CN116596158A - Regional pollution source emission total prediction method - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 41
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 45
- 231100000719 pollutant Toxicity 0.000 claims abstract description 45
- 230000005540 biological transmission Effects 0.000 claims abstract description 12
- 239000000126 substance Substances 0.000 claims abstract description 8
- 230000008859 change Effects 0.000 claims abstract description 7
- 238000000605 extraction Methods 0.000 claims abstract description 4
- 239000003245 coal Substances 0.000 claims description 37
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 18
- 238000005286 illumination Methods 0.000 claims description 11
- 238000004088 simulation Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 9
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 6
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 6
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 5
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 3
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 3
- 239000001569 carbon dioxide Substances 0.000 claims description 3
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 3
- 239000000446 fuel Substances 0.000 claims description 3
- 238000010248 power generation Methods 0.000 claims 1
- 230000007613 environmental effect Effects 0.000 abstract description 4
- 238000012544 monitoring process Methods 0.000 abstract description 2
- 230000007423 decrease Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 238000010438 heat treatment Methods 0.000 description 3
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 2
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- 238000004590 computer program Methods 0.000 description 2
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- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 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 1
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 1
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 1
- 208000003443 Unconsciousness Diseases 0.000 description 1
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- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide Inorganic materials O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
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- 238000012795 verification Methods 0.000 description 1
- 239000012855 volatile organic compound Substances 0.000 description 1
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G—PHYSICS
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The application relates to the technical field of environmental monitoring, and discloses a method for predicting total emission amount of regional pollution sources, which comprises the following steps: step one: firstly, receiving current comprehensive live data of an area to be predicted, and carrying out feature extraction on the comprehensive live data to obtain a space-time tensor. According to the regional pollution source emission total prediction method, the relation between the emission list and the pollutant concentration is accurately reflected based on the trained NN-CTM, then pollutant concentration data observed by the earth surface and satellites are input into the model, a new emission list is estimated through an error back propagation method, compared with a traditional chemical transmission model CTM, the regional pollution source emission total prediction method is efficient and differentiable, so that the emission list can be updated based on an error gradient between a concentration observed value and an analog value, the new emission list is compared with an original list, the change rate between the new emission list and the original list is obtained, the correlation of data characteristics and the pollutant concentration is improved, and the accuracy of pollution source emission total prediction is improved.
Description
Technical Field
The application relates to the technical field of environmental monitoring, in particular to a method for predicting total emission amount of regional pollution sources.
Background
With the rapid development of socioeconomic performance and the rise of environmental pressure of resources, because the environment is seriously threatened due to the influence of pollution discharge, many factors and areas of unsafe environment are revealed, if the early warning capability cannot be kept up, people can be worried about and unconscious when sudden pollution events occur, and serious threat and damage are brought to life health safety, economic life operation, social stability and environmental safety of people, and even serious social influence is brought.
The accurate verification of the emission list is an important premise for effectively simulating the air pollution control policy, but because pollution sources are numerous and the emission amount is continuously changed, the investigation efficiency of the emission source based on the traditional bottom-up method is quite low, the traditional method is seriously dependent on macroscopic statistics data, the timeliness and the precision guarantee are lacking, the inaccurate emission list also becomes a main limiting factor which currently influences the air quality prediction and the simulation precision, and therefore, a regional pollution source emission total amount prediction method is provided for solving the problems.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides the regional pollution source emission total quantity prediction method, which has the advantages of being capable of accurately and precisely predicting the emission total quantity of the pollution source, and the like, solves the problems that the traditional method is seriously dependent on macroscopic statistical data, lacks timeliness and precision guarantee, and the inaccurate emission list becomes a main limiting factor which currently influences the air quality prediction and simulation precision.
In order to achieve the purpose of accurate prediction, the application provides the following technical scheme: the regional pollution source emission total prediction method comprises the following steps:
step one: firstly, receiving current comprehensive live data of an area to be predicted, and carrying out feature extraction on the comprehensive live data to obtain a space-time tensor;
step two: constructing a neural network-based chemical transmission model NN-CTM to simulate the generation process of atmospheric pollutants, and simulating the pollutant concentration in the final air by combining illumination, geography and meteorological information;
step three: accurately reflecting the relation between the emission list and the pollutant concentration based on the trained NN-CTM, inputting pollutant concentration data observed by the earth surface and satellites in the model, and estimating a new emission list by an error back propagation method;
step four: comparing the new discharge list with the original list to obtain the change rate between the new discharge list and the original list, selecting a typical area, and comparing the pollutant concentration simulation value of the discharge list after adjustment with the data of the area observation station;
step five: and analyzing the corrected pollutant emission data in each state to form complete pollutant source emission total data, and predicting the emission value of future pollution.
Preferably, the meteorological information comprises wind power data and wind direction data, the illumination information comprises illumination time data and illumination angle data, and the geographic information comprises local factors and ground feature factors.
Preferably, the space-time tensor is used for representing the space-time variation of the characteristic elements corresponding to the comprehensive live data in the region to be predicted.
Preferably, the simulation error value of the transmission model NN-CTM is +/-3%, and the simulation speed of the transmission model NN-CTM is 1000 times of that of the original numerical model.
Preferably, the pollution sources include methane, carbon dioxide, sulfur dioxide, nitrogen oxides, and carbon monoxide.
Preferably, the emissions list includes predictions of coal consumption, including industrial and residential coal consumption.
Preferably, coal supply data and coal demand data are acquired for the amount of coal consumption, wherein the coal supply data comprise the amount of coal production and the amount of coal inlet, and the coal demand data comprise the amount of electricity generation and the amount of coal consumption.
Preferably, the month total supply amount and month total consumption amount are calculated according to the coal supply data and the coal demand data, a month supply and demand difference table of coal is generated, the proportion of the month thermal power consumption amount before the last month to the total consumption amount is calculated according to the month thermal power consumption amount before the last month and the total consumption amount, the proportion of the month thermal power consumption amount before the last month to the total consumption amount is predicted by utilizing a unitary regression function, and further the pollution total amount discharged in the coal consumption process is predicted.
Compared with the prior art, the application provides a method for predicting the total emission amount of regional pollution sources, which has the following beneficial effects:
according to the regional pollution source emission total prediction method, a chemical transmission model NN-CTM based on a neural network is constructed to simulate the generation process of atmospheric pollutants, the pollutant concentration in the final air is simulated by combining illumination, geography and meteorological information, the relation between an emission list and the pollutant concentration is accurately reflected based on the trained NN-CTM, further, pollutant concentration data observed by earth surface and satellites are input into the model, a new emission list is estimated by an error back propagation method, compared with a traditional chemical transmission model CTM, the novel emission list is efficient and differentiable, so that the emission list can be updated based on the error gradient between a concentration observation value and an analog value, the change rate between the novel emission list and the original list is obtained by comparing the novel emission list with the original list, a typical region is selected, the pollutant concentration analog value of the adjusted emission list is compared with the data of an observation station of the region, the correlation of the data characteristic and the pollutant concentration is effectively improved, and the accuracy of the pollution source emission total prediction is improved.
Drawings
FIG. 1 is a schematic view of NN-CTM model architecture of the present application;
FIG. 2 is a graph showing the variation of the main pollutant discharge amount in different areas of the present application.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly and completely described below in conjunction with the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The regional pollution source emission total prediction method comprises the following steps:
step one: firstly, receiving current comprehensive live data of a region to be predicted, and carrying out feature extraction on the comprehensive live data to obtain a space-time tensor;
step two: constructing a neural network-based chemical transmission model NN-CTM to simulate the generation process of atmospheric pollutants, and simulating the pollutant concentration in the final air by combining illumination, geography and meteorological information;
step three: accurately reflecting the relation between the emission list and the pollutant concentration based on the trained NN-CTM, inputting pollutant concentration data observed by the earth surface and satellites in the model, and estimating a new emission list by an error back propagation method;
step four: comparing the new discharge list with the original list to obtain the change rate between the new discharge list and the original list, selecting a typical area, and comparing the pollutant concentration simulation value of the discharge list after adjustment with the data of the area observation station;
step five: and analyzing the corrected pollutant emission data in each state to form complete pollutant source emission total data, and predicting the emission value of future pollution.
In an embodiment: in the case where the emission list includes prediction of the amount of coal consumed, the average rate of increase of the industrial amount of coal consumed is set to be α, and the average rate of increase of the industrial total yield value is set to be β, and the calculation is performed by the following formula:
wherein: e-prediction of annual Industrial coal consumption, X10 4 t/a；
E 0 -reference year industrial coal consumption, X10 4 t/a；
M-forecast annual Industrial Total yield, X10 4 t/a；
M 0 -reference year industrial total yield, X10 0 t/a；
t-forecast year;
t 0 -reference year.
If the above two expressions are changed to the α, β expression, the calculation is performed by the following formula:
thus, the modulus of elasticity of industrial coal consumption can be expressed as:
for prediction of civil coal consumption, the calculation is performed by the following formula:
E s ＝A s ·S
wherein: e (E) s Prediction of annual heating coal consumption, X10 4 t/a；
S, predicting a heating area in a year, and square meter;
A s -heating coal consumption coefficient, t/-square meter;
in an embodiment: the pollutant emission quantity is predicted to be various pollutants emitted to the atmosphere by fuel combustion and various pollutants emitted to the atmosphere in the process production process, the sum of the two parts is the total pollutant emission quantity, the atmosphere pollution in China is mainly soot type pollution, and the main pollutants in the industrial coal consumption process are methane, carbon dioxide, sulfur dioxide, oxynitride and carbon monoxide. The main pollutants are determined from practice, and are generally determined to be sulfur dioxide and oxynitride.
The prediction of sulfur dioxide emissions is calculated by the following formula:
wherein: g so2 Predicting annual sulfur dioxide emission, t/year;
b, coal quantity, t/year;
s, total sulfur content in coal,%;
in an embodiment: the oxynitride increases by about 3.5-4.0% in 1, 10 months, while decreases by more than 10% in 7 months; the ammonia gas emission increases in 1 month and decreases in the other three months, with the maximum decrease in 10 months; the emission of sulfur dioxide is reduced by about 10% in all months; emissions of volatile organic compounds also decrease and are greater in magnitude than sulfur dioxide by about 20%, which may be associated with an overestimation of ozone by CTM; the emission of PM2.5 per time increased by less than 5% in all months.
In an embodiment: by choosing 5 typical regions: the dense jingjingji (BTH), yangtze River Delta (YRD), zhujiang river delta (PRD), sichuan basin (SCH), and northwest China (NWC) where there is insufficient constraint due to lack of observation data, and the average discharge amount of 5 emissions for 4 months in each typical area was calculated as the change before and after adjustment.
In an embodiment, one or more kinds of weather parameter data required by people can be obtained from data issued by a Global Forecast System (GFS), then a weather data statistical table is generated according to the obtained one or more kinds of weather parameter data so as to be capable of obtaining data of temperature, wind direction, wind speed, ground air pressure and humidity parameters of different areas of the world, specifically, when the data is obtained, only the data required by people can be downloaded from the data issued by the Global Forecast System (GFS) without downloading redundant data, thus, data screening is not required in the later processing, and of course, the data issued by the Global Forecast System (GFS) can be downloaded firstly, and the data required by people can be selected before the weather data statistical table is generated, so that the workload of processing the later data, such as the temperature, precipitation, wind direction and wind speed data, can be reduced, the wind direction and wind speed data can be extracted as wind direction forecast data,
in the examples, the simulated values of the concentration of the pollutants using the adjusted emission list (N-Emis) were compared with the data of 612 observation stations, and it was found that the Mean Absolute Error (MAE) of the concentrations of nitrogen dioxide, sulfur dioxide, ozone and PM2.5 was reduced from 7.39 to 5.91 (20.03%), 3.64 to 3.22 (11.54%), 14.33 to 11.56 (19.33%) ppbv, and from 18.94 to 16.67 (11.99%) μgm-3, respectively, and as a result, the reliability of N-Emis and the utility of the study application of the machine learning method were demonstrated.
The beneficial effects of the application are as follows: according to the regional pollution source emission total prediction method, a chemical transmission model NN-CTM based on a neural network is constructed to simulate the generation process of atmospheric pollutants, the pollutant concentration in the final air is simulated by combining illumination, geography and meteorological information, the relation between the pollutant concentration and the pollutant concentration is accurately reflected based on the trained NN-CTM, further, pollutant concentration data observed by earth surface and satellites are input into the model, a new emission list is estimated by an error back propagation method, compared with a traditional chemical transmission model CTM, the novel emission list is efficient and differentiable, so that the emission list can be updated based on the error gradient between a concentration observation value and a simulation value, the change rate between the new emission list and the original list is obtained by comparing the novel emission list with the original list, a typical region is selected, the pollutant concentration simulation value of the adjusted emission list is compared with the data of an observation station of the region, the correlation of data characteristics and the pollutant concentration is effectively improved, the accuracy of the prediction of the pollution source emission total is improved, the traditional method is seriously dependent on statistics and the data, the quality of the statistics and the accuracy is greatly improved, and the current emission accuracy is not influenced, and the accuracy is limited, and the accuracy of the emission total prediction is greatly limited.
It will be apparent to those skilled in the art that embodiments of the application may be provided as a method, system, or computer program product, and that the application thus may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects, and that the application may take the form of a computer program product on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. The regional pollution source emission total amount prediction method is characterized by comprising the following steps of:
step one: firstly, receiving current comprehensive live data of an area to be predicted, and carrying out feature extraction on the comprehensive live data to obtain a space-time tensor;
step two: constructing a neural network-based chemical transmission model NN-CTM to simulate the generation process of atmospheric pollutants, and simulating the pollutant concentration in the final air by combining illumination, geography and meteorological information;
step three: accurately reflecting the relation between the emission list and the pollutant concentration based on the trained NN-CTM, inputting pollutant concentration data observed by the earth surface and satellites in the model, and estimating a new emission list by an error back propagation method;
step four: comparing the new discharge list with the original list to obtain the change rate between the new discharge list and the original list, selecting a typical area, and comparing the pollutant concentration simulation value of the discharge list after adjustment with the data of the area observation station;
step five: and analyzing the corrected pollutant emission data in each state to form complete pollutant source emission total data, and predicting the emission value of future pollution.
2. The method of claim 1, wherein the weather information includes wind data and wind direction data, the illumination information includes illumination time data and illumination angle data, and the geographic information includes local factors and ground object factors.
3. The method for predicting total emissions of a regional pollution source of claim 1, wherein the spatiotemporal tensor is used to characterize the spatiotemporal variation of the characteristic elements corresponding to the comprehensive live data within the region to be predicted.
4. The method according to claim 1, wherein the simulation error value of the transmission model NN-CTM is ±3%, and the simulation speed of the transmission model NN-CTM is 1000 times that of the original numerical model.
5. The method of claim 1, wherein the pollution source comprises methane, carbon dioxide, sulfur dioxide, nitrogen oxides, and carbon monoxide.
6. The method of claim 1, wherein the emissions schedule includes predictions of fuel consumption, including industrial and residential fuel consumption.
7. The regional pollution source total emission prediction method of claim 6, wherein coal supply data and coal demand data are acquired for the amount of coal consumption, wherein the coal supply data comprise coal yield and coal intake amount, and the coal demand data comprise power generation amount and coal consumption amount.
8. The regional pollution source emission total prediction method according to claim 7, wherein a monthly total supply amount and a monthly total consumption amount are calculated according to the coal supply data and the coal demand data, a monthly supply-demand difference table of coal is generated, the proportion of the monthly thermal power consumption before the last month to the total consumption amount is calculated according to the monthly thermal power consumption before the last month and the total consumption amount, and the proportion of the monthly thermal power consumption before the last month to the total consumption amount is predicted by utilizing a unitary regression function, so that the pollution total amount emitted in the coal consumption process is predicted.
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| CN111523717A (en) * | 2020-04-15 | 2020-08-11 | 北京工业大学 | Inversion estimation method for atmospheric pollutant emission list |
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| CN111523717A (en) * | 2020-04-15 | 2020-08-11 | 北京工业大学 | Inversion estimation method for atmospheric pollutant emission list |
| CN112905560A (en) * | 2021-02-02 | 2021-06-04 | 中国科学院地理科学与资源研究所 | Air pollution prediction method based on multi-source time-space big data deep fusion |
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