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Estimation of the human impact on nutrient loads carried by the Elbe River
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Mathematics, Statistics . Linköping University, The Institute of Technology.
GKSS Research Centre, Institute for Coastal Research, Geesthacht, Germany.
2004 (English)In: Environmental Monitoring and Assessment, ISSN 0167-6369 (print) 1573-2959 (online), Vol. 96, no 1-3, 15-33 p.Article in journal (Refereed) Published
Abstract [en]

The reunification of Germany led to dramatically reduced emissions of nitrogen (N) and phosphorus (P) to the environment. The aim of the present study was to examine how these exceptional decreases influenced the amounts of nutrients carried by the Elbe River to the North Sea. In particular, we attempted to extract anthropogenic signals from time series of riverine loads of nitrogen and phosphorus by developing a normalization technique that enabled removal of natural fluctuations caused by several weather-dependent variables. This analysis revealed several notable downward trends. The normalized loads of total-N and NO3-N exhibited an almost linear trend, even though the nitrogen surplus in agriculture dropped dramatically in 1990 and then slowly increased. Furthermore, the decrease in total-P loads was found to be considerably smaller close to the mouth of the river than further upstream. Studying the predictive ability of different normalization models showed the following: (i) nutrient loads were influenced primarily by water discharge; (ii) models taking into account water temperature, load of suspended particulate matter, and salinity were superior for some combinations of sampling sites and nutrient species; semiparametric normalization models were almost invariably better than ordinary regression models.

Place, publisher, year, edition, pages
2004. Vol. 96, no 1-3, 15-33 p.
Keyword [en]
Elbe River, nitrogen, normalization, phosphorus, trend detection
National Category
URN: urn:nbn:se:liu:diva-13603DOI: 10.1023/B:EMAS.0000031722.88972.62OAI: diva2:21035
Available from: 2005-12-16 Created: 2005-12-16 Last updated: 2009-05-19
In thesis
1. Monotonic and Semiparametric Regression for the Detection of Trends in Environmental Quality Data
Open this publication in new window or tab >>Monotonic and Semiparametric Regression for the Detection of Trends in Environmental Quality Data
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Natural fluctuations in the state of the environment can long conceal or distort important trends in the human impact on our ecosystems. Accordingly, there is increasing interest in statistical normalisation techniques that can clarify the anthropogenic effects by removing meteorologically driven fluctuations and other natural variation in time series of environmental quality data. This thesis shows that semi- and nonparametric regression methods can provide effective tools for applying such normalisation to collected data. In particular, it is demonstrated how monotonic regression can be utilised in this context. A new numerical algorithm for this type of regression can accommodate two or more discrete or continuous explanatory variables, which enables simultaneous estimation of a monotonic temporal trend and correction for one or more covariates that have a monotonic relationship with the response variable under consideration. To illustrate the method, a case study of mercury levels in fish is presented, using body length and weight as covariates. Semiparametric regression techniques enable trend analyses in which a nonparametric representation of temporal trends is combined with parametrically modelled corrections for covariates. Here, it is described how such models can be employed to extract trends from data collected over several seasons, and this procedure is exemplified by discussing how temporal trends in the load of nutrients carried by the Elbe River can be detected while adjusting for water discharge and other factors. In addition, it is shown how semiparametric models can be used for joint normalisation of several time series of data.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2005. 53 p.
Linköping Studies in Statistics, ISSN 1651-1700 ; 7Linköping Studies in Arts and Science, ISSN 0282-9800 ; 343
Normalisation, Monotonic, Semiparametric, Temporal trends, fluctuations, global, local, Matematisk statistik, Icke-parametriska metoder
National Category
Probability Theory and Statistics
urn:nbn:se:liu:diva-5124 (URN)91-85457-70-1 (ISBN)
Public defence
2005-12-16, BL32, B-huset, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Available from: 2005-12-16 Created: 2005-12-16 Last updated: 2014-09-05Bibliographically approved

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