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Self-feedback LSTM regression model for real-time particle source apportionment.

Wei Wang1, Weiman Xu2, Shuai Deng2

  • 1Trusted AI System Laboratory, College of Computer Science, Nankai University, Tianjin 300350, China; KLMDASR, Tianjin Key Laboratory of Network and Data Security Technology, Tianjin 300350, China.

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|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning model, the self-feedback long short-term memory (SF-LSTM) network, for real-time atmospheric particulate matter source apportionment. The SF-LSTM model effectively addresses complex pollution scenarios and outperforms traditional methods.

Keywords:
Particle source apportionmentRegressionSelf-feedback LSTM networkTime series

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Data Science

Background:

  • Global concern over atmospheric particulate matter pollution is rising.
  • Advanced particle collection techniques necessitate improved real-time source apportionment methods.
  • Existing methods struggle with complex scenarios like secondary and similar pollution sources.

Purpose of the Study:

  • To establish new constraints for atmospheric particulate matter source apportionment.
  • To develop a non-linear regression model capable of handling complex pollution circumstances.
  • To propose and evaluate a novel deep learning network for source contribution approximation.

Main Methods:

  • Analysis of potential constraints in single particle source apportionment.
  • Development of a three-step self-feedback long short-term memory (SF-LSTM) network.
  • Implementation of four loss functions within the scoring module to represent apportionment restraints.
  • Utilizing a regeneration module for non-linear calculation of source contributions.

Main Results:

  • The SF-LSTM model demonstrates superior performance over conventional regression methods across four evaluation indicators: residual sum of squares, stability, sparsity, and negativity.
  • SF-LSTM achieves better results in short time-resolution analysis, particularly under the stability restraint.
  • The deep learning approach effectively handles complex atmospheric pollution scenarios.

Conclusions:

  • The proposed SF-LSTM network offers a robust and effective solution for real-time atmospheric particulate matter source apportionment.
  • This deep learning model provides significant advancements in handling complex pollution sources and improving apportionment accuracy.
  • SF-LSTM represents a promising tool for environmental monitoring and pollution control strategies.