University of Oulu

Lyons, K. J., Ikonen, J., Hokajärvi, A.-M., Räsänen, T., Pitkänen, T., Kauppinen, A., Kujala, K., Rossi, P. M., & Miettinen, I. T. (2023). Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: A comparison with conventional methods. In Science of The Total Environment (Vol. 864, p. 161199). Elsevier BV. https://doi.org/10.1016/j.scitotenv.2022.161199

Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis : a comparison with conventional methods

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Author: Lyons, Kevin J.1; Ikonen, Jenni2; Hokajärvi, Anna-Maria2;
Organizations: 1Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland
2Expert Microbiology Unit, Finnish Institute for Health and Welfare, Kuopio, Finland
3Preventos Informatics Oy, Kuopio, Finland
4Department of Environmental Technology, Savonia University of Applied Sciences, Kuopio, Finland
5Department of Food Hygiene and Environmental Health, Faculty of Veterinary Medicine, University of Helsinki, Helsinki, Finland
6Animal Health Diagnostic Unit, Laboratory and Research Division, Finnish Food Authority, Helsinki, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe20230904116764
Language: English
Published: Elsevier, 2023
Publish Date: 2023-09-04
Description:

Abstract

Groundwater provides much of the world's potable water. Nevertheless, groundwater quality monitoring programmes often rely on a sporadic, slow, and narrowly focused combination of periodic manual sampling and laboratory analyses, such that some water quality deficiencies go undetected, or are detected too late to prevent adverse consequences. In an effort to address this shortcoming, we conducted enhanced monitoring of untreated groundwater quality over 12 months (February 2019–February 2020) in four shallow wells supplying potable water in Finland. We supplemented periodic manual sampling and laboratory analyses with (i) real-time online monitoring of physicochemical and hydrological parameters, (ii) analysis of stable water isotopes from groundwater and nearby surface waters, and (iii) microbial community analysis of groundwater via amplicon sequencing of the 16S rRNA gene and 16S rRNA. We also developed an early warning system (EWS) for detecting water quality anomalies by automating real-time online monitoring data collection, transfer, and analysis – using electrical conductivity (EC) and turbidity as indirect water quality indicators. Real-time online monitoring measurements were largely in fair agreement with periodic manual measurements, demonstrating their usefulness for monitoring water quality; and the findings of conventional monitoring, stable water isotopes, and microbial community analysis revealed indications of surface water intrusion and faecal contamination at some of the studied sites. With further advances in technology and affordability expected into the future, the supplementary methods used here could be more widely implemented to enhance groundwater quality monitoring – by contributing new insights and/or corroborating the findings of conventional analyses.

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Series: Science of the total environment
ISSN: 0048-9697
ISSN-E: 1879-1026
ISSN-L: 0048-9697
Volume: 864
Article number: 161199
DOI: 10.1016/j.scitotenv.2022.161199
OADOI: https://oadoi.org/10.1016/j.scitotenv.2022.161199
Type of Publication: A1 Journal article – refereed
Field of Science: 1172 Environmental sciences
318 Medical biotechnology
218 Environmental engineering
Subjects:
Funding: This work received significant financial support from the Finnish Ministry of Agriculture and Forestry (Maa- ja metsätalousministeriö), as a Blue Bioeconomy Government Key Project entitled “Ensuring the safety of groundwater through real-time monitoring”. K.J.L. was additionally supported by personal grants from the Land and Water Technology Support Association (Maa- ja vesitekniikan tuki ry) (project ID: 4261 Vesihuolto) and the KAUTE Foundation (Kaupallisten ja teknillisten tieteiden tukisäätiö KAUTE) (project ID: 20201135).
Dataset Reference: Data will be made available on request.
Copyright information: © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/