KR20050004316A - Prediction method of polluted air dispersion using multiple meteorological data - Google Patents

Abstract

PURPOSE: A prediction method of polluted air dispersion is provided to improve the accuracy of predicted air quality results by maximally utilizing meteorological data of the direction and speed of wind changed by a local topography. CONSTITUTION: The polluted air dispersion is predicted by the method of calculating a pollutant concentration in an object area from a meteorological data of a meteorological observatory using gaussian plume dispersion model. Specifically, the method comprises performing the prediction of individual pollutant concentration by utilizing a plurality of meteorological data observed at multiple meteorological observatories scattered in the object area and adding up all of the data to calculate overall pollutant concentration of the object area. The meteorological observatory for providing meteorological data about a pollution source of a predetermined position is selected using a positional function by the space division method.

Description

Prediction method of polluted air dispersion using multiple meteorological data}

The present invention relates to an air pollution spread prediction method using a plurality of meteorological observation data which improves the prediction accuracy of the Gaussian smoke diffusion model used for environmental impact assessment and increases the application area.

Environmental impact assessments related to air pollution use the air diffusion model to predict the current state of air quality in the target area or to predict regional air quality changes due to the establishment of air pollution emission facilities.

Korea has been using the Gaussian smoke diffusion model recommended by the US Environmental Protection Agency among the air diffusion models for the above purposes. It is widely used.

However, the Gaussian smoke diffusion model is a method of predicting the spread of air pollution throughout the region by using weather data from selected representative stations in the region. It is very unsuitable for the terrain and weather environment of our country, and inevitably there is a problem that a considerable error occurs in the prediction result.

Accordingly, the present invention has been made to solve the technical problems that occur when applying the Gaussian smoke diffusion model of the recommended atmosphere diffusion model of the US Environmental Protection Agency in our country as described above, the object of the present invention is a local area such as a mountain or valley The present invention provides a method for predicting the spread of air pollution using multiple weather observation data, which can improve the accuracy of the air quality prediction results by utilizing the weather observation data of the wind direction and wind speed transformed by the terrain.

In addition, another object of the present invention is to provide an air pollution spread prediction method using a plurality of meteorological observation data that can provide an effective pollution degree calculation method for obtaining a prediction result considering the influence of meteorological conditions by local type as much as possible. have.

1 is a schematic diagram showing a meteorological station and a spatially divided partial region.

2 is a total dust surface concentration distribution map of a target region by air pollution diffusion prediction method using a plurality of weather observation data carried out to verify the effectiveness of the present invention.

Figure 3 is a correlation analysis between the predicted concentration and the measured concentration by air pollution diffusion prediction method using a plurality of weather observation data carried out to verify the effectiveness of the present invention.

4 is a total dust surface concentration distribution map of a target region by air pollution diffusion prediction method using conventional single meteorological observation data for FIG.

FIG. 5 is a correlation analysis diagram between the predicted concentration and the measured concentration by the air pollution diffusion prediction method using the conventional single meteorological observation data of FIG. 3.

In order to achieve the object as described above, the present invention is divided into a plurality of target areas according to the location of the meteorological station, and by using the weather observation data obtained from the meteorological station of each divided region to reduce the air pollution of the entire target area. It is characterized by the prediction.

That is, according to the present invention, when using meteorological observation data obtained from a plurality of meteorological stations in the target area, the target area is divided by a space division method, and the meteorological observation data of the corresponding area is divided for the pollutant included in each corresponding area. The calculation results are applied and the prediction results are combined to provide the prediction result considering the influence of the weather condition by local type as much as possible.

In addition, the present invention is to calculate the pollution concentration using the Gaussian smoke diffusion model using the weather observation data of the weather station,

Provides a method for predicting air pollution spread using multiple meteorological observation data that estimates individual pollutant concentrations by using meteorological data observed from multiple side stations and adds them together to calculate the pollutant concentration of the entire area.

Here, the present invention uses a plurality of meteorological observation data selected by a position function by a spatial division method in selecting meteorological observation data of the rear and rear stations applied to the pollutant at any position when constructing the input data of the Gaussian smoke diffusion model.

The location function by the spatial division method is a function of generating a space dividing line by connecting a position that minimizes the distance to the weather station, and through this function, an area including each weather station is appropriately divided.

In addition, the present invention is characterized in selecting the meteorological observation data of the measurement station is the minimum distance from the pollution source to the measurement station in selecting the meteorological observation data of the weather station applied to the pollution source.

In other words, when the target area is divided by the spatial partitioning method, the weather data of the weather station corresponding to the area is used for the pollutant existing in each divided area.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

As illustrated in FIG. 1, when six meteorological stations including ①, ②, ③, ④, ⑤, and ⑥ exist within the selected area for the environmental impact assessment by air pollution diffusion, according to this embodiment, The target area is divided into subregions as many as the number of meteorological stations present in the area, but divided into subregions each containing one meteorological station.

Each of the divided regions is referred to as I, II, III, IV, V, VI in the order of the meteorological station. For example, as shown in FIG. 1, the partial region I includes the meteorological station 1, and the subregions II, III, IV, V, and VI are the same.

Here, the spatial division method according to the present invention divides an area by a dividing line generated by applying a function that minimizes the distance to the weather station, and the distance of the effectiveness of the meteorological elements measured at the weather station is separated from the weather station. As the physical properties decrease according to.

On the other hand, in order to operate the Gaussian diffusion model, weather data, emission data from pollutant sources and topographical data are required as input conditions.

According to the present invention, the target area is divided into sub-regions including a meteorological station as illustrated in FIG. 1, and the emission data of the pollutant and the meteorological data of the meteorological station in each sub-region are used as input conditions. However, the topographical data is calculated using atmospheric diffusion for the whole area using the topographical data of the whole area.

And the pollution degree by the air diffusion of the whole area is computed by overlapping and adding the pollution degree prepared for each partial area.

Example

In order to demonstrate the effect of improving the prediction accuracy of the pollution degree calculation result by air diffusion according to the present invention, the pollution degree calculation of the target area where the industrial complex is concentrated was performed.

FIG. 1 shows the topographic map of the target area and the location of the weather station within the area. The target area is divided into six sub-regions by the spatial division method according to the present invention.

FIG. 2 inputs emission information of pollutant sources present in the partitions and weather data of meteorological stations present in the areas for each partition, and calculates pollution levels in the entire area by inputting topographic data for the entire area. In addition, it shows the ground contamination distribution summed up by overlaying the calculation results for individual partitions.

Compared with FIG. 2, FIG. 4 shows a ground pollution distribution map by the conventional atmospheric diffusion model performed using only a single weather observation data, and this result uses only the weather data of ⑥, the most representative weather station. It is calculated.

Comparing FIG. 2 and FIG. 4, the ground pollution distribution diagrams of the respective methods show a very large difference. In FIG. 2 predicted by the present invention, a high concentration was predicted in the same region as the measured result, whereas the conventional single In the prediction section using weather observation data, Figure 4 confirms that high concentrations occur in completely different areas.

Figure 3 shows the correlation analysis between the pollution measured in the target area and the calculation result applying the spatial division method according to the present invention, the horizontal axis is the air diffusion model predicted value, the vertical axis is the air pollution measured value. The correlation by correlation analysis is 1.067, which is very close to 1.0, which is the most ideal correlation, which proves that the prediction accuracy according to the present invention is excellent.

Compared to FIG. 3, FIG. 5 shows a case where the conventional single meteorological observation data is used, and the correlation coefficient by the prediction method using the conventional single meteorological observation data is 0.8695, indicating that there is no correlation at all. You can see that this is impossible.

When the air diffusion model is used by applying the air pollution spread prediction method using the multiple weather observation data according to the present invention as described above, the existing pollution map for the entire target area is prepared using only the weather observation data of one point. Compared to the method, the wind direction and wind speed modified by the local type can be more realistically reflected to improve the accuracy of the prediction result of the pollutant concentration, and the area of the target area that can be analyzed can be expanded.

In addition, the present invention is suitable for most industrial zones in Korea, where the air pollution source is scattered, and the multi-phase gas phase according to the present invention is not applicable even though the diffusion prediction based on the existing single meteorological observation data is not applicable. Diffusion prediction based on observation data enables accurate prediction through correlation analysis.

Therefore, by applying the atmospheric diffusion model according to the present invention to the environmental impact assessment it is possible to provide a more reliable evaluation results.

Claims (3)

In calculating the concentration of pollutants in the target area using Gaussian smoke diffusion model using the weather observation data of the stations, Air pollution using plural meteorological observation data, characterized by performing individual pollutant concentration predictions by using meteorological observation data observed from a plurality of weather stations scattered in the target area, and summing them up to calculate the pollutant concentration of the entire target area. Spread prediction method. The air pollution spread prediction method using a plurality of meteorological observation data according to claim 1, characterized in that the climatic station for providing meteorological observation data for a pollutant at any position is selected by a position function by a spatial division method. The method of claim 2, wherein the location function by the spatial division method is a function of obtaining a minimum distance from a source to a measurement station, and the weather observation data for the source is provided from a meteorological station corresponding to the area. Air pollution spread prediction method using data.