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Libiseller, Claudia
Publications (10 of 16) Show all publications
Krook, J., Mårtensson, A., Eklund, M. & Libiseller, C. (2008). Swedish recovered wood waste: Linking regulation and contamination. Waste Management, 28(3), 638-648
Open this publication in new window or tab >>Swedish recovered wood waste: Linking regulation and contamination
2008 (English)In: Waste Management, ISSN 0956-053X, E-ISSN 1879-2456, Vol. 28, no 3, p. 638-648Article in journal (Refereed) Published
Abstract [en]

In Sweden, large amounts of wood waste are generated annually from construction and demolition activities, but also from other discarded products such as packaging and furniture. A large share of this waste is today recovered and used for heat production. However, previous research has found that recovered wood waste (RWW) contains hazardous substances, which has significant implications for the environmental performance of recycling. Improved sorting is often suggested as a proper strategy to decrease such implications. In this study, we aim to analyse the impacts of waste regulation on the contamination of RWW. The occurrence of industrial preservative-treated wood, which contains several hazardous substances, was used as an indicator for contamination. First the management of RWW during 1995–2004 was studied through interviews with involved actors. We then determined the occurrence of industrial preservative-treated wood in RWW for that time period for each supplier (actor). From the results, it can be concluded that a substantially less contaminated RWW today relies on extensive source separation. The good news is that some actors, despite several obstacles for such upstream efforts, have already today proved capable of achieving relatively efficient separation. In most cases, however, the existing waste regulation has not succeeded in establishing strong enough incentives for less contaminated waste in general, nor for extensive source separation in particular. One important factor for this outcome is that the current market forces encourage involved actors to practice weak quality requirements and to rely on end-of-pipe solutions, rather than put pressure for improvements on upstream actors. Another important reason is that there is a lack of communication and oversight of existing waste regulations. Without such steering mechanisms, the inherent pressure from regulations becomes neutralized.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-14073 (URN)10.1016/j.wasman.2007.03.010 (DOI)
Available from: 2006-10-09 Created: 2006-10-09 Last updated: 2019-06-13
Libiseller, C., Grimvall, A., Waldén, J. & Saari, H. (2005). Meteorological normalisation and non-parametric smoothing for quality assessment and trend analysis of tropospheric ozone data. Environmental Monitoring & Assessment, 100(1-3), 33-52
Open this publication in new window or tab >>Meteorological normalisation and non-parametric smoothing for quality assessment and trend analysis of tropospheric ozone data
2005 (English)In: Environmental Monitoring & Assessment, ISSN 0167-6369, E-ISSN 1573-2959, Vol. 100, no 1-3, p. 33-52Article 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.

Keywords
background ozone, level shifts, natural fluctuation, seasonal variation, temporal trend
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-24479 (URN)10.1007/s10661-005-7059-2 (DOI)6595 (Local ID)6595 (Archive number)6595 (OAI)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2017-12-13
Libiseller, C. & Grimvall, A. (2005). Meteorological normalisation of wet deposition data using wind sector information. In: The 16th International Conference on Quantitative Methods for the Environmental Sciences,2005.
Open this publication in new window or tab >>Meteorological normalisation of wet deposition data using wind sector information
2005 (English)In: The 16th International Conference on Quantitative Methods for the Environmental Sciences,2005, 2005Conference paper, Published paper (Other academic)
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-30922 (URN)16595 (Local ID)16595 (Archive number)16595 (OAI)
Available from: 2009-10-09 Created: 2009-10-09
Libiseller, C. (2004). Comparison of Methods for Normalisation and Trend Testing of Water Quality Data. In: The joint meeting of TIES 2004 and Accuracy 2004,2004.
Open this publication in new window or tab >>Comparison of Methods for Normalisation and Trend Testing of Water Quality Data
2004 (English)In: The joint meeting of TIES 2004 and Accuracy 2004,2004, 2004Conference paper, Published paper (Other academic)
Abstract [en]

To correctlyassesstrends in water quality data, influencing variables suchasdischarge or temperature must betaken into account. This canbedone by using i one-step procedures like the PartialMann-KendallPMK test ormultiple regression, or ii two-steptechniques thatinclude a normalisation followed by a trend test ontheresiduals.Which approach is most appropriate depends strongly ontherelationship between the response variableunder considerationandthe influencing variables. For example, PMK tests can besuperiorif there are long andvarying time lags in the waterqualityresponse. Two-step procedures are particularly useful whentheshape of thetemporal trend is the primary interest, but they canbemisleading if one of the influencing variables itselfexhibitsatrend or long-term tendency. The present study discussestheadvantages and disadvantages of some trendtesting techniques,usingSwedish water quality data to illustrate the properties ofthemethods.

Keywords
long-term changes in covariate, non-monotonic changes, long memory effects, seasonal variation
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22685 (URN)1977 (Local ID)1977 (Archive number)1977 (OAI)
Available from: 2009-10-07 Created: 2009-10-07
Libiseller, C., Grimvall, A. & Wahlin, K. (2004). Power Simulations using Data generated by Process-based Deterministic Models. In: COMPSTAT 2004,2004.
Open this publication in new window or tab >>Power Simulations using Data generated by Process-based Deterministic Models
2004 (English)In: COMPSTAT 2004,2004, 2004Conference paper, Published paper (Other academic)
Abstract [en]

Power analysis is an integral part of statistical hypothesis testing, and, when neither exactpower computations nor reasonable approximations are feasible, Monte Carlo simulations providea viable alternative. However, generating data for such simulations is often an intricate task, especially when the hypothesis testing is based on non-normal multivariate data withcomplex dependencies. Here, we show how process-based deterministic models can be employed to generate data with adequate statistical dependencies for realistic power simulations. In particular, we usedthe Integrated Nitrogen in Catchments INCA model to produce bivariate time series ofnitrogen concentration and water discharge data that included plausible temporal trends andrealistic cross-correlations, seasonal patterns, and memory effects. The random variation in thegenerated data was achieved by running the INCA model with various sets of weather data that were obtained by block resampling from a given time series of observed air temperatureand precipitation data. The assortment of temporal trends was created by altering the anthropogenic input of nitrogen to the catchment under consideration.Two tests for temporal trends in nitrogen concentration were compared: i a partial Mann-Kendall test in which water discharge was treated as a covariate; ii a two-stage procedurein which we first used a semi-parametric regression technique to remove the impact of natural fluctuations in water discharge, and we subsequently applied an ordinary Mann-Kendall testto the obtained residuals. Our simulations demonstrated that the two tests had comparablepower, but also that they involved empirical significance levels that were much higher than the nominal levels, possibly due to substantial serial dependence in the data.

Keywords
artificial data, environmental trends, partial Mann-Kendall test, semi-parametric regression, normalisation
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22689 (URN)1984 (Local ID)1984 (Archive number)1984 (OAI)
Available from: 2009-10-07 Created: 2009-10-07
Libiseller, C. (2003). Considering meteorological variation in assessments of environmental quality trends. (Doctoral dissertation). Linköping: Linköpings universitet
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. p. 41
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
Libiseller, C. & Grimvall, A. (2003). Model selection for local and regional meteorological normalisation of background concentrations of tropospheric ozone. Atmospheric Environment, 37(28), 3923-3931
Open this publication in new window or tab >>Model selection for local and regional meteorological normalisation of background concentrations of tropospheric ozone
2003 (English)In: Atmospheric Environment, ISSN 1352-2310, E-ISSN 1873-2844, Vol. 37, no 28, p. 3923-3931Article in journal (Refereed) Published
Abstract [en]

Meteorological normalisation of time series of air quality data aims to extract anthropogenic signals by removing natural fluctuations in the collected data. We showed that the currently used procedures to select normalisation models can cause over-fitting to observed data and undesirable smoothing of anthropogenic signals. A simulation study revealed that the risk of such effects is particularly large when: (i) the observed data are serially correlated, (ii) the normalisation model is selected by leave-one-out cross-validation, and (iii) complex models, such as artificial neural networks, are fitted to data. When the size of the test sets used in the cross-validation was increased, and only moderately complex linear models were fitted to data, the over-fitting was less pronounced. An empirical study of the predictive ability of different normalisation models for tropospheric ozone in Finland confirmed the importance of using appropriate model selection strategies. Moderately complex regional models involving contemporaneous meteorological data from a network of stations were found to be superior to single-site models as well as more complex regional models involving both contemporaneous and time-lagged meteorological data from a network of stations.

National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22635 (URN)10.1016/S1352-2310(03)00502-8 (DOI)1919 (Local ID)1919 (Archive number)1919 (OAI)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2017-12-13
Libiseller, C. (2003). Model selection of ozone normalisation using regional-scale meteorological variables. In: SPRUCE VI,2003.
Open this publication in new window or tab >>Model selection of ozone normalisation using regional-scale meteorological variables
2003 (English)In: SPRUCE VI,2003, 2003Conference paper, Published paper (Other academic)
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22954 (URN)2324 (Local ID)2324 (Archive number)2324 (OAI)
Available from: 2009-10-07 Created: 2009-10-07
Grimvall, A. & Libiseller, C. (2003). Model selection strategies for normalisation of environmental data. In: Agriculture, climate change and economic consequences - from description to mitigation,2003.
Open this publication in new window or tab >>Model selection strategies for normalisation of environmental data
2003 (English)In: Agriculture, climate change and economic consequences - from description to mitigation,2003, 2003Conference paper, Published paper (Other academic)
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22946 (URN)2315 (Local ID)2315 (Archive number)2315 (OAI)
Available from: 2009-10-07 Created: 2009-10-07
Libiseller, C. (2003). Regional approaches to normalisation of background ozone. In: The ISI International Conference on Environmental Statistics and Health,2003.
Open this publication in new window or tab >>Regional approaches to normalisation of background ozone
2003 (English)In: The ISI International Conference on Environmental Statistics and Health,2003, 2003Conference paper, Published paper (Other academic)
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22957 (URN)2327 (Local ID)2327 (Archive number)2327 (OAI)
Note
J. Mateu, D. Holland, and W. Gonzalez-Manteiga editors: The ISI International Conference on Environmental Statistics and Health: Conference Proceedings: Santiago de Compostela, July, 16-18, 2003Available from: 2009-10-07 Created: 2009-10-07
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