Abstract
Water quality monitoring is essential to understanding the complex dynamics of water ecosystems, the impact of human infrastructure on them and to ensure the safe use of water resources for drinking, recreation and transport. High frequency in-situ monitoring systems are being increasingly employed in water quality monitoring schemes due to their much finer temporal measurement scales possible and reduced cost associated with manual sampling, manpower and time needed to process results compared to traditional grab-sampling. Modelling water quality data at higher frequency reduces uncertainty and allows for the capture of transient events, although due to potential constraints of data storage, inducement of noise, and power conservation it is worthwhile not using an excessively high sampling frequency. In this study, high frequency data recorded in Bristol's Floating Harbour as part of the local UKRIC Urban Observatory activities is presented to analyse events not captured by the current manual sampling and laboratory analysis scheme. The frequency components of the time-series are analysed to work towards understanding the necessary sampling frequency of temperature, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), turbidity and conductivity as indicators of water quality. This study is the first of its kind to explore a statistical approach for determining the optimum sampling frequency for different water quality parameters using a high frequency dataset. Furthermore, it provides practical tools to understand how different sampling frequencies are representative of the water quality changes.
| Original language | English |
|---|---|
| Article number | 791595 |
| Number of pages | 14 |
| Journal | Frontiers in Sustainable Cities |
| Volume | 3 |
| DOIs | |
| Publication status | Published - 31 Jan 2022 |
Bibliographical note
Funding Information:Many thanks to Bristol City Council's Harbour Master's office for their kind aid in deploying the sensors in the harbour and to Dr. Yiheng Chen that helped with the data collection.
Funding Information:
This work was funded as part of the Water Informatics Science and Engineering Centre for Doctoral Training (WISE CDT) under a grant from the Engineering and Physical Sciences Research Council (EPSRC), grant number EP/L016214/1. This work was supported in part by the Engineering and Physical Sciences Research Council’s (ESPRC) UK Collaboratorium for Research in Infrastructure & Cities (UKCRIC): Urban Observatories grant (ref. EP/P016782/1).
Publisher Copyright:
Copyright © 2022 Coraggio, Han, Gronow and Tryfonas.
Research Groups and Themes
- Water and Environmental Engineering
Keywords
- sampling frequency
- water quality
- monitoring network design
- high frequency data analysis
- wireless sensor network (WSN)
Access to Document
- Full-text PDF (final published version)
This is the final published version of the article (version of record). It first appeared online via Frontiers Media at https://doi.org/10.3389/frsc.2021.791595 . Please refer to any applicable terms of use of the publisher.