Recent Advances in the Analysis of Real-time Water Quality Data Collected in Newfoundland and Labrador

Abstract

A real-time water quality monitoring (RTWQM) network was established in the province of Newfoundland and Labrador in late 2001. The network has changed the way river health is assessed in the province and a great deal has been learned in recent years about using this innovation in resource management. This paper summarizes three new developments carried out in recent years using RTWQM data. First, regression models are developed using real-time data as a surrogate for the concentration of important indicators of water quality that have traditionally been determined through manual grab sample collection. Second, regression models are developed for the prediction of water temperature and dissolved oxygen at the real-time water quality stations. A graphical approach is presented that links air temperature to these two important indicators of water quality. Third, control charts are investigated as a means of analyzing the data collected by the network. These charts have traditionally been used in the manufacturing and processing industries, where their usefulness as a quality control tool hinges upon the assumption that observations from the process being monitored are independent random variables. RTWQM measurements are autocorrelated over time and this lack of independence poses a challenge for control chart design. While a time-series approach is suitable for studying short subsets of the data (e.g. hourly measurements collected over the course of 3 to 5 days), the resulting chart does not clearly show when the health of an aquatic ecosystem is being threatened. Replacing the traditional control chart limit lines in favor of water quality criterion limits that better represent the concerns of resource managers is a much more suitable approach to analyzing real-time data.

Un rseau de suivi en temps rel de la qualit de l'eau (RTWQM) a t tabli Terre-Neuve-et-Labrador vers la fin de 2001. Le rseau a rvolutionn la faon dont on value la sant des rivires dans la province. De nombreuses connaissances ont t acquises ces dernires annes propos de l'utilisation de cette innovation dans la gestion des ressources. Cette tude rsume trois nouveaux domaines de recherche et dveloppement effectus dans les dernires annes en utilisant des donnes du RTWQM. (1) Des modles de rgression sont dvelopps en utilisant des donnes en temps rel comme substitut pour la concentration d'indicateurs importants de la qualit d'eau qui a t traditionnellement dtermine par le prlvement manuel d'chantillons. (2) Des modles de rgression sont dvelopps afin de prdire la temprature de l'eau et de l'oxygne dissous aux stations en temps rel. Une nouvelle approche graphique est prsente, qui cre un lien visuel entre la temprature de l'air et ces deux indicateurs importants de la qualit de l'eau. (3) Des diagrammes de contrle sont tudis dans le but d'examiner les donnes recueillies par le rseau. Ces diagrammes ont t habituellement utiliss dans les industries manufacturires et de procds - o leur utilit en tant qu'outil de contrle de la qualit repose sur la supposition que les observations du processus examin sont des variables indpendantes et alatoires. Des mesures du RTWQ sont auto-corrles au fil du temps et ce manque d'indpendance pose un dfi considrable pour la conception d'un tableau de contrle. Quoiqu'un modle de srie chronologique soit convenable pour tudier des sous-ensembles de donnes (p. ex. des mesures prises toutes les heures durant trois cinq jours), le tableau qui en rsulte ne montre pas clairement quel moment la sant d'un cosystme aquatique est en danger. Le remplacement de lignes de contrle graphique traditionnel par des critres de qualit de l'eau qui reprsentent mieux les proccupations des gestionnaires des ressources est beaucoup plus adapt l'analyse des donnes en temps rel.

Acknowledgements

Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Newfoundland and Labrador Department of Environment and Conservation, the Institute for Biodiversity, Ecosystem Science and Sustainability (IBES), and the School of Graduate Studies at Memorial University. The insight and assistance of Ms. Renee Paterson of the Department of Environment and Conservation are gratefully acknowledged.