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Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models.

Daniel Carreres-Prieto1, Juan T García1, Fernando Cerdán-Cartagena2

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Summary
This summary is machine-generated.

Real-time wastewater monitoring using spectral analysis can significantly reduce analysis costs. Genetic algorithms accurately estimate five key pollutants (COD, BOD5, TSS, TN, NO3-N) with low error rates.

Keywords:
LED spectrophotometernutrientsorganic mattersuspended solidswastewater pollutant characterization

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Water Quality Management

Background:

  • Increasing demand for real-time pollution monitoring in sanitation systems.
  • Need to comply with EU environmental policies and European Green Deal targets.
  • Current analytical methods for wastewater pollutants are time-consuming and costly.

Purpose of the Study:

  • To develop and validate rapid, cost-effective methods for estimating key wastewater pollutants.
  • To assess the feasibility of using spectral analysis and computational models for pollution load estimation.
  • To reduce the duration and cost of wastewater analysis without pre-treatment or chemical addition.

Main Methods:

  • Full-scale wastewater treatment plant field-sampling campaign.
  • Spectral response measurement of wastewater samples (380-700 nm).
  • Development of multivariate linear regression and machine learning genetic algorithms for pollutant estimation.

Main Results:

  • Genetic algorithms accurately estimated five pollutants (COD, BOD5, TSS, TN, NO3-N) in raw and treated wastewater with <4% error.
  • Multivariate linear regression models were limited to raw water and estimated COD and TSS with <0.5% error.
  • The developed methods eliminate the need for sample pre-treatment and chemical addition, reducing analysis time and cost.

Conclusions:

  • Spectral analysis combined with genetic algorithms offers a highly accurate and efficient method for real-time wastewater pollution monitoring.
  • The developed models support optimized operation of sanitation systems and compliance with environmental regulations.
  • This approach provides a significant advancement in cost-effective and rapid water quality assessment.