CN108229092A - Increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process - Google Patents
Increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process Download PDFInfo
- Publication number
- CN108229092A CN108229092A CN201810019607.2A CN201810019607A CN108229092A CN 108229092 A CN108229092 A CN 108229092A CN 201810019607 A CN201810019607 A CN 201810019607A CN 108229092 A CN108229092 A CN 108229092A
- Authority
- CN
- China
- Prior art keywords
- wet
- value
- concentration
- liquid phase
- formula
- 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.)
- Granted
Links
- 239000007791 liquid phase Substances 0.000 title claims abstract description 40
- 238000004088 simulation Methods 0.000 title claims abstract description 31
- 239000000126 substance Substances 0.000 title claims abstract description 30
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 21
- 238000005137 deposition process Methods 0.000 title claims abstract description 19
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 32
- 231100000719 pollutant Toxicity 0.000 claims abstract description 32
- 238000006243 chemical reaction Methods 0.000 claims abstract description 15
- 230000008021 deposition Effects 0.000 claims abstract description 15
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 claims abstract description 11
- 229910002651 NO3 Inorganic materials 0.000 claims abstract description 9
- 238000004062 sedimentation Methods 0.000 claims abstract description 9
- QAOWNCQODCNURD-UHFFFAOYSA-L Sulfate Chemical compound [O-]S([O-])(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-L 0.000 claims abstract description 8
- 150000003863 ammonium salts Chemical class 0.000 claims abstract description 8
- 239000000926 atmospheric chemistry Substances 0.000 claims abstract description 5
- 239000006185 dispersion Substances 0.000 claims abstract description 5
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 35
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 claims description 35
- 238000000151 deposition Methods 0.000 claims description 14
- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide Inorganic materials O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 claims description 14
- 239000007789 gas Substances 0.000 claims description 13
- 238000000034 method Methods 0.000 claims description 13
- 238000001556 precipitation Methods 0.000 claims description 13
- 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 claims description 11
- 238000009792 diffusion process Methods 0.000 claims description 10
- QAOWNCQODCNURD-UHFFFAOYSA-N Sulfuric acid Chemical compound OS(O)(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-N 0.000 claims description 9
- 229910017604 nitric acid Inorganic materials 0.000 claims description 7
- 239000013626 chemical specie Substances 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 239000000463 material Substances 0.000 claims description 5
- GRYLNZFGIOXLOG-UHFFFAOYSA-N Nitric acid Chemical compound O[N+]([O-])=O GRYLNZFGIOXLOG-UHFFFAOYSA-N 0.000 claims description 4
- 238000000469 dry deposition Methods 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 4
- 239000005022 packaging material Substances 0.000 claims description 3
- HNBFUFIYQWYCDM-UHFFFAOYSA-N oxygen(2-) sulfane titanium(4+) Chemical compound [O--].[O--].S.[Ti+4] HNBFUFIYQWYCDM-UHFFFAOYSA-N 0.000 claims 1
- 238000001311 chemical methods and process Methods 0.000 abstract description 5
- 229910052757 nitrogen Inorganic materials 0.000 abstract description 5
- 229910052717 sulfur Inorganic materials 0.000 abstract description 5
- 230000001568 sexual effect Effects 0.000 abstract description 4
- 239000003738 black carbon Substances 0.000 abstract description 3
- 230000004044 response Effects 0.000 abstract description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 abstract description 2
- 229910052799 carbon Inorganic materials 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 5
- 239000012071 phase Substances 0.000 description 4
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 3
- 239000000443 aerosol Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 239000013618 particulate matter Substances 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 239000000428 dust Substances 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 101100323157 Arabidopsis thaliana LAP1 gene Proteins 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000003916 acid precipitation 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
- 239000012491 analyte Substances 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- TXKMVPPZCYKFAC-UHFFFAOYSA-N disulfur monoxide Inorganic materials O=S=S TXKMVPPZCYKFAC-UHFFFAOYSA-N 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000008187 granular material Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000005180 public health Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- XTQHKBHJIVJGKJ-UHFFFAOYSA-N sulfur monoxide Chemical compound S=O XTQHKBHJIVJGKJ-UHFFFAOYSA-N 0.000 description 1
- 229910021653 sulphate ion Inorganic materials 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Classifications
-
- 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
-
- 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
- G01N33/0068—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 using a computer specifically programmed
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/10—Analysis or design of chemical reactions, syntheses or processes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2219/00—Indexing scheme relating to application aspects of data processing equipment or methods
- G06F2219/10—Environmental application, e.g. waste reduction, pollution control, compliance with environmental legislation
Landscapes
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Medicinal Chemistry (AREA)
- Physics & Mathematics (AREA)
- Food Science & Technology (AREA)
- Biochemistry (AREA)
- Combustion & Propulsion (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Crystallography & Structural Chemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computing Systems (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Treating Waste Gases (AREA)
Abstract
The invention discloses a kind of atmospheric pollution simulation prediction algorithms for increasing liquid phase chemical and wet deposition process, its chemical model based on CALGRID, under the premise of the influence for considering Atmospheric Chemistry reaction, atmospheric transport and dispersion, sedimentation, ground face source and overhead emission source, continue pollutant concentration variation item caused by introducing liquid-phase chemistry and wet deposition process, not only preferably simulated including SO so as to increase the CALGRID of sexual intercourse chemical process2、NO2、PM10、PM2.5Deng conventional primary pollution, secondary pollution, sulfate, nitrate, ammonium salt, black carbon, organic carbon etc. have also well simulated the wet deposition amount of S, N.Improved CALGRID patterns have high-resolution, high-timeliness, can conveniently and efficiently provide the response relation between source and receptor, as a result reliably.
Description
Technical field
The invention belongs to changing for air environmental pollution analyte detection technical field more particularly to Mesoscale photochemical pollution
Into type prediction model.
Background technology
Air quality model is a series of objects such as transmission, diffusion, conversion and removing after atmospheric environment is discharged into pollutant
On the basis of the understanding of reason and chemical process, using the research method and computer technology of the subjects such as meteorology, environment, physics, chemistry,
The method of air concentration distribution of pollutants situation and variation tendency on simulation and forecast different spaces scale is realized, in air quality
Forecast, Air Pollution Control, Environmental Planning and Management, urban construction and public health etc. have important practical application valency
Value has vast potential for future development.
CALGRID is developed by the ARB (Air Resources Board) of California, USA, is the mesoscale of Euler's type
Atmospheric photochemical model has better effects for the simulation of secondary pollution such as ozone, is primarily adapted for use under the conditions of clear sky
Photochemically reactive simulation contains atmospheric transport and dispersion, gas-phase chemical reaction, the point upper thread source of anthropogenic discharge, dry deposition
Etc. processes.
With the progress faster of urbanization and the fast development of social economy, with acid rain, photochemical pollution, gray haze pollutant
The air combined pollution that superposition is characterized that intercouples becomes the more and more prominent atmospheric environment problem in China.In recent years, it is right
The Forming Mechanism and synthesis of the regional atmospheric combined pollution of the key cities such as Jing-jin-ji region, Yangtze River Delta, Delta of the Pearl River group are anti-
The research controlled is the hot issue of China environmental protection research.It is therefore desirable to air quality model can be to various air pollutants
(trace gas, aerosol etc.) under different scale (region, city etc.) different type pollution course (gas phase, liquid phase, it is non-
Phase) it is simulated.In order to preferably utilize CALGRID models that the gas chemistry process of trace gas, aerosol is appreciated and understood
Microphysical chemical process and liquid-phase chemistry provide theories integration with improvement for the regulation and control of air combined pollution, need
CALGRID will can not only be simulated and be solved the problems, such as the ozone pollution related with gas-phase chemical reaction, can also rationally describe aerosol
Physical and chemical process, liquid-phase chemistry and the wet deposition process related with precipitation in sexual intercourse water.
CALGRID patterns lack to the liquid-phase chemistry in sexual intercourse water and the wet deposition process related with precipitation
Description.Liquid phase chemical and wet removing in air are the important components of Atmospheric Chemistry, all related with cloud and precipitation.Cloud and drop
The cyclic process of conveying, conversion and removing of the water to the minimum gas in air and suspended particles in an atmosphere plays important work
With being mainly manifested in：(1) pollutant is made effectively to be mixed in cloud there are strong rising and down draft in cumulus；(2)
The absorption of water dust and raindrop to gaseous pollutant and the removing to particulate generate wet deposition；(3) pollutant is by water dust
A series of dissolving, ionization and liquid phase oxidation reaction will occur after being absorbed with raindrop wherein.CALGRID wants accurate simulation big
The concentration of polluted gas and particulate matter component in gas, it is necessary to include above-mentioned cloud and precipitation chemistry process.
Invention content
In view of the problems of the existing technology, the present invention provides a kind of air for increasing liquid phase chemical and wet deposition process
POLLUTION SIMULATION prediction algorithm.
In order to solve the above technical problems, present invention employs following technical schemes：A kind of increase liquid phase chemical and wet deposition
The atmospheric pollution simulation prediction algorithm of process, includes the following steps：
Step 1：Chemical model based on CALGRID considers Atmospheric Chemistry reaction, atmospheric transport and dispersion, sedimentation, ground
Face source and the influence of overhead emission source, and pollutant concentration variation item caused by introducing liquid-phase chemistry and wet deposition process,
Chemical species concentration equation of change such as formula (1) is obtained,
On the right side of formula (1) in multinomial, first itemFor second order DIFFUSION IN TURBULENCE item, Section 2For diffusion
, Section 3 (P-L)GASChange item, Section 4 CHEM for gas chemistryaqBecome for pollutant concentration caused by liquid-phase chemistry
Change item, Section 5 EANTFor artificial discharge of pollutant sources item, Section 6Change for the species concentration as caused by dry deposition, the
SevenChange for the species concentration as caused by wet deposition；Wherein, C is chemical species mean concentration, and V is three-dimensional wind vector
Average magnitude, K is turbulent diffusivity, in formula, second order DIFFUSION IN TURBULENCE itemIt is closed by turbulent diffusivity K theories
Conversion obtains；
Step 2：Liquid-phase chemistry in its cloud, n+1 are considered to three kinds of nitric oxide, nitrogen dioxide, sulfur dioxide substances
The material concentration of moment substance liquid phase in cloud is solved by formula (2)：
Cn+1,i=(1- Δs t × Nz×Ka)×Cn (2)
CnThe material concentration of substance liquid phase in cloud during for the n moment, Δ t is integrates time step, NzFor total amount of cloud, KaFor liquid phase
Chemical conversion rate；
Step 3：Species concentration caused by wet deposition is changed, the species of consideration include nitric oxide, nitrogen dioxide, two
Sulfur oxide, nitric acid, sulfuric acid, PM10, sulfate, nitrate, ammonium salt, OC, EC, SOA, obtained first by empirical equation (3) wet clear
Removal rates Kw：
In formula (3), PrFor precipitation rate, a and b are empirical；
Then, concentration of the species at n+1 moment after wet reset procedure is obtained by formula (4)：
Cn+1,i=(1- Δs t × Kw)×Cn (4)
In formula, Cn+1Represent the pollutant concentration at current time, CnRepresent the pollutant concentration at upper moment, Δ t represents integration
Time step, KwFor wet clearance rate；
And in precipitation, the wet clearing amount of each pollutant is asked for by formula (5)：
In formula (5),For the wet deposition amount of n+1 moment time steps,For the wet heavy of n moment time steps
Drop amount, KwFor wet clearance rate, CnRepresent the pollutant concentration at upper moment, Δ Z indicates the pattern level of precipitation.
Further, the value of the empirical a and b in step 3 in the solution formula (3) of the rate of settling are as follows：
For SO2, when in summer, the value of a and b are respectively 0.14 and 0.12；When in spring or autumn, a
Value with b is respectively 0.036 and 0.53；When in winter, the value of a and b are respectively 0.009 and 0.70；
For SO4 2-, when in summer, the value of a and b are respectively 0.39 and 0.06；When in spring or autumn, a
Value with b is respectively 0.091 and 0.27；When in winter, the value of a and b are respectively 0.021 and 0.70.
Further, for NO and NO2Wet clearance rate value, be SO2Wet clearance rate a quarter.
Further, for HNO3The value of wet clearance rate is SO2Wet clearance rate half.
Further, for NO3 -、NH4 +Wet clearance rate value, with SO4 2-Wet clearance rate it is equal.
Further, for PM10Wet clearance rate is asked for, and empirical a and b value is respectively 1.26 Hes in formula (3)
0.79。
Further, for OC, EC and SOA wet clearance rate value, be taken as PM10The two of the value of wet clearance rate/
One.
Scheme explanation
Fig. 1 (a) is that the nested grid of pattern of the embodiment of the present invention sets schematic diagram；
Fig. 1 (b) is simulated domain of the embodiment of the present invention and its topographic map；
Fig. 2 is the groundlevel concentration of the Delta of Pearl River ozone in 2006 obtained using simulation and forecast algorithm of the present invention
Figure；
Fig. 3 (a) is the PM25 particulate matters of the Delta of Pearl River in 2006 obtained using simulation and forecast algorithm of the present invention
Groundlevel concentration distribution map；
Fig. 3 (b) is the sulphate particle of the Delta of Pearl River in 2006 obtained using simulation and forecast algorithm of the present invention
The groundlevel concentration distribution map of object；
Fig. 3 (c) is the nitrate granules of the Delta of Pearl River in 2006 obtained using simulation and forecast algorithm of the present invention
The groundlevel concentration distribution map of object；
Fig. 3 (d) is the ammonium salt particulate matter of the Delta of Pearl River in 2006 obtained using simulation and forecast algorithm of the present invention
Groundlevel concentration distribution map；
Fig. 4 (a) is the N sedimentation spirograms of the Delta of Pearl River in 2006 obtained using simulation and forecast algorithm of the present invention；
Fig. 4 (b) is the S sedimentation spirograms of the Delta of Pearl River in 2006 obtained using simulation and forecast algorithm of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings and with specific embodiment, the present invention is furture elucidated.It should be understood that these embodiments are only used for
It the bright present invention rather than limits the scope of the invention, after the present invention has been read, those skilled in the art are to of the invention
The modification of various equivalent forms falls within the application range as defined in the appended claims.
Increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process, include the following steps：
Step 1：Chemical model based on CALGRID considers Atmospheric Chemistry reaction, atmospheric transport and dispersion, sedimentation, ground
Face source and the influence of overhead emission source, and pollutant concentration variation item caused by introducing liquid-phase chemistry and wet deposition process,
Chemical species concentration equation of change such as formula (1) is obtained,
On the right side of formula (1) in multinomial, first itemFor second order DIFFUSION IN TURBULENCE item, Section 2For diffusion
, Section 3 (P-L)GASChange item, Section 4 CHEM for gas chemistryaqBecome for pollutant concentration caused by liquid-phase chemistry
Change item, Section 5 EANTFor artificial discharge of pollutant sources item, Section 6Change for the species concentration as caused by dry deposition, the
SevenChange for the species concentration as caused by wet deposition；Wherein, C is chemical species mean concentration, and V is three-dimensional wind vector
Average magnitude, K is turbulent diffusivity, in formula, second order DIFFUSION IN TURBULENCE itemIt is closed by turbulent diffusivity K theories
Conversion obtains.Mainly pollutant concentration changes item CHEM caused by liquid-phase chemistry belowaqWith the object as caused by wet deposition
Kind concentration variation itemCarry out solution explanation：
CHEMaqEmploy simplified method, to nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), it examines
Consider liquid-phase chemistry in its cloud：If there is cloud, convert by a certain percentage.Wherein, the liquid phase chemical conversion ratio of sulfur dioxide takes
For 10% per hour, the liquid phase chemical conversion ratio of nitrogen oxides (nitric oxide and nitrogen dioxide) is taken as 10% per hour daytime,
Night takes 2% per hour.In this way, nitric oxide (NO), nitrogen dioxide (NO in air2), sulfur dioxide (SO2) concentration variation
It can be expressed as：
Cn+1=(1- Δs t × Nz×Ka)×Cn (2)
(2) in formula, Cn+1Represent pollutant (NO, the NO at current time2And SO2) concentration, CnRepresent the pollutant at upper moment
Concentration, Δ t represent integration time step, NzRepresent total amount of cloud, KaRepresent liquid phase chemical conversion ratio.
For wet removing, nitric oxide, nitrogen dioxide, sulfur dioxide, nitric acid (HNO are considered3), sulfuric acid (H2SO4)、PM10、
Sulfate (SO4 2-), nitrate (NO3 -), ammonium salt (NH4 +), the wet reset procedure of OC, EC, SOA etc., using simplified processing side
Method introduces wet clearance rate Kw.Empirically formula indicates：
Kw=a × Prb (3)
(3) in formula, PrFor precipitation rate (mm/hr), exported by Meteorological Models.A, b is empirical, for SO2And SO4 2-,
A, b values are shown in Table 1.NO and NO2Wet clearance rate be taken as SO2Wet clearance rate a quarter, HNO3Take SO2Two/
One, NO3 -、NH4 +It takes and SO4 2-It is equal.PM10A, b value take 1.26 and 0.79.The wet clearance rate of OC, EC and SOA are taken as PM10
Half.
1 SO of table2And SO4 2-Wet clearance rate calculation formula in a, b value
Then, nitric oxide, nitrogen dioxide, sulfur dioxide, nitric acid, sulfuric acid, PM in air10, sulfate, nitrate, ammonium salt,
OC, EC, SOA etc. can be calculated via the concentration after wet deposition process by following formula,
Cn+1=(1- Δs t × Kw)×Cn (4)
(4) in formula, Cn+1Represent the pollutant concentration at current time, CnRepresent the pollutant concentration at upper moment, Δ t is represented
Integrate time step, KwFor wet clearance rate.And in precipitation, the wet clearing amount of each pollutant is：
(5) in formula,The wet deposition amount of current time step and next time step, K are represented respectivelywIt is wet
Clearance rate, CnRepresent the pollutant concentration at upper moment, Δ Z indicates the pattern level of precipitation.It is that the application present invention calculates below
The specific embodiment of method：
The liquid phase chemical of above-mentioned simplification and the algorithm of wet deposition are applied in CALGRID, to the Delta of the Pearl River in 2006
Area major pollutants and S, N sedimentation simulated, examine model long-play stability and result it is effective
Property.The simulation period of selection is whole year in 2006.The simulated domain (21.8N~24N112.16E~114.82E) of selection includes
10 areas such as Guangdong Province Guangzhou, Shenzhen, Foshan, Zhuhai, Dongguan, middle mountain, Huizhou, Qingyuan City, Zhaoqing, Jiangmen and Hong Kong, Macao
Two special administrative regions.Meteorological field is provided by Mesoscale Meteorology WRF, shares four layers of nesting, and the nested region of innermost layer is
The simulated domain of air quality model.Fig. 1 is shown in specific grid setting.Used source emission inventory be ZhangQiang and
The East Asia Region source emission data in 2006 of D.G.Streets etc., spatial resolution 0.5o。
Fig. 2 is the O of simulation3Average annual concentration, it is seen that the concentration of most area is in 45 μ g/m3Left and right, great Zhi areas concentration surpass
Cross 70 μ g/m3, the larger region of concentration is in one band of Guangzhou Foshan.Comparing Delta of the Pearl River air quality observation report in 2006 can
Know, the concentration that area source-receptor response model is simulated is close with observed result, and (most area concentration value is in 40~50 μ
g/m3Left and right, big value is in 70 μ g/m3Left and right).
Fig. 3 gives the groundlevel concentration distribution of the particulate matter of simulation, it is seen that the larger area distribution of concentration is in Guangzhou, Foshan
PM in a generation, with Delta of the Pearl River air quality observation report in 200610Distribution it is similar.Table 2 gives monitoring station and mould
The average value in each website each month of formula simulation, GZ1 refer to Lu Hu parks (Guangzhou), and GZ2 refers to ten thousand hectares of sand (Guangzhou), and GZ3 refers to a day lake
(Guangzhou), ZH refer to Tang Jia (Zhuhai), and FS1 refers to Shuande Party school (Foshan), and FS2 refers to Hui Jingcheng (Foshan), and ZQ refers in city (Zhaoqing), HZ
Refer to lower Pu (Huizhou), HK refers to Tsuen Wan (Hong Kong).It can be seen that error is smaller between simulation and observation.
Table 2-1 Guangdong and Hongkong Delta of the Pearl River regional air monitoring network portion website PM10Monitoring materials
Table 2-2 CALGRID pattern look-up tables part lattice point PM10Concentration
Fig. 4 is the spatial distribution of S, N sedimentation of simulation, it is seen that its distribution has corresponded to SO2, NH3And sulfate, nitrate,
The spatial distribution of ammonium salt.The result of CALGRID simulations is so preferable that have reacted the sedimentations of S and N compounds.
Generally, the CALGRID for increasing sexual intercourse chemical process not only preferably simulates conventional primary pollution
(SO2、NO2、PM10、PM2.5Deng), secondary pollution (O3Deng), sulfate (SO4 2-), nitrate (NO3 -), ammonium salt (NH4 +), black carbon
(BC), organic carbon (OC) etc. has also well simulated the wet deposition amount of S, N.Improved CALGRID patterns have high-resolution
Rate, high-timeliness can conveniently and efficiently provide the response relation between source and receptor, as a result reliably.
Claims (7)
1. a kind of atmospheric pollution simulation prediction algorithm for increasing liquid phase chemical and wet deposition process, it is characterised in that including following step
Suddenly：
Step 1：Chemical model based on CALGRID considers Atmospheric Chemistry reaction, atmospheric transport and dispersion, sedimentation, ground face source
With the influence of overhead emission source, and pollutant concentration variation item caused by introducing liquid-phase chemistry and wet deposition process obtains
Chemical species concentration equation of change such as formula (1),
On the right side of formula (1) in multinomial, first itemFor second order DIFFUSION IN TURBULENCE item, Section 2For diffusion term,
Three (P-L)GASChange item, Section 4 CHEM for gas chemistryaqChange item for pollutant concentration caused by liquid-phase chemistry,
Section 5 EANTFor artificial discharge of pollutant sources item, Section 6Change for the species concentration as caused by dry deposition, Section 7Change for the species concentration as caused by wet deposition；Wherein, C is chemical species mean concentration, and V is the flat of three-dimensional wind vector
Measuring, K is turbulent diffusivity, in formula, second order DIFFUSION IN TURBULENCE itemIt is closed and converted by turbulent diffusivity K theories
It obtains；
Step 2：Liquid-phase chemistry in its cloud, n+1 moment are considered to three kinds of nitric oxide, nitrogen dioxide, sulfur dioxide substances
The material concentration of substance liquid phase in cloud is solved by formula (2)：
Cn+1,i=(1- Δs t × Nz×Ka)×Cn (2)
CnThe material concentration of substance liquid phase in cloud during for the n moment, Δ t is integrates time step, NzFor total amount of cloud, KaFor liquid phase chemical
Conversion ratio；
Step 3：Species concentration caused by wet deposition is changed, the species of consideration include nitric oxide, nitrogen dioxide, titanium dioxide
Sulphur, nitric acid, sulfuric acid, PM10, sulfate, nitrate, ammonium salt, OC, EC, SOA, first pass through empirical equation (3) obtain it is wet remove speed
Rate Kw：
In formula (3), PrFor precipitation rate, a and b are empirical；
Then, concentration of the species at n+1 moment after wet reset procedure is obtained by formula (4)：
Cn+1,i=(1- Δs t × Kw)×Cn (4)
In formula, Cn+1Represent the pollutant concentration at current time, CnRepresenting the pollutant concentration at upper moment, Δ t represents integration time step,
KwFor wet clearance rate；
And in precipitation, the wet clearing amount of each pollutant is asked for by formula (5)：
In formula (5),For the wet deposition amount of n+1 moment time steps,For the wet deposition amount of n moment time steps,
KwFor wet clearance rate, CnRepresent the pollutant concentration at upper moment, Δ Z indicates the pattern level of precipitation.
2. increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process, feature according to claim 1
It is：The value of empirical a and b in step 3 in the solution formula (3) of the rate of settling are as follows：
For SO2, when in summer, the value of a and b are respectively 0.14 and 0.12；When in spring or autumn, a and b's
Value is respectively 0.036 and 0.53；When in winter, the value of a and b are respectively 0.009 and 0.70；
For SO4 2-, when in summer, the value of a and b are respectively 0.39 and 0.06；When in spring or autumn, a and b
Value be respectively 0.091 and 0.27；When in winter, the value of a and b are respectively 0.021 and 0.70.
3. increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process, feature according to claim 2
It is：For NO and NO2Wet clearance rate value, be SO2Wet clearance rate a quarter.
4. increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process, feature according to claim 2
It is：For HNO3The value of wet clearance rate is SO2Wet clearance rate half.
5. increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process, feature according to claim 2
It is：For NO3 -、NH4 +Wet clearance rate value, with SO4 2-Wet clearance rate it is equal.
6. increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process, feature according to claim 2
It is：For PM10Wet clearance rate is asked for, and empirical a and b value is respectively 1.26 and 0.79 in formula (3).
7. increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process, feature according to claim 6
It is：：For the value of the wet clearance rate of OC, EC and SOA, it is taken as PM10The half of the value of wet clearance rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810019607.2A CN108229092B (en) | 2018-01-09 | 2018-01-09 | Atmospheric pollution simulation prediction algorithm for increasing liquid phase chemistry and wet sedimentation process |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810019607.2A CN108229092B (en) | 2018-01-09 | 2018-01-09 | Atmospheric pollution simulation prediction algorithm for increasing liquid phase chemistry and wet sedimentation process |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108229092A true CN108229092A (en) | 2018-06-29 |
| CN108229092B CN108229092B (en) | 2023-09-12 |
Family
ID=62641593
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810019607.2A Active CN108229092B (en) | 2018-01-09 | 2018-01-09 | Atmospheric pollution simulation prediction algorithm for increasing liquid phase chemistry and wet sedimentation process |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108229092B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111157038A (en) * | 2018-11-08 | 2020-05-15 | 中国石油化工股份有限公司 | Measuring and calculating method for measuring pollutant discharge amount in to-be-measured area |
| CN112036036A (en) * | 2020-08-31 | 2020-12-04 | 中国科学院西北生态环境资源研究院 | Method for acquiring glacier black carbon concentration change process |
| CN112861327A (en) * | 2021-01-21 | 2021-05-28 | 山东大学 | Atmospheric chemical overall process online analysis system for atmospheric super station |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040253160A1 (en) * | 2003-06-10 | 2004-12-16 | Jones Kenneth Mitchell | Method of neutralizing acid rain |
| US20080108122A1 (en) * | 2006-09-01 | 2008-05-08 | State of Oregon acting by and through the State Board of Higher Education on behalf of Oregon | Microchemical nanofactories |
| CN102701252A (en) * | 2012-05-23 | 2012-10-03 | 贵州红星发展股份有限公司 | Barium carbonate, preparation method thereof and carbonizer |
| KR101382507B1 (en) * | 2012-10-19 | 2014-04-10 | 사단법인대기환경모델링센터 | Air quality forecast and management system |
| CN105512485A (en) * | 2015-12-14 | 2016-04-20 | 中国科学院大气物理研究所 | Novel method for estimating environment capacity of fine particles and precursors of fine particles |
| CN106290163A (en) * | 2016-07-21 | 2017-01-04 | 中国科学院化学研究所 | Dinitrogen pentoxide and concentration of nitric acid on-line monitoring system and monitoring method in a kind of air |
| CN107064029A (en) * | 2017-02-14 | 2017-08-18 | 中国科学院化学研究所 | A kind of nitrous acid and concentration of nitric acid on-line measurement system and measuring method |
-
2018
- 2018-01-09 CN CN201810019607.2A patent/CN108229092B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040253160A1 (en) * | 2003-06-10 | 2004-12-16 | Jones Kenneth Mitchell | Method of neutralizing acid rain |
| US20080108122A1 (en) * | 2006-09-01 | 2008-05-08 | State of Oregon acting by and through the State Board of Higher Education on behalf of Oregon | Microchemical nanofactories |
| CN102701252A (en) * | 2012-05-23 | 2012-10-03 | 贵州红星发展股份有限公司 | Barium carbonate, preparation method thereof and carbonizer |
| KR101382507B1 (en) * | 2012-10-19 | 2014-04-10 | 사단법인대기환경모델링센터 | Air quality forecast and management system |
| CN105512485A (en) * | 2015-12-14 | 2016-04-20 | 中国科学院大气物理研究所 | Novel method for estimating environment capacity of fine particles and precursors of fine particles |
| CN106290163A (en) * | 2016-07-21 | 2017-01-04 | 中国科学院化学研究所 | Dinitrogen pentoxide and concentration of nitric acid on-line monitoring system and monitoring method in a kind of air |
| CN107064029A (en) * | 2017-02-14 | 2017-08-18 | 中国科学院化学研究所 | A kind of nitrous acid and concentration of nitric acid on-line measurement system and measuring method |
Non-Patent Citations (5)
| Title |
|---|
| MIN XIE等: "Application of photochemical indicators to evaluate ozone nonlinear chemistry and pollution control countermeasure in China", ATMOSPHERIC ENVIRONMENT, pages 466 - 473 * |
| 杨建祥, 陈, 何莉, 姜振远: "南宁市酸沉降来源的研究", 电力环境保护, no. 02 * |
| 王体健，李宗恺，南方: "区域酸性沉降的数值研究──Ⅰ．模式", 大气科学, no. 05 * |
| 聂邦胜: "国内外常用的空气质量模式介绍", 海洋技术, pages 118 - 121 * |
| 谢旻: "区域空气质量模拟中查表法的应用研究", 环境科学, pages 1409 - 1417 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111157038A (en) * | 2018-11-08 | 2020-05-15 | 中国石油化工股份有限公司 | Measuring and calculating method for measuring pollutant discharge amount in to-be-measured area |
| CN112036036A (en) * | 2020-08-31 | 2020-12-04 | 中国科学院西北生态环境资源研究院 | Method for acquiring glacier black carbon concentration change process |
| CN112036036B (en) * | 2020-08-31 | 2021-03-02 | 中国科学院西北生态环境资源研究院 | Method for acquiring glacier black carbon concentration change process |
| CN112861327A (en) * | 2021-01-21 | 2021-05-28 | 山东大学 | Atmospheric chemical overall process online analysis system for atmospheric super station |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108229092B (en) | 2023-09-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Feng et al. | Summertime ozone formation in Xi'an and surrounding areas, China | |
| Bessagnet et al. | Aerosol modeling with CHIMERE—preliminary evaluation at the continental scale | |
| He et al. | Analyses of winter circulation types and their impacts on haze pollution in Beijing | |
| Ma et al. | Air pollutant emission characteristics and HYSPLIT model analysis during heating period in Shenyang, China | |
| He et al. | Numerical model-based artificial neural network model and its application for quantifying impact factors of urban air quality | |
| Fan et al. | Process analysis of regional aerosol pollution during spring in the Pearl River Delta region, China | |
| Xie et al. | The characteristics of hourly wind field and its impacts on air quality in the Pearl River Delta region during 2013–2017 | |
| Deng et al. | The MR-CA models for analysis of pollution sources and prediction of PM 2.5 | |
| Li et al. | The analysis and application of a new hybrid pollutants forecasting model using modified Kolmogorov–Zurbenko filter | |
| CN108229092A (en) | Increase liquid phase chemical and the atmospheric pollution simulation prediction algorithm of wet deposition process | |
| CN108052704A (en) | Mesoscale photochemical pollution simulation and forecast algorithm with Grid Nesting function | |
| Sun et al. | WRF-Chem simulation of a severe haze episode in the Yangtze River Delta, China | |
| Kwok et al. | Tracking emission sources of sulfur and elemental carbon in Hong Kong/Pearl River Delta region | |
| Jo et al. | A case study of heavy PM2. 5 secondary formation by N2O5 nocturnal chemistry in Seoul, Korea in January 2018: Model performance and error analysis | |
| Yan et al. | Impacts of synoptic circulations on summertime ozone pollution in Guanzhong Basin, northwestern China | |
| CN113156395A (en) | Aerosol laser radar data fusion method and system | |
| Ren et al. | Meteorological and chemical causes of heavy pollution in winter in Hohhot, Inner Mongolia Plateau | |
| Zhu et al. | Shifts of formation regimes and increases of atmospheric oxidation led to ozone increase in North China Plain and Yangtze River Delta from 2016 to 2019 | |
| CN108009330A (en) | Increase the Mesoscale photochemical pollution simulation and forecast algorithm of Meteorological Models interface | |
| Long et al. | Numerical simulation of the influence of major meteorological elements on the concentration of air pollutants during rainfall over Sichuan Basin of China | |
| CN108318622A (en) | Increase the atmospheric pollution simulation prediction algorithm of Organic aerosol chemical process | |
| CN108170635A (en) | A kind of Mesoscale photochemical pollution combined process analysis method | |
| Mao et al. | Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method | |
| Lazaridis et al. | Mesoscale modeling of combined aerosol and photo-oxidant processes in the Eastern Mediterranean | |
| CN108319808A (en) | Increase the atmospheric pollution simulation prediction algorithm of inorganic salts aerosol chemistry process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |