CN109858686A - A kind of ground emission inventories inverting optimization method based on EnKF - Google Patents
A kind of ground emission inventories inverting optimization method based on EnKF Download PDFInfo
- Publication number
- CN109858686A CN109858686A CN201910030086.5A CN201910030086A CN109858686A CN 109858686 A CN109858686 A CN 109858686A CN 201910030086 A CN201910030086 A CN 201910030086A CN 109858686 A CN109858686 A CN 109858686A
- Authority
- CN
- China
- Prior art keywords
- inverting
- inventory
- observation
- enkf
- error
- 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
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000005457 optimization Methods 0.000 title claims abstract description 34
- 230000008859 change Effects 0.000 claims abstract description 13
- 230000000694 effects Effects 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 5
- 230000006872 improvement Effects 0.000 claims abstract description 3
- 230000008569 process Effects 0.000 claims description 17
- 239000003344 environmental pollutant Substances 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 14
- 231100000719 pollutant Toxicity 0.000 claims description 14
- 238000004458 analytical method Methods 0.000 claims description 12
- 238000005259 measurement Methods 0.000 claims description 10
- 239000002243 precursor Substances 0.000 claims description 8
- 230000015556 catabolic process Effects 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 5
- 238000001311 chemical methods and process Methods 0.000 claims description 5
- 241000282339 Mustela Species 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 3
- 238000004513 sizing Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 description 39
- 238000011160 research Methods 0.000 description 12
- 230000004807 localization Effects 0.000 description 10
- 241000894007 species Species 0.000 description 8
- 238000009826 distribution Methods 0.000 description 7
- 239000000523 sample Substances 0.000 description 7
- 239000000443 aerosol Substances 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 230000007423 decrease Effects 0.000 description 5
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000009792 diffusion process Methods 0.000 description 3
- 235000013399 edible fruits Nutrition 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000005553 drilling Methods 0.000 description 2
- 238000005183 dynamical system Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 239000007791 liquid phase Substances 0.000 description 2
- 239000012071 phase Substances 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 241000487918 Acacia argyrodendron Species 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 241000196324 Embryophyta Species 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 229910002651 NO3 Inorganic materials 0.000 description 1
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 description 1
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 1
- 235000017284 Pometia pinnata Nutrition 0.000 description 1
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 1
- QAOWNCQODCNURD-UHFFFAOYSA-L Sulfate Chemical compound [O-]S([O-])(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-L 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 238000001856 aerosol method Methods 0.000 description 1
- 239000000809 air pollutant Substances 0.000 description 1
- 231100001243 air pollutant Toxicity 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 150000003863 ammonium salts Chemical class 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 235000011222 chang cao shi Nutrition 0.000 description 1
- 239000003610 charcoal Substances 0.000 description 1
- 239000013626 chemical specie Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000005137 deposition process Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000006477 desulfuration reaction Methods 0.000 description 1
- 230000023556 desulfurization Effects 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- 239000003292 glue Substances 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000002706 hydrostatic effect Effects 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000006303 photolysis reaction Methods 0.000 description 1
- 230000015843 photosynthesis, light reaction Effects 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 235000002639 sodium chloride Nutrition 0.000 description 1
- 239000011780 sodium chloride Substances 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 238000003892 spreading Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000001851 vibrational circular dichroism spectroscopy Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of ground emission inventories inverting optimization method based on EnKF, comprising: building EnKF inventory Inversion System is simultaneously coupled to WRF-CMAQ modular system；Inventory after daily inverting is re-applied to same day model predictions and provides initial fields, the average inventory as final optimization pass of more days inverting inventories for the DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM in inverting in next day；Assess inventory regional change and uncertainty；Forecast when inventory after optimization is carried out long within the assimilation period is forecast to compare with source list, improvement effect of the inventory after assessment optimization to forecast.The present invention constructs set inverting assimilation system by EnKF methodology to optimize SO simultaneously2、NOX、PM2.5Emission inventories effectively correct for discharge error and improve model predictions.
Description
Technical field
The invention belongs to field of environment protection, especially a kind of emission inventories inverting optimization method.
Background technique
Conventional exhaust inventory usually passes through on-site inspection method and obtains, since the distribution and variation of pollution sources are sufficiently complex,
Establishment inventory usually requires longer time and a large amount of manpower and is estimated to check each pollutant and establish model,
Emission inventories data are fixed and invariable in a long time, so can be influenced by industrial expansion or policy, actual discharge
Fluctuation is big, causes the biggish error of mode.
Summary of the invention
Goal of the invention: a kind of ground emission inventories inverting optimization method based on EnKF is provided, is deposited with solving the prior art
The above problem.
A kind of technical solution: ground emission inventories inverting optimization method based on EnKF, comprising:
Step 1, building EnKF inventory Inversion System are simultaneously coupled to WRF-CMAQ modular system；
Step 2, by the inventory after daily inverting be re-applied to the same day model predictions be inverting in next day in set it is pre-
Report provides initial fields, the average inventory as final optimization pass of more days inverting inventories；
Step 3, assessment inventory regional change and uncertainty；It is pre- when inventory after optimization is carried out long within the assimilation period
Report forecasts to compare with source list, improvement effect of the inventory after assessment optimization to forecast.
According to an aspect of the present invention, the step of building EnKF inventory Inversion System includes:
Step 11 generates source emission set sample
For source list, δ x is random perturbation field, and i is set sample number；
Step 12 considers PM2.5The influence of precursor, while to SO2、NOxDischarge carry out inverting correct, calculate source row
The background error covariance matrix put For source emission ensemble average before inverting,
N is natural number；
Step 13 is updated based on aggregate error statistics and measurement vector, the inventory after inverting by following equation:
Wherein, H Observation Operators, R are observation error covariance matrix, and K is gain matrix, determine ambient field and observation
Weight,Ensemble average for the analysis field of ith member, i.e. emission inventories after optimization, all members is estimated to be optimal
Meter, provides precision higher emission inventories for inverting next time.
According to an aspect of the present invention, further include step 14, carry out inverting using average daily concentration, assimilation window is set as 1
It, the influence to avoid model predictions diurnal variation error to inverting；Super observation is carried out to observation based on optimal estimation theory
Processing,
M observation y of same mesh point will be located atiPredicted value corresponding with n memberConstitute a super observation
Value and corresponding predicted value, i=1,2 ..., m；K=1,2 ..., n, it is assumed that the observation error of different website different moments is mutual
It is independent, yiCorresponding observation standard deviation is ri, then new to constitute observation ynew, observe standard deviation rnewAnd corresponding forecast
Meet following relationship:
According to an aspect of the present invention, further include step 15, single website successively assimilated using sequence assimilation mode, i.e.,
Assimilate the analysis field updated after an observation to continue to assimilate as the ambient field assimilated next time.
According to an aspect of the present invention, observation error covariance R includes measurement error and representive error,
Representive error
ε0For measurement error, ε0=ermax+ermin* Π0, wherein Π0For observation, ermax is elementary error；
γ is tuning scale factor, and Δ x is sizing grid, and L indicates the radius of influence of observation website, is equipped with observation bit
It closes, total observation error is defined as
According to an aspect of the present invention, CMAQ model system includes:
Chemical Transport module, transmission process, chemical process and infall process for simulating pollution object；
Primary condition module and boundary condition module, for providing pollutant initial fields and boundary field for CCTM；
Photochemical breakdown rate module, for calculating photochemical breakdown rate；
Meteorological-chemical interface module, is meteorologic model and the interface of CCTM, can for meteorological data to be converted into CCTM
The data format of reading.
The utility model has the advantages that constructing set inverting assimilation system by EnKF methodology to optimize SO simultaneously2、NOX、PM2.5Discharge is clear
It is single, it effectively corrects for discharge error and improves model predictions.
Detailed description of the invention
Fig. 1 is the principle of the present invention schematic diagram.
Fig. 2 is 10-m wind speed (WS10), the 2-m positioned at the Nanjing (a, c, e) and the observation of (b, d, f) Shanghai website with simulation
Temperature (T2) and 2-m relative humidity (RH2) time series comparison diagram.
Fig. 3 be China's Mainland, the region NCP, YRD and SCB be averaged SO2, NOX, a PM2.5 inverting discharge ratio
Factor time variation diagram.
Fig. 4 is inventory and source list (a, c, e) discharge difference (106g/d) and (b, d, f) discharge ratio after inverting
Factor space distribution map.
Fig. 5 is SO of 25 to 30 December in 2015 using inventory after inverting and source list simulation2Concentration and
RMSE comparison diagram.
Fig. 6 is NO of 25 to 30 December in 2015 using inventory after inverting and source list simulation2Concentration and
RMSE comparison diagram.
Fig. 7 is PM of 25 to 30 December in 2015 using inventory after inverting and source list simulation2.5Concentration and
RMSE comparison diagram.
Fig. 8 is inventory simulation (a, b) SO after source list and inverting2, (c, d) NO2, (e, f) PM2.5Average deviation pair
Than figure.
Fig. 9 is assimilation and does not assimilate SO2、NO2Observe the PM of inverting2.5(a) inventory difference (106G/d it) and (b) discharges
Scale factor spatial distribution map.
Figure 10 is SO2、NO2And PM2.5Inverting after localization scale becomes 180,108 and 180km from 108,72,108km
PM2.5(a) inventory difference (106G/d scale factor spatial distribution map) and (b) is discharged.
Specific embodiment
Details and principle of the invention is described in detail below.
As shown in Figure 1, constructing EnKF inventory Inversion System first and being coupled to WRF-CMAQ mode.Due to WRF-CMAQ
The offline coupling of model, ignoring the meteorological mutual feedback with chemistry influences, for the simulation error for avoiding Extreme Weather Events, this reality
It applies example and chooses the research period as 25 to 30 December in 2015.Secondly to a PM in emission inventories2.5And precursor SO2、
NOXIt carries out random perturbation 30 collection and is merged into row set forecast, be set as one to avoid the influence of discharge diurnal variation from assimilating window
It.Inventory after daily inverting is re-applied to same day model predictions and provides initial fields for the DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM in inverting in next day,
The average inventory as final optimization pass of more days inverting inventories.Assess inventory regional change and uncertainty；It will be clear after optimization
Forecast when singly carrying out long within the assimilation period is forecast to compare with source list, raising effect of the inventory after assessment optimization to forecast
Fruit.Aerosol concentration and component are forecast using WRF-CMAQ offline area air quality model.WRF modular system is state, the U.S.
Family's Center for Atmospheric Research (NCAR), U.S.National Oceanic and atmosphere office (NOAA) and NCEP combine multiple scientific research institution's exploitations
Mesoscale high-resolution weather research of new generation and Forecast Mode, can accurately reproduce the various synoptic process in real atmosphere, extensively
It is general to be applied to atmospheric science research and operational forecast demand.Research of the WRF model integrated so far in mesoscale at
Fruit, including two dynamic cores: the system knot of a data assimilation system and support a parallel computation and the system expandability
Structure.WRF horizontal direction uses Arakawa C grid, and vertical direction then uses terrain following mass coordinate by spatial spreading
Change, is complete compressible non-hydrostatic atmospheric model, can preferably improve the simulation and forecast to Study of Meso Scale Weather.WRF model
Inner parameter scheme is more richer than other mesoscale models, and the physical process considered is also in further detail.It is introduced in mode
Physical parameter scheme mainly includes microphysical processes scheme, cumulus convection scheme, PBL scheme, long wave shortwave radiation mould
Quasi- scheme and land face scheme etc..Simulation and real-time prediction experiments have shown that, WRF modular system is in terms of forecasting various weather conditions
Do well.
The third generation prediction of air quality model that CMAQ is developed by US Gov Env Protection Agency uses " atmosphere "
Design concept, physical and chemical process complicated between atmosphere difference pollutant is combined together consideration, meteorologic model is not only simulated
Driving under the conveying of pollutant, diffusion, conversion and transition process influence, combined dirty between region and City-scale
Contaminate the various chemical processes of object in an atmosphere, including liquid-phase chemistry, heterogeneous phase chemistry process, aerosol processes and dry and wet
Influence of the deposition process to concentration distribution, to substantially increase the precision of CMAQ simulation.CMAQ can be in different spaces scale model
In enclosing while the various problem of environmental pollutions such as the various air pollutants such as PM, ozone and acid deposition, visibility are simulated, may be used also
To be used to study pollutant sources, mechanism of production, diffusion, the area transmissions and assessment pollution reduction effect of pollutant are disclosed
The influence of fruit, Environment Controlling Strategy to air quality, to work out best decision.CMAQ model is designed using high modularization,
It is mainly made of 5 parts, core is Chemical Transport module CCTM (CMAQ Chemical-Transport Model
It Processor), can be with the transmission process, chemical process and infall process etc. of simulating pollution object；Primary condition module I CON
(Initial Conditions Processor) and boundary condition module BCON (Boundary Conditions
Processor pollutant initial fields and boundary field) are provided for CCTM；Photochemical breakdown rate module J PROC (Photolysis
Rate Processor) calculate photochemical breakdown rate；Meteorology-chemistry interface module MCIP (Meteorology-Chemistrry
Interface Processor) it is meteorologic model and the interface of CCTM, meteorological data is converted into the data that CCTM can be read
Format.For CMAQ relative to online coupling model, computational efficiency is higher, has been widely used in the research and business of air pollution
Forecast work.
WRF pattern simulation region, East and West direction include 169 mesh points, and north-south includes 129 mesh points, which covers
East Asia most area is covered, resolution ratio is 36km × 36km；Vertical direction is divided into 51 layers, a height of 50hPa of top of model.CMAQ
Each few three grids of the every side ratio WRF model of the simulated domain of model, it is vertical on be compressed to 15 layers from 51 WRF layers, wherein
Seven layers are located at boundary layer, and remaining in free convection layer.Temporal resolution is 6 hours, 1 ° × 1 ° of spatial resolution.Gas is molten
Glue initial fields are from the forecast for recycling 6 hours before.It is further noted that artificial source emission was worked out from Tsinghua University
The artificial source emission inventory (MIX) in Asia in 2010 (Li et al., 2017), provide five industries (electric power, industry, it is civilian, hand over
Logical, agricultural) 0.25 ° of spatial resolution monthly gridding emissions data.Biological source emission passes through MEGAN model (Guenther
Et al., 2006) off-line calculation.AERO4 aerosol module is used in the present embodiment, includes PM2.5Seven aerosol species,
Including organic carbon, black wood charcoal, sulfate, nitrate, ammonium salt, sea salt and also unformed aerosol species.
The random sample that the error for the ambient field that EnKF is inputted according to mode generates one group of finite aggregate is not true to represent it
It is qualitative, B matrix is calculated by model predictions result, not only solves what Kalman filtering faced in high dimensional pattern system
B matrix stores and calculates the excessive problem of cost, so that B matrix is developed with the update of dynamic mode, both can be applied to height
Nonlinear modular system is spent, adjoint mode need not be also write, be easier implementation and application.A committed step of EnKF
It is to generate discharge set sample to provide Ensemble simulation, different from Ensemble Numerical Weather Prediction generating mode, emission source does not need full
The foot certain condition of continuity and equilibrium condition, therefore the present embodiment generates set disturbance using Monte Carlo method.Source emission collection
Sample is closed to obtain by disturbance source list:
Wherein,For source list, δ X is random perturbation field, and in the present embodiment, perturbation amplitude is set as source list
15%, value is set as 0, i for set sample number, for computational efficiency and assimilation effect, this reality is better balanced lower than 0 after disturbance
It tests set number and is set as 30.In addition, the present embodiment considers PM2.5The influence of precursor, while to SO2、 NOXDischarge carry out it is anti-
It drills and corrects, since the correlation for directly acquiring each state variable is more difficult, so state variable is mutual between assuming
It is independent.The background error covariance matrix of source emission is obtained according to following equation:
Wherein,For source emission ensemble average before inverting.Inventory based on aggregate error statistics and measurement vector, after inverting
It is updated by following equation:
Wherein, H Observation Operators, R are observation error covariance matrix, and K is gain matrix, determine ambient field and observation
Weight,Ensemble average for the analysis field of ith member, i.e. emission inventories after optimization, all members is estimated to be optimal
Meter, provides precision higher emission inventories, y is observation matrix for inverting next time.
To avoid influence of the model predictions diurnal variation error to inverting, inverting is carried out using average daily concentration, assimilation window is set
It is 1 day.To reduce the influence for calculating cost and representive error, observation is carried out based on optimal estimation theory " super
Observation " processing, i.e., will be located at m observation y of same mesh pointiPredicted value corresponding with n member(i=1,2 ...,
m；K=1,2 ..., n) constitute a super observation and corresponding predicted value.Assuming that the observation of different website different moments misses
It is poor mutually indepedent, yiCorresponding observation standard deviation is ri, then new to constitute observation ynew, observe standard deviation rnewAnd it is corresponding pre-
ReportMeet following relationship:
Biggish matrix is stored and converted when to avoid analysis, it is contemplated that mutually indepedent between observation data, this implementation
Example successively assimilates single website using sequence assimilation mode, that is, assimilates the analysis field updated after an observation as next time
The ambient field of assimilation continues to assimilate.A major defect of EnKF is influenced by sampling error and mode error, and meeting exists
Introduced between two independent variables false correlation cause analytical error covariance underestimate and to cause state variable to generate false
Analysis increment.In order to reduce the influence for the false increment that finite aggregate generates, analysis is confined to see using localization scheme
It surveys in a certain range of grid, advantage is to facilitate the weak influence for eliminating that observation generates at a distance.Optimal localization scale and same
It is related to change the retention time of selection, dynamical system and the chemical species of window in an atmosphere, it is contemplated that NOxIt survives in an atmosphere
Period, localization scale was set as apart from the distance for observing 2 grids of website, and PM at one day or so2.5And SO2It is set as 3
The distance of grid.EnKF another serious problem in assimilation process is filtering divergence, and it is too small and neglect to show as set deviation
Depending on observation information.One method is expansion background error covariance, but due to lacking physical basis and in the area far from observation
Domain introduces the linearly increasing of background error falseness, keeps the effect of this method limited.In the present embodiment, we are using every
Identical disturbance all is carried out to source emission in the inverting of one step.In addition the covariance caused by sampling error in refutation process is
Negative value (indicating that forecast concentration is negative with source emission amount correlation) is set as 0.Unreasonable correction value in order to prevent, it is anti-every time
The discharge Dynamic gene control drilled is between 0.2~5 times of original discharge.
Observe data and error: firstly, for PM2.5, only measured value is located at 6~425 μ g/m3Within range
Data just assimilate, and for SO2、NO2, the threshold range of setting is respectively 1~400 μ g/m3, 1~185 μ g/m3, it is more than this
A range is considered as unreasonable is removed.Then pass through | O (t)-O (t ± 1) |≤m (t) carries out time continuity inspection
Excluding outlier, wherein O (t) and O (t+1) are value of the observation at t the and t+1 moment, and m (t) is according to Jiang et a1. (2013)
Experience is set as 50+0.15O (t).In addition, PM2.5、SO2、NO2Observation and predicted value deviation be greater than 150,150,300 μ g/
m3When also do not assimilate.
Observation error covariance R comes from measurement error and representive error, and usual biggish measured value has biggish measurement
Error, according to the research of Schwartz et al. (2012), measurement error is defined as ε0=ermax+ermin* Π0, wherein
Π0For observation, ermax is elementary error, PM2.5、SO2、NO2It is respectively set to 1.5,1.0,1.0 μ g/m3, ermin setting
It is 0.0075,0.005,0.005.Representive error εrAfter parametrization, obtain,
Wherein, γ is tuning scale factor, is set as 0.5 according to sensitivity experiments, Δ x is sizing grid (36km), L
The radius of influence for indicating observation website, related (being reduced to 3km) with observation position, total observation error is defined as
In short, WRF/CMAQ/EnKF assimilation Inversion System target is exactly assimilation observation data, Lai Youhua emission inventories make
Discharge of the inventory of optimization closer to the current generation.EnKF emission inventories inverting is to PM2.5The influence of forecast is studied.Based on EnKF
Method and WRF-CMAQ mode construct area alignment inventory inverting optimization system.Assimilate 25 days to 2015 December in 2015 first
On December 30, ground SO2, NO2, PM2.5Data are observed, inverting optimizes SO2、 NO2With a PM2.5The emission inventories of discharge, grind
Study carefully the regional change of inventory after inverting, and carries out pollutant concentration forecast in the inventory of optimization input WRF-CMAQ, assessment is instead
Inventory after drilling is to raising PM2.5The influence of forecast.
Embodiment
Have chosen the average 2-m temperature of the meteorological observation website of CHINESE REGION 36,2-m relative humidity and 10-m wind speed pair
The analogue value is assessed.Table 1, which gives, compares statistical result.All in all, the 10-m wind speed of simulation and 2-m temperature are than seeing
Measured value is slightly lower, and BIAS is respectively -1m/s and 4.3%, and 2-m temperature is slightly higher, and BIAS is 0.4 DEG C.10-m wind speed 2-m temperature and
The CORR of 2-m relative humidity is respectively 0.62,0.96 and 0.82, and it is good that this illustrates that the meteorological field of simulation and observation field have
Consistency.
Table 1
As shown in Fig. 2, giving the time sequence of the meteorological field of the simulation and observation in two website Nanjing, East China and Shanghai
The comparison of column.The simulation of temperature and relative humidity and observation more coincide on the whole, and wind speed is slightly lower, and the wind speed underestimated may
Lead to positive deviation of simulation.It should be noted that since observation data precision is 1m/s, and air speed value itself is smaller, therefore can
Very big statistic bias can be will cause.Research in, the wind speed of simulation is respectively in NCP and entire regional error
27.4% and 22.6%, using 36km resolution analog regional, mean wind speed has been over-evaluated more than 30%, and uses nested
The wind speed of 12km resolution analog NCP is over-evaluated and is reduced to 10% hereinafter, therefore may be helped using the simulation of high-resolution nesting
In the raising of wind speed simulation.On the whole, the meteorological simulation of the present embodiment does not have apparent systematic error, can reproduce meteorological field
Time-varying process.
As shown in figure 3, SO2、NOXWith a PM2.5It discharges and carries out inverting by adjusting daily priori emission inventories.Such as
Shown in figure, three species are in discharge scale factor (inventory/source list after the inverting) estimation analyzed every time, for SO2, four
A region is respectively less than 1 in the scale factor of all window invertings, shows SO2Exist in all areas and significantly over-evaluate,
In, the SCB range of decrease is the most obvious, after inverting twice, stablizes 10% or so of source list.And NOXIt is emitted on entire big
Land region, the discharge of the area NCP and YRD are basically unchanged, and scale factor is respectively less than 1 after the inverting of the area YRD, and discharge decreases.
For a PM2.5Inverting is discharged, although the whole continent average emission is basically unchanged, three area alignment variations are obvious.Instead
After drilling, NCP discharge is overall in rising trend, and YRD and SCB discharge are on a declining curve, wherein the PM in the area YRD2.5Discharge drop
It is low more obvious.There is apparent fluctuation in three species different windows in refutation process, however, fluctuation range is relatively existing
There is technology smaller using assimilation window inverting CO discharge in one hour, these are the result shows that inverting discharge estimation can in different windows
There can be higher uncertainty, rapidly fluctuation may be intrinsic with random model error (for example, meteorological field) and reality
Discharge variation it is related.From the point of view of three regions, NCP fluctuation is bigger, may be related with complicated regional pollution.
Under limited observation, the time change for studying discharge is relatively difficult, and the purpose of the application is to determine and drop
Low SO2、NOX、PM2.5The error of priori emission inventories.Therefore, after to continuous assessment 25 to 30 December in 2015,
Time is carried out to all analyses in all assimilation windows and space average obtains the estimation of the discharge after inverting, is post-processed in this way
Be conducive to filter out the time change of influence and the discharge estimation of Model by Stochastic Model Error.Average three species row after final inverting
Estimation and scale factor are put as shown in figure 3, table 1 while giving and discharge Variant statistical before and after inverting.
For SO2Discharge, in addition to northeast and the Northwest, national most area is in different degrees of downward trend,
Generally, China's Mainland averagely reduces 32.2%.NCP, YRD, SCB, Central China and the discharge of Pearl River Delta area reduce more bright
Aobvious, some areas discharge is reduced to former 50% or less discharge.Wherein, it is dropped respectively after the inverting of the area NCP, YRD and SCB
40.3%, 71.6%, 84%.The prior art is it has also been found that Chongqing region SO2In the presence of significantly over-evaluating, and it is anti-by EnKF methodology
Drill Chongqing region SO2Discharge, result are similar with the application.In addition, also there is a lot of other evidences to show recent years (especially
After being 2010-2011) since flue gas desulfurization, SO is widely used in Chinese power plant2Discharge reduces rapidly, such as from bottom to top
Emission inventories estimation and based on moonscope etc..From 2010 to 2014 year, East China SO2Vertical column density
30% or so are reduced, and a year discharge is reduced up to 50~60% from 2007-2008 to 2014.NOXIt is relatively small to discharge difference,
The faint increase in central and west regions, and there is smaller reduction in eastern region, wherein the YRD range of decrease is obvious, reduces than source emission rate
30.1%.NOXDischarge is mainly derived from motor vehicle and plant emissions, based on emission inventories and satellite methods table from bottom to top
Bright interior discharge nationwide before 2010 generally increases.And in western part of China, until discharge in 2013 still increases.In
State's government goal is in 12 period NOXDischarge reduces by 10%, and moonscope is shown in East China NO in 2011XDischarge
Reach peak, and is begun to decline during 2012.Relative to 2010, east China area NOX, VCDs is under 2014
Drop 20% or so, is consistent with the application substantially.For PM2.5Discharge, spatial distribution and PM2.5Analyze field spatial distribution more
Unanimously, the reliability that the application uses inversion method is further demonstrated.On the whole, discharge increase be mainly distributed on North China and
Northeast and the Northwest, and discharge reduce be mainly distributed on Henan and on the south southern area, the whole country averagely increase
12.5% PM2.5Discharge, wherein the area NCP increases 33.4%, may partially influence since winter heating etc. is uncertain.
And in the area YRD and SCB, PM2.5Discharge reduces 88.3%, 41.5% respectively.Table 2
Emission inventories inverting optimizes the influence to forecast: in order to probe into the influence that inverting emission inventories simulate pollutant,
Mode is re-entered into the inventory after optimization, and is compared with source list simulation.Using original during respectively studying
The SO that inventory is simulated after inventory and optimization2、NO2、PM2.5Concentration and RMSE.It can be seen that being used in different research areas
The SO that inventory is simulated after inverting2、NO2And PM2.5It is substantially better than source list simulation, in addition, it is different from initial fields assimilation, it uses
Inventory after inverting can constantly improve model simulation results in the case where outside drain is stablized.For SO2Simulation, it is entire in
State continent BIAS is by 20.2 μ g/m3It is reduced to -6.7 μ g/m3, that is to say, that error reduces 67%, in NCP, YRD and SCB,
BIAS is by 36.7,46.2,96 μ g/m3It is reduced to -10.5, -1.7,1.3 μ g/m3, reduce 71.4% respectively, 96.3%,
98.6% error, hence it is evident that the system of correcting for is over-evaluated.For NO2Simulation, although continent BIAS statistics it is constant, RMSE and
CORR increases, and three sub-regions source lists simulate BIAS very little, but are still effectively increased using the inventory after inverting
Simulation, BIAS is respectively by 2.5,17.0, -7.7 μ g/m3Drop to -1.2,2.7, -3.1 μ g/m3.For PM2.5Simulation, four areas
The BIAS in domain is respectively by -9.6, -34.1,20.9,36.5 μ g/m3Drop to -4.9, -17.6,9.7 μ g/m3、27.2μg/m3, remove
Outside SCB, other three region differences all have dropped 50% or so substantially.For SCB, although inventory compares source list after inverting
41.5% is had dropped, but BIAS only reduces 25.5%, however it remains more serious over-evaluates, this may be with the meteorology of simulation
And chemical deviation is in relation to (4.4 discuss).In addition, all species RMSE in four regions are declined, and CORR removes YRD
NO2It has dropped other than 0.01, also there is different degrees of raising.On the whole, the application is clear by the discharge of EnKF methodology inverting
List can effectively improve forecast result.
Table 3
In order to further verify influence of the source emission of assimilation to pollutant simulation is improved, three kinds of pollutants are in each city
Forecast result, for SO2, existed using the analog result of source list in East China, Central China and SCB, Pearl River Delta area
Significantly over-evaluate, and exist in northeast and the Northwest and significantly underestimate, using the emission inventories after assimilation, removes Gansu, mountain
Outside the province, the BIAS of the regional analog result of other overwhelming majority is substantially in 10 μ g/m in west3Within.Although the SO of latter two province of inverting2
Discharge has certain increase, and there are still bigger to underestimate for the analog result after inverting, it is thus possible to also need the row of further increasing
It puts.For NO2Simulation effectively corrects for central and east simulation using the emission inventories after inverting and over-evaluates, and analog result is also distributed
In 10 μ g/m3Within, and for west area, however it remains certain minus deviation.PM2.5Although the Emission Optimization effectively reduces
Over-evaluating for southern most area and underestimating for northern area, but big deviation is still present in Jing-jin-ji region, SCB and Xinjiang
Etc. ground.Due to the complexity of Beijing-tianjin-hebei Region pollution, meteorological simulation error, chemical reaction missing and PM2.5Non-linear two
It is secondary generate etc. factors all limit inverting as a result, and for Xinjiang region, anthropogenic discharge is relatively fewer, although new after inverting
Boundary most area improves discharge, but is affected by the non-artificial discharge such as dust storm, therefore still deposit in Xinjiang most area
Underestimate serious.On the whole, using the inventory of inverting after assimilation, it is nationwide in have 85.8% respectively, 82.5%,
85.4% city improves SO2、NO2And PM2.5Simulation.
In short, constructing set inverting assimilation system by EnKF methodology to optimize SO simultaneously2、NOX、PM2.5Emission inventories,
It effectively corrects for discharge error and improves model predictions.Although but PM after the regional inverting of SCB2.5Discharge reduces 41.5%,
But analog result still have it is biggish over-evaluate, error only reduces by 25.5%, and multiple city BIAS are higher than 40 μ g/m3.Pass through gas
Image field assessment, discovery this area WRF 10-m wind speed simulation is seriously underestimated, in addition, to over-evaluate 37.7%, 2-m relatively wet for 2-m temperature
Degree underestimates 17.5%.The wind speed underestimated is unfavorable for PM2.5Diffusion, cause the lasting accumulation of simulation, and the humidity over-evaluated promotes
The moisture absorption growth of aerosol and liquid-phase conversion, the temperature over-evaluated then enhance chemical reaction rate, promote PM2.5Not medium well
At.Although WRF parameter used by the application is reliable in most areas analog result, in region with a varied topography, more slightly
Rough resolution ratio can may still have biggish error, this increases the uncertainty of inverting inventory to a certain extent.Cause
This, needs to further increase resolution ratio to improve mode expression, especially in regions with complex terrain.
The PM of the area NCP inverting optimization2.5Discharge increases 33.4% compared with source list, which is changed to a certain extent
Learn model PM2.5The incomplete influence of heterogeneous phase chemistry process of generation.
The application is to reduce a large amount of precursors to discharge error to PM of inverting2.5Influence, take while assimilating inverting
PM2.5And secondary aerosol species precursor SO2、NOXThe strategy of discharge.Fig. 7 gives assimilation and does not assimilate SO2、 NO2Observation is anti-
The sensitivity experiments drilled, it can be seen that there are significant differences in Middle Eastern.When the discharge of gaseous precursors object is there are when error,
Also error can be generated to the concentration of the secondary inorganic matter generated in forecasting process, and then influences PM2.5Concentration, refutation process is then
It can be by adjusting a PM2.5Discharge make up the error of precursor discharge, therefore very it is necessary to combine a variety of correlations of assimilation
Pollutant.In addition, false increment is introduced in the analysis process to avoid observing at a distance, only in localization range scale
Observation carries out inverting estimation, according to factors such as species service life and atmospheric transmission conditions.The application due to Research scale relatively
Greatly, the atmospheric conditions of different zones are not quite similar, to global SO2、NO2、PM2.5The localization scale of unified setting is respectively 108,
72,108km, Fig. 8 give localization scale and are respectively set as PM when 180,108 and 180km2.5The discharge of discharge and the application
Difference and scale factor.As can be seen that discharge inverting has larger sensibility to localization scale, since localization scale becomes larger,
Archeus inverting less than place can also generate increment, therefore, all in all northern area discharge than the application discharge more
By force, southern weaker.It is following how by model dynamical system and localization scale further combined with according to atmospheric conditions determination
Different influence scale threshold values, obtaining more accurately assessment result, there is still a need for more studied and thought deeply.
The application by EnKF methodology construct set inverting assimilation system be coupled in WRF-CMAQ mode come and meanwhile it is excellent
Change SO during 25 to 30 December in 20152、NOX、PM2.5Emission inventories, analysis compare the space change of inventory after inverting
Change, and carries out forecast verifying with the inventory after inverting.As a result as follows:
Different pollutants discharge variation difference in different zones.In addition to northeast and the Northwest, national most area
SO2Discharge is in different degrees of downward trend, wherein dropped 40.3% after the inverting of the area NCP, YRD and SCB respectively,
71.6%, 84%.For NOX, central and west regions faint increase relatively small with source list difference is discharged in discharge after inverting,
And there is smaller reduction in eastern region, wherein the YRD range of decrease is obvious, reduces by 30.1% than source emission rate.For PM2.5Discharge increases
Add and be mainly distributed on North China and northeast and the Northwest, and reduce be mainly distributed on Henan and on the south southern area, on the whole,
The whole country averagely increases 12.5%PM2.5Discharge, wherein the area NCP increases 33.4%, and regional in YRD and SCB,
PM2.5Discharge reduces 88.3%, 41.5% respectively.
In different research areas, SO is simulated using the inventory after inverting2、NO2And PM2.5It is substantially better than source list mould
It is quasi-.For SO2Simulation, China's Mainland, NCP, YRD and SCB BIAS reduce 67%, 71.4%, 96.3%, 98.6%, it is bright
It is aobvious to correct for system and over-evaluate.For NO2Simulation, although continent BIAS is constant, RMSE and CORR increase, in addition, three
The BIAS of subregion reduces 52%, 84.1%, 59.7% respectively.In addition to SCB, other three regions are using clear after inverting
Single analog PM2.5Deviation all has dropped 50% or so substantially.For SCB, although inventory has dropped than source list after inverting
41.5%, but influenced by meteorological simulation error, BIAS only reduces 25.5%, however it remains serious to over-evaluate.Generally, it removes
The area YRD NO2CORR outside, analog result increases.Using the inventory of inverting after assimilation, have respectively in the whole country
85.8%, 82.5%, 85.4% city improves SO2、 NO2And PM2.5Simulation.
Utilize EnKF methodology Simultaneous Inversion SO 25 to 30 December in 20152、NOx、PM2.5Emission inventories.It analyzes excellent
The Spatial Variation of the relatively primitive inventory of inventory after change.Inventory after being then based on inverting simulates PM2.5Change in time and space, point
EnKF inverting emission inventories are analysed to PM2.5The influence of forecast.The result shows that the inventory after inverting can significantly improve PM2.5Forecast
Effect, China's Mainland, NCP, YRD, SCB BIAS reduce by 49%, 48.4%, 53.6% and 25.5% respectively.
It is further to note that specific technical features described in the above specific embodiments, in not lance
In the case where shield, it can be combined in any appropriate way.In order to avoid unnecessary repetition, the present invention is to various
No further explanation will be given for possible combination.
Claims (6)
1. a kind of ground emission inventories inverting optimization method based on EnKF characterized by comprising
Step 1, building EnKF inventory Inversion System are simultaneously coupled to WRF-CMAQ modular system；
Step 2, by the inventory after daily inverting be re-applied to the same day model predictions be inverting in next day in DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM mention
For initial fields, the average inventory as final optimization pass of more days inverting inventories；
Step 3, assessment inventory regional change and uncertainty；Forecast when inventory after optimization is carried out long within the assimilation period,
It forecasts to compare with source list, improvement effect of the inventory after assessment optimization to forecast.
2. the ground emission inventories inverting optimization method according to claim 1 based on EnKF, which is characterized in that the structure
The step of building EnKF inventory Inversion System include:
Step 11 generates source emission set sample
For source list, δ X is random perturbation field, and i is set sample number；
Step 12 considers PM2.5The influence of precursor, while to SO2、NOXDischarge carry out inverting correct, calculate the back of source emission
Scape error co-variance matrix For source emission ensemble average before inverting, n is nature
Number；
Step 13 is updated based on aggregate error statistics and measurement vector, the inventory after inverting by following equation:
Wherein, H Observation Operators, R are observation error covariance matrix, and K is gain matrix, determine the weight of ambient field and observation,Ensemble average for the analysis field of ith member, i.e. emission inventories after optimization, all members is optimal estimation, under
Inverting provides the higher emission inventories of precision.
3. the ground emission inventories inverting optimization method based on EnKF stated according to claim 2, which is characterized in that further include step
Rapid 14, inverting is carried out using average daily concentration, assimilation window is set as 1 day, to avoid model predictions diurnal variation error to the shadow of inverting
It rings；Super inspection process is carried out to observation based on optimal estimation theory,
M observation y of same mesh point will be located atiPredicted value corresponding with n memberConstitute super observation and
Corresponding predicted value, i=1,2 ..., m；K=1,2 ..., n, it is assumed that the observation error of different website different moments is mutually indepedent,
yiCorresponding observation standard deviation is ri, then new to constitute observation ynew, observe standard deviation rnewAnd corresponding forecastUnder satisfaction
Column relationship:
4. the ground emission inventories inverting optimization method according to claim 3 based on EnKF, which is characterized in that further include
Step 15 successively assimilates single website using sequence assimilation mode, that is, assimilates under the analysis field conduct updated after an observation
The ambient field once assimilated continues to assimilate.
5. the ground emission inventories inverting optimization method according to claim 3 based on EnKF, which is characterized in that observation misses
Poor covariance R includes measurement error and representive error,
Representive error
ε0For measurement error, ε0=ermax+ermin* Π0, wherein II0For observation, ermax is elementary error；
γ is tuning scale factor, and Δ x is sizing grid, and L indicates the radius of influence of observation website, related with observation position, always
Observation error be defined as
6. the ground emission inventories inverting optimization method according to claim 1 based on EnKF, which is characterized in that CMAQ mould
Type system includes:
Chemical Transport module, transmission process, chemical process and infall process for simulating pollution object；
Primary condition module and boundary condition module, for providing pollutant initial fields and boundary field for CCTM；
Photochemical breakdown rate module, for calculating photochemical breakdown rate；
Meteorological-chemical interface module, is meteorologic model and the interface of CCTM, for meteorological data to be converted into what CCTM can be read
Data format.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910030086.5A CN109858686A (en) | 2019-01-14 | 2019-01-14 | A kind of ground emission inventories inverting optimization method based on EnKF |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910030086.5A CN109858686A (en) | 2019-01-14 | 2019-01-14 | A kind of ground emission inventories inverting optimization method based on EnKF |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109858686A true CN109858686A (en) | 2019-06-07 |
Family
ID=66894600
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910030086.5A Pending CN109858686A (en) | 2019-01-14 | 2019-01-14 | A kind of ground emission inventories inverting optimization method based on EnKF |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109858686A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110909483A (en) * | 2019-12-03 | 2020-03-24 | 河北先河环保科技股份有限公司 | Point source atmospheric pollutant emission list verification method based on gridding data |
| CN111353634A (en) * | 2020-02-12 | 2020-06-30 | 中国人民解放军国防科技大学 | Sulfur dioxide emission source optimization method based on mode initial field assimilation |
| CN111487216A (en) * | 2020-05-06 | 2020-08-04 | 北京中科锐景科技有限公司 | Carbon dioxide flux inversion method and system |
| CN111651873A (en) * | 2020-05-22 | 2020-09-11 | 上海市环境科学研究院 | Urban atmosphere VOCs emission list inversion method, system, terminal and storage medium |
| CN111723482A (en) * | 2020-06-17 | 2020-09-29 | 南京大学 | Based on satellite CO2Method for observing and inverting surface carbon flux by using column concentration |
| CN112036038A (en) * | 2020-08-31 | 2020-12-04 | 中科三清科技有限公司 | Emission source emission amount adjusting and optimizing method, device, equipment and storage medium |
| CN114169179A (en) * | 2021-12-14 | 2022-03-11 | 重庆市生态环境大数据应用中心 | Air quality background concentration analysis method |
| CN112132336B (en) * | 2020-09-22 | 2024-02-20 | 南京创蓝科技有限公司 | Quarterly prediction method for PM2.5 concentration |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060229813A1 (en) * | 2005-03-30 | 2006-10-12 | Tobiska William K | Ionospheric forecast system (IFS) |
| CN106442236A (en) * | 2015-07-30 | 2017-02-22 | 中国科学院遥感与数字地球研究所 | Ground PM2.5 inversion method and system based on satellite remote sensing |
| CN107273995A (en) * | 2016-04-08 | 2017-10-20 | 株式会社日立制作所 | Urban Air Pollution Methods |
| CN108228538A (en) * | 2017-12-29 | 2018-06-29 | 航天科工智慧产业发展有限公司 | SO in a kind of city2The Forecasting Methodology of concentration value |
| CN108519340A (en) * | 2018-03-28 | 2018-09-11 | 刘诚 | A kind of satellite remote sensing contamination gas scapus concentration spatial and temporal distributions inversion algorithm |
| CN109060617A (en) * | 2018-07-06 | 2018-12-21 | 南京信息工程大学 | The simulation and analysis method that different agricultural emission intensity scenes influence PM2.5 concentration |
-
2019
- 2019-01-14 CN CN201910030086.5A patent/CN109858686A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060229813A1 (en) * | 2005-03-30 | 2006-10-12 | Tobiska William K | Ionospheric forecast system (IFS) |
| CN106442236A (en) * | 2015-07-30 | 2017-02-22 | 中国科学院遥感与数字地球研究所 | Ground PM2.5 inversion method and system based on satellite remote sensing |
| CN107273995A (en) * | 2016-04-08 | 2017-10-20 | 株式会社日立制作所 | Urban Air Pollution Methods |
| CN108228538A (en) * | 2017-12-29 | 2018-06-29 | 航天科工智慧产业发展有限公司 | SO in a kind of city2The Forecasting Methodology of concentration value |
| CN108519340A (en) * | 2018-03-28 | 2018-09-11 | 刘诚 | A kind of satellite remote sensing contamination gas scapus concentration spatial and temporal distributions inversion algorithm |
| CN109060617A (en) * | 2018-07-06 | 2018-12-21 | 南京信息工程大学 | The simulation and analysis method that different agricultural emission intensity scenes influence PM2.5 concentration |
Non-Patent Citations (3)
| Title |
|---|
| SHUZHUANG FENG, FEI JIANG, ZIQIANG JIANG, HENGMAO WANG, ZHE CAI,: "Impact of 3DVAR assimilation of surface PM2.5 observations on PM2.5 forecasts over China during wintertime", 《ATMOSPHERIC ENVIRONMENT》 * |
| 冯震、李兴华: "WRF-CMAQ模式在内蒙古本地化应用", 《内蒙古科技与经济》 * |
| 吴钲、谢旻、高阳华、芦华、赵磊、高松: "利用集合均方根卡尔曼滤波反演重庆大区SO2源排放", 《环境科学研究》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110909483A (en) * | 2019-12-03 | 2020-03-24 | 河北先河环保科技股份有限公司 | Point source atmospheric pollutant emission list verification method based on gridding data |
| CN110909483B (en) * | 2019-12-03 | 2023-09-08 | 河北先河环保科技股份有限公司 | Point source atmospheric pollutant emission list verification method based on gridding data |
| CN111353634B (en) * | 2020-02-12 | 2022-09-20 | 中国人民解放军国防科技大学 | Sulfur dioxide emission source optimization method based on mode initial field assimilation |
| CN111353634A (en) * | 2020-02-12 | 2020-06-30 | 中国人民解放军国防科技大学 | Sulfur dioxide emission source optimization method based on mode initial field assimilation |
| CN111487216A (en) * | 2020-05-06 | 2020-08-04 | 北京中科锐景科技有限公司 | Carbon dioxide flux inversion method and system |
| CN111487216B (en) * | 2020-05-06 | 2023-01-24 | 北京中科锐景科技有限公司 | Carbon dioxide flux inversion method and system |
| CN111651873A (en) * | 2020-05-22 | 2020-09-11 | 上海市环境科学研究院 | Urban atmosphere VOCs emission list inversion method, system, terminal and storage medium |
| CN111651873B (en) * | 2020-05-22 | 2023-03-14 | 上海市环境科学研究院 | Urban atmosphere VOCs emission list retrieval method, system, terminal and storage medium |
| CN111723482A (en) * | 2020-06-17 | 2020-09-29 | 南京大学 | Based on satellite CO2Method for observing and inverting surface carbon flux by using column concentration |
| CN111723482B (en) * | 2020-06-17 | 2023-11-21 | 南京大学 | Satellite CO-based 2 Method for inverting surface carbon flux by column concentration observation |
| CN112036038B (en) * | 2020-08-31 | 2021-07-23 | 中科三清科技有限公司 | Emission source emission amount adjusting and optimizing method, device, equipment and storage medium |
| CN112036038A (en) * | 2020-08-31 | 2020-12-04 | 中科三清科技有限公司 | Emission source emission amount adjusting and optimizing method, device, equipment and storage medium |
| CN112132336B (en) * | 2020-09-22 | 2024-02-20 | 南京创蓝科技有限公司 | Quarterly prediction method for PM2.5 concentration |
| CN114169179A (en) * | 2021-12-14 | 2022-03-11 | 重庆市生态环境大数据应用中心 | Air quality background concentration analysis method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109858686A (en) | A kind of ground emission inventories inverting optimization method based on EnKF | |
| Wei et al. | Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China | |
| CN111508569B (en) | Target soil property content prediction method based on soil transfer function | |
| Yin et al. | Surface ozone at Nam Co in the inland Tibetan Plateau: variation, synthesis comparison and regional representativeness | |
| Monteil et al. | The regional EUROpean atmospheric transport inversion COMparison, EUROCOM: first results on European wide terrestrial carbon fluxes for the period 2006–2015 | |
| Tang et al. | Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions | |
| Itahashi et al. | Seasonal source contributions of tropospheric ozone over East Asia based on CMAQ–HDDM | |
| CN112884079A (en) | Method for estimating near-surface nitrogen dioxide concentration based on Stacking integrated model | |
| Mendoza-Dominguez et al. | Estimation of emission adjustments from the application of four-dimensional data assimilation to photochemical air quality modeling | |
| CN111487216A (en) | Carbon dioxide flux inversion method and system | |
| CN111353634B (en) | Sulfur dioxide emission source optimization method based on mode initial field assimilation | |
| Alonzo et al. | Modelling the variability of the wind energy resource on monthly and seasonal timescales | |
| Ding et al. | Space-based NO x emission estimates over remote regions improved in DECSO | |
| CN108764527B (en) | Screening method for soil organic carbon library time-space dynamic prediction optimal environment variables | |
| Trieu et al. | Evaluation of summertime surface ozone in Kanto area of Japan using a semi-regional model and observation | |
| Sun et al. | Spatial-temporal prediction of air quality based on recurrent neural networks | |
| Feng et al. | High-resolution simulation of wintertime fossil fuel CO2 in Beijing, China: Characteristics, sources, and regional transport | |
| CN114724647A (en) | High spatial and temporal resolution CO2Flux inversion system and method | |
| CN114169179A (en) | Air quality background concentration analysis method | |
| Guo et al. | Construction and application of a regional kilometer-scale carbon source and sink assimilation inversion system (CCMVS-R) | |
| CN117219183A (en) | High coverage near ground NO in cloudy rain areas 2 Concentration estimation method and system | |
| Yang et al. | Estimating spatiotemporal dynamics of evapotranspiration and assessing the cause for its increase in China | |
| Chen et al. | Estimation and variation analysis of secondary inorganic aerosols across the Greater Bay Area in 2005 and 2015 | |
| Alfaro et al. | CFSR-NCEP Performance for weather data forecasting in the Pernambuco Semiarid, Brazil | |
| Mendoza-Dominguez et al. | Emission strength validation using four-dimensional data assimilation: Application to primary aerosol and precursors to ozone and secondary aerosol |
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 |