An outlier screening method based on reconstruction of particulate matter components

By reconstructing atmospheric particulate matter composition data and identifying outliers, the limitations of existing technologies in screening atmospheric particulate matter composition data have been overcome. This has enabled quantitative analysis of particulate matter composition and accurate identification of outliers, improving the stability and efficiency of data processing.

CN121030619BActive Publication Date: 2026-06-26CHINA NAT ENVIRONMENTAL MONITORING CENT
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA NAT ENVIRONMENTAL MONITORING CENT
Filing Date
2025-10-24
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies struggle to quantitatively analyze the various components of atmospheric particulate matter and cannot reconstruct these complex data into more representative features, resulting in limitations in screening abnormal particulate matter component data.

Method used

By acquiring ionic component data, carbon component data, and inorganic element component data from component monitoring stations, the component data is reconstructed after data preprocessing. Anomalies are identified using sliding time windows and correlation coefficients to determine target PM2.5 data and screen for abnormal data.

Benefits of technology

It improves the quantitative analysis capability of atmospheric particulate matter components, enhances the understanding of the relationships between particulate matter components, improves the stability and accuracy of abnormal data identification, optimizes computational efficiency, and meets the real-time processing requirements of online monitoring data.

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Abstract

The application provides an abnormal value screening method based on particle component reconstruction, and relates to the technical field of environmental monitoring. The method comprises the following steps: obtaining standard ion component data, standard carbon component data and standard inorganic element component data; obtaining multiple reconstruction component data; determining target PM2.5 data; determining suspicious reconstruction component data judgment results of the multiple reconstruction component data at each moment; determining single-variable time sequence abnormal point judgment results of each kind of reconstruction component data at each moment; determining multivariate time sequence abnormal point judgment results of the multiple reconstruction component data at each moment; and determining whether each kind of reconstruction component data at each moment is abnormal data. According to the application, the component reconstruction characteristics under different pollution levels can be evaluated, the understanding of the relationship between particle components by the model is enhanced, the stability and accuracy of identifying whether the reconstruction component data is abnormal data are improved, the calculation efficiency is optimized, and the environmental monitoring auditors are assisted to make decisions.
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