Die Lösung von Nutzungskonflikten aufgrund flächenhafter Bodenkontaminationen mit Hilfe Geografischer Informationssysteme und Mitteln der Geostatistik

Goal and Scope

By means of Geographical Information Systems (GIS) and other geostatistical methods, the extensive heterogeneous data materials of large area soil contaminations are re-evaluated to eliminate exploitation conflicts and investment obstacles.

Methods

The mapping of relevant frame criteria generates the basis to solve the conflicts, such as types of exploitation of field areas, development plans, protected areas, flooded areas, etc. as well as their geo-referencing in the Geographical Information System. The critical examination of data age and quality as well as the geostatistical evaluation by means of semivariogram analysis and Kriging technique are essential prerequisites for the determination of areas related to utility and exceeding test values.

Results and Conclusions

As a result, one can modify the hazardous situation and can provide recommendations for an optimal elimination of hazards, mostly in the framework of scheduled measures in terms of town planning.

Outlook

On the basis of the results obtained, further exploitation or monitoring work can clearly be optimized.     

Exploratory analysis of data sets with missing elements and outliers

The main goal of the presented paper was to develop a general strategy allowing exploration of contaminated data sets with missing elements, based on application of robust PLS for initial estimation of missing elements. Using robust distance, the outlying elements were identified. After their identification and replacing by missing elements, the expectation-maximization algorithm (which can be built in into different computational procedures, such as principal component analysis and its generalisation to the N-way data-the TUCKER3 model) was used for construction of the final model.     

Analyse der Immissionsstruktur der BTXE-Aromaten und der Ru?parameter in Innenstadtbereichen

Zusammenfassung Ziel und Hintergrund  In einem vier Thüringer St?dte umfassenden, stra?enverkehrsbezogenen, Immissionsmessprogramm wurden Immissionsparameter erfasst und statistisch aufzubereitet, um Zusammenh?nge zwischen dem Verkehrsgeschehen und der Immission zu erkennen und deren lokale Auspr?gung für die betrachteten Standorte zu beschreiben Methoden  Dazu, wurden die aromatischen, Kohlenwasserstoffe Benzol, Toluol Ethylbenzol, o-, m- und p-Xylol, die Ru?parameter Gesamtkohlenstoff, elementarer Kohlenstoff und organischer Kohlenstoff über zehn Monate bestimmt. Die Beprobung erstreckte sich jeweils über 24 Stunden und erfolgte an, Wochentagen und Wochenenden sowie in der kalten und warmen Jahreszeit. Zeitgleich verlief die Ermittlung der Anzahl, Art und Geschwindigkeit der Fahrzeuge an, der Messtelle. Zur Untersuchung der Daten wurden die deskriptive Statistik, die Analyse der Korrelation, die Cluster- und die Hauptkomponentenanalyse eingestzt. Ergebnisse  Für alle Standorte l?sst sich mit Hilfe der Korrelations-und Regressionsanalyse zeigen, dass der Stra?enverkehr unter Berücksichtigung seiner Merkmale die Parameter der Ru?immission und der-struktur beeinflusst. Im Gegensatz dazu ist eine direkte Verkehrsabh?ngigkeit der BTXE-Immission oder-struktur mit diesen Mitteln nicht nachzuweisen. Zwischen den Immissions-konzentrationen der BTXE-Aromaten und der Ru?komponenten lassen sich jedoch Korrelationen mit hoher Signifikanz finden. Die Auspr?gung der Korrelationen differient unter den Standorten. Mit Hilfe der Clusteranalyse lassen sich die Erhebungsgebiete gruppieren, mit der Hauptkomponentenanalyse k?nnen verkehrsabh?ngige und verkehrsunabh?ngige Faktoren extrahiert werden. Schlussfolgerungen, Empfehlungen und Ausblick  Clusteranalyse und Hauptkomponentenanalyse sind geeignet, die ?rtlichen Gegebenheiten zu beschreiben und Standorte untereinander zu vergleichen. Die neu eingeführten Indexparameter der BTXE-Aromaten k?nnen als Indikatoren technischer Ver?nderungen im Bereich der Emissionen aus Fahrzeugen, Haushalten und der Industrie herangezogen werden k?nnen.

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OnlineFirst: 28. April 2003     

Improvement of sampling strategies for randomly distributed hotspots in soil applying a computerized simulation considering the concept of uncertainty

Introduction  

The pollution of soil and environment as a result of human activity is a major problem. Nowadays, the determination of local contaminations is of interest for environmental remediation. These hotspots can have various toxic effects on plants, animals, humans, and the whole ecological system. However, economical and juridical consequences are also possible, e.g., high costs for remediation measures.     

Small-scale variability of metals in soil and composite sampling

Soil pollution data is also strongly scattering at small scale. Sampling of composite samples, therefore, is recommended for pollution assessment. Different statistical methods are available to provide information about the accuracy of the sampling process. Autocorrelation and variogram analysis can be applied to investigate spatial relationships. Analysis of variance is a useful method for homogeneity testing. The main source of the total measurement uncertainty is the uncertainty arising from sampling. The sample mass required for analysis can also be estimated using an analysis of variance. The number of increments to be taken for a composite sample can be estimated by means of simple statistical formulae. Analytical results of composite samples obtained from different fusion procedures of increments can be compared by means of multiple mean comparison. The applicability of statistical methods and their advantages are demonstrated for a case study investigating metals in soil at a very small spatial scale. The paper describes important statistical tools for the quantitative assessment of the sampling process. Detailed results clearly depend on the purpose of sampling, the spatial scale of the object under investigation and the specific case study, and have to be determined for each particular case.     

Characterization of elemental pollution and its variations in sediments and suspended particulate matter from the River Elbe via multi-element analysis combined with chemometric data evaluation

Sediments and suspended particulate matter taken from about 100 sampling sites along the River Elbe from the source to the mouth during the period 1992 and 1998 have been analyzed for the concentrations of more than 60 elements in each sample. The analytical data were subsequently processed by means of multivariate statistics in order to characterize the charge of the River Elbe with inorganic pollutants to elucidate pollution trends. Using factor analysis 18 elements were found to be enriched by human activities. By means of cluster analysis--using the 18 anthropogenically influenced elements as variables--the sampling sites were aggregated into groups having similar element distributions. Thus, the entire stretch of the River Elbe was divided into three sections of characteristic elemental pollution. Finally, the trends in anthropogenic pollution within the characteristic regions since the early nineties were assessed. It turned out that most of the anthropogenically influenced elements showed a decrease in the upper and middle parts of the river, whereas no decrease could be verified in the estuary region.     

Chemometric characterization of soil and plant pollution: Part 1: Multivariate data analysis and geostatistical determination of relationship and spatial structure of inorganic contaminants in soil

Geostatistical and multivariate methods of data analysis are used to describe patterns of soil pollution with inorganic contaminants in Celje County, Slovenia. Groups of contaminants and polluted sites were identified using cluster analysis and confirmed with multidimensional variance and discriminant analysis. Factor analysis yields an identification of not directly observable relationships between the contaminants. The spatial structure and distribution of contaminants were assessed by applying semivariogram analysis and kriging interpolation method. Zinc, Cd and Cu were identified as a pollutant emitted from the zinc smelter, Pb also from other sources, and Cr and Ni mostly from geological parent material.