Optimising punctual water sampling with an on-the-fly algorithm based on multiparameter high-frequency measurements.

Area of Science:

• Environmental Science
• Water Quality Monitoring
• Ecological Sampling

Background:

• Aquatic system sampling methods significantly impact data representativeness, especially in dynamic environments.
• High-frequency (HF) sampling offers better representativeness than spot sampling but generates large analytical workloads.
• Current methods struggle to efficiently capture data during transient events like storms or spills.

Purpose of the Study:

• To develop and validate a novel methodology for optimizing sampling frequency in aquatic systems.
• To reduce the number of samples required for accurate environmental monitoring using automated systems.
• To improve the efficiency and cost-effectiveness of high-frequency aquatic studies.

Main Methods:

• Coupling an automated sampler with a high-frequency (HF) multiparameter probe.
• Implementing an optimized sampling algorithm (OSA) that determines sampling relevance in real-time.
• Evaluating the OSA's performance using a case study with various physicochemical parameters.

Main Results:

• The OSA significantly reduced the number of collected samples while maintaining data representativeness.
• Physicochemical parameter variability was preserved, with Pearson correlations exceeding 0.96.
• Multivariate data structure was maintained, indicated by Tucker congruence values over 0.99 for PCA axes.

Conclusions:

• The developed OSA effectively optimizes sampling strategies for dynamic aquatic environments.
• This method simplifies HF studies, enabling better monitoring of brief, impactful events.
• The approach reduces human and financial costs associated with environmental monitoring studies.