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A novel method for micropollutant quantification using deep learning and multi-objective optimization.

Daeun Yun1, Daeho Kang2, Jiyi Jang1

  • 1School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan 44919, South Korea.

Water Research
|February 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a cost-effective deep learning method to estimate micropollutant concentrations in water. The approach uses standard solutions to accurately quantify these environmental contaminants, aiding public health protection.

Keywords:
Convolutional neural networkDeep learningHigh resolution mass spectrometryMicropollutantSurrogate method

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Area of Science:

  • Environmental Chemistry
  • Analytical Chemistry
  • Computational Science

Background:

  • Micropollutants (MPs) in aquatic ecosystems pose risks to public health, necessitating effective monitoring.
  • Current quantification methods like high-resolution mass spectrometry (HRMS) with stable isotope-labeled (SIL) standards are expensive.

Purpose of the Study:

  • To develop a rapid, cost-effective analytical approach for estimating micropollutant concentrations.
  • To leverage deep learning (DL) and multi-objective optimization for MP quantification.

Main Methods:

  • Utilized deep learning models (DarkNet-53, ResNet-50) and multi-objective optimization.
  • Employed standard solutions to estimate concentrations of 18 MPs, considering repeatability and natural organic matter.
  • Investigated the use of internal standards for quantifying non-target substances.

Main Results:

  • The DarkNet-53 DL model, using nine standard solutions, demonstrated the highest performance in estimating MP concentrations.
  • ResNet-50 showed the lowest performance among the tested DL models.
  • The approach successfully reduced instrumental error and matrix effects.

Conclusions:

  • Deep learning models show significant capability for estimating micropollutant concentrations in aquatic systems.
  • This method offers a promising, cost-effective alternative to traditional quantification techniques.