Title :

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

Author(s) :

Mougin, Jeremy [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Superville, Pierre-Jean [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Billon, Gabriel [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Journal title :

Water Res

Abbreviated title :

Water Res

Volume number :

221

Pages :

118750

Publication date :

2022-06-26

ISSN :

1879-2448

English keyword(s) :

Sampling
Algorithm
High frequency
On line
Monitoring
River

HAL domain(s) :

Chimie/Chimie théorique et/ou physique

English abstract : [en]

The way in which aquatic systems is sampled has a strong influence on our understanding of them, especially when they are highly dynamic. High frequency sampling has the advantage over spot sampling for representativeness ...
Show more >The way in which aquatic systems is sampled has a strong influence on our understanding of them, especially when they are highly dynamic. High frequency sampling has the advantage over spot sampling for representativeness but leads to a high amount of analysis. This study proposes a new methodology to choose when sampling accurately with an automated sampler coupled with a high frequency (HF) multiparameter probe. After each HF measurement, an optimised sampling algorithm (OSA) determines on-the-fly the relevance of taking a new sample in relation to previous waters already collected. Once the OSA was optimised, considering the number of HF parameters and their variabilities, it was demonstrated through a study case that the number of samples could be significantly reduced, while still covering periods of low and high variabilities. The comparison between the total HF dataset and the sampled subdataset shows that physicochemical parameter variability is preserved (Pearson correlations > 0.96) as well as the multiparameter variability (PCA axes remained similar with Tucker congruence > 0.99). This algorithm simplifies HF studies by making it easier to take samples during brief phenomena such as storms or accidental spills that are often poorly monitored. In addition, it optimises the number of samples to be taken to correctly describe a system and thus reduce the human and financial costs of these environmental studies.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS

Collections :

• Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Research team(s) :

Physicochimie de l’Environnement (PCE)

Submission date :

2024-02-28T22:25:23Z
2024-03-13T12:55:55Z