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Meteorological normalisation and non-parametric smoothing for quality assessment and trend analysis of tropospheric ozone data
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
Finnish Meteorological Institute, Helsinki, Finland.
Finnish Meteorological Institute, Helsinki, Finland.
2005 (English)In: Environmental Monitoring & Assessment, ISSN 0167-6369, E-ISSN 1573-2959, Vol. 100, no 1-3, 33-52 p.Article in journal (Refereed) Published
Abstract [en]

Despite extensive efforts to ensure that sampling and installation and maintenance of instruments are as efficient as possible when monitoring air pollution data, there is still an indisputable need for statistical post processing (quality assessment). We examined data on tropospheric ozone and found that meteorological normalisation can reveal (i) errors that have not been eliminated by established procedures for quality assurance and control of collected data, as well as (ii) inaccuracies that may have a detrimental effect on the results of statistical tests for temporal trends. Moreover, we observed that the quality assessment of collected data could be further strengthened by combining meteorological normalisation with non-parametric smoothing techniques for seasonal adjustment and detection of sudden shifts in level. Closer examination of apparent trends in tropospheric ozone records from EMEP (European Monitoring and Evaluation Programme) sites in Finland showed that, even if potential raw data errors were taken into account, there was strong evidence of upward trends during winter and early spring.

Place, publisher, year, edition, pages
2005. Vol. 100, no 1-3, 33-52 p.
Keyword [en]
background ozone, level shifts, natural fluctuation, seasonal variation, temporal trend
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-24479DOI: 10.1007/s10661-005-7059-2Local ID: 6595OAI: oai:DiVA.org:liu-24479DiVA: diva2:244799
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2017-12-13
In thesis
1. Considering meteorological variation in assessments of environmental quality trends
Open this publication in new window or tab >>Considering meteorological variation in assessments of environmental quality trends
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Time series of environmental data are collected to monitor the effectiveness of new emission reduction policies or to determine the general state of the environment. However, small gradual changes in such variables can easily be concealed by large fluctuations caused by prevailing weather conditions. Hence, there is a real need for procedures that facilitate separation of such natural variation from anthropogenic effects.

Taking meteorological or hydrological variables into consideration in a trend analysis can be done in several ways. The technique chosen to accomplish this objective depends on characteristics of the data set, for example the length of the time series and sampling frequencies, and the kind of relationships that exist between the response variable and the covariates. Two different approaches were examined in the studies underlying this thesis: multivariate non-parametric tests and parametric normalisation procedures. The non-parametric trend test proposed here was newly desinged, thus it was also necessary to conduct simulation studies to examine the performance of this method. By comparison, normalisation techniques have been used over the past few decades mainly to adjust for the impact of meteorological effects on air quality data. The choice of explanatory variables for such procedures was studied: first by examining variable selection procedures based on cross-validation, paying special attention to serially correlated response data; and secondly by considering variables derived from complex physics-based models as alternatives to measured variables. A number of other aspects that might influence the ability to detect trends were also explored, including level shifts due to instrument malfunctions.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2003. 41 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 3
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22755 (URN)2073 (Local ID)91-7373-615-5 (ISBN)2073 (Archive number)2073 (OAI)
Public defence
2003-04-25, Sal Key 1, Keyhuset, Linköpings universitet, Linköping, 10:15 (Swedish)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2012-12-14

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Libiseller, ClaudiaGrimvall, Anders

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