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Considering meteorological variation in assessments of environmental quality trends
Linköping University, Department of Mathematics, Statistics. Linköping University, Faculty of Health Sciences.
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: urn:nbn:se:liu:diva-22755Local ID: 2073ISBN: 91-7373-615-5 (print)OAI: oai:DiVA.org:liu-22755DiVA: diva2:243068
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
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, 71-84 p.Article 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.

Keyword
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. 11 p.
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, 3923-3931 p.Article 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, 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.

Keyword
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

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