A port atmospheric pollution source spatial fine analysis method

By performing meteorological normalization analysis and positive definite matrix factorization on the mass concentration data of air pollutants in ports, and combining the Bayesian multivariate receptor model and spatial interpolation method, the problem of meteorological conditions affecting the analysis of air pollution sources in ports was solved. This enabled the generation of high-resolution spatial distribution maps of pollution sources, improving the accuracy and interpretability of the analysis.

CN121723063BActive Publication Date: 2026-06-09CHINA WATERBORNE TRANSPORT RES INST
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA WATERBORNE TRANSPORT RES INST
Filing Date
2025-11-24
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing receptor model methods cannot effectively isolate the influence of meteorological conditions in the analysis of atmospheric pollution sources in ports, resulting in low accuracy of analysis results. They can only achieve point-based analysis and have poor interpretability.

Method used

By performing meteorological normalization analysis on the mass concentration data of various air pollutants from multiple monitoring points, the pollution source classes and spatial distribution matrices are extracted using a positive definite matrix factor decomposition model. Combined with a Bayesian multivariate receptor model and spatial interpolation method, a high-resolution spatial distribution map of pollution source emission intensity is generated.

Benefits of technology

It effectively filters meteorological factors, improves the accuracy and interpretability of pollution source analysis, and upgrades from point analysis to fine spatial characterization, providing scientific and reliable technical support.

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Abstract

The application provides a port atmospheric pollution source space fine analysis method, comprising: meteorological normalization analysis is carried out on the mass concentration data of various atmospheric pollutants of multiple monitoring points of the port respectively, and a demeteorized concentration sequence is obtained; the demeteorized concentration sequence is input into a positive definite matrix factor decomposition model, and a pollution source class with spatial uniformity distribution characteristics is extracted; a priori pollution source space distribution matrix is obtained based on prior knowledge composed of the pollution source class, a pollution source space distribution matrix, port pollution source emission information and activity level information; the demeteorized concentration sequence and the a priori pollution source space distribution matrix are input into a Bayesian multivariate receptor model, and a pollution source emission intensity space distribution and a time sequence are obtained; and based on the pollution source emission intensity and the spatial topological relationship of the multiple monitoring points, a high-resolution spatial distribution map of the pollution source emission intensity is generated. The application strips the meteorological influence, and realizes fine space description of the pollution source.
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