CN115097547A - Atmospheric heavy pollution forecasting method based on combination of numerical mode and statistical analysis - Google Patents
Atmospheric heavy pollution forecasting method based on combination of numerical mode and statistical analysis Download PDFInfo
- Publication number
- CN115097547A CN115097547A CN202210787303.7A CN202210787303A CN115097547A CN 115097547 A CN115097547 A CN 115097547A CN 202210787303 A CN202210787303 A CN 202210787303A CN 115097547 A CN115097547 A CN 115097547A
- Authority
- CN
- China
- Prior art keywords
- pollution
- pollutant
- concentration
- statistical analysis
- air
- 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.)
- Pending
Links
- 238000007619 statistical method Methods 0.000 title claims abstract description 32
- 238000013277 forecasting method Methods 0.000 title claims abstract description 8
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 116
- 231100000719 pollutant Toxicity 0.000 claims abstract description 116
- 239000007789 gas Substances 0.000 claims description 37
- 238000000034 method Methods 0.000 claims description 29
- 238000002798 spectrophotometry method Methods 0.000 claims description 21
- 241000699670 Mus sp. Species 0.000 claims description 19
- 238000004458 analytical method Methods 0.000 claims description 18
- 241000699666 Mus <mouse, genus> Species 0.000 claims description 17
- WSFSSNUMVMOOMR-UHFFFAOYSA-N Formaldehyde Chemical compound O=C WSFSSNUMVMOOMR-UHFFFAOYSA-N 0.000 claims description 12
- 230000000694 effects Effects 0.000 claims description 10
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 claims description 9
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 claims description 6
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 6
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 6
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 6
- YRKCREAYFQTBPV-UHFFFAOYSA-N acetylacetone Chemical compound CC(=O)CC(C)=O YRKCREAYFQTBPV-UHFFFAOYSA-N 0.000 claims description 6
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 6
- 239000000428 dust Substances 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 6
- 230000037213 diet Effects 0.000 claims description 4
- 235000005911 diet Nutrition 0.000 claims description 4
- VRZJGENLTNRAIG-UHFFFAOYSA-N 4-[4-(dimethylamino)phenyl]iminonaphthalen-1-one Chemical compound C1=CC(N(C)C)=CC=C1N=C1C2=CC=CC=C2C(=O)C=C1 VRZJGENLTNRAIG-UHFFFAOYSA-N 0.000 claims description 3
- MQTPMDMFXZTBDK-UHFFFAOYSA-N [Na+].Cl[O-].OC(=O)C1=CC=CC=C1O Chemical compound [Na+].Cl[O-].OC(=O)C1=CC=CC=C1O MQTPMDMFXZTBDK-UHFFFAOYSA-N 0.000 claims description 3
- LSVVNVHHHMEPJZ-UHFFFAOYSA-L [Na+].[Na+].[O-]S(=O)(=O)SS([O-])(=O)=O Chemical compound [Na+].[Na+].[O-]S(=O)(=O)SS([O-])(=O)=O LSVVNVHHHMEPJZ-UHFFFAOYSA-L 0.000 claims description 3
- 229910021529 ammonia Inorganic materials 0.000 claims description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- YNLAOSYQHBDIKW-UHFFFAOYSA-M diethylaluminium chloride Chemical compound CC[Al](Cl)CC YNLAOSYQHBDIKW-UHFFFAOYSA-M 0.000 claims description 3
- 238000002848 electrochemical method Methods 0.000 claims description 3
- 239000008098 formaldehyde solution Substances 0.000 claims description 3
- 238000004868 gas analysis Methods 0.000 claims description 3
- 238000004817 gas chromatography Methods 0.000 claims description 3
- 229910052739 hydrogen Inorganic materials 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 claims description 3
- 229910052753 mercury Inorganic materials 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 229910052700 potassium Inorganic materials 0.000 claims description 3
- 238000007789 sealing Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 208000024891 symptom Diseases 0.000 claims description 3
- 208000032140 Sleepiness Diseases 0.000 claims description 2
- 206010041349 Somnolence Diseases 0.000 claims description 2
- 238000011109 contamination Methods 0.000 claims description 2
- 230000003031 feeding effect Effects 0.000 claims 1
- 230000006461 physiological response Effects 0.000 claims 1
- 235000018770 reduced food intake Nutrition 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 description 3
- 235000012631 food intake Nutrition 0.000 description 2
- 229910052698 phosphorus Inorganic materials 0.000 description 2
- 230000011514 reflex Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005293 physical law Methods 0.000 description 1
- 230000007958 sleep Effects 0.000 description 1
- 230000037321 sleepiness Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
- G01N33/0067—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display by measuring the rate of variation of the concentration
-
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- 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
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Optimization (AREA)
- Environmental & Geological Engineering (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Food Science & Technology (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Algebra (AREA)
- Bioinformatics & Computational Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Geometry (AREA)
- Combustion & Propulsion (AREA)
- Evolutionary Computation (AREA)
- Operations Research (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Computer Hardware Design (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Sampling And Sample Adjustment (AREA)
Abstract
The invention discloses an atmospheric heavy pollution forecasting method based on combination of a numerical mode and statistical analysis, which comprises the following steps of; step S1: establishing an atmospheric pollution statistical analysis platform, receiving atmospheric pollution data information, and setting a minimum atmospheric pollution threshold value, wherein the atmospheric pollution statistical analysis platform is connected with a forecasting system in a data mode; step S2: setting a pollution range according to the concentration of pollutants, respectively setting a first pollution index, a second pollution index and a third pollution index, setting the range of the pollution indexes, and setting the maximum pollution index; step S3: analyzing each pollutant type of the polluted gas in the polluted gas; step S4: the invention can collect and analyze the pollutant concentration, measure the content of each pollutant in the pollutant and reduce the error.
Description
Technical Field
The invention belongs to the field of numerical modes, relates to a statistical analysis technology, and particularly relates to an atmospheric heavy pollution forecasting method based on the combination of the numerical modes and the statistical analysis.
Background
The numerical model is a customized representation (prediction) of global or regional historical weather (future days) using a very large computer, applying geofluid dynamic equations and related physical laws, and statistical analysis is one aspect of Business Intelligence (BI) that involves collecting and reviewing business data and trend reports.
In the prior art, an air quality forecasting business system of most cities in China is carried out by adopting one or more methods of potential forecasting, statistical forecasting and numerical forecasting, the air quality forecasting system and the method have high forecasting accuracy on middle-low concentration polluted weather, but have high forecasting error on an atmospheric heavy pollution process with increasingly obvious regional characteristics, the cause of pollutants cannot be accurately analyzed when the pollutants are broadcasted, and the concentration of the pollutants is usually directly measured by a monitoring instrument, so that a large error exists.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an atmospheric heavy pollution forecasting method based on the combination of a numerical mode and statistical analysis.
The technical problem to be solved by the invention is as follows:
(1) the existing method for directly measuring the concentration of pollutants by a monitoring instrument has the problem of great error;
(2) the existing method can not accurately analyze the reason of the pollutants when broadcasting the pollutants, and can not accurately analyze the content of the pollutants in the air by directly measuring the concentration of the pollutants through a monitoring instrument.
The purpose of the invention can be realized by the following technical scheme:
an atmospheric heavy pollution forecasting method based on combination of numerical patterns and statistical analysis, the method comprises the following steps;
step S1: establishing an atmospheric pollution statistical analysis platform, receiving atmospheric pollution data information, and setting a minimum atmospheric pollution threshold value, wherein the atmospheric pollution statistical analysis platform is connected with a forecasting system in a data mode;
step S2: setting a pollution range according to the concentration of pollutants, respectively setting a first pollution index, a second pollution index and a third pollution index, setting the range of the pollution indexes, and setting the maximum pollution index;
step S3: analyzing various pollutant types of the polluted gas in the polluted gas, sealing the air in a certain volume through a container, obtaining the concentration of pollutants in the certain volume, calculating the proportion of each pollutant, and sequencing the concentrations of the pollutants from high to low;
step S4: analyzing the cause of the pollutant, publishing the pollutant with the largest proportion by a forecasting system, publishing the harm of the pollutant and publishing how to effectively avoid the harm of the pollutant.
Further, in step S1, a pollutant analysis module, a pollutant statistics module and a pollutant concentration determination module are arranged based on the atmospheric pollution statistics and analysis platform, the pollutant concentration module detects the pollutant concentration, the detection information is fed back to the statistics and analysis platform terminal, the pollutant analysis module analyzes the pollutant, the pollutant statistics module receives the analyzed pollutant information, the pollutant quantity is counted, the counted database information is transmitted to the statistics and analysis platform terminal, the statistics and analysis platform terminal receives data, the received data information is compared with a minimum pollution threshold value, if the pollutant concentration is smaller than the minimum pollution threshold value, the pollutant is in a safety range, and if the pollutant concentration is greater than the minimum pollution threshold value, the pollutant is in a pollution range.
Furthermore, the boundary values of the pollution ranges are respectively set according to the concentrations of the pollution ranges, young mice are selected for experiments based on the pollution concentrations in the range volumes, A and A culture boxes with different volumes are respectively selected, the culture boxes sequentially comprise A, A and A, the volume of the culture boxes is from large to small, the A, A and A are larger than A, B and B are arranged in each culture box, pollutants are collected through a gas collecting hood, pollutants with the same volume and concentration are respectively introduced into the culture boxes with different volumes through the one-way valves, the pollutants with the same concentration correspondingly obtained in the culture boxes are B, B, the mice were observed for life reactions.
Furthermore, the food activity of the young mouse in 9 incubators is observed within 20-50 min, the young mouse has no uncomfortable symptoms in A1 and A2 incubators, the young mouse has poor activity in A3, A4, A5 and A6 incubators, the young mouse has poor activity and reduced food consumption in A7 and A8 incubators, and the young mouse sleeps in A9 incubators, so that the concentration of the first pollution index is set between B1 and B2, the concentration of the second pollution index is set between B3 and B6, the concentration of the third pollution index is set between B6 and B8, and the concentration of the maximum pollution index is set to be Q, so that Q is more than B8.
Furthermore, all the young mice are in a healthy state, after the experiment is finished, the young mice are placed in a pollution-free incubator for 24 hours, the activity and the diet condition of each young mouse are observed, and the pollution index of pollutants is set according to different physiological reactions of the young mice in each incubator.
Further, in step S3, various gases in the pollutant are detected by the gas pollution statistical analysis platform, and in a polluted environment, the air in a certain volume is sealed by the sealed container, wherein the volume of the sealed container is 1dm 3 A dust detector is arranged in the closed container and is used for detecting the dust concentration in the closed container, air in the closed container is pumped out through an air collector, an air sample is directly collected in the proper air collector and then is taken back to a laboratory for analysis, various kinds of air can be respectively measured, and if formaldehyde exists in the air, the air can be measured through a gas chromatography method, an AHMT spectrophotometry method, an electrochemical method and an acetylacetone spectrophotometry method; if ozone exists in the air, the ozone can be measured by an ultraviolet spectrophotometry and a sodium thiodisulfonate spectrophotometry, and if ammonia exists in the air, the ozone can be measured by an indophenol blue spectrophotometry and a sodium hypochlorite-salicylic acid spectrophotometry; if carbon monoxide exists in the air, the carbon monoxide can be measured by a non-spectroscopic infrared gas analysis method and a mercury replacement method; if sulfur dioxide exists in the air, the measurement can be carried out by a spectrophotometric method of absorbing pararosaniline hydrochloride by formaldehyde solution; the presence of nitric oxide in air can be measured by non-dispersive infrared methods.
Further, the contaminated mixture was measured to be represented by letters N, M, K, H and P, respectively, and 1dm was measured, respectively 3 The amounts of (a) are Nqwr, Mqwr, Kqwr, Hqwr, and Pqwr, the proportion of N gas Nzb is Nqwr/(Nqwr + Mqwr + Kqwr + Hqwr + Pqwr), the proportion of M gas Mzb is Mqwr/(Nqwr + Mqwr + Kqwr + Pqwr), the proportion of K gas Kzb is Kqwr/(Nqwr + Mqwr + Kqwr), the proportion of H gas Hzb is Hqwr/(Nqwr + Kqwr + qwr + Pqwr), the proportion of P gas Pzb is Pqwr + Kqwr + Pqwr), and the proportions of Nqwr + Kqwr + pqzr are sequentially larger than the magnitudes of pzzr, Kqwr + kqzr + pqzr, and pzzb are sequentially larger than the magnitudes of pzzr.
Further, in step S4, the atmospheric pollution statistical analysis platform estimates the generation source of the pollutant according to the detected pollutant gas and the concentration of the pollutant gas, and combines the time of the local city and the urban condition, and predicts the data information through the prediction system, and suggests the city to enhance the task of taking out the pollutant and to disclose the harm of the pollutant gas to the human body.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention can collect and analyze the concentration of the pollutants, measure the content of each pollutant in the pollutants and reduce errors;
2. the invention can accurately broadcast the pollutants, accurately analyze the reasons of the pollutants, accurately analyze the content of the pollutants in the air and improve the accuracy of pollution broadcast.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a method schematic diagram of an atmospheric heavy pollution forecasting method based on combination of a numerical mode and statistical analysis.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a method for forecasting heavy atmospheric pollution based on a combination of a numerical model and statistical analysis includes the following steps;
step S1: establishing an atmospheric pollution statistical analysis platform, receiving atmospheric pollution data information, and setting a minimum atmospheric pollution threshold value, wherein the atmospheric pollution statistical analysis platform is connected with a forecasting system in a data mode;
set up pollutant analysis module based on atmospheric pollution statistics analysis platform, pollutant statistics module and pollutant concentration survey module, pollutant concentration module detects the pollutant concentration, to detect information feedback to statistics analysis platform terminal, pollutant analysis module analyzes the pollutant, pollutant statistics module receives the pollutant information of analysis, it makes statistics of the pollutant quantity, carry statistics analysis platform terminal with the database information of statistics, statistics analysis platform terminal received data compares the data information of receipt with minimum pollution threshold value, if pollutant concentration is less than minimum pollution threshold value, then belong to the safety range, if pollutant concentration is greater than minimum pollution threshold value, then belong to the pollution range.
Respectively setting boundary values of the pollution ranges according to the concentrations of the pollutants in the pollution ranges, selecting young mice for experiments based on the concentrations of the pollutants in the range volumes, respectively selecting A1, A2, A3, A4, A5, A6, A7, A8 and A9 incubators with different volumes, wherein the volumes of the incubators are sequentially from large to small, A1 & gtA 2 & gtA 3 & gtA 4 & gtA 5 & gtA 6 & gtA 7 & gtA 8 & gtA 9, a check valve is arranged on each incubator, gas in the incubator flows out from the interior of the paper incubator, oxygen for the young mice to live is introduced through the check valve, the young mice can live through the oxygen, 2-3 young mice are placed in each incubator, the pollutants are trapped through a gas collection cover, the pollutants with the same volume and the same concentration are introduced into the incubators with different volumes through the check valve, and the corresponding concentrations of the pollutants in the incubators are B1, B2, B3 and 3, B3, B4, B5, B6, B7, B8 and B9, and therefore, B1 < B2 < B3 < B4 < B5 < B6 < B7 < B8 < B9, and the living reaction of the mice is observed.
The method comprises the steps of respectively observing the diet activity conditions of young mice in 9 incubators within 20-50 min, observing that the young mice have no uncomfortable symptoms in A1 and A2 incubators, and have poor activity conditions in A3, A4, A5 and A6 incubators, poor activity conditions and reduced food consumption conditions in A7 and A8 incubators, and the problem of sleepiness of the young mice in A9 incubators, so that the concentration of a first pollution index is set to be between B1 and B2, the concentration of a second pollution index is set to be between B3 and B6, the concentration of a third pollution index is set to be between B6 and B8, and the concentration of the maximum pollution index is set to be Q, wherein Q is greater than B8, and the lowest pollution threshold DQ is less than B1.
Selecting the young mice to be tested to be in a healthy state, after the experiment is finished, placing the young mice in a pollution-free incubator for 24 hours, observing the activity and diet condition of each young mouse, and setting the pollution index of pollutants according to different physiological reactions of the young mice in each incubator.
Step S2: setting a pollution range according to the concentration of pollutants, respectively setting a first pollution index, a second pollution index and a third pollution index, setting the range of the pollution indexes, and setting the maximum pollution index;
step S3: analyzing each pollutant type of the polluted gas in the polluted gas, sealing the air in a certain volume through a container, obtaining the concentration of the pollutants in the certain volume, calculating the proportion of each pollutant, and sequencing the pollutant concentrations from high to low;
various gases in the pollutants are detected through a gas pollution statistical analysis platform, and air in a certain volume is sealed through a closed container under a polluted environment, wherein the volume of the closed container is 1dm 3 A dust detector is arranged in the closed container and is used for detecting the dust concentration in the closed container, air in the closed container is pumped out through an air collector, an air sample is directly collected in the proper air collector and then is taken back to a laboratory for analysis, various kinds of air can be respectively measured, and if formaldehyde exists in the air, the air can be measured through a gas chromatography method, an AHMT spectrophotometry method, an electrochemical method and an acetylacetone spectrophotometry method; if ozone exists in the air, the ozone can be measured by an ultraviolet spectrophotometry and a sodium thiodisulfonate spectrophotometry, and if ammonia exists in the air, the ozone can be measured by an indophenol blue spectrophotometry and a sodium hypochlorite-salicylic acid spectrophotometry; if carbon monoxide exists in the air, the carbon monoxide can be measured by a non-spectroscopic infrared gas analysis method and a mercury replacement method; if sulfur dioxide exists in the air, the measurement can be carried out by a spectrophotometric method that the formaldehyde solution absorbs pararosaniline hydrochloride; the presence of nitric oxide in air can be measured by non-dispersive infrared methods.
The determination of the contamination mixture gave a letter designation N, M, K, H and P, respectively, for 1dm 3 When the amounts of Nqwr, Mqwr, Kqwr, Hqwr and Pqwr are given, the ratio of N gas to Nzb-Nqwr/(Nqwr + Mqwr + Kqwr + Hqwr + Pqwr), the share ratio of M gas Mzb-Mqwr/(Nqwr + Mqwr + Hqwr + Pqwr), the share ratio of K gas Kzb-Kqwr/(Nqwr + Mqwr + Kqwr + qwr), the share ratio of H gas Hzb-Hqwr/(Nqwr + Mqwr + Kqwr + Hqwr + pqr), the share ratio of P gas Pzb-Pqwr/(Nqwr + Mqwr + qwr + pqr), and the magnitudes of Mzb, Nzb, zkb, and Hzb Pzb are sequentially increased to be smaller.
Step S4: analyzing the generation reason of pollutants, publishing the pollutants with the largest proportion through a forecasting system, publishing the harm of the pollutants, publishing how the harm of the pollutants is effectively avoided, estimating the generation source of the pollutants by an atmospheric pollution statistical analysis platform according to the detected polluted gas and the concentration of the polluted gas by combining the time of a local city and the urban condition, forecasting data information through the forecasting system, recommending the city to strengthen the taking out work of the pollutants, and publishing the harm of the polluted gas to a human body.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula of the latest real situation obtained by collecting a large amount of data and performing software simulation, the preset parameters in the formula are set by the technical personnel in the field according to the actual situation, the weight coefficient and the scale coefficient are specific numerical values obtained by quantizing each parameter, and the subsequent comparison is convenient.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms 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 utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. An atmospheric heavy pollution forecasting method based on a numerical mode and statistical analysis combination is characterized by comprising the following steps;
step S1: establishing an atmospheric pollution statistical analysis platform, receiving atmospheric pollution data information, and setting an atmospheric minimum pollution threshold value, wherein the atmospheric pollution statistical analysis platform is connected with a forecasting system in a data manner;
step S2: setting a pollution range according to the concentration of pollutants, respectively setting a first pollution index, a second pollution index and a third pollution index, setting the range of the pollution indexes, and setting the maximum pollution index;
step S3: analyzing various pollutant types of the polluted gas in the polluted gas, sealing the air in a certain volume through a container, obtaining the concentration of pollutants in the certain volume, calculating the proportion of each pollutant, and sequencing the concentrations of the pollutants from high to low;
step S4: analyzing the cause of the pollutant, publishing the pollutant with the largest proportion by a forecasting system, publishing the harm of the pollutant and publishing how to effectively avoid the harm of the pollutant.
2. The method as claimed in claim 1, wherein the step S1 is performed by configuring a pollutant analysis module, a pollutant statistics module and a pollutant concentration determination module based on the atmospheric pollution statistical analysis platform, the pollutant concentration module detects the pollutant concentration and feeds the detected information back to the statistical analysis platform terminal, the pollutant analysis module analyzes the pollutant, the pollutant statistics module receives the analyzed pollutant information, counts the pollutant quantity, and transmits the statistical database information to the statistical analysis platform terminal, the statistical analysis platform terminal receives data to compare the received data information with the minimum pollution threshold, if the pollutant concentration is less than the minimum pollutant threshold, the method belongs to the safe range, if the pollutant concentration is greater than the minimum pollution threshold, it falls within the scope of contamination.
3. The method for forecasting the atmosphere heavy pollution based on the combination of the numerical mode and the statistical analysis as claimed in claim 2, characterized in that the boundary values of the pollution ranges are respectively set according to the concentrations of the pollution ranges, young mice are selected for experiments based on the concentrations of the pollution in the ranges, and a1, a2, A3, a4, a5, A6, a7, A8 and a9 incubators with different volumes are respectively selected, the volume of the incubator sequentially from large to small is A1 > A2 > A3 > A4 > A5 > A6 > A7 > A8 > A9, a check valve is arranged on the incubator, oxygen for the young mice to live is introduced into the incubator through the check valve, 2-3 young mice are placed in each incubator, the pollutants are trapped through a gas collection hood, the pollutants with the same concentration are introduced into the incubators with different volumes through the check valve, and the pollutants with the same concentration obtained by the corresponding pollutant in incubator is B1, B2, B3, B4, B5, B6, B7, B8 and B9, so that the living reaction of the mouse is observed, wherein B1 is more than B2 and more than B3 and more than B4 and more than B5 and more than B6 and more than B7 and more than B8 and more than B9.
4. The method for forecasting the atmospheric heavy pollution based on the combination of the numerical model and the statistical analysis as claimed in claim 3, wherein the feeding activity of the young mouse in 9 incubators is observed within 20-50 min, the young mouse is observed to have no uncomfortable symptoms in A1 and A2 incubators, the young mouse has poor activity in A3, A4, A5 and A6 incubators, the young mouse has poor activity and reduced food intake in A7 and A8 incubators, and the young mouse is somnolence in A9 incubators, so that the concentration of the first pollution index is set to be between B1 and B2, the concentration of the second pollution index is set to be between B3 and B6, the concentration of the third pollution index is set to be between B6 and B8, and the concentration of the maximum pollution index is set to be Q, and Q is greater than B8.
5. The method as claimed in claim 4, wherein the young mouse is in a healthy state, and after the experiment is completed, the young mouse is placed in a non-pollution incubator for 24 hours, and then the liveness and diet status of each young mouse are observed, and the pollution index of the pollutant is set according to the different physiological responses of the young mouse in each incubator.
6. The method for forecasting atmospheric heavy pollution based on the combination of numerical model and statistical analysis of claim 1, wherein in step S3, each gas in the pollutant is detected by a gas pollution statistical analysis platform, and under the polluted environment, the air in a certain volume is sealed by a closed container, and the volume of the closed container is 1dm 3 A dust detector is arranged in the closed container and is used for detecting the dust concentration in the closed container, air in the closed container is pumped out through an air collector, an air sample is directly collected in the proper air collector and then is taken back to a laboratory for analysis, various kinds of air can be respectively measured, and if formaldehyde exists in the air, the air can be measured through a gas chromatography method, an AHMT spectrophotometry method, an electrochemical method and an acetylacetone spectrophotometry method; if ozone exists in the air, the ozone can be measured by an ultraviolet spectrophotometry and a sodium thiodisulfonate spectrophotometry, and if ammonia exists in the air, the ozone can be measured by an indophenol blue spectrophotometry and a sodium hypochlorite-salicylic acid spectrophotometry; if carbon monoxide exists in the air, the carbon monoxide can be measured by a non-spectroscopic infrared gas analysis method and a mercury replacement method; if sulfur dioxide exists in the air, the measurement can be carried out by a spectrophotometric method of absorbing pararosaniline hydrochloride by formaldehyde solution; the presence of nitric oxide in air can be measured by non-dispersive infrared methods.
7. The method of claim 6, wherein the measured mixture of pollutants is represented by N, M, K, H letters and P, respectively, and 1dm is measured respectively 3 The amounts of (a) are Nqwr, Mqwr, Kqwr, Hqwr, and Pqwr, the proportion of N gas Nzb is Nqwr/(Nqwr + Mqwr + Kqwr + Pqwr), the proportion of M gas Mzb is Mqwr/(Nqwr + Mqwr + Kqwr + Pqwr), the proportion of K gas Kzb is Kqwr/(Nqwr + Mqwr + Kqwr + qwr), the proportion of H gas Hzb is Hqwr/(Nqwr + Mqwr + Kqwr + qwr + Pqwr), and the proportion of P gas Pzb is pqr/(Pqwr + M + Mqwr + qwr + Pqwr)qwr + Kqwr + Hqwr + Pqwr), Mzb, Nzb, Kzb, Hzb, and Pzb are compared in size, and arranged in order of size.
8. The method according to claim 1, wherein in step S4, the atmospheric pollution statistical analysis platform estimates the generation source of the pollutant according to the detected pollutant and the concentration of the pollutant, in combination with the time of the local city and the city condition, and predicts the data information through a prediction system, thereby suggesting that the city take out the pollutant more intensively and disclosing the harm of the pollutant to the human body.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210787303.7A CN115097547A (en) | 2022-07-04 | 2022-07-04 | Atmospheric heavy pollution forecasting method based on combination of numerical mode and statistical analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210787303.7A CN115097547A (en) | 2022-07-04 | 2022-07-04 | Atmospheric heavy pollution forecasting method based on combination of numerical mode and statistical analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115097547A true CN115097547A (en) | 2022-09-23 |
Family
ID=83296635
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210787303.7A Pending CN115097547A (en) | 2022-07-04 | 2022-07-04 | Atmospheric heavy pollution forecasting method based on combination of numerical mode and statistical analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115097547A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103163278A (en) * | 2013-01-31 | 2013-06-19 | 北京工业大学 | Atmospheric heavy pollution forecast method based on combination of numerical model and statistic analysis |
| KR20150031577A (en) * | 2013-09-16 | 2015-03-25 | 주식회사 에니텍 | Method of Inverse Calculation for The Amount of Air Pollutants Emissions |
| CN105891433A (en) * | 2015-12-29 | 2016-08-24 | 北京师范大学 | Method for identifying underground water type drinking water source particular pollutants |
| CN106651036A (en) * | 2016-12-26 | 2017-05-10 | 东莞理工学院 | Air quality forecasting system |
| KR20170077696A (en) * | 2015-12-28 | 2017-07-06 | 주식회사 세스코 | System and method for management of air quality |
| US20180073759A1 (en) * | 2016-09-13 | 2018-03-15 | Board Of Trustees Of Michigan State University | Intelligent Sensing System For Indoor Air Quality Analytics |
| CN110989043A (en) * | 2019-12-25 | 2020-04-10 | 中科三清科技有限公司 | Air quality index grade probability forecasting method and device and storage medium |
| CN111103405A (en) * | 2019-12-24 | 2020-05-05 | 象辑知源（武汉）科技有限公司 | Air quality forecast early warning system |
| CN111768038A (en) * | 2020-06-30 | 2020-10-13 | 平安国际智慧城市科技股份有限公司 | Pollutant monitoring method and device, terminal equipment and storage medium |
-
2022
- 2022-07-04 CN CN202210787303.7A patent/CN115097547A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103163278A (en) * | 2013-01-31 | 2013-06-19 | 北京工业大学 | Atmospheric heavy pollution forecast method based on combination of numerical model and statistic analysis |
| KR20150031577A (en) * | 2013-09-16 | 2015-03-25 | 주식회사 에니텍 | Method of Inverse Calculation for The Amount of Air Pollutants Emissions |
| KR20170077696A (en) * | 2015-12-28 | 2017-07-06 | 주식회사 세스코 | System and method for management of air quality |
| CN105891433A (en) * | 2015-12-29 | 2016-08-24 | 北京师范大学 | Method for identifying underground water type drinking water source particular pollutants |
| US20180073759A1 (en) * | 2016-09-13 | 2018-03-15 | Board Of Trustees Of Michigan State University | Intelligent Sensing System For Indoor Air Quality Analytics |
| CN106651036A (en) * | 2016-12-26 | 2017-05-10 | 东莞理工学院 | Air quality forecasting system |
| CN111103405A (en) * | 2019-12-24 | 2020-05-05 | 象辑知源（武汉）科技有限公司 | Air quality forecast early warning system |
| CN110989043A (en) * | 2019-12-25 | 2020-04-10 | 中科三清科技有限公司 | Air quality index grade probability forecasting method and device and storage medium |
| CN111768038A (en) * | 2020-06-30 | 2020-10-13 | 平安国际智慧城市科技股份有限公司 | Pollutant monitoring method and device, terminal equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112485319B (en) | Atmospheric environment navigation monitoring method, information processing method and device and monitoring vehicle | |
| CN109781809B (en) | Artificial intelligent calculating method for formaldehyde concentration | |
| CN107607450A (en) | A kind of air quality surveillance method and relevant device | |
| CN103245757A (en) | Gas environment emergency detection system | |
| CN116187613B (en) | Big data-based carbon emission flow real-time monitoring system and method thereof | |
| CN113283072B (en) | Water body COD detection method suitable for multiple scene conditions | |
| US20130262008A1 (en) | Measurement of light-absorption qualities in the visible spectrum using a camera | |
| CN112364940A (en) | Atmospheric pollutant source analysis method based on multi-source data, storage medium and equipment | |
| Forello et al. | Gaining knowledge on source contribution to aerosol optical absorption properties and organics by receptor modelling | |
| CN102778445A (en) | Intelligent analyzer and detection method for standard state dry basis | |
| CN115097547A (en) | Atmospheric heavy pollution forecasting method based on combination of numerical mode and statistical analysis | |
| CN110471098B (en) | Radon radiation dose detection method based on carbon quantum dot fluorescence biosensor | |
| CN102539508A (en) | System and method for monitoring malodorous gas of stall | |
| Mi et al. | [Retracted] Residential Environment Pollution Monitoring System Based on Cloud Computing and Internet of Things | |
| KR20130029868A (en) | Continuous water vapor content measurement system of stack gas emissions | |
| CN113075149B (en) | Data correction test method for preventing detection result from drifting and presenting descending rule | |
| Levy Zamora et al. | Identifying optimal co-location calibration periods for low-cost sensors | |
| CN202794032U (en) | Standard state dry basis intelligent analyzer | |
| Li et al. | Prediction of aerosolized fungal spores by determining respiratory intensity of the growing colony | |
| CN114707119B (en) | Method and system for quantifying atmospheric ozone pollution source based on domestic hyper-spectral satellite | |
| CN110687061A (en) | Formaldehyde solution detection method based on spectrometer | |
| CN206095727U (en) | Organic waste gas sampling instrument | |
| CN217425195U (en) | Infrared alcohol detection system with anti-interference performance | |
| CN118112085A (en) | Malodorous gas and odor monitoring and accurate tracing method | |
| CN112397203B (en) | Environment-exposed PM2.5 heavy metal health risk evaluation method and system |
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 |