Towards a water quality database for raw and validated data with emphasis on structured metadata

Author:

Plana Queralt12,Alferes Janelcy13,Fuks Kevin1,Kraft Tobias14,Maruéjouls Thibaud15,Torfs Elena12,Vanrolleghem Peter A.12

Affiliation:

1. modelEAU, Université Laval, 1065, avenue de la Médecine, Québec, QC, G1 V 0A6, Canada

2. CentrEau, Quebec Water Research Centre, Université Laval, 1065, avenue de la Médecine, Québec, QC, G1 V 0A6, Canada

3. s::can Messtechnik GmbH, Brigittagasse 22-24, 1200 Vienna, Austria

4. AF Toscano AG, Raetusstrasse 12, CH-7000 Chur, Switzerland

5. Le LyRE, Suez Eau France SAS, Domaine du Haut-Carré 43, rue Pierre Noailles Bâtiment C4, 33400 Talence, France

Abstract

Abstract On-line continuous monitoring of water bodies produces large quantities of high frequency data. Long-term quality control and applicability of these data require rigorous storage and documentation. To carry out these activities successfully, a database has to be built. Such a database should provide the simplicity to store and document all relevant data and should be easy to use for further data evaluation and interpretation. In this paper, a comprehensive database structure for water quality data is proposed. Its goal is to centralize the data, standardize their format, provide easy access, and, especially, document all relevant information (metadata) associated with the measurements in an efficient way. The emphasis on data documentation enables the provision of detailed information not only on the history of the measurements (e.g., where, how, when and by whom was the value measured) but also on the history of the equipment (e.g., sensor maintenance, calibration/validation history), personnel (e.g., experience), projects, sampling sites, etc. As such, the proposed database structure provides a robust and efficient tool for functional data storage and access, allowing future use of data collected at great expense.

Publisher

IWA Publishing

Subject

Water Science and Technology

Reference12 articles.

1. Scientific data management with open source tools – An urban drainage example,2012

2. Towards the automation of water quality monitoring networks,2010

3. EPA 2016 STOrage and RETrieval Data Warehouse. US Environmental Protection Agency. https://www.epa.gov/waterdata (accessed 13 December 2016).

4. Scientific data management in the coming decade;ACM SIGMOD Record,2005

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