CN114820258A - Method for estimating self-cleaning capacity of atmosphere based on standard reaching of fine particulate matters - Google Patents
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Abstract
The invention provides a method for estimating atmosphere self-cleaning capacity based on particulate matter standard reaching, which overcomes the defects that the existing atmosphere capacity algorithm considers the source-receptor relationship as a linear relationship, considers the perfect chemical process, and seriously does not accord with the actual situation of the calculation result, and the like, takes the fine particulate matter standard reaching as the constraint, calculates the atmosphere self-cleaning capacity from the physical and chemical space-time evolution process of pollutants, and can realize the fine dynamic calculation of the atmosphere self-cleaning capacity with high space-time resolution. The method is mainly used in the field of national atmospheric pollution prevention and control, and provides technical support for determining the upper limit of the atmospheric environment bearing, effectively formulating measures, optimally configuring the atmospheric self-cleaning capacity in time and space and promoting the further improvement of the atmospheric environment quality.
Description
Technical Field
The invention relates to the technical field of air pollution prevention and control, in particular to a method for estimating self-cleaning capacity of atmosphere based on fine particulate matter reaching standards.
Background
The self-cleaning capacity of the atmosphere refers to the capacity of removing pollutants by the self-movement of the atmosphere (such as physical and chemical processes of diffusion, dilution, sedimentation, chemical conversion and the like) in a certain space boundary under a given air quality control target. The self-cleaning capacity of the atmosphere refers to the capacity of removing pollutants by the self-movement of the atmosphere (such as physical and chemical processes of diffusion, dilution, sedimentation, chemical conversion and the like) in a certain space boundary under a given air quality control target. The self-purification capacity of the atmosphere reflects the natural purification capacity of the atmosphere, is mainly influenced by meteorological conditions (wind, temperature, humidity, precipitation and boundary layer height), terrain, underlying surface types and the like, and is irrelevant to the source emission space pattern. For example, strong wind can enhance the atmospheric diffusion and dilution capacity, the atmospheric pollutants can be removed by precipitation, the pollutant removal capacity is enhanced, and the atmospheric self-cleaning capacity is larger; the adverse temperature causes the vertical exchange of the atmosphere to be blocked, the pollutant removal capacity is weakened, and the self-cleaning capacity of the atmosphere is smaller. The atmosphere self-purification capacity is mainly used for describing the difference of the atmosphere self-purification capacity of each unit in different stages in the pollution process, and the main influence of various factors such as emission, transportation and meteorological conditions in different stages on the atmosphere pollution can be distinguished carefully by comparing the atmosphere self-purification capacity with the emission. At present, the research on the self-cleaning capacity of the atmosphere is only available, but most of the research is an accounting method for the capacity of the atmosphere environment.
At present, an accounting method for atmospheric environment capacity is continuously improved and promoted along with the evolution of environmental problems and the improvement of environmental theoretical cognitive level, and the existing common accounting methods comprise an A value method, a linear optimization method, a model simulation method and the like.
The A-value method is based on the box model principle: assuming that the environmental capacity is in a direct proportion relation with the self-purification capacity of the atmospheric environment and the area, only natural factors are considered, emission source characteristics and a chemical conversion process are not reflected, the method is suitable for verifying the atmospheric environment capacity in an ideal state and is not suitable for PM 2.5 、O 3 The environmental capacity under the constraint of reaching the standard is achieved, and the advantages are simple and convenient.
The linear optimization method comprises the following steps: calculating the atmospheric environment capacity based on a linear optimization theory, linking the pollution source and the diffusion process thereof with a control point, taking the concentration standard of a target control point as a constraint, and determining the maximum allowable emission of the source through a multi-source model, a mathematical programming method and the like. The linear optimization method is mainly suitable for an area with a smaller scale, can reflect the response relation of emission-receptor, and can carry out optimization configuration on the capacity of the atmospheric environment, but the method is restricted by linear hypothesis and cannot process the problem of secondary atmospheric pollution with nonlinear characteristics.
An iterative simulation optimization method: establishing a multi-target nonlinear optimization model based on a dynamic space transmission matrix, an industry contribution matrix and a precursor contribution matrix to obtain a plurality of optimized emission reduction schemes, and obtaining pollutant emission which meets the air quality standard through iterative simulation, namely the air environment capacity. The method can give consideration to the influences of natural factors such as weather and terrain and artificial factors such as pollution sources on the atmospheric environment capacity, effectively overcomes the defects of the traditional method, can reflect the complex atmospheric physical and chemical processes, but is originally based on the ideal assumption that the space and the industry distribution characteristics of the pollution source emission do not change obviously, cannot optimize and configure the atmospheric environment capacity, and has complex technology and huge calculated amount.
Atmospheric environment capacity mode algorithm: the atmospheric environment capacity is considered to be the maximum allowable discharge meeting the fine particulate matter standard, and the atmospheric environment capacity based on the fine particulate matter standard and the precursor is obtained through calculation of an air quality mode. The method divides the atmospheric environment capacity into two parts of atmospheric dynamic capacity and static capacity, and the dynamic capacity considers the joint action of the actual pollutant concentration and a given pollutant concentration constraint threshold on the processes of transportation, diffusion, sedimentation, chemical conversion and the like. The calculation of this method represents the maximum allowable emissions and is not the scavenging capability imparted by the natural conditions.
Disclosure of Invention
The invention aims to provide a method for estimating the self-cleaning capacity of the atmosphere based on the standard reaching of fine particles, so as to solve the problems in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for estimating the self-cleaning capacity of the atmosphere based on the standard reaching of fine particles comprises the following steps:
s1, acquiring vegetation types and underlying surface contents of the target area to be estimated, and inputting the acquired vegetation type data and underlying surface data into a WRF (wre forecasting center) mesoscale weather mode;
s2, establishing a target area stereo model for the target area to be estimated, and acquiring the horizontal plane area S of the target area v The vertical cross-sectional area of the boundary line of the target region is S h Boundary layer height H;
s3, simulating and outputting a wind profile and the height of a boundary layer by a WRF mesoscale weather mode, and then acquiring meteorological parameters of any grid in a target area, including a horizontal wind component v, a vertical wind speed w and a turbulent flow vertical diffusion coefficient k z ；
S4, inputting the atmospheric observation data and the atmospheric pollutant emission source data into the nested grid air quality mode, and combining the meteorological parameters obtained in the step S3 to obtain the dry sedimentation rate v of the atmospheric pollutants d The wet sedimentation coefficient lambda of the atmospheric pollutant and the chemical consumption rate ps of the atmospheric pollutant;
s5, respectively acquiring horizontal conveying flux ^ integral (c.v.S) of the atmospheric pollutants in the boundary layer of the target area outside the target area based on the parameters h ) Vertical conveying quantity (w.s) of atmospheric pollutants outside boundary layer in target region v C), the amount of dry sedimentation of atmospheric pollutants in the target region ^ v d ·c·s v ) The wet settling amount of the atmospheric pollutants in the target area
And chemical consumption of atmospheric pollutants in the atmosphere of the boundary layer of the target area
S6, calculating to obtain the self-cleaning air volume A by the following formula sp ：
Wherein, t 0 And t 1 Is the initial and end time of the calculation, and c is the target concentration threshold for atmospheric pollutants.
Preferably, the horizontal wind component v and the vertical wind speed w obtained in step S3 are positive values when the wind direction blows away from the target area, and are otherwise 0; when the wind blows vertically upwards, the vertical wind speed w is a positive value, and otherwise is 0.
Preferably, the vegetation types in step S1 are divided into 24 types:
urban and construction land, dryland farmland and pasture, irrigation farmland and pasture, hybrid paddy and pasture, mixed farmland and grassland, mixed farmland and woodland, grassland, shrub, mixed shrub and grassland, diluted tropical tree grassland, deciduous broad forest, evergreen coniferous forest, mixed forest, water body, herbaceous marsh, forest marsh, barren sparse vegetation, herbaceous orchids, woody orchids, bare ground frozen origin, snowy or icy ground
Preferably, the method can also perform the atmospheric self-cleaning capacity on any area in the target area, and comprises the following steps:
1) carrying out grid division on the three-dimensional model of the target area, dividing a horizontal plane into m grids, and dividing the grid horizontal plane into n layers; calculating the length delta l of the inner boundary of the grid of any grid object with the horizontal number of i and the vertical number of j ij Mesh thickness Δ h ij And the area of the grid S ij ；
2) Acquiring meteorological parameters of the grid, including horizontal wind component v of vertical boundary line ij Vertical wind speed w ij Dry settling rate v of atmospheric pollutants dij Wet sedimentation coefficient ^ of atmospheric pollutants ij Chemical consumption rate ps ij Target concentration threshold c of atmospheric pollutants ij And the vertical diffusion coefficient k of the grid turbulence ij ；
3) The self-cleaning capacity of the atmosphere of the grid is calculated by adopting the following formula:
wherein, c i(j+1) Is a grid atmospheric pollutant concentration threshold value with the horizontal number of i and the vertical number of j in the target area, delta t is unit calculation time, Un is a unit conversion factor, and the value is converted from mu g/s to ton/d and is 8.64 multiplied by 10 -8 。
The invention has the beneficial effects that:
the invention provides a method for estimating self-purification capacity of atmosphere based on up-to-standard particulate matter, which overcomes the defects that the source-receptor relation is considered as a linear relation, a perfect chemical process is considered, a calculation result is seriously inconsistent with an actual condition and the like in the conventional atmosphere capacity algorithm, calculates the self-purification capacity of atmosphere from the physicochemical space-time evolution process of pollutants by taking up the up-to-standard fine particulate matter as a constraint, and can realize high-space-time resolution fine dynamic calculation of the self-purification capacity of atmosphere. The method is mainly used in the field of national atmospheric pollution prevention and control, and provides technical support for determining the upper limit of the atmospheric environment bearing, effectively formulating measures, optimally configuring the atmospheric self-cleaning capacity in time and space and promoting the further improvement of the atmospheric environment quality.
Drawings
FIG. 1 is a flow chart of a method for estimating the self-cleaning capacity of the atmosphere based on the attainment of fine particulate matter as provided in example 1;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The embodiment provides a method for estimating the self-cleaning capacity of the atmosphere based on the fine particulate matter reaching the standard, as shown in fig. 1, comprising the following steps:
s1, acquiring the vegetation type and the underlying surface content of the target area to be estimated, and inputting the acquired vegetation type data and the underlying surface data into a WRF (write-once-filter) mesoscale weather mode;
s2, establishing a target area stereo model for the target area to be estimated, and acquiring the horizontal plane area S of the target area v The vertical cross-sectional area of the boundary line of the target region is S h Boundary layer height H;
s3, simulating and outputting a wind profile and the height of a boundary layer by a WRF mesoscale weather mode, and then acquiring meteorological parameters of any grid in a target area, including a horizontal wind component v, a vertical wind speed w and a turbulent flow vertical diffusion coefficient k z ；
S4, inputting the atmospheric observation data and the atmospheric pollutant emission source data into the nested grid air quality mode, and combining the meteorological parameters obtained in the step S3 to obtain the dry sedimentation rate v of the atmospheric pollutants d The wet sedimentation coefficient lambda of the atmospheric pollutant and the chemical consumption rate ps of the atmospheric pollutant;
s5, respectively acquiring horizontal conveying flux ^ integral (c.v.S) of the atmospheric pollutants in the boundary layer of the target area outside the target area based on the parameters h ) Vertical conveying quantity (w.s) of atmospheric pollutants outside boundary layer in target region v C), the amount of dry sedimentation of atmospheric pollutants in the target region ^ v d ·c·s v ) The wet settling amount of the atmospheric pollutants in the target area
And chemical consumption of atmospheric pollutants in the atmosphere of the boundary layer of the target area
S6, calculating to obtain the self-cleaning air volume A by the following formula sp ：
Wherein, t 0 And t 1 Is the calculated initial and end time, target concentration threshold c of atmospheric pollutants.
In the horizontal wind component v and the vertical wind speed w obtained in step S3 in this embodiment, when the wind direction blows off the target area, the horizontal wind component is a positive value, and otherwise, the horizontal wind component is 0; when the wind blows vertically upwards, the vertical wind speed w is a positive value, and otherwise is 0.
The vegetation types in step S1 in this embodiment are divided into 24 types:
urban and construction land, dry land farmland and pasture, irrigation farmland and pasture, hybrid paddy and dry land and pasture, hybrid farmland and grassland, hybrid farmland and woodland, grassland, shrub, hybrid shrub and grassland, tropical dilute tree grassland, deciduous broad forest, evergreen coniferous forest, hybrid forest, water, herbaceous marsh, forest marsh, barren vegetation, herbaceous orchids, woody orchids, bare ground frozen land, snow or ice.
Due to different vegetation types, the wind profile and the boundary layer height output by adopting the WRF mesoscale weather mode are different.
Example 2
The embodiment provides a method for estimating the self-cleaning capacity of the atmosphere of any area in a target area, which comprises the following steps:
1) acquiring vegetation types and underlying surface contents of a target area to be estimated, and inputting the acquired vegetation type data and the underlying surface data into a WRF (wrenching free-ranging) mesoscale weather mode;
carrying out grid division on the three-dimensional model of the target area, dividing a horizontal plane into m grids, and dividing the grid horizontal plane into n layers; calculating the length delta l of the inner boundary of the grid of any grid object with the horizontal number of i and the vertical number of j ij Mesh thickness Δ h ij And the area of the grid S ij ；
2) Simulating and outputting a wind profile and the height of a boundary layer by a WRF mesoscale weather mode, and acquiring meteorological parameters of the grid, including a horizontal wind component v of a vertical boundary line ij Vertical wind speed w ij Dry settling rate v of atmospheric pollutants dij Wet sedimentation coefficient ^ of atmospheric pollutants ij Chemical consumption rate ps ij And grid turbulence vertical diffusion systemNumber k ij ；
3) The self-cleaning capacity of the atmosphere of the grid is calculated by adopting the following formula:
wherein, c i(j+1) Is a grid atmospheric pollutant concentration threshold value with the horizontal number of i and the vertical number of j in the target area, delta t is unit calculation time, Un is a unit conversion factor, and the value is converted from mu g/s to ton/d and is 8.64 multiplied by 10 -8 。
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention provides a method for estimating atmosphere self-cleaning capacity based on particulate matter standard reaching, which overcomes the defects that the existing atmosphere capacity algorithm considers the source-receptor relationship as a linear relationship, considers the perfect chemical process, and seriously does not accord with the actual situation of the calculation result, and the like, takes the fine particulate matter standard reaching as the constraint, calculates the atmosphere self-cleaning capacity from the physical and chemical space-time evolution process of pollutants, and can realize the fine dynamic calculation of the atmosphere self-cleaning capacity with high space-time resolution.
The method can serve the field of national atmospheric pollution prevention and control, and provides technical support for determining the upper limit of the atmospheric environment bearing, effectively formulating measures, optimally configuring the atmospheric self-cleaning capacity in time and space and promoting the further improvement of the atmospheric environment quality.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (4)
1. A method for estimating the self-cleaning capacity of the atmosphere based on the standard reaching of fine particles is characterized by comprising the following steps:
s1, acquiring vegetation types and underlying surface contents of the target area to be estimated, and inputting the acquired vegetation type data and underlying surface data into a WRF (wre forecasting center) mesoscale weather mode;
s2, establishing a target area stereo model for the target area to be estimated, and acquiring the horizontal plane area S of the target area v The vertical cross-sectional area of the boundary line of the target region is S h Boundary layer height H;
s3, simulating and outputting a wind profile and the height of a boundary layer by a WRF mesoscale weather mode, and then acquiring meteorological parameters of any grid in a target area, including a horizontal wind component v, a vertical wind speed w and a turbulent flow vertical diffusion coefficient k z ；
S4, inputting the atmospheric observation data and the atmospheric pollutant emission source data into the nested grid air quality mode, and combining the meteorological parameters obtained in the step S3 to obtain the dry sedimentation rate v of the atmospheric pollutants d The wet sedimentation coefficient lambda of the atmospheric pollutant and the chemical consumption rate ps of the atmospheric pollutant;
s5, respectively acquiring horizontal conveying flux ^ integral (c.v.S) of the atmospheric pollutants in the boundary layer of the target area outside the target area based on the parameters h ) Vertical conveying quantity (w.s) of atmospheric pollutants outside boundary layer in target region v C), the amount of dry sedimentation of atmospheric pollutants in the target region ^ v d ·c·s v ) The wet settling amount of the atmospheric pollutants in the target area
And chemical consumption of atmospheric pollutants in the atmosphere of the boundary layer of the target area
S6, calculating to obtain the self-cleaning air volume A by the following formula sp ：
Where c is the target concentration threshold for atmospheric pollutants, t 0 And t 1 Are the initial and end times of the calculation.
2. The method for estimating atmospheric self-cleaning capacity based on fine particulate matter reaching standards according to claim 1, wherein the horizontal wind component v and the vertical wind speed w obtained in the step S3 are positive when the wind direction blows away from the target area, and are otherwise 0; when the wind blows vertically upwards, the vertical wind speed w is a positive value, and otherwise is 0.
3. The method of estimating self-cleaning capacity of atmosphere based on fine particulate matter reaching standards according to claim 1, wherein the method further comprises the steps of:
1) carrying out grid division on the three-dimensional model of the target area, dividing a horizontal plane into m grids, and dividing the grid horizontal plane into n layers; calculating the length delta l of the inner boundary of the grid of any grid object with the horizontal number of i and the vertical number of j ij Mesh thickness Δ h ij And a grid area S ij ；
2) Acquiring meteorological parameters of the grid, including horizontal wind components v of vertical boundary lines ij Vertical wind speed w ij Dry settling rate v of atmospheric pollutants dij Wet sedimentation coefficient ^ of atmospheric pollutants ij Chemical consumption rate ps ij And the vertical diffusion coefficient k of the grid turbulence ij ；
3) The self-cleaning capacity of the atmosphere of the grid is calculated by adopting the following formula:
wherein, c ij Is a target concentration threshold of atmospheric pollutants, c i(j+1) Is a grid atmospheric pollutant concentration threshold value with the horizontal number of i and the vertical number of j in the target area, delta t is unit calculation time, Un is a unit conversion factor, and the value is converted from mu g/s to ton/d and is 8.64 multiplied by 10 -8 。
4. The method for estimating self-cleaning capacity of atmosphere based on fine particulate matter reaching standards according to claim 1, wherein the vegetation types in the step S1 are 24 types:
urban and construction land, dry land farmland and pasture, irrigation farmland and pasture, hybrid paddy and dry land and pasture, hybrid farmland and grassland, hybrid farmland and woodland, grassland, shrub, hybrid shrub and grassland, tropical dilute tree grassland, deciduous broad forest, evergreen coniferous forest, hybrid forest, water, herbaceous marsh, forest marsh, barren vegetation, herbaceous orchids, woody orchids, bare ground frozen land, snow or ice.
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| CN117709208A (en) * | 2024-02-05 | 2024-03-15 | 四川国蓝中天环境科技集团有限公司 | Atmospheric environment capacity calculation method for artificially discharging pollutants |
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| US20100161383A1 (en) * | 2008-12-23 | 2010-06-24 | Glen Ores Butler | Profit optimizer |
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