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Missing component iterative inversion calibration nesting-PMF source apportionment algorithm

An iterative inversion and source analysis technology, applied in the field of iterative inversion calibration nesting of missing components—PMF source analysis algorithm, which can solve the problems of increased source class collinearity, missing chemical components to identify components, etc., to improve accuracy performance, optimizing the effect of source parsing results

Active Publication Date: 2018-12-25
NANKAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the lack of important identification components in the chemical components of the particulate matter monitored by the existing online monitoring technology, such as the crustal identification components Si and Al. The problem of increased collinearity of source classes

Method used

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  • Missing component iterative inversion calibration nesting-PMF source apportionment algorithm
  • Missing component iterative inversion calibration nesting-PMF source apportionment algorithm
  • Missing component iterative inversion calibration nesting-PMF source apportionment algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] See attached figure 1 , this example uses online monitoring data and factor analysis model for model calculation, the specific steps are as follows:

[0031] Step 1. Build factor analysis model input data. The input data is formed based on online monitoring data of particulate matter concentration and its chemical components monitored by different instruments, including water-soluble ions, carbon components, element concentrations, and particulate matter concentrations.

[0032] Measuring PM with Particulate Matter On-line Monitoring Instrument 2.5 concentration.

[0033] Carbon components, including OC and EC concentrations, were measured using a semi-continuous OC / EC instrument.

[0034] Measurement of water-soluble ions, including NH, using an online ion chromatography analyzer 4 + 、Na + , Mg 2+ 、K + , Ca 2+ , SO 4 2- , NO 3 - , Cl - and other concentrations.

[0035] Use the heavy metal online analyzer to monitor elements, including K, Ca, V, Cr, Mn, ...

Embodiment 2

[0067] See attached figure 1 , this example uses online monitoring data and factor analysis model for model calculation, the specific steps are as follows:

[0068] Step 1. Build factor analysis model input data. The input data include water-soluble ions, carbon components, element concentrations, and particle concentrations.

[0069] Measuring PM with Particulate Matter On-line Monitoring Instrument 2.5 concentration.

[0070] Carbon components, including OC and EC concentrations, were measured using a semi-continuous OC / EC instrument.

[0071] Measurement of water-soluble ions, including NH, using an online ion chromatography analyzer 4 + 、Na + , Mg 2+ 、K + , Ca 2+ , SO 4 2- , NO 3 - , Cl - and other concentrations.

[0072] Use the heavy metal online analyzer to monitor elements, including K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Ag, Cd, Sn, Sb, Ba, Au, Hg, Tl , Pb, Bi, etc. (the component category of each input data will change according to the act...

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Abstract

The invention provides a missing component iterative inversion calibration nesting-PMF source apportionment algorithm. The method comprises the following steps of: using an on-line monitoring instrument to construct multi-component online data and inputting the multi-component online data into a positive definite factor matrix decomposition model (PMF); selecting the number of factors and settinga model calculation parameter; performing model calculation, extracting the factors, and calculating each factor contribution; combining a measured source profile, a factor profile and factor contributions to perform inverse calculation on a receptor Si and a receptor Al to obtain reconstructed receptor data and reconstructed receptor matrix X1 of Si and Al, respectively; inputting the reconstructed receptor matrix X1 into the model again for calculation to obtain a new factor profile and factor contributions, and combining the measured source profile to perform inverse calculation on the receptor Si and the receptor Al to obtain the reconstructed receptor data and a reconstructed receptor matrix X2 of Si and Al; repeating the above steps until the reconstructed receptor data satisfying the restriction condition is obtained. According to the missing component iterative inversion calibration nesting-PMF source apportionment algorithm provided by the invention, the actual receptor data can be restored to a certain extent, and the accuracy of the model calculation can be improved.

Description

technical field [0001] The invention relates to the field of atmospheric particle source analysis, in particular to a missing component iterative inversion calibration nesting-PMF source analysis algorithm. Background technique [0002] China's air pollution is relatively serious, using scientific source apportionment methods to judge PM 2.5 Provenance is key to control and governance. In order to control severe air pollution, the United States took the lead in conducting research on the source analysis of particulate matter in the 1970s, and related research in Europe also made significant progress in the 1990s. my country's source apportionment work started in the 1980s. [0003] Currently PM 2.5 Source apportionment is mainly based on offline filter sampling, and the sampling time is generally 24 hours or longer. The acquisition period from sample collection, chemical group analysis to model results is long, and data averaged within a certain period of time cannot capt...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/00
CPCG01N33/0062
Inventor 史国良董世豪彭杏冯银厂
Owner NANKAI UNIV
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