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Identifying and quantifying multiple pollution sources in estuaries using fluorescence spectra and gradient-based

Zhuangming Zhao1, Min Xu2, Yu Yan2

  • 1South China Institute of Environmental Sciences, the Ministry of Ecology and Environment of PRC, Guangzhou 510655, China; Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519085, China.

Marine Pollution Bulletin
|November 17, 2024
PubMed
Summary
This summary is machine-generated.

A new intelligent method uses deep learning to identify and quantify water pollution sources in estuaries. Combining excitation-emission matrix (EEM) fluorescence spectra with gradient input improves accuracy in complex water mixtures.

Keywords:
Convolutional neural network (CNN)Deep learningEstuaryFluorescence spectroscopyMulti-source pollution

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Data Science

Background:

  • Estuarine areas face complex water pollution challenges from multiple sources.
  • Accurate identification and quantification of these sources are crucial for effective environmental management.
  • Traditional methods may struggle with the intricate spectral signatures of mixed pollutants.

Purpose of the Study:

  • To develop an intelligent deep learning-based method for identifying and quantifying water pollution sources in estuarine environments.
  • To evaluate the effectiveness of different input data types for the deep learning model.
  • To assess the model's performance under varying conditions of mixed pollution and background water composition.

Main Methods:

  • Characterization of excitation-emission matrix (EEM) fluorescence spectra for seven end-members (seawater, rainwater, five pollution sources).
  • Development of a deep learning model utilizing EEM fluorescence spectra.
  • Comparison of model performance using original EEM input versus combined EEM and gradient input.

Main Results:

  • The combined EEM and gradient input significantly enhanced classification and quantification accuracy compared to EEM alone.
  • Despite a decrease in accuracy with more mixed pollution sources, the combined input consistently improved performance by 3.1% to 6.8%.
  • Even with less than 70% seawater and rainwater, the combined input achieved higher accuracy (61.3%) and lower root mean square error (11.4%) for pollution source proportion estimation.

Conclusions:

  • Deep learning, particularly with combined spectral and gradient data, offers a powerful approach for water pollution source identification in estuaries.
  • The proposed intelligent method demonstrates improved accuracy and robustness in complex estuarine water matrices.
  • This technique provides a valuable tool for environmental monitoring and the management of estuarine pollution.