CN111027778A - Regional atmospheric environment risk monitoring point distribution optimization method based on multi-objective planning - Google Patents
Regional atmospheric environment risk monitoring point distribution optimization method based on multi-objective planning Download PDFInfo
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Abstract
The invention provides a regional atmospheric environment risk monitoring point distribution optimization method based on multi-objective planning. The regional atmospheric characteristic pollutants are screened aiming at the prominent atmospheric pollution problem in the target region, the environmental risk source, the risk control mechanism and the environmental risk receptor in the region are comprehensively considered to establish a target function, a multi-target planning model is established, and a monitoring distribution optimization scheme is obtained through calculation.
Description
Technical Field
The invention belongs to the technical field of regional atmospheric environment risk assessment, and particularly relates to a regional atmospheric environment risk monitoring and point distribution optimization method based on multi-objective planning.
Background
China has been in the pastThe rapid development of the air conditioner for decades also causes serious air pollution problems. PM (particulate matter)2.5、O3The negative effects of various atmospheric pollutants on the health of people are receiving wide attention and attention. The method has the advantages that monitoring point positions are scientifically and reasonably arranged, regional atmospheric environment monitoring is carried out, and the method has important significance for accurately and comprehensively mastering the space-time distribution characteristics of atmospheric pollutants, accurately monitoring the crowd health risks of air pollution and making scientific and feasible management strategies.
Since the end of the 80 s in the 20 th century, scholars in relevant fields of China developed exploration of atmospheric environment monitoring and optimization stationing methods, and the methods mainly adopted can be classified into an empirical method, a statistical method, a model method, a comprehensive method and the like. In general, the multi-target point distribution model is more reasonable than a single-target method. However, the currently adopted objective function is mostly based on an environment management mode aiming at improving the environmental quality, is not targeted to the management control of long-term chronic health risks, and is not completely matched with the actual need of preventing and solving the important risks of the ecological environment at present. Therefore, an atmospheric environment monitoring and stationing method which better meets the regional atmospheric environment risk management and control requirements needs to be established.
Disclosure of Invention
The invention aims to overcome the technical problem that the existing atmospheric environment monitoring and stationing technology is not guided by regional atmospheric environment risk control, and seeks a regional atmospheric environment risk monitoring-oriented site selection optimization method, namely, the regional atmospheric environment risk monitoring and stationing optimization method based on multi-objective planning is provided.
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
The invention adopts the following technical scheme:
the method for optimizing the regional atmospheric environment risk monitoring distribution points based on multi-objective planning comprises the following steps: constructing a multi-target model and constraint conditions; determining a weight coefficient; and carrying out optimization solution on the multi-objective model.
Wherein, the method also comprises the following steps:
and (3) area grid division: establishing a coordinate system in a target area, dividing grid units in the established coordinate system, and numbering the parts of the target area falling in the grid units;
screening characteristic pollutants: and constructing a characteristic pollutant screening method and an index system according to the pre-collected environmental data, and screening out the most concerned characteristic pollutant of the atmospheric environmental risk of the target area.
The process of constructing the multi-target model and the constraint conditions comprises the following steps:
constructing three sub-objective functions from the perspective of an environmental risk source, a risk control mechanism and an environmental risk receptor, wherein the environmental risk source is characterized by the standard pollution load of atmospheric environment and the like, the risk control mechanism is characterized by the diffusion range of an industrial pollution source, and the environmental risk receptor is characterized by the population number and the land type of a grid unit;
using the minimum and maximum monitoring points and the minimum distance between two monitoring points as constraint conditions;
the multi-objective model is represented as:
s.t.
Rab＞Rc
wherein i represents the number of the grid cells, and L represents the total number of the grid cells; j represents the number of the sub-targets, and M represents the total number of the sub-targets; k represents the sub-index number of a certain sub-target, such as the environment risk receptor is characterized by two sub-indexes of the number of human mouths and the type of land used in a grid unit, and N represents the total number of the sub-indexes of the certain sub-target; p is a radical ofi∈{0，11 when the p grid is selected, and 0 if not; z is a radical ofjkA standard value after the target value is subjected to non-dimensionalization; wjA weight coefficient representing the sub-target j; n is1The minimum number of monitoring points; n is2The number of monitoring points is the maximum; rabRepresenting the distance between any two monitoring points; rcIndicating a limiting minimum distance between monitoring points.
Wherein, the atmospheric environment equivalent standard pollution load L of n atmospheric pollutants in each grid unit range is calculated by the following formula:
wherein E isiRepresents the annual emission of atmospheric pollutants i, ESiIs the atmospheric emission standard of pollutant i.
The industrial pollution source diffusion range is calculated based on the position of the industrial pollution source and the wind direction and the wind frequency of a target area; wherein, a sector area with a 90-degree angle downwind of the pollution source is taken as an affected area, and the affected degree is divided by the distance from the pollution source.
The grid cell population number and the land type are calculated respectively based on a grid population data product and a land type data product, and different land types are assigned differently.
The coordinate system established in the target area is the coordinate system established by taking the longitude of the most west side point of the target area as the Y axis, the northward side as the positive direction, the latitude of the most south side point of the target area as the X axis, the eastward side as the positive direction and the intersection point of the two coordinate axes as the origin.
The grid unit refers to a grid unit divided by 1km x 1km in an established coordinate system.
And determining the weight coefficient by constructing a judgment matrix by using an analytic hierarchy process and providing a triangular fuzzy number complementary judgment matrix for the sub-target set.
And the optimization solution of the multi-target model is to select the lingo software to carry out optimization solution by using a hidden enumeration method.
The invention has the following beneficial effects: according to the method, regional atmospheric characteristic pollutants are screened aiming at the problem of prominent atmospheric pollution in a region, the environmental risk source, the risk control mechanism and the environmental risk receptor in the region are comprehensively considered to establish the objective function, the multi-objective planning model is constructed, the defect that a plurality of monitoring and point distributing methods are not guided by regional atmospheric environmental risk control is overcome, and the method has certain operability and practicability.
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FIG. 1 is a flow diagram of a regional atmospheric environment risk monitoring point distribution optimization method based on multi-objective programming.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others.
As shown in FIG. 1, in some illustrative embodiments, a multi-objective planning-based regional atmospheric environmental risk monitoring stationing optimization method is provided, including:
101: environmental data is collected.
And investigating the atmospheric environment risk source in the range of the 10km buffer area of the target area, and mainly knowing the information such as the position of the atmospheric environment risk source, the pollutant emission condition and the like.
Geographic information such as topographic data and meteorological data is collected.
And collecting atmospheric environment risk receptor information such as population distribution, land types and the like.
Collecting the existing atmospheric environment monitoring data, the atmospheric environment pollution petition number and related research literature of the target area.
102: and (5) carrying out region meshing.
And establishing a coordinate system in the target area, dividing grid units in the established coordinate system, and numbering the parts of the target area falling in the grid units in an Arabic numeral sequence.
The coordinate system established in the target area is the coordinate system established by taking the longitude line of the most west side point of the target area as the Y axis, the northward side as the positive direction, the latitude line of the most south side point of the target area as the X axis, the eastward side as the positive direction and the intersection point of the two coordinate axes as the origin. Thus, the target area is located in the first quadrant of the established coordinate system.
The grid unit is a grid unit divided by 1km × 1km in an established coordinate system, that is, the grid unit is a square with a side length of 1 km. Since the shape of the target area is not fixed, the target area does not exist in some divided unit grids, and the part of the target area falling in the grid unit is numbered when the target area is numbered.
103: and (4) screening characteristic pollutants.
According to the pre-collected environmental data, namely according to the information such as the pollutant emission condition, the existing atmospheric environmental condition, the atmospheric environmental pollution petition complaint and the like collected in the step 101, in combination with the relevant hazard properties of pollutants, a characteristic pollutant screening method and an index system are constructed, and the characteristic pollutant most concerned by the atmospheric environmental risk of the target area is screened out. The index system determines evaluation indexes, then provides evaluation standards and scores, scores each evaluation index according to the actual situation of a target area to obtain a final score, and the feature pollutants concerned can be obtained by taking higher scores.
An exemplary characteristic contaminant screening score index system is shown in the following table, which is an example of a characteristic contaminant screening score index system.
104: and constructing a multi-target model and constraint conditions.
Three sub-objective functions are constructed from the perspective of an environmental risk source, a risk control mechanism and an environmental risk receptor, respectively.
The environmental risk source is characterized by the atmospheric environment and other standard pollution loads.
The risk control mechanism is characterized by the spread of industrial pollution sources.
The environmental risk receptors are characterized by the number of population and right of way type within the grid cell.
The atmospheric environment equivalent pollution load L of n atmospheric pollutants in each grid unit range is calculated by the following formula:
in the above formula, EiRepresents the annual emission of atmospheric pollutants i, ESiIs the atmospheric emission standard of pollutant i.
The industrial pollution source diffusion range is calculated based on the position of the industrial pollution source and the wind direction and the wind frequency of a target area, and the calculation mode belongs to the prior art and is not described herein again. The downwind direction of the pollution source is the dominant wind direction all year round, wherein a sector area with a 90-degree angle of the downwind direction of the pollution source is taken as an affected area, the affected degree is divided by the distance from the pollution source, and the downwind direction of the pollution source is respectively expressed by 3km, 6km, 9km and more than 9km as a main affected area, a secondary affected area, a possible affected area and a basically unaffected area.
The grid cell population number and the land types are calculated respectively based on a grid population data product and a land type data product, different land types are subjected to different assignments, and example assignment tables of the different land types are as follows:
because the indexes have difference in magnitude and unit, the indexes are standardized by a range difference method, dimensionless and uniformly converted into a range of [0, 1 ].
And taking the minimum and maximum monitoring points and the minimum distance between two monitoring points as constraint conditions. Determining the minimum monitoring points according to the requirements of the distribution monitoring points in the technical Specification (trial) for distribution of environmental air quality monitoring point locations (HJ 664- "2013"), and determining the maximum monitoring points according to the economic cost constraint. The minimum distance between any two monitoring points is limited from the viewpoint of data redundancy.
In summary, the multi-objective model is represented as:
s.t.
Rab＞Rc
in the above formula, i represents the number of the grid cells, and L represents the total number of the grid cells; j represents the number of the sub-targets, and M represents the total number of the sub-targets; k represents the sub-index number of a certain sub-target, such as the environment risk receptor is characterized by two sub-indexes of the number of human mouths and the type of land used in a grid unit, and N represents the total number of the sub-indexes of the certain sub-target; p is a radical ofiE is {0, 1}, when the p grid is selected, 1 is selected, otherwise 0 is selected; z is a radical ofjkA standard value after the target value is subjected to non-dimensionalization; wjA weight coefficient representing the sub-target j; n is1The minimum number of monitoring points; n is2The number of monitoring points is the maximum; rabRepresenting the distance between any two monitoring points; rcIndicating a limiting minimum distance between monitoring points.
105: a weight coefficient is determined.
And determining the weight coefficient by constructing a judgment matrix by using an analytic hierarchy process and providing a triangular fuzzy number complementary judgment matrix for the sub-target set.
106: and (6) solving the model.
And carrying out optimization solving on the multi-target model to select the lingo software to carry out optimization solving by using a hidden enumeration method.
The method screens regional atmospheric characteristic pollutants aiming at the problem of prominent atmospheric pollution in a target region, establishes a target function by comprehensively considering an environmental risk source, a risk control mechanism and an environmental risk receptor in the region, constructs a multi-target planning model, and calculates to obtain a monitoring distribution optimization scheme.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
Claims (10)
1. The regional atmospheric environment risk monitoring and stationing optimization method based on multi-objective programming is characterized by comprising the following steps of:
constructing a multi-target model and constraint conditions;
determining a weight coefficient;
and carrying out optimization solution on the multi-objective model.
2. The multi-objective planning-based regional atmospheric environmental risk monitoring stationing optimization method of claim 1, further comprising, before the method:
and (3) area grid division: establishing a coordinate system in a target area, dividing grid units in the established coordinate system, and numbering the parts of the target area falling in the grid units;
screening characteristic pollutants: and constructing a characteristic pollutant screening method and an index system according to the pre-collected environmental data, and screening out the most concerned characteristic pollutant of the atmospheric environmental risk of the target area.
3. The multi-objective planning-based regional atmospheric environment risk monitoring point distribution optimization method according to claim 2, wherein the process of constructing the multi-objective model and the constraint conditions comprises the following steps:
constructing three sub-objective functions from the perspective of an environmental risk source, a risk control mechanism and an environmental risk receptor, wherein the environmental risk source is characterized by the standard pollution load of atmospheric environment and the like, the risk control mechanism is characterized by the diffusion range of an industrial pollution source, and the environmental risk receptor is characterized by the population number and the land type of a grid unit;
using the minimum and maximum monitoring points and the minimum distance between two monitoring points as constraint conditions;
the multi-objective model is represented as:
s.t.
Rab＞Rc
wherein i represents the number of the grid cells, and L represents the total number of the grid cells; j represents the number of the sub-targets, and M represents the total number of the sub-targets; k represents the sub-index number of a certain sub-target, such as the environment risk receptor is characterized by two sub-indexes of the number of human mouths and the type of land used in a grid unit, and N represents the total number of the sub-indexes of the certain sub-target; p is a radical ofiE is {0, 1}, when the p grid is selected, 1 is selected, otherwise 0 is selected; z is a radical ofjkA standard value after the target value is subjected to non-dimensionalization; wjA weight coefficient representing the sub-target j; n is1The minimum number of monitoring points; n is2The number of monitoring points is the maximum; rabRepresenting the distance between any two monitoring points; rcIndicating a limiting minimum distance between monitoring points.
4. The multi-objective programming based regional atmospheric environmental risk monitoring and stationing optimization method of claim 3, wherein the atmospheric environmental isocontour pollution load L of n atmospheric pollutants in each grid cell range is calculated by the following formula:
wherein E isiRepresents the annual emission of atmospheric pollutants i, ESiIs the atmospheric emission standard of pollutant i.
5. The multi-objective planning-based regional atmospheric environment risk monitoring and point placement optimization method of claim 4, wherein the industrial pollution source diffusion range is calculated based on the position of an industrial pollution source and the wind direction and the wind frequency of a target region; wherein, a sector area with a 90-degree angle downwind of the pollution source is taken as an affected area, and the affected degree is divided by the distance from the pollution source.
6. The multi-objective programming based regional atmospheric environment risk monitoring and stationing optimization method of claim 5, wherein the grid cell population number and the land type are calculated based on a grid population data product and a land type data product respectively, and different land types are assigned with different values.
7. The method for optimizing regional atmospheric environment risk monitoring and point placement based on multi-objective planning of claim 6, wherein the coordinate system established in the target region is a coordinate system established by taking the meridian of the west-most side point of the target region as the Y axis, the north-facing direction as the positive direction, the latitude of the south-most side point of the target region as the X axis, the east-facing direction as the positive direction, and the intersection of two coordinate axes as the origin.
8. The multi-objective planning-based regional atmospheric environment risk monitoring and point placement optimization method of claim 7, wherein the grid units are grid units divided by 1km x 1km in the established coordinate system.
9. The multi-objective programming based regional atmospheric environment risk monitoring and point placement optimization method of claim 8, wherein the determining of the weight coefficients is to construct a judgment matrix by using an analytic hierarchy process, and to provide a triangular fuzzy number complementation judgment matrix for the sub-target sets.
10. The multi-objective programming based regional atmospheric environment risk monitoring and point placement optimization method of claim 9, wherein the optimization solution of the multi-objective model is an optimization solution performed by selecting lingo software and using a hidden enumeration method.
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Application publication date: 20200417 |