Coked soil pollution spatial distribution prediction optimization method and system

Abstract

The invention provides a coking soil pollution spatial distribution prediction optimization method and system. The scheme comprises the following steps: acquiring a smoke diffusion range by utilizing acquisition equipment, and calculating a pollutant coordinate, a discharge rate, ground meteorological data, a discharge height and a discharge flow; inputting ground meteorological data and sounding meteorological data in sequence as a meteorological data source; acquiring a meteorological background initial value, terrain elevation data and pollution source data, generating site features and comprehensive pollution source data, inputting the data into a pollutant propagation model, and generating the pollutant concentration of any point; and obtaining a pollutant propagation model, obtaining a target lateral diffusion coefficient and a target transverse diffusion coefficient corresponding to the lowest error, and generating a heavy metal pollutant drawing according to a pollutant result. According to the scheme, an enterprise emission particulate matter simulation result is calculated through the atmospheric diffusion model and serves as a collaborative variable to be applied to the collaborative Crisgin interpolation method, and the prediction precision of heavy metal pollutants of soil around a coking plant is improved.

Description

technical field [0001] The invention relates to the technical field of meteorological prediction, and more specifically, to a method and system for predicting and optimizing the spatial distribution of coking soil pollution. Background technique [0002] Coking polluted soil has strong spatial variability, the difference between the maximum and minimum of soil pollutant content can be as high as 730 times, and the coefficient of variation can be as high as 398%. In order to obtain the spatial distribution characteristics of the pollutant content, the prior art mainly obtains the spatial distribution information of the pollutant content based on a spatial interpolation model. [0003] Prior to the technology of the present invention, the existing researches mainly improved the interpolation accuracy by screening the interpolation model, combining category auxiliary variables and geostatistics and other methods. However, the existing methods are limited to optimizing the pred...

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Problems solved by technology

However, the existing methods are limited to optimizing the prediction effect through mathematical analysis, and lack the inherent law of coking soil pollution, that is, the study of the driving mechanism of soil pollution. As a result, the prediction results are limited to the characteristics of soil pollution close to specific research companies, and it is difficult to apply directly Compared with the spatial distribution pattern of soil pollution of other polluting enterprises, the prediction results obtained by applying it to different research objects are quite different

[0004] The reason for these defects in the existing foundation is that the spatial interpolation model is based on the theory of spatial autocorrelation, that is, the values ​​at adjacent spatial positions have correlation and similar characteristics. It is difficult to predict the variation form, and it is difficult to provide support for the remediation and treatment of coking soil

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