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Uncertainty in water quality data and its implications for trend detection: lessons from Swedish environmental data
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
2008 (English)In: Environmental Science and Policy, ISSN 1462-9011, E-ISSN 1873-6416, Vol. 11, no 2, 115-124 p.Article in journal (Refereed) Published
Abstract [en]

The demands on monitoring systems have gradually increased, and interpretation of the data is often a matter of controversy. As an example of this, we investigated water quality monitoring and the eutrophication issue in Sweden. Our results demonstrate that powerful statistical tools for trend analysis can reveal flaws in the data and lead to new and revised interpretations of environmental data. In particular, we found strong evidence that long-term trends in measured nutrient concentrations can be more extensively influenced by changes in sampling and laboratory practices than by actual changes in the state of the environment. On a more general level, our findings raise important questions regarding the need for new paradigms for environmental monitoring and assessment. Introduction of a system in which conventional quality assurance is complemented with thorough statistical follow-up of reported values would represent a first step towards recognizing that environmental monitoring and assessment should be transformed from being a system for sampling and laboratory analyses into a system for interpreting information to support policy development.

Place, publisher, year, edition, pages
2008. Vol. 11, no 2, 115-124 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-44473DOI: 10.1016/j.envsci.2007.12.001Local ID: 76781OAI: oai:DiVA.org:liu-44473DiVA: diva2:265335
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13Bibliographically approved
In thesis
1. Roadmap for trend detection and assessment of data quality
Open this publication in new window or tab >>Roadmap for trend detection and assessment of data quality
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Regular measurements of the state of the environment constitute a cornerstone of environmental management. Without the support of long time series of reliable data, we would know much less about changes that occur in the environment and their causes. The present research aimed to explore how improved techniques for data analysis can help reveal flawed data and extract more information from environmental monitoring programmes. Based on our results, we propose that the organization of such monitoring should be transformed from a system for measuring and collecting data to an information system where resources have been reallocated to data analysis. More specifically, this thesis reports improved methods for joint analysis of trends in multiple time series and detection of artificial level shifts in the presence of smooth trends. Furthermore, special consideration is given to methods that automatically detect and adapt to the interdependence of the collected data. The current work resulted in a roadmap describing the process of proceeding from a set of observed concentrations to arrive at conclusions about the quality of the data and existence of trends therein. Improvements in existing software accompanied the development of new statistical procedures.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2008. 81 + papers 1-5 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 10Linköping Studies in Arts and Science, ISSN 0282-9800 ; 454
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-43109 (URN)71719 (Local ID)978-91-7393-792-4 (ISBN)71719 (Archive number)71719 (OAI)
Public defence
2008-10-10, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Supervisors
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2014-09-25Bibliographically approved

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Wahlin, KarlGrimvall, Anders

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