CN108829642B - Method for analyzing environmental quality data by using characteristic radar map - Google Patents
Method for analyzing environmental quality data by using characteristic radar map Download PDFInfo
- Publication number
- CN108829642B CN108829642B CN201810371190.6A CN201810371190A CN108829642B CN 108829642 B CN108829642 B CN 108829642B CN 201810371190 A CN201810371190 A CN 201810371190A CN 108829642 B CN108829642 B CN 108829642B
- Authority
- CN
- China
- Prior art keywords
- characteristic
- value
- map
- pollution
- radar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 230000007613 environmental effect Effects 0.000 title claims abstract description 29
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 50
- 231100000719 pollutant Toxicity 0.000 claims abstract description 50
- 238000001228 spectrum Methods 0.000 claims abstract description 21
- 238000004458 analytical method Methods 0.000 claims abstract description 10
- 239000000356 contaminant Substances 0.000 claims description 29
- 238000004364 calculation method Methods 0.000 claims description 15
- 239000011362 coarse particle Substances 0.000 claims description 12
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 8
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 6
- 229910052698 phosphorus Inorganic materials 0.000 claims description 6
- 239000011574 phosphorus Substances 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 239000002689 soil Substances 0.000 claims description 5
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 4
- 229910052757 nitrogen Inorganic materials 0.000 claims description 4
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- ZOXJGFHDIHLPTG-UHFFFAOYSA-N Boron Chemical compound [B] ZOXJGFHDIHLPTG-UHFFFAOYSA-N 0.000 claims description 2
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 claims description 2
- KRHYYFGTRYWZRS-UHFFFAOYSA-M Fluoride anion Chemical compound [F-] KRHYYFGTRYWZRS-UHFFFAOYSA-M 0.000 claims description 2
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims description 2
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims description 2
- 229910052785 arsenic Inorganic materials 0.000 claims description 2
- RQNWIZPPADIBDY-UHFFFAOYSA-N arsenic atom Chemical compound [As] RQNWIZPPADIBDY-UHFFFAOYSA-N 0.000 claims description 2
- 229910052796 boron Inorganic materials 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 229910052742 iron Inorganic materials 0.000 claims description 2
- WPBNNNQJVZRUHP-UHFFFAOYSA-L manganese(2+);methyl n-[[2-(methoxycarbonylcarbamothioylamino)phenyl]carbamothioyl]carbamate;n-[2-(sulfidocarbothioylamino)ethyl]carbamodithioate Chemical compound [Mn+2].[S-]C(=S)NCCNC([S-])=S.COC(=O)NC(=S)NC1=CC=CC=C1NC(=S)NC(=O)OC WPBNNNQJVZRUHP-UHFFFAOYSA-L 0.000 claims description 2
- 239000002480 mineral oil Substances 0.000 claims description 2
- 235000010446 mineral oil Nutrition 0.000 claims description 2
- 239000003208 petroleum Substances 0.000 claims description 2
- 239000011591 potassium Substances 0.000 claims description 2
- 229910052700 potassium Inorganic materials 0.000 claims description 2
- 239000012286 potassium permanganate Substances 0.000 claims description 2
- 150000003839 salts Chemical class 0.000 claims description 2
- 239000000126 substance Substances 0.000 claims 1
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 30
- JCXJVPUVTGWSNB-UHFFFAOYSA-N Nitrogen dioxide Chemical compound O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 description 29
- 230000008859 change Effects 0.000 description 8
- 238000012544 monitoring process Methods 0.000 description 8
- MGWGWNFMUOTEHG-UHFFFAOYSA-N 4-(3,5-dimethylphenyl)-1,3-thiazol-2-amine Chemical compound CC1=CC(C)=CC(C=2N=C(N)SC=2)=C1 MGWGWNFMUOTEHG-UHFFFAOYSA-N 0.000 description 3
- 229910002091 carbon monoxide Inorganic materials 0.000 description 3
- 230000002354 daily effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 239000000809 air pollutant Substances 0.000 description 2
- 231100001243 air pollutant Toxicity 0.000 description 2
- 238000011109 contamination Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 239000003570 air Substances 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002436 steel type Substances 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/206—Drawing of charts or graphs
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Algebra (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Organic Low-Molecular-Weight Compounds And Preparation Thereof (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for analyzing environmental quality data by using a characteristic radar chart, which mainly comprises the following four steps: 1. normalizing the data to eliminate the dimension of the pollutant concentration; 2. drawing a bottom map of the characteristic radar map, wherein the bottom map of the characteristic radar map comprises three lines in a polygon shape, the line 1 is in a regular polygon shape, and the numerical value is 1; the line 2 is an unspecified polygon, and the numerical value is more than 1; the line 3 is an unspecified polygon, and the numerical value is less than 1; 3. drawing a characteristic radar chart by the ratio of the pollution characteristic component spectrum of a specific time or characteristic station to the average pollution characteristic component spectrum in a certain period or a certain area; 4. and the characteristic radar chart is superposed on the base chart of the characteristic radar chart to form a complete chart. The method realizes intuitive and quick cause analysis and display by comparing the pollution characteristics at a specific time with the average pollution characteristics in a certain period or comparing the pollution characteristics at a specific site with the average pollution characteristics in a region in a manner of drawing a characteristic radar map.
Description
Technical Field
The invention belongs to the field of environmental quality analysis and monitoring, and particularly relates to a method for analyzing air, soil and water quality data by using a characteristic radar map.
Background
The environmental quality monitoring is an environmental monitoring content, mainly monitors the distribution and concentration of pollutants in the environment to determine the environmental quality condition, and can provide an essential basis for environmental quality evaluation and environmental influence evaluation through the historical data of timed and fixed-point environmental quality monitoring; and important basic data are provided for scientific research on the pollutant migration and transformation rules.
The existing analysis method for monitoring data is to analyze pollution characteristics by using concentration change or a simple ratio method, and because the pollutant concentration change and concentration values of different pollutants have magnitude-order change in numerical value, when the data are directly used for analysis, tiny characteristic change is covered under the background of huge data change, and the difference of the pollution characteristics in time series or space cannot be seen.
Disclosure of Invention
In order to solve the problems, the invention provides a method for analyzing environmental quality data by using a characteristic radar map, which is used for carrying out normalization processing on original conventional monitoring data, deducting the difference of the pollutant concentration change and the magnitude of the concentration values of different pollutants on numerical values, directly representing the change characteristics of the pollutants on a time sequence and a space by designing the characteristic radar map, and providing convenience for real-time and dynamic guidance of an environmental management department for decision making by using the environmental quality conventional monitoring data.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
a method for analyzing environmental quality data using a signature radar map, comprising the steps of:
(1) Data normalization, namely, performing percentage on the concentration of the pollutants according to a proportion to form a pollution characteristic component spectrum, so as to eliminate the dimension of the concentration of the pollutants;
the calculation formula is as follows:
i is different time;
j is a different contaminant;
(2) Calculating the standard value, the upper limit and the lower limit of each pollutant in a certain period or a certain area
The standard value is the ratio of the average pollution characteristic component spectrum of a certain period or a certain area to the average pollution characteristic component spectrum, is a dimensionless number, has a numerical value of 1, and has the calculation formula as follows:
the upper limit is the ratio of the average value of the pollution characteristic component spectrum in a certain period or a certain area plus the standard deviation thereof to the average value of the pollution characteristic component spectrum, the numerical value is more than 1, and the calculation formula is as follows:
the lower limit is the ratio of the mean value of the average pollution characteristic component spectrum in a certain period or a certain area minus the standard deviation thereof to the mean value of the pollution characteristic component spectrum, the numerical value is less than 1, and the calculation formula is as follows:
(3) Drawing of base map of characteristic radar chart
The characteristic radar map base map comprises three polygons, wherein one polygon is a regular polygon which corresponds to the standard value, and the other two polygons respectively represent the upper limit and the lower limit; the angles of each polygon respectively represent a contaminant, the angles representing the same contaminant lying on the same line through the center point;
(4) Drawing of time-specific or site-specific characteristic radar maps
Calculating the pollutant concentration characteristic value of a specific time or a specific site, drawing the characteristic value into a characteristic radar chart of a characteristic polygon by the same method of drawing a base chart, wherein the characteristic value is the ratio of the pollution characteristic component spectrum normalized concentration of the specific time or the specific site to the pollution characteristic component spectrum mean value of a certain period or a certain area, and the calculation formula is as follows:
(5) And superposing the characteristic radar map of a specific time or a specific station on the base map of the characteristic radar map to form a complete analysis map, and judging the pollution condition according to the position of each corner in a characteristic polygon in the characteristic radar map and the position condition of the pollutant of the corresponding corner.
As a preferred method for analyzing environmental quality data by using a characteristic radar chart, when a base chart is drawn, the upper limit value and the lower limit value of each pollutant are respectively drawn on the base chart by using the center of a regular polygon corresponding to a standard value as an origin and the distance from the origin to the angle of the regular polygon as a standard value 1, and then the upper limit value and the lower limit value of each pollutant are respectively connected into a closed polygon by using straight lines.
As a preferable example of another method for analyzing environmental quality data using a characteristic radar chart according to the present invention, when the base chart is drawn, a plurality of regular polygons for reference of data are drawn in equal proportion, with the center of the regular polygon corresponding to the standard value as the center.
As a preferred aspect of still another method of analyzing environmental quality data using a characteristic radar chart of the present invention, it is used for analyzing air quality or soil quality or water quality.
As a preferred method of analyzing environmental quality data using a signature radar chart according to the present invention, the pollutants include PM2.5, SO2, CO, coarse particles, and NO2 when used for analyzing air quality.
Preferably, the contaminant further comprises NO.
The air pollutants are not limited to the above-mentioned six types, but may also include water-soluble ions, elements, carbonaceous components, and other air pollutants.
As a preferable preference of the method for analyzing the environmental quality data by using the characteristic radar chart, when the method is used for analyzing the water quality, the pollutants comprise dissolved oxygen, potassium permanganate index, COD, ammonia nitrogen, total phosphorus, total nitrogen, petroleum and the like.
As a preferable example of the method for analyzing environmental quality data using a characteristic radar chart according to the present invention, when the method is used for analyzing soil quality, the pollutants include iron, manganese, total potassium, organic matters, total nitrogen, available phosphorus, total phosphorus, moisture, total arsenic, available boron, fluoride, chloride, mineral oil, and total salt amount.
The method realizes intuitive and quick cause analysis and display by comparing the pollution characteristic at a specific time with the average pollution characteristic in a certain period or the pollution characteristic at a specific site with the average pollution characteristic in a region in a way of drawing a characteristic radar map. The technical problem that tiny characteristic changes are covered due to the fact that the pollutant concentration changes and the concentration values of different pollutants have magnitude-order changes on numerical values can be solved, so that the difference of pollution characteristics on a time sequence or a space is obtained, and support is provided for environmental management and decision making. When each contaminant in the signature radar map exceeds the upper limit outward and exceeds the lower limit inward, it indicates that the corresponding contaminant has changed significantly from the average signature over a certain period or area. And clearly judging the type of pollution in time sequence and space according to the obvious change, thereby judging a pollution source and providing support for environmental management and decision.
Drawings
The invention is further illustrated by the non-limiting examples given in the accompanying drawings;
FIG. 1 is a radar chart showing the characteristics of six contaminants in example 1 of the present invention;
FIG. 2 is a radar chart of the characteristics of five contaminants in example 1;
FIG. 3 is a characteristic radar chart of PM2.5 concentration curves and five pollutants in 2018, month 2, day 16 to day 19 in Beijing, example 2;
FIG. 4 is a characteristic radar chart and classification of five pollutants of Jingjin Ji at 11/3/2018 and surrounding areas in example 3;
mainly accords with the description: 1, regular polygon corresponding to standard value, 2, polygon corresponding to lower limit value, 3, polygon corresponding to upper limit value, 4, polygon corresponding to specific time or specific station, 5 and reference polygon.
Detailed Description
In order that those skilled in the art can better understand the present invention, the following technical solutions are further described with reference to the accompanying drawings and examples.
Example 1
In this embodiment, the air quality of the city in Cangzhou city in 2014 is taken as an example, and a characteristic radar map is used for data analysis. The air quality monitoring data includes daily averages of PM2.5, SO2, NO2, CO, coarse particulate, NO.
(1) Firstly, carrying out data normalization on data monitored every day, and carrying out percentage conversion on the daily average values of PM2.5, SO2, NO2, CO, coarse particles and NO according to a proportion to form a pollution characteristic component spectrum;
the calculation formula is as follows:
i is different time;
j is a different contaminant;
(2) Calculating the standard values, upper limits and lower limits of PM2.5, SO2, NO2, CO, coarse particles and NO in the whole year in 2014
Wherein, the calculation formula of the standard value is as follows:
the upper limit is calculated by the formula:
the lower limit is calculated as:
the calculation results are shown in table one.
Watch 1
SO2 | NO | NO2 | CO | PM2.5 | Coarse particles | |
Mean value Zi | 0.171988377 | 0.036596676 | 0.156457779 | 0.005924 | 0.394174 | 0.234041 |
Standard deviation of Si | 0.08575579 | 0.034050275 | 0.051655211 | 0.002894 | 0.12969 | 0.125819 |
Upper limit of | 1.498613868 | 1.930419876 | 1.330154316 | 1.488526 | 1.329016 | 1.537594 |
Lower limit of | 0.501386132 | 0.069580124 | 0.669845684 | 0.511474 | 0.670984 | 0.462406 |
Standard value | 1 | 1 | 1 | 1 | 1 | 1 |
(3) Drawing of base map of drawing characteristic radar chart
As shown in fig. 1, a regular hexagon is drawn on a base map as a graph corresponding to a standard value, each corner of the regular hexagon represents a pollutant, the distance from the center of the regular hexagon to the corner of the regular hexagon is taken as a standard value 1, the upper limit value and the lower limit value of each pollutant are drawn on the graph, the upper limit value and the lower limit value of each pollutant are connected into two closed hexagons by straight lines, and the corners representing the same pollutant are located on the same straight line.
In order to mark the upper limit value of each pollutant, a regular polygon corresponding to the standard value can be scaled on the base map as a reference hexagon.
(4) Drawing of characteristic radar map of No. 1/19 in 2014
Calculating the characteristic value of the pollutant concentration at the time according to the formula
Wherein QUOTE -characteristic value of jth contaminant of ith time or ith station; the calculation results are shown in table two:
watch two
SO2 | NO | NO2 | CO | PM2.5 | Coarse particles | |
2014/1/19 | 0.96 | 0.44 | 0.76 | 1.01 | 1.44 | 0.54 |
The feature value is plotted into a feature radar chart of the feature polygon in the same way as the base chart is plotted, as shown in fig. 1.
(5) The characteristic radar map of No. 1/19 in 2014 is superposed on the base map of the characteristic radar map to form a complete analysis map, as shown in FIG. 1, and the condition of the pollutant corresponding to each corner in the characteristic polygon in the characteristic radar map is analyzed according to the position of the corresponding corner.
As can be seen from fig. 1, day 19/1 in 2014, the angle representing PM2.5 has exceeded the polygon corresponding to the upper limit value, the angle representing CO coincides with the regular hexagon corresponding to the standard value, and the angles representing coarse particles, SO2, NO, and NO2 are all between the hexagons representing the standard value and the lower limit value, SO as to determine that the atmospheric pollutants are mainly affected by secondary pollution on the day.
Because NO is unstable and is easily oxidized, the difference of the measured values at each time point is large, and therefore, in order to more accurately analyze the pollution conditions of other five items, the data of NO is deleted, the method is repeated, the data shown in table three below is calculated, and a radar characteristic diagram containing five pollutants is drawn according to the data, as shown in fig. 2.
Watch III
SO2 | NO2 | CO | PM2.5 | Coarse particles | |
2014/1/19 | 0.93 | 0.74 | 0.98 | 1.42 | 0.53 |
Upper limit of | 1.503299943 | 1.329881 | 1.466126 | 1.308742 | 1.501563 |
Lower limit of | 0.496700057 | 0.670119 | 0.533874 | 0.691258 | 0.498437 |
Standard value | 1 | 1 | 1 | 1 | 1 |
From fig. 2, it can be seen that only the angle representing PM2.5 exceeds the regular pentagon corresponding to the upper limit value, and the angles representing CO, NO2, SO2 and coarse particles are all between the hexagons representing the standard value and the lower limit value, SO as to more accurately judge that the atmospheric pollutants are mainly secondary pollution.
Example 2
The contamination of five contaminants (coarse particles, SO2, NO2, CO, PM 2.5) in beijing during the spring festival of 16-19/2/2018 was analyzed by the same calculation method as in example 1, and the daily atmospheric contamination characteristics were reflected by radar charts, whereby fig. 3 was obtained.
From fig. 3, it can be seen that beijing exhibits typical pollution characteristics of fireworks (PM 2.5 and SO2 are relatively high in occupancy ratio) in 16 days in 2 months (first year); subsequently (first two to first four), while PM2.5 is still high, SO2 gradually decreases and CO increases to some extent, indicating that the firework pollution effect gradually decreases after first two. Another significant feature during this pollution process is that the relative proportions of the coarse particles and NO2 are reduced, indicating that the activity of construction, freight transportation, etc. is reduced during the spring festival, reducing the impact of dust emission and motor vehicle emissions.
Example 3
The same calculation method as that of example 1 was used to analyze the pollution situation of five pollutants (coarse particles, SO2, NO2, CO, PM 2.5) in beijing jin Ji and surrounding areas, 3 and 11 months in 2018, radar maps were used to reflect the atmospheric pollution characteristics of different cities on the same day, and the pollution types of each city were classified.
The characteristic values of Beijing, baoding, cangzhou, gallery, tianjin and NO2 exceed the upper limit value, and the other four items are between the upper limit value and the lower limit value or close to the upper limit value, which indicates that the higher nitrogen dioxide is the main characteristic of cities in the day, and the main pollution source of the nitrogen dioxide is the tail gas of automobiles, so the type is classified into the type of the vehicle with the highest priority.
The characteristic values of five pollutants of the constant water, the stone house, the chenchen platform, the Yangquan and the zibo are divided into upper limit values and lower limit values which are in a comprehensive type.
The characteristic values of PM2.5 of Anyang, johnson, kaifeng, puyang and Zheng are all over the upper limit value, and the other four items are all between the upper limit value and the lower limit value or close to the upper limit value, which indicates that the higher PM2.5 is the main characteristic of cities on the day, so that the type is classified as secondary pollution type.
The characteristic values of coarse grains of lotus, jining, chatting and Taiyuan exceed the upper limit and are divided into coarse grain types.
The characteristic diagram of Bin and Jincheng is biased to sulfur dioxide, and the characteristic diagram is divided into a biased-burning coal type.
The characteristic values of sulfur dioxide, nitrogen dioxide and carbon monoxide in Tangshan exceed the upper limit, and the Tangshan is classified into a partial steel type.
The method for analyzing environmental quality data by using a characteristic radar chart provided by the invention is described in detail above. The description of the specific embodiments is only intended to facilitate an understanding of the method of the invention and its core ideas. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (6)
1. A method for analyzing environmental quality data using a signature radar map, comprising: comprises the following steps of (a) carrying out,
(1) Normalizing the data, namely performing percentile conversion on the concentration of the pollutants according to a proportion to obtain a pollution characteristic component spectrum;
the calculation formula is as follows:
i is different time;
j is a different contaminant;
(2) Calculating the standard value, the upper limit and the lower limit of each pollutant in a certain period or a certain area
The standard value is the ratio of the average pollution characteristic component spectrum in a certain period or a certain area to the average pollution characteristic component spectrum, and the numerical value is 1;
the upper limit is the ratio of the average value of the pollution characteristic component spectrum in a certain period or a certain area plus the standard deviation to the average value of the pollution characteristic component spectrum, the numerical value is more than 1, and the calculation formula is as follows:
the lower limit is the ratio of the mean value of the average pollution characteristic component spectrum in a certain period or a certain area minus the standard deviation thereof to the mean value of the pollution characteristic component spectrum, the numerical value is less than 1, and the calculation formula is as follows:
(3) Drawing of base map of characteristic radar chart
The characteristic radar map base map comprises three polygons, wherein one polygon is a regular polygon which corresponds to the standard value, and the other two polygons respectively represent the upper limit and the lower limit; the angles of each polygon respectively represent a contaminant, the angles representing the same contaminant lying on the same line through the center point;
when the base map is drawn, a standard value polygon map is drawn by taking the center of a regular polygon as an origin and taking the distance from the origin to the corners of the regular polygon as 1, the upper limit value and the lower limit value of each pollutant are respectively drawn on the map, and the upper limit value and the lower limit value of each pollutant are respectively connected into two closed polygons by straight lines;
when drawing the base map, drawing a plurality of regular polygons for data reference in equal proportion by taking the center of the regular polygon corresponding to the standard value as the center;
(4) Drawing of time-specific or site-specific characteristic radar maps
Calculating the pollutant concentration characteristic value of a specific time or a specific site, drawing the characteristic value into a characteristic radar chart of a characteristic polygon by the same method of drawing a base chart, wherein the characteristic value is the ratio of the pollution characteristic component spectrum normalized concentration of the specific time or the specific site to the pollution characteristic component spectrum mean value of a certain period or a certain area, and the calculation formula is as follows:
(5) And superposing the characteristic radar map of a specific time or a specific site on the base map of the characteristic radar map to form a complete analysis map, and judging the pollution condition according to the position of each corner in the characteristic polygon in the characteristic radar map and the position condition of the pollutant of the corresponding corner.
2. The method of analyzing environmental quality data using signature radar maps of claim 1, wherein: for analysis of air quality or soil quality or water quality.
3. The method of analyzing environmental quality data using signature radar maps of claim 2, wherein: for analysis of air quality, the pollutants include PM2.5, SO2, CO, coarse particles, NO2.
4. A method of analysing environmental quality data using signature radar maps according to claim 3, characterised by: the contaminants also include NO.
5. The method of analyzing environmental quality data using signature radar maps of claim 2, wherein: when the method is used for analyzing the water quality, the pollutants comprise dissolved oxygen, potassium permanganate index, COD (chemical oxygen demand), ammonia nitrogen, total phosphorus, total nitrogen and petroleum.
6. The method of analyzing environmental quality data using signature radar maps of claim 2, wherein: when the method is used for analyzing the soil quality, the pollutants comprise iron, manganese, total potassium, organic matters, total nitrogen, available phosphorus, total phosphorus, moisture, total arsenic, available boron, fluoride, chloride, mineral oil and total salt content.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810371190.6A CN108829642B (en) | 2018-04-24 | 2018-04-24 | Method for analyzing environmental quality data by using characteristic radar map |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810371190.6A CN108829642B (en) | 2018-04-24 | 2018-04-24 | Method for analyzing environmental quality data by using characteristic radar map |
Publications (2)
Publication Number | Publication Date |
---|---|
CN108829642A CN108829642A (en) | 2018-11-16 |
CN108829642B true CN108829642B (en) | 2022-11-22 |
Family
ID=64155104
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810371190.6A Active CN108829642B (en) | 2018-04-24 | 2018-04-24 | Method for analyzing environmental quality data by using characteristic radar map |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN108829642B (en) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110992357B (en) * | 2019-12-17 | 2023-04-14 | 云南中烟工业有限责任公司 | Radar chart analysis method, medium and single chip microcomputer for storing radar chart analysis method and respective application |
CN111611296B (en) * | 2020-05-20 | 2021-02-19 | 中科三清科技有限公司 | PM2.5Pollution cause analysis method and device, electronic equipment and storage medium |
CN111612064B (en) * | 2020-05-20 | 2021-02-19 | 中科三清科技有限公司 | PM2.5Method and device for tracing pollution air mass, electronic equipment and storage medium |
CN115902114A (en) * | 2022-12-07 | 2023-04-04 | 北京工业大学 | Small-scale atmospheric pollution tracing method based on semi-quantitative method |
CN116757555B (en) * | 2023-08-14 | 2023-11-03 | 中科三清科技有限公司 | Method, device, electronic equipment and storage medium for determining pollution characteristic type |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2002063539A1 (en) * | 2001-02-08 | 2002-08-15 | Aeroqual Limited | Air quality monitoring system |
CN105403664A (en) * | 2015-10-19 | 2016-03-16 | 电力规划设计总院 | WRF-CHEM-based large point pollution source atmosphere environment influence evaluating method |
CN105424840A (en) * | 2015-12-28 | 2016-03-23 | 周俊杰 | On-line continuous environmental air quality automatic monitoring system and peculiar smell source tracing method |
CN106339974A (en) * | 2016-10-21 | 2017-01-18 | 常州市环境监测中心 | Method for establishing urban air pollutant emission list |
CN106649960A (en) * | 2016-10-12 | 2017-05-10 | 环境保护部环境规划院 | Three-dimensional iterative computing method for environment capacity of multiple atmospheric pollutants |
CN107340364A (en) * | 2017-05-31 | 2017-11-10 | 北京市环境保护监测中心 | Polluted space analysis method and device based on magnanimity air pollution concentration data |
-
2018
- 2018-04-24 CN CN201810371190.6A patent/CN108829642B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2002063539A1 (en) * | 2001-02-08 | 2002-08-15 | Aeroqual Limited | Air quality monitoring system |
CN105403664A (en) * | 2015-10-19 | 2016-03-16 | 电力规划设计总院 | WRF-CHEM-based large point pollution source atmosphere environment influence evaluating method |
CN105424840A (en) * | 2015-12-28 | 2016-03-23 | 周俊杰 | On-line continuous environmental air quality automatic monitoring system and peculiar smell source tracing method |
CN106649960A (en) * | 2016-10-12 | 2017-05-10 | 环境保护部环境规划院 | Three-dimensional iterative computing method for environment capacity of multiple atmospheric pollutants |
CN106339974A (en) * | 2016-10-21 | 2017-01-18 | 常州市环境监测中心 | Method for establishing urban air pollutant emission list |
CN107340364A (en) * | 2017-05-31 | 2017-11-10 | 北京市环境保护监测中心 | Polluted space analysis method and device based on magnanimity air pollution concentration data |
Non-Patent Citations (2)
Title |
---|
利用雷达图表征环境质量状况的综合评价方法探讨;毛欣等;《第十一次全国环境监测学术交流会暨山东省第一次环境监测学术交流会》;20180228;第331-333页 * |
北京PM2.5中有机氮的污染特征与来源研究;张倩;《中国优秀硕士学位论文全文数据库 工程科技I辑》;20160715;B027-208 * |
Also Published As
Publication number | Publication date |
---|---|
CN108829642A (en) | 2018-11-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108829642B (en) | Method for analyzing environmental quality data by using characteristic radar map | |
Munir | Analysing temporal trends in the ratios of PM2. 5/PM10 in the UK | |
Lestari et al. | Source apportionment of particulate matter at urban mixed site in Indonesia using PMF | |
Querol et al. | Source origin of trace elements in PM from regional background, urban and industrial sites of Spain | |
Watson et al. | Chemical mass balance | |
Reche et al. | Urban NH3 levels and sources in six major Spanish cities | |
Munir et al. | Quantifying temporal trends of atmospheric pollutants in Makkah (1997–2012) | |
Viana et al. | Chemical characterisation of PM episodes in NE Spain | |
Linnik et al. | Geochemical assessment and spatial analysis of heavy metals pollution around coal-fired power station | |
Escrig et al. | Application of optimally scaled target factor analysis for assessing source contribution of ambient PM10 | |
Lv et al. | Geochemical and statistical analysis of trace metals in atmospheric particulates in Wuhan, central China | |
Wang et al. | Mixing state of ambient aerosols in Nanjing city by single particle mass spectrometry | |
Tsai et al. | Comparing physicochemical properties of ambient particulate matter of hot spots in a highly polluted air quality zone | |
Kuo et al. | Identifying the factors influencing PM2. 5 in southern Taiwan using dynamic factor analysis | |
CN115963041A (en) | Method for evaluating relationship between concentration of fine particulate matters and carbon emission in atmospheric environment | |
CN111899817A (en) | Pollutant source analysis method and device | |
Loranger et al. | Manganese contamination in Montreal in relation with traffic density | |
Istiqomah et al. | Particulate air pollution in Indonesia: quality index, characteristic, and source identification | |
Xing et al. | Spatial and temporal changes analysis of air quality before and after the COVID-19 in Shandong Province, China | |
Wu et al. | Haze emission efficiency assessment and governance for sustainable development based on an improved network data envelopment analysis method | |
Shen et al. | Multi-type air pollutant emission inventory of non-road mobile sources in China for the period 1990-2017 | |
Brimblecombe | Trends in secondary inorganic particles in Hong Kong, 1995–2020 | |
Akagbue et al. | Comprehensive Assessment and Remediation Strategies for Air Pollution: Current Trends and Future Prospects; A Case Study in Bompai Industrial Area, Kano State, Nigeria. | |
Zajusz-Zubek et al. | Dendrograms, heat maps and principal component analysis–the practical use of statistical methods for source apportionment of trace elements in PM10 | |
Nakatsubo et al. | Estimate of regional and broad-based sources for PM2. 5 collected in an industrial area of Japan |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |