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  • 1.
    Grimvall, Anders
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Libiseller, Claudia
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Model selection strategies for normalisation of environmental data2003In: Agriculture, climate change and economic consequences - from description to mitigation,2003, 2003Conference paper (Other academic)
  • 2.
    Krook, Joakim
    et al.
    Linköping University, Department of Management and Engineering, Environmental Technique and Management . Linköping University, The Institute of Technology.
    Mårtensson, Anders
    Linköping University, Department of Management and Engineering, Environmental Technique and Management . Linköping University, The Institute of Technology.
    Eklund, Mats
    Linköping University, Department of Management and Engineering, Environmental Technique and Management . Linköping University, The Institute of Technology.
    Libiseller, Claudia
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Swedish recovered wood waste: Linking regulation and contamination2008In: Waste Management, ISSN 0956-053X, E-ISSN 1879-2456, Vol. 28, no 3, p. 638-648Article in journal (Refereed)
    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.

  • 3.
    Libiseller, Claudia
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Comparison of Methods for Normalisation and Trend Testing of Water Quality Data2004In: The joint meeting of TIES 2004 and Accuracy 2004,2004, 2004Conference 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.

  • 4.
    Libiseller, Claudia
    Linköping University, Department of Mathematics, Statistics. Linköping University, Faculty of Health Sciences.
    Considering meteorological variation in assessments of environmental quality trends2003Doctoral 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.

    List of papers
    1. Performance of partial Mann–Kendall tests for trend detection in the presence of covariates
    Open this publication in new window or tab >>Performance of partial Mann–Kendall tests for trend detection in the presence of covariates
    2002 (English)In: Environmetrics, ISSN 1180-4009, E-ISSN 1099-095X, Vol. 13, no 1, p. 71-84Article in journal (Refereed) Published
    Abstract [en]

    Trend analyses of time series of environmental data are often carried out to assess the human impact on the environment under the influence of natural fluctuations in temperature, precipitation, and other factors that may affect the studied response variable. We examine the performance of partial Mann–Kendall (PMK) tests, i.e. trend tests in which the critical region is determined by the conditional distribution of one Mann-Kendall (MK) statistic for monotone trend, given a set of other MK statistics. In particular, we examine the impact of incorporating information regarding covariates in the Hirsch–Slack test for trends in serially correlated data collected over several seasons. Monte Carlo simulation of the performance of PMK tests demonstrates that the gain in power due to incorporation of relevant covariates can be large compared to the loss in power caused by irrelevant covariates. Furthermore, we have found that the asymptotic normality of the test statistics in such tests enables rapid and reliable determination of critical regions, unless the sample size is very small (n < 10) or the different MK statistics are very strongly correlated. A case study of water quality trends shows that PMK tests can detect and correct for rather complex relationships between river water quality and water discharge. The generic character of the PMK tests makes them particularly useful for scanning large sets of data for temporal trends.

    Keywords
    Covariates, Mann-Kendall tests, Natural fluctuations, Non-parametric tests, Trend detection, Water quality
    National Category
    Social Sciences
    Identifiers
    urn:nbn:se:liu:diva-47097 (URN)10.1002/env.507 (DOI)
    Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13
    2. Variance reduction for trend analysis of hydrochemical data from brackish waters
    Open this publication in new window or tab >>Variance reduction for trend analysis of hydrochemical data from brackish waters
    2003 (English)Report (Other academic)
    Abstract [en]

    We propose one parametric and one non-parametric method for detection of monotone trends in nutrient concentrations in brackish waters. Both methods take into account that temporal variation in the quality of such waters can be strongly influenced by mixing of salt and fresh water, thus salinity is used as a classification variable in the trend analysis. With the non-parametric approach, Mann-Kendall statistics are calculated for each salinity level, and the parametric method involves the use of bootstrap estimates of the trend slope in a time series regression model. In both cases, tests for each salinity level are combined in an overall trend test.

    Place, publisher, year, edition, pages
    Linköping: Linköpings universitet, 2003. p. 11
    Series
    LiU-MAT-R, ISSN 0349-246X ; 2
    National Category
    Mathematics
    Identifiers
    urn:nbn:se:liu:diva-22757 (URN)2076 (Local ID)2076 (Archive number)2076 (OAI)
    Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2012-12-14
    3. Model selection for local and regional meteorological normalisation of background concentrations of tropospheric ozone
    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
    4. Meteorological normalisation and non-parametric smoothing for quality assessment and trend analysis of tropospheric ozone data
    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
    5. Meteorological normalisation of tropospheric ozone using back trajectories
    Open this publication in new window or tab >>Meteorological normalisation of tropospheric ozone using back trajectories
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    The objective of meteorological normalisation of air quality measurements is to extract anthropogenic signals by removing meteorologically driven fluctuations in the collected data. We found that standard normalisation procedures involving regression of air quality on local meteorological data can be improved by incorporating information on four-day back trajectories of the sampled air masses. A case study of tropospheric ozone data revealed that the most efficient normalisation was achieved by including selected trajectory coordinates directly in multivariate regression models. Summarising the trajectories into clusters or sector values prior to the normalisation indicated that there was a slight loss of information.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-86417 (URN)
    Available from: 2012-12-14 Created: 2012-12-14 Last updated: 2012-12-14
  • 5.
    Libiseller, Claudia
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Model selection of ozone normalisation using regional-scale meteorological variables2003In: SPRUCE VI,2003, 2003Conference paper (Other academic)
  • 6.
    Libiseller, Claudia
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Regional approaches to normalisation of background ozone2003In: The ISI International Conference on Environmental Statistics and Health,2003, 2003Conference paper (Other academic)
  • 7.
    Libiseller, Claudia
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Meteorological normalisation of wet deposition data using wind sector information2005In: The 16th International Conference on Quantitative Methods for the Environmental Sciences,2005, 2005Conference paper (Other academic)
  • 8.
    Libiseller, Claudia
    et al.
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Grimvall, Anders
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Model selection for local and regional meteorological normalisation of background concentrations of tropospheric ozone2003In: Atmospheric Environment, ISSN 1352-2310, E-ISSN 1873-2844, Vol. 37, no 28, p. 3923-3931Article in journal (Refereed)
    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.

  • 9.
    Libiseller, Claudia
    et al.
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Grimvall, Anders
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Performance of partial Mann–Kendall tests for trend detection in the presence of covariates2002In: Environmetrics, ISSN 1180-4009, E-ISSN 1099-095X, Vol. 13, no 1, p. 71-84Article in journal (Refereed)
    Abstract [en]

    Trend analyses of time series of environmental data are often carried out to assess the human impact on the environment under the influence of natural fluctuations in temperature, precipitation, and other factors that may affect the studied response variable. We examine the performance of partial Mann–Kendall (PMK) tests, i.e. trend tests in which the critical region is determined by the conditional distribution of one Mann-Kendall (MK) statistic for monotone trend, given a set of other MK statistics. In particular, we examine the impact of incorporating information regarding covariates in the Hirsch–Slack test for trends in serially correlated data collected over several seasons. Monte Carlo simulation of the performance of PMK tests demonstrates that the gain in power due to incorporation of relevant covariates can be large compared to the loss in power caused by irrelevant covariates. Furthermore, we have found that the asymptotic normality of the test statistics in such tests enables rapid and reliable determination of critical regions, unless the sample size is very small (n < 10) or the different MK statistics are very strongly correlated. A case study of water quality trends shows that PMK tests can detect and correct for rather complex relationships between river water quality and water discharge. The generic character of the PMK tests makes them particularly useful for scanning large sets of data for temporal trends.

  • 10.
    Libiseller, Claudia
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Wahlin, Karl
    MAI .
    Power Simulations using Data generated by Process-based Deterministic Models2004In: COMPSTAT 2004,2004, 2004Conference 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.

  • 11.
    Libiseller, Claudia
    et al.
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Grimvall, Anders
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Waldén, J.
    Finnish Meteorological Institute, Helsinki, Finland.
    Paatera, J.
    Finnish Meteorological Institute, Helsinki, Finland.
    Meteorological normalisation of tropospheric ozone using back trajectoriesManuscript (preprint) (Other academic)
    Abstract [en]

    The objective of meteorological normalisation of air quality measurements is to extract anthropogenic signals by removing meteorologically driven fluctuations in the collected data. We found that standard normalisation procedures involving regression of air quality on local meteorological data can be improved by incorporating information on four-day back trajectories of the sampled air masses. A case study of tropospheric ozone data revealed that the most efficient normalisation was achieved by including selected trajectory coordinates directly in multivariate regression models. Summarising the trajectories into clusters or sector values prior to the normalisation indicated that there was a slight loss of information.

  • 12.
    Libiseller, Claudia
    et al.
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Grimvall, Anders
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Waldén, Jari
    Finnish Meteorological Institute, Helsinki, Finland.
    Saari, Helena
    Finnish Meteorological Institute, Helsinki, Finland.
    Meteorological normalisation and non-parametric smoothing for quality assessment and trend analysis of tropospheric ozone data2005In: Environmental Monitoring & Assessment, ISSN 0167-6369, E-ISSN 1573-2959, Vol. 100, no 1-3, p. 33-52Article in journal (Refereed)
    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.

  • 13.
    Libiseller, Claudia
    et al.
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Nordgaard, Anders
    Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
    Variance reduction for trend analysis of hydrochemical data from brackish waters2003Report (Other academic)
    Abstract [en]

    We propose one parametric and one non-parametric method for detection of monotone trends in nutrient concentrations in brackish waters. Both methods take into account that temporal variation in the quality of such waters can be strongly influenced by mixing of salt and fresh water, thus salinity is used as a classification variable in the trend analysis. With the non-parametric approach, Mann-Kendall statistics are calculated for each salinity level, and the parametric method involves the use of bootstrap estimates of the trend slope in a time series regression model. In both cases, tests for each salinity level are combined in an overall trend test.

  • 14.
    Libiseller, Claudia
    et al.
    Linköping University, Department of Mathematics, Statistics . Linköping University, Faculty of Arts and Sciences.
    Nordgaard, Anders
    Linköping University, Department of Mathematics, Statistics . Linköping University, Faculty of Arts and Sciences.
    Variance Reduction for Trend Analysis of Hydrochemical Data in Brackish Waters2002In: Environmental Communication in the Information Society: Proceedings of the 16th Conference “Informatics for Environmental Protection” Sept. 25-27, 2002, Vienna, Austria / [ed] Pillmann W., Tochtermann K, Vienna: ISEP , 2002Conference paper (Refereed)
  • 15.
    Nordgaard, Anders
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Libiseller, Claudia
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Variance reduction for trend analysis2002In: NORDSTAT 2002, Stockholm, Sweden, 2002Conference paper (Other academic)
    Abstract [en]

    The concentrations of nutrients and other substances in a water body can be strongly influenced by random fluctuations in the mixing of waters of different origin. Hence, the water quality at given site can exhibit a large temporal variation that makes it difficult to extract anthropogenic signals from collected data. In this paper, we examine how the human impact on nutrient concentrations in such water bodies can be clarified by replacing conventional time series or geostatistical approaches by trend detection techniques in which we analyse the variation in nutrient concentrations with salinity and time. The general principles for the trend detection are illustrated with data from the Baltic Sea. The statistical significance of temporal changes in nutrient concentrations can be assessed by using parametric and nonparametric trend tests. In the recent past a nonparametric trend test with correction for covariates was proposed (Libiseller and Grimvall, 2002). This test, however, can best be applied if trends are monotone in time, which is not necessarily fulfilled for the original data. We therefore suggest that an overall trend test is computed as the weighted sum of trend test statistics computed for different salinity levels. By this means we receive a rather homogeneous time series in each subset, which considerably improves the power of the trend test. In the parametric approach we suggest a regression model, with Total Phosphorus concentration as the dependent variable and time (months) as the explaining variable. The residuals from this model output are most likely non-independent and non-normally distributed, and we will therefore apply bootstrap assessment of the estimated parameters.

  • 16. Stålnacke, P.
    et al.
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Libiseller, Claudia
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Laznik, M.
    Kokorite, I.
    Trends in nutrient concentrations in Latvian rivers and the response to the dramatic change in agriculture2003In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 283, no 1-4, p. 184-205Article in journal (Refereed)
    Abstract [en]

    In recent years, the use of fertilisers in the Baltic countries (Estonia, Latvia, and Lithuania) has decreased at an unprecedented rate. The import of mineral fertilisers and feed stuff became almost non-existent, and extensive slaughtering of livestock reduced the amount of manure. In Latvia, the purchase of mineral fertilisers decreased by a factor of 15 between 1987 and 1996 and the number of livestock decreased with a factor of almost 4 during the same time period. Such abrupt and comprehensive changes in land use have never before occurred in the history of modern European agriculture. Here, the impact that this dramatic reduction has had on concentrations of nutrients in Latvian rivers is examined. To discern temporal changes, statistical analyses were undertaken on time series of nutrient concentrations and relationships between concentrations and runoff at 12 sampling sites in ten Latvian rivers covering drainage areas from 334 to 64,000 km2. Considering the study period 1987-1998, only four of the 12 sites showed statistically significant downward trends (one-sided test at the 5% level) in the dissolved inorganic nitrogen (DIN = NO3-N + NO2-N + NH4-N) data. There are probably two main explanations for the weak DIN trends. Firstly, long water-transit times in the soilwater and groundwater may have caused substantial time lag between changes in input and output of nitrate in the studied catchments. Secondly, the loss of DIN might have been dominated by mineralisation of large pools of organic nitrogen that have accumulated over several years. These inferences are supported by (i) a hydrograph recession analysis and (ii) indications of DIN transformation processes, presumably denitrification, in smaller streams and channels, based on measurements in small agricultural catchments (1-4 km2) in Estonia and Latvia. Formal testing of trends in phosphorus data revealed that marked drops occurred in riverine concentrations at six sites in 1987-1998. A joint analysis of concentration time series for all sampling sites for 1987-1998 showed weak statistical significance for downward trends in NH4-N, NO 3-N, and DIN (p ? 0.04) and substantial significance for PO 4-P (p < 0.01). Thus, the extensive decrease in agricultural intensity that began in the early 1990s has led to only a slow and limited (especially regarding nitrogen) response in Latvian rivers. The difference noted between nitrogen and phosphorus also suggests that factors other than reduced fertiliser application influenced the inertia of the water quality response. Our findings, along with those obtained in similar studies, show that large cuts in nutrient inputs do not necessarily cause an immediate response, particularly in medium-sized and large catchment areas.

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