the data of the atmosphere monitoring network, combines the space and time information, can grasp pollution dynamics at a higher granularity, extracts useful information such as trend, peak value and the like by carrying out combined excavation on a plurality of air quality micro-station data, further improves the robustness to random noise, analyzes the pollution propagation direction with the highest probability, can provide effective auxiliary information for law enforcement activities of related departments, avoids risks in advance, and inhibits further propagation of pollution.
Claims (5)
1. The pollution occurrence and propagation sensing method based on intensive air quality micro-station monitoring is characterized by comprising the following steps of:
s1, acquiring air quality micro-station monitoring data and mining to obtain peak value data;
s2, gridding the monitoring area, correcting and filtering peak data in the grid based on fixed site data, and obtaining processed peak data; clustering the processed peak data to generate a local pollution area;
s3, comparing peak data of all air quality micro-stations in each local pollution area, and calculating the most likely pollution center point through a maximum likelihood estimation method;
s4, calculating a pollution propagation direction through the space relation between the peak value data and the pollution center point and the pollutant time sequence data among the peak value data;
the specific method of the step S3 comprises the following substeps:
s3-1, setting peak weights according to the pollutant concentration for a single local pollution area, and simulating coordinate points around each peak according to the peak weights to obtain a peak coordinate point set; wherein the concentration of the contaminant is proportional to the peak weight, and the peak weight is proportional to the number of coordinate points;
s3-2, calculating a central coordinate point of the peak coordinate point set by a maximum likelihood estimation method based on the fact that the peak coordinate points obey two-dimensional Gaussian distribution, and taking the central coordinate point as the most likely pollution central point;
the specific method for calculating the central coordinate point of the peak coordinate point set by the maximum likelihood estimation method in the step S3-2 is as follows:
according to the formula:
acquiring a center coordinate pointThe method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps ofnThe total number of peak coordinate points in the peak coordinate point set is the total number of peak coordinate points; />Is the firstjCoordinate values of the peak coordinate points; />Standard deviation of normal distribution; pi is the circumference ratio; />The base 10 logarithm is shown;likelihood functions for the center coordinate points;
the specific method of step S4 comprises the following sub-steps:
s4-1, marking the central point as sittingAcquiring a peak data coordinate nearest to the center point coordinateThe construction direction is->To->Vector of->；
S4-2, searching for a satisfaction vectorVector->The included angle of (2) is smaller than 30 DEG, and the distance is +.>The nearest peak data coordinate pointThe method comprises the steps of carrying out a first treatment on the surface of the Wherein vector->Is +.>To->；
S4-3, obtaining peak value data coordinatesMonitoring sequence of the corresponding monitoring station +.>And peak data coordinates->Monitoring sequence of the corresponding monitoring station +.>The method comprises the steps of carrying out a first treatment on the surface of the The monitoring stations comprise an air quality micro station and a fixed monitoring station; />For corresponding monitoring stationmMonitoring data for each hour; />Is the hysteresis order; />For the corresponding monitoring site->Monitoring data for each hour;
s4-4, pair monitoring sequenceAnd->Performing a Grangel causal test, and determining the monitoring sequence when the test result is less than a set significance level>The change of (2) causes the monitoring sequence->Will->To->The direction is taken as the primary pollution propagation direction;
s4-5, dividing one peak value data coordinate which is selected to be nearest to the central point coordinate, and obtaining an unselected peak value data coordinate set;
s4-6, traversing all peak data coordinates in the unselected peak data coordinate set by adopting the same method as the steps S4-1 to S4-5 to obtain a plurality of preliminary pollution propagation directions;
s4-7, selecting the direction with the minimum corresponding Grangel causal test in all the preliminary pollution propagation directions as the final pollution propagation direction.
2. The pollution occurrence and propagation sensing method based on intensive air quality micro-station monitoring as claimed in claim 1, wherein the specific method of step S1 comprises the following sub-steps:
s1-1, acquiring the serial numbers, the point positions and the original monitoring data of the air quality micro-stations;
s1-2, for each pollutant data in the original monitoring data of each air quality micro station, performing burr filtering on the pollutant data by a Savitzky-Golay filter to obtain smooth data of each pollutant;
s1-3, searching the peak value of the smooth data of each pollutant according to significance by using a signal processing find_peaks method to obtain a peak value set of each pollutant data of each air quality micro station;
s1-4, reserving a point position higher than the average value of the peak points in the peak value set of each pollutant data of each air quality micro station, and acquiring longitude and latitude information of the point position to obtain peak value data.
3. The method for pollution occurrence and propagation sensing based on dense air quality micro-station monitoring of claim 2, wherein the division window width of the Savitzky-Golay filter in step S1-2 is 9 and the order is 2.
4. The method of claim 2, wherein the distance parameter during the peak of the smoothed data for each contaminant is set to 30 using the process find_peaks method in steps S1-3.
5. The pollution occurrence and propagation sensing method based on intensive air quality micro-station monitoring as claimed in claim 1, wherein the specific method of step S2 comprises the following sub-steps:
s2-1, meshing a monitoring area, and if a fixed monitoring station exists within 2km of a mesh where the air quality micro station is located, according to the formula:
acquiring the corrected peak value of the air quality micro stationContaminant monitoring value in dataThe method comprises the steps of carrying out a first treatment on the surface of the Otherwise, directly reserving a pollutant monitoring value in peak data of the corresponding air quality micro station; wherein->A monitoring value for a fixed monitoring site; />Monitoring values for contaminants in peak data of air quality micro-stations located within 2km of a fixed monitoring site in the same grid; />Weights for fixed monitoring sites; />Weight for air quality micro-station +.>，/>The method comprises the steps of carrying out a first treatment on the surface of the The further apart an air quality micro-station is from a corresponding stationary monitoring station, the +.>The smaller the value of (2);
s2-2, carrying out air index calculation on pollutant monitoring values of the air quality micro station and the fixed monitoring station, and reserving station data with the air index exceeding 50 as a pollution point;
s2-3, clustering all pollution points according to longitude and latitude by a K-means method, and marking each class after clustering as a pollution area to obtain a local pollution area.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108318630A (en) * | 2018-02-06 | 2018-07-24 | 济宁中科云天环保科技有限公司 | A kind of urban air-quality real-time monitoring system and method for early warning |
| CN108469273A (en) * | 2018-02-27 | 2018-08-31 | 济宁中科云天环保科技有限公司 | High in the clouds data joint debugging calibration method based on machine learning algorithm |
| CN109325616A (en) * | 2018-09-01 | 2019-02-12 | 哈尔滨工程大学 | It is a kind of to be returned based on Gaussian process and the fine particle that combines of glowworm swarm algorithm is predicted and source tracing method |
| CN110766191A (en) * | 2019-08-27 | 2020-02-07 | 东华理工大学 | Newly-added PM2.5 fixed monitoring station site selection method based on space-time kriging interpolation |
| CN111220782A (en) * | 2020-03-26 | 2020-06-02 | 泛测（北京）环境科技有限公司 | Particle pollutant detection alarm system in air atmosphere on road |
| CN114359002A (en) * | 2022-03-21 | 2022-04-15 | 四川国蓝中天环境科技集团有限公司 | Atmospheric pollution small-scale tracing method and system based on mobile monitoring trend mining |
| CN114997499A (en) * | 2022-06-07 | 2022-09-02 | 浙江工业大学 | Urban particulate matter concentration space-time prediction method under semi-supervised learning |
| WO2022217839A1 (en) * | 2021-04-14 | 2022-10-20 | 江南大学 | Air quality prediction method based on deep spatiotemporal similarity |
| CN115269675A (en) * | 2022-06-15 | 2022-11-01 | 浙江工业大学 | Time-space analysis method for atmospheric pollution in industrial park |
| CN115561408A (en) * | 2022-11-17 | 2023-01-03 | 河北先河环保科技股份有限公司 | Air pollution early warning method and device, electronic equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20220099256A (en) * | 2021-01-06 | 2022-07-13 | 삼성전자주식회사 | Device for outputting distribution for each wafer defect characteristic and its operation method |
-
2024
- 2024-01-18 CN CN202410075193.0A patent/CN117591907B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108318630A (en) * | 2018-02-06 | 2018-07-24 | 济宁中科云天环保科技有限公司 | A kind of urban air-quality real-time monitoring system and method for early warning |
| CN108469273A (en) * | 2018-02-27 | 2018-08-31 | 济宁中科云天环保科技有限公司 | High in the clouds data joint debugging calibration method based on machine learning algorithm |
| CN109325616A (en) * | 2018-09-01 | 2019-02-12 | 哈尔滨工程大学 | It is a kind of to be returned based on Gaussian process and the fine particle that combines of glowworm swarm algorithm is predicted and source tracing method |
| CN110766191A (en) * | 2019-08-27 | 2020-02-07 | 东华理工大学 | Newly-added PM2.5 fixed monitoring station site selection method based on space-time kriging interpolation |
| CN111220782A (en) * | 2020-03-26 | 2020-06-02 | 泛测（北京）环境科技有限公司 | Particle pollutant detection alarm system in air atmosphere on road |
| WO2022217839A1 (en) * | 2021-04-14 | 2022-10-20 | 江南大学 | Air quality prediction method based on deep spatiotemporal similarity |
| CN114359002A (en) * | 2022-03-21 | 2022-04-15 | 四川国蓝中天环境科技集团有限公司 | Atmospheric pollution small-scale tracing method and system based on mobile monitoring trend mining |
| CN114997499A (en) * | 2022-06-07 | 2022-09-02 | 浙江工业大学 | Urban particulate matter concentration space-time prediction method under semi-supervised learning |
| CN115269675A (en) * | 2022-06-15 | 2022-11-01 | 浙江工业大学 | Time-space analysis method for atmospheric pollution in industrial park |
| CN115561408A (en) * | 2022-11-17 | 2023-01-03 | 河北先河环保科技股份有限公司 | Air pollution early warning method and device, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于簇核心区域的聚类算法研究及在大气污染中的应用;谢越;中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑;20240115(第第2024 年01期期);B027-1153 * |
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