Digitalizing river aquatic ecosystems.

Area of Science:

• Environmental Science
• Ecotoxicology
• Computational Biology

Background:

• Traditional river health assessments use limited water quality indices and organism data.
• Current methods fail to comprehensively capture the biotic and abiotic status of river ecosystems.
• Emerging pollutants pose significant risks to aquatic environments, often overlooked by conventional assessments.

Purpose of the Study:

• To develop and validate a novel, integrated approach for evaluating ecological and health risks in river aquatic ecosystems.
• To enhance the comprehensiveness of river health assessments beyond traditional metrics.
• To provide a robust framework for informed river management strategies.

Main Methods:

• Detailed physicochemical characterization including emerging pollutant determination.
• Comprehensive biological characterization using DNA/RNA sequencing.
• Application of supervised machine learning for classification and health assessment of river ecosystems.

Main Results:

• The proposed method enables detailed characterization of river ecosystems, integrating physicochemical and biological data.
• Machine learning algorithms successfully classified river ecosystem health based on the comprehensive data.
• The approach offers a more holistic and accurate assessment of ecological risks.

Conclusions:

• The novel methodology significantly advances river ecosystem health assessment by integrating advanced molecular techniques and machine learning.
• This integrated approach provides a transformative tool for understanding and managing riverine environments.
• The findings support the application of this methodology for effective river management and conservation efforts.