Statistical models for evaluating suspected artefacts in long-term environmental monitoring data

Environmental Monitoring and Assessment
Claudia von BrömssenKerstin McEwan

Abstract

Long-term water quality monitoring is of high value for environmental management as well as for research. Artificial level shifts in time series due to method improvements, flaws in laboratory practices or changes in laboratory are a common limitation for analysis, which, however, are often ignored. Statistical estimation of such artefacts is complicated by the simultaneous existence of trends, seasonal variation and effects of other influencing factors, such as weather conditions. Here, we investigate the performance of generalised additive mixed models (GAMM) to simultaneously identify one or more artefacts associated with artificial level shifts, longitudinal effects related to temporal trends and seasonal variation, as well as to model the serial correlation structure of the data. In the same model, it is possible to estimate separate residual variances for different periods so as to identify if artefacts not only influence the mean level but also the dispersion of a series. Even with an appropriate statistical methodology, it is difficult to quantify artificial level shifts and make appropriate adjustments to the time series. The underlying temporal structure of the series is especially important. As long as there is no pr...Continue Reading

References

Apr 25, 2014·Environmental Monitoring and Assessment·Anders GrimvallGöran Lindström
Nov 19, 2014·Ambio·Jens FölsterAnders Wilander
Jan 21, 2015·Journal of Environmental Quality·Jorge A GuzmanAlexandria G McCombs
Sep 16, 2017·The Science of the Total Environment·Brian J HuserJens Fölster

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Citations

May 15, 2020·Environmental Monitoring and Assessment·Radislav Vaisman
Oct 11, 2020·Environmental Pollution·Stefano CrocchiantiDavid Cappelletti

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Software Mentioned

Autocorr
mgcv
GAMM
R

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